Consistency of predictive signature genes and classifiers generated using different microarray platforms.

Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80-90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.

Genomic indicators in the blood predict drug-induced liver injury.

Genomic biomarkers for the detection of drug-induced liver injury (DILI) from blood are urgently needed for monitoring drug safety. We used a unique data set as part of the Food and Drug Administration led MicroArray Quality Control Phase-II (MAQC-II) project consisting of gene expression data from the two tissues (blood and liver) to test cross-tissue predictability of genomic indicators to a form of chemically induced liver injury. We then use the genomic indicators from the blood as biomarkers for prediction of acetaminophen-induced liver injury and show that the cross-tissue predictability of a response to the pharmaceutical agent (accuracy as high as 92.1%) is better than, or at least comparable to, that of non-therapeutic compounds. We provide a database of gene expression for the highly informative predictors, which brings biological context to the possible mechanisms involved in DILI. Pathway-based predictors were associated with inflammation, angiogenesis, Toll-like receptor signaling, apoptosis, and mitochondrial damage. The results show for the first time and support the hypothesis that genomic indicators in the blood can serve as potential diagnostic biomarkers predictive of DILI.

Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.

Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine.

Algorithms for network analysis in systems-ADME/Tox using the MetaCore and MetaDrug platforms.

The authors have previously applied two integrated platforms, MetaCore and MetaDrug, for the assembly and analysis of human biological networks as a useful method for the integration and functional interpretation of high-throughput experimental data. The present study demonstrates in detail the specific algorithms that are used in both software platforms. Using a standard set of genes as input, namely CYP3A4 (an enzyme), PXR (a nuclear hormone receptor), MDR1 (a transporter) and hERG (an ion channel) related to the absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) of xenobiotics, we have now generated networks with each algorithm. The relative advantages and disadvantages of these algorithms are explained using these examples as well as appropriate instances of utility to illustrate further the particular circumstances for their use. In addition, the benefits of the different network algorithms are identified when compared with algorithms available in other products, where this information is available.

A meiotic recombination checkpoint controlled by mitotic checkpoint genes.

In budding yeast, meiotic recombination occurs at about 200 sites per cell and involves DNA double-strand break (DSB) intermediates. Here we provide evidence that a checkpoint control requiring the mitotic DNA-damage checkpoint genes RAD17, RAD24 and MEC1 ensures that meiotic recombination is complete before the first meiotic division (MI). First, RAD17, RAD24 and MEC1 are required for the meiotic arrest caused by blocking the repair of DSBs with a mutation in the recA homologue DMC1. Second, mec1 and rad24 single mutants (DMC1+) appear to undergo MI before all recombination events are complete. Curiously, the mitosis-specific checkpoint gene RAD9 is not required for meiotic arrest of dmc1 mutants. This shows that although mitotic and meiotic control mechanisms are related, they differ significantly. Rad17 and Rad24 proteins may contribute directly to formation of an arrest signal by association with single-strand DNA in mitosis and meiosis.

Tn10-mediated inversions fuse uridine phosphorylase (udp) and rRNA genes of Escherichia coli.

Two strains carrying metE::Tn10 insertions (upstream of the udp gene) were used to isolate mutants of Escherichia coli overexpressing udp. These strains differ in their gene order; one contains an inversion between the rrnD and rrnE rRNA operons. Selection was based on the ability of overexpressed Udp to complement thymine auxotrophy. Chromosomal rearrangements that connect the udp gene and promoters of different rrn operons were obtained by this selection. Seven of 14 independent mutants selected in one of the initial strains contained similar inversions of the metE-rrnD segment of the chromosome (about 12% of its length). Another mutant contained traces of a more complicated event, inversion between rrnB and rrnG operons, which was followed by reinversion of the segment between metE and the hybrid rrnG/B operon. Similar inversions (udp-rrn) in a strain already carrying an rrnE-rrnD inversion flip the chromosomal segment between metE and rrnD/E in the opposite direction. In this case, inversions are also accompanied by duplications of the chromosomal region between the rrnA and hybrid udp-rrnD/E operons. PCR amplification with a set of oligonucleotides from the rrn, Tn5, and met genes was used for more detailed mapping. Amplified fragments of the rearranged chromosomes connecting rrnD sequences and insertion elements were sequenced, and inversion endpoints were established.