CN116313155A - Disease associated evolution system and method based on lipidomic method - Google Patents
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Abstract
The invention provides a disease associated evolution system and method based on a lipidomic method and a biological data measurement chip set. The system includes a lipid sample analysis module, a lipid data grouping module, a lipid remapping module, and a lipid evolution database. The biological lipid assay chip set comprises a data analysis circuit, a data grouping circuit and a data remapping circuit, wherein the biological lipid assay chip set is installed on a computer terminal comprising a memory and a processor, the computer terminal is connected with a cloud lipid evolution database, and the biological lipid assay chip set and the processor execute a computer program stored in the memory and are used for realizing part or all of the steps of the disease association method. According to the technical scheme, corresponding possible associated diseases can be accurately evolved aiming at lipid components in the biological sample, more sample inputs can be provided based on a remapping process when the sample size is insufficient, and the evolution accuracy is improved.
Description
Technical Field
The invention belongs to the technical field of lipidomics, and particularly relates to a disease-associated evolution system and method based on a lipidomic method and a biological data measurement chip set for realizing the method.
Background
At the end of the twentieth century, metagenomics (metabolomics) has developed vigorously following genomics, transcriptomics, proteomics. Metabonomics is a discipline that examines the whole and its law of variation of endogenous metabolites produced by biological systems (cells, tissues or fluids) upon external stimuli or disturbances. The general workflow of metabolomics includes collection of samples, sample pretreatment, sample analysis and detection, data processing (data pretreatment, multivariate statistical analysis), metabolite identification, and biological interpretation. The most commonly used metabonomic study samples at present are cellular, urine, blood, tissue samples of organisms.
Lipids refer to a class of compounds that are insoluble in water and readily soluble in organic solvents. Lipid compounds are one of the important constituent substances of cells and also one of the main storage substances in living bodies. Lipid compounds also have important biological functions such as signal mediation, energy metabolism, and substance transport.
Lipidomics (lipidomics) is an important branch of metabonomics, an emerging discipline for systematic analysis of lipids in biological systems (cells, tissues or fluids), aimed at studying the overall metabolism of endogenous lipids and their law of variation after external stimuli or disturbances. The main content of the lipidomic study includes: 1. research is carried out on the overall profile of the overall lipid metabolite and the dynamic variation thereof, the variation and trend of the overall lipid metabolism level are evaluated by a multivariate statistical method, and endogenous small molecule differential lipids are screened and found and can be used as potential biomarkers; 2. the relationship between the change rule of differential lipid and biological process (such as disease induction and drug intervention) is studied, and the mechanism of disease occurrence or drug intervention is revealed through channel analysis.
For example, chinese patent publication CN112185462a proposes a classification device based on lipid biomarkers and application thereof. In particular, a lipid biomarker-based classification device includes a data acquisition module configured to acquire a characteristic dataset of a patient; a model matching module configured to query the classification model library and determine a matched classification model based on the medication features; the classification model is obtained by training a preset classification algorithm through the expression quantity of lipid biomarkers in blood of different types of patients; and a patient classification module configured to obtain a classification result of the patient for the pulmonary tuberculosis based on the classification model and the expression level of the lipid biomarker in the blood.
However, although the association of lipid change laws with biological processes (such as disease induction) is recognized by those skilled in the art, the prior art does not give an effective technical solution, particularly based on how lipidomics realize the evolution of association of different lipid sample data with disease data; particularly in the case of limited sample input in the current lipidomic analysis, evolution accuracy is usually not high, and the method is one of the technical problems to be solved in the field.
Disclosure of Invention
In order to solve the technical problems, the invention provides a disease-associated evolution system and method based on a lipidomic method and a biological data measurement chip set. The system includes a lipid sample analysis module, a lipid data grouping module, a lipid remapping module, and a lipid evolution database. The biological lipid assay chip set comprises a data analysis circuit, a data grouping circuit and a data remapping circuit, wherein the biological lipid assay chip set is installed on a computer terminal comprising a memory and a processor, the computer terminal is connected with a cloud lipid evolution database, and the biological lipid assay chip set and the processor execute a computer program stored in the memory and are used for realizing part or all of the steps of the method.
