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1. Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort CollaborativeResearch in context

Author

Timothy Bergquist, Johanna Loomba, Emily Pfaff, Fangfang Xia, Zixuan Zhao, Yitan Zhu, Elliot Mitchell, Biplab Bhattacharya, Gaurav Shetty, Tamanna Munia, Grant Delong, Adbul Tariq, Zachary Butzin-Dozier, Yunwen Ji, Haodong Li, Jeremy Coyle, Seraphina Shi, Rachael V. Philips, Andrew Mertens, Romain Pirracchio, Mark van der Laan, John M. Colford, Jr., Alan Hubbard, Jifan Gao, Guanhua Chen, Neelay Velingker, Ziyang Li, Yinjun Wu, Adam Stein, Jiani Huang, Zongyu Dai, Qi Long, Mayur Naik, John Holmes, Danielle Mowery, Eric Wong, Ravi Parekh, Emily Getzen, Jake Hightower, Jennifer Blase, Ataes Aggarwal, Joseph Agor, Amera Al-Amery, Oluwatobiloba Aminu, Adit Anand, Corneliu Antonescu, Mehak Arora, Sayed Asaduzzaman, Tanner Asmussen, Mahdi Baghbanzadeh, Frazier Baker, Bridget Bangert, Laila Bekhet, Jenny Blase, Brian Caffo, Hao Chang, Zeyuan Chen, Jiandong Chen, Jeffrey Chiang, Peter Cho, Robert Cockrell, Parker Combs, Ciara Crosby, Ran Dai, Anseh Danesharasteh, Elif Yildirim, Ryan Demilt, Kaiwen Deng, Sanjoy Dey, Rohan Dhamdhere, Andrew Dickson, Phoebe Dijour, Dong Dinh, Richard Dixon, Albi Domi, Souradeep Dutta, Mirna Elizondo, Zeynep Ertem, Solomon Feuerwerker, Danica Fliss, Jennifer Fowler, Sunyang Fu, Kelly Gardner, Neil Getty, Mohamed Ghalwash, Logan Gloster, Phil Greer, Yuanfang Guan, Colby Ham, Samer Hanoudi, Jeremy Harper, Nathaniel Hendrix, Leeor Hershkovich, Junjie Hu, Yu Huang, Tongtong Huang, Junguk Hur, Monica Isgut, Hamid Ismail, Grant Izmirlian, Kuk Jang, Christianah Jemiyo, Hayoung Jeong, Xiayan Ji, Ming Jiang, Sihang Jiang, Xiaoqian Jiang, Yuye Jiang, Akin Johnson, Zach Analyst, Saarthak Kapse, Uri Kartoun, Dukka KC, Zahra Fard, Tim Kosfeld, Spencer Krichevsky, Mike Kuo, Dale Larie, Lauren Lederer, Shan Leng, Hongyang Li, Jianfu Li, Tiantian Li, Xinwen Liang, Hengyue Liang, Feifan Liu, Daniel Liu, Gang Luo, Ravi Madduri, Vithal Madhira, Shivali Mani, Farzaneh Mansourifard, Robert Matson, Vangelis Metsis, Pablo Meyer, Catherine Mikhailova, Dante Miller, Christopher Milo, Gourav Modanwal, Ronald Moore, David Morgenthaler, Rasim Musal, Vinit Nalawade, Rohan Narain, Saideep Narendrula, Alena Obiri, Satoshi Okawa, Chima Okechukwu, Toluwanimi Olorunnisola, Tim Ossowski, Harsh Parekh, Jean Park, Saaya Patel, Jason Patterson, Chetan Paul, Le Peng, Diana Perkins, Suresh Pokharel, Dmytro Poplavskiy, Zach Pryor, Sarah Pungitore, Hong Qin, Salahaldeen Rababa, Mahbubur Rahman, Elior Rahmani, Gholamali Rahnavard, Md Raihan, Suraj Rajendran, Sarangan Ravichandran, Chandan Reddy, Abel Reyes, Ali Roghanizad, Sean