CN109583662B - Health risk evaluation method for binary mixture of atmospheric pollution - Google Patents
Health risk evaluation method for binary mixture of atmospheric pollution Download PDFInfo
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Abstract
A new method for evaluating health risks of binary mixtures of atmospheric pollution comprises the following steps: acquiring the concentration of pollutants in the atmosphere from literature or actual environment monitoring, selecting pollutants with similar concentrations as binary mixed components to be evaluated, and converting the actual environment exposure concentration into in vitro equivalent concentration; obtaining transcriptome data of each pollutant and in-vitro biological test information of a specific channel from a public database or literature, and calculating a mixed effect value, a corrected relative efficiency factor and a combined toxicity action mode of each pollutant; monte Carlo simulation is carried out on the in-vitro equivalent concentration of the pollutants, relative efficiency factors are corrected in a coupling mode, and the health risk of the mixed pollutants is evaluated. The method is established on the basis of the environmental exposure concentration of the pollutant monomer and the biological test data, can evaluate the health risk of the pollutant monomer without depending on the toxicity data of the mixture, saves the cost of manpower and material resources, and has wide application prospect in the fields of the evaluation of the actual environmental mixed exposure health risk, the evaluation of environmental safety and the like.
Description
Technical Field
The invention relates to the technical field of health risk evaluation of pollution mixed exposure, in particular to a health risk evaluation method of an atmospheric pollution binary mixture.
Background
The environmental pollution problem is never only the pollution problem of a single chemical. The various pollutants in the real environment are often present in a mixed form and produce combined toxic effects when exposed to the human population. Because the combined action of mixed pollution produces complex and changeable health effects, the calculation and evaluation of combined toxicity and health risks thereof are always the difficulties and focuses of scientific attention. The National Institute of Environmental Health Sciences (NIEHS) as early as 2013 pointed out that elucidating the Health effects of Environmental mixtures should be one of the priority studies in the future. Atmospheric Particulate Matter (PM) 2.5 ) Is a natural mixed system. With atmospheric particulates (particulate)For example, the ash haze generally relates to pollution of various chemical components such as sulfate, nitrogen oxides, Polycyclic Aromatic Hydrocarbons (PAHs), Polychlorinated Biphenyls (PCBs), toxic heavy metals, and the like. Even the major organic contaminant component, polycyclic aromatic hydrocarbons, occur in the atmosphere as a mixture of several species, including the well-known human carcinogen benzo [ a ]]Pyrene (benzoa)]pyrene, BaP). The International Agency for Research on Cancer (IARC) classified outdoor air pollution and atmospheric fine particulates as carcinogenic to humans in 2013 (http:// www.iarc.fr/en/media-center/iarcnews/pdf/pr221_ e.pdf). However, PM 2.5 The influence of exposure on human health not only has significant difference among different regions due to different pollution compositions in specific regions, but also has different health influence on atmospheric pollutants from different sources in the same region. Kan et al in 2005 indicated that the health impact of air pollution in china is different from that in developed countries. Laden et al found PM from automotive, coal-fired sources 2.5 Increase by 10 mu g/m 3 The death rate of the crowd is respectively increased by 3.4 percent and 1.1 percent, and the health hazard of the fine particles from the motor vehicle is higher. Therefore, accurate prediction of the combined toxicity of the air pollution mixture is a prerequisite for scientific assessment of the accumulated health risk.
Unfortunately, current non-experimental evaluation of the combined toxicity effects of mixtures has progressed far less than the search for single pollutant toxicity prediction methods. For example, there have been studies using machine learning classification algorithms to classify and predict the specific toxicity of a single chemical in a toxicity database. No matter whether an isobologram or a factorial analysis method is adopted, toxicity experiment data of a pollution mixture is still indispensable in the establishment of the existing combined toxicity prediction model. The current evaluation of the health risk of atmospheric pollution still relies on the simple additive hypothesis at the mixture level, focusing on the carcinogenic risk of polycyclic aromatic hydrocarbon mixtures relative to BaP, while ignoring the possibility of the pollutants to act synergistically leading to underestimated risk. The combined toxicity of contaminating mixtures, especially those with complex mechanisms, is more difficult to predict analytically due to the differences in their targets and modes of action in vivo. How to achieve combined toxicity calculation evaluation that is completely independent of mixture toxicity experiments is a great challenge and opportunity for pollutant toxicity prediction.
In conclusion, the toxicity prediction method of a single compound is gradually mature and widely applied to the risk assessment of environmental chemicals, and the complex combined toxicity effect generated by mixed chemicals is a difficult problem in environmental toxicology research, and a non-experimental assessment method is urgently needed to be developed. Due to the limitation of a combined toxicity research means, the evaluation of the health risk of the air pollution mixture is still limited to the working assumption that the toxicity of the pollution components is simply added, and the problem caused by risk underestimation is difficult to avoid. The development of a rapid toxicity prediction evaluation method which is close to the combined action mode of components in an environment real mixed system is urgently needed, so that the health risk and the potential threat of pollutants can be evaluated more objectively.
