Assessing transmission attribution risk from simulated sequencing data in HIV molecular epidemiology

BACKGROUND

HIV molecular epidemiology (ME) is the analysis of sequence data together with individual-level clinical, demographic, and behavioral data to understand HIV epidemiology. The use of ME has raised concerns regarding identification of the putative source in direct transmission events. This could result in harm ranging from stigma to criminal prosecution in some jurisdictions. Here we assessed the risks of ME using simulated HIV genetic sequencing data.

METHODS

We simulated social networks of men-who-have-sex-with-men, calibrating the simulations to data from San Diego. We used these networks to simulate consensus and next-generation sequence (NGS) data to evaluate the risks of identifying direct transmissions using different HIV sequence lengths, and population sampling depths. To identify the source of transmissions, we calculated infector probability and used phyloscanner software for the analysis of consensus and NGS data, respectively.

RESULTS

Consensus sequence analyses showed that the risk of correctly inferring the source (direct transmission) within identified transmission pairs was very small and independent of sampling depth. Alternatively, NGS analyses showed that identification of the source of a transmission was very accurate, but only for 6.5% of inferred pairs. False positive transmissions were also observed, where one or more unobserved intermediaries were present when compared to the true network.

CONCLUSION

Source attribution using consensus sequences rarely infers direct transmission pairs with high confidence but is still useful for population studies. In contrast, source attribution using NGS data was much more accurate in identifying direct transmission pairs, but for only a small percentage of transmission pairs analyzed.

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.

Development and validation of a random forest algorithm for source attribution of animal and human Salmonella Typhimurium and monophasic variants of S. Typhimurium isolates in England and Wales utilising whole genome sequencing data

Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random forest (RF) algorithm to predict the primary sources of human clinical Salmonella Typhimurium (S. Typhimurium) and monophasic variants (monophasic S. Typhimurium) isolates. To this end, we utilised single nucleotide polymorphism diversity in the core genome MLST alleles obtained from 1,061 laboratory-confirmed human and animal S. Typhimurium and monophasic S. Typhimurium isolates as inputs into a RF model. The algorithm was used for supervised learning to classify 399 animal S. Typhimurium and monophasic S. Typhimurium isolates into one of eight distinct primary source classes comprising common livestock and pet animal species: cattle, pigs, sheep, other mammals (pets: mostly dogs and horses), broilers, layers, turkeys, and game birds (pheasants, quail, and pigeons). When applied to the training set animal isolates, model accuracy was 0.929 and kappa 0.905, whereas for the test set animal isolates, for which the primary source class information was withheld from the model, the accuracy was 0.779 and kappa 0.700. Subsequently, the model was applied to assign 662 human clinical cases to the eight primary source classes. In the dataset, 60/399 (15.0%) of the animal and 141/662 (21.3%) of the human isolates were associated with a known outbreak of S. Typhimurium definitive type (DT) 104. All but two of the 141 DT104 outbreak linked human isolates were correctly attributed by the model to the primary source classes identified as the origin of the DT104 outbreak. A model that was run without the clonal DT104 animal isolates produced largely congruent outputs (training set accuracy 0.989 and kappa 0.985; test set accuracy 0.781 and kappa 0.663). Overall, our results show that RF offers considerable promise as a suitable methodology for epidemiological tracking and source attribution for foodborne pathogens.

A Review on Microbiological Source Attribution Methods of Human Salmonellosis: From Subtyping to Whole-Genome Sequencing

Salmonella is one of the main causes of human foodborne illness. It is endemic worldwide, with different animals and animal-based food products as reservoirs and vehicles of infection. Identifying animal reservoirs and potential transmission pathways of Salmonella is essential for prevention and control. There are many approaches for source attribution, each using different statistical models and data streams. Some aim to identify the animal reservoir, while others aim to determine the point at which exposure occurred. With the advance of whole-genome sequencing (WGS) technologies, new source attribution models will greatly benefit from the discriminating power gained with WGS. This review discusses some key source attribution methods and their mathematical and statistical tools. We also highlight recent studies utilizing WGS for source attribution and discuss open questions and challenges in developing new WGS methods. We aim to provide a better understanding of the current state of these methodologies with application to Salmonella and other foodborne pathogens that are common sources of illness in the poultry and human sectors.

Source Attribution of Antibiotic Resistance Genes in Estuarine Aquaculture: A Machine Learning Approach

Aquaculture located in urban river estuaries, where other anthropogenic activities may occur, has an impact on and may be affected by the environment where they are inserted, namely by the exchange of antimicrobial resistance genes. The latter may ultimately, through the food chain, represent a source of resistance genes to the human resistome. In an exploratory study of the presence of resistance genes in aquaculture sediments located in urban river estuaries, two machine learning models were applied to predict the source of 34 resistome observations in the aquaculture sediments of oysters and gilt-head sea bream, located in the estuaries of the Sado and Lima Rivers and in the Aveiro Lagoon, as well as in the sediments of the Tejo River estuary, where Japanese clams and mussels are collected. The first model included all 34 resistomes, amounting to 53 different antimicrobial resistance genes used as source predictors. The most important antimicrobial genes for source attribution were tetracycline resistance genes tet(51) and tet(L); aminoglycoside resistance gene aadA6; beta-lactam resistance gene blaBRO-2; and amphenicol resistance gene cmx_1. The second model included only oyster sediment resistomes, amounting to 30 antimicrobial resistance genes as predictors. The most important antimicrobial genes for source attribution were the aminoglycoside resistance gene aadA6, followed by the tetracycline genes tet(L) and tet(33). This exploratory study provides the first information about antimicrobial resistance genes in intensive and semi-intensive aquaculture in Portugal, helping to recognize the importance of environmental control to maintain the integrity and the sustainability of aquaculture farms.

Campylobacteriosis in Finland: Passive Surveillance in 2004-2021 and a Pilot Case-Control Study with Whole-Genome Sequencing in Summer 2022

Campylobacteriosis causes a significant disease burden in humans worldwide and is the most common type of zoonotic gastroenteritis in Finland. To identify infection sources for domestic Campylobacter infections, we analyzed Campylobacter case data from the Finnish Infectious Disease Register (FIDR) in 2004-2021 and outbreak data from the National Food- and Waterborne Outbreak Register (FWO Register) in 2010-2021, and conducted a pilot case-control study (256 cases and 756 controls) with source attribution and patient sample analysis using whole-genome sequencing (WGS) in July-August 2022. In the FIDR, 41% of the cases lacked information on travel history. Based on the case-control study, we estimated that of all cases, 39% were of domestic origin. Using WGS, 22 clusters of two or more cases were observed among 185 domestic cases, none of which were reported to the FWO register. Based on this case-control study and source attribution, poultry is an important source of campylobacteriosis in Finland. More extensive sampling and comparison of patient, food, animal, and environmental isolates is needed to estimate the significance of other sources. In Finland, campylobacteriosis is more often of domestic origin than FIDR notifications indicate. To identify the domestic cases, travel information should be included in the FIDR notification, and to improve outbreak detection, all domestic patient isolates should be sequenced.

Source attribution of campylobacteriosis in Australia, 2017-2019

Campylobacter jejuni and Campylobacter coli infections are the leading cause of foodborne gastroenteritis in high-income countries. Campylobacter colonizes a variety of warm-blooded hosts that are reservoirs for human campylobacteriosis. The proportions of Australian cases attributable to different animal reservoirs are unknown but can be estimated by comparing the frequency of different sequence types in cases and reservoirs. Campylobacter isolates were obtained from notified human cases and raw meat and offal from the major livestock in Australia between 2017 and 2019. Isolates were typed using multi-locus sequence genotyping. We used Bayesian source attribution models including the asymmetric island model, the modified Hald model, and their generalizations. Some models included an "unsampled" source to estimate the proportion of cases attributable to wild, feral, or domestic animal reservoirs not sampled in our study. Model fits were compared using the Watanabe-Akaike information criterion. We included 612 food and 710 human case isolates. The best fitting models attributed >80% of Campylobacter cases to chickens, with a greater proportion of C. coli (>84%) than C. jejuni (>77%). The best fitting model that included an unsampled source attributed 14% (95% credible interval [CrI]: 0.3%-32%) to the unsampled source and only 2% to ruminants (95% CrI: 0.3%-12%) and 2% to pigs (95% CrI: 0.2%-11%) The best fitting model that did not include an unsampled source attributed 12% to ruminants (95% CrI: 1.3%-33%) and 6% to pigs (95% CrI: 1.1%-19%). Chickens were the leading source of human Campylobacter infections in Australia in 2017-2019 and should remain the focus of interventions to reduce burden.

