Metagenomics for Pathogen Detection During a Mass Mortality Event in Songbirds.

Mass mortality events in wildlife can be indications of an emerging infectious disease. During the spring and summer of 2021, hundreds of dead passerines were reported across the eastern US. Birds exhibited a range of clinical signs including swollen conjunctiva, ocular discharge, ataxia, and nystagmus. As part of the diagnostic investigation, high-throughput metagenomic next-generation sequencing was performed across three molecular laboratories on samples from affected birds. Many potentially pathogenic microbes were detected, with bacteria forming the largest proportion; however, no singular agent was consistently identified, with many of the detected microbes also found in unaffected (control) birds and thus considered to be subclinical infections. Congruent results across laboratories have helped drive further investigation into alternative causes, including environmental contaminants and nutritional deficiencies. This work highlights the utility of metagenomic approaches in investigations of emerging diseases and provides a framework for future wildlife mortality events.

PoMeLo: a systematic computational approach to predicting metabolic loss in pathogen genomes.

BACKGROUND: Genome streamlining, the process by which genomes become smaller and encode fewer genes over time, is a common phenomenon among pathogenic bacteria. This reduction is driven by selection for minimized energy expenditure in a nutrient-rich environment. As pathogens evolve to become more reliant on the host, metabolic genes and resulting capabilities are lost in favor of siphoning metabolites from the host. Characterizing genome streamlining, gene loss, and metabolic pathway degradation can be useful in assessing pathogen dependency on host metabolism and identifying potential targets for host-directed therapeutics. RESULTS: PoMeLo (Predictor of Metabolic Loss) is a novel evolutionary genomics-guided computational approach for identifying metabolic gaps in the genomes of pathogenic bacteria. PoMeLo leverages a centralized public database of high-quality genomes and annotations and allows the user to compare an unlimited number of genomes across individual genes and pathways. PoMeLo runs locally using user-friendly prompts in a matter of minutes and generates tabular and visual outputs for users to compare predicted metabolic capacity between groups of bacteria and individual species. Each pathway is assigned a Predicted Metabolic Loss (PML) score to assess the magnitude of genome streamlining. Optionally, PoMeLo places the results in an evolutionary context by including phylogenetic relationships in visual outputs. It can also initially compute phylogenetically-weighted mean genome sizes to identify genome streamlining events. Here, we describe PoMeLo and demonstrate its use in identifying metabolic gaps in genomes of pathogenic Treponema species. CONCLUSIONS: PoMeLo represents an advance over existing methods for identifying metabolic gaps in genomic data, allowing comparison across large numbers of genomes and placing the resulting data in a phylogenetic context. PoMeLo is freely available for academic and non-academic use at https://github.com/czbiohub-sf/pomelo .

Building up a genomic surveillance platform for SARS-CoV-2 in the middle of a pandemic: a true North-South collaboration.

Next-generation sequencing technology has revolutionised pathogen surveillance over the last two decades. However, the benefits are not equitably distributed, with developing countries lagging far behind in acquiring the required technology and analytical capacity. Recent declines in the cost associated with sequencing-equipment and running consumables have created an opportunity for broader adoption. During the COVID-19 pandemic, rapid diagnostics development and DNA sequencing revolutionised the ability to diagnose and sequence SARS-CoV-2 rapidly. Socioeconomic inequalities substantially impact the ability to sequence SARS-CoV-2 strains and undermine a developing country's pandemic preparedness. Low- and middle-income countries face additional challenges in establishing, maintaining and expanding genomic surveillance. We present our experience of establishing a genomic surveillance system at the Aga Khan University, Karachi, Pakistan. Despite being at a leading health sciences research institute in the country, we encountered significant challenges. These were related to collecting standardised contextual data for SARS-CoV-2 samples, procuring sequencing reagents and consumables, and challenges with library preparation, sequencing and submission of high-quality SARS-CoV-2 genomes. Several technical roadblocks ensued during the implementation of the genomic surveillance framework, which were resolved in collaboration with our partners. High-quality genome sequences were then deposited on open-access platforms per the best practices. Subsequently, these efforts culminated in deploying Pakistan's first SARS-CoV-2 phyllo surveillance map as a Nextstrain build. Our experience offers lessons for the successful development of Genomic Surveillance Infrastructure in resource-limited settings struck by a pandemic.

