Analytical method, analytical device and computer medium for enrichment of microbial collection

Abstract

The present disclosure relates to an analysis method, an analysis device and a computer medium for enrichment of a microbial set. The analysis method includes receiving a microbial set to be analyzed input by a user. The analysis method also includes a first processor using a second microbial set knowledge base to determine the enrichment information of the microbial set to be analyzed, and the enrichment information includes associated human genes and significance parameters. Among them, the second microbial set knowledge base is generated based on the first microbial set knowledge base of microbial classification and combined with the association information between significant microorganisms and human genes. The analysis method also includes the first processor sorting the associated human genes according to the significance parameters. It is possible to efficiently and systematically utilize the relevant public information of microbial research results to accurately analyze the mechanism of various diseases acting on genes through specific microbial communities.

Description

Technical Field

[0001] The present disclosure relates to the technical field of bioinformatics, and more particularly, to an analysis method, an analysis device, and a computer medium for analyzing microbial information, especially microbial enrichment. Background Art

[0002] Research has found that the symbiotic microbiome (microbial collection) can influence various host phenotypic traits by regulating host physiological functions (such as immunity and metabolism), many of which are associated with the occurrence and progression of various complex human diseases. Host-microbiome interactions occur through changes in microbial collections or individual microorganisms.

[0003] Microorganisms can influence host phenotypes by regulating gene expression or cell signaling in relevant host cells / tissues, but the regulatory mechanisms by which microorganisms influence host physiological functions remain unclear. Current studies suggest that this host-microbiome interaction can be achieved through microbial metabolites, and that the host immune system is regulated by microbial metabolites in the gut microbiome.

[0004] Although some studies, such as but not limited to the Human Functional Genome Project (HFGP), have attempted to elucidate the mechanisms of action of the microbiome at the cellular level, these studies generally rely on a large amount of experimental and resource investment, are prone to empirical bias, and the mechanisms of action obtained are relatively limited. For example, a study of 500 healthy Dutch adults in the HFGP found that gut microbiome characteristics were associated with the production of various cytokines: in the study, two microbial metabolites, triphenols and palmitoleic acid, were experimentally verified to regulate the production of IFNγ and TNFγ in peripheral blood mononuclear cells, respectively. In further studies, the microbial-derived metabolite ascorbic acid was also identified as a selective inhibitor of activated CD4+ effector T cells (including cells that produce IL-17A-, IL-4-, and IFN4).

[0005] Although some databases have been established that classify microbiome species based on their compositional differences in human diseases, such as Disbiome, which covers 190 human diseases and 800 microorganisms in 674 published studies, and also includes databases that classify microorganisms based on genome, protein family, and species classification information. However, there has been no systematic study of which human genes these diseases may act on through microorganisms and the mechanisms of action. With the rapid development of microbiome research, although it is possible to identify microbial species associated with human diseases through differential abundance analysis, existing research requires a lot of manpower and resource investment. The established databases are relatively isolated, lack structured knowledge, and cannot efficiently and systematically utilize the latest relevant public information of research results, resulting in lagging and significant limitations in the enrichment analysis of microbial sets for various diseases. Summary of the Invention

[0006] The present disclosure is provided to solve the above-mentioned problems existing in the prior art.

[0007] The present disclosure aims to provide a computer-implemented analysis method, analysis device, analysis system, and computer medium for microbial set enrichment, which can efficiently and systematically utilize relevant public information on microbial research results to determine significant association information between microorganisms of various genera and species and human genes. This significant association information can be structured to generate a microbial set knowledge base, thereby facilitating the analysis of the mechanisms by which various diseases act on genes through specific microbial communities.

[0008] According to the first embodiment of the present disclosure, a method for analyzing the enrichment of a microbial set is provided. The analysis method includes receiving a microbial set to be analyzed input by a user. The analysis method also includes a first processor using a second microbial set knowledge base to determine the enrichment information of the microbial set to be analyzed, wherein the enrichment information includes associated human genes and significance parameters. The second microbial set knowledge base is generated based on the first microbial set knowledge base of microbial classification and combined with the association information between significant microorganisms and human genes. The analysis method also includes the first processor sorting the associated human genes according to the significance parameters.

[0009] According to the second embodiment of the present disclosure, there is provided an analysis device for enrichment of a microbial set, which includes a first interface and a first processor. The first interface can be configured to receive a microbial set to be analyzed input by a user. The first processor can be configured to: determine the enrichment information of the microbial set to be analyzed using a second microbial set knowledge base, wherein the enrichment information includes associated human genes and significance parameters. The second microbial set knowledge base is generated based on the first microbial set knowledge base of microbial classification and combined with the association information between significant microorganisms and human genes. The first processor can also be configured to sort the associated human genes according to the significance parameters.

[0010] According to the third scheme of the present disclosure, an analysis system for enrichment of a microbial set is provided. The analysis system may include an analysis device according to various embodiments of the present disclosure, a second interface, a third interface, a second processor, and a third processor. The second interface may be configured to: obtain the genus and species names of microorganisms from a database of 16SrRNA genes for the bacterial and archaeal kingdoms. The third interface may be configured to obtain a microbial phylogenetic tree. The second processor may be configured to: query a corresponding abstract list from the literature abstracts of a public literature database based on the acquired genus and species names of microorganisms; identify human gene information from the queried abstract list, and determine the association information between microorganisms and human genes; and determine the association strength of each piece of association information between microorganisms and human genes, and accordingly screen each piece of association information to determine significant association information between microorganisms and human genes. The third processor may be configured to: construct a directed acyclic graph based on the acquired microbial phylogenetic tree; and construct a first microbial set knowledge base for microbial classification based on the directed acyclic graph.

[0011] By utilizing the analysis methods, analysis devices, analysis systems, and computer media for enrichment of microbial collections according to the various embodiments of the present disclosure, it is possible to efficiently and systematically utilize the relevant public information on microbial research results and accurately analyze the mechanisms by which various diseases act on genes through specific microbial communities. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In the drawings, which are not necessarily drawn to scale, the same reference numerals may describe similar components in different views. The same reference numerals with letter suffixes or different letter suffixes may represent different instances of similar components. The accompanying drawings generally illustrate various embodiments by way of example and not limitation, and together with the description and claims, serve to illustrate the disclosed embodiments. Where appropriate, the same reference numerals are used throughout the drawings to refer to the same or similar parts. Such embodiments are illustrative and are not intended to be exhaustive or exclusive of the embodiments of the present apparatus or method.

[0013] Figure 1 A flowchart showing a first example of an analysis method for enrichment of a microbial collection according to an embodiment of the present disclosure;

[0014] FIG2( a ) shows a flow chart of processing significant association information between microorganisms and human genes determined based on public information in a public literature database according to an embodiment of the present disclosure;

[0015] FIG2( b ) shows a diagram of the evaluation results of significant association information between the determined microorganisms and human genes visualized by t-SNE;

[0016] Figure 3 A flowchart illustrating a process of constructing a first microorganism set knowledge base for microorganism classification according to an embodiment of the present disclosure;

[0017] Figure 4 A flowchart showing a second example of an analysis method for enrichment of a microbial collection according to an embodiment of the present disclosure;

[0018] Figure 5 A comparative diagram showing the differential abundance of microorganisms in wild-type mice and aSyn-overexpressing mice after feces from Parkinson's disease patients and healthy individuals were implanted into the body;

[0019] Figure 6 A schematic diagram showing a framework of an analysis device for enriching a microbial collection according to an embodiment of the present disclosure; and

[0020] Figure 7 A schematic diagram illustrating the configuration of an analysis system for enriching a microbial collection according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0021] To help those skilled in the art better understand the technical solutions of the present disclosure, the present disclosure is described in detail below with reference to the accompanying drawings and specific embodiments, but this does not limit the present disclosure. Note that the terms "first," "second," and "third" are used in this disclosure only for distinction and are not intended to limit quantity or function.

[0022] Figure 1 A flow chart showing a first example of an analysis method for enrichment of a microbial collection according to an embodiment of the present disclosure. Figure 1As shown, the analysis method begins at step 101: receiving a user input of a microbial set to be analyzed. The microbial set to be analyzed can be a predefined set of microbial sets, such as, but not limited to, disease-associated microbial sets, disease-specific microbial sets, and any one or more microbial sets with differential abundance between a disease and other diseases or a healthy host. In some embodiments, the microbial set to be analyzed can be customized by the user based on prior knowledge. In some embodiments, the microbial set to be analyzed can also be pre-established for various diseases based on research findings or published information on the association between various diseases and microbes, and presented to the user via an interface, such as a menu, allowing the user to select a disease of interest and enter the microbial set to be analyzed. This method eliminates the need for the user to master the association between various diseases and microbes, allowing for automated microbial set enrichment analysis for the disease of interest. This is highly user-friendly and helps maintain consistency and robustness in enrichment analysis results across different users for the same disease of interest. Such prior knowledge, research findings, or published information can be, for example, but not limited to, the Disbiome database, which contains information on compositional differences in the microbiome of human diseases.

[0023] In step 102, the first processor may use a second microbial set knowledge base to determine enrichment information for the microbial set to be analyzed. The enrichment information may include associated human genes and significance parameters. The second microbial set knowledge base is generated based on the first microbial set knowledge base of microbial classification and incorporates significant association information between microbes and human genes, thereby providing structured knowledge of how microbes from various species interact with human genes.

[0024] In step 103, the first processor may sort the associated human genes according to the significance parameter. In some embodiments, the significance parameter may include at least one of a p-value, a q-value, and an OR value, and a combination score c may be obtained based on a combination of at least two values, and the associated human genes may be sorted based on the combination score. In some embodiments, the significant association information between microorganisms and human genes may have a deviation in the degree of attention paid to each human gene. For example, the data source used as the basis for calculating the significant association information, such as but not limited to a public literature database, may itself have a deviation in the degree of attention paid to each human gene. In this way, a correction value may be determined based on the deviation in the degree of attention paid to each human gene. Accordingly, an enrichment parameter may be determined based on the significance parameter and the correction value; and then the associated human genes may be sorted based on the determined enrichment parameter. In this way, the deviation in the enrichment parameter caused by the deviation in the degree of attention paid to each human gene may be corrected, thereby obtaining a more accurate enrichment parameter.

