AssayResult: 75.85

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 4.78

StandardErrorMean: 3.38

Comment: Exact values reported in “Source Data” file.

AssayResult: 94.33

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 9.99

StandardErrorMean: 7.07

Comment: Exact values reported in “Source Data” file.

AssayResult: 102.58

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 2.19

StandardErrorMean: 1.55

Comment: Exact values reported in “Source Data” file.

AssayResult: 21.79

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 1.84

StandardErrorMean: 1.3

Comment: Exact values reported in “Source Data” file.

AssayResult: 95.86

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 1.62

StandardErrorMean: 1.15

Comment: Exact values reported in “Source Data” file.

AssayResult: 91.21

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 7.32

StandardErrorMean: 5.18

Comment: Exact values reported in “Source Data” file.

AssayResult: 10.59

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 0.6

StandardErrorMean: 0.43

Comment: Exact values reported in “Source Data” file.

AssayResult: 76.97

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 3.1

StandardErrorMean: 2.19

Comment: Exact values reported in “Source Data” file.

AssayResult: 7.92

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 1.32

StandardErrorMean: 0.94

Comment: Exact values reported in “Source Data” file.

AssayResult: 38.85

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 3.05

StandardErrorMean: 2.15

Comment: Exact values reported in “Source Data” file.

AssayResult: 16.89

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 3.12

StandardErrorMean: 2.21

Comment: Exact values reported in “Source Data” file.

AssayResult: 17.62

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 1.76

StandardErrorMean: 1.25

Comment: Exact values reported in “Source Data” file.

AssayResult: 86.41

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 17.62

StandardErrorMean: 12.46

Comment: Exact values reported in “Source Data” file.

AssayResult: 6.16

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 1.27

StandardErrorMean: 0.9

Comment: Exact values reported in “Source Data” file.

AssayResult: 95.16

AssayResultAssertion: Not reported

ReplicateCount: 3

StandardDeviation: 16.94

StandardErrorMean: 9.78

Comment: Exact values reported in “Source Data” file.

AssayResult: 7.08

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 0.54

StandardErrorMean: 0.38

Comment: Exact values reported in “Source Data” file.

AssayResult: 14.01

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 0.53

StandardErrorMean: 0.38

Comment: Exact values reported in “Source Data” file.

AssayResult: 74.49

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 2.29

StandardErrorMean: 1.62

Comment: Exact values reported in “Source Data” file.

AssayResult: 6.53

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 0.29

StandardErrorMean: 0.21

Comment: Exact values reported in “Source Data” file.

AssayResult: 30.27

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 1.04

StandardErrorMean: 0.74

Comment: Exact values reported in “Source Data” file.

AssayResult: 7.63

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 0.51

StandardErrorMean: 0.36

Comment: Exact values reported in “Source Data” file.

AssayResult: 63.4

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 5.92

StandardErrorMean: 4.18

Comment: Exact values reported in “Source Data” file.

AssayResult: 60.28

AssayResultAssertion: Not reported

ReplicateCount: 3

StandardDeviation: 0.14

StandardErrorMean: 0.1

Comment: Exact values reported in “Source Data” file.

AssayResult: 17.35

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 6.21

StandardErrorMean: 4.39

Comment: Exact values reported in “Source Data” file.

AssayResult: 106.23

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 14.57

StandardErrorMean: 10.3

Comment: Exact values reported in “Source Data” file.

AssayResult: 75.71

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 22.31

StandardErrorMean: 15.77

Comment: Exact values reported in “Source Data” file.

AssayResult: 6.66

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 0.28

StandardErrorMean: 0.2

Comment: Exact values reported in “Source Data” file.

AssayResult: 6.1

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 2.11

StandardErrorMean: 1.49

Comment: Exact values reported in “Source Data” file.

AssayResult: 84.07

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 2.47

StandardErrorMean: 1.75

Comment: Exact values reported in “Source Data” file.

AssayResult: 100.07

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 6.18

StandardErrorMean: 4.37

Comment: Exact values reported in “Source Data” file.

AssayResult: 91.6

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 8.2

StandardErrorMean: 5.8

Comment: Exact values reported in “Source Data” file.

AssayResult: 82.83

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 4.82

StandardErrorMean: 3.41

Comment: Exact values reported in “Source Data” file.

AssayResult: 87.35

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 11.94

StandardErrorMean: 8.44

Comment: Exact values reported in “Source Data” file.

AssayResult: 83.25

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 5.27

StandardErrorMean: 3.73

Comment: Exact values reported in “Source Data” file.

AssayResult: 7.03

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 2.68

StandardErrorMean: 1.9

Comment: Exact values reported in “Source Data” file.

AssayResult: 77.45

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 6.2

StandardErrorMean: 4.38

Comment: Exact values reported in “Source Data” file.

AssayResult: 9.92

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 1.93

StandardErrorMean: 1.37

Comment: Exact values reported in “Source Data” file.

AssayResult: 95.02

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 0.08

StandardErrorMean: 0.06

Comment: Exact values reported in “Source Data” file.

AssayResult: 10.4

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 3.22

StandardErrorMean: 2.28

Comment: Exact values reported in “Source Data” file.

AssayResult: 7.75

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 2.59

StandardErrorMean: 1.83

Comment: Exact values reported in “Source Data” file.

AssayResult: 75.45

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 4.03

StandardErrorMean: 2.85

Comment: Exact values reported in “Source Data” file.

AssayResult: 98.55

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 5.74

StandardErrorMean: 4.06

Comment: Exact values reported in “Source Data” file.

AssayResult: 62.31

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 11.49

StandardErrorMean: 8.13

Comment: Exact values reported in “Source Data” file.

AssayResult: 66.19

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 21.26

StandardErrorMean: 15.03

Comment: Exact values reported in “Source Data” file.

AssayResult: 105.41

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 9.45

StandardErrorMean: 6.68

Comment: Exact values reported in “Source Data” file.

AssayResult: 7.82

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 2.31

StandardErrorMean: 1.64

Comment: Exact values reported in “Source Data” file.

AssayResult: 92.32

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 2.26

StandardErrorMean: 1.6

Comment: Exact values reported in “Source Data” file.

AssayResult: 44.9

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 9.75

StandardErrorMean: 6.89

Comment: Exact values reported in “Source Data” file.

AssayResult: 97.61

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 0.97

StandardErrorMean: 0.68

Comment: Exact values reported in “Source Data” file.

AssayResult: 11.28

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 1.24

StandardErrorMean: 0.87

Comment: Exact values reported in “Source Data” file.

AssayResult: 86.67

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 2.24

StandardErrorMean: 1.58

Comment: Exact values reported in “Source Data” file.

HGVS: NM_024675.3:c.1010T>C p.(L337S)

AssayGeneralClass: BAO:0000062 patch clamp

AssayMaterialUsed: CLO:0037237 HEK293-derived cell

AssayDescription: HEK293T-derived cells stably expressing wild type or variant SCN5A were patch clamped and currents elicited in response to activation, inactivation, and recovery from inactivation were recorded, as well as late current measurements.

AssayReadOutDescription: Peak current density relative to wild type, which was set to 100%

AssayRange: %

AssayNormalRange: Peak current density 75-125% of wild type

AssayAbnormalRange: Peak current density 10-50% of wildtype

AssayIndeterminateRange: Peak current density 50-75% of wildtype

ValidationControlPathogenic: 0

ValidationControlBenign: 10

Replication: At least 2 independent transfections and at least 10 replicate cells per variant (see ReplicateCount in FunctionalAssayResult annotations for each variant).

StatisticalAnalysisDescription: Two-tailed t tests or two-tailed Mann-Whitney U tests were used to compare variant parameters between groups of variants, while differences in dispersion between groups were tested with Levene’s test.

AssayResult: 100

AssayResultAssertion: Normal

ReplicateCount: 471

StandardErrorMean: 3.7

ControlType: Normal; wild type

Comment: This variant (wildtype) had normal function. All other variant parameters were normalized to the values of wildtype. (Personal communication: A. Glazer)

AssayResult: 59.3

AssayResultAssertion: Indeterminate

ReplicateCount: 30

StandardErrorMean: 8.3

Comment: This variant had mild loss of function (peak current >50% and <75% of wildtype), therefore it was considered inconclusive and neither abnormal nor normal in vitro function. (Personal communication: A. Glazer)

AssayResult: 28.4

AssayResultAssertion: Abnormal

ReplicateCount: 13

StandardErrorMean: 8.6

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 45.3

AssayResultAssertion: Abnormal

ReplicateCount: 31

StandardErrorMean: 5.1

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1).

