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bioRxiv2022

Climate stress and its impact on livestock health, farming livelihoods and antibiotic use in Karnataka, India

Understanding the impact of climate change on livestock health is critical to safeguarding global food supplies and economies. Informed by ethnographic research with Indian farmers, veterinarians, and poultry industry representatives, we evidence that both precipitation and vapour pressure are key climate variables that relate to outbreaks of haemorrhagic septicaemia (HS), anthrax (AX), and black quarter (BQ) across the Indian state of Karnataka. We also identify temperature and maximum temperature to be negatively correlated with the same diseases, indicating that a cooling (but still hot) climate with wetter, humid conditions is a prime risk factor for future outbreaks. Principal component analyses have revealed the SW India monsoon and winter periods to be the most strongly correlated with HS, AX and BQ outbreaks. We identify vapour pressure, a proxy for humidity, as having a positive relationship with these specific livestock diseases. The negative relationship between temperature and these diseases, combined with the positive correlation with rainfall and humidity, allow us to classify climate-associated risk using a combination of gridded meteorological time series and epidemiological outbreak data covering the same region and timespan of 1987-2020. Risk maps were constructed following concerns over the growing impact of climate pressures raised by farmers during ethnographic study. Informed by their insights, we used current climate data and future climate projections as a risk classification tool to assess how disease risk varies in Karnataka in the present and possible future scenarios. Despite a relatively limited epidemiological dataset, clear relationships between precipitation, vapour pressure, and temperature with HS, AX and BQ, along with outbreak high-risk zones were defined. This methodology can be replicated to investigate other diseases (including in humans and plants) and other regions, irrespective of scale, as long as the climate and epidemiological data cover similar time periods. This evidence highlights the need for greater consideration of climate change in One Health research and policy and puts forward a case for, we argue, greater alignment between UNFCCC and One Health policy, for example, within the Tripartite Agreement (between OIE, FOA and WHO) on antimicrobial resistance as disease risk cannot be considered independent of climate change. One Health Impact StatementThis research aims to investigate the relationship between factors related to climate (surface temperatures, rainfall, humidity) and outbreaks of livestock-related bacterial diseases. This is especially relevant to the One Health approach as it attempts to integrate findings between not only the science of disease but also the science of climate change as a driver of disease, and address problems that could arise within the public and private sectors (local farming, livestock health, government policy etc.). Providing spatial context to climate-associated disease risk across the Indian state of Karnataka will benefit local farmers that may already be, or transitioning to, more intensive livestock farming along with policy makers and private sector companies who are planning for future investments. This transdisciplinary approach springboards from ethnographic observations of famers lived experiences of challenges to their livelihoods and facilitates the use of climate datasets that may not have been primarily collected for or used by disease-related studies to map long-term epidemiological risk. This demonstrates the pragmatic impact that such transdisciplinary projects can have by providing interpretations of observed risks to animal health (highlighted by social scientists during engagement with practitioner communities) that Earth Scientists were then able to quantify, proving links that would be otherwise not have been evidenced. Using disease data sourced from local institutions, including Government of India facilitates as well academic research laboratories, can plan the application of pragmatic solutions to local farmers who are primarily impacted by the findings of the research.

scientific communication and education↗

bioRxiv2022

Bayesian inference for spatio-temporal stochastic transmission of plant disease in the presence of roguing: a case study to estimate the dispersal distance of Flavescence doree

Estimating the distance at which pathogens disperse from one season to the next is crucial for designing efficient control strategies for invasive plant pathogens and a major milestone in the reduction of pesticide use in agriculture. However, we still lack such estimates for many diseases, especially for insect-vectored pathogens, such as Flavescence doree (FD). FD is a quarantine disease threatening European vineyards. Its management is based on mandatory insecticide treatments and the removal of infected plants identified during annual surveys. This paper introduces a general statistical framework to model the epidemiological dynamics of FD in a mechanistic manner that can take into account missing hosts in surveyed fields (resulting from infected plant removals). We parameterized the model using Markov chain Monte Carlo (MCMC) and data augmentation from surveillance data gathered in Bordeaux vineyards. The data mainly consist of two snapshot maps of the infectious status of all the plants in three adjacent fields during two consecutive years. We demonstrate that heavy-tailed dispersal kernels best fit the spread of FD and that on average, 50% (resp. 80%) of new infection occurs within 10.5 (resp. 22.2) meters from the source plant. These values are in agreement with estimates of the flying capacity of Scaphoideus titanus, the leafhopper vector of FD, reported in the literature using mark-capture techniques. Simulations of simple control scenarios using the fitted model suggest that cryptic infection hampered FD management. Future efforts should explore whether strategies relying on reactive host removal can improve FD management. Author summaryThe dispersal of pathogen propagules is an important feature of spatial epidemiology that has a major impact on the incidence and distribution of disease in a population. In agriculture, properly characterising the dispersal of emerging disease is of great importance in designing science-based control strategies that allow pesticide use to be reduced. Although field epidemiological surveys can provide informative data, they are by nature rare while resulting from the interactions between disease spread and the undergoing surveillance and control. Here, we take advantage of a general statistical framework to model the epidemiological dynamics of Flavescence doree (FD), a quarantine disease threatening European vineyards, in a mechanistic manner that can take into account missing hosts in surveyed fields (resulting from infected plant removals). We parameterized the model with a Bayesian approach using mainly two snapshot maps of the infectious status of all plants in three adjacent fields during two consecutive years. We demonstrate that on average, 50% (resp. 80%) of new FD infection occurs within 10.5 (resp. 22.2) meters of the source plant. Although FD mainly spreads locally from one year to the next, our results also indicate frequent long-distance dispersal events, a feature crucial to consider when designing control strategies.