Nontarget Chemical Composition of Surface Waters May Reflect Ecosystem Processes More than Discrete Source Contributions

We investigated environmental, landscape, and microbial factors that could structure the spatiotemporal variability in the nontarget chemical composition of four riverine systems in the Oregon Coast Range, USA. We hypothesized that the nontarget chemical composition in river water would be structured by broad-scale landscape gradients in each watershed. Instead, only a weak relationship existed between the nontarget chemical composition and land cover gradients. Overall, the effects of microbial communities and environmental variables on chemical composition were nearly twice as large as those of the landscape, and much of the influence of environmental variables on the chemical composition was mediated through the microbial community (i.e., environment affects microbes, which affect chemicals). Therefore, we found little evidence to support our hypothesis that chemical spatiotemporal variability was related to broad-scale landscape gradients. Instead, we found qualitative and quantitative evidence to suggest that chemical spatiotemporal variability of these rivers is controlled by changes in microbial and seasonal hydrologic processes. While the contributions of discrete chemical sources are undeniable, water chemistry is undoubtedly impacted by broad-scale continuous sources. Our results suggest that diagnostic chemical signatures can be developed to monitor ecosystem processes, which are otherwise challenging or impossible to study with existing off-the-shelf sensors.

Analysis of reservoir sources of Campylobacter isolates to free-range broilers in Denmark

Campylobacter is a common cause of food poisoning in many countries, with broilers being the main source. Organic and free-range broilers are more frequently Campylobacter-positive than conventionally raised broilers and may constitute a higher risk for human infections. Organic and free-range broilers may get exposed to Campylobacter from environmental reservoirs and livestock farms, but the relative importance of these sources is unknown. The aim of the study was to describe similarities and differences between the genetic diversity of the Campylobacter isolates collected from free-range/organic broilers with those isolated from conventional broilers and other animal hosts (cattle, pigs, and dogs) in Denmark to make inferences about the reservoir sources of Campylobacter to free-range broilers. The applied aggregated surveillance data consisted of sequenced Campylobacter isolates sampled in 2015 to 2017 and 2018 to 2021. The data included 1,102 isolates from free-range (n = 209), conventional broilers (n = 577), cattle (n = 261), pigs (n = 30), and dogs (n = 25). The isolates were cultivated from either fecal material (n = 434), food matrices (n = 569), or of nondisclosed origin (n = 99). Campylobacter jejuni (94.5%) dominated and subtyping analysis found 170 different sequence types (STs) grouped into 75 clonal complexes (CCs). The results suggest that CC-21 and CC-45 are the most frequent CCs found in broilers. The relationship between the CCs in the investigated sources showed that the different CCs were shared by most of the animals, but not pigs. The ST-profiles of free-range broilers were most similar to that of conventional broilers, dogs and cattle, in that order. The similarity was stronger between conventional broilers and cattle than between conventional and free-range broilers. The results suggest that cattle may be a plausible reservoir of C. jejuni for conventional and free-range broilers, and that conventional broilers are a possible source for free-range broilers or reflect a dominance of isolates adapted to the same host environment. Aggregated data provided valuable insight into the epidemiology of Campylobacter sources for free-range broilers, but time-limited sampling of isolates from different sources within a targeted area would hold a higher predictive value.

Knowledge About the Source of Emotion Predicts Emotion-Regulation Attempts, Strategies, and Perceived Emotion-Regulation Success

People's ability to regulate emotions is crucial to healthy emotional functioning. One overlooked aspect in emotion-regulation research is that knowledge about the source of emotions can vary across situations and individuals, which could impact people's ability to regulate emotion. Using ecological momentary assessments (N = 396; 7 days; 5,466 observations), we measured adults' degree of knowledge about the source of their negative emotions. We used language processing to show that higher reported knowledge led to more concrete written descriptions of the source. We found that higher knowledge of the source predicted more emotion-regulation attempts; increased the use of emotion-regulation strategies that target the source (cognitive reappraisal, situation modification) versus strategies that do not (distraction, emotional eating); predicted greater perceived success in regulating emotions; and greater well-being. These patterns were evident both within and between persons. Our findings suggest that pinpointing the source of emotions might play an important role in emotion regulation.

Animal sources of antimicrobial-resistant bacterial infections in humans: a systematic review

Bacterial antimicrobial resistance (AMR) is among the leading global health challenges of the century. Animals and their products are known contributors to the human AMR burden, but the extent of this contribution is not clear. This systematic literature review aimed to identify studies investigating the direct impact of animal sources, defined as livestock, aquaculture, pets, and animal-based food, on human AMR. We searched four scientific databases and identified 31 relevant publications, including 12 risk assessments, 16 source attribution studies, and three other studies. Most studies were published between 2012 and 2022, and most came from Europe and North America, but we also identified five articles from South and South-East Asia. The studies differed in their methodologies, conceptual approaches (bottom-up, top-down, and complex), definitions of the AMR hazard and outcome, the number and type of sources they addressed, and the outcome measures they reported. The most frequently addressed animal source was chicken, followed by cattle and pigs. Most studies investigated bacteria-resistance combinations. Overall, studies on the direct contribution of animal sources of AMR are rare but increasing. More recent publications tailor their methodologies increasingly towards the AMR hazard as a whole, providing grounds for future research to build on.

Comparison of Source Attribution Methodologies for Human Campylobacteriosis

Campylobacter spp. are the most common cause of bacterial gastrointestinal infection in humans both in Denmark and worldwide. Studies have found microbial subtyping to be a powerful tool for source attribution, but comparisons of different methodologies are limited. In this study, we compare three source attribution approaches (Machine Learning, Network Analysis, and Bayesian modeling) using three types of whole genome sequences (WGS) data inputs (cgMLST, 5-Mers and 7-Mers). We predicted and compared the sources of human campylobacteriosis cases in Denmark. Using 7mer as an input feature provided the best model performance. The network analysis algorithm had a CSC value of 78.99% and an F1-score value of 67%, while the machine-learning algorithm showed the highest accuracy (98%). The models attributed between 965 and all of the 1224 human cases to a source (network applying 5mer and machine learning applying 7mer, respectively). Chicken from Denmark was the primary source of human campylobacteriosis with an average percentage probability of attribution of 45.8% to 65.4%, representing Bayesian with 7mer and machine learning with cgMLST, respectively. Our results indicate that the different source attribution methodologies based on WGS have great potential for the surveillance and source tracking of Campylobacter. The results of such models may support decision makers to prioritize and target interventions.

Performance of Continuous Emission Monitoring Solutions under a Single-Blind Controlled Testing Protocol

Continuous emission monitoring (CM) solutions promise to detect large fugitive methane emissions in natural gas infrastructure sooner than traditional leak surveys, and quantification by CM solutions has been proposed as the foundation of measurement-based inventories. This study performed single-blind testing at a controlled release facility (release from 0.4 to 6400 g CH₄/h) replicating conditions that were challenging, but less complex than typical field conditions. Eleven solutions were tested, including point sensor networks and scanning/imaging solutions. Results indicated a 90% probability of detection (POD) of 3-30 kg CH₄/h; 6 of 11 solutions achieved a POD < 6 kg CH₄/h, although uncertainty was high. Four had true positive rates > 50%. False positive rates ranged from 0 to 79%. Six solutions estimated emission rates. For a release rate of 0.1-1 kg/h, the solutions' mean relative errors ranged from -44% to +586% with single estimates between -97% and +2077%, and 4 solutions' upper uncertainty exceeding +900%. Above 1 kg/h, mean relative error was -40% to +93%, with two solutions within ±20%, and single-estimate relative errors were from -82% to +448%. The large variability in performance between CM solutions, coupled with highly uncertain detection, detection limit, and quantification results, indicates that the performance of individual CM solutions should be well understood before relying on results for internal emissions mitigation programs or regulatory reporting.

Volatile Methyl Siloxanes and Other Organosilicon Compounds in Residential Air

Volatile methyl siloxanes (VMS) are ubiquitous in indoor environments due to their use in personal care products. This paper builds on previous work identifying sources of VMS by synthesizing time-resolved proton-transfer reaction time-of-flight mass spectrometer VMS concentration measurements from four multiweek indoor air campaigns to elucidate emission sources and removal processes. Temporal patterns of VMS emissions display both continuous and episodic behavior, with the relative importance varying among species. We find that the cyclic siloxane D5 is consistently the most abundant VMS species, mainly attributable to personal care product use. Two other cyclic siloxanes, D3 and D4, are emitted from oven and personal care product use, with continuous sources also apparent. Two linear siloxanes, L4 and L5, are also emitted from personal care product use, with apparent additional continuous sources. We report measurements for three other organosilicon compounds found in personal care products. The primary air removal pathway of the species examined in this paper is ventilation to the outdoors, which has implications for atmospheric chemistry. The net removal rate is slower for linear siloxanes, which persist for days indoors after episodic release events. This work highlights the diversity in sources of organosilicon species and their persistence indoors.