Disclosure of Invention
In view of the above, the present invention provides a method for evaluating health risks of binary mixtures of air pollution, so as to at least partially solve at least one of the above technical problems.
As one aspect of the invention, a combined toxicity prediction method of a binary mixture is provided, wherein the binary mixture comprises two components with similar molar concentrations, and the method comprises the following steps:
step 1: obtaining transcriptome data exposed by the two components respectively and in-vitro test data of test end points corresponding to specific targets or channels from a public database or documents, selecting one component with the highest toxicity of the two components as a reference body, and the other component as a non-reference body, and obtaining the mutual gene expression similarity of the reference body and the non-reference body and a monomer dose-effect curve equation of the test end points of the reference body and the non-reference body corresponding to different targets or channels respectively;
step 2: respectively obtaining in-vitro test half effective concentrations of a corresponding target spot or a channel non-reference body and a reference body according to the monomer dose-effect curve equation, and further obtaining relative efficiency factors of the non-reference body relative to the reference body;
and step 3: respectively calculating the gene residual effect of the reference body or the non-reference body on the non-reference body or the reference body exposed later when the reference body or the non-reference body is exposed earlier according to the gene expression similarity and the monomer dose-effect curve equation, obtaining a mixed effect numerical value of the binary mixture according to the gene residual effect and the monomer effect of the reference body and the non-reference body, and further obtaining a correction relative efficiency factor of the binary mixture relative to the reference body;
and 4, step 4: the ratio of the variation between the relative potency factor of the non-reference body relative to the reference body and the corrected relative potency factor of the binary mixture relative to the reference body is used to characterize the magnitude and mode of action of the combined toxic effect of the binary mixture.
As another aspect of the present invention, there is provided a method for evaluating health risk of an atmospheric pollution binary mixture, comprising the steps of:
step A, collecting atmospheric pollutant data through literature or actual environment monitoring, obtaining a plurality of pollutant concentrations in an atmospheric sample in a period of time, and selecting two pollutants with similar molarity as a binary mixture to be evaluated;
step B, using the joint toxicity prediction method in the steps 1-4, taking the pollutant with the highest toxicity in the binary mixture as a reference body, taking the other pollutant as a non-reference body, and respectively obtaining a corrected relative efficiency factor of the binary mixture relative to the reference body and a relative efficiency factor of the non-reference body relative to the reference body;
step C, taking the atmospheric concentration of the non-reference body in the binary mixture in the actual environment as the environmental exposure concentration, and converting the environmental exposure concentration into the in vitro equivalent concentration;
and D, utilizing the corrected relative efficacy factor of the binary mixture relative to the reference body and the relative efficacy factor of the non-reference body relative to the reference body, combining the in vitro equivalent concentration of the non-reference body and the toxicity onset concentration of the reference body, and carrying out binary mixture health risk assessment aiming at the corresponding target or passage.
Based on the technical scheme, the method for evaluating the health risk of the binary mixture of atmospheric pollution has the following beneficial effects:
(1) in the aspect of the combined toxicity evaluation of the environmental health risk evaluation of the mixture, a complex toxicity mechanism reflected by the change of a pollutant gene expression profile is fully considered, and the combined toxicity action mode of a binary mixture such as synergy and antagonism is obtained by mapping the complex toxicity mechanism to in-vitro test data of a specific passage or target point, so that the combined toxicity action mode is closer to the real situation than the mixed effect evaluation value obtained by a simple adding model of the toxicity of pollution components in the existing environmental health risk evaluation;
(2) the invention provides a technical scheme of environmental pollution health risk assessment based on joint toxicity prediction for the first time, the existing transcriptome and biological test information are fully combined, the joint toxicity of an equal proportion binary mixture is estimated by a gene residual effect method, the in vitro equivalent concentration simulation data of pollutants is coupled to carry out mixed exposure health risk assessment, the distribution range of uncertain factors in the in vitro equivalent concentration of the pollutants is obtained by applying simulation sampling, and finally, a scientific and reasonable health risk assessment value is obtained;
(3) the combined toxicity evaluation method is directly established on transcriptome data and in vitro activity test data of a pollutant monomer, can predict the combined toxicity of the binary mixture and further evaluate the health risk without obtaining toxicity test data of a mixed system in advance, saves the cost of manpower and material resources, is quick and high in efficiency, and has wide application prospect in the fields of actual environment mixture health risk evaluation, environment safety evaluation, mixture toxicity prediction and the like.
Drawings
FIG. 1 is a schematic diagram of health risk assessment using combined toxicity prediction of atmospheric pollutants according to example 1 of the present invention;
FIG. 2 is a graph showing dose-effect curves of the polycyclic aromatic hydrocarbons benzo [ a ] anthracene (BaA) and benzo [ a ] pyrene (BaP) in example 1 of the present invention;
FIG. 3 is a schematic diagram of the health risk distribution density and cumulative probability of BaA monomer and binary mixtures in example 1 of the present invention.
Detailed Description
In order that the objects, technical solutions and advantages of the present invention will become more apparent, the present invention will be further described in detail with reference to the accompanying drawings in conjunction with the following specific embodiments.
