Biology subjects

Machado, D.

Publications and source records attributed to Machado, D..

16 recordsLinked to original sources

bioRxiv2026

Genome-scale metabolic model atlas of the zoonotic pathogen Streptococcus suis

Streptococcus suis is a Gram-positive bacterium with a dual role as a commensal member of the porcine nasal microbiota and a pathogen causing systemic disease in pigs and humans. Mounting evidence suggests that metabolism is a key driver of S. suis pathogenicity. Given the species high genetic variability, we hypothesize that differences in metabolic networks could explain the diverse pathogenic phenotypes observed across different strains. To test this, we generated an atlas of over 3000 strain-specific and automatically curated genome-scale metabolic models that cover the breadth of pathogenic and commensal S. suis lineages. Using this model atlas, we performed the first species-level examination of metabolic traits in S. suis. Our simulations, supported by experimental validation, revealed three key insights. First, while metabolic traits are broadly conserved in S. suis, there are nevertheless lineage-dependent differences in amino acid auxotrophies and carbon utilization patterns that point towards distinct in vivo niches. Second, most strains are predicted to grow in different plausible in vivo environments regardless of their virulence phenotype, suggesting that metabolism is a weak barrier to systemic infection. Third, by systematically predicting reaction essentiality in more than 15 million reaction-strain-condition combinations, we identify a subset of 17 reactions, largely in nucleotide metabolism, that are conditionally essential in vivo and may serve as new targets for the development of new antimicrobials or vaccines. Overall, this study provides a valuable new resource for broadly examining S. suis metabolism and its role in pathogenicity.

Systems Biology↗

bioRxiv2026

Cross-feeding enables robust coexistence between four bacterial species

AbstractMicrobial diversity is often assumed to be limited by the number of available resources, yet many communities persist well beyond that expectation. Understanding the mechanisms that enable such coexistence remains a central question in microbial ecology. Here, using a four-species bacterial consortium, we asked whether coexistence can emerge from interactions between species rather than from the external environment alone. Across 31 simple nutrient conditions, including 16 single-resource environments, all four species persisted and repeatedly reached stable coexistence. We then chose 27 additional conditions to further probe the boundaries of coexistence by varying resource concentrations, temporal dynamics, nutrient complexity and relief of auxotrophy-associated dependencies, and only observed the extinction of one species in one of these conditions. Although the community composition in each environment was largely shaped by species fitness on the supplied resources, experimental assays and consumer-resource modeling showed that the coexistence was not explained by resource supply, but rather by cross-feeding and niche partitioning of metabolic byproducts. These metabolic interactions were strong enough to sustain coexistence even for species unable to use the supplied resources directly. Furthermore, robust coexistence across environments appears to be an emergent property of microbial communities, ingrained in members metabolic byproduct profiles and niche differences. Our findings demonstrate how microbes can increase the chemical complexity of their environment sufficiently to maintain coexistence well beyond what is expected from external resource supply. SignificanceUnderstanding the drivers of microbial diversity is essential for managing natural ecosystems and designing synthetic microbiomes. This study challenges the conventional application of the competitive exclusion principle, demonstrating that a four-species consortium can coexist across 31 chemically and metabolically diverse one- and two-carbon source environments. By systematically testing and ruling out alternative stabilizing mechanisms, we show that co-existence is an emergent property of the consortium, sustained by metabolic cross-feeding and niche partitioning. Guided by computational models, we identify hallmarks of robust co-existence in simple environments, including high variance in resource affinities and growth on partner-derived metabolites. Our work demonstrates how microbes modify their environment to sustain high diversity and provides principles for designing synthetic microbiomes that persist across environments.

microbiology↗

bioRxiv2025

A database of over 15.000 strain design publications reveals a conserved set of metabolic engineering targets across microbial hosts and products

