Single-tube protocol for culture-independent spoligotyping of Mycobacterium tuberculosis based on MeltArray.

IMPORTANCE: Spacer oligonucleotide typing (spoligotyping), the first-line genotyping assay for Mycobacterium tuberculosis (MTB), plays a fundamental role in the investigation of its epidemiology and evolution. In this study, we established a single-tube spoligotyping assay using MeltArray, a highly multiplex polymerase chain reaction (PCR) approach that runs on a real-time PCR thermocycler. The MeltArray protocol included an internal positive control, gyrB, to indicate the abundance of MTB via the quantification cycle and 43 spacers to identify the spoligotype via melting curve analysis. The entire protocol was completed in a single step within 2.5 hours. The lowest detectable copy number for the tested strains was 20 copies/reaction and thus sufficient for analyzing both culture and sputum samples. We conclude that MeltArray-based spoligotyping could be used immediately in low- and middle-income countries with a high tuberculosis burden, given its easy access, improved throughput, and potential applicability to clinical samples.

Bridging the gap between molecular and genomic epidemiology in tuberculosis: inferring MIRU-VNTR patterns from genomic data.

The transition from MIRU-VNTR-based epidemiology studies in tuberculosis (TB) to genomic epidemiology has transformed how we track transmission. However, short-read sequencing is poor at analyzing repetitive regions such as the MIRU-VNTR loci. This causes a gap between the new genomic data and the large amount of information stored in historical databases. Long-read sequencing could bridge this knowledge gap by allowing analysis of repetitive regions. However, the feasibility of extracting MIRU-VNTRs from long reads and linking them to historical data has not been evaluated. In our study, an in silico arm, consisting of inference of MIRU patterns from long-read sequences (using MIRUReader program), was compared with an experimental arm, involving standard amplification and fragment sizing. We analyzed overall performance on 39 isolates from South Africa and confirmed reproducibility in a sample enriched with 62 clustered cases from Spain. Finally, we ran 25 consecutive incident cases, demonstrating the feasibility of correctly assigning new clustered/orphan cases by linking data inferred from genomic analysis to MIRU-VNTR databases. Of the 3,024 loci analyzed, only 11 discrepancies (0.36%) were found between the two arms: three attributed to experimental error and eight to misassigned alleles from long-read sequencing. A second round of analysis of these discrepancies resulted in agreement between the experimental and in silico arms in all but one locus. Adjusting the MIRUReader program code allowed us to flag potential in silico misassignments due to suboptimal coverage or unfixed double alleles. Our study indicates that long-read sequencing could help address potential chronological and geographical gaps arising from the transition from molecular to genomic epidemiology of tuberculosis. IMPORTANCE: The transition from molecular epidemiology in tuberculosis (TB), based on the analysis of repetitive regions (VNTR-based genotyping), to genomic epidemiology transforms in the precision with which we track transmission. However, short-read sequencing, the most common method for performing genomic analysis, is poor at analyzing repetitive regions. This means that we face a gap between the new genomic data and the large amount of information stored in historical databases, which is also an obstacle to cross-national surveillance involving settings where only molecular data are available. Long-read sequencing could help bridge this knowledge gap by allowing analysis of repetitive regions. Our study demonstrates that MIRU-VNTR patterns can be successfully inferred from long-read sequences, allowing the correct assignment of new cases as clustered/orphan by linking new data extracted from genomic analysis to historical MIRU-VNTR databases. Our data may provide a starting point for bridging the knowledge gap between the molecular and genomic eras in tuberculosis epidemiology.

Development and implementation of a core genome multilocus sequence typing scheme for Yersinia enterocolitica: a tool for surveillance and outbreak detection.