AssayResult: 37.2

AssayResultAssertion: Abnormal

ReplicateCount: 26

StandardErrorMean: 3.8

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 23.1

AssayResultAssertion: Abnormal

ReplicateCount: 33

StandardErrorMean: 3.2

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 89.5

AssayResultAssertion: Normal

ReplicateCount: 29

StandardErrorMean: 14.6

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 0.9

AssayResultAssertion: Abnormal

ReplicateCount: 18

StandardErrorMean: 0.5

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 5.4

AssayResultAssertion: Abnormal

ReplicateCount: 19

StandardErrorMean: 1.5

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 14.8

AssayResultAssertion: Abnormal

ReplicateCount: 27

StandardErrorMean: 2.5

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 78.9

AssayResultAssertion: Normal

ReplicateCount: 38

StandardErrorMean: 7.2

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 43.3

AssayResultAssertion: Abnormal

ReplicateCount: 14

StandardErrorMean: 12.2

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 59.7

AssayResultAssertion: Indeterminate

ReplicateCount: 41

StandardErrorMean: 6.3

Comment: This variant had mild loss of function (peak current >50% and <75% of wildtype), therefore it was considered inconclusive and neither abnormal nor normal in vitro function. (Personal communication: A. Glazer)

AssayResult: 10.2

AssayResultAssertion: Abnormal

ReplicateCount: 12

StandardErrorMean: 3.4

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 0.3

AssayResultAssertion: Abnormal

ReplicateCount: 24

StandardErrorMean: 0.3

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 0

AssayResultAssertion: Abnormal

ReplicateCount: 11

StandardErrorMean: 0

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 3

AssayResultAssertion: Abnormal

ReplicateCount: 16

StandardErrorMean: 1.5

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 32.6

AssayResultAssertion: Abnormal

ReplicateCount: 10

StandardErrorMean: 6.2

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 36

AssayResultAssertion: Abnormal

ReplicateCount: 14

StandardErrorMean: 6

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 13.9

AssayResultAssertion: Abnormal

ReplicateCount: 15

StandardErrorMean: 2.8

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 3.5

AssayResultAssertion: Abnormal

ReplicateCount: 29

StandardErrorMean: 0.8

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 0.2

AssayResultAssertion: Abnormal

ReplicateCount: 25

StandardErrorMean: 0.2

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 102.6

AssayResultAssertion: Normal

ReplicateCount: 31

StandardErrorMean: 16.5

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 1.6

AssayResultAssertion: Abnormal

ReplicateCount: 15

StandardErrorMean: 0.7

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 12

AssayResultAssertion: Abnormal

ReplicateCount: 10

StandardErrorMean: 2.2

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 102.4

AssayResultAssertion: Normal

ReplicateCount: 39

StandardErrorMean: 15.5

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 47

AssayResultAssertion: Indeterminate

ReplicateCount: 10

StandardErrorMean: 15.5

Comment: This variant had a mix of multiple abnormalities: a partial loss of function of peak current (10-50% of wildtype) and a gain of function >10mV shift in activation voltage. Therefore it was considered to have inconclusive in vitro properties (neither normal nor abnormal in vitro function). (Personal communication: A. Glazer)

AssayResult: 114.7

AssayResultAssertion: Normal

ReplicateCount: 42

StandardErrorMean: 15.2

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 36

AssayResultAssertion: Abnormal

ReplicateCount: 19

StandardErrorMean: 5.9

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 121.4

AssayResultAssertion: Normal

ReplicateCount: 34

StandardErrorMean: 13.2

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 1.1

AssayResultAssertion: Abnormal

ReplicateCount: 27

StandardErrorMean: 0.8

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 29.8

AssayResultAssertion: Abnormal

ReplicateCount: 13

StandardErrorMean: 5.7

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 3.2

AssayResultAssertion: Abnormal

ReplicateCount: 16

StandardErrorMean: 0.5

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 0.8

AssayResultAssertion: Abnormal

ReplicateCount: 23

StandardErrorMean: 0.6

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 0

AssayResultAssertion: Abnormal

ReplicateCount: 43

StandardErrorMean: 0

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 16

AssayResultAssertion: Abnormal

ReplicateCount: 26

StandardErrorMean: 2.3

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 2.9

AssayResultAssertion: Abnormal

ReplicateCount: 20

StandardErrorMean: 2.1

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 117.2

AssayResultAssertion: Abnormal

ReplicateCount: 36

StandardErrorMean: 11.7

Comment: This variant had normal peak current and increased late current (>1% of peak), therefore it was considered a GOF variant (in vitro features consistent with Long QT Syndrome Type 3). (Personal communication: A. Glazer)

AssayResult: 21

AssayResultAssertion: Abnormal

ReplicateCount: 12

StandardErrorMean: 5.1

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 38.9

AssayResultAssertion: Abnormal

ReplicateCount: 27

StandardErrorMean: 7.2

Comment: This variant had partial loss of function of peak current (10-50% of wildtype) and a >10mV loss of function shift in Vhalf activation, therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 120.5

AssayResultAssertion: Normal

ReplicateCount: 41

StandardErrorMean: 10.5

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 105.3

AssayResultAssertion: Normal

ReplicateCount: 41

StandardErrorMean: 10.8

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 77.5

AssayResultAssertion: Normal

ReplicateCount: 30

StandardErrorMean: 8.6

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 41.7

AssayResultAssertion: Abnormal

ReplicateCount: 15

StandardErrorMean: 10.8

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 63.8

AssayResultAssertion: Indeterminate

ReplicateCount: 25

StandardErrorMean: 10.1

Comment: This variant had mild loss of function (peak current >50% and <75% of wildtype), therefore it was considered inconclusive and neither abnormal nor normal in vitro function. (Personal communication: A. Glazer)

AssayResult: 0.9

AssayResultAssertion: Abnormal

ReplicateCount: 12

StandardErrorMean: 0.6

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 68.1

AssayResultAssertion: Indeterminate

ReplicateCount: 18

StandardErrorMean: 8.7

Comment: This variant had mild loss of function (peak current >50% and <75% of wildtype), therefore it was considered inconclusive and neither abnormal nor normal in vitro function. (Personal communication: A. Glazer)

AssayResult: 32

AssayResultAssertion: Abnormal

ReplicateCount: 31

StandardErrorMean: 5

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 1.2

AssayResultAssertion: Abnormal

ReplicateCount: 11

StandardErrorMean: 0.7

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 3.4

AssayResultAssertion: Abnormal

ReplicateCount: 22

StandardErrorMean: 0.8

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 0

AssayResultAssertion: Abnormal

ReplicateCount: 39

StandardErrorMean: 0

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 0.6

AssayResultAssertion: Abnormal

ReplicateCount: 25

StandardErrorMean: 0.4

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 28.5

AssayResultAssertion: Abnormal

ReplicateCount: 21

StandardErrorMean: 7.6

Comment: This variant had partial loss of function of peak current (10-50% of wildtype) and a >10mV loss of function shift in Vhalf activation, therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 113.2

AssayResultAssertion: Normal

ReplicateCount: 30

StandardErrorMean: 13.9

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 0

AssayResultAssertion: Abnormal

ReplicateCount: 24

StandardErrorMean: 0

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 1.3

AssayResultAssertion: Abnormal

ReplicateCount: 67

StandardErrorMean: 0.3

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 0.8

AssayResultAssertion: Abnormal

ReplicateCount: 14

StandardErrorMean: 0.6

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 34.2

AssayResultAssertion: Abnormal

ReplicateCount: 14

StandardErrorMean: 6.7

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 109.6

AssayResultAssertion: Normal

ReplicateCount: 11

StandardErrorMean: 19.8

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 117.8

AssayResultAssertion: Normal

ReplicateCount: 15

StandardErrorMean: 14.5

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 39

AssayResultAssertion: Abnormal

ReplicateCount: 16

StandardErrorMean: 6.4

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 119.6

AssayResultAssertion: Normal

ReplicateCount: 22

StandardErrorMean: 19.5

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 0.2

AssayResultAssertion: Abnormal

ReplicateCount: 15

StandardErrorMean: 0.2

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 32.8

AssayResultAssertion: Abnormal

ReplicateCount: 16

StandardErrorMean: 5

Comment: This variant had partial loss of function of peak current (10-50% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 89.4

AssayResultAssertion: Normal

ReplicateCount: 26

StandardErrorMean: 12.7

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 85.1

AssayResultAssertion: Normal

ReplicateCount: 35

StandardErrorMean: 10.6

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 103.2

AssayResultAssertion: Normal

ReplicateCount: 33

StandardErrorMean: 12.7

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 120.5

AssayResultAssertion: Normal

ReplicateCount: 33

StandardErrorMean: 13.6

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 94.8

AssayResultAssertion: Normal

ReplicateCount: 33

StandardErrorMean: 12.6

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 109.1

AssayResultAssertion: Normal

ReplicateCount: 26

StandardErrorMean: 14.8

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 101

AssayResultAssertion: Normal

ReplicateCount: 41

StandardErrorMean: 8.9

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 104.3

AssayResultAssertion: Normal

ReplicateCount: 30

StandardErrorMean: 16.3

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 105.8

AssayResultAssertion: Normal

ReplicateCount: 36

StandardErrorMean: 12.7

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 103.2

AssayResultAssertion: Normal

ReplicateCount: 37

StandardErrorMean: 21.8

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 51.9

AssayResultAssertion: Indeterminate

ReplicateCount: 12

StandardErrorMean: 18.8

Comment: This variant had a mild loss of function in peak current (50-75% of wildtype). It had unmeasured late current, but has been previously reported to have high late current (GOF feature). Therefore it was considered to meet neither the abnormal or normal functional parameter. (Personal communication: A. Glazer)

