A probiotic protein v2016 and its use in the preparation of a medicament for treating obesity

Abstract

This invention discloses a probiotic protein V2016 and its application in the preparation of drugs for treating obesity, belonging to the field of biomedical technology. This invention provides a probiotic protein V2016, the amino acid sequence of which is shown in SEQ ID NO.1. Verification has shown that protein V2016 can efficiently and stably bind to the intestinal receptor ICAM-2, thereby significantly upregulating GLP-1 expression, mediating weight loss, reduced fat accumulation, and improved glucose tolerance; simultaneously, it can also reduce hepatic lipid deposition and inflammation, and promote thermogenesis activation in adipose tissue; at the intestinal microecological level, protein V2016 can improve the intestinal microenvironment, promote the enrichment of butyrate-producing bacteria in the intestine, thereby playing a role in regulating metabolism. In summary, the protein V2016 provided by this invention can be applied to the development of novel functional foods or weight-loss drugs.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a probiotic protein V2016 and its application in the preparation of drugs for treating obesity. Background Technology

[0002] Obesity and its associated metabolic disorders, including insulin resistance and cardiovascular metabolic complications, have become a global epidemic, imposing a significant economic burden on public health systems. Currently used weight-loss drugs generally suffer from problems such as single-target effects and significant side effects. Developing novel, safe, and effective metabolic regulators has significant clinical and economic value. Given the crucial role of gut microbiota in the development of obesity, probiotic therapy—which involves supplementing the body with exogenous beneficial microorganisms to correct dysbiosis and restore metabolic homeostasis—has become an important direction in obesity intervention research.

[0003] Akkermansia myxophilus ( Akkermansia muciniphila AKK bacteria can prevent diet-induced obesity without affecting appetite and eating habits by altering adipose tissue metabolism and intestinal permeability. It has the potential to be a next-generation probiotic drug.

[0004] P9 protein is an 84 kDa protein secreted by AKK bacteria. It interacts with intercellular adhesion molecule 2 (ICAM-2) of intestinal epithelial cells, thereby inducing intestinal cells to secrete glucagon-like peptide-1 (GLP-1). By promoting thermogenesis in brown adipose tissue, it achieves the purpose of weight loss and can be used as a target for the treatment of metabolic diseases.

[0005] As a hot topic in probiotic research, the discovery of its related functional proteins remains to be explored. Currently, apart from the first reported P9 protein from a standard strain, only one study documented its potential use in alleviating prostate inflammation and fibrosis; no further reports on P9 protein have been found. With the development of metagenomics, a large number of AKK bacterium genomes have been successfully assembled; however, whether these genomes encode more active or functionally differentiated P9 homologous proteins remains unclear and requires systematic analysis and experimental confirmation. Furthermore, while classic probiotic therapy offers a safe and potentially safe microbiome-based approach to obesity intervention, its limited efficacy, unclear mechanisms, and unstable colonization restrict its potential as a first-line or core treatment strategy. This situation prompts research to move towards greater precision: on the one hand, to delve deeper into next-generation probiotics with clear and powerful metabolic regulatory functions; on the other hand, to move beyond the concept of live bacteria and explore the direct application of their active metabolites or key components. Therefore, in-depth research into the genomes of animal gut microbiota, and the discovery of safer protein formulations that can upregulate the expression of key metabolic regulatory genes, promote thermogenesis in adipose tissue, and thus exert a weight-loss effect, has significant scientific value and practical production implications. Summary of the Invention

[0006] The purpose of this invention is to provide a probiotic protein V2016 and its application in the preparation of drugs for treating obesity, so as to solve the problems existing in the prior art. The protein V2016 discovered by this invention can inhibit fat formation, promote the expression of GLP-1, UCP1, and PGC-1α thermogenic genes, enrich butyrate-producing bacteria, regulate intestinal microbial levels, achieve multiple effects, and can be applied to the development of novel functional foods or weight loss drugs.

[0007] To achieve the above objectives, the present invention provides the following solution: The present invention provides a probiotic protein V2016, the amino acid sequence of which is shown in SEQ ID NO.1.

[0008] The present invention also provides the use of the probiotic protein V2016 in the preparation of a medicament for treating obesity.

[0009] Optionally, the probiotic protein V2016 can improve glucose tolerance.

[0010] Optionally, the probiotic protein V2016 can reduce lipid deposition and inflammation in the liver.

[0011] Optionally, the probiotic protein V2016 can promote thermogenic activation of adipose tissue.

