Application of black rice in inhibition of colorectal cancer

Abstract

The invention relates to the technical field of natural food, in particular to application of black rice in inhibition of colorectal cancer. According to the application, the black rice is used as a treatment medicine for inhibiting or relieving colorectal cancer; according to the application, low-abundance bacteroides simplex is used as a biomarker of colorectal cancer; a large number of experiments show that the survival time of ApcMin / + and AOM / DSS model mice eating the black rice for a long time is long, the number and volume of colorectal tumors in the ApcMin / + model mice eating the black rice for a long time are obviously smaller than those of a control group, and the connection between colon cells is relatively normal, which indicates that the black rice diet not only can improve the intestinal permeability of the ApcMin / + model mice, but also can improve the intestinal permeability of the AOM / DSS model mice. In addition, the black rice can also protect chemical barriers and immune barriers of intestinal tracts, and meanwhile, the level of inflammation in serum of an ApcMin / + model mouse with black rice diet is lower, which indicates that the black rice can be used as a medicine for treating colorectal cancer tumors and can delay development of the colorectal cancer tumors.

Description

Technical Field

[0001] This application relates to the technical field of natural foods, and particularly to the application of black rice in inhibiting colorectal cancer. Background Art

[0002] Colorectal cancer (CRC) is a common malignant tumor that causes huge losses and burdens to the public health and medical systems. At the same time, the colorectum is also the third most common cancer globally, second only to lung cancer and breast cancer. Currently, the epidemiological data of colorectal cancer shows regional differences, which are usually related to differences in eating habits, lifestyles, genetic factors, and medical resources. The global burden of colorectal cancer not only includes morbidity and mortality but also involves socioeconomic impacts and the pressure on medical resources. To reduce the global burden of colorectal cancer, prevention and control strategies become particularly important. Among them, early detection is one of the key measures to reduce the mortality rate of colorectal cancer. Through screening methods, lesions can be detected early and treated. Currently, the comprehensive screening of colorectal cancer includes fecal occult blood testing, colonoscopy, and virtual colonoscopy, etc.

[0003] The gut microbiota is widely known as the "forgotten organ" and maintains the stable state of the intestine through various complex mechanisms, playing an important role in human health and diseases. The intestine contains approximately 10 14The resident microbiota, including archaea, fungi, protozoa, and viruses, are collectively referred to as the "gut microbiome"; at the phylum level, the microbiome in the colon is typically dominated by Bacteroidetes, Firmicutes, Fusobacteria, Proteobacteria, and Actinobacteria, among which Gram-negative Bacteroidetes and Gram-positive Firmicutes are the major bacterial groups in healthy hosts. The gut microbiome is involved in maintaining both mucosal homeostasis and epithelial barrier function, while chronic inflammation and harmful metabolites are induced and produced by gut microbiota dysbiosis, leading to the development of diseases and tumors. It has been reported that several microorganisms are differentially enriched in tumor tissues compared to normal tissues and in fecal samples from CRC patients compared to healthy control subjects, such as Streptococcus, Fusobacterium nucleatum, Escherichia coli, and Escherichia faecalis, which are more abundant in CRC patients than in healthy individuals, while genera such as Clostridium, Faecalibacterium, and Bifidobacterium are typically depleted in CRC patients, indicating that different combinations of microorganisms and their functional genes can act synergistically; in addition, changes in pathogenic and protective bacterial populations may be potential causes of colorectal tumorigenesis. The microbiome of CRC patients is typically enriched in pro-inflammatory opportunistic pathogens and microorganisms associated with metabolic disorders and lacks butyrate-producing bacteria, which are crucial for maintaining gut homeostasis. Gut microbiota dysbiosis may induce epithelial cell proliferation, disrupt the epithelial barrier, perturb host immunity, and cause inflammation, thereby triggering CRC tumorigenesis. At the same time, the gut microbiome may produce microbial metabolites that interact with the host immune system and induce the release of genotoxic virulence factors, thus promoting the development of CRC.

[0004] Currently, CRC is prevented by detecting and removing precancerous lesions, specifically through fecal occult blood test (FOBT) and fecal immunochemical test (FIT) for screening. When the test is positive and accompanied by other clinical symptoms, colonoscopy is performed, which can improve the accuracy of preventive detection. However, the above-mentioned FOBT and FIT have low sensitivity to early and late tumors. Pathological imbalance of the gut microbiome usually exists in CRC patients, which has been proven to be closely related to the occurrence and development of cancer; the microbiome changes occurring in the early stage of CRC also highlight the potential of using specific bacterial species as non-invasive diagnostic biomarkers for CRC. In addition, the analysis of the microbial communities in fecal and mucosal samples shows that specific changes in the gut microbiome are related to different stages of CRC. These specific microbial markers distinguish CRC from healthy controls, indicating the diagnostic potential of gut microbiota in CRC at different stages of development. For example, metagenomic data of colorectal cancer patients show that Fusobacterium nucleatum is enriched in CRC tissues compared with normal tissues. The level of Fusobacterium nucleatum not only increases with the increase of malignancy but is also related to metastasis. Therefore, combining Fusobacterium nucleatum with FIT can increase the sensitivity of FIT detection from 73.1% to 92.3%. In addition, the combination of Fusarium nuclear, Bacteroides clarus, and Clostridium with FIT can also improve the sensitivity and specificity of diagnosing CRC and colorectal adenomas. At the same time, existing studies have shown that Fusarium nuclear, Fusarium oxysporum, and Actinomyces odontolyticus are significantly enriched in multiple polypoid adenomas or stage 0 colorectal cancer, suggesting that these bacteria contribute to the diagnosis of early colorectal cancer.

[0005] Although some CRC-specific gut microbiota have been identified in current research, it is still far from achieving precise screening. The research goal of gut microbiota is to narrow down the characteristics of the entire microbiome to the differences in specific species or sets of species that can be used as indicators. This can be achieved by developing specific tests to detect the presence and abundance of these indicator species without evaluating the entire microbiome, thus significantly reducing the cost and time of the diagnostic work. Given the close relationship between the gut microbiota and the occurrence and development of CRC, researchers are increasingly concerned about microbiome-related therapies to help prevent and treat colorectal cancer.

[0006] Given the important role played by food nutrition in the health of the body, the relationship between food and human health and diseases has attracted increasing attention and widespread concern. By studying the effects of specific components of food on the health of the body and clarifying the relationship between food components and related diseases, it has become a current research hotspot. Existing studies have shown that the consumption of whole grains or foods containing dietary fiber is negatively correlated with the risk of colon cancer and rectal cancer. Black rice is a special grain known for its health benefits, and the fiber content in black rice is higher than that of polished white rice commonly consumed in daily life. However, there is currently no research on using black rice in small molecules for colorectal cancer. Summary of the Invention

[0007] This application provides an application of black rice in inhibiting colorectal cancer to fill the gap in the existing technology where there is no research on using black rice in small molecules for colorectal cancer.

[0008] In the first aspect, this application provides an application of black rice as a therapeutic drug for colorectal cancer, and the application includes using black rice as a therapeutic drug for inhibiting or slowing down colorectal cancer.

[0009] Optionally, the therapeutic drug includes at least one of the following:

[0010] Drugs that improve intestinal permeability, drugs that inhibit the number of intestinal tumors, drugs that inhibit the size of intestinal tumors, drugs that inhibit the level of inflammatory factors in the serum, and drugs that upregulate the level of anti-inflammatory factors.

[0011] Optionally, the therapeutic drug further includes: drugs that increase the abundance of beneficial bacteria in the intestinal microbiome.

[0012] Optionally, the beneficial bacteria include Bacteroides uniformis and / or Lactobacillus, and there is a positive co-occurrence relationship between Bacteroides uniformis and Lactobacillus.

[0013] Optionally, the mass ratio of the black rice to the therapeutic drug ≥ 50%.

[0014] Optionally, the application further includes using cyanidin-3-O-glucoside of black rice as a therapeutic drug for inhibiting or slowing down colorectal cancer.

[0015] In the second aspect, this application provides an application of Bacteroides uniformis as a biomarker in colorectal cancer, and the application includes using low-abundance Bacteroides uniformis as a biomarker for colorectal cancer.

[0016] Optionally, the area under the receiver operating characteristic curve of Bacteroides uniformis ≥ 0.734.

[0017] In the third aspect, this application provides a reagent for screening or assisting in screening colorectal cancer, and the reagent includes an agent for detecting the abundance of Bacteroides uniformis.

[0018] In a fourth aspect, the present application provides a reagent for evaluating or assisting in evaluating the prognosis of colorectal cancer, and the reagent includes an agent for detecting the abundance of Bacteroides uniformis.

[0019] The above technical solutions provided by the embodiments of the present application have the following advantages compared with the prior art:

[0020] The application of black rice provided by the embodiments of the present application in the treatment of colorectal cancer. Through a large number of experiments, it is found that the survival time of Apc Min / + and AOM / DSS model mice fed black rice for a long time is long, and the Apc Min / + model mice fed black rice for a long time. Min / + The number and volume of colorectal tumors in the model mice are significantly smaller than those in the control group, and the intercellular junctions in the colon are relatively normal. This indicates that a black rice diet can not only improve the intestinal permeability of Apc Min / + model mice, but also protect the chemical and immune barriers of the intestine. At the same time, the inflammatory level in the serum of Apc Description of the Drawings

[0021] The drawings here are incorporated into the specification and form a part of this specification, showing the embodiments in line with the present application, and are used together with the specification to explain the principles of the present application.

[0022] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a graph showing the results of the effect of a black rice diet provided by the embodiments of the present application on the lifespan of colorectal model mice. Among them, Figure 1 A is a feeding pattern diagram of Apc Min / + model mice, Figure 1 B is a feeding pattern diagram of AOM / DSS model mice, Figure 1 C is the survival curve of Apc Min / + model mice fed with the control diet (n = 35) and the black rice diet (n = 28), Figure 1 D is the survival curve of AOM / DSS model mice fed with the control diet (n = 30) and the black rice diet (n = 27);

[0024] Figure 2 It is for the black rice diet provided by the embodiments of the present application to delay ApcMin / + Diagram of the development of CRC in mice, where Figure 2 A is a diagram showing the comparison of the body weights of mice in the black rice diet group and the control diet group at 22 weeks of age, Figure 2 B is a comparison diagram of the intestinal tissue staining results of mice in the black rice diet group and the control diet group at 22 weeks of age, as well as the statistics of the number and size of intestinal tumors, Figure 2 C is a comparison diagram of the HE staining pictures of the intestinal tissues of mice in the black rice diet group and the control diet group at 22 weeks of age and the statistical comparison of the staining results, Figure 2 D is a comparison diagram of the Ki-67 immunohistochemical results and statistical data of mice in the black rice diet group and the control diet group at 22 weeks of age, Figure 2 E is a comparison diagram of the PCNA protein electrophoresis results and statistical data of mice in the black rice diet group and the control diet group at 22 weeks of age, Figure 2 F is a quantitative diagram of the LPS level in the serum of mice in the black rice diet group and the control diet group at 22 weeks of age, Figure 2 G is a transmission electron micrograph of the intestinal tissues of mice in the black rice diet group and the control diet group at 22 weeks of age;

[0025] Figure 3 This is the diagram of the improvement of the intestinal Apc by the black rice diet provided in the embodiment of the present application Min / + Diagram of the results of the improvement of the intestinal barrier function of Apc mice by the black rice diet provided in the embodiment of the present application, where Figure 3 A is a diagram of the PAS staining results and statistical data of mice in the black rice diet group and the control diet group at 22 weeks of age, Figure 3 B is an immunohistochemical result diagram of the intestinal barrier protein ZO-1 of mice in the black rice diet group and the control diet group at 22 weeks of age, Figure 3 C is an immunohistochemical result diagram of the intestinal barrier protein Claudin-3 of mice in the black rice diet group and the control diet group at 22 weeks of age, Figure 3 D is an immunohistochemical result diagram of the intestinal barrier protein Occludin of mice in the black rice diet group and the control diet group at 22 weeks of age, Figure 3 E is a diagram of the expression level data of the intestinal barrier proteins ZO-1, Occludin, and Claudin-3 in mice in the 22-week black rice diet group and the control group;

