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Article

Based on the Co-Evolution of lncRNAs-Microbiota and Metabolites in Rumen Epithelium to Analyze the Adaptation Characteristics of Tibetan Sheep to Nutrient Stress in the Cold Season

by
Xiu Liu
1,*,
Xinyu Guo
1,
Yuzhu Sha
1,
Yanyu He
2,
Pengyang Shao
1,
Jiang Hu
1,
Jiqing Wang
1,
Shaobin Li
1 and
Zhiyun Hao
1
1
College of Animal Science and Technology/Gansu Key Laboratory of Herbivorous Animal Biotechnology, Gansu Agricultural University, Lanzhou 730070, China
2
School of Fundamental Sciences, Massey University, Palmerston North 4410, New Zealand
*
Author to whom correspondence should be addressed.
Fermentation 2023, 9(10), 892; https://doi.org/10.3390/fermentation9100892
Submission received: 4 September 2023 / Revised: 30 September 2023 / Accepted: 2 October 2023 / Published: 4 October 2023
(This article belongs to the Section Microbial Metabolism, Physiology & Genetics)
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Abstract

:
Based on the serious phenomenon of Tibetan sheep “growing strong in warm seasons and losing weight in cold seasons”, this study explores the regulation of lncRNAs, microbiota, and metabolites in the cold season adaptation of Tibetan sheep from the perspective of the co-evolution of the host genome (first genome) and microbiome (second genome). RNA-seq results showed that 172 DE lncRNAs were identified in the rumen epithelium of Tibetan sheep in warm and cold seasons, of which 87 DE lncRNAs were significantly up-regulated in cold seasons. KEGG enrichment showed that target genes of up-regulated lncRNAs were significantly enriched in TNF signaling and oxidative phosphorylation pathways. LncRNA-mRNA regulatory network indicated that DE lncRNAs were involved in nutrient stress in the cold season by targeting ATP1B2, CADPS, TLR5, and UGT1A6. Correlation analysis showed some lncRNAs were significantly correlated with acetic acid, propionic acid, butyric acid, and rumen epithelial histomorphology and had a negative correlation with Butyrivibrio-2 and Succiniclasticum (p < 0.05). In addition, differential metabolites bilirubin and lncRNAs were co-enriched in the bile secretion pathway. lncRNAs played an important role in the adaptation process of Tibetan sheep in the cold season, and mediate the host to participate in nutrient absorption, energy utilization, and immune response, indicating that the host genome and microbial genome promote Tibetan sheep to adapt to nutrient stress in the cold season through co-evolution.

1. Introduction

Tibetan sheep are the dominant breed and important genetic resource on the Qinghai-Tibet Plateau, and also the main economic source of local herdsmen. Tibetan sheep live in the Qinghai–Tibet Plateau with an altitude of more than 2500 m, and their grazing behavior is closely related to the changes in alpine herbage vegetation phenology [1]; furthermore, their growth and development are seriously affected by the plateau environment. In the grassed stage (the warm season corresponding to the alpine grazing area), the forage grass has high nutritional value and is rich in a large amount of protein, carbohydrates, and other nutrients, while in the dry grass stage (the cold season), the forage yield was seriously insufficient and the nutrition was insufficient, resulting in a serious seasonal imbalance and in a phenomenon of “rejuvenation in the warm season and loss of fat in the cold season” of Tibetan sheep. Therefore, how to efficiently adapt to nutrient stress in the cold season was the key measure to improve the productivity of Tibetan sheep. Rumen was the main place for forage fermentation of ruminants, and rumen microbiota was an important factor affecting the host’s energy acquisition and storage from the diet [2]. In addition to energy substrates available in arterial blood supply, such as glucose, glutamine, and free fatty acids, rumen epithelium can also use directly absorbed VFAs (Volatile fatty acids) as an energy source [3], especially butyric acid, which can further promote the growth of rumen papillae [4]. Rumen epithelium also contains specific proteins with structural and nutritional importance responsible for nutrient transport and metabolism, cell growth, and signal transmission [5]. Microbiota and hosts co-evolved in the structure and function to form interactive complexes, namely micro-ecosystems. According to the principles of system ecology, the more energy channels a system has, the higher its stability will be [6,7]. The higher the composition and richness of microbial species, the more channels the system can flow and the stronger the system function [8]. Microecosystems and hosts can adapt to the harsh plateau environment through co-evolution, in which diet was the main force shaping the composition and activity of intestinal microbiota [9], and the change of feeding habits was the core driving force of evolution. It had been found that the changes in dietary intake of high-altitude animals would change their enterotype, thereby mediating the development of nutritional homeostasis of high-altitude animals, which can better use nitrogen and energy to adapt to the cold season [10]. In conclusion, host and microbial genomes co-evolved to characterize the development of host and endosymbiont integration and associated cellular mechanisms by influencing metabolic collaboration and energy-related genes, such as the transport and uptake of VFAs [11,12].
Long chain non-coding RNA (lncRNA) was an autonomously transcribed non-coding RNA with a length greater than 200 bp. Studies have shown that lncRNA has a relatively low expression level [13], compared with protein-coding genes; lncRNA has strong spatial and temporal expression specificity and tissue specificity [14]. LncRNAs acted as “sponges” of competitive endogenous RNAs or miRNAs to regulate gene expression, thereby reducing the availability of miRNAs to targeted mRNAs [15]. According to the localization of lncRNAs and their specific interactions with DNA, RNA, and proteins, lncRNAs regulated chromatin function, the assembly and function of membrane-less nuclear bodies, and changed the stability and translation of cytoplasmic mRNAs, and interfered with signaling pathways [15]. Based on multi-omics studies, the mechanism of host-microbial interaction and its role in regulating rumen development in ruminants has been explored through the interaction between the early microbiome and the host transcriptome [16]. Zhong et al. found that cis and trans target genes of DE lncRNAs in four different developmental stages of goat rumen epithelium were mainly enriched in pathways related to the growth and metabolism of rumen cells, which provided a basis for the study of genetic regulation and molecular mechanism of ruminal development in ruminants [17]. Lu et al. explored the hypoxia adaptability of Tibetan sheep by studying lncRNA in the lung tissue of high-altitude and low-altitude Hu sheep [18]. However, studies on lncRNAs in rumen epithelial tissues of Tibetan sheep in warm and cold seasons were rarely reported. Therefore, the purpose of this study was to analyze the DE lncRNAs in rumen epithelial tissues of Tibetan sheep in warm and cold seasons and analyze the correlation between lncRNAs and its rumen microbiota and metabolites to reveal the adaptation of Tibetan sheep to nutrient stress in cold seasons, and provide a basis for the study of Tibetan sheep’s adaptability to the plateau.

