Biology subjects

Germain, P.-L.

Publications and source records attributed to Germain, P.-L..

At least 19 recordsLinked to original sources

bioRxiv2026

High-throughput identification of chromatin-opening transcription factors

Cell fate transitions involve major changes to the chromatin landscape, some of which are thought to deepen the basin around a newly adopted cell state, stabilizing gene expression programs that might otherwise be transient. Transcription factors (TFs) are central to this process, binding a subset of their genome-wide motif occurrences to create accessible regions that can act as cell type-specific enhancers. However, apart from a few well-studied cases, it is not clear which of the ~1,600 mammalian TFs, when newly expressed, can directly or indirectly initiate newly accessible sites, nor why only some motif occurrences gain accessibility. Here, we applied pooled CRISPR activation (CRISPRa) to upregulate endogenous TFs to physiological levels in mouse embryonic stem cells (mESCs). After screening guides targeting 240 developmental TFs for transcriptional response, we selected 60 and jointly profiled gene expression and chromatin accessibility in ~140,000 single cells. Some TFs (e.g. Cdx2, Sox10) appeared to initiate new accessibility on their own, predominantly at distal sites that are closed in mESCs but coincide with cell type-specific enhancers later in development. Others appeared to partner with "co-TFs", either by leveraging preexisting mESC heterogeneity (e.g. {uparrow}Grhl1 in Tfap2c+ mESCs [->] opening of sites bearing motifs for both), or by hierarchical activation (e.g. {uparrow}Sox7 [->] {uparrow}Gata6 [->] opening of sites bearing either or both motifs). Such dependencies shape which sites are used and which fates are reachable, and may explain the differential behavior of TFs with nearly identical consensus motifs. Thus, the capacity of a newly expressed TF to open chromatin strongly depends on the cell type, cell state and gene regulatory network it lands in. Charting these dependencies will inform how cell fate transitions are initiated in development, homeostasis and disease, and how to engineer them in silico, in vitro and in vivo.

genomics↗

bioRxiv2026

Addressing technical variations in ATAC-seq data and improving motif accessibility analyses

Tagmentation-based methods such as ATAC-seq and Cut&Tag have provided easy ways to profile the epigenome in low-input samples and even single cells. In this contribution, we discuss forms of bias (i.e. technical variations) in tagmentation-based data, in particular ATAC-seq, and introduce three R/bioconductor packages to facilitate bulk and single-cell epigenomic data analysis, with a special focus on motif accessibility analysis. The weightedMotifAccess package uses weight models to enable motif accessibility analysis, including transcription factor footprint information. The betterChromVAR package provides a novel, analytical re-implementation of the popular chromVAR method that offers substantial speed improvements, eliminates stochasticity, and offers additional features. Based on this, we also propose a method, CVnorm, that outperforms alternatives in normalizing technical bias in peak count data. The computational efficiency of these tools further enables a new framework for systematically investigating synergistic and antagonistic interactions between transcription factor motifs. Finally, the epiwraps package streamlines the visualization, normalization, and summarization of epigenomic data.

bioinformatics↗

bioRxiv2026

scDblFinder in Python with GPU support

High-throughput single-cell sequencing provides a scalable solution for characterizing cells and profiling gene expression for hundreds to millions of cells. However, this process gives rise to doublets, which can lead to inaccurate conclusions drawn from the data. A number of packages have therefore been developed to help accurately detect them, and in particular scDblFinder has been shown to outperform alternatives in the detection of doublets in single-cell (RNA) sequencing data. Being implemented in R, however, its adoption has been more limited in the Python community. Here, we present scDblFinderPy, a Python-based implementation of the scDblFinder R method, and show that it obtains similar performances. Furthermore, we include in it optional GPU support, thus further speeding up the process.

bioinformatics↗

bioRxiv2026

Large-scale prediction of transcription factor binding across human cell types informs regulatory genomics and reveals promiscuous occupancy associated with chromatin contacts

Our understanding of the mechanisms regulating gene expression has been hampered by our limited knowledge of which transcription factors (TFs) bind where in the genome, which is highly cell type-specific. While genome-wide TF binding can be experimentally assayed for individual TFs in individual cell types, profiling the full combinations of over 1600 TFs in hundreds of cell types is beyond practical reach. In this work, we developed a streamlined platform, TFBlearner, to train TF-specific models and predict bindings based on ATAC-seq data. We focused on biologically-motivated feature engineering and harnessed TF cooperativity and binding similarity across cell types to achieve state of the art binding predictions in unseen cell types in a scalable fashion. This enabled us to generate a compendium of binding predictions for 1108 Chromatin-associated proteins, of which 960 TFs, across 43 human cell types including widely-used cell lines and 36 physiological cell types representing all major human cell lineages. We show how the models additionally provide biological insights on the TFs, and show how the binding predictions can be used in downstream tasks such as TF activity inference. Our study additionally led to the observation of high promiscuity in TF occupancy. To investigate aspecific occupancy, we characterized crowded or high-occupancy (HOT) regions across cell types, providing evidence of their functionality, and reporting important cell type-specificity. Finally, we show that, across cell types, crowded regions engage in more 3D contacts, and that most TF occupancy at crowded promoters can be explained as tethered bindings from distal regulatory elements.

