Global chemical effects of the microbiome include new bile-acid conjugations.

A mosaic of cross-phylum chemical interactions occurs between all metazoans and their microbiomes. A number of molecular families that are known to be produced by the microbiome have a marked effect on the balance between health and disease1-9. Considering the diversity of the human microbiome (which numbers over 40,000 operational taxonomic units10), the effect of the microbiome on the chemistry of an entire animal remains underexplored. Here we use mass spectrometry informatics and data visualization approaches11-13 to provide an assessment of the effects of the microbiome on the chemistry of an entire mammal by comparing metabolomics data from germ-free and specific-pathogen-free mice. We found that the microbiota affects the chemistry of all organs. This included the amino acid conjugations of host bile acids that were used to produce phenylalanocholic acid, tyrosocholic acid and leucocholic acid, which have not previously been characterized despite extensive research on bile-acid chemistry14. These bile-acid conjugates were also found in humans, and were enriched in patients with inflammatory bowel disease or cystic fibrosis. These compounds agonized the farnesoid X receptor in vitro, and mice gavaged with the compounds showed reduced expression of bile-acid synthesis genes in vivo. Further studies are required to confirm whether these compounds have a physiological role in the host, and whether they contribute to gut diseases that are associated with microbiome dysbiosis.

Novel Pan-ERR Agonists Ameliorate Heart Failure Through Enhancing Cardiac Fatty Acid Metabolism and Mitochondrial Function.

BACKGROUND: Cardiac metabolic dysfunction is a hallmark of heart failure (HF). Estrogen-related receptors ERRα and ERRγ are essential regulators of cardiac metabolism. Therefore, activation of ERR could be a potential therapeutic intervention for HF. However, in vivo studies demonstrating the potential usefulness of ERR agonist for HF treatment are lacking, because compounds with pharmacokinetics appropriate for in vivo use have not been available. METHODS: Using a structure-based design approach, we designed and synthesized 2 structurally distinct pan-ERR agonists, SLU-PP-332 and SLU-PP-915. We investigated the effect of ERR agonist on cardiac function in a pressure overload-induced HF model in vivo. We conducted comprehensive functional, multi-omics (RNA sequencing and metabolomics studies), and genetic dependency studies both in vivo and in vitro to dissect the molecular mechanism, ERR isoform dependency, and target specificity. RESULTS: Both SLU-PP-332 and SLU-PP-915 significantly improved ejection fraction, ameliorated fibrosis, and increased survival associated with pressure overload-induced HF without affecting cardiac hypertrophy. A broad spectrum of metabolic genes was transcriptionally activated by ERR agonists, particularly genes involved in fatty acid metabolism and mitochondrial function. Metabolomics analysis showed substantial normalization of metabolic profiles in fatty acid/lipid and tricarboxylic acid/oxidative phosphorylation metabolites in the mouse heart with 6-week pressure overload. ERR agonists increase mitochondria oxidative capacity and fatty acid use in vitro and in vivo. Using both in vitro and in vivo genetic dependency experiments, we show that ERRγ is the main mediator of ERR agonism-induced transcriptional regulation and cardioprotection and definitively demonstrated target specificity. ERR agonism also led to downregulation of cell cycle and development pathways, which was partially mediated by E2F1 in cardiomyocytes. CONCLUSIONS: ERR agonists maintain oxidative metabolism, which confers cardiac protection against pressure overload-induced HF in vivo. Our results provide direct pharmacologic evidence supporting the further development of ERR agonists as novel HF therapeutics.

Extent to which array genotyping and imputation with large reference panels approximate deep whole-genome sequencing.

