Abstract
This study multidimensionally investigates the comprehensive effects of
Lactobacillus plantarum (LP)-fermented feed on growth performance,
intestinal health, and metabolic regulation in Pacific abalone
(Haliotis discus hannai). The results demonstrate that LP fermentation
significantly alters feed’s physical properties and nutritional
profile, softening texture, increasing viscosity, and emitting an
acidic aroma. Notably, it enhanced contents of cis-9-palmitoleic acid,
α-linolenic acid (ALA), and functional amino acids (GABA, L-histidine,
and L-asparagine), indicating that fermentation optimized ω-3 fatty
acid accumulation and amino acid profiles through the modulation of
fatty acid metabolic pathways, thereby improving feed biofunctionality
and stress-resistant potential. Further analyses revealed that
fermented feed markedly improved intestinal morphology in abalone,
promoting villus integrity and upregulating tight junction proteins
(ZO-1, Claudin) to reinforce intestinal barrier function. Concurrently,
it downregulated inflammatory cytokines (TNF-α, NF-κB, IL-16) while
upregulating anti-inflammatory factors (TLR4) and antioxidant-related
genes (NRF2/KEAP1 pathway), synergistically mitigating intestinal
inflammation and enhancing antioxidant capacity. Sequencing and
untargeted metabolomics unveiled that fermented feed substantially
remodeled gut microbiota structure, increasing Firmicutes abundance
while reducing Bacteroidetes, with the notable enrichment of beneficial
genera such as Mycoplasma. Metabolite profiling highlighted the
significant activation of lipid metabolism, tryptophan pathway, and
coenzyme A biosynthesis. A Spearman correlation analysis identified
microbiota–metabolite interactions (such as Halomonas’ association with
isethionic acid) potentially driving growth performance via metabolic
microenvironment regulation. In conclusion, LP-fermented feed enhances
abalone growth, immune response, and aquaculture efficiency through
multi-dimensional synergistic mechanisms (nutritional optimization,
intestinal homeostasis regulation, microbiota–metabolome crosstalk),
providing critical theoretical foundations for aquafeed development and
probiotic applications in aquaculture.
Keywords: Haliotis discus hannai, Lactobacillus plantarum, fermented
feed, growth, intestinal health
1. Introduction
Haliotis discus hannai, a pivotal subspecies within the genus Haliotis
(family Haliotidae) [[34]1], predominantly inhabits rocky substrates
across tropical to temperate marine zones [[35]2]. Within these
habitats, specific water temperature, transparency, and light intensity
constitute critical ecological determinants, and wild populations
establish stable ecological niches in algae- and kelp-rich environments
[[36]3], exhibiting trophic specialization through macroalgal
consumption. Despite these specific habitat requirements in the wild,
H. discus hannai is recognized as the cornerstone species in China’s
abalone aquaculture industry. H. discus hannai is the most extensively
farmed abalone species in northern China, especially in the Shandong,
Liaoning, and Fujian provinces. It constitutes > 70% of China’s farmed
abalone output due to its rapid growth, superior flesh quality, and
adaptability to controlled environments [[37]4]. However,
industrial-scale farming faces dual challenges: escalating biosecurity
risks from pathogenic infections, and systemic nutritional deficits.
Vibriosis and pseudomonad-induced pathologies (such as pustule disease
and abdominal rupture) have emerged as primary biosecurity threats
[[38]5,[39]6,[40]7,[41]8], with elevated water temperatures during
summer exacerbating pathogen proliferation, immunosuppression, and mass
mortality events. Concurrently, high-density farming amplifies pathogen
transmission via hydrodynamic vectors. Traditional algal-based feeds
fail to meet protein–lipid requirements, while artificial formulations,
despite storage stability advantages, suffer from suboptimal cellulose
digestibility and imbalanced amino acid profiles, collectively
impairing growth performance and elevating production costs [[42]9].
These intertwined constraints necessitate innovative technological
interventions.
Probiotics, as bioactive microbial preparations, demonstrate
multifaceted therapeutic potential through host–microbiome crosstalk
modulation. Their tripartite mechanisms encompass the following: gut
microbiota regulation via the competitive exclusion of pathogens
(Vibrio harveyi) and short-chain fatty acid (SCFA)-mediated enteric
homeostasis [[43]10]; nutrient metabolism enhancement through the
enzymatic hydrolysis of recalcitrant feed components (cereal
proteins/polysaccharides), achieving 8–34% weight gain improvement
[[44]11,[45]12,[46]13]; and immunomodulation via microbial-associated
molecular patterns (MAMPs) activating pattern recognition receptors
(PRRs), thereby triggering TLR4/NRF2/KEAP1 signaling cascades to
suppress pro-inflammatory cytokines (TNF-α, IL-16) [[47]14]. Notably,
Shewanella-supplemented diets reduce V. harveyi mortality from 77% to
27% [[48]15], while Bacillus strains enhance hemocyte vitality and
apoptosis resistance [[49]16]. Furthermore, probiotics exhibit an
environmental bioremediation capacity, degrading 99% of nitrites and
ammonia nitrogen in aquaculture systems [[50]17]. Lactobacillus
plantarum (LP) was selected as the focal probiotic agent in this study
due to its (1) well-documented safety (GRAS/QPS status) and stability
in feed matrices, (2) robust enzymatic repertoire (proteases,
carbohydrases) that enables the efficient degradation of complex
plant-derived antinutrients, (3) potent SCFA (particularly lactate)
production capacity, which acidifies the gut lumen to inhibit Vibrio
spp. colonization, and (4) proven immunostimulatory effects in aquatic
species via the upregulation of antioxidant pathways and antimicrobial
peptide expression [[51]17]. Its resilience to industrial processing
(lyophilization, pelleting) further enhances its practical
applicability in aquaculture feed production.
The synergistic integration of probiotic engineering with feed
biotechnology presents transformative potential. LP-fermented feeds
degrade antinutritional factors, enrich γ-aminobutyric acid (GABA) and
α-linolenic acid (ALA) profiles [[52]18], and activate the tryptophan
metabolism. Probiotic formulations achieve mortality reduction [[53]19]
and reshape gut microbiomes by elevating Firmicutes abundance,
fostering nutrient assimilation and immune priming [[54]20]. The
primary goal of this investigation is to rigorously evaluate the
efficacy of a novel LP-fermented feed in (1) augmenting growth
performance and feed utilization efficiency, (2) enhancing resistance
against Vibrio harveyi challenges, and (3) modulating key
immune-physiological and metabolic responses (e.g., microbiome
structure, antioxidant status). This paradigm not only truncates
traditional 3–4 year cultivation cycles, but enables precision health
management through microbiome–metabolome axis elucidation
(Halomonas–hydroxyethyl sulfonate metabolic coupling).
2. Materials and Methods
2.1. Preparation and Quality Analysis of Fermented Feed
2.1.1. Bacterial Culture Preparation and Fermentation Process
The LP-NDMJ-4 bacterial suspension (viable count: 7 × 10^8 CFU/mL,
isolated and preserved by our research center, Fuzhou, China) was mixed
with soybean meal substrate (crude protein ≥ 45%, Shandong Sanwei
Soybean Protein Co., Ltd., Linyi, China) at a 5% (v/v) inoculum ratio,
and the moisture content was adjusted to 15%. The mixture was loaded
into breathable membrane fermentation feed bags (specifications: 30 cm
× 40 cm, air permeability: 5000 mL/(m^2·h); Qingdao Haida Biotechnology
Group Co., Ltd., Qingdao, China) and fermented at 37 °C for 3 days in a
constant temperature incubator (SPX-250B, Shanghai Yiheng Scientific
Instruments Co., Ltd., Shanghai, China). Post-fermentation, samples
were collected for quality assessment.
