49 results on '"Duvallet, C."'
Search Results
2. SARS-CoV-2 titers in wastewater are higher than expected from clinically confirmed cases
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Wu, FQ, primary, Xiao, A, additional, Zhang, JB, additional, Gu, XQ, additional, Lee, WL, additional, Kauffman, K, additional, Hanage, WP, additional, Matus, M, additional, Ghaeli, N, additional, Endo, N, additional, Duvallet, C, additional, Moniz, K, additional, Erickson, TB, additional, Chai, PR, additional, Thompson, J, additional, and Alm, EJ, additional
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- 2020
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3. (m,k)-Firm Constraints and Derived Data Management for the Qos Enhancement in Distributed Real-Time DBMS
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Salem, M. B., Bouazizi, E., Duvallet, C., Bouaziz, R., Multimedia, InfoRmation systems and Advanced Computing Laboratory (MIRACL), Faculté des Sciences Economiques et de Gestion de Sfax (FSEG Sfax), Université de Sfax - University of Sfax-Université de Sfax - University of Sfax, Equipe Réseaux d'interactions et Intelligence Collective (RI2C - LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Normandie Université (NU), and Duvallet, Claude
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Software DRTDBMS ,[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] ,QoS management ,[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB] ,[INFO]Computer Science [cs] ,[INFO] Computer Science [cs] ,Feedback control scheduling ,Derived data - Abstract
International audience; Distributed Real-Time DBMS (DRTDBMS) is a collection of RTDBMS running on sites connected together via communication's networks for transaction processing. This system is characterized by the data distribution and the unpredictable transactions. In addition, in this system, the presence of several sites raises the problem of the unbalanced load between those nodes. In order to enhance the performance of DRTDBMS with taking into account those problems, Quality of Service (QoS) based approaches are the most appropriate. Distributed Feedback Scheduling Control Architecture (DFCSA) is proposed for managing the QoS in this system. In DRTDBMS, the results produced in time with less precision are sometimes preferable than exact results obtained in delay, inaccuracy can be tolerated. In order to take this assertion, in this paper, we extend the DFCSA by using the (m,k)-firm constraints, which take into account the imprecise results, using three data replication policies. The obtained architecture is called (m,k)-firm-User-DFCS. The second contribution consists of taking into account the real-time derived data on (m,k)-firm-User-DFCS architecture, always, using three data replication policies. The obtained architecture is called Derived Data Management (DDM)-(m,k)-firm-User-DFCS. Then, we are focusing on two ways of service optimization in DRTDBMS. We are interested in (1) the space optimization in which we propose to apply three replication data policies, and (2) the QoS optimization in which we propose to take into account the real-time derived data and (m,k)-firm constraints.
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- 2019
4. A fairness DASH-DMS architecture for adaptive streaming video in wireless network
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Harrouch, H., Kaddes, M., Majed ABDOULI, Duvallet, C., Bouaziz, R., Multimedia, InfoRmation systems and Advanced Computing Laboratory (MIRACL), Faculté des Sciences Economiques et de Gestion de Sfax (FSEG Sfax), Université de Sfax - University of Sfax-Université de Sfax - University of Sfax, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Equipe Réseaux d'interactions et Intelligence Collective (RI2C - LITIS), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), and Duvallet, Claude
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[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM] ,[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM] ,ComputingMilieux_MISCELLANEOUS - Abstract
International audience
- Published
- 2016
5. Video Replication over DASH in Distributed Multimedia Systems
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Harrouch, H., Kaddes, M., Duvallet, C., Majed ABDOULI, Bouaziz, R., Duvallet, Claude, Multimedia, InfoRmation systems and Advanced Computing Laboratory (MIRACL), Faculté des Sciences Economiques et de Gestion de Sfax (FSEG Sfax), Université de Sfax - University of Sfax-Université de Sfax - University of Sfax, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU), Equipe Réseaux d'interactions et Intelligence Collective (RI2C - LITIS), and Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie)
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[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM] ,[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM] ,ComputingMilieux_MISCELLANEOUS - Abstract
International audience
- Published
- 2015
6. A UML/MARTE Profile for Real-Time Databases
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Louati, N., primary, Bouaziz, R., additional, Duvallet, C., additional, and Sadeg, B., additional
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- 2012
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7. Feedback architecture for multimedia systems.
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Alaya, B., Duvallet, C., and Sadeg, B.
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- 2010
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8. How to Model a Real-Time Database?
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Idoudi, N., Duvallet, C., Bouaziz, R., Sadeg, B., and Gargouri, F.
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- 2009
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9. Structural Model of Real-Time Databases: An Illustration.
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Idoudi, N., Duvallet, C., Sadeg, B., Bouaziz, R., and Gargouri, F.
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- 2008
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10. Management of QoS and data freshness in RTDBSs using feedback control scheduling and data versions.
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Bouazizi, E., Duvallet, C., and Sadeg, B.
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- 2005
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11. Multi versions data for improvement of QoS in RTDBS.
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Bouazizi, E., Duvallet, C., and Sadeg, B.
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- 2005
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12. Structural model of real-time databases
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Idoudi, N., Duvallet, C., Sadeg, B., Bouaziz, R., and Faiez Gargouri
13. Management of QoS and Data Freshness in RTDBSs using Feedback Control Scheduling and Data Versions
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Bouazizi, E., primary, Duvallet, C., additional, and Sadeg, B., additional
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14. Multi-Versions Data for Improvement of QoS in RTDBS
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Bouazizi, E., primary, Duvallet, C., additional, and Sadeg, B., additional
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15. Assessment of sewer connectivity in the United States and its implications for equity in wastewater-based epidemiology.
