Idiosyncratic adverse drug reactions are unpredictable, dose-independent and potentially life threatening; this makes them a major factor contributing to the cost and uncertainty of drug development. Clinical data suggest that many such reactions involve immune mechanisms, and genetic association studies have identified strong linkages between drug hypersensitivity reactions to several drugs and specific HLA alleles. One of the strongest such genetic associations found has been for the antiviral drug abacavir, which causes severe adverse reactions exclusively in patients expressing the HLA molecular variant B*57:01. Abacavir adverse reactions were recently shown to be driven by drug-specific activation of cytokine-producing, cytotoxic CD8 + T cells that required HLA-B*57:01 molecules for their function; however, the mechanism by which abacavir induces this pathologic T-cell response remains unclear. Here we show that abacavir can bind within the F pocket of the peptide-binding groove of HLA-B*57:01, thereby altering its specificity. This provides an explanation for HLA-linked idiosyncratic adverse drug reactions, namely that drugs can alter the repertoire of self-peptides presented to T cells, thus causing the equivalent of an alloreactive T-cell response. Indeed, we identified specific self-peptides that are presented only in the presence of abacavir and that were recognized by T cells of hypersensitive patients. The assays that we have established can be applied to test additional compounds with suspected HLAlinked hypersensitivities in vitro. Where successful, these assays could speed up the discovery and mechanistic understanding of HLA-linked hypersensitivities, and guide the development of safer drugs.3D structure | small molecule | binding site A bacavir is a nucleoside analog that suppresses HIV replication. In approximately 8% of recipients, abacavir is associated with significant immune-mediated drug hypersensitivity, which is strongly associated with the presence of the HLA-B*57:01 allele (1, 2). Three complementary models for the mechanism of immune-mediated severe adverse drug reactions have traditionally been discussed (3, 4). The hapten (or prohapten) model states that drugs and their metabolites are too small to be immunogenic on their own, but rather act like haptens and modify certain self-proteins in the host that lead to immune recognition of the resulting hapten-self-peptide complexes as de novo antigens (5-7). The pharmacologic interaction with immune receptors (p-i) model states that drugs can induce the formation of HLA-drug complexes that can activate T-cell immune responses directly without requiring a specific peptide ligand (8). The danger model, which is in principle compatible with other models, states that danger signals other than the drug itself (e.g., chemical, physical, or viral stress) are required to overcome immune tolerance barriers that otherwise suppress drug hypersensitivity reactions (7).None of these existing models provides a convincing mechanism explaining how abacav...

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

The concept that proteins and small RNAs can move to and function in distant body parts is well established. However, non-cell-autonomy of small RNA molecules raises the question: To what extent are protein-coding messenger RNAs (mRNAs) exchanged between tissues in plants? Here we report the comprehensive identification of 2,006 genes producing mobile RNAs in Arabidopsis thaliana. The analysis of variant ecotype transcripts that were present in heterografted plants allowed the identification of mRNAs moving between various organs under normal or nutrient-limiting conditions. Most of these mobile transcripts seem to follow the phloem-dependent allocation pathway transporting sugars from photosynthetic tissues to roots via the vasculature. Notably, a high number of transcripts also move in the opposite, root-to-shoot direction and are transported to specific tissues including flowers. Proteomic data on grafted plants indicate the presence of proteins from mobile RNAs, allowing the possibility that they may be translated at their destination site. The mobility of a high number of mRNAs suggests that a postulated tissue-specific gene expression profile might not be predictive for the actual plant body part in which a transcript exerts its function.

Traditionally, most drugs have been discovered using phenotypic or target-based screens. Subsequently, their indications are often expanded on the basis of clinical observations, providing additional benefit to patients. This review highlights computational techniques for systematic analysis of transcriptomics (Connectivity Map, CMap), side effects, and genetics (genome-wide association study, GWAS) data to generate new hypotheses for additional indications. We also discuss data domains such as electronic health records (EHRs) and phenotypic screening that we consider promising for novel computational repositioning methods.

Drug repositioning helps fully explore indications for marketed drugs and clinical candidates. Here we show that the clinical side-effects (SEs) provide a human phenotypic profile for the drug, and this profile can suggest additional disease indications. We extracted 3,175 SE-disease relationships by combining the SE-drug relationships from drug labels and the drug-disease relationships from PharmGKB. Many relationships provide explicit repositioning hypotheses, such as drugs causing hypoglycemia are potential candidates for diabetes. We built Naïve Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was above 0.8 in 92% of these models. The method was extended to predict indications for clinical compounds, 36% of the models achieved AUC above 0.7. This suggests that closer attention should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to rationally explore the repositioning potential based on this “clinical phenotypic assay”.

In plants, protein-coding mRNAs can move via the phloem vasculature to distant tissues, where they may act as non-cellautonomous signals. Emerging work has identified many phloem-mobile mRNAs, but little is known regarding RNA motifs triggering mobility, the extent of mRNA transport, and the potential of transported mRNAs to be translated into functional proteins after transport. To address these aspects, we produced reporter transcripts harboring tRNA-like structures (TLSs) that were found to be enriched in the phloem stream and in mRNAs moving over chimeric graft junctions. Phenotypic and enzymatic assays on grafted plants indicated that mRNAs harboring a distinctive TLS can move from transgenic roots into wild-type leaves and from transgenic leaves into wild-type flowers or roots; these mRNAs can also be translated into proteins after transport. In addition, we provide evidence that dicistronic mRNA:tRNA transcripts are frequently produced in Arabidopsis thaliana and are enriched in the population of graft-mobile mRNAs. Our results suggest that tRNA-derived sequences with predicted stem-bulge-stem-loop structures are sufficient to mediate mRNA transport and seem to be necessary for the mobility of a large number of endogenous transcripts that can move through graft junctions.

Highlights d m 5 C methylation is highly enriched in transcripts moving from shoot to root d TCTP1 and HSC70.1 mRNAs are not graft mobile in RNA methylation-deficient mutants d TCTP1 is translated after transport in distinct root cells and affects root growth

Identifying new indications for existing drugs (drug repositioning) is an efficient way of maximizing their potential. Adverse drug reaction (ADR) is one of the leading causes of death among hospitalized patients. As both new indications and ADRs are caused by unexpected chemical–protein interactions on off-targets, it is reasonable to predict these interactions by mining the chemical–protein interactome (CPI). Making such predictions has recently been facilitated by a web server named DRAR-CPI. This server has a representative collection of drug molecules and targetable human proteins built up from our work in drug repositioning and ADR. When a user submits a molecule, the server will give the positive or negative association scores between the user’s molecule and our library drugs based on their interaction profiles towards the targets. Users can thus predict the indications or ADRs of their molecule based on the association scores towards our library drugs. We have matched our predictions of drug–drug associations with those predicted via gene-expression profiles, achieving a matching rate as high as 74%. We have also successfully predicted the connections between anti-psychotics and anti-infectives, indicating the underlying relevance of anti-psychotics in the potential treatment of infections, vice versa. This server is freely available at http://cpi.bio-x.cn/drar/.