genomics↗

bioRxiv2026

miR-495-3p inhibition in mice rescues mTORC1 hyperactivation-driven autistic-like behaviors

Defective social behavior and cognitive functions are hallmarks of Autism spectrum disorders (ASD). The molecular mechanisms keeping social behavior in a physiological range are largely unknown. We recently found that conditional knockout (cKO) of tmiRNA cluster miR-379-410 in mouse hippocampal neurons leads to hypersocial behavior. Therefore, inhibiting miR-379-410 members might represent a strategy to promote sociability in ASD. As an ASD model, we chose knockdown (KD) of the ASD risk gene Tsc1, a key negative regulator of mTORC1. Acute Tsc1 knockdown (KD) in hippocampal neurons was sufficient to induce hyposociability and memory deficits in adult wild-type mice. In contrast, Tsc1 KD had no effect on sociability in miR-379-410 cKO mice, indicating a requirement of miR-379-410. Furthermore, Tsc1 KD led to upregulation of tmiR-495-3p, and inhibition of this miRNA by antisense oligonucleotides was sufficient to prevent hyposociability and memory impairments. Our findings suggest that miR-495-3p is a key downstream effector of the Tsc1/mTORC1 pathway in sociability, and that targeting miR-495-3p represents a therapeutic avenue for restoring social and cognitive impairments in ASD without affecting mTORC1 homeostasis.

neuroscience↗

bioRxiv2025

Bulk-based hypothesis weighing to increase power in single-cell differential expression analysis

Due to the costs of single-cell sequencing, sample sizes are often relatively limited, sometimes leading to poorly reproducible results. In many contexts, however, larger bulk RNAseq data is available for the same conditions or experimental paradigm, which can be used as additional evidence of a generalizable differential expression pattern. Here, we show how such data can be used, via bulk-based hypothesis weighing (bbhw), to increase the power and robustness of single-cell differential state analysis. We find that all methods improve performance, with the best results obtained by applying a grouped Benjamini-Hochberg procedure on bins based on proportion-adjusted significance (PAS). These methods are implemented in the muscat package, and should be applicable to a broader range of scenarios.

bioinformatics↗

bioRxiv2025

Distinct molecular mechanisms of stress habituation in the mouse hippocampus

Chronic stress is a risk factor for neuropsychiatric disorders, making the ability to adapt to repeated stress a crucial determinant of mental health. On a molecular level, it remains unclear whether repeated exposure to stress is characterized by habituation - a decreased responsiveness to the same stimulus - or by the emergence of new, adaptive responses. Here, we explore how the tightly regulated molecular response triggered by acute restraint stress becomes altered after repeated restraint exposure. Transcriptomic sampling of the mouse hippocampus at multiple time points revealed that repeated stress leads to widespread habituation, damping stress-induced gene expression of all stress-responsive genes. However, we find no evidence for the emergence of new response profiles or alterations in baseline gene expression. Using single-cell multi-omics, we show that these findings hold true across cell types, and we reveal cell type specific patterns of habituation. Transcriptomic and chromatin accessibility profiles identify two distinct mechanisms that contribute to the observed habituation patterns: an early cAMP-associated mechanism that is related to blunted transcription after chronic stress, and a late corticosterone-dependent mechanism that is linked to a shortened transcriptional response. These extensive data are integrated, along with our previous work, into an interactive app, providing a uniquely detailed molecular resource that characterizes the acute stress response and the process of habituation across the genome.

neuroscience↗

bioRxiv2025

A mixture of endocrine disrupting chemicals linked to lower birth weight induces adipogenesis and transcriptional changes related to birth weight alterations and diabetes

