The evaluation of protein localization changes on a systematic level is a powerful tool for understanding how cells respond to environmental, chemical, or genetic perturbations. To date, work in understanding these proteomic responses through high-throughput imaging has catalogued localization changes independently for each perturbation. To distinguish changes that are targeted responses to the specific perturbation or more generalized programs, we developed a scalable approach to visualize the localization behavior of proteins across multiple experiments as a quantitative pattern. By applying this approach to 24 experimental screens consisting of nearly 400,000 images, we differentiated specific responses from more generalized ones, discovered nuance in the localization behavior of stress-responsive proteins, and formed hypotheses by clustering proteins that have similar patterns. Previous approaches aim to capture all localization changes for a single screen as accurately as possible, whereas our work aims to integrate large amounts of imaging data to find unexpected new cell biology. Cells are busy structures in which proteins move from one place to the other to perform their roles. To survive, organisms must constantly adapt to changes, such as mutations, new environments, or encountering drugs. Often, these adaptive responses involve groups of proteins traveling to new locations in the cell. Tracking these movements is useful to understand which biological processes cells activate to cope with modifications.To study these mechanisms, scientists conduct experiments where they expose cells to one type of change – for example they mutate one gene, or give one drug. They then follow the proteins in the cell using powerful microscopes.These instruments can take thousands of pictures of cells every day, which results in large amounts of data. The images are then analyzed, which is often subjective, time-consuming or cannot easily be repeated on other experiments. This prevents researchers from considering more than one experiment at the time, and it makes it difficult to compare how cells respond across many different types of changes.Here, Lu et al. combine and analyze 400,000 images from 24 experiments conducted on yeasts; they use a computational method that automatically measures the differences in the locations of the proteins between experiments. This new large-scale approach reveals aspects of cell biology that are not obvious from the results of any one experiment alone. For example, responses that are specific to one type of change can be distinguished from the ones that occur each time cells are exposed to new conditions. It also becomes possible, for the first time, to group together proteins that move in the same way across different types of changes, and to infer their roles. For instance, a protein was shown to be ‘pulsing’ – moving quickly between two specific compartments in the cell – based on how it shared certain features with other pulsing proteins.This computational approach is not limited to experiments on yeasts, and could be used on studies in human cells as well. Drug companies could then compare at a large scale how different treatments affect protein localization. The ability to control the subcellular localization of proteins has long been understood as an important component of a cell’s regulatory toolkit in response to perturbations , such asThe growing quantity and diversity of imaging experiments examining protein localization in yeast open the possibility of integrating proteomic information from different image screens. Integrative strategies are widely appreciated for the analysis of high-throughput transcript profiles, such as RNA-seq or microarray data, providing mutual context and hypothesis discovery that would have not been evident in independent analyses of the datasets , and three time-points each of wild-type cells subjected to rapamycin (RAP), hydroxyurea (HU), and α-factor (αF) treatment . For eaFinally, we grouped proteins by clustering our concatenated protein change profiles. The heat map in We manually evaluated images for localization changes predicted by our protein change profiles for five distinct clusters and recorded the proportion of proteins with localization changes visible to a human evaluator . In geneTo investigate the hypothesis that our clusters of protein localization change profiles contain proteins that are linked functionally, we evaluated clusters that showed strong signals for perturbations. We ignored clusters that had strong signals in the other untreated wild-type screens , the general trend was preserved.These similarities in their protein change profile patterns led us to hypothesize that Msn2, Dot6, and Rtg3 may be regulated by a common mechanism. Msn2 is the most studied of the three transcription factors, and exhibits a stochastic oscillation (or pulsing) between the cytoplasm and nucleus that is regulated by the cAMP-protein Kinase A (PKA) pathway . MoreoveGiven our discovery that Rtg3 shares a protein change profile with Dot6 and Msn2, we decided to revisit the condition-specific dynamics of Rtg3 nuclear localization. To test whether Rtg3 also shows condition-specific pulsing dynamics, we produced time-lapse movies of Rtg3 in standard growth media and in medium containing rapamycin.We observed Rtg3-GFP protein pulses  during growth in standard medium. We found that, as predicted by our cluster analysis, rapamycin treatment increased the duration of Rtg3 accumulation in the nucleus. In standard medium, single cells showed frequent oscillation of Rtg3 between the nucleus and cytoplasm, while under rapamycin treatment, single cells typically showed a prolonged period of nuclear localization in our movies. Quantification of the dynamics of Rtg3-GFP pulsing  revealedWe found evidence of localization changes for many ribosomal subunits, with their protein change profiles predicting that subsets of ribosomal proteins may respond specifically to one perturbation. As an example, we show a set of ribosomal protein subunits in A growing body of research suggests that specific ribosomal subunits may have extra-ribosomal functions ranging from ribosomal biogenesis to DNA repair and adhesive growth . For exaWe observed a cluster of proteins that had strong protein change profile patterns in both the rapamycin and hydroxyurea perturbations, but with different patterns in each perturbation. Following rapamycin treatment, the direction of localization changes indicated that the proteins were becoming closer to the cell center and further from the cell edge relative to those in the untreated cell, whereas in following hydroxyurea treatment, the same proteins were predicted to relocalize further from the cell center and closer to the cell edge. Evaluation of the images of cells expressing these proteins  showed tNext, we explored the relationship between our protein change profile clusters and other high-throughput datasets.First, we compared the associations between proteins found in our protein localization change analysis to those found in transcript and protein abundance data, by comparing the protein profile similarities in each data source. We expect our clusters to overlap partially with the clusters retrieved from these other datasets, such as in cases where protein localization changes are being driven by or forming a feedback loop with transcript and abundance changes, but we anticipated that at least some protein localization changes are an independent layer of regulation. We constructed profiles for transcript changes and protein abundance changes for the perturbations that we tested in We found little correlation between protein localization change profile similarity and transcript profile similarity . Some clΔrpd3 perturbation, and Cluster E, which contains stress-response proteins . Although many proteins that show similar localization change profiles have similar protein abundance change profiles, there are vast numbers of proteins with similar abundance change profiles that do not show similar localization change profiles. This suggests that similarity in protein localization changes is much more specific than similarity in protein abundance. We found that two clusters of protein localization change profiles had low distances in their protein abundance change profiles relative to the distribution of protein abundance change profile distances: Cluster J, which contains proteins that change abundance specifically in the proteins .Δrpd3 screen has a higher ratio of proteins with both large protein abundance and protein localization changes than the other perturbations. Finally, we directly compared the protein localization change profile with the transcript change and protein abundance change profiles for three clusters  could be differentiated in subtle ways. Our work integrates multiple datasets as a compact visualization, allowing for a deeper understanding of the subtle conditional and time-dependent dynamics of proteins.We addressed the question of how to scale analyses to tens of high-throughput microscopy screens. Previous approaches act on a single screen, scaling to tens of thousands of images. By integrating data from multiple screens, we show how to scale a single analysis to hundreds of thousands of images, an order of a magnitude increase in the scale of data.Our approach detects a novel reciprocal pattern of localization changes for membrane proteins following hydroxyurea or rapamycin treatment. These protein responses are notable because they exhibit different localization changes depending upon the perturbation. Although we do not have a clear hypothesis for these changes, the observation was made clear by our visualization, which shows trends in localization changes simultaneously for both perturbations. That this pattern occurred in multiple membrane or Golgi-localized proteins, increases our confidence that it reflects important, yet currently unknown biology. Without clustering based on quantitatively comparable localization change profiles, the prevalence of the pattern would not have been apparent.Our analysis is unbiased by prior biological knowledge. Despite this, we find many specific examples of known responses to perturbations, and thus strong evidence that the patterns we identify reflect underlying biology. The unbiased nature of our approach permits the association of new genes with previously known responses. We found that Crz1 was clustered with proteins in the mating response in the α-factor perturbation, a result that was initially unexpected given that Crz1 is not part of the canonical mating response . HoweverSimilarly, we identified pulsatile dynamics in the transcription factor Rtg3 in standard media in this study, and confirmed that these dynamics were altered by rapamycin. Our results contradict a previous proteome-wide screen that suggested that Rtg3 does not pulse . HoweverAs a method that exploits high-throughput imaging data, our results must be interpreted with the understanding that false positives and negatives may emerge from various steps in our data acquisition and analysis. First, occasional contamination and failures in the transformation process may occur. In this work, we found some proteins with bimodal expression in their population of cells. Our change detection algorithm correctly detects the resulting difference in protein expression in the images, but there is a possibility that these images are of mixed genetic strains. Determining whether variability in protein expression is biological or technical from the images alone is non-trivial: a previous quality-control experiment on six proteins with cell-to-cell variability in the GFP collection indicated that