Identification of Oocyst-Driven Toxoplasma gondii Infections in Humans and Animals through Stage-Specific Serology-Current Status and Future Perspectives

The apicomplexan zoonotic parasite Toxoplasma gondii has three infective stages: sporozoites in sporulated oocysts, which are shed in unsporulated form into the environment by infected felids; tissue cysts containing bradyzoites, and fast replicating tachyzoites that are responsible for acute toxoplasmosis. The contribution of oocysts to infections in both humans and animals is understudied despite being highly relevant. Only a few diagnostic antigens have been described to be capable of discriminating which parasite stage has caused an infection. Here we provide an extensive overview of the antigens and serological assays used to detect oocyst-driven infections in humans and animals according to the literature. In addition, we critically discuss the possibility to exploit the increasing knowledge of the T. gondii genome and the various 'omics datasets available, by applying predictive algorithms, for the identification of new oocyst-specific proteins for diagnostic purposes. Finally, we propose a workflow for how such antigens and assays based on them should be evaluated to ensure reproducible and robust results.

in Tennessee and Pennsylvania

Campylobacteriosis is the most common bacterial foodborne illness in the United States and is frequently associated with foods of animal origin. The goals of this study were to compare clinical and non-clinical Campylobacter populations from Tennessee (TN) and Pennsylvania (PA), use phylogenetic relatedness to assess source attribution patterns, and identify potential outbreak clusters. Campylobacter isolates studied (n = 3080) included TN clinical isolates collected and sequenced for routine surveillance, PA clinical isolates collected from patients at the University of Pennsylvania Health System facilities, and non-clinical isolates from both states for which sequencing reads were available on NCBI. Phylogenetic analyses were conducted to categorize isolates into species groups and determine the population structure of each species. Most isolates were C. jejuni (n = 2132, 69.2%) and C. coli (n = 921, 29.9%), while the remaining were C. lari (0.4%), C. upsaliensis (0.3%), and C. fetus (0.1%). The C. jejuni group consisted of three clades; most non-clinical isolates were of poultry (62.7%) or cattle (35.8%) origin, and 59.7 and 16.5% of clinical isolates were in subclades associated with poultry or cattle, respectively. The C. coli isolates grouped into two clades; most non-clinical isolates were from poultry (61.2%) or swine (29.0%) sources, and 74.5, 9.2, and 6.1% of clinical isolates were in subclades associated with poultry, cattle, or swine, respectively. Based on genomic similarity, we identified 42 C. jejuni and one C. coli potential outbreak clusters. The C. jejuni clusters contained 188 clinical isolates, 19.6% of the total C. jejuni clinical isolates, suggesting that a larger proportion of campylobacteriosis may be associated with outbreaks than previously determined.

[Chemome profiling of Pien-Tze-Huang by online pressurized liquid extraction-ultra-high performance liquid chromatography-ion trap-time-of-flight mass spectrometry]

Pien-Tze-Huang is one of the most famous traditional Chinese medicine prescriptions and consists of several precious medicinal materials, such as Notoginseng Radix et Rhizoma, Bovis Calculus, Snake Gall, and Moschus. However, its formula has not been completely revealed. It is mainly applied for the treatment of acute and chronic viral hepatitis, carbuncle, and boils caused by blood stasis, unknown swelling, bruises, and various inflammation disorders. The chemical composition of Pien-Tze-Huang is extremely complicated. Thus far, extensive attention has been paid to the principal chemical families in Pien-Tze-Huang, such as ginsenosides, bile acids, and muscone derivatives. Comprehensive chemical profiling, although of immense importance for systematic quality control, has not been achieved. Therefore, we configured a platform, namely online pressurized liquid extraction-ultra-high-performance liquid chromatography-ion trap-time-of-flight mass spectrometry (online PLE-UHPLC-IT-TOF-MS), to characterize the chemical profile of Pien-Tze-Huang in detail as well as to conduct source attribution, aiming to clarify the chemome of Pien-Tze-Huang and to provide a reliable method for quality assessment. A sub-microgram amount of Pien-Tze-Huang powder (0.3 mg) was placed in a hollow guard column, which was subsequently filled with clear silica gel. Filter membranes were used to seal the extraction vessel. The vessel was then placed in an adapted guard column holder and maintained in a thermal column oven (70 ℃). Metal tubing was used to connect the outlet of the guard column holder to the mass spectrometer. The extraction phase was maintained for 3 min by employing 0.1%(v/v) formic acid aqueous solution as the extraction solvent with a flow rate of 0.2 mL/min. Moreover, a six-port two-position electronic valve was introduced to automatically switch the system from extraction to elution phases. Within the elution phase, 0.1%(v/v) formic acid aqueous solution and acetonitrile composed the mobile phase, and the extracts were eluted with a gradient program. Because of the elevated temperature and pressure, the physical and chemical properties of water, especially polarity and solubility, were modified. Therefore, warm water could be an eligible green solvent to achieve wide polarity-spanned extraction. In addition, IT-TOF-MS was employed to acquire tandem mass spectrometry information. The mass fragmentation pathways of saponins and bile acids were carefully studied. Finally, according to authentic compounds, mass fragmentation pathways, reference information in the literature, and accessible databanks, a total of 73 signals were observed from Pien-Tze-Huang, of which 71 components were tentatively identified and assigned. Among them, 36 were from Notoginseng Radix et Rhizoma, 15 from Snake Gall, and 9 from Bovis Calculus, while the occurrences of the other 11 components were synergistically contributed by both Bovis Calculus and Snake Gall, through retrieving the in-house chemical database that was built by considering all accessible chemical information from Notoginseng Radix et Rhizoma, Bovis Calculus, Snake Gall, and Moschus. The other two compounds were assigned as unknown compounds. However, none of the components were assigned to Moschus because they mainly contained hydrophobic compounds, such as cycloketones, cholesterol, and sterols, among others, and it was difficult to detect them with the current measurement program. The extraction efficiency of online PLE was assessed by comparing it with the efficiency obtained from ultrasonication at the same time. According to base peak ion current chromatograms (BPCs) and mass spectrometry information, the efficiency of online PLE was greater than that of ultrasonic extraction, even through direct analysis. Online PLE-UHPLC-IT-TOF-MS is not only a tool fit for the concept of green analytical chemistry, but also a reliable analytical pipeline for the direct characterisation of other complicated matrixes. Above all, this study clarified the chemome of Pien-Tze-Huang and provided meaningful information for the quality control of this famous TCM prescription.

Improvements to the Success of Outbreak Investigations of Legionnaires' Disease: 40 Years of Testing and Investigation in New York State

Since 1978, the New York State Department of Health's public health laboratory, Wadsworth Center (WC), in collaboration with epidemiology and environmental partners, has been committed to providing comprehensive public health testing for Legionella in New York. Statewide, clinical case counts have been increasing over time, with the highest numbers identified in 2017 and 2018 (1,022 and 1,426, respectively). Over the course of more than 40 years, the WC Legionella testing program has continuously implemented improved testing methods. The methods utilized have transitioned from solely culture-based methods for organism recovery to development of a suite of reference testing services, including identification and characterization by PCR and pulsed-field gel electrophoresis (PFGE). In the last decade, whole-genome sequencing (WGS) has further refined the ability to link outbreak strains between clinical specimens and environmental samples. Here, we review Legionnaires' disease outbreak investigations during this time period, including comprehensive testing of both clinical and environmental samples. Between 1978 and 2017, 60 outbreaks involving clinical and environmental isolates with matching PFGE patterns were detected in 49 facilities from the 157 investigations at 146 facilities. However, 97 investigations were not solved due to the lack of clinical or environmental isolates or PFGE matches. We found 69% of patient specimens from New York State (NYS) were outbreak associated, a much higher rate than observed in other published reports. The consistent application of new cutting-edge technologies and environmental regulations has resulted in successful investigations resulting in remediation efforts. IMPORTANCE Legionella, the causative agent of Legionnaires' disease (LD), can cause severe respiratory illness. In 2018, there were nearly 10,000 cases of LD reported in the United States (https://www.cdc.gov/legionella/fastfacts.html; https://wonder.cdc.gov/nndss/static/2018/annual/2018-table2h.html), with actual incidence believed to be much higher. About 10% of patients with LD will die, and as high as 90% of patients diagnosed will be hospitalized. As Legionella is spread predominantly through engineered building water systems, identifying sources of outbreaks by assessing environmental sources is key to preventing further cases LD.