[0012] Optionally, the probiotic protein V2016 can improve the gut microenvironment and correct obesity-related gut microbiota imbalance.

[0013] The present invention also provides the use of the probiotic protein V2016 in the preparation of health products that help control body fat.

[0014] The present invention also provides a medicament for treating obesity, the medicament comprising the probiotic protein V2016.

[0015] Optionally, the drug may also contain pharmaceutically acceptable excipients.

[0016] The present invention also provides a health product that helps control body fat, the health product comprising the probiotic protein V2016.

[0017] The present invention discloses the following technical effects: This invention identifies a novel candidate protein, V2016, from *Akkermansia myxophilus* using bioinformatics techniques, and validates it through in vivo and in vitro experiments. Results show that protein V2016 can efficiently and stably bind to the intestinal receptor ICAM-2, thereby significantly upregulating the expression of glucagon-like peptide-1 (GLP-1), mediating weight loss, reduced fat accumulation, and improved glucose tolerance. It also reduces hepatic lipid deposition and inflammation, and promotes thermogenesis in adipose tissue. At the gut microbiota level, protein V2016 can improve the gut microenvironment, promote the enrichment of butyrate-producing bacteria in the gut, and thus play a role in regulating metabolism. In summary, the protein V2016 discovered in this invention can inhibit fat formation, promote the expression of GLP-1, UCP1, and PGC-1α thermogenic genes, enrich butyrate-producing bacteria, and regulate the gut microbiota, achieving multiple effects. It can be applied to the development of novel functional foods or weight-loss drugs.

[0018] Meanwhile, the protein V2016 provided by this invention is a protein preparation, which, compared with bacterial cells, has the characteristics of being natural and non-toxic, and has broad application prospects. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A phylogenetic analysis diagram of Akkermansia based on genome annotation assembled from gut metagenomics; Figure 2 Phylogenetic analysis diagram of 240 candidate P9 proteins; Figure 3Visualize the three-dimensional structure and molecular docking results of the P9 protein; Figure 4 Visualize the three-dimensional structure and molecular docking results of protein V2016; Figure 5 The graph shows the root mean square deviation (RMSD) results for protein V2016. Figure 6 Figure 1 shows the SASA analysis results for the radius of rotation Rg and the solvent-accessible surface area. Figure 7 The root mean square fluctuation (RMSF) of protein V2016 residues. Figure 8 The graph shows the evolution trajectory of the ligand binding distance of protein V2016 and the dynamic fluctuations in the number of hydrogen bonds. Figure 9 Principal component analysis (PCA) and free energy landscape results; Figure 10 Prokaryotic expression and purification of protein V2016; M: protein molecular weight standard; Lane 1: DE3 expression; 2: uninduced protein; 3: induced protein; 4: total induced protein in the supernatant after sonication; 5: total induced protein in the precipitate after sonication; 6: purified protein; Figure 11 The image shows the results of cell experiments on protein V2016. Figure 12 This is a dose-dependent experimental diagram of protein V2016; Figure 13 The graph shows the changes in body weight and oral glucose tolerance (OGTT) results in mice after intervention with protein V2016. Figure 14 The results of morphological observation and lipid droplet quantitative analysis of H&E staining of liver tissue pathological sections; Figure 15 To determine the expression levels of inflammatory factors IL-6 and TNF-α in liver tissue using qPCR; Figure 16 To determine the expression levels of the thermogenic factors GLP-1, PCSK-1, and gcg, the metabolic factor PCSK-2, and the inflammatory factors IL-1 and IL-6 in ileal tissue by qPCR; Figure 17 The weight of white adipose tissue and brown adipose tissue and their mass ratio (BAT / WAT). Figure 18 Morphological observation and quantitative analysis of lipid droplets after H&E staining of pathological sections of white adipose tissue (left image) and brown adipose tissue (right image); Figure 19 The expression levels of the thermogenic factors PRDM16 and UCP1 in brown adipose tissue were determined by qPCR. Figure 20 The expression levels of Cidea and PGC-1α, adipogenesis / mitochondrial biosynthesis factors in brown adipose tissue, were determined by qPCR. Figure 21 Abundance of gut microbiota in mice treated with protein V2016; Figure 22 Chao1 index, Pielou index, Shannon index, and Simpson index of mouse populations treated with protein V2016; Figure 23 Principal coordinate analysis (PCoA) based on Bray-Curtis distance for mouse populations treated with protein V2016. Figure 24 Figure showing the results of linear discriminant analysis (LDA > 4) of LEfSe in mice treated with protein V2016. Detailed Implementation

[0021] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0022] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0023] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0024] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0025] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0026] Example 1 Screening of protein V2016 1. Data screening of Akkermansia myxophilus 1.1 Gut metagenomic database annotation Gut metagenomic data were collected, and the acquired data were annotated and classified. The GTDB-Tk tool (version 2.3.3) was used for phylogenetic delineation, and the "skip_ani_screen" parameter was set to improve computational efficiency, ensuring that each assembled genome was accurately classified to the species-level taxonomic unit, thereby effectively distinguishing closely related species. Genera annotated as Akkermansia myxophilus were selected for further screening.

