Microbiome Analysis for Wastewater Surveillance during COVID-19

Interpretation of indoor air surveillance for respiratory infections: a prospective longitudinal observational study in a childcare setting

BACKGROUND

Sampling the air in indoor congregate settings, where respiratory pathogens are ubiquitous, may constitute a valuable yet underutilised data source for community-wide surveillance of respiratory infections. However, there is a lack of research comparing air sampling and individual sampling of attendees. Therefore, it remains unclear how air sampling results should be interpreted for the purpose of surveillance.

METHODS

In this prospective observational study, we compared the presence and concentration of several respiratory pathogens in the air with the number of attendees with infections and the pathogen load in their nasal mucus. Weekly for 22 consecutive weeks, we sampled the air in a single childcare setting in Belgium. Concurrently, we collected the paper tissues used to wipe the noses of 23 regular attendees: children aged zero to three and childcare workers. All samples were tested for 29 respiratory pathogens using PCR.

FINDINGS

Air sampling sensitively detected most respiratory pathogens found in nasal mucus. Some pathogens (SARS-CoV-2, Pneumocystis jirovecii) were found repeatedly in the air, but rarely in nasal mucus, whilst the opposite was true for others (Human coronavirus NL63). All three pathogens with a clear outbreak pattern (Human coronavirus HKU-1, human parainfluenza virus 3 and 4) were found in the air one week before or concurrent with the first detection in paper tissue samples. The presence and concentration of pathogens in the air was best predicted by the pathogen load of the most infectious case. However, air pathogen concentrations also correlated with the number of attendees with infections. Detection and concentration in the air were associated with CO2 concentration, a marker of ventilation and occupancy.

INTERPRETATION

Our results suggest that air sampling could provide sensitive, responsive epidemiological indicators for the surveillance of respiratory pathogens. Using air CO2 concentrations to normalise such signals emerges as a promising approach.

FUNDING

KU Leuven; DURABLE project, under the EU4Health Programme of the European Commission; Thermo Fisher Scientific.

Evaluation of tomato brown rugose fruit virus as a microbial source tracking marker for human sewage in Thailand

Tomato brown rugose fruit virus (ToBRFV) has emerged as a major plant pathogen with the potential to spread through contaminated wastewater, posing risks to agriculture and public health. This study evaluated ToBRFV as a human-specific microbial source tracking (MST) marker in Thailand, comparing its performance to crAssphage. Using qPCR assays, ToBRFV was detected in 62.5 % of building sewage samples (n = 16) and 100.0 % of wastewater treatment plant (WWTP) influent samples (n = 16). Notably, ToBRFV showed minimal cross-detection in non-human fecal samples (35 pooled samples), collected from cows, pigs, chickens, ducks, and goats, with only one detection in a pig fecal-source sample, demonstrating high specificity to human sewage. Concentrations in WWTP influent were significantly higher (mean: 5.19 ± 5.05; range: 3.96-5.62 log10 copies/100 mL) than in building sewage (mean: 4.36 ± 4.40; range: 2.33-4.85 log10 copies/100 mL) (p < 0.001). ToBRFV concentrations were significantly lower than crAssphage in building sewage but higher in WWTP influents. Additionally, ToBRFV and crAssphage exhibited moderate correlations in both building sewage and WWTP influent samples. These results suggest that ToBRFV could serve as a valuable MST marker for identifying human contamination in water bodies, complementing established markers. While ToBRFV's broader utility across diverse geographic regions remains to be fully validated, this study highlights its potential as a reliable indicator of human sewage in environmental surveillance.

Culture-Dependent and -Independent Wastewater Surveillance for Multiple Pathogenic Yeasts

Wastewater surveillance is a promising tool to monitor potential outbreaks and determine the disease burden within a community. This system has been extensively used to monitor polio and COVID-19 infection levels, yet few attempts have been made to apply it to monitoring pathogenic yeast. This study aimed to investigate the application of wastewater surveillance for potentially pathogenic yeast in wastewater treatment plant influent. This was done by comparing culture-dependent data with culture-independent data and investigating the fluconazole concentration in wastewater. Additional studies on the growth of isolated strains were conducted. We found that a multiplex PCR system to detect multiple yeasts holds promise as a molecular detection tool for wastewater surveillance. Culture-dependent results indicated that Candida spp. specifically C. krusei and C. glabrata, were most prominent. Growth studies supported that these species grow well in this environment while the less frequently isolated yeasts grew poorly. The data from culture-dependent and independent techniques showed some correlation, with similar species being identified with both, further promoting the use of molecular tools for surveillance. This study highlights the presence of potentially pathogenic yeasts in wastewater, which may indicate the prevalence of these yeasts in the environment or community. This wastewater may also be a potential source of infection for persons encountering it due to poor wastewater management.

Assessment of Bacterial Community Structure, Associated Functional Role, and Water Health in Full-Scale Municipal Wastewater Treatment Plants

The present study collected wastewater samples from fourteen (14) full-scale wastewater treatment plants (WWTPs) at different treatment stages, namely, primary, secondary, and tertiary, to understand the impact of WWTP processes on the bacterial community structure, their role, and their correlation with environmental variables (water quality parameters). The findings showed that the bacterial communities in the primary, secondary, and tertiary treatment stages are more or less similar. They are made up of 42 phyla, 84 classes, 154 orders, 212 families, and 268 genera. Proteobacteria, Bacteroidetes, Cloacimonetes, Firmicutes, Euryarchaeota, Verrucomicrobia, Cyanobacteria, Desulfomicrobium, Thauera, Zavarzinia, and Nitrospirae, among others, dominated the bacterial community structure in all treatment stages. The biochemical oxygen demand was 7-12 times, chemical oxygen demand (COD) was 6 times, and total suspended solids (TSS) was 3.5 times higher in the wastewater than what the Central Pollution Control Board (CPCB) in New Delhi, India, allows as standard discharge. The correlation analysis using the Pearson r matrix and canonical correspondence analysis (CCA) also confirmed the fact that these water quality parameters (especially BOD and COD) play a pivotal role in deciphering the community structure in WWTPs.

Whole genome sequencing approaches for taxonomic profiling and evaluation of wastewater quality

Tracking metagenomic abundance in wastewater is undoubtedly a powerful tool to detect emerging variants and improve community health. However, there are a few factors that limit environmental water-based genomic monitoring: sampling variability, incomplete coverage, genetic fragmentation, degradation, data analysis and interpretation. The decreasing costs of high-throughput sequencing and high-end supercomputers have increased the use and accuracy of genomic data for microbial detection and monitoring in wastewater samples within any given region. To better understand the microbial dynamics and to determine the target sequencing throughput required to establish taxa that may pose as bio-indicators of an epidemiological outbreak, wastewater samples were collected from distinct locations within the Emirate of Abu Dhabi, United Arab Emirates using appropriate sampling methods. A reference database of ∼27,000 known species was developed and used for further analysis. The results showed that 15 % of data in each sample matched any of ∼27,000 known bacterial, viral, fungal, or protozoan species. Despite the high fraction of unclassified data (85 %), more than 2000 species from >800 genera across >30 phyla were detected in each sample. Both 5 Gb and 10 Gb of sequenced data detected the top ∼2000 species with highest abundance. Doubling the target sequencing throughput (i.e., 10 Gb vs 5 Gb) detected ∼500 additional low-abundance species per sample however it did not affect the overall sample composition or translate into higher per-sample species diversity captured. There was a marginal increase in the number of species detected in each sample beyond 0.20 Gb of classified data. Overall, the results indicate that sequencing to a 3 Gb throughput detects nearly 95 % of all species in the samples.

