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bioRxiv2026

Lineage-specific tprK diversification and Treponema pallidum transmission dynamics in Buenos Aires, Argentina

BackgroundSyphilis rates are rising globally, with increases in congenital syphilis in South America particularly concerning. The characterization of contemporary South American Treponema pallidum (Tp) strains is crucial to syphilis vaccine development, yet few genomic epidemiology studies have focused on this region. Here, we performed whole genome sequencing (WGS) of Tp from Buenos Aires, Argentina, as well as deep sequencing of the hypervariable tprK locus, which is critical to Tp immune evasion. MethodsPeople with primary, secondary, or congenital syphilis were enrolled at two clinics in Buenos Aires between October 2018 and January 2023, including individuals associated with intra-household transmission. Hybrid capture WGS was performed and a core genome phylogeny generated. K-mer-based methods using full-length tprK PacBio long reads were used to uncover differences in diversity and detect Tp transmission. FindingsTp genomes were recovered from 70 individuals in Buenos Aires and primarily belonged to globally dominant SS14 sublineage-1 and Nichols sublineage-8, as did Tp from Brazil (n=8). Peruvian samples (n=3) all belonged to sublineage-1. Two individuals from Argentina had co-infections with Nichols- and SS14-lineage strains. Macrolide resistance via A2058G occurred in 27/70 (38.6%) samples. Across 56 samples, tprK allelic diversity was significantly increased in secondary syphilis, oral lesions, and SS14-lineage strains compared to primary syphilis, anogenital lesions, and Nichols-lineage strains, respectively. Increased diversity in SS14-lineage strains is driven by an enhanced repertoire of V7-specific donor sequences. tprK sequences from intra-household transmissions were more similar than unrelated samples with identical core genomes. InterpretationTp circulating in South America is closely related to dominant global sublineages. Increased tprK diversity in the SS14 lineage may influence Tps ability to escape host immunity. tprK profiling is a promising tool to elucidate syphilis transmission networks. This study underscores the utility of genomics to yield insights into Tp pathogenesis. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAlthough whole genome sequencing (WGS) and genomic epidemiology have contributed to an understanding of the global diversity of Treponema pallidum (Tp), very few strains from South America have been sequenced to date. On January 6th, 2026, we performed a PubMed search including terms "syphilis genomic epidemiology", "South America", "Treponema pallidum", and "Argentina". We excluded studies of ancient Tp samples found in South American archeological sites. A single Tp sample from Argentina was originally sequenced in 2016 and included in subsequent analyses of global diversity. Nine Peruvian samples were previously sequenced in a study of global diversity on six continents. Thirty-three samples from Cali, Colombia were predominantly SS14-lineage and genotypic macrolide resistance was found in half of strains. Additionally, studies of Tp in Buenos Aires using a multi-locus sequence typing approach have shown circulation of strains belonging to both Nichols and SS14 lineages, with an increasing rate of macrolide resistance over time. Five previous studies have examined tprK in clinical specimens and consistently shown increased tprK diversity in specimens associated with secondary syphilis compared to primary syphilis lesions. One study has shown that loss of tprK donor cassettes is associated with reduced tprK diversity, consistent with results from in vitro experiments. A single study has shown that tprK sequence content is more similar in samples with a suspected epidemiologic link, though no prior studies have looked at tprK diversity in the context of known syphilis transmission events. Added value of this studyThis study is the first to use WGS to comprehensively examine Tp transmission in a large South American city, yielding 96 samples from 70 individuals. We also add the first contemporary Tp genomes from Brazil. In contrast to findings from England, Australia, and other high-income countries, no Tp sublineages are associated with demographic groups or sexual networks in Buenos Aires. Two of seventy patients were co-infected with both Nichols- and SS14-lineage strains, showing the previously unappreciated frequency of conditions that permit inter-strain recombination-driven diversification of Tp. This study also reveals novel aspects of Tp pathogenesis, including lineage-specific differences in tprK diversity. We also develop methods for the analysis of tprK relatedness between samples and demonstrate that tprK sequences are more similar in samples from individuals within intra-household syphilis transmission chains compared to those from epidemiologically unrelated individuals. Implications of all the available evidenceTp strains circulating in Buenos Aires are genetically similar to those circulating worldwide and in Brazil and Peru but are noteworthy for the low (but rising) rate of macrolide resistance. Lineage-specific patterns of tprK antigenic variation could result in differences between Nichols- and SS14-lineage strains interactions with the host immune system. Finally, we show that tprK profiling holds promise to identify samples from within a syphilis transmission chain and could play an important role in public health.

microbiology↗
bioRxiv2017

Estimation of the dispersal distances of an aphid-borne virus in a patchy landscape