2. Methods

2.1. Experimental Design and Sample Collection

Tibetan sheep come from the same herd in Zuogaimanma Township (Latitude: 34.992207, Longitude: 103.051869), Hezuo City, Gannan Tibetan Autonomous Prefecture, Gansu Province (Altitude: 3300 m), and were selected as laboratory subjects after obtaining the consent of herdsmen. Twelve one-year-old Tibetan ewes with similar body weights (35.12 ± 1.43 kg) and good health conditions were selected, and all of them were in the local traditional natural grazing management state without any supplementary feeding. Samples were collected in the warm season of 2019 (July, n = 6) and the cold season of 2019 (December, n = 6). The experimental animals were treated in strict accordance with the requirements of the Ethics Committee. Before grazing in the morning, the test sheep were anesthetized only with pentobarbital sodium at a dose of 30 mg/kg body weight, diluted with normal saline into a 10% solution, and injected intravenously. After the sheep were completely anesthetized and slaughtered using the traditional local method of carotid bloodletting, the animals did not suffer, struggle, or scream for any length of time, thus ensuring the integrity of the collected organ tissue, and then the rumen organs were removed for sample collection, and the rumen abdominal sac tissue blocks (1 cm × 1 cm) were collected and rinsed with normal saline. At the same time, a small piece of the abdominal rumen sac was clipped, the contents were quickly rinsed off with PBS, and then the epithelial tissue was separated with blunt scissors, which was quickly stored in liquid nitrogen for the subsequent extraction of total RNA. Meantime, morphological sections of rumen epithelium were made. In addition, rumen contents were collected for microbial sequencing, metabolome analysis, and the determination of fermentation parameters (VFAs).

2.2. Observation of Rumen Epithelial Tissue Morphology and Determination of VFAs, Microbiota, and Metabolites

The epithelial tissue samples of the rumen abdominal sac were collected and fixed in a 4% paraformaldehyde solution, followed by hematoxylin-eosin staining. In addition, rumen contents of Tibetan sheep in cold and warm seasons were collected to determine fermentation parameters VFA and 16S rRNA, and the microbial structure and function were analyzed. The specific methods were referred to the research of Liu et al. [19]. The methods and results of rumen microbiota metabolites determination were shown by Liu et al. [20].

2.3. Construction of lncRNAs Libraries

A total of 1.5 μg RNA samples were used for rRNA removal using the Ribo-Zero rRNA Removal Kit (Epicenter, Madison, WI, USA). Sequencing libraries were generated using the NEBNextR UltraTM-directed RNA Library Preparation Kit from Illumina (NEB, Beijing, China), and index codes were added to the attribute sequences for each sample. The first cDNA was synthesized using random hexamer primers and reverse transcriptase. After the second cDNA synthesis was performed using DNA polymerase I and RNase H, the remaining overhanging portion was converted to blunt ends by exonuclease/polymerase activity. The insertion fragments with a length of 150~200 bp were selected preferentially, and the library fragments were purified with AMPure XP Beads (Beckman Coulter, Beverly, MA, USA). PCR amplification was then performed by incubating 3 μL of USER enzyme (NEB, Beijing, China) with size-selected, adapter-linked cDNA for 15 min at 37 °C. Then, PCR amplification was performed using Phusion High-fidelity DNA polymerase, universal PCR primers, and Index (X) primers. Finally, PCR products were purified by the AMPureXP system and the library quality was evaluated on a meteorological chromatography analyzer (Agilent 2100, Palo Alto, USA) and qPCR instrument (ABI, Leuven, USA). After cluster generation, library preparations were sequenced on the IlluminaHiseq platform to generate paired-end reads.

