A comparative urinary proteomic and metabolomic analysis between renal aa amyloidosis and membranous nephropathy with clinicopathologic correlations

Urinary omics has become a powerful tool for elucidating pathophysiology of glomerular diseases. However, no urinary omics analysis has been performed yet on renal AA amyloidosis. Here, we performed a comparative urine proteomic and metabolomic analysis between recently diagnosed renal AA amyloidosis (AA) and membranous nephropathy (MN) patients. Urine samples of 22 (8 AA, 8 MN and 6 healthy control) patients were analyzed with nLC-MS/MS and GC/MS for proteomic and metabolomic studies, respectively. Pathological specimens were scored for glomerulosclerosis and tubulointerstitial fibrosis grades. Functional enrichment analysis between AA and control groups showed enrichment in cell adhesion related sub-domains. Uromodulin (UMOD) was lower, whereas ribonuclease 1 (RNase1) and α-1-microglobulin/bikunin precursor (AMBP) were higher in AA compared to MN group. Correlations were demonstrated between UMOD-proteinuria (r = -0.48, p = 0.03) and AMBP-eGFR (r = -0.69, p = 0.003) variables. Metabolomic analysis showed myo-inositol and urate were higher in AA compared to MN group. A positive correlation was detected between RNase1 and urate independent of eGFR values (r = 0.63, p = 0.01). Enrichment in cell adhesion related domains suggested a possible increased urinary shear stress due to amyloid fibrils. UMOD, AMBP and myo-inositol were related with tubulointerstitial damage, whereas RNase1 and urate were believed to be related with systemic inflammation in AA amyloidosis. SIGNIFICANCE: Urinary omics studies have become a standard tool for biomarker studies. However, no urinary omics analysis has been performed yet on renal AA amyloidosis. Here, we performed a comparative urinary omics analysis between recently diagnosed renal AA amyloidosis (AA), membranous nephropathy (MN) patients and healthy controls. Pathological specimens were scored with glomerulosclerosis (G) and tubulointerstitial fibrosis (IF) grades to consolidate the results of the omics studies and correlation analyzes. Functional enrichment analysis showed enrichment in cell adhesion related sub-domains due to downregulation of cadherins; which could be related with increased urinary shear stress due to amyloid deposition and disruption of tissue micro-architecture. In comparative proteomic analyzes UMOD was lower, whereas RNase1 and AMBP were higher in AA compared to MN group. Whereas in metabolomic analyzes; myo-inositol, urate and maltose were higher in AA compared to MN group. Correlations were demonstrated between UMOD-proteinuria (r = -0.48, p = 0.03), AMBP-eGFR (r = -0.69, p = 0.003) and between RNase1-Urate independent of eGFR values (r = 0.63, p = 0.01). This study is the first comprehensive urinary omics analysis focusing on renal AA Amyloidosis to the best of our knowledge. Based on physiologic roles and clinicopathologic correlations of the molecules; UMOD, AMBP and myo-inositol were related with tubulointerstitial damage, whereas RNase1 and urate were believed to be increased with systemic inflammation and endothelial damage in AA amyloidosis.

Plasma Proteomic Analysis Reveals the Potential Role of Lectin and Alternative Complement Pathways in IgA Vasculitis Pathogenesis

BACKGROUND

IgA vasculitis (IgAV) is the most common form of childhood vasculitis. A better understanding of its pathophysiology is required to identify new potential biomarkers and treatment targets.

OBJECTIVE

to assess the underlying molecular mechanisms in the pathogenesis of IgAV using an untargeted proteomics approach.

METHODS

Thirty-seven IgAV patients and five healthy controls were enrolled. Plasma samples were collected on the day of diagnosis before any treatment was initiated. We used nano-liquid chromatography-tandem mass spectrometry (nLC-MS/MS) to investigate the alterations in plasma proteomic profiles. For the bioinformatics analyses, databases including Uniprot, PANTHER, KEGG, Reactome, Cytoscape, and IntAct were used.