[0027] By integrating publicly available AKK strain genomes and performing redundancy-free clustering using dRep (threshold ≥99.5%), a high-quality, non-redundant dataset containing 2725 genomes was constructed, covering multiple species including *A. muciniphila*, *A. massiliensis*, and *A. biwaensis*. Figure 1 ).

[0028] 1.2 Hypothesis Protein Screening A library was constructed using existing P9 data. AKK sequences obtained through Prodiga translation were screened, and Blastp was used to align with the P9 protein database, selecting sequences with sequence similarity >30%. Subsequently, dRep software (version 2.00, sequence consistency threshold set to >95%) was used to cluster and remove redundancy from all predicted protein sequences, ultimately obtaining 240 high-confidence candidate P9 homologous protein sequences. Phylogenetic analysis ( Figure 2 The results show that these candidate proteins do not cluster in the evolutionary branch (B-P9) to which the known P9 protein belongs, but are mainly distributed in other P9-like protein genome groups (corresponding to groups C, D, and E) with an average nucleotide identity (ANI) of 80-95%. This indicates that they are not orthologs of the known P9 protein, and likely represent a novel protein family within the genus AKK that has not yet been functionally identified, possessing independent evolutionary and functional significance.

[0029] 2. Bioinformatics analysis of the target protein 2.1 Bioinformatics Analysis Phylogenetic trees were constructed using iqtree with the AKK dataset and the putative protein sequence set; the three-dimensional structure of the putative protein was predicted using RoseTTAFold; the molecular docking binding energy between intercellular adhesion molecule 2 (ICAM-2) and the putative protein was predicted using HDOCK; and the docking site was visualized using PyMOL. Protein sequences with docking scores higher than those of the standard P9 protein, such as V2016, were selected. Figures 3-4 (Table 1).

[0030] Table 1. Molecular docking binding energy scores of outer membrane proteins V2016 and P9 with ICAM-2 2.2 Molecular Dynamics Simulation Molecular dynamics simulations were performed on the GROMACS 2023 software platform, using the CHARMM36 force field and TIP3P water model. After energy minimization and stepwise equilibrium were achieved, the system underwent a 100 ns production simulation in a non-thermo-barrier (NPT) system, with the simulation temperature and pressure maintained at 300 K and 1 bar, respectively. To assess the stability and conformational changes of the system during the simulation, the root mean square deviation of the protein backbone atoms was calculated to monitor the overall structural drift; the radius of gyration was used to examine the compactness of the complex; and the solvent exposure dynamics of the protein surface were analyzed based on the solvent-accessible surface area. Furthermore, principal component analysis was performed on the C-α atom trajectories after removing translational and rotational motions to extract their dominant collective motion patterns. For interaction analysis, hydrogen bonds were identified using geometric criteria, i.e., the distance between donor and acceptor atoms ≤ 3.5 Å and the bond angle ≥ 120°. Simultaneously, the hydration characteristics and dynamic hydration patterns around the complex interface were further examined by tracking the trajectories of water molecules.

[0031] The results show that the system remains stable overall during the simulation and exhibits dynamic bonding characteristics. Root mean square deviation (RMSD) analysis of the backbone atoms shows that... Figure 5 The system exhibits multiple stable conformational substates, and the overall system tends to converge and reach equilibrium in the later stages of the simulation. The radius of gyration Rg and the solvent-accessible surface area SASA (… Figure 6 The fluctuations are gentle, exhibiting dynamic binding characteristics. The root mean square fluctuations of the residues ( Figure 7 The data shows a highly flexible region around 250, suggesting its potential involvement in conformational recognition and rearrangement. The evolution trajectory of ligand binding distance (…) Figure 8 The results showed that the complex stabilized at 2.25 nm after 20 ns, and the number of hydrogen bonds fluctuated dynamically, reaching a peak of 10 at 80 ns, indicating continuous conformational optimization at the binding interface. Principal component analysis and free energy landscape ( Figure 9This further reveals the existence of four main conformational substates in the system, confirming the conformational selectivity and polymorphism of its binding process. Furthermore, the active site hydration network undergoes dynamic rearrangement during binding; changes in SASA indicate a process from dehydration to partial rehydration, which contributes to the eventual stability of the complex.