Metagenomic sequencing of post-mortem tissue samples for the identification of pathogens associated with neonatal deaths.

Postmortem minimally invasive tissue sampling together with the detailed review of clinical records has been shown to be highly successful in determining the cause of neonatal deaths. However, conventional tests including traditional culture methods and nucleic acid amplification tests have periodically proven to be insufficient to detect the causative agent in the infectious deaths. In this study, metagenomic next generation sequencing was used to explore for putative pathogens associated with neonatal deaths in post-mortem blood and lung tissue samples, in Soweto, South Africa. Here we show that the metagenomic sequencing results corroborate the findings using conventional methods of culture and nucleic acid amplifications tests on post-mortem samples in detecting the pathogens attributed in the causal pathway of death in 90% (18/20) of the decedents. Furthermore, metagenomic sequencing detected a putative pathogen, including Acinetobacter baumannii, Klebsiella pneumoniae, Escherichia coli, and Serratia marcescens, in a further nine of 11 (81%) cases where no causative pathogen was identified. The antimicrobial susceptibility profile was also determined by the metagenomic sequencing for all pathogens with numerous multi drug resistant organism identified. In conclusion, metagenomic sequencing is able to successfully identify pathogens contributing to infection associated deaths on postmortem blood and tissue samples.

MultiSero: An Open-Source Multiplex-ELISA Platform for Measuring Antibody Responses to Infection.

A multiplexed enzyme-linked immunosorbent assay (ELISA) that simultaneously measures antibody binding to multiple antigens can extend the impact of serosurveillance studies, particularly if the assay approaches the simplicity, robustness, and accuracy of a conventional single-antigen ELISA. Here, we report on the development of multiSero, an open-source multiplex ELISA platform for measuring antibody responses to viral infection. Our assay consists of three parts: (1) an ELISA against an array of proteins in a 96-well format; (2) automated imaging of each well of the ELISA array using an open-source plate reader; and (3) automated measurement of optical densities for each protein within the array using an open-source analysis pipeline. We validated the platform by comparing antibody binding to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) antigens in 217 human sera samples, showing high sensitivity (0.978), specificity (0.977), positive predictive value (0.978), and negative predictive value (0.977) for classifying seropositivity, a high correlation of multiSero determined antibody titers with commercially available SARS-CoV-2 antibody tests, and antigen-specific changes in antibody titer dynamics upon vaccination. The open-source format and accessibility of our multiSero platform can contribute to the adoption of multiplexed ELISA arrays for serosurveillance studies, for SARS-CoV-2 and other pathogens of significance.

Metagenomic next-generation sequencing to characterize potential etiologies of non-malarial fever in a cohort living in a high malaria burden area of Uganda.

Causes of non-malarial fevers in sub-Saharan Africa remain understudied. We hypothesized that metagenomic next-generation sequencing (mNGS), which allows for broad genomic-level detection of infectious agents in a biological sample, can systematically identify potential causes of non-malarial fevers. The 212 participants in this study were of all ages and were enrolled in a longitudinal malaria cohort in eastern Uganda. Between December 2020 and August 2021, respiratory swabs and plasma samples were collected at 313 study visits where participants presented with fever and were negative for malaria by microscopy. Samples were analyzed using CZ ID, a web-based platform for microbial detection in mNGS data. Overall, viral pathogens were detected at 123 of 313 visits (39%). SARS-CoV-2 was detected at 11 visits, from which full viral genomes were recovered from nine. Other prevalent viruses included Influenza A (14 visits), RSV (12 visits), and three of the four strains of seasonal coronaviruses (6 visits). Notably, 11 influenza cases occurred between May and July 2021, coinciding with when the Delta variant of SARS-CoV-2 was circulating in this population. The primary limitation of this study is that we were unable to estimate the contribution of bacterial microbes to non-malarial fevers, due to the difficulty of distinguishing bacterial microbes that were pathogenic from those that were commensal or contaminants. These results revealed the co-circulation of multiple viral pathogens likely associated with fever in the cohort during this time period. This study illustrates the utility of mNGS in elucidating the multiple potential causes of non-malarial febrile illness. A better understanding of the pathogen landscape in different settings and age groups could aid in informing diagnostics, case management, and public health surveillance systems.