Understanding the genetic basis of human diseases and traits is dependent on the identification and accurate genotyping of genetic variants. Deep whole-genome sequencing (WGS), the gold standard technology for SNP and indel identification and genotyping, remains very expensive for most large studies. Here, we quantify the extent to which array genotyping followed by genotype imputation can approximate WGS in studies of individuals of African, Hispanic/Latino, and European ancestry in the US and of Finnish ancestry in Finland (a population isolate). For each study, we performed genotype imputation by using the genetic variants present on the Illumina Core, OmniExpress, MEGA, and Omni 2.5M arrays with the 1000G, HRC, and TOPMed imputation reference panels. Using the Omni 2.5M array and the TOPMed panel, ≥90% of bi-allelic single-nucleotide variants (SNVs) are well imputed (r2 > 0.8) down to minor-allele frequencies (MAFs) of 0.14% in African, 0.11% in Hispanic/Latino, 0.35% in European, and 0.85% in Finnish ancestries. There was little difference in TOPMed-based imputation quality among the arrays with >700k variants. Individual-level imputation quality varied widely between and within the three US studies. Imputation quality also varied across genomic regions, producing regions where even common (MAF > 5%) variants were consistently not well imputed across ancestries. The extent to which array genotyping and imputation can approximate WGS therefore depends on reference panel, genotype array, sample ancestry, and genomic location. Imputation quality by variant or genomic region can be queried with our new tool, RsqBrowser, now deployed on the Michigan Imputation Server.

Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk.

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.

Author Correction: Exome sequencing of Finnish isolates enhances rare-variant association power.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Exome sequencing of Finnish isolates enhances rare-variant association power.

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

Ventilatory efficiency measured during sub-maximal cardiopulmonary exercise testing predicts postoperative outcomes following heart transplantation.

Cardiopulmonary exercise testing is commonly used to evaluate patients for heart transplantation. We assessed the utility of ventilatory efficiency (VE/VCO2 ) to predict perioperative outcomes following heart transplantation. We retrospectively reviewed all patients undergoing cardiopulmonary exercise testing prior to heart transplantation at our center. Spearman's coefficient showed a correlation between VE/VCO2 and ICU free days in the first 30-days post-transplant (R = -.37, p < .01). A VE /VCO2 cut-off >35 was associated with significantly lower median ICU-free days (23.0 vs. 27 days; p < .01) and a higher likelihood of postoperative morbidity (OR = 5.64, 95% CI = 1.75-18.16; p < .01). Multiple regression analysis controlling for peak oxygen consumption and right heart catheter parameters showed VE/VCO2 >35 is independently associated with lower ICU-free days (p < .01) and postoperative morbidity (p = .02). Peak oxygen consumption <15 mL/min/kg was not associated with higher ICU or hospital-free days. VE/VCO2 >35 independently predicts early postoperative morbidity in patients undergoing heart transplantation.

Exercise induced bronchodilation: a phenomenon more common, greater magnitude and more prolonged in older adults than in adolescents.

OBJECTIVE: There are few studies in clinically healthy subjects describing and quantifying exercise-induced bronchodilation (EIBd). This study aimed to describe and compare the magnitude and time course changes in post-exercise forced expired volume at the first second (FEV1) in healthy adolescents, younger adults, and older adults. METHODS: Adolescent (n = 73, aged 10-17 years), younger adult (n = 35, aged 18-25 years), and older adult (n = 25, aged 35-66 years) subjects with normal spirometry z-scores completed a maximal cardiopulmonary exercise test using the standardized exponential exercise test protocol on a cycle ergometer performed at stable temperature and humidity. Spirometry was performed pre-exercise and at 1-, 3-, 5-, and 10-minutes post-exercise to determine the percentage change in FEV1 compared to baseline. EIBd was defined as a ≥ 5% increase in post-exercise FEV1. RESULTS: Increases in FEV1 at one-minute post-exercise were observed in the adolescents (1.3%) and young adults (6.0%) with FEV1 returning to baseline after ten minutes. Compared to the adolescents, the older adults showed significantly greater and sustained increases in FEV1 at 1-, 3-, 5-, and 10-minutes post-exercise (6.4, 4.6, 4.7, and 3.8%, p < 0.05). At 1-minute post exercise a significantly greater proportion of younger adults (54%, p < 0.01) and older adults (64%, p < 0.01) demonstrated EIBd compared to the adolescent group (15%). CONCLUSION: Healthy older adults had a higher prevalence, greater magnitude and more prolonged EIBd compared to healthy adolescent and young adult subjects.

