Common variants of ZNF750, RPTOR and TRAF3IP2 genes and psoriasis risk

Psoriasis vulgaris is a genetically heterogenous disease with unclear molecular background. We assessed the association of psoriasis and its main clinical phenotypes with common variants of three potential psoriasis susceptibility genes: ZNF750, RPTOR and TRAF31P2. We genotyped 10 common variants in a cohort of 1,034 case–control individuals using Taqman genotyping assays and sequencing. Minor alleles of all four TRAF3IP2 variants were more frequent among cases. The strongest, significant association was observed for rs33980500 (OR = 2.5, p = 0.01790). Minor allele of this SNP was always present in two haplotypes found to be associated with increased psoriasis risk: rs13196377_G + rs13190932_G + rs33980500_T + rs13210247_A (OR = 2.7, p = 0.0054) and rs13196377_A + rs13190932_A + rs33980500_T + rs13210247_G (OR = 1.8, p = 0.0008). Analyses of clinically relevant phenotypes revealed association of rs33980500 with pustular psoriasis (OR = 1.2, p = 0.0109). We observed significant connection of severity of cutaneous disease with variation at rs13190932 and suggestive with three remaining TRAF3IP2 SNPs. Another positive associations were found between age of onset and familial aggregation of disease: smoking and younger age of onset, smoking and occurrence of pustular psoriasis, nail involvement and arthropatic psoriasis, nail involvement and more severe course of psoriasis. We found no statistically significant differences in the prevalence of the examined variants of RPTOR and ZNF750 genes among our cases and controls. We have replicated the association of TRAF3IP2-_rs33980500 variant with the susceptibility to psoriasis. We have found new associations with clinically relevant subphenotypes such as pustular psoriasis or moderate-to-severe cases. We ascertain no connection of RPTOR and ZNF750 variants with psoriasis or its subphenotypes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00403-013-1407-9) contains supplementary material, which is available to authorized users

Standard methods for molecular research in Apis mellifera

From studies of behaviour, chemical communication, genomics and developmental biology, among many others, honey bees have long been a key organism for fundamental breakthroughs in biology. With a genome sequence in hand, and much improved genetic tools, honey bees are now an even more appealing target for answering the major questions of evolutionary biology, population structure, and social organization. At the same time, agricultural incentives to understand how honey bees fall prey to disease, or evade and survive their many pests and pathogens, have pushed for a genetic understanding of individual and social immunity in this species. Below we describe and reference tools for using modern molecular-biology techniques to understand bee behaviour, health, and other aspects of their biology. We focus on DNA and RNA techniques, largely because techniques for assessing bee proteins are covered in detail in Hartfelder et al. (2013). We cover practical needs for bee sampling, transport, and storage, and then discuss a range of current techniques for genetic analysis. We then provide a roadmap for genomic resources and methods for studying bees, followed by specific statistical protocols for population genetics, quantitative genetics, and phylogenetics. Finally, we end with three important tools for predicting gene regulation and function in honey bees: Fluorescence in situ hybridization (FISH), RNA interference (RNAi), and the estimation of chromosomal methylation and its role in epigenetic gene regulation.Fundação para a Ciência e Tecnologi

The Honey Bee Epigenomes: Differential Methylation of Brain DNA in Queens and Workers

Using genome-wide methylation profiles in honey bee queen and worker brains to understand how contrasting organismal outputs are generated from the same genotype

Epigenetic Gene Promoter Methylation at Birth Is Associated With Child’s Later Adiposity

Objective: fixed genomic variation explains only a small proportion of the risk of adiposity. In animal models, maternal diet alters offspring body composition, accompanied by epigenetic changes in metabolic control genes. Little is known about whether such processes operate in humans.Research design and methods: using Sequenom MassARRAY we measured the methylation status of 68 CpGs 5? from five candidate genes in umbilical cord tissue DNA from healthy neonates. Methylation varied greatly at particular CpGs: for 31 CpGs with median methylation ?5% and a 5–95% range ?10%, we related methylation status to maternal pregnancy diet and to child’s adiposity at age 9 years. Replication was sought in a second independent cohort.Results: in cohort 1, retinoid X receptor-? (RXRA) chr9:136355885+ and endothelial nitric oxide synthase (eNOS) chr7:150315553+ methylation had independent associations with sex-adjusted childhood fat mass (exponentiated regression coefficient [?] 17% per SD change in methylation [95% CI 4–31], P = 0.009, n = 64, and ? = 20% [9–32], P < 0.001, n = 66, respectively) and %fat mass (? = 10% [1–19], P = 0.023, n = 64 and ? =12% [4–20], P = 0.002, n = 66, respectively). Regression analyses including sex and neonatal epigenetic marks explained >25% of the variance in childhood adiposity. Higher methylation of RXRA chr9:136355885+, but not of eNOS chr7:150315553+, was associated with lower maternal carbohydrate intake in early pregnancy, previously linked with higher neonatal adiposity in this population. In cohort 2, cord eNOS chr7:150315553+ methylation showed no association with adiposity, but RXRA chr9:136355885+ methylation showed similar associations with fat mass and %fat mass (? = 6% [2–10] and ? = 4% [1–7], respectively, both P = 0.002, n = 239).Conclusions: our findings suggest a substantial component of metabolic disease risk has a prenatal developmental basis. Perinatal epigenetic analysis may have utility in identifying individual vulnerability to later obesity and metabolic diseas

