https://en.wikipedia.org/wiki/Albert_Tangora

A couple of my classes require this for papers, and I like the policy becuase it protects students from being accused of suspected AI use.

I like that she might use our homework as examples. This makes me feel like I want to truly do my best and be proud of my work and for others to see it as well.

I think having extensions available for work will be great to have for some papers. Sometime's I get worried I won't have enough time to complete something due to work, life, etc. So knowing I have this available is good.

Take your time with your writing, make it to the best ability. I really enjoy writing, especially fiction and poems so I usually take longer than planned. But this is why I can ask for an extension and not feel bad about doing so.

I appreciate these statements used in regard to AI use. Even though the course is online and distance learning, the wording gives it a closer feel, where students are supported and expected to complete their best work.

Like early days of rhetcomp but with corporate funding pressure

Of course

① PREANESTHESIC EVALUATION AND PATIENT PREPARATION Preanestezik değerlendirme ve hasta hazırlığı

② It is an important clinical examination to reduce mortality and morbidity before the application of surgical or diagnostic anesthesia. Cerrahi veya tanısal anestezi uygulanmadan önce mortalite ve morbiditeyi azaltmak amacıyla yapılan önemli bir klinik değerlendirmedir.

③ To ensure that the patient is brought to optimal conditions before the operation Hastanın operasyon öncesinde en uygun (optimal) koşullara getirilmesini sağlamak amacıyla yapılır.

This is a very important thing to remember. You must always ask questions. But sometimes, it's hard to know which questions to ask. So, I'm glad this is here to remind you of the simple questions, they don't need to be the most intricate questions.

Im glad the writing mentions this. More specifically, it mentions to use an annotation method that works best for you and makes sense to you. There are so many different styles. What is most important is that you choose a way to annotate that when you go back you know exactly why you highlighted it.

I often read for my own enjoyment and entertainment so when it comes to having to active read it is very different. I'm glad there is a reminder of the steps here to show that it does take a little more time but, active reading is simple once you get the hang of it and have had practice.

Reading a text is different from reading a book. You have to learn how to not only read for entertainment. Reading a text is having to analyze and process all the information so that we can understand writing style and reasoning for the order and formation of the text.

Actively reading is all about questions. Instead of reading the text to gain knowledge, you would read to to continue getting new information that wasn't there when you read it the first time. I like how they define active reading in this text because of how similar it is to active listening. It's all about precessing and actually thinking about the reasoning behind it.

It is important to learn how to truly analyze a text and figure out what are the key points that you're supposed to be focusing on. Active reading is important when it comes to writing about a specific reading.

I believe that it is important to ask yourself questions as you read because it allows you to get a better understanding of the text and have a strong concept so you can go back and annotate what you have read.

This is important because it helps you read more critically instead of just accepting what is on the page. When you understand why a writer made certain choices and whether they were effective, you can better evaluate the argument, notice strengths and weaknesses, and improve your own writing.

I always do this when going over an essay or reading someone else's. I want to make sure I do not miss anything important or a mistake that one might have made. It also makes me have a clear concept of the essay.

I find it very important whenever I am reading something to read it twice. I often have to listen to books as well cause I usually miss multiple important pieces if I read it, especially only once. Going over it twice helps me retain the information.

This will have to be a constant reminder for me. Sometimes I struggle to keep a positive attitude when reading non-fiction works or school assigned pieces. I need to work on following the steps to ensure that I have a quality reading experience and retain what I need.

I am thrilled that annotation is part of our course load with this class. Annotating is a skill that I know I lack in. I had such a hard time writing down my thoughts and always assumed I would "just remember" what I needed on a first read. This is not typically the case.

I found this to be important for me when reading articles. Knowing if something is written for the general public's view or rather for a scientific view is important to my retention. Also with older pieces I find it helpful to know a general frame of reference.

Becoming more familiar with different types of writing is something that is crucial to improving active reading. If one is reading the same format, then there is not an opportunity to retain new skills by reading more styles of writing.

In short, this is strictly opinionated.

I understand the author is supporting the use of fountain pens greatly, but this passage stirs the pot, and ultimately can push the audience away if they prefer another pen instead.

This right here is a great statement to demonstrate how our brains are shrinking. We are not meant for technology, and it suppresses us.

A digital approach latches onto some humanity and passion through data. It is cryptic, yet, this flows into society today

This is a contradiction. In reality, we are adapting to technology, whether it is what we are intended for or not. The fountain pen is being used much less than traditional style pens now a-days and are outdated.

I used to use one. At first, it was sharp and harsh. Over time, it matched my fluidity in every stroke.

we are being tested with AI videos even looking realistic

this can create serious confusion

I.e., it's proof that they care

① Any substance can cause allergy or anaphylaxis. Herhangi bir madde alerjiye veya anafilaksiye neden olabilir.

② First exposure leads to immune memory formation. İlk maruziyet bağışıklık hafızasının oluşmasına yol açar.

③ Re-exposure to the same antigen triggers mediator release from mast cells, basophils, and platelets, resulting in a systemic reaction. Aynı antijene tekrar maruziyet, mast hücreleri, bazofiller ve trombositlerden mediyatör salınımını tetikler ve bunun sonucunda sistemik bir reaksiyon gelişir.

There can be different forms of testing a theory with a sociology paper

This is the simple definition for sociology

This is a clear example of response to a rhetorical situation.

Hastanın solunumsal anormalliklerinin şiddetini ve uygulanan girişimlere verdiği yanıtı değerlendirebilir.

Minimal ve moderat sedasyon uygulanan hastaların çoğu için, tek başına pulse oksimetre ile izlem, mekanik monitörizasyon yöntemi olarak yeterlidir.

Moderat ve derin sedasyonda, yalnızca hasta takibiyle görevli (ayrı) bir gözlemci, hastayı kesintisiz olarak izlemelidir.

Minimal sedasyonda, işlemi yapan hekimin aralıklı (intermittent) olarak yaptığı hasta takibi genellikle yeterlidir.

① To relieve anxiety, fear, and restlessness Anksiyeteyi, korkuyu ve huzursuzluğu azaltmak

② To increase the pain threshold Ağrı eşiğini yükseltmek

③ To reduce discomfort caused by position Pozisyona bağlı rahatsızlığı azaltmak

④ To ensure immobility Hareketsizliği sağlamak

⑤ To minimize hemodynamic changes Hemodinamik değişiklikleri (nabız, tansiyon dalgalanmalarını) en aza indirmek

⑥ To ensure patient safety Hasta güvenliğini sağlamak

⑦ To provide adequate analgesia, sedation, and amnesia Yeterli analjezi, sedasyon ve amnezi sağlamak

Derin sedasyon, bilinç düzeyinde belirgin bir baskılanma ile karakterizedir; bu durumda anlamlı motor yanıt ancak tekrarlayan veya ağrılı uyaranlarla elde edilebilir.

Hipoksi (kandaki oksijen düzeyinin düşmesi) ve/veya hipoventilasyonun (yetersiz solunum) görülme sıklığı, kullanılan ilaca bağlı olarak değişir ve %10 ile %30 arasında olabilir.

Bu durum, “bilinçli sedasyon (conscious sedation)” terimiyle en yakından ilişkilidir.

Orta (moderat) sedasyon, bilinç düzeyinde azalma ile karakterizedir; bu durumda hasta basit sözel ya da dokunsal uyaranlara yavaş fakat anlamlı motor yanıt verir.

Esas olarak anksiyeteyi azaltmak ve işlem sırasında hastanın konforunu sağlamak amacıyla uygulanır.

① Patient Cooperation: During the procedure, it is important for the patient to follow instructions. Hasta iş birliği: İşlem sırasında hastanın verilen talimatlara uyması önemlidir.

② Minimal sedation reduces anxiety while keeping the patient conscious. Minimal sedasyon, hastayı bilinçli halde tutarken kaygıyı azaltır.

① Determine the desired level of sedation. Uygulanacak sedasyon düzeyi (minimal, bilinçli, derin vb.) önceden belirlenmelidir.

② Prepare appropriate monitoring and rescue equipment. Hastayı izlemek ve olası acil durumlara müdahale etmek için uygun monitörizasyon ve kurtarma (acil) ekipmanları hazırlanmalıdır.

③ Administer analgesics prior to sedation. Sedasyondan önce ağrı kesiciler (analjezikler) uygulanmalıdır.

④ Titrate the agent to achieve the desired level of sedation. Kullanılan ilaç, istenen sedasyon düzeyine ulaşılana kadar yavaş ve kontrollü şekilde doz artırılarak verilmelidir (titrasyon).

⑤ Observe and monitor until the patient returns to their baseline mental state. Hasta, işlem öncesindeki normal bilinç ve mental durumuna tamamen dönene kadar gözlenmeli ve izlenmelidir.

İşlem yapılırken hastaya sakinleştirici (sedatif) veya bilinçten koparıcı (disosiyatif) ilaçlar verilir. Bu ilaçlar ağrı kesiciyle birlikte ya da tek başına kullanılabilir.

İşlem sırasında sedatif veya disosiyatif ajanların, analjeziklerle birlikte ya da analjezikler olmaksızın uygulanması yoluyla hastanın rahatsızlık hissetmesini önlerken, kardiyorespiratuvar fonksiyonların korunmasına izin veren bir sedasyon durumudur.

Pay attention to the beginning of sections

Good hints for reading

The aftermath of COVID-19 caused inflation to happen with many goods and services. Im sure it also affected insurance plans. How would a reform of health care act on or address the constant inflation and higher probability of low income for many families to make health care affordable? It looks like an issue that continues without an end unless inflation is stopped. But how would inflation stop? Until we "catch up" from what the economy lost during COVID-19?

I agree with this portion of the text. Whenever someone heard a person died because of COVID-19, they simply thought it was because of the disease itself. However, people didn't normally take other factors into account like the financial need to provide care or receive care, underlying health issues of the person that COVID-19 simply made worse, or even the lack of health care within ones residential area. It wasn't simply because of COVID-19. I wonder what the other common health issue was for those who had COVID-19 and passed away?

This section introduces the main research methods medical sociologists use to gather data about health, illness, and health care.

The researcher must carefully plan how to test the hypothesis, including deciding who to study, how to choose participants, and what type of data to collect.

This explains that research begins with theory, which leads to hypotheses. Hypotheses are specific predictions that can be tested using data, making them central to the scientific research process.

Support from major research institutions demonstrates that medical sociology was viewed as valuable and credible. This funding helped integrate sociological research into medical and mental health studies.

This sentence defines medical sociology. It explains that the field uses sociological theories and methods to study health and disease rather than viewing them as purely medical or biological issues.

The author emphasizes that vaccine skepticism follows clear social patterns. Factors like income, education level, and living in rural areas influence whether someone believes vaccines are unsafe, showing how inequality affects health beliefs.

This section explains that people often try to understand health outcomes by looking only at personal choices or medical conditions. While these factors matter, the author argues that focusing only on individuals ignores the broader social forces that shape those experiences.

Includes healthcare overload + people avoiding care.

Death numbers underestimate the real impact.

Researched forms a hypothesis, designs a study, draws conclusions from data

As healthcare becomes more expensive, adequate healthcare is harder to maintain

Brought recognition to the field

Health insurance became increasingly complex

Increased interest in psychosociological basis

Shifted focus from immunology to social conditions

Someone’s opinions and experiences with covid are highly based on where they live, work, and who they are surrounded by.

Sociology definition

Deaths from covid were not very widespread and were more prevalent in specific age and race groups.

This is interesting to me because it shows how even though King was a hallmark of the civil rights movement, it was his very dealth that propelled the movement further.

New term Discourse Communtiy

The first step entering your academic discourse community.

Chapter 2: Discourse Communities and Conventions Discourse Communities and Conventions A discourse community is a group of people who share basic values and assumptions and ways of communicating their goals. In the academic world, discourse communities are defined by field and subfield.

https://www.reddit.com/r/typewriters/comments/1qej21g/bulk_ribbon_source/

We are a small typewriter shop based in the Netherlands.

Usually we would order bulk ribbon from USA and pay 100+ euro for shipping costs and duties. The shipping mostly cost us more than the product itself and made it much harder for us financially.

We would even combine orders with other local typewriter shops to cut the costs down.

So we now found an alternative. Make it ourselves.

After much testing we have found the perfect ink saturation. No bleeding. No oversaturation. Plus the ribbon should last 3-5 years when properly stored.

We are happy to now offer 320m cotton reels (black) for other typewriter enthusiasts and shops based in Europe (we do ship worldwide, it is just much more affordable to buy local).

A ribbon can make or break your typing. I really dislike light print or oversaturated ribbons. So zero compromise in quality.

Comme énoncé dans le commentaire plus haut, je propose de supprimer cette note de bas page dont la référence est déjà énoncée à l'intérieur du texte.

Writing is iterative and each draft should be adjusted for audience clarity.

Editing is not just grammar, it’s also about ensuring the reader can follow and use your info too.

Different readers need different explanations.

Also Identify the audience type before writing to know depth, vocabulary, and examples.

Always keep readers’ needs and background in mind.

I chose this part because it helps explain that tests and assignments affect students emotionally, not just academically. It helps me understand how students feel about assessments, and that can change their confidence, effort, and motivation. This makes sense to me; then again, we are still students.

I chose this part because it explains why classroom assessments are so important compared to other types of measurements. This helped me realize that assessments are not only about collecting data from our students, but about motivating our students and helping them succeed. This shows that what teachers do in the classroom has a big impact on student success.

I chose this part because it connects teaching to different types of assessments. It helped me confirm that assessment is not just done with a test at the end of a unit, but something that happens before, during, and after lessons. This makes sense because teachers need different kinds of information at different times. Every student is different and learns differently, so it is important to be able to reach all of your students.

Many are intimidated. Even experienced writers have to seek help and support.

Something I need to learn.

Think critically before acting out of emotion, avoid sending harsh messages.

Tutoring is available

This textbook will cover ways to communicate effectively as you develop insight into your own style, writing process, grammatical choices, and rhetorical situations.

In the context of this fic, this sentence exists as in universe mantra regarding Technoblade and his immortality. A fan of the source material however would recognize this as Technoblade's (the content creator) signature catchphrase, which originally was coined in regards to his prowess in Minecraft PVP (Player vs Player) mini games.

A few years after this fic was posted controversies came out regarding certain content creators that directly inspired some of these characters. While the characters in this fic are for the most part detached from the source material, this may alter the extent that a newer audience may be able to connect with this fic.

'In the context of academia, neoliberalism is an ideology that shifts the primary purpose of universities from serving the public good (societal well-being, democratic citizenship, social equity) to providing private benefits to individuals. ' (Google search results)

on of the authors of the RMP textbook!

https://web.archive.org/web/20260116145616/https://world.hey.com/martijnaslander/emoji-zijn-goed-voor-je-brein-de-meeste-mensen-laten-dat-flink-onbenut-5c1119fe

[[Martijn Aslander p]] on the use of emoji in pkm

Andries Lohmeijer PE1BMC https://pe1bmc.nl doet een sessie over antennes op Meetkoppel. Workshop to complement the lecture

Andries Lohmeijer PE1BMC https://pe1bmc.nl doet een sessie over antennes op Meetkoppel

In this statement, he explains that happiness is found more in those who live in satisfaction other than working all the time. It interesting how different cultures measure success in their own specific way. For example, it seems like the Gaspesian man would take contentment and "comfort" over possessions gained. It would be interesting to see how different European culture would be if they practiced a culture this man is describing.

This Gaspesian man makes a great point. What is the purpose of voyaging out and risking life to explore when they have all the resources they need? He explains the "convenience" of it all. Every human contains some sort of curiosity. I have to wonder, how would the world look like if that curiosity was avoided. What if humans choose comfort over curiosity and didn't go out and explore?

The importance within this passage stresses how the settlers have to be "knit together." Sticking together in hardships and holding peace creates the best path for a community. This can be applied to our everyday lives and relationships because when voices are heard, there is more respect created. I was wondering how different the community would be if they didn't stick together through the hardships. What if families were more self-centered and independent?

This was a very interesting statement that Columbus gave. It seems like he only is looking at the natives in a way that is dismissing their culture. Especially in this quote, "They should be good servants and intelligent," this shows the reader how he saw the natives. I am just wondering if history would look different today if the culture of the natives was held higher by the Europeans? It seems like Columbus is only eyeing the natives for usefulness, like serving the people of Europe.

I found it very interesting that a small Water-beetle was said to create the earth. This is very intriguing because whenever I think of a creator in my opinion, I think of a powerful figure, not a water-beetle. The persistence of forming the earth with "Soft mud," was very inspiring because this shows the beetle's willingness and persistence. Even though the beetle was little and not necessarily the strongest creature, little by little, he created an island which was called earth. I wonder what compelled the Cherokee to believe a water-beetle created the earth. I love how this story shows how a small creature can make a big impact though.

After reading through this creation story of the Salinan Indians, I learned that the Bald Eagle held high authority over all the animals. He brought life and creation into the world, for example, he took, "Clay and modeled the figure of a man." I noticed that the humans were created by materials on earth, like clay. I found this really interesting because humans are born, but according to this creation story and their culture, it shows how humans are connected to the earth. I wonder if the Salinan Indians treated life on earth or earth's materials differently due to their creation story.

Unfortunately, this can be a lot of work. In fact, it is often much more work than building and training a model.

There seems to be a lot of tell-tell signs of over and under fitting depending on the different scores. Try and understand how these work.

How do we know?

Christopher Houston Carson (December 24, 1809 – May 23, 1868) was an American frontiersman, fur trapper, wilderness guide, Indian agent and U.S. Army officer. He became an American frontier legend in his own lifetime through biographies and news articles; exaggerated versions of his exploits were the subject of dime novels. His understated nature belied confirmed reports of his fearlessness, combat skills, tenacity, as well as profound effect on the westward expansion of the United States.

https://en.wikipedia.org/wiki/Kit_Carson

Daniel Boone (November 2 [O.S. October 22] 1734 – September 26, 1820) was an American pioneer and frontiersman whose exploits made him one of the first folk heroes of the United States. He became famous for his exploration and settlement of Kentucky, which was then beyond the western borders of the Thirteen Colonies. In 1775, Boone founded the Wilderness Road through the Cumberland Gap and into Kentucky, in the face of resistance from Native Americans. He founded Boonesborough, one of the first English-speaking settlements west of the Appalachian Mountains.

https://en.wikipedia.org/wiki/Daniel_Boone

August Gottlieb Spangenberg (15 July 1704 – 18 September 1792) was a German theologian, minister, and bishop of the Moravian Church. As successor to Nicolaus Zinzendorf as bishop of the Moravian Church, he helped develop and lead international Moravian missions in colonial-era Province of Pennsylvania and stabilized Moravian theology and organization.

https://en.wikipedia.org/wiki/August_Gottlieb_Spangenberg

The Lewis and Clark Expedition, also known as the Corps of Discovery Expedition, was the United States expedition to cross the newly acquired western portion of the country after the Louisiana Purchase. The Corps of Discovery was a select group of U.S. Army and civilian volunteers under the command of Captain Meriwether Lewis and his close friend Second Lieutenant William Clark. Clark, along with 30 others, set out from Camp Dubois (Camp Wood), Illinois, on May 14, 1804, met Lewis and ten other members of the group in St. Charles, Missouri, then went up the Missouri River. The expedition crossed the Continental Divide of the Americas near the Lemhi Pass, eventually coming to the Columbia River, and the Pacific Ocean in 1805. The return voyage began on March 23, 1806, at Fort Clatsop, Oregon, ending six months later on September 23.

https://en.wikipedia.org/wiki/Lewis_and_Clark_Expedition

The War of 1812 was fought by the United States and its allies against the United Kingdom and its allies in North America. It began when the United States declared war on Britain on 18 June 1812. Although peace terms were agreed upon in the December 1814 Treaty of Ghent, the war did not officially end until the peace treaty was ratified by the United States Congress on 17 February 1815.

https://en.wikipedia.org/wiki/War_of_1812

The Albany Congress (June 19 – July 11, 1754), also known as the Albany Convention of 1754, was a meeting of representatives sent by the legislatures of seven of the British colonies in British America: Connecticut, Maryland, Massachusetts, New Hampshire, New York, Pennsylvania, and Rhode Island. Those not in attendance included Newfoundland, Nova Scotia, New Jersey, Virginia, Georgia, North Carolina, and South Carolina. Representatives met daily at the City Hall in Albany, New York, from June 19 to July 11, 1754, to discuss better relations with the Native American tribes and common defensive measures against the French threat from Canada in the opening stage of the French and Indian War, the North American front of the Seven Years' War between Great Britain and France.

https://en.wikipedia.org/wiki/Albany_Congress

René-Robert Cavelier, Sieur de La Salle (November 22, 1643 – March 19, 1687), was a French explorer and fur trader in North America. He explored the Great Lakes region of the United States and Canada, and the Mississippi River. He is best known for an early 1682 expedition in which he canoed the lower Mississippi River from the mouth of the Illinois River to the Gulf of Mexico.

https://en.wikipedia.org/wiki/Ren%C3%A9-Robert_Cavelier,_Sieur_de_La_Salle

South Pass is a route across the Continental Divide, in the Rocky Mountains in southwestern Wyoming. Though it approaches a mile and a half high, South Pass is the lowest point on the Continental Divide between the Central and Southern Rocky Mountains. The passes furnish a natural crossing point of the Rockies. The historic pass became the route for emigrants on the Oregon, California, and Mormon trails to the West during the 19th century. It was designated as a U.S. National Historic Landmark on January 20, 1961.

https://en.wikipedia.org/wiki/South_Pass_(Wyoming)

The Cumberland Gap is a pass in the eastern United States through the long ridge of the Cumberland Mountains, within the Appalachian Mountains and near the tripoint of Kentucky, Virginia, and Tennessee. At an elevation of 1,631 feet (497 m) above sea level, it is famous in American colonial history for its role as a key passageway through the lower central Appalachians.

https://en.wikipedia.org/wiki/Cumberland_Gap

Achille Loria (2 March 1857 – 6 November 1943) was an Italian political economist. He was educated at the lyceum of his native city and the universities of Bologna, Pavia, Rome, Berlin, and London and graduated at the University of Bologna (1877).

https://en.wikipedia.org/wiki/Achille_Loria#:~:text=Achille%20Loria%20(2%20March%201857,was%20an%20Italian%20political%20economist.&text=He%20was%20educated%20at%20the,University%20of%20Bologna%20(1877).

