Novel Nested-Seq Approach for SARS-CoV-2 Real-Time Epidemiology and In-Depth Mutational Profiling in Wastewater

A scoping review of global SARS-CoV-2 wastewater-based epidemiology in light of COVID-19 pandemic

Recently, wastewater-based epidemiology (WBE) research has experienced a strong impetus during the Coronavirus disease 2019 (COVID-19) pandemic. However, a few technical issues related to surveillance strategies, such as standardized procedures ranging from sampling to testing protocols, need to be resolved in preparation for future infectious disease outbreaks. This review highlights the study characteristics, potential use of WBE and overview of methods, as well as methods utilized to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) including its variant in wastewater. A literature search was performed electronically in PubMed and Scopus according to PRISMA guidelines for relevant peer-reviewed articles published between January 2020 and March 2022. The search identified 588 articles, out of which 221 fulfilled the necessary criteria and are discussed in this review. Most global WBE studies were conducted in North America (n = 75, 34 %), followed by Europe (n = 68, 30.8 %), and Asia (n = 43, 19.5 %). The review also showed that most of the application of WBE observed were to correlate SARS-CoV-2 ribonucleic acid (RNA) trends in sewage with epidemiological data (n = 90, 40.7 %). The techniques that were often used globally for sample collection, concentration, preferred matrix recovery control and various sample types were also discussed. Overall, this review provided a framework for researchers specializing in WBE to apply strategic approaches to their research questions in achieving better functional insights. In addition, areas that needed more in-depth analysis, data collection, and ideas for new initiatives were identified.

Spike-Seq: An amplicon-based high-throughput sequencing approach for the sensitive detection and characterization of SARS-CoV-2 genetic variations in environmental samples

Ever since the outbreak of COVID-19 disease in Wuhan, China, different variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified. Wastewater-based epidemiology (WBE), an approach that has been successfully applied in numerous case studies worldwide, offers a cost-effective and rapid way for monitoring trends of SARS-Cov-2 in the community level without selection bias. Despite being a gold-standard procedure, WBE is a challenging approach due to the sample instability and the moderate efficiency of SARS-CoV-2 concentration in wastewater. In the present study, we introduce Spike-Seq, a custom amplicon-based approach for the S gene sequencing of SARS-CoV-2 in wastewater samples, which enables not only the accurate identification of the existing Spike-related genetic markers, but also the estimation of their frequency in the investigated samples. The implementation of Spike-Seq involves the combination of nested PCR-based assays that efficiently amplify the entire nucleotide sequence of the S gene and next-generation sequencing, which enables the variant detection and the estimation of their frequency. In the framework of the current work, Spike-Seq was performed to investigate the mutational profile of SARS-CoV-2 in samples from the Wastewater Treatment Plant (WWTP) of Athens, Greece, which originated from multiple timepoints, ranging from March 2021 until July 2022. Our findings demonstrate that Spike-Seq efficiently detected major genetic markers of B.1.1.7 (Alpha), B.1.617.2 (Delta) as well as B.1.1.529 (Omicron) variants in wastewater samples and provided their frequency levels, showing similar variant distributions with the published clinical data from the National Public Health organization. The presented approach can prove to be a useful tool for the detection of SARS-CoV-2 in challenging wastewater samples and the identification of the existing genetic variants of S gene.

Effectiveness of environmental surveillance of SARS-CoV-2 as an early-warning system: Update of a systematic review during the second year of the pandemic

The aim of this updated systematic review was to offer an overview of the effectiveness of environmental surveillance (ES) of SARS-CoV-2 as a potential early-warning system (EWS) for COVID-19 and new variants of concerns (VOCs) during the second year of the pandemic. An updated literature search was conducted to evaluate the added value of ES of SARS-CoV-2 for public health decisions. The search for studies published between June 2021 and July 2022 resulted in 1,588 publications, identifying 331 articles for full-text screening. A total of 151 publications met our inclusion criteria for the assessment of the effectiveness of ES as an EWS and early detection of SARS-CoV-2 variants. We identified a further 30 publications among the grey literature. ES confirms its usefulness as an EWS for detecting new waves of SARS-CoV-2 infection with an average lead time of 1-2 weeks for most of the publication. ES could function as an EWS for new VOCs in areas with no registered cases or limited clinical capacity. Challenges in data harmonization and variant detection require standardized approaches and innovations for improved public health decision-making. ES confirms its potential to support public health decision-making and resource allocation in future outbreaks.

