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Article

Investigating the Potential Mechanism of Methane Mitigation in Seaweed Gracilaria lemaneiformis via 16S rRNA Gene Sequencing and LC/MS-Based Metabolomics

by
Yi Sun
1,2,†,
Shuai Li
1,†,
Tongjun Guo
3,
Xiong Tong
1,
Zhifei Zhang
1,
Yufeng Yang
2,
Qing Wang
2,*,
Dagang Li
1 and
Li Min
1,3,4,*
1
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Ministry of Agriculture Key Laboratory of Animal Nutrition and Feed Science in South China, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
2
Department of Ecology, Jinan University/Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangzhou 510632, China
3
Key Laboratory of Xinjiang Feed Biotechnology, Feed Research Institute, Xinjiang Academy of Animal Science, Urumqi 830000, China
4
Agri-Food and Biosciences Institute, Hillsborough BT26 6DR, UK
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2025, 15(16), 1768; https://doi.org/10.3390/agriculture15161768
Submission received: 13 July 2025 / Revised: 14 August 2025 / Accepted: 16 August 2025 / Published: 18 August 2025
(This article belongs to the Special Issue Impact of Forage Quality and Grazing Management on Ruminant Nutrition)
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Abstract

Methane (CH4), originating from ruminants, is a major source of greenhouse gas emissions in the agriculture industry. This study aimed to determine the potential of red seaweed Gracilaria lemaneiformis (G. lemaneiformis) as an anti-methanogenic feed additive for cattle. Three supplementation levels of seaweed (2%, 5%, and 10% of dry matter) were evaluated for their effects on gas production and rumen fermentation characteristics during 48 h in vitro fermentation. The results revealed a significant decrease in total gas production (TGP), CO2, CH4, ammonia nitrogen (NH3-N), and volatile fatty acid (VFA) concentrations, with no differences in pH or dry matter disappearance (DMD). Notably, compared with the control group without seaweed, supplementation with 2% G. lemaneiformis effectively reduces CH4 emissions by 27.5% (p < 0.05). Supplementation with 2% G. lemaneiformis decreased the abundance of methanogens g_norank_f_Methanomethylophilaceae, responsible for CH4 generation, and increased the populations of bacteria (Kandleria and Succinivibrio) that compete with methanogens for substrates. Furthermore, upregulating the levels of 13(S)-HOTrE and 9(S)-HOTrE (polyunsaturated fatty acids) could inhibit methanogenic activity. Additionally, lower VFA concentrations will provide less raw materials for methane synthesis, thus further inhibiting methanogenesis. In summary, G. lemaneiformis, as a red seaweed with important economic value, can not only be applied to enhance marine carbon sinks but can also serve as a promising candidate for mitigating biomethane emissions in cattle.

1. Introduction

Achieving low-carbon agricultural economies is a universal objective for all nations, particularly those with substantial populations of cattle and sheep. It has been reported that the livestock sector contributes 14.5% to total anthropogenic greenhouse gas emissions globally [1], and enteric methane (CH4) from ruminants accounts for approximately 40% of this [2]. CH4 is the primary potent greenhouse gas emitted by ruminants such as sheep and cattle. Its global warming potential is 28 times greater than that of carbon dioxide (CO2) [2]. Therefore, it is necessary to focus more on mitigating ruminants’ methane emissions to effectively alleviate global warming.
In rumen, CH4 is a metabolite from the symbiotic interaction between fiber-degrading protozoa, bacteria, and methanogens. Methanogens use hydrogen (H2), CO2, acetate, methanol, and methylamine to generate CH4 (through three pathways: hydrogenotrophic, acetoclastic, and methylotrophic) [3]. To maintain normal fermentation, H2 generated in the rumen needs to be converted into CH4, which is released through ruminant belching, thus contributing to greenhouse emissions and energy loss [4]. In addition to being transformed to CH4, metabolic H2 can also be used in propionate production, competing with methanogenesis [5]. Thus, changes in acetate and propionate concentrations can partially reflect methane production. Theoretically, CH4 inhibition might be promoted by reduced acetate and increased propionate levels in the rumen [6].
Extensive studies have investigated the efficacy of feed additives in mitigating CH4 emissions by affecting microorganisms in the rumen system and altering the metabolic pathways of precursors substances [7,8]. Among the studied substances, seaweed exhibit significant potential as a marine resource for efficiently mitigating methane emissions [9]. Recent research has indicated that some species of red and brown seaweed, such as Chorda filum and Asparagopsis taxiformis, demonstrate anti-methanogenic properties [5,9,10]. However, many other species have not been evaluated for their effects on methane inhibition, including Gracilaria lemaneiformis (G. lemaneiformis). This species belongs to the genus Gracilaria (Phylum Rhodophyta) and has been successfully cultivated at scale in China, offering substantial biomass potential as a sustainable bioresource [11]. Previous studies showed that some Gracilaria species can effectively mitigate methane production by modulating rumen microbial communities and fermentation patterns [12,13,14]. G. lemaneiformis is an important member of the Gracilaria genus, but does it exhibit similar methanogenesis-inhibiting properties? To answer this question, this study aimed to (i) evaluate the methane mitigation efficacy of G. lemaneiformis and (ii) elucidate its methane-regulating mechanisms.

2. Materials and Methods

2.1. Seaweed Collection and Preparation

G. lemaneiformis was collected on 20 October 2022, on Dong Shan Island, a coastal region of Fujian Province, China (23°33′ N–23°47′ N, 117°17′ E–117°35′ E). The seaweed underwent thorough rinsing with water to remove impurities. The seaweed was then dried at 65 °C for 48 h to ensure thorough dehydration. The seaweed samples were processed using a pulverizer to obtain a fine powder, which was then screened through a 1 mm sieve to ensure uniformity. G. lemaneiformis powder was stored at −20 °C.

