A Comprehensive Guide to Quality Assessment and Data Submission for Genomic Surveillance of Enteric Pathogens.

This document outlines the steps necessary to assemble and submit the standard data package required for contributing to the global genomic surveillance of enteric pathogens. Although targeted to GenomeTrakr laboratories and collaborators, these protocols are broadly applicable for enteric pathogens collected for different purposes. There are five protocols included in this chapter: (1) quality control (QC) assessment for the genome sequence data, (2) validation for the contextual data, (3) data submission for the standard pathogen package or Pathogen Data Object Model (DOM) to the public repository, (4) viewing and querying data at NCBI, and (5) data curation for maintaining relevance of public data. The data are available through one of the International Nucleotide Sequence Database Consortium (INSDC) members, with the National Center for Biotechnology Information (NCBI) being the primary focus of this document. NCBI Pathogen Detection is a custom dashboard at NCBI that provides easy access to pathogen data plus results for a standard suite of automated cluster and genotyping analyses important for informing public health and regulatory decision-making.

Enhanced echovirus 11 genomic surveillance in neonatal infections in Spain following a European alert reveals new recombinant forms linked to severe cases, 2019 to 2023.

BackgroundIn 2023, a European alert was issued regarding an increase in severe enterovirus (EV) neonatal infections associated with echovirus 11 (E11) new lineage 1.AimTo analyse E11-positive cases between 2019 and 2023 to investigate whether the new lineage 1 circulated in Spain causing severe neonatal infections.MethodsEV-positive samples from hospitalised cases are sent for typing to the National Reference Enterovirus Laboratory. Available samples from 2022-23 were subjected to metagenomic next-generation sequencing.ResultsOf 1,288 samples genotyped, 103 were E11-positive (98 patients: 6 adults, 33 neonates, 89 children under 6 years; male to female ratio 1.9). E11 detection rate was similar before and after detection of the new lineage 1 in Spain in June 2022 (9.7% in 2019 vs 10.6% in 2023). The proportion of E11-infected ICU-admitted neonates in 2019-2022 (2/7) vs 2022-2023 (5/12) did not significantly differ (p = 0.65). In severe neonatal infections, 4/7 E11 strains were not linked to the new lineage 1. The three novel E11 recombinant genomes were associated with severe (n = 2) and non-severe (n = 1) cases from 2022-2023 and clustered outside the new lineage 1. Coinfecting pathogenic viruses were present in four of 10 E11-positive samples.ConclusionThe emergence of the new lineage 1 is not linked with an increase in incidence or severity of neonatal E11 infections in Spain. The detection of two novel E11 recombinants associated with severe disease warrants enhancing genomic and clinical surveillance.

AMRColab - a user-friendly antimicrobial resistance detection and visualization tool.

Antimicrobial resistance (AMR) poses a significant threat to global public health, with the potential to cause millions of deaths annually by 2050. Effective surveillance of AMR pathogens is crucial for monitoring and predicting their behaviour in response to antibiotics. However, many public health professionals lack the necessary bioinformatics skills and resources to analyse pathogen genomes effectively. To address this challenge, we developed AMRColab, an open-access bioinformatics analysis suite hosted on Google Colaboratory. AMRColab enables users with limited or no bioinformatics training to detect and visualize AMR determinants in pathogen genomes using a 'plug-and-play' approach. The platform integrates established bioinformatics tools such as AMRFinderPlus and hAMRonization, allowing users to analyse, compare and visualize trends in AMR pathogens easily. A trial run using methicillin-resistant Staphylococcus aureus (MRSA) strains demonstrated AMRColab's effectiveness in identifying AMR determinants and facilitating comparative analysis across strains. A workshop was conducted and feedback from participants indicated high confidence in using AMRColab and a willingness to incorporate it into their research. AMRColab's user-friendly interface and modular design make it accessible to a diverse audience, including medical laboratory technologists, medical doctors and public health scientists, regardless of their bioinformatics expertise. Future improvements to AMRColab will include enhanced visualization tools, multilingual support and the establishment of an online community platform. AMRColab represents a significant step towards democratizing AMR surveillance and empowering public health professionals to combat AMR effectively.

Influenza outbreak during the surge of SARS-CoV-2 omicron in a metropolitan area from southern Brazil: genomic surveillance.

