Biology subjects

Song, H.-S.

Publications and source records attributed to Song, H.-S..

11 recordsLinked to original sources

bioRxiv2024

Diversity in Chemical Subunits and Linkages: A Key Molecular Determinant of Microbial Richness, Community Structure, Interactions, and Substrate Utilization

Dietary fibers play a significant role in shaping the composition and function of microbial communities in the human colon. Our understanding of the specific chemical traits of dietary fibers that influence microbial diversity, interactions, and function remains limited. Towards filling this knowledge gap, we developed a novel measure, termed Chemical Subunits and Linkages (CheSL) Shannon diversity, to characterize the effects of carbohydrate complexity on human fecal bacteria cultured in vitro under controlled, continuous flow conditions using media that systematically varied in carbohydrate composition. Our analysis revealed that CheSL Shannon diversity demonstrated a strong Pearson correlation with microbial richness across multiple fecal samples and study designs. Additionally, we observed that microbial communities in media with higher CheSL Shannon diversity scores exhibited greater peptide utilization, and more connected, reproducible structures in computationally inferred microbial interaction networks. Together, these findings demonstrate that CheSL Shannon diversity can be a useful tool to quantify the effects of carbohydrate complexity on microbial diversity, metabolic potential, and interactions. Furthermore, our work highlights how robust and stable community data can be generated by engineering media composition and structure. These studies provide a valuable framework for future research on microbial community interactions and their potential impacts on host health. IMPORTANCEFor the human adult gut microbiota, higher microbial diversity strongly correlates with positive health outcomes. This correlation is likely due to increased community resilience that results from functional redundancy that can occur within diverse communities. While previous studies have shown that dietary fibers influence microbiota composition and function, we lack a complete mechanistic understanding of how differences in the composition of fibers are likely to functionally impact microbiota diversity. To address this need, we developed Chemical Subunits and Linkages (CheSL) Shannon diversity, a novel measure that describes carbohydrate complexity. Using this measure, we were able to correlate changes in carbohydrate complexity with alterations in microbial diversity and interspecies interactions. Overall, these analyses provide new perspectives on dietary optimization strategies to improve human health.

microbiology↗

bioRxiv2024

Modeling Microbial Regulatory Feedback in Organic Matter Decomposition Identifies Copiotrophic Traits as Key Drivers of Positive Priming

Microbial decomposition of complex soil organic matter (OM) is often regulated by labile organic carbon inputs, a phenomenon known as priming, which plays a critical role in belowground biogeochemical cycling. However, the strength and direction of microbial priming of soil OM pools varies significantly across ecosystems. A generalizable mechanistic framework explaining the factors that lead to accelerated (positive priming) or impeded (negative priming) rates of OM decomposition is still lacking. In this work, we conceptualize priming as a microbial feedback loop that optimizes the costs and benefits of maximizing growth rate, specifically, the cost of exoenzyme synthesis for decomposing complex OM versus the benefit of energy acquisition from labile OM. We examined the impacts of microbial growth traits and interactions on priming by employing a cybernetic modelling approach, which predicts complex microbial growth patterns by accounting for dynamic metabolic regulations. We simulated microbial priming across ecological community configurations composed of degraders and non-degraders with either oligotrophic or copiotrophic growth traits, resulting in seven combinations that included both single functional groups (degraders with either growth trait) and binary functional groups (combinations of degraders and non-degraders, or degraders only, with differing or common traits). Configurations with only non-degraders were excluded, as they are irrelevant for studying priming in OM decomposition. Monte Carlo simulations for these scenarios revealed: (1) positive priming is prevalent, while negative priming occurs sporadically under specific parameter settings; (2) positive priming is more frequently observed in microbial systems with copiotrophic degraders than those with oligotrophic degraders; (3) the presence of copiotrophic non-degraders suppresses positive priming, whereas the presence of oligotrophic non-degraders promotes it; and (4) the temporal dynamics of priming is also influenced by microbial growth traits and interactions. These findings highlight the driving role of microbial functional traits and interactions in priming. Most strikingly, our simulations predicted a dramatic positive priming effect triggered by the addition of a small amount (i.e., less than 10%) of labile OM, with no notable changes observed beyond this point. As we used a generalized microbial model, we hypothesize that our findings may reflect common features of OM priming across diverse microbial systems and environments. Overall, this work, combining new theories and models, significantly enhances our understanding of priming by providing model-generated and empirically testable hypotheses on the mechanisms governing it.

