Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

A Genetic Engineering Solution to the ��Arginine Conversion Problem�� in Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC)

Stable isotope labeling by amino acids in cell culture (SILAC) provides a straightforward tool for quantitation in proteomics. However, one problem associated with SILAC is the in vivo conversion of labeled arginine to other amino acids, typically proline. We found that arginine conversion in the fission yeast Schizosaccharomyces pombe occurred at extremely high levels, such that labeling cells with heavy arginine led to undesired incorporation of label into essentially all of the proline pool as well as a substantial portion of glutamate, glutamine, and lysine pools. We found that this can be prevented by deleting genes involved in arginine catabolism using methods that are highly robust yet simple to implement. Deletion of both fission yeast arginase genes or of the single ornithine transaminase gene, together with a small modification to growth medium that improves arginine uptake in mutant strains, was sufficient to abolish essentially all arginine conversion. We demonstrated the usefulness of our approach in a large scale quantitative analysis of proteins before and after cell division; both up- and down-regulated proteins, including a novel protein involved in septation, were successfully identified. This strategy for addressing the “arginine conversion problem” may be more broadly applicable to organisms amenable to genetic manipulation.Stable isotope labeling by amino acids in cell culture (SILAC)¹ (1) is one of the key methods for large scale quantitative proteomics (2, 3). In SILAC experiments, proteins are metabolically labeled by culturing cells in media containing either normal (“light”) or heavy isotope-labeled amino acids, typically lysine and arginine. Peptides derived from the light and heavy cells are thus distinguishable by mass spectrometry and can be mixed for accurate quantitation. SILAC is also possible at the whole-organism level (4).An inherent problem in SILAC is the metabolic conversion of labeled arginine to other amino acids, as this complicates quantitative analysis of peptides containing these amino acids. Arginine conversion to proline is well described in mammalian cells, although the extent of conversion varies among cell types (5). When conversion is observed, typically 10–25% of the total proline pool is found to contain label (6–11). Arginine conversion has also been reported in SILAC experiments with budding yeast Saccharomyces cerevisiae (3, 12, 13).Because more than 50% of tryptic peptides in large data sets contain proline (7), it is not practical simply to disregard proline-containing peptides during quantitation. Several methods have been proposed to either reduce arginine conversion or correct for its effects on quantitation. In some cell types, arginine conversion can be prevented by lowering the concentration of exogenous arginine (6, 14–16) or by adding exogenous proline (9). However, these methods can involve significant changes to growth media and may need to be tested for each experimental condition used. Given the importance of arginine in many metabolic pathways, careful empirical titration of exogenous arginine concentration is required to minimize negative effects on cell growth (14). In addition, low arginine medium can lead to incomplete arginine labeling, although the reasons for this are not entirely clear (7). An alternative strategy is to omit labeled arginine altogether (3, 13, 17), but this reduces the number of quantifiable peptides. Correction methods include using two different forms of labeled arginine (7) or computationally compensating for proline-containing peptides (11, 12, 18). Ultimately, none of these methods address the problem at its root, the utilization of arginine in cellular metabolism.To develop a differential proteomics work flow for the fission yeast Schizosaccharomyces pombe, we sought to adapt SILAC for use in this organism, a widely used model eukaryote with excellent classical and reverse genetics. Here we describe extremely high conversion of labeled arginine to other amino acids in fission yeast as well as a novel general solution to the problem that should be applicable to other organisms. As proof of principle, we quantitated changes in protein levels before and after cell division on a proteome-wide scale. We identified both up- and down-regulated proteins, including a novel protein involved in septation.     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]