Novel susceptibility loci identified in a genome-wide association study of type 2 diabetes complications in population of Latvia

Funding Information: The study was supported by European Regional Development Fund (ERDF) On Implementation of Activity 1.1.1.2 “Post-doctoral Research Aid” of the Specific Aid Objective 1.1.1 “To increase the research and innovative capacity of scientific institutions of Latvia and the ability to attract external financing, investing in human resources and infrastructure” of the Operational Programme “Growth and Employment”. Project No 1.1.1.2/VIAA/2/18/287 “Identification of clinical subgroups of Type 2 diabetes mellitus and application of pharmacogenetics in the development of personalized antidiabetic therapy”. The funding body was not involved in the design of the study, collection, analysis or interpretation of data, or writing the manuscript. Publisher Copyright: © 2021, The Author(s).Background: Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally. Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications. Methods: We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications. Results: The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02–3.64) and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87–3.34), rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71–3.01), rs6032 (F5, OR = 2.12; 95% CI = 1.63–2.77), rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69–3.01) and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66–2.87) and ophthalmic complications. By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy. Conclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions. Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations.publishersversionPeer reviewe

Significantly altered peripheral blood cell DNA methylation profile as a result of immediate effect of metformin use in healthy individuals

Funding Information: The work was supported by the European Regional Development Fund under the project “Investigation of interplay between multiple determinants influencing response to metformin: search for reliable predictors for efficacy of type 2 diabetes therapy” (Project Nr.: 1.1.1.1/16/A/091). Publisher Copyright: © 2018 The Author(s).Background: Metformin is a widely prescribed antihyperglycemic agent that has been also associated with multiple therapeutic effects in various diseases, including several types of malignancies. There is growing evidence regarding the contribution of the epigenetic mechanisms in reaching metformin's therapeutic goals; however, the effect of metformin on human cells in vivo is not comprehensively studied. The aim of our study was to examine metformin-induced alterations of DNA methylation profiles in white blood cells of healthy volunteers, employing a longitudinal study design. Results: Twelve healthy metformin-naïve individuals where enrolled in the study. Genome-wide DNA methylation pattern was estimated at baseline, 10 h and 7 days after the start of metformin administration. The whole-genome DNA methylation analysis in total revealed 125 differentially methylated CpGs, of which 11 CpGs and their associated genes with the most consistent changes in the DNA methylation profile were selected: POFUT2, CAMKK1, EML3, KIAA1614, UPF1, MUC4, LOC727982, SIX3, ADAM8, SNORD12B, VPS8, and several differentially methylated regions as novel potential epigenetic targets of metformin. The main functions of the majority of top-ranked differentially methylated loci and their representative cell signaling pathways were linked to the well-known metformin therapy targets: regulatory processes of energy homeostasis, inflammatory responses, tumorigenesis, and neurodegenerative diseases. Conclusions: Here we demonstrate for the first time the immediate effect of short-term metformin administration at therapeutic doses on epigenetic regulation in human white blood cells. These findings suggest the DNA methylation process as one of the mechanisms involved in the action of metformin, thereby revealing novel targets and directions of the molecular mechanisms underlying the various beneficial effects of metformin. Trial registration: EU Clinical Trials Register, 2016-001092-74. Registered 23 March 2017, https://www.clinicaltrialsregister.eu/ctr-search/trial/2016-001092-74/LV.Peer reviewe

Metformin targets intestinal immune system signaling pathways in a high-fat diet-induced mouse model of obesity and insulin resistance

IntroductionResearch findings of the past decade have highlighted the gut as the main site of action of the oral antihyperglycemic agent metformin despite its pharmacological role in the liver. Extensive evidence supports metformin’s modulatory effect on the composition and function of gut microbiota, nevertheless, the underlying mechanisms of the host responses remain elusive. Our study aimed to evaluate metformin-induced alterations in the intestinal transcriptome profiles at different metabolic states. MethodsThe high-fat diet-induced mouse model of obesity and insulin resistance of both sexes was developed in a randomized block experiment and bulk RNA-Seq of the ileum tissue was the method of choice for comparative transcriptional profiling after metformin intervention for ten weeks. ResultsWe found a prominent transcriptional effect of the diet itself with comparatively fewer genes responding to metformin intervention. The overrepresentation of immune-related genes was observed, including pronounced metformin-induced upregulation of immunoglobulin heavy-chain variable region coding Ighv1-7 gene in both high-fat diet and control diet-fed animals. Moreover, we provide evidence of the downregulation NF-kappa B signaling pathway in the small intestine of both obese and insulin-resistant animals as well as control animals after metformin treatment. Finally, our data pinpoint the gut microbiota as a crucial component in the metformin-mediated downregulation of NF-kappa B signaling evidenced by a positive correlation between the Rel and Rela gene expression levels and abundances of Parabacteroides distasonis, Bacteroides spp., and Lactobacillus spp. in the gut microbiota of the same animals. DiscussionOur study supports the immunomodulatory effect of metformin in the ileum of obese and insulin-resistant C57BL/6N mice contributed by intestinal immunoglobulin responses, with a prominent emphasis on the downregulation of NF-kappa B signaling pathway, associated with alterations in the composition of the gut microbiome

