Viral activation and ecological restructuring characterize a microbiome axis of spaceflight-associated immune activation

Maintenance of astronaut health during spaceflight will require monitoring and potentially modulating their microbiomes, which play a role in some space-derived health disorders. However, documenting the response of microbiota to spaceflight has been difficult thus far due to mission constraints that lead to limited sampling. Here, we executed a six-month longitudinal study centered on a three-day flight to quantify the high-resolution microbiome response to spaceflight. Via paired metagenomics and metatranscriptomics alongside single immune profiling, we resolved a microbiome "architecture" of spaceflight characterized by time-dependent and taxonomically divergent microbiome alterations across 750 samples and ten body sites. We observed pan-phyletic viral activation and signs of persistent changes that, in the oral microbiome, yielded plaque-associated pathobionts with strong associations to immune cell gene expression. Further, we found enrichments of microbial genes associated with antibiotic production, toxin-antitoxin systems, and stress response enriched universally across the body sites. We also used strain-level tracking to measure the potential propagation of microbial species from the crew members to each other and the environment, identifying microbes that were prone to seed the capsule surface and move between the crew. Finally, we identified associations between microbiome and host immune cell shifts, proposing both a microbiome axis of immune changes during flight as well as the sources of some of those changes. In summary, these datasets and methods reveal connections between crew immunology, the microbiome, and their likely drivers and lay the groundwork for future microbiome studies of spaceflight.

REDD1 (regulated in development and DNA damage 1) modulates the glucocorticoid receptor function in keratinocytes

Glucocorticoids (GCs) are widely used for the treatment of inflammatory skin diseases despite significant adverse effects including skin atrophy. Effects of GCs are mediated by the glucocorticoid receptor (GR), a well-known transcription factor. Previously, we discovered that one of the GR target genes, REDD1, is causatively involved in skin atrophy. Here, we investigated its role in GR function using HaCaT REDD1 knockout (KO) keratinocytes. We found large differences in transcriptome of REDD1 KO and control Cas9 cells in response to glucocorticoid fluocinolone acetonide (FA): both the scope and amplitude of response were significantly decreased in REDD1 KO. The status of REDD1 did not affect GR stability/degradation during self-desensitization, and major steps in GR activation-its nuclear import and phosphorylation at activating Ser211. However, the amount of GR phosphorylated at Ser226 that may play negative role in GR signalling, was increased in the nuclei of REDD1 KO cells. GR nuclear import and transcriptional activity also depend on the composition of GR chaperone complex: exchange of chaperone FKBP51 (FK506-binding protein 5) for FKBP52 (FK506-binding protein 4) being a necessary step in GR activation. We found the increased expression and abnormal nuclear translocation of FKBP51 in both untreated and FA-treated REDD1 KO cells. Overall, our results suggest the existence of a feed-forward loop in GR signalling mediated by its target gene REDD1, which has translational potential for the development of safer GR-targeted therapies.

The viral hypothesis: how herpesviruses may contribute to Alzheimer's disease

The hypothesis that infectious agents, particularly herpesviruses, contribute to Alzheimer's disease (AD) pathogenesis has been investigated for decades but has long engendered controversy. In the past 3 years, several studies in mouse models, human tissue models, and population cohorts have reignited interest in this hypothesis. Collectively, these studies suggest that many of the hallmarks of AD, like amyloid beta production and neuroinflammation, can arise as a protective response to acute infection that becomes maladaptive in the case of chronic infection. We place this work in its historical context and explore its etiological implications.

Systems modeling of metabolic dysregulation in neurodegenerative diseases

Neurodegenerative diseases (NDDs) encompass a wide range of conditions that arise owing to progressive degeneration and the ultimate loss of nerve cells in the brain and peripheral nervous system. NDDs such as Alzheimer's, Parkinson's, and Huntington's diseases negatively impact both length and quality of life, due to lack of effective disease-modifying treatments. Herein, we review the use of genome-scale metabolic models, network-based approaches, and integration with multiomics data to identify key biological processes that characterize NDDs. We describe powerful systems biology approaches for modeling NDD pathophysiology by leveraging in silico models that are informed by patient-derived multiomics data. These approaches can enable mechanistic insights into NDD-specific metabolic dysregulations that can be leveraged to identify potential metabolic markers of disease and predisease states.

miR155 regulation of behavior, neuropathology, and cortical transcriptomics in Alzheimer's disease

MicroRNAs are recognized as important regulators of many facets of physiological brain function while also being implicated in the pathogenesis of several neurological disorders. Dysregulation of miR155 is widely reported across a variety of neurodegenerative conditions, including Alzheimer's disease (AD), Parkinson's disease, amyotrophic lateral sclerosis, and traumatic brain injury. In previous work, we observed that experimentally validated miR155 gene targets were consistently enriched among genes identified as differentially expressed across multiple brain tissue and disease contexts. In particular, we found that human herpesvirus-6A (HHV-6A) suppressed miR155, recapitulating reports of miR155 inhibition by HHV-6A in infected T-cells, thyrocytes, and natural killer cells. In earlier studies, we also reported the effects of constitutive deletion of miR155 on accelerating the accumulation of Aβ deposits in 4-month-old APP/PSEN1 mice. Herein, we complete the cumulative characterization of transcriptomic, electrophysiological, neuropathological, and learning behavior profiles from 4-, 8- and 10-month-old WT and APP/PSEN1 mice in the absence or presence of miR155. We also integrated human post-mortem brain RNA-sequences from four independent AD consortium studies, together comprising 928 samples collected from six brain regions. We report that gene expression perturbations associated with miR155 deletion in mouse cortex are in aggregate observed to be concordant with AD-associated changes across these independent human late-onset AD (LOAD) data sets, supporting the relevance of our findings to human disease. LOAD has recently been formulated as the clinicopathological manifestation of a multiplex of genetic underpinnings and pathophysiological mechanisms. Our accumulated data are consistent with such a formulation, indicating that miR155 may be uniquely positioned at the intersection of at least four components of this LOAD "multiplex": (1) innate immune response pathways; (2) viral response gene networks; (3) synaptic pathology; and (4) proamyloidogenic pathways involving the amyloid β peptide (Aβ).