Adiponectin GWAS loci harboring extensive allelic heterogeneity exhibit distinct molecular consequences.

Loci identified in genome-wide association studies (GWAS) can include multiple distinct association signals. We sought to identify the molecular basis of multiple association signals for adiponectin, a hormone involved in glucose regulation secreted almost exclusively from adipose tissue, identified in the Metabolic Syndrome in Men (METSIM) study. With GWAS data for 9,262 men, four loci were significantly associated with adiponectin: ADIPOQ, CDH13, IRS1, and PBRM1. We performed stepwise conditional analyses to identify distinct association signals, a subset of which are also nearly independent (lead variant pairwise r2<0.01). Two loci exhibited allelic heterogeneity, ADIPOQ and CDH13. Of seven association signals at the ADIPOQ locus, two signals colocalized with adipose tissue expression quantitative trait loci (eQTLs) for three transcripts: trait-increasing alleles at one signal were associated with increased ADIPOQ and LINC02043, while trait-increasing alleles at the other signal were associated with decreased ADIPOQ-AS1. In reporter assays, adiponectin-increasing alleles at two signals showed corresponding directions of effect on transcriptional activity. Putative mechanisms for the seven ADIPOQ signals include a missense variant (ADIPOQ G90S), a splice variant, a promoter variant, and four enhancer variants. Of two association signals at the CDH13 locus, the first signal consisted of promoter variants, including the lead adipose tissue eQTL variant for CDH13, while a second signal included a distal intron 1 enhancer variant that showed ~2-fold allelic differences in transcriptional reporter activity. Fine-mapping and experimental validation demonstrated that multiple, distinct association signals at these loci can influence multiple transcripts through multiple molecular mechanisms.

Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits.

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL; <1 Mb from TSS, FDR < 1%). Compared to primary eQTL signals, secondary eQTL signals were located further from transcription start sites, had smaller effect sizes, and were less enriched in adipose tissue regulatory elements compared to primary signals. Among 2,843 cardiometabolic GWAS signals, 262 colocalized by LD and conditional analysis with 318 transcripts as primary and conditionally distinct secondary cis-eQTLs, including some across ancestries. Of cardiometabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulating lipid traits had the highest percentage of colocalized eQTLs (15% and 14%, respectively). Among alleles associated with increased cardiometabolic GWAS risk, approximately half (53%) were associated with decreased gene expression level. Mediation analyses of colocalized genes and cardiometabolic traits within the 434 individuals provided further evidence that gene expression influences variant-trait associations. These results identify hundreds of candidate genes that may act in adipose tissue to influence cardiometabolic traits.

ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition-implications for COVID-19.

BACKGROUND: COVID-19 severity varies widely. Although some demographic and cardio-metabolic factors, including age and obesity, are associated with increasing risk of severe illness, the underlying mechanism(s) are uncertain. SUBJECTS/METHODS: In a meta-analysis of three independent studies of 1471 participants in total, we investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 (ACE2), measured by RNA-Seq, which acts as a receptor for SARS-CoV-2 cellular entry. RESULTS: Lower adipose tissue ACE2 expression was associated with multiple adverse cardio-metabolic health indices, including type 2 diabetes (T2D) (P = 9.14 × 10-6), obesity status (P = 4.81 × 10-5), higher serum fasting insulin (P = 5.32 × 10-4), BMI (P = 3.94 × 10-4), and lower serum HDL levels (P = 1.92 × 10-7). ACE2 expression was also associated with estimated proportions of cell types in adipose tissue: lower expression was associated with a lower proportion of microvascular endothelial cells (P = 4.25 × 10-4) and higher proportion of macrophages (P = 2.74 × 10-5). Despite an estimated heritability of 32%, we did not identify any proximal or distal expression quantitative trait loci (eQTLs) associated with adipose tissue ACE2 expression. CONCLUSIONS: Our results demonstrate that individuals with cardio-metabolic features known to increase risk of severe COVID-19 have lower background ACE2 levels in this highly relevant tissue. Reduced adipose tissue ACE2 expression may contribute to the pathophysiology of cardio-metabolic diseases, as well as the associated increased risk of severe COVID-19.

