Empowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility

Haynes, Winston; Vallania, Francesco; Liu, Charles; Bongen, Erika; Tomczak, Aurelie; Andres-Terrè, Marta; Lofgren, Shane; Tam, Andrew; Deisseroth, Cole A.; Li, Matthew; Sweeney, Timothy E.; Khatri, Purvesh

doi:10.1142/9789813207813_0015

Cited by 89 publications

(107 citation statements)

References 29 publications

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“…Then, for each disease, the we applied a multi-cohort analysis framework to identify disease gene signatures. This framework 28 has been shown to identify reproducible gene signatures 29 across multiple independent cohorts in different disease conditions, including bacterial and viral infections, organ transplantation, and cancer for identifying diagnostic and prognostic disease signatures, novel drug targets and repurposing FDA-approved drugs 26 , 29 – 34 . Next, we performed a GO enrichment analysis via traditional over-representation statistical methods for each disease gene signature, producing a set of enriched GO terms.…”

Section: Resultsmentioning

confidence: 99%

Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations

Tomczak

Mortensen

Winnenburg

et al. 2018

Sci Rep

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132

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Gene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high throughput molecular data and generating hypotheses about underlying biological phenomena of experiments. However, the two building blocks of this analysis — the ontology and the annotations — evolve rapidly. We used gene signatures derived from 104 disease analyses to systematically evaluate how enrichment analysis results were affected by evolution of the GO over a decade. We found low consistency between enrichment analyses results obtained with early and more recent GO versions. Furthermore, there continues to be a strong annotation bias in the GO annotations where 58% of the annotations are for 16% of the human genes. Our analysis suggests that GO evolution may have affected the interpretation and possibly reproducibility of experiments over time. Hence, researchers must exercise caution when interpreting GO enrichment analyses and should reexamine previous analyses with the most recent GO version.

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Section: Resultsmentioning

confidence: 99%

Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations

Tomczak

Mortensen

Winnenburg

et al. 2018

Sci Rep

Self Cite

132

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“…Having access to detailed phenotypic data can improve the models and allow us to explore other relevant questions in association with the microbial genera. Finally, the clinical definition of PTB may be different depending on the exact obstetrical definition that was used in the individual studies, however, the majority of the cohorts we investigated focused on spontaneous PTB and the signals that are robust despite the potential patient heterogeneity are more likely to be real as we have seen from prior gene expression meta-analysis studies (Dudley et al, 2009;Haynes et al, 2017;Sweeney et al, 2017). As more data becomes publicly available, we hope that additional standardization and meta-data availability will help address some of the aforementioned issues.…”

Section: Discussionmentioning

confidence: 99%

Meta-Analysis of Vaginal Microbiome Data Provides New Insights Into Preterm Birth

et al. 2020

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Preterm birth (PTB) is defined as the birth of an infant before 37 weeks of gestational age. It is the leading cause of perinatal morbidity and mortality worldwide. In this study, we present a comprehensive meta-analysis of vaginal microbiome in PTB. We integrated raw longitudinal 16S rRNA vaginal microbiome data from five independent studies across 3,201 samples and were able to gain new insights into the vaginal microbiome state in women who deliver preterm in comparison to those who deliver at term. We found that women who deliver prematurely show higher within-sample variance in vaginal microbiome abundance, with the most significant difference observed during the first trimester. Modeling the data longitudinally revealed a number of microbial genera as associated with PTB, including several that were previously known and two newly identified by this meta-analysis: Olsenella and Clostridium sensu stricto. New hypotheses emerging from this integrative analysis can lead to novel diagnostics to identify women who are at higher risk for PTB and potentially inform new therapeutic interventions.

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“…Given that aberrant IFN signalling has been implicated in a broad range of infectious or autoimmune diseases, we further asked whether the IFN interactome could be used to interpret existing molecular datasets relevant to human pathologies. We drew on a resource of multi-cohort gene expression meta-analyses for 103 diseases [36,37] to identify genes with reproducible evidence of differential expression in eleven diseases characterized by an elevated IFN transcriptional signature [8] , including viral infections, systemic and discoid lupus erythematosus, rheumatoid arthritis, sarcoidosis, and Sjogren's syndrome. We mapped protein-protein interactions for dozens to hundreds of differentially expressed genes from each disease ( Figure 3I); notably, interactions for many such gene products were identified exclusively in the IFN-stimulated condition.…”

Section: Biological Relevance Of the Ifn Interactomementioning

confidence: 99%

Dynamic rewiring of the human interactome by interferon signalling

Kerr

Skinnider

Madero

et al. 2019

Preprint

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Background:The type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes (ISGs). Transcriptomic and biochemical approaches have established comprehensive catalogues of ISGs across species and cell types, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to delineate the effects of IFN signalling on the human proteome, culminating in the use of protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network. Results:We identified >27,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer ISG protein synthesis.Conclusions : Our map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing ISGs in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease. interaction network in differential and physiologically relevant contexts at the proteome scale represents a longstanding challenge [16] .Here, we apply a combination of quantitative proteomic approaches to chart the molecular landscape of type I IFN signalling, culminating in the use of protein correlation profiling (PCP)[17] to map interferon-induced rearrangements in the human interactome. The resulting protein-protein interaction network, encompassing over 27,000 interactions, reveals widespread rewiring of physical interactions and places known ISGs in an IFN-dependent functional context.We find evidence that the most evolutionarily conserved subset of ISGs are induced to physically interact in response to IFN stimulation, and experimentally validate the role of one such interaction in modulating STAT1 transcription. We develop statistical methods for differential network analysis to characterize interactome rewiring at the functional level, leading us to identify alterations in ribosome composition induced by interferon signalling that selectively downregulate ISG synthesis in order to fine-tune the IFN response. Collectively, this differential network map of the IFN-induced interactome provides a resource to mechanistically dissect the IFN response in the context of viral infection and autoimmune disease. RESULTS Proteome-wide analysis of the type I IFN responseWhereas the transcriptional response to IFN s...

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Empowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility

Cited by 89 publications

References 29 publications

Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations

Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations

Meta-Analysis of Vaginal Microbiome Data Provides New Insights Into Preterm Birth

Dynamic rewiring of the human interactome by interferon signalling

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