Identification of an early transcriptomic signature of insulin resistance and related diseases in lymphomonocytes of healthy subjects

Molecular mechanisms linking type 2 diabetes mellitus and late-onset Alzheimer's disease: A systematic review and qualitative meta-analysis

Research evidence indicating common metabolic mechanisms through which type 2 diabetes mellitus (T2DM) increases risk of late-onset Alzheimer's dementia (LOAD) has accumulated over recent decades. The aim of this systematic review is to provide a comprehensive review of common mechanisms, which have hitherto been discussed in separate perspectives, and to assemble and evaluate candidate loci and epigenetic modifications contributing to polygenic risk linkages between T2DM and LOAD. For the systematic review on pathophysiological mechanisms, both human and animal studies up to December 2023 are included. For the qualitative meta-analysis of genomic bases, human association studies were examined; for epigenetic mechanisms, data from human studies and animal models were accepted. Papers describing pathophysiological studies were identified in databases, and further literature gathered from cited work. For genomic and epigenomic studies, literature mining was conducted by formalised search codes using Boolean operators in search engines, and augmented by GeneRif citations in Entrez Gene, and other sources (WikiGenes, etc.). For the systematic review of pathophysiological mechanisms, 923 publications were evaluated, and 138 gene loci extracted for testing candidate risk linkages. 3 57 publications were evaluated for genomic association and descriptions of epigenomic modifications. Overall accumulated results highlight insulin signalling, inflammation and inflammasome pathways, proteolysis, gluconeogenesis and glycolysis, glycosylation, lipoprotein metabolism and oxidation, cell cycle regulation or survival, autophagic-lysosomal pathways, and energy. Documented findings suggest interplay between brain insulin resistance, neuroinflammation, insult compensatory mechanisms, and peripheral metabolic dysregulation in T2DM and LOAD linkage. The results allow for more streamlined longitudinal studies of T2DM-LOAD risk linkages.

Glucose dysregulation in antipsychotic-naive first-episode psychosis: in silico exploration of gene expression signatures

Antipsychotic (AP)-naive first-episode psychosis (FEP) patients display early dysglycemia, including insulin resistance and prediabetes. Metabolic dysregulation may therefore be intrinsic to psychosis spectrum disorders (PSDs), independent of the metabolic effects of APs. However, the potential biological pathways that overlap between PSDs and dysglycemic states remain to be identified. Using meta-analytic approaches of transcriptomic datasets, we investigated whether AP-naive FEP patients share overlapping gene expression signatures with non-psychiatrically ill early dysglycemia individuals. We meta-analyzed peripheral transcriptomic datasets of AP-naive FEP patients and non-psychiatrically ill early dysglycemia subjects to identify common gene expression signatures. Common signatures underwent pathway enrichment analysis and were then used to identify potential new pharmacological compounds via Integrative Library of Integrated Network-Based Cellular Signatures (iLINCS). Our search results yielded 5 AP-naive FEP studies and 4 early dysglycemia studies which met inclusion criteria. We discovered that AP-naive FEP and non-psychiatrically ill subjects exhibiting early dysglycemia shared 221 common signatures, which were enriched for pathways related to endoplasmic reticulum stress and abnormal brain energetics. Nine FDA-approved drugs were identified as potential drug treatments, of which the antidiabetic metformin, the first-line treatment for type 2 diabetes, has evidence to attenuate metabolic dysfunction in PSDs. Taken together, our findings support shared gene expression changes and biological pathways associating PSDs with dysglycemic disorders. These data suggest that the pathobiology of PSDs overlaps and potentially contributes to dysglycemia. Finally, we find that metformin may be a potential treatment for early metabolic dysfunction intrinsic to PSDs.

