Viral infection engenders bona fide and bystander subsets of lung-resident memory B cells through a permissive mechanism.

Lung-resident memory B cells (MBCs) provide localized protection against reinfection in respiratory airways. Currently, the biology of these cells remains largely unexplored. Here, we combined influenza and SARS-CoV-2 infection with fluorescent-reporter mice to identify MBCs regardless of antigen specificity. We found that two main transcriptionally distinct subsets of MBCs colonized the lung peribronchial niche after infection. These subsets arose from different progenitors and were both class switched, somatically mutated, and intrinsically biased in their differentiation fate toward plasma cells. Combined analysis of antigen specificity and B cell receptor repertoire segregated these subsets into "bona fide" virus-specific MBCs and "bystander" MBCs with no apparent specificity for eliciting viruses generated through an alternative permissive process. Thus, diverse transcriptional programs in MBCs are not linked to specific effector fates but rather to divergent strategies of the immune system to simultaneously provide rapid protection from reinfection while diversifying the initial B cell repertoire.

Meningeal macrophages protect against viral neuroinfection.

The surface of the central nervous system (CNS) is protected by the meninges, which contain a dense network of meningeal macrophages (MMs). Here, we examined the role of tissue-resident MM in viral infection. MHC-II- MM were abundant neonatally, whereas MHC-II+ MM appeared over time. These barrier macrophages differentially responded to in vivo peripheral challenges such as LPS, SARS-CoV-2, and lymphocytic choriomeningitis virus (LCMV). Peripheral LCMV infection, which was asymptomatic, led to a transient infection and activation of the meninges. Mice lacking macrophages but conserving brain microglia, or mice bearing macrophage-specific deletion of Stat1 or Ifnar, exhibited extensive viral spread into the CNS. Transcranial pharmacological depletion strategies targeting MM locally resulted in several areas of the meninges becoming infected and fatal meningitis. Low numbers of MHC-II+ MM, which is seen upon LPS challenge or in neonates, corelated with higher viral load upon infection. Thus, MMs protect against viral infection and may present targets for therapeutic manipulation.

Human germinal center transcriptional programs are de-synchronized in B cell lymphoma.

Most adult B cell lymphomas originate from germinal center (GC) B cells, but it is unclear to what extent B cells in overt lymphoma retain the functional dynamics of GC B cells or are blocked at a particular stage of the GC reaction. Here we used integrative single-cell analysis of phenotype, gene expression and variable-region sequence of the immunoglobulin heavy-chain locus to track the characteristic human GC B cell program in follicular lymphoma B cells. By modeling the cyclic continuum of GC B cell transitional states, we identified characteristic patterns of synchronously expressed gene clusters. GC-specific gene-expression synchrony was lost in single lymphoma B cells. However, distinct follicular lymphoma-specific cell states co-existed within single patient biopsies. Our data show that lymphoma B cells are not blocked in a GC B cell state but might adopt new dynamic modes of functional diversity, which opens the possibility of novel definitions of lymphoma identity.

Reticular Fibroblasts Expressing the Transcription Factor WT1 Define a Stromal Niche that Maintains and Replenishes Splenic Red Pulp Macrophages.

Located within red pulp cords, splenic red pulp macrophages (RPMs) are constantly exposed to the blood flow, clearing senescent red blood cells (RBCs) and recycling iron from hemoglobin. Here, we studied the mechanisms underlying RPM homeostasis, focusing on the involvement of stromal cells as these cells perform anchoring and nurturing macrophage niche functions in lymph nodes and liver. Microscopy revealed that RPMs are embedded in a reticular meshwork of red pulp fibroblasts characterized by the expression of the transcription factor Wilms' Tumor 1 (WT1) and colony stimulating factor 1 (CSF1). Conditional deletion of Csf1 in WT1+ red pulp fibroblasts, but not white pulp fibroblasts, drastically altered the RPM network without altering circulating CSF1 levels. Upon RPM depletion, red pulp fibroblasts transiently produced the monocyte chemoattractants CCL2 and CCL7, thereby contributing to the replenishment of the RPM network. Thus, red pulp fibroblasts anchor and nurture RPM, a function likely conserved in humans.

High-Dimensional Single-Cell Analysis Identifies Organ-Specific Signatures and Conserved NK Cell Subsets in Humans and Mice.