Delta SARS-CoV-2 variant is entirely substituted by the omicron variant during the fifth COVID-19 wave in Attica region

Wastewater analysis is the most attractive alternative way for the quantification and variant profiling of SARS-CoV-2. Infection dynamics can be monitored by RT-qPCR assays while NGS can provide evidence for the presence of existing or new emerging SARS-CoV-2 variants. Herein, apart from the infection dynamic in Attica since June 1st, 2021, the monitoring of 9 mutations of the omicron and 4 mutations of the delta SARS-CoV-2 variants, utilizing both novel Nested-Seq and RT-PCR, is reported and the substitution of the delta variant (B.1.617.2) by the omicron variant (B.1.1.529) in Attica, Greece within approximately one month is highlighted. The key difference between the two methodologies is discovery power. RT-PCR can only detect known sequences cost-effectively, while NGS is a hypothesis-free approach that does not require prior knowledge to detect novel genes. Overall, the potential of wastewater genomic surveillance for the early discovery and monitoring of variants important for disease management at the community level is underlined. This is the first study, reporting the SARS-CoV-2 infection dynamic for an extended time period and the first attempt to monitor two of the most severe variants with two different methodologies in Greece.

Pathogenesis and Mechanisms of SARS-CoV-2 Infection in the Intestine, Liver, and Pancreas

The novel coronavirus, SARS-CoV-2, rapidly spread worldwide, causing an ongoing global pandemic. While the respiratory system is the most common site of infection, a significant number of reported cases indicate gastrointestinal (GI) involvement. GI symptoms include anorexia, abdominal pain, nausea, vomiting, and diarrhea. Although the mechanisms of GI pathogenesis are still being examined, viral components isolated from stool samples of infected patients suggest a potential fecal-oral transmission route. In addition, viral RNA has been detected in blood samples of infected patients, making hematologic dissemination of the virus a proposed route for GI involvement. Angiotensin-converting enzyme 2 (ACE2) receptors serve as the cellular entry mechanism for the virus, and these receptors are particularly abundant throughout the GI tract, making the intestine, liver, and pancreas potential extrapulmonary sites for infection and reservoirs sites for developing mutations and new variants that contribute to the uncontrolled spread of the disease and resistance to treatments. This transmission mechanism and the dysregulation of the immune system play a significant role in the profound inflammatory and coagulative cascades that contribute to the increased severity and risk of death in several COVID-19 patients. This article reviews various potential mechanisms of gastrointestinal, liver, and pancreatic injury.

Real-Time Learning and Monitoring System in Fighting against SARS-CoV-2 in a Private Indoor Environment

The SARS-CoV-2 virus has posed formidable challenges that must be tackled through scientific and technological investigations on each environmental scale. This research aims to learn and report about the current state of user activities, in real-time, in a specially designed private indoor environment with sensors in infection transmission control of SARS-CoV-2. Thus, a real-time learning system that evolves and updates with each incoming piece of data from the environment is developed to predict user activities categorized for remote monitoring. Accordingly, various experiments are conducted in the private indoor space. Multiple sensors, with their inputs, are analyzed through the experiments. The experiment environment, installed with microgrids and Internet of Things (IoT) devices, has provided correlating data of various sensors from that special care context during the pandemic. The data is applied to classify user activities and develop a real-time learning and monitoring system to predict the IoT data. The microgrids were operated with the real-time learning system developed by comprehensive experiments on classification learning, regression learning, Error-Correcting Output Codes (ECOC), and deep learning models. With the help of machine learning experiments, data optimization, and the multilayered-tandem organization of the developed neural networks, the efficiency of this real-time monitoring system increases in learning the activity of users and predicting their actions, which are reported as feedback on the monitoring interfaces. The developed learning system predicts the real-time IoT data, accurately, in less than 5 milliseconds and generates big data that can be deployed for different usages in larger-scale facilities, networks, and e-health services.

