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Background: Obesity predisposes individuals to multiple cardiometabolic disorders, including type 2 diabetes (T2D). As body mass index (BMI) cannot reliably differentiate fat from lean mass, the metabolically detrimental abdominal obesity has been estimated using waist-hip ratio (WHR). Waist-hip ratio adjusted for body mass index (WHRadjBMI) in turn is a well-established sex-specific marker for abdominal fat and adiposity, and a predictor of adverse metabolic outcomes, such as T2D. However, the underlying genes and regulatory mechanisms orchestrating the sex differences in obesity and body fat distribution in humans are not well understood. Methods: We searched for genetic master regulators of WHRadjBMI by employing integrative genomics approaches on human subcutaneous adipose RNA sequencing (RNA-seq) data (n ~ 1400) and WHRadjBMI GWAS data (n ~ 700,000) from the WHRadjBMI GWAS cohorts and the UK Biobank (UKB), using co-expression network, transcriptome-wide association study (TWAS), and polygenic risk score (PRS) approaches. Finally, we functionally verified our genomic results using gene knockdown experiments in a human primary cell type that is critical for adipose tissue function. Results: Here, we identified an adipose gene co-expression network that contains 35 obesity GWAS genes and explains a significant amount of polygenic risk for abdominal obesity and T2D in the UKB (n = 392,551) in a sex-dependent way. We showed that this network is preserved in the adipose tissue data from the Finnish Kuopio Obesity Study and Mexican Obesity Study. The network is controlled by a novel adipose master transcription factor (TF), TBX15, a WHRadjBMI GWAS gene that regulates the network in trans. Knockdown of TBX15 in human primary preadipocytes resulted in changes in expression of 130 network genes, including the key adipose TFs, PPARG and KLF15, which were significantly impacted (FDR < 0.05), thus functionally verifying the trans regulatory effect of TBX15 on the WHRadjBMI co-expression network. Conclusions: Our study discovers a novel key function for the TBX15 TF in trans regulating an adipose co-expression network of 347 adipose, mitochondrial, and metabolically important genes, including PPARG, KLF15, PPARA, ADIPOQ, and 35 obesity GWAS genes. Thus, based on our converging genomic, transcriptional, and functional evidence, we interpret the role of TBX15 to be a main transcriptional regulator in the adipose tissue and discover its importance in human abdominal obesity. [ABSTRACT FROM AUTHOR]

Reverse causality has made it difficult to establish the causal directions between obesity and prediabetes and obesity and insulin resistance. To disentangle whether obesity causally drives prediabetes and insulin resistance already in non-diabetic individuals, we utilized the UK Biobank and METSIM cohort to perform a Mendelian randomization (MR) analyses in the non-diabetic individuals. Our results suggest that both prediabetes and systemic insulin resistance are caused by obesity (p = 1.2×10−3 and p = 3.1×10−24). As obesity reflects the amount of body fat, we next studied how adipose tissue affects insulin resistance. We performed both bulk RNA-sequencing and single nucleus RNA sequencing on frozen human subcutaneous adipose biopsies to assess adipose cell-type heterogeneity and mitochondrial (MT) gene expression in insulin resistance. We discovered that the adipose MT gene expression and body fat percent are both independently associated with insulin resistance (p≤0.05 for each) when adjusting for the decomposed adipose cell-type proportions. Next, we showed that these 3 factors, adipose MT gene expression, body fat percent, and adipose cell types, explain a substantial amount (44.39%) of variance in insulin resistance and can be used to predict it (p≤2.64×10−5 in 3 independent human cohorts). In summary, we demonstrated that obesity is a strong determinant of both prediabetes and insulin resistance, and discovered that individuals' adipose cell-type composition, adipose MT gene expression, and body fat percent predict their insulin resistance, emphasizing the critical role of adipose tissue in systemic insulin resistance. Author summary: Obesity is a global health epidemic predisposing to type 2 diabetes (T2D) and other cardiometabolic disorders. Previous studies have shown that obesity has a causal effect on T2D; however, it remains unknown whether obesity causes prediabetes and insulin resistance already in non-diabetic individuals. By utilizing almost half a million individuals from the UK Biobank and the Finnish METSIM cohort, we identified a significant causal effect of obesity on prediabetes and insulin resistance among the non-diabetic individuals. Next, we investigated the role of subcutaneous adipose tissue in these obesogenic effects. We discovered that the adipose mitochondrial gene expression and body fat percent are independently associated with insulin resistance after adjusting for the tissue heterogeneity. For the latter, we estimated the adipose cell type proportions by utilizing single-nucleus RNA sequencing of frozen adipose tissue biopsies. Moreover, we established a prediction model to estimate insulin resistance using body fat percent and adipose RNA-sequencing data, which enlightens the importance of adipose tissue in insulin resistance and provides a helpful tool to impute the insulin resistance for existing adipose RNA-sequencing cohorts. Overall, we discover the potential causal effect of obesity on prediabetes and insulin resistance and the key role of adipose tissue in insulin resistance. [ABSTRACT FROM AUTHOR]

