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Article

Calm Hu Sheep Have a Different Microbiome Profile and Higher Energy Utilization Efficiency Than Nervous Hu Sheep

by
Feifan Wu
1,2,
Luoyang Ding
1,2,
Jiasheng Wang
1,
Qiaoqing Chen
1,
Asmita Thapa
1,
Jianyu Mao
1 and
Mengzhi Wang
1,*
1
Laboratory of Metabolic Manipulation of Herbivorous Animal Nutrition, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
2
School of Agriculture and Environment (M087), University of Western Australia Institute of Agriculture, The University of Western Australia, Crawley, WA 6009, Australia
*
Author to whom correspondence should be addressed.
Fermentation 2023, 9(5), 470; https://doi.org/10.3390/fermentation9050470
Submission received: 11 April 2023 / Revised: 9 May 2023 / Accepted: 12 May 2023 / Published: 13 May 2023
(This article belongs to the Special Issue In Vitro Fermentation, 2nd Edition)
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Abstract

:
In sheep, temperament is known to affect animal welfare and the quality of animal products. While the composition of the gut microbiota is different between depressed patients and healthy human patients, in sheep, the influence of temperament on ruminal microbial species and abundance remains unknown. This study investigated the effects of temperament on parameters of rumen fermentation and microbial composition of rumen contents of Hu ram lambs. Using the pen score test, 6 lambs that scored 2 points or below (calm) and 6 lambs that scored 4 points or more (nervous) were selected from 100 ram lambs. The sheep were fed a standard diet for 60 days and rumen samples were collected at slaughter. The concentrations of propionic acid, isovaleric acid, valeric acid, and the ammonia nitrogen concentration were different between the calm and the nervous groups (p < 0.05). At the phylum level, there were significant differences in Bacteroidetes, Tenericutes, and Spirochetes (p < 0.05); and at the genus level, there were significant differences in the Christensenellaceae R-7 group, Treponema 2, Fibrobacter, and Ruminococcaceae UCG-003 (p < 0.05). The present study suggests that differences in the rumen microbiota between the calm group and the nervous group could have an impact on the metabolism of carbohydrates and polysaccharides and explain why Calm Hu sheep have a higher energy utilization efficiency than nervous Hu sheep. More studies are needed to further understand the effect of temperament on specific pathways of the rumen microbiota.

1. Introduction

In animals, temperament commonly refers to social or aggressive behavioral and physiological differences that are observed in individual animals in response to stressors or environmental challenges [1,2,3]. Temperament can be assessed by quantifying the response of animals to standardized tests [4,5,6]. For example, reactions caused by human operation or environmental stimulation include escape responses to human and environmental conditions [7], aggressive behaviors between individuals [8], emotional responses to stress [9], cognitive responses to new stimuli [10], and the calf-protecting behavior of cows [11]. Overall, animals with a calm temperament show less fear and anxiety, while nervous animals show higher levels of fear and anxiety [12]. Thus, temperament is considered as an objective measure of the degree of fear and anxiety that an animal experiences in the face of threats [13,14].
In farm animals, temperament can also affect animal welfare and productivity [15]. Cattle with a quiet temperament have higher feed efficiency than those with a nervous temperament [16]. Several studies have shown that production animals that are calm and docile grow faster, are easier to transport and feed, and have better meat quality than do individuals that are nervous and aggressive [4,17,18,19,20,21]. Calmer cows had better ADG, higher feed efficiency, and pregnancy rates than nervous cows [19], while irritable cattle had compromised performance in a feedlot, and poor carcass and meat quality traits than did calmer cattle [22]. In sheep, selection for temperament affects the sexual and maternal behavior of the female and the survival of newborn lambs [23].
Animal temperament can be influenced by many factors such as genetics and aspects of the environment. The temperament of farm animals such as sheep, cattle, and quail have a strong genetic basis. In sheep, the temperament of young lambs born to calm or nervous mothers is not affected by the genotype of the nurturing mother, as demonstrated by cross-breeding experiments [24]. Temperament, at least in part, determines how an individual responds to stressful situations and can vary considerably across species and sexes. Some studies have shown that temperament is related to the experience of stress during rearing [25]. In response to environmental stress, calm animals show a sluggish response while nervous animals are overreactive [23]. In association with the different behavioral responses, animals with calm or nervous temperaments have different cortisol responses to stressors [26].
In recent years, research on the gastrointestinal microbiota has become a new research hotspot. The gastrointestinal tract harbors a complex microbial network and its composition reflects the constant co-evolution of these microorganisms with the environment of the host [27]. In humans, the composition of the gut microbiota is altered in people with depression [28,29] and the abundance of specific genera is correlated with behavioral characteristics that are linked to personality [30]. Similarly, in animals, the gut microbiome can influence the stress response, anxiety, and depressive-like behaviors, as well as social behavior and communication [31,32]. There is also evidence that the relationship operates both ways, because as well as the gastrointestinal microbiome being a probable cause of temperament, physiological and behavioral changes that are associated with temperament can affect the gut microbiome [33]. In mice, the intestinal microbiota may be involved in depression-like behavior by altering glycerophospholipid metabolism in the gut–brain axis [34]. The composition of intestinal microbiota is different between mice with depression behavior and healthy mice [35]. In addition, the loss of intestinal microbiota can induce depression-like behavior in mice [35].
All the above studies show that, at least in monogastric animals, there is an interaction between temperament and the gastrointestinal microbiota. In ruminant animals, rumen function has a critical impact on the production and health of the host ruminant. Bacteria, which are the most abundant, diverse, and metabolically active ruminal microbes, enable the ruminant to ferment plant proteins and polysaccharides to generate the nutrients that are necessary for maintenance and growth [36,37]. However, it is not known if, such as in monogastrics, the gastrointestinal microbiota of ruminants is affected by temperament. Using Hu sheep, a breed of sheep that are fed all year round in Taihu Lake basin in China, we explored the relationship between temperament and the rumen microbiome. The rumen microbial composition was quantified using high-throughput sequencing technology and associated with the nutritional and physiological function of the Hu sheep with different temperament.

