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Disease and parasitism cause major welfare, environmental and economic concerns for global aquaculture. In this review, we examine the status and potential of technologies that exploit genetic variation in host resistance to tackle this problem. We argue that there is an urgent need to improve understanding of the genetic mechanisms involved, leading to the development of tools that can be applied to boost host resistance and reduce the disease burden. We draw on two pressing global disease problems as case studies—sea lice infestations in salmonids and white spot syndrome in shrimp. We review how the latest genetic technologies can be capitalised upon to determine the mechanisms underlying inter- and intra-species variation in pathogen/parasite resistance, and how the derived knowledge could be applied to boost disease resistance using selective breeding, gene editing and/or with targeted feed treatments and vaccines. Gene editing brings novel opportunities, but also implementation and dissemination challenges, and necessitates new protocols to integrate the technology into aquaculture breeding programmes. There is also an ongoing need to minimise risks of disease agents evolving to overcome genetic improvements to host resistance, and insights from epidemiological and evolutionary models of pathogen infestation in wild and cultured host populations are explored. Ethical issues around the different approaches for achieving genetic resistance are discussed. Application of genetic technologies and approaches has potential to improve fundamental knowledge of mechanisms affecting genetic resistance and provide effective pathways for implementation that could lead to more resistant aquaculture stocks, transforming global aquaculture

Recent Advances in Pig Molecular Genetics and Breeding Abstract Reducing harmful aggressive behaviour remains a major challenge in pig production. Social network analysis (SNA) showed the potential in providing novel behavioural traits that describe the direct and indirect role of individual pigs in pen level aggression. Our objectives were to (1) estimate the genetic parameters of these SNA traits, and (2) quantify the genetic associations between the SNA traits and commonly used performance measures: growth, feed intake, feed efficiency, and carcass traits. Animals were video recorded for 24h post-mixing. The observed fighting behaviour of each animal was used as input for the SNA. A Bayesian approach was performed to estimate the genetic parameters of SNA traits and their association with the performance traits. The heritability estimates for all SNA traits ranged from 0.01 to 0.35. The genetic correlations between SNA and performance traits were non-significant, except for weighted degree with Hot Carcass Weight, and for both betweenness and closeness centrality with Test Daily Gain, Final Body Weight and Hot Carcass Weight. Our results suggest that SNA traits are amenable for selective breeding. Integrating these traits with other behaviour and performance traits may potentially help in building up future strategies for simultaneously improving welfare and performance in commercial pig farms.

Recent breakthroughs in gene-editing technologies that can render individual animals fully resistant to infections may offer unprecedented opportunities for controlling future epidemics in farm animals. Yet, their potential for reducing disease spread is poorly understood as the necessary theoretical framework for estimating epidemiological effects arising from gene-editing applications is currently lacking. Here, we develop semistochastic modeling approaches to investigate how the adoption of gene editing may affect infectious disease prevalence in farmed animal populations and the prospects and time scale for disease elimination. We apply our models to the porcine reproductive and respiratory syndrome (PRRS), one of the most persistent global livestock diseases to date. Whereas extensive control efforts have shown limited success, recent production of gene-edited pigs that are fully resistant to the PRRS virus have raised expectations for eliminating this deadly disease. Our models predict that disease elimination on a national scale would be difficult to achieve if gene editing was used as the only disease control. However, from a purely epidemiological perspective, disease elimination may be achievable within 3 to 6 y, if gene editing were complemented with widespread and sufficiently effective vaccination. Besides strategic distribution of genetically resistant animals, several other key determinants underpinning the epidemiological impact of gene editing were identified.

Objective: Acute Pancreatitis (AP) is sudden onset pancreas inflammation that causes systemic injury with a wide and markedly heterogeneous range of clinical consequences. Here, we hypothesised that this observed clinical diversity corresponds to diversity in molecular subtypes that can be identified in clinical and multi-omics data.Summary Background data: Observational cohort study. n=57 for the discovery cohort (clinical, transcriptomics, proteomics and metabolomics data) and n=312 for the validation cohort (clinical and metabolomics data).Methods: We integrated co-incident transcriptomics, proteomics, and metabolomics data at serial time points between admission to hospital and up to 48 hours after recruitment from a cohort of patients presenting with acute pancreatitis. We systematically evaluated four different metrics for patient similarity using unbiased mathematical, biological and clinical measures of internal and external validity. We next compared the AP molecular endotypes with previous descriptions of endotypes in a critically ill population with acute respiratory distress syndrome (ARDS).Results: Our results identify four distinct and stable AP molecular endotypes. We validated our findings in a second independent cohort of patients with AP.We observed that two endotypes in AP recapitulate disease endotypes previously reported in ARDS.Conclusions: Our results show that molecular endotypes exist in AP and reflect biological patterns that are also present in ARDS, suggesting that generalisable patterns exist in diverse presentations of critical illness.

Potential interactions among co-circulating viral strains in host populations are often overlooked in the study of virus transmission. However, these interactions probably shape transmission dynamics by influencing host immune responses or altering the relative fitness among co-circulating strains. In this Review, we describe multi-strain dynamics from ecological and evolutionary perspectives, outline scales in which multi-strain dynamics occur and summarize important immunological, phylogenetic and mathematical modelling approaches used to quantify interactions among strains. We also discuss how host-pathogen interactions influence the co-circulation of pathogens. Finally, we highlight outstanding questions and knowledge gaps in the current theory and study of ecological and evolutionary dynamics of multi-strain viruses.

