Pharmacogenetic investigation of efficacy response to mepolizumab in eosinophilic granulomatosis with polyangiitis.

Treatment of patients with the rare disease eosinophilic granulomatosis with polyangiitis (EGPA) with mepolizumab, a monoclonal antibody to interleukin-5 (IL-5) that reduces blood eosinophil counts, as an add-on therapy to glucocorticoid treatment, results in more accrued weeks in remission, reductions in glucocorticoid use and reductions in relapse rate. However, treatment response varies across a continuum. Therefore, to investigate if large genetic effects could identify responders, the impact of genetic variants on efficacy in EGPA subjects taking mepolizumab and glucocorticoids was assessed in this post hoc study. Using linear regression and a negative binomial model, genetic variant association with three endpoints (accrued duration of remission, average oral glucocorticoid dose, and frequency of relapse) was tested in 61 EGPA subjects dosed with mepolizumab from MIRRA, a phase 3 trial. Candidate gene and genome-wide approaches were used. The candidate gene analysis was designed to investigate drug target effects with eight gene regions selected that were focused on the intersection of the glucocorticoid response (steroidal response) and IL-5 response mechanisms and recognizing potential overlap between EGPA and severe eosinophilic asthma diseases for which mepolizumab is used. The sample size was insufficient to enable testing of rare variants for effects. No genetic variant from either the candidate gene analysis or the GWAS associated with any endpoint. Thresholds to declare significance were p < 0.0008 (candidate variant) and p < 2.5 × 10-8 (genome-wide) analyses. Large genetic effects on mepolizumab-treatment response were not identified which could help differentiate responders from non-responders.

Cardiomyocyte Homeodomain-Interacting Protein Kinase 2 Maintains Basal Cardiac Function via Extracellular Signal-Regulated Kinase Signaling.

BACKGROUND: Cardiac kinases play a critical role in the development of heart failure, and represent potential tractable therapeutic targets. However, only a very small fraction of the cardiac kinome has been investigated. To identify novel cardiac kinases involved in heart failure, we used an integrated transcriptomics and bioinformatics analysis and identified Homeodomain-Interacting Protein Kinase 2 (HIPK2) as a novel candidate kinase. The role of HIPK2 in cardiac biology is unknown. METHODS: We used the Expression2Kinase algorithm for the screening of kinase targets. To determine the role of HIPK2 in the heart, we generated cardiomyocyte (CM)-specific HIPK2 knockout and heterozygous mice. Heart function was examined by echocardiography, and related cellular and molecular mechanisms were examined. Adeno-associated virus serotype 9 carrying cardiac-specific constitutively active MEK1 (TnT-MEK1-CA) was administrated to rescue cardiac dysfunction in CM-HIPK2 knockout mice. RESULTS: To our knowledge, this is the first study to define the role of HIPK2 in cardiac biology. Using multiple HIPK2 loss-of-function mouse models, we demonstrated that reduction of HIPK2 in CMs leads to cardiac dysfunction, suggesting a causal role in heart failure. It is important to note that cardiac dysfunction in HIPK2 knockout mice developed with advancing age, but not during development. In addition, CM-HIPK2 knockout mice and CM-HIPK2 heterozygous mice exhibited a gene dose-response relationship of CM-HIPK2 on heart function. HIPK2 expression in the heart was significantly reduced in human end-stage ischemic cardiomyopathy in comparison to nonfailing myocardium, suggesting a clinical relevance of HIPK2 in cardiac biology. In vitro studies with neonatal rat ventricular CMscorroborated the in vivo findings. Specifically, adenovirus-mediated overexpression of HIPK2 suppressed the expression of heart failure markers, NPPA and NPPB, at basal condition and abolished phenylephrine-induced pathological gene expression. An array of mechanistic studies revealed impaired extracellular signal-regulated kinase 1/2 signaling in HIPK2-deficient hearts. An in vivo rescue experiment with adeno-associated virus serotype 9 TnT-MEK1-CA nearly abolished the detrimental phenotype of knockout mice, suggesting that impaired extracellular signal-regulated kinase signaling mediated apoptosis as the key factor driving the detrimental phenotype in CM-HIPK2 knockout mice hearts. CONCLUSIONS: Taken together, these findings suggest that CM-HIPK2 is required to maintain normal cardiac function via extracellular signal-regulated kinase signaling.

Genetic effects on efficacy to fluticasone propionate/salmeterol treatment in COPD.

