Biology subjects

Vignoli, A.

Publications and source records attributed to Vignoli, A..

6 recordsLinked to original sources

bioRxiv2025

Mesodermal-specific MECP2 expression in Drosophila induces visceral and skeletal muscle defects rescued by butyrate supplementation

BackgroundPatients affected by Rett syndrome (RTT) and MECP2 duplication syndrome (MDS) experience disabling muscle weakness and gastrointestinal dysmotility of unclear origin. Whether these defects arise cell-autonomously, rather than secondarily to neural dysfunction, and which developmental windows are most vulnerable to MeCP2 disfunction remains unresolved. MeCP2 is a dosage-sensitive transcriptional regulator, whose functions are tightly linked to chromatin states. Because short-chain fatty acids (SCFAs) are known to inhibit histone deacetylases (HDACs), a tractable in vivo model is needed to test the effect of HDAC modulation on muscle defects. MethodsWe misexpressed human MECP2 in the Drosophila melanogaster mesoderm that gives rise to skeletal and visceral muscles. We analyzed quantitatively their morphology and function. To assess the effects of SCFA supplementation, we also supplemented diets with sodium butyrate (NaB), Lalbaay(R), a NaB-containing supplement, acetate (AcOH), and valproate (VPA). FindingsMECP2 misexpression caused pre-eclosion lethality, thinning of larval skeletal fibers with nuclear mispositioning and altered mitochondria. Functionally, it reduced locomotion, decreased food transit and gut peristalsis. Phenotypes were strongest when expression began during development. NaB and VPA supplementation rescue most of these phenotypes, consistent with their histone-deacetylase (HDAC) activity. Defects were not observed upon comparable misexpression of an RTT-associated MeCP2 loss-of-function variant, indicating that they might be relevant to pathogenesis of MECP2-related disorders. InterpretationOur genetic in vivo analysis models peripheral effects of MeCP2 dysregulation and their amelioration, supporting the possibility of HDAC-targeted strategies for MECP2-related muscle and gastrointestinal dysfunction.

genetics↗

bioRxiv2025

KODAMA enables self-guided weakly supervised learning in spatial transcriptomics

Spatial transcriptomics provides researchers with a powerful tool to investigate gene expression patterns within their native positional context in tissues, revealing intricate cellular relationships. However, analyzing spatial transcriptomics data presents unique challenges due to their high dimensionality and complexity. Several competitive tools have emerged, each aiming to integrate spatial dependencies into their workflow. Yet, the overall performance of these approaches remains constrained by limitations in prediction accuracy and compatibility with specific data types. Here, we introduce the third version of the KODAMA algorithm, specifically tailored for spatial transcriptomics analysis. This upgraded version incorporates a novel approach that effectively reduces data dimensionality while preserving spatial information. At its core, the KODAMA algorithm employs parallel iterations that enforce spatial constraints throughout an embedded clustering process. Our method provides the flexibility to simultaneously analyze multiple samples, streamlining analysis workflows. Additionally, it extends beyond traditional 2D analysis to accommodate multidimensional datasets, including 3D spatial information. KODAMA seamlessly integrates into various pipelines, such as Seurat and Giotto. Extensive evaluations of KODAMA on 10x Visium, Visium HD, and image-based 3D datasets demonstrate its high accuracy in spatial domain prediction. Comparative analyses against alternative dimensionality reduction techniques and spatial analysis tools consistently validate and highlight KODAMAs superior performance in unraveling the spatial organization of cellular components across both single and integrated tissue samples.

bioinformatics↗

bioRxiv2025

Evidence of distal regulations orchestrated by RNAs initiating at short tandem repeats

Short Tandem Repeats (STRs), also called microsatellites, correspond to tandemly repeated short DNA motifs (1 to 6 bp) and are one of the most polymorphic and abundant repetitive elements in the human genome. Variations of their length (i.e. number of consecutive repeats) have been implicated in gene expression regulation (termed expression(e)STRs). Using captrapping followed by long read sequencing, we discovered that STRs can host transcription start sites, the presence of which depends mainly on STR flanking sequences. Here, we investigate the effect of SNPs located in these sequences and ask whether STR-initiating RNAs have regulatory potential. First, we develop fully interpretable deep learning-based models, called Modular Neural Networks, able to predict, for each STR class, the level of RNAs using 101bp-long sequences encompassing STRs. Analysis of MNN filters allows us to identify multiple regulatory elements and candidate transcription factors. Second, leveraging genome sequencing and gene expression data from the Genotype-Tissue Expression project, we use the output of our models to link the levels of STR-initiating RNAs to the expression of nearby genes. We identify 14,340 significant associations (coined RNA(r)STRs) and illustrate how this novel resource can help interpret non-coding variants associated with complex traits and diseases. Third, we unveil an intricate transcriptional interplay between STR-initiating RNAs and Alu repeats that may couple their regulatory actions, extending both the nature and the functional importance of non-coding transcription and shedding new light on the complexity of distal regulations orchestrated by repeated sequences.

