Application of Akk bacteria in noninvasive early diagnosis of secondary lymphedema

Abstract

The invention is applicable to the technical field of biological medicine, and provides application of Akk bacteria in noninvasive early diagnosis of secondary lymphedema, the Akk bacteria are symbolic bacteria of normal people or animals, and when the symbolic bacteria of people or animals suffering from secondary lymphedema are changed, the abundance of the Akk bacteria is obviously reduced. By analyzing intestinal flora change caused by secondary lymphedema, a random forest sample classifier based on Akk bacteria abundance detection is successfully constructed, and the overall accuracy reaches 90% and is significantly higher than the baseline accuracy by 50%. The method not only realizes early noninvasive diagnosis of secondary lymphedema, but also provides a new biomarker detection means for clinic, can effectively solve the problem of lack of an effective noninvasive early diagnosis method in the prior art, and has a wide application prospect.

Description

Technical Field

[0001] The invention belongs to the technical field of biomedicine, and in particular relates to an application of Akk bacteria in the non-invasive early diagnosis of secondary lymphedema. Background Art

[0002] Secondary lymphedema (SL) is a chronic progressive disease caused by damage or dysfunction of the lymphatic system, which is common in cases of surgery, radiotherapy or infection. It manifests as accumulation of lymph and tissue fluid, accumulation of adipose tissue, fibrosis, immune disorder, inflammatory infiltration and other pathological conditions. Existing clinical diagnostic methods for SL mainly rely on imaging examinations (such as magnetic resonance lymphangiography) and clinical symptom assessment (such as limb circumference measurement), which often have the disadvantages of strong invasiveness, long diagnosis time and insufficient accuracy. In recent years, the study of intestinal flora has gradually become a hot topic in the biomedical field. Studies have shown that the composition of intestinal microbiota is closely related to the occurrence and development of various diseases, such as colorectal cancer, liver cancer and Alzheimer's disease. Akkermansia, as an important genus of intestinal bacteria, plays an important role in maintaining intestinal health and immune function.

[0003] At present, early diagnosis technology for secondary lymphedema is still relatively scarce, and there is a lack of effective non-invasive detection methods. Although traditional diagnostic methods such as lymphangiography and ultrasound examination can provide certain diagnostic information, they often require high technical requirements and equipment support, and impose a certain physical burden on patients. With the development of high-throughput sequencing technology, 16S rRNA sequencing has become an important tool for analyzing intestinal microbiota, which can provide rich information on microbial composition. Studies have shown that Akk bacteria usually have a high abundance in healthy individuals, while in various disease states, its abundance often decreases significantly, which provides a theoretical basis for its application as a biomarker.

[0004] Although studies have shown that intestinal flora imbalance is associated with a variety of diseases, no targeted methods have been widely used in the early diagnosis of secondary lymphedema. Existing diagnostic methods often cannot achieve early, non-invasive detection, resulting in patients being diagnosed only when the disease develops to a more serious stage, thus missing the best time for treatment. In addition, the lack of systematic research on changes in intestinal flora has resulted in the potential application of Akk bacteria in secondary lymphedema not being fully explored. Therefore, the development of a non-invasive early diagnosis method based on Akk bacteria abundance detection can effectively solve the shortcomings of existing technologies and provide new ideas and methods for the early identification of secondary lymphedema. Summary of the invention

[0005] The purpose of the present invention is to provide an application of Akk bacteria in the non-invasive early diagnosis of secondary lymphedema, aiming to solve the problems raised in the background technology.

[0006] In view of the above problems, the present invention is implemented by providing an application of Akk bacteria in the preparation of a non-invasive early diagnosis kit for secondary lymphedema.

[0007] Preferably, the Akk bacteria is a characteristic bacterial genus of normal humans or animals, and the characteristic bacterial genus of humans or animals suffering from secondary lymphedema changes, and the abundance of Akk bacteria decreases significantly.

[0008] Another object of the present invention is to provide an application of Akk bacteria in constructing a random forest sample classifier, wherein the random forest sample classifier is used for non-invasive early diagnosis of secondary lymphedema based on the abundance of Akk bacteria.

[0009] Another object of the present invention is to provide a random forest sample classifier based on Akk bacteria abundance detection, wherein the random forest sample classifier is used for non-invasive early diagnosis of secondary lymphedema based on the abundance of Akk bacteria.

[0010] Preferably, the method for constructing the random forest sample classifier comprises the following steps: Constructing animal models; Obtain the intestinal flora data of animals according to the animal model; Process the intestinal flora data, calculate the abundance of each genus, and identify species with significant differences; Based on the random forest algorithm, the processed intestinal flora data were used as input features to construct a random forest sample classifier.