2. Materials and Methods

2.1. Selection for Temperament

In 2017, one hundred 5-month-old Hu ram lambs with similar body weights (30 ± 2 kg) were selected from a commercial farm (Huai’an, China).
The temperament of the individual lambs was scored on a 1 to 5 scale using the pen score test described by Kunkle et al. (1986) as cited by Hammond et al. (1996) [38,39]. Briefly, the sheep were kept in pen of 50 sheep and the individual response to the approach of an evaluator was made using the scoring scale: 1 = nonaggressive (docile): walks slowly, can approach closely, not excited by humans or facilities; 2 = slightly aggressive: runs along fences, will stand in corner if humans stay away, may pace fence; 3 = moderately aggressive: runs along fences, head up, and will run if humans move closer, stops before hitting gates and fences, avoids humans; 4 = aggressive: runs, stays in back of group, head high, and very aware of humans, may run into fences and gates even with some distance, will likely run into fences if alone in pen; and 5 = very aggressive: excited, runs into fences, runs over humans, and anything else in path, “crazy”. The scoring was done by two evaluators, and each evaluator repeated the scoring on each individual animal twice. Sheep with an average of 2 trials score of 2 points or lower were classified as calm and those sheep with an average of 2 trials score of 4 points or more were classified as nervous. Six ram lambs with the lowest score for calm temperament and six ram lambs with the highest score for nervous temperament were moved to individual pens for the duration of the experiment.

2.2. Animal Feeding Management

Each sheep was kept in an individual pen (1.25 m × 1 m) for the total duration of the feeding trial (7 + 60 days, see below). The feeding and management conditions were the same for all twelve animals in the two groups.
The basic feed was formulated according to the NRC Sheep Feeding Standard of the United States (2007). During the feeding period, the diet was fed in the form of TMR with a concentrate to forage ratio of 60:40. The feed samples were fried in an oven at 135 °C for 3 h to obtain the DM (AOAC, 1990 [40]; Method No. 930.15). The total N was detected using the Kjeldahl method; the crude protein content was calculated as 6.25 × N (Method No. 984.13); the ether extract (EE) was measured using the Soxhlet system (Method No. 954.02); the acid detergent fiber (ADF); and neutral detergent fiber (NDF) of diet were analysed using method described by Soest, Robertson, and Lewis (1991) [41]. Digestible energy, calcium, and total phosphorus were calculated from the composition of ingredients (Table 1).
The feed refusals were measured daily before the morning feeding. Each daily feed was about 3% of the body weight with the amount offered being adjusted daily to provide a slight surplus each day. The sheep were fed twice a day, at 6:00 am and 6:00 pm. The sheep were free to eat and drink at all times.
The sheep were acclimated to the diet for a pre-trial period of seven days and then the experimental feeding trial lasted for 60 days. During the experiment, the feed remaining each day was collected and weighed, and the dry matter intake of each experimental animal was recorded. After the 60-day feeding trial, the ram lambs were transported approximately 50 km to an abattoir where they were kept in a lairage and supplied only with water for 24 h before slaughter, as per industry practice. The animals were slaughtered and processed by professional butchers.

2.3. Sample Collection and Processing

Fresh rumen content was collected from each sheep within 15 min after slaughter using an aseptic technique as previously validated [42,43]. The pH of the rumen fluid was measured using a portable pH meter (PB-21, Beijing Sartorius Scientific Instruments, Beijing, China). Samples of rumen content were filtered through four layers of sterilized gauze to collect filtrate, and distributed into 10 mL centrifuge tubes and stored at −20 °C. In addition, samples of unfiltered rumen content were stored in liquid nitrogen for the determination of rumen microbial diversity.

2.4. Measurement Indexes and Methods

2.4.1. Determination of Rumen Fermentation Parameters

Concentrations of volatile fatty acid (VFA) in the rumen fluid were determined by gas chromatography using a Shimadzu GC-14B (Kyoto, Japan) fitted with a capillary column (CP-WAX52 CB, 30 m × 0.53 m × 1 µm). The rumen contents were thawed at 4 °C and centrifuged at 12,000× g for 5 min. One milliliter of supernatant was taken and added to 0.2 mL of 20% metaphosphoric acid containing 60 mM Crotunic acid (internal standard method), filtered through a 0.22 μm needle filter, and analyzed by gas chromatography. The temperature of the injector and detector was 200 °C. The initial column temperature was 100 °C, and the temperature was raised to 150 °C at 3 °C/min. The sensitivity was set to 101, and the attenuation was 25 [44]. The concentration of ammonia nitrogen was determined by phenol-sodium hypochlorite colorimetry [45].

2.4.2. DNA Extraction and High-Throughput Sequencing

The total microbial DNA of the rumen fluid from each animal was extracted using a fecal genome extraction kit (Tiangen Biotechnology Co., Ltd., Beijing, China). The concentration and purity of total DNA (OD 260/280 and OD 260/230) was determined by an ultramicro spectrophotometer (NanoDrop-1000, Thermiel), and DNA quality was determined by agarose-gel electrophoresis.
The DNA was stored in a −20 °C refrigerator until high-throughput sequencing. A Novaseq-PE 250 sequencing platform was used in the sequencing system (SanDiego, CA, USA), and the sequencing company was Nanjing Jisi Huiyuan Biotechnology Co., Ltd. (Nanjing, China). Specific primers of the 16S v4–v5 region were designed to amplify specific regions, and fragments of about 420 bp were amplified. The fragments were sequenced using a Hiseq 2500 platform.
The raw data obtained from the sequencer were spliced and filtered. Then, OTUs (operational taxonomic units), clustering, and species classification analysis were performed. The sequence of each OTU was annotated to obtain the corresponding species information and the species-based abundance distribution. Multi-sequence alignment of OTUs was performed to construct the phylogenetic relationship.

2.5. Statistical and Abundance Analyses

The effect of temperament on pH, fermentation parameters, phylum strains, and genus present in the rumen fluid were analyzed using t-tests performed in SPSS 13.0 software for Windows. The community structure was compared between temperaments using a principal component analysis. The bar graph of KEGG function was made by GraphPad Prism 6.0 software. The Software package R (V3.1.1.) was used to build the Venn diagram, PCA, and the graphs of abundance of microbiome composition. The results are presented as the mean ± SEM. Significance was considered when p ≤ 0.05.

3. Results

3.1. The Effect of Temperament on Growth and Feed Utilization

There was no difference in the body weight of the two groups of lambs at the start of the experiment and after 67 days of dietary treatment (Table 2). There was no effect of temperament on the average daily weight gain or the daily feed intake of dry matter (Table 2).

3.2. Rumen Fermentation Parameters

The total volatile fatty acids, as well as the proportions of individual acids making up the total, pH, and ammonia nitrogen concentrations are presented in Table 3. The concentrations of propionic acid, isovaleric acid, and valeric acid were significantly higher in the nervous group than in the calm group (Table 3). There were no significant differences in the proportion of acetic acid, isobutyric acid, isobutyric acid, butyrate acid, total VFA concentration, acetic acid, or pH between the calm group and the nervous group (p > 0.05). The concentration of ammonia nitrogen was significantly higher in the calm group than it was in the nervous group (p < 0.05).