AssayResult: 64.8

AssayResultAssertion: Abnormal

ReplicateCount: 31

StandardErrorMean: 11.1

Comment: This variant had a mild loss of function in peak current (50-75% of wildtype). It also had a very large increase in recovery from inactivation (>10-fold slower). Therefore it was considered to have a partial loss of function (in vitro function consistent with Brugada Syndrome). (Personal communication: A. Glazer)

AssayResult: 2.2

AssayResultAssertion: Abnormal

ReplicateCount: 16

StandardErrorMean: 1

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1. (Personal communication: A. Glazer)

AssayResult: 114.3

AssayResultAssertion: Abnormal

ReplicateCount: 16

StandardErrorMean: 22.4

Comment: This variant had normal peak current and increased late current (>1% of peak), therefore it was considered a GOF variant (in vitro features consistent with Long QT Syndrome Type 3). (Personal communication: A. Glazer)

AssayResult: 23.2

AssayResultAssertion: Abnormal

ReplicateCount: 14

StandardErrorMean: 7.1

Comment: This variant had partial loss of function of peak current (10-50% of wildtype) and a >10mV loss of function shift in Vhalf activation, therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 113

AssayResultAssertion: Normal

ReplicateCount: 17

StandardErrorMean: 28.6

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 0.1

AssayResultAssertion: Abnormal

ReplicateCount: 19

StandardErrorMean: 0.1

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1. (Personal communication: A. Glazer)

AssayResult: 86.7

AssayResultAssertion: Normal

ReplicateCount: 28

StandardErrorMean: 8.6

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

AssayResult: 0.7

AssayResultAssertion: Abnormal

ReplicateCount: 17

StandardErrorMean: 0.6

Comment: This variant had loss of function of peak current (<10% of wildtype), therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

AssayResult: 115.6

AssayResultAssertion: Normal

ReplicateCount: 19

StandardErrorMean: 24.7

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

HGVS: NM_198056.2:c.1003T>C p.(Cys335Arg)

AssayGeneralClass: BAO:0010044 targeted transcriptional assay

AssayMaterialUsed: BTO:0000773 lymphoblastoid cell line derived from control individuals or individuals with germline TP53 variants

AssayDescription: Comparative transcriptomic analysis using RNA-Seq to compare EBV cell lines of wild type and pathogenic TP53 in the context of genotoxic stress induced by doxorubicin treatment. 10 biomarkers corresponding to p53 targets were measured to determine a functionality score.

AdditionalDocument: PMID: 23172776

AssayReadOutDescription: In the treated condition, the peak height of each of the 10  p53 target genes was measured and divided by the sum of the heights of the three control genes. This value was then divided by the same ratio calculated in the untreated condition. In the assay, the mean of the 10 values defines the p53 functionality score. The final p53 functionality score is the mean of the scores obtained in RT-MLPA and RT-QMPSF assays.

AssayRange: An arbitrary functionality score was calculated from the induction score of the 10 p53 targets.

AssayNormalRange: N/A

AssayAbnormalRange: N/A

AssayIndeterminateRange: N/A

AssayNormalControl: wild type TP53

AssayAbnormalControl: LFS patient cells

ValidationControlPathogenic: 8 Individuals with dominant-negative TP53 missense variants, 10 Individuals with null TP53 variants, and 13 Individuals with other TP53 missense variants

ValidationControlBenign: 3 patients with wild type TP53

Replication: experiments were performed in triplicates.

StatisticalAnalysisDescription: Differentially expressed genes between doxorubicin-treated and untreated cells were arbitrarily defined using, as filters, a P<0.01 and fold-change cutoffs >2 or <2, for up and down regulation, respectively. The resultant signal information was analyzed using one-way analysis of variance (ANOVA, P= 0.001), assuming normality but not equal variances with a Benjamani–Hochberg correction for multiple comparisons using three groups: controls, null, and missense mutations.

SignificanceThreshold: P=0.001

Comment: statistical analysis and P value from previous publication.

Reviewer #1 (Public Review):

In this work, Panigrahi et. al. develop a powerful deep-learning-based cell segmentation platform (MiSiC) capable of accurately segmenting bacteria cells densely packed within both homogenous and heterogeneous cell populations. Notably, MiSiC can be easily implemented by a researcher without the need for high-computational power. The authors first demonstrate MiSiC's ability to accurately segment cells with a variety of shapes including rods, crescents and long filaments. They then demonstrate that MiSiC is able to segment and classify dividing and non-dividing Myxococcus cells present in a heterogenous population of E. coli and Myxococcus. Lastly, the authors outline a training workflow with which MiSiC can be trained to identify two different cell types present in a mixed population using Myxococcus and E. coli as examples.

While we believe that MiSiC is a very powerful and exciting tool that will have a large impact on the bacterial cell biological community, we feel explanations of how to use the algorithm should be more greatly emphasized. To help other scientists use MiSiC to its fullest potential, the range of applications should be clarified. Furthermore, any inherent biases in MiSiC should be discussed so that users can avoid them.

Major Concerns:

1) It is unclear to us how a MiSiC user should choose/tune the value for the noise variance parameter. What exactly should be considered when choosing the noise variance parameter? Some possibilities include input image size, cell size (in pixels), cell density, and variance in cell size. Is there a recommended range for the parameter? These questions along with our second minor correction can be addressed with a paragraph in the Discussion section.

2) Could the authors expand on using algorithms like watershed, conditional random fields, or snake segmentation to segment bacteria when there is not enough edge information to properly separate them? How accurate are these methods at segmenting the cells? Should other MiSiC parameters be tuned to increase the accuracy when implementing these methods?

3) Can the MiSiC's ability to accurately segment phase and brightfield images be quantitatively compared against each other and against fluorescent images for overall accuracy? A figure similar to Fig. 2C, with the three image modalities instead of species would nicely complement Fig. 2A. If the segmentation accuracy varies significantly between image modalities, a researcher might want to consider the segmentation accuracy when planning their experiments. If the accuracy does not vary significantly, that would be equally useful to know.

4) The ability of MiSiC to segment dense clusters of cells is an exciting advancement for cell segmentation algorithms. However, is there a minimum cell density required for robust segmentation with MiSiC? The algorithm should be applied to a set of sparsely populated images in a supplemental figure. Is the algorithm less accurate for sparse images (perhaps reflected by an increase in false-positive cell identifications)? Any possible biases related to cell density should be noted.

5) It is exciting to see the ability of MiSiC to segment single cells of M. xanthus and E. coli species in densely packed colonies (Fig. 4b). Although three morphological parameters after segmentation were compared with ground truth, the comparison was conducted at the ensemble level (Fig. 4c). Could the authors use the Mx-GFP and Ec-mCherry fluorescence as a ground truth at the single cell level to verify the results of segmentation? For example, for any Ec cells identified by MiSiC in Fig. 4b, provide an index of whether its fluorescence is red or green. This single-cell level comparison is most important for the community.

Reviewer #1 (Public Review):

In this paper, authors did a fine job of combining phylogenetics and molecular methods to demonstrate the parallel evolution across vRNA segments in two seasonal influenza A virus subtypes. They first estimated phylogenetic relationships between vRNA segments using Robinson-Foulds distance and identified the possibility of parallel evolution of RNA-RNA interactions driving the genomic assembly. This is indeed an interesting mechanism in addition to the traditional role for proteins for the same. Subsequently, they used molecular biology to validate such RNA-RNA driven interaction by demonstrating co-localization of vRNA segments in infected cells. They also showed that the parallel evolution between vRNA segments might vary across subtypes and virus lineages isolated from distinct host origins. Overall, I find this to be excellent work with major implications for genome evolution of infectious viruses; emergence of new strains with altered genome combination.

Comments:

I am wondering if leaving out sequences (not resolving well) in the phylogenic analysis interferes with the true picture of the proposed associations. What if they reflect the evolutionary intermediates, with important implications for the pathogen evolution which is lost in the analyses?

Lines 50-51: Can you please elaborate? I think this might be useful for the reader to better understand the context. Also, a brief description on functional association between different known fragments might instigate curiosity among the readers from the very beginning. At present, it largely caters to people already familiar with the biology of influenza virus.

Lines 95-96 Were these strains all swine-origin? More details on these lineages will be useful for the readers.

Lines 128-132: I think it will be nice to talk about these hypotheses well in advance, may be in the Introduction, with more functional details of viral segments.

Lines 134-136: Please rephrase this sentence to make it more direct and explain the why. E.g. "... parallel evolution between PB1 and HA is likely to be weaker than that of PB1 and PA" .

Lines 222-223: Please include a set of hypotheses to explain you results? Please add a perspective in the discussion on how this contribute might to the pandemic potential of H1N!?.

Lines 287-288: I am wondering how likely is this to be true for H1N1.

