Gain-of-Function R225W Mutation in Human AMPKγ3 Causing Increased Glycogen and Decreased Triglyceride in Skeletal Muscle

BACKGROUND: AMP-activated protein kinase (AMPK) is a heterotrimeric enzyme that is evolutionarily conserved from yeast to mammals and functions to maintain cellular and whole body energy homeostasis. Studies in experimental animals demonstrate that activation of AMPK in skeletal muscle protects against insulin resistance, type 2 diabetes and obesity. The regulatory gamma(3) subunit of AMPK is expressed exclusively in skeletal muscle; however, its importance in controlling overall AMPK activity is unknown. While evidence is emerging that gamma subunit mutations interfere specifically with AMP activation, there remains some controversy regarding the impact of gamma subunit mutations. Here we report the first gain-of-function mutation in the muscle-specific regulatory gamma(3) subunit in humans. METHODS AND FINDINGS: We sequenced the exons and splice junctions of the AMPK gamma(3) gene (PRKAG3) in 761 obese and 759 lean individuals, identifying 87 sequence variants including a novel R225W mutation in subjects from two unrelated families. The gamma(3) R225W mutation is homologous in location to the gamma(2)R302Q mutation in patients with Wolf-Parkinson-White syndrome and to the gamma(3)R225Q mutation originally linked to an increase in muscle glycogen content in purebred Hampshire Rendement Napole (RN-) pigs. We demonstrate in differentiated muscle satellite cells obtained from the vastus lateralis of R225W carriers that the mutation is associated with an approximate doubling of both basal and AMP-activated AMPK activities. Moreover, subjects bearing the R225W mutation exhibit a approximately 90% increase of skeletal muscle glycogen content and a approximately 30% decrease in intramuscular triglyceride (IMTG). CONCLUSIONS: We have identified for the first time a mutation in the skeletal muscle-specific regulatory gamma(3) subunit of AMPK in humans. The gamma(3)R225W mutation has significant functional effects as demonstrated by increases in basal and AMP-activated AMPK activities, increased muscle glycogen and decreased IMTG. Overall, these findings are consistent with an important regulatory role for AMPK gamma(3) in human muscle energy metabolism

Bat accelerated regions identify a bat forelimb specific enhancer in the HoxD locus

Author Summary: The limb is a classic example of vertebrate homology and is represented by a large range of morphological structures such as fins, legs and wings. The evolution of these structures could be driven by alterations in gene regulatory elements that have critical roles during development. To identify elements that may contribute to bat wing development, we characterized sequences that are conserved between vertebrates, but changed significantly in the bat lineage. We then overlapped these sequences with predicted developing limb enhancers as determined by ChIP-seq, finding 166 bat accelerated sequences (BARs). Five BARs that were tested for enhancer activity in mice all drove expression in the limb. Testing the mouse orthologous sequence showed that three had differences in their limb enhancer activity as compared to the bat sequence. Of these, BAR116 was of particular interest as it is located near the HoxD locus, an essential gene complex required for proper spatiotemporal patterning of the developing limb. The bat BAR116 sequence drove robust forelimb expression but the mouse BAR116 sequence did not show enhancer activity. These experiments correspond to analyses of HoxD gene expressions in developing bat limbs, which had strong forelimb versus weak hindlimb expression for Hoxd10 - 11 . Combined, our studies highlight specific genomic regions that could be important in shaping the morphological differences that led to the development of the bat wing

Structural Relationships between Highly Conserved Elements and Genes in Vertebrate Genomes

Large numbers of sequence elements have been identified to be highly conserved among vertebrate genomes. These highly conserved elements (HCEs) are often located in or around genes that are involved in transcription regulation and early development. They have been shown to be involved in cis-regulatory activities through both in vivo and additional computational studies. We have investigated the structural relationships between such elements and genes in six vertebrate genomes human, mouse, rat, chicken, zebrafish and tetraodon and detected several thousand cases of conserved HCE-gene associations, and also cases of HCEs with no common target genes. A few examples underscore the potential significance of our findings about several individual genes. We found that the conserved association between HCE/HCEs and gene/genes are not restricted to elements by their absolute distance on the genome. Notably, long-range associations were identified and the molecular functions of the associated genes do not show any particular overrepresentation of the functional categories previously reported. HCEs in close proximity are found to be linked with different set of gene/genes. The results reflect the highly complex correlation between HCEs and their putative target genes

