A Study on the Origin of Peroxisomes: Possibility of Actinobacteria Symbiosis

The origin of peroxisomes as having developed from the endoplasmic reticulum (ER) was proposed on the basis of the similarity between some peroxisomal proteins and ER proteins, and the localization of some peroxisomal proteins on the ER. To study the evolutionary distance between peroxisomes and ER and Prokaryotes, we carried out a phylogenetic analysis of CDC48 (cell division control 48) and its homologs, including ER-localized CDC48, CDC48 homologs in Prokaryotes and peroxisome-localized PEX1 and PEX6. A similarity search analysis of peroxisomal protein sequences to prokaryotic protein sequences using BLAST at several thresholds (E-values) was also done. We propose Actinobacteria symbiosis for the origin of peroxisomes based on the following evidence: (1) PEX1 and PEX6 are close in distance to CDC48 homologs in Actinobacteria, and these distances are closer than to ER-localized CDC48. (2) Actinobacteria proteins show the highest degree of similarity to peroxisomal proteins compared with other prokaryotes

Precise Similarity of Many Human Proteins to Proteins of Prokarya

	 Proteins originated in early forms of life and have long survived, because they have always been required. Some recognizably similar proteins are found in all sequence comparisons between species, no matter how distant, including prokaryotes and eukaryotes. Reported here are observations on the relationships of human proteins to the proteins of 458 prokaryotes for which protein libraries are available. Each of these libraries includes a protein that matches a human protein with a BLAST score of 573 or more, indicating excellent conservation of certain amino acid sequences. A majority of these proteins also match a yeast protein and other eukaryote proteins with comparable accuracy, indicating that protein conservation is responsible in most cases rather than the horizontal transfer (HGT) between eukaryotes and prokaryotes. Rare examples of HGT are apparently also seen.
	Very many significant matches are seen as the criterion is opened, including 20,596 human proteins that match at least one prokaryote protein with expectation of 10-3 or less. Individual prokaryote proteins accurately match parts of many modern human proteins that have a wide range of functions showing directly that many proteins of different functions have evolved from an ancestral protein by duplication, rearrangement and divergence of function. The implication is that most or all modern proteins derive from the proteins of the last common ancestor with prokaryotes through many such events. 
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Hanks-Type Serine/Threonine Protein Kinases and Phosphatases in Bacteria: Roles in Signaling and Adaptation to Various Environments

Reversible phosphorylation is a key mechanism that regulates many cellular processes in prokaryotes and eukaryotes. In prokaryotes, signal transduction includes two-component signaling systems, which involve a membrane sensor histidine kinase and a cognate DNA-binding response regulator. Several recent studies indicate that alternative regulatory pathways controlled by Hanks-type serine/threonine kinases (STKs) and serine/threonine phosphatases (STPs) also play an essential role in regulation of many different processes in bacteria, such as growth and cell division, cell wall biosynthesis, sporulation, biofilm formation, stress response, metabolic and developmental processes, as well as interactions (either pathogenic or symbiotic) with higher host organisms. Since these enzymes are not DNA-binding proteins, they exert the regulatory role via post-translational modifications of their protein targets. In this review, we summarize the current knowledge of STKs and STPs, and discuss how these enzymes mediate gene expression in prokaryotes. Many studies indicate that regulatory systems based on Hanks-type STKs and STPs play an essential role in the regulation of various cellular processes, by reversibly phosphorylating many protein targets, among them several regulatory proteins of other signaling cascades. These data show high complexity of bacterial regulatory network, in which the crosstalk between STK/STP signaling enzymes, components of TCSs, and the translational machinery occurs. In this regulation, the STK/STP systems have been proved to play important roles

Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation

Gene expression is controlled primarily by interactions between transcription factor proteins (TFs) and the regulatory DNA sequence, a process that can be captured well by thermodynamic models of regulation. These models, however, neglect regulatory crosstalk: the possibility that non-cognate TFs could initiate transcription, with potentially disastrous effects for the cell. Here we estimate the importance of crosstalk, suggest that its avoidance strongly constrains equilibrium models of TF binding, and propose an alternative non-equilibrium scheme that implements kinetic proofreading to suppress erroneous initiation. This proposal is consistent with the observed covalent modifications of the transcriptional apparatus and would predict increased noise in gene expression as a tradeoff for improved specificity. Using information theory, we quantify this tradeoff to find when optimal proofreading architectures are favored over their equilibrium counterparts.Comment: 5 pages, 3 figure

Biased amino acid composition in warm-blooded animals

Among eubacteria and archeabacteria, amino acid composition is correlated with habitat temperatures. In particular, species living at high temperatures have proteins enriched in the amino acids E-R-K and depleted in D-N-Q-T-S-H-A. Here, we show that this bias is a proteome-wide effect in prokaryotes, and that the same trend is observed in fully sequenced mammals and chicken compared to cold-blooded vertebrates (Reptilia, Amphibia and fish). Thus, warm-blooded vertebrates likely experienced genome-wide weak positive selection on amino acid composition to increase protein thermostability

Investigation of compounds essential for the origin of life

Nucleic acid sequencing as a technique to determine the chemical and biological evolution of certain prokaryotic metabolic pathways is discussed. Protein in data and a microbiological organization of the prokaryotes is included

Phylogeny of Prokaryotes and Chloroplasts Revealed by a Simple Composition Approach on All Protein Sequences from Complete Genomes Without Sequence Alignment

The complete genomes of living organisms have provided much information on their phylogenetic relationships. Similarly, the complete genomes of chloroplasts have helped to resolve the evolution of this organelle in photosynthetic eukaryotes. In this paper we propose an alternative method of phylogenetic analysis using compositional statistics for all protein sequences from complete genomes. This new method is conceptually simpler than and computationally as fast as the one proposed by Qi et al. (2004b) and Chu et al. (2004). The same data sets used in Qi et al. (2004b) and Chu et al. (2004) are analyzed using the new method. Our distance-based phylogenic tree of the 109 prokaryotes and eukaryotes agrees with the biologists tree of life based on 16S rRNA comparison in a predominant majority of basic branching and most lower taxa. Our phylogenetic analysis also shows that the chloroplast genomes are separated to two major clades corresponding to chlorophytes s.l. and rhodophytes s.l. The interrelationships among the chloroplasts are largely in agreement with the current understanding on chloroplast evolution

Prediction of Metabolic Pathways Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations