Biology subjects

Jacques, P.-E.

Publications and source records attributed to Jacques, P.-E..

12 recordsLinked to original sources

bioRxiv2026

Reducing growth-medium complexity reveals nutrient-responsive programs in a near-minimal bacterium

Mesoplasma florum is a fast-growing, near-minimal bacterium and an emerging model for systems and synthetic biology. However, its dependence on complex serum-containing media limits experimental control and complicates the interpretation of cellular phenotypes. Here, we developed CMRL-AT, a serum-free, quasi-defined medium that supports rapid growth comparable to the commonly used ATCC 1161 medium. Despite supporting similar biomass, CMRL-AT profoundly reshaped the M. florum transcriptome, with approximately one-third of the annotated protein-encoding genes being differentially expressed relative to ATCC 1161. These changes revealed distinct physiological programs associated with rapid growth in complex medium and higher nutrient acquisition in CMRL-AT, illustrating how medium composition alters the functional priorities of a near-minimal cell. Transcriptome profiling across six energy sources further uncovered distinct sugar-responsive expression programs. Combining these responses with transcription-unit organization, protein-domain predictions, and metabolic context resolved fructose- and sucrose-responsive modules, and allowed the assignment of previously ambiguous phosphotransferase system components to specific sugar-utilization pathways. CMRL-AT provides an experimental framework to help resolving gene functions, refining metabolic models, and designing reduced genomes adapted to defined environments.

systems biology↗

bioRxiv2026

Taxonomic profilers and their influence on metagenomic diversity analyses

Estimating taxonomic profiles is a central task in microbiome research. Several bioinformatic tools have been developed for this purpose, differing in algorithmic strategy, reference database flexibility, sensitivity parameters, and the type of abundance they estimate. As a result, taxonomic profiles carry an unwanted methodological signal whose driving characteristics remains understudied. While benchmarks have evaluated the performance of some of these tools, they rely on simulated data; little work has been done to compare them using real metagenomes in the presence of noise and uncharacterised diversity. Overall, the impact of taxonomic profiler choice and parameterisation on scientific conclusions remains poorly understood. First, we provide a much-needed characterisation of four taxonomic profilers to help researchers better understand the available bioinformatic tools and inform their methodological choices. Then, we leverage 1,211 shotgun metagenomes from eight datasets to compare these taxonomic profilers across 13 methodological designs. Based on diversity indices, we found substantial variability in estimated taxonomic composition depending on methodological features such as reference database and algorithmic strategy. Alpha diversity analysis was substantially sensitive totool choice (particularly among k-mer-based tools) and reference database. Beta diversity showed sensitivity to both database and parameter choices, yet this variability barely affected statistical inference. Our findings highlight the sensitivity of taxonomic diversity analyses to taxonomic profiling methodology and the importance for researchers to consider assessing the robustness of their results to choice of tool, parameter, and reference database. Crucially, differences in sample diversity across methodologies are symptomatic of differences in estimated taxonomic composition, which can affect any analysis based on taxonomic abundances. Overall, this study underscores the importance of tool selection and parametrisation, and of conducting sensitivity analyses to support robust and reliable scientific conclusions. AUTHOR SUMMARYMicrobiome research relies on bioinformatic tools to determine which microbes are present in a sample and estimate their relative abundances, a process known as taxonomic profiling. Because this task requires comparing tens of millions of DNA sequences against thousands of microbial genomes, a wide range of computational strategies have been developed to make profiling accurate and efficient. As a result, many taxonomic profilers are now available, yet do not provide the same results. Although previous studies have evaluated their precision and sensitivity, less attention has been given to how the choice of profiler may influence the scientific conclusions drawn from microbiome data. Here, we selected four widely used taxonomic profilers to examine whether researchers would reach the same biological conclusions when comparing microbiomes across groups of samples, such as healthy and diseased individuals. We show that different profilers can lead to different biological conclusions and identify key tool characteristics that contribute to these discrepancies. Moreover, we provide a comparative overview of these four profilers, offering practical guidance for researchers seeking to choose the most appropriate tool for their study. These findings highlight the importance of software choice in microbiome research and support more transparent and reproducible data analysis practices.