genomics↗

bioRxiv2025

An nf-core framework for the systematic comparison of alternative modeling tools: the multiple sequence alignment case study

The computational complexity of many key bioinformatics problems has resulted in numerous alternative heuristic solutions, where no single approach consistently outperforms all others. This creates difficulties for users trying to identify the most suitable tool for their dataset and for developers managing and evaluating alternative methods. As data volumes grow, deploying these methods becomes increasingly difficult, highlighting the need for standardized frameworks for seamless tool deployment and comparison in HPC environments. Multiple sequence aligners (MSAs) rank among the most commonly employed modeling techniques in bioinformatics, playing a crucial role in applications such as protein structure prediction, phylogenetic reconstruction, and variant effect prediction. The NP-hardness of MSAs makes them a major example of problems where heuristics stand central, as no optimal solution can be currently obtained, within the limits of operational computational requirements. Here, we present a pilot design of an nf-core framework for streamlined tool deployment and rigorous performance evaluation focusing on the MSAs software ecosystem. By integrating the most popular MSA tools and focusing on a modular, and extensible architecture, we aspire to provide a key platform supporting MSA deployment, evaluation, and algorithmics development to the MSA community, and a proof-of-principle to the wider bioinformatics community.

bioinformatics↗

bioRxiv2023

Gut microbiota profile in CDKL5 deficiency disorder patients as a potential marker of clinical severity

CDKL5 deficiency disorder (CDD) is a neurodevelopmental condition characterized by global developmental delay, early-onset seizures, intellectual disability, visual and motor impairments. Unlike Rett Syndrome (RTT), CDD lacks a clear regression period. CDD patients frequently encounter gastrointestinal (GI) disturbances and exhibit signs of subclinical immune dysregulation. However, the underlying causes of these conditions remain elusive. Emerging studies indicate a potential connection between neurological disorders and gut microbiota, an area completely unexplored in CDD. We conducted a pioneering study, analyzing fecal microbiota composition in CDD patients and their healthy relatives. Notably, differences in intestinal bacterial diversity and composition were identified in CDD patients. We further investigated microbiota changes based on the severity of GI issues, seizure frequency, sleep disorders, food intake type, impairment in neuro-behavioral features (assessed through the RTT Behaviour Questionnaire - RSBQ), and ambulation capacity. Our findings hint at a potential connection between CDD, microbiota, and symptom severity. This study marks the first exploration of the gut-microbiota-brain axis in CDD patients. It adds to the growing body of research emphasizing the role of the gut microbiota in neurodevelopmental disorders and opens doors to potential interventions that target intestinal microbes with the aim of improving the lives of CDD patients.

microbiology↗

bioRxiv2022

Defects in lipid homeostasis reflect the function of TANGO2 in Acyl-CoA metabolism

We show that TANGO2, which lacks a transmembrane domain localizes predominantly to mitochondria and transiently to endoplasmic reticulum (ER) and lipid droplets (LDs). Evaluation of lipids in HepG2 cells lacking TANGO2 revealed an increase in the size of lipid droplets and reactive oxygen species production. There is also a marked increase lysophosphatidic acid (LPA) and a concomitant decrease in its biosynthetic precursor phosphatidic acid (PA). These changes are exacerbated in nutrient starved cells. Based on our data, we suggest that the principle function of TANGO2 is in acyl-CoA metabolism, which is necessary for the acylation of LPA to generate PA. This defect subsequently affects metabolism of many other fatty acids. These data help explain the physiological consequence of TANGO2 that induce acute metabolic crisis including rhabdomyolysis, cardiomyopathy and cardiac arrhythmias often leading to fatality upon starvation and stress.

cell biology↗