Consensus gene modules strategy identifies candidate blood-based biomarkers for primary Sjögren's disease.

Primary Sjögren disease (pSD) is an autoimmune disease characterized by lymphoid infiltration of exocrine glands leading to dryness of the mucosal surfaces and by the production of autoantibodies. The pathophysiology of pSD remains elusive and no treatment with demonstrated efficacy is available yet. To better understand the biology underlying pSD heterogeneity, we aimed at identifying Consensus gene Modules (CMs) that summarize the high-dimensional transcriptomic data of whole blood samples in pSD patients. We performed unsupervised gene classification on four data sets and identified thirteen CMs. We annotated and interpreted each of these CMs as corresponding to cell type abundances or biological functions by using gene set enrichment analyses and transcriptomic profiles of sorted blood cell subsets. Correlation with independently measured cell type abundances by flow cytometry confirmed these annotations. We used these CMs to reconcile previously proposed patient stratifications of pSD. Importantly, we showed that the expression of modules representing lymphocytes and erythrocytes before treatment initiation is associated with response to hydroxychloroquine and leflunomide combination therapy in a clinical trial. These consensus modules will help the identification and translation of blood-based predictive biomarkers for the treatment of pSD.

PLEX.I: a tool to discover features in multiplex networks that reflect clinical variation.

Molecular profiling technologies, such as RNA sequencing, offer new opportunities to better discover and understand the molecular networks involved in complex biological processes. Clinically important variations of diseases, or responses to treatment, are often reflected, or even caused, by the dysregulation of molecular interaction networks specific to particular network regions. In this work, we propose the R package PLEX.I, that allows quantifying and testing variation in the direct neighborhood of a given node between networks corresponding to different conditions or states. We illustrate PLEX.I in two applications in which we discover variation that is associated with different responses to tamoxifen treatment and to sex-specific responses to bacterial stimuli. In the first case, PLEX.I analysis identifies two known pathways i) that have already been implicated in the same context as the tamoxifen mechanism of action, and ii) that would have not have been identified using classical differential gene expression analysis.

Capturing the dynamics of microbial interactions through individual-specific networks.

Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.

Identification of new candidate drugs for primary Sjögren's syndrome using a drug repurposing transcriptomic approach.

OBJECTIVES: To date, no immunomodulatory drug has demonstrated its efficacy in primary SS (pSS). We sought to analyse potential commonalities between pSS transcriptomic signatures and signatures of various drugs or specific knock-in or knock-down genes. METHODS: Gene expression from peripheral blood samples of patients with pSS was compared with that of healthy controls in two cohorts and three public databases. In each of the five datasets, we analysed the 150 most up- and downregulated genes between pSS patients and controls with regard to the differentially expressed genes resulting from the biological action on nine cell lines of 2837 drugs, 2160 knock-in and 3799 knock-down genes in the Connectivity Map database. RESULTS: We analysed 1008 peripheral blood transcriptomes from five independent studies (868 patients with pSS and 140 healthy controls). Eleven drugs could represent potential candidate drugs, with histone deacetylases and PI3K inhibitors among the most significantly associated. Twelve knock-in genes were associated with a pSS-like profile and 23 knock-down genes were associated with a pSS-revert profile. Most of those genes (28/35, 80%) were interferon-regulated. CONCLUSION: This first drug repositioning transcriptomic approach in SS confirms the interest of targeting interferons and identifies histone deacetylases and PI3K inhibitors as potential therapeutic targets.

Variability of Primary Sjögren's Syndrome Is Driven by Interferon-α and Interferon-α Blood Levels Are Associated With the Class II HLA-DQ Locus.

