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Chua FJD, Kim SY, Hill E, Cai JW, Lee WL, Gu X, Afri Affandi SA, Kwok WCG, Ng W, Leifels M, Armas F, Chandra F, Chen H, Alm EJ, Tay M, Wong CCJ, Ng LC, Wuertz S, Thompson JR. Co-incidence of BA.1 and BA.2 at the start of Singapore's Omicron wave revealed by Community and University Campus wastewater surveillance. Sci Total Environ 2023; 875:162611. [PMID: 36871716 DOI: 10.1016/j.scitotenv.2023.162611] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 02/28/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
Wastewater surveillance (WWS) has been globally recognised to be a useful tool in quantifying SARS-CoV-2 RNA at the community and residential levels without biases associated with case-reporting. The emergence of variants of concern (VOCs) have given rise to an unprecedented number of infections even though populations are increasingly vaccinated. This is because VOCs have been reported to possess higher transmissibility and can evade host immune responses. The B.1.1.529 lineage (Omicron) has severely disrupted global plans to return to normalcy. In this study, we developed an allele-specific (AS) RT-qPCR assay which simultaneously targets the stretch of deletions and mutations in the spike protein from position 24-27 for quantitative detection of Omicron BA.2. Together with previous assays that detect mutations associated with Omicron BA.1 (deletion at position 69 and 70) and all Omicron (mutation at position 493 and 498), we report the validation and time series of these assays from September 2021 to May 2022 using influent samples from two wastewater treatment plants and across four University campus sites in Singapore. Viral RNA concentrations at the treatment plants corroborate with locally reported clinical cases, AS RT-qPCR assays revealed co-incidence of Omicron BA.1 and BA.2 on 12 January 2022, almost two months after initial BA.1 detection in South Africa and Botswana. BA.2 became the dominant variant by the end of January 2022 and completely displaced BA.1 by mid-March 2022. University campus sites were similarly positive for BA.1 and/or BA.2 in the same week as first detection at the treatment plants, where BA.2 became rapidly established as the dominant lineage within three weeks. These results corroborate clinical incidence of the Omicron lineages in Singapore and indicate minimal silent circulation prior to January 2022. The subsequent simultaneous spread of both variant lineages followed strategic relaxation of safe management measures upon meeting nationwide vaccination goals.
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Affiliation(s)
- Feng Jun Desmond Chua
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore
| | - Se Yeon Kim
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore
| | - Eric Hill
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore
| | - Jia Wei Cai
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore
| | - Wei Lin Lee
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), 138602, Singapore
| | - Xiaoqiong Gu
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), 138602, Singapore
| | - Siti Aisyah Afri Affandi
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore
| | - Wee Chiew Germaine Kwok
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore
| | - Weijie Ng
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore
| | - Mats Leifels
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore
| | - Federica Armas
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), 138602, Singapore
| | - Franciscus Chandra
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), 138602, Singapore
| | - Hongjie Chen
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), 138602, Singapore
| | - Eric J Alm
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), 138602, Singapore; Centre for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Martin Tay
- Environmental Health Institute, National Environmental Agency, 138667, Singapore
| | | | - Lee Ching Ng
- Environmental Health Institute, National Environmental Agency, 138667, Singapore; School of Biological Sciences, Nanyang Technological University, 637551, Singapore
| | - Stefan Wuertz
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore; School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore
| | - Janelle R Thompson
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 637551, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), 138602, Singapore; Asian School of the Environment, Nanyang Technological University, 637459, Singapore.
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Armas F, Chandra F, Lee WL, Gu X, Chen H, Xiao A, Leifels M, Wuertz S, Alm EJ, Thompson J. Contextualizing Wastewater-Based surveillance in the COVID-19 vaccination era. Environ Int 2023; 171:107718. [PMID: 36584425 PMCID: PMC9783150 DOI: 10.1016/j.envint.2022.107718] [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] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 12/16/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
SARS-CoV-2 wastewater-based surveillance (WBS) offers a tool for cost-effective oversight of a population's infections. In the past two years, WBS has proven to be crucial for managing the pandemic across different geographical regions. However, the changing context of the pandemic due to high levels of COVID-19 vaccination warrants a closer examination of its implication towards SARS-CoV-2 WBS. Two main questions were raised: 1) Does vaccination cause shedding of viral signatures without infection? 2) Does vaccination affect the relationship between wastewater and clinical data? To answer, we review historical reports of shedding from viral vaccines in use prior to the COVID-19 pandemic including for polio, rotavirus, influenza and measles infection and provide a perspective on the implications of different COVID-19 vaccination strategies with regard to the potential shedding of viral signatures into the sewershed. Additionally, we reviewed studies that looked into the relationship between wastewater and clinical data and how vaccination campaigns could have affected the relationship. Finally, analyzing wastewater and clinical data from the Netherlands, we observed changes in the relationship concomitant with increasing vaccination coverage and switches in dominant variants of concern. First, that no vaccine-derived shedding is expected from the current commercial pipeline of COVID-19 vaccines that may confound interpretation of WBS data. Secondly, that breakthrough infections from vaccinated individuals contribute significantly to wastewater signals and must be interpreted in light of the changing dynamics of shedding from new variants of concern.
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Affiliation(s)
- Federica Armas
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Franciscus Chandra
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Wei Lin Lee
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Xiaoqiong Gu
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Hongjie Chen
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Amy Xiao
- Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | - Mats Leifels
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
| | - Stefan Wuertz
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
| | - Eric J Alm
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; Asian School of the Environment, Nanyang Technological University, Singapore.
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Lee WL, Gu X, Armas F, Leifels M, Wu F, Chandra F, Chua FJD, Syenina A, Chen H, Cheng D, Ooi EE, Wuertz S, Alm EJ, Thompson J. Monitoring human arboviral diseases through wastewater surveillance: Challenges, progress and future opportunities. Water Res 2022; 223:118904. [PMID: 36007397 DOI: 10.1016/j.watres.2022.118904] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 07/19/2022] [Accepted: 07/23/2022] [Indexed: 05/21/2023]
Abstract
Arboviral diseases are caused by a group of viruses spread by the bite of infected arthropods. Amongst these, dengue, Zika, west nile fever and yellow fever cause the greatest economic and social impact. Arboviral epidemics have increased in frequency, magnitude and geographical extent over the past decades and are expected to continue increasing with climate change and expanding urbanisation. Arboviral prevalence is largely underestimated, as most infections are asymptomatic, nevertheless existing surveillance systems are based on passive reporting of loosely defined clinical syndromes with infrequent laboratory confirmation. Wastewater-based surveillance (WBS), which has been demonstrated to be useful for monitoring diseases with significant asymptomatic populations including COVID19 and polio, could be a useful complement to arboviral surveillance. We review the current state of knowledge and identify key factors that affect the feasibility of monitoring arboviral diseases by WBS to include viral shedding loads by infected persons, the persistence of shed arboviruses and the efficiency of their recovery from sewage. We provide a simple model on the volume of wastewater that needs to be processed for detection of arboviruses, in face of lower arboviral shedding rates. In all, this review serves to reflect on the key challenges that need to be addressed and overcome for successful implementation of arboviral WBS.
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Affiliation(s)
- Wei Lin Lee
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Xiaoqiong Gu
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Federica Armas
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Mats Leifels
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore
| | - Fuqing Wu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, Center for Infectious Disease, University of Texas School of Public Health, Houston, TX, USA
| | - Franciscus Chandra
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Feng Jun Desmond Chua
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore
| | - Ayesa Syenina
- Program in Emerging Infectious Diseases, Duke-NUS Medical School, Singapore 169857, Singapore; Viral Research and Experimental Medicine Centre (ViREMiCS), SingHealth Duke-NUS Academic Medical Centre, Singapore 169856, Singapore
| | - Hongjie Chen
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Dan Cheng
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore
| | - Eng Eong Ooi
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore; Program in Emerging Infectious Diseases, Duke-NUS Medical School, Singapore 169857, Singapore; Viral Research and Experimental Medicine Centre (ViREMiCS), SingHealth Duke-NUS Academic Medical Centre, Singapore 169856, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore
| | - Stefan Wuertz
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore; School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Eric J Alm
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore; Asian School of the Environment, Nanyang Technological University, Singapore 637459, Singapore.