2.2. Experimental Design and Treatments

The basal substrate (roughage/concentrate = 60:40, with corn straw as roughage) was approximately 0.5 g—dry matter (DM) basis. The nutrient compositions of G. lemaneiformis and the substrates are presented in Table 1, and the mineral content of G. lemaneiformis is shown in Table 2.
This study utilized a randomized block design. Seaweed powder was supplemented at three levels (2%, 5%, and 10% DM) in the basal substrate, and a control group was established without G. lemaneiformis supplementation (CON). The three supplementation levels were chosen based on a previous study [15]. Six biological replicates were set up for each group, with all treatments conducted simultaneously in the same incubation batch. The experiment was repeated twice, and six samples were randomly selected for subsequent experimental analysis.

2.3. In Vitro Incubation

Three healthy Holstein cattle (age, 2.8 ± 0.2 years; body weight, 508.7 ± 14.3 kg) were selected as rumen fluid donors. The animals were housed in a well-ventilated barn and fed at 7:00 and 17:00 daily (the animal diet composition is detailed in Table 3), with continuous access to clean drinking water. Rumen fluid was collected 2 h after morning feeding. Rumen fluid (2000 mL) was collected via oral stomach tubing according to the method described by Wang et al. [16]. The collected rumen fluid was sequentially filtered through four layers of sterile gauze to remove particulate matter. Then, the filtrates from the three cows were pooled into a CO2-filled thermos preheated to 39 °C to maintain anaerobic conditions and immediately transported to the laboratory. The collected rumen fluid was mixed with a warm buffer (39 °C) at a 1:2 volume ratio under continuous CO2 flushing. The buffer (1000 mL) was prepared as follows: 8.75 g NaHCO3, 1.00 g NH4HCO3, 1.43 g Na2HPO4, 1.55 g KH2PO4, 0.15 g MgSO4·7H2O, 0.52 g Na2S, 0.017 g CaCl·2H2O, 0.015 g MnCl2·4H2O, 0.002 g CoCl·6H2O, 0.012 g FeCl3·6H2O, 1.25 mL resazurin. Then, 75 mL of the buffered rumen fluid was accurately dispensed into the fermentation flasks containing the base substrate and seaweed powder, and the flasks were sealed with butyl rubber stoppers and aluminum caps. Subsequently, these samples were continuously incubated in a shaking water bath at 39 °C at 85 rpm for 48 h.

2.4. Sample Collection

Gas samples from each fermentation flask were collected using a syringe at 2, 4, 8, 12, 24, and 48 h of fermentation and stored in an air pocket for subsequent gas composition analysis. Upon completing the experiment, the fermentation flasks were immediately placed in ice to terminate the fermentation. The content from the fermentation flask was filtered through a nylon bag, and the bag dried at 65 °C for 24 h. The following filtrate samples were taken: 2 mL was collected for volatile fatty acids (VFA) determination, 1 mL for ammonia nitrogen (NH3-N) concentration analysis, and 5 mL for 16S rRNA sequencing and metabolomics analysis.

2.5. Gas Analysis

The total gas production (TGP) was defined as the cumulative volume of gas produced in each fermentation flask over all incubation time points. The concentrations of CH4 and CO2 were determined using a gas chromatograph (SP-2060T, Tianpu, Beijing, China) equipped with a 5A stainless-steel column (Φ3 mm × 3 m, with 60–80 mesh Chromosorb tensors, Tianpu, Beijing, China) and a Tbx-01 stainless-steel column (Φ3 mm × 1 m, with 60–80 mesh Chromosorb tensors, Tianpu, Beijing, China). The injection volume was set at 1 mL, while the carrier gas, high-purity argon (content ≥ 99.999%), was maintained at a flow rate of 30 mL/min. The air pressure was adjusted to 0.5 MPa. Both the detector (TCD) and column temperatures were set to 100 °C.

2.6. Fermentation Parameter Determination

The pH of the fermentation fluid was measured immediately after the experiment using a PHS-25 pH meter. NH3-N and dry matter disappearance (DMD) were quantified as described by Choi [7]. VFA profiling was performed on an Agilent 6890N GC system equipped with FID detection. The temperature program was initiated at 120 °C for 3 min, followed by a 10 °C/min ramp to 180 °C (1 min hold). Operational parameters included the following: The carrier gas was N2 with a flow rate of 2.0 mL/min. The FID temperature was maintained at 250 °C. A 0.4 μL sample was injected through an injector, with the injector base temperature set at 220 °C. The split ratio was 40:1.

2.7. DNA Extraction, 16S rRNA Sequencing, and Bioinformatics

Total microbial genomic DNA was isolated from rumen fluid samples using an MP-soil kit (MP Biomedicals, Shanghai, China) in accordance with the manufacturer’s protocol. The DNA was then stored at −80 °C for preservation. For PCR amplification, bacterial primers 338F_806R and archaeal primers 349F_806R were utilized. The PCR reaction mixture included 2×Pro Taq (10 μL), forward primer (5 μM, 0.8 μL), reverse primer (5 μM, 0.8 μL), and 10 ng/μL template DNA. The PCR amplification cycling conditions were as follows: initial denaturation at 95°C for 3 min, followed by 29 cycles of denaturing at 95 °C for 30 s, annealing at 53 °C for 30 s, and extension at 72 °C for 45 s. A final extension step was performed at 72 °C for 10 min, and the reaction was terminated at 10 °C. The obtained PCR products were purified and quantified, and the equimolarly pooled purified amplicons underwent paired-end sequencing on the Illumina PE300 platform (Illumina, San Diego, CA, USA). The generated raw sequencing data were archived in the NCBI Sequence Read Archive Database under Accession Number SRP510199.

