Biology subjects

Bosio, M.

Publications and source records attributed to Bosio, M..

6 recordsLinked to original sources

bioRxiv2025

Sorting F. graminearum core effector candidates shows multiple fungal proteins that target the wheat cell nucleus during Fusarium Head Blight

Effectors are small molecules secreted by microbial pathogens that disrupt host basal functioning and responses during infection by targeting various plant susceptibility factors. This study reports a candidate selection approach for identifying novel, potential plant nuclear localized effectors from Fusarium graminearum secretory proteins. From a dataset of core secretory proteins conserved across several Fusarium strains, candidates were selected based on predicted nuclear localization, structural characteristics, and expression profiles during infection. Transient expression in Nicotiana benthamiana confirmed accumulation in the plant nucleus, that were further confirmed in wheat protoplasts. One of these proteins was selected for yeast two-hybrid (Y2H) screening to identify wheat protein targets, using a Fusarium-infected wheat spike cDNA library specifically generated for this study. The screening identified a high confident interaction with a nuclear-localized wheat beta-amylase 2. The structural modeling of the protein complex between beta-amylase 2 and the putative effector was used to predict interacting amino acid residues and informed a deletion analysis to disrupt the interaction. This research identifies a F. graminearum secretory core protein that interacts with a conserved wheat beta-amylase 2, showcasing a method to select pathogenicity factors conserved across multiple pathogens and host plants, with implications for developing broad-spectrum resistance strategies. HighlightThis study proposes a method based on in silico and in vivo screens to identify interacting pairs between core pathogenicity effectors localized in the nucleus and susceptibility factors in plants.

plant biology↗

bioRxiv2023

Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes

Exploring the molecular basis of disease severity in rare disease scenarios is a challenging task provided the limitations on data availability. Causative genes have been described for Congenital Myasthenic Syndromes (CMS), a group of diverse minority neuromuscular junction (NMJ) disorders; yet a molecular explanation for the phenotypic severity differences remains unclear. Here, we present a workflow to explore the functional relationships between CMS causal genes and altered genes from each patient, based on multilayer network analysis of protein-protein interactions, pathways and metabolomics. Our results show that CMS severity can be ascribed to the personalized impairment of extracellular matrix components and postsynaptic modulators of acetylcholine receptor (AChR) clustering. We explore this in more detail for one of the proteins not previously associated with the NMJ, USH2A. Loss of the zebrafish USH2A ortholog revealed some effects on early movement and gross NMJ morphology. This work showcases how coupling multilayer network analysis with personalized -omics information provides molecular explanations to the varying severity of rare diseases; paving the way for sorting out similar cases in other rare diseases.

bioinformatics↗

bioRxiv2020

Efficient and Flexible Integration of Variant Characteristics in Rare Variant Association Studies Using Integrated Nested Laplace Approximation

Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 75% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the Rare Variant Genome Wide Association Study (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A. Data availability and implementationAll relevant data are within the manuscript and pipeline implementation on https://github.com/hanasusak/rvGWAS Author summaryComplex diseases are characterized by being related to genetic factors and environmental factors such as air pollution, diet etc. that together define the susceptibility of each individual to develop a given disease. Much effort has been applied to advance the knowledge of the genetic bases of such diseases, specially in the discovery of frequent genetic variants in the population increasing disease risk. However, these variants usually explain a little part of the etiology of such diseases. Previous studies have shown that rare variants, i.e. variants present in less than 1% of the population, may explain the rest of the variability related to genetic aspects of the disease. Genome sequencing offers the opportunity to discover rare variants, but powerful statistical methods are needed to discriminate those variants that induce susceptibility to the disease. Here we have developed a powerful and flexible statistical approach for the detection of rare variants associated with a disease and we have integrated it into a computer tool that is easy and intuitive for the researchers and clinicians to use. We have shown that our approach outperformed other common statistical methods specially in a situation where these variants explain just a small part of the disease. The discovery of these rare variants will contribute to the knowledge of the molecular mechanism of complex diseases.

