Abstract

   The gut microbiota plays a crucial role in maintaining the host’s
   metabolism and can influence the host’s productivity. Both dietary
   composition and gender have distinct effects on the composition of the
   gut microbiota. Therefore, to investigate the differences in the
   structure and function of the gut microbiota between female and male
   goats, we analyzed their fecal microbiota and metabolites when fed a
   10% crude protein diet at four different energy levels—7.01, 8.33,
   9.66, and 10.98 MJ/kg DM. Four non-pregnant female and four male
   Leizhou goats (all 8 months of age) were used in the experiment, with
   an average body weight of 10.3 ± 0.8 kg for females and 13.6 ± 1.1 kg
   for males (mean ± SD). The animals were assigned to two separate 4 × 4
   Latin square designs according to their gender, each consisting of four
   treatments and four 28-day periods, including 25 days of a dietary
   adaptation period and 3 days of fecal sample collection per period. The
   data were analyzed using the SAS statistical package and Pearson’s
   correlation analysis. The dominant phyla for all samples were
   Firmicutes and Bacteroidota, regardless of dietary energy levels or
   gender. Among fecal bacteria, unclassified_f_Lachnospiraceae was the
   dominant genus in the female goats, and Oscillospiraceae_UCG-005 was
   the dominant genus in the male goats. The relative abundance of
   unclassified_f_Lachnospiraceae (p < 0.001), Bacteroides (p = 0.007),
   norank_f_Ruminococcaceae (p = 0.024), Mediterraneibacter (p = 0.001),
   and norank_f_Muribaculaceae (p = 0.008) was greater in the female goats
   than in the male goats. In contrast, the relative abundance of
   Oscillospiraceae_UCG-005 (p < 0.001), Ruminococcus (p = 0.035),
   Monoglobus (p = 0.006), Oscillospiraceae-NK4A214_group (p = 0.008),
   norank_f_F082 (p < 0.001), and Prevotellaceae_UCG-003 (p < 0.001) was
   lower in the female goats than in the male goats. The volcano plot
   showed that there were 153, 171, 171, and 183 differential metabolites
   between the female and male goats at dietary energy levels of 7.01,
   8.33, 9.66, and 10.98 MJ/kg DM, respectively. Numerous correlations
   were observed between differential metabolites and microflora genera.
   We concluded that the non-pregnant female and male goats exhibited
   distinct metabolic abilities when consuming a 10% crude protein diet at
   four different energy levels. Interestingly, in the female and male
   goats, the fecal microbiota also showed some differing responses to the
   energy levels. These results provide a gender-based reference for
   formulating low-protein dietary strategies for 8-month-old Leizhou
   goats.

1. Introduction

   Ruminants primarily rely on a diverse and robust bacterial community
   for the efficient digestion of dietary fibers, which subsequently
   produce volatile fatty acids, microbial proteins, ammonia, and other
   metabolites essential for growth and reproduction [[44]1,[45]2]. In
   addition, gut bacterial communities play a vital role in the overall
   feed digestion and nutrient absorption in ruminants [[46]3,[47]4]. A
   number of factors influence bacterial communities’ composition. A
   previous study showed that the relative abundances (RAs) of
   Bacteroidota, Verrucomicrobiota, Cyanobacteria, Prevotella, and
   Lachnospiraceae_NK4A136_ group were lower, while Firmicutes and
   Desulfobacterota, and Ruminococcus, Saccharofermentans,
   Succiniclasticum, Christensenellaceae_R_7_group, and
   Candidatus_Sacch-arimonas were greater in female compared to male
   Qinchuan cattle [[48]5]. Similarly, Guo et al. reported that
   Fibrobacteres and Spirochaetes had significantly greater RAs in female
   than in male Tibetan goats [[49]6]. At the genus level, the RAs of
   Fibrobacter, Ruminococcus_1, and Pyramidobacter were significantly
   higher in female than in male Tibetan goats. These studies provided a
   gender reference for ruminants based on their bacterial communities.

   Dietary composition and nutritional levels strongly influence ruminant
   gut microbial communities and their diversity, richness, and evenness.
   Wang et al. reported that rumen bacterial diversity decreased with
   increasing dietary energy levels in sheep, with changes in the
   abundance of some bacteria (e.g., Prevotellaceae, Muribaculaceae,
   Saccharofermentans, Prevotella, and Succiniclasticum) [[50]7]. Liu et
   al. observed that a rumen-protected fat supplement (a high-energy
   additive) reduced the RAs of Ruminococcus, Rikenellaceae_RC9_gut_group,
   Treponema, norank_f_norank_o_RF39, Eubacterium_siraeum_group, and
   Ruminococcus_torques_group and increased the RAs of Bacteroides,
   norank_f_norank_o_Clostridia_UCG-014,
   norank_f_Eubacterium_coprostanoligenes_group,
   Eubacterium_ruminantium_group, norank_f_Oscillospirale-UCG-010,
   Oscillospiraceae_UCG-002, and Family_XIII_AD3011_ group [[51]8]. In
   dairy cows, Wei et al. reported that increasing dietary crude protein
   (CP) increased the RA of Paeniclostridium and decreased the RA of
   Lachnospiraceae_NK3A20_group [[52]9]. A previous study reported no
   difference in the ACE, Chao, Shannon, Simpson, and Sobs indices in
   goats when they consumed rumen-protected fat [[53]8]. However, the
   Chao, Shannon, and Sobs indices and coverage were greater in dairy cows
   when they consumed a low-protein diet with rumen-protected lysine than
   without rumen-protected lysine [[54]9]. Over the past few decades,
   scientists have mainly focused on rumen microorganisms. However, the
   hindgut serves as a secondary fermentation chamber in ruminants, where
   approximately 17% of digestible fiber is fermented, producing
   substantial amounts of short-chain fatty acids, including acetate,
   propionate, and butyrate—significant energy sources for the host.
   Hence, in the present study, we will focus on the fecal bacterial
   communities of ruminants consuming low-protein diets with varying
   energy levels.

   Metabolomics provides a comprehensive readout of the host–microbiome
   metabolic interface under dietary interventions. By profiling
   small-molecule metabolites and pathways, metabolomics complements
   sequencing-based microbiome studies, revealing functional consequences
   of microbial shifts [[55]10]. For instance, it has been reported that
   the microbiome and both its own and the host’s metabolome can regulate
   growth performance and production in dairy goats [[56]11], sheep
   [[57]12], crossbred cattle [[58]13], and dairy cows [[59]14]. Zhu et
   al. reported that the levels of 78 metabolites in the rumen fluid of
   Anhui white goats were altered (74 decreased, 4 increased) by dietary
   CP levels, and most of those at low-protein levels were substrates for
   microbial protein synthesis [[60]15]. Ma et al. identified 14
   metabolites that were consistently altered in Saanen goats when
   consuming a high-concentrate diet with a concentrate-to-forage ratio of
   70:30 compared to the control diet with a ratio of 30:70. These spanned
   carbohydrate metabolism, amino acids, fatty acids, biogenic amines, and
   organic acids [[61]16]. In the present study, metabolomics was also
   used to better reveal the effect of dietary energy levels on fecal
   microbiota in Leizhou goats when consuming a low-protein diet.

   The Leizhou goat, also known as the Hainan black goat, is an indigenous
   tropical breed from southern China. It is well-adapted to hot, humid
   environments and produces exceptionally high-quality meat containing
   more than 22.6% protein, including most essential amino acids, while
   being low in fat (<3%) and cholesterol (<60 mg/kg)
   [[62]17,[63]18,[64]19]. In addition, Hainan black goat meat is regarded
   as one of the “four famous foods” in Hainan [[65]20]. Despite these
   desirable traits, the average growth performance and carcass yield of
   Leizhou goats remain low relative to market demand. Numerous studies
   have shown that increasing dietary energy levels can improve the
   average daily gain of Leizhou goats [[66]8], grazing lactating yaks
   [[67]21], and Hulunbuir lambs [[68]22]. Dietary energy level and gender
   can affect the fecal microbiota; however, little is known about how
   their interaction influences the fecal microbiota in goats. The primary
   objective of this study was to compare the fecal microbiota and
   metabolomic profiles of male and female Leizhou goats fed a 10% crude
   protein diet with four energy levels.