2.8. Non-Targeted Metabolomics Analysis and Data Processing

Sample analysis was conducted via liquid chromatography–tandem mass spectrometry (LC-MS/MS) employing a Thermo UHPLC-Q Exactive HF-X platform (Waters Corporation, Milford, CT, USA). Chromatographic separation was achieved using a Waters ACQUITY HSS T3 column (2.1 mm internal diameter × 100 mm length, 1.8 μm particle size). The mobile phase system consisted of two components: (A) an aqueous solution containing 5% acetonitrile and 0.1% formic acid; (B) an organic phase comprising acetonitrile–isopropanol (1:1, v/v) with 0.1% formic acid. Analytical conditions included a 3 μL injection volume and isothermal column maintenance at 40 °C.
Mass spectrometry analysis was performed using a high-resolution Q Exactive HF-X system. Then, LC/MS raw data were processed through Progenesis QI v3.0 (Waters Corporation, Milford, USA) for feature detection and alignment. Following data preprocessing, spectral features were consolidated through peak grouping and duplicate removal. Simultaneously, compounds were annotated by matching mass spectra against three major metabolomics databases: Majorbio Database, Metlin, and HMDB.

2.9. Statistical Analysis

The raw data were statistically analyzed using IBM SPSS 25. Three supplementation levels of G. lemaneiformis were considered as fixed factors, while pH, DMD, TGP, CH4, and CO2 concentrations, total and individual VFA, and NH3-N concentration were treated as dependent variables. One-way ANOVA with LSD post hoc testing was performed to assess differences among groups. Statistical significance was set at p < 0.05 and high significance at p < 0.01. Dose–methane relationships were analyzed via linear regression (Origin 2025). Correlations between methane production and fermentation parameters were calculated in IBM SPSS 25 (r- and p-values) and visualized with Chiplot (https://www.chiplot.online/, accessed on 9 August 2025). Bioinformatics analysis for the microbiota and metabolomics was conducted using the Majorbio Cloud platform (https://cloud.majorbio.com, accessed on 1–30 April 2024).

3. Results

3.1. Effects of G. lemaneiformis on Rumen Fermentation Characteristics

The rumen fermentation characteristics are shown in Table 4. Before the experiment, the pH values of the rumen fluid, buffer, and buffered rumen fluid were measured as 6.57, 7.24, and 7.23, respectively. After 48 h of fermentation, pH and DMD showed no statistically significant differences at any of supplementation levels. Following the supplementation of G. lemaneiformis, there was a decrease in NH3-N content (p < 0.01).
Supplementation with 5% G. lemaneiformis did not affect the TVFA content, whereas significant reductions were observed in the other groups, including in acetate, butyrate, isobutyrate, and valerate concentrations in the seaweed-supplemented groups. The propionate concentration showed no difference. Notably, the acetate to propionate ratio (AP ratio) significantly decreased following G. lemaneiformis supplementation, and this change was linked to CH4 production.

3.2. Effects of G. lemaneiformis on Rumen Gas Composition

Total gas production (TGP) and CO2 production were significantly reduced with the supplementation of G. lemaneiformis (Figure 1). As shown in Figure 1A, during the 48 h fermentation period, the TGP in all treatment groups increased steadily, with the control group exhibiting higher gas production than the treated groups. Compared to the control group, supplementation with G. lemaneiformis significantly reduced TGP by at least 10.90 mL (6.8%) in absolute terms (Figure 1B) and 39.13 mL (12.3%) per gram of substrate (mL/g DM) (Figure 1C). Similarly, CO2 production was significantly reduced by at least 9.6 mL (8.3%) (Figure 1D) and 34.99 mL/g (15.1%) (Figure 1E). In summary, G. lemaneiformis supplementation reduced the TGP by 12.3–15.5% and CO2 by 15.1–16.6% on a mL/g DM basis (p < 0.01).
The CH4 production characteristics are presented in Figure 2. Supplementation with G. lemaneiformis significantly reduced CH4 by at least 4.03 mL (25.3%) (Figure 2A) and 7.76 mL/g (27.5%) compared to the control group (Figure 2B). G. lemaneiformis significantly decreased CH4 emissions by 27.5%, 32.6%, and 32.0% at the three supplementation levels, respectively, on a mL/g DM basis. Compared to the 2% supplementation level, CH4 emissions were lower at the 5% and 10% supplementation levels. Moreover, the proportion of CH4 in TGP also decreased with G. lemaneiformis supplementation (p < 0.01) (Figure 2C).
As shown in Figure 2D, CH4 production decreased in a dose-dependent manner with incremental supplementation of G. lemaneiformis, showing a significant negative correlation (Pearson’s r = −0.82, p < 0.001). Additionally, CH4 production was positively correlated with CO2, acetate, isobutyrate, butyrate, valerate, TVFA, and the AP ratio (p < 0.05), while it showed a negative correlation with pH (p < 0.01) (Figure 2E).
Collectively, the group with 2% G. lemaneiformis supplementation exhibited the optimal result in this study due to the decrease in CH4 and the lowest economic cost, with a similar influence on ruminal fermentation compared to the other groups. Subsequently, 16S rRNA gene sequencing and LC/MS-based metabolomics analysis were conducted on the 2% and CON groups to investigate the mechanisms underlying methane emission reductions.