Microbial biotechnology has the potential to address several societal issues through the sustainable production of industrially relevant compounds. Despite decades of successful cases, rational engineering of microbial metabolism is still a complex process due to the fine balance between nutrient supply, allocation of cellular resources, energy demand and redox balancing. In this work, we implemented a text-mining workflow for metabolic engineering and compiled a database of experimentally validated strain design strategies from over 15.000 research articles, which includes information on host strain, target compounds, and gene modifications. This large dataset reveals trends on the selection of suitable hosts for different kinds of products and the respective gene targets. Despite the wide variety of microbes and products, we observe a conserved set of target metabolic genes associated with central carbon metabolism, especially in upper glycolysis, pentose-phosphate pathway, citric acid cycle, and fermentative pathways. The most distinguishing feature among strain design strategies seems not to be which genes are targeted, but rather the direction in which they are modified (increased or decreased expression). Controlling flux at key branching points and redox balancing reactions is thus a critical engineering step to steer metabolism. Our collection of 25 years of literature can provide a stepping stone for starting new strain design projects without reinventing the wheel.

systems biology↗

bioRxiv2025

Activation of nuclear receptors correlates with tuberculosis severity and is a target for host-directed therapy

The immune response to Mycobacterium tuberculosis is accompanied by metabolic adaptations that fuel host immunity, but that are exploited by the pathogen to ensure persistence and growth. Nuclear receptors, such as liver-X-receptors (LXR), orchestrate macrophage immunometabolic adaptations to infection and globally associate with tuberculosis (TB) protection. Here, we show that the "signalling by nuclear receptors" (SNR) pathway is detected in the whole blood of TB patients and that its expression correlates with disease severity. Accordingly, we also show that the activation of the LXR pathway progressively increases in the lungs of M. tuberculosis-infected C57BL/6 and C3HeB/FeJ mice. Pharmacologic activation of LXR, specifically at the chronic stage of infection, improved infection outcomes and significantly prolonged the survival of the highly susceptible C3HeB/FeJ mice. Common to both mouse models and to in vitro macrophage infections, LXR activation enhanced bacterial control together with an increase in extracellular cholesterol levels. We propose that progressive LXR activation is required to fine-tune host cholesterol availability during M. tuberculosis infections and restrict access to this nutrient during chronic stages of infections. Collectively, we identify the SNR pathway as a potential biomarker of TB severity and timely LXR activation as a candidate host-directed therapy.

immunology↗

bioRxiv2025

Genome-scale metabolic modeling of Ruminiclostridium cellulolyticum: a microbial cell factory for valorization of lignocellulosic biomass

The development of sustainable biotechnological processes requires a transition from the traditional fermentation of refined substrates towards the valorization of waste materials such as lignocellulosic biomass. Although these so-called recalcitrant substrates cannot be degraded by model industrial organisms, they can be degraded by microbial consortia through a process of anaerobic digestion, where different community members are able to break down polysaccharides of varied complexity. Among these microbes, Ruminiclostridium cellulolyticum stands out as a promising candidate for fermentation of lignocellulose due to its ability to degrade both cellulose and hemicellulose. In this work, we present an updated genome-scale metabolic model for R. cellulolyticum strain H10. The model was manually curated with experimental data and the pathways for degradation of cellulose and hemicellulose (arabinoxylan and xyloglucan) were reconstructed and annotated with full detail. The model enables the simulation of the fermentation profile of lignocellulosic materials of various compositions, facilitating the use of this organism as a potential workhorse for sustainable biotechnology, and it provides a valuable template for the reconstruction and optimization of lignocellulose degradation pathways in related organisms.

systems biology↗

bioRxiv2024

Uptake and survival of Leishmania amazonensis in Acanthamoeba castellanii: An infection organism model?

Leishmaniasis is a neglected tropical disease. Parasite strategies and the evaluation of drugs and vaccines are inferred from studies carried out using mouse models and macrophages. The development of model organisms with no ethical restrictions will contribute to our knowledge of leishmaniasis. Acanthamoeba castellanii, a free-living protozoan, is known to interact with various microorganisms. In this study, the interaction between the amoeba A. castellanii and the trypanosomatid Leishmania amazonensis was investigated by combining quantitative kinetics analysis, optical, fluorescence, electronic, confocal, and live video microcopy. We sought to standardize protocols for the co-culture; the optimal experimental conditions were: RPMI medium + 10% SFB at 26{degrees}C. L. amazonensis invades A. castellanii through its acanthopods, and the promastigotes interact with the trophozoites via their flagellum, which also occurs when parasites infect mammalian macrophages. The forms of L. amazonensis inside the amoeba become rounded and lose their flagellum; they are similar to amastigotes. These round forms were isolated from trophozoites after 3 h of co-culture and differentiated into promastigotes, demonstrating their viability inside amoeba. The percentage of amoebas with L. amazonensis was reduced overtime. Thus, considering that A. castellanii can clear Leishmania, this interaction could serve as an effective model of cellular leishmanicidal mechanisms.

cell biology↗

bioRxiv2023

A benchmark of optimization solvers for genome-scale metabolic modeling

Genome-scale metabolic modeling is a powerful framework for predicting metabolic phenotypes of any organism with an annotated genome. For two decades, this framework has been used for rational design of microbial cell factories. In the last decade, the range of applications has exploded, and new frontiers have emerged, including the study of the gut microbiome and its health implications, and the role of microbial communities in global ecosystems. However, all the critical steps in this framework, from model construction to simulation, require the use of powerful linear optimization solvers, with the choice often relying on commercial solvers for their well-known computational efficiency. In this work, I benchmark a total of six solvers (two commercial and four open-source) and measure their performance to solve linear and mixed-integer linear problems of increasing complexity. Although commercial solvers are still the fastest, at least two open-source solvers show comparable performance. These results show that genome-scale metabolic modeling does not need to be hindered by commercial licensing schemes and can become a truly open science framework for solving urgent societal challenges.

systems biology↗

bioRxiv2022

The genomic basis of the plant island syndrome in Darwin's giant daisies

Oceanic archipelagos comprise multiple disparate environments over small geographic areas and are isolated from other biotas. These conditions have led to some of the most spectacular adaptive radiations, which have been key to our understanding of evolution, and offer a unique chance to characterise the genomic basis underlying rapid and pronounced phenotypic changes. Repeated patterns of evolutionary change in plants on oceanic archipelagos, i.e. the plant island syndrome, include changes in leaf morphology, acquisition of perennial life-style, and change of ploidy. Here, we describe the genome of the critically endangered and Galapagos endemic Scalesia atractyloides Arnot., obtaining a chromosome-resolved 3.2-Gbp assembly with 43,093 candidate gene models. Using a combination of fossil transposable elements, k-mer spectra analyses and orthologue assignment, we identify the two ancestral subgenomes and date their divergence and the polyploidization event, concluding that the ancestor of all Scalesia species on the Galapagos was an allotetraploid. There are a comparable number of genes and transposable elements across the two subgenomes, and while their synteny has been mostly conserved, we find multiple inversions that may have facilitated adaptation. We identify clear signatures of selection across genes associated with vascular development, life-growth, adaptation to salinity and changes in flowering time, thus finding compelling evidence for a genomic basis of island syndrome in Darwins giant daisy radiation. This work advances understanding of factors influencing subgenome divergence in polyploid genomes, and characterizes the quick and pronounced genomic changes in a specular and diverse radiation of an iconic island plant radiation.

evolutionary biology↗

bioRxiv2020

Polarization of microbial communities between competitive and cooperative metabolism

