WO2019134586A1 - Method and system for identifying gene-regulatory chromatin interaction and application thereof - Google Patents

Abstract

The present invention relates to a method for identifying gene-regulatory chromatin interaction by using various genome analysis technologies, for example RNA-seq, ChIP-seq, and Hi-C, to characterize and analyze a target property of a sample undergoing state transition (such as cell differentiation) in multiple aspects. In particular, the present invention relates to a method for identifying chromatin interaction and a related gene associated with sample state transition during the transition.

Description

Method, system and application for identifying gene regulatory chromatin interaction

cross reference

The present application claims the priority of the invention as "identification method, system and application of gene-regulated chromatin interaction" on January 5, 2018, to Chinese Patent Application No. 201810011140.7 of the Chinese Patent Office, the contents of which are incorporated by reference. Incorporated herein.

Technical field

The present invention relates to a method for identifying gene-regulated chromatin interactions, and more particularly to a method for identifying chromatin interactions and corresponding effector genes that are capable of affecting sample transitions during sample state transitions.

Background technique

Chromatin conformation plays a key role in the regulation of gene expression. Studies have found that mitotic interphase chromosomes occupy a specific domain, and gene transcription is closely related to the relative position of genes relative to the nuclear fibrosis and chromosome domains. Recent studies using high-throughput chromosome conformation capture (Hi-C) have revealed that genomes are organized into hundreds of kilobases to one megabase Topologically Associating Domain (TAD), and in TAD The chromatin region is more likely to interact with other regions within the same TAD than with regions other than TAD. And between different cell types, most of the TAD positions remain unchanged and show evolutionary conservation.

The genes in the same TAD showed coordinated changes in the face of hormone stimulation or during differentiation, indicating that TAD is not only a structural structural unit, but also a functional unit of transcriptional regulation. In addition, long-term chromatin interactions mediated by specific or non-coding RNAs in TAD link long-range regulatory regions, such as enhancers and gene promoters, making long-range regulation of gene expression possible.

For example, in cell differentiation, often accompanied by differences in the expression of key genes and large changes in the three-dimensional structure or conformation of chromatin, there is currently no effective means for determining how changes in chromatin structure and key genes are involved in this process. The interrelationship of expressions and other behaviors, and how this correlation affects state changes such as cell differentiation. Therefore, there is an urgent need in the art for a new method that can effectively analyze and identify chromatin interactions with regulatory functions or influence or regulation of chromatin interaction during state transitions, which plays an important role in state transition. Key genes or regulatory factors.

Summary of the invention

After long-term research, the inventors have obtained a method for correlating chromatin interaction, expression level and/or recognition site of a specific gene with gene regulation inside chromatin, thereby completing the present invention.

In a first aspect, the invention relates to a method for identifying a sample state transition effector gene whose expression is affected by a change in chromatin interaction in a state transition of a sample, comprising the steps of:

(1) comparing the samples in the first state and the second state, thereby obtaining at least the following difference information: the difference in the identifiable behavior of the gene, and the difference in chromatin interactions existing in the transcriptional regulatory region of the gene, and

(2) correlating the difference information obtained in the step (1) to obtain a difference in the identifiable behavior of the gene related to the chromatin interaction difference in the transcriptional regulatory region in the state transition, thereby identifying the effector gene.

In one embodiment, wherein the sample is a cell.

In another embodiment, wherein the gene recognizable behavioral difference comprises a difference in gene expression amount and/or a difference in binding pattern distribution in a gene regulatory region genomic sequence; preferably, the difference in gene expression amount is mRNA expression Difference in amount or difference in protein expression.

In another embodiment, wherein the chromatin interaction difference present in the transcriptional regulatory region of the gene in step (1) is obtained by the following steps:

(a) identifying the site of the promoter and/or enhancer in the active state of the sample genome;

(b) Identify the areas where all chromatin interactions occur;

(c) integrating the information obtained in step (a) and step (b) to obtain a chromatin interaction between the promoter and the enhancer in an activated state, ie, a chromatin interaction present in the transcriptional regulatory region;

(d) Comparing chromatin interactions between different samples in the transcriptional regulatory region, resulting in differences in chromatin interactions present in the transcriptional regulatory regions of the gene.

In another embodiment, wherein the genetically identifiable behavior difference in step (1) is obtained by the following steps:

i) obtaining a sample in a first state and a second state;

Ii) taking a portion of the sample in the first state and the second state, respectively performing transcriptional expression analysis, and comparing the difference in mRNA expression between the samples; preferably, the transcriptional expression analysis is performed by RNA sequencing, ie, RNA-seq method.

In another embodiment, further comprising:

Iii) taking a portion of the sample in the first state and the second state, respectively analyzing the chromatin open region sequence, preferably using the ATAC-seq method, and analyzing the transcription factor binding motif distributed in the chromatin open region sequence, preferably The difference in distribution of the bonded phantoms between the samples in the first state and the second state is further compared.

In another embodiment, wherein the chromatin interaction differences present in the transcriptional regulatory region of the gene in step (1) are analyzed by the following steps:

Iv) taking a portion of the sample in the first state and the second state, identifying promoter and/or enhancer information respectively in an activated state, preferably using the ChIP-seq method, which is used in the ChIP-seq method Preferably, the antibody is a binding antibody of H3K4me3 and H3K27ac, which forms a signal peak in combination with H3K4me3 and H3K27ac, respectively, representing promoter and enhancer sites in an activated state;

v) taking another sample in the first state and the second state, using chromatin conformation capture technology, preferably using high-throughput chromatin conformation capture techniques, such as Hi-C method, in situ Hi-C method, BL-Hi -C method or ChIA-PET method to obtain information on genome-wide chromatin interaction; or to obtain information on local chromatin interaction using 4C or 5C method;

Vi) dividing the reference genomic sequence into regions of a certain size, preferably between 1 and 40 kb, for example 1 kb, 5 kb, 10 kb, 15 kb, 20 kb, 25 kb, 30 kb, 35 kb or 40 kb, based on step iv) Promoter and enhancer information of the obtained active state, by separately obtaining a region containing the active promoter and enhancer sites, preferably the region including the promoter is named as a gene region, and the region containing the enhancer sequence Named the regulatory area;

Subsequently, in combination with the frequency signal of the chromatin interaction obtained in step v), a chromatin interaction frequency signal occurring between the gene region and the regulatory region is identified, thereby obtaining a chromatin interaction associated with gene regulation; Comparison of gene regulation-related chromatin interactions between samples in a state and a second state, with statistically significant differences, identified as differential gene-regulatory interactions, including relative In the sample in the first state, the enhanced gene-regulated chromosomal interaction and/or the attenuated gene-regulated chromosomal interaction in the sample in the second state.