PURPOSE: No studies have investigated genetic effects on quality of life (QoL) measurements like improvements in the St George's Respiratory Questionnaire (SGRQ) scores for chronic obstructive pulmonary disease treatments with fluticasone propionate/salmeterol (FSC). Therefore, in addition to testing genetic effects on change from baseline in trough forced expiratory volume in 1 s (FEV1), genetic associations that may predict SGRQ response to FSC treatment were investigated in this analysis. METHODS: This post hoc exploratory genome-wide genetic analysis included subjects from 10 clinical trials: NCT01772134, NCT01772147, NCT00633217, NCT01817764, NCT01879410, NCT01822899, NCT01323621, NCT01342913, NCT01323634, and NCT01706328. The Genetics Analysis Population (subjects who provided written consent, a blood sample for genetic research, and were successfully genotyped) included 2005/2900 subjects in the intent-to-treat sample, who received FSC, for testing association with change from baseline in trough FEV1 and 1188/2005 subjects for testing SGRQ responses (change from baseline SGRQ score and categorical response by SGRQ score with Responders achieving >4 unit decrease at end of study treatment). MAIN FINDINGS: One locus on chromosome 20 with seven variants with low minor allele frequencies significantly associated with change from baseline SGRQ score. The binary SGRQ response provided similar trends for association but did not attain genome-wide significance levels. No genetic association was detected with change from baseline in trough FEV1. CONCLUSIONS: Common variants are unlikely to play a role in response to FSC.

Activation of the Amino Acid Response Pathway Blunts the Effects of Cardiac Stress.

BACKGROUND: The amino acid response (AAR) is an evolutionarily conserved protective mechanism activated by amino acid deficiency through a key kinase, general control nonderepressible 2. In addition to mobilizing amino acids, the AAR broadly affects gene and protein expression in a variety of pathways and elicits antifibrotic, autophagic, and anti-inflammatory activities. However, little is known regarding its role in cardiac stress. Our aim was to investigate the effects of halofuginone, a prolyl-tRNA synthetase inhibitor, on the AAR pathway in cardiac fibroblasts, cardiomyocytes, and in mouse models of cardiac stress and failure. METHODS AND RESULTS: Consistent with its ability to inhibit prolyl-tRNA synthetase, halofuginone elicited a general control nonderepressible 2-dependent activation of the AAR pathway in cardiac fibroblasts as evidenced by activation of known AAR target genes, broad regulation of the transcriptome and proteome, and reversal by l-proline supplementation. Halofuginone was examined in 3 mouse models of cardiac stress: angiotensin II/phenylephrine, transverse aortic constriction, and acute ischemia reperfusion injury. It activated the AAR pathway in the heart, improved survival, pulmonary congestion, left ventricle remodeling/fibrosis, and left ventricular function, and rescued ischemic myocardium. In human cardiac fibroblasts, halofuginone profoundly reduced collagen deposition in a general control nonderepressible 2-dependent manner and suppressed the extracellular matrix proteome. In human induced pluripotent stem cell-derived cardiomyocytes, halofuginone blocked gene expression associated with endothelin-1-mediated activation of pathologic hypertrophy and restored autophagy in a general control nonderepressible 2/eIF2α-dependent manner. CONCLUSIONS: Halofuginone activated the AAR pathway in the heart and attenuated the structural and functional effects of cardiac stress.

In vivo regulation of gene expression and T helper type 17 differentiation by RORγt inverse agonists.

The orphan nuclear receptor, retinoic acid receptor-related orphan nuclear receptor γt (RORγt), is required for the development and pathogenic function of interleukin-17A-secreting CD4(+) T helper type 17 (Th17) cells. Whereas small molecule RORγt antagonists impair Th17 cell development and attenuate autoimmune inflammation in vivo, the broader effects of these inhibitors on RORγt-dependent gene expression in vivo has yet to be characterized. We show that the RORγt inverse agonist TMP778 acts potently and selectively to block mouse Th17 cell differentiation in vitro and to impair Th17 cell development in vivo upon immunization with the myelin antigen MOG35-55 plus complete Freund's adjuvant. Importantly, we show that TMP778 acts in vivo to repress the expression of more than 150 genes, most of which fall outside the canonical Th17 transcriptional signature and are linked to a variety of inflammatory pathologies in humans. Interestingly, more than 30 genes are related with SMAD3, a transcription factor involved in the Th17 cell differentiation. These results reveal novel disease-associated genes regulated by RORγt during inflammation in vivo, and provide an early read on potential disease indications and safety concerns associated with pharmacological targeting of RORγt.

Systematic prediction of drug combinations based on clinical side-effects.

Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures.

Mining emerging biomedical literature for understanding disease associations in drug discovery.