2.1.2. Feed Quality Assessment
Sensory Evaluation and pH Measurement
The sensory attributes (aroma, color, and appearance) of fermented and
unfermented feeds were comparatively scored. For pH measurement, 3 g of
sample was mixed with 30 mL of ultrapure water (Milli-Q system, Merck
Millipore, Darmstadt, Germany), incubated at 37 °C with shaking
(THZ-98A thermostatic shaker, Shanghai Zhicheng Analytical Instruments
Co., Ltd., Shanghai, China) for 30 min, and analyzed using a pH meter
(FE28, Mettler Toledo, Greifensee, Switzerland).
Nutritional Profiling (Gas Chromatography–Mass Spectrometry, GC-MS Analysis)
Samples were extracted with isopropanol (High Performance Liquid
Chromatography HPLC grade, Sigma-Aldrich, St. Louis, MO, USA)-n-hexane
(HPLC grade, Sinopharm Chemical Reagent Co., Ltd., Shanghai, China)
(2:3 v/v, containing 0.2 mg/L internal standard), followed by liquid
nitrogen grinding (JXFSTPRP-CL grinding instrument, Shanghai Jingxin
Technology, Shanghai, China), ice-water bath ultrasonication (KQ-500DE,
Kunshan Shumei Ultrasonic Instrument Co., Ltd., Kunshan, China) for 5
min, and centrifugation at 12,000× g for 15 min at 4 °C (Centrifuge
5424R, Eppendorf, Hamburg, Germany). The supernatant was dried under
nitrogen gas (TurboVap LV, Biotage, Uppsala, Sweden), derivatized with
methanol:trimethylsilyl diazomethane (1:2 v/v, Sigma-Aldrich),
redissolved in n-hexane, and analyzed via GC-MS (7890B-5977B, Agilent
Technologies, Santa Clara, CA, USA).
Amino Acid Analysis (UHPLC-MS/MS)
Samples were extracted with acetonitrile (HPLC grade, Merck, Darmstadt,
Germany)–methanol (HPLC grade, Sigma-Aldrich)–water (Milli-Q) (2:2:1
v/v, containing isotope-labeled internal standards), subjected to
liquid nitrogen grinding, ultrasonication, and centrifugation. The
supernatant was analyzed using UltraHigh Performance Liquid
Chromatography tandem mass spectrometry (UHPLC-MS/MS) (Vanquish
Horizon-Q Exactive HF-X, Thermo Fisher Scientific, Waltham, MA, USA)
for amino acid composition.
2.2. Experimental Animals and Husbandry
2.2.1. Experimental Design
A total of 300 juvenile abalone (H. discus hannai) with similar body
weights (19 ± 1.5 g) were obtained from Fujian Xiapu Rixing Aquaculture
Cooperative (Ningde, China) and divided into three groups: the CK
group, fed an unfermented basal diet (formulation: 15% fish meal, 40%
kelp powder, 40% soybean meal, 5% spirulina; all ingredients purchased
from Qingdao Haida Biotechnology Group); the HD group, fed on natural
kelp feed (Fujian Xiapu Kelp Aquaculture Base, Ningde, China); and the
LP group, fed L. plantarum NDMJ-4 fermented feed. Each group consisted
of 100 individual abalone, with a 90-day rearing period. All animal
experiments were conducted in accordance with the requirements of the
Animal Ethics Committee of the Fujian Academy of Agricultural Sciences
Institute of Animal Husbandry and Veterinary Medicine (MTLLSC
2024-001-2).
2.2.2. Rearing Conditions
Water temperature (22–28 °C) was monitored using a temperature logger
(HOBO MX2201, Onset Computer, Bourne, MA, USA), pH (7.0–8.1) measured
using a pH meter (FE28, Mettler Toledo), and dissolved oxygen (6.0–8.5
mg/L) was calibrated using a portable dissolved oxygen meter (Pro20,
YSI, Yellow Springs, OH, USA). Feed was administered every 5 days (500
g per cage) using aquaculture cages (40 cm × 30 cm × 12 cm, Fujian
Xiapu Fishing Gear Factory, Ningde, China).
2.2.3. Sample Collection
At the end of the trial, 20 abalones per group were randomly selected.
Body weight was measured using an electronic balance (ME104E, Mettler
Toledo), and shell length/width were determined using a vernier caliper
(500-196-30, Mitutoyo, Kawasaki, Japan). Intestinal tissues were
divided into two portions: one portion from 10 abalones per group
stored at −80 °C (DW-HL668 ultra-low temperature freezer, Zhongke
Meiling, Hefei, China) for DNA extraction and subsequent microbiota
analysis, and the other fixed in 4% paraformaldehyde (Servicebio,
Wuhan, China) for HE staining.
2.3. Growth Performance Evaluation
The following indices were calculated:
Weight Gain Rate (WGR):
[MATH: Final weight−Initial weightInitial weight×100% :MATH]
Survival Rate (SR):
[MATH: =Number of survivorsInitial number×100% :MATH]
Specific Growth Rate (SGR)
[MATH: =Final weight−Initial weightExperimental days×100% :MATH]
Feed Conversion Ratio (FCR):
[MATH: =Total feed intakeTotal weight gain×100% :MATH]
2.4. Intestinal Histomorphology and Barrier Function Analysis
2.4.1. Intestinal Tissue Fixation and HE Staining
Abalone intestinal tissues were rinsed with physiological saline
(Sinopharm Chemical Reagent Co., Ltd., Shanghai, China), and 1 cm
segments were fixed in 4% paraformaldehyde (pH 7.4, Servicebio, Wuhan,
China) at 4 °C for 24 h. Tissues were dehydrated in graded ethanol
(70%, 80%, 90%, 100%; analytical grade, Sinopharm), cleared in xylene
(analytical grade, Sinopharm), embedded in paraffin wax (Leica
Biosystems, Wetzlar, Germany), and sectioned at 5 μm thickness.
Sections were stained with hematoxylin (Harris method, Servicebio) for
5 min, differentiated in 1% acid ethanol, blued in running water, and
counterstained with eosin (Servicebio) for 1 min. Neutral resin
(Servicebio) was used for mounting. Intestinal villus structure, goblet
cell distribution, and inflammatory infiltration were observed under a
Nikon ECLIPSE Ci microscope (Nikon, Tokyo, Japan), with images captured
using the NIS-Elements D 5.11 system (Nikon).