In particular, in a first aspect of the invention, there is provided a disease-associated evolution system based on a lipidomic method, the system comprising a lipid sample analysis module, a lipid data grouping module, a lipid remapping module and a lipid evolution database;
the lipid sample analysis module is used for carrying out attribute analysis on an input lipid sample, wherein the attribute analysis comprises identification of a collection source and a collection method of the lipid sample;
as one of the improvements of the present invention, the lipid data grouping module is configured to group lipid sample data based on the attribute analysis result of the lipid sample analysis module, and the collection method of the lipid sample data in each group is the same;
the lipid evolution database provides a lipid structure database, a gene/protein database, and a lipid-disease association tool library for analyzing the association of sample lipid data with potential diseases;
as a further improvement of the present invention, the lipid remapping module receives the lipid evolution database and outputs a correlation analysis result, and when the correlation analysis result meets a predetermined condition, the lipid remapping module re-acquires the attribute analysis result from the lipid sample analysis module, and after performing remapping processing on the attribute analysis result, inputs the remapping processing result into the lipid data grouping module for re-grouping;
the remapping treatment comprises mixing partial original lipid samples with the same acquisition sources to obtain a mixed lipid sample.
As the more specific key technical means for embodying the improvement, after grouping the lipid sample data, inputting the lipid component data of each group into the lipid structure database and the gene/protein database in parallel to obtain a lipid structure recognition result and a gene protein recognition result respectively;
inputting the lipid structure recognition result and the gene protein recognition result into the lipid-disease association tool library;
the lipid-disease association tool library adopts at least two association analysis methods to obtain a first association analysis result and a second association analysis result.
In a second aspect of the invention, a method of disease-associated evolution based on a lipidomic approach is provided, which method can be implemented based on the disease-associated evolution system of the first aspect described above.
In a specific implementation, the method includes:
s800: collecting different types of biological samples;
s801: selecting a corresponding lipid acquisition method to extract lipid component data in the biological sample based on the type of the biological sample;
s802: after carrying out attribute analysis on all lipid component data, obtaining a plurality of grouped lipid sample data;
s803: taking each of the grouped lipid sample data as input to a lipid evolution database;
s804: judging whether the output result of the lipid evolution database meets a preset condition for the input of the set of sample data;
if yes, entering the next step;
otherwise, taking the next group of grouped lipid sample data as input of the lipid evolution database, and returning to the step S803;
s805: performing remapping treatment on all lipid component data to obtain a remapped mixed lipid sample; returning to step S802.
As a further improvement of the process,
the remapping process of step S805 specifically includes:
determining remapping parameters based on the output of the lipid evolution database,
and mixing partial original lipid samples with the same acquisition sources based on the remapping parameters to obtain a remapping mixed lipid sample.
In a third aspect of the present invention, there is also provided a biological data assay chipset comprising a data parsing circuit, a data grouping circuit and a data remapping circuit, the biological lipid assay chipset being mounted on a computer terminal comprising a memory and a processor, the computer terminal being connected to a cloud lipid evolution database, a computer program stored in the memory being executed by the biological lipid assay chipset and the processor for carrying out part or all of the steps of the method of the second aspect.
According to the technical scheme, corresponding possible associated diseases can be accurately evolved aiming at lipid components in the biological sample, more sample inputs can be provided based on a remapping process when the sample size is insufficient, and the evolution accuracy is improved.
Further advantages of the invention will be further elaborated in the description section of the embodiments in connection with the drawings.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a sub-module combination architecture of a disease-associated evolution system based on a lipidomic approach in accordance with one embodiment of the present invention;
FIG. 2 is a flow chart of the system of FIG. 1 for deriving correlation analysis results;
FIG. 3 is a schematic diagram of a further embodiment of the correlation results depicted in FIG. 2;
FIG. 4 is a flow chart of a disease-associated evolution method based on a lipidomic approach implemented based on the system described in FIG. 1;
fig. 5 is a schematic diagram of a hardware architecture of a biological data assay chipset according to an embodiment of the present invention.