Infectious diseases are a major threat to the sustainable production of high-producing animals. Control efforts, such as vaccination or breeding approaches often target improvements to individual resilience to infections, i.e., they strengthen an animal's ability to cope with infection, rather than preventing infection per se. There is increasing evidence for the contribution of non-clinical carriers (animals that become infected and are infectious but do not develop clinical signs) to the overall health and production of livestock populations for a wide range of infectious diseases. Therefore, we strongly advocate a shift of focus from increasing the disease resilience of individual animals to herd disease resilience as the appropriate target for sustainable disease control in livestock. Herd disease resilience not only captures the direct effects of vaccination or host genetics on the health and production performance of individuals but also the indirect effects on the environmental pathogen load that herd members are exposed to. For diseases primarily caused by infectious pathogens shed by herd members, these indirect effects on herd resilience are mediated both by individual susceptibility to infection and by characteristics (magnitude of infectiousness, duration of infectious period) that influence pathogen shedding from infected individuals. We review what is currently known about how vaccination and selective breeding affect herd disease resilience and its underlying components, and outline the changes required for improvement. To this purpose, we also seek to clarify and harmonise the terminology used in the different animal science disciplines to facilitate future collaborative approaches to infectious disease control in livestock.

Objectives: To develop a Critical Appraisal tool for non-computational-specialist public health professionals to assess the quality and relevance of modelling studies about Test and Trace (and Protect – TTP) programmes’ impact on COVID-19 transmission.Study Design: Decision-making tool developmentMethods: Using Tugwell et al.’s Health Care Effectiveness equation as a conceptual framework, combined with a purposive search of the relevant early modeling literature, we developed six critical appraisal questions for the rapid assessment of modeling studies related to the evaluation of TTP programmes’ effectiveness.Results: By applying the Critical Appraisal tool to selected recent COVID-19 modeling studies we demonstrate how models can be evaluated using the six questions to evaluate internal and external validity, and relevance.Conclusions: These six critical appraisal questions are able to discriminate between modeling studies of higher and lower quality and relevance to evaluating TTP programmes’ impact. However, these questions require independent validation in a larger and systematic sample of relevant modeling studies which have appeared in later stages of the pandemic.

The genetic diversity and frequent emergence of novel genetic variants of porcine reproductive and respiratory syndrome virus type-2 (PRRSV) hinders control efforts, yet drivers of macro-evolutionary patterns of PRRSV remain poorly documented. Utilizing a comprehensive database of &gt, 20,000 orf5 sequences, our objective was to classify variants according to the phylogenetic structure of PRRSV co-circulating in the U.S., quantify evolutionary dynamics of sub-lineage emergence, and describe potential antigenic differences among sub-lineages. We subdivided the most prevalent lineage (Lineage 1, accounting for approximately 60% of available sequences) into eight sub-lineages. Bayesian coalescent SkyGrid models were used to estimate each sub-lineage’s effective population size over time. We show that a new sub-lineage emerged every 1 to 4 years and that the time between emergence and peak population size was 4.5 years on average (range: 2–8 years). A pattern of sequential dominance of different sub-lineages was identified, with a new dominant sub-lineage replacing its predecessor approximately every 3 years. Consensus amino acid sequences for each sub-lineage differed in key GP5 sites related to host immunity, suggesting that sub-lineage turnover may be linked to immune-mediated competition. This has important implications for understanding drivers of genetic diversity and emergence of new PRRSV variants in the U.S.

Survival during an epidemic is partly determined by host genetics. While quantitative 10 genetic studies typically consider survival as an indicator for disease resistance (an 11 individual’s propensity to avoid becoming infected or diseased), mortality rates of 12 populations undergoing an epidemic are also affected by endurance (the propensity of 13 diseased individual to survive the infection) and infectivity (i.e. the propensity of an 14 infected individual to transmit disease). Few studies have demonstrated genetic 15 variation in disease endurance, and no study has demonstrated genetic variation in 16 host infectivity, despite strong evidence for considerable phenotypic variation in this 17 trait. Here we propose an experimental design and statistical models for estimating 18 genetic diversity in all three host traits. Using an infection model in fish we provide, for 19 the first time, direct evidence for genetic variation in host infectivity, in addition to 20 variation in resistance and endurance. We also demonstrate how genetic differences in 21 these three traits contribute to survival. Our results imply that animals can evolve 22 different disease response types affecting epidemic survival rates, with important 23 implications for understanding and controlling epidemics.

Infectious diseases have a huge impact on animal health, production and welfare, and human health. Understanding the role of host genetics in disease spread is important for developing disease control strategies that efficiently reduce infection incidence and risk of epidemics. While heritable variation in disease susceptibility has been targeted in livestock breeding, emerging evidence suggests that there is additional genetic variation in host infectivity, but the potential benefits of including infectivity into selection schemes are currently unknown. A Susceptible-Infected-Recovered epidemiological model incorporating polygenic genetic variation in both susceptibility and infectivity was combined with quantitative genetics selection theory to assess the non-linear impact of genetic selection on field measures of epidemic risk and severity. Response to 20 generations of selection was calculated in large simulated populations, exploring schemes differing in accuracy and intensity. Assuming moderate genetic variation in both traits, 50% selection on susceptibility required seven generations to reduce the basic reproductive number R0 from 7.64 to the critical threshold of R0

