Structure and expression of two nuclear receptor genes in marsupials: insights into the evolution of the antisense overlap between the α-thyroid hormone receptor and Rev-erbα

<p>Abstract</p> <p>Background</p> <p>Alternative processing of α-thyroid hormone receptor (TRα, NR1A1) mRNAs gives rise to two functionally antagonistic nuclear receptors: TRα1, the α-type receptor, and TRα2, a non-hormone binding variant that is found only in mammals. TRα2 shares an unusual antisense coding overlap with mRNA for Rev-erbα (NR1D1), another nuclear receptor protein. In this study we examine the structure and expression of these genes in the gray short-tailed opossum, <it>Monodelphis domestica</it>, in comparison with that of eutherian mammals and three other marsupial species, <it>Didelphis virginiana, Potorous tridactylus </it>and <it>Macropus eugenii</it>, in order to understand the evolution and regulatory role of this antisense overlap.</p> <p>Results</p> <p>The sequence, expression and genomic organization of mRNAs encoding TRα1 and Rev-erbα are very similar in the opossum and eutherian mammals. However, the sequence corresponding to the TRα2 coding region appears truncated by almost 100 amino acids. While expression of TRα1 and Rev-erbα was readily detected in all tissues of <it>M. domestica </it>ages 0 days to 18 weeks, TRα2 mRNA was not detected in any tissue or stage examined. These results contrast with the widespread and abundant expression of TRα2 in rodents and other eutherian mammals. To examine requirements for alternative splicing of TRα mRNAs, a series of chimeric minigenes was constructed. Results show that the opossum TRα2-specific 5' splice site sequence is fully competent for splicing but the sequence homologous to the TRα2 3' splice site is not, even though the marsupial sequences are remarkably similar to core splice site elements in rat.</p> <p>Conclusions</p> <p>Our results strongly suggest that the variant nuclear receptor isoform, TRα2, is not expressed in marsupials and that the antisense overlap between TRα and Rev-erbα thus is unique to eutherian mammals. Further investigation of the TRα and Rev-erbα genes in marsupial and eutherian species promises to yield additional insight into the physiological function of TRα2 and the role of the associated antisense overlap with Rev-erbα in regulating expression of these genes.</p

Common Polymorphisms at the <i>CYP17A1 </i>Locus Associate With Steroid Phenotype:Support for Blood Pressure Genome-Wide Association Study Signals at This Locus

Genome-wide association studies implicate the CYP17A1 gene in human blood pressure regulation although the causative polymorphisms are as yet unknown. We sought to identify common polymorphisms likely to explain this association. We sequenced the CYP17A1 locus in 60 normotensive individuals and observed 24 previously identified single-nucleotide polymorphisms with minor allele frequency &gt;0.05. From these, we selected, for further studies, 7 polymorphisms located ≤2 kb upstream of the CYP17A1 transcription start site. In vitro reporter gene assays identified 3 of these (rs138009835, rs2150927, and rs2486758) as having significant functional effects. We then analyzed the association between the 7 polymorphisms and the urinary steroid metabolites in a hypertensive cohort (n=232). Significant associations included that of rs138009835 with aldosterone metabolite excretion; rs2150927 associated with the ratio of tetrahydrodeoxycorticosterone to tetrahydrodeoxycortisol, which we used as an index of 17α-hydroxylation. Linkage analysis showed rs138009835 to be the only 1 of the 7 polymorphisms in strong linkage disequilibrium with the blood pressure–associated polymorphisms identified in the previous studies. In conclusion, we have identified, characterized, and investigated common polymorphisms at the CYP17A1 locus that have functional effects on gene transcription in vitro and associate with corticosteroid phenotype in vivo. Of these, rs138009835—which we associate with changes in aldosterone level—is in strong linkage disequilibrium with polymorphisms linked by genome-wide association studies to blood pressure regulation. This finding clearly has implications for the development of high blood pressure in a large proportion of the population and justifies further investigation of rs138009835 and its effects

Genetic evidence for a normal-weight "metabolically obese" phenotype linking insulin resistance, hypertension, coronary artery disease, and type 2 diabetes