Yersinia enterocolitica (Y. enterocolitica) is the most frequent etiological agent of yersiniosis and has been responsible for several national outbreaks in Norway and elsewhere. A standardized high-resolution method, such as core genome Multilocus Sequence Typing (cgMLST), is needed for pathogen traceability at the national and international levels. In this study, we developed and implemented a cgMLST scheme for Y. enterocolitica. We designed a cgMLST scheme in SeqSphere + using high-quality genomes from different Y. enterocolitica biotype sublineages. The scheme was validated if more than 95% of targets were found across all tested Y. enterocolitica: 563 Norwegian genomes collected between 2012 and 2022 and 327 genomes from public data sets. We applied the scheme to known outbreaks to establish a threshold for identifying major complex types (CTs) based on the number of allelic differences. The final cgMLST scheme included 2,582 genes with a median of 97.9% (interquartile range 97.6%-98.8%) targets found across all tested genomes. Analysis of outbreaks identified all outbreak strains using single linkage clustering at four allelic differences. This threshold identified 311 unique CTs in Norway, of which CT18, CT12, and CT5 were identified as the most frequently associated with outbreaks. The cgMLST scheme showed a very good performance in typing Y. enterocolitica using diverse data sources and was able to identify outbreak clusters. We recommend the implementation of this scheme nationally and internationally to facilitate Y. enterocolitica surveillance and improve outbreak response in national and cross-border outbreaks.

Coronavirus GenBrowser for monitoring the transmission and evolution of SARS-CoV-2.

Genomic epidemiology is important to study the COVID-19 pandemic, and more than two million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequences were deposited into public databases. However, the exponential increase of sequences invokes unprecedented bioinformatic challenges. Here, we present the Coronavirus GenBrowser (CGB) based on a highly efficient analysis framework and a node-picking rendering strategy. In total, 1,002,739 high-quality genomic sequences with the transmission-related metadata were analyzed and visualized. The size of the core data file is only 12.20 MB, highly efficient for clean data sharing. Quick visualization modules and rich interactive operations are provided to explore the annotated SARS-CoV-2 evolutionary tree. CGB binary nomenclature is proposed to name each internal lineage. The pre-analyzed data can be filtered out according to the user-defined criteria to explore the transmission of SARS-CoV-2. Different evolutionary analyses can also be easily performed, such as the detection of accelerated evolution and ongoing positive selection. Moreover, the 75 genomic spots conserved in SARS-CoV-2 but non-conserved in other coronaviruses were identified, which may indicate the functional elements specifically important for SARS-CoV-2. The CGB was written in Java and JavaScript. It not only enables users who have no programming skills to analyze millions of genomic sequences, but also offers a panoramic vision of the transmission and evolution of SARS-CoV-2.

High performance Legionella pneumophila source attribution using genomics-based machine learning classification.

Fundamental to effective Legionnaires' disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent, Legionella pneumophila. Genomics has revolutionized pathogen surveillance, but L. pneumophila has a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here, we present a powerful machine learning approach that assigns the geographical source of Legionnaires' disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534 L. pneumophila genome sequences, including 149 genomes linked to 20 previously reported Legionnaires' disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmental L. pneumophila genomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance-agreement with epidemiological information. Our multivariate statistical learning approach maximizes the use of genomic variation data and is thus well-suited for supporting Legionnaires' disease outbreak investigations.IMPORTANCEIdentifying the sources of Legionnaires' disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila, the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires' disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires' disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission.

From single DNA adducts measurement to DNA adductomics in molecular epidemiology of cancer.

Environmental carcinogens exert their carcinogenic effects by forming DNA adducts. This type of DNA damage can also be formed endogenously as a result of, e.g., oxidative damage. Unrepaired  DNA adducts may induce mutations in critical genes, leading to the initiation of chemical carcinogenesis. Therefore,  detection, identification, and quantification of DNA adducts is essential for cancer risk assessment. Over the last 50 years, the major DNA adducts formed by different classes of environmental carcinogens were characterized. With the development of techniques such as 32P-postlabeling, their measurement was implemented into molecular epidemiology. Advances in liquid chromatography-tandem mass spectrometry (LC-MS ) made the measurement of adducts more precise  and allowed to gain knowledge about their identity and structures. Therefore,  opened the way to  DNA adductomics, the  "omics" approach investigating DNA adducts comprehensively, similarly to proteomics. This review presents the historical perspective of DNA adducts research and the emerging field of adductomics.

