Stochastic cell fate and longevity of offspring

Objective: Cellular decision-making is a key process in which cells with similar genetic and environmental background make dissimilar decisions. This stochastic process, which happens in prokaryotic and eukaryotic cells including stem cells, causes cellular diversity and phenotypic variation. In addition, fitness predicts and describes changes in the genetic composition of populations throughout the evolutionary history. Fitness may thus be defined as the ability to adapt and produce surviving offspring. Here, we present a mathematical model to predict the fitness of a cell and to address the fundamental issue of phenotypic variation. We study a basic decision-making scenario where a bacteriophage lambda reproduces in E. coli, using both the lytic and the lysogenic pathways. In the lytic pathway, the bacteriophage replicates itself within the host bacterium. This fast replication overcrowds and in turn destroys the host bacterium. In the lysogenic pathway, however, the bacteriophage inserts its DNA into the host genome, and is replicated simultaneously with the host genome

Materials and Methods: In this prospective study, a mathematical predictive model was developed to estimate fitness as an index of survived offspring. We then leverage experimental data to validate the predictive power of our proposed model. A mathematical model based on game theory was also generated to elucidate a rationale behind cell decision

Results: Our findings indicate that a rational decision that is aimed to maximize life expectancy of offspring is almost identical to bacteriophage behavior reported based on experimental data. The results also showed that stochastic decision on cell fate maximizes the expected number of survived offspring

Conclusion: We present a mathematical framework for analyzing a basic phenotypic variation problem and explain how bacteriophages maximize offspring longevity based on this model. We also introduce a mathematical benchmark for other investigations of phenotypic variation that exists in eukaryotes including stem cell differentiation

In silico perspectives on the prediction of the PLP's epitopes involved in multiple sclerosis

Background: Multiple sclerosis [MS] is the most common autoimmune disease of the central nervous system [CNS]. The main cause of the MS is yet to be revealed, but the most probable theory is based on the molecular mimicry that concludes some infections in the activation of T cells against brain auto-antigens that initiate the disease cascade

Objectives: The Purpose of this research is the prediction of the auto-antigen potency of the myelin proteolipid protein [PLP] in multiple sclerosis

Materials and Methods: As there wasn't any tertiary structure of PLP available in the Protein Data Bank [PDB] and in order to characterize the structural properties of the protein, we modeled this protein using prediction servers. Meta prediction method, as a new perspective in silico, was performed to find PLPs epitopes. For this purpose, several T cell epitope prediction web servers were used to predict PLPs epitopes against Human Leukocyte Antigens [HLA]. The overlap regions, as were predicted by most web servers were selected as immunogenic epitopes and were subjected to the BLASTP against microorganisms

Results: Three common regions, AA[58-74], AA[161-177], and AA[238-254] were detected as immunodominant regions through meta-prediction. Investigating peptides with more than 50% similarity to that of candidate epitope AA[58-74] in bacteria showed a similar peptide in bacteria [mainly consistent with that of clostridium and mycobacterium] and spike protein of Alphacoronavirus 1, Canine coronavirus, and Feline coronavirus. These results suggest that cross reaction of the immune system to PLP may have originated from a bacteria or viral infection, and therefore molecular mimicry might have an important role in the progression of MS

Conclusions: Through reliable and accurate prediction of the consensus epitopes, it is not necessary to synthesize all PLP fragments and examine their immunogenicity experimentally [in vitro]. In this study, the best encephalitogenic antigens were predicted based on bioinformatics tools that may provide reliable results for researches in a shorter time and at a lower cost

Comparative analysis of prostate cancer gene regulatory networks via hub type variation

Prostate cancer is one of the most widespread cancers in men and is fundamentally a genetic disease. Identifying regulators in cancer using novel systems biology approaches will potentially lead to new insight into this disease. It was sought to address this by inferring gene regulatory networks [GRNs]. Moreover, dynamical analysis of GRNs can explain how regulators change among different conditions, such as cancer subtypes. In our approach, independent gene regulatory networks from each prostate state were reconstructed using one of the current state-of-art reverse engineering approaches. Next, crucial genes involved in this cancer were highlighted by analyzing each network individually and also in comparison with each other. In this paper, a novel network-based approach was introduced to find critical transcription factors involved in prostate cancer. The results led to detection of 38 essential transcription factors based on hub type variation. Additionally, experimental evidence was found for 29 of them as well as 9 new transcription factors. The results showed that dynamical analysis of biological networks may provide useful information to gain better understanding of the cell

Analysis of candidate genes has proposed the role of y chromosome in human prostate cancer

Prostate cancer, a serious genetic disease, has known as the first widespread cancer in men, but the molecular changes required for the cancer progression has not fully understood. Availability of high-throughput gene expression data has led to the development of various computational methods, for identification of the critical genes, have involved in the cancer. In this paper, we have shown the construction of co-expression networks, which have been using Y-chromosome genes, provided an alternative strategy for detecting of new candidate, might involve in prostate cancer. In our approach, we have constructed independent co-expression networks from normal and cancerous stages have been using a reverse engineering approach. Then we have highlighted crucial Y chromosome genes involved in the prostate cancer, by analyzing networks, based on party and date hubs. Our results have led to the detection of 19 critical genes, related to prostate cancer, which 12 of them have previously shown to be involved in this cancer. Also, essential Y chromosome genes have searched based on reconstruction of sub-networks which have led to the identification of 4 experimentally established as well as 4 new Y chromosome genes might be linked putatively to prostate cancer. Correct inference of master genes, which mediate molecular, has changed during cancer progression would be one of the major challenges in cancer genomics. In this paper, we have shown the role of Y chromosome genes in finding of the prostate cancer susceptibility genes. Application of our approach to the prostate cancer has led to the establishment of the previous knowledge about this cancer as well as prediction of other new genes