PublishedJournal ArticleResearch Support, Non-U.S. Gov'tThe mechanisms that predispose to hypertension, coronary artery disease (CAD), and type 2 diabetes (T2D) in individuals of normal weight are poorly understood. In contrast, in monogenic primary lipodystrophy-a reduction in subcutaneous adipose tissue-it is clear that it is adipose dysfunction that causes severe insulin resistance (IR), hypertension, CAD, and T2D. We aimed to test the hypothesis that common alleles associated with IR also influence the wider clinical and biochemical profile of monogenic IR. We selected 19 common genetic variants associated with fasting insulin-based measures of IR. We used hierarchical clustering and results from genome-wide association studies of eight nondisease outcomes of monogenic IR to group these variants. We analyzed genetic risk scores against disease outcomes, including 12,171 T2D cases, 40,365 CAD cases, and 69,828 individuals with blood pressure measurements. Hierarchical clustering identified 11 variants associated with a metabolic profile consistent with a common, subtle form of lipodystrophy. A genetic risk score consisting of these 11 IR risk alleles was associated with higher triglycerides (β = 0.018; P = 4 × 10(-29)), lower HDL cholesterol (β = -0.020; P = 7 × 10(-37)), greater hepatic steatosis (β = 0.021; P = 3 × 10(-4)), higher alanine transaminase (β = 0.002; P = 3 × 10(-5)), lower sex-hormone-binding globulin (β = -0.010; P = 9 × 10(-13)), and lower adiponectin (β = -0.015; P = 2 × 10(-26)). The same risk alleles were associated with lower BMI (per-allele β = -0.008; P = 7 × 10(-8)) and increased visceral-to-subcutaneous adipose tissue ratio (β = -0.015; P = 6 × 10(-7)). Individuals carrying ≥17 fasting insulin-raising alleles (5.5% population) were slimmer (0.30 kg/m(2)) but at increased risk of T2D (odds ratio [OR] 1.46; per-allele P = 5 × 10(-13)), CAD (OR 1.12; per-allele P = 1 × 10(-5)), and increased blood pressure (systolic and diastolic blood pressure of 1.21 mmHg [per-allele P = 2 × 10(-5)] and 0.67 mmHg [per-allele P = 2 × 10(-4)], respectively) compared with individuals carrying ≤9 risk alleles (5.5% population). Our results provide genetic evidence for a link between the three diseases of the "metabolic syndrome" and point to reduced subcutaneous adiposity as a central mechanism

Testing the role of predicted gene knockouts in human anthropometric trait variation

National Heart, Lung, and Blood Institute (NHLBI) S.L. is funded by a Canadian Institutes of Health Research Banting doctoral scholarship. G.L. is funded by Genome Canada and Génome Québec; the Canada Research Chairs program; and the Montreal Heart Institute Foundation. C.M.L. is supported by Wellcome Trust (grant numbers 086596/Z/08/Z, 086596/Z/08/A); and the Li Ka Shing Foundation. N.S. is funded by National Institutes of Health (grant numbers HL088456, HL111089, HL116747). The Mount Sinai BioMe Biobank Program is supported by the Andrea and Charles Bronfman Philanthropies. GO ESP is supported by NHLBI (RC2 HL-103010 to HeartGO, RC2 HL-102923 to LungGO, RC2 HL-102924 to WHISP). The ESP exome sequencing was performed through NHLBI (RC2 HL-102925 to BroadGO, RC2 HL- 102926 to SeattleGO). EGCUT work was supported through the Estonian Genome Center of University of Tartu by the Targeted Financing from the Estonian Ministry of Science and Education (grant number SF0180142s08); the Development Fund of the University of Tartu (grant number SP1GVARENG); the European Regional Development Fund to the Centre of Excellence in Genomics (EXCEGEN) [grant number 3.2.0304.11-0312]; and through FP7 (grant number 313010). EGCUT were further supported by the US National Institute of Health (grant number R01DK075787). A.K.M. was supported by an American Diabetes Association Mentor-Based Postdoctoral Fellowship (#7-12-MN- 02). The BioVU dataset used in the analyses described were obtained from Vanderbilt University Medical Centers BioVU which is supported by institutional funding and by the Vanderbilt CTSA grant ULTR000445 from NCATS/NIH. Genome-wide genotyping was funded by NIH grants RC2GM092618 from NIGMS/OD and U01HG004603 from NHGRI/NIGMS. Funding to pay the Open Access publication charges for this article was provided by a block grant from Research Councils UK to the University of Cambridge

Testing the role of predicted gene knockouts in human anthropometric trait variation