Impact of Technological Developments on Infectious Disease Epidemiology: Lessons From the First 100 Years of the American Journal of Epidemiology.

Technological developments in laboratory and epidemiologic methods, combined with increasing computing power, have synergistically increased our understanding of the epidemiology of infectious disease. Using historical examples from the first 100 years of the American Journal of Epidemiology, we illustrate how these developments provided the foundation for the rapid detection of the agent causing coronavirus disease 2019 (COVID-19), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), from its transmission efficiency and modalities, risk factors, and natural history to the evaluation of new vaccines and treatments to control its spread and impact. Comparisons with timelines for elucidation of the epidemiology, natural history, and control of other infectious diseases, including viral hepatitis, humbly remind us of how much past discoveries have paved the way for more rapid discovery of and response to new pathogens. We close with some comments on a potential future role of the Journal in infectious disease epidemiology.

The changing epidemiology of Neisseria gonorrhoeae genogroups and antimicrobial resistance in Queensland, Australia, 2010-15: a case series analysis of unique Neisseria gonorrhoeae isolates.

BACKGROUND: Neisseria gonorrhoeae (NG) can lead to serious reproductive and sexual health outcomes, and the annual number of NG notifications in Australia increased steadily from 10329 in 2010 to 29549 by 2020. Australian populations most affected are urban men who have sex with men and First Nations peoples living in remote areas, and a resurgence in urban heterosexuals has been observed since 2012. METHODS: A case series analysis of Queensland NG isolates (2010-15) exploring temporal trends and antimicrobial resistance by demographic and geographic distribution and genotype was performed. Proportions describe age, sex, strain, genogroup (NG multi-antigen sequence typing), region, swab site, antimicrobial sensitivity and isolate rates per 100000 population. Dominant genogroups were identified. RESULTS: Among 3953 isolates, the median age was 25years (IQR 20-34years) and most (n =2871/3915, 73%) were men. Brisbane city (68.8) and Far North Queensland (54.1) excluding Cairns showed the highest rates. Forty-six genogroups were documented, seven (G2992, G6876, G1415, G4186, G5, G1407 and G6937) comprised half of all isolates. The predominant male genogroup was G2992 (16%), and G6876 (20%) for females; G5 was predominantly male from 2010 to 2011, but equal in both sexes from 2012 to 2015. CONCLUSION: Considerable temporal, geographical and demographical diversity was observed in Queensland NG isolates, which has public health implications. Certain genogroups are more transient than others, and evidence suggests bridging from male-dominant networks to heterosexual networks. Molecular surveillance can enhance tracking the epidemiology and movement of NG in Australia, highlighting the necessity of genotyping to expose potentially prevalent strains circulating in undetected or underrepresented networks by current screening methods.

Fundamental Identifiability Limits in Molecular Epidemiology.

Viral phylogenies provide crucial information on the spread of infectious diseases, and many studies fit mathematical models to phylogenetic data to estimate epidemiological parameters such as the effective reproduction ratio (Re) over time. Such phylodynamic inferences often complement or even substitute for conventional surveillance data, particularly when sampling is poor or delayed. It remains generally unknown, however, how robust phylodynamic epidemiological inferences are, especially when there is uncertainty regarding pathogen prevalence and sampling intensity. Here, we use recently developed mathematical techniques to fully characterize the information that can possibly be extracted from serially collected viral phylogenetic data, in the context of the commonly used birth-death-sampling model. We show that for any candidate epidemiological scenario, there exists a myriad of alternative, markedly different, and yet plausible "congruent" scenarios that cannot be distinguished using phylogenetic data alone, no matter how large the data set. In the absence of strong constraints or rate priors across the entire study period, neither maximum-likelihood fitting nor Bayesian inference can reliably reconstruct the true epidemiological dynamics from phylogenetic data alone; rather, estimators can only converge to the "congruence class" of the true dynamics. We propose concrete and feasible strategies for making more robust epidemiological inferences from viral phylogenetic data.