only two had mixed genotypes, while two had variability despite identical genotypes and another two were inconclusive . In addiSecond, segmentation and other image transformations can introduce artifacts. We found a cluster of bud tip proteins whose localization was predicted to change in the α-factor perturbation (Cluster Q). On manual evaluation, we observed that our segmentation algorithm incorrectly segmented the bud tip of cells in these images as a bud cell ; while tThird, we observed that our clustering method can introduce false positives into our clusters. We observed that some of our clusters of protein change profiles had a few protein change profiles with weaker signals  in one screen to being expressed in a recognizable localization in the other screen as localization changes. We elected to retain these signals. This strategy is consistent with previous work that has generally considered these cases to be localization changes .The protein change profile for individual perturbations is concatenated into a concatenated profile. We clustered these profiles using the open-source Cluster 3.0 package , with hiTo visualize these resultant clustered matrices as heat maps, we used Java Treeview . Blue vaEnrichment analyses of the clusters in rpd3Δ , we show protein–protein pairs formed between the proteins with the strongest changes in either protein localization changes or transcript changes. We required that a protein have either two log-fold changes above an absolute value of 2.0 in the transcript change profile, or eight values above an absolute value of 5.0 in the localization change profile. This filter resulted in 310 proteins with a transcript change, and 440 proteins with a localization change, for a total of 655 non-redundant proteins between the two lists. We then computed the correlation distance between the transcript change profile and the protein localization change profile for each pair of proteins in this list, which we visualized as the scatterplot in To compare protein localization change profile similarity with transcript change profile similarity for these perturbations, we constructed transcript change profiles using four separate genomic expression microarray experiments for the rpd3Δ , hydroxyrpd3Δ , rapamycrpd3Δ , and α-frpd3Δ  perturbaTo compare protein localization change profile similarity with protein abundance change profile similarity, we constructed protein abundance change profiles using the same image data that we used to construct the protein localization change profiles. For each protein in each image screen, we measured the average intensity of the GFP signal in single cells. We calculated the difference between this measurement and that in the WT2 screen, and concatenated these differences into a profile. Similar to the transcript change profile similarity, we required that a protein have either two changes above an absolute value of 50 in the protein abundance change profile, or eight values above an absolute value of 5.0 in the localization change profile. This filter resulted in 318 proteins with a protein abundance change, and 440 proteins with a localization change, for a total of 686 non-redundant proteins between the two datasets. We then computed the correlation distance between the protein abundance change profile and the protein localization change profile for each pair of proteins in this list, which we visualized as the scatterplot in Sacchromyces cerevisiae proteome were taken from the Biogrid  The authors indicate they derive descriptors even when a single cell is available. Was this decision taken based on a general optimization scheme or is it arbitrary? What fraction of the data corresponds to 1 to 5 cells? One should show that this decision brings more signal than noise.2) A significant fraction of strains in the GFP library show fluorescence levels that are close to auto–fluorescence, and in that respect can be hard to analyze. How do such strains affect this work? What fraction of the strains in the matrix of Figure 1 are in the "top–50% abundance class?"3) More generally, to what extent does profile similarity depend on signal intensity or protein abundance?4) How was the number of features decided upon? Each feature described in Figure S1 has 10 pixels associated with it (and therefore I assume 10 parameters). Why are 10 features necessary when, e.g., considering "distance to the cell edge." Could one feature, or perhaps an average of the features facilitate interpretation while being equally informative in the profile comparison? It would be necessary to establish an objective criterion that can be optimized – such as "number of changes detected across different conditions versus across replicates of the same condition." . A detailed depiction of the features employed is needed.5) The approach is validated using a set of 20 pictures, which may not account for global effects. A more general benchmark would, therefore, make sense in that respect too (see comment #4).6) In the validation, the authors infer a false positive rate of ~50%. They should provide a sense of what the origin of these false positives is, and how it may impact the conclusions. For example, that proteins in the same clusters to not exhibit similar transcription profile, physical interactions, or localization.7) An analysis of a limited number of clusters is presented in Table 1, where GO enrichment is carried out. Are these the only clusters showing functional enrichment? The global nature of this work begs for a more general analysis of GO enrichment, i.e., by defining clusters across the entire matrix and analyzing GO enrichment for all of them, not a handful only. Even if a few are described in Table 1, the results can be provided as Supplementary data. In addition, "numbers" can be added to Table 1 (number of proteins in the cluster and number corresponding to the annotation being enriched).8) Figure 4C contains cells that do not appear to contain any green fluorescence, while others show a strong signal. Such bimodal behavior is worrying as it may not be biological. Perhaps that some cells have lost their fluorescence during the SGA process for example. Is the localization profile affected by such cases? Is such bimodal behavior widespread? If so it should be accounted for in the analyses, and such cases would need to be filtered or flagged.9) Global "quality control" figures should be shown, suggestions for this are given below:– Subsection “An unsupervised analysis of protein localization changes in over 280,000 images”: More details should be presented regarding this sentence. Can a distribution of "localization change" score be shown for replicates of the same condition and different conditions? More of such "sanity check" plots would help get a better sense of how the method is doing. (See comment #1)– The consistency of protein abundance (or rank abundance) across the different screen should be controlled.– The association between change in localization, expression–change, and PPIs is shown in Figure 6 for a few cases, but it would be more valuable to show it in a systematic fashion, for example by showing (i) how Z–score for profile change in the data from Figure 1 correlate with a Z–score for abundance change, or (ii) How profile similarity relates to other measures of functional relatedness? E.g., protein interaction profile similarity, genetic interaction profile similarity, expression–profile similarity, etc. Such a bird's eye view of the data is lacking and would add much to this work.– The last analysis suggests that proteins with different localizations can exhibit a similar change. This is counter–intuitive, and more data could be provided to examine the origin of such cases– Looking at Figure 6C, why is cluster I, which contains ribosomal proteins, not enriched in physical interactions? If using BioGrid one does not need to limit oneself to low–throughput data. A large and high–quality dataset can be obtained by taking all physical interactions supported by more than one PMID. Lastly, the "clusters circles" of Figure 6C do not overlay with points.10) The authors identified Rtg3 to be a pulsing transcription factor through cluster analysis and conducted time–lapse movies to confirm such dynamics. In the cited reference , pulsing was defined as TFs that exhibit dynamic shuttling between nucleus and cytoplasm during the steady–state response to added stresses. In Figure 3, the authors didn't indicate when the stress was added and whether the cells on average have reached steady–state. The authors didn't specify what stress condition each cell represent in Figure 3. Lastly, to analyze "longest pulse duration"  does not provide a robust measure. The mean/median duration should be presented.11) Analysis of protein level change. In Figure 6A, the authors compared localization pattern with microarray pattern. However, it would be more natural to compare the protein expression pattern with microarray pattern, and more importantly protein expression with localization changes. It is indeed always difficult to merge different data from different laboratories, but protein expression is readily available from the images as the sum of GFP intensities in the cell. Essential revisions:Globally, all reviewers found that the method shows an important potential. Numerous comments point to analyses needing to be more thorough, sometimes requiring a more systematic examination. Considering that the data underlying the kinase deletions appear to be of lower quality, I suggest cutting that part. Figures are often hard to follow, so particular attention needs to be given to the presentation of the results. Please find below a consolidated list of the reviewer's comments.1) The authors indicate they derive descriptors even when a single cell is available. Was this decision taken based on a general optimization scheme or is it arbitrary? What fraction of the data corresponds to 1 to 5 cells? One should show that this decision brings more signal than noise.We believe that this comment was based on a lack of clarity in the description of our previously published methods. We require at least one mother–bud pair in each of the 10 bins that we define, which track mother and bud cells separately over 5 stages in the cell cycle. This means that we require a minimum of 10 cells per sample, distributed across the cell cycle. In practice, the lowest number of cells we observe in the reference untreated wild–type with this filter is 18, and only 13 of 3955 proteins have fewer than 25 cells.We have edited subsection “An unsupervised analysis of protein localization changes in over 280,000 images” and included a new figure  to better explain our image analysis pipeline and included the details about the numbers of cells in subsection “Image Analysis and Quantification of Localization Changes”. We hope that this is clearer in the revised manuscript.These decisions are based on the desire to avoid filtering out proteins from the analysis because of missing data as we add more screens to the analysis. We believe that including as many proteins as possible is important because we wish to provide as much as possible a proteome–scale analysis. In addition, for technical reasons, our localization change detection algorithm  actually works better when we have more proteins in the analysis. For example, changing the minimum requirement from 1 to 5 cells per bin filters out an additional 730 (18.4%) proteins in the reference untreated wild–type.2) A significant fraction of strains in the GFP library show fluorescence levels that are close to auto–fluorescence, and in that respect can be hard to analyze. How do such strains affect this work? What fraction of the strains in the matrix