America's first transcontinental railroad (known originally as the "Pacific Railroad" and later as the "Overland Route") was a 1,911-mile (3,075 km) continuous railroad line built between 1863 and 1869 that connected the existing eastern U.S. rail network at Council Bluffs, Iowa, with the Pacific coast at the Oakland Long Wharf on San Francisco Bay.

https://en.wikipedia.org/wiki/First_transcontinental_railroad

Francis Joseph Grund (September 19, 1805 – September 29, 1863) was a Bohemian-born American journalist and author who wrote such works as The Americans in Their Moral, Social, and Political Relations (1837).

https://en.wikipedia.org/wiki/Francis_Grund

American Fur Company, enterprise incorporated in New York state (April 6, 1808) by John Jacob Astor, which dominated the fur trade of the central and western United States during the first third of the 19th century. The company absorbed or crushed its rivals during its search for furs in the Great Lakes region, Missouri River valley, Rocky Mountains, and Oregon. Explorations by the firm’s traders and trappers, directed chiefly from its office in St. Louis, did much to prepare the frontier for settlement.

https://www.britannica.com/money/American-Fur-Company

The 1820 United States census was the fourth census conducted in the United States. It was conducted on August 7, 1820. The 1820 census included six new states: Louisiana, Indiana, Mississippi, Illinois, Alabama and Maine. The total population was determined to be 9,638,453, of which 1,538,022 were slaves.

https://en.wikipedia.org/wiki/1820_United_States_census

The Royal Proclamation of 1763 was issued by King George III of Great Britain on 7 October 1763. It followed the Treaty of Paris (1763), which formally ended the Seven Years' War and transferred French territory in North America to Great Britain.[1] The proclamation at least temporarily forbade all new settlements west of a line drawn along the Appalachian Mountains, which was delineated as an Indian Reserve.[2] Exclusion from the vast region of Trans-Appalachia created discontent between Britain and colonial land speculators and potential settlers.

https://en.wikipedia.org/wiki/Royal_Proclamation_of_1763

The Knights of the Golden Horseshoe Expedition, also known as the Transmontane Expedition,[1] took place in 1716 in the British Colony of Virginia. The Royal Governor and a number of prominent citizens traveled westward, across the Blue Ridge Mountains on an exploratory expedition.

https://en.wikipedia.org/wiki/Knights_of_the_Golden_Horseshoe_Expedition

Major-General Alexander Spotswood (12 December 1676 – 7 June 1740) was a British army officer, explorer and colonial administrator who served as the governor of Virginia from 1710 to 1722. After an unsatisfactory military career, in 1710 he was appointed as Virginia's governor, a post he held for twelve years. During that period, Spotswood engaged in the exploration of the territories beyond the western border, of which he was the first to see the economic potentials.

https://en.wikipedia.org/wiki/Alexander_Spotswood

Hermann Eduard von Holst (June 19, 1841 – January 20, 1904) was a German-American historian and author. Von Holst emigrated to the United States and wrote extensively on the Constitution of the United States, largely from an anti-slavery perspective.

https://en.wikipedia.org/wiki/Hermann_Eduard_von_Holst

John C. Calhoun (born March 18, 1782, Abbeville district, South Carolina, U.S.—died March 31, 1850, Washington, D.C.) was an American political leader who was a congressman, the secretary of war, the seventh vice president (1825–32), a senator, and the secretary of state of the United States.

https://www.britannica.com/biography/John-C-Calhoun

The 1790 United States census was the first United States census. It recorded the population of the whole United States as of Census Day, August 2, 1790, as mandated by Article 1, Section 2, of the Constitution and applicable laws. In the first census, the population of the United States was enumerated to be 3,929,214 inhabitants.

https://en.wikipedia.org/wiki/1790_United_States_census

On leaving document as object behind, to be replaced by a diff perception of knowledge objects. Have to read in more detail to see what it actually posits.

• [ ] return

EC has communicated that Greenland as part of the Kingdom of Denmark is covered by the mutual defence clause in the EU Treaty of Lisbon

eLife Assessment

This work identifies a novel, conserved link between glycolysis and sulfur metabolism that governs fungal morphogenesis and virulence. The compelling evidence, integrating multiple approaches, provides an important conceptual advance. A future mechanistic dissection of how sulfur metabolites interface with known pathways is encouraged.

Reviewer #1 (Public review):

Summary:

Fungal survival and pathogenicity rely on the ability to undergo reversible morphological transitions, which is often linked to nutrient availability. In this study, the authors uncover a conserved connection between glycolytic activity and sulfur amino acid biosynthesis that drives morphogenesis in two fungal model systems. By disentangling this process from canonical cAMP signaling, the authors identify a new metabolic axis that integrates central carbon metabolism with developmental plasticity and virulence.

Strengths:

The study integrates different experimental approaches, including genetic, biochemical, transcriptomic and morphological analyses and convincingly demonstrates that perturbations in glycolysis alters sulfur metabolic pathways and thus impacts pseudohyphal and hyphal differentiation. Overall, this work offers new and important insights into how metabolic fluxes are intertwined with fungal developmental programs and therefore opens new perspectives to investigate morphological transitioning in fungi.

Importantly, in the revised version the authors now substantiate the transcriptomic findings by RT-qPCR analyses in the pfk1ΔΔ and adh1ΔΔ strains, demonstrating that genetic disruption of glycolytic flux generally mirrors the effects of 2-deoxyglucose treatment. The manuscript's discussion has also been strengthened by explicitly addressing why cysteine and methionine differ in their ability to rescue filamentation in S. cerevisiae versus C. albicans, highlighting species-specific differences in sulfur uptake and transsulfuration pathways.

Overall, this revised manuscript provides compelling evidence for a previously unrecognized coupling between glycolysis and sulfur metabolism that shapes fungal morphogenesis and virulence. It opens new perspectives on metabolic control of fungal development and raises interesting mechanistic questions for future work.

Comments on revisions:

The authors have incorporated all of my suggested changes and addressed all raised concerns.

Reviewer #2 (Public review):

Summary:

The manuscript investigates the interplay between glycolysis and sulfur metabolism in regulating fungal morphogenesis and virulence. Using both Saccharomyces cerevisiae and Candida albicans, the authors demonstrate that glycolytic flux is essential for morphogenesis under nitrogen-limiting conditions, acting independently of the established cAMP-PKA pathway. Transcriptomic and genetic analyses reveal that glycolysis influences the de novo biosynthesis of sulfur-containing amino acids, specifically cysteine and methionine. Notably, supplementation with sulfur sources restores morphogenetic and virulence defects in glycolysis-deficient mutants, thereby linking core carbon metabolism with sulfur assimilation and fungal pathogenicity.

Strengths:

The work identifies a previously uncharacterized link between glycolysis and sulfur metabolism in fungi, bridging metabolic and morphogenetic regulation which is an important conceptual advance and fungal pathogenicity. Demonstrating that adding cysteine supplementation rescues virulence defects in animal model connects basic metabolism to infection outcomes that add on biomedical importance.

Comments on revisions:

The authors have sufficiently addressed my concern and provided a clear justification for their proposed model including the limitations of performing the mechanistic assays at this stage. I am satisfied with the response and have no further comments

Reviewer #3 (Public review):

This study investigates the connection between glycolysis and the biosynthesis of sulfur-containing amino acids in controlling fungal morphogenesis, using Saccharomyces cerevisiae and C. albicans as model organisms. The authors identify a conserved metabolic axis that integrates glycolysis with cysteine/methionine biosynthetic pathways to influence morphological transitions. This work broadens the current understanding of fungal morphogenesis, which has largely focused on gene regulatory networks and cAMP-dependent signaling pathways, by emphasizing the contribution of metabolic control mechanisms.

Strengths:

The delineation of how glycolytic flux regulates fungal morphogenesis through a cAMP-independent mechanism is an advancement. The coupling of glycolysis with the de novo biosynthesis of sulfur-containing amino acids, a requirement for morphogenesis, introduces a novel and unexpected layer of regulation.

Demonstrating this mechanism in both S. cerevisiae and C. albicans strengthens the argument for its evolutionary conservation and biological importance.

The ability to rescue the morphogenesis defect through supplementation of sulfur-containing amino acids provides a functional validation.

Weaknesses:

cAMP addition rescued the pseudohyphal differentiation defect exhibited by the ΔΔgpa2 strain. More clarity is needed on how this mechanism is mechanistically distinct from the metabolic control - whether cAMP acts in parallel or downstream to sulfur-containing amino acids biosynthesis has to be characterized. Supplementation of cysteine and methionine bypasses glycolytic regulation; the link between these amino acids and their role in fungal morphogenesis is not completely characterized.

The demonstrated link between glycolysis and sulfur amino acid biosynthesis, along with its implications for virulence in C. albicans, is important for understanding fungal adaptation, as mentioned in the article; however, the downstream effects of Met4 activation were not fully characterized. How does Cysteine/Methionine rescue morphogenesis? The author's response figure 1 shows that there are no significant transcriptional changes in the expression of cAMP-PKA pathway-associated genes, which alone could not completely explain the role of gpa2 in morphogenesis, because exogenous cAMP can restore pseudohyphal differentiation in the ΔΔgpa2 background (Revised Fig. 1L). This implies that gpa2's function in morphogenesis is an additional, or possibly a metabolic or post-transcriptional, layer of regulation, and its connection to sulfur-containing amino acids remains to be elucidated.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:

Fungal survival and pathogenicity rely on the ability to undergo reversible morphological transitions, which are often linked to nutrient availability. In this study, the authors uncover a conserved connection between glycolytic activity and sulfur amino acid biosynthesis that drives morphogenesis in two fungal model systems. By disentangling this process from canonical cAMP signaling, the authors identify a new metabolic axis that integrates central carbon metabolism with developmental plasticity and virulence.

Strengths:

The study integrates different experimental approaches, including genetic, biochemical, transcriptomic, and morphological analyses, and convincingly demonstrates that perturbations in glycolysis alter sulfur metabolic pathways and thus impact pseudohyphal and hyphal differentiation. Overall, this work offers new and important insights into how metabolic fluxes are intertwined with fungal developmental programs and therefore opens new perspectives to investigate morphological transitioning in fungi.

We thank the reviewer for finding this study to be of importance and for appreciating our multipronged approach to substantiate our finding that perturbations in glycolysis alter sulfur metabolism and thus impact pseudohyphal and hyphal differentiation in fungi.

Weaknesses:

A few aspects could be improved to strengthen the conclusions. Firstly, the striking transcriptomic changes observed upon 2DG treatment should be analyzed in S. cerevisiae adh1 and pfk1 deletion strains, for instance, through qPCR or western blot analyses of sulfur metabolism genes, to confirm that observed changes in 2DG conditions mirror those seen in genetic mutants. Secondly, differences between methionine and cysteine in their ability to rescue the mutant phenotype in both species are not mentioned, nor discussed in more detail. This is especially important as there seem to be differences between S. cerevisiae and C. albicans, which might point to subtle but specific metabolic adaptations.

The authors are also encouraged to refine several figure elements for clarity and comparability (e.g., harmonized axes in bar plots), condense the discussion to emphasize the conceptual advances over a summary of the results, and shorten figure legends.

We are grateful for this valuable and constructive feedback, and we agree with the reviewer on the necessity of performing RT-qPCR analysis of sulfur metabolism genes in ∆∆pfk1 and ∆∆adh1 strains of S. cerevisiae to validate our RNA-Seq results using 2DG. We have performed this experiment, and our results show that several genes involved in the de novo biosynthesis of sulfur-containing amino acids are downregulated in both the ∆∆pfk1 and ∆∆adh1 strains, corroborating the downregulation of sulfur metabolism genes in the 2DG treated samples. This new data is now included in the revised manuscript as Supplementary Figure 2C. 

Furthermore, we acknowledge the reviewer’s point regarding the significance of comparing the differences in the ability of methionine and cysteine to rescue filamentation defects exhibited by the mutants, between S. cerevisiae and C. albicans. The observed differences between S. cerevisiae and C. albicans likely highlight species-specific metabolic adaptations within the sulfur assimilation pathway.  While both yeasts employ the transsulfuration pathway to interconvert these sulfur-containing amino acids, the precise regulatory points including the specific enzymes, their compartmentalization, and transcriptional control are not identical. For instance, differences in the feedback inhibition mechanisms or the expression levels of key transsulfuration enzymes between S. cerevisiae and C. albicans could explain the variations in the phenotypic rescue experiments (Chebaro et al., 2017; Lombardi et al., 2024; Rouillon et al., 2000; Shrivastava et al., 2021; Thomas and Surdin-Kerjan, 1997). Furthermore, the species-specific differences in amino acid transport systems (permeases) adds another layer of complexity. S. cerevisiae primarily uses multiple, low-affinity permeases for cysteine transport (Gap1, Bap2, Bap3, Tat1, Tat2, Agp1, Gnp1, Yct1), while relying on a limited set of high-affinity transporters (like Mup1) for methionine transport, with the added complexity that its methionine transporters can also transport cysteine (Düring-Olsen et al., 1999; Huang et al., 2017; Kosugi et al., 2001; Menant et al., 2006). In contrast, C. albicans utilizes a high-affinity transporters for the uptake of both amino acids, employing Cyn1 specifically for cysteine and Mup1 for methionine, indicating a greater reliance on dedicated transport mechanisms for these sulfur-containing molecules in the pathogenic yeast (Schrevens et al., 2018; Yadav and Bachhawat, 2011). A combination of the aforesaid factors could be the potential reason for the differences in the ability of cysteine and methionine to rescue filamentation in S. cerevisiae and C. albicans.

Finally, we have enhanced the quantitative rigor and clarity of the data presentation in the revised manuscript by implementing Y-axis uniformity across all relevant bar graphs to facilitate a more robust and direct comparative analysis. We have also condensed the discussion to emphasize the conceptual advances and have shortened the figure legends as per the reviewer suggestions

Reviewer #2 (Public review):

Summary:

This manuscript investigates the interplay between glycolysis and sulfur metabolism in regulating fungal morphogenesis and virulence. Using both Saccharomyces cerevisiae and Candida albicans, the authors demonstrate that glycolytic flux is essential for morphogenesis under nitrogen-limiting conditions, acting independently of the established cAMP-PKA pathway. Transcriptomic and genetic analyses reveal that glycolysis influences the de novo biosynthesis of sulfur-containing amino acids, specifically cysteine and methionine. Notably, supplementation with sulfur sources restores morphogenetic and virulence defects in glycolysis-deficient mutants, thereby linking core carbon metabolism with sulfur assimilation and fungal pathogenicity.

Strengths:

The work identifies a previously uncharacterized link between glycolysis and sulfur metabolism in fungi, bridging metabolic and morphogenetic regulation, which is an important conceptual advance and fungal pathogenicity. Demonstrating that adding cysteine supplementation rescues virulence defects in animal models connects basic metabolism to infection outcomes, which adds to biomedical importance.

We would like to thank the reviewer for the positive comments on our work. We are pleased that they recognize the novel metabolic link between glycolysis and sulfur metabolism as a key conceptual advance in fungal morphogenesis. 

Weaknesses:

The proposed model that glycolytic flux modulates Met30 activity post-translationally remains speculative. While data support Met4 stabilization in met30 deletion strains, the mechanism of Met30 modulation by glycolysis is not demonstrated.

We thank the reviewer for this valuable feedback. The activity of the SCF^Met30 E3 ubiquitin ligase, mediated by the F box protein Met30, is dynamically regulated through both proteolytic degradation and its dissociation from the SCF complex, to coordinate sulfur metabolism and cell cycle progression (Smothers et al., 2000; Yen et al., 2005). Our transcriptomic (RNA-seq analysis) and protein expression analysis (Fig. 3J) confirms that Met30 expression is not differentially regulated in the presence of 2DG, effectively eliminating changes in synthesis or SCF^Met30 proteasomal degradation as the dominant regulatory mechanism. This observation is consistent with the established paradigm wherein stress signals, such as cadmium (Cd²⁺) exposure, rapidly inactivates the SCF^Met30 E3 ubiquitin ligase via the dissociation of Met30 from the Skp1 subunit of the SCF complex (Lauinger et al., 2024; Yen et al., 2005). We therefore propose that active glycolytic flux modulates SCF^Met30 activity post-translationally, specifically by triggering Met30 detachment from the SCF complex. This mechanism would stabilize the primary substrate, the transcription factor Met4, thus promoting the biosynthesis of sulfur-containing amino acids. Mechanistic validation of this hypothesis, particularly the assessment of Met30 dissociation from the SCF^Met30 complex via immunoprecipitation (IP), is technically challenging. Since these experiments will involve isolation of cells from colonies undergoing pseudohyphal differentiation, on solid media (given that pseudohyphal differentiation does not occur in liquid media that is limiting for nitrogen (Gancedo, 2001; Gimeno et al., 1992)), current cell yields (OD₆₀₀≈1 from ≈80-100 colonies) are significantly below the amount of cells that is needed to obtain the required amount of total protein concentration, for standard pull down assays (OD<Sub>600</sub>≈600-800 is required to achieve 1-2 mg/ml of total protein which is the standard requirement for pull down protocols in S. cerevisiae (Lauinger et al., 2024)).

Given that the primary objective of our study is to establish the novel regulatory link between glycolysis and sulfur metabolism in the context of fungal morphogenesis, we would like to explore these crucial mechanistic details, in depth, in a subsequent study.

Reviewer #3 (Public review):

This study investigates the connection between glycolysis and the biosynthesis of sulfur-containing amino acids in controlling fungal morphogenesis, using Saccharomyces cerevisiae and C. albicans as model organisms. The authors identify a conserved metabolic axis that integrates glycolysis with cysteine/methionine biosynthetic pathways to influence morphological transitions. This work broadens the current understanding of fungal morphogenesis, which has largely focused on gene regulatory networks and cAMP-dependent signaling pathways, by emphasizing the contribution of metabolic control mechanisms. However, despite the novel conceptual framework, the study provides limited mechanistic characterization of how the sulfur metabolism and glycolysis blockade directly drive morphological outcomes. In particular, the rationale for selecting specific gene deletions, such as Met32 (and not Met4), or the Met30 deletion used to probe this pathway, is not clearly explained, making it difficult to assess whether these targets comprehensively represent the metabolic nodes proposed to be critical. Further supportive data and experimental validation would strengthen the claims on connections between glycolysis, sulfur amino acid metabolism, and virulence.

Strengths:

(1) The delineation of how glycolytic flux regulates fungal morphogenesis through a cAMP-independent mechanism is a significant advancement. The coupling of glycolysis with the de novo biosynthesis of sulfur-containing amino acids, a requirement for morphogenesis, introduces a novel and unexpected layer of regulation.

(2) Demonstrating this mechanism in both S. cerevisiae and C. albicans strengthens the argument for its evolutionary conservation and biological importance.

(3) The ability to rescue the morphogenesis defect through exogenous supplementation of sulfur-containing amino acids provides functional validation.

(4) The findings from the murine Pfk1-deficient model underscore the clinical significance of metabolic pathways in fungal infections.

We are grateful for this comprehensive and insightful summary of our work. We deeply appreciate the reviewer's recognition of the key conceptual breakthroughs regarding the metabolic regulation of fungal morphogenesis and the clinical relevance of our findings.

Weaknesses:

(1) While the link between glycolysis and sulfur amino acid biosynthesis is established via transcriptomic and proteomic analysis, the specific regulation connecting these pathways via Met30 remains to be elucidated. For example, what are the expression and protein levels of Met30 in the initial analysis from Figure 2? How specific is this effect on Met30 in anaerobic versus aerobic glycolysis, especially when the pentose phosphate pathway is involved in the growth of the cells when glycolysis is perturbed ?