Discovery and Genomic Characterization of a Novel Henipavirus, Angavokely Virus, from Fruit Bats in Madagascar.

The genus Henipavirus (family Paramyxoviridae) currently comprises seven viruses, four of which have demonstrated prior evidence of zoonotic capacity. These include the biosafety level 4 agents Hendra (HeV) and Nipah (NiV) viruses, which circulate naturally in pteropodid fruit bats. Here, we describe and characterize Angavokely virus (AngV), a divergent henipavirus identified in urine samples from wild, Madagascar fruit bats. We report the nearly complete 16,740-nucleotide genome of AngV, which encodes the six major henipavirus structural proteins (nucleocapsid, phosphoprotein, matrix, fusion, glycoprotein, and L polymerase). Within the phosphoprotein (P) gene, we identify an alternative start codon encoding the AngV C protein and a putative mRNA editing site where the insertion of one or two guanine residues encodes, respectively, additional V and W proteins. In other paramyxovirus systems, C, V, and W are accessory proteins involved in antagonism of host immune responses during infection. Phylogenetic analysis suggests that AngV is ancestral to all four previously described bat henipaviruses-HeV, NiV, Cedar virus (CedV), and Ghanaian bat virus (GhV)-but evolved more recently than rodent- and shrew-derived henipaviruses, Mojiang (MojV), Gamak (GAKV), and Daeryong (DARV) viruses. Predictive structure-based alignments suggest that AngV is unlikely to bind ephrin receptors, which mediate cell entry for all other known bat henipaviruses. Identification of the AngV receptor is needed to clarify the virus's potential host range. The presence of V and W proteins in the AngV genome suggest that the virus could be pathogenic following zoonotic spillover. IMPORTANCE Henipaviruses include highly pathogenic emerging zoonotic viruses, derived from bat, rodent, and shrew reservoirs. Bat-borne Hendra (HeV) and Nipah (NiV) are the most well-known henipaviruses, for which no effective antivirals or vaccines for humans have been described. Here, we report the discovery and characterization of a novel henipavirus, Angavokely virus (AngV), isolated from wild fruit bats in Madagascar. Genomic characterization of AngV reveals all major features associated with pathogenicity in other henipaviruses, suggesting that AngV could be pathogenic following spillover to human hosts. Our work suggests that AngV is an ancestral bat henipavirus that likely uses viral entry pathways distinct from those previously described for HeV and NiV. In Madagascar, bats are consumed as a source of human food, presenting opportunities for cross-species transmission. Characterization of novel henipaviruses and documentation of their pathogenic and zoonotic potential are essential to predicting and preventing the emergence of future zoonoses that cause pandemics.

Case Report: Cambodian National Malaria Surveillance Program Detection of Plasmodium knowlesi.

Despite recent success in reducing the regional incidence of Plasmodium falciparum malaria, cases of zoonotic malaria are on the rise in Southeast Asia. The Cambodian National Malaria Surveillance Program has previously relied on rapid diagnostic tests and blood smear microscopy with confirmatory polymerase chain reaction (PCR) testing in a subset of cases to further distinguish P. falciparum, P. malariae, P. ovale, and P. vivax species. Here, metagenomic next-generation sequencing identified P. knowlesi mono-infection in six Cambodian patients initially diagnosed with P. malariae by blood smear microscopy in February-May 2020. These findings of recent human infections with P. knowlesi in Cambodia led to the incorporation of P. knowlesi-specific PCR diagnostics to national malaria surveillance efforts.

Workshop-based learning and networking: a scalable model for research capacity strengthening in low- and middle-income countries.