of Figure 1 are in the "top–50% abundance class?"Our localization change algorithm automatically handles low intensity proteins where the GFP signal is not recognizable. Low intensity proteins show a distinctive pattern in our feature space  and can be handled identically to proteins with a distinct localization: If the protein remains low intensity in the perturbation, the protein will have the same pattern of features and will not be identified as having a localization change.On the other hand, if proteins change from being low intensity in the wild–type to having a recognizable localization in the perturbation (or vice versa), these will be identified as having a localization change. We elected to retain these signals. There is some ambiguity on whether these changes are “truly” localization changes or instead should be thought of as “abundance” changes, but we believe that this shows that the distinction between “abundance” and “localization” is somewhat arbitrary.In addition, we believe our perspective is in line with previous localization change detection work has generally considered these cases to be localization changes: Tkach et al., (2012) includes localization changes where the protein abundance is at auto–fluorescence levels in untreated cells, Chong et al., (2015) also includes localization changes of this nature. We have added a brief discussion of abundance and localization changes to the revised manuscript to subsection “Image Analysis and Quantification of Localization Changes”.For all proteins that we analyzed in this study, 50% of proteins have a protein abundance in the reference untreated wild–type greater than 41.0 A.U. For the 1159 proteins that we show in Figure 2, 529 (or 45.6%) of proteins have a protein abundance greater than 41.0 A.U. We have added these numbers to subsection “Clustering and Visualization of Protein Change Profile.”3) More generally, to what extent does profile similarity depend on signal intensity or protein abundance?For most screens, the strongest localization change signals do not co–occur with a significant abundance change, and the strongest abundance changes do not co–occur with a significant localization change. We have added Figure 7—figure supplement 1, which shows scatterplots of the protein localization change profile magnitude compared to the average protein abundance change for some screens.rpd3Δ perturbation is an exception. This screen shows more localization changes that co–occur with a strong change in abundance. These results are consistent with previous work; while Chong et al., (2015) report little correlation between abundance and localization changes for the hydroxyurea and rapamycin perturbations, 17 of the 31 localization changes that they detect for the rpd3Δ perturbation also significantly change in abundance.The We have reported these findings in subsection “Cluster associations found by our unsupervised exploration of localization changes are complementary to other high–throughput experiments” of the revised manuscript.4) How was the number of features decided upon? Each feature described in Figure S1 has 10 pixels associated with it (and therefore I assume 10 parameters). Why are 10 features necessary when, e.g., considering "distance to the cell edge." Could one feature, or perhaps an average of the features facilitate interpretation while being equally informative in the profile comparison? It would be necessary to establish an objective criterion that can be optimized – such as "number of changes detected across different conditions versus across replicates of the same condition." . A detailed depiction of the features employed is needed.Once again, we believe there is confusion due to the lack of clarity in the description of the methods in the submitted version of the manuscript.The 10 features result from the separation our cells into 10 bins (5 cell cycle stages for the bud and 5 cell cycle stages for the mother cells). The features shown for proteins are an average of the single cells in each bin. The repeated features in the protein localization change profile are designed to show if the protein localization change is dependent on cell cycle and/or on mother/bud cell type. We have clarified this in the revised manuscript .Feature selection and optimization are key areas for research in automated microscopy image analysis. It is likely that they could lead to easier interpretation of localization changes. However, since our current manuscript is focused on data integration from multiple screens, we believe that it is outside the scope to begin consideration of these challenging data analysis problems.5) The approach is validated using a set of 20 pictures, which may not account for global effects. A more general benchmark would, therefore, make sense in that respect too (see comment #4).We take the point that 20 pictures were not convincing. We have therefore expanded our manual validation set to 80 pairs of images, recorded in Supplementary Data 2. We analyzed the images in Clusters E, F, J, Q, and S, shown in Figure 1. For each set of images, we have reported whether a localization change was visible to the human observer, and quantified false positive rates with respect to these observations.We thank the reviewer for the suggestion to develop a general benchmark for protein localization changes under perturbations. This would be useful to the community to develop new statistical approaches. The images that we have manually inspected as a part of this study could form the seed for such a data resource, and we have made them available in Supplementary Data 2. However, we believe that further development of this resource is beyond the scope of the current manuscript.6) In the validation, the authors infer a false positive rate of ~50%. They should provide a sense of what the origin of these false positives is, and how it may impact the conclusions. For example, that proteins in the same clusters to not exhibit similar transcription profile, physical interactions, or localization.To address the origin of the false positives, we used the set of manually inspected images (discussed in response to point #5). We found at least three causes for false positives: (1) protein change profiles with weaker signals that are clustered among the profiles with stronger signals. These are true “statistical” false positives where the noise passes the threshold that we chose and happens to fall in a pattern similar enough to the strong signals. (2) localization changes are too subtle, complex, or borderline to identify consistently by human eye. These often include cases where only a subset of cells in the image show the localization change, and/or the localization changes in in the quantitative appearance of an organelle. While these are false positives in the sense that a human observer would not call them localization changes, it is likely that they are simply subtle biological effects. (3) technical issues, particularly related to mis–segmentation of cells with previously unseen morphology. We believe that our methods to a surprisingly good job at dealing with changes in cell morphology, but they are not perfect.A table of all our manual annotations can be found in Supplementary Data 2, and images of these proteins are available on CYCLoPS. We have added some supplementary figures to address this question and discussed all of these cases along with specific examples and images in the revised manuscript. Figure 2—figure supplement 1 shows examples of case 1 and Figure 2—figure supplement 2 shows examples of case 2; we discuss these in the result section entitled “An unsupervised analysis of protein localization changes in over 280,000 images”. Supplementary Data 5 shows an example of case 3, and we discuss this as a source of false positives in the Discussion section.To address whether the results of the global comparisons with other data could be explained by the presence of false positives, we examined cases where localization change patterns did not co–occur with transcript or protein abundance patterns, or with protein interactions and shared subcellular localization, in the context of the clusters that we had manually identified the false positives. We found that the false positives could not completely explain the lack of co–occurrence for any cluster. We report these results in the result section entitled “Cluster associations found by our unsupervised exploration of localization changes are complementary to other high–throughput experiments” with reference to Figure 2—figure supplement 2; we believe that verification in this limited set is the best we can do to eliminate the possibility that our global results are explained by false positives.7) An analysis of a limited number of clusters is presented in Table 1, where GO enrichment is carried out. Are these the only clusters showing functional enrichment? The global nature of this work begs for a more general analysis of GO enrichment, i.e., by defining clusters across the entire matrix and analyzing GO enrichment for all of them, not a handful only. Even if a few are described in Table 1, the results can be provided as sup. Data. In addition, "numbers" can be added to Table 1 (number of proteins in the cluster and number corresponding to the annotation being enriched).We have added the numbers of proteins in the table as requested. We have now included any significant GO terms for all of the manually identified clusters in Source Data 1.Presenting GO enrichments for automatically chosen clusters could be misleading because global effects  that differentially effect proteins with different subcellular localization patterns might still be significant. For example, clusters showing differences between untreated wild–type screens show strong GO enrichments. We have therefore decided to stick with manually identified clusters, staying true to the use of clustering as exploratory data analysis to identify patterns.8) Figure 4C contains cells that do not appear to contain any green fluorescence, while others show a strong signal. Such bimodal behavior is worrying as it may not be biological. Perhaps that some cells have lost their fluorescence during the SGA process for example. Is the localization profile affected by such cases? Is such bimodal behavior widespread? If so it should be accounted for in the analyses, and such cases would need to be filtered or flagged.We agree that some of our detected localization changes may be due to contamination or technical issues in the transformation: occasional errors are inevitable in high–throughput screens. We have discussed contamination as a source of false positives in the Discussion section of the revised manuscript. We note that this issue is a limitation of the GFP collection. In these cases, our analysis correctly identifies that there is a localization change in the images.Unfortunately, it is not possible to confirm from the images alone if bimodal behavior is caused by a technical issue. In a previous study, we performed quality control experiments on 6 proteins with highly variable expression in the GFP collection indicated that only 2 had mixed genotypes, while 2 had variability despite identical genotypes, and another 2 were inconclusive .iki3Δ perturbation needs further confirmation and we have removed these examples from our results in subsection “Stress–responsive transcription factors that exhibit stochastic pulsatility may be controlled by the same mechanism”.We therefore decided not to filter bimodally–expressed proteins because this may unnecessarily exclude real and interesting biological findings. Instead, we advocate generation of independent GFP–tagged strains to verify the localization change, as we have done for our Rtg3 experiment. Indeed, the specific case of some ribosomal subunits being expressed at very low levels in some cells in the 9) Global "quality control" figures should be shown, suggestions for this are given below:– Subsection “An unsupervised analysis of protein localization changes in over 280,000 images”: More details should be presented regarding this sentence. Can a distribution of "localization change" score be shown for replicates of the same condition and different conditions? More of such "sanity check" plots would help get a better sense of how the method is doing. (See comment #1)rpd3Δ and the iki3Δ replicates, as well as comparisons to different conditions. We find that the replicates exhibit strong correlation, particularly for proteins that show strong signals, while different perturbations are poorly correlated.We thank the reviewers for this useful comment. We have now included a comparison of localization change profiles for replicates as Supplementary Data 1 and discuss these results in subsection “An unsupervised analysis of protein localization changes in over 280,000 images”. We analyse the correlation between the – The consistency of protein abundance (or rank abundance) across the different screen should be controlled.We compared the protein abundance across all the screens and we find R2 > 0.79 with the untreated wildtype. We have reported this result in subsection “Experimental Strains and Image Acquisition”.