Method and system for synchronously detecting host chromatin openness and in-vivo microbiome based on transposase

Abstract

The invention discloses a method and system for synchronously detecting host chromatin openness and in-vivo microbiome based on transposase, and belongs to the technical field of biological sequencing data analysis. According to the method, transposase is used for selectively fragmenting an open chromatin region of a host, and a microbial genome is almost randomly cut, so that synchronous enrichment of host and microbial DNA is realized; after high-throughput sequencing library construction and double-end sequencing, sequencing data is split into host source and non-host source reads through bioinformatics analysis, host chromatin state and microorganism composition are analyzed respectively, and a microorganism-host epigenetic regulation network is constructed; the invention further provides a matched DNA sequencing library and an analysis system, multi-scene research of infectious diseases, intestinal microecology, tumor microenvironment and the like is supported, a public database can be reanalyzed, and potential microbial interaction signals are mined. According to the method, the host-microorganism interaction research efficiency is remarkably improved, and a high-sensitivity and integrated technical scheme is provided for epigenetic regulation mechanism analysis.

Description

Technical Field

[0001] This invention belongs to the field of biological sequencing data analysis technology, specifically relating to a method and system for simultaneously detecting host chromatin accessibility and in vivo microbiome based on transposases. Background Technology

[0002] Host-microbe interactions play a crucial role in infection, immunity, and disease progression. In recent years, endogenous microorganisms (such as bacteria and fungi) have been detected in various tumor tissues, such as colorectal cancer and gastric cancer. These microorganisms do not exist in isolation and have a profound impact on the occurrence and development of tumors (Battaglia, TW, et al. (2024). A pan-cancer analysis of the microbiome in metastatic cancer. Cell187, 2324-2335 e2319.; Yang, L., et al. (2023). Intratumoral microbiota: roles in cancer initiation, development and therapeutic efficacy. Signal TransductTarget Ther 8, 35.). Studies have also shown that microorganisms can serve as carriers for tumor therapy, opening up new avenues for cancer treatment (Chang, Z., et al. (2025). Bacterial immunotherapy leveraging IL-10R hysteresis for both phagocytosis evasion and tumor immunity revitalization. Cell 188, 1842-1857). e1820.; Kreitz, J., et al. (2023). Programmable protein delivery with a bacterial contractile injection system. Nature 616, 357-364.). Epigenetic regulation is an important pathway for host cells to respond to microorganisms, accurately reflecting gene expression activity and the state of the cell. In colorectal cancer, *Fusobacterium nucleatum* can drive hypermethylation in the promoter region of tumor suppressor genes and significantly enhance glycolytic activity by increasing H3K27ac deposition (Wang, N., and Fang, JY (2023). *Fusobacterium nucleatum*, a key pathogenic factor and microbial biomarker for colorectal cancer. Trends Microbiol 31, 159-172.).Among various epigenetic indicators, chromatin openness contains a particularly rich amount of information. It not only reflects regions of the genome in an active transcriptional state and accurately identifies cis-regulatory elements, but more importantly, it reveals the intrinsic mechanisms of gene expression that determine cellular identity and function. These studies demonstrate that combining microbial detection with host chromatin openness analysis is crucial for deeply elucidating the underlying mechanisms of host-microbe interactions.