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Yu Q, Olesen SW, Duvallet C, and Grad YH
- Abstract
Wastewater-based epidemiology is a promising public health tool that can yield a more representative view of the population than case reporting. However, only about 80% of the U.S. population is connected to public sewers, and the characteristics of populations missed by wastewater-based epidemiology are unclear. To address this gap, we used publicly available datasets to assess sewer connectivity in the U.S. by location, demographic groups, and economic groups. Data from the U.S. Census' American Housing Survey revealed that sewer connectivity was lower than average when the head of household was American Indian and Alaskan Native, White, non-Hispanic, older, and for larger households and those with higher income, but smaller geographic scales revealed local variations from this national connectivity pattern. For example, data from the U.S. Environmental Protection Agency showed that sewer connectivity was positively correlated with income in Minnesota, Florida, and California. Data from the U.S. Census' American Community Survey and Environmental Protection Agency also revealed geographic areas with low sewer connectivity, such as Alaska, the Navajo Nation, Minnesota, Michigan, and Florida. However, with the exception of the U.S. Census data, there were inconsistencies across datasets. Using mathematical modeling to assess the impact of wastewater sampling inequities on inferences about epidemic trajectory at a local scale, we found that in some situations, even weak connections between communities may allow wastewater monitoring in one community to serve as a reliable proxy for an interacting community with no wastewater monitoring, when cases are widespread. A systematic, rigorous assessment of sewer connectivity will be important for ensuring an equitable and informed implementation of wastewater-based epidemiology as a public health monitoring system., Competing Interests: SWO and CD are former employees of Biobot Analytics, Inc., where CD also holds stocks and bonds. YHG is a board member of Day Zero Diagnostics and an advisor/consultant to GSK, plc., (Copyright: © 2024 Yu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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- 2024
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16. Structured Ethical Review for Wastewater-Based Testing in Support of Public Health.
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Bowes DA, Darling A, Driver EM, Kaya D, Maal-Bared R, Lee LM, Goodman K, Adhikari S, Aggarwal S, Bivins A, Bohrerova Z, Cohen A, Duvallet C, Elnimeiry RA, Hutchison JM, Kapoor V, Keenum I, Ling F, Sills D, Tiwari A, Vikesland P, Ziels R, and Mansfeldt C
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- Humans, Public Health, Retrospective Studies, SARS-CoV-2, Wastewater, Ethical Review, COVID-19 epidemiology
- Abstract
Wastewater-based testing (WBT) for SARS-CoV-2 has rapidly expanded over the past three years due to its ability to provide a comprehensive measurement of disease prevalence independent of clinical testing. The development and simultaneous application of WBT measured biomarkers for research activities and for the pursuit of public health goals, both areas with well-established ethical frameworks. Currently, WBT practitioners do not employ a standardized ethical review process, introducing the potential for adverse outcomes for WBT professionals and community members. To address this deficiency, an interdisciplinary workshop developed a framework for a structured ethical review of WBT. The workshop employed a consensus approach to create this framework as a set of 11 questions derived from primarily public health guidance. This study retrospectively applied these questions to SARS-CoV-2 monitoring programs covering the emergent phase of the pandemic (3/2020-2/2022 ( n = 53)). Of note, 43% of answers highlight a lack of reported information to assess. Therefore, a systematic framework would at a minimum structure the communication of ethical considerations for applications of WBT. Consistent application of an ethical review will also assist in developing a practice of updating approaches and techniques to reflect the concerns held by both those practicing and those being monitored by WBT supported programs.
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- 2023
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17. Structured Ethical Review for Wastewater-Based Testing.
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Bowes DA, Darling A, Driver EM, Kaya D, Maal-Bared R, Lee LM, Goodman K, Adhikari S, Aggarwal S, Bivins A, Bohrerova Z, Cohen A, Duvallet C, Elnimeiry RA, Hutchison JM, Kapoor V, Keenum I, Ling F, Sills D, Tiwari A, Vikesland P, Ziels R, and Mansfeldt C
- Abstract
Wastewater-based testing (WBT) for SARS-CoV-2 has rapidly expanded over the past three years due to its ability to provide a comprehensive measurement of disease prevalence independent of clinical testing. The development and simultaneous application of the field blurred the boundary between measuring biomarkers for research activities and for pursuit of public health goals, both areas with well-established ethical frameworks. Currently, WBT practitioners do not employ a standardized ethical review process (or associated data management safeguards), introducing the potential for adverse outcomes for WBT professionals and community members. To address this deficiency, an interdisciplinary group developed a framework for a structured ethical review of WBT. The workshop employed a consensus approach to create this framework as a set of 11-questions derived from primarily public health guidance because of the common exemption of wastewater samples to human subject research considerations. This study retrospectively applied the set of questions to peer- reviewed published reports on SARS-CoV-2 monitoring campaigns covering the emergent phase of the pandemic from March 2020 to February 2022 (n=53). Overall, 43% of the responses to the questions were unable to be assessed because of lack of reported information. It is therefore hypothesized that a systematic framework would at a minimum improve the communication of key ethical considerations for the application of WBT. Consistent application of a standardized ethical review will also assist in developing an engaged practice of critically applying and updating approaches and techniques to reflect the concerns held by both those practicing and being monitored by WBT supported campaigns., Synopsis: Development of a structured ethical review facilitates retrospective analysis of published studies and drafted scenarios in the context of wastewater-based testing.
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- 2023
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18. Towards a soft three-level voting model (Soft T-LVM) for fake news detection.
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Jlifi B, Sakrani C, and Duvallet C
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Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)
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- 2022
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19. Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States.