[0026] Figure 4 This is the diagram of the reduction of the serum inflammation level of Apc mice by the black rice diet provided in the embodiment of the present application Min / + Diagram of the results of the reduction of the serum inflammation level of Apc mice by the black rice diet provided in the embodiment of the present application, where Figure 4 A is a diagram of the results of the level of the anti-inflammatory factor IL-4 in the serum of mice in the black rice diet group and the control diet group at 22 weeks of age, Figure 4 B is a diagram of the results of the level of the anti-inflammatory factor IL-10 in the serum of mice in the black rice diet group and the control diet group at 22 weeks of age, Figure 4 C is a diagram of the results of the level of the pro-inflammatory factor IL-6 in the serum of mice in the black rice diet group and the control diet group at 22 weeks of age, Figure 4Figure showing the levels of the pro-inflammatory factor TNF-α in the sera of 22-week-old mice in the black rice diet group and the control diet group;

[0027] Figure 5 Figure showing the results of the effect of black rice diet provided in the embodiments of the present application on the development of CRC in the AOM / DSS mouse model. Among them, Figure 5 A is a figure showing the body weights of AOM / DSS model mice in the black rice diet group and the control diet group before sacrifice (n = 9 per group); Figure 5 B is a comparison figure of the intestinal tissue staining results, as well as the statistical data of the tumor number and volume, of AOM / DSS model mice in the black rice diet group and the control diet group at the time of sacrifice; Figure 5 C is a comparison figure of the HE staining results of the colon tissues of AOM / DSS model mice in the black rice diet group and the control diet group for pathological diagnosis, as well as the statistical data; Figure 5 D is a comparison figure of the Ki-67 immunohistochemical staining results of the colon tissues of AOM / DSS model mice in the black rice diet group and the control diet group, as well as the quantitative analysis of the Ki-67 index; Figure 5 E is a comparison figure of the Western blot analysis results of the PCNA protein expression levels in the colon tissues of AOM / DSS model mice in the black rice diet group and the control diet group, as well as the quantitative analysis results; Figure 5 F is a comparison figure of the LPS concentrations in the sera of AOM / DSS model mice in the black rice diet group and the control diet group; Figure 5 G is a schematic diagram of the intercellular junctions of AOM / DSS model mice captured by transmission electron microscopy; Figure 5 H is a figure showing the PAS staining results of the colonic mucus cells of AOM / DSS model mice in the black rice diet group and the control diet group; Figure 5 I is an immunohistochemical staining figure of the adhesion molecule ZO-1 in AOM / DSS model mice in the black rice diet group and the control diet group; Figure 5 J is an immunohistochemical staining figure of the adhesion molecule Claudin-3 in AOM / DSS model mice in the black rice diet group and the control diet group; Figure 5 K is an immunohistochemical staining figure of the adhesion molecule Occludin in AOM / DSS model mice in the black rice diet group and the control diet group; Figure 5 L is a figure showing the quantitative analysis results of the adhesion molecules ZO-1, Claudin-3, and Occludin in AOM / DSS model mice in the black rice diet group and the control diet group; Figure 5 M is a figure showing the levels of the anti-inflammatory factor IL-4 in AOM / DSS model mice in the black rice diet group and the control diet group; Figure 5 N is a figure showing the levels of the anti-inflammatory factor IL-10 in AOM / DSS model mice in the black rice diet group and the control diet group; Figure 5Figure showing the levels of the pro-inflammatory factor IL-6 in AOM / DSS model mice in the black rice diet group and the control diet group. Figure 5 Figure showing the levels of the pro-inflammatory factor TNF-α in AOM / DSS model mice in the black rice diet group and the control diet group;

[0028] Figure 6 This is the figure showing the improvement of Apc by black rice diet provided in the embodiments of the present application Min / + Figure showing the increase in the abundance of beneficial bacteria in the gut microbiota composition of Apc mice improved by black rice diet provided in the embodiments of the present application. Among them, Figure 6 Figure A shows the results of α-diversity assessment of wild-type mice (WT), control diet mice, and black rice diet mice using Shannon and Simpson indices at 14 weeks (WT_14, CD_14, and BRD_14) and 22 weeks (WT_22, CD_22, and BRD_22). Figure 6 Figure B shows the results of β-diversity analysis using Bray-Curtis distance metric. Figure 6 Figure C shows the marked microbial abundances between Apc mice in the black rice diet group and the control diet group, or between wild-type mice in the control diet group and Apc Min / + mice. Min / + Figure showing the marked microbial abundances between Apc mice in the black rice diet group and the control diet group, or between wild-type mice in the control diet group and Apc Figure 6 Figure D shows the relative abundances of Bacteroides uniformis and Escherichia coli. Figure 6 Figure E shows the schematic diagram of Spearman correlation coefficients between microorganisms;

[0029] Figure 7 This is the figure showing the improvement of the gut microbiota composition of AOM / DSS mice by black rice diet and the increase in the abundance of beneficial bacteria provided in the embodiments of the present application. Among them, Figure 7 Figure A shows the results of α-diversity assessment of wild-type mice (WT), control diet mice, and black rice diet mice using Shannon and Simpson indices at the first DSS treatment (C_1 and B_1) and the third DSS treatment (C_3 and B_3). Figure 7 Figure B shows the results of β-diversity analysis using Bray-Curtis distance metric. Figure 7 Figure C shows the marked microbial abundances between AOM / DSS model mice in the black rice diet group and the control diet group, and between wild-type mice in the control diet group and AOM / DSS mice. Figure 7 Figure D shows the relative abundances of Bacteroides uniformis and Escherichia coli. Figure 7 Figure E shows the schematic diagram of Spearman correlation coefficients between microorganisms;

[0030] Figure 8 This is the figure showing the enrichment results of Bacteroides uniformis in healthy human guts provided in the embodiments of the present application. Among them, Figure 8 Figure A shows the information on the number of colorectal cancer patients and healthy people in six sets of public data.Figure 8 Table B shows the influence degree of confounding factors on disease status;

[0031] Figure 9 This is a schematic diagram of the relative abundance of Bacteroides uniformis in the population and the ROC curve for Bacteroides uniformis to predict CRC status provided by the embodiments of the present application. Among them, Figure 9 A is the relative abundance of Bacteroides uniformis in the population after meta-analysis, Figure 9 B is the relative abundance of Bacteroides uniformis in the population of six sets of data, Figure 9 C is the ROC curve graph for Bacteroides uniformis to predict CRC status;

[0032] Figure 10 This is a result graph showing that Bacteroides uniformis is regulated by anthocyanins and inhibits the proliferation of CRC cancer cell lines. Among them, Figure 10 A is a schematic diagram of the detection results of targeted anthocyanin metabolites, Figure 10 B is a comparison graph of the number of bacteria after Bacteroides uniformis is treated with black rice extract and C3G, Figure 10 C is a schematic diagram of the cell growth curves of colon cancer cell lines HCT116 and SW620 under the treatment of Bacteroides uniformis and Escherichia coli. Detailed implementation manners

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0034] Unless otherwise specifically stated, all kinds of raw materials, reagents, instruments, and equipment used in the present application can be obtained through market purchase or can be prepared by existing methods.

[0035] The creative thinking of the present application is:

[0036] The occurrence and development of colorectal cancer (CRC) is a multi-stage process. Nearly 70% to 90% of CRCs originate from polyps. This process starts from abnormal crypts and evolves into polyps, also known as pre-tumor lesions. In particular, the DNA damage of cells increases over time, which may lead to the appearance of highly dysplastic features, thus significantly increasing the risk of developing into invasive cancer. If these polyps are not removed, they may grow into or outside the walls of the colon and rectum and eventually develop into CRC within about 10 to 15 years. In addition, after the formation of new blood vessels, cancer cells are easily introduced into the lymphatic and circulatory systems, thus spreading to distant organs. Stem cells or stem-like cells are generally considered to be the origin cells of most CRCs. These special cells gradually accumulate many genetic mutations and epigenetic alterations. The bottom of the colonic crypt is considered to be an important origin and development site of cancer stem cells, which is crucial for tumor occurrence and maintenance.

[0037] Despite the latest progress in the diagnosis and treatment of CRC, CRC remains one of the most commonly diagnosed and fatal cancers. In developing countries that are gradually adopting Western lifestyles, the incidence of CRC has increased significantly. Therefore, diet, as a modifiable risk factor for CRC, plays an important role in influencing the occurrence and development of the disease. In recent years, relevant epidemiological investigations, meta-analysis results, and analysis of animal experiment data have shown that different dietary patterns have a significant impact on the incidence of CRC. Mediterranean diet, ketogenic diet, vegetarian diet, and restricted diet can reduce the risk of developing CRC by improving and regulating the homeostasis of the gut microbiome. On the contrary, diets that usually involve a large intake of red meat, processed meat, high-fructose corn syrup, and processed by unhealthy cooking methods tend to increase the risk of CRC. Currently, the gut microbiota generally affects the occurrence of CRC through three main pathways:

[0038] (i) Abnormal proliferation of pathogenic bacteria can directly cause cancer or lead to the proliferation of opportunistic microorganisms in the tumor-associated microenvironment;

[0039] (ii) Derivatives of microorganisms, such as metabolites or genotoxins, contribute to the occurrence of CRC;

[0040] (iii) Host-microbe interactions contribute to the activation of oncogenic signaling pathways, ultimately accelerating the progression of CRC. For example, Peptostreptococcus anaerobius (an anaerobic bacterium) can activate the PI3K-Akt and NF-κB signaling pathways to enhance the release of pro-inflammatory factors in the epithelium.

[0041] Meanwhile, the gut microbiome may produce microbial metabolites that interact with the host immune system and induce the release of genotoxic virulence factors, thus promoting the development of CRC; two genotoxins produced by Escherichia and E. coli include cytolethal distending toxin (CDT) and colicin. The former is carcinogenic and can induce double-strand DNA breaks through its deoxyribonuclease activity, while the latter causes DNA cross-linking and double-strand DNA breaks. In addition, host-microbe interactions contribute to the activation of oncogenic signaling pathways, thus promoting CRC progression. Fusobacterium nucleatum has been shown to bind to E-cadherin on the surface of colon cells through FadA adhesion, thereby inducing inflammation and carcinogenic responses. Anaerotruncus is a type of anaerobic bacterium that is selectively enriched in the fecal and mucosal microbiota of patients with colorectal cancer (CRC). In addition, Anaerotruncus also interacts with TLR2 and TLR4 of colon cancer cells, leading to an increase in intracellular reactive oxygen species (ROS) levels, thereby stimulating cholesterol synthesis and promoting cell proliferation. Studies in recent years have shown that gut microbiota are important influencing factors in the relationship between diet and colorectal cancer. Therefore, diet is expected to reduce the incidence of colorectal cancer by regulating the composition of gut microbiota.

[0042] Existing studies have shown that the consumption of whole grains or foods containing dietary fiber is negatively correlated with the risk of colon and rectal cancer. Due to dietary fiber and bioactive compounds such as polyphenols, flavonoids (quercetin, kaempferol, apigenin), isoflavones, caffeic acid, and resveratrol, they can act as chemopreventive agents for preventing colorectal cancer in vitro and in vivo, respectively. Dietary fiber can bind to the carcinogens of secondary bile acids, increase fecal volume (diluting carcinogens), and prevent carcinogens from directly contacting the colon for a long time. Black rice is a special grain known for its health benefits. It has a long history of cultivation. As a functional food, black rice is known as the "medicinal rice" and "longevity rice", and it is a precious type in the world's rice germplasm resources. Black rice is rich in anthocyanins in its epidermis, thus showing a black color; anthocyanins, also known as anthocyanosides, are a class of water-soluble natural pigments in plants, belonging to the flavonoid substances in phenolic compounds, and are widely present in nature. Anthocyanins have anti-inflammatory, antioxidant, and chemoprotective effects. In addition, anthocyanins also have physiological health functions, including reducing the risk of obesity, diabetes, and cardiovascular diseases, as well as slowing down the occurrence and development of colorectal cancer tumors; the fiber content in black rice is higher than that of polished white rice commonly consumed daily, which can bind bile acids and carcinogens and repair the colon mucosal epithelium. Dietary fiber can not only reduce fecal transit time but also promote the fermentation of short-chain fatty acids (SCFAs) by the colon microbiota, thereby stimulating the repair and maintaining the health of the colon wall.

