SARS-CoV-2 surveillance in Italy through phylogenomic inferences based on Hamming distances derived from pan-SNPs, -MNPs and -InDels

Dynamic of SARS-CoV-2 variants circulation in Tunisian pediatric population, during successive waves, from March 2020 to September 2022

The emergence of SARS-CoV-2 variants has led to several cases among children. However, limited information is available from North African countries. This study describes the SARS-CoV-2 strains circulating in Tunisian pediatric population during successive waves. A total of 447 complete sequences were obtained from individuals aged from 13 days to 18 years, between March 2020 and September 2022: 369 sequences generated during this study and 78 ones, available in GISAID, previously obtained from Tunisian pediatric patients. These sequences were compared with 354 and 274 ones obtained from Tunisian adults and a global dataset, respectively. The variant circulation dynamics of predominant variants were investigated during the study period using maximum-likelihood phylogenetic analysis. Among the studied population, adolescents were the predominant age group, comprising 55.26% of cases. Twenty-three lineages were identified; seven of which were not previously reported in Tunisia. Phylogenetic analysis showed a close relationship between the sequences from Tunisian adults and children. The connections of sequences from other countries were variable according to variants: close relationships were observed for Alpha, B1.160 and Omicron variants, while independent Tunisian clusters were observed for Delta and B.1.177 lineages. These findings highlight the pivotal role of children in virus transmission and underscore the impact of vaccination on virus spread. Vaccination of children, with booster doses, may be considered for better management of future emergences.

Evaluation of next generation sequencing approaches for SARS-CoV-2

Within public health control strategies for SARS-CoV-2, whole genome sequencing (WGS) is essential for tracking viral spread and monitoring the emergence of variants which may impair the effectiveness of vaccines, diagnostic methods, and therapeutics. In this manuscript different strategies for SARS-CoV-2 WGS including metagenomic shotgun (SG), library enrichment by myBaits® Expert Virus-SARS-CoV-2 (Arbor Biosciences), nCoV-2019 sequencing protocol, ampliseq approach by Swift Amplicon® SARS-CoV-2 Panel kit (Swift Biosciences), and Illumina COVIDSeq Test (Illumina Inc.), were evaluated in order to identify the best approach in terms of results, labour, and costs. The analysis revealed that Illumina COVIDSeq Test (Illumina Inc.) is the best choice for a cost-effective, time-consuming production of consensus sequences.

SARS-CoV-2 excretion kinetics in nasopharyngeal and stool samples from the pediatric population

Background

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for serious respiratory infections in humans. Even in the absence of respiratory symptoms, gastrointestinal (GI) signs were commonly reported in adults and children. Thus, oral-fecal transmission was suspected as a possible route of infection. The objective of this study was to describe RNA shedding in nasopharyngeal and stool samples obtained from asymptomatic and symptomatic children and to investigate virus viability.

Methods

This study included 179 stool and 191 nasopharyngeal samples obtained from 71 children, which included symptomatic (n = 64) and asymptomatic (n = 7) ones. They were collected every 7 days from the onset of the infection until negativation. Viral RNA was detected by real-time RT-PCR, targeting the N and ORF1 genes. Whole-genome sequencing was performed for positive cases. Viral isolation was assessed on Vero cells, followed by molecular detection confirmation.

Results

All cases included in this study (n = 71) were positive in their nasopharyngeal samples. SARS-CoV-2 RNA was detected in 36 stool samples obtained from 15 out of 71 (21.1%) children; 13 were symptomatic and two were asymptomatic. Excretion periods varied from 7 to 21 days and 7 to 14 days in nasopharyngeal and fecal samples, respectively. Four variants were detected: Alpha (n = 3), B.1.160 (n = 3), Delta (n = 7), and Omicron (n = 1). Inoculation of stool samples on cell culture showed no specific cytopathic effect. All cell culture supernatants were negative for RT-qPCR.

Conclusion

Our study demonstrated nasopharyngeal and fecal shedding of SARS-CoV-2 RNA by children up to 21 and 14 days, respectively. Fecal shedding was recorded in symptomatic and asymptomatic children. Nevertheless, SARS-CoV-2 was not isolated from positive stool samples.