Influenza circulation was significantly affected in 2020-21 by the COVID-19 pandemic. During this time, few influenza cases were recorded. However, in the summer of 2021-22, an increase in atypical influenza cases was observed, leading to the resurgence of influenza in the southernmost state of Brazil, Rio Grande do Sul (RS). The present study aimed to identify the circulation of FLUAV, FLUBV and SARS-CoV-2 and characterize the influenza genomes in respiratory samples using high-throughput sequencing technology (HTS). Respiratory samples (n = 694) from patients in RS were selected between July 2021 and August 2022. The samples were typed using reverse transcriptase real-time PCR (RT-qPCR) and showed 32% (223/694) of the samples to be positive for SARS-CoV-2, 7% for FLUAV (H3) (49/694). FLUBV was not detected. RT-qPCR data also resulted in FLUAV and SARS-CoV-2 co-infections in 1.7% (4/223) of samples tested. Whole genome sequencing of FLUAV produced 15 complete genomes of the H3N2 subtype, phylogenetically classified in the 3C.2a1b.2a.2a.3 subclade and revealing the dominance of viruses in the southern region of Brazil. Mutation analysis identified 72 amino acid substitutions in all genes, highlighting ongoing genetic evolution with potential implications for vaccine effectiveness, viral fitness, and pathogenicity. This study underscores limitations in current surveillance systems, advocating for comprehensive data inclusion to enhance understanding of influenza epidemiology in southern Brazil. These findings contribute valuable insights to inform more effective public health responses and underscore the critical need for continuous genomic surveillance.

Large scale analysis of the SARS-CoV-2 main protease reveals marginal presence of nirmatrelvir-resistant SARS-CoV-2 Omicron mutants in Ontario, Canada, December 2021-September 2023.

Background: In response to the COVID-19 pandemic, a new oral antiviral called nirmatrelvir-ritonavir (PaxlovidTM) was authorized for use in Canada in January 2022. In vitro studies have reported mutations in Mpro protein that may be associated with the development of nirmatrelvir resistance. Objectives: To survey the prevalence, relevance and temporal patterns of Mpro mutations among SARS-CoV-2 Omicron lineages in Ontario, Canada. Methods: A total of 93,082 Mpro gene sequences from December 2021 to September 2023 were analyzed. Reported in vitro Mpro mutations were screened against our database using in-house data science pipelines to determine the nirmatrelvir resistance. Negative binomial regression was conducted to analyze the temporal trends in Mpro mutation counts over the study time period. Results: A declining trend was observed in non-synonymous mutations of Mpro sequences, showing a 7.9% reduction (95% CI: 6.5%-‬9.4%; p<0.001) every 30 days. The P132H was the most prevalent mutation (higher than 95%) in all Omicron lineages. In vitro nirmatrelvir-resistant mutations were found in 3.12% (n=29/929) Omicron lineages with very low counts, ranging from one to 19. Only two mutations, A7T (n=19) and M82I (n=9), showed temporal presence among the BA.1.1 in 2022 and the BQ.1.2.3 in 2022, respectively. Conclusion: The observations suggest that, as of September 2023, no significant or widespread resistance to nirmatrelvir has developed among SARS-CoV-2 Omicron variants in Ontario. This study highlights the importance of creating automated monitoring systems to track the emergence of nirmatrelvir-resistant mutations within the SARS-CoV-2 virus, utilizing genomic data generated in real-time.

Local patterns of spread of influenza A H3N2 virus in coastal Kenya over a 1-year period revealed through virus sequence data.

The patterns of spread of influenza A viruses in local populations in tropical and sub-tropical regions are unclear due to sparsity of representative spatiotemporal sequence data. We sequenced and analyzed 58 influenza A(H3N2) virus genomes sampled between December 2015 and December 2016 from nine health facilities within the Kilifi Health and Demographic Surveillance System (KHDSS), a predominantly rural region, covering approximately 891 km2 along the Kenyan coastline. The genomes were compared with 1571 contemporaneous global sequences from 75 countries. We observed at least five independent introductions of A(H3N2) viruses into the region during the one-year period, with the importations originating from Africa, Europe, and North America. We also inferred 23 virus location transition events between the nine facilities included in the study. International virus imports into the study area were captured at the facilities of Chasimba, Matsangoni, Mtondia, and Mavueni, while all four exports from the region were captured from the Chasimba facility, all occurring to Africa destinations. A strong spatial clustering of virus strains at all locations was observed associated with local evolution. Our study shows that influenza A(H3N2) virus epidemics in local populations appear to be characterized by limited introductions followed by significant local spread and evolution. Knowledge of the viral lineages that circulate within specific populations in understudied tropical and subtropical regions is required to understand the full diversity and global ecology of influenza viruses and to inform vaccination strategies within these populations.