A Novel Approach to Safer Glucocorticoid Receptor-Targeted Anti-lymphoma Therapy via REDD1 (Regulated in Development and DNA Damage 1) Inhibition

Glucocorticoids are widely used for therapy of hematologic malignancies. Unfortunately, chronic treatment with glucocorticoids commonly leads to adverse effects including skin and muscle atrophy and osteoporosis. We found recently that REDD1 (regulated in development and DNA damage 1) plays central role in steroid atrophy. Here, we tested whether REDD1 suppression makes glucocorticoid-based therapy of blood cancer safer. Unexpectedly, approximately 50% of top putative REDD1 inhibitors selected by bioinformatics screening of Library of Integrated Network-Based Cellular Signatures database (LINCS) were PI3K/Akt/mTOR inhibitors. We selected Wortmannin, LY294002, and AZD8055 for our studies and showed that they blocked basal and glucocorticoid-induced REDD1 expression. Moreover, all PI3K/mTOR/Akt inhibitors modified glucocorticoid receptor function shifting it toward therapeutically important transrepression. PI3K/Akt/mTOR inhibitors enhanced anti-lymphoma effects of Dexamethasone in vitro and in vivo, in lymphoma xenograft model. The therapeutic effects of PI3K inhibitor+Dexamethasone combinations ranged from cooperative to synergistic, especially in case of LY294002 and Rapamycin, used as a previously characterized reference REDD1 inhibitor. We found that coadministration of LY294002 or Rapamycin with Dexamethasone protected skin against Dexamethasone-induced atrophy, and normalized RANKL/OPG ratio indicating a reduction of Dexamethasone-induced osteoporosis. Together, our results provide foundation for further development of safer and more effective glucocorticoid-based combination therapy of hematologic malignancies using PI3K/Akt/mTOR inhibitors.

Do infections have a role in the pathogenesis of Alzheimer disease?

The idea that infectious agents in the brain have a role in the pathogenesis of Alzheimer disease (AD) was proposed nearly 30 years ago. However, this theory failed to gain substantial traction and was largely disregarded by the AD research community for many years. Several recent discoveries have reignited interest in the infectious theory of AD, culminating in a debate on the topic at the Alzheimer's Association International Conference (AAIC) in July 2019. In this Viewpoint article, experts who participated in the AAIC debate weigh up the evidence for and against the infectious theory of AD and suggest avenues for future research and drug development.

Alterations in innate immunity and epithelial cell differentiation are the molecular pillars of hidradenitis suppurativa

BACKGROUND

The large unmet need of hidradenitis suppurativa/acne inversa (HS) therapy requires the elucidation of disease-driving mechanisms and tissue targeting.

OBJECTIVE

Robust characterization of the underlying HS mechanisms and detection of the involved skin compartments.

METHODS

Hidradenitis suppurativa/acne inversa molecular taxonomy and key signalling pathways were studied by whole transcriptome profiling. Dysregulated genes were detected by comparing lesional and non-lesional skin obtained from female HS patients and matched healthy controls using the Agilent array platform. The differential gene expression was confirmed by quantitative real-time PCR and targeted protein characterization via immunohistochemistry in another set of female patients. HS-involved skin compartments were also recognized by immunohistochemistry.

RESULTS

Alterations to key regulatory pathways involving glucocorticoid receptor, atherosclerosis, HIF1α and IL17A signalling as well as inhibition of matrix metalloproteases were detected. From a functional standpoint, cellular assembly, maintenance and movement, haematological system development and function, immune cell trafficking and antimicrobial response were key processes probably being affected in HS. Sixteen genes were found to characterize HS from a molecular standpoint (DEFB4, MMP1, GJB2, PI3, KRT16, MMP9, SERPINB4, SERPINB3, SPRR3, S100A8, S100A9, S100A12, S100A7A (15), KRT6A, TCN1, TMPRSS11D). Among the proteins strongly expressed in HS, calgranulin-A, calgranulin-B and serpin-B4 were detected in the hair root sheath, koebnerisin and connexin-32 in stratum granulosum, transcobalamin-1 in stratum spinosum/hair root sheath, small prolin-rich protein-3 in apocrine sweat gland ducts/sebaceous glands-ducts and matrix metallopeptidase-9 in resident monocytes.

CONCLUSION

Our findings highlight a panel of immune-related drivers in HS, which influence innate immunity and cell differentiation in follicular and epidermal keratinocytes as well as skin glands.

Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits

Background

Genetic loss-of-function variants (LoFs) associated with disease traits are increasingly recognized as critical evidence for the selection of therapeutic targets. We integrated the analysis of genetic and clinical data from 10,511 individuals in the Mount Sinai BioMe Biobank to identify genes with loss-of-function variants (LoFs) significantly associated with cardiovascular disease (CVD) traits, and used RNA-sequence data of seven metabolic and vascular tissues isolated from 600 CVD patients in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study for validation. We also carried out in vitro functional studies of several candidate genes, and in vivo studies of one gene.

Results

We identified LoFs in 433 genes significantly associated with at least one of 10 major CVD traits. Next, we used RNA-sequence data from the STARNET study to validate 115 of the 433 LoF harboring-genes in that their expression levels were concordantly associated with corresponding CVD traits. Together with the documented hepatic lipid-lowering gene, APOC3, the expression levels of six additional liver LoF-genes were positively associated with levels of plasma lipids in STARNET. Candidate LoF-genes were subjected to gene silencing in HepG2 cells with marked overall effects on cellular LDLR, levels of triglycerides and on secreted APOB100 and PCSK9. In addition, we identified novel LoFs in DGAT2 associated with lower plasma cholesterol and glucose levels in BioMe that were also confirmed in STARNET, and showed a selective DGAT2-inhibitor in C57BL/6 mice not only significantly lowered fasting glucose levels but also affected body weight.

