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    ItemOpen Access
    HIPAA and the Leak of "Deidentified" EHR Data.
    (The New England journal of medicine, 2021-06-05) Mandl, Kenneth D; Perakslis, Eric D
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    International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci.
    (Nature communications, 2019-10) Nievergelt, Caroline M; Maihofer, Adam X; Klengel, Torsten; Atkinson, Elizabeth G; Chen, Chia-Yen; Choi, Karmel W; Coleman, Jonathan RI; Dalvie, Shareefa; Duncan, Laramie E; Gelernter, Joel; Levey, Daniel F; Logue, Mark W; Polimanti, Renato; Provost, Allison C; Ratanatharathorn, Andrew; Stein, Murray B; Torres, Katy; Aiello, Allison E; Almli, Lynn M; Amstadter, Ananda B; Andersen, Søren B; Andreassen, Ole A; Arbisi, Paul A; Ashley-Koch, Allison E; Austin, S Bryn; Avdibegovic, Esmina; Babić, Dragan; Bækvad-Hansen, Marie; Baker, Dewleen G; Beckham, Jean C; Bierut, Laura J; Bisson, Jonathan I; Boks, Marco P; Bolger, Elizabeth A; Børglum, Anders D; Bradley, Bekh; Brashear, Megan; Breen, Gerome; Bryant, Richard A; Bustamante, Angela C; Bybjerg-Grauholm, Jonas; Calabrese, Joseph R; Caldas-de-Almeida, José M; Dale, Anders M; Daly, Mark J; Daskalakis, Nikolaos P; Deckert, Jürgen; Delahanty, Douglas L; Dennis, Michelle F; Disner, Seth G; Domschke, Katharina; Dzubur-Kulenovic, Alma; Erbes, Christopher R; Evans, Alexandra; Farrer, Lindsay A; Feeny, Norah C; Flory, Janine D; Forbes, David; Franz, Carol E; Galea, Sandro; Garrett, Melanie E; Gelaye, Bizu; Geuze, Elbert; Gillespie, Charles; Uka, Aferdita Goci; Gordon, Scott D; Guffanti, Guia; Hammamieh, Rasha; Harnal, Supriya; Hauser, Michael A; Heath, Andrew C; Hemmings, Sian MJ; Hougaard, David Michael; Jakovljevic, Miro; Jett, Marti; Johnson, Eric Otto; Jones, Ian; Jovanovic, Tanja; Qin, Xue-Jun; Junglen, Angela G; Karstoft, Karen-Inge; Kaufman, Milissa L; Kessler, Ronald C; Khan, Alaptagin; Kimbrel, Nathan A; King, Anthony P; Koen, Nastassja; Kranzler, Henry R; Kremen, William S; Lawford, Bruce R; Lebois, Lauren AM; Lewis, Catrin E; Linnstaedt, Sarah D; Lori, Adriana; Lugonja, Bozo; Luykx, Jurjen J; Lyons, Michael J; Maples-Keller, Jessica; Marmar, Charles; Martin, Alicia R; Martin, Nicholas G; Maurer, Douglas; Mavissakalian, Matig R; McFarlane, Alexander; McGlinchey, Regina E; McLaughlin, Katie A; McLean, Samuel A; McLeay, Sarah; Mehta, Divya; Milberg, William P; Miller, Mark W; Morey, Rajendra A; Morris, Charles Phillip; Mors, Ole; Mortensen, Preben B; Neale, Benjamin M; Nelson, Elliot C; Nordentoft, Merete; Norman, Sonya B; O'Donnell, Meaghan; Orcutt, Holly K; Panizzon, Matthew S; Peters, Edward S; Peterson, Alan L; Peverill, Matthew; Pietrzak, Robert H; Polusny, Melissa A; Rice, John P; Ripke, Stephan; Risbrough, Victoria B; Roberts, Andrea L; Rothbaum, Alex O; Rothbaum, Barbara O; Roy-Byrne, Peter; Ruggiero, Ken; Rung, Ariane; Rutten, Bart PF; Saccone, Nancy L; Sanchez, Sixto E; Schijven, Dick; Seedat, Soraya; Seligowski, Antonia V; Seng, Julia S; Sheerin, Christina M; Silove, Derrick; Smith, Alicia K; Smoller, Jordan W; Sponheim, Scott R; Stein, Dan J; Stevens, Jennifer S; Sumner, Jennifer A; Teicher, Martin H; Thompson, Wesley K; Trapido, Edward; Uddin, Monica; Ursano, Robert J; van den Heuvel, Leigh Luella; Van Hooff, Miranda; Vermetten, Eric; Vinkers, Christiaan H; Voisey, Joanne; Wang, Yunpeng; Wang, Zhewu; Werge, Thomas; Williams, Michelle A; Williamson, Douglas E; Winternitz, Sherry; Wolf, Christiane; Wolf, Erika J; Wolff, Jonathan D; Yehuda, Rachel; Young, Ross McD; Young, Keith A; Zhao, Hongyu; Zoellner, Lori A; Liberzon, Israel; Ressler, Kerry J; Haas, Magali; Koenen, Karestan C
