Biology subjects

Borelli, T. C.

Publications and source records attributed to Borelli, T. C..

7 recordsLinked to original sources

bioRxiv2025

ClusterApp to visualize, organize, and navigate metabolomics data

BackgroundClustering analysis is a foundational step in exploratory data analysis workflows, with dimensionality reduction methods commonly used to visualize multidimensional data in lower-dimensional spaces and infer sample clustering. Principal Component Analysis (PCA) is widely applied in metabolomics but is often suboptimal for clustering visualization. Metabolomics data often require specialized manipulations such as blank removal, quality control adjustments, and data transformations that demand efficient visualization tools. However, the lack of user-friendly tools for clustering without computational expertise presents a challenge for metabolomics researchers. ClusterApp addresses this gap as a web application that performs Principal Coordinate Analysis (PCoA), expanding clustering alternatives in metabolomics. Built on a QIIME 2 Docker image, it enables PCoA computation and Emperor plot visualization. The app supports data input from GNPS, GNPS2, or user-provided spreadsheets. Freely available, ClusterApp can be locally installed as a Docker image or integrated into Jupyter notebooks, offering accessibility and flexibility to diverse users. ResultsTo demonstrate the data preprocessing techniques available in ClusterApp, we analyzed two Liquid Chromatography coupled to Tandem Mass Spectrometry (LC-MS/MS) metabolomics datasets: one exploring metabolomic differences in mouse tissue samples and another investigating coral life history stages. Among the dissimilarity measures available, the Bray-Curtis measure effectively highlighted key metabolomic variations and patterns across both datasets. Targeted filtering significantly enhanced data reliability by retaining biologically relevant features, 10,617 in the coral dataset and 7,341 in the mouse dataset while eliminating noise. The combination of Total Ion Current (TIC) normalization and auto-scaling improved clustering resolution, revealing distinct separations in tissue types and life stages. ClusterApps flexible features, such as customizable blank removal and group selection, provided tailored analyses, enhancing visualization and interpretation of metabolomic profiles. ConclusionClusterApp addresses the need for accessible, dynamic tools for exploratory data analysis in metabolomics. By coupling data transformation capabilities with PCoA on multiple dissimilarity matrices, it provides a versatile solution for clustering analysis. Its web interface and Docker-based deployment offer flexibility, accommodating a wide range of use cases through graphical or programmatic interactions. ClusterApp empowers researchers to uncover meaningful patterns and relationships in metabolomics data without requiring cumbersome data manipulation or advanced bioinformatics expertise.

bioinformatics↗

bioRxiv2024

Semi-automated monitoring of longitudinal microbial metabolic dynamics: A study case for lignin degradation

This study introduces a low-cost, open-source, semi-automated bioreactor system, developed using Arduino and Raspberry Pi, for longitudinal microbial culture studies. The system protocol includes detailed instructions for custom-designed components, specifications for easily purchasable parts, and open-source code for a web-based control interface. It was validated for sterility and tested in a case study involving the cultivation of Phanerochaete chrysosporium and Trichoderma reesei to assess their metabolic dynamics, both in isolation and co-culture, for lignin degradation. Using mass spectrometry coupled with gas chromatography, several lignin degradation intermediates were annotated, including 4-hydroxybenzoic acid, vanillic acid, and ferulic acid, along with their temporal variation profiles over 25 days. The results highlighted potential synergy between the two fungal species in lignin breakdown, while also demonstrating the bioreactors flexibility and suitability for diverse biotechnological applications, particularly in the field of bio-degradation and waste valorization.

microbiology↗

bioRxiv2024

Characterization of a marine bacteria through a novel metabologenomics approach

Exploiting microbial natural products is a key pursuit of the bioactive compound discovery field. Recent advances in modern analytical techniques have increased the volume of microbial genomes and their encoded biosynthetic products measured by mass spectrometry-based metabolomics. However, connecting multi-omics data to uncover metabolic processes of interest is still challenging. This results in a large portion of genes and metabolites remaining unannotated. Further exacerbating the annotation challenge, databases and tools for annotation and omics integration are scattered, requiring complex computations to annotate and integrate omics datasets. Here we performed a two-way integrative analysis combining genomics and metabolomics data to describe a new approach to characterize the marine bacterial isolate BRA006 and to explore its biosynthetic gene cluster (BGC) content as well as the bioactive compounds detected by metabolomics. We described BRA006 genomic content and structure by comparing Illumina and Oxford Nanopore MinION sequencing approaches. Digital DNA:DNA hybridization (dDDH) taxonomically assigned BRA006 as a potential new species of the Micromonospora genus. Starting from LC-ESI(+)-HRMS/MS data, and mapping the annotated enzymes and metabolites belonging to the same pathways, our integrative analysis allowed us to correlate the compound Brevianamide F to a new BGC, previously assigned to other function.

biochemistry↗

bioRxiv2024

DeepSEA: an alignment-free deep learning tool for functional annotation of antimicrobial resistance proteins

Antimicrobial resistance (AMR) is one of the most concerning modern threats as it places a greater burden on health systems than HIV and malaria combined. Current surveillance strategies for tracking antimicrobial resistance (AMR) rely on genomic comparisons and depend on sequence alignment with strict similarity cutoffs of greater than 95%. Therefore, these methods have high false-negative error rates due to a lack of reference sequences with a representative coverage of AMR protein diversity. Deep learning (DL) has been used as an alternative to sequence alignment, as artificial neural networks (ANNs) can extract abstract features from data and therefore limit the need for sequence comparisons. Here, a convolutional neural network (CNN) was trained to differentiate antimicrobial resistance proteins from nonresistance proteins and functionally annotate them in nine resistance classes. Our model demonstrated higher recall values (>0.9) than the alignment-based approach for all protein classes tested. Additionally, our CNN architecture allowed us to investigate internal states and explain the model classification regarding protein domain feature importance related to antimicrobial molecule inactivation. Finally, we built an open-source bioinformatic tool that can be used to annotate antimicrobial resistance proteins and provide information on protein domains without sequence alignment.

