Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 5 de 5
Filter
Add filters

Language
Document Type
Year range
1.
PubMed; 2022.
Preprint in English | PubMed | ID: ppcovidwho-332635

ABSTRACT

Importance: Genomic footprints of pathogens shed by infected individuals can be traced in environmental samples. Analysis of these samples can be employed for noninvasive surveillance of infectious diseases. Objective: To evaluate the efficacy of environmental surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for predicting COVID-19 cases in a college dormitory. Design: Using a prospective experimental design, air, surface swabs, and wastewater samples were collected from a college dormitory from March to May 2021. Students were randomly screened for COVID-19 during the study period. SARS-CoV-2 in environmental samples was concentrated with electronegative filtration and quantified using Volcano 2 nd Generation-qPCR. Descriptive analyses were conducted to examine the associations between time-lagged SARS-CoV-2 in environmental samples and clinically diagnosed COVID-19 cases. Setting: This study was conducted in a residential dormitory at the University of Miami, Coral Gables campus, FL, USA. The dormitory housed about 500 students. Participants: Students from the dormitory were randomly screened, for COVID-19 for 2-3 days / week while entering or exiting the dormitory. Main Outcome: Clinically diagnosed COVID-19 cases were of our main interest. We hypothesized that SARS-CoV-2 detection in environmental samples was an indicator of the presence of local COVID-19 cases in the dormitory, and SARS-CoV-2 can be detected in the environmental samples several days prior to the clinical diagnosis of COVID-19 cases. Results: SARS-CoV-2 genomic footprints were detected in air, surface swab and wastewater samples on 52 (63.4%), 40 (50.0%) and 57 (68.6%) days, respectively, during the study period. On 19 (24%) of 78 days SARS-CoV-2 was detected in all three sample types. Clinically diagnosed COVID-19 cases were reported on 11 days during the study period and SARS-CoV-2 was also detected two days before the case diagnosis on all 11 (100%), 9 (81.8%) and 8 (72.7%) days in air, surface swab and wastewater samples, respectively. Conclusion: Proactive environmental surveillance of SARS-CoV-2 or other pathogens in a community/public setting has potential to guide targeted measures to contain and/or mitigate infectious disease outbreaks. Key Points: Question: How effective is environmental surveillance of SARS-CoV-2 in public places for early detection of COVID-19 cases in a community?Findings: All clinically confirmed COVID-19 cases were predicted with the aid of 2 day lagged SARS-CoV-2 in environmental samples in a college dormitory. However, the prediction efficiency varied by sample type: best prediction by air samples, followed by wastewater and surface swab samples. SARS-CoV-2 was also detected in these samples even on days without any reported cases of COVID-19, suggesting underreporting of COVID-19 cases. Meaning: SARS-CoV-2 can be detected in environmental samples several days prior to clinical reporting of COVID-19 cases. Thus, proactive environmental surveillance of microbiome in public places can serve as a mean for early detection of location-time specific outbreaks of infectious diseases. It can also be used for underreporting of infectious diseases.

2.
PubMed; 2022.
Preprint in English | PubMed | ID: ppcovidwho-331037

ABSTRACT

IMPORTANCE: Genomic footprints of pathogens shed by infected individuals can be traced in environmental samples, which can serve as a noninvasive method for infectious disease surveillance. OBJECTIVE: To determine the efficacy of predicting COVID-19 cases using the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) found in air, surface swabs and wastewater samples. DESIGN: A prospective experimental design utilizing randomized surveillance of air, surface, and wastewater samples was performed from March to May 2021. SARS-CoV-2 in environmental samples was concentrated with electronegative filtration and quantified using Volcano 2 (nd) Generation-qPCR. Descriptive analyses were conducted to examine the associations between time-lagged SARS-CoV-2 in environmental samples and clinically diagnosed COVID-19 cases. SETTING: This study was conducted in a residential dormitory at the University of Miami, Coral Gables campus. PARTICIPANTS: Random air and surface swab samples were collected in high-traffic areas of a college dormitory, housing roughly 500 students, with the number of individuals contributing at any point in time. Wastewater was collected from the dormitory where individuals from the resident population as well as any visitors of the building contributed to the sewer system. Students from the dormitory were randomly screened for COVID-19 for 2-3 days / week. MAIN OUTCOME: SARS-CoV-2 detection in environmental samples was an indicator of the presence of local COVID-19 cases and a 2-day lead indicator for a potential outbreak at the dormitory building scale. The hypothesis being tested was formulated prior to the data collection. RESULTS: A total of 445 air, surface swab and wastewater samples were collected, and these data were aggregated by day. SARS-CoV-2 genomic footprints were detected in air, surface swab and wastewater samples on 52 (63.4%), 40 (50.0%) and 57 (68.6%) days, respectively, during the study period. On 19 (24%) of 78 days SARS-CoV-2 was detected in all three sample types. Clinically diagnosed COVID-19 cases were reported on 11 days during the study period and SARS-CoV-2 was also detected two days before the case diagnosis on all 11 (100%), 9 (81.8%) and 8 (72.7%) days in air, surface swab and wastewater samples, respectively. CONCLUSION: Proactive environmental surveillance of SARS-CoV-2 or other pathogens in a community/public setting has potential to guide targeted measures to contain and/or mitigate infectious disease outbreaks. KEY POINTS: Question: Could environmental surveillance provide a means of early detection for SARS-CoV-2 in high population communities, such as college campuses, or even cities?Findings: In this surveillance study, SARS-CoV-2 was detected from air, surface swab, and wastewater samples of a college dormitory in 165 (30%) of 445 total samples collected. When assessed alone, each sample type offered a means for predicting COVID-19 clinical cases (∼90%), however when aggregated, provided a prediction rate of roughly 100% for detecting SARS-CoV-2 within the community.Meaning: The findings suggest that the early detection of SARS-CoV-2 from environmental sampling including active air, surface swab, and wastewater surveillance provide an accurate and early means of detecting COVID-19 infection within high population communities.