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Lee WL, Armas F, Guarneri F, Gu X, Formenti N, Wu F, Chandra F, Parisio G, Chen H, Xiao A, Romeo C, Scali F, Tonni M, Leifels M, Chua FJD, Kwok GW, Tay JY, Pasquali P, Thompson J, Alborali GL, Alm EJ. Rapid displacement of SARS-CoV-2 variant Delta by Omicron revealed by allele-specific PCR in wastewater. Water Res 2022; 221:118809. [PMID: 35841797 PMCID: PMC9250349 DOI: 10.1016/j.watres.2022.118809] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/18/2022] [Accepted: 07/01/2022] [Indexed: 05/06/2023]
Abstract
On November 26, 2021, the B.1.1.529 COVID-19 variant was classified as the Omicron variant of concern (VOC). Reports of higher transmissibility and potential immune evasion triggered flight bans and heightened health control measures across the world to stem its distribution. Wastewater-based surveillance has demonstrated to be a useful complement for clinical community-based tracking of SARS-CoV-2 variants. Using design principles of our previous assays that detect SARS-CoV-2 variants (Alpha and Delta), we developed an allele-specific RT-qPCR assay which simultaneously targets the stretch of mutations from Q493R to Q498R for quantitative detection of the Omicron variant in wastewater. We report their validation against 10-month longitudinal samples from the influent of a wastewater treatment plant in Italy. SARS-CoV-2 RNA concentrations and variant frequencies in wastewater determined using these variant assays agree with clinical cases, revealing rapid displacement of the Delta variant by the Omicron variant within three weeks. These variant trends, when mapped against vaccination rates, support clinical studies that found the rapid emergence of SARS-CoV-2 Omicron variant being associated with an infection advantage over Delta in vaccinated persons. These data reinforce the versatility, utility and accuracy of these open-sourced methods using allele-specific RT-qPCR for tracking the dynamics of variant displacement in communities through wastewater for informed public health responses.
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Affiliation(s)
- Wei Lin Lee
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Federica Armas
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Flavia Guarneri
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "Bruno Ubertini" (IZSLER), Italy
| | - Xiaoqiong Gu
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Nicoletta Formenti
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "Bruno Ubertini" (IZSLER), Italy
| | - Fuqing Wu
- Center for Infectious Disease, Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas School of Public Health, Houston, TX, USA
| | - Franciscus Chandra
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Giovanni Parisio
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "Bruno Ubertini" (IZSLER), Italy
| | - Hongjie Chen
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA; Department of Biological Engineering, Massachusetts Institute of Technology, USA
| | - Claudia Romeo
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "Bruno Ubertini" (IZSLER), Italy
| | - Federico Scali
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "Bruno Ubertini" (IZSLER), Italy
| | - Matteo Tonni
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "Bruno Ubertini" (IZSLER), Italy
| | - Mats Leifels
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
| | - Feng Jun Desmond Chua
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
| | - Germaine Wc Kwok
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
| | - Joey Yr Tay
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Paolo Pasquali
- Dipartimento di Sicurezza Alimentare, Nutrizione e Sanità Pubblica Veterinaria, Istituto Superiore di Sanità, Italy
| | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; Asian School of the Environment, Nanyang Technological University, Singapore.
| | - Giovanni Loris Alborali
- Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "Bruno Ubertini" (IZSLER), Italy
| | - Eric J Alm
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA; Department of Biological Engineering, Massachusetts Institute of Technology, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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5
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Wu F, Lee WL, Chen H, Gu X, Chandra F, Armas F, Xiao A, Leifels M, Rhode SF, Wuertz S, Thompson J, Alm EJ. Making waves: Wastewater surveillance of SARS-CoV-2 in an endemic future. Water Res 2022; 219:118535. [PMID: 35605390 PMCID: PMC9062764 DOI: 10.1016/j.watres.2022.118535] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 05/28/2023]
Abstract
Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor the emergence and spread of SARS-CoV-2 infections in populations during the COVID-19 pandemic. Coincident with the global vaccination efforts, the world is also enduring new waves of SARS-CoV-2 variants. Reinfections and vaccine breakthroughs suggest an endemic future where SARS-CoV-2 continues to persist in the general population. In this treatise, we aim to explore the future roles of wastewater surveillance. Practically, WBS serves as a relatively affordable and non-invasive tool for mass surveillance of SARS-CoV-2 infection while minimizing privacy concerns, attributes that make it extremely suited for its long-term usage. In an endemic future, the utility of WBS will include 1) monitoring the trend of viral loads of targets in wastewater for quantitative estimate of changes in disease incidence; 2) sampling upstream for pinpointing infections in neighborhoods and at the building level; 3) integrating wastewater and clinical surveillance for cost-efficient population surveillance; and 4) genome sequencing wastewater samples to track circulating and emerging variants in the population. We further discuss the challenges and future developments of WBS to reduce inconsistencies in wastewater data worldwide, improve its epidemiological inference, and advance viral tracking and discovery as a preparation for the next viral pandemic.
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Affiliation(s)
- Fuqing Wu
- Center for Infectious Disease, Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas School of Public Health, Houston, TX, USA.
| | - Wei Lin Lee
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Hongjie Chen
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Xiaoqiong Gu
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Franciscus Chandra
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Federica Armas
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Amy Xiao
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mats Leifels
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
| | | | - Stefan Wuertz
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
| | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; Asian School of the Environment, Nanyang Technological University, Singapore
| | - Eric J Alm
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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6
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Xiao A, Wu F, Bushman M, Zhang J, Imakaev M, Chai PR, Duvallet C, Endo N, Erickson TB, Armas F, Arnold B, Chen H, Chandra F, Ghaeli N, Gu X, Hanage WP, Lee WL, Matus M, McElroy KA, Moniz K, Rhode SF, Thompson J, Alm EJ. Metrics to relate COVID-19 wastewater data to clinical testing dynamics. Water Res 2022; 212:118070. [PMID: 35101695 PMCID: PMC8758950 DOI: 10.1016/j.watres.2022.118070] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 11/29/2021] [Accepted: 01/11/2022] [Indexed: 05/02/2023]
Abstract
Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. 24-hour composite wastewater samples were collected from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and SARS-CoV-2 RNA concentrations were measured using RT-qPCR. The relationship between wastewater copy numbers of SARS-CoV-2 gene fragments and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater copy numbers of SARS-CoV-2 gene fragments and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. The WC ratio increases after key events, providing insight into the balance between disease spread and public health response. Time lag and transfer function analysis showed that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity, which allows for more timely case detection and reporting. These three metrics could help further integrate wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.
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Affiliation(s)
- Amy Xiao
- Department of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA
| | - Fuqing Wu
- Department of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA
| | - Mary Bushman
- Harvard T.H. Chan School of Public Health, Harvard University USA
| | - Jianbo Zhang
- Department of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA
| | | | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School USA; The Fenway Institute, Fenway Health, Boston, MA USA; The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology USA; Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute USA
| | | | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School USA; Harvard Humanitarian Initiative, Harvard University USA
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Brian Arnold
- Department of Computer Science, Princeton University USA; Center for Statistics and Machine Learning, Princeton University USA
| | - Hongjie Chen
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Franciscus Chandra
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - William P Hanage
- Harvard T.H. Chan School of Public Health, Harvard University USA
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | | | - Katya Moniz
- Department of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA
| | | | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; Asian School of the Environment, Nanyang Technological University, Singapore
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA; Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Broad Institute of MIT and Harvard, Cambridge, MA USA.
