Biology subjects

Droit, A.

Publications and source records attributed to Droit, A..

32 records · Page 2Linked to original sources

bioRxiv2021

MRG proteins are shared by multiple protein complexes with distinct functions

MRG15/MORF4L1 is a highly conserved protein in eukaryotes that contains a chromodomain recognizing H3K36me3 in chromatin. Intriguingly, it has been reported in the literature to interact with several different factors involved in chromatin modifications, gene regulation, alternative mRNA splicing and DNA repair by homologous recombination. In order to get a complete and reliable picture of associations in physiological conditions, we used genome editing and tandem affinity purification to analyze the stable native interactome of human MRG15, its paralog MRGX/MORF4L2 that lacks the chromodomain, and MRGBP (MRG-binding protein) in isogenic K562 cells. We found stable interchangeable association of MRG15 and MRGX with the NuA4/TIP60 histone acetyltransferase/chromatin remodeler, Sin3B histone deacetylase/demethylase, ASH1L histone methyltransferase and PALB2/BRCA2 DNA repair protein complexes. These associations were further confirmed and analyzed by CRISPR-tagging of endogenous proteins and comparison of expressed isoforms. Importantly, based on structural information, point mutations could be introduced that can specifically disrupt MRG15 association with some complexes but not others. Most interestingly, we also identified a new abundant native complex formed by MRG15/X-MRGBP-BRD8-EP400NL that is functionally similar to the yeast TINTIN (Trimer Independent of NuA4 for Transcription Interactions with Nucleosomes) complex. Our results show that EP400NL, being homologous to the N-terminal region of NuA4/TIP60 subunit EP400, creates TINTIN by competing for BRD8 association. Functional genomics indicate that human TINTIN plays a role in transcription of specific genes. This is most likely linked to the H4ac-binding bromodomain of BRD8 along the H3K36me3-binding chromodomain of MRG15 on the coding region of transcribed genes. Taken together, our data provide a complete detailed picture of human MRG proteins-associated protein complexes which is essential to understand and correlate their diverse biological functions in chromatin-based nuclear processes. HighlightsO_LIMRG15 and MRGX are stably associated with several different protein complexes important for genome expression and stability. C_LIO_LISeveral MRG-containing complexes are chromatin modifiers. C_LIO_LISpecific point mutations in the MRG domain differentially affect associated complexes. C_LIO_LIA major human complex homologous to the yeast TINTIN complex is identified. C_LIO_LIThe protein EP400NL competes with EP400 to functionally separate TINTIN from the NuA4/TIP60 complex. C_LIO_LITINTIN contains a bromodomain and a chromodomain to regulate transcription. C_LI

molecular biology↗

bioRxiv2021

Copy number variations with adaptive potential in caribou (Rangifer tarandus): genome architecture and new annotated genome assembly

BackgroundRangifer tarandus (caribou and reindeer) has experienced recent drastic population size reductions throughout its circumpolar distribution. In efforts aimed at preserving caribou in North America and reindeer in Eurasia, genetic diversity conservation is of utmost importance, particularly the adaptive genetic diversity. To facilitate genomic studies of the caribou population, we improved genome assembly and annotation by combining long-read, linked-read and RNA sequencing technologies. As copy number variations (CNVs) are known to impact phenotype and are therefore likely to play a key role in adaptation, we investigated CNVs among the genomes of individuals representing three ecotypes of caribou (migratory, boreal and mountain). ResultsUsing de novo transcriptome assembly and similarity with annotated human gene sequences, we identified 17,394 robust gene models embedded in a new highly contiguous genome assembly made of 13,994 scaffolds and presenting the highest N50 reported to date. A BUSCO analysis supported the high accuracy of this assembly, 90% of which being represented by only 131 scaffolds. Genome level comparisons with domestic ruminant species showed high synteny within this clade. A total of 1,698 large CNVs (length > 1kb) were identified, including 332 overlapping coding sequences annotated for functions related to immunity, musculoskeletal development or metabolism regulation and others. While the CNV distribution over the genome revealed 31 CNV hotspots, 43 large CNVs were particularly distinctive of the migratory and sedentary ecotypes and included genes annotated for functions related to cardiac development, fatty acid regulation, cold responses, locomotory behavior or environmental perception (hearing and sight), that can be related to the expected adaptations. ConclusionsThis work includes the first publicly available annotation of the Rangifer tarandus genome and the first genome assembly allowing genome architecture analyses. This robust annotation based on truly expressed sequences showed a distribution overlapping many CNVs that are promising candidates given the annotations supporting their involvement in adaptation. This new highly contiguous assembly will allow relative localization of genetic variations and features and will be a valuable resource for molecular tool development and genomic studies aimed at describing and preserving this species.