[0043] Although there have been some reports on the health effects of black rice, there is still a lack of research on the molecular mechanisms of black rice as a whole food. Given the important role of food nutrition in maintaining body health, the relationship between food and human health and diseases has attracted increasing attention. Studying the effects of specific food components on body health, clarifying the relationship between food components and related diseases, and then preventing and improving specific diseases through dietary intervention have now become one of the popular research fields internationally.

[0044] An embodiment of the present application provides an application of black rice as a therapeutic drug for colorectal cancer, and the application includes using black rice as a therapeutic drug for inhibiting or slowing down colorectal cancer.

[0045] In some alternative embodiments, the therapeutic drug includes at least one of the following:

[0046] Drugs for improving intestinal permeability, drugs for inhibiting the number of intestinal tumors, drugs for inhibiting the size of intestinal tumors, drugs for inhibiting the levels of inflammatory factors in serum, and drugs for upregulating the levels of anti-inflammatory factors.

[0047] In the embodiments of the present application, by defining the specific types of therapeutic drugs, it can be clarified that black rice has the effects of improving intestinal permeability, inhibiting the number of intestinal tumors, inhibiting the size of intestinal tumors, inhibiting inflammatory factors in serum, and upregulating the levels of anti-inflammatory factors. Therefore, it can be clearly stated that a black rice diet can delay the development of colorectal cancer tumors.

[0048] In some alternative embodiments, the therapeutic drug further includes: drugs for increasing the abundance of beneficial bacteria in the intestinal microbiome.

[0049] In some alternative embodiments, the beneficial bacteria include Bacteroides uniformis and / or Lactobacillus, and Bacteroides uniformis and Lactobacillus show a positive co-occurrence relationship.

[0050] In the embodiments of the present application, through experiments, it is found that Bacteroides uniformis and Lactobacillus are enriched in the black rice diet mouse model. At the same time, there is a positive co-linearity relationship between Bacteroides uniformis and Lactobacillus johnsonii and Lactobacillus reuteri. Currently, Lactobacillus is considered a promising tool for treating colorectal cancer in vitro and in vivo. Therefore, this indicates that Bacteroides uniformis may also have the effect of delaying the progression of colorectal cancer.

[0051] In some alternative embodiments, the mass ratio of the black rice to the therapeutic drug ≥ 50%.

[0052] In the embodiments of the present application, by defining the mass ratio of black rice and the therapeutic drug, the dosage standard of black rice as a therapeutic drug can be further clarified, so that it can be determined that black rice has the effects of improving intestinal permeability, inhibiting the number of intestinal tumors, inhibiting the size of intestinal tumors, inhibiting inflammatory factors in serum, and upregulating the level of anti-inflammatory factors.

[0053] In some alternative embodiments, the application further includes using cyanidin-3-O-glucoside in black rice as a therapeutic drug for inhibiting or slowing down colorectal cancer.

[0054] In the embodiments of the present application, since black rice contains anthocyanins and cyanidin-3-O-glucoside (C3G) is the main component of black rice anthocyanins, based on the beneficial activity of anthocyanins, it is shown that C3G can promote the reproduction of Bacteroides uniformis, thereby increasing the abundance of Bacteroides uniformis, and further realizing the delay of the progression of colorectal cancer by black rice diet.

[0055] Based on a general inventive concept, the embodiments of the present application provide an application of Bacteroides uniformis as a biomarker in colorectal cancer, and the application includes using low-abundance Bacteroides uniformis as a biomarker for colorectal cancer.

[0056] The application of Bacteroides uniformis as a biomarker in colorectal cancer is realized based on the above application of black rice as a therapeutic drug for colorectal cancer. The specific principle of the application of black rice as a therapeutic drug for colorectal cancer can be referred to the above embodiments. Since the application of Bacteroides uniformis as a biomarker in colorectal cancer adopts some or all of the technical solutions of the above embodiments, it at least has all the beneficial effects brought by the technical solutions of the above embodiments, and will not be elaborated here one by one.

[0057] In some alternative embodiments, the area under the receiver operating characteristic curve of the Bacteroides uniformis

[0058] ≥0.734.

[0059] In the embodiments of the present application, since the receiver operating characteristic curve (ROC) is a tool for evaluating the performance of a classifier at different thresholds, when evaluating the predictive ability of bacteria, the receiver operating characteristic curve can be used to measure the trade-off between the true positive rate (also known as sensitivity) and the false positive rate of the classifier. The closer the area under the ROC curve AUC (Area Under the Curve) is to 1, the more accurate the classification result. When the AUC is between 0.7 and 0.9, the classification result has a certain degree of accuracy. Therefore, by defining the area under the receiver operating characteristic curve of Bacteroides uniformis to be above 0.734, it can be determined that Bacteroides uniformis is a beneficial bacterium that is conservative and has potential screening ability.

[0060] Based on a general inventive concept, the embodiments of the present application provide a reagent for screening or assisting in the screening of colorectal cancer, and the reagent includes an agent for detecting the abundance of Bacteroides uniformis.

[0061] This reagent is realized based on the application of the above-mentioned Bacteroides uniformis as a biomarker in colorectal cancer. The specific principle of the application of the Bacteroides uniformis as a biomarker in colorectal cancer can be referred to the above embodiments. Since this reagent adopts some or all of the technical solutions of the above embodiments, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, and will not be elaborated one by one here.

[0062] Based on a general inventive concept, the embodiments of the present application provide a reagent for evaluating or assisting in the evaluation of the prognosis of colorectal cancer, and the reagent includes an agent for detecting the abundance of Bacteroides uniformis.

[0063] This reagent is realized based on the application of the above-mentioned Bacteroides uniformis as a biomarker in colorectal cancer. The specific principle of the application of the Bacteroides uniformis as a biomarker in colorectal cancer can be referred to the above embodiments. Since this reagent adopts some or all of the technical solutions of the above embodiments, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, and will not be elaborated one by one here.

[0064] The following further elaborates the present application in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present application and not to limit the scope of the present application. The experimental methods without specific conditions noted in the following embodiments are usually determined according to industry standards. If there is no corresponding industry standard, they are carried out according to general international standards, conventional conditions, or the conditions recommended by the manufacturer.

[0065] Example 1

[0066] I. Preliminary preparation

[0067] 1. Experimental animals

[0068] Purchase 4-week-old Apc Min / + mice and 4-week-old C57BL / 6J wild-type mice from the Animal Experiment Center of Huazhong Agricultural University. All animal operations follow the relevant regulations of Huazhong Agricultural University on animal research.

[0069] 2. Black rice feed

[0070] The purified diet formula for mice was designed according to the AIN-93M diet, and the black rice used was the "Heishuai" strain from Hanzhong, Shaanxi. First, the components of the rice produced in the current year were determined to determine the contents of its protein, calcium, phosphorus and other components. Subsequently, the formula was balanced using the reference components provided by the AIN-93M diet, which was produced by Shenzhen Ruidi Diet Technology Co., Ltd. in China. First, the rice was mixed with the feed and then ground into powder at 4 °C. Then it was evenly mixed with components such as casein, and extrusion and granulation were carried out at room temperature. After granulation, the diet was dried at 32 °C for 18 h to a moisture content of 12%, vacuum-packed, irradiated for sterilization, and stored at -20 °C. The rice used for diet production is produced once a year, and the raw materials are provided by the company. Diet production is carried out once every three months to ensure freshness and quality.

[0071] 3. Main instruments and equipment: As shown in Table 1

[0072] Table 1 Main instruments and equipment

[0073]

[0074]

[0075] 4. Main reagents and reagent kits: As shown in Table 2.

[0076] Table 2 Main reagents and reagent kits

[0077] Names of main reagents Manufacturer or company Chloroform Equipment Section, Huazhong Agricultural University Isopropanol Equipment Section, Huazhong Agricultural University Absolute ethanol Equipment Section, Huazhong Agricultural University Paraffin Equipment Section, Huazhong Agricultural University 10% formalin solution Equipment Section, Huazhong Agricultural University Fetal bovine serum Gibco Hematoxylin staining solution Beijing Zhongshan Jinqiao Biotechnology Co., Ltd. Eosin solution Beijing Zhongshan Jinqiao Biotechnology Co., Ltd. Ki-67 monoclonal antibody Abcam, USA ZO-1 monoclonal antibody Abcam, USA Fecal DNA kit OMEGA, USA Trizol Reagent Applied Biosystems, USA Taq DNA polymerase Promega, USA Taq DNA Buffer Promega, USA dNTP mixture HyTest Ltd, Finland Primer Wuhan Qingke Innovation Biotechnology Co., Ltd. DNA marker TaKaRa Reverse transcription kit Thermo Scientific Neutral resin Beijing Zhongshan Jinqiao Biotechnology Co., Ltd. DAB chromogenic kit Beijing Zhongshan Jinqiao Biotechnology Co., Ltd. AxyPrep DNA gel extraction kit OMEGA, USA Transfer buffer Gibco

[0078] 5. Preparation of main solutions:

[0079] (1) PBS buffer solution: Measure 2000 mL of distilled water, add the pre-purchased phosphate buffer powder to the distilled water, stir repeatedly. After the powder is completely dissolved, measure the pH value of the solution. A pH value in the range of 7.2 - 7.4 is considered qualified. The contents of each component are as follows: Na 2 HPO 4 : 1.22 g, NaCl: 16.03 g, KCl: 0.40 g, KH 2 PO 4 : 0.38 g.

[0080] (2) Citric acid tissue antigen retrieval solution: According to the required amount of antigen retrieval solution, take the citric acid tissue antigen retrieval solution (100×) and dilute it at a volume ratio of 1:100; for example: take 10 mL of the citric acid tissue antigen retrieval solution (100×) and add 990 mL of distilled water to make 1000 mL of the citric acid tissue antigen retrieval solution.

[0081] (3) DAB Chromogenic Solution: The DAB chromogenic solution needs to be freshly prepared before use. If there is a little precipitation during preparation, it can be filtered and used, which will not affect the staining effect and quality. Preparation method: In the equipped small test tube, add 1 mL of DAB buffer solution, 1 drop (50 μL) each of DAB substrate (20×) and DAB chromogen (20×) in sequence to prepare the DAB chromogenic solution. The prepared DAB chromogenic solution should be stored in the dark and is valid within 60 minutes.

[0082] (4) Preparation method of periodic acid-alcohol solution: Periodic acid (HIO 4 ·2H 2 O): 0.4 g, 95% alcohol: 35 mL, M / 5 sodium acetate (2.72 g + distilled water 100 mL): 5 mL, distilled water: 10 mL. Store in the refrigerator, use a brown bottle, and it can be used for two weeks.

[0083] (5) Schiff's solution: Add 0.5 g of basic fuchsin to 100 mL of distilled water, shake the Erlenmeyer flask constantly for 5 minutes to dissolve it completely. After cooling to 50 °C, filter and add 10 mL of hydrochloric acid, then cool to 25 °C, add 0.5 g - 1 g of sodium metabisulfite, let it stand at room temperature for at least 24 hours, and then store it sealed in the refrigerator.

[0084] (6) Preparation of Schiff's alcohol solution: Schiff's solution 11.5 mL, 1N HCl: 0.5 mL, pure alcohol 23 mL.

[0085] (7) Carnoy's fixative: Pure alcohol 60 mL, glacial acetic acid 10 mL, chloroform 30 mL.

[0086] II. Test methods

[0087] 1. Mouse feeding:

[0088] Mice in each group were adaptively fed with standard diet and drinking water for one week; among them, the feed for feeding mice was strictly irradiated and sterilized, and the water bottles, breeding cages and bedding were all sterilized by high-pressure steam before use. The breeding environment was SPF level, the environmental temperature was set at 24 °C ± 1 °C, and the automatic lighting device was adjusted according to the daylight time. All procedures followed the guidelines approved by the Animal Experiment Ethics Committee of Huazhong Agricultural University.