There is increasing evidence that endocrine disrupting chemicals (EDCs) are contributing to the rise in metabolic disorders and obesity. Humans are constantly exposed to numerous EDCs, thus human exposure entails complex EDC mixtures. In this study, we examined the effects of an EDC mixture, mixture G, composed of four phthalate esters, triclosan, and three poly- och perfluorinated alkyl substances. Mixture G had previously been defined based on its association with lower birth weight in a pregnancy cohort, where low birth weight is an early risk factor for metabolic morbidities later in life. Here, we studied its effects on adipogenesis and uncovered their underlying transcriptional changes. Human mesenchymal stem cells (hMSCs) were exposed to mixture G in concentrations and mixing ratios that reflect those measured in human serum. Mixture G induced adipogenesis in hMSCs, as evidenced by a dose-dependent increase in lipid droplet accumulation after 14-21 days. Notably, significant adipogenic effects were observed at concentrations comparable to those detected in humans. RNA-sequencing upon exposure for 48 h revealed dose-dependent transcriptional changes in over 1000 genes. Mixture G-induced differentially expressed genes (DEGs) showed significant overlap with genes involved in osteogenesis, with glucocorticoid-regulated genes, and with genes associated with birth weight alterations and diabetes type II. These results indicate that exposure to an environmentally relevant EDC mixture induces adipogenesis and leads to transcriptional alterations that might change the balance between adipogenic and osteogenic differentiation as well as the functionality of MSCs, possibly via interference with glucocorticoid signalling. Thus our findings underscore the role of EDCs as metabolic disruptors and shed light on the molecular mechanisms underlying their potential contribution to the development of metabolic disorders. HighlightsO_LIAn endocrine disruptor mixture linked to lower birth weight increases adipogenesis C_LIO_LIThe mixture induced transcriptomic changes at low doses C_LIO_LIAffected genes are associated with birth weight and diabetes type II C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/638050v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@12428bforg.highwire.dtl.DTLVardef@ef28b3org.highwire.dtl.DTLVardef@159024corg.highwire.dtl.DTLVardef@a605b7_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2024

On metrics for subpopulation detection in single-cell and spatial omics data

Benchmarks are crucial to understanding the strengths and weaknesses of the growing number of tools for single-cell and spatial omics analysis. A key task is to distinguish subpopulations within complex tissues, where evaluation typically relies on external clustering validation metrics. Different metrics often lead to inconsistencies between rankings, highlighting the importance of understanding the behavior and biological implications of each metric. In this work, we provide a framework for systematically understanding and selecting validation metrics for single-cell data analysis, addressing tasks such as creating cell embeddings, constructing graphs, clustering, and spatial domain detection. Our discussion centers on the desirable properties of metrics, focusing on biological relevance and potential biases. Using this framework, we not only analyze existing metrics, but also develop novel ones. Delving into domain detection in spatial omics data, we develop new external metrics tailored to spatially-aware measurements. Additionally, a Bioconductor R package, poem, implements all the metrics discussed. While we focus on single-cell omics, much of the discussion is of broader relevance to other types of high-dimensional data.

bioinformatics↗

bioRxiv2024

A Comprehensive and Robust Multiplex-DIA Workflow Profiles Protein Turnover Regulations Associated with Cisplatin Resistance

Measuring protein turnover is essential for understanding cellular biological processes and advancing drug discovery. The multiplex DIA mass spectrometry (DIA-MS) approach, combined with dynamic SILAC labeling (pulse-SILAC, or pSILAC), has proven to be a reliable method for analyzing protein turnover and degradation kinetics. Previous multiplex DIA-MS workflows have employed various strategies, including leveraging the highest isotopic labeling channels of peptides to enhance the detection of isotopic MS signal pairs or clusters. In this study, we introduce an improved and robust workflow that integrates a novel machine learning strategy and channel-specific statistical filtering, enabling dynamic adaptation to systematic or temporal variations in channel ratios. This allows comprehensive profiling of protein turnover throughout the pSILAC experiment without relying solely on the highest channel signals. Additionally, we developed KdeggeR, a data processing and analysis package optimized for pSILAC-DIA experiments, which estimates and visualizes peptide and protein degradation rates and dynamic profiles. Our integrative workflow was benchmarked on both 2-channel and 3-channel standard DIA datasets and workflow to an aneuploid cancer cell model before and after cisplatin resistance development demonstrated a strong negative correlation between transcript regulation and protein degradation for major protein complex subunits. We also identified specific protein turnover signatures associated with cisplatin resistance.

systems biology↗

bioRxiv2024

microRNA-138-5p suppresses excitatory synaptic strength at the cerebellar input layer