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7
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Gu X, Sim JX, Lee WL, Cui L, Chan YF, Chang ED, Teh YE, Zhang AN, Armas F, Chandra F, Chen H, Zhao S, Lee Z, Thompson JR, Ooi EE, Low JG, Alm EJ, Kalimuddin S. Gut Ruminococcaceae levels at baseline correlate with risk of antibiotic-associated diarrhea. iScience 2022; 25:103644. [PMID: 35005566 PMCID: PMC8718891 DOI: 10.1016/j.isci.2021.103644] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/19/2021] [Accepted: 12/13/2021] [Indexed: 12/11/2022] Open
Abstract
Antibiotic-associated diarrhea (AAD) affects a significant proportion of patients receiving antibiotics. We sought to understand if differences in the gut microbiome would influence the development of AAD. We administered a 3-day course of amoxicillin-clavulanate to 30 healthy adult volunteers, and analyzed their stool microbiome, using 16S rRNA gene sequencing, at baseline and up to 4 weeks post antibiotic administration. Lower levels of gut Ruminococcaceae were significantly and consistently observed from baseline until day 7 in participants who developed AAD. Overall, participants who developed AAD experienced a greater decrease in microbial diversity. The probability of AAD could be predicted based on qPCR-derived levels of Faecalibacterium prausnitzii at baseline. Our findings suggest that a lack of gut Ruminococcaceae influences development of AAD. Quantification of F. prausnitzii in stool prior to antibiotic administration may help identify patients at risk of AAD, and aid clinicians in devising individualized treatment regimens to minimize such adverse effects.
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Affiliation(s)
- Xiaoqiong Gu
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 1 Create Way, Singapore 138602, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Jean X.Y. Sim
- Department of Infectious Diseases, Singapore General Hospital, Academia Level 3, 20 College Road, Singapore 169856, Singapore
| | - Wei Lin Lee
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 1 Create Way, Singapore 138602, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Liang Cui
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 1 Create Way, Singapore 138602, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Yvonne F.Z. Chan
- Department of Infectious Diseases, Singapore General Hospital, Academia Level 3, 20 College Road, Singapore 169856, Singapore
| | - Ega Danu Chang
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 1 Create Way, Singapore 138602, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Yii Ean Teh
- Department of Infectious Diseases, Singapore General Hospital, Academia Level 3, 20 College Road, Singapore 169856, Singapore
| | - An-Ni Zhang
- Department of Biological Engineering, Massachusetts Institute of Technology, 21 Ames Street, Cambridge, MA 02142, USA
| | - Federica Armas
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 1 Create Way, Singapore 138602, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Franciscus Chandra
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 1 Create Way, Singapore 138602, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Hongjie Chen
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 1 Create Way, Singapore 138602, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Shijie Zhao
- Department of Biological Engineering, Massachusetts Institute of Technology, 21 Ames Street, Cambridge, MA 02142, USA
| | - Zhanyi Lee
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 1 Create Way, Singapore 138602, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Janelle R. Thompson
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore
- Asian School of the Environment, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
| | - Eng Eong Ooi
- Program in Emerging Infectious Diseases, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore
- Viral Research and Experimental Medicine Center, SingHealth Duke-NUS Academic Medical Centre (ViREMiCS), 20 College Road, Singapore 169856, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, 12 Science Drive 2, Singapore 117549, Singapore
| | - Jenny G. Low
- Department of Infectious Diseases, Singapore General Hospital, Academia Level 3, 20 College Road, Singapore 169856, Singapore
- Program in Emerging Infectious Diseases, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore
- Viral Research and Experimental Medicine Center, SingHealth Duke-NUS Academic Medical Centre (ViREMiCS), 20 College Road, Singapore 169856, Singapore
| | - Eric J. Alm
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, 1 Create Way, Singapore 138602, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, 21 Ames Street, Cambridge, MA 02142, USA
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Building E25-321, Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Shirin Kalimuddin
- Department of Infectious Diseases, Singapore General Hospital, Academia Level 3, 20 College Road, Singapore 169856, Singapore
- Program in Emerging Infectious Diseases, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore
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8
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Wu F, Xiao A, Zhang J, Moniz K, Endo N, Armas F, Bonneau R, Brown MA, Bushman M, Chai PR, Duvallet C, Erickson TB, Foppe K, Ghaeli N, Gu X, Hanage WP, Huang KH, Lee WL, Matus M, McElroy KA, Nagler J, Rhode SF, Santillana M, Tucker JA, Wuertz S, Zhao S, Thompson J, Alm EJ. SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases. Sci Total Environ 2022; 805:150121. [PMID: 34534872 PMCID: PMC8416286 DOI: 10.1016/j.scitotenv.2021.150121] [Citation(s) in RCA: 135] [Impact Index Per Article: 67.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 05/18/2023]
Abstract
Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we quantify the SARS-CoV-2 concentration and track its dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral load as a convolution of back-dated new clinical cases with the average population-level viral shedding function. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. This finding suggests that SARS-CoV-2 concentrations in wastewater may be primarily driven by viral shedding early in infection. This work shows that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and infer early viral shedding dynamics for newly infected individuals, which are difficult to capture in clinical investigations.
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Affiliation(s)
- Fuqing Wu
- Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA
| | - Amy Xiao
- Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA
| | - Jianbo Zhang
- Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA
| | - Katya Moniz
- Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA
| | | | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Richard Bonneau
- Center for Data Science NYU, Center for Social Media and Politics, New York University, USA
| | - Megan A Brown
- Center for Data Science NYU, Center for Social Media and Politics, New York University, USA
| | - Mary Bushman
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, USA; The Fenway Institute, Fenway Health, Boston, MA, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, USA; Harvard Humanitarian Initiative, Harvard University, USA
| | | | | | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | | | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | | | - Jonathan Nagler
- Center for Data Science NYU, Center for Social Media and Politics, New York University, USA
| | - Steven F Rhode
- Massachusetts Water Resources Authority, Boston, MA, USA
| | - Mauricio Santillana
- Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | - Joshua A Tucker
- Center for Data Science NYU, Center for Social Media and Politics, New York University, USA
| | - Stefan Wuertz
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; School of Civil and Environmental Enginering, Nanyang Technological University, Singapore
| | - Shijie Zhao
- Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA
| | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; Asian School of the Environment, Nanyang Technological University, Singapore
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA; Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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9
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Wu F, Xiao A, Zhang J, Moniz K, Endo N, Armas F, Bushman M, Chai PR, Duvallet C, Erickson TB, Foppe K, Ghaeli N, Gu X, Hanage WP, Huang KH, Lee WL, McElroy KA, Rhode SF, Matus M, Wuertz S, Thompson J, Alm EJ. Wastewater surveillance of SARS-CoV-2 across 40 U.S. states from February to June 2020. Water Res 2021; 202:117400. [PMID: 34274898 PMCID: PMC8249441 DOI: 10.1016/j.watres.2021.117400] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 06/11/2021] [Accepted: 06/28/2021] [Indexed: 05/18/2023]
Abstract
Wastewater-based disease surveillance is a promising approach for monitoring community outbreaks. Here we describe a nationwide campaign to monitor SARS-CoV-2 in the wastewater of 159 counties in 40 U.S. states, covering 13% of the U.S. population from February 18 to June 2, 2020. Out of 1,751 total samples analyzed, 846 samples were positive for SARS-CoV-2 RNA, with overall viral concentrations declining from April to May. Wastewater viral titers were consistent with, and appeared to precede, clinical COVID-19 surveillance indicators, including daily new cases. Wastewater surveillance had a high detection rate (>80%) of SARS-CoV-2 when the daily incidence exceeded 13 per 100,000 people. Detection rates were positively associated with wastewater treatment plant catchment size. To our knowledge, this work represents the largest-scale wastewater-based SARS-CoV-2 monitoring campaign to date, encompassing a wide diversity of wastewater treatment facilities and geographic locations. Our findings demonstrate that a national wastewater-based approach to disease surveillance may be feasible and effective.
