Quantitative Method for the Impact of Organic Carbon Dark Matter

Abstract

The present application relates to the influence of organic carbon dark matter quantitative method. Obtain samples under different ecological environment conditions, determine the sample organic carbon composition, environmental variable, and divide the organic carbon molecules into known substances and dark matter; Select the environmental variable for analysis, group the samples according to the environmental variable change, and establish the coexistence network of known substances and the coexistence network of known substances and dark matter for each group of samples based on correlation analysis; Select the network topology parameter for analysis, construct the evaluation index according to the difference between the statistical values of the topology parameters of the two networks, and quantitatively analyze the influence of dark matter on the community. The method of the present application can accurately obtain the quantitative comparison results of the influence of organic carbon dark matter under different ecological systems and environmental changes, and clarify the response characteristics of the influence of dark matter under the background of global change.

Description

Technical Field

[0001] The present invention belongs to the field of environmental ecology, and specifically relates to a method for quantifying the influence of organic carbon dark matter. Background Art

[0002] Dark matter in nature generally refers to biological or non-biological substances that have not been identified, recognized or classified under current conditions, such as organic carbon dark matter. Although the development of high-resolution mass spectrometry technology has enabled us to have a deeper understanding of complex organic carbon molecules. For example, ultra-high-resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICRMS) technology can quantitatively identify the composition structure of organic carbon at the molecular level. However, based on the existing molecular database, there is still a large amount of organic carbon dark matter to be discovered. It is worth noting that information such as the composition and characteristics of these dark matter is usually not available, and therefore they are often excluded by default in traditional analysis. This makes us lack a comprehensive and accurate understanding of organic carbon, especially how these dark matter affects existing known substances (bright matter). Network analysis, as an emerging analytical method, can be used to characterize the relationship between known species and dark matter, and is expected to help solve the problem of the inability to understand the role and importance of organic carbon dark matter. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for quantifying the influence of organic carbon dark matter.

[0004] The above-mentioned purpose of the present invention is achieved through the following technical solutions:

[0005] A method to quantify the influence of organic carbon dark matter, including:

[0006] Obtain samples under different ecological conditions, measure the organic carbon composition and environmental variables of the samples, and classify organic carbon molecules into known matter and dark matter;

[0007] Select environmental variables for analysis, group samples according to the changes in environmental variables, and establish a coexistence network of known substances and a coexistence network of known substances and dark matter for each group of samples based on correlation analysis;

[0008] The network topology parameters used for analysis were selected, and evaluation indicators were constructed based on the differences in the statistical values ​​of the topology parameters of the two networks to quantitatively analyze the influence of dark matter on the community.

[0009] As a preferred embodiment, based on the existing database, organic carbon molecules are divided into known matter and dark matter according to whether they can be identified, recognized or classified.

[0010] As a preferred embodiment, the topological parameter is degree centrality, closeness centrality or betweenness centrality; preferably degree centrality. In each network, the degree centrality (dv) of a node (organic carbon molecule) v refers to the number of edges connected to v.

[0011] As a preferred implementation, the statistical value is an average value.

[0012] As a preferred embodiment, the two networks are compared and the statistical values ​​of the network topology parameters are used to construct a quantitative influence index iDME for analyzing the influence of dark matter on the community. The iDME is calculated as follows:

[0013]

[0014] Among them, iDME (%) is the influence of organic carbon dark matter on the community. DK and M KK They represent the topological parameters of a single known matter and dark matter coexistence network, and the known matter coexistence network, respectively, and are the mean of the topological parameters of all nodes in a single network. is the mean topological parameter of the coexistence network of known matter and dark matter (for example, for m known matter and dark matter coexistence networks, Refers to the mean of the topological parameters of the network of m known matter and dark matter coexisting, such as the mean of degree centrality); is the mean value of the topological parameters of the coexistence network of known substances;

[0015] The positive or negative values ​​of iDME are used to determine whether dark matter has a positive or negative impact on the community, and the absolute value of iDME is used to determine the strength of the impact. A value of zero indicates that dark matter has no significant impact.

