SPREAD: Spatiotemporal Pathogen Relationships and Epidemiological Analysis Dashboard.

In the scope of public health, the rapid identification and control of infectious disease outbreaks are a paramount concern. Traditional surveillance methods often face challenges in effectively combining genetic, geographical, and temporal data, which is crucial for a comprehensive understanding of disease transmission dynamics. Addressing this critical need, the Spatiotemporal Phylogenomic Research and Epidemiological Analysis Dashboard (SPREAD) emerges as an innovative standalone web-based application. SPREAD integrates several modules for detailed genomic relationships, pinpointing genetically close pathogens, and spatial mapping, providing in-depth views of how diseases spread across populations and territories, with significant advantage to manage both bacteria and viruses based on allele and variant calling, respectively. Designed for broad accessibility, SPREAD operates seamlessly within web browsers, eliminating the need for sophisticated IT infrastructure and facilitating its use across various public health contexts. Its intuitive interface ensures that users can effortlessly navigate complex datasets, facilitating widespread access to advanced surveillance capabilities. Through its initial deployments, SPREAD has proven instrumental in quickly identifying transmission clusters, significantly aiding in the formulation of prompt and targeted public health responses. Through the integration of state-of-the-art technology with a focus on user-centered design, SPREAD offers a promising solution that highlights the potential of digital health innovations.

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.

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.

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.

Full Genome Characterization of Respiratory Syncytial Virus Causing a Fatal Infection in an Immunocompromised Patient in Tunisia.

Human orthopneumovirus (HRSV) is a virus belonging to the Pneumovirus genus that causes lower respiratory tract infections (LRTI) in infants worldwide. In Tunisia, thousands of infants hospitalized for LRTI are found to be positive for HRSV but no whole genome sequences of HRSV strains circulating in this country are available thus far. In this study, five nasal swab samples collected at different time points from a three-month-old female baby with severe immunodeficiency that was hospitalized for acute bronchiolitis were investigated by next generation sequencing. The Tunisian sequences from this study originated from samples collected in 2021, belong to the ON1 genotype of HRSV-A, and are clustered with European sequences from 2019 and not from 2020 or 2021. This is most likely related to local region-specific transmission of different HRSV-A variants due to the COVID-19 related travel restrictions. Overall, this is the first report describing the whole genome sequence of HRSV from Tunisia. However, more sequence data is needed to better understand the genetic diversity and transmission dynamic of HRSV.

A European-wide dataset to uncover adaptive traits of Listeria monocytogenes to diverse ecological niches.

Listeria monocytogenes (Lm) is a ubiquitous bacterium that causes listeriosis, a serious foodborne illness. In the nature-to-human transmission route, Lm can prosper in various ecological niches. Soil and decaying organic matter are its primary reservoirs. Certain clonal complexes (CCs) are over-represented in food production and represent a challenge to food safety. To gain new understanding of Lm adaptation mechanisms in food, the genetic background of strains found in animals and environment should be investigated in comparison to that of food strains. Twenty-one partners, including food, environment, veterinary and public health laboratories, constructed a dataset of 1484 genomes originating from Lm strains collected in 19 European countries. This dataset encompasses a large number of CCs occurring worldwide, covers many diverse habitats and is balanced between ecological compartments and geographic regions. The dataset presented here will contribute to improve our understanding of Lm ecology and should aid in the surveillance of Lm. This dataset provides a basis for the discovery of the genetic traits underlying Lm adaptation to different ecological niches.

In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes.