Recent breakthroughs in gene-editing technologies that can render individuals fully resistant to infections may offer unprecedented opportunities for controlling future epidemics. Yet, their potential for reducing disease spread are poorly understood as the necessary theoretical framework for estimating epidemiological effects arising from gene editing applications is currently lacking. Here, we develop semi-stochastic modelling approaches to investigate how the adoption of gene editing may affect infectious disease prevalence in farmed animal populations and the prospects and time-scale for disease elimination. We apply our models to the Porcine Reproductive and Respiratory Syndrome PRRS, one of the most persistent global livestock diseases to date. Whereas extensive control efforts have shown limited success, recent production of gene-edited pigs that are fully resistant to the PRRS virus have raised expectations for eliminating this deadly disease.Our models predict that disease elimination on a national scale would be difficult to achieve if gene editing was used as the only disease control. However, when complemented with vaccination, the introduction of 10% of genetically resistant animals in a fraction of herds could be sufficient for eliminating the disease within 3-6 years. Besides strategic distribution of genetically resistant animals, several other key determinants underpinning the epidemiological impact of gene-editing were identified.

Social network analysis (SNA) has provided novel traits that describe the role of individual pigs in aggression. The objectives were to (1) estimate the genetic parameters for these SNA traits, (2) quantify the genetic association between SNA and skin lesion traits, and (3) investigate the possible response to selection for SNA traits on skin lesion traits. Pigs were video recorded for 24 h post-mixing. The observed fight and bullying behaviour of each animal was used as input for the SNA. Skin lesions were counted on different body parts at 24 h (SL24h) and 3 weeks (SL3wk) post-mixing. A Bayesian approach estimated the genetic parameters of SNA traits and their association with skin lesions. SNA traits were heritable (h2 = 0.09 to 0.26) and strongly genetically correlated (rg > 0.88). Positive genetic correlations were observed between all SNA traits and anterior SL24h, except for clustering coefficient. Our results suggest that selection for an index that combines the eigenvector centrality and clustering coefficient could potentially decrease SL24h and SL3wk compared to selection for each trait separately. This study provides a first step towards potential integration of SNA traits into a multi-trait selection index for improving pigs’ welfare.

Contest behaviour, and in particular the propensity to attack an unfamiliar conspecific, is influenced by an individual’s aggressiveness, as well as by experience of winning and losing (so called ‘winner–loser effects’). Individuals vary in aggressiveness and susceptibility to winner–loser effects but the relationship between these drivers of contest behaviour has been poorly investigated. Here we hypothesise that the winner–loser effect on initiation of agonistic behaviour (display, non-damaging aggression, biting and mutual fighting) is influenced by aggressiveness. Pigs (n = 255) were assayed for aggressiveness (tendency to attack in resident−intruder tests) and then experienced two dyadic contests (age 10 and 13 weeks). Agonistic behaviour, up to reciprocal fighting, in contest 2 was compared between individuals of different aggressiveness in the RI test and experiences of victory or defeat in contest 1. Winner–loser effects were more influential than aggressiveness in determining initiation of agonistic behaviour. After accruing more skin lesions in contest 1, individuals were less likely to engage in escalated aggression in contest 2. The interaction between aggressiveness and winner–loser experience did not influence contest behaviour. The results suggest that aggressiveness does not compromise learning from recent contest experience and that reducing aggressiveness is unlikely to affect how animals experience winning and losing.

Bovine Tuberculosis (bTB) caused by Mycobacterium bovis is a livestock disease of global economic and public health importance. There are currently no effective vaccines available for livestock and so control relies on animal level surveillance and pasteurisation of dairy products. A new alternative control approach is to exploit the genetic variability of the host; recent studies have demonstrated that breeding Bos taurus cattle for increased resistance to bTB is feasible. The utility of such an approach is still unknown for the Bos indicus cattle population. This study aims to assess genetic variation in bTB resistance and the underlying genomic architecture in Bos indicus breeds in Cameroon.We conducted a cross-sectional study of slaughter cattle in Cameroon and genotyped a sample of 213 cattle. Their genomic diversity was characterised using PCA, hierarchical clustering and admixture analysis. We assessed genetic variation in bTB resistance using heritability analysis and compared quantitative trait loci.Previous studies had found that breed was an important factor in explaining the epidemiology of bTB, with Fulani cattle appearing to be more susceptible than mixed breeds. However, we show that the apparent phenotypic differences in visual appearance between the breeds was not reflected by clear genomic differences. At the genetic level, cattle belonging to different hierarchical genomic clusters differed in their susceptibility to bTB. There was evidence of a genomic association between M. bovis infection status with specific SNPs.We highlight the need to understand the challenges faced by livestock in specific settings both in terms of pathogens and the environment, in addition to their intended purpose and how they fit into a defined management system. It is only at this point livestock keepers can then make informed breeding choices, not only for resistance to disease but also for increasing production.

