Biology subjects

Thibodeau, A.

Publications and source records attributed to Thibodeau, A..

9 recordsLinked to original sources

bioRxiv2026

Mandatory use of mock communities highlighted by the descriptive comparison of Epi2Me 16S and EMU bioinformatic workflows for full-length 16S rRNA Nanopore sequencing.

Full-length 16S rRNA gene sequencing using Oxford Nanopore Technologies has emerged as a promising approach to improve species-level resolution in microbiota studies. However, the accuracy of taxonomic assignment remains highly dependent on the bioinformatics s used to process Nanopore long-read data. Therefore, the only way to ensure a good level of certainty in obtained results is to use positive controls in the form of mock communities in the experimental designs. In this study, we compared the performance of Epi2Me 16S (using Minimap2 or Kraken2) workflows provided by Oxford Nanopore Technologies and an EMU workflow for full-length 16S rRNA gene analysis. Using a commercial mock community sequenced across multiple Nanopore runs, taxonomic assignment accuracy and reproducibility was evaluated. Epi2Me-Kraken2 exhibited 18 % of incorrect genus-level assignments and failed to identify 3 species present in the mock community. While Epi2Me-Minimap2 achieved an excellent genus-level classification, reporting 9 % of sequences assigned to a genus not in the mock community, species-level assignments were inconsistent for several community members such as Listeria. In contrast, EMU provided accurate and consistent species-level taxonomic profiles, with all species correctly identified while keeping the number of genus absent from the mock community at 1.2%. ImportanceThese results highlight that Epi2Me integrated workflows are not the best option for specie-level taxonomic assignation. More importantly, this paper underscores the importance of routine inclusion of positive controls for microbiota studies, in the form of mock communities, as a critical safeguard for accurate data interpretation. Without the use of a mock community, a paper published would be at risk of reporting wrong observations and inaccurate conclusions.

bioinformatics↗

bioRxiv2026

Lifestyle intervention is associated with attenuation of ER stress/inflammation and enhancement of naive immune cell identity in older adults with metabolic disease

Chronic inflammation and cellular stress are hallmarks of aging, obesity, and type 2 diabetes (T2D), but whether these programs can be modulated by lifestyle intervention in late life, particularly in the presence of established metabolic disease, remain unknown. We profiled circulating immune cells from older adults with obesity and T2D (ages 66-83 years; n = 9) before and after a 6-month lifestyle intervention combining caloric restriction with exercise training. Participants showed substantial weight loss ([~]7%) alongside improvements in glycemic control, insulin sensitivity, and physical performance. Longitudinal single-cell transcriptomic and epigenomic profiling identified two major changes. First, intervention was associated with downregulation of inflammatory and endoplasmic reticulum (ER) stress transcriptional programs, with the most pronounced effects observed in CD14+ monocytes. DDIT3 (CHOP) was transcriptionally and epigenetically downregulated and its inferred regulatory network encompassed multiple inflammatory mediators. Second, naive CD4+ T, naive Treg, and naive B cells exhibited an upregulation of naive cell identity genes, with naiveness scores increasing after intervention, which declines with age in an independent healthy adult cohort. Together, these findings suggest that lifestyle intervention is associated with coordinated remodeling of both innate and adaptive immune compartments in older adults, revealing substantial plasticity of the aging immune system especially targeting ER stress, inflammation, and naive lymphocyte identity programs.

immunology↗

bioRxiv2025

Single-cell map of the healthy human immune system across the lifespan reveals unique infant immune signatures

The human immune system undergoes continuous remodeling from infancy through old age, yet the timing and trajectory of these changes across the lifespan remain poorly defined. To address this, we profiled peripheral blood mononuclear cells from 95 healthy individuals (ages 2 months to 88 years), including infants (n=27), children (n=23), adults (n=18), and older adults (n=27) using scRNA-seq and snATAC-seq. MAIT and {gamma}{delta} T cells showed a "Rise and fall" pattern, which rise in childhood, peak in young adulthood, and decline with age. CD8+ T cells were the most affected by aging with decreasing naive T cells and increasing GzK+ CD8+ T cells and TEMRA cells. Infants had lower myeloid/lymphoid ratio, with a distinct composition marked by increased frequencies of CD16+ monocytes and plasmacytoid dendritic cells and reduced frequencies of CD14+ monocytes and conventional DCs. Their adaptive immune compartment also displayed unique features, including constitutive interferon-stimulated gene expression in T and B cells, and an expanded SOX4+ populations in naive CD4+, naive CD8+ and {gamma}{delta} T cells, comprising [~]30% of the naive T cell pool. SOX4+ naive CD4+ T cells displayed a Th2 epigenetic signature. This map provides critical insights into human immune system dynamics across the lifespan, emphasizing unique features of the infant immune system.

immunology↗

bioRxiv2025

CMV reshapes lymphoid immunity in aging: a single-cell atlas with predictive modeling