Characterising the spatio-temporal dynamics of pathogens in natura is key to ensuring their efficient prevention and control. However, it is notoriously difficult to estimate dispersal parameters at scales that are relevant to real epidemics. Epidemiological surveys can provide informative data, but parameter estimation can be hampered when the timing of the epidemiological events is uncertain, and in the presence of interactions between disease spread, surveillance, and control. Further complications arise from imperfect detection of disease, and from the computationally intractable number of data on individual hosts arising from landscape-level surveys. Here, we present a Bayesian framework that overcomes these barriers by integrating over associated uncertainties in a model explicitly combining the processes of disease dispersal, surveillance and control. Using a novel computationally efficient approach to account for patch geometry, we demonstrate that disease dispersal distances can be estimated accurately in a fragmented landscape when disease control is ongoing. Applying this model to data for an aphid-borne virus (Plum pox virus) surveyed for 15 years over 600 orchards, we obtain the first estimate of the distribution of the flight distances of infectious aphids at the landscape scale. Most infectious aphids leaving a tree land beyond the bounds of a 1-ha orchard (50% of flights terminate within about 90 m). Moreover, long-distance flights are not rare (10% of flights exceed 1 km). By their impact on our quantitative understanding of winged aphids dispersal, these results can inform the design of management strategies for plant viruses, which are mainly aphid-borne.\n\nAuthor SummaryIn spatial epidemiology, dispersal kernels quantify how the probability of pathogen dissemination varies with distance. Spatial models of pathogen spread are sensitive to kernel parameters; yet these parameters have rarely been estimated using field data gathered at relevant scales. Robust estimation is rendered difficult by practical constraints limiting the number of surveyed individuals, and uncertainties concerning their disease status. Here, we present a framework that overcomes these barriers and permits inference for a between-patch transmission model. Extensive simulations show that dispersal kernels can be estimated from epidemiological surveillance data. When applied to such data collected from more than 600 orchards during 15 years of a plant virus epidemic our approach enables the estimation of the dispersal kernel of infectious winged aphids. This kernel is long-tailed, as 50% of the infectious aphids leaving a tree terminate their infectious flight within 90 m and 10% beyond 1 km. This first estimate of flight distances at the landscape scale for aphids-a group of vectors transmitting numerous viruses-is crucial for the science-based design of control strategies targeting plant virus epidemics.

epidemiology↗
bioRxiv2017

A data-driven model for the assessment of age-dependent patterns of Tuberculosis burden and impact evaluation of novel vaccines.

In the case of tuberculosis (TB), the capabilities of epidemic models to produce quantitatively robust forecasts are limited by multiple hindrances. Among these, understanding the complex relationship between disease epidemiology and populations' age structure has been highlighted as one of the most relevant. TB dynamics depends on age in multiple ways, some of which are traditionally simplified in the literature. That is the case of the heterogeneities in contact intensity among different age-strata that are common to all air-borne diseases, but still typically neglected in the TB case. Furthermore, whilst demographic structures of many countries are rapidly aging, demographic dynamics is pervasively ignored when modeling TB spreading. In this work, we present a TB transmission model that incorporates country-specific demographic prospects and empirical contact data around a data-driven description of TB dynamics. Using our model, we find that the inclusion of demographic dynamics is followed by an increase in the burden levels prospected for the next decades in the areas of the world that are most hit by the disease nowadays. Similarly, we show that considering realistic patterns of contacts among individuals in different age-strata reshapes the transmission patterns reproduced by the models, a result with potential implications for the design of age-focused epidemiological interventions.\n\nSignificance StatementEven though tuberculosis (TB) is acknowledged as a strongly age-dependent disease, it remains unclear how TB epidemics would react, in the following decades, to the generalized aging that human populations are experiencing worldwide. This situation is partly caused by the limitations of current transmission models at describing the relationship between demography and TB transmission. Here, we present a data-driven epidemiological model that, unlike previous approaches, explicitly contemplates relevant aspects of the coupling between agestructure and TB dynamics, such as demographic evolution and contact heterogeneities. Using our model, we identify substantial biases in epidemiological forecasts rooted in an inadequate description of these aspects, both at the level of aggregated incidence and mortality rates and their distribution across age-strata.

epidemiology↗
bioRxiv2019

Upscaling surveillance of tick-borne pathogens in the French Caribbean islands

Despite the high burden of vector-borne disease in (sub)-tropical areas, few information are available regarding the diversity of tick and tick-borne pathogens circulating in the Caribbean. Management and control of vector-borne disease require actual epidemiological data to better assess and anticipate the risk of (re)-emergence of tick-borne diseases in the region. To simplify and reduce the costs of such large-scale surveys, we implemented a high-throughput microfluidic real-time PCR system suitable for the screening of the main bacterial and parasitic genera involved in tick-borne disease and potentially circulating in the area. We used the new screening tool to perform an exploratory epidemiological study on 132 specimens of Amblyomma variegatum and 446 of Rhipicephalus microplus collected in Guadeloupe and Martinique. Not only the system was able to detect the main pathogens of the area- Ehrlichia ruminantium, Rickettsia africae, Anaplasma marginale, Babesia bigemina, Babesia bovis- but the system also provided evidence of unsuspected microorganisms in Caribbean ticks, belonging to the Anaplasma, Ehrlichia, Borrelia and Leishmania genera. Our study demonstrated how high-throughput microfluidic real-time PCR technology can assist large-scale epidemiological studies, providing a rapid overview of tick-borne pathogen and microorganism diversity, and opening up new research perspectives for the epidemiology of tick-borne pathogens.