We are grateful for the insightful feedback provided by the reviewer. S. cerevisiae is a Crabtree positive organism that primarily uses anaerobic glycolysis to metabolize glucose, under glucose-replete conditions (Barford and Hall, 1979; De Deken, 1966) and our pseudohyphal differentiation assays are performed in glucose-rich conditions (Gimeno et al., 1992). Furthermore, perturbation of glycolysis is known to induce compensatory upregulation of the Pentose Phosphate Pathway (PPP) (Ralser et al., 2007) and we have also observed the upregulation of the gene that encodes for transketolase-1 (Tkl1), a key enzyme in the PPP, in our RNA-seq data. Importantly, our transcriptomic (RNA-seq analysis) and protein expression analysis (Fig. 3J) confirms that Met30 expression is not differentially regulated in the presence of 2DG, effectively eliminating changes in synthesis or SCF^Met30 proteasomal degradation as the dominant regulatory mechanism.  This aligns with the established paradigm wherein stress signals, such as cadmium (Cd²⁺) exposure, rapidly inactivates SCF^Met30 E3 ubiquitin ligase via Met30 dissociation from the Skp1 subunit of the complex (Lauinger et al., 2024; Yen et al., 2005). We therefore propose that active glycolytic flux modulates SCF^Met30 activity post-translationally, specifically by triggering Met30 detachment from the SCF complex. This mechanism would stabilize the primary substrate, the transcription factor Met4, thus promoting the biosynthesis of sulfur-containing amino acids. Further experiments are required to delineate the specific role of pentose phosphate pathway in the aforesaid proposed regulation of the Met30 activity under glycolysis perturbation and this will be explored in our subsequent study.

(2) Including detailed metabolite profiling could have strengthened the metabolic connection and provided additional insights into intermediate flux changes, i.e., measuring levels of metabolites to check if cysteine or methionine levels are influenced intracellularly. Also, it is expected to see how Met30 deletion could affect cell growth. Data on Met30 deletion and its effect on growth are not included, especially given that a viable heterozygous Met30 strain has been established. Measuring the cysteine or methionine levels using metabolomic analysis would further strengthen the claims in every section.

We are grateful to the reviewer for this constructive feedback. To address the potential impact of met30 deletion on cell growth, we have included new data (Suppl. Fig. 4A) demonstrating that the deletion of a single copy of met30 in diploid S. cerevisiae does not compromise overall cell growth under nitrogen-limiting conditions as the ∆met30 strain grows similar to the wild-type strain. 

Our pseudohyphal/hyphal differentiation assays show that the defects induced by glycolytic perturbation is fully rescued by the exogenous supplementation of sulfur-containing amino acids, cysteine or methionine. Since these data conclusively demonstrate that the primary metabolic limitation caused by the perturbation of glycolysis, which leads to filamentation defects is sulfur metabolism, we posit that performing comprehensive metabolic profiling would primarily reconfirm the aforesaid results. We believe that our in vitro and in vivo sulfur add-back experiments sufficiently substantiate the novel regulatory metabolic link between glycolysis and sulfur metabolism.

(3) In comparison with the previous bioRxiv (doi: https://doi.org/10.1101/2025.05.14.654021) of this article in May 2025 to the recent bioRxiv of this article (doi: https://doi.org/10.1101/2025.05.14.654021), there have been some changes, and Met30 deletion has been recently included, and the chemical perturbation of glycolysis has been added as new data. Although the changes incorporated in the recent version of the article improved the illustration of the hypothesis in Figure 6, which connects glycolysis to Sulfur metabolism, the gene expression and protein levels of all genes involved in the illustrated hypothesis are not consistently shown. For example, in some cases, the Met4 expression is not shown (Figure 4), and the Met30 expression is not shown during profiling (gene expression or protein levels) throughout the manuscript. Lack of consistency in profiling the same set of key genes makes understanding more complicated.

We thank the reviewer for this feedback which helps us to clarify the scope of our transcriptomic analysis. Our decision to focus our RT-qPCR experiments on downstream targets, while excluding met4 and met30 from the RT-qPCR analysis, is based on their known regulatory mechanisms. Met4 activity is predominantly regulated by post-translational ubiquitination by the SCFMet30 complex followed by its degradation (Rouillon et al., 2000; Shrivastava et al., 2021; Smothers et al., 2000)  while Met30 activity is primarily regulated by its auto-degradation or its dissociation from the SCFMet30 complex (Lauinger et al., 2024; Smothers et al., 2000; Yen et al., 2005).  Consistent with this, our RNA-Seq results indicate that neither met4 nor met30 transcripts are differentially expressed, in response to 2DG addition. For all our RT-qPCR analysis in S. cerevisiae and C. albicans, we have consistently used the same set of sulfur metabolism genes and these include met32, met3, met5, met10 and met17. Our data on protein expression analysis of Met30 in S. cerevisiae (Fig. 3J) confirms that Met30 expression is not differentially regulated in the presence of 2DG, effectively eliminating changes in synthesis or SCFMet30 proteasomal degradation as the dominant regulatory mechanism.

(4) The demonstrated link between glycolysis and sulfur amino acid biosynthesis, along with its implications for virulence in C. albicans, is important for understanding fungal adaptation, as mentioned in the article; however, the Met4 activation was not fully characterized, nor were the data presented when virulence was assessed in Figure 4. Why is Met4 not included in Figure 4D and I? Especially, according to Figure 6, Met4 activation is crucial and guides the differences between glycolysis-active and inactive conditions.

We thank the reviewer for their input. As the Met4 transcription factor in C. albicans is primarily regulated post-translationally through its degradation and inactivation by the SCFMet30 E3 ubiquitin ligase complex (Shrivastava et al., 2021), we opted to monitor the transcriptional status of downstream targets of Met4 (i.e., genes directly regulated by Met4), as these are the genes that exhibit the most direct and functionally relevant transcriptional changes in response to the altered Met4 levels.

(5) Similarly, the rationale behind selecting Met32 for characterizing sulfur metabolism is unclear. Deletion of Met32 resulted in a significant reduction in pseudohyphal differentiation; why is this attributed only to Met32? What happens if Met4 is deleted? It is not justified why Met32, rather than Met4, was chosen. Figure 6 clearly hypothesizes that Met4 activation is the key to the mechanism.

We sincerely thank the reviewer for this insightful query regarding our selection of the met32 for our gene deletion experiments. The choice of ∆∆met32 strain was strategically motivated by its unique phenotypic properties within the de novo biosynthesis of sulfur-containing amino acids pathway. While deletions of most the genes that encode for proteins involved in the de novo biosynthesis of sulfurcontaining amino acids, result in auxotrophy for methionine or cysteine, ∆∆met32 strain does not exhibit this phenotype (Blaiseau et al., 1997). This key distinction is attributed to the functional redundancy provided by the paralogous gene, met31 (Blaiseau et al., 1997). Crucially, given that the deletion of the central transcriptional regulator, met4, results in cysteine/methionine auxotrophy, the use of the ∆∆met32 strain provides an essential, viable experimental model for investigating the role of sulfur metabolism during pseudohyphal differentiation in S. cerevisiae.

(6) The comparative RT-qPCR in Figure 5 did not account for sulfur metabolism genes, whereas it was focused only on virulence and hyphal differentiation. Is there data to support the levels of sulfur metabolism genes?

We thank the reviewer for this feedback. We wish to respectfully clarify that the data pertaining to expression of sulfur metabolism genes in the presence of 2DG or in the ∆∆pfk1 strain in C. albicans are already included and discussed within the manuscript. These results can be found in Figure 4, panels D and I, respectively.

(7) To validate the proposed interlink between sulfur metabolism and virulence, it is recommended that the gene sets (illustrated in Figure 6) be consistently included across all comparative data included throughout the comparisons. Excluding sulfur metabolism genes in Figure 5 prevents the experiment from demonstrating the coordinated role of glycolysis perturbation → sulfur metabolism → virulence. The same is true for other comparisons, where the lack of data on Met30, Met4, etc., makes it hard.to connect the hypothesis. It is also recommended to check the gene expression of other genes related to the cAMP pathway and report them to confirm the cAMP-independent mechanism. For example, gap2 deletion was used to confirm the effects of cAMP supplementation, but the expression of this gene was not assessed in the RNA-seq analysis in Figure 2. It would be beneficial to show the expression of cAMP-related genes to completely confirm that they do not play a role in the claims in Figure 2.

We thank the reviewer for this valuable feedback. The transcriptional analysis of the sulfur metabolism genes in the presence of 2DG and the ∆∆pfk1 strain is shown in Figures 4D and 4I.

Our RNA-seq analysis (Author response image 1) confirms that there is no significant transcriptional change in the expression of cAMP-PKA pathway associated genes (Log2 fold change ≥ 1 for upregulated genes and Log2 fold change ≤ -1 for downregulated genes) in 2DG treated cells compared to the untreated control cells, reinforcing our conclusion that the glycolytic regulation of fungal morphogenesis is mediated through a cAMP-PKA pathway independent mechanism.

Author response image 1.

(8) Although the NAC supplementation study is included in the new version of the article compared to the previous version in BioRxiv (May 2025), the link to sulfur metabolism is not well characterized in Figure 5 and their related datasets. The main focus of the manuscript is to delineate the role of sulfur metabolism; hence, it is anticipated that Figure 5 will include sulfur-related metabolic genes and their links to pfk1 deletion, using RT-PCR measurements as shown for the virulence genes.

We thank the reviewer for this question. The relevant data are indeed present within the current submission. We respectfully direct the reviewer's attention to Figure 4, panels D and I, where the data pertaining to expression of sulfur metabolism genes in the presence of 2DG or in the ∆∆pfk1 strain in C. albicans can be found.

(9) The manuscript would benefit from more information added to the introduction section and literature supports for some of the findings reported earlier, including the role of (i) cAMP-PKA and MAPK pathways, (ii) what is known in the literature that reports about the treatment with 2DG (role of Snf1, HXT1, and HXT3), as well as how gpa2 is involved. Some sentences in the manuscripts are repetitive; it would be beneficial to add more relevant sections to the introduction and discussion to clarify the rationale for gene choices.

We thank the reviewer for this valuable feedback. We have incorporated these changes in our revised manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Line 107: As morphological transitions are indeed a conserved phenomenon across fungal species, hosts & environmental niches, the authors could refer to a few more here (infection structures like appressoria; fruiting bodies, etc.).

We thank the reviewer for this valuable feedback. We have incorporated these changes in our revised manuscript.

Line 119/120: That's a bit misleading in my opinion. Gpr1 acts as a key sensor of external carbon, while Ras proteins control the cAMP pathway as intracellular sensory proteins. That should be stated more clearly. cAMP is the output and not the sensor.

We appreciate the reviewer's detailed attention to this signaling network. We have revised the manuscript to precisely reflect this established signaling hierarchy for maximum clarity.

(2) Line 180: ..differentiation

We thank the reviewer for this valuable feedback. We have incorporated this change in our revised manuscript.

(3) Figure 1 panels C & F. The authors should provide the same scale for all experiments. Otherwise, the interpretation can be difficult. The same applies to the different bar plots in Figure 4. Have the authors quantified pseudohyphal differentiation in the cAMP add-back assays? I agree that the chosen images look convincing, but they don't reflect quantitative analyses.

We thank the reviewer for detailed and constructive feedback. We have changed the Y-axis and made it more uniform to improve the clarity of our data presentation in the revised manuscript.

We have also incorporated the quantitative analysis of the cAMP add-back assays in S. cerevisiae, in Figure 2 Panel L.

(4) Line 367/68: "cysteine or methionine was able to completely rescue". Here, the authors should phrase their wording more carefully. Figure 3C shows the complete rescue of the phenotype qualitatively, but Figure 3D clearly shows that there are differences between the supplementation of cysteine and methionine, with the latter not fully restoring the phenotype.

We sincerely appreciate the reviewer's meticulous attention to the data interpretation. We fully agree that the initial phrasing in lines 367/368 requires adjustment, as Figure 3D establishes a quantitative difference in the efficiency of phenotypic rescue between cysteine and methionine supplementation. We have revised the text to articulate this difference.

(5) Line 568: Here, apparently, the ability to rescue the differentiation phenotype is reversed compared to the experiment with S. cerevisiae. Cysteine only results in ~20% hyphal cells, while methionine restores to wild-type-like hyphal formation. Can the authors comment on where these differences might originate from? Is there a difference in the uptake of cysteine vs. methionine in the two species or consumption rates?

We thank the reviewer for their detailed and constructive feedback. We believe this phenotypic difference can be due to the distinct metabolic prioritization of sulfur amino acids in C. albicans. Methionine is a known trigger for hyphal differentiation in C. albicans and serves as the immediate precursor for the universal methyl donor, S-adenosylmethionine (SAM) (Schrevens et al., 2018). (Kraidlova et al., 2016). The morphological transition to hyphae involves a complex regulatory cascade which requires high rates of methylation, and this requires a rapid and direct conversion of methionine into SAM (Kraidlova et al., 2016; Schrevens et al., 2018). Cysteine, however, must first be converted into methionine via the transsulfuration pathway to produce SAM, making it metabolically less efficient for these aforesaid processes.

Reviewer #2 (Recommendations for the authors):

The study's comprehensive experimental approach with integrating pharmacological inhibition, genetic manipulation, transcriptomics, and infection animal model, provides strong evidence for a conserved mechanism, though some aspects need further clarification.

Major Comments:

(1) While the data suggest that glycolysis affects Met30 activity post-translationally, the underlying mechanism remains speculative. The authors should perform co-immunoprecipitation or ubiquitination assays to confirm whether glycolytic perturbation alters Met30-SCF complex interactions or Met4 ubiquitination levels.

We thank the reviewer for this valuable feedback. The activity of the SCF^Met30 E3 ubiquitin ligase, mediated by the F box protein Met30, is dynamically regulated through both proteolytic degradation and its dissociation from the SCF complex, to coordinate sulfur metabolism and cell cycle progression (Smothers et al., 2000; Yen et al., 2005). Our transcriptomic (RNA-seq analysis) and protein expression analysis (Fig. 3J) confirms that Met30 expression is not differentially regulated in the presence of 2DG, effectively eliminating changes in synthesis or SCF^Met30 proteasomal degradation as the dominant regulatory mechanism. This observation is consistent with the established paradigm wherein stress signals, such as cadmium (Cd²⁺) exposure, rapidly inactivates the SCF^Met30 E3 ubiquitin ligase via the dissociation of Met30 from the Skp1 subunit of the SCF complex (Lauinger et al., 2024; Yen et al., 2005). We therefore propose that active glycolytic flux modulates SCF^Met30 activity post-translationally, specifically by triggering Met30 detachment from the SCF complex. This mechanism would stabilize the primary substrate, the transcription factor Met4, thus promoting the biosynthesis of sulfur-containing amino acids. Mechanistic validation of this hypothesis, particularly the assessment of Met30 dissociation from the SCF^Met30complex via immunoprecipitation (IP), is technically challenging. Since these experiments will involve isolation of cells from colonies undergoing pseudohyphal differentiation, on solid media (given that pseudohyphal differentiation does not occur in liquid media that is limiting for nitrogen (Gancedo, 2001; Gimeno et al., 1992)), current cell yields (OD⁶⁰⁰≈1 from ≈80-100 colonies) are significantly below the amount of cells that is needed to obtain the required amount of total protein concentration, for standard pull down assays (OD600≈600-800 is required to achieve 1-2 mg/ml of total protein which is the standard requirement for pull down protocols in S. cerevisiae (Lauinger et al., 2024)).

Given that the primary objective of our study is to establish the novel regulatory link between glycolysis and sulfur metabolism in the context of fungal morphogenesis, we would like to explore these crucial mechanistic details, in depth, in a subsequent study.

(2) 2DG can exert pleiotropic effects unrelated to glycolytic inhibition (e.g., ER stress, autophagy induction). The authors are encouraged to perform complementary metabolic flux analyses, such as quantification of glycolytic intermediates or ATP levels, to confirm specific glycolytic inhibition.

We appreciate the reviewer's concern regarding the potential pleiotropic effects of 2DG. While we acknowledge that 2DG may induce secondary cellular stress, we are confident that the observed phenotypes are robustly attributed to glycolytic inhibition based on our complementary genetic evidence. Specifically, the deletion strains ∆∆pfk1 and ∆∆adh1, which genetically perturb distinct steps in glycolysis, recapitulate the phenotypic results observed with 2DG treatment. Given this strong congruence between chemical inhibition and specific genetic deletions of key glycolytic enzymes, we are confident that our observed phenotypes are predominantly driven by the perturbation of the glycolytic pathway by 2DG.

(3) The differential rescue effects (cysteine-only in inhibitor assays vs. both cysteine and methionine in genetic mutants) require further explanation. The authors should discuss potential differences in metabolic interconversion or amino acid transport that may account for this observation.

We thank the reviewer for their valuable feedback. One explanation for the observed differential rescue effects of cysteine and methionine can be due to the distinct amino acid transport systems used by S. cerevisiae to transport these amino acids. S. cerevisiae primarily uses multiple, lowaffinity permeases (Gap1, Bap2, Bap3, Tat1, Tat2, Agp1, Gnp1, Yct1) for cysteine transport, while relying on a limited set of high-affinity transporters (like Mup1) for methionine transport, with the added complexity that its methionine transporters can also transport cysteine (Düring-Olsen et al., 1999; Huang et al., 2017; Kosugi et al., 2001; Menant et al., 2006). Hence, it is likely that cysteine uptake could be happening at a higher efficiency in S. cerevisiae compared to methionine uptake. Therefore, to achieve a comparable functional rescue by exogenous supplementation of methionine, it is necessary to use a higher concentration of methionine. When we performed our rescue experiments using higher concentrations of methionine, we did not see any rescue of pseudohyphal differentiation in the presence of 2DG and in fact we noticed that, at higher concentrations of methionine, the wild-type strain failed to undergo pseudohyphal differentiation even in the absence of 2DG. This is likely due to the fact that increasing the methionine concentration raises the overall nitrogen content of the medium, thereby making the medium less nitrogen-starved. This presents a major experimental constraint, as pseudohyphal differentiation is strictly dependent on nitrogen limitation, and the elevated nitrogen resulting from the higher methionine concentration can inhibit pseudohyphal differentiation.

(4) NAC may influence host redox balance or immune responses. The discussion should consider whether the observed virulence rescue could partly result from host-directed effects.

We thank the reviewer for this valuable feedback. We acknowledge the role of NAC in host directed immune response. It is important to note that, in the context of certain bacterial pathogens, NAC has been reported to augment cellular respiration, subsequently increasing Reactive Oxygen Species (ROS) generation, which contributes to pathogen clearance (Shee et al., 2022). Interestingly, in our study, NAC supplementation to the mice was given prior to the infection and maintained continuously throughout the duration of the experiment. This continuous supply of NAC likely contributes to the rescue of virulence defects exhibited by the ∆∆pfk1 strain (Fig. 5I and J). Essentially, NAC likely allows the mutant to fully activate its essential virulence strategies (including morphological switching), to cause a successful infection in the host. As per the reviewer suggestion, this has been included in the discussion section of the manuscript.

Reviewer #3 (Recommendations for the authors):

Most of the comments related to improving the manuscript have been provided in the public review. Here are some specifics for the authors to consider:

(1) It is important to clarify the rationale for choosing specific gene deletions over other key genes (e.g., Met32 and Met30) and explain why Met4 was not included, given its proposed central role in Figure 6.

We sincerely thank the reviewer for this insightful query regarding our selection of the met32 for our gene deletion experiments. The choice of ∆∆met32 strain was strategically motivated by its unique phenotypic properties within the de novo biosynthesis of sulfur-containing amino acids pathway. While deletions of most the genes that encode for proteins involved in the de novo biosynthesis of sulfurcontaining amino acids, result in auxotrophy for methionine or cysteine, ∆∆met32 strain does not exhibit this phenotype (Blaiseau et al., 1997). This key distinction is attributed to the functional redundancy provided by the paralogous gene, met31 (Blaiseau et al., 1997). Crucially, given that the deletion of the central transcriptional regulator, met4, results in cysteine/methionine auxotrophy, the use of the ∆∆met32 strain provides an essential, viable experimental model for investigating the role of sulfur metabolism during pseudohyphal differentiation in S. cerevisiae.

(2) Comparison of consistent gene and protein expression data (Met30, Met4, Met32) across all relevant figures and analyses would strengthen the mechanistic connection in a better way. Some data that might help connect the sections is not included; please see the public review for more details.

We thank the reviewer for this valuable input, which helps us to clarify the scope of our transcriptomic analysis. Our decision to focus our RT-qPCR experiments on downstream targets, while excluding Met4 and Met30 from the RT-qPCR analysis, is based on their known regulatory mechanisms. Met4 activity is predominantly regulated by post-translational ubiquitination by the SCFMet30 complex followed by its degradation (Rouillon et al., 2000; Shrivastava et al., 2021; Smothers et al., 2000)  while Met30 activity is primarily regulated by its auto-degradation or its dissociation from the SCFMet30 complex (Lauinger et al., 2024; Smothers et al., 2000; Yen et al., 2005).  Consistent with this, our RNA-Seq results indicate that neither met4 nor met30 transcripts are differentially expressed, in response to 2DG addition. For all our RT-qPCR analysis in S. cerevisiae and C. albicans, we have consistently used the same set of sulfur metabolism genes and these include met32, met3, met5, met10 and met17. Our data on protein expression analysis of Met30 in S, cerevisiae (Fig. 3J) confirms that Met30 expression is not differentially regulated in the presence of 2DG, effectively eliminating changes in synthesis or SCFMet30 proteasomal degradation as the dominant regulatory mechanism.

(3) Suggested to include metabolomic profiling (cysteine, methionine, and intermediate metabolites) to substantiate the proposed metabolic flux between glycolysis and sulfur metabolism.