Natural killer (NK) cells are innate lymphoid cells (ILCs) involved in antimicrobial and antitumoral responses. Several NK cell subsets have been reported in humans and mice, but their heterogeneity across organs and species remains poorly characterized. We assessed the diversity of human and mouse NK cells by single-cell RNA sequencing on thousands of individual cells isolated from spleen and blood. Unbiased transcriptional clustering revealed two distinct signatures differentiating between splenic and blood NK cells. This analysis at single-cell resolution identified three subpopulations in mouse spleen and four in human spleen, and two subsets each in mouse and human blood. A comparison of transcriptomic profiles within and between species highlighted the similarity of the two major subsets, NK1 and NK2, across organs and species. This unbiased approach provides insight into the biology of NK cells and establishes a rationale for the translation of mouse studies to human physiology and disease.

Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests.

Understanding the relationship between genetic variations and variations in complex and quantitative phenotypes remains an ongoing challenge. While Genome-wide association studies (GWAS) have become a vital tool for identifying single-locus associations, we lack methods for identifying epistatic interactions. In this article, we propose a novel method for higher-order epistasis detection using mixed effect conditional inference forest (epiMEIF). The proposed method is fitted on a group of single nucleotide polymorphisms (SNPs) potentially associated with the phenotype and the tree structure in the forest facilitates the identification of n-way interactions between the SNPs. Additional testing strategies further improve the robustness of the method. We demonstrate its ability to detect true n-way interactions via extensive simulations in both cross-sectional and longitudinal synthetic datasets. This is further illustrated in an application to reveal epistatic interactions from natural variations of cardiac traits in flies (Drosophila). Overall, the method provides a generalized way to identify higher-order interactions from any GWAS data, thereby greatly improving the detection of the genetic architecture underlying complex phenotypes.

Protein synthesis inhibition and GADD34 control IFN-ß heterogeneous expression in response to dsRNA.

In innate immune responses, induction of type-I interferons (IFNs) prevents virus spreading while viral replication is delayed by protein synthesis inhibition. We asked how cells perform these apparently contradictory activities. Using single fibroblast monitoring by flow cytometry and mathematical modeling, we demonstrate that type-I IFN production is linked to cell's ability to enter dsRNA-activated PKR-dependent translational arrest and then overcome this inhibition by decreasing eIF2α phosphorylation through phosphatase 1c cofactor GADD34 (Ppp1r15a) expression. GADD34 expression, shown here to be dependent on the IRF3 transcription factor, is responsible for a biochemical cycle permitting pulse of IFN synthesis to occur in cells undergoing protein synthesis inhibition. Translation arrest is further demonstrated to be key for anti-viral response by acting synergistically with MAVS activation to amplify TBK1 signaling and IFN-ß mRNA transcription, while GADD34-dependent protein synthesis recovery contributes to the heterogeneous expression of IFN observed in dsRNA-activated cells.

MoonDB 2.0: an updated database of extreme multifunctional and moonlighting proteins.

MoonDB 2.0 (http://moondb.hb.univ-amu.fr/) is a database of predicted and manually curated extreme multifunctional (EMF) and moonlighting proteins, i.e. proteins that perform multiple unrelated functions. We have previously shown that such proteins can be predicted through the analysis of their molecular interaction subnetworks, their functional annotations and their association to distinct groups of proteins that are involved in unrelated functions. In MoonDB 2.0, we updated the set of human EMF proteins (238 proteins), using the latest functional annotations and protein-protein interaction networks. Furthermore, for the first time, we applied our method to four additional model organisms - mouse, fly, worm and yeast - and identified 54 novel EMF proteins in these species. In addition to novel predictions, this update contains 63 human and yeast proteins that were manually curated from literature, including descriptions of moonlighting functions and associated references. Importantly, MoonDB's interface was fully redesigned and improved, and its entries are now cross-referenced in the UniProt Knowledgebase (UniProtKB). MoonDB will be updated once a year with the novel EMF candidates calculated from the latest available protein interactions and functional annotations.

Protein complex scaffolding predicted as a prevalent function of long non-coding RNAs.

The human transcriptome contains thousands of long non-coding RNAs (lncRNAs). Characterizing their function is a current challenge. An emerging concept is that lncRNAs serve as protein scaffolds, forming ribonucleoproteins and bringing proteins in proximity. However, only few scaffolding lncRNAs have been characterized and the prevalence of this function is unknown. Here, we propose the first computational approach aimed at predicting scaffolding lncRNAs at large scale. We predicted the largest human lncRNA-protein interaction network to date using the catRAPID omics algorithm. In combination with tissue expression and statistical approaches, we identified 847 lncRNAs (∼5% of the long non-coding transcriptome) predicted to scaffold half of the known protein complexes and network modules. Lastly, we show that the association of certain lncRNAs to disease may involve their scaffolding ability. Overall, our results suggest for the first time that RNA-mediated scaffolding of protein complexes and modules may be a common mechanism in human cells.