[0025] Through the analysis method of microbial set enrichment according to various embodiments of the present disclosure, more accurate and comprehensive structured knowledge is provided on how microorganisms of various species act on human genes. Gene-level pathogenic mechanisms are automatically inferred based on the microbial community characteristics associated with the disease. Moreover, the enrichment information including associated human genes and significance parameters can accurately and quantitatively explain the effects of microorganisms of various species on host phenotypes.

[0026] FIG2( a) shows a flowchart of the processing of significant association information between microorganisms and human genes determined based on public information from a public literature database according to an embodiment of the present disclosure. As shown in FIG2( a), in step 201, the genus and species names of microorganisms are obtained from a database of 16S rRNA genes for the bacterial kingdom and the archaeal kingdom. For example, the database of 16S rRNA genes for the bacterial kingdom and the archaeal kingdom can adopt the GreenGenes database, but is not limited thereto. The GreenGenes database is used as an example for explanation in this disclosure, but the description of this disclosure based on the GreenGenes database can also be applied to other existing databases of 16S rRNA genes for the bacterial kingdom and the archaeal kingdom, such as the Silva database, which will not be repeated here. The classification information of the GreenGenes database can be parsed to obtain the genus and species names of 1,085 microorganisms in the bacterial kingdom and the archaeal kingdom.

[0027] In step 202, the second processor searches for a corresponding abstract list from the literature abstracts of the public literature database based on the name of the genus and species of the acquired microorganism. In some embodiments, the public literature database may include the PubMed database, but is not limited thereto. For example, Web of Science may also be used, as long as the literature therein records the research results of microorganisms associated with human genes. The literature abstract usually records the core research results of the literature. For example, if the research results involve information on the association between microorganisms and human genes, the name of the microorganism and the name of the human gene will usually be recorded in the abstract, and the amount of data only accounts for a very small part of the total data volume of the literature, thereby significantly improving the efficiency of the query work while also ensuring the pertinence of the query results. For example, Geneshot can be used to query the standard microbial names in the Greengene classification at the genus and species levels of the PubMed abstract, thereby returning a list of relevant abstracts.

[0028] In step 203, the second processor identifies human gene information from the queried summary list and determines the association information between the microorganism and the human gene. In some embodiments, human gene information can be identified from the queried summary list via a named entity recognition (NER) tool (such as, but not limited to, Tagger, etc.), and the identified human gene information is normalized based on the human gene name of the Human Genome Database (HGNC), thereby determining the association information between the microorganism and the normalized human gene. This method can be used to query and record 970 microbial species names that are returned at least once, and a total of 8,865 different human genes are identified.

[0029] In step 204, the second processor determines the strength of each piece of association information between the microorganism and the human gene. In step 205, each piece of association information is screened to identify significant association information between the microorganism and the human gene. In some embodiments, the association strength can be compared with a threshold to eliminate incidental associations, i.e., to delete non-significant associations, thereby obtaining significant association information between the microorganism and the human gene.

[0030] In some embodiments, the association strength of each piece of association information between a microorganism and a human gene is calculated using the following formula (1):

[0031]

[0032] Among them, i is the serial number of the microorganism, j is the serial number of the human gene, and is the size of the set.

[0033] In some embodiments, the screening results showed that 42,944 microbe-gene association pairs were obtained, covering 752 microbes and 2,045 human genes. Among them, the human genes most closely associated with microbes were genes related to human immunity and inflammatory response, such as genes encoding cytokines including TNF, IL10, and IL6, and genes related to innate immune response, such as Toll-like receptors (TLRs) and the innate immune signaling adaptor MYD88 (see Table 1 below).

[0034] Table 1: Statistics of human genes closely related to microorganisms in the PubMed database

[0035]

[0036]

[0037] In addition, genes that have no obvious role in immunity also show associations with many microorganisms, such as the proto-oncogene FOS and the apoptosis-related cysteine ​​protease CASP3. In addition, by processing the significant association information between microorganisms and human genes determined based on the public abstract information of the PubMed database disclosed in the present invention, the genera and species of microorganisms most closely associated with human genes were also found, including model microorganisms (such as Escherichia coli and Saccharomyces cerevisiae), common commensal bacteria (such as Staphylococcus aureus and Lactobacillus) and well-known microbial pathogens (such as Salmonella enterica, Pseudomonas aeruginosa and Helicobacter pylori) (see Table 2 below).

[0038] Table 2: Statistics of microorganisms most closely related to human genes in PubMed literature

[0039]

[0040]

[0041] In some embodiments, the t-SNE machine learning algorithm is used to reduce the dimensionality of the significant association relationship information between the obtained microorganisms and human genes, thereby deducing the relevant microbial genera and species based on their potential functional association spectrum with human genes. By identifying the phylum information on t-SNE, several microbial clusters at the same phylum level, such as Firmicutes and Proteobacteria, are obtained, as shown in Figure 2(b). It can be seen that the set of microorganisms associated with the same human gene is more significantly enriched than random species in certain taxonomic evolutionary branches (e.g., phylum level), thereby confirming the good quality of the significant association relationship between the microorganisms and human genes obtained by the processing according to various embodiments of the present disclosure.

[0042] Figure 3 FIG. 1 is a flowchart showing a process of constructing a first microorganism set knowledge base for microorganism classification according to an embodiment of the present disclosure. Figure 3 As shown, in step 301, a microbial phylogenetic tree is obtained. In step 302, a third processor constructs a directed acyclic graph based on the obtained microbial phylogenetic tree. In step 303, the third processor constructs a first microbial set knowledge base for microbial classification based on the directed acyclic graph.

[0043] In some embodiments, in step 301, a database of 16S rRNA genes for the Bacteria and Archaea kingdoms can be downloaded, such as, but not limited to, the Greengenes database. In some embodiments, the same database of 16S rRNA genes for the Bacteria and Archaea kingdoms used for significant associations between microorganisms and human genes can be used to better match the constructed first microbial knowledge base with the significant associations, thereby improving the efficiency of generating the second microbial knowledge base based on the first microbial knowledge base and incorporating significant association information. The following description uses the Greengenes database as an example.

[0044] The phylogenetic tree in the Greengenes database can be filtered. Since the present disclosure focuses on data from 16s rDNA, two kingdoms, namely, viruses and viroids, can be filtered out from the phylogenetic tree to obtain a microbial phylogenetic tree. In some embodiments, suffixes including "_noname" and "_unclassified" can also be deleted to standardize the strings describing microorganisms in the Greengenes database.

[0045] In some embodiments, in step 302, the filtered microbial phylogenetic tree can be used to construct a directed acyclic graph (DAG). For example, the DAG can be constructed based on a DAG algorithm, for example, using taxa at each taxonomic level (excluding the strain level and above) in the Greengenes database as nodes of the DAG.

[0046] In some embodiments, in step 303, the microbial phylogenetic tree can be converted into a collection of microbial assemblies by enumerating all leaf nodes from parent nodes at each species taxonomic level, thereby constructing a first microbial assemblies knowledge base based on species taxonomy. In other embodiments, other methods can be used to convert the microbial phylogenetic tree into a collection of microbial assemblies, such as, but not limited to, sequence alignment based on a reference genome.

[0047] Figure 4A flowchart of a second example of an analysis method for enrichment of a microbial set according to an embodiment of the present disclosure is shown. The analysis method is mainly divided into three parts: a first process 401 of constructing a first microbial set knowledge base for microbial classification, a second process 402 of determining significant association information between microorganisms and human genes based on public information in a public literature database, and a third process 403 of determining the enrichment information of the microbial set to be analyzed based on the microbial set to be analyzed input by the user and performing corresponding sorting. The first process 401 can adopt the process of constructing a first microbial set knowledge base for microbial classification according to various embodiments of the present disclosure, and the second process 402 can adopt the process of determining significant association information between microorganisms and human genes based on public information in a public literature database according to various embodiments of the present disclosure, which will not be repeated here.

[0048] The third process 403 begins at step 404, where a user input of a microbial set to be analyzed is received. In some embodiments, a second microbial set knowledge base may also be received at step 404, where the second microbial set knowledge base is generated based on the first microbial set knowledge base of microbial classification and in combination with significant association information between microorganisms and human genes. In some embodiments, the third process 403 may include a step of generating the second microbial set knowledge base, for example, receiving the first microbial set knowledge base of microbial classification from the first process 401 and significant association information between microorganisms and human genes from the second process 402, and combining the two to generate the second microbial set knowledge base.

[0049] In step 405, the background information of each microorganism in the microorganism set to be analyzed is annotated using the second microorganism set knowledge base (including human genes closely associated therewith), and each microorganism that has been annotated with background information is independently iteratively tested to obtain enrichment information, wherein the enrichment information includes associated human genes and significance parameters. In some embodiments, the independent iterative test can adopt a test method such as Fisher's test, but is not limited thereto, and a test method such as Stouffer's test can also be adopted. In some embodiments, the significance parameter includes but is not limited to a p-value, and the number of iterations can be, for example, about 1,000 to 2,000 times.

[0050] The p-value can be corrected to obtain a q-value (or an OR value, not shown). In some embodiments, the associated human genes can be ranked using, for example, the p-value (or q-value, OR value, etc.). In some embodiments, a total score c can be calculated based on at least two of the p-value, q-value, and OR value (step 406), and the associated human genes can be ranked based on the total score c (step 407).

[0051] The total score c can be calculated using a variety of methods. For example, the expected rank deviation z score can be calculated using the same algorithm as the gene enrichment analysis software (Enrichr). The p-value obtained from the Fisher test can be combined with the expected rank deviation z score to obtain the total score c. For example, the total score can be calculated according to formula (2).

[0052] c=log 10 (p)·z Formula (2)

[0053] By introducing the expected rank deviation z score, the deviation of large sample sets such as Fisher test can be effectively corrected.

[0054] By applying the analysis method for enriching microbial sets according to various embodiments of the present disclosure to the Disbiome database, the good effect of the analysis method was fully confirmed. The processing time can be controlled within two hours. The results of the automatic analysis are fully consistent with the research results on the association between specific diseases and genes reported in some recent relevant literature.

[0055] For example, by applying the analysis method for microbial set enrichment according to various embodiments of the present disclosure to the Disbiome database, several disease-gene associations mediated by microorganisms were automatically discovered, see Table 3.

[0056] Table 3: Main gene-disease associations identified by enrichment analysis based on the Disbiome database

[0057]

[0058] A notable finding was the significant enrichment of microbes associated with nonalcoholic fatty liver disease (NAFLD) and the SREBF1 and LPL genes (see Table 3). SREBF1 is a known regulator of cholesterol and fatty acid synthesis in the liver, encoding a sterol regulatory element-binding transcription factor. Overexpression of SREBF1 has been shown to cause NAFLD in mice. Lipoprotein lipase (LPL) plays a role in the pathophysiology of NAFLD: lipoprotein metabolism is a major pathway for lipid homeostasis in hepatocytes. This significant enrichment analysis finding was also validated by recent studies on nonalcoholic steatohepatitis, which found that upregulation of LPL in hepatic stellate cells has been shown to exacerbate liver fibrosis in nonalcoholic steatohepatitis (NASH), which can be considered a subtype of NAFLD. Therefore, the abnormal abundance of microbes associated with both SREBF1 and LPL in NAFLD patients warrants further investigation.

[0059] Another notable finding was the significant enrichment of microorganisms associated with the ATG16L1, CCL11, and FUT2 genes in patients with Crohn's disease, a form of inflammatory bowel disease (IBD). This suggests a role for these microorganisms in the pathology of Crohn's disease. This significant enrichment finding is consistent with comparative biological experiments with healthy controls, which revealed significantly elevated levels of CCL11, an eosinophil-specific chemotactic factor that has been shown to be a major mediator of eosinophil recruitment in the colon, in the serum of Crohn's disease patients. This significant enrichment finding also aligns with recent findings regarding fucosyltransferase 2 (FUT2), whose genetic polymorphisms have been associated with Crohn's disease in multiple independent genome-wide association studies from diverse populations. Furthermore, this significant enrichment finding is corroborated by recent research in Crohn's disease, which has highlighted the role of autophagy-related 16-like 1 (ATG16L1) in Crohn's disease. Therefore, microorganisms related to the above three types of substances may play a key role in the occurrence and development of Crohn's disease.

[0060] It can be seen that the analysis method of microbial set enrichment according to the embodiment of the present disclosure can not only trace the relationship between human genes and human diseases, especially for metabolic disorders such as NAFLD, Crohn's disease, etc., which further confirms the hypothesis that host-microbiome interactions are mediated by microbial metabolites. The analysis method of microbial set enrichment according to the embodiment of the present disclosure may also be able to find human genes related to various human diseases (such as NAFLD) in advance and comprehensively before actual biological experiments, reflecting the indispensable role of the intestinal microbiome in the occurrence and development of inflammatory bowel disease, thereby providing a useful reference for subsequent biological experimental research.

[0061] Furthermore, the analysis method for enrichment of microbial collections according to the embodiments of the present disclosure has also achieved outstanding results in the study of Parkinson's disease (PD) pathology.

[0062] The 16S microbiome data of PD patients and healthy people were reanalyzed, and the microorganisms with differential abundance in the two groups were analyzed using the microbial set enrichment analysis method according to the embodiment of the present disclosure. Specifically, the 16S dataset was first downloaded from Qiita (project ID: 10483). Then, ANCOM was used to download the DA (differential abundance) microbial information (such as the DA information) of the feces of PD patients and healthy people implanted into wild-type mice and aSyn overexpressing mice in the previous report. Figure 5(As shown in Figure 2, where light grey indicates aSyn overexpression and dark grey indicates wild type) and using this DA microbiome information as input, we then used microbiome enrichment analysis based on the second microbiome knowledge base to rank human genes enriched in DA microbes associated with PD.

[0063] Among the DA-enriched genes (as shown in Table 4 ), some immune-related genes were found, such as IL10, FOXP3, DEFB4A, CCL2, and CCR2.

[0064] Table 4. Major human genes found by enrichment analysis of DA microbiota from feces of PD and healthy individuals implanted into mice

[0065]

[0066] This suggests that DA microorganisms in PD can affect the immune system by regulating genes involved in various types of immune responses, which is consistent with the findings of Sampson et al. in their 2016 study of PD through biological experiments that intestinal flora affect neuroimmune responses in PD mouse models.