Spatial and temporal dynamics of West Nile virus between Africa and Europe

It is unclear whether West Nile virus (WNV) circulates between Africa and Europe, despite numerous studies supporting an African origin and high transmission in Europe. We integrated genomic data with geographic observations and phylogenetic and phylogeographic inferences to uncover the spatial and temporal viral dynamics of WNV between these two continents. We focused our analysis towards WNV lineages 1 (L1) and 2 (L2), the most spatially widespread and pathogenic WNV lineages. Our study shows a Northern-Western African origin of L1, with back-and-forth exchanges between West Africa and Southern-Western Europe; and a Southern African origin of L2, with one main introduction from South Africa to Europe, and no back introductions observed. We also noticed a potential overlap between L1 and L2 Eastern and Western phylogeography and two Afro-Palearctic bird migratory flyways. Future studies linking avian and mosquito species susceptibility, migratory connectivity patterns, and phylogeographic inference are suggested to elucidate the dynamics of emerging viruses.

Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data

BACKGROUND

Genomic data-based machine learning tools are promising for real-time surveillance activities performing source attribution of foodborne bacteria such as Listeria monocytogenes. Given the heterogeneity of machine learning practices, our aim was to identify those influencing the source prediction performance of the usual holdout method combined with the repeated k-fold cross-validation method.

METHODS

A large collection of 1 100 L. monocytogenes genomes with known sources was built according to several genomic metrics to ensure authenticity and completeness of genomic profiles. Based on these genomic profiles (i.e. 7-locus alleles, core alleles, accessory genes, core SNPs and pan kmers), we developed a versatile workflow assessing prediction performance of different combinations of training dataset splitting (i.e. 50, 60, 70, 80 and 90%), data preprocessing (i.e. with or without near-zero variance removal), and learning models (i.e. BLR, ERT, RF, SGB, SVM and XGB). The performance metrics included accuracy, Cohen's kappa, F1-score, area under the curves from receiver operating characteristic curve, precision recall curve or precision recall gain curve, and execution time.

RESULTS

The testing average accuracies from accessory genes and pan kmers were significantly higher than accuracies from core alleles or SNPs. While the accuracies from 70 and 80% of training dataset splitting were not significantly different, those from 80% were significantly higher than the other tested proportions. The near-zero variance removal did not allow to produce results for 7-locus alleles, did not impact significantly the accuracy for core alleles, accessory genes and pan kmers, and decreased significantly accuracy for core SNPs. The SVM and XGB models did not present significant differences in accuracy between each other and reached significantly higher accuracies than BLR, SGB, ERT and RF, in this order of magnitude. However, the SVM model required more computing power than the XGB model, especially for high amount of descriptors such like core SNPs and pan kmers.

CONCLUSIONS

In addition to recommendations about machine learning practices for L. monocytogenes source attribution based on genomic data, the present study also provides a freely available workflow to solve other balanced or unbalanced multiclass phenotypes from binary and categorical genomic profiles of other microorganisms without source code modifications.

ReporTree: a surveillance-oriented tool to strengthen the linkage between pathogen genetic clusters and epidemiological data

BACKGROUND

Genomics-informed pathogen surveillance strengthens public health decision-making, playing an important role in infectious diseases' prevention and control. A pivotal outcome of genomics surveillance is the identification of pathogen genetic clusters and their characterization in terms of geotemporal spread or linkage to clinical and demographic data. This task often consists of the visual exploration of (large) phylogenetic trees and associated metadata, being time-consuming and difficult to reproduce.

RESULTS

We developed ReporTree, a flexible bioinformatics pipeline that allows diving into the complexity of pathogen diversity to rapidly identify genetic clusters at any (or all) distance threshold(s) or cluster stability regions and to generate surveillance-oriented reports based on the available metadata, such as timespan, geography, or vaccination/clinical status. ReporTree is able to maintain cluster nomenclature in subsequent analyses and to generate a nomenclature code combining cluster information at different hierarchical levels, thus facilitating the active surveillance of clusters of interest. By handling several input formats and clustering methods, ReporTree is applicable to multiple pathogens, constituting a flexible resource that can be smoothly deployed in routine surveillance bioinformatics workflows with negligible computational and time costs. This is demonstrated through a comprehensive benchmarking of (i) the cg/wgMLST workflow with large datasets of four foodborne bacterial pathogens and (ii) the alignment-based SNP workflow with a large dataset of Mycobacterium tuberculosis. To further validate this tool, we reproduced a previous large-scale study on Neisseria gonorrhoeae, demonstrating how ReporTree is able to rapidly identify the main species genogroups and characterize them with key surveillance metadata, such as antibiotic resistance data. By providing examples for SARS-CoV-2 and the foodborne bacterial pathogen Listeria monocytogenes, we show how this tool is currently a useful asset in genomics-informed routine surveillance and outbreak detection of a wide variety of species.