epidemiology↗
bioRxiv2016

Polygenic analysis of schizophrenia and 19 immune diseases reveals modest pleiotropy

Epidemiological studies indicate that many immune diseases occur at different rates among people with schizophrenia compared to the general population. Here, we evaluated whether this phenotypic correlation between immune diseases and schizophrenia might be explained by shared genetic risk factors (genetic correlation). We used data from a large genome-wide association study (GWAS) of schizophrenia (N=35,476 cases and 46,839 controls) to compare the genetic architecture of schizophrenia to 19 immune diseases. First, we evaluated the association with schizophrenia of 581 variants previously reported to be associated with immune diseases at genome-wide significance. We identified three variants with pleiotropic effects, located in regions associated with both schizophrenia and immune disease. Our analyses provided the strongest evidence of pleiotropy at rs1734907 ([~]85kb upstream of EPHB4), a variant which was associated with increased risk of both Crohns disease (OR = 1.16, P = 1.67x10-13) and schizophrenia (OR = 1.07, P = 7.55x10-6). Next, we investigated genome-wide sharing of common variants between schizophrenia and immune diseases using polygenic risk scores (PRS) and cross-trait LD Score regression (LDSC). PRS revealed significant genetic overlap with schizophrenia for narcolepsy (p=4.1x10-4), primary biliary cirrhosis (p=1.4x10-8), psoriasis (p=3.6x10-5), systemic lupus erythematosus (p=2.2x10-8), and ulcerative colitis (p=4.3x10-4). Genetic correlations between these immune diseases and schizophrenia, estimated using LDSC, ranged from 0.10 to 0.18 and were consistent with the expected phenotypic correlation based on epidemiological data. We also observed suggestive evidence of sex-dependent genetic correlation between schizophrenia and multiple sclerosis (interaction p=0.02), with genetic risk scores for multiple sclerosis associated with greater risk of schizophrenia among males but not females. Our findings suggest that shared genetic risk factors contribute to the epidemiological co-occurrence of schizophrenia and certain immune diseases, and suggest that in some cases this genetic correlation is sex-dependent. Author SummaryImmune diseases occur at different rates among patients with schizophrenia compared to the general population. While the reasons for this phenotypic correlation are unclear, shared genetic risk (genetic correlation) has been proposed as a contributing factor. Prior studies have estimated the genetic correlation between schizophrenia and a handful of immune diseases, with conflicting results. Here, we performed a comprehensive cross-disorder investigation of schizophrenia and 19 immune diseases. We identified three individual genetic variants associated with both schizophrenia and immune diseases, including a variant near EPHB4 - a gene whose protein product guides the migration of lymphocytes towards infected cells in the immune system and the migration of neuronal axons in the brain. We demonstrated significant genome-wide genetic correlation between schizophrenia and narcolepsy, primary biliary cirrhosis, psoriasis, systemic lupus erythematosus, and ulcerative colitis. Finally, we identified a potential sex-dependent pleiotropic effect between schizophrenia and multiple sclerosis. Our findings point to shared genetic risk for schizophrenia and at least a subset of immune diseases, which likely contributes to their epidemiological co-occurrence. These results raise the possibility that the same genetic variants may exert their effects on neurons or immune cells to influence the development of psychiatric and immune disorders, respectively.

Genetics↗
bioRxiv2020

A cautionary note on the use of machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices

Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise parasite population structure. Many of the methods used to characterise structure are unsupervised machine learning algorithms which depend on a genetic distance matrix, notably principal coordinates analysis (PCoA) and hierarchical agglomerative clustering (HAC). PCoA and HAC are sensitive to both the definition of genetic distance and algorithmic specification. Importantly, neither algorithm infers malaria parasite ancestry. As such, PCoA and HAC can inform (e.g. via exploratory data visualisation and hypothesis generation), but not answer comprehensively, key questions about malaria parasite ancestry. We illustrate the sensitivity of PCoA and HAC using 393 Plasmodium falciparum whole genome sequences collected from Cambodia and neighbouring regions (where antimalarial resistance has emerged and spread recently) and we provide tentative guidance for the use and interpretation of PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scientific question; (ii) a clear justification of analytical methods used to answer the scientific question along with discussion of any inferential limitations; (iii) publicly available genetic distance matrices when downstream analyses depend on them; and (iv) sensitivity analyses. To bridge the inferential disconnect between the output of non-inferential unsupervised learning algorithms and the scientific questions of interest, tailor-made statistical models are needed to infer malaria parasite ancestry. In the absence of such models speculative reasoning should feature only as discussion but not as results. Author summaryGenetic epidemiology studies of malaria attempt to characterise what is happening in malaria parasite populations. In particular, they are an important tool to track the spread of drug resistance and to validate genetic makers of drug resistance. To make sense of parasite genetic data, researchers usually characterise the population structure using statistical methods. This is most often done as a two step process. The first is a data reduction step, whereby the data are summarised into a distance matrix (each entry represents the genetic distance between two isolates) and then the distance matrix is input into an unsupervised machine learning algorithm. Principal coordinates analysis and hierarchical agglomerative clustering are the two most popular unsupervised machine learning algorithms used for this purpose in malaria genetic epidemiology. We illustrate that this procedure is sensitive to the choice of genetic distance and to the specification of the algorithms. These unsupervised methods are useful for exploratory data analysis but cannot be used to infer historical events. We provide some guidance on how to make genetic epidemiology analyses more transparent and reproducible.

