(C) PLOS One This story was originally published by PLOS One and is unaltered. . . . . . . . . . . Virome Data Explorer: A web resource to longitudinally explore respiratory viral infections, their interactions with other pathogens and host transcriptomic changes in over 100 people [1] ['Marta Galanti', 'Department Of Environmental Health Sciences', 'Mailman School Of Public Health', 'Columbia University', 'New York', 'United States Of America', 'Juan Angel Patiño-Galindo', 'Program For Mathematical Genomics', 'Department Of Systems Biology', 'Columbia University Irving Medical Center'] Date: 2024-01 Viral respiratory infections are an important public health concern due to their prevalence, transmissibility, and potential to cause serious disease. Disease severity is the product of several factors beyond the presence of the infectious agent, including specific host immune responses, host genetic makeup, and bacterial coinfections. To understand these interactions within natural infections, we designed a longitudinal cohort study actively surveilling respiratory viruses over the course of 19 months (2016 to 2018) in a diverse cohort in New York City. We integrated the molecular characterization of 800+ nasopharyngeal samples with clinical data from 104 participants. Transcriptomic data enabled the identification of respiratory pathogens in nasopharyngeal samples, the characterization of markers of immune response, the identification of signatures associated with symptom severity, individual viruses, and bacterial coinfections. Specific results include a rapid restoration of baseline conditions after infection, significant transcriptomic differences between symptomatic and asymptomatic infections, and qualitatively similar responses across different viruses. We created an interactive computational resource (Virome Data Explorer) to facilitate access to the data and visualization of analytical results. Competing interests: RR is a member of the SAB of Diatech Pharmacogenetics and Flahy and founder of Genotwin. JS and Columbia University disclose partial ownership of SK Analytics. JS discloses consulting for BNI. DSR is now employee of "Merck & Co., Inc., Kenilworth, NJ, USA" and may hold stock. IF is now employee of Abbvie and OE is currently employee of Genotwin. All other authors have no competing interests. In order to illustrate the utility of our dataset for future research and beyond, here we depict the transcriptomic changes that occurred during infection, accounting for differences in symptomatology, the type of viral infection and the occurrence of coinfection with respiratory bacteria. We find the up-regulation of hundreds of genes functionally associated with immune response during viral infection. Further, we identify a gene expression signature that is not only capable of differentiating between symptomatic and asymptomatic infections, but also between positive asymptomatic individuals and negative individuals (either symptomatic or asymptomatic). We also depict differences between viruses, with influenza leading to greater changes in gene expression than other viruses, such as coronavirus (CoV) or rhinovirus. Finally, we find evidence of interactions, at the transcriptomic level, between common respiratory bacteria and viruses. Here, we leverage a unique dataset generated from comprehensive longitudinal surveillance of common respiratory viruses over the course of 19 months in the Manhattan borough of New York City. For our study, we collected epidemiological data, including self-reported respiratory symptoms, from 214 participants. Importantly, we also processed over 4,000 nasal swabs, analyzed corresponding qPCR-based respiratory virus panel assays, and sequenced over 800 samples for host transcriptome characterization and quantification of respiratory viruses, pathogenic bacteria, and fungi. Using these data, we created an interactive web resource, publicly available website The Virome of Manhattan Project: Virome Data Explorer to visualize longitudinal transcriptomic changes driven by respiratory viruses in naturally occurring infections. Transcriptomic analysis provides the opportunity to simultaneously characterize host and respiratory pathogen interactions [ 4 , 5 ]. This approach has been validated using peripheral blood, nasopharyngeal samples [ 6 , 7 ], and lung cell samples [ 8 – 10 ]. Most existing studies focusing on transcriptomic response to respiratory infections consist of either in vitro analyses [ 8 – 12 ], challenge studies in which participants