bioinformatics↗

bioRxiv2019

SmartRNASeqCaller: improving germline variant calling from RNAseq

BackgroundTranscriptomics data, often referred as RNA-Seq, are increasingly being adopted in clinical practice due to the opportunity to answer several questions with the same data - e.g. gene expression, splicing, allele-specific expression even without matching DNA. Indeed, recent studies showed how RNA-Seq can contribute to decipher the impact of germline variants. These efforts allowed to dramatically improved the diagnostic yield in specific rare disease patient cohorts. Nevertheless, RNA-Seq is not routinely adopted for germline variant calling in the clinic. This is mostly due to a combination of technical noise and biological processes that affect the reliability of results, and are difficult to reduce using standard filtering strategies.\n\nResultsTo provide reliable germline variant calling from RNA-Seq for clinical use, such as for mendelian diseases diagnosis, we developed SmartRNASeqCaller: a Machine Learning system focused to reduce the burden of false positive calls from RNA-Seq. Thanks to the availability of large amount of high quality data, we could comprehensively train SmartRNASeqCaller using a suitable features set to characterize each potential variant.\n\nThe model integrates information from multiple sources, capturing variant-specific characteristics, contextual information, and external sources of annotation. We tested our tool against state-of-the-art workflows on a set of 376 independent validation samples from GIAB, Neuromics, and GTEx consortia. SmartRNASeqCaller remarkably increases precision of RNA-Seq germline variant calls, reducing the false positive burden by 50% without strong impact on sensitivity. This translates to an average precision increase of 20.9%, showing a consistent effect on samples from different origins and characteristics.\n\nConclusionsSmartRNASeqCaller shows that a general strategy adopted in different areas of applied machine learning can be exploited to improve variant calling. Switching from a naive hard-filtering schema to a more powerful, data-driven solution enabled a qualitative and quantitative improvement in terms of precision/recall performances. This is key for the intended use of SmartRNASeqCaller within clinical settings to identify disease-causing variants.

bioinformatics↗

bioRxiv2019

Tracking of antibiotic resistance transfer and rapid plasmid evolution in a hospital setting by Nanopore sequencing

BackgroundInfection of patients with multidrug-resistant (MDR) bacteria often leave very limited or no treatment options. The transfer of antimicrobial resistance genes (ARG) carrying plasmids between bacterial species by horizontal gene transfer represents an important mode of expansion of ARGs. Here, we evaluated the application of Nanopore sequencing technology in a hospital setting for monitoring the transfer and rapid evolution of antibiotic resistance plasmids within and across multiple species.\n\nResultsIn 2009 we experienced an outbreak with an extensively multidrug resistant P. aeruginosa harboring the carbapenemase enzyme blaIMP-8, and in 2012 the first Citrobacter freundii and Citrobacter werkmanii harboring the same enzyme were detected. Using Nanopore and Illumina sequencing we conducted a comparative analysis of all blaIMP-8 bacteria isolated in our hospital over a 6-year period (n = 54). We developed the computational platforms pathoLogic and plasmIDent for Nanopore-based characterization of clinical isolates and monitoring of ARG transfer, comprising de-novo assembly of genomes and plasmids, polishing, QC, plasmid circularization, ARG annotation, comparative genome analysis of multiple isolates and visualization of results. Using plasmIDent we identified a 40 kb plasmid carrying blaIMP-8 in P. aeruginosa and C. freundii, verifying that plasmid transfer had occurred. Within C. freundii the plasmid underwent further evolution and plasmid fusion, resulting in a 164 kb mega-plasmid, which was transferred to C. werkmanii. Moreover, multiple rearrangements of the multidrug resistance gene cassette were detected in P. aeruginosa, including deletions and translocations of complete ARGs.\n\nConclusionPlasmid transfer, plasmid fusion and rearrangement of the multidrug resistance gene cassette mediated the rapid evolution of opportunistic pathogens in our hospital. We demonstrated the feasibility of tracking plasmid evolution dynamics and ARG transfer in clinical settings in a timely manner. The approach will allow for successful countermeasures to contain not only clonal, but also plasmid mediated outbreaks.

genomics↗

bioRxiv2017

Germline determinants of the somatic mutation landscape in 2,642 cancer genomes

Cancers develop through somatic mutagenesis, however germline genetic variation can markedly contribute to tumorigenesis via diverse mechanisms. We discovered and phased 88 million germline single nucleotide variants, short insertions/deletions, and large structural variants in whole genomes from 2,642 cancer patients, and employed this genomic resource to study genetic determinants of somatic mutagenesis across 39 cancer types. Our analyses implicate damaging germline variants in a variety of cancer predisposition and DNA damage response genes with specific somatic mutation patterns. Mutations in the MBD4 DNA glycosylase gene showed association with elevated C>T mutagenesis at CpG dinucleotides, a ubiquitous mutational process acting across tissues. Analysis of somatic structural variation exposed complex rearrangement patterns, involving cycles of templated insertions and tandem duplications, in BRCA1-deficient tumours. Genome-wide association analysis implicated common genetic variation at the APOBEC3 gene cluster with reduced basal levels of somatic mutagenesis attributable to APOBEC cytidine deaminases across cancer types. We further inferred over a hundred polymorphic L1/LINE elements with somatic retrotransposition activity in cancer. Our study highlights the major impact of rare and common germline variants on mutational landscapes in cancer.

genomics↗