Supervised Machine Learning Enables Geospatial Microbial Provenance

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset—the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85–89% to 90–94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF)

A history of the MetaSUB consortium: Tracking urban microbes around the globe

The MetaSUB Consortium, founded in 2015, is a global consortium with an interdisciplinary team of clinicians, scientists, bioinformaticians, engineers, and designers, with members from more than 100 countries across the globe. This network has continually collected samples from urban and rural sites including subways and transit systems, sewage systems, hospitals, and other environmental sampling. These collections have been ongoing since 2015 and have continued when possible, even throughout the COVID-19 pandemic. The consortium has optimized their workflow for the collection, isolation, and sequencing of DNA and RNA collected from these various sites and processing them for metagenomics analysis, including the identification of SARS-CoV-2 and its variants. Here, the Consortium describes its foundations, and its ongoing work to expand on this network and to focus its scope on the mapping, annotation, and prediction of emerging pathogens, mapping microbial evolution and antibiotic resistance, and the discovery of novel organisms and biosynthetic gene clusters.ISSN:2589-004

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.ISSN:0092-8674ISSN:1097-417

Lessons learned from SARS-CoV-2 measurements in wastewater

Standardized protocols for wastewater-based surveillance (WBS) for the RNA of SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, are being developed and refined worldwide for early detection of disease outbreaks. We report here on lessons learned from establishing a WBS program for SARS-CoV-2 integrated with a human surveillance program for COVID-19. We have established WBS at three campuses of a university, including student residential dormitories and a hospital that treats COVID-19 patients. Lessons learned from this WBS program address the variability of water quality, new detection technologies, the range of detectable viral loads in wastewater, and the predictive value of integrating environmental and human surveillance data. Data from our WBS program indicated that water quality was statistically different between sewer sampling sites, with more variability observed in wastewater coming from individual buildings compared to clusters of buildings. A new detection technology was developed based upon the use of a novel polymerase called V2G. Detectable levels of SARS-CoV-2 in wastewater varied from 102 to 106 genomic copies (gc) per liter of raw wastewater (L). Integration of environmental and human surveillance data indicate that WBS detection of 100 gc/L of SARS-CoV-2 RNA in wastewater was associated with a positivity rate of 4% as detected by human surveillance in the wastewater catchment area, though confidence intervals were wide (β ~ 8.99 ∗ ln(100); 95% CI = 0.90–17.08; p < 0.05). Our data also suggest that early detection of COVID-19 surges based on correlations between viral load in wastewater and human disease incidence could benefit by increasing the wastewater sample collection frequency from weekly to daily. Coupling simpler and faster detection technology with more frequent sampling has the potential to improve the predictive potential of using WBS of SARS-CoV-2 for early detection of the onset of COVID-19.[Display omitted]•Lessons learned from wastewater surveillance for SARS-CoV-2 are reported.•A new innovative detection method, V2G-qPCR, was evaluated.•100 gc/L of SARS-CoV-2 in wastewater associate with a 4% positivity rate•SARS-CoV-2 in wastewater was a 4-day lead indicator.•More frequent sampling is recommended for model development

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities

Relationships between SARS-CoV-2 in Wastewater and COVID-19 Clinical Cases and Hospitalizations, with and without Normalization against Indicators of Human Waste

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in wastewater has been used to track community infections of coronavirus disease-2019 (COVID-19), providing critical information for public health interventions. Since levels in wastewater are dependent upon human inputs, we hypothesize that tracking infections can be improved by normalizing wastewater concentrations against indicators of human waste [Pepper Mild Mottle Virus (PMMoV), β-2 Microglobulin (B2M), and fecal coliform]. In this study, we analyzed SARS-CoV-2 and indicators of human waste in wastewater from two sewersheds of different scales: a University campus and a wastewater treatment plant. Wastewater data were combined with complementary COVID-19 case tracking to evaluate the efficiency of wastewater surveillance for forecasting new COVID-19 cases and, for the larger scale, hospitalizations. Results show that the normalization of SARS-CoV-2 levels by PMMoV and B2M resulted in improved correlations with COVID-19 cases for campus data using volcano second generation (V2G)-qPCR chemistry (rs = 0.69 without normalization, rs = 0.73 with normalization). Mixed results were obtained for normalization by PMMoV for samples collected at the community scale. Overall benefits from normalizing with measures of human waste depend upon qPCR chemistry and improves with smaller sewershed scale. We recommend further studies that evaluate the efficacy of additional normalization targets

Geospatially-resolved public-health surveillance via wastewater sequencing

Wastewater, which contains everything from pathogens to pollutants, is a geospatially-and temporally-linked microbial fingerprint of a given population. As a result, it can be leveraged for monitoring multiple dimensions of public health across locales and time. Here, we integrate targeted and bulk RNA sequencing (n=1,419 samples) to track the viral, bacterial, and functional content over geospatially distinct areas within Miami Dade County from 2020-2022. First, we used targeted amplicon sequencing (n=966) to track diverse SARS-CoV-2 variants across space and time, and we found a tight correspondence with clinical caseloads from University students (N = 1,503) and Miami-Dade County hospital patients (N = 3,939 patients), as well as an 8-day earlier detection of the Delta variant in wastewater vs. in patients. Additionally, in 453 metatranscriptomic samples, we demonstrate that different wastewater sampling locations have clinically and public-health-relevant microbiota that vary as a function of the size of the human population they represent. Through assembly, alignment-based, and phylogenetic approaches, we also detect multiple clinically important viruses (e.g.,norovirus) and describe geospatial and temporal variation in microbial functional genes that indicate the presence of pollutants. Moreover, we found distinct profiles of antimicrobial resistance (AMR) genes and virulence factors across campus buildings, dorms, and hospitals, with hospital wastewater containing a significant increase in AMR abundance. Overall, this effort lays the groundwork for systematic characterization of wastewater to improve public health decision making and a broad platform to detect emerging pathogens

Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions.

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including &gt;25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies