Adding Ethanol to the Batch and Continuous Transplantation Co-Culture of Maize Straw Fermented by Rumen Fluid for the Production of Caproic Acid

Abstract

In this study, to enhance the concentration of caproic acid generated from maize straw fermentation and clarify the structures of bacterial and fungal communities within the serially subcultured rumen microbial fermentation system, maize straw was used as the substrate. In a continuous subculture system, the impacts of ethanol addition on pH and gas production were explored, with a focus on the caproic acid yield in the final (eighth generation) generation and alterations in bacterial and fungal communities. The results showed that the relative abundances of unidentified_Clostridiales, Shuttleworthia, and Syntrophococcus in ethanol-driven caproic acid production were enriched by 5.36-fold, 2.61-fold, and 2.25-fold, respectively. This consequently increased the concentration of caproic acid in the fermentation broth to 1492 mg/L, representing a 3.7-fold increase. These findings are highly significant for the high-value utilization of maize straw waste to produce caproic acid via the carboxylic acid platform using rumen microorganisms in industrial processing.

1. Introduction

Lignocellulosic fibers stand out as the most intensively researched biomass for alternative resource utilization. Agricultural waste, in particular, is a highly valuable lignocellulosic resource due to its abundant availability, high yield, and relatively low cost [1]. Maize, as the world’s second-largest agricultural crop, generates substantial straw waste, with China alone producing over 10 million tons annually [2]. The conversion of maize stover into value-added products and the alleviation of environmental impacts from straw accumulation are urgent challenges that require innovative solutions. Microbial anaerobic fermentation offers a viable approach to transform agricultural waste into a variety of industrial products, including organic acids, biofuels, enzymes, and lipids [3,4,5,6]. Optimizing microbial fermentation technology for the utilization of maize stover is a strategic direction for creating a cleaner and more sustainable environment.

Anaerobic fermentation of straw is a four-stage process comprising hydrolysis, acidogenesis, acetogenesis, and methanogenesis [7]. During the hydrolysis stage, complex macromolecules such as cellulose, starch, proteins, and lipids are broken down into simpler monomers–sugars, amino acids, and fatty acids. These monomers are then converted into short-chain fatty acids (SCFAs), ethanol, hydrogen (H₂), and carbon dioxide (CO₂) in the acidogenesis phase. Acetogenesis involves the conversion of SCFAs and ethanol into acetate, which is subsequently converted to methane (CH₄) either directly or through the reduction of CO₂. Methane production is an effective and sustainable method for processing large amounts of lignocellulosic waste to meet global energy demands. SCFAs are more valuable than biomethane as they can be further converted into high-value-added chemicals, such as bioplastics and biofuels including biodiesel [8,9,10]. Additionally, SCFAs can be transformed into medium-chain fatty acids (MCFAs) through beta-oxidation, a process that is gaining attention for its potential to yield products of higher economic value [11,12].

Rumen microorganisms are well-known for their remarkable degradation abilities, especially in breaking down plant materials rich in fiber, such as maize straw. The rumen’s lignocellulosic biomass decomposition efficiency is estimated to be threefold higher compared to traditional anaerobic digesters [13]. The rumen microbiota converts biomass into SCFAs, supplying nutrients for ruminants [14], and also produces gases like methane and carbon dioxide. The synthesis of SCFAs and gaseous byproducts is achieved through synergistic interactions within diverse microbial communities [15]. The formation of SCFAs requires the participation of electron donors (EDs) and electron acceptors (EAs), initiating from acetic acid and extending the carbon chain via reverse β-oxidation [16]. Ethanol, a well-known ED in fermentation processes, is oxidized to generate acetyl-CoA, which can engage in reverse β-oxidation. Additionally, the acetyl-CoA produced can promote chain elongation from acetic acid to butyric acid and facilitate the hexanoic acid pathway from butyric acid [17,18,19]. Ma et al. significantly increased the caproic acid production to 1473 mg by adding ethanol as an ED to the co-fermentation system of rice straw and rumen fluid, and found that the conversion rate of segmented addition was nearly twice as high as that of one-time addition (17.69% vs. 7.38%) [20]. Although our previous research has demonstrated that ethanol, as an electron donor, can increase the elongation rate of SCFA chains in the mixed culture of in vitro rumen-fermented switchgrass [21], there are still two issues that remain unclear at present: (1) the exact effect of adding ethanol on the relatively large biomass of maize straw and (2) whether caproic acid production from straw fermentation can be increased by domesticating rumen microorganisms through continuous batch culture.