[0032] Example 2: Prokaryotic expression of the target protein V2016 1. Ligation of the target gene and the vector To construct the pET-28a(+)V2016 recombinant expression vector, primers P9-F1, P9-R1, V2016-F1, V2016-R1, V2016-F2, and V2016-R2 were designed with EcoRI and BamHI restriction sites (Table 2).

[0033] For the target sequence, polymerase chain reaction (PCR) was used for amplification. The total reaction volume was 10 μL: 5 μL 5-fold buffer, 1 μL bacterial template, 0.5 μL each of forward and reverse primers, and sterile water was added to make up the volume. The reaction tubes were placed in a PCR instrument and the following program was run: 95℃ pre-denaturation for 5 min; 95℃ denaturation for 30 s, 58℃ annealing for 30 s and 72℃ extension for 1 min, for 35 cycles; and 72℃ extension for 10 min.

[0034] After the reaction, the sample was verified on a 1% agarose gel. The target gene was then obtained using a gel extraction kit (Sangon Biotech (Shanghai) DNA extraction kit). The target gene and vector (pET-28a(+)) were double-digested with restriction endonucleases EcoRI and BamHI. After digestion, the digested DNA was recovered, and the concentration of the recovered product was measured using a Nanodrop 2000. The ligation system was calculated based on the digested product concentration and a vector:target gene ratio of 3:1. After mixing, ligation was performed at 16℃ for 2 hours.

[0035] Table 2 Primer sequences (H is human, M is mouse). 2. Transformed expression strain The ligated recombinant plasmid pET-28a(+)V2016 was transformed into *E. coli* strain DH5α. Prepared competent cells were immediately placed on ice. The ligation product (no more than 1 / 10 of the transformation volume) was added to the competent cells, gently mixed, and placed on ice for 45 min. The ligation system was then heat-shocked in a 42°C water bath for 90 s, and quickly transferred to ice for 3-5 min. 1 mL of LB broth was added to the competent cells, and the cells were cultured at 37°C with shaking for 1 h. After 1 h, the cells were centrifuged at 8000 rpm for 15 min. The supernatant was discarded, leaving approximately 100 µL of culture medium in a tube. The culture medium and precipitate were mixed thoroughly, and an appropriate amount of culture was spread onto LB broth containing Kansas protein. + In solid culture medium, place the culture upright for 30 min, then incubate at 37°C for no more than 17 h. Pick colonies and place them in 1 mL of LB (containing Kansas) solution. + In EP tubes containing Kansas medium, incubate on a shaker for 4 h, then inoculate into 20 mL LB (containing Kansas medium) + Incubate overnight in liquid culture medium; for bacterial preservation: add 500 µL of bacterial culture to 500 µL of 50% glycerol, mix well, and store at -80℃. Use a plasmid miniprep kit to extract recombinant plasmids from the cloning strain DH5a and transform them into Escherichia coli strain DE3 for expression.

[0036] 3. Inducing protein expression The expressed DE3 strain was inoculated into 20 mL LB (containing Kans) + Incubate overnight in liquid culture medium, then take 4 mL of the overnight culture and add it to 400 mL LB (containing Kans) + In liquid culture medium, incubate at 37°C in a shaker until the logarithmic growth phase (OD50). 600 =0.6~0.8), add inducer (0.1‰ IPTG), and induce expression at 37℃ for 18 h. Centrifuge the induced bacterial cells at 4℃, 8000 rpm for 10 min, collect the bacterial cell pellet, wash with PBS, add 20 mL of non-denaturing lysis buffer (50 mM Tris and 500 mM NaCl, adjusted pH to 7.5 with hydrochloric acid), resuspend, incubate overnight at 4℃, and then use an ultrasonic cell disruptor to obtain the lysate at 40% power for a total time of 30 min (3 s working / 5 s rest).