ecology↗

bioRxiv2023

Gut microbiota and fecal short chain fatty acids differ with adiposity and country of origin: The METS-Microbiome Study

The relationship between the gut microbiota, short chain fatty acid (SCFA) metabolism, and obesity remains unclear due to conflicting reports from studies with limited statistical power. Additionally, this association has rarely been explored in large scale diverse populations. Here, we investigated associations between fecal microbial composition, predicted metabolic potential, SCFA concentrations, and obesity in a large (N = 1,934) adult cohort of African-origin spanning the epidemiologic transition, from Ghana, South Africa, Jamaica, Seychelles, and the United States (US). The greatest gut microbiota diversity and total fecal SCFA concentration was found in the Ghanaian population, while the lowest levels were found in the US population, respectively representing the lowest and the highest end of the epidemiologic transition spectrum. Country-specific bacterial taxa and predicted-functional pathways were observed, including an increased prevalence of Prevotella, Butyrivibrio, Weisella and Romboutsia in Ghana and South Africa, while Bacteroides and Parabacteroides were enriched in Jamaican and the US populations. Importantly, VANISH taxa, including Butyricicoccus and Succinivibrio, were significantly enriched in the Ghanaian cohort, reflecting the participants traditional lifestyles. Obesity was significantly associated with lower SCFA concentrations, a decrease in microbial richness, and dissimilarities in community composition, and reduction in the proportion of SCFA synthesizing bacteria including Oscillospira, Christensenella, Eubacterium, Alistipes, Clostridium and Odoribacter. Further, the predicted proportions of genes in the lipopolysaccharide (LPS) synthesis pathway were enriched in obese individuals, while genes associated with butyrate synthesis via the dominant pyruvate pathway were significantly reduced in obese individuals. Using machine learning, we identified features predictive of metabolic state and country of origin. Country of origin could accurately be predicted by the fecal microbiota (AUC = 0.97), whereas obesity could not be predicted as accurately (AUC = 0.65). Participant sex (AUC = 0.75), diabetes status (AUC = 0.63), hypertensive status (AUC = 0.65), and glucose status (AUC = 0.66) could all be predicted with different success. Interestingly, within country, the predictive accuracy of the microbiota for obesity was inversely correlated to the epidemiological transition, being greatest in Ghana (AUC = 0.57). Collectively, our findings reveal profound variation in the gut microbiota, inferred functional pathways, and SCFA synthesis as a function of country of origin. While obesity could be predicted accurately from the microbiota, the variation in accuracy in parallel with the epidemiological transition suggests that differences in the microbiota between obesity and non-obesity may be larger in low-to-middle countries compared to high-income countries. Further examination of independent study populations using multi-omic approaches will be necessary to determine the factors that drive this association.

microbiology↗

bioRxiv2023

High-throughput phenogenotyping of Mycobacteria tuberculosis clinical strains reveals bacterial determinants of treatment outcomes