According to literature research on a calculation model for evaluating the health risk of air pollution, the related method or technology has defects. Based on a simple additive toxicity prediction model, the mixed effect between pollutants is neglected on the assumption that the pollutants act independently. Often underestimating toxic effects. The invention aims to provide a mixture combined toxicity prediction method based on combination of transcriptome data and specific channel in-vitro biological test information, which is independent of a priori mixed toxicity test and evaluates the atmospheric pollution health by coupling atmospheric pollutant exposure data.
The basic principle of the invention is that for a specific target or passage, the mixed effect caused by the simultaneous exposure of two pollutants can be regarded as the comprehensive influence of the gene residual effect generated by the exposure of one monomer and the exposure of the other monomer; therefore, the mixing effect of the binary mixture can be estimated by combining the data of the binary mixture in the gene expression similarity and the in vitro test data of the binary mixture monomer. The combined toxicity profile and potency of the binary mixtures are characterized by the ratio of variation between the relative potency factor of the contaminant monomer and the corrected relative potency factor of the binary mixture. Finally, the distribution density and the cumulative probability of the health risks of the atmospheric mixed pollution are obtained through the corrected relative efficiency factors and the atmospheric pollution exposure data of the binary mixture.
The literature and patent retrieval results show that the invention firstly provides a calculation solution for environmental pollution health risk assessment based on joint toxicity prediction. Before the completion of the invention, reports of using in-vitro equivalent concentration simulation data of pollutants based on transcriptome data and biological test information for evaluating the health risk of the atmospheric pollution mixture are not found.
Firstly, the invention provides a method for joint toxicity prediction of a binary mixture comprising two components in close molar concentrations, for example in a molar ratio of (1: 1.2) to (1.2: 1), comprising the steps of:
step 1: obtaining transcriptome data exposed by the two components respectively and monomer in-vitro test data of a test endpoint corresponding to a specific target point or a specific channel from an open database or a literature, and selecting one component with the highest toxicity of the two components as a reference body ref and the other component as a non-reference body i according to literature reports or the in-vitro test data, wherein:
the transcriptome data was used to obtain the mutual gene expression similarity between the reference ref and non-reference i:
wherein G is i And G ref Set of differential genes, r, for non-reference i and reference ref, respectively i，ref The ratio of the number of common difference genes between the non-reference body i and the reference body ref to the total difference genes of the reference body ref, r ref，i The ratio of the number of common difference genes of the non-reference body i and the reference body ref to the number of total difference genes of the non-reference body i is represented by 0 ≦ r i，ref ，r ref，i ≤1；
And obtaining a monomer dose-effect curve equation of the reference body ref and the non-reference body i corresponding to different target points or channels respectively by using monomer in-vitro activity test data, namely the proportion of target point or channel response under different administration doses.
Step 2: and respectively obtaining the half effective concentrations of the non-reference body i and the reference body ref of the corresponding target point or passage k according to the monomer dose-effect curve equation, and calculating the ratio of the half effective concentrations to obtain the relative efficiency factor of the non-reference body i relative to the reference body ref.
Specifically, the method comprises the following steps:
respectively obtaining the monomer concentrations of the non-reference body i and the reference body ref when the trigger target point or the channel k responds 50%, namely the half effective concentration
And
calculating the ratio of the half effective concentration of the reference body ref to the non-reference body i, namely the relative efficiency factor of the non-reference body i relative to the reference body ref
And step 3: and respectively calculating the gene residual effect of the reference body ref or the non-reference body i on the non-reference body i or the reference body i exposed later when the reference body ref or the non-reference body i is exposed in advance according to the gene expression similarity and the monomer dose-effect curve equation, obtaining a mixed effect value of the binary mixture according to the gene residual effect and the monomer effect of the reference body ref and the non-reference body i, and further obtaining a corrected relative efficiency factor of the binary mixture relative to the reference body ref.