We hypothesize that the addition of ethanol can promote the elongation of fatty acid chains during the fermentation of maize straw by rumen fluid through the reverse β-oxidation pathway. Additionally, continuous subculturing for domesticating rumen microorganisms will enhance the production of caproic acid by optimizing the composition of the microbial community and metabolic pathways. By investigating the underlying mechanisms and the impact on microbial communities, the research provides valuable insights into the utilization of agricultural waste resources.

2. Materials and Methods

2.1. Substrate and Rumen Fluid

The experimental procedures concerning animals were approved by the Animal Care Committee at Yangzhou University (Yangzhou, China). The maize straw was obtained from the Animal Nutrition and Feed Engineering Technology Research Center, Yangzhou University, Jiangsu Province, China. Maize straw was dried at 65 °C for 48 h, then ground through a 0.50 mm sieve, and sealed in hermetic/air-tight bags for later use. Ruminal fluid was obtained from two dry-lactation Holstein cows with permanent ruminal fistulas fed twice daily (8:00 and 16:00) with a 40:60 concentrate to roughage ratio diet and free access to water at the experimental farm of Yangzhou University (Gaoyou, China). Ruminal contents were collected before morning feeding, filtered through 4 layers of sterile gauze and brought back to the laboratory immediately and placed in a 39 °C water bath [22].

2.2. Sequential Batch Culture

To mimic conventional in vitro fermentation protocols and perceived use of mixed ruminal inocula for industrial SCFA and MCFA production in “open-culture” reactor microbiomes, all experiments were conducted under non-aseptic conditions, without sterilization of vessels, medium components, or transfer equipment [23]. The saliva buffer was prepared according to Menke’s method: 1.10 mg L⁻¹ CaCl₂·2H₂O, 0.83 mg L⁻¹ MnCl₂·4H₂O, 0.08 mg L⁻¹ CoCl₂·6H₂O, 0.67 mg L⁻¹ FeCl₃·6H₂O, 5.83 mg L⁻¹ NaHCO₃, 0.95 mg L⁻¹ Na₂HPO₄, 1.03 mg L⁻¹ KH₂PO₄, and 0.10 mg L⁻¹ MgSO₄·7H₂O, all reagents are from Sinopharm Chemical Reagent Co., Ltd., Shanghai, China [24]. Maize straw (0.80 g) as fermentation substrate was weighed into 128 mL volume glass anaerobic culture flask before inoculation. An amount of 15 mL buffer and 2 mL rumen fluid were mixed and O₂ was removed by continuously flushing with CO₂. An amount of 0.15 mL of 2.5% sodium sulfide solution (w/v) was added as reduced reagent. An amount of 0.2 mL of anhydrous ethanol was supplemented in the flask, sealed with a rubber stopper with an aluminum cap and incubated at 39 °C for 72 h. The control (CON) group consisted of flasks containing substrates without ethanol addition. The ethanol (ETH) and CON groups each consisted of six replicate bottles, totaling twelve bottles. The gas volume at 0, 2, 4, 6, 12, 24, 48, and 72 h was recorded to calculate gas production. The pH value and VFA production of fermentation fluid were tested at 72 h.

All continuous subculture procedures were similar to batch culture. At the first 72 h (first generation), 2 mL of culture fluid was transferred into new 128 mL flasks containing fresh culture media and substrates. The pH value and gas production were monitored during the culture period. After seven batch transfers, samples were collected for the analysis of pH, VFA, and bacterial composition, corresponding to the eighth generation.

2.3. Sampling and Analysis

The pH value of the fermentation liquid was determined by pH meter (PHS-25 produced by Shanghai Electronic Instrument Co., Ltd., Shanghai, China). The pressure values in the flasks at each time point were determined using pressure gauge (barometer DPG1000B15PSG-5, CeComp Electronics Inc., Libertyville, IL, USA) [21,24]. The following calculation equation was applied:

Vgas = Vj × Ppsi × 0.068004084,

(1)

where Vgas is the gas volume at 39 °C, mL, Vj is the vial volume headspace of liquid (110 mL in this study), mL, and Ppsi is the pressure of the vial, psi.