Improved Detection of Rare Genetic Variants for Diseases

Technology advances have promoted gene-based sequencing studies with the aim of identifying rare mutations responsible for complex diseases. A complication in these types of association studies is that the vast majority of non-synonymous mutations are believed to be neutral to phenotypes. It is thus critical to distinguish potential causative variants from neutral variation before performing association tests. In this study, we used existing predicting algorithms to predict functional amino acid substitutions, and incorporated that information into association tests. Using simulations, we comprehensively studied the effects of several influential factors, including the sensitivity and specificity of functional variant predictions, number of variants, and proportion of causative variants, on the performance of association tests. Our results showed that incorporating information regarding functional variants obtained from existing prediction algorithms improves statistical power under certain conditions, particularly when the proportion of causative variants is moderate. The application of the proposed tests to a real sequencing study confirms our conclusions. Our work may help investigators who are planning to pursue gene-based sequencing studies

Methods for adjusting population structure and familial relatedness in association test for collective effect of multiple rare variants on quantitative traits

Because of the low frequency of rare genetic variants in observed data, the statistical power of detecting their associations with target traits is usually low. The collapsing test of collective effect of multiple rare variants is an important and useful strategy to increase the power; in addition, family data may be enriched with causal rare variants and therefore provide extra power. However, when family data are used, both population structure and familial relatedness need to be adjusted for the possible inflation of false positives. Using a unified mixed linear model and family data, we compared six methods to detect the association between multiple rare variants and quantitative traits. Through the analysis of 200 replications of the quantitative trait Q2 from the Genetic Analysis Workshop 17 data set simulated for 697 subjects from 8 extended families, and based on quantile-quantile plots under the null and receiver operating characteristic curves, we compared the false-positive rate and power of these methods. We observed that adjusting for pedigree-based kinship gives the best control for false-positive rate, whereas adjusting for marker-based identity by state slightly outperforms in terms of power. An adjustment based on a principal components analysis slightly improves the false-positive rate and power. Taking into account type-1 error, power, and computational efficiency, we find that adjusting for pedigree-based kinship seems to be a good choice for the collective test of association between multiple rare variants and quantitative traits using family data

Discovery of Rare Variants via Sequencing: Implications for the Design of Complex Trait Association Studies

There is strong evidence that rare variants are involved in complex disease etiology. The first step in implicating rare variants in disease etiology is their identification through sequencing in both randomly ascertained samples (e.g., the 1,000 Genomes Project) and samples ascertained according to disease status. We investigated to what extent rare variants will be observed across the genome and in candidate genes in randomly ascertained samples, the magnitude of variant enrichment in diseased individuals, and biases that can occur due to how variants are discovered. Although sequencing cases can enrich for casual variants, when a gene or genes are not involved in disease etiology, limiting variant discovery to cases can lead to association studies with dramatically inflated false positive rates

A study of the distribution of phylogenetically conserved blocks within clusters of mammalian homeobox genes

Genome sequencing efforts of the last decade have produced a large amount of data, which has enabled whole-genome comparative analyses in order to locate potentially functional elements and study the overall patterns of phylogenetic conservation. In this paper we present a statistically based method for the characterization of these patterns in mammalian DNA sequences. We have applied this approach to the study of exceptionally well conserved homeobox gene clusters (Hox), based on an alignment of six species, and we have constructed a map of Hox cataloguing the conserved fragments, along with their locations in relation to the genes and other landmarks, sometimes showing unexpected layouts

Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay

The integrative analysis of highâ throughput reporter assays, machine learning, and profiles of epigenomic chromatin state in a broad array of cells and tissues has the potential to significantly improve our understanding of noncoding regulatory element function and its contribution to human disease. Here, we report results from the CAGI 5 regulation saturation challenge where participants were asked to predict the impact of nucleotide substitution at every base pair within five diseaseâ associated human enhancers and nine diseaseâ associated promoters. A library of mutations covering all bases was generated by saturation mutagenesis and altered activity was assessed in a massively parallel reporter assay (MPRA) in relevant cell lines. Reporter expression was measured relative to plasmid DNA to determine the impact of variants. The challenge was to predict the functional effects of variants on reporter expression. Comparative analysis of the full range of submitted prediction results identifies the most successful models of transcription factor binding sites, machine learning algorithms, and ways to choose among or incorporate diverse datatypes and cellâ types for training computational models. These results have the potential to improve the design of future studies on more diverse sets of regulatory elements and aid the interpretation of diseaseâ associated genetic variation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151884/1/humu23797_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151884/2/humu23797.pd