LocusZoom.js: interactive and embeddable visualization of genetic association study results.

SUMMARY: LocusZoom.js is a JavaScript library for creating interactive web-based visualizations of genetic association study results. It can display one or more traits in the context of relevant biological data (such as gene models and other genomic annotation), and allows interactive refinement of analysis models (by selecting linkage disequilibrium reference panels, identifying sets of likely causal variants, or comparisons to the GWAS catalog). It can be embedded in web pages to enable data sharing and exploration. Views can be customized and extended to display other data types such as phenome-wide association study (PheWAS) results, chromatin co-accessibility, or eQTL measurements. A new web upload service harmonizes datasets, adds annotations, and makes it easy to explore user-provided result sets. AVAILABILITY AND IMPLEMENTATION: LocusZoom.js is open-source software under a permissive MIT license. Code and documentation are available at: https://github.com/statgen/locuszoom/. Installable packages for all versions are also distributed via NPM. Additional features are provided as standalone libraries to promote reuse. Use with your own GWAS results at https://my.locuszoom.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle.

We integrate comeasured gene expression and DNA methylation (DNAme) in 265 human skeletal muscle biopsies from the FUSION study with >7 million genetic variants and eight physiological traits: height, waist, weight, waist-hip ratio, body mass index, fasting serum insulin, fasting plasma glucose, and type 2 diabetes. We find hundreds of genes and DNAme sites associated with fasting insulin, waist, and body mass index, as well as thousands of DNAme sites associated with gene expression (eQTM). We find that controlling for heterogeneity in tissue/muscle fiber type reduces the number of physiological trait associations, and that long-range eQTMs (>1 Mb) are reduced when controlling for tissue/muscle fiber type or latent factors. We map genetic regulators (quantitative trait loci; QTLs) of expression (eQTLs) and DNAme (mQTLs). Using Mendelian randomization (MR) and mediation techniques, we leverage these genetic maps to predict 213 causal relationships between expression and DNAme, approximately two-thirds of which predict methylation to causally influence expression. We use MR to integrate FUSION mQTLs, FUSION eQTLs, and GTEx eQTLs for 48 tissues with genetic associations for 534 diseases and quantitative traits. We identify hundreds of genes and thousands of DNAme sites that may drive the reported disease/quantitative trait genetic associations. We identify 300 gene expression MR associations that are present in both FUSION and GTEx skeletal muscle and that show stronger evidence of MR association in skeletal muscle than other tissues, which may partially reflect differences in power across tissues. As one example, we find that increased RXRA muscle expression may decrease lean tissue mass.

A Common Type 2 Diabetes Risk Variant Potentiates Activity of an Evolutionarily Conserved Islet Stretch Enhancer and Increases C2CD4A and C2CD4B Expression.

Genome-wide association studies (GWASs) and functional genomics approaches implicate enhancer disruption in islet dysfunction and type 2 diabetes (T2D) risk. We applied genetic fine-mapping and functional (epi)genomic approaches to a T2D- and proinsulin-associated 15q22.2 locus to identify a most likely causal variant, determine its direction of effect, and elucidate plausible target genes. Fine-mapping and conditional analyses of proinsulin levels of 8,635 non-diabetic individuals from the METSIM study support a single association signal represented by a cluster of 16 strongly associated (p < 10-17) variants in high linkage disequilibrium (r2 > 0.8) with the GWAS index SNP rs7172432. These variants reside in an evolutionarily and functionally conserved islet and ß cell stretch or super enhancer; the most strongly associated variant (rs7163757, p = 3 × 10-19) overlaps a conserved islet open chromatin site. DNA sequence containing the rs7163757 risk allele displayed 2-fold higher enhancer activity than the non-risk allele in reporter assays (p < 0.01) and was differentially bound by ß cell nuclear extract proteins. Transcription factor NFAT specifically potentiated risk-allele enhancer activity and altered patterns of nuclear protein binding to the risk allele in vitro, suggesting that it could be a factor mediating risk-allele effects. Finally, the rs7163757 proinsulin-raising and T2D risk allele (C) was associated with increased expression of C2CD4B, and possibly C2CD4A, both of which were induced by inflammatory cytokines, in human islets. Together, these data suggest that rs7163757 contributes to genetic risk of islet dysfunction and T2D by increasing NFAT-mediated islet enhancer activity and modulating C2CD4B, and possibly C2CD4A, expression in (patho)physiologic states.