BackgroundCombatting the tuberculosis (TB) epidemic caused by Mycobacterium tuberculosis (Mtb) necessitates a better understanding of the factors contributing to patient clinical outcomes and transmission. While host and environmental factors have been evaluated, the impact of Mtb genetic background and phenotypic diversity is underexplored. Previous work has made associations between Mtb genetic lineages and some clinical and epidemiological features, but the bacterial traits underlying these connections are largely unknown. MethodsWe developed a high-throughput functional genomics platform for defining genotype-phenotype relationships across a panel of Mtb clinical isolates. These phenotypic fitness profiles function as intermediate traits which can be linked to Mtb genetic variants and associated with clinical and epidemiological outcomes. We applied this approach to a collection of 158 Mtb strains from a study of Mtb transmission in Ho Chi Minh City, Vietnam. Mtb strains were genetically tagged in multiplicate, which allowed us to pool the strains and assess in vitro competitive fitness using deep sequencing across a set of 14 host-relevant antibiotic and metabolic conditions. Phylogenetic and monogenic associations with these intermediate traits were identified and then associated with clinical outcomes. FindingsMtb clinical strains have a broad range of growth and drug response dynamics that can be clustered by their phylogenetic relationships. We identified novel monogenic associations with Mtb fitness in various metabolic and antibiotic conditions. Among these, we find that mutations in Rv1339, a phosphodiesterase, which were identified through their association with slow growth in glycerol, are further associated with treatment failure. We also identify a previously uncharacterized subclade of Lineage 1 strains (L1.1.1.1) that is phenotypically distinguished by slow growth under most antibiotic and metabolic stress conditions in vitro. This clade is associated with cavitary disease, treatment failure, and demonstrates increased transmission potential. InterpretationHigh-throughput phenogenotyping of Mtb clinical strains enabled bacterial intermediate trait identification that can provide a mechanistic link between Mtb genetic variation and patient clinical outcomes. Mtb strains associated with cavitary disease, treatment failure, and transmission potential display intermediate phenotypes distinguished by slow growth under various antibiotic and metabolic conditions. These data suggest that Mtb growth regulation is an adaptive advantage for host bacterial success in human populations, in at least some circumstances. These data further suggest markers for the underlying bacterial processes that govern these clinical outcomes. FundingNational Institutes of Allergy and Infectious Diseases: P01 AI132130 (SS, SMF); P01 AI143575 (XW, SMF); U19 AI142793 (QL, SMF); 5T32AI132120-03 (SS); 5T32AI132120-04 (SS); 5T32AI049928-17 (SS) Wellcome Trust Fellowship in Public Health and Tropical Medicine: 097124/Z/11/Z (NTTT) National Health and Medical Research Council (NHMRC)/A*STAR joint call: APP1056689 (SJD) The funding sources had no involvement in study methodology, data collection, analysis, and interpretation nor in the writing or submission of the manuscript. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe used different combinations of the words mycobacterium tuberculosis, tuberculosis, clinical strains, intermediate phenotypes, genetic barcoding, phenogenomics, cavitary disease, treatment failure, and transmission to search the PubMed database for all studies published up until January 20th, 2022. We only considered English language publications, which biases our search. Previous work linking Mtb determinants to clinical or epidemiological data has made associations between bacterial lineage, or less frequently, genetic polymorphisms to in vitro or in vivo models of pathogenesis, transmission, and clinical outcomes such as cavitary disease, treatment failure, delayed culture conversion, and severity. Many of these studies focus on the global pandemic Lineage 2 and Lineage 4 Mtb strains due in part to a deletion in a polyketide synthase implicated in host-pathogen interactions. There are a number of Mtb GWAS studies that have led to novel genetic determinants of in vitro drug resistance and tolerance. Previous Mtb GWAS analyses with clinical outcomes did not experimentally test any predicted phenotypes of the clinical strains. Published laboratory-based studies of Mtb clinical strains involve relatively small numbers of strains, do not identify the genetic basis of relevant phenotypes, or link findings to the corresponding clinical outcomes. There are two recent studies of other pathogens that describe phenogenomic analyses. One study of 331 M. abscessus clinical strains performed one-by-one phenotyping to identify bacterial features associated with clearance of infection and another details a competition experiment utilizing three barcoded Plasmodium falciparum clinical isolates to assay antimalarial fitness and resistance. Added value of this studyWe developed a functional genomics platform to perform high-throughput phenotyping of Mtb clinical strains. We then used these phenotypes as intermediate traits to identify novel bacterial genetic features associated with clinical outcomes. We leveraged this platform with a sample of 158 Mtb clinical strains from a cross sectional study of Mtb transmission in Ho Chi Minh City, Vietnam. To enable high-throughput phenotyping of large numbers of Mtb clinical isolates, we applied a DNA barcoding approach that has not been previously utilized for the high-throughput analysis of Mtb clinical strains. This approach allowed us to perform pooled competitive fitness assays, tracking strain fitness using deep sequencing. We measured the replicative fitness of the clinical strains in multiplicate under 14 metabolic and antibiotic stress condition. To our knowledge, this is the largest phenotypic screen of Mtb clinical isolates to date. We performed bacterial GWAS to delineate the Mtb genetic variants associated with each fitness phenotype, identifying monogenic associations with several conditions. We then defined Mtb phenotypic and genetic features associated with clinical outcomes. We find that a subclade of Mtb strains, defined by variants largely involved in fatty acid metabolic pathways, share a universal slow growth phenotype that is associated with cavitary disease, treatment failure and increased transmission potential in Vietnam. We also find that mutations in Rv1339, a poorly characterized phosphodiesterase, also associate with slow growth in vitro and with treatment failure in patients. Implications of all the available evidencePhenogenomic profiling demonstrates that Mtb strains exhibit distinct growth characteristics under metabolic and antibiotic stress conditions. These fitness profiles can serve as intermediate traits for GWAS and association with clinical outcomes. Intermediate phenotyping allows us to examine potential processes by which bacterial strain differences contribute to clinical outcomes. Our study identifies clinical strains with slow growth phenotypes under in vitro models of antibiotic and host-like metabolic conditions that are associated with adverse clinical outcomes. It is possible that the bacterial intermediate phenotypes we identified are directly related to the mechanisms of these outcomes, or they may serve as markers for the causal yet unidentified bacterial determinants. Via the intermediate phenotyping, we also discovered a surprising diversity in Mtb responses to the new anti-mycobacterial drugs that target central metabolic processes, which will be important in considering roll-out of these new agents. Our study and others that have identified Mtb determinants of TB clinical and epidemiological phenotypes should inform efforts to improve diagnostics and drug regimen design.

