The adaptability of livestock to changing environments is based in particular on their genetic characteristics but also on the farming conditions to which they are subjected. However, this last point is poorly documented and little is known about its contribution to environmental challenges. The study by Quéméner and colleagues [1] addresses this question by assessing the effect of two hygiene conditions (good vs poor) on the distribution of cell populations present in adipose and muscle tissues of pigs divergently selected for feed efficiency [2].

The working hypothesis is that degraded housing conditions would be at the origin of an hyper stimulation of the immune system that can influence the homeostasis of adipose tissue and skeletal muscle and consequently modulate the cellular content of these tissues. Cellular compositions are thus interesting intermediate phenotypes for quantifying complex traits. The study uses pigs divergently selected for residual feed intake (RFI+ and RFI-) to assess whether there is a genetic effect associated with the observed phenotypes. 

The study characterized different stromal cell populations based on the expression of surface markers: CD45 to separate hematopoietic lineages and markers associated with the stem properties of mesenchymal cells: CD56, CD34, CD38 and CD140a. The authors observed that certain subpopulations are differentially enriched according to the hygiene condition (good vs poor) in adipose and skeletal tissue (CD45-CD56-) sometimes with an associated (genetic) lineage effect. This pioneering study validates a number of tools for characterizing cell subpopulations present in porcine adipose and muscle tissue. It confirms that housing conditions can have an effect on intermediate phenotypes such as intra-tissue cell populations. This pioneering work will pave the way to better understand the effects of livestock systems on tissue biology and animal phenotypes and to characterize the nature and function of progenitor cells present in muscle and adipose tissue.

[1] Quéméner A, Dessauge F, Perruchot MH, Le Floc’h N, Louveau I. 2022. The impact of housing conditions on porcine mesenchymal stromal/stem cell populations differ between adipose tissue and skeletal muscle. bioRxiv 2021.06.08.447546, ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2021.06.08.447546 

[2] Gilbert H, Bidanel J-P, Gruand J, Caritez J-C, Billon Y, Guillouet P, Lagant H, Noblet J, Sellier P. 2007. Genetic parameters for residual feed intake in growing pigs, with emphasis on genetic relationships with carcass and meat quality traits. Journal of Animal Science 85:3182–3188. https://doi.org/10.2527/jas.2006-590.

One of the key questions in pig industry nowadays is how health and performance of piglets can be improved by sow nutrition and milk composition. The levels of dietary fibers in sow’s gestation diet have positive effects observed on the litters. However, the composition of dietary fibers and the organization of polysaccharides within the cell wall in the different plants determine their physicochemical properties and, thereby, their behaviour in the gut of the sows and the subsequent physiological response of the animals. Hemicelluloses are polysaccharides constituents of the cell walls of plants, which are fermented in the gut to produce volatile fatty acids (VFA). These VFA can serve as energy source for milk synthesis and can thereby influence the development of suckling piglets. Palumbo and colleagues (1) proposed an original experimental design to compare diets with similar fiber contents but different hemicellulose levels, thanks to varying the sources of fibers used in the dietary formulations. Effects were studied on performance and health of lactating sows and their piglets during suckling period and until post-weaning. The dietary treatments had no effect on the total number of piglets weaned and, consequently, on litter weight at weaning. Milk yield was not influenced by the dietary treatments, but milk composition (lactose content, copper and threonine proportions) was affected by the level of hemicellulose in the maternal diets. With a decreasing hemicellulose level in sow diet, milk lactose content linearly decreased, whereas the copper and threonine contents linearly increased. There was no effect on piglet performance during the lactation period. During the second week of post-weaning, a quadratic increase in the incidence of diarrhoea and the number of days with diarrhoea for suckling piglets was observed with decreasing hemicellulose level in diet. Interestingly, the observed effects were partly different for piglets born with a low body weight. Indeed, there was a linear decrease in the incidence of diarrhoea and days with diarrhoea with decreased hemicellulose level in the maternal diet for those piglets, together with increased growth performance from birth to two weeks post-weaning. The authors postulated that the improved growth performance and the lower incidence of diarrhoea observed in small piglets during post-weaning period may be related to the increased abundance of threonine and copper and increased concentration of total VFA in milk of sows fed a diet with reduced hemicellulose levels. This study confirms the importance of early-life interventions to improve the post-weaning development and health of this sub-population of piglets.

Reference

(1) Palumbo F, Bee G, Trevisi P, and Girard M. (2022). Decreasing the level of hemicelluloses in sow's lactation diet affects the milk composition and post-weaning performance of low birthweight piglets. agriRxiv 2022.00116, ver 4 (R3), peer-reviewed and recommended by PCI Animal Science.   https://doi.org/10.31220/agriRxiv.2022.00116 

In this paper, Madouasse et al. (2021) present a generalisable Bayesian method for calculating the probability that a herd is free from disease, based on its prior disease status, and using data (herd status over time over a sufficient number of herds to inform the model) and reasonable prior estimates of the sensitivity and specificity of tests being used to determine animal infection status.  Where available, the modelling approach can also include relevant additional risk factors. 

By bringing all these factors together, it allows for most countries to use the same analytical approach on their data, with differences across datasets expressed in terms of the uncertainty around the central estimates. 

Having a single methodology that generates both a central estimate of disease freedom, and uncertainty thus provides the opportunity (given typically available data) to compare the probability of freedom across different systems. This is relevant in terms of the context of trade (since international trade of livestock in many cases depends on disease freedom). It is also important when evaluating, for example, transnational burdens of disease - and with different regulations in place in different countries, this is invaluable and can be used, for example, to assess risks of zoonotic infection including for zoonotic infection emergence. In the BVD example provided, the point is made that, since regular testing would probably pick up infection rapidly, the addition of risk factors is most valuable where testing is infrequent. This emphasizes the advantages of direct incorporation of risk factors into a single modelling framework. 

From a technical point of view, the analysis compares two different packages for the Markov Chain Monte Carlo (MCMC) implementation necesary to run the model. They show that, while there are some slight systematic differences, the estimates provided by the two methods are similar to each other; as one method is approximate but substantially more stable and generally much more computationally efficient, this is an important outcome. Both implementations are freely available and  with relevant additional software made similarly available by the authors. This is extremely welcome and should encourage its general adoption across different countries. 

No single model can of course account for everything. In particular, the reliance on past data means that there is an implicit assumption common to all purely statistical methods that the underlying risks have not changed. Thus projections to altered circumstances (changing underlying risk factors or systematic changes in testing or test performance) cannot so easily be incorporated, since these factors are complicated by the dynamics of infection that lie outside the modelling approach. Of course the well known quote from George Box that "all models are wrong" applies here - the generality of approach, statistical robustness and open source philosophy adopted make this model very useful indeed.  

Madouasse A, Mercat M, van Roon A, Graham D, Guelbenzu M, Santman Berends I, van Schaik G, Nielen M, Frössling J, Ågren E, Humphry RW, Eze J, Gunn GJ, Henry MK, Gethmann J, More SJ, Toft N, Fourichon C (2021) A modelling framework for the prediction of the herd-level probability of infection from longitudinal data. bioRxiv, 2020.07.10.197426, ver. 6 peer-reviewed and recommended by PCI Animal Science. https://doi.org/10.1101/2020.07.10.197426