genomics↗

bioRxiv2021

Cis-Regulatory Hubs: a Relevant 3D Model to Study the Genetics of Complex Diseases with an Application to Schizophrenia

The 3-dimensional (3D) conformation of the chromatin creates complex networks of noncoding regulatory regions (distal elements) and promoters impacting gene regulation. Despite the importance of the role of noncoding regions in complex diseases, little is known about their interplay within regulatory hubs and implication in multigenic diseases like schizophrenia. Here we show that cis-regulatory hubs (CRHs) in neurons highlight functional interactions between distal elements and promoters, providing a model to explain epigenetic mechanisms involved in complex diseases. CRHs represent a new 3D model, where distal elements interact to create a complex network of active genes. In a disease context, CRHs highlighted strong enrichments in schizophrenia-associated genes, schizophrenia-associated SNPs and schizophrenia heritability compared to equivalent structures. Finally, CRHs exhibit larger proportions of genes differentially expressed in schizophrenia compared to promoter-distal element pairs or TADs. CRHs thus capture causal regulatory processes improving the understanding of complex disease etiology such as schizophrenia. These multiple lines of genetic and statistical evidence support CRHs as 3D models to study dysregulation of gene expression in complex diseases more generally. Summary BlurbGenes and their regulatory elements are organized within 3D networks in neurons which model functional structures and explain schizophrenia genetic etiology.

genetics↗

bioRxiv2021

GFI1 tethers the NuRD complex to open and transcriptionally active chromatin in myeloid progenitors

GFI1 is a SNAG-domain, DNA binding transcriptional repressor which controls myeloid differentiation, in particular the formation of neutrophils. Here we show that GFI1 interacts with the chromodomain helicase CHD4 and other components of the "Nucleosome remodeling and deacetylase" (NuRD) complex. In granulo-monocytic precursors, GFI1, CHD4 or GFI1/CHD4 complexes occupy sites of open chromatin enriched for histone marks associated with active transcription suggesting that GFI1 recruits the NuRD complex to target genes that are regulated by active or bivalent promoters and active enhancers. Our data also show that GFI1 and GFI1/CHD4 complexes occupy promoters of different sets of genes that are either enriched for IRF1 or SPI-1 consensus sites, respectively. During neutrophil differentiation, overall chromatin closure and depletion of H3K4me2 occurs at different degrees depending on whether GFI1, CHD4 or both are present, indicating that GFI1 affects the chromatin remodeling activity of the NuRD complex. Moreover, GFI1/CHD4 complexes regulate chromatin openness and histone modifications differentially to enable regulation of target genes affecting the signaling pathways of the immune response or nucleosome organization or cellular metabolic processes.

molecular biology↗

bioRxiv2020

Extensive and accurate benchmarking of DIA acquisition methods and software tools using a complex proteomic standard

Over the past decade, the data-independent acquisition mode has gained popularity for broad coverage of complex proteomes by LC-MS/MS and quantification of low-abundance proteins. However, there is no consensus in the literature on the best data acquisition parameters and processing tools to use for this specific application. Here, we present the most comprehensive comparison of DIA workflows on Orbitrap instruments published so far in the field of proteomics. Using a standard human 48 proteins mixture (UPS1 - Sigma) at 8 different concentrations in an E. coli proteome background, we tested 36 workflows including 4 different DIA window acquisition schemes and 6 different software tools (DIA-NN, DIA-Umpire, OpenSWATH, ScaffoldDIA, Skyline and Spectronaut) with or without the use of a DDA spectral library. Based on the number of proteins identified, quantification linearity and reproducibility, as well as sensitivity and specificity in 28 pairwise comparisons of different UPS1 concentrations, we summarize the major considerations and propose guidelines for choosing the DIA workflow best suited for LC-MS/MS proteomic analyses. Our 96 DIA raw files and software outputs have been deposited on ProteomeXchange for testing or developing new DIA processing tools.