[0011] Preferably, the intestinal flora data is obtained by 16S rRNA sequencing.

[0012] Preferably, the method for processing intestinal flora data includes 16S rRNA high-throughput sequencing technology and LEfSe analysis method.

[0013] The present invention successfully constructed a random forest sample classifier based on Akk bacteria abundance detection by analyzing the changes in intestinal flora caused by secondary lymphedema, with an overall accuracy of 90%, significantly higher than the baseline accuracy of 50%. This method not only realizes the early non-invasive diagnosis of secondary lymphedema, but also provides a new biomarker detection method for clinical use, which can effectively solve the problem of the lack of effective non-invasive early diagnosis methods in the prior art and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1The following are the results of the construction and detection of the secondary lymphedema model of the mouse tail; among them, a is the appearance of the mouse tail, b is the indocyanine green near-infrared imaging of the mouse tail, c is the dynamic monitoring of the mouse tail volume, d is the volume change of the mouse, e is the RT-qPCR result of the lymphatic vessel marker molecules in the mouse tail tissue, f is the HE staining of the mouse tail tissue and the statistical graph of the skin and fat thickness, and g is the HE staining of the mouse heart, liver, spleen, lung and kidney.

[0015] Figure 2 This is the result of α-diversity analysis of mouse intestinal flora.

[0016] Figure 3 This is the result of β-diversity analysis of mouse intestinal flora.

[0017] Figure 4 This is the result of LEfSe analysis of mouse intestinal flora.

[0018] Figure 5 This is the result of random forest analysis of mouse intestinal flora.

[0019] Figure 6 This is the analysis result of species composition of mouse intestinal flora. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] The purpose of the embodiments of the present invention is to solve the problem of the lack of early non-invasive diagnostic methods for secondary lymphedema. Existing diagnostic methods such as imaging examinations and clinical symptom assessments have defects such as strong invasiveness, long diagnosis time, and insufficient accuracy, which leads to patients being diagnosed only when the disease develops to a more serious stage, thus missing the best time for treatment. In addition, the prior art lacks systematic research on changes in intestinal flora, and fails to fully explore the potential application of Akkermansia as a biomarker in the early diagnosis of secondary lymphedema. Therefore, the embodiments of the present invention provide a non-invasive early diagnosis method based on Akkermansia abundance detection by analyzing changes in intestinal flora, which provides new ideas and methods for the early identification of secondary lymphedema.

[0022] The embodiment of the present invention uses 16S rRNA high-throughput sequencing technology to sequence the intestinal flora of mice with secondary lymphedema. The results show that Akk bacteria is a characteristic bacterial genus of normal mice, and the abundance of Akk bacteria in the intestinal flora of mice with secondary lymphedema is significantly reduced. Therefore, the embodiment of the present invention applies the detection of Akk bacteria abundance in the intestinal flora to the non-invasive early diagnosis of secondary lymphedema, which opens up new ideas and methods for the diagnosis of secondary lymphedema and effectively solves the problem of lack of early diagnosis methods for secondary lymphedema.

[0023] Specifically, in one embodiment of the present invention, a random forest sample classifier based on Akk bacteria abundance detection is provided, and the random forest sample classifier is used for non-invasive early diagnosis of secondary lymphedema based on the abundance of Akk bacteria.

[0024] Among them, the construction method of the random forest sample classifier includes the following steps: S1. Construction of animal model; S2. Obtaining intestinal flora data of animals according to the animal model; the intestinal flora data is obtained by 16S rRNA sequencing; S3. Process the intestinal flora data, calculate the abundance of each bacterial genus and identify species with significant differences; the methods for processing the intestinal flora data include 16S rRNA high-throughput sequencing technology and LEfSe analysis method; S4. Based on the random forest algorithm, the processed intestinal flora data is used as input features to construct a random forest sample classifier.

[0025] In another embodiment of the present invention, a non-invasive early diagnosis method based on the detection of Akkermansia abundance is provided to facilitate effective identification in the early stages of secondary lymphedema.

[0026] In another embodiment of the present invention, a mouse tail secondary lymphedema model was constructed, and the lymphatic vessels were cut off and the skin was removed by tail circumcision surgery to verify the validity of the model.

[0027] In another embodiment of the present invention, the lymphatic flow status of the mouse tail is detected, and the lymphatic flow disorder and skin fat accumulation are analyzed to evaluate the severity of lymphedema.

[0028] In another embodiment of the present invention, the intestinal flora of mice was analyzed by 16S rRNA high-throughput sequencing technology to evaluate its α diversity and β diversity to explore the relationship between changes in the intestinal flora and secondary lymphedema.

