Restoration of mRNA Expression of Solute Carrier Proteins in Liver of Diet-Induced Obese Mice by Metformin

Metabolic basis of solute carrier transporters in treatment of type 2 diabetes mellitus

Solute carriers (SLCs) constitute the largest superfamily of membrane transporter proteins. These transporters, present in various SLC families, play a vital role in energy metabolism by facilitating the transport of diverse substances, including glucose, fatty acids, amino acids, nucleotides, and ions. They actively participate in the regulation of glucose metabolism at various steps, such as glucose uptake (e.g., SLC2A4/GLUT4), glucose reabsorption (e.g., SLC5A2/SGLT2), thermogenesis (e.g., SLC25A7/UCP-1), and ATP production (e.g., SLC25A4/ANT1 and SLC25A5/ANT2). The activities of these transporters contribute to the pathogenesis of type 2 diabetes mellitus (T2DM). Notably, SLC5A2 has emerged as a valid drug target for T2DM due to its role in renal glucose reabsorption, leading to groundbreaking advancements in diabetes drug discovery. Alongside SLC5A2, multiple families of SLC transporters involved in the regulation of glucose homeostasis hold potential applications for T2DM therapy. SLCs also impact drug metabolism of diabetic medicines through gene polymorphisms, such as rosiglitazone (SLCO1B1/OATP1B1) and metformin (SLC22A1-3/OCT1-3 and SLC47A1, 2/MATE1, 2). By consolidating insights into the biological activities and clinical relevance of SLC transporters in T2DM, this review offers a comprehensive update on their roles in controlling glucose metabolism as potential drug targets.

Membrane transporters in cell physiology, cancer metabolism and drug response

By controlling the passage of small molecules across lipid bilayers, membrane transporters influence not only the uptake and efflux of nutrients, but also the metabolic state of the cell. With more than 450 members, the Solute Carriers (SLCs) are the largest transporter super-family, clustering into families with different substrate specificities and regulatory properties. Cells of different types are, therefore, able to tailor their transporter expression signatures depending on their metabolic requirements, and the physiological importance of these proteins is illustrated by their mis-regulation in a number of disease states. In cancer, transporter expression is heterogeneous, and the SLC family has been shown to facilitate the accumulation of biomass, influence redox homeostasis, and also mediate metabolic crosstalk with other cell types within the tumour microenvironment. This Review explores the roles of membrane transporters in physiological and malignant settings, and how these roles can affect drug response, through either indirect modulation of sensitivity or the direct transport of small-molecule therapeutic compounds into cells.

Intratumor microbiome derived glycolysis-lactate signatures depicts immune heterogeneity in lung adenocarcinoma by integration of microbiomic, transcriptomic, proteomic and single-cell data

Introduction

Microbiome plays roles in lung adenocarcinoma (LUAD) development and anti-tumor treatment efficacy. Aberrant glycolysis in tumor might promote lactate production that alter tumor microenvironment, affecting microbiome, cancer cells and immune cells. We aimed to construct intratumor microbiome score to predict prognosis of LUAD patients and thoroughly investigate glycolysis and lactate signature's association with LUAD immune cell infiltration.

Methods

The Cancer Genome Atlas-LUAD (TCGA-LUAD) microbiome data was downloaded from cBioPortal and analyzed to examine its association with overall survival to create a prognostic scoring model. Gene Set Enrichment Analysis (GSEA) was used to find each group's major mechanisms involved. Our study then investigated the glycolysis and lactate pattern in LUAD patients based on 19 genes, which were correlated with the tumor microenvironment (TME) phenotypes and immunotherapy outcomes. We developed a glycolysis-lactate risk score and signature to accurately predict TME phenotypes, prognosis, and response to immunotherapy.

Results

Using the univariate Cox regression analysis, the abundance of 38 genera were identified with prognostic values and a lung-resident microbial score (LMS) was then developed from the TCGA-LUAD-microbiome dataset. Glycolysis hallmark pathway was significantly enriched in high-LMS group and three distinct glycolysis-lactate patterns were generated. Patients in Cluster1 exhibited unfavorable outcomes and might be insensitive to immunotherapy. Glycolysis-lactate score was constructed for predicting prognosis with high accuracy and validated in external cohorts. Gene signature was developed and this signature was elevated in epithelial cells especially in tumor mass on single-cell level. Finally, we found that the glycolysis-lactate signature levels were consistent with the malignancy of histological subtypes.

Discussion

Our study demonstrated that an 18-microbe prognostic score and a 19-gene glycolysis-lactate signature for predicting prognosis of LUAD patients. Our LMS, glycolysis-lactate score and glycolysis-lactate signature have potential roles in precision therapy of LUAD patients.