bioinformatics↗

bioRxiv2020

Interpretation of network-based integration from multi-omics longitudinal data

Cost reduction of high-throughput technologies has enabled the monitoring of the same biological sample across multiple omics studies and multiple timepoints. The goal is to combine longitudinal multi-omics data to detect temporal relationships between molecules and interactions between omics layers. This can finally lead to uncover new regulation mechanisms and interactions that could be responsible for causing complex phenotype or disease. However multi-omics integration of diverse omics data is still challenging due to heterogeneous data and designs. Moreover, interpretation of multi-omics models is the key to understand biological systems. We propose a generic analytic and integration framework for multi-omics longitudinal datasets that consists of multi-omics kinetic clustering and multi-layer network-based analysis. This frame-work was successfully applied to two case studies with different experimental designs and omics data collected. The first case studied transcriptomic and proteomic changes during cell cycle in human HeLa cells, while the second focused on maize transcriptomic and metabolomic response to aphid feeding. Propagation analysis on multi-layer networks identifies regulatory mechanisms and function prediction for both case studies. Our framework has led to the identification of new multi-layer interactions involved in key biological functions that cannot be revealed with single omics analysis and interplay in the kinetics that could help identify novel biological mechanisms.

systems biology↗

bioRxiv2020

Towards CNN Representations for Small Mass Spectrometry Data Classification: From Transfer Learning to Cumulative Learning

Rapid and accurate clinical diagnosis of pathological conditions remains highly challenging. A very important component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some popular Machine Learning (ML) approaches have been investigated for this purpose but these ML models require time-consuming preprocessing steps such as baseline correction, denoising, and spectrum alignment to remove non-sample-related data artifacts. They also depend on the tedious extraction of handcrafted features, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn efficient representations from raw data without the need for costly preprocessing. However, their effectiveness drastically decreases when the number of available training samples is small, which is a common situation in medical applications. Transfer learning strategies extend an accurate representation model learnt usually on a large dataset containing many categories, to a smaller dataset with far fewer categories. In this study, we first investigate transfer learning on a 1D-CNN we have designed to classify MS data, then we develop a new representation learning method when transfer learning is not powerful enough, as in cases of low-resolution or data heterogeneity. What we propose is to train the same model through several classification tasks over various small datasets in order to accumulate generic knowledge of what MS data are, in the resulting representation. By using rat brain data as the initial training dataset, a representation learning approach can have a classification accuracy exceeding 98% for canine sarcoma cancer cells, human ovarian cancer serums, and pathogenic microorganism biotypes in 1D clinical datasets. We show for the first time the use of cumulative representation learning using datasets generated in different biological contexts, on different organisms, in different mass ranges, with different MS ionization sources, and acquired by different instruments at different resolutions. Our approach thus proposes a promising strategy for improving MS data classification accuracy when only small numbers of samples are available as a prospective cohort. The principles demonstrated in this work could even be beneficial to other domains (astronomy, archaeology...) where training samples are scarce.

bioinformatics↗

bioRxiv2019

Male sex exacerbates the plasticity and pathogenicity of Th17 cells in a chronic model of CNS autoimmunity.

Sex differences in the incidence and severity of multiple sclerosis (MS) have long been recognized. However, the underlying cellular and molecular mechanisms for why male sex is associated with more aggressive and debilitating disease remain poorly defined. Using an T cell adoptive transfer model of chronic EAE, we find that male Th17 cells induced disease of increased severity relative to female Th17 cells, irrespective of whether transferred to male or female recipients. Throughout the disease course, a greater frequency of male Th17 cells produced the heterodox cytokine IFN{gamma}, a hallmark of pathogenic Th17 responses. Intriguingly, sex chromosomal complement, and not hormones, were responsible for the increased pathogenicity of male Th17 cells and an X-linked immune regulator, Jarid1c, was downregulated in both pathogenic male Th17 and CD4+ T cells from men with MS. Together, our data indicate that male sex critical regulates Th17 cell plasticity and pathogenicity via sex chromosomal complement.

