Biology subjects

Varoquaux, N.

Publications and source records attributed to Varoquaux, N..

13 recordsLinked to original sources

bioRxiv2026

Conserved lifestyle-associated chromosome architectures across mammalian symbionts

Bacterial chromosome biology has largely focused on bacteria that are either free-living or facultatively associated with eukaryotes. Therefore, it is not known how obligate animal symbionts organize their chromosomes. Here, we studied the chromosome organization of three species of multicellular Neisseriaceae that colonize the oral cavity of mammals, Alysiella filiformis, Simonsiella muelleri and Conchiformibius steedae. DNA fluorescence in situ hybridization showed that - irrespective of their ploidy - their chromosomes are longitudinally configured with the origin of DNA replication consistently localized at their host-attached poles throughout the cell cycle. Immunolocalization, ChIP-seq and EMSA implicated ParBS complexes in maintaining this stable chromosome orientation. Moreover, chromosome conformation capture across two species and three growth conditions further revealed conserved lifestyle-associated chromosome architectures, including planktonic-specific ParB-associated chromatin loops and condition-specific chromatin frontiers. Together, our findings show that obligate mammalian symbionts maintain stable longitudinal chromosome orientations irrespective of ploidy while remodeling higher-order chromosome architecture according to physiological state. Distinct yet conserved chromosome architectures characterize exponential, stationary and surface-associated growth, indicating that bacterial genome folding reflects lifestyle and environmental context.

microbiology↗

bioRxiv2026

COCOA-Tree: Phylogenetic visualization and comparative analysis of coevolving residues

The evolutionary co-occurrence of amino acid changes between protein residues underlies key structural and functional properties of protein families. Building on these coevolutionary patterns, methods have been developed to identify groups of residues associated with enzyme functionalities, such as Statistical Coupling Analysis (SCA) or Specificity-Determining Position (SDP) methods. These methods and their variations differ in the metrics used to quantify coevolution, residues weighting schemes, and corrections introduced to mitigate noise and phylogenetic biases. Yet, systematic comparisons across methods are rarely performed, and the evolutionary origins of the coevolutionary patterns highlighted by each approach are seldom addressed, limiting our ability to disentangle functional from phylogenetic contributions. To address these issues, we introduce COCOA-Tree, a Python library for SCA-like dimensionality-reduction analyses. COCOA-Tree supports custom metrics and enables visualization of coevolutionary patterns on phylogenetic trees. We also provide guidance to map results onto 3D structures in PyMOL. Using COCOA-Tree, we reanalyze published datasets and uncover previously unnoticed evolutionary properties of groups of coevolving residues detected by SCA, known as sectors. In particular, in the well-studied S1A serine protease family, we show that two of the three known sectors exhibit qualitatively distinct levels of sequence conservation depending on the enzymatic functions and on the phylogenetic clades to which the proteins belong. We further show that different coevolution metrics often identify qualitatively distinct groups of coevolving residues, although they yield consistent results for mildly conserved residues. Finally, as an example of the versatility of COCOA-Tree, we provide an example of visualizing pairs of residues detected by Direct Coupling Analysis (DCA) methods on a phylogenetic tree, highlighting a rich diversity of co-evolutionary patterns. Overall, we expect COCOA-Tree to help identify residues that control protein function and thereby improve our capacity for functional engineering and our understanding of the principles governing protein evolution. COCOA-Tree website: https://tree-timc.github.io/cocoatree

