Engineering microbial chemical factories using metabolic models

Recent advances in genome annotation and synthetic biology for the development of microbial chassis

This article provides an overview of microbial host selection, synthetic biology, genome annotation, metabolic modeling, and computational methods for predicting gene essentiality for developing a microbial chassis. This article focuses on lactic acid bacteria (LAB) as a microbial chassis and strategies for genome annotation of the LAB genome. As a case study, Lactococcus lactis is chosen based on its well-established therapeutic applications such as probiotics and oral vaccine development. In this article, we have delineated the strategies for genome annotations of lactic acid bacteria. These strategies also provide insights into streamlining genome reduction without compromising the functionality of the chassis and the potential for minimal genome chassis development. These insights underscore the potential for the development of efficient and sustainable synthetic biology systems using streamlined microbial chassis with minimal genomes.

Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases

The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.

Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes

Succinic acid (SA) is one of the top candidate value-added chemicals that can be produced from biomass via microbial fermentation. A considerable number of cell factories have been proposed in the past two decades as native as well as non-native SA producers. Actinobacillus succinogenes is among the best and earliest known natural SA producers. However, its industrial application has not yet been realized due to various underlying challenges. Previous studies revealed that the optimization of environmental conditions alone could not entirely resolve these critical problems. On the other hand, microbial in silico metabolic modeling approaches have lately been the center of attention and have been applied for the efficient production of valuable commodities including SA. Then again, literature survey results indicated the absence of up-to-date reviews assessing this issue, specifically concerning SA production. Hence, this review was designed to discuss accomplishments and future perspectives of in silico studies on the metabolic capabilities of SA producers. Herein, research progress on SA and A. succinogenes, pathways involved in SA production, metabolic models of SA-producing microorganisms, and status, limitations and prospects on in silico studies of A. succinogenes were elaborated. All in all, this review is believed to provide insights to understand the current scenario and to develop efficient mathematical models for designing robust SA-producing microbial strains.

Proteome Regulation Patterns Determine Escherichia coli Wild-Type and Mutant Phenotypes

It is generally recognized that proteins constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. To account for the growth condition-dependent proteome in the constraint-based metabolic modeling of Escherichia coli, we consolidated a coarse-grained protein allocation approach with the explicit consideration of enzymatic constraints on reaction fluxes. Besides representing physiologically relevant wild-type phenotypes and flux distributions, the resulting protein allocation model (PAM) advances the predictability of the metabolic responses to genetic perturbations. A main driver of mutant phenotypes was ascribed to inherited regulation patterns in protein distribution among metabolic enzymes. Moreover, the PAM correctly reflected metabolic responses to an augmented protein burden imposed by the heterologous expression of green fluorescent protein. In summary, we were able to model the effects of important and frequently applied metabolic engineering approaches on microbial metabolism. Therefore, we want to promote the integration of protein allocation constraints into classical constraint-based models to foster their predictive capabilities and application for strain analysis and engineering purposes.

IMPORTANCE Predictive metabolic models are important, e.g., for generating biological knowledge and designing microbes with superior performance for target compound production. Yet today’s whole-cell models either show insufficient predictive capabilities or are computationally too expensive to be applied to metabolic engineering purposes. By linking the inherent genotype-phenotype relationship to a complete representation of the proteome, the PAM advances the accuracy of simulated phenotypes and intracellular flux distributions of E. coli. Being equally computationally lightweight as classical stoichiometric models and allowing for the application of established in silico tools, the PAM and related simulation approaches will foster the use of a model-driven metabolic research. Applications range from the investigation of mechanisms of microbial evolution to the determination of optimal strain design strategies in metabolic engineering, thus supporting basic scientists and engineers alike.