Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes.

Pseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic diversity and the functional metabolic landscape of P. aeruginosa clinical isolates. To better understand the metabolic repertoire of P. aeruginosa in infection, we deeply profiled a representative set from a library of 971 clinical P. aeruginosa isolates with corresponding patient metadata and bacterial phenotypes. The genotypic clustering based on whole-genome sequencing of the isolates, multilocus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective P. aeruginosa pangenome metabolic repertoire. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection.

Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment.

With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.

Erratum: Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling.

[This corrects the article DOI: 10.1016/j.isci.2022.104483.].

Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes.

Pseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic diversity and the functional metabolic landscape of P. aeruginosa clinical isolates. To better understand the metabolic repertoire of P. aeruginosa in infection, we deeply profiled a representative set from a library of 971 clinical P. aeruginosa isolates with corresponding patient metadata and bacterial phenotypes. The genotypic clustering based on whole-genome sequencing of the isolates, multi-locus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective P. aeruginosa pangenome metabolic repertoire. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection.

Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling.

Pancreatic ductal adenocarcinoma (PDAC) is a major research focus because of its poor therapy response and dismal prognosis. PDAC cells adapt their metabolism to the surrounding environment, often relying on diverse nutrient sources. Because traditional experimental techniques appear exhaustive to find a viable therapeutic strategy, a highly curated and omics-informed PDAC genome-scale metabolic model was reconstructed using patient-specific transcriptomics data. From the model-predictions, several new metabolic functions were explored as potential therapeutic targets in addition to the known metabolic hallmarks of PDAC. Significant downregulation in the peroxisomal beta oxidation pathway, flux modulation in the carnitine shuttle system, and upregulation in the reactive oxygen species detoxification pathway reactions were observed. These unique metabolic traits of PDAC were correlated with potential drug combinations targeting genes with poor prognosis in PDAC. Overall, this study provides a better understanding of the metabolic vulnerabilities in PDAC and will lead to novel effective therapeutic strategies.

Staphylococcal ClpXP protease targets the cellular antioxidant system to eliminate fitness-compromised cells in stationary phase.

The transition from growth to stationary phase is a natural response of bacteria to starvation and stress. When stress is alleviated and more favorable growth conditions return, bacteria resume proliferation without a significant loss in fitness. Although specific adaptations that enhance the persistence and survival of bacteria in stationary phase have been identified, mechanisms that help maintain the competitive fitness potential of nondividing bacterial populations have remained obscure. Here, we demonstrate that staphylococci that enter stationary phase following growth in media supplemented with excess glucose, undergo regulated cell death to maintain the competitive fitness potential of the population. Upon a decrease in extracellular pH, the acetate generated as a byproduct of glucose metabolism induces cytoplasmic acidification and extensive protein damage in nondividing cells. Although cell death ensues, it does not occur as a passive consequence of protein damage. Instead, we demonstrate that the expression and activity of the ClpXP protease is induced, resulting in the degeneration of cellular antioxidant capacity and, ultimately, cell death. Under these conditions, inactivation of either clpX or clpP resulted in the extended survival of unfit cells in stationary phase, but at the cost of maintaining population fitness. Finally, we show that cell death from antibiotics that interfere with bacterial protein synthesis can also be partly ascribed to the corresponding increase in clpP expression and activity. The functional conservation of ClpP in eukaryotes and bacteria suggests that ClpP-dependent cell death and fitness maintenance may be a widespread phenomenon in these domains of life.

Transcriptomic data-driven discovery of global regulatory features of rice seeds developing under heat stress.

Plants respond to abiotic stressors through a suite of strategies including differential regulation of stress-responsive genes. Hence, characterizing the influences of the relevant global regulators or on stress-related transcription factors is critical to understand plant stress response. Rice seed development is highly sensitive to elevated temperatures. To elucidate the extent and directional hierarchy of gene regulation in rice seeds under heat stress, we developed and implemented a robust multi-level optimization-based algorithm called Minimal Regulatory Network identifier (MiReN). MiReN could predict the minimal regulatory relationship between a gene and its potential regulators from our temporal transcriptomic dataset. MiReN predictions for global regulators including stress-responsive gene Slender Rice 1 (SLR1) and disease resistance gene XA21 were validated with published literature. It also predicted novel regulatory influences of other major regulators such as Kinesin-like proteins KIN12C and STD1, and WD repeat-containing protein WD40. Out of the 228 stress-responsive transcription factors identified, we predicted de novo regulatory influences on three major groups (MADS-box M-type, MYB, and bZIP) and investigated their physiological impacts during stress. Overall, MiReN results can facilitate new experimental studies to enhance our understanding of global regulatory mechanisms triggered during heat stress, which can potentially accelerate the development of stress-tolerant cultivars.

Investigation of microbial community interactions between Lake Washington methanotrophs using -------genome-scale metabolic modeling.