Identification of seven novel loci associated with amino acid levels using single-variant and gene-based tests in 8545 Finnish men from the METSIM study.

Comprehensive metabolite profiling captures many highly heritable traits, including amino acid levels, which are potentially sensitive biomarkers for disease pathogenesis. To better understand the contribution of genetic variation to amino acid levels, we performed single variant and gene-based tests of association between nine serum amino acids (alanine, glutamine, glycine, histidine, isoleucine, leucine, phenylalanine, tyrosine, and valine) and 16.6 million genotyped and imputed variants in 8545 non-diabetic Finnish men from the METabolic Syndrome In Men (METSIM) study with replication in Northern Finland Birth Cohort (NFBC1966). We identified five novel loci associated with amino acid levels (P = < 5×10-8): LOC157273/PPP1R3B with glycine (rs9987289, P = 2.3×10-26); ZFHX3 (chr16:73326579, minor allele frequency (MAF) = 0.42%, P = 3.6×10-9), LIPC (rs10468017, P = 1.5×10-8), and WWOX (rs9937914, P = 3.8×10-8) with alanine; and TRIB1 with tyrosine (rs28601761, P = 8×10-9). Gene-based tests identified two novel genes harboring missense variants of MAF <1% that show aggregate association with amino acid levels: PYCR1 with glycine (Pgene = 1.5×10-6) and BCAT2 with valine (Pgene = 7.4×10-7); neither gene was implicated by single variant association tests. These findings are among the first applications of gene-based tests to identify new loci for amino acid levels. In addition to the seven novel gene associations, we identified five independent signals at established amino acid loci, including two rare variant signals at GLDC (rs138640017, MAF=0.95%, Pconditional = 5.8×10-40) with glycine levels and HAL (rs141635447, MAF = 0.46%, Pconditional = 9.4×10-11) with histidine levels. Examination of all single variant association results in our data revealed a strong inverse relationship between effect size and MAF (Ptrend<0.001). These novel signals provide further insight into the molecular mechanisms of amino acid metabolism and potentially, their perturbations in disease.

Common, low-frequency, and rare genetic variants associated with lipoprotein subclasses and triglyceride measures in Finnish men from the METSIM study.

Lipid and lipoprotein subclasses are associated with metabolic and cardiovascular diseases, yet the genetic contributions to variability in subclass traits are not fully understood. We conducted single-variant and gene-based association tests between 15.1M variants from genome-wide and exome array and imputed genotypes and 72 lipid and lipoprotein traits in 8,372 Finns. After accounting for 885 variants at 157 previously identified lipid loci, we identified five novel signals near established loci at HIF3A, ADAMTS3, PLTP, LCAT, and LIPG. Four of the signals were identified with a low-frequency (0.005

Genetic regulatory signatures underlying islet gene expression and type 2 diabetes.

Genome-wide association studies (GWAS) have identified >100 independent SNPs that modulate the risk of type 2 diabetes (T2D) and related traits. However, the pathogenic mechanisms of most of these SNPs remain elusive. Here, we examined genomic, epigenomic, and transcriptomic profiles in human pancreatic islets to understand the links between genetic variation, chromatin landscape, and gene expression in the context of T2D. We first integrated genome and transcriptome variation across 112 islet samples to produce dense cis-expression quantitative trait loci (cis-eQTL) maps. Additional integration with chromatin-state maps for islets and other diverse tissue types revealed that cis-eQTLs for islet-specific genes are specifically and significantly enriched in islet stretch enhancers. High-resolution chromatin accessibility profiling using assay for transposase-accessible chromatin sequencing (ATAC-seq) in two islet samples enabled us to identify specific transcription factor (TF) footprints embedded in active regulatory elements, which are highly enriched for islet cis-eQTL. Aggregate allelic bias signatures in TF footprints enabled us de novo to reconstruct TF binding affinities genetically, which support the high-quality nature of the TF footprint predictions. Interestingly, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprints. Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions. Together, these results suggest that common regulatory variations have shaped islet TF footprints and the transcriptome and that a confluent RFX regulatory grammar plays a significant role in the genetic component of T2D predisposition.