[0029] In another embodiment of the present invention, the significantly different species between the N group and the L group were identified by LEfSe analysis, confirming the importance of Akkermansia as a landmark species in secondary lymphedema.

[0030] In another embodiment of the present invention, a random forest sample classifier is constructed using a random forest algorithm, and the accuracy and species importance of the model are evaluated to achieve effective diagnosis of secondary lymphedema.

[0031] Example 1: This example provides a method for constructing a mouse tail secondary lymphedema model. C57BL / 6 mice were selected as experimental subjects. The specific steps are as follows: Selection of experimental animals: Select healthy C57BL / 6 mice of any gender, weighing between 20-25 g, and ensure that the mice are acclimatized to the environment for at least one week before the experiment.

[0032] Surgical preparation: Under sterile conditions, anesthetize the mouse using an appropriate anesthetic (e.g., isoflurane or ketamine) for general anesthesia, ensuring that the mouse is pain-free during the procedure.

[0033] Mouse tail circumcision surgery: Circumcision surgery is performed on the mouse tail. The specific operation is as follows: Use a scalpel to cut off the lymphatic vessels on both sides and remove about 3 mm of skin to ensure that the lymphatic vessels are completely cut off.

[0034] Model verification: After surgery, the morphological changes of the mouse tail were observed, the swelling of the tail was recorded, and subsequent physiological index tests were performed.

[0035] Example 2: The effectiveness of the above mouse tail secondary lymphedema model was verified, and the specific steps were as follows: Morphological examination: By observing the appearance of the mouse tail, the degree of swelling of the mouse tail in the experimental group (L group) was recorded compared with that in the normal group (N group). Figure 1 As shown in a and c.

[0036] Lymphatic flow status detection: Near-infrared imaging technology was used to detect the lymphatic flow status of the mouse tail and evaluate lymphatic flow disorders. Figure 1 As shown in b.

[0037] Molecular biological detection: Mouse tail tissue was extracted, and the mRNA expression of lymphatic vessel markers (such as VEGF-C, VEGFR-3, and LYVE-1) was detected using qPCR technology. The differences between group N and group L were compared. The results are as follows: Figure 1 As shown in e.

[0038] Detection of tissue morphological changes: The thickness of the dermis and fat in the tail of mice was measured by tissue sectioning and microscopic observation, and the comparison results with those of group N were recorded, such as Figure 1 As shown in f.

[0039] Health status assessment: Regularly monitor the weight of mice and the health status of organs such as heart, liver, spleen, lung and kidney, and record the significant differences with group N. The results are as follows: Figure 1 As shown in d and g.

[0040] Example 3: After the above model verification is completed, the analysis of mouse intestinal flora is performed, and the specific steps are as follows: Sample collection: Collect fecal samples from mice and ensure that the samples are stored under sterile conditions.

[0041] 16S rRNA high-throughput sequencing: DNA was extracted from the collected fecal samples, and 16S rRNA high-throughput sequencing technology was used to analyze the intestinal flora to obtain mouse intestinal flora data.

[0042] α diversity analysis: Chao1, observed species, Shannon, Simpson, Faith's PD, Pielou's evenness, Good's coverage and other indices were used for α diversity analysis to compare the intestinal flora diversity between group N and group L. The results are as follows Figure 2 shown.

[0043] β diversity analysis: Bray-Curtis distance combined with PCoA method was used to perform β diversity analysis to evaluate the differences in community structure between group N and group L. The results are as follows Figure 3 shown.

[0044] Example 4: LEfSe analysis was performed on the above mouse intestinal flora data to identify significantly different species. The specific steps are as follows: Data processing: The mouse intestinal flora data obtained by 16S rRNA sequencing were processed to calculate the abundance of each species.

[0045] LDA value distribution analysis: LEfSe analysis method was used to calculate the LDA values ​​between the groups, identify the significantly different species between the N group and the L group, and confirm the importance of Akk bacteria as a landmark species in secondary lymphedema. The results are as follows Figure 4 shown.

[0046] Embodiment 5: This embodiment provides a method for constructing a random forest sample classifier, which specifically includes the following steps: Model construction: The random forest algorithm was used to construct a classifier model using the above-processed intestinal flora data as input features.

[0047] Model evaluation: The accuracy and species importance of the model were evaluated, and the overall accuracy was recorded to be 90%, which was significantly higher than the baseline accuracy of 50%. The results are as follows Figure 5 shown.

[0048] Species importance analysis: Analyze the importance of Akk bacteria in the model and confirm its key role in the diagnosis of secondary lymphedema, such as Figure 5 As shown in a and b.

