Impact of high-wheat bran diet on sows’ microbiota, performances and progeny’s growth and health

Finding alternatives to antimicrobial growth promoters is part of the goal of improving sustainability in pig production. Dietary fibres are considered as health-promoting substances acting on pigs’ microbiota. This study aimed to investigate whether the enrichment of sows’ diet with high levels of wheat bran (WB) could impact the performances of sows and piglets’ health. Seven sows were fed a control diet (CON) and 8 sows a WB diet from day 43 of gestation (WB 240 g/kg DM) until the end of the lactation period (WB 140 g/kg DM). Diets were formulated to be iso-energetic and iso-nitrogenous by changing the proportions of some ingredients. Faeces were sampled at different time points (before treatment, during treatment: in gestation and lactation) to determine microbiota composition (sequencing with Illumina MiSeq). Milk was sampled weekly to determine lactose, fat and protein concentration by mid-infrared technology and IgA and IgG contents by ELISA. Before weaning (d26-27), piglets were euthanized, intestinal contents and tissues sampled for further analyses. Zootechnical performances of sows and piglets were recorded. Statistical analyses were performed using the SAS MIXED procedure and repeated measurements. Treatment never impacted piglets’ weight (P=0.51). Sows’ ingestion during the lactation period was comparable between both treatments until the last 4 days of lactation where the percentage of target ingestion was significantly (P<0.001) lower for the WB (66%) compared to the CON group (89%). No effect on sows’ backfat and weight changes was observed. An increased abundance of Lactobacillus spp. in feces of the WB group was observed in gestation before and after diet change (8.8% vs 15.1% of total bacteria). However, for the overall genera changes between treatments, it only seems to occur for minor groups of bacteria. Milk protein, fat, IgG and IgA were not affected by treatment, but a time-effect (P<0.001) was observed while treatment impacted (P<0.05) lactose content. In conclusion, sows’ performances were not affected by the high WB diet and more research on the piglets’ samples is foreseen

Improving taste and flavor in dairy product through milk analysis of free fatty by mid-infrared (MIR) spectrometry

editorial reviewe

Prediction of pregnancy state from milk mid-infrared (MIR) spectroscopy in dairy cows