Evaluation of Modified Two-Tiered Testing Algorithms for Lyme Disease Laboratory Diagnosis Using Well-Characterized Serum Samples.

Standard two-tiered testing (STTT) is the recommended algorithm for laboratory diagnosis of Lyme disease (LD). Several limitations are associated with STTT that include low sensitivity in the early stages of disease, as well as technical complexity and subjectivity associated with second-tier immunoblotting; therefore, modified two-tiered testing (MTTT) algorithms that utilize two sequential first-tier tests and eliminate immunoblotting have been evaluated. Recently, a novel MTTT that uses a VlsE chemiluminescence immunoassay followed by a C6 enzyme immunoassay has been proposed. The purpose of this study was to evaluate the performance of the VlsE/C6 MTTT using well-characterized serum samples. Serum samples from the CDC Lyme Serum Repository were tested using three MTTTs, VlsE/C6, whole-cell sonicate (WCS)/C6, and WCS/VlsE, and three STTTs (immunoblotting preceded by three different first-tier assays: VlsE, C6, and WCS). Significant differences were not observed between the results of the MTTTs assessed; however, the VlsE/C6 MTTT resulted in the highest specificity (100%) when other diseases were tested and the lowest sensitivity (75%) for LD samples. Significant differences were present between the results for various MTTTs and STTTs evaluated. Specifically, all MTTTs resulted in higher sensitivities than the STTTs for all LD groups combined and were significantly more accurate (i.e., higher proportion of correct classifications) for this group, with the exception of the WCS/ViraStripe STTT. Additionally, when other diseases were tested, only the results of the VlsE/C6 MTTT differed significantly from those of the WCS/ViraStripe STTT, with the VlsE/C6 MTTT resulting in a 6.2% higher accuracy. Overall, the VlsE/C6 MTTT offers an additional laboratory testing algorithm for LD with equivalent or enhanced performance compared to that of the other MTTTs and STTTs evaluated in this study.

REV-ERB and ROR: therapeutic targets for treating myopathies.

Muscle is primarily known for its mechanical roles in locomotion, maintenance of posture, and regulation of cardiac and respiratory function. There are numerous medical conditions that adversely affect muscle, myopathies that disrupt muscle development, regeneration and protein turnover to detrimental effect. Skeletal muscle is also a vital secretory organ that regulates thermogenesis, inflammatory signaling and directs context specific global metabolic changes in energy substrate preference on a daily basis. Myopathies differ in the causative factors that drive them but share common features including severe reduction in quality of life and significantly increased mortality all due irrefutably to the loss of muscle mass. Thus far clinically viable approaches for preserving muscle proteins and stimulating new muscle growth without unwanted side effects or limited efficacy has been elusive. Over the last few decades, evidence has emerged through in vitro and in vivo studies that suggest the nuclear receptors REV-ERB and ROR might modulate pathways involved in myogenesis and mitochondrial biogenesis. Hinting that REV-ERB and ROR might be targeted to treat myopathies. However there is still a need for substantial investigation into the roles of these nuclear receptors in in vivo rodent models of degenerative muscle diseases and acute injury. Although exciting, REV-ERB and ROR have somewhat confounding roles in muscle physiology and therefore more studies utilizing in vivo models of skeletal muscle myopathies are needed. In this review we highlight the molecular forces driving some of the major degenerative muscular diseases and showcase two promising molecular targets that may have the potential to treat myopathies: ROR and REV-ERB.

Re-sequencing expands our understanding of the phenotypic impact of variants at GWAS loci.

Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20-30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5' and 3' untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants.