Progressive leukoencephalopathy impairs neurobehavioral development in sialin-deficient mice

Slc17a5−/− mice represent an animal model for the infantile form of sialic acid storage disease (SASD). We analyzed genetic and histological time-course expression of myelin and oligodendrocyte (OL) lineage markers in different parts of the CNS, and related this to postnatal neurobehavioral development in these mice. Sialin-deficient mice display a distinct spatiotemporal pattern of sialic acid storage, CNS hypomyelination and leukoencephalopathy. Whereas few genes are differentially expressed in the perinatal stage (p0), microarray analysis revealed increased differential gene expression in later postnatal stages (p10–p18). This included progressive upregulation of neuroinflammatory genes, as well as continuous down-regulation of genes that encode myelin constituents and typical OL lineage markers. Age-related histopathological analysis indicates that initial myelination occurs normally in hindbrain regions, but progression to more frontal areas is affected in Slc17a5−/− mice. This course of progressive leukoencephalopathy and CNS hypomyelination delays neurobehavioral development in sialin-deficient mice. Slc17a5−/− mice successfully achieve early neurobehavioral milestones, but exhibit progressive delay of later-stage sensory and motor milestones. The present findings may contribute to further understanding of the processes of CNS myelination as well as help to develop therapeutic strategies for SASD and other myelination disorders

Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein–ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD. The targets and genes are linked via PPAR signalling pathway and induction of apoptosis, generating a hypothesis for the MoA of thiazolidinediones. Our analysis show one or more underlying MoA for compounds and were well-substantiated with literature

A genetics-led approach defines the drug target landscape of 30 immune-related traits

Most candidate drugs currently fail later-stage clinical trials, largely due to poor prediction of efficacy on early target selection1. Drug targets with genetic support are more likely to be therapeutically valid2,3, but the translational use of genome-scale data such as from genome-wide association studies for drug target discovery in complex diseases remains challenging4,5,6. Here, we show that integration of functional genomic and immune-related annotations, together with knowledge of network connectivity, maximizes the informativeness of genetics for target validation, defining the target prioritization landscape for 30 immune traits at the gene and pathway level. We demonstrate how our genetics-led drug target prioritization approach (the priority index) successfully identifies current therapeutics, predicts activity in high-throughput cellular screens (including L1000, CRISPR, mutagenesis and patient-derived cell assays), enables prioritization of under-explored targets and allows for determination of target-level trait relationships. The priority index is an open-access, scalable system accelerating early-stage drug target selection for immune-mediated disease

A genetics-led approach defines the drug target landscape of 30 immune-related traits

Most candidate drugs currently fail later-stage clinical trials, largely due to poor prediction of efficacy on early target selection1. Drug targets with genetic support are more likely to be therapeutically valid2,3, but the translational use of genome-scale data such as from genome-wide association studies for drug target discovery in complex diseases remains challenging4,5,6. Here, we show that integration of functional genomic and immune-related annotations, together with knowledge of network connectivity, maximizes the informativeness of genetics for target validation, defining the target prioritization landscape for 30 immune traits at the gene and pathway level. We demonstrate how our genetics-led drug target prioritization approach (the priority index) successfully identifies current therapeutics, predicts activity in high-throughput cellular screens (including L1000, CRISPR, mutagenesis and patient-derived cell assays), enables prioritization of under-explored targets and allows for determination of target-level trait relationships. The priority index is an open-access, scalable system accelerating early-stage drug target selection for immune-mediated disease