In another embodiment, the step (2) specifically includes the following steps:

a) Combine differences in gene expression levels with gene-regulated chromatin interactions, and select genes that are present in enhanced or diminished gene-regulated chromatin interactions with significant changes in expression in different states of the sample. ;or

b) Combine the difference in transcription factor binding motif distribution in the genomic transcriptional regulatory region with the information on the difference in gene regulatory chromatin interaction, and select the enhanced or weakened gene regulatory chromatin in the different states of the sample. a gene whose internal action, transcription factor binding motif distribution also undergoes significant changes; or

c) Combine the difference in gene expression, the difference in transcription factor binding motif distribution with the information on the difference in gene regulatory chromatin interaction, and choose to enhance or attenuate gene regulatory chromatin in different states of the sample. In the internal and genomic transcriptional regulatory regions, genes whose binding motif distribution changes significantly and the expression level also changes significantly.

In another embodiment, a step of functionally studying the screened effector genes to determine their function is further included.

In another embodiment, it further comprises the step of identifying a gene-regulated chromatin interaction, a chromatin interaction that is capable of affecting the expression of the effector gene identified in step (2), as a gene-regulated chromatin interaction .

In another embodiment, wherein the sample state transition is achieved by chemical agent induction, natural differentiation, and/or physical stimulation.

In a second aspect, the invention relates to a method of identifying a chromatin interaction capable of modulating a state transition of a sample, comprising the steps of any of the first aspects.

In a third aspect, the invention relates to a method of identifying a regulatory factor involved in a chromatin interaction involved in a state transition of a sample, comprising the steps of any of the first aspects.

In a fourth aspect, the invention relates to a method of identifying a substance capable of modulating chromatin interaction, comprising: identifying an effector gene or gene regulation of a chromatin interaction using any of the embodiments of the first aspect The chromatin interacts, and then the substance to be tested is contacted with the sample to analyze changes in the effector gene or gene regulatory interaction.

In a fifth aspect, the invention relates to an identification system for a sample state transition effector gene whose expression is affected by changes in chromatin interactions in a state transition of a sample, comprising the following modules:

(1) Gene identifiable behavior difference analysis module;

(2) an analysis module for differences in chromatin interactions in the transcriptional regulatory region;

(3) an effect gene identification module;

The system is capable of obtaining genetically identifiable behavioral differences associated with differences in chromatin interactions, thereby obtaining sample state transition effector genes that are affected by chromatin interactions.

Preferably, the system further comprises a gene regulatory chromatin interaction recognition module to identify gene regulatory chromatin interactions that are capable of affecting expression of the effector gene.

In one embodiment, wherein the gene recognizable behavioral difference analysis module is capable of analyzing a difference in gene expression levels and/or a difference in transcription factor binding motif distribution in a genomic sequence of a transcriptional regulatory region of the gene.

In another embodiment, an analysis module in which the chromatin interaction differences of the transcriptional regulatory regions are capable of performing the following analysis:

(a) identifying the site of the promoter and/or enhancer in the active state of the sample genome;

(b) Identify the areas where all chromatin interactions occur;

(c) integrating the information obtained in step (a) and step (b) to obtain a chromatin interaction between the promoter and the enhancer in an activated state, ie, a chromatin interaction present in the transcriptional regulatory region;

(d) Comparison of chromatin interactions between different samples in the transcriptional regulatory region to obtain differences in chromatin interactions present in the transcriptional regulatory regions.

In yet another embodiment, the effector gene identification module is capable of performing the following analysis:

a) Combine differences in gene expression levels with gene-regulated chromatin interactions, and select genes that are present in enhanced or diminished gene-regulated chromatin interactions with significant changes in expression in different states of the sample. ;or

b) Combine the difference in transcription factor binding motif distribution in the genomic transcriptional regulatory region with the information on the difference in gene regulatory chromatin interaction, and select the enhanced or weakened gene regulatory chromatin in the different states of the sample. a gene whose internal action, transcription factor binding motif distribution also undergoes significant changes; or

c) Combine the difference in gene expression, the difference in transcription factor binding motif distribution with the information on the difference in gene regulatory chromatin interaction, and choose to enhance or attenuate gene regulatory chromatin in different states of the sample. In the internal and genomic transcriptional regulatory regions, genes whose binding motif distribution changes significantly and the expression level also changes significantly.

In a sixth aspect, the present invention relates to a detection kit comprising the reagents used in the methods of the first to fourth aspects of the invention.

The method of the present invention establishes an analysis and identification method between chromatin conformation and transcriptional regulation by integrating a plurality of histological test methods and results. The method can be applied to the analysis of multiple biological processes, such as cell differentiation, ontogeny, cell variability, disease treatment, etc., so that chromatin interactions and regulatory factors having important influence on the above processes can be identified at the chromatin conformation level. .

DRAWINGS

Figure 1 shows the overall flow of one embodiment of the present invention.

Figure 2 is a graph showing the change in mRNA expression of HL-60 cells induced by All-trans-retinoic acid (abbreviated as ATRA) compared with the control group in one embodiment of the present invention, and Figure 2A shows the dimension. The change in gene expression after formic acid induction, Figure 2B shows the enrichment of differentially expressed genes in different GO classifications.

Figure 3 shows the change in the frequency of chromatin interactions within the TAD after induction with ATRA (Fig. 3A) and the relationship between frequency changes and differentially expressed genes (Fig. 3B).

Figure 4 shows the relationship between changes in the frequency of chromatin interactions in TAD and changes in H3K4me3 and H3K27ac modifications.

Figure 5 shows the chromatin interaction differences associated with gene expression regulation obtained in one embodiment of the present invention, wherein Figure 5A shows the changes in the H3K4me3 signal and the H3K27ac signal in the ATRA-treated and control groups, and Figure 5B shows the ATRA. The specific and shared peaks of the control group and the control group were separated from the transcription start site, respectively. Figure 5C shows the search for chromatin interactions related to gene regulation. Figure 5D shows the H3K27ac in the gene region of the Gain and Loss groups. Signals, as well as relative comparison of H3K4me3 signals in the gene region, Figure 5E shows the differentially expressed genes in the Gain and Loss groups.

Figure 6 shows a comparison of the results of ATAC-seq determination of chromatin open regions after induction by ATRA in one embodiment of the invention. Figures 6A and 6B show the specific peaks of each group and the distribution of peaks in the genome. Figures 6C and 6D show that in the regulatory region and the gene region, the Gain group and the Loss group interact with the ATRA-treated group and the control group, respectively. Sexual and shared chromatin open signal distribution, in which the Gain group is more enriched in the ATRA-treated group-specific signal, while the Loss group is more enriched in the control-specific signal, and it can be seen that the regulatory region is more enriched than the gene region. Specific signal. Figure 6E shows transcription factor binding motifs enriched in chromatin open regions in the control and ATRA induction groups.

Figure 7 shows a comparison of the distribution of transcription factors (TFs) with binding motifs in the open chromatin region of the Gain group (Figure 7A) and the Loss group (Figure 7B), from which it can be seen that the GATA binding motif is between the two groups. There are specific differences.