Resource competition and metabolic cross-feeding are among the main drivers of microbial community assembly. Yet, the degree to which these two conflicting forces are reflected in the composition of natural communities has not been systematically investigated. Here, we use genome-scale metabolic modeling to assess resource competition and metabolic cooperation potential in large co-occurring groups, with up to 40 member species, across thousands of habitats. Our analysis revealed two distinct community types, clustering at opposite ends in a trade-off landscape between competition and cooperation. On one end lie highly cooperative communities, characterized by smaller genomes and multiple auxotrophies, reminiscent of the black queen hypothesis. At the other end lie highly competitive communities, conforming to the red queen hypothesis, featuring larger genomes and overlapping nutritional requirements. While the latter are mainly present in soils, the former are found both in free-living and host-associated habitats. Community-scale flux simulations showed that, while the competitive communities can better resist species invasion but not nutrient shift, the cooperative communities are susceptible to species invasion but resilient to nutrient change. In accord, we show, through analyzing an additional independent dataset, the colonization of the human gut by probiotic species is positively associated with the presence of cooperative species in the recipient microbiome. Together, our analysis highlights the bifurcation between competition and cooperation in the assembly of natural communities and its implications for community modulation.

microbiology↗

bioRxiv2020

Oncogenic memory underlying minimal residual disease in breast cancer

Tumor relapse is responsible for most breast cancer related deaths1,2. The disease recurrence stems from treatment refractory cancer cells that persist as minimal residual disease (MRD) for years following initial therapy3. Yet, the molecular characteristics defining the malignancy of MRD remain elusive due to difficulties in observing these rare cells in patients or in model organisms. Here, we use a tractable organoid system and multi-omics analysis to show that the dormant MRD cells retain metabolic peculiarities reminiscent of the tumor state. While the MRD cells were distinct from both normal and tumor cells at a global transcriptomic level, their metabolomic and lipidomic profile markedly resembled that of the tumor state. The MRD cells particularly exhibited a de-regulated urea cycle and elevated glycolysis. We find the latter being crucial for their survival and could be selectively targeted using a small molecule inhibitor of glycolytic activity. We validated these metabolic peculiarities of the MRD cells in corresponding tissues obtained from the mouse model as well as in transcriptomic data from patients following neo-adjuvant therapy. Together, our results show that the treatment surviving MRD cells retain features of the tumor state over an extended period suggestive of an oncogenic memory. In accord, we found striking similarity in DNA methylation profiles between the tumor and the MRD cells. The distinction of MRD from normal breast cells comes as a surprise, considering their phenotypic similarity with regards to proliferation and polarized epithelial organization. The metabolic aberrances of the MRD cells offer a therapeutic opportunity towards tackling emergence of breast tumor recurrence in post-treatment care.

cancer biology↗

bioRxiv2020

Benchmarking kinetic models of Escherichia coli metabolism

Predicting phenotype from genotype is the holy grail of quantitative systems biology. Kinetic models of metabolism are among the most mechanistically detailed tools for phenotype prediction. Kinetic models describe changes in metabolite concentrations as a function of enzyme concentration, reaction rates, and concentrations of metabolic effectors uniquely enabling integration of multiple omics data types in a unifying mechanistic framework. While development of such models for Escherichia coli has been going on for almost twenty years, multiple separate models have been established and systematic independent benchmarking studies have not been performed on the full set of models available. In this study we compared systematically all recently published kinetic models of the central carbon metabolism of Escherichia coli. We assess the ease of use of the models, their ability to include omics data as input, and the accuracy of prediction of central carbon metabolic flux phenotypes. We conclude that there is no clear winner among the models when considering the resulting tradeoffs in performance and applicability to various scenarios. This study can help to guide further development of kinetic models, and to demonstrate how to apply such models in real-world setting, ultimately enabling the design of efficient cell factories. Author summaryKinetic modeling is a promising method to predict cell metabolism. Such models provide mechanistic description of how concentrations of metabolites change in the cell as a function of time, cellular environment and the genotype of the cell. In the past years there have been several kinetic models published for various organisms. We want to assess how reliably models of Escherichia coli metabolism could predict cellular metabolic state upon genetic or environmental perturbations. We test selected models in the ways that represent common metabolic engineering practices including deletion and overexpression of genes. Our results suggest that all published models have tradeoffs and the model to use should be chosen depending on the specific application. We show in which cases users could expect the best performance from published models. Our benchmarking study should help users to make a better informed choice and also provides systematic training and testing dataset for model developers.

systems biology↗

bioRxiv2019

Emergence of stable coexistence in a complex microbial community through metabolic cooperation and spatio-temporal niche partitioning

