Broadcasters, receivers, functional groups of metabolites, and the link to heart failure by revealing metabolomic network connectivity.

BACKGROUND AND OBJECTIVE: Blood-based small molecule metabolites offer easy accessibility and hold significant potential for insights into health processes, the impact of lifestyle, and genetic variation on disease, enabling precise risk prevention. In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. METHODS: We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. RESULTS: We identified metabolites associated with higher and lower risk of HF incidence, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. These associations were not confounded by the other metabolites due to uncovering the connectivity among metabolites and adjusting each association for the confounding metabolites. Examples of our findings include the direct influence of asparagine on glycine, both of which were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids, which are not synthesized in the human body and are obtained directly from the diet. CONCLUSION: Metabolites may play a critical role in linking genetic background and lifestyle factors to HF incidence. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates studying complex conditions like HF.

Personalized iPSC-Derived Dopamine Progenitor Cells for Parkinson's Disease.

We report the implantation of patient-derived midbrain dopaminergic progenitor cells, differentiated in vitro from autologous induced pluripotent stem cells (iPSCs), in a patient with idiopathic Parkinson's disease. The patient-specific progenitor cells were produced under Good Manufacturing Practice conditions and characterized as having the phenotypic properties of substantia nigra pars compacta neurons; testing in a humanized mouse model (involving peripheral-blood mononuclear cells) indicated an absence of immunogenicity to these cells. The cells were implanted into the putamen (left hemisphere followed by right hemisphere, 6 months apart) of a patient with Parkinson's disease, without the need for immunosuppression. Positron-emission tomography with the use of fluorine-18-L-dihydroxyphenylalanine suggested graft survival. Clinical measures of symptoms of Parkinson's disease after surgery stabilized or improved at 18 to 24 months after implantation. (Funded by the National Institutes of Health and others.).

GenoPheno: cataloging large-scale phenotypic and next-generation sequencing data within human datasets.

Precision medicine promises to revolutionize treatment, shifting therapeutic approaches from the classical one-size-fits-all to those more tailored to the patient's individual genomic profile, lifestyle and environmental exposures. Yet, to advance precision medicine's main objective-ensuring the optimum diagnosis, treatment and prognosis for each individual-investigators need access to large-scale clinical and genomic data repositories. Despite the vast proliferation of these datasets, locating and obtaining access to many remains a challenge. We sought to provide an overview of available patient-level datasets that contain both genotypic data, obtained by next-generation sequencing, and phenotypic data-and to create a dynamic, online catalog for consultation, contribution and revision by the research community. Datasets included in this review conform to six specific inclusion parameters that are: (i) contain data from more than 500 human subjects; (ii) contain both genotypic and phenotypic data from the same subjects; (iii) include whole genome sequencing or whole exome sequencing data; (iv) include at least 100 recorded phenotypic variables per subject; (v) accessible through a website or collaboration with investigators and (vi) make access information available in English. Using these criteria, we identified 30 datasets, reviewed them and provided results in the release version of a catalog, which is publicly available through a dynamic Web application and on GitHub. Users can review as well as contribute new datasets for inclusion (Web: https://avillachlab.shinyapps.io/genophenocatalog/; GitHub: https://github.com/hms-dbmi/GenoPheno-CatalogShiny).

Comprehensive characterization of putative genetic influences on plasma metabolome in a pediatric cohort.

BACKGROUND: The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype-metabotype associations. However, these associations have not been characterized in children. RESULTS: We conducted the largest genome by metabolome-wide association study to date of children (N = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h2) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h2 (> 0.8) for 15.9% of features and low h2 (< 0.2) for most of features (62.0%). The features with high h2 were enriched for amino acid and nucleic acid metabolism, while carbohydrate and lipid concentrations showed low h2. For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p < 3.5 × 10-12 (= 5 × 10-8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; and ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride (m/z 781.7483, retention time (RT) 89.3 s); CALN1 and Tridecanol (m/z 283.2741, RT 27.6). A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for dADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network. CONCLUSION: Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene-environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene-environment interaction toward healthy aging trajectories.

Inhibition of mevalonate metabolism by statins augments the immunoregulatory phenotype of vascular endothelial cells and inhibits the costimulation of CD4+ T cells.

