Research perspectives on animal health in the era of artificial intelligence.

Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009-2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.

Phosphorus in agricultural soils: drivers of its distribution at the global scale.

Phosphorus (P) availability in soils limits crop yields in many regions of the World, while excess of soil P triggers aquatic eutrophication in other regions. Numerous processes drive the global spatial distribution of P in agricultural soils, but their relative roles remain unclear. Here, we combined several global data sets describing these drivers with a soil P dynamics model to simulate the distribution of P in agricultural soils and to assess the contributions of the different drivers at the global scale. We analysed both the labile inorganic P (PILAB ), a proxy of the pool involved in plant nutrition and the total soil P (PTOT ). We found that the soil biogeochemical background corresponding to P inherited from natural soils at the conversion to agriculture (BIOG) and farming practices (FARM) were the main drivers of the spatial variability in cropland soil P content but that their contribution varied between PTOT vs. PILAB . When the spatial variability was computed between grid cells at half-degree resolution, we found that almost all of the PTOT spatial variability could be explained by BIOG, while BIOG and FARM explained 38% and 63% of PILAB spatial variability, respectively. Our work also showed that the driver contribution was sensitive to the spatial scale characterizing the variability (grid cell vs. continent) and to the region of interest (global vs. tropics for instance). In particular, the heterogeneity of farming practices between continents was large enough to make FARM contribute to the variability in PTOT at that scale. We thus demonstrated how the different drivers were combined to explain the global distribution of agricultural soil P. Our study is also a promising approach to investigate the potential effect of P as a limiting factor for agroecosystems at the global scale.

Adaptive diversification in heterogeneous environments.

The role of environmental heterogeneity in the evolution of biological diversity has been studied only for simple types of heterogeneities and dispersals. This article broadens previous results by considering heterogeneities and dispersals that are structured by several environmental factors. It studies the evolution of a metapopulation, living in a network of patches connected by dispersal, under the effects of mutation, selection and migration. First, it is assumed that patches are equally connected and that they carry habitats characterized by several factors exerting selection pressures on several individual traits. Habitat factors may vary in the environment independently or they may be correlated. It is shown that correlations between habitat factors promote adaptive diversification and that this effect may be modified by trait interactions on survival. Then, it is assumed that patches are structured by two crossed factors, called the row and column factors, such that patches are more connected when they occur in the same row or in the same column. Environmental patterns in which each habitat appears in each row the same number of times and appears in each column the same number of times are found to hinder adaptive diversification.

Sampling Strategies for Evaluating the Rate of Adventitious Transgene Presence in Non-Genetically Modified Crop Fields.

According to E.U. regulations, the maximum allowable rate of adventitious transgene presence in non-genetically modified (GM) crops is 0.9%. We compared four sampling methods for the detection of transgenic material in agricultural non-GM maize fields: random sampling, stratified sampling, random sampling + ratio reweighting, random sampling + regression reweighting. Random sampling involves simply sampling maize grains from different locations selected at random from the field concerned. The stratified and reweighting sampling methods make use of an auxiliary variable corresponding to the output of a gene-flow model (a zero-inflated Poisson model) simulating cross-pollination as a function of wind speed, wind direction, and distance to the closest GM maize field. With the stratified sampling method, an auxiliary variable is used to define several strata with contrasting transgene presence rates, and grains are then sampled at random from each stratum. With the two methods involving reweighting, grains are first sampled at random from various locations within the field, and the observations are then reweighted according to the auxiliary variable. Data collected from three maize fields were used to compare the four sampling methods, and the results were used to determine the extent to which transgene presence rate estimation was improved by the use of stratified and reweighting sampling methods. We found that transgene rate estimates were more accurate and that substantially smaller samples could be used with sampling strategies based on an auxiliary variable derived from a gene-flow model.

Epidemics in markets with trade friction and imperfect transactions.

Market trade-routes can support infectious-disease transmission, impacting biological populations and even disrupting trade that conduces the disease. Epidemiological models increasingly account for reductions in infectious contact, such as risk-aversion behaviour in response to pathogen outbreaks. However, responses in market dynamics clearly differ from simple risk aversion, as are driven by other motivation and conditioned by "friction" constraints (a term we borrow from labour economics). Consequently, the propagation of epidemics in markets of, for example livestock, is frictional due to time and cost limitations in the production and exchange of potentially infectious goods. Here we develop a coupled economic-epidemiological model where transient and long-term market dynamics are determined by trade friction and agent adaptation, and can influence disease transmission. The market model is parameterised from datasets on French cattle and pig exchange networks. We show that, when trade is the dominant route of transmission, market friction can be a significantly stronger determinant of epidemics than risk-aversion behaviour. In particular, there is a critical level of friction above which epidemics do not occur, which suggests some epidemics may not be sustained in highly frictional markets. In addition, friction may allow for greater delay in removal of infected agents that still mitigates the epidemic and its impacts. We suggest that policy for minimising contagion in markets could be adjusted to the level of market friction, by adjusting the urgency of intervention or by increasing friction through incentivisation of larger-volume less-frequent transactions that would have limited effect on overall trade flow. Our results are robust to model specificities and can hold in the presence of non-trade disease-transmission routes.