[0016] As a preferred embodiment, the method further includes decomposing the influence quantitative index iDME into an internal influence component intra-iDME and an interactive influence component inter-iDME;

[0017]

[0018]

[0019] Where K1 and K2 are two components of the known matter coexistence network (KK network), and K1 and D are two components of the known matter and dark matter coexistence network (DK network). K1 and K2 are the known matter part of the network, D is the dark matter part of the network, and K1 is the common part of the two networks. are the topological parameters of the K2 interior and the interaction between K1 and K2 in a single known substance coexistence network; M DD 、 are the topological parameters of the D interior and the interaction between D and K1 in the network of coexistence of a single known matter and dark matter; is the topological parameter inside K1 in the two networks. Taking the coexistence network of a single known substance as an example, is the mean of the topological parameters of all nodes inside K1 when only the internal node connections of K1 are considered; is the mean of the topological parameters of all nodes inside K2 when only the internal node connections of K2 are considered; is the mean of the topological parameters of all nodes in K1 and K2 when only the nodes between K1 and K2 are connected.

[0020] That is That is in, is the mean of the topological parameters inside K1 in the two networks (for example, for a coexistence network of m known substances, Refers to the mean of topological parameters inside K1 in a network of m known substances coexisting, such as the mean of degree centrality). are the mean values ​​of the topological parameters inside K2 and the interaction between K1 and K2 in the coexistence network of known substances; They are the mean values ​​of the topological parameters inside D and the interaction part between D and K1 in the coexistence network of known matter and dark matter, respectively.

[0021] intra-iDME (%) is the internal influence of organic carbon dark matter, that is, the mean value of topological parameters (such as degree centrality) inside D in the DK network. Compared with the mean topological parameter of K2 in KK network Increase or decrease, the impact on the topological parameters of the community (whole network); inter-iDME (%) is the interaction between organic carbon dark matter and known substances, that is, the mean topological parameter of the interaction between D and K1 in the DK network Compared with the mean topological parameter of the interaction between K2 and K1 in the KK network The impact of increasing or decreasing the topological parameters of the community (entire network).

[0022] As a preferred embodiment, m organic carbon molecules are randomly extracted from each group of samples, and a coexistence network of m known substances and a coexistence network of m known substances and dark matter are established for each group of samples.

[0023] As a preferred embodiment, when selecting organic carbon molecules for establishing a network from each group of samples, the selected organic carbon molecules appear in at least n% of the samples, where n% is a preset ratio.

[0024] As a preferred embodiment, the total number of organic carbon molecules in the two networks is the same, and the known substances in the known substance and dark matter coexistence network are all included in the corresponding group of known substance coexistence networks to ensure the comparability of the networks.

[0025] As a preferred embodiment, the method further includes establishing a correlation model between the evaluation index and the environmental variables of the sample using regression analysis, and analyzing the response characteristics of the influence of dark matter as the environmental variables change.

[0026] In the method of the present invention, the samples under different ecological and environmental conditions are natural environment samples collected in the field or experimental samples under different ecological and environmental conditions. Environmental variables include temperature, nutrients, etc., or other global environmental change factors that can change the ecosystem, such as organic pollutants, microplastics and heavy metals. The molecular composition of the organic carbon can be obtained by high-resolution mass spectrometry technology, such as ultra-high resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and other technologies. The composition data is usually a matrix with samples as rows and organic carbon molecules as columns. This method can also be applied to other organisms or non-organisms with the same or similar composition data, such as microorganisms, when there is dark matter that has not been identified, recognized or classified. The KK network and DK network can be established based on correlation analysis methods such as SparCC, Pearson, and Spearman.

[0027] Based on the network analysis method, the present invention constructs two types of organic carbon molecular networks, namely the coexistence network of known substances and the coexistence network of known substances and dark matter. By comparing the differences in the topological parameters of the two networks, a quantitative index iDME of the influence of organic carbon dark matter is established, and iDME is decomposed into an internal influence component intra-iDME and an interactive influence component inter-iDME. Compared with traditional analysis that usually excludes dark matter, the establishment of the new index of the present invention helps to fully and accurately understand the composition of organic carbon molecules, clarify the importance of dark matter and its influence on the community, and reveal its mechanism of action. By combining the iDME index and its components with environmental variables, it helps to clarify the response characteristics of the influence of organic carbon dark matter with changes in environmental factors or reveal potential environmental driving mechanisms. Therefore, this method can accurately obtain quantitative comparison results of the influence of organic carbon dark matter under different ecosystems and environmental changes, and reveal its mechanism of action, providing a new research method and research idea for a comprehensive understanding of the importance of organic carbon dark matter and its response mechanism in the context of global change. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 1 is a flow chart for constructing the iDME index and its components intra-iDME and inter-iDME in the present invention.