microbiology↗

bioRxiv2024

Detecting superspreaders in wildlife reservoirs of disease

Bovine tuberculosis (bTB) has significant socio-economic and welfare impacts on the cattle industry worldwide. In the United Kingdom and Ireland, disease control is complicated by the presence of infection in wildlife, principally the European badger. Control strategies tend to be applied to whole populations, but better-targeted management of key sources of transmission, be they individuals or groups, may be more efficient. Mechanistic transmission models can be used to better understand key epidemiological drivers of disease spread and identify high-risk individuals and groups as long as they can be adequately fitted to observed data. However, this is a significant challenge, especially within wildlife populations, because monitoring relies on imperfect diagnostic test information, and even under systematic surveillance efforts (such as capture-mark-recapture sampling) epidemiological events are only partially observed. To this end we develop a stochastic compartmental model of bTB transmission, and fit this to individual-level data from a unique > 40-year longitudinal study of 2,391 badgers using a recently developed individual forward filtering backward sampling algorithm. Modelling challenges are further compounded by spatio-temporal meta-population structures and age-dependent mortality. We develop a novel estimator for the individual effective reproduction number that provides quantitative evidence for the presence of superspreader badgers, despite the population-level effective reproduction number being less than one. We also infer measures of the hidden burden of infection in the host population through time; the relative likelihoods of competing routes of transmission; effective and realised infectious periods; and longitudinal measures of diagnostic test performance. This modelling framework provides an efficient and generalisable way to fit state-space models to individual-level data in wildlife populations, which allows identification of high-risk individuals and exploration of important epidemiological questions about bTB and other wildlife diseases. Author summaryWild animals commonly harbour infectious diseases with risk of spillover to humans and livestock. We fitted an individual-level stochastic spatial meta-population model of bovine tuberculosis (bTB) transmission to data from a long-term longitudinal study of the European badger (Meles meles). Our framework provides an efficient and generalisable means of fitting state-space models to individual-level data, to identify high-risk individuals and explore important epidemiological questions. We develop a novel estimator for the individual effective reproduction number, providing quantitative evidence for the presence of superspreader badgers (those individuals most responsible for onward transmission of infection), despite the population-level effective reproduction number being less than one. Predicting the hidden burden of infection in individuals and social groups is critical for disease management but challenging in practice, since monitoring relies on imperfect surveillance and diagnostic testing, however control of bTB in badgers could be substantially increased by targeting interventions at high-risk groups.

ecology↗

bioRxiv2024

Social ageing can protect against infectious disease in a group-living primate

The benefits of social living are well established, but sociality also comes with costs, including infectious disease risk. This cost-benefit ratio of sociality is expected to change across individuals lifespans, which may drive changes in social behaviour with age. To explore this idea, we combine data from a group-living primate for which social ageing has been described with epidemiological models to show that having lower social connectedness when older can protect against the costs of a hypothetical, directly transmitted endemic pathogen. Assuming no age differences in epidemiological characteristics (susceptibility to, severity, and duration of infection), older individuals suffered lower infection costs, which was explained largely because they were less connected in their social networks than younger individuals. This benefit of social ageing depended on epidemiological characteristics and was greatest when infection severity increased with age. When infection duration increased with age, social ageing was beneficial only when pathogen transmissibility was low. Older individuals benefited most from having a lower frequency of interactions (strength) and network embeddedness (closeness) and benefited less from having fewer social partners (degree). Our study provides a first examination of the epidemiology of social ageing, demonstrating the potential for pathogens to influence evolutionary dynamics of social ageing in natural populations.

animal behavior and cognition↗

bioRxiv2024

Bacterial strain nomenclature in the genomic era: Life Identification Numbers using a gene-by-gene approach

Unified strain taxonomies are needed for the epidemiological surveillance of bacterial pathogens and international communication in microbiological research. Core genome multilocus sequence typing (cgMLST) holds great promise for standardized high-resolution strain genotyping. However, this approach faces challenges including classification instability and disconnection of new nomenclature from widely adopted classical MLST identifiers. This essay discusses the cgMLST-based Life Identification Number (LIN) method, recently proposed as a stable multilevel strain taxonomy system applicable to most bacterial pathogens. We describe how LIN codes are implemented and used in practice for precise strain definitions and epidemiological tracking. Glossary Multilocus sequence typing (MLST)A genotyping method applied mostly to microbial strains to study population structure and epidemiology, based on comparing the nucleotide sequences of a small number (typically seven) of housekeeping protein-coding genes. In MLST, allele numbers are assigned to each sequence variant (allele) of a given gene. The MLST genotype of a bacterial strain is defined by the combination of the allele numbers observed at the genes that are included in the genotyping scheme. A sequence type (ST) is assigned to each unique combination of alleles, called an MLST profile. MLST was invented in 1998 and became a de-facto standard taxonomy of bacterial strains, albeit at low resolution. Core genome MLSTAn extension of MLST that analyzes sequence variation across hundreds to thousands of conserved (core) genes, shared by all strains of a species, providing higher resolution typing for genomic epidemiology and evolutionary studies. cgMLST schemes typically comprise 2000 to 4000 genes, depending on the genome size and genetic variation (in terms of presence/absence of genes) within bacterial species. A core genome sequence type (cgST) can be assigned to unique cgMLST profiles, i.e., a unique combination of cgMLST allelic numbers. Whole Genome Sequencing (WGS)A method that determines the complete DNA sequence of an organisms genome in a single process, providing comprehensive information for comparative genetic analyses based on cgMLST or other analytic methods. Single Nucleotide Polymorphisms (SNPs)Variations at a single base position in the DNA sequence among individuals isolates, strains or species, used as genetic markers for studying for example, evolutionary relationships or strain identity. Average nucleotide identity (ANI)A measure of genomic similarity between two organisms, calculated as the average percentage of identical nucleotides in orthologous genomic regions; commonly used to assess species-level relatedness in prokaryotes. TaxonomyHere, we apply the word taxonomy to bacterial strains as a system of classifying, naming and identifying strains based on shared genetic characteristics as defined by e.g., cgMLST.

