The fragility of the US position necessitated neutrality

primary vs secondary orality; further examples of Ong's preference to primary

Formatting

better link https://www.nytimes.com/2025/03/17/climate/trump-eliminates-epa-science.html

This might be a better link: https://apnews.com/article/trump-epa-climate-zeldin-power-plants-feb184286a7a9419aefddce293362e6b

Heading should be "Major Rollsbacks"?

In 36 of 73 countries, local artists make up less than 30% of the national charts. Most of these nations stream more music from a single foreign country than from all their local artists combined. That’s nearly half of all countries studied, highlighting widespread cultural colonization through streaming.

I feel this reads better:

"To explore this, music education platform Skoove teamed up with data experts DataPulse Research to analyze Spotify’s Top 200 weekly charts across 73 countries for over a year. The result shows which countries streamed local artists versus international acts, revealing how much each of their 'Top 200' is dominated by homegrown talent compared to global performers."

Also, can "for over a year" be more specific by writing in weeks, since it's a weekly chart?

There should be a '?'

I rewrote this paragraph as follows:

"Music embodies the essence of a nation's culture. When local artists break out, they become sources of pride for their communities. Yet, streaming data reveals a striking divide: while India overwhelmingly supports its homegrown talent, other countries flood their charts with international hits. Why do some nations passionately protect their musical identity while others embrace global sounds?"

The EPA has no mandate to prioritize corporate profits over the health and safety of Americans.

I think this is a very fair standard, as it requires students to put in a significant amount of effort while also encouraging their curiosity in the subjects by providing a tangible reward for going beyond the bare minimum and reassuring them that their time will not be wasted.

this is the definition

Definition

definition

definition

This is the definition

I do agree with this though, it helps to focus on the details rather than general ideas.

I was about to ask what makes a poem good but then I continued reading and saw this insane checklist… This is the type of stuff that drives people away from writing (in general)!

eLife Assessment

This important report describes the changing antiviral activity of IFIT1 across mammals and in response to distinct viruses, likely as a result of past arms races. One of the main strengths of the manuscript is the breadth of mammalian IFIT1 orthologs and viruses that were tested, as well as the thoroughness of the positive selection analysis. Overall the evidence is convincing, and the discussion conveys well the limitations due to physical interactions with other IFITs that are not accounted for.

Reviewer #2 (Public review):

McDougal et al. describe the surprising finding that IFIT1 proteins from different mammalian species inhibit replication of different viruses, indicating that evolution of IFIT1 across mammals has resulted in host species-specific antiviral specificity. Before this work, research into the antiviral activity and specificity of IFIT1 had mostly focused on the human ortholog, which was described to inhibit viruses including vesicular stomatitis virus (VSV) and Venezuelan equine encephalitis virus (VEEV) but not other viruses including Sindbis virus (SINV) and parainfluenza virus type 3 (PIV3). In the current work, the authors first perform evolutionary analyses on IFIT1 genes across a wide range of mammalian species and reveal that IFIT1 genes have evolved under positive selection in primates, bats, carnivores, and ungulates. Based on these data, they hypothesize that IFIT1 proteins from these diverse mammalian groups may show distinct antiviral specificities against a panel of viruses. By generating human cells that express IFIT1 proteins from different mammalian species, the authors show a wide range of antiviral activities of mammalian IFIT1s. Most strikingly, they find several IFIT1 proteins that have completely different antiviral specificities relative to human IFIT1, including IFIT1s that fail to inhibit VSV or VEEV, but strongly inhibit PIV3 or SINV. These results indicate that there is potential for IFIT1 to inhibit a much wider range of viruses than human IFIT1 inhibits. Electrophoretic mobility shift assays (EMSAs) suggest that some of these changes in antiviral specificity can be ascribed to changes in direct binding of viral RNAs. Interestingly, they also find that chimpanzee IFIT1, which is >98% identical to human IFIT1, fails to inhibit any tested virus. Replacing three residues from chimpanzee IFIT1 with those from human IFIT1, one of which has evolved under positive selection in primates, restores activity to chimpanzee IFIT1. Together, these data reveal a vast diversity of IFIT1 antiviral specificity encoded by mammals, consistent with an IFIT1-virus evolutionary "arms race".

Overall, this is a very interesting and well-written manuscript that combines evolutionary and functional approaches to provide new insight into IFIT1 antiviral activity and species-specific antiviral immunity. The conclusion that IFIT1 genes in several mammalian lineages are evolving under positive selection is supported by the data. The virology results, which convincingly show that IFIT1s from different species have distinct antiviral specificity, are the most surprising and exciting part of the paper. As such, this paper will be interesting for researchers studying mechanisms of innate antiviral immunity, as well as those interested in species-specific antiviral immunity. Moreover, it may prompt others to test a wide range of orthologs of antiviral factors beyond those from humans or mice, which could further the concept of host-specific innate antiviral specificity. Additional areas for improvement, which are mostly to clarify the presentation of data and conclusions, are described below.

Strengths:

(1) This paper is a very strong demonstration of the concept that orthologous innate immune proteins can evolve distinct antiviral specificities. Specifically, the authors show that IFIT1 proteins from different mammalian species are able to inhibit replication of distinct groups of viruses, which is most clearly illustrated in Figure 4G. This is an unexpected finding, as the mechanism by which IFIT1 inhibits viral replication was assumed to be similar across orthologs. While the molecular basis for these differences remains unresolved, this is a clear indication that IFIT1 evolution functionally impacts host-specific antiviral immunity and that IFIT1 has the potential to inhibit a much wider range of viruses than previously described.

(2) By revealing these differences in antiviral specificity across IFIT1 orthologs, the authors highlight the importance of sampling antiviral proteins from different mammalian species to understand what functions are conserved and what functions are lineage- or species-specific. These results might therefore prompt similar investigations with other antiviral proteins, which could reveal a previously undiscovered diversity of specificities for other antiviral immunity proteins.

(3) The authors also surprisingly reveal that chimpanzee IFIT1 shows no antiviral activity against any tested virus despite only differing from human IFIT1 by eight amino acids. By mapping this loss of function to three residues on one helix of the protein, the authors shed new light on a region of the protein with no previously known function.

(4) Combined with evolutionary analyses that indicate that IFIT1 genes are evolving under positive selection in several mammalian groups, these functional data indicate that IFIT1 is engaged in an evolutionary "arms race" with viruses, which results in distinct antiviral specificities of IFIT1 proteins from different species.

Weaknesses:

(1) Some of the results and discussion text could be more focused on the model of evolution-driven changes in IFIT1 specificity. In particular, the majority of the residue mapping is on the chimpanzee protein, where it would appear that this protein has lost all antiviral function, rather than changing its antiviral specificity like some other examples in this paper. As such, the connection between the functional mapping of individual residues with the positive selection analysis and changes in antiviral specificity is not present. While the model that changes in antiviral specificity have been positively selected for is intriguing, it is not supported by data in the paper.

(2) The strength of the differences in antiviral specificity could be highlighted to a greater degree. Specifically, the text describes a number of interesting examples of differences in inhibition of viruses from Figure 3C and 3D, and 4C-F. The revised version has added some clarity by at least providing raw data for 3C and 3D for the reader to make their own comparisons, but it is still difficult to quickly assess which are the most interesting comparisons to make (e.g. for future mapping of residues that might be important).

Reviewer #3 (Public review):

Summary:

This manuscript by McDougal et al, demonstrates species-specific activities of diverse IFIT1 orthologs, and seeks to utilize evolutionary analysis to identify key amino acids under positive selection that contribute to antiviral activity of this host factor. While the authors identify amino acid residues important for antiviral activity of some orthologs, and propose a possible mechanism by which these residues may function, the significance or applicability of these findings to other orthologs is unclear. However, the subject matter is of interest to the field, and these findings contribute to the body of knowledge regarding IFIT1 evolution.

Strengths:

Assessment of multiple IFIT1 orthologs shows the wide variety of antiviral activity of IFIT1, and identification of residues outside of the known RNA binding pocket in the protein suggests additional novel mechanisms which may regulate IFIT1 activity.

Weaknesses:

Given that there appears to be very little overlap observed in orthologs that inhibited the viruses tested, it's possible that other amino acids may be key drivers of antiviral activity in these other orthologs. Thus, it's difficult to conclude whether the findings that residues 362/4/6 are important for IFIT1 activity can be broadly applied to other orthologs, or whether these are unique to human and chimpanzee IFIT1. While additional molecular studies of the impact of these mutations on IFIT1 function (e.g. impact on IFIT complex formation) would lend further insight, as it stands, these findings demonstrate a role for these residues in IFIT1 activity.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

McDougal et al. aimed to characterize the antiviral activity of mammalian IFIT1 orthologs. They first performed three different evolutionary selection analyses within each major mammalian clade and identified some overlapping positive selection sites in IFIT1. They found that one site that is positively selected in primates is in the RNA-binding exit tunnel of IFIT1 and is tolerant of mutations to amino acids with similar biochemical properties. They then tested 9 diverse mammalian IFIT1 proteins against VEEV, VSV, PIV3, and SINV and found that each ortholog has distinct antiviral activities. Lastly, they compared human and chimpanzee IFIT1 and found that the determinant of their differential anti-VEEV activity may be partly attributed to their ability to bind Cap0 RNA. 

Strengths: 

The study is one of the first to test the antiviral activity of IFIT1 from diverse mammalian clades against VEEV, VSV, PIV3, and SINV. Cloning and expressing these 39 IFIT1 orthologs in addition to single and combinatorial mutants is not a trivial task. The positive connection between anti-VEEV activity and Cap0 RNA binding is interesting, suggesting that differences in RNA binding may explain differences in antiviral activity. 

Weaknesses: 

The evolutionary selection analyses yielded interesting results, but were not used to inform follow-up studies except for a positively selected site identified in primates. Since positive selection is one of the two major angles the authors proposed to investigate mammalian IFIT1 orthologs with, they should integrate the positive selection results with the rest of the paper more seamlessly, such as discussing the positive selection results and their implications, rather than just pointing out that positively selected sites were identified. The paper should elaborate on how the positive selection analyses PAML, FUBAR, and MEME complement one another to explain why the tests gave them different results. Interestingly, MEME which usually provides more sites did not identify site 193 in primates that was identified by both PAML and FUBAR. The authors should also provide the rationale for choosing to focus on the 3 sites identified in primates only. One of those sites, 193, was also found to be positively selected in bats, although the authors did not discuss or integrate that finding into the study. In Figure 1A, they also showed a dN/dS < 1 from PAML, which is confusing and would suggest negative selection instead of positive selection. Importantly, since the authors focused on the rapidly evolving site 193 in primates, they should test the IFIT1 orthologs against viruses that are known to infect primates to directly investigate the impact of the evolutionary arms race at this site on IFIT1 function. 

We thank the reviewer for their assessment and for acknowledging the breadth of our dataset regarding diverse IFIT1s, number of viruses tested, and the functional data that may correlate biochemical properties of IFIT1 orthologous proteins with antiviral function. We have expanded the introduction and results sections to better explain and distinguish between PAML, FUBAR, and MEME analyses. Furthermore, we have expanded the discussion to incorporate the observation that site 193 is rapidly evolving in bats, as well as the observation that nearby sites to the TPR4 loop were identified as rapidly evolving in all clades of mammals tested. We also do observe an overall gene dN/dS of <1, however this is simply the average across all codons of the entire gene and does not rule out positive selection at specific sites. This is observed for other restriction factors, as many domains are undergoing purifying selection to retain core functions (e.g enzymatic function, structural integrity) while other domains (e.g. interfaces with viral antagonists or viral proteins) show strong positive selection. Specific examples include the restriction factors BST-2/Tetherin (PMID: 19461879) and MxA (PMID: 23084925). Furthermore, we agree that testing more IFIT1-sensitive viruses that naturally infect primates with our IFIT1 193 mutagenesis library would shed light on the influence of host-virus arms races at this site. However, VEEV naturally does also infect humans as well as at least one other species of primate (PMID: 39983680).