CONCLUSIONS

In summary, ReporTree is a pan-pathogen tool for automated and reproducible identification and characterization of genetic clusters that contributes to a sustainable and efficient public health genomics-informed pathogen surveillance. ReporTree is implemented in python 3.8 and is freely available at https://github.com/insapathogenomics/ReporTree .

One health system supporting surveillance during COVID-19 epidemic in Abruzzo region, southern Italy

The Istituti Zooprofilattici Sperimentali (IZSs) are public health institutes dealing with the aetiology and pathogenesis of infectious diseases of domestic and wild animals. During Coronavirus Disease 2019 epidemic, the Italian Ministry of Health appointed the IZSs to carry out diagnostic tests for the detection of SARS-CoV-2 in human samples. In particular, the IZS of Abruzzo and Molise (IZS-Teramo) was involved in the diagnosis of SARS-CoV-2 through testing nasopharyngeal swabs by Real Time RT-PCR. Activities and infrastructures were reorganised to the new priorities, in a "One Health" framework, based on interdisciplinary, laboratory promptness, accreditation of the test for the detection of the RNA of SARS-CoV-2 in human samples, and management of confidentiality of sensitive data. The laboratory information system - SILAB - was implemented with a One Health module for managing data of human origin, with tools for the automatic registration of information improving the quality of the data. Moreover, the "National Reference Centre for Whole Genome Sequencing of microbial pathogens - database and bioinformatics analysis" - GENPAT - formally established at the IZS-Teramo, developed bioinformatics workflows and IT dashboard with ad hoc surveillance tools to support the metagenomics-based SARS-CoV-2 surveillance, providing molecular sequencing analysis to quickly intercept the variants circulating in the area. This manuscript describes the One Health system developed by adapting and integrating both SILAB and GENPAT tools for supporting surveillance during COVID-19 epidemic in the Abruzzo region, southern Italy. The developed dashboard permits the health authorities to observe the SARS-CoV-2 spread in the region, and by combining spatio-temporal information with metagenomics provides early evidence for the identification of emerging space-time clusters of variants at the municipality level. The implementation of the One Health module was designed to be easily modelled and adapted for the management of other diseases and future hypothetical events of pandemic nature.

COVID-19 infection-associated platelet and neutrophil activation is blunted by previous anti-SARS-CoV-2 vaccination

The effectiveness of vaccination against SARS-CoV-2 in preventing COVID-19 or in reducing severe illness in subjects hospitalized for COVID-19 despite vaccination has been unequivocally shown. However, no studies so far have assessed if subjects who get COVID-19 despite vaccination are protected from SARS-CoV-2-induced platelet, neutrophil and endothelial activation, biomarkers associated with thrombosis and worse outcome. In this pilot study, we show that previous vaccination blunts COVID-19-associated platelet activation, assessed by circulating platelet-derived microvesicles and soluble P-selectin, and neutrophil activation, assessed by circulating neutrophil extracellular trap (NET) biomarkers and matrix metalloproteinase-9, and reduces COVID-19-associated thrombotic events, hospitalization in intensive-care units and death.

SARS-CoV-2 Delta VOC in a Paucisymptomatic Dog, Italy

Since the very beginning of the COVID-19 pandemic, SARS-CoV-2 detection has been described in several animal species. A total of 625 outbreaks in animals have been reported globally, affecting 17 species in 32 countries and the human source of infection has been recognized including pet owners, zookeepers, and farmers. In this report, we describe the case of a paucisymptomatic dog in Italy infected with SARS-CoV-2 from a household with three confirmed human cases of COVID-19 living in Pesaro (Marche region, Italy). The dog showed high viral RNA titers in the nasal and oropharyngeal swabs. In the nasal swab, SARS-CoV-2 RNA lasted for a least a week. By sequencing, the strain was assigned to the AY.23 lineage (PANGO), one of the sub-lineages of the major SARS-CoV-2 Delta variant of concern (VOC). Although we did not process the swabs of the three human cases, we strongly suspect a human origin for the dog infection. In this regard, AY.23 sequences, although never released thus far in the Marche region, were detected in the neighboring regions. Our findings highlight once more the need for a One Health approach for SARS-CoV-2 surveillance, management, and control, thus preventing viral spillover from animals to humans.