Molecular characterization of novel and selective peroxisome proliferator-activated receptor alpha agonists with robust hypolipidemic activity in vivo.

The nuclear receptor peroxisome proliferator-activated receptor alpha (PPARalpha) is recognized as the primary target of the fibrate class of hypolipidemic drugs and mediates lipid lowering in part by activating a transcriptional cascade that induces genes involved in the catabolism of lipids. We report here the characterization of three novel PPARalpha agonists with therapeutic potential for treating dyslipidemia. These structurally related compounds display potent and selective binding to human PPARalpha and support robust recruitment of coactivator peptides in vitro. These compounds markedly potentiate chimeric transcription systems in cell-based assays and strikingly lower serum triglycerides in vivo. The transcription networks induced by these selective PPARalpha agonists were assessed by transcriptional profiling of mouse liver after short- and long-term treatment. The induction of several known PPARalpha target genes involved with fatty acid metabolism were observed, reflecting the expected pharmacology associated with PPARalpha activation. We also noted the down-regulation of a number of genes related to immune cell function, the acute phase response, and glucose metabolism, suggesting that these compounds may have anti-inflammatory action in the mammalian liver. Whereas these compounds are efficacious in acute preclinical models, extended safety studies and further clinical testing will be required before the full therapeutic promise of a selective PPARalpha agonist is realized.

Comparing 2-nt 3' overhangs against blunt-ended siRNAs: a systems biology based study.

In this study, we formulate a computational reaction model following a chemical kinetic theory approach to predict the binding rate constant for the siRNA-RISC complex formation reaction. The model allowed us to study the potency difference between 2-nt 3' overhangs against blunt-ended siRNA molecules in an RNA interference (RNAi) system. The rate constant predicted by this model was fed into a stochastic simulation of the RNAi system (using the Gillespie stochastic simulator) to study the overall potency effect. We observed that the stochasticity in the transcription/translation machinery has no observable effects in the RNAi pathway. Sustained gene silencing using siRNAs can be achieved only if there is a way to replenish the dsRNA molecules in the cell. Initial findings show about 1.5 times more blunt-ended molecules will be required to keep the mRNA at the same reduced level compared to the 2-nt overhang siRNAs. However, the mRNA levels jump back to saturation after a longer time when blunt-ended siRNAs are used. We found that the siRNA-RISC complex formation reaction rate was 2 times slower when blunt-ended molecules were used pointing to the fact that the presence of the 2-nt overhangs has a greater effect on the reaction in which the bound RISC complex cleaves the mRNA.

Assessing the translatability of in vivo cardiotoxicity mechanisms to in vitro models using causal reasoning.

Drug-induced cardiac toxicity has been implicated in 31% of drug withdrawals in the USA. The fact that the risk for cardiac-related adverse events goes undetected in preclinical studies for so many drugs underscores the need for better, more predictive in vitro safety screens to be deployed early in the drug discovery process. Unfortunately, many questions remain about the ability to accurately translate findings from simple cellular systems to the mechanisms that drive toxicity in the complex in vivo environment. In this study, we analyzed translatability of cardiotoxic effects for a diverse set of drugs from rodents to two different cell systems (rat heart tissue-derived cells (H9C2) and primary rat cardiomyocytes (RCM)) based on their transcriptional response. To unravel the altered pathway, we applied a novel computational systems biology approach, the Causal Reasoning Engine (CRE), to infer upstream molecular events causing the observed gene expression changes. By cross-referencing the cardiotoxicity annotations with the pathway analysis, we found evidence of mechanistic convergence towards common molecular mechanisms regardless of the cardiotoxic phenotype. We also experimentally verified two specific molecular hypotheses that translated well from in vivo to in vitro (Kruppel-like factor 4, KLF4 and Transforming growth factor beta 1, TGFB1) supporting the validity of the predictions of the computational pathway analysis. In conclusion, this work demonstrates the use of a novel systems biology approach to predict mechanisms of toxicity such as KLF4 and TGFB1 that translate from in vivo to in vitro. We also show that more complex in vitro models such as primary rat cardiomyocytes may not offer any advantage over simpler models such as immortalized H9C2 cells in terms of translatability to in vivo effects if we consider the right endpoints for the model. Further assessment and validation of the generated molecular hypotheses would greatly enhance our ability to design predictive in vitro cardiotoxicity assays.

Transcriptomic analysis of genetically defined autism candidate genes reveals common mechanisms of action.

BACKGROUND: Austism spectrum disorder (ASD) is a heterogeneous behavioral disorder or condition characterized by severe impairment of social engagement and the presence of repetitive activities. The molecular etiology of ASD is still largely unknown despite a strong genetic component. Part of the difficulty in turning genetics into disease mechanisms and potentially new therapeutics is the sheer number and diversity of the genes that have been associated with ASD and ASD symptoms. The goal of this work is to use shRNA-generated models of genetic defects proposed as causative for ASD to identify the common pathways that might explain how they produce a core clinical disability. METHODS: Transcript levels of Mecp2, Mef2a, Mef2d, Fmr1, Nlgn1, Nlgn3, Pten, and Shank3 were knocked-down in mouse primary neuron cultures using shRNA constructs. Whole genome expression analysis was conducted for each of the knockdown cultures as well as a mock-transduced culture and a culture exposed to a lentivirus expressing an anti-luciferase shRNA. Gene set enrichment and a causal reasoning engine was employed to identify pathway level perturbations generated by the transcript knockdown. RESULTS: Quantification of the shRNA targets confirmed the successful knockdown at the transcript and protein levels of at least 75% for each of the genes. After subtracting out potential artifacts caused by viral infection, gene set enrichment and causal reasoning engine analysis showed that a significant number of gene expression changes mapped to pathways associated with neurogenesis, long-term potentiation, and synaptic activity. CONCLUSIONS: This work demonstrates that despite the complex genetic nature of ASD, there are common molecular mechanisms that connect many of the best established autism candidate genes. By identifying the key regulatory checkpoints in the interlinking transcriptional networks underlying autism, we are better able to discover the ideal points of intervention that provide the broadest efficacy across the diverse population of autism patients.

Kinetic analysis of genomewide gene expression reveals molecule circuitries that control T cell activation and Th1/2 differentiation.

The global gene expression profiling of early T helper (Th) 1 and Th2 differentiation reveals that this process can be divided into two stages, activation and differentiation. The activation stage is manifested in coordinated mobilization of the replication machinery, a process that we hypothesize may be responsible for establishing genomewide opening of transcription loci. The molecular programs underlying the differentiation stage consist of highly regulated expression of functional groups of genes that are important for the biological properties of Th1/2 cells and transcription factors that are likely important in establishing terminal differentiation of these cells. The kinetics of expression pattern of a number of transcription factors shed new light on the molecular events that shape the outcome of Th1/2 differentiation.