We thank the reviewer for this valuable input. Our pseudohyphal/hyphal differentiation assays show that the defects induced by glycolytic perturbation is fully rescued by the exogenous supplementation of sulfur-containing amino acids, cysteine or methionine. Since these data conclusively demonstrate that the primary metabolic limitation caused by the perturbation of glycolysis, which leads to filamentation defects, is sulfur metabolism, we posit that performing comprehensive metabolic profiling would primarily reconfirm the aforesaid results. We believe that our in vitro and in vivo sulfur add-back experiments sufficiently substantiate the novel regulatory metabolic link between glycolysis and sulfur-metabolism.

(4) Data on the effects of Met30 deletion on cell growth are currently not included, and relevant controls should be included to ensure observed phenotypes are not due to general growth defects.

We are grateful to the reviewer for this constructive feedback. To address the potential impact of met30 deletion on cell growth, we have included new data (Suppl. Fig. 4A) demonstrating that the deletion of a single copy of met30 in diploid S. cerevisiae does not compromise overall growth under nitrogen-limiting conditions as the ∆met30 strain grows similar to the wild-type strain.

(5) Expanding RT-qPCR and data from transcriptomic analyses to include sulfur metabolism genes and key cAMP pathway genes to confirm the proposed cAMP-independent mechanism during virulence characterization is necessary.

We thank the reviewer for this valuable feedback. The transcriptional analysis of the sulfur metabolism genes in the presence of 2DG and the ∆∆pfk1 strain is shown in Figures 4D and 4I. 

In order to confirm that glycolysis is critical for fungal morphogenesis in a cAMP-PKA pathway independent manner under nitrogen-limiting conditions in C. albicans, we performed cAMP add-back assays. Interestingly, corroborating our S. cerevisiae data, the exogenous addition of cAMP failed to rescue hyphal differentiation defect caused by the perturbation of glycolysis through 2DG addition or by the deletion of the pfk1 gene, under nitrogen-limiting condition in C. albicans. This data is now included in Suppl. Fig. 5B.

(6) Enhancing the introduction and discussion by providing a clearer rationale for gene selection and more detailed references to established pathways (cAMP-PKA, MAPK, Snf1/HXT regulation, gpa2 involvement) is needed to reinstate the hypothesis.

We thank the reviewer for this valuable feedback. We have incorporated these changes in our revised manuscript.

(7) Reducing redundancy in the text and improving figure consistency, particularly by ensuring that the gene sets depicted in Figure 6 are represented across all datasets, would strengthen the interconnections among sections.

We thank the reviewer for this valuable feedback.  We have incorporated these changes in our revised manuscript.

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eLife Assessment

This study presents DeepTX, a valuable methodological tool that integrates mechanistic stochastic models with single-cell RNA sequencing data to infer transcriptional burst kinetics at genome scale. The approach is broadly applicable and of interest to subfields such as systems biology, bioinformatics, and gene regulation. The evidence supporting the findings is solid, with appropriate validation on synthetic data and thoughtful discussion of limitations related to identifiability and model assumptions.

Joint Public Review:

In this work, the authors present DeepTX, a computational tool for studying transcriptional bursting using single-cell RNA sequencing (scRNA-seq) data and deep learning. The method aims to infer transcriptional burst dynamics-including key model parameters and the associated steady-state distributions-directly from noisy single-cell data. The authors apply DeepTX to datasets from DNA damage experiments, revealing distinct regulatory patterns: IdU treatment in mouse stem cells increases burst size, promoting differentiation, while 5FU alters burst frequency in human cancer cells, driving apoptosis or survival depending on dose. These findings underscore the role of burst regulation in mediating cell fate responses to DNA damage.

The main strength of this study lies in its methodological contribution. DeepTX integrates a non-Markovian mechanistic model with deep learning to approximate steady-state mRNA distributions as mixtures of negative binomial distributions, enabling genome-scale parameter inference with reduced computational cost. The authors provide a clear discussion of the framework's assumptions, including reliance on steady-state data and the inherent unidentifiability of parameter sets, and they outline how the model could be extended to other regulatory processes.

The revised manuscript addresses the original concerns raised by the reviewers, particularly those related to sample size requirements, distributional assumptions, and the biological interpretation of the inferred parameters. The authors have also included an extensive discussion of the limitations of the methodological framework, including the constraints associated with relying on snapshot data, as well as a broader contextualisation of DeepTX within the landscape of existing tools that link mechanistic modelling and single-cell transcriptomics.

Overall, this work represents a valuable contribution to the integration of mechanistic models with high-dimensional single-cell data. It will be of interest to researchers in systems biology, bioinformatics, and computational modelling.

Comments on revisions:

We thank the authors for their thorough revision and for carefully addressing the points raised in the previous review. At this stage, the reviewers have no further concerns.

Author response:

The following is the authors’ response to the previous reviews.

Joint Public Review:

In this work, the authors present DeepTX, a computational tool for studying transcriptional bursting using single-cell RNA sequencing (scRNA-seq) data and deep learning. The method aims to infer transcriptional burst dynamics-including key model parameters and the associated steady-state distributions-directly from noisy single-cell data. The authors apply DeepTX to datasets from DNA damage experiments, revealing distinct regulatory patterns: IdU treatment in mouse stem cells increases burst size, promoting differentiation, while 5FU alters burst frequency in human cancer cells, driving apoptosis or survival depending on dose. These findings underscore the role of burst regulation in mediating cell fate responses to DNA damage.

The main strength of this study lies in its methodological contribution. DeepTX integrates a non-Markovian mechanistic model with deep learning to approximate steady-state mRNA distributions as mixtures of negative binomial distributions, enabling genome-scale parameter inference with reduced computational cost. The authors provide a clear discussion of the framework's assumptions, including reliance on steady-state data and the inherent unidentifiability of parameter sets, and they outline how the model could be extended to other regulatory processes.

The revised manuscript addresses many of the original concerns, particularly regarding sample size requirements, distributional assumptions, and the biological interpretation of inferred parameters. However, the framework remains limited by the constraints of snapshot data and cannot yet resolve dynamic heterogeneity or causality. The manuscript would also benefit from a broader contextualisation of DeepTX within the landscape of existing tools linking mechanistic modelling and single-cell transcriptomics. Finally, the interpretation of pathway enrichment analyses still warrants clarification.

Overall, this work represents a valuable contribution to the integration of mechanistic models with highdimensional single-cell data. It will be of interest to researchers in systems biology, bioinformatics, and computational modelling.

Recommendations for the authors:

We thank the authors for their thorough revision and for addressing many of the points raised during the initial review. The revised manuscript presents an improved and clearer account of the methodology and its implications. However, several aspects would benefit from further clarification and refinement to strengthen the presentation and avoid overstatement.

(1) Contextualization within the existing literature

The manuscript would benefit from placing DeepTX more clearly in the context of other computational tools developed to connect mechanistic modelling and single-cell RNA sequencing data. This is an active area of research with notable recent contributions, including Sukys and Grima (bioRxiv, 2024), Garrido-Rodriguez et al. (PLOS Comp Biol, 2021), and Maizels (2024). Positioning DeepTX in relation to these and other relevant efforts would help readers appreciate its specific advances and contributions.

We sincerely thank you for this valuable suggestion. We agree that situating DeepTX within the broader landscape of computational approaches linking mechanistic modeling and single-cell RNA sequencing data will clarify its contributions and advances. In this revised version, we have explicitly discussed the comparison and relation of DeepTX in the context of this active area using an individual paragraph in the Discussion section.

Specifically, we mentioned that the DeepTX research paradigm contributes to a growing line of area aiming to link mechanistic models of gene regulation with scRNA-seq data. Maizels provided a comprehensive review of computational strategies for incorporating dynamic mechanisms into single-cell transcriptomics (Maizels RJ, 2024). In this context, RNA velocity is one of the most important examples as it infers short-term transcriptional trends based on splicing kinetics and deterministic ODEs model. However, such approaches are limited by their deterministic assumptions and cannot fully capture the stochastic nature of gene regulation. DeepTX can be viewed as an extension of this framework to stochastic modelling, explicitly addressing transcriptional bursting kinetics under DNA damage. Similarly, DeepCycle, developed by Sukys and Grima (Sukys A & Grima R, 2025), investigates transcriptional burst kinetics during the cell cycle, employing a stochastic age-dependent model and a neural network to infer burst parameters while correcting for measurement noise. By contrast, MIGNON integrates genomic variation data and static transcriptomic measurements into a mechanistic pathway model (HiPathia) to infer pathway-level activity changes, rather than gene-level stochastic transcriptional dynamics (Garrido-Rodriguez M et al., 2021). In this sense, DeepTX and MIGNON are complementary, with DeepTX resolving burst kinetics at the single-gene level and MIGNON emphasizing pathway responses to genomic perturbations, which could inspire future extensions of DeepTX that incorporate sequence-level information.

(2) Interpretation of GO analysis

The interpretation of the GO enrichment results in Figure 4D should be revised. While the text currently associates the enriched terms with signal transduction and cell cycle G2/M phase transition, the most significant terms relate to mitotic cell cycle checkpoint signaling. This distinction should be made clear in the main text, and the conclusions drawn from the GO analysis should be aligned more closely with the statistical results.

We sincerely appreciate you for the insightful comment. We have carefully re-examined the GO enrichment results shown in Figure 4D and agree that the most significantly enriched terms correspond to mitotic cell cycle checkpoint signaling and signal transduction in response to DNA damage, rather than general G2/M phase transition processes. Accordingly, we have revised the main text to highlight the biological significance of mitotic cell cycle checkpoint signaling.

Specifically, we now emphasize two key points: DNA damage and mitotic checkpoint activation are closely interconnected. (1) The mitotic checkpoint serves as a crucial safeguard to ensure accurate chromosome segregation and maintain genomic stability under DNA damage conditions. Activation of the mitotic checkpoint can influence cell fate decisions and differentiation potential (Kim EM & Burke DJ, 2008; Lawrence KS et al., 2015). (2) Sustained activation of the spindle assembly checkpoint (SAC) has been reported to induce mitotic slippage and polyploidization, which in turn may enhance the differentiation potential of embryonic stem cells  (Mantel C et al., 2007). These revisions ensure that our interpretation is consistent with the statistical enrichment results and better reflect the underlying biological processes implicated by the data.

(3) Justification for training on simulated data

The decision to train the model on simulated data should be clearly justified. While the advantage of having access to ground-truth parameters is understood, the manuscript would benefit from a discussion of the limitations of this approach, particularly in terms of generalizability to real datasets. Moreover, it is worth noting that many annotated scRNA-seq datasets are publicly available and could, in principle, be used to complement the training strategy.

We thank you for this insightful comment. We chose to train DeepTXsolver on simulated data because no experimental dataset currently provides genome-wide transcriptional burst kinetics with known ground truth, which is essential for supervised learning. Simulation enables us to (i) generate large, fully annotated datasets spanning the biologically relevant parameter space, (ii) expose the solver to diverse bursting regimes (e.g., low/high burst frequency, small/large burst size, unimodal/bimodal distributions), and (iii) quantitatively benchmark model accuracy, parameter identifiability, and robustness prior to deployment on real scRNA-seq data.

We acknowledge, however, that simulation-based training has inherent limitations in terms of generalizability. Real biological systems may deviate from the idealized bursting model, exhibit more complex noise structures, or display parameter distributions that differ from those in simulations. Moreover, the lack of ground-truth parameters in experimental scRNA-seq datasets prevents an absolute evaluation of inference accuracy. In the future work, publicly available annotated scRNA-seq datasets could be used to complement this simulation-based training strategy and enhance generalizability. We have revised the manuscript to explicitly discuss both the rationale for using simulated data and the potential limitations of this approach.

(4) Benchmarking against external methods

The performance of DeepTX is primarily compared to a prior method from the same group. To strengthen the methodological claims, it would be preferable to include benchmarking against additional established tools from the broader literature. This would offer a more objective evaluation of the performance gains attributed to DeepTX.

We thank you for this constructive suggestion. We fully agree that benchmarking DeepTX against additional established tools from the broader literatures would provide a more comprehensive and objective evaluation of DeepTX . In the revised manuscript, we have included comparative analyses with other widely used methods, including nnRNA (From Shahrezaei group (Tang W et al., 2023)), txABC (from our group (Luo S et al., 2023)), txBurst (from Sandberg group (Larsson AJM et al., 2019)), txInfer (from Junhao group (Gu J et al., 2025)) (Supplementary Figure S4). The comparative results indicate that our method demonstrates superior performance in both efficiency and accuracy.

(5) Interpretation of Figures 4-6

The revised figures are clear and informative; however, the associated interpretations in the main text remain too strong relative to the type of analysis performed. For instance, in Figure 4, it is suggested that changes in burst size are linked to DNA damage-induced signalling cascades that affect cell cycle progression and fate decisions. While this is a plausible hypothesis, GO and GSEA analyses are correlative by nature and not sufficient to support such a mechanistic claim on their own. These analyses should be presented as exploratory, and the strength of the conclusions drawn should be tempered accordingly. Similar caution should be applied to the interpretations of Figures 5 and 6.

We thank you for this important comment. In the revised manuscript, we have carefully moderated the interpretation of the GO and GSEA results in Figures 4, 5, and 6. Specifically, we now present these analyses as exploratory and emphasize their correlative nature, avoiding causal claims that go beyond the scope of the data. The text has been rephrased to highlight the observed associations rather than implying direct causal relationships.

For Figure 4, we emphasize that while it is tempting to hypothesize that enhanced burst size may contribute to DNA damage-related checkpoint activation and thereby influence cell cycle progression and differentiation, our current results only indicate an association between burst size enhancement and pathways involved in DNA damage response and checkpoint signaling.

For Figure 5, we emphasize that although our GO analysis cannot establish causality, the results are consistent with an association between 5-FU-induced changes in burst kinetics and pathways related to oxidative stress and apoptosis. Based on this, we propose a model outlining a potential process through which DNA damage may ultimately lead to cellular apoptosis.

For Figure 6, we emphasize that these enrichment results suggest that high-dose 5FU treatment may be associated with processes such as telomerase activation and mitochondrial function maintenance, both of which have been implicated in cell survival and apoptosis evasion in previous experimental studies. For example, prior work indicates that hTERT translocation can activate telomerase pathways to support telomere maintenance and reduce oxidative stress, which is thought to contribute to apoptosis resistance. While our enrichment analysis cannot establish causality, the observed transcriptional bursting changes are consistent with these reported survival-associated mechanisms.

(6) Discussion section framing

The initial paragraphs of the discussion section make broad biological claims about the role of transcriptional bursting in cellular decision-making. While transcriptional bursting is undoubtedly relevant, the manuscript would benefit from a more cautious framing. It would be more appropriate to foreground the methodological contributions of DeepTX, and to present the biological insights as hypotheses or observations that may guide future experimental investigation, rather than as established conclusions.

We thank you for this insightful comment. We have revised the discussion to clarify and appropriately temper our claims regarding transcriptional bursting. First, we now explicitly recognize that transcriptional bursting is one of multiple contributors to cellular variability, rather than the sole or dominant factor driving cellular decision-making. Second, we have restructured the opening of the discussion to prioritize the methodological contributions of DeepTX, highlighting its strength as a framework for inferring genomewide burst kinetics from scRNA-seq data. Finally, the biological insights derived from our analysis are now presented as correlative observations and potential hypotheses, which may inform and guide future experimental investigations, rather than as definitive mechanistic conclusions.

Small Comments

(1) Presentation of discrete distributions: In several figures (e.g., Figure 2B and Supplementary Figures S4, S6, and S8), the comparisons between empirical mRNA distributions and DeepTX-inferred distributions are visually represented using connecting lines, which may give the impression that continuous distributions are being compared to discrete ones. Given the focus on transcriptional bursting, a process inherently tied to discrete stochastic events, this representation could be misleading. The figure captions and visual style should be revised to clarify that all distributions are discrete and to avoid potential confusion. In general, it is recommended to avoid connecting points in discrete distributions with lines, as this can suggest interpolation or comparison with continuous distributions. This applies to Figures 2A and 2B in particular.

We thank you for this valuable suggestion. To prevent any potential misinterpretation of discrete distributions as continuous ones, we have revised the visual representation of the empirical and DeepTXinferred mRNA distributions in Figures 2B, and Supplementary Figures S4, S6, and S8. Specifically, we have replaced the line plots with step plots, which more accurately capture the discrete nature of transcriptional bursting. Additionally, we have updated the figure captions to clearly state that all distributions are discrete.

(2) Transcription is always a multi-step process. While the manuscript aims to model additional complexity introduced by DNA damage, the current phrasing (e.g., on page 5) could be read as implying that transcription becomes multi-step only under damage conditions. This should be clarified.

We thank you for this helpful observation. We agree that transcription is inherently a multi-step process under all conditions. To avoid any possible misunderstanding, we have revised the text to clarify this point.

Specifically, we now explain that many previous studies have employed simplified two-state models to approximate transcriptional dynamics, however, the gene expression process is inherently a multi-step process, which particularly cannot be neglected under conditions of DNA damage. DNA damage can result in slowing or even stopping the RNA pol II movement and cause many macromolecules to be recruited for damage repair. This process will affect the spatially localized behavior of the promoter, causing the dwell time of promoter inactivation and activation that cannot be approximated by a simple two state. Our work adopts a multi-step model because it is more appropriate for capturing the additional complexity introduced by DNA damage.

(3) The first sentence of the discussion section overstates the importance of transcriptional bursting. While it is a key source of variability, it is not the only nor always the dominant one. Furthermore, its role in DNA damage response remains an emerging hypothesis rather than a general principle. The claims in this section should be moderated accordingly.

We thank you for this valuable feedback. In the revised discussion, we have moderated the statements in the opening paragraph to better reflect the current understanding. Specifically, we now acknowledge that transcriptional bursting represents one of multiple sources of variability and is not always the dominant contributor. In addition, we have reframed the role of transcriptional bursting in DNA damage response as an emerging hypothesis, rather than a general principle. To further address this concern, we replaced conclusion-like statements with more cautious, hypothesis-oriented phrasing, presenting our observations as potential directions for future experimental validation.

References

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Sukys, A., Grima, R. 2025. Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data. Nucleic Acids Research 53. DOI: https://dx.doi.org/10.1093/nar/gkaf295, PMID: 40240003

Garrido-Rodriguez, M., Lopez-Lopez, D., Ortuno, F.M., Peña-Chilet, M., Muñoz, E., Calzado, M.A., Dopazo, J. 2021. A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways. PLoS Computational Biology 17: e1008748. DOI: https://dx.doi.org/10.1371/journal.pcbi.1008748, PMID: 33571195

Kim, E.M., Burke, D.J. 2008. DNA damage activates the SAC in an ATM/ATR-dependent manner, independently of the kinetochore. PLoS Genet 4: e1000015. DOI: https://dx.doi.org/10.1371/journal.pgen.1000015, PMID: 18454191

Lawrence, K.S., Chau, T., Engebrecht, J. 2015. DNA damage response and spindle assembly checkpoint function throughout the cell cycle to ensure genomic integrity. PLoS Genet 11: e1005150.DOI: https://dx.doi.org/10.1371/journal.pgen.1005150, PMID: 25898113

Mantel, C., Guo, Y., Lee, M.R., Kim, M.K., Han, M.K., Shibayama, H., Fukuda, S., Yoder, M.C., Pelus, L.M., Kim, K.S., Broxmeyer, H.E. 2007. Checkpoint-apoptosis uncoupling in human and mouse embryonic stem cells: a source of karyotpic instability. Blood 109: 4518-4527. DOI: https://dx.doi.org/10.1182/blood-2006-10-054247, PMID: 17289813

Tang, W., Jørgensen, A.C.S., Marguerat, S., Thomas, P., Shahrezaei, V. 2023. Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics. Bioinformatics 39. DOI: https://dx.doi.org/10.1093/bioinformatics/btad395, PMID: 37354494

Luo, S., Zhang, Z., Wang, Z., Yang, X., Chen, X., Zhou, T., Zhang, J. 2023. Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model. Royal Society Open Science 10: 221057. DOI: https://dx.doi.org/10.1098/rsos.221057, PMID: 37035293

Larsson, A.J.M., Johnsson, P., Hagemann-Jensen, M., Hartmanis, L., Faridani, O.R., Reinius, B., Segerstolpe, A., Rivera, C.M., Ren, B., Sandberg, R. 2019. Genomic encoding of transcriptional burst kinetics. Nature 565: 251-254. DOI: https://dx.doi.org/10.1038/s41586-018-0836-1, PMID: 30602787

Gu, J., Laszik, N., Miles, C.E., Allard, J., Downing, T.L., Read, E.L. 2025. Scalable inference and identifiability of kinetic parameters for transcriptional bursting from single cell data. Bioinformatics. DOI: https://dx.doi.org/10.1093/bioinformatics/btaf581, PMID: 41131798.

eLife Assessment

This study provides important insights into mural cell dynamics and vascular pathology using a zebrafish model of cerebral small vessel disease. The authors present convincing evidence that partial loss of foxf2 function results in progressive, cell-autonomous defects in pericytes accompanied by endothelial abnormalities across the lifespan. By leveraging advanced in vivo imaging and genetic approaches, the work establishes zebrafish as a powerful and relevant model for dissecting the cellular mechanisms underlying cerebral small vessel disease.

