Functions Of Uncharacterized Genes Research Articles

Diguanylate Cyclases and Phosphodiesterases Required for Basal-Level c-di-GMP in Pseudomonas aeruginosa as Revealed by Systematic Phylogenetic and Transcriptomic Analyses.

Cyclic diguanosine monophosphate (c-di-GMP) is an important second messenger involved in bacterial switching from motile to sessile lifestyles. In the opportunistic pathogen Pseudomonas aeruginosa, at least 40 genes are predicted to encode proteins for the making and breaking of this signal molecule. However, there is still paucity of information concerning the systemic expression pattern of these genes and the functions of uncharacterized genes. In this study, we analyzed the phylogenetic distribution of genes from P. aeruginosa that were predicted to have a GGDEF domain and found five genes (PA5487, PA0285, PA0290, PA4367, and PA5017) with highly conserved distribution across 52 public complete pseudomonad genomes. PA5487 was further characterized as a typical diguanylate cyclase (DGC) and was named dgcH A systemic analysis of the gene expression data revealed that the expression of dgcH is highly invariable and that dgcH probably functions as a conserved gene to maintain the basal level of c-di-GMP, as reinforced by gene expression analyses. The other four conserved genes also had an expression pattern similar to that of dgcH The functional analysis suggested that PA0290 encoded a DGC, while the others functioned as phosphodiesterases (PDEs). Our data revealed that there are five DGC and PDE genes that maintain the basal level of c-di-GMP in P. aeruginosaIMPORTANCEPseudomonas aeruginosa is an opportunistic pathogen that can cause infections in animals, humans, and plants. The formation of biofilms by P. aeruginosa is the central mode of action to persist in hosts and evade immune and antibiotic attacks. Cyclic-di-GMP (c-di-GMP) is an important second messenger involved in the regulation of biofilm formation. In P. aeruginosa PAO1 strain, there are around 40 genes that encode enzymes for making and breaking this dinucleotide. A major missing piece of information in this field is the phylogeny and expression profile of those genes. Here, we took a systemic approach to investigate this mystery. We found that among 40 c-di-GMP metabolizing genes, 5 have well-conserved phylogenetic distribution and invariable expression profiles, suggesting that there are enzymes required for the basal level of c-di-GMP in P. aeruginosa This study thus provides putative therapeutic targets against P. aeruginosa infections.

A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities.

Improved efforts are necessary to define the functional product of cancer mutations currently being revealed through large-scale sequencing efforts. Using genome-scale pooled shRNA screening technology, we mapped negative genetic interactions across a set of isogenic cancer cell lines and confirmed hundreds of these interactions in orthogonal co-culture competition assays to generate a high-confidence genetic interaction network of differentially essential or differential essentiality (DiE) genes. The network uncovered examples of conserved genetic interactions, densely connected functional modules derived from comparative genomics with model systems data, functions for uncharacterized genes in the human genome and targetable vulnerabilities. Finally, we demonstrate a general applicability of DiE gene signatures in determining genetic dependencies of other non-isogenic cancer cell lines. For example, the PTEN(-/-) DiE genes reveal a signature that can preferentially classify PTEN-dependent genotypes across a series of non-isogenic cell lines derived from the breast, pancreas and ovarian cancers. Our reference network suggests that many cancer vulnerabilities remain to be discovered through systematic derivation of a network of differentially essential genes in an isogenic cancer cell model.

Reverse engineering cellular networks

We describe a computational protocol for the ARACNE algorithm, an information-theoretic method for identifying transcriptional interactions between gene products using microarray expression profile data. Similar to other algorithms, ARACNE predicts potential functional associations among genes, or novel functions for uncharacterized genes, by identifying statistical dependencies between gene products. However, based on biochemical validation, literature searches and DNA binding site enrichment analysis, ARACNE has also proven effective in identifying bona fide transcriptional targets, even in complex mammalian networks. Thus we envision that predictions made by ARACNE, especially when supplemented with prior knowledge or additional data sources, can provide appropriate hypotheses for the further investigation of cellular networks. While the examples in this protocol use only gene expression profile data, the algorithm's theoretical basis readily extends to a variety of other high-throughput measurements, such as pathway-specific or genome-wide proteomics, microRNA and metabolomics data. As these data become readily available, we expect that ARACNE might prove increasingly useful in elucidating the underlying interaction models. For a microarray data set containing approximately 10,000 probes, reconstructing the network around a single probe completes in several minutes using a desktop computer with a Pentium 4 processor. Reconstructing a genome-wide network generally requires a computational cluster, especially if the recommended bootstrapping procedure is used.