bioinformatics↗

bioRxiv2026

Development of a microRNA clock for gestational age in the general population

IntroductionDNA methylation (DNAm) has shown promise as a biological marker of gestational age (GA). Here, we aimed to characterize how plasma circulating miRNAs - another epigenetic mechanism regulating gene expression - associate with GA at birth, and to construct a miRNA-based GA clock (miRClock-GA). MethodsWe leveraged 2083 umbilical cord plasma-derived circulating miRNAs from Generation R (N=1695). First, we performed linear regressions to identify miRNome-wide significant miRNAs associated with GA. Second, we applied elastic net regression to construct miRClock-GA. These steps were validated in Gen3G (N=213). Finally, we computed age acceleration (miRClock-AA) and evaluated association of miRClock-GA and miRClock-AA with child developmental outcomes up to 17 years of age, including comparisons with DNAmClocks. FindingsWe identified 123 miRNAs associated with GA, with miR-150-5p showing the strongest positive association ( B=0.244, SE=0.036, P=2.3e-11) and miR-373-3p the strongest negative association ( B=-0.255, SE=0.065, P=8.6e-5). MiRClock-GA correlated consistently with GA in Generation R train (rrange=0.62-0.72) and test sets (rrange=0.45-0.52) and in the independent validation cohort (rGen3G =0.33). Correlations between miRClock-GA and DNAmClocks were weak to moderate (rrange=0.28-0.42). MiRClock-AA explained significant variance in birthweight and childhood BMI beyond clinical GA. ConclusionsThis study reveals widespread associations between circulating miRNAs and GA, supports miRClock-GA as a consistent, well-performing biological marker of GA, with miRClock-AA predicting birthweight and childhood BMI beyond GA itself. Our findings provide a broader perspective on the potential utility of miRNAs as early markers of development. FundingE.U. Horizon Europe Research and Innovation Programme (FAMILY,No.101057529); European Research Council (TEMPO,No.101039672). Full funding in Acknowledgements. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSEpigenetic clocks have emerged as powerful tools for assessing individual differences in biological ageing. Existing epigenetic clocks are typically constructed using DNA methylation (DNAm) data and are developed to estimate whether an individuals epigenetic age deviates relative to their chronological age. In adults, higher predicted biological epigenetic age than chronological age (accelerated epigenetic ageing) is an established indicator of mortality risk and other age-related morbidities. MicroRNAs are another epigenetic mechanism associated with chronological age and age-related phenotypes across species such as C. elegans, mice, primates and humans. Recently, a plasma-derived, cell-free circulating miRNA clock was developed in a sample of older European adults, with accelerated miRNA age predicting biological age-related conditions such as frailty and multi-system blood biomarkers. However, miRNA-based estimations of biological gestational age at birth have not been previously done and their relationship to child health outcomes are unknown. Added value of this studyTo gain a broader perspective on the role of epigenetic markers in biological gestational age at birth and the potential utility of miRNAs as early markers of (altered) development, we leveraged cord blood plasma circulating miRNA data collected at birth in a large population-based cohort. First, we identified 123 miRNAs associated with gestational age. Second, we used this information to construct a miRNA-based epigenetic clock for gestational age (miRClock-GA) and validated this clock in an independent cohort. MiRClock-GA showed moderate correlations with both gestational age and existing DNAm-based gestational clocks. Third, we found that miRClock-GA correlated cross-sectionally with birthweight, as well as prospectively with several adaptive, behavioural, cognitive, and growth outcomes measured up to age 17y, after taking into account maternal influences such as maternal education level, smoking, pre-pregnancy BMI, and age. Accelerated ageing of miRClock-GA (miRClock-AA) explained variance in birthweight and childhood BMI beyond clinical GA, after accounting for maternal influences. Implications of all available evidenceOur study provides the first large-scale evidence that circulating miRNAs in cord blood plasma show widespread associations with gestational age at birth and can be used to derive a robust biological marker of GA (MiRClock-GA), which explains a substantial proportion of the variation in clinical GA. MiRClock-AA provides information beyond GA in some child outcomes, such as BMI. These findings extend adult research, supporting the potential of miRNAs as promising markers for early risk assessment and health monitoring in a developmental context.

genomics↗

bioRxiv2025

Comparative Analysis of Gene Importance in Escherichia coli Across Growth Conditions