Cytomegalovirus (CMV) is a common herpesvirus that establishes lifelong latency and becomes increasingly prevalent with age. We systematically characterized CMV-associated immune remodeling by analyzing six human cohorts (two newly built) using single-cell RNA sequencing, T cell receptor (TCR) sequencing, and flow cytometry. Beyond the well-known expansion of CD4/CD8 TEMRA, adaptive NK, and {gamma}{delta} T cells, CMV(+) adults exhibited increased frequencies of GZMK CD8 T cells and atypical B cells, alongside a reduction of CD56dim NK cells. Longitudinal profiling of an individual who seroconverted revealed rapid CMV-driven shifts in circulating immune cell frequencies. Single-cell TCR data analyzed using a large database of CMV-associated clones combined with predictive modelling (CMVerify), identified novel CMV-specific clonal expansions reproduced across two independent cohorts. In the CD8 lineage, CMV-specific clones were enriched in GZMK CD8 and CD8 TEMRA cells, while in the CD4 lineage, Th1 cells showed clonal expansion alongside CD4 TEMRA cells. This integrative study revealed how latent CMV alters the cellular and clonal landscape, defining GZMK CD8 and Th1 cells as newly recognized elements of response to CMV in humans.

immunology↗

bioRxiv2024

Comparative Multi-omic Mapping of Human Pancreatic Islet Endoplasmic Reticulum and Cytokine Stress Responses Provides Insights into Type 2 Diabetes Genetics

Endoplasmic reticulum (ER) and inflammatory stress responses are two pathophysiologic factors contributing to islet dysfunction and failure in Type 2 Diabetes (T2D). However, how human islet cells respond to these stressors and whether T2D-associated genetic variants modulate these responses is unknown. To fill this knowledge gap, we profiled transcriptional (RNA-seq) and epigenetic (ATAC-seq) remodeling in human islets exposed to ex vivo ER (thapsigargin) or inflammatory (IL-1{beta}+IFN-{gamma}) stress. 5,427 genes ([~]32%) were associated with stress responses; most were stressor-specific, including upregulation of genes mediating unfolded protein response (e.g. DDIT3, ATF4) and NFKB signaling (e.g. NFKB1, NFKBIA) in ER stress and cytokine-induced inflammation respectively. Islet single-cell RNA-seq profiling revealed strong but heterogeneous beta cell ER stress responses, including a distinct beta cell subset that highly expressed apoptotic genes. Epigenetic profiling uncovered 14,968 stress-responsive cis-regulatory elements (CREs; [~]14%), the majority of which were stressor-specific, and revealed increased accessibility at binding sites of transcription factors that were induced upon stress (e.g. ATF4 for ER stress, IRF8 for cytokine-induced inflammation). Eighty-six stress-responsive CREs overlapped known T2D-associated variants, including 20 residing within CREs that were more accessible upon ER stress. Among these, we linked the rs6917676 T2D risk allele (T) to increased in vivo accessibility of an islet ER stress-responsive CRE and allele-specific beta cell nuclear factor binding in vitro. We showed that MAP3K5, the only ER stress-responsive gene in this locus, promotes beta cell apoptosis. Consistent with its pro-apoptotic and putative diabetogenic roles, MAP3K5 expression inversely correlated with beta cell abundance in human islets and was induced in beta cells from T2D donors. Together, this study provides new genome-wide insights into human islet stress responses and putative mechanisms of T2D genetic variants.

genomics↗

bioRxiv2022

Epigenetic Memory of COVID-19 in Innate Immune Cells and Their Progenitors

Severe coronavirus disease 2019 (COVID-19) is characterized by systemic inflammation and can result in protracted symptoms. Robust systemic inflammation may trigger persistent changes in hematopoietic cells and innate immune memory through epigenetic mechanisms. We reveal that rare circulating hematopoietic stem and progenitor cells (HSPC), enriched from human blood, match the diversity of HSPC in bone marrow, enabling investigation of hematopoiesis and HSPC epigenomics. Following COVID-19, HSPC retain epigenomic alterations that are conveyed, through differentiation, to progeny innate immune cells. Epigenomic changes vary with disease severity, persist for months to a year, and are associated with increased myeloid cell differentiation and inflammatory or antiviral programs. Epigenetic reprogramming of HSPC may underly altered immune function following infection and be broadly relevant, especially for millions of COVID-19 survivors. One Sentence SummaryTranscriptomic and epigenomic analysis of blood reveal sustained changes in hematopoiesis and innate immunity after COVID-19. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/479588v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@1ffe42dorg.highwire.dtl.DTLVardef@dd4868org.highwire.dtl.DTLVardef@1bcae8borg.highwire.dtl.DTLVardef@674e85_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

A read count-based method to detect multiplets and their cellular origins from snATAC-seq data