Conclusion

In sum, by integrating genetic and electronic medical record data, and leveraging one of the world’s largest human RNA-sequence datasets (STARNET), we identified known and novel CVD-trait related genes that may serve as targets for CVD therapeutics and as such merit further investigation.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0542-3) contains supplementary material, which is available to authorized users.

Systems Pharmacology Identifies an Arterial Wall Regulatory Gene Network Mediating Coronary Artery Disease Side Effects of Antiretroviral Therapy

BACKGROUND

Antiretroviral therapy (ART) for HIV infection increases risk for coronary artery disease (CAD), presumably by causing dyslipidemia and increased atherosclerosis. We applied systems pharmacology to identify and validate specific regulatory gene networks through which ART drugs may promote CAD.

METHODS

Transcriptional responses of human cell lines to 15 ART drugs retrieved from the Library of Integrated Cellular Signatures (overall 1127 experiments) were used to establish consensus ART gene/transcriptional signatures. Next, enrichments of differentially expressed genes and gene-gene connectivity within these ART-consensus signatures were sought in 30 regulatory gene networks associated with CAD and CAD-related phenotypes in the Stockholm Atherosclerosis Gene Expression study.

RESULTS

Ten of 15 ART signatures were significantly enriched both for differential expression and connectivity in a specific atherosclerotic arterial wall regulatory gene network (AR-RGN) causal for CAD involving RNA processing genes. An atherosclerosis in vitro model of cholestryl ester-loaded foam cells was then used for experimental validation. Treatments of these foam cells with ritonavir, nelfinavir, and saquinavir at least doubled cholestryl ester accumulation ( P=0.02, 0.0009, and 0.02, respectively), whereas RNA silencing of the AR-RGN top key driver, PQBP1 (polyglutamine binding protein 1), significantly curbed cholestryl ester accumulation following treatment with any of these ART drugs by >37% ( P<0.05).

CONCLUSIONS

By applying a novel systems pharmacology data analysis framework, 3 commonly used ARTs (ritonavir, nelfinavir, and saquinavir) were found altering the activity of AR-RGN, a regulatory gene network promoting foam cell formation and risk of CAD. Targeting AR-RGN or its top key driver PQBP1 may help reduce CAD side effects of these ART drugs.

Critical period plasticity-related transcriptional aberrations in schizophrenia and bipolar disorder

Childhood critical periods of experience-dependent plasticity are essential for the development of environmentally appropriate behavior and cognition. Disruption of critical periods can alter development of normal function and confer risk for neurodevelopmental disorders. While genes and their expression relevant to neurodevelopment are associated with schizophrenia, the molecular relationship between schizophrenia and critical periods has not been assessed systematically. Here, we apply a transcriptome-based bioinformatics approach to assess whether genes associated with the human critical period for visual cortex plasticity, a well-studied model of cortical critical periods, are aberrantly expressed in schizophrenia and bipolar disorder. Across two dozen datasets encompassing 522 cases and 374 controls, we find that the majority show aberrations in expression of genes associated with the critical period. We observed both hyper- and hypo-critical period plasticity phenotypes at the transcriptome level, which partially mapped to drug candidates that reverse the disorder signatures in silico. Our findings indicate plasticity aberrations in schizophrenia and their treatment may need to be considered in the context of subpopulations with elevated and others reduced plasticity. Future work should leverage ongoing consortia RNA-sequencing efforts to tease out the sources of plasticity-related transcriptional aberrations seen in schizophrenia, including true biological heterogeneity, interaction between normal development/aging and the disorder, and medication history. Our study also urges innovation towards direct assessment of visual cortex plasticity in humans with schizophrenia to precisely deconstruct the role of plasticity in this disorder.

Correction: Integrative approach to sporadic Alzheimer's disease: deficiency of TYROBP in cerebral Aβ amyloidosis mouse normalizes clinical phenotype and complement subnetwork molecular pathology without reducing Aβ burden

This article was originally published under standard licence, but has now been made available under a CC BY 4.0 license. The PDF and HTML versions of the paper have been modified accordingly.

Deletion of the glucocorticoid receptor chaperone FKBP51 prevents glucocorticoid-induced skin atrophy

FKBP51 (FK506-binding protein 51) is a known co-chaperone and regulator of the glucocorticoid receptor (GR), which usually attenuates its activity. FKBP51 is one of the major GR target genes in skin, but its role in clinical effects of glucocorticoids is not known. Here, we used FKBP51 knockout (KO) mice to determine FKBP51's role in the major adverse effect of topical glucocorticoids, skin atrophy. Unexpectedly, we found that all skin compartments (epidermis, dermis, dermal adipose and CD34+ stem cells) in FKBP51 KO animals were much more resistant to glucocorticoid-induced hypoplasia. Furthermore, despite the absence of inhibitory FKBP51, the basal level of expression and glucocorticoid activation of GR target genes were not increased in FKBP51 KO skin or CRISPR/Cas9-edited FKBP51 KO HaCaT human keratinocytes. FKBP51 is known to negatively regulate Akt and mTOR. We found a significant increase in AktSer473 and mTORSer2448 phosphorylation and downstream pro-growth signaling in FKBP51-deficient keratinocytes in vivo and in vitro. As Akt/mTOR-GR crosstalk is usually negative in skin, our results suggest that Akt/mTOR activation could be responsible for the lack of increased GR function and resistance of FKBP51 KO mice to the steroid-induced skin atrophy.

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

BACKGROUND

Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR).

METHODS

The data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes.

RESULTS

Using an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)).

