The hookworm Necator americanus is the predominant soil-transmitted human parasite. Adult worms feed on blood in the small intestine, causing iron deficiency anaemia, malnutrition, growth and development stunting in children, and severe morbidity and mortality during pregnancy in women. Characterization of the first hookworm genome sequence (244 Mb, 19,151 genes) identified genes orchestrating the hookworm's invasion of the human host, genes involved in blood feeding and development, and genes encoding proteins that represent new potential drug targets against hookworms. N. americanus has undergone a considerable and unique expansion of immunomodulator proteins, some of which we highlight as potential novel treatments against inflammatory diseases. We also utilize a protein microarray to demonstrate a post-genomic application of the hookworm genome sequence. This genome provides an invaluable resource to boost ongoing efforts towards fundamental and applied post-genomic research, including the development of new methods to control hookworm and human immunological diseases.

Human saliva is clinically informative of both oral and general health. Since next generation shotgun sequencing (NGS) is now widely used to identify and quantify bacteria, we investigated the bacterial flora of saliva microbiomes of two healthy volunteers and five datasets from the Human Microbiome Project, along with a control dataset containing short NGS reads from bacterial species representative of the bacterial flora of human saliva. GENIUS, a system designed to identify and quantify bacterial species using unassembled short NGS reads was used to identify the bacterial species comprising the microbiomes of the saliva samples and datasets. Results, achieved within minutes and at greater than 90% accuracy, showed more than 175 bacterial species comprised the bacterial flora of human saliva, including bacteria known to be commensal human flora but also Haemophilus influenzae, Neisseria meningitidis, Streptococcus pneumoniae, and Gamma proteobacteria. Basic Local Alignment Search Tool (BLASTn) analysis in parallel, reported ca. five times more species than those actually comprising the in silico sample. Both GENIUSand BLAST analyses of saliva samples identified major genera comprising the bacterial flora of saliva, but GENIUS provided a more precise description of species composition, identifying to strain in most cases and delivered results at least 10,000 times faster. Therefore, GENIUS offers a facile and accurate system for identification and quantification of bacterial species and/or strains in metagenomic samples.

For most prokaryotic organisms, amino acid biosynthesis represents a significant portion of their overall energy budget. The difference in the cost of synthesis between amino acids can be striking, differing by as much as 7-fold. Two prokaryotic organisms, Escherichia coli and Bacillus subtilis, have been shown to preferentially utilize less costly amino acids in highly expressed genes, indicating that parsimony in amino acid selection may confer a selective advantage for prokaryotes. This study confirms those findings and extends them to 4 additional prokaryotic organisms: Chlamydia trachomatis, Chlamydophila pneumoniae AR39, Synechocystis sp. PCC 6803, and Thermus thermophilus HB27. Adherence to codon-usage biases for each of these 6 organisms is inversely correlated with a coding region's average amino acid biosynthetic cost in a fashion that is independent of chemoheterotrophic, photoautotrophic, or thermophilic lifestyle. The obligate parasites C. trachomatis and C. pneumoniae AR39 are incapable of synthesizing many of the 20 common amino acids. Removing auxotrophic amino acids from consideration in these organisms does not alter the overall trend of preferential use of energetically inexpensive amino acids in highly expressed genes.

Prokaryotic organisms preferentially utilize less energetically costly amino acids in highly expressed genes. Studies have shown that the proteome of Saccharomyces cerevisiae also exhibits this behavior, but only in broad terms. This study examines the question of metabolic efficiency as a proteome-shaping force at a finer scale, examining whether trends consistent with cost minimization as an evolutionary force are present independent of protein function and amino acid physicochemical property, and consistently with respect to amino acid biosynthetic costs. Inverse correlations between the average amino acid biosynthetic cost of the protein product and the levels of gene expression in S. cerevisiae are consistent with natural selection to minimize costs. There are, however, patterns of amino acid usage that raise questions about the strength (and possibly the universality) of this selective force in shaping S. cerevisiae's proteome.

Nematode.net (http://nematode.net) has been a publicly available resource for studying nematodes for over a decade. In the past 3 years, we reorganized Nematode.net to provide more user-friendly navigation through the site, a necessity due to the explosion of data from next-generation sequencing platforms. Organism-centric portals containing dynamically generated data are available for over 56 different nematode species. Next-generation data has been added to the various data-mining portals hosted, including NemaBLAST and NemaBrowse. The NemaPath metabolic pathway viewer builds associations using KOs, rather than ECs to provide more accurate and fine-grained descriptions of proteins. Two new features for data analysis and comparative genomics have been added to the site. NemaSNP enables the user to perform population genetics studies in various nematode populations using next-generation sequencing data. HelmCoP (Helminth Control and Prevention) as an independent component of Nematode.net provides an integrated resource for storage, annotation and comparative genomics of helminth genomes to aid in learning more about nematode genomes, as well as drug, pesticide, vaccine and drug target discovery. With this update, Nematode.net will continue to realize its original goal to disseminate diverse bioinformatic data sets and provide analysis tools to the broad scientific community in a useful and user-friendly manner.