Submitted 2020-07-14 | Accepted 2020-08-18 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.224-232Pregnancy assessment is a very important tool for the reproductive management in efficient and profitable dairy farms. Nowadays, mid-infrared (MIR) spectroscopy is the method of choice in the routine milk recording system for quality control and to determine standard milk components. Since it is well known that there are changes in milk yield and composition during pregnancy, the aim of this study was to develop a discriminant model to predict the pregnancy state from routinely recorded MIR spectral data. The data for this study was from the Austrian milk recording system. Test day records of Fleckvieh, Brown Swiss and Holstein Friesian cows between 3 and 305 days of lactation were included in the study. As predictor variables, the first derivative of 212 selected MIR spectral wavenumbers were used. The data set contained roughly 400,000 records from around 40,000 cows and was randomly split into calibration and validation set by farm. Prediction was done with Partial Least Square Discriminant Analysis. Indicators of model fit were sensitivity, specificity, balanced accuracy and Area Under Receiver Operating Characteristic Curve (AUC). In a first approach, one discriminant model for all cows across the whole lactation and gestation lengths was applied. The sensitivity and specificity of this model in validation were 0.856 and 0.836, respectively. Splitting up the results for different lactation stages showed that the model was not able to predict pregnant cases before the third month of lactation and vice versa not able to predict non-pregnancy after the third month of lactation. Consequently, in the second approach a prediction model for each different (expected) pregnancy stage and lactation stage was developed. Balanced accuracies ranged from 0.523 to 0.918. Whether prediction accuracies from this study are sufficient to provide farmers with an additional tool for fertility management, it needs to be explored in discussions with farmers and breeding organizations.Keywords: MIR spectroscopy, pregnancy prediction, dairy cow, PLSReferencesBalhara, A. K., Gupta, M., Singh, S., Mohanty, A. K., & Singh, I. (2013). Early pregnancy diagnosis in bovines: Current status and future directions. The Scientific World Journal, 2013. hhttps://doi.org/10.1155/2013/958540Bekele, N., Addis, M., Abdela, N., & Ahmed, W. M. (2016). Pregnancy Diagnosis in Cattle for Fertility Management: A Review. Global Veterinaria, 16(4), 355–364. https://doi.org/10.5829/idosi.gv.2016.16.04.103136Benedet, A., Franzoi, M., Penasa, M., Pellattiero, E., & De Marchi, M. (2019). Prediction of blood metabolites from milk mid-infrared spectra in early-lactation cows. Journal of Dairy Science, 102(12), 11298–11307. https://doi.org/10.3168/jds.2019-16937Delhez, P., Ho, P. N., Gengler, N., Soyeurt, H., & Pryce, J. E. (2020). Diagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy? Journal of Dairy Science, 103(4), 3264–3274. https://doi.org/10.3168/jds.2019-17473Egger-Danner, C., Fürst, C., Mayerhofer, M., Rain, C., & Rehling, C. (2018). ZuchtData Jahresbericht 2018. Vienna. [Online]. Available at: https://zar.at/Downloads/Jahresberichte/ZuchtData-Jahresberichte.html. [Accessed: 2020, May 15].Gengler, N., Tijani, A., Wiggans, G. R., & Misztal, I. (1999). Estimation of (Co)variance function coefficients for test day yield with a expectation-maximization restricted maximum likelihood algorithm. Journal of Dairy Science, 82(8), 1849.e1-1849.e23. https://doi.org/10.3168/jds.S0022-0302(99)75417-2Grelet, C., Fernández Pierna, J. A., Dardenne, P., Baeten, V., & Dehareng, F. (2015). Standardization of milk mid-infrared spectra from a European dairy network. Journal of Dairy Science, 98(4), 2150–2160. https://doi.org/10.3168/jds.2014-8764Grelet, C., Bastin, C., Gelé, M., Davière, J. B., Johan, M., Werner, A., Reding, R., Fernandes Pierna, J. A., Colinet, F. G., Dardenne, P., Gendler, N., Soyeurt, H. & Dehareng, F. (2016). Development of Fourier transform mid-infrared calibrations to predict acetone, β-hydroxybutyrate, and citrate contents in bovine milk through a European dairy network. Journal of Dairy Science, 99(6), 4816–4825. https://doi.org/10.3168/jds.2015-10477Hirpa, A., Yehualaw, B., Wube, A., Asnake, A., Jemberu, A., Medicine, V., & Box, P. O. (2018). Review on Pregnancy Diagnosis in Dairy Cows, 9(2), 45–55. https://doi.org/10.5829/idosi.jri.2018.45.55Ho, P. N., Bonfatti, V., Luke, T. D. W., & Pryce, J. E. (2019). Classifying the fertility of dairy cows using milk mid-infrared spectroscopy. Journal of Dairy Science. https://doi.org/10.3168/jds.2019-16412Humblot, P. (2001). Monitor Pregnancy and Determine the Timing , Frequencies and Sources of Embryonic Mortality in Ruminants. Theriogenology, 56(01), 1417–1433.Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28(5), 1–26.Lainé, A., Bel Mabrouk, H., Dale, L. M., Bastin, C., & Gengler, N. (2014). How to use mid-infrared spectral information from milk recording system to detect the pregnancy status of dairy cows. Communications in Agricultural and Applied Biological Sciences, 79(1), 33–38.Lainé, A., Bastin, C., Grelet, C., Hammami, H., Colinet, F. G., Dale, L. M., Gillon, A., Vandenplas, J., Deharend, F. & Gengler, N. (2017). Assessing the effect of pregnancy stage on milk composition of dairy cows using mid-infrared spectra. Journal of Dairy Science, 100(4), 2863–2876.https://doi.org/10.3168/jds.2016-11736Lantz, B. (2015). Machine Learning with R. Machine Learning (Second Edi). Packt Publishing Ltd. https://doi.org/10.1002/9781119642183.ch14Mineur, A., Köck, A., Grelet, C., Gengler, N., Egger-Danner, C., & Sölkner, J. (2017). First Results in the Use of Milk Mid-infrared Spectra in the Detection of Lameness in Austrian Dairy Cows Genomic evaluation View project MACSUR View project. Agriculturae Conspectus Scientifi Cus, Vol. 82(No. 2 (163-166)), (163-166). Retrieved from https://www.researchgate.net/publication/325450513Olori, V. E., Brotherstone, S., Hill, W. G., & McGuirk, B. J. (1997). Effect of gestation stage on milk yield and composition in Holstein Friesian dairy cattle. Livestock Production Science, 52(2), 167–176. https://doi.org/10.1016/S0301-6226(97)00126-7Pohler, K. G., Franco, G. A., Reese, S. T., Dantas, F. G., Ellis, M. D., & Payton, R. R. (2016). Past, present and future of pregnancy detection methods. Applied Reproductive Strategies in Beef Cattle 7-8 September 2016, 251–259.Rienesl, L., Khayatzadeh, N., Köck, A., Dale, L., Werner, A., Grelet, C., Gengler, N., Auer, F-J., Egger-Danner, C., Massart, X. & Sölkner, J. (2019). Mastitis detection from milk mid-infrared (MIR) spectroscopy in dairy cows. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 67(5), 1221–1226. https://doi.org/10.11118/actaun201967051221Santos, J. E. P., Thatcher, W. W., Chebel, R. C., Cerri, R. L. A., & Galvão, K. N. (2004). The effect of embryonic death rates in cattle on the efficacy of estrus synchronization programs. Animal Reproduction Science, 82–83, 513–535. https://doi.org/10.1016/j.anireprosci.2004.04.015SAS Institute Inc. (2017). SAS software 9.4. SAS Institute Inc., Cary, NC, USA.Soyeurt, H., Dehareng, F., Gengler, N., McParland, S., Wall, E., Berry, D. P., Coffey, P. & Dardenne, P. (2011). Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. Journal of Dairy Science, 94(4), 1657–1667. https://doi.org/10.3168/jds.2010-3408Soyeurt, H., Bastin, C., Colinet, F. G., Arnould, V. M.-R., Berry, D. P., Wall, E., Dehareng, F., Nguyen, H. N., Pardenne, P., Schefers, J., Vandenplas, J., Weigel, K., Coffey, M., Théron, L., Detilleux, J., Reding, E., Gengler, N. & McParland, S. (2012). Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis. Animal, 6(11), 1830–1838. https://doi.org/10.1017/s1751731112000791Toffanin, V., De Marchi, M., Lopez-Villalobos, N., & Cassandro, M. (2015). Effectiveness of mid-infrared spectroscopy for prediction of the contents of calcium and phosphorus, and titratable acidity of milk and their relationship with milk quality and coagulation properties. International Dairy Journal, 41, 68–73. https://doi.org/10.1016/j.idairyj.2014.10.002Vanlierde, A., Vanrobays, M.-L., Dehareng, F., Froidmont, E., Soyeurt, H., McParland, S., S., Lewis, E., Deighton, M. H., Grandl, F., Kreuzer, M., Gredler, B., Dardenne, P. & Gengler, N. (2015). Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra. Journal of Dairy Science, 98(8), 5740–5747. https://doi.org/10.3168/jds.2014-8436Vanlierde, A., Soyeurt, H., Gengler, N., Colinet, F. G., Froidmont, E., Kreuzer, M., Grandl, F., Bell, M., Lund, P., Olijhoek, D. W., Eugéne M., Martin, C., Kuhla, B. & Dehareng, F. (2018). Short communication: Development of an equation for estimating methane emissions of dairy cows from milk Fourier transform mid-infrared spectra by using reference data obtained exclusively from respiration chambers. Journal of Dairy Science, 101(8). https://doi.org/10.3168/jds.2018-14472