Reviewer #1 (Public review):

Summary:

The paper by Graff et al. investigates the function of foxf2 in zebrafish to understand the progression of cerebral small vessel disease. The authors use a partial loss of foxf2 (zebrafish possess two foxf2 genes, foxf2a and foxf2b, and the authors mainly analyze homozygous mutants in foxf2a) to investigate the role of foxf2 signaling in regulating pericyte biology. The find that the number of pericytes is reduced in foxf2a mutants and that the remaining pericytes display alterations in their morphologies. The authors further find that mutant animals can develop to adulthood but that in adult animals, both endothelial and pericyte morphologies are affected. They also show that mutant pericytes can partially repopulate the brain after genetic ablation.

Strengths:

The paper is well written and easy to follow. The authors now include pericyte marker gene analysis and solid quantifications of the observed phenotypes.

Weaknesses:

None left.

Reviewer #2 (Public review):

Summary:

This study investigates the developmental and lifelong consequences of reduced foxf2 dosage in zebrafish, a gene associated with human stroke risk and cerebral small vessel disease (CSVD). The authors show that a ~50% reduction in foxf2 function through homozygous loss of foxf2a leads to a significant decrease in brain pericyte number, along with striking abnormalities in pericyte morphology-including enlarged soma and extended processes-during larval stages. These defects are not corrected over time but instead persist and worsen with age, ultimately affecting the surrounding endothelium. The study also makes an important contribution by characterizing pericyte behavior in wild-type zebrafish using a clever pericyte-specific Brainbow approach, revealing novel interactions such as pericyte process overlap not previously reported in mammals.

Strengths:

This work provides mechanistic insight into how subtle, developmental changes in mural cell biology and coverage of the vasculature can drive long-term vascular pathology. The authors make strong use of zebrafish imaging tools, including longitudinal analysis in transgenic lines to follow pericyte number and morphology over larval development and then applied tissue clearing and whole brain imaging at 3 and 11 months to further dissect the longitudinal effects of foxf2a loss. The ability to track individual pericytes in vivo reveals cell-intrinsic defects and process degeneration with high spatiotemporal resolution. Their use of a pericyte-specific Zebrabow line also allows, for the first time, detailed visualization of pericyte-pericyte interactions in the developing brain, highlighting structural features and behaviors that challenge existing models based on mouse studies. Together, these findings make the zebrafish a valuable model for studying the cellular dynamics of CSVD.

Weaknesses:

I originally suggested quantifying pericyte coverage across brain regions to address potential lineage-specific effects due to the distinct developmental origins of forebrain (neural crest-derived) and hindbrain (mesoderm-derived) pericytes. However, I appreciate the authors' response referencing recent work from their lab (Ahuja, 2024), which demonstrates that both neural crest and mesoderm contribute to pericyte lineages in the midbrain and hindbrain. The convergence of these lineages into a shared transcriptional state by 30 hpf, as shown by their single-cell RNA-seq data, makes it unlikely that regional quantification would provide meaningful lineage-specific insight. I agree with the authors that lineage tracing experiments often suffer from low sample sizes, and their updated findings challenge earlier compartmental models of pericyte origin. I therefore appreciate their rationale for not pursuing regional quantification and consider this concern addressed. Furthermore, my other two points regarding quantification of foxf2 levels and overall vascular changes have been thoroughly addressed in the revised manuscript. These additions significantly strengthen the paper's conclusions and improve the overall rigor of the study.

Reviewer #3 (Public review):

Summary:

The goal of the work by Graff, et al. is to model CSVD in the zebrafish using foxf2a mutants. The mutants show loss of cerebral pericyte coverage that persists through adulthood, but it seems foxf2a does not regulate the regenerative capacity of these cells. The findings are interesting and build on previous work from the group. Limitations of the work include little mechanistic insight into how foxf2a alters pericyte recruitment/differentiation/survival/proliferation in this context, and the overlap of these studies with previous work in fox2a/b double mutants. However, the data analysis is clean and compelling and the findings will contribute to the field.

Comments on revisions:

The authors have addressed all of my original concerns.

Author response:

The following is the authors’ response to the original reviews.

eLife Assessment

This study presents valuable findings that advance our understanding of mural cell dynamics and vascular pathology in a zebrafish model of cerebral small vessel disease. The authors provide compelling evidence that partial loss of foxf2 function leads to progressive, cell-intrinsic defects in pericytes and associated endothelial abnormalities across the lifespan, leveraging powerful in vivo imaging and genetic tools. The strength of evidence could be further improved by additional mechanistic insight and quantitative or lineage-tracing analyses to clarify how pericyte number and identity are affected in the mutant model.

Thank you to the reviewers for insightful comments and for the time spent reviewing the manuscript. We have strengthened the data through responding to the comments.

Public Reviews:

Reviewer #1 (Public review):

The paper by Graff et al. investigates the function of foxf2 in zebrafish to understand the progression of cerebral small vessel disease. The authors use a partial loss of foxf2 (zebrafish possess two foxf2 genes, foxf2a and foxf2b, and the authors mainly analyze homozygous mutants in foxf2a) to investigate the role of foxf2 signaling in regulating pericyte biology. They find that the number of pericytes is reduced in foxf2a mutants and that the remaining pericytes display alterations in their morphologies. The authors further find that mutant animals can develop to adulthood, but that in adult animals, both endothelial and pericyte morphologies are affected. They also show that mutant pericytes can partially repopulate the brain after genetic ablation.

(1) Weaknesses: The results are mainly descriptive, and it is not clear how they will advance the field at their current state, given that a publication on mice has already examined the loss of foxf2 phenotype on pericyte biology (Reyahi, 2015, Dev. Cell).

The Reyahi paper was the earliest report of foxf2 mutant brain pericytes and remains illuminating. The work was very well technically executed. Our manuscript expands and at times, contradicts, their findings. We realized that we did not fully discuss this in our discussion, and this has now been updated. The biggest difference between the two studies is in the direction of change in pericytes after foxf2 knockout, a major finding in both papers. This is where it is important to understand the differences in methods. Reyahi et al., used a conditional knockout under Wnt1:Cre which will ablate pericytes derived from neural crest, but not those derived from mesoderm, nor will it affect foxf2 expression in endothelial cells. Our model is a full constitutive knockout of the gene in all brain pericytes and endothelial cells. For GOF, Reyahi used a transgenic model with a human FOXF2 BAC integrated into the mouse germline.

Both studies are important. We do not know enough about human phenotypes in patients with strokeassociated human FOXF2 SNVs to know the direction of change in pericyte numbers. We showed that the SNVs reduce FOXF2 gene expression in vitro (Ryu, 2022). Here we demonstrate dosage sensitivity in fish (showing phenotypes when 1 of 4 foxf2a + foxf2b alleles are lost, Figure 1F), supporting that slight reductions of FOXF2 in humans could lead to severe brain vessel phenotypes. For this reason, our work is complementary to the previously published work and suggests that future studies should focus on understanding the role of dosage, cell autonomy, and human pericyte phenotypes with respect to FOXF2. While some experiments are parallel in mouse and fish, we go further to look at cell death and regeneration, and to understand the consequences on the whole brain vasculature.

(2) Reyahi et al. showed that loss of foxf2 in mice leads to a marked downregulation of pdgfrb expression in perivascular cells. In contrast to expectation, perivascular cell numbers were higher in mutant animals, but these cells did not differentiate properly. The authors use a transgenic driver line expressing gal4 under the control of the pdgfrb promoter and observe a reduction in pericyte (pdgfrb-expressing) cells in foxf2a mutants. In light of the mouse data, this result might be due to a similar downregulation of pdgfrb expression in fish, which would lead to a downregulation of gal4 expression and hence reduced labelling of pericytes. The authors show a reduction of pdgfrb expression also in zebrafish in foxf2b mutants (Chauhan et al., The Lancet Neurology 2016).

Reyahi detected more pericytes in the Wnt1:Cre mouse, while we detected fewer in the foxf2a (and foxf2a;foxf2b) mutants. This may be because of different methods. For instance, because the mouse knockout is not a constitutive Foxf2 knockout, the observed increase in pericytes may be because mesodermal-derived pericytes proliferate more highly when the neural crest-derived pericytes are absent. Or does endothelial foxf2 activate pericyte proliferation when foxf2 is lost in some pericytes? It is also possible that mouse foxf2 has a different role from its fish ortholog. Despite these differences, there are common conclusions from both models. For instance, both mouse and fish show foxf2 controls capillary pericyte numbers, albeit in different directions. Both show hemorrhage and loss of vascular stability as a result. Both papers identify the developmental window as critical for setting up the correct numbers of pericytes.  

As the reviewer suggested, it was important to test whether pdgfrb is downregulated in fish as it is in mice. To do this, we measured expression of pdgfrb in foxf2 mutants using hybridization chain reaction (HCR) of pdgfrb in foxf2 mutants. The results show no change in pdgfrb mRNA in foxf2a mutants at two independent experiments (Fig S3). Independently, we integrated pdgfrb transgene intensity (using a single allele of the transgene so there are no dose effects) in foxf2a mutants vs. wildtype. We found no difference (Fig S3) suggesting that pdgfrb is a reliable reporter for counting pericytes in the foxf2a knockout. The reviewer is correct that we previously showed downregulation of pdgfrb in foxf2b mutants at 4 dpf using colorimetric ISH. foxf2a and foxf2b are unlinked, independent genes (~400 M years apart in evolution) and may have different regulation.

(3) It would be important to clarify whether, also in zebrafish, foxf2a/foxf2b mutants have reduced or augmented numbers of perivascular cells and how this compares to the data in the mouse.  

We discuss methodological differences between Reyahi and our work in point (1) above. The reduction in pericytes in foxf2a;foxf2b mutants has been previously published (Ryu, 2022, Supplemental Figure 1) and shown again here in Supplemental Figure 2). Numbers are reduced in double mutants up to 10 dpf, suggesting no recovery. Further, in response to reviewer comments, we have quantified pericytes in the whole fish brain (Figure 3E-G) and show reduced pericytes in the adult, reduced vessel network length, and importantly that the pericyte density is reduced. In aggregate, our data shows pericyte reduction at 5 developmental stages from embryo through adult. The reason for different results from the mouse is unknown and may reflect a technical difference (constitutive vs Wnt1:Cre) or a species difference.  

(4) The authors should perform additional characterization of perivascular cells using marker gene expression (for a list of markers, see e.g., Shih et al. Development 2021) and/or genetic lineage tracing.

This is a good point. We have added HCR analysis of additional markers. Results show co-expression of foxf2a, foxf2b, nduf4la2 and pdgfrb in brain pericytes (Fig 2, Fig S3).

(5) The authors motivate using foxf2a mutants as a model of reduced foxf2 dosage, "similar to human heterozygous loss of FOXF2". However, it is not clear how the different foxf2 genes in zebrafish interact with each other transcriptionally. Is there upregulation of foxf2b in foxf2a mutants and vice versa? This is important to consider, as Reyahi et al. showed that foxf2 gene dosage in mice appears to be important, with an increase in foxf2 gene dosage (through transgene expression) leading to a reduction in perivascular cell numbers.

We agree that dosage is a very important concept and show phenotypes in foxf2a heterozygotes (Fig 1F). To test the potential compensation from foxf2b, we have added qPCR for foxf2b in foxf2a mutants as well as HCR of foxf2b in foxf2a mutants (Fig S3C,D). There is no change in foxf2b expression in foxf2a mutants. We discuss dosage in our discussion.

(6) Figures 3 and 4 lack data quantification. The authors describe the existence of vascular defects in adult fish, but no quantifiable parameters or quantifications are provided. This needs to be added.

This query was technically challenging to address, but very worthwhile. We have not seen published methods for quantifying brain pericytes along with the vascular network (certainly not in zebrafish adults), so we developed new methods of analyzing whole brain vascular parameters of cleared adult brains (Figure S6) using a combination of segmentation methods for pericytes, endothelium and smooth muscle. We have added another author (David Elliott) as he was instrumental in designing methods. We find a significant decrease in vessel network length in foxf2a mutants at 3 month and 6 months (Figures 3F and 4G). Similarly, we show a lower number of brain pericytes in foxf2a mutants (Figure 3E). Finally, we added whole brain analysis of smooth muscle coverage (Figure 4) and show no change in vSMC number or coverage of vessels at 5 and 10 dpf or adult, respectively, pointing to pericytes being the cells most affected. Thank you, this query pushed us in a very productive direction. These methods will be extremely useful in the future!

(7) The analysis of pericyte phenotypes and morphologies is not clear. On page 6, the authors state: "In the wildtype brain, adult pericytes have a clear oblong cell body with long, slender primary processes that extend from the cytoplasm with secondary processes that wrap around the circumference of the blood vessel." Further down on the same page, the authors note: "In wildtype adult brains, we identified three subtypes of pericytes, ensheathing, mesh and thin-strand, previously characterized in murine models." In conclusion, not all pericytes have long, slender primary processes, but there are at least three different sub-types? Did the authors analyze how they might be distributed along different branch orders of the vasculature, as they are in the mouse?

We have reworded the text on page 5/6 to be clearer that embryonic pericytes are thin strand only. Additional pericyte subtypes develop later are seen in the mature vasculature of the adult. We could not find a way to accurately analyze pericyte subtypes in the adult brain. The imaging analysis to count pericytes used soma as machine learning algorithms have been developed to count nuclei but not analyze processes.

(8) Which type of pericyte is affected in foxf2a mutant animals? Can the authors identify the branch order of the vasculature for both wildtype and mutant animals and compare which subtype of pericyte might be most affected? Are all subtypes of pericytes similarly affected in mutant animals? There also seems to be a reduction in smooth muscle cell coverage.

Please see the response to (7) about pericyte subtypes. In response to the reviewer’s query, we have now analyzed vSMCs in the embryonic and adult brain. In the embryonic brain we see no statistical differences in vSMC number at 5 and 10 dpf (Figure 4). In the adult, vSMC length (total length of vSMCs in a brain) and vSMC coverage (proportion of brain vessels with vSMCs) are not significantly different. This data is important because it suggests that foxf2a has a more important role in pericytes than in vSMCs.

(9) Regarding pericyte regeneration data (Figure 7): Are the values in Figure 7D not significantly different from each other (no significance given)?

Any graphs missing bars have no significance and were left off for clarity. We have stated this in the statistical methods.  

(10) In the discussion, the authors state that "pericyte processes have not been studied in zebrafish".

Ando et al. (Development 2016) studied pericyte processes in early zebrafish embryos, and Leonard et al. (Development 2022) studied zebrafish pericytes and their processes in the developing fin. We apologize, this was not meant to say that pericyte processes had not been studied before, we have reworded this to make clear the intent of the sentence. We were trying to emphasize that we are the first to quantify processes at different stages, especially  in foxf2 mutants. Processes change morphology over development, especially after 5 dpf, something that our data captures. Our images are of stages that have not been previously characterized. We added a reference to Mae et al., who found similar process length changes in a mouse knockout of a different gene, and to Leonard who previously showed overlap of processes in a different context in fish.

Reviewer #2 (Public review):

Summary:

This study investigates the developmental and lifelong consequences of reduced foxf2 dosage in zebrafish, a gene associated with human stroke risk and cerebral small vessel disease (CSVD). The authors show that a ~50% reduction in foxf2 function through homozygous loss of foxf2a leads to a significant decrease in brain pericyte number, along with striking abnormalities in pericyte morphologyincluding enlarged soma and extended processes-during larval stages. These defects are not corrected over time but instead persist and worsen with age, ultimately affecting the surrounding endothelium. The study also makes an important contribution by characterizing pericyte behavior in wild-type zebrafish using a clever pericyte-specific Brainbow approach, revealing novel interactions such as pericyte process overlap not previously reported in mammals.

Strengths:

This work provides mechanistic insight into how subtle, developmental changes in mural cell biology and coverage of the vasculature can drive long-term vascular pathology. The authors make strong use of zebrafish imaging tools, including longitudinal analysis in transgenic lines to follow pericyte number and morphology over larval development, and then applied tissue clearing and whole brain imaging at 3 and 11 months to further dissect the longitudinal effects of foxf2a loss. The ability to track individual pericytes in vivo reveals cell-intrinsic defects and process degeneration with high spatiotemporal resolution. Their use of a pericyte-specific Zebrabow line also allows, for the first time, detailed visualization of pericytepericyte interactions in the developing brain, highlighting structural features and behaviors that challenge existing models based on mouse studies. Together, these findings make the zebrafish a valuable model for studying the cellular dynamics of CSVD.

Weaknesses:

(11) While the findings are compelling, several aspects could be strengthened. First, quantifying pericyte coverage across distinct brain regions (forebrain, midbrain, hindbrain) would clarify whether foxf2a loss differentially impacts specific pericyte lineages, given known regional differences in developmental origin, with forebrain pericytes being neural crest-derived and hindbrain pericytes being mesoderm-derived.

In recently published work from our lab, we published that both neural crest and mesodermal cells contribute to pericytes in both the mid and hindbrain, and could not confirm earlier work suggesting more rigid compartmental origins (Ahuja, 2024). In the Ahuja, 2024 paper we noted that lineage experiments are often limited by n’s which is why this may not have been discovered before. This makes us skeptical that counting different regions will allow us to interpret data about neural crest and mesoderm. Further, Ahuja 2024 shows that pericyte intermediate progenitors from both mesoderm and neural crest are indistinguishable at 30 hpf through single cell sequencing and have converged on a common phenotype.  

(12) Second, measuring foxf2b expression in foxf2a mutants would better support the interpretation that total FOXF2 dosage is reduced in a graded fashion in heterozygote and homozygote foxf2a mutants.

We have done both qPCR for foxf2b in foxf2a mutants and HCR (quantitative ISH). This is now reported in Fig S3. 

(13) Finally, quantifying vascular density in adult mutants would help determine whether observed endothelial changes are a downstream consequence of prolonged pericyte loss. Correlating these vascular changes with local pericyte depletion would also help clarify causality.

We have added this data to Figure 3 and 4. Please also see response (6).

Reviewer #3 (Public review):

Summary:

The goal of the work by Graff et al. is to model CSVD in the zebrafish using foxf2a mutants. The mutants show loss of cerebral pericyte coverage that persists through adulthood, but it seems foxf2a does not regulate the regenerative capacity of these cells. The findings are interesting and build on previous work from the group. Limitations of the work include little mechanistic insight into how foxf2a alters pericyte recruitment/differentiation/survival/proliferation in this context, and the overlap of these studies with previous work in fox2a/b double mutants. However, the data analysis is clean and compelling, and the findings will contribute to the field.

(14) Please make Figures 5C and 5E red-green colorblind friendly.

Thank you. We have changed the colors to light blue and yellow to be colorblind friendly.

Reviewer #3 (Recommendations for the authors):

(15) I'm not sure this reviewer totally agrees with the assessment that foxf2a loss of function, while foxf2b remains normal, is the same as FOXF2 heterozygous loss of function in humans. The discussion of the gene dosage needs to be better framed, and the authors should carry out qPCR to show that foxf2b levels are not altered in the foxf2a mutant background.

We have added data on foxf2b expression in foxf2a mutants to Fig S3. We have updated the results.

(16) Figure 4/SF7- is the aneurysm phenotype derived from the ECs or pericytes? Cell-type-specific rescues would be interesting to determine if phenotypes are rescued, especially the developmental phenotypes (it is appreciated that carrying out rescue experiments until adulthood is complex). When is the earliest time point that aneurysm-like structures are seen?

This is a fascinating question, especially as we show that endothelial cells (vessel network length) are affected in the adult mutants. The foxf2a mutants that we work with here are constitutive knockouts. While a strategy to rescue foxf2a in specific lineages is being developed in the laboratory this will require a multi-generation breeding effort to get drivers, transgenes and mutants on the same background, and these fish are not currently available. Thank you for this comment- it is something we want to follow up on.

(17) Figure 5 - This is very nice analysis.

Thank you! We think it is informative too.

(18) Figure 6 - needs to contain control images

We have added wildtype images to figure 6A.

(19) Figure 7- vessel images should be shown to demonstrate the specificity of NTR treatment to the pericytes.

We have added the vessel images to Figure 7. We apologize for the omission.

That's a good question. This is a must. Working in a diverse school district that has special ed schools, Regular ed magnet schools, and part time magnet schools, the curriculum for each of these types of schools can be a challenge especially with our special ed schools. Not all digital tools would be appropriate for all students. In fact, often digital tools have to be identified for a single student due to the student's needs and therefore our teachers have to adapt to provide the best learning experience the student should have.

eLife Assessment

This valuable study uses fiber photometry, implantable lenses, and optogenetics, to show that a subset of subthalamic nucleus neurons are active during movement, and that active but not passive avoidance depends in part on STN projections to substantia nigra. The strength of the evidence for these claims is solid and this paper may be of interest to basic and applied behavioural neuroscientists working on movement or avoidance.

Reviewer #1 (Public review):

Summary:

The manuscript presents a robust set of experiments that provide new insights into the role of STN neurons during active and passive avoidance tasks. These forms of avoidance have received comparatively less attention in the literature than the more extensively studied escape or freezing responses, despite being extremely relevant to human behaviour and more strongly influenced by cognitive control.

Strengths:

Understanding the neural infrastructure supporting avoidance behaviour would be a fundamental milestone in neuroscience. The authors employ sophisticated methods to delineate the role of STN neurons during avoidance behaviours. The work is thorough and the evidence presented is compelling. Experiments are carefully constructed, well-controlled, and the statistical analyses are appropriate.