Reviewer #1 (Public Review):

In this paper, the authors tried to investigate complex roles of immune cells during acute myocardial infarction (AMI) by examining immune cells in blood samples from acute coronary syndrome (ACS) patients. They found an increase in the circulating levels of CD14+HLA-DRneg/low monocytes and CD16+CD66b+CD10neg neutrophils in the blood of ACS patients compared to healthy people, all of which were correlated with elevated levels of inflammatory markers in serum. Those findings were then further explored at a mechanistic level by using in vitro and in vivo experiments. Interestingly, the researchers also found that high cytomegalovirus (CMV) antibody titers could affect the immunoregulatory mechanisms in AMI patients. Taken together, the findings of the researchers could potentially contribute to the development of a more effective strategy to prevent cardiac deterioration and cardiovascular adverse events after AMI.

Strengths:

This paper contains novel insight regarding role of neutrophil and monocyte subset in pathophysiology of AMI. Although the increased level of CD10neg subsets of neutrophils in AMI patients has recently been reported (Marechal, P., et al. 2020. Neutrophil phenotypes in coronary artery disease. Journal of Clinical Medicine), the current paper aptly complemented the previous findings obtained by using its in vitro and in vivo mice model. This study also has robust methods to support their conclusion.

Weakness:

To further improve the strength of their conclusion, the experiments investigating the effects of immunoregulatory function of immature neutrophils and HLA-DRneg/low monocytes subsets would be advised.

Reviewer #1 (Public Review):

The manuscript by He et al. reveals a novel role for PKC-theta, following T cell receptor (TCR) stimulation, in regulating the nuclear translocation of several key activation-dependent transcription factors by regulating the assembly of key components of the nuclear pore complex (NPC). The authors make use of T cell lines and primary T cells to show that following TCR stimulation, PKC-theta phosphorylates RanGAP1 to promote its interaction with Ubc9 and increase the sumoylation of RanGAP1, which, in turn, enhances assembly of the RanBP2 subcomplex of the NPC that then promotes the nuclear import of AP-1, NFAT and NFB. These conclusions are well supported by a rigorous experimental approach, which included the use of PKC-theta deficient, sumoyltion-defective, kinase-dead, and constitutively active mutants, and RanGAP1-deficient cells.

Reviewer #1 (Public Review):

This paper uses a large breeding colony of guppies to measure genetic correlations between hormonal stress responses and behavior in an open-field test. Although we know a lot about the mechanisms of hormone-mediated behavior, we know less about variation in hormonal systems, particularly genetic variation. Understanding how hormones relate genetically to the behaviors they mediate is particularly important because it helps us understand how the entire hormone-behavior system evolves. A priori, we would expect genetic correlations between hormones and the behavior they underlie, such that selection on the hormone would lead to a response in the behavior and vice versa. However, evidence for this pattern is rare.

Here, the authors show that stress-induced levels of cortisol are repeatable and heritable. Interestingly, they also show that individuals show a lower stress response to later stress and slightly less variation, indicating a G X E interaction. There was a significant genetic correlation between the hormonal response and one of the behaviors measured in the open field test, and the hormone loaded positively in the first genetic principal component along with all the behaviors. This is evidence of an correlated suite of traits that would evolve together in response to selection.

This is an important study, because evidence of genetic variation in hormonal systems, not to mention genetic covariation with hormone-mediated traits, is rare. The results presented here provide insight into how a hormone-behavior complex might adapt to a changing environment. They are also relevant to ideas about the maintenance of variation in coping styles in natural populations.

Reviewer #1 (Public Review):

Using various voltage and concentration protocols in a heterologous expression system, the authors provide compelling evidence for strong block of GluR1 AMPA receptors by intracellular NASPM, and unlike spermine, the block is independent of auxiliary subunit expression. The authors also show that intracellular NASPM provides a more complete block than spermine of synaptic currents in GluR2-KO neurons.

Overall the manuscript contains high quality data that is clearly presented. It seems likely that this approach will be useful for assessing the contribution of CP-AMPARs in various scenarios. However, currently the authors have fallen short of providing a comprehensive analysis of the use of NASPM to differentiate between CP and CI AMPARs in intact systems containing multiple AMPAR subunits and auxiliary proteins.

Reviewer #1 (Public Review):

In "Asymptomatic Bordetella pertussis infections in a longitudinal cohort of young African infants and their mothers", the authors analyze longitudinal data from a cohort in Zambia of infant/mother pairs to investigate the evidence for subclinical and asymptomatic infections in both pairs as well as the use of IS481 qPCR cycle threshold (CT) values in providing evidence for pertussis infection. Overall, the manuscript lacks substantial statistical support or clear evidence of some of the patterns they are stating and would require a substantial revision to justify their conclusions. The majority of the manuscript relies on 8 infant/mother pairs where they have evidence of pertussis infection and rely on the dense sampling to investigate infection dynamics. However, this is a very small sample size and further, based on the results displayed in Figure 1, it is not obvious that the data has a very pattern that warrant their assertions.

Major comments:

The main results and conclusions are highly reliant on details from eight mother/infant pairs. However, Figure 1 does not show a clear picture of the fade-in/fade-out. The authors go into great detail describing each of these 8 pairs, however based on the figure and text there does not appear to be clear evidence of an underlying pattern. While there are some instances with a combination of higher/lower IS481 CT values, it does not appear to have a clear pattern. For example, what are possible explanations for time periods between samples with evidence of IS481 and those without (such as pair A, C, D, E, F and H)? There also does not appear to be a clear pattern of symptoms in any of these samples (aside from having fewer symptoms in the mothers than infants). Further, it is not obvious how similar these observed (such as a mixture of times of high or low values often preceded or followed by times when IS481 was not detected) is similar to different to the rest of the cohort (in contrast to those who have a definitive positive NP sample during a symptomatic visit). The main results are primarily a descriptive analysis of these 8 mother/infant pairs with little statistical analyses or additional support.

The authors do not provide evidence or detail about what is known about the variability in IS481 CT values, amongst individuals, or over time, or pre/post vaccination. Without this information, it is not clear how informative some of this variability is versus how much variability in these values is expected. I think particularly in Figure 1, how many of the individuals have periods between times when IS481 evidence was observed when it was not observed, is concerning that these data (at this granular a level) are measuring true infection dynamics. Adding in additional information about the distribution and patterns of these values for the other cohort members would also provide valuable insight into how Figure 1 should be interpreted in this context. As it stands, the authors do not provide sufficient interpretation and evidence for having relevant infection arcs.

It appears that Figure 2A was created using only 8 data points (from the infant data values). If so, this level of extrapolation from such few data points does not provide enough evidence to support to the results in the text (particularly about evidence for fade-in/fade-out population-level dynamics). Also, in Figure 2, it is not clear to me the added value of Figure 2C and the main goal of this figure.

The authors have created a measure called, evidence for infection (EFI), which is a summary measure of their IS481 CT values across the study. However, it is not clear why the authors are only considering an aggregated (sum) value which loses any temporality or relationship with symptoms/antibiotic use. For example, the values may have been high earlier in the study, but symptoms were unrelated to that evidence for infection - or visa versa. This seems to be an important factor - were these possible undiagnosed, asymptomatic, or mild symptomatic pertussis infections? It is not clear why the authors only focus on a sum value for EFI versus other measures (such as multiple values above or below certain thresholds, etc.) to provide additional support and evidence for their results.

It is not clear why the authors have emphasized the novelty and large proportion of asymptomatic infections observed in these data. For example, there have been household studies of pertussis (see https://academic.oup.com/cid/article-abstract/70/1/152/5525423?redirectedFrom=PDF which performed a systematic review that included this topic) that have also found such evidence. While cross-sectional surveys may be commonly used in practice, it is not clear that there is no other type of study that provides any evidence for asymptomatic infections. Further, it is not clear why the authors refer to widespread asymptomatic pertussis when a large proportion of individuals with evidence for pertussis infection had symptoms. Would it not be undiagnosed pertussis if it is associated with clinical symptomatology?

Reviewer #1 (Public Review):

Galdadas et al. applied a combinatorial approach of equilibrium and nonequilibrium molecular dynamics methods to study two important members of the Class A β-lactamase enzyme family in detail. Authors carefully chose two representative enzymes from this family, TEM-1 and KPC-2 in this study. Understanding of the nature of the communication pathways between allosteric ligand binding site and the active site has been the main focus of this study. Another very interesting finding of this study was the position of clinical variants that was precisely mapped along the allosteric communication pathway. This approach certainly has broad utility as it can be applied to study long-range communications in enzymes that are triggered by binding of a ligand (drug candidate) to an alternative/remote site, and also in cases where certain mutations occur far away from the active site but lead to drug resistance.

Overall, the manuscript is well written, and the conclusions are mostly well supported by data.

Reviewer #1 (Public Review):

The authors aimed to survey a large transfusion database in Sweden to catalog associations between ABO/RhD blood group antigens and a wide variety of clinical phenotypes in a systematic, unbiased and comprehensive manor. They succeed at surveying over 1200 phenotypes in over 5 million people and identify 49 statistically significant associations for ABO blood group and point out a couple novel associations. Their statistical methods are appropriate and help eliminate potential false positive associations. The strengths of this study are the unbiased survey of a large database and the appropriate corrections for multiple observations which allow the authors to explore a large number of associations without loosing site of what is really a significant association.