Pre-Weaning Inulin Supplementation Alters the Ileal Transcriptome in Pigs Regarding Lipid Metabolism.

peer reviewedPrebiotics, such as inulin, are non-digestible compounds that stimulate the growth of beneficial microbiota, which results in improved gut and overall health. In this study, we were interested to see if, and how, the ileal transcriptome altered after inulin administration in the pre-weaning period in pigs. Seventy-two Piétrain-Landrace newborn piglets were divided into three groups: (a) a control (CON) group (n = 24), (b) an inulin (IN)-0.5 group (n = 24), and (c) an IN-0.75 group (n = 24). Inulin was provided as a solution and administered twice a day. At week 4, eight piglets per group, those closest to the average in body weight, were sacrificed, and ileal scrapings were collected and analyzed using 3' mRNA massively parallel sequencing. Only minor differences were found, and three genes were differentially expressed between the CON and IN-0.5 group, at an FDR of 10%. All three genes were downregulated in the IN-0.5 group. When comparing the CON group with the IN-0.75 group, five genes were downregulated in the IN-0.75 group, including the three genes seen earlier as differentially expressed between CON and IN-0.5. No genes were found to be differential expressed between IN-0.5 and IN-0.75. Validation of a selection of these genes was done using qRT-PCR. Among the downregulated genes were Angiopoietin-like protein 4 (ANGPTL4), Aquaporin 7 (AQP7), and Apolipoprotein A1 (APOA1). Thus, although only a few genes were found to be differentially expressed, several of them were involved in lipid metabolism, belonging to the peroxisome proliferator-activated receptor (PPAR) signaling pathway and known to promote lipolysis. We, therefore, conclude that these lipid metabolism genes expressed in the ileum may play an important role when supplementing piglets with inulin early in life, before weaning

Early life programming of piglets' microbiota and gut health by maternal dietary fibre supplementation