Below we individually address the reviewers' claims of inaccurate data interpretation.

Some of the data interpretation is not accurate. For example: 

(1) Lines 232-234: "...western blot analysis revealed that the expression of IFIT1 orthologs was relatively uniform, except for the higher expression of orca IFIT1 and notably lower expression of pangolin IFIT1 (Figure 4B)." In fact, most of the orthologs are not expressed in a "relatively uniform" manner e.g. big brown bat vs. shrew are quite different. 

We have now included quantification of the western blots to allow the reader to compare infection results with the infection data (Updated Figure 4B and 4G). We have also removed the phrase “relatively uniform” from the text and have instead included text describing the quantified expression differences.

(2) Line 245: "...mammalian IFIT1 species-specific differences in viral suppression are largely independent of expression differences." While it is true that there is no correlation between protein expression and antiviral activity in each species, the authors cannot definitively conclude that the species-specific differences are independent of expression differences. Since the orthologs are clearly not expressed in the same amounts, it is impossible to fully assess their true antiviral activity. At the very least, the authors should acknowledge that the protein expression can affect antiviral activity. They should also consider quantifying the IFIT1 protein bands and normalizing each to GAPDH for readers to better compare protein expression and antiviral activity. The same issue is in Line 267. 

We have now included quantification and normalization of the western blots to allow the reader to compare infection results with the infection data (Updated Figure 4B and 4G). Furthermore, we acknowledge in the text that expression differences may affect antiviral potency in infection experiments.

(3) Line 263: "SINV... was modestly suppressed by pangolin, sheep, and chinchilla IFIT1 (Figure 4E)..." The term "modestly suppressed" does not seem fitting if there is 60-70% infection in cells expressing pangolin and chinchilla IFIT1. 

We have modified the text to say “significantly suppressed” rather than “modestly suppressed.”

(4) The study can be significantly improved if the authors can find a thread to connect each piece of data together, so the readers can form a cohesive story about mammalian IFIT1. 

We appreciate the reviewer’s suggestion and have tried to make the story including more cohesive through commentary on positive selection and by using the computational analysis to first inform potential evolutionary consequences of IFIT1 functionality first by an intraspecies (human) approach, and then later an interspecies approach with diverse mammals that have great sequence diversity. Furthermore, we point out that almost all IFIT1s tested in the ortholog screen were also included in our computational analysis allowing for the potential to connect functional observations with those seen in the evolutionary analyses.

Reviewer #2 (Public review): 

McDougal et al. describe the surprising finding that IFIT1 proteins from different mammalian species inhibit the replication of different viruses, indicating that the evolution of IFIT1 across mammals has resulted in host speciesspecific antiviral specificity. Before this work, research into the antiviral activity and specificity of IFIT1 had mostly focused on the human ortholog, which was described to inhibit viruses including vesicular stomatitis virus (VSV) and Venezuelan equine encephalitis virus (VEEV) but not other viruses including Sindbis virus (SINV) and parainfluenza virus type 3 (PIV3). In the current work, the authors first perform evolutionary analyses on IFIT1 genes across a wide range of mammalian species and reveal that IFIT1 genes have evolved under positive selection in primates, bats, carnivores, and ungulates. Based on these data, they hypothesize that IFIT1 proteins from these diverse mammalian groups may show distinct antiviral specificities against a panel of viruses. By generating human cells that express IFIT1 proteins from different mammalian species, the authors show a wide range of antiviral activities of mammalian IFIT1s. Most strikingly, they find several IFIT1 proteins that have completely different antiviral specificities relative to human IFIT1, including IFIT1s that fail to inhibit VSV or VEEV, but strongly inhibit PIV3 or SINV. These results indicate that there is potential for IFIT1 to inhibit a much wider range of viruses than human IFIT1 inhibits. Electrophoretic mobility shift assays (EMSAs) suggest that some of these changes in antiviral specificity can be ascribed to changes in the direct binding of viral RNAs. Interestingly, they also find that chimpanzee IFIT1, which is >98% identical to human IFIT1, fails to inhibit any tested virus. Replacing three residues from chimpanzee IFIT1 with those from human IFIT1, one of which has evolved under positive selection in primates, restores activity to chimpanzee IFIT1. Together, these data reveal a vast diversity of IFIT1 antiviral specificity encoded by mammals, consistent with an IFIT1-virus evolutionary "arms race". 

Overall, this is a very interesting and well-written manuscript that combines evolutionary and functional approaches to provide new insight into IFIT1 antiviral activity and species-specific antiviral immunity. The conclusion that IFIT1 genes in several mammalian lineages are evolving under positive selection is supported by the data, although there are some important analyses that need to be done to remove any confounding effects from gene recombination that has previously been described between IFIT1 and its paralog IFIT1B. The virology results, which convincingly show that IFIT1s from different species have distinct antiviral specificity, are the most surprising and exciting part of the paper. As such, this paper will be interesting for researchers studying mechanisms of innate antiviral immunity, as well as those interested in species-specific antiviral immunity. Moreover, it may prompt others to test a wide range of orthologs of antiviral factors beyond those from humans or mice, which could further the concept of host-specific innate antiviral specificity. Additional areas for improvement, which are mostly to clarify the presentation of data and conclusions, are described below. 

Strengths: 

(1) This paper is a very strong demonstration of the concept that orthologous innate immune proteins can evolve distinct antiviral specificities. Specifically, the authors show that IFIT1 proteins from different mammalian species are able to inhibit the replication of distinct groups of viruses, which is most clearly illustrated in Figure 4G. This is an unexpected finding, as the mechanism by which IFIT1 inhibits viral replication was assumed to be similar across orthologs. While the molecular basis for these differences remains unresolved, this is a clear indication that IFIT1 evolution functionally impacts host-specific antiviral immunity and that IFIT1 has the potential to inhibit a much wider range of viruses than previously described. 

(2) By revealing these differences in antiviral specificity across IFIT1 orthologs, the authors highlight the importance of sampling antiviral proteins from different mammalian species to understand what functions are conserved and what functions are lineage- or species-specific. These results might therefore prompt similar investigations with other antiviral proteins, which could reveal a previously undiscovered diversity of specificities for other antiviral immunity proteins. 

(3) The authors also surprisingly reveal that chimpanzee IFIT1 shows no antiviral activity against any tested virus despite only differing from human IFIT1 by eight amino acids. By mapping this loss of function to three residues on one helix of the protein, the authors shed new light on a region of the protein with no previously known function. 

(4) Combined with evolutionary analyses that indicate that IFIT1 genes are evolving under positive selection in several mammalian groups, these functional data indicate that IFIT1 is engaged in an evolutionary "arms race" with viruses, which results in distinct antiviral specificities of IFIT1 proteins from different species. 

Weaknesses: 

(1) The evolutionary analyses the authors perform appear to indicate that IFIT1 genes in several mammalian groups have evolved under positive selection. However, IFIT1 has previously been shown to have undergone recurrent instances of recombination with the paralogous IFIT1B, which can confound positive selection analyses such as the ones the authors perform. The authors should analyze their alignments for evidence of recombination using a tool such as GARD (in the same HyPhy package along with MEME and FUBAR). Detection of recombination in these alignments would invalidate their positive selection inferences, in which case the authors need to either analyze individual non-recombining domains or limit the number of species to those that are not undergoing recombination. While it is likely that these analyses will still reveal a signature of positive selection, this step is necessary to ensure that the signatures of selection and sites of positive selection are accurate. 

(2) The choice of IFIT1 homologs chosen for study needs to be described in more detail. Many mammalian species encode IFIT1 and IFIT1B proteins, which have been shown to have different antiviral specificity, and the evolutionary relationship between IFIT1 and IFIT1B paralogs is complicated by recombination. As such, the assertion that the proteins studied in this manuscript are IFIT1 orthologs requires additional support than the percent identity plot shown in Figure 3B. 

(3) Some of the results and discussion text could be more focused on the model of evolution-driven changes in IFIT1 specificity. In particular, the chimpanzee data are interesting, but it would appear that this protein has lost all antiviral function, rather than changing its antiviral specificity like some other examples in this paper. As such, the connection between the functional mapping of individual residues with the positive selection analysis is somewhat confusing. It would be more clear to discuss this as a natural loss of function of this IFIT1, which has occurred elsewhere repeatedly across the mammalian tree. 

(4) In other places in the manuscript, the strength of the differences in antiviral specificity could be highlighted to a greater degree. Specifically, the text describes a number of interesting examples of differences in inhibition of VSV versus VEEV from Figure 3C and 3D, but it is difficult for a reader to assess this as most of the dots are unlabeled and the primary data are not uploaded. A few potential suggestions would be to have a table of each ortholog with % infection by VSV and % infection by VEEV. Another possibility would be to plot these data as an XY scatter plot. This would highlight any species that deviate from the expected linear relationship between the inhibition of these two viruses, which would provide a larger panel of interesting IFIT1 antiviral specificities than the smaller number of species shown in Figure 4. 

We thank the reviewer for their fair assessment of our manuscript. As the reviewer requested, we performed GARD analysis on our alignments used for PAML, FUBAR, and MEME (New Supp Fig 1). By GARD, we found 1 or 2 predicted breakpoints in each clade. However, much of the sequence was after or between the predicted breakpoints. Therefore, we were able to reanalyze for sites undergoing positive selection in the large region of the sequence that do not span the breakpoints. We were able to validate almost all sites originally identified as undergoing positive selection still exhibit signatures of positive selection taking these breakpoints into account: primates (11/12), bats (14/16), ungulates (30/37), and carnivores (2/4). To further validate our positive selection analysis, we used Recombination Detection Program 4 (RDP4) to remove inferred recombinant sequences from the primate IFIT1 alignment and performed PAML, FUBAR, and MEME. Once again, the sites in our original anlaysis were largely validated by this method. Importantly, sites 170, 193, and 366 in primates, which are discussed in our manuscript, were found to be undergoing positive selection in 2 of the 3 analyses using alignments after the indicated breakpoint in GARD and after removal of recombinant sequences by RDP4. We have updated the text to acknowledge IFIT1/IFIT1B recombination more clearly and include the GARD analysis as well as PAML, FUBAR, and MEME reanalysis taking into account predicted breakpoints by GARD and RDP4. Furthermore, to increase evidence that the sequences used in this study for both computational and functional analysis are IFIT1 orthologs rather than IFIT1B, we have included a maximum likelihood tree after aligning coding sequences on the C-terminal end (corresponding to bases 907-1437 of IFIT1). In Daughtery et al. 2016 (PMID: 27240734) this strategy was used to distinguish between IFIT1 and IFITB. All sequences used in our study grouped with IFIT1 sequences (including many confirmed IFIT1 sequences used in Daughterty et al.) rather than IFIT1B sequences or IFIT3. This new data, including the GARD, RDP4, and maximum likelihood tree is included as a new Supplementary Figure 1.