2.4.2. Intestinal Barrier-Related Gene Expression Analysis
Intestinal tissues (20 mg) were homogenized in liquid nitrogen, lysed
with 1 mL NucleoZOL (Macherey-Nagel, Düren, Germany), and incubated for
5 min. After adding 0.2 mL chloroform (HPLC grade, Sigma-Aldrich),
samples were vortexed for 15 s and centrifuged at 12,000× g for 15 min
at 4 °C (Eppendorf Centrifuge 5424R). The aqueous phase was mixed with
isopropanol (Sigma-Aldrich) to precipitate RNA. RNA pellets were washed
twice with 75% ethanol, dissolved in DEPC-treated water
(Sigma-Aldrich), and quantified for purity (A260/A280 > 1.8, NanoDrop
2000, Thermo Fisher Scientific) and concentration (Qubit 4.0
Fluorometer, Thermo Fisher). Reverse transcription was performed using
the Yeasen Hifair^® III 1st Strand cDNA Synthesis Kit (Yeasen,
Shanghai, China) in a 20 μL reaction: 4 μL 5× gDNA Digester Mix, 4 μL
5× Hifair^® Buffer, 1 μL Oligo(dT) primer, 1 μL Random Hexamers, and 1
μL Enzyme Mix. The reaction conditions were 25 °C for 5 min, 55 °C for
15 min, and 85 °C for 5 min. qRT-PCR was conducted in a 10 μL system
containing 0.8 μL cDNA, 0.5 μM primers ([55]Table 1), and 5 μL 2× SYBR
Green Master Mix (Yeasen) on a Roche LightCycler 96 qPCR system (Roche,
Basel, Switzerland) with the following program: 94 °C for 30 s
(pre-denaturation), 40 cycles of 94 °C for 5 s, 60 °C for 15 s, and 72
°C for 10 s, followed by melt curve analysis (65–95 °C, 0.5 °C/s).
Relative gene expression was calculated using the 2−ΔΔCt method with
GAPDH as the reference, and three technical replicates were performed
per group.
Table 1.
Effects of LP-fermented soybean meal on sensory parameters.
Group Color Odor Texture Agglomeration pH
CK Light yellow Bean cake aroma Firm Loose granules 6.5
LP Brownish-yellow Sour alcoholic aroma Soft Agglomerated 6.7
[56]Open in a new tab
CK: control group; LP: LP-fermented group.
2.5. Gut Microbiota Diversity and Composition Analysis
2.5.1. Microbial Genomic DNA Extraction and Sequencing
Total DNA was extracted from intestinal contents (50 mg) using the
CTAB/SDS method. Samples were homogenized with 500 μL lysis buffer (100
mM Tris-HCl, 40 mM EDTA, pH 8.0, Sigma-Aldrich) and 0.5 g glass beads
(0.1 mm diameter, BioSpec Products, Bartlesville, USA), followed by
incubation with 20 μL proteinase K (20 mg/mL, Qiagen, Hilden, Germany)
at 65 °C for 1 h. After chloroform:isoamyl alcohol (24:1,
Sigma-Aldrich) extraction and isopropanol precipitation, DNA pellets
were washed with 75% ethanol and dissolved in TE buffer (10 mM
Tris-HCl, 1 mM EDTA, pH 8.0, Sigma-Aldrich). DNA integrity was assessed
via 1% agarose gel electrophoresis (Biowest, Barcelona, Spain),
quantified using Qubit, and the V3-V4 region of 16S rDNA was amplified
with primers 338F (ACTCCTACGGGAGGCAGCAG) and 806R
(GGACTACHVGGGTWTCTAAT) (Sangon Biotech, Shanghai, China). PCR products
were purified with AMPure XP beads (Beckman Coulter, Brea, CA, USA) and
sequenced on the Illumina NovaSeq 6000 platform (Illumina, San Diego,
CA, USA) in PE250 mode (≥50,000 reads per sample).
2.5.2. Bioinformatics Analysis
Raw sequencing data were quality-controlled using Fastp v0.23.2 (BGI,
Shenzhen, China) (Q20 ≥ 90%, removal of non-specific sequences). OTUs
were clustered at 97% similarity using UPARSE v11 (Robert C. Edgar,
Berkeley, CA, USA), chimeras were removed with UCHIME, and taxonomic
annotation was performed using the SILVA v138 database
([57]https://www.arb-silva.de/). Alpha diversity indices (Shannon,
Chao1, Simpson) were calculated via QIIME2 (Greg Caporaso, Flagstaff,
AZ, USA). Beta diversity was analyzed using PCoA based on Unweighted
Unifrac distances (phyloseq package in R, R Foundation R.4.3.8, Vienna,
Austria). LefSe analysis (linear discriminant analysis (LDA) > 4,
Kruskal–Wallis test p < 0.05) identified differential taxa, visualized
using the R package ggplot2 (3.5.0).
2.6. Non-Targeted Metabolomic Analysis on Gastric and Intestinal Content
Samples
2.6.1. Metabolite Extraction and LC-MS Detection
Liquid nitrogen-ground intestinal tissues (100 mg) were extracted with
500 μL pre-cooled 80% methanol (containing 0.1% formic acid, HPLC
grade, Sigma-Aldrich), vortexed for 1 min (Vortex-Genie 2, Scientific
Industries, Bohemia, NY, USA), ultrasonicated in an ice bath (40 kHz,
KQ-500DE, Kunshan Shumei, Kunshan, China) for 10 min, and centrifuged
at 15,000× g for 20 min at 4 °C (Eppendorf). The supernatant (300 μL)
was diluted with 120 μL ultrapure water (Milli-Q system, Merck
Millipore) to 53% methanol, centrifuged again, and 200 μL was injected
for analysis. Chromatographic separation was performed on a Thermo
Scientific Hypersil Gold C18 column (2.1 mm × 100 mm, 1.9 μm, Thermo
Fisher) with mobile phase A (0.1% formic acid in water) and B (0.1%
formic acid in methanol). The gradient program was 0–1.5 min (2% B), 3
min (85% B), and 10 min (100% B), followed by equilibration at 2% B.
Metabolites were detected using a Thermo Q Exactive HF-X mass
spectrometer (Thermo Fisher) with a HESI ion source (positive/negative
modes), spray voltage 3.5 kV (+)/2.8 kV (−), full scan range m/z
70–1050 (resolution: 120,000), and MS/MS in Top 10 mode.
2.6.2. Metabolite Identification and Pathway Analysis
Raw data were converted to mzXML format using ProteoWizard
(ProteoWizard Foundation, Cambridge, MA, USA). Peak extraction,
alignment, and normalization were performed using XCMS (Scripps
Research, San Diego, CA, USA; parameters: bw = 5, ppm = 10, minfrac =
0.5). Metabolites were annotated via HMDB ([58]http://www.hmdb.ca/,
accessed on 26 June 2025), KEGG ([59]https://www.kegg.jp//, accessed on
26 June 2025), and METLIN (Scripps Research) databases (mass error < 5
ppm, retention time deviation < 0.2 min, MS/MS similarity > 80%).
Differential metabolites were subjected to KEGG pathway enrichment
analysis (MetaboAnalyst 5.0, [60]https://www.metaboanalyst.ca//,
accessed on 26 June 2025; p < 0.05, FDR-corrected), visualized as
volcano plots, heatmaps, and pathway bubble charts using the R package
ggplot2(3.5.0).