Figure 8A shows that transcriptional factor-target gene regulatory network analysis indicates that GATA2 is located in the network core hub, and Figure 8B shows that ATRA induces HL-60 cell differentiation, between GATA2 and other ATAC-seq-rich transcription factors. Interaction relationship.

Figure 9A and Figure 9B show the GATA2 gene region and regulatory region interaction and GATA2 gene expression changes after ATRA induction, respectively; Figure 9C and Figure 9D show the ZBTB16 gene region and regulatory region interaction and ZBTB16 gene expression changes after ARTA induction, respectively;

Figure 10A shows the results of the 4C chromatin conformation capture upstream of the GATA2 gene, Figure 10B shows the in situ FISH results verifying the chromatin loop structure, and Figure 10C shows the Pearson correlation coefficients for the red and green fluorescence distributions in the FISH experiment.

Fig. 11A shows the results of the 4C chromatin conformation capture of the ZBTB16 gene region, and Fig. 11B and Fig. 11C show the models of Gata2 and Zbtb16 in the induced differentiation, respectively, which explain the relationship between chromatin structure, transcription factor binding and gene expression.

Detailed ways

The terms used in this application have the same meaning as the term in the prior art. In order to clearly indicate the meaning of the terms used, the specific meanings of some terms in this application are given below. In the event of a conflict between the definitions of this term and the general meaning of the term, the definitions herein prevail.

definition

The term "sample", also referred to as "sample", refers to any subject that can be analyzed, as long as the subject of the analysis contains chromatin and the expression product of the gene (eg, mRNA and/or protein), the sample may be a eukaryotic cell. For example, animal cells, plant cells, fungal cells, and the like, and sometimes lysates of cells may also be included.

The term "state transition" refers to a change in the nature or morphology of a sample by a particular additive induction or internal natural process for the same sample. For example, the differentiation of chemical agents, physical stimulation or differentiation of cells in natural physiological processes, such as the natural differentiation process of cells triggered by the action of external hormones or other signaling molecules or genes or proteins inside cells. In one embodiment, "at least two samples in different states" are formed by state transitions in the present invention.

The "sample in the first state" and the "sample in the second state" refer to the samples of the two different states obtained after the state transition process. In some embodiments, the "sample in the first state" is the sample before the state transition, and the "sample in the second state" is the sample after the state transition.

The term "effector gene" refers to a gene involved in the process of sample state transition. The effector gene may be the cause of the state transition of the sample, that is, the process by which the gene can initiate a state transition. For example, in a cell differentiation induction model, the gene may be directly In response to the addition of the induced gene, which triggers the differentiation of the cell; in addition, the gene can also be a link in the middle of the state transition process, or simply as a result of a state transition. It should be noted that a "gene" herein may refer to a gene, and may also refer to an expression product of a gene, such as an mRNA transcript or a protein. In one embodiment, the effector gene may be a transcription factor.

The term "transcriptional regulatory region", or regulatory region, refers to a region within the genomic DNA that is located upstream or downstream of the gene, for example, 10 kb-1 Mb, 50 kb-500 kb, 100 kb-200 kb, including promoters, enhancers. A region of a site where a trans-acting factor (eg, a transcription factor) binds.

In one embodiment of the invention, the analysis of the transcriptional regulatory region plays an important role in the selection of a target effector gene from a large number of alternative candidate genes, such as determining whether an interacting promoter and enhancer are present in the transcriptional regulatory region of the gene. , and whether the transcriptional regulatory region is an open sequence, and which binding motifs are present; whether the interaction between the enhancer and the promoter is altered in the samples of different states, and whether the distribution of the binding motif is Changes have also occurred, and combined with other information (such as the amount of gene expression) can effectively obtain effector genes.

The term "gene regulatory-associated chromatin interactions", also known as "chromosome interactions present in transcriptional regulatory regions", refers to sequences within different regulatory elements of the transcriptional regulatory region of a gene, such as promoters and enhancers. , the occurrence of chromatin interactions.

In one embodiment of the invention, the reference genomic sequence is divided into regions of a size that can be adjusted according to the depth of the chromatin conformational analysis, such as the depth of sequencing, preferably in the range of 1 kb to 40 kb, for example 1 kb. 2kb, 3kb, 4kb, 5kb, 6kb, 7kb, 8kb, 9kb, 10kb, 11kb, 12kb, 13kb, 14kb, 15kb, 16kb, 17kb, 18kb, 19kb, 20kb, 21kb, 22kb, 23kb, 24kb, 25kb, 26kb 27 kb, 28 kb, 29 kb, 30 kb, 31 kb, 32 kb, 33 kb, 34 kb, 35 kb, 36 kb, 37 kb, 38 kb, 39 kb or 40 kb. In a specific embodiment, the region is 40 kb in size.

Next, based on the promoter and enhancer information of the active state obtained in the aforementioned step iv), the regions containing the active promoter and the enhancer site are respectively obtained by alignment, and the region containing the promoter is preferably named as the gene region. , the region containing the enhancer sequence is named as a regulatory region; subsequently, combined with the signal of chromatin interaction in step v), the chromatin interaction frequency signal between the gene region and the regulatory region is analyzed (ie, between specific regions) The number of chromatin interactions, for example, can be expressed as the number of reads in the Hi-C data where the two ends fall within a specific region, respectively. When there is a identifiable intensity contact signal between the gene region and the regulatory region, it is considered to have gene regulation. Related chromatin interactions.

The term "gene-regulated chromatin interaction differences" is sometimes referred to herein as "differences in chromatin interactions in transcriptional regulatory regions", "differences in chromatin interactions in transcriptional regulatory regions" or "existing in transcription" The difference in chromatin interactions in the regulatory region, the meaning of which is the same. The method is obtained by comparing the gene regulation-related chromatin interactions of the samples in the first state and the second state, wherein there is a significant difference, which is identified as a differential gene-regulatory interaction. ), wherein "significance" preferably means statistically significant. For example, when a hypothesis test is employed, p < 0.05 or p < 0.01.

The term "gene-regulated chromatin interaction" refers to those chromatin-related chromatin interactions that are significantly different between a sample in a first state and a sample in a second state, ie, staining-related staining. As part of the qualitative interaction, the association between chromatin interactions and gene regulation is more certain in the identified gene-regulated chromatin interactions.

In fact, the "identity of gene-regulated chromatin interactions" identified above can be considered as a comparison of "gene-regulated chromatin interactions". Depending on the type of "difference", gene-regulated chromatin interactions are also divided into two types: the enhanced gene regulatory interaction of the sample in the second state relative to the sample in the first state (in the present invention) In the embodiment, this type of interaction is also classified as a Gain group) and/or attenuated gene regulatory interactions (in the embodiment of the invention, this type of interaction is also classified as the Loss group).

