Computational prediction and biochemical characterization of novel RNA aptamers to Rift Valley fever virus nucleocapsid protein.

Rift Valley fever virus (RVFV) is a potent human and livestock pathogen endemic to sub-Saharan Africa and the Arabian Peninsula that has potential to spread to other parts of the world. Although there is no proven effective and safe treatment for RVFV infections, a potential therapeutic target is the virally encoded nucleocapsid protein (N). During the course of infection, N binds to viral RNA, and perturbation of this interaction can inhibit viral replication. To gain insight into how N recognizes viral RNA specifically, we designed an algorithm that uses a distance matrix and multidimensional scaling to compare the predicted secondary structures of known N-binding RNAs, or aptamers, that were isolated and characterized in previous in vitro evolution experiment. These aptamers did not exhibit overt sequence or predicted structure similarity, so we employed bioinformatic methods to propose novel aptamers based on analysis and clustering of secondary structures. We screened and scored the predicted secondary structures of novel randomly generated RNA sequences in silico and selected several of these putative N-binding RNAs whose secondary structures were similar to those of known N-binding RNAs. We found that overall the in silico generated RNA sequences bound well to N in vitro. Furthermore, introduction of these RNAs into cells prior to infection with RVFV inhibited viral replication in cell culture. This proof of concept study demonstrates how the predictive power of bioinformatics and the empirical power of biochemistry can be jointly harnessed to discover, synthesize, and test new RNA sequences that bind tightly to RVFV N protein. The approach would be easily generalizable to other applications.

An extremely halophilic proteobacterium combines a highly acidic proteome with a low cytoplasmic potassium content.

Halophilic archaea accumulate molar concentrations of KCl in their cytoplasm as an osmoprotectant and have evolved highly acidic proteomes that function only at high salinity. We examined osmoprotection in the photosynthetic Proteobacteria Halorhodospira halophila and Halorhodospira halochloris. Genome sequencing and isoelectric focusing gel electrophoresis showed that the proteome of H. halophila is acidic. In line with this finding, H. halophila accumulated molar concentrations of KCl when grown in high salt medium as detected by x-ray microanalysis and plasma emission spectrometry. This result extends the taxonomic range of organisms using KCl as a main osmoprotectant to the Proteobacteria. The closely related organism H. halochloris does not exhibit an acidic proteome, matching its inability to accumulate K(+). This observation indicates recent evolutionary changes in the osmoprotection strategy of these organisms. Upon growth of H. halophila in low salt medium, its cytoplasmic K(+) content matches that of Escherichia coli, revealing an acidic proteome that can function in the absence of high cytoplasmic salt concentrations. These findings necessitate a reassessment of two central aspects of theories for understanding extreme halophiles. First, we conclude that proteome acidity is not driven by stabilizing interactions between K(+) ions and acidic side chains but by the need for maintaining sufficient solvation and hydration of the protein surface at high salinity through strongly hydrated carboxylates. Second, we propose that obligate protein halophilicity is a non-adaptive property resulting from genetic drift in which constructive neutral evolution progressively incorporates weakly stabilizing K(+)-binding sites on an increasingly acidic protein surface.

Metabolic and translational efficiency in microbial organisms.

Metabolic efficiency, as a selective force shaping proteomes, has been shown to exist in Escherichia coli and Bacillus subtilis and in a small number of organisms with photoautotrophic and thermophilic lifestyles. Earlier attempts at larger-scale analyses have utilized proxies (such as molecular weight) for biosynthetic cost, and did not consider lifestyle or auxotrophy. This study extends the analysis to all currently sequenced microbial organisms that are amenable to these analyses while utilizing lifestyle specific amino acid biosynthesis pathways (where possible) to determine protein production costs and compensating for auxotrophy. The tendency for highly expressed proteins (with adherence to codon usage bias as a proxy for expressivity) to utilize less biosynthetically expensive amino acids is taken as evidence of cost selection. A comprehensive analysis of sequenced genomes to identify those that exhibit strong translational efficiency bias (389 out of 1,700 sequenced organisms) is also presented.

A genetic optimization approach for isolating translational efficiency bias.

The study of codon usage bias is an important research area that contributes to our understanding of molecular evolution, phylogenetic relationships, respiratory lifestyle, and other characteristics. Translational efficiency bias is perhaps the most well-studied codon usage bias, as it is frequently utilized to predict relative protein expression levels. We present a novel approach to isolating translational efficiency bias in microbial genomes. There are several existent methods for isolating translational efficiency bias. Previous approaches are susceptible to the confounding influences of other potentially dominant biases. Additionally, existing approaches to identifying translational efficiency bias generally require both genomic sequence information and prior knowledge of a set of highly expressed genes. This novel approach provides more accurate results from sequence information alone by resisting the confounding effects of other biases. We validate this increase in accuracy in isolating translational efficiency bias on 10 microbial genomes, five of which have proven particularly difficult for existing approaches due to the presence of strong confounding biases.