Core Genome MLST for Source Attribution of Campylobacter coli

Campylobacter species are among the leading foodborne bacterial agents of human diarrheal illness. The majority of campylobacteriosis has been attributed to Campylobacter jejuni (85% or more), followed by Campylobacter coli (5–10%). The distribution of C. jejuni and C. coli varies by host organism, indicating that the contribution to human infection may differ between isolation sources. To address the relative contribution of each source to C. coli infections in humans, core genome multilocus sequence type with a 200-allele difference scheme (cgMLST₂₀₀) was used to determine cgMLST type for 3,432 C. coli isolated from food animals (n = 2,613), retail poultry meats (n = 389), human clinical settings (n = 285), and environmental sources (n = 145). Source attribution was determined by analyzing the core genome with a minimal multilocus distance methodology (MMD). Using MMD, a higher proportion of the clinical C. coli population was attributed to poultry (49.6%) and environmental (20.9%) sources than from cattle (9.8%) and swine (3.2%). Within the population of C. coli clinical isolates, 70% of the isolates that were attributed to non-cecal retail poultry, dairy cattle, beef cattle and environmental waters came from two cgMLST₂₀₀ groups from each source. The most common antibiotic resistance genes among all C. coli were tetO (65.6%), bla_OXA–193 (54.2%), aph(3′)-IIIa (23.5%), and aadE-Cc (20.1%). Of the antibiotic resistance determinants, only one gene was isolated from a single source: bla_OXA–61 was only isolated from retail poultry. Within cgMLST₂₀₀ groups, 17/17 cgMLST₂₀₀-435 and 89/92 cgMLST₂₀₀-707 isolates encoded for aph(3’)-VIIa and 16/16 cgMLST₂₀₀-319 harbored aph(2’)-If genes. Distribution of bla_OXA alleles showed 49/50 cgMLST₂₀₀-5 isolates contained bla_OXA–498 while bla_OXA–460 was present in 37/38 cgMLST₂₀₀-650 isolates. The cgMLST₂₀₀-514 group revealed both ant(6)-Ia and sat4 resistance genes in 23/23 and 22/23 isolates, respectively. Also, cgMLST₂₀₀-266 and cgMLST₂₀₀-84 had GyrAT86I mutation with 16/16 (100%) and 14/15 (93.3%), respectively. These findings illustrate how cgMLST and MMD methods can be used to evaluate the relative contribution of known sources of C. coli to the human burden of campylobacteriosis and how cgMLST typing can be used as an indicator of antimicrobial resistance in C. coli.

Improved Constraints on Global Methane Emissions and Sinks Using δ 13C-CH4

We study the drivers behind the global atmospheric methane (CH4) increase observed after 2006. Candidate emission and sink scenarios are constructed based on proposed hypotheses in the literature. These scenarios are simulated in the TM5 tracer transport model for 1984-2016 to produce three-dimensional fields of CH4 and δ 13C-CH4, which are compared with observations to test the competing hypotheses in the literature in one common model framework. We find that the fossil fuel (FF) CH4 emission trend from the Emissions Database for Global Atmospheric Research 4.3.2 inventory does not agree with observed δ 13C-CH4. Increased FF CH4 emissions are unlikely to be the dominant driver for the post-2006 global CH4 increase despite the possibility for a small FF emission increase. We also find that a significant decrease in the abundance of hydroxyl radicals (OH) cannot explain the post-2006 global CH4 increase since it does not track the observed decrease in global mean δ 13C-CH4. Different CH4 sinks have different fractionation factors for δ 13C-CH4, thus we can investigate the uncertainty introduced by the reaction of CH4 with tropospheric chlorine (Cl), a CH4 sink whose abundance, spatial distribution, and temporal changes remain uncertain. Our results show that including or excluding tropospheric Cl as a 13 Tg/year CH4 sink in our model changes the magnitude of estimated fossil emissions by ∼20%. We also found that by using different wetland emissions based on a static versus a dynamic wetland area map, the partitioning between FF and microbial sources differs by 20 Tg/year, ∼12% of estimated fossil emissions.

Genomic Characterization of Campylobacter jejuni Adapted to the Guinea Pig (Cavia porcellus) Host

Campylobacter jejuni is the leading bacterial cause of gastroenteritis worldwide with excessive incidence in low-and middle-income countries (LMIC). During a survey for C. jejuni from putative animal hosts in a town in the Peruvian Amazon, we were able to isolate and whole genome sequence two C. jejuni strains from domesticated guinea pigs (Cavia porcellus). The C. jejuni isolated from guinea pigs had a novel multilocus sequence type that shared some alleles with other C. jejuni collected from guinea pigs. Average nucleotide identity and phylogenetic analysis with a collection of C. jejuni subsp. jejuni and C. jejuni subsp. doylei suggest that the guinea pig isolates are distinct. Genomic comparisons demonstrated gene gain and loss that could be associated with guinea pig host specialization related to guinea pig diet, anatomy, and physiology including the deletion of genes involved with selenium metabolism, including genes encoding the selenocysteine insertion machinery and selenocysteine-containing proteins.

Expectancy changes the self-monitoring of voice identity

Self‐voice attribution can become difficult when voice characteristics are ambiguous, but functional magnetic resonance imaging (fMRI) investigations of such ambiguity are sparse. We utilized voice‐morphing (self‐other) to manipulate (un‐)certainty in self‐voice attribution in a button‐press paradigm. This allowed investigating how levels of self‐voice certainty alter brain activation in brain regions monitoring voice identity and unexpected changes in voice playback quality. FMRI results confirmed a self‐voice suppression effect in the right anterior superior temporal gyrus (aSTG) when self‐voice attribution was unambiguous. Although the right inferior frontal gyrus (IFG) was more active during a self‐generated compared to a passively heard voice, the putative role of this region in detecting unexpected self‐voice changes during the action was demonstrated only when hearing the voice of another speaker and not when attribution was uncertain. Further research on the link between right aSTG and IFG is required and may establish a threshold monitoring voice identity in action. The current results have implications for a better understanding of the altered experience of self‐voice feedback in auditory verbal hallucinations.

Bayesian Source Attribution of Salmonella Typhimurium Isolates From Human Patients and Farm Animals in England and Wales

The purpose of the study was to apply a Bayesian source attribution model to England and Wales based data on Salmonella Typhimurium (ST) and monophasic variants (MST), using different subtyping approaches based on sequence data. The data consisted of laboratory confirmed human cases and mainly livestock samples collected from surveillance or monitoring schemes. Three different subtyping methods were used, 7-loci Multi-Locus Sequence Typing (MLST), Core-genome MLST, and Single Nucleotide Polymorphism distance, with the impact of varying the genetic distance over which isolates would be grouped together being varied for the latter two approaches. A Bayesian frequency matching method, known as the modified Hald method, was applied to the data from each of the subtyping approaches. Pigs were found to be the main contributor to human infection for ST/MST, with approximately 60% of human cases attributed to them, followed by other mammals (mostly horses) and cattle. It was found that the use of different clustering methods based on sequence data had minimal impact on the estimates of source attribution. However, there was an impact of genetic distance over which isolates were grouped: grouping isolates which were relatively closely related increased uncertainty but tended to have a better model fit.

Genome-Wide Identification of Host-Segregating Single-Nucleotide Polymorphisms for Source Attribution of Clinical Campylobacter coli Isolates

Campylobacter is among the most common causes of gastroenteritis worldwide. Campylobacter jejuni and Campylobacter coli are the most common species causing human disease. DNA sequence-based methods for strain characterization have focused largely on C. jejuni, responsible for 80 to 90% of infections, meaning that C. coli epidemiology has lagged behind. Here, we have analyzed the genome of 450 C. coli isolates to determine genetic markers that can discriminate isolates sampled from 3 major reservoir hosts (chickens, cattle, and pigs). These markers then were applied to identify the source of infection of 147 C. coli strains from French clinical cases. Using STRUCTURE software, 259 potential host-segregating markers were revealed by probabilistic characterization of single-nucleotide polymorphism (SNP) frequency variation in strain collections from three different hosts. These SNPs were found in 41 genes or intergenic regions, mostly coding for proteins involved in motility and membrane functions. Source attribution of clinical isolates based on the differential presence of these markers confirmed chickens as the most common source of C. coli infection in France.IMPORTANCE Genome-wide and source attribution studies based on Campylobacter species have shown their importance for the understanding of foodborne infections. Although the use of multilocus sequence typing based on 7 genes from C. jejuni is a powerful method to structure populations, when applied to C. coli, results have not clearly demonstrated its robustness. Therefore, we aim to provide more accurate data based on the identification of single-nucleotide polymorphisms. Results from this study reveal an important number of host-segregating SNPs, found in proteins involved in motility, membrane functions, or DNA repair systems. These findings offer new, interesting opportunities for further study of C. coli adaptation to its environment. Additionally, the results demonstrate that poultry is potentially the main reservoir of C. coli in France.

