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Curator: @dhovakimyan1
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ATCC CRL-4001
DOI: 10.7554/eLife.96414
Resource: (ATCC Cat# CRL-4001, RRID:CVCL_6573)
Curator: @dhovakimyan1
SciCrunch record: RRID:CVCL_6573
ATCC CCL-2
DOI: 10.7554/eLife.96414
Resource: (TKG Cat# TKG 0331, RRID:CVCL_0030)
Curator: @dhovakimyan1
SciCrunch record: RRID:CVCL_0030
ATCC CCL-171
DOI: 10.7554/eLife.96414
Resource: (KCLB Cat# 10171, RRID:CVCL_0440)
Curator: @dhovakimyan1
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RRID:Addgene_10825
DOI: 10.7554/eLife.96414
Resource: RRID:Addgene_10825
Curator: @scibot
SciCrunch record: RRID:Addgene_10825
CVCL_A304
DOI: 10.7554/eLife.96414
Resource: None
Curator: @scibot
SciCrunch record: RRID:CVCL_A304
FreeSurfer
DOI: 10.7554/eLife.106464.1
Resource: FreeSurfer (RRID:SCR_001847)
Curator: @areedewitt04
SciCrunch record: RRID:SCR_001847
MotionCor2
DOI: 10.7554/eLife.106455.1
Resource: MotionCor2 (RRID:SCR_016499)
Curator: @areedewitt04
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DOI: 10.7554/eLife.106303.1
Resource: GenScript (RRID:SCR_002891)
Curator: @areedewitt04
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RRID:SCR_003070
DOI: 10.7554/eLife.98634
Resource: ImageJ (RRID:SCR_003070)
Curator: @scibot
SciCrunch record: RRID:SCR_003070
RRID:ZIRC_ZL1
DOI: 10.7554/eLife.98634
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Curator: @scibot
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DOI: 10.7554/eLife.98634
Resource: GraphPad Prism (RRID:SCR_002798)
Curator: @scibot
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DOI: 10.7554/eLife.91609
Resource: Thermo Q Exactive Plus Hybrid Quadrupole-Orbitrap Mass Spectrometer (RRID:SCR_020556)
Curator: @scibot
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DOI: 10.7554/eLife.91609
Resource: EASY-nLC 1000 Liquid Chromatograph (RRID:SCR_014993)
Curator: @scibot
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DOI: 10.7554/eLife.91609
Resource: (Synaptic Systems Cat# 173 004, RRID:AB_10641162)
Curator: @scibot
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DOI: 10.7554/eLife.91609
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DOI: 10.7554/eLife.91609
Resource: Affinity Designer (RRID:SCR_016952)
Curator: @scibot
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DOI: 10.7554/eLife.91609
Resource: (Vector Laboratories Cat# H-1400, RRID:AB_2336787)
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Resource: (Molecular Probes Cat# A-11076, RRID:AB_141930)
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Resource: GraphPad Prism (RRID:SCR_002798)
Curator: @scibot
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DOI: 10.7554/eLife.91609
Resource: (Thermo Fisher Scientific Cat# N21482, RRID:AB_2620170)
Curator: @scibot
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DOI: 10.7554/eLife.91609
Resource: GuPPy (RRID:SCR_022353)
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DOI: 10.7554/eLife.91609
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SciCrunch record: RRID:Addgene_100845
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DOI: 10.7554/eLife.91609
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Resource: Python Programming Language (RRID:SCR_008394)
Curator: @scibot
SciCrunch record: RRID:SCR_008394
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DOI: 10.7554/eLife.88588
Resource: (Sigma-Aldrich Cat# F3165, RRID:AB_259529)
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DOI: 10.7554/eLife.88588
Resource: GraphPad Prism (RRID:SCR_002798)
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DOI: 10.7554/eLife.88588
Resource: Fiji (RRID:SCR_002285)
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DOI: 10.7554/eLife.88588
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DOI: 10.7554/eLife.88588
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DOI: 10.7554/eLife.88588
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DOI: 10.7554/eLife.88588
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Curator: @scibot
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DOI: 10.7554/eLife.88588
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DOI: 10.7554/eLife.101984
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Curator: @scibot
SciCrunch record: RRID:SCR_002798
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DOI: 10.7554/eLife.101984
Resource: MaxQuant (RRID:SCR_014485)
Curator: @scibot
SciCrunch record: RRID:SCR_014485
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DOI: 10.7554/eLife.101984
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Curator: @scibot
SciCrunch record: RRID:SCR_008520
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DOI: 10.7554/eLife.101984
Resource: ConsensusPathDB (RRID:SCR_002231)
Curator: @scibot
