Biology subjects

Matias, C.

Publications and source records attributed to Matias, C..

6 recordsLinked to original sources

bioRxiv2026

Fox: Predicting rates of host-symbiont co-evolution events with association-aware attention

Motivation: Hosts and their symbionts share a history of associations in which lineages cospeciate, switch hosts, duplicate, or are lost. Quantifying the relative frequencies of these four events is a central goal of cophylogenetics, but existing tools typically require a fully resolved, correctly rooted host tree, often alongside a symbiont tree, propagating tree-inference errors into the reconciliation and scaling poorly to large systems. Results: We introduce Fox, a transformer neural network architecture that infers the relative frequencies of the four cophylogenetic events directly from a pair of host and symbiont multiple sequence alignments (MSAs) and their sequence mapping, with no tree-reconstruction or reconciliation step. Fox is trained under a simulation-based inference scheme: it learns event frequencies from alignments generated by a coevolutionary process, paying its computational cost once at training time and processing any new system in a single forward pass. On held-out data from its training simulator, Fox recovers event frequencies accurately and with near-zero bias. On additional synthetic data from an independent cophylogeny simulator, its error roughly doubled, but is still very low allowing to track the true frequencies. Most importantly, when compared to the Approximate Bayesian Computation (ABC)-based method AmoCoala on these data, it reduces mean absolute error by 45-76% for cospeciations, host switches and duplications and shows similar error for losses, while running more than three orders of magnitude faster. Applied to the empirical cockroach-Blattabacterium system, Fox recovers the expected cospeciation-dominated profile, showing that behaviour learned on simulated data transfers to real sequences. These results confirm that the coevolutionary signal usually extracted from trees can be read directly, accurately, and fast in the alignments themselves

bioinformatics↗

bioRxiv2026

Cophylogeny simulators are not interchangeable: similarities, differences and structural biases in synthetic host-symbiont

Synthetic data are becoming increasingly important for computational studies of cophylogeny, including machine learning inference, benchmarking, and method testing. Several generators have been proposed to produce such data, but each relies on different assumptions about host-symbiont coevolution. These assumptions are often implicit and rarely examined, even though results can depend strongly on the synthetic model being used. In this article, we present a systematic structural analysis of representative cophylogeny generators under controlled scenarios. The goal is to make their assumptions explicit and to understand how these choices shape the synthetic data they produce as well as the conclusions that may be drawn from them.

bioinformatics↗

bioRxiv2025

Deficient Cardiolipin Remodeling Alters Muscle Fiber Composition and Neuromuscular Connectivity in Barth Syndrome

BackgroundBarth syndrome (BTHS) is a rare X-linked mitochondrial disorder caused by mutations in the TAFAZZIN gene, which disrupts cardiolipin (CL) remodeling and mitochondrial function. While cardiac manifestations of BTHS are well characterized, the mechanisms underlying skeletal muscle weakness and fatigability are poorly understood. MethodsWe investigated neuromuscular and mitochondrial alterations in a novel murine model (TazPM) carrying a patient-derived D75H point mutation in Tafazzin. This mutation preserves protein abundance but abolishes enzymatic activity. Skeletal muscle function was assessed via weightlifting and hanging tests. Muscle fiber composition and neuromuscular junction (NMJ) integrity were evaluated using immunofluorescence, western blotting, and in vivo electrophysiology. Mitochondrial morphology was examined by transmission electron microscopy, and bioenergetics were quantified using ultra-performance liquid chromatography. Stress signaling was assessed by western blotting. ResultsMale TazPM mice exhibited elevated monolysocardiolipin and reduced mature CL levels, confirming deficient transacylase activity. These mice exhibited lower muscle strength and endurance, smaller muscle fibers of all types, and a shift toward fast-twitch type 2B fibers, which are more susceptible to fatigue. Electrophysiological analysis revealed a 60% reduction in motor unit number and an increase in average single motor unit potential, indicating motor neuron remodeling. NMJ protein analysis showed decreased MUSK and DOK7 and increased CHRNA1, suggesting impaired NMJ integrity. Despite mitochondrial structural abnormalities and reduced expression of key mitochondrial proteins (NDUFB8, MCU, TMEM65), resting ATP, phosphocreatine, and adenine nucleotide ratios were unchanged in both glycolytic and oxidative muscles. However, stress signaling pathways were markedly activated, including phosphorylation of eIF2, increased CHOP, DELE1, p53 expression, and altered Wnt/{beta}-catenin signaling components. ConclusionsDeficiency of Tafazzin enzymatic activity in skeletal muscle is sufficient to result in widespread neuromuscular remodeling, including fiber size/type shifts, motor unit loss, NMJ dysregulation, and stress pathway activation, without overt energetic failure at rest. These findings suggest that myopathy in BTHS arises not solely from mitochondrial ATP insufficiency but rather from cumulative structural and signaling disruptions.

