Mikhail A. Kulyashov

Genome-scale metabolic models are widely used to investigate cellular metabolism, but their interpretation and comparison are limited by the lack of reproducible pathway-level visualizations with a common spatial organization. This study presents BioEMMA, a Python-based tool for the automated generation of model-specific metabolic pathway maps in the Escher JSON format using coordinate information from curated KEGG pathway maps. BioEMMA parses KGML files, map reaction and metabolite identifiers to model database namespaces, filters pathway elements according to an input SBML model, adds non-primary metabolites, reconstructs Escher-compatible layouts, and supports flux visualization. The tool was integrated into a reproducible BioUML workflow for metabolic model reconstruction. BioEMMA was evaluated using the e_coli_core model and the KEGG glycolysis/gluconeogenesis pathway while generating a model-specific map with overlaid FBA fluxes. It was then applied to compare E. coli reconstructions generated by gapseq, ModelSEEDpy, and Reconstructor across three central carbon metabolism pathways. To broaden the evaluation, BioEMMA was applied using 87 prokaryotic BiGG models and three eukaryotic models. The analysis revealed pathway-specific differences in reaction coverage, shared and model-specific reactions, and predicted flux activity. BioEMMA therefore provides a reproducible framework for pathway-level visualization and comparison of genome-scale metabolic reconstructions within a common spatial coordinate system.

Aerobic methanotrophs mediate methane oxidation contributing to a major biological sink that limits CH₄ release to the atmosphere in oxygenated environments and serve as promising platforms for biotechnological applications. In natural and engineered environments, these bacteria rarely exist in isolation but form stable associations with heterotrophic satellites that utilize methanotrophic metabolites, remove inhibitory intermediates, and provide essential growth factors. Such interactions enhance methane oxidation efficiency and community stability, yet the metabolic mechanisms underlying them remain poorly resolved. This review summarizes current knowledge on both natural and synthetic aerobic methanotrophic consortia, focusing on the composition, functions, and biotechnological relevance of satellite microorganisms. We systematically examine available mathematical frameworks—from ecological and statistical models to genome-scale metabolic reconstructions and dynamic flux balance analysis—applied to methanotrophs and their satellites. Our analysis reveals that while genome-scale metabolic models have been developed for model heterotrophic species, only a few correspond to experimentally identified methanotroph satellites, and community-level reconstructions remain limited. The lack of curated and experimentally validated models restricts the predictive power of current approaches. Addressing these limitations will require not only targeted reconstruction of satellite metabolism, but also the combined use of complementary computational methods followed by experimental verification. Such an integrative strategy will be essential for understanding methanotrophic community organization and function and, more broadly, other microbial consortia with complex metabolic interactions. Addressing these limitations through targeted reconstruction of satellite metabolism and integration of existing models will be key to advancing quantitative understanding of methanotrophic community organization and function.

Background/Objectives: Genome-scale metabolic models (GSM) are key tools for predicting microbial physiology, yet species within the genus Lentilactobacillus remain largely unexplored. Lentilactobacillus kefiri DH5 is an obligately heterofermentative lactic acid bacterium with unique redox metabolism, but no curated GSM model exists for this species. This study aimed to generate the first GSM model for L. kefiri DH5, evaluate multiple reconstruction tools, and characterize metabolic features underlying its heterofermentative metabolism. Methods: Draft GSM models were generated from the L. kefiri DH5 genome annotation using five reconstruction tools. For each tool, gap-filling was performed on a CDM, followed by quality assessment using the MEMOTE. Manual curation was performed using the COBRApy library. Results: Among the five reconstructions, the KBase-derived draft demonstrated the highest quality and production potential for metabolites characteristic of heterofermentative fermentation. During manual curation of this model, reaction directions in central carbon metabolism and amino acid pathways were corrected. Analysis further identified an alternative NADH-regenerating glucose shunt via D-gluconate, supported by omics data and enzyme promiscuity considerations. Incorporation of this pathway resolved the redox imbalance and allowed the model to reproduce metabolic exchange profiles characteristic of obligate heterofermenters. Conclusions: We developed the first manually curated genome-scale model of L. kefiri DH5 and showed that the choice of reconstruction tool substantially affects model quality and predictive power. We also proposed an alternative glucose assimilation shunt via gluconolactone, which resolved the redox imbalance in the model and enabled representation of the heterofermentative metabolism.