[0003] Currently, in the field of tumor microbiome research, studies have utilized whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) to analyze the microorganisms present in tumors (Battaglia, TW, et al. (2024). A pan-cancer analysis of the microbiome in metastatic cancer. Cell 187, 2324-2335e2319.; Narunsky-Haziza, L., et al. (2022). Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions. Cell 185, 3789-3806e3717.). However, both methods have significant limitations in practical applications. During WGS, because the host genome is much larger than the microbial genome, microbial signals are difficult to highlight within the vast host background signal, posing a significant challenge to microbial detection. Traditional RNA-seq sequencing relies on a poly(A) enrichment step. However, most bacterial transcriptions lack poly(A) tails, resulting in a very low percentage of microbial reads in the sequencing results, severely impacting detection sensitivity and accuracy. Furthermore, most microbial reads detected by RNA-seq are concentrated in the rRNA regions of microbial genes, limiting the comprehensive capture of microbial genomic information. Additionally, neither WGS nor RNA-seq can simultaneously analyze the host's epigenetic state, hindering in-depth analysis of the host's epigenetic regulatory response to microorganisms in tumor tissues. This presents a significant obstacle to a comprehensive understanding of host-microbe interaction mechanisms. Currently, the commonly used method for detecting host chromatin accessibility is ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing). Although some studies have observed microbial-derived sequencing reads in ATAC-seq data, these findings remain at the observational level, lacking a complete analytical methodology and system for in-depth understanding, and failing to achieve effective data utilization. Summary of the Invention

[0004] To address the limitations of existing whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) technologies in host microbiome detection, such as low sensitivity, limited functional information capture, and the inability to simultaneously analyze host epigenetic status, which restricts the understanding of host-microbe interaction mechanisms, this invention aims to provide a method and system for simultaneously detecting host chromatin accessibility and the in vivo microbiome based on transposase-mediated simultaneous detection technology. This transposase-mediated simultaneous detection technology enables efficient joint analysis of the microbiome and host chromatin accessibility, overcoming the bottleneck in in-depth mechanism research.

[0005] To achieve the above objectives, the present invention employs the following technical solution: This invention provides a method for simultaneous detection of host chromatin accessibility and in vivo microbiome based on transposase-accessible chromatin and microbiome sequencing (TACM-seq), comprising the following steps: Step 1: Extract cell nuclei from the mixed sample containing host cells and microorganisms to obtain the processed mixed sample; Step 2: The treated mixed sample is co-incubated with the transposase mixture. The transposase selectively inserts into and cuts open chromatin regions of the host; while the whole genome of the microorganism, lacking nucleosome structure, is randomly fragmented and labeled by the transposase to obtain DNA fragments treated with transposase. Step 3: Construct a high-throughput sequencing library from the DNA fragments treated with transposase, perform paired-end sequencing, and obtain mixed sequencing data of the host and microorganisms; Step 4: Based on the mixed sequencing data, the mixed sequencing data is compared with the host reference genome and divided into host-derived reads and non-host-derived reads; Chromatin openness analysis was performed on host-origin reads; By comparing non-host source reads with microbial genome databases, the species of microorganisms can be identified and the relative abundance of microorganisms can be quantified. By correlating microbial abundance information with changes in host chromatin openness, differentially open regions can be identified, linked to host gene function, and the regulatory mechanisms of microorganisms on host epigenetics can be obtained.

[0006] The transposase is Tn5 transposase, and the transposase reaction temperature is 36~38℃ for 20~40 min.

[0007] Preferably, the transposase reaction is carried out in a buffer containing digitalin and polysorbate-20 (Tween-20). Enrichment of the host open chromatin region and the microbial genome is achieved by fragmenting the host open chromatin region and the random region of the microbial genome.

[0008] The sample processing includes: lysing the cell membrane of host cells with a mild detergent to preserve the integrity of the microorganisms, or mechanically homogenizing tissue samples and avoiding density gradient centrifugation to reduce microbial loss.

[0009] In step 4, sequence alignment tools are used to align the sequencing reads with the host reference genome; based on the reads aligned to the host genome, peak identification algorithms are used to identify open chromatin regions, and statistical analysis methods are used to perform differential openness analysis; and species classification algorithms are used to identify microorganisms for the reads not aligned to the host genome.

[0010] Preferably, the host chromatin analysis uses the Bowtie2 tool to align the host genome and employs MACS2 for peak position identification.

[0011] Preferably, the tool for identifying microorganisms is to compare microbial genome databases using Kraken2.

[0012] Preferably, the openness analysis uses DiffBind to perform statistical analysis on openness differences.

[0013] This invention provides a DNA sequencing library for simultaneous detection, the library being constructed by the method and comprising: DNA fragments originating from host-open chromatin regions; DNA fragments derived from the entire genome of microorganisms; Sequencing adapter sequences support paired-end sequencing on high-throughput sequencing platforms.