Long term study on the fate and environmental risks of favipiravir in wastewater treatment plants and comparison with COVID-19 cases

In recent years especially during COVID-19, the increased usage of antiviral drugs has led to increased interest in monitoring their presence in wastewater worldwide. In this study, it was examined the occurrence, fate and environmental risks of favipiravir which is used for COVID-19 treatment in two wastewater treatment plants (WWTPs) with different treatment processes in Istanbul, Turkey. Favipiravir was measured in WWTPs influent samples, effluent samples and sludge samples with maximum concentrations of 97 μg/L, 64.11 μg/L and 182.47 μg/g, respectively. Favipiravir had removal efficiency below 55 % for both WWTPs. Mass balance analysis showed that favipiravir removal in WWTPs mainly attributed to biodegradation/biotransformation. Statistical analysis revealed a significant correlation between favipiravir concentration and COVID-19 incidence in Istanbul. The microbial distribution analysis indicated that comparison of collected COVID-19 pandemic sludge and post-pandemic period sludge samples, a noteworthy reduction in the Chloroflexi and Actinobacteriota phyla at the phylum level was observed. Environmental risk assessment using risk quotients ranged from 168 to 704, indicating that the presence of this antiviral drug posed significant ecological risks to aquatic organisms. The study concluded that WWTPs were releasing antiviral drugs into the environment, thereby posing risks to both the aquatic ecosystem and public health. The results of this study demonstrate the persistence of favipiravir in WWTPs and offer crucial supporting data for further research into the advancement of wastewater treatment technology. Also, this study shows wastewater based monitoring is supplementary and early warning system for determining the occurrence of antiviral drugs.

Re-vitalizing wastewater: Nutrient recovery and carbon capture through microbe-algae synergy using omics-biology

Increasing amounts of wastewater is the most pervasive and challenging environmental problem globally. Conventional treatment methods are costly and entail huge energy, carbon consumption and greenhouse gas emissions. Owing to their unique ability of carbon capturing and resource recovery, microalgae-microbiome based treatment is a potential approach and is widely used for carbon-neutral wastewater treatment. Microalgae-bacteria synergy (i.e., the functionally beneficial microbial synthetic communities) performs better and enhances carbon-sequestration and nutrient recovery from wastewater treatment plants. This review presents a comprehensive information regarding the potential of microalgae-microbiome as a sustainable agent for wastewater and discusses synergistic approaches for effective nutrient removal. Moreover, this review discusses, the role of omics-biology and Insilco approaches in unravelling and understanding the algae-microbe synergism and their response toward wastewater treatment. Finally, it discusses various microbiome engineering approaches for developing the effective microalgae-bacteria partners for carbon sequestration and nutrient recovery from wastewater, and summarizes future research perspectives on microalgae-microbiome based bioremediation.

Metaviromic reveals the dynamics and diversity of the virosphere in wastewater samples from Natal, Brazil

The COVID-19 pandemic underscored the significance of omics technology and Wastewater-Based Epidemiology for epidemic preparedness. This study investigates the virosphere in wastewater samples from Natal (Brazil), aiming to understand its structure, relationships, and potential. Metaviromic analysis was used on DNA and RNA from weekly samples collected over a year (June/2021 to May/2022) from three wastewater treatment plants. The virosphere showed stability, particularly in viruses infecting microorganisms and plants. However, an alternation of representatives of viruses that infect animals has been observed. Among the most abundant viruses infecting microorganisms are genera associated with the bacterial genera Escherichia, Pseudomonas, and Caulobacte. Regarding the viruses infecting plants, Sobemovirus and Tobamovirus are the most abundant genera. Odontoglossum ringspot virus was identified as a possible RNA virus biomarker. Among DNA viruses infecting animals, genera Bocaparvovirus and Mastadenovirus are the most prevalent. Intriguingly, some Poxviridae family members were observed in the samples. Co-occurrence network analysis identified potential biomarkers like Volepox virus, Anatid herpesvirus 1, and Caviid herpesvirus 2. Among RNA viruses affecting animals, Mamastrovirus, Rotavirus, and Norovirus genera were the most abundant pathogens. Furthermore, members of the Coronaviridae family exhibited a high degree of centrality values in the co-occurrence network, even connecting with unclassified viruses. The study emphasizes the importance of research in understanding the roles of unclassified viruses. In addition, we observed an association between Coronaviridae reads, rainfall, and the number of reported COVID-19 cases. Our study highlights the diversity and complexity of the viral community in wastewater and the need for research to understand better the ecological roles unclassified viruses play. Such advances will significantly contribute to our preparedness and response to future viral threats. Furthermore, our study contributes to knowledge of virosphere dynamics, offering insights that can contribute to the direction of future public health policies and interventions.

Exploring microbial worlds: a review of whole genome sequencing and its application in characterizing the microbial communities

The classical microbiology techniques have inherent limitations in unraveling the complexity of microbial communities, necessitating the pivotal role of sequencing in studying the diversity of microbial communities. Whole genome sequencing (WGS) enables researchers to uncover the metabolic capabilities of the microbial community, providing valuable insights into the microbiome. Herein, we present an overview of the rapid advancements achieved thus far in the use of WGS in microbiome research. There was an upsurge in publications, particularly in 2021 and 2022 with the United States, China, and India leading the metagenomics research landscape. The Illumina platform has emerged as the widely adopted sequencing technology, whereas a significant focus of metagenomics has been on understanding the relationship between the gut microbiome and human health where distinct bacterial species have been linked to various diseases. Additionally, studies have explored the impact of human activities on microbial communities, including the potential spread of pathogenic bacteria and antimicrobial resistance genes in different ecosystems. Furthermore, WGS is used in investigating the microbiome of various animal species and plant tissues such as the rhizosphere microbiome. Overall, this review reflects the importance of WGS in metagenomics studies and underscores its remarkable power in illuminating the variety and intricacy of the microbiome in different environments.