[0089] 2. Modeling and feeding of mice with conventional colorectal cancer model:

[0090] In the azoxymethane / dextran sulfate sodium (AOM / DSS) model, 4-week-old C57BL / 6 mice (Hunan SJA Laboratory Animal Co., Ltd.) were taken for one week of adaptive feeding, and then randomly divided into 2 groups according to body weight and fed with black rice feed (Readydietech Co., Ltd., Shenzhen, China) and control feed respectively, with 45 mice in each group.

[0091] At 8 weeks of age, mice were intraperitoneally injected with 10 mg / kg of AOM (Merck, Darmstadt, Germany), and then administered 3 cycles of DSS (MP Biomedicals, Solon, OH) to mimic colitis-associated CRC. Each cycle was 7 days, with drinking water containing 2.0% DSS, and regular drinking water for 14 days.

[0092] The Apc Min / + mice were randomly divided into two groups: the black rice group and the control group, with 45 mice in each group. The AOM / DSS or Apc Min / + model mice were harvested on the 126th and 154th days, respectively. According to European Directive 2010 / 63, same-sex animals (4 mice per cage) should be housed in pairs during the test period to ensure their social needs are met; the cages of mice in the same group were randomly arranged, and the mice were placed in a temperature-controlled specific pathogen-free environment with a 12 h light / dark cycle. During the whole experiment, mice were not randomly culled, and all procedures were in accordance with the guidelines approved by the Animal Experiment Ethics Committee of Huazhong Agricultural University.

[0093] 3. Specimen collection and preservation:

[0094] In the 14th and 22nd weeks of feeding the Apc Min / + mice, a certain amount of ice cubes and sterile cryotubes were prepared to collect fresh feces of the mice. The collected feces were put into sterile cryotubes and quickly transferred to an -80 °C refrigerator for preservation, to be used for intestinal flora detection. Detection was carried out using metagenomic and metabolomic sequencing methods, and bioinformatics analysis was performed on the detection results.

[0095] In the periods after the first DSS treatment and the third DSS treatment during the AOM / DSS mouse model establishment, a certain amount of ice cubes and sterile cryotubes were prepared to collect fresh feces of the mice. The collected feces were put into sterile cryotubes and quickly transferred to an -80 °C refrigerator for preservation, to be used for intestinal flora detection. Detection was carried out using metagenomic and metabolomic sequencing methods, and bioinformatics analysis was performed on the detection results.

[0096] After collecting blood from the orbital sinus of the mice, the mice were sacrificed by spinal cord transection. The entire intestinal segment from the stomach to the anus was isolated. The esophagus and stomach were separated by cutting the gastroesophageal junction, and the rectum near the anal margin was cut, leaving the rectum. The stomach, duodenum, small intestine, colon, and rectum were all removed. The removed organs were gently rinsed in PBS buffer to remove intestinal contents. The small intestine segment and large intestine segment were placed on a glass slide, longitudinally incised with surgical scissors and spread out flat. Filter paper was used to absorb the liquid. The size of the adenomas was measured with a ruler, the number of intestinal adenomas in each mouse was recorded, and photographs were taken according to the distribution and diameter of the adenomas. The intestine was rolled up from one end along the longitudinal axis of the intestine with forceps, and tissue pins were passed through the intestinal tissue for fixation to prevent the rolled-up intestinal tissue from spreading. Then it was placed in a solution containing 10% formalin, and the tissue containing the formalin solution was stored at room temperature for subsequent immunohistochemical staining, PAS staining, HE staining and other experiments. The Apc Min / + The tissues of the small intestine and large intestine segments of Apc

[0097] 4. HE staining:

[0098] Isolate Apc Min / + The small intestine and large intestine segments of mice and WT mice were fixed with 4% paraformaldehyde, and paraffin sections were made for HE staining to observe the histological morphology of the small and large intestines. The Image J software was used to analyze the differences in the integrity of the intestinal tissue structure between female and male Apc Min / + female and male mice.

[0099] The specific steps for the production of HE-stained paraffin sections are as follows:

[0100] (1) Specimen collection and fixation: Fresh tissue blocks of animals (usually no more than 0.5 cm thick) were put into the pre-prepared fixative (10% formalin, Bouin's fixative) to denature and coagulate the proteins of tissues and cells, so as to prevent autolysis after cell death or decomposition by bacteria, thus maintaining the original morphological structure of cells.

[0101] (2) Dehydration and clearing: Generally, alcohol with a low concentration to a high concentration was used as a dehydrating agent to gradually remove the water in the tissue block. Then the tissue block was placed in xylene, a clearing agent that is soluble in both alcohol and paraffin, to clear the tissue block, replacing the alcohol in the tissue block with xylene before it could be infiltrated with wax and embedded.

[0102] (3) Impregnation with wax and embedding: Place the cleared tissue blocks into the melted paraffin in a wax melting box and keep it warm; after the paraffin has completely penetrated the tissue blocks, perform embedding: First, prepare a container (such as folding a small paper box), pour in the melted paraffin, and quickly pick up the tissue blocks saturated with paraffin and place them into it. Let it cool and solidify into a block; only when the embedded tissue blocks become hard can they be cut into very thin sections on a microtome.

[0103] (4) Sectioning and mounting: Fix the embedded wax blocks on the microtome and cut them into thin sections, generally 5μm - 8μm thick. The cut thin sections often wrinkle and need to be flattened in warm water and then mounted on glass slides and dried in an incubator at 45°C.

[0104] (5) Dewaxing: Hematoxylin and Eosin (HE) staining is commonly used to increase the color differences of various parts of the tissue cell structure for easy observation; among them, hematoxylin (H) is a basic dye that can stain the cell nucleus and ribosomes in the cell blue - purple, and the structures stained by basic dyes are basophilic; eosin (E) is an acidic dye that can stain the cytoplasm red or light red, and the structures stained by acidic dyes are acidophilic; before staining, the paraffin in the sections must be removed with xylene, and then through alcohol from high concentration to low concentration, and finally into distilled water, then it can be stained.

[0105] (6) Staining:

[0106] 1) Place the sections that have been put into distilled water into an aqueous hematoxylin solution and stain for several minutes.

[0107] 2) Differentiate in acid water and ammonia water for several seconds each.

[0108] 3) Rinse with running water for 1h and then place in distilled water for a moment.

[0109] 4) Dehydrate in 70% and 90% alcohol for 10 minutes each.

[0110] 5) Stain in alcoholic eosin staining solution for 2 - 3 minutes.

[0111] 6) Dehydration and clearing: The stained sections are dehydrated with absolute alcohol and then cleared with xylene.

[0112] 7) Sealing: Drop Canada balsam on the cleared sections, cover with a coverslip for sealing. After the balsam is slightly dry, attach a label, and the section specimens can be used.

[0113] Result analysis: HE is a method for examining tumor cells. Under the microscope, the nuclei of the cells appear purple - blue, the cytoplasm appears light rose - red, and the red blood cells appear light red.

[0114] 5. PAS staining:

[0115] Paraffin sections of large intestine sections Colon sections were dewaxed and washed with distilled water; incubated with 1% periodic acid solution (Sigma-Aldrich) for 10 min, then incubated with Schiff's reagent (Sigma-Aldrich) for 40 min, then incubated with hematoxylin dye for 5 min, and rinsed with running water for 5 min; finally, dehydrated, transparentized, and sealed as usual.

[0116] PAS positive cells are red and cell nuclei are blue. PAS staining is used to detect the number of goblet cells in intestinal tissue and evaluate the intestinal mucus barrier function.

[0117] 6.ELISA analysis:

[0118] Before killing and sampling, the mice were bled through the eye sockets. The blood was allowed to stand at room temperature for 30 minutes and then centrifuged at 3000 rpm for 20 minutes. The supernatant was taken and the serum was stored at -80°C after aliquoting to avoid repeated freezing and thawing. The ELISA kit was used to detect the level of gastrointestinal cancer marker CA199 in the blood of mice in different groups. The specific experimental steps are as follows:

[0119] Take out the required strips from the aluminum foil bag after equilibration at room temperature for 60 minutes; add 50 μL of the sample to be tested to the sample well and nothing to the blank well; add 100 μL of the detection antibody labeled with horseradish peroxidase (HRP) to each well of the standard well and the sample well except the blank well, seal the reaction wells with a sealing film, and incubate at 37°C in a water bath or incubator for 60 minutes; discard the liquid, pat dry on absorbent paper, fill each well with washing solution (350 μL), let stand for 1 minute, shake off the washing solution, pat dry on absorbent paper, and repeat the washing process 5 times (or use a plate washer); add 50 μL of substrate A and B to each well, incubate at 37°C in the dark for 15 minutes; add 50 μL of stop solution to each well, and measure the OD value of each well at a wavelength of 450 nm within 15 minutes.

[0120] With the OD value of the measured standard as the horizontal axis and the concentration value of the standard as the vertical axis, the standard curve is drawn using relevant software, and a linear regression equation is obtained. The OD value of the sample is substituted into the equation to calculate the concentration of the sample.

[0121] 7. Immunohistochemical staining:

[0122] (1) Using the strept avidin-biotin complex (SABC) method, the specific experimental steps are as follows:

[0123] Step 1: Dewaxing and Hydration: Sequentially immerse the glass slide with colon tissue in xylene solution I for 10 min, xylene solution II for 10 min, absolute ethanol I for 10 min, absolute ethanol II for 10 min, 95% ethanol solution for 10 min, 90% ethanol solution for 10 min, 80% ethanol solution for 10 min, and 75% ethanol solution for 10 min. After complete dewaxing, rinse the glass slide with running water for 5 min, then immerse it in distilled water for 1 min, and take out the glass slide.

[0124] Step 2: Blocking: Put the dewaxed tissue section into H 2 O 2 solution with a concentration of 3%, soak for 20 min, remove the endogenous peroxidase, and then rinse with running water 2 times, 5 min each time.

[0125] Step 3: Antigen Retrieval: Put the blocked tissue section into citrate buffer, adjust the pH = 6.0, and heat to retrieve the antigen at medium heat (92°C - 98°C).

[0126] Step 4: Serum Blocking: After gradually cooling to room temperature, rinse with PBS 3 times, 5 min each time, and wipe with absorbent paper to remove the liquid around the tissue section. Quickly add 5% BSA blocking solution, transfer to a wet box, and place at room temperature for 20 min.

[0127] Step 5: Add Primary Antibody (volume ratio 1:1000): Take out the glass slide with the tissue section from the wet box, and remove the excess liquid with absorbent paper. Drop the primary antibody and incubate overnight at 4°C.

[0128] Step 6: Add Secondary Antibody (volume ratio 1:1000): Take out the wet box the next day after overnight incubation, place at room temperature for 30 min, rinse with PBS 3 times, 5 min each time, wipe with absorbent paper to remove the liquid around the tissue section, add biotinylated secondary antibody, put it back into the wet box, and incubate in an incubator at 37°C for 30 min.

[0129] Step 7: Drop SABC: Take out the glass slide with the tissue section, rinse with PBS solution 3 times, 5 min each time, gently shake to remove the excess liquid on the surface of the glass slide. Then add SABC reagent with a dropper, put it back into the wet box, and incubate in an incubator at 37°C for 30 min.

[0130] Step 8: Color Development: Take out the glass slide with the tissue section from the incubator, rinse with PBS 3 times, 5 min each time; under room temperature conditions, perform DAB color development and observe the intensity of the color development reaction under a microscope.

[0131] Step 9: Counterstaining: Rinse the slide with running water for 15 min until complete, then immerse it in distilled water for 1 min, and counterstain with hematoxylin for about 90 s.

[0132] Step 10: Differentiation with hydrochloric acid alcohol: After counterstaining, carefully rinse the slide with running water for 15 min, then immerse it in distilled water for 1 min; take out the slide, differentiate it with hydrochloric acid alcohol for 2 s, and then carefully wash it with running water again for 15 min to fully blue the tissue section, and take out the slide. Dehydration and clearing: Sequentially place the blued tissue section into 75% ethanol solution, 80% ethanol solution, 95% ethanol solution, absolute ethanol I, absolute ethanol II, and xylene for 5 min each.