3.3. The Effect of Temperament on Rumen Microbial Structure

3.3.1. Rumen Bacterial Sequencing OTUs and Alpha Diversities

Of the 1414 OTUs detected overall, only 11 OTUs were unique to the calm group, and 20 to the nervous group. The other 1383 were shared between the groups (Figure 1). The detected OTU coverage index was greater than 97.67% for the treatment. There was no significant difference in Chao index between the two groups (p > 0.05). Shannon’s index and Simpson’s index were significantly lower in the calm group than in the nervous group (p < 0.05; Table 4). A principal component analysis of β diversity showed that there were significant differences in the bacterial composition between the two groups (p < 0.05; Figure 2).

3.3.2. The Effect of Temperament on the Relative Abundance of the Rumen Bacterial Community at the Phylum and Genus Level

Our taxonomic analysis showed that at the phylum level the bacterial community was predominantly Firmicutes, Bacteroidetes, Proteobacteria, Spirochaetes, and Tenericutes (Figure 3). At the genus level, the dominant groups were Prevotella and Prevotellaceae UCG-001 of phylum Bacteroidetes; Christensenellaceae R-7 group, Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-003 and Fibrobacter of phylum Firmicutes; and Treponema 2 of the phylum Spirochaetes (Figure 4).
At the phylum level, the relative abundance of Bacteroidetes and Spirochetes was significantly higher in the nervous group than that in the calm group (p < 0.05; Table 5). The relative abundance of Firmicutes was significantly higher in the calm group than in the nervous group (p < 0.05; Table 5). There was no significant difference in the relative abundance of Proteobacteria between the two groups (p > 0.05; Table 5).
At the genus level, there were no significant differences in the relative abundance of Prevotella, Prevotellaceae UCG-001, Ruminococcaceae UCG-010, and Ruminococcaceae NK4A214 between the two groups (p > 0.05; Table 6). The relative abundance of Christensenellaceae R-7 group in the calm group was significantly higher than in the nervous group (p < 0.05; Table 6). The relative abundance of Treponema 2, Fibrobacter, and Ruminococcaceae UCG-003 in the nervous group was significantly higher than in the calm group (p < 0.05; Table 6).

3.4. Prediction of Bacterial Functions via KEGG

The predictions of bacterial function from KEGG pathways are shown in Figure 5A,B. The genes that were identified are related to metabolism, genetic information processing, and environmental information processing. Based on metabolic pathways, most of the predicted genes are involved in carbohydrate metabolism and amino acid metabolism.
A comparative analysis of KEGG function prediction (Figure 6) showed that most of the functional genes that were different between the two temperament groups are related to carbohydrate metabolism and polysaccharide synthesis and metabolism. The genes related to carbohydrate metabolism were genes involved in N-glycan biosynthesis, mutual transformation of pentose and glucuronic acid, starch, and sucrose metabolism, glycosaminoglycan degradation, isoquinoline biogenic base biosynthesis, galactose metabolism, and glycosaminoglycan degradation. In addition, genes involved in lipoic acid metabolism, lipopolysaccharide biosynthesis, and lipopolysaccharide biosynthesis were also different between the two temperament groups.

4. Discussion

The aim of this study was to assess the effect of temperament on the rumen microbiome and associated fermentation parameters in Hu Sheep when they were fed a standard ration. Our results suggest that there is a strong interaction between temperament and the rumen bacterial population. Differences in the bacterial population at both the phylum and genus levels between the two temperaments could explain the differences in metabolite profiles that we observed in our study. In addition, the differences in bacterial abundance suggest that some specific metabolic pathways, such as pathways in carbohydrate metabolism, could be different between calm and nervous sheep. Being a correlational study, it is not possible for us to conclude whether the temperament of sheep affects their rumen microbiome or whether the bacterial population affects the temperament. To our knowledge, the present study is the first demonstration of an interaction between temperament and the microbiome in sheep.
Rumen function was different between the groups as shown by the specific differences in fermentation parameters between the two temperament groups. While the pH of the rumen fluid was not different between the two temperament groups, the rumen ammonia nitrogen was higher in the calm group than it was in the nervous group. The ruminal pH value of Hu sheep was maintained between 6.51 and 6.71 in both groups, which would benefit the decomposition of fiber and protein synthesis by bacteria [46,47]. Interestingly, the differences in the ruminal concentration of ammonia nitrogen between the two temperament groups suggest that bacterial activity was lower in the calm sheep because most bacterial species are known to be able to utilize ammonia for the synthesis of nitrogenous compounds [48]. The differences in bacterial activity could have affected the balance states of protein degradation and microbial protein synthesis and ultimately the concentration of ammonia nitrogen [49]. It has to be noted that the concentration of ammonia nitrogen measured in the present study (2 to 4 mg/100 mL) was low compared to published values for the optimal concentration of ruminal ammonia nitrogen to synthesize microbial protein (8 to 30 mg/100 mL) [50]. It is possible that the time of sampling or the duration of storage of the samples prior to analysis might have led to a loss of rumen ammonia from the samples. However, the difference in ammonia nitrogen that we identified between the groups is probably reliable because the rumen samples were all collected within one hour and stored and processed together.
The concentrations of propionic acid in the rumen fluid of calm Hu sheep were significantly lower than they were in the nervous Hu sheep, while the concentration of acetic acid was similar between the two temperament groups. Therefore, the greater ratio of acetic acid to propionic acid (Calm: ~6:1, Nervous: ~4.5:1) indicates that there was a change in rumen fermentation mode towards propionic acid fermentation in the calm Hu sheep. Overall, the specific differences in fermentation parameters that we observed in the present study concurs with our finding that the microbiome was different between the two temperament groups.
The bacterial population was different between the two temperament groups and the differences were observed at the phylum level and at the genus level suggested that there are differences in metabolic pathways between the calm and nervous sheep. The bacterial population was less diverse (Shannon index) and less rich (Simpson index) in the calm sheep than in the nervous sheep, but the number of species (Chao value) was not different between the two temperament groups.
The difference in the relative abundance of the two most dominant phyla, Bacteroidetes and Firmicutes, suggests that the temperament of Hu sheep could be linked to these two phyla in the rumen. The relative ratio of Bacteroidetes and Firmicutes was different between the calm and the nervous sheep, suggesting differences in metabolic pathways between the two temperaments. In cattle, both of those phyla are involved in carbohydrate and protein metabolism [51,52]. In the present study, the relative abundance of Bacteroidetes was significantly higher in the nervous group than it was in the calm group. The different abundance of Bacteroidetes should result in a better efficiency of degradation of carbohydrate and protein and non-fiber plant polysaccharides [53] in the nervous sheep. The nervous Hu sheep could have had a higher carbohydrate metabolism and decomposition rate than the calm Hu sheep. The abundance of Bacteroidetes can be affected by the type of plant fiber that is ingested [54]. It could be possible that the nervous Hu sheep ate less fiber substances and more non-fiber substances during feeding, which would have induced an increase in the Bacteroidetes population. Interestingly, since the pH of the rumen fluid was similar between the two temperament groups, the decreased abundance of Bacteroidetes in the calm sheep was not due to a decrease of rumen pH value which is known to negatively affect the size of the Bacteroidetes population [52]. By contrast, the relative abundance of Firmicutes was significantly higher in the calm group than it was in the nervous group. Firmicutes are involved mainly in carbohydrate metabolism and are well suited to a wide range of environmental conditions [55]. So, the greater abundance of Firmicutes suggests that the calm sheep might be better adapted to a wider range of environmental conditions.
Amongst the least abundant phyla, the relative abundance of Spirochetes was significantly higher in the nervous group than it was in the calm group. The phylum Spirochetes is known to ferment plant polymers such as pectin, xylan, and arabinogalactan [56]. Spirochetes can also interact with other cellulolytic bacteria [57]. We suggest that the nervous Hu sheep had a better absorption and possibly utilization of carbohydrates from the feedstuff than did the calm Hu sheep. Although there was no difference in the relative abundance of Proteobacteria between the calm and nervous groups, Proteobacteria was the third most abundant phylum, and plays an important role in rumen metabolism [58,59,60].
Our analysis of the microbiome at the genus level supports the notion that temperament affected the capacity of sheep to process plant materials by affecting the interactions between rumen microorganisms. The relative abundance of Fibrobacter, Treponema 2, and Ruminococcaceae UCG-003 was significantly higher in the nervous group than in the calm group. Fibrobacter are known to facilitate the degradation of cellulose and hemicellulose in the rumen [61,62]. In the rumen, Treponema are associated with the degradation of hemicellulose but do not utilize cellulose [63]. Treponema can affect the number of cellulolytic bacteria and then use soluble sugars that are released from cellulose by cellulolytic bacteria to produce metabolites such as succinate, acetate, and formate [63]. Altogether, Treponema enhances cellulose decomposition and, therefore, the rate of degradation of cellulose is higher in the nervous than the calm Hu sheep. Similarly, studies have provided evidence supporting the involvement of Ruminococcaceae in fiber degradation and ruminal biohydrogenation [64,65,66]. Overall, it is highly possible that the nervous Hu sheep are better at decomposing cellulose than the calm sheep. Lastly, the relative abundance of Christensenellaceae R-7 group in the calm group was significantly higher than that of the nervous group. Christensenellaceae R-7 group belongs to the Firmicutes phylum which are often the dominant bacteria promoting the decomposition of cellulose by gastrointestinal microorganisms [54,67]. Interestingly, the relative abundance of Christensenellaceae, and the gut microbiome can be affected by several factors including host genetics [68]. Therefore, we can speculate that the calm Hu sheep have more Christensenellaceae R-7 group due to an impact of their temperament genetics. Further studies are required to identify the mechanisms that connect temperament genetics to the abundance of Christensenellaceae. In depth studies are needed to better understand the specific pathways that underly the interactions between the rumen microbiota and temperament in sheep.