Microbial diversity, genomics, and phage-host interactions of cyanobacterial harmful algal blooms

The occurrence of cyanobacterial harmful algal blooms (cyanoHABs) is related to their physical and chemical environment. However, less is known about their associated microbial interactions and processes. In this study, cyanoHABs were analyzed as a microbial ecosystem, using 1 year of 16S rRNA sequencing and 70 metagenomes collected during the bloom season from Lake Okeechobee (Florida, USA). Biogeographical patterns observed in microbial community composition and function reflected ecological zones distinct in their physical and chemical parameters that resulted in bloom "hotspots" near major lake inflows. Changes in relative abundances of taxa within multiple phyla followed increasing bloom severity. Functional pathways that correlated with increasing bloom severity encoded organic nitrogen and phosphorus utilization, storage of nutrients, exchange of genetic material, phage defense, and protection against oxidative stress, suggesting that microbial interactions may promote cyanoHAB resilience. Cyanobacterial communities were highly diverse, with picocyanobacteria ubiquitous and oftentimes most abundant, especially in the absence of blooms. The identification of novel bloom-forming cyanobacteria and genomic comparisons indicated a functionally diverse cyanobacterial community with differences in its capability to store nitrogen using cyanophycin and to defend against phage using CRISPR and restriction-modification systems. Considering blooms in the context of a microbial ecosystem and their interactions in nature, physiologies and interactions supporting the proliferation and stability of cyanoHABs are proposed, including a role for phage infection of picocyanobacteria. This study displayed the power of "-omics" to reveal important biological processes that could support the effective management and prediction of cyanoHABs.

IMPORTANCE

Cyanobacterial harmful algal blooms pose a significant threat to aquatic ecosystems and human health. Although physical and chemical conditions in aquatic systems that facilitate bloom development are well studied, there are fundamental gaps in the biological understanding of the microbial ecosystem that makes a cyanobacterial bloom. High-throughput sequencing was used to determine the drivers of cyanobacteria blooms in nature. Multiple functions and interactions important to consider in cyanobacterial bloom ecology were identified. The microbial biodiversity of blooms revealed microbial functions, genomic characteristics, and interactions between cyanobacterial populations that could be involved in bloom stability and more coherently define cyanobacteria blooms. Our results highlight the importance of considering cyanobacterial blooms as a microbial ecosystem to predict, prevent, and mitigate them.

Antimicrobial resistance landscape in a metropolitan city context using open drain wastewater-based metagenomic analysis

One Health concept recognizes the inextricable interactions of diverse ecosystems and their subsequent effect on human, animal and plant health. Antimicrobial resistance (AMR) is a major One Health concern and is predicted to cause catastrophes if appropriate measures are not implemented. To understand the AMR landscape in a south Indian metropolitan city, metagenomic analysis of open drains was performed. The data suggests that in January 2022, macrolide class of antibiotics contributed the highest resistance of 40.1% in the city, followed by aminoglycoside- 24.4%, tetracycline- 11.3% and lincosamide- 6.7%. The 'mutations in the 23S rRNA gene conferring resistance to macrolide antibiotics' were the major contributor of resistance with a prevalence of 39.7%, followed by '16s rRNA with mutation conferring resistance to aminoglycoside antibiotics'- 22.2%, '16S rRNA with mutation conferring resistance to tetracycline derivatives'- 9.2%, and '23S rRNA with mutation conferring resistance to lincosamide antibiotics'- 6.7%. The most prevalent antimicrobial resistance gene (ARG) 'mutations in the 23S rRNA gene conferring resistance to macrolide antibiotics' was present in multiple pathogens including Escherichia coli, Campylobacter jejuni, Acinetobacter baumannii, Streptococcus pneumoniae, Pseudomonas aeruginosa, Neisseria gonorrhoeae, Klebsiella pneumoniae and Helicobacter pylori. Most of the geographical locations in the city showed a similar landscape for AMR. Considering human mobility and anthropogenic activities, such an AMR landscape could be common across other regions too. The data indicates that pathogens are evolving and acquiring antibiotic resistance genes to evade antibiotics of multiple major drug classes in diverse hosts. The outcomes of the study are relevant not only in understanding the resistance landscape at a broader level but are also important for identifying the resistant drug classes, the mechanisms of gaining resistance and for developing new drugs that target specific pathways. This kind of surveillance protocol can be extended to regions in other developing countries to assess and combat the problem of antimicrobial resistance.

Tracking diarrhea viruses and mpox virus using the wastewater surveillance network in Hong Kong

The wastewater surveillance network successfully established for COVID-19 showed great potential to monitor other infectious viruses, such as norovirus, rotavirus and mpox virus. In this study, we established and validated detection methods for these viruses in wastewater. We developed a supernatant-based method to detect RNA viruses from wastewater samples and applied it to the monthly diarrhea viruses (norovirus genogroup I & II, and rotavirus) surveillance in wastewater treatment plants (WWTPs) at a city-wide level for 16 months. Significant correlations were observed between the diarrhea viruses concentrations in wastewater and detection rates in faecal specimens by clinical surveillance. The highest norovirus concentration in wastewater was obtained in winter, consistent with the seasonal pattern of norovirus outbreak in Hong Kong. Additionally, we established a pellet-based method to monitor DNA viruses in wastewater and detected weak signals for mpox virus in wastewater from a WWTP serving approximately 16,700 people, when the first mpox patient in Hong Kong was admitted to the hospital within the catchment area. Genomic sequencing provided confirmatory evidence for the validity of the results. Our findings emphasized the efficacy of the wastewater surveillance network in WWTPs as a cost-effective tool to track the transmission trend of diarrhea viruses and to provide sensitive detection of novel emerging viruses such as mpox virus in low-prevalence areas. The developed methods and surveillance results provide confidence for establishing robust wastewater surveillance programs to control infectious diseases in the post-pandemic era.

Saliva-based detection of SARS-CoV-2: a bibliometric analysis of global research

Saliva has emerged as a promising noninvasive biofluid for the diagnosis of oral and systemic diseases, including viral infections. During the coronavirus disease 2019 (COVID-19) pandemic, a growing number of studies focused on saliva-based detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Taking advantage of the WoS core collection (WoSCC) and CiteSpace, we retrieved 1021 articles related to saliva-based detection of SARS-CoV-2 and conducted a comprehensive bibliometric analysis. We analyzed countries, institutions, authors, cited authors, and cited journals to summarize their contribution and influence and analyzed keywords to explore research hotspots and trends. From 2020 to 2021, research focused on viral transmission via saliva and verification of saliva as a reliable specimen, whereas from 2021 to the present, the focus of research has switched to saliva-based biosensors for SARS-CoV-2 detection. By far, saliva has been verified as a reliable specimen for SARS-CoV-2 detection, although a standardized procedure for saliva sampling and processing is needed. Studies on saliva-based detection of SARS-CoV-2 will promote the development of saliva-based diagnostics and biosensors for viral detection. Collectively, our findings could provide valuable information to help scientists perceive the basic knowledge landscapes on saliva-based detection of SARS-CoV-2, the past and current research hotspots, and future opportunities.