Metformin strongly affects transcriptome of peripheral blood cells in healthy individuals

Funding Information: The study was supported by the European Regional Development Fund under the project ?Investigation of interplay between multiple determinants influencing response to metformin: search for reliable predictors for efficacy of type 2 diabetes therapy? (Project No.: 1.1.1.1/16/A/091, https://ec.europa.eu/regional_policy/en/funding/ erdf/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors would like to thank all the volunteers for their participation and acknowledge the Genome Database of the Latvian Population for providing biological material and data. Publisher Copyright: © 2019 Ustinova et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Metformin is a commonly used antihyperglycaemic agent for the treatment of type 2 diabetes mellitus. Nevertheless, the exact mechanisms of action, underlying the various therapeutic effects of metformin, remain elusive. The goal of this study was to evaluate the alterations in longitudinal whole-blood transcriptome profiles of healthy individuals after a one-week metformin intervention in order to identify the novel molecular targets and further prompt the discovery of predictive biomarkers of metformin response. Next generation sequencing-based transcriptome analysis revealed metformin-induced differential expression of genes involved in intestinal immune network for IgA production and cytokine-cytokine receptor interaction pathways. Significantly elevated faecal sIgA levels during administration of metformin, and its correlation with the expression of genes associated with immune response (CXCR4, HLA-DQA1, MAP3K14, TNFRSF21, CCL4, ACVR1B, PF4, EPOR, CXCL8) supports a novel hypothesis of strong association between metformin and intestinal immune system, and for the first time provide evidence for altered RNA expression as a contributing mechanism of metformin’s action. In addition to universal effects, 4 clusters of functionally related genes with a subject-specific differential expression were distinguished, including genes relevant to insulin production (HNF1B, HNF1A, HNF4A, GCK, INS, NEUROD1, PAX4, PDX1, ABCC8, KCNJ11) and cholesterol homeostasis (APOB, LDLR, PCSK9). This inter-individual variation of the metformin effect on the transcriptional regulation goes in line with well-known variability of the therapeutic response to the drug.publishersversionPeer reviewe

Contemporary Challenges and Solutions

CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.publishersversionpublishe

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies

Effects of antidiabetic medicament metformin on human gut microbiome

Metformīns ir plaši lietots medikaments 2. tipa cukura diabēta ārstēšanā. Pastāv hipotēze, ka metformīna ietekme uz organismu daļēji varētu tikt skaidrota ar tā mijiedarbību ar zarnu mikrobiomu. Bakalaura darba mērķis bija noskaidrot, kā metformīns ietekmē zarnu mikrobioma kompozīciju. Darba ietvaros tika izdalīta mikrobiālā DNS no astoņu cilvēku fēču paraugiem, kas bija ievākti dažādos laika posmos no metformīna lietošanas uzsākšanas, un, izmantojot lielapjoma paralēlo sekvenēšanu, veikta 16S rRNS V3 reģiona analīze, lai salīdzinātu mikrobioma kompozīciju. Pēc nedēļu ilgas metformīna lietošanas tika novērotas būtiskas izmaiņas vairāku taksonomisko vienību īpatsvarā un mikrobioma iekšējās daudzveidības samazināšanās. Darbs veikts no 10.2014. līdz 05.2015. Latvijas Biomedicīnas pētījumu un studiju centrā.Metformin is widely used for treatment of type 2 diabetes. There is a hypothesis that the effects of metformin are partially explained by its interaction with the gut microbiome. The aim of this work was to investigate the effects of metformin on composition of gut microbiome. In the course of this study the microbial DNA was extracted from stool samples gained from eight individuals in three time points of metformin therapy. After the extraction the analysis of the 16S rRNA V3 region employing massive parallel sequencing was carried out to compare the composition of the microbiome. As the result significant changes in abundances of various taxonomic units and reduced species richness was found after a weeklong metformin therapy. The work was carried out in the Latvian Biomedical Research and Study Centre during the period from 10.2014 to 05.2015

Changes in the human gut microbiome composition and DNA methylation profiles that are associated with the administration of metformin