[0067] The microbial enrichment analysis method according to the disclosed embodiments was also used to investigate the enrichment of two proinflammatory cytokines with elevated expression in the brains of patients with Parkinson's disease (PD), including tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). IL-6 was found to have a small enrichment in DA microbes (p-value = 9.7e-3; composite score = 4.42). This result suggests that changes in microbial abundance in PD may upregulate proinflammatory cytokines, such as IL-6, thereby inducing the neuroinflammatory state of PD.

[0068] In order to further study the role of the clustering effect of the main genes enriched in DA microorganisms in neuroinflammation, Enrichr was also used to perform gene function enrichment analysis, and it was found that these genes were significantly enriched in microglia-related genes (p value = 5.8e-6), which is consistent with the findings of Sampson et al. in their study on PD through biological experiments in 2016: PD has a specific intestinal microbial community, and the intestinal microbial community regulates microglial activation.

[0069] In a reanalysis of the dataset, the microbiome enrichment analysis method disclosed herein was applied to the DA microbiome of PD, resulting in the identification of human genes associated with microbiome enrichment, many of which are related to immunity. This result suggests the important role of the gut microbiome in neuroimmune responses. Furthermore, gene function enrichment analysis revealed that many human genes are involved in microglia development, which is consistent with the final findings of various studies that have undergone extensive animal experiments.

[0070] Figure 6 FIG. 6 is a schematic diagram showing a framework of a microorganism collection enrichment analysis device 600 according to an embodiment of the present disclosure. Figure 6 As shown, the microbial set enrichment analysis device 600 may include a communication interface 601 and a first processor 602. The communication interface 601 may be configured to: receive a microbial set to be analyzed input by a user. The first processor 602 may be configured to: determine the enrichment information of the microbial set to be analyzed using a second microbial set knowledge base; the enrichment information includes associated human genes and significance parameters; and sort the associated human genes according to the significance parameters. The second microbial set knowledge base is generated based on the first microbial set knowledge base of microbial classification and combined with the association information between significant microorganisms and human genes.

[0071] In some embodiments, the communication interface 601 may include a network adapter, a cable connector, a serial connector, a USB connector, a parallel connector, a high-speed data transmission adapter (such as optical fiber, USB 3.0, Thunderbolt interface, etc.), a wireless network adapter (such as a WiFi adapter), a telecommunications (3G, 4G / LTE, etc.) adapter, etc. The microorganism collection enrichment analysis device 600 can be connected to other components through the communication interface 601.

[0072] In some embodiments, the microbial collection enrichment analysis device 600 can be connected to the Figure 7 The first processor 602 may further be configured to generate a second microbial set knowledge base based on the first microbial set knowledge base of microbial classification and in combination with significant association information between microorganisms and human genes.

[0073] In some embodiments, the communication interface 601 can be configured to receive an existing second microorganism set knowledge base.

[0074] In some embodiments, the communication interface 601 can be configured to obtain the name of the genus and species of microorganisms from a database of 16SrRNA genes for the bacterial and archaeal kingdoms and obtain a microbial phylogenetic tree. Accordingly, the first processor 602 can be configured to: based on the name of the genus and species of the microorganisms obtained, query the corresponding abstract list from the literature abstracts of the public literature database; identify human gene information from the queried abstract list and determine the association information between microorganisms and human genes; determine the association strength of each piece of association information between microorganisms and human genes, and screen each piece of association information accordingly to determine the significant association information between microorganisms and human genes. In some embodiments, the first processor 602 can be further configured to: construct a directed acyclic graph based on the obtained microbial phylogenetic tree; construct a first microorganism set knowledge base for microbial classification based on the directed acyclic graph; and generate the second microorganism set knowledge base based on the first microorganism set knowledge base for microbial classification, combined with the significant association information between microorganisms and human genes.

[0075] like Figure 6 As shown in , the medical image detection apparatus 400 may include a communication interface 601 , a first processor 602 , a memory 603 , a storage 604 and a display 605 .

[0076] In some embodiments, the first processor 602 may be a processing device comprising one or more general-purpose processing devices, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), and the like. More specifically, the processor may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor that runs other instruction sets, or a processor that runs a combination of instruction sets. The processor may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a system on a chip (SoC), and the like. The first processor 602 may be communicatively coupled to the memory 603 and configured to execute computer-executable instructions stored thereon to perform various steps of the analysis method for enriching a microbial collection according to various embodiments of the present disclosure. In some embodiments, the analysis method for enriching a microbial collection according to various embodiments of the present disclosure may also be implemented by programs stored on the storage 604, which may be loaded into the memory 603 for execution by the first processor 602.

[0077] In some embodiments, memory 603 / storage 604 may be a non-transitory computer-readable medium, such as read-only memory (ROM), random access memory (RAM), phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), electrically erasable programmable read-only memory (EEPROM), other types of random access memory (RAM), flash disks or other forms of flash memory, cache, registers, static memory, compact disk read-only memory (CD-ROM), digital versatile disks (DVD) or other optical storage, cassette tapes or other magnetic storage devices, or any other possible non-transitory medium used to store information or instructions that can be accessed by a computer device.