As the ability to synthesize complete genomes becomes increasingly accessible, the question of what should compose those sequences is becoming more prevalent. However, identifying genes essential for the survival of an organism is challenging, as gene essentiality is a nuanced concept that heavily depends on context. In this study, we identified growth medium-specific important genes by performing transposon mutagenesis in Escherichia coli BW25113 and sequencing mutant populations at multiple time points in three growth media. Our analysis revealed a core set of 412 important genes shared across all conditions, along with distinct medium-specific gene sets of varying sizes. By analyzing temporal variations in read counts for each gene, we identified additional sets of genes whose inactivation causes a lighter impact on fitness. We used this dataset to define medium-specific gene modules required to sustain robust growth under each condition. Our study underscores the context-dependent nature of gene essentiality and marks a step toward refining the concept from a universal list to a more nuanced, condition-specific framework, which will be invaluable for future genome design efforts. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/679994v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1a6863corg.highwire.dtl.DTLVardef@1b6e594org.highwire.dtl.DTLVardef@9a879dorg.highwire.dtl.DTLVardef@13d5df5_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

Leveraging the largest harmonized epigenomic data collection for metadata prediction validated and augmented over 350,000 public epigenomic datasets

Epigenomic data found in public databases often suffer from issues of non-standardization and incompleteness in their associated metadata. There are currently no automated approaches to validate or correct missing or inaccurate information listed in databases. To tackle this challenge, we harnessed the extensive harmonized data and metadata provided by the EpiATLAS project of the International Human Epigenome Consortium (IHEC) to train EpiClass, a suite of machine learning classifiers that can predict key metadata ([~]98% accuracy), including experimental assay, donor sex, biospecimen and sample cancer status. The development of these classifiers enabled the identification of a few mislabeled and low-quality datasets in the EpiATLAS project, while also completing with high-confidence most of the missing metadata. These classifiers were also validated on ENCODE datasets absent from the initial training, then applied to assess more than 350,000 human ChIP-Seq and RNA-Seq datasets from public repositories. Overall, this effort not only validated the accuracy of the vast majority of assays reported by the original authors, but also unveiled [~]500 datasets with discrepancies, in particular through data swap within series of experiments. More importantly, EpiClass also supplied high-confidence predictions for over 320,000 metadata attributes of the biological sample such as the sex, cancer status and biomaterial type, which had been originally omitted in the majority of cases. Our work introduces the first systematic approach for metadata correction and augmentation, enhancing the quality and reliability of publicly available epigenomic data.

bioinformatics↗

bioRxiv2025

Long-read transcriptome assembly reveals vast isoform diversity in the placenta associated with metabolic and endocrine function

The placenta is critical for fetal development and mediates the effects of pregnancy complications on offspring metabolic health, yet it is often poorly characterized in genomic studies. Existing transcriptomic analyses rely on adult tissue-based references, which overlook developmentally important isoform diversity. We used largest-in-class long-read RNA-seq (N=72) to create a comprehensive placental transcriptome reference, identifying 37,661 high-confidence isoforms (14,985 novel) across 12,302 genes (2,759 novel). Contrary to characterizations of the placenta as a "transcriptomic void," we found transcriptional breadth and complexity comparable to adult tissues, with extraordinary splicing diversity in genes controlling obesity, lactogen production and growth, including 108 distinct CSH1 (placental lactogen) isoforms. This improved reference offers two advantages: First, it reduced inferential uncertainty in isoform quantification by 30% and increased the yield of high-confidence transcripts. Applying this reference to short-read RNA-seq datasets (N=344) of gestational diabetes mellitus (GDM), we found that placental transcription mediated 36% of GDM effects on birth weight, with novel CSH1 isoforms identified as key mediators. We further uncovered ancestry-specific effects, with distinct CSH1 isoforms mediating larger effects in European (24.4%) than Asian (13.4%) populations. Our results establish that utilizing long-read-based, tissue-specific transcriptomic annotations is critical, enabling isoform-resolved analyses that provide greater sensitivity than conventional gene-level approaches for understanding placental function and context-specific variation across diverse biobanks.

genomics↗

bioRxiv2025

SnoBIRD: A tool to identify C/D box snoRNAs and refine their annotation across all eukaryotes