Progress in malaria genomic surveillance using long-read sequencing.

Recent studies by Girgis et al. and de Cesare et al. promise to advance malaria genomic surveillance using inexpensive and portable long-read amplicon-sequencing technologies. These technologies allow rapid characterization of drug-resistance markers, antigenic diversity, and diagnostic target loci from dried blood spots, providing new tools for surveillance in endemic regions and informing interventions to combat malaria more effectively.

Exploring Dengue Infection in a Vaccinated Individual: Preliminary Molecular Diagnosis and Sequencing Insights.

This study examines a case involving a 7-year-old child who developed dengue symptoms following Qdenga vaccination. Despite initial negative diagnostic results, molecular analysis confirmed an infection with DENV4. Next-generation sequencing detected viral RNA from both DENV2 and DENV4 serotypes, which were identified as vaccine-derived strains using specific primers. Phylogenetic analysis further confirmed that these sequences belonged to the Qdenga vaccine rather than circulating wild-type viruses. This case underscores the critical need for precise diagnostic interpretation in vaccinated individuals to avoid misdiagnosis and to strengthen public health surveillance. A comprehensive understanding of vaccine-induced viremia is essential for refining dengue surveillance, improving diagnostic accuracy, and informing public health strategies in endemic regions.

Influence of SARS-CoV-2 variants on COVID-19 epidemiological and clinical profiles: a comparative analysis of two waves of cases.

BACKGROUND: The COVID-19 pandemic has been the most significant health challenge of the last century. Multiple and successive waves of COVID-19 cases, driven particularly by the emergence of new SARS-CoV-2 variants, have kept the world in a constant state of alert. METHODS: We present an observational, descriptive, cross-sectional study aimed at identifying SARS-CoV-2 variants circulating during two local waves of COVID-19 cases in southern Bahia, Brazil (late 2021 and late 2022), and analyzing the association between the detected variants and the epidemiological and clinical characteristics of the disease. For this purpose, data and nasopharyngeal samples from individuals in southern Bahia, Brazil, with suspected COVID-19 were included. Viral detection was performed by RT-qPCR, and SARS-CoV-2 variants were identified by next-generation viral sequencing. RESULTS: A total of 368 nasopharyngeal samples were tested. Approximately 23% of the samples from late 2021 tested positive for SARS-CoV-2, while in 2022, the positivity rate was about 56%. All sequenced samples from 2021 were identified as the Delta variant, while in 2022, all samples were classified as the Omicron variant. Overall, individuals who tested positive for SARS-CoV-2 in 2022 were younger than those who tested positive in 2021. Moreover, we observed significant differences in the clinical spectrum of SARS-CoV-2 infection when comparing the two periods. Individuals who presented with anosmia/ageusia were more likely to test positive for SARS-CoV-2 infection in 2021 but not in 2022. Additionally, fever, dry cough, pharyngalgia, headache, and rhinorrhea were more frequent among individuals infected with the Omicron variant than among those infected with the Delta variant. CONCLUSIONS: The profile of COVID-19 in southern Bahia differed when analyzing two distinct waves of the pandemic in the region. These differences are likely related to the variants, which may differ in transmissibility and virulence, thereby altering the dynamics of the pandemic. This underscores the importance of genomic surveillance in better understanding the behavior of viral infections.

Towards integrated malaria molecular surveillance in Africa.

Integrated malaria molecular surveillance (iMMS) systems are essential for Africa's expanding malaria genomics initiatives. Here we highlight a few initiatives and demonstrate how iMMS can support evidence-based decisions and policies for National Malaria Programs and other malaria control stakeholders. We conclude with key considerations for advancing these malaria genomics initiatives towards sustainable iMMS.