– The association between change in localization, expression–change, and PPIs is shown in Figure 6 for a few cases, but it would be more valuable to show it in a systematic fashion, for example by showing (i) how Z–score for profile change in the data from Figure 1 correlate with a Z–score for abundance change, or (ii) How profile similarity relates to other measures of functional relatedness? E.g., protein interaction profile similarity, genetic interaction profile similarity, expression–profile similarity, etc. Such a bird's eye view of the data is lacking and would add much to this work.We thank the reviewers for this excellent suggestion. The suggested analysis of comparing profile similarity better demonstrates the point that we were trying to make in this section, which was that the associations that we found through clustering protein localization change profiles could not entirely be found by clustering other similar high–throughput datasets. We have now included the suggested analysis as Figure 7, which compares protein localization change profile similarity with both protein abundance change, and transcriptional profile similarity, in the revised manuscript.– The last analysis suggests that proteins with different localizations can exhibit a similar change. This is counter–intuitive, and more data could be provided to examine the origin of such casesBecause our analysis captures relative movements in protein localization, it does not depend upon the protein sharing a localization in the untreated wild–type. For example, we show that a subset of proteins moved to the vacuole under rapamycin treatment in subsection “Membrane proteins exhibit a reciprocal pattern of localization changes following hydroxyurea or rapamycin treatment”, despite proteins showing different localizations in the untreated wild–type. We have now provided a clearer representation of this phenomenon using pie charts in Figure 8B, and discussed the origin of heterogeneity for three clusters, S, J and T in the text.– Looking at Figure 6C, why is cluster I, which contains ribosomal proteins, not enriched in physical interactions? If using BioGrid one does not need to limit oneself to low–throughput data. A large and high–quality dataset can be obtained by taking all physical interactions supported by more than one PMID. Lastly, the "clusters circles" of Figure 6C do not overlay with points.We thank the reviewer for pointing out this problem.We have expanded the enrichment analysis for BIOGRID to incorporate all physical interactions supported by more than one PMID. We note that this does not change the number of interactions substantially: we originally analyzed 47,498 non–redundant low–throughput physical interactions and incorporating high–throughput physical interactions supported by more than one PMID only adds 7,656 more interactions.Upon closer analysis, we found that BIOGRID contains very few interactions between ribosomal subunits: we found only 41 physical interactions between ribosomal subunits even before filtering for redundancy. We therefore decided to also check for enrichment for protein associations that had a “binding” interaction type in the STRING database. The ribosomal clusters are now enriched with the STRING database, but other clusters have lower enrichment in STRING compared to BIOGRID. In the revised manuscript, we show enrichment in both databases side–by–side in Figure 8A.As we are unable to explain these inconsistencies in BIOGRID and STRING, we have downgraded our claims about protein–protein interactions. Instead of stating that our clusters are not enriched for physical protein–protein interactions in general, we state that we cannot find enrichment in these databases.Finally, figure 6C no longer appears in the revised manuscript.10) The authors identified Rtg3 to be a pulsing transcription factor through cluster analysis and conducted time–lapse movies to confirm such dynamics. In the cited reference , pulsing was defined as TFs that exhibit dynamic shuttling between nucleus and cytoplasm during the steady–state response to added stresses. In Figure 3, the authors didn't indicate when the stress was added and whether the cells on average have reached steady–state. The authors didn't specify what stress condition each cell represent in Figure 3. Lastly, to analyze "longest pulse duration"  does not provide a robust measure. The mean/median duration should be presented.We have the edited the subsection “Stress–responsive transcription factors that exhibit stochastic pulsatility may be controlled by the same mechanism” to clarify this.We have now indicated when stress (Rapamycin treatment) was added in the caption of Figure 4. Since we observe pulsing in standard media (unstressed cells) over many hours, we are confident that these cells have reached steady state, and that pulsing occurs at steady–state.Our finding is that Rapamycin alters the pulsing behavior by inducing accumulation of protein in the nucleus, and we have clarified this in the new manuscript. Our observations suggest that the pulsatile dynamics of Rtg3 are similar to Msn2, which also exhibits pulsing under many constant conditions, but accumulates in the nucleus under rapamycin treatment.Finally, we have now presented an analysis that compares the average duration of nuclear localization, instead of the largest pulse in Figure 4C and find similar results. In order to do so, we needed to reliably identify all the pulses in each trace, and we therefore devised a more robust method for estimating nuclear localization. We describe this briefly in the Materials and methods section; the traces in Figure 4 have been updated with measurements using this new method.11) Analysis of protein level change. In Figure 6A, the authors compared localization pattern with microarray pattern. However, it would be more natural to compare the protein expression pattern with microarray pattern, and more importantly protein expression with localization changes. It is indeed always difficult to merge different data from different laboratories, but protein expression is readily available from the images as the sum of GFP intensities in the cell.We have now incorporated protein expression into our analyses. We now show a comparison of protein abundance profile similarity to protein localization similarity in Figure 7 and discussed the results in the test. Our cluster–by–cluster comparison of protein localization change profiles also now shows both the transcript and the protein abundance profiles .We have also included scatterplots comparing the magnitude of the protein localization change profile with the protein abundance change for some screens in Figure 7—figure supplement 1. For most screens, the strongest localization change signals do not co–occur with a significant abundance change, and the strongest abundance changes do not co–occur with a significant localization change.


Recent advances in sequence technology result in large datasets of sequence variants. For the human genome, several tools are available to predict the impact of these variants on gene and protein functions. However, for model organisms such as yeast such tools are lacking, specifically to predict the effect of protein sequence altering variants on the protein level. We present a python framework that enables users to map in a fully automated fashion large set of variants to protein functional regions and post-translationally modified residues. Furthermore, we provide the user with the possibility to retrieve predicted functional information on modified residues from other resources for example that are predicted to play a role in protein-protein interactions. The results are complemented by statistical tests to highlight the significance of underlying functions and pathways affected by mutations. We show the application of this package on a yeast dataset derived from a recent evolutionary experiment on adaptation to ethanol.https://github.com/CSB-KUL/yMap and is implemented in Python.The package is available from Bioinformatics online. Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) potentially disrupt normal protein functions. Several computational tools exist today that can predict the damaging effects of nsSNPs on protein structure and functions. For example, PolyPhen predicts the result of amino acid substitutions on the structure and function of human proteins , but it Yeast has been an excellent model to study systems biology. It helped considerably in understanding different molecular phenomena including protein-protein interaction and network biology. The exponential increase in yeast PTM data has left a gap between the identification and the functional annotation of PTM residues . We inclBefore you can initiate a variant analysis, you need to run a sub module of the yMap package to download and filter all the data needed to perform the mapping . The major functions of the package can be categorized as following:Residue level annotation: The data function downloads UniProt files for the yeast proteome, extracts annotations on residue level and stores these in a tab-separated file. It also downloads a gff file with the yeast genome sequence and annotation from SGD. Annotations of PTMs from PTMfunc , feature, feature type and source of data. The report is complemented with enrichments file and a BioGrid ids containing file.All the analyses are summarized in a We have re-analyzed variants from evolutionary experiments on adaption to high levels of ethanol (We present an automated method to analyze the impact of nsSNPs on protein level in yeast for the first time. The python package for yeast genotype to phenotype mapping yMap focuses on mutations in functional and structural protein regions. This package can be used to analyze mutations that affect important functional regions including PTMs, protein domains, active and binding sites, DNA-protein binding motifs, PPI, PTMs crosstalk and hotspots and the secondary structural regions. The method can facilitate the interpretation of large-scale variant data and the correlation of genotype with phenotype.This work has been supported by the KU Leuven Research Fund.Conflict of Interest: none declared.Supplementary DataClick here for additional data file.


