Multi-omics profiling: the way towards precision medicine in metabolic diseases

ProMENDA: an updated resource for proteomic and metabolomic characterization in depression

Depression is a prevalent mental disorder with a complex biological mechanism. Following the rapid development of systems biology technology, a growing number of studies have applied proteomics and metabolomics to explore the molecular profiles of depression. However, a standardized resource facilitating the identification and annotation of the available knowledge from these scattered studies associated with depression is currently lacking. This study presents ProMENDA, an upgraded resource that provides a platform for manual annotation of candidate proteins and metabolites linked to depression. Following the establishment of the protein dataset and the update of the metabolite dataset, the ProMENDA database was developed as a major extension of its initial release. A multi-faceted annotation scheme was employed to provide comprehensive knowledge of the molecules and studies. A new web interface was also developed to improve the user experience. The ProMENDA database now contains 43,366 molecular entries, comprising 20,847 protein entries and 22,519 metabolite entries, which were manually curated from 1370 human, rat, mouse, and non-human primate studies. This represents a significant increase (more than 7-fold) in molecular entries compared to the initial release. To demonstrate the usage of ProMENDA, a case study identifying consistently reported proteins and metabolites in the brains of animal models of depression was presented. Overall, ProMENDA is a comprehensive resource that offers a panoramic view of proteomic and metabolomic knowledge in depression. ProMENDA is freely available at https://menda.cqmu.edu.cn .

One-pot method for preparing DNA, RNA, and protein for multiomics analysis

Typical multiomics studies employ separate methods for DNA, RNA, and protein sample preparation, which is labor intensive, costly, and prone to sampling bias. We describe a method for preparing high-quality, sequencing-ready DNA and RNA, and either intact proteins or mass-spectrometry-ready peptides for whole proteome analysis from a single sample. This method utilizes a reversible protein tagging scheme to covalently link all proteins in a lysate to a bead-based matrix and nucleic acid precipitation and selective solubilization to yield separate pools of protein and nucleic acids. We demonstrate the utility of this method to compare the genomes, transcriptomes, and proteomes of four triple-negative breast cancer cell lines with different degrees of malignancy. These data show the involvement of both RNA and associated proteins, and protein-only dependent pathways that distinguish these cell lines. We also demonstrate the utility of this multiomics workflow for tissue analysis using mouse brain, liver, and lung tissue.

The triggering pathway, the metabolic amplifying pathway, and cellular transduction in regulation of glucose-dependent biphasic insulin secretion

Introduction: Insulin secretion is a highly regulated process critical for maintaining glucose homeostasis. This abstract explores the intricate interplay between three essential pathways: The Triggering Pathway, The Metabolic Amplifying Pathway, and Cellular Transduction, in orchestrating glucose-dependent biphasic insulin secretion.Mechanism: During the triggering pathway, glucose metabolism in pancreatic beta-cells leads to ATP production, closing ATP-sensitive potassium channels and initiating insulin exocytosis. The metabolic amplifying pathway enhances insulin secretion via key metabolites like NADH and glutamate, enhancing calcium influx and insulin granule exocytosis. Additionally, the cellular transduction pathway involves G-protein coupled receptors and cyclic AMP, modulating insulin secretion.Result and Conclusion: These interconnected pathways ensure a dynamic insulin response to fluctuating glucose levels, with the initial rapid phase and the subsequent sustained phase. Understanding these pathways' complexities provides crucial insights into insulin dysregulation in diabetes and highlights potential therapeutic targets to restore glucose-dependent insulin secretion.

Applying proteomics in metabolic dysfunction-associated steatotic liver disease: From mechanism to biomarkers

Metabolic dysfunction-associated steatotic liver disease (MASLD), which represents the most common cause of liver disease, is emerging as a major health problem around the world. However, the molecular events that underline the pathogenesis and the progression of MASLD remain to be fully elucidated. Advanced stages of MASLD is strongly associated with liver-related outcomes and overall mortality. Despite this, highly accurate, sensitive, and non-invasive diagnostic tools are currently not aviailable, yet no FDA approved drugs for MASLD. The advance of proteomics has enable the study of protein expression, post-translational modifications (PTMs), subcellular distribution, and interactions. In this review, we discuss insights gained from the recent proteomics studies that shed new light on the pathogenesis, diagnosis and potential theraputic targets of MASLD.