This study sheds light on a topic of interest to many scientists. The ABO gene encodes a glycosyltransferase enzyme that has 4 major haplotypes in human populations and results in a specific pattern of posttranslational modification of plasma proteins and blood cells including erythrocytes. Proteins decorated with an H antigen can receive additional carbohydrate antigens from ABO transferase intracellularly. The common A allele transfers UDP-GalNAc while the B allele transfers UDP-Gal. The A2 allele is hypomorphic compared to the A allele and transfers lower amounts of UDP-GalNAc and the common O allele is a null resulting in no transferase activity.

The allele frequencies of these common alleles varies by ancestry and has geographic differences. Variation at ABO is unconstrained with many rare variants contributing to the four common haplotypes at ABO. Interestingly, geographically specific selective pressures may have led to allele frequency differences. For example. ~40-50% of individuals are homozygous for the null (type O) allele. These null haplotypes are more common in individuals of Latino or African ancestry while 'A' haplotypes are slightly more common in individuals of European origin and 'B' alleles are more common in individuals of Asian and African ancestry. Overall, O is more common than A or B alleles. An unbiased survey of phenotype frequencies by blood type allows for confirmation of previous associations and discovery of novel associations. In this largely European ancestry cohort, blood type A is the most common (45-47%) while blood type O is second most common at 38-39%.

Limitations of Phenome-wide Association Studies (PheWAS) like the one presented in this manuscript should be noted. Associations with complex phenotypes or those with small effect size will not be detected even in a large cohort such as the SCANDAT. This study is also biased toward associations with phenotypes more common in the Scandinavian population. This may present associations related to the population substructure and not a direct association with ABO. In genome-wide association studies this can be addressed through multiple methods but it is not clear how the authors correct for population structure in this study. Likewise, the insight into the mechanistic reasons for ABO associations is not a strength of this study and will await subsequent studies for many phenotypes. Mechanistic insight might be particularly interesting for the novel associations uncovered by this study.

Reviewer #1 (Public Review):

In this manuscript, the authors use single cell RNA sequencing to investigate cell-type specific eQTL within C. elegans. This relies on the well known ability to genotype individuals via their transcriptome allowing the authors to generate both phenotypes and genotypes from single cell transcriptomes. This identifies a blend of cis and trans-eQTL that are cell type specific and starts to provide numerical observations to the communities expectation of cell type specificity.

The use of simultaneous single cell sequencing on a diversity of individuals is a unique method that is absolutely essential to get around the vast scale issues that are presented when contemplating single cell eQTL within multicellular organisms. However, an unfortunate outcome of this approach that the cell-autonomy of the eQTL cannot be studied. Instead the cell types have to be considered completely independent of each other.

The authors conduct an analysis of eQTL per each cell type to get at specificity. This identifies a number of eQTL found in only a single cell type but these binary tests can have an ascertainment issue that may be over-estimating the cell type specificity. Optimally, this would be conducted by incorporating the different cell types as different environments within a single eQTL model but given the different sample sizes, this may not be feasible. Alternatively an investigation of how eQTLs specific to one cell type are or are not found by shifting the detection threshold in the other tissues could test this possibility.

Reviewer #1 (Public Review):

This manuscript describes the curation of a training dataset, CEM500K, of cellular electron microscope (EM) data including STEM, TEM of sections, electron tomography, serial section and array tomography SEM, block-face and focused-ion beam SEM. Using CEM500K to train an unsupervised deep learning algorithm, MoCoV2, the authors present segmentation results on a number of publically available benchmark datasets. They show that the standard Intersection-over-Union scores obtained with the CEM500K-trained MoCoV2 model, referred to as CEM500K-moco, equal or exceed the scores of benchmark segmentation results. They also demonstrate the robustness of CEM500K-moco's performance with respect to input image transformations, including rotation, Gaussian blur and noise, brightness, contrast and scale. The authors make the remarkable discovery that MoCoV2 spontaneously learned to use organelles as "landmarks" to identify important features in images, simulating human behavior to some degree.

Reviewer #1 (Public Review):

The submitted manuscript 'Distinct higher-order representations of natural sounds in human and ferret auditory cortex' by Landemard and colleagues seeks to investigate the neural representations of sound in the ferret auditory cortex. Specifically, they examine the stages of processing via manipulating the complexity and sound structure of stimuli. The authors create synthetic auditory stimuli that are statistically equivalent to natural sounds in their cochlear representation, temporal modulation structure, spectral modulation structure, and spectro-temporal modulation structure. The authors use functional ultrasound imaging (fUS) which allowed for the measurement of the hemodynamic signal at much finer spatial scales than fMRI, making it particularly suitable for the ferret. The authors then compare their results to work done in humans that has previously been published (e.g. Norman-Haignere and McDermott, 2018) and find that: 1. While human non-primary auditory cortex demonstrates a significant difference between natural speech/music sounds and their synthetic counterparts, the ferret non-primary auditory cortex does not. 2. For each sound manipulation in humans, the dissimilarity increases as the distance from the primary auditory cortex increases, whereas for ferrets it does not. 3. While ferrets behaviorally respond to con-specific vocalizations, the ferret auditory cortex does not demonstrate the same hierarchical processing stream as humans do.

Overall, I find the approach (especially the sound manipulations) excellent and the overall finding quite intriguing. My only concern, is that it is essentially a null-result. While this result will be useful to the literature, there is always the concern that a lack of finding could also be due to other factors.

Major points:

1) What if the stages in the ferret are wrong? The authors use 4 different manipulations thought to reflect key elements of sound structure and/or the relevant hierarchy of the processing stages of the auditory cortex, but it's possible that the dimensions in the ferret auditory cortex are along a different axis than spectro/temporal modulations. While I do not expect the authors to attempt every possible axis, it would be beneficial to discuss.

2) For the ferret vocalizations, it is possible that a greater N would allow for a clearer picture of whether or not the activation is greater than speech/music? While it is clear that any difference would be subtle and probably require a group analysis, this would help settle this result/issue (at least at the group level).

3) Relatedly, did the magnitude of this effect increase outside the auditory cortex?

4) It would be useful to have a measure of the noise floor for each plot and/or species for NSE analyses. This would make it easier to distinguish whether, for instance, in 2A-D, an NSE of 0.1 (human primary) vs. an NSE of 0.042 (ferret primary) should be interpreted as a bit more than double, or both close to the noise floor (which is what I presume).

Reviewer #1 (Public Review):

In this study, the authors set out to address the interesting question of how activating septal cholinergic neurons affects learning and memory of reward locations. The work provides compelling evidence showing that activation of septal cholinergic cells at reward zones suppresses sharp wave-ripples and impairs memory performance in freely behaving animals. The data are properly controlled and analyzed, and the results support the conclusions. The results shed new light on the functional significance of cholinergic projections in reward learning. Future follow-up studies designed to selectively activate cholinergic projections specifically at times when sharp wave-ripples occur will be interesting to determine the importance of cholinergic sharp wave-ripple suppression for these effects.

Reviewer #1 (Public Review):

Here, Houri-Ze'evi, et al. treated progeny of parents that had inherited small RNA response (silencing of an artificial, single-copy GL-expressed gfp with anti-gfp dsRNA) with 3 stresses – heat shock, hyperosmolarity, or starvation – starting at L1, and examined gfp silencing. All three treatments reduced silencing (visible as increased GFP fluorescence) in subsequent generations (F1-F3). The authors tested resetting of endogenous (endo-siRNA) and piRNAs using an endo-siRNA sensor target sequence and piRNA recognition sites, respectively. Again, all 3 stressors reset both in the same generation, but did not reset the effect transgenerationally, suggesting that exogenous RNAi resetting functions through a different mechanism than endogenous.

Next, they tested adults, which also led to resetting. However, only the F1 generation, not F2, is susceptible to resetting (how? Why?), revealing a critical period for resetting susceptibility. Reversal of the stress with RNAi treatment does not result in resetting, nor does simpy changing conditions. The authors then went on to examine mutants that might be defective in stress responses or in resetting; MAPK genes and skn-1 are required for resetting. Small RNA-seq from stressed worms and their progeny showed a decrease overall with stresses, and reveals some potential classes of genes, including targets of the mutator genes, and overlap with classic stress response pathways (dauer, IIS). Overall, this work presents some interesting phenomena and moves towards explaining how it might work through the identification of a critical period and some genes that are required.

In this version, the authors have added more information regarding the relationship between MAPK and SKN-1, and transcriptional targets. Most importantly, they have performed tissue-specific rescue of sek-1; in neurons, this rescues, but intestine did not.

These data add to prior work from the Rechavi lab and others in the field, which together address the interplay of small RNAs, response to stress, and transgenerational inheritance.