Description of the embodiments
Referring to fig. 1, a submodule combination architecture diagram of a disease-associated evolution system based on a lipidomic approach according to one embodiment of the present invention.
In fig. 1, the system includes a lipid sample analysis module, a lipid data grouping module, a lipid remapping module, and a lipid evolution database.
More specifically, the lipid sample analysis module is used for carrying out attribute analysis on the input lipid sample, wherein the attribute analysis comprises identification of a collection source and a collection method of the lipid sample.
Correspondingly, although not shown in fig. 1, in practical implementation, as a further preference, the system further comprises a lipid acquisition subsystem; the lipid acquisition subsystem selects a corresponding lipid acquisition method to extract lipid component data based on the type of the lipid biological sample; types of biological samples include biological plasma, biological cell tissue, and biological fluids.
In various embodiments of the invention, the disease-associated evolution based on the lipidomic approach follows the general course of the study of lipidomic. This will be described briefly below.
The current flow of lipidomics in disease diagnosis is generally divided into three steps: defining medical problems; the lipidomic analysis comprises the collection and preparation of samples, separation and identification and data information processing; and (5) analyzing results and screening biomarkers. The most important link is the lipidomic analysis, i.e. the invention involves a critical part.
Lipids can be collected from biological plasma, biological tissue and biological fluids, and the corresponding lipid collection methods include gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, electrospray ionization mass spectrometry.
As a more specific description, the lipid-lipid collection-analysis method mainly includes gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), electrospray ionization mass spectrometry, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), nuclear Magnetic Resonance (NMR), and the like.
In various embodiments of the present invention, in order to achieve subsequent evolution analysis and remapping analysis, the inventors decided to employ gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, electrospray ionization mass spectrometry through multiple comparisons; it is particularly noted that the present invention does not employ magnetic resonance (NMR) in order to maintain subsequent sample evolution mixing.
More specifically, for biological plasma, a liquid chromatography-combined method is adopted; electrospray ionization mass spectrometry is adopted for biological plasma; electrospray ionization mass spectrometry was used for biological cell tissues.
Wherein, further, for biological cell tissue, purification treatment is needed, including sample pretreatment and sample derivatization treatment.
Based on the above, the lipid data grouping module is configured to group lipid sample data based on the attribute analysis result of the lipid sample analysis module, where the collection method of the lipid sample data in each group is the same.
More specifically, the lipid data grouping module groups lipid sample data based on the attribute analysis result of the lipid sample analysis module, and specifically includes: lipid component data extracted for the same type of biological sample are grouped.
After grouping the lipid sample data, inputting each grouped lipid component data into the lipid structure database and the gene/protein database in parallel to respectively obtain a lipid structure identification result and a gene protein identification result;
and the lipid remapping module receives the lipid evolution database and outputs a correlation analysis result, and when the correlation analysis result meets a preset condition, the lipid remapping module reacquires the attribute analysis result from the lipid sample analysis module, carries out remapping processing on the attribute analysis result, and then inputs the remapping processing result into the lipid data grouping module for regrouping.
More specifically, on the basis of fig. 1, see fig. 2.
The lipid evolution database provides a lipid structure database, a gene/protein database, and a lipid-disease association tool library for analyzing the association of sample lipid data with underlying diseases.
The lipid evolution database can further form a lipid-disease association tool library through literature grabbing and indexing or manual labeling based on a lipid structure database and a gene/protein database provided by the existing open-source online lipid database.
The open source online Lipid database may be, for example, lipid Maps, which is the research effort of the national institute of integrated medical (NIGMS). Lipid Maps can provide Lipid structure databases and gene/protein databases, including Lipid standard Lipid pathway MS analysis tools, structure mapping tools, etc., from which 168 tens of thousands of lipids can be identified, and can provide structure and MS information for over 1 ten thousand lipids.
See fig. 3 based on fig. 2.
Inputting the lipid structure recognition result and the gene protein recognition result into the lipid-disease association tool library;
the lipid-disease association tool library adopts at least two association analysis methods to obtain a first association analysis result and a second association analysis result.
The first correlation analysis result comprises a first lipid structure-disease correlation data pair and a first degree of correlation;
the second correlation analysis result includes a second gene/protein-disease correlation data pair and a second degree of correlation.
Then, the correlation analysis result satisfies a predetermined condition, specifically including one or a combination of the following conditions:
the first correlation analysis value is below a first threshold and the second correlation analysis value is below a second threshold;
the degree of overlap of the first lipid structure-disease associated data pair and the second gene/protein-disease associated data pair is below a third threshold.
The association analysis value is association degree, wherein the association degree represents the association degree between lipid-diseases or genes/proteins-diseases, the higher the association degree is, the more evolution association or marking relationship exists, and the association degree can be normalized to a value between 0 and 1;
the degree of overlap refers to the degree of overlap of the disease signature occurring in the first lipid structure-disease associated data pair and the disease signature occurring in the second gene/protein-disease associated data pair, e.g., overlap is considered if a disease a occurs in both the first lipid structure-disease associated data pair and the second gene/protein-disease associated data pair. The ratio of all such overlapping diseases in all data pairs is calculated as a measure of the degree of overlap, expressed in percent.
The first to third thresholds may be determined in advance according to actual needs. Preferably, both the first threshold and the second threshold are greater than 0.65; the third threshold is greater than the first threshold and the second threshold.
On the basis of fig. 1-3, referring to fig. 4, a disease-related evolution method based on a lipidomic approach is presented.
In fig. 4, the method comprises the steps of:
s800: collecting different types of biological samples;
s801: selecting a corresponding lipid acquisition method to extract lipid component data in the biological sample based on the type of the biological sample;
s802: after carrying out attribute analysis on all lipid component data, obtaining a plurality of grouped lipid sample data;
s803: taking each of the grouped lipid sample data as input to a lipid evolution database;
s804: judging whether the output result of the lipid evolution database meets a preset condition for the input of the set of sample data;
if yes, entering the next step;
otherwise, taking the next group of grouped lipid sample data as input of the lipid evolution database, and returning to the step S803;
s805: performing remapping treatment on all lipid component data to obtain a remapped mixed lipid sample; returning to step S802.
The remapping process of step S805 specifically includes:
determining remapping parameters based on the output of the lipid evolution database,
and mixing partial original lipid samples with the same acquisition sources based on the remapping parameters to obtain a remapping mixed lipid sample.
The remapping parameters here characterize how much of the original lipid sample from the same acquisition source is mixed. The remapping parameter is between 100% and 0%. When the remapping parameter is 100%, this means that all raw lipid samples from the same collection source are mixed.
In actual calculation, the remapping parameter may be determined based on a threshold difference between the output result of step S804 and a predetermined condition.
Fig. 5 also shows a biological data assay chipset comprising a data parsing circuit, a data grouping circuit and a data remapping circuit, the biological lipid assay chipset being mounted on a computer terminal comprising a memory and a processor, the computer terminal being connected to a cloud lipid evolution database, a computer program stored in the memory being executed by the biological lipid assay chipset and the processor for carrying out part or all of the steps of the method as described in fig. 4.
In fig. 5, the computer terminal includes a plurality of memories and processors to execute a plurality of parallel executable methods or steps of the method in parallel, further speeding up the efficiency.
The evolution model shows that the method can accurately evolve corresponding possible associated diseases aiming at lipid components in biological samples; meanwhile, when the primary evolution result shows that the sample size is insufficient, more sample inputs can be provided based on the remapping process, and the evolution accuracy is improved.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A disease-associated evolution system based on a lipidomic approach, the system comprising a lipid sample analysis module, a lipid data grouping module, a lipid remapping module, and a lipid evolution database;
the method is characterized in that:
the lipid sample analysis module is used for carrying out attribute analysis on the input lipid sample, wherein the attribute analysis comprises identification of a collection source and a collection method of the lipid sample;
the lipid data grouping module is used for grouping lipid sample data based on the attribute analysis result of the lipid sample analysis module, and the collection method of the lipid sample data in each grouping is the same;
the lipid evolution database provides a lipid structure database, a gene/protein database, and a lipid-disease association tool library for analyzing the association of sample lipid data with potential diseases;
the lipid remapping module receives the lipid evolution database and outputs a correlation analysis result, when the correlation analysis result meets a preset condition, the lipid remapping module reacquires the attribute analysis result from the lipid sample analysis module, and after carrying out remapping treatment on the attribute analysis result, the lipid remapping module inputs the remapping treatment result into the lipid data grouping module for regrouping;
the remapping treatment comprises mixing partial original lipid samples with the same acquisition sources to obtain a mixed lipid sample.
2. A disease-associated evolution system based on a lipidomic approach as claimed in claim 1, wherein:
the system further comprises a lipid acquisition subsystem;
the lipid acquisition subsystem selects a corresponding lipid acquisition method to extract lipid component data based on the type of the lipid biological sample;
types of biological samples include biological plasma, biological cell tissue, and biological fluids.
3. A disease-associated evolution system based on a lipidomic approach as claimed in claim 2, wherein:
the lipid acquisition method comprises a gas chromatography-mass spectrometry method, a liquid chromatography-mass spectrometry method and an electrospray ionization mass spectrometry method.
4. A disease-associated evolution system based on a lipidomic approach as claimed in claim 2, wherein:
the lipid data grouping module groups lipid sample data based on the attribute analysis result of the lipid sample analysis module, and specifically includes:
lipid component data extracted for the same type of biological sample are grouped.
5. A disease-associated evolution system based on a lipidomic approach as claimed in claim 1 or 2 or 4, characterized in that:
after grouping the lipid sample data, inputting each grouped lipid component data into the lipid structure database and the gene/protein database in parallel to respectively obtain a lipid structure identification result and a gene protein identification result;
inputting the lipid structure recognition result and the gene protein recognition result into the lipid-disease association tool library;
the lipid-disease association tool library adopts at least two association analysis methods to obtain a first association analysis result and a second association analysis result.
6. The disease-associated evolution system based on a lipidomic approach as claimed in claim 5, wherein:
the first correlation analysis result comprises a first lipid structure-disease correlation data pair and a first degree of correlation;
the second correlation analysis result includes a second gene/protein-disease correlation data pair and a second degree of correlation.
7. The disease-associated evolution system based on a lipidomic approach as claimed in claim 6, wherein:
the correlation analysis result meets the preset conditions and specifically comprises one or a combination of the following conditions:
the first correlation analysis value is below a first threshold and the second correlation analysis value is below a second threshold;
the degree of overlap of the first lipid structure-disease associated data pair and the second gene/protein-disease associated data pair is below a third threshold.
8. A method of disease-related evolution based on a lipidomic approach, the method comprising the steps of:
s800: collecting different types of biological samples;
s801: selecting a corresponding lipid acquisition method to extract lipid component data in the biological sample based on the type of the biological sample;
s802: after carrying out attribute analysis on all lipid component data, obtaining a plurality of grouped lipid sample data;
s803: taking each of the grouped lipid sample data as input to a lipid evolution database;
s804: judging whether the output result of the lipid evolution database meets a preset condition for the input of each group of sample data;
if yes, entering the next step;
otherwise, taking the next group of grouped lipid sample data as input of the lipid evolution database, and returning to the step S803;
s805: performing remapping treatment on all lipid component data to obtain a remapped mixed lipid sample; returning to step S802.
9. A method of disease-associated evolution based on a lipidomic approach as claimed in claim 8, wherein:
the remapping process of step S805 specifically includes:
determining remapping parameters based on the output of the lipid evolution database,
and mixing partial original lipid samples with the same acquisition sources based on the remapping parameters to obtain a remapping mixed lipid sample.
10. A biological data assay chipset comprising a data resolution circuit, a data grouping circuit and a data remapping circuit, the biological lipid assay chipset being mounted on a computer terminal comprising a memory and a processor, the computer terminal being connected to a cloud lipid evolution database, a computer program stored in the memory being executed by the biological lipid assay chipset and the processor for carrying out part or all of the steps of the method of any one of claims 8 or 9.
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