Microbial communities in nature often feature complex compositional dynamics yet also stable coexistence of diverse species. The mechanistic underpinnings of such dynamic stability remain unclear as system-wide studies have been limited to small engineered communities or synthetic assemblies. Here we show how kefir, a natural milk-fermenting community, realizes stable coexistence through spatio-temporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microbes) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a dynamic fashion with early members opening metabolic niches for the followers. Through large-scale mapping of metabolic preferences and inter-species interactions, we show how microbes poorly suited for milk survive in, and even dominate, the community through metabolic cooperation and uneven partitioning between the grain and the liquid phase. Overall, our findings reveal how spatio-temporal dynamics promote stable coexistence and have implications for deciphering and modulating complex microbial ecosystems.

microbiology↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗

bioRxiv2018

Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism and model-guided re-engineering. Recent applications of metabolic models have also demonstrated their usefulness in unraveling cross-feeding within microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging far behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual cura-tion required to obtain good quality models and thus physiologically meaningful simulation results. Here, we present an automated tool - CarveMe - for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation-ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We build reconstructions for two model organisms, Escherichia coli and Bacillus subtillis, as well as a collection of human gut bacteria, and show that CarveMe models perform similarly to manually curated models in reproducing experimental phenotypes. Finally, we demonstrate the scalability of CarveMe through reconstructing 5587 bacterial models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.

bioinformatics↗

bioRxiv2017

Zika virus infection induces synthesis of Digoxin in glioblastoma cells

Recently, microcephaly cases have increased in Americas and have been matter of concern due to Zika virus (ZIKV) recent outbreak. Previous studies have shown that ZIKV-infected progenitor neuronal cells present morphological abnormalities and increased rates of cell death, which may be indicators of microcephaly causes. As recent studies indicate Zika virus tropism for brain cells, how would a glioblastoma (GBM) lineage behave under ZIKV infection, considering GBM the most common and malignant brain tumor in adults, presenting extreme chemoresistance and high morbidity and mortality rates? The current trend of using genetically engineered oncolytic pathogens as a safe way to eliminate tumors is under development, with trials already in course. Therefore, the present study evaluated the possible oncolytic effects and metabolomic alterations of Zika virus infection at human malignant M059J glioblastoma cells. Microscopic evaluation was performed using optical microscopy, which showed cytopathic effects induced by ZIKV at GBM cells. For the metabolomics study, both control and infected cell cultures were submitted to MALDI-MSI analysis. Mass spectrometry data were submitted to PLS-DA statistical analysis, and distinct biomarkers were elected for each infected groups. This study brings light to unexpectedly induced metabolic changes, as endogenous Digoxin as important biomarker for ZIKV-GBM group, associated with cytopathic effects induced by viral infection. These results evidences that genetically engineered ZIKV might be a potential new strategy for neural cancer management through the induction of endogenous digoxin synthesis in glioblastoma cells.

neuroscience↗

bioRxiv2017

Post-translational regulation of metabolism in fumarate hydratase deficient cancer cells

Deregulated signal transduction pathways and energy metabolism are hallmarks of cancer and both play a fundamental role in the process of tumorigenesis. While it is increasingly recognised that signalling and metabolism are highly interconnected, the underpinning mechanisms of their co-regulation are still largely unknown. Here we designed and acquired proteomics, phosphoproteomics, and metabolomics experiments in fumarate hydratase (FH) deficient cells and developed a computational modelling approach to identify putative regulatory phosphorylation-sites of metabolic enzymes. We identified previously reported functionally relevant phosphosites and potentially novel regulatory residues in enzymes of the central carbon metabolism. In particular, we show that pyruvate dehydrogenase (PDHA1) enzymatic activity is inhibited by increased phosphorylation in FH-deficient cells. Our work provides a novel approach to investigate how post-translational modifications of enzymes regulate metabolism and could have important implications for understanding the metabolic transformation of FH-deficient cancers.

systems biology↗