Abstract

Background

   Mineral elements play a crucial role in supporting the life activities
   and physiological functions of animals. However, numerous studies have
   revealed that in some geographical areas and certain grazing
   situations, grazing livestock frequently suffers from mineral element
   deficiencies due to the loss of mineral elements from grassland
   forages, such as selenium (Se). To shed fresh light on this issue, this
   study aims to investigate the impact of dietary Se deficiency and
   supplementation on the liver of grazing sheep in these challenging
   conditions.

Method

   This study involved 28 grazing Mongolian Wu Ranke sheep with an average
   body weight of about 32.20 ± 0.37 kg, which were divided into the Se
   treatment group and the control group. The Se treatment group was fed
   with the low-Se diet for 60 days and then continued to be fed with the
   high-Se diet for 41 days. The liver concentration of minerals,
   transcriptomic analysis, and untargeted metabolomic analysis were
   conducted to assess the impact of Se deficiency and supplementation on
   the liver of grazing sheep.

Results

   Dietary Se deficiency and supplementation significantly reduced and
   elevated liver concentration of Se, respectively (p < 0.05). Gene
   functional enrichment analysis suggested that dietary Se deficiency
   might impair protein synthesis efficiency, while Se supplementation was
   found to enhance liver protein synthesis in grazing sheep. AGAP1, ERN1,
   MAL2, NFIC, and RERG were identified as critical genes through the
   weighted gene correlation network analysis, the quantitative real-time
   polymerase chain reaction, and the receiver operating characteristic
   curve validation that could potentially serve as biomarkers.
   Metabolomics analysis revealed that dietary Se deficiency significantly
   reduced the abundance of metabolites such as 5-hydroxytryptamine, while
   dietary Se supplementation significantly elevated the abundance of
   metabolites such as 5-hydroxytryptophan (p < 0.05).

Conclusion

   Integrative analysis of the transcriptome and metabolome revealed that
   dietary Se deficiency led to reduced hepatic antioxidant and
   anti-inflammatory capacity, whereas Se supplementation increased the
   hepatic antioxidant and anti-inflammatory capacity in grazing Wu Ranke
   sheep. These findings provide new insights into the effects of dietary
   Se deficiency and supplementation on the liver of grazing sheep,
   potentially leading to improved overall health and well-being of
   grazing livestock.

   Keywords: deficiency, metabolomic, selenium, sheep, supplementation,
   transcriptomics

1. Introduction

   Sheep are regarded as a highly significant livestock species in terms
   of global food production, as they offer valuable resources such as
   fur, meat, and goat’s milk for humans ([29]1, [30]2). Trace essential
   minerals, serving as essential cofactors and enzyme components in
   diverse biological processes, play a pivotal role in the immune,
   oxidative, and energy metabolism of sheep. It mainly includes zinc
   (Zn), copper (Cu), cobalt (Co), magnesium (Mn), selenium (Se), etc.
   These micronutrients are crucial for the growth and development of
   sheep ([31]3, [32]4). Nevertheless, numerous studies have revealed that
   grazing livestock in predominantly grazing areas suffer from mineral
   intake deficiencies. This is mainly caused by the loss of mineral
   elements from the soil, which limits forage from providing grazing
   livestock with enough mineral nutrients ([33]5). Livestock husbandry is
   a predominant sector in the Inner Mongolia Autonomous Region of China,
   serving as the primary industry and source of revenue for local herders
   ([34]6). Our previous studies have also identified mineral deficiencies
   in Inner Mongolia grazing sheep, particularly Se ([35]7).

   Se is an essential trace mineral element for sheep, with antioxidant,
   anti-inflammatory, anti-carcinogenic, and anti-parasitic properties
   ([36]8). It is one of the most important essential trace elements in
   livestock production. As a constituent of 25 selenoproteins, Se plays a
   significant role in reproduction, thyroid hormone metabolism, and
   antioxidant defense ([37]9). Ruminants exhibit lower Se absorption
   efficiency compared to monogastric animals and poultry, resulting in
   frequent occurrences of Se deficiency without additional
   supplementation in the diet ([38]10). The initial report of Se
   deficiency in ruminants dates back to 1957, with the main symptoms
   being muscle tremors, difficulty swallowing, and increased heart rate,
   known as “white muscle disease” ([39]11). Moreover, dietary Se
   deficiency negatively impacts reproductive efficiency and mammary
   health ([40]11). Transcriptomic analyses reveal that dietary Se
   deficiency resulted in excessive mitochondrial fission and decreased
   expression of the Mfn2 and Opa1 in calf liver, as well as increased
   apoptosis and necroptosis in human uterine smooth muscle cells, and
   down-regulation of 19 Se proteins, including GPX1, GPX2, and GPX3
   ([41]12, [42]13). Our previous findings also demonstrated that dietary
   Se deficiency impaired liver, myocardium, and pancreas function in
   grazing sheep, which could be appropriately alleviated by supplementing
   with Se ([43]7). Much research has illustrated that the liver serves as
   a primary organ for Se storage ([44]10). However, there are fewer
   studies on the effects of Se on the liver of grazing sheep. It is still
   unclear how selenium deficiency and supplementation impact gene
   expression and metabolite fluctuations in the liver.

