POST: A framework for set-based association analysis in high-dimensional data.

Evaluating the differential expression of a set of genes belonging to a common biological process or ontology has proven to be a very useful tool for biological discovery. However, existing gene-set association methods are limited to applications that evaluate differential expression across k⩾2 treatment groups or biological categories. This limitation precludes researchers from most effectively evaluating the association with other phenotypes that may be more clinically meaningful, such as quantitative variables or censored survival time variables. Projection onto the Orthogonal Space Testing (POST) is proposed as a general procedure that can robustly evaluate the association of a gene-set with several different types of phenotypic data (categorical, ordinal, continuous, or censored). For each gene-set, POST transforms the gene profiles into a set of eigenvectors and then uses statistical modeling to compute a set of z-statistics that measure the association of each eigenvector with the phenotype. The overall gene-set statistic is the sum of squared z-statistics weighted by the corresponding eigenvalues. Finally, bootstrapping is used to compute a p-value. POST may evaluate associations with or without adjustment for covariates. In simulation studies, it is shown that the performance of POST in evaluating the association with a categorical phenotype is similar to or exceeds that of existing methods. In evaluating the association of 875 biological processes with the time to relapse of pediatric acute myeloid leukemia, POST identified the well-known oncogenic WNT signaling pathway as its top hit. These results indicate that POST can be a very useful tool for evaluating the association of a gene-set with a variety of different phenotypes. We have developed an R package named POST which is freely available in Bioconductor.

Diversity of hydrolases from hydrothermal vent sediments of the Levante Bay, Vulcano Island (Aeolian archipelago) identified by activity-based metagenomics and biochemical characterization of new esterases and an arabinopyranosidase.

A metagenomic fosmid expression library established from environmental DNA (eDNA) from the shallow hot vent sediment sample collected from the Levante Bay, Vulcano Island (Aeolian archipelago) was established in Escherichia coli. Using activity-based screening assays, we have assessed 9600 fosmid clones corresponding to approximately 350 Mbp of the cloned eDNA, for the lipases/esterases/lactamases, haloalkane and haloacid dehalogenases, and glycoside hydrolases. Thirty-four positive fosmid clones were selected from the total of 120 positive hits and sequenced to yield ca. 1360 kbp of high-quality assemblies. Fosmid inserts were attributed to the members of ten bacterial phyla, including Proteobacteria, Bacteroidetes, Acidobateria, Firmicutes, Verrucomicrobia, Chloroflexi, Spirochaetes, Thermotogae, Armatimonadetes, and Planctomycetes. Of ca. 200 proteins with high biotechnological potential identified therein, we have characterized in detail three distinct α/ß-hydrolases (LIPESV12_9, LIPESV12_24, LIPESV12_26) and one new α-arabinopyranosidase (GLV12_5). All LIPESV12 enzymes revealed distinct substrate specificities tested against 43 structurally diverse esters and 4 p-nitrophenol carboxyl esters. Of 16 different glycosides tested, the GLV12_5 hydrolysed only p-nitrophenol-α-(L)-arabinopyranose with a high specific activity of about 2.7 kU/mg protein. Most of the α/ß-hydrolases were thermophilic and revealed a high tolerance to, and high activities in the presence of, numerous heavy metal ions. Among them, the LIPESV12_24 was the best temperature-adapted, retaining its activity after 40 min of incubation at 90 °C. Furthermore, enzymes were active in organic solvents (e.g., >30% methanol). Both LIPESV12_24 and LIPESV12_26 had the GXSXG pentapeptides and the catalytic triads Ser-Asp-His typical to the representatives of carboxylesterases of EC 3.1.1.1.