2.7. Correlation Analysis
Correlation analysis among 19 gut microbial taxa, 19 differential
metabolites, and key seawater indicators was performed as follows:
Microbial taxa were identified via 16S rRNA sequencing of abalone
gastrointestinal contents based on abundance variations. Metabolites
were quantified via metabolomics, while seawater indicators were
assessed using environmental monitoring tools (such as Lianchuan Cloud
platform). Spearman’s rank correlation analysis evaluated monotonic
relationships between variables, visualized via heatmaps to highlight
interaction patterns.
2.8. Bioinformatics and Statistical Analysis
Data were analyzed using GraphPad Prism 9.0 (GraphPad Software, San
Diego, CA, USA). One-way ANOVA was applied, and the results are
expressed as mean ± standard deviation. A p-value < 0.05 was considered
statistically significant.
3. Results
3.1. Analysis of Feed Quality Before and After Fermentation
3.1.1. Sensory Evaluation of Fermented Feed
As shown in [61]Table 1 and [62]Figure 1, the color of LP-fermented
feed exhibited minimal variation compared to the control group, both
presenting a brownish-yellow hue. Post-fermentation, the aroma of
soybean meal transitioned from its original bean cake scent to a
distinctive sour and alcoholic fragrance. The addition of fermentation
broth increased feed moisture content, resulting in a softer texture
with minor agglomeration, although no significant pH alteration was
observed (pH 6.5 vs. 6.7).
Figure 1.
[63]Figure 1
[64]Open in a new tab
Granular state of soybean meal before and after fermentation. (A)
Pre-fermented soybean meal. (B) Fermented soybean meal.
3.1.2. Impact of Probiotic Fermentation on the Nutritional Profile of Soybean
Meal
As indicated in [65]Table 2, LP fermentation notably enhanced specific
fatty acid contents. The LP group demonstrated a two-fold increase in
cis-9-palmitoleic acid (106.85 ± 21.37 mg/kg vs. 48.32 ± 9.18 mg/kg in
CK) and a 14% elevation in α-linolenic acid (ALA) content (292.6 ±
55.59 mg/kg vs. 256.6 ± 33.36 mg/kg). However, docosahexaenoic acid
(DHA) levels decreased by 11.8% (66.47 ± 13.29 mg/kg vs. 75.33 ± 10.55
mg/kg), warranting further investigation into fermentation-induced
metabolic pathway alterations. A marginal increase was observed in
cis-11,14,17-eicosatrienoic acid (2.47 ± 0.3 mg/kg vs. 1.97 ± 0.28
mg/kg).
Table 2.
Composition of unsaturated fatty acids in soybean meal (mg/kg).
Free Fatty Acid CK LP p-Value
Trans-9-myristoleic acid 0.2 ± 0.04 0.1 ± 0.02 0.063
Trans-9-palmitoleic acid 1.1 ± 0.18 2.38 ± 0.33 0.082
Cis-9-palmitoleic acid 48.32 ± 9.18 106.85 ± 21.37 0.023
Trans-9-octadecenoic acid 3.08 ± 0.46 3.7 ± 0.7 0.079
Cis-11-octadecenoic acid 122.78 ± 18.42 124.87 ± 24.97 0.057
Cis-13-erucic acid 5.35 ± 0.75 5.42 ± 0.87 0.065
Trans-13-docosenoic acid 3.49 ± 0.45 3.62 ± 0.36 0.073
Cis-15-nervonic acid 3.11 ± 0.4 3.42 ± 0.55 0.056
A-linolenic acid (ala) 256.6 ± 33.36 292.6 ± 55.59 0.017
Cis-11,14,17-eicosatrienoic acid 1.97 ± 0.28 2.47 ± 0.3 0.045
Cis-4,7,10,13,16,19-docosahexaenoic acid (dha) 75.33 ± 10.55 66.47 ±
13.29 0.014
Cis-9,12-linoleic acid (la) 23.58 ± 4.24 23.27 ± 3.72 0.061
Γ-linolenic acid (gla) 2.57 ± 0.28 2.33 ± 0.33 0.078
Cis-8,11,14-dihomo-γ-linolenic acid (dgla) 2.44 ± 0.49 2.47 ± 0.47
0.089
Arachidonic acid (aa) 23.56 ± 3.53 21.48 ± 2.79 0.051
Cis-7,10,13,16-docosatetraenoic acid 4.53 ± 0.91 4.72 ± 0.52 0.091
[66]Open in a new tab
Data presented as mean ± standard deviation. CK: control group; LP:
LP-fermented group.
Concerning saturated fatty acids ([67]Table 3), caprylic acid (C8:0)
content decreased by 20.53% in the LP group, while lauric acid (C12:0),
tridecanoic acid (C13:0), and myristic acid (C14:0) exhibited dramatic
increases of 357.14%, 163.64%, and 51.42%, respectively.
Table 3.
Composition of saturated fatty acids in soybean meal (mg/kg).
Free Fatty Acid CK LP p-Value
Caprylic acid (C8:0) 3.41 ± 0.55 2.71 ± 0.33 0.013
Lauric acid (C12:0) 0.21 ± 0.03 0.96 ± 0.11 0.011
Tridecanoic acid (C13:0) 0.11 ± 0.01 0.29 ± 0.04 0.032
Myristic acid (C14:0) 30.34 ± 4.25 45.94 ± 9.19 0.017
Pentadecanoic acid (C15:0) 9.31 ± 1.58 13.28 ± 2.66 0.025
Palmitic acid (C16:0) 1520.93 ± 273.77 1685.45 ± 219.11 0.043
Heptadecanoic acid (C17:0) 18.18 ± 3.45 21.92 ± 3.29 0.041
Stearic acid (C18:0) 345.27 ± 65.6 350.38 ± 63.07 0.069
[68]Open in a new tab
Data presented as mean ± standard deviation. ND: not detected; CK:
control group; LP: LP-fermented group.
3.1.3. Amino Acid Profile Modifications Induced by Probiotic Fermentation
Fermentation significantly elevated amino acid levels in the LP group
compared to CK([69]Table 4). The most pronounced increases were
observed in L-histidine (+38.69%), L-asparagine (+36.10%),
γ-aminobutyric acid (+32.88%), and L-Arginine (+29.51%).
Table 4.
Effects of probiotic additives on amino acid content in feed before and
after fermentation (g/kg).
Amino Acid CK LP p-Value
β-Alanine 0.26 ± 0.01 0.32 ± 0.01 0.045
γ-Aminobutyric acid (GABA) 14.72 ± 0.48 19.56 ± 0.41 0.021
L-Proline 8.68 ± 0.08 9.35 ± 0.13 0.056
L-Asparagine 73.43 ± 2.57 99.94 ± 6.77 0.023
L-Glutamine 10.09 ± 0.35 12.79 ± 0.78 0.062
L-Methionine 46.28 ± 0.86 51.73 ± 0.39 0.051
L-Histidine 9.77 ± 0.09 13.55 ± 0.4 0.008
3-Methyl-L-histidine 0.41 ± 0.01 0.49 ± 0.01 0.078
1-Methyl-L-histidine 0.20 ± 0.01 0.23 ± 0.01 0.098
L-Arginine 23.21 ± 0.13 30.28 ± 0.58 0.015
L-Tryptophan 7.54 ± 0.24 7.81 ± 0.16 0.049
[70]Open in a new tab
Data presented as mean ± standard deviation. CK: control group; LP:
LP-fermented group.