genetics↗
bioRxiv2020

Genomic analysis of the diversity, antimicrobial resistance and virulence potential of Campylobacter jejuni and Campylobacter coli strains from a private health care center in Central Chile

Campylobacter jejuni and Campylobacter coli are the leading cause of human gastroenteritis in the industrialized world and an emerging threat in developing countries. The incidence of campylobacteriosis in South America is greatly underestimated, mostly due to the lack of adequate diagnostic methods. Accordingly, there is limited genomic and epidemiological data from this region. In the present study, we performed a genome-wide analysis of the genetic diversity, virulence, and antimicrobial resistance of the largest collection of clinical C. jejuni and C. coli strains from Chile available to date (n=81), collected in 2017-2019 in Santiago, Chile. This culture collection accounts for over a third of the genome sequences available of clinical strains from South America. cgMLST analysis identified high genetic diversity as well as 13 novel STs and alleles in both C. jejuni and C. coli. Pangenome and virulome analyses showed a differential distribution of virulence factors, including both plasmid and chromosomally encoded T6SSs and T4SSs. Resistome analysis predicted widespread resistance to fluoroquinolones, but low rates of erythromycin resistance. This study provides valuable genomic and epidemiological data and highlights the need for further genomic epidemiology studies in Chile and other South American countries to better understand molecular epidemiology and antimicrobial resistance of this emerging intestinal pathogen. AUTHOR SUMMARYCampylobacter is the leading cause of bacterial gastroenteritis worldwide and an emerging and neglected pathogen in South America. In this study, we performed an in-depth analysis of the genome sequences of 69 C. jejuni and 12 C. coli clinical strains isolated from Chile, which account for over a third of the sequences from clinical strains available from South America. We identified a high genetic diversity among C. jejuni strains and the unexpected identification of clade 3 C. coli strains, which are infrequently isolated from humans in other regions of the world. Most strains harbored the virulence factors described for Campylobacter. While [~]40% of strains harbored mutation in the gyrA gene described to confer fluoroquinolone resistance, very few strains encoded the determinants linked to macrolide resistance, currently used for the treatment of campylobacteriosis. Our study contributes to our knowledge of this important foodborne pathogen providing valuable data from South America.

microbiology↗
bioRxiv2020

A community-driven resource for genomic surveillance of Neisseria gonorrhoeae at Pathogenwatch

BackgroundAntimicrobial resistant (AMR) Neisseria gonorrhoeae is an urgent threat to public health, as strains resistant to at least one of the two last line antibiotics used in empiric therapy of gonorrhoea, ceftriaxone and azithromycin, have spread internationally. Whole genome sequencing (WGS) data can be used to identify new AMR clones, transmission networks and inform the development of point-of-care tests for antimicrobial susceptibility, novel antimicrobials and vaccines. Community driven tools that provide an easy access to and analysis of genomic and epidemiological data is the way forward for public health surveillance. MethodsHere we present a public health focussed scheme for genomic epidemiology of N. gonorrhoeae at Pathogenwatch (https://pathogen.watch/ngonorrhoeae). An international advisory group of experts in epidemiology, public health, genetics and genomics of N. gonorrhoeae was convened to inform on the utility of current and future analytics in the platform. We implement backwards compatibility with MLST, NG-MAST and NG-STAR typing schemes as well as an exhaustive library of genetic AMR determinants linked to a genotypic prediction of resistance to eight antibiotics. A collection of over 12,000 N. gonorrhoeae genome sequences from public archives has been quality-checked, assembled and made public together with available metadata for contextualization. ResultsAMR prediction from genome data revealed specificity values over 99% for azithromycin, ciprofloxacin and ceftriaxone and sensitivity values around 99% for benzylpenicillin and tetracycline. A case study using the Pathogenwatch collection of N. gonorrhoeae public genomes showed the global expansion of an azithromycin resistant lineage carrying a mosaic mtr over at least the last 10 years, emphasizing the power of Pathogenwatch to explore and evaluate genomic epidemiology questions of public health concern. ConclusionsThe N. gonorrhoeae scheme in Pathogenwatch provides customized bioinformatic pipelines guided by expert opinion that can be adapted to public health agencies and departments with little expertise in bioinformatics and lower resourced settings with internet connection but limited computational infrastructure. The advisory group will assess and identify ongoing public health needs in the field of gonorrhoea, particularly regarding gonococcal AMR, in order to further enhance utility with modified or new analytic methods.

genomics↗
bioRxiv2020

Limited genetic diversity of blaCMY-2-containing IncI1-pST12 plasmids from Enterobacteriaceae of human and broiler chicken origin in the Netherlands