BACKGROUND: Whole genome sequencing analyzed by core genome multi-locus sequence typing (cgMLST) is widely used in surveillance of the pathogenic bacteria Listeria monocytogenes. Given the heterogeneity of available bioinformatics tools to define cgMLST alleles, our aim was to identify parameters influencing the precision of cgMLST profiles. METHODS: We used three L. monocytogenes reference genomes from different phylogenetic lineages and assessed the impact of in vitro (i.e. tested genomes, successive platings, replicates of DNA extraction and sequencing) and in silico parameters (i.e. targeted depth of coverage, depth of coverage, breadth of coverage, assembly metrics, cgMLST workflows, cgMLST completeness) on cgMLST precision made of 1748 core loci. Six cgMLST workflows were tested, comprising assembly-based (BIGSdb, INNUENDO, GENPAT, SeqSphere and BioNumerics) and assembly-free (i.e. kmer-based MentaLiST) allele callers. Principal component analyses and generalized linear models were used to identify the most impactful parameters on cgMLST precision. RESULTS: The isolate's genetic background, cgMLST workflows, cgMLST completeness, as well as depth and breadth of coverage were the parameters that impacted most on cgMLST precision (i.e. identical alleles against reference circular genomes). All workflows performed well at ≥40X of depth of coverage, with high loci detection (> 99.54% for all, except for BioNumerics with 97.78%) and showed consistent cluster definitions using the reference cut-off of ≤7 allele differences. CONCLUSIONS: This highlights that bioinformatics workflows dedicated to cgMLST allele calling are largely robust when paired-end reads are of high quality and when the sequencing depth is ≥40X.

New Sequence Types and Antimicrobial Drug-Resistant Strains of Streptococcus suis in Diseased Pigs, Italy, 2017-2019.

Streptococcus suis is a pathogen associated with severe diseases in pigs and humans. Human infections have a zoonotic origin in pigs. To assess circulating strains, we characterized the serotypes, sequence types, and antimicrobial susceptibility of 78 S. suis isolates from diseased farmed pigs in Italy during 2017-2019. Almost 60% of infections were caused by serotypes 1/2 and 9. All but 1 of the serotype 2 and 1/2 isolates were confined to a single cluster, and serotype 9 isolates were distributed along the phylogenetic tree. Besides sequence type (ST) 1, the serotype 2 cluster included ST7, which caused severe human infections in China in 1998 and 2005. A large proportion of serotype 9 isolates, assigned to ST123, were resistant to penicillin. The emergence of this clone threatens the successful treatment of S. suis infection. Characterizing S. suis isolates from pigs will promote earlier detection of emerging clones.

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

BACKGROUND: Faced with the ongoing global pandemic of coronavirus disease, the 'National Reference Centre for Whole Genome Sequencing of microbial pathogens: database and bioinformatic analysis' (GENPAT) formally established at the 'Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise' (IZSAM) in Teramo (Italy) is in charge of the SARS-CoV-2 surveillance at the genomic scale. In a context of SARS-CoV-2 surveillance requiring correct and fast assessment of epidemiological clusters from substantial amount of samples, the present study proposes an analytical workflow for identifying accurately the PANGO lineages of SARS-CoV-2 samples and building of discriminant minimum spanning trees (MST) bypassing the usual time consuming phylogenomic inferences based on multiple sequence alignment (MSA) and substitution model. RESULTS: GENPAT constituted two collections of SARS-CoV-2 samples. The first collection consisted of SARS-CoV-2 positive swabs collected by IZSAM from the Abruzzo region (Italy), then sequenced by next generation sequencing (NGS) and analyzed in GENPAT (n = 1592), while the second collection included samples from several Italian provinces and retrieved from the reference Global Initiative on Sharing All Influenza Data (GISAID) (n = 17,201). The main results of the present work showed that (i) GENPAT and GISAID detected the same PANGO lineages, (ii) the PANGO lineages B.1.177 (i.e. historical in Italy) and B.1.1.7 (i.e. 'UK variant') are major concerns today in several Italian provinces, and the new MST-based method (iii) clusters most of the PANGO lineages together, (iv) with a higher dicriminatory power than PANGO lineages, (v) and faster that the usual phylogenomic methods based on MSA and substitution model. CONCLUSIONS: The genome sequencing efforts of Italian provinces, combined with a structured national system of NGS data management, provided support for surveillance SARS-CoV-2 in Italy. We propose to build phylogenomic trees of SARS-CoV-2 variants through an accurate, discriminant and fast MST-based method avoiding the typical time consuming steps related to MSA and substitution model-based phylogenomic inference.

