A Novel Statistical Algorithm for Gene Expression Analysis Helps Differentiate Pregnane X Receptor-Dependent and Independent Mechanisms of Toxicity

Genome-wide gene expression profiling has become standard for assessing potential liabilities as well as for elucidating mechanisms of toxicity of drug candidates under development. Analysis of microarray data is often challenging due to the lack of a statistical model that is amenable to biological variation in a small number of samples. Here we present a novel non-parametric algorithm that requires minimal assumptions about the data distribution. Our method for determining differential expression consists of two steps: 1) We apply a nominal threshold on fold change and platform p-value to designate whether a gene is differentially expressed in each treated and control sample relative to the averaged control pool, and 2) We compared the number of samples satisfying criteria in step 1 between the treated and control groups to estimate the statistical significance based on a null distribution established by sample permutations. The method captures group effect without being too sensitive to anomalies as it allows tolerance for potential non-responders in the treatment group and outliers in the control group. Performance and results of this method were compared with the Significant Analysis of Microarrays (SAM) method. These two methods were applied to investigate hepatic transcriptional responses of wild-type (PXR+/+) and pregnane X receptor-knockout (PXR−/−) mice after 96 h exposure to CMP013, an inhibitor of β-secretase (β-site of amyloid precursor protein cleaving enzyme 1 or BACE1). Our results showed that CMP013 led to transcriptional changes in hallmark PXR-regulated genes and induced a cascade of gene expression changes that explained the hepatomegaly observed only in PXR+/+ animals. Comparison of concordant expression changes between PXR+/+ and PXR−/− mice also suggested a PXR-independent association between CMP013 and perturbations to cellular stress, lipid metabolism, and biliary transport

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses

Dynamic changes in eIF4F-mRNA interactions revealed by global analyses of environmental stress responses

BACKGROUND: Translation factors eIF4E and eIF4G form eIF4F, which interacts with the messenger RNA (mRNA) 5' cap to promote ribosome recruitment and translation initiation. Variations in the association of eIF4F with individual mRNAs likely contribute to differences in translation initiation frequencies between mRNAs. As translation initiation is globally reprogrammed by environmental stresses, we were interested in determining whether eIF4F interactions with individual mRNAs are reprogrammed and how this may contribute to global environmental stress responses. RESULTS: Using a tagged-factor protein capture and RNA-sequencing (RNA-seq) approach, we have assessed how mRNA associations with eIF4E, eIF4G1 and eIF4G2 change globally in response to three defined stresses that each cause a rapid attenuation of protein synthesis: oxidative stress induced by hydrogen peroxide and nutrient stresses caused by amino acid or glucose withdrawal. We find that acute stress leads to dynamic and unexpected changes in eIF4F-mRNA interactions that are shared among each factor and across the stresses imposed. eIF4F-mRNA interactions stabilised by stress are predominantly associated with translational repression, while more actively initiating mRNAs become relatively depleted for eIF4F. Simultaneously, other mRNAs are insulated from these stress-induced changes in eIF4F association. CONCLUSION: Dynamic eIF4F-mRNA interaction changes are part of a coordinated early translational control response shared across environmental stresses. Our data are compatible with a model where multiple mRNA closed-loop complexes form with differing stability. Hence, unexpectedly, in the absence of other stabilising factors, rapid translation initiation on mRNAs correlates with less stable eIF4F interactions

The structure and function of Alzheimer's gamma secretase enzyme complex

The production and accumulation of the beta amyloid protein (Aβ) is a key event in the cascade of oxidative and inflammatory processes that characterizes Alzheimer’s disease (AD). A multi-subunit enzyme complex, referred to as gamma (γ) secretase, plays a pivotal role in the generation of Aβ from its parent molecule, the amyloid precursor protein (APP). Four core components (presenilin, nicastrin, aph-1, and pen-2) interact in a high-molecular-weight complex to perform intramembrane proteolysis on a number of membrane-bound proteins, including APP and Notch. Inhibitors and modulators of this enzyme have been assessed for their therapeutic benefit in AD. However, although these agents reduce Aβ levels, the majority have been shown to have severe side effects in pre-clinical animal studies, most likely due to the enzymes role in processing other proteins involved in normal cellular function. Current research is directed at understanding this enzyme and, in particular, at elucidating the roles that each of the core proteins plays in its function. In addition, a number of interacting proteins that are not components of γ-secretase also appear to play important roles in modulating enzyme activity. This review will discuss the structural and functional complexity of the γ-secretase enzyme and the effects of inhibiting its activity

Molecular cloning of a family of retroviral sequences found in chimpanzee but not human DNA.

A number of retrovirus-like sequences have been cloned from chimpanzee DNA which constitute the chimpanzee homologs of the endogenous colobus type C virus CPC-1. One of the clones contains a nearly complete viral genome, but others have sustained deletions of 1 to 2 kilobases in the polymerase gene. The pattern of related sequences detected in other primate species is consistent with the genetic transmission of these sequences for millions of years. However, the appropriately related sequences have not been detected in human, gibbon, or orangutan DNAs. These results suggest either that this family of sequences has been deleted from humans, gibbons, and orangutans, or that the genes were recently acquired in the chimpanzee and gorilla lineages

A mechanism of translational repression by competition of Paip2 with eIF4G for poly(A) binding protein (PABP) binding

The eukaryotic mRNA 3′ poly(A) tail and the 5′ cap cooperate to synergistically enhance translation. This interaction is mediated by the cap-binding protein eIF4E, the poly(A) binding protein (PABP), and eIF4G, a scaffolding protein that bridges between eIF4E and PABP to bring about the circularization of the mRNA. The translational repressor, Paip2 (PABP-interacting protein 2), inhibits translation by promoting the dissociation of PABP from poly(A). Here we report on the existence of an alternative mechanism by which Paip2 inhibits translation by competing with eIF4G for binding to PABP. We demonstrate that Paip2 can abrogate the translational activity of PABP, which is tethered to the 3′ end of the mRNA. Thus, Paip2 can inhibit translation by a previously unrecognized mechanism, which is independent of its ability to disrupt PABP–poly(A) interaction