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Affiliation(s)
- Fuqing Wu
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Amy Xiao
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jianbo Zhang
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Katya Moniz
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Mary Bushman
- Harvard T.H. Chan School of Public Health, Harvard University, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, USA; The Fenway Institute, Fenway Health, Boston, MA, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, USA; The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, USA; Harvard Humanitarian Initiative, Harvard University
| | | | | | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - William P Hanage
- Harvard T.H. Chan School of Public Health, Harvard University, USA
| | | | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | - Steven F Rhode
- Massachusetts Water Resources Authority, Boston, MA, USA
| | | | - Stefan Wuertz
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; Civil and Environmental Engineering, Nanyang Technological University, Singapore
| | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; Asian School of the Environment, Nanyang Technological University, Singapore
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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10
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Armas F, Di Stasi A, Mardirossian M, Romani AA, Benincasa M, Scocchi M. Effects of Lipidation on a Proline-Rich Antibacterial Peptide. Int J Mol Sci 2021; 22:7959. [PMID: 34360723 PMCID: PMC8347091 DOI: 10.3390/ijms22157959] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 07/21/2021] [Accepted: 07/22/2021] [Indexed: 01/04/2023] Open
Abstract
The emergence of multidrug-resistant bacteria is a worldwide health problem. Antimicrobial peptides have been recognized as potential alternatives to conventional antibiotics, but still require optimization. The proline-rich antimicrobial peptide Bac7(1-16) is active against only a limited number of Gram-negative bacteria. It kills bacteria by inhibiting protein synthesis after its internalization, which is mainly supported by the bacterial transporter SbmA. In this study, we tested two different lipidated forms of Bac7(1-16) with the aim of extending its activity against those bacterial species that lack SbmA. We linked a C12-alkyl chain or an ultrashort cationic lipopeptide Lp-I to the C-terminus of Bac7(1-16). Both the lipidated Bac-C12 and Bac-Lp-I forms acquired activity at low micromolar MIC values against several Gram-positive and Gram-negative bacteria. Moreover, unlike Bac7(1-16), Bac-C12, and Bac-Lp-I did not select resistant mutants in E. coli after 14 times of exposure to sub-MIC concentrations of the respective peptide. We demonstrated that the extended spectrum of activity and absence of de novo resistance are likely related to the acquired capability of the peptides to permeabilize cell membranes. These results indicate that C-terminal lipidation of a short proline-rich peptide profoundly alters its function and mode of action and provides useful insights into the design of novel broad-spectrum antibacterial agents.
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Affiliation(s)
- Federica Armas
- Department of Life Sciences, University of Trieste, 34127 Trieste, Italy; (F.A.); (A.D.S.); (M.M.); (M.B.)
- Area Science Park, Padriciano, 34149 Trieste, Italy
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore
| | - Adriana Di Stasi
- Department of Life Sciences, University of Trieste, 34127 Trieste, Italy; (F.A.); (A.D.S.); (M.M.); (M.B.)
| | - Mario Mardirossian
- Department of Life Sciences, University of Trieste, 34127 Trieste, Italy; (F.A.); (A.D.S.); (M.M.); (M.B.)
- Department of Medical Sciences, University of Trieste, 34129 Trieste, Italy
| | | | - Monica Benincasa
- Department of Life Sciences, University of Trieste, 34127 Trieste, Italy; (F.A.); (A.D.S.); (M.M.); (M.B.)
| | - Marco Scocchi
- Department of Life Sciences, University of Trieste, 34127 Trieste, Italy; (F.A.); (A.D.S.); (M.M.); (M.B.)
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11
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Xiao A, Wu F, Bushman M, Zhang J, Imakaev M, Chai PR, Duvallet C, Endo N, Erickson TB, Armas F, Arnold B, Chen H, Chandra F, Ghaeli N, Gu X, Hanage WP, Lee WL, Matus M, McElroy KA, Moniz K, Rhode SF, Thompson J, Alm EJ. Metrics to relate COVID-19 wastewater data to clinical testing dynamics. medRxiv 2021:2021.06.10.21258580. [PMID: 34159339 PMCID: PMC8219106 DOI: 10.1101/2021.06.10.21258580] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. We collected 24-hour composite wastewater samples from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and measured SARS-CoV-2 RNA concentrations using RT-qPCR. We show that the relationship between wastewater viral titers and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater viral titers and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. We find that the WC ratio increases after key events, providing insight into the balance between disease spread and public health response. We also find that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity. These three metrics could complement a framework for integrating wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.
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Affiliation(s)
- Amy Xiao
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | - Fuqing Wu
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | - Mary Bushman
- Harvard T.H. Chan School of Public Health, Harvard University
| | - Jianbo Zhang
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | | | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women’s Hospital, Harvard Medical School
- The Fenway Institute, Fenway Health, Boston, MA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology
- Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute
| | | | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women’s Hospital, Harvard Medical School
- Harvard Humanitarian Initiative, Harvard University
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Brian Arnold
- Department of Computer Science, Princeton University
- Center for Statistics and Machine Learning, Princeton University
| | - Hongjie Chen
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Franciscus Chandra
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | | | - Katya Moniz
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | | | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
- Broad Institute of MIT and Harvard, Cambridge, MA
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12
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Wu F, Xiao A, Zhang J, Moniz K, Endo N, Armas F, Bushman M, Chai PR, Duvallet C, Erickson TB, Foppe K, Ghaeli N, Gu X, Hanage WP, Huang KH, Lee WL, Matus M, McElroy KA, Rhode SF, Wuertz S, Thompson J, Alm EJ. Wastewater Surveillance of SARS-CoV-2 across 40 U.S. states. medRxiv 2021:2021.03.10.21253235. [PMID: 33758888 PMCID: PMC7987047 DOI: 10.1101/2021.03.10.21253235] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Wastewater-based disease surveillance is a promising approach for monitoring community outbreaks. Here we describe a nationwide campaign to monitor SARS-CoV-2 in the wastewater of 159 counties in 40 U.S. states, covering 13% of the U.S. population from February 18 to June 2, 2020. Out of 1,751 total samples analyzed, 846 samples were positive for SARS-CoV-2 RNA, with overall viral concentrations declining from April to May. Wastewater viral titers were consistent with, and appeared to precede, clinical COVID-19 surveillance indicators, including daily new cases. Wastewater surveillance had a high detection rate (>80%) of SARS-CoV-2 when the daily incidence exceeded 13 per 100,000 people. Detection rates were positively associated with wastewater treatment plant catchment size. To our knowledge, this work represents the largest-scale wastewater-based SARS-CoV-2 monitoring campaign to date, encompassing a wide diversity of wastewater treatment facilities and geographic locations. Our findings demonstrate that a national wastewater-based approach to disease surveillance may be feasible and effective.
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Affiliation(s)
- Fuqing Wu
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | - Amy Xiao
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | - Jianbo Zhang
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | - Katya Moniz
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | | | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Mary Bushman
- Harvard T.H. Chan School of Public Health, Harvard University
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women’s Hospital, Harvard Medical School
- The Fenway Institute, Fenway Health, Boston, MA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women’s Hospital, Harvard Medical School
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology
- Harvard Humanitarian Initiative, Harvard University
| | | | | | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | | | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | | | | | - Stefan Wuertz
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Civil and Environmental Engineering, Nanyang Technological University, Singapore
| | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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13
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Mardirossian M, Sola R, Beckert B, Valencic E, Collis DWP, Borišek J, Armas F, Di Stasi A, Buchmann J, Syroegin EA, Polikanov YS, Magistrato A, Hilpert K, Wilson DN, Scocchi M. Peptide Inhibitors of Bacterial Protein Synthesis with Broad Spectrum and SbmA-Independent Bactericidal Activity against Clinical Pathogens. J Med Chem 2020; 63:9590-9602. [PMID: 32787108 DOI: 10.1021/acs.jmedchem.0c00665] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Proline-rich antimicrobial peptides (PrAMPs) are promising lead compounds for developing new antimicrobials; however, their narrow spectrum of action is limiting. PrAMPs kill bacteria binding to their ribosomes and inhibiting protein synthesis. In this study, 133 derivatives of the PrAMP Bac7(1-16) were synthesized to identify the crucial residues for ribosome inactivation and antimicrobial activity. Then, five new Bac7(1-16) derivatives were conceived and characterized by antibacterial and membrane permeabilization assays, X-ray crystallography, and molecular dynamics simulations. Some derivatives displayed broad spectrum activity, encompassing Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumanii, Pseudomonas aeruginosa, and Staphylococcus aureus. Two peptides out of five acquired a weak membrane-perturbing activity while maintaining the ability to inhibit protein synthesis. These derivatives became independent of the SbmA transporter, commonly used by native PrAMPs, suggesting that they obtained a novel route to enter bacterial cells. PrAMP-derived compounds could become new-generation antimicrobials to combat antibiotic-resistant pathogens.