systems biology↗

bioRxiv2024

GPR43 in Eosinophils Prevents the Emergence of Pathogenic Siglec-Fhi Neutrophils in Allergic Airway Inflammation

Eosinophils are major effector cells in type 2 immune responses, contributing to host defense and allergic diseases. They also play critical roles in maintaining tissue homeostasis by regulating various immune cell types. However, evidence of the crosstalk between eosinophils and neutrophils is limited. Here, we show that eosinophils directly associate with neutrophils in the lungs of asthma-induced mice. Eosinophil-specific deficiency of the short-chain fatty acid receptor GPR43 results in hyperactivation of eosinophils and increases the expression of neutrophil chemoattractants and PECAM-1, thus enhancing the interaction between eosinophils and neutrophils. This binding event exposes the neutrophils to eosinophil-derived IL-4 and GM-CSF, which induces the conversion of conventional neutrophils into more pathogenic Siglec-Fhi neutrophils that strongly enhance Th17 cell differentiation and aggravate asthma symptoms. These results reveal GPR43 as a critical regulator of eosinophils and highlight that eosinophils have a hitherto little-known ability to directly modulate neutrophil differentiation and function. One Sentence SummaryEosinophils directly recruit neutrophils and induce their differentiation into a pathogenic Siglec-Fhi subset in allergic airway inflammation.

immunology↗

bioRxiv2023

ModelSEED v2: High-throughput genome-scale metabolic model reconstruction with enhanced energy biosynthesis pathway prediction

Since the release of ModelSEED in 2010, the systems biology research community has used the ModelSEED genome-scale metabolic model reconstruction pipeline to build over 200,000 draft metabolic reconstructions that support hundreds of publications. Here we describe the first comprehensive update to this reconstruction tool, with new features such as (i) a dramatically improved representation of energy metabolism, which ensures that models produce accurate amounts of ATP per mol of nutrient consumed; (ii) a new template for Archaea model reconstruction; and (iii) a significantly improved curation of all metabolic pathways with mappings to RAST subsystems annotations. We applied the improved pipeline to build and analyze core and genome-scale models for Archaea and Bacteria genomes in KEGG. The new ModelSEED pipeline generates larger models that possess more reactions and genes and require fewer gap-filled reactions. In addition, we see conserved patterns in the ATP biosynthesis mechanism across phylogeny, and identify clades where our understanding of energy biosynthesis is still poor. ModelSEED v2 is available through KBase and the ModelSEED website (https://modelseed.org/), which supports reconstruction, gap-filling, flux balance analysis and model export through a modern web interface.

systems biology↗

bioRxiv2023

Coupling Flux Balance Analysis with Reactive Transport Modeling through Machine Learning for Rapid and Stable Simulation of Microbial Metabolic Switching

Integration of genome-scale metabolic networks with reactive transport models (RTMs) is an advanced simulation technique that enables predicting the changes of microbial growth and metabolism in space and time. Despite promising demonstrations in the past, computational inefficiency has been pointed out as a critical issue to overcome because it requires repeated implementation of linear programming (LP) to get flux balance analysis (FBA) solutions in every time step and every spatial grid. To address this challenge, we propose a new simulation method where we train/validate artificial neural networks (ANNs) using FBA solutions randomly sampled and incorporate the resulting reduced-order FBA model (represented as algebraic equations) into RTMs as source/sink terms. We demonstrate the efficiency of our method via a case study of Shewanella oneidensis MR-1 strain. During the aerobic growth on lactate, S. oneidensis produces metabolic byproducts (such as pyruvate and acetate), which are subsequently consumed as alternative carbon sources when the preferred ones are depleted. Simulating such intricate dynamics posed a considerable challenge, which we overcame by adopting the cybernetic approach that describes metabolic switches as the outcome of dynamic competition among multiple growth options. In both zero-dimensional batch and one-dimensional column configurations, the ANN-based reduced-order models achieved substantial reduction of computational time by several orders of magnitude compared to the original LP-based FBA models. Importantly, the ANN models produced robust solutions without any special measures to prevent numerical instability. These developments significantly promote our ability to utilize genome-scale networks in complex, multi-physics, and multi-dimensional ecosystem modeling.