L and ID are surveyed for representative proteome organisms and protein sets from the three domains of life. The two-dimensional maps (designated as fingerprints here) from the protein distribution densities in the LD space defined by ln(L) and ID are then constructed. The fingerprints for different organisms and protein sets are found to be distinct with each other, and they can therefore be used for comparative studies. As a test case, phylogenetic trees have been constructed based on the protein distribution densities in the fingerprints of proteomes of organisms without performing any protein sequence comparison and alignments. The phylogenetic trees generated are biologically meaningful, demonstrating that the protein distributions in the LD space may serve as unique phylogenetic signals of the organisms at the proteome level.The existence of complete genome sequences makes it important to develop different approaches for classification of large-scale data sets and to make extraction of biological insights easier. Here, we propose an approach for classification of complete proteomes/protein sets based on protein distributions on some basic attributes. We demonstrate the usefulness of this approach by determining protein distributions in terms of two attributes: protein lengths and protein intrinsic disorder contents (ID). The protein distributions based on  Determination of complete genome sequences for a number of organisms has offered an unprecedented opportunity for biological community and transformed biology into a discipline that depends significantly on how to classify and interpret large-scale data sets and to extract biological insights from these data sets. The traditional ways of thinking and approaches from the pregenomic era  are of fundamental importance in the postgenomic era. Nevertheless, new approaches based on some global features of omics data sets need to be explored in order to make classification and comparison of large-scale data sets easier. For proteomes, this may be achieved, for instance, through identification of key parameters or attributes of proteins and comparison of protein distributions within complete proteomes of different organisms or protein sets in terms of such parameters or attributes.L hereafter) and intrinsic disorder content (protein disorder ID hereafter). It had been proposed that the protein sizes, folding rates, and many other physical properties could be associated or even determined by L [L compared to the prokaryotic proteomes [L may be different for proteomes and for different protein sets suggest that such distributions may be used to classify proteomes of different organisms or protein sets. They may also be used in the future to help understand the properties of proteomes in different disease states, as there seems to be a wide variability of predicted disorder among different diseases [In this paper, we adapt this approach and use two parameters of proteins for the purpose of classifying complete proteomes of different organisms  and protein sets: the length of protein amino acid (aa) sequence , and the proteome data of two giant DNA viruses (termed giruses in literature). We noticed that the eukaryotic proteomes do not always exhibit averagely longer proteins than the prokaryotic proteomes. Our observation on protein disorder agrees well with the previous finding, that is, the average disorder contents in eukaryotic proteins are indeed higher than those in prokaryotic proteins. The two-dimensional maps (designated as fingerprints here) based on the protein distribution densities in the LD space defined by ln(L) and ID for the representative proteomes of different organisms and protein sets were constructed, and these fingerprints show distinct patterns for different organisms and protein sets. The features and relationships among the fingerprints are analyzed and compared. To test if our classification of proteomes of different organisms and protein sets proposed here is meaningful, we generated phylogenetic trees based on the protein distribution densities in the fingerprints of proteomes of different organisms without performing any protein sequence comparison and alignments. The phylogenetic trees generated in this way were found to be meaningful, as they contain important information of evolution. Thus, the proposed approach may represent a useful and simple way for proteome classification and comparison. In present study, for each protein-encoding gene locus only the prime protein has been used, therefore, the protein densities  to 34,350 aa (titin of human). For the protein length comparison, as pointed out previously [always have averagely longer proteins than those in the prokaryotic proteomes, as previously suggested [Amborella trichopoda has a median protein length of 218, shorter than all prokaryotes (Archaea and bacteria) surveyed here. In addition, Giardia intestinalis in the Eukaryota domain has an even shorter median protein length of 147. The average Ls show the same trend as the median values  from the proteomes of different organisms and protein sets listed in n values .N. equitans and bacterium Rickettsiales have the lowest disorder content at the protein level  for the systems examined. As the smallest known archaeon, N. equitans is an obligate symbiont on the other archaeon I. hospitalis, which is the smallest known free-living archaeon [Rickettsiales, on the other hand, was suggested to be a living candidate that is close to the ancient endosymbiotic alphaproteobacteria that were merged into an archaeon and eventually transferred into the mitochondria of the first eukaryotic cell [Nevertheless, the proteins in a eukaryotic proteome do have a significantly higher intrinsic disorder in average (41.1 ± 6.4%) compared to those in a prokaryotic proteome (15.6 ± 6.5%), consistent with previous studies , 28. Thiarchaeon . The fretic cell . These tN. equitans and alphaproteobacterium Rickettsiales, as well as the mitochondrial set , the Mimivirus and Pandoravirus, were also analyzed. The numbers of proteins encoded in these two giruses are comparable to the prokaryotic proteomes. The disorder content of the proteome of the Mimivirus is larger than that of the prokaryotes, but smaller than that of the eukaryotes surveyed here. However, the Pandoravirus has a proteome with disorder content close to that of the eukaryotes.Finally, the viral and plasmid gene sets were analyzed. The viral gene set contains 237,463 genes collected from 4942 strains and the plasmid set contains 95,214 genes cultivated from 985 bacteria. Interestingly, the proteins from these two sets yield similar trends in both length and disorder distributions.L) in all proteomes and protein data sets have been observed. In this analysis, all proteins of a proteome or protein set have been ranked hierarchically from the shortest to the longest, and the proteins then distribute linearly on ln(L) . Similar linear distribution trend is observed for the percentage of residues located in the IDPR, ID (L). Consistent with a previous report , exponenIDPR, ID . TherefoL) and ID, and these two attributes need to be independent of each other. Therefore, we calculated the correlation coefficients (CCs) between ln(L) and ID of proteins in all proteomes and protein sets (C. reinhardtii (TableL) and ID, therefore, may have been driven by the evolutionary processes rather than a cause-and-effect relationship. As such, the validity of the protein LD space and the related architecture of protein distributions in the LD space  should be discussed in an evolutionary framework (see below).We defined a two-dimensional LD space with the two attributes, ln(ein sets . Pearsonein sets  andS1 aH. sapiens) and Arabidopsis thaliana, respectively. Using proteomes form the two representative animal and plant, the protein distributions of proteomes in the LD space were converted to the protein-density contour maps in The most thoroughly annotated animal and plant genomes may be those of human (L) < 6.4 or ~300 < L < 600) and relatively low disorder contents (ID < 0.3) whereas the animal proteome contains more long and disordered proteins . This may partly explain the slightly positive correlations between ln(L) and ID in the animal proteomes but negative correlations in the plant proteomes. The protein distribution contour maps of other proteomes and gene sets can be found in Figure At a first glance, the plant proteome has more proteins of medium lengths  < 4.6), among which 294  are considered disordered (ID > 0.5); in the A. thaliana proteome, 888 (3.2%) out of 2292 short proteins are disordered. On the other hand, in the H. sapiens proteome, 1135 (5.6%) out of 2384 long proteins  ≥ 6.9) are disordered; whereas, in the A. thaliana proteome, 306 (1.1%) out of 1157 long proteins are disordered. Therefore, a significant difference between the animal (H. sapiens) and the plant  could be recognized as that the former has more long disordered proteins, whereas the latter has more short disordered proteins. This difference shown in It is straightforward to visualize the differences of these two proteomes using the differential contour in A. thaliana proteome are lower than those of the H. sapiens proteome [A. thaliana compared to H. sapiens , some of the sIDPs had been lost whereas long IDPs were enriched. Here, GO annotations of the plant genes were adopted from the plant comparative genomics database PLAZA 3.0 [A recent report also indicates that the overall disorder contents of the proteome , which wproteome . Based o sapiens , we focuproteome . However sapiens . Based oLAZA 3.0 .architectural differences among proteomes, the LD space was divided into M × N blocks and then, the distance between two species A and B was calculated using a Euclidian-type formula based on the protein distributions in all blocks  the eukaryotes are clearly separated from the prokaryotes and (2) plants and animals are grouped together, even the eudicot plants  and monocot plants (O. sativa and A. comosus) are separated. The tree in A. trichopoda before the other plant species and after P. patents. Interestingly, it is consistent with our understanding of the plants-fungi-animals phylogenetic relationships [By dividing the LD space with = N = 10 , the dis= N = 10  were calionships  and stayionships . Based oTo the best of our knowledge, this is the first time to classify proteomes and protein sets based on the protein distribution densities in the LD space (fingerprints), and a detailed comparison with the previous work is therefore not straightforward. Nevertheless, the survey of protein distributions in terms