Distinguishing epidemiologically related and unrelated plasmids is essential to confirm plasmid transmission. We compared IncI1-pST12 plasmids from both human and livestock origin and explored the degree of sequence similarity between plasmids from Enterobacteriaceae with different epidemiological links. Short-read sequence data of Enterobacteriaceae cultured from humans and broilers were screened for the presence of both a blaCMY-2 gene and an IncI1-pST12 replicon. Isolates were long-read sequenced on a MinION sequencer (OxfordNanopore Technologies). After plasmid reconstruction using hybrid assembly, pairwise single nucleotide polymorphisms (SNP) were determined. The plasmids were annotated, and a pan-genome was constructed to compare genes variably present between the different plasmids. Nine Escherichia coli sequences of broiler origin, four Escherichia coli sequences and one Salmonella enterica sequence of human origin were selected for the current analysis. A circular contig with the IncI1-pST12 replicon and blaCMY-2 gene was extracted from the assembly graph of all fourteen isolates. Analysis of the IncI1-pST12 plasmids revealed a low number of SNP differences (range of 0-9 SNPs). The range of SNP differences overlapped in isolates with different epidemiological links. One-hundred and twelve from a total of 113 genes of the pan-genome were present in all plasmid constructs. NGS-analysis of blaCMY--2-containing IncI1-pST12 plasmids isolated from Enterobacteriaceae with different epidemiological links show a high degree of sequence similarity in terms of SNP differences and the number of shared genes. Therefore, statements on the horizontal transfer of these plasmids based on genetic identity should be made with caution.Competing Interest StatementThe authors have declared no competing interest.View Full Text

microbiology↗
bioRxiv2020

Species detection within the Echinococcus granulosus sensu lato complex by novel probe-based Real-Time PCRs

Infections with eggs of Echinococcus granulosus sensu lato (s.l.) can cause cystic echinococcosis in intermediate host animals and humans. Upon ingestion of viable eggs, oncospheres hatch from the eggs and subsequently develop into fluid-filled larval cysts, most frequently in the liver or the lungs. The slowly growing cysts progressively interfere with organ function. The risk of infection is determined by the host range of the parasite, its pathogenicity and other epidemiologically relevant parameters, which differ significantly among the five species within the E. granulosus s.l. complex. It is therefore essential to diagnose the correct species within E. granulosus s.l. to help understand specific disease epidemiology and to facilitate effective implementation of control measures. For this purpose, simple, fast and cost-effective typing techniques are needed. We developed quantitative Real-Time Polymerase Chain Reactions (qPCRs) and corresponding sequence-specific hydrolysis DNA probes to target polymorphic regions in the mitochondrial genome of E. granulosus s.l.. In a single-step typing approach, we distinguished E. granulosus s.l. members in four epidemiologically relevant subgroups. These were E. granulosus sensu stricto (G1, G3), E. equinus (G4), E. ortleppi (G5) and the E. canadensis cluster (G6 to G8 and G10). The technique also allowed identification and differentiation of these species from other Echinococcus or Taenia taxa for samples isolated from cysts or faeces. Single-step genotyping techniques for the molecular diagnosis of Echinococcus spp. by qPCRs may not only improve diagnostic performance, but also our knowledge on the epidemiology of the parasites and help controlling the various agents of cystic echinococcosis.