AH, Fernandez S, Huang AT, Vatanaprasan P, Saethang T, Luk-in S, Storer RJ, Ounjai P, Devanga Ragupathi NK, Kanthawee P, Ngamwongsatit N, Badavath VN, Thuptimdang W, Leelahavanichkul A, Kanjanabuch T, Miyanaga K, Cui L, Nanbo A, Shibuya K, Kupwiwat R, Sano D, Furukawa T, Sei K, Higgins PG, Kicic A, Singer AC, Chatsuwan T, Trowsdale S, Abe S, Ishikawa H, Amarasiri M, Modchang C, Wannigama DL. Exploring indoor and outdoor dust as a potential tool for detection and monitoring of COVID-19 transmission

This study investigated the potential of using SARS-CoV-2 viral concentrations in dust as an additional surveillance tool for early detection and monitoring of COVID-19 transmission. Dust samples were collected from 8 public locations in 16 districts of Bangkok, Thailand, from June to August 2021. SARS-CoV-2 RNA concentrations in dust were quantified, and their correlation with community case incidence was assessed. Our findings revealed a positive correlation between viral concentrations detected in dust and the relative risk of COVID-19. The highest risk was observed with no delay (0-day lag), and this risk gradually decreased as the lag time increased. We observed an overall decline in viral concentrations in public places during lockdown, closely associated with reduced human mobility. The effective reproduction number for COVID-19 transmission remained above one throughout the study period, suggesting that transmission may persist in locations beyond public areas even after the lockdown measures were in place.

Wastewater-based surveillance as a tool for public health action: SARS-CoV-2 and beyond

Wastewater-based surveillance (WBS) has undergone dramatic advancement in the context of the coronavirus disease 2019 (COVID-19) pandemic. The power and potential of this platform technology were rapidly realized when it became evident that not only did WBS-measured SARS-CoV-2 RNA correlate strongly with COVID-19 clinical disease within monitored populations but also, in fact, it functioned as a leading indicator. Teams from across the globe rapidly innovated novel approaches by which wastewater could be collected from diverse sewersheds ranging from wastewater treatment plants (enabling community-level surveillance) to more granular locations including individual neighborhoods and high-risk buildings such as long-term care facilities (LTCF). Efficient processes enabled SARS-CoV-2 RNA extraction and concentration from the highly dilute wastewater matrix. Molecular and genomic tools to identify, quantify, and characterize SARS-CoV-2 and its various variants were adapted from clinical programs and applied to these mixed environmental systems. Novel data-sharing tools allowed this information to be mobilized and made immediately available to public health and government decision-makers and even the public, enabling evidence-informed decision-making based on local disease dynamics. WBS has since been recognized as a tool of transformative potential, providing near-real-time cost-effective, objective, comprehensive, and inclusive data on the changing prevalence of measured analytes across space and time in populations. However, as a consequence of rapid innovation from hundreds of teams simultaneously, tremendous heterogeneity currently exists in the SARS-CoV-2 WBS literature. This manuscript provides a state-of-the-art review of WBS as established with SARS-CoV-2 and details the current work underway expanding its scope to other infectious disease targets.

Building Environmental and Sociological Predictive Intelligence to Understand the Seasonal Threat of SARS-CoV-2 in Human Populations

Current modeling practices for environmental and sociological modulated infectious diseases remain inadequate to forecast the risk of outbreak(s) in human populations, partly due to a lack of integration of disciplinary knowledge, limited availability of disease surveillance datasets, and overreliance on compartmental epidemiological modeling methods. Harvesting data knowledge from virus transmission (aerosols) and detection (wastewater) of SARS-CoV-2, a heuristic score-based environmental predictive intelligence system was developed that calculates the risk of COVID-19 in the human population. Seasonal validation of the algorithm was uniquely associated with wastewater surveillance of the virus, providing a lead time of 7-14 days before a county-level outbreak. Using county-scale disease prevalence data from the United States, the algorithm could predict COVID-19 risk with an overall accuracy ranging between 81% and 98%. Similarly, using wastewater surveillance data from Illinois and Maryland, the SARS-CoV-2 detection rate was greater than 80% for 75% of the locations during the same time the risk was predicted to be high. Results suggest the importance of a holistic approach across disciplinary boundaries that can potentially allow anticipatory decision-making policies of saving lives and maximizing the use of available capacity and resources.

Metabolites: a converging node of host and microbe to explain meta-organism

Meta-organisms encompassing the host and resident microbiota play a significant role in combatting diseases and responding to stress. Hence, there is growing traction to build a knowledge base about this ecosystem, particularly to characterize the bidirectional relationship between the host and microbiota. In this context, metabolomics has emerged as the major converging node of this entire ecosystem. Systematic comprehension of this resourceful omics component can elucidate the organism-specific response trajectory and the communication grid across the ecosystem embodying meta-organisms. Translating this knowledge into designing nutraceuticals and next-generation therapy are ongoing. Its major hindrance is a significant knowledge gap about the underlying mechanisms maintaining a delicate balance within this ecosystem. To bridge this knowledge gap, a holistic picture of the available information has been presented with a primary focus on the microbiota-metabolite relationship dynamics. The central theme of this article is the gut-brain axis and the participating microbial metabolites that impact cerebral functions.

Multi-omic approaches for host-microbiome data integration

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

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.

Anthelmintic resistance in soil-transmitted helminths: One-Health considerations

The One-Health approach recognizes the intricate connection between human, animal, and environmental health, and that cooperative effort from various professionals provides comprehensive awareness and potential solutions for issues relating to the health of people, animals, and the environment. This approach has increasingly gained appeal as the standard strategy for tackling emerging infectious diseases, most of which are zoonoses. Treatment with anthelmintics (AHs) without a doubt minimizes the severe consequences of soil-transmitted helminths (STHs); however, evidence of anthelmintic resistance (AR) development to different helminths of practically every animal species and the distinct groups of AHs is overwhelming globally. In this regard, the correlation between the application of anthelmintic drugs in both human and animal populations and the consequent development of anthelmintic resistance in STHs within the context of a One-Health framework is explored. This review provides an overview of the major human and animal STHs, treatment of the STHs, AR development and drug-related factors contributing towards AR, One-Health and STHs, and an outline of some One-Health strategies that may be used in combating AR.

Genomic diversity of Vibrio spp. and metagenomic analysis of pathogens in Florida Gulf coastal waters following Hurricane Ian

IMPORTANCE

Evidence suggests warming temperatures are associated with the spread of potentially pathogenic Vibrio spp. and the emergence of human disease globally. Following Hurricane Ian, the State of Florida reported a sharp increase in the number of reported Vibrio spp. infections and deaths. Hence, monitoring of pathogens, including vibrios, and environmental parameters influencing their occurrence is critical to public health. Here, DNA sequencing was used to investigate the genomic diversity of Vibrio parahaemolyticus and Vibrio vulnificus, both potential human pathogens, in Florida coastal waters post Hurricane Ian, in October 2022. Additionally, the microbial community of water samples was profiled to detect the presence of Vibrio spp. and other microorganisms (bacteria, fungi, protists, and viruses) present in the samples. Long-term environmental data analysis showed changes in environmental parameters during and after Ian were optimal for the growth of Vibrio spp. and related pathogens. Collectively, results will be used to develop predictive risk models during climate change.