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Affiliation(s)
- Mario Mardirossian
- Department of Medical Sciences, University of Trieste, 34125 Trieste, Italy
| | - Riccardo Sola
- Department of Life Sciences, University of Trieste, 34127 Trieste, Italy
| | - Bertrand Beckert
- Institut für Biochemie und Molekularbiologie, University of Hamburg, 20146 Hamburg, Germany
| | - Erica Valencic
- Institute for Maternal and Child Health-IRCCS "Burlo Garofolo", 30137 Trieste, Italy
| | | | | | - Federica Armas
- Department of Life Sciences, University of Trieste, 34127 Trieste, Italy
| | - Adriana Di Stasi
- Department of Life Sciences, University of Trieste, 34127 Trieste, Italy
| | - Jan Buchmann
- Institut für Biochemie und Molekularbiologie, University of Hamburg, 20146 Hamburg, Germany.,Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois 60607, United States
| | - Egor A Syroegin
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois 60607, United States
| | - Yury S Polikanov
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois 60607, United States.,Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, Illinois 60607, United States
| | | | - Kai Hilpert
- Institute of Infection and Immunology, St. George's, University of London, SW 17 0RE London, U.K
| | - Daniel N Wilson
- Institut für Biochemie und Molekularbiologie, University of Hamburg, 20146 Hamburg, Germany
| | - Marco Scocchi
- Department of Life Sciences, University of Trieste, 34127 Trieste, Italy
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14
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Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, Kauffman K, Hanage W, Matus M, Ghaeli N, Endo N, Duvallet C, Poyet M, Moniz K, Washburne AD, Erickson TB, Chai PR, Thompson J, Alm EJ. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases. mSystems 2020. [PMID: 32694130 DOI: 10.1101/2020.04.%2005.20051540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/24/2023] Open
Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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Affiliation(s)
- Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Kathryn Kauffman
- University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | | | - Mathilde Poyet
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Katya Moniz
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Humanitarian Institute, Cambridge, Massachusetts, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Fenway Institute, Boston, Massachusetts, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Janelle Thompson
- Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Eric J Alm
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Campus for Research Excellence and Technological Enterprise, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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15
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Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, Kauffman K, Hanage W, Matus M, Ghaeli N, Endo N, Duvallet C, Poyet M, Moniz K, Washburne AD, Erickson TB, Chai PR, Thompson J, Alm EJ. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases. mSystems 2020. [PMID: 32694130 DOI: 10.1101/2020.06.15.20117747v2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2023] Open
Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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Affiliation(s)
- Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Kathryn Kauffman
- University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | | | - Mathilde Poyet
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Katya Moniz
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Humanitarian Institute, Cambridge, Massachusetts, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Fenway Institute, Boston, Massachusetts, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Janelle Thompson
- Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Eric J Alm
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Campus for Research Excellence and Technological Enterprise, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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16
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Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, Kauffman K, Hanage W, Matus M, Ghaeli N, Endo N, Duvallet C, Poyet M, Moniz K, Washburne AD, Erickson TB, Chai PR, Thompson J, Alm EJ. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases. mSystems 2020. [PMID: 32660974 DOI: 10.1016/j.solener.2019.02.027] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [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] [Indexed: 05/03/2023] Open
Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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Affiliation(s)
- Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Kathryn Kauffman
- University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | | | - Mathilde Poyet
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Katya Moniz
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Humanitarian Institute, Cambridge, Massachusetts, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Fenway Institute, Boston, Massachusetts, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Janelle Thompson
- Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Eric J Alm
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Campus for Research Excellence and Technological Enterprise, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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17
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Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, Kauffman K, Hanage W, Matus M, Ghaeli N, Endo N, Duvallet C, Poyet M, Moniz K, Washburne AD, Erickson TB, Chai PR, Thompson J, Alm EJ. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases. mSystems 2020. [PMID: 32694130 DOI: 10.1101/2020.04.05.20051540] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [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] [Indexed: 05/12/2023] Open
Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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Affiliation(s)
- Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Kathryn Kauffman
- University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | | | - Mathilde Poyet
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Katya Moniz
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Humanitarian Institute, Cambridge, Massachusetts, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Fenway Institute, Boston, Massachusetts, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Janelle Thompson
- Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Eric J Alm
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Campus for Research Excellence and Technological Enterprise, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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18
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Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, Kauffman K, Hanage W, Matus M, Ghaeli N, Endo N, Duvallet C, Poyet M, Moniz K, Washburne AD, Erickson TB, Chai PR, Thompson J, Alm EJ. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases. mSystems 2020. [PMID: 32694130 DOI: 10.1128/2fmsystems.00614-20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023] Open
Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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Affiliation(s)
- Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Kathryn Kauffman
- University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | | | - Mathilde Poyet
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Katya Moniz
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Humanitarian Institute, Cambridge, Massachusetts, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Fenway Institute, Boston, Massachusetts, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Janelle Thompson
- Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Eric J Alm
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Campus for Research Excellence and Technological Enterprise, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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19
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Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, Kauffman K, Hanage W, Matus M, Ghaeli N, Endo N, Duvallet C, Poyet M, Moniz K, Washburne AD, Erickson TB, Chai PR, Thompson J, Alm EJ. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases. mSystems 2020. [PMID: 32694130 DOI: 10.1101/2020.04.05.200515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023] Open
Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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Affiliation(s)
- Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Kathryn Kauffman
- University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | | | - Mathilde Poyet
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Katya Moniz
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Humanitarian Institute, Cambridge, Massachusetts, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Fenway Institute, Boston, Massachusetts, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Janelle Thompson
- Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Eric J Alm
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Campus for Research Excellence and Technological Enterprise, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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20
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Wu F, Xiao A, Zhang J, Moniz K, Endo N, Armas F, Bonneau R, Brown MA, Bushman M, Chai PR, Duvallet C, Erickson TB, Foppe K, Ghaeli N, Gu X, Hanage WP, Huang KH, Lee WL, Matus M, McElroy KA, Nagler J, Rhode SF, Santillana M, Tucker JA, Wuertz S, Zhao S, Thompson J, Alm EJ. SARS-CoV-2 titers in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases. medRxiv 2020:2020.06.15.20117747. [PMID: 32607521 PMCID: PMC7325186 DOI: 10.1101/2020.06.15.20117747] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we use longitudinal wastewater analysis to track SARS-CoV-2 dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. Viral titers in wastewater increased exponentially from mid-March to mid-April, after which they began to decline. Viral titers in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral titers as a convolution of back-dated new clinical cases with the viral shedding function of an individual. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. Finally, we found that wastewater viral titers at the neighborhood level correlate better with demographic variables than with population size. This work suggests that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and may shed light on infection characteristics that are difficult to capture in clinical investigations, such as early viral shedding dynamics.
