Expression Patterns of Protein Kinases Correlate with Gene Architecture and Evolutionary Rates

Protein kinase (PK) genes comprise the third largest superfamily that occupy ∼2% of the human genome. They encode regulatory enzymes that control a vast variety of cellular processes through phosphorylation of their protein substrates. Expression of PK genes is subject to complex transcriptional regulation which is not fully understood.Our comparative analysis demonstrates that genomic organization of regulatory PK genes differs from organization of other protein coding genes. PK genes occupy larger genomic loci, have longer introns, spacer regions, and encode larger proteins. The primary transcript length of PK genes, similar to other protein coding genes, inversely correlates with gene expression level and expression breadth, which is likely due to the necessity to reduce metabolic costs of transcription for abundant messages. On average, PK genes evolve slower than other protein coding genes. Breadth of PK expression negatively correlates with rate of non-synonymous substitutions in protein coding regions. This rate is lower for high expression and ubiquitous PKs, relative to low expression PKs, and correlates with divergence in untranslated regions. Conversely, rate of silent mutations is uniform in different PK groups, indicating that differing rates of non-synonymous substitutions reflect variations in selective pressure. Brain and testis employ a considerable number of tissue-specific PKs, indicating high complexity of phosphorylation-dependent regulatory network in these organs. There are considerable differences in genomic organization between PKs up-regulated in the testis and brain. PK genes up-regulated in the highly proliferative testicular tissue are fast evolving and small, with short introns and transcribed regions. In contrast, genes up-regulated in the minimally proliferative nervous tissue carry long introns, extended transcribed regions, and evolve slowly.PK genomic architecture, the size of gene functional domains and evolutionary rates correlate with the pattern of gene expression. Structure and evolutionary divergence of tissue-specific PK genes is related to the proliferative activity of the tissue where these genes are predominantly expressed. Our data provide evidence that physiological requirements for transcription intensity, ubiquitous expression, and tissue-specific regulation shape gene structure and affect rates of evolution

Fast Statistical Alignment

We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment—previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches—yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/

progressiveMauve: Multiple Genome Alignment with Gene Gain, Loss and Rearrangement

Multiple genome alignment remains a challenging problem. Effects of recombination including rearrangement, segmental duplication, gain, and loss can create a mosaic pattern of homology even among closely related organisms.We describe a new method to align two or more genomes that have undergone rearrangements due to recombination and substantial amounts of segmental gain and loss (flux). We demonstrate that the new method can accurately align regions conserved in some, but not all, of the genomes, an important case not handled by our previous work. The method uses a novel alignment objective score called a sum-of-pairs breakpoint score, which facilitates accurate detection of rearrangement breakpoints when genomes have unequal gene content. We also apply a probabilistic alignment filtering method to remove erroneous alignments of unrelated sequences, which are commonly observed in other genome alignment methods. We describe new metrics for quantifying genome alignment accuracy which measure the quality of rearrangement breakpoint predictions and indel predictions. The new genome alignment algorithm demonstrates high accuracy in situations where genomes have undergone biologically feasible amounts of genome rearrangement, segmental gain and loss. We apply the new algorithm to a set of 23 genomes from the genera Escherichia, Shigella, and Salmonella. Analysis of whole-genome multiple alignments allows us to extend the previously defined concepts of core- and pan-genomes to include not only annotated genes, but also non-coding regions with potential regulatory roles. The 23 enterobacteria have an estimated core-genome of 2.46Mbp conserved among all taxa and a pan-genome of 15.2Mbp. We document substantial population-level variability among these organisms driven by segmental gain and loss. Interestingly, much variability lies in intergenic regions, suggesting that the Enterobacteriacae may exhibit regulatory divergence.The multiple genome alignments generated by our software provide a platform for comparative genomic and population genomic studies. Free, open-source software implementing the described genome alignment approach is available from http://gel.ahabs.wisc.edu/mauve

Low Enzymatic Activity Haplotypes of the Human Catechol-O-Methyltransferase Gene: Enrichment for Marker SNPs