Dengue virus surveillance in Nepal yields the first on-site whole genome sequences of isolates from the 2022 outbreak.

BACKGROUND: The 4 serotypes of dengue virus (DENV1-4) can each cause potentially deadly dengue disease, and are spreading globally from tropical and subtropical areas to more temperate ones. Nepal provides a microcosm of this global phenomenon, having met each of these grim benchmarks. To better understand DENV transmission dynamics and spread into new areas, we chose to study dengue in Nepal and, in so doing, to build the onsite infrastructure needed to manage future, larger studies. METHODS AND RESULTS: During the 2022 dengue season, we enrolled 384 patients presenting at a hospital in Kathmandu with dengue-like symptoms; 79% of the study participants had active or recent DENV infection (NS1 antigen and IgM). To identify circulating serotypes, we screened serum from 50 of the NS1+ participants by RT-PCR and identified DENV1, 2, and 3 - with DENV1 and 3 codominant. We also performed whole-genome sequencing of DENV, for the first time in Nepal, using our new on-site capacity. Sequencing analysis demonstrated the DENV1 and 3 genomes clustered with sequences reported from India in 2019, and the DENV2 genome clustered with a sequence reported from China in 2018. CONCLUSION: These findings highlight DENV's geographic expansion from neighboring countries, identify China and India as the likely origin of the 2022 DENV cases in Nepal, and demonstrate the feasibility of building onsite capacity for more rapid genomic surveillance of circulating DENV. These ongoing efforts promise to protect populations in Nepal and beyond by informing the development and deployment of DENV drugs and vaccines in real time.

Molecular characterisation of human rabies in Tanzania and Kenya: a case series report and phylogenetic investigation.

BACKGROUND: Rabies remains a major public health problem in low- and middle-income countries. However, human rabies deaths are rarely laboratory-confirmed or sequenced, especially in Africa. Five human rabies deaths from Tanzania and Kenya were investigated and the causative rabies viruses sequenced, with the aim of identifying implications for rabies control at individual, healthcare and societal levels. CASE PRESENTATION: The epidemiological context and care of these cases was contrasting. Four had a clear history of being bitten by dogs, while one had an unclear biting history. Two individuals sought medical attention within a day of being bitten, whereas three sought care only after developing rabies symptoms. Despite seeking medical care, none of the cases received complete post-exposure prophylaxis: one patient received only tetanus vaccination, one did not complete the post-exposure vaccination regimen, one followed an off-label vaccination schedule, and two did not receive any post-exposure vaccinations before the onset of symptoms. These cases highlight serious gaps in health-seeking behaviour, and in health systems providing appropriate care following risky exposures, including in the accessibility and effectiveness of post-exposure prophylaxis as it is administered in the region. CONCLUSIONS: The viral genomic and epidemiological data confirms dog-mediated rabies as the cause of each of these deaths. The phylogenetic investigation highlights the transboundary circulation of rabies within domestic dog populations, revealing distinct rabies virus clades with evidence of regional spread. These findings underscore the importance of coordinated cross-border control efforts between the two countries. Urgent action is needed to improve awareness around the need for emergency post-exposure vaccines that should be accessible in local communities and administered appropriately, as well as investment in coordinated dog vaccination to control dog-mediated rabies, the underlying cause of these deaths.

Genetic and structural characterization of dengue virus involved in the 2023 autochthonous outbreaks in central Italy.

Dengue virus (DENV) has been expanding its range to temperate areas that are not usually affected, where the spread of vectors has been facilitated by global trade and climate change. In Europe, there have been many cases of DENV imported from other regions in the past few years, leading to local outbreaks of DENV among people without travel history. Here we describe the epidemiological and molecular investigations of three transmission events locally acquired DENV infections caused by serotypes 1, 2, and 3, respectively, in the Latium Region from August to November 2023. Next-generation or Sanger sequencing were used to obtain the whole genomes, or the complete E-gene of the viruses, respectively. The structure of the DENV-1 and DENV-3 sequences was analysed to identify amino acid changes that were not found in the closest related sequences. The major cluster was supported by DENV-1 (originated in South America), with 42 autochthonous infections almost occurring in the eastern area of Rome, probably due to a single introduction followed by local sustained transmission. Seven DENV-1 subclusters have been identified by mutational and phylogenetic analysis. Structural analysis indicated changes whose meaning can be explained by the adaptation of the virus to human hosts and vectors and their interactions with antibodies and cell receptors.