The statin family of therapeutics is widely used clinically as cholesterol lowering agents, and their effects to target intracellular mevalonate production is a key mechanism of action. In this study, we performed full transcriptomic RNA sequencing and qPCR to evaluate the effects of mevalonate on the immunoregulatory phenotype of endothelial cells (EC). We find that mevalonate-dependent gene regulation includes a reduction in the expression of multiple pro-inflammatory genes including TNFSF4 (OX40-L) and TNFSF18 (GITR-L) and a co-incident induction of immunoregulatory genes including LGALS3 (Galectin-3) and LGALS9 (Galectin-9). In functional assays, pretreatment of EC with simvastatin to inhibit mevalonate metabolism resulted in a dose-dependent reduction in the costimulation of CD45RO+ CD4+ T cell proliferation as well as IL-2, IFNγ and IL-6 production versus vehicle-treated EC. In contrast, pre-treatment of EC with L-mevalonate in combination with simvastatin reversed phenotypic and functional responses. Collectively, these results indicate that relative mevalonate metabolism by EC is critical to sustain EC-dependent mechanisms of immunity. Our findings have broad relevance for the repurposing of statins as therapeutics to augment immunoregulation and/or to inhibit local tissue pro-inflammatory cytokine production following transplantation.

WEScover: selection between clinical whole exome sequencing and gene panel testing.

BACKGROUND: Whole exome sequencing (WES) is widely adopted in clinical and research settings; however, one of the practical concerns is the potential false negatives due to incomplete breadth and depth of coverage for several exons in clinically implicated genes. In some cases, a targeted gene panel testing may be a dependable option to ascertain true negatives for genomic variants in known disease-associated genes. We developed a web-based tool to quickly gauge whether all genes of interest would be reliably covered by WES or whether targeted gene panel testing should be considered instead to minimize false negatives in candidate genes. RESULTS: WEScover is a novel web application that provides an intuitive user interface for discovering breadth and depth of coverage across population-scale WES datasets, searching either by phenotype, by targeted gene panel(s) or by gene(s). Moreover, the application shows metrics from the Genome Aggregation Database to provide gene-centric view on breadth of coverage. CONCLUSIONS: WEScover allows users to efficiently query genes and phenotypes for the coverage of associated exons by WES and recommends use of panel tests for the genes with potential incomplete coverage by WES.

Genetic variation analyses indicate conserved SARS-CoV-2-host interaction and varied genetic adaptation in immune response factors in modern human evolution.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a pandemic as of early 2020. Upon infection, SARS-CoV-2 attaches to its receptor, that is, angiotensin-converting enzyme 2 (ACE2), on the surface of host cells and is then internalized into host cells via enzymatic machineries. This subsequently stimulates immune response factors. Since the host immune response and severity of COVID-19 vary among individuals, genetic risk factors for severe COVID-19 cases have been investigated. Our research group recently conducted a survey of genetic variants among SARS-CoV-2-interacting molecules across populations, noting near absence of difference in allele frequency spectrum between populations in these genes. Recent genome-wide association studies have identified genetic risk factors for severe COVID-19 cases in a segment of chromosome 3 that involves six genes encoding three immune-regulatory chemokine receptors and another three molecules. The risk haplotype seemed to be inherited from Neanderthals, suggesting genetic adaptation against pathogens in modern human evolution. Therefore, SARS-CoV-2 uses highly conserved molecules as its virion interaction, whereas its immune response appears to be genetically biased in individuals to some extent. We herein review the molecular process of SARS-CoV-2 infection as well as our further survey of genetic variants of its related immune effectors. We also discuss aspects of modern human evolution.

T Cell-Specific Adaptor Protein Regulates Mitochondrial Function and CD4+ T Regulatory Cell Activity In Vivo following Transplantation.

The T cell-specific adaptor protein (TSAd), encoded by the SH2D2A gene, is an intracellular molecule that binds Lck to elicit signals that result in cytokine production in CD4+ T effector cells (Teff). Nevertheless, using Sh2d2a knockout (KO; also called TSAd-/-) mice, we find that alloimmune CD4+ Teff responses are fully competent in vivo. Furthermore, and contrary to expectations, we find that allograft rejection is accelerated in KO recipients of MHC class II-mismatched B6.C-H-2bm12 heart transplants versus wild-type (WT) recipients. Also, KO recipients of fully MHC-mismatched cardiac allografts are resistant to the graft-prolonging effects of costimulatory blockade. Using adoptive transfer models, we find that KO T regulatory cells (Tregs) are less efficient in suppressing Teff function and they produce IFN-γ following mitogenic activation. In addition, pyrosequencing demonstrated higher levels of methylation of CpG regions within the Treg-specific demethylated region of KO versus WT Tregs, suggesting that TSAd, in part, promotes Treg stability. By Western blot, Lck is absent in the mitochondria of KO Tregs, and reactive oxygen species production by mitochondria is reduced in KO versus WT Tregs. Full transcriptomic analysis demonstrated that the key mechanism of TSAd function in Tregs relates to its effects on cellular activation rather than intrinsic effects on mitochondria/metabolism. Nevertheless, KO Tregs compensate for a lack of activation by increasing the number of mitochondria per cell. Thus, TSAd serves as a critical cell-intrinsic molecule in CD4+Foxp3+ Tregs to regulate the translocation of Lck to mitochondria, cellular activation responses, and the development of immunoregulation following solid organ transplantation.