Carbohydrate metabolism genes and pathways in insects: insights from the honey bee genome

Carbohydrate-metabolizing enzymes may have particularly interesting roles in the honey bee, Apis mellifera, because this social insect has an extremely carbohydrate-rich diet, and nutrition plays important roles in caste determination and socially mediated behavioural plasticity. We annotated a total of 174 genes encoding carbohydrate-metabolizing enzymes and 28 genes encoding lipid-metabolizing enzymes, based on orthology to their counterparts in the fly, Drosophila melanogaster, and the mosquito, Anopheles gambiae. We found that the number of genes for carbohydrate metabolism appears to be more evolutionarily labile than for lipid metabolism. In particular, we identified striking changes in gene number or genomic organization for genes encoding glycolytic enzymes, cellulase, glucose oxidase and glucose dehydrogenases, glucose-methanol-choline (GMC) oxidoreductases, fucosyltransferases, and lysozymes

Diet and Cell Size Both Affect Queen-Worker Differentiation through DNA Methylation in Honey Bees (Apis mellifera, Apidae)

Young larvae of the honey bee (Apis mellifera) are totipotent; they can become either queens (reproductives) or workers (largely sterile helpers). DNA methylation has been shown to play an important role in this differentiation. In this study, we examine the contributions of diet and cell size to caste differentiation.We measured the activity and gene expression of one key enzyme involved in methylation, Dnmt3; the rates of methylation in the gene dynactin p62; as well as morphological characteristics of adult bees developed either from larvae fed with worker jelly or royal jelly; and larvae raised in either queen or worker cells. We show that both diet type and cell size contributed to the queen-worker differentiation, and that the two factors affected different methylation sites inside the same gene dynactin p62.We confirm previous findings that Dnmt3 plays a critical role in honey bee caste differentiation. Further, we show for the first time that cell size also plays a role in influencing larval development when diet is kept the same

Finding the missing honey bee genes: lessons learned from a genome upgrade

BACKGROUND: The first generation of genome sequence assemblies and annotations have had a significant impact upon our understanding of the biology of the sequenced species, the phylogenetic relationships among species, the study of populations within and across species, and have informed the biology of humans. As only a few Metazoan genomes are approaching finished quality (human, mouse, fly and worm), there is room for improvement of most genome assemblies. The honey bee (Apis mellifera) genome, published in 2006, was noted for its bimodal GC content distribution that affected the quality of the assembly in some regions and for fewer genes in the initial gene set (OGSv1.0) compared to what would be expected based on other sequenced insect genomes. RESULTS: Here, we report an improved honey bee genome assembly (Amel_4.5) with a new gene annotation set (OGSv3.2), and show that the honey bee genome contains a number of genes similar to that of other insect genomes, contrary to what was suggested in OGSv1.0. The new genome assembly is more contiguous and complete and the new gene set includes ~5000 more protein-coding genes, 50% more than previously reported. About 1/6 of the additional genes were due to improvements to the assembly, and the remaining were inferred based on new RNAseq and protein data. CONCLUSIONS: Lessons learned from this genome upgrade have important implications for future genome sequencing projects. Furthermore, the improvements significantly enhance genomic resources for the honey bee, a key model for social behavior and essential to global ecology through pollination.Funding for the project was provided by a grant to RG from the National Human Genome Research Institute, National Institutes of Health (NHGRI, NIH) U54 HG003273. Contributions from members of the CGE lab were supported by Agriculture and Food Research Initiative Competitive grant no. 2010- 65205-20407 from the USDA National Institute of Food Agriculture. AKB was supported by a Clare Luce Booth Fellowship at Georgetown University