   Transcriptomics and metabolomics are valuable tools for investigating
   changes in gene expression and metabolite profiles in animals.
   Therefore, the experiment divided 28 grazing Mongolian Wu Ranke sheep
   into control group and Se treatment group. The Se treatment group was
   fed with deficient multi-nutrient salts diet for 60 days and then fed
   with supplement multi-nutrient salts diet for 41 days. The liver
   weight, concentration of essential mineral elements, transcriptomics,
   and metabolomics were analyzed after deficient and supplement
   treatments to investigate the effects of Se deficiency and
   supplementation on the liver of grazing sheep. The aim of this study is
   to provide fresh light on the hepatic impact of dietary Se deficiency
   and supplementation, while also providing data guidance for the
   sensible mineral supplementation of grazing sheep.

2. Materials and methods

2.1. Animal ethics

   All animal procedures were approved and conducted in strict accordance
   with the requirements set out by the Inner Mongolia University Animal
   Care and Use Committee (IMU-2020-sheep-040).

2.2. Experimental design

   A total of 28 4-month-old female grazing Wu Ranke sheep, with an
   average body weight of about 32.20 ± 0.37 kg, were purchased from Abaga
   Banner, Xilin Gol League, Inner Mongolia Autonomous Region, China. All
   sheep were housed individually and fed with crushed oats, natural
   grass, and multi-nutrient salts according to the National Research
   Council’s (NRC, 2007) suggestion ([45]7).

   The feeding experiment design is shown in [46]Figure 1. Only crushed
   oats and natural grass were fed to grazing Wu Ranke sheep during the
   28 days pre-feeding period. The 28 Wu Ranke sheep were randomly
   separated into the Se deficient group (LSe) and the control group of
   the Se deficient treatment period (LCG) at the end of the pre-feeding
   period. LSe and LCG were fed with Se deficient multi-nutrient salt and
   standard multi-nutrient salt for 60 days, respectively. Subsequently, 7
   sheep were chosen at random from each group to be slaughtered. The
   remaining 7 sheep in the LSe and LCG groups were provided with Se
   supplement multi-nutrient salt and standard multi-nutrient salt,
   respectively. They were then slaughtered after 41 days, forming the Se
   supplement group (SSe) and the control group of the Se supplement
   treatment period (SCG). The formulation of multi-nutrient salts for
   each group is shown in [47]Supplementary Table S1 and nutrient content
   in oats and natural grass is shown in [48]Supplementary Table S2.

Figure 1.

   [49]Figure 1
   [50]Open in a new tab

   Feeding experimental design of the selenium treatment group and control
   group in 28 grazing Wu Ranke sheep. LSe, the selenium deficient group;
   LCG, the control group of the selenium deficient treatment period; SSe,
   the selenium supplement group; SCG, the control group of the selenium
   supplement treatment period.

2.3. Sample collection

   The Wu Ranke sheep were fasted for 12 h before slaughter. Liver samples
   were collected at the end of the Se deficient treatment period (day 88)
   and the Se supplement treatment period (day 129) during the feeding
   period. The liver samples were weighed for fresh weight, followed by
   snap-freezing in liquid nitrogen and subsequent storage at −80°C for
   future laboratory analysis.

2.4. Detection of essential mineral elements in liver

   The liver samples were digested using a microwave digester (REVO,
   Labtech, Beijing, China), and the digestion procedure can be found in
   [51]Supplementary Table S3. Analysis of the concentrations of 10
   essential mineral elements in the liver, including phosphorus (P),
   sulfur (S), potassium (K), calcium (Ca), manganese (Mn), iron (Fe),
   cobalt (Co), copper (Cu), zinc (Zn), and selenium (Se), was performed
   using a TXRF spectrometer equipped with a molybdenum (Mo) X-ray tube
   (S4 T-STAR, Bruker Nano GmbH, Berlin, Germany). Each sample was
   measured for 300 s, and the results for all liver minerals were
   calibrated against a standard curve to ensure accuracy ([52]7).

2.5. Transcriptomic analysis

2.5.1. RNA extraction and Illumina sequencing

   Total RNA was extracted and purified from each liver sample using the
   TRIzol reagent (Ambion, TX, United States) as per the manufacturer’s
   protocol ([53]14). The RNA Nano 6,000 Assay Kit was applied to the
   Bioanalyzer 2,100 system (Agilent Technologies, CA, United States) to
   assess the RNA quality. Five sheep livers were randomly selected from
   each group to construct cDNA libraries using the NEBNext^® Ultra™ RNA
   Library Prep Kit for Illumina^®. The cDNA libraries from 20 Wu Ranke
   sheep livers (5 sheep per group) for sequencing using the Illumina
   Novaseq platform and 150 bp paired-end reads were generated ([54]15).

2.5.2. Quality control, reads mapping to the reference genome, and
quantification of gene expression level

   The raw data was first assessed for quality using FastQC
   software.[55]^1 The result provided information about the base
   composition of the sequence and the quality of the corresponding
   sequence ([56]16).

   The reference genome and gene annotation files were downloaded for
   sheep (Oar_rambouillet_v1.0) from the Ensembl website.[57]^2 Salmon
   software was used to construct the reference genome index, and
   separately align the reads to the reference genome ([58]17). Gene
   expression of each transcript was calculated and expressed as
   transcripts per kilobase million reads (TPM) ([59]18).

2.5.3. Identification of differentially expressed genes

   Differentially expressed genes (DEGs) were identified between LSe and
   LCG, as well as SSe and SCG, using the DESeq2 R package. The
   significance of the DEGs was determined based on criteria of
   |log2foldchange| ≥ 1 and a false discovery rate (FDR) < 0.05 (the
   p-value adjusted by the Benjamini-Hochberg method) ([60]19).

