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]     

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]     

PTIP Regulates 53BP1 and SMC1 at the DNA Damage Sites

Although PTIP is implicated in the DNA damage response, through interactions with 53BP1, the function of PTIP in the DNA damage response remain elusive. Here, we show that RNF8 controls DNA damage-induced nuclear foci formation of PTIP, which in turn regulates 53BP1 localization to the DNA damage sites. In addition, SMC1, a substrate of ATM, could not be phosphorylated at the DNA damage sites in the absence of PTIP. The PTIP-dependent pathway is important for DNA double strand breaks repair and DNA damage-induced intra-S phase checkpoint activation. Taken together, these results suggest that the role of PTIP in the DNA damage response is downstream of RNF8 and upstream of 53BP1. Thus, PTIP regulates 53BP1-dependent signaling pathway following DNA damage.The DNA damage response pathways are signal transduction pathways with DNA damage sensors, mediators, and effectors, which are essential for maintaining genomic stability (1–3). Following DNA double strand breaks, histone H2AX at the DNA damage sites is rapidly phosphorylated by ATM/ATR/DNAPK (4–10), a family homologous to phosphoinositide 3-kinases (11, 12). Subsequently, phospho-H2AX (γH2AX) provides the platform for accumulation of a larger group of DNA damage response factors, such as MDC1, BRCA1, 53BP1, and the MRE11·RAD50·NBS1 complex (13, 14), at the DNA damage sites. Translocalization of these proteins to the DNA double strand breaks (DSBs)³ facilitates DNA damage checkpoint activation and enhances the efficiency of DNA damage repair (14, 15).Recently, PTIP (Pax2 transactivation domain-interacting protein, or Paxip) has been identified as a DNA damage response protein and is required for cell survival when exposed to ionizing radiation (IR) (1, 16–18). PTIP is a 1069-amino acid nuclear protein and has been originally identified in a yeast two-hybrid screening as a partner of Pax2 (19). Genetic deletion of the PTIP gene in mice leads to early embryonic lethality at embryonic day 8.5, suggesting that PTIP is essential for early embryonic development (20). Structurally, PTIP contains six tandem BRCT (BRCA1 carboxyl-terminal) domains (16–18, 21). The BRCT domain is a phospho-group binding domain that mediates protein-protein interactions (17, 22, 23). Interestingly, the BRCT domain has been found in a large number of proteins involved in the cellular response to DNA damages, such as BRCA1, MDC1, and 53BP1 (7, 24–29). Like other BRCT domain-containing proteins, upon exposure to IR, PTIP forms nuclear foci at the DSBs, which is dependent on its BRCT domains (16–18). By protein affinity purification, PTIP has been found in two large complexes. One includes the histone H3K4 methyltransferase ALR and its associated cofactors, the other contains DNA damage response proteins, including 53BP1 and SMC1 (30, 31). Further experiments have revealed that DNA damage enhances the interaction between PTIP and 53BP1 (18, 31).To elucidate the DNA damage response pathways, we have examined the upstream and downstream partners of PTIP. Here, we report that PTIP is downstream of RNF8 and upstream of 53BP1 in response to DNA damage. Moreover, PTIP and 53BP1 are required for the phospho-ATM association with the chromatin, which phosphorylates SMC1 at the DSBs. This PTIP-dependent pathway is involved in DSBs repair.     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[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]     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[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]     

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]