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Keehner J, Abeles SR, Longhurst CA, Horton LE, Myers FE, Riggs-Rodriguez L, Ahmad M, Baxter S, Boussina A, Cantrell K, Cardenas P, De Hoff P, El-Kareh R, Holland J, Ikeda D, Kurashige K, Laurent LC, Lucas A, Pride D, Sathe S, Tran AR, Vasylyeva TI, Yeo G, Knight R, Wertheim JO, Torriani FJ. Integrated Genomic and Social Network Analyses of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in the Healthcare Setting. Clin Infect Dis 2024:ciad738. [PMID: 38227643 DOI: 10.1093/cid/ciad738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Infection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2. METHODS We performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs. RESULTS Among 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001). CONCLUSIONS IP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission.
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Affiliation(s)
- Jocelyn Keehner
- Division of Infectious Diseases, Department of Medicine, University of California-SanFrancisco, San Francisco, California, USA
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
| | - Shira R Abeles
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
- Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health, San Diego, California, USA
| | - Christopher A Longhurst
- Division of Biomedical Informatics, Department of Medicine, UC San Diego Health, La Jolla, California, USA
- Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
| | - Lucy E Horton
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
- Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health, San Diego, California, USA
- Vaccine Research and Development Unit, Pfizer Inc, San Diego, California, USA
| | - Frank E Myers
- Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health, San Diego, California, USA
| | - Lindsay Riggs-Rodriguez
- Population Health Services Organization-Programs and Strategy, UC San Diego Health, San Diego, California, USA
| | - Mohammed Ahmad
- Information Services EMR, UC San Diego Health, San Diego, California, USA
| | - Sally Baxter
- Division of Biomedical Informatics at the University of California-San Diego, San Diego, California, USA
| | - Aaron Boussina
- Division of Biomedical Informatics, University of California-San Diego, La Jolla, California, USA
| | - Kalen Cantrell
- Department of Computer Science & Engineering, Jacobs School of Engineering, University of California, San Diego, California, USA
| | - Priscilla Cardenas
- UC San Diego Health's Contact Tracing Team, Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health, San Diego, California, USA
| | - Peter De Hoff
- Sanford Consortium of Regenerative Medicine, University of California-San Diego, La Jolla, California, USA
- Expedited COVID Identification Environment Laboratory, Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
- Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, UC San Diego Health, San Diego, California, USA
| | - Robert El-Kareh
- Division of Biomedical Informatics, Department of Medicine, UC San Diego Health, La Jolla, California, USA
- Division of Hospital Medicine, Department of Medicine, UC San Diego Health, La Jolla, California, USA
| | - Jennifer Holland
- Analytics and Population Health Department, UC San Diego Health, San Diego, California, USA
| | - Daryn Ikeda
- UC San Diego Health's Contact Tracing Team, Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health, San Diego, California, USA
| | - Kirk Kurashige
- Analytics and Population Health Department, UC San Diego Health, San Diego, California, USA
| | - Louise C Laurent
- Sanford Consortium of Regenerative Medicine, University of California-San Diego, La Jolla, California, USA
- Expedited COVID Identification Environment Laboratory, Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
- Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, UC San Diego Health, San Diego, California, USA
| | - Andrew Lucas
- Information Services EMR, UC San Diego Health, San Diego, California, USA
| | - David Pride
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
- Department of Pathology, UC San Diego Health, La Jolla, California, USA
| | - Shashank Sathe
- Sanford Consortium of Regenerative Medicine, University of California-San Diego, La Jolla, California, USA
- Department of Cellular and Molecular Medicine, University of California-San Diego, La Jolla, California, USA
- Expedited COVID Identification Environment Laboratory, Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
| | - Allen R Tran
- Information Services EMR, UC San Diego Health, San Diego, California, USA
| | - Tetyana I Vasylyeva
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
| | - Gene Yeo
- Sanford Consortium of Regenerative Medicine, University of California-San Diego, La Jolla, California, USA
- Department of Cellular and Molecular Medicine, University of California-San Diego, La Jolla, California, USA
- Expedited COVID Identification Environment Laboratory, Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
| | - Rob Knight
- Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
- Department of Bioengineering, University of California-San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California-San Diego, La Jolla, California, USA
- Expedited COVID Identification Environment Laboratory, Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
- Center for Microbiome Innovation, University of California-San Diego, La Jolla, California, USA
| | - Joel O Wertheim
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
| | - Francesca J Torriani
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
- Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health, San Diego, California, USA
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Ortiz-Barbosa GS, Torres-Martínez L, Manci A, Neal S, Soubra T, Khairi F, Trinh J, Cardenas P, Sachs JL. No disruption of rhizobial symbiosis during early stages of cowpea domestication. Evolution 2022; 76:496-511. [PMID: 35014694 DOI: 10.1111/evo.14424] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/15/2021] [Accepted: 11/26/2021] [Indexed: 11/26/2022]
Abstract
Modern agriculture intensely selects aboveground plant structures, while often neglecting belowground features, and evolutionary tradeoffs between these traits are predicted to disrupt host control over microbiota. Moreover, drift, inbreeding, and relaxed selection for symbiosis in crops might degrade plant mechanisms that support beneficial microbes. We studied the impact of domestication on the nitrogen fixing symbiosis between cowpea and root-nodulating Bradyrhizobium. We combined genome-wide analyses with a greenhouse inoculation study to investigate genomic diversity, heritability, and symbiosis trait variation among wild and early-domesticated cowpea genotypes. Cowpeas experienced modest decreases in genome-wide diversity during early domestication. Nonetheless, domesticated cowpeas responded efficiently to variation in symbiotic effectiveness, by forming more root nodules with nitrogen-fixing rhizobia and sanctioning non-fixing strains. Domesticated populations invested a larger proportion of host tissues into root nodules than wild cowpeas. Unlike soybean and wheat, cowpea showed no compelling evidence for degradation of symbiosis during domestication. Domesticated cowpeas experienced a less severe bottleneck than these crops and the low nutrient conditions in Africa where cowpea landraces were developed likely favored plant genotypes that gain substantial benefits from symbiosis. Breeders have largely neglected symbiosis traits, but artificial selection for improved plant responses to microbiota could increase plant performance and sustainability. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- G S Ortiz-Barbosa
- Department of Microbiology & Plant Pathology, University of California, Riverside, CA
| | - L Torres-Martínez
- Department of Evolution Ecology and Organismal Biology, University of California, Riverside, CA
| | - A Manci
- Department of Microbiology & Plant Pathology, University of California, Riverside, CA
| | - S Neal
- Department of Evolution Ecology and Organismal Biology, University of California, Riverside, CA
| | - T Soubra
- Department of Evolution Ecology and Organismal Biology, University of California, Riverside, CA
| | - F Khairi
- Department of Evolution Ecology and Organismal Biology, University of California, Riverside, CA
| | - J Trinh
- Department of Evolution Ecology and Organismal Biology, University of California, Riverside, CA
| | - P Cardenas
- Department of Evolution Ecology and Organismal Biology, University of California, Riverside, CA
| | - J L Sachs
- Department of Microbiology & Plant Pathology, University of California, Riverside, CA.,Department of Evolution Ecology and Organismal Biology, University of California, Riverside, CA.,Institute of Integrative Genome Biology, University of California, Riverside, CA
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