molecular biology↗
bioRxiv2020

The Nature of Genetic Susceptibility to Multiple Sclerosis

OBJECTIVETo explore the nature of MS-susceptibility and, by extension, other complex-genetic diseases. BACKGROUNDBasic-epidemiological parameters of MS (e.g., prevalence, recurrence-risks for siblings and twins, time-dependent changes in sex-ratio, etc.) are well-established. Moreover, >200 genetic-loci are unequivocally MS-associated, especially the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype-association. DESIGN/METHODSWe define the "genetically-susceptible" subset-(G) to include everyone with any non-zero life-time chance of developing MS. We analyze, mathematically, the implications that these epidemiological observations have regarding genetic susceptibility. In addition, we use the sex-ratio change (observed over a 35-year interval), to derive the relationship between MS-probability and an increasing likelihood of a suitable environmental-exposure. RESULTSWe demonstrate that genetic-susceptibitly is restricted to less than 4.7% of populations across Europe and North America. Among carriers of the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype, fewer than 20% are even in the subset-(G). Women are less likely to be susceptible than men although their MS-penetrance is considerably greater. Response-curves for MS-probability increase with an increasing likelihood of a suitable environmental-exposure, especially among women. These environmental response-curves plateau at under 50% for women and at a significantly lower level for men. CONCLUSIONSMS is fundamentally a genetic disorder. Despite this, a suitable environmental-exposure is also critical for disease-pathogenesis. Genetic-susceptibility requires specific combinations of non-additive genetic risk-factors. For example, the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype, by itself, poses no MS-risk. Moreover, the fact that environmental-response-curves plateau below 50%, indicates that disease-pathogenesis is partly stochastic. By extension, other diseases for which monozygotic-twin recurrence-risks greatly exceed disease-prevalence (e.g., rheumatoid arthritis, diabetes, and celiac disease), must have a similar genetic basis. Author SummaryWe define a "genetic-susceptible" subset (G) of the general population (Z) to include everyone with any non-zero chance of developing MS over their life-time. Using well-established epidemiological data from across Europe and North America, we establish that genetic-susceptibility is confined to less than 4.7% of these populations. Thus, the large majority of individuals have no chance whatsoever of developing MS, irrespective of any environmental conditions that they may experience during their lifetimes. In this sense, MS is fundamentally a genetic disorder. And, indeed, more than 200 genetic-loci, in multiple genomic locations have now been well-established to be associated with MS. Notably, however, the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 or (H+) haplotype, which has, by far, the strongest MS-association of any, has a carrier frequency in the population of 23% in North America and Europe. Therefore, with genetic susceptibility in the population being less than 4.7%, more than 80% of (H+)-haplotype carriers, must not be genetically-susceptible and, thus, have no chance of developing MS. In this circumstance, genetic susceptibility to MS must arise from a combination of this haplotype with "susceptible states" at other genetic loci. By itself, the (H+)-haplotype poses no risk. Indeed, genetic-susceptibility, generally, seems to require specific combinations of non-additive genetic risk-factors. Naturally, the conclusion that MS is fundamentally genetic does not preclude the possibility the environmental events are also critical to disease-pathogenesis. Using epidemiological data about the world-wide increase in the (F:M) sex-ratio for MS to construct (for men and women separately) the response curves relating an increasing likelihood of MS to an increasing likelihood of a sufficient environmental exposure (i.e., an exposure sufficient to cause MS in a susceptible individual). This analysis provides insight to both disease-susceptibility and disease-pathogenesis. First, men are more likely to be susceptible than women although susceptible women are considerably more likely to actually develop MS. Second, men seem to have a lower environmental threshold than women for developing MS. Nevertheless, women are more responsive to changes in the environmental conditions compared to men. Third, even with a maximal environmental exposure, susceptible women never exceed a 50% chance of developing MS. By contrast, susceptible men have a significantly lower likelihood (<10% chance) of developing MS. This indicates that stochastic factors must also be critical in disease pathogenesis. Finally, the nature of genetic susceptibility developed here for MS is applicable to many other complex genetic disorders. Indeed, for any disease, in which the proband-wise MZ-twin concordance rate greatly exceeds the disease-prevalence in the population (e.g., type I diabetes, rheumatoid arthritis, and celiac disease), only a small fraction of the population can possibly be genetically susceptible, as defined.

genetics↗
bioRxiv2020

Evolution of virulence in emerging epidemics: inference from an evolution experiment

Inference using mathematical models of infectious disease dynamics is a powerful tool to analyse epidemiological data and elucidate pathogen life cycles. Key epidemiological parameters can be estimated from demographic time series by computing the likelihood of alternative models of pathogen transmission. Here we use this inference approach to analyze data from an evolution experiment in which we monitored both the epidemiology and the evolution of the temperate bacteriophage{lambda} during an epidemic. We estimate parameter values for all the life-history traits of two distinct strains of the virus. In particular, we estimate the ability of the two virus strains to modulate plastically the rate of lysogenization with the multiplicity of infection. Our work illustrates how inference from experimental evolution data can feedback on the development of models aiming to predict the epidemiology and evolution of infectious diseases.

evolutionary biology↗
bioRxiv2020

A skyline birth-death process for inferring the population size from a reconstructed tree with occurrences

Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, population size through time for clades of interest, based on molecular sequence data. In the fields of epidemiology and macroevolution these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of "total-evidence" analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, i.e. lacking morphological and molecular data, which we call occurrences, provide invaluable information to reconstruct past population sizes. Here, we present new methodological developments around the Fossilized Birth-Death Process enabling us to (i) efficiently incorporate occurrence data while remaining computationally tractable and scalable; (ii) consider piecewise-constant birth, death and sampling rates; and (iii) reconstruct past population sizes, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical datasets stemming from the fields of epidemiology and macroevolution. In epidemiology, we apply our model to the Covid-19 outbreak on the Diamond Princess ship. We infer the total prevalence throughout the outbreak, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we present an empirical case study of cetaceans. We infer the diversity trajectory using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. Our case studies highlight that the advances we present allow us to further bridge the gap between between epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics.

evolutionary biology↗
bioRxiv2021

Cluster-specific gene marker enhance Shigella and Enteroinvasive Escherichia coli in silico serotyping