bioinformatics↗

bioRxiv2025

Global distribution of isoprenoid quinones across Bacteria

Isoprenoid quinones represent a class of redox lipids involved in many critical cellular functions, including ATP synthesis through electron transport chains. They thus occupy a pivotal role in the bioenergetics of all three domains of life. The diversity of quinone types observed across microbial taxa has long supported their use as chemotaxonomic markers in microbial systematics. More recently, variations in quinone repertoires have been linked to metabolic adaptations and a novel quinone was discovered. Despite a revived interest in the role of quinones, a unified perspective on the distribution of quinones in Bacteria is currently lacking. In this study, quinone biosynthetic pathways were systematically annotated in 26,264 high quality genomes of bacterial species, and specific information on quinones produced by over 6,000 bacterial species was extracted by text mining the abstracts of thousands of articles. The results were mapped onto a phylogenetic tree, providing the most comprehensive overview of quinone distribution in Bacteria to date. This enabled us to highlight the surprisingly dynamic evolutionary history of the two menaquinone-producing pathways. Moreover, the identification and experimental validation of a deeply branching ubiquinone pathway in Desulfobacterota represents the first occurrence of such a pathway outside the Pseudomonadota and provides insights into the nature of the ancestral UQ pathway. The updated compendium of bacterial quinones is a valuable resource to facilitate the prediction of quinone structural features from genomic data, to establish correlations between quinone structures and cellular traits, and to explore the evolution of quinone repertoires in connection with the diversification of microbial metabolisms.

microbiology↗

bioRxiv2025

DNA methylation and immune infiltration mediate the impact of tobacco exposure on pancreatic adenocarcinoma outcome: a high-dimensional mediation analysis

MotivationHigh-dimensional mediation analysis is critical for dissecting causal pathways in complex diseases. However, existing methods struggle to handle censored survival outcomes and unobserved confounders in epigenetic data. There is a pressing need for scalable, statistically robust frameworks to identify epigenetic mediators of exposure-outcome relationships, especially in cancer research where survival data are central and molecular pathways are highly complex and interconnected. ResultsWe introduce HDMAX2-surv, a two-step framework extending HDMAX2 to survival analysis. HDMAX2-surv integrates (i) latent factor modeling to adjust for unobserved confounders, (ii) flexible survival models (Aalen additive hazards and accelerated failure-time models). Simulations demonstrated superior performance over state-of-the-art methods (e.g., HIMA) in mediator selection and effect estimation. We integrated this approach with causal discovery frameworks and immune deconvolution algorithms to dissect multi-pathway mediation mechanisms. Applied to TCGA pancreatic adenocarcinoma data (n=112), HDMAX2-surv identified 36 aggregated methylated regions (AMRs) mediating tobacco exposure effects on survival, including immune-mediated pathways undetectable via gene expression alone. Availability and implementationHDMAX2-surv is implemented in R and available on GitHub (https://github.com/bcm-uga/tims-pdac), with documentation and example scripts for reproducibility.

bioinformatics↗

bioRxiv2025

Multi-season analysis reveals hundreds of drought-responsive genes in sorghum

Persistent drought affects global crop production and is becoming more severe in many parts of the world in recent decades. Deciphering how plants respond to drought will facilitate the development of flexible mitigation strategies. Sorghum bicolor L. Moench (sorghum), a major cereal crop and an emerging bioenergy crop, exhibits remarkable resilience to drought. To better understand the molecular traits that underlie sorghums remarkable drought tolerance, we undertook a large-scale sorghum gene expression profiling effort, totaling nearly 1,500 transcriptome profiles, across a 3-year field study with replicated plots in Californias Central Valley. This study included time-resolved gene expression data from roots and leaves of two sorghum genotypes, BTx642 and RTx430, with different pre-flowering and post-flowering drought-tolerance adaptations under control and drought conditions. Quantification of genotype-specific drought tolerance effects was enabled by de novo sequencing, assembly, and annotation of both BTx642 and RTx430 genomes. These reference-quality genomes were used to construct a pan-gene set for characterizing conserved and genotype-specific expression. By integrating time-resolved transcriptomic responses to drought in the field across three consecutive years, we identified a set of drought-responsive genes that responded similarly in all three years of our field study. This expansive dataset represents a unique resource for sorghum and drought research communities and provides a methodological framework for the integration of multi-faceted time-resolved transcriptomic datasets.

genomics↗

bioRxiv2023

Stable chromosome configuration and loop-based polarization in animal symbionts

Chromosome partitioning precedes the division of the cytoplasm, and its evolution is linked with the positioning of the division plane. So far, bacterial chromosome biology has heavily focused on transversally dividing, free-living ones. Here, we determined the chromosome organization of three longitudinally dividing Neisseriaceae exclusively inhabiting the oral cavity of mammals. We showed that in all three multicellular bacteria the origin of DNA replication is invariably located at the host-attached (proximal) pole. Next, 3C-seq revealed loop-based folding of the ori region in Alysiella filiformis and Simonsiella muelleri. Moreover, genes involved in cell motility, piliation and signal transduction mechanisms were specifically looped when transcriptionally and translationally active cells adhered to a substrate, but not when cultured in liquid. Overall, we propose that proximal positioning of the ori and loop-based folding of its surrounding DNA may mediate localized translation of proteins involved in host colonization.