RESULTS

Among the 418 proteins identified in the nLC-MS/MS analysis, 20 had significantly different expressions in IgAV patients. Among them, 15 were upregulated and 5 were downregulated. According to the KEGG pathway and function classification analysis, complement and coagulation cascades were the most enriched pathways. GO analyses showed that the differentially expressed proteins were mainly involved in defense/immunity proteins and the metabolite interconversion enzyme family. We also investigated molecular interactions in the identified 20 proteins of IgAV patients. We extracted 493 interactions from the IntAct database for the 20 proteins and used Cytoscape for the network analyses.

CONCLUSION

Our results clearly suggest the role of the lectin and alternate complement pathways in IgAV. The proteins defined in the pathways of cell adhesion may serve as biomarkers. Further functional studies may lead the way to better understanding of the disease and new therapeutic options for IgAV treatment.

Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.

Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging

BACKGROUND

Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.

RESULTS

Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels.

CONCLUSION

This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

SAT0497 A PILOT PROTEOMIC ANALYSIS OF PLASMA BIOMARKERS IN IgA VASCULITIS

Background:

IgA vasculitis/ Henoch Schönlein Purpura (IgAV/HSP) is the most common vasculitis of childhood, characterized by the IgA1 immune deposits in the small vessels. Although it is very common, the understanding of its molecular pathogenesis is still very limited.

Objectives:

We aimed to analyse plasma proteomes of IgAV/HSP patients using nano liquid chromatography – tandem mass spectrometry (nLC-MS/MS) to investigate the disease pathogenesis.

Methods:

IgAV/HSP was diagnosed according to the Ankara criteria in 2008 (1). Five active IgAV/HSP patients and two age and gender-matched health controls were enrolled in this pilot study. Serum samples from subjects were collected on the same day of IgAV/HSP diagnosis and before steroid or other immunosuppressive treatment initiated. Sample preparation was carried out using PreOmics İST Kit. We investigated the alteration of serum proteome using the nano LC-MS/MS approach. Bruker raw files were analyzed using the proteomics software Max Quant (1.6.7.0). The human reference proteome set from UniProt was used to identify proteins. Proteomic data were analyzed with Perseus 1.6.7.0.

Results:

The data file includes peptide and protein identification, accession numbers, protein and gene names, sequence coverage and label free quantification (LFQ) values of each sample. 345 proteins were reported per sample. Identifications from the reverse decoy database, identified by site only and known contaminants were excluded. Data were log transformed. Two sample T-test was performed between groups. We identified 23 significantly different expressed proteins (Table 1). Mainly the differentially expressed proteins were in the Ig and complement pathway, innate immune inflammatory, and were among the structural cytoskeletal filaments. The levels of Complement C3, Apolipoprotein E, Glyceraldehyde-3-phosphate dehydrogenase, Filamin-A, Alpha-1B-glycoprotein, Tubulin beta-1 chain, Lipopolysaccharide-binding protein, Ig mu chain C region were significantly higher in IGAV patients.

Table 1.

List of differentially expressed proteins identified in IgAV compared to healthy controls

Majority protein IDsProtein namesGene namesp-valueO75822Eukaryotic translation initiation factor 3 subunit JEIF3J0,004P05106;H3BM21Integrin beta-3ITGB30,008P02743Serum amyloid P-componentAPCS0,015A0A0J9YX35unknown0,015A2NJV5;A0A075B6S2unknownIGKV A18;IGKV2D-290,021P02679Fibrinogen gamma chainFGG0,022A0A0C4DH36unknownIGHV3-380,024P0DP03;P01768Ig heavy chain V-III region CAM0,024O14791Apolipoprotein L1APOL10,025P01024Complement C3C30,030P02675Fibrinogen beta chain; Fibrinopeptide BFGB0,030P01860Ig gamma-3 chain C regionIGHG30,031A0A075B7D8unknownIGHV3OR15-70,034P02649Apolipoprotein EAPOE0,035P04406Glyceraldehyde-3-phosphate dehydrogenaseGAPDH0,037A0A075B6R2;A0A087WW49unknownIGHV4-40,039P21333Filamin-AFLNA0,043P04217Alpha-1B-glycoproteinA1BG0,045P02790HemopexinHPX0,046A0A0C4DH25unknownIGKV3D-200,047Q9H4B7Tubulin beta-1 chainTUBB10,049P18428Lipopolysaccharide-binding proteinLBP0,049P01871Ig mu chain C regionIGHM0,050

Conclusion:

This pilot proteomic study may provide us a perspective in the pathogenesis of IgAV (HSP).

