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.

Multilocus sequence typing schemes for the emerging swine pathogen Mycoplasma hyosynoviae.

Mycoplasma (M.) hyosynoviae is a commensal of the upper respiratory tract in swine, which has the potential to spread systemically, usually resulting in arthritis in fattening pigs and gilts. To date, very little is known about the epidemiology of M. hyosynoviae, mainly due to a lack of suitable typing methods. Therefore, this study aimed to develop both a conventional multi locus sequence typing (MLST) and a core genome (cg) MLST scheme. The development of the cgMLST was based on whole genome sequences of 64 strains isolated from pigs and wild boars during routine diagnostics as well as nine publicly available genomes. A cgMLST scheme containing 390 target genes was established using the Ridom© SeqSphere+ software. Using this scheme as a foundation, seven housekeeping genes were selected for conventional MLST based on their capability to reflect genome wide relatedness and subsequently, all 73 strains were typed by applying both methods. Core genome MLST results revealed a high diversity of the studied strain population and less than 100 allele differences between epidemiologically unrelated strains were only detected for four isolates from the US. On the other hand, seven clonal clusters (≤ 12 allele differences) comprising 20 isolates were identified. Comparison of the two typing methods resulted in highly congruent phylogenetic trees and an Adjusted Rand Coefficient of 0.893, while cgMLST showed marginally higher resolution when comparing closely related isolates, indicated by a slightly higher Simpson's ID (0.992) than conventional MLST (Simpson's ID = 0.990). Overall, both methods seem well suited for epidemiological analyses for scientific as well as diagnostic purposes. While MLST is faster and cheaper, cgMLST can be used to further differentiate closely related isolates.

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.

Global genetic diversity of measles virus (Paramyxoviridae: Morbillivirus: Morbillivirus hominis): historical aspects and current state.

Monitoring the circulation of the measles virus and studying its genetic diversity is an important component of the measles elimination program. A methodological approach to molecular genetic studies and their interpretation in the measles surveillance was developed in the early 2000s. During its development, clear areas of circulation of each genotype of the virus were identified, therefore, the determination of viruses' genotypes was proposed to monitor circulation and identify transmission pathways. However, in the future, due to a significant decrease in the number of active genotypes, an approach based on sub-genotyping was proposed: determining not only the genotype of the virus, but also its genetic lineage/genetic variant. The Global Measles and Rubella Laboratory Network (GMRLN) systematically monitors the circulation of the measles virus at the sub-genotypic level, depositing the results in a specialized database MeaNS2. It is this database that is the most complete and reliable source of information about the genetic characteristic of measles viruses. This review presents both historical information and the latest data on the global genetic diversity of the measles virus.

Introduction and benchmarking of pyMLST: open-source software for assessing bacterial clonality using core genome MLST.

Core genome multilocus sequence typing (cgMLST) has gained in popularity for bacterial typing since whole-genome sequencing (WGS) has become affordable. We introduce here pyMLST, a new complete, stand-alone, free and open source pipeline for cgMLST analysis. pyMLST can create or import a core genome database. For each gene, the first allele is aligned against the bacterial genome of interest using BLAT. Incomplete genes are aligned using MAFT. All data are stored in a SQLite database. pyMLST accepts assembly genomes or raw data (with the option pyMLST-KMA) as input. To evaluate our new tool, we selected three genome collections of major bacterial pathogens (Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus) and compared them with pyMLST, pyMLST-KMA, ChewBBACA, SeqSphere and the variant calling approach. We compared the sensitivity, precision and false-positive rate for each method with those of the variant calling approach. Minimal spanning trees were generated with each type of software to evaluate their interest in the context of a bacterial outbreak. We found that pyMLST-KMA is a convenient screening method to avoid assembling large bacterial collections. Our data showed that pyMLST (free, open source, available in Galaxy and pipeline ready) performed similarly to the commercial SeqSphere and performed better than ChewBBACA and pyMLST-KMA.

Molecular epidemiology, antimicrobial susceptibility, and toxin production of clinical Clostridioides difficile isolates from a teaching hospital in Northern China.