CONCLUSIONS

Large scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness.

Abstract B023: Identification and biochemical validation of piperlongumine as a potent therapeutic against neuroendocrine prostate cancer

Introduction and Objectives: Recent studies have highlighted the existence of a highly lethal and drug-resistant variant of prostate cancer (PCa) termed neuroendocrine prostate cancer (NEPC). We have used a genomics-based drug-repositioning approach to identify piperlongumine, a natural product constituent of the fruit of the long pepper (Piper longum), as a potential therapeutic against NEPC. The efficacy of this drug was tested in the NEPC cell line model NCI-H660 and compared to several other PCa cell lines in a modified WST-1 assay. Preclinical testing in mouse xenograft models of NEPC was also undertaken. Finally, the ability of piperlongumine to inhibit p-STAT3 signaling and promote apoptosis in cell lines and extracted tumors were measured by Western blot analyses.

Methods: PCa cell lines (LNCaP, 22Rv1, Du145, PC3, H660, and RWPE) were grown in complete media (RPM1 10% FBS, Advanced DMEM 5% FBS, or Keratinocyte-SFM) and treated with piperlongumine for 3 or 7 days. IC50 values were generated from WST assay data using Prism6. Nude mice (n=5) were injected with 1.5x106 H660 cells on each flank, and intraperitoneally administered with either 2.5mg/kg piperlongumine (n=3) or DMSO (n=2) daily for 3 weeks after tumors formed 200mm3 in volume. Tumor volume was measured daily with calipers. Western blot analyses were performed on protein lysates extracted from tumors and PCa cells treated with 0-10 μM of drug.

Results: Piperlongumine was highly effective in inhibiting the growth of H660 cells

(IC50 = 0.4 μM) compared to LNCaP, 22Rv1, Du145, PC3, and RWPE cells. On average, the growth rate of untreated tumors was 2.7 times greater than those treated was piperlongumine. Treated H660 cells exhibited significant inhibition of p-STAT3 and increases in cPARP1 levels while other cell lines displayed little to no fluctuation.

Conclusions: Using drug-repositioning approaches we have identified a lead compound that inhibits the growth of the highly drug-resistant NEPC cell line NCI-H660. Remarkably, piperlongumine happens to be a water-soluble plant product with no known side effects.

Citation Format: Kamlesh K. Yadav, Khader Shameer, Jennifer A. Stockert, Cordelia Elaiho, Ben Readhead, Shalini S. Yadav, Joel Dudley, Ashutosh K. Tewari. Identification and biochemical validation of piperlongumine as a potent therapeutic against neuroendocrine prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B023.

Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning

Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.

Multiscale Analysis of Independent Alzheimer's Cohorts Finds Disruption of Molecular, Genetic, and Clinical Networks by Human Herpesvirus

Investigators have long suspected that pathogenic microbes might contribute to the onset and progression of Alzheimer's disease (AD) although definitive evidence has not been presented. Whether such findings represent a causal contribution, or reflect opportunistic passengers of neurodegeneration, is also difficult to resolve. We constructed multiscale networks of the late-onset AD-associated virome, integrating genomic, transcriptomic, proteomic, and histopathological data across four brain regions from human post-mortem tissue. We observed increased human herpesvirus 6A (HHV-6A) and human herpesvirus 7 (HHV-7) from subjects with AD compared with controls. These results were replicated in two additional, independent and geographically dispersed cohorts. We observed regulatory relationships linking viral abundance and modulators of APP metabolism, including induction of APBB2, APPBP2, BIN1, BACE1, CLU, PICALM, and PSEN1 by HHV-6A. This study elucidates networks linking molecular, clinical, and neuropathological features with viral activity and is consistent with viral activity constituting a general feature of AD.

Rapamycin Modulates Glucocorticoid Receptor Function, Blocks Atrophogene REDD1, and Protects Skin from Steroid Atrophy

Glucocorticoids have excellent therapeutic properties; however, they cause significant adverse atrophogenic effects. The mTORC1 inhibitor REDD1 has been recently identified as a key mediator of glucocorticoid-induced atrophy. We performed computational screening of a connectivity map database to identify putative REDD1 inhibitors. The top selected candidates included rapamycin, which was unexpected because it inhibits pro-proliferative mTOR signaling. Indeed, rapamycin inhibited REDD1 induction by glucocorticoids dexamethasone, clobetasol propionate, and fluocinolone acetonide in keratinocytes, lymphoid cells, and mouse skin. We also showed blunting of glucocorticoid-induced REDD1 induction by either catalytic inhibitor of mTORC1/2 (OSI-027) or genetic inhibition of mTORC1, highlighting role of mTOR in glucocorticoid receptor signaling. Moreover, rapamycin inhibited glucocorticoid receptor phosphorylation, nuclear translocation, and loading on glucocorticoid-responsive elements in REDD1 promoter. Using microarrays, we quantified a global effect of rapamycin on gene expression regulation by fluocinolone acetonide in human keratinocytes. Rapamycin inhibited activation of glucocorticoid receptor target genes yet enhanced the repression of pro-proliferative and proinflammatory genes. Remarkably, rapamycin protected skin against glucocorticoid-induced atrophy but had no effect on the glucocorticoid anti-inflammatory activity in different in vivo models, suggesting the clinical potential of combining rapamycin with glucocorticoids for the treatment of inflammatory diseases.

