Lyplal1 is dispensable for normal fat deposition in mice

Genome-wide identification of quantitative trait loci and candidate genes for seven carcass traits in a four-way intercross porcine population

BACKGROUND

Carcass traits are essential economic traits in the commercial pig industry. However, the genetic mechanism of carcass traits is still unclear. In this study, we performed a genome-wide association study (GWAS) based on the specific-locus amplified fragment sequencing (SLAF-seq) to study seven carcass traits on 223 four-way intercross pigs, including dressing percentage (DP), number of ribs (RIB), skin thinkness (ST), carcass straight length (CSL), carcass diagonal length (CDL), loin eye width (LEW), and loin eye thickness (LET).

RESULTS

A total of 227,921 high-quality single nucleotide polymorphisms (SNPs) were detected to perform GWAS. A total of 30 SNPs were identified for seven carcass traits using the mixed linear model (MLM) (p < 1.0 × 10- 5), of which 9 SNPs were located in previously reported quantitative trait loci (QTL) regions. The phenotypic variation explained (PVE) by the significant SNPs was from 2.43 to 16.32%. Furthermore, 11 candidate genes (LYPLAL1, EPC1, MATN2, ZFAT, ZBTB10, ZNF704, INHBA, SMYD3, PAK1, SPTBN2, and ACTN3) were found for carcass traits in pigs.

CONCLUSIONS

The GWAS results will improve our understanding of the genetic basis of carcass traits. We hypothesized that the candidate genes associated with these discovered SNPs would offer a biological basis for enhancing the carcass quality of pigs in swine breeding.

Multiple Linear Regression Allows Weighted Burden Analysis of Rare Coding Variants in an Ethnically Heterogeneous Population

Weighted burden analysis has been used in exome-sequenced case-control studies to identify genes in which there is an excess of rare and/or functional variants associated with phenotype. Implementation in a ridge regression framework allows simultaneous analysis of all variants along with relevant covariates, such as population principal components. In order to apply the approach to a quantitative phenotype, a weighted burden score is derived for each subject and included in a linear regression analysis. The weighting scheme is adjusted in order to apply differential weights to rare and very rare variants and a score is derived based on both the frequency and predicted effect of each variant. When applied to an ethnically heterogeneous dataset consisting of 49,790 exome-sequenced UK Biobank subjects and using body mass index as the phenotype, the method produces a very inflated test statistic. However, this is almost completely corrected by including 20 population principal components as covariates. When this is done, the top 30 genes include a few which are quite plausibly associated with the phenotype, including LYPLAL1 and NSDHL. This approach offers a way to carry out gene-based analyses of rare variants identified by exome sequencing in heterogeneous datasets without requiring that data from ethnic minority subjects be discarded. This research has been conducted using the UK Biobank Resource.

Discovery of small-molecule enzyme activators by activity-based protein profiling

Activity-based protein profiling (ABPP) has been used extensively to discover and optimize selective inhibitors of enzymes. Here, we show that ABPP can also be implemented to identify the converse – small-molecule enzyme activators. Using a kinetically controlled, fluorescence polarization-ABPP assay, we identify compounds that stimulate the activity of LYPLAL1 – a poorly characterized serine hydrolase with complex genetic links to human metabolic traits. We apply ABPP-guided medicinal chemistry to advance a lead into a selective LYPLAL1 activator suitable for use in vivo. Structural simulations coupled to mutational, biochemical, and biophysical analyses indicate that this compound increases LYPLAL1’s catalytic activity likely by enhancing the efficiency of the catalytic triad charge-relay system. Treatment with this LYPLAL1 activator confers beneficial effects in a mouse model of diet-induced obesity. These findings reveal a new mode of pharmacological regulation for this large enzyme family and suggest that ABPP may aid discovery of activators for additional enzyme classes.

Gene-by-Sex Interactions in Mitochondrial Functions and Cardio-Metabolic Traits

We studied sex differences in over 50 cardio-metabolic traits in a panel of 100 diverse inbred strains of mice. The results clearly showed that the effects of sex on both clinical phenotypes and gene expression depend on the genetic background. In support of this, genetic loci associated with the traits frequently showed sex specificity. For example, Lyplal1, a gene implicated in human obesity, was shown to underlie a sex-specific locus for diet-induced obesity. Global gene expression analyses of tissues across the panel implicated adipose tissue "beiging" and mitochondrial functions in the sex differences. Isolated mitochondria showed gene-by-sex interactions in oxidative functions, such that some strains (C57BL/6J) showed similar function between sexes, whereas others (DBA/2J and A/J) showed increased function in females. Reduced adipose mitochondrial function in males as compared to females was associated with increased susceptibility to obesity and insulin resistance. Gonadectomy studies indicated that gonadal hormones acting in a tissue-specific manner were responsible in part for the sex differences.

The genetics of adiposity

Genome-wide discovery efforts have identified more than 500 genetic loci associated with adiposity traits. The vast majority of these loci were found through large-scale meta-analyses for body mass index (BMI) and waist-to-hip ratio (WHR), and in European ancestry populations. However, alternative approaches, focusing on non-European ancestry populations, more refined adiposity measures, and low-frequency (minor allele frequency (MAF)<5%) coding variants, identified additional novel loci that had not been identified before. Loci associated with overall obesity implicate pathways that act in the brain, whereas loci associated with fat distribution point to pathways involved in adipocyte biology. Pinpointing the causal gene within each locus remains challenging, but is a critical step towards translation of genome-wide association study (GWAS) loci into new biology. Ultimately, new genes may provide pharmacological targets for the development of weight loss drugs.