Systematically evaluating the exponentially growing body of scientific literature has become a critical task that every drug discovery organization must engage in in order to understand emerging trends for scientific investment and strategy development. Developing trends analysis uses the number of publications within a 3-year window to determine concepts derived from well-established disease and gene ontologies to aid in recognizing and predicting emerging areas of scientific discoveries relevant to that space. In this chapter, we describe such a method and use obesity and psoriasis as use-case examples by analyzing the frequency of disease-related MeSH terms in PubMed abstracts over time. We share how our system can be used to predict emerging trends at a relatively early stage and we analyze the literature-identified genes for genetic associations, druggability, and biological pathways to explore any potential biological connections between the two diseases that could be utilized for drug discovery.

Integrative clinical transcriptomics analyses for new therapeutic intervention strategies: a psoriasis case study.

Psoriasis is a chronic inflammatory skin disease with complex pathological features and unmet pharmacotherapy needs. Here, we present a framework for developing new therapeutic intervention strategies for psoriasis by utilizing publicly available clinical transcriptomics data sets. By exploring the underlying molecular mechanisms of psoriasis, the effects of subsequent perturbation of these mechanisms by drugs and an integrative analysis, we propose a psoriasis disease signature, identify potential drug repurposing opportunities and present novel target selection methodologies. We anticipate that the outlined methodology or similar approaches will further support biomarker discovery and the development of new drugs for psoriasis.

Applications of Connectivity Map in drug discovery and development.

Genome-wide expression profiling of gene transcripts has been successfully applied in biomedical discovery for over a decade. Based on the premises of this technology, Connectivity Map provides a data-driven and systematic approach for discovering associations among genes, chemicals and biological conditions such as diseases. Since its first introduction in 2006, the approach has shown emerging promises in uncovering avenues for drug discovery and development such as in identifying and suggesting new indications for existing drugs and elucidating mode of actions for novel chemicals in addition to potentially predicting side effects.

Sex differences in global mRNA content of human skeletal muscle.

Women oxidize more fat as compared to men during endurance exercise and several groups have shown that the mRNA content of selected genes related to fat oxidation are higher in women (e.g. hormone sensitive lipase, beta-hydroxyacyl-CoA dehydrogenase, CD36). One of the possible mechanisms is that women tend to have a higher area percentage of type I skeletal muscle fibers as compared with men. Consequently, we hypothesized that sex would influence the basal mRNA and protein content for genes involved in metabolism and the determination of muscle fiber type. Muscle biopsies from the vastus lateralis were collected from healthy men and women. We examined mRNA content globally using Affymetrix GeneChips, and selected genes were examined and/or confirmed by RT-PCR. Furthermore, we examined protein content by Western blot analysis. Stringent gene array analysis revealed 66 differentially expressed genes representing metabolism, mitochondrial function, transport, protein biosynthesis, cell proliferation, signal transduction pathways, transcription and translation. Stringent gene array analysis and RT-PCR confirmed that mRNA for; acyl-coenzyme A acyltransferase 2 (ACAA2), trifunctional protein beta (HADHB), catalase, lipoprotein lipase (LPL), and uncoupling protein-2 (UCP-2) were higher in women. Targeted gene analysis revealed that myosin heavy chain I (MHCI), peroxisome proliferator-activated receptor (PPAR)delta were higher in women compared with men. Surprisingly, there were no significant sex based differences in protein content for HADHB, ACAA2, catalase, PPARdelta, and MHC1. In conclusion, the differences in the basal mRNA content in resting skeletal muscle suggest that men and women are transcriptionally "primed" for known physiological differences in metabolism however the mechanism behind sex differences in fiber type remains to be determined.

Inferring novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships.

BACKGROUND: Discovering that drug entities already approved for one disease are effective treatments for other distinct diseases can be highly beneficial and cost effective. To do this predictively, our conjecture is that a semantic infrastructure linking mechanistic relationships between pharmacologic entities and multidimensional knowledge of biological systems and disease processes will be highly enabling. RESULTS: To develop a knowledge framework capable of modeling and interconnecting drug actions and disease mechanisms across diverse biological systems contexts, we designed a Disease-Drug Correlation Ontology (DDCO), formalized in OWL, that integrates multiple ontologies, controlled vocabularies, and data schemas and interlinks these with diverse datasets extracted from pharmacological and biological domains. Using the complex disease Systemic Lupus Erythematosus (SLE) as an example, a high-dimensional pharmacome-diseasome graph network was generated as RDF XML, and subjected to graph-theoretic proximity and connectivity analytic approaches to rank drugs versus the compendium of SLE-associated genes, pathways, and clinical features. Tamoxifen, a current candidate therapeutic for SLE, was the highest ranked drug. CONCLUSION: This early stage demonstration highlights critical directions to follow that will enable translational pharmacotherapeutic research. The uniform application of Semantic Web methodology to problems in data integration, knowledge representation, and analysis provides an efficient and potentially powerful means to allow mining of drug action and disease mechanism relationships. Further improvements in semantic representation of mechanistic relationships will provide a fertile basis for accelerated drug repositioning, reasoning, and discovery across the spectrum of human disease.