Non-random distribution of extensive chromosome rearrangements in Brassica napus depends on genome organization.

Chromosome rearrangements are common, but their dynamics over time, mechanisms of occurrence and the genomic features that shape their distribution and rate are still poorly understood. We used allohaploid Brassica napus (AC, n = 19) as a model to analyze the effect of genomic features on the formation and diversity of meiotically driven chromosome rearrangements. We showed that allohaploid B. napus meiosis leads to extensive new structural diversity. Almost every allohaploid offspring carried a unique combination of multiple rearrangements throughout the genome, and was thus structurally differentiated from both its haploid parent and its sister plants. This large amount of genome reshuffling was remarkably well-tolerated in the heterozygous state, as neither male nor female fertility were strongly reduced, and meiosis behavior was normal in most cases. We also used a quantitative statistical model, which accounted for 75% of the observed variation in rearrangement rates, to show that the distribution of meiotically driven chromosome rearrangements was not random but was shaped by three principal genomic features. In descending order of importance, the rate of marker loss increased strongly with genetic distance from the centromere, the degree of collinearity between chromosomes, and the genome of origin (A < C). Overall, our results demonstrate that B. napus accumulates a large number of genetic changes, but these rearrangements are not randomly distributed in the genome. The structural genetic diversity produced by the allohaploid pathway and its role in the evolution of polyploid species compared to diploid meiosis are discussed.

Influence of cultivated landscape composition on variety resistance: an assessment based on wheat leaf rust epidemics.

In plant pathology, the idea of designing variety management strategies at the scale of cultivated landscapes is gaining more and more attention. This requires the identification of effects that take place at large scales on host and pathogen populations. Here, we show how the landscape varietal composition influences the resistance level (as measured in the field) of the most grown wheat varieties by altering the structure of the pathogen populations. For this purpose, we jointly analysed three large datasets describing the wheat leaf rust pathosystem (Puccinia triticina/Triticum aestivum) at the country scale of France with a Bayesian hierarchical model. We showed that among all compatible pathotypes, some were preferentially associated with a variety, that the pathotype frequencies on a variety were affected by the landscape varietal composition, and that the observed resistance level of a variety was linked to the frequency of the most aggressive pathotypes among all compatible pathotypes. This data exploration establishes a link between the observed resistance level of a variety and landscape composition at the national scale. It illustrates that the quantitative aspects of the host-pathogen relationship have to be considered in addition to the major resistance/virulence factors in landscape epidemiology approaches.

Modelling the effect of heterogeneity of shedding on the within herd Coxiella burnetii spread and identification of key parameters by sensitivity analysis.

Coxiella burnetii is the bacterium responsible for Q fever, a worldwide zoonosis. Ruminants, especially cattle, are recognized as the most important source of human infections. Although a great heterogeneity between shedder cows has been described, no previous studies have determined which features such as shedding route and duration or the quantity of bacteria shed have the strongest impact on the environmental contamination and thus on the zoonotic risk. Our objective was to identify key parameters whose variation highly influences C. burnetii spread within a dairy cattle herd, especially those related to the heterogeneity of shedding. To compare the impact of epidemiological parameters on different dynamical aspects of C. burnetii infection, we performed a sensitivity analysis on an original stochastic model describing the bacterium spread and representing the individual variability of the shedding duration, routes and intensity as well as herd demography. This sensitivity analysis consisted of a principal component analysis followed by an ANOVA. Our findings show that the most influential parameters are the probability distribution governing the levels of shedding, especially in vaginal mucus and faeces, the characteristics of the bacterium in the environment (i.e. its survival and the fraction of bacteria shed reaching the environment), and some physiological parameters related to the intermittency of shedding (transition probability from a non-shedding infected state to a shedding state) or to the transition from one type of shedder to another one (transition probability from a seronegative shedding state to a seropositive shedding state). Our study is crucial for the understanding of the dynamics of C. burnetii infection and optimization of control measures. Indeed, as control measures should impact the parameters influencing the bacterium spread most, our model can now be used to assess the effectiveness of different control strategies of Q fever within dairy cattle herds.

Characterization of the insoluble proteome of Lactococcus lactis by SDS-PAGE LC-MS/MS leads to the identification of new markers of adaptation of the bacteria to the mouse digestive tract.