Vaccination is the main tool for controlling infectious diseases in livestock. Yet current vaccines only provide partial protection raising concerns about vaccine effectiveness in the field.Two successive transmission trials were performed involving 52 pigs to evaluate the effectiveness of a Porcine Reproductive and Respiratory Syndrome (PRRS) vaccinal strain candidate against horizontal transmission of a virulent heterologous strain. PRRS virus, above the specified limit of detection, was observed in serum and nasal secretions for all but one pig (the exception only tested positive for serum), indicating that vaccination did not protect pigs from becoming infected and shedding the heterologous strain. However, vaccination delayed the onset of viraemia, reduced the duration of shedding and significantly decreased viral load throughout infection. Serum antibody profiles indicated that 4 out of 13 (31%) vaccinates in one trial had no serological response (NSR).A Bayesian epidemiological model was fitted to the data to assess the impact of vaccination and presence of NSRs on PRRS virus transmission dynamics. Despite little evidence for reduction in the transmission rate, vaccinated animals were on average slower to become infectious, experienced a shorter infectious period and recovered faster. The overall PRRSV transmission potential, represented by the reproductive ratio R0 was lower for the vaccinated animals, although there was substantial overlap in the credibility intervals for both groups. Model selection suggests that transmission parameters of vaccinated pigs with NSR were more similar to those of unvaccinated animals. The presence of NSRs in a population, however, seemed to only marginally affect the transmission dynamics.The results suggest that even when vaccination can’t prevent infection, it can still have beneficial impacts on the transmission dynamics and contribute to reducing a herd’s R0. However, biosecurity and other measures need to be considered to decrease contact rates and lower R0 below 1.

Background Fighting and controlling epidemic and endemic diseases represents a considerable cost to livestock production. Much research is dedicated to breeding disease resilient livestock, but this is not yet a common objective in practical breeding programs. In this paper, we investigate how future breeding programs may benefit from recent research on disease resilience. Main body We define disease resilience in terms of its component traits resistance (R: the ability of a host animal to limit within-host pathogen load (PL)) and tolerance (T: the ability of an infected host to limit the damage caused by a given PL), and model the host's production performance as a reaction norm on PL, depending on R and T. Based on this, we derive equations for the economic values of resilience and its component traits. A case study on porcine respiratory and reproductive syndrome (PRRS) in pigs illustrates that the economic value of increasing production in infectious conditions through selection for R and T can be more than three times higher than by selection for production in disease-free conditions. Although this reaction norm model of resilience is helpful for quantifying its relationship to its component traits, its parameters are difficult and expensive to quantify. We consider the consequences of ignoring R and T in breeding programs that measure resilience as production in infectious conditions with unknown PL—particularly, the risk that the genetic correlation between R and T is unfavourable (antagonistic) and that a trade-off between them neutralizes the resilience improvement. We describe four approaches to avoid such antagonisms: (1) by producing sufficient PL records to estimate this correlation and check for antagonisms—if found, continue routine PL recording, and if not found, shift to cheaper proxies for PL; (2) by selection on quantitative trait loci (QTL) known to influence both R and T in favourable ways; (3) by rapidly modifying towards near-complete resistance or tolerance, (4) by re-defining resilience as the animal's capacity to resist (or recover from) the perturbation caused by an infection, measured as temporal deviations of production traits in within-host longitudinal data series. Conclusions All four alternatives offer promising options for genetic improvement of disease resilience, and most rely on technological and methodological developments and innovation in automated data generation.

[EN] Robustness has become a highly desirable breeding goal in the globalized agricultural market. Both genotype-by-environment interaction (G x E) and micro-environmental sensitivity are important robustness components of aquaculture production, in which breeding stock is often disseminated to different environments. The objectives of this study were (i) to quantify the degree of G x E by assessing the growth performance of Genetically Improved Farmed Tilapia (GIFT) across three countries (Malaysia, India and China) and (ii) to quantify the genetic heterogeneity of environmental variance for body weight at harvest (BW) in GIFT as a measure of micro-environmental sensitivity. Selection for BW was carried out for 13 generations in Malaysia. Subsets of 60 full-sib families from Malaysia were sent to China and India after five and nine generations respectively. First, a multi-trait animal model was used to analyse the BW in different countries as different traits. The results indicate a strong G x E. Second, a genetically structured environmental variance model, implemented using Bayesian inference, was used to analyse micro-environmental sensitivity of BW in each country. The analysis revealed the presence of genetic heterogeneity of both BW and its environmental variance in all environments. The presence of genetic variation in residual variance of BW implies that the residual variance can be modified by selection. Incorporating both G x E and micro-environmental sensitivity information may help in selecting robust genotypes with high performance across environments and resilience to environmental fluctuations., Dr. Agha was funded by Newton Grant, British Research Council. Dr. Doeschl-Wilson's contribution was funded by the BBSRC Institute Strategic Programme Grants [BB/J004235/1 (ISP1) and BBS/E/D/30002276 (ISP3)]. Dr. Ibanez-Escriche's contribution was funded by the Marie-Curie Fellowships (H2020-MSCA-IF2014_ST-653216).

Early play fighting mimics later aggression in many species, and may, therefore, be expected to reduce costs from later aggressive interactions. Using social network analysis (SNA) the effect of a central play fighting network position on later skin lesions from aggression was assessed in domestic pigs. Piglets (n = 263) were kept in litter groups or socialised pre-weaning with another litter to enhance play fighting experience. Play fighting was recorded for 1.5 h per day over 6 days pre-weaning. Play fighting network centrality was quantified using measures of individual network position and entire network structure (degree, eigenvector, betweenness, clustering coefficient). Skin lesions from aggression were counted after a dyadic contest and at 24 h and 3 weeks following group mixing. Pigs with play fighting interactions with many partners experienced fewer lesions from the dyadic contest (in-degree, p = 0.01) and tended to received fewer lesions 3 weeks after group mixing (degree, p = 0.088) but no other play fighting centrality measures affected the number of lesions at any point. The benefits of play fighting were therefore limited to specific aggressive social contexts. The tendency of socialised piglets to play fight with non-littermates did not affect subsequent lesions. We advocate the use of SNA over approaches that only consider dyadic interactions to further our understanding of the influence of early social group interactions on later life experience.