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DOI: 10.7554/eLife.101984
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Curator: @scibot
SciCrunch record: RRID:SCR_002285
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DOI: 10.7554/eLife.101984
Resource: Gene Set Enrichment Analysis (RRID:SCR_003199)
Curator: @scibot
SciCrunch record: RRID:SCR_003199
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DOI: 10.7554/eLife.101984
Resource: Systems Transcriptional Activity Reconstruction (RRID:SCR_005622)
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DOI: 10.7554/eLife.101984
Resource: Ingenuity Pathway Analysis (RRID:SCR_008653)
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DOI: 10.7554/eLife.101984
Resource: Bowtie (RRID:SCR_005476)
Curator: @scibot
SciCrunch record: RRID:SCR_016368
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DOI: 10.7554/eLife.101984
Resource: Cuffdiff (RRID:SCR_001647)
Curator: @scibot
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DOI: 10.7554/eLife.101984
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DOI: 10.7554/eLife.101984
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DOI: 10.7554/eLife.101984
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DOI: 10.7554/eLife.101984
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DOI: 10.7554/eLife.101984
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DOI: 10.7554/eLife.101984
Resource: (BD Biosciences Cat# 610456, RRID:AB_397829)
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DOI: 10.7554/eLife.101984
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SciCrunch record: RRID:IMSR_JAX:007676
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DOI: 10.7554/eLife.101984
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SciCrunch record: RRID:Addgene_52961
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DOI: 10.7554/eLife.101984
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SciCrunch record: RRID:IMSR_JAX:003554
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DOI: 10.7554/eLife.101984
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DOI: 10.7554/eLife.101984
Resource: (IMSR Cat# JAX_005584,RRID:IMSR_JAX:005584)
Curator: @scibot
SciCrunch record: RRID:IMSR_JAX:005584
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DOI: 10.7554/eLife.101984
Resource: None
Curator: @scibot
SciCrunch record: RRID:MGI:3629214
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DOI: 10.7554/eLife.101984
Resource: (IMSR Cat# JAX_023047,RRID:IMSR_JAX:023047)
Curator: @scibot
SciCrunch record: RRID:IMSR_JAX:023047
These observations suggest that the pleiotropic effects of yellow on male mating success might result from effects of yellow in the adult CNS, particularly in fru-expressing neurons.
This sentence really shows how complex and interconnected gene function can be. It's not just that the yellow gene affects pigmentation, it might also influence behavior through its role in specific brain cells.
It seemed worthwhile therefore to examine more closely one example of a gene mutation affecting behavior and to ask two questions, (1) how does it bring about its effect? [and], (2) what part might it play in evolution?”
Even back in 1956, Bastock was asking questions that are still relevant today, not just about how a gene affects behavior, but also what that means in terms of evolution.
The study reveals the importance of scientists considering that genes that affect behavior may do so by changing anatomy rather than by altering the brain
This is a reminder to scientists (and us) that behavior isn’t just about brain chemistry; sometimes it’s about how the body is built.
Yellow protein is expressed in sex combs (Hinaux et al., 2018, Figure 3G,H), where it is presumably required for synthesis of black dopamine melanin in the sex comb ‘teeth’.
Melanization = adding dark pigment (melanin) to certain body parts. In this case, melanization makes the sex combs stronger and more rigid, helping males grab females. Without yellow, the sex combs are lighter and weaker.
t expression of yellow in fru-expressing cells is neither necessary nor sufficient for yellow’s effect on male mating success.
Yellow doesn’t need to be in brain cells to affect mating; the problem is somewhere else.
The yellow males lack melanin pigments in their sex combs, which changes their structure.
Key shift from neural to anatomical explanation, behavioral defect actually caused by structural changes in sex combs, not brain chemistry.
However, we found that suppressing yellow expression in the larval CNS, dopaminergic neurons, or serotonergic neurons (Figure 2—figure supplement 3, FET, P values ranging from 0.45 to 1), or in all neurons (Figure 2E, FET, p=1 in all cases) or all glia (Figure 2F, FET, p=1), had no significant effect on male mating success.