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Duvallet C, Wu F, McElroy KA, Imakaev M, Endo N, Xiao A, Zhang J, Floyd-O'Sullivan R, Powell MM, Mendola S, Wilson ST, Cruz F, Melman T, Sathyanarayana CL, Olesen SW, Erickson TB, Ghaeli N, Chai P, Alm EJ, and Matus M
- Abstract
Wastewater-based epidemiology has emerged as a promising technology for population-level surveillance of COVID-19. In this study, we present results of a large nationwide SARS-CoV-2 wastewater monitoring system in the United States. We profile 55 locations with at least six months of sampling from April 2020 to May 2021. These locations represent more than 12 million individuals across 19 states. Samples were collected approximately weekly by wastewater treatment utilities as part of a regular wastewater surveillance service and analyzed for SARS-CoV-2 RNA concentrations. SARS-CoV-2 RNA concentrations were normalized to pepper mild mottle virus, an indicator of fecal matter in wastewater. We show that wastewater data reflect temporal and geographic trends in clinical COVID-19 cases and investigate the impact of normalization on correlations with case data within and across locations. We also provide key lessons learned from our broad-scale implementation of wastewater-based epidemiology, which can be used to inform wastewater-based epidemiology approaches for future emerging diseases. This work demonstrates that wastewater surveillance is a feasible approach for nationwide population-level monitoring of COVID-19 disease. With an evolving epidemic and effective vaccines against SARS-CoV-2, wastewater-based epidemiology can serve as a passive surveillance approach for detecting changing dynamics or resurgences of the virus., Competing Interests: The authors declare the following competing financial interest(s): C.D., K.A.M., N.E., M.I., R.F.-O., M.M.P., S.M., S.T.W., F.C., T.M., C.L.S., and S.W.O. are current or former employees of Biobot Analytics, Inc. E.J.A. is scientific advisor to Biobot Analytics, Inc., (© 2022 The Authors. Published by American Chemical Society.)
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- 2022
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20. Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques.
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Baaijens JA, Zulli A, Ott IM, Nika I, van der Lugt MJ, Petrone ME, Alpert T, Fauver JR, Kalinich CC, Vogels CBF, Breban MI, Duvallet C, McElroy KA, Ghaeli N, Imakaev M, Mckenzie-Bennett MF, Robison K, Plocik A, Schilling R, Pierson M, Littlefield R, Spencer ML, Simen BB, Hanage WP, Grubaugh ND, Peccia J, and Baym M
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- Humans, Wastewater, RNA, Viral genetics, Transcriptome, SARS-CoV-2 genetics, COVID-19
- Abstract
Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable., (© 2022. The Author(s).)
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- 2022
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21. MicrobiomeCensus estimates human population sizes from wastewater samples based on inter-individual variability in gut microbiomes.
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Zhang L, Chen L, Yu XA, Duvallet C, Isazadeh S, Dai C, Park S, Frois-Moniz K, Duarte F, Ratti C, Alm EJ, and Ling F
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- Humans, Pandemics, Population Density, Sewage, Wastewater, COVID-19, Gastrointestinal Microbiome genetics
- Abstract
The metagenome embedded in urban sewage is an attractive new data source to understand urban ecology and assess human health status at scales beyond a single host. Analyzing the viral fraction of wastewater in the ongoing COVID-19 pandemic has shown the potential of wastewater as aggregated samples for early detection, prevalence monitoring, and variant identification of human diseases in large populations. However, using census-based population size instead of real-time population estimates can mislead the interpretation of data acquired from sewage, hindering assessment of representativeness, inference of prevalence, or comparisons of taxa across sites. Here, we show that taxon abundance and sub-species diversisty in gut-associated microbiomes are new feature space to utilize for human population estimation. Using a population-scale human gut microbiome sample of over 1,100 people, we found that taxon-abundance distributions of gut-associated multi-person microbiomes exhibited generalizable relationships with respect to human population size. Here and throughout this paper, the human population size is essentially the sample size from the wastewater sample. We present a new algorithm, MicrobiomeCensus, for estimating human population size from sewage samples. MicrobiomeCensus harnesses the inter-individual variability in human gut microbiomes and performs maximum likelihood estimation based on simultaneous deviation of multiple taxa's relative abundances from their population means. MicrobiomeCensus outperformed generic algorithms in data-driven simulation benchmarks and detected population size differences in field data. New theorems are provided to justify our approach. This research provides a mathematical framework for inferring population sizes in real time from sewage samples, paving the way for more accurate ecological and public health studies utilizing the sewage metagenome., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: E.J.A has an equity stake in Biobot Analytics. C. Duvallet is employed by Biobot Analytics.
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- 2022
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22. Metrics to relate COVID-19 wastewater data to clinical testing dynamics.
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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, and Alm EJ
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- Benchmarking, Humans, RNA, Viral, SARS-CoV-2, Wastewater, Wastewater-Based Epidemiological Monitoring, COVID-19, Pandemics
- 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., (Copyright © 2022. Published by Elsevier Ltd.)
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- 2022
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23. SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases.
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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, and Alm EJ
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- Humans, RNA, Viral, Virus Shedding, Wastewater, COVID-19, SARS-CoV-2
- 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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: MM and NG are cofounders of Biobot Analytics. EJA is advisor to Biobot Analytics. CD, KAM, KF, and NE are employees at Biobot Analytics, and all these authors hold shares in the company. PRC and TBE have a financial interest in Biobot Analytics, a company engaged in the collection and analysis of wastewater to develop epidemiological data. PRC and TBE's interests were reviewed and are managed by Brigham and Women's Hospital and Mass General Brigham in accordance with their conflict-of-interest policies., (Copyright © 2021. Published by Elsevier B.V.)
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- 2022
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24. "Waste Not, Want Not" - Leveraging Sewer Systems and Wastewater-Based Epidemiology for Drug Use Trends and Pharmaceutical Monitoring.
