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]     

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]     

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]     

Genome Scale Reconstruction of a Salmonella Metabolic Model: COMPARISON OF SIMILARITY AND DIFFERENCES WITH A COMMENSAL Escherichia coli STRAIN*

Salmonella are closely related to commensal Escherichia coli but have gained virulence factors enabling them to behave as enteric pathogens. Less well studied are the similarities and differences that exist between the metabolic properties of these organisms that may contribute toward niche adaptation of Salmonella pathogens. To address this, we have constructed a genome scale Salmonella metabolic model (iMA945). The model comprises 945 open reading frames or genes, 1964 reactions, and 1036 metabolites. There was significant overlap with genes present in E. coli MG1655 model iAF1260. In silico growth predictions were simulated using the model on different carbon, nitrogen, phosphorous, and sulfur sources. These were compared with substrate utilization data gathered from high throughput phenotyping microarrays revealing good agreement. Of the compounds tested, the majority were utilizable by both Salmonella and E. coli. Nevertheless a number of differences were identified both between Salmonella and E. coli and also within the Salmonella strains included. These differences provide valuable insight into differences between a commensal and a closely related pathogen and within different pathogenic strains opening new avenues for future explorations.Salmonella is a major cause of human and animal enteric disease. Salmonella consists of two species, bongori and enterica, and the latter can be further divided into subspecies (I-VI). The majority of human and animal infections are caused by S. enterica subspecies I, of which Salmonella typhimurium and Salmonella enteritidis are the most prevalent causes of human inflammatory gastroenteritis, often referred to as food poisoning (1). The recent availability of genome sequences of bacterial pathogens, including Salmonella, provides an opportunity to interrogate these organisms using a systems biology approach. By contrasting the genotype-phenotype relationship of pathogens such as Salmonella against closely related commensals such as an Escherichia coli K12 insights can be revealed into how these pathogens have adapted to their environmental niche(s). Salmonella and E. coli K12 share ∼85% of their genome (2–6). DNA microarray and genome sequencing studies have highlighted regions of the genome that are conserved between these closely related bacteria and those that are different. Many of the differences are attributable to the acquisition of virulence factors, although a significant proportion of their genome codes is for metabolic genes (2–8).A genome scale model consists of a stoichiometric reconstruction of all reactions known to act in the metabolism of an organism along with a set of accompanying constraints on the flux of each reaction in the system (9, 10). These models define the organism''s global metabolic space, network structural properties, and flux distribution potential (9, 10). Therefore constraint-based models can help predict cellular phenotypes given particular environmental conditions. Genome scale models have been useful in understanding the metabolic properties of a variety of organisms including E. coli, Bacillus subtilis, Pseudomonas putida, and Lactobacillus (9–12). Genome scale models can be validated in various ways such as continuous culture experiments, substrate utilization assays, specific gene mutations, and isotopic carbon measurements. The high through-put phenotype microarray (PM)³ system that is available through Biolog (Hayward, CA) is ideal to use for substrate utilization assays as it provides a comprehensive large-scale phenotyping technology to assess gene function at the cellular level (13).The aim of this work was to construct a Salmonella genome scale model. The model highlights the similarities and differences between pathogenic bacteria such as S. typhimurium and S. enteritidis and the commensal E. coli K12 laboratory strains. The model was validated using the PM system and literature-derived (i.e. bibliomic) information. The substrate utilization assays also highlighted current knowledge gaps that will require further experimental data that can be used in the future for refining and extending the model.     

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]     

Proteomes of Host Cell Membranes Modified by Intracellular Activities of Salmonella enterica