At each sampling time, the fermentation mixture was collected using a syringe, and the samples were pretreated for determination later. Briefly, fermentation liquid was centrifuged (12,000 r/min, 20 min), and 1 mL supernatant was mixed with 0.2 mL A total of 20% of metaphosphoric acid (containing 60 mM crotonic acid) was kept overnight in a refrigerator at −20 °C. After thawing, the acidified samples were centrifuged (12,000 r/min, 10 min, 4 °C) and the supernatant was filtered through a 0.22 μm water phase filter membrane. The percentages of the individual VFA (Acetic, propionic, butyric, valeric, and caproic acid were measured by gas chromatography (GC-3800, Shanghai Kechuang Chromatography Instrument Co., Ltd., Shanghai, China) and column type, the same as the gas assay above. The temperature of the injector, column, and detector was 200 °C, 110 °C, and 200 °C, respectively. The carrier gas was nitrogen, with 50 mL min⁻¹ flow rate and 1 μL injection volume. In addition to quantifying g/L individual concentrations of individual and total SCFA, total alkyl concentrations (TAL) and average SCFA chain lengths (ACL) were calculated as previously described [12,21]:

(1) Total mM alkyl groups, the sum of all methyl and methylene groups in SCFA, i.e., total mM alkyl groups (hereafter designated TAL) = C₂ + (2 × C₃) + (3 × C₄) + (4 × C₅) + (5 × C₆), where Cx corresponds to mM concentration of SCFA of carbon number x.

(2) Average SCFA chain length (hereafter designated ACL) = (total mM alkyl groups/total mM SCFA) + 1, where the 1 represents the nonenergetic carbon (carboxyl group) of the SCFA molecule.

The amplification primers for 16S rDNA sequencing were 515F and 806R of the 16S V4 region (515F: 5′-GTGCCAGCMGCCGCGGTAA-3′) and (806R: 5′-GGACTACHVGGGTWTCTAAT-3′). The ITS amplicon sequencing primers are ITS5-1737F to ITS2-2043R (1737F: 5′-GGAAGTAAAAGTCGTAACAAGG-3′, 2043R: 5′-GCTGCGTTCTTCAT CGATGC-3′). The PCR products were sent to Novogene Co., Ltd. (Beijing, China) for high-throughput sequencing analysis. Microbial sequencing was analyzed as elaborated previously [25]. In brief, the raw data were read using FASTP (Version 0.14.1), followed by denoising with the DADA2 module in QIIME2 (Version 1.9.1). Sequences with an abundance of less than 5 were filtered out to obtain the final amplicon sequence variants (ASVs) and feature table. Finally, ASVs were compared with the SILVA (version 138.1) database to determine species information. Based on the ASV data, QIIME2 was used to calculate α-diversity indices, including Ace, Chao1, Shannon, and Simpson indices.

2.4. Statistical Analysis

The significance of differences was determined using independent samples of T-tests and univariate general linear models in the SPSS 26.0 software. The test results were expressed as “mean ± standard deviation”, with p-value less than 0.05, indicating significant differences. To reveal the intrinsic associations between microbial communities and fermentation products, canonical correlation analysis (CCA) and visualization of the results were performed using Chiplot (https://www.chiplot.online/) (accessed on 27 January 2025) [26]. It should be noted that due to the insufficient readings of ITS, one sample had to be removed from the ETH group.

3. Results and Discussion

3.1. Effects of Ethanol on Continuous Subcultured Culture of Maize Straw with Rumen Liquid

3.1.1. pH and Gas Production

Using rumen fluid as the inoculum and 72 h fermentation fluid for continuous subculture, maize stover was fermented to produce acid. Ethanol was added as an electron donor (ED) to promote chain elongation via β-oxidation for caproic acid production. The variations in pH and gas production during anaerobic fermentation across different generations are shown in Figure 1. The addition of ethanol and eight consecutive subculturing cycles of the enriched cultures in the same medium led to significant changes in the fermentation pH and total gas production. In the continuous subculturing processes, the pH values at the end of each cycle generally ranged from 5.2 to 6.7 (mean = 5.81), and each culture cycle produced 80~120 mL (mean = 99.5 mL) of gas. The significant increase in the pH of the fermentation broth with ethanol addition compared to the CON group might be due to the effective use of ED to promote organic acid chain elongation, thereby reducing the H⁺ ion concentration in the fermentation broth [18,27,28].