Core Genome Multilocus Sequence Typing Scheme for Improved Characterization and Epidemiological Surveillance of Pathogenic Brucella.

Brucellosis poses a significant burden to human and animal health worldwide. Robust and harmonized molecular epidemiological approaches and population studies that include routine disease screening are needed to efficiently track the origin and spread of Brucella strains. Core genome multilocus sequence typing (cgMLST) is a powerful genotyping system commonly used to delineate pathogen transmission routes for disease surveillance and control. Except for Brucella melitensis, cgMLST schemes for Brucella species are currently not established. Here, we describe a novel cgMLST scheme that covers multiple Brucella species. We first determined the phylogenetic breadth of the genus using 612 Brucella genomes. We selected 1,764 genes that were particularly well conserved and typeable in at least 98% of these genomes. We tested the new scheme on 600 genomes and found high agreement with the whole-genome-based single nucleotide polymorphism (SNP) analysis. Next, we applied the scheme to reanalyze the genome of Brucella strains from epidemiologically linked outbreaks. We demonstrated the applicability of the new scheme for high-resolution typing required in outbreak investigations as previously reported with whole-genome SNP methods. We also used the novel scheme to define the global population structure of the genus using 1,322 Brucella genomes. Finally, we demonstrated the possibility of tracing distribution of Brucella strains by performing cluster analysis of cgMLST profiles and found nearly identical cgMLST profiles in different countries. Our results show that sequencing depth of more than 40-fold is optimal for allele calling with this scheme. In summary, this study describes a novel Brucella-wide cgMLST scheme that is applicable in Brucella molecular epidemiology and helps in accurately tracking and thus controlling the sources of infection. The scheme is publicly accessible and should represent a valuable resource for laboratories with limited computational resources and bioinformatics expertise.

The global dissemination of bacterial infections necessitates the study of reverse genomic epidemiology.

Whole genome sequencing (WGS) has revolutionized the genotyping of bacterial pathogens and is expected to become the new gold standard for tracing the transmissions of bacterial infectious diseases for public health purposes. Traditional genomic epidemiology often uses WGS as a verification tool, namely, when a common source or epidemiological link is suspected, the collected isolates are sequenced for the determination of clonal relationships. However, increasingly frequent international travel and food transportation, and the associated potential for the cross-border transmission of bacterial pathogens, often lead to an absence of information on bacterial transmission routes. Here we introduce the concept of 'reverse genomic epidemiology', i.e. when isolates are inspected by genome comparisons to be sufficiently similar to one another, they are assumed to be a consequence of infection from a common source. Through BacWGSTdb (http://bacdb.org/BacWGSTdb/), a database we have developed for bacterial genome typing and source tracking, we have found that almost the entire analyzed 20 bacterial species exhibit the phenomenon of cross-border clonal dissemination. Five networks were further identified in which isolates sharing nearly identical genomes were collected from at least five different countries. Three of these have been documented as real infectious disease outbreaks, therefore demonstrating the feasibility and authority of reverse genomic epidemiology. Our survey and proposed strategy would be of potential value in establishing a global surveillance system for tracing bacterial transmissions and outbreaks; the related database and techniques require urgent standardization.

MicrobeTrace: Retooling molecular epidemiology for rapid public health response.

Outbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions. We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. MicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully operational without an internet connection. Using publicly available data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. Users can email microbetrace@cdc.gov for support. The source code is available at https://github.com/cdcgov/microbetrace.

Next-Generation Sequencing (NGS) in COVID-19: A Tool for SARS-CoV-2 Diagnosis, Monitoring New Strains and Phylodynamic Modeling in Molecular Epidemiology.

