Control of exuT activity for galacturonate transport by the negative regulator ExuR in Erwinia chrysanthemi EC16

Novel Metabolic Pathways and Regulons for Hexuronate Utilization in Proteobacteria

We used comparative genomics to reconstruct d-galacturonic and d-glucuronic acid catabolic pathways and associated transcriptional regulons involving the tripartite ATP-independent periplasmic (TRAP) family transporters that bind hexuronates in proteobacteria. The reconstructed catabolic network involves novel transcription factors, catabolic enzymes, and transporters for utilization of both hexuronates and aldarates (d-glucarate and meso-galactarate). The reconstructed regulons for a novel GntR family transcription factor, GguR, include the majority of hexuronate/aldarate utilization genes in 47 species from the Burkholderiaceae, Comamonadaceae, Halomonadaceae, and Pseudomonadaceae families. GudR, GulR, and UdhR are additional local regulators of some hexuronate/aldarate utilization genes in some of the above-mentioned organisms. The predicted DNA binding motifs of GguR and GudR regulators from Ralstonia pickettii and Polaromonas were validated by in vitro binding assays. Genes from the GulR- and GguR-controlled loci were differentially expressed in R. pickettii grown on hexuronates and aldarates. By a combination of bioinformatics and experimental techniques we identified a novel variant of the oxidative pathway for hexuronate utilization, including two previously uncharacterized subfamilies of lactone hydrolases (UxuL and UxuF). The genomic context of respective genes and reconstruction of associated pathways suggest that both enzymes catalyze the conversion of d-galactaro- and d-glucaro-1,5-lactones to the ring-opened aldarates. The activities of the purified recombinant enzymes, UxuL and UxuF, from four proteobacterial species were directly confirmed and kinetically characterized. The inferred novel aldarate-specific transporter from the tripartite tricarboxylate transporter (TTT) family transporter TctC was confirmed to bind d-glucarate in vitro This study expands our knowledge of bacterial carbohydrate catabolic pathways by identifying novel families of catabolic enzymes, transcriptional regulators, and transporters.IMPORTANCE Hexuronate catabolic pathways and their transcriptional networks are highly variable among different bacteria. We identified novel transcriptional regulators that control the hexuronate and aldarate utilization genes in four families of proteobacteria. By regulon reconstruction and genome context analysis we identified several novel components of the common hexuronate/aldarate utilization pathways, including novel uptake transporters and catabolic enzymes. Two novel families of lactonases involved in the oxidative pathway of hexuronate catabolism were characterized. Novel transcriptional regulons were validated via in vitro binding assays and gene expression studies with Polaromonas and Ralstonia species. The reconstructed catabolic pathways are interconnected with each other metabolically and coregulated via the GguR regulons in proteobacteria.

Complete Genome Sequence Analysis of Enterobacter sp. SA187, a Plant Multi-Stress Tolerance Promoting Endophytic Bacterium

Enterobacter sp. SA187 is an endophytic bacterium that has been isolated from root nodules of the indigenous desert plant Indigofera argentea. SA187 could survive in the rhizosphere as well as in association with different plant species, and was able to provide abiotic stress tolerance to Arabidopsis thaliana. The genome sequence of SA187 was obtained by using Pacific BioScience (PacBio) single-molecule sequencing technology, with average coverage of 275X. The genome of SA187 consists of one single 4,429,597 bp chromosome, with an average 56% GC content and 4,347 predicted protein coding DNA sequences (CDS), 153 ncRNA, 7 rRNA, and 84 tRNA. Functional analysis of the SA187 genome revealed a large number of genes involved in uptake and exchange of nutrients, chemotaxis, mobilization and plant colonization. A high number of genes were also found to be involved in survival, defense against oxidative stress and production of antimicrobial compounds and toxins. Moreover, different metabolic pathways were identified that potentially contribute to plant growth promotion. The information encoded in the genome of SA187 reveals the characteristics of a dualistic lifestyle of a bacterium that can adapt to different environments and promote the growth of plants. This information provides a better understanding of the mechanisms involved in plant-microbe interaction and could be further exploited to develop SA187 as a biological agent to improve agricultural practices in marginal and arid lands.

A selC-associated genomic island of the extraintestinal avian pathogenic Escherichia coli strain BEN2908 is involved in carbohydrate uptake and virulence

The complete nucleotide sequence and genetic organization of a new genomic island (AGI-3) isolated from the extraintestinal avian pathogenic Escherichia coli strain BEN2908 is reported. This 49,600-bp island is inserted at the selC locus and contains putative mobile genetic elements such as a phage-related integrase gene, transposase genes, and direct repeats. AGI-3 shows a mosaic structure of five modules. Some of these modules are present in other E. coli strains and in other pathogenic bacterial species. The gene cluster aec-35 to aec-37 of module 1 encodes proteins associated with carbohydrates assimilation such as a major facilitator superfamily transporter (Aec-36), a glycosidase (Aec-37), and a putative transcriptional regulator of the LacI family (Aec-35). The aec-35 to aec-37 cluster was found in 11.6% of the tested pathogenic and nonpathogenic E. coli strains. When present, the aec-35 to aec-37 cluster is strongly associated with the selC locus (97%). Deletion of the aec-35-aec-37 region affects the assimilation of seven carbohydrates, decreases the growth rate of the strain in minimal medium containing galacturonate or trehalose, and attenuates the virulence of E. coli BEN2908 for chickens.

The effect of GeneChip gene definitions on the microarray study of cancers

The Affymetrix GeneChip is a popular microarray platform for genome-wide expression profiling and has been widely used in functional genomics especially in the classification of cancers. Due to the updating of genome data, much of the genome information with which the chips were designed is out-of-date and it has been reported that many of the genes/transcripts on the chips differ from their original definition when mapping the probes to the new genome information. Dai et al. have reported that the updated definition can cause as much as 30-50% discrepancy in the genes selected as differentially expressed on a heart tissue expression profiling dataset. Understanding the nature of this difference is therefore very important for the utilization of the data. In this work, with a large cancer dataset as an example, we compared two major definitions and investigated their effects on classification, clustering, discovery of differentially expressed genes and gene-set-based analysis. Results show that the two definitions agree well on clustering and classification results but genes and gene sets discovered as differentially expressed or enriched can be very different. Discoveries based on the Affymetrix definition can cover most of those based on the new definition, but tend to have more false positives.

A sequence-based identification of the genes detected by probesets on the Affymetrix U133 plus 2.0 array

One of the biggest problems facing microarray experiments is the difficulty of translating results into other microarray formats or comparing microarray results to other biochemical methods. We believe that this is largely the result of poor gene identification. We re-identified the probesets on the Affymetrix U133 plus 2.0 GeneChip array. This identification was based on the sequence of the probes and the sequence of the human genome. Using the BLAST program, we matched probes with documented and postulated human transcripts. This resulted in the redefinition of approximately 37% of the probes on the U133 plus 2.0 array. This updated identification specifically points out where the identification is complicated by cross-hybridization from splice variants or closely related genes. More than 5000 probesets detect multiple transcripts and therefore the exact protein affected cannot be readily concluded from the performance of one probeset alone. This makes naming difficult and impacts any downstream analysis such as associating gene ontologies, mapping affected pathways or simply validating expression changes. We have now automated the sequence-based identification and can more appropriately annotate any array where the sequence on each spot is known.