2.4. Prediction of LncRNAs

The prediction of new lncRNAs includes two parts: basic screening and potential coding capability screening. (1) Sequence information of transcript was obtained by basic screening; (2) after screening for potential coding ability, transcripts with potential coding ability were removed from the previous basic screening, and the remaining ones were newly predicted lncRNAs. Because lncRNAs did not encode proteins, the coding potential of the transcript was screened to determine whether it had coding potential, so as to determine whether the transcript was a lncRNA. The most widely used coding potential analysis methods were used to screen the above candidate lncRNAs, including CPC analysis, CNCI analysis, CPAT analysis, and pfam protein domain analysis.

2.5. Identification of DE lncRNAs

StringTie (1.3.1) was used to calculate FPKMs of lncRNAs and coding genes in each sample. DESeq R package (1.10.1) was used for differential expression analysis between the two groups. DESeq detected the p < 0.01 and log2 (Fold change) > 1 genes that were designated as DE genes. In the process of DE lncRNAs detection, Fold Change ≥ 2 and FDR < 0.01 was used as the screening criterion. Fold Change was the ratio of expression between two groups.

2.6. Functional Enrichment of DE lncRNAs

Functional enrichment analysis of cis/trans target genes of DE lncRNAs between samples was performed to predict the statistical enrichment of DE genes in the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway. The topGO R package was used to perform Gene Ontology (GO) enrichment analysis of DE genes. ClusterProfiler [21] was used to enrich and analyze the biological processes, molecular functions, and cell components of genes.

2.7. Prediction of LncRNAs Target Genes

Based on the interaction modes between lncRNAs and their target genes, two prediction methods were used: (1) lncRNAs regulated the expression of their adjacent genes, and the adjacent genes within 100 KB of lncRNAs were predicted as their target genes; (2) The correlation analysis of lncRNA and mRNA expression between samples was used to predict the trans-target genes of lncRNAs. Perl script was used to identify the adjacent genes within 100 kb upstream and downstream of lncRNAs as cis-target genes. The trans-target genes of lncRNAs were predicted by analyzing the correlation between lncRNAs and mRNA expression levels between samples. The Pearson correlation coefficient method was used to analyze the correlation between lncRNAs and mRNA among samples, and the genes with absolute correlation values greater than 0.9 and significant p values less than 0.01 were selected as the trans-target genes of lncRNAs.

2.8. Construction of lncRNA-mRNA Targeting Network

Based on Illumina HiSeq miRNA sequencing data from the same samples, a circRNA-miRNA co-expression network was established according to miRNA-binding sites predicted by miRanda. Cytoscape software v3.8.2 was used to construct a lncRNA-mRNA co-expression network.

2.9. RT-qPCR Analysis

Three primers were designed to amplify lncRNA-specific reverse splicing points (Table 1), which were used to verify the authenticity of lncRNA and the reliability of RNA-Seq data. RT-qPCR was performed in triplicate using a 2 × ChamQ SYBR qPCR Master (Vazyme, Nanjing, China) on Applied Biosystems QuantStudio®6 Flex (Thermo Lifetech, CA, USA). The relative expression levels of these genes were analyzed using the 2−ΔΔCT method and β-actin was used as an internal reference gene.

2.10. Data Analysis

All statistical analyses were performed using IBM SPSS 22.0 (SPSS, Inc., Chicago, IL, USA). The correlation between DE lncRNAs, VFAs, morphology was analyzed using SPSS 25 (Chicago, IL, USA) and Origin 9.1 (Northampton, MA, USA) software, and the Spearman correlation test was used for correlation analysis. Statistical significance level p < 0.05. Genus-level microbiota (Top20) and DE lncRNAs were selected for cluster analysis (correlation threshold < 0.1, p < 0.05). The functional pathway map with the co-enrichment of differential metabolites and DE lncRNAs target genes was used to explore the interaction between differential lncRNAs and differential metabolites.

3. Results

3.1. Overview of Sequencing Data of lncRNAs in Tibetan Sheep Rumen Epithelium Tissue

A total of 341,259,053 reads were obtained in this study, with Q30% greater than 94.79% and GC content ranging from 48.28% to 51.21% (Table 2). In addition, this study obtained a total of 101.42 Gb of clean data for long-chain non-coding RNA analysis, and the clean data of each sample reached 15.56 Gb. The comparison efficiency between reads of each sample and reference genome was 62.53–87.21% (Table 3).

3.2. Identification and DE lncRNAs in Tibetan Sheep Rumen Epithelium

A total of 8762 lncRNAs were identified in this study (Figure 1A), of which 2803 were intronic lncRNAs (32%), 291 were sense lncRNAs (3.3%), 4436 were intergenic lncRNAs (50.6%), and 1232 were antisense lncRNAs (14.1%) (Figure 1B). Using Fold Change ≥ 2 and FDR < 0.01 as screening criteria, a total of 172 DE lncRNAs were identified, among which 87 were significantly up-regulated and 85 were down-regulated in the cold season (Figure 1C). Hierarchical cluster analysis was performed on the screened DE lncRNAs, and the expression patterns among individuals in the group were basically consistent, indicating good repeatability and large differences between groups (Figure 1D).

