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Microorganisms are ubiquitous in the biosphere, playing a crucial role in both biogeochemistry of the planet and human health. However, identifying these microorganisms and defining their function are challenging. Widely used approaches in comparative metagenomics, 16S amplicon sequencing and whole genome shotgun sequencing (WGS), have provided access to DNA sequencing analysis to identify microorganisms and evaluate diversity and abundance in various environments. However, advances in parallel high-throughput DNA sequencing in the past decade have introduced major hurdles, namely standardization of methods, data storage, reproducible interoperability of results, and data sharing. The National Ecological Observatory Network (NEON), established by the National Science Foundation, enables all researchers to address queries on a regional to continental scale around a variety of environmental challenges and provide high-quality, integrated, and standardized data from field sites across the U.S. As the amount of metagenomic data continues to grow, standardized procedures that allow results across projects to be assessed and compared is becoming increasingly important in the field of metagenomics. We demonstrate the feasibility of using publicly available NEON soil metagenomic sequencing datasets in combination with open access Metagenomics Rapid Annotation using the Subsystem Technology (MG-RAST) server to illustrate advantages of WGS compared to 16S amplicon sequencing. Four WGS and four 16S amplicon sequence datasets, from surface soil samples prepared by NEON investigators, were selected for comparison, using standardized protocols collected at the same locations in Colorado between April-July 2014. The dominant bacterial phyla detected across samples agreed between sequencing methodologies. However, WGS yielded greater microbial resolution, increased accuracy, and allowed identification of more genera of bacteria, archaea, viruses, and eukaryota, and putative functional genes that would have gone undetected using 16S amplicon sequencing. NEON open data will be useful for future studies characterizing and quantifying complex ecological processes associated with changing aquatic and terrestrial ecosystems.

Vibrios are among the most common bacteria that inhabit surface waters throughout the world and are responsible for a number of severe infections both in humans and animals. Several reports recently showed that human Vibrio illnesses are increasing worldwide including fatal acute diarrheal diseases, such as cholera, gastroenteritis, wound infections, and septicemia. Many scientists believe this increase may be associated with global warming and rise in sea surface temperature (SST), although not enough evidence is available to support a causal link between emergence of Vibrio infections and climate warming. The effect of increased SST in promoting spread of vibrios in coastal and brackish waters is considered a causal factor explaining this trend. Field and laboratory studies carried out over the past 40 years supported this hypothesis, clearly showing temperature promotes Vibrio growth and persistence in the aquatic environment. Most recently, a long-term retrospective microbiological study carried out in the coastal waters of the southern North Sea provided the first experimental evidence for a positive and significant relationship between SST and Vibrio occurrence over a multidecadal time scale. As a future challenge, macroecological studies of the effects of ocean warming on Vibrio persistence and spread in the aquatic environment over large spatial and temporal scales would conclusively support evidence acquired to date combined with studies of the impact of global warming on epidemiologically relevant variables, such as host susceptibility and exposure. Assessing a causal link between ongoing climate change and enhanced growth and spread of vibrios and related illness is expected to improve forecast and mitigate future outbreaks associated with these pathogens.