2. Materials and Methods

2.1. Animal, Experimental Design, and Treatments

   Eight Leizhou goats (8 months of age; four non-pregnant females and
   four males) were used in the experiment, with an average body weight of
   10.3 ± 0.8 kg for females and 13.6 ± 1.1 kg for males (mean ± SD).
   Animals were assigned to two separate 4 × 4 Latin square designs based
   on gender, each consisting of four treatments and four 28-day periods,
   including 25 days of adaptation and 3 days of fecal sample collection
   per period. The goats were individually housed in metabolic cages
   equipped with feed bunkers and individual water troughs. Prior to the
   experimental trial, all goats underwent a 14-day adaptation period. All
   goats were fed a low-protein total mixed ration with four different
   energy levels: 7.01 (low energy, LE), 8.33 (middle–low energy, MLE),
   9.66 (middle–high energy, MHE), and 10.98 (high energy, HE) MJ/kg DM.
   Respectively, the diets provided 0.85, 1.00, 1.15, and 1.30 times the
   maintenance energy requirement of goats according to the Nutrient
   Requirements of Meat-Type Sheep and Goats (NY/T 816-2021) [[69]23]. The
   dietary ingredients and chemical composition of the experimental diets
   are shown in [70]Supplementary Table S1. The four diets were weighed
   and provided to the goats twice daily (at 08:00 and 16:30) throughout
   the experimental period, alongside free access to feed and water.

2.2. Sample Collections

   Before the morning feeding from d 26 to d 28 in each period, the fecal
   samples (approximately 5 g) were collected from the rectums of each
   goat following the method described by Wuytack et al. [[71]24]. The
   fecal samples were collected into a 10 mL centrifuge tube, frozen in
   liquid nitrogen immediately, and stored at −80 °C until further omics
   analysis.

2.3. Fecal Bacterial Communities’ Analysis

   The fecal samples underwent bacterial community analysis at Majorbio
   Bio-Pharm Technology Co., Ltd. (Shanghai, China). Genomic DNA was
   extracted from the specimens using a commercial fecal DNA extraction
   kit (product number: DP328, Tiangen Biotech, Beijing, China) according
   to the manufacturer’s protocols. The DNA quality control included
   concentration measurement using a Nanodrop spectrophotometer (Thermo
   Scientific, Wilmington, DE, USA) and integrity assessment using the
   FlashGel system (Lonza, Rockland, Inc., Pessac, France). Subsequent
   amplification targeted the V3–V4 regions of bacterial 16S rRNA using
   primers 338/806R under standard PCR conditions [[72]8], with qualified
   PCR products sequenced on the Illumina MiSeq platform (2 × 300
   paired-end sequencing runs).

   For V3 and V4 amplification, the primer pair 338F
   (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) was
   employed. Reactions utilized TransStart FastPfu DNA polymerase
   (TransGen Biotech, Beijing, China) in an ABI geneAmp&reg (product
   number: 9700; Applied Biosystems, Foster City, CA, USA), with PCR
   reaction conditions as follows: 95 °C for 3 min, then 27 cycles at 95
   °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s, with a final extension
   at 72 °C for 10 min, and 10 °C until halted by the user. Triplicate
   20-uL reactions contained 10 μL of 2 × Pro Taq, 0.8 μL of each primer
   (5 μM), 0.4 μL of FastPfu Polymerase, and 10 ng/uL of template DNA,
   with ddH[2]O added until 20 µL was reached. The amplified products were
   gel-purified (2% agarose) using the AxyPrep DNA Gel Extraction Kit
   (Axygen, Union City, NJ, USA) and then quantified using QuantiFluor-ST
   (Promega, Madison, WI, USA).

   Bioinformatic processing of sequencing raw data was performed using the
   QIIME package (V 1.91) following established methods [[73]25]. Raw
   sequences underwent adapter trimming and ambiguous read removal,
   followed by FLASH assembly (overlap > 10 bp; mismatch < 20%) and
   UCHIME-based chimera removal. The OTU clustering at 97% similarity used
   USEARCH, with taxonomic classification via RDP classifier (v11.5)
   against the Silva 138.2 database. To study the species diversity of the
   32 samples, alpha diversity was used on six indices, including Sobs,
   Shannon, Simpson, ACE, Chao, and Good-coverage. The six indices of the
   32 samples were calculated using QIIME (Quantitative Insights into
   Microbial Ecology) (Version 1.7.0). Linear discriminant analysis effect
   size (LEfSe) was determined for bacterial difference analysis in fecal
   bacteria between gender and among energy levels by coupling the
   Kruskal–Wallis Test for statistical significance with additional tests
   assessing biological consistency and effect relevance. Linear
   discriminant analysis scores were used to estimate the influence of the
   abundance of each species on the differential effect, using the free
   online Majorbio Cloud Platform ([74]www.Majorbio.com, accessed on 18
   May 2025) to generate graphical visualizations. Taxa with an LDA Score
   > 3 were considered as exhibiting a significant effect size.

2.4. Metabolites Extraction, UHPLC-MS/MS, and Metabolomic Analysis

   Prior to the metabolomics profiling, the fecal samples were
   cryopreserved at −80 °C at Majorbio Bio-Pharm Technology Co., Ltd.
   (Shanghai, China). Aliquots (50.0 ± 0.1 mg) of fecal sample were
   homogenized in a 2-mL centrifuge tube containing a 6 mm diameter
   grinding bead, before adding 400 μL of ice-cold methanol:water (v:v,
   4:1) extraction solvent. Mechanical disruption was performed using a
   high-throughput tissue grinder at a low temperature (60 Hz, −20 °C).
   Subsequent processing involved: (1) vortex-assisted low-temperature
   ultrasonication (40 kHz, 5 °C) for 30 min; (2) thermal equilibration at
   −20 °C for 30 min to maximize metabolite solubility; (3) centrifuged at
   13,000× g for 15 min (4 °C); (4) the supernatant was filtered using
   0.22-μm nylon membranes. Processed extracts were stored at −80 °C until
   LC-MS analysis.

   The chromatographic separation was achieved using an ACQUITY HSS T3
   column (100 mm × 2.1 mm, 1.8 μm; Waters, Milford, CT, USA) on an AB
   SCIEX UPLC-TripleTOF system. The mobile phases consisted of solvent A,
   which was mixed with 0.1% formic acid in water: acetonitrile according
   to a volume ratio of 95:5, and solvent B, which was mixed with 0.1%
   formic acid in acetonitrile: isopropanol:water following a volume ratio
   of 47.5:47.5. The flow rate was kept at 0.40 mL/min and the column
   temperature was set at 40 °C.

   Mass spectrometry was conducted using a Thermo UHPLC-Q Exactive system
   (Thermo Fisher Scientific Inc., Beijing, China) with a dual-polarity
   electrospray ionization (ESI) source. The optimal conditions were set
   as follows: the heater temperature was 400 °C; capillary temperature
   was 320 °C; sheath gas flow rate, 40 arb; aux gas flow rate, 10 arb;
   ion-spray voltage floating (ISVF), −2800 V in negative mode and 3500 V
   in positive mode; and normalized collision energy, 20–40–60 V rolling
   for MS/MS. The total MS resolution was 70,000, and MS/MS resolution was
   17,500. The data-dependent acquisition (DDA) mode was adopted for data
   acquisition. The detection was carried out within a mass range from 70
   to 1050 m/z.

