@article{he_tiezzi_jiang_howard_huang_gray_choi_maltecca_2022, title={Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine}, volume={100}, ISSN={["1525-3163"]}, url={https://doi.org/10.1093/jas/skac231}, DOI={10.1093/jas/skac231}, abstractNote={Abstract The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 d of age). Rectal swabs were taken from each animal at 158 ± 4 d of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including 4 kernel functions (Linear Kernel, LK; Polynomial Kernel, PK; Gaussian Kernel, GK; Arc-cosine Kernel with one hidden layer, AK1), 2 dissimilarity methods (Bray-Curtis, BC; Jaccard, JA), and 2 ordination methods (Metric Multidimensional Scaling, MDS; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine.}, number={9}, journal={JOURNAL OF ANIMAL SCIENCE}, author={He, Yuqing and Tiezzi, Francesco and Jiang, Jicai and Howard, Jeremy and Huang, Yijian and Gray, Kent and Choi, Jung-Woo and Maltecca, Christian}, year={2022}, month={Sep} } @article{he_tiezzi_howard_huang_gray_maltecca_2022, title={Exploring the role of gut microbiota in host feeding behavior among breeds in swine}, volume={22}, ISSN={["1471-2180"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85122218151&partnerID=MN8TOARS}, DOI={10.1186/s12866-021-02409-6}, abstractNote={Abstract Background The interplay between the gut microbiota and feeding behavior has consequences for host metabolism and health. The present study aimed to explore gut microbiota overall influence on feeding behavior traits and to identify specific microbes associated with the traits in three commercial swine breeds at three growth stages. Feeding behavior measures were obtained from 651 pigs of three breeds (Duroc, Landrace, and Large White) from an average 73 to 163 days of age. Seven feeding behavior traits covered the information of feed intake, feeder occupation time, feeding rate, and the number of visits to the feeder. Rectal swabs were collected from each pig at 73 ± 3, 123 ± 4, and 158 ± 4 days of age. DNA was extracted and subjected to 16 S rRNA gene sequencing. Results Differences in feeding behavior traits among breeds during each period were found. The proportion of phenotypic variances of feeding behavior explained by the gut microbial composition was small to moderate (ranged from 0.09 to 0.31). A total of 21, 10, and 35 amplicon sequence variants were found to be significantly (q-value < 0.05) associated with feeding behavior traits for Duroc, Landrace, and Large White across the three sampling time points. The identified amplicon sequence variants were annotated to five phyla, with Firmicutes being the most abundant. Those amplicon sequence variants were assigned to 28 genera, mainly including Christensenellaceae_R-7_group, Ruminococcaceae_UCG-004, Dorea, Ruminococcaceae_UCG-014, and Marvinbryantia. Conclusions This study demonstrated the importance of the gut microbial composition in interacting with the host feeding behavior and identified multiple archaea and bacteria associated with feeding behavior measures in pigs from either Duroc, Landrace, or Large White breeds at three growth stages. Our study provides insight into the interaction between gut microbiota and feeding behavior and highlights the genetic background and age effects in swine microbial studies. }, number={1}, journal={BMC MICROBIOLOGY}, author={He, Yuqing and Tiezzi, Francesco and Howard, Jeremy and Huang, Yijian and Gray, Kent and Maltecca, Christian}, year={2022}, month={Jan} } @article{he_tiezzi_jiang_howard_huang_gray_choi_maltecca_2022, title={Use of Host Feeding Behavior and Gut Microbiome Data in Estimating Variance Components and Predicting Growth and Body Composition Traits in Swine}, volume={13}, ISSN={["2073-4425"]}, url={https://doi.org/10.3390/genes13050767}, DOI={10.3390/genes13050767}, abstractNote={The purpose of this study was to investigate the use of feeding behavior in conjunction with gut microbiome sampled at two growth stages in predicting growth and body composition traits of finishing pigs. Six hundred and fifty-one purebred boars of three breeds: Duroc (DR), Landrace (LR), and Large White (LW), were studied. Feeding activities were recorded individually from 99 to 163 days of age. The 16S rRNA gene sequences were obtained from