3.2. Production Performance
Production performance refers to the comprehensive evaluation of
abalone growth, survival rate, and feed utilization efficiency during
aquaculture, serving as a critical indicator for assessing rearing
conditions, feed quality, and management efficacy. As detailed in
[71]Table 5, the growth performance of abalone varied significantly
across feed treatment groups. The weight gain rate in the LP group
(421%) was significantly higher than that in the CK (201%) and HD
(263%) groups.
Table 5.
Growth performance indexes of H. discus hannai.
Group HD CK LP p-Value
Total weight gain rate (%) 421% 201% 263% 0.017
Survival rate (%) 69% 45.33% 61.66% 0.031
Daily weight gain rate (%) 19% 9.5% 12.44% 0.018
Feed conversion ratio (FCR) 2633.12 1586.37 1205.36 0.014
[72]Open in a new tab
Data presented as mean ± standard deviation. HD: natural feed group;
CK: control group; LP: LP-fermented group.
The feed conversion ratio (FCR), a key metric for evaluating feed
utilization efficiency, was calculated as 2633.12 for the LP group,
lower than both the CK (1586.37) and HD (1205.36) groups. In terms of
survival rate, the LP group achieved 61.66%, outperforming the CK group
(45.33%), but remaining lower than the HD group (69%). Notably, while
the total weight gain rate of the LP group (263%) substantially
exceeded that of the CK group (201%), it was markedly lower than the HD
group’s 421%.
3.3. Determination of Abalone Quality
The experiment compared changes in amino acid content in abalone muscle
among the HD group (kelp-based feed), LP group (LP-fermented feed), and
CK group (basal feed) ([73]Table 6). The results reveal that the LP
group exhibited higher concentrations of most amino acids in abalone
muscle compared to both the HD and CK groups. Specifically, histidine,
phenylalanine, and cystine levels in the LP group were 0.21 g/kg, 0.41
g/kg, and 0.11 g/kg, respectively. These values represented increases
of 0.10 g/kg, 0.09 g/kg, and 0.10 g/kg compared to the CK group (0.11
g/kg, 0.32 g/kg, and 0.09 g/kg), and increments of 0.03 g/kg, 0.04
g/kg, and 0.10 g/kg relative to the HD group (0.18 g/kg, 0.37 g/kg, and
0.01 g/kg). These findings indicate that fermented feed enhances the
umami-related components in abalone muscle.
Table 6.
Effects of different diets on amino acid content of abalone muscle
quality.
Amino Acid g/KG HD LP CK p-Value
Aspartic acid 1.21 ± 0.12 1.26 ± 0.16 1.19 ± 0.12 0.058
Threonine 0.53 ± 0.08 0.57 ± 0.14 0.56 ± 0.16 0.121
Serine 0.62 ± 0.12 0.62 ± 0.15 0.54 ± 0.07 0.087
Glutamic acid 2.04 ± 0.55 2.1 ± 0.55 2.03 ± 0.26 0.042
Glycine 1.15 ± 0.12 1.02 ± 0.29 0.97 ± 0.16 0.036
Alanine 0.77 ± 0.2 0.76 ± 0.15 0.72 ± 0.2 0.231
Cystine 0.01 ± 0 0.11 ± 0.02 0.09 ± 0.01 0.011
Lavandulin 0.51 ± 0.14 0.55 ± 0.1 0.46 ± 0.12 0.063
Methionine 0.29 ± 0.07 0.3 ± 0.03 0.25 ± 0.04 0.079
Isoleucine 0.47 ± 0.08 0.51 ± 0.13 0.47 ± 0.12 0.092
Leucine 0.82 ± 0.19 0.89 ± 0.24 0.88 ± 0.17 0.152
Tyrosine 0.36 ± 0.1 0.4 ± 0.09 0.34 ± 0.07 0.035
Phenylalanine 0.37 ± 0.1 0.41 ± 0.09 0.32 ± 0.04 0.012
Lysine 0.72 ± 0.07 0.82 ± 0.22 0.76 ± 0.21 0.055
Histidine 0.18 ± 0.03 0.21 ± 0.06 0.11 ± 0.02 0.034
Arginine 1.31 ± 0.3 1.29 ± 0.35 1.21 ± 0.3 0.048
Proline 0.55 ± 0.09 0.5 ± 0.07 0.44 ± 0.04 0.053
[74]Open in a new tab
Data presented as mean ± standard deviation. HD: natural feed group;
CK: control group; LP: LP-fermented group.
3.4. Effects of LP-Fermented Feed on Intestinal Morphology of Abalone
A comparative analysis of intestinal tissue structure ([75]Figure 2)
revealed significant morphological differences in abalone intestines
across feed treatment groups. In the HD group, the intestinal
architecture remained relatively intact, characterized by
well-developed villi and a continuous mucosal layer. The abundant
villous structure effectively expanded the nutrient absorption surface
area, thereby enhancing dietary nutrient utilization and promoting
growth. These findings suggest that kelp-based feed may exert
protective effects on intestinal integrity, supporting barrier function
and nutrient assimilation. In contrast, the CK group exhibited
disorganized intestinal folds with shortened, sparse villi and
localized structural damage, indicating the limited benefits of basal
feed on intestinal health. Such compromised morphology likely reduced
nutrient absorption efficiency, ultimately impairing growth
performance.
Figure 2.
[76]Figure 2
[77]Open in a new tab
Section of the intestinal tissue of Abalone discus humphead (HE
staining, 40×). HD: natural feed group; CK: control group; LP:
LP-fermented group.
The LP group demonstrated markedly improved intestinal integrity
compared to CK, with significantly elongated and denser villi and no
observable structural defects. Although slightly inferior to the HD
group, the LP group’s intestinal morphology surpassed that of CK,
highlighting that LP NDHJ-4 fermentation enhanced digestive capacity
and intestinal health in H. discus hannai through structural
optimization.
3.5. Effects of LP-Fermented Feed on Intestinal Barrier Function, Cytokines,
and Antioxidant Factors in Abalone
A comparative analysis of intestinal tight junction protein mRNA
expression across feed treatment groups ([78]Figure 3) revealed
distinct molecular responses. The LP group exhibited significantly
elevated Claudin mRNA expression compared to both CK and HD groups (p <
0.001), with no significant difference observed between CK and HD (ns).
Similarly, ZO-1 mRNA expression was markedly higher in the LP group
than in CK and HD (p < 0.01), indicating enhanced tight junction
assembly and intestinal barrier integrity in the fermented feed group.
Figure 3.
[79]Figure 3
[80]Open in a new tab
Effects of feeding different diets on the intestinal tissue and
cytokine mRNA expression of H. discus hannai. **: p < 0.01; ***: p <
0.001; ns = not significant (p ≥ 0.05).
Inflammatory and immune-related cytokine profiling demonstrated the
regulatory effects of LP treatment. While TLR4 mRNA expression showed
no difference between CK and HD, it was significantly upregulated in LP
(p < 0.01). Conversely, TNF-α mRNA expression in LP decreased sharply
compared to CK and HD (p < 0.01), with no intergroup difference between
CK and HD. Notably, NF-κB mRNA levels were lowest in LP, while HD
displayed the highest expression (p < 0.001 vs. both groups), and CK
levels exceeded LP (p < 0.01). Additionally, IL-16 mRNA expression in
LP was reduced by 42% compared to CK and HD (p < 0.01), further
suggesting that fermented feed mitigates inflammatory signaling.