Shigella and enteroinvasive Escherichia coli (EIEC) cause human bacillary dysentery with similar invasion mechanisms and share similar physiological, biochemical and genetic characteristics. The ability to differentiate Shigella and EIEC from each other is important for clinical diagnostic and epidemiologic investigations. The existing genetic signatures may not discriminate between Shigella and EIEC. However, phylogenetically, Shigella and EIEC strains are composed of multiple clusters and are different forms of E. coli. In this study, we identified 10 Shigella clusters, 7 EIEC clusters and 53 sporadic types of EIEC by examining over 17,000 publicly available Shigella/EIEC genomes. We compared Shigella and EIEC accessory genomes to identify the cluster-specific gene markers or marker sets for the 17 clusters and 53 sporadic types. The gene markers showed 99.63% accuracy and more than 97.02% specificity. In addition, we developed a freely available in silico serotyping pipeline named Shigella EIEC Cluster Enhanced Serotype Finder (ShigEiFinder) by incorporating the cluster-specific gene markers and established Shigella/EIEC serotype specific O antigen genes and modification genes into typing. ShigEiFinder can process either paired end Illumina sequencing reads or assembled genomes and almost perfectly differentiated Shigella from EIEC with 99.70% and 99.81% cluster assignment accuracy for the assembled genomes and mapped reads respectively. ShigEiFinder was able to serotype over 59 Shigella serotypes and 22 EIEC serotypes and provided a high specificity with 99.40% for assembled genomes and 99.38% for mapped reads for serotyping. The cluster markers and our new serotyping tool, ShigEiFinder (https://github.com/LanLab/ShigEiFinder), will be useful for epidemiologic and diagnostic investigations. Impact statementThe differentiation of Shigella strains from enteroinvasive E. coli (EIEC) is important for clinical diagnosis and public health epidemiologic investigations. The similarities between Shigella and EIEC strains make this differentiation very difficult as both share common ancestries within E. coli. However, Shigella and EIEC are phylogenetically separated into multiple clusters, making high resolution separation using cluster specific genomic markers possible. In this study, we identified 17 Shigella or EIEC clusters including five that were newly identified through examination of over 17,000 publicly available Shigella and EIEC genomes. We further identified an individual or a set of cluster-specific gene markers for each cluster using comparative genomic analysis. These markers can then be used to classify isolates into clusters and were used to develop an in silico pipeline, ShigEiFinder (https://github.com/LanLab/ShigEiFinder) for accurate differentiation, cluster typing and serotyping of Shigella and EIEC from Illumina sequencing reads or assembled genomes. This study will have broad application from understanding the evolution of Shigella/EIEC to diagnosis and epidemiology. Data summarySequencing data have been deposited at the National Center for Biotechnology Information under BioProject number PRJNA692536. RepositoriesRaw sequence data are available from NCBI under the BioProject number PRJNA692536.

microbiology↗
bioRxiv2021

Constructing and validating a transferable epidemic risk index in data scarce environments using open data: a case study for dengue in the Philippines

Epidemics are among the most costly and destructive natural hazards globally. To reduce the impacts of infectious disease outbreaks, the development of a risk index for infectious diseases can be effective, by shifting infectious disease control from emergency response to early detection and prevention. In this study, we introduce a methodology to construct and validate an epidemic risk index using only open data, with a specific focus on scalability. The external validation of our risk index makes use of distance sampling to correct for underreporting of infections, which is often a major source of biases, based on geographical accessibility to health facilities. We apply this methodology to assess the risk of dengue in the Philippines. The results show that the computed dengue risk correlates well with standard epidemiological metrics, i.e. dengue incidence (p = 0.002). Here, dengue risk constitutes of the two dimensions susceptibility and exposure. Susceptibility was particularly associated with dengue incidence (p = 0.047) and dengue case fatality rate (CFR) (p = 0.029). Exposure had lower correlations to dengue incidence (p = 0.211) and CFR (p = 0.163). Highest risk indices were seen in the south of the country, mainly among regions with relatively high susceptibility to dengue outbreaks. Our findings reflect that the modelled epidemic risk index is a strong indication of sub-national dengue disease patterns and has therefore proven suitability for disease risk assessments in the absence of timely epidemiological data. The presented methodology enables the construction of a practical, evidence-based tool to support public health and humanitarian decision-making processes with simple, understandable metrics. The index overcomes the main limitations of existing indices in terms of construction and actionability. Author summaryO_LIWhy Was This Study Done? - Epidemics are among the most costly and destructive natural hazards occurring globally; currently, the response to epidemics is still focused on reaction rather than prevention or preparedness. - The development of an epidemic risk index can support identifying high-risk areas and can guide prioritization of preventive action and humanitarian response. - While several frameworks for epidemic risk assessment exist, they suffer from several limitations, which resulted in limited uptake by local health actors - such as governments and humanitarian relief workers - in their decision-making processes C_LIO_LIWhat Did the Researchers Do and Find? - In this study, we present a methodology to develop epidemic risk indices, which overcomes the major limitations of previous work: strict data requirements, insufficient geographical granularity, validation against epidemiological data. - We take as a case study dengue in the Philippines and develop an epidemic risk index; we correct dengue incidence for underreporting based on accessibility to healthcare and show that it correlates well with the risk index (Pearson correlation coefficient 0.69, p-value 0.002). C_LIO_LIWhat Do These Findings Mean? - Our methodology enables the development of disease-specific epidemic risk indices at a sub-national level, even in countries with limited data availability; these indices can guide local actors in programming prevention and response activities. - Our findings on the case study show that the epidemic risk index is a strong indicator of sub-national dengue disease patterns and is therefore suitable for disease risk assessments in the absence of timely and complete epidemiological data. C_LI

ecology↗
bioRxiv2021

A new phylodynamic model of Mycobacterium bovis transmission in a multi-host system uncovers the role of the unobserved reservoir.