Intracellular pathogens need to establish a growth-stimulating host niche for survival and replication. A unique feature of the gastrointestinal pathogen Salmonella enterica serovar Typhimurium is the creation of extensive membrane networks within its host. An understanding of the origin and function of these membranes is crucial for the development of new treatment strategies. However, the characterization of this compartment is very challenging, and only fragmentary knowledge of its composition and biogenesis exists. Here, we describe a new proteome-based approach to enrich and characterize Salmonella-modified membranes. Using a Salmonella mutant strain that does not form this unique membrane network as a reference, we identified a high-confidence set of host proteins associated with Salmonella-modified membranes. This comprehensive analysis allowed us to reconstruct the interactions of Salmonella with host membranes. For example, we noted that Salmonella redirects endoplasmic reticulum (ER) membrane trafficking to its intracellular niche, a finding that has not been described for Salmonella previously. Our system-wide approach therefore has the potential to rapidly close gaps in our knowledge of the infection process of intracellular pathogens and demonstrates a hitherto unrecognized complexity in the formation of Salmonella host niches.Bacterial pathogens have evolved sophisticated mechanisms enabling them to invade, reside in, and proliferate in a large range of eukaryotic hosts. This often involves hijacking the host phagosomal system, interfering with the host cell signaling and trafficking machinery, and establishing a replication niche to avoid clearance (1). Whereas some pathogens escape phagosomes and replicate in the host cytoplasm, most of the described pathogens replicate in membrane-bound, vacuole-like compartments (2). Such intracellular niches of various pathogens are diverse, and biogenesis often depends on the delivery of bacterial effector proteins into the host cell cytoplasm.Salmonella enterica, the causative agent of localized gastroenteritis and the life-threatening systemic infection known as typhoid fever, forms so-called Salmonella-containing vacuoles (SCVs)¹ inside host cells (3). SCVs mature through continuous interactions with endocytic and recycling pathways, accompanied by a spatial shift from the side of internalization to the juxtanuclear position close to the microtubule-organizing center (4, 5). Whereas the initial maturation steps are similar to the canonical phagosome biogenesis, the formation of an extensive tubular membrane network extending from the mature SCV is unique to Salmonella-infected host cells. This network contains various tubular structures such as Salmonella-induced filaments (SIFs), sorting nexin tubules, Salmonella-induced secretory carrier membrane protein 3 tubules, and lysosome-associated membrane protein 1-negative tubules (5–7), distinguishable by individual organelle marker proteins. For instance, tubules decorated with lysosome-associated membrane protein 1 (LAMP1) are known as SIFs (8, 9). In this paper we refer to all host membranes modified by intracellular Salmonella as Salmonella-modified membranes (SMMs).In general, the appearance of SMMs coincides with the onset of bacterial replication, and both phenomena are dependent on the translocation of effector proteins of the Salmonella Pathogenicity Island 2 (SPI2)-encoded type III secretion system (T3SS) (10, 11). These effector proteins manipulate a large number of host cell processes and force the host cell to create a suitable microenvironment for Salmonella (7, 12, 13). Although many Salmonella effector proteins have been described (14), much less is known about the host proteins that are manipulated to foster bacterial growth.A systematic proteome-wide analysis would be an important step toward understanding the mechanisms used by Salmonella to reorganize the host cell endosomal system during intracellular proliferation. However, one major challenge is the need to distinguish host proteins directed toward the Salmonella-induced compartments from those that are present independent of an infection.In this report we describe a novel method for the enrichment of SMMs and utilize a comparative strategy to identify proliferation-relevant host proteins. This systematic characterization of the SMM proteome provides new insights into the cellular origin and biogenesis of SMMs and identifies host cell proteins modified by the activity of intracellular Salmonella.     