Application of Whole-Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium

Prevention of the emergence and spread of foodborne diseases is an important prerequisite for the improvement of public health. Source attribution models link sporadic human cases of a specific illness to food sources and animal reservoirs. With the next generation sequencing technology, it is possible to develop novel source attribution models. We investigated the potential of machine learning to predict the animal reservoir from which a bacterial strain isolated from a human salmonellosis case originated based on whole-genome sequencing. Machine learning methods recognize patterns in large and complex data sets and use this knowledge to build models. The model learns patterns associated with genetic variations in bacteria isolated from the different animal reservoirs. We selected different machine learning algorithms to predict sources of human salmonellosis cases and trained the model with Danish Salmonella Typhimurium isolates sampled from broilers (n = 34), cattle (n = 2), ducks (n = 11), layers (n = 4), and pigs (n = 159). Using cgMLST as input features, the model yielded an average accuracy of 0.783 (95% CI: 0.77-0.80) in the source prediction for the random forest and 0.933 (95% CI: 0.92-0.94) for the logit boost algorithm. Logit boost algorithm was most accurate (valid accuracy: 92%, CI: 0.8706-0.9579) and predicted the origin of 81% of the domestic sporadic human salmonellosis cases. The most important source was Danish produced pigs (53%) followed by imported pigs (16%), imported broilers (6%), imported ducks (2%), Danish produced layers (2%), Danish produced cattle and imported cattle (<1%) while 18% was not predicted. Machine learning has potential for improving source attribution modeling based on sequence data. Results of such models can inform risk managers to identify and prioritize food safety interventions.

Comparing serotyping with whole-genome sequencing for subtyping of non-typhoidal Salmonella enterica: a large-scale analysis of 37 serotypes with a public health impact in the USA

Serotyping has traditionally been used for subtyping of non-typhoidal Salmonella (NTS) isolates. However, its discriminatory power is limited, which impairs its use for epidemiological investigations of source attribution. Whole-genome sequencing (WGS) analysis allows more accurate subtyping of strains. However, because of the relative newness and cost of routine WGS, large-scale studies involving NTS WGS are still rare. We aimed to revisit the big picture of subtyping NTS with a public health impact by using traditional serotyping (i.e. reaction between antisera and surface antigens) and comparing the results with those obtained using WGS. For this purpose, we analysed 18 282 sequences of isolates belonging to 37 serotypes with a public health impact that were recovered in the USA between 2006 and 2017 from multiple sources, and were available at the National Center for Biotechnology Information (NCBI). Phylogenetic trees were reconstructed for each serotype using the core genome for the identification of genetic subpopulations. We demonstrated that WGS-based subtyping allows better identification of sources potentially linked with human infection and emerging subpopulations, along with providing information on the risk of dissemination of plasmids and acquired antimicrobial resistance genes (AARGs). In addition, by reconstructing a phylogenetic tree with representative isolates from all serotypes (n=370), we demonstrated genetic variability within and between serotypes, which formed monophyletic, polyphyletic and paraphyletic clades. Moreover, we found (in the entire data set) an increased detection rate for AARGs linked to key antimicrobials (such as quinolones and extended-spectrum cephalosporins) over time. The outputs of this large-scale analysis reveal new insights into the genetic diversity within and between serotypes; the polyphyly and paraphyly of certain serotypes may suggest that the subtyping of NTS to serotypes may not be sufficient. Moreover, the results and the methods presented here, leading to differentiation between genetic subpopulations based on their potential risk to public health, as well as narrowing down the possible sources of these infections, may be used as a baseline for subtyping of future NTS infections and help efforts to mitigate and prevent infections in the USA and globally.

Source Tracking Based on Core Genome SNV and CRISPR Typing of Salmonella enterica Serovar Heidelberg Isolates Involved in Foodborne Outbreaks in Québec, 2012

Whole-genome sequencing (WGS) is the method of choice for bacterial subtyping and it is rapidly replacing the more traditional methods such as pulsed-field gel electrophoresis (PFGE). Here we used the high-resolution core genome single nucleotide variant (cgSNV) typing method to characterize clinical and food from Salmonella enterica serovar Heidelberg isolates in the context of source attribution. Additionally, clustered regularly interspaced short palindromic repeats (CRISPR) analysis was included to further support this method. Our results revealed that cgSNV was highly discriminatory and separated the outbreak isolates into distinct clusters (0-4 SNVs). CRISPR analysis was also able to distinguish outbreak strains from epidemiologically unrelated isolates. Specifically, our data clearly demonstrated the strength of these two methods to determine the probable source(s) of a 2012 epidemiologically characterized outbreak of S. Heidelberg. Using molecular cut-off of 0-10 SNVs, the cgSNV analysis of 246 clinical and food isolates of S. Heidelberg collected in Québec, in the same year of the outbreak event, revealed that retail and abattoir chicken isolates likely represent an important source of human infection to S. Heidelberg. Interestingly, the isolates genetically related by cgSNV also harbored the same CRISPR as outbreak isolates and clusters. This indicates that CRISPR profiles can be useful as a complementary approach to determine source attribution in foodborne outbreaks. Use of the genomic analysis also allowed to identify a large number of cases that were missed by PFGE, indicating that most outbreaks are probably underestimated. Although epidemiological information must still support WGS-based results, cgSNV method is a highly discriminatory method for the resolution of outbreak events and the attribution of these events to their respective sources. CRISPR typing can serve as a complimentary tool to this analysis during source tracking.

Core Genome Multilocus Sequence Typing for Food Animal Source Attribution of Human Campylobacter jejuni Infections

Campylobacter jejuni is a major foodborne pathogen and common cause of bacterial enteritis worldwide. A total of 622 C. jejuni isolates recovered from food animals and retail meats in the United States through the National Antimicrobial Resistance Monitoring System between 2013 and 2017 were sequenced using an Illumina MiSeq. Sequences were combined with WGS data of 222 human isolates downloaded from NCBI and analyzed by core genome multilocus sequence typing (cgMLST) and traditional MLST. cgMLST allelic difference (AD) thresholds of 0, 5, 10, 25, 50, 100 and 200 identified 828, 734, 652, 543, 422, 298 and 197 cgMLST types among the 844 isolates, respectively, and traditional MLST identified 174 ST. The cgMLST scheme allowing an AD of 200 (cgMLST₂₀₀) revealed strong correlation with MLST. cgMLST₂₀₀ showed 40.5% retail chicken isolates, 56.5% swine, 77.4% dairy cattle and 78.9% beef cattle isolates shared cgMLST sequence type with human isolates. All ST-8 had the same cgMLST₂₀₀ type (cgMLST₂₀₀-12) and 74.3% of ST-8 and 75% cgMLST₂₀₀-12 were confirmed as sheep abortion virulence clones by PorA analysis. Twenty-nine acquired resistance genes, including 21 alleles of bla_OXA, tetO, aph(3′)-IIIa, ant(6)-Ia, aadE, aad9, aph(2′)-Ig, aph(2′)-Ih, sat4 plus mutations in gyrA, 23SrRNA and L22 were identified. Resistance genotypes were strongly linked with cgMLST₂₀₀ type for certain groups including 12/12 cgMLST₂₀₀-510 with the A103V substitution in L22 and 10/11 cgMLST₂₀₀-608 with the T86I GyrA substitution associated with macrolide and quinolone resistance, respectively. In summary, the cgMLST₂₀₀ threshold scheme combined with resistance genotype information could provide an excellent subtyping scheme for source attribution of human C. jejuni infections.

AB_SA: Accessory genes-Based Source Attribution - tracing the source of Salmonella enterica Typhimurium environmental strains

The partitioning of pathogenic strains isolated in environmental or human cases to their sources is challenging. The pathogens usually colonize multiple animal hosts, including livestock, which contaminate the food-production chain and the environment (e.g. soil and water), posing an additional public-health burden and major challenges in the identification of the source. Genomic data opens up new opportunities for the development of statistical models aiming to indicate the likely source of pathogen contamination. Here, we propose a computationally fast and efficient multinomial logistic regression source-attribution classifier to predict the animal source of bacterial isolates based on 'source-enriched' loci extracted from the accessory-genome profiles of a pangenomic dataset. Depending on the accuracy of the model's self-attribution step, the modeller selects the number of candidate accessory genes that best fit the model for calculating the likelihood of (source) category membership. The Accessory genes-Based Source Attribution (AB_SA) method was applied to a dataset of strains of Salmonella enterica Typhimurium and its monophasic variant (S. enterica 1,4,[5],12:i:-). The model was trained on 69 strains with known animal-source categories (i.e. poultry, ruminant and pig). The AB_SA method helped to identify 8 genes as predictors among the 2802 accessory genes. The self-attribution accuracy was 80 %. The AB_SA model was then able to classify 25 of the 29 S. enterica Typhimurium and S. enterica 1,4,[5],12:i:- isolates collected from the environment (considered to be of unknown source) into a specific category (i.e. animal source), with more than 85 % of probability. The AB_SA method herein described provides a user-friendly and valuable tool for performing source-attribution studies in only a few steps. AB_SA is written in R and freely available at https://github.com/lguillier/AB_SA.