We also agree with the reviewer that it is possible that chimpanzee IFIT1 has lost antiviral function due to the residues 364 and 366 that differ from human IFIT1. We have updated the discussion sections to include the possibility that chimpanzee IFIT1 is an example of a natural loss of function that has occurred in other species over evolution as well as the potential consequences of this occurrence. Regarding highlighting the strength of differences in antiviral activity between IFIT1 orthologs, we have included several updates to strengthen the ability of the reader to assess these differences. First, we have included a supplementary table that includes the infection data for each ortholog from the VEEV and VSV screen to allow for readers to evaluate ranked antiviral activity of the species that suppress these viruses. In addition, the silhouettes next to the dot plots indicate the top ranked hits in order of viral inhibition (with the top being the most inhibitory) giving the reader a visual representation in the figure of top antiviral orthologs during our screen. We have also updated the figure legend to inform the reader of this information.

Reviewer #3 (Public Review):  

Summary: 

This manuscript by McDougal et al, demonstrates species-specific activities of diverse IFIT1 orthologs and seeks to utilize evolutionary analysis to identify key amino acids under positive selection that contribute to the antiviral activity of this host factor. While the authors identify amino acid residues as important for the antiviral activity of some orthologs and propose a possible mechanism by which these residues may function, the significance or applicability of these findings to other orthologs is unclear. However, the subject matter is of interest to the field, and these findings could be significantly strengthened with additional data.

Strengths:

Assessment of multiple IFIT1 orthologs shows the wide variety of antiviral activity of IFIT1, and identification of residues outside of the known RNA binding pocket in the protein suggests additional novel mechanisms that may regulate IFIT1 activity.

Weaknesses:

Consideration of alternative hypotheses that might explain the variable and seemingly inconsistent antiviral activity of IFIT1 orthologs was not really considered. For example, studies show that IFIT1 activity may be regulated by interaction with other IFIT proteins but was not assessed in this study.

Given that there appears to be very little overlap observed in orthologs that inhibited the viruses tested, it's possible that other amino acids may be key drivers of antiviral activity in these other orthologs. Thus, it's difficult to conclude whether the findings that residues 362/4/6 are important for IFIT1 activity can be broadly applied to other orthologs, or whether these are unique to human and chimpanzee IFIT1. Similarly, while the hypothesis that these residues impact IFIT1 activity in an allosteric manner is an attractive one, there is no data to support this.  

We thank the reviewer for their fair assessment of our manuscript. To address the weaknesses that the reviewer has pointed out we have expanded the discussion to more directly address alternate hypotheses, such as the possibility of IFIT1 activity being regulated by interaction with other IFIT proteins. Furthermore, we expanded the discussion to include an alternate hypothesis for the role of residues 364 and 366 in primate IFIT1 besides allosteric regulation. In addition, we did not intend to claim or imply that residues 364/6 are the key drivers of antiviral activity for all IFITs tested. However, we speculate that within primates these residues may play a key role as these residues differ between chimpanzee IFIT1 (which lacks significant antiviral activity towards the viruses tested in this study) and human IFIT1 (which possesses significant antiviral activity). In addition, these residues seem to be generally conserved in primate species, apart from chimpanzee IFIT1. We have included changes to the text to more clearly indicate that we highlight the importance of these residues specifically for primate IFIT1, but not necessarily for all IFIT1 proteins in all clades.

Reviewer #1 (Recommendations for the authors): 

(1) The readers would benefit from a more detailed background on the concept and estimation of positive selection for the readers, including the M7/8 models in PAML. 

We have included more information in the text to provide a better background for the concepts of positive selection and how PAML tests for this using M7 and M8 models.

(2) Presentation of data 

a) Figure 3C and 3D: is there a better way to present the infection data so the readers can tell the ranked antiviral activity of the species that suppress VEEV? 

We have included a supplementary table that includes the infection data for each ortholog from the VEEV and VSV screen to allow for readers to evaluate ranked antiviral activity of the species that suppress these viruses. In addition, the silhouettes next to the dot plots indicate the top ranked hits in order of viral inhibition (with the top being the most inhibitory). We have updated the figure legend to inform the reader of this information as well.

b) Figure 4C and 4D: consider putting the western blot in Supplementary Figure 1 underneath the infection data or with the heatmap so readers can compare it with the antiviral activity. 

We have also included quantification of the western blots performed to evaluate IFIT1 expression during the experiments shown in Figure 4C and 4D in an updated Figure 4B. We have also included normalized expression values with the heatmap shown in an updated Figure 4G so the reader can evaluate potential impact of protein expression on antiviral activity for all infection experiments shown in figure 4.

(3) Line 269-270: as a rationale for narrowing the species to human, black flying fox, and chimp IFIT1, human and black flying fox were chosen because they strongly inhibit VEEV, but pangolin wasn't included even though it had the strongest anti-VEEV activity? 

The rationale for narrowing the species to human, black flying fox, and chimpanzee IFIT1 was related to the availability of biological tools, high quality genome/transcriptome sequencing databases, and other factors. Specifically human and chimp IFIT1 are closely related but have variable antiviral activities, making their comparison highly relevant. Bats are well established as reservoirs for diverse viruses, whereas the reservoir status of many other mammals is less well defined. Furthermore, purifying large amounts of high quality IFIT1 protein after bacterial expression was another limitation to functional studies. We have added this information into the manuscript text.

(4) Figure 5A: to strengthen the claim that "species-specific antiviral activities of IFIT1s can be partly explained by RNA binding potential", it would be good to include one more positive and one more negative control. In other words, test the cap0 RNA binding activity of an IFIT1 ortholog that strongly inhibits VEEV and an ortholog that does not. It would also be good to discuss why chimp IFIT1 still shows dose-dependent RNA binding yet it is one of the weakest at inhibiting VEEV. 

We appreciate the reviewer's suggestion to include more controls and expand the dataset. While we understand the potential value of expanding the dataset, we believe that human IFIT1 serves as a robust positive control and human IFIT1 R187 (RNA-binding deficient) serves as an established negative control. Future experiments with other purified IFITs from other species will indeed strengthen evidence linking IFIT1 species-specific activity and RNA-binding.

Regarding chimpanzee IFIT1, we acknowledge there appears to be some dose-dependent Cap0 RNA-binding. However, the binding affinity is much weaker than that of human or black flying fox IFIT1. We speculate that during viral infection reduced binding affinity could impair the ability of chimpanzee IFIT1 to efficiently sequester viral RNA and inhibit viral translation. This reduction in binding affinity may, therefore, allow the cell to be overwhelmed by the exponential increase in viral RNA during replication resulting in an ineffective antiviral IFIT1. In the literature, a similar phenomenon is observed by Hyde et. al (PMID: 24482115). In this study, the authors test mouse Ifit1 Cap0 RNA binding by EMSA of the 5’ UTR sequence of VEEV RNA containing an A or G at nucleotide position 3. EMSA shows binding of both the A3 and G3 Cap0 VEEV RNA sequences, however stronger Ifit1 binding is observed for A3 Cap0 RNA sequence. The consequences of the reduced Ifit1 binding of the G3 Cap0 VEEV RNA are observed in vitro by a substantial increase in viral titers produced from cells as well as an increase in protein produced in a luciferase-based translation assay. The authors also show in vivo relevance of this reduction of Ifit1 binding as WT B6 mice infected with VEEV containing the A3 UTR exhibited 100% survival, while WT B6 mice infected with VEEV containing the G3 UTR survived at a rate of only ~25%. Therefore, the literature supports that a decrease in Cap0 RNA binding by an IFIT protein (while still exhibiting Cap0 RNA binding) observed by EMSA can result in considerable alterations of viral infection both in vitro and in vivo.

Minor: 

(1) Line 82: "including 5' triphosphate (5'-ppp-RNA), or viral RNAs..." having a comma here will make the sentence clearer. 

We have improved the clarity of this sentence. It now reads, “IFIT1 binds uncapped 5′triphosphate RNA (5′-ppp-RNA) and capped but unmethylated RNA (Cap0, an m⁷G cap lacking 2′-O methylation).”

(2) Line 100: "...similar mechanisms have been at least partially evolutionarily conserved in IFIT proteins to restrict viral infection by IFIT proteins". 

We have updated the text to improve clarity by revising the sentence to “VEEV TC-83 is sensitive to human IFIT1 and mouse Ifit1B, indicating at least partial conservation of antiviral function by IFIT proteins."

(3) Line 109: "signatures of rapid evolution or positive selection" would put positive selection second because that is the more technical term that can benefit from the more layperson term (rapid evolution). 

We have updated this sentence incorporating this suggestion. “Positive selection, or rapid evolution, is denoted by a high ratio of nonsynonymous to synonymous substitutions (dN/dS >1).”

(4) Lines 116-117: "However, this was only assessed in a few species" would benefit from a citation. 

We have inserted the citation.

(5) Line 127 heading: "IFIT1 is rapidly evolving in mammals" would be more accurate to say "in major clades of mammals". 

We have updated the text to include this suggestion.

(6) Line 165: "IFIT1 L193 mutants". 

We have updated the text to rephrase this for clarity.

(7) Line 170: two strains of VEEV were mentioned in the Intro, so it would be good to specify which strain of VEEV was used?

We have updated the text to clarify the VEEV strain. In this study, all experiments were performed using the VEEV TC-83 strain.

(8) Line 174: "Indeed, all mutants at position 193, whether hydrophobic or positively charged, inhibited VEEV similarly to the WT..." It should read "all hydrophobic and positively charged mutants inhibited VEEV similarly to the WT...". 

We corrected as suggested. 

(9) Line 204: what are "control cells"? Cells that are mock-infected, or cells without IFIT1? 

We have updated the text to improve clarity. What we refer to as control cells, were cells expressing an empty vector control rather than an IFIT1.

(10) Need to clarify n=2 and n=3 replicates throughout the manuscript. Does that refer to three independent experiments? Or an experiment with triplicate wells/samples? 

We have updated the text to say “independent experiments” instead of “biological replicates” to prevent any confusion.  All n=2 or n=3 replicates denote independent experiments.

(11) Line 254: "dominant antiviral effector against the related human parainfluenza virus type 5..." 

We have updated the text to improve clarity.

(12) Line 271: "The black flying fox (Pteropus alecto), is a model megabat species..." scientific name was italicized here but not elsewhere. Remove comma.

We have updated the text accordingly.

(13) Line 293: "...chimpanzee IFIT1 lacked these properties" but chimp IFIT1 can bind cap0 RNA, just at a lower level. 

We have updated the text to acknowledge that chimpanzee IFIT1 can bind cap0 RNA, albeit at a lower level than human IFIT1.

(14) Figure 6B: please fix the x-axis labels. They're very cramped. 

We have updated the x-axis labels for figure 6B and figure 6D to improve clarity.

(15) Line 609: "...trimmed and aligned"? 

Our phrasing is to indicate that coding sequences were aligned, and gaps were removed to reduce the chance of false positive signal by underrepresented codons such as gaps or short insertions. We have removed “trimmed” from the text and changed the text to say “aligned sequences” to increase clarity.

Reviewer #2 (Recommendations for the authors): 

(1) Numbers less than 10 should be spelled out throughout the manuscript (e.g. line 138). 

We have updated the text to reflect the request.

(2) Line 165: "expression of IFIT1 193 mutants" should be rephrased. 

We have updated the text to rephrase this sentence for clarity.

(3) A supplemental table or file should be included that contains the accession number and species names of sequences used for evolutionary analyses and for functional testing. In addition, the alignments that were used for positive selection can be included.  

We have included a supplemental file containing accession numbers, species names for evolutionary analysis and functional studies. In addition, this table includes the infection data for each IFIT1 homolog for the screen performed in figure 3.