References:

[1]Ozen S, Pistorio A, Iusan SM, Bakkaloglu A, Herlin T, Brik R, et al. EULAR/PRINTO/PRES criteria for Henoch-Schonlein purpura, childhood polyarteritis nodosa, childhood Wegener granulomatosis and childhood Takayasu arteritis: Ankara 2008. Part II: final classification criteria. Ann Rheum Dis 2010;69(5):798e806.

Disclosure of Interests:

Selcan Demir: None declared, Melis Sardan: None declared, Idil Yet: None declared, Erdal Sag Grant/research support from: Novartis and SOBI financially supported the HELIOS registry during the establishment of infrastructure, Yelda Bilginer Grant/research support from: Novartis and SOBI financially supported the HELIOS registry during the establishment of infrastructure, Ömür Celikbicak: None declared, Seza Özen Consultant of: Novartis, Pfizer, Speakers bureau: SOBI, Novartis

Metabolomics reveals a link between homocysteine and lipid metabolism and leukocyte telomere length: the ENGAGE consortium

Telomere shortening has been associated with multiple age-related diseases such as cardiovascular disease, diabetes, and dementia. However, the biological mechanisms responsible for these associations remain largely unknown. In order to gain insight into the metabolic processes driving the association of leukocyte telomere length (LTL) with age-related diseases, we investigated the association between LTL and serum metabolite levels in 7,853 individuals from seven independent cohorts. LTL was determined by quantitative polymerase chain reaction and the levels of 131 serum metabolites were measured with mass spectrometry in biological samples from the same blood draw. With partial correlation analysis, we identified six metabolites that were significantly associated with LTL after adjustment for multiple testing: lysophosphatidylcholine acyl C17:0 (lysoPC a C17:0, p-value = 7.1 × 10⁻⁶), methionine (p-value = 9.2 × 10⁻⁵), tyrosine (p-value = 2.1 × 10⁻⁴), phosphatidylcholine diacyl C32:1 (PC aa C32:1, p-value = 2.4 × 10⁻⁴), hydroxypropionylcarnitine (C3-OH, p-value = 2.6 × 10⁻⁴), and phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4, p-value = 9.0 × 10⁻⁴). Pathway analysis showed that the three phosphatidylcholines and methionine are involved in homocysteine metabolism and we found supporting evidence for an association of lipid metabolism with LTL. In conclusion, we found longer LTL associated with higher levels of lysoPC a C17:0 and PC ae C38:4, and with lower levels of methionine, tyrosine, PC aa C32:1, and C3-OH. These metabolites have been implicated in inflammation, oxidative stress, homocysteine metabolism, and in cardiovascular disease and diabetes, two major drivers of morbidity and mortality.

Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health

Background

Tobacco smoking is a risk factor for multiple diseases, including cardiovascular disease and diabetes. Many smoking-associated signals have been detected in the blood methylome, but the extent to which these changes are widespread to metabolically relevant tissues, and impact gene expression or metabolic health, remains unclear.

Methods

We investigated smoking-associated DNA methylation and gene expression variation in adipose tissue biopsies from 542 healthy female twins. Replication, tissue specificity, and longitudinal stability of the smoking-associated effects were explored in additional adipose, blood, skin, and lung samples. We characterized the impact of adipose tissue smoking methylation and expression signals on metabolic disease risk phenotypes, including visceral fat.

Results

We identified 42 smoking-methylation and 42 smoking-expression signals, where five genes (AHRR, CYP1A1, CYP1B1, CYTL1, F2RL3) were both hypo-methylated and upregulated in current smokers. CYP1A1 gene expression achieved 95% prediction performance of current smoking status. We validated and replicated a proportion of the signals in additional primary tissue samples, identifying tissue-shared effects. Smoking leaves systemic imprints on DNA methylation after smoking cessation, with stronger but shorter-lived effects on gene expression. Metabolic disease risk traits such as visceral fat and android-to-gynoid ratio showed association with methylation at smoking markers with functional impacts on expression, such as CYP1A1, and at tissue-shared smoking signals, such as NOTCH1. At smoking-signals, BHLHE40 and AHRR DNA methylation and gene expression levels in current smokers were predictive of future gain in visceral fat upon smoking cessation.

Conclusions

Our results provide the first comprehensive characterization of coordinated DNA methylation and gene expression markers of smoking in adipose tissue. The findings relate to human metabolic health and give insights into understanding the widespread health consequence of smoking outside of the lung.

Electronic supplementary material

The online version of this article (10.1186/s13148-018-0558-0) contains supplementary material, which is available to authorized users.

GWAS of epigenetic aging rates in blood reveals a critical role for TERT

DNA methylation age is an accurate biomarker of chronological age and predicts lifespan, but its underlying molecular mechanisms are unknown. In this genome-wide association study of 9907 individuals, we find gene variants mapping to five loci associated with intrinsic epigenetic age acceleration (IEAA) and gene variants in three loci associated with extrinsic epigenetic age acceleration (EEAA). Mendelian randomization analysis suggests causal influences of menarche and menopause on IEAA and lipoproteins on IEAA and EEAA. Variants associated with longer leukocyte telomere length (LTL) in the telomerase reverse transcriptase gene (TERT) paradoxically confer higher IEAA (P < 2.7 × 10-11). Causal modeling indicates TERT-specific and independent effects on LTL and IEAA. Experimental hTERT-expression in primary human fibroblasts engenders a linear increase in DNA methylation age with cell population doubling number. Together, these findings indicate a critical role for hTERT in regulating the epigenetic clock, in addition to its established role of compensating for cell replication-dependent telomere shortening.

Genetic variation at 16q24.2 is associated with small vessel stroke

Objective

Genome‐wide association studies (GWAS) have been successful at identifying associations with stroke and stroke subtypes, but have not yet identified any associations solely with small vessel stroke (SVS). SVS comprises one quarter of all ischemic stroke and is a major manifestation of cerebral small vessel disease, the primary cause of vascular cognitive impairment. Studies across neurological traits have shown that younger‐onset cases have an increased genetic burden. We leveraged this increased genetic burden by performing an age‐at‐onset informed GWAS meta‐analysis, including a large younger‐onset SVS population, to identify novel associations with stroke.

Methods

We used a three‐stage age‐at‐onset informed GWAS to identify novel genetic variants associated with stroke. On identifying a novel locus associated with SVS, we assessed its influence on other small vessel disease phenotypes, as well as on messenger RNA (mRNA) expression of nearby genes, and on DNA methylation of nearby CpG sites in whole blood and in the fetal brain.

Results

We identified an association with SVS in 4,203 cases and 50,728 controls on chromosome 16q24.2 (odds ratio [OR; 95% confidence interval {CI}] = 1.16 [1.10–1.22]; p = 3.2 × 10⁻⁹). The lead single‐nucleotide polymorphism (rs12445022) was also associated with cerebral white matter hyperintensities (OR [95% CI] = 1.10 [1.05–1.16]; p = 5.3 × 10⁻⁵; N = 3,670), but not intracerebral hemorrhage (OR [95% CI] = 0.97 [0.84–1.12]; p = 0.71; 1,545 cases, 1,481 controls). rs12445022 is associated with mRNA expression of ZCCHC14 in arterial tissues (p = 9.4 × 10⁻⁷) and DNA methylation at probe cg16596957 in whole blood (p = 5.3 × 10⁻⁶).

Interpretation

16q24.2 is associated with SVS. Associations of the locus with expression of ZCCHC14 and DNA methylation suggest the locus acts through changes to regulatory elements. Ann Neurol 2017;81:383–394

Heritable components of the human fecal microbiome are associated with visceral fat

BACKGROUND

Variation in the human fecal microbiota has previously been associated with body mass index (BMI). Although obesity is a global health burden, the accumulation of abdominal visceral fat is the specific cardio-metabolic disease risk factor. Here, we explore links between the fecal microbiota and abdominal adiposity using body composition as measured by dual-energy X-ray absorptiometry in a large sample of twins from the TwinsUK cohort, comparing fecal 16S rRNA diversity profiles with six adiposity measures.

