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Maternal asthma in pregnancy is associated with adverse perinatal and child health outcomes; however, mechanisms are poorly understood.The PRogramming of Intergenerational Stress Mechanisms (PRISM) prospective pregnancy cohort characterized asthma history during pregnancy via questionnaires and quantified placental DNAm using the Illumina Infinium HumanMethylation450 BeadChip. We performed epigenome-wide association analyses (n = 223) to estimate associations between maternal active or inactive asthma, as compared to never asthma, and placental differentially methylated positions (DMPs) and differentially variable positions (DVPs). Models adjusted for maternal pre-pregnancy body mass index, smoking status, parity, age and education level and child sex. P-values were FDR-adjusted.One hundred and fifty-nine (71.3%) pregnant women reported no history of asthma (never asthma), 15 (6.7%) reported inactive, and 49 (22%) reported active antenatal asthma. Women predominantly self-identified as Black/Hispanic Black [88 (39.5%)] and Hispanic/non-Black [42 (18.8%)]. We identified 10 probes at FDR0.05 and 4 probes at FDR0.10 characterized by higher variability in maternal active asthma compared to never asthma mapping to GPX3, LHPP, PECAM1, ATAD3C, and ARHGEF4 and 2 probes characterized by lower variation mapping to CHMP4A and C5orf24. Amongst women with inactive asthma, we identified 52 probes, 41 at FDR0.05 and an additional 11 at FDR0.10, with higher variability compared to never asthma; BMP4, LHPP, PHYHIPL, and ZSCAN23 were associated with multiple DVPs. No associations were observed with DMPs.We observed alterations in placental DNAm in women with antenatal asthma, as compared to women without a history of asthma. Further research is needed to understand the impact on fetal development.

The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. Here, we report initial findings from the Asthma Mobile Health Study, a research study, including recruitment, consent, and enrollment, conducted entirely remotely by smartphone. We achieved secure bidirectional data flow between investigators and 7,593 participants from across the United States, including many with severe asthma. Our platform enabled prospective collection of longitudinal, multidimensional data (e.g., surveys, devices, geolocation, and air quality) in a subset of users over the 6-month study period. Consistent trending and correlation of interrelated variables support the quality of data obtained via this method. We detected increased reporting of asthma symptoms in regions affected by heat, pollen, and wildfires. Potential challenges with this technology include selection bias, low retention rates, reporting bias, and data security. These issues require attention to realize the full potential of mobile platforms in research and patient care.

Genomic characterization has allowed for the differentiation of different tumor types based upon the abundance of gene transcripts. However, owing to the many layers of regulation between transcript and the post-translationally modified protein, the functional moiety of the cell, it has been challenging to extrapolate biology from these transcriptional differences. We hypothesized that a comparative analysis of the proteome and phosphoproteome across 7 childhood brain tumors would yield a deeper understanding of the differences in their functional biology. We performed tandem mass tag labeling and triple mass spectrometry of 226 fresh frozen tumor samples collected in a single institution representing the histologic diagnoses of: high grade astrocytoma (27), low grade astrocytoma (97), ganglioglioma (20), ependymoma (32), medulloblastoma (22), atypical teratoid rhabdoid tumor (12), and craniopharyngioma (16). Among these samples were 22 pairs from pre/post recurrence. Across this sample set, we quantified 9155 proteins and 13632 phospho sites. After data preprocessing including normalization, batch correction and missing value imputation, we performed consensus clustering and showed that protein profiles can effectively distinguish major histology types. Additional regression based differential analyses revealed groups of proteins and pathways showing distinct activity patterns across different histologic types. Further leveraging the WGS data, we characterized the functional impact of mutation and fusion features of the glial tumors. Specifically, focusing on the largest cohort, low grade glioma, we analyzed the proteomic ramifications of BRAF status: V600E, BRAF-fusion and wild type. Moreover, from the primary and recurrent tumor pairs, we discovered the upregulation of proteins associated with immune evasion in more advanced LGG tumors. The incorporation of the proteomic dimension into large scale efforts at tumor characterization adds functional insight that can help drive translational efforts.

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

We performed a comprehensive proteogenomic analysis across seven childhood brain tumors for a deeper understanding of their functional biology. Whole genome sequencing, RNAseq, quantitative proteomic profiling and phosphoproteomics were performed on 219 fresh frozen tumor samples representing the histologic diagnoses of: low grade astrocytoma (93), ependymoma (32), high grade astrocytoma (26), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16) and atypical teratoid rhabdoid tumor (12). Unsupervised clustering analysis based on proteomics data reveals eight clusters with distinct protein profiles and pathway activities. While some clusters coincide with histologic diagnoses, a couple of clusters appear to be a mixture of different diagnoses, including one cluster consisting of “aggressive” tumors characterized by poor survival and high stemness scores. By integrating proteomic data with RNAseq and WGS data, we characterize the impact of mutations (H3K27M, BRAFV600E, BRAF fusion) and CNVs upon the proteome across various diagnoses. Multiomics based kinase-substrate association analysis and co-expression network analysis reveal targetable active kinase networks within these tumors. Proteomic data reveals unique biology associated with H3K27M mutation status in HGG and BRAF aberrations in LGG. Characterization of the tumor microenvironment through deconvolution analyses based on multi-omics data reveals 5 distinct tumor clusters associated with different populations of infiltrating immune cells and the relative activity of the immune system based upon the expression of pro-inflammation or immunosuppressive markers. This study reports the first large-scale deep comprehensive proteogenomic analysis crossing traditional histologic boundaries to uncover foundational pediatric brain tumor biology including functional insight that helps drive translational efforts.

