Kinetic, metabolic, and statistical analytics: addressing metabolic transport limitations among organelles and microbial communities

Promoting lignocellulosic biorefinery by machine learning: progress, perspectives and challenges

The lignocellulosic biorefinery involves pretreatment, enzymatic hydrolysis, mixed sugar fermentation, and optional anaerobic digestion. This pipeline could be effectively implemented through machine learning (ML)-guided process optimization and strain modification rather than experimental or experience-based ones. This review takes a holistic perspective on the entire pipeline, discussing how ML could aid lignocellulosic, while other published work has focused on individual modules within the pipeline. This review also explores the model construction and evaluation strategies and highlights the emerging potential of transfer learning and hybrid ML models to address data insufficiency and improve model interpretability. Furthermore, challenges and future prospects of ML in lignocellulosic biorefinery will be elaborated in this review. Integrating ML into lignocellulosic biorefinery offers a promising pathway towards sustainable and competitive biorefinery systems.

Inverse-Vaccines for Rheumatoid Arthritis Re-establish Metabolic and Immunological Homeostasis in Joint Tissues

Rheumatoid arthritis (RA) causes immunological and metabolic imbalances in tissue, exacerbating inflammation in affected joints. Changes in immunological and metabolic tissue homeostasis at different stages of RA are not well understood. Herein, the changes in the immunological and metabolic profiles in different stages in collagen induced arthritis (CIA), namely, early, intermediate, and late stage is examined. Moreover, the efficacy of the inverse-vaccine, paKG(PFK15+bc2) microparticle, to restore tissue homeostasis at different stages is also investigated. Immunological analyses of inverse-vaccine-treated group revealed a significant decrease in the activation of pro-inflammatory immune cells and remarkable increase in regulatory T-cell populations in the intermediate and late stages compared to no treatment. Also, glycolysis in the spleen is normalized in the late stages of CIA in inverse-vaccine-treated mice, which is similar to no-disease tissues. Metabolomics analyses revealed that metabolites UDP-glucuronic acid and L-Glutathione oxidized are significantly altered between treatment groups, and thus might provide new druggable targets for RA treatment. Flux metabolic modeling identified amino acid and carnitine pathways as the central pathways affected in arthritic tissue with CIA progression. Overall, this study shows that the inverse-vaccines initiate early re-establishment of homeostasis, which persists through the disease span.

The complex world of kefir: Structural insights and symbiotic relationships

Kefir milk, known for its high nutritional value and health benefits, is traditionally produced by fermenting milk with kefir grains. These grains are a complex symbiotic community of lactic acid bacteria, acetic acid bacteria, yeasts, and other microorganisms. However, the intricate coexistence mechanisms within these microbial colonies remain a mystery, posing challenges in predicting their biological and functional traits. This uncertainty often leads to variability in kefir milk's quality and safety. This review delves into the unique structural characteristics of kefir grains, particularly their distinctive hollow structure. We propose hypotheses on their formation, which appears to be influenced by the aggregation behaviors of the community members and their alliances. In kefir milk, a systematic colonization process is driven by metabolite release, orchestrating the spatiotemporal rearrangement of ecological niches. We place special emphasis on the dynamic spatiotemporal changes within the kefir microbial community. Spatially, we observe variations in species morphology and distribution across different locations within the grain structure. Temporally, the review highlights the succession patterns of the microbial community, shedding light on their evolving interactions.Furthermore, we explore the ecological mechanisms underpinning the formation of a stable community composition. The interplay of cooperative and competitive species within these microorganisms ensures a dynamic balance, contributing to the community's richness and stability. In kefir community, competitive species foster diversity and stability, whereas cooperative species bolster mutualistic symbiosis. By deepening our understanding of the behaviors of these complex microbial communities, we can pave the way for future advancements in the development and diversification of starter cultures for food fermentation processes.

Top-down and bottom-up microbiome engineering approaches to enable biomanufacturing from waste biomass

Growing environmental concerns and the need to adopt a circular economy have highlighted the importance of waste valorization for resource recovery. Microbial consortia-enabled biotechnologies have made significant developments in the biomanufacturing of valuable resources from waste biomass that serve as suitable alternatives to petrochemical-derived products. These microbial consortia-based processes are designed following a top-down or bottom-up engineering approach. The top-down approach is a classical method that uses environmental variables to selectively steer an existing microbial consortium to achieve a target function. While high-throughput sequencing has enabled microbial community characterization, the major challenge is to disentangle complex microbial interactions and manipulate the structure and function accordingly. The bottom-up approach uses prior knowledge of the metabolic pathway and possible interactions among consortium partners to design and engineer synthetic microbial consortia. This strategy offers some control over the composition and function of the consortium for targeted bioprocesses, but challenges remain in optimal assembly methods and long-term stability. In this review, we present the recent advancements, challenges, and opportunities for further improvement using top-down and bottom-up approaches for microbiome engineering. As the bottom-up approach is relatively a new concept for waste valorization, this review explores the assembly and design of synthetic microbial consortia, ecological engineering principles to optimize microbial consortia, and metabolic engineering approaches for efficient conversion. Integration of top-down and bottom-up approaches along with developments in metabolic modeling to predict and optimize consortia function are also highlighted.

ONE-SENTENCE SUMMARY

This review highlights the microbial consortia-driven waste valorization for biomanufacturing through top-down and bottom-up design approaches and describes strategies, tools, and unexplored opportunities to optimize the design and stability of such consortia.

Quantitative and analytical tools to analyze the spatiotemporal population dynamics of microbial consortia

Microorganisms occupy almost every niche on earth. They play critical roles in maintaining ecological balance, atmospheric C/N cycle, and human health. Microbes live in consortia with metabolite exchange or signal communication. Quantitative and analytical tools are becoming increasingly important to study microbial consortia dynamics. We argue that a combined reductionist and holistic approach will be important to understanding the assembly rules and spatiotemporal population dynamics of the microbial community (MICOM). Reductionism allows us to reconstruct complex MICOM from isolated or simple synthetic consortia. Holism allows us to understand microbes as a community with cooperation and competition. Here we review the recent development of quantitative and analytical tools to uncover the underlying principles in microbial communities that govern their spatiotemporal change and interaction dynamics. Mathematical models and analytical tools will continue to provide essential knowledge and expand our capability to manipulate and control microbial consortia.

Genome-scale modeling of yeast metabolism: retrospectives and perspectives

Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.