[0049] Example 6: Comprehensively compare the advantages of Akk bacteria as diagnostic marker species compared with other bacterial genera, the specific steps are as follows: Comparison of iconic species: The most abundant iconic species at the genus level in group N is Akk bacteria, and the most abundant iconic species at the genus level in group L is Lactobacillus. Figure 4 shown.

[0050] Random forest model construction bacterial genus importance score: The most important bacterial genus involved in the construction of random forest sample classifiers in group N is Akk bacteria, and the most important bacterial genus involved in the construction of random forest sample classifiers in group L is Adlercreutzia. Figure 5 As shown in b.

[0051] Species composition analysis: The most abundant genus in group N was Akk bacteria, and the most abundant genus in group L was Lactobacillus. Figure 6 shown.

[0052] Therefore, based on the above results, Akk bacteria are stable and characteristic in different comparative analyses and are the best bacterial genus choice for diagnosing secondary lymphedema.

[0053] Through the above implementation scheme, the embodiment of the present invention successfully constructed a random forest sample classifier based on Akk bacteria abundance detection, which provided a new method and idea for the early non-invasive diagnosis of secondary lymphedema, significantly improved the accuracy and efficiency of diagnosis, and has broad clinical application prospects.

[0054] Comparative example: 1. Compared with traditional diagnostic methods: For example, enhanced magnetic resonance lymphangiography requires injection of contrast agent under the patient's skin or at the finger web, and this diagnostic method is time-consuming and costly; compared with this traditional diagnostic method, the solution provided by the embodiment of the present invention has the advantages of being non-invasive, simple, low-cost, and low time consumption. Compared with other traditional non-invasive diagnostic methods, such as ultrasound, CT, bioelectrical impedance measurement, etc., the solution provided by the embodiment of the present invention can predict the risk of disease by detecting the abundance of Akk bacteria in the intestinal flora, and realize the early diagnosis of secondary lymphedema.

[0055] 2. Comparison with other intestinal bacterial genera: such as CN116855618A, a group of marker fecal bacterial genera for the diagnosis of obsessive-compulsive disorder in children and adolescents and their applications. Based on the comparison and analysis of intestinal microorganisms of patients with obsessive-compulsive disorder and healthy controls, the differential flora between the two groups was obtained [a combination of 30 intestinal flora: Bacteroides, Parabacteroides, Sutterella, Subdoligranulum, Mitsuokella, Hydrogenophaga, Monoglobus, Pseudoxanthomonas, Pseudaminobacter, Paenarthrobacter, Nocardioides, Opitutus, Devosia, Bordetella, Agromyc es, Aeromicrobium, Luteimonas, Chiayiivirga, Bilophila, Ramlibacter, Haliangium, Altererythrobacter, Lysobacter, IMCC26134, Sandaracinus, Bosea, Erysipelatoclostridium, Niastella, Chryseolinea and Arenimonas], combined with high-quality data on the relative abundance of differential microbiota between patients with obsessive-compulsive disorder and healthy controls as the training set, to conduct risk assessment and early diagnosis of patients with obsessive-compulsive disorder.

[0056] Compared with the invention, the embodiment of the present invention is also based on the comparison and analysis of the differences in the intestinal flora of the control group mice and the intestinal flora of the secondary lymphedema mice, and obtains the differential flora between the two groups; however, the embodiment of the present invention scores the importance of the differential flora, narrows the range of the differential flora, and finds the Akkermansia signature genus that has a significant impact on the disease. It can predict and diagnose disease risks by detecting a single genus in fecal samples, greatly improving the efficiency of disease diagnosis and reducing detection costs.

[0057] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. Application of Akk bacteria in the preparation of a non-invasive early diagnosis kit for secondary lymphedema.

2. The use according to claim 1, characterized in that: The Akk bacteria is a characteristic bacterial genus of normal humans or animals. The characteristic bacterial genus of humans or animals suffering from secondary lymphedema changes, and the abundance of Akk bacteria decreases significantly.

3. The application of Akk bacteria in constructing a random forest sample classifier is characterized in that: The random forest sample classifier is used for non-invasive early diagnosis of secondary lymphedema based on the abundance of Akk bacteria.

4. The use according to claim 3, characterized in that: The construction method of the random forest sample classifier The following steps are involved: Constructing animal models; Obtain the intestinal flora data of animals according to the animal model; Process the intestinal flora data, calculate the abundance of each genus, and identify species with significant differences; Based on the random forest algorithm, the processed intestinal flora data were used as input features to construct a random forest sample classifier.

5. The use according to claim 4, characterized in that: The intestinal flora data were obtained by 16S rRNA sequencing.

6. The use according to claim 4, characterized in that: Methods for processing intestinal flora data include 16SrRNA high-throughput sequencing technology and LEfSe analysis.

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