Specifically, the present step includes the following substeps:
substep 31: obtaining the percentage f of the non-reference body i and the reference body ref in triggering the target point or path k response under the in vitro test exposure concentration C by using the monomer dose-response curve equation of the non-reference body i and the reference body ref i c，k 、
Wherein the content of the first and second substances,
represents the percentage of k-max response, set to 100%;
and
the monomer concentrations of non-reference i and non-reference ref, respectively, at which 50% of the target or pathway k response is initiated; n represents the monomer dose-response curve equation coefficient;
substep 32: calculation of the residual Effect of genes on specific targets or pathways k at concentrations C for equivalent reference ref exposure if non-reference i is first exposed
Can be calculated from equation (4 a):
wherein r is i，ref The gene similarity of the non-reference body i relative to the reference body ref;
twice the percentage of reference ref exposure that elicits a particular target or via k response;
representing the effect of a biologically functionally equivalent portion of reference ref and non-reference i;
representing the effect of a different part of the biological function of the reference body ref relative to the non-reference body i;
similarly, exposure to an equivalent amount of non-reference i when reference ref is first exposed elicits a residual effect on the gene for a particular target or pathway k
Can be calculated from equation (4 b):
wherein r is ref，i The gene similarity of the reference body ref relative to the non-reference body i;
the percentage of a particular target or pathway k response elicited for twice the amount of non-reference i exposure;
representing the effect of a biologically functionally equivalent part of the non-reference i and reference ref entities; (1-r) ref，i )f i c，k Representing the effect of a different part of the biological function of the non-reference body i relative to the reference body ref;
substep 33: calculating the specific mixing effect values at concentration C for equal amounts of non-reference i and reference ref sequentially exposed to induce a specific target or via k
Monomeric effects f by non-reference i i c，k And residual effect of gene
And calculating to obtain:
similarly, at concentration C, the sequential exposure of equal amounts of reference ref and non-reference i triggers a specific mixed-effect value for a particular target or via k
Through a maleEquation (5b) calculates:
substep 34: taken together, the sequential exposure of the non-reference i and reference ref at concentration C gives rise to a mixed effect value at a particular target or via k
Sequential exposure to reference ref and non-reference i induces a mixed effect value at a particular target or via k
Calculating to obtain the value of the mixing effect caused by the simultaneous equal exposure of the non-reference body i and the reference body ref
Substep 35: 0 to 20 times the number of the sub-steps 31 to 34
Multiple (e.g., 10000) concentrations C in the concentration range, with equal amounts of non-reference i and reference ref exposed simultaneously, induce a mixed effect value in the target or via k response
Obtaining a dose-effect relationship for the binary mixture;
substep 36: obtained according to substep 35
Monomers in binary mixtures corresponding in time
And calculating a corrected relative potency factor for the binary mixture relative to the reference mass ref
As shown in formula (7):
and 4, step 4: the magnitude and mode of action of the combined toxic effects of the binary mixtures can be characterized by the ratio of the change between the relative potency factor for the non-reference body i relative to the reference body ref and the corrected relative potency factor for the binary mixtures relative to the reference body ref, as in equation (8). That is, the rate of change of toxicity of a particular target or pathway k induced by a binary mixture versus an equivalent amount of monomer exposure in the presence/absence of a reference ref:
wherein if
The larger the absolute value, the more significant the change in toxicity of the reference body ref and the non-reference body i when co-exposed in the presence of the reference body ref, compared to the non-reference body i;
if the number is positive, the toxicity of the non-reference body i is enhanced in the presence of the reference body ref, and a synergistic effect is generated;
if the number is negative, the toxicity of the non-reference body i is weakened in the presence of the reference body ref, and an antagonistic effect is generated;
if 0, it indicates that there is no change in toxicity of the non-reference substance i in the presence of the reference substance ref, and the binary systemIs a simple additive effect.
The invention also provides an evaluation method for the health risk of the binary mixture of atmospheric pollution based on the binary mixture combined toxicity prediction method, which comprises the following steps:
step A, collecting data of atmospheric pollutants through literature or actual environment monitoring to obtain a plurality of pollutant concentrations in an atmospheric sample in a period of time, selecting a group of pollutants as a mixed component to be evaluated, and further selecting two pollutants with a molar concentration ratio of 1: 1.2-1.2: 1 as a binary mixture to be evaluated.
Step B, using the combined toxicity prediction method to obtain the corrected relative efficacy factors of the binary mixture relative to the reference body ref by using the pollutant with the highest toxicity in the binary mixture as the reference body ref and the other pollutant as the non-reference body i
And the relative potency factor of the non-reference body i relative to the reference body ref
Step C, taking the atmospheric concentration of the non-reference body i in the binary mixture in the actual environment as the environmental exposure concentration, and converting the environmental exposure concentration into the in vitro equivalent concentration;
in particular, the in vitro equivalent concentration EC of each non-reference body i in the binary mixture to be evaluated i Comprises the following steps:
wherein QC i Atmospheric concentration in the actual environment, i.e. environmental exposure concentration, of a pollutant; AB is the respiratory rate of Chinese adults; f is the proportion of contaminants contacting the alveolar region, if QC i If the gas phase concentration of the contaminant is, f is 1, if QC i F is the particle phase concentration of the pollutants, and is the alveolar deposition fraction of the particles; SA is the alveolar region surface area(ii) a PSA is the surface area of a single hole of a cell culture plate in an in vitro test experiment; v is the single well volume of the cell culture plate in the in vitro test experiment.
Step D, in vitro equivalent concentration EC of binding non-reference body i i Correcting the relative efficiency factor
Relative efficiency factor
And the concentration of the reference substance ref at which toxicity is effective
Binary mixture health risk assessment is performed for a specific target or pathway k.