Rouffa, Xiaoyang Ruan, Arpita Saha, Sahil Sawant, Melody Schiaffino, Diego Seira, Saurav Sengupta, Ruslan Shalaev, Linh Shinguyen, Karnika Singh, Soumya Sinha, Damien Socia, Halen Stalians, Charalambos Stavropoulos, Jan Strube, Devika Subramanian, Jiehuan Sun, Ju Sun, Chengkun Sun, Prathic Sundararajan, Salmonn Talebi, Edward Tawiah, Jelena Tesic, Mikaela Thiess, Raymond Tian, Luke Torre-Healy; Ming-Tse Tsai, David Tyus, Madhurima Vardhan, Benjamin Walzer, Jacob Walzer, Junda Wang, Lu Wang, Will Wang, Jonathan Wang, Yisen Wang, Chad Weatherly, Fanyou Wu, Yifeng Wu, Hao Yan, Zhichao Yang, Biao Ye, Rui Yin, Changyu Yin, Yun Yoo, Albert You, June Yu, Martin Zanaj, Zachary Zaiman, Kai Zhang, Xiaoyi Zhang, Tianmai Zhang, Degui Zhi, Yishan Zhong, Huixue Zhou, Andrea Zhou, Yuanda Zhu, Sophie Zhu, Meredith Adams, Caleb Alexander, Benjamin Amor, Alfred Anzalone, Benjamin Bates, Will Beasley, Tellen Bennett, Mark Bissell, Eilis Boudreau, Samuel Bozzette, Katie Bradwell, Carolyn Bramante, Don Brown, Penny Burgoon, John Buse, Tiffany Callahan, Kenrick Cato, Scott Chapman, Christopher Chute, Jaylyn Clark, Marshall Clark, Will Cooper, Lesley Cottrell, Karen Crowley, Mariam Deacy, Christopher Dillon, David Eichmann, Mary Emmett, Rebecca Erwin-Cohen, Patricia Francis, Evan French, Rafael Fuentes, Davera Gabriel, Joel Gagnier, Nicole Garbarini, Jin Ge, Kenneth Gersing, Andrew Girvin, Valery Gordon, Alexis Graves, Justin Guinney, Melissa Haendel, J.W. Hayanga, Brian Hendricks, Wenndy Hernandez, Elaine Hill, William Hillegass, Stephanie Hong, Dan Housman, Robert Hurley, Jessica Islam, Randeep Jawa, Steve Johnson, Rishi Kamaleswaran, Warren Kibbe, Farrukh Koraishy, Kristin Kostka, Michael Kurilla, Adam Lee, Harold Lehmann, Hongfang Liu, Charisse Madlock-Brown; Sandeep Mallipattu, Amin Manna, Federico Mariona, Emily Marti, Greg Martin, Jomol Mathew, Diego Mazzotti, Julie McMurry, Hemalkumar Mehta, Sam Michael, Robert Miller, Leonie Misquitta, Richard Moffitt, Michele Morris, Kimberly Murray, Lavance Northington, Shawn O’Neil, Amy Olex, Matvey Palchuk, Brijesh Patel, Rena Patel, Philip Payne, Jami Pincavitch, Lili Portilla, Fred Prior, Saiju Pyarajan, Lee Pyles, Nabeel Qureshi, Peter Robinson, Joni Rutter, Ofer Sadan, Nasia Safdar, Amit Saha, Joel Saltz, Mary Saltz, Clare Schmitt, Soko Setoguchi, Noha Sharafeldin, Anjali Sharathkumar, Usman Sheikh, Hythem Sidky, George Sokos, Andrew Southerland, Heidi Spratt, Justin Starren, Vignesh Subbian, Christine Suver, Cliff Takemoto, Meredith Temple-O'Connor, Umit Topaloglu, Satyanarayana Vedula, Anita Walden, Kellie Walters, Cavin Ward-Caviness, Adam Wilcox, Ken Wilkins, Andrew Williams, Chunlei Wu, Elizabeth Zampino, Xiaohan Zhang, and Richard Zhu