SARS-CoV-2 has presented a rapidly accelerating global public health crisis. The ability to detect and analyze viral RNA from minimally invasive patient specimens is critical to the public health response. Metagenomic next-generation sequencing (mNGS) offers an opportunity to detect SARS-CoV-2 from nasopharyngeal (NP) swabs. This approach also provides information on the composition of the respiratory microbiome and its relationship to coinfections or the presence of other organisms that may impact SARS-CoV-2 disease progression and prognosis. Here, using direct Oxford Nanopore long-read third-generation metatranscriptomic and metagenomic sequencing of NP swab specimens from 50 patients under investigation for COVID-19, we detected SARS-CoV-2 sequences by applying the CosmosID bioinformatics platform. Further, we characterized coinfections and detected a decrease in the diversity of the microbiomes in these patients. Statistically significant shifts in the microbiome were identified among COVID-19-positive and -negative patients, in the latter of whom a higher abundance of Propionibacteriaceae and a reduction in the abundance of Corynebacterium accolens were observed. Our study also corroborates the growing evidence that increased SARS-CoV-2 RNA detection from NP swabs is associated with the early stages of disease rather than with severity of disease. This work illustrates the utility of mNGS for the detection and analysis of SARS-CoV-2 from NP swabs without viral target enrichment or amplification and for the analysis of the respiratory microbiome. Metagenomic next-generation sequencing (mNGS) offers an agnostic approach for emerging pathogen detection directly from clinical specimens. In contrast to targeted methods, mNGS also provides valuable information on the composition of the microbiome and might uncover coinfections that may associate with disease progression and impact prognosis. To evaluate the use of mNGS for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and/or other infecting pathogens, we applied direct Oxford Nanopore long-read third-generation metatranscriptomic and metagenomic sequencing. Nasopharyngeal (NP) swab specimens from 50 patients under investigation for CoV disease 2019 (COVID-19) were sequenced, and the data were analyzed by the CosmosID bioinformatics platform. Further, we characterized coinfections and the microbiome associated with a four-point severity index. SARS-CoV-2 was identified in 77.5% (31/40) of samples positive by RT-PCR, correlating with lower cycle threshold (Ct) values and fewer days from symptom onset. At the time of sampling, possible bacterial or viral coinfections were detected in 12.5% of SARS-CoV-2-positive specimens. A decrease in microbial diversity was observed among COVID-19-confirmed patients (Shannon diversity index, P  = 0.0082; Chao richness estimate, P  = 0.0097; Simpson diversity index, P  = 0.018), and differences in microbial communities were linked to disease severity ( P  = 0.022). Furthermore, statistically significant shifts in the microbiome were identified among SARS-CoV-2-positive and -negative patients, in the latter of whom a higher abundance of Propionibacteriaceae ( P  = 0.028) and a reduction in the abundance of Corynebacterium accolens ( P  = 0.025) were observed. Our study corroborates the growing evidence that increased SARS-CoV-2 RNA detection from NP swabs is associated with the early stages rather than the severity of COVID-19. Further, we demonstrate that SARS-CoV-2 causes a significant change in the respiratory microbiome. This work illustrates the utility of mNGS for the detection of SARS-CoV-2, for diagnosing coinfections without viral target enrichment or amplification, and for the analysis of the respiratory microbiome. IMPORTANCE SARS-CoV-2 has presented a rapidly accelerating global public health crisis. The ability to detect and analyze viral RNA from minimally invasive patient specimens is critical to the public health response. Metagenomic next-generation sequencing (mNGS) offers an opportunity to detect SARS-CoV-2 from nasopharyngeal (NP) swabs. This approach also provides information on the composition of the respiratory microbiome and its relationship to coinfections or the presence of other organisms that may impact SARS-CoV-2 disease progression and prognosis. Here, using direct Oxford Nanopore long-read third-generation metatranscriptomic and metagenomic sequencing of NP swab specimens from 50 patients under investigation for COVID-19, we detected SARS-CoV-2 sequences by applying the CosmosID bioinformatics platform. Further, we characterized coinfections and detected a decrease in the diversity of the microbiomes in these patients. Statistically significant shifts in the microbiome were identified among COVID-19-positive and -negative patients, in the latter of whom a higher abundance of Propionibacteriaceae and a reduction in the abundance of Corynebacterium accolens were observed. Our study also corroborates the growing evidence that increased SARS-CoV-2 RNA detection from NP swabs is associated with the early stages of disease rather than with severity of disease. This work illustrates the utility of mNGS for the detection and analysis of SARS-CoV-2 from NP swabs without viral target enrichment or amplification and for the analysis of the respiratory microbiome.

Climate change is having increasingly profound effects on human health, notably those associated with the occurrence, distribution, and transmission of infectious diseases. The number of disparate ecological parameters and pathogens affected by climate change are vast and expansive. Disentangling the complex relationship between these variables is critical for the development of effective countermeasures against its effects. The pathogen Vibrio vulnificus , a naturally occurring aquatic bacterium that causes fulminant septicemia, represents a quintessential climate-sensitive organism. In this review, we use V . vulnificus as a model organism to elucidate the intricate network of interactions between climatic factors and pathogens, with the objective of identifying common patterns by which climate change is affecting their disease burden. Recent findings indicate that in regions native to V . vulnificus or related pathogens, climate-driven natural disasters are the chief contributors to their disease outbreaks. Concurrently, climate change is increasing the environmental suitability of areas non-endemic to their diseases, promoting a surge in their natural populations and transmission dynamics, thus elevating the risk of new outbreaks. We highlight potential risk factors and climatic drivers aggravating the threat of V . vulnificus transmission under both scenarios and propose potential measures for mitigating its impact. By defining the mechanisms by which climate change influences V . vulnificus disease burden, we aim to shed light on the transmission dynamics of related disease-causing agents, thereby laying the groundwork for early warning systems and broadly applicable control measures.