RESULTS

We profile six adiposity measures in 3666 twins and estimate their heritability, finding novel evidence for strong genetic effects underlying visceral fat and android/gynoid ratio. We confirm the association of lower diversity of the fecal microbiome with obesity and adiposity measures, and then compare the association between fecal microbial composition and the adiposity phenotypes in a discovery subsample of twins. We identify associations between the relative abundances of fecal microbial operational taxonomic units (OTUs) and abdominal adiposity measures. Most of these results involve visceral fat associations, with the strongest associations between visceral fat and Oscillospira members. Using BMI as a surrogate phenotype, we pursue replication in independent samples from three population-based cohorts including American Gut, Flemish Gut Flora Project and the extended TwinsUK cohort. Meta-analyses across the replication samples indicate that 8 OTUs replicate at a stringent threshold across all cohorts, while 49 OTUs achieve nominal significance in at least one replication sample. Heritability analysis of the adiposity-associated microbial OTUs prompted us to assess host genetic-microbe interactions at obesity-associated human candidate loci. We observe significant associations of adiposity-OTU abundances with host genetic variants in the FHIT, TDRG1 and ELAVL4 genes, suggesting a potential role for host genes to mediate the link between the fecal microbiome and obesity.

CONCLUSIONS

Our results provide novel insights into the role of the fecal microbiota in cardio-metabolic disease with clear potential for prevention and novel therapies.

Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms

Metabolomic profiling is a powerful approach to characterize human metabolism and help understand common disease risk. Although multiple high-throughput technologies have been developed to assay the human metabolome, no technique is capable of capturing the entire human metabolism. Large-scale metabolomics data are being generated in multiple cohorts, but the datasets are typically profiled using different metabolomics platforms. Here, we compared analyses across two of the most frequently used metabolomic platforms, Biocrates and Metabolon, with the aim of assessing how complimentary metabolite profiles are across platforms. We profiled serum samples from 1,001 twins using both targeted (Biocrates, n = 160 metabolites) and non-targeted (Metabolon, n = 488 metabolites) mass spectrometry platforms. We compared metabolite distributions and performed genome-wide association analyses to identify shared genetic influences on metabolites across platforms. Comparison of 43 metabolites named for the same compound on both platforms indicated strong positive correlations, with few exceptions. Genome-wide association scans with high-throughput metabolic profiles were performed for each dataset and identified genetic variants at 7 loci associated with 16 unique metabolites on both platforms. The 16 metabolites showed consistent genetic associations and appear to be robustly measured across platforms. These included both metabolites named for the same compound across platforms as well as unique metabolites, of which 2 (nonanoylcarnitine (C9) [Biocrates]/Unknown metabolite X-13431 [Metabolon] and PC aa C28:1 [Biocrates]/1-stearoylglycerol [Metabolon]) are likely to represent the same or related biochemical entities. The results demonstrate the complementary nature of both platforms, and can be informative for future studies of comparative and integrative metabolomics analyses in samples profiled on different platforms.

Genetic and environmental impacts on DNA methylation levels in twins

Epigenetics describes the study of cellular modifications that can modify the expression of genes without changing the DNA sequence. DNA methylation is one of the most stable and prevalent epigenetic mechanisms. Twin studies have been a valuable model for unraveling the genetic and epigenetic epidemiology of complex traits, and now offer a potential to dissect the factors that impact DNA methylation variability and its biomedical significance. The twin design specifically allows for the study of genetic, environmental and lifestyle factors, and their potential interactions, on epigenetic profiles. Furthermore, genetically identical twins offer a unique opportunity to assess nongenetic impacts on epigenetic profiles. Here, we summarize recent findings from twin studies of DNA methylation profiles across tissues, to define current knowledge regarding the genetic and nongenetic factors that influence epigenetic variation.

A Metabolome-Wide Association Study of Kidney Function and Disease in the General Population

Small molecules are extensively metabolized and cleared by the kidney. Changes in serum metabolite concentrations may result from impaired kidney function and can be used to estimate filtration (e.g., the established marker creatinine) or may precede and potentially contribute to CKD development. Here, we applied a nontargeted metabolomics approach using gas and liquid chromatography coupled to mass spectrometry to quantify 493 small molecules in human serum. The associations of these molecules with GFR estimated on the basis of creatinine (eGFRcr) and cystatin C levels were assessed in ≤1735 participants in the KORA F4 study, followed by replication in 1164 individuals in the TwinsUK registry. After correction for multiple testing, 54 replicated metabolites significantly associated with eGFRcr, and six of these showed pairwise correlation (r≥0.50) with established kidney function measures: C-mannosyltryptophan, pseudouridine, N-acetylalanine, erythronate, myo-inositol, and N-acetylcarnosine. Higher C-mannosyltryptophan, pseudouridine, and O-sulfo-L-tyrosine concentrations associated with incident CKD (eGFRcr <60 ml/min per 1.73 m(2)) in the KORA F4 study. In contrast with serum creatinine, C-mannosyltryptophan and pseudouridine concentrations showed little dependence on sex. Furthermore, correlation with measured GFR in 200 participants in the AASK study was 0.78 for both C-mannosyltryptophan and pseudouridine concentration, and highly significant associations of both metabolites with incident ESRD disappeared upon adjustment for measured GFR. Thus, these molecules may be alternative or complementary markers of kidney function. In conclusion, our study provides a comprehensive list of kidney function-associated metabolites and highlights potential novel filtration markers that may help to improve the estimation of GFR.

coMET: visualisation of regional epigenome-wide association scan results and DNA co-methylation patterns

Background

Epigenome-wide association scans (EWAS) are an increasingly powerful and widely-used approach to assess the role of epigenetic variation in human complex traits. However, this rapidly emerging field lacks dedicated visualisation tools that can display features specific to epigenetic datasets.

Result

We developed coMET, an R package and online tool for visualisation of EWAS results in a genomic region of interest. coMET generates a regional plot of epigenetic-phenotype association results and the estimated DNA methylation correlation between CpG sites (co-methylation), with further options to visualise genomic annotations based on ENCODE data, gene tracks, reference CpG-sites, and user-defined features. The tool can be used to display phenotype association signals and correlation patterns of microarray or sequencing-based DNA methylation data, such as Illumina Infinium 450k, WGBS, or MeDIP-seq, as well as other types of genomic data, such as gene expression profiles. The software is available as a user-friendly online tool from http://epigen.kcl.ac.uk/cometand as an R Bioconductor package. Source code, examples, and full documentation are also available from GitHub.

Conclusion

Our new software allows visualisation of EWAS results with functional genomic annotations and with estimation of co-methylation patterns. coMET is available to a wide audience as an online tool and R package, and can be a valuable resource to interpret results in the fast growing field of epigenetics. The software is designed for epigenetic data, but can also be applied to genomic and functional genomic datasets in any species.

An integrated epigenomic analysis for type 2 diabetes susceptibility loci in monozygotic twins

DNA methylation has a great potential for understanding the aetiology of common complex traits such as Type 2 diabetes (T2D). Here we perform genome-wide methylated DNA immunoprecipitation sequencing (MeDIP-seq) in whole-blood-derived DNA from 27 monozygotic twin pairs and follow up results with replication and integrated omics analyses. We identify predominately hypermethylated T2D-related differentially methylated regions (DMRs) and replicate the top signals in 42 unrelated T2D cases and 221 controls. The strongest signal is in the promoter of the MALT1 gene, involved in insulin and glycaemic pathways, and related to taurocholate levels in blood. Integrating the DNA methylome findings with T2D GWAS meta-analysis results reveals a strong enrichment for DMRs in T2D-susceptibility loci. We also detect signals specific to T2D-discordant twins in the GPR61 and PRKCB genes. These replicated T2D associations reflect both likely causal and consequential pathways of the disease. The analysis indicates how an integrated genomics and epigenomics approach, utilizing an MZ twin design, can provide pathogenic insights as well as potential drug targets and biomarkers for T2D and other complex traits.

Type 2 diabetes (T2D) is a highly heterogeneous disease with a strong genetic component. Here the authors examine genome-wide methylation patterns in T2D-discordant, T2D-concordant and healthy concordant monozygotic twin pairs, and identify DNA methylation signals that may represent new biomarkers or drug targets for T2D.