[0037] 4. Protein purification Centrifuge the dissolved material at low speed to collect the supernatant. Filter the supernatant through a 0.22 μm filter membrane. Add 10 volumes of ddH2O to wash the nickel column to remove the 20% ethanol used to store the nickel column. Add 10 volumes of elution buffer and washing buffer sequentially. Add 5 mL of the filtered supernatant. Continue to add 10 volumes of washing buffer to remove impurities. Collect the effluent as a control. Add 10 volumes of elution buffer to elute the target protein and collect the effluent. Detection was performed using 12% SDS-PAGE electrophoresis. Bacterial samples were centrifuged at 12,000 rpm for 1 min, the supernatant was removed, and 40 μL ddH2O and 10 μL 5× protein loading buffer were added. The mixture was then incubated in a boiling water bath for 10 min. During loading, the following were added in sequence: protein marker, uninduced DE3-expressing bacterial culture, uninduced protein, induced protein, total induced protein in the supernatant after sonication, total induced protein in the precipitate after sonication, and purified protein V2016. Electrophoresis was performed at 80 V for 30 min at room temperature, followed by electrophoresis at 120 V for 100 min. Visualization was achieved by Coomassie brilliant blue staining.

[0038] SDS-PAGE results ( Figure 10 The results showed that the target protein V2016, with a size of approximately 76 kDa (the standard P9 protein is approximately 84 kDa), was successfully obtained in high purity. The amino acid sequence of protein V2016 is shown in SEQ ID NO.1.

[0039] SEQ ID NO.1: .

[0040] Example 3: In vitro functional assay of protein V2016 1. Protein treatment The protein solution obtained by concentration and purification through an ultrafiltration tube (10 kDa) was then added PBS solution three times to replace the protein in the PBS solution. Finally, the concentration was measured using a BCA protein concentration measurement kit (Sangon Biotech (Shanghai)) and the protein solution concentration was determined to be 1 μg / μL with PBS. The solution was then stored at -20℃ for later use.

[0041] 2. Measurement of GLP-1 expression levels H716 human colon cancer cells were seeded into 12-well plates, with 6 × 10⁶ cells per well. 5Cells were cultured in RPMI 1640 medium (containing 10% fetal bovine serum and 1% penicillin-streptomycin) at 37°C and 5% CO2. When the cells reached 80%–90% confluence, they were starved for 2 h, then the medium was changed, and 2 mL of protein V2016 at a concentration of 50 μg / mL was added for 2 h. A blank control group and a P9 protein group (Yoon HS, Cho CH, Yun MS, et al. Akkermansia muciniphila secretes a glucagon-like peptide-1-inducing protein that improves glucose homeostasis and ameliorates metabolic disease in mice[J]. Nature Microbiology, 2021, 6(5): 563–573.) were set up as controls. Cells were collected by centrifugation, RNA was extracted, reverse transcribed, and the secretion of GLP-1 was detected by qPCR (with GAPDH as an internal control, primers are shown in Table 2).

[0042] The qPCR reaction system consisted of 10 μL: 5 μL 2×SYBR Green premix (Sangon Biotech (Shanghai)), 0.15 μL each of GLP-1 upstream and downstream primers, 1 μL cDNA template, and nuclease-free water to make up the difference. qPCR reaction program: 95℃ pre-denaturation for 2 min, 95℃ denaturation for 20 s, 58℃ annealing and extension for 20 s, 60 cycles (fluorescence signal is collected during this stage), and a melting curve is generated after the reaction to continuously collect fluorescence signal to verify the specificity of the product. Finally, based on the Ct values ​​recorded by the instrument and combined with the internal reference gene, through 2 -ΔΔCt The algorithm performs relative quantitative analysis of the expression levels of the target gene. The results show that, compared with the standard P9 protein, V2016 has the most significant induction effect on GLP-1 expression. Figure 11 (p<0.0001).

[0043] 3. Dose-dependent assay of protein V2016 H716 human colon cancer cells were seeded into 12-well plates, with 6 × 10⁶ cells per well. 5Cells were cultured in RPMI 1640 medium (containing 10% fetal bovine serum and 1% penicillin-streptomycin) at 37°C and 5% CO2. When the cells reached 80%–90% confluence, they were starved for 2 hours, followed by medium replacement with 2 mL of protein V2016 at concentrations of 50, 75, 100, 125, and 150 μg / mL, respectively, for 2 hours. Cells were centrifuged to collect RNA, which was then reverse transcribed. qPCR was used to detect GLP-1 secretion levels (reaction system and procedure as described in "2. Measurement of GLP-1 Expression").

[0044] The results showed that GLP-1 expression levels gradually increased with increasing V2016 concentration, exhibiting a significant dose-dependent effect, and reaching a peak induction effect at 100 μg / mL (p<0.01). However, when the concentration exceeded this threshold, the promoting effect did not continue to increase, but instead slightly decreased, suggesting that this effect may be due to receptor saturation or intracellular feedback regulation mechanisms. Figure 12 ).