Many livestock and human vaccines are leaky because they block symptoms but do not prevent infection or onward transmission. This leakiness is concerning because it increases vaccination coverage required to prevent disease spread and can promote evolution of increased pathogen virulence. Despite leakiness, vaccination may reduce pathogen load, affecting disease transmission dynamics. However, the impacts on post-transmission disease development and infectiousness in contact individuals are unknown. Here, we use transmission experiments involving Marek disease virus (MDV) in chickens to show that vaccination with a leaky vaccine substantially reduces viral load in both vaccinated individuals and unvaccinated contact individuals they infect. Consequently, contact birds are less likely to develop disease symptoms or die, show less severe symptoms, and shed less infectious virus themselves, when infected by vaccinated birds. These results highlight that even partial vaccination with a leaky vaccine can have unforeseen positive consequences in controlling the spread and symptoms of disease., "Leaky" vaccination of chickens infected with Marek“s disease virus reduced symptoms and lowered mortality in the unvaccinated contact birds infected by them. The cause was transmission of lower virus loads from vaccinated than unvaccinated birds; this benefit of leaky vaccination is rarely taken into consideration.

Markov chain Monte Carlo (MCMC) is widely used for Bayesian inference in models of complex systems. Performance, however, is often unsatisfactory in models with many latent variables due to so-called poor mixing, necessitating development of application specific implementations. This paper introduces "posterior-based proposals" (PBPs), a new type of MCMC update applicable to a huge class of statistical models (whose conditional dependence structures are represented by directed acyclic graphs). PBPs generates large joint updates in parameter and latent variable space, whilst retaining good acceptance rates (typically 33%). Evaluation against other approaches (from standard Gibbs / random walk updates to state-of-the-art Hamiltonian and particle MCMC methods) was carried out for widely varying model types: an individual-based model for disease diagnostic test data, a financial stochastic volatility model, a mixed model used in statistical genetics and a population model used in ecology. Whilst different methods worked better or worse in different scenarios, PBPs were found to be either near to the fastest or significantly faster than the next best approach (by up to a factor of 10). PBPs therefore represent an additional general purpose technique that can be usefully applied in a wide variety of contexts., 54 pages, 11 figures, 2 tables

Selective breeding for improving host responses to infectious pathogens is a promising option for disease control. In fact, disease resilience, the ability of a host to survive or cope with infectious challenge, has become a highly desirable breeding goal. However, resilience is a complex trait composed of two different host defence mechanisms, namely resistance (the ability of a host to avoid becoming infected or diseased) and endurance (the ability of an infected host to survive the infection). While both could be targeted for genetic improvement, it is currently unknown how they contribute to survival, as reliable estimates of genetic parameters for both traits obtained simultaneously are scarce. A difficulty lies in obtaining endurance phenotypes for genetic analyses. In this study, we present the results from an innovative challenge test carried out in turbot whose design allowed disentangling the genetic basis of resistance and endurance to Philasterides dicentrarchi, a parasite causing scuticociliatosis that leads to substantial economic losses in the aquaculture industry. A noticeable characteristic of the parasite is that it causes visual signs that can be used for disentangling resistance and endurance. Our results showed the existence of genetic variation for both traits (heritability = 0.26 and 0.12 for resistance and endurance, respectively) and for the composite trait resilience (heritability = 0.15). The genetic correlation between resistance and resilience was very high (0.90) indicating that both are at a large extent the same trait, but no significant genetic correlation was found between resistance and endurance. A total of 18,125 SNPs obtained from 2b-RAD sequencing enabled genome-wide association analyses for detecting QTLs controlling the three traits. A candidate QTL region on linkage group 19 that explains 33% of the additive genetic variance was identified for resilience. The region contains relevant genes related to immune response and defence mechanisms. Although no significant associations were found for resistance, the pattern of association was the same as for resilience. For endurance, one significant association was found on linkage group 2. The accuracy of genomic breeding values was also explored for resilience, showing that it increased by 12% when compared with the accuracy of pedigree-based breeding values. To our knowledge, this is the first study in turbot disentangling the genetic basis of resistance and endurance to scuticociliatosis. This work was supported by the European Union’s Seventh Framework Programme (KBBE.2013.1.2-659 10) under Grant Agreement No. 613611, Ministerio de Ciencia, Innovación y Universidades, Spain (Grant CGL2016-75904-C2-2-P), and Fondos FEDER. SI

Acute pancreatitis (AP) is sudden onset pancreas inflammation that causes multiple organ dysfunction syndrome (MODS) and death in certain individuals who develop AP yet minimal systemic inflammation in others. Here, we show that this observed diversity in systemic response and outcome is accompagnied by diversity in molecular subtypes that can be identified using computational analysis of clinical and multiomic data. We integrated co-incident whole blood transcriptomic, plasma proteomic, and serum metabolomic data at serial time points from a cohort of patients presenting with AP and systematically evaluated four different metrics for patient similarity, using unbiased mathematical, biological and clinical measures of internal and external validity. Our results identify four distinct and stable AP endotypes that are characterized by pathway and biomarker combination stereotypes into hypermetabolic, hepatopancreaticobiliary, catabolic and innate immune endotypes. The catabolic endotype in AP strikingly recapitulates a disease endotype previously reported in acute respiratory distress syndrome, a recognized complication of AP. Our findings demonstrate that clinically-relevant and generalizable endotypes exist in AP.