Removal of intI1, ARGs, and SARS-CoV-2 and changes in bacterial communities in four sewage treatment facilities

Currently, discharge regulations for wastewater treatment plants (WWTPs) are based on conventional parameters, but more is needed to ensure safe water reuse. In particular, emerging pollutants, as antimicrobials and antibiotic resistance genes (ARGs), are not considered. This research focuses on the fate of emerging biological contaminants during wastewater treatment in Mexico City. intI1 and the ARGs cphA-02, OXA-10 and sul1 were analyzed by qPCR; pathogenic bacteria species were characterized by high throughput sequencing of complete 16S rRNA gene, and fragments of SARS-CoV-2 were quantified by RT-qPCR. Conventional parameters (chemical oxygen demand and coliform bacteria) were also determined. Two sampling campaigns (rainy and dry seasons) were carried out in four municipal WWTPs in Mexico City, representing five biological treatment processes: conventional activated sludge, extended aeration activated sludge, membrane bioreactor, direct anaerobic digestion, and constructed wetland, followed by ultraviolet light or chlorine disinfection. In most cases, gene fragments of SARS-CoV-2 were eliminated below the detection limit of RT-qPCR. The abundance of intI1 positively correlated with the sul1, OXA-10, and cphA-02 abundances; intI1 and the ARGs here studied were partially removed in the WWTPs, and in most cases, the number of copies per second discarded in the sludge were higher those in the effluent. The treatment processes decreased the abundance of dominant bacterial groups in the raw wastewater, while enriching bacterial groups in the effluent and the biological sludge, with possible pollutant removal capabilities. Bacterial communities in the raw wastewater showed the predominance of the genus Arcobacter (from 62.4 to 86.0 %) containing potentially pathogenic species. Additionally, DNA of some species persisted after the treatment processes: A. johnsonii, A. junii, A. caviae, A. hydrophila, A. veronii, A. butzleri, A. cryaerophilus, Chryseobacterium indologenes, Hafnia paralvei, M. osloensis, Pseudomonas putida and Vibrio cholerae, which deserves special attention in future regulation for safe water reuse.

Application of high-throughput sequencing technologies and analytical tools for pathogen detection in urban water systems: Progress and future perspectives

The ubiquitous presence of pathogenic microorganisms, such as viruses, bacteria, fungi, and protozoa, in urban water systems poses a significant risk to public health. The emergence of infectious waterborne diseases mediated by urban water systems has become one of the leading global causes of mortality. However, the detection and monitoring of these pathogenic microorganisms have been limited by the complexity and diversity in the environmental samples. Conventional methods were restricted by long assay time, high benchmarks of identification, and narrow application sceneries. Novel technologies, such as high-throughput sequencing technologies, enable potentially full-spectrum detection of trace pathogenic microorganisms in complex environmental matrices. This review discusses the current state of high-throughput sequencing technologies for identifying pathogenic microorganisms in urban water systems with a concise summary. Furthermore, future perspectives in pathogen research emphasize the need for detection methods with high accuracy and sensitivity, the establishment of precise detection standards and procedures, and the significance of bioinformatics software and platforms. We have compiled a list of pathogens analysis software/platforms/databases that boast robust engines and high accuracy for preference. We highlight the significance of analyses by combining targeted and non-targeted sequencing technologies, short and long reads technologies, sequencing technologies, and bioinformatic tools in pursuing upgraded biosafety in urban water systems.

Metaproteomic Analysis of Nasopharyngeal Swab Samples to Identify Microbial Peptides in COVID-19 Patients

During the COVID-19 pandemic, impaired immunity and medical interventions resulted in cases of secondary infections. The clinical difficulties and dangers associated with secondary infections in patients necessitate the exploration of their microbiome. Metaproteomics is a powerful approach to study the taxonomic composition and functional status of the microbiome under study. In this study, the mass spectrometry (MS)-based data of nasopharyngeal swab samples from COVID-19 patients was used to investigate the metaproteome. We have established a robust bioinformatics workflow within the Galaxy platform, which includes (a) generation of a tailored database of the common respiratory tract pathogens, (b) database search using multiple search algorithms, and (c) verification of the detected microbial peptides. The microbial peptides detected in this study, belong to several opportunistic pathogens such as Streptococcus pneumoniae, Klebsiella pneumoniae, Rhizopus microsporus, and Syncephalastrum racemosum. Microbial proteins with a role in stress response, gene expression, and DNA repair were found to be upregulated in severe patients compared to negative patients. Using parallel reaction monitoring (PRM), we confirmed some of the microbial peptides in fresh clinical samples. MS-based clinical metaproteomics can serve as a powerful tool for detection and characterization of potential pathogens, which can significantly impact the diagnosis and treatment of patients.

AMAnD: an automated metagenome anomaly detection methodology utilizing DeepSVDD neural networks

The composition of metagenomic communities within the human body often reflects localized medical conditions such as upper respiratory diseases and gastrointestinal diseases. Fast and accurate computational tools to flag anomalous metagenomic samples from typical samples are desirable to understand different phenotypes, especially in contexts where repeated, long-duration temporal sampling is done. Here, we present Automated Metagenome Anomaly Detection (AMAnD), which utilizes two types of Deep Support Vector Data Description (DeepSVDD) models; one trained on taxonomic feature space output by the Pan-Genomics for Infectious Agents (PanGIA) taxonomy classifier and one trained on kmer frequency counts. AMAnD's semi-supervised one-class approach makes no assumptions about what an anomaly may look like, allowing the flagging of potentially novel anomaly types. Three diverse datasets are profiled. The first dataset is hosted on the National Center for Biotechnology Information's (NCBI) Sequence Read Archive (SRA) and contains nasopharyngeal swabs from healthy and COVID-19-positive patients. The second dataset is also hosted on SRA and contains gut microbiome samples from normal controls and from patients with slow transit constipation (STC). AMAnD can learn a typical healthy nasopharyngeal or gut microbiome profile and reliably flag the anomalous COVID+ or STC samples in both feature spaces. The final dataset is a synthetic metagenome created by the Critical Assessment of Metagenome Annotation Simulator (CAMISIM). A control dataset of 50 well-characterized organisms was submitted to CAMISIM to generate 100 synthetic control class samples. The experimental conditions included 12 different spiked-in contaminants that are taxonomically similar to organisms present in the laboratory blank sample ranging from one strain tree branch taxonomic distance away to one family tree branch taxonomic distance away. This experiment was repeated in triplicate at three different coverage levels to probe the dependence on sample coverage. AMAnD was again able to flag the contaminant inserts as anomalous. AMAnD's assumption-free flagging of metagenomic anomalies, the real-time model training update potential of the deep learning approach, and the strong performance even with lightweight models of low sample cardinality would make AMAnD well-suited to a wide array of applied metagenomics biosurveillance use-cases, from environmental to clinical utility.

