Biomarker for predicting weight loss

Abstract

The present application relates to a biomarker for predicting weight loss in a subject scheduled to undergo gastric resection, the biomarker comprising bacteria of the genus Veilonella and / or bacteria of the genus Escherichia.

Description

Biomarkers for predicting weight loss

[0001] This paper relates to biomarkers for predicting weight loss.

[0002] Gastrectomy is a surgical procedure that removes all or part of the stomach. It can be used for various treatments, such as treating gastric cancer when a tumor is found in the stomach, or bariatric surgery to control weight over a short or long term by controlling the amount of food consumed.

[0003] In this regard, after gastrectomy, the stomach capacity is reduced, so undigested food can suddenly go down to the intestines after a meal, causing dumping syndrome such as nausea, vomiting, and malnutrition. In some patients, weight loss has been confirmed, but research is being conducted on ways to detect and prevent this in advance.

[0004] The technology underlying this invention is disclosed in U.S. Patent Publication No. 2019-0062811. While this patent discloses which bacterial species are dominant in a patient and promote weight loss, it does not identify the bacterial species mentioned in the present invention.

[0005] The present invention aims to solve the problems of the above-mentioned prior art and to provide a biomarker for predicting weight loss.

[0006] In addition, it is an object of the present invention to provide a composition capable of measuring the above biomarker.

[0007] However, the technical tasks that the embodiments of the present invention seek to achieve are not limited to the technical tasks described above, and other technical tasks may exist.

[0008] As a technical means for achieving the above-mentioned technical task, the first aspect of the present invention relates to a biomarker for predicting weight loss in a subject for gastrectomy, and specifically to a biomarker for predicting weight loss comprising a bacterium of the genus Veillonella and / or a bacterium of the genus Escherichia.

[0009] According to one embodiment of the present invention, the Veillonella genus bacteria and Escherichia genus bacteria may be contained in the organs of the subject or in the feces of the subject, but are not limited thereto.

[0010] According to one embodiment of the present invention, the organ of the subject may include, but is not limited to, bacteria of the genus Bacteroides and bacteria of the genus Prevotella.

[0011] According to one embodiment of the present invention, the organ may contain, but is not limited to, more bacteria of the genus Prevotella than bacteria of the genus Bacteroides.

[0012] According to one embodiment of the present invention, the number of bacteria of the genus Veillonella and the number of bacteria of the genus Escherichia may increase after gastrectomy, but is not limited thereto.

[0013] According to one embodiment of the present invention, the Veillonella bacterium and the Escherichia bacterium may be associated with health-related indicators of the subject, but are not limited thereto.

[0014] According to one embodiment of the present invention, the health-related indicator may include, but is not limited to, one selected from the group consisting of weight, body mass index (BMI), blood sugar, cholesterol, albumin, hemoglobin, protein, lymphocytes, white blood cells, and combinations thereof.

[0015] According to one embodiment of the present invention, the Veillonella genus bacteria may include, but are not limited to, Veillonella atypica (V. atypica).

[0016] According to one embodiment of the present invention, the Escherichia genus bacteria may include, but are not limited to, Escherichia coli (E. coli).

[0017] In addition, the second aspect of the present invention relates to a composition for predicting weight loss, comprising a preparation capable of measuring the number of bacteria of the genus Veillonella and / or bacteria of the genus Escherichia contained in the organ or feces of a subject for gastrectomy.

[0018] The above-described problem-solving methods are merely exemplary and should not be construed as limiting the present invention. In addition to the exemplary embodiments described above, additional embodiments may be included in the drawings and detailed description of the invention.

[0019] According to the aforementioned means for solving the problem of the present invention, weight loss due to gastrectomy syndrome after gastrectomy can be prevented or suppressed by analyzing a biomarker for predicting weight loss according to the present invention.

[0020] In addition, the biomarker for predicting weight loss can be used in the treatment of various diseases including gastrectomy, and for example, can be used in various surgeries such as gastric cancer surgery and bariatric surgery for dieting.

[0021] However, the effects that can be obtained from this center are not limited to the effects described above, and other effects may exist.

[0022] Figures 1a and 1b show principal component analysis of the distribution of intestinal microorganisms before and after gastrectomy and before endoscopic submucosal dissection.

[0023] Figures 2a and 2b show changes in intestinal microflora before and after gastrectomy.

[0024] Figure 3 shows the microorganisms at different MAG levels depending on the intestinal type of the gastric resection subject.

[0025] Figure 4 shows that the intestinal microflora distribution of gastric resection subjects is divided into two intestinal types.

[0026] Figures 5a and 5b show changes in health-related indicators according to the intestinal type of gastric resection subjects.

[0027] Figures 6a to 6d illustrate changes in biomarkers according to one embodiment of the present invention.

[0028] Figures 7a to 7c illustrate changes in biomarkers according to one embodiment of the present invention.

[0029] Figure 8 shows changes in biomarkers according to one embodiment of the present invention.

[0030] Figures 9a and 9b illustrate the biosynthetic pathway of a biomarker according to one embodiment of the present invention.

[0031] Figure 10 shows changes in biomarkers according to one embodiment of the present invention.

[0032] Below, with reference to the attached drawings, an embodiment of the present invention is described in detail so that a person having ordinary knowledge in the technical field to which the present invention pertains can easily carry out the present invention.

[0033] However, the present invention can be implemented in various different forms and is not limited to the embodiments described herein. In order to clearly explain the present invention in the drawings, parts irrelevant to the description have been omitted, and similar parts have been designated with similar drawing reference numerals throughout the specification.

[0034] Throughout this specification, when a part is said to be "connected" to another part, this includes not only cases where it is "directly connected" but also cases where it is "electrically connected" with another element in between.

[0035] Throughout this specification, when it is said that a member is located “on,” “above,” “upper,” “lower,” “lower” or “lower” another member, this includes not only cases where the member is in contact with the other member, but also cases where another member exists between the two members.

[0036] Throughout this specification, whenever a part is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise specifically stated.

[0037] Below, biomarkers for predicting weight loss according to one implementation example and embodiment of the present invention are described.

[0038] As a technical means for achieving the above-mentioned technical task, the first aspect of the present invention relates to a biomarker for predicting weight loss in a subject for gastrectomy, and specifically to a biomarker for predicting weight loss comprising a bacterium of the genus Veillonella and / or a bacterium of the genus Escherichia.

[0039] According to one embodiment of the present invention, the Veillonella genus bacteria and Escherichia genus bacteria may be contained in the organs of the subject or in the feces of the subject, but are not limited thereto.

[0040] The above-mentioned Veillonella and Escherichia bacteria are known as oral bacteria, but can also be found in the intestines or feces. Specifically, the intestinal barrier of the Prevotella group (gastrectomy subjects with a high number of Prevotella bacteria in the intestines) was confirmed to be weaker than the intestinal barrier of the Bacteroides group (gastrectomy subjects with a high number of Bacteroides bacteria in the intestines), and it was confirmed that many Veillonella and Escherichia bacteria were found in the intestines or feces of the Prevotella group.

[0041] Gastrectomy is a surgical procedure to remove part or all of the stomach. It can be performed for patients with gastric diseases such as gastric cancer or gastric ulcers, or for dieting purposes such as reducing food intake. Gastrectomy involves removing part or all of the stomach, and the remaining section is then anastomosed to another organ (such as the small intestine). This can lead to post-gastrectomy syndromes such as weight loss and vitamin deficiency-related edema. This process can also alter the gut flora due to increased intestinal oxygenation and bile diversion.

[0042] In general, the intestinal types of gastrectomy candidates can be broadly classified into three groups: those dominated by Ruminococcus bacteria, those dominated by Bacteroides bacteria (Bacteroides group), and those dominated by Prevotella bacteria (Prevotella group). In this regard, it was confirmed that gastrectomy candidates with a predominance of Bacteroides bacteria experienced less weight loss than those with a predominance of Prevotella bacteria.

