Biology subjects

Helaers, R.

Publications and source records attributed to Helaers, R..

3 recordsLinked to original sources

bioRxiv2026

Mixed acid fermentation products from Lachnospira eligens counteract myotube atrophy

IntroductionAcute myeloid leukemia (AML) is a hematological malignancy associated with muscle wasting. As the relative abundance of Lachnospira eligens was reduced in patients with AML compared to healthy individuals and correlated positively with muscle strength, we hypothesized that L. eligens positively impacts the muscle through the production of small metabolites reaching the systemic circulation. MethodsL. eligens levels were analyzed in two additional independent cohorts. Six L. eligens isolates were characterized through whole-genome sequencing to select clinically relevant strains. The composition of their culture supernatant was analyzed by metabolomics. The impact of L. eligens supernatant on dexamethasone- and interleukin-6-atrophied murine myotubes was assessed. Bioactive metabolites and their production mechanism were identified using among others bioactivity-guided fractionation. The underlying mechanism was also explored on the host side through myotubes transcriptome analysis and metabolic flux analysis. The relevance of bioactive metabolites and their production mechanism was evaluated through clinical data and samples analyses and in a mouse model of leukemia. ResultsThe levels of L. eligens are reduced in independent cohorts of patients with AML and its supernatant counteracts myotube atrophy. This anti-atrophic effect, conserved between strains of the same species, depends on the occurrence of mixed acid fermentation (MAF) in anoxic culture conditions and the presence of its acid end-products acetate, formate and D-lactate. Consistent with those results, blood levels of acetate are decreased and the relative abundance of fecal bacteria capable of performing aerobic respiration is increased in patients with AML. However, bacterial supernatant failed to prevent muscle atrophy and weakness in leukemic mice, likely due to insufficient sustained elevation of acid end-products in the blood. ConclusionThis work reveals the anti-atrophic effect of MAF end-products on myotubes and suggests the importance of considering gut electron acceptor levels (e.g. O2) in disorders affecting muscle health. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/729780v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@112529eorg.highwire.dtl.DTLVardef@1ee1929org.highwire.dtl.DTLVardef@b5f389org.highwire.dtl.DTLVardef@187bc40_HPS_FORMAT_FIGEXP M_FIG Mixed acid fermentation products from Lachnospira eligens counteract myotube atrophy. Our study suggests that gut anaerobiosis is disrupted in treatment-naive patients with acute myeloid leukemia (AML), leading to decreased circulating acetate levels and a reduced relative abundance of L. eligens, which significantly correlated with muscle strength. In line with this framework, in vitro experiments demonstrate that the culture supernatant of L. eligens, which contains mixed acid fermentation (MAF) end-products such as acetate, effectively counteracts C2C12 myotube atrophy in the presence of pro-atrophying stimuli. Further mechanistic experiments indicate a causal role for MAF end-products in this anti-atrophying effect. Created with BioRender.com. Legend: solid frames: experimental results; dashed frames: hypothetical conclusions derived from results; black solid arrow: established correlation; black dashed arrows: hypothetical causation. C_FIG

microbiology↗

bioRxiv2025

A simple workflow to identify novel Small Linear Motif (SLiM)-mediated interactions with AlphaFold