Subjects
Long COVID, PASC, Machine learning, COVID-19, Evaluation, Community challenge, Medicine, Medicine (General), R5-920
Abstract

Summary: Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Published
2024
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4. Contributors

Author

Shakuntala Baichoo, K.D. Blizinsky, S. Chandrasekharan, Melek Chaouch, Emile R. Chimusa, Jorge L. Contreras, Deanna Cross, Pieter De Smet, Faisal M. Fadlelmola, Christopher Fields, Robert R. Freimuth, Elena Ghanaim, Anisah Ghoorah, Zoe Gill, Melissa Haendel, S. Jooma, Samar Kamal Kassim, Melissa Konopko, Judit Kumuthini, Michael Lang, Upendo Masamu, Catherine A. McCarty, Jennifer B. McCormick, Robert P. Milius, Nicola Mulder, Zahra Mungloo-Dilmohamud, Mullai Murugan, Liberata A. Mwita, James C. O'Leary, Angela Page, Jyotishman Pathak, Dimitri Patrinos, Fouzia Radouani, Verena Ras, J.A. Reusch, Laura Lyman Rodriguez, Chaimae Samtal, Raphael Z. Sangeda, Dassen Sathan, Milaine S.S. Tchamga, Mahsa Shabani, May Terry, Kimberly A. Thomson, John Van Horn, Gordon Wells, Lyndon Zass, and Ma'n H. Zawati

Published
2023
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5. Characterizing Long COVID: Deep Phenotype of a Complex Condition

Author

Joel H. Saltz, Tellen D. Bennett, Emily R. Pfaff, George D. Vavougios, Sebastian Köhler, Joel J. Gagnier, Melissa Haendel, Chen Liang, Madeline A Rock, Peter N. Robinson, Anupam A Sule, Farrukh M. Koraishy, Carolyn T. Bramante, Julian Solway, Hongfang Liu, Umit Topaloglu, Heidi Spratt, Douglas S McNair, Tiffany J. Callahan, Teshamae S. Monteith, Wesley Kimble, Lauren E Chan, Julie A. McMurry, James Brian Byrd, Casey S. Greene, Richard A. Moffitt, Alfred J. Anzalone, Ann M. Parker, Ben Coleman, Mallory A Perry, Charisse R. Madlock-Brown, Robert A Schuff, Feifan Liu, Rachel R Deer, Vithal Madhira, Hannah E. Davis, Marc D. Basson, Timothy Bergquist, Halie M. Rando, Ramakanth Kavuluru, Diego R. Mazzotti, Nicole Vasilevsky, Anthony Solomonides, Liwei Wang, Leigh C. Carmody, Nicolas Matentzoglu, Eilis A. Boudreau, Justin T. Reese, Gary S. Stein, William B. Hillegass, Christopher G. Chute, and Haitao Chu

Subjects
medicine.medical_specialty, Medicine (General), Medical terminology, phenotyping, Coronavirus disease 2019 (COVID-19), media_common.quotation_subject, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), General Biochemistry, Genetics and Molecular Biology, Terminology, Presentation, Post-Acute COVID-19 Syndrome, R5-920, Human Phenotype Ontology, medicine, Relevance (law), Humans, In patient, Intensive care medicine, long COVID, Organ system, media_common, Original Research, business.industry, SARS-CoV-2, Foundation (evidence), COVID-19, General Medicine, Natural history, Layperson, of post-acute sequelae of SARS-CoV-2, Medicine, business, human phenotype ontology
Abstract

Background Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. Methods The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. Findings We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. Interpretation Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. Funding U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411 .

Published
2021

6. De Novo Variants in WDR37 Are Associated with Epilepsy, Colobomas, Dysmorphism, Developmental Delay, Intellectual Disability, and Cerebellar Hypoplasia