Similar to other droplet-based single cell assays, single nucleus ATAC-seq (snATAC-seq) data harbor multiplets that confound downstream analyses. Detecting multiplets in snATAC-seq data is particularly challenging due to its sparsity and trinary nature (0 reads: closed chromatin, 1: open in one allele, 2: open in both alleles), yet offers a unique opportunity to infer multiplets when >2 uniquely aligned reads are observed at multiple loci. Here, we implemented the first read count-based multiplet detection method, ATAC-DoubletDetector, that detects multiplets independently of cell-type. Using PBMC and pancreatic islet datasets, ATAC-DoubletDetector captured simulated heterotypic multiplets (different cell-types) with [~]0.60 recall, showing [~]24% improvement over state of the art. ATAC-DoubletDetector detected homotypic multiplets with [~]0.61 recall, representing the first method to detect multiplets originating from the same cell type. Using our novel clustering-based algorithm, multiplets were annotated to their cellular origins with [~]85% accuracy. Application of ATAC-DoubletDetector will improve downstream analysis of snATAC-seq.

bioinformatics↗

bioRxiv2020

CoRE-ATAC: A Deep Learning model for the functional Classification of Regulatory Elements from single cell and bulk ATAC-seq data

Cis-Regulatory elements (cis-REs) include promoters, enhancers, and insulators that regulate gene expression programs via binding of transcription factors. ATAC-seq technology effectively identifies active cis-REs in a given cell type (including from single cells) by mapping accessible chromatin at base-pair resolution. However, these maps are not immediately useful for inferring specific functions of cis-REs. For this purpose, we developed a deep learning framework (CoRE-ATAC) with novel data encoders that integrate DNA sequence (reference or personal genotypes) with ATAC-seq cut sites and read pileups. CoRE-ATAC was trained on 4 cell types (n=6 samples/replicates) and accurately predicted known cis-RE functions from 7 cell types (n=40 samples) that were not used in model training (mean average precision=0.80). CoRE-ATAC enhancer predictions from 19 human islet samples coincided with genetically modulated gain/loss of enhancer activity, which was confirmed by massively parallel reporter assays (MPRAs). Finally, CoRE-ATAC effectively inferred cis-RE function from aggregate single nucleus ATAC-seq (snATAC) data from human blood-derived immune cells that overlapped with known functional annotations in sorted immune cells, which established the efficacy of these models to study cis-RE functions of rare cells without the need for cell sorting. ATAC-seq maps from primary human cells reveal individual- and cell-specific variation in cis-RE activity. CoRE-ATAC increases the functional resolution of these maps, a critical step for studying regulatory disruptions behind diseases.Author Summary Non-coding DNA sequences serve different functional roles to regulate gene expression. For these sequences to be active, they must be accessible for proteins and other factors to bind in order to carry out a specific regulatory function. Even so, mutations within these sequences or other regulatory events may modulate their activity or regulatory function. It is therefore critical that we identify these non-coding sequences and their specific regulatory function to fully understand how specific genes are regulated. Current sequencing technologies allow us to identify accessible sequences via chromatin accessibility maps from low cell numbers, enabling the study of clinical samples. However, determining the functional role associated with these sequences remains a challenge. Towards this goal, we harnessed the power of deep learning to unravel the intricacies of chromatin accessibility maps to infer their associated gene regulatory functions. We demonstrate that our method, CoRE-ATAC, can infer regulatory functions in diverse cell types, captures activity differences modulated by genetic mutations, and can be applied to accessibility maps of single cell clusters to infer regulatory functions of rare cell populations. These inferences will further our understanding of how genes are regulated and enable the study of these mechanisms as they relate to disease.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2018

EndoC-βH1 multi-genomic profiling defines gene regulatory programs governing human pancreatic β cell identity and function

EndoC-{beta}H1 is emerging as a critical human beta cell model to study the genetic and environmental etiologies of beta cell function, especially in the context of diabetes. Comprehensive knowledge of its molecular landscape is lacking yet required to fully take advantage of this model. Here, we report extensive chromosomal (spectral karyotyping), genetic (genotyping), epigenetic (ChIP-seq, ATAC-seq), chromatin interaction (Hi-C, Pol2 ChIA-PET), and transcriptomic (RNA-seq, miRNA-seq) maps of this cell model. Integrated analyses of these maps define known (e.g., PDX1, ISL1) and putative (e.g., PCSK1, mir-375) beta cell-specific chromatin interactions and transcriptional cis-regulatory networks, and identify allelic effects on cis-regulatory element use and expression.\n\nImportantly, comparative analyses with maps generated in primary human islets/beta cells indicate substantial preservation of chromatin looping, but also highlight chromosomal heterogeneity and fetal genomic signatures in EndoC-{beta}H1. Together, these maps, and an interactive web application we have created for their exploration, provide important tools for the broad community in the design and success of experiments to probe and manipulate the genetic programs governing beta cell identity and (dys)function in diabetes.

genomics↗