OBJECTIVE: Primary Sjögren's syndrome (SS) is the second most frequent systemic autoimmune disease, affecting 0.1% of the general population. To characterize the molecular and clinical variabilities among patients with primary SS, we integrated transcriptomic, proteomic, cellular, and genetic data with clinical phenotypes in a cohort of 351 patients with primary SS. METHODS: We analyzed blood transcriptomes and genotypes of 351 patients with primary SS who were participants in a multicenter prospective clinical cohort. We replicated the transcriptome analysis in 3 independent cohorts (n = 462 patients). We determined circulating interferon-α (IFNα) and IFNγ protein concentrations using digital single molecular arrays (Simoa). RESULTS: Transcriptome analysis of the prospective cohort showed a strong IFN gene signature in more than half of the patients; this finding was replicated in the 3 independent cohorts. Because gene expression analysis did not discriminate between type I IFN and type II IFN, we used Simoa to demonstrate that the IFN transcriptomic signature was driven by circulating IFNα and not by IFNγ protein levels. IFNα protein levels, detectable in 75% of patients, were significantly associated with clinical and immunologic features of primary SS disease activity at enrollment and with increased frequency of systemic complications over the 5-year follow-up. Genetic analysis revealed a significant association between IFNα protein levels, a major histocompatibility (MHC) class II haplotype, and anti-SSA antibody. Additional cellular analysis revealed that an MHC class II HLA-DQ locus acts through up-regulation of HLA class II molecules on conventional dendritic cells. CONCLUSION: We identified the predominance of IFNα as a driver of primary SS variability, with IFNα demonstrating an association with HLA gene polymorphisms.

A shared disease-associated oligodendrocyte signature among multiple CNS pathologies.

Alzheimer's disease (AD) is a complex neurodegenerative disease, perturbing neuronal and non-neuronal cell populations. In this study, using single-cell transcriptomics, we mapped all non-immune, non-neuronal cell populations in wild-type and AD model (5xFAD) mouse brains. We identified an oligodendrocyte state that increased in association with brain pathology, which we termed disease-associated oligodendrocytes (DOLs). In a murine model of amyloidosis, DOLs appear long after plaque accumulation, and amyloid-beta (Aß) alone was not sufficient to induce the DOL signature in vitro. DOLs could be identified in a mouse model of tauopathy and in other murine neurodegenerative and autoimmune inflammatory conditions, suggesting a common response to severe pathological conditions. Using quantitative spatial analysis of mouse and postmortem human brain tissues, we found that oligodendrocytes expressing a key DOL marker (SERPINA3N/SERPINA3 accordingly) are present in the cortex in areas of brain damage and are enriched near Aß plaques. In postmortem human brain tissue, the expression level of this marker correlated with cognitive decline. Altogether, this study uncovers a shared signature of oligodendrocytes in central nervous system pathologies.

An overview of machine learning methods for monotherapy drug response prediction.

For an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses. Here, we provide a uniquely wide and deep systematic review of the rapidly evolving literature on monotherapy drug response prediction, with a systematic characterization and classification that comprises more than 70 ML methods in 13 subclasses, their input and output data types, modes of evaluation, and code and software availability. ML experts are provided with a fundamental understanding of the biological problem, and how ML methods are configured for it. Biologists and biomedical researchers are introduced to the basic principles of applicable ML methods, and their application to the problem of drug response prediction. We also provide systematic overviews of commonly used data sources used for training and evaluation methods.

Differential levels of IFNα subtypes in autoimmunity and viral infection.

Type I interferons are essential for host response to viral infections, while dysregulation of their response can result in autoinflammation or autoimmunity. Among IFNα (alpha) responses, 13 subtypes exist that signal through the same receptor, but have been reported to have different effector functions. However, the lack of available tools for discriminating these closely related subtypes, in particular at the protein level, has restricted the study of their differential roles in disease. We developed a digital ELISA with specificity and high sensitivity for the IFNα2 subtype. Application of this assay, in parallel with our previously described pan-IFNα assay, allowed us to study different IFNα protein responses following cellular stimulation and in diverse patient cohorts. We observed different ratios of IFNα protein responses between viral infection and autoimmune patients. This analysis also revealed a small percentage of autoimmune patients with high IFNα2 protein measurements but low pan-IFNα measurements. Correlation with an ISG score and functional activity showed that in this small sub group of patients, IFNα2 protein measurements did not reflect its biological activity. This unusual phenotype was partly explained by the presence of anti-IFNα auto-antibodies in a subset of autoimmune patients. This study reports ultrasensitive assays for the study of IFNα proteins in patient samples and highlights the insights that can be obtained from the use of multiple phenotypic readouts in translational and clinical studies.

