Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment

Gastric cancer is the fifth most diagnosed cancer in the world, affecting more than a million people and causing nearly 783,000 deaths each year. The prognosis of advanced gastric cancer remains extremely poor despite the use of surgery and adjuvant therapy. Therefore, understanding the mechanism of gastric cancer development, and the discovery of novel diagnostic biomarkers and therapeutics are major goals in gastric cancer research. Here, we review recent progress in application of omics technologies in gastric cancer research, with special focus on the utilization of systems biology approaches to integrate multi-omics data. In addition, the association between gastrointestinal microbiota and gastric cancer are discussed, which may offer insights in exploring the novel microbiota-targeted therapeutics. Finally, the application of data-driven systems biology and machine learning approaches could provide a predictive understanding of gastric cancer, and pave the way to the development of novel biomarkers and rational design of cancer therapeutics

Advances in Metabolic Engineering of Saccharomyces cerevisiae for Cocoa Butter Equivalent Production

Cocoa butter is extracted from cocoa beans, and it is mainly used as the raw material for the production of chocolate and cosmetics. Increased demands and insufficient cocoa plants led to a shortage of cocoa butter supply, and there is therefore much interesting in finding an alternative cocoa butter supply. However, the most valuable component of cocoa butter is rarely available in other vegetable oils. Saccharomyces cerevisiae is an important industrial host for production of chemicals, enzyme and pharmaceuticals. Advances in synthetical biology and metabolic engineering had enabled high-level of triacylglycerols (TAG) production in yeast, which provided possible solutions for cocoa butter equivalents (CBEs) production. Diverse engineering strategies focused on the fatty acid-producing pathway had been applied in S. cerevisiae, and the key enzymes determining the TAG structure were considered as the main engineering targets. Recent development in phytomics and multi-omics technologies provided clues to identify potential targeted enzymes, which are responsible for CBE production. In this review, we have summarized recent progress in identification of the key plant enzymes for CBE production, and discussed recent and future metabolic engineering and synthetic biology strategies for increased CBE production in S. cerevisiae

Modeling the metabolic dynamics at the genome-scale by optimized yield analysis

The hybrid cybernetic model (HCM) approach is a dynamic modeling framework that integrates enzyme synthesis and activity regulation. It has been widely applied in bioreaction engineering, particularly in the simulation of microbial growth in different mixtures of carbon sources. In a HCM, the metabolic network is decomposed into elementary flux modes (EFMs), whereby the network can be reduced into a few pathways by yield analysis. However, applying the HCM approach on conventional genome-scale metabolic models (GEMs) is still a challenge due to the high computational demands. Here, we present a HCM strategy that introduced an optimized yield analysis algorithm (opt-yield-FBA) to simulate metabolic dynamics at the genome-scale without the need for EFMs calculation. The opt-yield-FBA is a flux-balance analysis (FBA) based method that can calculate optimal yield solutions and yield space for GEM. With the opt-yield-FBA algorithm, the HCM strategy can be applied to get the yield spaces and avoid the computational burden of EFMs, and it can therefore be applied for developing dynamic models for genome-scale metabolic networks. Here, we illustrate the strategy by applying the concept to simulate the dynamics of microbial communities

Machine learning for data integration in human gut microbiome

Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to characterize the microbial ecosystems, which led to an explosion of different types of molecular profiling data, such as metagenomics, metatranscriptomics and metabolomics. For analysis of such data, machine learning algorithms have shown to be useful for identifying key molecular signatures, discovering potential patient stratifications, and particularly for generating models that can accurately predict phenotypes. In this review, we first discuss how dysbiosis of the intestinal microbiota is linked to human disease development and how potential modulation strategies of the gut microbial ecosystem can be used for disease treatment. In addition, we introduce categories and workflows of different machine learning approaches, and how they can be used to perform integrative analysis of multi-omics data. Finally, we review advances of machine learning in gut microbiome applications and discuss related challenges. Based on this we conclude that machine learning is very well suited for analysis of gut microbiome and that these approaches can be useful for development of gut microbe-targeted therapies, which ultimately can help in achieving personalized and precision medicine

Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma

BACKGROUND. Identifying factors conferring responses to therapy in cancer is critical to select the best treatment for patients. For immune checkpoint inhibition (ICI) therapy, mounting evidence suggests that the gut microbiome can determine patient treatment outcomes. However, the extent to which gut microbial features are applicable across different patient cohorts has not been extensively explored. METHODS. We performed a meta-analysis of 4 published shotgun metagenomic studies (Ntot = 130 patients) investigating differential microbiome composition and imputed metabolic function between responders and nonresponders to ICI. RESULTS. Our analysis identified both known microbial features enriched in responders, such as Faecalibacterium as the prevailing taxa, as well as additional features, including overrepresentation of Barnesiella intestinihominis and the components of vitamin B metabolism. A classifier designed to predict responders based on these features identified responders in an independent cohort of 27 patients with the area under the receiver operating characteristic curve of 0.625 (95% CI: 0.348–0.899) and was predictive of prognosis (HR = 0.35, P = 0.081). CONCLUSION. These results suggest the existence of a fecal microbiome signature inherent across responders that may be exploited for diagnostic or therapeutic purposes

Metabolic engineering of human gut microbiome: Recent developments and future perspectives

Many studies have demonstrated that the gut microbiota is associated with human health and disease. Manipulation of the gut microbiota, e.g. supplementation of probiotics, has been suggested to be feasible, but subject to limited therapeutic efficacy. To develop efficient microbiota-targeted diagnostic and therapeutic strategies, metabolic engineering has been applied to construct genetically modified probiotics and synthetic microbial consortia. This review mainly discusses commonly adopted strategies for metabolic engineering in the human gut microbiome, including the use of in silico, in vitro, or in vivo approaches for iterative design and construction of engineered probiotics or microbial consortia. Especially, we highlight how genome-scale metabolic models can be applied to advance our understanding of the gut microbiota. Also, we review the recent applications of metabolic engineering in gut microbiome studies as well as discuss important challenges and opportunities

Whole-genome sequencing reveals high-risk clones of Pseudomonas aeruginosa in Guangdong, China

The ever-increasing prevalence of infections produced by multidrug-resistant or extensively drug-resistant Pseudomonas aeruginosa is commonly linked to a limited number of aptly-named epidemical \u27high-risk clones\u27 that are widespread among and within hospitals worldwide. The emergence of new potential high-risk clone strains in hospitals highlights the need to better and further understand the underlying genetic mechanisms for their emergence and success. P. aeruginosa related high-risk clones have been sporadically found in China, their genome sequences have rarely been described. Therefore, the large-scale sequencing of multidrug-resistance high-risk clone strains will help us to understand the emergence and transmission of antibiotic resistances in P. aeruginosa high-risk clones. In this study, 212 P. aeruginosa strains were isolated from 2 tertiary hospitals within 3 years (2018-2020) in Guangdong Province, China. Whole-genome sequencing, multi-locus sequence typing (MLST) and antimicrobial susceptibility testing were applied to analyze the genomic epidemiology of P. aeruginosa in this region. We found that up to 130 (61.32%) of the isolates were shown to be multidrug resistant, and 196 (92.45%) isolates were Carbapenem-Resistant Pseudomonas aeruginosa. MLST analysis demonstrated high diversity of sequence types, and 18 reported international high-risk clones were identified. Furthermore, we discovered the co-presence of exoU and exoS genes in 5 collected strains. This study enhances insight into the regional research of molecular epidemiology and antimicrobial resistance of P. aeruginosa in China. The high diversity of clone types and regional genome characteristics can serve as a theoretical reference for public health policies and help guide measures for the prevention and control of P. aeruginosa resistance

Genome-scale insights into the metabolic versatility of Limosilactobacillus reuteri

