In recent years, RNA-sequencing (RNA-seq) has emerged as a powerful technology for transcriptome profiling. For a given gene, the number of mapped reads is not only dependent on its expression level and gene length, but also the sequencing depth. To normalize these dependencies, RPKM (reads per kilobase of transcript per million reads mapped) and TPM (transcripts per million) are used to measure gene or transcript expression levels. A common misconception is that RPKM and TPM values are already normalized, and thus should be comparable across samples or RNA-seq projects. However, RPKM and TPM represent the relative abundance of a transcript among a population of sequenced transcripts, and therefore depend on the composition of the RNA population in a sample. Quite often, it is reasonable to assume that total RNA concentration and distributions are very close across compared samples. Nevertheless, the sequenced RNA repertoires may differ significantly under different experimental conditions and/or across sequencing protocols; thus, the proportion of gene expression is not directly comparable in such cases. In this review, we illustrate typical scenarios in which RPKM and TPM are misused, unintentionally, and hope to raise scientists’ awareness of this issue when comparing them across samples or different sequencing protocols.

In response to DNA damage, the ATM protein kinase activates signal transduction pathways essential for coordinating cell cycle progression with DNA repair. In the human disease ataxia-telangiectasia, mutation of the ATM gene results in multiple cellular defects, including enhanced sensitivity to ionizing radiation (IR). This phenotype highlights ATM as a potential target for novel inhibitors that could be used to enhance tumor cell sensitivity to radiotherapy. A targeted compound library was screened for potential inhibitors of the ATM kinase, and CP466722 was identified. The compound is nontoxic and does not inhibit phosphatidylinositol 3-kinase (PI3K) or PI3K-like protein kinase family members in cells. CP466722 inhibited cellular ATM-dependent phosphorylation events and disruption of ATM function resulted in characteristic cell cycle checkpoint defects. Inhibition of cellular ATM kinase activity was rapidly and completely reversed by removing CP466722. Interestingly, clonogenic survival assays showed that transient inhibition of ATM is sufficient to sensitize cells to IR and suggests that therapeutic radiosensitization may only require ATM inhibition for short periods of time. The ability of CP466722 to rapidly and reversibly regulate ATM activity provides a new tool to ask questions about ATM function that could not easily be addressed using genetic models or RNA interference technologies. [Cancer Res 2008; 68(18):7466-74]

Phenotypic drug discovery approaches can positively affect the translation of preclinical findings to patients. However, not all phenotypic assays are created equal. A critical question then follows: What are the characteristics of the optimal assays? We analyze this question and propose three specific criteria related to the disease relevance of the assay-system, stimulus, and end point-to help design the most predictive phenotypic assays.

We present a new more general way to combine ab initio quantum mechanical calculations with classical mechanical free energy perturbation approach to calculate the energetics of enzyme-catalyzed reactions and the same reaction in solution. This approach, which enables enzyme and solution reactions to be compared without the use of empirical parameters, is applied to the formation of the tetrahedral intermediate in trypsin, but it should be generally applicable to any enzymatic reaction. Critical to the accurate calculation of the reaction energetics in solution is the estimate of the free energy to assemble the reacting groups, where the approach recently published by Hermans and Wang (J. Am. Chem. Soc. 1997, 119, 2707) was used. A central aspect of this new approach is the use of the RESP protocol to calculate the charge distribution of structures along the reaction pathway, which enables us to circumvent problems in partitioning the charge across a residue that is being divided into QM and MM parts. The classical mechanical free energy calculations are implemented with two different approaches, “Cartesian mapping” and “flexible FEP”. The similarity of the results found by using these two approaches supports the robustness of the calculated free energies. The calculated free energies are in quite good agreement with available experimental data for the activation free energies in the enzyme and aqueous phase reactions.

Fully phosphorothioate antisense oligonucleotides (ASOs) with locked nucleic acids (LNAs) improve target affinity, RNase H activation and stability. LNA modified ASOs can cause hepatotoxicity, and this risk is currently not fully understood. In vitro cytotoxicity screens have not been reliable predictors of hepatic toxicity in non-clinical testing; however, mice are considered to be a sensitive test species. To better understand the relationship between nucleotide sequence and hepatotoxicity, a structure–toxicity analysis was performed using results from 2 week repeated-dose-tolerability studies in mice administered LNA-modified ASOs. ASOs targeting human Apolipoprotien C3 (Apoc3), CREB (cAMP Response Element Binding Protein) Regulated Transcription Coactivator 2 (Crtc2) or Glucocorticoid Receptor (GR, NR3C1) were classified based upon the presence or absence of hepatotoxicity in mice. From these data, a random-decision forest-classification model generated from nucleotide sequence descriptors identified two trinucleotide motifs (TCC and TGC) that were present only in hepatotoxic sequences. We found that motif containing sequences were more likely to bind to hepatocellular proteins in vitro and increased P53 and NRF2 stress pathway activity in vivo. These results suggest in silico approaches can be utilized to establish structure–toxicity relationships of LNA-modified ASOs and decrease the likelihood of hepatotoxicity in preclinical testing.