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Environmental and genetic factors cause defects in pancreatic islets driving type 2 diabetes (T2D) together with the progression of multi-tissue insulin resistance. Mass spectrometry proteomics on samples from five key metabolic tissues of a cross-sectional cohort of 43 multi-organ donors provides deep coverage of their proteomes. Enrichment analysis of Gene Ontology terms provides a tissue-specific map of altered biological processes across healthy, prediabetes (PD), and T2D subjects. We find widespread alterations in several relevant biological pathways, including increase in hemostasis in pancreatic islets of PD, increase in the complement cascade in liver and pancreatic islets of PD, and elevation in cholesterol biosynthesis in liver of T2D. Our findings point to inflammatory, immune, and vascular alterations in pancreatic islets in PD that are hypotheses to be tested for potential contributions to hormonal perturbations such as impaired insulin and increased glucagon production. This multi-tissue proteomic map suggests tissue-specific metabolic dysregulations in T2D.

Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between EMC4 and TMEM30A, which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.

Aim: The aim of this study was to explore the benefits of data integration from different platforms for single nucleus transcriptomics profiling to characterize cell populations in human liver. Methods: We generated single-nucleus RNA sequencing data from Chromium 10X Genomics and Drop-seq for a human liver sample. We utilized state of the art bioinformatics tools to undertake a rigorous quality control and to integrate the data into a common space summarizing the gene expression variation from the respective platforms, while accounting for known and unknown confounding factors. Results: Analysis of single nuclei transcriptomes from both 10X and Drop-seq allowed identification of the major liver cell types, while the integrated set obtained enough statistical power to separate a small population of inactive hepatic stellate cells that was not characterized in either of the platforms. Conclusions: Integration of droplet-based single nucleus transcriptomics data enabled identification of a small cluster of inactive hepatic stellate cells that highlights the potential of our approach. We suggest single-nucleus RNA sequencing integrative approaches could be utilized to design larger and cost-effective studies.

Recent studies suggested that dysregulated YY1 plays a pivotal role in many liver diseases. To obtain a detailed view of genes and pathways regulated by YY1 in the liver, we carried out RNA sequencing in HepG2 cells after YY1 knockdown. A rigid set of 2,081 differentially expressed genes was identified by comparing the YY1-knockdown samples (n = 8) with the control samples (n = 14). YY1 knockdown significantly decreased the expression of several key transcription factors and their coactivators in lipid metabolism. This is illustrated by YY1 regulating PPARA expression through binding to its promoter and enhancer regions. Our study further suggest that down-regulation of the key transcription factors together with YY1 knockdown significantly decreased the cooperation between YY1 and these transcription factors at various regulatory regions, which are important in regulating the expression of genes in hepatic lipid metabolism. This was supported by the finding that the expression of SCD and ELOVL6, encoding key enzymes in lipogenesis, were regulated by the cooperation between YY1 and PPARA/RXRA complex over their promoters.

The stearoyl-CoA desaturase 1 (SCD1) gene at 10q24.31 encodes the rate limiting enzyme SCD1 that catalyzes the biosynthesis of monounsaturated fatty acids (MUFAs) from saturated fatty acids (SFAs). Dysregulated SCD1 activity has been observed in many human diseases including non-alcoholic fatty liver disease (NAFLD), obesity, hypertension, hyperlipidemia, metabolic syndrome and several types of cancer. HNF4A is a central regulator of glucose and lipid metabolism and previous studies suggested that it is deeply involved in regulating the SCD1 activity in the liver. However, the underlying mechanisms on whether and how SCD1 is regulated by HNF4A have not been explored in detail. In this study, we found that HNF4A regulates SCD1 expression by directly binding to the key regulatory regions in the SCD1 locus. Knocking down of HNF4A significantly downregulated the expression of SCD1. Variants rs55710213 and rs56334587 in intron 5 of SCD1 directly reside in a canonical HNF4A binding site. The GG haplotype of rs55710213 and rs56334587 is associated with decreased SCD1 activity by disrupting the binding of HNF4A, which further decreased the enhancer activity and SCD1 expression. In conclusion, our study demonstrated that SCD1 is directly regulated by HNF4A, which may be helpful in the understanding of the altered metabolic pathways in many diseases associated with dysregulated SCD1 or HNF4A or both.