   To monitor instrumental stability, pooled quality control (QC) samples
   were generated by combining equal-volume aliquots from all experimental
   samples. QC injections were interspersed at 8–10 sample intervals
   throughout the analytical sequence, enabling systematic evaluation of
   retention time shifts (<0.1 min), signal drift (RSD < 15%), and feature
   reproducibility. Principal component analysis (PCA) and partial least
   squares discriminant analysis (PLS-DA) were conducted using the
   multivariate statistical analysis module. Volcano plots were created
   using the differential analysis module, and differentially abundant
   metabolites were identified using t-tests and Variable Importance in
   Projection (VIP) scores (p < 0.05, VIP scores > 1). The differentially
   abundant metabolites were then subjected to Kyoto Encyclopedia of Genes
   and Genomes (KEGG) enrichment analysis, and the biological pathway most
   relevant to the experimental treatment was obtained using Fisher’s
   exact test. All metabolites were analyzed by Shanghai Majorbio
   Bio-pharm Technology Co., Ltd. (Shanghai, China) and the data were
   analyzed using the free online Majorbio Cloud Platform
   ([75]www.Majorbio.com, accessed on 18 May 2025).

2.5. Statistics Analysis

   The data were first organized using Excel 2023. Subsequent statistical
   analyses were performed using the SAS statistical package (SAS version
   9.4, SAS Inst. Inc., Cary, NC, USA). The model was: Y = μ + EL + P + G
   + e, where Y = dependent variable; μ = treatment mean value; EL =
   effect of dietary energy level; P = period; G = effect of gender; and e
   = residual error. Polynomial contrasts were conducted to determine the
   effects of the increasing energy fiber level on fecal bacterial
   communities at genus (RA > 0.50%) and phylum (RA > 0.50%) using the SAS
   statistical package (SAS version 9.4, SAS Inst. Inc., Cary, NC, USA).
   Data were presented as mean ± standard error of the mean (SEM).
   Pearson’s correlation analysis was used to assess the relationship
   between fecal microbial taxa and metabolites. Statistical significance
   was set at p < 0.05.

3. Results

3.1. Sequencing Metrics for the Fecal Microbiota of Goats

   A total of 2,253,409 raw sequences were generated from 32 fecal
   samples, consisting of 1,163,748 sequences from female goats and
   1,089,661 sequences from male goats. Based on 97% nucleotide sequence
   identity, 24,525 OTUs were obtained. The flower plot shows that 1173
   OTUs were shared across the eight groups, accounting for 37.8%, 38.7%,
   36.1%, and 42.5% of the total OTUs in the female goats and 40.2%,
   38.2%, 35.1%, and 32.4% in the male goats receiving diets with 7.01,
   8.33, 9.66, and 10.98 MJ/kg DM, respectively. Additionally, the numbers
   of specific OTUs in the LE, MLE, MHE, and HE groups were 379, 307, 384,
   and 274 for female goats and 264, 252, 432, and 278 for male goats,
   respectively ([76]Figure 1).

Figure 1.

   [77]Figure 1
   [78]Open in a new tab

   Venn diagram of OTUs of fecal microbial communities between female and
   male goats when consuming a 10% crude protein diet at 4 energy levels.
   F-LE, female goats fed a low energy diet (=7.01 MJ/kg DM) group (n =
   4); F-MLE, female goats fed a middle–low energy diet (=8.33 MJ/kg DM)
   group (n = 4); F-MHE, in the female goats fed a middle–high energy diet
   (=9.66 MJ/kg DM) group (n = 4); F-HE, female goats fed a high energy
   diet (=10.98 MJ/kg DM) group (n = 4); M-LE, male goats fed a low energy
   diet (=7.01 MJ/kg DM) group (n = 4); M-MLE, male goats fed a middle–low
   energy (=8.33 MJ/kg DM) diet group (n = 4); M-MHE, male goats fed a
   middle–high energy diet (=9.66 MJ/kg DM) group (n = 4); M-HE, male
   goats fed a high energy diet (=10.98 MJ/kg DM) group(n = 4).

   The Shannon index was lower (p = 0.041) and the Simpson index was
   greater (p = 0.024) in the female goats than in the male goats. The
   Sobs (p = 0.035), ACE (p = 0.030), and Chao indices (p = 0.027)
   increased quadratically with rising dietary energy levels ([79]Figure
   2).

Figure 2.

   [80]Figure 2
   [81]Open in a new tab

   Comparison of diversity score indices of fecal bacteria in goats when
   consuming a 10% crude protein diet at 4 energy levels. (A) Sobs index
   in the fecal bacteria; (B) Shannon index in the fecal bacteria; (C)
   Simpson index in the fecal bacteria; (D) Ace index in the fecal
   bacteria; (E) Chao index in the fecal bacteria (F) Coverage index in
   the fecal bacteria. F-LE, female goats fed a low energy diet (=7.01
   MJ/kg DM) group (n = 4); F-MLE, female goats fed a middle–low energy
   diet (=8.33 MJ/kg DM) group (n = 4); F-MHE, in the female goats fed a
   middle–high energy diet (=9.66 MJ/kg DM) group (n = 4); F-HE, female
   goats fed a high energy diet (=10.98 MJ/kg DM) group (n = 4); M-LE,
   male goats fed a low energy diet (=7.01 MJ/kg DM) group (n = 4); M-MLE,
   male goats fed a middle–low energy (=8.33 MJ/kg DM) diet group (n = 4);
   M-MHE, male goats fed a middle–high energy diet (=9.66 MJ/kg DM) group
   (n = 4); M-HE, male goats fed a high energy diet (=10.98 MJ/kg DM)
   group(n = 4).

   A total of 18 phyla were identified. Across all samples, Firmicutes was
   the most abundant phylum, with relative abundances of 71.1%, 82.7%,
   78.2%, and 70.6% in the female goats, and 70.3%, 78.7%, 77.4%, and
   71.5% in the male goats for diets containing 7.01, 8.33, 9.66, and
   10.98 MJ/kg DM, respectively ([82]Table 1). Bacteroidota was the second
   most abundant phylum with relative abundances of 21.9%, 14.7%, 18.2%,
   and 21.2% in the female goats and 24.8%, 17.0%, 17.7%, and 21.7% in the
   male goats for diets containing 7.01, 8.33, 9.66, and 10.98 MJ /kg DM,
   respectively. There were no differences between female and male goats
   in terms of the relative abundances (RAs) of Firmicutes, Bacteroidota,
   Spirochaetota, Verrucomicrobiota, and Patescibacteria, or their
   interaction with energy levels. The RA of Firmicutes increased
   quadratically (p < 0.001), and that of Spirochaetota increased linearly
   (p < 0.001), whereas the RA of Bacteroidota (p< 0.001) and other minor
   phyla (p < 0.001) decreased quadratically with increasing dietary
   energy levels.

Table 1.

   Relative abundance of fecal bacterial community at phylum level (0.5% <
   RA) in goats when consuming a 10% crude protein diet at 4 energy
   levels.
   Items Gender Dietary Energy Levels SEM p-Values
   LE MLE MHE HE G E G × E E-L E-Q
   Firmicutes Female 71.1 82.7 78.2 70.6 0.93 0.285 <0.001 0.427 0.691
   <0.001
   Male 70.3 78.7 77.4 71.5
   Bacteroidota Female 21.9 14.7 18.2 21.2 0.71 0.223 <0.001 0.621 0.439
   <0.001
   Male 24.8 17.0 17.7 21.7
   Spirochaetota Female 0.46 0.92 1.79 3.94 0.234 0.126 <0.001 0.059
   <0.001 0.316
   Male 0.69 2.12 2.49 3.30
   Verrucomicrobiota Female 0.57 0.31 0.52 1.44 0.202 0.285 0.418 0.211
   0.649 0.146
   Male 2.23 0.72 0.94 0.67
   Patescibacteria Female 2.55 0.42 0.09 0.08 0.274 0.365 0.130 0.603
   0.038 0.213
   Male 0.80 0.24 0.09 0.05
   Others Female 3.34 0.90 1.17 2.82 0.195 0.128 <0.001 0.008 0.245 <0.001
   Male 1.21 1.27 1.41 2.71
   [83]Open in a new tab

   LE = low energy group, 7.01 MJ/kg DM; MLE = middle–low energy group,
   8.33 MJ/kg DM; MHE = middle–high energy group, 9.66 MJ/kg DM; HE = high
   energy group, 10.98 MJ/kg DM; SEM = standard error of the means. G =
   effect of female and male; E = effect of different energy levels; G ×
   E= effect of interaction between G and E; E-L = Linear effect of
   dietary energy levels; E-Q = Quadratic effect of dietary energy levels.