3.3. Effects of G. lemaneiformis on Microbial Community

The microbial diversity of bacteria and archaea in the rumen fluid is presented in Figure 3. Compared with the CON group, there were no differences in the abundance of bacteria (Figure 3A,B) and archaea (Figure 3E,F) according to the Ace and Chao 1 indexes in the 2% group. However, the Simpson’s index indicated that bacterial diversity was significantly higher in the 2% group (p = 0.02) (Figure 3D), whereas archaeal diversity was not affected (Figure 3G,H).
The β diversity of bacteria and archaea in the CON and 2% groups was visualized using principal coordinate analysis (PCoA) plots (Figure 4). Within bacteria, a clear separation was noted between the CON and 2% groups (R = 0.8778, p = 0.003), whereas the archaeal community composition showed no difference between the two groups (R = 0.05, p = 0.282).
The compositions of the top 30 abundant genera in the samples, along with their percentages, are presented in Figure 5, with other low-abundance genera classified as “others”. Among bacterial communities, the relative abundance of Firmicutes, Bacteroidota, and Proteobacteria together contributed 93%. At the genus level, norank_f_F082 was dominant, and it decreased when G. lemaneiformis was supplemented (p < 0.01). Within the archaeal communities, Euryarchaeota was the dominant phylum, accounting for 95% and 92% in the CON and 2% groups, respectively. At the genus level, the prevalent species was Methanobrevibacter, and its abundance decreased with G. lemaneiformis supplementation (p > 0.05).
Based on microbiome composition analysis at the genus level, 15 bacteria and 1 archaeon in total were identified as differentially abundant between the two groups (p < 0.05) (Figure 6). Notably, only four bacteria exhibited higher abundances in the CON group. Among all bacteria, variations in the relative abundances of norank__f__F082, Succinivibrio, and Kandleria were more pronounced. In addition, the relative abundance of the archaeal genus g__norank__Methanomethylophilaceae was found to be lower in the 2% group (p < 0.01).
The relationships between environmental factors (including TGP, CH4, CO2, pH, DMD, TVFA, acetate, propionate, butyrate, isobutyrate, valerate, AP, and NH3-N) and the top 20 genera were analyzed at the genus level (Figure 7). Among these genera, Kandleria, Succinivibrio, and Oribacterium showed negative correlations with most environmental factors, except for propionate, pH, and DMD. Moreover, there was a significantly positive correlation between norank_f_F082 and TGP, CO2, CH4, and AP (Figure 7A). Notably, variations in VFA and CO2 were strongly correlated with variations in CH4. According to Figure 7B, CH4 was also positively correlated with norank_f__Methanomethylophilaceae and Methanobrevibacter, whereas unclassified_k_norank_d_Archaea showed a negative correlation with CH4.

3.4. Effects of G. lemaneiformis on Rumen Metabolites and Metabolic Pathways

In this experiment, a total of 859 metabolites were detected. The PLS-DA score indicated a significant separation in metabolites between the two groups (Figure 8A). In this model, R2X and R2Y were 0.594 and 0.995, respectively, and Q2 was 0.903. These values can be used to filter differential metabolites (Figure 8B). There were 131 differential metabolites (Supplementary Table S1), satisfying the conditions of p < 0.05, VIP > 1, and FC ≥ 1, including 87 upregulated and 44 downregulated metabolites (Figure 8C). The differential metabolites were labeled according to the KEGG database, and KEGG enrichment analysis was conducted on these metabolites (Figure 8D). The results show that metabolites are prominently enriched in pathways such as carbohydrate metabolism, lipid metabolism, digestive system, and amino acid metabolism.