[0045] Example 4: In vivo functional assay of protein V2016 1. Mouse glucose tolerance test Eight-week-old male C57BL / 6 mice (initial weight 22±2 g, n=24) were randomly divided into three groups (n=8). Treatment was administered via gavage: a normal control group (standard maintenance diet), a high-fat diet model group (HFD+PBS 100 μL / mouse / day), and a protein intervention group (protein V2016 1 μg / μL / 100 μL / mouse / day). Administration lasted for 45 days.

[0046] Mice were fasted for 12 hours before the experiment (with free access to water). After weighing their fasting body weight, the dosage volume was calculated based on a glucose dose of 2 g / kg. Mice were administered 20% glucose solution by gavage (time recorded as 0 min). Blood samples were collected via tail vein sampling at 0 min (fasting baseline) and at 15, 30, 60, 90, and 120 min after administration. Blood glucose concentrations at each time point were measured using a portable blood glucose meter and accompanying test strips. Throughout the experiment, the ambient temperature was kept constant to minimize stress interference with blood glucose levels.

[0047] 2. Mouse experiment Eight-week-old male C57BL / 6 mice (initial weight 22±2 g, n=24) were randomly divided into three groups (n=8). The mice were administered the diet via gavage: a normal control group (standard maintenance diet), a high-fat diet model group (HFD+PBS 100 μL / mouse / day), and a protein intervention group (protein V2016 1 μg / μL / mouse / day). All mice were kept in an SPF-grade environment and fed a maintenance diet for one week, followed by a 45-day experiment. Body weight changes were monitored every 6 days.

[0048] At the end of the experiment, mice were euthanized by cervical dislocation, with 5 individuals randomly selected from each group. Liver, adipose tissue, and ileum were isolated and accurately weighed. Partial portions of liver, brown adipose tissue, and white adipose tissue were fixed in 4% paraformaldehyde and then paraffin-embedded, sectioned, and subjected to H&E staining analysis by a professional institution.

[0049] Simultaneously, 50-100 mg of tissue samples were weighed, ground in liquid nitrogen, and total RNA was extracted using the TRIzol method. RNA quality was verified by NanoDrop and agarose gel electrophoresis. 260 / A 280 Qualified samples with expression values ​​between 2.0 and 2.2 were reverse transcribed into cDNA using a PrimeScript RT kit. Using β-actin as an internal control, the expression levels of target genes (IL-6, TNF-α, GLP-1, PCSK1, gcg, PCSK2, IL-18, PRDM16, UCP1, Cidea, and PGC-1α) were detected by SYBR Green qRT-PCR. Data were analyzed using a 23... -ΔΔCt The method is used for relative quantitative analysis.

[0050] The primers are listed in Table 2. The reaction system and procedure for qRT-PCR are the same as in "2. Measurement of GLP-1 expression".

[0051] The results showed that, compared with the high-fat diet group, the protein V2016 treatment group significantly slowed down the rate of weight gain (p<0.05), demonstrating a clear weight loss effect. In the glucose tolerance test, the blood glucose response curve of the protein V2016 treatment group was closer to that of the normal control group, indicating that it has a positive effect on improving glucose homeostasis. Figure 13 ).

[0052] Liver histological analysis ( Figure 14 The results showed that lipid droplet accumulation in hepatocytes of mice treated with V2016 was significantly less than that in the high-fat diet group. Simultaneously, the expression levels of the key pro-inflammatory cytokine IL-6 and TNF-α in the liver of this group were significantly reduced, indicating that V2016 helps alleviate liver inflammation induced by a high-fat diet. Figure 15In ileal tissue, protein V2016 treatment significantly increased GLP-1 expression levels and also significantly upregulated the gene expression of its processing enzyme (PCSK1) and key synthetic precursor (gcg), but had no significant effect on the transcriptional levels of PCSK2, IL-18, and IL-6. Figure 16 ).

[0053] Next, the weights of brown and white adipose tissue in the three groups of mice were systematically assessed. The results showed that the weights of both types of adipose tissue were significantly reduced in the protein-treated group. Further analysis of the adipose tissue percentage revealed a significant difference between the treatment group and the high-fat diet group, while the percentage was similar to that of the normal control group. Figure 17 ).