of each of the two attributes is consistent with the work published previously. We noticed that the eukaryotic proteomes do not always exhibit averagely longer proteins than the prokaryotic proteomes. Our observation on protein disorder agrees well with the previous finding, that is, the average disorder contents in eukaryotic proteins are indeed higher than those in prokaryotic proteins. We have also generated phylogenetic trees based on the protein distribution densities in the fingerprints of proteomes, and this allows us to make some comparisons of the results that we obtained here with the knowledge in the field and to examine the consistency and differences with earlier investigations. Such comparison may also provide certain alternative views that were generated through this unique approach.It has been in the debate over the years concerning if viruses should be included in the tree of life , 41 or iThe phylogenetic tree with the viral and organelle  gene sets is shown in N. equitans and alphaproteobacterium Rickettsiales, owing to that majorities of the proteins in these proteomes and protein set are highly ordered . However, we found that 19 (out of 23) mitochondrial IDPs have unknown functions involved in unknown biological processes  and protein intrinsic disorder contents (D) as the basic attributes, other attributes (not limited to those from proteins) may be introduced as well. One can imagine that one of the properties for the attributes would be that protein distributions in terms of the new attributes would be different for different proteomes (protein sets) so that the purpose of classification of proteomes (protein sets) can be achieved.Our two-dimensional contour maps (or proteome fingerprints) based on the protein distribution densities in the LD space show distinct patterns for different organisms and protein sets and may therefore be used for classification of proteomes and protein sets. The phylogenetic trees generated based on the protein distribution densities from the fingerprints were found to be meaningful, as they seem to contain important information of evolution. Thus, the proposed approach and its further extension may represent a useful and alternative way for proteome classification and comparison. It should be pointed out that although in the present work we used protein lengths  contain unknown “P. trichocarpa) proteome [P. trichocarpa genome, there are three versions  of the proteomes, of which the v03 proteome has 41,434 primary proteins and 31,579 splicing alternatives . Using the primary proteins of all three versions and the full proteome of the v03 version as separated entries, a phylogenetic tree was constructed . IDpep is also termed as the “rough definition” of the disorder contents in [The PONDR-VSL2 algorithm  was appltents in  and rangΧ is the total number of amino acids and iD is the PONDR score of the ith residue of the proteome or protein set. IDres corresponds to the definition adapted in [pep and IDres are listed in pep (simplified as ID in the main text) had been chosen to act as one of the attributes of the LD space.It had been suggested that the total proteome information content (PIC) could be defined as the total number of amino acids of the primary proteins (longest isoform at each gene locus) that the proteome carries . In accoapted in . Both IDX was first divided into M × N blocks  and N for ID. This separation is reasonable because both ln(L) and ID exhibit linearity (ijth block (i in ln(L) and j in ID%) is calculated as Xij = nij/ntot, where ijn is the number of proteins in the ijth block and ntot = ∑l=1M∑d=1Nnld is the total number of proteins in the proteome of species X. Normalization of the protein density is realized by default since ∑Xij = 1.To generate the fingerprints, the LD space of species ks e.g., , M for linearity . Then, tABr is the distance between A and B and ijX (X = A or B) is the protein density in the ijth block. The calculated distance matrix is converted to the phylogenetic tree using the neighbor-joining method by the T-REX web server [M and N and detailed block separations may serve as variables to fine tune the final phylogenetic tree. As a proof of concept, the reconstructed phylogenetic tree using M = N = 10 is shown in Using the protein densities, the distance between two organisms A and B can be calculated using the Euclidean equation:b server . M and NL)) → calculations of the protein densities in all blocks  and protein length of primary proteins (logarithm, ln(l blocks  → calcull blocks  → recons


Previous studies showed that prolonged exposure to fluoride (F-) and aluminum (Al3+) ions is associated with numerous diseasesincluding neurological disorders. They don't have any known biological function. But they can bind with proteins that interact withions similar to them. Such unwanted interactions affect the normal biological function of the target proteins, as well as theirdownstream protein-protein interactions. Several studies show the detrimental effects posed by them including Alzheimer's disease.However, their target proteins have never been reported. Here, we have screened for the human protein targets subjected to F- andAl3+ interactions by using data-driven prediction tools. We have identified 20 different proteins that directly bind with them . In addition, protein-protein interaction has been explored to find the proteins thatindirectly interact with F- and Al3+. We have found 86 indirect targets for F- and 90 for Al3+. Furthermore, 19 common protein targetshave been identified, including proteins (9 out of 19) associated with neurodegenerative disorders. However, wet lab experiments arebeyond our scopes to validate the binding networks. Additional studies must be warranted. Fluorine (F) and aluminum (Al) are widely used in additives.Fluorine commonly appears as fluoride ion (F-) that reacts withother positively charged cations . Sodium Aluminium is the most abundant metal in nature having noknown biological function . It is aThere is evidence that fluoride and aluminum individuallycontribute to the development of different disorders. Fluoridecauses detrimental effects on proteins expressed in soft tissuesincluding the blood, brain, and liver  by penetThe first step in the prediction of the potential protein targets offluoride and aluminum is to find the network of interactingproteins with F- and Al3+ ions in human. We have used theSTITCH 5.0 (http://stitch.embl.de/) web server to find the protein targets.The STITCH database aims to incorporate dispersed databases,texts, and prediction methods into a single, easy-to-use resource.The STITCH offers a comprehensive overview of chemical andprotein interaction network for numerous organisms includingbinding affinities between them. The predicted interaction isglobal for an individual organism and does not distinguish tissuespecificity . Target The protein sequences identified with the STRING 10.5 analysiswere downloaded in FASTA format from NCBI protein database(http://www.ncbi.nlm.nih.gov/protein). Two FASTA files weredownloaded, containing 86 and 90 proteins for fluoride andaluminum, respectively. Additionally, numerous data-driventools further analyzed these proteins.Gene ontology (GO) of the retrieved protein sequences wasanalyzed by Blast 2 GO software (https://www.blast2go.com/). This toThe Venny 2.1 (http://bioinfogp.cnb.csic.es/tools/venny/) webserver  has beenThe organs expressing the 19 common genes and their expressionlevel have been determined from the NCBI(https://www.ncbi.nlm.nih.gov/gene/) database that usesRPKM (Reads Per Kilo-base per Million mapped reads) toquantify gene expression level.The STITCH 5.0 predicts the association for ion-protein networkand their binding affinities. The STITCH 5.0 has predicted 20human proteins for fluoride and aluminum interaction (10proteins for each) . The STRThrough the Blast 2 GO software, we executed a series of stepsfor a list of target protein sequences of fluoride and aluminum,including protein Blast, mapping, and scanning motifs by InterPro Scan. The Gene Ontology (GO) has been performed toannotate the biological processes, molecular function, andcellular component for all the proteins found by the STITCH 5.0and STRING 10.5 , 3, 4, aThe Venny 2.1 web server has been used to detect the 19 commonprotein targets of fluoride and aluminum .Our findings suggest that fluoride and aluminum both havedirect (ion-protein interaction) and indirect (protein-proteininteraction) protein targets in the human. They directly bind with20 proteins (both have 10 targets each), while the indirect bindingnetwork has 86 and 90 proteins for fluoride and aluminum,respectively. They have different binding affinities with theirtarget, for instance, fluoride has a strong binding affinity withENO2 compared to GAPDH and aluminum can strongly bindwith GPX1 compared to SNCA. The GO annotation for theidentified proteins demonstrates their association with numerousbiological processes, cellular components, and molecularfunctions in the human. Both the ions have 19 common proteintargets. We have further analyzed their expression site andexpression level. RPKM data shows different expression level ofthe common target proteins of fluoride and aluminum in thehuman, for example, ALB  is highly expressed in theliver and SCN10A (Sodium Channel 10 Alpha subunit) is barelyexpressed in the testis. Most of the common target proteins areexpressed in the brain. Fluoride and aluminum could havedetrimental effects on many organs, including the spleen, liver,adrenal and prostate glands, small intestine, heart, and primarilythe brain. The accumulation of aluminum-fluoride (AlF3)complex in the brain causes prolonged neurotoxicity, synapticloss, and neurodegeneration . AlF3 coHere, we find that APP or Amyloid beta (A4) precursor proteincan interact with F- and Al3+. APP is a single-pass transmembraneprotein highly expressed in the brain and is a key factor in thedevelopment of Alzheimer's disease . It is pEffects of fluoride and aluminum on living organisms especiallyon the humans come to light through long-term exposure to theseions. To minimize the exposure to fluoride and aluminum, publicawareness is a must, for instance, parents should pay closeattention to their children while they brush their teeth. Inaddition, companies should make toothpaste tasteless. Weshould avoid boiling the water in aluminum vessels, preservingacidic fruits and vinegar in the aluminum containers, preparingacidic fruit juice in the aluminum coated juicer, and use ofautomatic coffee machines etc.  to lowerThe authors have declared that no competing interest exists.