4. Discussion

Previous research indicated that the supplementation of seaweed in ruminants’ feed can affect rumen fermentation parameters, CH4 production, microbial composition, and metabolic pathways [17]. In the present study, rumen fermentation parameters and gas production were affected in all treatment groups with G. lemaneiformis supplementation, with no significant differences observed among these treatment groups. Therefore, we selected the CON group and the group supplemented with 2% G. lemaneiformis to explore the mechanisms of methane emission reduction, and the subsequent discussions primarily revolve around these two groups.
pH, NH3-N, and VFA are important indicators of rumen stability during fermentation [18]. pH serves as a crucial factor influencing enzyme activity, microbial growth, VFA production, and even CH4 generation [19]. The pH is generally maintained at 5.5–7.5 in rumen, and TVFA is one of the crucial factors influencing pH changes. However, the present results indicate that changes in TVFA concentrations were not consistently associated with alterations in pH. Specifically, the pH did not increase despite the significant decrease in TVFA concentrations after the addition of G. lemaneiformis. This phenomenon may be attributed to the accumulation of organic acids other than VFA, such as lactic and formic acid, or other acidic substances produced via microbial metabolism [20]. Kandleria is a lactate producer [21], and an increase in its abundance leads to a reduction in pH. Similarly, Butyrivibrio is a producer of butyrate and formate [22]. However, a decrease in butyrate concentration was observed when the abundance of Butyrivibrio increased. This probably suggests that G. lemaneiformis may have driven Butyrivibrio to favor the formate production pathway. pH changes represent a dynamic and complex process. Fortunately, the pH remained within the normal range with G. lemaneiformis supplementation, which is beneficial for microbial activity.
G. lemaneiformis supplementation led to a significant reduction in individual VFA concentrations. Specifically, acetate, butyrate, isobutyrate, and valerate were significantly decreased, while propionate showed no difference with G. lemaneiformis supplementation. These changes are strongly associated with CH4 production. The initial stage of acetate formation in the rumen releases metabolic H2, which can act as a hydrogen donor for methanogens and promote CH4 formation. Propionate serves as a hydrogen sink and competes with CH4 to metabolize H2 [23]. Therefore, CH4 emission was negatively correlated with the rumen propionate concentration and positively correlated with the acetate concentration and AP ratio [24]. Interestingly, these trends were also observed in this study—i.e., a significant decrease in the AP ratio with G. lemaneiformis supplementation—indicating that H2 may be partially redistributed to propionate, thus reducing the quantity of H2 available for methanogenesis.
Similarly, G. lemaneiformis supplementation led to a significant reduction in NH3-N concentration, and similar results have been reported in other studies after CH4 inhibitor supplementation [14,25]. NH3-N is a product of protein digestion, and a decrease in its concentration indicates that rumen microbes inhibit protein hydrolysis and amino acid deamination or enhance NH3-N utilization. Nevertheless, the decrease in NH3-N output in the rumen could have environmental benefits by reducing nitrogen emissions from ruminants [25].
In the present study, the cumulative gas production curve did not exhibit a clear plateau phase, as the production might continue to increase toward the end of the incubation period. This pattern suggests that the assay duration may have been insufficient for fermentation to reach its maximum point, or that there was still substrate available for degradation. As a result, kinetic parameters such as maximum gas volume and lag time represent values within the observed fermentation window rather than the absolute maximum achievable under prolonged incubation. In this context, a considerable reduction in TGP, CH4, and CO2 production was found when supplementing G. lemaneiformis at the 2% level after 48 h of fermentation. The results showed an approximate absolute decrease of 10.5% in TGP, 26.0% in CH4 production, and 13.4% in CO2 production. When expressed per mL/g, the decreases in TGP, CH4, and CO2 production were 12.3%, 27.5%, and 15.1%, respectively. At the 2% supplementation level, dried G. lemaneiformis exhibited a more effective CH4 inhibition effect than many other seaweeds. For example, Wasson et al. reported that among 67 seaweed surveyed (including red, brown, and green seaweed), except for Asparagopsis taxiformis (A. taxiformis) (which reduced CH4 emissions by 99%), only seven reduced CH4 emissions by between 7.3% and 17.0% at 2% supplementation [10]. This disparity in inhibition effects can be attributed to variations in seaweed species and their chemical compositions [26]. The ability of A. taxiformis to mitigate rumen CH4 production can be attributed to the abundance of bromoform components. These components can inhibit the activity of coenzymes vitamin B12 (cobalamin) and methyl coenzyme M reductase (MCR), a key catalase in the final step of methanogenesis [27]. In an in vivo study, a decrease in bromoform concentrations with storage time was found to subsequently decrease CH4 production [28]. Therefore, it is not surprising that A. taxiformis, with a high number of halogenated compounds, had a superior effect regarding their anti-methanogenic potential compared to other species.
Compared to A. taxiformis, the key bioactive compounds responsible for the anti-methanogenic effects of Gracilaria species have not yet been fully characterized. Studies report that the principal bioactive compounds in Gracilaria include steroids, terpenoids, eiconoids, saponins, and alkaloids [29,30]. Terpenoids are also the primary active components of essential oils, which impair the energy metabolism of archaea, resulting in methane reduction rates of up to 26% [31]. Saponins have demonstrated an ability to inhibit methane emissions in in vitro studies by reducing the abundance of methanogens and protozoa [32]. They interact with bacterial lipid bilayers to form complexes that increase membrane permeability, thereby compromising membrane integrity and inducing microbial cell lysis [33]. However, their primary effect targets protozoa, and this impact is transient [31]. Alkaloids exhibit antimicrobial properties and can alter the microbial community structure [34]. The ability of G. lemaneiformis to reduce methane production may result from the synergistic effects of multiple active compounds. However, current research on the anti-methanogenic effects of these bioactive compounds has predominantly focused on terrestrial plants. The efficacy of these substances in marine seaweed such as G. lemaneiformis remains to be verified through further experimentation.
Methanogens are the only known group of microorganisms that generate energy through methanogenesis [35], and their abundance directly influences the rate of methanogenesis [36]. In correlation analysis, Methanobrevibacter and g__norank_f__Methanomethylophilacea showed a significantly positive correlation with CH4 production. Methanobrevibicter, classified as a hydrogenotrophic methanogen, is the dominant archaea genus involved in methanogenesis, while g__norank_f__Methanomethylophilaceae functions as a methylotrophic methanogen [3]. In the 2% group, Methanobrevibacter abundance was comparatively lower (92% vs. 89%). Despite the decrease in Methanobrevibacter abundance lacking statistical significance, its presence as a dominant species still played a role in mitigating CH4 production. Similarly, a significant decrease in the abundance of g__norank_f__Methanomethylophilaceae (0.3% vs. 0.1%) directly alleviated CH4 emissions.
Methanogenic archaea are the predominant contributors to CH4 production, but other rumen bacteria also regulate the amount of CH4 generated [37]. In this trial, significant distinctions in ruminal bacterial diversity and community composition were detected between the CON and 2% groups. This finding indicates that bacteria might also exert a crucial influence in the CH4 inhibition process mediated by G. lemaneiformis. Microbial sequencing analysis showed that norank_f_F082 abundance was significantly reduced in the 2% group. This bacterium is associated with the digestion of non-structural carbohydrates such as soluble sugar and starch [38]. These carbohydrates produce pyruvate through the glycolytic pathway, which in turn generates H2, CO2, and VFAs, including acetate, propionate, and butyrate [38]. In this study, it was observed that the decrease in norank_f_F082 abundance was consistent with the reduction in acetate and butyrate concentrations, which serve as raw materials for CH4 production. Hence, the decrease in norank_f_F082 abundance after the supplementation of 2% G. lemaneiformis may result in diminished availability of substrates for methanogenesis.
In addition, higher abundances of Kandleria and Succinivibrio were observed in this study, and this is typical of low-methanogenic rumen [39,40]. Mitigating CH4 production results in the accumulation of H2, which has a negative impact on the regeneration of NAD+ within the glycolytic pathway [41]. Therefore, organisms may be forced to directly regenerate NAD+ using alternative H2 binding reactions, such as the succinic acid production pathway mediated by Succinivibrio and the lactate production pathway mediated by Kandleria [40]. In this way, the microbiome can adapt to the new environment, and alternative H2 binding pathways, such as propionate production mediated by lactate or succinate, may be observed in the rest of the microbiome [42]. In this study, an increase in Succinivibrio and Kandleria abundances was observed when CH4 emissions decreased. H2 can be used to competitively inhibit methanogens. Meanwhile, Kandleria employs an alternative approach to reduce the substrates available for CH4 generation. Kandleria primarily produces lactic acid, which can then be converted to butyrate in the rumen. This pathway results in a lower production of H2 compared to traditional direct fermentation, in which carbohydrates are converted to butyrate [21,43]. As a consequence, the increased abundance of Succinivibrio and Kandleria competes with CH4 formation.
Metabolomics analysis has been extensively employed to elucidate changes in metabolites and metabolic networks in organisms. In the present study, LC/MS-based metabolomics analysis indicated a close relationship between methane inhibition and some metabolites after supplementation with 2% G. lemaneiformis. KEGG enrichment analysis showed that ruminal differential metabolites were enriched in lipid metabolism, including linoleic acid metabolism and alpha-linoleic acid metabolism after 2% G. lemaneiformis supplementation. Two polyunsaturated fatty acids (PUFAs), 13(S)-HOTrE (C18:3) and 9(S)-HOTrE (C18:2), were significantly upregulated in the above two pathways, respectively. Interestingly, among fatty acids, C12:0, C18:3, and other PUFAs have a greater potential to inhibit methanogenesis compared to saturated fatty acids [8]. PUFA can decrease the abundance and metabolic activities of rumen methanogens and alter their species composition [44]. Furthermore, PUFAs also act as a hydrogen sink through biohydrogenation [45]. Therefore, upregulation of 13(S)-HOTrE and 9(S)-HOTrE contributes to a reduction in CH4 emissions. Additionally, the decrease in the abundance of methanogens such as Methanobrevibacter and g__norank_f__Methanomethylophilaceae may also be attributed to an increase in lipid metabolites that inhibit methanogenic activity in the 2% group [44]. Additionally, previous studies showed that inhibiting CH4 production induces changes in amino acid metabolism, such as upregulating tryptophan metabolism [46,47]. A similar result was observed in this study. However, the potential relationship between CH4 inhibition and amino acid metabolism remains unclear and needs to be explored through more detailed experiments.

