179 research outputs found
Identification of differentially expressed sense and antisense transcript pairs in breast epithelial tissues
Background: More than 20% of human transcripts have naturally occurring antisense products (or natural antisense transcripts – NATs), some of which may play a key role in a range of human diseases. To date, several databases of in silico defined human sense-antisense (SAS) pairs have appeared, however no study has focused on differential expression of SAS pairs in breast tissue. We therefore investigated the expression levels of sense and antisense transcripts in normal and malignant human breast epithelia using the Affymetrix HG-U133 Plus 2.0 and Almac Diagnostics Breast Cancer DSA microarray technologies as well as massively parallel signature sequencing (MPSS) data.
Results: The expression of more than 2500 antisense transcripts were detected in normal breast duct luminal cells and in primary breast tumors substantially enriched for their epithelial cell content by DSA microarray. Expression of 431 NATs were confirmed by either of the other two technologies. A corresponding sense transcript could be identified on DSA for 257 antisense transcripts. Of these SAS pairs, 163 have not been previously reported. A positive correlation of differential expression between normal and malignant breast samples was observed for most SAS pairs. Orientation specific RT-QPCR of selected SAS pairs validated their expression in several breast cancer cell lines and solid breast tumours.
Conclusion: Disease-focused and antisense enriched microarray platforms (such as Breast Cancer DSA) confirm the assumption that antisense transcription in the human breast is more prevalent than previously anticipated. Expression of a proportion of these NATs has already been confirmed by other technologies while the true existence of the remaining ones has to be validated. Nevertheless, future studies will reveal whether the relative abundances of antisense and sense transcripts have regulatory influences on the translation of these mRNAs
Regional fat depot masses are influenced by protein-coding gene variants
Waist-to-hip ratio (WHR) is a prominent cardiometabolic risk factor that increases cardio-metabolic disease risk independently of BMI and for which multiple genetic loci have been identified. However, WHR is a relatively crude proxy for fat distribution and it does not capture all variation in fat distribution. We here present a study of the role of coding genetic variants on fat mass in 6 distinct regions of the body, based on dual-energy X-ray absorptiometry imaging on more than 17k participants. We find that the missense variant CCDC92(S70C), previously associated with WHR, is associated specifically increased leg fat mass and reduced visceral but not subcutaneous central fat. The minor allele-carrying transcript of CCDC92 is constitutively more highly expressed in adipose tissue samples. In addition, we identify two coding variants in SPATA20 and UQCC1 that are associated with arm fat mass. SPATA20(K422R) is a low-frequency variant with a large effect on arm fat only, and UQCC1(R51Q) is a common variant reaching significance for arm but showing similar trends in other subcutaneous fat depots. Our findings support the notion that different fat compartments are regulated by distinct genetic factors.Peer reviewe
Characterization of the genetic determinants of context-specific DNA methylation in primary monocytes
To better understand inter-individual variation in sensitivity of DNA methylation (DNAm) to immune activity, we characterized effects of inflammatory stimuli on primary monocyte DNAm (n = 190). We find that monocyte DNAm is site-dependently sensitive to lipopolysaccharide (LPS), with LPS-induced demethylation occurring following hydroxymethylation. We identify 7,359 high-confidence immune-modulated CpGs (imCpGs) that differ in genomic localization and transcription factor usage according to whether they represent a gain or loss in DNAm. Demethylated imCpGs are profoundly enriched for enhancers and colocalize to genes enriched for disease associations, especially cancer. DNAm is age associated, and we find that 24-h LPS exposure triggers approximately 6 months of gain in epigenetic age, directly linking epigenetic aging with innate immune activity. By integrating LPS-induced changes in DNAm with genetic variation, we identify 234 imCpGs under local genetic control. Exploring shared causal loci between LPS-induced DNAm responses and human disease traits highlights examples of disease-associated loci that modulate imCpG formation
MicroRNA Expression in Abdominal and Gluteal Adipose Tissue Is Associated with mRNA Expression Levels and Partly Genetically Driven
