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Meiosis requires the formation of programmed DNA double strand breaks (DSBs), essential for fertility and for generating genetic diversity. DSBs are induced by the catalytic activity of the TOPOVIL complex formed by SPO11 and TOPOVIBL. To ensure genomic integrity, DNA cleavage activity is tightly regulated, and several accessory factors (REC114, MEI4, IHO1, and MEI1) are needed for DSB formation in mice. How and when these proteins act is not understood. Here, we show that REC114 is a direct partner of TOPOVIBL, and identify their conserved interacting domains by structural analysis. We then analyse the role of this interaction by monitoring meiotic DSBs in female and male mice carrying point mutations in TOPOVIBL that decrease or disrupt its binding to REC114. In these mutants, DSB activity is strongly reduced genome-wide in oocytes, and only in sub-telomeric regions in spermatocytes. In addition, in mutant spermatocytes, DSB activity is delayed in autosomes. These results suggest that REC114 is a key member of the TOPOVIL catalytic complex, and that the REC114/TOPOVIBL interaction ensures the efficiency and timing of DSB activity. TOPOVIBL and REC114 are required for meiotic DNA double-strand breaks (DSBs). This study shows that TOPOVIBL forms a complex with REC114 and mice carrying mutations that disrupt the interaction show DSB defects with distinct outcomes in males and females.

The intratumoral microenvironment, or stroma, is of major importance in the pathobiology of pancreatic ductal adenocarcinoma (PDA), and specific conditions in the stroma may promote increased cancer aggressiveness. We hypothesized that this heterogeneous and evolving compartment drastically influences tumor cell abilities, which in turn influences PDA aggressiveness through crosstalk that is mediated by extracellular vesicles (EVs). Here, we have analyzed the PDA proteomic stromal signature and identified a contribution of the annexin A6/LDL receptor-related protein 1/thrombospondin 1 (ANXA6/LRP1/TSP1) complex in tumor cell crosstalk. Formation of the ANXA6/LRP1/TSP1 complex was restricted to cancer-associated fibroblasts (CAFs) and required physiopathologic culture conditions that improved tumor cell survival and migration. Increased PDA aggressiveness was dependent on tumor cell-mediated uptake of CAF-derived ANXA6+ EVs carrying the ANXA6/LRP1/TSP1 complex. Depletion of ANXA6 in CAFs impaired complex formation and subsequently impaired PDA and metastasis occurrence, while injection of CAF-derived ANXA6+ EVs enhanced tumorigenesis. We found that the presence of ANXA6+ EVs in serum was restricted to PDA patients and represents a potential biomarker for PDA grade. These findings suggest that CAF-tumor cell crosstalk supported by ANXA6+ EVs is predictive of PDA aggressiveness, highlighting a therapeutic target and potential biomarker for PDA.

Moonlighting proteins are a subclass of multifunctional proteins whose functions are unrelated. Although they may play important roles in cells, there has been no large-scale method to identify them, nor any effort to characterize them as a group. Here, we propose the first method for the identification of ‘extreme multifunctional’ proteins from an interactome as a first step to characterize moonlighting proteins. By combining network topological information with protein annotations, we identify 430 extreme multifunctional proteins (3% of the human interactome). We show that the candidates form a distinct sub-group of proteins, characterized by specific features, which form a signature of extreme multifunctionality. Overall, extreme multifunctional proteins are enriched in linear motifs and less intrinsically disordered than network hubs. We also provide MoonDB, a database containing information on all the candidates identified in the analysis and a set of manually curated human moonlighting proteins. Proteins are sometimes implicated in separate and seemingly unrelated processes, so called moonlighting functions. Here the authors use bioinformatics tools to identify extreme multifunctional proteins and define a signature of extreme multifunctionality.

Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression). However, this diversity is classically not considered and the different interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected to retain more information. Here we assessed aggregation, consensus and multiplex-modularity approaches to detect communities from multiple network sources. By simulating random networks, we demonstrated that the multiplex-modularity method outperforms the aggregation and consensus approaches when network layers are incomplete or heterogeneous in density. Application to a multiplex biological network containing 4 layers of physical or functional interactions allowed recovering communities more accurately annotated than their aggregated counterparts. Overall, taking into account the multiplexity of biological networks leads to better-defined functional modules. A user-friendly graphical software to detect communities from multiplex networks, and corresponding C source codes, are available at GitHub ( https://github.com/gilles-didier/MolTi ).

The high mortality rate in septic shock patients is likely due to environmental and genetic factors, which influence the host response to infection. Two genome-wide association studies (GWAS) on 832 septic shock patients were performed. We used integrative bioinformatic approaches to annotate and prioritize the sepsis-associated single nucleotide polymorphisms (SNPs). An association of 139 SNPs with death based on a false discovery rate of 5% was detected. The most significant SNPs were within the CISH gene involved in cytokine regulation. Among the 139 SNPs associated with death and the 1311 SNPs in strong linkage disequilibrium with them, we investigated 1439 SNPs within non-coding regions to identify regulatory variants. The highest integrative weighted score (IW-score) was obtained for rs143356980, indicating that this SNP is a robust regulatory candidate. The rs143356980 region is located in a non-coding region close to the CISH gene. A CRISPR-Cas9-mediated deletion of this region and specific luciferase assays in K562 cells showed that rs143356980 modulates the enhancer activity in K562 cells. These analyses allowed us to identify several genes associated with death in patients with septic shock. They suggest that genetic variations in key genes, such as CISH, perturb relevant pathways, increasing the risk of death in sepsis patients.