3.3. Functional Characteristics of DE lncRNAs

As shown in Figure 2, GO results showed that target genes of DE lncRNAs were significantly enriched in functions related to immune defense in cis-mRNA pairs. The GO terms involved the flavonoid biosynthetic process (GO: 0009813) and regulation of the type I interferon-mediated signaling pathway (GO: 0060338) and other pathways. In Trans-mRNA pairs, most target genes were significantly enriched in groups related to nutrient utilization and energy metabolism in warm and cold seasons. The GO terms related to pathways such as heparin-binding (GO: 0008201) and ATPase activity, coupled to the transmembrane movement of substances (GO: 0042626). The KEGG pathway further analyzed the potential functional signaling pathways of target genes of Tibetan sheep rumen epithelium tissue in warm and cold seasons. In cis-mRNA pairs, DE lncRNAs target genes were significantly enriched in starch and sucrose metabolism, steroid hormone biosynthesis, drug metabolism-other enzymes, porphyrin and chlorophyll metabolism, and pentose and glucuronate interconversions; in trans mRNA pairs, the target genes of DE lncRNAs were significantly enriched in MAPK signaling pathway, thyroid hormone signaling pathway, TNF signaling pathway and bile secretion, ECM–receptor interaction, fanconi anemia pathway, Epstein–Barr virus infection and other related pathways. The up-regulated target genes were significantly enriched in the AMPK signaling pathway, PPAR signaling pathway, TNF signaling pathway, and bile secretion pathway. Down-regulated target genes were significantly enriched in Epstein–Barr virus infection, lysine degradation, oxidative phosphorylation, and protein processing in the endoplasmic reticulum and other related pathways (Figure 3).

3.4. Construction of lncRNA-mRNA Regulatory Network

In order to further analyze the biological function of DE lncRNAs, we combined the sequencing results of our previous mRNA [20], and the lncRNA-mRNA regulatory network was constructed. As shown in Figure 4, the five most significantly up-regulated (MSTRG.123203.4, MSTRG.113717.2, MSTRG.44204.2, MSTRG.60784.1, MSTRG.103129.28) and four most significantly down-regulated (MSTRG.18675.1, MSTRG.3973.1, MSTRG.83755.1, MSTRG.99282.1) in the rumen epithelial tissues of Tibetan sheep in the cold season were randomly selected, and their target genes were predicted and selected to construct the lncRNA–mRNA interaction network.

3.5. The DE lncRNA Was Verified by RT-qPCR

As shown in Figure 5, to verify the RNA-seq results, three DE lncRNAs were randomly selected and verified by RT-qPCR in warm and cold seasons. The results showed that the RT-qPCR expression pattern of the selected genes was consistent with the RNA-seq analysis, indicating the reliability and accuracy of the RNA-seq method used in the study.

3.6. Association Analysis of lncRNAs with VFAs and Tissue Morphological Characteristics in Rumen Epithelium

As shown in Figure 6, The correlation analysis of DE lncRNAs in rumen epithelial and VFAs showed that lncRNAs (MSTRG.123203.4, MSTRG.44204.2, MSTRG.60784.1, MSTRG.145005.2) were significantly positively correlated with acetic acid, propionic acid, and total VFAs in the cold season (p < 0.05). Butyric acid was positively and significantly correlated with lncRNAs (MSTRG.103129.28, MSTRG.145005.2, MSTRG.120061.1, MSTRG.56302.1 and MSTRG.44204.2) (p < 0.05). The significant down-regulation of lncRNAs in the cold season (MSTRG.83755.1) was significantly negatively correlated with acetic acid, propionic acid, and total VFAs (p < 0.05).
The correlation analysis between DE lncRNAs and tissue micromorphology showed that lncRNAs (MSTRG.44204.2, MSTRG.103129.28, MSTRG.120061.1, MSTRG.56302.1) were significantly positively correlated with muscle thickness and stratum corneum thickness in the cold season (p < 0.05). The significant up-regulated of lncRNA in the cold season (MSTRG.113717.2) was positively correlated with nipple width, granular layer thickness and spinous layer thickness (p < 0.05), and negatively correlated with basal layer thickness (p < 0.05). The significant down-regulation of lncRNA (MSTRG.3973.1) in the cold season was negatively correlated with muscle thickness, papillary width, and stratum corneum thickness (p < 0.05), and positively correlated with basal layer thickness (p < 0.05).

