Genome-Scale Metabolic Model Analysis of Metabolic Differences between Lauren Diffuse and Intestinal Subtypes in Gastric Cancer

Applications of genome-scale metabolic models to the study of human diseases: A systematic review

BACKGROUND AND OBJECTIVES

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

RESULTS

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

CONCLUSIONS

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

1-Deoxynojirimycin containing Morus alba leaf-based food modulates the gut microbiome and expression of genes related to obesity

BACKGROUND

Obesity is a serious disease with an alarmingly high incidence that can lead to other complications in both humans and dogs. Similar to humans, obesity can cause metabolic diseases such as diabetes in dogs. Natural products may be the preferred intervention for metabolic diseases such as obesity. The compound 1-deoxynojirimycin, present in Morus leaves and other sources has antiobesity effects. The possible antiobesity effect of 1-deoxynojirimycin containing Morus alba leaf-based food was studied in healthy companion dogs (n = 46) visiting the veterinary clinic without a history of diseases. Body weight, body condition score (BCS), blood-related parameters, and other vital parameters of the dogs were studied. Whole-transcriptome of blood and gut microbiome analysis was also carried out to investigate the possible mechanisms of action and role of changes in the gut microbiome due to treatment.

RESULTS

After 90 days of treatment, a significant antiobesity effect of the treatment food was observed through the reduction of weight, BCS, and blood-related parameters. A whole-transcriptome study revealed differentially expressed target genes important in obesity and diabetes-related pathways such as MLXIPL, CREB3L1, EGR1, ACTA2, SERPINE1, NOTCH3, and CXCL8. Gut microbiome analysis also revealed a significant difference in alpha and beta-diversity parameters in the treatment group. Similarly, the microbiota known for their health-promoting effects such as Lactobacillus ruminis, and Weissella hellenica were abundant (increased) in the treatment group. The predicted functional pathways related to obesity were also differentially abundant between groups.

CONCLUSIONS

1-Deoxynojirimycin-containing treatment food have been shown to significantly improve obesity. The identified genes, pathways, and gut microbiome-related results may be pursued in further studies to develop 1-deoxynojirimycin-based products as candidates against obesity.

The emerging role of glycolysis and immune evasion in gastric cancer

Gastric cancer (GC) is the fifth most common malignancy and the third leading cause of cancer-related deaths worldwide. Similar to other types of tumors, GC cells undergo metabolic reprogramming and switch to a "predominantly glycolytic" metabolic pattern to promote its survival and metastasis, also known as "the Warburg effect", which is characterized by enhanced glucose uptake and lactate production. A large number of studies have shown that targeting cancer cells to enhanced glycolysis is a promising strategy, that can make cancer cells more susceptible to other conventional treatment methods of treatment, including chemotherapy, radiotherapy and immunotherapy, and so on. Therefore, this review summarizes the metabolic characteristics of glycolysis in GC cells and focuses on how abnormal lactate concentration can lead to immunosuppression through its effects on the differentiation, metabolism, and function of infiltrating immune cells, and how targeting this phenomenon may be a potential strategy to improve the therapeutic efficacy of GC.

Lipid metabolism characterization in gastric cancer identifies signatures to predict prognostic and therapeutic responses

Purpose: Increasing evidence has elucidated the significance of lipid metabolism in predicting therapeutic efficacy. Obviously, a systematic analysis of lipid metabolism characterizations of gastric cancer (GC) needs to be reported. Experimental design: Based on two proposed computational algorithms (TCGA-STAD and GSE84437), the lipid metabolism characterization of 367 GC patients and its systematic relationship with genomic characteristics, clinicopathologic features, and clinical outcomes of GC were analyzed in our study. Differentially expressed genes (DEGs) were identified based on the lipid metabolism cluster. At the same time, we applied single-factor Cox regression and random forest to screen signature genes to construct a prognostic model, namely, the lipid metabolism score (LMscore). Next, we deeply explored the predictive value of the LMscore for GC. To verify the specific changes in lipid metabolism, a total of 90 serum, 30 tumor, and non-tumor adjacent tissues from GC patients, were included for pseudotargeted metabolomics analysis via SCIEX triple quad 5500 LC-MS/MS system. Results: Five lipid metabolism signature genes were identified from a total of 3,104 DEGs. The LMscore could be a prognosticator for survival in different clinicopathological GC cohorts. As well, the LMscore was identified as a predictive biomarker for responses to immunotherapy and chemotherapeutic drugs. Additionally, significant changes in sphingolipid metabolism and sphingolipid molecules were discovered in cancer tissue from GC patients by pseudotargeted metabolomics. Conclusion: In conclusion, multivariate analysis revealed that the LMscore was an independent prognostic biomarker of patient survival and therapeutic responses in GC. Depicting a comprehensive landscape of the characteristics of lipid metabolism may help to provide insights into the pathogenesis of GC, interpret the responses of gastric tumors to therapies, and achieve a better outcome in the treatment of GC. In addition, significant alterations of sphingolipid metabolism and increased levels of sphingolipids, in particular, sphingosine (d16:1) and ceramide, were discovered in GC tissue by lipidome pseudotargeted metabolomics, and most of the sphingolipid molecules have the potential to be diagnostic biomarkers for GC.