This review discusses the current testing methodologies for COVID-19 diagnosis and explores next-generation sequencing (NGS) technology for the detection of SARS-CoV-2 and monitoring phylogenetic evolution in the current COVID-19 pandemic. The review addresses the development, fundamentals, assay quality control and bioinformatics processing of the NGS data. This article provides a comprehensive review of the obstacles and opportunities facing the application of NGS technologies for the diagnosis, surveillance, and study of SARS-CoV-2 and other infectious diseases. Further, we have contemplated the opportunities and challenges inherent in the adoption of NGS technology as a diagnostic test with real-world examples of its utility in the fight against COVID-19.

Identification of Hidden Population Structure in Time-Scaled Phylogenies.

Population structure influences genealogical patterns, however, data pertaining to how populations are structured are often unavailable or not directly observable. Inference of population structure is highly important in molecular epidemiology where pathogen phylogenetics is increasingly used to infer transmission patterns and detect outbreaks. Discrepancies between observed and idealized genealogies, such as those generated by the coalescent process, can be quantified, and where significant differences occur, may reveal the action of natural selection, host population structure, or other demographic and epidemiological heterogeneities. We have developed a fast non-parametric statistical test for detection of cryptic population structure in time-scaled phylogenetic trees. The test is based on contrasting estimated phylogenies with the theoretically expected phylodynamic ordering of common ancestors in two clades within a coalescent framework. These statistical tests have also motivated the development of algorithms which can be used to quickly screen a phylogenetic tree for clades which are likely to share a distinct demographic or epidemiological history. Epidemiological applications include identification of outbreaks in vulnerable host populations or rapid expansion of genotypes with a fitness advantage. To demonstrate the utility of these methods for outbreak detection, we applied the new methods to large phylogenies reconstructed from thousands of HIV-1 partial pol sequences. This revealed the presence of clades which had grown rapidly in the recent past and was significantly concentrated in young men, suggesting recent and rapid transmission in that group. Furthermore, to demonstrate the utility of these methods for the study of antimicrobial resistance, we applied the new methods to a large phylogeny reconstructed from whole genome Neisseria gonorrhoeae sequences. We find that population structure detected using these methods closely overlaps with the appearance and expansion of mutations conferring antimicrobial resistance. [Antimicrobial resistance; coalescent; HIV; population structure.].

Molecular epidemiology of respiratory syncytial virus in children in sub-Saharan Africa.

OBJECTIVES: This study investigated the molecular epidemiology of respiratory syncytial virus (RSV) among febrile children with acute respiratory tract infection in Ghana, Gabon, Tanzania and Burkina Faso between 2014 and 2017 as well as the evolution and diversification of RSV strains from other sub-Saharan countries. METHODS: Pharyngeal swabs were collected at four study sites (Agogo, Ghana: n = 490; Lambaréné, Gabon: n = 182; Mbeya, Tanzania: n = 293; Nouna, Burkina Faso: n = 115) and analysed for RSV and other respiratory viruses using rtPCR. For RSV-positive samples, sequence analysis of the second hypervariable region of the G gene was performed. A dataset of RSV strains from sub-Saharan Africa (2011-2017) currently available in GenBank was compiled. Phylogenetic analysis was conducted to identify the diversity of circulating RSV genotypes. RESULTS: In total, 46 samples were tested RSV positive (Ghana n = 31 (6.3%), Gabon n = 4 (2.2%), Tanzania n = 9 (3.1%) and Burkina Faso n = 2 (1.7%)). The most common RSV co-infection was with rhinovirus. All RSV A strains clustered with genotype ON1 strains with a 72-nucleotide duplication and all RSV B strains belonged to genotype BAIX. Phylogenetic analysis of amino acid sequences from sub-Saharan Africa revealed the diversification into 11 different ON1 and 22 different BAIX lineages and differentiation of ON1 and BAIX strains into potential new sub-genotypes, provisionally named ON1-NGR, BAIX-KEN1, BAIX-KEN2 and BAIX-KEN3. CONCLUSION: The study contributes to an improved understanding of the molecular epidemiology of RSV infection in sub-Saharan Africa. It provides the first phylogenetic data for RSV from Tanzania, Gabon and Burkina Faso and combines it with RSV strains from all other sub-Saharan countries currently available in GenBank.