   At the genus level, a total of 316 bacterial taxa were identified. The
   three most dominant genera were Christensenellaceae_R-7_group,
   Oscillospiracea_UCG-005, and unclassified_f_Lachnospiraceae ([84]Table
   2). For Christensenellaceae_R-7_group, the RAs were 7.55%, 11.72%,
   14.24%, and 10.46% in the female goats, and 10.10%, 10.42%, 9.94%, and
   11.36% in the male goats for diets containing 7.01, 8.33, 9.66, and
   10.98 MJ/kg DM, respectively. The RA of Oscillospiracea_UCG-005 was
   11.40%, 14.00%, 8.43%, and 7.57% in the female goats, and 13.15, 13.26,
   10.74, and 10.63% in the male goats for diets containing 7.01, 8.33,
   9.66, and 10.98 MJ/kg DM, respectively. The RA of
   unclassified_f_Lachnospiraceae was 9.52%, 13.93%, 12.32%, and 13.43% in
   the female goats, and 8.06%, 9.94%, 11.28%, and 7.42% in the male goats
   for diets containing 7.01, 8.33, 9.66, and 10.98 MJ/kg DM,
   respectively. There was a significant interaction between sex and
   dietary energy level on the RAs of Christensenellaceae_R-7_group,
   Oscillospiraceae_UCG-005, unclassified_f_Lachnospiraceae,
   norank_f_[Eubacterium]_coprostanoligenes_group,
   Rikenellaceae_RC9_gut_group, norank_f_Ruminococcaceae,
   Prevotellaceae_UCG-003. The RAs of unclassified_f_Lachnospiraceae (p <
   0.001), Bacteroides (p = 0.007), norank_f_Ruminococcaceae (p = 0.024),
   Mediterraneibacter (p = 0.001), and norank_f_Muribaculaceae (p = 0.008)
   were greater, whereas the RAs of Oscillospiraceae_UCG-005 (p < 0.001),
   Ruminococcus (p = 0.035), Monoglobus (p = 0.006),
   Oscillospiraceae-NK4A214_group (p = 0.008), norank_f_F082 (p < 0.001),
   and Prevotellaceae_UCG-003 (p < 0.001) were lower in the female goats
   than in male goats. The RAs of Oscillospiraceae_UCG-005 (p < 0.001),
   Monoglobus (p = 0.019), Prevotellaceae_UCG-004 (p = 0.002),
   unclassified_c_Clostridia(p = 0.001), norank_f_Ruminococcaceae (p <
   0.001), and others (p = 0.001) decreased linearly, whereas the RAs of
   norank_f_[Eubacterium]_coprostanoligenes_group (p < 0.001),
   Ruminococcus(p < 0.001), Treponema (p < 0.001),
   Oscillospiraceae-UCG-002 (p = 0.024), [Eubacterium]_siraeum_group (p =
   0.002), norank_f_Muribaculaceae (p < 0.001), and
   [Eubacterium]_ruminantium_group (p = 0.002) increased linearly with
   increasing dietary energy levels. The RAs of
   Christensenellaceae_R-7_group (p = 0.047),
   unclassified_f_Lachnospiraceae (p = 0.013), Rikenellaceae_RC9_gut_group
   (p = 0.009), and norank_o_RF39 (p = 0.020) increased quadratically,
   whereas the RA of Mediterraneibacter (p = 0.018) decreased
   quadratically with increasing dietary energy levels.

Table 2.

   Relative abundance of fecal bacterial community at genus level (0.5% <
   RA) in goats when consuming a 10% crude protein diet at 4 energy
   levels.
   Items Gender Dietary Energy Levels SEM p-Values
   LE MLE MHE HE G E G × E E-L E-Q
   unclassified_f__
   Lachnospiraceae Female 9.52 13.93 12.32 13.42 0.46 <0.001 0.001 0.008
   0.223 0.013
   Male 8.06 9.94 11.28 7.41
   Oscillospiraceae
   _UCG-005 Female 11.40 14.00 8.43 7.57 0.42 <0.001 <0.001 0.005 <0.001
   0.086
   Male 13.15 13.26 10.74 10.63
   Christensenellacea
   e_R-7_group Female 7.55 11.72 14.24 10.46 0.45 0.459 0.026 0.015 0.059
   0.047
   Male 10.10 10.42 9.94 11.36
   norank_f__[Eubacterium]_
   coprostanoligenes_group Female 3.11 5.70 10.56 6.28 0.48 0.086 <0.001
   0.007 <0.001 0.002
   Male 4.51 6.30 8.67 9.53
   Rikenellaceae_RC9
   _gut_group Female 6.53 7.11 5.37 7.50 0.25 0.913 0.014 0.002 0.515
   0.009
   Male 8.45 4.83 6.49 6.58
   Ruminococcus Female 1.86 4.64 3.79 4.97 0.29 0.035 <0.001 0.446 <0.001
   0.232
   Male 2.49 4.64 4.64 6.58
   norank_o__Clostridia_
   UCG-014 Female 2.28 3.28 3.93 3.96 0.30 0.379 0.265 0.457 0.327 0.101
   Male 3.03 5.21 4.06 3.25
   Bacteroides Female 4.80 3.05 6.07 5.91 0.31 0.007 0.045 0.059 0.856
   0.278
   Male 4.79 3.00 3.59 2.79
   Monoglobus Female 2.44 2.15 2.99 1.94 0.22 0.006 0.003 0.073 0.019
   0.060
   Male 4.08 3.87 3.90 1.46
   Prevotellaceae_UCG
   -004 Female 4.56 1.35 2.27 1.67 0.29 0.726 0.008 0.325 0.002 0.235
   Male 3.74 3.10 2.27 1.45
   Treponema Female 0.46 0.91 1.77 3.90 0.24 0.178 <0.001 0.103 <0.001
   0.385
   Male 0.68 2.12 2.46 3.23
   norank_f__UCG-010 Female 1.71 1.91 2.46 1.46 0.12 0.395 0.326 0.554
   0.455 0.125
   Male 2.22 2.24 2.07 1.82
   Akkermansia Female 0.25 0.26 0.41 1.39 0.20 0.352 0.503 0.123 0.745
   0.168
   Male 2.06 0.62 0.60 0.48
   unclassified_c__
   Clostridia Female 2.62 2.09 1.24 0.73 0.22 0.950 0.013 0.769 0.001
   0.419
   Male 2.03 2.70 1.22 0.62
   Alistipes Female 2.12 0.98 0.77 2.08 0.14 0.665 0.068 0.173 0.272 0.017
   Male 2.02 1.63 1.45 1.30
   norank_o__RF39 Female 0.90 1.43 1.37 1.65 0.10 0.944 0.039 0.064 0.160
   0.020
   Male 0.96 1.44 2.04 0.86
   Lachnospiraceae_
   AC2044_group Female 0.85 1.86 1.44 1.89 0.13 0.372 0.764 0.196 0.420
   0.679
   Male 1.48 1.12 1.38 1.14
   norank_f__Rumin-
   ococcaceae Female 3.75 1.34 0.69 0.86 0.19 0.024 <0.001 0.002 <0.001
   0.003
   Male 1.67 1.15 0.66 1.18
   Oscillospiraceae-NK4A214_group Female 1.35 0.89 1.00 0.99 0.06 0.008
   0.143 0.524 0.231 0.084
   Male 1.43 1.32 1.18 1.40
   Oscillospiraceae-
   UCG-002 Female 0.68 0.99 1.11 1.90 0.12 0.999 0.170 0.473 0.024 0.796
   Male 0.97 1.00 1.45 1.25
   norank_f__F082 Female 0.85 0.25 0.31 0.53 0.14 0.001 0.098 0.134 0.219
   0.111
   Male 0.96 0.71 1.35 2.06
   Mediterraneibacter Female 1.52 0.99 0.59 1.24 0.08 0.001 0.018 0.154
   0.767 0.018
   Male 0.55 0.55 0.44 0.85
   Prevotellaceae_UCG-003 Female 0.06 0.02 0.02 0.07 0.10 <0.001 <0.001
   <0.001 0.145 0.050
   Male 0.64 0.64 0.19 1.59
   [Eubacterium]_siraeum
   _group Female 0.32 0.72 0.85 0.89 0.05 0.790 0.004 0.360 0.002 0.017
   Male 0.52 0.81 0.85 0.69
   unclassified_f__Oscill-ospiraceae Female 0.81 0.66 0.61 0.55 0.06 0.551
   0.521 0.983 0.153 0.729
   Male 0.87 0.67 0.75 0.62
   g__Family_XIII_
   AD3011_group Female 0.58 0.65 0.87 0.53 0.04 0.810 0.300 0.718 0.812
   0.152
   Male 0.67 0.62 0.69 0.56
   norank_o__Bacteroidales Female 1.01 0.15 1.37 0.51 0.11 0.263 0.077
   0.108 0.261 0.468
   Male 0.97 0.46 0.29 0.40
   Ruminiclostridium Female 0.72 2.56 0.32 0.11 0.21 0.202 0.080 0.102
   0.348 0.063
   Male 0.12 0.51 0.91 0.19
   norank_f__Muriba-
   culaceae Female 0.25 0.80 0.67 1.62 0.09 0.008 <0.001 0.133 <0.001
   0.718
   Male 0.10 0.64 0.52 0.85
   [Eubacterium]_ruminan
   tium_group Female 0.17 0.30 0.72 0.72 0.07 0.213 0.004 0.696 0.002
   0.296
   Male 0.41 0.40 1.04 0.67
   others Female 24.96 13.31 11.44 12.71 0.80 0.487 <0.001 <0.001 0.001
   <0.001
   Male 16.27 14.06 12.86 17.15
   [85]Open in a new tab

   LE = low energy group, 7.01 MJ/kg DM; MLE = middle–low energy group,
   8.33 MJ/kg DM; MHE = middle–high energy group, 9.66 MJ/kg DM; HE = high
   energy group, 10.98 MJ/kg DM.; SEM = standard error of the means. G =
   effect of female and male; E = effect of different energy levels; G × E
   = effect of interaction between G and E; E-L = linear effect of dietary
   energy levels; E-Q= quadratic effect of dietary energy levels.

   Differential microbiota influenced by dietary energy level were further
   identified using linear discriminant analysis effect size (LEfSe;
   [86]Figure 3A,B), with a default LDA score threshold of ±2.0. A total
   of 10, 5, 2, and 10 differential taxa were detected in the female
   goats, and 10, 6, 2, and 2 differential taxa were detected in male
   goats receiving diets with energy levels of 7.01, 8.33, 9.66, and 10.98
   MJ/kg DM, respectively. At the genus levels, the bacteria biomarkers in
   the LE group were g_norank_f_Ruminococcaceae,
   g_Candidatus_Saccharimonas, g_[Eubacterium]_sulci_group,
   g_Lachnospiraceae_UCG-008, g_norank_f_Erysipelotrichaceae, and
   g_Saccharofermentans for female goats and g_Monoglobus,
   g_dgA-11_gut_group, g_Oscillospira, g_Hornefia, and
   g_Defluviitaleaceae_UCG-011 for male goats. In the MLE group, the
   biomarkers were g_UCG-005 and g_norank_f_Lachnospiraceae in the female
   goats, and g_Candidatus_Soleaferra, g_UCG-009, g_norank-c_Clostridia,
   and g_norank-c_Clostridia in male goats. In the MHE group, the
   biomarkers included g_norank_o_Bacteroidales in the female goats, and
   g_Lachnospiraceae_NC2004_group and g_[Eubacterium]_xylanophilum_group
   in male goats. In the HE group, the biomarkers were g_Treponema and
   g_Lachnospiaceae_UCG-004 in the female goats, and F-082 and
   g_norank_f_F082 in the male goats.

Figure 3.

   [87]Figure 3

   [88]Figure 3
   [89]Open in a new tab

   Linear discriminant analysis effect size (LEfSe) analysis. (A) The
   cladogram diagram shows the microbial species with significant
   differences between female and male goats when consuming a 10% crude
   protein diet with different energy levels. (B) Species with significant
   differences between female and male goats when consuming a 10% crude
   protein diet with different energy levels that have an LDA score
   greater than the estimated value (>2.0). Histogram bar length
   represents the LDA effect size; taxonomic prefixes represent
   classification ranks: phylum = p_, class = c_, order = o_, family = f_,
   and genus = g_. F-LE, female goats fed a low energy diet (=7.01 MJ/kg
   DM) group (n = 4); F-MLE, female goats fed a middle–low energy diet
   (=8.33 MJ/kg DM) group (n = 4); F-MHE, in the female goats fed a
   middle–high energy diet (=9.66 MJ/kg DM) group (n = 4); F-HE, female
   goats fed a high energy diet (=10.98 MJ/kg DM) group (n = 4); M-LE,
   male goats fed a low energy diet (=7.01 MJ/kg DM) group (n = 4); M-MLE,
   male goats fed a middle–low energy (=8.33 MJ/kg DM) diet group (n = 4);
   M-MHE, male goats fed a middle–high energy diet (=9.66 MJ/kg DM) group
   (n = 4); M-HE, male goats fed a high energy diet (=10.98 MJ/kg DM)
   group(n = 4).

3.2. Fecal Metabolomics Profiling Between Female and Male Goats When
Consuming a Low-Protein Diet with Different Energy Levels

   To provide a comprehensive overview of the differences in fecal
   metabolite profiles between female and male goats consuming a
   low-protein diet with varying energy levels, principal component
   analysis (PCA) and partial least squares discriminant analysis (PLS-DA)
   were conducted. The scatterplots of both PCA and PLS-DA suggested clear
   separations based on gender and dietary energy levels, particularly
   along principal components 1 and 2, indicating substantial differences
   in fecal metabolomic profiles ([90]Figure 4).

Figure 4.

   [91]Figure 4
   [92]Open in a new tab

   PCA (A) and PLA-DA (B) of fecal microbial metabolites in female and
   male goats when consuming a 10% crude protein diet with different
   energy levels. LE-F, female goats fed a low energy diet (=7.01 MJ/kg
   DM) group (n = 4); MLE-F, female goats fed a middle–low energy diet
   (=8.33 MJ/kg DM) group (n = 4); MHE-F, in the female goats fed a
   middle–high energy diet (=9.66 MJ/kg DM) group (n = 4); HE-F, female
   goats fed a high energy diet (=10.98 MJ/kg DM) group (n = 4); LE-M,
   male goats fed a low energy diet (=7.01 MJ/kg DM) group (n = 4); MLE-M,
   male goats fed a middle–low energy (=8.33 MJ/kg DM) diet group (n = 4);
   MHE-M, male goats fed a middle–high energy diet (=9.66 MJ/kg DM) group
   (n = 4); HE-M, male goats fed a high energy diet (=10.98 MJ/kg DM)
   group (n = 4).