Significance of wastewater surveillance in detecting the prevalence of SARS-CoV-2 variants and other respiratory viruses in the community - A multi-site evaluation

Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome in wastewater has proven to be useful for tracking the trends of virus prevalence within the community. The surveillance also provides precise and early detection of any new and circulating variants, which aids in response to viral outbreaks. Site-specific monitoring of SARS-CoV-2 variants provides valuable information on the prevalence of new or emerging variants in the community. We sequenced the genomic RNA of viruses present in the wastewater samples and analyzed for the prevalence of SARS-CoV-2 variants as well as other respiratory viruses for a period of one year to account for seasonal variations. The samples were collected from the Reno-Sparks metropolitan area on a weekly basis between November 2021 to November 2022. Samples were analyzed to detect the levels of SARS-CoV-2 genomic copies and variants identification. This study confirmed that wastewater monitoring of SARS-CoV-2 variants can be used for community surveillance and early detection of circulating variants and supports wastewater-based epidemiology (WBE) as a complement to clinical respiratory virus testing as a healthcare response effort. Our study showed the persistence of the SARS-CoV-2 virus throughout the year compared to a seasonal presence of other respiratory viruses, implicating SARS-CoV-2's broad genetic diversity and strength to persist and infect susceptible hosts. Through secondary analysis, we further identified antimicrobial resistance (AMR) genes in the same wastewater samples and found WBE to be a feasible tool for community AMR detection and monitoring.

Identification and genome sequencing of an influenza H3N2 variant in wastewater from elementary schools during a surge of influenza A cases in Las Vegas, Nevada

Real-time surveillance of infectious diseases at schools or in communities is often hampered by delays in reporting due to resource limitations and infrastructure issues. By incorporating quantitative PCR and genome sequencing, wastewater surveillance has been an effective complement to public health surveillance at the community and building-scale for pathogens such as poliovirus, SARS-CoV-2, and even the monkeypox virus. In this study, we asked whether wastewater surveillance programs at elementary schools could be leveraged to detect RNA from influenza viruses shed in wastewater. We monitored for influenza A and B viral RNA in wastewater from six elementary schools from January to May 2022. Quantitative PCR led to the identification of influenza A viral RNA at three schools, which coincided with the lifting of COVID-19 restrictions and a surge in influenza A infections in Las Vegas, Nevada, USA. We performed genome sequencing of wastewater RNA, leading to the identification of a 2021-2022 vaccine-resistant influenza A (H3N2) 3C.2a1b.2a.2 subclade. We next tested wastewater samples from a treatment plant that serviced the elementary schools, but we were unable to detect the presence of influenza A/B RNA. Together, our results demonstrate the utility of near-source wastewater surveillance for the detection of local influenza transmission in schools, which has the potential to be investigated further with paired school-level influenza incidence data.

Analysis of metatranscriptomic methods to enable wastewater-based biosurveillance of all infectious diseases

Introduction

Wastewater-based surveillance emerged during the COVID-19 pandemic as an efficient way to quickly screen large populations, monitor infectious disease transmission over time, and identify whether more virulent strains are becoming more prevalent in the region without burdening the health care system with individualized testing. Ohio was one of the first states to implement wastewater monitoring through its Ohio Coronavirus Wastewater Monitoring Network (OCWMN), originally tracking the prevalence of COVID-19 by quantitative qPCR from over 67 sites across the state. The OCWMN evolved along with the pandemic to include sequencing the SARS-CoV-2 genome to assess variants of concern circulating within the population. As the pandemic wanes, networks such as OCWMN can be expanded to monitor other infectious diseases and outbreaks of interest to the health department to reduce the burden of communicable diseases. However, most surveillance still utilizes qPCR based diagnostic tests for individual pathogens, which is hard to scale for surveillance of multiple pathogens.

Methods

Here we have tested several genomic methods, both targeted and untargeted, for wastewater-based biosurveillance to find the most efficient procedure to detect and track trends in reportable infectious diseases and outbreaks of known pathogens as well as potentially novel pathogens or variants on the rise in our communities. RNA extracts from the OCWMN were provided weekly from 10 sites for 6 weeks. Total RNA was sequenced from the samples on the Illumina NextSeq and on the MinION to identify pathogens present. The MinION long read platform was also used to sequence SARS-CoV-2 with the goal of reducing the complexity of variant calling in mixed populations as occurs with short Illumina reads. Finally, a targeted hybridization approach was tested for compatibility with wastewater RNA samples.

Results and discussion

The data analyzed here provides a baseline assessment that demonstrates that wastewater is a rich resource for infectious disease epidemiology and identifies technology gaps and potential solutions to enable this resource to be used by public health laboratories to monitor the infectious disease landscape of the regions they serve.

Tracing COVID-19 Trails in Wastewater: A Systematic Review of SARS-CoV-2 Surveillance with Viral Variants

The emergence of new variants of SARS-CoV-2 associated with varying infectivity, pathogenicity, diagnosis, and effectiveness against treatments challenged the overall management of the COVID-19 pandemic. Wastewater surveillance (WWS), i.e., monitoring COVID-19 infections in communities through detecting viruses in wastewater, was applied to track the emergence and spread of SARS-CoV-2 variants globally. However, there is a lack of comprehensive understanding of the use and effectiveness of WWS for new SARS-CoV-2 variants. Here we systematically reviewed published articles reporting monitoring of different SARS-CoV-2 variants in wastewater by following the PRISMA guidelines and provided the current state of the art of this study area. A total of 80 WWS studies were found that reported different monitoring variants of SARS-CoV-2 until November 2022. Most of these studies (66 out of the total 80, 82.5%) were conducted in Europe and North America, i.e., resource-rich countries. There was a high variation in WWS sampling strategy around the world, with composite sampling (50/66 total studies, 76%) as the primary method in resource-rich countries. In contrast, grab sampling was more common (8/14 total studies, 57%) in resource-limited countries. Among detection methods, the reverse transcriptase polymerase chain reaction (RT-PCR)-based sequencing method and quantitative RT-PCR method were commonly used for monitoring SARS-CoV-2 variants in wastewater. Among different variants, the B1.1.7 (Alpha) variant that appeared earlier in the pandemic was the most reported (48/80 total studies), followed by B.1.617.2 (Delta), B.1.351 (Beta), P.1 (Gamma), and others in wastewater. All variants reported in WWS studies followed the same pattern as the clinical reporting within the same timeline, demonstrating that WWS tracked all variants in a timely way when the variants emerged. Thus, wastewater monitoring may be utilized to identify the presence or absence of SARS-CoV-2 and follow the development and transmission of existing and emerging variants. Routine wastewater monitoring is a powerful infectious disease surveillance tool when implemented globally.