[0078] In some embodiments, the storage 604 can store various data that appear in the analysis method of microbial set enrichment according to each embodiment of the present disclosure, such as but not limited to: a second microbial set knowledge base; a database of 16S rRNA genes for the bacterial and archaeal kingdoms, a first microbial set knowledge base for microbial classification, significant association information between microorganisms and human genes, a list of queried summaries, a microbial phylogenetic tree, significance parameters, associated diseases, and the like.

[0079] In some embodiments, the first processor 602 may be further configured to present a visualization of the associated human genes and their rankings, the corresponding at least one significance parameter, and / or other data on a display 605. The display 605 may include a liquid crystal display (LCD), a light emitting diode display (LED), a plasma display, or any other type of display, and provide a graphical user interface (GUI) presented on the display for user input and image / data display.

[0080] Figure 7 A schematic diagram of the configuration of an analysis system for enriching a microbial collection according to an embodiment of the present disclosure is shown, wherein Figure 6 The same configuration as shown is not repeated here. Figure 7 As shown, the system may include a microorganism-human gene association information determining device 606, a first microorganism set knowledge base construction device 608, and a microorganism set enrichment analysis device 600. The microorganism-human gene association information determining device 606 may include a second processor 607, and the first microorganism set knowledge base construction device 608 may include a third processor 609.

[0081] In some embodiments, the second processor 607 can be configured to: obtain the genus and species names of microorganisms from a database of 16S rRNA genes for the bacterial and archaeal kingdoms; based on the obtained genus and species names of microorganisms, query a corresponding abstract list from the literature abstracts in a public literature database; identify human gene information from the queried abstract list and determine the association information between microorganisms and human genes; and determine the association strength of each piece of association information between microorganisms and human genes, and accordingly screen each piece of association information to determine significant association information between microorganisms and human genes. In some embodiments, the third processor 609 can be configured to: obtain a microorganism phylogenetic tree; construct a directed acyclic graph based on the obtained microorganism phylogenetic tree; and construct a first microorganism set knowledge base for microorganism classification based on the directed acyclic graph.

[0082] The microorganism-human gene association information determining device 606 and the first microorganism collection knowledge base building device 608 can adopt similar configurations to the microorganism collection enrichment analysis device 600, and will not be described in detail here. Figure 7 The following description uses the separate first processor 602, second processor 607, and third processor 609 as an example, but the present disclosure is not limited thereto. In some embodiments, the first processor 602, second processor 607, and third processor 609 can also be integrated. Accordingly, the entire system can be integrated into a single microorganism collection enrichment analysis device 600 for implementation.

[0083] Furthermore, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present disclosure with equivalent elements, modifications, omissions, combinations (e.g., solutions that intersect various embodiments), adaptations, or changes. The elements in the claims are to be interpreted broadly based on the language employed in the claims and are not limited to the examples described in this specification or during the prosecution of this application, which examples are to be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, with the true scope and spirit being indicated by the following claims and the full scope of their equivalents.

[0084] The above description is intended to be illustrative and not restrictive. For example, the above examples (or one or more schemes thereof) can be used in combination with each other. For example, a person of ordinary skill in the art may use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features can be grouped together to simplify the present disclosure. This should not be interpreted as an intention that a disclosed feature that is not required to be protected is necessary for any claim. On the contrary, the subject matter of the present invention may be less than all the features of a specific disclosed embodiment. Thus, the following claims are incorporated into the specific embodiments as examples or embodiments, wherein each claim is independently a separate embodiment, and it is considered that these embodiments can be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the appended claims and the full scope of equivalents to which these claims are entitled.

Claims

1. A method for analyzing microbial enrichment, characterized in that: The analysis method comprises receiving a microorganism set to be analyzed input by a user; The first processor determines enrichment information of the microbial set to be analyzed using a second microbial set knowledge base, wherein the enrichment information includes associated human genes and significance parameters, and the second microbial set knowledge base is generated based on the first microbial set knowledge base of microbial classification and in combination with association information between significant microorganisms and human genes; The first processor ranks the associated human genes according to the significance parameter; The association information between the significant microorganisms and human genes is determined by the following steps: The genus and species names of microorganisms were obtained from the 16S rRNA gene database for the Bacteria and Archaea kingdoms; The second processor searches for a corresponding abstract list from the literature abstracts in the public literature database based on the acquired genus and species names of the microorganisms; The second processor identifies human gene information from the retrieved summary list and determines association information between the microorganism and the human gene; The second processor determines the correlation strength of each piece of correlation information between the microorganism and the human gene, and accordingly screens each piece of correlation information to determine significant correlation information between the microorganism and the human gene.

2. The analysis method according to claim 1, characterized in that The first microorganism set knowledge base of microorganism classification is further constructed by the following steps: Obtain microbial phylogenetic trees; A third processor constructs a directed acyclic graph based on the obtained microbial phylogenetic tree; The third processor constructs a first microorganism set knowledge base for microorganism classification based on the directed acyclic graph.