    The risk of posttraumatic stress disorder (PTSD) following trauma is heritable, but robust common variants have yet to be identified. In a multi-ethnic cohort including over 30,000 PTSD cases and 170,000 controls we conduct a genome-wide association study of PTSD. We demonstrate SNP-based heritability estimates of 5-20%, varying by sex. Three genome-wide significant loci are identified, 2 in European and 1 in African-ancestry analyses. Analyses stratified by sex implicate 3 additional loci in men. Along with other novel genes and non-coding RNAs, a Parkinson's disease gene involved in dopamine regulation, PARK2, is associated with PTSD. Finally, we demonstrate that polygenic risk for PTSD is significantly predictive of re-experiencing symptoms in the Million Veteran Program dataset, although specific loci did not replicate. These results demonstrate the role of genetic variation in the biology of risk for PTSD and highlight the necessity of conducting sex-stratified analyses and expanding GWAS beyond European ancestry populations.
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    ItemOpen Access
    Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score.
    (The British journal of surgery, 2021-11) COVIDSurg Collaborative
    To support the global restart of elective surgery, data from an international prospective cohort study of 8492 patients (69 countries) was analysed using artificial intelligence (machine learning techniques) to develop a predictive score for mortality in surgical patients with SARS-CoV-2. We found that patient rather than operation factors were the best predictors and used these to create the COVIDsurg Mortality Score (https://covidsurgrisk.app). Our data demonstrates that it is safe to restart a wide range of surgical services for selected patients.
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    ItemOpen Access
    Quantification of DNA cleavage specificity in Hi-C experiments.
    (Nucleic Acids Res, 2016-01-08) Meluzzi, Dario; Arya, Gaurav
    Hi-C experiments produce large numbers of DNA sequence read pairs that are typically analyzed to deduce genomewide interactions between arbitrary loci. A key step in these experiments is the cleavage of cross-linked chromatin with a restriction endonuclease. Although this cleavage should happen specifically at the enzyme's recognition sequence, an unknown proportion of cleavage events may involve other sequences, owing to the enzyme's star activity or to random DNA breakage. A quantitative estimation of these non-specific cleavages may enable simulating realistic Hi-C read pairs for validation of downstream analyses, monitoring the reproducibility of experimental conditions and investigating biophysical properties that correlate with DNA cleavage patterns. Here we describe a computational method for analyzing Hi-C read pairs to estimate the fractions of cleavages at different possible targets. The method relies on expressing an observed local target distribution downstream of aligned reads as a linear combination of known conditional local target distributions. We validated this method using Hi-C read pairs obtained by computer simulation. Application of the method to experimental Hi-C datasets from murine cells revealed interesting similarities and differences in patterns of cleavage across the various experiments considered.