are inoculated with a viral agent in a controlled setting [ 13 – 17 ], or observational studies tracking symptomatic natural infection [ 6 , 18 – 20 ]. Challenge studies are informative because they can capture both symptomatic and asymptomatic infections and can be conducted in a very controlled manner with sampling of participants at specific and frequent time points. However, induced infections are not necessarily representative of naturally acquired infections and the reduced size of these experiments (typically involving 10 to 15 subjects), the age of the participants (typically avoiding very young and older individuals), and the lack of bacterial coinfections makes them prone to biases that are difficult to control. Observational transcriptomic studies, on the other hand, typically follow cases after participants manifest respiratory symptoms and are thus biased towards symptomatic and severe infections often resulting in hospitalization [ 6 , 7 , 9 , 21 ]. This is a major limitation, given that previous analyses have found that the majority of respiratory infections are asymptomatic or mild [ 22 ] and predominantly medically unattended [ 23 ]. Respiratory infections are a major cause of disease worldwide, impose enormous societal and public health costs, and are a leading cause of death in young children [ 1 , 2 ]. The recent emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has underscored the need for better understanding of the transmissibility, pathogenicity, and host/pathogen interactions of the many different viruses and bacteria that cause respiratory infections. While some viruses like influenza and respiratory syncytial virus (RSV) are more routinely tracked and frequently studied, a comprehensive analysis of host/pathogen interactions is lacking for many of the other common viruses that circulate widely and cause respiratory disease. Understanding the mechanisms of host response is essential for appropriately understanding the diseases caused by these infectious agents, directing surveillance, treating infections, and developing targeted therapeutics against severe disease. Rather than direct viral effects, host response to infection is often the predominant factor determining symptom severity [ 3 ]. We then applied the expression signature score (factor score F 10 ), calculated with the top 10 DE genes in the longitudinal analysis to the 5 groups of viral infection plus baseline. The score F 10 significantly distinguished influenza samples from all other groups ( Fig 6D ) with the exception of coinfections CoV+HRV (Wilcoxon rank sum test, p = 0.02 with HRV, p = 0.03 with CoV, p = 0.04 with RSV). Results are consistent when using the metrics F 25 and F 100 , respectively, calculated with the top 25 and top 100 genes (see S6 Fig ). Consistent with our previous results from longitudinal data, the CibersortX analyses stratified by virus revealed that viral infections lead to significant (Padj <0.05, Mann–Whitney tests) increases of activated CD4 T cells (Fold-Changes for CoV: 1.8—and HRV: 1.76), activated NK cells (FC for Flu: 2.06, HRV: 2.44, CoV+HRV: 12.44), and Macrophages M1 (significant for all viruses except RSV, FC ranging between 3.79 -CoV- and 6.79 CoV+HRV). We also found trends towards a decrease in plasma cells in all viruses except RSV (FC between 0.25 -Flu- and 0.68 -HRV-). Compared to HRV and CoV, influenza-infected samples had the highest relative abundance of activated Mast cells (mean relative abundances: flu = 0.088, HRV = 0.044, CoV = 0.032), M1 macrophages (mean relative abundances: flu = 0.055, HRV = 0.033, CoV = 0.031), NK activated cells (mean relative abundances: flu = 0.015, HRV = 0.013, CoV = 0.009), and activated CD4T cells (mean relative abundances: flu = 0.043, HRV = 0.037, CoV = 0.038) ( Fig 6C , S17 and S20 Data files). (a) Log 2-fold change +/- SE of the expression of the top 30 DE genes across infection type (rhinovirus, coronavirus, coinfection rhinovirus/coronavirus, influenza, and RSV) with respect to negative samples. The top 30 genes were selected as the genes with lowest padj in the LRT comparing the full model (design~ Batch+ParticipantID+Infection_type) to the reduced model, in which the term Infection_type was removed. The significance was not corrected for FDR. (b) Comparison across different viruses of the top immune-related biological processes enriched during infection. Enrichment is quantified by the median zscore of samples that tested positive for each virus (colored dots) and compared to the median zscore across