Pasha: a versatile R package for piling chromatin HTS data.

UNLABELLED: We describe an R package designed for processing aligned reads from chromatin-oriented high-throughput sequencing experiments. Pasha (preprocessing of aligned sequences from HTS analyses) allows easy manipulation of aligned reads from short-read sequencing technologies (ChIP-seq, FAIRE-seq, MNase-Seq, …) and offers innovative approaches such as ChIP-seq reads elongation, nucleosome midpoint piling strategy for positioning analyses, or the ability to subset paired-end reads by groups of insert size that can contain biologically relevant information. AVAILABILITY AND IMPLEMENTATION: Pasha is a multi-platform R package, available on CRAN repositories under GPL-3 license (https://cran.r-project.org/web/packages/Pasha/). CONTACTS: rfenouil@gmail.com or jean-christophe.andrau@igmm.cnrs.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BubbleGUM: automatic extraction of phenotype molecular signatures and comprehensive visualization of multiple Gene Set Enrichment Analyses.

BACKGROUND: Recent advances in the analysis of high-throughput expression data have led to the development of tools that scaled-up their focus from single-gene to gene set level. For example, the popular Gene Set Enrichment Analysis (GSEA) algorithm can detect moderate but coordinated expression changes of groups of presumably related genes between pairs of experimental conditions. This considerably improves extraction of information from high-throughput gene expression data. However, although many gene sets covering a large panel of biological fields are available in public databases, the ability to generate home-made gene sets relevant to one's biological question is crucial but remains a substantial challenge to most biologists lacking statistic or bioinformatic expertise. This is all the more the case when attempting to define a gene set specific of one condition compared to many other ones. Thus, there is a crucial need for an easy-to-use software for generation of relevant home-made gene sets from complex datasets, their use in GSEA, and the correction of the results when applied to multiple comparisons of many experimental conditions. RESULT: We developed BubbleGUM (GSEA Unlimited Map), a tool that allows to automatically extract molecular signatures from transcriptomic data and perform exhaustive GSEA with multiple testing correction. One original feature of BubbleGUM notably resides in its capacity to integrate and compare numerous GSEA results into an easy-to-grasp graphical representation. We applied our method to generate transcriptomic fingerprints for murine cell types and to assess their enrichments in human cell types. This analysis allowed us to confirm homologies between mouse and human immunocytes. CONCLUSIONS: BubbleGUM is an open-source software that allows to automatically generate molecular signatures out of complex expression datasets and to assess directly their enrichment by GSEA on independent datasets. Enrichments are displayed in a graphical output that helps interpreting the results. This innovative methodology has recently been used to answer important questions in functional genomics, such as the degree of similarities between microarray datasets from different laboratories or with different experimental models or clinical cohorts. BubbleGUM is executable through an intuitive interface so that both bioinformaticians and biologists can use it. It is available at http://www.ciml.univ-mrs.fr/applications/BubbleGUM/index.html .

ShIVA: a user-friendly and interactive interface giving biologists control over their single-cell RNA-seq data.

Single-cell technologies have revolutionised biological research and applications. As they continue to evolve with multi-omics and spatial resolution, analysing single-cell datasets is becoming increasingly complex. For biologists lacking expert data analysis resources, the problem is even more crucial, even for the simplest single-cell transcriptomics datasets. We propose ShIVA, an interface for the analysis of single-cell RNA-seq and CITE-seq data specifically dedicated to biologists. Intuitive, iterative and documented by video tutorials, ShIVA allows biologists to follow a robust and reproducible analysis process, mostly based on the Seurat v4 R package, to fully explore and quantify their dataset, to produce useful figures and tables and to export their work to allow more complex analyses performed by experts.

Characterizing intergenic transcription at RNA polymerase II binding sites in normal and cancer tissues.

Intergenic transcription in normal and cancerous tissues is pervasive but incompletely understood. To investigate this, we constructed an atlas of over 180,000 consensus RNA polymerase II (RNAPII)-bound intergenic regions from 900 RNAPII chromatin immunoprecipitation sequencing (ChIP-seq) experiments in normal and cancer samples. Through unsupervised analysis, we identified 51 RNAPII consensus clusters, many of which mapped to specific biotypes and revealed tissue-specific regulatory signatures. We developed a meta-clustering methodology to integrate our RNAPII atlas with active transcription across 28,797 RNA sequencing (RNA-seq) samples from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Encyclopedia of DNA Elements (ENCODE). This analysis revealed strong tissue- and disease-specific interconnections between RNAPII occupancy and transcriptional activity. We demonstrate that intergenic transcription at RNAPII-bound regions is a novel per-cancer and pan-cancer biomarker. This biomarker displays genomic and clinically relevant characteristics, distinguishing cancer subtypes and linking to overall survival. Our results demonstrate the effectiveness of coherent data integration to uncover intergenic transcriptional activity in normal and cancer tissues.