Network Approach to Source Attribution of Salmonella enterica Serovar Typhimurium and Its Monophasic Variant

Salmonella enterica subspecies enterica serovar Typhimurium and its monophasic variant are among the most common Salmonella serovars associated with human salmonellosis each year. Related infections are often due to consumption of contaminated meat of pig, cattle, and poultry origin. In order to evaluate novel microbial subtyping methods for source attribution, an approach based on weighted networks was applied on 141 human and 210 food and animal isolates of pigs, broilers, layers, ducks, and cattle collected in Denmark from 2013 to 2014. A whole-genome SNP calling was performed along with cgMLST and wgMLST. Based on these genomic input data, pairwise distance matrices were built and used as input for construction of a weighted network where nodes represent genomes and links to distances. Analyzing food and animal Typhimurium genomes, the coherence of source clustering ranged from 89 to 90% for animal source, from 84 to 85% for country, and from 63 to 65% for year of isolation and was equal to 82% for serotype, suggesting animal source as the first driver of clustering formation. Adding human isolate genomes to the network, a percentage between 93.6 and 97.2% clustered with the existing component and only a percentage between 2.8 and 6.4% appeared as not attributed to any animal sources. The majority of human genomes were attributed to pigs with probabilities ranging from 83.9 to 84.5%, followed by broilers, ducks, cattle, and layers in descending order. In conclusion, a weighted network approach based on pairwise SNPs, cgMLST, and wgMLST matrices showed promising results for source attribution studies.

Composite Machine Learning Algorithm for Material Sourcing

This study developed a composite machine learning algorithm for attribution of materials of forensic interest (like ammonium nitrate) to original sources. k-nearest neighbor and random forest models were used for source elimination and classification, respectively, in a two-step, composite algorithm based on particle color, size/shape, and trace element concentration features. Novel approaches for simulation to supplement within-source reference features based on empirically measured multi-lot analyses, an improved hold-one-lot-out method for cross-validation, an assessment of the likelihood of the presence of a reference sample, fusion of the source probabilities from the respective classification models, and the calculation of metrics for assessing ensemble sourcing performance are described. Excellent sourcing predictions were obtained; the sourcing algorithm identified the correct source as the top choice 89% of the time, and the correct source was identified to be an average of 2.7 times more likely than the most likely incorrect source.

Global and regional source attribution of Shiga toxin-producing Escherichia coli infections using analysis of outbreak surveillance data

Shiga toxin-producing Escherichia coli (STEC) infections pose a substantial health and economic burden worldwide. To target interventions to prevent foodborne infections, it is important to determine the types of foods leading to illness. Our objective was to determine the food sources of STEC globally and for the six World Health Organization regions. We used data from STEC outbreaks that have occurred globally to estimate source attribution fractions. We categorised foods according to their ingredients and applied a probabilistic model that used information on implicated foods for source attribution. Data were received from 27 countries covering the period between 1998 and 2017 and three regions: the Americas (AMR), Europe (EUR) and Western-Pacific (WPR). Results showed that the top foods varied across regions. The most important sources in AMR were beef (40%; 95% Uncertainty Interval 39-41%) and produce (35%; 95% UI 34-36%). In EUR, the ranking was similar though with less marked differences between sources (beef 31%; 95% UI 28-34% and produce 30%; 95% UI 27-33%). In contrast, the most common source of STEC in WPR was produce (43%; 95% UI 36-46%), followed by dairy (27%; 95% UI 27-27%). Possible explanations for regional variability include differences in food consumption and preparation, frequency of STEC contamination, the potential of regionally predominant STEC strains to cause severe illness and differences in outbreak investigation and reporting. Despite data gaps, these results provide important information to inform the development of strategies for lowering the global burden of STEC infections.

Source Attribution of Salmonella in Macadamia Nuts to Animal and Environmental Reservoirs in Queensland, Australia

Salmonella enterica is a common contaminant of macadamia nut kernels in the subtropical state of Queensland (QLD), Australia. We hypothesized that nonhuman sources in the plantation environment contaminate macadamia nuts. We applied a modified Hald source attribution model to attribute Salmonella serovars and phage types detected on macadamia nuts from 1998 to 2017 to specific animal and environmental sources. Potential sources were represented by Salmonella types isolated from avian, companion animal, biosolids-soil-compost, equine, porcine, poultry, reptile, ruminant, and wildlife samples by the QLD Health reference laboratory. Two attribution models were applied: model 1 merged data across 1998-2017, whereas model 2 pooled data into 5-year time intervals. Model 1 attributed 47% (credible interval, CrI: 33.6-60.8) of all Salmonella detections on macadamia nuts to biosolids-soil-compost. Wildlife and companion animals were found to be the second and third most important contamination sources, respectively. Results from model 2 showed that the importance of the different sources varied between the different time periods; for example, Salmonella contamination from biosolids-soil-compost varied from 4.4% (CrI: 0.2-11.7) in 1998-2002 to 19.3% (CrI: 4.6-39.4) in 2003-2007, and the proportion attributed to poultry varied from 4.8% (CrI: 1-11) in 2008-2012 to 24% (CrI: 11.3-40.7) in 2013-2017. Findings suggest that macadamia nuts were contaminated by direct transmission from animals with access to the plantations (e.g., wildlife and companion animals) or from indirect transmission from animal reservoirs through biosolids-soil-compost. The findings from this study can be used to guide environmental and wildlife sampling and analysis to further investigate routes of Salmonella contamination of macadamia nuts and propose control options to reduce potential risk of human salmonellosis.

Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms

This Opinion considers the application of whole genome sequencing (WGS) and metagenomics for outbreak investigation, source attribution and risk assessment of food‐borne pathogens. WGS offers the highest level of bacterial strain discrimination for food‐borne outbreak investigation and source‐attribution as well as potential for more precise hazard identification, thereby facilitating more targeted risk assessment and risk management. WGS improves linking of sporadic cases associated with different food products and geographical regions to a point source outbreak and can facilitate epidemiological investigations, allowing also the use of previously sequenced genomes. Source attribution may be favoured by improved identification of transmission pathways, through the integration of spatial‐temporal factors and the detection of multidirectional transmission and pathogen–host interactions. Metagenomics has potential, especially in relation to the detection and characterisation of non‐culturable, difficult‐to‐culture or slow‐growing microorganisms, for tracking of hazard‐related genetic determinants and the dynamic evaluation of the composition and functionality of complex microbial communities. A SWOT analysis is provided on the use of WGS and metagenomics for Salmonella and Shigatoxin‐producing Escherichia coli (STEC) serotyping and the identification of antimicrobial resistance determinants in bacteria. Close agreement between phenotypic and WGS‐based genotyping data has been observed. WGS provides additional information on the nature and localisation of antimicrobial resistance determinants and on their dissemination potential by horizontal gene transfer, as well as on genes relating to virulence and biological fitness. Interoperable data will play a major role in the future use of WGS and metagenomic data. Capacity building based on harmonised, quality controlled operational systems within European laboratories and worldwide is essential for the investigation of cross‐border outbreaks and for the development of international standardised risk assessments of food‐borne microorganisms.

Critical Orientation in the Jungle of Currently Available Methods and Types of Data for Source Attribution of Foodborne Diseases

With increased interest in source attribution of foodborne pathogens, there is a need to sort and assess the applicability of currently available methods. Herewith we reviewed the most frequently applied methods for source attribution of foodborne diseases, discussing their main strengths and weaknesses to be considered when choosing the most appropriate methods based on the type, quality, and quantity of data available, the research questions to be addressed, and the (epidemiological and microbiological) characteristics of the pathogens in question. A variety of source attribution approaches have been applied in recent years. These methods can be defined as top–down, bottom–up, or combined. Top–down approaches assign the human cases back to their sources of infection based on epidemiological (e.g., outbreak data analysis, case-control/cohort studies, etc.), microbiological (i.e., microbial subtyping), or combined (e.g., the so-called ‘source-assigned case-control study’ design) methods. Methods based on microbial subtyping are further differentiable according to the modeling framework adopted as frequency-matching (e.g., the Dutch and Danish models) or population genetics (e.g., Asymmetric Island Models and STRUCTURE) models, relying on the modeling of either phenotyping or genotyping data of pathogen strains from human cases and putative sources. Conversely, bottom–up approaches like comparative exposure assessment start from the level of contamination (prevalence and concentration) of a given pathogen in each source, and then go upwards in the transmission chain incorporating factors related to human exposure to these sources and dose-response relationships. Other approaches are intervention studies, including ‘natural experiments,’ and expert elicitations. A number of methodological challenges concerning all these approaches are discussed. In absence of an universally agreed upon ‘gold’ standard, i.e., a single method that satisfies all situations and needs for all pathogens, combining different approaches or applying them in a comparative fashion seems to be a promising way forward.

