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]     

Increased Lipid Accumulation in the Chlamydomonas reinhardtii sta7-10 Starchless Isoamylase Mutant and Increased Carbohydrate Synthesis in Complemented Strains

The accumulation of bioenergy carriers was assessed in two starchless mutants of Chlamydomonas reinhardtii (the sta6 [ADP-glucose pyrophosphorylase] and sta7-10 [isoamylase] mutants), a control strain (CC124), and two complemented strains of the sta7-10 mutant. The results indicate that the genetic blockage of starch synthesis in the sta6 and sta7-10 mutants increases the accumulation of lipids on a cellular basis during nitrogen deprivation relative to that in the CC124 control as determined by conversion to fatty acid methyl esters. However, this increased level of lipid accumulation is energetically insufficient to completely offset the loss of cellular starch that is synthesized by CC124 during nitrogen deprivation. We therefore investigated acetate utilization and O₂ evolution to obtain further insights into the physiological adjustments utilized by the two starchless mutants in the absence of starch synthesis. The results demonstrate that both starchless mutants metabolize less acetate and have more severely attenuated levels of photosynthetic O₂ evolution than CC124, indicating that a decrease in overall anabolic processes is a significant physiological response in the starchless mutants during nitrogen deprivation. Interestingly, two independent sta7-10:STA7 complemented strains exhibited significantly greater quantities of cellular starch and lipid than CC124 during acclimation to nitrogen deprivation. Moreover, the complemented strains synthesized significant quantities of starch even when cultured in nutrient-replete medium.Microalgae are able to efficiently convert sunlight, water, and CO₂ into a variety of products suitable for renewable energy applications, including H₂, carbohydrates, and lipids (11, 12, 16, 21, 38, 41, 44). The unicellular green alga Chlamydomonas reinhardtii has emerged as a model organism for studying algal physiology, photosynthesis, metabolism, nutrient stress, and the synthesis of bioenergy carriers (12, 15, 19, 24, 32). During acclimation to nitrogen deprivation, C. reinhardtii cells accumulate significant quantities of starch and form lipid bodies (4, 5, 8, 26, 28, 30, 34, 43, 46, 48). Despite the significance of these products in algal physiology and in biofuels applications, the metabolic, enzymatic, and regulatory mechanisms controlling the partitioning of metabolites into these distinct carbon stores in algae are poorly understood. Several C. reinhardtii starch mutants with various phenotypic changes in starch content and structure have been isolated (2,–4). Two of these, the sta6 and sta7 mutants, contain single-gene disruptions that result in “starchless” phenotypes with severely attenuated levels of starch granule accumulation (2, 4, 34, 39, 40, 48).The disrupted loci in the two isolated starchless mutants are distinct and each mutant has a unique phenotype (7, 40). In the sta6 mutant, the small, catalytic subunit of ADP-glucose pyrophosphorylase (AGPase-SS) is disrupted (2, 4, 48), and this mutant accumulates less than 1% of the starch observed in wild-type (WT) cells under conditions of nitrogen deprivation. The sta7 mutant contains a disrupted isoamylase gene (7, 8, 10, 39, 40) and also has severely attenuated levels of starch, but it accumulates a soluble glycogen-like product (4, 9). In this study, we conducted an examination of the unique physiological acclimations that are utilized by these mutants to adapt to the loss of starch synthesis. As the genetic lesions in these two mutants are distinct and block starch synthesis via two very different mechanisms, we investigated the physiological consequences of starch inhibition in both of these mutants from a holistic bioenergy perspective, which included photosynthetic parameters and the overall yields of lipids and carbohydrates, the two primary bioenergy carriers in C. reinhardtii. Specifically, we examined whether the inability to synthesize starch would result in the accumulation of additional lipid, alter cellular growth or cell size, affect acetate utilization, and/or influence photosynthetic O₂ evolution. Our data indicate that both the sta6 (BAFJ5) and sta7 (sta7-10) mutants accumulate more lipid than the CC124 control during nitrogen deprivation. However, the additional lipid does not completely offset the loss of starch synthesis from a complete energetic perspective. Increased lipid accumulation during nitrogen stress has also been reported for a variety of starch mutants in recent papers (26, 27, 46). A significant feature in both of the starchless mutants studied here is that O₂ evolution and acetate utilization are diminished during nitrogen stress, which is undesirable from an overall bioenergy perspective. Remarkably, complementation of sta7-10 with genomic DNA encoding the wild-type isoamylase gene resulted in cells that were larger than those of the sta6, sta7-10, and CC124 strains, exhibited the highest total lipid levels during nitrogen deprivation, and overaccumulated starch even in nutrient-replete medium.     

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]