An antioxidant pathway analysis revealed that Keap1 mRNA expression
peaked in CK, but was suppressed in LP and HD (p < 0.001). Although
Nrf-2 mRNA levels showed no difference between CK and LP, HD exhibited
significantly higher Nrf-2 expression than CK (p < 0.05), highlighting
divergent antioxidant activation mechanisms.
3.6. Effects of Fermented Feed on the Intestinal Microbiota of Abalone
3.6.1. OTU Distribution and Diversity Analysis
The Venn diagram ([81]Figure 4) illustrates the similarity of
operational taxonomic unit (OTU) composition among three abalone
intestinal microbiota groups. A total of 46,574 OTUs were identified
across all groups, with 3063 OTUs shared among all three groups.
Specifically, the CK group contained 13,159 OTUs (4879 unique), the HD
group exhibited the highest richness with 17,963 OTUs (10,191 unique),
and the LP group comprised 16,318 OTUs (9795 unique). This hierarchical
distribution indicates that the HD group demonstrated the highest OTU
richness, followed by the LP and CK groups.
Figure 4.
[82]Figure 4
[83]Open in a new tab
Effects of fermented feed on gut microbiota composition in abalone.
(A1) Venn diagram of OTU distribution. (A2) Analysis of Beta diversity.
(A3) Analysis of LDA effect size of the different groups. (B1–B5)
Changes in microbial flora at the portal level. (C1–C9) Changes in
microbial flora at the genus level. * p < 0.05; **: p < 0.01; ***: p <
0.001; ns = not significant (p ≥ 0.05).
Alpha diversity indices were analyzed to evaluate microbial community
characteristics ([84]Table 7). The HD group showed significantly higher
Observed_species, Shannon, and Simpson indices compared to the CK and
LP groups (p < 0.05), while no significant differences were observed
between the CK and LP groups. Notably, the CK group exhibited the
highest Chao1 and ACE indices, reflecting distinct patterns in species
richness estimation. These results suggest that LP and HD feed
modulates intestinal microbiota by enhancing microbial diversity
(Shannon: 4.12 in CK vs. 4.27 in LP vs. 4.79 in HD) while maintaining
comparable species richness to basal feed.
Table 7.
Alpha diversity indices of intestinal microbiota in H. discus hannai.
Group Observed Species Chao1 ACE Shannon Simpson
CK 2413.40 4580.73 5097.43 4.12 0.89
LP 2358.60 2886.44 3168.66 4.27 0.89
HD 2916.10 3905.55 4343.13 4.79 0.92
p-value 0.047 0.015 0.027 0.032 0.159
[85]Open in a new tab
HD: natural feed group; CK: control group; LP: LP-fermented group.
As illustrated in the PCoA plot (Unweighted Unifrac distance), each
point represents an individual abalone sample, with the color
corresponding to experimental groups (HD, LP, CK). The analysis
revealed distinct clustering patterns: samples within the same group
exhibited tight aggregation, while intergroup points were spatially
separated without overlap. This clear segregation (PERMANOVA, * p <
0.001) indicates that dietary interventions significantly restructured
the intestinal microbial community composition of abalone.
3.6.2. Effects of Fermented Feed on Gut Microbiota Composition in Abalone
Following species annotation processing, a comparative analysis of
microbial communities was conducted based on relative abundance at both
the phylum and genus levels. A total of 37 bacterial phyla and 1156
bacterial genera were identified across all intestinal samples. Based
on annotation results, the top 10 phyla accounting for over 99% of
total annotated species in abalone gut microbiota from different marine
areas were selected for analysis.
[86]Figure 4 illustrates the phylum-level effects of fermented feed on
abalone gut microbiota. The dominant phyla included Proteobacteria,
Tenericutes, Bacteroidetes, Firmicutes, Actinobacteria, Planctomycetes,
and Verrucomicrobia. Significant intergroup differences were observed
in Acidobacteria, Spirochaetes, and Chloroflexi (p < 0.05).
Notably, Proteobacteria showed no significant variation among the
experimental groups. The LP group exhibited the significant
upregulation of Tenericutes (p = 0.017) and downregulation of
Bacteroidetes (p = 0.023) compared to controls. Although Firmicutes
abundance showed no statistical difference from the CK group, it was
significantly lower than the HD group (p = 0.035).
At the genus level ([87]Figure 4C), the dominant genera comprised
Curvibacter, Mycoplasma, Asinibacterium, Mycoplasmopsis, Ochrobactrum,
Pandoraea, Hydrotalea, and Pelagibacterium. The LP group demonstrated
3.08-fold and 14.42-fold increases in Mycoplasma and Ochrobactrum
abundance, respectively, versus CK group, concomitant with 2.35-fold
reduction in Pelagibacterium. Curvibacter, Asinibacterium, Pandoraea,
and Hydrotalea showed no significant differences from the CK group, but
were notably reduced compared to the HD group.
Linear discriminant analysis (LDA > 4) revealed 31 differentially
abundant species across groups ([88]Figure 4(A3)). The CK, HD, and LP
groups contained seven, fourteen, and ten discriminant species,
respectively, contributing to microbial structure variation. Notably,
Lactobacillus plantarum-fermented feed enhanced differential microbiota
diversity in H. discus hannai compared to the CK group. Although the LP
group showed slightly lower diversity than the seaweed-fed HD group,
its increased discriminant species count relative to the CK group
indicates probiotic-fermented feed’s modulatory effect on intestinal
microbial diversity.
3.7. Metabolite Identification and Quantification
A total of 2478 compounds were identified through LC-MS-based
metabolomic profiling combined with an in-house database. The annotated
metabolites were classified into 14 major categories ([89]Figure 5):
lipids and lipid-like molecules (877), organoheterocyclic compounds
(391), organic oxides (167), organic acids and derivatives (460),
organohalides (3), organosulfur compounds (11), organonitrogen
compounds (52), 1,3-dipolar organic compounds (1), homogeneous
non-metallic compounds (1), hydrocarbons and derivatives (7), alkaloids
and derivatives (55), lignans/neolignans (8),
nucleosides/nucleotides/analogs (52), benzenoids (243),
phenylpropanoids/polyketides (120), and others (30).
Figure 5.
[90]Figure 5
[91]Open in a new tab
Effects of fermented feed on metabolites composition in abalone gut.
(A) Donut plot of metabolite classification and proportion. (B) Venn
diagram of differential metabolites in each comparison group. (C) KEGG
diagram of differential metabolic pathways in each comparison group.
(D1–D6) OPLS-DA score diagram of different comparison groups. (E–G)
Differential metabolite volcano plot.
3.7.1. OPLS-DA Modeling
Untargeted LC-MS/MS metabolomics was performed on ten intestinal
samples per group. Orthogonal Partial Least Squares Discriminant
Analysis (OPLS-DA) with 1000 permutation tests (to mitigate overfitting
risks) revealed distinct clustering patterns among the three groups
([92]Figure 4B). The clear spatial separation (R2Y > 0.98 for all
pairwise comparisons) and absence of intergroup overlap confirmed
significant metabolic profile differences. Model validity was further
supported by high predictive capacity: CK vs. HD (R2Y = 0.99, Q2 =
0.693), CK vs. LP (R2Y = 0.993, Q2 = 0.951), and HD vs. LP (R2Y =
0.985, Q2 = 0.922). All Q2 values exceeded the 0.5 threshold,
confirming robust model reliability ([93]Figure 4C).