Science education and research have the potential to drive profound change in low- and middle-income countries (LMICs) through encouraging innovation, attracting industry, and creating job opportunities. However, in LMICs, research capacity is often limited, and acquisition of funding and access to state-of-the-art technologies is challenging. The Alliance for Global Health and Science (the Alliance) was founded as a partnership between the University of California, Berkeley (USA) and Makerere University (Uganda), with the goal of strengthening Makerere University's capacity for bioscience research. The flagship program of the Alliance partnership is the MU/UCB Biosciences Training Program, an in-country, hands-on workshop model that trains a large number of students from Makerere University in infectious disease and molecular biology research. This approach nucleates training of larger and more diverse groups of students, development of mentoring and bi-directional research partnerships, and support of the local economy. Here, we describe the project, its conception, implementation, challenges, and outcomes of bioscience research workshops. We aim to provide a blueprint for workshop implementation, and create a valuable resource for bioscience research capacity strengthening in LMICs.

Discovering disease-causing pathogens in resource-scarce Southeast Asia using a global metagenomic pathogen monitoring system.

SignificanceMetagenomic pathogen sequencing offers an unbiased approach to characterizing febrile illness. In resource-scarce settings with high biodiversity, it is critical to identify disease-causing pathogens in order to understand burden and to prioritize efforts for control. Here, metagenomic next-generation sequencing (mNGS) characterization of the pathogen landscape in Cambodia revealed diverse vector-borne and zoonotic pathogens irrespective of age and gender as risk factors. Identification of key pathogens led to changes in national program surveillance. This study is a "real world" example of the use of mNGS surveillance of febrile individuals, executed in-country, to identify outbreaks of vector-borne, zoonotic, and other emerging pathogens in a resource-scarce setting.

Full Genome Nobecovirus Sequences From Malagasy Fruit Bats Define a Unique Evolutionary History for This Coronavirus Clade.

Bats are natural reservoirs for both Alpha- and Betacoronaviruses and the hypothesized original hosts of five of seven known zoonotic coronaviruses. To date, the vast majority of bat coronavirus research has been concentrated in Asia, though coronaviruses are globally distributed; indeed, SARS-CoV and SARS-CoV-2-related Betacoronaviruses in the subgenus Sarbecovirus have been identified circulating in Rhinolophid bats in both Africa and Europe, despite the relative dearth of surveillance in these regions. As part of a long-term study examining the dynamics of potentially zoonotic viruses in three species of endemic Madagascar fruit bat (Pteropus rufus, Eidolon dupreanum, Rousettus madagascariensis), we carried out metagenomic Next Generation Sequencing (mNGS) on urine, throat, and fecal samples obtained from wild-caught individuals. We report detection of RNA derived from Betacoronavirus subgenus Nobecovirus in fecal samples from all three species and describe full genome sequences of novel Nobecoviruses in P. rufus and R. madagascariensis. Phylogenetic analysis indicates the existence of five distinct Nobecovirus clades, one of which is defined by the highly divergent ancestral sequence reported here from P. rufus bats. Madagascar Nobecoviruses derived from P. rufus and R. madagascariensis demonstrate, respectively, Asian and African phylogeographic origins, mirroring those of their fruit bat hosts. Bootscan recombination analysis indicates significant selection has taken place in the spike, nucleocapsid, and NS7 accessory protein regions of the genome for viruses derived from both bat hosts. Madagascar offers a unique phylogeographic nexus of bats and viruses with both Asian and African phylogeographic origins, providing opportunities for unprecedented mixing of viral groups and, potentially, recombination. As fruit bats are handled and consumed widely across Madagascar for subsistence, understanding the landscape of potentially zoonotic coronavirus circulation is essential for mitigation of future zoonotic threats.

Metagenomic characterization of swine slurry in a North American swine farm operation.

Modern day large-scale, high-density farming environments are inherently susceptible to viral outbreaks, inadvertently creating conditions that favor increased pathogen transmission and potential zoonotic spread. Metagenomic sequencing has proven to be a useful tool for characterizing the microbial burden in both people, livestock, and environmental samples. International efforts have been successful at characterizing pathogens in commercial farming environments, especially swine farms, however it is unclear whether the full extent of microbial agents have been adequately captured or is representative of farms elsewhere. To augment international efforts we performed metagenomic next-generation sequencing on nine swine slurry and three environmental samples from a United States of America (U.S.A.) farm operation, characterized the microbial composition of slurry, and identified novel viruses. We assembled a remarkable total of 1792 viral genomes, of which 554 were novel/divergent. We assembled 1637 Picobirnavirus genome segments, of which 538 are novel. In addition, we discovered 10 new viruses belonging to a novel taxon: porcine Statoviruses; which have only been previously reported in human, macaques, mouse, and cows. We assembled 3 divergent Posaviruses and 3 swine Picornaviruses. In addition to viruses described, we found other eukaryotic genera such as Entamoeba and Blastocystis, and bacterial genera such as Listeria, Treponema, Peptoclostridium and Bordetella in the slurry. Of these, two species Entamoeba histolytica and Listeria monocytogenes known to cause human disease were detected. Further, antimicrobial resistance genes such as tetracycline and MLS (macrolide, lincosamide, streptogramin) were also identified. Metagenomic surveillance in swine fecal slurry has great potential for novel and antimicrobial resistant pathogen detection.