Reviewer #2 (Public review):

Summary:

Zhou, Sajid et al. present a study investigating the STN involvement in signaled movement. They use fiber photometry, implantable lenses, and optogenetics during active avoidance experiments to evaluate this. The data are useful for the scientific community and the overall evidence for their claims is solid, but many aspects of the findings are confusing. The authors present a huge collection of data, it is somewhat difficult to extract the key information and the meaningful implications resulting from these data.

Strengths:

The study is comprehensive in using many techniques and many stimulation powers and frequencies and configurations.

Reviewer #3 (Public review):

Summary:

The authors use calcium recordings from STN to measure STN activity during spontaneous movement and in a multi-stage avoidance paradigm. They also use optogenetic inhibition and lesion approaches to test the role of STN during the avoidance paradigm. The paper reports a large amount of data and makes many claims, some seem well supported to this Reviewer, others not so much.

Strengths:

Well-supported claims include data showing that during spontaneous movements, especially contraversive ones, STN calcium activity is increased using bulk photometry measurements. Single-cell measures back this claim but also show that it is only a minority of STN cells that respond strongly, with most showing no response during movement, and a similar number showing smaller inhibitions during movement.

Photometry data during cued active avoidance procedures show that STN calcium activity sharply increases in response to auditory cues, and during cued movements to avoid a footshock. Optogenetic and lesion experiments are consistent with an important role for STN in generating cue-evoked avoidance. And a strength of these results is that multiple approaches were used.

[Editors' note: The authors provided a good explanation regarding the difference between interpreting 'caution' in the healthy vs impaired situation, and this addressed one of the remaining major concerns from the last round of review.]

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

One possible remaining conceptual concern that might require future work is determining whether STN primarily mediates higher-level cognitive avoidance or if its activation primarily modulates motor tone.

Our results using viral and electrolytic lesions (Fig. 11) and optogenetic inhibition of STN neurons (Fig. 10) show that signaled active avoidance is virtually abolished, and this effect is reproduced when we selectively inhibit STN fibers in the midbrain (Fig. 12). Inhibition of STN projections in either the substantia nigra pars reticulata (SNr) or the midbrain reticular tegmentum (mRt) eliminates cued avoidance responses while leaving escape responses intact. Importantly, mice continue to escape during US presentation after lesions or during photoinhibition, demonstrating that basic motor capabilities and the ability to generate rapid defensive actions are preserved.

These findings argue against the idea that STN’s role in avoidance reflects a nonspecific suppression or facilitation of motor tone, even if the STN also contributes to general movement control. Instead, they show that STN output is required for generating “cognitively” guided cued actions that depend on interpreting sensory information and applying learned contingencies to decide when to act. Thus, while STN activity can modulate movement parameters, the loss-of-function results point to a more selective role in supporting cued, goal-directed avoidance behavior rather than a general adjustment of motor tone.

Reviewer #2 (Public review):

All previous weaknesses have been addressed. The authors should explain how inhibition of the STN impairing active avoidance is consistent with the STN encoding cautious action. If 'caution' is related to avoid latency, why does STN lesion or inhibition increase avoid latency, and therefore increase caution? Wouldn't the opposite be more consistent with the statement that the STN 'encodes cautious action'?

The reviewer’s interpretation treats any increase in avoidance latency as evidence of “more caution,” but this holds only when animals are performing the avoidance behavior normally. In our intact animals, avoidance rates remain high across AA1 → AA2 → AA3, and the active avoidance trials (CS1) used to measure latency are identical across tasks (e.g., in AA2 the only change is that intertrial crossings are punished). Under these conditions, changes in latency genuinely reflect adjustments in caution, because the behavior itself is intact, actions remain tightly coupled to the cue, and the trials are identical.

This logic does not apply when STN function is disrupted. STN inhibition or lesions reduce avoidance to near chance levels; the few crossings that do occur are poorly aligned to the CS and many likely reflect random movement rather than a cued avoidance response. Once performance collapses, latency can no longer be assumed to reflect the same cognitive process. Thus, interpreting longer latencies during STN inactivation as “more caution” would be erroneous, and we never make that claim.

A simple analogy may help clarify this distinction. Consider a pedestrian deciding when to cross the street after a green light. If the road is deserted (like AA1), the person may step off the curb quickly. If the road is busy with many cars that could cause harm (like AA2), they may wait longer to ensure that all cars have stopped. This extra hesitation reflects caution, not an inability to cross. However, if the pedestrian is impaired (e.g., cannot clearly see the light, struggles to coordinate movements, or cannot reliably make decisions), a delayed crossing would not indicate greater caution—it would reflect a breakdown in the ability to perform the behavior itself. The same principle applies to our data: we interpret latency as “caution” only when animals are performing the active avoidance behavior normally, success rates remain high, and the trial rules are identical. Under STN inhibition or lesion, when active avoidance collapses, the latency of the few crossings that still occur can no longer be interpreted as reflecting caution. We have added these points to the Discussion.

Reviewer #3 (Public review):

Original Weaknesses:

I found the experimental design and presentation convoluted and some of the results over-interpreted.

We appreciate the reviewer’s comment, but the concern as stated is too general for us to address in a concrete way. The revised manuscript has been substantially reorganized, with simplified terminology, streamlined figures, and removal of an entire set of experiments to avoid over-interpretation. We are confident that the experimental design and results are now presented clearly and without extrapolation beyond the data. If there are specific points the reviewer finds convoluted or over-interpreted, we would be happy to address them directly.

As presented, I don't understand this idea that delayed movement is necessarily indicative of cautious movements. Is the distribution of responses multi-modal in a way that might support this idea; or do the authors simply take a normal distribution and assert that the slower responses represent 'caution'? Even if responses are multi-modal and clearly distinguished by 'type', why should readers think this that delayed responses imply cautious responding instead of say: habituation or sensitization to cue/shock, variability in attention, motivation, or stress; or merely uncertainty which seems plausible given what I understand of the task design where the same mice are repeatedly tested in changing conditions. This relates to a major claim (i.e., in the title).

We appreciate the reviewer’s question and address each component directly.

(1) What we mean by “caution” and how it is operationalized

In our study, caution is defined operationally as a systematic increase in avoidance latency when the behavioral demand becomes higher, while the trial structure and required response remain unchanged. Specifically, CS1 trials are identical in AA1, AA2, and AA3. Thus, when mice take longer to initiate the same action under more demanding contexts, the added time reflects additional evaluation before acting—consistent with longestablished interpretations of latency shifts in cognitive psychology (see papers by Donders, Sternberg, Posner) and interpretations of deliberation time in speed-accuracy tradeoff literature.

(2) Why this interpretation does not rely on multi-modal response distributions We do not claim that “cautious” responses form a separate mode in the latency distribution. The distributions are unimodal, and caution is inferred from conditiondependent shifts in these distributions across identical trials, not from the existence of multiple peaks (see Zhou et al, 2022). Latency shifts across conditions with identical trial structure are widely used as behavioral indices of deliberation or caution.

(3) Why alternative explanations (habituation/sensitization, motivation, attention, stress, uncertainty) do not account for these latency changes

Importantly, nothing changes in CS1 trials between AA1 and AA2 with respect to the cue, shock, or required response. Therefore:

- Habituation/sensitization to the cue or shock cannot explain the latency shift (the stimuli and trial type are unchanged). We have previously examined cue-evoked orienting responses and their habituation in detail (Zhou et al., 2023), and those measurements are dissociable from the latency effects described here.

- Motivation or attention are unlikely to change selectively for identical CS1 trials when the task manipulation only adds a contingency to intertrial crossings.

- Uncertainty also does not increase for CS1 trials, they remain fully predictable and unchanged between conditions.

- Stress is too broad a construct to be meaningful unless clearly operationalized; moreover, any stress differences that arise from task structure would covary with caution rather than replace the interpretation.

(4) Clarifying “types” of responses

The reviewer’s question about “response types” appears to conflate behavioral latencies with the neuronal response “types” defined in the manuscript. The term “type” in this paper refers to neuronal activation derived from movement-based clustering, not to distinct behavioral categories of avoidance, which we term modes.

In sum, we interpret increased CS1 latency as “caution” only when performance remains intact and trial structure is identical between conditions; under those criteria, latency reliably reflects additional cognitive evaluation before acting, rather than nonspecific changes in sensory processing, motivation, etc.

Related to the last, I'm struggling to understand the rationale for dividing cells into 'types' based their physiological responses in some experiments.

There is longstanding precedent in systems neuroscience for classifying neurons by their physiological response patterns, because neurons that respond similarly often play similar functional roles. For example, place cells, grid cells, direction cells, in vivo, and regular spiking, burst firing, and tonic firing in vitro are all defined by characteristic activity patterns in response to stimuli rather than anatomy or genetics alone. In the same spirit, our classifications simply reflect clusters of neurons that exhibit similar ΔF/F dynamics around behaviorally relevant events, such as movement sensitivity or avoidance modes. This is a standard analytic approach used in many studies. Thus, our rationale is not arbitrary: the “classes” and “types” arise from data-driven clustering of physiological responses, consistent with widespread practice, and they help reveal functional distinctions within the STN that would otherwise remain obscured.

In several figures the number of subjects used was not described. This is necessary. Also necessary is some assessment of the variability across subjects.

All the results described include the number of animals. To eliminate uncertainty, we now also include this information in figure legends.

The only measure of error shown in many figures relates trial-to-trial or event variability, which is minimal because in many cases it appears that hundreds of trials may have been averaged per animal, but this doesn't provide a strong view of biological variability (i.e., are results consistent across animals?).

The concern appears to stem from a misunderstanding of what the mixed-effects models quantify. The figure panels often show session-averaged traces for clarity, all statistical inferences in the paper are made at the level of animals, not trials. Mixed-effects modeling is explicitly designed for hierarchical datasets such as ours, where many trials are nested within sessions, which are themselves nested within animals.

In our models, animal is the clustering (random) factor, and sessions are nested within animals, so variability across animals is directly estimated and used to compute the population-level effects. This approach is not only appropriate but is the most stringent and widely recommended method for analyzing behavioral and neural data with repeated measures. In other words, the significance tests and confidence intervals already fully incorporate biological variability across animals.

Thus, although hundreds of trials per animal may be illustrated for visualization, the inferences reflect between-animal consistency, not within-animal trial repetition. The fact that the mixed-effects results are robust across animals supports the biological reliability of the findings.

It is not clear if or how spread of expression outside of target STN was evaluated, and if or how or how many mice were excluded due to spread or fiber placements. Inadequate histological validation is presented and neighboring regions that would be difficult to completely avoid, such as paraSTN may be contributing to some of the effects.

The STN is a compact structure with clear anatomical boundaries, and our injections were rigorously validated to ensure targeting specificity. As detailed in the Methods, every mouse underwent histological verification, and injections were quantified using the Brain Atlas Analyzer app (available on OriginLab), which we developed to align serial sections to the Allen Brain Atlas. This approach provides precise, slice-by-slice confirmation of viral spread. We have performed thousands of AAV injections and probe implants in our lab, incorporating over the years highly reliable stereotaxic procedures with multiple depth and angle checks and tools. For this study specifically, fewer than 10% of mice were excluded due to off-target expression or fiber/lesion placement. None of the included cases showed spread into adjacent structures.

Regarding paraSTN: anatomically, paraSTN is a very small extension contiguous with STN. Our study did not attempt to dissociate subregions within STN, and the viral expression patterns we report fall within the accepted boundaries of STN. Importantly, none of our photometry probes or miniscope lenses sampled paraSTN, so contributions from that region are extremely unlikely to account for any of our neural activity results.

Finally, our paper employs five independent loss-of-function approaches—optogenetic inhibition of STN neurons, selective inhibition of STN projections to the midbrain (in two sites: SNr and mRt), and STN lesions (electrolytic and viral). All methods converge on the same conclusion, providing strong evidence that the effects we report arise from manipulation of STN itself rather than from neighboring regions.

Raw example traces are not provided.

We do not think raw traces are useful here. All figures contain average traces to reflect the average activity of the estimated populations, which are already clustered per classes and types.

The timeline of the spontaneous movement and avoidance sessions were not clear, nor the number of events or sessions per animal and how this was set. It is not clear if there was pre-training or habituation, if many or variable sessions were combined per animal, or what the time gaps between sessions was, or if or how any of these parameters might influence interpretation of the results.

As noted, we have enhanced the description of the sessions, including the number of animals and sessions, which are daily and always equal per animals in each group of experiments. The sessions are part of the random effects in the model. In addition, we now include schematics to facilitate understanding of the procedures.  

Comments on revised version:

The authors removed the optogenetic stimulation experiments, but then also added a lot of new analyses. Overall the scope of their conclusions are essentially unchanged. Part of the eLife model is to leave it to the authors discretion how they choose to present their work. But my overall view of it is unchanged. There are elements that I found clear, well executed, and compelling. But other elements that I found difficult to understand and where I could not follow or concur with their conclusions.

We respectfully disagree with the assertion that the scope of our conclusions remains unchanged. The revised manuscript differs in several fundamental ways:

(1) Removal of all optogenetic excitation experiments

These experiments were a substantial portion of the original manuscript, and their removal eliminated an entire set of claims regarding the causal control of cautious responding by STN excitation. The revised manuscript no longer makes these claims.

(2) Addition of analyses that directly address the reviewers’ central concerns The new analyses using mixed-effects modeling, window-specific covariates, and movement/baseline controls were added precisely because reviewers requested clearer dissociation of sensory, motor, and task-related contributions. These additions changed not only the presentation but the interpretation of the neural signals. We now conclude that STN encodes movement, caution, and aversive signals in separable ways—not that it exclusively or causally regulates caution.

(3) Clear narrowing of conclusions

Our current conclusions are more circumscribed and data-driven than in the original submission. For example, we removed all claims that STN activation “controls caution,” relying instead on loss-of-function data showing that STN is necessary for performing cued avoidance—not for generating cautious latency shifts. This is a substantial conceptual refinement resulting directly from the review process.

(4) Reorganization to improve clarity

Nearly every section has been restructured, including terminology (mode/type/class), figure organization, and explanations of behavioral windows. These revisions were implemented to ensure that readers can follow the logic of the analyses.

We appreciate the reviewer’s recognition that several elements were clear and compelling. For the remaining points they found difficult to understand, we have addressed each one in detail in the response and revised the manuscript accordingly. If there are still aspects that remain unclear, we would welcome explicit identification of those points so that we can clarify them further.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) Show individual data points on bar plots

- partially addressed. Individual data points are still not shown.

Wherever feasible, we display individual data points (e.g., Figures 1 and 2) to convey variability directly. However, in cases where figures depict hundreds of paired (repeatedmeasures) data points, showing all points without connecting them would not be appropriate, while linking them would make the figures visually cluttered and uninterpretable. All plots and traces include measures of variability (SEM), and the raw data will be shared on Dryad. When error bars are not visible, they are smaller than the trace thickness or bar line—for example, in Figure 5B, the black circles and orange triangles include error bars, but they are smaller than the symbol size.

Also, to minimize visual clutter, only a subset of relevant comparisons is highlighted with asterisks, whereas all relevant statistical results, comparisons, and mouse/session numbers are fully reported in the Results section, with statistical analyses accounting for the clustering of data within subjects and sessions.

(2) The active avoidance experiments are confusing when they are introduced in the results section. More explanation of what paradigms were used and what each CS means at the time these are introduced would add clarity. For example AA1, AA2 etc are explained only with references to other papers, but a brief description of each protocol and a schematic figure would really help.

- partially addressed. A schematic figure showing the timeline would still be helpful.

As suggested, we have added an additional panel to Fig. 5A with a schematic describing

AA1-3 tasks. In addition, the avoidance protocols are described briefly but clearly in the Results section (second paragraph of “STN neurons activate during goal-directed avoidance contingencies”) and in greater detail in the Methods section. As stated, these tasks were conducted sequentially, and mice underwent the same number of sessions per procedure, which are indicated. All relevant procedural information has been included in these sections. Mice underwent daily sessions and learnt these tasks within 1-2 sessions, progressing sequentially across tasks with an equal number of sessions per task (7 per task), and the resulting data were combined and clustered by mouse/session in the statistical models.

(3) How do the Class 1, 2, 3 avoids relate to Class 1 , 2, 3 neural types established in Figure 3? It seems like they are not related, and if that is the case they should be named something different from each other to avoid confusion.

-not sufficiently addressed. The new naming system of neural 'classes' and 'types' helps with understanding that these are completely different ways of separating subpopulations within the STN. However, it is still unclear why the authors re-type the neurons based on their relation to avoids, when they classify the neurons based on their relationship to speed earlier. And it is unclear whether these neural classes and neural types have anything to do with each other. Are the neural Types related to the neural classes in any way? and what is the overlap between neural types vs classes? Which separation method is more useful for functionally defining STN populations?

The remaining confusion stems from treating several independent analyses as if they were different versions of the same classification. In reality, each analysis asks a distinct question, and the resulting groupings are not expected to overlap or correspond. We clarify this explicitly below.

- Movement onset neuron classes (Class A, B, C; Fig. 3):

These classes categorize neurons based on how their ΔF/F changes around spontaneous movement onset. This analysis identifies which neurons encode the initiation and direction of movement. For instance, Class B neurons (15.9%) were inhibited as movement slowed before onset but did not show sharp activation at onset, whereas Class C neurons (27.6%) displayed a pronounced activation time-locked to movement initiation. Directional analyses revealed that Class C neurons discharged strongly during contraversive turns, while Class B neurons showed a weaker ipsiversive bias. Because neurons were defined per session and many of these recordings did not include avoidance-task sessions, these movement-onset classes were not used in the avoidance analyses.

- Movement-sensitivity neuron classes (Class 1, 2, 3, 4; Fig. 7):

These classes categorize neurons based on the cross-correlation between ΔF/F and head speed, capturing how each neuron’s activity scales with movement features across the entire recording session. This analysis identifies neurons that are strongly speed-modulated, weakly speed-modulated, or largely insensitive to movement. These movement-sensitivity classes were then carried forward into the avoidance analyses to ask how neurons with different kinematic relationships participate during task performance; for example, whether neurons that are insensitive to movement nonetheless show strong activation during avoidance actions.

- Avoidance modes (Mode 1, 2, 3; Fig. 8)

Here we classify actions, not neurons. K-means clustering is applied to the movementspeed time series during CS1 active avoidance trials only, which allows us to identify distinct action modes or variants—fast-onset versus delayed avoidance responses. This action-based classification ensures that we compare neural activity across identical movements, eliminating a major confound in studies that do not explicitly separate action variants. First, we examine how population activity differs across these avoidance modes, reflecting neural encoding of the distinct actions themselves. Second, within each mode, we then classify neurons into “types,” which simply describes how different neurons activate during that specific avoidance action (as noted next).

- Neuron activation types within each mode (Type a, b, c; Fig.9)

This analysis extends the mode-based approach by classifying neuronal activation patterns only within each specific avoidance mode. For each mode, we apply k-means clustering to the ΔF/F time series to identify three activation types—e.g., neurons showing little or no response, neurons showing moderate activation, and neurons showing strong or sharply timed activation. Because all trials within a mode have identical movement profiles, these activation types capture the variability of neural responses to the same avoidance behavior. Importantly, these activation “types” (a, b,

c) are not global neuron categories. They do not correspond to, nor are they intended to map onto, the movement-based neuron classes defined earlier. Instead, they describe how neurons differ in their activation during a particular behavioral mode—that is, within a specific set of behaviorally matched trials. Because modes are defined at the trial level, the neurons contributing to each mode can differ: some neurons have trials belonging to one mode, others to two or all three. Thus, Type a/b/c groupings are not fixed properties of neurons. To prevent confusion, we refer to them explicitly as neuronal activation types, emphasizing that they characterize mode-specific response patterns rather than global cell identities.

In conclusion, the categorizations serve entirely different analytical purposes and should not be interpreted as competing classifications. The mode-specific “types” do not reclassify or replace the movement-sensitivity classes; they capture how neurons differ within a single, well-defined avoidance action, while the movement classes reflect how neurons relate to movements in general. Each classification relates to different set of questions and overlap between them is not expected.

To make this as clear as possible we added the following paragraph to the Results:  

“To avoid confusion between analyses, it is important to note that the movement-sensitivity classes defined here (Class 1–4; Fig. 7) are conceptually distinct from both the movementonset classes (Class A–C; Fig. 3) and the neuronal activation “types” introduced later in the avoidance-mode analysis. The Class 1–4 grouping reflects how neurons relate to movement across the entire session, based on their cross-correlation with speed. The onset classes A–C capture neural activity specifically around spontaneous movement initiation during general exploration. In contrast, the later activation “types” are derived within each avoidance mode and describe how neurons differ in their activation patterns during identical CS1 avoidance responses. These classifications answer different questions about STN function and are not intended to correspond to one another.”