[Effects of wearing a lumbosacral support on velocity and torque of lumbar spine during three-dimensional trunk motion]

The use of back support is one of the common methods aimed to prevent low back pain. The purpose of the present study was to investigate the effect of wearing a lumbosacral support on lumbar spine velocity and torque in six directions during combined trunk motion. In this interventional study, 30 young healthy men were selected simply from convenient samples. They were asked to stand in Isostation B200 system and perform three-dimensional trunk motion against a resistance of 50% of maximal voluntary contraction torque while wearing or not wearing a lumbosacral support. Under each condition of test, five successive motions of trunk were performed in downward direction [as flexion, right lateral flexion, and right rotation] and upward direction [as extension, left lateral flexion, and left rotation], and the variables of average velocity and average torque were recorded during motion. With the use of a lumbosacral support, average velocity was significantly increased in flexion [P=0.015] and extension [P=0.005], but no significant changes were found in other directions [P>0/05]. Back support decreased average torque of right rotation significantly [P=0.006], but did not significantly changed this variable in other directions [P>0/05]. Wearing a lumbosacral support can increase velocity in sagittal plane. Decreased rotation torque of trunk, as a result of using a back support, may reduce the twisting forces on lumbar spine joints

Knowledge-based potentials in protein fold recognition

An accurate potential function is essential for protein folding problem and structure prediction. Two different types of potential energy functions are currently in use. The first type is based on the law of physics and second type is referred to as statistical potentials or knowledge based potentials. In the latter type, the energy function is extracted from statistical analysis of experimental data of known protein structures. By increasing the amount of three dimensional protein structures, this approach is growing rapidly. There are various forms of knowledge based potentials depending on how statistics are calculated and how proteins are modeled. In this review, we explain how the knowledge based potentials are extracted by using known protein structures and briefly compare many of the potentials in theory

Comparison of hubs in effective normal and tumor protein interaction networks

Cancer is caused by genetic abnormalities, such as mutation of ontogenesis or tumor suppressor genes which alter downstream signaling pathways and protein-protein interactions. Comparison of protein interactions in cancerous and normal cells can be of help in mechanisms of disease diagnoses and treatments. We constructed protein interaction networks of cancerous and normal cells. These protein interaction networks are constructed using gene-expression profiles measured from different samples of cancerous and normal tissues from four different parts of the body including colon, prostate, lung, and central nervous system. We used pattern recognition techniques to construct these networks. We calculated ten graph related parameters including closeness centrality, graph diameter, index of aggregation, entropy of edge distribution, connectivity, number of edges divided by the number of vertices, entropy, graph centrality, sum of the wiener number, and modified vertex distance numbers for each of the cancerous and normal protein interaction networks. We have also compared number of edges and hubs of the both cancerous and normal resultant protein interaction networks. Our results show that in the studied tissue samples, effective normal protein interaction networks are denser in number of edges and hubs compared with their corresponding effective cancerous protein interaction networks. Number of hubs in effective cancerous protein interaction networks decreases dramatically in comparison with normal tissues. This can be used as a symptom for identification of cancerous tissues

Helix segment assignment in proteins using fuzzy logic

The automatic assignment of protein secondary structure from three dimensional coordinates is an essential step in the characterization of protein structure. Although, the recognition of secondary structures such as alpha-helices and beta-sheets seem straightforward, but there are many different definitions, each regarding different criteria. We have developed a new algorithm for protein helix assignment, by using fuzzy logic based on backbone torsion angles. In this method, each residue takes a number from 0 to 100 that indicates the helical membership degree of that residue. This method can be converted to a classical method whenever we assume that any residue with a membership degree greater than 83 is a helix. Comparison of the results with structures reported in protein data bank [PDB], dictionary of secondary structure of proteins [DSSP] and structure identification [STRIDE] for 324 proteins indicate that our algorithm works as well as DSSP showing 93% agreement. We believe that the fuzzy secondary structure assignment has more advantages than the other classical approaches used for protein structure comparisons and alignments

Cloning and Characterization of cbhII gene from trichoderma parceramosum and its experssion in pichia pastoris

The genomic and cDNA clones encoding cellobiohydrolase II [cbhII] have been isolated and sequenced from a native Iranian isolate of Trichoderma parceramosum, a high cellulolytic enzymes producer isolate. This represents the first report of cbhII gene from this organism. Comparison of genomic and cDNA sequences indicates this gene contains three short introns and also an open reading frame coding for a protein of 470 amino acids. The deduced amino acid sequence includes a putative signal peptide, cellulose binding domain, linker region, and catalytic domain. Homology between this sequence and other reported Trichoderma CBHII proteins and also structural prediction of this enzyme are discussed. The coding sequence of cbhII gene was cloned in pPIC9 expression vector and expressed in Pichia pastoris GS115. The expression was confirmed by Northern dot blot, RT-PCR and enzyme activity staining