Small nucleolar RNAs (snoRNAs), a group of noncoding RNAs present amongst all eukaryotes, are most extensively characterized for their regulation of ribosome biogenesis and splicing. Despite their central roles, current snoRNA annotations remain incomplete. Several eukaryote genome annotations contain few or no snoRNAs, and none distinguish expressed snoRNAs from their pseudogenes--a recently characterized snoRNA subclass with distinct features and expression levels. To address this, we developed SnoBIRD, a BERT-based C/D box snoRNA predictor trained on snoRNAs spanning all eukaryote kingdoms. We show that SnoBIRD outperforms existing tools and is the only predictor capable of identifying snoRNA pseudogenes using biologically relevant signal. Applied on the fission yeast and human genomes, we demonstrate that only SnoBIRD scales well with genome size in terms of runtime, and we identify and experimentally validate several new SnoBIRD-predicted C/D box snoRNAs. By running SnoBIRD on multiple eukaryote genomes, we identify hundreds of novel snoRNA candidates and highlight SnoBIRDs usefulness to determine the evolutionary paths of snoRNAs distributed across different species. Overall, SnoBIRD represents a user-friendly and efficient tool for reliably predicting C/D box snoRNAs and their pseudogenes across any eukaryote genome. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/646650v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@99b390org.highwire.dtl.DTLVardef@dbe375org.highwire.dtl.DTLVardef@32358aorg.highwire.dtl.DTLVardef@58fb3a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

The Single-Stranded DNA-Binding Factor SUB1/PC4 Alleviates Replication Stress at Telomeres and is a Vulnerability of ALT Cancer Cells

AO_SCPLOWBSTRACTC_SCPLOWTo achieve replicative immortality, cancer cells must activate telomere maintenance mechanisms. In 10-15% of cancers, this is enabled by recombination-based alternative lengthening of telomeres pathways (ALT). ALT cells display several hallmarks including heterogeneous telomere length, extrachromosomal telomeric repeats and ALT-associated PML bodies. ALT cells also have high telomeric replication stress (RS) enhanced by fork-stalling structures (R-loops, G4s) and altered chromatin states. In ALT cells, telomeric RS promotes telomere elongation but above a certain threshold becomes detrimental to cell survival. Manipulating RS at telomeres has thus been proposed as a therapeutic strategy against ALT cancers. Through analysis of genome-wide CRISPR fitness screens, we identified ALT-specific vulnerabilities and describe here our characterization of the roles of SUB1, a ssDNA-binding protein, as a novel regulator of telomere stability. SUB1 depletion further increases RS at ALT telomeres, profoundly impairing ALT cell growth without impacting telomerase-positive cancer cells. During RS, SUB1 is recruited to stalled forks and ALT telomeres via its ssDNA-binding domain. This recruitment is potentiated by RPA depletion, suggesting that these factors may compete for ssDNA. The viability of ALT cells and their resilience towards RS also requires ssDNA-binding by SUB1. SUB1 depletion accelerates cell death induced by FANCM depletion, triggering unsustainable levels of telomeric damage specifically in ALT cells. Finally, combining SUB1 depletion with RS-inducing drugs rapidly induces replication catastrophe in ALT cells. Altogether, our work identifies SUB1 as a new ALT susceptibility with important roles in the mitigation of RS at ALT telomeres and suggests new therapeutic strategies for a host of still poorly managed cancers. SO_SCPLOWIGNIFICANCEC_SCPLOW SO_SCPLOWTATEMENTC_SCPLOWCurrently, there are few treatment options for ALT cancers with chemotherapy still occupying center stage despite often limited efficacy. ALT cancer cells experience high levels of replication stress at telomeres and its enhancement (e.g. via ATR inhibition) is a promising therapeutic strategy. Sensitivity to ATR inhibition varies amongst ALT cell lines/tumors warranting the development of additional ways to modulate telomeric replication stress. Here we identify SUB1, a single-stranded DNA-binding protein, as a vulnerability of ALT cells. SUB1 localizes to ALT telomeres and mitigates deleterious replication stress. SUB1 depletion synergizes with ATR inhibition and FANCM downregulation suggesting that co-targeting SUB1 with other regulators of replication stress at telomeres may kill ALT cancer cells more effectively.

cancer biology↗

bioRxiv2024

Maternal obesity and offspring neurodevelopment are associated with hypoxic gene expression in term human placenta