3.1.2. Ethanol Promotes the Production of Caproic Acid and Chain Extension

Adding ethanol to the fermentation of maize stover with rumen fluid led to acetate dominance, with propionate, butyrate, valerate, and caproate contents (in g/L) being successively lower (Figure 2). Prior to subculturing, although ethanol addition to the fermentation could reduce propionate production and increase caproate production, it did not significantly increase the total VFA yield or the average chain length (ACL) (CON: 2.87 vs. ETH: 2.87). However, after subculturing, consistent with expectations, the ETH group exhibited reduced production of propionate (CON: 1.66 g/L vs. ETH: 0.18 g/L), butyrate (CON: 1.25 g/L vs. ETH: 0.51 g/L), and valerate (CON: 0.58 g/L vs. ETH: 0.24 g/L), along with enhanced caproate production increased by 3.7-fold (CON: 0.4 g/L vs. ETH: 1.49 g/L) and an extended average VFA chain length (CON: 3.09 vs. ETH: 3.66). Additionally, valerate production and ACL extension were influenced by the interaction between ethanol addition and subculturing.

Early studies have shown that ethanol, as an electron donor, generates acetyl-CoA by supplying reducing equivalents like NADH and FADH₂. Acetyl-CoA is a key intermediate in the reverse β-oxidation (RBO) cycle for carbon chain elongation [12,29,30,31]. In our experiment, ethanol was metabolized by rumen microorganisms into intermediate products such as acetyl-CoA, which participated in caproate synthesis, thereby increasing caproate production. The average chain length of VFA naturally increases with the elevation of caproic acid production, as caproic acid has a longer carbon chain. Upon ethanol addition to the maize stover–rumen fluid fermentation, a similar RBO pathway may be activated, prompting microorganisms to convert more substrates into caproate while reducing propionate and butyrate production. Moreover, during subculturing, microorganisms continuously adapt to metabolic product changes and further adjust their metabolic pathways [21,32]. As the number of subcultures increases, microbial ethanol utilization efficiency improves, potentially yielding more caproate. Enhanced feedback inhibition amplifies the impact on valerate production and ACL, thus forming an interactive effect between ethanol addition and subculturing.

3.2. Microbial Community Analysis

3.2.1. Alpha and Beta Diversity Analysis

Ethanol significantly reduced the ACE, Chao1, Shannon, and Simpson indices of bacteria (

Table 1. Effect of ethanol supplementation on the alpha diversity and species richness of microorganisms in vitro fermented maize straw.

Items	Treatments		p-Value
	CON	ETH
Bacterial 1
Ace	320.79 ± 7.17	242.43 ± 8.17	<0.001
Chao1	322.50 ± 7.94	249.25 ± 18.11	0.004
Shannon	5.49 ± 0.08	4.57 ± 0.08	<0.001
Simpson	0.9564 ± 0.003	0.9141 ± 0.007	<0.001
Fungi 2
Ace	986.72 ± 42.64	1111.47 ± 47.04	0.081
Chao1	986.28 ± 42.30	1121.72 ± 52.29	0.072
Shannon	6.60 ± 0.12	7.13 ± 0.19	0.035
Simpson	0.9686 ± 0.003	0.9739 ± 0.004	0.314

¹ Each value represents the mean ± SE of 6 replicates (n = 6); ² In the ETH group, fungal data are presented as the mean ± standard error (SE) of 5 replicates (n = 5).

). Overall, ethanol addition had no significant effect on fungal species richness (Ace and Chao1 indices) but significantly increased the fungal community’s Shannon diversity index (p = 0.035). This indicates that ethanol has a potential positive effect on fungal community diversity under the experimental conditions, yet has little impact on community dominance. It suggests that ethanol reduces bacterial species number and diversity, simplifying the community structure. Additionally, certain species became dominant in the community, suppressing other species’ growth. This finding is consistent with our previous research on wheat straw and ethanol in vitro co-culture, where ethanol increased valeric and caproic acid production, and bacteria played a more crucial role than fungi [22]. On the other hand, ethanol-caused reduction in bacterial richness and diversity implies that some bacteria could not survive [21,22,33]. The decrease in pH and gas production might be associated with these changes in bacterial richness and diversity.