Analyzing taxonomic classification using extensible Markov models.

MOTIVATION: As next generation sequencing is rapidly adding new genomes, their correct placement in the taxonomy needs verification. However, the current methods for confirming classification of a taxon or suggesting revision for a potential misplacement relies on computationally intense multi-sequence alignment followed by an iterative adjustment of the distance matrix. Due to intra-heterogeneity issues with the 16S rRNA marker, no classifier is available for sub-genus level, which could readily suggest a classification for a novel 16S rRNA sequence. Metagenomics further complicates the issue by generating fragmented 16S rRNA sequences. This article proposes a novel alignment-free method for representing the microbial profiles using extensible Markov models (EMMs) with an extended Karlin-Altschul statistical framework similar to the classic alignment paradigm. We propose a log odds (LODs) score classifier based on Gumbel difference distribution that confirms correct classifications with statistical significance qualifications and suggests revisions where necessary. RESULTS: We tested our method by generating a sub-genus level classifier with which we re-evaluated classifications of 676 microbial organisms using the NCBI FTP database for the 16S rRNA. The results confirm current classification for all genera while ascertaining significance at 95%. Furthermore, this novel classifier isolates heterogeneity issues to a mere 12 strains while confirming classifications with significance qualification for the remaining 98%. The models require less memory than that needed by multi-sequence alignments and have better time complexity than the current methods. The classifier operates at sub-genus level, and thus outperforms the naive Bayes classifier of the RNA Database Project where much of the taxonomic analysis is available online. Finally, using information redundancy in model building, we show that the method applies to metagenomic fragment classification of 19 Escherichia coli strains. AVAILABILITY AND IMPLEMENTATION: Source code and binaries freely available for download at http://lyle.smu.edu/IDA/EMMSA/, implemented in JAVA and supported on MS Windows.

Automated isolation of translational efficiency bias that resists the confounding effect of GC(AT)-content.

Genomic sequencing projects are an abundant source of information for biological studies ranging from the molecular to the ecological in scale; however, much of the information present may yet be hidden from casual analysis. One such information domain, trends in codon usage, can provide a wealth of information about an organism's genes and their expression. Degeneracy in the genetic code allows more than one triplet codon to code for the same amino acid, and usage of these codons is often biased such that one or more of these synonymous codons are preferred. Detection of this bias is an important tool in the analysis of genomic data, particularly as a predictor of gene expressivity. Methods for identifying codon usage bias in genomic data that rely solely on genomic sequence data are susceptible to being confounded by the presence of several factors simultaneously influencing codon selection. Presented here is a new technique for removing the effects of one of the more common confounding factors, GC(AT)-content, and of visualizing the search-space for codon usage bias through the use of a solution landscape. This technique successfully isolates expressivity-related codon usage trends, using only genomic sequence information, where other techniques fail due to the presence of GC(AT)-content confounding influences.

Do amino acid biosynthetic costs constrain protein evolution in Saccharomyces cerevisiae?

Prokaryotic organisms preferentially utilize less energetically costly amino acids in highly expressed genes. Studies have shown that the proteome of Saccharomyces cerevisiae also exhibits this behavior, but only in broad terms. This study examines the question of metabolic efficiency as a proteome-shaping force at a finer scale, examining whether trends consistent with cost minimization as an evolutionary force are present independent of protein function and amino acid physicochemical property, and consistently with respect to amino acid biosynthetic costs. Inverse correlations between the average amino acid biosynthetic cost of the protein product and the levels of gene expression in S. cerevisiae are consistent with natural selection to minimize costs. There are, however, patterns of amino acid usage that raise questions about the strength (and possibly the universality) of this selective force in shaping S. cerevisiae's proteome.

Amino acid cost and codon-usage biases in 6 prokaryotic genomes: a whole-genome analysis.

For most prokaryotic organisms, amino acid biosynthesis represents a significant portion of their overall energy budget. The difference in the cost of synthesis between amino acids can be striking, differing by as much as 7-fold. Two prokaryotic organisms, Escherichia coli and Bacillus subtilis, have been shown to preferentially utilize less costly amino acids in highly expressed genes, indicating that parsimony in amino acid selection may confer a selective advantage for prokaryotes. This study confirms those findings and extends them to 4 additional prokaryotic organisms: Chlamydia trachomatis, Chlamydophila pneumoniae AR39, Synechocystis sp. PCC 6803, and Thermus thermophilus HB27. Adherence to codon-usage biases for each of these 6 organisms is inversely correlated with a coding region's average amino acid biosynthetic cost in a fashion that is independent of chemoheterotrophic, photoautotrophic, or thermophilic lifestyle. The obligate parasites C. trachomatis and C. pneumoniae AR39 are incapable of synthesizing many of the 20 common amino acids. Removing auxotrophic amino acids from consideration in these organisms does not alter the overall trend of preferential use of energetically inexpensive amino acids in highly expressed genes.