A systematic review of source attribution of human campylobacteriosis using multilocus sequence typing

BackgroundCampylobacter is a leading global cause of bacterial gastroenteritis, motivating research to identify sources of human infection. Population genetic studies have been increasingly applied to this end, mainly using multilocus sequence typing (MLST) data.ObjectivesThis review aimed to summarise approaches and findings of these studies and identify best practice lessons for this form of genomic epidemiology.MethodsWe systematically reviewed publications using MLST data to attribute human disease isolates to source. Publications were from January 2001, when this type of approach began. Searched databases included Scopus, Web of Science and PubMed. Information on samples and isolate datasets used, as well as MLST schemes and attribution algorithms employed, was obtained. Main findings were extracted, as well as any results' validation with subsequent correction for identified biases. Meta-analysis is not reported given high levels of heterogeneity.ResultsOf 2,109 studies retrieved worldwide, 25 were included, and poultry, specifically chickens, were identified as principal source of human infection. Ruminants (cattle or sheep) were consistently implicated in a substantial proportion of cases. Data sampling and analytical approaches varied, with five different attribution algorithms used. Validation such as self-attribution of isolates from known sources was reported in five publications. No publication reported adjustment for biases identified by validation.ConclusionsCommon gaps in validation and adjustment highlight opportunities to generate improved estimates in future genomic attribution studies. The consistency of chicken as the main source of human infection, across high income countries, and despite methodological variations, highlights the public health importance of this source.

Development of food safety risk assessment tools based on molecular typing and WGS of Campylobacter jejuni genome

The ‘learning‐by‐doing’ EU‐FORA fellowship programme in the development of risk assessment tools based on molecular typing and WGS of Campylobacter jejuni genome was structured into two main activities: the primary one focused on training on risk assessment methodology and the secondary one in starting and enhancing the cooperation between the hosting and home organisations, or other joint activities. The primary activities had three subsequent work packages (WPs): WP1 data organisation, WP2 cluster and association analyses, and WP3 development of risk assessment models. The secondary activities have branched into one workshop and the initiation of a cooperation programme between the hosting and home organisations. In the last quarter, the fellow had contributed to the characterisation of some pathogens in possible response to a changing climate, part of the CLEFSA project. The fellow attended various forms of training: online and on‐site courses, and also participated at several conferences and meetings for improving his knowledge and skills, contributing to performing the Campylobacter risk assessment and source attribution.

A Modular Bayesian Salmonella Source Attribution Model for Sparse Data

Several statistical models for salmonella source attribution have been presented in the literature. However, these models have often been found to be sensitive to the model parameterization, as well as the specifics of the data set used. The Bayesian salmonella source attribution model presented here was developed to be generally applicable with small and sparse annual data sets obtained over several years. The full Bayesian model was modularized into three parts (an exposure model, a subtype distribution model, and an epidemiological model) in order to separately estimate unknown parameters in each module. The proposed model takes advantage of the consumption and overall salmonella prevalence of the studied sources, as well as bacteria typing results from adjacent years. The latter were used for a smoothed estimation of the annual relative proportions of different salmonella subtypes in each of the sources. The source-specific effects and the salmonella subtype-specific effects were included in the epidemiological model to describe the differences between sources and between subtypes in their ability to infect humans. The estimation of these parameters was based on data from multiple years. Finally, the model combines the total evidence from different modules to proportion human salmonellosis cases according to their sources. The model was applied to allocate reported human salmonellosis cases from the years 2008 to 2015 to eight food sources.

Zoonotic Source Attribution of Salmonella enterica Serotype Typhimurium Using Genomic Surveillance Data, United States

Increasingly, routine surveillance and monitoring of foodborne pathogens using whole-genome sequencing is creating opportunities to study foodborne illness epidemiology beyond routine outbreak investigations and case–control studies. Using a global phylogeny of Salmonella enterica serotype Typhimurium, we found that major livestock sources of the pathogen in the United States can be predicted through whole-genome sequencing data. Relatively steady rates of sequence divergence in livestock lineages enabled the inference of their recent origins. Elevated accumulation of lineage-specific pseudogenes after divergence from generalist populations and possible metabolic acclimation in a representative swine isolate indicates possible emergence of host adaptation. We developed and retrospectively applied a machine learning Random Forest classifier for genomic source prediction of Salmonella Typhimurium that correctly attributed 7 of 8 major zoonotic outbreaks in the United States during 1998–2013. We further identified 50 key genetic features that were sufficient for robust livestock source prediction.

Extending statistical models for source attribution of zoonotic diseases: a study of campylobacteriosis

Preventing and controlling zoonoses through the design and implementation of public health policies requires a thorough understanding of transmission pathways. Modelling jointly the epidemiological data and genetic information of microbial isolates derived from cases provides a methodology for tracing back the source of infection. In this paper, the attribution probability for human cases of campylobacteriosis for each source, conditional on the extent to which each case resides in a rural compared to urban environment, is estimated. A model that incorporates genetic data and evolutionary processes is applied alongside a newly developed genetic-free model. We show that inference from each model is comparable except for rare microbial genotypes. Further, the effect of 'rurality' may be modelled linearly on the logit scale, with increasing rurality leading to the increasing likelihood of ruminant-sourced campylobacteriosis.

Salmonella source attribution in a subtropical state of Australia: capturing environmental reservoirs of infection

Salmonellosis is a leading cause of hospitalisation due to gastroenteritis in Australia. A previous source attribution analysis for a temperate state in Australia attributed most infections to chicken meat or eggs. Queensland is in northern Australia and includes subtropical and tropical climate zones. We analysed Queensland notifications for salmonellosis and conducted source attribution to compare reservoir sources with those in southern Australia. In contrast to temperate Australia, most infections were due to non-Typhimurium serotypes, with particularly high incidence in children under 5 years and strong seasonality, peaking in summer. We attributed 65.3% (95% credible interval (CrI) 60.6-73.2) of cases to either chicken meat or eggs and 15.5% (95% CrI 7.0-19.5) to nuts. The subtypes with the strongest associations with nuts were Salmonella Aberdeen, S. Birkenhead, S. Hvittingfoss, S. Potsdam and S. Waycross. All five subtypes had high rates of illness in children under 5 years (ranging from 4/100 000 to 23/100 000), suggesting that nuts may be serving as a proxy for environmental transmission in the model. Australia's climatic range allows us to conduct source attribution in different climate zones with similar food consumption patterns. This attribution provides evidence for environment-mediated transmission of salmonellosis in sub-tropical regions.

Attribution of human infections with Shiga toxin-producing Escherichia coli (STEC) to livestock sources and identification of source-specific risk factors, The Netherlands (2010-2014)

Shiga toxin-producing Escherichia coli (STEC) is a zoonotic pathogen of public health concern whose sources and transmission routes are difficult to trace. Using a combined source attribution and case-control analysis, we determined the relative contributions of four putative livestock sources (cattle, small ruminants, pigs, poultry) to human STEC infections and their associated dietary, animal contact, temporal and socio-econo-demographic risk factors in the Netherlands in 2010/2011-2014. Dutch source data were supplemented with those from other European countries with similar STEC epidemiology. Human STEC infections were attributed to sources using both the modified Dutch model (mDM) and the modified Hald model (mHM) supplied with the same O-serotyping data. Cattle accounted for 48.6% (mDM) and 53.1% (mHM) of the 1,183 human cases attributed, followed by small ruminants (mDM: 23.5%; mHM: 25.4%), pigs (mDM: 12.5%; mHM: 5.7%) and poultry (mDM: 2.7%; mHM: 3.1%), whereas the sources of the remaining 12.8% of cases could not be attributed. Of the top five O-serotypes infecting humans, O157, O26, O91 and O103 were mainly attributed to cattle (61%-75%) and O146 to small ruminants (71%-77%). Significant risk factors for human STEC infection as a whole were the consumption of beef, raw/undercooked meat or cured meat/cold cuts. For cattle-attributed STEC infections, specific risk factors were consuming raw meat spreads and beef. Consuming raw/undercooked or minced meat were risk factors for STEC infections attributed to small ruminants. For STEC infections attributed to pigs, only consuming raw/undercooked meat was significant. Consuming minced meat, raw/undercooked meat or cured meat/cold cuts were associated with poultry-attributed STEC infections. Consuming raw vegetables was protective for all STEC infections. We concluded that domestic ruminants account for approximately three-quarters of reported human STEC infections, whereas pigs and poultry play a minor role and that risk factors for human STEC infection vary according to the attributed source.