The Clinical Genome and Ancestry Report: An interactive web application for prioritizing clinically implicated variants from genome sequencing data with ancestry composition.

Genome sequencing is positioned as a routine clinical work-up for diverse clinical conditions. A commonly used approach to highlight candidate variants with potential clinical implication is to search over locus- and gene-centric knowledge databases. Most web-based applications allow a federated query across diverse databases for a single variant; however, sifting through a large number of genomic variants with combination of filtering criteria is a substantial challenge. Here we describe the Clinical Genome and Ancestry Report (CGAR), an interactive web application developed to follow clinical interpretation workflows by organizing variants into seven categories: (1) reported disease-associated variants, (2) rare- and high-impact variants in putative disease-associated genes, (3) secondary findings that the American College of Medical Genetics and Genomics recommends reporting back to patients, (4) actionable pharmacogenomic variants, (5) focused reports for candidate genes, (6) de novo variant candidates for trio analysis, and (7) germline and somatic variants implicated in cancer risk, diagnosis, treatment and prognosis. For each variant, a comprehensive list of external links to variant-centric and phenotype databases are provided. Furthermore, genotype-derived ancestral composition is used to highlight allele frequencies from a matched population since some disease-associated variants show a wide variation between populations. CGAR is an open-source software and is available at https://tom.tch.harvard.edu/apps/cgar/.

The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system.

PURPOSE: Clinicians and researchers must contextualize a patient's genetic variants against population-based references with detailed phenotyping. We sought to establish globally scalable technology, policy, and procedures for sharing biosamples and associated genomic and phenotypic data on broadly consented cohorts, across sites of care. METHODS: Three of the nation's leading children's hospitals launched the Genomic Research and Innovation Network (GRIN), with federated information technology infrastructure, harmonized biobanking protocols, and material transfer agreements. Pilot studies in epilepsy and short stature were completed to design and test the collaboration model. RESULTS: Harmonized, broadly consented institutional review board (IRB) protocols were approved and used for biobank enrollment, creating ever-expanding, compatible biobanks. An open source federated query infrastructure was established over genotype-phenotype databases at the three hospitals. Investigators securely access the GRIN platform for prep to research queries, receiving aggregate counts of patients with particular phenotypes or genotypes in each biobank. With proper approvals, de-identified data is exported to a shared analytic workspace. Investigators at all sites enthusiastically collaborated on the pilot studies, resulting in multiple publications. Investigators have also begun to successfully utilize the infrastructure for grant applications. CONCLUSIONS: The GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.

Correction: The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Highly differentiated cytotoxic T cells in inclusion body myositis.

Inclusion body myositis is a late onset treatment-refractory autoimmune disease of skeletal muscle associated with a blood autoantibody (anti-cN1A), an HLA autoimmune haplotype, and muscle pathology characterized by cytotoxic CD8+ T cell destruction of myofibres. Here, we report on translational studies of inclusion body myositis patient muscle compared with a diverse set of other muscle disease samples. Using available microarray data on 411 muscle samples from patients with inclusion body myositis (n = 40), other muscle diseases (n = 265), and without neuromuscular disease (normal, n = 106), we identified a signature of T-cell cytotoxicity in inclusion body myositis muscle coupled with a signature of highly differentiated CD8 T-cell effector memory and terminally differentiated effector cells. Further, we examined killer cell lectin-like receptor G1 (KLRG1) as a marker of this population of cells, demonstrated the correlation of KLRG1 gene expression with lymphocyte cytotoxicity across 28 870 human tissue samples, and identified the presence of KLRG1 on pathogenic inclusion body myositis muscle invading T cells and an increase in KLRG1 expressing T cells in inclusion body myositis blood. We examined inclusion body myositis muscle T-cell proliferation by Ki67 immunohistochemistry demonstrating that diseased muscle-invading T cells are minimally or non-proliferative, in accordance with known properties of highly differentiated or terminally differentiated T cells. We found low expression of KLRG1 on infection-protective human lymphoid tissue central memory T cells and autoimmune-protective human blood regulatory T cells. Targeting highly differentiated cytotoxic T cells could be a favourable approach to treatment of inclusion body myositis.