[0054] Morphological analysis and lipid droplet area quantification of adipose tissue sections showed that the lipid droplet size in the white adipose tissue of the protein-treated group mice was significantly reduced; the lipid droplets in their brown adipose tissue also showed a similar shrinkage trend, and the phenotype of both adipose tissues was closer to that of the normal group. Figure 18 ).

[0055] At the molecular level, the expression levels of key thermogenesis and differentiation-related genes in brown adipose tissue were detected by quantitative PCR. The results showed that V2016 treatment significantly upregulated the expression levels of the brown adipose tissue marker PRDM16 and the key thermogenesis factor UCP1. Figure 19 In addition, decreased expression of the lipid droplet-associated protein Cidea and increased expression of the mitochondrial biosynthesis regulator PGC-1α were also detected. Figure 20 ).

[0056] Example 5: Intestinal function assay of protein V2016 1. Extraction and quality testing of total DNA from gut microbiota Using EZNA ® Following strict adherence to the instructions, total microbial community DNA was extracted from 50 mg fecal samples of the three groups of mice in Example 4 using the Soil DNA Kit, yielding purified DNA solutions. DNA concentration and purity were determined using NanoDrop. 260 / A 280 The ratio was determined and DNA integrity was detected by 1% agarose gel electrophoresis.

[0057] 2. PCR amplification and library construction of 16S rRNA gene amplicon Using qualified genomic DNA as a template, PCR amplification of the V3-V4 hypervariable region of the 16S rRNA gene was performed using 341F and 806R specific primers (see Table 2).

[0058] The reaction mixture consisted of 25 μL: 12.5 μL of 2×KAPA HiFi HotStart ReadyMix, 0.5 μL each of forward and reverse primers (10 μM), 10 ng of template DNA, and sterile water to make up the volume. The PCR reaction program was: 95℃ pre-denaturation for 3 min; 95℃ denaturation for 30 s, 55℃ annealing for 30 s, 72℃ extension for 30 s, for 25 cycles; and a final extension at 72℃ for 5 min. Three parallel reactions were performed for each sample to increase amplification uniformity.

[0059] PCR products were recovered and purified using AxyPrepDNA to remove primer dimers and non-specific products. Equal volumes of purified amplicon products from each sample were mixed to construct sequencing libraries. Fragment size distribution of the libraries was quality controlled using an Agilent 2100 bioanalyzer.

[0060] 3. High-throughput sequencing After the DNA samples pass amplification and testing, the PCR products are mixed and purified. Then, the library preparation process includes end repair, A-tailing, sequencing adapter addition, and purification. Once the libraries pass testing, different libraries are pooled according to their effective concentration and target data volume requirements before paired-end sequencing. The sequencing service is provided by a professional company (Novogene).

[0061] 4. Bioinformatics and Statistical Analysis The raw data from the microarray was first subjected to quality control: the qiime demux plugin was used to remove low-quality bases and adapter sequences, and the paired-end sequences were merged into a single sequence using FLASH software based on overlap. Subsequent analysis was mainly performed on the QIIME2 (Quantitative Insights Into Microbial Ecology, version 2022.8) platform.

[0062] (1) Sequence processing and OTU / ASV clustering: The deblur plugin was used to remove noise and chimeras to obtain high-precision amplicon sequence variants (ASVs). Species annotation of representative ASV sequences was performed based on the SILVA (Release 138.2) database.

[0063] (2) α diversity analysis: The Chao1 index (richness estimate), Pielou index (evenness), Shannon index and Simpson index (species diversity) were calculated, and the Kruskal-Wallis test was used to compare the differences between groups.

[0064] (3) β-diversity analysis: Differences in microbial community structure among samples were calculated based on Bray-Curtis distance and UniFrac distance (weighted and unweighted), and visualized using principal coordinate analysis (PCoA). Permutation multivariate analysis of variance (PERMANOVA, Adonis test) was used to assess the statistical significance of differences in community structure among groups.

[0065] Species composition and differential analysis: Community composition was analyzed at the phylum, class, order, family, and genus levels. LEfSe (Linear Discriminant Analysis Effect Size) analysis was used to identify biomarker species with significant intergroup differences (LDA score > 4.0, p < 0.05).