Deciphering the state of immune silence in fatal COVID-19 patients.

Since the beginning of the SARS-CoV-2 pandemic, COVID-19 appeared as a unique disease with unconventional tissue and systemic immune features. Here we show a COVID-19 immune signature associated with severity by integrating single-cell RNA-seq analysis from blood samples and broncho-alveolar lavage fluids with clinical, immunological and functional ex vivo data. This signature is characterized by lung accumulation of naïve lymphoid cells associated with a systemic expansion and activation of myeloid cells. Myeloid-driven immune suppression is a hallmark of COVID-19 evolution, highlighting arginase-1 expression with immune regulatory features of monocytes. Monocyte-dependent and neutrophil-dependent immune suppression loss is associated with fatal clinical outcome in severe patients. Additionally, our analysis shows a lung CXCR6+ effector memory T cell subset is associated with better prognosis in patients with severe COVID-19. In summary, COVID-19-induced myeloid dysregulation and lymphoid impairment establish a condition of 'immune silence' in patients with critical COVID-19.

Bystander IFN-γ activity promotes widespread and sustained cytokine signaling altering the tumor microenvironment.

The cytokine IFN-γ produced by tumor-reactive T cells is a key effector molecule with pleiotropic effects during anti-tumor immune responses. While IFN-γ production is targeted at the immunological synapse, its spatiotemporal activity within the tumor remains elusive. Here, we report that while IFN-γ secretion requires local antigen recognition, IFN-γ diffuses extensively to alter the tumor microenvironment in distant areas. Using intravital imaging and a reporter for STAT1 translocation, we provide evidence that T cells mediate sustained IFN-γ signaling in remote tumor cells. Furthermore, tumor phenotypic alterations required several hours of exposure to IFN-γ, a feature that disfavored local IFN-γ activity over diffusion and bystander activity. Finally, single-cell RNA-seq data from melanoma patients also suggested bystander IFN-γ activity in human tumors. Thus, tumor-reactive T cells act collectively to create large cytokine fields that profoundly modify the tumor microenvironment.

Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients.

Viruses are a constant threat to global health as highlighted by the current COVID-19 pandemic. Currently, lack of data underlying how the human host interacts with viruses, including the SARS-CoV-2 virus, limits effective therapeutic intervention. We introduce Viral-Track, a computational method that globally scans unmapped single-cell RNA sequencing (scRNA-seq) data for the presence of viral RNA, enabling transcriptional cell sorting of infected versus bystander cells. We demonstrate the sensitivity and specificity of Viral-Track to systematically detect viruses from multiple models of infection, including hepatitis B virus, in an unsupervised manner. Applying Viral-Track to bronchoalveloar-lavage samples from severe and mild COVID-19 patients reveals a dramatic impact of the virus on the immune system of severe patients compared to mild cases. Viral-Track detects an unexpected co-infection of the human metapneumovirus, present mainly in monocytes perturbed in type-I interferon (IFN)-signaling. Viral-Track provides a robust technology for dissecting the mechanisms of viral-infection and pathology.

LGP2 binds to PACT to regulate RIG-I- and MDA5-mediated antiviral responses.

The retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs) RIG-I, MDA5, and LGP2 stimulate inflammatory and antiviral responses by sensing nonself RNA molecules produced during viral replication. Here, we investigated how LGP2 regulates the RIG-I- and MDA5-dependent induction of type I interferon (IFN) signaling and showed that LGP2 interacted with different components of the RNA-silencing machinery. We identified a direct protein-protein interaction between LGP2 and the IFN-inducible, double-stranded RNA binding protein PACT. The LGP2-PACT interaction was mediated by the regulatory C-terminal domain of LGP2 and was necessary for inhibiting RIG-I-dependent responses and for amplifying MDA5-dependent responses. We described a point mutation within LGP2 that disrupted the LGP2-PACT interaction and led to the loss of LGP2-mediated regulation of RIG-I and MDA5 signaling. These results suggest a model in which the LGP2-PACT interaction regulates the inflammatory responses mediated by RIG-I and MDA5 and enables the cellular RNA-silencing machinery to coordinate with the innate immune response.

Single-Cell Analysis of Diverse Pathogen Responses Defines a Molecular Roadmap for Generating Antigen-Specific Immunity.

The immune system generates pathogen-tailored responses. The precise innate immune cell types and pathways that direct robust adaptive immune responses have not been fully characterized. By using fluorescent pathogens combined with massively parallel single-cell RNA-seq, we comprehensively characterized the initial 48 h of the innate immune response to diverse pathogens. We found that across all pathogens tested, most of the lymph node cell types and states showed little pathogen specificity. In contrast, the rare antigen-positive cells displayed pathogen-specific transcriptional programs as early as 24 h after immunization. In addition, mycobacteria activated a specific NK-driven IFNγ response. Depletion of NK cells and IFNγ showed that IFNγ initiated a monocyte-specific signaling cascade, leading to the production of major chemokines and cytokines that promote Th1 development. Our systems immunology approach sheds light on early events in innate immune responses and may help further development of safe and efficient vaccines.

The Cytoscape Automation app article collection.

Cytoscape is the premiere platform for interactive analysis, integration and visualization of network data. While Cytoscape itself delivers much basic functionality, it relies on community-written apps to deliver specialized functions and analyses. To date, Cytoscape's CyREST feature has allowed researchers to write workflows that call basic Cytoscape functions, but provides no access to its high value app-based functions. With Cytoscape Automation, workflows can now call apps that have been upgraded to expose their functionality. This article collection is a resource to assist readers in quickly and economically leveraging such apps in reproducible workflows that scale independently to large data sets and production runs.

A Blood RNA Signature Detecting Severe Disease in Young Dengue Patients at Hospital Arrival.

Background: Early detection of severe dengue can improve patient care and survival. To date, no reliable single-gene biomarker exists. We hypothesized that robust multigene signatures exist. Methods: We performed a prospective study on Cambodian dengue patients aged 4 to 22 years. Peripheral blood mononuclear cells (PBMCs) were obtained at hospital admission. We analyzed 42 transcriptomic profiles of patients with secondary dengue infected with dengue serotype 1. Our novel signature discovery approach controls the number of included genes and captures nonlinear relationships between transcript concentrations and severity. We evaluated the signature on secondary cases infected with different serotypes using 2 datasets: 22 PBMC samples from additional patients in our cohort and 32 whole blood samples from an independent cohort. Results: We identified an 18-gene signature for detecting severe dengue in patients with secondary infection upon hospital admission with a sensitivity of 0.93 (95% confidence interval [CI], .82-.98), specificity of 0.67 (95% CI, .53-.80), and area under the receiver operating characteristic curve (AUC) of 0.86 (95% CI, .75-.97). At validation, the signature had empirical AUCs of 0.85 (95% CI, .69-1.00) and 0.83 (95% CI, .68-.98) for the PBMCs and whole blood datasets, respectively. Conclusions: The signature could detect severe dengue in secondary-infected patients upon hospital admission. Its genes offer new insights into the pathogenesis of severe dengue.

Network module identification-A widespread theoretical bias and best practices.