Deficiency of TYROBP, an adapter protein for TREM2 and CR3 receptors, is neuroprotective in a mouse model of early Alzheimer's pathology

Conventional genetic approaches and computational strategies have converged on immune-inflammatory pathways as key events in the pathogenesis of late onset sporadic Alzheimer’s disease (LOAD). Mutations and/or differential expression of microglial specific receptors such as TREM2, CD33, and CR3 have been associated with strong increased risk for developing Alzheimer’s disease (AD). DAP12 (DNAX-activating protein 12)/TYROBP, a molecule localized to microglia, is a direct partner/adapter for TREM2, CD33, and CR3. We and others have previously shown that TYROBP expression is increased in AD patients and in mouse models. Moreover, missense mutations in the coding region of TYROBP have recently been identified in some AD patients. These lines of evidence, along with computational analysis of LOAD brain gene expression, point to DAP12/TYROBP as a potential hub or driver protein in the pathogenesis of AD. Using a comprehensive panel of biochemical, physiological, behavioral, and transcriptomic assays, we evaluated in a mouse model the role of TYROBP in early stage AD. We crossed an Alzheimer’s model mutant APP^KM670/671NL/PSEN1^Δexon9(APP/PSEN1) mouse model with Tyrobp^−/− mice to generate AD model mice deficient or null for TYROBP (APP/PSEN1; Tyrobp^+/− or APP/PSEN1; Tyrobp^−/−). While we observed relatively minor effects of TYROBP deficiency on steady-state levels of amyloid-β peptides, there was an effect of Tyrobp deficiency on the morphology of amyloid deposits resembling that reported by others for Trem2^−/− mice. We identified modulatory effects of TYROBP deficiency on the level of phosphorylation of TAU that was accompanied by a reduction in the severity of neuritic dystrophy. TYROBP deficiency also altered the expression of several AD related genes, including Cd33. Electrophysiological abnormalities and learning behavior deficits associated with APP/PSEN1 transgenes were greatly attenuated on a Tyrobp-null background. Some modulatory effects of TYROBP on Alzheimer’s-related genes were only apparent on a background of mice with cerebral amyloidosis due to overexpression of mutant APP/PSEN1. These results suggest that reduction of TYROBP gene expression and/or protein levels could represent an immune-inflammatory therapeutic opportunity for modulating early stage LOAD, potentially leading to slowing or arresting the progression to full-blown clinical and pathological LOAD.

Abstract 3250: Computational drug repositioning and biochemical validation of piperlongumine as a potent therapeutic agent for neuroendocrine prostate cancer

Introduction and Objectives: Neuroendocrine prostate cancer (NEPC) is a highly lethal and drug-resistant variant of prostate cancer (PCa). We have used a genomics-based drug-repositioning approach and identified compounds with therapeutic activity against NEPC. One of the compounds- piperlongumine (PL) is a natural product constituent of the fruit of the Long pepper (Piper longum). The efficacy of this drug was tested in the NEPC cell line model NCI-H660 and compared to several other PCa cell lines in a modified WST-1 assay. Pre-clinical testing in mouse xenograft models of NEPC was also undertaken. Finally, the ability of piperlongumine to inhibit p-STAT3 signaling and promote apoptosis was measured by western blot analyses.

Methods: PCa cell lines (LNCaP, 22Rv1, Du145, PC3, H660 and RWPE) were grown in complete media (RPM1 10%FBS, Advanced DMEM 5%FBS or Keratinocyte-SFM) and treated with piperlongumine for 3 or 7 days. IC50 values were generated from WST assay data using Prism6. Nude mice (n=5) were injected with 1.5x106 H660 cells on each flank, and intraperitoneally administered with either 2.5mg/kg PL (n=3) or DMSO (n=2) daily for 3 weeks after tumors formed 200mm3 in volume. Tumor volume was measured daily with calipers. Western blot analyses were performed on protein lysates extracted from tumors and PCa cells treated with 0-10 μM of drug.

Results: PL was highly effective in inhibiting the growth of H660 cells (IC50 = 0.4 μM) compared to LNCaP, 22Rv1, Du145, PC3, and RWPE cells. This was consistent with an increased level of cPARP1 in H660 cells when compared to other prostate cancer cell lines. On average, the growth rate of untreated tumors was 2.7 times greater than those treated with PL. Similar to previous reports suggesting PL to inhibit STAT3 activity, treated H660 cells exhibited inhibition of STAT3 phosphorylation.

Conclusions: Using computational drug repositioning approaches we have identified several lead compounds for NEPC. One of the compound piperlongumine, a water-soluble plant product with no known side effects inhibits the growth of the highly drug-resistant NEPC cell line H660.

Source of Funding: Deane Prostate Health, Icahn School of Medicine at Mount Sinai. Both Shalini S Yadav and Kamlesh K Yadav are supported by the Prostate Cancer Foundation Young Investigator Awards. Research in Dudley's lab is supported by grants from National Institutes of Health R01 DK098242 and U54 CA189201.

Note: This abstract was not presented at the meeting.

Citation Format: Kamlesh Kumar Yadav, Khader Shameer, Ben Readhead, Jennifer A. Stockert, Cordelia Elaiho, Shalini S. Yadav, Benjamin S. Glicksberg, Kipp W. Johnson, Christine Becker, Andrew Kasarskis, Ashutosh K. Tewari, Joel T. Dudley. Computational drug repositioning and biochemical validation of piperlongumine as a potent therapeutic agent for neuroendocrine prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3250. doi:10.1158/1538-7445.AM2017-3250

Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine

The traditional paradigm of cardiovascular disease research derives insight from large-scale, broadly inclusive clinical studies of well-characterized pathologies. These insights are then put into practice according to standardized clinical guidelines. However, stagnation in the development of new cardiovascular therapies and variability in therapeutic response implies that this paradigm is insufficient for reducing the cardiovascular disease burden. In this state-of-the-art review, we examine 3 interconnected ideas we put forth as key concepts for enabling a transition to precision cardiology: 1) precision characterization of cardiovascular disease with machine learning methods; 2) the application of network models of disease to embrace disease complexity; and 3) using insights from the previous 2 ideas to enable pharmacology and polypharmacology systems for more precise drug-to-patient matching and patient-disease stratification. We conclude by exploring the challenges of applying a precision approach to cardiology, which arise from a deficit of the required resources and infrastructure, and emerging evidence for the clinical effectiveness of this nascent approach.