5. Conclusions

The differences between temperaments at the phylum and genus levels suggest that sheep with different temperaments could have a different carbohydrate and polysaccharide metabolism. The nervous sheep could be more likely to metabolize carbohydrates and more inclined to metabolize polysaccharides into glycosaminoglycan and lipopolysaccharides.

Author Contributions

M.W. designed the experiments and revised the manuscript. F.W., J.W., Q.C., A.T. and J.M. conducted the experiments. L.D. provided the experimental reagents and the equipment. F.W. analyzed the data and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the International Academic Exchange Fund for Graduate Students of Yangzhou University, the National 14th and 13th Five-Year Plan Key Research and Development Program (2021YFD1600702, 2017YFD0800200), the key program of State Key Laboratory of Sheep Genetic Improvement and Healthy Production (2021ZD07; NCG202232), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China.

Institutional Review Board Statement

The animal study protocol was approved by the Yangzhou University Animal Care and Use Committee of Jiangsu Province (China). Approval Code: SYXK (Su) 2016-0019. Approval Date: 6 April 2016.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be made available by request to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sutherland, M.A.; Rogers, A.R.; Verkerk, G.A. The effect of temperament and responsiveness towards humans on the behavior, physiology and milk production of multi-parous dairy cows in a familiar and novel milking environment. Physiol. Behav. 2012, 107, 329–337. [Google Scholar] [CrossRef] [PubMed]
  2. Lyons, D.M. Individual differences in temperament of dairy goats and the inhibition of milk ejection. Appl. Anim. Behav. Sci. 1989, 22, 269–282. [Google Scholar] [CrossRef]
  3. Beausoleil, N.J.; Blache, D.; Stafford, K.J.; Mellor, D.J.; Noble, A.D.L. Exploring the basis of divergent selection for ‘temperament’ in domestic sheep. Appl. Anim. Behav. Sci. 2008, 109, 261–274. [Google Scholar] [CrossRef]
  4. Burrow, H.M. Measurements of temperament and their relationships with performance traits of beef cattle. Anim. Breed. Abstr. 1997, 65, 477–495. [Google Scholar]
  5. Paredes-Sánchez, F.A.; Sifuentes-Rincón, A.M.; Casas, E.; Arellano-Vera, W.; Parra-Bracamonte, G.M.; Riley, D.G.; Welsh, T.H., Jr.; Randel, R.D. Novel genes involved in the genetic architecture of temperament in Brahman cattle. PLoS ONE 2020, 15, e0237825. [Google Scholar] [CrossRef] [PubMed]
  6. Tulloh, N.M. Behaviour of cattle in yards. II. A study of temperament. Anim. Behav. 1961, 9, 25–30. [Google Scholar] [CrossRef]
  7. dos Santos, F.C.; Peixoto, M.G.C.D.; Fonseca, P.A.D.S.; Pires, M.D.F.; Ventura, R.V.; Rosse, I.D.C.; Bruneli, F.A.T.; Machado, M.A.; Carvalho, M.R.S. Identification of candidate genes for reactivity in guzerat (bos indicus) cattle: A genome-wide association study. PLoS ONE 2017, 12, e0169163. [Google Scholar] [CrossRef]
  8. Schütz, K.E.; Rogers, A.R.; Poulouin, Y.A.; Cox, N.R.; Tucker, C.B. The amount of shade influences the behavior and physiology of dairy cattle. J. Dairy Sci. 2010, 93, 125–133. [Google Scholar] [CrossRef]
  9. Reefmann, N.; Wechsler, B.; Gygax, L. Behavioural and physiological assessment of positive and negative emotion in sheep. Anim. Behav. 2009, 78, 651–659. [Google Scholar] [CrossRef]
  10. Graunke, K.L.; Nürnberg, G.; Repsilber, D.; Puppe, B.; Langbein, J. Describing Temperament in an Ungulate: A Multidimensional Approach. PLoS ONE 2013, 8, e74579. [Google Scholar] [CrossRef]
  11. Vallée, A.; Daures, J.; van Arendonk, J.A.M.; Bovenhuis, H. Genome-wide association study for behavior, type traits, and muscular development in Charolais beef cattle1. J. Anim. Sci. 2016, 94, 2307–2316. [Google Scholar] [CrossRef] [PubMed]
  12. Blache, D.; Martin, G.; Maloney, S.K.; Martin, G. Towards Ethically Improved Animal Experimentation in the Study of Animal Reproduction. Reprod. Domest. Anim. 2008, 43, 8–14. [Google Scholar] [CrossRef] [PubMed]
  13. Boissy, A. Fear and Fearfulness in Animals. Q. Rev. Biol. 1995, 70, 165–191. [Google Scholar] [CrossRef] [PubMed]
  14. Boulos, Z.; Terman, M. Food availability and daily biological rhythms. Neurosci. Biobehav. Rev. 1980, 4, 119–131. [Google Scholar] [CrossRef]
  15. Costilla, R.; Kemper, K.E.; Byrne, E.M.; Porto-Neto, L.R.; Carvalheiro, R.; Purfield, D.C.; Doyle, J.L.; Berry, D.P.; Moore, S.S.; Wray, N.R.; et al. Genetic control of temperament traits across species: Association of autism spectrum disorder risk genes with cattle temperament. Genet. Sel. Evol. 2020, 52, 51. [Google Scholar] [CrossRef]
  16. Nkrumah, J.D.; Crews, D.H., Jr.; Basarab, J.A.; Price, M.A.; Okine, E.K.; Wang, Z.; Li, C.; Moore, S.S. Genetic and phenotypic relationships of feeding behavior and temperament with performance, feed efficiency, ultrasound, and carcass merit of beef cattle1. J. Anim. Sci. 2007, 85, 2382–2390. [Google Scholar] [CrossRef]