Research needs for optimising wastewater-based epidemiology monitoring for public health protection

Wastewater-based epidemiology (WBE) is an unobtrusive method used to observe patterns in illicit drug use, poliovirus, and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The pandemic and need for surveillance measures have led to the rapid acceleration of WBE research and development globally. With the infrastructure available to monitor SARS-CoV-2 from wastewater in 58 countries globally, there is potential to expand targets and applications for public health protection, such as other viral pathogens, antimicrobial resistance (AMR), pharmaceutical consumption, or exposure to chemical pollutants. Some applications have been explored in academic research but are not used to inform public health decision-making. We reflect on the current knowledge of WBE for these applications and identify barriers and opportunities for expanding beyond SARS-CoV-2. This paper critically reviews the applications of WBE for public health and identifies the important research gaps for WBE to be a useful tool in public health. It considers possible uses for pathogenic viruses, AMR, and chemicals. It summarises the current evidence on the following: (1) the presence of markers in stool and urine; (2) environmental factors influencing persistence of markers in wastewater; (3) methods for sample collection and storage; (4) prospective methods for detection and quantification; (5) reducing uncertainties; and (6) further considerations for public health use.

Wastewater Sequencing-An Innovative Method for Variant Monitoring of SARS-CoV-2 in Populations

The SARS-CoV-2 outbreak has already affected more than 555 million people, and 6.3 million people have died. Due to its high infectivity, it is crucial to track SARS-CoV-2 outbreaks early to prevent the spread of infection. Wastewater monitoring appears to be a powerful and effective tool for managing epidemiological situations. Due to emerging mutations of SARS-CoV-2, there is a need to monitor mutations in order to control the pandemic. Since the sequencing of randomly chosen individuals is time-consuming and expensive, sequencing of wastewater plays an important role in revealing the dynamics of infection in a population. The sampling method used is a crucial factor and significantly impacts the results. Wastewater can be collected as a grab sample or as a 24 h composite sample. Another essential factor is the sample volume, as is the method of transport used. This review discusses different pretreatment procedures and RNA extraction, which may be performed using various methods, such as column-based extraction, TRIzol, or magnetic extraction. Each of the methods has its advantages and disadvantages, which are described accordingly. RT-qPCR is a procedure that confirms the presence of SARS-CoV-2 genes before sequencing. This review provides an overview of currently used methods for preparing wastewater samples, from sampling to sequencing.

SARS-CoV-2 wastewater surveillance data can predict hospitalizations and ICU admissions

We measured SARS-CoV-2 RNA load in raw wastewater in Attica, Greece, by RT-qPCR for the environmental surveillance of COVID-19 for 6 months. The lag between RNA load and pandemic indicators (COVID-19 hospital and intensive care unit (ICU) admissions) was calculated using a grid search. Our results showed that RNA load in raw wastewater is a leading indicator of positive COVID-19 cases, new hospitalization and admission into ICUs by 5, 8 and 9 days, respectively. Modelling techniques based on distributed/fixed lag modelling, linear regression and artificial neural networks were utilized to build relationships between SARS-CoV-2 RNA load in wastewater and pandemic health indicators. SARS-CoV-2 mutation analysis in wastewater during the third pandemic wave revealed that the alpha-variant was dominant. Our results demonstrate that clinical and environmental surveillance data can be combined to create robust models to study the on-going COVID-19 infection dynamics and provide an early warning for increased hospital admissions.