ChagasDB: 80 years of publicly available data on the molecular host response to Trypanosoma cruzi infection in a single database.

Chagas disease is a parasitical disease caused by Trypanosoma cruzi which affects ∼7 million people worldwide. Per year, ∼10 000 people die from this pathology. Indeed, ∼30% of humans develop severe chronic forms, including cardiac, digestive or neurological disorders, for which there is still no treatment. In order to facilitate research on Chagas disease, a manual curation of all papers corresponding to 'Chagas disease' referenced on PubMed has been performed. All deregulated molecules in hosts (all mammals, humans, mice or others) following T. cruzi infection were retrieved and included in a database, named ChagasDB. A website has been developed to make this database accessible to all. In this article, we detail the construction of this database, its contents and how to use it. Database URL https://chagasdb.tagc.univ-amu.fr.

Peyer's patch phagocytes acquire specific transcriptional programs that influence their maturation and activation profiles.

Peyer's patches (PPs) are secondary lymphoid organs in contact with the external environment via the intestinal lumen, thus combining antigen sampling and immune response initiation sites. Therefore, they provide a unique opportunity to study the entire process of phagocyte differentiation and activation in vivo. Here, we deciphered the transcriptional and spatial landscape of PP phagocyte populations from their emergence in the tissue to their final maturation state at homeostasis and under stimulation. Activation of monocyte-derived Lysozyme-expressing dendritic cells (LysoDCs) differs from that of macrophages by their upregulation of conventional DC (cDC) signature genes such as Ccr7 and downregulation of typical monocyte-derived cell genes such as Cx3cr1. We identified gene sets that distinguish PP cDCs from the villus ones and from LysoDCs. We also identified key immature, early, intermediate, and late maturation markers of PP phagocytes. Finally, exploiting the ability of the PP interfollicular region to host both villous and subepithelial dome emigrated cDCs, we showed that the type of stimulus, the subset, but also the initial location of cDCs shape their activation profile and thus direct the immune response. Our study highlights the importance of targeting the right phagocyte subset at the right place and time to manipulate the immune response.

Genetic architecture of natural variation of cardiac performance from flies to humans.

Deciphering the genetic architecture of human cardiac disorders is of fundamental importance but their underlying complexity is a major hurdle. We investigated the natural variation of cardiac performance in the sequenced inbred lines of the Drosophila Genetic Reference Panel (DGRP). Genome-wide associations studies (GWAS) identified genetic networks associated with natural variation of cardiac traits which were used to gain insights as to the molecular and cellular processes affected. Non-coding variants that we identified were used to map potential regulatory non-coding regions, which in turn were employed to predict transcription factors (TFs) binding sites. Cognate TFs, many of which themselves bear polymorphisms associated with variations of cardiac performance, were also validated by heart-specific knockdown. Additionally, we showed that the natural variations associated with variability in cardiac performance affect a set of genes overlapping those associated with average traits but through different variants in the same genes. Furthermore, we showed that phenotypic variability was also associated with natural variation of gene regulatory networks. More importantly, we documented correlations between genes associated with cardiac phenotypes in both flies and humans, which supports a conserved genetic architecture regulating adult cardiac function from arthropods to mammals. Specifically, roles for PAX9 and EGR2 in the regulation of the cardiac rhythm were established in both models, illustrating that the characteristics of natural variations in cardiac function identified in Drosophila can accelerate discovery in humans.

Blood DNA methylation marks discriminate Chagas cardiomyopathy disease clinical forms.

Chagas disease is a parasitic disease from South America, affecting around 7 million people worldwide. Decades after the infection, 30% of people develop chronic forms, including Chronic Chagas Cardiomyopathy (CCC), for which no treatment exists. Two stages characterized this form: the moderate form, characterized by a heart ejection fraction (EF) ≥ 0.4, and the severe form, associated to an EF < 0.4. We propose two sets of DNA methylation biomarkers which can predict in blood CCC occurrence, and CCC stage. This analysis, based on machine learning algorithms, makes predictions with more than 95% accuracy in a test cohort. Beyond their predictive capacity, these CpGs are located near genes involved in the immune response, the nervous system, ion transport or ATP synthesis, pathways known to be deregulated in CCCs. Among these genes, some are also differentially expressed in heart tissues. Interestingly, the CpGs of interest are tagged to genes mainly involved in nervous and ionic processes. Given the close link between methylation and gene expression, these lists of CpGs promise to be not only good biomarkers, but also good indicators of key elements in the development of this pathology.