3.2.2. Community Structure of Bacteria and Fungi

The top 10 most abundant bacterial and fungal phyla in terms of relative abundance are shown in Figure 3A,C. Compared with the CON group, ethanol significantly increased the relative abundance of Firmicutes (p = 0.001) and significantly decreased those of Bacteroidetes (p = 0.009), Actinobacteria (p = 0.004), and Spirochaetes (p = 0.001). Ethanol had no significant effect on the relative abundances of Basidiomycota (p = 0.673) and Ascomycota (p = 0.454), the major components of the fungal community (

Table A1. Effects of ethanol addition on the relative abundances of bacterial and fungal communities at the phylum level in in vitro fermented maize straw (with an average relative abundance of at least one treatment ≥ 1%).

Phylum	Treatments		p-Value
	CON 1	ETH 2
Bacterial
Firmicutes	54.11 ± 2.42	78.58 ± 5.01	0.001
Bacteroidetes	27.89 ± 1.99	15.58 ± 3.21	0.009
Actinobacteria	14.35 ± 1.40	4.31 ± 2.27	0.004
Spirochaetes	1.72 ± 0.26	0.08 ± 0.03	0.001
Proteobacteria	1.39 ± 0.18	0.82 ± 0.20	0.061
Fungi
Basidiomycota	56.39 ± 1.56	57.34 ± 1.47	0.673
Ascomycota	16.97 ± 1.02	15.59 ± 1.51	0.454

¹ Each value represents the mean ± SE of 6 replicates (n = 6); ² In the ETH group, fungal data are presented as the mean ± standard error (SE) of 5 replicates (n = 5).

). Firmicutes are often involved in carbohydrate metabolism during rumen fermentation; Bacteroidetes play a role in polysaccharide degradation [34,35]; Actinobacteria can decompose complex organic substances (such as polysaccharides, cellulose, starch, chitin, etc.) and promote nutrient cycling [36]; and Spirochaetes act synergistically with other microorganisms (such as Fibrobacteres) and participate in lignocellulose degradation [37]. Such significant changes in dominant bacterial phyla indicate that ethanol can profoundly affect the bacterial metabolic networks and functions in fermentation.

The top 20 bacterial and fungal genera in terms of relative abundance are shown in Figure 3B,D. Ethanol addition induced significant shifts in multiple genera within Firmicutes (

Table A2. Effects of ethanol addition on the relative abundances of bacterial and fungal communities at the genus level in in vitro fermented maize straw (with an average relative abundance of at least one treatment ≥ 0.5%).

Phylum	Genus	Treatments		p-Value
		CON 1	ETH 2
Bacterial
Firmicutes	unidentified_Clostridiales	3.91 ± 0.58	20.97 ± 3.75	0.006
	Shuttleworthia	4.09 ± 0.62	10.7 ± 1.89	0.016
	unidentified_Lachnospiraceae	8.27 ± 1.21	5.00 ± 2.29	0.237
	Syntrophococcus	3.74 ± 0.50	8.42 ± 1.75	0.043
	Solobacterium	5.98 ± 0.60	1.44 ± 0.29	<0.001
	Mitsuokella	3.26 ± 0.79	0.03 ± 0.01	0.010
	Succiniclasticum	3.60 ± 0.33	1.84 ± 0.60	0.027
	Dialister	0.02 ± 0.00	1.77 ± 0.69	0.053
	Lactobacillus	1.20 ± 0.23	1.75 ± 0.36	0.226
	Oribacterium	1.36 ± 0.19	1.16 ± 0.25	0.547
Bacteroidetes	unidentified_Prevotellaceae	15.29 ± 1.34	11.69 ± 2.5	0.233
	unidentified_Rikenellaceae	2.88 ± 0.80	0.02 ± 0.01	0.016
Actinobacteria	Pseudoscardovia	13.36 ± 1.42	3.41 ± 2.24	0.004
Spirochaetes	unidentified_Spirochaetaceae	1.69 ± 0.26	0.08 ± 0.03	0.001
Fungi
Basidiomycota	Wallemia	23.00 ± 4.00	22.00 ± 2.22	0.841
	Hannaella	9.63 ± 0.95	10.42 ± 1.73	0.682
	Symmetrospora	8.24 ± 1.27	4.93 ± 0.22	0.044
	Tricholoma	2.11 ± 0.85	3.78 ± 1.2	0.274
	Geminibasidium	1.75 ± 0.82	2.08 ± 0.56	0.762
	Papiliotrema	3.11 ± 0.34	2.52 ± 0.71	0.452
	Sterigmatomyces	2.18 ± 0.37	2.04 ± 0.43	0.810
	Erythrobasidium	1.48 ± 0.41	2.18 ± 0.20	0.184
	Russula	0.64 ± 0.29	1.37 ± 0.57	0.259
Ascomycota	Cladosporium	5.84 ± 0.46	5.82 ± 1.32	0.984
	Aspergillus	4.83 ± 0.23	2.58 ± 0.49	0.002
	Acremonium	1.13 ± 0.14	0.47 ± 0.13	0.008
	Pichia	0.51 ± 0.15	1.03 ± 0.64	0.403