Molecular evolution and pathogenicity of chicken anemia virus isolates in China.

Chicken infectious anemia (CIA), caused by chicken anemia virus (CAV), is an important immunosuppressive disease that seriously threatens the global poultry industry. Here, we isolated and identified 30 new CAV strains from CAV-positive flocks. The VP1 genes of these strains were sequenced and analyzed at the nucleotide and amino acid levels and were found to have very similar nucleotide sequences (> 97% identity); however, they showed 93.9-100.0% sequence identity to the VP1 genes of 55 reference strains. Furthermore, alignment of the deduced amino acid sequences revealed some unique mutations. Phylogenetic analysis indicated the division of VP1 amino acid sequences into two groups (A and B) and four subgroups (A1, A2, A3 and A4). Interestingly, 22 of the newly isolated strains and some Asian reference strains belonged to the A1 group, whereas the remaining eight new isolates belonged to the A3 group. To evaluate the pathogenicity of the epidemic CAV strains from China, the representative strains CAV-JL16/8901 and CAV-HeN19/3001 and the reference strain Cux-1 were selected for animal experiments. Chickens infected with the isolates and reference strain all showed thymus atrophy and bone marrow yellowing. The mortality rates for CAV-JL16/8901, CAV-HeN19/3001, and the reference strain was 30%, 20%, and 0%, respectively, indicating that the epidemic strains pose a more serious threat to chickens. We not only analyzed the molecular evolution of the epidemic strains but also showed for the first time that the epidemic strains in China are more pathogenic than reference strain Cux-1. Effective measures should be established to prevent the spread of CIA in China.

Detection and genetic characteristics of porcine bocavirus in central China.

To investigate the epidemic profile and genetic diversity of porcine bocavirus (PBoV), 281 clinical samples, including 236 intestinal tissue samples and 45 fecal samples were collected from diarrheic piglets on 37 different pig farms in central China, and two SYBR Green I-based quantitative PCR assays were developed to detect PBoV1/2 and PBoV3/4/5, respectively. One hundred forty-eight (52.67%) of the 281 clinical samples were positive for PBoV1/2, 117 (41.63%) were positive for PBoV3/4/5, 55 (19.57%) were positive for both PBoV1/2 and PBoV3/4/5, and 86.49% (32/37) of the pig farms were positive for PBoV. Overall, the prevalence of PBoV was 74.73% (210/281) in central China. Subsequently, nearly full-length genomic sequences of two PBoV strains (designated CH/HNZM and PBoV-TY) from two different farms were determined. Phylogenetic analysis demonstrated that the two PBoV strains obtained in this study belonged to the PBoV G2 group and had a close relationship to 10 other PBoV G2 strains but differed genetically from PBoV G1, PBoV G3, and seven other bocaviruses. CH/HNZM and PBoV-TY were closely related to the PBoV strain GD18 (KJ755666), which may be derived from the PBoV strains 0912/2012 (MH558677) and 57AT-HU (KF206160) through recombination. Compared with reference strain ZJD (HM053694)-China, more amino acid variation was found in the NS1 proteins of CH/HNZM and PBoV-TY. These data extend our understanding of the molecular epidemiology and evolution of PBoV.

Retrospective surveillance of porcine circovirus 4 in pigs in Inner Mongolia, China, from 2016 to 2018.