The Lactobacillaceae family encompasses microorganisms of exceptional ecological and biotechnological importance, serving as central agents in food fermentations, health applications, and nutrient cycling across diverse environments. Despite their broad functional and phylogenetic diversity, the global distribution and ecological specialization of Lactobacillaceae are not yet fully understood. In this study, we performed a comprehensive analysis of over 2 million records from the NCBI database to survey and trace the ecological landscape of Lactobacillaceae across thousands of distinct habitats. Our results reveal that food products and animal hosts represent the primary ecological niches for members of this family. The examined taxa exhibit a broad spectrum of ecological strategies, ranging from generalists with wide environmental adaptability to specialists with strict niche preferences. Notably, our findings highlight a profound geographical and ecological sampling bias, with unclassified taxids frequent in animal gastrointestinal tracts, soils, and especially in living plant tissues—habitats identified as promising frontiers for discovering novel biodiversity. The obtained results emphasize the urgent need for expanded sampling efforts in underexplored geographic regions such as Africa, Antarctica, the Arctic, South America, and Central Asia to capture a more complete picture of Lactobacillaceae diversity. The study underscores the necessity of implementing standardized, metadata-rich data deposition practices to enable unbiased, large-scale ecological and evolutionary analyses. Ultimately, these insights not only deepen our fundamental knowledge of Lactobacillaceae diversity but also provide a strategic framework for future bioprospecting, fostering the discovery of novel strains and expanding the biotechnological potential of this influential bacterial family.

Novel bioinformatics approaches for large-scale gene expression data analysis provide an opportunity to reconstruct transcriptional regulatory networks that control cellular response to different environmental stimulus for non-model microbial systems. In this study, we used published transcriptomic datasets for two closely related methanotrophs, Methylotuvimicrobium alcaliphilum 20Z^R and Methylotuvimicrobium buryatense 5GB1C, in different growth conditions and a combination of bioinformatics methods in order to reveal co-expressed and functionally related genes regulated by one or two potential or experimentally verified transcription factors. The clustering and subsequent functional-structural analysis of the transcriptomic datasets revealed 49 potential transcription factors (TFs) common to both methanotrophic cultures. In addition to known global regulators, that control expression of methane utilization genes, such as mxaB and rpoN, two novel factors were predicted to control cellular metabolism by activation pathways for iron uptake (MEALZ_RS12170, FeoC, a [Fe–S]-dependent transcriptional regulator) and lanthanide (Ln)-dependent growth (MEALZ_RS09670, OmpR-family, a two-component system, response regulator). FeoC regulates a cluster that was down-expressed in methane and oxygen-limited growth conditions, which correlated with reduced cellular respiration. The study suggests that OmpR plays a role in regulating genes associated with lanthanide (Ln)-dependent metabolism, including an (Ln)-dependent methanol dehydrogenase xoxF and associated formaldehyde activating enzyme, fae2. Overall, the research revises the regulatory networks in two biotechnology-relevant methanotrophic chassis and highlights several novel factors that control their gene expression.

Biotechnology continues to drive innovation in the production of pharmaceuticals, biofuels, and other valuable compounds, leveraging the power of microbial systems for enhanced yield and sustainability. Genome-scale metabolic (GSM) modeling has become an essential approach in this field, which enables a guide for targeting genetic modifications and the optimization of metabolic pathways for various industrial applications. While single-species GSM models have traditionally been employed to optimize strains like Escherichia coli and Lactococcus lactis, the integration of these models into community-based approaches is gaining momentum. Herein, we present a pipeline for community metabolic modeling with a user-friendly GUI, applying it to analyze interactions between Methylococcus capsulatus, a biotechnologically important methanotroph, and Escherichia coli W3110 under oxygen- and nitrogen-limited conditions. We constructed models with unmodified and homoserine-producing E. coli strains using the pipeline implemented in the original BioUML platform. The E. coli strain primarily utilized acetate from M. capsulatus under oxygen limitation. However, homoserine produced by E. coli significantly reduced acetate secretion and the community growth rate. This homoserine was taken up by M. capsulatus, converted to threonine, and further exchanged as amino acids. In nitrogen-limited modeling conditions, nitrate and ammonium exchanges supported the nitrogen needs, while carbon metabolism shifted to fumarate and malate, enhancing E. coli TCA cycle activity in both cases, with and without modifications. The presence of homoserine altered cross-feeding dynamics, boosting amino acid exchanges and increasing pyruvate availability for M. capsulatus. These findings suggest that homoserine production by E. coli optimizes resource use and has potential for enhancing microbial consortia productivity.

Biotechnology continues to drive innovation in the production of pharmaceuticals, biofuels, and other valuable compounds, leveraging the power of microbial systems for enhanced yield and sustainability. Genome-scale metabolic (GSM) modeling has become an essential approach in this field that enables a guide for target genetic modifications and optimization of metabolic pathways for various industrial applications. While single-species GSM models have traditionally been employed to optimize strains like Escherichia coli and Lactococcus lactis, the integration of these models into community-based approaches is gaining momentum. Constraint-based metabolic community (CBMc) models, which simulate the interactions between multiple microbial species, offer a more comprehensive understanding of metabolic networks and their potential for improvement of biotechnological processes on pilot- and industrial scales. However, there are few platforms that effectively support both single-species GSM and CBMc models, limiting the accessibility and application of these advanced modeling techniques. In this study, we demonstrate the use of the BioUML platform, one of the few tools that provides simultaneous investigation of both single-species and community models. We harnessed the platform to reconstruct and analyze microbial interactions within a synthetic consortium of widely used biotechnology microorganisms: methanotroph, Methylococcus capsulatus Bath and engineered Escherichia coli strains under oxygen- and nitrogen- limited conditions. Our research highlights the potential of E. coli to reduce the accumulation of inhibitory by-products like acetate, thereby enhancing the metabolic efficiency and growth of M. capsulatus. The study underscores the utility of CBMc models in optimization of microbial consortia for biotechnological applications, offering new strategies for the sustainable production of biofuels, single-cell proteins, and other high-value compounds.

Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria.

BioUML (https://www.biouml.org)—is a web-based integrated platform for systems biology and data analysis. It supports visual modelling and construction of hierarchical biological models that allow us to construct the most complex modular models of blood pressure regulation, skeletal muscle metabolism, COVID-19 epidemiology. BioUML has been integrated with git repositories where users can store their models and other data. We have also expanded the capabilities of BioUML for data analysis and visualization of biomedical data: (i) any programs and Jupyter kernels can be plugged into the BioUML platform using Docker technology; (ii) BioUML is integrated with the Galaxy and Galaxy Tool Shed; (iii) BioUML provides two-way integration with R and Python (Jupyter notebooks): scripts can be executed on the BioUML web pages, and BioUML functions can be called from scripts; (iv) using plug-in architecture, specialized viewers and editors can be added. For example, powerful genome browsers as well as viewers for molecular 3D structure are integrated in this way; (v) BioUML supports data analyses using workflows (own format, Galaxy, CWL, BPMN, nextFlow). Using these capabilities, we have initiated a new branch of the BioUML development—u-science—a universal scientific platform that can be configured for specific research requirements.

The Gene Transcription Regulation Database (GTRD; http://gtrd.biouml.org/) contains uniformly annotated and processed NGS data related to gene transcription regulation: ChIP-seq, ChIP-exo, DNase-seq, MNase-seq, ATAC-seq and RNA-seq. With the latest release, the database has reached a new level of data integration. All cell types (cell lines and tissues) presented in the GTRD were arranged into a dictionary and linked with different ontologies (BRENDA, Cell Ontology, Uberon, Cellosaurus and Experimental Factor Ontology) and with related experiments in specialized databases on transcription regulation (FANTOM5, ENCODE and GTEx). The updated version of the GTRD provides an integrated view of transcription regulation through a dedicated web interface with advanced browsing and search capabilities, an integrated genome browser, and table reports by cell types, transcription factors, and genes of interest.