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.

The COVID-19 pandemic is marked by the successive emergence of new SARS-CoV-2 variants, lineages, and sublineages that outcompete earlier strains, largely due to factors like increased transmissibility and immune escape. We propose DeepAutoCoV, an unsupervised deep learning anomaly detection system, to predict future dominant lineages (FDLs). We define FDLs as viral (sub)lineages that will constitute >10% of all the viral sequences added to the GISAID, a public database supporting viral genetic sequence sharing, in a given week. DeepAutoCoV is trained and validated by assembling global and country-specific data sets from over 16 million Spike protein sequences sampled over a period of ~4 years. DeepAutoCoV successfully flags FDLs at very low frequencies (0.01%-3%), with median lead times of 4-17 weeks, and predicts FDLs between ~5 and ~25 times better than a baseline approach. For example, the B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than a year before it was considered for an updated COVID-19 vaccine. Furthermore, DeepAutoCoV outputs interpretable results by pinpointing specific mutations potentially linked to increased fitness and may provide significant insights for the optimization of public health 'pre-emptive' intervention strategies.

Emergence of multidrug-resistant Providencia rettgeri clone in food-producing animals: A public health threat.

The occurrence of carbapenemases encoding genes in Providencia rettgeri is a critical public health concern since this species has intrinsic resistance to several antimicrobials, including polymyxins. The identification of this multidrug-resistant (MDR) pathogen outside the hospital setting has become increasingly frequent, and raises an alert for the global health agencies, as they indicate a possible spread of such pathogens. Herein, we described three MDR P. rettgeri isolates carrying a diversity of antimicrobial resistance genes (ARGs) isolated from stool samples of swine and bovine in Brazil. Molecular analysis revealed that all isolates belonged to the same clone. The whole genome sequencing (WGS) of a representative isolate (PVR-188) was performed by MiSeq Illumina® platform, while the assembling and annotation was achieved using SPAdes and Prooka, respectively. The WGS analyses indicated the presence of ARGs that confer resistance to ß-lactams (bla NDM-1, bla CTX-M-2), quinolones (qnrD1), aminoglycosides (aadA2, aadA1, aph(3')-Via), phenicol (catB2), sulfonamides (sul1, sul2), and trimethoprim (dfrA12, dfrA1). The presence of three plasmid replicons (Col3M, IncQ1, and IncT) was detected, but no phage sequences were found. The phylogenetic analyses confirmed the genomic relationship of the PVR-188 with P. rettgeri isolates recovered from animals and humans in the USA and Malaysia. In conclusion, we report the occurrence of MDR P. rettgeri clone colonizing the gut microbiota of food-producing animals in Brazil, revealing the spread of this pathogen beyond hospital boundaries.

Genomic surveillance as a scalable framework for precision phage therapy against antibiotic-resistant pathogens.

Phage therapy is gaining increasing interest in the fight against critically antibiotic-resistant nosocomial pathogens. However, the narrow host range of bacteriophages hampers the development of broadly effective phage therapeutics and demands precision approaches. Here, we combine large-scale phylogeographic analysis with high-throughput phage typing to guide the development of precision phage cocktails targeting carbapenem-resistant Acinetobacter baumannii, a top-priority pathogen. Our analysis reveals that a few strain types dominate infections in each world region, with their geographical distribution remaining stable within 6 years. As we demonstrate in Eastern Europe, this spatiotemporal distribution enables preemptive preparation of region-specific phage collections that target most local infections. Finally, we showcase the efficacy of phage cocktails against prevalent strain types using in vitro and animal infection models. Ultimately, genomic surveillance identifies patients benefiting from the same phages across geographical scales, thus providing a scalable framework for precision phage therapy.

Wastewater genomic surveillance to track infectious disease-causing pathogens in low-income countries: Advantages, limitations, and perspectives.