microbiology↗

bioRxiv2024

A phylogenetic host range index reveals contrasted relationships between phage virulence and specialisation

Phages are typically known for having a limited host range, targeting various strains within a specific bacterial species. However, factors like the phylogeny or epidemiology of host bacteria are often disregarded, despite their potential influence on phage specialization and virulence. This research utilizes a new "phylogenetic host range index" that accounts for the genetic diversity of bacterial hosts, to classify phages into specialists and generalists accurately. We provide evidence that the CRISPR-Cas immune system of bacteria more frequently targets generalist phages than specialist phages. We explore the hypothesis that generalist phages might exhibit lower virulence than specialist ones due to potential evolutionary trade-offs between host range breadth and virulence. Importantly, contrasted correlations between phage virulence and host range depend on the epidemiological context. A trade-off was confirmed in a homogeneous bacterial epidemiology situation, but not in more complex epidemiological scenario, where no apparent costs were detected for phages adapted to a wide range of hosts. This study highlights the need for genetic analyses in phage host range and of investigating ecological trade-offs that could improve their applications in biocontrol or therapy.

microbiology↗

bioRxiv2025

Life identification number (LIN) codes for the genomic taxonomy of Corynebacterium diphtheriae strains

BackgroundCorynebacterium diphtheriae, which causes diphtheria, remains a public health concern especially in regions with low vaccination coverage. While advances in genomic typing, such as core-genome Multi-Locus Sequence Typing (cgMLST, based on 1305 genes), have improved our ability for strain identification, a standardized and stable genomic taxonomy is still lacking. This study aimed to establish a consistent classification and nomenclature for C. diphtheriae strains using cgMLST-based Life Identification Number (LIN) codes. MethodsComparing 1,665 genomes from C. diphtheriae and its closely related species C. belfantii and C. rouxii, we observed population-level genetic discontinuities in cgMLST profiles dissimilarities, and established hierarchical taxonomic levels based on optimal allelic difference thresholds. Ten-level LIN codes were defined, encompassing broad population structure subdivisions and fine-scale epidemiological levels. The LIN code system was implemented into the BIGSdb-Pasteur platform, and nicknames derived from the 7-loci MLST sequence types were given to sublineages and clonal groups. ResultscgMLST genetic thresholds were first defined at species (minimum of 1,240 allelic differences) and lineage levels (1,035 differences). Sublineages (SL), clonal groups (ClG), and genetic clusters (GC) were next defined with progressively finer allelic mismatch thresholds (500, 55, and 25 differences, respectively). A broad population diversity of C. diphtheriae was uncovered, with the distinction of >400 SLs and >1,000 GCs. For epidemiological purposes, five shallow-level thresholds (8, 4, 2 ,1, and 0 allelic mismatches were defined, completing the 10-level LIN code taxonomy. We illustrate LIN codes applicability to investigate the genetic diversity and transmission chains of relevant clusters, such as SL8 (the 1990s ex-USSR outbreak) or SL384 (involved in outbreaks in Yemen and Europe). ConclusionsThe cgMLST-based LIN code system provides a stable genomic taxonomy for strains of C. diphtheriae, C. rouxii and C. belfantii. By defining ten hierarchical levels of resolution, this system effectively captures its phylogenetic diversity, facilitating population biology research and epidemiological surveillance. The public availability of this system from the BIGSdb-Pasteur platform provides a standardized framework for diphtheria genomic epidemiology with potential to harmonize global surveillance of the resurgence of diphtheria.

microbiology↗

bioRxiv2025

A new typing scheme demonstrates high discriminatory power for Treponema pallidum subspecies

The global resurgence of treponematoses, particularly syphilis, poses a growing public health challenge. Despite recent advances in sequencing technologies, obtaining complete Treponema pallidum genome sequences for epidemiological studies remains time-consuming and challenging due to the difficulty related to procuring clinical samples with sufficient treponemal burden to fulfil the sequencing requirements. There is an urgent need for rapid, cost-effective and accessible typing methods suitable for laboratories with Sanger sequencing resources. Based on the analysis of 121 T. pallidum genomes from geographically diverse regions, we selected seven highly variable genes to form the basis of this new typing system. These seven genes show high discrimination capacity, identifying many allelic profiles among T. pallidum isolates. Importantly, the scheme employs a single-step PCR protocol for the amplification and sequencing of all seven targets enabling straightforward implementation in standard laboratory settings. The MLST was validated using a diverse set of T. pallidum clinical samples from across the globe. A significant proportion of the tested samples showed macrolide resistance, emphasizing the need for epidemiological surveillance. Utilizing this new tool, we have analyzed the genetic variation within and between populations of T. pallidum, considering the geographical origin of the samples. Population structure analysis revealed distinct genetic clusters, underlining complex transmission dynamics of T. pallidum, shaped by local epidemiological factors. The MLST scheme is publicly accessible through the PubMLST database, encouraging widespread adoption in standard laboratories due to this database being user-friendly, intuitive, and fast to implement. The novel MLST scheme offers a promising tool to advance the study of the molecular epidemiology of T. pallidum, facilitate tracking transmission, and establish a global surveillance network with the overall goal of strengthening public health interventions for syphilis control.