MicroRNAs are small, highly conserved non-coding RNAs that negatively regulate mRNA translation and stability. In the brain, microRNAs contribute to neuronal development, synaptogenesis, and synaptic plasticity. The microRNA 138-5p (miR-138-5p) controls inhibitory synaptic transmission in the hippocampus and is highly expressed in cerebellar excitatory neurons. However, its role in cerebellar synaptic transmission remains unkwown. Here, we investigated excitatory transmission within the cerebellum in mice expressing a sponge construct that sequesters endogenous miR-138-5p. Mossy fiber stimulation-evoked excitatory postsynaptic currents (EPSCs) in granule cells were significantly larger compared with controls. Furthermore, we observed larger miniature EPSC amplitudes, suggesting increased postsynaptic AMPA receptor numbers. High-frequency train stimulations revealed enhanced short-term depression following miR-138-5p downregulation. Together with computational modelling, this suggests a negative regulation of presynaptic release probability. Overall, our results demonstrate that miR-138-5p suppresses synaptic strength through pre- and postsynaptic mechanisms, providing a powerful mechanism for tuning excitatory synaptic input into the cerebellum.

neuroscience↗

bioRxiv2024

NanoTag - an IgG-free method for mapping DNA-protein interactions

Genome-wide profiling of DNA-protein interactions in cells can provide important information about mechanisms of gene regulation. Most current methods for genome-wide profiling of DNA-bound proteins, such as ChIP-seq and CUT&Tag, use conventional IgG antibodies to bind target protein(s), which limits their applicability to targets for which high affinity and high specificity antibodies are available. Here we describe NanoTag, a novel method derived from CUT&Tag that is IgG-free and uses a nanobody to profile DNA-protein interactions. NanoTag is based on an anti-GFP nanobody-Tn5 transposase fusion that allows mapping GFP-tagged proteins associated with chromatin in a fast and cost-effective manner. We demonstrate the utility of NanoTag by profiling the histone mark H3K4me3 via its binding partner TATA box-binding protein-associated factor 3 (TAF3) and the transcription factors Nanog and CTCF in mouse embryonic stem cells expressing GFP-tagged targets. For the targets examined, NanoTag data shows high correlation to CUT&Tag data and displays a similarly high signal-to-noise ratio. Overall, NanoTag provides a flexible, IgG-free and cost-effective method to generate high resolution DNA-binding profiles in cells or tissues.

molecular biology↗

bioRxiv2024

A functional screen uncovers circular RNAs regulating excitatory synaptogenesis in hippocampal neurons

Circular RNAs (circRNAs) are an expanding class of largely unexplored RNAs which are prominently enriched in the mammalian brain. Here, we systematically interrogated their role in excitatory synaptogenesis of rat hippocampal neurons using RNA interference. Thereby, we identified seven circRNAs as negative regulators of excitatory synapse formation, many of which contain high-affinity microRNA binding sites. Knockdown of one of these candidates, circRERE, surprisingly promoted the formation of electrophysiologically silent synapses. Mechanistically, circRERE knockdown resulted in a preferential upregulation of synaptic mRNAs containing binding sites for miR-128-3p because of a reduced protective interaction between miR-128-3p and circRERE. Accordingly, overexpression of circRERE rescued exaggerated synapse formation upon circRERE knockdown in a miR-128-3p binding site-specific manner. Overall, our results uncover circRERE-mediated stabilization of miR-128-3p as a novel mechanism to restrict the formation of silent excitatory synaptic co-clusters and more generally implicate circRNA-dependent microRNA regulation in the control of synapse development and function.

neuroscience↗

bioRxiv2024

Early life stress-induced miR-708-5p affects bipolar disorder-associated phenotypes through neuronatin downregulation

Mood-disorders (MDs) are caused by a complex interplay of genetic and environmental (GxE) risk factors. However, the molecular pathways engaged by GxE risk factors to trigger specific MD-associated endophenotypes are still poorly understood. Here, by using unbiased small RNA sequencing in peripheral blood mononuclear cells (PBMCs), we identified the BD-associated miR-708-5p as one of the most strongly upregulated microRNAs in peripheral blood of both healthy human subjects with a high genetic or environmental (early life stress) predisposition to develop MDs. miR-708 is also upregulated in the hippocampus of rats which underwent juvenile social isolation, a rodent model of early life stress. Furthermore, ectopic overexpression of miR-708-5p in the hippocampus of adult male mice is sufficient to elicit MD-associated behavioural endophenotypes, demonstrating a causal role for elevated miR-708-5p levels in MD development. We further show that miR-708-5p directly targets Neuronatin (Nnat), an endoplasmic reticulum (ER) resident protein involved in calcium homeostasis. Consequently, restoring Nnat expression in the hippocampus of miR-708-5p overexpressing mice rescues miR-708-5p dependent behavioural phenotypes. Finally, miR-708-5p is strongly upregulated in PBMCs derived from patients diagnosed with MD, in particular BD males. Peripheral expression of miR-708-5p, in conjunction with the previously identified miR-499-5p, allows to differentiate male BD patients from patients suffering from major depressive disorder (MDD) and healthy controls. In summary, we describe a functional role for the miR-708-5p/Nnat pathway in MD etiology and identify miR-708-5p as a potential biomarker for the differential diagnosis of MDs.