Biological processes often manifest themselves as coordinated changes across modules, i.e., sets of interacting genes. Commonly, the high dimensionality of genome-scale data prevents the visual identification of such modules, and straightforward computational search through a set of known pathways is a limited approach. Therefore, tools for the data-driven, computational, identification of modules in gene interaction networks have become popular components of visualization and visual analytics workflows. However, many such tools are known to result in modules that are large, and therefore hard to interpret biologically. Here, we show that the empirically known tendency towards large modules can be attributed to a statistical bias present in many module identification tools, and discuss possible remedies from a mathematical perspective. In the current absence of a straightforward practical solution, we outline our view of best practices for the use of the existing tools.

Common and phylogenetically widespread coding for peptides by bacterial small RNAs.

BACKGROUND: While eukaryotic noncoding RNAs have recently received intense scrutiny, it is becoming clear that bacterial transcription is at least as pervasive. Bacterial small RNAs and antisense RNAs (sRNAs) are often assumed to be noncoding, due to their lack of long open reading frames (ORFs). However, there are numerous examples of sRNAs encoding for small proteins, whether or not they also have a regulatory role at the RNA level. METHODS: Here, we apply flexible machine learning techniques based on sequence features and comparative genomics to quantify the prevalence of sRNA ORFs under natural selection to maintain protein-coding function in 14 phylogenetically diverse bacteria. Importantly, we quantify uncertainty in our predictions, and follow up on them using mass spectrometry proteomics and comparison to datasets including ribosome profiling. RESULTS: A majority of annotated sRNAs have at least one ORF between 10 and 50 amino acids long, and we conservatively predict that 409±191.7 unannotated sRNA ORFs are under selection to maintain coding (mean estimate and 95% confidence interval), an average of 29 per species considered here. This implies that overall at least 10.3±0.5% of sRNAs have a coding ORF, and in some species around 20% do. 165±69 of these novel coding ORFs have some antisense overlap to annotated ORFs. As experimental validation, many of our predictions are translated in published ribosome profiling data and are identified via mass spectrometry shotgun proteomics. B. subtilis sRNAs with coding ORFs are enriched for high expression in biofilms and confluent growth, and S. pneumoniae sRNAs with coding ORFs are involved in virulence. sRNA coding ORFs are enriched for transmembrane domains and many are predicted novel components of type I toxin/antitoxin systems. CONCLUSIONS: We predict over two dozen new protein-coding genes per bacterial species, but crucially also quantified the uncertainty in this estimate. Our predictions for sRNA coding ORFs, along with predicted novel type I toxins and tools for sorting and visualizing genomic context, are freely available in a user-friendly format at http://disco-bac.web.pasteur.fr. We expect these easily-accessible predictions to be a valuable tool for the study not only of bacterial sRNAs and type I toxin-antitoxin systems, but also of bacterial genetics and genomics.

Detecting multi-way epistasis in family-based association studies.

The era of genome-wide association studies (GWAS) has led to the discovery of numerous genetic variants associated with disease. Better understanding of whether these or other variants interact leading to differential risk compared with individual marker effects will increase our understanding of the genetic architecture of disease, which may be investigated using the family-based study design. We present M-TDT (the multi-locus transmission disequilibrium test), a tool for detecting family-based multi-locus multi-allelic effects for qualitative or quantitative traits, extended from the original transmission disequilibrium test (TDT). Tests to handle the comparison between additive and epistatic models, lack of independence between markers and multiple offspring are described. Performance of M-TDT is compared with a multifactor dimensionality reduction (MDR) approach designed for investigating families in the hypothesis-free genome-wide setting (the multifactor dimensionality reduction pedigree disequilibrium test, MDR-PDT). Other methods derived from the TDT or MDR to investigate genetic interaction in the family-based design are also discussed. The case of three independent biallelic loci is illustrated using simulations for one- to three-locus alternative hypotheses. M-TDT identified joint-locus effects and distinguished effectively between additive and epistatic models. We showed a practical example of M-TDT based on three genes already known to be implicated in malaria susceptibility. Our findings demonstrate the value of M-TDT in a hypothesis-driven context to test for multi-way epistasis underlying common disease etiology, whereas MDR-PDT-based methods are more appropriate in a hypothesis-free genome-wide setting.