microbiology↗

bioRxiv2025

Coinfection with malaria alters the dynamics and fitness of an intestinal nematode

Infections with soil transmitted helminths (STHs) are highly prevalent in humans living in the intertropical region. While, in most cases, STHs can establish chronic infections, the dynamics of the infection can be altered when other parasites exploit the same host. These changes can have consequences in terms of the health of the host, the epidemiology of the disease (e.g., the duration of the infection and the inter-host transmission success) and the fitness of the parasite. Here, we investigated if the coinfection with Plasmodium yoelii alters the dynamics and the fitness of the murine nematode Heligmosomoides polygyrus. We found that, compared to single infected mice, coinfection produced an increase in the number of excreted eggs, while the biomass of adult worms in the intestine did not differ between single infected and coinfected mice. Moreover, the increase in egg excretion was also observed when Plasmodium infected hosts that had been harboring the nematode during the past four weeks (i.e., when the population size of adult worms can only decrease due to mortality). Therefore, the enhanced shedding of eggs reflects a plastic adjustment of worm fecundity to the environment provided by a coinfected host. This plastic response was modulated by the host Th2 immunity, as coinfection inhibited IL-4 and IL-13 gene expression, plasma levels of IL-5 and IL-13, and the expansion of GATA-3+ CD4+ T cells in the spleen. In agreement with this, experimentally inhibiting IL-13 with neutralizing antibodies reproduced the results observed in coinfected mice (an increase in egg excretion), while the administration of recombinant IL-13 reduced egg shedding. Interestingly, coinfection had a net positive effect on parasite fitness as shown by a longer persistence within the host and higher cumulative number of eggs excreted up to 99 days post-infection. Although the gene expression of Th2 cytokines was lower at day 99 p.i., coinfected mice still had a downregulated expression compared to single infected hosts. These results show that coinfection with Plasmodium has the potential to affect the epidemiology of STHs by increasing the number of eggs excreted over the whole infectious period and maintaining a larger environmental reservoir of transmissible stages. Author SummaryCoinfection between soil-transmitted helminths and malaria is common in several countries of the intertropical region, especially among the most vulnerable populations. Coinfection has the potential to worsen the symptoms caused by malaria, therefore it is important to understand what are the epidemiological and ecological factors that promote the occurrence of coinfection. Transmission of soil-transmitted helminths usually requires human contact with transmissible stages (parasitic eggs or larvae) in the environment; therefore, high egg excretion in the feces of infected people is a key factor contributing to maintain a reservoir of infective stages from which humans can get infected. In this study, we experimentally investigated whether coinfection with malaria alters the dynamics (egg excretion, infection persistence) of a murine intestinal nematode. We found that hosts infected with malaria and subsequently infected with the nematode, excreted more nematode eggs for a longer period, compared to single infected hosts. These changes were mediated by an impaired Th2 immune response in coinfected hosts. These results suggest that malaria coinfection produces positive feedback on key epidemiological traits of the nematode that can further enhance the risk of malaria/helminths cooccurrence.

evolutionary biology↗

bioRxiv2025

Outbreak Investigation of Phytobacter diazotrophicus Using Fourier Transform Infrared Spectroscopy compared to cgMLST

IntroductionPhytobacter diazotrophicus is an emerging opportunistic pathogen associated with hospital outbreaks and a reservoir of antimicrobial resistance genes; however, its clonal epidemiology remains poorly understood. Accurate identification and rapid clonal typing are crucial for infection control. Core genome MLST (cgMLST) is the genomic gold standard; however, it is both costly and time-consuming. Fourier-transform infrared (FT-IR) spectroscopy using the IR Biotyper(R) emerges as a rapid and accessible typing alternative. ObjectiveThis study evaluated the performance of the IR Biotyper(R) in discriminating P. diazotrophicus outbreak isolates, compared to cgMLST. MethodologyEight epidemiologically relevant isolates, including two from a total parenteral nutrition (TPN) outbreak and four from a hemodialysis clinic, along with two outliers, were analyzed. Its identification was confirmed by API20E, MALDI-TOF MS, nifL gene qPCR, and WGS. Both cgMLST, based on 7,755 conserved genes, and the Fourier Transform Infrared (FT-IR) Spectroscopy by IR Biotyper(R) were used for clonal typing, with the spectral cutoff empirically determined based on known clonal isolates. ResultsBoth methodologies demonstrated remarkable agreement in detecting clonal clusters. The two TPN outbreak isolates showed 23 allele differences (ADs) by cgMLST and consistently clustered according to the IR Biotyper. Similarly, the four isolates from the outbreak at hemodialysis clinic formed homogeneous clusters by both methods (5-27 ADs by cgMLST). The outlier isolates were consistently discriminated. ConclusionFT-IR spectroscopy (IR Biotyper(R)) proved promising and complementary to cgMLST for typing P. diazotrophicus in outbreak scenarios, offering a rapid and cost-effective alternative for detecting clonal events. Its applicability in routine epidemiological surveillance is significant. Statement of ImportancePhytobacter diazotrophicus is an emerging opportunistic pathogen of increasing concern in clinical environments, associated with hospital outbreaks and carrying antimicrobial resistance genes. Rapid detection and precise clonal characterization are essential for infection control and epidemiological surveillance. However, these are challenging for less-understood species. Genomic methods, such as cgMLST, are the gold standard, but their cost and time limitations hinder routine application in outbreak response. This study evaluated the IR Biotyper system, a rapid and accessible phenotypic tool that uses Fourier Transform Infrared Spectroscopy, compared to cgMLST for clonal typing of P. diazotrophicus. Our findings demonstrate a remarkable agreement between the two methods in identifying clonal clusters. This validation suggests the potential of the IR Biotyper as an effective tool for real-time screening and active surveillance of P. diazotrophicus in clinical settings. This enables a more agile response to outbreaks and aids in managing antimicrobial resistance, reserving high-resolution genomic analyses for more in-depth investigations.

