Effect of codon message on xylanase thermal activity

A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: A xylanase temperature dependence case study

Growing industrial utilization of enzymes and the increasing availability of metagenomic data highlight the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose, a component of lignocellulose. The optimum temperature of enzymes is an essential factor to be considered when choosing appropriate biocatalysts for a particular purpose. Therefore, in silico prediction of this attribute is a significant cost and time-effective step in the effort to characterize novel enzymes. The objective of this study was to develop a computational method to predict the thermal dependence of xylanases. This tool was then implemented for targeted screening of putative xylanases with specific thermal dependencies from metagenomic data and resulted in the identification of three novel xylanases from sheep and cow rumen microbiota. Here we present thermal activity prediction for xylanase, a new sequence-based machine learning method that has been trained using a selected combination of various protein features. This random forest classifier discriminates non-thermophilic, thermophilic, and hyper-thermophilic xylanases. The model's performance was evaluated through multiple iterations of sixfold cross-validations as well as holdout tests, and it is freely accessible as a web-service at arimees.com.

Success of alignment-free oligonucleotide (k-mer) analysis confirms relative importance of genomes not genes in speciation and phylogeny

The utility of DNA sequence substrings (k-mers) in alignment-free phylogenetic classification, including that of bacteria and viruses, is increasingly recognized. However, its biological basis eludes many 21st century practitioners. A path from the 19th century recognition of the informational basis of heredity to the modern era can be discerned. Crick’s DNA ‘unpairing postulate’ predicted that recombinational pairing of homologous DNAs during meiosis would be mediated by short k-mers in the loops of stem-loop structures extruded from classical duplex helices. The complementary ‘kissing’ duplex loops – like tRNA anticodon–codon k-mer duplexes – would seed a more extensive pairing that would then extend until limited by lack of homology or other factors. Indeed, this became the principle behind alignment-based methods that assessed similarity by degree of DNA–DNA reassociation in vitro. These are now seen as less sensitive than alignment-free methods that are closely consistent, both theoretically and mechanistically, with chromosomal anti-recombination models for the initiation of divergence into new species. The analytical power of k-mer differences supports the theses that evolutionary advance sometimes serves the needs of nucleic acids (genomes) rather than proteins (genes), and that such differences can play a role in early speciation events.

Deciphering the rationale behind specific codon usage pattern in extremophiles

Protein stability is affected at different hierarchies – gene, RNA, amino acid sequence and structure. Gene is the first level which contributes via varying codon compositions. Codon selectivity of an organism differs with normal and extremophilic milieu. The present work attempts at detailing the codon usage pattern of six extremophilic classes and their harmony. Homologous gene datasets of thermophile-mesophile, psychrophile-mesophile, thermophile-psychrophile, acidophile-alkaliphile, halophile-nonhalophile and barophile-nonbarophile were analysed for filtering statistically significant attributes. Relative abundance analysis, 1–9 scale ranking, nucleotide compositions, attribute weighting and machine learning algorithms were employed to arrive at findings. AGG in thermophiles and barophiles, CAA in mesophiles and psychrophiles, TGG in acidophiles, GAG in alkaliphiles and GAC in halophiles had highest preference. Preference of GC-rich and G/C-ending codons were observed in halophiles and barophiles whereas, a decreasing trend was reflected in psychrophiles and alkaliphiles. GC-rich codons were found to decrease and G/C-ending codons increased in thermophiles whereas, acidophiles showed equal contents of GC-rich and G/C-ending codons. Codon usage patterns exhibited harmony among different extremophiles and has been detailed. However, the codon attribute preferences and their selectivity of extremophiles varied in comparison to non-extremophiles. The finding can be instrumental in codon optimization application for heterologous expression of extremophilic proteins.

Comparative genomics of Mycobacterium reveals evolutionary trends of M. avium complex

Mycobacterium is gram positive, slow growing, disease causing Actinobacteria. Beside potential pathogenic species, Mycobacterium also contains opportunistic pathogens as well as free living non-pathogenic species. Disease related various analyses on Mycobacterium tuberculosis are very widespread. However, genomic study of overall Mycobacterium species for understanding the selection pressure on genes as well as evolution of the organism is still illusive. MLSA and 16s rDNA based analysis has been generated for 241 Mycobacterium strains and a detailed analysis of codon and amino acid usage bias of mycobacterial genes, their functional analysis have been done. Further the evolutionary features of M. avium complex also have been revealed. Mycobacterial genes are moderately GC rich showed higher expression level in PPs and significant negative correlation with biosynthetic cost of proteins. Translational selection pressure was observed in mycobacterial genes. MAC showed close relationship with NPs and higher evolutionary rate in MAC revealed their constant evolving nature.

The survival mechanisms of thermophiles at high temperatures: an angle of omics

Thermophiles are referred to as microorganisms with optimal growth temperatures of >60 °C. Over the past few years, a number of studies have been conducted regarding thermophiles, especially using the omics strategies. This review provides a systematic view of the survival physiology of thermophiles from an "omics" perspective, which suggests that the adaptive ability of thermophiles is based on a cooperative mode with multi-dimensional regulations integrating genomics, transcriptomics, and proteomics.