Cracking the regulatory code

Ward, Michelle C.; Gilad, Yoav

doi:10.1038/550190a

Cited by 18 publications

(17 citation statements)

References 7 publications

(8 reference statements)

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“…New insights regarding human IGF2 gene expression-GTEx has collected gene expression data by RNA-sequencing from many non-diseased human tissues (63)(64)(65), with 80 -388 donors per tissue (GTEX release 7). Presented in Fig.…”

Section: Resultsmentioning

confidence: 99%

The complex genetics of human insulin-like growth factor 2 are not reflected in public databases

Rotwein

2018

Journal of Biological Chemistry

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Section: Resultsmentioning

confidence: 99%

The complex genetics of human insulin-like growth factor 2 are not reflected in public databases

Rotwein

2018

Journal of Biological Chemistry

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“…There is also strong evidence for multiple eQTL signals across this haplotype which includes three genes, i.e. CCDC92, DNAH10 and ZNF664[17]. Previous GWAS studies have also associated SNPs in this haplotype with a reduction in insulin resistance[11], improvements in metabolic syndrome[18], reduced WHRadjBMI[3, 4], increased adiponectin levels[19] and with increased plasma HDL-cholesterol and reduced triglyceride concentrations[20–22].…”

Section: Resultsmentioning

confidence: 99%

“…Whilst this study focusses on exonic coding variants, recent studies have highlighted the need for caution when dissociating the analysis of such variants from surrounding eQTL signals[23]. To that end we also sought to investigate the reported eQTL signals at this locus, for both CCDC92 and ZNF664 (GTEx project[17] and[11] using allele-specific qPCR; a method that allows us to assess expressed allelic imbalance in heterozygous individuals and thus an eQTL. This showed a highly statistically significant increased expression of transcripts found on the minor allele haplotype for both genes (ASAT 5.8%, GSAT 4.9%, Fig 3 A and B).…”

Section: Resultsmentioning

confidence: 99%

Regional fat depot masses are influenced by protein-coding gene variants

Neville

Wittemans

Pinnick

et al. 2019

Preprint

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23 With the identification of a large number of genetic loci associated with human fat 24 distribution and its importance for metabolic health, the question arises as to what 25 the genetic drivers for discrete fat depot expansion might be. To date most studies 26 have focussed on conventional anthropometric measures such as waist-to-hip ratio 27 (WHR) adjusted for body mass index. We searched for genetic loci determining 28 discrete fat depots mass size using an exome-wide approach in 3 large cohorts.29 Here we report an exome-wide analysis of non-synonymous genetic variants in 30 17,212 participants in which regional fat masses were quantified using dual-energy 31 X-ray absorptiometry. The missense variant CCDC92 S70C, previously associated with 32 WHR, is associated specifically with reduced visceral and increased leg fat masses.33 Allele-specific expression analysis shows that the deleterious minor allele carrying 34 transcript also has a constitutively higher expression. In addition, we identify two 35 variants associated with the transcriptionally distinct fat depot arm fat (SPATA20 K422R 36 and UQCC1 R51Q ). SPATA20 K422R , a rare novel locus with a large effect size specific 37 to arm, and UQCC1 R51Q , a common variant exome-wide significant in arm but 38 showing similar trends in other subcutaneous fat depots. In terms of the 39 understanding of human fat distribution, these findings suggest distinct regulation of 40 discrete fat depot expansion. 42Author summary 43 Human fat storing tissues are heterogeneous and comprise functionally and 44 structurally distinct regional fat depots, the relative size of which appear to have 45 significant implications for health. Whilst it is known that inter-individual differences in 46 fat distribution have genetic drivers, studies to date have focussed on crude 3 47 anthropometric approximations of region fat masses rather than precise measures.48 Here we describe an exome-wide analysis of a large collection of men and women 49 who have undergone body scanning using dual-energy X-ray absorptiometry (DXA) 50 to better define regional fat masses and identify new genetic drivers for human fat 51 distribution. With this approach we identify three gene regions associated with 52 distinct fat depots which can help to explain the variation in fat distribution between 53 people and may lead to a better understanding of the depot specific fat 54 tissue expansion.4 56 57 58 Beyond associations with chronic disease and overall obesity, as defined by body-59 mass index (BMI), it is becoming increasingly apparent that there is an even stronger 60 relationship between body fat distribution and cardio-metabolic disease [1, 2]. For 61 example, Yusuf et al. [1] showed that waist-to-hip ratio (WHR) is a stronger predict of 62 myocardial infarction than BMI. To date, the overwhelming majority genome and 63 exome-wide association studies on fat distribution have focussed on waist and hip 64 circumference and WHR [3, 4]. While these measures are easy and cheap to obtain 65 on a large sca...