[0014] A system for implementing the method of simultaneously detecting host chromatin accessibility and in vivo microbiome based on transposases includes: Sample processing module: integrates cell lysis, homogenization, and transposase reaction units; Sequencing module: used for high-throughput sequencing of host open chromatin DNA and microbial DNA in mixed samples; Data processing module: includes a data splitting unit for separating host and microbial sequencing reads; The microbiome analysis unit is used to perform species classification and abundance quantification. Host analysis unit, used to detect differences in open chromatin regions, associated gene functions, and transcription factor motifs; Association and integration module: Based on microbial abundance and host epigenetic changes, construct a host-microbe interaction network model.

[0015] The data analysis module comes pre-installed with standardized processes, supporting raw data quality control, host contamination filtering, microbial identification and quantification, and microbial functional annotation.

[0016] Preferably, the sequencing module supports paired-end sequencing with a read length of 150 bp.

[0017] Preferably, the data processing module integrates a Fastp quality filter for high-throughput sequencing data quality control, Bowtie2 short sequence alignment tool version 2 for host genome alignment, Model Alignment and Peak Discovery Algorithm version 2 (MACS2) for peak analysis, DiffBind for differential peak analysis, HOMER for transcription factor motif enrichment analysis, and Kraken2 for species classification and identification.

[0018] The correlation integration module includes statistical modeling tools for correlation analysis or regulatory mechanism analysis.

[0019] The association and integration module includes a visualization interface that outputs bar charts of microbial species and abundance, as well as heatmaps of changes in host gene promoter chromatin openness and corresponding microbial abundance.

[0020] The method for simultaneous detection of host chromatin accessibility and in vivo microbiome based on transposase, or the DNA sequencing library for simultaneous detection, or the application of the system, includes: Infectious disease research: elucidating the regulatory mechanisms by which pathogens (such as Salmonella) affect host chromatin accessibility; And / or gut microbiome research: revealing the types and abundance of symbiotic communities and their impact on host gene expression and epigenetics; And / or cancer microenvironment analysis: exploring the association between tumor-associated microbes and host epigenetic responses; And / or reanalysis of public databases: mining potential tumor-associated microbial signals from open chromatin sequencing data in the Cancer Genome Atlas (TCGA) database.

[0021] In infection models, host immune-related genes are located by differentially open regions, and regulatory pathways are verified by combining changes in microbial abundance. In the reanalysis of public data on open sequencing of tumor chromatin, microbial-origin sequences are identified by unmapped reads, and tumor function-related microorganisms and their effects on tumor epigenetics are screened.

[0022] Compared with the prior art, the present invention has the following beneficial effects: The method provided by this invention utilizes transposase-mediated synchronous processing technology, taking advantage of the limited open regions of host chromatin and the fully open nature of the microbial genome, to achieve simultaneous analysis of microbial detection sensitivity and host epigenetic status. This addresses the core problems of microbial signals being easily submerged by host background, incomplete capture of functional information, and inability to correlate epigenetic regulation. Nucleus extraction creates homogeneous conditions for mixed samples, ensuring that transposases can enter the host cell nucleus or microorganisms. During co-incubation of the transposase mixture, selective cleavage of open chromatin regions of the host (occupying only a small portion of the host genome, approximately 0.5%–2%) is performed, while random cleavage of the entire microbial genome DNA is conducted. Because microorganisms lack nucleosome structures, their DNA is fully open, a characteristic that significantly enriches microbial DNA during sequencing, effectively increasing the proportion of microbial signals and avoiding poly(A)-dependent bias. By constructing a high-throughput sequencing library and performing paired-end sequencing, a sequence containing both host and microbial DNA is generated. Hybrid sequencing data of microbial information provides a unified data source for subsequent isolation and analysis. Alignment of the hybrid sequencing data with the host reference genome separates host and non-host reads, enabling precise extraction of microbial signals. Chromatin accessibility analysis is then performed on host-derived reads to identify differential regions and correlate them with gene functions, thus analyzing the host epigenetic state. Simultaneously, non-host-derived reads are compared with microbial databases to complete species identification and abundance quantification, comprehensively capturing microbial genomic information. Finally, through correlation analysis between microbial abundance and changes in host accessibility, a mechanism bridge is directly established to understand the epigenetic regulation of the host by microorganisms. Through stepwise analysis and correlation integration, microbial detection and host epigenetic analysis are seamlessly integrated, forming a complete chain from sample processing to mechanism analysis, overcoming the bottleneck of traditional methods that cannot simultaneously achieve both microbial sensitivity and host epigenetic analysis.