The term "gene identifiable behavioral difference" refers to a difference that can be qualitatively or quantitatively observed in relation to the nature, state, etc. of a gene in a sample of different states. Wherein the "gene" is not a specific part or a pre-selected gene by human intervention, but in a quantitative or qualitative analysis, a whole set of genes with identifiable behavioral differences is observed. Sometimes, for the sake of clarity, the above "gene" can be defined as a candidate gene or a candidate gene, but it should be noted that even the expressions of "alternative gene" or "candidate gene" are not used herein to indicate these. Alternative genes or candidate genes are part of a pre-selected range that needs to be artificially selected.

The term "binding motif" refers to an element which is present on genomic DNA and which can be bound by a trans-acting factor such as a transcription factor to regulate a target gene, for example, to regulate expression of an effector gene in the present invention.

The term "difference in the distribution of bound phantoms" refers to the difference in the number, position or presence of the whole or part of the bound phantom between samples in different states, or the portion located in the region of interest or The difference in the number, location, and presence or absence of a particular binding motif.

The term "chromatin interaction" refers to long-range interactions between different chromatin sites, thereby forming a high-level conformation of chromatin to maintain chromatin structure or to promote gene expression.

The term "chromatin interaction frequency", also referred to herein as "Hi-C interaction frequency" or Hi-C contact frequency, refers to the different regions found in chromatin interactions when performing chromatin conformational analysis. The signal of the interaction is expressed as the number of reads in the Hi-C data where the two ends fall within a specific area.

The term "chromatin open region sequence" refers to a DNA sequence that is exposed in chromatin due to nucleosome binding or the like and can be bound by a trans-acting factor such as a transcription factor.

The term "ChIP-seq" refers to a technique that combines immunoprecipitation (ChIP) with high-throughput sequencing to efficiently detect DNA segments that interact with histones, transcription factors, etc., across the genome. The principle is as follows: Firstly, the DNA fragment of the target protein is specifically enriched by chromatin immunoprecipitation (ChIP), and purified and library constructed; then, the enriched DNA fragment is subjected to high-throughput sequencing. The obtained millions of sequence reads are then accurately mapped to the genome, that is, DNA segment information that interacts with histones, transcription factors, and the like in a genome-wide range is obtained.

The term "chromatin conformation capture technology" refers to all techniques that enable the relationship between different spatial positions of chromatin to establish chromatin three-dimensional structure information, including the common 3C technology, namely Chromosome Conformation Capture, which also includes high-throughput Sequencing chromatin conformation capture technology.

The term "high-throughput chromatin conformation capture technology" refers to the combination of high-throughput sequencing technology and bioinformatics analysis methods to efficiently analyze the spatial position of the entire chromatin DNA in the genome-wide range and achieve high resolution. A method for chromatin three-dimensional structure and chromatin interaction information. In this paper, the technique includes at least Hi-C, Hi-C based improved technology in situ Hi-C, and BL-Hi-C obtained after further introduction of bridge-linker based on the in situ Hi-C method. In addition, the ChIA-PET method is also a high-throughput chromatin conformation capture technique.

The term "ATAC-seq" refers to a technique for studying chromatin accessibility in molecular biology. It consists of two parts, the ATAC experiment and high-throughput sequencing. The key part of the ATAC-seq experiment is the transposase Tn5 to the sample genome. The role of DNA. The transposon preferentially incorporates a genomic region that is generally free of nucleosomes (nuclear bodies) or exposed DNA segments. Therefore, the enrichment of certain locus sequences in the genome indicates that there are no nucleosomes in the region, and is in a loosely exposed state in which nuclear machinery such as DNA-binding proteins can enter, providing information on the transcriptional active state of the chromatin segment. ATAC-seq employs a mutated multi-active transposase that allows for efficient cleavage of exposed DNA and simultaneous ligation of specific sequences. The ligation-ligated DNA fragment was isolated and amplified by PCR for high-throughput sequencing.

Example

The following examples are exemplified by ATRA-induced HL-60 differentiation, and exemplarily illustrate how the disclosed method finds genes that have important regulation during the above differentiation process and performs corresponding analyses. It should be noted that those skilled in the art can understand that the methods in the present disclosure are not limited to the methods exemplified in the embodiments, but are applicable to any sample in two different states, and the related target regulatory genes are searched. And analysis.

Example 1 Cell Culture and ATRA Induction

HL60 cells were purchased from the National Experimental Cell Resource Sharing Platform (Beijing Union Medical College, China). The cells were maintained in RPMI-1640 medium (Gibco, supplemented with 10% fetal calf serum (FBS, Gibco, USA), 50 units/mL penicillin and streptomycin (Gibco, USA) and non-essential amino acids (Gibco, USA). In the United States.

For granulocyte differentiation, 2×10 ⁵ /ml HL-60 cells were treated with 1 μM ATRA (1 mM stock solution in ethanol, Sigma, USA) for 4 days (referred to as ATRA group); cells treated with equal amounts of ethanol were called controls. group. The medium was changed on the second day while adding ATRA/ethanol.

Example 2 Obtaining RNA differential expression information of control and ATRA cells

Method: RNA seq

process

Total RNA was extracted from control cells and ATRA-treated cells using the TRIZOL (Ambion, USA) method. Library construction and sequencing were performed by Annoyouda (China).

For RNA-seq analysis, the adaptor sequence was first removed, followed by Bowtie to compare the data back to the reference genome hg19, filtering out the sequencing reads of the ribosomal RNA. After the above steps, the remaining read data was compared with the transcriptome data RSEM v1.2.7 and quantitative analysis was performed. The annotated file was downloaded from the Human Genome 19 version of the genome browser at the University of California, Santa Cruz (UCSC). Differential gene expression was calculated from the mean of gene-wise dispersion estimates using a Deseq2 version 1.4.5 software package. A gene with a significant difference in expression was determined based on an adjusted p-value equal to 0.01 and a fold change value of greater than 0.9 after log2. Gene Ontology analysis uses DAVID. RNA-seq plots (plots) were drawn using the Rg 3.3.1 version of the ggplot2 software package.

result

Differentially expressed genes (DEGs) analysis showed 941 up-regulated genes and 611 down-regulated genes after ATRA induction (Fig. 2A). GO analysis showed that the "immune response" and "leukocyte activation" genes were significantly enriched, which is in fact consistent with the terminal differentiation process of neutrophils (Fig. 2B).

Example 3 The level of chromatin interaction in TAD is closely related to gene expression level, gene promoter and enhancer activity.

Method: BL-Hi-C

process

Library construction: Cells were treated with 1% formaldehyde to crosslink proteins and proteins and DNA in cells, then heavy using lysis buffer (50 mM HEPES-KOH, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100 and 0.1% SDS) Hanging. The genome was then digested with the enzyme HaeIII into fragments with blunt ends. The blunt ends of the DNA fragments were treated with adenine and ligated with a biotin-containing bridge linker at 16 °C for 4 hours, and the unligated DNA fragments were digested with exonuclease (NEB). Next, the cells were digested with proteinase K (Ambion) overnight, and DNA was extracted and purified using phenol-chloroform (Solarbio) in combination with ethanol precipitation. Then, the DNA was fragmented using a S220 focused sonicator (Covaris), and the biotin-labeled DNA fragment was bound by streptavidin-coated Dynabeads M280 (Thermo Fisher). Libraries prepared from magnetic beads were subjected to Illumina sequencing and amplified by PCR. After purification with AMPure XP beads (Beckman, Germany), sequencing was performed using an Illumina HiSeq 2500 sequencer.