A novel circovirus designated "porcine circovirus type 4" (PCV4) was recently reported in pigs with severe clinical disease in Hunan Province, China. Relatively little is known about the molecular epidemiology of this recently discovered virus. In order to assess the prevalence of PCV4 infection in pigs and to analyze its genomic characteristics, 1683 clinical samples were collected in Inner Mongolia, China, from 2016 to 2018. The overall infection rate of PCV4 was 1.6% (27/1683) at the sample level and 21.6% (11/51) at the farm level, with rates ranging from 3.2% (1/31) to 20.0% (6/30) on different PCV4-positive pig farms. In addition, the PCV4 infection rates at both the sample and farm level increased from 2016 to 2018. This also showed that PCV4 was present in pigs in 2016 in China and therefore did not arrive later than this date. Additionally, our findings showed that PCV4 infections had no association with PCV2 or PCV3 infections. We sequenced the complete genomes of three PCV4 strains and found that the PCV4 strains had a high degree of genetic stability but shared less than 80% sequence identity with other circoviruses. We identified six amino acid mutations in the Rep protein and seven in the Cap protein. Phylogenetic analysis based on Cap and Rep sequences confirmed that the PCV4 strains grouped in an independent branch. Our findings provide important information about the prevalence and genetic characteristics of PCV4 strains.

Molecular epidemiology of GII noroviruses in outpatients with acute gastroenteritis in Shandong Province, China.

Noroviruses have been recognized as the most important causative agents of acute gastroenteritis. The present study was carried out to investigate the molecular epidemiological features of genotype II (GII) norovirus in outpatients with acute gastroenteritis in Shandong province in China from July 2017 to June 2018. In total, 151 (10.30%) samples were positive for NoV GII strains by RT-PCR. Eight genotypes were detected: GII.2, GII.3, GII.4, GII.6, GII.7, GII.12, GII.13 and GII.17. GII.4 (43.71%) was the most prevalent genotype, and the dominant strains belonged to the group of Sydney-2012 strains. GII.17 (27.15%), which has become the main cause of outbreaks of acute gastroenteritis in China, also accounted for a high proportion. Meanwhile, three recombinant types (GII.P17-GII.7, GII.P3-GII.4 and GII.P12-GII.4) were observed and authenticated using Simplot software. The results showed that GII norovirus was the main cause of acute gastroenteritis in Shandong province. GII.4 and GII.17 were the dominant genotypes. Continuous observation and identification of emerging genotypes are necessary for understanding the evolution of the virus, control of infection, and development of vaccines.

Molecular detection of coccidian Apicomplexa Parasites isolated from wild crab-eating and pampas foxes through novel TaqMan™ probes: a contribution to their molecular epidemiology.

Neospora caninum, Toxoplasma gondii and Hammondia spp. are coccidian parasites similar in morphology. Molecular techniques are necessary to detect parasite DNA isolated from stool samples in wild canids because they were reported as definitive hosts of N. caninum life cycle. The objective of this study was to develop a highly sensitive and accurate molecular method for the identification of coccidian Apicomplexa parasites in crab-eating fox (Cerdocyon thous) and pampas fox (Lycalopex gymnocercus). Tissue samples from road-killed animals (pampas fox = 46, crab-eating fox = 55) and feces (pampas fox = 84, crab-eating fox = 2) were collected, and species were diagnosed through molecular assay. PCR was used for the amplification of a fragment of the coccidian Apicomplexa nss-rRNA gene. Additionally, we developed a novel real-time PCR TaqMan™ probe approach to detect T. gondii- Hammondia spp. and N. caninum. This is the first report of N. caninum DNA in pampas fox feces (n = 1), thus it was also detected from pampas fox tissues (n = 1). Meanwhile, T. gondii was found in tissues of pampas (n = 1) and crab-eating (n = 1) foxes and H. triffittae in one crab-eating fox tissue. Despite the low percentage (2.5%) of positive samples, the molecular method developed in this study proved to be highly sensitive and accurate allowing to conduct an extensive monitoring analysis for these parasites in wildlife.