[0043] The above-mentioned Veillonella and Escherichia genera are known as oral bacteria that can survive under aerobic conditions, but the inventors of the present invention confirmed that Veillonella and Escherichia bacteria proliferated in the intestinal microflora of a gastrectomy subject in which the above-mentioned Prevotella bacteria were dominant after gastrectomy, and through this, confirmed that the dominance of Veillonella and / or Escherichia bacteria among intestinal bacteria can be analyzed and utilized as a biomarker for predicting weight loss.

[0044] According to one embodiment of the present invention, the organ may contain, but is not limited to, more bacteria of the genus Prevotella than bacteria of the genus Bacteroides.

[0045] According to one embodiment of the present invention, the relative distribution of metagenome-assembled genomes (MAGs) corresponding to the genus Veillonella bacteria and MAGs corresponding to the genus Escherichia bacteria may increase after gastrectomy, but is not limited thereto.

[0046] For example, the relative distribution of MAG (total: 100 standard) in V. atypica, a bacterium of the genus Veillonella, was investigated, and for MAG.795, it changed from 0.0074 to 0.1612, for MAG.796, from 0.0083 to 0.0876, for MAG.797, from 0.0063 to 0.0725, and for MAG.798, from 0.0168 to 0.2561.

[0047] In addition, the relative distribution of MAG in E. coli, which is a bacterium of the genus Escherichia, was investigated, and it changed from 0.3923 to 1.5892 for MAG.821, from 0.0010 to 0.0387 for MAG.822, and from 0.3005 to 1.2016 for MAG.823.

[0048] The intestinal microbial environment may change due to the above-mentioned gastrectomy, and at this time, the intestinal microbial environment of the Bacteroides group (for example, microbial diversity or number of individuals) and the intestinal microbial environment of the Prevotella group may change in the same or different directions before and after the gastrectomy.

[0049] According to one embodiment of the present invention, the Veillonella genus bacteria may include, but are not limited to, Veillonella atypica (V. atypica).

[0050] According to one embodiment of the present invention, the Escherichia genus bacteria may include, but are not limited to, Escherichia coli (E. coli).

[0051] According to one embodiment of the present invention, the Veillonella bacterium and the Escherichia bacterium may be associated with health-related indicators of the subject, but are not limited thereto.

[0052] According to one embodiment of the present invention, the health-related indicator may include, but is not limited to, one selected from the group consisting of weight, body mass index (BMI), blood sugar, cholesterol, albumin, hemoglobin, protein, lymphocytes, white blood cells, and combinations thereof.

[0053] As described later, in the case of the Bacteroides group after gastric resection, the average amount of weight loss, BMI reduction, albumin reduction, and white blood cell reduction was relatively smaller than in the Prevotella group, while the average amount of blood sugar reduction and hemoglobin reduction was larger, and there was no significant difference in the amount of protein reduction, lymphocyte reduction, and BUN reduction.

[0054] In addition, the second aspect of the present invention relates to a composition for predicting weight loss, comprising a preparation capable of measuring the distribution / predominance of bacteria of the genus Veillonella and / or bacteria of the genus Escherichia contained in the feces of a subject for gastric resection.

[0055] The present invention will be described in more detail through the following examples; however, the following examples are for illustrative purposes only and are not intended to limit the scope of the present invention.

[0056] [Example 1]

[0057] Stool specimens and clinical information of the experimental group for health-related indicator analysis were obtained after receiving consent and approval from the Institutional Review Board of Ajou University Hospital. Specifically, 82 patients diagnosed with gastric cancer (GC) and scheduled for gastrectomy or endoscopic submucosal dissection (ESD) were enrolled. Patients who underwent radical gastrectomy for GC at Ajou University Hospital in Suwon between October 2017 and January 2020 were enrolled. However, patients with a history of gastric or colonic surgery, patients diagnosed with other malignant tumors after treatment, patients treated for inflammatory bowel disease, or patients who received antibiotics, probiotics, H2 receptor antagonists, or proton pump inhibitors within 1 month prior to stool specimen collection were excluded.

[0058] At this time, the average age of the patients was 54.7 years, 51 (62.1%) were male, and the average body mass index was 24.4 kg / m 2 Five patients were treated for diabetes. Subsequently, the nutritional status of the patients was investigated, including body weight, serum protein, albumin, cholesterol, blood nitrogen, hemoglobin level, and white blood cell count.

