Epidemiology, genetics, and subtyping of preserved ratio impaired spirometry (PRISm) in COPDGene.

BackgroundPreserved Ratio Impaired Spirometry (PRISm), defined as a reduced FEV1 in the setting of a preserved FEV1/FVC ratio, is highly prevalent and is associated with increased respiratory symptoms, systemic inflammation, and mortality. Studies investigating quantitative chest tomographic features, genetic associations, and subtypes in PRISm subjects have not been reported.MethodsData from current and former smokers enrolled in COPDGene (n = 10,192), an observational, cross-sectional study which recruited subjects aged 45-80 with ≥10 pack years of smoking, were analyzed. To identify epidemiological and radiographic predictors of PRISm, we performed univariate and multivariate analyses comparing PRISm subjects both to control subjects with normal spirometry and to subjects with COPD. To investigate common genetic predictors of PRISm, we performed a genome-wide association study (GWAS). To explore potential subgroups within PRISm, we performed unsupervised k-means clustering.ResultsThe prevalence of PRISm in COPDGene is 12.3%. Increased dyspnea, reduced 6-minute walk distance, increased percent emphysema and decreased total lung capacity, as well as increased segmental bronchial wall area percentage were significant predictors (p-value <0.05) of PRISm status when compared to control subjects in multivariate models. Although no common genetic variants were identified on GWAS testing, a significant association with Klinefelter's syndrome (47XXY) was observed (p-value < 0.001). Subgroups identified through k-means clustering include a putative "COPD-subtype", "Restrictive-subtype", and a highly symptomatic "Metabolic-subtype".ConclusionsPRISm subjects are clinically and genetically heterogeneous. Future investigations into the pathophysiological mechanisms behind and potential treatment options for subgroups within PRISm are warranted.Trial registrationClinicaltrials.gov Identifier: NCT000608764

Human Cancer Long Non-Coding RNA Transcriptomes

Once thought to be a part of the ‘dark matter’ of the genome, long non-coding RNAs (lncRNAs) are emerging as an integral functional component of the mammalian transcriptome. LncRNAs are a novel class of mRNA-like transcripts which, despite no known protein-coding potential, demonstrate a wide range of structural and functional roles in cellular biology. However, the magnitude of the contribution of lncRNA expression to normal human tissues and cancers has not been investigated in a comprehensive manner. In this study, we compiled 272 human serial analysis of gene expression (SAGE) libraries to delineate lncRNA transcription patterns across a broad spectrum of normal human tissues and cancers. Using a novel lncRNA discovery pipeline we parsed over 24 million SAGE tags and report lncRNA expression profiles across a panel of 26 different normal human tissues and 19 human cancers. Our findings show extensive, tissue-specific lncRNA expression in normal tissues and highly aberrant lncRNA expression in human cancers. Here, we present a first generation atlas for lncRNA profiling in cancer

Applications of VirScan to broad serological profiling of bat reservoirs for emerging zoonoses

IntroductionBats are important providers of ecosystem services such as pollination, seed dispersal, and insect control but also act as natural reservoirs for virulent zoonotic viruses. Bats host multiple viruses that cause life-threatening pathology in other animals and humans but, themselves, experience limited pathological disease from infection. Despite bats’ importance as reservoirs for several zoonotic viruses, we know little about the broader viral diversity that they host. Bat virus surveillance efforts are challenged by difficulties of field capture and the limited scope of targeted PCR- or ELISA-based molecular and serological detection. Additionally, virus shedding is often transient, thus also limiting insights gained from nucleic acid testing of field specimens. Phage ImmunoPrecipitation Sequencing (PhIP-Seq), a broad serological tool used previously to comprehensively profile viral exposure history in humans, offers an exciting prospect for viral surveillance efforts in wildlife, including bats.MethodsHere, for the first time, we apply PhIP-Seq technology to bat serum, using a viral peptide library originally designed to simultaneously assay exposures to the entire human virome.ResultsUsing VirScan, we identified past exposures to 57 viral genera—including betacoronaviruses, henipaviruses, lyssaviruses, and filoviruses—in semi-captive Pteropus alecto and to nine viral genera in captive Eonycteris spelaea. Consistent with results from humans, we find that both total peptide hits (the number of enriched viral peptides in our library) and the corresponding number of inferred past virus exposures in bat hosts were correlated with poor bat body condition scores and increased with age. High and low body condition scores were associated with either seropositive or seronegative status for different viruses, though in general, virus-specific age-seroprevalence curves defied assumptions of lifelong immunizing infection, suggesting that many bat viruses may circulate via complex transmission dynamics.DiscussionOverall, our work emphasizes the utility of applying biomedical tools, like PhIP-Seq, first developed for humans to viral surveillance efforts in wildlife, while highlighting opportunities for taxon-specific improvements

Canvass: a crowd-sourced, natural-product screening library for exploring biological space