each pig at 123 ± 4 and 158 ± 4 days of age. When pigs reached market weight, body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content were measured on live animals. Three models including feeding behavior (Model_FB), gut microbiota (Model_M), or both (Model_FB_M) as predictors, were investigated. Prediction accuracies were evaluated through cross-validation across genetic backgrounds using the leave-one-breed-out strategy and across rearing environments using the leave-one-room-out approach. The proportions of phenotypic variance of growth and body composition traits explained by feeding behavior ranged from 0.02 to 0.30, and from 0.20 to 0.52 when using gut microbiota composition. Overall prediction accuracy (averaged over traits and time points) of phenotypes was 0.24 and 0.33 for Model_FB, 0.27 and 0.19 for Model_M, and 0.40 and 0.35 for Model_FB_M for the across-breed and across-room scenarios, respectively. This study shows how feeding behavior and gut microbiota composition provide non-redundant information in predicting growth in swine.}, number={5}, journal={GENES}, publisher={MDPI AG}, author={He, Yuqing and Tiezzi, Francesco and Jiang, Jicai and Howard, Jeremy T. and Huang, Yijian and Gray, Kent and Choi, Jung-Woo and Maltecca, Christian}, year={2022}, month={May} } @article{telles_simon_scallan_gould_papich_he_lee_lidbury_steiner_kathrani_et al._2021, title={Evaluation of gastrointestinal transit times and pH in healthy cats using a continuous pH monitoring system}, volume={12}, ISSN={["1532-2750"]}, DOI={10.1177/1098612X211062096}, abstractNote={Objectives The aim of this study was to characterize gastrointestinal (GI) transit times and pH in healthy cats. Methods GI transit times and pH were measured in six healthy, colony-housed, purpose-bred spayed female cats using a continuous, non-invasive pH monitoring system in a sequential order design. For the first period (‘pre-feeding’), food was withheld for 20 h, followed by oral administration of a pH capsule. Five hours post-capsule administration, cats were meal-fed by offering them their daily allowance of food for 1 h. For the second period (‘post-feeding’), food was withheld for 24 h and cats were fed for 1 h, after which a pH capsule was orally administered. Studies in both periods were repeated three times. GI transit times and pH were compared between the two periods. Results The median transit times for the pre- and post-feeding periods, respectively, were: gastric – 94 mins (range 1–4101) and 1068 mins (range 484–5521); intestinal – 1350 mins (range 929–2961) and 1534 mins (range 442–2538); and GI – 1732 mins (range 1105–5451) and 2795 mins (range 926–6563). The median GI pH values for the first and second periods, respectively, were: esophageal – 7.0 (range 3.5–7.8) and 4.5 (range 2.9–6.4); gastric – 2.7 (range 1.7–6.2) and 2.0 (range 1.1–3.3); intestinal – 8.2 (range 7.6–8.7) and 7.8 (range 6.7–8.5); first-hour small intestinal – 8.2 (range 7.4–8.7) and 8.3 (range 7.9–8.6); and last-hour large intestinal – 8.5 (range 7.0–8.9) and 7.8 (range 6.3–8.7). Gastric ( P <0.0020) and intestinal pH ( P <0.0059) were significantly increased in the pre-feeding period compared with the post-feeding period. Conclusions and relevance Gastric and intestinal pH differed significantly when the capsule was administered 5 h prior to feeding compared with 1 h after feeding. Transit times for both periods showed high degrees of intra- and inter-individual variability. }, journal={JOURNAL OF FELINE MEDICINE AND SURGERY}, author={Telles, Naila J. and Simon, Bradley T. and Scallan, Elizabeth M. and Gould, Emily N. and Papich, Mark G. and He, Yuqing and Lee, Mu-Tien and Lidbury, Jonathan A. and Steiner, Jorg M. and Kathrani, Aarti and et al.