Whole Genome Sequencing Characterization of HEV3-e and HEV3-f Subtypes among the Wild Boar Population in the Abruzzo Region, Italy: First Report.

Hepatitis E virus (HEV) is an emergent zoonotic pathogen, causing worldwide acute and chronic hepatitis in humans. HEV comprises eight genotypes and several subtypes. HEV genotypes 3 and 4 (HEV3 and HEV4) are zoonotic. In Italy, the most part of HEV infections (80%) is due to autochthonous HEV3 circulation of the virus, and the key role played by wild animals is generally accepted. Abruzzo is an Italian region officially considered an HEV "hot spot", with subtype HEV3-c being up to now the only one reported among wild boars. During the year 2018-2019, a group of wild boars in Abruzzo were screened for HEV; positive RNA liver samples were subjected to HEV characterization by using the whole genome sequencing (WGS) approach methodology. This represents the first report about the detection of HEV-3 subtypes e and f in the wild boar population in this area. Since in Italy human infections from HEV 3-e and f have been associated with pork meat consumption, our findings deserve more in-depth analysis with the aim of evaluating any potential correlation between wild animals, the pork chain production and HEV human infections.

Draft Genome Sequence of Listeria monocytogenes Serovar 1/2a Strain IZSAM_Lm_14-16064, Isolated from an Italian Cooked Ham in 2014.

In this report, the draft genome sequence of Listeria monocytogenes serovar 1/2a strain IZSAM_Lm_14-16064, isolated in Italy from a cooked ham, is announced. The genome is similar to that of a clinical strain isolated in 2014.

Proteomic analysis of Brucella melitensis and Brucella ovis for identification of virulence factor using bioinformatics approachs.

The genus Brucella includes several genetically monomorphic species but with different phenotypic and virulence characteristics. In this study, proteins of two Brucella species, B. melitensis type strain 16 M and B. ovis REO198 were compared by proteomics approach, in order to explain the phenotypic and pathophysiological differences among Brucella species and correlate them with virulence factors. Protein extracts from the two Brucella species were separated by SDS-PAGE and 5 areas, which resulted qualitatively and quantitatively different, were analyzed by nLC-MS/MS. A total of 880 proteins (274 proteins of B. melitensis and 606 proteins of B. ovis) were identified; their functional and structural features were analyzed by bioinformatics tools. Four unique peptides belonging to 3 proteins for B. ovis and 10 peptides derived from 7 proteins for B. melitensis were chosen for the high amount of predicted B-cell epitopes exposed to the solvent. Among these proteins, outer-membrane immunogenic protein (N8LTS7) and 25 kDa outer-membrane immunogenic protein (Q45321), respectively of B. ovis and B. melitensis, could be interesting candidates for improving diagnostics tests and vaccines. Moreover, 8 and 13 outer and periplasmic non homologue proteins of B. ovis and B. melitensis were identified to screen the phenotypic differences between the two Brucella strains. These proteins will be used to unravel pathogenesis and ameliorate current diagnostic assays.

A "One-Health" approach for diagnosis and molecular characterization of SARS-CoV-2 in Italy.

The current pandemic is caused by a novel coronavirus (CoV) called SARS-CoV-2 (species Severe acute respiratory syndrome-related coronavirus, subgenus Sarbecovirus, genus Betacoronavirus, family Coronaviridae). In Italy, up to the 2nd of April 2020, overall 139,422 confirmed cases and 17,669 deaths have been notified, while 26,491 people have recovered. Besides the overloading of hospitals, another issue to face was the capacity to perform thousands of tests per day. In this perspective, to support the National Health Care System and to minimize the impact of this rapidly spreading virus, the Italian Ministry of Health involved the Istituti Zooprofilattici Sperimentali (IZSs), Veterinary Public Health Institutes, in the diagnosis of SARS-CoV-2 by testing human samples. The Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise is currently testing more than 600 samples per day and performing whole genome sequencing from positive samples. Sequence analysis of these samples suggested that different viral variants may be circulating in Italy, and so in Abruzzo region. CoVs, and related diseases, are well known to veterinarians since decades. The experience that veterinarians operating within the Public Health system gained in the control and characterization of previous health issues of livestock and poultry including avian flu, bluetongue, foot and mouth disease, responsible for huge economic losses, is certainly of great help to minimize the impact of this global crisis.