[0059] Of the 82 patients, 8 patients underwent total gastrectomy with Roux-en-Y reconstruction, 4 patients underwent proximal gastrectomy with double-lumen reconstruction, and the remaining 70 patients underwent distal subtotal gastrectomy with Billroth I or II reconstruction. D1+ or D2 lymphadenectomy was performed in all patients according to the treatment guidelines for GC. All patients were diagnosed with metastatic disease based on the surgical findings, and all underwent curative resection. Most patients were diagnosed with stage I disease by postoperative pathology, 5 patients with stage II disease, and 4 patients with stage III disease. Except for 1 patient, most patients with stage II or III disease were followed up with adjuvant chemotherapy for 6 months and 1 year after surgery. During the same period, 11 patients who underwent ESD for early-stage GC were enrolled as negative controls, and they were curatively resected by endoscopic resection, and none required further surgery.

[0060] Stool samples from the enrolled patients were collected 1 week before surgical or endoscopic resection, and patients were regularly followed up every 3 or 6 months after surgical or endoscopic resection. To assess tumor recurrence, computed tomography, serum tumor marker analysis, and gastroscopy were performed. Patients' nutritional status was assessed at a 1-year follow-up visit, and patients with no confirmed recurrence were provided with a stool sample once more. The stool samples were stored at -80°C until DNA extraction for shotgun metagenomic sequencing.

[0061] [Example 2]: Shotgun metagenome sequencing

[0062] Genomic DNA was extracted from stool samples using the PowerFecal DNA extraction kit according to the manufacturer's recommendations, and a sequencing library consisting of 1.0 μg of DNA per stool sample was prepared using the TruSeq Nano DNA library preparation kit (Illumina, USA). Genomic DNA was then randomly fragmented to approximately 350 bp using a Covaris cracker (Covaris, USA). The DNA fragments were then blunted, A-tailed, and ligated with full-length adapters for Illumina sequencing by further PCR amplification. The libraries were purified using AMPure XP (Beckman Coulter, USA), analyzed for size distribution on an Agilent 2100 Bioanalyzer (Agilent Technologies), and quantitatively quantified by qPCR. Subsequently, they were sequenced using the Illumina Novaseq platform with 150 bp paired-end reads.

[0063] [Example 3]: Reading preprocessing and assembly

[0064] A total of 1.8 Tbp was generated from 180 samples. Subsequently, FaQC (version 1.36) was used to remove low-quality sequences (i.e., reads with a Phred score <20) from the raw data, and FastUniq was used to remove duplicate reads. Reads mapped to the human genome reference (GRCh38) were removed using bowtie2 (version 2.2.3), and functional profiling of the sequences was performed using HUMAnN3.

[0065] Subsequently, for metagenomic binning, reads were assembled using MEGAHIT (version v1.1.3), and read mapping to the assembled contigs was performed using Minimap2 (version 2.12-r828-dirty). Contig coverage calculations were performed using Samtools (version 1.8) and Bedtools (version 2.25.0), respectively. Subsequently, the metagenome-assembled genome (MAG) was reconstructed using MetaBAT2, and low-quality genome bins were refined using Additional Clustering Refiner (ACR), obtaining a total of 1,308 bins. In the upper bin set, the completeness and contamination of each bin were assessed using CheckM (v1.0.11), and bins with completeness ≥ 50% and contamination ≤ 10% were deduplicated using dRep at 99% average nucleotide identity (ANI), ultimately obtaining a total of 839 MAGs (metagenome-assembled genomes).

[0066] [Example 4]: Classification and functional characterization of MAG

[0067] GTDB-Tk (Genome Taxonomy Database Toolkit) was used to assign taxonomic classifications to MAGs. Specifically, Prokka was used to predict the protein coding sequences (CDSs) of MAGs. The functions of each MAG were characterized using GhostKOALA based on KEGG orthology (KO) assignments.

[0068] The presence of bacterial virulence factors (VFs) in MAGs was detected using BLASTp against the Virulence Factors Database (VFDB). Only genes with ≥60% identity and >50% sequence coverage were used for further analysis, and BAM files from the previous metagenomic binning step were converted to reads per kilobase per million (RPKM) using the 'jgi_summarize_bam_contig_depth' script from MetaBAT2 and featureCounts to calculate relative gene abundance.

