Comparative Analysis of the Global Transcriptome of Anopheles funestus from Mali, West Africa

Background: Anopheles funestus is a principal vector of malaria across much of tropical Africa and is considered one of the most efficient of its kind, yet studies of this species have lagged behind those of its broadly sympatric congener, An. gambiae. In aid of future genomic sequencing of An. funestus, we explored the whole body transcriptome, derived from mixed stage progeny of wild-caught females from Mali, West Africa. Principal Findings: Here we report the functional annotation and comparative genomics of 2,005 expressed sequence tags (ESTs) from An. funestus, which were assembled with a previous EST set from adult female salivary glands from the same mosquito. The assembled ESTs provided for a nonredundant catalog of 1,035 transcripts excluding mitochondrial sequences. Conclusions/Significance: Comparison of the An. funestus and An. gambiae transcriptomes using computational and macroarray approaches revealed a high degree of sequence identity despite an estimated 20–80 MY divergence time between lineages. A phylogenetically broader comparative genomic analysis indicated that the most rapidly evolving proteins – those involved in immunity, hematophagy, formation of extracellular structures, and hypothetical conserved proteins – are those that probably play important roles in how mosquitoes adapt to their nutritional and externa

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

<p>Abstract</p> <p>Background</p> <p>The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions.</p> <p>Results</p> <p>In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification.</p> <p>Conclusion</p> <p>High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.</p

Highly-multiplexed barcode sequencing: an efficient method for parallel analysis of pooled samples

Next-generation sequencing has proven an extremely effective technology for molecular counting applications where the number of sequence reads provides a digital readout for RNA-seq, ChIP-seq, Tn-seq and other applications. The extremely large number of sequence reads that can be obtained per run permits the analysis of increasingly complex samples. For lower complexity samples, however, a point of diminishing returns is reached when the number of counts per sequence results in oversampling with no increase in data quality. A solution to making next-generation sequencing as efficient and affordable as possible involves assaying multiple samples in a single run. Here, we report the successful 96-plexing of complex pools of DNA barcoded yeast mutants and show that such ‘Bar-seq’ assessment of these samples is comparable with data provided by barcode microarrays, the current benchmark for this application. The cost reduction and increased throughput permitted by highly multiplexed sequencing will greatly expand the scope of chemogenomics assays and, equally importantly, the approach is suitable for other sequence counting applications that could benefit from massive parallelization

Zebrafish Mutants calamity and catastrophe Define Critical Pathways of Gene–Nutrient Interactions in Developmental Copper Metabolism

Nutrient availability is an important environmental variable during development that has significant effects on the metabolism, health, and viability of an organism. To understand these interactions for the nutrient copper, we used a chemical genetic screen for zebrafish mutants sensitive to developmental copper deficiency. In this screen, we isolated two mutants that define subtleties of copper metabolism. The first contains a viable hypomorphic allele of atp7a and results in a loss of pigmentation when exposed to mild nutritional copper deficiency. This mutant displays incompletely penetrant skeletal defects affected by developmental copper availability. The second carries an inactivating mutation in the vacuolar ATPase that causes punctate melanocytes and embryonic lethality. This mutant, catastrophe, is sensitive to copper deprivation revealing overlap between ion metabolic pathways. Together, the two mutants illustrate the utility of chemical genetic screens in zebrafish to elucidate the interaction of nutrient availability and genetic polymorphisms in cellular metabolism

Evidence-Based Annotation of Gene Function in Shewanella oneidensis MR-1 Using Genome-Wide Fitness Profiling across 121 Conditions

Most genes in bacteria are experimentally uncharacterized and cannot be annotated with a specific function. Given the great diversity of bacteria and the ease of genome sequencing, high-throughput approaches to identify gene function experimentally are needed. Here, we use pools of tagged transposon mutants in the metal-reducing bacterium Shewanella oneidensis MR-1 to probe the mutant fitness of 3,355 genes in 121 diverse conditions including different growth substrates, alternative electron acceptors, stresses, and motility. We find that 2,350 genes have a pattern of fitness that is significantly different from random and 1,230 of these genes (37% of our total assayed genes) have enough signal to show strong biological correlations. We find that genes in all functional categories have phenotypes, including hundreds of hypotheticals, and that potentially redundant genes (over 50% amino acid identity to another gene in the genome) are also likely to have distinct phenotypes. Using fitness patterns, we were able to propose specific molecular functions for 40 genes or operons that lacked specific annotations or had incomplete annotations. In one example, we demonstrate that the previously hypothetical gene SO_3749 encodes a functional acetylornithine deacetylase, thus filling a missing step in S. oneidensis metabolism. Additionally, we demonstrate that the orphan histidine kinase SO_2742 and orphan response regulator SO_2648 form a signal transduction pathway that activates expression of acetyl-CoA synthase and is required for S. oneidensis to grow on acetate as a carbon source. Lastly, we demonstrate that gene expression and mutant fitness are poorly correlated and that mutant fitness generates more confident predictions of gene function than does gene expression. The approach described here can be applied generally to create large-scale gene-phenotype maps for evidence-based annotation of gene function in prokaryotes

Mining for genotype-phenotype relations in Saccharomyces using partial least squares