Advantages of omics approaches for elucidating metabolic changes in diabetic peripheral neuropathy

Various animal and cell culture models of diabetes mellitus (DM) have been established and utilized to study diabetic peripheral neuropathy (DPN). The divergence of metabolic abnormalities among these models makes their etiology complicated despite some similarities regarding the pathological and neurological features of DPN. Thus, this study aimed to review the omics approaches toward DPN, especially on the metabolic states in diabetic rats and mice induced by chemicals (streptozotocin and alloxan) as type 1 DM models and by genetic mutations (MKR, db/db and ob/ob) and high-fat diet as type 2 DM models. Omics approaches revealed that the pathways associated with lipid metabolism and inflammation in dorsal root ganglia and sciatic nerves were enriched and controlled in the levels of gene expression among these animal models. Additionally, these pathways were conserved in human DPN, indicating the pivotal pathogeneses of DPN. Omics approaches are beneficial tools to better understand the association of metabolic changes with morphological and functional abnormalities in DPN.

Opportunities and challenges of 5G network technology toward precision medicine

Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.

Uncovering the relationship between gut microbial dysbiosis, metabolomics, and dietary intake in type 2 diabetes mellitus and in healthy volunteers: a multi-omics analysis

Type 2 Diabetes Mellitus has reached epidemic levels globally, and several studies have confirmed a link between gut microbial dysbiosis and aberrant glucose homeostasis among people with diabetes. While the assumption is that abnormal metabolomic signatures would often accompany microbial dysbiosis, the connection remains largely unknown. In this study, we investigated how diet changed the gut bacteriome, mycobiome and metabolome in people with and without type 2 Diabetes.1 Differential abundance testing determined that the metabolites Propionate, U8, and 2-Hydroxybutyrate were significantly lower, and 3-Hydroxyphenyl acetate was higher in the high fiber diet compared to low fiber diet in the healthy control group. Next, using multi-omics factor analysis (MOFA2), we attempted to uncover sources of variability that drive each of the different groups (bacterial, fungal, and metabolite) on all samples combined (control and DM II). Performing variance decomposition, ten latent factors were identified, and then each latent factor was tested for significant correlations with age, BMI, diet, and gender. Latent Factor1 was the most significantly correlated. Remarkably, the model revealed that the mycobiome explained most of the variance in the DM II group (12.5%) whereas bacteria explained most of the variance in the control group (64.2% vs. 10.4% in the DM II group). The latent Factor1 was significantly correlated with dietary intake (q < 0.01). Further analyses of the impact of bacterial and fungal genera on Factor1 determined that the nine bacterial genera (Phocaeicola, Ligilactobacillus, Mesosutterella, Acidaminococcus, Dorea A, CAG-317, Caecibacter, Prevotella and Gemmiger) and one fungal genus (Malassezia furfur) were found to have high factor weights (absolute weight > 0.6). Alternatively, a linear regression model was fitted per disease group for each genus to visualize the relationship between the factor values and feature abundances, showing Xylose with positive weights and Propionate, U8, and 2-Hydroxybutyrate with negative weights. This data provides new information on the microbially derived changes that influence metabolic phenotypes in response to different diets and disease conditions in humans.

Functional roles of sphingolipids in immunity and their implication in disease

Sphingolipids, which are components of cellular membranes and organ tissues, can be synthesized or degraded to modulate cellular responses according to environmental cues, and the balance among the different sphingolipids is important for directing immune responses, regardless of whether they originate, as intra- or extracellular immune events. Recent progress in multiomics-based analyses and methodological approaches has revealed that human health and diseases are closely related to the homeostasis of sphingolipid metabolism, and disease-specific alterations in sphingolipids and related enzymes can be prognostic markers of human disease progression. Accumulating human clinical data from genome-wide association studies and preclinical data from disease models provide support for the notion that sphingolipids are the missing pieces that supplement our understanding of immune responses and diseases in which the functions of the involved proteins and nucleotides have been established. In this review, we analyze sphingolipid-related enzymes and reported human diseases to understand the important roles of sphingolipid metabolism. We discuss the defects and alterations in sphingolipid metabolism in human disease, along with functional roles in immune cells. We also introduce several methodological approaches and provide summaries of research on sphingolipid modulators in this review that should be helpful in studying the roles of sphingolipids in preclinical studies for the investigation of experimental and molecular medicines.

