Ana Viñuela’s research while affiliated with University of Dundee and other places

Background Abdominal aortic aneurysms (AAAs) are focal dilatations of the abdominal aorta that expand progressively, increasing their risk of rupture. Rupture of an AAA is associated with high mortality rates, but the mechanisms underlying the initiation, expansion, and rupture of AAAs are not yet fully understood. We aimed to characterize the pathophysiology of AAAs and identify new genes associated with AAA initiation and progression. Methods and Results This study used RNA sequencing data on 140 samples, becoming the largest RNA sequencing data set for differential expression studies of AAAs. We performed differential expression analyses and analyses of differential splicing between dilated and nondilated aortic tissue samples, and between AAAs of different diameters. We identified 3002 differentially expressed genes between AAAs and controls that were independent of ischemic time, 1425 of which were new. Additionally, 8 genes ( EXTL3 , ZFR , DUSP8 , DISP1 , USP33 , VPS37C , ZNF784 , RFX1 ) were differentially expressed between AAAs of varying diameters and between AAAs and control samples. Finally, 7 genes ( SPP1 , FHL1 , GNAS , MORF4L2 , HMGN1 , ARL1 , RNASE4 ) with differential splicing patterns were also differentially expressed genes between AAAs and controls, suggesting that splicing differences in these genes may contribute to the observed expression changes and disease development. Conclusions This study identifies new genes and splicing patterns associated with AAAs and validates previous relevant pathways on AAAs. These findings contribute to the understanding of the complex mechanisms underlying AAAs and may provide potential targets to limit AAA progression and mortality risk.

Identification of genes and proteins mediating the activity of GWAS variants requires molecular data from disease relevant tissues, but these may be difficult to collect. Using multiple gene expression reference datasets and GWAS summary statistics for T2D we identified 1,818 unique genes associated with T2D. Comparing the performance of different reference datasets, we found that sample size, and not the relevance of the tissue to the disease, was the critical factor in identifying relevant genes. Genes implicated using a well powered expression dataset were also more likely to have multiple lines of genetic evidence. A targeted proteomics reference dataset from plasma samples showed similar power to identify T2D related proteins as gene expression with the same sample size. Accounting for BMI reduces power across all tissues and phenotypes by ~30%, suggesting that many GWAS links to T2D are mediated by BMI, potentially implicating insulin resistance related effects. Finally, using data from smaller GWAS studies with precisely defined T2D subtypes uncovers genes directly relevant to that subtype, such as LST1, an immune response gene for Severe Autoimmune Diabetes and TRMT2A, involved in beta-cell apoptosis, for Severe Insulin Deficient Diabetes. Our work demonstrates the benefits of well powered reference datasets in accessible tissues and well-defined disease subtypes when studying complex diseases involving multiple tissues.

Aims/hypothesis Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the metabolomics to find their association with the glycaemic spectrum and find a causal relationship between metabolites and type 2 diabetes. Methods As part of the Innovative Medicines Initiative - Diabetes Research on Patient Stratification (IMI-DIRECT) consortium, 3000 plasma samples were measured with the Biocrates AbsoluteIDQ p150 Kit and Metabolon analytics. A total of 911 metabolites (132 targeted metabolomics, 779 untargeted metabolomics) passed the quality control. Multivariable linear and logistic regression analysis estimates were calculated from the concentration/peak areas of each metabolite as an explanatory variable and the glycaemic status as a dependent variable. This analysis was adjusted for age, sex, BMI, study centre in the basic model, and additionally for alcohol, smoking, BP, fasting HDL-cholesterol and fasting triacylglycerol in the full model. Statistical significance was Bonferroni corrected throughout. Beyond associations, we investigated the mediation effect and causal effects for which causal mediation test and two-sample Mendelian randomisation (2SMR) methods were used, respectively. Results In the targeted metabolomics, we observed four (15), 34 (99) and 50 (108) metabolites (number of metabolites observed in untargeted metabolomics appear in parentheses) that were significantly different when comparing normal glucose regulation vs impaired glucose regulation/prediabetes, normal glucose regulation vs type 2 diabetes, and impaired glucose regulation vs type 2 diabetes, respectively. Significant metabolites were mainly branched-chain amino acids (BCAAs), with some derivatised BCAAs, lipids, xenobiotics and a few unknowns. Metabolites such as lysophosphatidylcholine a C17:0, sum of hexoses, amino acids from BCAA metabolism (including leucine, isoleucine, valine, N-lactoylvaline, N-lactoylleucine and formiminoglutamate) and lactate, as well as an unknown metabolite (X-24295), were associated with HbA1c progression rate and were significant mediators of type 2 diabetes from baseline to 18 and 48 months of follow-up. 2SMR was used to estimate the causal effect of an exposure on an outcome using summary statistics from UK Biobank genome-wide association studies. We found that type 2 diabetes had a causal effect on the levels of three metabolites (hexose, glutamate and caproate [fatty acid (FA) 6:0]), whereas lipids such as specific phosphatidylcholines (PCs) (namely PC aa C36:2, PC aa C36:5, PC ae C36:3 and PC ae C34:3) as well as the two n-3 fatty acids stearidonate (18:4n3) and docosapentaenoate (22:5n3) potentially had a causal role in the development of type 2 diabetes. Conclusions/interpretation Our findings identify known BCAAs and lipids, along with novel N-lactoyl-amino acid metabolites, significantly associated with prediabetes and diabetes, that mediate the effect of diabetes from baseline to follow-up (18 and 48 months). Causal inference using genetic variants shows the role of lipid metabolism and n-3 fatty acids as being causal for metabolite-to-type 2 diabetes whereas the sum of hexoses is causal for type 2 diabetes-to-metabolite. Identified metabolite markers are useful for stratifying individuals based on their risk progression and should enable targeted interventions. Graphical Abstract