Author

Oguz Kanca, Jonathan C. Andrews, Pei-Tseng Lee, Chirag Patel, Stephen R. Braddock, Anne M. Slavotinek, Julie S. Cohen, Cynthia S. Gubbels, Kimberly A. Aldinger, Judy Williams, Maanasa Indaram, Ali Fatemi, Timothy W. Yu, Pankaj B. Agrawal, Gilbert Vezina, Cas Simons, Joanna Crawford, C. Christopher Lau, Wendy K. Chung, Thomas C. Markello, William B. Dobyns, David R. Adams, William A. Gahl, Michael F. Wangler, Shinya Yamamoto, Hugo J. Bellen, May Christine V. Malicdan, Maria T. Acosta, Pankaj Agrawal, Mercedes E. Alejandro, Patrick Allard, Justin Alvey, Ashley Andrews, Euan A. Ashley, Mahshid S. Azamian, Carlos A. Bacino, Guney Bademci, Eva Baker, Ashok Balasubramanyam, Dustin Baldridge, Jim Bale, Deborah Barbouth, Gabriel F. Batzli, Pinar Bayrak-Toydemir, Alan H. Beggs, Gill Bejerano, Jonathan A. Bernstein, Gerard T. Berry, Anna Bican, David P. Bick, Camille L. Birch, Stephanie Bivona, John Bohnsack, Carsten Bonnenmann, Devon Bonner, Braden E. Boone, Bret L. Bostwick, Lorenzo Botto, Lauren C. Briere, Elly Brokamp, Donna M. Brown, Matthew Brush, Elizabeth A. Burke, Lindsay C. Burrage, Manish J. Butte, John Carey, Olveen Carrasquillo, Ta Chen Peter Chang, Hsiao-Tuan Chao, Gary D. Clark, Terra R. Coakley, Laurel A. Cobban, Joy D. Cogan, F. Sessions Cole, Heather A. Colley, Cynthia M. Cooper, Heidi Cope, William J. Craigen, Precilla D'Souza, Surendra Dasari, Mariska Davids, Jyoti G. Dayal, Esteban C. Dell'Angelica, Shweta U. Dhar, Naghmeh Dorrani, Daniel C. Dorset, Emilie D. Douine, David D. Draper, Laura Duncan, David J. Eckstein, Lisa T. Emrick, Christine M. Eng, Cecilia Esteves, Tyra Estwick, Liliana Fernandez, Carlos Ferreira, Elizabeth L. Fieg, Paul G. Fisher, Brent L. Fogel, Irman Forghani, Laure Fresard, Rena A. Godfrey, Alica M. Goldman, David B. Goldstein, Jean-Philippe F. Gourdine, Alana Grajewski, Catherine A. Groden, Andrea L. Gropman, Melissa Haendel, Rizwan Hamid, Neil A. Hanchard, Nichole Hayes, Frances High, Ingrid A. Holm, Jason Hom, Alden Huang, Yong Huang, Rosario Isasi, Fariha Jamal, Yong-hui Jiang, Jean M. Johnston, Angela L. Jones, Lefkothea Karaviti, Emily G. Kelley, Dana Kiley, David M. Koeller, Isaac S. Kohane, Jennefer N. Kohler, Deborah Krakow, Donna M. Krasnewich, Susan Korrick, Mary Koziura, Joel B. Krier, Jennifer E. Kyle, Seema R. Lalani, Byron Lam, Brendan C. Lanpher, Ian R. Lanza, Jozef Lazar, Kimberly LeBlanc, Brendan H. Lee, Hane Lee, Roy Levitt, Shawn E. Levy, Richard A. Lewis, Sharyn A. Lincoln, Pengfei Liu, Xue Zhong Liu, Nicola Longo, Sandra K. Loo, Joseph Loscalzo, Richard L. Maas, Ellen F. Macnamara, Calum A. MacRae, Valerie V. Maduro, Marta M. Majcherska, Laura A. Mamounas, Teri A. Manolio, Rong Mao, Ronit Marom, Gabor Marth, Beth A. Martin, Martin G. Martin, Julian A. Martínez-Agosto, Shruti Marwaha, Thomas May, Jacob McCauley, Allyn McConkie-Rosell, Colleen E. McCormack, Alexa T. McCray, Thomas O. Metz, Matthew Might, Eva Morava-Kozicz, Paolo M. Moretti, Marie Morimoto, John J. Mulvihill, David R. Murdock, Avi Nath, Stan F. Nelson, J. Scott Newberry, John H. Newman, Sarah K. Nicholas, Donna Novacic, Devin Oglesbee, James P. Orengo, Laura Pace, Stephen Pak, J. Carl Pallais, Christina G.S. Palmer, Jeanette C. Papp, Neil H. Parker, John A. Phillips, Jennifer E. Posey, John H. Postlethwait, Lorraine Potocki, Barbara N. Pusey, Aaron Quinlan, Archana N. Raja, Genecee Renteria, Chloe M. Reuter, Lynette Rives, Amy K. Robertson, Lance H. Rodan, Jill A. Rosenfeld, Robb K. Rowley, Maura Ruzhnikov, Ralph Sacco, Jacinda B. Sampson, Susan L. Samson, Mario Saporta, Judy Schaechter, Timothy Schedl, Kelly Schoch, Daryl A. Scott, Lisa Shakachite, Prashant Sharma, Vandana Shashi, Kathleen Shields, Jimann Shin, Rebecca Signer, Catherine H. Sillari, Edwin K. Silverman, Janet S. Sinsheimer, Kathy Sisco, Kevin S. Smith, Lilianna Solnica-Krezel, Rebecca C. Spillmann, Joan M. Stoler, Nicholas Stong, Jennifer A. Sullivan, Shirley Sutton, David A. Sweetser, Holly K. Tabor, Cecelia P. Tamburro, Queenie K.-G. Tan, Mustafa Tekin, Fred Telischi, Willa Thorson, Cynthia J. Tifft, Camilo Toro, Alyssa A. Tran, Tiina K. Urv, Matt Velinder, Dave Viskochil, Tiphanie P. Vogel, Colleen E. Wahl, Nicole M. Walley, Chris A. Walsh, Melissa Walker, Jennifer Wambach, Jijun Wan, Lee-kai Wang, Patricia A. Ward, Katrina M. Waters, Bobbie-Jo M. Webb-Robertson, Daniel Wegner, Monte Westerfield, Matthew T. Wheeler, Anastasia L. Wise, Lynne A. Wolfe, Jeremy D. Woods, Elizabeth A. Worthey, John Yang, Amanda J. Yoon, Guoyun Yu, Diane B. Zastrow, Chunli Zhao, and Stephan Zuchner