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    ItemOpen Access
    Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics.
    (Nature medicine, 2021-03) Muus, Christoph; Luecken, Malte D; Eraslan, Gökcen; Sikkema, Lisa; Waghray, Avinash; Heimberg, Graham; Kobayashi, Yoshihiko; Vaishnav, Eeshit Dhaval; Subramanian, Ayshwarya; Smillie, Christopher; Jagadeesh, Karthik A; Duong, Elizabeth Thu; Fiskin, Evgenij; Torlai Triglia, Elena; Ansari, Meshal; Cai, Peiwen; Lin, Brian; Buchanan, Justin; Chen, Sijia; Shu, Jian; Haber, Adam L; Chung, Hattie; Montoro, Daniel T; Adams, Taylor; Aliee, Hananeh; Allon, Samuel J; Andrusivova, Zaneta; Angelidis, Ilias; Ashenberg, Orr; Bassler, Kevin; Bécavin, Christophe; Benhar, Inbal; Bergenstråhle, Joseph; Bergenstråhle, Ludvig; Bolt, Liam; Braun, Emelie; Bui, Linh T; Callori, Steven; Chaffin, Mark; Chichelnitskiy, Evgeny; Chiou, Joshua; Conlon, Thomas M; Cuoco, Michael S; Cuomo, Anna SE; Deprez, Marie; Duclos, Grant; Fine, Denise; Fischer, David S; Ghazanfar, Shila; Gillich, Astrid; Giotti, Bruno; Gould, Joshua; Guo, Minzhe; Gutierrez, Austin J; Habermann, Arun C; Harvey, Tyler; He, Peng; Hou, Xiaomeng; Hu, Lijuan; Hu, Yan; Jaiswal, Alok; Ji, Lu; Jiang, Peiyong; Kapellos, Theodoros S; Kuo, Christin S; Larsson, Ludvig; Leney-Greene, Michael A; Lim, Kyungtae; Litviňuková, Monika; Ludwig, Leif S; Lukassen, Soeren; Luo, Wendy; Maatz, Henrike; Madissoon, Elo; Mamanova, Lira; Manakongtreecheep, Kasidet; Leroy, Sylvie; Mayr, Christoph H; Mbano, Ian M; McAdams, Alexi M; Nabhan, Ahmad N; Nyquist, Sarah K; Penland, Lolita; Poirion, Olivier B; Poli, Sergio; Qi, CanCan; Queen, Rachel; Reichart, Daniel; Rosas, Ivan; Schupp, Jonas C; Shea, Conor V; Shi, Xingyi; Sinha, Rahul; Sit, Rene V; Slowikowski, Kamil; Slyper, Michal; Smith, Neal P; Sountoulidis, Alex; Strunz, Maximilian; Sullivan, Travis B; Sun, Dawei; Talavera-López, Carlos; Tan, Peng; Tantivit, Jessica; Travaglini, Kyle J; Tucker, Nathan R; Vernon, Katherine A; Wadsworth, Marc H; Waldman, Julia; Wang, Xiuting; Xu, Ke; Yan, Wenjun; Zhao, William; Ziegler, Carly GK; NHLBI LungMap Consortium; Human Cell Atlas Lung Biological Network
    Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.
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    ItemOpen Access
    Systematic comparison of published host gene expression signatures for bacterial/viral discrimination.
    (Genome medicine, 2022-02-21) Bodkin, Nicholas; Ross, Melissa; McClain, Micah T; Ko, Emily R; Woods, Christopher W; Ginsburg, Geoffrey S; Henao, Ricardo; Tsalik, Ephraim L

    Background

    Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another.

    Methods

    This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies.

    Results

    Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69-0.97 for viral classification. Signature size varied (1-398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months-1 year and 2-11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets.

    Conclusions

    In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature's size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.