Plasma metabolomic response to high-carbohydrate meals of differing glycaemic load in overweight women

BACKGROUND

Metabolomic dysregulation following a meal in overweight individuals with the Metabolic Syndrome (MetS) involves multiple pathways of nutrient storage and oxidation.

OBJECTIVE

The aim of the current study was to perform an acute cross-over intervention to examine the interactive actions of meal glycaemic load (GL) on the dynamic responses of the plasma metabolome in overweight females.

METHODS

Postmenopausal women [63 ± 1.23y; Healthy (n = 20) and MetS (n = 20)] ingested two differing high-carbohydrate test meals (73 g carbohydrate; 51% energy) composed of either low glycemic index (LGI) or high (HGI) foods in a randomised sequence. Plasma metabolome was analysed using liquid chromatography-mass spectrometry (LC-MS).

RESULTS

In the overweight women with MetS, there were suppressed postprandial responses for several amino acids (AAs), including phenylalanine, leucine, valine, and tryptophan, p < 0.05), irrespective of the meal type. Meal GL exerted a limited impact on the overall metabolomic response, although the postprandial levels of alanine were higher with the low GL meal and uric acid was greater following the high GL meal (p < 0.05).

CONCLUSIONS

MetS participants exhibited reduced differences in the concentrations of a small set of AAs and a limited group of metabolites implicated in energy metabolism following the meals. However, the manipulation of meal GL had minimal impact on the postprandial metabolome. This study suggests that the GL of a meal is not a major determinant of postprandial response, with a greater impact exerted by the metabolic health of the individual. Trial registration Australia New Zealand Clinical Trials Registry: ACTRN12615001108505 (21/10/2015).

Quantitative Proteomic Analysis of Serum Reveals MST1 as a Potential Candidate Biomarker in Spontaneously Diabetic Cynomolgus Monkeys

The prevalence of type 2 diabetes (T2DM) is increasing globally, creating essential demands for T2DM animal models for the study of disease pathogenesis, prevention, and therapy. A non-human primate model such as cynomolgus monkeys can develop T2DM spontaneously in an age-dependent way similar to humans. In this study, a data-independent acquisition-based quantitative proteomics strategy was employed to investigate the serum proteomic profiles of spontaneously diabetic cynomolgus monkeys compared with healthy controls. The results revealed significant differences in protein abundances. A total of 95 differentially expressed proteins (DEPs) were quantitatively identified in the current study, among which 31 and 64 proteins were significantly upregulated and downregulated, respectively. Bioinformatic analysis revealed that carbohydrate digestion and absorption was the top enriched pathway by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-protein interaction network analysis demonstrated that MST1 was identified as the most connected protein in the network and could be considered as the hub protein. MST1 was significantly and inversely associated with FSG and HbA1c. Furthermore, recent lines of evidence also indicate that MST1 acts as a crucial regulator in regulating hepatic gluconeogenesis to maintain metabolic homeostasis while simultaneously suppressing the inflammatory processes. In conclusion, our study provides novel insights into serum proteome changes in spontaneously diabetic cynomolgus monkeys and points out that the dysregulation of several DEPs may play an important role in the pathogenesis of T2DM.