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Affiliation(s)
- Fuqing Wu
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | - Amy Xiao
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | - Jianbo Zhang
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | - Katya Moniz
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | | | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Richard Bonneau
- Center for Data Science NYU, Center for Social Media and Politics, New York University
| | - Megan A Brown
- Center for Data Science NYU, Center for Social Media and Politics, New York University
| | - Mary Bushman
- Harvard T.H. Chan School of Public Health, Harvard University
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women’s Hospital, Harvard Medical School
- The Fenway Institute, Fenway Health, Boston, MA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women’s Hospital, Harvard Medical School
- Harvard Humanitarian Initiative, Harvard University
| | | | | | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | | | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | | | - Jonathan Nagler
- Center for Data Science NYU, Center for Social Media and Politics, New York University
| | | | - Mauricio Santillana
- Harvard T.H. Chan School of Public Health, Harvard University
- Department of Pediatrics, Harvard Medical School. Boston, MA
- Computational Health Informatics Program. Boston Children’s Hospital, Boston, MA
| | - Joshua A Tucker
- Center for Data Science NYU, Center for Social Media and Politics, New York University
| | - Stefan Wuertz
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Civil and Environmental Engineering, Nanyang Technological University, Singapore
| | - Shijie Zhao
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
| | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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21
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Mardirossian M, Sola R, Beckert B, Collis DWP, Di Stasi A, Armas F, Hilpert K, Wilson DN, Scocchi M. Proline-Rich Peptides with Improved Antimicrobial Activity against E. coli, K. pneumoniae, and A. baumannii. ChemMedChem 2019; 14:2025-2033. [PMID: 31692278 PMCID: PMC6973051 DOI: 10.1002/cmdc.201900465] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 10/16/2019] [Indexed: 01/08/2023]
Abstract
Proline-rich antimicrobial peptides (PrAMPs) are promising agents to combat multi-drug resistant pathogens due to a high antimicrobial activity, yet low cytotoxicity. A library of derivatives of the PrAMP Bac5(1-17) was synthesized and screened to identify which residues are relevant for its activity. In this way, we discovered that two central motifs -PIRXP- cannot be modified, while residues at N- and C- termini tolerated some variations. We found five Bac5(1-17) derivatives bearing 1-5 substitutions, with an increased number of arginine and/or tryptophan residues, exhibiting improved antimicrobial activity and broader spectrum of activity while retaining low cytotoxicity toward eukaryotic cells. Transcription/translation and bacterial membrane permeabilization assays showed that these new derivatives still retained the ability to strongly inhibit bacterial protein synthesis, but also acquired permeabilizing activity to different degrees. These new Bac5(1-17) derivatives therefore show a dual mode of action which could hinder the selection of bacterial resistance against these molecules.
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Affiliation(s)
| | - Riccardo Sola
- Department of Life SciencesUniversity of Trieste34128TriesteItaly
| | - Bertrand Beckert
- Institute for Biochemistry and Molecular BiologyUniversity of Hamburg20146HamburgGermany
| | | | - Adriana Di Stasi
- Department of Life SciencesUniversity of Trieste34128TriesteItaly
| | - Federica Armas
- Department of Life SciencesUniversity of Trieste34128TriesteItaly
| | - Kai Hilpert
- St GeorgesUniversity of LondonLondonSW17 0REUK
| | - Daniel N. Wilson
- Institute for Biochemistry and Molecular BiologyUniversity of Hamburg20146HamburgGermany
| | - Marco Scocchi
- Department of Life SciencesUniversity of Trieste34128TriesteItaly
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22
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Mardirossian M, Sola R, Beckert B, Collis DWP, Di Stasi A, Armas F, Hilpert K, Wilson DN, Scocchi M. Front Cover: Proline‐Rich Peptides with Improved Antimicrobial Activity against
E. coli
,
K. pneumoniae
, and
A. baumannii
(ChemMedChem 24/2019). ChemMedChem 2019. [DOI: 10.1002/cmdc.201900670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
| | - Riccardo Sola
- Department of Life SciencesUniversity of Trieste 34128 Trieste Italy
| | - Bertrand Beckert
- Institute for Biochemistry and Molecular BiologyUniversity of Hamburg 20146 Hamburg Germany
| | | | - Adriana Di Stasi
- Department of Life SciencesUniversity of Trieste 34128 Trieste Italy
| | - Federica Armas
- Department of Life SciencesUniversity of Trieste 34128 Trieste Italy
| | - Kai Hilpert
- St GeorgesUniversity of London London SW17 0RE UK
| | - Daniel N. Wilson
- Institute for Biochemistry and Molecular BiologyUniversity of Hamburg 20146 Hamburg Germany
| | - Marco Scocchi
- Department of Life SciencesUniversity of Trieste 34128 Trieste Italy
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23
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Armas F, Pacor S, Ferrari E, Guida F, Pertinhez TA, Romani AA, Scocchi M, Benincasa M. Design, antimicrobial activity and mechanism of action of Arg-rich ultra-short cationic lipopeptides. PLoS One 2019; 14:e0212447. [PMID: 30789942 PMCID: PMC6383929 DOI: 10.1371/journal.pone.0212447] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 02/01/2019] [Indexed: 01/01/2023] Open
Abstract
The increasing emergence of multidrug-resistant microorganisms represents one of the greatest challenges in the clinical management of infectious diseases, and requires the development of novel antimicrobial agents. To this aim, we de novo designed a library of Arg-rich ultra-short cationic antimicrobial lipopeptides (USCLs), based on the Arg-X-Trp-Arg-NH2 peptide moiety conjugated with a fatty acid, and investigated their antibacterial potential. USCLs exhibited an excellent antimicrobial activity against clinically pathogenic microorganisms, in particular Gram-positive bacteria, including multidrug resistant strains, with MIC values ranging between 1.56 and 6.25 μg/mL. The capability of the two most active molecules, Lau-RIWR-NH2 and Lau-RRIWRR-NH2, to interact with the bacterial membranes has been predicted by molecular dynamics and verified on liposomes by surface plasmon resonance. Both compounds inhibited the growth of S. aureus even at sub MIC concentrations and induced cell membranes permeabilization by producing visible cell surface alterations leading to a significant decrease in bacterial viability. Interestingly, no cytotoxic effects were evidenced for these lipopeptides up to 50–100 μg/mL in hemolysis assay, in human epidermal model and HaCaT cells, thus highlighting a good cell selectivity. These results, together with the simple composition of USCLs, make them promising lead compounds as new antimicrobials.
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Affiliation(s)
- Federica Armas
- Department of Life Sciences, University of Trieste, Trieste, Italy
- Area Science Park, Padriciano, Trieste, Italy
| | - Sabrina Pacor
- Department of Life Sciences, University of Trieste, Trieste, Italy
| | - Elena Ferrari
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Filomena Guida
- Department of Life Sciences, University of Trieste, Trieste, Italy
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Thelma A. Pertinhez
- Department of Medicine and Surgery, University of Parma, Parma, Italy
- Transfusion Medicine Unit, AUSL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | | | - Marco Scocchi
- Department of Life Sciences, University of Trieste, Trieste, Italy
| | - Monica Benincasa
- Department of Life Sciences, University of Trieste, Trieste, Italy
- * E-mail:
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24
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Djemal SE, Camperio C, Armas F, Siala M, Smaoui S, Messadi-Akrout F, Gdoura R, Marianelli C. Detection of a streptomycin-resistant Mycobacterium bovis strain through antitubercular drug susceptibility testing of Tunisian Mycobacterium tuberculosis complex isolates from cattle. BMC Vet Res 2018; 14:296. [PMID: 30268120 PMCID: PMC6162935 DOI: 10.1186/s12917-018-1623-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Accepted: 09/24/2018] [Indexed: 01/06/2023] Open
Abstract
Background A rising isolation trend of drug-resistant M. bovis from human clinical cases is documented in the literature. Here we assessed Mycobacterium tuberculosis complex isolates from cattle for drug susceptibility by the gold standard agar proportion method and a simplified resazurin microtitre assay (d-REMA). A total of 38 M. tuberculosis complex strains, including M. bovis (n = 36) and M. caprae (n = 2) isolates, from cattle in Tunisia were tested against isoniazid, rifampin, streptomycin, ethambutol, kanamycin and pyrazinamide. Results M. caprae isolates were found to be susceptible to all test drugs. All M. bovis strains were resistant to pyrazinamide, as expected. In addition, one M. bovis isolate showed high-level resistance to streptomycin (MIC > 500.0 μg/ml). Concordant results with the two methods were found. The most common target genes associated with streptomycin resistance, namely the rrs, rpsL and gidB genes, were DNA sequenced. A non-synonymous mutation at codon 43 (K43R) was found in the rpsL gene. To the best of our knowledge, this is the first report describing the isolation of a streptomycin-resistant M. bovis isolate from animal origin. Conclusions Antitubercular drug susceptibility testing of M. bovis isolates from animals should be performed in settings where bTB is endemic in order to estimate the magnitude of the risk of drug-resistant tuberculosis transmission to humans.