Long-chain polyunsaturated fatty acids (LC-PUFAs) influence human health in several areas, including cardiovascular disease, diabetes, fatty liver disease, and cancer. ELOVL2 encodes one of the key enzymes in the in vivo synthesis of LC-PUFAs from their precursors. Variants near ELOVL2 have repeatedly been associated with levels of LC-PUFA-derived metabolites in genome-wide association studies (GWAS), but the mechanisms behind these observations remain poorly defined. In this study, we found that rs953413, located in the first intron of ELOVL2, lies within a functional FOXA and HNF4α cooperative binding site. The G allele of rs953413 increases binding of FOXA1/FOXA2 and HNF4α to an evolutionarily conserved enhancer element, conferring allele-specific upregulation of the rs953413-associated gene ELOVL2. The expression of ELOVL2 was significantly downregulated by both FOXA1 and HNF4α knockdown and CRISPR/Cas9-mediated direct mutation to the enhancer element. Our results suggest that rs953413 regulates LC-PUFAs metabolism by altering ELOVL2 expression through FOXA1/FOXA2 and HNF4α cooperation.

Alteration of various metabolites has been linked to type 2 diabetes (T2D) and insulin resistance. However, identifying significant associations between metabolites and tissue-specific phenotypes requires a multi-omics approach. In a cohort of 42 subjects with different levels of glucose tolerance (normal, prediabetes and T2D) matched for age and body mass index, we calculated associations between parameters of whole-body positron emission tomography (PET)/magnetic resonance imaging (MRI) during hyperinsulinemic euglycemic clamp and non-targeted metabolomics profiling for subcutaneous adipose tissue (SAT) and plasma. Plasma metabolomics profiling revealed that hepatic fat content was positively associated with tyrosine, and negatively associated with lysoPC(P-16:0). Visceral adipose tissue (VAT) and SAT insulin sensitivity (Ki), were positively associated with several lysophospholipids, while the opposite applied to branched-chain amino acids. The adipose tissue metabolomics revealed a positive association between non-esterified fatty acids and, VAT and liver Ki. Bile acids and carnitines in adipose tissue were inversely associated with VAT Ki. Furthermore, we detected several metabolites that were significantly higher in T2D than normal/prediabetes. In this study we present novel associations between several metabolites from SAT and plasma with the fat fraction, volume and insulin sensitivity of various tissues throughout the body, demonstrating the benefit of an integrative multi-omics approach., De 2 första författarna delar förstaförfattarskapet

Visceral adipose tissue (VAT)-fat stored around the internal organs-has been suggested as an independent risk factor for cardiovascular and metabolic disease1-3, as well as all-cause, cardiovascular-specific and cancer-specific mortality4,5. Yet, the contribution of genetics to VAT, as well as its disease-related effects, are largely unexplored due to the requirement for advanced imaging technologies to accurately measure VAT. Here, we develop sex-stratified, nonlinear prediction models (coefficient of determination = 0.76; typical 95% confidence interval (CI) = 0.74-0.78) for VAT mass using the UK Biobank cohort. We performed a genome-wide association study for predicted VAT mass and identified 102 novel visceral adiposity loci. Predicted VAT mass was associated with increased risk of hypertension, heart attack/angina, type 2 diabetes and hyperlipidemia, and Mendelian randomization analysis showed visceral fat to be a causal risk factor for all four diseases. In particular, a large difference in causal effect between the sexes was found for type 2 diabetes, with an odds ratio of 7.34 (95% CI = 4.48-12.0) in females and an odds ratio of 2.50 (95% CI = 1.98-3.14) in males. Our findings bolster the role of visceral adiposity as a potentially independent risk factor, in particular for type 2 diabetes in Caucasian females. Independent validation in other cohorts is necessary to determine whether the findings can translate to other ethnicities, or outside the UK.

Type 2 diabetes (T2D) mellitus is a complex metabolic disease commonly caused by insulin resistance in several tissues. We performed a matched two-dimensional metabolic screening in tissue samples from 43 multi-organ donors. The intra-individual analysis was assessed across five key metabolic tissues (serum, visceral adipose tissue, liver, pancreatic islets and skeletal muscle), and the inter-individual across three different groups reflecting T2D progression. We identified 92 metabolites differing significantly between non-diabetes and T2D subjects. In diabetes cases, carnitines were significantly higher in liver, while lysophosphatidylcholines were significantly lower in muscle and serum. We tracked the primary tissue of origin for multiple metabolites whose alterations were reflected in serum. An investigation of three major stages spanning from controls, to pre-diabetes and to overt T2D indicated that a subset of lysophosphatidylcholines was significantly lower in the muscle of pre-diabetes subjects. Moreover, glycodeoxycholic acid was significantly higher in liver of pre-diabetes subjects while additional increase in T2D was insignificant. We confirmed many previously reported findings and substantially expanded on them with altered markers for early and overt T2D. Overall, the analysis of this unique dataset can increase the understanding of the metabolic interplay between organs in the development of T2D.