3. The analysis method according to claim 2, characterized in that The significance parameter includes at least one of a p-value, a q-value and an OR value, and sorting the associated human genes according to the significance parameter also includes: determining a correction value based on the attention deviation of each human gene in a public literature database; determining an enrichment parameter based on the significance parameter and the correction value; and sorting the associated human genes based on the determined enrichment parameter.

4. The analysis method according to any one of claims 1 to 3, characterized in that Identifying human gene information from the queried summary list and determining the association information between microorganisms and human genes further includes: identifying human gene information from the queried summary list via a named entity recognition (NER) tool, and normalizing the identified human gene information based on the human gene names of the human genome database, thereby determining the association information between the microorganisms and the normalized human genes.

5. The analysis method according to any one of claims 1 to 3, characterized in that The correlation strength of each piece of correlation information between microorganisms and human genes is calculated using the following formula (1): Among them, i is the serial number of the microorganism, j is the serial number of the human gene, and || is the size of the set.

6. The analysis method according to claim 2 or 3, characterized in that Obtaining microbial phylogenetic trees includes: Get a database of 16S rRNA genes for the Bacteria and Archaea kingdoms; The species development tree in the database of 16S rRNA genes for the bacterial kingdom and the archaeal kingdom is filtered to remove the developmental tree branches of the viral kingdom and the viroid kingdom.

7. The analysis method according to claim 2, characterized in that The first microorganism set knowledge base for microorganism classification based on the directed acyclic graph includes: Using taxonomic units at each taxonomic level above the strain level in the microbial phylogenetic tree as nodes; All leaf nodes from parent nodes of each classification level are enumerated to convert the microbial phylogenetic tree into a first microbial set knowledge base of the microbial classification.

8. The analysis method according to claim 1, characterized in that The first processor and the second processor are integrated or separated.

9. The analysis method according to claim 2, characterized in that The first processor and the third processor are integrated or separated.

10. An analytical device for enrichment of microorganisms, characterized in that: include: The first interface is configured to: receive a microorganism set to be analyzed input by a user: The first processor is configured as follows: Determining enrichment information of the microbial set to be analyzed using a second microbial set knowledge base, the enrichment information including associated human genes and significance parameters, wherein the second microbial set knowledge base is generated based on the first microbial set knowledge base of microbial classification and in combination with association information between significant microorganisms and human genes; and ranking the associated human genes according to the significance parameter; The association information between the significant microorganisms and human genes is determined by the following steps: The genus and species names of microorganisms were obtained from the 16S rRNA gene database for the Bacteria and Archaea kingdoms; The second processor searches for a corresponding abstract list from the literature abstracts in the public literature database based on the acquired genus and species names of the microorganisms; The second processor identifies human gene information from the retrieved summary list and determines association information between the microorganism and the human gene; The second processor determines the correlation strength of each piece of correlation information between the microorganism and the human gene, and accordingly screens each piece of correlation information to determine significant correlation information between the microorganism and the human gene.

11. The analysis device according to claim 10, characterized in that The first interface is further configured to receive the second microorganism set knowledge base.

12. The analysis device according to claim 10, characterized in that The first interface is further configured to: receive the association information between the significant microorganisms and human genes from the second processor, and receive the first microorganism set knowledge base of microorganism classification from the third processor; The first processor is further configured to generate the second microorganism set knowledge base based on the first microorganism set knowledge base of microorganism classification and in combination with the association information between significant microorganisms and human genes.

13. The analysis device according to claim 10, characterized in that The first interface is further configured to: obtain a microbial phylogenetic tree; The first processor is further configured to: Construct a directed acyclic graph based on the obtained microbial phylogenetic tree; Constructing a first microorganism set knowledge base of microorganism classification based on the directed acyclic graph; as well as The second microbial set knowledge base is generated based on the first microbial set knowledge base of microbial classification and combined with the association information between significant microorganisms and human genes.

14. A microbial enrichment analysis system, characterized in that: include: An analysis device according to any one of claims 10 to 13; The second interface is configured to: obtain the genus and species names of microorganisms from a database of 16S rRNA genes for the bacterial kingdom and the archaeal kingdom; The third interface is configured to: obtain a microbial phylogenetic tree; The second processor is configured as: Based on the names of the genus and species of the obtained microorganisms, a corresponding abstract list is queried from the literature abstracts in the public literature database; Identify human gene information from the queried abstract list and determine the association information between microorganisms and human genes; as well as Determine the strength of association between each piece of association information between microorganisms and human genes, and screen each piece of association information based on the strength of association between each piece of association information to determine significant association information between microorganisms and human genes; as well as The third processor is configured as: constructing a directed acyclic graph based on the obtained microbial phylogenetic tree; and A first microorganism set knowledge base of microorganism classification is constructed based on the directed acyclic graph.

15. A non-transitory computer-readable storage medium having executable instructions stored thereon, wherein when the executable instructions are executed by a processor, the method for analyzing the enrichment of a microbial collection according to any one of claims 1 to 9 is implemented.

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