[0023] This invention is the first to recognize that data generated using TACM-seq (Transposase-Accessible Chromatin and Microbiome sequencing) technology, while performing host epigenetic analysis, contains microbial information that has the great potential for systematic development for highly sensitive microbial detection. More importantly, this invention is the first to reveal and verify the significant advantages of this method over traditional whole-genome sequencing and RNA sequencing in terms of microbial detection sensitivity and coverage, and pioneers its application in functional studies to elucidate host-microbe interactions in complex tissues such as tumors.

[0024] The DNA sequencing library provided by this invention for simultaneous detection integrates host open chromatin region DNA fragments, microbial whole-genome DNA fragments, and sequencing adapter sequences to construct a unified data carrier. This enables simultaneous host epigenetic analysis and microbiome detection within a single library, effectively avoiding interference from host background signals in microbiome detection and supporting subsequent data separation and correlation analysis. The restricted source of the host fragments reduces background noise, the whole-genome coverage of the microbial fragments enhances signal strength, and the paired-end sequencing of the sequencing adapters ensures data separability, collectively forming an efficient data foundation for the joint analysis of host chromatin openness and the microbiome.

[0025] The system provided by this invention constructs a complete analytical chain from sample processing to mechanism analysis through modular integrated design, addressing the challenge of simultaneous detection in host-microbe interaction studies. The sample processing module integrates cell lysis, homogenization, and transposase reaction units, creating uniform conditions for mixed samples and ensuring that transposases can effectively enter the host cell nucleus or microorganisms. This allows subsequent transposase processing to selectively cleave open chromatin regions of the host and randomly cleave the whole-genome DNA of the microorganisms, laying the foundation for high-sensitivity microbial detection. The sequencing module performs high-throughput sequencing of the open chromatin DNA and microbial DNA of the mixed samples, generating mixed sequencing data containing host and microbial information. This provides a unified data source for subsequent separation and analysis, avoiding the problem of microbial signal overwhelmance caused by the large host background signal in traditional methods. The data processing module includes a data splitting unit to separate the mixed sequencing data into host and microbial sequencing reads, achieving accurate extraction of microbial signals and ensuring that microbial analysis is not interfered with by the host background. The microbiome analysis unit performs species classification and abundance quantification, comprehensively capturing microbial genomic information and solving the problem of incomplete functional information capture caused by poly(A) dependence in traditional RNA-seq and other methods. The host analysis unit detects differences in open chromatin regions, associated gene functions, and transcription factor motifs, analyzing changes in host epigenetic status and revealing gene expression regulation mechanisms. The association and integration module constructs a host-microbe interaction network model based on microbial abundance and host epigenetic changes, directly establishing a bridge between microbial mechanisms and host epigenetic regulation. These technical features work together: the sample processing module provides suitable samples for sequencing, the sequencing module generates raw data, the data processing module separates data streams into independent analysis paths, the microbiome and host analysis units process microbial and host data respectively to obtain complete information, and the association and integration module combines the two to form a closed loop, thus seamlessly connecting microbial detection and host epigenetic analysis, overcoming the bottleneck of traditional methods that cannot simultaneously achieve both microbial sensitivity and host epigenetic analysis.

