Biology subjects

da Silva, R. R.

Publications and source records attributed to da Silva, R. R..

13 recordsLinked to original sources

bioRxiv2026

Ecological Interactions Drive Metabolomic Diversification in Amazonian Pseudonocardia Symbionts

Fungus-growing ants engage in a multipartite symbiosis, including Pseudonocardia bacteria that produce antifungal metabolites to protect their fungal cultivar from the specialized pathogen Escovopsis. While different bioactive metabolites have been reported from ant-associated Pseudonocardia, most studies have focused on a limited number of strains, leaving the extent of chemical diversity across broader ecological contexts less resolved. Here, we investigated the antagonistic potential and metabolomic repertoires of 36 Pseudonocardia strains isolated from Amazonian Paratrachymyrmex ants. Pairwise bioactivity assays against two Escovopsis isolates revealed striking variability, with inhibition generally stronger and more diverse against the pathogenic fungus originating from the same ant genus. Untargeted LC-MS/MS metabolomics coupled with 16S rRNA-based phylogenetic analyses showed that closely related strains harbored highly divergent chemical profiles, underscoring a decoupling between taxonomy and metabolite output. Detailed analyses of selected isolates revealed the production of structurally diverse metabolites, including dentigerumycin analogs, provipeptide A, {beta}-carbolines, and tetracycline-related compounds. Co-culture analysis uncovered metabolites absent in monocultures, including lichenysins, pepstatins, and hallobacillins, as well as conserved attinimicin, whose production was enhanced under pathogen challenge. These results highlight that both strain-specific metabolic repertoires and interaction-induced chemistry contribute to the defensive arsenal of Pseudonocardia. Together, our findings likely demonstrate that ecological pressures and local adaptation, rather than phylogeny alone, drive metabolomic diversification in this defensive symbiosis. Beyond their potential for novel bioactive compound discovery, these results provide insights into the chemical basis of multipartite symbioses, the dynamics of defensive mutualisms, and the ecological forces shaping microbial diversity in underexplored environments such as the Amazon. IMPORTANCEMicrobial symbionts are central to host defense and natural product discovery, yet the factors driving their chemical diversification remain unclear. The fungus-growing ant-Pseudonocardia-Escovopsis system offers a powerful model to study how ecological context shapes microbial metabolism. By systematically characterizing multiple Amazonian Pseudonocardia strains, we show that antagonistic capacity and metabolomic repertoires vary widely, even among strains with highly similar 16S rRNA gene sequences, revealing a pronounced discordance between 16S-based phylogenetic relatedness and specialized metabolite production. These findings highlight the likely importance of ecological pressures and local adaptation in shaping metabolomic output, emphasizing symbiotic actinobacteria as both key ecological players and promising sources of antifungal natural products.

microbiology↗

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

How the microbiome liberates iron from heme

The microbiome is a hidden organ system with diverse metabolic roles. Using Bacteroides thetaiotaomicron (B theta) as a model, we asked how microbiome species process iron, the quintessential biometal supporting respiration and life as we know it, when iron is provided in complex with protoporphyrin IX (PPIX) as heme. An intestinal transporter for heme has not been unequivocally identified. Canonical pathways for reclaiming heme iron from host cells or the diet require O2, which is unavailable in the GI tract and many pathological microenvironments. HmuS, a homolog of cobalamine-(vitamin B12) and chlorophyll-building chelatases, is widespread in anaerobic microbial ecosystems. In this work, we provide direct physiological, biochemical, and structural evidence for the anaerobic removal of iron from heme by HmuS, a de-chelatase that deconstructs heme to PPIX and Fe(II). We show that heme can be used as a sole iron source by B theta, yielding PPIX, and that hmuS inactivation under these conditions is lethal. Iron removal from heme depends on the B theta membrane fraction, NADH, and O2 exclusion. Absorbance spectra indicate that heterologously expressed HmuS is isolated with a non-substrate heme bound and can accommodate multiple heme equivalents under saturating conditions. Solution of the cryoEM structure reveals heme and two cations at sites that are conserved across the HmuS family and the chelatase superfamily, respectively. The proposed structure-based mechanism for iron removal further links biosynthetic and biodegradative pathways for heme, chlorophyll, and vitamin B12, three ancient and biologically crucial metallocofactors. Significance StatementIron in its heme and non-heme forms is essential for nearly all life. This work defines a microbial mechanism for metabolizing heme that is widespread in gastrointestinal and other anaerobic ecosystems. To date, an intestinal cellular transporter for heme has not been unequivocally identified. However, heme, abundant in both red meat diets and animal cells, is converted by the bacterial HmuS enzyme into a non-heme form from which the iron can be repurposed. HmuS enzymes form a distinct de-chelatase subset of a superfamily of chelatases that catalyze metal insertion into chlorophyll and vitamin B12, indicating convergence of metabolic pathways for biosynthesis and degradation of three of lifes essential metallocofactors. Understanding HmuS function supports engineered approaches to directing microbiome-host metabolism and offsetting heme-associated pathologies including anemia, inflammation, and cancer.