3.7.2. Screening of Differential Metabolites
Differential metabolites were identified using Variable Importance in
Projection (VIP) scores from the first principal component of
Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) models
combined with independent t-test p-values. Among the 3254 detected
metabolites, 959 showed significant intergroup variations (VIP > 1.0, p
< 0.05), including 664 upregulated and 405 downregulated compounds in
LP compared to CK ([94]Table 8). The key metabolic pathways impacted by
fermented feed included lipid biosynthesis (23.1%), amino acid
metabolism (18.7%), and secondary metabolite production (14.5%).
Table 8.
Comparative analysis of differential metabolites across experimental
groups.
Comparison Group Total Differential Metabolites (Diff_All) Upregulated
Metabolites (Diff_Up) Downregulated Metabolites (Diff_Down)
CK vs. HD 494 286 208
CK vs. LP 959 664 405
HD vs. LP 963 460 503
[95]Open in a new tab
HD: natural feed group; CK: control group; LP: LP-fermented group.
3.7.3. Classification of Differential Metabolites
In the CK vs. HD comparison, 494 differential metabolites were
identified, including 167 lipids and lipid-like molecules, 96
organoheterocyclic compounds, 35 organic oxides, 93 organic
acids/derivatives, 1 organohalide, 2 organosulfur compounds, 11
organonitrogen compounds, 2 hydrocarbons, 14 alkaloids/derivatives, 1
lignan/neolignan, 12 nucleosides/nucleotides/analogs, 32 benzenoids, 24
phenylpropanoids/polyketides, and 4 others, with 286 upregulated and
208 downregulated metabolites.
The CK vs. LP comparison revealed 959 differential metabolites: 346
lipids/lipid-like molecules, 185 organoheterocyclic compounds, 68
organic oxides, 153 organic acids/derivatives, 3 organohalides, 4
organosulfur compounds, 14 organonitrogen compounds, 2 hydrocarbons, 18
alkaloids/derivatives, 4 lignans/neolignans, 18
nucleosides/nucleotides/analogs, 89 benzenoids, 46
phenylpropanoids/polyketides, and 9 others, showing 664 upregulated and
405 downregulated species.
For HD vs. LP, 963 differential metabolites were detected: 355
lipids/lipid-like molecules, 173 organoheterocyclic compounds, 65
organic oxides, 155 organic acids/derivatives, 2 organohalides, 5
organosulfur compounds, 13 organonitrogen compounds, 4 hydrocarbons, 15
alkaloids/derivatives, 2 lignans/neolignans, 1 homogeneous non-metallic
compound, 17 nucleosides/nucleotides/analogs, 99 benzenoids, 50
phenylpropanoids/polyketides, and 7 others, with 460 upregulated and
503 downregulated metabolites.
Cross-group analysis identified lipids/lipid-like molecules,
organoheterocyclic compounds, organic acids/derivatives, and benzenoids
as the predominant differential metabolite categories. A core set of
149 shared differential metabolites was observed across all comparisons
([96]Figure 5).
3.7.4. KEGG Pathway Enrichment Analysis
KEGG pathway analysis (FDR-adjusted p < 0.05) revealed the significant
enrichment of differential metabolites in ovarian steroidogenesis (p =
0.018), tryptophan metabolism (p = 0.026), and pantothenate/CoA
biosynthesis (p = 0.042) ([97]Figure 4). These pathways are
hypothesized to regulate growth performance and immune responses in H.
discus hannai.
3.7.5. Shared Differential Metabolites Across Groups
Fourteen high-confidence differential metabolites (VIP > 1.0, p < 0.05)
were identified between the CK and experimental groups (HD/LP) after
filtering unnamed compounds ([98]Table 9). Three metabolites were
downregulated in the LP/HD groups: cis-11,14,17-eicosatrienoic acid,
thermozymocidin, and nabilone. Eleven metabolites exhibited upregulated
trends, including the key intermediates of lipid remodeling
(lysophosphatidylcholine 18:1) and amino acid derivatives
(N-acetylglutamine).
Table 9.
High-confidence differential metabolites.
Metabolite Name log2 FC p-Value VIP Value Trend
HD vs. CK LP vs. CK HD vs. CK LP vs. CK HD vs. CK LP vs. CK
Artemether 0.98 2.98 <0.05 <0.01 1.29 1.90 ↑
Pyropheophorbide A 2.00 6.93 <0.01 <0.01 1.97 1.90 ↑
cis-11,14,17-Eicosatrienoic acid −1.12 −2.59 <0.05 <0.01 1.22 1.66 ↓
Fumaroylcarnitine 0.81 2.81 <0.01 <0.01 1.69 1.86 ↑
Formylfuranobenzopyrone 1.31 6.28 <0.03 <0.01 1.43 1.90 ↑
Thermocidin −0.56 −3.26 <0.01 <0.01 2.09 1.93 ↓
L-β-Homoserine 0.59 1.57 <0.01 <0.01 1.96 1.84 ↑
Azithromide 0.91 4.37 <0.01 <0.01 1.98 1.81 ↑
Hydroxyethylsulfonic acid 0.87 5.97 <0.01 <0.01 1.86 1.96 ↑
D-Gulonic acid gamma-lactone 1.03 4.77 <0.01 <0.01 2.06 1.96 ↑
2-Deoxy-beta-L-erythro-pentafuranose 1.15 4.35 <0.01 <0.01 2.32 1.95 ↑
Nabilone −1.52 −3.90 <0.01 <0.01 2.36 1.88 ↓
3-Phenethyl-2′-deoxyuridine 2.13 10.88 <0.01 <0.01 1.77 1.95 ↑
4-Fluoro-3-phenoxybenzoic acid 3.70 5.81 <0.01 <0.01 2.53 1.91 ↑
[99]Open in a new tab
↑ indicates upregulation, ↓ indicates downregulation HD vs. CK: High
Dose vs. Control. LP vs. CK: Low Dose vs. Control; FC: Fold Change;
VIP: Variable Importance in Projection. Note: Some metabolite names
have been translated using standard IUPAC nomenclature and common
biochemical naming conventions.
3.8. Correlation Analysis of Differential Microbial Genera, Metabolites, and
Production Performance in H. discus hannai
To elucidate the potential mechanisms by which LP-fermented feed
modulates abalone production performance through intestinal
microbiota–metabolite crosstalk, a Spearman correlation analysis was
conducted between the differential bacterial genera, metabolites,
growth indices (total weight gain rate), and survival rate. The
association heatmap ([100]Figure 6) reveals distinct interaction
patterns, where the red and blue regions denote positive and negative
correlations, respectively, reflecting the potential microbial
regulation of metabolic pathways and host physiology.
Figure 6.
[101]Figure 6
[102]Open in a new tab
Correlation analysis of differential bacterial genera with other
indexes. * p < 0.05, ** p < 0.01.