3.7. Analysis of lncRNAs-Microbiota-Metabolite Interaction in Rumen Epithelium Tissue

Combined with previous studies in rumen microbiota and their metabolites in Tibetan sheep in warm and cold seasons [19,20], this study analyzed the interaction between lncRNAs and microbiota in rumen epithelium, and found that there was a significant correlation between lncRNAs and microbiota. As shown in Figure 7A, the two significantly down-regulated lncRNAs in the cold season were negatively correlated with Rikenelleaceae-RC9-gut-group and Ruminococcus 1 (p < 0.05). There was a significant positive correlation with Butyrivibrio-2, Ruminococceaceae-NK4A214-group, and Succiniclasticum (p < 0.05) in which there was an extremely significant correlation with Butyrivibrio-2 and Succiniclasticum (p < 0.01). In addition, the other seven lncRNAs significantly up-regulated in the cold season were positively correlated with the Rikenelleaceae-RC9-gut-group and Ruminococcus 1 (p < 0.05). There was a significant positive correlation with Butyrivibrio-2, Ruminococcaceae-NK4A214-group, and Succiniclasticum (p < 0.05). In addition, MSTRG.66762.1 was positively correlated with Prevotella_1 (p < 0.05), and negatively correlated with Prevotalleaceae-UCG-001 (p < 0.05).
As shown in Figure 7B, in the tryptophan biosynthesis pathway of the KO00380 pathway, metabolites such as tryptophan and kynurenine were significantly up-regulated in the cold season, and target genes IDO1, IDO2, and AFMID were involved in regulating the production of tryptophan and kynurenine. In the KO00330 pathway, the target gene GATM was involved in the production of guanidine acetate and creatine from arginine, and the metabolites guanidine acetate and creatine were significantly up-regulated in the cold season. In the KO00230 pathway, differential metabolites aicar, xanthosine, urate, and target genes ATIC, NT5M, and NT5DC4 were co-enriched in histidine metabolism and glycine, serine and threonine metabolism pathways. In the KO00860 pathway, the target genes UGT1A4, UGT1A6, and UGT1A9 were involved in the regulation of the differential metabolites bilirubin to produce D-urobilinogen and D-urobilin.