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Erickson TB, Endo N, Duvallet C, Ghaeli N, Hess K, Alm EJ, Matus M, and Chai PR
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- COVID-19 epidemiology, Humans, Pandemics, SARS-CoV-2, Substance-Related Disorders epidemiology, Biomarkers analysis, Illicit Drugs analysis, Pharmaceutical Preparations analysis, Substance Abuse Detection methods, Wastewater chemistry, Wastewater-Based Epidemiological Monitoring, Water Pollutants, Chemical analysis
- Abstract
During the current global COVID-19 pandemic and opioid epidemic, wastewater-based epidemiology (WBE) has emerged as a powerful tool for monitoring public health trends by analysis of biomarkers including drugs, chemicals, and pathogens. Wastewater surveillance downstream at wastewater treatment plants provides large-scale population and regional-scale aggregation while upstream surveillance monitors locations at the neighborhood level with more precise geographic analysis. WBE can provide insights into dynamic drug consumption trends as well as environmental and toxicological contaminants. Applications of WBE include monitoring policy changes with cannabinoid legalization, tracking emerging illicit drugs, and early warning systems for potent fentanyl analogues along with the resurging wave of stimulants (e.g., methamphetamine, cocaine). Beyond drug consumption, WBE can also be used to monitor pharmaceuticals and their metabolites, including antidepressants and antipsychotics. In this manuscript, we describe the basic tenets and techniques of WBE, review its current application among drugs of abuse, and propose methods to scale and develop both monitoring and early warning systems with respect to measurement of illicit drugs and pharmaceuticals. We propose new frontiers in toxicological research with wastewater surveillance including assessment of medication assisted treatment of opioid use disorder (e.g., buprenorphine, methadone) in the context of other social burdens like COVID-19 disease., (© 2021. The Author(s).)
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- 2021
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25. Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification.
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Baaijens JA, Zulli A, Ott IM, Petrone ME, Alpert T, Fauver JR, Kalinich CC, Vogels CBF, Breban MI, Duvallet C, McElroy K, Ghaeli N, Imakaev M, Mckenzie-Bennett M, Robison K, Plocik A, Schilling R, Pierson M, Littlefield R, Spencer M, Simen BB, Hanage WP, Grubaugh ND, Peccia J, and Baym M
- Abstract
Effectively monitoring the spread of SARS-CoV-2 variants is essential to efforts to counter the ongoing pandemic. Wastewater monitoring of SARS-CoV-2 RNA has proven an effective and efficient technique to approximate COVID-19 case rates in the population. Predicting variant abundances from wastewater, however, is technically challenging. Here we show that by sequencing SARS-CoV-2 RNA in wastewater and applying computational techniques initially used for RNA-Seq quantification, we can estimate the abundance of variants in wastewater samples. We show by sequencing samples from wastewater and clinical isolates in Connecticut U.S.A. between January and April 2021 that the temporal dynamics of variant strains broadly correspond. We further show that this technique can be used with other wastewater sequencing techniques by expanding to samples taken across the United States in a similar timeframe. We find high variability in signal among individual samples, and limited ability to detect the presence of variants with clinical frequencies <10%; nevertheless, the overall trends match what we observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in variant prevalence in situations where clinical sequencing is unavailable or impractical.
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- 2021
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26. Wastewater surveillance of SARS-CoV-2 across 40 U.S. states from February to June 2020.
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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, and Alm EJ
- Subjects
- Disease Outbreaks, Humans, RNA, Viral, Wastewater, COVID-19, SARS-CoV-2
- 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., (Copyright © 2021. Published by Elsevier Ltd.)
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- 2021
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27. Making waves: Defining the lead time of wastewater-based epidemiology for COVID-19.
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Olesen SW, Imakaev M, and Duvallet C
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- Humans, Lead, SARS-CoV-2, Wastewater, COVID-19, Wastewater-Based Epidemiological Monitoring
- Abstract
Individuals infected with SARS-CoV-2, the virus that causes COVID-19, may shed the virus in stool before developing symptoms, suggesting that measurements of SARS-CoV-2 concentrations in wastewater could be a "leading indicator" of COVID-19 prevalence. Multiple studies have corroborated the leading indicator concept by showing that the correlation between wastewater measurements and COVID-19 case counts is maximized when case counts are lagged. However, the meaning of "leading indicator" will depend on the specific application of wastewater-based epidemiology, and the correlation analysis is not relevant for all applications. In fact, the quantification of a leading indicator will depend on epidemiological, biological, and health systems factors. Thus, there is no single "lead time" for wastewater-based COVID-19 monitoring. To illustrate this complexity, we enumerate three different applications of wastewater-based epidemiology for COVID-19: a qualitative "early warning" system; an independent, quantitative estimate of disease prevalence; and a quantitative alert of bursts of disease incidence. The leading indicator concept has different definitions and utility in each application., (Copyright © 2021. Published by Elsevier Ltd.)
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- 2021
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28. Analysis of 39 drugs and metabolites, including 8 glucuronide conjugates, in an upstream wastewater network via HPLC-MS/MS.
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Foppe KS, Kujawinski EB, Duvallet C, Endo N, Erickson TB, Chai PR, and Matus M
- Subjects
- Limit of Detection, Linear Models, Reproducibility of Results, Tandem Mass Spectrometry methods, Chromatography, High Pressure Liquid methods, Glucuronides analysis, Pharmaceutical Preparations analysis, Wastewater chemistry, Water Pollutants, Chemical analysis
- Abstract
Pharmaceutical compounds ingested by humans are metabolized and excreted in urine and feces. These metabolites can be quantified in wastewater networks using wastewater-based epidemiology (WBE) methods. Standard WBE methods focus on samples collected at wastewater treatment plants (WWTPs). However, these methods do not capture more labile classes of metabolites such as glucuronide conjugates, products of the major phase II metabolic pathway for drug elimination. By shifting sample collection more upstream, these unambiguous markers of human exposure are captured before hydrolysis in the wastewater network. In this paper, we present an HPLC-MS/MS method that quantifies 8 glucuronide conjugates in addition to 31 parent and other metabolites of prescription and synthetic opioids, overdose treatment drugs, illicit drugs, and population markers. Calibration curves for all analytes are linear (r
2 > 0.98), except THC (r2 = 0.97), and in the targeted range (0.1-1,000 ng mL-1 ) with lower limits of quantification (S/N = 9) ranging from 0.098 to 48.75 ng mL-1 . This method is fast with an injection-to-injection time of 7.5 min. We demonstrate the application of the method to five wastewater samples collected from a manhole in a city in eastern Massachusetts. Collected wastewater samples were filtered and extracted via solid-phase extraction (SPE). The SPE cartridges are eluted and concentrated in the laboratory via nitrogen-drying. The method and case study presented here demonstrate the potential and application of expanding WBE to monitoring labile metabolites in upstream wastewater networks., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)- Published
- 2021
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29. Metrics to relate COVID-19 wastewater data to clinical testing dynamics.