To grasp the epidemiological trend and drug resistance mechanisms of Clostridioides difficile (C. diff) in Beijing, 302 C. diff isolates were obtained from patients with diarrhea. The sequence types (STs) from mainstream strains were all susceptible to metronidazole, vancomycin, piperacillin/tazobactam, meropenem, and tigecycline but almost resistant to ciprofloxacin and clindamycin. The missense mutation of GyrA/GyrB and RpoB resulted in fluoroquinolone and rifamycin resistance, respectively. Toxigenic strains from clade IV were likely to be missed due to the deficiency of tcdA gene. Four tcdC genotypes were first detected in strains from clade III and IV. The truncating mutation of TcdC disabled its function working as a toxin suppressor. In conclusion, the molecular epidemiology of C. diff in Beijing is different from other regions of China. The antimicrobial resistance patterns and toxin-producing abilities of strains with different STs varied greatly, which suggests that continuous surveillance and control are meaningful and urgent.

Real-Time Nanopore Q20+ Sequencing Enables Extremely Fast and Accurate Core Genome MLST Typing and Democratizes Access to High-Resolution Bacterial Pathogen Surveillance.

Next-generation whole-genome sequencing is essential for high-resolution surveillance of bacterial pathogens, for example, during outbreak investigations or for source tracking and escape variant analysis. However, current global sequencing and bioinformatic bottlenecks and a long time to result with standard technologies demand new approaches. In this study, we investigated whether novel nanopore Q20+ long-read chemistry enables standardized and easily accessible high-resolution typing combined with core genome multilocus sequence typing (cgMLST). We set high requirements for discriminatory power by using the slowly evolving bacterium Bordetella pertussis as a model pathogen. Our results show that the increased raw read accuracy enables the description of epidemiological scenarios and phylogenetic linkages at the level of gold-standard short reads. The same was true for our variant analysis of vaccine antigens, resistance genes, and virulence factors, demonstrating that nanopore sequencing is a legitimate competitor in the area of next-generation sequencing (NGS)-based high-resolution bacterial typing. Furthermore, we evaluated the parameters for the fastest possible analysis of the data. By combining the optimized processing pipeline with real-time basecalling, we established a workflow that allows for highly accurate and extremely fast high-resolution typing of bacterial pathogens while sequencing is still in progress. Along with advantages such as low costs and portability, the approach suggested here might democratize modern bacterial typing, enabling more efficient infection control globally.

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.

Rapid Identification of Pseudomonas aeruginosa International High-Risk Clones Based on High-Resolution Melting Analysis.

The Pseudomonas aeruginosa population has a nonclonal epidemic structure. It is generally composed of a limited number of widespread clones selected from a background of many rare and unrelated genotypes recombining at high frequency. Due to the increasing prevalence of nosocomial infections caused by multidrug-resistant/extensively drug-resistant (MDR/XDR) strains, it is advisable to implement infection control measures. Pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST) are considered the gold standard methods in bacterial typing, despite being limited by cost, staff, and instrumental demands. Here, we present a novel mini-MLST scheme for P. aeruginosa rapid genotyping based on high-resolution melting analysis. Using the proposed mini-MLST scheme, 3,955 existing sequence types (STs) were converted into 701 melting types (MelTs), resulting in a discriminatory power of D = 0.993 (95% confidence interval [CI], 0.992 to 0.994). Whole-genome sequencing of 18 clinical isolates was performed to support the newly designed mini-MLST scheme. The clonal analysis of STs belonging to MelTs associated with international high-risk clones (HRCs) performed by goeBURST software revealed that a high proportion of the included STs are highly related to HRCs and have also been witnessed as responsible for serious infections. Therefore, mini-MLST provides a clear warning for the potential spread of P. aeruginosa clones recognized as MDR/XDR strains with possible serious outcomes. IMPORTANCE In this study, we designed a novel mini-MLST typing scheme for Pseudomonas aeruginosa. Its great discriminatory power, together with ease of performance and short processing time, makes this approach attractive for prospective typing of large isolate sets. Integrating the novel P. aeruginosa molecular typing scheme enables the development and spread of MDR/XDR high-risk clones to be investigated.

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.

Epidemiological Investigation of Hospital Transmission of Corynebacterium striatum Infection by Core Genome Multilocus Sequence Typing Approach.