   The volcano plots show that a total of 153, 171, 171, and 183
   differential metabolites were identified between female and male goats
   fed diets containing 7.01, 8.33, 9.66, and 10.98 MJ/kg DM, respectively
   ([93]Figure 5). Among these, 78, 143, 111, and 119 metabolites were
   upregulated, while 75, 28, 60, and 64 were downregulated at the
   respective energy levels. PLS-DA analysis further confirmed the
   distinct metabolic profiles between genders, with corresponding model
   performance parameters as follows: R^2X = 0.222, R^2Y = 0.852, and Q^2
   = 0.277, R^2X = 0.211, R^2Y = 0.848, and Q^2 = 0.223, R^2X = 0.160,
   R^2Y = 0.971, and Q^2 = 0.219, R^2X = 0.221, R^2Y = 0.855, and Q^2 =
   0.300 for the 7.01, 8.33, 9.66, and 10.98 MJ/kg DM, respectively
   ([94]Figure 6 and [95]Figure 7).

Figure 5.

   [96]Figure 5
   [97]Open in a new tab

   Volcano map of fecal metabolites of female and male goats when
   consuming a 10% crude protein diet with different energy levels. (A)
   Volcano map of fecal metabolites of female and male goats when
   consuming a 10% crude protein diet in the 7.01 MJ/kg group; (B) volcano
   map of fecal metabolites of female and male goats when consuming a 10%
   crude protein diet in the 8.33 MJ/kg group; (C) volcano map of fecal
   metabolites of female and male goats when consuming a 10% crude protein
   diet in the 9.66 MJ/kg group; (D) volcano map of fecal metabolites of
   female and male goats when consuming a 10% crude protein diet in the
   10.98 MJ/kg group; LE-F, female goats fed a low energy diet (=7.01
   MJ/kg DM) group (n = 4); MLE-F, female goats fed a middle–low energy
   diet (=8.33 MJ/kg DM) group (n = 4); MHE,-F in the female goats fed a
   middle–high energy diet (=9.66 MJ/kg DM) group (n = 4); HE-F, female
   goats fed a high energy diet (=10.98 MJ/kg DM) group (n = 4); LE-M,
   male goats fed a low energy diet (=7.01 MJ/kg DM) group (n = 4); MLE-M,
   male goats fed a middle–low energy (=8.33 MJ/kg DM) diet group (n = 4);
   MHE-M, male goats fed a middle–high energy diet (=9.66 MJ/kg DM) group
   (n = 4); HE-M, male goats fed a high energy diet (=10.98 MJ/kg DM)
   group (n = 4).

Figure 6.

   [98]Figure 6
   [99]Open in a new tab

   Principal component analysis (PCA) of fecal metabolites of female and
   male goats when consuming a 10% crude protein diet at four energy
   levels. (A) Results of PCA of fecal metabolites of female and male
   goats when consuming a 10% crude protein diet in the 7.01 MJ/kg group;
   (B) results of PCA of fecal metabolites of female and male goats when
   consuming a 10% crude protein diet in the 8.33 MJ/kg group; (C) results
   of PCA of fecal metabolites of female and male goats when consuming a
   10% crude protein diet in the 9.66 MJ/kg group; (D) results of PCA of
   fecal metabolites of female and male goats when consuming a 10% crude
   protein diet in the 10.98 MJ/kg group; LE-F, female goats fed a low
   energy diet (=7.01 MJ/kg DM) group (n = 4); MLE-F, female goats fed a
   middle–low energy diet (=8.33 MJ/kg DM) group (n = 4); MHE-F, in the
   female goats fed a middle–high energy diet (=9.66 MJ/kg DM) group (n =
   4); HE-F, female goats fed a high energy diet (=10.98 MJ/kg DM) group
   (n = 4); LE-M, male goats fed a low energy diet (=7.01 MJ/kg DM) group
   (n = 4); MLE-M, male goats fed a middle–low energy (=8.33 MJ/kg DM)
   diet group (n = 4); MHE-M, male goats fed a middle–high energy diet
   (=9.66 MJ/kg DM) group (n = 4); HE-M, male goats fed a high energy diet
   (=10.98 MJ/kg DM) group(n = 4).

Figure 7.

   [100]Figure 7
   [101]Open in a new tab

   PLS-DA of fecal microbial metabolites of female and male goats when
   consuming a 10% crude protein diet with different energy levels. (A)
   Results of PLS-DA of fecal metabolites of female and male goats when
   consuming a 10% crude protein diet in the 7.01 MJ/kg group; (B) results
   of PLS-DA of fecal metabolites of female and male goats when consuming
   a 10% crude protein diet in the 8.33 MJ/kg group; (C) results of PLS-DA
   of fecal metabolites of female and male goats when consuming a 10%
   crude protein diet in the 9.66 MJ/kg group; (D) results of PLS-DA of
   fecal metabolites of female and male goats when consuming a 10% crude
   protein diet in the 10.98 MJ/kg group. LE-F, female goats fed a low
   energy diet (=7.01 MJ/kg DM) group (n = 4); MLE-F, female goats fed a
   middle–low energy diet (=8.33 MJ/kg DM) group (n = 4); MHE-F, in the
   female goats fed a middle–high energy diet (=9.66 MJ/kg DM) group (n =
   4); HE-F, female goats fed a high energy diet (=10.98 MJ/kg DM) group
   (n = 4); LE-M, male goats fed a low energy diet (=7.01 MJ/kg DM) group
   (n = 4); MLE-M, male goats fed a middle–low energy (=8.33 MJ/kg DM)
   diet group (n = 4); MHE-M, male goats fed a middle–high energy diet
   (=9.66 MJ/kg DM) group (n = 4); HE-M, male goats fed a high energy diet
   (=10.98 MJ/kg DM) group(n = 4).

   To further elucidate the metabolic pathways associated with sex-related
   differences, KEGG pathway enrichment analysis was performed on the
   differential metabolites between female and male goats at each energy
   level. The results showed that in the LE group, the differential
   metabolites were significantly enriched in pathways such as bile
   secretion, mineral absorption, glycine, serine, and threonine and
   nitrogen metabolism. In the MLE group, the enriched pathways included
   biosynthesis of plant secondary metabolites, vitamin digestion and
   absorption, and alpha-Linolenic acid metabolism. In the MHE group,
   significant enrichment was observed in pathways such as tyrosine
   metabolism, flavone and flavonol biosynthesis, and alpha-Linolenic acid
   metabolism. In the HE group, the differential metabolites were
   significantly enriched in pathways including toluene degradation,
   degradation of flavonoids, and fatty acid biosynthesis ([102]Figure 8
   and [103]Figure 9).

Figure 8.

   [104]Figure 8
   [105]Open in a new tab

   Effect of KEGG enrichment analysis on female and male goats when
   consuming a 10% crude protein diet with different energy levels. (A)
   Results of KEGG enrichment analysis of fecal metabolites of female and
   male goats when consuming a 10% crude protein diet in the 7.01 MJ/kg
   group; (B) results of KEGG enrichment analysis of fecal metabolites of
   female and male goats when consuming a 10% crude protein diet in the
   8.33 MJ/kg group; (C) results of KEGG enrichment analysis of fecal
   metabolites of female and male goats when consuming a 10% crude protein
   diet in the 9.66 MJ/kg group; (D) results of KEGG enrichment analysis
   of fecal metabolites of female and male goats when consuming a 10%
   crude protein diet in the 10.98 MJ/kg group; LE-F, female goats fed a
   low energy diet (=7.01 MJ/kg DM) group (n = 4); MLE-F, female goats fed
   a middle–low energy diet (=8.33 MJ/kg DM) group (n = 4); MHE-F, in
   female goats fed a middle–high energy diet (=9.66 MJ/kg DM) group (n =
   4); HE-F, female goats fed a high energy diet (=10.98 MJ/kg DM) group
   (n = 4); LE-M, male goats fed a low energy diet (=7.01 MJ/kg DM) group
   (n = 4); MLE-M, male goats fed a middle–low energy (=8.33 MJ/kg DM)
   diet group (n = 4); MHE-M, male goats fed a middle–high energy diet
   (=9.66 MJ/kg DM) group (n = 4); HE-M, male goats fed a high energy diet
   (=10.98 MJ/kg DM) group(n = 4).