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Affiliation(s)
- Saif Eddine Djemal
- Department of Life Sciences, Research Laboratory of Environmental Toxicology-Microbiology and Health (LR17ES06), Faculty of Sciences, University of Sfax, Sfax, Tunisia
| | - Cristina Camperio
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161, Rome, Italy
| | - Federica Armas
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161, Rome, Italy
| | - Mariam Siala
- Department of Life Sciences, Research Laboratory of Environmental Toxicology-Microbiology and Health (LR17ES06), Faculty of Sciences, University of Sfax, Sfax, Tunisia.,Department of Biology, Preparatory Institute for Engineering Studies, University of Sfax, Sfax, Tunisia
| | - Salma Smaoui
- Department of Microbiology, Regional Hygiene Care Mycobacteriology Laboratory, Hedi-Chaker University Hospital, Sfax, Tunisia.,Department of Biology B, Faculty of Pharmacy, University of Monastir, Monastir, Tunisia.,Department of Microbiology, National Reference Laboratory of Mycobacteria, Research Unit (UR12SP18), A. Mami University Hospital of Pneumology, Ariana, Tunisia
| | - Feriele Messadi-Akrout
- Department of Biology, Preparatory Institute for Engineering Studies, University of Sfax, Sfax, Tunisia.,Department of Microbiology, Regional Hygiene Care Mycobacteriology Laboratory, Hedi-Chaker University Hospital, Sfax, Tunisia
| | - Radhouane Gdoura
- Department of Life Sciences, Research Laboratory of Environmental Toxicology-Microbiology and Health (LR17ES06), Faculty of Sciences, University of Sfax, Sfax, Tunisia
| | - Cinzia Marianelli
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161, Rome, Italy.
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25
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Mignacca SA, Dore S, Spuria L, Zanghì P, Amato B, Duprè I, Armas F, Biasibetti E, Camperio C, Lollai SA, Capucchio MT, Cannas EA, Di Marco Lo Presti V, Marianelli C. Intramammary infusion of a live culture of Lactococcus lactis in ewes to treat staphylococcal mastitis. J Med Microbiol 2017; 66:1798-1810. [PMID: 29134942 DOI: 10.1099/jmm.0.000641] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
PURPOSE Alternatives to antibiotic therapy for mastitis in ruminants are needed. We present an evaluation, in two trials, of the efficacy of an intramammary infusion of a live culture of Lactococcus lactis for the treatment of subclinical and clinical mastitis in ewes. METHODOLOGY In total, 67 animals were enrolled: 19 lactating ewes (study 1), including healthy (N=6) and coagulase-negative staphylococci (CNS)-infected ewes (N=13); and 48 lactating ewes (study 2) with either CNS mastitis (N=32), or Staphylococcus aureus mastitis (N=16), for a total of 123 mammary glands. Intramammary infusions were performed with either L. lactis or PBS for 3 (study 1) or 7 (study 2) consecutive days. Antibiotic-treated and untreated control glands were included. Milk samples for microbiology, somatic cell analysis and milk production were collected before and after treatment.Results/Key findings.L. lactis rapidly activated the mammary glands' innate immune response and initiated an inflammatory response as evidenced by the recruitment of polymorphonuclear neutrophils and increased somatic cell counts. But while leading to a transient clearance of CNS in the gland, this response caused mild to moderate clinical cases of mastitis characterized by abnormal milk secretions and udder inflammation. Moreover, S. aureus infections did not improve, and CNS infections tended to relapse. CONCLUSION Under our experimental conditions, the L. lactis treatment led to a transient clearance of the pathogen in the gland, but also caused mild to moderate clinical cases of mastitis. We believe it is still early to implement bacterial formulations as alternatives in treating mastitis in ruminants and further experimentation is needed.
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Affiliation(s)
| | - Simone Dore
- National Reference Center for Sheep and Goat Mastitis, Istituto Zooprofilattico Sperimentale della Sardegna, Sassari, Italy
| | - Liliana Spuria
- Department of Veterinary Sciences, University of Turin, Turin, Italy
| | - Pietro Zanghì
- Istituto Zooprofilattico Sperimentale della Sicilia, Barcellona Pozzo di Gotto, Italy
| | - Benedetta Amato
- Istituto Zooprofilattico Sperimentale della Sicilia, Barcellona Pozzo di Gotto, Italy
| | - Ilaria Duprè
- National Reference Center for Sheep and Goat Mastitis, Istituto Zooprofilattico Sperimentale della Sardegna, Sassari, Italy
| | - Federica Armas
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy.,Department of Science, Roma Tre University, Rome, Italy
| | - Elena Biasibetti
- Department of Veterinary Sciences, University of Turin, Turin, Italy
| | - Cristina Camperio
- Department of Veterinary Sciences, University of Turin, Turin, Italy.,Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
| | - Stefano A Lollai
- National Reference Center for Sheep and Goat Mastitis, Istituto Zooprofilattico Sperimentale della Sardegna, Sassari, Italy
| | | | - Eugenia Agnese Cannas
- National Reference Center for Sheep and Goat Mastitis, Istituto Zooprofilattico Sperimentale della Sardegna, Sassari, Italy
| | | | - Cinzia Marianelli
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
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26
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Camperio C, Armas F, Biasibetti E, Frassanito P, Giovannelli C, Spuria L, D’Agostino C, Tait S, Capucchio MT, Marianelli C. A mouse mastitis model to study the effects of the intramammary infusion of a food-grade Lactococcus lactis strain. PLoS One 2017; 12:e0184218. [PMID: 28873396 PMCID: PMC5584933 DOI: 10.1371/journal.pone.0184218] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Accepted: 08/21/2017] [Indexed: 11/26/2022] Open
Abstract
Lactococcus lactis is one of the most important microorganisms in the dairy industry and has “generally recognized as safe” (GRAS) status. L. lactis belongs to the group of lactic acid bacteria (LAB) and is encountered in a wide range of environments. Recently, the use of the intramammary infusion of a live culture of LAB has been investigated as a new antibiotic alternative for treating mastitis in dairy ruminants. Controversial results are described in literature regarding its efficacy and safety. In this study we conducted in-depth investigation of the mammary gland immune response induced by intramammary inoculum of a live culture of L. lactis LMG 7930 using the mouse mastitis model. Overnight cultures either of L. lactis (≈ 107 CFU) or of the mastitis pathogens Staphylococcus chromogenes (≈ 105 CFU) or S. aureus (≈ 102 CFU/ml) were injected into the mouse inguinal glands. A double injection, consisting of S. chromogenes first and then L. lactis, was also investigated. Bacterial recovery from the gland and inflammatory cell infiltration were assessed. L. lactis-treated and control glands were analysed for proinflammatory cytokine production. Microbiological results showed that L. lactis was able to survive in the mammary gland 24 h post infection, as were the mastitis pathogens S. chromogenes and S. aureus. L. lactis reduced S. chromogenes survival in the glands and increased its own survival ability by coexisting with the pathogen. Histology showed that L. lactis-treated glands presented variable histological features, ranging from undamaged tissue with no inflammatory cell infiltrate to severe PMN infiltrate with focal areas of tissue damage. S. aureus-treated glands showed the most severe histological grade of inflammation despite the fact that the inoculum size was the smallest. In contrast, most S. chromogenes-treated glands showed normal structures with no infiltration or lesions. Significant increases in IL-1β and TNF-α levels were also found in L. lactis-inoculated glands. The above findings seem to suggest that food-grade L. lactis at a high-inoculum dose such as an overnight culture may elicit a suppurative inflammatory response in the mammary gland, thus becoming a potential mastitis-causing pathogen. Because of the unpredictable potential of L. lactis in acting as a potential mastitis pathogen, this organism cannot be considered a safe treatment for bovine mastitis.