systems biology↗

bioRxiv2022

Kinetics-based Inference of Environment-Dependent Microbial Interactions and Their Dynamic Variation

Microbial communities in nature are dynamically evolving as member species change their interactions subject to environmental variations. Accounting for such context-dependent dynamic variations in interspecies interactions is critical for predictive ecological modeling. In the absence of generalizable theoretical foundations, we lack a fundamental understanding of how microbial interactions are driven by environmental factors, significantly limiting our capability to predict and engineer community dynamics and function. To address this issue, we propose a novel theoretical framework that allows us to represent interspecies interactions as an explicit function of environmental variables (such as substrate concentrations) by combining growth kinetics and a generalized Lotka-Volterra model. A synergistic integration of these two complementary models leads to the prediction of alterations in interspecies interactions as the outcome of dynamic balances between positive and negative influences of microbial species in mixed relationships. This unique capability of our approach was experimentally demonstrated using a synthetic consortium of two Escherichia coli mutants that are metabolically dependent (due to an inability to synthesize essential amino acids), but competitively growing on a shared substrate. The analysis of the E. coli binary consortium using our model not only showed how interactions between the two amino acid auxotrophic mutants are controlled by the dynamic shifts in limiting substrates, but also enabled quantifying previously uncharacterizable complex aspects of microbial interactions such as asymmetry in interactions. Our approach can be extended to other ecological systems to model their environment-dependent interspecies interactions from growth kinetics. IMPORTANCEModeling of environment-controlled interspecies interactions through separate identification of positive and negative influences of microbes in mixed relationships is a new capability that can significantly improve our ability to understand, predict, and engineer complex dynamics of microbial communities. Moreover, robust prediction of microbial interactions as a function of environmental variables can serve as valuable benchmark data to validate modeling and network inference tools in microbial ecology, the development of which has often been impeded due to the lack of ground truth information on interactions. While demonstrated against microbial data, the theory developed in this work is readily applicable to general community ecology to predict interactions among microorganisms such as plants and animals, as well as microorganisms.

bioinformatics↗

bioRxiv2020

Disturbance Triggers Non-Linear Microbe-Environment Feedbacks

Conceptual frameworks linking microbial community membership, properties, and processes with the environment and emergent function have been proposed but remain untested. Here we refine and test a recent conceptual framework using hyporheic zone sediments exposed to wetting/drying transitions. Throughout the system we found threshold-like responses to the duration of desiccation. Membership of the putatively active community--but not the whole community--responded due to enhanced deterministic selection (an emergent community property). Concurrently, the thermodynamic properties of organic matter became less favorable for oxidation (an environmental component) and respiration decreased (a microbial process). While these responses were step functions of desiccation, we observed continuous monotonic relationships among community assembly, respiration, and organic matter thermodynamics. Placing the results in context of our conceptual framework points to previously unrecognized internal feedbacks that are initiated by disturbance, mediated by thermodynamics, and that cause the impacts of disturbance to be dependent on the history of disturbance.

ecology↗

bioRxiv2020

Naringenin as an antibacterial reagent controlling of biofilm formation and fatty acid metabolism in MRSA

MRSA is Methicillin-resistant Staphylococcus aureus and they are widespread and making trouble in treatment in communities and surgical areas. MRSA have been adapted to antibiotics so that they can block the access of antibiotics physically or chemically deactivate it or modify the precursor of the target. Flavonoids are secondary metabolites which are naturally produced by plant or fungus and they are acting generally as pigment, quorum sensing molecules, antibiotics to other competitive microorganisms. Their natural origins and multiple activities have drawn much attention to be developed as the potential drugs since flavonoids could be a good candidate to overcome antibiotic resistant bacteria. Among various flavonoids, we found out naringenin has antibacterial activity on MRSA and {Delta}agr mutants which are more resistance than MRSA to beta-lactam antibiotics by decreasing biofilm formation dramatically and decreasing the secretion of fatty acid. It also showed high synergetic activity with oxacillin to both antibacterial activity and biofilm inhibition. Considering the number of flavonoids, our experiments expand the possibility of the use of flavonoids to MRSA.