microbiology↗

bioRxiv2018

A quantitative evaluation of MIRU-VNTR typing against whole-genome sequencing for identifying Mycobacterium tuberculosis transmission: A prospective observational cohort study

SummaryO_ST_ABSBackgroundC_ST_ABSMycobacterial Interspersed Repetitive Unit-Variable Number Tandem Repeat (MIRU-VNTR) typing is widely used in high-income countries for Mycobacterium tuberculosis typing. Whole-genome sequencing (WGS) is known to deliver greater specificity, but no quantitative prospective comparison has yet been undertaken.\n\nMethodsWe studied isolates from the English Midlands, sampled consecutively between 1 January 2012 and 31 December 2015. In addition to routinely performed MIRU-VNTR typing, DNA was extracted from liquid cultures and sequenced using Illumina technology. Demographic and epidemiological data were extracted from the Enhanced Tuberculosis Surveillance system maintained by Public Health England. Closely related samples, defined using a threshold of five single nucleotide variants (SNVs), were compared to samples with identical MIRU-VNTR profiles, with shared epidemiological risk factors, and to those with both characteristics.\n\nFindings1,999 patients were identified for whom at least one M. tuberculosis isolate had been MIRU-VNTR typed and sequenced. Comparing epidemiological risk factors with close genetic relatedness, only coresidence had a positive predictive value of over 5%. Excluding co-resident individuals, 18.6% of patients with identical MIRU-VNTR profiles were within 5 SNVs. Where patients also shared social risk factors and ethnic group, this rose to 48%. Only 8% of MIRU-VNTR linked pairs in lineage 1 were within 5 SNV, compared to 31% in lineage 4.\n\nInterpretationIn the setting studied, MIRU-VNTR typing and epidemiological risk factors are poorly predictive of close genomic relatedness, assessed by SNV. MIRU-VNTR performance varies markedly by lineage.\n\nFundingPublic Health England, National Institute of Health Research Oxford Biomedical Research Centre.

microbiology↗

bioRxiv2018

SciLMM: Computing heritability with millions of individuals

The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human populations in scales much larger than previously possible. Linear mixed models (LMMs) are routinely used to analyze extremely large animal and plant pedigrees for the purposes of selective breeding. However, LMMs have not been previously applied to analyze population-scale human family trees. Here, we present Sparse Cholesky factorIzation LMM (Sci-LMM), a modeling framework for studying population-scale family trees that combines techniques from the animal and plant breeding literature and from human genetics literature. The proposed framework can construct a matrix of relationships between trillions of pairs of individuals and fit the corresponding LMM in several hours. We demonstrate the capabilities of Sci-LMM via simulation studies and by estimating the heritability of longevity and of reproductive fitness (quantified via number of children) in a large pedigree spanning millions of individuals and over five centuries of human history. Sci-LMM provides a unified framework for investigating the epidemiological history of human populations via genealogical records.\n\nAuthor SummaryThe advent of online genealogy services allows the assembly of population-scale family trees, spanning millions of individuals and centuries of human history. Such datasets enable answering genetic epidemiology questions on unprecedented scales. Here we present Sci-LMM, a pedigree analysis framework that combines techniques from animal and plant breeding research and from human genetics research for large-scale pedigree analysis. We apply Sci-LMM to analyze population-scale human genealogical records, spanning trillions of relationships. We have made both Sci-LMM and an anonymized dataset of millions of individuals freely available to download, making the analysis of population-scale human family trees widely accessible to the research community. Together, these resources allow researchers to investigate genetic and epidemiological questions on an unprecedented scale.

genetics↗

bioRxiv2018

Bayesian phylodynamic inference with complex models

Population genetic modeling can enhance Bayesian phylogenetic inference by providing a realistic prior on the distribution of branch lengths and times of common ancestry.The parameters of a population genetic model may also have intrinsic importance, and simultaneous estimation of a phylogeny and model parameters has enabled phylodynamic inference of population growth rates, reproduction numbers, and effective population size through time. Phylodynamic inference based on pathogen genetic sequence data has emerged as useful supplement to epidemic surveillance, however commonly-used mechanistic models that are typically fitted to non-genetic surveillance data are rarely fitted to pathogen genetic data due to a dearth of software tools, and the theory required to conduct such inference has been developed only recently. We present a framework for coalescent-based phylogenetic and phylodynamic inference which enables highly-flexible modeling of demographic and epidemiological processes. This approach builds upon previous structured coalescent approaches and includes enhancements for computational speed, accuracy, and stability. A flexible markup language is described for translating parametric demographic or epidemiological models into a structured coalescent model enabling simultaneous estimation of demographic or epidemiological parameters and time-scaled phylogenies. We demonstrate the utility of these approaches by fitting compartmental epidemiological models to Ebola virus and Influenza A virus sequence data, demonstrating how important features of these epidemics, such as the reproduction number and epidemic curves, can be gleaned from genetic data. These approaches are provided as an open-source package PhyDyn for the BEAST phylogenetics platform.