The emergence of infectious diseases, particularly those caused by respiratory pathogens like COVID-19 and influenza viruses, poses a significant threat to public health, especially in the context of climate change. Vulnerable variants and major pathogenicities are appearing, leading to a wide range of illnesses and increased morbidity. Wastewater genomic surveillance represents a cost-effective and a crucial tool for tracking infectious diseases, particularly in regions where clinical testing resources might be limited or inadequate. However, there are numerous limitations that need to be addressed in order to enhance its effectiveness for monitoring a wide range of pathogens. The current study uses this approach for the first time in Morocco to monitor the epidemiology of SARS-CoV-2 and Influenza A, B and RSV virus infections during the third wave of COVID-19 caused by the Omicron variant. The virome was concentrated from wastewater collected from two sewersheds of two cities, Agadir and Inezgane, using the the polyethylene glycol (PEG)/NaCl method. All 26 samples from both cities exhibited positive results for SARS-CoV-2, indicating varying viral loads. In the case of the Influenza A virus, four samples tested positive in Inezgane. However, no detection of Influenza B or RSV was observed in any of the samples. The estimated SARS-CoV-2 viral RNA copy numbers observed were then used to estimate the number of infected individuals using the SEIR model. The estimated number of cases correlates positively with the number of reported cases. Next Generation Sequencing showed that samples contain the following two variants: BA.1 and BA.2 that have been detected in clinical samples. In the case of Influenza A, clinical samples revealed a mild presence of the influenza virus subtype A(H3N2). This study demonstrates the effectiveness and feasibility of wastewater genomic surveillance in monitoring pathogens such as SARS-CoV-2 in Morocco. This approach can become an even more powerful tool for monitoring and predicting the spread of infectious diseases by addressing several key considerations. These include enhancing data collection methods, making environmental corrections for factors affecting RNA stability in wastewater, and refining mathematical models to improve their accuracy in predicting the number of infected cases. Incorporating statistical and machine learning models can further enhance the precision of these predictions.

Retrospective Analysis of Omicron in Minas Gerais, Brazil: Emergence, Dissemination, and Diversification.

Brazil is one of the countries most affected by COVID-19, with the highest number of deaths recorded. Brazilian Health Institutions have reported four main peaks of positive COVID-19 cases. The last two waves were characterized by the emergence of the VOC Omicron and its sublineages. This study aimed to conduct a retrospective surveillance study illustrating the emergence, dissemination, and diversification of the VOC Omicron in 15 regional health units (RHUs) in MG, the second most populous state in Brazil, by combining epidemiological and genomic data. A total of 5643 confirmed positive COVID-19 samples were genotyped using the panels TaqMan SARS-CoV-2 Mutation and 4Plex SC2/VOC Bio-Manguinhos to define mutations classifying the BA.1, BA.2, BA.4, and BA.5 sublineages. While sublineages BA.1 and BA.2 were more prevalent during the third wave, BA.4 and BA.5 dominated the fourth wave in the state. Epidemiological and viral genome data suggest that age and vaccination with booster doses were the main factors related to clinical outcomes, reducing the number of deaths, irrespective of the Omicron sublineages. Complete genome sequencing of 253 positive samples confirmed the circulation of the BA.1, BA.2, BA.4, and BA.5 subvariants, and phylogenomic analysis demonstrated that the VOC Omicron was introduced through multiple international events, followed by transmission within the state of MG. In addition to the four subvariants, other lineages have been identified at low frequency, including BQ.1.1 and XAG. This integrative study reinforces that the evolution of Omicron sublineages was the most significant factor driving the highest peaks of positive COVID-19 cases without an increase in more severe cases, prevented by vaccination boosters.

Evidence of Lineage 1 and 3 West Nile Virus in Person with Neuroinvasive Disease, Nebraska, USA, 2023.

West Nile virus (WNV) is the most common cause of human arboviral disease in the contiguous United States, where only lineage 1 (L1) WNV had been found. In 2023, an immunocompetent patient was hospitalized in Nebraska with West Nile neuroinvasive disease and multisystem organ failure. Testing at the Centers for Disease Control and Prevention indicated an unusually high viral load and acute antibody response. Upon sequencing of serum and cerebrospinal fluid, we detected lineage 3 (L3) and L1 WNV genomes. L3 WNV had previously only been found in Central Europe in mosquitoes. The identification of L3 WNV in the United States and the observed clinical and laboratory features raise questions about the potential effect of L3 WNV on the transmission dynamics and pathogenicity of WNV infections. Determining the distribution and prevalence of L3 WNV in the United States and any public health and clinical implications is critical.