  17. Haskell, M.J.; Esimm, G.; Turner, S.P. Genetic selection for temperament traits in dairy and beef cattle. Front. Genet. 2014, 5, 368. [Google Scholar] [CrossRef]
  18. Fordyce, G.; Goddard, M.; Seifert, G. The measurement of temperament in cattle and the effect of experience and genotype. In Proceedings of the Australian Society for Animal Production; Pergamon Press: Sydney, NSW, Australia, 1982; pp. 329–332. [Google Scholar]
  19. Voisinet, B.D.; Grandin, T.; Tatum, J.D.; O’Connor, S.F.; Struthers, J.J. Feedlot cattle with calm temperaments have higher average daily gains than cattle with excitable temperaments. J. Anim. Sci. 1997, 75, 892–896. [Google Scholar] [CrossRef]
  20. Cooke, R.F.; Kunkle, B.E. Interdisciplinary Beef Symposium: Temperament and acclimation to human handling influence growth, health, and reproductive responses in Bos taurus and Bos indicus cattle1. J. Anim. Sci. 2014, 92, 5325–5333. [Google Scholar] [CrossRef]
  21. Cooke, R.F.; Moriel, P.; Cappellozza, B.I.; Miranda, V.F.B.; Batista, L.F.D.; Colombo, E.A.; Ferreira, V.S.M.; Miranda, M.F.; Marques, R.S.; Vasconcelos, J.L.M. Effects of temperament on growth, plasma cortisol concentrations and puberty attainment in Nelore beef heifers. Animal 2019, 13, 1208–1213. [Google Scholar] [CrossRef]
  22. Francisco, C.L.; Resende, F.D.; Benatti, J.M.B.; Castilhos, A.M.; Cooke, R.F.; Jorge, A.M. Impacts of temperament on Nellore cattle: Physiological responses, feedlot performance, and carcass characteristics. J. Anim. Sci. 2015, 93, 5419–5429. [Google Scholar] [CrossRef] [PubMed]
  23. Blache, D.; Bickell, S.L. Temperament and reproductive biology: Emotional reactivity and reproduction in sheep Biologia temperamental e reprodutiva: Reatividade emocional e reprodução em ovinos. Rev. Bras. De Zootec. 2010, 39, 401–408. [Google Scholar] [CrossRef]
  24. Bickell, S.; Poindron, P.; Nowak, R.; Chadwick, A.; Ferguson, D.; Blache, D. Genotype rather than non-genetic behavioural transmission determines the temperament of Merino lambs. Anim. Welf. 2009, 18, 459–466. [Google Scholar] [CrossRef]
  25. Sebastian, T.; Watts, J.M.; Stookey, J.M.; Buchanan, F.; Waldner, C. Temperament in beef cattle: Methods of measurement and their relationship to production. Can. J. Anim. Sci. 2011, 91, 557–565. [Google Scholar] [CrossRef]
  26. Rietema, S.E.; Blackberry, M.A.; Maloney, S.K.; Martin, G.B.; Hawken, P.A.R.; Blache, D. Twenty-four-hour profiles of metabolic and stress hormones in sheep selected for a calm or nervous temperament. Domest. Anim. Endocrinol. 2015, 53, 78–87. [Google Scholar] [CrossRef] [PubMed]
  27. Ley, R.E.; Peterson, D.A.; Gordon, J.I. Ecological and Evolutionary Forces Shaping Microbial Diversity in the Human Intestine. Cell 2006, 124, 837–848. [Google Scholar] [CrossRef] [PubMed]
  28. Jiang, H.; Ling, Z.; Zhang, Y.; Mao, H.; Ma, Z.; Yin, Y.; Wang, W.; Tang, W.; Tan, Z.; Shi, J.; et al. Altered fecal microbiota composition in patients with major depressive disorder. Brain Behav. Immun. 2015, 48, 186–194. [Google Scholar] [CrossRef]
  29. Naseribafrouei, A.; Hestad, K.; Avershina, E.; Sekelja, M.; Linløkken, A.; Wilson, R.; Rudi, K. Correlation between the human fecal microbiota and depression. Neurogastroenterol. Motil. 2014, 26, 1155–1162. [Google Scholar] [CrossRef]
  30. Johnson, K.V.-A. Gut microbiome composition and diversity are related to human personality traits. Hum. Microbiome J. 2020, 15, 100069. [Google Scholar] [CrossRef]
  31. Cryan, J.F.; Dinan, T.G. Mind-altering microorganisms: The impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 2012, 13, 701–712. [Google Scholar] [CrossRef]
  32. Sharon, G.; Sampson, T.R.; Geschwind, D.H.; Mazmanian, S.K. The Central Nervous System and the Gut Microbiome. Cell 2016, 167, 915–932. [Google Scholar] [CrossRef] [PubMed]
  33. Berg, G.; Rybakova, D.; Fischer, D.; Cernava, T.; Vergès, M.-C.C.; Charles, T.; Chen, X.; Cocolin, L.; Eversole, K.; Corral, G.H.; et al. Microbiome definition re-visited: Old concepts and new challenges. Microbiome 2020, 8, 103. [Google Scholar] [CrossRef] [PubMed]
  34. Wu, M.; Tian, T.; Mao, Q.; Zou, T.; Zhou, C.-J.; Xie, J.; Chen, J.-J. Associations between disordered gut microbiota and changes of neurotransmitters and short-chain fatty acids in depressed mice. Transl. Psychiatry 2020, 10, 350. [Google Scholar] [CrossRef] [PubMed]
  35. Zheng, P.; Zeng, B.; Zhou, C.; Liu, M.; Fang, Z.; Xu, X.; Zeng, L.; Chen, J.-J.; Fan, S.-H.; Du, X.; et al. Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host’s metabolism. Mol. Psychiatry 2016, 21, 786–796. [Google Scholar] [CrossRef] [PubMed]