biochemistry↗

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

Optimizing the detection of biological signals through a semi-automated feature selection tool

Untargeted metabolomics is often used in studies that aim to trace the metabolic profile in a broad context, with the data-dependent acquisition (DDA) mode being the most commonly used method. However, this approach has the limitation that not all detected ions are fragmented in the data acquisition process, in addition to the lack of specificity regarding the process of fragmentation of biological signals. The present work aims to extend the detection of biological signals and contribute to overcoming the fragmentation limits of the DDA mode with a dynamic procedure that combines experimental and in silico approaches. Metabolomic analysis was performed on three different species of actinomycetes using liquid chromatography coupled to mass spectrometry. The data obtained were preprocessed by the MZmine software and processed by the custom package, RegFilter. RegFilter allowed the coverage of the entire chromatographic run and the selection of precursor ions for fragmentation that were previously missed in DDA mode. Most of the ions selected by the tool could be annotated through three levels of annotation, presenting biological relevant candidates. In addition, the tool offers the possibility of creating local spectral libraries curated according to the users interests. Thus, the adoption of a dynamic analysis flow using RegFilter allowed for detection optimization of biological signals, previously absent in the DDA mode. In addition, this workflow enables the creation and search of in-house tailored custom libraries.

bioinformatics↗

bioRxiv2023

Semi-automated retrieval of chemical and phylogenetic information from natural products literature

Natural products (NPs) are metabolites of great importance due to their fundamental biological role in performing specialized activities, ranging from basic cellular functions to complex ecological interactions. These metabolites have contributed to innovating fields such as agriculture and medicine due to their optimized biological activities, a consequence of evolution. A key factor in ensuring that isolated NPs are novel is to search scientific literature and compare pre-existing chemical entities with the new isolate. Unfortunately, articles are typically not machine-readable, a problem that hinders efficient searching and increases the chances of unintended rediscovery. In addition, the time required to add new compound discoveries to compound databases hinders computational studies on cell metabolism and Quantitative Structure-Activity Relationships (QSAR). Here, we present a modularized tool that uses text mining techniques to retrieve chemical entities and taxonomic mentions present in scientific literature, called NPMINE (Natural Products MINIng). We were able to analyze 55,382 scientific articles from some of the most important applied chemistry journals from Brazil and the world, consistently recovering the expected taxonomic and structural information. This processing resulted in 120,970 unique InChI Keys potentially associated with 21,526 unique species mentioned. Using the PubChem BioAssay database we show how QSAR models can be used to mine active leads. The results indicate that NPMINE not only facilitates natural products cataloging, but also assists in biological source assignment and structure-activity relationships, a time-consuming task, typically performed in low throughput.

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↗

bioRxiv2022

Mining novel cis-regulatory elements from the emergent host Rhodosporidium toruloides using transcriptomic data

The demand for robust microbial cell factories that can produce valuable biomaterials while being resistant to stresses imposed by current bioprocesses is rapidly growing. R. toruloides is an emerging host that presents desirable features for bioproduction, since it can grow in a wide range of substrates and tolerate a variety of toxic compounds. In order to explore R. toruloides suitability for application as a cell factory in biorefineries, we sought to understand the transcriptional responses of this yeast when growing under experimental settings that simulated those used in biofuels-related industries. Thus, we performed RNA sequencing of the oleaginous, carotenogenic yeast in different contexts. The first ones were stress-related: two conditions of high temperature (37 {degrees}C and 42 {degrees}C) and two ethanol concentrations (2% and 4%), while the other was using the inexpensive and abundant sugarcane juice as substrate. Using transcriptomic data, differential expression and functional analysis were implemented to select differentially expressed genes and enriched pathways from each set-up. A reproducible bioinformatics workflow was developed for mining new regulatory elements. We then predicted, for the first time in this yeast, binding motifs for several transcription factors, including HAC1, ARG80, RPN4, ADR1, and DAL81. Most of the putative transcription factors uncovered here were involved in stress responses and found in the yeast genome. Our method for motif discovery provides a new realm of possibilities in the study of gene regulatory networks, not only for the emerging host R. toruloides, but for other organisms of biotechnological importance.