Longitudinal metatranscriptomic sequencing of Southern California wastewater representing 16 million people from August 2020-21 reveals widespread transcription of antibiotic resistance genes

Municipal wastewater provides a representative sample of human fecal waste across a catchment area and contains a wide diversity of microbes. Sequencing wastewater samples provides information about human-associated and medically important microbial populations, and may be useful to assay disease prevalence and antimicrobial resistance (AMR). Here, we present a study in which we used untargeted metatranscriptomic sequencing on RNA extracted from 275 sewage influent samples obtained from eight wastewater treatment plants (WTPs) representing approximately 16 million people in Southern California between August 2020 - August 2021. We characterized bacterial and viral transcripts, assessed metabolic pathway activity, and identified over 2,000 AMR genes/variants across all samples. Because we did not deplete ribosomal RNA, we have a unique window into AMR carried as ribosomal mutants. We show that AMR diversity varied between WTPs (as measured through PERMANOVA, P < 0.001) and that the relative abundance of many individual AMR genes/variants increased over time (as measured with MaAsLin2, P_adj < 0.05). Similarly, we detected transcripts mapping to human pathogenic bacteria and viruses suggesting RNA sequencing is a powerful tool for wastewater-based epidemiology and that there are geographical signatures to microbial transcription. We captured the transcription of gene pathways common to bacterial cell processes, including central carbon metabolism, nucleotide synthesis/salvage, and amino acid biosynthesis. We also posit that due to the ubiquity of many viruses and bacteria in wastewater, new biological targets for microbial water quality assessment can be developed. To the best of our knowledge, our study provides the most complete longitudinal metatranscriptomic analysis of a large population's wastewater to date and demonstrates our ability to monitor the presence and activity of microbes in complex samples. By sequencing RNA, we can track the relative abundance of expressed AMR genes/variants and metabolic pathways, increasing our understanding of AMR activity across large human populations and sewer sheds.

Indian sewage microbiome has unique community characteristics and potential for population-level disease predictions

Sewage wastewater pollutes water and poses a public health issue but it could also prove useful in certain research domains. Sewage is a complex niche relevant for research concerning 'one-health', human health, pollution and antibiotic resistance. Indian gut microbiome is also understudied due to sampling constraints and sewage could be used to explore it. Ostensibly, Indian sewage needs to be studied and here, we performed a cross-sectional pan-India sewage sampling to generate the first comprehensive Indian sewage microbiome. Indian sewage showed predominance of Burkholderiaceae, Rhodocyclaceae, Veillonellaceae, Prevotellaceae, etc. and has high representation of gut microbes. The identified gut microbes have overrepresentation of Veillonellaceae, Rikenellaceae, Streptococcaceae, and Bacillaceae. Imputed metagenomics of sewage microbiome indicated dominance of transport, motility, peptidases, amino acid metabolism, and antibiotic resistance genes. Microbiome-disease associations drawn using simple decision tree and random forest analysis identified specific microbes as potential predictors of diabetes and obesity in a city. Altogether, we generated the first Indian sewage microbiome and our non-invasive, high-throughput workflow could be emulated for future research, wastewater-based epidemiology and designing policies concerning public health.

Sequencing and Variant Detection of Eight Abundant Plant-Infecting Tobamoviruses across Southern California Wastewater

Tobamoviruses are agriculturally relevant viruses that cause crop losses and have infected plants in many regions of the world. These viruses are frequently found in municipal wastewater, likely coming from human diet and industrial waste across wastewater catchment areas. As part of a large wastewater-based epidemiology study across Southern California, we analyzed RNA sequence data from 275 influent wastewater samples obtained from eight wastewater treatment plants with a catchment area of approximately 16 million people from July 2020 to August 2021. We assembled 1,083 high-quality genomes, enumerated viral sequencing reads, and detected thousands of single nucleotide variants from eight common tobamoviruses: bell pepper mottle virus, cucumber green mottle mosaic virus, pepper mild mottle virus, tobacco mild green mosaic virus, tomato brown rugose fruit virus, tomato mosaic virus, tomato mottle mosaic virus, and tropical soda apple mosaic virus. We show that single nucleotide variants had amino acid-altering consequences along with synonymous mutations, which represents potential evolution with functional consequences in genomes of these viruses. Our study shows the importance of wastewater sequencing to monitor the genomic diversity of these plant-infecting viruses, and we suggest that our data could be used to continue tracking the genomic variability of such pathogens. IMPORTANCE Diseases caused by viruses in the genus Tobamovirus cause crop losses around the world. As with other viruses, mutation occurring in the virus's genomes can have functional consequences and may alter viral infectivity. Many of these plant-infecting viruses have been found in wastewater, likely coming from human consumption of infected plants and produce. By sequencing RNA extracted from influent wastewater obtained from eight wastewater treatment plants in Southern California, we assembled high-quality viral genomes and detected thousands of single nucleotide variants from eight tobamoviruses. Our study shows that Tobamovirus genomes vary at many positions, which may have important consequences when designing assays for the detection of these viruses by agricultural or environmental scientists.

Pierre Schneider B, Detrick E. SARS-CoV-2 surveillance with environmental surface sampling in public areas

Contaminated surfaces are one of the ways that coronavirus disease 2019 (COVID-19) may be transmitted. SARS-CoV-2 can be detected on environmental surfaces; however, few environmental sampling studies have been conducted in nonclinical settings. The objective of this study was to detect SARS-CoV-2 RNA on environmental surfaces in public areas in Las Vegas, Nevada. In total, 300 surface samples were collected from high-touch surfaces from high-congregate public locations and from a public health facility (PHF) that was visited by COVID-19 patients. Environmental samples were analyzed with quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR) using SARS-CoV-2 specific primers and probes for three target genes. Results showed that 31 out of 300 (10.3%) surface samples tested positive for SARS-CoV-2, 24 at the PHF and 7 in high-congregate public locations. Concentrations ranged from 102 to 106 viral particles per 3 ml sample on a wide variety of materials. The data also showed that the N gene assay had greater sensitivity compared to the S and ORF gene assays. Besides frequently touched surfaces, SARS-CoV-2 was detected in restrooms, on floors and surfaces in contact with floors, as well as in a mop water sample. The results of this study describe the extent and distribution of environmental SARS-CoV-2 contamination in public areas in Las Vegas, Nevada. A method using the N gene PCR assay was developed for SARS-CoV-2 environmental monitoring in public areas. Environmental monitoring with this method can determine the specific sites of surface contamination in the community and may be beneficial for prevention of COVID-19 indirect transmission, and evaluation and improvement of infection control practices in public areas, public health facilities, universities, and businesses.