Utility of Human Relevant Preclinical Animal Models in Navigating NAFLD to MAFLD Paradigm

Fatty liver disease is an emerging contributor to disease burden worldwide. The past decades of work established the heterogeneous nature of non-alcoholic fatty liver disease (NAFLD) etiology and systemic contributions to the pathogenesis of the disease. This called for the proposal of a redefinition in 2020 to that of metabolic dysfunction-associated fatty liver disease (MAFLD) to better reflect the current understanding of the disease. To date, several clinical cohort studies comparing NAFLD and MAFLD hint at the relevancy of the new nomenclature in enriching for patients with more severe hepatic injury and extrahepatic comorbidities. However, the underlying systemic pathogenesis is still not fully understood. Preclinical animal models have been imperative in elucidating key biological mechanisms in various contexts, including intrahepatic disease progression, interorgan crosstalk and systemic dysregulation. Furthermore, they are integral in developing novel therapeutics against MAFLD. However, substantial contextual variabilities exist across different models due to the lack of standardization in several aspects. As such, it is crucial to understand the strengths and weaknesses of existing models to better align them to the human condition. In this review, we consolidate the implications arising from the change in nomenclature and summarize MAFLD pathogenesis. Subsequently, we provide an updated evaluation of existing MAFLD preclinical models in alignment with the new definitions and perspectives to improve their translational relevance.

Genetics of Type 2 Diabetes: Past, Present, and Future

Diabetes has reached epidemic proportions worldwide. Currently, approximately 537 million adults (20–79 years) have diabetes, and the total number of people with diabetes is continuously increasing. Diabetes includes several subtypes. About 80% of all cases of diabetes are type 2 diabetes (T2D). T2D is a polygenic disease with an inheritance ranging from 30 to 70%. Genetic and environment/lifestyle factors, especially obesity and sedentary lifestyle, increase the risk of T2D. In this review, we discuss how studies on the genetics of diabetes started, how they expanded when genome-wide association studies and exome and whole-genome sequencing became available, and the current challenges in genetic studies of diabetes. T2D is heterogeneous with respect to clinical presentation, disease course, and response to treatment, and has several subgroups which differ in pathophysiology and risk of micro- and macrovascular complications. Currently, genetic studies of T2D focus on these subgroups to find the best diagnoses and treatments for these patients according to the principles of precision medicine.

Genetic and Molecular Characterization Revealed the Prognosis Efficiency of Histone Acetylation in Pan-Digestive Cancers

The imbalance between acetylation and deacetylation of histone proteins, important for epigenetic modifications, is closely associated with various diseases, including cancer. However, knowledge regarding the modification of histones across the different types of digestive cancers is still lacking. The purpose of this research was to analyze the role of histone acetylation and deacetylation in pan-digestive cancers. We systematically characterized the molecular alterations and clinical relevance of 13 histone acetyltransferase (HAT) and 18 histone deacetylase (HDAC) genes in five types of digestive cancers, including esophageal carcinoma, gastric cancer, hepatocellular carcinoma, pancreatic cancer, and colorectal cancer. Recurrent mutations and copy number variation (CNV) were extensively found in acetylation-associated genes across pan-digestive cancers. HDAC9 and KAT6A showed widespread copy number amplification across five pan-digestive cancers, while ESCO2, EP300, and HDAC10 had prevalent copy number deletions. Accordingly, we found that HAT and HDAC genes correlated with multiple cancer hallmark-related pathways, especially the histone modification-related pathway, PRC2 complex pathway. Furthermore, the expression pattern of HAT and HDAC genes stratified patients with clinical benefit in hepatocellular carcinoma and pancreatic cancer. These results indicated that acetylation acts as a key molecular regulation of pan-digestive tumor progression.

Akt: A Potential Drug Target for Metabolic Syndrome

The serine/threonine kinase Akt, also known as protein kinase B (PKB), is one of the key factors regulating glucose and lipid energy metabolism, and is the core focus of current research on diabetes and metabolic diseases. Akt is mostly expressed in key metabolism-related organs and it is activated in response to various stimuli, including cell stress, cell movement, and various hormones and drugs that affect cell metabolism. Genetic and pharmacological studies have shown that Akt is necessary to maintain the steady state of glucose and lipid metabolism and a variety of cellular responses. Existing evidence shows that metabolic syndrome is related to insulin resistance and lipid metabolism disorders. Based on a large number of studies on Akt-related pathways and reactions, we believe that Akt can be used as a potential drug target to effectively treat metabolic syndrome.