microbiology↗

bioRxiv2020

Representing Organic Matter Thermodynamics in Biogeochemical Reactions via Substrate-Explicit Modeling

Predictive biogeochemical modeling requires data-model integration that enables explicit representation of the sophisticated roles of microbial processes that transform substrates. Data from high-resolution organic matter (OM) characterization are increasingly available and can serve as a critical resource for this purpose, but their incorporation into biogeochemical models is often prohibited due to an over-simplified description of reaction networks. To fill this gap, we proposed a new concept of biogeochemical modeling--termed substrate-explicit modeling--that enables parameterizing OM-specific oxidative degradation pathways and reaction rates based on the thermodynamic properties of OM pools. The resulting kinetic models are characterized by only two parameters regardless of the complexity of OM profiles, which can greatly facilitate the integration with reactive transport models for ecosystem simulations by alleviating the difficulty in parameter identification. For every detected organic molecule in a given sample, our approach provides a systematic way to formulate reaction kinetics from chemical formula, which enables the evaluation of the impact of OM character on biogeochemical processes across conditions. In a case study of two sites with distinct OM thermodynamics, our method not only predicted oxidative degradation to be primarily driven by thermodynamic efficiency of OM consistent with experimental rate measurements, but also revealed previously unknown critically important aspects of biogeochemical reactions, including their condition-specific response to carbon and/or oxygen limitations. Lastly, we showed that the proposed substrate-explicit modeling approach can be synergistically combined with enzyme-explicit approach to provide improved predictions. This result led us to present integrative biogeochemical modeling as a unifying framework that can ideally describe the dynamic interplay among microbes, enzymes, and substrates to address advanced questions and hypotheses in future studies. Altogether, the new modeling concept we propose in this work provides a foundational platform for unprecedented predictions of biogeochemical and ecosystem dynamics through enhanced integration with diverse experimental data and extant modeling approaches.

systems biology↗

bioRxiv2020

Carbon limitation leads to thermodynamic regulation of aerobic metabolism

Organic matter (OM) metabolism in freshwater ecosystems is a critical source of uncertainty in global biogeochemical cycles, yet aquatic OM cycling remains poorly understood. Here, we present the first work to explicitly test OM thermodynamics as a key regulator of aerobic respiration, challenging long-held beliefs that organic carbon and oxygen concentrations are the primary determinants of respiration rates. We pair controlled microcosm experiments with ultrahigh-resolution OM characterization to demonstrate a clear relationship between OM thermodynamic favorability and aerobic respiration under carbon limitation. We also demonstrate a shift in the regulation of aerobic respiration from OM thermodynamics to nitrogen content when carbon is in excess, highlighting a central role for OM thermodynamics in aquatic biogeochemical cycling particularly in carbon-limited ecosystems. Our work therefore illuminates a structural gap in aquatic biogeochemical models and presents a new paradigm in which OM thermodynamics and nitrogen content interactively govern aerobic respiration.

ecology↗

bioRxiv2018

Lactobacillus acidophilus disrupts collaborative multispecies bile acid metabolism

Bile acids are metabolic links between hosts and their gut microbiomes, yet little is known about the roles they play in microbe-to-microbe interactions. Here we present a study designed to investigate the effect that a common probiotic, Lactobacillus acidophilus, has on microbial interactions that lead to formation of secondary bile acids. A model microbial consortium was built from three human gut isolates, Clostridium scindens, Collinsella aerofaciens, and Blautia obeum, and cultured under different bile acid and probiotic treatments. A multi-omics platform that included mass spectrometry-based metabolomics and activity-based proteomic probes was used to produce two major results. The first, was that an uncommon secondary bile acid - ursocholate - was produced by a multi-species chemical synthesis pathway. This result highlights a new microbe-to-microbe interaction mediated by bile acids. The second finding was that the probiotic strain, L. acidophilus, quenched the observed interactions and effectively halted consortial synthesis of ursocholate. Little is known about the role that ursocholate plays in human health and development. However, we did discover that a decrease in ursocholate abundance corresponded with successful weight loss in patients after gastric bypass surgery versus those who did not lose weight after surgery. Hence, this study uncovered basic knowledge that may aid future designs of custom probiotic therapies to combat obesity.

microbiology↗