2.5.4. Weighted gene co-expression network analysis

   The Weighted Gene Co-expression Network Analysis (WGCNA) was a
   bioinformatic analysis technique that efficiently examines the
   relationship between genes and phenotypic data. The co-expression
   networks for the LSe, SSe, LCG, and SCG were constructed using
   DESeq2-normalized gene expression data by the WGCNA R package. The
   highly co-expressed gene modules were inferred using WGCNA for 9,253
   genes in the Wu Ranke sheep of these groups. The PickSoftThreshold was
   utilized to determine the optimal soft threshold for selecting and
   validating gene co-expression modules. To identify modules based on
   topological overlap, the matrix data was converted into an adjacency
   matrix and subsequently clustered. Clustering dendrograms were
   generated by computation of module eigengene (ME) and merging related
   modules in the tree based on ME. In this experiment, the hepatic
   concentration of Se served as the phenotypic information used to screen
   for hub genes associated with phenotypes ([61]20).

2.5.5. Screening of the critical genes

   The candidate critical genes were identified as those genes that were
   identical to DEGs and hub genes. Subsequently, the receiver operating
   characteristic (ROC) curve model was verified using the pROC R package
   for the selected critical genes ([62]20).

2.5.6. Quantitative real-time polymerase chain reaction validation

   Gene expression levels were assessed through the utilization of
   quantitative real-time polymerase chain reaction (qRT-PCR). The Reverse
   Transcription Kit was used to reverse-transcribe total RNA into cDNA
   (R222, Vazyme, Nanjing, China). The qRT-PCR was carried out using the
   SYBR Green master mix (Q311, Vazyme, Nanjing, China). Real-time
   detection of SYBR Green fluorescence was performed using a qTOWER 2.2
   Real-Time PCR System (Analytik Jena, Jena, Germany). The GAPDH gene was
   amplified as an internal control. The relative quantification values
   for the critical genes were determined using the 2^−ΔΔCt method
   ([63]21). Gene-specific primers were designed using Primer-BLAST[64]^3
   and were listed in [65]Supplementary Table S4.

2.5.7. Functional enrichment analysis

   The ClusterProfiler R package was employed to conduct Gene Ontology
   (GO) enrichment analysis of the DEGs. The GSEA R package was utilized
   to perform Gene Set Enrichment Analysis (GSEA) on the GO enrichment
   dataset. GO enrichments with a false discovery rate (FDR) less than
   0.05 were considered statistically significant. GSEA enrichments with
   p-values less than 0.01 were considered significant ([66]20, [67]22).

2.6. Metabolite extraction and untargeted metabolomic analysis

   A total of 28 liver samples (7 sheep per group) were subjected to
   metabolite extraction using standard procedures ([68]23). The extracted
   samples were then analyzed using a Vanquish UHPLC system coupled with
   an Orbitrap Q ExactiveTM HF mass spectrometer (ThermoFisher, MA, United
   States). The chromatographic and mass spectrometric conditions are
   shown in [69]Supplementary Table S5.

   The raw data files obtained from UHPLC–MS/MS analysis were processed
   using Compound Discoverer 3.1 (CD3.1, ThermoFisher) software. This
   involved peak alignment, peak picking, and quantification for each
   metabolite. Subsequently, the peak intensities were normalized to the
   total spectral intensity. The normalized data were used to predict the
   molecular formula based on additive ions, molecular ion peaks, and
   fragment ions. And then peaks were matched with the mzCloud, mzVault,
   and MassList databases to obtain accurate qualitative and relative
   quantitative results.

   Differential metabolites (DEMs) between LSe and LCG, as well as SSe and
   SCG, were identified based on the following criteria: variable
   importance in projection (VIP) ≥ 1, |log2 fold change| ≥ 1, and
   p < 0.05. The enriched pathways associated with these DEMs were further
   investigated using the Kyoto Encyclopedia of Genes and Genomes (KEGG)
   database.[70]^4 The functions of the enriched pathways associated with
   the differential metabolites (DEMs) were investigated using the Kyoto
   Encyclopedia of Genes and Genomes (KEGG) database (see text footnote
   4). The metabolic pathways with a p-value less than 0.05 were
   considered to exhibit statistically significant enrichment ([71]24,
   [72]25).

2.7. Statistical analysis

   The normality of the data distribution was assessed using the
   Shapiro–Wilk test. The independent samples t-test was performed to
   determine the significant difference between liver weight, liver
   mineral element concentration, and gene expression between the control
   group and treatment group using GraphPad Prism (v.9.3.1). The results
   were presented as mean ± standard error (SE), and differences with a
   p-value less than 0.05 were considered statistically significant.
   Pearson’s correlation analysis was conducted between critical genes and
   DEMs using GraphPad Prism (version 9.3.1). Correlations were considered
   significant if the p-value was less than 0.05 and the absolute value of
   the correlation coefficient (r) was greater than 0.7 ([73]21). The
   results were visualized using R (version 4.1.2), and vector drawing was
   performed using Adobe Illustrator 2020.

3. Results

3.1. Liver fresh weight and mineral element concentration

   As depicted in [74]Figure 2, dietary Se deficiency and supplementation
   had no impact on the liver weight of Wu Ranke sheep.

Figure 2.

   [75]Figure 2
   [76]Open in a new tab

   Fresh weight of grazing sheep liver. ^NS p > 0.05, * p < 0.05, **
   p < 0.01, *** p < 0.001. LSe, the selenium deficient group; LCG, the
   control group of the selenium deficient treatment period; SSe, the
   selenium supplement group; SCG, the control group of the selenium
   supplement treatment period.