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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, and Alm EJ
- 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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- 2021
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30. Wastewater Surveillance of SARS-CoV-2 across 40 U.S. states.
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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, and Alm EJ
- 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., Competing Interests: Competing Interests MM and NG are cofounders of Biobot Analytics. EJA is advisor to Biobot Analytics. CD, KAM, KF, and NE are employees at Biobot Analytics, and all these authors hold shares in the company.
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- 2021
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31. Standardizing data reporting in the research community to enhance the utility of open data for SARS-CoV-2 wastewater surveillance.
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McClary-Gutierrez JS, Aanderud ZT, Al-Faliti M, Duvallet C, Gonzalez R, Guzman J, Holm RH, Jahne MA, Kantor RS, Katsivelis P, Kuhn KG, Langan LM, Mansfeldt C, McLellan SL, Grijalva LMM, Murnane KS, Naughton CC, Packman AI, Paraskevopoulos S, Radniecki TS, Roman FA Jr, Shrestha A, Stadler LB, Steele JA, Swalla BM, Vikesland P, Wartell B, Wilusz CJ, Wong JCC, Boehm AB, Halden RU, Bibby K, and Vela JD
- Abstract
SARS-CoV-2 RNA detection in wastewater is being rapidly developed and adopted as a public health monitoring tool worldwide. With wastewater surveillance programs being implemented across many different scales and by many different stakeholders, it is critical that data collected and shared are accompanied by an appropriate minimal amount of metainformation to enable meaningful interpretation and use of this new information source and intercomparison across datasets. While some databases are being developed for specific surveillance programs locally, regionally, nationally, and internationally, common globally-adopted data standards have not yet been established within the research community. Establishing such standards will require national and international consensus on what metainformation should accompany SARS-CoV-2 wastewater measurements. To establish a recommendation on minimum information to accompany reporting of SARS-CoV-2 occurrence in wastewater for the research community, the United States National Science Foundation (NSF) Research Coordination Network on Wastewater Surveillance for SARS-CoV-2 hosted a workshop in February 2021 with participants from academia, government agencies, private companies, wastewater utilities, public health laboratories, and research institutes. This report presents the primary two outcomes of the workshop: (i) a recommendation on the set of minimum meta-information that is needed to confidently interpret wastewater SARS-CoV-2 data, and (ii) insights from workshop discussions on how to improve standardization of data reporting., Competing Interests: Conflicts of interest C. D. is an employee of Biobot Analytics, Inc. P. K. is the founder of Venthic Technologies. B. M. S. is an employee of IDEXX Laboratories, Inc. R. U. H. is a cofounder of AquaVitas, LLC and the nonprofit project OneWaterOneHealth of the Arizona State University Foundation.
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- 2021
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32. Wastewater network infrastructure in public health: Applications and learnings from the COVID-19 pandemic.
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Sharara N, Endo N, Duvallet C, Ghaeli N, Matus M, Heussner J, Olesen SW, Alm EJ, Chai PR, and Erickson TB
- Abstract
Accurate estimates of COVID-19 burden of infections in communities can inform public health strategy for the current pandemic. Wastewater based epidemiology (WBE) leverages sewer infrastructure to provide insights on rates of infection by measuring viral concentrations in wastewater. By accessing the sewer network at various junctures, important insights regarding COVID-19 disease activity can be gained. The analysis of sewage at the wastewater treatment plant level enables population-level surveillance of disease trends and virus mutations. At the neighborhood level, WBE can be used to describe trends in infection rates in the community thereby facilitating local efforts at targeted disease mitigation. Finally, at the building level, WBE can suggest the presence of infections and prompt individual testing. In this critical review, we describe the types of data that can be obtained through varying levels of WBE analysis, concrete plans for implementation, and public health actions that can be taken based on WBE surveillance data of infectious diseases, using recent and successful applications of WBE during the COVID-19 pandemic for illustration., Competing Interests: Competing interests: We have read the journal’s policy and the authors of this manuscript have the following competing interests: NS, NE, CD, NG, MM, JH, SWO are employees of Biobot Analytics. NG and MM are cofounders of Biobot Analytics. NS, NE, CD, NG, MM, JH, SWO all receive salary from Biobot Analytics. PRC, TBE, EJA are members of the scientific advisory board for Biobot Analytics. NS, MM, EJA have significant financial interest in Biobot Analytics. PRC and TBE also have a financial interest in Biobot Analytics. PRC and TBE’s interests were reviewed and are managed by Brigham and Women’s Hospital and Mass General Brigham in accordance with their conflict of interest policies.
- Published
- 2021
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33. Predicting human health from biofluid-based metabolomics using machine learning.
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Evans ED, Duvallet C, Chu ND, Oberst MK, Murphy MA, Rockafellow I, Sontag D, and Alm EJ
- Subjects
- Databases, Factual, Health Status, Humans, Machine Learning, Metabolomics methods, Models, Theoretical
- Abstract
Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified-where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.
