Biology subjects

Pavao, A.

Publications and source records attributed to Pavao, A..

7 recordsLinked to original sources

bioRxiv2025

Deconvoluting Metabolomic Flux in Living Cells with Real-time 13C J-coupling-edited Proton High-Resolution Magic Angle Spinning (HRMAS) NMR

Carbon-13 (13C) NMR provides a powerful approach to non-invasively investigate complex cellular metabolism. However, limitations arise per 13C NMRs reduced sensitivity as compared to proton (1H) NMR, even when using 13C tracer molecules to enrich signal, and particularly when investigating metabolic processes in low volume samples. To overcome these limitations, we developed a 1H NMR method that harnesses 1H NMRs sensitivity with 13C NMRs specificity by capturing 13C NMR information in the 1H spectrum via 13C-1H J-couplings (1H[13C-Jed]). This approach improves 13C signal detection 13.9-fold and incorporates additional 1H NMR information to investigate complex metabolism on a cellular scale. Here, we demonstrate application of 1H[13C- Jed] NMR to deconvolute complex cellular metabolism in the anaerobic pathogen Clostridioides difficile. Analyses enabled precise tracking of branching metabolic reactions under anoxic and reducing conditions over time in a 30{micro}L volume, and improved predictions of the pathogens genome scale metabolism. 1H[13C-Jed] NMR provides methodological advancements to investigate dynamic cellular metabolism and its role in complex biologic processes.

biophysics↗

bioRxiv2023

HRMAS 13C NMR and genome-scale metabolic modeling identify threonine as a preferred dual redox substrate for Clostridioides difficile

Stickland-fermenting Clostridia preferentially ferment amino acids to generate energy and anabolic substrates for growth. In gut ecosystems, these species prefer dual redox substrates, particularly mucin-abundant leucine. Here, we establish how theronine, a more prevalent, mucin-abundant substrate, supports dual redox metabolism in the pathogen Clostridioides difficile. Real-time, High-Resolution Magic Angle Spinning NMR spectroscopy, with dynamic flux balance analyses, inferred dynamic recruitment of four distinct threonine fermentation pathways, including ones with intermediate accrual that supported changing cellular needs for energy, redox metabolism, nitrogen cycling, and growth. Model predictions with 13C isotopomer analyses of [U-13C]threonine metabolites inferred threonines reduction to butyrate through the reductive leucine pathway, a finding confirmed by deletion of the hadA 2-hydroxyisocaproate CoA transferase. In vivo metabolomic and metatranscriptomic analyses illustrate how threonine metabolism in C. difficile and the protective commensal Paraclostridium bifermentans impacts pathogen colonization and growth, expanding the range of dual-redox substrates that modulate host risks for disease.

microbiology↗

bioRxiv2023

Central in vivo mechanisms by which C. difficile's proline reductase drives efficient metabolism, growth, and toxin production

Clostridioides difficile (CD) is a sporulating and toxin-producing nosocomial pathogen that opportunistically infects the gut, particularly in patients with depleted microbiota after antibiotic exposure. Metabolically, CD rapidly generates energy and substrates for growth from Stickland fermentations of amino acids, with proline being a preferred reductive substrate. To investigate the in vivo effects of reductive proline metabolism on C. difficiles virulence in an enriched gut nutrient environment, we evaluated wild-type and isogenic{Delta} prdB strains of ATCC43255 on pathogen behaviors and host outcomes in highly susceptible gnotobiotic mice. Mice infected with the{Delta} prdB mutant demonstrated extended survival via delayed colonization, growth and toxin production but ultimately succumbed to disease. In vivo transcriptomic analyses demonstrated how the absence of proline reductase activity more broadly disrupted the pathogens metabolism including failure to recruit oxidative Stickland pathways, ornithine transformations to alanine, and additional pathways generating growth-promoting substrates, contributing to delayed growth, sporulation, and toxin production. Our findings illustrate the central role for proline reductase metabolism to support early stages of C. difficile colonization and subsequent impact on the pathogens ability to rapidly expand and cause disease.

microbiology↗

bioRxiv2022

Elucidating dynamic anaerobe metabolism with HRMAS 13C NMR and genome-scale metabolic modeling.