[0029] Figure 2 The coexistence network of known matter (left picture) and the coexistence network of known matter and dark matter (right picture) established based on single sample grouping.

[0030] Figure 3 It is the response characteristic of the iDME index calculated by the method of the present invention as chlorophyll a changes.

[0031] Figure 4 The present invention is used to calculate the response characteristics of the two components of the iDME index, intra-iDME and inter-iDME, as chlorophyll a changes. DETAILED DESCRIPTION

[0032] The present invention will be further described below with reference to specific implementation cases.

[0033] This case study conducted a field microcosm experiment on lake sediment samples, examining the effects of organic carbon dark matter on communities under two global environmental changes: rising temperature and nutrient enrichment. This research, primarily conducted at Balggesvarri Mountain in Norway, serves as an empirical case study for presentation and discussion.

[0034] Mountainous areas provide a natural temperature gradient, with temperatures decreasing with increasing altitude. Specifically, the study area was set up with five altitude (temperature) gradients, from low to high, and ten nutrient gradients at each altitude, with three replicates for each nutrient. At the end of the experiment, 150 samples were collected for organic carbon composition analysis, and environmental variables such as water temperature, pH, and sediment chlorophyll a content were measured.

[0035] According to the method of the present invention ( Figure 1 ), ① Based on the environmental samples after cultivation, the coexistence network of known substances and the coexistence network of known substances and dark matter were constructed respectively ( Figure 2 ); ② Calculate the quantitative index of influence of organic carbon dark matter iDME; ③ Further, decompose it into two components, namely internal influence intra-iDME and interactive influence inter-iDME. Finally, through the regression analysis method, the iDME ( Figure 3 ) and its components intra-iDME and inter-iDME ( Figure 4 )Response characteristics along the changes of chlorophyll a.

[0036] The specific implementation steps are:

[0037] Step 1: Collect environmental samples to obtain organic carbon molecular composition and environmental variables.

[0038] Sediment samples were collected, and dissolved organic carbon was extracted. The molecular composition of organic carbon was obtained by ultra-high resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Environmental variables were obtained by physicochemical analysis.

[0039] Step 2, based on existing databases, the organic carbon molecules were divided into known substances and dark matter.

[0040] In this case, the organic carbon molecules that were not identified, recognized or classified were defined as dark matter, and the organic carbon molecules that were identified, recognized or classified were defined as known substances.

[0041] Step 3, the samples were grouped according to the environmental variables. In each group, based on SparCC correlation analysis, the organic carbon molecule network was constructed.

[0042] In this case, the chlorophyll a content was selected as the main environmental variable, because the chlorophyll a content represented the primary productivity and also reflected the combined effects of experimental control factors (temperature and nutrient salt). The environmental samples were sorted according to the chlorophyll a content from low to high, and each 50 samples were divided into 1 group, with an interval of 5 samples between each group (for example, 1-50 is group 1, 6-55 is group 2, and so on). A total of 21 groups were generated, and the mean chlorophyll a content of each group of samples was calculated.

[0043] From each group of samples, a certain number of organic carbon molecules can be randomly selected m times, and the coexistence network of m known substances (KK network) and the coexistence network of m known substances and dark matter (DK network) can be established by correlation analysis method. In each random selection, in order to have comparability, all known substances in the DK network should be included in the KK network, and the total number of organic carbon molecules in the two networks should be consistent. The selected organic carbon molecules in the two networks should exist in at least a certain proportion of samples in each group. The number of known substances and dark matter in the DK network can be determined according to the actual proportion of known substances and dark matter in each group of samples or other proportions such as 1:1.

[0044] Here, in order to facilitate calculation, 999 times of random selection were performed, and 200 organic carbon molecules were selected each time. The selected organic carbon molecules should exist in at least 15 (30%) samples, and the proportion of known substances and dark matter in the DK network was selected as 1:1, i.e. both were 100 molecules. By SparCC correlation analysis, 999 coexistence networks of known substances and coexistence networks of known substances and dark matter were established without distinguishing the positive and negative correlation coefficients (which can also be used to establish networks that distinguish the positive and negative correlation coefficients, and the network topology parameters are calculated respectively).

[0045] Step 4: Calculate the quantitative index of dark matter influence iDME using the aforementioned iDME calculation formula, and use regression analysis to statistically analyze the response characteristics of the iDME index as chlorophyll a changes.