Hi-C data analysis: First, the bridge linker sequence (sequence: CGCGATATCTTATCTGACT or GTCAGATAAGATATCGCGT) in the read is removed, and if the complete link divides the read into two segments, the 5' segment is retained. Second, the reads processed as described above are mapped to the human genome hg19 version, while the duplicate fragments are removed. Third, based on the DNA strand information to estimate the distance threshold of the pair of readings, combined with the information of the pair of pairs and the chain, the interaction pairs are divided into the following types: complete fragments (readings do not occur internally), self-joining, Inter-chromosomal connections and intrachromosomal connections. For a restriction endonuclease that recognizes 4 base pairs, the threshold for intrachromosomal ligation is approximately 3 kb.

Hi-C data correction: The iterative correction method (ICE) was used to correct the system bias, followed by a 40 kb resolution interaction matrix.

For the TAD classification, we use the cluster-based Hi-C domain search (CHDF) method.

Calculation of TAD interaction fold change: To calculate the fold change in TAD internal and external Hi-C counts upon ATRA induction, the fold change between replicates was first calculated. For each TAD, the fold change of the control and ATRA group cells was combined to generate a background distribution (internal and external fold changes were calculated separately). The fold change between ATRA treated cells and control cells is then introduced into the background distribution to obtain p values based on their position in the background distribution. A TAD with a p-value <0.05 in both replicates was defined as a significantly altered TAD.

Method: ChIP-seq library construction and data analysis

process

The ATRA-treated HL-60 cells and control cells were cross-linked with 1% formaldehyde. Then, the cell membrane was lysed using lysis buffer (50 mM HEPES-KOH, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 0.1% sodium deoxycholate and 1% SDS). Chromatin was resuspended in FA lysis buffer (50 mM HEPES-KOH, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 0.1% sodium deoxycholate and 0.1% SDS) followed by sonication processor (Cole-Parmer) , United States) for fragmentation. Immunoprecipitation was performed using overnight treatment with Dynabeads (Thermo Fisher) pre-incubated with H3K4me3 and H3K27ac antibodies (Abeam, England). After washing and purifying the DNA, library construction was performed using the TruePrep DNA Library Preparation Kit (Vazyme, China) according to the experimental manual provided by the manufacturer. The library was sequenced using an Illumina HiSeq 2500 sequencer.

For the ChIP-Seq analysis, the linker sequence was first removed. The sequencing reads were then aligned to the human genome hg19 version using Bowtie. The histone modification peak was generated using the MACS2 software call and the parameter was set to '-g hs--nomodel--broad'. The peaks present in both replicates (bedtools software, 1 bp minimal overlap) were considered confidence peaks. Then, the confidence peaks obtained by the control and ATRA-treated cells were compared to distinguish the ATRA-treated group-specific peaks, the control-group-specific peaks, and the overlapping peaks. Peak comparisons were made using the interactBed software in bedtools.

analysis

The TAD containing the expressed gene (n=3362) was sorted according to the change of the internal Hi-C interaction frequency of TAD, and these TADs were classified according to the statistical distribution of Hi-C interaction frequency, and the distribution of the expressed genes in these TADs was calculated. (Fig. 3A, 3B). The results showed that after TTRA induction, TAD was more likely to have differentially expressed genes in TAD, which showed significant changes in internal interactions compared to TAD, which did not change significantly in internal interactions. Moreover, TAD with increased internal chromatin interaction enriches the differentially expressed genes, while TAD with reduced internal chromatin interactions exhibits differentially expressed differentially expressed genes (Fig. 3B). The above results show that there is a positive correlation between the change in gene expression in TAD and the frequency of internal chromatin interaction.

Further, in order to qualitatively characterize the epigenetic state within the TAD, we performed ChIP-seq in control and ATRA-treated cells, respectively, using antibodies against H3K4me3 and H3K27ac, which primarily labeled active promoters and enhancers. By calculating the ChIP-seq signal change in the TAD, it was found that the levels of H3K4me3 and H3K27ac in the TAD with increased internal chromatin interaction also increased, while the TAD with reduced chromatin interaction showed an opposite change (Fig. 4). This suggests that changes in epigenetic activity within TAD may affect chromatin conformational variation and differential gene expression.

It can be seen from the present example that the level of chromatin interaction in the TAD is very closely related to gene regulation, and can be further used to find the target functional gene.

Example 4 "differential gene-regulatory chromatin interactions" can effectively indicate differences in gene expression

On the basis of Example 3, in order to better quantify the TAD internal chromatin interaction level and apply it to the identification of genes, the ChIP-seq data in Example 3 was first used, and 12295 was identified in the ATRA group cells. A H3K4me3 peak and a 12493 H3K27ac peak identified 14263 H3K4me3 and 22149 H3K27ac peaks in the control cells (Fig. 5A). The H3K4me3 peak represents the active promoter, and the H3K27ac peak represents the active transcriptional region and enhancer. By calculating the distance from the closest transcription start site (TSS) of each H3K27ac peak, we found that the ATRA group and/or control-specific peaks are often farther away from TSS than the common peaks of both (Figure 5B). , indicating a more distant regulatory change after ATRA induction.

Chromatin interactions can pull distal regulatory elements, promoters, and transcription initiation sites for transcription initiation, in order to further determine how changes in chromatin interaction affect gene expression and which genes are affected Expression, carried out the following steps:

The entire gene was first assembled into a 40 kb bin. Next, if the bin contains a promoter that expresses the gene (ie, the H3K4me3 peak), the bin is labeled as "gene region"; if the H3K27ac peak at the distal end of the promoter is included (Fig. 5C), the bin is labeled as "regulatory region" ". Then, the Hi-C interaction relationship (Hi-C read) between the gene region and the regulatory region is used to express the intensity of chromatin interactions associated with gene regulation. Thus, we generated a chromatin interaction matrix using Hi-C data at a resolution of 40 kb, and the counts in the corresponding elements of the matrix represent the intensity of interaction between the gene and the regulatory region. For example, if the i-th bin is a gene region and the j-th bin is a regulatory region, the count of [i, j] represents a chromatin interaction associated with gene regulation. The chromatin interactions associated with gene regulation within the same TADs serve as input for differential interaction analysis. The difference interaction analysis is based on the MA curve method and the random sampling model, namely:

The Hi-C experiment is considered to be a sampling of chromatin interactions in most cells, so the reads of the interaction between the two regions in the Hi-C data follow a binomial distribution. Let C ₁ and C ₂ represent the counts of specific gene-regulated chromatin interactions obtained from control and ATRA-treated cells, respectively, having a C _i - binomial distribution (n _i , p _i ), i=1, 2, where n _i represents the total number of Hi-C counts and p _i represents the probability of counting from the regulatory chromatin interaction of the gene. We define M = (log ₂ C _{1 -} log ₂ C ₂ )/2, and A = (log ₂ C ₁ + log ₂ C ₂ )/2. Under the assumption of random sampling, assuming A = a (a is an observation of A), the conditional distribution of M follows an approximate normal distribution. For each gene-regulated chromatin interaction on the MA map, we performed a hypothesis test of H ₀ : p ₁ = p ₂ and H ₁ : p ₁ ≠p ₂ . The p-value is then assigned based on the conditional normal distribution. The analysis was performed using the DE Gseq software package of version 3.3.1 with the parameter set to "MARS".