¹ Each value represents the mean ± SE of 6 replicates (n = 6); ² In the ETH group, fungal data are presented as the mean ± standard error (SE) of 5 replicates (n = 5).

). Specifically, the relative abundances of unidentified_Clostridiales (p = 0.006), Shuttleworthia (p = 0.016), and Syntrophococcus (p = 0.043) were markedly higher in the ETH group than in the CON group, indicating their potential roles in ethanol-associated metabolic processes and specific fermentation pathways. In contrast, the relative abundances of Solobacterium (p < 0.001) and Mitsuokella (p = 0.01) were substantially lower in the ETH group, suggesting ethanol-mediated growth inhibition and potential disruption of their original functions in the fermentation system. Within Bacteroidetes, the abundance of unidentified_Rikenellaceae (p = 0.016) was significantly reduced in the ETH group, whereas unidentified_Prevotellaceae abundance showed no significant difference (p = 0.233). In Actinobacteria and Spirochaetes, the relative abundances of Pseudoscardovia (p = 0.004) and unidentified_Spirochaetaceae (p = 0.001) were notably lower in the ETH group, indicating ethanol strongly inhibited these genera and likely interfered with their roles in lignocellulose degradation and nutrient cycling. For the fungal community at the genus level, ethanol significantly reduced the relative abundance of Symmetrospora (Basidiomycota, p = 0.044) in the ETH group, which may affect the metabolic functions of Basidiomycota during fermentation. However, most Basidiomycota genera (e.g., Wallemia [p = 0.841], Hannaella [p = 0.682]) exhibited no significant differences, indicating their stability and ethanol resistance in the fermentation system. In Ascomycota, Aspergillus (p = 0.002) and Acremonium (p = 0.008) showed substantially lower abundances in the ETH group, suggesting ethanol suppressed these genera and potentially altered metabolic processes such as substrate conversion. Overall, ethanol had a more limited impact on the fungal community at the genus level, with most genera showing stable abundances.

PCoA results show that ethanol remarkably alters the bacterial community structure in the fermentation broth but has no significant effect on the fungal community (Figure 4A,E). SIMPER analysis revealed that changes in Lactobacillus, Megasphaera, Shuttleworthia, and unidentified_Clostridiales were key drivers of bacterial community differentiation between groups; Wallemia, Pholiota, and Aspergillus were the primary genera contributing to fungal community differences (Figure 4B,F). LEfSe results further indicated that unidentified_Clostridiales, Shuttleworthia, and Syntrophococcus were biomarker bacteria for the ETH group, while Solobacterium, Mitsuokella, unidentified_Rikenellaceae, and Pseudoscardovia characterized the CON group. Aspergillus emerged as the key taxon differentiating the fungal community structure (Figure 4C,D,G,H).

