Biology subjects

Passeri, I.

Publications and source records attributed to Passeri, I..

5 recordsLinked to original sources

bioRxiv2026

From code to natural language: MErlin - a multiomics toolkit for bacterial epigenomics delivered as Claude agent skill.

Interpreting a bacterial methylome is a multi-omics problem. It requires integrating modified-base calls with genome annotation, motif inventories, methyltransferase genotypes, transcript abundance, replichore position and, increasingly, chromosome conformation. These data types are commonly generated in incompatible formats, use inconsistent sequence and gene identifiers, and originate from different analytical workflows. The relevant algorithms are available, but assembling them into a coherent and statistically defensible analysis remains a substantial data-integration and interface problem. We present MErlin (Methylation-driven Expression & Regulation Linkage in Interacting Nuclear-domains), a multi-omics toolkit comprising seventeen composable modules, from basecalled modBAM files to ranked gene-level evidence and a self-contained HTML report. MErlin is distributed both as a conventional Python package and as an agent skill: a structured, version-controlled layer of procedural knowledge that enables a compatible large language model (LLM) assistant to select and operate the audited package without generating a new analysis implementation for each request. This design treats natural language as an interface to fixed analytical operations rather than as a substitute for tested scientific software. The skill encodes module-selection rules, mandatory preflight checks, questions that require human input, design-to-inference constraints, and interpretation guidance. We describe MErlin's architecture and statistics, validate it against a synthetic dataset with planted ground truth, and illustrate the conversational interface on a real methylome-transcriptome comparison in Pseudoalteromonas haloplanktis TAC125. MErlin is open source and available at https://github.com/IacopoPasseri/MErlin

bioinformatics↗

bioRxiv2026

Wild rice Oryza rufipogon outperforms cultivated rice in stimulating beneficial bacterial endophytes.

Beneficial interactions between plants and microorganisms strongly influence plant health and productivity, and root exudates play a central role in shaping these associations. This study analyzed the transcriptional responses of the bacterial endophytes Enterobacter asburiae RCA24 and Kosakonia sacchari RCA25 to root exudates from two commercial Italian rice accessions (Oryza sativa Baldo and Vialone Nano) and from an accession of the wild progenitor of tropical rice, Oryza rufipogon. Bacterial transcriptome analyses revealed that RCA24 responds differently to O. sativa varieties and that RCA25 was more stimulated by O. rufipogon. Changes in bacterial gene expression were mainly related to central metabolism, stress response, and signal transduction, highlighting a precise pattern of interaction. On the other hand, transcriptome analysis of inoculated rice revealed that RCA24 triggered broader transcriptional changes in plants than RCA25. Differentially expressed genes were related, especially in shoots, to defense responses, hormone-mediated signaling, and ribosome biogenesis, revealing that plants discriminate bacterial strains in a genotype-specific manner at the transcriptional level. Our findings suggest that traits beneficial to plant-soil microbiota interactions present in O. rufipogon and lost during domestication and diversification could be identified and reintroduced into modern rice varieties to improve sustainable field performance through beneficial microbial associations.

plant biology↗

bioRxiv2023

The pan-epigenome of the symbiotic nitrogen fixing bacterium Sinorhizobium meliloti unravels unexpected variability of DNA-methylation profiles in closely related strains

