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Heath, A.

Publications and source records attributed to Heath, A..

5 recordsLinked to original sources

Evolution and diversification dynamics of butterflies

Butterflies are a diverse and charismatic insect group that are thought to have diversified via coevolution with plants and in response to dispersals following key geological events. These hypotheses have been poorly tested at the macroevolutionary scale because a comprehensive phylogenetic framework and datasets on global distributions and larval hosts of butterflies are lacking. We sequenced 391 genes from nearly 2,000 butterfly species to construct a new, phylogenomic tree of butterflies representing 92% of all genera and aggregated global distribution records and larval host datasets. We found that butterflies likely originated in what is now the Americas, [~]100 Ma, shortly before the Cretaceous Thermal Maximum, then crossed Beringia and diversified in the Paleotropics. The ancestor of modern butterflies likely fed on Fabaceae, and most extant families were present before the K/Pg extinction. The majority of butterfly dispersals occurred from the tropics (especially the Neotropics) to temperate zones, largely supporting a "cradle" pattern of diversification. Surprisingly, host breadth changes and shifts to novel host plants had only modest impacts.

evolutionary biology↗

Pex11 β knockdown decreases peroxisome abundance and reverses the inhibitory effect of palmitate on pancreatic beta-cell function.

AimsReactive oxygen species generated by the peroxisomes and mitochondria contribute to lipotoxicity in pancreatic beta-cells. Through targeted Pex11{beta} knockdown and peroxisome depletion, our aim was to investigate the specific contribution of peroxisomes to palmitate mediated pancreatic beta-cell dysfunction. MethodsMIN6 cells were transfected with probes targeted against Pex11{beta}, a regulator of peroxisome abundance, or with scrambled control probes. Peroxisome abundance was measured by PMP-70 protein expression. 48hrs post transfection, cells were incubated with or without 250M palmitate for a further 48hrs before measurement of reactive oxygen species, mitochondrial respiratory function, and glucose stimulated insulin secretion. ResultsPex11{beta} knockdown decreased target gene expression by more than 80% compared with the scrambled control (P<0.001), leading to decreased PMP-70 expression (p<0.01). Pex11{beta} knockdown decreased palmitate mediated generation of reactive oxygen species (P<0.001), but with no effect on mitochondrial respiratory function. At 25mM glucose, palmitate treatment decreased insulin secretion in the control cells (2.54{+/-}0.25 vs 7.07{+/-}0.83 [mean{+/-}SEM] ng/hr/g protein; P<0.001), with a similar pattern in the Pex11{beta} knockdown cells. However, in the presence of palmitate, insulin secretion was significantly higher in the Pex11{beta} knockdown versus control cells (4.04{+/-}0.46 vs 2.54{+/-}0.25 ng/hr/g protein; p<0.05). ConclusionPex11{beta} knockdown decreased peroxisome abundance, decreased palmitate mediated ROS generation, and reversed the inhibitory effect of palmitate on insulin secretion. These findings highlight a specific and independent role for peroxisomes in pancreatic beta-cell lipotoxicity.

cell biology↗

Uniform Genomic Data Analysis in the NCI Genomic Data Commons

The goal of the National Cancer Institute (NCI) Genomic Data Commons (GDC) is to provide the cancer research community with a data repository of uniformly processed genomic and associated clinical data that enables data sharing and collaborative analysis in the support of precision medicine. The initial GDC dataset include genomic, epigenomic, proteomic, clinical and other data from the NCI TCGA and TARGET programs. Data production for the GDC started in June, 2015 using an OpenStack-based private cloud. By June of 2016, the GDC had analyzed more than 50,000 raw sequencing data inputs, as well as multiple other data types. Using the latest human genome reference build GRCh38, the GDC generated a variety of data types from aligned reads to somatic mutations, gene expression, miRNA expression, DNA methylation status, and copy number variation. In this paper, we describe the pipelines and workflows used to process and harmonize the data in the GDC. The generated data, as well as the original input files from TCGA and TARGET, are available for download and exploratory analysis at the GDC Data Portal and Legacy Archive (https://gdc.cancer.gov/).

bioinformatics↗

Comprehensive cross-disorder analyses of CNTNAP2 suggest it is unlikely to be a primary risk gene for psychiatric disorders.