National Heart, Lung, and Blood Institute (NHLBI) S.L. is funded by a Canadian Institutes of Health Research Banting doctoral scholarship. G.L. is funded by Genome Canada and Génome Québec; the Canada Research Chairs program; and the Montreal Heart Institute Foundation. C.M.L. is supported by Wellcome Trust (grant numbers 086596/Z/08/Z, 086596/Z/08/A); and the Li Ka Shing Foundation. N.S. is funded by National Institutes of Health (grant numbers HL088456, HL111089, HL116747). The Mount Sinai BioMe Biobank Program is supported by the Andrea and Charles Bronfman Philanthropies. GO ESP is supported by NHLBI (RC2 HL-103010 to HeartGO, RC2 HL-102923 to LungGO, RC2 HL-102924 to WHISP). The ESP exome sequencing was performed through NHLBI (RC2 HL-102925 to BroadGO, RC2 HL- 102926 to SeattleGO). EGCUT work was supported through the Estonian Genome Center of University of Tartu by the Targeted Financing from the Estonian Ministry of Science and Education (grant number SF0180142s08); the Development Fund of the University of Tartu (grant number SP1GVARENG); the European Regional Development Fund to the Centre of Excellence in Genomics (EXCEGEN) [grant number 3.2.0304.11-0312]; and through FP7 (grant number 313010). EGCUT were further supported by the US National Institute of Health (grant number R01DK075787). A.K.M. was supported by an American Diabetes Association Mentor-Based Postdoctoral Fellowship (#7-12-MN- 02). The BioVU dataset used in the analyses described were obtained from Vanderbilt University Medical Centers BioVU which is supported by institutional funding and by the Vanderbilt CTSA grant ULTR000445 from NCATS/NIH. Genome-wide genotyping was funded by NIH grants RC2GM092618 from NIGMS/OD and U01HG004603 from NHGRI/NIGMS. Funding to pay the Open Access publication charges for this article was provided by a block grant from Research Councils UK to the University of Cambridge

Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution.

To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist-hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9x10(-11)) and MSRA (WC, P = 8.9x10(-9)). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6x10(-8)). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity

Testing the role of predicted gene knockouts in human anthropometric trait variation

National Heart, Lung, and Blood Institute (NHLBI) S.L. is funded by a Canadian Institutes of Health Research Banting doctoral scholarship. G.L. is funded by Genome Canada and Génome Québec; the Canada Research Chairs program; and the Montreal Heart Institute Foundation. C.M.L. is supported by Wellcome Trust (grant numbers 086596/Z/08/Z, 086596/Z/08/A); and the Li Ka Shing Foundation. N.S. is funded by National Institutes of Health (grant numbers HL088456, HL111089, HL116747). The Mount Sinai BioMe Biobank Program is supported by the Andrea and Charles Bronfman Philanthropies. GO ESP is supported by NHLBI (RC2 HL-103010 to HeartGO, RC2 HL-102923 to LungGO, RC2 HL-102924 to WHISP). The ESP exome sequencing was performed through NHLBI (RC2 HL-102925 to BroadGO, RC2 HL- 102926 to SeattleGO). EGCUT work was supported through the Estonian Genome Center of University of Tartu by the Targeted Financing from the Estonian Ministry of Science and Education (grant number SF0180142s08); the Development Fund of the University of Tartu (grant number SP1GVARENG); the European Regional Development Fund to the Centre of Excellence in Genomics (EXCEGEN) [grant number 3.2.0304.11-0312]; and through FP7 (grant number 313010). EGCUT were further supported by the US National Institute of Health (grant number R01DK075787). A.K.M. was supported by an American Diabetes Association Mentor-Based Postdoctoral Fellowship (#7-12-MN- 02). The BioVU dataset used in the analyses described were obtained from Vanderbilt University Medical Centers BioVU which is supported by institutional funding and by the Vanderbilt CTSA grant ULTR000445 from NCATS/NIH. Genome-wide genotyping was funded by NIH grants RC2GM092618 from NIGMS/OD and U01HG004603 from NHGRI/NIGMS. Funding to pay the Open Access publication charges for this article was provided by a block grant from Research Councils UK to the University of Cambridge

Focused Ion Beam Fabrication

Contains reports on thirteen research projects and a list of publications.Defense Advanced Research Projects Agency/U.S. Army Research Office Contract DAAL03-88-K-0108National Science Foundation Grant ECS 89-21728MIT Lincoln Laboratory Innovative Research ProgramSEMATECH Contract 90-MC-503Micrion Contract M08774U.S. Army Research Office Contract DAAL03-87-K-0126IBM Corporatio