In prokaryotes, DNA methylation has been found to be involved in several mechanisms, such as DNA repair, DNA-protein interactions, gene expression, cell cycle progression and self-DNA recognition (the Restriction-Modification systems). Studies on representatives from the same bacterial species have found that genome-wide DNA methylation patterns can be highly variable and may affect phenotypic variation and gene transfer among closely related strains. However, broader evolutionary studies on such epigenomic variation in bacteria are still scarce. Here, we addressed this point by performing an epigenomic analysis on 21 strains of the facultative plant symbiotic nitrogen-fixing alphaproteobacterium Sinorhizobium meliloti. Strains of these species are characterized by a divided (multipartite) genome structure, including a chromosome, a chromid and a (more recently acquired) megaplasmid. Since these strains display extensive genomic and phenotypic variation, they are good models to test evolutionary hypotheses on the relationships among epigenomic signatures, genome structure evolution and phenotypic switches. Results showed the presence of a wide pan-epigenome with 16 DNA methylated motifs, including both 4mC and 6mA palindromic and nonpalindromic motifs. While 9 motifs have been found methylated by all strains, the remaining had differential methylation between S. meliloti strains, constituting a dispensable epigenome. Differences in frequency of methylation were found among replicons, with the megaplasmid and the additional plasmids displaying several motifs with different methylation frequency with respect to the chromosome and the chromid. Moreover, differences between coding, upstream and intergenic regions, were found, suggesting that DNA methylation at specific motifs may play a role in gene regulation and consequently in phenotypic variability among strains. Altogether, our data indicate the presence of a large epigenomic diversity in S. meliloti, with epigenome signatures differing between replicons, reflecting their timing of evolutionary acquisition in S. meliloti genomes and suggesting a role of DNA methylation in the variability of gene expression among strains.

genomics↗

bioRxiv2022

Modelling the metabolic consequences of antimicrobial exposure

Besides genetic mutations, the metabolic state of bacterial cells represents another driving factor in the emergence of antimicrobial resistance and in the actual efficacy of treatments. In this direction, studying how bacteria reprogram their metabolism when facing antimicrobial exposure is crucial to enhance our ability to limit the development and spread of antibiotic resistance. Here we have studied the metabolic consequences of antimicrobial exposure in bacteria using an integrated approach that exploits transcriptomics and computational modelling. Specifically, we asked whether common metabolic strategies emerge during the exposure to antimicrobials, regardless of the kind of antimicrobial used or, on the contrary, antimicrobial-specific pathways exist. To this purpose, we have used an heterogeneous dataset from six published studies on Escherichia coli exposed to different concentrations/types of compounds. We show that experimental condition, not antimicrobial exposure, is the factor that influences the most the resulting metabolic networks. However, despite condition-dependent metabolic signatures being evident, specific changes in flux distributions by antimicrobial exposed cells could be identified. In particular, purine and pyrimidine biosynthesis, and cofactor and prosthetic group biosynthesis were commonly affected by all considered antimicrobials. This suggests the presence of general metabolic strategies to face the stress posed by antimicrobial exposure and that, in turn, may represent an untapped resource for the fight against microbial infections. Finally, our analysis predicted an overall metabolic rewiring following bacteriostatic vs. bactericidal drug exposure that is in line with the current knowledge about the effects of these two classes of compounds on microbial metabolic phenotypes. IMPORTANCEA mechanistic understanding of microbial metabolic reprogramming during antimicrobial exposure is key to facilitate the discovery of new resistance mechanisms and to identify novel areas of intervention to face microbial infections. This study shows how the integration of transcriptomic data and genome-scale metabolic modelling can be used to address this critical issue, and to trace general metabolic strategies exploited by bacteria to face the stress posed by antimicrobial drugs.

systems biology↗

bioRxiv2022

Extraction and analysis of methylation features from Pacific Biosciences SMRT reads using MeStudio

MotivationDNA methylation is the most relevant epigenetic information, present in eukaryotes and prokaryotes, and is related to several biological phenomena, from cellular differentiation to control of gene flow, pathogenesis and virulence. The widespread use of third-generation sequencing technologies allows direct and easy detection of genome-wide methylation profiles, offering increasing opportunities to understand and exploit the epigenomics landscape. ResultsWe introduce MeStudio, a pipeline which allows to analyse and combine genome-wide methylation profiles with genomic features. Outputs report the presence of DNA methylation in coding sequences, noncoding sequences, intergenic sequences, and sequences upstream to CDS. We show the usage and performances of MeStudio on a set of single-molecule real time sequencing outputs from the bacterial species Sinorhizobium meliloti. Availability and ImplementationMeStudio is written in Python, Bash and C and is freely available under an open source GPLv3 license at https://github.com/combogenomics/MeStudio Supplementary informationSupplementary data are available at Bioinformatics online. Contactcombo.unifi@gmail.com

bioinformatics↗