The microbiological content of drinking water traditionally is determined by employing culture-dependent methods that are unable to detect all microorganisms, especially those that are not culturable. High-throughput sequencing now makes it possible to determine the microbiome of drinking water. Thus, the natural microbiota of water and water distribution systems can now be determined more accurately and analyzed in significantly greater detail, providing comprehensive understanding of the microbial community of drinking water applicable to public health. In this study, shotgun metagenomic analysis was performed to determine the microbiological content of drinking water and to provide a preliminary assessment of tap, drinking fountain, sparkling natural mineral, and non-mineral bottled water. Predominant bacterial species detected were members of the phyla Actinobacteria and Proteobacteria, notably the genera Alishewanella, Salmonella, and Propionibacterium in non-carbonated non-mineral bottled water, Methyloversatilis and Methylibium in sparkling natural mineral water, and Mycobacterium and Afipia in tap and drinking fountain water. Fecal indicator bacteria, i.e., Escherichia coli or enterococci, were not detected in any samples examined in this study. Bacteriophages and DNA encoding a few virulence-associated factors were detected but determined to be present only at low abundance. Antibiotic resistance markers were detected only at abundance values below our threshold of confidence. DNA of opportunistic plant and animal pathogens was identified in some samples and these included bacteria (Mycobacterium spp.), protozoa (Acanthamoeba mauritaniensis and Acanthamoeba palestinensis), and fungi (Melampsora pinitorqua and Chryosporium queenslandicum). Archaeal DNA (Candidatus Nitrosoarchaeum) was detected only in sparkling natural mineral water. This preliminary study reports the complete microbiome (bacteria, viruses, fungi, and protists) of selected types of drinking water employing whole-genome high-throughput sequencing and bioinformatics. Investigation into activity and function of the organisms detected is in progress.

Background One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited. Results In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages. Conclusions This study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

Human saliva is clinically informative of both oral and general health. Since next generation shotgun sequencing (NGS) is now widely used to identify and quantify bacteria, we investigated the bacterial flora of saliva microbiomes of two healthy volunteers and five datasets from the Human Microbiome Project, along with a control dataset containing short NGS reads from bacterial species representative of the bacterial flora of human saliva. GENIUS, a system designed to identify and quantify bacterial species using unassembled short NGS reads was used to identify the bacterial species comprising the microbiomes of the saliva samples and datasets. Results, achieved within minutes and at greater than 90% accuracy, showed more than 175 bacterial species comprised the bacterial flora of human saliva, including bacteria known to be commensal human flora but also Haemophilus influenzae, Neisseria meningitidis, Streptococcus pneumoniae, and Gamma proteobacteria. Basic Local Alignment Search Tool (BLASTn) analysis in parallel, reported ca. five times more species than those actually comprising the in silico sample. Both GENIUS and BLAST analyses of saliva samples identified major genera comprising the bacterial flora of saliva, but GENIUS provided a more precise description of species composition, identifying to strain in most cases and delivered results at least 10,000 times faster. Therefore, GENIUS offers a facile and accurate system for identification and quantification of bacterial species and/or strains in metagenomic samples.

Vibrio parahaemolyticus, autochthonous to estuarine, marine, and coastal environments throughout the world, is the causative agent of food-borne gastroenteritis. More than 80 serotypes have been described worldwide, based on antigenic properties of the somatic (O) and capsular (K) antigens. Serovar O3:K6 emerged in India in 1996 and subsequently was isolated worldwide, leading to the conclusion that the first V. parahaemolyticus pandemic had taken place. Most strains of V. parahaemolyticus isolated from the environment or seafood, in contrast to clinical strains, do not produce a thermostable direct hemolysin (TDH) and/or a TDH-related hemolysin (TRH). Type 3 secretion systems (T3SSs), needle-like apparatuses able to deliver bacterial effectors into host cytoplasm, were identified as triggering cytotoxicity and enterotoxicity. Type 6 secretion systems (T6SS) predicted to be involved in intracellular trafficking and vesicular transport appear to play a role in V. parahaemolyticus virulence. Recent advances in V. parahaemolyticus genomics identified several pathogenicity islands (VpaIs) located on either chromosome in both epidemic and pandemic strains and comprising additional colonization factors, such as restriction-modification complexes, chemotaxis proteins, classical bacterial surface virulence factors, and putative colicins. Furthermore, studies indicate strains lacking toxins and genomic regions associated with pathogenicity may also be pathogenic, suggesting other important virulence factors remain to be identified. The unique repertoire of virulence factors identified to date, their occurrence and distribution in both epidemic and pandemic strains worldwide are described, with the aim of highlighting the complexity of V. parahaemolyticus pathogenicity as well as its dynamic genome.