To understand how miRNAs contribute to the molecular phenotype of adipose tissues and related traits, we performed global miRNA expression profiling in subcutaneous abdominal and gluteal adipose tissue of 70 human subjects and characterised which miRNAs were differentially expressed between these tissues. We found that 12% of the miRNAs were significantly differentially expressed between abdominal and gluteal adipose tissue (FDR adjusted p<0.05) in the primary study, of which 59 replicated in a follow-up study of 40 additional subjects. Further, 14 miRNAs were found to be associated with metabolic syndrome case-control status in abdominal tissue and three of these replicated (primary study: FDR adjusted p<0.05, replication: p<0.05 and directionally consistent effect). Genome-wide genotyping was performed in the 70 subjects to enable miRNA expression quantitative trait loci (eQTL) analysis. Candidate miRNA eQTLs were followed-up in the additional 40 subjects and six significant, independent cis-located miRNA eQTLs (primary study: p<0.001; replication: p<0.05 and directionally consistent effect) were identified. Finally, global mRNA expression profiling was performed in both tissues to enable association analysis between miRNA and target mRNA expression levels. We find 22% miRNAs in abdominal and 9% miRNAs in gluteal adipose tissue with expression levels significantly associated with the expression of corresponding target mRNAs (FDR adjusted p<0.05). Taken together, our results indicate a clear difference in the miRNA molecular phenotypic profile of abdominal and gluteal adipose tissue, that the expressions of some miRNAs are influenced by cis-located genetic variants and that miRNAs are associated with expression levels of their predicted mRNA targets
Association of prolactin receptor (PRLR) variants with prolactinomas
Prolactinomas are the most frequent type of pituitary tumors, which represent 10–20% of all intracranial neoplasms in humans. Prolactinomas develop in mice lacking the prolactin receptor (PRLR), which is a member of the cytokine receptor superfamily that signals via Janus kinase-2-signal transducer and activator of transcription-5 (JAK2-STAT5) or phosphoinositide 3-kinase-Akt (PI3K-Akt) pathways to mediate changes in transcription, differentiation and proliferation. To elucidate the role of the PRLR gene in human prolactinomas, we determined the PRLR sequence in 50 DNA samples (35 leucocytes, 15 tumors) from 46 prolactinoma patients (59% males, 41% females). This identified six germline PRLR variants, which comprised four rare variants (Gly57Ser, Glu376Gln, Arg453Trp and Asn492Ile) and two low-frequency variants (Ile76Val, Ile146Leu), but no somatic variants. The rare variants, Glu376Gln and Asn492Ile, which were in complete linkage disequilibrium, and are located in the PRLR intracellular domain, occurred with significantly higher frequencies (P 1.3-fold, P < 0.02) and proliferation (1.4-fold, P < 0.02), but did not affect pSTAT5 signaling. Treatment of cells with an Akt1/2 inhibitor or everolimus, which acts on the Akt pathway, reduced Asn492Ile signaling and proliferation to WT levels. Thus, our results identify an association between a gain-of-function PRLR variant and prolactinomas and reveal a new etiology and potential therapeutic approach for these neoplasms
Genome-wide characterization of circulating metabolic biomarkers
Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases
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Identification and Functional Characterization of <i>G6PC2</i> Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the <i>G6PC2-ABCB11</i> Locus
Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights
Higher thyrotropin leads to unfavorable lipid profile and somewhat higher cardiovascular disease risk: evidence from multi-cohort Mendelian randomization and metabolomic profiling.
BACKGROUND: Observational studies suggest interconnections between thyroid status, metabolism, and risk of coronary artery disease (CAD), but causality remains to be proven. The present study aimed to investigate the potential causal relationship between thyroid status and cardiovascular disease and to characterize the metabolomic profile associated with thyroid status. METHODS: Multi-cohort two-sample Mendelian randomization (MR) was performed utilizing genome-wide significant variants as instruments for standardized thyrotropin (TSH) and free thyroxine (fT4) within the reference range. Associations between TSH and fT4 and metabolic profile were investigated in a two-stage manner: associations between TSH and fT4 and the full panel of 161 metabolomic markers were first assessed hypothesis-free, then directional consistency was assessed through Mendelian randomization, another metabolic profile platform, and in individuals with biochemically defined thyroid dysfunction. RESULTS: Circulating TSH was associated with 52/161 metabolomic markers, and fT4 levels were associated with 21/161 metabolomic markers among 9432 euthyroid individuals (median age varied from 23.0 to 75.4 years, 54.5% women). Positive associations between circulating TSH levels and concentrations of very low-density lipoprotein subclasses and components, triglycerides, and triglyceride content of lipoproteins were directionally consistent across the multivariable regression, MR, metabolomic platforms, and for individuals with hypo- and hyperthyroidism. Associations with fT4 levels inversely reflected those observed with TSH. Among 91,810 CAD cases and 656,091 controls of European ancestry, per 1-SD increase of genetically determined TSH concentration risk of CAD increased slightly, but not significantly, with an OR of 1.03 (95% CI 0.99-1.07; p value 0.16), whereas higher genetically determined fT4 levels were not associated with CAD risk (OR 1.00 per SD increase of fT4; 95% CI 0.96-1.04; p value 0.59). CONCLUSIONS: Lower thyroid status leads to an unfavorable lipid profile and a somewhat increased cardiovascular disease risk
Genome-wide characterization of circulating metabolic biomarkers
Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1,2,3,4,5,6,7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8,9,10,11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases
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