bioinformatics↗

bioRxiv2018

GI-16 lineage (624/I or Q1), there and back again: the history of one of the major threat for poultry farming of our era

The genetic variability of Infectious bronchitis virus (IBV) is one of the main challenges for its control, hindering not only the development of effective vaccination strategies but also its classification and, consequently, epidemiology understanding. The 624/I and Q1 genotypes, now recognized to be part of the GI-16 lineage, represent an excellent example of the practical consequences of IBV molecular epidemiology limited knowledge. In fact, being their common origin unrecognized for a long time, independent epidemiological pictures were drawn for the two genotypes. To fix this misinterpretation, the present study reconstructs the history, population dynamics and spreading patterns of GI-16 lineage as a whole using a phylodynamic approach. A collection of worldwide available hypervariable region 1 and 2 (HVR12) and 3 (HVR3) sequences of the S1 protein was analysed together with 258 HVR3 sequences obtained from samples collected in Italy (the country where this genotype was initially identified) since 1963. The results demonstrate that after its emergence at the beginning of the XX century, GI-16 was able to persist until present days in Italy. Approximately in the late 1980s, it migrated to Asia, which became the main nucleus for further spreading to Middle East, Europe and especially South America, likely through multiple introduction events. A remarkable among-country diffusion was also demonstrated in Asia and South America. Interestingly, although most of the recent Italian GI-16 strains originated from ancestral viruses detected in the same country, a couple were closely related to Chinese ones, supporting a backward viral flow from China to Italy.\n\nBesides to the specific case-study results, this work highlights the misconceptions that originate from the lack of a unified nomenclature and poor molecular epidemiology data generation and sharing. This shortcoming appears particularly relevant since the described scenario could likely be shared by many other IBV genotypes and pathogens in general.

evolutionary biology↗

bioRxiv2018

rPinecone: Define sub-lineages of a clonal expansion via a phylogenetic tree

The ability to distinguish between pathogens is a fundamental requirement to understand the epidemiology of infectious diseases. Phylogenetic analysis of genomic data can provide a powerful platform to identify lineages within bacterial populations, and thus inform outbreak investigation and transmission dynamics. However, resolving differences between pathogens associated with low variant (LV) populations carrying low median pairwise single nucleotide variant (SNV) distances, remains a major challenge. Here we present rPinecone, an R package designed to define sub-lineages within closely related LV populations. rPinecone uses a root-to-tip directional approach to define sub-lineages within a phylogenetic tree according to SNV distance from the ancestral node. The utility of this program was demonstrated using genomic data of two LV populations: a hospital outbreak of methicillin-resistant Staphylococcus aureus and endemic Salmonella Typhi from rural Cambodia. rPinecone identified the transmission branches of the hospital outbreak and geographically-confined lineages in Cambodia. Sub-lineages identified by rPinecone in both analyses were phylogenetically robust. It is anticipated that rPinecone can be used to discriminate between lineages of bacteria from LV populations where other methods fail, enabling a deeper understanding of infectious disease epidemiology for public health purposes.\n\nDATA SUMMARYO_LISource code for rPinecone is available on GitHub under the open source licence GNU GPL 3; (url: https://github.com/alexwailan/rpinecone).\nC_LIO_LINewick format files for both phylogenetic trees have been deposited in Figshare; (url: https://doi.org/10.6084/m9.figshare.7022558)\nC_LIO_LIGeographical analysis of the S. Typhi Dataset using Microreact is available at https://microreact.org/project/r1IqkrN1X.\nC_LIO_LIAccession numbers, meta data and sample lineage results of both datasets used in this paper are listed in the supplementary tables.\nC_LI\n\nI/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {boxtimes}\n\nIMPACT STATEMENTWhole genome sequence data from bacterial pathogens is increasingly used in the epidemiological investigation of infectious disease, both in outbreak and endemic situations. However, distinguishing bacterial species which are both very similar and which are likely to come from a small geographical and temporal range presents a major technical challenge for epidemiologists. rPinecone was designed to address this challenge and utilises phylogenetic data to define lineages within bacterial populations that have limited variation. This approach is therefore of great interest to epidemiologists as it adds a further level of clarity above and beyond that which is offered by existing approaches which have not been designed to consider bacterial isolates containing variation that only transiently exist, but which is epidemiologically informative. rPinecone has the flexibility to be applied to multiple pathogens and has direct application for investigations of clinical outbreaks and endemic disease to understand transmission dynamics or geographical hotspots of disease.

genomics↗

bioRxiv2019

Identification of hidden population structure in time-scaled phylogenies

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

evolutionary biology↗