DEPTOR modulates activation responses in CD4+ T cells and enhances immunoregulation following transplantation.

DEPTOR is an evolutionarily conserved cell-intrinsic binding partner of mTOR that functions as a negative regulator of signaling responses. In this study, we show that DEPTOR is expressed within CD4+ T cells, and we observed that its relative level of expression modulates differentiation as well as glucose utilization within CD4+ T effectors in vitro. Using knock-in mice, we also find that induced expression of DEPTOR within CD4+ T regulatory cells stabilizes Foxp3 expression, shifts metabolism toward oxidative phosphorylation, and increases survival and suppressive function. In vivo, fully MHC mismatched cardiac allograft survival is significantly prolonged in knock-in recipients and sustained recipient expression of DEPTOR in combination with costimulatory blockade induces long-term graft survival. Furthermore, we show that the induced expression of DEPTOR in CD4+ T effectors fails to inhibit acute allograft rejection. Rather, prolonged survival is dominantly mediated via induced expression and function of DEPTOR within recipient CD4+ T regulatory cells. These collective findings identify DEPTOR as a novel protein that functions in CD4+ T cells to augment immunoregulation in vitro and in vivo.

Learning a Comorbidity-Driven Taxonomy of Pediatric Pulmonary Hypertension.

RATIONALE: Pediatric pulmonary hypertension (PH) is a heterogeneous condition with varying natural history and therapeutic response. Precise classification of PH subtypes is, therefore, crucial for individualizing care. However, gaps remain in our understanding of the spectrum of PH in children. OBJECTIVE: We seek to study the manifestations of PH in children and to assess the feasibility of applying a network-based approach to discern disease subtypes from comorbidity data recorded in longitudinal data sets. METHODS AND RESULTS: A retrospective cohort study comprising 6 943 263 children (<18 years of age) enrolled in a commercial health insurance plan in the United States, between January 2010 and May 2013. A total of 1583 (0.02%) children met the criteria for PH. We identified comorbidities significantly associated with PH compared with the general population of children without PH. A Bayesian comorbidity network was constructed to model the interdependencies of these comorbidities, and network-clustering analysis was applied to derive disease subtypes comprising subgraphs of highly connected comorbid conditions. A total of 186 comorbidities were found to be significantly associated with PH. Network analysis of comorbidity patterns captured most of the major PH subtypes with known pathological basis defined by the World Health Organization and Panama classifications. The analysis further identified many subtypes documented in only a few case studies, including rare subtypes associated with several well-described genetic syndromes. CONCLUSIONS: Application of network science to model comorbidity patterns recorded in longitudinal data sets can facilitate the discovery of disease subtypes. Our analysis relearned established subtypes, thus validating the approach, and identified rare subtypes that are difficult to discern through clinical observations, providing impetus for deeper investigation of the disease subtypes that will enrich current disease classifications.

An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy.

This study was designed to analyze urinary proteins associated with ovarian cancer (OC) and investigate the potential urinary biomarker panel to predict malignancy in women with pelvic masses. We analyzed 23 biomarkers in urine samples obtained from 295 patients with pelvic masses scheduled for surgery. The concentration of urinary biomarkers was quantitatively assessed by the xMAP bead-based multiplexed immunoassay. To identify the performance of each biomarker in predicting cancer over benign tumors, we used a repeated leave-group-out cross-validation strategy. The prediction models using multimarkers were evaluated to develop a urinary ovarian cancer panel. After the exclusion of 12 borderline tumors, the urinary concentration of 17 biomarkers exhibited significant differences between 158 OCs and 125 benign tumors. Human epididymis protein 4 (HE4), vascular cell adhesion molecule (VCAM), and transthyretin (TTR) were the top three biomarkers representing a higher concentration in OC. HE4 demonstrated the highest performance in all samples withOC(mean area under the receiver operating characteristic curve (AUC) 0.822, 95% CI: 0.772-0.869), whereas TTR showed the highest efficacy in early-stage OC (AUC 0.789, 95% CI: 0.714-0.856). Overall, HE4 was the most informative biomarker, followed by creatinine, carcinoembryonic antigen (CEA), neural cell adhesion molecule (NCAM), and TTR using the least absolute shrinkage and selection operator (LASSO) regression models. A multimarker panel consisting of HE4, creatinine, CEA, and TTR presented the best performance with 93.7% sensitivity (SN) at 70.6% specificity (SP) to predict OC over the benign tumor. This panel performed well regardless of disease status and demonstrated an improved performance by including menopausal status. In conclusion, the urinary biomarker panel with HE4, creatinine, CEA, and TTR provided promising efficacy in predicting OC over benign tumors in women with pelvic masses. It was also a non-invasive and easily available diagnostic tool.