negative samples (black cross). Data underlying panel 6b are zscores reported in S19 Data . (c) Cibersort analysis of the relative abundance of cell types across infection type. Significant pairwise comparisons in cell abundance among viral infections are highlighted in S20 Data . (d) Score factor F 10 across the 717 samples calculated with the top 10 genes differentially expressed in the longitudinal analysis and applied to the 6 viral infection groups: negative (Neg), HRV, CoV, CoV+HRV, influenza (FLU), and RSV. Boxplots show the median (red line) and interquartiles (blue lines) of the distribution. Whiskers of boxplots extend to all non-outliers in the distribution. Outliers are shown as red “+”. Panels (a), (c), and (d) are based on data in S17 Data . CoV, coronavirus; LRT, likelihood ratio test; RSV, respiratory syncytial virus. To identify specific transcriptional signatures associated with specific viral infections, we compared the transcriptional profiles of the 5 most frequently isolated respiratory viral infections (rhinovirus, coronaviruses, coinfection rhinovirus/coronaviruses, influenza, and RSV) using a total of 717 samples. Other infection types were excluded from this analysis due to a low number of representative samples (i.e., less than 5 samples infected with a given virus). We found transcriptomic changes from baseline in previously identified immune genes for all infection types ( Fig 6A ). Fig 6A shows the log fold change in the 30 genes that showed the strongest change in expression across the infection type groups (see Methods ). Pairwise comparison between baseline (Neg) and individual viruses are shown in S16 Data . Although many overexpressed genes were common to the different viruses, we observed differences across viruses in the magnitude of the transcriptomic response. Influenza was associated with a larger increase in the expression of immune-related genes. In contrast, RSV infections displayed the lowest change in expression from baseline ( Fig 6A ). Similar results were obtained when the analysis was repeated with only negative asymptomatic samples as baseline group ( S18 Data ). This observation is confirmed when comparing with Gene Set Variation Analysis (GSVA) the enrichment of immune-related processes across different viral infections (see Methods ). The gene sets were selected as the immune processes that were overrepresented (p < 0.05) in the longitudinal and symptomatic analysis, and include activation of immune response, response to interferon and antiviral activity ( Fig 6B ). In a similar way, we also assessed the interaction between symptomatology in viral infections and bacterial abundance. At gene expression level, we only found significant interactions between symptomatology and S. pneumoniae. According to GSEA, the abundance of these bacteria in symptomatic people infected with respiratory viruses was associated with higher expression of transcripts related to adaptive immune response (humoral immune response, B cell activate) as well as leukocyte proliferation and NK activation ( S5 Fig and S15 Data). Within the participants infected with a respiratory virus, the abundance of bacteria was significantly different in symptomatic versus asymptomatic people only for S. salivarius and S. oralis, where there was a significant decrease of these bacteria in symptomatic samples (Mann–Whitney test: corrected p-value <0.05; S5 Fig ). We found significant viral/bacterial interactions, at the gene expression level, for Haemophilus influenzae and 2 species of the genus Streptococcus: S. pneumoniae and S. salivarus (padjs <0.05 in DESEQ2 analyses). According to the GSEA results, the abundance of these bacteria in people infected with respiratory viruses was associated with higher expression of genes related to activation of lymphocytes (B cells, NK cells, and granulocytes) and interleukin (IL1, IL6) production ( Fig 5A and S14 Data ). Although we observed significant interactions at the transcriptomic level, abundance of these bacteria was not significantly different between positive and negative samples ( Fig 5B and S14 Data ). We tested for potential viral and bacterial interactions during the course of natural infection. For this, we detected reads assigned to a number of bacterial species, common to the human respiratory tract, that can cause opportunistic diseases. We then assessed whether such bacterial abundance (measured as normalized fraction of reads) had a differential effect, at the host transcriptomic level, in individuals before