BACKGROUND: The role of methane in global warming has become paramount to the environment and the human society, especially in the past few decades. Methane cycling microbial communities play an important role in the global methane cycle, which is why the characterization of these communities is critical to understand and manipulate their behavior. Methanotrophs are a major player in these communities and are able to oxidize methane as their primary carbon source. RESULTS: Lake Washington is a freshwater lake characterized by a methane-oxygen countergradient that contains a methane cycling microbial community. Methanotrophs are a major part of this community involved in assimilating methane from lake water. Two significant methanotrophic species in this community are Methylobacter and Methylomonas. In this work, these methanotrophs are computationally studied via developing highly curated genome-scale metabolic models. Each model was then integrated to form a community model with a multi-level optimization framework. The competitive and mutualistic metabolic interactions among Methylobacter and Methylomonas were also characterized. The community model was next tested under carbon, oxygen, and nitrogen limited conditions in addition to a nutrient-rich condition to observe the systematic shifts in the internal metabolic pathways and extracellular metabolite exchanges. Each condition showed variations in the methane oxidation pathway, pyruvate metabolism, and the TCA cycle as well as the excretion of formaldehyde and carbon di-oxide in the community. Finally, the community model was simulated under fixed ratios of these two members to reflect the opposing behavior in the two-member synthetic community and in sediment-incubated communities. The community simulations predicted a noticeable switch in intracellular carbon metabolism and formaldehyde transfer between community members in sediment-incubated vs. synthetic condition. CONCLUSION: In this work, we attempted to predict the response of a simplified methane cycling microbial community from Lake Washington to varying environments and also provide an insight into the difference of dynamics in sediment-incubated microcosm community and synthetic co-cultures. Overall, this study lays the ground for in silico systems-level studies of freshwater lake ecosystems, which can drive future efforts of understanding, engineering, and modifying these communities for dealing with global warming issues.

Metabolic Modeling Elucidates the Transactions in the Rumen Microbiome and the Shifts Upon Virome Interactions.

The complex microbial ecosystem within the bovine rumen plays a crucial role in host nutrition, health, and environmental impact. However, little is known about the interactions between the functional entities within the system, which dictates the community structure and functional dynamics and host physiology. With the advancements in high-throughput sequencing and mathematical modeling, in silico genome-scale metabolic analysis promises to expand our understanding of the metabolic interplay in the community. In an attempt to understand the interactions between microbial species and the phages inside rumen, a genome-scale metabolic modeling approach was utilized by using key members in the rumen microbiome (a bacteroidete, a firmicute, and an archaeon) and the viral phages associated with them. Individual microbial host models were integrated into a community model using multi-level mathematical frameworks. An elaborate and heuristics-based computational procedure was employed to predict previously unknown interactions involving the transfer of fatty acids, vitamins, coenzymes, amino acids, and sugars among the community members. While some of these interactions could be inferred by the available multi-omic datasets, our proposed method provides a systemic understanding of why the interactions occur and how these affect the dynamics in a complex microbial ecosystem. To elucidate the functional role of the virome on the microbiome, local alignment search was used to identify the metabolic functions of the viruses associated with the hosts. The incorporation of these functions demonstrated the role of viral auxiliary metabolic genes in relaxing the metabolic bottlenecks in the microbial hosts and complementing the inter-species interactions. Finally, a comparative statistical analysis of different biologically significant community fitness criteria identified the variation in flux space and robustness of metabolic capacities of the community members. Our elucidation of metabolite exchange among the three members of the rumen microbiome shows how their genomic differences and interactions with the viral strains shape up a highly sophisticated metabolic interplay and explains how such interactions across kingdoms can cause metabolic and compositional shifts in the community and affect the health, nutrition, and pathophysiology of the ruminant animal.

Computational Approaches on Stoichiometric and Kinetic Modeling for Efficient Strain Design.

Engineering biological systems that are capable of overproducing products of interest is the ultimate goal of any biotechnology application. To this end, stoichiometric (or steady state) and kinetic models are increasingly becoming available for a variety of organisms including prokaryotes, eukaryotes, and microbial communities. This ever-accelerating pace of such model reconstructions has also spurred the development of optimization-based strain design techniques. This chapter highlights a number of such frameworks developed in recent years in order to generate testable hypotheses (in terms of genetic interventions), thus addressing the challenges in metabolic engineering. In particular, three major methods are covered in detail including two methods for designing strains (i.e., one stoichiometric model-based and the other by integrating kinetic information into a stoichiometric model) and one method for analyzing microbial communities.

d-OptCom: Dynamic multi-level and multi-objective metabolic modeling of microbial communities.

Most microbial communities change with time in response to changes and/or perturbations in environmental conditions. Temporal variations in interspecies metabolic interactions within these communities can significantly affect their structure and function. Here, we introduce d-OptCom, an extension of the OptCom procedure, for the dynamic metabolic modeling of microbial communities. It enables capturing the temporal dynamics of biomass concentration of the community members and extracellular concentration of the shared metabolites, while integrating species- and community-level fitness functions. The applicability of d-OptCom was demonstrated by modeling the dynamic co-growth of auxotrophic mutant pairs of E. coli and by computationally assessing the dynamics and composition of a uranium-reducing community comprised of Geobacter sulfurreducens, Rhodoferax ferrireducens, and Shewanella oneidensis. d-OptCom was also employed to examine the impact of lactate vs acetate addition on the relative abundance of uranium-reducing species. These studies highlight the importance of simultaneously accounting for both species- and community-level fitness functions when modeling microbial communities, and demonstrate that the incorporation of uptake kinetic information can substantially improve the prediction of interspecies flux trafficking. Overall, this study paves the way for the dynamic multi-level and multi-objective analysis of microbial ecosystems.