BUSCADOR RECOLECTA

Raw_SNP_data Structure_SNP_data 64_ind_29504_SNPs Dataset used for calculating genome diversity parameters 44_ind_29504_SNPs Dataset applied in the genome-wide association studies 76_ind_8_SNPs Dataset used in the validation of genome-wide association studies GWAS_TB_standard_44 GWAS summary statistics for infected vs infected MTC individuals GWAS_TB_stratified_44 GWAS summary statistics for stratified analysis of infected vs. uninfected MTC individuals GWAS_TB_standard_76 GWAS summary statistics for the validation test between infected vs. uninfected MTC individuals GWAS_TB_stratified_76 GWAS summary statistics for the validation test, stratified analysis, between infected vs. uninfected MTC individuals GWAS_Season_standard_44 GWAS summary statistics of 2002/06 vs. 2009/12 seasons GWAS_Season_stratified_44 GWAS summary statistics of stratified analysis for 2002/06 vs. 2009/12 seasons. GWAS_Season_standard_76 GWAS summary statistics for the validation test between 2002/06 vs. 2009/12 seasons. GWAS_Season_stratified_76 GWAS summary statistics for the validation test, stratified analysis, between 2002/06 vs. 2009/12 seasons., Tuberculosis (TB) affects a wide range of host species worldwide. Understanding host-pathogen co-evolution remains a global challenge owing to complex interactions among host genetic factors, pathogen traits and environmental conditions. We used an endemic wild boar population that had undergone a huge increase in Mycobacterium bovis infection prevalence, from 45% in 2002/06 to 83% in 2009/12, to understand the effects of host genetics on host TB outcomes and disease dynamics. Host genomic variation was characterized using a high-density single nucleotide polymorphism (SNP) array, while host TB phenotype was assessed using both gross pathology and mycobacterial culture. Two complementary genome-wide association (GWAS) analyses were conducted: (i) infected-uninfected; and (ii) 2002/06–2009/12. The SNPs with the highest allelic frequency differences between time-periods and TB outcomes were identified and validated in a large dataset. In addition, we quantified the expression levels of some of their closest genes. These analyses highlighted various SNPs (i.e. rs81465339, rs81394585, rs81423166) and some of the closest genes (i.e. LOC102164072, BDNF/NT-3, NTRK2, CDH8, IGSF21) as candidates for host genetic susceptibility. In addition to TB-driven selection, our findings outline the putative role of demographic events in shaping genomic variation in natural populations and how population crashes and drift may impact host genetic susceptibility to TB over time., Peer reviewed

Fig. S1. Ddiagram of flow of participants used in this study., Identification as a study of diagnostic accuracy using at least one measure of accuracy (such as sensitivity, specificity, predictive values, or AUC). Structured summary of study design, methods, results, and conclusions (for specific guidance, see STARD for Abstracts)., Peer reviewed

The datasets are reported in the supplementary material Tables S1-S4., Table S1. Summary of the distributions of the proportion of the population harvested each year and supporting data. Figure S1. Histogram and probability density of the estimated proportion of the population harvested each year, autocorrelation and trace plots. Table S2. Summary of the estimated distributions of abundance and supporting data at each site. The Geweke test was -0.557 (P=0.577) for the wild boar and 1.085 (P=0.278) for the red deer models. Figure S2. Histogram and probability density of the abundance estimated at each site, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. Table S3. Summary of the estimated distributions and supporting references for the sensitivity and specificity of the diagnostic tests. Figure S3. Histogram and probability density of the estimated sensitivity and specificity of the diagnostic tests employed, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. Table S4. Summary of the estimated distributions of the true prevalence by host species and supporting data at each study site. Figure S4. Histogram and probability density of the estimated true prevalence at each site, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. Figure S5. Histogram and probability density of the estimated probability of Mycobacterium tuberculosis complex excretion from infected wild boar and red deer, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. Table S5. Summary of the estimated distributions of the R0 by host species at each study site. Figure S6. Histogram and probability density of the estimated R0 by host species at each study site, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. For intelligibility, only R0 estimates <200 were plotted. Table S6. Probability of animal tuberculosis being a single-host or multi-host disease at each site. Probabilities derived from the 100,000 estimates of the R0 of Mycobacterium tuberculosis complex in each host species. Single maintenance host: only one species with R0>1; Obligatory multi-host: R0 both species<1; Facultative multi-host: R0 both species >1. Highest probability for each site is highlighted in bold. Figure S7. Diagnostic plots of the generalised linear models. Diagnostic plots for the wild boar, red deer, and multi-host models with the whole dataset and after removing one influential outlier (site 15). Table S7. Summary of the generalised linear model selection. Models with ΔAICc<2 from the most supported model. Figure S8. Basic reproduction number estimated for the wild boar and red deer in each of the study sites. Kernel density of 100 estimates of the R0 of Mycobacterium tuberculosis complex in the wild boar and red deer drawn randomly from the 100,000 estimates at each site. Axes in square root scale, R0=1 as blue horizontal and vertical lines., Peer reviewed

Data File S1. Serum proteomics analysis; Data File S2. Analysis of immunoglobulin proteins underrepresented and overrepresented in infected cohorts when compared to PCR– individuals; Data File S3. Human-autoantibody general survey in PCR– and PCR+ cohorts; Data File S4. Analysis of selected non-immunoglobulin proteins underrepresented and overrepresented in infected cohorts when compared to PCR– individuals., Peer reviewed

Data S1. Serum proteomics analysis; Data S2. Analysis of immunoglobulin proteins differentially represented in TB+ elephants when compared to TB− animals; Data S3. Gene Ontology (GO) annotations for non-immunoglobulin proteins; Data S4. Most significant pathways identified by Reactome analysis of non-immunoglobulin proteins; Data S5. Protein sequence alignment between M. tuberculosis P9WNK5|ESXB_MYCTU ESAT-6-like protein EsxB and identified mycobacterial proteins with predicted antigen binding regions to immunoglobulin proteins., Peer reviewed

Files include the data necessary for the estimation of the population density by REM (DataREMZenodo.xlsx), data on indirect interactions recorded by camera traps at the pilot monitoring site level (DataIntZenodo.xlsx), and the metadata of the images (Metadata_imagesbyPP.xlsx)., This study aims to report the implementation of the first nationwide pilot trial of balanced IWM in Europe, using a network of 11 pilot monitoring sites in Spain. The insights obtained in this pilot IWM trial will help to further develop a comprehensive IWM and serve as a reference for implementing IWM systems in other regions. For this purpose, a grid of 20 camera traps (Browning Strike Force HD ProX, Browning Arms Company®, Morgan, Utah, USA) was deployed for two months at each study site (n=11). REM was applied to wild boar (Sus scrofa), red deer (Cervus elaphus), and red fox (Vulpes vulpes) as the most frequently detected species. Relative abundance indexes, namely trapping rate and relative occupancy index, were calculated for the remaining, less frequently detected species., Peer reviewed

Supplementary information: Table S1: Detailed information on the 94 whole genomes retrieved from GenBank and used in this study., Peer reviewed

Supplementary Material 1: Supplementary Table S1. Data from sampled tortoises, their morphologic characteristics and identification of PCR-positive results. Supplementary Table S2. Tick infestation in Testudo graeca populations through their distribution range: infestation prevalence (%), infestation intensity (tick/infected tortoise) and host density (tortoise/ha)., Peer reviewed