The FADS1 and FADS2 genes in the FADS cluster encode the rate-limiting enzymes in the synthesis of long-chain polyunsaturated fatty acids (LC-PUFAs). Genetic variation in this region has been associated with a large number of diseases and traits many of them correlated to differences in metabolism of PUFAs. However, the causative variants leading to these associations have not been identified. Here we find that the multiallelic rs174557 located in an AluYe5 element in intron 1 of FADS1 is functional and lies within a PATZ1 binding site. The derived allele of rs174557, which is the common variant in most populations, diminishes binding of PATZ1, a transcription factor conferring allele-specific downregulation of FADS1 The PATZ1 binding site overlaps with a SP1 site. The competitive binding between the suppressive PATZ1 and the activating complex of SP1 and SREBP1c determines the enhancer activity of this region, which regulates expression of FADS1.

Somatic mutations drive cancer and there are established ways to study those in coding sequences. It has been shown that some regulatory mutations are over-represented in cancer. We develop a new strategy to find putative regulatory mutations based on experimentally established motifs for transcription factors (TFs). In total, we find 1,552 candidate regulatory mutations predicted to significantly reduce binding affinity of many TFs in hepatocellular carcinoma and affecting binding of CTCF also in esophagus, gastric, and pancreatic cancers. Near mutated motifs, there is a significant enrichment of (1) genes mutated in cancer, (2) tumor-suppressor genes, (3) genes in KEGG cancer pathways, and (4) sets of genes previously associated to cancer. Experimental and functional validations support the findings. The strategy can be applied to identify regulatory mutations in any cell type with established TF motifs and will aid identifications of genes contributing to cancer.

Genome-wide association studies (GWAS) have identified a large number of disease-associated SNPs, but in few cases the functional variant and the gene it controls have been identified. To systematically identify candidate regulatory variants, we sequenced ENCODE cell lines and used public ChIP-seq data to look for transcription factors binding preferentially to one allele. We found 9962 candidate regulatory SNPs, of which 16 % were rare and showed evidence of larger functional effect than common ones. Functionally rare variants may explain divergent GWAS results between populations and are candidates for a partial explanation of the missing heritability. The majority of allele-specific variants (96 %) were specific to a cell type. Furthermore, by examining GWAS loci we found >400 allele-specific candidate SNPs, 141 of which were highly relevant in our cell types. Functionally validated SNPs support identification of an SNP in SYNGR1 which may expose to the risk of rheumatoid arthritis and primary biliary cirrhosis, as well as an SNP in the last intron of COG6 exposing to the risk of psoriasis. We propose that by repeating the ChIP-seq experiments of 20 selected transcription factors in three to ten people, the most common polymorphisms can be interrogated for allele-specific binding. Our strategy may help to remove the current bottleneck in functional annotation of the genome.

Genome-wide association studies (GWAS) point to regions with associated genetic variants but rarely to a specific gene and therefore detailed knowledge regarding the genes contributing to complex traits and diseases remains elusive. The functional role of GWAS-SNPs is also affected by linkage disequilibrium with many variants on the same haplotype and sometimes in the same regulatory element almost equally likely to mediate the effect. Using ChIP-seq data on many transcription factors, we pinpointed genetic variants in HepG2 and HeLa-S3 cell lines which show a genome-wide significant difference in binding between alleles. We identified a collection of 3713 candidate functional regulatory variants many of which are likely drivers of GWAS signals or genetic difference in expression. A recent study investigated many variants before finding the functional ones at the GALNT2 locus, which we found in our genome-wide screen in HepG2. This illustrates the efficiency of our approach.

Background: Plasma levels of high-density lipoprotein cholesterol (HDL-C) have been associated to cardiovascular disease. The high heritability of HDL-C plasma levels has been an incentive for several genome wide association studies (GWASs) which identified, among others, variants in the first intron of the GALNT2 gene strongly associated to HDL-C levels. However, the lead GWAS SNP associated to HDL-C levels in this genomic region, rs4846914, is located outside of transcription factor (TF) binding sites defined by chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq) experiments in the ENCODE project and is therefore unlikely to be functional. In this study we apply a bioinformatics approach which rely on the premise that ChIP-seq reads can identify allele specific binding of a TF at cell specific regulatory elements harboring allele specific SNPs (AS-SNPs). EMSA and luciferase assays were used to validate the allele specific binding and to test the enhancer activity of the regulatory element harboring the AS-SNP rs4846913 as well as the neighboring rs2144300 which are in high LD with rs4846914. Findings: Using luciferase assays we found that rs4846913 and the neighboring rs2144300 displayed allele specific enhancer activity. We propose that an inhibitor binds preferentially to the rs4846913-C allele with an inhibitory boost from the synergistic binding of other TFs at the neighboring SNP rs2144300. These events influence the transcription level of GALNT2. Conclusions: The results suggest that rs4846913 and rs2144300 drive the association to HDL-C plasma levels through an inhibitory regulation of GALNT2 rather than the reported lead GWAS SNP rs4846914.