One third of women in the United States are affected by obesity during pregnancy. Maternal obesity (MO) is associated with an increased risk of neurodevelopmental and metabolic disorders in the offspring. The placenta, located at the maternal-fetal interface, is a key organ determining fetal development and likely contributes to programming of long-term offspring health. We profiled the term placental transcriptome in humans (pre-pregnancy BMI 35+ [MO condition] or 18.5-25 [lean condition]) using single-nucleus RNA-seq to compare expression profiles in MO versus lean conditions, and to reveal potential mechanisms underlying offspring disease risk. We recovered 62,864 nuclei of high quality from 10 samples each from the maternal-facing and fetal-facing sides of the placenta. On both sides in several cell types, MO was associated with upregulation of hypoxia response genes. On the maternal-facing side only, hypoxia gene expression was associated with offspring neurodevelopmental measures, in Gen3G, an independent pregnancy cohort with bulk placental tissue RNA-seq. We leveraged Gen3G to determine genes that correlated with impaired neurodevelopment and found these genes to be most highly expressed in extravillous trophoblasts (EVTs). EVTs further showed the strongest correlation between neurodevelopment impairment gene scores (NDIGSs) and the hypoxia gene score. We reanalyzed gene expression of cultured EVTs, and found increased NDIGSs associated with exposure to hypoxia. Among EVTs, accounting for the hypoxia gene score attenuated 44% of the association between BMI and NDIGSs. These data suggest that hypoxia in EVTs may be a key process in the neurodevelopmental programming of fetal exposure to MO.

genomics↗

bioRxiv2021

CanDIG: Secure Federated Genomic Queries and Analyses Across Jurisdictions

Rapid expansions of bioinformatics and computational biology have broadened the collection and use of -omics data including genomic, transcriptomic, methylomic and a myriad of other health data types, in the clinic and the laboratory. Both clinical and research uses of such data require co-analysis with large datasets, for which participant privacy and the need for data custodian controls must remain paramount. This is particularly challenging in multi-jurisdictional settings, such as Canada, where health privacy and security requirements are often heterogeneous. Data federation presents a solution to this, allowing for integration and analysis of large datasets from various sites while abiding by local policies. The Canadian Distributed Infrastructure for Genomics platform (CanDIG) enables federated querying and analysis of -omics and health data while keeping that data local and under local control. It builds upon existing infrastructures to connect five health and research institutions across Canada, relies heavily on standards and tooling brought together by the Global Alliance for Genomics and Health (GA4GH), implements a clear division of responsibilities among its participants and adheres to international data sharing standards. Participating researchers and clinicians can therefore contribute to and quickly access a critical mass of -omics data across a national network in a manner that takes into account the multi-jurisdictional nature of our privacy and security policies. Through this, CanDIG gives medical and research communities the tools needed to use and analyze the ever-growing amount of -omics data available to them in order to improve our understanding and treatment of various conditions and diseases. CanDIG is being used to make genomic and phenotypic data available for querying across Canada as part of data sharing for five leading pan-Canadian projects including the Terry Fox Comprehensive Cancer Care Centre Consortium Network (TF4CN) and Terry Fox PRecision Oncology For Young peopLE (PROFYLE), and making data from provincial projects such as POG (Personalized Onco- Genomics) more widely available.

genomics↗

bioRxiv2020

Physiological System Dysregulation in Gene Expression Correlates Negatively with Age

We attempted to identify gene expression systems that dysregulate with age in human peripheral blood samples across five public datasets. Dysregulation of gene ontology (GO)-defined systems was measured using the Mahalanobis distance (DM), a measure of multivariant aberrance. We expected many weak positive DM-age correlations, indicating loss of homeostatic control. Out of the 5180 GO terms tested, we found 230 systems that replicated in at least three datasets. Surprisingly, all 230 systems showed negative DM-age correlations, in contrast to findings with clinical biomarkers. These systems were mostly metabolic functions related to small molecules and nitrogen compounds, transport functions, biosynthetic processes and response to stress functions. These results suggest a loss of responsiveness in these gene expression systems during the aging process, and contrast to some previous literature showing increased gene expression heterogeneity with age.

cell biology↗

bioRxiv2018

BOFdat: generating biomass objective function stoichiometric coefficients from experimental data

Genome-scale models (GEMs) rely on a biomass objective function (BOF) to predict phenotype from genotype. Here we present BOFdat, a Python package that offers functions to generate biomass objective function stoichiometric coefficients (BOFsc) from macromolecular cell composition and relative abundances of macromolecules obtained from omic datasets. Growth-associated and non-growth associated maintenance (GAM and NGAM) costs can also be calculated by BOFdat.\n\nBOFdat is freely available on the Python Package Index (pip install BOFdat). The source code and an example usage (Jupyter Notebook and example files) are available on GitHub (https://github.com/jclachance/BOFdat). The documentation and API are available through ReadTheDocs (https://bofdat.readthedocs.io).\n\nContactjean-christophe.lachance@usherbrooke.ca, zaking@eng.ucsd.edu, pierre-etienne.jacques@usherbrooke.ca

systems biology↗