  36. Deusch, S.; Camarinha-Silva, A.; Conrad, J.; Beifuss, U.; Rodehutscord, M.; Seifert, J. A structural and functional elucidation of the rumen microbiome influenced by various diets and microenvironments. Front. Microbiol. 2017, 8, 1605. [Google Scholar] [CrossRef]
  37. Seshadri, R.; Leahy, S.C.; Attwood, G.T.; Teh, K.H.; Lambie, S.C.; Cookson, A.L.; Eloe-Fadrosh, E.A.; Pavlopoulos, G.A.; Hadjithomas, M.; Varghese, N.J.; et al. Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection. Nat. Biotechnol. 2018, 36, 359–367. [Google Scholar] [CrossRef] [PubMed]
  38. Kunkle, W.E.; Baker, F.S.; Palmer, A.Z. Factors Affecting Performance of Florida Steers and Heifers in the Feedlot; University of Florida: Gainesville, FL, USA, 1986; p. 87. [Google Scholar]
  39. Hammond, A.; Olson, T.; Chase Jr, C.; Bowers, E.; Randel, R.; Murphy, C.; Vogt, D.; Tewolde, A. Heat tolerance in two tropically adapted Bos taurus breeds, Senepol and Romosinuano, compared with Brahman, Angus, and Hereford cattle in Florida. J. Anim. Sci. 1996, 74, 295–303. [Google Scholar] [CrossRef]
  40. Baur, F.J.; Ensminger, L.G. Association of official analytical chemists. J. Am. Oil Chem. Soc. 1977, 54, 171–172. [Google Scholar] [CrossRef]
  41. Van Soest, P.J.; Robertson, J.B.; Lewis, B.A. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 1991, 74, 3583–3597. [Google Scholar] [CrossRef]
  42. Fortina, R.; Patrucco, S.G.; Barbera, S.; Tassone, S. Rumen Fluid from Slaughtered Animals: A Standardized Procedure for Sampling, Storage and Use in Digestibility Trials. Methods Protoc. 2022, 5, 59. [Google Scholar] [CrossRef]
  43. Chaudhry, A.S. Slaughtered cattle as source of rumen fluid to evaluate supplements for in vitro degradation of grass nuts and barley Straw. Open Vet. Sci. J. 2008, 2, 16–22. [Google Scholar] [CrossRef]
  44. Kristensen, N.B. Quantification of whole blood Short-chain fatty acids by gas chromatographic determination of plasma 2-chloroethyl derivatives and correction for dilution space in erythrocytes. Acta Agric. Scand. Sect. A Anim. Sci. 2000, 50, 231–236. [Google Scholar] [CrossRef]
  45. Weatherburn, M.W. Phenol-hypochlorite reaction for determination of ammonia. Anal. Chem. 1967, 39, 971–974. [Google Scholar] [CrossRef]
  46. Enemark, J.M.D. The monitoring, prevention and treatment of sub-acute ruminal acidosis (SARA): A review. Vet. J. 2008, 176, 32–43. [Google Scholar] [CrossRef] [PubMed]
  47. Morgante, M.; Gianesella, M.; Casella, S.; Ravarotto, L.; Stelletta, C.; Giudice, E. Blood gas analyses, ruminal and blood pH, urine and faecal pH in dairy cows during subacute ruminal acidosis. Comp. Clin. Pathol. 2008, 18, 229–232. [Google Scholar] [CrossRef]
  48. Jin, D.; Zhao, S.; Zheng, N.; Beckers, Y.; Wang, J. Urea metabolism and regulation by rumen bacterial urease in ruminants—A review. Ann. Anim. Sci. 2018, 18, 303–318. [Google Scholar] [CrossRef]
  49. Santoso, B.; Kilmaskossu, A.; Sambodo, P. Effects of saponin from Biophytum petersianum Klotzsch on ruminal fermentation, microbial protein synthesis and nitrogen utilization in goats. Anim. Feed. Sci. Technol. 2007, 137, 58–68. [Google Scholar] [CrossRef]
  50. McDonald, P.; Edwards, R.A.; Greenhalge, J.F.; Morgan, C.A.; Sinclair, L.A.; Wilkinson, R.G. Animal Nutrition, 6th ed.; Pearson: London, UK, 2002. [Google Scholar]
  51. de Oliveira, M.N.V.; Jewell, K.A.; Freitas, F.S.; Benjamin, L.A.; Tótola, M.R.; Borges, A.C.; Moraes, C.A.; Suen, G. Characterizing the microbiota across the gastrointestinal tract of a Brazilian Nelore steer. Vet. Microbiol. 2013, 164, 307–314. [Google Scholar] [CrossRef]
  52. Hook, S.E.; Steele, M.A.; Northwood, K.S.; Dijkstra, J.; France, J.; Wright, A.-D.G.; McBride, B.W. Impact of subacute ruminal acidosis (SARA) adaptation and recovery on the density and diversity of bacteria in the rumen of dairy cows. FEMS Microbiol. Ecol. 2011, 78, 275–284. [Google Scholar] [CrossRef]
  53. Pitta, D.W.; Pinchak, W.E.; Dowd, S.; Dorton, K.; Yoon, I.; Min, B.R.; Fulford, J.D.; Wickersham, T.A.; Malinowski, D.P. Longitudinal shifts in bacterial diversity and fermentation pattern in the rumen of steers grazing wheat pasture. Anaerobe 2014, 30, 11–17. [Google Scholar] [CrossRef]
  54. Spence, C.; Wells, W.G.; Smith, C.J. Characterization of the Primary Starch Utilization Operon in the Obligate Anaerobe Bacteroides fragilis: Regulation by Carbon Source and Oxygen. J. Bacteriol. 2006, 188, 4663–4672. [Google Scholar] [CrossRef] [PubMed]
  55. Lamendella, R.; Santo Domingo, J.W.; Ghosh, S.; Martinson, J.; Oerther, D.B. Comparative fecal metagenomics unveils unique functional capacity of the swine gut. BMC Microbiol. 2011, 11, 103. [Google Scholar] [CrossRef] [PubMed]
  56. Paster, B.J.; Canale-Parola, E. Physiological diversity of rumen spirochetes. Appl. Environ. Microbiol. 1982, 43, 686–693. [Google Scholar] [CrossRef]
  57. Stanton, T.B.; Canale-Parola, E. Enumeration and selective isolation of rumen spirochetes. Appl. Environ. Microbiol. 1979, 38, 965–973. [Google Scholar] [CrossRef] [PubMed]
