Biology subjects

Andre, O.

Publications and source records attributed to Andre, O..

8 recordsLinked to original sources

bioRxiv2025

ECM-stiffness mediated persistent fibroblast activation requires integrin and formin dependent chromatin remodeling

Transient activation of fibroblasts into contractile myofibroblasts is essential for extracellular matrix (ECM) production and remodeling during wound healing and tissue regeneration. While ECM-dependent mechanisms mediating transient activation is well studied, how fibroblasts switch from transient to a persistently activated state and drive fibrosis and aberrant tissue repair in diseases such as cancer is less understood. Here, we show that human cancer-associated fibroblasts (CAFs) switch from transient to persistently activated states upon prolonged exposure to stiff ECMs and stiffness-dependent secreted factors. This switch is accompanied by activation of ECM-stiffness-dependent mechanotransduction pathways and changes in the nuclear architecture and its association with chromatin. Mechanistically, we identify two pathways required for this switch-ECM ligand binding and complete activation of {beta}1 integrins smoothens the nuclear lamina during prolonged exposure, increases nuclear YAP, and reduces lamin-chromatin contacts while in parallel, exposure to the stiff ECM activates the formin mDia2 and independent of alterations in the nuclear architecture alters lamin-chromatin coupling, likely through its role in assembling nuclear actin. Importantly, we find that blocking either pathway prevents persistent myofibroblast activation, which is rescued by inhibition of histone deacetylases, indicating that dynamic chromatin modifications act downstream of these ECM-dependent pathways to maintain the persistently activated state. These findings link integrin-based ECM sensing to chromatin remodeling and fibroblast memory, with implications for stromal plasticity in the tumor microenvironment.

cell biology↗

bioRxiv2025

Smart Microscopy: Current Implementations and a Roadmap for Interoperability

Smart microscopy is transforming life sciences by automating experimental imaging workflows and enabling real-time adaptation based on feedback from images and other data streams. This shift increases throughput, improves reproducibility, and expands the functional capabilities of microscopes. However, the current landscape is highly fragmented. Academic researchers often develop custom solutions for specific scientific needs, while industry offerings are typically proprietary and tied to specific hardware. This diversity, while fostering innovation, also creates major challenges in interoperability, reproducibility, and standardization, which slows progress and adaption. This article presents a collaborative effort between academic and industry leaders to survey the current state of smart microscopy, highlight representative implementations, and identify common technical and organizational barriers. We propose a framework for greater interoperability based on shared standards, modular software design, and community-driven development. Our goal is to support collaboration across the field and lay the groundwork for a more connected, reusable, and accessible smart microscopy ecosystem. We conclude with a call to action for researchers, hardware developers, and institutions to join in building an open, interoperable foundation that will unlock the full potential of smart microscopy in life science research.

cell biology↗

bioRxiv2024

Cardiomyocyte antisense transcription regulates exon usage in the elastic spring region of Titin to modulate sarcomere function

BackgroundThe spring-like sarcomere protein Titin (TTN) is a key determinant of cardiac passive stiffness and diastolic function. Alternative splicing of TTN I-band exons produce protein isoforms with variable size and elasticity, but the mechanisms regulating TTN exon skipping and isoform composition in the human heart are not well studied. Non-coding RNA transcripts from the antisense strand of protein-coding genes have been shown to regulate alternative splicing of the sense gene. The TTN gene locus harbours >80 antisense transcripts with unknown function in the human heart. The aim of this study was to determine if TTN antisense transcripts play a role in alternative splicing of TTN. MethodsRNA-sequencing and RNA in situ hybridization (ISH) of cardiac tissue from unused organ donor hearts (n=7) and human induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs) were used to determine the expression and localization of TTN antisense transcripts. The effect of siRNA-mediated knock down of TTN antisense transcripts on TTN exon usage in iPS-CMs was determined using RNA-sequencing. Live cell imaging with sarcomere tracking was used to analyze the effect of antisense transcript knock down on sarcomere length, organization and contraction dynamics. RNA ISH, immunofluorescence and high content microscopy was performed in iPS-CMs to study the interaction between antisense transcripts, TTN mRNA and splice factor protein RBM20. ResultsIn mapping TTN antisense transcription, we found that TTN-AS1-276 was the predominant transcript in the human heart and that it was mainly localized in cardiomyocyte nuclear chromatin. Knock down of TTN-AS1-276 in human iPS-CMs resulted in decreased interaction between the splicing factor RBM20 and TTN pre-mRNA, decreased TTN I-band exon skipping, and markedly lower expression of the less elastic TTN isoform N2B. The effect on TTN exon usage was independent of sense-antisense exon overlap and polymerase II elongation rate. Furthermore, knockdown resulted in longer sarcomeres with preserved alignment, improved fractional shortening and relaxation times. ConclusionsWe demonstrate a role for the cardiac TTN antisense transcript TTN-AS1-276 in facilitating alternative splicing of TTN and regulating sarcomere properties. This transcript could constitute a target for improving cardiac passive stiffness and diastolic function in conditions such as heart failure with preserved ejection fraction.

molecular biology↗

bioRxiv2022

Data-driven microscopy allows for automated targeted acquisition of relevant data withhigher fidelity

Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM), that uses population-wide cell characterization to enable data-driven high-fidelity imaging of relevant phenotypes. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As proof-of-concept, we apply DDM with plugins for improved high-content screening and live adaptive microscopy. DDM also allows for easy correlative imaging in other systems with a plugin that uses the spatial relationship of the sample population for automated registration. We believe DDM will be a valuable approach for reducing human bias, increasing reproducibility, and placing singlecell characteristics in the context of the sample population when interpreting microscopy data, leading to an overall increase in data fidelity.