and during a viral infection. This was done by performing differential gene expression analyses, using samples representative of longitudinal viral episodes (PRE and DUR1 samples). We also statistically compared (with DESEQ2) the expression profiles of SP, AP, and SN to AN. The log2FC from a selection of the top 25 overexpressed genes in SP (from a total of 1,456 overexpressed genes; see S9 Data and S10 Data) shows a clear hierarchical structure in the 4 groups, with the log2FC of symptomatic positives on average 2 times that of the log2FC for asymptomatic positives ( Fig 4C ). We then applied the expression signature score (factor score F 10 ), calculated by combining the expression of the top 10 DE genes in the longitudinal analysis (CXCL11, CXCL10, CCL9, IFI6, IFIT3, CCL8, SLAMF7, BATF2, RSAD2, GBP1) to the 4 groups AN, SN, AP, and SP. The score F 10 was significantly different among the groups of negative samples, positive asymptomatic, and positive symptomatic samples. In particular, the mean score for SP and AP was statistically different (Wilcoxon rank sum test, p = 0.02) and the mean score for AP samples was statistically different from both AN and SN (Wilcoxon rank sum test, p < 10 −32 and p < 10 −13 , respectively). Results are consistent when using the metrics F 25 and F 100 , respectively, calculated with the top 25 and top 100 genes (see S4 Fig ). (a) Volcano plots of differentially expressed genes between SP samples and AP samples. The cutoff for fold change is >|2|; the default cutoff for P-value is 10e-6 . (b) Score factor F 10 across the 699 samples calculated with the top 10 genes differentially expressed in the longitudinal analysis and applied to the 4 symptom-infection groups (AN, SN, AP, and SP). Samples expression values are normalized using the DESEQ2 normalization factor. Boxplots show the median (red line) and interquartiles (blue lines) of the distribution. Whiskers of boxplots extend to all non-outliers in the distribution. Outliers are shown as red “+”. (c) Log fold changes +/-SE for top DE genes in SP, AP, and SN when compared to AN samples assumed as baseline. The list of genes was selected as the top 25 DE genes in SP vs. AN. Panel (a) is based on data in S9 Data . Panels (b and c) are based on data in S10 Data . AN, asymptomatic negative; AP, asymptomatic positive; SN, symptomatic negative; SP, symptomatic positive. We found 516 genes differentially expressed (419 overexpressed) in SP compared with AP ( Fig 4A and S9 Data ). The 81 genes that were at least 2-fold up-regulated at significance level padj <0.05 are well-known modulators of host immune response and included components of interferon pathways (MX1, IFITs, and IFIs groups), cytokines of the CXCL and CCL groups, genes with known antiviral activity (MX1, ISG15, RSAD2, and the OASs and DDXs groups), and genes associated with inflammation, such as IL6. These results were consistent when different thresholds were considered for the symptomatic classification: 506 (342 overexpressed) when using a symptom score threshold of >19 in SP versus AP ( S11 Data ). In this latter design, there were 43 SP samples, 166 AP, 26 SN, and 464 AN. To understand how asymptomatic infections differ from symptomatic ones, we compared the transcriptomic profiles of symptomatic and asymptomatic samples. We used a set of 699 samples (see sample selection in Methods ), of which 209 were positive for at least 1 respiratory virus. This set of samples, featuring a median of 8 samples per individual, allowed comparison of the signal across symptom level and virus positivity status while controlling for the differences across the 81 contributing individuals. The samples were divided into 4 categories based on diagnosis (positive versus negative for respiratory viruses) and symptoms (symptomatic versus asymptomatic). Overall, there were 63 symptomatic positive (SP) samples, 146 asymptomatic positive (AP), 67 symptomatic negative (SN), and 423 asymptomatic negative (AN). We also compared the transcriptomic signal, quantified by F 10 and F 100 , across normalized symptomatic and asymptomatic episodes (an episode was symptomatic if the weekly symptom score associated with any DURING sample was above 9 and asymptomatic otherwise, see Methods ). The transcriptomic signal of the acute phase increased more markedly in symptomatic than in asymptomatic episodes (p = 0.01 and p = 0.003, Wilcoxon rank sum test); however, the recovery phase of POST1 and POST2 did not differ significantly across the 2 ( Figs 3E and S3 ). In the next