Dbf4-Cdc7 Phosphorylation of Mcm2 Is Required for Cell Growth

The Dbf4-Cdc7 kinase (DDK) is required for the activation of the origins of replication, and DDK phosphorylates Mcm2 in vitro. We find that budding yeast Cdc7 alone exists in solution as a weakly active multimer. Dbf4 forms a likely heterodimer with Cdc7, and this species phosphorylates Mcm2 with substantially higher specific activity. Dbf4 alone binds tightly to Mcm2, whereas Cdc7 alone binds weakly to Mcm2, suggesting that Dbf4 recruits Cdc7 to phosphorylate Mcm2. DDK phosphorylates two serine residues of Mcm2 near the N terminus of the protein, Ser-164 and Ser-170. Expression of mcm2-S170A is lethal to yeast cells that lack endogenous MCM2 (mcm2Δ); however, this lethality is rescued in cells harboring the DDK bypass mutant mcm5-bob1. We conclude that DDK phosphorylation of Mcm2 is required for cell growth.The Cdc7 protein kinase is required throughout the yeast S phase to activate origins (1, 2). The S phase cyclin-dependent kinase also activates yeast origins of replication (3–5). It has been proposed that Dbf4 activates Cdc7 kinase in S phase, and that Dbf4 interaction with Cdc7 is essential for Cdc7 kinase activity (6). However, it is not known how Dbf4-Cdc7 (DDK)² acts during S phase to trigger the initiation of DNA replication. DDK has homologs in other eukaryotic species, and the role of Cdc7 in activation of replication origins during S phase may be conserved (7–10).The Mcm2-7 complex functions with Cdc45 and GINS to unwind DNA at a replication fork (11–15). A mutation of MCM5 (mcm5-bob1) bypasses the cellular requirements for DBF4 and CDC7 (16), suggesting a critical physiologic interaction between Dbf4-Cdc7 and Mcm proteins. DDK phosphorylates Mcm2 in vitro with proteins purified from budding yeast (17, 18) or human cells (19). Furthermore, there are mutants of MCM2 that show synthetic lethality with DBF4 mutants (6, 17), suggesting a biologically relevant interaction between DBF4 and MCM2. Nevertheless, the physiologic role of DDK phosphorylation of Mcm2 is a matter of dispute. In human cells, replacement of MCM2 DDK-phosphoacceptor residues with alanines inhibits DNA replication, suggesting that Dbf4-Cdc7 phosphorylation of Mcm2 in humans is important for DNA replication (20). In contrast, mutation of putative DDK phosphorylation sites at the N terminus of Schizosaccharomyces pombe Mcm2 results in viable cells, suggesting that phosphorylation of S. pombe Mcm2 by DDK is not critical for cell growth (10).In budding yeast, Cdc7 is present at high levels in G₁ and S phase, whereas Dbf4 levels peak in S phase (18, 21, 22). Furthermore, budding yeast DDK binds to chromatin during S phase (6), and it has been shown that Dbf4 is required for Cdc7 binding to chromatin in budding yeast (23, 24), fission yeast (25), and Xenopus (9). Human and fission yeast Cdc7 are inert on their own (7, 8), but Dbf4-Cdc7 is active in phosphorylating Mcm proteins in budding yeast (6, 26), fission yeast (7), and human (8, 10). Based on these data, it has been proposed that Dbf4 activates Cdc7 kinase in S phase and that Dbf4 interaction with Cdc7 is essential for Cdc7 kinase activity (6, 9, 18, 21–24). However, a mechanistic analysis of how Dbf4 activates Cdc7 has not yet been accomplished. For example, the multimeric state of the active Dbf4-Cdc7 complex is currently disputed. A heterodimer of fission yeast Cdc7 (Hsk1) in complex with fission yeast Dbf4 (Dfp1) can phosphorylate Mcm2 (7). However, in budding yeast, oligomers of Cdc7 exist in the cell (27), and Dbf4-Cdc7 exists as oligomers of 180 and 300 kDa (27).DDK phosphorylates the N termini of human Mcm2 (19, 20, 28), human Mcm4 (10), budding yeast Mcm4 (26), and fission yeast Mcm6 (10). Although the sequences of the Mcm N termini are poorly conserved, the DDK sites identified in each study have neighboring acidic residues. The residues of budding yeast Mcm2 that are phosphorylated by DDK have not yet been identified.In this study, we find that budding yeast Cdc7 is weakly active as a multimer in phosphorylating Mcm2. However, a low molecular weight form of Dbf4-Cdc7, likely a heterodimer, has a higher specific activity for phosphorylation of Mcm2. Dbf4 or DDK, but not Cdc7, binds tightly to Mcm2, suggesting that Dbf4 recruits Cdc7 to Mcm2. DDK phosphorylates two serine residues of Mcm2, Ser-164 and Ser-170, in an acidic region of the protein. Mutation of Ser-170 is lethal to yeast cells, but this phenotype is rescued by the DDK bypass mutant mcm5-bob1. We conclude that DDK phosphorylation of Ser-170 of Mcm2 is required for budding yeast growth.