genomics↗

bioRxiv2023

A new take on missing value imputation for bottom-up label-free LC-MS/MS proteomics

Label-free bottom-up proteomics using mass spectrometry and liquid chromatography has long been established as one of the most popular high-throughput analysis workflows for proteome characterization. However, it produces data hindered by complex and heterogeneous missing values, which imputation has long remained problematic. To cope with this, we introduce Pirat, an algorithm that harnesses this challenge using an original likelihood maximization strategy. Notably, it models the instrument limit by learning a global censoring mechanism from the data available. Moreover, it estimates the covariance matrix between enzymatic cleavage products (i.e., peptides or precursor ions), while offering a natural way to integrate complementary transcriptomic information when multi-omic assays are available. Our benchmarking on several datasets covering a variety of experimental designs (number of samples, acquisition mode, missingness patterns, etc.) and using a variety of metrics (differential analysis ground truth or imputation errors) shows that Pirat outperforms all pre-existing imputation methods. Beyond the interest of Pirat as an imputation tool, these results pinpoint the need for a paradigm change in proteomics imputation, as most pre-existing strategies could be boosted by incorporating similar models to account for the instrument censorship or for the correlation structures, either grounded to the analytical pipeline or arising from a multi-omic approach.

bioinformatics↗

bioRxiv2021

Unveiling the links between peptide identification and differential analysis FDR controls by means of a practical introduction to knockoff filters

In proteomic differential analysis, FDR control is often performed through a multiple test correction (i.e., the adjustment of the original p-values). In this protocol, we apply a recent and alternative method, based on so-called knockoff filters. It shares interesting conceptual similarities with the target-decoy competition procedure, classically used in proteomics for FDR control at peptide identification. To provide practitioners with a unified understanding of FDR control in proteomics, we apply the knockoff procedure on real and simulated quantitative datasets. Leveraging these comparisons, we propose to adapt the knockoff procedure to better fit the specificities of quantitive proteomic data (mainly very few samples). Performances of knockoff procedure are compared with those of the classical Benjamini-Hochberg procedure, hereby shedding a new light on the strengths and weaknesses of target-decoy competition.

bioinformatics↗

bioRxiv2021

Inference of genome 3D architecture by modeling overdispersion of Hi-C data

We address the challenge of inferring a consensus 3D model of genome architecture from Hi-C data. Existing approaches most often rely on a two step algorithm: first convert the contact counts into distances, then optimize an objective function akin to multidimensional scaling (MDS) to infer a 3D model. Other approaches use a maximum likelihood approach, modeling the contact counts between two loci as a Poisson random variable whose intensity is a decreasing function of the distance between them. However, a Poisson model of contact counts implies that the variance of the data is equal to the mean, a relationship that is often too restrictive to properly model count data. We first confirm the presence of overdispersion in several real Hi-C data sets, and we show that the overdispersion arises even in simulated data sets. We then propose a new model, called Pastis-NB, where we replace the Poisson model of contact counts by a negative binomial one, which is parametrized by a mean and a separate dispersion parameter. The dispersion parameter allows the variance to be adjusted independently from the mean, thus better modeling overdispersed data. We compare the results of Pastis-NB to those of several previously published algorithms: three MDS-based methods (ShRec3D, ChromSDE, and Pastis-MDS) and a statistical methods based on a Poisson model of the data (Pastis-PM). We show that the negative binomial inference yields more accurate structures on simulated data, and more robust structures than other models across real Hi-C replicates and across different resolutions. A Python implementation of Pastis-NB is available at https://github.com/hiclib/pastis under the BSD license Supplementary information is available at https://nellev.github.io/pastisnb/

bioinformatics↗

bioRxiv2020

Dynamics of the compartmentalized Streptomyces chromosome during metabolic differentiation