Overview of Transcriptomic Research on Type 2 Diabetes: Challenges and Perspectives

Type 2 diabetes (T2D) is a common chronic disease whose etiology is known to have a strong genetic component. Standard genetic approaches, although allowing for the detection of a number of gene variants associated with the disease as well as differentially expressed genes, cannot fully explain the hereditary factor in T2D. The explosive growth in the genomic sequencing technologies over the last decades provided an exceptional impetus for transcriptomic studies and new approaches to gene expression measurement, such as RNA-sequencing (RNA-seq) and single-cell technologies. The transcriptomic analysis has the potential to find new biomarkers to identify risk groups for developing T2D and its microvascular and macrovascular complications, which will significantly affect the strategies for early diagnosis, treatment, and preventing the development of complications. In this article, we focused on transcriptomic studies conducted using expression arrays, RNA-seq, and single-cell sequencing to highlight recent findings related to T2D and challenges associated with transcriptome experiments.

MOUSSE: Multi-Omics Using Subject-Specific SignaturEs

Simple Summary

Modern profiling technologies have led to relevant progress toward precision medicine and disease management. A new trend in patient classification is to integrate multiple data types for the same subjects to increase the chance of identifying meaningful phenotype groups. However, these methodologies are still in their infancy, with their performance varying widely depending on the biological conditions analyzed. We developed MOUSSE, a new unsupervised and normalization-free tool for multi-omics integration able to maintain good clustering performance across a wide range of omics data. We verified its efficiency in clustering patients based on survival for ten different cancer types. The results we obtained show a higher average score in classification performance than ten other state-of-the-art algorithms. We have further validated the method by identifying a list of biological features potentially involved in patient survival, finding a high degree of concordance with the literature.

Abstract

High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival.

Mechanism of tanshinones and phenolic acids from Danshen in the treatment of coronary heart disease based on co-expression network

Background

The tanshinones and phenolic acids in Salvia miltiorrhiza (also named Danshen) have been confirmed for the treatment of coronary heart disease (CHD), but the action mechanisms remain elusive.

Methods

In the current study, the co-expression protein interaction network (Ce-PIN) was used to illustrate the differences between the tanshinones and phenolic acids of Danshen in the treatment of CHD. By integrating the gene expression profile data and protein-protein interactions (PPIs) data, the Ce-PINs of tanshinones and phenolic acids were constructed. Then, the Ce-PINs were analyzed by gene ontology enrichment analyzed based on the optimal algorithm.

Results

It turned out that Danshen is able to treat CHD by regulating the blood circulation, immune response and lipid metabolism. However, phenolic acids may regulate the blood circulation by Extracellular calcium-sensing receptor (CaSR), Endothelin-1 receptor (EDNRA), Endothelin-1 receptor (EDNRB), Kininogen-1 (KNG1), tanshinones may regulate the blood circulation by Guanylate cyclase soluble subunit alpha-1 (GUCY1A3) and Guanylate cyclase soluble subunit beta-1 (GUCY1B3). In addition, both the phenolic acids and tanshinones may regulate the immune response or inflammation by T-cell surface glycoprotein CD4 (CD4), Receptor-type tyrosine-protein phosphatase C (PTPRC).

Conclusion

Through the same targets of the same biological process and different targets of the same biological process, the tanshinones and phenolic acids synergistically treat coronary heart disease.

Research Progress of Mechanisms and Drug Therapy For Atherosclerosis on Toll-Like Receptor Pathway

Recent reports have established atherosclerosis (AS) as a major factor in the pathogenetic process of cardiovascular diseases such as ischemic stroke and coronary heart disease. Although the possible pathogenesis of AS remains to be elucidated, a large number of investigations strongly suggest that the inhibition of toll-like receptors (TLRs) alleviates the severity of AS to some extent by suppressing vascular inflammation and the formation of atherosclerotic plaques. As pattern recognition receptors, TLRs occupy a vital position in innate immunity, mediating various signaling pathways in infective and sterile inflammation. This review summarizes the available data on the research progress of AS and the latest antiatherosclerotic drugs associated with TLR pathway.

Overview of genomics and post-genomics research on type 2 diabetes mellitus: Future perspectives and a framework for further studies

In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes. Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies. We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease. We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.

Correction: Identification of an early transcriptomic signature of insulin resistance and related diseases in lymphomonocytes of healthy subjects

[This corrects the article DOI: 10.1371/journal.pone.0182559.].