[0133] Mounting: Mount the slide with neutral balsam, and observe and photograph it under the microscope after it dries.

[0134] (2) Result determination

[0135] Use the tissue section that is only dropped with PBS solution but not added with the primary antibody as a negative control. Use the tissue section added with the known antigen expression as a positive control. Observe the staining condition of each tissue section under the microscope. The matrix area shows light blue, the negative cell nuclei show blue, the gaps show white, and the Ki-67 positive is that the cell nuclei are stained brown-yellow. Continuously count 100 crypts and count the number of cells with brown-yellow cell nuclei. The statistical result is the percentage of positive cells.

[0136] 8. Immunofluorescence:

[0137] (1) Wash the cells 3 times with PBS until clean.

[0138] (2) Fix the cells with 4% paraformaldehyde for 20 min, and wash the cells 3 times with PBS for 5 min each time.

[0139] (3) Permeabilize the cells with 0.5% Triton X-100 at room temperature for 20 min, and wash the cells 3 times with PBS for 5 min each time.

[0140] (4) After sucking out the PBS, block the cells with the blocking solution at room temperature for 1 h.

[0141] (5) After sucking out the blocking solution, add a sufficient amount of diluted primary antibody, incubate overnight at 4°C or for 2 h at 37°C, and wash the cells 3 times with PBS for 5 min each time.

[0142] Note: The following steps are all operated in a relatively dark place away from light.

[0143] (6) After sucking out the PBS, add a sufficient amount of diluted fluorescent secondary antibody, incubate at 37°C for 1 h, and wash the cells 3 times with PBS for 5 min each time.

[0144] (7) After aspirating PBS, incubate the cells with DAPI stain for 5 min to stain the nuclei of the cells, and then wash with PBS three times, 5 min each time;

[0145] (8) The washed cells were observed under an OLYMPUS IX5I fluorescence inverted microscope and images were collected.

[0146] 9. Real-time fluorescence quantitative PCR:

[0147] Isolation of Apc Min / + The small intestine and large intestine segments of mice were ground with liquid nitrogen to extract tissue RNA and protein. Real-time quantitative PCR and Western blot were used to detect the expression of genes related to intestinal barrier function, including ZO-1, Occludin and Claudin3 genes in Apc Min / + Expression levels in male and female mice.

[0148] (1) Realtime-PCR technology:

[0149] Step 1: Extraction and reverse transcription of tissue RNA Specific steps:

[0150] 1) Take an appropriate amount of mouse colon tissue (about 5 mg), treat it with DEPC water, put it in a mortar, grind the tissue into powder in liquid nitrogen, scrape all the tissue powder with a spoon, add it to an RNase-free EP tube, and then add 1 mL of Trizol lysis solution.

[0151] 2) Place on ice for 5 min to allow it to completely melt, then add 200 μL of chloroform, cover the EP tube tightly, and shake vigorously for 15 s to mix thoroughly.

[0152] 3) Place in an ice bath for 5 minutes, then place in a centrifuge and centrifuge at 12000 rpm for 15 minutes; aspirate 400 μL of the upper aqueous phase and place in another EP tube, then add an equal volume of isopropanol to the EP tube, shake well and allow to precipitate for 10 minutes at room temperature.

[0153] 4) Centrifuge at 4°C for 15 min, set the speed to 12,000 rpm, discard the supernatant, add anhydrous ethanol, and centrifuge at the same speed for 5 min. Discard the supernatant again, add 1 mL of 70% ethanol solution, and centrifuge at the same speed for 5 min.

[0154] 5) Discard the supernatant, let stand at room temperature for 10 min, and add appropriate amount of DEPC water.

[0155] 6) Total RNA concentration determination: Pipette 2 μL of the mixed solution from the EP tube. Using ultraviolet spectrophotometry, measure the OD values for RNA concentration at wavelengths of 260 nm and 280 nm. Thus, obtain the RNA content, and ensure that the measured A260 / A280 ratio is within the range of 1.8 - 2.0.

[0156] 7) Refer to the reverse transcription kit instructions to establish a 20 μL reverse transcription system. The experimental procedure is as follows:

[0157] Perform the operation on ice. Take 1 μg - 5 μg of total RNA (determined according to the RNA concentration), add 2 μL each of Oligo(dT) and Super Pure dNTP to a nuclease-free EP tube, add DEPC water to 14.5 μL, incubate in a 70 °C water bath for 5 min, and then in an ice bath for 2 min. Sequentially add the RNA enzyme inhibitor: 0.5 μL, M-MLV: 1 μL, and 5×First-Strand Buffer: 4 μL, and mix well by shaking. Incubate at 42 °C for 50 min for amplification, then raise the temperature to 95 °C and maintain for 5 min to terminate the reaction.

[0158] Step Two: PCR primer synthesis:

[0159] Use the Nucleotied BLAST database (http: / / blast.ncbi.nlm.nih.gov / Blast.cgi) to verify the set primer sequences in the database according to the reference literature. The primer synthesis is completed by Genewiz Biotechnology Company, and the primer sequences are shown in Table 3.

[0160] Table 3 Primer names and sequences

[0161]

[0162] Step Three: The Realtime-PCR reaction system is shown in Table 4.

[0163] Table 4 PCR reaction system

[0164] Components of reaction system Volume (μL) SYBR Green I Mix 10 Forward primer (10 μmol / L) 0.5 Reverse primer (10 μmol / L) 0.5 Control diet NA template 1 <![CDATA[Add ddH 2 O to a volume of]]> 20

[0165] Step Four: Set the Realtime-PCR reaction conditions. Pre-denature at 95 °C for 10 min, denature at 95 °C for 10 s, anneal at 60 °C for 30 s, extend at 72 °C for 20 s, and repeat the above reactions 40 times. After the amplification is completed, automatically read the melting curve. Raise the temperature at a rate of 0.1 °C per second from 65 °C to 95 °C while performing fluorescence monitoring.

[0166] Step 5: Results analysis: Relative mRNA expression was performed using the standard ΔΔCT method to calculate the fold change of each sample normalized to the housekeeping gene and PCR quantification was analyzed.

[0167] 10.Western blot:

[0168] Extraction of colon tissue protein: Take out part of the mouse colon tissue stored in a -80℃ refrigerator, put it into a homogenizer, and operate on ice. Prepare a lysis solution containing protease inhibitors at a volume ratio of 1:100, add an appropriate amount of lysis solution according to the weight of the colon tissue, homogenize, and stand at room temperature for 30 minutes to completely lyse the colon tissue protein, and then collect the lysed tissue protein. Centrifuge for 10 minutes at a speed of 13000rpm, and collect the supernatant to detect the protein concentration.

[0169] Step 1: Tissue protein concentration determination

[0170] Preparation of BCA working solution: Prepare BCA reagent and Cu reagent in a volume ratio of 50:1.

[0171] Dilution of protein standard BSA: Take out a 0.5mL EP tube, use a pipette to draw 10μL of BSA standard, put it into the EP tube, draw 90μL of PBS again and add it to the EP tube, shake it gently, and the final concentration is 0.5mg / mL of dilution. The standard is added to the standard well according to the concentration gradient (0μL, 2μL, 4μL, 6μL, 12μL, 16μL, 20μL). The remaining amount of PBS added is 20μL-V standard. Dilute the sample tissue 50 times, measure 20μL and add it to the 96-well plate; add 200μL of working solution to all wells, let it stand for 30min, and control the temperature at 37℃.

[0172] Microplate reader determination: Use a spectrophotometer to determine the wavelength at 562nm and record the absorbance value. Calculate according to the standard curve to obtain the protein concentration value in each group of samples. The obtained concentration can be expanded 50 times to calculate the protein concentration value of the real tissue sample.

[0173] Prepare a buffer solution containing β-mercaptoethanol, calculate the liquid volume required for 100 μg of tissue protein, add it to each loading well, then add 4x loading buffer, and use PBS to correct and fill the loading wells with insufficient volume according to the total amount of liquid in the loading wells (20 μL system), put it in boiling water for 10 minutes for denaturation, cool naturally at room temperature, centrifuge, and let it stand for use.

[0174] Step 2: Gel preparation. Align the glass plates and clamp them tightly in the holder. During the operation, ensure that the two glasses are aligned to prevent glue leakage. Prepare the separating gel according to the experimental arrangement, shake it immediately after adding TEMED, and then pour the gel. Add anhydrous ethanol to seal the gel until the liquid level is flush with the glass plate. After about 45 minutes, pour off the anhydrous ethanol on the upper layer of the gel and blot the remaining liquid with absorbent paper. Prepare 5% stacking gel according to the previous method, shake it immediately after adding TEMED, and then pour the gel. Fill the remaining space with stacking gel and then insert the comb into the stacking gel until the gel solidifies;

[0175] Step 3: Electrophoresis. Add the sample into the electrophoresis wells and perform SDS-PAGE gel electrophoresis. The voltage for the stacking gel is 80V, and the separating gel is 120V. Stop the electrophoresis when the bromophenol blue just runs out;

[0176] Step 4: Blotting. First, prepare 6 pieces of 7 cm × 9 cm filter paper and a PVDF membrane with appropriate size of 0.22 μm. The PVDF membrane should be activated with methanol before use. Place the blotting clip, two sponge pads, a glass rod, filter paper, and the activated PVDF membrane in a basin containing transfer buffer. Then open the clip and keep the black side horizontal. Place a sponge and three layers of filter paper on the pad. Carefully peel off the separating gel and cover it on the filter paper, then cover the membrane on the gel, and use a glass rod to remove the air bubbles. Cover three layers of filter paper on the membrane and remove the air bubbles. Finally, cover another sponge pad. Transfer at 200 mA, and the transfer time depends on the size of the target protein;

[0177] Step 5: Immunoreaction. Incubate the transferred membrane on a shaker at room temperature with 5% non-fat milk (prepared with TBST) for 1 h to block it. Then dilute the primary antibody and incubate it overnight at 4°C. The next day, wash it three times with TBST on a shaker at room temperature, 10 min each time. Dilute the secondary antibody 3000 times with TBST, incubate it at room temperature for 30 min, and then wash it three times with TBST on a shaker at room temperature, 10 min each time;

[0178] Step 6: ECL chemiluminescence detection. Mix equal volumes of reagent A and reagent B in a centrifuge tube. Place the membrane with the protein side up and fully contact it with this mixture for 2 min, then remove all the residual liquid, wrap it with plastic wrap, and put it into the GE Image Quant LAS4000mini for imaging analysis. Quantify the gray value using ImageJ software. The relative expression level of the target protein = gray value of the target protein / gray value of the internal reference protein.

[0179] 11. Co-culture of bacteria and cells:

[0180] Take out the cell cryopreservation tube from liquid nitrogen, dissolve it by swirling in a 37°C water bath, transfer the cell cryopreservation solution to a cell culture flask with a pipette, add 3 mL of the prepared cell culture medium, pipette gently to mix evenly, and shake the culture flask crosswise to make the cells evenly distributed. Then incubate the cells at 37°C in an incubator with 5% CO2 Cultivate in an incubator and change the medium after 6 h.

[0181] Change the culture medium in a timely manner according to the growth status of the cells: When the cell culture medium turns yellow, the culture medium should be changed, generally once every 24 h. After the cells grow to a density of 80% - 90%, digest and passage them.

[0182] Cell passage process: Aspirate the old culture medium, rinse twice with 1×PBS, add 200 μL of 0.25% trypsin to make it fully contact the cells, place it in the incubator for digestion for about 2 min. After the cells shrink and become round, quickly add about 10 mL of cell culture medium to the culture flask to terminate the digestion of trypsin. Then use a pipette to blow the cell wall to make the cells fall off, and continue to blow the culture medium until it becomes a single-cell suspension. Finally, inoculate it into 2 cell flasks at a ratio of 1:2 and continue to culture.

[0183] Seed CRC cells in a 24-well plate (20,000 cells / well) containing Eagle medium (Gibco BRL, Grand Island, New York) with 10% fetal bovine serum. Expose the cells to the functional strain and infect for 4 h under anaerobic (oxygen-tolerant) conditions. Then replace the medium containing bacteria with Eagle medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin 40 μg / mL for co-culture. Digest the cells with trypsin and count the number of cells every day.