L1000CDS2: LINCS L1000 characteristic direction signatures search engine

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS². The L1000CDS² search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS² search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS² to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS², we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS² we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS² tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000 resource.

Molecular systems evaluation of oligomerogenic APP(E693Q) and fibrillogenic APP(KM670/671NL)/PSEN1(Δexon9) mouse models identifies shared features with human Alzheimer's brain molecular pathology

Identification and characterization of molecular mechanisms that connect genetic risk factors to initiation and evolution of disease pathophysiology represent major goals and opportunities for improving therapeutic and diagnostic outcomes in Alzheimer's disease (AD). Integrative genomic analysis of the human AD brain transcriptome holds potential for revealing novel mechanisms of dysfunction that underlie the onset and/or progression of the disease. We performed an integrative genomic analysis of brain tissue-derived transcriptomes measured from two lines of mice expressing distinct mutant AD-related proteins. The first line expresses oligomerogenic mutant APP(E693Q) inside neurons, leading to the accumulation of amyloid beta (Aβ) oligomers and behavioral impairment, but never develops parenchymal fibrillar amyloid deposits. The second line expresses APP(KM670/671NL)/PSEN1(Δexon9) in neurons and accumulates fibrillar Aβ amyloid and amyloid plaques accompanied by neuritic dystrophy and behavioral impairment. We performed RNA sequencing analyses of the dentate gyrus and entorhinal cortex from each line and from wild-type mice. We then performed an integrative genomic analysis to identify dysregulated molecules and pathways, comparing transgenic mice with wild-type controls as well as to each other. We also compared these results with datasets derived from human AD brain. Differential gene and exon expression analysis revealed pervasive alterations in APP/Aβ metabolism, epigenetic control of neurogenesis, cytoskeletal organization and extracellular matrix (ECM) regulation. Comparative molecular analysis converged on FMR1 (Fragile X Mental Retardation 1), an important negative regulator of APP translation and oligomerogenesis in the post-synaptic space. Integration of these transcriptomic results with human postmortem AD gene networks, differential expression and differential splicing signatures identified significant similarities in pathway dysregulation, including ECM regulation and neurogenesis, as well as strong overlap with AD-associated co-expression network structures. The strong overlap in molecular systems features supports the relevance of these findings from the AD mouse models to human AD.

Systems Genetic Analyses Highlight a TGFβ-FOXO3 Dependent Striatal Astrocyte Network Conserved across Species and Associated with Stress, Sleep, and Huntington's Disease

Recent systems-based analyses have demonstrated that sleep and stress traits emerge from shared genetic and transcriptional networks, and clinical work has elucidated the emergence of sleep dysfunction and stress susceptibility as early symptoms of Huntington's disease. Understanding the biological bases of these early non-motor symptoms may reveal therapeutic targets that prevent disease onset or slow disease progression, but the molecular mechanisms underlying this complex clinical presentation remain largely unknown. In the present work, we specifically examine the relationship between these psychiatric traits and Huntington's disease (HD) by identifying striatal transcriptional networks shared by HD, stress, and sleep phenotypes. First, we utilize a systems-based approach to examine a large publicly available human transcriptomic dataset for HD (GSE3790 from GEO) in a novel way. We use weighted gene coexpression network analysis and differential connectivity analyses to identify transcriptional networks dysregulated in HD, and we use an unbiased ranking scheme that leverages both gene- and network-level information to identify a novel astrocyte-specific network as most relevant to HD caudate. We validate this result in an independent HD cohort. Next, we computationally predict FOXO3 as a regulator of this network, and use multiple publicly available in vitro and in vivo experimental datasets to validate that this astrocyte HD network is downstream of a signaling pathway important in adult neurogenesis (TGFβ-FOXO3). We also map this HD-relevant caudate subnetwork to striatal transcriptional networks in a large (n = 100) chronically stressed (B6xA/J)F2 mouse population that has been extensively phenotyped (328 stress- and sleep-related measurements), and we show that this striatal astrocyte network is correlated to sleep and stress traits, many of which are known to be altered in HD cohorts. We identify causal regulators of this network through Bayesian network analysis, and we highlight their relevance to motor, mood, and sleep traits through multiple in silico approaches, including an examination of their protein binding partners. Finally, we show that these causal regulators may be therapeutically viable for HD because their downstream network was partially modulated by deep brain stimulation of the subthalamic nucleus, a medical intervention thought to confer some therapeutic benefit to HD patients. In conclusion, we show that an astrocyte transcriptional network is primarily associated to HD in the caudate and provide evidence for its relationship to molecular mechanisms of neural stem cell homeostasis. Furthermore, we present a unified systems-based framework for identifying gene networks that are associated with complex non-motor traits that manifest in the earliest phases of HD. By analyzing and integrating multiple independent datasets, we identify a point of molecular convergence between sleep, stress, and HD that reflects their phenotypic comorbidity and reveals a molecular pathway involved in HD progression.

Dysregulation of miRNA-9 in a Subset of Schizophrenia Patient-Derived Neural Progenitor Cells

Converging evidence indicates that microRNAs (miRNAs) may contribute to disease risk for schizophrenia (SZ). We show that microRNA-9 (miR-9) is abundantly expressed in control neural progenitor cells (NPCs) but also significantly downregulated in a subset of SZ NPCs. We observed a strong correlation between miR-9 expression and miR-9 regulatory activity in NPCs as well as between miR-9 levels/activity, neural migration, and diagnosis. Overexpression of miR-9 was sufficient to ameliorate a previously reported neural migration deficit in SZ NPCs, whereas knockdown partially phenocopied aberrant migration in control NPCs. Unexpectedly, proteomic- and RNA sequencing (RNA-seq)-based analysis revealed that these effects were mediated primarily by small changes in expression of indirect miR-9 targets rather than large changes in direct miR-9 targets; these indirect targets are enriched for migration-associated genes. Together, these data indicate that aberrant levels and activity of miR-9 may be one of the many factors that contribute to SZ risk, at least in a subset of patients.

Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach

BACKGROUND

Therapeutic treatments for schizophrenia do not alleviate symptoms for all patients and efficacy is limited by common, often severe, side-effects. Genetic studies of disease can identify novel drug targets, and drugs for which the mechanism has direct genetic support have increased likelihood of clinical success. Large-scale genetic studies of schizophrenia have increased the number of genes and gene sets associated with risk. We aimed to examine the overlap between schizophrenia risk loci and gene targets of a comprehensive set of medications to potentially inform and improve treatment of schizophrenia.

METHODS

We defined schizophrenia risk loci as genomic regions reaching genome-wide significance in the latest Psychiatric Genomics Consortium schizophrenia genome-wide association study (GWAS) of 36 989 cases and 113 075 controls and loss of function variants observed only once among 5079 individuals in an exome-sequencing study of 2536 schizophrenia cases and 2543 controls (Swedish Schizophrenia Study). Using two large and orthogonally created databases, we collated drug targets into 167 gene sets targeted by pharmacologically similar drugs and examined enrichment of schizophrenia risk loci in these sets. We further linked the exome-sequenced data with a national drug registry (the Swedish Prescribed Drug Register) to assess the contribution of rare variants to treatment response, using clozapine prescription as a proxy for treatment resistance.

FINDINGS

We combined results from testing rare and common variation and, after correction for multiple testing, two gene sets were associated with schizophrenia risk: agents against amoebiasis and other protozoal diseases (106 genes, p=0·00046, pcorrected =0·024) and antipsychotics (347 genes, p=0·00078, pcorrected=0·046). Further analysis pointed to antipsychotics as having independent enrichment after removing genes that overlapped these two target sets. We noted significant enrichment both in known targets of antipsychotics (70 genes, p=0·0078) and novel predicted targets (277 genes, p=0·019). Patients with treatment-resistant schizophrenia had an excess of rare disruptive variants in gene targets of antipsychotics (347 genes, p=0·0067) and in genes with evidence for a role in antipsychotic efficacy (91 genes, p=0·0029).

INTERPRETATION

Our results support genetic overlap between schizophrenia pathogenesis and antipsychotic mechanism of action. This finding is consistent with treatment efficacy being polygenic and suggests that single-target therapeutics might be insufficient. We provide evidence of a role for rare functional variants in antipsychotic treatment response, pointing to a subset of patients where their genetic information could inform treatment. Finally, we present a novel framework for identifying treatments from genetic data and improving our understanding of therapeutic mechanism.

FUNDING

US National Institutes of Health.

Cross-Tissue Regulatory Gene Networks in Coronary Artery Disease

Inferring molecular networks can reveal how genetic perturbations interact with environmental factors to cause common complex diseases. We analyzed genetic and gene expression data from seven tissues relevant to coronary artery disease (CAD) and identified regulatory gene networks (RGNs) and their key drivers. By integrating data from genome-wide association studies, we identified 30 CAD-causal RGNs interconnected in vascular and metabolic tissues, and we validated them with corresponding data from the Hybrid Mouse Diversity Panel. As proof of concept, by targeting the key drivers AIP, DRAP1, POLR2I, and PQBP1 in a cross-species-validated, arterial-wall RGN involving RNA-processing genes, we re-identified this RGN in THP-1 foam cells and independent data from CAD macrophages and carotid lesions. This characterization of the molecular landscape in CAD will help better define the regulation of CAD candidate genes identified by genome-wide association studies and is a first step toward achieving the goals of precision medicine.

Computational and experimental advances in drug repositioning for accelerated therapeutic stratification

Drug repositioning is an important component of therapeutic stratification in the precision medicine paradigm. Molecular profiling and more sophisticated analysis of longitudinal clinical data are refining definitions of human diseases, creating needs and opportunities to re-target or reposition approved drugs for alternative indications. Drug repositioning studies have demonstrated success in complex diseases requiring improved therapeutic interventions as well as orphan diseases without any known treatments. An increasing collection of available computational and experimental methods that leverage molecular and clinical data enable diverse drug repositioning strategies. Integration of translational bioinformatics resources, statistical methods, chemoinformatics tools and experimental techniques (including medicinal chemistry techniques) can enable the rapid application of drug repositioning on an increasingly broad scale. Efficient tools are now available for systematic drug-repositioning methods using large repositories of compounds with biological activities. Medicinal chemists along with other translational researchers can play a key role in various aspects of drug repositioning. In this review article, we briefly summarize the history of drug repositioning, explain concepts behind drug repositioning methods, discuss recent computational and experimental advances and highlight available open access resources for effective drug repositioning investigations. We also discuss recent approaches in utilizing electronic health record for outcome assessment of drug repositioning and future avenues of drug repositioning in the light of targeting disease comorbidities, underserved patient communities, individualized medicine and socioeconomic impact.

REDD1 functions at the crossroads between the therapeutic and adverse effects of topical glucocorticoids

Cutaneous atrophy is the major adverse effect of topical glucocorticoids; however, its molecular mechanisms are poorly understood. Here, we identify stress-inducible mTOR inhibitor REDD1 (regulated in development and DNA damage response 1) as a major molecular target of glucocorticoids, which mediates cutaneous atrophy. In REDD1 knockout (KO) mice, all skin compartments (epidermis, dermis, subcutaneous fat), epidermal stem, and progenitor cells were protected from atrophic effects of glucocorticoids. Moreover, REDD1 knockdown resulted in similar consequences in organotypic raft cultures of primary human keratinocytes. Expression profiling revealed that gene activation by glucocorticoids was strongly altered in REDD1 KO epidermis. In contrast, the down-regulation of genes involved in anti-inflammatory glucocorticoid response was strikingly similar in wild-type and REDD1 KO mice. Integrative bioinformatics analysis of our and published gene array data revealed similar changes of gene expression in epidermis and in muscle undergoing glucocorticoid-dependent and glucocorticoid-independent atrophy. Importantly, the lack of REDD1 did not diminish the anti-inflammatory effects of glucocorticoids in preclinical model. Our findings suggest that combining steroids with REDD1 inhibitors may yield a novel, safer glucocorticoid-based therapies.