(4) Similarly having 3 different cell types (a,b,c) in the active avoidance seems unrelated to the original classification of cell types (1,2,3), and these are different for each class of avoid. This is very confusing and it is unclear how any of these types relate to each other. Presumable the same mouse has all three classes of avoids, so there are recording from each cell during each type of avoid. So the authors could compare one cell during each avoid and determine whether it relates to movement or sound or something else. It is interesting that types a,b,c have the exact same proportions in each class of avoid, and really makes it important to investigate if these are the exact same cells or not. Also, these mice could be recorded during open field so the original neural classification (class 1, 2,3) could be applied to these same cells and then the authors can see whether each cell type defined in the open field has different response to the different avoid types. As it stands, the paper simply finds that during movement and during avoidance behaviors different cells in the STN do different things. - Similarly, the authors somewhat addressed the neural types issue, but figure 9 still has 9 different neural types and it is unclear whether the same cells that are type 'a' in mode 1 avoids are also type 'a' in mode 2 avoids, or do some switch to type b? Is there consistency between cell types across avoid modes? The authors show that type 'c' neurons are differentially elevated in mode 3 vs 2, but also describes neurons as type '2c' and statistically compare them to type '1c' neurons. Are these the same neurons? or are type 2c neurons different cells vs type 1c neurons? This is still unclear and requires clarification to be interpretable.

We believe the remaining confusion arises from treating the different classification schemes as if they were alternative labels applied to the same neurons, when in fact they serve entirely separate analytical purposes and may not include the same neurons (see previous point). Because these classifications answer different questions, they are not expected to overlap, nor is overlap required for the interpretations we draw. It is therefore not appropriate to compare a neuron’s “type” in one avoidance mode to its movement class, or to ask whether types a/b/c across different modes are “the same cells,” since modes are defined by trial-level movement clustering rather than by neuron identity. Importantly, Types a/b/c are not intended as a new global classification of neurons; they simply summarize the variability of neuronal responses within each behaviorally matched mode. We agree that future studies could expand our findings, but that is beyond the already wide scope of the present paper. Our current analyses demonstrate a key conceptual point: when movement is held constant (via modes), STN neurons still show heterogeneous, outcome- and caution-related patterns, indicating encoding that cannot be reduced to movement alone.

Relatedly, was the association with speed used to define each neural "class" done in the active avoidance context or in a separate (e.g. open field) experiment? This is not clear in the text.

The cross-correlation classes were derived from the entire recording session, which included open-field and avoidance tasks recordings. The tasks include long intertrial periods with spontaneous movements. We found no difference in classes when we include only a portion of the session, such as the open field or if we exclude the avoidance interval where actions occur.

Finally, in figure 7, why is there a separate avoid trace for each neural class? With the GRIN lens, the authors are presumably getting a sample of all cell types during each avoid, so why do the avoids differ depending on the cell type recorded?

The entire STN population is not recorded within a single session; each session contributes only a subset of neurons to the dataset. Consequently, each neural class is composed of neurons drawn from partially non-overlapping sets of sessions, each with its own movement traces. For this reason, we plot avoidance traces separately for each neural class to maintain strict within-session correspondence between neural activity and the behavior collected in the same sessions. This prevents mixing behavioral data across sessions that did not contribute neurons to that class and ensures that all neural– behavioral comparisons remain appropriately matched. We have clarified this rationale in the revised manuscript. We note that averaging movement across classes—as is often done—would obscure these distinctions and would not preserve the necessary correspondence between neural activity and behavior. This is also clarified in Results.

(5) The use of the same colors to mean two different things in figure 9 is confusing. AA1 vs AA2 shouldn't be the same colors as light-naïve vs light signaling CS.

-addressed, but the authors still sometimes use the same colors to mean different things in adjacent figures (e.g. the red, blue, black colors in figure 1 and figure 2 mean totally different things) and use different colors within the same figure to represent the same thing (Figure 9AB vs Figure 9CD). This is suboptimal.

Following the reviewer’s suggestion, in Figure 2, we changed the colors, so readers do not assume they are related to Fig. 1.

In Figure 9, we changed the colors in C,D to match the colors in A,B.

(6) The exact timeline of the optogenetics experiments should be presented as a schematic for understandability. It is not clear which conditions each mouse experienced in which order. This is critical to the interpretation of figure 9 and the reduction of passive avoids during STN stimulation. Did these mice have the CS1+STN stimulation pairing or the STN+US pairing prior to this experiment? If they did, the stimulation of the STN could be strongly associated with either punishment or with the CS1 that predicts punishment. If that is the case, stimulating the STN during CS2 could be like presenting CS1+CS2 at the same time and could be confusing. The authors should make it clear whether the mice were naïve during this passive avoid experiment or whether they had experienced STN stimulation paired with anything prior to this experiment.

-addressed

(7) Similarly, the duration of the STN stimulation should be made clear on the plots that show behavior over time (e.g. Figure 9E).

-addressed

(8) There is just so much data and so many conditions for each experiment here. The paper is dense and difficult to read. It would really benefit readability if the authors put only the key experiments and key figure panels in the main text and moved much of the repetative figure panels to supplemental figures. The addition of schematic drawings for behavioral experiment timing and for the different AA1, AA2, AA3 conditions would also really improve clarity.

-partially addressed. The paper is still dense and difficult to read. No experimental schematics were added.

As suggested, we now added the schematic to Fig. 5A.  

New Comments:

(9) Description of the animals used and institutional approval are missing from the methods.

The information on animal strains and institutional approval is already included in the manuscript. The first paragraph of the Methods section states:

“… All procedures were reviewed and approved by the institutional animal care and use committee and conducted in adult (>8 weeks) male and female mice. …”

Additionally, the next subsection, “Strains and Adeno-Associated Viruses (AAVs),” fully specifies all mouse lines used. We therefore believe that the required descriptions of animals and institutional approval are already present and meet standard reporting.

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eLife Assessment

The authors combine a modeling approach, using a digital twin, with electrophysiological evidence in two species to assess the role of inhibition in shaping selectivity in the visual cortex. The results provide an important advance beyond the classic view of sensory coding by proving compelling evidence that many neurons in visual areas exhibit dual-feature selectivity. Overall, the work exceptionally showcases how in silico experiments can generate concrete hypotheses about neuronal coding that are difficult to discover experimentally.

Reviewer #1 (Public review):

This manuscript used deep learning to highlight the role of inhibition in shaping selectivity in primary and higher visual cortex. The findings hint at hitherto unknown axes of structured inhibition operating in cortical networks with a potentially key role in object recognition.

The multi-species approach of testing the model in macaque and mouse is excellent, as it improves the chances that the observed findings are a general property of mammalian visual cortex. However, it would be useful to delineate any notable differences between these species, which are to be expected given their lifestyle.

The overall performance of the model appears to be excellent in V1, with over 80% performance, but it falls substantially in V4. It would be important to consider the implications of this finding; for example, in the context of studying temporal lobe structures that are central to recognizing objects. Would one expect that model performance decreases further here, and what measures could be taken to avoid this? Or is this type of model better restricted to V1 or even LGN?

While the manuscript delineates novel axes of inhibitory interactions, it remains unclear what exactly these axes are and how they arise. What are the steps that need to be taken to make progress along these lines?

Reviewer #2 (Public review):

The classic view of sensory coding states that (excitatory) neurons are active to some preferred stimuli and otherwise silent. In contrast, inhibitory neurons are considered broadly tuned. Due to the gigantic potential image space, it is hard to comprehensively map the tuning of individual neurons. In this tour de force study, Franke et al. combine electrophysiological recordings in macaque (V1, V4) and mouse (V1, LM, LI) visual cortex with large-scale screens based on digital twin models, as well as beautiful systems identification (most/least activating stimuli). Based on these digital twins, they discover dual-feature selectivity (which they validate both in macaques and mice). Dual-feature selectivity involves a bidirectional modulation of firing rates around an elevated baseline. Neurons are excited by specific preferred features and systematically suppressed by distinct, non-preferred features. This tuning was identified by excellently combining advances in AI & high-throughput ephys.

The study is comprehensive and convincing. Overall, this work showcases how in silico experiments can generate concrete hypotheses about neuronal coding that are difficult to discover experimentally, but that can be experimentally validated! I think this work is of substantial interest to the neuroscience community. I'm sure it will motivate many future experimental and computational studies. In particular, it will be of great interest to understand when and how the brain leverages dual-feature selectivity. The discussion of the article is already an interesting starting point for these considerations.

Strengths:

(1) Using computational models to predict neuronal responses allowed them to go through millions of images, which may not be possible in vivo.

(2) The cross-species and cross-area consistency of the results is another major strength. Pointing out that the results may be a fundamental strategy of mammalian cortical processing.

(3) They show that the feature causing peak excitation in one neuron often drives suppression in another. This may be an efficient coding scheme where the population covers the visual manifold. I'd like to understand better why the authors believe that this shows that there are low-dimensional subspaces based on preferred and non-preferred stimulus features (vs. many more, but some axes are stronger).

Author response:

We thank the reviewers for their constructive and helpful feedback on our manuscript. We are delighted that they found the study to be "comprehensive and convincing" and a "tour de force" in its combination of electrophysiological recordings with large-scale digital twin screening. We appreciate that the reviewers highlighted the strengths of our multi-species approach and the "cross-species and cross-area consistency" of the results, noting that the work showcases how in silico experiments can generate concrete, experimentally validatable hypotheses.

The reviewers also raised several important points that we plan to address in the final version of the manuscript to improve clarity and interpretation. These center on:

Model performance in V4: Reviewer #1 raised questions regarding the comparative drop in model performance in V4 and the implications for the validity of the results (including the use of "high confidence" neurons and a request for clarification on the number of animals in the V4 dataset).

Species differences: Both reviewers noted the value of the macaque-mouse comparison but requested a more explicit delineation of the differences between these species given their distinct ethological niches.

The nature of inhibitory dimensions: The reviewers asked for further details on how to identify these inhibitory dimensions and the specific relationship between excitation and inhibition. We believe unraveling these mechanisms represents an exciting direction for future work, and we will explicitly mention this in the Discussion section of the final manuscript, alongside a clearer contextualization with prior literature.

Technical clarifications: Reviewer #2 requested clarifications on specific technical details, such as the skewness thresholds used for sparsity analysis.

In the final version of the manuscript, we will address these points by adding necessary clarifications to the text—including confirming the animal cohort details—explicitly contrasting the mouse and macaque data to highlight coding differences, and expanding our discussion. We will also ensure all technical inquiries, such as those regarding skewness and reference citations, are fully resolved.

We believe addressing these points will significantly strengthen the manuscript.

eLife Assessment

This paper represents a valuable contribution to our understanding of how LFP oscillations and beta band coordination between the hippocampus and prefrontal cortex of rats may relate to learning. Enthusiasm for the reported results was moderated by the concern that some key analyses need to be done, and highly relevant details about task, data, and statistics were missing. Consequently, the reviewers considered the evidence to be incomplete in this version of the manuscript.

Reviewer #1 (Public review):

Wang, Zhou et al. investigated coordination between the prefrontal cortex (PFC) and the hippocampus (Hp), during reward delivery, by analyzing beta oscillations. Beta oscillations are associated with various cognitive functions, but their role in coordinating brain networks during learning is still not thoroughly understood. The authors focused on the changes in power, peak frequencies, and coherence of beta oscillations in two regions when rats learn a spatial task over days. Inconsistent with the authors' hypothesis, beta oscillations in those two regions during reward delivery were not coupled in spectral or temporal aspects. They were, however, able to show reverse changes in beta oscillations in PFC and Hp as the animal's performance got better. The authors were also able to show a small subset of cell populations in PFC that are modulated by both beta oscillations in PFC and sharp wave ripples in Hp. A similarly modulated cell population was not observed in Hp. These results are valuable in pointing out distinct periods during a spatial task when two regions modulate their activity independently from each other.

The authors included a detailed analysis of the data to support their conclusions. However, some clarifications would help their presentation, as well as help readers to have a clear understanding.

(1) The crucial time point of the analysis is the goal entry. However, it needs a better explanation in the methods or in figures of what a goal entry in their behavioral task means.

(2) Regarding Figure 2, the authors have mentioned in the methods that PFC tetrodes have targeted both hemispheres. It might be trivial, but a supplementary graph or a paragraph about differences or similarities between contralateral and ipsilateral tetrodes to Hp might help readers.

(3) The authors have looked at changes in burst properties over days of training. For the coincidence of beta bursts between PFC and Hp, is there a change in the coincidence of bursts depending on the day or performance of the animal?

(4) Regarding the changes in performance through days as well as variance of the beta burst frequency variance (Figures 3C and 4C); was there a change in the number of the beta bursts as animals learn the task, which might affect variance indirectly?

(5) In the behavioral task, within a session, animals needed to alternate between two wells, but the central arm (1) was in the same location. Did the authors alternate the location of well number 1 between days to different arms? It is possible that having well number 1 in the same location through days might have an effect on beta bursts, as they would get more rewards in well number 1?

(6) The animals did not increase their performance in the F maze as much as they increased it in the Y maze. It would be more helpful to see a comparison between mazes in Figure 5 in terms of beta burst timing. It seems like in Y maze, unrewarded trials have earlier beta bursts in Y maze compared to F maze. Also, is there a difference in beta burst frequencies of rewarded and unrewarded trials?

(7) For individual cell analysis, the authors recorded from Hp and the behavioral task involved spatial learning. It would be helpful to readers if authors mention about place field properties of the cells they have recorded from. It is known that reward cells firing near reward locations have a higher rate to participate in a sharp wave ripple. Factoring in the place field properties of the cells into the analysis might give a clearer picture of the lack of modulation of HP cells by beta and sharp wave ripples.

Reviewer #2 (Public review):

(1) When presenting the power spectra for the representative example (Figure 1), it would be appropriate to display a broader frequency band-including delta, theta, and gamma (up to ~100 Hz), rather than only the beta band. What was the rat's locomotor state (e.g., running speed) after entering the reward location, during which the LFPs were recorded? If the rats stopped at the goal but still consumed the reward (i.e., exhibited very low running speed), theta rhythms might still occasionally occur, and sharp-wave ripples (SWRs) could be observed during rest. Do beta bursts also occur during navigation prior to goal entry? It would be beneficial to display these rhythmic activities continuously across both the navigation and goal entry phases. Additionally, given that the hippocampal theta rhythm is typically around 7-8 Hz, while a peak at approximately 15-16 Hz is visible in the power spectra in Figure 1C, the authors should clarify whether the 22 Hz beta activity represents a genuine oscillation rather than a harmonic of the theta rhythm.

(2) The authors claim that beta activity is independent between CA1 and PFC, based on the low coherence between these regions. However, it is challenging to discern beta-specific coherence in CA1; instead, coherence appears elevated across a broader frequency band (Figure 2 and Figure 2-1D). An alternative explanation could be that the uncoupled beta between CA1 and PFC results from low local beta coherence within CA1 itself.

(3) In Figure 2-1E-F, visual inspection of the box plots reveals minimal differences between PFC-Ind and PFC-Coin/CA1-Coin conditions, despite reported statistical significance. It may be necessary to verify whether the significance arises from a large sample size.

(4) In Figure 3 and Figure 4, although differences in power and frequency appear to change significantly across days, these changes are not easily discernible by visual inspection. It is worth considering whether these variations are related to increased task familiarity over days, potentially accompanied by higher running speeds.

(5) The stronger spiking modulation by local beta oscillations shown in Figure 6 could also be interpreted in the context of uncoupled beta between CA1 and PFC. In this analysis, only spikes occurring during beta bursts should be included, rather than all spikes within a trial. The authors should verify the dataset used and consider including a representative example illustrating beta modulation of single-unit spiking.

(6) As observed in Figure 7D, CA1 beta bursts continue to occur even after 2.5 seconds following goal entry, when SWRs begin to emerge. Do these oscillations alternate over time, or do they coexist with some form of cross-frequency coupling?

Reviewer #3 (Public review):

Summary:

This paper explored the role of beta rhythms in the context of spatial learning and mPFC-hippocampal dynamics. The authors characterized mPFC and hippocampal beta oscillations, examining how their coordination and their spectral profiles related to learning and prefrontal neuronal firing. Rats performed two tasks, a Y-maze and an F-maze, with the F-maze task being more cognitively demanding. Across learning, prefrontal beta oscillation power increased while beta frequency decreased. In contrast, hippocampal beta power and beta frequency decreased. This was particularly the case for the well-performed and well-learned Y-maze paradigm. The authors identified the timing of beta oscillations, revealing an interesting shift in beta burst timing relative to reward entry as learning progressed. They also discovered an interesting population of prefrontal neurons that were tuned to both prefrontal beta and hippocampal sharp-wave ripple events, revealing a spectrum of SWR-excited and SWR-inhibited neurons that were differentially phase locked to prefrontal beta rhythms.

In sum, the authors set out to examine how beta rhythms and their coordination were related to learning and goal occupancy. The authors identified a set of learning and goal-related correlates at the level of LFP and spike-LFP interactions, but did not report on spike-behavioral correlates.

Strengths:

Pairing dual recordings of medial prefrontal cortex (mPFC) and CA1 with learning of spatial memory tasks is a strength of this paper. The authors also discovered an interesting population of prefrontal neurons modulated by both beta and CA1 sharp-wave ripple (SWR) events, showing a relationship between SWR-excited and SWR-inhibited neurons and beta oscillation phase.

Weaknesses:

The authors report on a task where rats were performing sub-optimally (F-maze), weakening claims. Likewise, it is questionable as to whether mPFC and hippocampus are dually required to perform a no-delay Y-maze task at day 5, where rats are performing near 100%. There would be little reason to suspect strong oscillatory coupling when task performance is poor and/or independent of mPFC-HPC communication (Jones and Wilson, 2005), potentially weakening conclusions about independent beta rhythms. Moreover, there is little detail provided about sample sizes and how data sampling is being performed (e.g., rats, sessions, or trials), raising generalizability concerns.

Author response:

Public Reviews:.

Reviewer #1 (Public review):

Wang, Zhou et al. investigated coordination between the prefrontal cortex (PFC) and the hippocampus (Hp), during reward delivery, by analyzing beta oscillations. Beta oscillations are associated with various cognitive functions, but their role in coordinating brain networks during learning is still not thoroughly understood. The authors focused on the changes in power, peak frequencies, and coherence of beta oscillations in two regions when rats learn a spatial task over days. Inconsistent with the authors' hypothesis, beta oscillations in those two regions during reward delivery were not coupled in spectral or temporal aspects. They were, however, able to show reverse changes in beta oscillations in PFC and Hp as the animal's performance got better. The authors were also able to show a small subset of cell populations in PFC that are modulated by both beta oscillations in PFC and sharp wave ripples in Hp. A similarly modulated cell population was not observed in Hp. These results are valuable in pointing out distinct periods during a spatial task when two regions modulate their activity independently from each other.

The authors included a detailed analysis of the data to support their conclusions. However, some clarifications would help their presentation, as well as help readers to have a clear understanding.

(1) The crucial time point of the analysis is the goal entry. However, it needs a better explanation in the methods or in figures of what a goal entry in their behavioral task means.

We appreciate Reviewer 1 pointing out this shortcoming and will clarify the description in the revised manuscript. Each goal is located at the end of the arm, and is equipped with a reward delivery unit. The unit has an infrared sensor. The rat breaks the infrared beam when it enters the goal.

(2) Regarding Figure 2, the authors have mentioned in the methods that PFC tetrodes have targeted both hemispheres. It might be trivial, but a supplementary graph or a paragraph about differences or similarities between contralateral and ipsilateral tetrodes to Hp might help readers.

We will provide the requested analysis in the full revision. We saw both hemispheres had similar properties.

(3) The authors have looked at changes in burst properties over days of training. For the coincidence of beta bursts between PFC and Hp, is there a change in the coincidence of bursts depending on the day or performance of the animal?

We will provide the requested analysis in the full revision.

(4) Regarding the changes in performance through days as well as variance of the beta burst frequency variance (Figures 3C and 4C); was there a change in the number of the beta bursts as animals learn the task, which might affect variance indirectly?

The analysis we can do here is to control for differences in the number of bursts for each category (days/performance quintile) by resampling the data to match the burst count between categories.

(5) In the behavioral task, within a session, animals needed to alternate between two wells, but the central arm (1) was in the same location. Did the authors alternate the location of well number 1 between days to different arms? It is possible that having well number 1 in the same location through days might have an effect on beta bursts, as they would get more rewards in well number 1?

The central arm remained the same across days since we needed the animals to learn the alternation task. In our experience, the animal needs a few days to learn the alternation rule when we switch the central arm location. For this experiment, we were interested in the initial learning process, and we kept the central constant. Switching the central arm location is a great suggestion for a follow up experiment where we can understand the effects of reward contingency change has on beta bursts.

(6) The animals did not increase their performance in the F maze as much as they increased it in the Y maze. It would be more helpful to see a comparison between mazes in Figure 5 in terms of beta burst timing. It seems like in Y maze, unrewarded trials have earlier beta bursts in Y maze compared to F maze. Also, is there a difference in beta burst frequencies of rewarded and unrewarded trials?

We will add this analysis in the revised manuscript.

(7) For individual cell analysis, the authors recorded from Hp and the behavioral task involved spatial learning. It would be helpful to readers if authors mention about place field properties of the cells they have recorded from. It is known that reward cells firing near reward locations have a higher rate to participate in a sharp wave ripple. Factoring in the place field propertiesd of the cells into the analysis might give a clearer picture of the lack of modulation of HP cells by beta and sharp wave ripples.

This is a great suggestion, and we will address this in the full revision.

Reviewer #2 (Public review):

We thank Reviewer 2 for their helpful comments and will address these in full in the revision. These are great suggestions to provide greater detail on the spectral and behavioral data at the goal.

(1) When presenting the power spectra for the representative example (Figure 1), it would be appropriate to display a broader frequency band-including delta, theta, and gamma (up to ~100 Hz), rather than only the beta band.

