Biology subjects

Chan, S. H. J.

Publications and source records attributed to Chan, S. H. J..

6 recordsLinked to original sources

bioRxiv2024

ADToolbox: Incorporating Metagenomics Data for Improved Prediction of Anaerobic Digestion Dynamics

Handling the global food waste requires sustainable solutions. Anaerobic digestion (AD) is a common approach for waste remediation and bioenergy production which has been traditionally used mainly for methane production. Shifting the focus from methane to volatile fatty acids (VFAs), such as acetic, propionic, and butyric acids, offers promising alternatives due to their diverse applications and higher added value. Achieving desired AD product distributions requires a comprehensive understanding of factors like temperature, pH, feedstock composition, and the complex microbial dynamics inherent in AD. Various AD modeling approaches exist, from simple equations to complex flux balance analysis (FBA) and machine learning (ML) model. The Anaerobic Digestion Model No. 1 (ADM 1) is a commonly used kinetic model, striking a reasonable balance between parameter requirements and biochemical details involved in the model. Yet, it falls short in capturing specific VFAs like caproic acid and integrating microbial information directly. We present ADToolbox, a Python package for modeling AD metabolism. ADToolbox incorporates metagenomic information into an enhanced ADM model. The model accommodates a more detailed feedstock degradation model and VFA and methanogenesis metabolism. ADToolbox provides a variety of interfaces such as command line interface and an interactive web interface line interface, and a Python API, facilitating large-scale metagenomic analyses and modeling simulations. In this article we indicate that prioritizing the microbial aspect of AD enhances flexibility and predictive power in terms of VFA production accuracy, contributing to sustainable waste management strategies. Explore ADToolbox at https://chan-csu.github.io/ADToolbox for detailed documentation.

bioengineering↗

bioRxiv2023

Microbial Interactions from a New Perspective: Reinforcement Learning Reveals New insights into microbiome evolution

Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities. Here we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, that treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulation policies will stabilize in the dynamic ecosystem of interest in presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions.

systems biology↗

bioRxiv2022

kb_DRAM: Annotating and functional profiling of genomes with DRAM in KBase

SummaryAnnotation is predicting the location of and assigning function to genes in a genome. DRAM is a tool developed to annotate bacterial, archaeal and viral genomes derived from pure cultures or metagenomes. DRAM distills multiple gene annotations to summaries of functional potential. Despite these benefits, a downside of DRAM is processing requires large computation resources, which limits its accessibility, and it did not integrate with downstream metabolic modelling tools. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated in the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point and click interface, as well as generate genome scale metabolic models from these DRAM annotations. Availability and ImplementationThe kb_DRAM software is available at https://github.com/shafferm/kb_DRAM. The kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. A narrative with examples of running all KBase apps is available at https://kbase.us/n/88325/84/. ContactMichael Shaffer, michael.t.shaffer@colostate.edu; Kelly Wrighton, kelly.wrighton@colostate.edu Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Metabolomics and proteomics of L. rhamnosus GG and E. coli Nissle probiotic supernatants identify distinct pathways that mediate growth suppression of antimicrobial-resistant pathogens

Probiotics merit testing as alternatives to conventional antibiotics and are receiving increased attention for efficacy against multi-drug resistant pathogen infections. This study hypothesis was that the Gram-positive probiotic, L. rhamnosus GG (LGG) and Gram-negative E. coli Nissle (ECN) secrete distinct proteins and metabolites to suppress pathogen growth. LGG and ECN cell free supernatants were tested in a dose-dependent manner for differential growth suppression of Salmonella Typhimurium, Escherichia coli, and Klebsiella oxytoca that harbor antimicrobial resistance (AMR). Across supernatant doses, LGG was 6.27% to 20.55% more effective than ECN at suppressing AMR pathogen growth. Proteomics and metabolomics were performed to identify pathways that distinguished LGG and ECN for antimicrobial functions. From the 667 detected metabolites in probiotic cell free supernatants, 304 metabolites had significantly different relative abundance between LGG and ECN, and only 5 and 6 unique metabolites were identified for LGG and ECN respectively. LGG and ECN differences involved amino acid, energy and nucleotide metabolism. Proteomics analysis of ECN and LGG cell free supernatants identified distinctions in 87 proteins, where many were related to carbohydrate and energy metabolism. Integration of genome-proteome-metabolome signatures from LGG and ECN with predictive metabolic modeling supported differential use of substrates by these two probiotics as drivers of antimicrobial actions. ECN metabolized a range of carbon sources, largely purines, whereas LGG consumed primarily carbohydrates. Understanding functional biosynthesis, utilization and secretion of bioactive metabolites and proteins from genetically distinct probiotics will guide strategic approaches for developing antibiotic alternatives and for controlling spread of multi-drug resistant pathogens. ImportanceProbiotics are practical alternatives for protection against antimicrobial resistant pathogens. Bioactive probiotics molecules merit further investigation using high throughput - omic approaches. This study identified functional differences between Gram-positive L. rhamnosus GG (LGG) and Gram-negative E. coli Nissle (ECN) probiotics that suppressed the growth of antimicrobial resistant S. Typhimurium, K. oxytoca, and E. coli. Proteomes and metabolomes of the probiotic cell free supernatants showed metabolic differences between LGG and ECN for mediating pathogen growth suppression. Metabolites distinguishing LGG versus ECN growth suppression included carbohydrates, lipids, amino acids, and nucleic acids. The metabolic flux differences between ECN and LGG, which coincided with observed separations in the proteomes and metabolomes, was hypothesized to explain the differential suppression of AMR pathogens. Integrated metabolite and protein signatures produced by each probiotic merit attention as adjuvant therapeutics for antimicrobial resistant infections.