bioinformatics↗

bioRxiv2022

Improving annotation propagation on molecular networks through random walks: Introducing ChemWalker

Annotation of the mass signals is still the biggest bottleneck for the untargeted mass spectrometry analysis of complex mixtures. Molecular networks are being increasingly adopted by the mass spectrometry community as a tool to annotate large scale experiments. We have previously shown that the process of propagating annotations from spectral library matches on molecular networks can be automated using Network Annotation Propagation (NAP). One of the limitations of NAP is that the information for the spectral matches is only propagated locally, to the first neighbor of a spectral match. Here we show that annotation propagation can be expanded to nodes not directly connected to spectral matches using random walks on graphs, introducing the ChemWalker python library. Similarly to NAP, ChemWalker relies on combinatorial in silico fragmentation results, performed by MetFrag, searching biologically relevant databases. Departing from the combination of a spectral network and the structural similarity among candidate structures, we have used MetFusion Scoring function to create a weight function, producing a weighted graph. This graph was subsequently used by the random walk to calculate the probability of walking through a set of candidates, departing from seed nodes (represented by spectral library matches). This approach allowed the information propagation to nodes not directly connected to the spectral library match. Compared to NAP, ChemWalker has a series of improvements, on running time, scalability and maintainability and is available as a stand alone python package. ChemWalker is freely available at https://github.com/computational-chemical-biology/ChemWalker.

bioinformatics↗

bioRxiv2020

Combining functional and structural genomics to track antibiotic resistance genes in mobile elements in clinical bacterial strains

The rise of multi-antibiotics resistant bacteria represents an emergent threat to human health. Here, we investigate antibiotic resistance mechanisms in bacteria of several species isolated from an intensive care unit in Brazil. We used whole-genome analysis to identify antibiotic resistance genes (ARGs) and plasmids in 35 strains of Gram-negative and Gram-positive bacteria, including the first genomic description of Morganella morganii and Ralstonia mannitolilytica clinical isolates from South America. We identify a high abundance of beta-lactamase genes in highly resistant organisms, including seven extended-spectrum {beta}-lactamases shared between organisms from different species. Additionally, we identify several ARGs-carrying plasmids indicating the potential for fast transmission of resistance mechanism between bacterial strains, comprising a novel IncFII plasmid recently introduced in Brazil from Asia. Through comparative genomic analysis, we demonstrate that some pathogens identified here are very distantly related to other bacteria isolated worldwide, demonstrating the potential existence of endemic bacterial pathogens in Brazil. Also, we uncovered at least two couples of (near)-identical plasmids exhibiting multi-drug resistance, suggesting that plasmids were transmitted between bacteria of the same or different species in the hospital studied. Finally, since many highly resistant strains carry several different ARGs, we used functional genomics to investigate which of them were indeed functional. In this sense, for three bacterial strains (Escherichia coli, Klebsiella pneumoniae, and M. morganii), we identify six beta-lactamase genes out of 15 predicted in silico as the main responsible for the resistance mechanisms observed, corroborating the existence of redundant resistance mechanisms in these organisms. ImportanceBig data and large-scale sequencing projects have revolutionized the field, achieving a greater understanding of ARGs identification and spreading at global level. However, given that microbiota and associated ARGs may fluctuate across geographic zones, hospital-associated infections within clinical units still remain underexplored in Brazil - the largest country in South America; 210 million inhabitants - and neighboring countries. This work highlighted the identification of several ARGs shared between species co-occurring simultaneously into a Brazilian hospital, some of them associated with large plasmids, mostly endowed with transposable elements. Also, genomic features of clinically underrepresented pathogens such M. morganii and B. cepacia were revealed. Taken together, our results demonstrate how structural and functional genomics can help to identify emerging mechanisms of shared antibiotic resistance in bacteria from clinical environments. Systematic studies as the one presented here should help to prevent outbreaks of novel multidrug resistance bacteria in healthcare facilities.

microbiology↗

bioRxiv2017

Big trouble with little inserts: boundaries in metagenomic screenings using lacZα based vectors

The vast biochemical repertoire found in microbial communities from a wide-range of environments allows screening and isolation of novel enzymes with improved catalytic features. In this sense, metagenomics approaches have been of high relevance for providing enzymes used in diverse industrial applications. For instance, glycosyl hydrolases, which catalyze the hydrolysis of carbohydrates to sugars, are essential for bioethanol production from renewable resources. In the current study, we have focused on the prospection of protease and glycosyl hydrolase activities from microbial communities inhabiting a soil sample by using the lacZ-based plasmid pSEVA232 in the generation of a screenable metagenomic library. For this, we used a functional screen based on skimmed milk agar and a pH indicator dye as previously reported in literature. Although we effectively identified nine positive clones in the screenings, subsequent experiments revealed that this phenotype was not because of the hydrolytic activity encoded in the metagenomic fragments, but rather due to the insertion of small metagenomic DNA fragments in frame within the coding region of the lacZ alpha gene present in the original vector. We concluded that the current method has a higher tendency for false positive recovery of clones, when used in combination with a lacZ-based vector. Finally, we discuss the molecular explanation for positive phenotype recovering and highlight the importance of reporting boundaries in metagenomic screenings methodologies.

molecular biology↗