(4) The discussion of potential functions of the C-terminus of IFIT1 should include possible interactions with other proteins. In particular, the C-terminus of IFIT1 has been shown to interact with IFIT3 in a way that modulates its activity (PMID: 29525521). Although residues 362-366 were not shown in that paper to interact with a fragment of IFIT3, it is possible that these residues may be important for interaction with full-length IFIT3 or some other IFIT1 binding partner. 

We thank the reviewer for their suggestion. We have expanded the discussion to explore the possibility that residues 364 and 366 of IFIT1 may be involved in IFIT1-IFIT3 interactions and consequently Cap0 RNA-binding and antiviral activity.

(5) The quantification of the EMSAs should be described in more detail. In particular, from looking at the images shown in Figure 5A, it would appear that human and chimpanzee IFIT1 show similar degrees of probe shift, while the human R187H panel shows no shifting at all. However, the quantification shows chimpanzee IFIT1 as being statistically indistinguishable from human R187H. Additional information on how bands were quantified and whether they were normalized to unshifted RNA would be helpful in attempting to resolve this visual discordance. 

EMSAs were quantified by determining Adj. Vol. Intensity in ImageLab (BioRad), which subtracts background signal, after imaging at the same exposure and SYBR Gold staining time. To determine Adj. Vol. Intensity, we drew a box (same size for each gel and lane for each replicate) for each lane above the free probe. These values were not normalized to unshifted RNA, however equal RNA was loaded. While the ANOVA shows no significant difference, between human R187H and chimpanzee IFIT1 band shift intensity, this is potentially due to the between group variance in the ANOVA. The increase in the AUC value for chimpanzee IFIT1 is 36.4% higher than R187H.

The AUC of Adj. Vol. Intensity of human IFIT1 band shift is roughly 2-fold more than that of chimpanzee IFIT1. We believe this matches with the visual representation as well, as human IFIT1 has a darker “upper” band in the shift, as well as a clear dark “lower” band that is not well defined in the chimpanzee shift. Furthermore, the upper band of the chimpanzee IFIT1 shift appears to be as intense in the 400nM as the upper band in the 240nM human IFIT1 lane, without taking into account the lower band seen for human IFIT1 as well. We included this quantification as kD was unable to be calculated due to no clear probe disappearance and we do not intend for this quantification to act as a substitute for binding affinity calculations, rather to aid the reader in data interpretation.

Reviewer #3 (Recommendations for the authors): 

(1) IFIT1 has been demonstrated to function in conjunction with other IFIT proteins, do you think the absence of antiviral activity is due to isolated expression of IFIT1 without these cofactors, and therefore might explain why there was little overlap observed in orthologs that inhibited the viruses tested (Figure 3, lines 209-210). 

We do not believe that isolated expression of IFIT1 without cofactors (such as orthologous IFIT proteins) would fully explain the disparities in antiviral activity as many IFIT1s that expressed inhibited either VSV or VEEV in our screen. However, we acknowledge that the expression of IFIT1 alone does create a limitation in our study as IFIT1 antiviral activity and RNA-binding can be modulated by interactions with other IFIT proteins. Therefore, we do believe that it is possible that co-expression of IFIT1 with other IFITs from a given species might potentially enhance antiviral activity. Future studies may shed light on this.

(2) Figure 5 - Calculating the Kd for each protein would be more informative. How does the binding affinity of these IFIT1 proteins compare to that which has previously been reported? 

We are unable to accurately determine kD as there is not substantial diminished signal of the free probe. Therefore, we are only able to compare IFIT1 protein binding between species without accurate mathematical calculation of binding affinity. Our result does appear similar to that of mouse Ifit1 binding to VEEV RNA (PMID: 24482115), in which the authors also do not calculate a kD for their RNA EMSA.

(3) Mutants 364 and 366 may not have direct contact with RNA, but RNA EMSA data presented suggest that the binding affinity may be different (though this is hard to conclude without Kd data). Additional biochemical data with these mutants might provide more insight here. 

We agree that further studies using 364 and 366 double mutant human and chimpanzee protein in EMSAs would provide additional biochemical data and provide insight into the role of these residues in direct RNA binding. We acknowledge this is a limitation of our study as we provide only genetic data demonstrating the importance of these residues.

(4) Given that there appears to be very little overlap observed in orthologs that inhibited the viruses tested, it's possible that other amino acids may be key drivers of antiviral activity in these other orthologs. Thus, it's difficult to conclude whether the findings that residues 362/4/6 are important for IFIT1 activity can be broadly applied to other orthologs. A more systematic assessment of the role of these mutations across multiple diverse orthologs would provide more insight here. Do other antiviral proteins show this trend (ie exhibit little overlap in orthologs that inhibit these viruses). What do you think might be driving this? 

We agree that other residues outside of 364 and 366 may be key drivers of antiviral activity across the IFTI1 orthologs tested. We do not hypothesize that this will broadly apply across IFIT1 from diverse clades of mammals as overall amino acid identity can differ by over 30%. However, based on the chimpanzee and human IFIT1 data, as well as sequence alignment within primates specifically, we believe these residues may be key for primate (but not necessarily other clades of mammals) IFIT1 antiviral activity.

Regarding if other antiviral proteins show little overlap in orthologs that inhibit a given virus, to our knowledge such a functional study with this large and divergent dataset of orthologs has not been performed. However, there are many examples of restriction factors exhibiting speciesspecific antiviral activity when ortholog screens have been performed. For example, HIV was reported to be suppressed by MX2 orthologs from human, rhesus macaque, and African green monkey, but not sheep or dog MX2 (PMID: 24760893). In addition, foamy virus was inhibited by the human and rhesus macaque orthologs of PHF11, but not the mouse and feline orthologs (PMID: 32678836). Furthermore, studies from our lab have shown variability in RTP4 ortholog antiviral activity inhibition towards viruses much as hepatitis C virus (HCV), West Nile virus (WNV), and Zika virus (ZIKV) (PMID: 33113352).

eLife Assessment

In this valuable contribution, the authors present a novel and versatile probabilistic tool for classifying tracking behaviors and understanding parameters for different types of single-particle motion. The software package will be broadly applicable to single-particle tracking studies. The methodology has been convincingly tested by computational comparisons and experimental data, although the mathematical foundation for the hypothesis testing method can be further strengthened.

Reviewer #1 (Public review):

Summary:

Weiss and co-authors presented a versatile probabilistic tool. aTrack helps in classifying tracking behaviors and understanding important parameters for different types of single particle motion types: Brwonian, Confined, or Directed motion. The tool can be used further to analyze populations of tracks and the number of motion states. This is a stand-alone software package, making it user-friendly for a broad group of researchers.

Strengths:

This manuscript presents a novel method for trajectory analysis.

Comments on revisions:

The authors have strengthened and improved the manuscript

Reviewer #2 (Public review):

Summary:

The authors present a software package "aTrack" for identification of motion types and parameter estimation in single-particle tracking data. The software is based on maximum likelihood estimation of the time-series data given an assumed motion model and likelihood ratio tests for model selection. They characterized the performance of the software mostly on simulated data and showed that it is applicable to experimental data.

Strengths:

Although many tools exist in the single-particle tracking (SPT) field, this particular software package is developed using an innovative mathematical model and a probabilistic approach. It also provide inference of motion types, which are critical to answer biological questions in SPT experiments.

(1) The authors adopt a novel mathematical framework, which is unique in the SPT field.

(2) The authors have validated their method extensively using simulated tracks and compared to existing methods when appropriate.

(3) The code is freely available

Weaknesses:

The authors did a good job during the revision to address most of the weaknesses in my (as well as other reviewer's) first round of review. Nevertheless, the following issue is still not fully addressed.<br /> The hypothesis testing method presented here lacks rigorous statistical foundation. The authors improved on this point after the revision, but in their newly added SI section "Statistical Test", only justified their choices using "hand-waving" arguments (i.e. there is not a single reference to proper statistical textbooks or earlier works in this important section). I understand that sometimes mathematical rigor comes later after some intuition-guided choices of critical parameters seems to work, but nevertheless need to point it out as a remaining weakness.

Author response:

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

Reviewer #1 (Public Review): 

Summary: 

Weiss and co-authors presented a versatile probabilistic tool. aTrack helps in classifying tracking behaviors and understanding important parameters for different types of single particle motion types: Brownian, Confined, or Directed motion. The tool can be used further to analyze populations of tracks and the number of motion states. This is a stand-alone software package, making it user-friendly for a broad group of researchers. 

Strengths: 

This manuscript presents a novel method for trajectory analysis. 

Weaknesses: 

(1) In the results section, is there any reason to choose the specific range of track length for determining the type of motion? The starting value is fine, and would be short enough, but do the authors have anything to report about how much is too long for the model? 

We chose to test the range of track lengths (five-to-hundreds of steps) to cover the broad range of scenarios arising from single proteins or fluorophores to brighter objects with more labels.  While there is no upper-limit per se, the computation time of our method scales linearly with track length, 100 time-points takes ~2 minutes to run on a standard consumer-level desktop CPU. We have added the following sentence to note the time-cost with trajectory length:  

“The recurrent formula enables our model computation time to scale linearly with the number of time points.”

(2) Robustness to model mismatches is a very important section that the authors have uplifted diligently. Understanding where and how the model is limited is important. For example, the authors mentioned the limitation of trajectory length, do the authors have any information on the trajectory length range at which this method works accurately? This would be of interest to readers who would like to apply this method to their own data. 

We agree that limitations are important to estimate, and trajectory length is an important consideration when choosing how to analyze a dataset. We report the categorization certainty, i.e. the likelihood differences, for a range of track lengths (Fig. 2 a,c, Fig. 3c-d, and Fig. 4 c,g.).

For example, here are the key plots from Fig. 2 quantifying the relative likelihoods, where being within the light region is necessary. The light areas represent a useful likelihood ratio.

We only performed analysis up to track lengths of 600 time steps but parameter estimations and significance can only improve when increasing the track length as long as the model assumptions are verified. The broader limitations and future opportunities for new methods are now expanded upon in the discussion, for example switching between states and model and state and model ambiguities (bound vs very slow diffusion vs very slow motion).

(3) aTrack extracts certain parameters from the trajectories to determine the motion types. However, it is not very clear how certain parameters are calculated. For example, is the diffusion coefficient D calculated from fitting, and how is the confinement factor defined and estimated, with equations? This information will help the readers to understand the principles of this algorithm.

We apologize for the confusion. All the model parameters are fit using the maximum likelihood approach. To make this point clearer in the manuscript, we have made three changes:

(1) We modified the following sentence to replace “determined” with "fit”:

“Finally, Maximum Likelihood Estimation (MLE) is used to fit the underlying parameter value”

(2) We added the following sentence in the main text :

“In our model, the velocity is the characteristic parameter of directed motion and the confinement factor represents the force within a potential well. More precisely, the confinement factor $l$ is defined such that at each time step the particle position is updated by $l$ times the distance particle/potential well center (see the Methods section for more details).”.

(3) We have added a new section in the methods, called Fitting Method, where we have added the explanation below:

“For the pure Brownian model, the parameters are the diffusion coefficient and the localization error. For the confinement model, the parameters are the diffusion coefficient, the localization error, confinement factor, and the diffusion coefficientof the potential well. For the directed model, the parameters are the diffusion coefficient, the localization error, the initial velocity and the acceleration variance.

These parameters are estimated using the maximum likelihood approach which consists in finding the parameters that maximize the likelihood. We realize this fitting step using gradient descent via a TensorFlow model. All the estimates presented in this article are obtained from a single set of initial parameters to demonstrate that the convergence capacity of aTrack is robust to the initial parameter values.”