Reviewer #1:

Single molecule localization microscopy (SMLM) has become an important method for understanding the subcellular distribution of fluorescently labelled biomolecules at length scales of a few tens of nanometers. A critical challenge has been to find out, whether and to what extent biomolecular clustering occurs. While methods have been published which address the problem of identifying biomolecular clusters in SMLM images, they still suffer from many user-defined parameters, which - if selected inappropriately - influence the obtained results substantially. The StormGraph-3D method proposed here addresses these issues, based on a comprehensive mathematical framework which reduces the number of user-defined input parameters. The method was evaluated using comprehensive simulations of data, which show its robustness compared to alternative approaches.

The methods part of the paper would benefit, however, from more realistic data of single molecule blinking behavior, and the evaluation of the consequences on the performance of the method. As the authors acknowledge, overcounting due to blinking has challenged data analysis previously, and gave rise to artifactual localization clusters that do not represent the underlying protein distribution. It would be of particular interest, which results in the method yielded for a random biomolecular distribution.

Reviewer #1:

The authors of Working Memory Gates Visual Input to Primate Prefrontal Neurons studied how working memory influences information transmitting from V4 to frontal eye field via extracellular recording and electrical stimulation on behaving primate. They found that V4 neurons target FEF neurons with both visual and motor properties, and its synaptic efficacy of V4 to FEF was enhanced by working memory. These findings are interesting and important to our understanding about how our brain acts during daily WM-related activity.

1) In classical working memory tasks, the task periods usually consist of fixation, cue, delay and then a response period. The neural activity during the delay period is typically considered to be a working memory-related signal. However, in the current study, the authors didn't point out whether only delay period activity was included in analysis when they compared synaptic efficacy between stimulation and non-stimulation trials, in Figure 4a. Because the differences of neuronal response during fixation period cannot be viewed as relevant to information held in working memory, it may be better if only neuronal activity in the delay period was included in their analysis.

2) Did the 96 visual-recipient FEF neurons exhibit working memory-related activity in their memory guided saccade task? The example neuron in Figure 3a didn't show significant difference between In and Out trials during the delay period. If the visual-recipient neuron didn't present working memory related activity, how could the authors say enhanced synaptic efficacy from V4 to FEF was caused by working memory?

3) Did the two example neurons in Figure 4c show adjusted values (subtracting the same measure during non-stimulated trials)? The authors mentioned in Method that Figure 4 showed adjusted values, but it may not be applicable for raster plot in Figure 4c. It may be helpful that using adjusted values show stimulation effects on evoked spike counts during memory In and Out trials.

4) Did the authors find some FEF cells showing elevated firing during delay period in outside-RF trials compared with baseline firing? These elevated firing was not caused by RF cue, may underlying working memory signal.

5) The sample size should be indicated in Figure 3b Venn diagram.

6) It's better to indicate electrical stimulus protocol in Figure 1.

Reviewer #1:

The present study sought to better characterize how listeners deal with competing speech streams from multiple talkers, that is, whether unattended speech in a multitalker environment competes for exclusively lower-level acoustic/phonetic resources or whether it competes for higher-level linguistic processing resources as well. The authors recorded MEG data and used hierarchical frequency tagging in an unattended speech stream presented to one ear while listeners were instructed to attend to stories presented in the other ear. The study found that when the irrelevant speech contained structured (linguistic) content, an increase in power at the phrasal level (1 Hz) was observed, but not at the word level (2 Hz) or the sentence level (0.5 Hz). This suggests that some syntactic information in the unattended speech stream is represented in cortical activity, and that there may be a disconnect between lexical (word level) processing and syntactic processing. Source analyses of the difference between conditions indicated activity in left inferior frontal and left posterior parietal cortices. Analysis of the source activity underlying the linear transformation of the stimulus and response revealed activation in the left inferior frontal (and nearby) cortex. Implications for the underlying mechanisms (whether attentional shift or parallel processing) are discussed. The results have important implications for the debate on the type and amount of representation that occurs to unattended speech streams.

The authors utilize clever tools which arguably provided a unique means to address the main research question, i.e., they used hierarchical frequency tagging for the distractor speech, which allowed them to assess linguistic representations at different levels (syllable-, word-, phrase-, and sentence-level). This technique enabled the authors to make claims about what level of language hierarchy the stimuli are being processed, depending on the observed frequency modulation in neural activity. These stimuli were presented during MEG recording, which let the authors assess changes in neurophysiological processing in near real time--essential for research on spoken language. Source analyses of these data provided information on the potential neural mechanisms involved in this processing. The authors also assessed a temporal response function (TRF) based on the speech envelope to determine the brain regions involved at these different levels for linguistic analysis of the distractor speech.

Critiques:

Speech manipulation:

In general, it is unclear what predictions to make regarding the frequency tagging of the unattended distractor speech. On the one hand, the imposed artificial rhythmicity (necessary for the frequency tagging approach) may make it easier for listeners to ignore the speech stream, and thus seeing an effect at higher-level frequency tags may be of greater note, although not entirely plausible. On the other hand, having the syllables presented at a consistent rate may make it easier for listeners to parse words and phrasal units because they know precisely when in time a word/phrase/sentence boundary is going to occur, allowing listeners to check on the irrelevant speech stream at predictable times. For both the frequency tagging and TRF electrophysiological results, the task-irrelevant structured speech enhancement could be interpreted as an infiltration of this information in the neural signal (as the authors suggest), but because the behavioral results are not different this latter interpretation is not easily supported. This pattern of results is difficult to interpret.

Behavioral Results:

Importantly, no behavioral difference in accuracy was observed between the two irrelevant speech conditions (structured vs. non-structured), which makes it difficult to interpret what impact the structured irrelevant speech had on attentive listening. If the structured speech truly "infiltrates" or "competes" for linguistic processing resources, the reader would assume a decrease in task accuracy in the structured condition. This behavioral pattern has been observed in other studies. This calls into questions the face validity of the stimuli and task being used.

Attention:

In this study activation of posterior parietal cortex was found, that could be indicative of a strong attentional manipulation, and that the task was in fact quite attentionally demanding in order for subjects to perform. This may align with the lack of behavioral difference between structured and non-structured irrelevant stimuli. Perhaps subjects attempted to divide their attention which may have been possible between speech that was natural and speech that was rather artificial. The current results may align with a recent proposal that inferior frontal activity may be distinguished by language selective and domain general patterns.

Lack of word level response:

A major concern is that the results do not seem to replicate from an earlier study with the same structured stimuli, i.e., the effects were seen for sentence and word level frequency tagging. As the authors discuss, it seems difficult to understand how a phrasal level of effect could be obtained without word-level processing, and so a response at the word level is expected.

Familiarization phase:

The study included a phase of familiarization with the stimuli, to get participants to understand the artificial speech. However it would seem that it is much easier for listeners to report back on structured rather than unstructured stimuli. This is relevant to understanding any potential differences between the two conditions. It is unclear if any quantification was made of performance/understanding at this phase. If there is no difference in the familiarization phase, this might explain why there was no difference in behavior during the actual task between the two conditions. Or, if there is a difference at the familiarization phase (i.e. structured sequences are more easily repeated back than non-structured sequences), this might help explain the neural data result at 1 Hz, given that some higher level of processing must have occurred for the structured speech (such as "chunking" into words/phrasal units).

Joint Public Review:

Eisele et al. evaluated the direct action of erythropoietin (EPO) on hematopoietic stem and progenitor cells that included hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs). They used cellular barcoding to enable in vivo tracking of cellular output and then used scRNA-seq to corroborate their findings. They observed the transiently promoted output of Myeloid-Erythroid (ME)-biased and Myeloid-B-cell (MB)-biased clones. Single-cell RNA sequencing analysis revealed that EPO acted mostly on MPP1 and MPP2. Based on these data, the authors concluded that EPO acts directly on MPPs and transiently modulates their output. Although the conceptual advance brought by this study is incremental as similar findings have been presented by previous studies, the integration and use of both barcoding and scRNA-seq adds strength to the conclusions reached in the present study.

Reviewer #1 (Public Review):

In this work, the authors set out to better understand the mechanisms by which the nematode C. elegans responds to bacterial pathogens.

Using behavioral assays and genetic manipulations, the authors find that C. elegans can rapidly learn to avoid the pathogen E. faecalis (E.f.). While recent studies from other groups have shown that small RNAs (sRNAs) produced by some pathogenic bacteria can trigger aversive learning, the authors find that this seems not to be the case for E. faecalis. Instead, they provide evidence that E. faecalis causes abdominal distention, and that this may provide the trigger for learning. Because the evidence for this is largely correlative, alternative explanations may still be possible. Further, the authors identify two TRPM-class ion channels whose function appears to be necessary for learned avoidance of E.f. The authors propose that one or both of these may mediate detection of abdominal distention, an interesting idea that merits further study. While the paper's title indicates that these channels "mediate" this function, this remains speculative.

The authors also find that wild-type C. elegans prefer olfactory stimuli from E.f. to those of their regular diet, E. coli, but that this pattern is reversed after exposure to E.f. This plasticity involves the function of the chemosensory neurons ASE, AWC, and AWB, as well as the cyclic-nucleotide-gated channel TAX-2/TAX-4. This finding provides important insight into the nature of the changes in neural circuit function that are triggered by pathogen exposure, leading to pathogen avoidance.