Corynebacterium striatum has recently received increasing attention due to its multiple antimicrobial resistances and its role as an invasive infection/outbreak agent. Recently, whole-genome sequencing (WGS)-based core genome multilocus sequence typing (cgMLST) has been used in epidemiological studies of specific human pathogens. However, this method has not been reported in studies of C. striatum. In this work, we aim to propose a cgMLST scheme for C. striatum. All publicly available C. striatum genomes, 30 C. striatum strains isolated from the same hospital, and 1 epidemiologically unrelated outgroup C. striatum strain were used to establish a cgMLST scheme targeting 1,795 genes (hereinafter referred to as 1,795-cgMLST). The genotyping results of cgMLST showed good congruence with core genome-based single-nucleotide polymorphism typing in terms of tree topology. In addition, the cgMLST provided a greater discrimination than the MLST method based on 6 housekeeping genes (gyrA, gyrB, hsp65, rpoB, secA1, and sodA). We established a clonal group (CG) threshold based on 104 allelic differences; a total of 56 CGs were identified from among 263 C. striatum strains. We also defined an outbreak threshold based on seven allelic differences that is capable of identifying closely related isolates that could give clues on hospital transmission. According to the results of analysis of drug-resistant genes and virulence genes, we identified CG4, CG5, CG26, CG28, and CG55 as potentially hypervirulent and multidrug-resistant CGs of C. striatum. This study provides valuable genomic epidemiological data on the diversity, resistance, and virulence profiles of this potentially pathogenic microorganism. IMPORTANCE Recently, WGS of many human and animal pathogens has been successfully used to investigate microbial outbreaks. The cgMLST schema are powerful genotyping tools that can be used to investigate potential epidemics and provide classification of the strains precise and reliable. In this study, we proposed the development of a cgMLST typing scheme for C. striatum, and then we evaluated this scheme for its applicability to hospital transmission investigations. This report describes the first cgMLST schema for C. striatum. The analysis of hospital transmission of C. striatum based on cgMLST methods has important clinical epidemiological significance for improving nosocomial infection monitoring of C. striatum and in-depth understanding of its nosocomial transmission routes.

spa Typing of Methicillin-Resistant Staphylococcus aureus Based on Whole-Genome Sequencing: the Impact of the Assembler.

Sequencing of the spa gene of methicillin-resistant Staphylococcus aureus (MRSA) is used for assigning spa types to e.g., detect transmission and control outbreaks. Traditionally, spa typing is performed by Sanger sequencing but has in recent years been replaced by whole-genome sequencing (WGS) in some laboratories. Spa typing by WGS involves de novo assembly of millions of short sequencing reads into larger contiguous sequences, from which the spa type is then determined. The choice of assembly program therefore potentially impacts the spa typing result. In this study, WGS of 1,754 MRSA isolates was followed by de novo assembly using the assembly programs SPAdes (with two different sets of parameters) and SKESA. The spa types were assigned and compared to the spa types obtained by Sanger sequencing, regarding the latter as the correct spa types. SPAdes with the two different settings resulted in assembly of the correct spa type for 84.8% and 97.6% of the isolates, respectively, while SKESA assembled the correct spa type in 98.6% of cases. The misassembled spa types were generally two spa repeats shorter than the correct spa type and mainly included spa types with repetition of the same repeats. WGS-based spa typing is thus very accurate compared to Sanger sequencing, when the best assembly program for this purpose is used. IMPORTANCE spa typing of methicillin-resistant Staphylococcus aureus (MRSA) is widely used by clinicians, infection control workers, and researchers both in local outbreak investigations and as an easy way to communicate and compare MRSA types between laboratories and countries. Traditionally, spa types are determined by Sanger sequencing, but in recent years a whole-genome sequencing (WGS)-based approach has become increasingly used. In this study, we compared spa typing by WGS using different methods for assembling the genome from short sequencing reads and compared to Sanger sequencing as the gold standard. We find substantial differences in correct assembly of spa types between the assembly methods. Our findings are therefore important for the quality of WGS based spa typing data being exchanged by clinical microbiology laboratories.

Molecular Epidemiology of Staphylococcus aureus in China Reveals the Key Gene Features Involved in Epidemic Transmission and Adaptive Evolution.