microbiology↗

bioRxiv2019

Bridging traditional evolutionary game theory and metabolic models for predicting Nash equilibrium of microbial metabolic interactions

Microbial metabolic interactions impact ecosystems, human health and biotechnological processes profoundly. However, their determination remains elusive, invoking an urgent need for predictive models that seamlessly integrate metabolic details with ecological and evolutionary principles which shape the interactions within microbial communities. Inspired by the evolutionary game theory, we formulated a bi-level optimization framework termed NECom for the prediction of Nash equilibria of microbial community metabolic models with significantly enhanced accuracy. NECom is free of a long hidden forced altruism setup in previous static algorithm while allowing for sensing and responding between microbial members that is missing in dynamic methods. We successfully predicted several classical games in the context of metabolic interactions that were falsely or incompletely predicted by existing methods, including prisoners dilemma, snowdrift game and mutualism. The results provided insights into why mutualism is favorable despite seemingly costly cross-feeding metabolites, and demonstrated the potential to predict heterogeneous phenotypes among the same species. NECom was then applied to a reported algae-yeast co-culture system that shares typical cross-feeding features of lichen, a model system of mutualism. More than 1200 growth conditions were simulated, of which 488 conditions correspond to 3221 experimental data points. Without fitting any ad-hoc parameters, an overall 63.5% and 81.7% reduction in root-mean-square error in predicted growth rates for the two species respectively was achieved when compared with the standard flux balance analysis. The simulation results further show that growth-limiting crossfeeding metabolites can be pinpointed by shadow price analysis to explain the predicted frequency-dependent growth pattern, offering insights into how stabilizing microbial interactions control microbial populations.

systems biology↗

bioRxiv2019

A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data

BackgroundRhodosporidium toruloides is a basidiomycetes yeast that can accumulate large amount of lipids and natively produce carotenoids. To better assess this non-model yeasts metabolic capabilities, we reconstruct a genome-scale model of R. toruloides IFO0880s metabolic network (iRhto1108) using recent functional genomics and phenotypic data in literature or generated herein.\n\nResultsThe model iRhto1108 accounts for 2,203 reactions, 1,985 metabolites and 1,108 genes. In this work, we integrate and supplement the current knowledge with in-house generated biomass composition and experimental measurements pertaining to the organisms metabolic capabilities. Phenotype-genotype relationship predictions were improved through manual curation of gene-protein-reaction rules for 543 reactions and validations with gene essentiality data leading to correct recapitulations of 84.5% of gene essentiality data (sensitivity of 94.3% and specificity of 53.8%). Organism-specific macromolecular composition and ATP maintenance requirements were experimentally measured for two separate growth conditions: (i) carbon and (ii) nitrogen limitations. Overall, iRhto1108 reproduced R. toruloidess utilization capabilities for 18 alternate substrates, matched measured wild-type growth yield, and recapitulated the viability of 772 out of 819 deletion mutants. As a demonstration to the models fidelity in guiding engineering interventions, the OptForce procedure was applied on iRhto1108 for the overproduction of triacylglycerol. Suggested interventions recapitulated many of the previously successfully implemented genetic modifications and put forth a few new ones.\n\nConclusioniRhto1108 offers a highly curated model for a non-model yeast supported by multiple layers of experimental data that can be used to inform genetic interventions.

systems biology↗