cell biology↗

bioRxiv2025

Loss of adenylosuccinate synthetase 1 in mice recapitulates features of ADSS1 myopathy

ADSS1 myopathy is an ultra-rare congenital myopathy characterized by progressive cardiac and skeletal muscle degeneration with childhood to adolescent onset. This autosomal recessive disease is caused by mutations in the ADSS1 gene, encoding the enzyme adenylosuccinate synthetase (AdSS1). AdSS1 plays a critical role in the adenine nucleotide cycle, which is important for energy metabolism in muscle cells. Enzymatic defects, engendered by loss-of-function mutations in ADSS1, lead to a bottleneck in the adenine nucleotide cycle, causing metabolic dysfunction which ultimately results in progressive muscle weakness, mobility impairment, and respiratory and cardiac dysfunction, often requiring the use of a ventilator. Despite its debilitating nature, there are currently no cures or targeted treatments available, and little research into possible therapeutic strategies has been done. With a limited patient profile encompassing fewer than 200 known patients worldwide, establishing a mouse model for ADSS1 myopathy is critical to understanding its pathogenesis and for developing future therapies. Here, we present and characterize the first mouse model of ADSS1 myopathy - a constitutive Adss1 knockout model - by (1) defining its natural history, (2) exploring its metabolic pathomechanisms, and (3) characterizing its histopathological features. We find that Adss1KO/KO mice have subtle motor deficits and present with histopathological features consistent with patient phenotypes. Overall, we show that despite a relatively mild phenotype, this novel mouse model has quantifiable pathological features that can be used to develop therapies for, and further probe pathophysiology of, ADSS1 myopathy.

genetics↗

bioRxiv2025

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features

BackgroundMetabolic networks are complex systems that describe the biochemical reactions within an organism through pairwise interactions between chemical compounds. While this representation is widely used to study biological function, it fails to capture the full structure of metabolic reactions, many of which involve more than two compounds. Hypergraphs offer a more natural representation, where nodes represent metabolites and hyperedges represent reactions involving multiple participants. Clustering such metabolic hypergraphs can reveal systematic differences among evolutionarily distinct organisms, providing insight into ecological constraints and evolutionary pressures. MethodsIn this study, we investigate how different graphs and hypergraphs embedding methods influence their unsupervised clustering, with the goal of capturing taxonomy-based classes. We apply 14 distinct embedding strategies to a large-scale dataset of 8,467 metabolic hypergraphs. Each embedding was followed by hierarchical clustering using a fixed linkage method. To assess performance, we compared the resulting clusters against known taxonomic groupings. ResultsOur findings show that the choice of hypergraph embedding has a significant effect on clustering outcomes. Among the tested methods, Bag of Hyperedges with Jaccard distance, Histogram Cosine Kernel, and a Hypergraph Auto-Encoder consistently performed best. We also advocate that the embedding method should be chosen based on the goal of the downstream task.

bioinformatics↗

bioRxiv2019

PPanGGOLiN: Depicting microbial diversity via a Partitioned Pangenome Graph

The use of comparative genomics for functional, evolutionary, and epidemiological studies requires methods to classify gene families in terms of occurrence in a given species. These methods usually lack multivariate statistical models to infer the partitions and the optimal number of classes and dont account for genome organization. We introduce a graph structure to model pangenomes in which nodes represent gene families and edges represent genomic neighborhood. Our method, named PPanGGOLiN, partitions nodes using an Expectation-Maximization algorithm based on multivariate Bernoulli Mixture Model coupled with a Markov Random Field. This approach takes into account the topology of the graph and the presence/absence of genes in pangenomes to classify gene families into persistent, cloud, and one or several shell partitions. By analyzing the partitioned pangenome graphs of isolate genomes from 439 species and metagenome-assembled genomes from 78 species, we demonstrate that our method is effective in estimating the persistent genome. Interestingly, it shows that the shell genome is a key element to understand genome dynamics, presumably because it reflects how genes present at intermediate frequencies drive adaptation of species, and its proportion in genomes is independent of genome size. The graph-based approach proposed by PPanGGOLiN is useful to depict the overall genomic diversity of thousands of strains in a compact structure and provides an effective basis for very large scale comparative genomics. The software is freely available at https://github.com/labgem/PPanGGOLiN. Author summaryMicroorganisms have the greatest biodiversity and evolutionary history on earth. At the genomic level, it is reflected by a highly variable gene content even among organisms from the same species which explains the ability of microbes to be pathogenic or to grow in specific environments. We developed a new method called PPanGGOLiN which accurately represent the genomic diversity of a species (i.e. its pangenome) using a compact graph structure. Based on this pangenome graph, we classify genes by a statistical method according to their occurrence in the genomes. This method allowed us to build pangenomes even for uncultivated species at an unprecedented scale. We applied our method on all available genomes in databanks in order to depict the overall diversity of hundreds of species. Overall, our work enables microbiologists to explore and visualize pangenomes alike a subway map.

bioinformatics↗