Mutations in interaction surfaces differentially impact E. coli Hfq association with small RNAs and their mRNA targets.

The RNA chaperone protein Hfq is required for the function of all small RNAs (sRNAs) that regulate mRNA stability or translation by limited base pairing in Escherichia coli. While there have been numerous in vitro studies to characterize Hfq activity and the importance of specific residues, there has been only limited characterization of Hfq mutants in vivo. Here, we use a set of reporters as well as co-immunoprecipitation to examine 14 Hfq mutants expressed from the E. coli chromosome. The majority of the proximal face residues, as expected, were important for the function of sRNAs. The failure of sRNAs to regulate target mRNAs in these mutants can be explained by reduced sRNA accumulation. Two of the proximal mutants, D9A and F39A, acted differently from the others in that they had mixed effects on different sRNA/mRNA pairs and, in the case of F39A, showed differential sRNA accumulation. Mutations of charged residues at the rim of Hfq interfered with positive regulation and gave mixed effects for negative regulation. Some, but not all, sRNAs accumulated to lower levels in rim mutants, suggesting qualitative differences in how individual sRNAs are affected by Hfq. The distal face mutants were expected to disrupt binding of ARN motifs found in mRNAs. They were more defective for positive regulation than negative regulation at low mRNA expression, but the defects could be suppressed by higher levels of mRNA expression. We discuss the implications of these observations for Hfq binding to RNA and mechanisms of action.

The entomopathogenic bacterial endosymbionts Xenorhabdus and Photorhabdus: convergent lifestyles from divergent genomes.

Members of the genus Xenorhabdus are entomopathogenic bacteria that associate with nematodes. The nematode-bacteria pair infects and kills insects, with both partners contributing to insect pathogenesis and the bacteria providing nutrition to the nematode from available insect-derived nutrients. The nematode provides the bacteria with protection from predators, access to nutrients, and a mechanism of dispersal. Members of the bacterial genus Photorhabdus also associate with nematodes to kill insects, and both genera of bacteria provide similar services to their different nematode hosts through unique physiological and metabolic mechanisms. We posited that these differences would be reflected in their respective genomes. To test this, we sequenced to completion the genomes of Xenorhabdus nematophila ATCC 19061 and Xenorhabdus bovienii SS-2004. As expected, both Xenorhabdus genomes encode many anti-insecticidal compounds, commensurate with their entomopathogenic lifestyle. Despite the similarities in lifestyle between Xenorhabdus and Photorhabdus bacteria, a comparative analysis of the Xenorhabdus, Photorhabdus luminescens, and P. asymbiotica genomes suggests genomic divergence. These findings indicate that evolutionary changes shaped by symbiotic interactions can follow different routes to achieve similar end points.

Global analysis of small RNA and mRNA targets of Hfq.

Hfq, a bacterial member of the Sm family of RNA-binding proteins, is required for the action of many small regulatory RNAs that act by basepairing with target mRNAs. Hfq binds this family of small RNAs efficiently. We have used co-immunoprecipitation with Hfq and direct detection of the bound RNAs on genomic microarrays to identify members of this small RNA family. This approach was extremely sensitive; even Hfq-binding small RNAs expressed at low levels were readily detected. At least 15 of 46 known small RNAs in E. coli interact with Hfq. In addition, high signals in other intergenic regions suggested up to 20 previously unidentified small RNAs bind Hfq; five were confirmed by Northern analysis. Strong signals within genes and operons also were detected, some of which correspond to known Hfq targets. Within the argX-hisR-leuT-proM operon, Hfq appears to compete with RNase E and modulate RNA processing and degradation. Thus Hfq immunoprecipitation followed by microarray analysis is a highly effective method for detecting a major class of small RNAs as well as identifying new Hfq functions.

Initial proteome analysis of model microorganism Haemophilus influenzae strain Rd KW20.

The proteome of Haemophilus influenzae strain Rd KW20 was analyzed by liquid chromatography (LC) coupled with ion trap tandem mass spectrometry (MS/MS). This approach does not require a gel electrophoresis step and provides a rapidly developed snapshot of the proteome. In order to gain insight into the central metabolism of H. influenzae, cells were grown microaerobically and anaerobically in a rich medium and soluble and membrane proteins of strain Rd KW20 were proteolyzed with trypsin and directly examined by LC-MS/MS. Several different experimental and computational approaches were utilized to optimize the proteome coverage and to ensure statistically valid protein identification. Approximately 25% of all predicted proteins (open reading frames) of H. influenzae strain Rd KW20 were identified with high confidence, as their component peptides were unambiguously assigned to tandem mass spectra. Approximately 80% of the predicted ribosomal proteins were identified with high confidence, compared to the 33% of the predicted ribosomal proteins detected by previous two-dimensional gel electrophoresis studies. The results obtained in this study are generally consistent with those obtained from computational genome analysis, two-dimensional gel electrophoresis, and whole-genome transposon mutagenesis studies. At least 15 genes originally annotated as conserved hypothetical were found to encode expressed proteins. Two more proteins, previously annotated as predicted coding regions, were detected with high confidence; these proteins also have close homologs in related bacteria. The direct proteomics approach to studying protein expression in vivo reported here is a powerful method that is applicable to proteome analysis of any (micro)organism.

Spectral analysis of distributions: finding periodic components in eukaryotic enzyme length data.

We introduce the spectral analysis of distributions (SAD), a method for detecting and evaluating possible periodicity in experimental data distributions (histograms) of arbitrary shape. SAD determines whether a given empirical distribution contains a periodic component. We also propose a system of probabilistic mixture distributions to model a histogram consisting of a smooth background together with peaks at periodic intervals, with each peak corresponding to a fixed number of subunits added together. This mixture distribution model allows us to estimate the parameters of the data and to test the statistical significance of the estimated peaks. The analysis is applied to the length distribution of eukaryotic enzymes.