section, we investigate the association between symptom severity and differential expression of immune-related genes in greater depth. The effect of respiratory viral infections was not significantly different across sex and age group, even though some differences in the transcriptomic profile of immune genes across groups existed at baseline (i.e., in no-infection condition). Specifically, in the absence of viral infections, children displayed a larger expression of immune genes compared to adults that was significant for 94 of the 100 genes of signature F 100 . S7 Data and S2 Fig show the infection effect in male versus female and in children versus teenagers versus adults, together with pairwise comparisons between different groups at baseline. The signature scores F N , based on the top 10, top 25, and top 100 differentially expressed genes between DURING1 and PRE (see Methods ) were able to distinguish infection and postinfection samples from the paired preinfection samples (see Fig 3D for F 10 and S1 Fig for F 25 and F 100 ). To pair samples, we restricted to the (n = 98) longitudinal episodes that included a PRE sample, and we normalized the DURING and POST scores, simply dividing them by the score of their respective PRE. Results showed that in addition to being markedly distinguishable from DURING samples (median DUR1 normalized score = 1.53, p < 10 −13 and median DUR2 normalized score = 1.33, p = 0.004), PRE samples were still weakly distinguishable from POST1 (median POST1 normalized score = 1.09, p = 0.03) but not from POST2 (median POST2 normalized score = 1.07, p = 0.12, significance assessed with Wilcoxon signed rank tests). These longitudinal data were further subjected to in silico cell sorting with CibersortX, in order to compare the relative abundance of different leukocytes. Our results suggest that onset of infection leads to an increase of proinflammatory macrophages M1 (Fold-Change = 4.4), activated CD4 T cells (FC = 1.8), and activated NK cells (FC = 2.9) (all Padj <0.05, Mann–Whitney tests) in nasopharyngeal samples. Soon after an infection is cleared, most changes are restored ( S4 Data , Fig 3B , and corresponding S5 Data ). We compared these findings with the changes in expression of gene markers for different immune cell types. In agreement with the results from Cibersort, samples during infection had higher levels of expression for genes such as Maf, Icos (CD4 T cell activation), Cd69, FCGR3 (NK cell activation), Cd80, CD86 (macrophages M), as well as markers of cytotoxicity (GZMB, GZMK) and inflammation (IFNG, TNF) ( Fig 3C and corresponding S5 Data ). Conversely, the difference between the PRE and POST1 groups was minor with no genes differentially expressed after FDR correction ( Fig 3A and S2 Data ). Furthermore, we did not see evidence of overexpression of recovery genes in POST1 versus DURING1 samples and despite some residual effects in the postinfection samples (i.e., the slower recovery of genes related to CD4 Th1 activation (MAF, ICOS)), the profile of POST samples closely resembles that of PRE samples ( Fig 3C ). We did not find obvious functional annotation for down-regulated genes during infection, although many transcripts are consistent with previous reports [ 15 ]. Panel (a): Volcano plots of differentially expressed genes between DURING1 and PRE, DURING1 and POST1, POST1 and PRE. The cutoff for fold change is >|2|; the default cutoff for p-value is 10^-6. (b) Relative abundance of immune cells, as predicted with Cibersort, across the PRE-DURING1-DURING2-POST1-POST2 samples. Significant pairwise comparisons are reported in S2 Data . (c) Heatmap of normalized expression of selected genes, known to be markers for immune cells, across PRE-DURING1-DURING2-POST1-POST2 samples. (d) Scores F 10 of longitudinal samples, calculated using the top 10 genes differentially expressed in the DURING1 vs. PRE comparison, normalized by the respective PRE sample in each episode. Red boxplot lines are the median of distributions, and blue boxes are the interquartile. Whiskers extend to all non-outliers of the distribution. Outliers are represented with red + symbol. For each group, we reported p-values of a Wilcoxon signed rank test for the hypothesis that the dataset comes from a distribution with median = 1. (e) Medians (straight lines) and interquartile (shaded areas) of longitudinal symptomatic (red) and asymptomatic (blue) episodes normalized by the respective PRE sample. Underlying data for panels (b–e) can be found in S5 Data . Our results show