[0184] 12. Detection of cell cycle:

[0185] (1) Cell collection: Digest the treated cell sample group with 0.25% trypsin containing EDTA to form a single-cell suspension. After terminating the digestion with DMEM + 10% FBS, centrifuge at 200 g for 3 min to precipitate the cells.

[0186] (2) Cell fixation: Suspend the centrifuged cells with 500 mL of pre-cooled PBS, centrifuge at 200 g for 3 min to precipitate the cells, discard the supernatant to fully remove the residual FBS and trypsin; add 500 mL of 70% ethanol pre-cooled at -20°C to suspend the cells, and fix at 4°C for 30 min or at -20°C overnight (here, when adding ethanol, pay attention to blowing the cells while adding to prevent the cells from clumping during fixation, which is difficult to disperse and affects subsequent detection).

[0187] (3) RNA removal: Centrifuge the fixed cells at 200 g for 5 min to precipitate the cells and discard the supernatant; suspend the cells with 500 mL of pre-cooled PBS, centrifuge at 200 g for 3 min to precipitate the cells, and discard the supernatant; incubate with 500 mL of 100 mg / mL RNase A at 37°C for 30 min to fully degrade the intracellular RNA.

[0188] (4) PI staining: Centrifuge the cells treated with RNase A at 200 g for 3 min to precipitate, and discard the supernatant; Suspend the cells with 200 mL of PI staining solution at a concentration of 50 mg / mL, and place them in the dark on ice for 30 min for staining.

[0189] (5) Instrument testing: Use a BD flow cytometer to test the samples. Pay attention to correcting the parameters before testing to optimize the experimental results; Collect 10,000 cells for each sample for cell cycle analysis.

[0190] (6) Result analysis: Analyze the collected cell cycle results using Summit 4.0 software, calculate the cell ratios of G1 / G0, S phase, and G2 / M, repeat the experiment 3 - 4 times, and statistically analyze the results.

[0191] III. Bioinformatics analysis:

[0192] 1. Programming languages and main software are shown in Tables 5 and 6.

[0193] Table 5 Main programming languages

[0194] Name Version Website Linux 4.15.0 http: / / www.gnu.org / / software / bash / Python 3.6.3 https: / / ww.python.org / R 4.2.1 https: / / www.r-project.org /

[0195] Table 6 Main bioinformatics software

[0196]

[0197] After obtaining the original sequencing data of this application, all analyses are carried out on a local workstation.

[0198] 2. Metagenomic data analysis:

[0199] (1) Library construction and sequencing:

[0200] Mail the collected fecal samples of mice to BGI (Wuhan, China) for the construction of PE libraries. The sequencing process and instruments are provided by BGI.

[0201] According to the conventional metagenomic analysis process, including data quality control of high-quality reads, host genome alignment, cleaning of host contamination, and then downstream abundance construction and species / function difference analysis. The specific analysis process of this study is as follows:

[0202] Sequence quality control: After the data is downloaded, call the KneadData process on the Linux server for metagenomic data quality control. Use Trimmomatic to shear the adapter sequences of reads, and remove reads with a length less than 50 bp, an average base quality value lower than 20 bp, and reads containing N bases to obtain better-quality sequences required for subsequent analysis.

[0203] Removing host contamination: Invoke the KneadData process to align the host genomic sequences to the corresponding database and remove the host sequences. Use Bowtie2 to align the genomic reads to the human reference genome (hg19). After removing the aligned host contamination reads, merge the paired-end reads for subsequent microbial species classification.

[0204] Taxonomic annotation: Invoke MetaPhlAn2 to calculate species abundances. Use Bowtie2 to compare with the microbial genomic data. Combine the obtained species abundance data into a table and classify it according to kingdom, phylum, class, order, family, genus, and species. And this application only conducts differential analysis for bacterial species.

[0205] Microbial diversity analysis: For the study of microbial community diversity or ecological diversity, diversity indices are usually used for evaluation and comparison. Commonly used diversity indices include alpha diversity analysis and beta diversity analysis. Alpha diversity refers to the diversity within a specific environmental area or ecosystem, mainly an indicator reflecting species richness and the evenness of individual distribution in the community. There are usually four indices to describe alpha diversity, namely: Observed species, Chao1, Shannon, and Simpson. Among them, Observed species refers to the number of OTUs (species) actually contained in the sample, and Chao1 refers to the estimated number of OTUs in the sample. Both of these indices can reflect the level of species number in the sample, and the higher these two indices, the higher the species richness in the sample. The Shannon and Simpson indices can reflect the abundance distribution of each species in the sample. The more OTUs in the sample and the more uniform the abundance distribution, the higher the alpha diversity index. Calculate the alpha diversity indices, the Shannon index and the Simpson index, using the diversity function of the vegan package (version 2.6 - 4) (Dixon 2003).

[0206] Beta diversity analysis: The relative abundance tables classified by species, genus, and phylum were used to calculate beta diversity respectively. The vegdist function in the vegan package (version 2.6 - 4) was used to calculate the distance between species. The Bray - Curtis - based distance was selected, and the principal co - ordinates analysis (PCoA) was performed using the pcoa function in the ape package (version 5.6.2). In the PCoA analysis, the differences between different groups were analyzed using permutational multivariate analysis of variation (PERMANOVA). The significance of the analysis was calculated using the adonis2 function in the pairwiseAdonis package (version 0.4). A P value < 0.05 was considered statistically significant.

[0207] Differential analysis: We performed differential analysis on mice in each group. The analysis method was the Wilcoxon rank - sum test, and the obtained P values were corrected to obtain FDR values. Screening was performed using FDR values and Log2 fold change (log2FC). Up - regulation was defined as FDR < 0.05 and log2FC ≥ 1, and down - regulation was defined as FDR < 0.05 and log2FC ≤ - 1.

[0208] Microbial genome and pathway annotation: Humann3 is a computational tool for functional group analysis, used to analyze metagenomic sequencing data, especially for gene function analysis of microbial communities.

[0209] 1) Bowtie2 was used to accelerate nucleic acid - level search;

[0210] 2) Diamond was used to accelerate translated protein - level search;

[0211] 3) Analyze the functional profiles of known and unknown biological analysis groups: The MetaPhlAn2 and ChocoPhlAn pan - genome databases can obtain functional profiles more quickly and accurately;

[0212] 4) Obtain results at the genome, gene, and pathway levels: The UniRef database provides the definition of gene families; the MetaCyc pathway database provides the definition of gene pathways; MinPath provides the defined minimal pathway sets.

[0213] 3. Metabolome data analysis:

[0214] (1) Untargeted metabolome detection:

[0215] The collected mouse fecal samples were mailed to Metware Biotechnology Co., Ltd. (Wuhan, China) for metabolome sequencing using liquid chromatography-mass spectrometry (LC-MS). The instrument platform for this LC-MS analysis was the AB SCIEX ultra-high performance liquid chromatography tandem time-of-flight mass spectrometry UPLC-TripleTOF system. The sequencing process and instruments were provided by Metware Biotechnology Co., Ltd.

[0216] (2)TM broad-target metabolome processing and analysis:

[0217] Data preprocessing: Some of the metabolome data obtained from TM broad-target had null values, and the null values in the original data were filled with grouped data. For each metabolite in each group, if the sum of the missing value numbers of the samples was greater than 70% of the total sample size, the metabolite was removed; if the sum of the missing value numbers was less than 70% of the total sample size, the missing values were filled with half of the minimum value of the metabolite, and the data matrix was normalized by total sum. Then, variables with a relative standard deviation (RSD) greater than 20% in the QC quality control samples were removed, and logarithmic normalization (log10) was performed.

[0218] Differential metabolite analysis: Principal component analysis (PCA) was performed using the R software package PCAtools (version 2.0.0), and orthogonal partial least squares discriminant analysis (OPLS-DA) was performed using the R software package ropls (version 1.30.0) to obtain the VIP (Variable Importance in Projection) value. The Wilcoxon rank-sum test was used to perform differential analysis on male and female mice, and the obtained P values were corrected to obtain FDR values. Differential metabolites were screened using the thresholds of FDR < 0.05 and VIP ≥ 1. MetOrigin (Yu et al. 2022) (http: / / metorigin.met-bioinformatics.cn / home / ) was used for the source analysis of differential metabolites and the pathway enrichment analysis of metabolites from different sources (Metabolite Pathway Enrichment Analysis, MPEA). Finally, differential metabolites and enriched pathways of microbial and microbial-host co-metabolism were selected. Min / + Differential metabolite analysis: Principal component analysis (PCA) was performed using the R software package PCAtools (version 2.0.0), and orthogonal partial least squares discriminant analysis (OPLS-DA) was performed using the R software package ropls (version 1.30.0) to obtain the VIP (Variable Importance in Projection) value. The Wilcoxon rank-sum test was used to perform differential analysis on male and female mice, and the obtained P values were corrected to obtain FDR values. Differential metabolites were screened using the thresholds of FDR < 0.05 and VIP ≥ 1. MetOrigin (Yu et al. 2022) (http: / / metorigin.met-bioinformatics.cn / home / ) was used for the source analysis of differential metabolites and the pathway enrichment analysis of metabolites from different sources (Metabolite Pathway Enrichment Analysis, MPEA). Finally, differential metabolites and enriched pathways of microbial and microbial-host co-metabolism were selected.

[0219] (3) Targeted metabolomics detection:

[0220] 1) Sample pretreatment:

[0221] i. Take out the sample and thaw it on ice (subsequent operations are all carried out on ice);

[0222] ii. After the sample is thawed, mix it evenly and transfer 50 μL to a centrifuge tube;

[0223] iii. Add 250 μL of methanol solution (containing 10 μL of internal standard working solution with a concentration of 250 ng / mL) to a centrifuge tube. After vortexing for 3 min to mix evenly, let it stand in a -20 °C refrigerator for 30 min;

[0224] iv. Centrifuge at 12,000 r / min for 10 min at 4 °C, and transfer 150 μL of the supernatant to a new centrifuge tube;

[0225] v. After centrifuging again, transfer 100 μL of the supernatant to an injection vial and temporarily store it in a -20 °C refrigerator for LC-MS / MS analysis;

[0226] 2) Chromatographic and mass spectrometric acquisition conditions:

[0227] The data acquisition instrument system mainly includes an ultra-high performance liquid chromatography (Ultra Performance Liquid Chromatography, UPLC) (ExionLC TM AD, https: / / sciex.com.cn / ) and a tandem mass spectrometry (Tandem Mass Spectrometry, MS / MS) ( 6500 + https: / / sciex.com.cn / ).

[0228] i. The liquid phase conditions mainly include:

[0229] ii. Chromatographic column: Waters HSS T3 C18 column (1.8 μm, 100 mm × 2.1 mm i.d.);

[0230] iii. Mobile phase: Phase A, ultrapure water (containing 0.1% formic acid); Phase B, acetonitrile (containing 0.1% formic acid);

[0231] iv. Flow rate 0.35 mL / min; column temperature 40 °C; injection volume 5 μL;

[0232] v. Mobile phase gradient: At 0 min, A / B is 90:10 (V / V); at 1 min, A / B is 90:10 (V / V); at 8 min, A / B is 5:95 (V / V); at 9.5 min, A / B is 5:95 (V / V); at 9.6 min, A / B is 90:10 (V / V); at 12 min, A / B is 90:10 (V / V).

[0233] The mass spectrometric conditions mainly include:

[0234] The temperature of the electrospray ionization (ESI) source is 550 °C, the mass spectrometry voltage is 5500 V in positive ion mode, -4500 V in negative ion mode, and the curtain gas (CUR) is 35 psi. In the Q-Trap 6500+, each ion pair is scanned and detected according to the optimized declustering potential (DP) and collision energy (CE).

[0235] 3) Qualitative and quantitative principles:

[0236] The MWDB (Metware Database) database is constructed based on the standards, and qualitative analysis is performed on the data detected by mass spectrometry.