Specifically, the present step includes the following substeps:
substep D1: the percentage of k-response at the initiating target or pathway is obtained from the equation of the monomer dose-response curve for the reference ref
Corresponding to the concentration of ref monomer
I.e. the concentration at which the reference body ref acts toxically;
can be derived by reference to the volumetric ref monomer dose-response curve equation, as in equation (10):
substep D2 activation ratio for the health risk of binary mixture of reference ref and non-reference i at the corresponding target point or pathway k
Characterization, representationComprises the following steps:
wherein EC i Is the in vitro equivalent concentration of non-reference body i;
correcting relative potency factors for the binary mixtures at the respective target points or pathways k relative to the reference body ref;
substep D3: similarly, the health risk of the non-reference body i at the corresponding target point or passage k is obtained, and the passage activation ratio is used
Characterization, expressed as:
wherein
Is the in vitro equivalent concentration of non-reference body i;
the relative performance factor of the non-reference i with respect to the reference ref at the corresponding target or via k.
Substep D4: for target or pathway k, concentration QC by environmental exposure to non-reference i i And carrying out Monte Carlo sampling simulation on the respiratory rate AB of the Chinese adult to obtain
And
the distribution density and the cumulative probability of (a) characterize the health risk of a binary mixture of the non-reference body i and the reference body ref. The greater the RA is at the 99 th percentile of cumulative probabilityLarge indicates greater health risk.
In order to clearly understand the technical contents of the present invention, the technical solutions of the present invention are further described below with reference to the following embodiments and the accompanying drawings. It should be noted that the following examples are only for illustrating the present invention and are not intended to limit the present invention.
Example 1
Polycyclic aromatic hydrocarbons are a typical toxic component commonly found in atmospheric particulates, and due to their known carcinogenic properties and adverse health effects, three polycyclic aromatic hydrocarbon analogs have been listed as the first ten of the priority pollutants by the american toxic and disease registration authority (atard, 2015). In this embodiment, by integrating transcriptome data and in vitro test data, a new method for evaluating health risk of an atmospheric pollution binary mixture is described by taking polycyclic aromatic hydrocarbon mixed pollution in atmospheric particulates as an example, and a flow is shown in fig. 1, and specifically includes the following steps:
(1) and collecting atmospheric particulate pollution data.
Concentrations of polycyclic aromatic hydrocarbons on the beijing atmospheric particulate phase were obtained from the literature from 8 months 2015 to 3 months 2016 (sci. total environ, 2017, 593-. Wherein the pollution levels of two polycyclic aromatic hydrocarbons, benzo [ a ] pyrene (BaP) and benzo [ a ] anthracene (BaA), are around 1: 1 during non-heating seasons, and Table 1 lists typical pollution data for 4 groups of concentrations in the range of (1: 1.2) to (1.2: 1). BaP and BaA are selected as binary mixtures to be evaluated, wherein BaP is widely existed and stable in the environment, is polycyclic aromatic hydrocarbon (IARC, 2010) which is only classified as human carcinogen in IARC, and is considered as the monomer with the largest toxicity in polycyclic aromatic hydrocarbon. BaP has been reported in the literature to modulate the efficiency of other monomers of PAH in forming DNA adducts in human cells (Toxicology, 2014, 321, 27-39.). Therefore, BaP was selected as a reference for the binary mixture and BaA as a non-reference, both of which have the chemical structures shown below.
(2) Binary mixtures combined with toxicity prediction.
First, gene chip data were obtained from the literature for lung tissue samples from 28-day male rats after oral gavage of BaP and BaA, respectively (Arch. toxicol., 2016, 90, 2461-. The raw data are numbered GSE43438 and GSE51321 in the GEO database (https:// www.ncbi.nlm.nih.gov/GEO /). Reading gene expression chip data by using an affy package of an R language and generating an expression matrix, carrying out standardization processing on the gene data by using an RMA function, carrying out differential gene extraction by using a limma package, and reserving a gene with a corrected p value (FDR) less than or equal to 0.05 as a gene with obvious differential expression. The number of genes differed between three compounds of BaP and BaA was 1736 and 169, respectively. Secondly, high throughput in vitro test data of BaA and BaP on three pathways, AhR (AEID: 806), p53 (AEID: 1321) and NF-. kappa.B (AEID: 1346), were obtained from a ToxCrat toxicity database (https:// operator. eta. gov/dashboard /) search based on its clearly regulated genes. AC of BaP and BaA 50 The (. mu.M) values and the monomer dose-response curve coefficients are shown in Table 2, and the monomer dose-response curves are shown in FIG. 2.
After high-throughput in vitro biological test data and gene expression data of the mixed components are obtained by adopting the method of the invention, the gene expression similarity r of the components BaP and BaA of the binary mixture is calculated according to the formulas (1a) and (1b) BaA，BaP And r Bap，BaA 0.005 and 0.050, respectively.
According to the formula (2), the relative potency factors of BaA monomer relative to BaP monomer on three pathways k of AhR, p53 and NF-kB are calculated by using BaP as a reference body
According to the formulas (3) to (6), the weight is respectively calculated to be 0 to 20 times
Percentage of BaP and BaA monomers responding on three pathways k AhR, p53 and NF- κ B under equal exposure to 10000 concentration points C equally spaced over the concentration range
And specific value of mixing effect produced by binary mixture
I.e. the dose-effect relationship of a binary mixture. And push back to obtain
Of a mixture of two-component compounds with respect to the BaP monomer
The results are shown in Table 3.