[0069] [Example 5]: Statistical Analysis

[0070] Enterotyping of gut microbiota based on the Partitioning Around Medoids (PAM) clustering algorithm was performed using the Cluster (2.1.3), ClusterSim (0.49-2), and ade4 (1.7-19) packages in the R environment.

[0071] To distinguish significantly different bacterial MAG between groups, t-test or analysis of variance (ANOVA) was performed, microbial diversity of MAG composition was calculated using the vegan package in R (v2.6-2), and the significance of differences between groups for microbial composition data of MAG was assessed using stat_compare_means of the ggpubr package (0.5.0). Statistical analysis was performed by performing principal coordinates analysis (PCoA) for all patients with gastric cancer using Bray-Curtis dissimilarity based on the relative abundance data of MAG using the vegdist function of the vegan package.

[0072] [Experimental Example 1]

[0073] Figures 1a and 1b show principal component analysis of the distribution of intestinal microorganisms before and after gastrectomy, and Figures 2a and 2b show changes in intestinal microorganisms before and after gastrectomy.

[0074] Stool samples from patients before endoscopic submucosal dissection were used as controls.

[0075] Referring to Figures 1a to 2b, changes in intestinal microorganisms due to gastrectomy are confirmed through MAG analysis, and in particular, it can be confirmed that microbial diversity differs significantly depending on the intestinal type.

[0076] [Experimental Example 2]

[0077] Figures 3 and 4 illustrate the intestinal types of gastrectomy subjects. Specifically, the samples were divided into two groups through PAM clustering, and Figure 3 depicts the different microorganisms when comparing the two groups. As a result of the analysis of the above samples, the samples can be classified into Bacteroides enterotype, Prevotella enterotype, and Ruminococcus enterotype through PAM clustering analysis, but in our hospital, the Bacteroides enterotype and Prevotella enterotype were selected for analysis.

[0078] In addition, the x-axis and y-axis in Fig. 4 correspond to a two-dimensional representation of the distribution of microorganisms divided into two intestinal types, and the green color in Fig. 4 represents the Bacteroides intestinal type, and the red color represents the Prevotella intestinal type.

[0079] Referring to Figure 3, in the case of the Prevotella dominant group (subjects undergoing gastrectomy in which Prevotella bacteria are dominant over Bacteroides bacteria in the intestine), it can be confirmed that there is less MAG corresponding to Bacteroides bacteria and more MAG corresponding to Prevotella bacteria.

[0080] [Experimental Example 3]

[0081] Figures 5a and 5b show changes in health-related indicators according to the intestinal type of gastric resection subjects.

[0082] Referring to FIGS. 5a and 5b, it was confirmed that, in the case of the Bacteroides dominant group after gastric resection, the weight loss, BMI reduction, albumin reduction, and white blood cell reduction were relatively smaller on average compared to the Prevotella dominant group, while the glucose (blood sugar) reduction and hemoglobin reduction were larger on average, and there was no significant difference in the protein reduction, lymphocyte reduction, and BUN reduction.

[0083] [Experimental Example 4]

[0084] Figures 6a to 6d and Figures 7a to 7c illustrate changes in biomarkers according to one embodiment of the present invention. Specifically, Figures 6a to 6d illustrate changes in the relative distribution (%) of V. atypica, and Figures 7a to 7c illustrate changes in the relative distribution (%) of E. coli.

[0085] Referring to Figures 6a to 7c, it can be confirmed that the degree of increase in the relative dominance of V. atypica and E. coli in the Prevotella-dominant group after gastrectomy is greater than that in the Bacteroides-dominant group. In addition, it can be confirmed through T test and Bonferroni test that the dominance of V. atypica and E. coli among Veillonella species significantly increases only in the Prevotella-dominant group after gastrectomy.

[0086] [Experimental Example 5]

[0087] Figure 8 shows changes in biomarkers according to one embodiment of the present invention.