<p>Abstract</p> <p>Background</p> <p>Multivariate approaches are important due to their versatility and applications in many fields as it provides decisive advantages over univariate analysis in many ways. Genome wide association studies are rapidly emerging, but approaches in hand pay less attention to multivariate relation between genotype and phenotype. We introduce a methodology based on a BLAST approach for extracting information from genomic sequences and Soft- Thresholding Partial Least Squares (ST-PLS) for mapping genotype-phenotype relations.</p> <p>Results</p> <p>Applying this methodology to an extensive data set for the model yeast <it>Saccharomyces cerevisiae</it>, we found that the relationship between genotype-phenotype involves surprisingly few genes in the sense that an overwhelmingly large fraction of the phenotypic variation can be explained by variation in less than 1% of the full gene reference set containing 5791 genes. These phenotype influencing genes were evolving 20% faster than non-influential genes and were unevenly distributed over cellular functions, with strong enrichments in functions such as cellular respiration and transposition. These genes were also enriched with known paralogs, stop codon variations and copy number variations, suggesting that such molecular adjustments have had a disproportionate influence on <it>Saccharomyces </it>yeasts recent adaptation to environmental changes in its ecological niche.</p> <p>Conclusions</p> <p>BLAST and PLS based multivariate approach derived results that adhere to the known yeast phylogeny and gene ontology and thus verify that the methodology extracts a set of fast evolving genes that capture the phylogeny of the yeast strains. The approach is worth pursuing, and future investigations should be made to improve the computations of genotype signals as well as variable selection procedure within the PLS framework.</p

Evolution under Fluctuating Environments Explains Observed Robustness in Metabolic Networks

A high level of robustness against gene deletion is observed in many organisms. However, it is still not clear which biochemical features underline this robustness and how these are acquired during evolution. One hypothesis, specific to metabolic networks, is that robustness emerges as a byproduct of selection for biomass production in different environments. To test this hypothesis we performed evolutionary simulations of metabolic networks under stable and fluctuating environments. We find that networks evolved under the latter scenario can better tolerate single gene deletion in specific environments. Such robustness is underlined by an increased number of independent fluxes and multifunctional enzymes in the evolved networks. Observed robustness in networks evolved under fluctuating environments was “apparent,” in the sense that it decreased significantly as we tested effects of gene deletions under all environments experienced during evolution. Furthermore, when we continued evolution of these networks under a stable environment, we found that any robustness they had acquired was completely lost. These findings provide evidence that evolution under fluctuating environments can account for the observed robustness in metabolic networks. Further, they suggest that organisms living under stable environments should display lower robustness in their metabolic networks, and that robustness should decrease upon switching to more stable environments

Off-Target Effects of Psychoactive Drugs Revealed by Genome-Wide Assays in Yeast

To better understand off-target effects of widely prescribed psychoactive drugs, we performed a comprehensive series of chemogenomic screens using the budding yeast Saccharomyces cerevisiae as a model system. Because the known human targets of these drugs do not exist in yeast, we could employ the yeast gene deletion collections and parallel fitness profiling to explore potential off-target effects in a genome-wide manner. Among 214 tested, documented psychoactive drugs, we identified 81 compounds that inhibited wild-type yeast growth and were thus selected for genome-wide fitness profiling. Many of these drugs had a propensity to affect multiple cellular functions. The sensitivity profiles of half of the analyzed drugs were enriched for core cellular processes such as secretion, protein folding, RNA processing, and chromatin structure. Interestingly, fluoxetine (Prozac) interfered with establishment of cell polarity, cyproheptadine (Periactin) targeted essential genes with chromatin-remodeling roles, while paroxetine (Paxil) interfered with essential RNA metabolism genes, suggesting potential secondary drug targets. We also found that the more recently developed atypical antipsychotic clozapine (Clozaril) had no fewer off-target effects in yeast than the typical antipsychotics haloperidol (Haldol) and pimozide (Orap). Our results suggest that model organism pharmacogenetic studies provide a rational foundation for understanding the off-target effects of clinically important psychoactive agents and suggest a rational means both for devising compound derivatives with fewer side effects and for tailoring drug treatment to individual patient genotypes

Sodium Selenide Toxicity Is Mediated by O2-Dependent DNA Breaks

Hydrogen selenide is a recurrent metabolite of selenium compounds. However, few experiments studied the direct link between this toxic agent and cell death. To address this question, we first screened a systematic collection of Saccharomyces cerevisiae haploid knockout strains for sensitivity to sodium selenide, a donor for hydrogen selenide (H2Se/HSe−/Se2−). Among the genes whose deletion caused hypresensitivity, homologous recombination and DNA damage checkpoint genes were over-represented, suggesting that DNA double-strand breaks are a dominant cause of hydrogen selenide toxicity. Consistent with this hypothesis, treatment of S. cerevisiae cells with sodium selenide triggered G2/M checkpoint activation and induced in vivo chromosome fragmentation. In vitro, sodium selenide directly induced DNA phosphodiester-bond breaks via an O2-dependent reaction. The reaction was inhibited by mannitol, a hydroxyl radical quencher, but not by superoxide dismutase or catalase, strongly suggesting the involvement of hydroxyl radicals and ruling out participations of superoxide anions or hydrogen peroxide. The •OH signature could indeed be detected by electron spin resonance upon exposure of a solution of sodium selenide to O2. Finally we showed that, in vivo, toxicity strictly depended on the presence of O2. Therefore, by combining genome-wide and biochemical approaches, we demonstrated that, in yeast cells, hydrogen selenide induces toxic DNA breaks through an O2-dependent radical-based mechanism