Calculating the corrected relative efficiency factor of the binary mixture relative to the BaP monomer according to the formulas (7) to (8)
And relative performance factor
And correcting the relative efficacy factor
Relative rate of change therebetween
Get on three paths
And
the values of (a) are 0.346, 0.15 and 0.14, respectively, and are all positive. Indicating that BaA toxicity was enhanced in the presence of BaP. Wherein, the prediction result on the p53 pathway is consistent with the toxicity research result of the binary mixture based on the experimental method (Mutat Res.2007, 620, 123-134; toxicology.2011, 279, 36-44).
(3) Binary mixture health risk assessment.
Binary mixed group to be evaluated according to formula (9)Environmental exposure concentration QC of classified pollutants BaA Conversion to in vitro equivalent concentration EC BaA . Wherein QC BaA Is the concentration of the contaminant BaA on the atmospheric particulates in the actual environment (nmol/m) 3 ) (Table 1); since the pollutant concentration data is derived from atmospheric particulates, f is the predicted alveolar deposition fraction of particulates (0.2350), and the predicted value is from the lung metrology model software MPPD v3.04(https:// www.ara.com/products/multiple-path-particulate-clinical-model-mpdd-v-304); SA is the alveolar region surface area (75 m) 2 ) (ii) a AB is the respiratory rate of Chinese adults (14.5-18 m) 3 Day); PSA is the surface area of a single well using 1536 well plates in the ToxPast in vitro test (0.023 cm) 2 ) (ii) a V is the single well volume (5. mu.L) of 1536 well plates used in the Toxcast in vitro assay.
Respectively calculating three paths of AhR, p53 and NF-kB according to the formula (10),
corresponding BaP monomer concentration
The concentration at which toxicity as a risk agent (reference substance) acts is shown in Table 3.
Equivalent in vitro concentration EC according to equation (11) BaA Associated with respective pathways
And
calculating to obtain the channel activation ratio of the binary mixture
And
to EC BaA Middle uncertain variable QC BaA And AB obtaining the distribution density and the cumulative probability of RA (representing health risk) through Monte Carlo sampling simulation, wherein the cumulative probability of RA is 99 th percentileRespectively, the following steps: 0.23,0.10,0.041
Wherein the Monte Carlo sampling simulation obtains 10000 environmental exposure concentrations QC of BaA BaA And sample of respiration rate AB, QC BaA The AB is obtained by sampling according to the uniform distribution of BaA environmental exposure concentration, and is obtained from the lognormal distribution of the respiration rate, and is used for describing the variation range of the respiration rate of Chinese population.
Similarly, BaA the monomer pathway activation ratio was calculated from equation (12)
And
the 99 th percentile of the cumulative probability of the distribution density and the cumulative probability of (2) is respectively: 0.10,0.026,0.009.
The distribution density and cumulative probability results for health risks for the binary mixture and BaA monomer are shown in fig. 3. From figure 3 it can be seen that BaA and the BaP mixture all have an increased health risk over BaA monomer in all three pathways. As can be seen from fig. 3, the distribution density of RA of the mixture over the three pathways compared to BaA monomer is on the right, indicating that BaA toxicity is enhanced in the presence of BaP and the mixture may pose a greater health risk. It can be seen that mixed toxicity prediction and health risk assessment can be performed by the method of the present invention through contamination exposure data and bioassay data of contaminant monomers, independent of bioassay data of the mixture. The method has wide application prospect in the fields of actual environment mixed exposure health risk evaluation, environment safety evaluation and the like, and is expected to provide technical support and guidance for chemical mixed pollution health risk evaluation.
In summary, the present invention combines pollutant combined toxicity mode of action prediction with actual environmental exposure data by making full use of existing in vitro bioassay data and transcriptome information to simulate cumulative health risk distributions for environmental mixtures. In particular, the method of the invention has no predictive assumption about the mode of action of a chemical mixture, the mixing effect being determined from in vitro dose-effect curves and transcriptome information estimates of the individual components in the mixture.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Attached table:
TABLE 1 concentration of polycyclic aromatic hydrocarbon monomer in atmospheric samples (nmol/m) 3 )
TABLE 2 AC of polycyclic aromatic hydrocarbon monomers on three pathways 50 Value of (mu M) and dose-response curve coefficient
TABLE 3 binary mixtures of polycyclic aromatic hydrocarbons on three pathways AC 50 (mu.M) and AC of BaP 10 (μM)
Claims (10)
1. A method for joint toxicity prediction of a binary mixture comprising two components in close molar proximity, comprising the steps of:
step 1: obtaining transcriptome data exposed by the two components respectively and in-vitro test data of test end points corresponding to specific targets or channels from a public database or documents, selecting one component with the highest toxicity of the two components as a reference body, and the other component as a non-reference body, and obtaining the mutual gene expression similarity of the reference body and the non-reference body and a monomer dose-effect curve equation of the test end points of the reference body and the non-reference body corresponding to different targets or channels respectively;
step 2: respectively obtaining in-vitro test half effective concentrations of a corresponding target spot or a channel non-reference body and a reference body according to the monomer dose-effect curve equation, and calculating the ratio of the effective concentrations to obtain relative efficiency factors of the non-reference body relative to the reference body;
and step 3: respectively calculating the gene residual effect of the reference body or the non-reference body on the non-reference body or the reference body exposed later when the reference body or the non-reference body is exposed earlier according to the gene expression similarity and the monomer dose-effect curve equation, obtaining a mixed effect numerical value of the binary mixture according to the gene residual effect and the monomer effect of the reference body and the non-reference body, and further obtaining a correction relative efficiency factor of the binary mixture relative to the reference body;
and 4, step 4: the ratio of the variation between the relative potency factor of the non-reference body relative to the reference body and the corrected relative potency factor of the binary mixture relative to the reference body is used to characterize the magnitude and mode of action of the combined toxic effect of the binary mixture.
