Elucidating the metabolic characteristics of pancreatic β-cells from patients with type 2 diabetes (T2D) using a genome-scale metabolic modeling

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.

Anti-diabetic drug discovery using the bioactive compounds of Momordica charantia by molecular docking and molecular dynamics analysis

Diabetes mellitus (DM) is a multifactorial life-threatening endocrine disease characterized by abnormalities in glucose metabolism. It is a chronic metabolic disease that involves multiple enzymes such as α-amylase and α-glucosidases. Inhibition of these enzymes has been identified as a promising method for managing diabetes, and researchers are currently focusing on discovering novel α-amylase and α-glucosidase inhibitors for diabetes therapy. Hence, we have selected 12 bioactive compounds from the Momordica charantia (MC) plant and performed a virtual screening and molecular dynamics investigation to identify natural inhibitors of α-amylase and α-glucosidases. Our in silico result revealed that phytocompound Rutin showed the highest binding affinity against α-amylase (1HNY) enzymes at (-11.68 kcal/mol), followed by Karaviloside II (-9.39), Momordicoside F (-9.19), Campesterol (-9.11. While docking against α-glucosidases (4J5T), Rutin again showed the greatest binding affinity (-11.93 kcal/mol), followed by Momordicine (-9.89), and Campesterol (-8.99). Molecular dynamics (MD) simulation research is currently the gold standard for drug design and discovery. Consequently, we conducted simulations of 100 nanoseconds (ns) to assess the stability of protein-ligand complexes based on parameters like RMSD, RMSF, RG, PCA, and FEL. The significance of our findings indicates that rutin from MC might serve as an effective natural therapeutic agent for diabetes management due to its strongest binding affinities with α-amylase and α-glucosidase enzymes. Further research in animals and humans is essential to validate the efficacy of these drug molecules.Communicated by Ramaswamy H. Sarma.

Pancreatic cancer pathology image segmentation with channel and spatial long-range dependencies

Based on deep learning, pancreatic cancer pathology image segmentation technology effectively assists pathologists in achieving improved treatment outcomes. However, compared to traditional image segmentation tasks, the large size of tissues in pathology images requires a larger receptive field. While methods based on dilated convolutions or attention mechanisms can enhance the receptive field, they cannot capture long-range feature dependencies. Directly applying self-attention mechanisms to capture long-range dependencies results in intolerable computational complexity. To address these challenges, we introduce a channel and spatial self-attention (CS) Module designed for efficiently capturing both channel and spatial long-range feature dependencies in pancreatic cancer pathological images. Specifically, the channel and spatial self-attention module consists of an adaptive channel self-attention module and a window-shift spatial self-attention module. The adaptive channel self-attention module adaptively pools features to a fixed size to capture long-range feature dependencies. While the window-shift spatial self-attention module captures spatial long-range dependencies in a window-based manner. Additionally, we propose a re-weighted cross-entropy loss to mitigate the impact of long-tail distribution on performance. Our proposed method surpasses state-of-the-art on both our Pancreatic Cancer Pathology Image (PCPI) dataset and the GlaS challenge dataset. The mDice and mIoU have achieved 73.93% and 59.42% in our PCPI dataset.

Peroxisomal hydrogen peroxide signaling: A new chapter in intracellular communication research

Hydrogen peroxide (H2O2), a natural metabolite commonly found in aerobic organisms, plays a crucial role in numerous cellular signaling processes. One of the key organelles involved in the cell's metabolism of H2O2 is the peroxisome. In this review, we first provide a concise overview of the current understanding of H2O2 as a molecular messenger in thiol redox signaling, along with the role of peroxisomes as guardians and modulators of cellular H2O2 balance. Next, we direct our focus toward the recently identified primary protein targets of H2O2 originating from peroxisomes, emphasizing their importance in unraveling the complex interplay between peroxisomal H2O2 and cell signaling. We specifically focus on three areas: signaling through peroxiredoxin redox relay complexes, calcium signaling, and phospho-signaling. Finally, we highlight key research directions that warrant further investigation to enhance our comprehension of the molecular and biochemical mechanisms linking alterations in peroxisomal H2O2 metabolism with disease.

Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer

Metabolism is a dynamic network of biochemical reactions that support systemic homeostasis amidst changing nutritional, environmental, and physical activity factors. The circulatory system facilitates metabolite exchange among organs, while the endocrine system finely tunes metabolism through hormone release. Endocrine disorders like obesity, diabetes, and Cushing's syndrome disrupt this balance, contributing to systemic inflammation and global health burdens. They accompany metabolic changes on multiple levels from molecular interactions to individual organs to the whole body. Understanding how metabolic fluxes relate to endocrine disorders illuminates the underlying dysregulation. Cancer is increasingly considered a systemic disorder because it not only affects cells in localized tumors but also the whole body, especially in metastasis. In tumorigenesis, cancer-specific mutations and nutrient availability in the tumor microenvironment reprogram cellular metabolism to meet increased energy and biosynthesis needs. Cancer cachexia results in metabolic changes to other organs like muscle, adipose tissue, and liver. This review explores the interplay between the endocrine system and systems-level metabolism in health and disease. We highlight metabolic fluxes in conditions like obesity, diabetes, Cushing's syndrome, and cancers. Recent advances in metabolomics, fluxomics, and systems biology promise new insights into dynamic metabolism, offering potential biomarkers, therapeutic targets, and personalized medicine.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.