Streptomyces are among the most prolific bacterial producers of specialized metabolites, including antibiotics. The linear chromosome is partitioned into a central region harboring core genes and two extremities enriched in specialized metabolite biosynthetic gene clusters (SMBGCs). The molecular mechanisms governing structure and function of these compartmentalized genomes remain mostly unknown. Here we show that in exponential phase, chromosome structure correlates with genetic compartmentalization: conserved, large and highly transcribed genes form boundaries that segment the central part of the chromosome into domains, whereas the terminal ends are transcriptionally, largely quiescent compartments with different structural features. Onset of metabolic differentiation is accompanied by remodeling of chromosome architecture from an open to a rather closed conformation, in which the SMBGCs are expressed forming new boundaries. Altogether, our results reveal that S. ambofaciens linear chromosome is partitioned into structurally distinct entities, indicating a link between chromosome folding, gene expression and genome evolution.

microbiology↗

bioRxiv2020

circHiC: circular visualization of Hi-C data and integration of genomic data

Genome wide contact frequencies obtained using Hi-C-like experiments have raised novel challenges in terms of visualization and rationalization of chromosome structuring phenomena. In bacteria, display of Hi-C data should be congruent with the circularity of chromosomes. However, standard representations under the form of square matrices or horizontal bands are not adapted to periodic conditions as those imposed by (most) bacterial chromosomes. Here, we fill this gap and propose a Python library, built upon the widely used Matplotlib library, to display Hi-C data in circular strips, together with the possibility to overlay genomic data. The proposed tools are light and fast, aiming to facilitate the exploration and understanding of bacterial chromosome structuring data. The library further includes the possibility to handle linear chromosomes, providing a fresh way to display and explore eukaryotic data. Availability and implementationThe package runs under Python 3 and is freely available at https://github.com/TrEE-TIMC/circHiC. The documentation can be found at https://tree-timc.github.io/circhic/; images obtained in different organisms are provided in the gallery section and are accompanied with codes. Contactivan.junier@univ-grenoble-alpes.fr, nelle.varoquaux@univ-grenoble-alpes.fr

bioinformatics↗

bioRxiv2019

Inferring diploid 3D chromatin structures from Hi-C data

The 3D organization of the genome plays a key role in many cellular processes, such as gene regulation, differentiation, and replication. Assays like Hi-C measure DNA-DNA contacts in a high-throughput fashion, and inferring accurate 3D models of chromosomes can yield insights hidden in the raw data. For example, structural inference can account for noise in the data, disambiguate the distinct structures of homologous chromosomes, orient genomic regions relative to nuclear landmarks, and serve as a framework for integrating other data types. Although many methods exist to infer the 3D structure of haploid genomes, inferring a diploid structure from Hi-C data is still an open problem. Indeed, the diploid case is very challenging, because Hi-C data typically does not distinguish between homologous chromosomes. We propose a method to infer 3D diploid genomes from Hi-C data. We demonstrate the accuarcy of the method on simulated data, and we also use the method to infer 3D structures for mouse chromosome X, confirming that the active homolog exhibits a bipartite structure, whereas the active homolog does not.

bioinformatics↗

bioRxiv2018

Changes in genome organization of parasite-specific gene families during the Plasmodium transmission stages

The development of malaria parasites throughout their various life cycle stages is controlled by coordinated changes in gene expression. We previously showed that the three-dimensional organization of the P. falciparum genome is strongly associated with gene expression during its replication cycle inside red blood cells. Here, we analyzed genome organization in the P. falciparum and P. vivax transmission stages. Major changes occurred in the localization and interactions of genes involved in pathogenesis and immune evasion, erythrocyte and liver cell invasion, sexual differentiation and master regulation of gene expression. In addition, we observed reorganization of subtelomeric heterochromatin around genes involved in host cell remodeling. Depletion of heterochromatin protein 1 (PfHP1) resulted in loss of interactions between virulence genes, confirming that PfHP1 is essential for maintenance of the repressive center. Overall, our results suggest that the three-dimensional genome structure is strongly connected with transcriptional activity of specific gene families throughout the life cycle of human malaria parasites.

systems biology↗