  58. Metzler-Zebeli, B.U.; Khol-Parisini, A.; Gruber, L.; Zebeli, Q. Microbial populations and fermentation profiles in rumen liquid and solids of Holstein cows respond differently to dietary barley processing. J. Appl. Microbiol. 2015, 119, 1502–1514. [Google Scholar] [CrossRef] [PubMed]
  59. Golder, H.; Denman, S.; McSweeney, C.; Wales, W.; Auldist, M.; Wright, M.; Marett, L.; Greenwood, J.; Hannah, M.; Celi, P.; et al. Effects of partial mixed rations and supplement amounts on milk production and composition, ruminal fermentation, bacterial communities, and ruminal acidosis. J. Dairy Sci. 2014, 97, 5763–5785. [Google Scholar] [CrossRef]
  60. Pitta, D.W.; Pinchak, W.E.; Indugu, N.; Vecchiarelli, B.; Sinha, R.; Fulford, J.D. Metagenomic analysis of the rumen microbiome of steers with wheat-induced frothy bloat. Front. Microbiol. 2016, 7, 689. [Google Scholar] [CrossRef]
  61. Jiang, W.; Wu, N.; Wang, X.; Chi, Y.; Zhang, Y.; Qiu, X.; Hu, Y.; Li, J.; Liu, Y. Dysbiosis gut microbiota associated with inflammation and impaired mucosal immune function in intestine of humans with non-alcoholic fatty liver disease. Sci. Rep. 2015, 5, 8096. [Google Scholar] [CrossRef]
  62. Neumann, A.P.; McCormick, C.A.; Suen, G. Fibrobacter communities in the gastrointestinal tracts of diverse hindgut-fermenting herbivores are distinct from those of the rumen: Ecology of Fibrobacter in hindgut-fermenting herbivores. Environ. Microbiol. 2017, 19, 3768–3783. [Google Scholar] [CrossRef]
  63. Stanton, T.B.; Canale-Parola, E. Treponema bryantii sp. nov., a rumen spirochete that interacts with cellulolytic bacteria. Arch. Microbiol. 1980, 127, 145–156. [Google Scholar] [CrossRef]
  64. Huws, S.A.; Kim, E.J.; Lee, M.R.F.; Scott, M.B.; Tweed, J.K.S.; Pinloche, E.; Wallace, R.J.; Scollan, N.D. As yet uncultured bacteria phylogenetically classified as Prevotella, Lachnospiraceae incertae sedis and unclassified Bacteroidales, Clostridiales and Ruminococcaceae may play a predominant role in ruminal biohydrogenation. Environ. Microbiol. 2011, 13, 1500–1512. [Google Scholar] [CrossRef] [PubMed]
  65. Gagen, E.J.; Padmanabha, J.; Denman, S.E.; McSweeney, C.S. Hydrogenotrophic culture enrichment reveals rumen Lachnospiraceae and Ruminococcaceae acetogens and hydrogen-responsive Bacteroidetes from pasture-fed cattle. FEMS Microbiol. Lett. 2015, 362, fnv104. [Google Scholar] [CrossRef] [PubMed]
  66. Opdahl, L.J.; Gonda, M.G.; St-Pierre, B. Identification of Uncultured Bacterial Species from Firmicutes, Bacteroidetes and CANDIDATUS Saccharibacteria as Candidate Cellulose Utilizers from the Rumen of Beef Cows. Microorganisms 2018, 6, 17. [Google Scholar] [CrossRef] [PubMed]
  67. Brulc, J.M.; Antonopoulos, D.A.; Miller, M.E.B.; Wilson, M.K.; Yannarell, A.C.; Dinsdale, E.A.; Edwards, R.E.; Frank, E.D.; Emerson, J.B.; Wacklin, P.; et al. Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc. Natl. Acad. Sci. USA 2009, 106, 1948–1953. [Google Scholar] [CrossRef]
  68. Waters, J.L.; Ley, R.E. The human gut bacteria Christensenellaceae are widespread, heritable, and associated with health. BMC Biol. 2019, 17, 83. [Google Scholar] [CrossRef] [PubMed]
Fermentation 09 00470 g001 550
Figure 1. The distribution of OTUs in Hu sheep with different temperaments.
Figure 1. The distribution of OTUs in Hu sheep with different temperaments.
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Figure 2. Principal component analysis of the β Diversity of the microbiome in Hu sheep with different temperaments.
Figure 2. Principal component analysis of the β Diversity of the microbiome in Hu sheep with different temperaments.
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Figure 3. Composition of rumen bacteria at the phylum level in Hu sheep with different temperaments.
Figure 3. Composition of rumen bacteria at the phylum level in Hu sheep with different temperaments.
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Figure 4. Composition of rumen bacteria at the genus level in Hu sheep with different temperaments.
Figure 4. Composition of rumen bacteria at the genus level in Hu sheep with different temperaments.
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Fermentation 09 00470 g005a 550Fermentation 09 00470 g005b 550
Figure 5. KEGG functional annotation cluster heat map by pathways (A) and by functional group (B).
Figure 5. KEGG functional annotation cluster heat map by pathways (A) and by functional group (B).
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Figure 6. Comparison of the of KEGG function prediction between Hu sheep with different temperaments. *: p < 0.05, **: p < 0.01.
Figure 6. Comparison of the of KEGG function prediction between Hu sheep with different temperaments. *: p < 0.05, **: p < 0.01.
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Table