[0237] Quantification is carried out using the multiple reaction monitoring (MRM) mode of triple quadrupole mass spectrometry. In the MRM mode, the quadrupole first screens the precursor ions (parent ions) of the target substance to exclude the ions corresponding to other molecular weight substances to initially exclude interference; the precursor ions are broken into multiple fragment ions after being induced to ionize in the collision cell, and then the required characteristic fragment ions are selected by filtering through the triple quadrupole to exclude non-target ion interference, making the quantification more accurate and the repeatability better. After obtaining the mass spectrometry analysis data of different samples, the chromatographic peaks of all target substances are integrated, and quantitative analysis is carried out through the standard curve.

[0238] 4) Standard curve:

[0239] Standard solution with different concentrations of 0.001 ng / mL, 0.002 ng / mL, 0.005 ng / mL, 0.01 ng / mL, 0.02 ng / mL, 0.05 ng / mL, 0.1 ng / mL, 0.2 ng / mL, 0.5 ng / mL, 1 ng / mL, 2 ng / mL, 5 ng / mL, 10 ng / mL, 20 ng / mL, 50 ng / mL, 100 ng / mL, 500 ng / mL, 1000 ng / mL is prepared, and the chromatographic peak intensity data of the corresponding quantitative signals of the standards at each concentration are obtained; with the concentration ratio of external standard to internal standard or the concentration of external standard (Concentration Ratio or Concentration) as the abscissa, and the area ratio of external standard to internal standard or the area of external standard (Area Ratio or Area) as the ordinate, the standard curves of different substances are plotted.

[0240] 5) Sample content:

[0241] Substitute the integrated peak areas of all detected samples into the linear equation of the standard curve for calculation. After further substituting into the calculation formula, the content data of this substance in the actual sample is finally obtained. Note: The unit conversion has been performed in the calculation formula, and the corresponding values can be directly substituted to obtain the sample content.

[0242] Content of tryptophan in liquid sample (ng / mL) = c * V1 / 1000 / V2

[0243] Meaning of each letter in the formula:

[0244] c is the concentration value (ng / mL) obtained by substituting the integrated peak area of the sample into the standard curve;

[0245] V1 is the total volume of the extraction solution (μL);

[0246] V2 is the volume of the sample taken (mL).

[0247] (4) Targeted metabolomics analysis:

[0248] Rank the total concentrations of compounds among different groups and select the top 5 compounds for visualization.

[0249] 4. Public cohort metagenomics analysis:

[0250] Public data of six sets of colorectal cancer metagenomes (Feng et al. 2015, Vogtmann et al. 2016, Hannigan et al. 2018, Thomas et al. 2019, Yachida et al. 2019) were downloaded from the curatedMetagenomicData R package (Pasolli et al. 2017) respectively. In the available datasets, sample data of adenomas, other diseases such as type 2 diabetes, fatty liver, and hypertension were excluded.

[0251] To ensure consistency and data quality, the above standard sample data was further strictly filtered: (1) Samples with low alignment reads (≤1000000) were subsequently excluded, which may be attributed to low sequencing depth and host read contamination; (2) Outliers and suspected contamination cases were removed, including those with high species content (species read count ≥ 50% of the total) and low species content (species read count ≤ 0.01% * 1 / n; N is the number of samples in different disease states for each study); (3) Species with low abundance were discarded (species read count ≤ 0.001 of the total).

[0252] Meta-analyse method was used for public data metagenomic analysis, and the identification method of main confounding factors was consistent with previous studies.

[0253] Three sets of cohorts were randomly selected, and the data were divided into training and prediction at a ratio of 3:7. The biomarkers were verified by random forest and 10-fold cross-validation was performed. The performance of the classification model was analyzed by the receiver operating characteristic (ROC) using the R package pROC, and the ROC curve was plotted.

[0254] 5. Association analysis:

[0255] In this study, the corr.test function of the psych package (version 2.2.9) was used to select the spearman method to calculate the correlation between the differential microbial species in the metagenome and the differential metabolites in the metabolome. The Heatmap function of the ComplexHeatmap package (version 2.14.0) was used to draw the association heatmap, and the correlation map was drawn for some differentially significant microbial species and differential metabolites.

[0256] 6. Transcriptome data analysis:

[0257] (1) All raw data were preprocessed by fasp and quality controlled by MutiQC to obtain high-quality sequences;

[0258] (2) The reference genomes and annotations of mice and humans were downloaded from the Ensembl database (https: / / ftp.ensembl.org / pub / );

[0259] (3) The index file of the reference genome was constructed by hisat2-built in Hisat2, the sequences were aligned to the corresponding reference genome, sorted by samtools, and the sam file was converted into a bam file;

[0260] (4) The featureCounts function in subread was used to count the number of reads for all samples;

[0261] (5) DESeq2 was used for differential expression analysis, and differentially expressed genes were screened with a threshold of padj < 0.05;

[0262] (6) The R package clusterProfiler was used to perform pathway enrichment such as GO, KEGG, and GSEA on the candidate markers to obtain key molecular pathways.

[0263] 7. Immune score calculation:

[0264] ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data) can calculate the immune score based on single-sample gene set enrichment analysis (ssGSEA) for evaluating the composition of immune cells.

[0265] 8. Data acquisition:

[0266] The dataset generated in this application can be obtained from the National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation (GSA:CRA008369, OMIX:OMIX001920), and is publicly available at https: / / bigd.big.ac.cn / . The code and all analysis results can be found at https: / / github.com / Yichel518 / Dietary-analysis-for-CRC.

[0267] IV. Statistical methods

[0268] Statistical analysis was performed using GraphPad Prism 8.0 (GraphPad Software Inc.) and R software version 4.0.3. The chi-square test was used for the comparison of categorical variables. The non-parametric Kruskal-Wallis test was used to compare the microbial differences among the three groups (WT, CD, and BRD groups). Standardized (Z-score) data were used for metabolite analysis. The covariate effects of species diversity were tested using multiple linear regression methods. The Euclidean distance of metabolites and the Bray-Curtis distance of bacteria were used for the between-group difference test (PERMANOVA). The false discovery rate (FDR) value was corrected using P<0.05 and Benjamini-Hochberg to indicate statistical significance. The Spearman rank correlation coefficient was used to estimate the correlation between microbes or microbe-metabolites. The area under the receiver operating characteristic curve (ROC) (AUC) was used to evaluate the diagnostic value of potential bacterial markers in differentiating CRC patients from the healthy group. The Youden index (J = Sensitivity + Specificity - 1) was used to determine the optimal cut-off value with the maximum disease detection sensitivity and specificity. P<0.05 was considered statistically significant.

[0269] III. Results and analysis:

[0270] 1. Black rice diet significantly prolonged the survival time of Min / + Apc and AOM / DSS model mice:

[0271] The scientific customization of black rice feed is a prerequisite for conducting research on the health effects of black rice. For example, it can meet the nutritional needs of mature mice and maintain the same nutrient concentration, energy as the control feed. Therefore, in this application, the standard feed formula AIN-93M was selected as the control diet (CD), which is a widely used purified diet made from refined ingredients and is commonly used to feed mature rodents. 50% of black rice was added to the control feed as the black rice diet (BRD) (the detailed feed nutritional formula is shown in Table 7).

[0272] Table 7 Feed Nutritional Formula Table

[0273] Percentage (%) Control diet Black rice-diet Protein 13 12 Carbohydrate 73 68 Fat 4 4 kcal / gm 3.8 3.59 Casein 14 8.10 L-Cystine 0.18 0.17 Corn Starch 49.57 10.14 Maltodextrin 10 12.5 11.63 Sucrose 10 9.31 Cellulose 5 4.09 Soybean Oil 4 2.23 t-Butylhydroquinone 0.008 0.01 Mineral Mix S10022M 3.5 3.26 Vitamin Mix V10037 1 0.93 Choline Bitartrate 0.25 0.15 Fiber (kJ / g) 0.5 1.6 C3G (μg / mg) 0 6.59

[0274] To rule out the preference of mice for the two feeds, the food intake of mice was monitored for a long time in this application (the feed was weighed every two days), and the food intake for one month was selected to compare the intake differences between the control and black rice diets (as Figure 1 shown). The results showed that there was no significant difference in the intake of the control diet and the black rice diet by mice within one month of feeding, indicating that mice have no preference for the intake of these two feeds and subsequent research can be carried out.

[0275] To study the effect of the black rice diet on the lifespan of colorectal model mice, in this application, colorectal cancer model Apc Min / + and AOM / DSS mice were randomly divided into a control diet group and a black rice diet group. Different diet treatments were given starting from one week of adaptive feeding until natural death (as Figure 1 A and Figure 1 B shown). The results showed that in Apc Min / + mice, the longest survival time of the control group mice was 221 days. In contrast, the black rice group mice were extended by 15.4% (the longest survival time: 255 days). The average survival time of the control group was 159 days, while the black rice group was extended by 20% (the average survival time: 190.8 days) (as Figure 1 C shown). In addition, in another CRC model AOM / DSS mice, the longest survival time of the control group mice was 245 days, while that of the black rice group mice was 257 days. The results of the average survival time showed that the control group was 137.3 days, while the black rice group was 177.7 days, and the average survival time was extended by 29.5% (as Figure 1 D shown).

[0276] 2. The black rice diet has a delaying effect on the tumor development of colorectal cancer model mice:

[0277] (1) The black rice diet delays the tumor development of Apc Min / + model mice:

[0278] To explore the protective effect of black rice diet on colorectal cancer mice, this application evaluated the tumor development status of two CRC models, Apc Min / + and AOM / DSS. For Apc Min / + mice, the body weights were measured at 22 weeks of age for each group of mice.

[0279] Epidemiological data show that obesity increases the risk of colorectal cancer by 30% - 70%. The results indicate that the body weight of mice in the black rice group was lower than that of the control group (as Figure 2 shown in A). Meanwhile, intestinal samples were collected for the statistics of the number of tumors. The number and volume of colorectal tumors were significantly smaller than those of the control group mice (as Figure 2 shown in B). HE sections showed that compared with the mice fed with the control diet, the proportions of mice with adenocarcinoma and high- and low-grade dysplasia were lower in the mice fed with the black rice diet (as Figure 2 shown in C). In addition, the results of colon immunohistochemistry of the mice fed with the black rice diet showed fewer Ki-67 positive cells (as Figure 2 shown in D), indicating a lower level of cell proliferation. At the same time, the expression of proliferating cell nuclear antigen (PCNA) in the mice fed with the black rice diet was significantly reduced (as Figure 2 shown in E), further indicating a lower level of cell proliferation compared with the control group. The above results show that the black rice diet can slow down the tumor development of colorectal cancer.

[0280] As a protective mechanism, the intestinal barrier prevents harmful substances from entering the blood circulation, thus avoiding the cascade reaction of pathophysiological changes; to test the effect of the black rice diet on the intestinal barrier function in the delayed development of colorectal cancer, this application evaluated the permeability and integrity of the intestinal barrier. Since the endotoxin lipopolysaccharide (LPS) is the main component of the outer membrane of Gram-negative bacteria, it can be used to evaluate the permeability of mouse colon cells. The results showed that the LPS level in the serum of the black rice group was lower than that of the control diet group (as Figure 2 shown in F); consistent with this, the intestinal barrier structure under transmission electron microscopy confirmed that the intercellular junctions of colon cells in the mice fed with the black rice diet were relatively normal, while those in the mice fed with the control diet were abnormal, including the expansion of the apical junction complex and the intercellular space of the paracellular space (as Figure 2 shown in G). The above results show that the black rice diet can improve the intestinal permeability of Apc Min / + mice.

[0281] Goblet cells are specialized epithelial cells that form the mucus barrier in the intestine and are crucial for the formation of the intestinal mucus barrier. This application counted the number of goblet cells. Periodic-Acid Schiff (PAS) staining of colon tissues showed that the average number of goblet cells in each crypt was more in the mice fed with the black rice diet than in the mice fed with the control diet (as Figure 3as shown in Figure A). In addition, the expression levels of tight junction proteins ZO-1, Occludin, and Claudin-3 in mice fed a black rice diet increased (as shown in Figures Figure 3 B to Figure 3 E). These results indicate that the black rice diet not only protects the intestinal structure but also protects the chemical and immune barriers of the intestine.