(4) The authors mentioned the scenario where a particle may experience several types of motion simultaneously. How do these motions simulated and what do they mean in terms of motion types? Are they mixed motion (a particle switches motion types in the same trajectory) or do they simply present features of several motion types? It is not intuitive to the readers that a particle can be diffusive (Brownian) and direct at the same time. 

In the text, we present an example where one can observe this type of motion to help the reader understand when this type of motion can be met: “Sometimes, particles undergo diffusion and directed motion simultaneously, for example, particles diffusing in a flowing medium (Qian 1991).”

This is simulated by the addition of two terms affecting the hidden position variable before adding a localization term to create the observed variable. In the analysis, this manifests as non-zero values for the diffusion coefficient and the linear velocity. For example, Figure 4g and the associated text, where a single particle moves with a directed component and a Brownian diffusion component at each step.

We did not simulate transitions between types of motion. Switching is not treated by this current model; however, this limitation is described in the discussion and our team and others are currently working on addressing this challenge.

Reviewer #2 (Public Review): 

Summary: 

The authors present a software package "aTrack" for identification of motion types and parameter estimation in single-particle tracking data. The software is based on maximum likelihood estimation of the time-series data given an assumed motion model and likelihood ratio tests for model selection. They characterized the performance of the software mostly on simulated data and showed that it is applicable to experimental data. 

Strengths: 

A potential advantage of the presented method is its wide applicability to different motion types. 

Weaknesses: 

(1) There has been a lot of similar work in this field. Even though the authors included many relevant citations in the introduction, it is still not clear what this work uniquely offers. Is it the first time that direct MLE of the time-series data was developed? Suggestions to improve would include (a) better wording in the introduction section, (b) comparing to other popular methods (based on MSD, step-size statistics (Spot-On, eLife 2018;7:e33125), for example) using the simulated dataset generated by the authors, (c) comparing to other methods using data set in challenges/competitions (Nat. Comm (2021) 12:6253).  

We thank the reviewer for this suggestion and agree that the explanation of the innovative aspects of our method in the introduction was not clear enough. We have now modified the introduction to better explain what is improved here compared to previous approaches.

“The main innovations of this model are: 1) it uses analytical recurrence formulas to perform the integration step for complex motion, improving speed and accuracy; 2) it handles both confined and directed motion; 3) anomalous parameters, such as the center of the potential well and the velocity vector are allowed to change through time to better represent tracks with changing directed motion or confinement area; and lastly 4) for a given track or set of tracks, aTrack can determine whether tracks can be statistically categorized as confined or directed, and the parameters that best describe their behavior, for example, diffusion coefficient, radius of confinement, and speed of directed motion.”

Regarding alternatives, we compare our method in the text to the best-performing algorithm of the

2021 Anomalous Diffusion (AnDi) Challenge challenge mentioned by the reviewer in Figure 6 (RANDI, Argun et al, arXiv, 2021, Muñoz-Gil et al, Nat Com. 2021). Notably, both methods performed similarly on fBm, but ours was more robust in cases where there were small differences between the process underlying the data and the model assumptions, a likely scenario in real datasets. Regarding Spot-On, this was not mentioned as it only deals with multiple populations of Brownian diffusers, preventing a quantitative comparison.

(2) The Hypothesis testing method presented here has a number of issues: first, there is no definition of testing statistics. Usually, the testing statistics are defined given a specific (Type I and/or Type II) error rate. There is also no discussion of the specificity and sensitivity of the testing results (i.e. what's the probability of misidentification of a Brownian trajectory as directed? etc).

We now explain our statistical approach and how to perform hypothesis testing with our metric in a new supplementary section, Statistical test. 

We use the likelihood ratio as a more conservative alternative to the p-value. In Fig S2, we show that our metric is an upper bound of the p-value and can be used to perform hypothesis testing with a chosen type I error rate. 

Related, it is not clear what Figure 2e (and other similar plots) means, as the likelihood ratio is small throughout the parameter space. Also, for likelihood ratio tests, the authors need to discuss how model complexity affects the testing outcome (as more complex models tend to be more "likely" for the data) and also how the likelihood function is normalized (normalization is not an issue for MLE but critical for ratio tests). 

We present the likelihood ratio as an upper bound of the p-value. Therefore, we can reject the null hypothesis if it is smaller than a given threshold, e.g. 0.05, but this number should be decreased if multiple tests are performed. The colorscale we show in the figure is meant to highlight the working range (light), and ambiguous range (dark) of the method.

As the reviewer mentions, we expect the alternative hypothesis to result in higher likelihoods than the simpler null hypothesis for null hypothesis tracks, but, as seen in the Fig S2, the likelihood ratio of a dataset corresponding to the null hypothesis is strongly skewed toward its upper limit 1. This means that for most of the tracks, the likelihood is not (or little) affected by the model complexity. The likelihoods of all the models are normalized so their integrals over the data equals 1/A with A the area of the field of view which is independent of the model complexity.

(3) Relating to the mathematical foundation (Figure 1b). The measured positions are drawn as direct arrows from the real position states: this infers instantaneous localization. In reality, there is motion blur which introduces a correlation of the measured locations. Motion blur is known to introduce bias in SPT analysis, how does it affect the method here? 

The reviewer raises an important point as our model does not explicitly consider motion blur. We have now added a paragraph that presents how our model performs in case of motion blur in the section called Robustness to model mismatches. This section and the corresponding new Supplemental Fig. S7 demonstrate that the estimated diffusion length is accurate so long as the static localization error is higher than the dynamic localization error. If the dynamic localization error is higher, our model systematically underestimates the diffusion length by a factor 0.81 = (2/3)^0.5 which can be corrected for with an added post-processing step.  

(4) The authors did not go through the interpretation of the figure. This may be a matter of style, but I find the figures ambiguous to interpret at times.  

We thank the reviewer for their feedback on improving the readability. To avoid overly repetitive and lengthy sections of text, we have opted for a concise approach. This allows us to present closely related panels at the same point in the text, while not ignoring important variations and tests. Considering this feedback and the reviewers, we have added more information and interpretation throughout our manuscript to improve interpretability.

(5) It is not clear to me how the classification of the 5 motion types was accomplished. 

We have modified the specific text related to this figure to describe an illustrative example to show how one could use aTrack on a dataset where not that much is known: First, we present the method to determine the number of states; second, we verify the parameter estimates correspond to the different states.  

Classifying individual tracks is possible. While not done in the section corresponding to Fig. 5, this is done in Fig. 7 and a new supplementary plot, Fig. S9b (shown below). In brief, this is accomplished with our method by computing the likelihood of each track given each state. The probability that a given track is in state k equals the likelihood of the track given the state divided by the sum of the likelihoods given the different states. 

(6) Figure 3. Caption: what is ((d_{est}-0.1)/0.1)? Also panel labeled as "d" should be "e". 

Thank you for bringing these errors to our attention, the panel and caption have been corrected.

Reviewer #3 (Public Review): 

Summary: 

In this work, Simon et al present a new computational tool to assess non-Brownian single-particle dynamics (aTrack). The authors provide a solid groundwork to determine the motion type of single trajectories via an analytical integration of multiple hidden variables, specifically accounting for localization uncertainty, directed/confined motion parameters, and, very novel, allowing for the evolution of the directed/confined motion parameters over time. This last step is, to the best of my knowledge, conceptually new and could prove very useful for the field in the future. The authors then use this groundwork to determine the motion type and its corresponding parameter values via a series of likelihood tests. This accounts for obtaining the motion type which is statistically most likely to be occurring (with Brownian motion as null hypothesis). Throughout the manuscript, aTrack is rigorously tested, and the limits of the methods are fully explored and clearly visualised. The authors conclude with allowing the characterization of multiple states in a single experiment with good accuracy and explore this in various experimental settings. Overall, the method is fundamentally strong, wellcharacterised, and tested, and will be of general interest to the single-particle-tracking field. 

Strengths: 

(1) The use of likelihood ratios gives a strong statistical relevance to the methodology. There is a sharp decrease in likelihood ratio between e.g. confinement of 0.00 and 0.05 and velocity of 0.0 and 0.002 (figure 2c), which clearly shows the strength of the method - being able to determine 2nm/timepoint directed movement with 20 nm loc. error and 100 nm/timepoint diffusion is very impressive. 

We apologize for the confusion, the directed tracks in Fig 2 have no Brownian-motion component, i.e. D=0. We have made this clearer in the main text. Specifically, this section of the text refers to a track in linear motion with 2 nm displacements per step. With 70 time points (69 steps), a single particle which moved from 138 nm with a localization error of 20 nm (95% uncertainty range of 80 nm) can be statistically distinguished from slow diffusive motion.

In Fig. 4g, we explore the capabilities of our method to detect if a diffusive particle also has a directed motion component. 

(2) Allowing the hidden variables of confinement and directed motion to change during a trajectory (i.e. the q factor) is very interesting and allows for new interpretations of data. The quantifications of these variables are, to me, surprisingly accurate, but well-determined. 

(3) The software is well-documented, easy to install, and easy to use. 

Weaknesses: 

(1) The aTrack principle is limited to the motions incorporated by the authors, with, as far as I can see, no way to add new analytical non-Brownian motion. For instance, being able to add a dynamical stateswitching model (i.e. quick on/off switching between mobile and non-mobile, for instance, repeatable DNA binding of a protein), could be of interest. I don't believe this necessarily has to be incorporated by the authors, but it might be of interest to provide instructions on how to expand aTrack.  

We agree that handling dynamic state switching is very useful and highlight this potential future direction in the discussion. The revised text reads:

“An important limitation of our approach is that it presumes that a given track follows a unique underlying model with fixed parameters. In biological systems, particles often transition from one motion type to another; for example, a diffusive particle can bind to a static substrate or molecular motor (46). In such cases, or in cases of significant mislinkings, our model is not suitable. However, this limitation can be alleviated by implicitly allowing state transitions with a hidden Markov Model (15) or alternatives such as change-point approaches (30, 47, 48), and spatial approaches (49).”

(2) The experimental data does not very convincingly show the usefulness of aTrack. The authors mention that SPBs are directed in mitosis and not in interphase. This can be quantified and studied by microscopy analysis of individual cells and confirming the aTrack direction model based on this, but this is not performed. Similarly, the size of a confinement spot in optical tweezers can be changed by changing the power of the optical tweezer, and this would far more strongly show the quantitative power of aTrack. 

We agree with the reviewer and have revised the biological experiment section significantly to better illustrate the potential of aTrack in various use cases.

Now, we show an experiment to quantify the effect of LatA, an actin inhibitor, on the fraction of directed tracks obtained with aTrack. We find that LatA significantly decreases directed motion while a LatA-resistant mutant is not affected (Fig7a-c).

As suggested by the reviewer, we have expanded the optical tweezer experiment by varying the laser power. As expected, increasing the laser power decreases the confinement radius.

(3) The software has a very strict limit on the number of data points per trajectory, which is a user input. Shorter trajectories are discarded, while longer trajectories are cut off to the set length. It is not explained why this is necessary, and I feel it deletes a lot of useful data without clear benefit (in experimental conditions).

We thank the reviewer for this recommendation; we have now modified the architecture of our model to enable users to consider tracks of multiple lengths. Note that the computation time is proportional to the longest track length times the number of tracks.  

Reviewer #2 (Recommendations For The Authors): 

Develop a better mathematical foundation for the likelihood ratio tests. 

We added more explanation of the likelihood ratio tests and their interpretation a new section entitled Statistical test in the supplementary information to address this recommendation.

Place this work in clearer contexts. 

We have now revised the introduction to better contextualize this work.