Epigenetic regulation of transcription factor binding motifs promotes Th1 response in Chagas disease cardiomyopathy.

Chagas disease, caused by the protozoan Trypanosoma cruzi, is an endemic parasitic disease of Latin America, affecting 7 million people. Although most patients are asymptomatic, 30% develop complications, including the often-fatal Chronic Chagasic Cardiomyopathy (CCC). Although previous studies have demonstrated some genetic deregulations associated with CCCs, the causes of their deregulations remain poorly described. Based on bulk RNA-seq and whole genome DNA methylation data, we investigated the genetic and epigenetic deregulations present in the moderate and severe stages of CCC. Analysis of heart tissue gene expression profile allowed us to identify 1407 differentially expressed transcripts (DEGs) specific from CCC patients. A tissue DNA methylation analysis done on the same tissue has permitted the identification of 92 regulatory Differentially Methylated Regions (DMR) localized in the promoter of DEGs. An in-depth study of the transcription factors binding sites (TFBS) in the DMRs corroborated the importance of TFBS's DNA methylation for gene expression in CCC myocardium. TBX21, RUNX3 and EBF1 are the transcription factors whose binding motif appears to be affected by DNA methylation in the largest number of genes. By combining both transcriptomic and methylomic analysis on heart tissue, and methylomic analysis on blood, 4 biological processes affected by severe CCC have been identified, including immune response, ion transport, cardiac muscle processes and nervous system. An additional study on blood methylation of moderate CCC samples put forward the importance of ion transport and nervous system in the development of the disease.

MetamORF: a repository of unique short open reading frames identified by both experimental and computational approaches for gene and metagene analyses.

The development of high-throughput technologies revealed the existence of non-canonical short open reading frames (sORFs) on most eukaryotic ribonucleic acids. They are ubiquitous genetic elements conserved across species and suspected to be involved in numerous cellular processes. MetamORF (https://metamorf.hb.univ-amu.fr/) aims to provide a repository of unique sORFs identified in the human and mouse genomes with both experimental and computational approaches. By gathering publicly available sORF data, normalizing them and summarizing redundant information, we were able to identify a total of 1 162 675 unique sORFs. Despite the usual characterization of ORFs as short, upstream or downstream, there is currently no clear consensus regarding the definition of these categories. Thus, the data have been reprocessed using a normalized nomenclature. MetamORF enables new analyses at locus, gene, transcript and ORF levels, which should offer the possibility to address new questions regarding sORF functions in the future. The repository is available through an user-friendly web interface, allowing easy browsing, visualization, filtering over multiple criteria and export possibilities. sORFs can be searched starting from a gene, a transcript and an ORF ID, looking in a genome area or browsing the whole repository for a species. The database content has also been made available through track hubs at UCSC Genome Browser. Finally, we demonstrated an enrichment of genes harboring upstream ORFs among genes expressed in response to reticular stress. Database URL  https://metamorf.hb.univ-amu.fr/.

Type 1 Innate Lymphoid Cells Limit the Antitumoral Immune Response.

Natural killer (NK) cells are known to be able to kill established tumor cell lines, but important caveats remain regarding their roles in the detection and elimination of developing primary tumors. Using a genetic model of selective ILC1 and NK cell deficiency, we showed that these cells were dispensable for tumor immunosurveillance and immunoediting in the MCA-induced carcinogenesis model. However, we were able to generate primary cell lines derived from MCA-induced tumors with graded sensitivity to NK1.1+ cells (including NK cells and ILC1). This differential sensitivity was associated neither with a modulation of intratumoral NK cell frequency, nor the capacity of tumor cells to activate NK cells. Instead, ILC1 infiltration into the tumor was found to be a critical determinant of NK1.1+ cell-dependent tumor growth. Finally, bulk tumor RNAseq analysis identified a gene expression signature associated with tumor sensitivity to NK1.1+ cells. ILC1 therefore appear to play an active role in inhibiting the antitumoral immune response, prompting to evaluate the differential tumor infiltration of ILC1 and NK cells in patients to optimize the harnessing of immunity in cancer therapies.