5. Conclusions

This study identified G. lemaneiformis as an effective rumen methane inhibitor. All three tested supplementation levels achieved desirable methane mitigation effects while showing comparable impacts on rumen fermentation parameters. Therefore, we recommend 2% DM (which reduces CH4 emissions by 27.5%) as the optimal supplementation level based on efficacy and economic feasibility. Methane mitigation mechanisms primarily involve (i) modulating volatile fatty acid profiles, (ii) inhibiting methanogen activities, and (iii) regulating rumen microbial metabolic pathways (Figure 9).
G. lemaneiformis directed rumen fermentation toward lower AP ratio, CH4 emissions, and NH3-N concentration without detrimental impact on pH and DMD. G. lemaneiformis might reduce CH4 emissions by modifying the community structure of microorganisms such as norank_f_F082, Kandleria and Succinivibrio, and inhibiting methanogens (Methanobrevibacter and g__norank_f__Methanomethylophilacea). Moreover, it upregulated the metabolites (13(S)-HOTrE and 9(S)-HOTrE) which might inhibit methanogens activities, and altered metabolic pathways associated with methanogenesis, particularly lipid metabolism and amino acid metabolism. Furthermore, G. lemaneiformis supplementation promotes metabolic H2 flow towards propionate production, which competes with methanogenesis.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture15161768/s1, Table S1: Differential Metabolites.

Author Contributions

Conceptualization, D.L. and L.M.; methodology, S.L.; formal analysis, Y.S.; investigation, Y.S. and S.L.; data curation, Y.S. and S.L.; writing—original draft preparation, Y.S. and S.L.; writing—review and editing, T.G., X.T., Z.Z., Y.Y., Q.W., D.L. and L.M.; supervision, Y.Y. and L.M.; project administration, Q.W.; funding acquisition, T.G. and L.M. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (SML2024SP028, SML2024SP002, SML2021SP203), Global Ocean Negative Carbon Emissions (ONCE) Project, Guangdong Modern Agro-industry Technology Research System (2024CXTD13), National Natural Science Foundation of China (32271684), China Scholarship Council (202408440440), Guangzhou Science and technology planning project (2023A04J0793), Open project of Xinjiang Key Laboratory of Feed Biotechnology for Herbivorous Livestock (XJSLSW-2023002), and the Fundamental Research Funds for the Central Universities (21625101).

Institutional Review Board Statement

This study was conducted in December 2023 at the Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China. The animal experiment protocol complied with the requirements of experimental animal welfare and ethics, as well as the regulations of the Ministry of Science and Technology of China. It was performed following European Directive 2010/63/EU and S.I. No. 543 of 2012.