}, year={2021}, month={Dec} } @article{maltecca_dunn_he_mcnulty_schillebeeckx_schwab_shull_fix_tiezzi_2021, title={Microbial composition differs between production systems and is associated with growth performance and carcass quality in pigs}, volume={3}, ISSN={["2524-4671"]}, url={https://doi.org/10.1186/s42523-021-00118-z}, DOI={10.1186/s42523-021-00118-z}, abstractNote={Abstract Background The role of the microbiome in livestock production has been highlighted in recent research. Currently, little is known about the microbiome's impact across different systems of production in swine, particularly between selection nucleus and commercial populations. In this paper, we investigated fecal microbial composition in nucleus versus commercial systems at different time points. Results We identified microbial OTUs associated with growth and carcass composition in each of the two populations, as well as the subset common to both. The two systems were represented by individuals with sizeable microbial diversity at weaning. At later times microbial composition varied between commercial and nucleus, with species of the genus Lactobacillus more prominent in the nucleus population. In the commercial populations, OTUs of the genera Lactobacillus and Peptococcus were associated with an increase in both growth rate and fatness. In the nucleus population, members of the genus Succinivibrio were negatively correlated with all growth and carcass traits, while OTUs of the genus Roseburia had a positive association with growth parameters. Lactobacillus and Peptococcus OTUs showed consistent effects for fat deposition and daily gain in both nucleus and commercial populations. Similarly, OTUs of the Blautia genus were positively associated with daily gain and fat deposition. In contrast, an increase in the abundance of the Bacteroides genus was negatively associated with growth performance parameters. Conclusions The current study provides a first characterization of microbial communities' value throughout the pork production systems. It also provides information for incorporating microbial composition into the selection process in the quest for affordable and sustainable protein production in swine. }, number={1}, journal={ANIMAL MICROBIOME}, publisher={Springer Science and Business Media LLC}, author={Maltecca, Christian and Dunn, Rob and He, Yuqing and McNulty, Nathan P. and Schillebeeckx, Constantino and Schwab, Clint and Shull, Caleb and Fix, Justin and Tiezzi, Francesco}, year={2021}, month={Aug} } @misc{he_maltecca_tiezzi_2021, title={Potential Use of Gut Microbiota Composition as a Biomarker of Heat Stress in Monogastric Species: A Review}, volume={11}, ISSN={["2076-2615"]}, url={https://doi.org/10.3390/ani11061833}, DOI={10.3390/ani11061833}, abstractNote={Heat stress is a current challenge for livestock production, and its impact could dramatically increase if global temperatures continue to climb. Exposure of agricultural animals to high ambient temperatures and humidity would lead to substantial economic losses because it compromises animal performance, productivity, health, and welfare. The gut microbiota plays essential roles in nutrient absorption, energy balance, and immune defenses through profound symbiotic interactions with the host. The homeostasis of those diverse gut microorganisms is critical for the host’s overall health and welfare status and also is sensitive to environmental stressors, like heat stress, reflected in altered composition and functionality. This article aims to summarize the research progress on the interactions between heat stress and gut microbiome and discuss the potential use of the gut microbiota composition as a biomarker of heat stress in monogastric animal species. A comprehensive understanding of the gut microbiota’s role in responding to or regulating physiological activities induced by heat stress would contribute to developing mitigation strategies.}, number={6}, journal={ANIMALS}, publisher={MDPI AG}, author={He, Yuqing and Maltecca, Christian and Tiezzi, Francesco}, year={2021}, month={Jun} } @article{he_tiezzi_maltecca_2020, title={249 Predicting body weight of finishing pigs using machine and deep learning algorithms}, volume={98}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skaa278.324}, DOI={10.1093/jas/skaa278.324}, abstractNote={Abstract Understanding and exploiting feeding patterns in swine could allow a reduced feed waste and minimized sorting losses. The objectives of this study were to evaluate the ability to predict whether a pig reached a target weight at finishing by using several algorithms and to compare the prediction using varying amounts of data during the growing period. Data were collected on 655 pigs from 75 to 166 days of age. Pigs were housed with 8 to 15 