[0282] Chronic inflammation is a recognized risk factor for colorectal cancer, and increased intestinal permeability may lead to chronic inflammation. In this study, the results of enzyme-linked immunosorbent assay (ELISA) showed that the black rice diet downregulated the expression of pro-inflammatory cytokines tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) and upregulated the expression of anti-inflammatory cytokines interleukin-4 (IL-4) and interleukin-10 (IL-10) (as shown in Figures Figure 4 A to Figure 4 D). The above results indicate that the black rice diet can reduce the inflammatory level in Apc Min / + mice.

[0283] (2) The black rice diet delays tumor development in AOM / DSS model mice:

[0284] To confirm these findings, a C57BL / 6 mouse model treated with AOM / DSS was used to determine whether the effect of the black rice diet is universal. The results showed that the body weight of mice in the black rice diet group was significantly lower than that of the control group (as shown in Figure Figure 5 A), and the number of intestinal tumors in this group was fewer and the tumor volume was smaller (as shown in Figure Figure 5 B), the proportion of adenocarcinoma and high- and low-grade dysplasia in the intestine (as shown in Figure Figure 5 C, the pathological score was based on the following criteria: 0 for normal, 1 for low-grade intraepithelial neoplasia, 2 for high-grade intraepithelial neoplasia, 3 for cancer), the proportion of ki-67 positive cells representing the cell proliferation level, and the expression of PCNA protein in cells were lower (as shown in Figures Figure 5 D and Figure 5 E), and intestinal permeability decreased (as shown in Figures Figure 5 F and Figure 5 G). In addition, the intestinal barrier structure in the black rice group was more complete, which was reflected in the increased number of goblet cells and the expression of tight junction proteins (as shown in Figures Figure 5 H to Figure 5 L), and lower inflammatory levels in the serum (as shown in Figures Figure 5 M to Figure 5 P). These consistent results indicate that the black rice diet may function in multiple CRC model mice - it can delay the development of CRC tumors.

[0285] 3. The black rice diet alters the intestinal microbiota composition and increases the abundance of the beneficial bacterium Bacteroides uniformis:

[0286] (1) Black rice diet altered the intestinal microbiota composition of Apc Min / + model mice and increased the abundance of the beneficial bacterium Bacteroides uniformis:

[0287] The intestinal microbiota composition depends on dietary substrates and often serves as a mediator of the pro-inflammatory and anti-inflammatory effects of food. Therefore, it is hypothesized that different dietary conditions may affect microbial community changes, and black rice diet may promote the enrichment of beneficial microorganisms. In the Apc Min / + model, although no differences in the "Simpson Index" (used to measure ecological species diversity) were observed between different diet groups (as shown in Figure 6 A, alpha-diversity analysis of wild-type mice, mice fed a control diet or a black rice diet using the Simpson Index; WT_14: n = 38, CD_14: n = 15, BRD_14: n = 14, WT_22: n = 14, CD_14: n = 22, BRD_22: n = 15), differences in microbial composition between different diet groups were detected using the Bray-Curtis metric (used to compare the similarity or difference between samples or communities) (as shown in Figure 6 B). To determine the specific bacterial changes associated with each diet group, the microbial composition of Apc Min / + mice fed a black rice diet and Apc Min / + mice fed a control diet, as well as wild-type (WT) mice fed a control diet and Apc Min / + mice fed a control diet, were compared for differences in microbial composition at two time points (14W represents the pre-cancerous stage, while 22W represents the tumor stage). It was observed that Lactobacillus johnsonii and Bacteroides uniformis were enriched in Apc Min / + mice fed a black rice diet (as shown in Figure 6 C and Figure 6 D). In addition, co-occurrence analysis showed (as shown in Figure 6 E, red lines represent positive co-occurrence relationships, blue lines represent negative co-occurrence relationships, and gray lines represent non-co-occurrence relationships) that Bacteroides uniformis had a positive co-linear relationship with Lactobacillus johnsonii and Lactobacillus reuteri among probiotics, and currently Lactobacillus is considered a promising tool for the treatment of colorectal cancer in vitro and in vivo. The above results suggest that Bacteroides uniformis may play a beneficial role in slowing down the progression of CRC.

[0288] (2) Black rice diet changed the intestinal microbiota composition in AOM / DSS model mice and increased the abundance of the beneficial bacterium Bacteroides uniformis:

[0289] In the AOM / DSS colon cancer model, the black rice diet group showed increased Shannon index and Simpson index both in the pre-cancer stage (after the first DSS treatment) and tumor stage (after the third DSS treatment) (as Figure 7 shown in A, the α-diversity of wild-type mice (WT), control diet-fed mice, and black rice diet-fed mice was evaluated using the Shannon and Simpson indices, respectively, after the first DSS treatment (C_1 and B_1) and the third DSS treatment (C_3 and B_3). WT: n = 9, CD_1: n = 26, BRD_1: n = 23, CD_3: n = 16, BRD_3: n = 13), representing higher α-diversity and suggesting a more stable intestinal microecology. Bray-Curtis distance analysis showed differences in intestinal microbiota composition between the black rice diet and the control diet (as Figure 7 shown in B), indicating that the black rice diet enhanced intestinal microbiota diversity and changed their composition. Similar to the Apc Min / + model, Bacteroides uniformis was enriched in wild-type mice fed the control diet and mice fed the black rice diet during the tumor stage, while pathogenic Escherichia coli (E. coli) decreased in the black rice diet group (as Figure 7 shown in C and Figure 7 D). In addition, co-occurrence analysis of microorganisms showed that Bacteroides uniformis had a positive co-occurrence relationship with the beneficial Lactobacillus johnsonii and a negative co-occurrence relationship with the harmful E. coli (as Figure 7 shown in E). The above results were consistent with the Apc Min / + model results, jointly indicating that Bacteroides uniformis has a potential protective role in the development of CRC.

[0290] 4. Bacteroides uniformis is enriched in the intestines of healthy humans:

[0291] (1) Collect public population cohort data:

[0292] To further explore whether Bacteroides uniformis is conserved in the intestines of humans and mice, this application collected six sets of public data for meta-analysis. The cohorts included 378 healthy human samples and 393 colorectal cancer patient samples from the United States, Italy, Australia, and Japan (as Figure 8 shown in A). Analysis of variance was performed on the disease status, age, BMI, region, cohort, alcohol, gender, and library size of all samples, and it was found that "cohort" was the main factor affecting the judgment of disease status (as Figure 8 shown in B).

[0293] (2)Bacteroides uniformis is enriched in the gut of healthy individuals and has good predictive ability:

[0294] In the differential analysis, the "cohort" factor was used as a confounding factor (including age, BMI, region, gender, alcohol, study cohort, and library size), and integrated analysis of six sets of data was performed. The results showed that compared with CRC patients, Bacteroides uniformis was enriched in the healthy human gut (as shown in Figure 9 A and Figure 9 B).

[0295] To evaluate the predictive ability of Bacteroides uniformis, samples from 3 cohorts were randomly selected and trained and predicted by the random forest method according to a sample ratio of 3:7. The results showed that the classification effect of Bacteroides uniformis was good (AUC = 0.734) (as shown in Figure 9 C). The above results indicate that Bacteroides uniformis is a beneficial bacterium that is conserved in humans and mice and has potential screening ability.

[0296] (3)Bacteroides uniformis is regulated by anthocyanins and inhibits the proliferation of two colorectal cancer cell lines:

[0297] To explore the relationship between black rice diet and Bacteroides uniformis, this application first analyzed the main types of anthocyanins in black rice, black rice feed, and black rice extract (showing the top five substances in black rice feed, black rice, and black rice extract respectively). The research results showed that cyanidin-3-O-glucoside (C3G) was the category with the highest content in black rice, black rice feed, and black rice extract (as shown in Figure 10 A), and then C3G was co-cultured with Bacteroides uniformis. The results showed that C3G promoted the proliferation of Bacteroides uniformis (as shown in Figure 10 B). In addition, Bacteroides uniformis was co-cultured with two colorectal cancer cell lines, SW620 and HCT116. The results showed that Bacteroides uniformis inhibited the growth of two different CRC cell lines, SW620 and HCT116 (as shown in Figure 10 C, with Bacteroides uniformis as the experimental group and Escherichia coli as the negative control group). These findings indicate that a black rice diet may more effectively protect beneficial microorganisms, thereby slowing the development of colorectal cancer.

[0298] In summary, for the application of black rice provided in the embodiments of this application as a therapeutic drug for colorectal cancer, through experiments, it was found that the number and volume of colorectal tumors in Apc Min / + model mice on a black rice diet were significantly smaller than those in the control group, and the intercellular connections in the colon were relatively normal. This indicates that a black rice diet can not only improve the intestinal permeability of Apc Min / + model mice, but also protect the chemical and immune barriers of the intestine. At the same time, the Apc Min / +The inflammatory level in the serum of the model mice was lower, indicating that black rice can be used as a therapeutic drug for colorectal cancer tumors and delay the development of colorectal cancer tumors.

[0299] The various embodiments of the present application may exist in the form of a range; it should be understood that the description in the form of a range is only for convenience and brevity and should not be construed as a rigid limitation on the scope of the present application; therefore, it should be considered that the described range description has specifically disclosed all possible sub-ranges and individual values within that range. For example, it should be considered that the range description from 1 to 6 has specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., and individual numbers within that range, such as 1, 2, 3, 4, 5, and 6, regardless of the range. Additionally, whenever a numerical range is indicated herein, it means including any cited number (fraction or integer) within the indicated range.

[0300] In the present application, unless otherwise stated, the orientation terms such as "upper" and "lower" specifically refer to the drawing directions in the figures. Additionally, in the description of the specification of the present application, the terms "include", "comprise", etc. mean "include but not limited to". In this text, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In this text, "and / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Where A and B can be singular or plural. In this text, "at least one" means one or more, and "a plurality" means two or more. "At least one kind", "at least one of the following items (pieces)" or similar expressions refer to any combination of these items, including any combination of single item (piece) or plural items (pieces). For example, "at least one of a, b, or c", or, "at least one of a, b, and c" can both represent: a, b, c, a - b (i.e., a and b), a - c, b - c, or a - b - c, where a, b, and c can be single or multiple respectively.

[0301] The above description is only the specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. Use of black rice as a therapeutic drug for colorectal cancer, Characterized in that, The said use includes using black rice as a therapeutic drug for inhibiting or slowing down colorectal cancer.

2. The use according to claim 1, Characterized in that, The said therapeutic drug includes at least one of the following: Drugs for improving intestinal permeability, drugs for inhibiting the number of intestinal tumors, drugs for inhibiting the size of intestinal tumors, drugs for inhibiting the level of inflammatory factors in serum, and drugs for upregulating the level of anti-inflammatory factors.

3. The use according to claim 1, Characterized in that, The said therapeutic drug also includes: drugs for increasing the abundance of beneficial bacteria in the gut microbiome.

4. The use according to claim 3, Characterized in that, The said beneficial bacteria include Bacteroides uniformis and / or Lactobacillus, and there is a positive co-occurrence relationship between Bacteroides uniformis and Lactobacillus.

5. The use according to claim 1, Characterized in that, The mass ratio of the said black rice to the said therapeutic drug is ≥50%.

6. The use according to claim 1, Characterized in that, The said use also includes using cyanidin-3-O-glucoside of black rice as a therapeutic drug for inhibiting or slowing down colorectal cancer.

7. Use of Bacteroides uniformis as a biomarker in colorectal cancer, Characterized in that, The said use includes using low-abundance Bacteroides uniformis as a biomarker for colorectal cancer.

8. The use according to claim 7, Characterized in that, The area under the receiver operating characteristic curve of the said Bacteroides uniformis is ≥0.

734.

9. A reagent for screening or assisting in screening colorectal cancer, Characterized in that, The said reagent includes a medicament for detecting the abundance of Bacteroides uniformis.

10. A reagent for evaluating or assisting in evaluating the prognosis of colorectal cancer, Characterized in that, The said reagent includes a medicament for detecting the abundance of Bacteroides uniformis.

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