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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.

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.

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.