- Published
- 2020
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34. Mapping Community Opioid Exposure Through Wastewater-Based Epidemiology as a Means to Engage Pharmacies in Harm Reduction Efforts.
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Duvallet C, Hayes BD, Erickson TB, Chai PR, and Matus M
- Subjects
- Community Pharmacy Services organization & administration, Humans, North Carolina, Opioid Epidemic, Pilot Projects, Analgesics, Opioid pharmacokinetics, Harm Reduction, Opioid-Related Disorders prevention & control, Wastewater-Based Epidemiological Monitoring
- Published
- 2020
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35. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases.
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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, and Alm EJ
- 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., (Copyright © 2020 Wu et al.)
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- 2020
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36. SARS-CoV-2 titers in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases.
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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, and Alm EJ
- 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.
- Published
- 2020
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37. Rapid Assessment of Opioid Exposure and Treatment in Cities Through Robotic Collection and Chemical Analysis of Wastewater.
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Endo N, Ghaeli N, Duvallet C, Foppe K, Erickson TB, Matus M, and Chai PR
- Subjects
- Adult, Feasibility Studies, Female, Humans, Male, Mass Spectrometry, Middle Aged, Naloxone analysis, Narcotic Antagonists analysis, North Carolina, Opioid-Related Disorders diagnosis, Opioid-Related Disorders therapy, Solid Phase Extraction, Substance-Related Disorders diagnosis, Substance-Related Disorders therapy, Urban Health, Analgesics, Opioid analysis, Environmental Monitoring instrumentation, Opioid Epidemic, Opioid-Related Disorders epidemiology, Robotics instrumentation, Substance-Related Disorders epidemiology, Wastewater analysis, Water Pollutants, Chemical analysis
- Abstract
Introduction: Accurate data regarding opioid use, overdose, and treatment is important in guiding community efforts at combating the opioid epidemic. Wastewater-based epidemiology (WBE) is a potential method to quantify community-level trends of opioid exposure beyond overdose data, which is the basis of most existing response efforts. However, most WBE efforts collect parent opioid compounds (e.g., morphine) at wastewater treatment facilities, measuring opioid concentrations across large catchment zones which typically represent an entire municipality. We sought to deploy a robotic sampling device at targeted manholes within a city to semi-quantitatively detect opioid metabolites (e.g., morphine glucuronide) at a sub-city community resolution., Methods: We deployed a robotic wastewater sampling platform at ten residential manholes in an urban municipality in North Carolina, accounting for 44.5% of the total municipal population. Sampling devices comprised a robotic sampling arm with in situ solid phase extraction, and collected hourly samples over 24-hour periods. We used targeted mass spectrometry to detect the presence of a custom panel of opioids, naloxone, and buprenorphine., Results: Ten sampling sites were selected to be a representative survey of the entire municipality by integrating sewer network and demographic GIS data. All eleven metabolites targeted were detected during the program. The average morphine milligram equivalent (MME) across the nine illicit and prescription opioids, as excreted and detected in wastewater, was 49.1 (standard deviation of 31.9) MME/day/1000-people. Codeine was detected most frequently (detection rate of 100%), and buprenorphine was detected least frequently (12%). The presence of naloxone correlated with city data of known overdoses reversed by emergency medical services in the prehospital setting., Conclusion: Wastewater-based epidemiology with smart sewer selection and robotic wastewater collection is feasible to detect the presence of specific opioids, naloxone, methadone, and buprenorphine within a city. These results suggest that wastewater epidemiology could be used to detect patterns of opioid exposure and may ultimately provide information for opioid use disorder (OUD) treatment and harm reduction programs.
- Published
- 2020
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38. Data detectives, self-love, and humility: a research parasite's perspective.
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Duvallet C
- Subjects
- Big Data, Data Analysis, Databases, Factual, Documentation, Humans, Data Mining methods, Data Mining standards, Information Dissemination, Research, Research Personnel
- Abstract
Secondary analysis solidifies and expands upon scientific knowledge through the re-analysis of existing datasets. However, researchers performing secondary analyses must develop specific skills to be successful and can benefit from adopting some computational best practices. Recognizing this work is also key to building and supporting a community of researchers who contribute to the scientific ecosystem through secondary analyses. The Research Parasite Awards are one such avenue, celebrating outstanding contributions to the rigorous secondary analysis of data. As the recipient of a 2019 Junior Research Parasite Award, I was asked to provide some perspectives on life as a research parasite, which I share in this commentary., (© The Author(s) 2020. Published by Oxford University Press.)
- Published
- 2020
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39. Framework for rational donor selection in fecal microbiota transplant clinical trials.
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Duvallet C, Zellmer C, Panchal P, Budree S, Osman M, and Alm EJ
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- Clostridioides difficile growth & development, Clostridioides difficile pathogenicity, Clostridium Infections microbiology, Fecal Microbiota Transplantation adverse effects, Gastrointestinal Microbiome genetics, Humans, Microbiota genetics, Retrospective Studies, Clostridium Infections therapy, Donor Selection methods, Fecal Microbiota Transplantation methods, Feces microbiology
- Abstract
Early clinical successes are driving enthusiasm for fecal microbiota transplantation (FMT), the transfer of healthy gut bacteria through whole stool, as emerging research is linking the microbiome to many different diseases. However, preliminary trials have yielded mixed results and suggest that heterogeneity in donor stool may play a role in patient response. Thus, clinical trials may fail because an ineffective donor was chosen rather than because FMT is not appropriate for the indication. Here, we describe a conceptual framework to guide rational donor selection to increase the likelihood that FMT clinical trials will succeed. We argue that the mechanism by which the microbiome is hypothesized to be associated with a given indication should inform how healthy donors are selected for FMT trials, categorizing these mechanisms into four disease models and presenting associated donor selection strategies. We next walk through examples based on previously published FMT trials and ongoing investigations to illustrate how donor selection might occur in practice. Finally, we show that typical FMT trials are not powered to discover individual taxa mediating patient responses, suggesting that clinicians should develop targeted hypotheses for retrospective analyses and design their clinical trials accordingly. Moving forward, developing and applying novel clinical trial design methodologies like rational donor selection will be necessary to ensure that FMT successfully translates into clinical impact., Competing Interests: EA is co-founder of Finch Therapeutics and is on the Board of Directors of OpenBiome. CZ, PP, SB, and MO are employed by OpenBiome. These commercial sources of funding do not alter our adherence to PLOS ONE policies on sharing data and materials.