   [77]Figure 3 illustrates the liver concentration of 10 mineral elements
   in the Se deficient and supplement treatments.

Figure 3.

   [78]Figure 3
   [79]Open in a new tab

   The concentration of liver mineral elements in selenium deficient
   treated and supplement treated compared with the control group. ^NS
   p > 0.05, * p < 0.05, ** p < 0.01, *** p < 0.001.

   Dietary Se deficient treatment caused a sharp decrease in the liver
   concentration of Se (0.000058 ± 0.0000011 mg/g vs.
   0.00041 ± 0.000026 mg/g, p < 0.05), while significantly elevating the
   liver concentration of P (3.75 ± 0.11 mg/g vs. 3.34 ± 0.12 mg/g,
   p < 0.05), S (2.50 ± 0.053 mg/g vs. 2.25 ± 0.056 mg/g, p < 0.05), K
   (4.35 ± 0.17 mg/g vs. 3.85 ± 0.10 mg/g, p < 0.05), Mn
   (0.0031 ± 0.00018 mg/g vs. 0.0023 ± 0.000078 mg/g, p < 0.05) and Zn
   (0.047 ± 0.0010 mg/g vs. 0.042 ± 0.0014 mg/g, p < 0.05). This treatment
   did not affect the liver concentration of Ca, Fe, Cu, and Co was
   undetectable below the lower limit of instrumental detection.

   Dietary Se supplement significantly elevated liver concentration of K
   (3.90 ± 0.11 mg/g vs. 3.44 ± 0.031 mg/g, p < 0.05), Mn
   (0.0029 ± 0.00014 mg/g vs. 0.0022 ± 0.00011 mg/g, p < 0.05), and Se
   (0.00073 ± 0.00012 mg/g vs. 0.00039 ± 0.000015 mg/g, p < 0.05), without
   affecting the concentration of P, S, Ca, Fe, Cu, Zn, and Co was
   undetectable below the instrumental detection limit.

3.2. Transcriptome analysis

3.2.1. Identification of DEGs

   [80]Supplementary Figure S1 demonstrated that the sequence quality and
   per sequence quality scores of LSe, SSe, LCG, and SCG reflect the high
   quality of the RNA-seq data. To identify DEGs, the gene expression data
   was normalized using DESeq2. The normalized data can be observed in
   [81]Supplementary Figure S2. Differential analysis revealed that a
   total of 53 DEGs were identified in LSe compared to LCG, of which 35
   were up-regulated and 18 were down-regulated ([82]Figure 4A). A total
   of 1 up-regulated DEGs and 1 down-regulated DEGs were identified in SSe
   compared to SCG ([83]Figure 4B). The information regarding the
   up-regulated and down-regulated DEGs can be found in [84]Supplementary
   Table S6.

Figure 4.

   [85]Figure 4
   [86]Open in a new tab

   DEGs identified between different treatment groups. (A) Volcano Plot of
   DEGs in the selenium deficient group. (B) Volcano Plot of DEGs in the
   selenium supplement group. The top 10 up-regulated and down-regulated
   gene names were tagged and sorted by FDR value. DEGs, differentially
   expressed genes; FDR, false discovery rate.

3.2.2. Function enrichment analysis of DEGs

   As shown in [87]Figure 5, the Go enrichment analysis revealed that the
   DEGs in LSe Wu Ranke sheep were significantly enriched in 20 terms
   compared with LCG, including cellular response to glucose stimulus,
   negative regulation of TOR signaling, apical part of cell, GTPase
   activator activity, and so on.

Figure 5.

   [88]Figure 5
   [89]Open in a new tab

   The Go enrichment of DEGs in the selenium supplement group. Go, gene
   ontology; DEGs, differentially expressed genes; FDR, false discovery
   rate.

   The Go enrichment analysis of DEGs in SSe Wu Ranke sheep indicated that
   DEGs were primarily significantly enriched in 6 terms, with
   down-regulated genes enriched in large ribosomal subunit, ribosome,
   structural constituent of ribosome, translation, and up-regulated genes
   enriched in GTPase activity and GTP binding.

   The results of GESA assays based on the Go enrichment data set are
   shown in [90]Figure 6. The top 5 significantly enriched terms sorted by
   normalized enrichment score (NES) in LCG were thiol-dependent
   deubiquitinase (NES, −1.83), flavin adenine dinucleotide binding (NES,
   −1.83), endoplasmic reticulum unfolded protein response (NES, −1.84),
   limb development (NES, −1.92) and negative regulation of viral genome
   replication (NES, −2.03) (p < 0.01). The top 5 significantly enriched
   terms in LSe were immune response (NES, 2.28), chemotaxis (NES, 2.17),
   chemokine activity (NES, 2.08), ribosome (NES, 2.07), and structural
   constituent of ribosome (NES, 2.04) (p < 0.01). The top 5 significantly
   enriched terms in SCG were melanocyte differentiation (NES, −1.77),
   vasodilation (NES, −1.80), programmed cell death (NES, −1.86),
   keratinocyte differentiation (NES, −1.92) and positive regulation of
   protein secretion (NES, −1.95) (p < 0.01). The top 5 significantly
   enriched terms in SSe were defense response to the bacterium (NES,
   1.83), chloride transmembrane transport (NES, 1.82), modulation of
   chemical synaptic transmission (NES, 1.80), chloride transport (NES,
   1.75), and protein secretion (NES, 1.74) (p < 0.01).

Figure 6.