Aim: Warfarin dose requirement is associated with VKORC1 rs9923231, and we studied whether it is a functional variant. Materials & methods: We selected variants in linkage disequilibrium with rs9923231 that bind transcription factors in an allele-specific way. Representative haplotypes were cloned or constructed, nuclear protein binding and transcriptional activity were evaluated. Results: rs56314408C>T and rs2032915C>T were detected in a liver enhancer in linkage disequilibrium with rs9923231. The rs56314408-rs2032915 C-C haplotype preferentially bound nuclear proteins and had higher transcriptional activity than T-T and the African-specific T-C. A motif for TFAP2A/C was disrupted by rs56314408T. No difference in transcriptional activity was detected for rs9923231G>A. Conclusion: Our results supported an activating role for rs56314408C, while rs9923231G>A had no evidence of being functional.

Background: Alteration of various metabolites has been linked to type 2 diabetes (T2D) and insulin resistance. However, identifying significant associations between metabolites and tissue-specific alterations is challenging and requires a multi-omics approach. In this study, we aimed at discovering associations of metabolites from subcutaneous adipose tissue (SAT) and plasma with the volume, the fat fraction (FF) and the insulin sensitivity (Ki) of specific tissues using [18F]FDG PET/MRI. Materials and Methods: In a cohort of 42 subjects with different levels of glucose tolerance (normal, prediabetes and T2D) matched for age and body-mass-index (BMI) we calculated associations between parameters of whole-body FDG PET/MRI during clamp and non-targeted metabolomics profiling for SAT and blood plasma. We also used a rule-based classifier to identify a large collection of prevalent patterns of co-dependent metabolites that characterize non-diabetes (ND) and T2D. Results: The plasma metabolomics profiling revealed that hepatic fat content was positively associated with tyrosine, and negatively associated with lysoPC(P-16:0). Ki in visceral adipose tissue (VAT) and SAT, was positively associated with several species of lysophospholipids while the opposite applied to branched-chain amino acids (BCAA) and their intermediates. The adipose tissue metabolomics revealed a positive association between non-esterified fatty acids and, VAT and liver Ki. On the contrary, bile acids and carnitines in adipose tissue were inversely associated with VAT Ki. Finally, we presented a transparent machine-learning model that predicted ND or T2D in “unseen” data with an accuracy of 78%. Conclusions: Novel associations of several metabolites from SAT and plasma with the FF, volume and insulin senstivity of various tissues throughout the body were discovered using PET/MRI and a new integrative multi-omics approach. A promising computational model that predicted ND and T2D with high certainty, su

The liver is the largest solid organ and a primary metabolic hub. In recent years, intact cell nuclei were used to perform single-nuclei RNA-seq (snRNA-seq) for tissues difficult to dissociate and for flash-frozen archived tissue samples to discover unknown and rare cell sub-populations. In this study, we performed snRNA-seq of a liver sample to identify sub-populations of cells based on nuclear transcriptomics. In 4,282 single nuclei we detected on average 1,377 active genes and we identified seven major cell types. We integrated data from 94,286 distal interactions (p<0.05) for 7,682 promoters from a targeted chromosome conformation capture technique (HiCap) and mass spectrometry (MS) proteomics for the same liver sample. We observed a reasonable correlation between proteomics and in silico bulk snRNA-seq (r=0.47) using tissue-independent gene-specific protein abundancy estimation factors. We specifically looked at genes of medical importance. The DPYD gene is involved in the pharmacogenetics of fluoropyrimidines toxicity and some of its variants are analyzed for clinical purposes. We identified a new putative polymorphic regulatory element, which may contribute to variation in toxicity. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and we investigated all known risk genes. We found a complex regulatory network for the SLC2A2 gene with 16 candidate enhancers. Three of them harbor somatic motif breaking and other mutations in HCC in the Pan Cancer Analysis of Whole Genomes dataset and are candidates to contribute to malignancy. Our results highlight the potential of a multi-omics approach in the study of human diseases.