Biology subjects

Brun, M.

Publications and source records attributed to Brun, M..

4 recordsLinked to original sources

bioRxiv2025

High-dimensional Immune Profiling Following Autologous Hematopoietic Stem Cell Transplantation in Relapsing-Remitting Multiple Sclerosis

Background and ObjectivesAutologous hematopoietic stem cell transplantation (aHSCT) induces durable remission in treatment-refractory relapsing-remitting multiple sclerosis (RRMS). However, to what extent post-transplant immune recovery reflects restoration of pre-treatment state versus selective remodeling remains incompletely understood. We used high-dimensional whole-blood immune profiling to characterize lineage-specific remodeling in patients with RRMS following aHSCT and to compare trajectories with healthy reference profiles. MethodsPeripheral blood from 25 patients with RRMS treated with aHSCT in the RAM-MS trial was analyzed longitudinally at baseline, 100 days, and 6, 12, and 24 months post-transplant. Samples were compared with those from 9 age- and sex-matched healthy controls using a 41-marker mass cytometry panel. Unsupervised clustering and multi-level modeling were used to quantify lineage-level abundance changes and phenotypic remodeling across adaptive and innate compartments. ResultsImmune recovery after aHSCT was characterized by selective and sustained reshaping of specific T-and B-cell subsets rather than uniform restoration of pre-treatment composition. Naive CD4+ and CD8+ T cells gradually repopulated, whereas memory CD4+ T cells remained reduced at 24 months. Memory CD8+ T cells showed a transient early expansion before declining toward baseline levels. Naive B cells expanded early and remained elevated, while memory B cells were durably reduced. B-cell reconstitution involved expansion of activation-associated naive populations alongside contraction of selected memory subsets. Across T-cell compartments, CD161-expressing chemokine receptor-enriched subsets were selectively depleted. Several of these alterations persisted despite partial normalization of overall cell counts. These trajectories were consistent across patients, supporting a robust and reproducible effect of aHSCT on immune architecture. DiscussionaHSCT induces durable, lineage-specific reorganization of adaptive immunity along a shared trajectory across patients, characterized by contraction of inflammatory T-cell programs and hierarchical restructuring of B-cell maturation rather than simple immune restoration.

immunology↗

bioRxiv2024

HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures

Accumulation processes, where many potentially coupled features are acquired over time, occur throughout the sciences, from evolutionary biology to disease progression, and particularly in the study of cancer progression. Existing methods for learning the dynamics of such systems typically assume limited (often pairwise) relationships between feature subsets, cross-sectional or untimed observations, small feature sets, or discrete orderings of events. Here we introduce HyperTraPS-CT (Hypercubic Transition Path Sampling in Continuous Time) to compute posterior distributions on continuous-time dynamics of many, arbitrarily coupled, traits in unrestricted state spaces, accounting for uncertainty in observations and their timings. We demonstrate the capacity of HyperTraPS-CT to deal with cross-sectional, longitudinal, and phylogenetic data, which may have no, uncertain, or precisely specified sampling times. HyperTraPS-CT allows positive and negative interactions between arbitrary subsets of features (not limited to pairwise interactions), supporting Bayesian and maximum-likelihood inference approaches to identify these interactions, consequent pathways, and predictions of future and unobserved features. We also introduce a range of visualisations for the inferred outputs of these processes and demonstrate model selection and regularisation for feature interactions. We apply this approach to case studies on the accumulation of mutations in cancer progression and the acquisition of anti-microbial resistance genes in tuberculosis, demonstrating its flexibility and capacity to produce predictions aligned with applied priorities.

bioinformatics↗

bioRxiv2024

Late-life dietary folate restriction reduces biosynthetic processes without compromising healthspan in mice

Folate is a vitamin required for cell growth and is present in fortified foods in the form of folic acid to prevent congenital abnormalities. The impact of low folate status on life-long health is poorly understood. We found that limiting folate levels with the folate antagonist methotrexate increased the lifespan of yeast and worms. We then restricted folate intake in aged mice and measured various health metrics, metabolites, and gene expression signatures. Limiting folate intake decreased anabolic biosynthetic processes in mice and enhanced metabolic plasticity. Despite reduced serum folate levels in mice with limited folic acid intake, these animals maintained their weight and adiposity late in life, and we did not observe adverse health outcomes. These results argue that the effectiveness of folate dietary interventions may vary depending on an individuals age and sex. A higher folate intake is advantageous during the early stages of life to support cell divisions needed for proper development. However, a lower folate intake later in life may result in healthier aging.

molecular biology↗

bioRxiv2017

HMMER Cut-off Threshold Tool (HMMERCTTER): Supervised Classification of Superfamily Protein Sequences with a reliable Cut-off Threshold

Protein superfamilies can be divided into subfamilies of proteins with different functional characteristics. Their sequences can be classified hierarchically, which is part of sequence function assignation. Typically, there are no clear subfamily hallmarks that would allow pattern-based function assignation by which this task is mostly achieved based on the similarity principle. This is hampered by the lack of a score cut-off that is both sensitive and specific.\n\nHMMER Cut-off Threshold Tool (HMMERCTTER) adds a reliable cut-off threshold to the popular HMMER. Using a high quality superfamily phylogeny, it clusters a set of training sequences such that the cluster-specific HMMER profiles show 100% precision and recall (P&R), thereby generating a specific threshold as inclusion cut-off. Profiles and threshold are then used as classifiers to screen a target dataset. Iterative inclusion of novel sequences to groups and the corresponding HMMER profiles results in high sensitivity while specificity is maintained by imposing 100% P&R. In three presented case studies of protein superfamilies, classification of large datasets with 100% P&R was achieved with over 95% coverage. Limits and caveats are presented and explained.\n\nHMMERCTTER is a promising protein superfamily sequence classifier provided high quality training datasets are used. It provides a decision support system that aids in the difficult task of sequence function assignation in the twilight zone of sequence similarity. A package containing source code and full dataset will be deposited at Github and is available for reviewers at: https://www.dropbox.com/s/aacao6ggcak30bg/Repo.tar.gz?dl=0\n\nAuthor summaryThe enormous amount of genome sequences made available in the last decade provide new challenges for scientists. An important step in genome sequence processing is function assignation of the encoded protein sequences, typically based on the similarity principle: The more similar sequences are, the more likely they encode the same function. However, evolution generated many protein superfamilies that consist of various subfamilies with different functional characteristics, such as substrate specificity, optimal activity conditions or the catalyzed reaction. The classification of superfamily sequences to their respective subfamilies can be performed based on similarity but since the different subfamilies also remain similar, it requires a reliable similarity score cut-off.\n\nWe present a tool that clusters training sequences and describes them in profiles that identify cluster members with higher similarity scores than non-cluster members, i.e. with 100% precision and recall. This defines a score cut-off threshold. Profiles and thresholds are then used to classify other sequences. Classified sequences are included in the profiles in order to improve sensitivity while maintaining specificity by imposing 100% precision and recall. Results on three case studies show that the tool can correctly classify complex superfamilies with over 95% coverage.\n\nHMMERCTTER is meant as a decision support system for the expert biologist rather than the computational biologist.

bioinformatics↗