Somatic mutation driven codon transition bias in human cancer

The structure-based cancer-related single amino acid variation prediction

Single amino acid variation (SAV) is an amino acid substitution of the protein sequence that can potentially influence the entire protein structure or function, as well as its binding affinity. Protein destabilization is related to diseases, including several cancers, although using traditional experiments to clarify the relationship between SAVs and cancer uses much time and resources. Some SAV prediction methods use computational approaches, with most predicting SAV-induced changes in protein stability. In this investigation, all SAV characteristics generated from protein sequences, structures and the microenvironment were converted into feature vectors and fed into an integrated predicting system using a support vector machine and genetic algorithm. Critical features were used to estimate the relationship between their properties and cancers caused by SAVs. We describe how we developed a prediction system based on protein sequences and structure that is capable of distinguishing if the SAV is related to cancer or not. The five-fold cross-validation performance of our system is 89.73% for the accuracy, 0.74 for the Matthews correlation coefficient, and 0.81 for the F1 score. We have built an online prediction server, CanSavPre ( http://bioinfo.cmu.edu.tw/CanSavPre/ ), which is expected to become a useful, practical tool for cancer research and precision medicine.

A Crosstalk on Codon Usage in Genes Associated with Leukemia

Leukemia is the outcome of aggregation of damaged white blood cells. Several genes were reported to be associated with the pathogenesis of leukemia. These genes were computationally analyzed to decipher their codon usage bias (CUB) and to identify the prime factors influencing the codon usage profile as no work was reported yet. The mean values of synonymous codon usage order (SCUO) parameter indicated low CUB of the genes. Significant positive association of SCUO with overall GC and positional GCs might signal the presence of mutational pressure. However, neutrality plot suggested the dominant role of natural selection across the genes. Along with natural selection, the role of mutation pressure was also prominent and that might be responsible for lower CUB (SCUO = 0.19) of genes. Low translational speed might permit accuracy in the process. A strong inverse relationship of translational rate was observed with CUB of genes and folding energy.

Synonymous codon usage and context analysis of genes associated with pancreatic cancer

Pancreatic cancer is a fatal disorder which originates in pancreas. Its mortality rate is increasing with time. Some studies also reported that pancreatic cancer would be ranked 2nd by the year 2030. Codon usage bias (CUB) arises when synonymous codons for each amino acid are not used randomly in the coding sequences of genes. We used bioinformatic methods to analyze the compositional properties, codon context and codon usage trend of the genes associated with pancreatic cancer as no work was reported yet. From the base composition analysis, the pancreatic cancer genes were found to be GC-rich and at the 3rd codon position the G/C ending codons were more preferred to A/T ending ones. The CUB was low in genes associated with pancreatic cancer. Correspondence analysis proposed that other than base constraints, CUB might also be affected by some other factors such as natural selection. Moreover, results of correlation analysis indicated that CUB and various GC contents i.e. GC, GC1, GC2, GC3 played important role in the release of free energy by transcripts of the genes associated with pancreatic cancer. The low compAI values of coding sequences suggested a low translation rate of the genes.

Linkage disequilibrium and haplotype analysis of Src and Yes1 genes in thyroid cancer

Purpose: This study was planned to examine the effects of Src and Yes1 single nucleotide polymorphism (SNPs) on the risk of thyroid cancer in 499 patients and 500 controls. Materials & methods: Three SNPs of Src gene and three SNPs of Yes1 gene were analyzed using Tetra-primer ARMS-PCR followed by sequencing. Results: rs121913314 of Src gene genotype TT showed 32-fold increased risk of thyroid cancer and rs2305994 of Yes1 genotypes TT and CT showed 2.7-fold and 16-fold increased risk in thyroid cancer (p < 0.0001). Haplotype analysis revealed that CATGCC, CATGCT, CATGTC, CATGTT, TATGCC and TATGTTA haplotypes are associated with thyroid cancer risk. Conclusion: Results showed that genotypes and allele distribution of Src and Yes1 genes are significantly linked with increased risk of thyroid cancer.

Physicochemical Foundations of Life that Direct Evolution: Chance and Natural Selection are not Evolutionary Driving Forces

The current framework of evolutionary theory postulates that evolution relies on random mutations generating a diversity of phenotypes on which natural selection acts. This framework was established using a top-down approach as it originated from Darwinism, which is based on observations made of complex multicellular organisms and, then, modified to fit a DNA-centric view. In this article, it is argued that based on a bottom-up approach starting from the physicochemical properties of nucleic and amino acid polymers, we should reject the facts that (i) natural selection plays a dominant role in evolution and (ii) the probability of mutations is independent of the generated phenotype. It is shown that the adaptation of a phenotype to an environment does not correspond to organism fitness, but rather corresponds to maintaining the genome stability and integrity. In a stable environment, the phenotype maintains the stability of its originating genome and both (genome and phenotype) are reproduced identically. In an unstable environment (i.e., corresponding to variations in physicochemical parameters above a physiological range), the phenotype no longer maintains the stability of its originating genome, but instead influences its variations. Indeed, environment- and cellular-dependent physicochemical parameters define the probability of mutations in terms of frequency, nature, and location in a genome. Evolution is non-deterministic because it relies on probabilistic physicochemical rules, and evolution is driven by a bidirectional interplay between genome and phenotype in which the phenotype ensures the stability of its originating genome in a cellular and environmental physicochemical parameter-depending manner.