systems biology↗

bioRxiv2022

Group A streptococci induce high-affinity M protein-fibronectin interaction when specific human antibodies are bound

Group A streptococcus (GAS) is a highly adapted, humanspecific pathogen that is known to manipulate the immune system through various mechanisms. GAS M protein constitutes a primary target of the immune system due to its spatial configuration and dominance on the bacterial surface. Antibody responses targeting the M protein have been shown to favor the conserved C region. Such antibodies circumvent antigenic escape and efficiently bind to various M types. The ability of GAS to bind to fibronectin (Fn), a high molecular weight glycoprotein of the extracellular matrix, has long been known to be essential for the pathogens evolutionary success and fitness. However, some strains lack the ability to efficiently bind Fn. Instead, they have been found to inefficiently bind Fn via the M protein A-B domains. Here, we show that human antibodies can induce a high-affinity Fn-binding state in M proteins, likely by enhancing the weak A-B domain binding. The antibodies bind to a conserved region of M proteins, and the high-affinity binding only occurs on the individual M proteins with bound specific antibodies. By allowing the binding of antibodies to a certain region in M, and thereby enhancing Fn-binding, GAS exploits the human humoral immune response to efficiently bind Fn without needing to waste energy on the production of additional proteins - potentially giving such strains an evolutionary advantage.

immunology↗

bioRxiv2021

Opsonization by non-neutralizing antibodies can confer protection to SARS-CoV-2 despite Spike-dependent modulation of phagocytosis

Spike-specific antibodies are central to effective COVID19 immunity. Research efforts have focused on antibodies that neutralize the ACE2-Spike interaction but not on non-neutralizing antibodies. Antibody-dependent phagocytosis is an immune mechanism enhanced by opsonization, where typically, more bound antibodies trigger a stronger phagocyte response. Here, we show that Spike-specific antibodies, dependent on concentration, can either enhance or reduce Spike-bead phagocytosis by monocytes independently of the antibody neutralization potential. Surprisingly, we find that both convalescent patient plasma and patient-derived monoclonal antibodies lead to maximum opsonization already at low levels of bound antibodies and is reduced as antibody binding to Spike protein increases. Moreover, we show that this Spike-dependent modulation of opsonization seems to affect the outcome in an experimental SARS-CoV-2 infection model. These results suggest that the levels of anti-Spike antibodies could influence monocyte-mediated immune functions and propose that non-neutralizing antibodies could confer protection to SARS-CoV-2 infection by mediating phago-cytosis.

immunology↗

bioRxiv2020

Distinct genetic bases for plant root responses to lipo-chitooligosaccharide signal molecules from distinct microbial origins

O_LILipo-chitooligosaccharides (LCOs) were originally found as symbiotic signals called Nod Factors (Nod-LCOs) controlling nodulation of legumes by rhizobia. More recently LCOs were also found in symbiotic fungi and, more surprisingly, very widely in the kingdom fungi including in saprophytic and pathogenic fungi. The LCO-V(C18:1, Fuc/MeFuc), hereafter called Fung-LCOs, are the LCO structures most commonly found in fungi. This raises the question of how legume plants, such as Medicago truncatula, can perceive and discriminate between Nod-LCOs and these Fung-LCOs. C_LIO_LITo address this question, we performed a Genome Wide Association Study on 173 natural accessions of Medicago truncatula, using a root branching phenotype and a newly developed local score approach. C_LIO_LIBoth Nod- and Fung-LCOs stimulated root branching in most accessions but there was very little correlation in the ability to respond to these types of LCO molecules. Moreover, heritability of root response was higher for Nod-LCOs than for Fung-LCOs. We identified 123 loci for Nod-LCO and 71 for Fung-LCO responses, but only one was common. C_LIO_LIThis suggests that Nod- and Fung-LCOs both control root branching but use different molecular mechanisms. The tighter genetic constraint of the root response to Fung-LCOs possibly reflects the ancestral origin of the biological activity of these molecules. C_LI

plant biology↗

bioRxiv2020

MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics

Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is currently the gold-standard technique to determine the full chemical diversity in biological samples. This approach still has many limitations, however; notably, the difficulty of estimating accurately the number of unique metabolites being profiled among the thousands of MS ion signals arising from chromatograms. Here, we describe a new workflow, MS-CleanR, based on the MS-DIAL/MS-FINDER suite, which tackles feature degeneracy and improves annotation rates. We show that implementation of MS-CleanR reduces the number of signals by nearly 80% while retaining 95% of unique metabolite features. Moreover, the annotation results from MS-FINDER can be ranked with respect to database chosen by the user, which improves identification accuracy. Application of MS-CleanR to the analysis of Arabidopsis thaliana grown in three different conditions improved class separation resulting from multivariate data analysis and lead to annotation of 75% of the final features. The full workflow was applied to metabolomic profiles from three strains of the leguminous plant Medicago truncatula that have different susceptibilities to the oomycete pathogen Aphanomyces euteiches; a group of glycosylated triterpenoids overrepresented in resistant lines were identified as candidate compounds conferring pathogen resistance. MS-CleanR is implemented through a Shiny interface for intuitive use by end-users (available at: https://github.com/eMetaboHUB/MS-CleanR).

bioinformatics↗