Population structure and attribution of human clinical Campylobacter jejuni isolates from central Europe to livestock and environmental sources

Campylobacter jejuni is among the most prevalent causes of human bacterial gastroenteritis worldwide. Domesticated animals and, especially, chicken meat are considered to be the main sources of infections. However, the contribution of surface waters and wildlife in C. jejuni transmission to humans is not well understood. We have evaluated the source attribution potential of a six-gene multiplex PCR (mPCR) method coupled with STRUCTURE analysis on a set of 410 C. jejuni strains isolated from environment, livestock, food and humans in central Europe. Multiplex PCR fingerprints were analysed using Subclade prediction algorithm to classify them into six distinct mPCR clades. A subset of C. jejuni isolates (70%) was characterized by multilocus sequence typing (MLST) demonstrating 74% congruence between mPCR and MLST. The correspondence analysis of mPCR clades and sources of isolation indicated three distinct groups in the studied C. jejuni population-the first one associated with isolates from poultry, the second one with isolates from cattle, and the third one with isolates from the environment. The STRUCTURE analysis attributed 7.2% and 21.7% of human isolates to environmental sources based on MLST and mPCR fingerprints, respectively.

Local genes for local bacteria: Evidence of allopatry in the genomes of transatlantic Campylobacter populations

The genetic structure of bacterial populations can be related to geographical locations of isolation. In some species, there is a strong correlation between geographical distance and genetic distance, which can be caused by different evolutionary mechanisms. Patterns of ancient admixture in Helicobacter pylori can be reconstructed in concordance with past human migration, whereas in Mycobacterium tuberculosis it is the lack of recombination that causes allopatric clusters. In Campylobacter, analyses of genomic data and molecular typing have been successful in determining the reservoir host species, but not geographical origin. We investigated biogeographical variation in highly recombining genes to determine the extent of clustering between genomes from geographically distinct Campylobacter populations. Whole‐genome sequences from 294 Campylobacter isolates from North America and the UK were analysed. Isolates from within the same country shared more recently recombined DNA than isolates from different countries. Using 15 UK/American closely matched pairs of isolates that shared ancestors, we identify regions that have frequently and recently recombined to test their correlation with geographical origin. The seven genes that demonstrated the greatest clustering by geography were used in an attribution model to infer geographical origin which was tested using a further 383 UK clinical isolates to detect signatures of recent foreign travel. Patient records indicated that in 46 cases, travel abroad had occurred <2 weeks prior to sampling, and genomic analysis identified that 34 (74%) of these isolates were of a non‐UK origin. Identification of biogeographical markers in Campylobacter genomes will contribute to improved source attribution of clinical Campylobacter infection and inform intervention strategies to reduce campylobacteriosis.

Population Genomics of Legionella longbeachae and Hidden Complexities of Infection Source Attribution

Legionella longbeachae is the primary cause of legionellosis in Australasia and Southeast Asia and an emerging pathogen in Europe and the United States; however, our understanding of the population diversity of L. longbeachae from patient and environmental sources is limited. We analyzed the genomes of 64 L. longbeachae isolates, of which 29 were from a cluster of legionellosis cases linked to commercial growing media in Scotland in 2013 and 35 were non–outbreak-associated isolates from Scotland and other countries. We identified extensive genetic diversity across the L. longbeachae species, associated with intraspecies and interspecies gene flow, and a wide geographic distribution of closely related genotypes. Of note, we observed a highly diverse pool of L. longbeachae genotypes within compost samples that precluded the genetic establishment of an infection source. These data represent a view of the genomic diversity of L. longbeachae that will inform strategies for investigating future outbreaks.

Genome-Wide Identification of Host-Segregating Epidemiological Markers for Source Attribution in Campylobacter jejuni

Campylobacter is among the most common worldwide causes of bacterial gastroenteritis. This organism is part of the commensal microbiota of numerous host species, including livestock, and these animals constitute potential sources of human infection. Molecular typing approaches, especially multilocus sequence typing (MLST), have been used to attribute the source of human campylobacteriosis by quantifying the relative abundance of alleles at seven MLST loci among isolates from animal reservoirs and human infection, implicating chicken as a major infection source. The increasing availability of bacterial genomes provides data on allelic variation at loci across the genome, providing the potential to improve the discriminatory power of data for source attribution. Here we present a source attribution approach based on the identification of novel epidemiological markers among a reference pan-genome list of 1,810 genes identified by gene-by-gene comparison of 884 genomes of Campylobacter jejuni isolates from animal reservoirs, the environment, and clinical cases. Fifteen loci involved in metabolic activities, protein modification, signal transduction, and stress response or coding for hypothetical proteins were selected as host-segregating markers and used to attribute the source of 42 French and 281 United Kingdom clinical C. jejuni isolates. Consistent with previous studies of British campylobacteriosis, analyses performed using STRUCTURE software attributed 56.8% of British clinical cases to chicken, emphasizing the importance of this host reservoir as an infection source in the United Kingdom. However, among French clinical isolates, approximately equal proportions of isolates were attributed to chicken and ruminant reservoirs, suggesting possible differences in the relative importance of animal host reservoirs and indicating a benefit for further national-scale attribution modeling to account for differences in production, behavior, and food consumption.IMPORTANCE Accurately quantifying the relative contribution of different host reservoirs to human Campylobacter infection is an ongoing challenge. This study, based on the development of a novel source attribution approach, provides the first results of source attribution in Campylobacter jejuni in France. A systematic analysis using gene-by-gene comparison of 884 genomes of C. jejuni isolates, with a pan-genome list of genes, identified 15 novel epidemiological markers for source attribution. The different proportions of French and United Kingdom clinical isolates attributed to each host reservoir illustrate a potential role for local/national variations in C. jejuni transmission dynamics.

A Comparative Exposure Assessment of Campylobacter in Ontario, Canada

To inform source attribution efforts, a comparative exposure assessment was developed to estimate the relative exposure to Campylobacter, the leading bacterial gastrointestinal disease in Canada, for 13 different transmission routes within Ontario, Canada, during the summer. Exposure was quantified with stochastic models at the population level, which incorporated measures of frequency, quantity ingested, prevalence, and concentration, using data from FoodNet Canada surveillance, the peer-reviewed and gray literature, other Ontario data, and data that were specifically collected for this study. Models were run with @Risk software using Monte Carlo simulations. The mean number of cells of Campylobacter ingested per Ontarian per day during the summer, ranked from highest to lowest is as follows: household pets, chicken, living on a farm, raw milk, visiting a farm, recreational water, beef, drinking water, pork, vegetables, seafood, petting zoos, and fruits. The study results identify knowledge gaps for some transmission routes, and indicate that some transmission routes for Campylobacter are underestimated in the current literature, such as household pets and raw milk. Many data gaps were identified for future data collection consideration, especially for the concentration of Campylobacter in all transmission routes.

Inferring source attribution from a multiyear multisource data set of Salmonella in Minnesota

Salmonella enterica is a global health concern because of its widespread association with foodborne illness. Bayesian models have been developed to attribute the burden of human salmonellosis to specific sources with the ultimate objective of prioritizing intervention strategies. Important considerations of source attribution models include the evaluation of the quality of input data, assessment of whether attribution results logically reflect the data trends and identification of patterns within the data that might explain the detailed contribution of different sources to the disease burden. Here, more than 12,000 non-typhoidal Salmonella isolates from human, bovine, porcine, chicken and turkey sources that originated in Minnesota were analysed. A modified Bayesian source attribution model (available in a dedicated R package), accounting for non-sampled sources of infection, attributed 4,672 human cases to sources assessed here. Most (60%) cases were attributed to chicken, although there was a spike in cases attributed to a non-sampled source in the second half of the study period. Molecular epidemiological analysis methods were used to supplement risk modelling, and a visual attribution application was developed to facilitate data exploration and comprehension of the large multiyear data set assessed here. A large amount of within-source diversity and low similarity between sources was observed, and visual exploration of data provided clues into variations driving the attribution modelling results. Results from this pillared approach provided first attribution estimates for Salmonella in Minnesota and offer an understanding of current data gaps as well as key pathogen population features, such as serotype frequency, similarity and diversity across the sources. Results here will be used to inform policy and management strategies ultimately intended to prevent and control Salmonella infection in the state.