Individuals differ widely in their contribution to the spread of infection within and across populations. Three key epidemiological host traits affect infectious disease spread: susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection to others) and recoverability (propensity to recover quickly). Interventions aiming to reduce disease spread may target improvement in any one of these traits, but the necessary statistical methods for obtaining risk estimates are lacking. In this paper we introduce a novel software tool called SIRE (standing for “Susceptibility, Infectivity and Recoverability Estimation”), which allows for the first time simultaneous estimation of the genetic effect of a single nucleotide polymorphism (SNP), as well as non-genetic influences on these three unobservable host traits. SIRE implements a flexible Bayesian algorithm which accommodates a wide range of disease surveillance data comprising any combination of recorded individual infection and/or recovery times, or disease diagnostic test results. Different genetic and non-genetic regulations and data scenarios (representing realistic recording schemes) were simulated to validate SIRE and to assess their impact on the precision, accuracy and bias of parameter estimates. This analysis revealed that with few exceptions, SIRE provides unbiased, accurate parameter estimates associated with all three host traits. For most scenarios, SNP effects associated with recoverability can be estimated with highest precision, followed by susceptibility. For infectivity, many epidemics with few individuals give substantially more statistical power to identify SNP effects than the reverse. Importantly, precise estimates of SNP and other effects could be obtained even in the case of incomplete, censored and relatively infrequent measurements of individuals’ infection or survival status, albeit requiring more individuals to yield equivalent precision. SIRE represents a new tool for analysing a wide range of experimental and field disease data with the aim of discovering and validating SNPs and other factors controlling infectious disease transmission., Author summary Effective approaches to reduce the spread of infectious disease transmission in populations are urgently needed. Reduction in disease spread is most effectively achieved by reducing, separately or in combination, individual (i) “susceptibility”, i.e. the relative risk to become infected when exposed to infectious individuals or material, (ii) “infectivity”, i.e. the propensity to transmit infection to others when infected, and/or by (iii) improving “recoverability”, i.e. the propensity to recover. However, to date it is impossible to assess how these three key epidemiological traits controlling disease transmission in a population are regulated by specific genes or interventions, as the necessary statistical methods for estimating genetic and non-genetic effects from available disease surveillance data don’t exist. This paper introduces a novel statistical method that can estimate, for the first time, genetic and non-genetic effects for host susceptibility, infectivity and recoverability simultaneously from a wide range of realistic disease surveillance data. The method has been incorporated into a user-friendly, freely available software tool called SIRE. SIRE can be applied to a range of experimental and field data and will help to move disease control significantly forward by simultaneously targeting multiple host traits affecting infectious disease spread.

The human population is growing, and as a result we need to produce more food whilst reducing the impact of farming on the environment. Selective breeding and genomic selection have had a transformational impact on livestock productivity, and now transgenic and genome-editing technologies offer exciting opportunities for the production of fitter, healthier and more-productive livestock. Here, we review recent progress in the application of genome editing to farmed animal species and discuss the potential impact on our ability to produce food.

BackgroundBreeding for disease resistance has become a highly desirable strategy for mitigating infectious disease problems in aquaculture. However, knowledge of the genetic relationship between resistance and other economically important traits, such as growth, is important to assess prior to including disease resistance into the breeding goal. Our study assessed the genetic correlations between growth and survival traits in a large bacterial infection challenge experiment. A population of 2,606 coho salmon individuals from 107 full-sibling families were challenged with the bacteria Piscirickettsia salmonis. Growth was measured as average daily gain prior (ADG0) and during (ADGi) the experimental infection and as harvest weight (HW). Resistance was measured as Survival time (ST) and binary survival (BS). Furthermore, individual measures of bacterial load (BL) were assessed as new resistance phenotypes and to provide an indication of genetic variation in tolerance in salmonid species.ResultsSignificant moderate heritabilities were estimated for ADG0 (0.30 ± 0.05), HW (0.38 ± 0.03), and for the survival traits ST (0.16 ± 0.03) and BS (0.18 ± 0.03). In contrast, heritabilities for ADGi and log-transformed BL were low (0.07 ± 0.02 (significant) and 0.04 ± 0.03, respectively), although these increased to moderate significant levels (0.20 ± 0.09 and 0.12 ± 0.05, respectively) when traits were assessed in survivors only. Significant and favorable genetic correlations were found between ADG0 and the growth traits ADGi (0.40 ± 0.16) and HW (0.64 ± 0.09), as well as with resistance as ST (0.43 ± 0.18), indicating that fish with higher genetic growth rate early on and prior to infection not only tend to maintain their genetic growth advantage until harvest, but also tend to grow faster and survive longer during infection. Furthermore, no robust unfavorable genetic correlations between ADG0 and any of the other traits considered in this study, in particular BL, was identified. Adding log BL as covariates into the models for growth under infection and survival provided an indication for genetic variation in tolerance.ConclusionsThese results suggest that selective breeding for early growth would be expected to simultaneously increase survival time and growth performance during an infection with Piscirickettsia salmonis after accounting for variation in bacterial load, and harvest weight in this coho salmon population, without negatively impacting on pathogen burden.