Compound heterozygous variants in NBAS as a cause of atypical osteogenesis imperfecta

Background Osteogenesis imperfecta (OI), the commonest inherited bone fragility disorder, affects 1 in 15,000 live births resulting in frequent fractures and reduced mobility, with significant impact on quality of life. Early diagnosis is important, as therapeutic advances can lead to improved clinical outcome and patient benefit. Report Whole exome sequencing in patients with OI identified, in two patients with a multi-system phenotype, compound heterozygous variants in NBAS (neuroblastoma amplified sequence). Patient 1: NBAS c.5741G > A p.(Arg1914His); c.3010C > T p.(Arg1004*) in a 10-year old boy with significant short stature, bone fragility requiring treatment with bisphosphonates, developmental delay and immunodeficiency. Patient 2: NBAS c.5741G > A p.(Arg1914His); c.2032C > T p.(Gln678*) in a 5-year old boy with similar presenting features, bone fragility, mild developmental delay, abnormal liver function tests and immunodeficiency. Discussion Homozygous missense NBAS variants cause SOPH syndrome (short stature; optic atrophy; Pelger-Huet anomaly), the same missense variant was found in our patients on one allele and a nonsense variant in the other allele. Recent literature suggests a multi-system phenotype. In this study, patient fibroblasts have shown reduced collagen expression, compared to control cells and RNAseq studies, in bone cells show that NBAS is expressed in osteoblasts and osteocytes of rodents and primates. These findings provide proof-of-concept that NBAS mutations have mechanistic effects in bone, and that NBAS variants are a novel cause of bone fragility, which is distinguishable from ‘Classical’ OI. Conclusions Here we report on variants in NBAS, as a cause of bone fragility in humans, and expand the phenotypic spectrum associated with NBAS. We explore the mechanism underlying NBAS and the striking skeletal phenotype in our patients

Genetic Evidence for a Normal-Weight "Metabolically Obese" Phenotype Linking Insulin Resistance, Hypertension, Coronary Artery Disease, and Type 2 Diabetes

The mechanisms that predispose to hypertension, coronary artery disease (CAD), and type 2 diabetes (T2D) in individuals of normal weight are poorly understood. In contrast, in monogenic primary lipodystrophy—a reduction in subcutaneous adipose tissue—it is clear that it is adipose dysfunction that causes severe insulin resistance (IR), hypertension, CAD, and T2D. We aimed to test the hypothesis that common alleles associated with IR also influence the wider clinical and biochemical profile of monogenic IR. We selected 19 common genetic variants associated with fasting insulin–based measures of IR. We used hierarchical clustering and results from genome-wide association studies of eight nondisease outcomes of monogenic IR to group these variants. We analyzed genetic risk scores against disease outcomes, including 12,171 T2D cases, 40,365 CAD cases, and 69,828 individuals with blood pressure measurements. Hierarchical clustering identified 11 variants associated with a metabolic profile consistent with a common, subtle form of lipodystrophy. A genetic risk score consisting of these 11 IR risk alleles was associated with higher triglycerides (β = 0.018; P = 4 × 10−29), lower HDL cholesterol (β = −0.020; P = 7 × 10−37), greater hepatic steatosis (β = 0.021; P = 3 × 10−4), higher alanine transaminase (β = 0.002; P = 3 × 10−5), lower sex-hormone-binding globulin (β = −0.010; P = 9 × 10−13), and lower adiponectin (β = −0.015; P = 2 × 10−26). The same risk alleles were associated with lower BMI (per-allele β = −0.008; P = 7 × 10−8) and increased visceral-to-subcutaneous adipose tissue ratio (β = −0.015; P = 6 × 10−7). Individuals carrying ≥17 fasting insulin–raising alleles (5.5% population) were slimmer (0.30 kg/m2) but at increased risk of T2D (odds ratio [OR] 1.46; per-allele P = 5 × 10−13), CAD (OR 1.12; per-allele P = 1 × 10−5), and increased blood pressure (systolic and diastolic blood pressure of 1.21 mmHg [per-allele P = 2 × 10−5] and 0.67 mmHg [per-allele P = 2 × 10−4], respectively) compared with individuals carrying ≤9 risk alleles (5.5% population). Our results provide genetic evidence for a link between the three diseases of the “metabolic syndrome” and point to reduced subcutaneous adiposity as a central mechanism