4. Discussion
This study multidimensionally evaluates the effects of soybean meal
fermented with Lactobacillus plantarum (LP) on feed quality, growth
performance, intestinal health, and metabolic regulation in abalone (H.
discus hannai). The results demonstrate that LP fermentation
significantly enhanced key fatty acids in soybean meal, including a
two-fold increase in cis-9-palmitoleic acid and a 14% elevation in
α-linolenic acid (ALA), alongside notable improvements in amino acids
such as L-histidine and L-asparagine.
Critically, these nutritional enhancements translated to significant
biological outcomes. Feeding with LP-fermented feed markedly improved
abalone growth rate (263% vs. 201% in the control group, CK) and
survival rate (61.66% vs. 45.33% in CK). Beyond growth metrics,
mechanistic investigations revealed profound effects on intestinal
physiology: LP fermentation enhanced intestinal morphology (elongated
villi and structural integrity), upregulated tight junction proteins
(Claudin and ZO-1), suppressed inflammatory cytokines (TNF-α, IL-16),
and activated antioxidant pathways (Keap1 downregulation), collectively
optimizing intestinal health.
Further elucidating the drivers of improved health and growth, LP-fed
abalone exhibited increased gut microbiota diversity, elevated
abundances of Mycoplasma and Ochrobactrum, and differential metabolites
enriched in tryptophan metabolism and the pantothenate/CoA biosynthesis
pathways, revealing a potential microbiota–metabolism axis driving
growth promotion.
Positioning these findings within existing knowledge, this study aligns
with prior research on probiotic-fermented feeds in aquaculture, but
also unveils novel insights. Regarding nutritional enhancement, earlier
studies consistently reported that microbial fermentation degrades
antinutritional factors in soybean meal and liberates free amino acids.
For instance, N. Muhamad Nor [[103]21] observed significant increases
in lysine and methionine content after lactic acid bacteria
fermentation, consistent with the marked elevation of L-histidine and
L-asparagine here. However, our work uniquely highlights the
paradoxical enrichment of ω-3 fatty acids (ALA) alongside DHA
reduction—a phenomenon seldom reported. This discrepancy may stem from
LP’s metabolic specificity: earlier studies suggested that lactic acid
bacteria preferentially degrade long-chain fatty acids via β-oxidation
[[104]22], potentially accelerating DHA catabolism, while ALA
accumulation could arise from bacterial biosynthesis or selective
substrate utilization [[105]23]. These nuanced findings underscore the
need for multi-omics approaches (e.g., lipidomics and transcriptomics)
to dissect fatty acid metabolic networks.
Transitioning to growth performance, while LP-fed abalone exhibited
higher weight gain than CK, it underperformed compared to the
seaweed-based HD group—a result partially congruent with [[106]24], who
demonstrated that fermented soybean meal could replace 30% of fishmeal
without compromising fish growth. The likely explanation for HD’s
superiority resides in its polysaccharide-rich matrix and trace element
content, unmatched by fermented feeds. Notably, a key advantage of the
LP diet emerged in efficiency: the significantly lower feed conversion
ratio (FCR) implies enhanced nutrient utilization, a mechanism
plausibly linked to the observed intestinal remodeling and microbiota
modulation. For example, villus elongation directly expands nutrient
absorption surfaces [[107]25], while upregulated tight junction
proteins fortify barrier integrity, mitigating leaky gut risks
[[108]26]. These observations not only corroborate the “gut
health–nutrient utilization” axis proposed in probiotic studies, but
extend it by integrating structural, immunological, and microbial
dimensions—a holistic perspective that was previously absent.
The gut microbiota findings further reveal both consonance and
divergence from the literature. The marked enrichment of Tenericutes
and Mycoplasma in LP-fed abalone contrasts with Ifra Ghori et al.
(2022) [[109]27], who reported the probiotic-driven suppression of
conditional pathogens in fish. Significantly, the 14-fold surge in
Ochrobactrum—a genus renowned for xenobiotic degradation
[[110]28]—suggests its role in detoxifying feed-derived contaminants,
indirectly bolstering host health. Nevertheless, comparative analysis
highlights a challenge: the higher OTU diversity in the HD group
underscores the ecological superiority of natural diets in maintaining
microbial stability, urging the future exploration of hybrid feeding
strategies.
Metabolomic insights add another layer of understanding, delineating
the multidimensional regulatory effects of LP fermentation.
Differential metabolites enriched in the tryptophan metabolism
(kynurenine pathway) align with suppressed TLR4/NF-κB signaling,
implying that tryptophan derivatives mitigate intestinal inflammation—a
mechanism known in mammals [[111]29], now extended to mollusks.
Crucially, the upregulation of artemotil and isethionic acid,
correlated with Wolinella abundance and growth metrics, provides direct
evidence for a “microbiota–metabolite–host” axis. This multi-omics
integration transcends conventional single-parameter analyses, offering
unprecedented resolution into probiotic mechanisms—an advancement
addressing critical gaps in earlier aquaculture research.
Finally, this study identifies some key limitations that point to
future directions: the metabolic mechanism driving DHA reduction
remains unclear, potentially addressable by stable isotope tracing; the
absence of long-term assessments on reproductive performance
necessitates extended trials; and the lack of functional gut microbiota
profiling (metagenomics) limits causal insights into
microbiota–metabolite interactions, highlighting the need to integrate
metagenomics with metabolic flux studies. And the potential for
enhanced resistance against specific pathogens like Vibrio harveyi
requires validation through dedicated challenge trials. Additionally,
exploring yeast co-fermentation strategies may further optimize fatty
acid composition and feed’s nutritional value.
5. Conclusions
This study multidimensionally deciphers how LP-fermented soybean meal
promotes abalone growth through three synergistic mechanisms: nutrient
fortification, intestinal health optimization, and
microbiota–metabolome network remodeling. These findings advance the
theoretical framework for probiotic applications in aquaculture by
establishing quantitative correlations between fermentation-induced
nutritional shifts (ALA accumulation) and host physiological responses.
Specifically, this study provides actionable insights for sustainable
feed development, including optimizing fermentation parameters to
enhance GABA production for improved stress resistance and modulating
microbiota-metabolite crosstalk to promote growth efficiency.
Author Contributions
Z.L.: conceptualization, methodology, software, investigation, formal
analysis, writing—original draft, writing—review and editing; L.K. and
C.H.: data curation, writing—original draft; S.P.: visualization and
investigation; M.Z.: resources and supervision; F.L.:
conceptualization, funding acquisition, resources, supervision. All
authors have read and agreed to the published version of the
manuscript.
Institutional Review Board Statement
This study was conducted in accordance with the Declaration of Helsinki
and was approved by the Ethical Committees of institute of
Biotechnology, Fujian Academy of Agricultural Sciences
(BI-AEC-20230307) on 7 March 2023.
Data Availability Statement
The datasets presented in this study can be found in online
repositories.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
The authors declare that financial support was received for the
research, authorship, and/or publication of this article. We are
grateful for the financial support from Fujian Provincial Special Fund
Project for Promoting the High-quality Development of Marine and
Fishery Industry in 2023 (FJHYF-L-2023-7).
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data
contained in all publications are solely those of the individual
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and/or the editor(s) disclaim responsibility for any injury to people
or property resulting from any ideas, methods, instructions or products
referred to in the content.
References