Staphylococcus aureus is a Gram-positive pathogen that causes various infections in humans and domestic animals. In China, S. aureus is the most common Gram-positive pathogen that causes clinical infections. However, there are few comprehensive genome-based molecular epidemiology studies to investigate the genotypic background of the major S. aureus clones that are epidemic in China. Here, four S. aureus isolates that were recovered from hospital personnel were sequenced. In combination with whole-genome sequencing (WGS) data of 328 S. aureus strains as references, we performed a comprehensive molecular epidemiology study to reveal the molecular epidemic characterization of S. aureus that is epidemic in China. It was found that 332 S. aureus isolates were phylogenetically categorized into 4 major epidemic groups with different epidemiology phenotypes. Each group has exclusive features in virulence genotypic profiles, antimicrobial resistance genotypic profiles, core and pangenome features representing the differences involved in genetic features, evolutionary processes, and potential future evolutionary directions. Moreover, a comparative core genome analysis of 332 S. aureus isolates indicated several key genes that contributed to differences in molecular epidemic characterization and promoted the adaptive evolutionary process of each group. This study provides a comprehensive understanding of molecular epidemiological characteristics and adaptive evolutionary directions of major S. aureus clones that are epidemic in China. IMPORTANCE Staphylococcus aureus is an important Gram-positive pathogen that is epidemic worldwide and causes various infections in humans and domestic animals. However, there has been relatively little research on comprehensive molecular epidemiology in China. In this research, we reconstructed the phylogenetic relationship based on whole-genome data of strains almost all over China, screened for resistance and virulence genes, and took core and pan genome analysis to perform a comprehensive molecular epidemiology study of S. aureus that is epidemic in China. Our results highlight that there are 4 major epidemic groups with different epidemiology phenotypes after phylogenetic categorization with exclusive genetic features in virulence genotypic profiles, antimicrobial-resistance genotypic profiles, and core and pangenome features, and we found key gene features involved in epidemic transmission and adaptive evolution. Our findings are critical in describing molecular characteristic profiles of S. aureus infection, which could update existing preventive measures and take appropriate strategies.

A core genome multilocus sequence typing (cgMLST) analysis of Mycoplasma bovis isolates.

Mycoplasma bovis (M. bovis) is an emerging major bovine pathogen, causing economic losses worldwide in the dairy and beef industry. Whole-genome sequencing (WGS) now allows high resolution for tracing clonal populations. Based on WGS, we developed the core genome multilocus sequence typing (cgMLST) scheme and applied it onto 151 genomes of clonal and non-clonal strains of M. bovis isolated from China, Australia, Israel, Denmark, Canada, and the USA. We used the complete genome of M. bovis PG45 as the reference genome. The pairwise genome comparison of these 151 genome sequences resulted in 478 cgMLST gene targets present in > 99.0 % clonal and non-clonal isolates with 100 % overlap and > 90 % sequence similarity. A total of 478 core genes were retained as cgMLST target genes of which an average of 90.4-99 % were present in 151 M. bovis genomes, while M. agalactiae (PG2) had 17.0 % and M. mycoides subsp. capri (PG3), M. ovipneumoniae (Y98), and M. arginine resulted in 0.0 % of good targets. When tested against the clonal and non-clonal strains, we found cgMLST clusters were congruent with the MLST-defined clonal groups, which had various degrees of diversity at the whole-genome level. Notably, cgMLST could distinguish between clonal and epidemiologically unrelated strains of the same clonal group, which could not be achieved using traditional MLST schemes. Our results showed that ninety-two M. bovis genomes from clonal group isolates had > 10 allele differences and unambiguously differentiated from unrelated outgroup strains. Additionally, cgMLST revealed that there might be several sub-clones of the emerging ST-52 clone. The cgMLST phylogenetic analysis results showed substantial agreement with geographical and temporal information. cgMLST enables the use of next-generation sequencing technology to bovine mycoplasma epidemiology at both the local and global levels. In conclusion, the novel cgMLST scheme not only showed discrimination resolution highly as compared with MLST and SNP cgMLST in sub-typing but also indicated the capability to reveal more population structure characteristics than MLST.

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.

[Progress in research of genomic epidemiology of pathogenic fungi].

Genomic epidemiology, based on whole-genome sequencing technology and bioinformatics analysis, can make up for the shortcomings of traditional molecular typing methods and provide a novel insight for the genetic evolution and transmission of pathogenic fungi. The combination of genetic information and epidemiological methods of pathogenic fungi can predict fungi transmission routes and risks, and provide a theoretical basis for the development of public health strategies for fungi infection prevention and control. This paper summarizes the development of molecular epidemiology and genomic epidemiology, as well as the application of genomic epidemiology methods in the analyses of genetic relationship, origin, evolution, drug resistance, virulence, and genome-wide association of pathogenic fungi, and discusses the development of pathogenic fungi genomic epidemiology.

Plasmodium falciparum genomic surveillance reveals spatial and temporal trends, association of genetic and physical distance, and household clustering.

