Inferring Causation in Yeast Gene Association Networks With Kernel Logistic Regression

Computational prediction of gene-gene associations is one of the productive directions in the study of bioinformatics. Many tools are developed to infer the relation between genes using different biological data sources. The association of a pair of genes deduced from the analysis of biological data becomes meaningful when it reflects the directionality and the type of reaction between genes. In this work, we follow another method to construct a causal gene co-expression network while identifying transcription factors in each pair of genes using microarray expression data. We adopt a machine learning technique based on a logistic regression model to tackle the sparsity of the network and to improve the quality of the prediction accuracy. The proposed system classifies each pair of genes into either connected or nonconnected class using the data of the correlation between these genes in the whole Saccharomyces cerevisiae genome. The accuracy of the classification model in predicting related genes was evaluated using several data sets for the yeast regulatory network. Our system achieves high performance in terms of several statistical measures.

Veterinary and human biobanking practices: enhancing molecular sample integrity

Animal models have historically informed veterinary and human pathophysiology. Next-generation genomic sequencing and molecular analyses using analytes derived from tissue require integrative approaches to determine macroanalyte integrity as well as morphology for imaging algorithms that can extend translational applications. The field of biospecimen science and biobanking will play critical roles in tissue sample collection and processing to ensure the integrity of macromolecules, aid experimental design, and provide more accurate and reproducible downstream genomic data. Herein, we employ animal experiments to combine protein expression analysis by microscopy with RNA integrity number and quantitative measures of morphologic changes of autolysis. These analyses can be used to predict the effect of preanalytic variables and provide the basis for standardized methods in tissue sample collection and processing. We also discuss the application of digital imaging with quantitative RNA and tissue-based protein measurements to show that genomic methods augment traditional in vivo imaging to support biospecimen science. To make these observations, we have established a time course experiment of murine kidney tissues that predicts conventional measures of RNA integrity by RIN analysis and provides reliable and accurate measures of biospecimen integrity and fitness, in particular for time points less than 3 hours post-tissue resection.

Cytokine receptor-like factor 1 (CRLF1) protects against 6-hydroxydopamine toxicity independent of the gp130/JAK signaling pathway

Oxidative stress is an important cause of cellular toxicity in the central nervous system and contributes to the pathology associated with neurodegenerative disorders including Parkinson’s disease. As such, elucidation of cellular mechanisms that enhance neuronal resistance to oxidative stress may provide new avenues for therapy. In this study we employed a simple two-state cellular model to identify genes that are associated with resistance to oxidative stress induced by 6-hydroxydopamine (6-OHDA). In this model, undifferentiated neuroblastoma cells display higher sensitivity to 6-OHDA than differentiated cells. By comparing the gene expression between these two states, we identified several genes whose expression is altered concomitant with changes in 6-OHDA sensitivity. This gene set includes cytokine receptor-like factor 1 (CRLF1), which is up-regulated during the differentiation process and has been previously implicated in neuroprotection. We show that the product of this gene is both necessary and sufficient for increased resistance to 6-OHDA in differentiated neuroblastoma cells, and that CRLF1 serves its protective role by a cell autonomous mechanism that is independent from its known role as a co-ligand for the ciliary neurotrophic factor receptor. These data provide an additional role for CRLF1 that could potentially explain its broad expression pattern and effects on cells lacking expression of this receptor.