Subjects
Male, 0301 basic medicine, Pediatrics, WD40 Repeats, Developmental Disabilities, Sequence Homology, 030105 genetics & heredity, Epilepsy, CHARGE syndrome, 0302 clinical medicine, Cerebellum, Intellectual disability, Child, Cerebellar hypoplasia, Genetics (clinical), Genetics, Microfilament Proteins, Body Dysmorphic Disorders, Null allele, Phenotype, Coloboma, Drosophila melanogaster, Female, Adult, medicine.medical_specialty, Protein family, Allele name, Locus (genetics), Biology, Nervous System Malformations, Young Adult, 03 medical and health sciences, WD40 repeat, Intellectual Disability, Report, medicine, Animals, Humans, Amino Acid Sequence, Allele, Gene, business.industry, Infant, Newborn, Correction, Infant, Regret, medicine.disease, Human genetics, 030104 developmental biology, Mutation, business, 030217 neurology & neurosurgery
Abstract

WD40 repeat-containing proteins form a large family of proteins present in all eukaryotes. Here, we identified five pediatric probands with de novo variants in WDR37, which encodes a member of the WD40 repeat protein family. Two probands shared one variant and the others have variants in nearby amino acids outside the WD40 repeats. The probands exhibited shared phenotypes of epilepsy, colobomas, facial dysmorphology reminiscent of CHARGE syndrome, developmental delay and intellectual disability, and cerebellar hypoplasia. The WDR37 protein is highly conserved in vertebrate and invertebrate model organisms and is currently not associated with a human disease. We generated a null allele of the single Drosophila ortholog to gain functional insights and replaced the coding region of the fly gene CG12333/wdr37 with GAL4. These flies are homozygous viable but display severe bang sensitivity, a phenotype associated with seizures in flies. Additionally, the mutant flies fall when climbing the walls of the vials, suggesting a defect in grip strength, and repeat the cycle of climbing and falling. Similar to wall clinging defect, mutant males often lose grip of the female abdomen during copulation. These phenotypes are rescued by using the GAL4 in the CG12333/wdr37 locus to drive the UAS-human reference WDR37 cDNA. The two variants found in three human subjects failed to rescue these phenotypes, suggesting that these alleles severely affect the function of this protein. Taken together, our data suggest that variants in WDR37 underlie a novel syndromic neurological disorder.

Published
2019

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