a strong immune signal during the acute phase of infection, followed by a rapid decay of transcriptomic signal after infection clearance: 6,594 genes were differentially expressed (padj <0.05) between the PRE and the DURING1 phase. Among the differentially expressed genes, those that were up-regulated during infection (n = 1,422) had a much stronger significance ( Fig 3A and S2 Data ) with 210 genes more than 2-fold up-regulated and 23 genes more than 4-fold up-regulated. Gene set enrichment analyses revealed that the most significant biological processes associated with viral infection were those related to adaptive immune response, cytokine production, acute inflammatory response, lymphocyte- and neutrophil-mediated immunity (Gene Set Enrichment Analysis (GSEA) adj, p-value <0.05; all enrichment scores >2.0; see S3 Data ). The 10 most significantly overexpressed genes (7.5*10 −53 1,400 genes overexpressed with respect to baseline preinfection samples. The steep decay of transcriptomic signal in postinfection samples was observed in both symptomatic and asymptomatic episodes and is in line with the rapid restoration shown in Zhai and colleagues at 21 days after symptom onset, when the transcriptomic profile of immunity genes in peripheral blood was not distinguishable from negative baseline, and with challenge studies that show a peak in gene expression 3 to 5 days postinoculation followed by a signal decrease [14,16,30]. We were not able to identify in the postinfection samples of our dataset the up-regulation of specific recovery genes found in [20], possibly due to a coarse sampling design (weekly, at most, but we allowed postinfections samples within 10 days from positive samples due to frequent sampling missed by participants). Zhai and colleagues employed a finer sampling design that enabled identification of a recovery phase between day 4 and day 6 following symptom onset. The majority of the infections in our sample set were caused by rhinoviruses and coronaviruses. Only 8% of positive samples presented multiple viral coinfections, with one case positive for 5 different viruses. One of the viruses detected, WU Polyomavirus, was only found in coinfections with other respiratory viruses, confirming previously reported of high coinfection rates for this specific virus [33]. After comparing the transcriptomic changes driven by the most frequently isolated viruses (CoV, rhinovirus, influenza, RSV, dual infections with CoV and rhinovirus) and negative baselines, we observed a notable overlap of overexpressed genes. Many of the shared overexpressed genes have also been previously identified in SARS-CoV-2 infections [34] and across multiple seasonal respiratory viruses [8,17,30,35]. Rather than qualitative differences across viruses, we detected differences in the magnitude of differential expression of shared DE genes. Infection with influenza virus, for which the vaccination status of participants was unknown, was associated with stronger signal compared to infection with the other viruses. Specifically, CXCL11, RSAD2, IL6, IFIT1 presented more than 3 times the log fold change in influenza positive compared to rhinovirus positive samples. Other studies have reported a larger transcriptional response to influenza than RSV [9], rhinovirus (Dissanayake and colleagues), and multiple non-influenza respiratory viruses [20,28,30]. We also found increased expression of immune-related pathways in infections with alphacoronaviruses compared with infections with betacoronaviruses. This is particularly interesting as in the same cohort we found higher frequency of re-infections with betacoronavirus compared to alphacoronaviruses [36], suggesting a possible link between repeat exposure to pathogens and host responses. In our cohort, RSV infections presented the smallest up-regulation of immune genes, possibly due to the demographic of RSV infections (all the sequenced RSV–positive samples belonged to adults). Typically, people at risk for severe disease with RSV are infants and older adults, and some studies focusing on these demographics have found stronger effects [7]. Volunteers reported, on a daily basis, the presence and intensity of symptoms related to respiratory infections. These data, together with testing regardless of respiratory symptoms enabled us to characterize the transcriptomic signal of asymptomatic viral respiratory infections. Our results revealed not only that there are significant differences between symptomatic and asymptomatic individuals who are positive for