This result was surprising because it shows that turning off yellow in key parts of the nervous system—including neurons involved in mood and movement—does not affect male mating behavior. This suggests that yellow doesn’t act in the brain or nerves to influence mating, challenging the original hypothesis.
We hypothesized that the MRS might contain an enhancer driving yellow expression and found that ChIP-seq data indicate the Doublesex (Dsx) transcription factor binds to this region in vivo (Clough et al., 2014).
This shows that the yellow gene might be turned on by a DNA region called the MRS, which is controlled by the Doublesex (Dsx) protein. Since Dsx helps control male and female behaviors in flies, this suggests that Dsx might directly control yellow to help males mate successfully.
(MRS)
Stands for Male Reproductive System. It includes the testes (sperm is made), seminal vesicles (store sperm), accessory glands (affects female behavior after mating), ejaculatory duct and bulb (transfer sperm to female)
insect pigment genes cause changes in the fly’s brain because these pigments are made from dopamine, a chemical messenger that acts in the brain.
These are actions or signals that males use to attract a mate, and convince those mates to reproduce offspring with them.
courtship behaviors
These are actions or signals that males use to attract a mate, and convince those mates to reproduce offspring with them.
RNA oligonucleotides
This is short, synthetic strands of RNA, typically ranging from 2 to 50 nucleotides in length, used in various molecular biology applications
template secondary structures.
This is the folded conformations that a DNA or RNA template can adopt due to intramolecular base pairing, forming structures like hairpins or stem-loops, It can interrupt or intervene with DNA replication, transcription, translation and etc.
no replicase ribozyme has been observed in existing life forms;
Most likely because they have been replaced by more efficient protein enzymes due to evolution.
ATCC (CRL-2593
DOI: 10.7554/eLife.106265.1
Resource: (ATCC Cat# CRL-2593, RRID:CVCL_5440)
Curator: @areedewitt04
SciCrunch record: RRID:CVCL_5440
MATLAB
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Psychophysics Toolbox
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SciCrunch record: RRID:SCR_002881
PyMol
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SciCrunch record: RRID:SCR_000305
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SciCrunch record: RRID:SCR_015872
Chimera
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SciCrunch record: RRID:SCR_004097
Jackson Lab (Stock No. 027410
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Environmental change is actually a complex of changes, both abiotic and biotic.
Abiotic factors like temperature and humidity affect D. melanogaster's growth and survival, with optimal conditions boosting development and extremes causing harm. Biotic factors like helpful microbes support health, while predators, pathogens, and competition can reduce survival.
Their decaying host resources are also home to many microbes, as well as to other arthropods
Many of the microbes include yeast and bacteria, and for the arthropods they include other Drosophila flies, mites, beetles and ants. These microbes and arthropods benefit each other. For example the Saccharomyces cerevisiae (baker’s yeast) is often found on rotting fruit; provides nutrients and volatiles that attract flies. The arthropods, such as the mites, beetles and ants, can often be harmful to the Drosophila flies, as they do compete for food and nutrients and often prey on eggs or larvae.
entomologist
A scientist who studies insects.
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While found in association with these other species, D. melanogaster colonizes the rotting fruits at a particular time during the decay trajectory. First to arrive is D. simulans, followed by D. melanogaster, and then the other species (Nunney, 1990, 1996): this is consistent with D. melanogaster having a higher ethanol tolerance than its relative D. simulans (McKenzie and Parsons 1972, 1974), which arrives earlier, when fewer volatiles have been produced by fermentation.
I never realized there was a specific trajectory for when different species can colonize rotting fruit.
The resultant 2007 publication by the Drosophila 12 Genomes Consortium et al., 2007, of 12 genomes and their analysis, has rapidly revolutionized and expanded the utility of the Drosophila system for studies ranging from computational biology and embryology, to evolution and human disease.
This highlights how genomic data that has been gathered from these flies has opened new interdisciplinary avenues, extending the relevance of fruit fly studies beyond basic biology to broader, even medical, fields.
While these culture conditions keep flies ‘healthy’ by laboratory standards, they do not represent the conditions that D. melanogaster experience in nature.
This sentence draws attention to the artificiality of lab environments and introduces the idea that scientific observations made in such controlled settings may not fully reflect natural behaviors or biology that one might find in the wild. It also implies the assumption that lab findings are universally applicable, reminding us that environmental context matters especially when it comes to genetics.