The contactin-associated protein-like 2 (CNTNAP2) gene is a member of the neurexin superfamily. CNTNAP2 was implicated in the cortical dysplasia-focal epilepsy (CDFE) syndrome, a recessive disease characterized by intellectual disability, epilepsy, language impairments and autistic features. Associated SNPs and heterozygous deletions in CNTNAP2 have also frequently been reported in autism, schizophrenia and other psychiatric or neurological disorders. We aim here to gain conclusive evidence for the role of CNTNAP2 in susceptibility to psychiatric disorders by the comprehensive analysis of large genomic datasets. In this study we used: i) summary statistics from the Psychiatric Genomics Consortium (PGC) GWAS; ii) examined all reported CNTNAP2 structural variants in patients and controls; iii) performed cross-disorder analysis of functional or previously associated SNPs; iv) and conducted burden tests for pathogenic rare variants using sequencing data (4,483 ASD and 6,135 schizophrenia cases, and 13,042 controls).\n\nIn a CNV mircroarray study, we previously identified a 131kb deletion in CNTNAP2 intron 1, removing a FOXP2 transcription factor binding site in an extended BD family. Here we perform a quantitative-PCR validation showing imperfect segregation with disease (5 bipolar disorder relatives). The distribution of CNVs across CNTNAP2 in psychiatric cases from previous reports was no different from controls of the database of genomic variants. Gene-based association testing did not implicate common variants in autism, schizophrenia or other psychiatric phenotypes. The association of proposed functional SNPs rs7794745 and rs2710102, reported to influence brain connectivity, was not replicated; nor did functional SNPs yield significant results in meta-analysis across psychiatric disorders. Disrupting CNTNAP2 rare variant burden was not higher in autism or schizophrenia compared to controls. This large comprehensive candidate gene study indicates that CNTNAP2 may not be a robust risk gene for psychiatric phenotypes.\n\nAUTHOR SUMMARYGenetic mutations that disrupt both copies of the CNTNAP2 gene lead to severe disease, characterized by profound intellectual disability, epilepsy, language difficulties and autistic traits. Researchers hypothesized that this gene may also be involved in autism given some overlapping clinical features with this disease. Indeed, several large DNA deletions affecting one of the two copies of CNTNAP2 were found in some patients with autism, and later also in patients with schizophrenia, bipolar disorder, ADHD and epilepsy, suggesting that this gene was involved in several psychiatric or neurologic diseases. Other studies considered genetic sequence variations that are common in the general population, and suggested that two such sequence variations in CNTNAP2 predispose to psychiatric diseases by influencing the functionality and connectivity of the brain. In the current study, we report the deletion of one copy of CNTNAP2 in a patient with bipolar disorder from an extended family where five relatives were affected with this condition. To better understand the involvement of CNTNAP2 in risk of mental illness, we performed several genetic analyses using a series of large publically available or in-house datasets, comprising many thousands of patients and controls. Despite the previous consideration of CNTNAP2 as a strong candidate gene for autism or schizophrenia, we show that neither common, deletion nor ultra-rare variants in CNTNAP2 are likely to play a major role in risk of psychiatric diseases.

genetics↗

Exome chip meta-analysis elucidates the genetic architecture of rare coding variants in smoking and drinking behavior

BackgroundSmoking and alcohol use behaviors in humans have been associated with common genetic variants within multiple genomic loci. Investigation of rare variation within these loci holds promise for identifying causal variants impacting biological mechanisms in the etiology of disordered behavior. Microarrays have been designed to genotype rare nonsynonymous and putative loss of function variants. Such variants are expected to have greater deleterious consequences on gene function than other variants, and significantly contribute to disease risk.\n\nMethodsIn the present study, we analyzed [~]250,000 rare variants from 17 independent studies. Each variant was tested for association with five addiction-related phenotypes: cigarettes per day, pack years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted single variant tests of all variants, and gene-based burden tests of nonsynonymous or putative loss of function variants with minor allele frequency less than 1%.\n\nResultsMeta-analytic sample sizes ranged from 70,847 to 164,142 individuals, depending on the phenotype. Known loci tagged by common variants replicated, but there was no robust evidence for individually associated rare variants, either in gene based or single variant tests. Using a modified method-of-moment approach, we found that all low frequency coding variants, in aggregate, contributed 1.7% to 3.6% of the phenotypic variation for the five traits (p<.05).\n\nConclusionsThe findings indicate that rare coding variants contribute to phenotypic variation, but that much larger samples and/or denser genotyping of rare variants will be required to successfully identify associations with these phenotypes, whether individual variants or gene- based associations.

genetics↗