immunology↗

bioRxiv2019

Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning

The identification of microbial species in biological samples is essential to many applications in health, food safety and environment. MALDI-TOF MS technology has become a tool of choice for microbial identification but it has several drawbacks including: it requires a long step of bacterial culture prior to analysis (24h), it has a low specificity and is not quantitative. We have developed a new strategy for identifying bacterial species in biological samples using specific LC-MS/MS peptidic signatures. In the first training step, deep proteome coverage of bacteria of interest is obtained in Data Independent Acquisition (DIA) mode, followed by the use of machine learning to define the peptides the most susceptible to distinguish each bacterial species from the others. Then, in the second step, this peptidic signature is monitored in biological samples using targeted proteomics. This method, which allows the bacterial identification from clinical specimens in less than 4h, has been applied to fifteen species representing 84% of all Urinary Tract Infections (UTI). More than 31000 peptides in 200 samples have been quantified by DIA and analyzed by machine learning to determine an 82 peptides signature and build prediction models able to classify the fifteen bacterial species. This peptidic signature was validated for its use in routine conditions using Parallel Reaction Monitoring on a capillary flow chromatography coupled to a Thermo Scientific Q Exactive HF-X instrument. Linearity and reproducibility of the method were demonstrated as well as its accuracy on donor specimens. Within 4h and without bacterial culture, our method was able to predict the predominant bacteria infecting a sample in 97% of cases and 100% above the 1x105 CFU/mL threshold commonly used by clinical laboratories. This work demonstrates the efficiency of our method for the rapid and specific identification of the bacterial species causing UTI and could be extended in the future to other biological specimens and to bacteria having specific virulence or resistance factors.

microbiology↗

bioRxiv2019

A generic multivariate framework for the integration of microbiome longitudinal studies with other data types

Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g. sparsity, compositional).\n\nWe propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial communities data, and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modelling, to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.

bioinformatics↗

bioRxiv2019

DSNetwork: An integrative approach to visualize predictions of variants’ deleteriousness

One of the most challenging tasks of the post-genome-wide association studies (GWAS) research era is the identification of functional variants among those associated with a trait for an observed GWAS signal. Several methods have been developed to evaluate the potential functional implications of genetic variants. Each of these tools has its own scoring system which forces users to become acquainted with each approach to interpret their results. From an awareness of the amount of work needed to analyze and integrate results for a single locus, we proposed a flexible and versatile approach designed to help the prioritization of variants by aggregating the predictions of their potential functional implications. This approach has been made available through a web interface called DSNetwork which acts as a single-point of entry to almost 60 reference predictors for both coding and non-coding variants and displays predictions in an easy-to-interpret visualization. We confirmed the usefulness of our methodology by successfully identifying functional variants in four breast cancer susceptibility loci. DSNetwork is an integrative web application implemented through the Shiny framework and available at: http://romix.genome.ulaval.ca/dsnetwork.\n\nAuthor summaryOver the past years, GWAS have enabled the identification of numerous susceptibility loci associated with complex traits (https://www.ebi.ac.uk/gwas/). However, many of those signals contain hundreds or even thousands of significantly associated variants among which only a few are really responsible of the phenotype. Substantial efforts have been made in the development of prediction methods to prioritize variants within GWAS-associated regions to go from statistical associations, to the identification of functional variants modulating gene expression, in order to ultimately gain insight into disease pathophysiology. Unfortunately, these numerous prediction tools generate contradictory predictions rendering the interpretation of results challenging. Some tools such as VEP [McLaren et al., 2016] report their scores using a color scheme, thus acknowledging the need to assist the user in the interpretation of predictor results. Nonetheless, the multiplication of approaches can often result in an extensive amount of data that is hard to synthesize. Aware of the challenge of evaluating the potential deleteriousness of variants in the context of fine mapping analyses, we created a customizable visualization approach that was implemented it in the decision support tool called DSNetwork for Decision Support Network. This tool enables quick access to gold standard and new predictors for both coding and non-coding variants through an easily interpretable visualization of these predictions for a set of variants.