The paper also examines a role for the neuropeptide receptor npr-1 in learned E.f. avoidance. Animals lacking npr-1 function are known to strongly avoid high (ambient) oxygen concentrations, and instead prefer the lower-oxygen environment of a bacterial lawn. The authors find that this oxygen avoidance overcomes any avoidance of E.f.; thus, npr-1 mutants do not avoid E.f. when tested with ambient oxygen, but they do avoid it in a low-oxygen environment. This indicates that npr-1 is not required for pathogen avoidance per se. Although the authors suggest that npr-1 may be a target of the learning process, this is not well justified by the data and it may be more likely that oxygen avoidance and pathogen avoidance are separate processes.

Together, these findings demonstrate that the mechanisms underlying learned pathogen avoidance in C. elegans differ substantially depending on the nature of the pathogen, and that worms likely use a combination of strategies to deal with these threats in the wild.

Reviewer #1 (Public Review):

In this manuscript the authors demonstrate that acute systemic inflammation induces a new system of rapid migration of granulocyte-macrophage progenitors and committed macrophage-dendritic progenitors but not other progenitors or stem cells from BM to lymphatic capillaries. This traffic is mediated by Ccl19/Cccr7 and is NfkB independent but Traf activation dependent. This type of trafficking is anti-inflammatory with promotion of early survival.

Specifically, authors work shows the traffic of DC-biased myeloid progenitors through direct transit from BM to bone lymphatic capillaries. This type of trafficking is highly activated in endotoxic inflammation. Giving LPS to mice results in massive mobilization of myeloid progenitors from the BM to lymph and retention in LN takes place. This happens rapidly and before the appearance of these cells in PB. This type pf LPS challenge induces Ccr7 expression on GMPs as well as secretion of CcL9 in the LN. Importantly, loss of CcL9 or neutralizing Ccr7 inhibits GMP/MDP migration to the LN and inflammation induce mortality.

The studies are well performed and the data supports the conclusions. The role of this signaling axis in the recruitment of GMPs/MDPs has not been investigated in this detail.

Reviewer #1 (Public Review):

The manuscript by Tindle et al describes generation of adult lung organoids (ALO) from human lung biopsies and their use to study the changes in gene expression as a result of SARS-CoV-2 infection. The main advantage of the use of organoids is the ability to generate many cell types that make up the lung. In this particular case the authors report the presence of AT1, AT2 cells, Basal cells, Goblet cells, Ciliated cells and Club cells. The authors were able to cultivate the cells at the air-liquid interface and to establish cultures of predominately proximal and predominately distal airway cells. The main finding is that proximal cells are more prone to viral infection, while distal cells are governing the exuberant inflammatory response, with both cells required for the exuberant response to occur. A useful information provided by the paper is the analysis gene signatures of various cellular models, in comparison to the infected human lung.

Reviewer #1 (Public Review):

This manuscript by Taylor et al. carefully investigates (1) ParB-ParA and (2) ParB-ParB interactions in the F Plasmid SopABC system using microfluidics, TIRF microscopy and magnetic tweezers.

(1) The work shows that the activation of ParA ATP hydrolysis requires a dimer of ParA to bind to two protomers of ParB. Surprisingly, ParB can bind to ParA either using the two protomers of a single dimer or two protomers from distinct dimers. The former occurs in the absence of ligands, the latter upon addition of either CTP or parS DNA, thus presumably corresponding to the state of ParB found in the cells near a parS site. The authors suggest that this is crucial for the precise timing of ParA-ParB anchoring and release.

(2) Magnetic tweezer experiments demonstrate nucleotide-dependent compaction of DNA by ParB. This compaction is strictly parS-sequence dependent and robust even at elevated DNA extension force (5 pN) and at relatively low ParB concentrations. This implies ParB dimer-ParB dimer interactions exclusively on parS DNA.

The conclusions are generally well supported by the data. Few control experiments are suggested.

Reviewer #1 (Public Review):

The paper "Insights from a Pan India Sero-Epidemiological survey (Phenome-India cohort) for SARS-CoV2" reports a longitudinal survey of about 10000 subjects from laboratories of the CSIR (India) who consented to be tested for antibodies to SARS-CoV-2, across August and September 2020. The methodology is a standard one, using the Roche kit to test for antibodies to the nucleocapsid antigen with a followup to detect neutralising antibodies using the GENScript Kit. A questionnaire for all participants asked about age, gender, pre-existing conditions and blood group, among other questions.

The principal results of the study were:

1) An overall seropositivity of 10.14% [95% CI: 9.6 - 10.7] but a large variation across locations

2) Virtually all of the seropositive exhibited neutralising activity

3) Seropositivity correlated with population density in different locations

4) A weak correlation was seen to changes in the test positivity across locations

5) A large asymptomatic fraction (~75%) who did not recall symptoms

6) Of those symptomatic, most reported mild flu-like symptoms with fever

7) A correlation with blood group, with seropositivity highest for AB, follow by B, O and A

8) A vegetarian diet correlated with reduced seropositivity

9) Antibody levels remained constant for 3 months across a sub-sample white neutralising activity was lost in ~30% of this subsample. Over a longer period, in a still smaller subsample of those tested at 3 months, anti-nucleocapsid antibody levels declines while neutralising antibody levels remained roughly constant

10) There is a reasonable agreement with the results of the second Indian serosurvey which obtained a seroprevalence of about 7% India-wise, although excluding urban hotspots.

The deficiencies of this study are:

1) This is a very specific cohort, largely urban, with - presumably - relatively higher levels of education. It is hard to see how this might translate into a general statement about the population

2) The presentation of Figure 1 was quite confusing, especially the colour coding

3) It is surprising that the state of Maharashtra shows only intermediate to low levels of seropositivity, given that the impact of the pandemic was largest there and especially in the city of Pune. There have been alternative serosurveys for Pune which found much higher levels of seropositivity from about the same period.

4) The statement "Seropositivity of 10% or more was associated with reductions in TPR which may mean declining transmission": For a disease with R of about 2, this would actually be somewhat early in the epidemic, so you wouldn't expect to see this in an indicator such as TPR. TPR is also strongly correlated with amounts of testing which isn't accounted for.

5) The correlation with vegetarianism is unusual - you might have argued that this could potentially protect against disease but that it might protect against infection is hard to credit. Much of South Asia is not particularly vegetarian but has seen significantly less impact

6) On the same point above, it is possible that social stratification associated with diet - direct employees being more likely to be vegetarian than contract workers - might be a confounder here, since outsourced staff seem to be at higher risk.

7) There may be correlations to places of residence that again act as confounders. If direct employees are provided official accommodation, they may simply have had less exposure, being more protected.

8) The correlations with blood group don't seem to match what is known from elsewhere

9) The statement that "declining cases may reflect persisting humeral immunity among sub-communities with higher exposure" is unsupported. What sub-communities?

Reviewer #1 (Public Review):

In the manuscript "Single-cell transcriptomics defines heterogeneity of epicardial cells and fibroblasts within the infarcted heart", the authors isolated epicardial stromal cells (EpiSC) and cardiac interstitial/stromal cells (termed active CSCs) from the same I/R heart and identified transcriptionally distinct subpopulation of EpiSCs via 10x genomics technology. They also performed transcriptome profile comparison between EpiSCs and aCSCs. This manuscript shows rigorous scientific investigation. Their isolation protocol is supported by their previous publication. Method section documented in detail of step-wise QC process of bioinformatics analysis. In summary, the analysis identified 11 clusters of EpiSC, some of which overlap with the well-established epicardial marker WT1 with confirmed in situ anatomical localization. When compared to aCSC, the two groups showed clear different function/states as expected. In the lineage tracing model, RNA velocity predicts cell hierarchy, cell-cell communication between populations, as well as cell cycle activity. Overall this manuscript provides a significant degree of information that can be helpful to the field.

Reviewer #1 (Public Review):

The work by Lutes et al. addresses how thymocytes undergo positive selection during their differentiation into mature T cells. The authors make use of several in vitro and in vivo model systems to the test whether developing thymocytes at the critical preselection CD4+CD8+ stage, expressing T cell receptors (TCRs) with different levels of putative self-reactivity, undergo different or similar differentiation events, in terms of migration, thymic epithelial cell engagement and temporal kinetics, and gene expression changes.

The authors selected three TCR-transgenes, which have increasing levels of self-reactivity, TG6, F5 and OT1, respectively, to test their hypothesis, that TCR signals during positive selection are not only sensed differently but lead to different outcomes that then define the functional status of mature T cells. The author's conclusions that thymocytes with low self-reactivity differentiate with distinct kinetics (migration, engagement and temporal) and express a different suite of genes than thymocytes that experience high self-reactivity is well supported by several elegant approaches, and convincing findings.

The authors clearly established that low to high TCR signaling outcomes affect the timing of positive selection, which is beautifully illustrated in Figures 3-6, and extend that work to non-TCR transgenic mice as well. Lastly, their findings from RNA-seq analyses shed light into the different genetic programs experienced by high-reactivity fast differentiating CD8 T cells as compared to low-reactivity slower differentiating cells, which appear to retain the expression a unique set of ion channels during later stages of their differentiation process.