Data Availability Statement

The data presented in this study are openly available in the NCBI Sequence Read Archive Database under Accession Number SRP510199.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Total gas production and carbon dioxide production of treatment diets in in vitro study. (A) The cumulative gas production curve; (B,C) total gas production characteristics; (D,E) carbon dioxide production characteristics. CON: a diet without G. lemaneiformis. a–c indicates p < 0.01.
Figure 1. Total gas production and carbon dioxide production of treatment diets in in vitro study. (A) The cumulative gas production curve; (B,C) total gas production characteristics; (D,E) carbon dioxide production characteristics. CON: a diet without G. lemaneiformis. a–c indicates p < 0.01.
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Figure 2. Methane production of treatment diets in in vitro study. CON: a diet without G. lemaneiformis. a–c indicates p < 0.01, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. (A–D) Methane production characteristics; (E) correlation analysis of methane with fermentation parameters. TGP, total gas production; DMD, dry matter digestibility; TVFA, total volatile fatty acids; AP, the acetate to propionate ratio; NH3-N, ammonia nitrogen.
Figure 2. Methane production of treatment diets in in vitro study. CON: a diet without G. lemaneiformis. a–c indicates p < 0.01, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. (A–D) Methane production characteristics; (E) correlation analysis of methane with fermentation parameters. TGP, total gas production; DMD, dry matter digestibility; TVFA, total volatile fatty acids; AP, the acetate to propionate ratio; NH3-N, ammonia nitrogen.
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Figure 3. Alpha diversity analysis of microbial communities based on OTU level in the rumen. CON: a diet without G. lemaneiformis. (A–D) Effects of 2% dried G. lemaneiformis on bacterial α-diversity. (E–H) Effects of 2% dried G. lemaneiformis on archaeal α-diversity. Student’s t-test was used to verify differences between two groups, while fdr was used to correct p values. * p < 0.05.
Figure 3. Alpha diversity analysis of microbial communities based on OTU level in the rumen. CON: a diet without G. lemaneiformis. (A–D) Effects of 2% dried G. lemaneiformis on bacterial α-diversity. (E–H) Effects of 2% dried G. lemaneiformis on archaeal α-diversity. Student’s t-test was used to verify differences between two groups, while fdr was used to correct p values. * p < 0.05.
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Figure 4. Principal coordinate analysis (PCoA) plots of Bray–Curtis dissimilarities for bacterial community of rumen samples. (A) β-diversity analysis of bacteria in rumen. (B) β-diversity analysis of archaea in rumen. Bray–Curtis was used to calculate the distance between samples, and ANOSIM was used to test differences between two groups.
Figure 4. Principal coordinate analysis (PCoA) plots of Bray–Curtis dissimilarities for bacterial community of rumen samples. (A) β-diversity analysis of bacteria in rumen. (B) β-diversity analysis of archaea in rumen. Bray–Curtis was used to calculate the distance between samples, and ANOSIM was used to test differences between two groups.
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Figure 5. Relative abundance of microbial in response to 2% dried G. lemaneiformis supplementation. (A) Relative abundance of bacteria communities at the phylum level and (B) relative abundance of bacteria communities at the genus level. (C) Relative abundance of archaea communities at the phylum level. (D) Relative abundance of archaea communities at the genus level.
Figure 5. Relative abundance of microbial in response to 2% dried G. lemaneiformis supplementation. (A) Relative abundance of bacteria communities at the phylum level and (B) relative abundance of bacteria communities at the genus level. (C) Relative abundance of archaea communities at the phylum level. (D) Relative abundance of archaea communities at the genus level.
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Figure 6. Wilcoxon’s rank-sum test bar plot reveals significant differences in rumen microbiota abundance between the CON group and 2% group at the genus level. (A) The rumen bacteria with significant differences between the CON and 2% groups at the genus level. (B) The rumen archaea with significant differences between the CON and 2% groups at the genus level. Wilcoxon’s rank-sum test was used to test difference between groups, with fdr to correct p values. * p < 0.05, ** p < 0.01.
Figure 6. Wilcoxon’s rank-sum test bar plot reveals significant differences in rumen microbiota abundance between the CON group and 2% group at the genus level. (A) The rumen bacteria with significant differences between the CON and 2% groups at the genus level. (B) The rumen archaea with significant differences between the CON and 2% groups at the genus level. Wilcoxon’s rank-sum test was used to test difference between groups, with fdr to correct p values. * p < 0.05, ** p < 0.01.
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Figure 7. Correlation analysis shows the relationships between the top 20 genera present in microbial communities and environmental factors (TGP, CH4, CO2, pH, DMD, TVFA, acetate, propionate, butyrate, isobutyrate, valerate, AP, and NH3-N). (A) Correlation between bacteria and environmental factors. (B) Correlation between archaea and environmental factors. TGP, total gas production; DMD, dry matter digestibility; TVFA, total volatile fatty acids; AP, acetate-to-propionate ratio; NH3-N, ammonia nitrogen. Spearman’s rank correlation coefficient was used to calculate the correlation between species and environmental factors, with average hierarchical clustering methods applied to environmental factors and species. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 7. Correlation analysis shows the relationships between the top 20 genera present in microbial communities and environmental factors (TGP, CH4, CO2, pH, DMD, TVFA, acetate, propionate, butyrate, isobutyrate, valerate, AP, and NH3-N). (A) Correlation between bacteria and environmental factors. (B) Correlation between archaea and environmental factors. TGP, total gas production; DMD, dry matter digestibility; TVFA, total volatile fatty acids; AP, acetate-to-propionate ratio; NH3-N, ammonia nitrogen. Spearman’s rank correlation coefficient was used to calculate the correlation between species and environmental factors, with average hierarchical clustering methods applied to environmental factors and species. * p < 0.05, ** p < 0.01, *** p < 0.001.
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Figure 8. Metabolite analysis revealed changes in metabolite and metabolic pathways after supplementation with 2% G. lemaneiformis. (A) Partial least squares discriminant analysis plot (PLS-DA) depicting rumen metabolite distribution. (B) PLA-DA permutation test to assess model reliability. (C) Volcano plot showing differential metabolites between the CON and 2% groups (yellow and red indicate increased and decreased significance, respectively). (D) KEGG pathway enrichment suggests that significant changes occurred in the pathways. Student’s t-test and two-tailed test were used to verify differential metabolites and correct p values. The BH test adjusted the p value of the KEGG pathway. * p < 0.05, ** p < 0.01.
Figure 8. Metabolite analysis revealed changes in metabolite and metabolic pathways after supplementation with 2% G. lemaneiformis. (A) Partial least squares discriminant analysis plot (PLS-DA) depicting rumen metabolite distribution. (B) PLA-DA permutation test to assess model reliability. (C) Volcano plot showing differential metabolites between the CON and 2% groups (yellow and red indicate increased and decreased significance, respectively). (D) KEGG pathway enrichment suggests that significant changes occurred in the pathways. Student’s t-test and two-tailed test were used to verify differential metabolites and correct p values. The BH test adjusted the p value of the KEGG pathway. * p < 0.05, ** p < 0.01.
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Figure 9. The mechanisms of CH4 emission mitigation by G. lemaneiformis in an in vitro study.
Figure 9. The mechanisms of CH4 emission mitigation by G. lemaneiformis in an in vitro study.
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Table 1. Nutrient composition of substrate and G. lemaneiformis.
Table 1. Nutrient composition of substrate and G. lemaneiformis.
ParameterRoughage 1Concentrate 2G. lemaneiformis
OM92.9%92.8%65.6%
Ash7.1%7.8%34.4%
CP7.1%20.5%25.0%
NDF38.5%16.6%14.6%
ADF21.7%6.2%5.5%
OM: organic matter; CP: crude protein; NDF: neutral detergent fiber; ADF: acid detergent fiber. 1 Roughage was corn straw, harvested in experimental fields. 2 Concentrate composition: 500 g/kg corn, 235 g/kg DDGS (distiller’s dried grains with solubles), 220 g/kg soybean meal, 10 g/kg stone powder, 9 g/kg dicalcium phosphate, 4 g/kg multivitamins, 10 g/kg salt, 1 g/kg multi-minerals, 10 g/kg baking soda, 1.5 g/kg mold inhibitor.
Table 2. Mineral content of G. lemaneiformis.
Table 2. Mineral content of G. lemaneiformis.
MineralContent (mg/kg)
Macro-minerals
Na11,650
Mg2670
Ca2250
K13,720
S7930
P1790
Trace elements
Al2660
B177.80
Cr2.65
Cu6.03
Co0.50
Fe1200
Mo0.24
Sb<0.01
Zn35.56
I7.78
Mn135.50
Se0.48
Toxic heavy metals
As6.03
Ba6.39
Cd0.6
Hg<0.01
Pb2.41
Ti43.82
Tl<0.01
Sn0.38
Sr20.83
Ni1.93
V2.53
Table 3. Base feeding grain composition and nutrient levels (dry matter basis).
Table 3. Base feeding grain composition and nutrient levels (dry matter basis).
ItemContent
Ingredients (%)
Corn35.00
DDGS16.45
Soybean meal15.40
Stone powder0.70
CaHPO40.60
Vitamins0.28
NaCl0.70
Minerals0.07
NaHCO30.70
Antifungal agent0.10
Whole plant silage corn30.00
Total100.00
Nutrient levels (%)
OM89.37
CP16.48
NDF50.33
ADF26.22
EE4.80
Ca0.90
P0.35
Nem (MJ/kg)6.71
DDGS: distiller’s grains protein feed; OM: organic matter; CP: crude protein; NDF: neutral detergent fiber; ADF: acid detergent fiber; EE: crude fat; Nem: net energy for maintenance.
Table 4. Rumen fermentation characteristics of the treatment diets in the in vitro study.
Table 4. Rumen fermentation characteristics of the treatment diets in the in vitro study.
ParameterCON2%5%10%SEMp Value
pH6.956.966.976.970.00330.056
DMD %0.850.850.850.840.00230.057
NH3-N mmol/L16.73 a11.81 c12.18 c13.47 b0.42<0.001
Volatile fatty acids (mmol/L)
TVFA83.33 a75.97 ab78.27 ab72.01 b1.27<0.01
Acetate46.82 a42.11 b43.09 b39.44 b0.76<0.01
Propionate22.3521.8522.8820.860.310.12
Isobutyrate0.80 a0.61 b0.63 b0.60 b0.19<0.01
Butyrate11.93 a10.17 b10.38 b9.86 b0.21<0.01
Valerate1.43 a1.29 b1.25 b1.22 b0.24<0.01
AP ratio2.10 a1.93 b1.88 c1.89 c0.018<0.001
CON: no seaweed supplemented; DMD: dry matter disappearance; NH3-N: ammonia nitrogen; AP ratio: the acetate to propionate ratio; SEM, standard error of the mean. a–c means p < 0.01.
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Sun, Y.; Li, S.; Guo, T.; Tong, X.; Zhang, Z.; Yang, Y.; Wang, Q.; Li, D.; Min, L. Investigating the Potential Mechanism of Methane Mitigation in Seaweed Gracilaria lemaneiformis via 16S rRNA Gene Sequencing and LC/MS-Based Metabolomics. Agriculture 2025, 15, 1768. https://doi.org/10.3390/agriculture15161768