C16H11BF2N2, monoclinic, P21/c (no. 14), a = 9.0978(14) Å, b = 18.580(3) Å, c = 15.476(2) Å, β = 99.516(5)°, V = 2580.0(7) Å3, Z = 8, Rgt(F) = 0.0406, wRref(F2) = 0.1307, T = 193 K. [ABSTRACT FROM AUTHOR]

Motivation: Mapping bias causes preferential alignment to the reference allele, forming a major obstacle in allele-specific expression (ASE) analysis. The existing methods, such as simulation and SNP-aware alignment, are either inaccurate or relatively slow. To fast and accurately count allelic reads for ASE analysis, we developed a novel approach, ASElux, which utilizes the personal SNP information and counts allelic reads directly from unmapped RNA-sequence (RNA-seq) data. ASElux significantly reduces runtime by disregarding reads outside single nucleotide polymorphisms (SNPs) during the alignment. Results: When compared to other tools on simulated and experimental data, ASElux achieves a higher accuracy on ASE estimation than non-SNP-aware aligners and requires a much shorter time than the benchmark SNP-aware aligner, GSNAP with just a slight loss in performance. ASElux can process 40 million read-pairs from an RNA-sequence (RNA-seq) sample and count allelic reads within 10 min, which is comparable to directly counting the allelic reads from alignments based on other tools. Furthermore, processing an RNA-seq sample using ASElux in conjunction with a general aligner, such as STAR, is more accurate and still ~4x faster than STAR + WASP, and ~33x faster than the lead SNP-aware aligner, GSNAP, making ASElux ideal for ASE analysis of largescale transcriptomic studies. We applied ASElux to 273 lung RNA-seq samples from GTEx and identified a splice-QTL rs11078928 in lung which explains the mechanism underlying an asthma GWAS SNP rs11078927. Thus, our analysis demonstrated ASE as a highly powerful complementary tool to cis-expression quantitative trait locus (eQTL) analysis. [ABSTRACT FROM AUTHOR]

C18H15BN2O2, monolinic, P21/n (no. 14), a = 13.61(2) Å, b = 7.389(11) Å, c = 16.34(2) Å, β = 113.91(2)°, V = 1502(4) Å3, Z = 4, Rgt(F) = 0.0544, wRref(F2) = 0.1651, T = 273 K. [ABSTRACT FROM AUTHOR]

C18H17BN2O2, monoclinic, P21/c (no. 14), a = 12.5782(14) Å, b = 13.6724(14) Å, c = 8.7113(9) Å, β = 97.819(6)°, V = 1484.2(3) Å3, Z = 4, Rgt(F) = 0.0482, wRref(F2) = 0.1357, T = 193 K. [ABSTRACT FROM AUTHOR]

C18H15BN2O2, monoclinic, P21/c (no. 14), a = 6.8522(9) Å, b = 8.4333(11) Å, c = 13.1908(18) Å, β = 95.880(5)°, V = 758.24(17) Å3, Z = 2, Rgt(F) = 0.0381, wRref(F2) = 0.1068, T = 193 K. CCDC no.: 2045109 [ABSTRACT FROM AUTHOR]

C17H13BN2O2, orthorhombic, Pbca (no. 61), a = 10.0820(14) Å, b = 13.1135(19) Å, c = 20.644(3) Å, V = 2729.4 Å3, Z = 8, Rgt(F) = 0.0373, wRref(F2) = 0.1005, T = 193 K. [ABSTRACT FROM AUTHOR]

Due to the growth of interest in single-cell genomics, computational methods for distinguishing true variants from artifacts are highly desirable. While special attention has been paid to false positives in variant or mutation calling from single-cell sequencing data, an equally important but often neglected issue is that of false negatives derived from allele dropout during the amplification of single cell genomes. In this paper, we propose a simple strategy to reduce the false negatives in single-cell sequencing data analysis. Simulation results show that this method is highly reliable, with an error rate of 4.94×10-5, which is orders of magnitude lower than the expected false negative rate (~34%) estimated from a single-cell exome dataset, though the method is limited by the low SNP density in the human genome. We applied this method to analyze the exome data of a few dozen single tumor cells generated in previous studies, and extracted cell specific mutation information for a small set of sites. Interestingly, we found that there are difficulties in using the classical clonal model of tumor cell growth to explain the mutation patterns observed in some tumor cells. [ABSTRACT FROM AUTHOR]

