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The primary means of classifying new functions for genes and proteins relies on Gene Ontology (GO), which defines genes/proteins using a controlled vocabulary in terms of their Molecular Function, Biological Process and Cellular Component. The challenge is to present this information to researchers to compare and discover patterns in multiple datasets using visually comprehensible and user-friendly statistical reports. Importantly, while there are many GO resources available for eukaryotes, there are none suitable for simultaneous, graphical and statistical comparison between multiple datasets. In addition, none of them supports comprehensive resources for bacteria. By using Streptococcus pneumoniae as a model, we identified and collected GO resources including genes, proteins, taxonomy and GO relationships from NCBI, UniProt and GO organisations. Then, we designed database tables in PostgreSQL database server and developed a Java application to extract data from source files and loaded into database automatically. We developed a PHP web application based on Model-View-Control architecture, used a specific data structure as well as current and novel algorithms to estimate GO graphs parameters. We designed different navigation and visualization methods on the graphs and integrated these into graphical reports. This tool is particularly significant when comparing GO groups between multiple samples (including those of pathogenic bacteria) from different sources simultaneously. Comparing GO protein distribution among up- or down-regulated genes from different samples can improve understanding of biological pathways, and mechanism(s) of infection. It can also aid in the discovery of genes associated with specific function(s) for investigation as a novel vaccine or therapeutic targets.Availability http://turing.ersa.edu.au/BacteriaGO.

Serum levels of miR-194 have been reported to predict prostate cancer recurrence after surgery, but its functional contributions to this disease have not been studied. Herein, it is demonstrated that miR-194 is a driver of prostate cancer metastasis. Prostate tissue levels of miR-194 were associated with disease aggressiveness and poor outcome. Ectopic delivery of miR-194 stimulated migration, invasion, and epithelial-mesenchymal transition in human prostate cancer cell lines, and stable overexpression of miR-194 enhanced metastasis of intravenous and intraprostatic tumor xenografts. Conversely, inhibition of miR-194 activity suppressed the invasive capacity of prostate cancer cell lines and Mechanistic investigations identified the ubiquitin ligase suppressor of cytokine signaling 2 (SOCS2) as a direct, biologically relevant target of miR-194 in prostate cancer. Low levels of correlated strongly with disease recurrence and metastasis in clinical specimens. SOCS2 downregulation recapitulated miR-194-driven metastatic phenotypes, whereas overexpression of a nontargetable SOCS2 reduced miR-194-stimulated invasion. Targeting of SOCS2 by miR-194 resulted in derepression of the oncogenic kinases FLT3 and JAK2, leading to enhanced ERK and STAT3 signaling. Pharmacologic inhibition of ERK and JAK/STAT pathways reversed miR-194-driven phenotypes. The GATA2 transcription factor was identified as an upstream regulator of miR-194, consistent with a strong concordance between GATA2 and miR-194 levels in clinical specimens. Overall, these results offer new insights into the molecular mechanisms of metastatic progression in prostate cancer..

The engineering of thermostable enzymes is receiving increased attention. The paper, detergent, and biofuel industries, in particular, seek to use environmentally friendly enzymes instead of toxic chlorine chemicals. Enzymes typically function at temperatures below 60°C and denature if exposed to higher temperatures. In contrast, a small portion of enzymes can withstand higher temperatures as a result of various structural adaptations. Understanding the protein attributes that are involved in this adaptation is the first step toward engineering thermostable enzymes. We employed various supervised and unsupervised machine learning algorithms as well as attribute weighting approaches to find amino acid composition attributes that contribute to enzyme thermostability. Specifically, we compared two groups of enzymes: mesostable and thermostable enzymes. Furthermore, a combination of attribute weighting with supervised and unsupervised clustering algorithms was used for prediction and modelling of protein thermostability from amino acid composition properties. Mining a large number of protein sequences (2090) through a variety of machine learning algorithms, which were based on the analysis of more than 800 amino acid attributes, increased the accuracy of this study. Moreover, these models were successful in predicting thermostability from the primary structure of proteins. The results showed that expectation maximization clustering in combination with uncertainly and correlation attribute weighting algorithms can effectively (100%) classify thermostable and mesostable proteins. Seventy per cent of the weighting methods selected Gln content and frequency of hydrophilic residues as the most important protein attributes. On the dipeptide level, the frequency of Asn-Glu was the key factor in distinguishing mesostable from thermostable enzymes. This study demonstrates the feasibility of predicting thermostability irrespective of sequence similarity and will serve as a basis for engineering thermostable enzymes in the laboratory.