We characterized the insoluble proteome of Lactococcus lactis using 1D electrophoresis-LC-MS/MS and identified 313 proteins with at least two different peptides. The identified proteins include 89 proteins having a predicted signal peptide and 25 predicted to be membrane-located. In addition, 67 proteins had alkaline isoelectric point values. Using spectra and peptide counts, we compared protein abundances in two different conditions: growth in rich medium, and after transit in the mouse digestive tract. We identified the large mechanosensitive channel and a putative cation transporter as membrane markers of bacterial adaptation to the digestive tract.

Sensitivity analysis to identify key parameters influencing Salmonella infection dynamics in a pig batch.

In the context of managed herds, epidemiological models usually take into account relatively complex interactions involving a high number of parameters. Some parameters may be uncertain and/or highly variable, especially epidemiological parameters. Their impact on the model outputs must then be assessed by a sensitivity analysis, allowing to identify key parameters. The prevalence over time is an output of particular interest in epidemiological models, so sensitivity analysis methods adapted to such dynamic output are needed. In this paper, such a sensitivity analysis method, based on a principal component analysis and on analysis of variance, is presented. It allows to compute a generalised sensitivity index for each parameter of a model representing Salmonella spread within a pig batch. The model is a stochastic discrete-time model describing the batch dynamics and movements between rearing rooms, from birth to slaughterhouse delivery. Four health states were introduced: Salmonella-free, seronegative shedder, seropositive shedder and seropositive carrier. The indirect transmission was modelled via an infection probability function depending on the quantity of Salmonella in the rearing room. Simulations were run according to a fractional factorial design enabling the estimation of main effects and two-factor interactions. For each of the 18 epidemiological parameters, four values were chosen, leading to 4096 scenarios. For each scenario, 15 replications were performed, leading to 61440 simulations. The sensitivity analysis was then conducted on the seroprevalence output. The parameters governing the infection probability function and residual room contaminations were identified as key parameters. To control the Salmonella seroprevalence, efficient measures should therefore aim at these parameters. Moreover, the shedding rate and maternal protective factor also had a major impact. Therefore, further investigation on the protective effect of maternal or post-infection antibodies would be needed.

Homeologous recombination plays a major role in chromosome rearrangements that occur during meiosis of Brassica napus haploids.

Chromosomal rearrangements can be triggered by recombination between distinct but related regions. Brassica napus (AACC; 2n = 38) is a recent allopolyploid species whose progenitor genomes are widely replicated. In this article, we analyze the extent to which chromosomal rearrangements originate from homeologous recombination during meiosis of haploid B. napus (n = 19) by genotyping progenies of haploid x euploid B. napus with molecular markers. Our study focuses on three pairs of homeologous regions selected for their differing levels of divergence (N1/N11, N3/N13, and N9/N18). We show that a high number of chromosomal rearrangements occur during meiosis of B. napus haploid and are transmitted by first division restitution (FDR)-like unreduced gametes to their progeny; half of the progeny of Darmor-bzh haploids display duplications and/or losses in the chromosomal regions being studied. We demonstrate that half of these rearrangements are due to recombination between regions of primary homeology, which represents a 10- to 100-fold increase compared to the frequency of homeologous recombination measured in euploid lines. Some of the other rearrangements certainly result from recombination between paralogous regions because we observed an average of one to two autosyndetic A-A and/or C-C bivalents at metaphase I of the B. napus haploid. These results are discussed in the context of genome evolution of B. napus.

Market analyses of livestock trade networks to inform the prevention of joint economic and epidemiological risks.

Conventional epidemiological studies of infections spreading through trade networks, e.g., via livestock movements, generally show that central large-size holdings (hubs) should be preferentially surveyed and controlled in order to reduce epidemic spread. However, epidemiological strategies alone may not be economically optimal when costs of control are factored in together with risks of market disruption from targeting core holdings in a supply chain. Using extensive data on animal movements in supply chains for cattle and swine in France, we introduce a method to identify effective strategies for preventing outbreaks with limited budgets while minimizing the risk of market disruptions. Our method involves the categorization of holdings based on position along the supply chain and degree of market share. Our analyses suggest that trade has a higher risk of propagating epidemics through cattle networks, which are dominated by exchanges involving wholesalers, than for swine. We assess the effectiveness of contrasting interventions from the perspectives of regulators and the market, using percolation analysis. We show that preferentially targeting minor, non-central agents can outperform targeting of hubs when the costs to stakeholders and the risks of market disturbance are considered. Our study highlights the importance of assessing joint economic-epidemiological risks in networks underlying pathogen propagation and trade.

PrBn, a major gene controlling homeologous pairing in oilseed rape (Brassica napus) haploids.