In developing epithelia, the core planar polarity proteins physically interact with each other and localize asymmetrically at opposite cell ends, forming intercellular complexes that link the polarity of neighboring cells. Using quantitative imaging to examine the composition of the core protein complex in vivo, we find that complex composition is unexpectedly plastic. The transmembrane proteins Frizzled and Flamingo form a stoichiometric nucleus in the complex, while the relative levels of the other four core proteins can vary independently. Exploring the functional consequences of this, we show that robust cell polarization is achieved over a range of complex stoichiometries but is dependent on maintaining appropriate levels of the components Frizzled and Strabismus. We propose that the core proteins assemble into signalosome-like structures, where stable association is not dependent on one-to-one interactions with binding partners, and signaling functions can act over a wide range of complex compositions.  •Planar polarity proteins form signalosome-like complexes of variable stoichiometry•Flamingo and Frizzled form a stoichiometric nucleus within the complex•Asymmetric localization of core proteins is robust to changes in complex composition•Core protein stability in complexes is not dependent on complex stoichiometry Strutt et al. show that the composition of the core planar polarity complex is highly plastic, with stable association of complex components occurring over a wide range of stoichiometries and with cell polarization being robust to changes in complex composition, suggesting a signalosome-like organization. During animal development, many epithelia are polarized in the plane of the tissue. One of the best-characterized systems that controls this planar polarity involves the core planar polarity proteins (known hereafter as the core proteins). These localize asymmetrically within cells at the level of the adherens junctions and control the production of polarized structures and polarized cell behavior .Drosophila pupal wing epithelium. Here, they localize asymmetrically on proximal and distal cell edges and regulate the orientation of an actin-rich trichome, which emerges from the distal end of each cell. The seven-pass transmembrane protein Frizzled (Fz) localizes distally, with the cytoplasmic proteins Dishevelled (Dsh) and Diego (Dgo); Strabismus , a four-pass transmembrane protein, localizes proximally with the cytoplasmic protein Prickle (Pk); and the atypical cadherin Flamingo  localizes both proximally and distally, where it can mediate homophilic adhesion between neighboring cells  promote clustering , so theiFinally, it is not known why asymmetry is robust to changes in core protein levels, as such changes might be expected to disrupt feedback interactions. Loss of Dsh ubiquitination leads to the accumulation of Dsh and the other core proteins at junctions ; neverthHere, we carried out a detailed study of core protein levels at cell junctions. We assumed that the proteins detected at junctions are all part of complexes, as genetic studies have shown that individual core proteins have little or no localization to junctions in the absence of their transmembrane partners . This alUsing this methodology, we have determined the relative concentrations of each of the core proteins at junctions in pupal wings in vivo. By manipulating gene dosage, we then investigated how modulating complex composition affects asymmetry. This gives us unexpected insights into the relationships among core complex composition, sorting into asymmetric junctional puncta, and the acquisition of cellular asymmetry, and it leads us to suggest that the complex is organized into signalosome-like structures.To determine the relative stoichiometry of the core planar polarity proteins in vivo, we tagged each of them with EGFP D and theFor this approach, the tagged proteins must all be expressed at endogenous levels. For Fmi, Fz, and Pk, the EGFP tag was inserted into the endogenous locus by in vivo homologous recombination. For Stbm, Dsh, and Dgo, the EGFP tag was inserted into a P[acman] rescue construct by recombineering and then integrated into the genome; the transgenic animals were then crossed into appropriate mutant backgrounds to maintain normal gene dosage.The EGFP-tagged proteins localized asymmetrically at junctions in the pupal wing, and trichome polarity in the adult fly wing was normal, suggesting that the proteins can replace endogenous protein function . WesternWe first measured the stoichiometry of the core proteins within puncta, as core proteins in puncta are predominantly stable and highly asymmetrically organized, consistent with ordered arrays of aligned complexes C. As a cComparing mean EGFP puncta intensity in each of the fly lines showed that for every two molecules of Fmi-EGFP within puncta, there are approximately two molecules of Fz-EGFP and EGFP-Dsh, six molecules of Stbm-EGFP, and one molecule of EGFP-Pk and EGFP-Dgo E. As morWe then investigated the degree of asymmetry of the core proteins within puncta by making twin clones and examining puncta intensity on clone boundaries, where tissue expressing tagged protein was adjacent to tissue expressing untagged protein. As previously observed , Fz-EGFPIn junctional domains that lack large puncta, core protein complex distribution is less ordered, and complexes are less stable . Puncta The similar relative core protein levels and corresponding complex composition in puncta and non-puncta were surprising, considering that Fz and Fmi show lower stability in non-puncta . We therStable proportions of the endogenously expressed tagged core proteins were determined using fluorescence recovery after photobleaching (FRAP). All six core proteins had a larger stable proportion in puncta than non-puncta, although the stable proportion varied for each protein B. IntereIn summary, overall core protein stoichiometry is similar regardless of whether complexes are concentrated in stable ordered domains (puncta) or more sparsely distributed in less ordered non-puncta regions. However, the stable and unstable proportions of the core proteins vary between puncta regions and non-puncta regions. This indicates an uncoupling between complex composition and protein stability, such that stability is not promoted by an altered complex composition but by the local concentration of complexes of the same orientation.As core complex stoichiometry is similar in puncta and non-puncta, one possibility was that a single optimal composition is necessary for junctional localization of the core protein complex. If this were true, stoichiometry would also be the same in tissue in which core protein localization is overall less asymmetric. At earlier stages of wing development, cells are undergoing junctional remodelling; the core proteins show only weak cellular asymmetry and form only small puncta .Interestingly, complex stoichiometry was different in earlier-stage wings. The relative amount of Stbm in puncta was lower in younger wings, while the relative amount of Dsh was increased C. SimilaWe next investigated what happens to stoichiometry if gene dosage of one of the core proteins is altered. First, we examined the effects of halving the dosage of the three cytoplasmic core proteins . Pupal wings expressing two copies of EGFP-tagged protein and no endogenous protein were compared to wings carrying one copy of tagged protein in a heterozygous null mutant background. In each case, the levels of EGFP-tagged protein at junctions was roughly halved in the heterozygous mutant . This waP[acman] constructs in wild-type instead of mutant backgrounds were then used to double gene dosage. Doubling Dsh or Dgo dosage caused the amount of protein in puncta to increase, consistent with idea that levels of these components are normally limiting D–S5F. AgComputational modeling has suggested that the cytoplasmic proteins play key roles in promoting feedback interactions between the core proteins e.g., . Howeverfmi dosage caused a reduction in protein levels, both in puncta and non-puncta  promoter, and we then examined their levels in clones lacking endogenous protein. In both cases, the levels of protein in puncta were modestly decreased , whereas protein complexes would be expected to be at least an order of magnitude smaller. Nevertheless, the lack of variation in our measurements between different puncta and in junctions overall suggests that there is little spatial variation in complex composition in cell junctions, and the figures we obtain can be regarded as plausible local averages of complex composition.The cytoplasmic core proteins depend on the transmembrane proteins for their recruitment to junctions . HoweverInterestingly, the features we describe in our cloud model bear a striking resemblance to those seen for higher-order assemblies of signaling molecules known as signalosomes . Such coAnother feature of signalosomes is that the polymerization of downstream signaling proteins is nucleated by a core that is often sub-stoichiometric —a role tOne of our original hypotheses was that the overall composition of the core complex might be different in highly polarized puncta, where protein stability is high, than in disordered non-puncta, where stability is lower. However, we find that the composition of the complex is very similar in puncta and non-puncta. Why then is core protein stability higher in puncta?cis interactions may then result in increased stability and lower turnover. An example of this is the immunological synapse, in which protein-protein interactions cause diffusional trapping and clustering of signaling molecules  pupal wings were dissected directly into sample buffer. One pupal wing equivalent was used per lane. A Bio-Rad ChemiDoc XRS+ was used for imaging, and band intensities from three biological replicates were quantified using ImageJ. Data were compared used unpaired t tests or ANOVA for multiple comparisons.2 were selected for puncta and non-puncta. After imaging, ROIs were manually reselected in ImageJ and quantitated. Control unbleached regions were also quantitated to control for acquisition bleaching. Data were corrected for acquisition bleaching and normalized against an average of the prebleached values and the first postbleach value. Data from ROIs in the same wing were averaged, and Prism (v6 GraphPad) was used to fit a one-phase exponential association curve for each wing. Data from several wings were then used to fit a final exponential association curve, and an extra sum-of-squares F test was performed to compare curve plateaux (Ymax) between puncta and non-puncta.For live imaging, pupae were prepared and imaged at 28 hr APF (unless otherwise stated) as previously described . For FRATo combine the FRAP data with the puncta stoichiometry data, the normalized stoichiometry data were multiplied by the stable and unstable proportions. Standard errors for each experiment were combined, and plotted as the square root of the sum of each error squared. To compare relative stable and unstable amounts in puncta and non-puncta, each dataset was normalized to 2 units of Fmi-EGFP, and two-way ANOVA with Holm-Šídák correction was performed.fmiE59 heterozygous background, prebleached values were averaged for each wing and multiplied by the stable proportion (1 − Ymax). Stable amounts were then averaged across wings and compared using unpaired t tests.To compare stable and unstable amounts between EGFP-tagged protein in a wild-type and Membrane masks were generated using Packing Analyzer , and autFor comparing intensity of individual puncta labeled with two different antibodies in the same wing, a puncta mask was generated as above for one channel, and this mask was then used to measure puncta intensity in both channels.For quantitating puncta in clones in fixed images, wild-type and mutant regions were separated, and puncta detection applied to each region separately. Puncta intensity was compared between control and mutant regions in the same wing using paired t tests.fmi-EGFP, fz-EGFP, and stbm-EGFP twin clones, puncta were detected over the whole image on the basis of Fmi staining. Puncta on proximal and distal cell ends were selected manually in ImageJ, and mean intensity and puncta area were measured for Fmi, Fz, or Stbm. Proximal and distal puncta intensity were averaged per wing and compared in the same wing using paired t tests.For measuring puncta asymmetry on the borders of A MATLAB script was used to determine the angle of maximum asymmetry for each cell (see H.S., J.G., and D.S. designed experiments and wrote MATLAB scripts. H.S. and J.G. conducted experiments and analyzed the data. H.S. and D.S. wrote the paper.