Identifying disease-causal genes using Semantic Web-based representation of integrated genomic and phenomic knowledge.

Most common chronic diseases are caused by the interactions of multiple factors including the influences and responses of susceptibility and modifier genes that are themselves subject to etiologic events, interactions, and environmental factors. These entities, interactions, mechanisms, and phenotypic consequences can be richly represented using graph networks with semantically definable nodes and edges. To use this form of knowledge representation for inferring causal relationships, it is critical to leverage pertinent prior knowledge so as to facilitate ranking and probabilistic treatment of candidate etiologic factors. For example, genomic studies using linkage analyses detect quantitative trait loci that encompass a large number of disease candidate genes. Similarly, transcriptomic studies using differential gene expression profiling generate hundreds of potential disease candidate genes that themselves may not include genetically variant genes that are responsible for the expression pattern signature. Hypothesizing that the majority of disease-causal genes are linked to biochemical properties that are shared by other genes known to play functionally important roles and whose mutations produce clinical features similar to the disease under study, we reasoned that an integrative genomics-phenomics approach could expedite disease candidate gene identification and prioritization. To approach the problem of inferring likely causality roles, we generated Semantic Web methods-based network data structures and performed centrality analyses to rank genes according to model-driven semantic relationships. Our results indicate that Semantic Web approaches enable systematic leveraging of implicit relations hitherto embedded among large knowledge bases and can greatly facilitate identification of centrality elements that can lead to specific hypotheses and new insights.

Mining human phenome to investigate modularity of complex disorders.

A principal goal for biomedical research is to improve our understanding of factors that control clinical disease phenotypes. Among genetically-determined diseases, identical mutations may exhibit substantial phenotype variance by individual and background strain, suggesting both environmental and genetic mutant allele interactions. Moreover, different diseases can share phenotypic features extensively. To test the hypothesis that phenotypic similarities and differences among diseases and disease subvariants may represent differential activation of correlated feature "disease phenotype modules", we systematically parsed Online Mendelian Inheritance in Man (OMIM) and Syndrome DB databases using the UMLS to construct a disease - clinical phenotypic feature matrix suitable for various clustering algorithms. Using Cardiovascular Syndromes as a model, our results demonstrate a critical role for representing both phenotypic generalization and specificity relationships for the ability to retrieve non-trivial associations among disease entities such as shared protein domains and pathway and ontology functions of associated causal genes.

Corticortophin releasing factor 2 receptor agonist treatment significantly slows disease progression in mdx mice.

BACKGROUND: Duchenne muscular dystrophy results from mutation of the dystrophin gene, causing skeletal and cardiac muscle loss of function. The mdx mouse model of Duchenne muscular dystrophy is widely utilized to evaluate the potential of therapeutic regimens to modulate the loss of skeletal muscle function associated with dystrophin mutation. Importantly, progressive loss of diaphragm function is the most consistent striated muscle effect observed in the mdx mouse model, which is the same as in patients suffering from Duchenne muscular dystrophy. METHODS: Using the mdx mouse model, we have evaluated the effect that corticotrophin releasing factor 2 receptor (CRF2R) agonist treatment has on diaphragm function, morphology and gene expression. RESULTS: We have observed that treatment with the potent CRF2R-selective agonist PG-873637 prevents the progressive loss of diaphragm specific force observed during aging of mdx mice. In addition, the combination of PG-873637 with glucocorticoids not only prevents the loss of diaphragm specific force over time, but also results in recovery of specific force. Pathological analysis of CRF2R agonist-treated diaphragm muscle demonstrates that treatment reduces fibrosis, immune cell infiltration, and muscle architectural disruption. Gene expression analysis of CRF2R-treated diaphragm muscle showed multiple gene expression changes including globally decreased immune cell-related gene expression, decreased extracellular matrix gene expression, increased metabolism-related gene expression, and, surprisingly, modulation of circadian rhythm gene expression. CONCLUSION: Together, these data demonstrate that CRF2R activation can prevent the progressive degeneration of diaphragm muscle associated with dystrophin gene mutation.