Association of SLC11A1 polymorphisms with anthropometric and biochemical parameters describing Type 2 Diabetes Mellitus

Diabetes, a leading cause of death globally, has different types, with Type 2 Diabetes Mellitus (T2DM) being the most prevalent one. It has been established that variations in the SLC11A1 gene impact risk of developing infectious, inflammatory, and endocrine disorders. This study is aimed to investigate the association between the SLC11A1 gene polymorphisms (rs3731864 G/A, rs3731865 C/G, and rs17235416 + TGTG/- TGTG) and anthropometric and biochemical parameters describing T2DM. Eight hundred participants (400 in each case and control group) were genotyped using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and amplification-refractory mutation system-PCR (ARMS-PCR) methods. Lipid profile, fasting blood sugar (FBS), hemoglobin A1c level, and anthropometric indices were also recorded for each subject. Findings revealed that SLC11A1-rs3731864 G/A, -rs17235416 (+ TGTG/- TGTG) were associated with T2DM susceptibility, providing protection against the disease. In contrast, SLC11A1-rs3731865 G/C conferred an increased risk of T2DM. We also noticed a significant association between SLC11A1-rs3731864 G/A and triglyceride levels in patients with T2DM. In silico evaluations demonstrated that the SLC11A2 and ATP7A proteins also interact directly with the SLC11A1 protein in Homo sapiens. In addition, allelic substitutions for both intronic variants disrupt or create binding sites for splicing factors and serve a functional effect. Overall, our findings highlighted the role of SLC11A1 gene variations might have positive (rs3731865 G/C) or negative (rs3731864 G/A and rs17235416 + TGTG/- TGTG) associations with a predisposition to T2DM.

Developmental programming: Impact of prenatal bisphenol-A exposure on liver and muscle transcriptome of female sheep

Gestational Bisphenol A (BPA) exposure leads to peripheral insulin resistance, and hepatic and skeletal muscle oxidative stress and lipotoxicity during adulthood in the female sheep offspring. To investigate transcriptional changes underlying the metabolic outcomes, coding and non-coding (nc) RNA in liver and muscle from 21-month-old control and prenatal BPA-treated (0.5 mg/kg/day from days 30 to 90 of gestation; Term: 147 days) female sheep were sequenced. Prenatal BPA-treatment dysregulated: expression of 194 genes (138 down, 56 up) in liver and 112 genes (32 down, 80 up) in muscle (FDR < 0.05 and abs log2FC > 0.5); 155 common gene pathways including mitochondrial-related genes in both tissues; 1415 gene pathways including oxidative stress and lipid biosynthetic process specifically in the liver (FDR < 0.01); 192 gene pathways including RNA biosynthetic processes in muscle (FDR < 0.01); 77 lncRNA (49 down, 28 up), 14 microRNAs (6 down, 8 up), 127 snoRNAs (63 down, 64 up) and 55 snRNAs (15 down, 40 up) in the liver while upregulating 6 lncRNA and dysregulating 65 snoRNAs (47 down, 18 up) in muscle (FDR < 0.1, abs log2FC > 0.5). Multiple ncRNA correlated with LCORL, MED17 and ZNF41 mRNA in liver but none of them in the muscle. Discriminant analysis identified (p < 0.05) PECAM, RDH11, ABCA6, MIR200B, and MIR30B in liver and CAST, NOS1, FASN, MIR26B, and MIR29A in muscle as gene signatures of gestational BPA exposure. These findings provide mechanistic clues into the development and/or maintenance of the oxidative stress and lipid accumulation and potential for development of mitochondrial and fibrotic defects contributing to the prenatal BPA-induced metabolic dysfunctions.

Metabolomic Analysis of Serum and Tear Samples from Patients with Obesity and Type 2 Diabetes Mellitus

Metabolomics strategies are widely used to examine obesity and type 2 diabetes (T2D). Patients with obesity (n = 31) or T2D (n = 26) and sex- and age-matched controls (n = 28) were recruited, and serum and tear samples were collected. The concentration of 23 amino acids and 10 biogenic amines in serum and tear samples was analyzed. Statistical analysis and Pearson correlation analysis along with network analysis were carried out. Compared to controls, changes in the level of 6 analytes in the obese group and of 10 analytes in the T2D group were statistically significant. For obesity, the energy generation, while for T2D, the involvement of NO synthesis and its relation to insulin signaling and inflammation, were characteristic. We found that BCAA and glutamine metabolism, urea cycle, and beta-oxidation make up crucial parts of the metabolic changes in T2D. According to our data, the retromer-mediated retrograde transport, the ethanolamine metabolism, and, consequently, the endocannabinoid signaling and phospholipid metabolism were characteristic of both conditions and can be relevant pathways to understanding and treating insulin resistance. By providing potential therapeutic targets and new starting points for mechanistic studies, our results emphasize the importance of complex data analysis procedures to better understand the pathomechanism of obesity and diabetes.