Solving for X: Evidence for sex-specific autism biomarkers across multiple transcriptomic studies.

Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers and characterize their function, we present an analysis of a single paired-end postmortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both postmortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.

Measuring coverage and accuracy of whole-exome sequencing in clinical context.

PURPOSE: To evaluate the coverage and accuracy of whole-exome sequencing (WES) across vendors. METHODS: Blood samples from three trios underwent WES at three vendors. Relative performance of the three WES services was measured for breadth and depth of coverage. The false-negative rates (FNRs) were estimated using the segregation pattern within each trio. RESULTS: Mean depth of coverage for all genes was 189.0, 124.9, and 38.3 for the three vendor services. Fifty-five of the American College of Medical Genetics and Genomics 56 genes, but only 56 of 63 pharmacogenes, were 100% covered at 10 × in at least one of the nine individuals for all vendors; however, there was substantial interindividual variability. For the two vendors with mean depth of coverage >120 ×, analytic positive predictive values (aPPVs) exceeded 99.1% for single-nucleotide variants and homozygous indels, and sensitivities were 98.9-99.9%; however, heterozygous indels showed lower accuracy and sensitivity. Among the trios, FNRs in the offspring were 0.07-0.62% at well-covered variants concordantly called in both parents. CONCLUSION: The current standard of 120 × coverage for clinical WES may be insufficient for consistent breadth of coverage across the exome. Ordering clinicians and researchers would benefit from vendors' reports that estimate sensitivity and aPPV, including depth of coverage across the exome.

Mitf is a master regulator of the v-ATPase, forming a control module for cellular homeostasis with v-ATPase and TORC1.

The v-ATPase is a fundamental eukaryotic enzyme that is central to cellular homeostasis. Although its impact on key metabolic regulators such as TORC1 is well documented, our knowledge of mechanisms that regulate v-ATPase activity is limited. Here, we report that the Drosophila transcription factor Mitf is a master regulator of this holoenzyme. Mitf directly controls transcription of all 15 v-ATPase components through M-box cis-sites and this coordinated regulation affects holoenzyme activity in vivo. In addition, through the v-ATPase, Mitf promotes the activity of TORC1, which in turn negatively regulates Mitf. We provide evidence that Mitf, v-ATPase and TORC1 form a negative regulatory loop that maintains each of these important metabolic regulators in relative balance. Interestingly, direct regulation of v-ATPase genes by human MITF also occurs in cells of the melanocytic lineage, showing mechanistic conservation in the regulation of the v-ATPase by MITF family proteins in fly and mammals. Collectively, this evidence points to an ancient module comprising Mitf, v-ATPase and TORC1 that serves as a dynamic modulator of metabolism for cellular homeostasis.

Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis.

Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells. © 2016 Wiley Periodicals, Inc.

ksRepo: a generalized platform for computational drug repositioning.

BACKGROUND: Repositioning approved drug and small molecules in novel therapeutic areas is of key interest to the pharmaceutical industry. A number of promising computational techniques have been developed to aid in repositioning, however, the majority of available methodologies require highly specific data inputs that preclude the use of many datasets and databases. There is a clear unmet need for a generalized methodology that enables the integration of multiple types of both gene expression data and database schema. RESULTS: ksRepo eliminates the need for a single microarray platform as input and allows for the use of a variety of drug and chemical exposure databases. We tested ksRepo's performance on a set of five prostate cancer datasets using the Comparative Toxicogenomics Database (CTD) as our database of gene-compound interactions. ksRepo successfully predicted significance for five frontline prostate cancer therapies, representing a significant enrichment from over 7000 CTD compounds, and achieved specificity similar to other repositioning methods. CONCLUSIONS: We present ksRepo, which enables investigators to use any data inputs for computational drug repositioning. ksRepo is implemented in a series of four functions in the R statistical environment under a BSD3 license. Source code is freely available at http://github.com/adam-sam-brown/ksRepo. A vignette is provided to aid users in performing ksRepo analysis.