Modelling microbial communities using biochemical resource allocation analysis

Deciphering and designing microbial communities by genome-scale metabolic modelling

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

Are microbes colimited by multiple resources?

Resource colimitation - the dependence of growth on multiple resources simultaneously - has become an important topic in microbiology due both to the development of systems approaches to cell physiology and ecology and to the relevance of colimitation to environmental science, biotechnology, and human health. Empirical tests of colimitation in microbes suggest that it may be common in nature. However, recent theoretical and empirical work has demonstrated the need for systematic measurements across resource conditions, in contrast to the factorial supplementation experiments used in most previous studies. The mechanistic causes of colimitation remain unclear in most cases and are an important challenge for future work, but we identify the alignment of resource consumption with the environment, interactions between resources, and biological and environmental heterogeneity as major factors. On the other hand, the consequences of colimitation are widespread for microbial physiology and ecology, especially the prediction and control of microbial growth, motivating continued consideration of this state in microbiology.

Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective

Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed.

From metagenomes to metabolism: Systematically assessing the metabolic flux feasibilities for "Candidatus Accumulibacter" species during anaerobic substrate uptake

With the rapid growing availability of metagenome assembled genomes (MAGs) and associated metabolic models, the identification of metabolic potential in individual community members has become possible. However, the field still lacks an unbiassed systematic evaluation of the generated metagenomic information to uncover not only metabolic potential, but also feasibilities of these models under specific environmental conditions. In this study, we present a systematic analysis of the metabolic potential in species of "Candidatus Accumulibacter", a group of polyphosphate-accumulating organisms (PAOs). We constructed a metabolic model of the central carbon metabolism and compared the metabolic potential among available MAGs for "Ca. Accumulibacter" species. By combining Elementary Flux Modes Analysis (EFMA) with max-min driving force (MDF) optimization, we obtained all possible flux distributions of the metabolic network and calculated their individual thermodynamic feasibility. Our findings reveal significant variations in the metabolic potential among "Ca. Accumulibacter" MAGs, particularly in the presence of anaplerotic reactions. EFMA revealed 700 unique flux distributions in the complete metabolic model that enable the anaerobic uptake of acetate and its conversion into polyhydroxyalkanoates (PHAs), a well-known phenotype of "Ca. Accumulibacter". However, thermodynamic constraints narrowed down this solution space to 146 models that were stoichiometrically and thermodynamically feasible (MDF > 0 kJ/mol), of which only 8 were strongly feasible (MDF > 7 kJ/mol). Notably, several novel flux distributions for the metabolic model were identified, suggesting putative, yet unreported, functions within the PAO communities. Overall, this work provides valuable insights into the metabolic variability among "Ca. Accumulibacter" species and redefines the anaerobic metabolic potential in the context of phosphate removal. More generally, the integrated workflow presented in this paper can be applied to any metabolic model obtained from a MAG generated from microbial communities to objectively narrow the expected phenotypes from community members.

Multiscale models driving hypothesis and theory-based research in microbial ecology

Hypothesis and theory-based studies in microbial ecology have been neglected in favour of those that are descriptive and aim for data-gathering of uncultured microbial species. This tendency limits our capacity to create new mechanistic explanations of microbial community dynamics, hampering the improvement of current environmental biotechnologies. We propose that a multiscale modelling bottom-up approach (piecing together sub-systems to give rise to more complex systems) can be used as a framework to generate mechanistic hypotheses and theories (in-silico bottom-up methodology). To accomplish this, formal comprehension of the mathematical model design is required together with a systematic procedure for the application of the in-silico bottom-up methodology. Ruling out the belief that experimentation before modelling is indispensable, we propose that mathematical modelling can be used as a tool to direct experimentation by validating theoretical principles of microbial ecology. Our goal is to develop methodologies that effectively integrate experimentation and modelling efforts to achieve superior levels of predictive capacity.

Microbial population dynamics decouple growth response from environmental nutrient concentration

How the growth rate of a microbial population responds to the environmental availability of chemical nutrients and other resources is a fundamental question in microbiology. Models of this response, such as the widely used Monod model, are generally characterized by a maximum growth rate and a half-saturation concentration of the resource. What values should we expect for these half-saturation concentrations, and how should they depend on the environmental concentration of the resource? We survey growth response data across a wide range of organisms and resources. We find that the half-saturation concentrations vary across orders of magnitude, even for the same organism and resource. To explain this variation, we develop an evolutionary model to show that demographic fluctuations (genetic drift) can constrain the adaptation of half-saturation concentrations. We find that this effect fundamentally differs depending on the type of population dynamics: Populations undergoing periodic bottlenecks of fixed size will adapt their half-saturation concentrations in proportion to the environmental resource concentrations, but populations undergoing periodic dilutions of fixed size will evolve half-saturation concentrations that are largely decoupled from the environmental concentrations. Our model not only provides testable predictions for laboratory evolution experiments, but it also reveals how an evolved half-saturation concentration may not reflect the organism's environment. In particular, this explains how organisms in resource-rich environments can still evolve fast growth at low resource concentrations. Altogether, our results demonstrate the critical role of population dynamics in shaping fundamental ecological traits.