peer-reviewed Post-mixing aggression in pigs is a harmful and costly behaviour which negatively impacts both animal welfare and farm efficiency. There is vast unexplained variation in the amount of acute and chronic aggression that dyadic behaviours do not fully explain. This study hypothesised that certain pen-level network properties may improve prediction of lesion outcomes due to the incorporation of indirect social interactions that are not captured by dyadic traits. Utilising current SNA theory, we investigate whether pen-level network properties affect the number of aggression-related injuries at 24 hours and 3 weeks post-mixing (24hr-PM and 3wk-PM). Furthermore we compare the predictive value of network properties to conventional dyadic traits. A total of 78 pens were video recorded for 24hr post-mixing. Each aggressive interaction that occurred during this time period was used to construct the pen-level networks. The relationships between network properties at 24hr and the pen level injuries at 24hr-PM and 3wk-PM were analysed using mixed models and verified using permutation tests. The results revealed that network properties at 24hr could predict long term aggression (3wk-PM) better than dyadic traits. Specifically, large clique formation in the first 24hr-PM predicted fewer injuries at 3wk-PM and high betweenness centralisation at 24hr-PM predicted increased rates of injury at 3wk-PM. This study demonstrates that network properties present during the first 24hr-PM have predictive value for chronic aggression, and have potential to allow identification and intervention for at risk groups.

International audience; Viremia rebound is a common but undesirable phenomenon for various diseases, especially for the Porcine Respiratory and Reproductive Syndrome (PRRS), a major concern for the swine industry. What causes some individuals to experience viremia rebound while others manage to steadily clear the virus has however been subject to much speculation. Hypotheses are the emergence of viral escape mutants, re-infection and differences in immune competence. To test this last hypothesis, we developed a mathematical model describing the within-host immune response to PRRS infection. We included the major mechanisms found to influence PRRS infection, as well as their regulations at the between-cell scale. We developed a rigorous ABC-like optimisation method to fit our model to an extensive set of experimental data, consisting of non-rebounder and rebounder viremia profiles. We then compared, between both profiles, the estimated parameter values, the resulting immune dynamics and the efficacy of the underlying immune mechanisms. Confronted to experimental data, our model successfully captured the between-host variations observed in viremia data, including rebounds. Moreover, we found that rebound was promoted by high apoptosis, high cell infection and low cytolysis by cytotoxic lymphocytes, while increasing neutralisation was very efficient to prevent rebounds. These results show that viremia rebound can occur as a result of differences in the immune competence alone and offers, for the first time, insights into potential causative immune mechanisms generating rebound.

Background High resistance (the ability of the host to reduce pathogen load) and tolerance (the ability to maintain high performance at a given pathogen load) are two desirable host traits for producing animals that are resilient to infections. For Porcine Reproductive and Respiratory Syndrome (PRRS), one of the most devastating swine diseases worldwide, studies have identified substantial genetic variation in resistance of pigs, but evidence for genetic variation in tolerance has so far been inconclusive. Resistance and tolerance are usually considered as static traits. In this study, we used longitudinal viremia measurements of PRRS virus infected pigs to define discrete stages of infection based on viremia profile characteristics. These were used to investigate host genetic effects on viral load (VL) and growth at different stages of infection, to quantify genetic variation in tolerance at these stages and throughout the entire 42-day observation period, and to assess whether the single nucleotide polymorphism (SNP) WUR10000125 (WUR) with known large effects on resistance confers significant differences in tolerance. Results Genetic correlations between resistance and growth changed considerably over time. Individuals that expressed high genetic resistance early in infection tended to grow slower during that time-period, but were more likely to experience lower VL and recovery in growth by the later stage. The WUR genotype was most strongly associated with VL at early- to mid-stages of infection, and with growth at mid- to late-stages of infection. Both, single-stage and repeated measurements random regression models identified significant genetic variation in tolerance. The WUR SNP was significantly associated only with the overall tolerance slope fitted through all stages of infection, with the genetically more resistant AB pigs for the WUR SNP being also more tolerant to PRRS. Conclusions The results suggest that genetic selection for improved tolerance of pigs to PRRS is possible in principle, but may be feasible only with genomic selection, requiring intense recording schemes that involve repeated measurements to reliably estimate genetic effects. In the absence of such records, consideration of the WUR genotype in current selection schemes appears to be a promising strategy to improve simultaneously resistance and tolerance of growing pigs to PRRS. Electronic supplementary material The online version of this article (10.1186/s12711-018-0420-z) contains supplementary material, which is available to authorized users.

The objective of this study was to investigate the genetic relationships between skin lesion traits in group housed growing pigs as a measure of short- (in a newly mixed group) and long- (in a socially stable group) term aggression and commonly used commercial performance measures: growth, feed intake, feed efficiency, and carcass traits. Data on 2,413 growing pigs (138 groups) were available. Pigs were mixed into new social groups of 18 animals, and skin lesions were counted 24 h (SL24h) and 5 wk (SL5wk) postmixing. The animal model was used to estimate genetic parameters for skin lesion traits, test daily gain, lifetime daily gain, daily feed intake, feed efficiency (calculated as test daily gain divided by daily feed intake), loin depth, back fat, and HCW. Skin lesions had a heritable component, ranging from 0.08 for anterior SL24h to 0.22 for central SL5wk and would, therefore, be suitable as a method of phenotyping aggression for selection purposes. Significant positive genetic correlations were found between SL24h and SL5wk (0.46 to 0.81). Positive genetic correlations were also found between SL24h (central and posterior body regions) or SL5wk (all body regions) and the production traits lifetime daily gain, test daily gain, and HCW (0.29 to 0.54). Central SL24h, anterior SL5wk, and posterior SL5wk were found to correlate positively with feed efficiency (0.39 to 0.50), suggesting that pigs with more lesions convert feed more efficiently. Where significant, the magnitude of phenotypic correlations was low but positive (0.07 to 0.10). These results suggest that, genetically, animals that receive many lesions show improved performance compared to those with few lesions, except for anterior SL24h, which had previously been shown to be genetically positively correlated with the initiation of nonreciprocal attacks. It may, therefore, be possible, via selection against anterior skin lesions at mixing, to reduce this form of 1-sided aggression without adversely affecting production traits.