Traditionally, testing for COVID-19 is done by detecting SARS-CoV-2 in samples collected from nasal swabs and/or saliva. However, SARS-CoV-2 can also be detected in feces of infected individuals. Wastewater surveillance (WS), when coupled with advanced molecular techniques, offers near real-time monitoring of community-wide transmission of SARS-CoV-2 and allows assessing and mitigating COVID-19 outbreaks, by evaluating the total microbial assemblage in a community. Composite wastewater samples (24 h) were collected weekly from a manhole between December 2020 and November 2021 in Maryland, USA. RT-qPCR results showed concentrations of SARS-CoV-2 RNA recovered from wastewater samples reflected incidence of COVID-19 cases. When a drastic increase in COVID-19 was detected in February 2021, samples were selected for microbiome analysis (DNA metagenomics, RNA metatranscriptomics, and targeted SARS-CoV-2 sequencing). Targeted SARS-CoV-2 sequencing allowed for detection of important genetic mutations, such as spike: K417N, D614G, P681H, T716I, S982A, and D1118H, commonly associated with increased cell entry and reinfection. Microbiome analysis (DNA and RNA) provided important insight with respect to human health-related factors, including detection of pathogens and their virulence/antibiotic resistance genes. Specific microbial species comprising the wastewater microbiome correlated with incidence of SARS-CoV-2 RNA, suggesting potential association with SARS-CoV-2 infection. Climatic conditions, namely, temperature, were related to incidence of COVID-19 and detection of SARS-CoV-2 in wastewater, having been monitored as part of an environmental risk score assessment carried out in this study. In summary, the wastewater microbiome provides useful public health information, and hence, a valuable tool to proactively detect and characterize pathogenic agents circulating in a community. In effect, metagenomics of wastewater can serve as an early warning system for communicable diseases, by providing a larger source of information for health departments and public officials. IMPORTANCE Traditionally, testing for COVID-19 is done by detecting SARS-CoV-2 in samples collected from nasal swabs and/or saliva. However, SARS-CoV-2 can also be detected in feces of infected individuals. Therefore, wastewater samples can be used to test all individuals of a community contributing to the sewage collection system, i.e., the infrastructure, such as gravity pipes, manholes, tanks, lift stations, control structures, and force mains, that collects used water from residential and commercial sources and conveys the flow to a wastewater treatment plant. Here, we profile community wastewater collected from a manhole, detect presence of SARS-CoV-2, identify genetic mutations of SARS-CoV-2, and perform COVID-19 risk score assessment of the study area. Using metagenomics analysis, we also detect other microorganisms (bacteria, fungi, protists, and viruses) present in the samples. Results show that by analyzing all microorganisms present in wastewater, pathogens circulating in a community can provide an early warning for contagious diseases.

Cholera remains a global public health threat in regions where social vulnerabilities intersect with climate and weather processes that impact infectious Vibrio cholerae . While access to safe drinking water and sanitation facilities limit cholera outbreaks, sheer cost of building such infrastructure limits the ability to safeguard the population. Here, using Yemen as an example where cholera outbreak was reported in 2016, we show how predictive abilities for forecasting risk, employing sociodemographical, microbiological, and climate information of cholera, can aid in combating disease outbreak. An epidemiological analysis using Bradford Hill Criteria was employed in near-real-time to understand a predictive model’s outputs and cholera cases in Yemen. We note that the model predicted cholera risk at least four weeks in advance for all governorates of Yemen with overall 72% accuracy (varies with the year). We argue the development of anticipatory decision-making frameworks for climate modulated diseases to design intervention activities and limit exposure of pathogens preemptively.