Bacillus subtilis Produces Amino Acids to Stimulate Protein Synthesis in Ruminal Tissue Explants via the Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Beta–Serine/Threonine Kinase–Mammalian Target of Rapamycin Complex 1 Pathway

Background

Bacillus subtilis is a probiotic strain that is widely used as a feed supplement for ruminants. In this study, one B. subtilis strain isolated from the ruminal fluid of Holstein dairy cows was used for an ex vivo study with ruminal tissue explants. The main goal was to assess the potential endosymbiotic links between B. subtilis and the ruminal epithelium using molecular analyses and amino acid profiling. The explant culture protocol was first optimized to determine the ideal conditions in terms of tissue viability before performing the actual experiments involving active and inactive bacteria with or without protein synthesis inhibitors, such as LY294002 (phosphatidylinositol 3-kinase inhibitor) or rapamycin [mammalian target of rapamycin (mTOR) inhibitor].

Results

The mRNA levels of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta (PIK3CB), serine/threonine kinase (AKT), mTOR, P70S6K1, and eukaryotic translation initiation factor 4E binding protein 1 were the highest (p < 0.01), while those of programmed cell death 4 were the lowest when the tissue was incubated with 10⁷ of B. subtilis. Compared with the inactivated bacteria, the expression levels of PIK3CB and AKT, and overall changes in mTOR and P70S6K1 were greater in rumen explants with living bacteria (p < 0.05). With an increase in B. subtilis concentration, the trends of protein and corresponding gene changes were consistent. There were differences in the concentrations of individual amino acids in the supernatants of living and inactivated bacterial culture groups, with most amino acids enriched in pathways, such as aminoacyl tRNA biosynthesis, cyanoamino acid metabolism, monobactam biosynthesis, or glycine, serine, and threonine metabolism. The addition of psilocybin upregulated the expression levels of PIK3CB and AKT. A significant decrease (p < 0.05) in PIK3CB and mTOR protein expression levels was detected after the addition of LY294002 and rapamycin. In addition, These responses were associated with the downregulation (p < 0.05) of AKT and P70S6K protein expression levels.

Conclusions

We confirmed that the in vivo ruminal tissue culture system is a suitable model for studying probiotic-induced alterations in tissue function. As such, this study provides a means for future mechanistic studies related to microbial regulation and the dietary supply of proteins. In addition, living and inactivated B. subtilis can promote protein synthesis in ruminal tissue explants by altering the expression levels of related factors in the PIK3CB–AKT–mTORC1 pathway, which could further aid in optimizing the feed efficiency and increasing the use of inactivated bacteria as additives in dairy cow farming.

Molecular insights into plant-microbe interactions for sustainable remediation of contaminated environment

The widespread distribution of organic and inorganic pollutants in water resources have increased due to rapid industrialization. Rhizospheric zone-associated bacteria along with endophytic bacteria show a significant role in remediation of various pollutants. Metaomics technologies are gaining an advantage over traditional methods because of their capability to obtain detailed information on exclusive microbial communities in rhizosphere of the plant including the unculturable microorganisms. Transcriptomics, proteomics, and metabolomics are functional methodologies that help to reveal the mechanisms of plant-microbe interactions and their synergistic roles in remediation of pollutants. Intensive analysis of metaomics data can be useful to understand the interrelationships of various metabolic activities between plants and microbes. This review comprehensively discusses recent advances in omics applications made hitherto to understand the mechanisms of plant-microbe interactions during phytoremediation. It extends the delivery of the insightful information on plant-microbiomes communications with an emphasis on their genetic, biochemical, physical, metabolic, and environmental interactions.

Microfluidic and mathematical modeling of aquatic microbial communities

Aquatic microbial communities contribute fundamentally to biogeochemical transformations in natural ecosystems, and disruption of these communities can lead to ecological disasters such as harmful algal blooms. Microbial communities are highly dynamic, and their composition and function are tightly controlled by the biophysical (e.g., light, fluid flow, and temperature) and biochemical (e.g., chemical gradients and cell concentration) parameters of the surrounding environment. Due to the large number of environmental factors involved, a systematic understanding of the microbial community-environment interactions is lacking. In this article, we show that microfluidic platforms present a unique opportunity to recreate well-defined environmental factors in a laboratory setting in a high throughput way, enabling quantitative studies of microbial communities that are amenable to theoretical modeling. The focus of this article is on aquatic microbial communities, but the microfluidic and mathematical models discussed here can be readily applied to investigate other microbiomes.