   [91]Figure 6
   [92]Open in a new tab

   The Top 5 GSEA enrichment of Go terms. (A) Enriched in the control
   group of the selenium deficient treatment period. (B) Enriched in the
   selenium deficient group. (C) Enriched in the control group of the
   selenium supplement treatment period. (D) Enriched in the selenium
   supplement group. GSEA, gene set enrichment analysis; Go, gene
   ontology.

3.2.3. Screening of critical genes

   The WGCNA analysis resulted in the construction of a turquoise module.
   Therefore, the correlation data between the liver concentration of Se
   and the turquoise module were utilized to screen hub genes ([93]Figure
   7A). Hub genes of SSe up-regulated DEGs and LSe down-regulated DEGs
   were identified based on genes with module membership (MM) greater than
   0.70 and gene significance (GS) greater than 0.64. A total of 270 hub
   genes were successfully screened ([94]Figure 7B). The same method was
   employed to screen for hub genes among SSe down-regulated DEGs and LSe
   up-regulated DEGs. However, no hub genes were identified through this
   process.

Figure 7.

   [95]Figure 7
   [96]Open in a new tab

   Screening of the candidate critical genes by WGCNA method. (A) Network
   dendrogram from co-expression topological overlap. Color bars show the
   correlation of gene expression with liver concentration of selenium.
   (B) The scatter plot of the association between the turquoise module
   and gene importance. The genes within the red box are identified as hub
   genes. (C) The Venn diagram of overlapping genes between DEGs and the
   hub genes as candidate critical genes. WGCNA, the weighted gene
   co-expression network analysis; DEGs, differentially expressed genes;
   LSe, the selenium deficient group; SSe, the selenium supplement group.

   Candidate critical genes were identified as the genes that overlapped
   between the DEGs and the hub genes. Ultimately, the 4 candidate hub
   genes were screened among the LSe down-regulated genes, and 1 candidate
   hub gene was screened among the SSe up-regulated genes ([97]Figure 7C).

   The gene expression of candidate critical genes in LSe and SSe is shown
   in [98]Figure 8A. Two candidate genes, nuclear factor I C (NFIC) and
   RAS like estrogen regulated growth inhibitor (RERG), were randomly
   selected for qRT-PCR gene expression validation. As depicted in
   [99]Figure 8B, the results demonstrated that the mRNA expression level
   of NFIC was significantly decreased and RERG was significantly
   increased compared with the control group (p < 0.05). The [100]Figure
   8C showed that the area under curve (AUC) values of the ROC models for
   ArfGAP with GTPase domain, ankyrin repeat and PH domain 1 (AGAP1),
   endoplasmic reticulum to nucleus signaling 1 (ERN1), mal, T cell
   differentiation protein 2 (MAL2), NFIC and RERG were 1, which indicated
   that RERG can be considered as critical gene for SSe and AGAP1, ERN1,
   MAL2, and NFIC can be considered as critical gene for LSe.

Figure 8.

   [101]Figure 8
   [102]Open in a new tab

   The validation of the critical genes. (A) The gene TPM counts of
   candidate critical genes. (B) The relative mRNA expression levels of
   NFIC and RERG in the liver by qRT-PCR. (C) The ROC validation for
   critical genes. ^NS p > 0.05, * p < 0.05, ** p < 0.01, *** p < 0.001.
   LSe, the selenium deficient group; LCG, the control group of the
   selenium deficient treatment period; SSe, the selenium supplement
   group; SCG, the control group of the selenium supplement treatment
   period; TPM, transcripts per kilobase million reads; qRT-PCR,
   quantitative real-time polymerase chain reaction; ROC, the receiver
   operating characteristic curve.

3.3. Metabolome analysis

3.3.1. Identification of DEMs

   The Partial Least Squares Discriminant Analysis (PLS-DA) illustrated
   distinct differences between LSe and LCG, as well as between SSe and
   SCG, as depicted in [103]Supplementary Figure S3. In [104]Figures
   9A,[105]B, a total of 6 DEMs were identified in LSe using the negative
   ion mode (NEG), consisting of 1 up-regulated and 5 down-regulated DEMs
   (p < 0.05). Additionally, 12 DEMs were identified in LSe using the
   positive ion mode (POS), comprising 5 up-regulated and 7 down-regulated
   DEMs (p < 0.05). These DEMs were primarily classified into organooxygen
   compounds and carboxylic acids and derivatives. In the SSe (NEG), 8
   DEMs were identified, including 1 up-regulated and 7 down-regulated
   DEMs, and 24 DEMs were identified in SSe (POS), including 14
   up-regulated and 10 down-regulated DEMs, as shown in [106]Figures
   9C,[107]D (p < 0.05). The DEMs of SSe were mainly classified into
   carboxylic acids and derivatives. The information regarding the
   up-regulated and down-regulated DEMs can be found in [108]Supplementary
   Table S7.

Figure 9.

   [109]Figure 9
   [110]Open in a new tab

   DEMs identified between different treatment groups. (A) Volcano Plot of
   DEMs in LSe of NEG. (B) Volcano Plot of DEMs in LSe of POS. (C) Volcano
   Plot of DEMs in SSe of NEG. (D) Volcano Plot of DEMs in SSe of POS.
   LSe, the selenium deficient group; SSe, the selenium supplement group;
   NEG, negative ion mode; POS, positive ion mode; DEMs, differential
   metabolites.

3.3.2. Function enrichment analysis of DEMs

   As depicted in [111]Figure 10A, the results of the KEGG enrichment
   analysis revealed that the DEMs in LSe (NEG) were enriched in a total
   of 15 metabolic pathways. Among these pathways, Phenylalanine, tyrosine
   and tryptophan biosynthesis, Carbon metabolism, Biosynthesis of amino
   acids, Phosphonate and phosphinate metabolism, Glycolysis /
   Gluconeogenesis, and Sulfur metabolism exhibited significant enrichment
   (p < 0.05).