B Correction to: Genome Med 13, 123 (2021) b B https://doi.org/10.1186/s13073-021-00939-2 b It was highlighted that in the original article [[1]] the UK Biobank Resource Application Number was incorrect (3934). Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes. [Extracted from the article]

C21H25EuN2O10, monoclinic, P21/n (no. 14), a = 14.1635(8) Å, b = 12.1381(6) Å, c = 14.4528(7) Å, β = 104.361(2)°, V = 2407.1(2) Å3, Z = 4, Rgt(F) = 0.0304, wRref(F2) = 0.0893, T = 296 K. [ABSTRACT FROM AUTHOR]

C32H50Eu2N16O24, monoclinic, P21/c (no. 14), a = 7.685(5) Å, b = 13.482(5) Å, c = 23.240(5) Å, β = 93.578(5)°, V = 2403.2(19) Å3, Z = 2, Rgt(F) = 0.0235, wRref(F2) = 0.0577, T = 293 K. [ABSTRACT FROM AUTHOR]

C16H12BBrN2, monoclinic, P21 (no. 4), a = 4.7061(3) Å, b = 10.4639(6) Å, c = 13.5675(8) Å, β = 97.512(2)°, V = 662.39(7) Å3, Z = 2, Rgt(F) = 0.0384, wRref(F2) = 0.0995, T = 189.98 K. [ABSTRACT FROM AUTHOR]

Genetic factors underlying coronary artery disease (CAD) have been widely studied using genome-wide association studies (GWASs). However, the functional understanding of the CAD loci has been limited by the fact that a majority of GWAS variants are located within non-coding regions with no functional role. High cholesterol and dysregulation of the liver metabolism such as non-alcoholic fatty liver disease confer an increased risk of CAD. Here, we studied the function of non-coding single-nucleotide polymorphisms in CAD GWAS loci located within liver-specific enhancer elements by identifying their potential target genes using liver cis -eQTL analysis and promoter Capture Hi-C in HepG2 cells. Altogether, 734 target genes were identified of which 121 exhibited correlations to liver-related traits. To identify potentially causal regulatory SNPs, the allele-specific enhancer activity was analyzed by (1) sequence-based computational predictions, (2) quantification of allele-specific transcription factor binding, and (3) STARR-seq massively parallel reporter assay. Altogether, our analysis identified 1,277 unique SNPs that display allele-specific regulatory activity. Among these, susceptibility enhancers near important cholesterol homeostasis genes (APOB, APOC1, APOE , and LIPA) were identified, suggesting that altered gene regulatory activity could represent another way by which genetic variation regulates serum lipoprotein levels. Using CRISPR-based perturbation, we demonstrate how the deletion/activation of a single enhancer leads to changes in the expression of many target genes located in a shared chromatin interaction domain. Our integrative genomics approach represents a comprehensive effort in identifying putative causal regulatory regions and target genes that could predispose to clinical manifestation of CAD by affecting liver function. [ABSTRACT FROM AUTHOR]

Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. We observe that snRNA-seq is commonly subject to contamination by high amounts of ambient RNA, which can lead to biased downstream analyses, such as identification of spurious cell types if overlooked. We present a novel approach to quantify contamination and filter droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: (1) human differentiating preadipocytes in vitro, (2) fresh mouse brain tissue, and (3) human frozen adipose tissue (AT) from six individuals. All three data sets showed evidence of extranuclear RNA contamination, and we observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq, our clustering strategy also successfully filtered single-cell RNA-seq data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem. [ABSTRACT FROM AUTHOR]

The 24-h rhythm of behavioral and physiological processes is a typical biological phenomenon regulated by a group of circadian rhythm genes. Dysfunction of the circadian rhythm can cause a wide range of problems, such as cancer and metabolic diseases. In recent decades, increased understanding of the roles of circadian rhythm genes in the bone remodeling process have been documented, including osteoblastic bone formation, osteoclastic bone resorption, and osteoblast/osteoclast communication. A timely review of the current findings may help to facilitate the new field of circadian rhythmic bone remodeling research. Targeted pharmacological modulation of circadian rhythm genes is a possible therapeutic approach through which to overcome bone remodeling problems in the future. [ABSTRACT FROM AUTHOR]