2. The combined toxicity prediction method of claim 1, wherein the molar concentration ratio of the reference body ref to the non-reference body i in the binary mixture is (1: 1.2) - (1.2: 1).
3. The combined toxicity prediction method of claim 1, wherein in step 1, the similarity of gene expression between the reference body ref and the non-reference body i is expressed as:
wherein G is i And G ref The differential gene sets of the non-reference body i and the reference body ref respectively,r i，ref the ratio of the number of common difference genes between the non-reference body i and the reference body ref to the total difference genes of the reference body ref, r ref，i The ratio of the number of common difference genes of the non-reference body i and the reference body ref to the number of total difference genes of the non-reference body i is represented by 0 ≦ r i，ref ，r ref，i ≤1。
4. The combined toxicity prediction method according to claim 1, wherein the step 2 specifically comprises:
respectively obtaining the monomer concentrations of the non-reference body i and the reference body ref when the corresponding target point or channel k responds 50%, namely the half effective concentration according to the monomer dose-effect curve equation
And
calculating the ratio of the half effective concentration of the reference body ref to the non-reference body i, namely the relative efficiency factor of the non-reference body i relative to the reference body ref
Comprises the following steps:
5. the combined toxicity prediction method according to claim 1, wherein step 3 comprises the following sub-steps:
substep 31: obtaining the percentage f of the non-reference body i and the reference body ref respectively triggering the target point or the path k response under the in vitro test exposure concentration C by the monomer dose-effect curve equation i c，k 、
Comprises the following steps:
wherein the content of the first and second substances,
represents the percentage of k max response, set to 100%;
and
the monomer concentrations of non-reference i and non-reference ref, respectively, at which 50% of the target or pathway k response is initiated; n represents the monomer dose-response curve equation coefficient;
substep 32: calculation of the Gene residual Effect of equivalent reference ref exposure at concentration C on specific target or pathway k if non-reference i is first exposed
And the residual effect of a gene at a particular target or pathway k on an equivalent amount of non-reference i exposure if reference ref is first exposed
Comprises the following steps:
wherein r is i，ref The degree of gene similarity of the non-reference body i relative to the reference body ref, r ref，i The gene similarity of the reference body ref relative to the non-reference body i;
twice the percentage of reference ref exposure that elicits a particular target or via k response;
the percentage of a particular target or pathway k response elicited for twice the amount of non-reference i exposure;
substep 33: calculating the specific mixing effect values at concentration C for equal amounts of non-reference i and reference ref sequentially exposed to induce a specific target or via k
And the sequential exposure of equal amounts of reference ref and non-reference i to induce a mixed effect value at a particular target or via k
Comprises the following steps:
substep 34: calculating the value of the mixing effect at concentration C of equal amounts of non-reference i and reference ref exposed simultaneously to induce a specific target or via k
Comprises the following steps:
substep 35: calculated according to substeps 31 to 34 is 0 to 20 times
Mixed effect values for the simultaneous exposure of equal amounts of non-reference i and reference ref at multiple concentrations C in equally spaced concentration ranges to induce a target or via k response
Obtaining a dose-effect relationship for the binary mixture;
substep 36: is calculated according to substep 35
Monomers in binary mixtures corresponding in time
And calculate the corrected relative potency of the binary mixture relative to the reference ref
Comprises the following steps:
6. the method of claim 1, wherein in step 4, the combined toxicity effect of the binary mixture is determined by the relative potency factor of the non-reference body i relative to the reference body ref according to the following formula
And binary mixtures relative to a referenceCorrected relative performance factor for body ref
The ratio of the changes between:
wherein if
The larger the absolute value of (a) indicates that the co-exposure of the reference body ref and the non-reference body i in the presence of the reference body ref produces a more significant change in the combined toxicity effect than the non-reference body i;
if the number is positive, the toxicity of the non-reference body i is enhanced in the presence of the reference body ref, and a synergistic effect is generated;
if the number is negative, the toxicity of the non-reference body i is weakened in the presence of the reference body ref, and an antagonistic effect is generated;
if 0, the toxicity of the non-reference system i is unchanged in the presence of the reference system ref, and the binary system has a simple additive effect.
7. A health risk evaluation method for an atmospheric pollution binary mixture is characterized by comprising the following steps:
step A, collecting atmospheric pollutant data through literature or actual environment monitoring, obtaining a plurality of pollutant concentrations in an atmospheric sample in a period of time, and selecting two pollutants with similar molarity as a binary mixture to be evaluated;
step B, using the joint toxicity prediction method of any one of claims 1 to 6, taking the pollutant with the highest toxicity in the binary mixture as a reference body, and taking another pollutant as a non-reference body, and respectively obtaining the corrected relative efficacy factor of the binary mixture relative to the reference body and the relative efficacy factor of the non-reference body relative to the reference body;
step C, taking the atmospheric concentration of the non-reference body in the binary mixture in the actual environment as the environmental exposure concentration, and converting the environmental exposure concentration into the in vitro equivalent concentration;
and D, utilizing the corrected relative efficacy factor of the binary mixture relative to the reference body and the relative efficacy factor of the non-reference body relative to the reference body, combining the in vitro equivalent concentration of the non-reference body and the toxicity onset concentration of the reference body, and carrying out binary mixture health risk assessment aiming at the corresponding target or passage.
8. The method for evaluating the health risk of the binary mixture of atmospheric pollution according to claim 7, wherein in the step A, two pollutants with molar concentration ratio of 1: 1.2-1.2: 1 are selected as binary mixed components to be evaluated.
9. The binary mixture of atmospheric pollution health risks assessment method according to claim 7, wherein in step C,
in vitro equivalent concentration EC of non-reference body i in the binary mixture to be evaluated i Comprises the following steps:
wherein QC i Is the atmospheric concentration of a pollutant in the actual environment, namely the environmental exposure concentration; AB is the respiratory rate of Chinese adults; f is the proportion of contaminants contacting the alveolar region, if QC i If the gas phase concentration of the contaminant is, f is 1, if QC i F is the particle phase concentration of the pollutants, and is the alveolar deposition fraction of the particles; SA is alveolar region surface area; SA is alveolar region surface area; PSA is a cell culture plate in-vitro test experimentThe surface area of the single hole of (a); v is the single well volume of the cell culture plate in the in vitro test experiment.
10. The method according to claim 7, characterized in that step D comprises in particular the following sub-steps:
substep D1, obtaining the percentage of k-response at the target site or pathway of initiation from the monomer dose-response curve equation of the reference ref
Corresponding to the concentration of ref monomer
I.e. the concentration at which the reference body ref acts toxically;
can be derived by reference to the monomer dose-response curve equation for the body ref:
substep D2 activation ratio for health risk of an equal ratio binary mixture of reference ref and non-reference i at the corresponding target point or pathway k
Characterization, expressed as:
wherein EC i Is the in vitro equivalent concentration of non-reference body i;
correcting relative potency factors for the binary mixtures at the respective target points or pathways k relative to the reference body ref;
substep D3: activation ratio for health risk of non-reference i monomers at corresponding target or pathway k
Characterization, expressed as:
wherein EC i Is the in vitro equivalent concentration of non-reference body i;
relative performance factor of non-reference i relative to reference ref at the corresponding target or via k;
substep D4: for target or pathway k, concentration QC by environmental exposure to non-reference i i And carrying out Monte Carlo sampling simulation on the respiratory rate AB of the Chinese adult to obtain
And
and a greater RA in the 99 th percentile of cumulative probability indicates a greater health risk.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102867116A (en) * | 2012-08-30 | 2013-01-09 | 常州大学 | Regional water environment priority control pollutant screening method based on health risk |
| CN104751373A (en) * | 2015-03-23 | 2015-07-01 | 国网重庆市电力公司电力科学研究院 | Environmental economic dispatching method considering polluted gas emission risk |
| CN106153510A (en) * | 2015-03-25 | 2016-11-23 | 日本电气株式会社 | The evaluation method of pollutant and device |
| CN107832579A (en) * | 2017-11-08 | 2018-03-23 | 中科宇图科技股份有限公司 | A kind of water environment healthy economy damage appraisement method and system of parameter localization |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102867116A (en) * | 2012-08-30 | 2013-01-09 | 常州大学 | Regional water environment priority control pollutant screening method based on health risk |
| CN104751373A (en) * | 2015-03-23 | 2015-07-01 | 国网重庆市电力公司电力科学研究院 | Environmental economic dispatching method considering polluted gas emission risk |
| CN106153510A (en) * | 2015-03-25 | 2016-11-23 | 日本电气株式会社 | The evaluation method of pollutant and device |
| CN107832579A (en) * | 2017-11-08 | 2018-03-23 | 中科宇图科技股份有限公司 | A kind of water environment healthy economy damage appraisement method and system of parameter localization |
Non-Patent Citations (2)
| Title |
|---|
| 城市空气质量健康风险评估研究进展;张莉等;《地理科学进展》;20060610(第03期);全文 * |
| 雾霾重污染期间北京居民对高浓度PM_(2.5)持续暴露的健康风险及其损害价值评估;谢元博等;《环境科学》;20140115(第01期);全文 * |
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