Background Limosilactobacillus reuteri (earlier known as Lactobacillus reuteri) is a well-studied lactic acid bacterium, with some specific strains used as probiotics, that exists in different hosts such as human, pig, goat, mouse and rat, with multiple body sites such as the gastrointestinal tract, breast milk and mouth. Numerous studies have confirmed the beneficial effects of orally administered specific L. reuteri strains, such as preventing bone loss and promoting regulatory immune system development. L. reuteri ATCC PTA 6475 is a widely used strain that has been applied in the market as a probiotic due to its positive effects on the human host. Its health benefits may be due, in part, to the production of beneficial metabolites. Considering the strain-specific effects and genetic diversity of L. reuteri strains, we were interested to study the metabolic versatility of these strains. Results In this study, we aimed to systematically investigate the metabolic features and diversities of L. reuteri strains by using genome-scale metabolic models (GEMs). The GEM of L. reuteri ATCC PTA 6475 was reconstructed with a template-based method and curated manually. The final GEM iHL622 of L. reuteri ATCC PTA 6475 contains 894 reactions and 726 metabolites linked to 622 metabolic genes, which can be used to simulate growth and amino acids utilization. Furthermore, we built GEMs for the other 35 L. reuteri strains from three types of hosts. The comparison of the L. reuteri GEMs identified potential metabolic products linked to the adaptation to the host. Conclusions The GEM of L. reuteri ATCC PTA 6475 can be used to simulate metabolic capabilities and growth. The core and pan model of 35 L. reuteri strains shows metabolic capacity differences both between and within the host groups. The GEMs provide a reliable basis to investigate the metabolism of L. reuteri in detail and their potential benefits on the host

Characterization of two β-galactosidases LacZ and WspA1 from Nostoc flagelliforme with focus on the latter’s central active region

The identification and characterization of new β-galactosidases will provide diverse candidate enzymes for use in food processing industry. In this study, two β-galactosidases, Nf-LacZ and WspA1, from the terrestrial cyanobacterium Nostoc flagelliforme were heterologously expressed in Escherichia coli, followed by purification and biochemical characterization. Nf-LacZ was characterized to have an optimum activity at 40\ua0\ub0C and pH 6.5, different from that (45\ua0\ub0C and pH 8.0) of WspA1. Two enzymes had a similar Michaelis constant (Km = 0.5\ua0mmol/liter) against the substrate o-nitrophenyl-β-D-galactopyranoside. Their activities could be inhibited by galactostatin bisulfite, with IC50 values of 0.59\ua0\ub5M for Nf-LacZ and 1.18\ua0\ub5M for WspA1, respectively. Gel filtration analysis suggested that the active form of WspA1 was a dimer, while Nf-LacZ was functional as a larger multimer. WspA1 was further characterized by the truncation test, and its minimum central region was found to be from residues 188 to 301, having both the glycosyl hydrolytic and transgalactosylation activities. Finally, transgenic analysis with the GFP reporter protein found that the N-terminus of WspA1 (35 aa) might play a special role in the export of WspA1 from cells. In summary, this study characterized two cyanobacterial β-galactosidases for potential applications in food industry

Developments in Fatty Acid-Derived Insect Pheromone Production Using Engineered Yeasts

The use of traditional chemical insecticides for pest control often leads to environmental pollution and a decrease in biodiversity. Recently, insect sex pheromones were applied for sustainable biocontrol of pests in fields, due to their limited adverse impacts on biodiversity and food safety compared to that of other conventional insecticides. However, the structures of insect pheromones are complex, and their chemical synthesis is not commercially feasible. As yeasts have been widely used for fatty acid-derived pheromone production in the past few years, using engineered yeasts may be promising and sustainable for the low-cost production of fatty acid-derived pheromones. The primary fatty acids produced by Saccharomyces cerevisiae and other yeasts are C16 and C18, and it is also possible to rewire/reprogram the metabolic flux for other fatty acids or fatty acid derivatives. This review summarizes the fatty acid biosynthetic pathway in S. cerevisiae and recent progress in yeast engineering in terms of metabolic engineering and synthetic biology strategies to produce insect pheromones. In the future, insect pheromones produced by yeasts might provide an eco-friendly pest control method in agricultural fields