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confidence: 99%

“…The prevalence and importance of disease-related genetic variants in non-protein-coding regions of the genome is well documented 19,20 . These variants may regulate how and when a gene is expressed 20 . Interestingly, analysis of RNA sequencing data from different human tissues held by the GTEx Portal database revealed highest expression of NBEAL1 in arteries (Supplemental Fig.…”

mentioning

confidence: 99%

NBEAL1 controls SREBP2 processing and cholesterol metabolism and is a susceptibility locus for coronary artery disease

Bindesbøll

Aas

Ögmundsdóttir

et al. 2020

Sci Rep

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Dysregulated cholesterol homeostasis promotes the pathology of atherosclerosis, myocardial infarction and strokes. Cellular cholesterol is mainly regulated at the transcriptional level by SREBP2, but also through uptake of extracellular cholesterol from low density lipoproteins (LDL) via expression of LDL receptors (LDLR) at the cell surface. Identification of the mechanisms involved in regulation of these processes are thus key to understand the pathology of coronary artery disease. Here, we identify the large and poorly characterized BEACH domain protein Neurobeachin-like (NBEAL) 1 as a Golgiassociated protein required for regulation of cholesterol metabolism. NBEAL1 is most abundantly expressed in arteries. Genetic variants in NBEAL1 are associated with decreased expression of NBEAL1 in arteries and increased risk of coronary artery disease in humans. We show that NBEAL1 regulates cholesterol metabolism by modulating LDLR expression in a mechanism involving interaction with SCAP and PAQR3 and subsequent SREBP2-processing. Thus, low expression of NBEAL1 may lead to increased risk of coronary artery disease by downregulation of LDLR levels. NBEAL1 belongs to a family of proteins that shares a highly conserved domain known as the BEACH (Beige and Chediak-Higashi) domain, found in nine human proteins. The cellular functions of most BEACH proteins still remain poorly defined more than a decade after the crystal structure of the BEACH domain was resolved 1,2 , but several family members are linked to membrane trafficking and/or modeling processes, including regulation of lysosome size, autophagy, apoptosis and granule size 3-6. Variants in genes encoding distinct BEACH proteins cause several human diseases, including grey platelet syndrome (NBEAL2) 7-9 , Chédiak-Higashi Syndrome (LYST) 10 and human primary microcephaly (ALFY/WDFY3) 11. NBEAL1 is one of the least understood BEACH proteins and we therefore aimed to elucidate its cellular localization and function, as well as a potential link to disease. Results and Discussion The NBEAL1 locus has previously been associated with coronary artery disease (CAD) (also called coronary heart disease or coronary atherosclerosis) 12,13. However, the culprit at the locus in relation to CAD has remained somewhat unclear. In order to characterize a possible role of NBEAL1 in the disease, we utilized data from the CAD Genome-wide Replication and Meta-analysis (CARDIoGRAMplusC4D) consortium that holds information on genome wide association data for CAD comprising 60,801 cases and 123,504 controls 14,15. NBEAL1 is located on chromosome 2 and using this data we performed a lookup of variants on this chromosome that associated with CAD (Supplemental Table 1). Interestingly, this revealed the strongest association within chromosome 2

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Cracking the regulatory code

Cited by 18 publications

References 7 publications

The complex genetics of human insulin-like growth factor 2 are not reflected in public databases

The complex genetics of human insulin-like growth factor 2 are not reflected in public databases

Regional fat depot masses are influenced by protein-coding gene variants

NBEAL1 controls SREBP2 processing and cholesterol metabolism and is a susceptibility locus for coronary artery disease

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