Anaerobic microbial metabolism drives critical functions within global ecosystems, host-microbiota interactions, and industrial applications, yet remains ill-defined. Here we advance versatile approaches to elaborate dynamic metabolism in living cells of obligate anaerobes, using the pathogen Clostridioides difficile, an amino acid and carbohydrate-fermenting Clostridia. High-Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy of C. difficile grown with uniformly labeled 13C substrates informed dynamic flux balance analysis (dFBA) of the pathogens genome-scale metabolism. Predictions identified metabolic integration of glycolytic and amino acid fermentation pathways at alanines biosynthesis, to support efficient energy generation, maintenance of redox balance, nitrogen handling, and biomass generation. Model predictions advanced an approach using the sensitivity of 13C NMR spectroscopy to simultaneously track cellular carbon and nitrogen flow, from [U-13C]glucose and [15N] leucine, confirming the formation of [13C,15N]alanine. We illustrate experimental and computational approaches to elaborate complex anaerobic metabolism for diverse applications.

microbiology↗

bioRxiv2022

Clostridium scindens colonization of gnotobiotic mice promotes a chronic unresolving infection with Clostridioides difficile

The commensal Clostridium scindens has been regarded as a promising bacteriotherapeutic against Clostridioides difficile infection due to its ability to consume host factors that can promote C. difficile growth, and its production of the antimicrobial compound 1-acetyl-{beta}-carboline. We investigated C. scindens protective effects against C. difficile using defined colonization studies in gnotobiotic mice. Mice infected with C. difficile develop lethal infection within 48 hours. In contrast, 88% of mice pre-colonized with C. scindens survived acute infection with delayed C. difficile colonization, lower biomass, and toxin B levels at 24 hours after infection. However, two weeks post-challenge, surviving mice showed comparable levels of cecal C. difficile vegetative and spore biomass and toxin B, as seen during acute infection. After two weeks, co-colonized mice exhibited mucosal colonic hyperplasia with focal pseudomembranes, modeling a chronic and recurrent infection state. Our findings illustrate how the commensal microbiota can modulate host and pathogen interactions leading to chonic C. difficile carriage and infection.

microbiology↗

bioRxiv2021

Real-time HRMAS 13C NMR of obligately anaerobic cells identifies new metabolic targets in the pathogen Clostridioides difficile

Anaerobic microbial metabolism drives critical aspects of host-microbiome interactions and supports many economically important industrial applications. Yet, the metabolic pathways of anaerobic bacteria and their associated constraints for maintaining energy and redox balance are often poorly described. We employ High-Resolution Magic Angle Spinning Carbon-13 (13C) Nuclear Magnetic Resonance spectroscopy with dynamic flux balance analysis to resolve real-time metabolism in living cells of the obligately anaerobic pathogen Clostridioides difficile. Using 13C-labeled carbon sources, we elaborate the time-dependent progression of reductive and oxidative anaerobic fermentation pathways. Analyses identified new integration points for redox and nitrogen coupling between carbohydrate and amino acid metabolism, particularly in the production of 13C-alanine from 13C-glucose to provide an ammonia sink from co-occurring amino acid fermentation. Analyses conducted in the presence or absence of selenium, a required co-factor for the proline Stickland reductase, demonstrate further capacity to modulate cellular metabolism and resulting metabolites. Findings informed a genome-scale metabolic model of C. difficile, identifying alanine and associated electron carrier pools as critical metabolic integration points in energy flow and biomass expansion. We illustrate use of HRMAS NMR as a new analytical platform to resolve complex interactions in anaerobic metabolism and inform new metabolic targets to counter C. difficile infections.

microbiology↗

bioRxiv2018

Forecasting bacterial survival-success and adaptive evolution through multi-omics stress response-mapping, network analyses and machine learning

Whether a bacterial pathogen establishes an infection and/or evolves antibiotic resistance depends on successful survival while experiencing stress from for instance the host immune system and/or antibiotics. Predictions on bacterial survival and adaptive outcomes could thus have great prognostic value. However, it is unknown what information is required to enable such predictions. By developing a novel network-based analysis method, a bacterium's phenotypic and transcriptional response can be objectively quantified in temporal 3D-feature space. The resulting trajectories can be interpreted as a degree of coordination, where a focused and coordinated response predicts bacterial survival-success, and a random uncoordinated response predicts survival-failure. These predictions extend to both antibiotic resistance and in vivo infection conditions and are applicable to both Gram-positive and Gram-negative bacteria. Moreover, through experimental evolution we show that the degree of coordination is an adaptive outcome - an uncoordinated response evolves into a coordinated response when a bacterium adapts to its environment. Most surprisingly, it turns out that phenotypic and transcriptional response data, network features and genome plasticity data can be used to train a machine learning model that is able to predict which genes in the genome will adapt under nutrient or antibiotic selection. Importantly, this suggests that deterministic factors help drive adaptation and that evolution is, at least partially, predictable. This work demonstrates that with the right information predictions on bacterial short-term survival and long-term adaptive outcomes are feasible, which underscores that personalized infectious disease diagnostics and treatments are possible, and should be developed.

microbiology↗