Yellow Fever Outbreak in Eastern Senegal, 2020-2021.

Yellow fever virus remains a major threat in low resource countries in South America and Africa despite the existence of an effective vaccine. In Senegal and particularly in the eastern part of the country, periodic sylvatic circulation has been demonstrated with varying degrees of impact on populations in perpetual renewal. We report an outbreak that occurred from October 2020 to February 2021 in eastern Senegal, notified and managed through the synergistic effort yellow fever national surveillance implemented by the Senegalese Ministry of Health in collaboration with the World Health Organization, the countrywide 4S network set up by the Ministry of Health, the Institut Pasteur de Dakar, and the surveillance of arboviruses and hemorrhagic fever viruses in human and vector populations implemented since mid 2020 in eastern Senegal. Virological analyses highlighted the implication of sylvatic mosquito species in virus transmission. Genomic analysis showed a close relationship between the circulating strain in eastern Senegal, 2020, and another one from the West African lineage previously detected and sequenced two years ago from an unvaccinated Dutch traveler who visited the Gambia and Senegal before developing signs after returning to Europe. Moreover, genome analysis identified a 6-nucleotide deletion in the variable domain of the 3'UTR with potential impact on the biology of the viral strain that merits further investigations. Integrated surveillance of yellow fever virus but also of other arboviruses of public health interest is crucial in an ecosystem such as eastern Senegal.

The COVID-19 epidemic in Madagascar: clinical description and laboratory results of the first wave, march-september 2020.

BACKGROUND: Following the first detection of SARS-CoV-2 in passengers arriving from Europe on 19 March 2020, Madagascar took several mitigation measures to limit the spread of the virus in the country. METHODS: Nasopharyngeal and/or oropharyngeal swabs were collected from travellers to Madagascar, suspected SARS-CoV-2 cases and contact of confirmed cases. Swabs were tested at the national reference laboratory using real-time RT-PCR. Data collected from patients were entered in an electronic database for subsequent statistical analysis. All distribution of laboratory-confirmed cases were mapped, and six genomes of viruses were fully sequenced. RESULTS: Overall, 26,415 individuals were tested for SARS-CoV-2 between 18 March and 18 September 2020, of whom 21.0% (5,553/26,145) returned positive. Among laboratory-confirmed SARS-CoV-2-positive patients, the median age was 39 years (IQR: 28-52), and 56.6% (3,311/5,553) were asymptomatic at the time of sampling. The probability of testing positive increased with age with the highest adjusted odds ratio of 2.2 [95% CI: 1.9-2.5] for individuals aged 49 years and more. Viral strains sequenced belong to clades 19A, 20A and 20B indicative of several independent introduction of viruses. CONCLUSIONS: Our study describes the first wave of the COVID-19 in Madagascar. Despite early strategies in place Madagascar could not avoid the introduction and spread of the virus. More studies are needed to estimate the true burden of disease and make public health recommendations for a better preparation to another wave.

IDseq-An open source cloud-based pipeline and analysis service for metagenomic pathogen detection and monitoring.