- Published
- 2019
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40. Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.
- Author
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Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS 2nd, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, and Caporaso JG
- Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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- 2019
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41. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.
- Author
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Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS 2nd, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, and Caporaso JG
- Subjects
- Databases, Factual, Humans, Computational Biology, Data Science, Microbiota, Software
- Published
- 2019
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42. A practical guide to methods controlling false discoveries in computational biology.
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Korthauer K, Kimes PK, Duvallet C, Reyes A, Subramanian A, Teng M, Shukla C, Alm EJ, and Hicks SC
- Subjects
- Computational Biology standards, Computer Simulation, Computational Biology methods
- Abstract
Background: In high-throughput studies, hundreds to millions of hypotheses are typically tested. Statistical methods that control the false discovery rate (FDR) have emerged as popular and powerful tools for error rate control. While classic FDR methods use only p values as input, more modern FDR methods have been shown to increase power by incorporating complementary information as informative covariates to prioritize, weight, and group hypotheses. However, there is currently no consensus on how the modern methods compare to one another. We investigate the accuracy, applicability, and ease of use of two classic and six modern FDR-controlling methods by performing a systematic benchmark comparison using simulation studies as well as six case studies in computational biology., Results: Methods that incorporate informative covariates are modestly more powerful than classic approaches, and do not underperform classic approaches, even when the covariate is completely uninformative. The majority of methods are successful at controlling the FDR, with the exception of two modern methods under certain settings. Furthermore, we find that the improvement of the modern FDR methods over the classic methods increases with the informativeness of the covariate, total number of hypothesis tests, and proportion of truly non-null hypotheses., Conclusions: Modern FDR methods that use an informative covariate provide advantages over classic FDR-controlling procedures, with the relative gain dependent on the application and informativeness of available covariates. We present our findings as a practical guide and provide recommendations to aid researchers in their choice of methods to correct for false discoveries.
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- 2019
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43. Aerodigestive sampling reveals altered microbial exchange between lung, oropharyngeal, and gastric microbiomes in children with impaired swallow function.
- Author
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Duvallet C, Larson K, Snapper S, Iosim S, Lee A, Freer K, May K, Alm E, and Rosen R
- Subjects
- Adolescent, Bronchoscopy, Child, Child, Preschool, Cross-Sectional Studies, Female, Gastroesophageal Reflux complications, Gastroscopy, Humans, Infant, Male, Pneumonia, Aspiration etiology, Prospective Studies, RNA, Bacterial genetics, RNA, Ribosomal, 16S genetics, Bacteria classification, Bacteria genetics, Deglutition, Gastroesophageal Reflux microbiology, Gastrointestinal Microbiome, Lung microbiology, Pneumonia, Aspiration microbiology
- Abstract
Background: Children with oropharyngeal dysphagia have impaired airway protection mechanisms and are at higher risk for pneumonia and other pulmonary complications. Aspiration of gastric contents is often implicated as a cause for these pulmonary complications, despite being supported by little evidence. The goal of this study is to determine the relative contribution of oropharyngeal and gastric microbial communities to perturbations in the lung microbiome of children with and without oropharyngeal dysphagia and aspiration., Methods: We conducted a prospective cohort study of 220 patients consecutively recruited from a tertiary aerodigestive center undergoing simultaneous esophagogastroduodenoscopy and flexible bronchoscopy. Bronchoalveolar lavage, gastric and oropharyngeal samples were collected from all recruited patients and 16S sequencing was performed. A subset of 104 patients also underwent video fluoroscopic swallow studies to assess swallow function and were categorized as aspiration/no aspiration. To ensure the validity of the results, we compared the microbiome of these aerodigestive patients to the microbiome of pediatric patients recruited to a longitudinal cohort study of children with suspected GERD; patients recruited to this study had oropharyngeal, gastric and/or stool samples available. The relationships between microbial communities across the aerodigestive tract were described by analyzing within- and between-patient beta diversities and identifying taxa which are exchanged between aerodigestive sites within patients. These relationships were then compared in patients with and without aspiration to evaluate the effect of aspiration on the aerodigestive microbiome., Results: Within all patients, lung, oropharyngeal and gastric microbiomes overlap. The degree of similarity is the lowest between the oropharynx and lungs (median Jensen-Shannon distance (JSD) = 0.90), and as high between the stomach and lungs as between the oropharynx and stomach (median JSD = 0.56 for both; p = 0.6). Unlike the oropharyngeal microbiome, lung and gastric communities are highly variable across people and driven primarily by person rather than body site. In patients with aspiration, the lung microbiome more closely resembles oropharyngeal rather than gastric communities and there is greater prevalence of microbial exchange between the lung and oropharynx than between gastric and lung sites (p = 0.04 and 4x10-5, respectively)., Conclusions: The gastric and lung microbiomes display significant overlap in patients with intact airway protective mechanisms while the lung and oropharynx remain distinct. In patients with impaired swallow function and aspiration, the lung microbiome shifts towards oropharyngeal rather than gastric communities. This finding may explain why antireflux surgeries fail to show benefit in pediatric pulmonary outcomes., Competing Interests: The authors have declared that no competing interests exist.
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- 2019
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44. Predictability and persistence of prebiotic dietary supplementation in a healthy human cohort.
- Author
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Gurry T, Gibbons SM, Nguyen LTT, Kearney SM, Ananthakrishnan A, Jiang X, Duvallet C, Kassam Z, and Alm EJ
- Subjects
- Adult, Gastrointestinal Microbiome physiology, Humans, Polymorphism, Single Nucleotide genetics, Young Adult, Dietary Supplements, Prebiotics
- Abstract
Dietary interventions to manipulate the human gut microbiome for improved health have received increasing attention. However, their design has been limited by a lack of understanding of the quantitative impact of diet on a host's microbiota. We present a highly controlled diet perturbation experiment in a healthy, human cohort in which individual micronutrients are spiked in against a standardized background. We identify strong and predictable responses of specific microbes across participants consuming prebiotic spike-ins, at the level of both strains and functional genes, suggesting fine-scale resource partitioning in the human gut. No predictable responses to non-prebiotic micronutrients were found. Surprisingly, we did not observe decreases in day-to-day variability of the microbiota compared to a complex, varying diet, and instead found evidence of diet-induced stress and an associated loss of biodiversity. Our data offer insights into the effect of a low complexity diet on the gut microbiome, and suggest that effective personalized dietary interventions will rely on functional, strain-level characterization of a patient's microbiota.
- Published
- 2018
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45. Correcting for batch effects in case-control microbiome studies.
- Author
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Gibbons SM, Duvallet C, and Alm EJ
- Subjects
- Case-Control Studies, Colorectal Neoplasms microbiology, Computational Biology, Computer Simulation, Data Interpretation, Statistical, Databases, Nucleic Acid statistics & numerical data, High-Throughput Nucleotide Sequencing statistics & numerical data, Humans, Meta-Analysis as Topic, Statistics, Nonparametric, Microbiota genetics, Oligonucleotide Array Sequence Analysis statistics & numerical data
- Abstract
High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses.
- Published
- 2018
- Full Text
- View/download PDF
46. Meta-analysis generates and prioritizes hypotheses for translational microbiome research.
- Author
-
Duvallet C
- Subjects
- Clinical Trials as Topic, Humans, Meta-Analysis as Topic, Translational Research, Biomedical trends, Dysbiosis therapy, Gastrointestinal Microbiome, Microbiota, Translational Research, Biomedical methods
- Published
- 2018
- Full Text
- View/download PDF
47. Meta-analysis of gut microbiome studies identifies disease-specific and shared responses.
- Author
-
Duvallet C, Gibbons SM, Gurry T, Irizarry RA, and Alm EJ
- Subjects
- Bacteria classification, Case-Control Studies, Feces microbiology, Humans, Population Density, Population Dynamics, Bacteria growth & development, Gastrointestinal Diseases microbiology, Gastrointestinal Microbiome, Gastrointestinal Tract microbiology
- Abstract
Hundreds of clinical studies have demonstrated associations between the human microbiome and disease, yet fundamental questions remain on how we can generalize this knowledge. Results from individual studies can be inconsistent, and comparing published data is further complicated by a lack of standard processing and analysis methods. Here we introduce the MicrobiomeHD database, which includes 28 published case-control gut microbiome studies spanning ten diseases. We perform a cross-disease meta-analysis of these studies using standardized methods. We find consistent patterns characterizing disease-associated microbiome changes. Some diseases are associated with over 50 genera, while most show only 10-15 genus-level changes. Some diseases are marked by the presence of potentially pathogenic microbes, whereas others are characterized by a depletion of health-associated bacteria. Furthermore, we show that about half of genera associated with individual studies are bacteria that respond to more than one disease. Thus, many associations found in case-control studies are likely not disease-specific but rather part of a non-specific, shared response to health and disease.
- Published
- 2017
- Full Text
- View/download PDF
48. dbOTU3: A new implementation of distribution-based OTU calling.
- Author
-
Olesen SW, Duvallet C, and Alm EJ
- Subjects
- Algorithms, Phylogeny
- Abstract
Distribution-based operational taxonomic unit-calling (dbOTU) improves on other approaches by incorporating information about the input sequences' distribution across samples. Previous implementations of dbOTU presented challenges for users. Here we introduce and evaluate a new implementation of dbOTU that is faster and more user-friendly. We show that this new implementation has theoretical and practical improvements over previous implementations of dbOTU, making the algorithm more accessible to microbial ecology and biomedical researchers.
- Published
- 2017
- Full Text
- View/download PDF
49. [Improvement of the reproducibility of the Elisa test for detecting anti-Trypanosoma congolese antibodies in cattle].
- Author
-
Bocquentin R and Duvallet C
- Subjects
- Animals, Burkina Faso, Cattle, Reproducibility of Results, Antibodies, Protozoan analysis, Enzyme-Linked Immunosorbent Assay veterinary, Trypanosoma congolense immunology
- Abstract
Simplicity and the potential automatization make the ELISA test a universal tool for the detection of antibodies, and, more recently, of antigens. But the reproducibility of results is not very good, due to many varying factors. We tried to improve the reproducibility of the ELISA test for the detection of anti-Trypanosoma congolense antibodies in cattle. For that, buffers are always used at room temperature to avoid temperature gradients in the plates. All volumes are increased to 200 microliters per well. Non-activated rabbit serum is used to block nonspecific sites and to reduce background signal. Better results were obtained with a homologous antigen (T. congolense) than with a heterologous one (T. evansi). Titration of the antigen concentration to be used must be checked for each new batch. A distribution is proposed which would permit a testing of each serum four times per plate and thus to calculate the mean and standard deviation for each serum. Finally, we propose that the reading should no longer be a function of time after contact between enzyme and substrat, but a function of the progress of the reaction measured in a target serum. Results show a very good reproducibility and in addition we propose a computerized monitoring system.
- Published
- 1990
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