bioinformatics↗

bioRxiv2021

ChiMera: An easy to use pipeline for Bacterial Genome Based Metabolic Network Reconstruction, Evaluation and Visualization

Several genome scale metabolic reconstruction tools have been developed in the last decades. They have helped to construct many metabolic models, which have contributed to a variety of fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a higher level of bioinformatic skills, and most of them are not scalable for multiple genomes. Moreover, the functionalities required to build models are generally scattered through multiple tools, requiring knowledge of their utilization. Here, we present ChiMera, which combines the most efficient tools in model reconstruction, prediction, and visualization. ChiMera uses CarveMe top-down approach based on genomic evidence to prune a global model with a high level of curation, generating a draft genome able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules of visualization implemented, predefined and universal. The first generates maps for the most important pathways, e.g., core-metabolism, fatty acid oxidation and biosynthesis, nucleotides and amino acids biosynthesis, glycolysis, and others. The second module produces a genome-wide metabolic map, which can be used to harvest KEGG pathway information for each compound in the model. A module of gene essentiality and knockout is also present. Overall, ChiMera combines model creation, gap-filling, FBA and metabolic visualization to create a simulation ready genome-scale model, helping genetic engineering projects, prediction of phenotypes, and other model-driven discoveries in a friendly manner.

bioinformatics↗

bioRxiv2020

The ant fungus garden acts as an external digestive system

Most animals digest their food within their own bodies, but some do not. Many species of ants grow fungus gardens that pre-digest food as an essential step of the ants nutrient uptake. To better understand this digestion process, we generated a 3D molecular map of an Atta texana fungus garden, revealing chemical modifications mediated by the fungus garden as plant material passes through.

systems biology↗

bioRxiv2018

Comprehensive mass spectrometry-guided plant specialized metabolite phenotyping reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae

Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped specialized metabolite diversity, which drives many other aspects of plant biodiversity. However, until recently, large-scale studies investigating specialized metabolite diversity in an evolutionary context have been limited by the impossibility to identify chemical structures of hundreds to thousands of compounds in a time-feasible manner. Here, we introduce a workflow for large-scale, semi-automated annotation of specialized metabolites, and apply it for over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5 % based on spectral library matches alone to 42.6 % of total MS/MS molecular features extending annotations from well-known plant compound classes into the dark plant metabolomics matter. To gain insights in substructural diversity within the plant family, we also extract patterns of co-occurring fragments and neutral losses, so-called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7-O-methyltransferase activity. Our workflow provides the foundations towards the automated, high-throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.

plant biology↗

bioRxiv2018

Untargeted Mass Spectrometry-Based Metabolomics Tracks Molecular Changes in Raw and Processed Foods and Beverages

A major aspect of our daily lives is the need to acquire, store and prepare our food. Storage and preparation can have drastic effects on the compositional chemistry of our foods, but we have a limited understanding of the temporal nature of processes such as storage, spoilage, fermentation and brewing on the chemistry of the foods we eat. Here, we performed a temporal analysis of the chemical changes in foods during common household preparations using untargeted mass spectrometry and novel data analysis approaches. Common treatments of foods such as home fermentation of yogurt, brewing of tea, spoilage of meats and ripening of tomatoes altered the chemical makeup through time, through both chemical and biological processes. For example, brewing tea altered its composition by increasing the diversity of molecules, but this change was halted after 4 min of brewing. The results indicate that this is largely due to differential extraction of the material from the tea and not modification of the molecules during the brewing process. This is in contrast to the preparation of yogurt from milk, spoilage of meat and the ripening of tomatoes where biological transformations directly altered the foods molecular composition. Comprehensive assessment of chemical changes using multivariate statistics showed the varied impacts of the different food treatments, while analysis of individual chemical changes show specific alterations of chemical families in the different food types. The methods developed here represent novel approaches to studying the changes in food chemistry that can reveal global alterations in chemical profiles and specific transformations at the chemical level.\n\nO_LSTHighlightsC_LSTO_LIWe created a reference data set for tomato, milk to yogurt, tea, coffee, turkey and beef.\nC_LIO_LIWe show that normal preparation and handling affects the molecular make-up.\nC_LIO_LITea preparation is largely driven by differential extraction.\nC_LIO_LIFormation of yogurt involves chemical transformations.\nC_LIO_LIThe majority of meat molecules are not altered in 5 days at room temperature.\nC_LI

biochemistry↗