Short linear motifs (SLiMs) are highly compact interaction modules embedded within disordered protein regions and are increasingly recognized for their central role in maintaining cellular homeostasis. Due to their small size, degeneracy and transient binding, SLiMs remain difficult to detect both experimentally and computationally. Here, we show that AlphaFold, used via ColabFold, offers a practical and accessible alternative for in-silico SLiM discovery. Unlike previous studies focused on structural accuracy, we evaluated AlphaFolds capacity to reveal SLiMs independently of model quality. To this end, we benchmarked several scoring metrics and showed that AlphaFold2 combined with MiniPAE yields the best performance, outperforming AlphaFold3 in this context. Building on these findings, we also provide a streamlined and cost-effective workflow for SLiM prediction requiring no installation or local computation. To overcome challenges associated with SLiM validation, we also introduce a highly sensitive detection method based on proximity labeling in living cells. This workflow was used to predict the occurrence of SLiMs that mediate binding to ribosomal protein S6 kinase A3 (RPS6KA3 or RSK2). By leveraging Colabfold and MiniPAE available through Colab notebooks, our approach provides a scalable and widely accessible strategy for identifying functional SLiMs in proteins of interest. MiniPAE can be accessed at https://github.com/martinovein/MiniPAE Short descriptionMartin Veinstein is a PhD student in Biomedical Sciences at the de Duve Institute, UCLouvain, Belgium. He specializes in Small Linear Motifs (SLiMs) in the context of host-virus interactions and has developed strong expertise in bioinformatics, structural biology, and predictive modeling. Victor J is a unfergradiate student at the ECAM Brussels Engineering School, Haute Ecole "ICHEC-ECAM-ISFSC", Brussels, Belgium. His activities span form September to November 2023. B.I. Iorga is a CNRS Research Director at the Institut de Chimie des Substances Naturelles in Gif-sur-Yvette, France. His research focuses among others on methodological developments in molecular modeling and the in-silico prediction of antibiotic resistance using machine learning and deep learning approaches. Raphael Helaers is a Senior Investigator and leads bioinformatics infrastructure at the de Duve Institute, UCLouvain, Belgium. He has developed strong expertise in next-generation sequencing and software development, along with a deep interest in biology, genetics, and evolution. Thomas Michiels is a Full Professor and researcher at the de Duve Institute, UCLouvain, Belgium. His research focuses on virus-mediated subversion of the innate immune response. Frederic Sorgeloos is an adjunct Professor at the INRS, Laval, Canada. He currently focuses on the subversion of cellular homeostasis through small linear peptides encoded by viral and bacterial pathogens. Short abstractVarious AlphaFold2/3 scoring metrics were systematically benchmarked for their ability to detect Small Linear Motifs (SLiMs) Based on this evaluation, a user-friendly and cost-effective in-silico workflow is proposed to identify novel SLiMs-targeting proteins The utility of this workflow is demonstrated through the prediction of previously uncharacterized SLiMs interacting with RSK kinases. A sensitive in-vitro assay is proposed to streamline the validation of low-affinity SLiM-target interactions. Together, our workflow and associated validation assay offer an integrated pipeline for the discovery and validation of SLiM-mediated protein-protein interactions.

bioinformatics↗

bioRxiv2017

DiGeST: Distributed Computing for Scalable Gene and Variant Ranking with Hadoop/Spark

BackgroundThe advent of next-generation sequencing technologies has opened new avenues for clinical genomics research. In particular, as sequencing costs continue to decrease, an ever-growing number of clinical genomics institutes now rely on DNA sequencing studies at varying scales - genome, exome, mendeliome - for uncovering disease-associated variants or genes, in both rare and non-rare diseases.\n\nA common methodology for identifying such variants or genes is to rely on genetic association studies (GAS), that test whether allele or genotype frequencies differ between two groups of individuals, usually diseased subjects and healthy controls. Current bioinformatics tools for performing GAS are designed to run on standalone machines, and do not scale well with the increasing size of study designs and the search for multi-locus genetic associations. More efficient distributed and scalable data analysis solutions are needed to address this challenge.\n\nResultsWe developed a Big Data solution stack for distributing computations in genetic association studies, that address both single and multi-locus associations. The proposed stack, called DiGeST (Distributed Gene/variant Scoring Tool) is divided in two main components: a Hadoop/Spark high-performance computing back-end for efficient data storage and distributed computing, and a Web front-end providing users with a rich set of options to filter, compare and explore exome data from different sample populations. Using exome data from the 1000 Genomes Project, we show that our distributed implementation smoothly scales with computing resources. We make the resulting software stack Open-Source, and provide virtualisation scripts to run the complete environment both on standalone machine or Hadoop-based cluster.\n\nConclusionsHadoop/Spark provides a powerful and well-suited distributed computing framework for genetic association studies. Our work illustrates the flexibility, ease of use and scalability of the framework, and more generally advocates for its wider adoption in bioinformatics pipelines.

bioinformatics↗