4. Discussion

The formation of common species between mammals and intestinal symbiotes may be the result of their co-evolution, and there was cohesion in the evolution of the mammalian host–gut microbiome entity [22]. Co-evolution between animal host genome (first genome) and microbial genome (second genome) has become a new focus in the study of animal adaptive evolution. In this study, lncRNAs were identified and analyzed in the rumen epithelium of Tibetan sheep, and it was found that the target genes of DE lncRNAs were significantly enriched in the MAPK signaling pathway, which was the main signaling pathway regulating lipid metabolism. The MAPK signaling pathway can transmit various stimulation signals in mammalian cells and cause appropriate mechanism responses of the body to cope with the external environment [23]. The activation of MAPK can inhibit fat synthesis and promote fatty acid oxidation [24], which is conducive to activating the absorption of nutrients and energy metabolism of Tibetan sheep in the cold season. The main role of the insulin signaling pathway was to control blood glucose levels, and Wang et al. found that the insulin signaling pathway was significantly affected by cold exposure [25]. This study found that the target genes for up-regulating lncRNAs in the cold season were significantly enriched in the TNF signaling pathway, which played an important role in inflammatory reaction and immunity in addition to participating in lipolysis with multiple pathways such as the MAPK signaling pathway [26]. Cytokines such as tumor necrosis factor (TNF) and interleukin 1 (IL1) can promote leukocyte extravasation by increasing the level of leukocyte adhesion molecules on endothelial cells, so as to participate in the body’s immunity [27], thus improving the immune defense ability of Tibetan sheep in the process of resisting harsh cold season environment. TRAF1 supported lymphocyte survival by promoting the signal transduction downstream of TNFRs [28], while TRAF3 had a positive regulatory function on T cells [29], which was conducive to improving the immune capacity of Tibetan sheep in the cold season. This study also found that the up-regulated lncRNAs target genes were significantly enriched in the oxidative phosphorylation pathway in the cold season, and oxidative phosphorylation can provide most ATP for life maintenance and was responsible for establishing and maintaining metabolic homeostasis [30], which was conducive to Tibetan sheep’s response to nutrient stress in the cold season. In addition, lncRNA–mRNA targeting network analysis showed that the target gene ATP1B2 was a candidate gene for heat-resistant traits and could encode a transmembrane carrier protein, which played an important role in Na-K transport and energy metabolism [31]. ATP1B2 provided energy for the membrane transport of metabolites, nutrients, and ions, and its polymorphism was related to milk yield, milk composition, and heat resistance [31]. The expression of ATP8A1 was related to feed intake [32], and ATP8A1 was also found to be up-regulated in the cold season in mRNA-related studies [20]. UCP3 was an important gene in the low-temperature adaptation of Tibetan pigs, which can significantly improve the oxidative respiration rate of adipose cells in Tibetan pigs and has potential heat production ability [33]. Lu et al. found that the expression level of UCP3 in Tibetan sheep was significantly higher than that in Hu sheep, indicating that Tibetan sheep could also increase body heat production through UCP3-mediated subcutaneous fat browning [18]. The significant up-regulation of lncRNA-targeting UCP3 in the cold season indicated that lncRNAs played an important role in responding to nutrient stress in Tibetan sheep in the cold season by regulating the expression of the target gene UCP3. CADPS was a calcium-dependent secretion activator gene, which had been identified to be related to feeding behavior [34]. The study found that the rumen was the main site for the active absorption of calcium ions in sheep, and there was a mechanism for the absorption of Ca2+/H+ exchange in the rumen epithelial cells that depended on VFAs [35], further indicating that Tibetan sheep in the cold season regulate the absorption of nutrients and energy in the rumen epithelial cells through the calcium ion pathway, so as to adapt to cold season nutrient stress. The target gene LIX1 was an S-palmitoylated protein derived from gastric mesenchymal progenitor cells [36].
The mRNA results showed that LIX1 in the rumen epithelium of Tibetan sheep was significantly down-regulated in the cold season and the down-regulation of LIX1 changed the ultrastructure of mitochondria [20], resulting in a significant reduction in respiration, and also weakened the production of mitochondrial reactive oxygen species (mtROS) [36], which was conducive to maintaining the energy loss of Tibetan sheep in the cold season. LIX1 also regulated the expression of fructose-1, 6-diphosphatase (FBP1), thereby increasing glucose consumption and lactate production [37]. LIX1 was first discovered in chicken embryos [38], but its research in ruminants needed to be further explored. Chen et al. [39] found that the rumen epithelium had evolved a gene expression pattern similar to that of esophageal epithelium, and PRD-SPRRII and TCHL2 were important structural genes of rumen epithelium. The LncRNA (MSTRG.3973.1) target gene PRD-SPRRII, which was predicted to be the main structural protein, can encode the protein of keratinized epidermal structure development and play an important role in the keratinization of the keratin-rich rumen surface [40]. Cuticle cells were highly keratinized and played a protective role as a defensive barrier against the physical environment in the rumen [41]. In this study, lncRNA (MSTRG.3973.1) was significantly negatively correlated with cuticle thickness, which was significantly higher in the cold season than in the warm season, and the significant down-regulation of MSTRG.3973.1 in the cold season may be conducive to the effective absorption of nutrients by rumen epithelium in cold season. The cuticle of the rumen epithelium has a certain barrier effect, and the numerous ruminal papillae in the ruminal epithelium can expand the ruminal surface area for effective absorption of nutrients [42]. This study found a significant positive correlation between lncRNA and nipple width, and that lncRNA could target SLC26A9, which was consistent with the results of Wang et al. [43]. Wang et al. [44] found that PLA2G3, SLC26A9, SLC34A3, and other genes were mostly involved in the ion transport pathway, and papilla length and width were positively correlated with the expression levels of genes (PLA2G3, SLC26A9, SLC34A3) in the ion transport pathway. These genes may influence rumen development by participating in ion transport pathways. It has been found that the microbiome can influence the expression of host genes by altering epigenetic programming such as histone modification [44]. At the same time, the microbiome also influences the differential gene expression of some transcription factors [45].
There was a significant correlation between lncRNAs in rumen epithelium and microbiota. Among them, the target gene TLR5 had a significant negative correlation with the microbiota Butyribrio-2 and Succiniclassicum, while Gram-negative bacteria, such as Butyribrio-2 produced bacterial flagella and flagellin, can specifically stimulate the highly expressed TLR5 in intestinal epithelial cells [46]. Liu et al. [47] found that TLR5 played an important role in the recognition of bacterial flagellin, can also recognize the host’s extracellular bacteria and bacterial products, and trigger the immune response, which was essential to maintain the host’s microbial homeostasis. In this study, lncRNAs such as MSTRG.113717.2 were significantly up-regulated in the cold season, and TLR5 was also found to be highly expressed in the cold season in mRNA studies [20], which further showed that TLR5 had a beneficial effect on maintaining rumen homeostasis in the cold season. Similarly, Liu et al. [48] also found that changes in the bacterial population of goat rumen epithelium were related to changes in expression of TLR. Another important feature of the co-evolution of host and microbial genomes was metabolic cooperation, which had mainly manifested in the biosynthesis of branched amino acids [11]. In this study, correlation analysis between lncRNAs and microbial metabolites showed that lncRNA target genes UGT1A3, UGT1A4, UGT1A6, UGT1A9, etc., were involved in the regulation of pathways related to bile secretion. The UGT1 gene family (phenol/bilirubin family) can synthesize enzymes related to gluconaldehyde [49], and the endogenous substrates of gluconaldehyde include bilirubin, bile acids, and steroids [50]. Metabolomics analysis found that up-regulated metabolites such as urobilinogen and bilirubin were enriched in bile secretion pathways during the cold season. Studies have found that bilirubin was a breakdown product of heme, produced by the bacterial community [51]. The structural similarities between urobilinogen and bilirubin led to the possibility that both had similar antioxidant activities [52]. Urobilinogen can effectively inhibit the oxidation of food components by free radicals in the intestine and reduce the damage of oxidative products to the intestinal wall [52], which is conducive to the normal growth and development of Tibetan sheep in the cold season. In conclusion, as shown in Figure 8, based on the difference analysis of lncRNAs, this study further explored the differences between the growth and development of Tibetan sheep in warm and cold seasons from the perspective of co-evolution of the host genome and microbial genome, aiming to reveal the regulatory mechanism of Tibetan sheep’s adaptation to nutrient stress in the cold season and provide new insights for relevant studies on the adaptability of plateau animals.