bioinformatics↗

bioRxiv2018

A type 2 diabetes disease module with a high collective influence for Cdk2 and PTPLAD1 is localized in endosomes

Despite the identification of many susceptibility genes our knowledge of the underlying mechanisms responsible for complex disease remains limited. Here, we identified a type 2 diabetes disease module in endosomes, and validate it for functional relevance on selected nodes. Using hepatic Golgi/endosomes fractions, we established a proteome of insulin receptor-containing endosomes that allowed the study of physical protein interaction networks on a type 2 diabetes background. The resulting collated network is formed by 313 nodes and 1147 edges with a topology organized around a few major hubs with Cdk2 displaying the highest collective influence. Overall, 88% of the nodes are associated with the type 2 diabetes genetic risk, including 101 new candidates. The Type 2 diabetes module is enriched with cytoskeleton and luminal acidification -dependent processes that are shared with secretion-related mechanisms. We identified new signaling pathways driven by Cdk2 and PTPLAD1 whose expression regulate the association of the insulin receptor with TUBA, TUBB, the actin component ACTB and the endosomal sorting markers Rab5c and Rab11a. Therefore, the interactome of internalized insulin receptors reveals the presence of a type 2 diabetes disease module enriched in new layers of feedback loops required for insulin signaling, clearance and islet biology.\n\nAuthor SummaryAccording to the local hypothesis each complex disease can be linked to a well-defined network called the disease module. A disease module can be defined by the topological properties of protein interaction networks. Given the complexity of the whole interaction map the existence of such disease modules remains largely to be tested. Here, we found a type 2 diabetes disease module in insulin receptor-containing endosomes. The disease module contains new pathways that are associated with both insulin signaling, clearance and secretion. Its co-functionality with islets biology may provide a mechanistic rationale for the exploration of personalized medicine and elaborate new drugs.

genomics↗

bioRxiv2018

Germline genomic landscapes of breast cancer patients significantly predict clinical outcomes

Germline genetic variants such as BRCA1/2 play an important role in tumorigenesis and clinical outcomes of cancer patients. However, only a small fraction (i.e., 5-10%) of inherited variants has been associated with clinical outcomes (e.g., BRCA1/2, APC, TP53, PTEN and so on). The challenge remains in using these inherited germline variants to predict clinical outcomes of cancer patient population. In an attempt to solve this issue, we applied our recently developed algorithm, eTumorMetastasis, which constructs predictive models, on exome sequencing data to ER+ breast (n=755) cancer patients. Gene signatures derived from the genes containing functionally germline genetic variants significantly distinguished recurred and non-recurred patients in two ER+ breast cancer independent cohorts (n=200 and 295, P=1.4x10-3). Furthermore, we found that recurred patients possessed a higher rate of germline genetic variants. In addition, the inherited germline variants from these gene signatures were predominately enriched in T cell function, antigen presentation and cytokine interactions, likely impairing the adaptive and innate immune response thus favoring a pro-tumorigenic environment. Hence, germline genomic information could be used for developing non-invasive genomic tests for predicting patients outcomes (or drug response) in breast cancer, other cancer types and even other complex diseases.

cancer biology↗

bioRxiv2018

eTumorMetastasis, a network-based algorithm predicts clinical outcomes using whole-exome sequencing data of cancer patients

Continual reduction in sequencing cost is expanding the accessibility of genome sequencing data for routine clinical applications. However, the lack of methods to construct machine learning-based predictive models using these datasets has become a crucial bottleneck for the application of sequencing technology in clinics. Here we developed a new algorithm, eTumorMetastasis, which transforms tumor functional mutations into network-based profiles, and identify network operational gene signatures (NOG signatures) which model the tipping point at which a tumor cell shifts from a state that doesnt favor recurrences to one that does. We showed that NOG signatures derived from genomic mutations of tumor founding clones (i.e., the most recent common ancestor of the cells within a tumor) significantly distinguished recurred and non-recurred breast tumors. These results imply that somatic mutations of tumor founders are association with tumor recurrence and can be used to predict clinical outcomes. Finally, the concepts underlying the eTumorMetastasis pave the way for the application of genome sequencing in predictions for other complex genetic diseases.

bioinformatics↗