Whole-Genome Sequence of Listeria monocytogenes Serovar 1/2a Strain IZSAM_Lm_15_17439_A144, Representative of a Human Outbreak in 2008.

Here, we report the genome sequence of Listeria monocytogenes serovar 1/2a strain IZSAM_Lm_15_17439_A144, isolated in Italy from a patient during a Listeria monocytogenes outbreak in 2008. This strain showed 98.9% sequence identity to a strain isolated in Canada in the same year.

Whole-Genome Sequence of a Reemerging Listeria monocytogenes Serovar 1/2a Strain in Central Italy.

We report the whole-genome sequence of a Listeria monocytogenes strain isolated from a child in central Italy. Interestingly, the sequence showed a difference of only 13 single-nucleotide polymorphisms (SNPs) from a strain responsible for a severe listeriosis outbreak that occurred between January 2015 and March 2016 in the same region.

Animal welfare monitoring and livestock traceability during transport.

The authors present an experimental project that aims to establish an effective navigation system in accordance with European Council Regulation 1/2005 concerning animal welfare during transport. The prototype created during the project consists of both hardware and software components. An onboard unit is installed at truck level. It collects and transmits real-time information of the animal transport to a remote receiver database. A Web/geographic information system (GIS) application is used to analyse and monitor the information received. The architecture of the hardware and software of the project is presented, focusing on the features of the Web-GIS application.

Quality management for the road transportation of livestock.

Transport can be a significant stress factor for livestock and can result in poor animal welfare and economic losses. Quality management measures are actively employed in fields different from animal welfare and could be applied to improve the welfare of animals and reduce the consequent losses during road transportation and related activities Training and education of staff comprise one possible measure, Web-geographic information system technology used to monitor the true state of transported animals is another innovation that promises major progress. With this technology, behavioural and environmental parameters can be monitored and registered in real time. The resulting data could be useful to control the transport environment and the conduct of staff. Although some parameters cannot be represented through numerical relationships, behavioural and environmental measurements can be used in a risk analysis system to minimise risks of poor welfare during animal transportation. The European Union Joint Research Centre and the Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise 'G. Caporale' in Teramo are working on an experimental project to prove the feasibility of a navigation system for long road journeys as referred to in Regulation (EC) 1/2005 of the European Union. Such a system enables the collection of data on transported animals and the verification that welfare requirements are being met at any given moment during the journey.

A Web-based geographic information system for monitoring animal welfare during long journeys.

Animal welfare protection during long journeys is mandatory according to European Union regulations designed to ensure that animals are transported in accordance with animal welfare requirements and to provide control bodies with a regulatory tool to react promptly in cases of non-compliance and to ensure a safe network between products, animals and farms. Regulation 1/2005/EC foresees recourse to a system of traceability within European Union member states. The Joint Research Centre of the European Commission (JRC) has developed a prototype system fulfilling the requirements of the Regulation which is able to monitor compliance with animal welfare requirements during transportation, register electronic identification of transported animals and store data in a central database shared with the other member states through a Web-based application. Test equipment has recently been installed on a vehicle that records data on vehicle position (geographic coordinates, date/time) and animal welfare conditions (measurements of internal temperature of the vehicle, etc.). The information is recorded at fixed intervals and transmitted to the central database. The authors describe the Web-based geographic information system, through which authorised users can visualise instantly the real-time position of the vehicle, monitor the sensor-recorded data and follow the time-space path of the truck during journeys.