The fact that this fly is an ecological generalist no doubt contributed to the facility with which it was initially propagated in the laboratory, rapidly becoming a popular model system.
This sentence highlights how D. melanogaster’s adaptability to a variety of environments (being an "ecological generalist") made it particularly well-suited for laboratory use and a very common species used for genetic testing.
From its first use in the laboratory in the early 1900s until the present day, Drosophila melanogaster has been central to major breakthroughs in genetics. The use of this fruit fly as a model organism
The phrase “central to major breakthroughs” emphasizes not just its utility, but its foundational role in some of biology’s most important discoveries. It introduces the idea that this fly has played a critical role in the development of genetics as a field.
The number of investigators using D. melanogaster as a model for studying human disease is steadily rising (Pfleger and Reiter, 2008), especially for more complex disorders, such as heart disease (Piazza and Wessells, 2011), mental and neurological illness (Pandey and Nichols, 2011), and obesity
Prior to reading this, I would not have known how closely related humans and D. melanogaster were related to each other to be able to study different diseases, especially complex diseases and obesity
Females lay their eggs in necrotic material
So, my guess is that the necrotic decomposition phase is able to host all life cycles of the eggs. However, why do they still lay eggs on fresh items and food if it has not started through the decomp phase?
From its first use in the laboratory in the early 1900s until the present day, Drosophila melanogaster has been central to major breakthroughs in genetics.
I had no clue how big of a role the fruit fly plays in genetic research. As i read more, I find out just how much they are studied in the lab versus in the wild.
Approximately 65% of human disease genes are estimated to have counterparts in D. melanogaster
This demonstrates the genetic similarity between D. melanogaster and humans, reinforcing its value as a model organism for studying human disease mechanisms.
Few studies of D. melanogaster have been done in the wild, but those that have reveal a different picture of wild flies.
This highlights the research gap between laboratory and field studies. Understanding wild populations is important to get a complete picture of the species' biology and evolution.
The genes that control these behavioural differences can hold clues to controlling the reproduction of economically and medically important insects, such as testse flies and mosquitoes
Understanding the genetic basis of reproductive behaviors could inform pest control strategies to reduce disease transmission and crop damage.
Reproductive behavior and biology, while extensively studied in the laboratory, is less well-understood in the wild.
Laboratory conditions often simplify or alter natural behaviors, so findings may not fully reflect what occurs in nature.
We hypothesize that these dynamic interactions, coupled with the potentially high metabolic cost of toxins (Nisani et al., 2012), have driven the evolution of a distinct venom composition in each developmental stage.
Interesting when reading up on this particular section, each pupal phase creates its own set of venom mixtures which helps it to balance energy and stay protected!
recombinant form
This refers to the biological molecule which is usually RNA, DNA or protein that is combined artificially from different sources. It is considered genetic engineering.
Our finding of different expression levels of toxins in different developmental stages and adult tissues strongly suggests that venom composition changes across development and that each arsenal of toxins might have been shaped by selection for different biotic interactions. As Nematostella develops from a non-predatory, swimming larva to an adult sessile predatory polyp that is 150-fold larger than the larva (Figure 1A), its interspecific interactions vastly change across development.
This makes sense because just as humans change and develop across different stages of life, venom composition would do the same
The results of the experiments showed that Nematostella mothers pass on a toxin to their eggs that makes them unpalatable to predators.
That is a very interesting finding because being able to pass along this toxin from mothers to their eggs is not something I think I have ever heard of to keep predators away.
Variation in expression patterns of the NEP3 family members and the fact that at least four different types of gland cells at distinct developmental stages and tissues express different toxins (Nv1, Nvlysin1b, NEP6 and NvePTx1) in Nematostella suggests a highly complex venom landscape in this species
Maybe this variety and diversity potentially keeps prey from getting used to the venom.
The results of the ISH and nCounter experiments indicated that NvePTx1 is maternally deposited at the RNA level.
It makes sense that the venom isn't made by the larvae with the mother providing this to them so they can hunt right away.
Strikingly, within 10 min from the start of the incubation 3 out of 8 Artemia were paralyzed or dead, and within 90 min 7 of 8 were dead (Video 1), whereas in a control group without planulae all Artemia were alive.
I find it impressive that such a small larva can kill something bigger. This shows it probably has very strong venom.
change dramatically between developmental stages of this species
I wonder if this variation in venom could be linked to specific prey types available at each stage.
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