5. Conclusions

Under the cold season’s nutritional stress conditions in the Qinghai–Tibet Plateau, the lncRNAs in the rumen epithelium of Tibetan sheep and the rumen microorganisms and their metabolites responded to the cold season’s nutritional stress by producing coevolution. The target gene of rumen epithelial DE lncRNAs enhanced the energy metabolism and immune defense ability of Tibetan sheep in the cold season by concentrating on KEGG pathways, such as the MAPK signal pathway and the oxidative phosphorylation pathway and targeting ATP8A1, UCP3, CADPS, etc., thus affecting the nutritional stress of Tibetan sheep in the cold season. The interaction of lncRNAs, such as rumen epithelium MSTRG.113717.2, MSTRG.3973.1, MSTRG.145005.2 with rumen microorganisms and their metabolites, showed that the Tibetan sheep host genome and rumen microbiome were critical to maintaining the host microbial homeostasis by recognizing the bacterial flagellin of Gram-negative bacteria in the rumen microbiome. Therefore, this study aimed to explore the adaptation and regulation of Tibetan sheep to the cold season’s nutritional stress through the interaction analysis of the host genome and intestinal microbiota, and provided a basis for the research on the cold season adaptation of ruminants in high altitude areas.

Author Contributions

X.L., Y.H. and X.G. designed the study. X.G., Y.S., P.S., J.H., J.W., S.L. and Z.H.; performed the experiments and collected samples. X.L. 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 Gansu Agricultural University Youth Mentor Support Fund project (GAU-QDFC-2022-06); National Natural Science Foundation Project of China (32260820 and 31860688) and the Discipline Team Project of Gansu Agricultural University (GAU-XKTD-2022-21). The funding bodies played no role in the design of the study and collection, analysis, interpretation of data, and in writing the manuscript.

Institutional Review Board Statement

The Ethical Institutional Review Board of the Animal Husbandry Committee of Gansu Agricultural University approved the conduct of the study (Approval No. GAU-LC-2020-27), and the selection and treatment of experimental animals were conducted in strict accordance with the ethical requirements, and obtain the informed consent of the animal owner.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Sequence Read Archive (SRA): SRR17883805–SRR17883810/SRR12719079–SRR12719088.

Acknowledgments

We thank members of the Gansu Key Laboratory of Herbivorous Animal Biotechnology labs for helpful discussions and comments. We thank Yanyu He and Jiang Hu, and all members of this paper for their help and valuable suggestions.

Conflicts of Interest

The authors declare no conflict of interest.