Figure 10.

   [112]Figure 10
   [113]Open in a new tab

   The KEGG pathways enrichment of DEMs in the LSe of NEG (A), LSe of POS
   (B), SSe of NEG (C), and SSe of POS (D). KEGG, the Kyoto Encyclopedia
   of Genes and Genomes; DEMs, differential metabolites; LSe, the selenium
   deficient group; SSe, the selenium supplement group; NEG, negative ion
   mode; POS, positive ion mode.

   As shown in [114]Figure 10B, the results of KEGG enrichment analysis
   indicated that the DEMs of LSe (POS) were enriched into a total of 12
   metabolic pathways, of which Gap junction, Calcium signaling pathway,
   Synaptic vesicle cycle, and Inflammatory mediator regulation of TRP
   channels were significantly enriched (p < 0.05).

   [115]Figure 10C showed that the KEGG pathway demonstrated that the DEMs
   in SSe (NEG) were enriched in 5 pathways, of which the Calcium
   signaling pathway was significantly enriched (p < 0.05).

   [116]Figure 10D illustrated the KEGG pathway analysis, which revealed
   that the DEMs in SSe (POS) were enriched in 15 pathways. Among them,
   the Porphyrin and chlorophyll metabolism and Phototransduction
   exhibited significant enrichment among these pathways (p < 0.05).

   The main secondary classifications of these KEGG pathways are Amino
   acid metabolism, Carbon metabolism, and Signal transduction.

3.4. Integrative analyses of transcriptome and metabolome

   Correlation analysis between LSe critical genes and LSe DEMs (NEG)
   revealed that AGAP1 and ERN1 were significantly and positively
   correlated with D-Erythrose 4-phosphate, NFIC was significantly and
   positively correlated with L-Cysteine-glutathione disulfide (p < 0.05)
   ([117]Figure 11A). In the LSe (POS), AGAP1, ERN1, MAL2, and NFIC were
   significantly and positively correlated with Gly-Tyr-Ala,
   Glycyl-L-leucine, and DL-Arginine. AGAP1 and NFIC were significantly
   and positively correlated with Serotonin and Gly-Phe, while
   significantly and negatively correlated with 2-Arachidonoyl glycerol.
   ERN1 was significantly and positively correlated with Gly-Phe, while
   significantly and negatively correlated with DL-o-Tyrosine. MAL2 was
   significantly and negatively correlated with DL-o-Tyrosine and
   2-Arachidonoyl glycerol (p < 0.05) ([118]Figure 11B).

Figure 11.

   [119]Figure 11
   [120]Open in a new tab

   The correlation analysis of critical genes and DEMs in the LSe of NEG
   (A), LSe of POS (B), SSe of NEG (C), and SSe of POS (D). * p < 0.05, **
   p < 0.01, *** p < 0.001. DEMs, differential metabolites; LSe, the
   selenium deficient group; SSe, the selenium supplement group; NEG,
   negative ion mode; POS, positive ion mode.

   Correlation analysis between SSe critical gene and SSe DEMs indicated
   that RERG exhibited a significant negative correlation with Reduced
   nicotinamide adenine dinucleotide in the NEG and a significant positive
   correlation with Guanosine monophosphate in the POS (p < 0.05)
   ([121]Figures 11C,[122]D).

4. Discussion

   Mineral elements play a vital role in the physiological functions and
   healthy development of sheep. Se, in particular, has been found to be
   crucial for the immune system, reproductive health, and antioxidant
   capacity of sheep ([123]9). While numerous studies have explored the
   addition of organic or inorganic Se to sheep diets, comprehensive
   investigations into the specific effects of Se deficiency or
   supplementation on the liver of grazing sheep are lacking ([124]10,
   [125]11, [126]26, [127]27). Therefore, this experiment delves into
   assessing liver weight, the concentration of mineral elements,
   transcriptomics, and metabolomics in grazing sheep under conditions of
   dietary Se deficiency and Se supplementation.

   The results showed that dietary Se treatment had no significant impact
   on liver weights in grazing sheep. This suggested that changes in liver
   weight may not be directly regulated by the dietary Se status. Previous
   studies have demonstrated that the liver was the primary storage for Se
   in animals ([128]10). The mineral elements results revealed a
   significant decrease in the liver concentration of Se in response to
   dietary Se deficiency. Conversely, dietary Se supplementation led to a
   significant increase in the liver concentration of Se. The findings of
   our experiment on liver mineral elements are consistent with previous
   studies, supporting that liver Se levels can assess the status of Se in
   grazing sheep. Furthermore, it was observed that dietary Se deficiency
   led to a significant increase in the liver concentration of S. This can
   be attributed to the similar chemical properties of S and Se, resulting
   in competition for the same transporter protein ([129]28).
   Consequently, when the concentration of Se in the liver decreased,
   there was a significant increase in the concentration of S. Meanwhile,
   the dietary supplement Se resulted in a significant increase in the
   liver concentration of K and Mn. This phenomenon is likely attributed
   to the essential role of Se as a component of numerous enzymes
   ([130]9). The supplementation of Se to the diet enhanced the activity
   of these enzymes, thereby promoting the absorption of K and Mn by the
   liver.