Post-weaning diarrhoea (PWD) is a widespread disease causing loss of weight and mortality of the piglets. To cure or prevent PWD, the treatment of pigs with antibiotics is frequent. The overuse of these substances led to the appearance of multi-resistant bacteria, raising public health issues. Thus, finding sustainable alternatives to antibiotics for PWD curation is of major importance. Most research focusses on the use of substances like prebiotics able to affect the microbiota of the piglets, as gut microbiota is responsible for the maturation of the intestinal immune system. Promoting a beneficial microbiota as early in life as possible is a good strategy for a better future health and a lower prevalence of PWD. Our hypothesis was that using dietary fibres (wheat bran and resistant starch) in the diet of sows would alter their microbiota and in turn affect their piglets’ microbiota and future health. In addition, the ability of the two fibre sources to alter milk composition, also affecting piglets’ performances and health, was tested. This hypothesis was challenged with two animal experiments. Results indicated that wheat bran (WB) and resistant starch (RS) had the ability to alter sows’ microbiota during gestation but not anymore during lactation, possibly limiting a differential microbial transfer to their offspring. These two dietary fibre slightly altered milk composition. Maternal wheat bran had the ability to increase the villus height and villus to crypt ratio in the small intestine of the progeny, while resistant starch increased the gene expression of tight junction proteins at weaning. These two fibre sources included in a high level in sows’ diets did not affect their performance or their piglets’, making their use in animal diets realistic. A second objective of the thesis was to unravel whether the diet of sows could program the metabolism of piglets for later life, using them as model for human. For this, piglets were challenged with a high fat diet in order to induce low-grade inflammation and/or obesity symptoms. After 7 weeks on a high fat diet, piglets had an increased backfat thickness and higher serum cholesterol levels. The main findings are that feeding sows resistant starch increased the total sum of short-chain fatty acids (SCFA) production in the caecum and colon of their progeny, which is beneficial but did not affect the microbiota of the pigs. Moreover, maternal RS diet seemed to increase the barrier function of the colon due to a higher gene expression of tight junction proteins while the maternal effects on intestinal inflammation were contradictory for TNF-α and IFN-γ. It seems thus that the maternal diet had the ability to decrease gut permeability. However, the high fat diet did not alter the microbiota of the pigs, nor was it affected by the maternal diet. In conclusion, using dietary fibre in sows’ diet had the ability to alter their own microbiota during gestation and milk composition, but the impact on the piglet’s microbiota was rather limited. It could be thus interesting to use these diets on piglets’ themselves after birth to promote the establishment of beneficial bacteria. Although effects on the microbiota were limited, the maternal diet seemed to affect some aspects of the health of their progeny in later life

Different sources of resistant starch in vitro show contrasting fermentation and SCFA profiles

Resistant starch (RS) is well known to be fermented in the caecum and the colon of animals, increasing the production of short-chain fatty acids (SCFA), especially butyrate. The latter is health-promoting, exhibiting anti-inflammatory effects on the gut and hence it has been postulated that dietary strategies should aim for an increased intestinal butyrate production in pig production. In this study, five different purified sources of RS (high-amylose maize, potato and pea starches) have been tested in vitro for their fermentation kinetics and SCFA profiles as a preliminary step to include one of these substrates in the diet of sows to modulate intestinal microbiota and fermentation patterns. Briefly, after an in vitro hydrolysis with porcine pepsin and pancreatin, undigested residues recovered by dialysis (1,000 kD) were fermented in vitro for 72 hours in a gas test using sows faeces as microbial inoculum. Six vials per RS source were fermented, 3 were used for determination of SCFA profiles at five consecutive time-points and 3 for the fermentation kinetics based on the monitoring of gas volume at regular intervals. SCFA production and profile was measured by high performance liquid chromatography, while fermentation kinetics was mathematically modelled to allow proper comparison between RS sources. All statistical analyses were performed with the MIXED procedure of SAS and repeated measurements for SCFA. All investigated parameters were influenced (p15% of total SCFA from 12 hours after the beginning of the fermentation onwards) and exhibited an extensive and rapid fermentation (highest final gas volume (A) and gas production rate and lowest time to reach A/2) while high amylose maize produced the lowest butyrate proportion (<6% of the total SCFA) during the slowest and lowest fermentation. Therefore, pea starch appears to be the most promising to be used in pig nutrition to modulate intestinal fermentation

The determinants of adapting forest management practices to climate change : Lessons from a survey of French private forest owners

International audienceClimate change seriously impacts forest ecosystems. In order to maintain a healthy and sustainable forest cover, adaptation strategies should be implemented. This article proposes to deepen our understanding of the decision-making process of private forest owners in terms of adaptation decisions towards climate change. In particular, we question whether or not French private forest owners have already implemented adaptation strategies and if yes, we identify the determinants of this decision. We focus on the identification of the determinants of the probability to adapt and on the determinants of adopting each strategy separately (early harvest, thinning, irregular silviculture). A survey of more than 900 French private forest owners was conducted for the purpose of collecting both (1) objective variables: characteristics of the owners and the property; and (2) subjective vari-ables: perception of climate change and impacts. The results reveal that both types of variables explain the adaptation decision. In addition, we show that the determinants are different from one adaptation strategy to another, meaning that the adaptation decision should not be thought of in general but, instead, strategy-by-strategy