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Affiliation(s)
- Cristina Camperio
- Department of Food Safety and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
- Department of Veterinary Sciences, University of Turin, Turin, Italy
| | - Federica Armas
- Department of Food Safety and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
- Department of Sciences, Roma Tre University, Rome, Italy
| | - Elena Biasibetti
- Department of Veterinary Sciences, University of Turin, Turin, Italy
| | - Paolo Frassanito
- Department of Food Safety and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
| | - Carlo Giovannelli
- Department of Food Safety and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
| | - Liliana Spuria
- Department of Veterinary Sciences, University of Turin, Turin, Italy
| | - Claudia D’Agostino
- Department of Food Safety and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
| | - Sabrina Tait
- Center for Gender-Specific Medicine, Istituto Superiore di Sanità, Rome, Italy
| | | | - Cinzia Marianelli
- Department of Food Safety and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
- * E-mail:
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Armas F, Camperio C, Coltella L, Selvaggini S, Boniotti MB, Pacciarini ML, Di Marco Lo Presti V, Marianelli C. Comparison of semi-automated commercial rep-PCR fingerprinting, spoligotyping, 12-locus MIRU-VNTR typing and single nucleotide polymorphism analysis of the embB gene as molecular typing tools for Mycobacterium bovis. J Med Microbiol 2017; 66:1151-1157. [DOI: 10.1099/jmm.0.000536] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Federica Armas
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
| | - Cristina Camperio
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
| | - Luana Coltella
- Microbiology Laboratory, Bambino Gesù Paediatric Hospital, Rome, Italy
| | | | - Maria Beatrice Boniotti
- National Reference Centre for Mycobacterium Bovis Tuberculosis, Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia Romagna, Brescia, Italy
| | - Maria Lodovica Pacciarini
- National Reference Centre for Mycobacterium Bovis Tuberculosis, Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia Romagna, Brescia, Italy
| | | | - Cinzia Marianelli
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
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Armas F, Camperio C, Marianelli C. In Vitro Assessment of the Probiotic Potential of Lactococcus lactis LMG 7930 against Ruminant Mastitis-Causing Pathogens. PLoS One 2017; 12:e0169543. [PMID: 28068371 PMCID: PMC5222591 DOI: 10.1371/journal.pone.0169543] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 12/19/2016] [Indexed: 11/26/2022] Open
Abstract
Mastitis in dairy ruminants is considered to be the most expensive disease to farmers worldwide. Recently, the intramammary infusion of lactic acid bacteria has emerged as a potential new alternative to antibiotics for preventing and treating bovine mastitis. In this study we have investigated in vitro the probiotic potential of Lactococcus lactis LMG 7930, a food-grade and nisin-producing strain, against mastitis-causing pathogens. We have characterized its carbohydrate fermentation and antibiotic susceptibility profiles, cell surface properties and antimicrobial activity, as well as its capabilities to adhere to and inhibit the invasion of pathogens into the bovine mammary epithelial cell line BME-UV1d. We found that L. lactis LMG 7930 was sensitive to tested drugs, according to the EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP), and showed an improved carbohydrate fermentation capacity compared to starter strains. Moreover, the strain exhibited antagonistic properties towards many of the pathogens tested. It presented medium surface hydrophobicity, a low basic property and no electron acceptor capability. It showed low auto-aggregation and no co-aggregation abilities towards any of the tested pathogens. The strain was one of the most adhesive to bovine mammary epithelial cells among tested bacteria, but its internalisation was low. The strain did not affect significantly pathogen invasion; however, a trend to decrease internalization of some pathogens tested was observed. In conclusion, our results suggest that this strain might be a promising candidate for the development of new strategies of mastitis control in ruminants. Future investigations are needed to evaluate its safety and efficacy under field conditions.
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Affiliation(s)
- Federica Armas
- Department of Food Safety and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
- Department of Sciences, Roma Tre University, Rome, Italy
| | - Cristina Camperio
- Department of Food Safety and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
- Department of Animal Pathology, Faculty of Veterinary Medicine, University of Turin, Turin, Italy
| | - Cinzia Marianelli
- Department of Food Safety and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
- * E-mail:
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Armas F, Furlanello T, Camperio C, Trotta M, Novari G, Marianelli C. Molecular characterization and drug susceptibility profile of a Mycobacterium avium subspecies avium isolate from a dog with disseminated infection. J Med Microbiol 2016; 65:278-285. [PMID: 26758809 DOI: 10.1099/jmm.0.000221] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Mycobacterium avium-intracellulare complex (MAC) infections have been described in many mammalian species, including humans and pets. We isolated and molecularly typed the causative agent of a rare case of disseminated mycobacteriosis in a dog. We identified the pathogen as M. avium subspecies avium by sequencing the partial genes gyrB and rpsA. Considering the zoonotic potential of this infection, and in an attempt to ensure the most effective treatment for the animal, we also determined the drug susceptibility profile of the isolate to the most common drugs used to treat MAC disease in humans. The pathogen was tested in vitro against the macrolide clarithromycin, as well as against amikacin, ciprofloxacin, rifampicin, ethambutol and linezolid, by the resazurin microdilution assay. It was found to be sensitive to all tested drugs apart from ethambutol. Despite the fact that the pathogen was sensitive to the therapies administered, the dog's overall clinical status worsened and the animal died shortly after antimicrobial susceptibility results became available. Nucleotide sequencing of the embB gene, the target gene most commonly associated with ethambutol resistance, showed new missense mutations when compared to sequences available in public databases. In conclusion, we molecularly identified the MAC pathogen and determined its drug susceptibility profile in a relatively short period of time (7 days). We also characterized new genetic mutations likely to have been involved in the observed ethambutol resistance. Our results confirmed the usefulness of both the gyrB and the rpsA genes as biomarkers for an accurate identification and differentiation of MAC pathogens.
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Affiliation(s)
- Federica Armas
- Department of Food Safety and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
| | | | - Cristina Camperio
- Department of Food Safety and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
| | | | | | - Cinzia Marianelli
- Department of Food Safety and Veterinary Public Health, Istituto Superiore di Sanità, Rome, Italy
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Marianelli C, Armas F, Boniotti MB, Mazzone P, Pacciarini ML, Di Marco Lo Presti V. Multiple drug-susceptibility screening in Mycobacterium bovis: new nucleotide polymorphisms in the embB gene among ethambutol susceptible strains. Int J Infect Dis 2015; 33:39-44. [DOI: 10.1016/j.ijid.2014.12.043] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Revised: 12/19/2014] [Accepted: 12/22/2014] [Indexed: 10/24/2022] Open
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Affiliation(s)
- F Armas
- Servicio de Medicina Nuclear, Hospital Insular de Las Palmas de Gran Canaria.
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Armas F, Hernandez M, Vega V, Gutierrez I, Jimenez P, Pavcovich M, Sánchez M, Garc´ía J, Nuñez V, Murias A. P72 Sentinel node biopsy after subareolar injection in early breast cancer. Breast 2007. [DOI: 10.1016/s0960-9776(07)70137-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Miguélez M, Linares Feria M, González A, Mesa MC, Armas F, Laynez P. [Human babesiosis in a patient after splenectomy]. Med Clin (Barc) 1996; 106:427-9. [PMID: 8637296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
A case of human babesiosis in a 34-year-old male patient with previous splenectomy is reported. This is the first description of babesiosis in a splenectomized patient in Spain. The clinical picture was mainly characterized by the presence of fever and hemolysis. The findings of a reactive hemophagocytic syndrome and lymph node enlargement, the description of which is exception in this entity are of note. The therapeutic aspects are reported emphasizing the good response to treatment with clindamycin and quinine sulphate which is unusual in the splenectomized cases reported in Europe. The diagnostic tods as well as the epidemiologic implications of the description of this zoonosis in Spain are discussed.
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Affiliation(s)
- M Miguélez
- Servicios de Medicina Interna, Hospital Nuestra Señora de la Candelaria
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