NCATS thanks Dingyin Tao for assistance with compound characterization. This research was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH). R.B.A. acknowledges support from NSF (CHE-1665145) and NIH (GM126221). M.K.B. acknowledges support from NIH (5R01GM110131). N.Z.B. thanks support from NIGMS, NIH (R01GM114061). J.K.C. acknowledges support from NSF (CHE-1665331). J.C. acknowledges support from the Fogarty International Center, NIH (TW009872). P.A.C. acknowledges support from the National Cancer Institute (NCI), NIH (R01 CA158275), and the NIH/National Institute of Aging (P01 AG012411). N.K.G. acknowledges support from NSF (CHE-1464898). B.C.G. thanks the support of NSF (RUI: 213569), the Camille and Henry Dreyfus Foundation, and the Arnold and Mabel Beckman Foundation. C.C.H. thanks the start-up funds from the Scripps Institution of Oceanography for support. J.N.J. acknowledges support from NIH (GM 063557, GM 084333). A.D.K. thanks the support from NCI, NIH (P01CA125066). D.G.I.K. acknowledges support from the National Center for Complementary and Integrative Health (1 R01 AT008088) and the Fogarty International Center, NIH (U01 TW00313), and gratefully acknowledges courtesies extended by the Government of Madagascar (Ministere des Eaux et Forets). O.K. thanks NIH (R01GM071779) for financial support. T.J.M. acknowledges support from NIH (GM116952). S.M. acknowledges support from NIH (DA045884-01, DA046487-01, AA026949-01), the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program (W81XWH-17-1-0256), and NCI, NIH, through a Cancer Center Support Grant (P30 CA008748). K.N.M. thanks the California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board for support. B.T.M. thanks Michael Mullowney for his contribution in the isolation, elucidation, and submission of the compounds in this work. P.N. acknowledges support from NIH (R01 GM111476). L.E.O. acknowledges support from NIH (R01-HL25854, R01-GM30859, R0-1-NS-12389). L.E.B., J.K.S., and J.A.P. thank the NIH (R35 GM-118173, R24 GM-111625) for research support. F.R. thanks the American Lebanese Syrian Associated Charities (ALSAC) for financial support. I.S. thanks the University of Oklahoma Startup funds for support. J.T.S. acknowledges support from ACS PRF (53767-ND1) and NSF (CHE-1414298), and thanks Drs. Kellan N. Lamb and Michael J. Di Maso for their synthetic contribution. B.S. acknowledges support from NIH (CA78747, CA106150, GM114353, GM115575). W.S. acknowledges support from NIGMS, NIH (R15GM116032, P30 GM103450), and thanks the University of Arkansas for startup funds and the Arkansas Biosciences Institute (ABI) for seed money. C.R.J.S. acknowledges support from NIH (R01GM121656). D.S.T. thanks the support of NIH (T32 CA062948-Gudas) and PhRMA Foundation to A.L.V., NIH (P41 GM076267) to D.S.T., and CCSG NIH (P30 CA008748) to C.B. Thompson. R.E.T. acknowledges support from NIGMS, NIH (GM129465). R.J.T. thanks the American Cancer Society (RSG-12-253-01-CDD) and NSF (CHE1361173) for support. D.A.V. thanks the Camille and Henry Dreyfus Foundation, the National Science Foundation (CHE-0353662, CHE-1005253, and CHE-1725142), the Beckman Foundation, the Sherman Fairchild Foundation, the John Stauffer Charitable Trust, and the Christian Scholars Foundation for support. J.W. acknowledges support from the American Cancer Society through the Research Scholar Grant (RSG-13-011-01-CDD). W.M.W.acknowledges support from NIGMS, NIH (GM119426), and NSF (CHE1755698). A.Z. acknowledges support from NSF (CHE-1463819). (Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH); CHE-1665145 - NSF; CHE-1665331 - NSF; CHE-1464898 - NSF; RUI: 213569 - NSF; CHE-1414298 - NSF; CHE1361173 - NSF; CHE1755698 - NSF; CHE-1463819 - NSF; GM126221 - NIH; 5R01GM110131 - NIH; GM 063557 - NIH; GM 084333 - NIH; R01GM071779 - NIH; GM116952 - NIH; DA045884-01 - NIH; DA046487-01 - NIH; AA026949-01 - NIH; R01 GM111476 - NIH; R01-HL25854 - NIH; R01-GM30859 - NIH; R0-1-NS-12389 - NIH; R35 GM-118173 - NIH; R24 GM-111625 - NIH; CA78747 - NIH; CA106150 - NIH; GM114353 - NIH; GM115575 - NIH; R01GM121656 - NIH; T32 CA062948-Gudas - NIH; P41 GM076267 - NIH; R01GM114061 - NIGMS, NIH; R15GM116032 - NIGMS, NIH; P30 GM103450 - NIGMS, NIH; GM129465 - NIGMS, NIH; GM119426 - NIGMS, NIH; TW009872 - Fogarty International Center, NIH; U01 TW00313 - Fogarty International Center, NIH; R01 CA158275 - National Cancer Institute (NCI), NIH; P01 AG012411 - NIH/National Institute of Aging; Camille and Henry Dreyfus Foundation; Arnold and Mabel Beckman Foundation; Scripps Institution of Oceanography; P01CA125066 - NCI, NIH; 1 R01 AT008088 - National Center for Complementary and Integrative Health; W81XWH-17-1-0256 - Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program; P30 CA008748 - NCI, NIH, through a Cancer Center Support Grant; California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board; American Lebanese Syrian Associated Charities (ALSAC); University of Oklahoma Startup funds; 53767-ND1 - ACS PRF; PhRMA Foundation; P30 CA008748 - CCSG NIH; RSG-12-253-01-CDD - American Cancer Society; RSG-13-011-01-CDD - American Cancer Society; CHE-0353662 - National Science Foundation; CHE-1005253 - National Science Foundation; CHE-1725142 - National Science Foundation; Beckman Foundation; Sherman Fairchild Foundation; John Stauffer Charitable Trust; Christian Scholars Foundation)Published versionSupporting documentatio

Challenges in quantifying changes in the global water cycle

Human influences have likely already impacted the large-scale water cycle but natural variability and observational uncertainty are substantial. It is essential to maintain and improve observational capabilities to better characterize changes. Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time-series over land but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols, and due to large climate variability presently limits confidence in attribution of observed changes

Genome-Wide Interaction-Based Association Analysis Identified Multiple New Susceptibility Loci for Common Diseases

Genome-wide interaction-based association (GWIBA) analysis has the potential to identify novel susceptibility loci. These interaction effects could be missed with the prevailing approaches in genome-wide association studies (GWAS). However, no convincing loci have been discovered exclusively from GWIBA methods, and the intensive computation involved is a major barrier for application. Here, we developed a fast, multi-thread/parallel program named “pair-wise interaction-based association mapping” (PIAM) for exhaustive two-locus searches. With this program, we performed a complete GWIBA analysis on seven diseases with stringent control for false positives, and we validated the results for three of these diseases. We identified one pair-wise interaction between a previously identified locus, C1orf106, and one new locus, TEC, that was specific for Crohn's disease, with a Bonferroni corrected P<0.05 (P = 0.039). This interaction was replicated with a pair of proxy linked loci (P = 0.013) on an independent dataset. Five other interactions had corrected P<0.5. We identified the allelic effect of a locus close to SLC7A13 for coronary artery disease. This was replicated with a linked locus on an independent dataset (P = 1.09×10−7). Through a local validation analysis that evaluated association signals, rather than locus-based associations, we found that several other regions showed association/interaction signals with nominal P<0.05. In conclusion, this study demonstrated that the GWIBA approach was successful for identifying novel loci, and the results provide new insights into the genetic architecture of common diseases. In addition, our PIAM program was capable of handling very large GWAS datasets that are likely to be produced in the future

Nrf2 status affects tumor growth, HDAC3 gene promoter associations, and the response to sulforaphane in the colon

BACKGROUND: The dietary agent sulforaphane (SFN) has been reported to induce nuclear factor erythroid 2 (NF-E2)-related factor 2 (Nrf2)-dependent pathways as well as inhibiting histone deacetylase (HDAC) activity. The current investigation sought to examine the relationships between Nrf2 status and HDAC expression in preclinical and translational studies. RESULTS: Wild type (WT) and Nrf2-deficient (Nrf2(−/+)) mice were treated with the colon carcinogen 1,2-dimethylhydrazine (DMH) followed by 400 ppm SFN in the diet (n = 35 mice/group). WT mice were more susceptible than Nrf2(−/+) mice to tumor induction in the colon. Tumors from WT mice had higher HDAC levels globally and locally on genes such as cyclin-dependant kinase inhibitor 2a (Cdkn2a/p16) that were dysregulated during tumor development. The average tumor burden was reduced by SFN from 62.7 to 26.0 mm(3) in WT mice and from 14.6 to 11.7 mm(3) in Nrf2(−/+) mice. The decreased antitumor activity of SFN in Nrf2(−/+) mice coincided with attenuated Cdkn2a promoter interactions involving HDAC3. HDAC3 knockdown in human colon cancer cells recapitulated the effects of SFN on p16 induction. Human subjects given a broccoli sprout extract supplement (200 μmol SFN equivalents), or reporting more than five cruciferous vegetable servings per week, had increased p16 expression that was inversely associated with HDAC3 in circulating peripheral blood mononuclear cells (PBMCs) and in biopsies obtained during screening colonoscopy. CONCLUSIONS: Nrf2 expression varies widely in both normal human colon and human colon cancers and likely contributes to the overall rate of tumor growth in the large intestine. It remains to be determined whether this influences global HDAC protein expression levels, as well as local HDAC interactions on genes dysregulated during human colon tumor development. If corroborated in future studies, Nrf2 status might serve as a biomarker of HDAC inhibitor efficacy in clinical trials using single agent or combination modalities to slow, halt, or regress the progression to later stages of solid tumors and hematological malignancies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13148-015-0132-y) contains supplementary material, which is available to authorized users