The production of multiple transcripts per gene is a process regulated by inherited genetic variants and epitranscriptomic modifications, and plays a prominent role in modulating complex traits and diseases. To simultaneously characterize the effect of genetic variants on transcript abundance and N6-methyladenosine (m6A) modifications, we produced long-read native poly(A) RNA-seq data for 60 genetically different lymphoblastoid cell lines (LCLs) from the 1000 Genomes/Geuvadis project. We identified a high diversity of both annotated (31%) and unannotated (61%) transcripts, with only a small proportion expressed across individuals (35% and 7%, respectively). In a genome-wide genetic analysis on transcripts, we identified 105 trQTLs, of which 76 were not detected as eQTLs using a larger published short-read RNAseq dataset (317 samples). A population wide characterization of m6A methylation DRACH motifs identified an average of 40.1 m6A modifications on 6,222 genes. Genetic association analysis of highly variable modifications from 1,155 genes identified m6A modification quantitative trait loci (m6A-QTLs) for 16 transcripts. Colocalization analysis of trQTL and m6A-QTLs, identified 33 candidate transcripts mediating GWAS traits, with 46.4% of the colocalized trQTLs implicating novel risk transcripts. Overall, the simultaneous characterization of transcripts and post-transcriptional modifications identified genetic effects on transcription often missed when using other sequencing technologies.

Context The role of glucagon-like peptide-1(GLP-1) in Type 2 diabetes (T2D) and obesity is not fully understood. Objective We investigate the association of cardiometabolic, diet and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D. Method We analysed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1(n=2127) individuals at risk of diabetes; cohort 2 (n=789) individuals with new-onset of T2D. Results Our multiple regression analysis reveals that fasting total GLP-1 is associated with an insulin resistant phenotype and observe a strong independent relationship with male sex, increased adiposity and liver fat particularly in the prediabetes population. In contrast, we showed that incremental GLP-1 decreases with worsening glycaemia, higher adiposity, liver fat, male sex and reduced insulin sensitivity in the prediabetes cohort. Higher fasting total GLP-1 was associated with a low intake of wholegrain, fruit and vegetables inpeople with prediabetes, and with a high intake of red meat and alcohol in people with diabetes. Conclusion These studies provide novel insights into the association between fasting and incremental GLP-1, metabolic traits of diabetes and obesity, and dietary intake and raise intriguing questions regarding the relevance of fasting GLP-1 in the pathophysiology T2D.

Background: Abdominal aortic aneurysms (AAA) are focal dilatations of the abdominal aorta. They are normally asymptomatic and progressively expand, increasing their risk of rupture. Rupture of an AAA is associated with high mortality rates, but the mechanisms underlying the initiation, expansion and rupture of AAA are not yet fully understood. This study aims to characterize and identify new genes associated with the pathophysiology of AAA through differential expression analyses between dilated and non-dilated aortic tissue samples, and between AAA of different diameters. Our study used RNA-seq data on 140 samples, becoming the largest RNA-seq dataset for differential expression studies of AAA. Results: We identified 7,454 differentially expressed genes (DEGs) between AAA and controls, 2,851 of which were new compared to previous microarray studies. Notably, a novel cluster on adenosine triphosphate synthesis regulation emerged as strongly associated with AAA. Additionally, exploring AAA of different diameters identified eight genes (EXTL3, ZFR, DUSP8, DISP1, USP33, VPS37C, ZNF784, RFX1) that overlapped with the DEGs between AAA and controls, implying roles in both disease onset and progression. Seven genes (SPP1, FHL1, GNAS, MORF4L2, HMGN1, ARL1, RNASE4) with differential splicing patterns were also DEGs between AAA and controls, suggesting that splicing differences contribute to the observed expression changes and the disease development. Conclusions: This study identified new genes and pathways associated with AAA onset and progression and validated previous relevant roles of inflammation and intracellular calcium regulation. These findings provide insights into the complex mechanisms underlying AAA and indicate potential targets to limit AAA progression and mortality risk.