pigs and a Feed Intake Recording Equipment in each pen. Feed consumption, occupation time, and body weight per visit were recorded when a pig visited the feeder. Lasso Regression (LS), a machine learning algorithm: Random Forest (RF), and a deep learning algorithm: Long-short Term Memory (LSTM) network, were used to forecast whether pigs can reach 129 kg at the finishing stage (159–166 d). Times of visits, a sum of feed consumption, a sum of occupation time in the feeder every day, and age were used as predictors. Data were split into 6 slices by 14 days and used to calibrate the models and their predictive ability was tested with data corresponding to the last 8 days of the study period. The greatest correlation coefficients were 0.799, 0.828, and 0.868 using slice 6 (145–158 d) to train the LS, RF, and LSTM, respectively. The LS and LSTM algorithms had a smaller root mean squared error, 0.863 and 0.895 compared to the RF with 1.375 in the prediction. Overall, LS and LSTM performed best. Predictions using data closest to the finishing stage proved better. This study connects the dynamics of feeding behavior and feed intake data to growth using prediction methods that will hopefully accelerate the mainstream application of electronic feeders in pig production systems.}, number={Supplement_4}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={He, Yuqing and Tiezzi, Francesco and Maltecca, Christian}, year={2020}, month={Nov}, pages={176–176} } @article{he_maltecca_tiezzi_soto_flowers_2020, title={Transcriptome analysis identifies genes and co-expression networks underlying heat tolerance in pigs}, volume={21}, ISSN={["1471-2156"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85083872520&partnerID=MN8TOARS}, DOI={10.1186/s12863-020-00852-4}, abstractNote={Abstract Background Heat stress adversely affects pig growth and reproduction performance by reducing feed intake, weight gain, farrowing rate, and litter size. Heat tolerance is an important characteristic in pigs, allowing them to mitigate the negative effects of heat stress on their physiological activities. Yet, genetic variation and signaling pathways associated with the biological processes of heat-tolerant pigs are currently not fully understood. This study examined differentially expressed genes and constructed gene co-expression networks on mRNAs of pigs under different heat-stress conditions using whole transcriptomic RNA-seq analyses. Semen parameters, including total sperm number per ejaculate, motility, normal morphology rate, droplets, and rejected ejaculate rate, were measured weekly on 12 boars for two time periods: thermoneutral (January to May), and heat stress (July to October). Boars were classified into heat-tolerant (n = 6) and heat-susceptible (n = 6) groups based on the variation of their ejaculate parameters across the two periods. RNA was isolated from the blood samples collected from the thermoneutral and heat stress periods for gene expression analysis. Results Under heat stress, a total of 66 differentially expressed genes (25 down-regulated, 41 up-regulated) were identified in heat-tolerant pigs compared to themselves during the thermoneutral period. A total of 1041 differentially expressed genes (282 down-regulated, 759 up-regulated) were identified in the comparison between heat-tolerant pigs and heat-susceptible pigs under heat stress. Weighted gene co-expression network analysis detected 4 and 7 modules with genes highly associated (r > 0.50, p < 0.05) with semen quality parameters in heat-tolerant and heat-susceptible pigs under the effects of heat stress, respectively. Conclusion This study utilized the sensitivity of semen to heat stress to discriminate the heat-tolerance ability of pigs. The gene expression profiles under the thermoneutral and heat stress conditions were documented in heat-tolerant and heat-susceptible boars. Findings contribute to the understanding of genes and biological mechanisms related to heat stress response in pigs and provide potential biomarkers for future investigations on the reproductive performance of pigs. }, number={1}, journal={BMC GENETICS}, author={He, Yuqing and Maltecca, Christian and Tiezzi, Francesco and Soto, Emmanuel Lozada and Flowers, William L.}, year={2020}, month={Apr} }