RESULTS: Based on whether the difference in Hi-C exposure between control and ATRA-treated cells was significant (p<0.001 based on Benjamini correction), differences in gene-regulated chromatin interactions were identified and 422 pairs of enhanced gene regulation were identified. Correlated chromatin interactions (which may be referred to simply as "Gain") and 330 chromatin interactions associated with attenuated (or decreased) gene regulation (which may be referred to simply as the "Loss" group) (Fig. 5C).

Further, in the Gain group, the fold change of the H3K27ac signal in the corresponding gene region and the fold change of the H3K4me3 signal in the corresponding gene region were significantly increased, while in the Loss group, the above two groups of signals showed opposite trends (Fig. 5D). These results can explain, at least in part, the positive correlation between active histone status and chromatin interaction intensity and positive correlation with differential gene expression. According to statistics, a total of 430 and 323 genes respectively involved enhanced gene regulation-associated chromatin interactions and decreased gene regulation-associated chromatin interactions; further combined with RNA expression data, it was found that when ATRA was induced 164 and 61 genes in the above genes showed differential expression (Fig. 5E), and the proportion was significantly higher than the overall ratio (1649 differentially expressed genes in 12266 genes), Gain group or Loss Group interactions can, to a large extent, result in up- or down-regulation of the corresponding genes involved.

Example 5: There is a correlation between the difference in gene regulatory chromatin interactions and the accessibility of chromatin

Method: ATAC-seq

Process

The control sample and the ATRA-treated sample were separately placed in ice in a lysis buffer (10 mM Tris-HCl (pH 7.4), 10 mM NaCl, 3 mM MgCl ₂ and NP-40) for 10 minutes to prepare a nucleus immediately after cell lysis. Rotate to remove the supernatant. The nuclei were then incubated with Tn5 transposome and labeling buffer for 30 minutes at 37 °C (Vazyme, China). After the labeling, the stop buffer was directly added to the reaction system to end the labeling reaction. A 12-cycle PCR was then performed to amplify the library. After the PCR reaction, the library was purified using 1.2 x AMP beads (Beckman, Germany) and the resulting library was sequenced using an Illumina HiSeq 2500 sequencer.

The linker sequences were removed and the sequences aligned into the human genome hg19 version. The ATAC-seq peak is called up using MACS2 using default parameters, followed by peak comparison. To identify sequence motifs rich in ATAC-seq peaks, use Mififs Genome.pl in the HOMER program. AnnotatePeaks.pl is used to identify specific peaks that contain certain motifs. The GREAT analysis of the ATAC-seq peak is described in the literature (McLean CY, Bristor D, Hiller M, Clarke SL, Schaar BT, Lowe CB, et al. GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol. 2010 May2; 28 (5 ): 495–501).

result

The ATAC-seq peaks in the region of the gene with differential gene regulatory chromatin interactions and in the regulatory region were calculated, and it was found that the enhanced (Gain) and attenuated (Loss) interactions were enriched in ATRA-specific and control-specific, respectively. Sex peaks (Figures 6C and 6D). The regulatory region showed a stronger enrichment tendency than the gene region, indicating that changes in TF binding in the open chromatin region, particularly in the distal regulatory region, regulate the formation of chromatin interactions.

To characterize the transcription factor binding status after ATRA induction, we performed a motif analysis of the specific ATAC-seq peaks of the control and ATRA-inducing groups using the HOMER software (see Example 5 for specific procedures). Most transcription factors including CTCF, PU.1, RUNX and CEBP were highly similar between ATRA-treated cells or control cells (Fig. 6E). The expression of PU.1 mRNA was slightly up-regulated by the ATAC data analysis (~1.9-fold); among the RUNX family members, only RUNX3 showed significant mRNA level changes (~3.2-fold, up-regulated) after ATRA treatment, indicating that it may be Modulation in ATRA induction. Notably, the GATA binding motif was only enriched in control cells (Fig. 6E), and the expression level of GATA2 mRNA was significantly downregulated (~0.06 fold) after differentiation, indicating that loss of GATA2 binding is associated with the ATRA induction process.

Example 6 Identification of key gene transcription associated with chromatin structural changes by integrating multi-omics data in an ATRA induction model

The experimental procedures and results analysis of Hi-C and ChIP-seq are described in detail in Examples 3 and 4 to obtain an enhanced gene regulation-associated chromatin interaction group (Gain) (Fig. 7A) or attenuated gene regulatory chromatin. Interaction group (Loss) (Fig. 7B). Motif analysis was then performed on the ATAC-seq peak using the HOMER software in the above two interaction groups (see Example 5 for details).

The results showed that the transcription factors such as PU.1 and RUNX JUNB with binding sites were enriched in the two groups, but there was no significant difference in enrichment between the Gain group and the Loss group, indicating that these transcription factors The association with altered chromatin interactions is lower. However, it is worth noting that GATA motif sequences (such as GATA1 and GATA2) are only significantly enriched in the regulatory region of the Loss group, suggesting that GATA transcription factors have a unique role in chromatin interactions, further binding RNA- According to the seq data, since the expression level of GATA2 mRNA is significantly down-regulated after differentiation (~0.06-fold), the loss of GATA2 binding may contribute to the ATRA induction process; thus, by synthesizing the above multi-omics data, GATA2 was successfully identified as a candidate. gene.

Example 7 Discovery of More Related Transcription Factors

To further describe the relationship between transcription factors and differentially expressed genes during differentiation, we mapped the above transcription factors and differentially expressed genes to the HTRI TF-Target network (Fig. 8A). In the subnet, we find that GATA2 is the most networked hub node. In addition, GATA2 showed interaction with most of the ATAC-seq-rich transcription factors and known key regulators of granulocyte differentiation (Fig. 8B). In conclusion, by integrating chromatin accessibility information and transcriptional regulatory networks, we found that GATA2 may play an important transcription factor in ATRA-induced HL-60 differentiation.

Example 8 ATRA induction reduces chromatin interactions between the GATA2 promoter and regulatory regions

Based on differential gene regulation chromatin interaction analysis, we observed a significant decrease in the interaction between the gene region containing Gata2 and the upstream regulatory region after ATRA stimulation (Fig. 9A and Fig. 9B). To describe in detail the chromatin conformation associated with Gata2, the Gata2 promoter was next used as a bait, and 4C chromatin conformation capture techniques were performed in control and ATRA treated cells.