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Fermentation 09 00892 g001
Figure 1. Identification and differential expression of lncRNAs. (A) Venn diagram of lncRNAs prediction; (B) statistical plot of lncRNAs; (C) volcano plot of differentially expressed lncRNAs; (D) clustering plot of differentially expressed lncRNAs.
Figure 1. Identification and differential expression of lncRNAs. (A) Venn diagram of lncRNAs prediction; (B) statistical plot of lncRNAs; (C) volcano plot of differentially expressed lncRNAs; (D) clustering plot of differentially expressed lncRNAs.
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Figure 2. Analysis of GO enriched pathways.
Figure 2. Analysis of GO enriched pathways.
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Figure 3. KEGG pathway analysis. (A) cis-target gene enrichment pathway; (B) trans-target gene enrichment pathway; (C) up-target gene enrichment pathway; (D) down-target gene enrichment pathway).
Figure 3. KEGG pathway analysis. (A) cis-target gene enrichment pathway; (B) trans-target gene enrichment pathway; (C) up-target gene enrichment pathway; (D) down-target gene enrichment pathway).
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Figure 4. lncRNA–mRNA targeting network diagram.
Figure 4. lncRNA–mRNA targeting network diagram.
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Figure 5. Comparison of RT-qPCR and RNA-seq results.
Figure 5. Comparison of RT-qPCR and RNA-seq results.
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Figure 6. Correlation analysis of lncRNAs with VFAs and histological micromorphology. Note: * The correlation was significant at 0.05. A. indicated MSTRG.123203.4, B. indicated MSTRG.113717.2, C. indicated MSTRG.44204.2, D. indicated MSTRG.3973.1, E. indicated MSTRG.83755.1, F. indicated MSTRG.60784.1, G. indicated MSTRG.103129.28, H. indicated MSTRG.145005.2, I. indicated MSTRG.120061.1, J. indicated MSTRG.56302.1. MLT: Muscular layer thickness; NH: nipple height; NW: nipple width; SCT: Thickness of stratum corneum; SGT: stratum granulosum thickness; SST: stratum spinosum thickness; BLT: basal layer thickness.
Figure 6. Correlation analysis of lncRNAs with VFAs and histological micromorphology. Note: * The correlation was significant at 0.05. A. indicated MSTRG.123203.4, B. indicated MSTRG.113717.2, C. indicated MSTRG.44204.2, D. indicated MSTRG.3973.1, E. indicated MSTRG.83755.1, F. indicated MSTRG.60784.1, G. indicated MSTRG.103129.28, H. indicated MSTRG.145005.2, I. indicated MSTRG.120061.1, J. indicated MSTRG.56302.1. MLT: Muscular layer thickness; NH: nipple height; NW: nipple width; SCT: Thickness of stratum corneum; SGT: stratum granulosum thickness; SST: stratum spinosum thickness; BLT: basal layer thickness.
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Figure 7. Analysis of interaction between lncRNAs and microbiota and metabolites. (A) Correlation heat map between LncRNA and microbiome; (B) LncRNA and metabolite enrichment pathway. Note: A. indicated MSTRG.123203.4, B. indicated MSTRG.113717.2, C. indicated MSTRG.44204.2, D. indicated MSTRG.3973.1, E. indicated MSTRG.83755.1, F. indicated MSTRG.60784.1, G. indicated MSTRG.103129.28, H. indicated MSTRG.145005.2, I. indicated MSTRG.120061.1, J. indicated MSTRG.56302.1. KEGG path maps are from the KEGG database and are licensed for copyright use. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 7. Analysis of interaction between lncRNAs and microbiota and metabolites. (A) Correlation heat map between LncRNA and microbiome; (B) LncRNA and metabolite enrichment pathway. Note: A. indicated MSTRG.123203.4, B. indicated MSTRG.113717.2, C. indicated MSTRG.44204.2, D. indicated MSTRG.3973.1, E. indicated MSTRG.83755.1, F. indicated MSTRG.60784.1, G. indicated MSTRG.103129.28, H. indicated MSTRG.145005.2, I. indicated MSTRG.120061.1, J. indicated MSTRG.56302.1. KEGG path maps are from the KEGG database and are licensed for copyright use. * p < 0.05, ** p < 0.01, *** p < 0.001.
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Figure 8. Regulation model of differential lncRNAs on nutrient stress in the cold season.
Figure 8. Regulation model of differential lncRNAs on nutrient stress in the cold season.
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Table 1. Primer information of DE genes.
Table 1. Primer information of DE genes.
LncRNAForward (5′ → 3′)Reverse (5′ → 3′)
MSTRG.99282.1TGCTTAAACTGGCCCCTCTGCACCACCGAAAGTCCTCCAA
MSTRG.118214.1GGGCCAGAGACAACTGGAAAGAATCGTCCAAGGAGACGCA
MSTRG.89480.2GACGAAAGAAAGGCAGCGTCCCCAGCTGGTTGTTCCTAGAG
β-actinAGCCTTCCTTCCTGGGCATGGAGGACAGCACCGTGTTGGCGTAGA
Table 2. Summary statistics of the RNA-seq data.
Table 2. Summary statistics of the RNA-seq data.
SampleRead SumBase SumGC (%)N (%)Q 20(%)Q 30(%)
cold153,859,43916,025,456,74848.37098.2794.99
cold260,990,36418,103,396,31448.85098.3495.04
cold361,090,36918,095,280,63848.28098.2294.79
warm152,304,30015,558,868,81650.68098.3795.12
warm254,632,26216,233,111,64251.21098.3895.17
warm358,382,31917,397,315,87650.83098.4295.34
Table 3. Statistical table of sequence alignment results between the sample sequencing data and the selected reference genome.
Table 3. Statistical table of sequence alignment results between the sample sequencing data and the selected reference genome.
SampleTotal ReadsMapped ReadsUniq Mapped ReadsMultiple Reads
cold1107,718,87893,204,904 (86.53%)73,478,583 (68.21%)19,726,321 (18.31%)
cold2121,980,728106,374,890 (87.21%)84,457,338 (69.24%)21,917,552 (17.97%)
cold3122,180,738106,430,676 (87.11%)82,687,313 (67.68%)23,743,363 (19.43%)
warm1104,608,60073,161,590 (69.94%)54,951,260 (52.53%)18,210,330 (17.41%)
warm2109,264,52468,325,730 (62.53%)53,242,812 (48.73%)15,082,918 (13.80%)
warm3116,764,63887,757,254 (75.16%)69,574,963 (59.59%)18,182,291 (15.57%)
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Liu, X.; Guo, X.; Sha, Y.; He, Y.; Shao, P.; Hu, J.; Wang, J.; Li, S.; Hao, Z. Based on the Co-Evolution of lncRNAs-Microbiota and Metabolites in Rumen Epithelium to Analyze the Adaptation Characteristics of Tibetan Sheep to Nutrient Stress in the Cold Season. Fermentation 2023, 9, 892. https://doi.org/10.3390/fermentation9100892

AMA Style

Liu X, Guo X, Sha Y, He Y, Shao P, Hu J, Wang J, Li S, Hao Z. Based on the Co-Evolution of lncRNAs-Microbiota and Metabolites in Rumen Epithelium to Analyze the Adaptation Characteristics of Tibetan Sheep to Nutrient Stress in the Cold Season. Fermentation. 2023; 9(10):892. https://doi.org/10.3390/fermentation9100892

Chicago/Turabian Style

Liu, Xiu, Xinyu Guo, Yuzhu Sha, Yanyu He, Pengyang Shao, Jiang Hu, Jiqing Wang, Shaobin Li, and Zhiyun Hao. 2023. "Based on the Co-Evolution of lncRNAs-Microbiota and Metabolites in Rumen Epithelium to Analyze the Adaptation Characteristics of Tibetan Sheep to Nutrient Stress in the Cold Season" Fermentation 9, no. 10: 892. https://doi.org/10.3390/fermentation9100892

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