Figure 9.

   [106]Figure 9

   [107]Figure 9
   [108]Open in a new tab

   Effect of differential abundance score of KEGG of female and male goats
   when consuming a 10% crude protein diet with different energy levels.
   (A) Results of differential abundance score of KEGG of fecal
   metabolites of female and male goats when consuming a 10% crude protein
   diet in the 7.01 MJ/kg group; (B) results of differential abundance
   score of KEGG of fecal metabolites of female and male goats when
   consuming a 10% crude protein diet in the 8.33 MJ/kg group; (C) results
   of differential abundance score of KEGG of fecal metabolites of female
   and male goats when consuming a 10% crude protein diet in the 9.66
   MJ/kg group; (D) results of differential abundance score of KEGG of
   fecal metabolites of female and male goats when consuming a 10% crude
   protein diet in the 10.98 MJ/kg group; LE-F, female goats fed a low
   energy diet (=7.01 MJ/kg DM) group (n = 4); MLE-F, female goats fed a
   middle–low energy diet (=8.33 MJ/kg DM) group (n = 4); MHE-F, in female
   goats fed a middle–high energy diet (=9.66 MJ/kg DM) group (n = 4);
   HE-F, female goats fed a high energy diet (=10.98 MJ/kg DM) group (n =
   4); LE-M, male goats fed a low energy diet (=7.01 MJ/kg DM) group (n =
   4); MLE-M, male goats fed a middle–low energy (=8.33 MJ/kg DM) diet
   group (n = 4); MHE-M, male goats fed a middle–high energy diet (=9.66
   MJ/kg DM) group (n = 4); HE-M, male goats fed a high energy diet
   (=10.98 MJ/kg DM) group (n = 4). * p < 0.05, ** p < 0.01, and *** p <
   0.001.

3.3. Correlation Analysis Between Fecal Metabolites and Bacteria

   Correlation analyses showed numerous significant associations between
   differential metabolites and microbial taxa ([109]Figure 10).
   Pantothenic acid was positively correlated with norank_f_Muribaculaceae
   and Treponema, while negatively correlated with
   Oscillospiraceae-NK4A214_group, Oscillospiraceae_UCG-005, Monoglobus,
   unclassified_c_Clostridia, and unclassified_f_Oscillospiraceae.
   Glycodeoxycholic acid showed positive correlations with
   norank_f_UCG-010, Oscillospiraceae_UCG-005, and Monoglobus. Similarly,
   glycocholic acid was positively correlated with
   Oscillospiraceae_UCG-005 and Monoglobus, but negatively correlated with
   Alistipes. Uracil was positively correlated with
   unclassified_f_Lachnospiraceae, norank_f_Muribaculaceae,
   Oscillospiraceae-UCG-002, Christensenellaceae_R-7_group,
   norank_f_[Eubacterium]_coprostanoligenes_group,
   [Eubacterium]_siraeum_group, Ruminococcus, Treponema. In contrast,
   uracil was negatively correlated with norank_f_Ruminococcaceae,
   Alistipes, Prevotellaceae_UCG-004, others, norank_f_F082,
   Prevotellaceae_UCG-003, norank_f_UCG-010,
   Oscillospiraceae-NK4A214_group, Oscillospiraceae_UCG-005, Monoglobus,
   Akkermansia, norank_o_ Bacteroidales, and unclassified_c_Clostridia.

Figure 10.

   [110]Figure 10
   [111]Open in a new tab

   Pearson’s rank correlation analysis of the fecal bacteria (at genus, RA
   > 0.50%) and fecal metabolites (Top 50). * p < 0.05, ** p < 0.01, and
   *** p < 0.001 according to Pearson’s rank correlation coefficient. The
   red and blue colors indicate positive and negative correlations,
   respectively.

4. Discussion

   In general, greater microbial diversity and richness are considered
   beneficial to host health and contribute to microbiota stability
   [[112]26,[113]27]. The richness and diversity of bacterial communities
   in the gastrointestinal tract can be influenced by several factors,
   including dietary composition and nutrient levels [[114]28,[115]29], as
   well as the age and species of animals [[116]27,[117]30,[118]31]. In
   this study, dietary energy level and gender did not affect the Sobs,
   Ace, Chao, or Coverage indices of the fecal bacterial community in
   goats. However, the Shannon index was lower, and the Simpson index was
   greater in the female goats than in males. A previous study in Tibetan
   goats reported numerically greater Shannon and Simpson indices in
   females than in males [[119]6].

   At the phylum level, Firmicutes and Bacteroidota were the dominant
   fecal bacteria across all dietary energy levels and in both male and
   female goats. These results are in agreement with previous studies in
   Boer goats [[120]30], Blond Adamellan goats [[121]32], and dairy goats
   [[122]33]. In the present study, there were no significant differences
   between female and male goats in the RAs of Firmicutes, Bacteroidota,
   Spirochaetota, Verrucomicrobiota, Patescibacteria, and others. In
   contrast, a previous study reported higher RAs of Fibrobacteres and
   Spirochaetes in female compared to male Tibetan goats [[123]6].
   Similarly, Pan et al. reported that the RAs of Bacteroidota,
   Verrucomicrobiota, and Cyanobacteria were higher, while those of
   Firmicutes and Desulfobacterota were lower in the rumen fluid samples
   of male compared to female Qinchuan cattle [[124]5]. Such discrepancies
   across studies may contribute to differences in dietary composition and
   among animal species. In the present study, the highest RA of
   Firmicutes and the lowest RA of Bacteroidota were observed in the 8.33
   MJ/kg DM group. This aligns with previous studies showing that
   bacterial communities could change in Shaanxi white cashmere goats
   [[125]34], Yunshang black goat [[126]35], and fattening male Hu lambs
   [[127]36] when consuming diets with different energy levels. In the
   rumen ecosystem, Spirochaetes are known to degrade cellulose, pectin,
   and phytic acid, contributing to the utilization of fermentable
   carbohydrates and the production of volatile fatty acids [[128]37]. In
   this study, the RA of Spirochaetota increased linearly with increasing
   dietary energy levels. Previous studies reported that the digestibility
   of neutral detergent fiber and acid detergent fiber decreased with
   increasing dietary energy levels in yaks [[129]38], Tibetan sheep
   [[130]39], and Small-tailed Han sheep [[131]39]. We infer that
   increased fecal fiber content at higher energy levels may support the
   proliferation of Spirochaetota, explaining the observed linear increase
   in their abundance.

   A previous study showed that Prevotella_1 was the dominant genus in the
   rumen and did not differ between male and female Tibetan goats
   [[132]22]. Interestingly, in the current study,
   unclassified_f__Lachnospiraceae was the dominant genus in the female
   goats, while Oscillospiraceae_UCG-005 dominated in male goats.
   Additionally, compared with male goats, in female goats, the RA of
   Oscillospiraceae_UCG-005 was lower, and that of
   unclassified_f__Lachnospiraceae was significantly greater. These
   differences in dominant fecal genera may be associated with dietary
   composition. One study reported that the RAs of Fibrobacter,
   Ruminococcus_1, Erysipelotrichaceae_UCG-004, Oscillospira, and
   Pyramidobacter were significantly higher, whereas those of Lachnospira
   and Ruminococcaceae_NK4A214_group were lower in female than in male
   Tibetan goats [[133]22]. In the current study, the RAs of several
   bacterial genera also increased or decreased in a gender-dependent
   manner, which suggests that fecal bacterial communities respond
   differently between sexes even when consuming the same diet. One study
   reported that an increased RA of Christensenellaceae_R-7_group was
   associated with improved growth performance in goats [[134]40]. In
   addition, numerous studies have shown that higher dietary energy levels
   enhance average daily gain in yaks [[135]38] and Small-tailed Han sheep
   [[136]39], which may partly explain the increased proportion of
   Christensenellaceae_R-7_group observed with rising dietary energy
   levels in the current study. The genus Muribaculaceae_unclassified
   remains relatively understudied. In the present study, the RA of
   norank_f__Muribaculaceae increased with increasing dietary energy
   levels. This finding is consistent with a study of Saanen dairy goats,
   where supplementation with 90 g of conjugated linoleic acid effectively
   increased dietary energy and also led to an increase in the abundance
   of Muribaculaceae [[137]41].