The specific process is as follows:

First, the cells were cross-linked with 1% formaldehyde, and the nuclei were separated by resuspending in lysis buffer (500 μl of 10 mM Tris-HCl pH 8.0, 10 mM NaCl, 0.2% Igepal CA-630, and 50 μl protease inhibitor), and then, The nuclei were washed by 1 x NEBuffer 2 (NEB, UK) and treated with 0.3% SDS at 65 °C. Subsequent digestion with HindIII at 37 °C overnight followed by proximal ligation at 4 °C for 4 hours. After the reverse cross-linking was treated with proteinase K (Ambion, USA), the DNA was purified by extracting a combined ethanol precipitate using phenol-chloroform (Solarbio). The second digestion step was carried out overnight using DpnII. Then, we performed a second ligation and ethanol precipitation to extract the DNA. DNA was finally purified using the QIAquick PCR Purification Kit (QIAGEN, Germany) according to the manufacturer's protocol. After the PCR reaction, we purified the 4C library using AMPure beads (Beckman, Germany) and sequenced the library using an Illumina HiSeq 2500 sequencer.

For 4C-seq data analysis, the adaptor sequence was first removed using the cutadapt software in SAMtools. Use Bowtie to match the read length to the human hg19 version of the genomic information. Then, using the RPM standardization, the corresponding data is processed using the r3Cseq packet in R 3.3.1.

result

In control cells, we found that the Gata2 promoter has strong chromatin interactions with three upstream enhancers indicated by the H3K27ac peak (ie, chr3: 128240590-128254410, 128262419-128292429 and 128309790-128334446). The position of enhancer E3 (approximately 80 kb upstream of the GATA2 promoter) is very close to the known enhancer, confirming the reliability of the 4C data. After ATRA induction, the intensity of interaction between the Gata2 promoter and the upstream region decreased to varying degrees (0.54, 0.46- and 0.4-fold for E1, E2 and E3, respectively), consistent with a decrease in H3K27ac (Fig. 10A). . Morphological analysis showed that these regions contained the PU.1/RUNX1 and GATA motif sequences, respectively, and an open chromatin state was observed only in the control cells (Fig. 10A). Deletion of binding of the key transcription factor (PU.1/RUNX1/GATA) in the distal regulatory region disrupts the chromatin loop and inhibits GATA2 expression. To further confirm the disappearance of the chromatin loop after ATRA induction, we performed three-dimensional DNA fluorescence in situ hybridization (FISH) in control and ATRA-treated cells, and the results showed that the control cells bind to the Gata2 promoter and the ATRA-treated cells. The probe signal of enhancer E3 has a larger overlap (Fig. 10B). Furthermore, the Pearson correlation coefficient between the two probes was higher in the control cells than in the ATRA-treated cells, further confirming the destruction of the chromatin loop caused by ATRA induction (Fig. 10C).

Example 9 Changes in chromatin interaction (loss of chromatin loop) inhibited ZBTB16 mRNA expression

In the previously mentioned transcriptional regulatory network (Fig. 8A), another important gene encoding the zinc finger protein ZBTB16 (also known as PLZF) involves differences in gene regulatory chromatin interactions. After ATRA induction, the expression level of this gene and chromatin interaction were significantly reduced (Fig. 9C). A previous study (Tang Z, Luo OJ, Li X, Zheng M, Zhu JJ, Szalaj P, et al. CTCF-Mediated Human 3D Genome Architecture Reveals Chromatin Topology for Transcription. Cell.2015 Dec17;163(7): 1611–1627) Three upstream and two downstream anchors bound by CTCF near the Zbtb16 gene locus were identified in K562 cells using ChIP-PET. To determine if the chromatin loops found by ChIA-PET were altered under ATRA induction, we used 5 ChIA-PET anchors as baits for 4C analysis (Figure 11A). The results of one of the 3' anchors showed a disappearance of the chromatin loop between 5' and 3' of Zbtb16, accompanied by a significant decrease in ZBTB16 mRNA (Fig. 9D). This result is consistent with previous predictions that the 5' and 3' loops of chromatin maintain high levels of gene expression. The ATAC-seq peak was observed at 5' of Zbtb16 in ATRA treated and control cells. Unexpectedly, only the peaks in the ATRA-treated cells were enriched for the PU.1 motif, indicating that PU.1 can bind to the 5' anchor after ATRA treatment.

Based on the results of Examples 8 and 9, we proposed two models to explain chromatin structure, transcription factor binding, and gene expression changes during ATRA-induced differentiation in the GATA2 and Zbtb16 regions. In the GATA2 region, PU.1, RUNX1 and GATA2 bind to the upstream enhancer, maintain the chromatin loop and promote transcription (Fig. 11B), causing loss of transcription factor binding after ATRA treatment, disrupting the chromatin loop and inhibiting Gata2 transcription. . In the Zbtb16 region, the 5' and 3' loops maintain continuous transcription of Zbtb16 in control cells. After ATRA treatment, in contrast to Gata2, binding of PU.1 resulted in disruption of the chromatin loop and thus inhibited transcription of Zbtb16 (Fig. 11C).

Claims (19)

1. A method for identifying a sample state transition effector gene whose expression is affected by a change in chromatin interaction in a state transition of a sample, comprising the steps of:

   (1) comparing the samples in the first state and the second state, thereby obtaining at least the following difference information: the difference in the identifiable behavior of the gene, and the difference in chromatin interactions existing in the transcriptional regulatory region of the gene, and

   (2) correlating the difference information obtained in the step (1) to obtain a difference in the identifiable behavior of the gene related to the chromatin interaction difference in the transcriptional regulatory region in the state transition, thereby identifying the effector gene.
2. The method of claim 1 wherein the sample is a cell.
3. The method according to claim 1 or 2, wherein said gene recognizable behavioral difference comprises a difference in gene expression amount and/or a difference in binding pattern distribution in a gene regulatory region genomic sequence; preferably, said gene expression amount The difference is the difference in mRNA expression or the difference in protein expression.
4. The method according to any one of claims 1 to 3, wherein the chromatin interaction difference existing in the transcriptional regulatory region of the gene in the step (1) is obtained by the following steps:

   (a) identifying the site of the promoter and/or enhancer in the active state of the sample genome;

   (b) Identify the areas where all chromatin interactions occur;

   (c) integrating the information obtained in step (a) and step (b) to obtain a chromatin interaction between the promoter and the enhancer in an activated state, that is, a chromatin interaction existing in the transcriptional regulatory region of the gene; with

   (d) Comparing chromatin interactions between different samples in the transcriptional regulatory region of the gene to obtain differences in chromatin interactions present in the transcriptional regulatory regions of the gene.
5. The method according to any one of claims 1 to 4, wherein the difference in the identifiable behavior of the gene in the step (1) is obtained by the following steps:

   i) obtaining samples in the first state and the second state;