[0066] 5. Results The effects of different interventions on gut microbiota were assessed using a 16S rRNA gene sequencing system. Results are as follows: Figure 21As shown, firstly, the top 10 most abundant bacterial genera were analyzed at the genus level. In the control group, the gut microbiota was dominated by Muribculaceae_Incertae_Sedis (72.44%), followed by Lachnospiraceae_NK4A136_group (13.72%). Compared with the control group, HFD significantly altered the gut microbiota structure: the abundance of Ileibacterium, which is positively associated with obesity, increased approximately 3-fold (from 5.36% to 16.02%), and the abundance of Ligilactobacillus increased approximately 22-fold (from 0.94% to 20.84%), while the abundance of the beneficial bacteria Lachnospiraceae_NK4A136_group decreased by approximately 80% (from 13.72% to 2.71%), which may be related to metabolic disorders and inflammatory states under a high-fat diet. V2016 intervention effectively reversed the aforementioned changes: the abundance of *Ileibacterium* and *Ligilactobacillus* decreased by approximately 36% and 70% respectively compared to the HFD group (to 10.28% and 6.17%, respectively). Simultaneously, V2016 intervention also promoted the recovery and enrichment of beneficial bacterial genera. On the one hand, it significantly promoted the enrichment of *Dubosiella* (from 6.00% in the HFD group to 15.17%, an increase of approximately 1.5 times). On the other hand, it partially recovered the *Desulfovibrionaceae_Incertae_Sedis* (related to dietary fiber degradation, from 11.64% to 39.22%) and *Lachnospiraceae_NK4A136_group* (related to butyrate production, from 2.71% to 5.19%), which had been depleted due to HFD. These results suggest that V2016 may correct obesity-related gut microbiota imbalance by improving the gut microenvironment and regulating the microbial metabolic network.

[0067] Principal coordinate analysis (PCoA) based on Bray-Curtis distance showed significant separation in the overall gut microbiota structure of the three groups. The microbiota structure of the HFD group deviated significantly from that of the Control group, while the sample points of the V2016 group were distributed between the two and closer to the Control group, indicating that the V2016 intervention helped to restore the disordered microbiota structure to a normal state. There were no statistically significant differences among the groups in species richness (Chao1 index) and evenness (Pielou index). Figure 22 ).

[0068] Further identification of biomarkers with high discriminative power between groups was performed using LEfSe (LDA Effect Size) analysis. Results are as follows: Figure 23As shown, the differentially expressed microbiota between the HFD and V2016 groups exhibited a clustered distribution phylogenetically. The significantly enriched groups in the HFD group were mainly concentrated in different branches of the phylum Actinomycetota, including the order Bifidobacteriales (containing Bifidobacterium) and the order Coriobacteriales (containing Coriobacteriaceae_UCG-002). Furthermore, Faecalibaculum (containing Firmicutes) was also characteristically enriched in the HFD group. In contrast, the characteristic microorganisms enriched in the V2016 group were mainly Dubosiella and Anaerorotruncus, belonging to Firmicutes, which are located in different phylogenetic branches than the differentially expressed microbiota in the HFD group. (LDA value distribution map) Figure 24 This difference was further quantified: the signature groups of the HFD group (such as Coriobacteriaceae_UCG-002, Bifidobacterium, and Faecalibaculum) all had high LDA values ​​(LDA Score > 4.0). Meanwhile, the characteristic groups of the V2016 group, Dubosiella (LDA Score > 4.0) and Anaerorotruncus, also exhibited significant LDA values.

[0069] The above results strongly demonstrate that HFD and V2016 interventions create distinctly different characteristic microbiota, suggesting that V2016 may play a role in alleviating obesity by regulating specific microbial groups.

[0070] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A probiotic protein V2016, characterized in that, The amino acid sequence of the probiotic protein V2016 is shown in SEQ ID NO.

1.

2. The use of the probiotic protein V2016 as described in claim 1 in the preparation of a medicament for treating obesity.

3. The application as described in claim 2, characterized in that, The probiotic protein V2016 can improve glucose tolerance.

4. The application as described in claim 2, characterized in that, The probiotic protein V2016 can reduce lipid deposition and inflammation in the liver.

5. The application as described in claim 2, characterized in that, The probiotic protein V2016 can promote thermogenesis activation in adipose tissue.

6. The application as described in claim 2, characterized in that, The probiotic protein V2016 can improve the gut microenvironment and correct obesity-related gut microbiota imbalance.

7. The use of the probiotic protein V2016 as described in claim 1 in the preparation of health products that help control body fat.

8. A drug for treating obesity, characterized in that, The drug comprises the probiotic protein V2016 as described in claim 1.

9. The medicament as described in claim 8, characterized in that, The drug also contains pharmaceutically acceptable excipients.

10. A health supplement that helps control body fat, characterized in that, The health product contains the probiotic protein V2016 as described in claim 1.

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