An organism’s protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome.https://github.com/fosterlab/PrInCE, where usage instructions can be found. An example dataset and output are also provided for testing purposes.Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at PrInCE is the first fast and easy-to-use data analysis pipeline that predicts interactomes and protein complexes from co-elution data. PrInCE allows researchers without bioinformatics expertise to analyze high-throughput co-elution datasets.The online version of this article (10.1186/s12859-017-1865-8) contains supplementary material, which is available to authorized users. The association of proteins into complexes is common across all domains of life , 2. IndeA proteome of 1500 proteins has over one million possible binary protein-protein interactions (PPIs) and many more potential higher-order complexes. Because of this combinatorial explosion, even relatively simple proteomes can yield rich, complex interactomes. High-throughput or high-content methods that identify many PPIs simultaneously are therefore valuable to efficiently map these networks. There are currently three general methods for doing this: The first, yeast-2 hybrid (Y2H), operates by incorporating modified bait and prey proteins in a genetically modified yeast cell, such that a PPI between bait and prey drives transcription of a reporter gene. Affinity purification mass spectrometry (AP-MS), a second technique, involves immunoprecipitation of proteins of interest (baits) [The third general approach, collectively termed co-fractionation approaches, involves resolving complexes by either chromatography or electrophoresis and assigning interacting partners based on the similarity of fractionation profiles –8. WhileGaussBuild.m); 2) correction for slight differences in the separation dimension between replicates (Alignment.m); 3) comparison of differences in protein amounts, i.e. fold changes, between conditions (FoldChange.m); 4) prediction of PPIs within each condition (Interactions.m); and 5) construction of protein complexes from the predicted PPIs (Complexes.m). The first two modules, i.e. GaussBuild.m and Alignment.m, are pre-processing steps, while the remaining three modules compute protein abundance changes and predict protein interactions and complexes  identification of Gaussian-like peaks in the co-fractionation profiles . If there are multiple replicates, this module also calculates significance using a paired t-test. Fold changes are calculated using data centered on the Gaussian peaks identified by GaussBuild.m [Within a single replicate, the protein abundance ratio, i.e. fold change, is calculated between conditions for each protein  pairs, where μ and σ are Gaussian parameters fitted to ci and cj. For example, if ci is fit by two Gaussians with  equal to  and , and cj is fit by one Gaussian with parameters , Co-apex score, PPI prediction begins by calculating the effective distance between the co-fractionation profiles of every pair of proteins. We use five distance measures to quantify different aspects of co-fractionation profile similarity. For all distance measures, a value close to zero signals high similarity between co-fractionation profiles. These five metrics are not exhaustive, but in practice we found there was little value in additional measures. For a pair of co-fractionation profiles > 2. The contribution of each feature to prediction performance depends on the dataset, although in general the most-informative (least-rejected) features are 1-Rcorr, P, and CA. Distance measures are combined across replicates (but not conditions) for each protein-protein pair. Class labels are assigned based on the reference database. Reference protein pairs that occur in the same complex are gold standard interactions (interacting or “intra-complex” label). Proteins that are found in the reference database individually but do not occur within the same complex are labeled non-interacting (“inter-complex”) and are false positive interactions [Combined with a reference database such as CORUM, these five distance measures can be used to predict novel PPIs. Our pipeline uses a machine learning classifier to do this , 15. Speractions . Novel ik-fold cross-validation scheme to avoid over-fitting. k = 15 is used as a tradeoff between computation time and classification accuracy. The classifier calculates an interaction probability for every protein pair. Self-interactions are not considered.The Naïve Bayes classifier returns the probability that putative protein pairs are interacting. Interaction probabilities are calculated separately for each experimental condition. We use a TP/(TP + FP), where TP and FP are the number of true positives and false positives. The desired precision is chosen by the user.By applying a threshold to interaction probability returned by the classifier, protein pairs are separated into predicted interactions and predicted non-interactions. The probability threshold is chosen so that the resulting interaction list has a desired ratio of true positives (intra-complex) and false positives (inter-complex), quantified as precision interaction score. A predicted interaction’s score is equal to the precision of all predicted interactions with an interaction probability greater than or equal to it. Although interaction probability and score are largely equivalent, interaction score has two advantages. First, interaction score is more human readable, since the dynamic range of predicted interaction probabilities is often quite small. Second, the use of interaction score makes it trivial to generate interaction lists with a desired precision.Finally, we express the confidence of each predicted interaction by reformulating interaction probability as an p and dens are optimized via grid search to produce the most reference-like complexes. p represents the number of unknown pairwise interactions, and dens is a threshold for the minimum density of a complex, where complex density is defined as the sum of weighted internal edges divided by N(N − 1)/2. Parameters are optimized to maximize either the matching ratio [Complexes are predicted from the list of pairwise interactions using the ClusterONE algorithm . The pring ratio  or geomeng ratio  between For this study, we tested PrInCE on four co-fractionation datasets, each composed of thousands of co-fractionation profiles Table . D1, D2,We tested how the choice of gold standard reference affects the interactions predicted by PrInCE. First, we predicted interactions using subsets of CORUM drawn under two different schemes. The first scheme was designed to test the effects of the size of the reference set: a fraction of CORUM complexes were drawn randomly  and interactions were predicted from dataset D1. The second scheme was designed to test whether interactions could be predicted consistently for different reference sets. To control the number of PPIs we performed a paired analysis, where we divided CORUM into two halves with equal numbers of gold standard PPIs in the data. These halves have no PPIs in common, and interactions were predicted from both halves using a single replicate of dataset D1. The first scheme was repeated 10 times, and the second Scheme 50 times. Second, we predicted interactions from all four datasets using two additional gold standard references: IntAct  and hu.M is consistent with other known evidence for protein interaction. To do so, we calculated the Spearman correlation coefficient between interaction score and these four other, independent measures of protein interaction: (i) whether protein pairs shared at least one Gene Ontology term within GO slim, a condensed version of the full GO ontology , 22; ; (iii) ap.org/, ; and . For these interacting versus non-interacting enrichment analyses, we imposed a 10% breadth cutoff on all annotation terms, such that only annotation terms common to less than 10% of all proteins in the sample were used. As in , we alsoThird, we re-estimated the precision of our predicted interaction lists using an independent, previously described method . Our defFinally, following the network analysis of , we explComplexes.m, that was not present in the previous version.PrInCE uses a machine learning approach to predict conditional interactomes from co-fractionation data. Four datasets were used to benchmark PrInCE versus a previous pipeline , which sPredicting protein-protein interactions (PPIs) is one of the primary functions of this pipeline. Figure By applying a threshold to interaction probabilities outputted by the classifier, a final interaction list can be generated at a precision specified by the user. For example, a more stringent list containing an estimated 75% true positives (white), or a more inclusive list with an estimated 50% true positives , meaning it requires examples of both interacting and non-interacting proteins, i.e. a gold standard reference of protein complexes. We sought to investigate how characteristics of the reference impact the interactions predicted by PrInCE. Using subsets of CORUM to simulate the effects of a smaller reference, we see that the number of predicted interactions can vary widely when using relatively small references  largely overlaps with the 20S proteasome contained in the CORUM reference database (black and purple). One member  was predicted to be participating in the complex. Notably, while P28065 is not in the CORUM database, it is annotated as a proteasomal protein. Thus, using co-elution as the only source of evidence, PrInCE predicted this known co-complex member of the 20S proteasome even though it was missing from the reference.Sorting co-fractionation profiles by their peak location Fig.  reveals PrInCE is also capable of predicting entirely novel protein complexes. For example, a four member complex was predicted in dataset D1, of which no proteins were in CORUM Fig. . ReassurTP/(TP + FP). In order to validate that we are separating signal from noise in a biologically meaningful way, we sought to establish the biological significance of interaction lists generated by PRInCE using independent evidence. First, we wanted to confirm that the measure we use to rank the confidence of predicted interactions, interaction score, is a useful way to identify which interactions are more likely to be true positives. To do so, we tested whether proteins in high score PPIs are more likely to share annotation terms than low score interactions. Indeed, for every GO-slim annotation category, as interaction score increased, so did the proportion of interactions sharing at least one annotation term   Fig. , share aHow do predicted interactions differ from predicted non-interactions? A well-performing pipeline returns predicted classes that are, at least by some measures, cleanly separated. To assess this, we first compared Jaccard indices , which mWe next used enrichment values to quantify the tendency for predicted interacting proteins to share annotation terms. In general, interacting proteins were about 10× more likely to share GO annotation terms than non-interacting proteins Fig. . FinallyCalculating the precision of the interactions predicted by PrInCE is crucial for minimizing the number of false positives. To estimate precision, both the numbers of true and false positives must be calculated. The reference database provides a list of true positive interactions (intra- complex). However, since no comparable database of false positive interactions exists, we make the assumption that pairs of interacting proteins which are both present in the reference, but not reported by the reference to interact, are false positives (inter-complex). Several of these false positives are likely to be true interactions that simply have not been previously discovered and thus not included in the reference, meaning that PrInCE likely underestimates the true precision of the interactions. Using the method outlined in  to re-esfragment the network by revealing islands of isolated subgraphs; removing the highest confidence interactions should lead to no fragmentation. Indeed, removing low confidence interactions first produced a network with a greater number  to a Support Vector Machine classifier  we found the Naive Bayes predicted similar or greater numbers of interactions at a given precision level, depending on the dataset (data not shown).E. coli datasets we recommend the IntAct database. Because false positives are defined as inter-complex pairs that are not also intra-complex pairs, gold standard databases of binary protein pairs, such as STRING, are not suitable. If neither CORUM nor IntAct are suitable, in general we recommend that the reference be large enough  between gold standard co-elution curves  SILAC datasets. A major strength of SILAC experiments is that they allow conditional experiments to be performed simultaneously, minimizing experimental variability between conditions. However, the analysis here of dataset D3, a surrogate for a non-SILAC labelled dataset, demonstrates that PrInCE is not limited to analyzing SILAC data. In fact, PrInCE can analyze any dataset with co-fractionation profiles for single proteins where co-fractionation is meaningful evidence of co-complex membership, and for which there exists a suitable reference.PrInCE provides a powerful tool for predicting interactomes from co-fractionation experiments. It greatly simplifies the task of analyzing co-fractionation datasets, requiring at most installation and simple command line tools. Building on preliminary versions of a bioinformatics treatment , 11, PrI