   Ii) taking a portion of the sample in the first state and the second state, respectively performing transcriptional expression analysis, and comparing the difference in mRNA expression between the samples; preferably, the transcriptional expression analysis is performed by RNA sequencing, ie, RNA-seq method.
6. The method of claim 5 further comprising:

   Iii) taking a portion of the sample in the first state and the second state, respectively analyzing the chromatin open region sequence, preferably using the ATAC-seq method, and analyzing the transcription factor binding motif distributed in the chromatin open region sequence, preferably The difference in distribution of transcription factor binding motifs between samples in the first state and the second state is further compared.
7. The method according to any one of claims 1 to 6, wherein the difference in chromatin interactions present in the transcriptional regulatory region of the gene in step (1) is analyzed by the following steps:

   Iv) taking a portion of the sample in the first state and the second state, identifying promoter and/or enhancer information respectively in an activated state, preferably using the ChIP-seq method, which is used in the ChIP-seq method Preferably, the antibody is a binding antibody of H3K4me3 and H3K27ac, which forms a signal peak in combination with H3K4me3 and H3K27ac, respectively, representing promoter and enhancer sites in an activated state;

   v) taking another sample in the first state and the second state, using chromatin conformation capture technology, preferably using high-throughput chromatin conformation capture techniques, such as Hi-C method, in situ Hi-C method, BL-Hi -C method or ChIA-PET method to obtain information on genome-wide chromatin interaction; or to obtain information on local chromatin interaction using 4C or 5C method;

   Vi) dividing the reference genomic sequence into regions of a certain size, preferably between 1 and 40 kb, for example 1 kb, 5 kb, 10 kb, 15 kb, 20 kb, 25 kb, 30 kb, 35 kb or 40 kb, based on step iv) Promoter and enhancer information of the obtained active state, by separately obtaining a region containing the active promoter and enhancer sites, preferably the region including the promoter is named as a gene region, and the region containing the enhancer sequence Named the regulatory area;

   Subsequently, combined with the frequency signal of the chromatin interaction obtained in step v), the chromatin interaction frequency signal occurring between the gene region and the regulatory region is identified, thereby obtaining a gene regulation-related chromatin interaction; Comparison of gene regulation-related chromatin interactions between samples of the state and the second state, with statistically significant differences, identified as differential gene-regulatory interactions, including relative The sample in the first state, the enhanced gene regulatory chromosome interaction and/or the attenuated gene regulatory chromosome interaction in the sample in the second state.
8. The method according to any one of claims 1 to 7, wherein step (2) specifically comprises the steps of:

   a) Combine differences in gene expression levels with gene-regulated chromatin interactions, and select genes that are present in enhanced or diminished gene-regulated chromatin interactions with significant changes in expression in different states of the sample. ;or

   b) Combine the difference in transcription factor binding motif distribution in the genomic transcriptional regulatory region with the information on the difference in gene regulatory chromatin interaction, and select the enhanced or weakened gene regulatory chromatin in the different states of the sample. a gene whose internal action, transcription factor binding motif distribution also undergoes significant changes; or

   c) Combine the difference in gene expression, the difference in transcription factor binding motif distribution with the information on the difference in gene regulatory chromatin interaction, and choose to enhance or attenuate gene regulatory chromatin in different states of the sample. In the internal and genomic transcriptional regulatory regions, genes whose binding motif distribution changes significantly and the expression level also changes significantly.
9. The method according to any one of claims 1 to 8, further comprising the step of performing a functional study on the screened effector gene to determine its function.
10. The method of any one of claims 1 to 9, further comprising the step of identifying a gene-regulated chromatin interaction, ie, a chromatin interaction capable of affecting the expression of the effector gene identified in step (2), As a gene-regulated chromatin interaction.
11. The method according to any one of claims 1 to 10, wherein the sample state transition is achieved by chemical agent induction, natural differentiation and/or physical stimulation.
12. A method of identifying a chromatin interaction that modulates a state transition of a sample, comprising the steps of any one of claims 1 to 11.
13. A method of identifying a regulatory factor involved in a chromatin interaction involved in a state transition of a sample, comprising the steps of any one of claims 1 to 11.
14. A method of identifying a substance that modulates a chromatin interaction, comprising: identifying an effector gene of a chromatin interaction using the method of any one of claims 1 to 9 or 11 or using the method of claim 10 or The method identifies a gene-regulated chromatin interaction, and then contacts the test substance with the sample to analyze changes in the effector gene or gene regulatory interaction.
15. An identification system for a sample state transition effector gene whose expression is affected by changes in chromatin interactions in a state transition of a sample, including the following modules:

    (1) Gene identifiable behavior difference analysis module;

    (2) an analysis module for the difference in gene regulatory chromatin interactions in the transcriptional regulatory region;

    (3) an effect gene identification module;

    The system is capable of obtaining genetically identifiable behavioral differences associated with differences in chromatin interactions, thereby obtaining sample state transition effector genes that are affected by chromatin interactions.

    Preferably, the system further comprises a gene regulatory chromatin interaction recognition module to identify gene regulatory chromatin interactions that are capable of affecting expression of the effector gene.
16. The system according to claim 15, wherein said gene recognizable behavior difference analysis module is capable of analyzing a difference in gene expression amount and/or a difference in transcription factor binding motif distribution in a genomic sequence of a transcription regulatory region of the gene.
17. The system according to claim 15 or 16, wherein the analysis module for the difference in chromatin interactions of the transcriptional regulatory region is capable of performing the following analysis:

    (a) identifying the site of the promoter and/or enhancer in the active state of the sample genome;

    (b) Identify the areas where all chromatin interactions occur;

    (c) integrating the information obtained in step (a) and step (b) to obtain a chromatin interaction between the promoter and the enhancer in an activated state, ie, a chromatin interaction present in the transcriptional regulatory region;

    (d) Comparison of chromatin interactions between different samples in the transcriptional regulatory region to obtain differences in chromatin interactions present in the transcriptional regulatory regions.
18. The system according to any one of claims 15 to 17, wherein the effector gene identification module is capable of performing the following analysis:

    a) Combine differences in gene expression levels with gene-regulated chromatin interactions, and select genes that are present in enhanced or diminished gene-regulated chromatin interactions with significant changes in expression in different states of the sample. ;or

    b) Combine the difference in transcription factor binding motif distribution in the genomic transcriptional regulatory region with the information on the difference in gene regulatory chromatin interaction, and select the enhanced or weakened gene regulatory chromatin in the different states of the sample. a gene whose internal action, transcription factor binding motif distribution also undergoes significant changes; or

    c) Combine the difference in gene expression, the difference in transcription factor binding motif distribution with the information on the difference in gene regulatory chromatin interaction, and choose to enhance or attenuate gene regulatory chromatin in different states of the sample. In the internal and genomic transcriptional regulatory regions, genes whose binding motif distribution changes significantly and the expression level also changes significantly.
19. A test kit comprising the reagent used in the method of any one of claims 1 to 14.

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