Improve manuscript clarity. 

Based on reviewer feedback and input from others, we have addressed this point throughout the article to improve readability.

Make the code available. 

The code is available on https://github.com/FrancoisSimon/aTrack, now including code for track generation.

Reviewer #3 (Recommendations For The Authors): 

(1) I believe the underlying model presented in Figure 1 is of substantial impact, especially when considering it as a simulation tool. I would suggest the authors make their method also available as a simulator (as far as I can tell, this is not explicitly done in their code repository, although logically the code required for the simulator should already be in the codebase somewhere). 

Thank you for this suggestion, the simulation scripts are now on the Github repository together with the rest of the analysis method. https://github.com/FrancoisSimon/aTrack

(2) The authors should explore and/or discuss the effects of wrong trajectory linking to their method. Throughout the text, fully correct trajectory linking is assumed and assessed, while in real experiments, it is often the case that trajectory linking is wrong, e.g. due to blinking emitters, imaging artefacts, high-density localizations, etc etc. This would have a major impact on the accuracy of trajectories, and it is extremely relevant to explore how this is translated to the output of aTrack. 

As the reviewer notes, our current model does not account for track mislinking. This limits the method to data with lower fluorophore-densities, which is the typical use-case for SPT. We have added a brief description of the issue into the discussion of limitations.  

(3) aTrack only supports 2D-tracking, but I don't believe there is a conceptual reason not to have this expanded to three dimensions. 

The stand-alone software is currently limited to 2D tracks, however, the aTrack Python package works for any number of dimensions (i.e. 1-3). Note that since the current implementation assumes a single localization error for all axes, more modifications may be required for some types of 3D tracking. See https://github.com/FrancoisSimon/aTrack for more details about aTrack implementations.

(4) Crucial information is missing in the experimental demonstrations. Especially in the NP-bacteria dataset, I miss scalebars, and information on the number of tracks. It is not explained why 5 different states are obtained - especially because I would naively expect three states: immobile NPs (e.g. stuck to glass), diffusing NPs, and NPs attached to bacteria, and thus directed. Figure 7e shows three diffusive states (why more than one?), no immobile states (why?), and two directed states (why?). 

We thank the reviewer for pointing out these issues. We have now added scalebars and more experimental details to the figure and text as well as modifying the plot to more clearly emphasize the directed nanoparticles that are attached to cells from the diffusive nanoparticles.  

Likely, our focal plane was too high to see the particles stuck on glass. The multiple diffusive states may be caused by different sizes of nanoparticle complexes, the multiple directed states can be caused by the fact that directed motion of the cell-attached-nanoparticles occasionally shows drastic changes of orientations. We have also clarified in the text how multiple states can help handle a heterogeneous population as was shown by Prindle et al. 2022, Microbiol Spectr. The characterization and phenotyping of microbial populations by nanoparticle tracking was published in Zapata et al. 2022, Nanoscale. 

(5) I don't think I agree that 'robustness to model mismatches' is a good thing. Very crudely, the fact that aTrack finds fractional Brownian motion to be normal Brownian motion is technically a downside - and this should be especially carefully positioned if (in the future) a fractional Brownian motion model would be added to aTrack. I think that the author's point can be better tested by e.g. widely varying simulated vs fitted loc precision/diffusion coefficient (which are somewhat interchangeable).

In this context, our intention in describing the robustness to “model mismatches” refers to classifying subdiffusion as subdiffusive irrespective of the exact subdiffusion motion physics (as well as superdiffusion), that is, to use aTrack how MSD analysis is often deployed. This is important in the context of real-world applications where simple mathematical models cannot perfectly represent real tracks with greater complexity. 

Inevitably, some fraction of tracks with a pure Brownian motion may appear to match with a fractional Brownian motion, and thus statistical tests are needed to determine if this is significant. In general, aTrack finds fBm to be normal Brownian motion only when the anomalous coefficient is near 1, i.e. when the two models are indeed the same. When analysing fBm tracks with anomalous coefficients of 0.5 or 1.5, aTrack find that these tracks are better explained by our confined diffusion model or directed motion model, respectively (Please see Fig. 6a, copied below). 

To better clarify our objective, the section now has a brief introduction that reads:

“One of the most important features of a method is its robustness to deviations from its assumptions. Indeed, experimental tracking data will inevitably not match the model assumptions to some degree, and models need to be resilient to these small deviations.”  

Smaller points: 

(1) It is not clear what a biological example is of rotational diffusion. 

We modified the text to better explain the use of rotational diffusion.

(2) The text in the section on experimental data should be expanded and clarified, there currently are multiple 'floating sentences' that stop halfway, and it does not clearly describe the biological relevance and observed findings.  

We thank the reviewer for pointing out this issue. We have reworked the experimental section to better and more clearly explain the biological relevance of the findings.

(3) Caption of figure 3: 'd' should be 'e'. 

(4) Caption of Figure 7: log-likelihood should be Lconfined - Lbrownian, I believe. 

(5) Equation number missing in SI first sentence. 

(6) Supplementary Figure 1 top part access should be Lc-Lb instead of Ld-Lb. 

We have made these corrections, thank you for bringing them to our attention.

Very interesting tool, but bringing my A game will be scary

I'm also in the camp that has never heard of this app/service/thing but it does seem handy. :)

eLife Assessment

The paper reports valuable findings about the mechanism of regulation of the heat shock response in plants that acts as a brake to prevent hyperactivation of the stress response, which have theoretical or practical implications for a subfield. The study presented by the authors provides solid methods, data, and analysis that broadly support the claims. This report presents helpful information regarding new spliced HSFs forms in Arabidopsis that highlights key information in the understanding of heat stress and plant growth.

Reviewer #2 (Public review):

Summary:

The authors report that Arabidopsis short HSFs S-HsfA2, S-HsfA4c, and S-HsfB1 confer extreme heat. They have truncated DNA binding domains that bind to a new heat-regulated element. Considering Short HSFA2, the authors have highlighted the molecular mechanism by which S-HSFs prevent HSR hyperactivation via negative regulation of HSP17.6B. The S-HsfA2 protein binds to the DNA binding domain of HsfA2, thus preventing its binding to HSEs, eventually attenuating HsfA2-activated HSP17.6B promoter activity. This report adds insights to our understanding of heat tolerance and plant growth.

Strengths:

(1) The manuscript represents ample experiments to support the claim.

(2) The manuscript covers a robust number of experiments and provides specific figures and graphs to in support of their claim.

(3) The authors have chosen a topic to focus on stress tolerance in changing environment.

(4) The authors have summarized the probable mechanism using a figure.

Weaknesses:

Quite minimum

(1) Fig. 3. the EMSA to reveal binding

(2) Alignment of supplementary figures 6-7.

Author response:

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

Reviewer #1 (Public review):

In the present work, Chen et al. investigate the role of short heat shock factors (S-HSF), generated through alternative splicing, in the regulation of the heat shock response (HSR). The authors focus on S-HsfA2, an HSFA2 splice variant containing a truncated DNA-binding domain (tDBD) and a known transcriptional-repressor leucin-rich domain (LRD). The authors found a two-fold effect of S-HsfA2 on gene expression. On the one hand, the specific binding of S-HsfA2 to the heat-regulated element (HRE), a novel type of heat shock element (HSE), represses gene expression. This mechanism was also shown for other S-HSFs, including HsfA4c and HsfB1. On the other hand, S-HsfA2 is shown to interact with the canonical HsfA2, as well as with a handful of other HSFs, and this interaction prevents HsfA2 from activating gene expression. The authors also identified potential S-HsfA2 targets and selected one, HSP17.6B, to investigate the role of the truncated HSF in the HSR. They conclude that S-HsfA2-mediated transcriptional repression of HSP17.6B helps avoid hyperactivation of the HSR by counteracting the action of the canonical HsfA2.

The manuscript is well written and the reported findings are, overall, solid. The described results are likely to open new avenues in the plant stress research field, as several new molecular players are identified. Chen et al. use a combination of appropriate approaches to address the scientific questions posed. However, in some cases, the data are inadequately presented or insufficient to fully support the claims made. As such, the manuscript would highly benefit from tackling the following issues:

(1) While the authors report the survival phenotypes of several independent lines, thereby strengthening the conclusions drawn, they do not specify whether the presented percentages are averages of multiple replicates or if they correspond to a single repetition. The number of times the experiment was repeated should be reported. In addition, Figure 7c lacks the quantification of the hsp17.6b-1 mutant phenotype, which is the background of the knock-in lines. This is an essential control for this experiment

For the seedling survival rates and gene expression levels, we added statistical analysis based on at least two independent experiments. Figure 6E of the revised manuscript shows the phenotypes of the WT, hsp17.6b-1, HSP17.6B-KI, and HSP17.6B-OE plants and the statistical analysis of their seedling survival rates after heat exposure.

(2) In Figure 1c, the transcript levels of HsfA2 splice variants are not evident, as the authors only show the quantification of the truncated variant. Moreover, similar to the phenotypes discussed above, it is unclear whether the reported values are averages and, if so, what is the error associated with the measurements. This information could explain the differences observed in the rosette phenotypes of the S-HsfA2-KD lines. Similarly, the gene expression quantification presented in Figures 4 and 5, as well as the GUS protein quantification of Figure 3F, also lacks this crucial information.

RT‒qPCR analysis of the expression of these genes from at least two independent experiments was performed. We also added these missing information to the figure legends.

(3) The quality of the main figures is low, which in some cases prevents proper visualization of the data presented. This is particularly critical for the quantification of the phenotypes shown in Figure 1b and for the fluorescence images in Figures 4f and 5b. Also, Figure 9b lacks essential information describing the components of the performed experiments.

We apologize; owing to the limitations of equipment and technology, we will attempt to obtain high-quality images in the future. A detailed description of Figure 9b is provided in the methods section.

(4) Mutants with low levels of S-HsfA2 yield smaller plants than the corresponding wild type. This appears contradictory, given that the proposed role of this truncated HSF is to counteract the growth repression induced by the canonical HSF. What would be a plausible explanation for this observation? Was this phenomenon observed with any of the other tested S-HSFs?

We found that the constitutive expression of S-HsfA2 inhibits Arabidopsis growth. Considering this, Arabidopsis plants do not produce S-HsfA2 under normal conditions to avoid growth inhibition. However, under heat stress, Arabidopsis plants generate S-HsfA2, which contributes to heat tolerance and growth balance. In the revised manuscript, we provided supporting data indicating that S-HsfA4c-GFP or S-HsfB1-RFP constitutive expression confers Arabidopsis extreme heat stress sensitivity but inhibits root growth (Supplemental Figure S8). Therefore, this phenomenon is also observed in S-HsfA4c-GFP or S-HsfB1-RFP.

(5) In some cases, the authors make statements that are not supported by the results:<br /> (i) the claim that only the truncated variant expression is changed in the knock-down lines is not supported by Figure 1c;

In three S-HsfA2-KD lines, RT‒PCR splicing analysis revealed that HsfA2-II but not HsfA2-III is easily detected. In the revised manuscript, we added RT‒qPCR analysis, and the results revealed that the abundance of HsfA2-III and HsfA2-II but not that of the full-length HsfA2 mRNA significantly decreased under extreme heat (Figure 1C). Considering that HsfA2-III but not HsfA2-II is a predominant splice variant under extreme heat (Liu et al., 2013), S-HsfA2-KD may lead to the knockdown of alternative HsfA2 splicing transcripts, especially HsfA2-III.

(ii) the increase in GUS signal in Figure 3a could also result from local protein production;

We included this possibility in the results analysis.