Table 1. Composition and nutrient levels of the basal diet (DM basis).
Table 1. Composition and nutrient levels of the basal diet (DM basis).
ItemsContent (%)
Silage corn24.80
Leymus chinensis15.20
Corn18.00
Wheat bran9.78
Soybean meal16.20
Brown rice8.52
Beer yeast4.02
Corn gluten meal1.20
Calcium dihydrogen phosphate0.72
Stone powder0.96
Salt0.30
Premix (1)0.30
Total100.00
Nutrient (2)
Digestible Energy (MJ/kg)13.12
Crude protein (%)20.72
Neutral detergent fiber (%)31.80
Acid detergent fiber (%)19.62
Ether extract (%)2.10
Calcium (%)0.74
Total phosphorus (%)0.51
(1): The premix provided per kg of diet: CuSO4 25 mg, FeSO4·H2O 75 mg, ZnSO4·H2O 105 mg, CoCL2 0.0024 mg, Na2SeO3 0.016 mg, VA 12,000 IU, VD3 10,000 IU, VE 25 mg, Nicotinic acid 36 mg, Choline 1000 mg. (2): Digestible energy, calcium, and total phosphorus were calculated from the composition of ingredients.
Table
Table 2. Bodyweight, average weight gain, and dry matter intake in Hu sheep with different temperaments.
Table 2. Bodyweight, average weight gain, and dry matter intake in Hu sheep with different temperaments.
ItemsCalm GroupNervous Groupp-Value
Bodyweight (kg)
Start on the experiment29.7 ± 2.230.0 ± 1.80.30
End of the experiment47.7 ± 0.2349.3± 1.630.20
Average daily weight gain (kg/d)0.26 ± 0.220.36 ± 0.050.49
Daily dry matter intake (kg/d)1.48 ± 0.171.51 ± 0.230.30
Table
Table 3. Rumen fermentation parameters in Hu sheep with different temperaments.
Table 3. Rumen fermentation parameters in Hu sheep with different temperaments.
ItemsCalm GroupNervous Groupp-Value
pH6.70 ± 0.106.57 ± 0.060.116
Acetic acid (mmol/L)15.86 ± 1.7116.52 ± 1.060.754
Propionic acid (mmol/L)2.71 ± 0.233.55 ± 0.280.045
Isobutyric acid (mmol/L)0.28 ± 0.030.36 ± 0.050.194
Butyrate acid (mmol/L)1.13 ± 0.181.41 ± 0.230.372
Isovaleric acid (mmol/L)0.30 ± 0.030.45 ± 0.040.018
Valeric acid (mmol/L)0.13 ± 0.010.21 ± 0.030.022
Total VFA concentration (mmol/L)20.43 ± 1.8322.50 ± 1.680.424
Acetic acid/acid ratio (%)5.00 ± 0.804.67 ± 0.080.134
Ammonia nitrogen concentration (mg/100 mL)3.68 ± 0.431.94 ± 0.090.007
Table
Table 4. Effect of temperament on the number of species (Chao value) and indices of biodiversity (Shannon and Simpson indices) in the microbial population in the rumen fluid of Hu sheep.
Table 4. Effect of temperament on the number of species (Chao value) and indices of biodiversity (Shannon and Simpson indices) in the microbial population in the rumen fluid of Hu sheep.
Alpha Diversity IndexCalm GroupNervous Groupp-Value
Chao value1288.51 ± 12.081279.92 ± 13.770.649
Shannon index7.78 ± 0.068.15 ± 0.03< 0.01
Simpson index0.9850 ± 0.0010.9907 ± 0.0002< 0.01
Table
Table 5. Relative abundance (%) of phylum strains of Hu sheep with different temperaments.
Table 5. Relative abundance (%) of phylum strains of Hu sheep with different temperaments.
PhylumCalm GroupNervous Groupp-Value
Bacteroidetes55.72 ± 0.6458.74 ± 1.040.033
Firmicutes34.78 ± 0.6529.63 ± 0.39< 0.01
Proteobacteria2.87 ± 0.443.30 ± 0.830.650
Spirochaetes2.04 ± 0.243.06 ± 0.120.004
Tenericutes2.69 ± 0.102.32 ± 0.080.020
Table
Table 6. Relative abundance (%) of genus of Hu sheep with different temperaments.
Table 6. Relative abundance (%) of genus of Hu sheep with different temperaments.
GenusCalm GroupNervous Groupp-Value
Prevotella 116.77 ± 1.4216.60 ± 0.700.917
Christensenellaceae R-7 group13.37 ± 0.826.91 ± 0.61< 0.01
Prevotellaceae UCG-0013.35 ± 0.102.98 ± 0.580.546
Ruminococcaceae NK4A214 group3.20 ± 0.323.03 ± 0.190.649
Treponema 21.56 ± 0.212.31 ± 0.600.006
Ruminococcaceae UCG-0101.40 ± 0.961.65 ± 0.750.067
Ruminococcaceae UCG-0031.05 ± 0.051.38 ± 0.100.014
Fibrobacter0.76 ± 0.191.87 ± 0.11< 0.01
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Wu, F.; Ding, L.; Wang, J.; Chen, Q.; Thapa, A.; Mao, J.; Wang, M. Calm Hu Sheep Have a Different Microbiome Profile and Higher Energy Utilization Efficiency Than Nervous Hu Sheep. Fermentation 2023, 9, 470. https://doi.org/10.3390/fermentation9050470

AMA Style

Wu F, Ding L, Wang J, Chen Q, Thapa A, Mao J, Wang M. Calm Hu Sheep Have a Different Microbiome Profile and Higher Energy Utilization Efficiency Than Nervous Hu Sheep. Fermentation. 2023; 9(5):470. https://doi.org/10.3390/fermentation9050470

Chicago/Turabian Style

Wu, Feifan, Luoyang Ding, Jiasheng Wang, Qiaoqing Chen, Asmita Thapa, Jianyu Mao, and Mengzhi Wang. 2023. "Calm Hu Sheep Have a Different Microbiome Profile and Higher Energy Utilization Efficiency Than Nervous Hu Sheep" Fermentation 9, no. 5: 470. https://doi.org/10.3390/fermentation9050470

APA Style

Wu, F., Ding, L., Wang, J., Chen, Q., Thapa, A., Mao, J., & Wang, M. (2023). Calm Hu Sheep Have a Different Microbiome Profile and Higher Energy Utilization Efficiency Than Nervous Hu Sheep. Fermentation, 9(5), 470. https://doi.org/10.3390/fermentation9050470

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