Mixing of growing pigs results in aggressive contests between group members. As aggression serves to establish dominance relationships, it is possible that increased initial aggression may facilitate the formation of social hierarchies. The objective of the study was to investigate whether there is a phenotypic link between behavioural traits of aggression at mixing and increased long-term group social stability. Aggressive behavioural traits were recorded for 24 h after mixing, whereas the numbers of skin lesions (anterior, central and posterior) were obtained 24 h (SL24h) and 3 weeks post-mixing (SL3wk) for 1,166 pigs. At the group level, aggressive behavioural traits were positively correlated with anterior SL24h (0.34 to 0.67; P

International audience

International audience; Understanding the mechanisms determining the variability in infection dynamics between hosts or strains for a given pathogen is a key issue to better understand and control infection spread. In particular, effective and constant reduction in pathogen load is desirable over infection profiles exhibiting rebounds for the health of the infected individuals and the entire herd. In this context, PRRS virus is of a particular interest. Indeed, (i) infection profiles either with or without rebound have been reported for various viral strains and host breeds; (ii) mechanisms responsible for the emergence of rebounds are unclear; (iii) PRRS virus infections are associated with highly variable global immune responses and mechanisms responsible for the infection dynamics are still poorly understood.We aimed at identifying immune mechanisms that could explain PRRSv infection rebounds using a mathematical modelling approach of the within-host dynamics. Compared to published immunological models, our model provides both an integrative and detailed view of the immune response, representing the mechanisms at the between-cell scale. We fitted the model to a set of viremia data following an experimental challenge of 240 pigs with the same dose of a virulent PRRS virus strain resulting in both rebounder (109) and non-rebounder (131) profiles. Within a profile, experimental data exhibited a wide between-host variability in infection dynamics. Between both profiles, the variability in infection dynamics preceding the rebound (i.e. during the first 20 days of the 42-day post inoculation observation period) was similar. We compared, between rebounders and non-rebounders, the set of estimated parameter values, the resulting immune dynamics and the activation levels of the underlying immune mechanisms. The activation levels were quantified by the cumulated number of viral particles or infected cells that were created or destroyed over the infection time (i.e. the flows) by mechanisms of interest: viral replication, phagocytosis of viral particles, cell infection, viral neutralisation, cytolysis (by natural killers and cytotoxic lymphocytes) and apoptosis (by TNFα antiviral cytokine) of infected cells.Compared to non-rebounders, rebounders were characterised by a higher level of immune response activation, due to higher rates of cell infection. They also exhibited higher flows of infected cell cytolysis and apoptosis, but similar viral neutralisation flows despite higher infection and viral replication flows. This points out an inadequate production of neutralising antibodies.These results would suggest that vaccines or genetic selection promoting a strong neutralising response, ideally associated with strong antiviral and cytolytic responses, should prevent against infection with rebound.

International audience

Breeding for disease resistance has become a highly desirable strategy for mitigating infectious disease problems in aquaculture. However, knowledge of the genetic relationship between resistance and other economically important traits, such as growth, is important to assess prior to including disease resistance into the breeding goal. Our study assessed the genetic correlations between growth and survival traits in a large bacterial infection challenge experiment. A population of 2606 coho salmon individuals from 107 full-sibling families were challenged with the bacteria Piscirickettsia salmonis. Growth was measured as average daily gain prior (ADG0) and during (ADGi) the experimental infection and as harvest weight (HW). Resistance was measured as Survival time (ST) and binary survival (BS). Furthermore, individual measures of bacterial load (BL) were assessed as new resistance phenotypes and to provide an indication of genetic variation in tolerance in salmonid species. Resistant families showed lower bacterial load than those susceptible to P. salmonis. Furthermore, some surviving fish belonging to resistant families, were considered as bacterial-free because their bacterial load was below the detection threshold. Adding logBL as a covariate into the models for growth under infection and survival indicated significant genetic variation in tolerance.Significant moderate heritabilities were estimated for ADG0 (0.30 ± 0.05), HW (0.38 ± 0.03), and for the survival traits ST (0.16 ± 0.03) and BS (0.18 ± 0.03). In contrast, heritabilities for ADGi and log-transformed BL were low (0.07 ± 0.02 (significant) and 0.04 ± 0.03, respectively), although these increased to moderate significant levels (0.20 ± 0.09 and 0.12 ± 0.05, respectively) when traits were assessed in survivors only. Significant favorable genetic correlations were found between ADG0 and ADGi (0.40 ± 0.16), HW (0.64 ± 0.09), and with resistance as ST (0.43 ± 0.18), indicating that fish with higher genetic growth rate early on and prior to infection not only tend to maintain their genetic growth advantage until harvest, but also tend to grow faster and survive longer during infection. Although a significant unfavorable correlation (−0.50 ± 0.13) between HW and ST was found, this value decreased to −0.35 ± 0.20 using uncensored data from non-survivors only. Similarly, no robust unfavorable genetic correlations between ADG0 and LogBL, or ADG0 and any of the other traits considered in this study, was identified. These results suggest that selective breeding for early growth, in the current coho salmon population, would be expected to simultaneously increase survival time and growth performance during an infection with Piscirickettsia salmonis, without negatively impacting on pathogen burden