SUMMARY To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology., Graphical Abstract, In Brief Comprehensive proteogenomic characterization in 103 treatment-naive clear cell renal cell carcinoma patient samples highlights tumor-specific alterations at the proteomic level that are unrevealed by transcriptomic profiling and proposes a revised subtyping scheme based on integrated omics analysis.

Widespread adoption of smart mobile platforms coupled with a growing ecosystem of sensors including passive location tracking and the ability to leverage external data sources create an opportunity to generate an unprecedented depth of data on individuals. Mobile health technologies could be utilized for chronic disease management as well as research to advance our understanding of common diseases, such as asthma. We conducted a prospective observational asthma study to assess the feasibility of this type of approach, clinical characteristics of cohorts recruited via a mobile platform, the validity of data collected, user retention patterns, and user data sharing preferences. We describe data and descriptive statistics from the Asthma Mobile Health Study, whereby participants engaged with an iPhone application built using Apple's ResearchKit framework. Data from 6346 U.S. participants, who agreed to share their data broadly, have been made available for further research. These resources have the potential to enable the research community to work collaboratively towards improving our understanding of asthma as well as mobile health research best practices.

In our recent Asthma Mobile Health Study (AMHS), thousands of asthma patients across the country contributed medical data through the iPhone Asthma Health App on a daily basis for an extended period of time. The collected data included daily self-reported asthma symptoms, symptom triggers, and real time geographic location information. The AMHS is just one of many studies occurring in the context of now many thousands of mobile health apps aimed at improving wellness and better managing chronic disease conditions, leveraging the passive and active collection of data from mobile, handheld smart devices. The ability to identify patient groups or patterns of symptoms that might predict adverse outcomes such as asthma exacerbations or hospitalizations from these types of large, prospectively collected data sets, would be of significant general interest. However, conventional clustering methods cannot be applied to these types of longitudinally collected data, especially survey data actively collected from app users, given heterogeneous patterns of missing values due to: 1) varying survey response rates among different users, 2) varying survey response rates over time of each user, and 3) non-overlapping periods of enrollment among different users. To handle such complicated missing data structure, we proposed a probability imputation model to infer missing data. We also employed a consensus clustering strategy in tandem with the multiple imputation procedure. Through simulation studies under a range of scenarios reflecting real data conditions, we identified favorable performance of the proposed method over other strategies that impute the missing value through low-rank matrix completion. When applying the proposed new method to study asthma triggers and symptoms collected as part of the AMHS, we identified several patient groups with distinct phenotype patterns. Further validation of the methods described in this paper might be used to identify clinically important patterns in large data sets with complicated missing data structure, improving the ability to use such data sets to identify at-risk populations for potential intervention.

Cocaine addiction is a major problem affecting all societal and economic classes for which there is no effective therapy. We hypothesized an effective anti-cocaine vaccine could be developed by using an adeno-associated virus (AAV) gene transfer vector as the delivery vehicle to persistently express an anti-cocaine monoclonal antibody in vivo, which would sequester cocaine in the blood, preventing access to cognate receptors in the brain. To accomplish this, we constructed AAVrh.10antiCoc.Mab, an AAVrh.10 gene transfer vector expressing the heavy and light chains of the high affinity anti-cocaine monoclonal antibody GNC92H2. Intravenous administration of AAVrh.10antiCoc.Mab to mice mediated high, persistent serum levels of high-affinity, cocaine-specific antibodies that sequestered intravenously administered cocaine in the blood. With repeated intravenous cocaine challenge, naive mice exhibited hyperactivity, while the AAVrh.10antiCoc.Mab-vaccinated mice were completely resistant to the cocaine. These observations demonstrate a novel strategy for cocaine addiction by requiring only a single administration of an AAV vector mediating persistent, systemic anti-cocaine passive immunity.

Motivation: Identification of expression Quantitative Trait Loci (eQTL), the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis. Methods: We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model is a combined multivariate regression and factor analysis, where the complete likelihood of the model is used to derive a ridge estimator for simultaneous factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects; it provides P-values and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of single nucleotide polymorphisms on a standard 8 core 2.6 G desktop. Results: By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and significantly outperforms all related hidden factor methods for eQTL analysis when there are eQTL with univariate and multivariate (pleiotropic) effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in the human lung for a study that included presumptive hidden factors. HEFT identified all of the cis-eQTL found by other hidden factor methods and 91 additional cis-eQTL. HEFT also identified a number of eQTLs with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer. Availability: Software is available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: jgm45@cornell.edu