We will show more examples of power spectra with a wider frequency range. We did examine the wider spectra and noticed power in the beta frequency band was more prominent than others.

What was the rat's locomotor state (e.g., running speed) after entering the reward location, during which the LFPs were recorded?

We will add the time aligned speed profile to the spectra and raw data examples. Because goal entry is defined as the time the animals break the infrared beam at the goal (response to Reviewer 1), the rat would have come to a stop.

If the rats stopped at the goal but still consumed the reward (i.e., exhibited very low running speed), theta rhythms might still occasionally occur, and sharp-wave ripples (SWRs) could be observed during rest.

We typically find low theta power in the hippocampus after the animal reaches the goal location and as it consumes reward. Reviewer 2 is correct about occasional theta power at the goal. We have observed this but mostly before the animal leaves the goal location. We did find SWRs during goal periods. One example is shown in Fig. 7A.

Do beta bursts also occur during navigation prior to goal entry?

We did not find consistent beta bursts in PFC or CA1 on approach to goal entry. We can provide the analyses in our full revision. In our initial exploratory analysis, we found beta bursts was most prominent after goal entry, which led us to focus on post-goal entry beta for this manuscript. However, beta oscillations in the hippocampus during locomotion or exploration has been reported (Ahmed & Mehta, 2012; Berke et al., 2008; França et al., 2014; França et al., 2021; Iwasaki et al., 2021; Lansink et al., 2016; Rangel et al., 2015).

It would be beneficial to display these rhythmic activities continuously across both the navigation and goal entry phases. Additionally, given that the hippocampal theta rhythm is typically around 7-8 Hz, while a peak at approximately 15-16 Hz is visible in the power spectra in Figure 1C, the authors should clarify whether the 22 Hz beta activity represents a genuine oscillation rather than a harmonic of the theta rhythm.

To ensure we fully address this concern, we can provide further spectral analysis in our revised manuscript to show theta power in CA1 is reduced after goal entry. We were initially concerned about the possibility that the 22Hz power in CA1 may be a harmonic rather than a standalone oscillation band. If these are harmonics of theta, we should expect to find coincident theta at the time of bursts in the beta frequency. In Fig. 1B, Fig. 2A, we show examples of the raw LFP traces from CA1. Here, the detected bursts are not accompanied by visible theta frequency activity. For PFC, we do not always see persistent theta frequency oscillations like CA1. In PFC, we found beta bursts were frequent and visually identifiable when examining the LFP. We provided examples of the PFC LFP (Fig. 1B, Fig. 1-1, and Fig. 2A). In these cases, we see clear beta frequency oscillations lasting several cycles and these are not accompanied by any oscillations in the theta frequency in the LFP trace.

(2) The authors claim that beta activity is independent between CA1 and PFC, based on the low coherence between these regions. However, it is challenging to discern beta-specific coherence in CA1; instead, coherence appears elevated across a broader frequency band (Figure 2 and Figure 2-1D). An alternative explanation could be that the uncoupled beta between CA1 and PFC results from low local beta coherence within CA1 itself.

This is a legitimate concern, and we used three methods to characterize coherence and coordination between the two regions. First, we calculated coherence for tetrode pairs for times when the animal was at goals (Fig. 2B), which provides a general estimation of coherence across frequencies but lack any temporal resolution. Second, we calculated burst aligned coherence (Fig. 2-1), which provides temporal resolution relative to the burst, but the multi-taper method is constrained by the time-frequency resolution trade off. Third, we quantified the timing between the burst peaks (Fig. 2D), which will describe timing differences but the peaks for the bursts may not be symmetric. Thus, each method has its own caveats, but we drew our conclusion from the combination of results from these three analyses, which pointed to similar conclusions.

Reviewer 2 is correct in pointing out the uniformly high coherence within CA1 across the frequency range we examined. When we inspected the raw LFP across multiple tetrodes in CA1, they were similar to each other (Fig. 2A). This likely reflects the uniformity in the LFP across recording sites in CA1, which is what we saw with coherence values across the frequency range (Fig. 2B). We found CA1 coherence between tetrode pairs within CA1 across the range, were statistically higher, compared to tetrode pairs in PFC (Fig. 2B and C), thus our results are unlikely to be explained by low beta coherence within CA1 itself. The burst aligned coherence using a multi-taper method also supports this. The coherence values within CA1 at the time of CA1 bursts is ~0.8-0.9.

(3) In Figure 2-1E-F, visual inspection of the box plots reveals minimal differences between PFC-Ind and PFC-Coin/CA1-Coin conditions, despite reported statistical significance. It may be necessary to verify whether the significance arises from a large sample size.

We will include the sample sizes for each of the boxplots, these should be the same as the power comparison in Fig. 2-1 A-C. The LFP within a one second window centered around the bursts are usually very similar, and the multi-taper method will return high coherence values. The p-values from statistical comparisons between the boxes are corrected using the Benjamini-Hochberg method.

(4) In Figure 3 and Figure 4, although differences in power and frequency appear to change significantly across days, these changes are not easily discernible by visual inspection. It is worth considering whether these variations are related to increased task familiarity over days, potentially accompanied by higher running speeds.

We agree with Reviewer 2 that familiarity increases across days, and the animal is likely running faster. The analysis for Fig. 3 and 4 includes only data from periods when the animal was at the goal and was not moving. We used linear mixed effects models to quantify the relationship between power, frequency and day or behavioral quintile.

(5) The stronger spiking modulation by local beta oscillations shown in Figure 6 could also be interpreted in the context of uncoupled beta between CA1 and PFC. In this analysis, only spikes occurring during beta bursts should be included, rather than all spikes within a trial. The authors should verify the dataset used and consider including a representative example illustrating beta modulation of single-unit spiking.

We agree with Reviewer 2 that the stronger modulation to local beta is another piece of evidence indicating uncoupled beta between the two regions. We appreciate this suggestion and will add examples illustrating beta modulation for single units. We want to clarify the spikes were only from periods when the animal is at the goal location on each trial and does not include the running period between goals.

(6) As observed in Figure 7D, CA1 beta bursts continue to occur even after 2.5 seconds following goal entry, when SWRs begin to emerge. Do these oscillations alternate over time, or do they coexist with some form of cross-frequency coupling?

This is a very interesting and helpful suggestion. Although we found SWRs generally appear later than beta bursts, it is possible the two are related on a finer timescale pointing to coordination. Our cross-correlation analysis between PFC and CA1 beta bursts only showed the relationship on the timescale of seconds. We will show a higher time-resolution version of this analysis in the revision.

Reviewer #3 (Public review):

Summary:

This paper explored the role of beta rhythms in the context of spatial learning and mPFC-hippocampal dynamics. The authors characterized mPFC and hippocampal beta oscillations, examining how their coordination and their spectral profiles related to learning and prefrontal neuronal firing. Rats performed two tasks, a Y-maze and an F-maze, with the F-maze task being more cognitively demanding. Across learning, prefrontal beta oscillation power increased while beta frequency decreased. In contrast, hippocampal beta power and beta frequency decreased. This was particularly the case for the well-performed and well-learned Y-maze paradigm. The authors identified the timing of beta oscillations, revealing an interesting shift in beta burst timing relative to reward entry as learning progressed. They also discovered an interesting population of prefrontal neurons that were tuned to both prefrontal beta and hippocampal sharp-wave ripple events, revealing a spectrum of SWR-excited and SWR-inhibited neurons that were differentially phase locked to prefrontal beta rhythms.

In sum, the authors set out to examine how beta rhythms and their coordination were related to learning and goal occupancy. The authors identified a set of learning and goal-related correlates at the level of LFP and spike-LFP interactions, but did not report on spike-behavioral correlates.

Strengths:

Pairing dual recordings of medial prefrontal cortex (mPFC) and CA1 with learning of spatial memory tasks is a strength of this paper. The authors also discovered an interesting population of prefrontal neurons modulated by both beta and CA1 sharp-wave ripple (SWR) events, showing a relationship between SWR-excited and SWR-inhibited neurons and beta oscillation phase.

Weaknesses:

Moreover, there is little detail provided about sample sizes and how data sampling is being performed (e.g., rats, sessions, or trials), raising generalizability concerns.

We appreciate Reviewer 3’s thoughtful suggestions for making our claims convincing. We will include information about sample sizes and address each detailed recommendation in the revised manuscript.

The authors report on a task where rats were performing sub-optimally (F-maze), weakening claims.

Our experiment was designed to allow us to examine within the same animal, a well-performed task (Y) and a less well-performed task (F). This contrast allows us to determine differences in neural correlates. We can further dissect the relevant differences to take advantage of this experiment design.

Likewise, it is questionable as to whether mPFC and hippocampus are dually required to perform a no-delay Y-maze task at day 5, where rats are performing near 100%.

We agree with Reviewer 3 that the mPFC and hippocampus may not be required when the animal reaches stable performance on day 5 (Deceuninck & Kloosterman, 2024). The data we collected spans the full range of early learning (day 1) to proficiency (day 5). We wanted to understand the dynamics of beta across these learning stages.

Recent studies suggest mPFC and hippocampus are likely to be needed, in some capacity, for learning continuous spatial alternation tasks on a range of maze geometries. Lesions, inactivation or waking activity perturbation of hippocampus or hippocampus and mPFC on the W maze alternation task slowed learning (Jadhav et al., 2012; Kim & Frank, 2009; Maharjan et al., 2018). More recently, optogenetic silencing of mPFC after sharp wave ripples on the Y maze alternation affected performance when the center arm was switched (den Bakker et al., 2023). The Y and F mazes in our study both share the continuous alternation rule, where the animal needed to avoid visiting a previously visited location on the outbound choice relative to the center, and always return to the center location.

Further, the performance characteristics on the outbound and inbound components of our Y task is similar to the W task. We have analyzed the “inbound” and “outbound” performance of the animals on the Y maze alternation task, and they are similar to the W maze alternation task. The “inbound” or reference location component is learned quickly whereas the ”outbound”, alternation component is learned slowly. We can add this analysis to the revised manuscript.

There would be little reason to suspect strong oscillatory coupling when task performance is poor and/or independent of mPFC-HPC communication (Jones and Wilson, 2005) potentially weakening conclusions about independent beta rhythms.

Although many studies have examined the oscillatory coupling properties at the theta frequency between mPFC-HPC (Hyman et al., 2005; Jones & Wilson, 2005; Siapas et al., 2005), our understanding of beta frequency coordination between the two regions is less established, especially at goal locations. Beta frequency coordination at goal locations may or may not follow similar properties to theta frequency coupling. In this manuscript we are reporting the properties of goal-location beta frequency activity in mPFC-HPC networks. We are not aware of prior work describing these properties at this stage of a spatial navigation task, especially their coordination in time.

References

Ahmed, O. J., & Mehta, M. R. (2012). Running speed alters the frequency of hippocampal gamma oscillations. J Neurosci, 32(21), 7373-7383. https://doi.org/10.1523/JNEUROSCI.5110-11.2012

Berke, J. D., Hetrick, V., Breck, J., & Greene, R. W. (2008). Transient 23-30 Hz oscillations in mouse hippocampus during exploration of novel environments. Hippocampus, 18(5), 519-529. https://doi.org/10.1002/hipo.20435

Deceuninck, L., & Kloosterman, F. (2024). Disruption of awake sharp-wave ripples does not affect memorization of locations in repeated-acquisition spatial memory tasks. Elife, 13. https://doi.org/10.7554/eLife.84004

den Bakker, H., Van Dijck, M., Sun, J. J., & Kloosterman, F. (2023). Sharp-wave-ripple-associated activity in the medial prefrontal cortex supports spatial rule switching. Cell Rep, 42(8), 112959. https://doi.org/10.1016/j.celrep.2023.112959

França, A. S., do Nascimento, G. C., Lopes-dos-Santos, V., Muratori, L., Ribeiro, S., Lobão-Soares, B., & Tort, A. B. (2014). Beta2 oscillations (23-30 Hz) in the mouse hippocampus during novel object recognition. Eur J Neurosci, 40(11), 3693-3703. https://doi.org/10.1111/ejn.12739

França, A. S. C., Borgesius, N. Z., Souza, B. C., & Cohen, M. X. (2021). Beta2 Oscillations in Hippocampal-Cortical Circuits During Novelty Detection. Front Syst Neurosci, 15, 617388. https://doi.org/10.3389/fnsys.2021.617388

Hyman, J. M., Zilli, E. A., Paley, A. M., & Hasselmo, M. E. (2005). Medial prefrontal cortex cells show dynamic modulation with the hippocampal theta rhythm dependent on behavior. Hippocampus, 15(6), 739-749. https://doi.org/10.1002/hipo.20106

Iwasaki, S., Sasaki, T., & Ikegaya, Y. (2021). Hippocampal beta oscillations predict mouse object-location associative memory performance. Hippocampus, 31(5), 503-511. https://doi.org/10.1002/hipo.23311

Jadhav, S. P., Kemere, C., German, P. W., & Frank, L. M. (2012). Awake hippocampal sharp-wave ripples support spatial memory. Science (New York, N.Y.), 336(6087), 1454-1458. https://doi.org/10.1126/science.1217230

Jones, M. W., & Wilson, M. A. (2005). Theta Rhythms Coordinate Hippocampal–Prefrontal Interactions in a Spatial Memory Task. PLoS Biology, 3(12). https://doi.org/10.1371/journal.pbio.0030402

Kim, S. M., & Frank, L. M. (2009). Hippocampal Lesions Impair Rapid Learning of a Continuous Spatial Alternation Task. PLoS ONE, 4(5). https://doi.org/10.1371/journal.pone.0005494

Lansink, C. S., Meijer, G. T., Lankelma, J. V., Vinck, M. A., Jackson, J. C., & Pennartz, C. M. (2016). Reward Expectancy Strengthens CA1 Theta and Beta Band Synchronization and Hippocampal-Ventral Striatal Coupling. J Neurosci, 36(41), 10598-10610. https://doi.org/10.1523/JNEUROSCI.0682-16.2016

Maharjan, D. M., Dai, Y. Y., Glantz, E. H., & Jadhav, S. P. (2018). Disruption of dorsal hippocampal - prefrontal interactions using chemogenetic inactivation impairs spatial learning. Neurobiol Learn Mem, 155, 351-360. https://doi.org/10.1016/j.nlm.2018.08.023

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Siapas, A. G., Lubenov, E. V., & Wilson, M. A. (2005). Prefrontal Phase Locking to Hippocampal Theta Oscillations. Neuron, 46(1), 141-151. https://doi.org/10.1016/j.neuron.2005.02.028.

DOI: 10.3389/fphar.2021.747975

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

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DOI: 10.1016/j.isci.2025.114567

Resource: RRID:IMSR_JAX:007909

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:007909

What is this?

Looking forward to reading and having conversations in the Open Learning Commons, the Indy Learning Commons

indyweb.link

google.search-open_learning_commons

around the latest salient synthesis of ideas we discussed 4 yours ago

This would server as a nice minimal viable exemplar of - open - commons based - local-first - person-first - permanent - evergreen - inter - personal - planetary

networked co-laboration initiated n the annotation margins

with a mutually beneficial interplay between the Centralized Web and the permanent interplnetary Autnonomous People Centered Web, the emergent alternative: IndyWeb

This is a pretty clever way to protest without getting in trouble. the donkey is just walking around with no idea it's part of a political statement, so you can't really punish it for anything. It's interesting how the protesters split up the different parts of protesting so that the actual messenger (the donkey) has no clue what's going on, while they get to stay anonymous and spread their message.

Transparent Peer Review

Download the complete Review Process [PDF] including:

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Note that this chapter was adapted from a conference paper. It has a certain conversational tone.

Please look up the author's scholarly biography before reading the article.

BIOLOGICAL AGE

FACTORS THAT DETERMINE THE RATE AT WHICH OUR BODY AGES: NUTRITION, LEVEL OF PHYSICAL ACTIVITY, SLEEPING HABITS, SMOKING, ALCOHOL CONSUMPTION, HOW WE MENTALLY HANDLE STRESS, & GENETIC HISTORY OF ANCESTORS

the gradual reduction in numbers or strength due to factors like resignation, retirement, or wear and tear

shared characteristics or trends among people born around the same time (a cohort) due to common historical, cultural, or environmental experiences that influence their lives, shaping their behaviors, values, and health outcomes differently from other generations

LIFE EXPECTANCY

LIFESPAN(LONGEVITY): REFERRING TO THE LENGTH OF TIME A SPECIES CAN EXIST UNDER THE MOST OPTIMAL CONDITIONS.

NEW AND STILL BEING EXPLORED SINCE IT DOESN'T APPLY TO ALL CULTURES IN THE SAME WAY

• EXAMPLES INCLUDE: RIGHT VS WRONG, WHAT TO EAT, HOW TO SPEAK

• IT TEACHES US: HOW TO LIVE IN A SOCIETY AND ADVANCE IN SOLUTIONS THAT CAN BENEFIT NEW GENERATIONS

• LEARNED FROM: PARENTS, SCHOOLS, HOUSES OF WORSHIP, MEDIA, FRIENDS, AND OTHERS THROUGHOUT A LIFETIME

UNIQUE EXPERIENCES MAY SHAPE OUR DEV--CHILD LOSING A PARENT AT A YOUNG AGE--NOT A TYPICAL EXPERIENCE OF THE AGE GROUP.

COHORT SHAPED BY TIME PERIOD THEY WERE BORN LEADING THEM TO LIVE LIFE EXPERIENCING SIMILAR CIRCUMSTANCES.

1. DEVELOPMENT IS MULTIDISCIPLINARY

Entering a new never seen before country must've been very exciting!

I like this Korean YouTuber's travel content generally and his YP review gets his trademark thorough treatment. He doesn't like YP all that much, but you can draw your own conclusions from the footage: https://youtu.be/nlreX8MIJvc?si=Rfx__exB84ldBNnD

I think the use of words in this sentence provides a look into the harsh realities of the sea voyages. However, I did find this primary source a little more confusing so I could be wrong.

This sentence just expresses to me the sad reality of what slavery entailed. It was all about a profit, no one cared about the feelings or emotions of others.

I've wondered what both the Native Americans and the Europeans reactions would be towards each other. Both cultures are very different, and I would imagine it would be very scary meeting someone from a totally different background than you for the first time.

This is microeconomics because it focuses on the choice of singular household rather than the economy whole. This illustrates economic way of thinking because it considers all the costs and trade offs of raising a child.

This sentence is amusing the Europeans are using comparisons to describe unfamiliar species to other European listeners. They definitely used interesting wording.

I wonder why he ordered that odd request?

I think that visiting an area you've never seen before for the first time must be a very exzotic experience. It would be so cool to see all the natural resources there.

In my previous History classes we had to learn how to read primary sources and determine reliability for them. This sentence clearly indicates that the whole excerpt is going to be biased. Theis author clearly admires Hawkins , and this positive language makes him sound heroic.

I wonder where they got the idea that cannibalism was an intricate part of Native American life? How did this get so misinterpreted?

I again see Europeans claiming land through religion and politics. This shows that religions seems to be a tool for exploration and control.

This sentence to me feels like Europeans believed planting a flag in the ground meant they owned the land. They seem to ignore all the evidence of the people who may live there.

I think it's interesting that Erik gave up this amazing voyage just because he was injured slightly. He just turned around and left the whole plan when one thing didn't go his way. I wonder if he was really injured, or it was an excuse not to partake in the adventure.

The scientific method of procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses.

This is more for you: Do you discourage test taking in academia generally speaking?

There was a cartoon of two women; one a muslim one with her body covered, and a white women with a bikini and sunglasses. I appreciate this part of the syllabus because it outlines the level of detail required to explain one's idea and individual perspective.

This reminds me of Lara Boyd's ted talk and how she Acknowledges healthy brain exercises. Activities together as classmates based off of a variety of interesting texts sounds like amazing weekly brain exercising!

The thought of innovating the way I participate in discourse and analyze discourse is exciting. My interpretation is we will be pushing ourselves to learn outside of our comfort zone in different ways throughout the semester.

This book generally seems up my alley, especially with the environmental disaster aspect! I recently read the book: I Cheerfully refuse and I imagine there will be some similarities.

I've always seem Schizophrenia as a taboo subject amongst societal discourse so I'm very curious and excited for this reading.

Paraphrase The author talks about how attractive a woman's voice can be but also be misleading relating to how Sirens use their songs to lure sailors in and killing them like women do to wallets, strength, and cause the loss of God.

Interpretation The paragraph displays a negative connotation of women even considering them dangerous by comparing them to Sirens.

5c It shows me the context of how women were viewed and valued in society back then

I really appreciate this being a section in the syllabus. I appreciate that the professor has taken the time to express that all students will be valued. I also appreciate the note of "no student is expected to speak for all members." I think that is just as important of a sentiment.

I am excited for a variety of assignments with lower stakes. In previous classes with only a few grades available. I tend to have much more stress and end up performing worse.

I find it very interesting that we must complete all assignments in Google Docs. While I understand it is for ensuring that work is original, I have never seen or heard of a professor using this method.

I find this to be a very fair policy. AI has a time and a place, but I believe it is not in a college writing course. I appreciate that the professor wishes the students to do well and will offer grace, but am glad that boundaries are set as well.

"However, exactly how many triplets code amino acids and how many have other functions we are unable to say." This makes more sense in relation to a comment I made at the very beginning of the paper. Still, the fact that the idea was even suggested during this period of time is impressive.