3.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-330706

ABSTRACT

With the emergence of SARS-CoV-2 variants that may increase transmissibility and/or cause escape from immune responses 1-3 , there is an urgent need for the targeted surveillance of circulating lineages. It was found that the B.1.1.7 (also 501Y.V1) variant first detected in the UK 4,5 could be serendipitously detected by the ThermoFisher TaqPath COVID-19 PCR assay because a key deletion in these viruses, spike DELTA69-70, would cause a "spike gene target failure" (SGTF) result. However, a SGTF result is not definitive for B.1.1.7, and this assay cannot detect other variants of concern that lack spike DELTA69-70, such as B.1.351 (also 501Y.V2) detected in South Africa 6 and P.1 (also 501Y.V3) recently detected in Brazil 7 . We identified a deletion in the ORF1a gene (ORF1a DELTA3675-3677) in all three variants, which has not yet been widely detected in other SARS-CoV-2 lineages. Using ORF1a DELTA3675-3677 as the primary target and spike DELTA69-70 to differentiate, we designed and validated an open source PCR assay to detect SARS-CoV-2 variants of concern 8 . Our assay can be rapidly deployed in laboratories around the world to enhance surveillance for the local emergence spread of B.1.1.7, B.1.351, and P.1.

4.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-296897

ABSTRACT

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has infected over 115 million people and caused over 2.5 million deaths worldwide. Yet, the molecular mechanisms underlying the clinical manifestations of COVID-19, as well as what distinguishes them from common seasonal influenza virus and other lung injury states such as Acute Respiratory Distress Syndrome (ARDS), remains poorly understood. To address these challenges, we combined transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues, matched with spatial protein and expression profiling (GeoMx) across 357 tissue sections. These results define both body-wide and tissue-specific (heart, liver, lung, kidney, and lymph nodes) damage wrought by the SARS-CoV-2 infection, evident as a function of varying viral load (high vs. low) during the course of infection and specific, transcriptional dysregulation in splicing isoforms, T cell receptor expression, and cellular expression states. In particular, cardiac and lung tissues revealed the largest degree of splicing isoform switching and cell expression state loss. Overall, these findings reveal a systemic disruption of cellular and transcriptional pathways from COVID-19 across all tissues, which can inform subsequent studies to combat the mortality of COVID-19, as well to better understand the molecular dynamics of lethal SARS-CoV-2 infection and other viruses.

5.
PUBMED; 2021.
Preprint in English | PUBMED | ID: ppcovidwho-293385

ABSTRACT

Metagenomic DNA sequencing is a powerful tool to characterize microbial communities but is sensitive to environmental DNA contamination, in particular when applied to samples with low microbial biomass. Here, we present contamination-free metagenomic DNA sequencing (Coffee-seq), a metagenomic sequencing assay that is robust against environmental contamination. The core idea of Coffee-seq is to tag the DNA in the sample prior to DNA isolation and library preparation with a label that can be recorded by DNA sequencing. Any contaminating DNA that is introduced in the sample after tagging can then be bioinformatically identified and removed. We applied Coffee-seq to screen for infections from microorganisms with low burden in blood and urine, to identify COVID-19 co-infection, to characterize the urinary microbiome, and to identify microbial DNA signatures of inflammatory bowel disease in blood.

SELECTION OF CITATIONS
SEARCH DETAIL