Catechol-O-methyltransferase (COMT) is an enzyme that plays a key role in the modulation of catechol-dependent functions such as cognition, cardiovascular function, and pain processing. Three common haplotypes of the human COMT gene, divergent in two synonymous and one nonsynonymous (val158met) position, designated as low (LPS), average (APS), and high pain sensitive (HPS), are associated with experimental pain sensitivity and risk of developing chronic musculoskeletal pain conditions. APS and HPS haplotypes produce significant functional effects, coding for 3- and 20-fold reductions in COMT enzymatic activity, respectively. In the present study, we investigated whether additional minor single nucleotide polymorphisms (SNPs), accruing in 1 to 5% of the population, situated in the COMT transcript region contribute to haplotype-dependent enzymatic activity. Computer analysis of COMT ESTs showed that one synonymous minor SNP (rs769224) is linked to the APS haplotype and three minor SNPs (two synonymous: rs6267, rs740602 and one nonsynonymous: rs8192488) are linked to the HPS haplotype. Results from in silico and in vitro experiments revealed that inclusion of allelic variants of these minor SNPs in APS or HPS haplotypes did not modify COMT function at the level of mRNA folding, RNA transcription, protein translation, or enzymatic activity. These data suggest that neutral variants are carried with APS and HPS haplotypes, while the high activity LPS haplotype displays less linked variation. Thus, both minor synonymous and nonsynonymous SNPs in the coding region are markers of functional APS and HPS haplotypes rather than independent contributors to COMT activity

Optimization of Duplex Stability and Terminal Asymmetry for shRNA Design

Prediction of efficient oligonucleotides for RNA interference presents a serious challenge, especially for the development of genome-wide RNAi libraries which encounter difficulties and limitations due to ambiguities in the results and the requirement for significant computational resources. Here we present a fast and practical algorithm for shRNA design based on the thermodynamic parameters. In order to identify shRNA and siRNA features universally associated with high silencing efficiency, we analyzed structure-activity relationships in thousands of individual RNAi experiments from publicly available databases (ftp://ftp.ncbi.nlm.nih.gov/pub/shabalin/siRNA/si_shRNA_selector/). Using this statistical analysis, we found free energy ranges for the terminal duplex asymmetry and for fully paired duplex stability, such that shRNAs or siRNAs falling in both ranges have a high probability of being efficient. When combined, these two parameters yield a ∼72% success rate on shRNAs from the siRecords database, with the target RNA levels reduced to below 20% of the control. Two other parameters correlate well with silencing efficiency: the stability of target RNA and the antisense strand secondary structure. Both parameters also correlate with the short RNA duplex stability; as a consequence, adding these parameters to our prediction scheme did not substantially improve classification accuracy. To test the validity of our predictions, we designed 83 shRNAs with optimal terminal asymmetry, and experimentally verified that small shifts in duplex stability strongly affected silencing efficiency. We showed that shRNAs with short fully paired stems could be successfully selected by optimizing only two parameters: terminal duplex asymmetry and duplex stability of the hypothetical cleavage product, which also relates to the specificity of mRNA target recognition. Our approach performs at the level of the best currently utilized algorithms that take into account prediction of the secondary structure of the target and antisense RNAs, but at significantly lower computational costs. Based on this study, we created the si-shRNA Selector program that predicts both highly efficient shRNAs and functional siRNAs (ftp://ftp.ncbi.nlm.nih.gov/pub/shabalin/siRNA/si_shRNA_selector/)

Arginylation regulates purine nucleotide biosynthesis by enhancing the activity of phosphoribosyl pyrophosphate synthase

Protein arginylation is an emerging post-translational modification that targets a number of metabolic enzymes, however the mechanisms and downstream effects of this modification are unknown. Here we show that lack of arginylation renders cells vulnerable to purine nucleotide synthesis inhibitors and affects the related glycine and serine biosynthesis pathways. We show that the purine nucleotide biosynthesis enzyme PRPS2 is selectively arginylated, unlike its close homologue PRPS1, and that arginylation of PRPS2 directly facilitates its biological activity. Moreover, selective arginylation of PRPS2 but not PRPS1 is regulated through a coding sequence-dependent mechanism that combines elements of mRNA secondary structure with lysine residues encoded near the N-terminus of PRPS1. This mechanism promotes arginylation-specific degradation of PRPS1 and selective retention of arginylated PRPS2 in vivo. We therefore demonstrate that arginylation affects both the activity and stability of a major metabolic enzyme