An urgent need for COP27: confronting converging crises

The last 12 months have provided further evidence of the potential for cascading ecological and socio-political crises that were warned of 12 months ago. Then a consensus statement from the Regional Action on Climate Change Symposium warned: "the Earth's climatic, ecological, and human systems are converging towards a crisis that threatens to engulf global civilization within the lifetimes of children now living." Since then, the consequences of a broad set of extreme climate events (notably droughts, floods, and fires) have been compounded by interaction with impacts from multiple pandemics (including COVID-19 and cholera) and the Russia-Ukraine war. As a result, new connections are becoming visible between climate change and human health, large vulnerable populations are experiencing food crises, climate refugees are on the move, and the risks of water, food, and climate disruption have been visibly converging and compounding. Many vulnerable populations now face serious challenges to adapt. In light of these trends, this year, RACC identifies a range of measures to be taken at global and regional levels to bolster the resilience of these populations in the face of such emerging crises. In particular, at all scales, there is a need for globally available local data, reliable analytic techniques, community capacity to plan adaptation strategies, and the resources (scientific, technical, cultural, and economic) to implement them. To date, the rate of growth of the support for climate change resilience lags behind the rapid growth of cascading and converging risks. As an urgent message to COP27, it is proposed that the time is now right to devote much greater emphasis, global funding, and support to the increasing adaptation needs of vulnerable populations.

A pilot surveillance report of SARS-CoV-2 rapid antigen test results among volunteers in Germany, 1st week of July 2022

Purpose

We hypothesized that SARS-CoV-2 infection numbers reported by governmental institutions are underestimated due to high dark figures as only results from polymerase chain reaction (PCR) tests are incorporated in governmental statistics and testing capacities were further restricted as of July, 2022.

Methods

A point prevalence investigation was piloted by rapid antigen testing (RAT) among participants of the VACCELERATE volunteer registry. 2400 volunteers were contacted, of which 500 received a RAT including instructions for self-testing in the first week of July, 2022. Results were self-reported via e-mail.

Results

419 valid RAT results were collected until July 7th, 2022. Between July-1 and July-7, 2022, 7/419 (1.67%) tests were positive. Compared to reports of the German Federal Government, our results suggest a more than twofold higher prevalence. Three out of seven positive individuals did not have a PCR test and are therefore likely not to be displayed in governmental statistics.

Conclusion

Our findings imply that the actual prevalence of SARS-CoV-2 may be higher than detected by current surveillance systems, so that current pandemic surveillance and testing strategies may be adapted.

Catching the Wave: Detecting Strain-Specific SARS-CoV-2 Peptides in Clinical Samples Collected during Infection Waves from Diverse Geographical Locations

The Coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulted in a major health crisis worldwide with its continuously emerging new strains, resulting in new viral variants that drive "waves" of infection. PCR or antigen detection assays have been routinely used to detect clinical infections; however, the emergence of these newer strains has presented challenges in detection. One of the alternatives has been to detect and characterize variant-specific peptide sequences from viral proteins using mass spectrometry (MS)-based methods. MS methods can potentially help in both diagnostics and vaccine development by understanding the dynamic changes in the viral proteome associated with specific strains and infection waves. In this study, we developed an accessible, flexible, and shareable bioinformatics workflow that was implemented in the Galaxy Platform to detect variant-specific peptide sequences from MS data derived from the clinical samples. We demonstrated the utility of the workflow by characterizing published clinical data from across the world during various pandemic waves. Our analysis identified six SARS-CoV-2 variant-specific peptides suitable for confident detection by MS in commonly collected clinical samples.

Microbiological and Clinical Findings of SARS-CoV-2 Infection after 2 Years of Pandemic: From Lung to Gut Microbiota

Early recognition and prompt management are crucial for improving survival in COVID-19 patients, and after 2 years of the pandemic, many efforts have been made to obtain an early diagnosis. A key factor is the use of fast microbiological techniques, considering also that COVID-19 patients may show no peculiar signs and symptoms that may differentiate COVID-19 from other infective or non-infective diseases. These techniques were developed to promptly identify SARS-CoV-2 infection and to prevent viral spread and transmission. However, recent data about clinical, radiological and laboratory features of COVID-19 at time of hospitalization could help physicians in early suspicion of SARS-CoV-2 infection and distinguishing it from other etiologies. The knowledge of clinical features and microbiological techniques will be crucial in the next years when the endemic circulation of SARS-CoV-2 will be probably associated with clusters of infection. In this review we provide a state of the art about new advances in microbiological and clinical findings of SARS-CoV-2 infection in hospitalized patients with a focus on pulmonary and extrapulmonary characteristics, including the role of gut microbiota.

Longitudinal metatranscriptomic sequencing of Southern California wastewater representing 16 million people from August 2020-21 reveals widespread transcription of antibiotic resistance genes

Municipal wastewater provides a representative sample of human fecal waste across a catchment area and contains a wide diversity of microbes. Sequencing wastewater samples provides information about human-associated and medically-important microbial populations, and may be useful to assay disease prevalence and antimicrobial resistance (AMR). Here, we present a study in which we used untargeted metatranscriptomic sequencing on RNA extracted from 275 sewage influent samples obtained from eight wastewater treatment plants (WTPs) representing approximately 16 million people in Southern California between August 2020 - August 2021. We characterized bacterial and viral transcripts, assessed metabolic pathway activity, and identified over 2,000 AMR genes/variants across all samples. Because we did not deplete ribosomal RNA, we have a unique window into AMR carried as ribosomal mutants. We show that AMR diversity varied between WTPs and that the relative abundance of many individual AMR genes/variants increased over time and may be connected to antibiotic use during the COVID-19 pandemic. Similarly, we detected transcripts mapping to human pathogenic bacteria and viruses suggesting RNA sequencing is a powerful tool for wastewater-based epidemiology and that there are geographical signatures to microbial transcription. We captured the transcription of gene pathways common to bacterial cell processes, including central carbon metabolism, nucleotide synthesis/salvage, and amino acid biosynthesis. We also posit that due to the ubiquity of many viruses and bacteria in wastewater, new biological targets for microbial water quality assessment can be developed. To the best of our knowledge, our study provides the most complete longitudinal metatranscriptomic analysis of a large population's wastewater to date and demonstrates our ability to monitor the presence and activity of microbes in complex samples. By sequencing RNA, we can track the relative abundance of expressed AMR genes/variants and metabolic pathways, increasing our understanding of AMR activity across large human populations and sewer sheds.

Pandemic's silver lining

The intense international focus on the COVID-19 pandemic has provided a unique opportunity to use a wide array of novel tools to carry out scientific studies on the SARS-CoV-2 virus. The value of these comparative studies extends far beyond their consequences for SARS-CoV-2, providing broad implications for health-related science. Here we specifically discuss the impacts of these comparisons on advances in vaccines, the analysis of host humoral immunity, and antibody discovery. As an extension, we also discuss potential synergies between these areas.Abbreviations: CoVIC: The Coronavirus Immunotherapeutic Consortium; EUA: Emergency Use Authorization.