BACKGROUND: Metagenomic next-generation sequencing (mNGS) has enabled the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample. Existing mNGS data analysis tools typically require bioinformatics expertise and access to local server-class hardware resources. For many research laboratories, this presents an obstacle, especially in resource-limited environments. FINDINGS: We present IDseq, an open source cloud-based metagenomics pipeline and service for global pathogen detection and monitoring (https://idseq.net). The IDseq Portal accepts raw mNGS data, performs host and quality filtration steps, then executes an assembly-based alignment pipeline, which results in the assignment of reads and contigs to taxonomic categories. The taxonomic relative abundances are reported and visualized in an easy-to-use web application to facilitate data interpretation and hypothesis generation. Furthermore, IDseq supports environmental background model generation and automatic internal spike-in control recognition, providing statistics that are critical for data interpretation. IDseq was designed with the specific intent of detecting novel pathogens. Here, we benchmark novel virus detection capability using both synthetically evolved viral sequences and real-world samples, including IDseq analysis of a nasopharyngeal swab sample acquired and processed locally in Cambodia from a tourist from Wuhan, China, infected with the recently emergent SARS-CoV-2. CONCLUSION: The IDseq Portal reduces the barrier to entry for mNGS data analysis and enables bench scientists, clinicians, and bioinformaticians to gain insight from mNGS datasets for both known and novel pathogens.

Rapid metagenomic characterization of a case of imported COVID-19 in Cambodia.

Rapid production and publication of pathogen genome sequences during emerging disease outbreaks provide crucial public health information. In resource-limited settings, especially near an outbreak epicenter, conventional deep sequencing or bioinformatics are often challenging. Here we successfully used metagenomic next generation sequencing on an iSeq100 Illumina platform paired with an open-source bioinformatics pipeline to quickly characterize Cambodia's first case of COVID-2019.

Complete Genome Sequence of a Novel Coronavirus (SARS-CoV-2) Isolate from Bangladesh.

The complete genome sequence of a novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) isolate obtained from a nasopharyngeal swab from a patient with COVID-19 in Bangladesh is reported.

Co-occurrence of Campylobacter Species in Children From Eastern Ethiopia, and Their Association With Environmental Enteric Dysfunction, Diarrhea, and Host Microbiome.

High Campylobacter prevalence during early childhood has been associated with stunting and environmental enteric dysfunction (EED), especially in low resource settings. This study assessed the prevalence, diversity, abundance, and co-occurrence of Campylobacter spp. in stools from children in a rural area of eastern Ethiopia and their association with microbiome, diarrhea, and EED in children. Stool samples (n = 100) were collected from randomly selected children (age range: 360-498 days) in five kebeles in Haramaya District, Ethiopia. Diarrhea, compromised gut permeability, and gut inflammation were observed in 48, 45, and 57% of children, respectively. Campylobacter prevalence and species diversity were assessed using PCR and meta-total RNA sequencing (MeTRS). The prevalence of Campylobacter spp. in the children's stools was 50% (41-60%) by PCR and 88% (80-93.6%) by MeTRS (P < 0.01). Further, seven Campylobacter species (Campylobacter jejuni, Campylobacter upsaliensis, Campylobacter hyointestinalis, Campylobacter coli, Campylobacter sp. RM6137, uncultured Campylobacter sp., and Campylobacter sp. RM12175) were detected by MeTRS in at least 40% of children stools in high abundance (>1.76-log read per million per positive stool sample). Four clusters of Campylobacter species (5-12 species per cluster) co-occurred in the stool samples, suggesting that Campylobacter colonization of children may have occurred through multiple reservoirs or from a reservoir in which several Campylobacter species may co-inhabit. No associations between Campylobacter spp., EED, and diarrhea were detected in this cross-sectional study; however, characteristic microbiome profiles were identified based on the prevalence of Campylobacter spp., EED severity, and diarrhea. Forty-seven bacterial species were correlated with Campylobacter, and 13 of them also correlated with gut permeability, gut inflammation and/or EED severity. Forty-nine species not correlated with Campylobacter were correlated with gut permeability, gut inflammation, EED severity and/or diarrhea. This study demonstrated that (1) in addition to C. jejuni and C. coli, multiple non-thermophilic Campylobacter spp. (i.e., Campylobacter hyointestinalis, Campylobacter fetus, and Campylobacter concisus) were frequently detected in the children's stools and (2) the Campylobacter, gut permeability, gut inflammation, EED severity, and diarrhea were associated with characteristic microbiome composition. Additional spatial and longitudinal studies are needed to identify environmental reservoirs and sources of infection of children with disparate Campylobacter species and to better define their associations with EED in low-income countries.