[0046] In each group, the mean values ​​of the topological parameters of 999 networks of two types are calculated, namely and On this basis, the iDME index of dark matter was calculated, and its response characteristics with changes in chlorophyll a were statistically analyzed using regression analysis methods.

[0047] Step 5: Decompose the iDME index into two components using the aforementioned calculation formulas for the intra-iDME component and the inter-iDME component, and statistically analyze the response characteristics of the two components with changes in chlorophyll a using regression analysis.

[0048] like Figure 3 As shown in the figure, through regression analysis, the variation pattern of the organic carbon iDME index with the mean chlorophyll a in the sample group was obtained. The results show that the iDME index of organic carbon in the study area is generally positive or negative, that is, the dark matter of organic carbon significantly affects the number of connections in the community (whole network). In addition, the effect of organic carbon dark matter on the number of connections in the community (whole network) changes from a negative effect to a positive effect as the chlorophyll a concentration increases, that is, the primary productivity increases. Figure 4 As shown in the figure, further regression analysis revealed how the two components of the organic carbon dark matter iDME index varied with the mean chlorophyll-a values ​​within the sample groups. The results indicate that when primary productivity is low, the effect of dark matter on the number of connections in the community (the entire network) is primarily through interaction, while when primary productivity is high, it is primarily through internal influence.

[0049] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Those skilled in the art will appreciate that modifications and variations may be made without departing from the spirit and scope of the present invention.

Claims

1. A method for quantifying the influence of organic carbon dark matter, characterized in that: include: Obtain samples under different ecological conditions, measure the organic carbon composition and environmental variables of the samples, and classify organic carbon molecules into known matter and dark matter based on whether the organic carbon molecules can be identified, recognized, or classified based on existing databases; dark matter refers to organic carbon molecules that have not been identified, recognized, or classified; Select environmental variables for analysis, group samples according to the changes in environmental variables, and establish a coexistence network of known substances and a coexistence network of known substances and dark matter for each group of samples based on correlation analysis; The network topology parameters used for analysis were selected, and evaluation indicators were constructed based on the differences in the statistical values ​​of the topology parameters of the two networks to quantitatively analyze the influence of dark matter on the community.

2. The method according to claim 1, characterized in that The topological parameter is degree centrality, closeness centrality or betweenness centrality.

3. The method according to claim 1, characterized in that The statistical value is the average value.

4. The method according to claim 1, wherein By comparing the two networks, the statistical values ​​of the network topology parameters are used to construct the influence quantitative index iDME to analyze the influence of dark matter on the community. The iDME calculation method is as follows: ； in is the mean value of the topological parameters of the network of coexistence of known matter and dark matter; is the mean value of the topological parameters of the coexistence network of known substances; use The positive or negative value of dark matter can be used to judge whether it has a positive or negative impact on the community. The absolute value of determines the strength of its influence.

5. The method according to claim 4, characterized in that It also includes decomposing the influence quantitative index iDME into internal influence components and interaction components ; ； ； Where K1 and K2 are two components of the known matter coexistence network; K1 and D are two components of the known matter and dark matter coexistence network; K1 and K2 are the known matter part of the network, D is the dark matter part of the network, and K1 is the common part of the two networks; That is , That is ;in, is the mean of the topological parameters inside K1 in the two networks, 、 are the mean values ​​of the topological parameters inside K2 and the interaction between K1 and K2 in the coexistence network of known substances; 、 They are the mean values ​​of the topological parameters inside D and the interaction part between D and K1 in the coexistence network of known matter and dark matter, respectively.

6. The method according to claim 1, characterized in that From each group of samples, m organic carbon molecules were randomly sampled, and a coexistence network of m known substances and a coexistence network of m known substances and dark matter were established for each group of samples.

7. The method according to claim 1 or 6, characterized in that When selecting organic carbon molecules for establishing a network from each group of samples, the selected organic carbon molecules appear in at least n% of the samples, where n% is a preset proportion.

8. The method according to claim 1, characterized in that The total number of organic carbon molecules in the two networks is the same, and the known substances in the known matter and dark matter coexistence network are all included in the known matter coexistence network of the corresponding group.

9. The method according to claim 1, characterized in that It also includes using regression analysis to establish a correlation model between the evaluation indicators and the environmental variables of the sample, and analyzing the response characteristics of the influence of dark matter as the environmental variables change.

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