However, what the expression of these ion channels means in terms of either supporting the slow progression or perhaps responsible for the slow progression is not directly addressed, and likely beyond the scope. Nevertheless, the authors posit as to the potential role(s) for the differently expressed gene subsets. Overall, the work is crisply executed, and the findings reveal new aspects as to how positive selection can be achieved by thymocytes expressing very different TCR reactivities.

Reviewer #1 (Public Review):

Nielsen and colleagues describe a large new multi-ome database containing combinations of absolute mRNA quantities, proteome and amino acid concentrations in a set of 14 yeast populations grown in various conditions in chemostats. Apart from being a valuable resource for colleagues, analysis of the data confirms the results of several previous seminal studies.

For example, the authors confirm the relatively high correlation between transcript and corresponding protein abundance. Moreover, it is shown that for most genes, changes in transcript abundance related to manipulated changes in growth rate largely reflected the availability of RNA polymerase II. Interestingly, this was not the case for genes involved in central carbon metabolism, suggesting that these are regulated separately, likely to maintain the cells' ATP levels. Similarly, manipulation of growth through the use of different nitrogen sources led to changes in transcription that correlated with certain amino-acid-derived metabolites (including nucleotides), but not with RNAPolII levels. Genes involved in central carbon metabolism are again an exception to this rule.

Reviewer #1 (Public Review):

This paper describes the development of a suite of viral vectors that allow expression (either on or off) of genes of interest depending on both Cre and Flp expression. They demonstrate that their system can solve the problem encountered with the other approach and use it for mapping axonal projections of the glutamatergic, LepR-expressing neurons and the consequences of chronic activation of these neurons on food intake and energy expenditure. The results are significant and clearly presented. The failure of the other system (INTRSECT) for their application is not clearly understood, but authors say that it may be due to low expression of Cre or Flp in these neurons; however, Supp Fig. 1 shows that it Lepr-Cre and Slc17a6-FLPo were sufficient to activate a transgenic reporter (Supp Fig. 1). The authors reveal that they probably could have used Nr5a1-Cre mice manipulate the activity of these VMH neurons. Nevertheless, it is worthwhile having multiple methods to attack a specific problem because of unforeseen complications with particular methods.

Reviewer #1 (Public Review):

The zebrafish has a rich history of enabling innovative microscopy techniques, and is also a well established system to model inflammation and infection by human pathogens. Consistent with this, Miskolci et al use zebrafish to test a novel imaging approach that has great potential to significantly impact the field of immunometabolism. Fluorescence lifetime is a label-free, non-invasive imaging approach to detect metabolic changes in situ, at the level of the single cell. In this report, Miskolci et al use fluorescence lifetime imaging of NAD(P)H and FAD to detect metabolic changes in zebrafish macrophages (with temporal and spatial resolution) in response to inflammatory and infectious cues.

Miskolci et al (eLife 2019) have previously characterized inflammatory and wound healing responses to distinct caudal fin injuries (tail wound, infection and tail wound, thermal injury). In this report, authors use these different injury models to show that fluorescence lifetime imaging can detect variations in macrophage metabolism. Although many interesting results are presented and future directions are proposed, the study in its current state is descriptive and lacks validation across different modalities. As a result, the reliability of fluorescence lifetime imaging in zebrafish macrophages is not yet convincing. Moreover, any metabolomic changes in macrophages are not functionally linked to zebrafish phenotypes (eg inflammation, bacterial burden, caudal fin regeneration).

Reviewer #1 (Public Review):

The manuscript presents a very nice and very detailed approach to illustrate the anatomical hierarchies and also some differences of signal transmission in the olfactory vs. thermosensory-/hygrosensory systems.

The authors first provide a complete description of the Drosophila olfactory system, from first, second and third-order neurons in the lateral horn. Using a generally applicable analysis methods, they extract information flow and layered organisation between olfactory input and descending interneurons. Among the results is the interesting finding that downstream of the mushroom body and lateral horn, output neurons converge to presumably regulate behavior. In an additional set of analyses, Schlegel et al. describe and quantify inter- vs. intraindividual stereotypy of neurons and motifs. They actually compare neurons from three hemispheres of two brains and show an astounding degree of similarity across brains. This is somewhat reassuring and helpful to the field of Drosophila connectomics.

While the many details and data make the manuscript a somewhat strenuous read, and the sheer flood of data could be a bit overwhelming, the data and findings are impressive and important.

1) The work is very complementary to the data presented by Li et al. on the mushroom body.

2) The structure and the step-by-step approach to showing increasingly complex circuitry and by defining different layers of the circuitry is very helpful for the reader to get an impression of the complexity of this brain.

3) Of significant importance and of use for the community are, in addition to the data, the described methods tools for data analysis.

4) Using this type of analysis, the authors test hypotheses and prevailing assumptions in the field. For instance, they find that in early layers of the olfactory system neurons tend to connect to the next higher layer, whereas neurons in higher layers interconnect or even connect back to earlier layers. This is a very interesting finding that might have important implications regarding top-down feedback and recurrent loops in olfactory processing.

5) Analysis of connectivity in the antennal lobe suggests that the system is highly lateralized. This finding also has important implications and helps to explain why flies might be able to discern left from right odor sources.

6) The manuscript shows many examples of what other scientists/readers of the manuscript could extract from the raw anatomical data. This will be very useful for the community beyond the data that is actually already shown in the manuscript.

7) The authors also compare their findings to the connectome/motifs identified for the larval olfactory system. There are many similarities as expected.

Reviewer #1 (Public Review):

Claudi et al. present a new tool for visualizing brain maps. In the era of new technologies to clear and analyze brains of model organisms, new tools are becoming increasingly important for researchers to interact with this data. Here, the authors report on a new tool for just this: exploring, visualizing, and rendering this high dimensional (and large) data. This tool will be of great interest to researchers who need to visualize multiple brains within several key model organisms.

The authors provide a nice overview of the tool, and the reader can quickly see its utility. What I would like to ask the authors to add is more information about computational resources and computing time for rendering; i.e. in the paper, they state "Brainrender uses vedo as the rendering engine (Musy et al., 2019), a state-of-the-art tool that enables fast, high quality rendering with minimal hardware requirements (e.g.: no dedicated GPU is needed)" - but would performance be improved with a GPU, runtimes, etc?

I would also be happy to see the limitations and directions expanded. For example, napari is a powerful n-dimensional viewer, how does performance compare (i.e. any plans for a napari plug in, or ImageJ plug in, or is this not compatible with this software's vision?). How does brain render compare (run time, computing wise) to Blender, for example, or another rendering tool standard in fields outside of neuroscience?

The methods are short (maybe check for all open source code citations are included, as needed), but they have excellent docs elsewhere; it would be nice to have minimal code examples in the methods though, i.e. "it's as easy as pip install brainrender" ... or such.

Lastly, I congratulate the authors on a clear paper, excellent documentation (https://docs.brainrender.info/), and I believe this is a very nice contribution to the community.

Reviewer #1 (Public Review):

The manuscript by Schrieber et al., explores whether inbreeding affects floral attractiveness to pollinators with additional factors of sex and origin in play, in male and female plants of Silene latifolia. The authors use a combination of spatial sampling, floral volatiles, flower color, and floral rewards coupled with the response of a specialized pollinator to these traits. Their results show that females are more affected by inbreeding and in general inbreeding negatively impacts the "composite nature" of floral traits. The manuscript is well written, the experiments are detailed and quite elaborate. For example., the methodology for flower color estimation is the most detailed effort in this area that I can remember. All the experiments in the manuscript show meticulous planning, with extensive data collection addressing minute details, including the statistics used. However, I do have some concerns that need to be addressed.

Core strengths: Detailed experimental design, elaborate data collection methods, well-defined methodology that is easy to follow. There is a logical flow for the experiments, and no details are missing in most of the experiemnts.

Weaknesses: A recent study has addressed some of the questions detailed in the manuscript. So, introduction needs to be tweaked to reflect this.

Some details and controls are missing in floral scent estimation. Flower age, a pesticide treatment of plants that could affect chemistry..needs to be better refined. While the study is laser-focused on floral traits, as the authors are aware inbreeding affects the total phenotype of the plants including fitness and defense traits. For example, there are quite a few studies that have shown how inbreeding affects the plant defense phenotype. This could be addressed in the introduction and discussion.

Reviewer #1 (Public Review):

In this study, Zhang et al. systematically analyze the effect of xanthohumol (XN) and TXN, a xanthohumol derivative, in a model of high-fat diet (HFD) feeding to mice, inducing several pathologies related to the metabolic syndrome. They authors convincingly show that XN and TXN attenuate HFD-induced weight gain, hepatic steatosis and lipid accumulation in adipose tissues. Furthermore, they newly show that XN and TXN bind to the PPARgamma ligand-binding domain pocket and that this inhibitory effect on PPARgamma is at least in part responsible for the observe beneficial effects.