INTRODUCTION Diabetes mellitus is a disease associated with abnormally high levels of glucose. It arises due to one of the two causes: insulin production inadequacy or inadequacy of cell sensitivity to insulin. Diabetes can be classified into 3 types. Types of Diabetes mellitus and their causes Type 1 Diabetes (T1D) – T1D occurs due to the inability of the pancreas to synthesize sufficient insulin.
There is no known cause of T1D and it constitutes about 3% of diagnosed diabetes cases. Type 2 Diabetes (T2D) – T2D occurs because of insufficient insulin levels or decreased uptake of insulin into cells. 95% of diagnosed diabetes cases are Type 2 diabetes. Obesity and a sedentary lifestyle are the leading causes of type 2 diabetes. Gestational Diabetes – Mostly observed in pregnant women who develop high blood sugar levels and affects around 10% of pregnancies.
This usually resolves around the time of child birth. Type 2 diabetes is the most common form of diabetes and constitutes about 87-91% of all cases. In 2017, 425 million adults worldwide had diabetes, with 72.
9 million diabetes cases reported in India. There are six categories of oral antidiabetic drugs (OADs) that are used for the treatment namely, biguanides, sulfonylureas, thiazolidinediones, meglitinides, dipeptidyl peptidase IV inhibitors, and ?-glucosidase inhibitors. Among them, metformin (Biguanide) is used as first-line treatment for Type 2 Diabetes. Metformin acts on the liver, kidney and intestines. Multiple studies in mice have demonstrated the role of metformin in reducing hepatic gluconeogenesis.
The variability in response to metformin by diabetes individuals has been previously documented with about 35% failing initially to achieve desired blood glucose levels on monotherapy. There are various risk factors which have been attributed to this variability in response to metformin drug. These include genetic, epigenetic and environmental risk factors. The genetic research aided by technologies like Genome-wide association studies (GWAS), next-generation DNA sequencing, have helped to highlight pharmacogenetically relevant biomarkers. Genetic polymorphisms in the MATE1 and MATE2 were shown to alter the effect of metformin in diabetes individuals. In SLC47A1 (MATE1), a common variant in the promoter region (?66T>C; rs2252281) decreases MATE1 expression and increases levels of metformin in hepatocytes. The C allele of rs2252281 was associated with significantly better glucose-lowering response to metformin.
A promoter region variant, rs12943590 in SLC47A2 (MATE2), has been shown to alter glucose-lowering response to metformin as well. From the Genome wide association studies (GWAS) in the Genetics of DARTS (GoDARTS) study, it was observed that a variant present in a region containing seven genes on chromosome 11 had a strong association with metformin response. Metformin uptake into hepatocytes is catalyzed by the organic cation transporter-1 (OCT1). According to recent studies, it has been shown that human OCT1 is highly polymorphic and can affect metformin uptake by cells. Ataxia telangiectasia mutated gene (ATM), which encodes a serine/threonine kinase can be considered as a potential candidate gene and possibly regulates enzymes in the metformin response pathway.
An inhibitor molecule of ATM used in the in vitro cellular studies was shown to be an OCT1 inhibitor as it prevented the entry of the drug into the hepatocytes and prevented activation of ATM. Several SNPs were identified during a candidate gene study of 40 genes involved in the Metformin target pathway. Nominally significant interactions were observed at SNPs which encode drug targets for metformin, in the gene encoding the AMPK kinase, STK11 and the AMPK subunit genes PRKAA1, PRKAA2, and PRKAB2. Less than 10% of published GWAS identify the genetic basis behind the variation in therapeutic drug responses and adverse drug reactions (ADRs).
The limitation of pharmacogenomics GWAS to only specific populations have limited our understanding of the mechanisms responsible for drug distribution, action and toxicity. Most pharmacogenomic-associated SNPs occur majorly in non-coding regions. Non-coding RNAs, miRNAs, epigenetic modifications like DNA methylation and histone modifications on the other hand, induce changes in the genome and alter the gene expression at the transcriptome level. The study of transcriptome levels in diabetes individuals who are undergoing metformin drug therapy will help better understand the metabolic pathways which are altered with respect to metformin drug response. In the present study, we aim to identify variations in the transcriptome levels of diabetes individuals who are undergoing metformin therapy.
AIM AND OBJECTIVES To analyse the differential gene expression data for prediction of metformin drug response in type 2 diabetes individuals. To achieve this, we have the following objectives: To perform whole transcriptome sequencing of metformin responsive and non-responsive Type 2 diabetes individuals. To identify differentially expressed genes in metformin responsive and non-responsive Type 2 Diabetes individuals. To carry out in silico network analysis for identification of altered metabolic pathways in metformin treated Type 2 Diabetes individuals. 3. METHODOLOGY Experimental work flow Study Participants: Ethical Clearance has been obtained from Institutional Ethical Committee of Kasturba Medical College and written informed consent will be taken from all the study participants.
Inclusion criteria for T2D and drug response criteria: Newly diagnosed Type 2 Diabetes individuals who will undergo metformin monotherapy will be enrolled for the study. The inclusion involves diagnosis of diabetes based on WHO criteria. Individuals with in the age of 25-65 years belonging to either gender (male or female) will be considered.
The study participants will be followed up and clinical variables will be assessed at the end of three months to classify individuals as responders and non-responders. Individuals with reduction of HbA1c by 1% or 40 mg/dl decrease in fasting glucose after three months of metformin treatment will be considered as responders whereas the individuals who fail to achieve the desired decrease in glucose profile will be considered as non-responders. Exclusion criteria for T2D and drug response criteria: Study participants who are advised for two or more oral hypoglycemic drugs will be excluded from the study.
Sample collection: Peripheral blood samples will be obtained through venipuncture. Anthropometric measurements and clinical data of Type 2 Diabetes individuals on metformin drug will be collected by self-assessment and available records. Biochemical Analysis: Quantification of lipids namely total cholesterol (TC), triglycerides (TG), very low density lipoprotein (VLDL), Low density lipoprotein (LDL), high density lipoprotein (HDL), will be performed by using clinical chemistry auto analyzer. (Hitachi 912).
RNA Isolation: The blood samples will be subjected to whole blood RNA isolation using TRIzol RNA isolation method. RNA-Seq: The isolated RNA will be subjected to whole transcriptome sequencing by RNA-seq NGS method. Statistical analysis: All statistical analysis will be performed using available NGS-pipelines and R statistical packages.
P-value <0.05 will be considered significant for all statistical evaluations. 4.
EXPECTED OUTCOME The study of differential gene expression in the transcriptomes of metformin drug responders and non-responders will help to identify pharmacologically important metabolic pathways. The altered metabolic pathways can be further utilized to determine a better course of treatment for type 2 diabetes individuals.