Multi-host pathogens are particularly difficult to control, especially when at least one of the hosts acts as a hidden reservoir. Deep sequencing of densely sampled pathogens has the potential to transform this understanding, but requires analytical approaches that jointly consider epidemiological and genetic data to best address this problem. While there has been considerable success in analyses of single species systems, the hidden reservoir problem is relatively under-studied. A well-known exemplar of this problem is bovine Tuberculosis, a disease found in British and Irish cattle caused by Mycobacterium bovis, where the Eurasian badger has long been believed to act as a reservoir but remains of poorly quantified importance except in very specific locations. As a result, the effort that should be directed at controlling disease in badgers is unclear. Here, we analyse densely collected epidemiological and genetic data from a cattle population but do not explicitly consider any data from badgers. We use a simulation modelling approach to show that, in our system, a model that exploits available cattle demographic and herd-to-herd movement data, but only considers the ability of a hidden reservoir to generate pathogen diversity, can be used to choose between different epidemiological scenarios. In our analysis, a model where the reservoir does not generate any diversity but contributes to new infections at a local farm scale are significantly preferred over models which generate diversity and/or spread disease at broader spatial scales. While we cannot directly attribute the role of the reservoir to badgers based on this analysis alone, the result supports the hypothesis that under current cattle control regimes, infected cattle alone cannot sustain M. bovis circulation. Given the observed close phylogenetic relationship for the bacteria taken from cattle and badgers sampled near to each other, the most parsimonious hypothesis is that the reservoir is the infected badger population. More broadly, our approach demonstrates that carefully constructed bespoke models can exploit the combination of genetic and epidemiological data to overcome issues of extreme data bias, and uncover important general characteristics of transmission in multi-host pathogen systems. Author summaryFor single host pathogens, pathogen genetic data have been transformative for understanding the transmission and control of many diseases, particuarly rapidly evolving RNA viruses. However garnering similar insights where pathogens are multi-host is more challenging, particularly when the evolution of the pathogen is slower and pathogen sampling often heavily biased. This is the case for Mycobacterium bovis, the causative agent of bovine Tuberculosis (bTB) and for which the Eurasian badger plays an as yet poorly understood role in transmission and spread. Here we have developed a computational model that incorporates M. bovis genetic data from cattle only with a highly abstracted model of an unobserved reservoir. Our research shows that a model in which the reservoir does not contribute to pathogen diversity, but is a source of infection in spatially localised areas around each farm, better describes the patterns of outbreaks observed in a population-level sample of a single M. bovis genotype in Northern Ireland over a period of 15 years, compared to models in which either the reservoir has no role, disease spread is spatially extensive, or where they generate considerable diversity on their own. While this reservoir model is not explicitly a model of badgers, its characteristics are consistent with other data that would suggest a reservoir consisting of infected badgers that contribute substantially to cattle infection, but could not maintain disease on their own.

ecology↗
bioRxiv2021

Genetic diversity and evolution of SARS-CoV-2 in Belgium during the first wave outbreak

SARS-CoV-2, the causative agent of COVID-19 was first detected in Belgium on 3rd February 2020, albeit the first epidemiological wave started in March and ended in June 2020. One year after the first epidemiological wave hit the country data analyses reveled the temporal and variant distribution of SARS-CoV-2 and its implication with Belgian epidemiological measures. In this study, 766 complete SARS-CoV-2 genomes of samples originating from the first epidemiological were sequenced to characterize the temporal and geographic distribution of the COVID-19 pandemic in Belgium through phylogenetic and variant analysis. Our analysis reveals the presence of the major circulating SARS-CoV-2 clades (G, GH and GR) and lineages circulating in Belgium at that time. Moreover, it contextualizes the density of SARS-CoV-2 cases over time with non-intervention measures taken to prevent the spread of SARS-CoV-2 in Belgium, specific international case imports and the functional implications of the most representative non-synonymous mutations present in Belgium between February to June 2020.

genomics↗
bioRxiv2022

Comparing transmission reconstruction models with Mycobacterium tuberculosis whole genome sequence data

Pathogen genomic epidemiology is now routinely used worldwide to interrogate infectious disease dynamics. Multiple computational tools that reconstruct transmission networks by coupling genomic data with epidemiological modelling have been developed. The resulting inferences are often used to inform outbreak investigations, yet to date, the performance of these transmission reconstruction tools has not been compared specifically for tuberculosis, a disease process with complex epidemiology that includes variable latency periods and within-host heterogeneity. Here, we carried out a systematic comparison of seven publicly available transmission reconstruction tools, evaluating their accuracy in predicting transmission events in both simulated and real-world Mycobacterium tuberculosis outbreaks. No tool was able to fully resolve transmission networks, though both the single-tree and multi-tree input implementations of TransPhylo identified the most epidemiologically supported transmission events and the fewest false positive links. We observed a high degree of variability in the transmission networks inferred by each approach. Our findings may inform an end-users choice of tools in future tuberculosis transmission analyses and underscore the need for caution when interpreting transmission networks produced using probabilistic approaches.

bioinformatics↗