   Fecal metabolomics complements microbial functional genomic
   interpretation by reflecting the metabolic outputs of gut microbiota.
   The results of the present study showed that fecal metabolite profiles
   differed between female and male goats when fed a low-protein diet with
   varying energy levels. In addition, even when consuming the same diet,
   distinct metabolomic differences were observed between the male and
   female goats. The differential metabolites were primarily classified as
   organic acids, lipids, and amino acids. Pan et al. reported that
   pathways related to amino acid and lipid metabolism were mainly
   enriched in female Qinchuan cattle, whereas carbohydrate metabolism and
   glycan biosynthesis and metabolism pathways were more enriched in males
   [[138]5]. Consistently, the KEGG pathway analyses performed in this
   study revealed gender-specific enrichment patterns under the same
   dietary energy levels. Specifically, the differentially enriched
   pathways included bile secretion, mineral absorption, glycine, serine,
   threonine metabolism, and nitrogen metabolism at 7.01 MJ/kg DM;
   biosynthesis of plant secondary metabolites, vitamin digestion and
   absorption, and alpha-linolenic acid metabolism at 8.33 MJ/kg DM;
   tryosine metabolism, flavone and flavonol biosynthesis and
   alpha-Linolenic acid metabolism at 9.66 MJ/kg DM; and toluene
   degradation, degradation of flavonoids, and fatty acid biosynthesis at
   10.98 MJ/kg DM. These findings suggest that gender influences fecal
   metabolite profiles by modulating metabolism pathways related to amino
   acid metabolism, as well as cofactors and vitamin metabolism.

   Pantothenic acid, an essential nutrient, plays a vital role in
   metabolism as a component of coenzyme A and acyl-carrier-proteins
   [[139]42,[140]43]. Yi et al. reported that pantothenic acid
   concentrations were downregulated in animals fed a
   concentrate-to-forage ratio of 65:35 compared to those fed a 50:50
   ratio, and found no correlation between rumen bacterial genera and
   pantothenic acid concentrations [[141]44]. However, Yoshii et al.
   [[142]43] reported that several intestinal commensal bacteria possess
   pantothenic acid biosynthesis pathways, including Bacteroides fragilis
   and Prevotella copri (Bacteroidetes), some Ruminococcus spp. (R.
   lactaris and R. torques) (Firmicutes), and Salmonella enterica and
   Helicobacter pylori (Proteobacteria), indicating their capacity to
   produce pantothenic acid endogenously. Our results showed that
   pantothenic acid was positively correlated with norank_f_Muribaculaceae
   and Treponema and negatively correlated with
   Oscillospiraceae-NK4A214_group, Oscillospiraceae_UCG-005, Monoglobus,
   unclassified_c_Clostridia, and unclassified_f_Oscillospiraceae. These
   findings suggest a selective association between pantothenic acid
   levels and specific microbial taxa in the goat hindgut. Previous
   studies have shown that glycodeoxycholic acid and glycocholic acid can
   alter bile acid metabolism and reshape intestinal microbiota [[143]45].
   We consistently found that glycodeoxycholic acid was positively
   associated with norank_f_UCG-010, Oscillospiraceae_UCG-005, and
   Monoglobus. Glycocholic acid was positively correlated with
   Oscillospiraceae_UCG-005 and Monoglobus, but negatively correlated with
   Alistipes. Uracil, which may serve as a microbial genetic marker in
   ruminants, is also implicated in enhancing microbial protein synthesis.
   Zhang et al. demonstrated that increased synchronization energy and
   nitrogen release in the rumen enhanced microbial protein synthesis
   [[144]46], accompanied by an increased abundance of Fibrobacter and
   Ruminobacter and decreased levels of Klebsiella and Succinivibrio. In
   the present study, uracil was positively correlated with
   Oscillospiraceae-UCG-002, Christensenellaceae_R-7_group,
   norank_f_[Eubacterium]_coprostanoligenes_group,
   [Eubacterium]_siraeum_group, Ruminococcus, and Treponema. In contrast,
   negative correlations were observed with norank_f_Ruminococcaceae,
   Alistipes, Prevotellaceae_UCG-004, norank_f_F082,
   Prevotellaceae_UCG-003, norank_f_UCG-010,
   Oscillospiraceae-NK4A214_group, Oscillospiraceae_UCG-005, Monoglobus,
   and Akkermansia. Taken together, these findings suggest that uracil has
   a substantial impact on the gut microbial ecosystem and serves as a key
   indicator of fecal metabolic profiles in response to dietary and
   host-related factors.

5. Conclusions

   This study investigated the effects of gender and dietary energy level
   on fecal metabolite profiles and microbiota composition in goats fed a
   low-protein diet. The results showed that
   unclassified_f_Lachnospiraceae was the dominant genus in female goats,
   while Oscillospiraceae_UCG-005 was the dominant genus in male goats.
   Additionally, distinct differences in fecal metabolite profiles were
   observed between female and male goats under varying dietary energy
   levels. It is concluded that the female and male goats exhibit
   different metabolic responses to diets containing 10% crude protein
   with varying energy levels. These findings provide a basis for
   developing gender-specific dietary guidelines for 8-month-old Leizhou
   goats under low-protein feeding conditions.

Supplementary Materials

   The following supporting information can be downloaded at:
   [145]https://www.mdpi.com/article/10.3390/ani15152174/s1, Table S1:
   Composition of dietary ingredients and nutritional levels of the 4
   treatments.
   [146]animals-15-02174-s001.zip^ (96.9KB, zip)

Author Contributions

   Conceptualization, H.L.; Data curation, H.L., X.Y., X.D. and J.C.;
   Funding acquisition, H.L. and K.W.; Investigation, W.P., A.C., Y.Y.,
   K.W., J.H. and H.Z.; Methodology, W.P., A.C. and J.C.; Software, J.C.;
   validation, H.L. and X.Y.; Formal analysis, J.C. and X.Y. Resources,
   J.H.; Supervision, H.Z.; Visualization, H.Z.; Project administration,
   H.L., H.Z.; Writing—Original Draft, H.L. and W.W.; Writing—Review &
   Editing, W.W., X.D. and H.Z. All authors have read and agreed to the
   published version of the manuscript.

Institutional Review Board Statement

   All procedures involved in the present experiment were approved by the
   Animal Ethics Committee of Zhanjiang Experimental Station (protocol no.
   ZES 202405001). The approval date was 29 April 2024.

Informed Consent Statement

   Not applicable.

Data Availability Statement

   Data may be provided upon request to the corresponding author
   (liuh2018@lzu.edu.cn).

Conflicts of Interest

   The authors declare no conflicts of interest.

Funding Statement

   This research was supported by the Hainan Provincial Natural Science
   Foundation of China (NO. 324QN301), Central Public-interest Scientific
   Institution Basal Research Fund (No: 1630012025809; 1630012025807),
   Special Research Project of Zhanjiang Experimental Station, Chinese
   Academy of Tropical Agricultural Sciences (NO. ZJSYZ2024002).

Footnotes

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References