Precise control of chromosome pairing is vital for conferring meiotic, and hence reproductive, stability in sexually reproducing polyploids. Apart from the Ph1 locus of wheat that suppresses homeologous pairing, little is known about the activity of genes that contribute to the cytological diploidization of allopolyploids. In oilseed rape (Brassica napus) haploids, the amount of chromosome pairing at metaphase I (MI) of meiosis varies depending on the varieties the haploids originate from. In this study, we combined a segregation analysis with a maximum-likelihood approach to demonstrate that this variation is genetically based and controlled mainly by a gene with a major effect. A total of 244 haploids were produced from F(1) hybrids between a high- and a low-pairing variety (at the haploid stage) and their meiotic behavior at MI was characterized. Likelihood-ratio statistics were used to demonstrate that the distribution of the number of univalents among these haploids was consistent with the segregation of a diallelic major gene, presumably in a background of polygenic variation. Our observations suggest that this gene, named PrBn, is different from Ph1 and could thus provide complementary information on the meiotic stabilization of chromosome pairing in allopolyploid species.

Pathogen population dynamics in agricultural landscapes: the Ddal modelling framework.

Modelling processes that occur at the landscape scale is gaining more and more attention from theoretical ecologists to agricultural managers. Most of the approaches found in the literature lack applicability for managers or, on the opposite, lack a sound theoretical basis. Based on the metapopulation concept, we propose here a modelling approach for landscape epidemiology that takes advantage of theoretical results developed in the metapopulation context while considering realistic landscapes structures. A landscape simulator makes it possible to represent both the field pattern and the spatial distribution of crops. The pathogen population dynamics are then described through a matrix population model both stage- and space-structured. In addition to a classical invasion analysis we present a stochastic simulation experiment and provide a complete framework for performing a sensitivity analysis integrating the landscape as an input factor. We illustrate our approach using an example to evaluate whether the agricultural landscape composition and structure may prevent and mitigate the development of an epidemic. Although designed for a fungal foliar disease, our modelling approach is easily adaptable to other organisms.

Recovering power in association mapping panels with variable levels of linkage disequilibrium.

Association mapping has permitted the discovery of major QTL in many species. It can be applied to existing populations and, as a consequence, it is generally necessary to take into account structure and relatedness among individuals in the statistical model to control false positives. We analytically studied power in association studies by computing noncentrality parameter of the tests and its relationship with parameters characterizing diversity (genetic differentiation between groups and allele frequencies) and kinship between individuals. Investigation of three different maize diversity panels genotyped with the 50k SNPs array highlighted contrasted average power among panels and revealed gaps of power of classical mixed models in regions with high linkage disequilibrium (LD). These gaps could be related to the fact that markers are used for both testing association and estimating relatedness. We thus considered two alternative approaches to estimating the kinship matrix to recover power in regions of high LD. In the first one, we estimated the kinship with all the markers that are not located on the same chromosome than the tested SNP. In the second one, correlation between markers was taken into account to weight the contribution of each marker to the kinship. Simulations revealed that these two approaches were efficient to control false positives and were more powerful than classical models.

Dynamics of adaptation in spatially heterogeneous metapopulations.

The selection pressure experienced by organisms often varies across the species range. It is hence crucial to characterise the link between environmental spatial heterogeneity and the adaptive dynamics of species or populations. We address this issue by studying the phenotypic evolution of a spatial metapopulation using an adaptive dynamics approach. The singular strategy is found to be the mean of the optimal phenotypes in each habitat with larger weights for habitats present in large and well connected patches. The presence of spatial clusters of habitats in the metapopulation is found to facilitate specialisation and to increase both the level of adaptation and the evolutionary speed of the population when dispersal is limited. By showing that spatial structures are crucial in determining the specialisation level and the evolutionary speed of a population, our results give insight into the influence of spatial heterogeneity on the niche breadth of species.

Genetic regulation of meiotic cross-overs between related genomes in Brassica napus haploids and hybrids.

Although the genetic regulation of recombination in allopolyploid species plays a pivotal role in evolution and plant breeding, it has received little recent attention, except in wheat (Triticum aestivum). PrBn is the main locus that determines the number of nonhomologous associations during meiosis of microspore cultured Brassica napus haploids (AC; 19 chromosomes). In this study, we examined the role played by PrBn in recombination. We generated two haploid x euploid populations using two B. napus haploids with differing PrBn (and interacting genes) activity. We analyzed molecular marker transmission in these two populations to compare genetic changes, which have arisen during meiosis. We found that cross-over number in these two genotypes was significantly different but that cross-overs between nonhomologous chromosomes showed roughly the same distribution pattern. We then examined genetic recombination along a pair of A chromosomes during meiosis of B. rapa x B. napus AAC and AACC hybrids that were produced with the same two B. napus genotypes. We observed significant genotypic variation in cross-over rates between the two AAC hybrids but no difference between the two AACC hybrids. Overall, our results show that PrBn changes the rate of recombination between nonhomologous chromosomes during meiosis of B. napus haploids and also affects homologous recombination with an effect that depends on plant karyotype.