   Transcriptome analysis can unveil a series of genes that display
   differential expression in grazing sheep when subjected to dietary Se
   deficiency or supplementation. Go enrichment analysis of DEGs revealed
   that dietary Se deficiency reduced the cellular response to glucose
   stimulus and decreased the efficiency of intra-Golgi vesicle-mediated
   transport. The Golgi vesicle was an essential organelle responsible for
   protein synthesis, modification, and transport within the cell
   ([131]29). When the efficiency of intra-Golgi vesicle-mediated
   transport was compromised, the process of protein synthesis was
   negatively impacted, subsequently affecting cell metabolism and normal
   function. Furthermore, our findings indicated that dietary
   supplementation with Se significantly increased GTPase activity and the
   abundance of GTP-binding proteins, leading to enhanced protein
   synthesis ([132]30). Moreover, GSEA analysis also demonstrated that the
   terms associated with protein secretion were significantly enriched in
   SSe. Meanwhile, the term related to flavin adenine dinucleotide (FAD)
   was found to be significantly enriched in LCG. FAD was a crucial
   coenzyme that played a vital role in regulating enzyme activities,
   participating in energy metabolism, and providing antioxidant
   protection ([133]31). The above results indicate that dietary Se
   deficiency can have negative effects on protein synthesis and reduce
   the antioxidant capacity of liver cells, whereas dietary Se
   supplementation has the potential to enhance the protein synthesis
   capacity of liver cells in Wu Ranke sheep.

   By employing WGCNA and DESeq2 analyses, four down-regulated genes
   AGAP1, ERN1, MAL2, and NFIC were screened as the critical gene for LSe,
   while up-regulated gene RERG was screened as the critical gene for SSe.
   Among them, AGAP1 was widely recognized as a gene involved in the
   regulation of membrane transport and protein transport. Additionally,
   it has been linked to psychiatric disorders such as autism and
   schizophrenia ([134]32). The NFI family is a crucial family of
   transcription factors involved in adenoviral DNA replication, which
   consists of 4 genes, including NFIA, NFIB, NFIC, and NFIX, in most
   vertebrates. Research has demonstrated that up-regulation of NFIC
   promotes adipocyte and osteoblast differentiation ([135]33, [136]34).
   RERG was a tumor suppressor gene. In mice, the RERG protein has been
   shown to effectively inhibit tumorigenesis ([137]35). It plays a
   critical role in regulating cell growth, differentiation, and survival.
   Moreover, RERG could be considered as a potential candidate gene
   associated with the promotion of goat breeding ([138]36). These 5 genes
   can serve as valuable biomarkers for evaluating the Se status in
   grazing sheep. Furthermore, the findings suggest that dietary Se
   deficiency may have adverse effects on the differentiation of
   adipocytes and osteoblasts, as well as protein transport, while dietary
   Se supplementation may suppress tumorigenesis in Wu Ranke sheep.

   Metabolomics analysis revealed that DEMs in LSe and SSe Wu Ranke sheep
   were predominantly classified as Organooxygen compounds and Carboxylic
   acids and derivatives. KEGG pathway enrichment analysis indicated that
   the DEMs were primarily enriched in metabolic pathways such as amino
   acid metabolism, carbohydrate metabolism, and signal transduction. This
   suggests that dietary Se deficiency and supplementation may affect
   amino acid metabolism and carbohydrate metabolism in the liver of Wu
   Ranke sheep.

   The abundance of several metabolites, including L-Cysteine-glutathione
   disulfide (L-CySSG), Phosphoenolpyruvic acid (PEP), Serotonin, and
   DL-arginine, were significantly down-regulated in the liver DEMs of LSe
   Wu Ranke sheep. Among them, L-CySSG served as a storage form of
   L-cysteine and has been found to possess potent hepatoprotective
   properties against liver damage ([139]37). PEP was a glycolytic and
   carbohydrate metabolite with a high-energy phosphoryl group, known for
   its cytoprotective and antioxidant activities ([140]38). Serotonin,
   also known as 5-hydroxytryptamine, was a major product of tryptophan
   metabolism and can be produced through the decarboxylation of
   5-hydroxytryptophan in the catalyze of tryptophan carboxylase. It has
   been shown that serotonin promotes liver regeneration following partial
   resection of the liver in animals ([141]39, [142]40). Additionally,
   DL-arginine has been shown to inhibit lipid peroxidation and suppress
   inflammatory responses ([143]41). The results of the integrative
   analysis of transcriptomics and metabolomics indicated that the
   down-regulated genes NFIC, ERN1, MAL2, and AGAP1 were able to
   positively regulate the production of amino acid metabolites,
   specifically Gly-Tyr-Ala, Glycyl-L-leucine, and DL-arginine.
   Furthermore, NFIC was found to positively regulate the production of
   L-CySSG and serotonin, while AGAP1 was able to positively regulate
   serotonin production. In light of these findings, the down-regulation
   of the above metabolites can be inferred that Se deficiency in the diet
   may lower the hepatic antioxidant capacity and anti-inflammatory
   response, rendering it susceptible to damage in Wu Ranke sheep.
   However, the specific functions and regulatory mechanisms of these
   observations will require further investigation.