Molecular epidemiology using genomic data can help identify relationships between malaria parasite population structure, malaria transmission intensity, and ultimately help generate actionable data to assess the effectiveness of malaria control strategies. Genomic data, coupled with geographic information systems data, can further identify clusters or hotspots of malaria transmission, parasite genetic and spatial connectivity, and parasite movement by human or mosquito mobility over time and space. In this study, we performed longitudinal genomic surveillance in a cohort of 70 participants over four years from different neighborhoods and households in Thiès, Senegal-a region of exceptionally low malaria transmission (entomological inoculation rate less than 1). Genetic identity (identity by state, IBS) was established using a 24-single nucleotide polymorphism molecular barcode, identity by descent was calculated from whole genome sequence data, and a hierarchical Bayesian regression model was used to establish genetic and spatial relationships. Our results show clustering of genetically similar parasites within households and a decline in genetic similarity of parasites with increasing distance. One household showed extremely high diversity and warrants further investigation as to the source of these diverse genetic types. This study illustrates the utility of genomic data with traditional epidemiological approaches for surveillance and detection of trends and patterns in malaria transmission not only by neighborhood but also by household. This approach can be implemented regionally and countrywide to strengthen and support malaria control and elimination efforts.

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.

Simultaneous detection and genetic characterization of porcine circovirus 2 and 4 in Henan province of China.

Porcine circovirus 4 (PCV4) was identified as a novel porcine circovirus in China in 2019. To investigate the prevalence and genetic characteristics of PCV2 and PCV4, 133 clinical samples (103 tissue samples and 30 serum samples) were collected from 30 different pig farms in Henan province of China, and a SYBR Green I-based duplex quantitative real-time polymerase chain reaction assay was established to detect PCV2 and PCV4 genomes simultaneously. The complete genome sequences of 20 PCV2 and 6 PCV4 strains from 19 and 6 clinical samples respectively were sequenced and analyzed. The results showed the detection limits of this assay were 80.2 copies/µL for PCV2 and 58.6 copies/µL for PCV4. The detection results of clinical samples revealed the PCV2 positive rate was 63.16% (84/133), the PCV4 positive rate was 33.33% (45/133), and the PCV2 and PCV4 co-infection positive rate was 21.05% (28/133). Among 20 PCV2 strains, 6 belonged to PCV2a, 6 belonged to PCV2b and 8 belonged to PCV2d. Co-infection with JZ1 (PCV2b) and JZ2 (PCV2d) strains was identified in one sample (JZ-1). Eleven putative recombination events were found through the recombination analysis, suggesting that the new PCV2 variant strains had circulated in Henan province, which contributes to our understanding of evolutionary characteristics of PCV2 in China. The possible genotypes of PCV4 strains were determined based on genomic sequences of 6 PCV4 strains in this study and 29 PCV4 reference strains available at GenBank. According to three different phylogenetic trees (ORF1, ORF2 and complete genome), all 35 PCV4 strains were clustered into two major genotypes (PCV4a and PCV4b), and 6 PCV4 strains in this study belonged to PCV4a. Additionally, the functional regions of PCV4 strains were predicted by comparison with other circoviruses, which are conducive to the further study of the biological functions of PCV4 genome.

Molecular subtyping for source tracking of Escherichia coli using core genome multilocus sequence typing at a food manufacturing plant.

When harmful bacteria are detected in the final product at a food manufacturing plant, it is necessary to identify and eliminate the source of contamination so that it does not occur again. In the current study, the source of contamination was tracked using core genome multilocus sequence typing (cgMLST) analysis in cases where Escherichia coli was detected in the final product at a food manufacturing plant. cgMLST analysis was performed on 40 strains of E. coli collected from the environment [floor (26 strains), drainage ditch (5 strains), container (4 strains), post-heating production line (1 strain)] and products [final product (3 strains) and intermediate product (1 strain)]. In total, 40 E. coli isolates were classified into 17 genogroups by cgMLST analysis. The 4 E. coli strains isolated from the intermediate and final products were classified into two genogroups (I and II). Certain isolates collected from the environment also belonged to those genogroups, it was possible to estimate the transmission of E. coli in the manufacturing plant. Thus, the dynamics of E. coli in the food manufacturing location were clarified by using cgMLST analysis. In conclusion, our results indicate that cgMLST analysis can be effectively used for hygiene management at food manufacturing locations.