Mechanistically, specific microorganisms utilize acetic, propionic, and butyric acids alongside ED (e.g., ethanol) to elongate carbon chains via fatty acid β-oxidation, generating valeric and caproic acids. Ethanol, as a critical ED for chain elongation, facilitates the production of medium-chain fatty acids [38]. Clostridiales, a well-documented ethanol-utilizing taxon, drives caproic acid synthesis from acetic acid through reverse β-oxidation [29,39,40]. Studies have shown that co-culturing Clostridium kluyveri with ruminal bacteria accelerates carbon chain elongation and enhances caproic acid production [12]. In this study, unidentified_Clostridiales was the dominant genus in ethanol-driven caproate production, with a relative abundance of 20.97% in the ETH group (5.36-fold higher than CON). Syntrophococcus (8.42% in ETH), capable of generating acetic acid from formic acid (a sugar oxidation product), provided essential precursors for caproate biosynthesis [41]. Shuttleworthia, with a 160% increase in relative abundance in ETH, likely participates in ethanol-dependent caproic acid metabolism, further supporting the observed chain elongation.

3.2.3. Correlation Between Microorganisms and Fatty Acid Production

To investigate the effects of ethanol on microbial community structure and its interaction with environmental factors, canonical correlation analysis (CCA) was performed. The results revealed that key bacterial genera including unidentified_Clostridiales, Syntrophococcus, and Shuttleworthia were strongly associated with caproic acid and average chain length (ACL), whereas pH was a critical environmental factor shaping the bacterial community structure (Figure 5A). Gas production-related parameters significantly influenced the fungal community structure, as shown in Figure 5B.

To further clarify the associations between the microbial community and fermentation indicators, Spearman correlation analysis was conducted to examine relationships between microbial genera and fermentation parameters. The results indicated that the fungal community exhibited weaker correlations with fatty acid production compared to the bacterial community. Specifically, Aspergillus (R = −0.836; p = 0.001) and Acremonium (R = −0.664; p = 0.026) in Ascomycota showed negative correlations with caproic acid production (Figure 5D).

Consistent with expectations, the bacterial community was closely associated with fatty acid production. In Firmicutes, unidentified_Clostridiales (R = 0.839; p < 0.001), Shuttleworthia (R = 0.748; p = 0.005), Syntrophococcus (R = 0.685; p = 0.014), and Dialister (R = 0.683; p = 0.014) exhibited significant positive correlations with caproate. Additionally, unidentified_Clostridiales (R = 0.797; p = 0.002), Syntrophococcus (R = 0.58; p = 0.048), and Dialister (R = 0.739; p = 0.006) were positively correlated with ACL, indicating their potential roles in caproic acid biosynthesis and carbon chain elongation. In contrast, Pseudoscardovia (Actinobacteria, R = −0.601; p = 0.039), Solobacterium (Firmicutes, R = −0.692; p = 0.013), Mitsuokella (Firmicutes, R = −0.741; p = 0.006), unidentified_Rikenellaceae (Firmicutes, R = −0.741; p = 0.006), and unidentified_Spirochaetaceae (Spirochaetes, R = −0.741; p = 0.006) showed negative correlations with caproate production, consistent with their low relative abundances in the ETH group. These observations may be attributed to ethanol-mediated antibacterial effects or poor adaptation to long-term in vitro subculturing. Notably, genera positively correlated with caproic acid production were negatively correlated with acetic, butyric, and valeric acid production, and vice versa, aligning with the fermentation results where ethanol promoted the elongation of short-chain fatty acids into caproic acid.

In conclusion, this study demonstrated that after eight generations of in vitro domestication, rumen microorganisms can still facilitate short-chain fatty acid chain elongation in maize straw fermentation through electron donor addition, thereby enhancing caproic acid production. Key bacterial genera unidentified_Clostridiales, Shuttleworthia, and Syntrophococcus play critical roles in this process. Future studies should focus on isolating and purifying these microorganisms to validate their functional contributions to caproic acid production.

4. Conclusions

Adding ethanol during in vitro rumen fermentation inhibits the production of acetic and propionic acids while enhancing caproic acid biosynthesis. This study demonstrates that ethanol enables rumen microorganisms to convert acetic and propionic acids into caproic acid when maize stover serves as the fermentation substrate. Specifically, the bacterial genera unidentified_Clostridiales, Shuttleworthia, and Syntrophococcus stably utilize ethanol to facilitate caproic acid production across multiple generations of in vitro subculturing. Rumen fungi exhibit minimal effect on the carbon chain elongation of volatile fatty acids, with bacterial communities playing a dominant role in this process. Future research should focus on further validation through pilot-scale or larger-scale trials to evaluate the stability and applicability of this strategy under actual production conditions, thus providing more direct references for industrial applications.