Modification of SMN2 exon 7 (E7) splicing is a validated therapeutic strategy against spinal muscular atrophy (SMA). However, a target-based approach to identify small-molecule E7 splicing modifiers has not been attempted, which could reveal novel therapies with improved mechanistic insight. Here, we chose as a target the stem-loop RNA structure TSL2, which overlaps with the 5′ splicing site of E7. A small-molecule TSL2-binding compound, homocarbonyltopsentin (PK4C9), was identified that increases E7 splicing to therapeutic levels and rescues downstream molecular alterations in SMA cells. High-resolution NMR combined with molecular modelling revealed that PK4C9 binds to pentaloop conformations of TSL2 and promotes a shift to triloop conformations that display enhanced E7 splicing. Collectively, our study validates TSL2 as a target for small-molecule drug discovery in SMA, identifies a novel mechanism of action for an E7 splicing modifier, and sets a precedent for other splicing-mediated diseases where RNA structure could be similarly targeted.

Gene signatures of drug-induced toxicity are of broad interest, but they are often identified from small-scale, single-time point experiments, and are therefore of limited applicability. To address this issue, we performed multivariate analysis of gene expression, cell-based assays, and histopathological data in the TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation system) database. Data mining highlights four genes—EGR1, ATF3, GDF15 and FGF21—that are induced 2 h after drug administration in human and rat primary hepatocytes poised to eventually undergo cytotoxicity-induced cell death. Modelling and simulation reveals that these early stress-response genes form a functional network with evolutionarily conserved structure and intrinsic dynamics. This is underlined by the fact that early induction of this network in vivo predicts drug-induced liver and kidney pathology with high accuracy. Our findings demonstrate the value of early gene-expression signatures in predicting and understanding compound-induced toxicity. The identified network can empower first-line tests that reduce animal use and costs of safety evaluation.

BackgroundTuberous sclerosis complex (TSC) is a genetic disease characterized by benign tumor growths in multiple organs and neurological symptoms induced by mTOR hyperfunction. Because the molecular pathology is highly complex and the etiology poorly understood, we employed a defined human neuronal model with a single mTOR activating mutation to dissect the disease-relevant molecular responses driving the neuropathology and suggest new targets for treatment.MethodsWe investigate the disease phenotype of TSC by neural differentiation of a human stem cell model that had been deleted for TSC2 by genome editing. Comprehensive genomic analysis was performed by RNA sequencing and ribosome profiling to obtain a detailed genome-wide description of alterations on both the transcriptional and translational level. The molecular effect of mTOR inhibitors used in the clinic was monitored and comparison to published data from patient biopsies and mouse models highlights key pathogenic processes.ResultsTSC2-deficient neural stem cells showed severely reduced neuronal maturation and characteristics of astrogliosis instead. Transcriptome analysis indicated an active inflammatory response and increased metabolic activity, whereas at the level of translation ribosomal transcripts showed a 5’UTR motif-mediated increase in ribosome occupancy. Further, we observed enhanced protein synthesis rates of angiogenic growth factors. Treatment with mTOR inhibitors corrected translational alterations but transcriptional dysfunction persisted.ConclusionsOur results extend the understanding of the molecular pathophysiology of TSC brain lesions, and suggest phenotype-tailored pharmacological treatment strategies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0347-3) contains supplementary material, which is available to authorized users.

BackgroundBoolean models are increasingly used to study biological signaling networks. In a Boolean network, nodes represent biological entities such as genes, proteins or protein complexes, and edges indicate activating or inhibiting influences of one node towards another. Depending on the input of activators or inhibitors, Boolean networks categorize nodes as either active or inactive. The formalism is appealing because for many biological relationships, we lack quantitative information about binding constants or kinetic parameters and can only rely on a qualitative description of the type “A activates (or inhibits) B”. A central aim of Boolean network analysis is the determination of attractors (steady states and/or cycles). This problem is known to be computationally complex, its most important parameter being the number of network nodes. Various algorithms tackle it with considerable success. In this paper we present an algorithm, which extends the size of analyzable networks thanks to simple and intuitive arguments.ResultsWe present lnet, a software package which, in fully asynchronous updating mode and without any network reduction, detects the fixed states of Boolean networks with up to 150 nodes and a good part of any present cycles for networks with up to half the above number of nodes. The algorithm goes through a complete enumeration of the states of appropriately selected subspaces of the entire network state space. The size of these relevant subspaces is small compared to the full network state space, allowing the analysis of large networks. The subspaces scanned for the analyses of cycles are larger, reducing the size of accessible networks. Importantly, inherent in cycle detection is a classification scheme based on the number of non-frozen nodes of the cycle member states, with cycles characterized by fewer non-frozen nodes being easier to detect. It is further argued that these detectable cycles are also the biologically more important ones. Furthermore, lnet also provides standard Boolean analysis features such as node loop detection.Conclusionslnet is a software package that facilitates the analysis of large Boolean networks. Its intuitive approach helps to better understand the network in question.

The aim of this study was to characterize the proteome of normal and malignant colonic tissue. We previously studied the colon proteome using 2-DE and MALDI-MS and identified 734 proteins (Roeßler, M., Rollinger, W., Palme S., Hagmann, M.-L., et al., Clin. Cancer Res. 2005, 11, 6550-6557).Here we report the identification of additional colon proteins from the same set of tissue samples using a complementary nano-flow 2-D-LC-ESI-MS. In total, 484 proteins were identified in colon. Of these, 252 had also been identified by the 2-DE/MALDI-MS approach, whereas 232 proteins were unique to the 2-D-LC-ESI-MS analysis. Comparing protein expression in neoplastic and normal colon tissue indicated elevated expression of several proteins in colorectal cancer, among them the well established tumor marker carcinoembryonic antigen, as well as calnexin, 40S ribosomal protein S15a, serpin H1, and S100A12. Overexpression of these proteins was confirmed by immunoblotting. Serum levels of S100A12 were determined by ELISA and were found to be strongly elevated in colorectal cancer patients compared to healthy individuals. We conclude, that 2-D-LC-ESI-MS is a powerful approach to identify and compare protein profiles of tissue samples, that it is complementary to 2-DE/MALDI-MS approaches and has the potential to identify novel biomarkers.