(iii) in Figure 6b, the deletion of the HRE abolishes heat responsiveness, rather than merely altering the level of response; and

In the revised manuscript, we added new data concerning the roles of HREs and HSEs in the response of the HSP17.6B promoter to heat stress (Figure 6A). These results suggest that the HRE and HSE elements are responsible for the response of the HSP17.6B promoter to heat stress and that the HRE negatively regulates the HSP17.6B promoter at 37°C, whereas the HSE is positive at 42°C.

(iv) the phenotypes in Figure 8b are not clear enough to conclude that HSP17.6B overexpressors exhibit a dwarf but heat-tolerant phenotype.

When grown in soil, the HSP17.6B-OE seedlings presented a dwarf phenotype compared with the WT control. Heat stress resulted in browning of the WT leaves, but the leaves of the HSP17.6B-OE plants remained green, suggesting that the HSP17.6B-OE seedlings also presented a heat-tolerant phenotype in the soil. These results are qualitative but not quantitative experimental data; therefore, the conclusions are adjusted in the abstract and results sections.

Reviewer #2 (Public review):

Summary:

The authors report that Arabidopsis short HSFs S-HsfA2, S-HsfA4c, and S-HsfB1 confer extreme heat. They have truncated DNA binding domains that bind to a new heat-regulated element. Considering Short HSFA2, the authors have highlighted the molecular mechanism by which S-HSFs prevent HSR hyperactivation via negative regulation of HSP17.6B. The S-HsfA2 protein binds to the DNA binding domain of HsfA2, thus preventing its binding to HSEs, eventually attenuating HsfA2-activated HSP17.6B promoter activity. This report adds insights to our understanding of heat tolerance and plant growth.

Strengths:

(1) The manuscript represents ample experiments to support the claim.

(2) The manuscript covers a robust number of experiments and provides specific figures and graphs in support of their claim.

(3) The authors have chosen a topic to focus on stress tolerance in a changing environment.

Weaknesses:

(1) One s-HsfA2 represents all the other s-Hsfs; S-HsfA4c, and S-HsfB1. s-Hsfs can be functionally different. Regulation may be positive or negative. Maybe the other s-hsfs may positively regulate for height and be suppressed by the activity of other s-hsfs.

In this study, we used S-HsfA2, S-HsfA4c, and S-HsfB1 data to support the view that “splice variants of HSFs generate new plant HSFs”. We also noted that S-HsfA2 cannot represent a traditional S-HSF. S-HsfA4c and S-HsfB1 may have functions other than S-HsfA2 because of their different C-terminal motifs or domains. Different S-HSFs might participate in the same biological process, such as heat tolerance, through the coregulation of downstream genes. We added this information to the discussion section.

(2) Previous reports on gene regulations by hsfs can highlight the mechanism.

In the introduction section, we included these references concerning HSFs and S-HSFs.

(3) The Materials and Methods section could be rearranged so that it is based on the correct flow of the procedure performed by the authors.

The materials and methods and results sections are arranged in the logical order.

(4) Graphical representation could explain the days after sowing data, to provide information regarding plant growth.

The days after sowing (DAS) for the age of the Arabidopsis seedlings are stated in the Materials and Methods section and figure legends.

(5) Clear images concerning GFP and RFP data could be used.

We provided high-quality images of S-HsfA2-GFP and the GFP control (Figure 3 in the revised manuscript).

Reviewing Editor comments:

The EMSA shown in Figures 2, 3, 4, and 5, which are critical to support the manuscript's claims, are of poor quality, without any repeats to support. In addition, there is not much information about how these EMSA were done. I suggest including better EMSA in a new version of this manuscript.

Thank you for your suggestion. We added the missing information, including the detailed EMSA method and experiment repeat times in the methods section and figure legends. We provide high-quality images of HRE probes binding to nuclear proteins (Figure 4E).

Reviewer  #1 (Recommendations for the authors):

(1) The paper is overall well-written, but it could greatly benefit from reorganizing the results subsections. Currently, there are entire subsections dedicated to supplementary figures (e.g., lines 177-191) and main figures split into different subsections (e.g., lines 237-246). It is recommended to organize all the information related to a main figure into a single subsection and to incorporate the description of the corresponding supplementary figures. This would imply a general reorganization of the figures, moving some information to the supplementary data (for instance, the data in Figure 4 could be supplementary to Figure 5) and vice versa (Supplementary Figure 4 should be incorporated into main Figure 2, as it presents very important results). Also, Figures 7 and 8 would be better presented if merged into a single figure/subsection.

Thank you for your suggestion. We have merged some figures into a single figure according to the main information. In the current version, there are 8 main figures, which includes a new figure.

(2) Survival phenotypes vary widely, making reliable statistical analysis challenging. The chlorophyll and fresh weight quantifications presented in figures such as Figure 5 appear to effectively describe the phenomenon and allow for statistical comparisons. Figures 1 and 7 would benefit from including these measurements if the variability in survival percentages is too high to calculate statistical differences reliably. Also, in Figure 8, all chlorophyll measurements should be normalized to fresh weight rather than seedling number due to the dwarfism observed in the overexpressor lines.

Thank you for pointing out your concerns. We added statistical analysis based on at least two independent experiments, including Figures 1 and 7, to the original manuscript. In Figure 8 in the original manuscript, chlorophyll measurements were normalized to fresh weight.

(3) Typos: in Figure 3a it should be "min" not "mim"; in Supplementary Figure 3, the GFP and merge images are swapped.

We apologize for these errors, and we have corrected them. Supplementary Figure 3 was replaced with new images and was included in Figure 3 in the revised manuscript.

Reviewer  #2 (Recommendations for the authors):

(1) The abstract states "How this process is prevented to ensure proper plant growth has not been determined." The authors can be the first to do this, by adding graphical data on the height difference in hSfA2-arabidopis and wild-type Arabidopsis.

Thank you and agree with you. We have added this information to the new working model (Figure 8)

(2) The authors claim that Arabidopsis S-HsfA2, S-HsfA4c, and S-HsfB1; but have used S-HsfA2 to understand the action. The mechanisms being unknown for S-HsfA4c, and S-HsfB1 cannot be represented by S-HsfA2 to represent the mechanism.

Thank you for your valuable comments. In this study, we used S-HsfA2, S-HsfA4c, and S-HsfB1 data to support the view that “splice variants of HSFs generate new plant HSFs”. We also noted that S-HsfA2 cannot represent a traditional S-HSF because S-HsfA4c and S-HsfB1 may have functions other than S-HsfA2. Therefore, we deleted “representative S-HSF” from the revised manuscript. In the future, we will conduct in-depth research on the relevant mechanisms of S-HsfA4c and S-HsfB1 under your guidance.

(3) The authors can include which of the HSFs interacted with other genes of Arabidopsis reported by other researchers are positively or negatively regulated in heat response/ growth or the balance.

In the introduction section, we included these genes. AtHsfA2, AtHsfA3, and BhHsf1 confer heat tolerance in Arabidopsis but also result in a dwarf phenotype in plants (Ogawa et al., 2007; Yoshida et al., 2008; Zhu et al., 2009).

(4) The authors have started from the subsection plant materials and growth conditions. It is unclear from where the authors have found these HSF mutant Arabidopsis? Is it a continuation of some other work? As a reader, I am utterly confused because of the arrangement of the materials and methods section.

We apologize for the lack of detailed information in the Materials and methods section. These mutants were purchased from AraShare (Fuzhou, China) and verified via PCR and RT‒qPCR. We added the missing information.

(5) Is the DAS - Days After Sowing - represented as a graph or table? This will add data to the plant growth section to clearly state the difference between the mutants and the wild-type.

In this study, the age of the Arabidopsis seedlings was calculated as days after sowing (DAS), as stated in the Materials and Methods section and figure legends.

(6) Heat stress treatment after gus staining looks absurd. Should it not follow after plant materials and growth conditions, which should ideally be after the plant transformation and cloning section? The initial step is definitely about plasmid construction. Kindly rearrange.

Thank you for your valuable suggestions. We have rearranged the logical order of the materials and methods.

(7) The expression of GFP and RFP was not clearly seen in the images. This could be because of the poor resolution of the images added.

We obtained high-quality images of S-HsfA2-GFP (Figure 3 in the revised manuscript).

(8) We live in an age where it is widely known that genes are not functioning independently but are coregulated and coregulate other proteins. The authors can address the role of these spliced variants on gene regulation and compare them with the HSFs.

We agree with your suggestion. In this study, HSP17.6B was identified as a direct gene of S-HsfA2 and HsfA2, which can partly explain the role of S-HsfA2 in heat resistance and growth balance. However, the mechanical mechanism by which S-HsfA2 regulates heat tolerance and growth balance may not be limited to HSP17.6B. On the basis of the current data, we propose that the putative S-HsfA2-DERB2A-HsfA3 module might be associated with the roles of S-HsfA2 in heat tolerance and growth balance. Please refer to the discussion section for a detailed explanation.

(9) Regulatory elements can be validated in relation to their interaction with proven HSFs.

Supplemental Figure S3 shows that His6-HsfA2 failed to bind to the HRE in vitro.

(10) The authors seem to be biased toward heat stress and have not worked enough on plant growth. Biochemical data and images on plant growth could be added to bring out the novelty of this manuscript.

Thank you for your suggestion. We added new data indicating that, compared with the wild-type control, S-HsfA2-GFP, S-HsfA4c-GFP, or S-HsfB1-GFP overexpression inhibited root length (Supplemental Figure 8).

(11) Line 251 on page 11 of the submitted manuscript says that the s-Hsfs were previously identified by Liu et al. (2013) yet in the abstract the authors claim that these s-HsFs are NEW kinds of HSF with a unique truncated DNA-binding domain (tDBD) that binds a NEW heat-regulated element (HRE).

In our previous report, several S-HSFs, including S-HsfA2, S-HsfA4, S-HsfB1, and S-HsfB2a, were identified primarily in Arabidopsis (Liu et al., 2013). In this study, we further characterized S-HsfA2, S-HsfA4, and S-HsfB1 and revealed several features of S-HSFs. Therefore, we claim that these S-HSFs are new kinds of HSFs.

(12) What are these NEW kinds of HRE? Which genes have these HRE? Was an in silico study conducted to study it or can any reports can be cited?

HREs, i.e., heat-regulated elements, are newly identified heat-responsive elements in this study. The sequences of HREs are partially related to traditional heat shock elements (HSEs). Because we did not identify the essential nucleic acids required for t-DBD binding to the HRE, we did not perform an in silico study.

(13) S-HSFs may interact with existing HSFs. Have the authors thought in this direction? It can have a role in positively regulating other sHSFs or regulating multiple expressing genes related to plant growth and other functions. This needs to be explored.

Thank you for this point. Given that the overexpression of Arabidopsis HsfA2 or HsfA3 inhibits growth under nonstress conditions, we discussed this direction from the perspective of the interaction of S-HsfA2 with HsfA2 or HsfA3 in the revised manuscript.

(14) The authors need to concentrate on the presentation and arrangement of both their materials and methods and result section and write them in a systematic manner (or following a workflow).

The materials, methods and results sections are arranged in logical order.

(15) The authors have used references in the results section which can be added to the discussion section to make it more accurate.

Thank you for your suggestions. We have moved some references to the discussion section, but the necessary references remain in the results section.

Rhetorical context: the situation that surrounds what is being read/said.

normal 11k

Ezt a mondatot töröljük ki pls!

A rögzítés során kitöltendő mezők az alábbiak (minden mező kitöltése kötelező):

Ezt a két oszlopot töröljük ki.