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

Background Human plasma contains a wide variety of circulating proteins. These proteins can be important clinical biomarkers in disease and also possible drug targets. Large scale genomics studies of circulating proteins can identify genetic variants that lead to relative protein abundance. Methods We conducted a meta-analysis on genome-wide association studies of autosomal chromosomes in 22,997 individuals of primarily European ancestry across 12 cohorts to identify protein quantitative trait loci (pQTL) for 92 cardiometabolic associated plasma proteins. Results We identified 503 (337 cis and 166 trans) conditionally independent pQTLs, including several novel variants not reported in the literature. We conducted a sex-stratified analysis and found that 118 (23.5%) of pQTLs demonstrated heterogeneity between sexes. The direction of effect was preserved but there were differences in effect size and significance. Additionally, we annotate trans-pQTLs with nearest genes and report plausible biological relationships. Using Mendelian randomization, we identified causal associations for 18 proteins across 19 phenotypes, of which 10 have additional genetic colocalization evidence. We highlight proteins associated with a constellation of cardiometabolic traits including angiopoietin-related protein 7 (ANGPTL7) and Semaphorin 3F (SEMA3F). Conclusion Through large-scale analysis of protein quantitative trait loci, we provide a comprehensive overview of common variants associated with plasma proteins. We highlight possible biological relationships which may serve as a basis for further investigation into possible causal roles in cardiometabolic diseases.

Objective Certain ethnicities such as South Asians and East Asians have higher rates of type 2 diabetes mellitus, in part, driven by insulin deficiency. Insulin deficiency can be due to beta-cell insufficiency, low beta-cell mass, or early cell death. Transcription factor XBP1 maintains beta-cell function and prevents early cell death by mitigating cellular endoplasmic reticulum stress. We examine the role of XBP1 expression in maintaining glucose homeostasis, glycaemic control, and response to diabetes therapeutics. Research Design and Methods Colocalisation analyses were used to determine if expression of XBP1 in pancreatic islets and type 2 diabetes shared common causal genetic variants. We identify a lead eQTL variant associated exclusively with XBP1 expression and examine its association HOMA-B and stimulated glucose in cohorts of newly diagnosed Asian Indians from Dr. Mohans Diabetes Specialities Centre, India (DMDSC) and the Telemedicine Project for Screening diabetes and complications in rural Tamil Nadu (TREND). We then examine longer term glycaemic control using HbA1c in Asian Indian cohorts, the Tayside Diabetes Study (TDS) of white European ancestry in Scoltand, and the Genes & Health (G&H) study of British South Asian Bangladeshi and Pakistani ancestry. Finally, we assess the effect of eQTL variant on drugs designed to improve insulin secretion (sulphonylureas and GLP1-RA). Results Variants affecting XBP1 expression in the pancreatic islets colocalised with variants associated with T2DM risk in East Asians but not in white Europeans. Lower expression of XBP1 was associated with higher risk of T2DM. rs7287124 was the lead eQTL variant and had a higher risk allele frequency in East (65%) and South Asians (50%) compared to white Europeans (25%). In 470 South Asian Indians, the variant was associated with lower beta-cell function and higher stimulated glucose (Beta log HOMAB =-0.14, P=5x10-3). Trans-ancestry meta-analysed effect of the variant in 179,668 individuals was 4.32 mmol/mol (95%CI:2.60,6.04, P=8x10-7) per allele. In 477 individuals with young onset diabetes with non-obese BMI, the per allele effect was 6.41 mmol/mol (95%CI:3.04, 9.79, P =2x10-4). Variant carriers showed impaired response to sulphonylureas. Conclusion XBP1 expression is a novel target for T2DM with particular value for individuals of under-researched ancestries who have greater risk of young, non-obese onset diabetes. The effect of XBP1 eQTL variant was found to be comparable with or greater that the effect of novel glucose-lowering therapies.