Metformīns ir pirmās izvēles medikaments 2. tipa cukura diabēta ārstēšanā. Tam raksturīga variabilitāte terapijas efektivitātē un biežas blaknes. Medikamenta iedarbību var ietekmēt tādi faktori kā mijiedarbība ar zarnu mikrobiomu un gēnu ekspresijas regulācija DNS metilēšanas līmenī. Maģistra darba mērķis bija noskaidrot, kādas izmaiņas metformīns ierosina cilvēka zarnu mikrobioma kompozīcijā un leikocītu metilēšanas profilā. Darbā tika veikta 16S rRNS amplikonu analīze 18 metformīna lietotāju fēču paraugos un DNS metilēšanas novērtēšana 12 indivīdiem. Metformīna ierosinātas izmaiņas gan zarnu mikrobiomā, gan metilēšanas profilā tika novērotas jau pirmo 24 stundu laikā, daļēji skaidrojot iespējamos blakņu cēloņus un pozitīvo efektu pamatā esošos mehānismus. Darbs izstrādāts 06.2015. – 05.2017. Latvijas Biomedicīnas pētījumu un studiju centrā.Metformin is a first line agent for treatment of type 2 diabetes. It has high efficacy variability and side effects are common. The effect of medicament can be influenced by such factors as interaction with gut microbiome and regulation of gene expression via DNA methylation. The aim of this work was to investigate the impact of metformin on composition of gut microbiome and methylation profile of leucocytes. Fecal samples from 18 participants were used in 16S rRNA analysis and DNA methylation was evaluated in 12 individuals. Metformin induced changes in gut microbiome and methylation profiles were observed within the first 24 hours, thus partly explaining the possible causes for side effects and mechanisms of positive effects. The work was carried out in the Latvian Biomedical Research and Study Centre during the period 06.2015 – 05.2017

METFORMIN EFFECTS ON GUT MICROBIOME AND EPIGENETICS IN TYPE 2 DIABETES PATIENTS AND HEALTHY INDIVIDUALS

Elektroniskā versija nesatur pielikumusMetformīna farmakodinamika ir plaši pētīta, tomēr detaļas joprojām ir neskaidras. Šī pētījuma mērķis bija identificēt taksonomiskos un funkcionālos zarnu mikrobioma biomarķierus, kā arī saimniekorganisma epiģenētiskās iezīmes metformīna farmakodinamikai, terapijas efektivitātei un tolerancei. Izmantojot zarnu mikrobioma lielapjoma paralēlo sekvenēšanu, mēs novērojām metformīna terapijas izraisītu būtisku un tūlītēju iekšējās daudzveidības samazināšanos, kā arī taksonomiskā profila izmaiņas jaundiagnosticētiem T2D pacientiem un veseliem indivīdiem. Mēs prezentējām pirmsterapijas mikrobioma taksonomisko sastāvu kā rīku terapijas efektivitātes un tolerances prognozēšanai. Dati par globālajām DNS metilēšanas izmaiņām metformīna lietošanas laikā parādīja pirmo un pašlaik vienīgo pētījumu, kurā analizēta longitudināla ietekme veselos indivīdos.The pharmacodynamic effects of the metformin have been widely studied, yet details remain obscure. The aim of this study was to identify taxonomic and functional gut microbiome biomarkers as well as epigenetic signatures of the host for metformin pharmacodynamics, therapy efficacy and tolerance. Using massive parallel sequencing of gut microbiome, we observed significant and immediate reduction of inner diversity and changes in the taxonomic profile caused by metformin in newly diagnosed T2D patients and healthy individuals. We also presented the baseline sample composition as a prediction tool for therapy efficacy and tolerance. Study on global DNA methylation changes during metformin use presented the first and currently the only study evaluating longitudinal effects in peripheral blood cells of healthy individuals

Association of metformin administration with gut microbiome dysbiosis in healthy volunteers.

BACKGROUND:Metformin is a widely used first-line drug for treatment of type 2 diabetes. Despite its advantages, metformin has variable therapeutic effects, contraindications, and side effects. Here, for the very first time, we investigate the short-term effect of metformin on the composition of healthy human gut microbiota. METHODS:We used an exploratory longitudinal study design in which the first sample from an individual was the control for further samples. Eighteen healthy individuals were treated with metformin (2 × 850 mg) for 7 days. Stool samples were collected at three time points: prior to administration, 24 hours and 7 days after metformin administration. Taxonomic composition of the gut microbiome was analyzed by massive parallel sequencing of 16S rRNA gene (V3 region). RESULTS:There was a significant reduction of inner diversity of gut microbiota observed already 24 hours after metformin administration. We observed an association between the severity of gastrointestinal side effects and the increase in relative abundance of common gut opportunistic pathogen Escherichia-Shigella spp. One week long treatment with metformin was associated with a significant decrease in the families Peptostreptococcaceae and Clostridiaceae_1 and four genera within these families. CONCLUSIONS:Our results are in line with previous findings on the capability of metformin to influence gut microbiota. However, for the first time we provide evidence that metformin has an immediate effect on the gut microbiome in humans. It is likely that this effect results from the increase in abundance of opportunistic pathogens and further triggers the occurrence of side effects associated with the observed dysbiosis. An additional randomized controlled trial would be required in order to reach definitive conclusions, as this is an exploratory study without a placebo control arm. Our findings may be further used to create approaches that improve the tolerability of metformin