Comparative transcriptomics of mountain pine beetle pheromone-biosynthetic tissues and functional analysis of CYP6DE3.

BACKGROUND: The mountain pine beetle (MPB, Dendroctonus ponderosae Hopkins) is a highly destructive pest of pine forests in western North America. During flight to a new host tree and initiation of feeding, mountain pine beetles release aggregation pheromones. The biosynthetic pathways of these pheromones are sex-specific and localized in the midgut and fat body, but the enzymes involved have not all been identified or characterized. RESULTS: We used a comparative RNA-Seq analysis between fed and unfed male and female MPB midguts and fat bodies to identify candidate genes involved in pheromone biosynthesis. The 13,407 potentially unique transcripts showed clear separation based on feeding state and gender. Gene co-expression network construction and examination using petal identified gene groups that were tightly connected. This, as well as other co-expression and gene ontology analyses, identified all four known pheromone biosynthetic genes, confirmed the tentative identification of four others from a previous study, and suggested nine novel candidates. One cytochrome P450 monooxygenase, CYP6DE3, identified as a possible exo-brevicomin-biosynthetic enzyme in this study, was functionally characterized and likely is involved in resin detoxification rather than pheromone biosynthesis. CONCLUSIONS: Our analysis supported previously characterized pheromone-biosynthetic genes involved in exo-brevicomin and frontalin biosynthesis and identified a number of candidate cytochrome P450 monooxygenases and a putative cyclase for further studies. Functional analyses of CYP6DE3 suggest its role in resin detoxification and underscore the limitation of using high-throughput data to tentatively identify candidate genes. Further functional analyses of candidate genes found in this study should lead to the full characterization of MPB pheromone biosynthetic pathways and the identification of molecular targets for possible pest management strategies.

Population genetic screening efficiently identifies carriers of autosomal dominant diseases.

Three inherited autosomal dominant conditions-BRCA-related hereditary breast and ovarian cancer (HBOC), Lynch syndrome (LS) and familial hypercholesterolemia (FH)-have been termed the Centers for Disease Control and Prevention Tier 1 (CDCT1) genetic conditions, for which early identification and intervention have a meaningful potential for clinical actionability and a positive impact on public health1. In typical medical practice, genetic testing for these conditions is based on personal or family history, ethnic background or other demographic characteristics2. In this study of a cohort of 26,906 participants in the Healthy Nevada Project (HNP), we first evaluated whether population screening could efficiently identify carriers of these genetic conditions and, second, we evaluated the impact of genetic risk on health outcomes for these participants. We found a 1.33% combined carrier rate for pathogenic and likely pathogenic (P/LP) genetic variants for HBOC, LS and FH. Of these carriers, 21.9% of participants had clinically relevant disease, among whom 70% had been diagnosed with relevant disease before age 65. Moreover, 90% of the risk carriers had not been previously identified, and less than 19.8% of these had documentation in their medical records of inherited genetic disease risk, including family history. In a direct follow-up survey with all carriers, only 25.2% of individuals reported a family history of relevant disease. Our experience with the HNP suggests that genetic screening in patients could identify at-risk carriers, who would not be otherwise identified in routine care.

Adding confidence to gene expression clustering.

It has been well established that gene expression data contain large amounts of random variation that affects both the analysis and the results of microarray experiments. Typically, microarray data are either tested for differential expression between conditions or grouped on the basis of profiles that are assessed temporally or across genetic or environmental conditions. While testing differential expression relies on levels of certainty to evaluate the relative worth of various analyses, cluster analysis is exploratory in nature and has not had the benefit of any judgment of statistical inference. By using a novel dissimilarity function to ascertain gene expression clusters and conditional randomization of the data space to illuminate distinctions between statistically significant clusters of gene expression patterns, we aim to provide a level of confidence to inferred clusters of gene expression data. We apply both permutation and convex hull approaches for randomization of the data space and show that both methods can provide an effective assessment of gene expression profiles whose coregulation is statistically different from that expected by random chance alone.

Genome-wide association analysis identifies genetic variations in subjects with myalgic encephalomyelitis/chronic fatigue syndrome.

Myalgic encephalomyelitis, also known as chronic fatigue syndrome or ME/CFS, is a multifactorial and debilitating disease that has an impact on over 4 million people in the United States alone. The pathogenesis of ME/CFS remains largely unknown; however, a genetic predisposition has been suggested. In the present study, we used a DNA single-nucleotide polymorphism (SNP) chip representing over 906,600 known SNPs to analyze DNA from ME/CFS subjects and healthy controls. To the best of our knowledge, this study represents the most comprehensive genome-wide association study (GWAS) of an ME/CFS cohort conducted to date. Here 442 SNPs were identified as candidates for association with ME/CFS (adjusted P-value<0.05). Whereas the majority of these SNPs are represented in non-coding regions of the genome, 12 SNPs were identified in the coding region of their respective gene. Among these, two candidate SNPs resulted in missense substitutions, one in a pattern recognition receptor and the other in an uncharacterized coiled-coil domain-containing protein. We also identified five SNPs that cluster in the non-coding regions of T-cell receptor loci. Further examination of these polymorphisms may help identify contributing factors to the pathophysiology of ME/CFS, as well as categorize potential targets for medical intervention strategies.

Gene expression associated with compatible viral diseases in grapevine cultivars.

Viral diseases affect grapevine cultures without inducing any resistance response. Thus, these plants develop systemic diseases and are chronically infected. Molecular events associated with viral compatible infections responsible for disease establishment and symptoms development are poorly understood. In this study, we surveyed viral infection in grapevines at a transcriptional level. Gene expression in the Vitis vinifera red wine cultivars Carménère and Cabernet-Sauvignon naturally infected with GLRaV-3 were evaluated using a genome-wide expression profiling with the Vitis vinifera GeneChip from Affymetrix. We describe numerous genes that are induced or repressed in viral infected grapevines leaves. Changes in gene expression involved a wide spectrum of biological functions, including processes of translation and protein targeting, metabolism, transport, and cell defense. Considering cellular localization, the membrane and endomembrane systems appeared with the highest number of induced genes, while chloroplastic genes were mostly repressed. As most induced genes associated with the membranous system are involved in transport, the possible effect of virus in this process is discussed. Responses of both cultivars are analyzed and the results are compared with published data from other species. This is the first study of global gene profiling in grapevine in response to viral infections using DNA microarray.

Expression profile of ovarian tumors: distinct signature of Sertoli-Leydig cell tumor.

Epithelial ovarian tumors are the most common subtype of ovarian cancer. In this study, we reveal distinct expression signatures of previously uncharacterized ovarian carcinoma subtypes, including endometrioid component of mixed ovarian tumor and Sertoli-Leydig tumor. Both subtypes were compared to the most common and well-characterized ovarian epithelial carcinoma of the serous type. These comparisons were performed by complementaryDNA (cDNA) microarrays allowing high-fidelity measurements of the expression levels of 39,360 human individual cDNA species representing both known and unknown human genes. Functional analysis of differentially expressed genes in Sertoli-Leydig tumor revealed an upregulation in sonic hedgehog pathway, deregulation of several metabolic pathways especially in amino acid metabolism and overexpression of genes associated with protein synthesis, including ribosomal genes.