respiratory viruses, but also that the transcriptomic signal of immune-related genes during asymptomatic/mild infection is strongly distinguishable from baseline (samples negative for respiratory viruses). This contrasts with previous studies that found no or little transcriptomic changes in asymptomatic or mild infections [13,17,28,30,37]. This discrepancy may partially depend on the lack of a uniform asymptomatic classification across studies. In our study, we only relied on self-reported symptoms and not signs (fever, for example, was reported as a symptom and did not require measurement) and parents reported symptoms for their small children. There was also very large variability of the average weekly symptom score across participants, with some individuals reporting no symptoms for the whole duration of the study and others with consistently very high scores [23]. To address this potential shortcoming, we ran several sensitivity analyses including different symptom score thresholds for classifying symptomatic and asymptomatic infection to ensure that our results were robust. The different model designs that we analyzed confirmed the presence of a strong transcriptomic response during asymptomatic and mild infections. RNA-seq not only allows measurement and comparison of gene expression among samples, but also can be used to detect exogenous RNAs. In this work, we leveraged the RNA-seq data derived from our volunteers to assess positivity to respiratory viruses (in combination with qPCR results) and to measure the relative abundance of bacteria from the respiratory tract. In the online resource, we have included relative abundance of these bacterial species (bacterial reads relative to all reads not mapping to human, as obtained from the Pandora pipeline), as inferred by running Kracken/Bracken. We found significant transcriptomic interactions between respiratory viruses and different bacteria (S. pneumoniae, S. salivarius, and H. influenzae). Specifically, the abundance of these bacteria in samples positive for respiratory viruses was associated with higher expression of genes related to lymphocyte activation and granulocyte activity (e.g., phagocytosis, neutrophil activation) than in the absence of virus. We also found that the abundance of S. pneumoniae in samples from symptomatic individuals infected with respiratory viruses had higher expression of adaptive immune response and NK cell activation pathways than asymptomatic ones. Previous studies in mice and cellular cultures have searched for changes in immune response driven by coinfection by respiratory viruses (e.g., influenza) and bacteria such as S. pneumoniae. They have found a synergistic activation of proinflammatory cytokines that would lead to accumulation of immune cells such as neutrophils [38–42]. The findings that we obtained from our cohort are thus in line with these works and evidence interactions related to immune response between viral infections and the presence of common bacteria from the respiratory tract. While previous studies have mostly focused on the interaction between viral infections and S. pneumonia, we have demonstrated that similar interactions occur with other respiratory bacteria. Our public RNA-seq dataset will enable other investigators to identify RNA reads from other organisms of interest. One limitation of our study is that we did not account for medicine uptake during the course of the study. Specifically, antimicrobials use may have impacted the abundance of bacterial species and anti-inflammatory agents may have affected expression profile of target genes [43]. Vaccination status of the participants was also not accounted for. In summary, we present a cohort study, consisting of hundreds of samples, that depicts the transcriptional changes driven by respiratory viral infection. In addition to the biological data derived from RNA-seq, for every volunteer we have collected information on other critical variables including age, sex, and daily symptoms. We have compiled these data to build a publicly available, user-friendly web-based resource where any user can compare, longitudinally over the course of 19 months, patterns of viral positivity, symptomatology and transcriptomic changes for the individuals enrolled. [END] --- [1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002089 Published and (C) by PLOS One Content appears here under this condition or license: Creative Commons - Attribution BY 4.0. via Magical.Fish Gopher News Feeds: gopher://magical.fish/1/feeds/news/plosone/