neuroscience↗

bioRxiv2024

On the identification of differentially-active transcription factors from ATAC-seq data

ATAC-seq has emerged as a rich epigenome profiling technique, and is commonly used to identify Transcription Factors (TFs) underlying given phenomena. A number of methods can be used to identify differentially-active TFs through the accessibility of their DNA-binding motif, however little is known on the best approaches for doing so. Here we benchmark several such methods using a combination of curated datasets with various forms of short-term perturbations on known TFs, as well as semi-simulations. We include both methods specifically designed for this type of data as well as some that can be repurposed for it. We also investigate variations to these methods, and identify three particularly promising approaches (a chromVAR-limma workflow with critical adjustments, monaLisa and a combination of GC smooth quantile normalization and multivariate modeling). We further investigate the specific use of nucleosome-free fragments, the combination of top methods, and the impact of technical variation. Finally, we illustrate the use of the top methods on a novel dataset to characterize the impact on DNA accessibility of TRAnscription Factor TArgeting Chimeras (TRAFTAC), which can deplete TFs - in our case NFkB - at the protein level. Author summaryTranscription factors regulate gene expression by binding sites in the genome that often harbor a specific DNA motif. The collective accessibility of these motif-matching regions, measured by technologies such as ATAC-seq, can be used to infer the activity of the corresponding transcription factors. Here we use curated datasets of 11 TF-specific perturbations as well as 116 semi-simulated datasets to benchmark various methods for identifying factors that differ in activity between experimental conditions. We investigate important variations in the analysis and make recommendations pertaining to such analysis. Finally, we illustrate the application of the top methods to characterize the effects of a novel method for perturbing transcription factors at the protein level.

bioinformatics↗

bioRxiv2023

A human-specific microRNA controls the timing of excitatory synaptogenesis

Neural circuit development in the human cortex is considerably prolonged in comparison to non-human primates, a trait that contributes to the remarkable cognitive capacity of modern humans. Here, we explore the regulatory role of non-coding RNAs, which dramatically expanded during brain evolution, in synapse development of human-induced pluripotent stem-cell derived neurons. We found that inhibition of a human-specific microRNA, miR-1229-3p, alters the trajectory of human neuronal maturation and enhances excitatory synaptic transmission. Transcriptome analysis following miR-1229 knockdown revealed a downregulation of mitochondrial DNA (mtDNA) encoded genes. We further show that miR-1229 regulates mitochondrial morphology, mtDNA abundance and matrix calcium concentration, and that stimulation of mitochondrial metabolism rescues decreased calcium buffering in miR-1229-3p depleted neurons. Accordingly, miR-1229 directly targets an entire network of genes involved in mitochondrial function and ER-associated protein homeostasis. Our findings reveal an important function of human-specific miR-1229-3p in developmental timing of human synaptogenesis and generally implicate non-coding RNAs in the control of human connectivity and cognition. One-Sentence SummaryA human-specific microRNA slows down the formation and maturation of neuronal synapses by reducing mitochondrial metabolism.

neuroscience↗

bioRxiv2023

Benchmarking computational methods for single-cell chromatin data analysis

Single-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices. We benchmarked 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluated the performance of each method at different data processing stages. This comprehensive approach allowed us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection. Our analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.

bioinformatics↗

bioRxiv2023

Harnessing PROTAC technology to combat stress hormone receptor activation

Counteracting the overactivation of glucocorticoid receptors (GR) is an important therapeutic goal in stress-related psychiatry and beyond. The only clinically approved GR antagonist lacks selectivity and induces unwanted side effects. To complement existing tools of small-molecule-based inhibitors, we present a highly potent, novel catalytically-driven GR degrader, KH-103, based on proteolysis-targeting chimera technology. This selective degrader enables immediate and reversible GR depletion that is independent of genetic manipulation and circumvents transcriptional adaptations to inhibition. KH-103 achieves passive inhibition, preventing agonistic induction of gene expression, and significantly averts the GRs genomic effects compared to two currently available inhibitors. Application in primary-neuron cultures revealed the dependency of a glucocorticoid-induced increase in spontaneous calcium activity on GR. Finally, we present a proof of concept for application in-vivo. KH-103 opens opportunities for a more lucid interpretation of GR functions with translational potential.

molecular biology↗