   The integrative analysis of transcriptomics and metabolomics of SSe
   indicated that the up-regulated gene RERG was able to positively
   regulate the production of guanosine monophosphate (GMP), while
   negatively regulating the production of reduced nicotinamide adenine
   dinucleotide (NADH). Among them, GMP was a nucleotide that served as a
   fundamental building block of RNA and DNA. It played a crucial role in
   various physiological processes, including protein synthesis and energy
   metabolism ([144]42). NADH acted as an electron transfer mediator in
   redox reactions in animals, which facilitated the production of
   mitochondrial ATP in the cellular respiratory chain ([145]43). The
   metabolomics result also revealed that the abundance of several
   metabolites, including L-glutathione oxidized (GSSG), epigallocatechin
   (EGC), and 5-hydroxytryptophan, were significantly up-regulated in the
   liver DEMs of SSe Wu Ranke sheep. Among them, GSSG was a tripeptide
   composed of glycine, cysteine, and glutamic acid. It played a crucial
   role in cellular antioxidant defense by converting between its oxidized
   and reduced forms through the catalytic action of glutathione
   reductase. The oxidation of glutathione to GSSG was an essential
   mechanism for cells to balance reactive oxygen species and reduce
   oxidative stress. This was particularly important as free radicals have
   the ability to react with intracellular molecules, causing cell damage
   and triggering apoptosis. By reacting with free radicals, GSSG helps
   protect cells from oxidative damage by reducing the number of free
   radicals ([146]44). EGC, a tea polyphenol, has been found to have
   significant preventive effects against cardiovascular disease and
   cancer in animal studies ([147]45). Moreover, when combined with
   curcumin, EGC exhibited inhibitory effects on inflammation in animals
   ([148]46). Additionally, 5-hydroxytryptophan served both as a drug and
   a dietary supplement. It can be decarboxylated to produce
   5-hydroxytryptamine, which is further converted into melatonin. As a
   monoamine neurotransmitter, it regulates various physiological
   processes including mood, cognition, learning, memory, and sleep.
   Furthermore, it possessed anti-inflammatory properties by reducing the
   production of pro-inflammatory mediators. Studies conducted on the
   liver of Holstein cows have shown that 5-hydroxytryptophan stimulated
   autocrine-paracrine adaptation to lactation. Interestingly, dietary Se
   deficiency significantly decreased the abundance of 5-hydroxytryptamine
   in the liver metabolites of Wu Ranke sheep. Conversely, dietary Se
   supplementation led to a significant increase in the abundance of
   5-hydroxytryptophan in the liver metabolites of Wu Ranke sheep. This
   suggested that dietary Se supplementation may enhance the antioxidant,
   anti-inflammatory, and lactation capacities of Wu Ranke sheep. However,
   further analysis is required to elucidate the specific mechanisms
   involved.

   In addition, there are several limitations in this study that should be
   acknowledged. Firstly, the feeding experiment could have been designed
   optimally for 30, 60, and 90 days to obtain more reasonable days for
   supplemental feeding. Secondly, the dosage of supplemental Se could be
   set to different concentration gradients to explore the optimal
   supplemental dose of grazing. In future experiments, we will further
   investigate the mechanisms through which critical genes influence the
   abundance of metabolites. Additionally, we also intend to delve deeper
   into the specific mechanisms by which hepatic critical genes regulate
   Se deficiency and supplementation in grazing sheep. Furthermore, we
   have plans for further research involving diverse grazing sheep breeds
   to validate and expand upon our findings, ultimately enhancing the
   depth and breadth of our research insights.

5. Conclusion

   This study aimed to investigate the effect of dietary Se deficiency and
   supplementation on the liver weight, the concentration of mineral
   elements, transcriptomic, and metabolomic in grazing Wu Ranke sheep.
   The findings revealed that the concentration of Se in the liver was
   found to be susceptible to variations in dietary Se levels. Go and GESA
   enrichment analysis suggested that dietary Se deficiency might impair
   protein synthesis efficiency, while Se supplementation was found to be
   beneficial in enhancing liver protein synthesis in grazing sheep.
   AGAP1, ERN1, MAL2, NFIC, and RERG were identified as critical genes
   through WGCNA, qRT-PCR and ROC validation that could potentially serve
   as biomarkers. Integrative analysis of the transcriptome and metabolome
   revealed that dietary Se deficiency led to reduced hepatic antioxidant
   capacity and anti-inflammatory response, whereas Se supplementation
   increased the hepatic antioxidant and anti-inflammatory capacity in
   grazing Wu Ranke sheep.

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 at: [149]https://www.ncbi.nlm.nih.gov/,
   PRJNA1031778.

Ethics statement

   The animal study was approved by Inner Mongolia University Animal Care
   and Use Committee. The study was conducted in accordance with the local
   legislation and institutional requirements.

Author contributions

   XJ: Data curation, Software, Writing – original draft. LiM: Software,
   Validation, Visualization, Writing – original draft. ZQ:
   Conceptualization, Resources, Methodology, Writing – review & editing.
   LaM: Writing – review & editing.

Funding Statement

   The author(s) declare financial support was received for the research,
   authorship, and/or publication of this article. This research was
   funded by the Research Support Program for the Introduction of
   High-level Talents at the Provincial Level in Inner Mongolia Autonomous
   Region (12000-15042245) and the Introduction of High-level Talents
   Research Start-up Grant in 2018 (30500-5185143).

Footnotes

   ^1[150]https://www.bioinformatics.babraham.ac.uk/projects/fastqc

   ^2[151]http://www.ensembl.org/index.html

   ^3[152]https://www.ncbi.nlm.nih.gov/tools/primer-blast/

   ^4[153]https://www.genome.jp/kegg/pathway.html

Conflict of interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Publisher’s note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

Supplementary material

   The Supplementary material for this article can be found online at:
   [154]https://www.frontiersin.org/articles/10.3389/fvets.2024.1358975/fu
   ll#supplementary-material
   [155]Data_Sheet_1.docx^ (784.4KB, docx)

References