Background Fusobacterium nucleatum is a gram-negative anaerobic species residing in the oral cavity and implicated in several inflammatory processes in the human body. Although F. nucleatum abundance is increased in inflammatory bowel disease subjects and is prevalent in colorectal cancer patients, the causal role of the bacterium in gastrointestinal disorders and the mechanistic details of host cell functions subversion are not fully understood. Results We devised a computational strategy to identify putative secreted F. nucleatum proteins ( Fuso Secretome) and to infer their interactions with human proteins based on the presence of host molecular mimicry elements . Fuso Secretome proteins share similar features with known bacterial virulence factors thereby highlighting their pathogenic potential. We show that they interact with human proteins that participate in infection-related cellular processes and localize in established cellular districts of the host–pathogen interface. Our network-based analysis identified 31 functional modules in the human interactome preferentially targeted by 138 Fuso Secretome proteins, among which we selected 26 as main candidate virulence proteins, representing both putative and known virulence proteins. Finally, six of the preferentially targeted functional modules are implicated in the onset and progression of inflammatory bowel diseases and colorectal cancer. Conclusions Overall, our computational analysis identified candidate virulence proteins potentially involved in the F. nucleatum —human cross-talk in the context of gastrointestinal diseases.

Germline silencing of transposable elements is essential for the maintenance of genome integrity. Recent results indicate that this repression is largely achieved through a RNA silencing pathway that involves Piwi-interacting RNAs (piRNAs). However the repressive mechanisms are not well understood. To address this question, we used the possibility to disrupt the repression of the Drosophila I element retrotransposon by hybrid dysgenesis. We show here that the repression of the functional I elements that are located in euchromatin requires proteins of the piRNA pathway, and that the amount of ovarian I element piRNAs correlates with the strength of the repression in the female germline. Antisense RNAs, which are likely used to produce antisense piRNAs, are transcribed by heterochromatic defective I elements, but efficient production of these antisense small RNAs requires the presence in the genome of euchromatic functional I elements. Finally, we demonstrate that the piRNA-induced silencing of the functional I elements is at least partially posttranscriptional. In a repressive background, these elements are still transcribed, but some of their sense transcripts are kept in nurse cell nuclear foci together with those of the Doc retrotransposon. In the absence of I element piRNAs, either in dysgenic females or in mutants of the piRNA silencing pathway, sense I element transcripts are transported toward the oocyte where retrotransposition occurs. Our results indicate that piRNAs are involved in a posttranscriptional gene-silencing mechanism resulting in RNA nuclear accumulation.

Background The increasing incidence of emerging infectious diseases is posing serious global threats. Therefore, there is a clear need for developing computational methods that can assist and speed up experimental research to better characterize the molecular mechanisms of microbial infections. Methods In this context, we developed mimicINT, an open-source computational workflow for large-scale protein-protein interaction inference between microbe and human by detecting putative molecular mimicry elements mediating the interaction with host proteins: short linear motifs (SLiMs) and host-like globular domains. mimicINT exploits these putative elements to infer the interaction with human proteins by using known templates of domain-domain and SLiM-domain interaction templates. mimicINT also provides (i) robust Monte-Carlo simulations to assess the statistical significance of SLiM detection which suffers from false positives, and (ii) an interaction specificity filter to account for differences between motif-binding domains of the same family. We have also made mimicINT available via a web server. Results In two use cases, mimicINT can identify potential interfaces in experimentally detected interaction between pathogenic Escherichia coli type-3 secreted effectors and human proteins and infer biologically relevant interactions between Marburg virus and human proteins. Conclusions The mimicINT workflow can be instrumental to better understand the molecular details of microbe-host interactions.

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.

Transcription factors achieve specificity by establishing intricate interaction networks that will change depending on the cell context. Capturing these interactions in live condition is however a challenging issue that requires sensitive and non-invasive methods. We present a set of fly lines, called 'multicolor BiFC library', which covers most of the Drosophila transcription factors for performing Bimolecular Fluorescence Complementation (BiFC). The multicolor BiFC library can be used to probe two different binary interactions simultaneously and is compatible for large-scale interaction screens. The library can also be coupled with established Drosophila genetic resources to analyze interactions in the developmentally relevant expression domain of each protein partner. We provide proof of principle experiments of these various applications, using Hox proteins in the live Drosophila embryo as a case study. Overall this novel collection of ready-to-use fly lines constitutes an unprecedented genetic toolbox for the identification and analysis of protein-protein interactions in vivo.