Szokásos angol/magyar issue.

The following fields must be filled:

Az angol oszlop az angol a magyar szöveg a magyar doksi tartalmazza.

Ezt a mondatot törölhetjük.

Az új magyar szöveg: "A hozamszámításokhoz szükséges hozamgörbét meghatározó pontokat az értékelés típusokat tartalmazó felület Yield curve pop-up ablakában lehet rögzíteni és megtekinteni."

?

'set of rules that govern the structure and arrangement of words, phrases and other elements in a language."

Did the "abode of Islam" not fit in with the idea of nation states and the growing number of secular empires that were emerging in the geographical region now known as the "Middle East"? Possibly, the European map makers and political leaders of the time did not like the idea of a growing and prosperous "abode of Islam". Especially since the European churches had undergone a lot of recent reforms and schisms during that time.

How have these borders grown?

While images cannot be included in these annotations, it is important to note how this site uses images to emphasize the text and set the general tone. The images are important for the text that references them, but also the general tone and casual text sets

More on the overall structure of the digital format: The blog-like structure of the website allows for constant updating, with readers being presented with the most recent post. For non-digital literature, this kind of structure is harder to get out, with things like newsletters or a weekly newspaper column being the closest alternative, both of which can require more connections or resource than just making a website.

This seems to bring another issue that it replaces true crtive ideas from humans and coming up with there own anserws and thoughts in school they are useing AI in place of there own thoughts and just fleshing out the Ais reapted idea.

Diagram figure 2.4 lists this as a micro environmental

With global trade uncertainty due to the U.S. tariffs its becoming more prominent for trade to focused more on the European & Asian markets.

While phone cameras often produce higher-quality images in terms of sharpness and convenience, I believe they feel less real. This is a good example of why images from a dedicated camera can feel more authentic and why some people say they even look better despite technically having lower resolution or fewer features than your phone.

From both a physics and practical standpoint, the larger lens of a dedicated camera captures more light, which makes it perform better in low-light situations. Personally, I prefer the experience of using a camera over a phone. It feels more intentional and personal, even if it's less convenient.

The mining boom often enriched service industries still as the rush generated massive speculation. In Colorado, the gold extracted was less than half of what investors poured into it..

And why did President Harding commute Eugene Debs sentence in 1921 what does this suggest about changing attitudes toward world war I dissent?

How did the Socialist Party of America gain influence in the early 1900s and what does their electoral success reveal about public sentiment at the time?

It's odd that people let farmers struggled with falling crop prices, leaving them in economic despair. With little political support, they felt abandoned by the government.

M1

supp

supp

supp

note avant le point

supprimer l'espace en trop

les exposants agrandissent légèrement l'interligne

supp

supp

supp

[« Enfin »]

(mettre des guillemets dans les crochets)

C'est un extrait de la conclusion

(voir Conclusion)

Critica 1: "Onwuegbuzie y Leech (2005) añaden que actualmente los estudios que utilizan un solo método de investigación se convierten en una amenaza para el adelanto de las Ciencias Sociales".

Se menciona que si un estudio es llevado a cabo bajo una sola metodología de investigación se considera una amenaza para las ciencias sociales. Pero yo creo que si un estudio se lleva a cabo bajo una sola metodología es porque esa metodología es la indicada para ejecutarse y cumplir los objetivos que se requieren.

Critica 2: "Brown (2014) afirma que tomar decisiones correctas con respecto al tipo de instrumentos que se van a utilizar en una investigación puede requerir de mucho tiempo y consideración. Por otro lado, a más de decidir qué instrumentos darán a conocer información confiable, es necesario buscar la forma de reunir datos que finalmente sean fáciles y eficientes al momento de recopilarlos y analizarlos. "

Critica 3: "Seidman (1998) recomienda a los investigadores que administren una secuencia de tres entrevistas con los mismos participantes para obtener suficiente información. La primera entrevista se aplica con el propósito de romper el hielo y crear un ambiente de empatía, a la vez que se realiza un rápido barrido de las áreas que serán investigadas posteriormente".

Al momento de realizar 2 o más entrevistas a los mismos participantes, se puede llegar a dar el caso de que los participantes se empiecen a presentar fatiga por el tiempo que lleva respondiendo preguntas, es por esto que esta opción a mí consideración puede que no sea muy eficiente dependiendo el tipo de persona.

Critica 4: " Luego del respectivo análisis de las transcripciones de las dos primeras entrevistas, el propósito de la tercera, según Seidman (1998), es cubrir ciertos temas que no fueron tratados en las sesiones previas"

El realizar una tercera entrevista para abaracra temas que no fueron tocados creo que no es la mejor opción, ya que el tema que no fue abordado se pudiera abordar en las 2 entrevistas anteriores para evitar una sobrecarga en el entrevistado.

Critica 5: "Mackey y Gass (2005) recomiendan a los investigadores novatos o principiantes que antes de conducir un análisis estadístico de datos, se familiaricen con la estadística de su estudio a través de cursos, textos o consultorías con expertos en el área".

En esta parte el autor tiene razón en que los investigadores novatos que trabajan con datos estadisiticos previamente indaguen en la estadistica para una mejor interpretación de datos.

5

Lo que el autor menciona sobre los cambios e innovación para realizar la recolección de datos cuantitativos hacen que las computadoras o software especializados nos permita realizarlo de una manera más eficiente

4

Estoy de acuerdo con el pensamiento sobre que el objetivo respecto a la recolección de datos en cualquier investigación consiste en reunir información para abordar las preguntas del estudio que se está realizando.

3

El autor hace mención de muchos autores, lo cual deja el objetivo del mismo autor un poco desfocalizada

The heart of the sky seems to be referring to God that controls the sky.

The snake in his hand, the bird on his back, and its tail down to the ground represent wind, knowledge, and creation? i believe so.

the God that represented wind, knowledge, and creation.

Could it be that when mankind or life began on Earth, it caused a "ripple" or "imbalance" to affect the natural world?

Upon further research and analysis this statement describes the world before creation where the sky and sea where clam or motionless

The statement might mean that nothing is truly valuable until it's bought together. "There is nothing whatever gathered together. It is at rest; not a single thing stirs. It is held back, kept at rest under the sky." Does rest indicate that the sky protects or makes sure everything stays in balance from straying away?

The 'maker' seems to be molding life into the world and bringing life to everything they create. Mother and Father, could the gods be seen as gender-fluid or non-binary?

Four sides-four corners-the four corners of the globe is what they might be referring to. North, South, East, and West Hemispheres.

Will the Mayans state every detail on how the sky is lit up?

Is the text stating that life began from a shadow and brought life into the world through light? The sea creates the illusion that the sun rises from the water at dawn and sets into the sea at dusk.

Their Gods created everything and accounted for every small detail of the natural world, as well as beyond, to the Mayans.

Names of divine entities present at the beginning of creation. Some of the Gods have more names than one.

Seems as if the Quiché Mayans sought to explain the world and its natural laws.

Quiché is an member of the indigenous people and their language

this is slightly more nuanced in a writing class. Some of the knowledge and skills you take out of it is about effort.

this speaks to the need for college students to understand how the rest of their life impacts the results they see in class assignments

focus on what works for individual students instead of rote recommendations

we need to figure out the right times and ways to allow students independent time to work things out while also providing tools, strategies, and other forms of guidance so that they are actively working towards a goal and not simply spinning their wheels in the name of grit

this is something I struggle with as an educator and writer. How to encourage dedication and effort, without an overreliance on the idea of "hard work"

this is especially true and needed for writing classes

how can we work on bridging the gap between encouraging effort and growth and emphasizing the need for excellence?

it sounds like there is a blacklash to the growth mindset, which is central to this argument

the title seems like it is the thesis statement for this article

Short term goals: I want to finish all classwork ahead of their due-dates, study for upcoming exams, and attend all lectures even if I'm "too tired" after work

Yes I am because I know I have family and friends that will support me.

keeping up with the work and studying.

Not sure yet because I'm still in highschool

8-12 years

ajouter la référence à la note 1 au bas du tableau

Remplacer "." par "," And add space around "-" for 2024: "2,85 - 3,05%"

Remove

remplacer les points par des virgules

remove

"moment"

Taux moyen pondéré d'actualisation

remplacer par: "recouvrement estimé relatif aux"

81,7 millions de dollars (82,9 millions de dollars au 31 mars 2024)

remplacer par: ", rajustées pour tenir compte de l'inflation, pour les projets prévus comprenant"

remove

remplacer par: "en raison du temps écoulé"

remove

ajouter "dans les bâtiments"

remplacer par: "obligations liées à la mise hors service d'immobilisations sont comme suit"

remplacer par: ", des obligations de fermeture et post-fermeture associées aux décharges, des activités de mise hors service liées aux navires, embarcations et autres véhicules et réservoirs de stockage souterrains."

"constaté"

Remove

est-ce qu'il y a de la place pour le mettre toute sur la même ligne?

"Total - Divers"

Est-ce qu'on peut mettre ceci sur la même ligne que le titre? il semble avoir de la place dans la colonne

"Total - Vente ..."

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2024-2025 s'élève à 51 881 756 $ (50 620 773 $ en 2024)

Needs a spacing over this

le renvoi ne fonctionne pas

é

ajouter un tiret entre les deux mots

mettre la note avant le ?

mettre la note avant le point

mettre la note avant le point

mettre la note avant le ?

mettre la note avant le ?

mettre la note avant le point

espace avant le crochet ouvrant

mettre entre crochet + bleu

la note rentre dans le footer si l'on ne développe pas les références peut-être préférer les Références toujours développées ?

il manque un espace avant la (

pas en bleu, en noir

ça ne renvoie pas à la note 8, est-ce normal ?

contrairement aux autres paragraphes, celui-ci est très proche de l'espace pour les notes de page. il touche presque la note 9

"I will tell the secret to you" she is referencing the song that is what the sirens tell them it makes them feel special and that they have to do something. They listen every time which is ultimately what gets them killed

Even thought mayans had mathematics and astrology skills and knowledge; religion still out ranked science

I wonder why it still remains a mystery

Is the text really lost or in a private book collectors collection.

An early community. I wonder if they also had a HOA; maybe in the for of some form of Tax

Pyramid temples similar to egyptians. Did Mayas and egyptians build three-dimentinal for a reason? Maybe they where easier to construct or less labor needed to be involved.

I wonder if other cultures throughout time represent the sun and moon similar to the mayans.From what i can think of Egyption Gods are similar

how?

what were they doing to be especially careful? A better description of methods would be helpful

Clear, logical inference.

I did not even think that there would be so many potential source of error to consider.

wouldn't you say the value is that of human art, art history, and culture, rather than just intrinsic value?

!!!!

ここはビルドは英語にした方が良いです

logic

President= ethos, credibility

right, but how do you justify spending money (an inherently human thing) on protecting nature rather than protecting people (i.e. social reforms)

i understand this argument, but I disagree; if done properly, nature is worth conserving for humans and for itself

eLife Assessment

This manuscript reports the development and characterization of iGABASnFR2, a genetically encoded GABA sensor that demonstrates substantially improved performance compared to its predecessor, iGABASnFR1. The work is comprehensive and methodologically rigorous, combining high-throughput mutagenesis, functional screening, structural analysis, biophysical characterization, and in vivo validation. The significance of the findings is fundamental, and the supporting evidence is compelling. iGABASnFR2 represents a notable advance in GABA sensor engineering, enabling enhanced imaging of GABA transmission both in brain slices and in vivo, and constitutes a timely, technically robust addition to the molecular toolkit for neuroscience research.