A targetable GATA2-IGF2 axis confers aggressiveness in lethal prostate cancer

Elucidating the determinants of aggressiveness in lethal prostate cancer may stimulate therapeutic strategies that improve clinical outcomes. We used experimental models and clinical databases to identify GATA2 as a regulator of chemotherapy resistance and tumorigenicity in this context. Mechanistically, direct upregulation of the growth hormone IGF2 emerged as a mediator of the aggressive properties regulated by GATA2. IGF2 in turn activated IGF1R and INSR as well as a downstream polykinase program. The characterization of this axis prompted a combination strategy whereby dual IGF1R/INSR inhibition restored the efficacy of chemotherapy and improved survival in preclinical models. These studies reveal a GATA2-IGF2 aggressiveness axis in lethal prostate cancer and identify a therapeutic opportunity in this challenging disease.

Automated detection of off-label drug use

Off-label drug use, defined as use of a drug in a manner that deviates from its approved use defined by the drug's FDA label, is problematic because such uses have not been evaluated for safety and efficacy. Studies estimate that 21% of prescriptions are off-label, and only 27% of those have evidence of safety and efficacy. We describe a data-mining approach for systematically identifying off-label usages using features derived from free text clinical notes and features extracted from two databases on known usage (Medi-Span and DrugBank). We trained a highly accurate predictive model that detects novel off-label uses among 1,602 unique drugs and 1,472 unique indications. We validated 403 predicted uses across independent data sources. Finally, we prioritize well-supported novel usages for further investigation on the basis of drug safety and cost.

Abstract B230: Targeting mitochondria for metastatic lung adenocarcinoma specific lethality

Introduction: Lung cancer is the leading cause of cancer deaths in the United States. Its ability to leave the primary tumor and establish inoperable metastasis impairs successful therapy and is a major contributor to its lethality. A genetically engineered mouse model recapitulates the human disease very well, incorporating conditional alleles of the Kras oncogene and p53 tumor suppressor. Recently, cell lines generated from non-metastatic primary lung tumors (TnonMet) and metastases (Met) in this model were shown to maintain their metastatic state and exhibit dramatic differences in gene expression programs. This unique set of cell lines from early stage non-metastatic tumors and late-stage metastases provides an opportunity to understand whether cancer progression also creates unique vulnerabilities.

Experimental design: A pooled lentiviral-shRNA library screen which targeted ∼20,000 genes with &gt;92,000 shRNA in two TnonMet and two Met cell lines was used to systematically uncover genes that are specifically required to sustain metastasis survival or growth. High-throughput sequencing of the shRNA in the cancer cell populations before and after 20 population doublings in culture identified shRNAs that disadvantaged each cell line. Gene-set enrichment algorithm was utilized to analyse the data. qRT-PCR, cell competition assays and mitochondria-specific dyes were used to verify the results and unravel the underlying mechanism.

Results and Conclusion: The analysis of our pooled shRNA screen revealed mitochondrial ribosomal protein (Mrp) complexes as the top two gene-sets; in fact individual shRNA targeting more than 15 Mrp genes selectively disadvantaging Met cells with minimal impact on TnonMet cells. These results were individually verified with qRT-PCR. Interestingly, Met cells showed increased expression of mitochondria encoded genes, pointing to a strong imbalance in mitochondrial homeostasis. Furthermore, mitochondria-targeted therapy blocking mitochondrial translation or replication significantly inhibited Met cell growth more than TnonMet cell growth. Taken together, these results point to mitochondria as a metastasis-specific therapeutic target in lung adenocarcinoma and need to be further investigated.

Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B230.

Citation Format: Barbara M. Gruener, Chen-Hua Chuang, Ian Linde, Shin-Heng Chiou, Ben Readhead, Joel Dudley, John Doench, David Root, Monte M. Winslow. Targeting mitochondria for metastatic lung adenocarcinoma specific lethality. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B230.

Translational Bioinformatics Approaches to Drug Development

SIGNIFICANCE

A majority of therapeutic interventions occur late in the pathological process, when treatment outcome can be less predictable and effective, highlighting the need for new precise and preventive therapeutic development strategies that consider genomic and environmental context. Translational bioinformatics is well positioned to contribute to the many challenges inherent in bridging this gap between our current reactive methods of healthcare delivery and the intent of precision medicine, particularly in the areas of drug development, which forms the focus of this review.

RECENT ADVANCES

A variety of powerful informatics methods for organizing and leveraging the vast wealth of available molecular measurements available for a broad range of disease contexts have recently emerged. These include methods for data driven disease classification, drug repositioning, identification of disease biomarkers, and the creation of disease network models, each with significant impacts on drug development approaches.

CRITICAL ISSUES

An important bottleneck in the application of bioinformatics methods in translational research is the lack of investigators who are versed in both biomedical domains and informatics. Efforts to nurture both sets of competencies within individuals and to increase interfield visibility will help to accelerate the adoption and increased application of bioinformatics in translational research.

FUTURE DIRECTIONS

It is possible to construct predictive, multiscale network models of disease by integrating genotype, gene expression, clinical traits, and other multiscale measures using causal network inference methods. This can enable the identification of the "key drivers" of pathology, which may represent novel therapeutic targets or biomarker candidates that play a more direct role in the etiology of disease.