Author Summary | UraA is an example of a symporter, and is responsible for the proton-driven uptake of uracil in bacteria like E. coli . |
Cardiolipin binding sites in UraA | That these were not seen in the crystal structure is perhaps not surprising given that the purifica-tion/ crystallization procedures extracts the majority of any bound lipids and that CL is a relatively minor (5%) component of the E. coli inner membrane. |
Cardiolipin binding sites in UraA | UraA is a member of the nucelobase/ascorbate transporter (NAT) family, of which there are 10 family members in E. coli . |
Cardiolipin binding sites in UraA | How conserved are these residues in the NAT family in E. coli ? |
Introduction | We explored these questions using the E. coli UraA H+-uracil symporter, the crystal structure of which was determined with bound uracil in the detergent n-nonyl-B-D-glucopyrano-side (NG; Fig. |
Introduction | Including UraA, there are ten members of the NCS2 family of transporters in E. coli . |
Introduction | Mutation of the conserved residues of the motif in the Aspergillus nidulans uric acid/xan-thine permease, UapA and the E. coli xanthine permease XanQ (Yng) have shown that residues within the NAT motif contribute to the substrate specificity [19]. |
Biomarker discovery through functional and network analysis | Another 3 genes are involved in the entero-bactin synthesis (entA, entE, fepA), a siderophore that has been very recently revealed to be related to the growth of E. coli in M9 [40]. |
Categorization of gene expression data | WT samples were identified from experiments that didn’t undergo genetic and environmental perturbations from the three platforms (7 for Affymetrix E. coli An-tisense Genome Array, 6 for Affymetrix E. coli Genome 2.0 Array, and 6 for RNA-Seq). |
Discussion | Single-molecule real-time (SMRT) sequencing technology has been recently applied to reading of genome-scale methyla-tion states in a pathogenic E. coli [51] and the technology would provide higher-resolution of molecular information of bacteria, enabling fine-scale predictive characterization based on it. |
Introduction | Indeed, after aggregating all high-throughput transcriptional data that is currently available for E. coli , the most well-studied model microbe, we are still limited to a few thousands microarray or RNA-Seq experiments that cover more than 30 strains, a dozen different media and a multitude of other genetic (knockout, over-expressions, re-wirings), or environmental (carbon limitation, chemicals, abiotic factors) perturbations. |
Introduction | Affymetrix E. coli Genome 2.0, Affymetrix E. coli Antisense) and technologies (e.g. |
Introduction | To achieve this, we have extended, normalized and annotated a compendium that was compiled recently [29] to incorporate all published high-quality Affymetrix mi-croarray and RNA-Seq datasets in E. coli (2258 samples in total, Fig. |
Methods | We downloaded 83 RNA-Seq E. coli transcriptional profiles from 17 different GEO entries [30] that correspond to 8 strains, LB and MOPS media in wild-type (WT), gene knockouts (KOs), double KOs and environmental perturbations. |
Methods | We integrated the RNA-Seq dataset (64 samples) to the E. coli Microarray Compendium (EcoMAC) that consists of 2198 microarrays of 4189 genes for which raw files were downloaded and normalized by RMA (robust multichip average) method [29]. |
Selection of most informative genes and functional enrichment analysis | Global map of genetic interactions for E. coli is reconstructed from [72] with pathway modules that functionally cluster genes based on the Pathway Ontology and transporter complexes curated in EcoCyc [73]. |