[0088] Referring to Figure 8, an increase in virulence factors after gastrectomy can be confirmed in the Prevotella-dominant group. In particular, bacteria with a high genome-wide virulence factor content, such as E. coli and Prevotella copri, were identified, and these were confirmed to be species that increased after gastrectomy in the Prevotella-dominant group. Therefore, weight loss due to gastrectomy syndrome in gastric cancer patients can be prevented by inhibiting the growth of Veillonella species (specifically Veillonella atypica) and E. coli after gastrectomy.

[0089] [Experimental Example 6]

[0090] Figures 9a and 9b illustrate the biosynthetic pathway of a biomarker according to one embodiment of the present invention. Specifically, in Figures 9a and 9b, * in significance means less than 0.05, ** means less than 0.01, and *** means less than 0.001.

[0091] Referring to Figure 9, a total of 60 pathways were identified to be possessed by V. atypica found in the intestines of patients who underwent gastrectomy, and among these, 58 pathways showed differences before and after gastrectomy in the Privotella-dominant group. At this time, the pathway in which V. atypica showed the highest abundance compared to other bacteria was identified as PWY-5005 (biotin biosynthesis II).

[0092] [Experimental Example 7]

[0093] Fig. 10 illustrates changes in biomarkers according to one embodiment of the present invention. Specifically, the description of 3.1e-09 in Fig. 10 indicates the adjusted P value as a result of statistical analysis of the difference between Pre-gastrectomy and Post-gastrectomy using the Wilcoxon test, and the y-axis indicates the relative dominance within the sample of the PWY-5005 pathway, expressed as a calculated value of RPKM, and is a unit expressing the relative dominance based on 1,000,000, taking into account sequencing differences, etc. In addition, points located outside the box in Fig. 10 indicate values ​​corresponding to outliers.

[0094] Referring to Figure 10, the relative dominance of the Prevotella group in the PWY-5005 pathway sample appears to increase less than that of the bacteroides group, but statistical analysis reveals a more significant increase in the Prevotella group.

[0095] The above description of the present invention is for illustrative purposes only, and those skilled in the art will readily appreciate that the present invention can be readily modified into other specific forms without altering the technical spirit or essential characteristics of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single entity may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined manner.

[0096] The scope of the present invention is indicated by the claims described below rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention.

Claims

1. Regarding biomarkers for predicting weight loss in patients undergoing gastrectomy, A biomarker for predicting weight loss comprising a bacterium of the genus Veillonella and / or a bacterium of the genus Escherichia.

2. In paragraph 1, The above-mentioned Veillonella genus bacteria and Escherichia genus bacteria are biomarkers for predicting weight loss, which are contained in the organs of the subject or in the feces of the subject.

3. In paragraph 2, A biomarker for predicting weight loss, wherein the organ of the subject comprises bacteria of the genus Bacteroides and bacteria of the genus Prevotella.

4. In paragraph 3, The above-mentioned organ is a biomarker for predicting weight loss, which contains more Prevotella bacteria than Bacteroides bacteria.

5. In paragraph 1, Biomarkers for predicting weight loss, wherein the intestinal distribution of the above-mentioned Veillonella bacteria and the intestinal distribution of the Escherichia bacteria increase after gastrectomy.

6. In paragraph 1, The above-mentioned Veillonella genus bacteria and the above-mentioned Escherichia genus bacteria are biomarkers for predicting weight loss, which are associated with health-related indicators of the subject.

7. In paragraph 6, A biomarker for predicting weight loss, wherein the health-related indicators include those selected from the group consisting of body weight, body mass index (BMI), blood sugar, cholesterol, albumin, hemoglobin, protein, lymphocytes, white blood cells, and combinations thereof.

8. In paragraph 1, A biomarker for predicting weight loss, wherein the Veillonella genus bacteria includes Veillonella atypica (V. atypica).

9. In paragraph 1, The above Escherichia genus bacteria are biomarkers for predicting weight loss, including Escherichia coli (E. coli).

10. A composition for predicting weight loss, comprising a preparation capable of measuring the number of bacteria of the genus Veillonella and / or bacteria of the genus Escherichia contained in the organ or feces of a subject for gastric resection.

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