AMA Style

Sun Y, Li S, Guo T, Tong X, Zhang Z, Yang Y, Wang Q, Li D, Min L. Investigating the Potential Mechanism of Methane Mitigation in Seaweed Gracilaria lemaneiformis via 16S rRNA Gene Sequencing and LC/MS-Based Metabolomics. Agriculture. 2025; 15(16):1768. https://doi.org/10.3390/agriculture15161768

Chicago/Turabian Style

Sun, Yi, Shuai Li, Tongjun Guo, Xiong Tong, Zhifei Zhang, Yufeng Yang, Qing Wang, Dagang Li, and Li Min. 2025. "Investigating the Potential Mechanism of Methane Mitigation in Seaweed Gracilaria lemaneiformis via 16S rRNA Gene Sequencing and LC/MS-Based Metabolomics" Agriculture 15, no. 16: 1768. https://doi.org/10.3390/agriculture15161768

APA Style

Sun, Y., Li, S., Guo, T., Tong, X., Zhang, Z., Yang, Y., Wang, Q., Li, D., & Min, L. (2025). Investigating the Potential Mechanism of Methane Mitigation in Seaweed Gracilaria lemaneiformis via 16S rRNA Gene Sequencing and LC/MS-Based Metabolomics. Agriculture, 15(16), 1768. https://doi.org/10.3390/agriculture15161768

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