[0026] The applications provided by this invention address the limitation in elucidating host-microbe interaction mechanisms by applying methods or systems for simultaneously detecting host chromatin accessibility and in vivo microbiome to multiple key research areas. In infectious disease research, by analyzing the regulatory mechanisms of pathogens on host chromatin accessibility, it directly analyzes the dynamic impact of microorganisms on the host's epigenetic state during infection, revealing how pathogens specifically regulate open regions of host chromatin to affect gene expression. In gut microbiome research, by revealing the types and abundance of symbiotic flora and their impact on host gene expression and epigenetics, it comprehensively captures the details of symbiotic microbe-host interactions in a homeostatic environment, avoiding the shortcomings of traditional methods that cannot correlate epigenetic changes due to weak microbial signals. In cancer microenvironment analysis, by exploring the correlation between tumor-associated microorganisms and host epigenetic responses, it focuses on the tumor microenvironment. How do microorganisms in the environment drive changes in host chromatin openness, thereby elucidating the epigenetic regulatory role of microorganisms in cancer progression? The reanalysis of public databases mines potential tumor-associated microbial signals from chromatin openness sequencing data in public databases such as TCGA, enabling the secondary use of historical data. Microbial information can be extracted from existing sequencing data without additional experiments, covering the entire research chain from pathogen infection and symbiotic homeostasis to tumor pathology and data re-mining. This ensures that simultaneous detection methods can be systematically applied to the analysis of host-microbe interaction mechanisms in different fields, thus overcoming the limitations of single technologies that cannot simultaneously achieve both microbial detection sensitivity and host epigenetic analysis. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the technical process of the present invention; Figure 2 The relative abundance of Salmonella Typhimurium reads detected by TACM-seq, WGS, and RNA-seq at each MOI is shown in Figure 1 (A). The normalized genome browser map of read coverage within a 50 kb region of the Salmonella Typhimurium genome is shown in Figure 2 (B). The whole genome coverage bar chart of Salmonella Typhimurium is shown in Figure 3 (C). The distribution bar chart of the sequencing reads of the method on the Salmonella Typhimurium genome is shown in Figure 4 (D). Figure 3 This invention presents the epigenetic responses of mouse macrophages Raw264.7 to different MOIs of Salmonella Typhimurium, detected by TACM-seq. The data includes a heatmap of host-differential open chromatin regions identified by TACM-seq at different infection concentrations (A), gene GO enrichment analysis results of genes associated with upregulated chromatin openness in macrophages treated with a 1:1 multiplicity of infection (B), and immune-related genes with increased chromatin openness (such as...). Il33 , Crtam and BlkThe genome browser map (C); Figure 4 This invention compares the performance of three methods—TACM-seq, WGS (whole genome sequencing), and RNA-seq—in the detection of synthetic microbial communities. The comparison includes: (A) a comparison of the detection sensitivity of four representative species in terms of relative abundance and genome coverage; (B) scatter plots of the abundance of all species at the genus level detected by each method in simulated and negative control samples, with TACM-seq showing a higher signal-to-noise ratio; and (C) bar charts showing the relative abundance of synthetic microbial communities and other contaminant species detected by each method, with TACM-seq detecting the lowest proportion of contaminant species signals. Figure 5 The relative abundance and genome coverage of Klebsiella pneumoniae detected in mouse liver by TACM-seq in this invention.

[0028] Figure 6 This invention reviews and analyzes a list of microorganisms (A) detected in tumor chromatin accessibility data that influence tumor epigenetic status. High abundance of *Fusobacterium nucleatum* in colorectal cancer leads to WNT signaling pathway inhibitory factors. DKK1 and VANGL2 The promoter openness of genes is downregulated, thereby promoting the continued openness of the WNT signaling pathway (B). Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] The RAW264.7 macrophage cell line (Shanghai Fuheng Biotechnology Co., Ltd.), Salmonella typhimurium (ATCC14028, Shanghai Xuanke Biotechnology Co., Ltd.), Tn5 transposase (nearshore protein, N248-01A), BALB / c mice (Beijing Vital River), and Klebsiella pneumoniae (Shanghai Xuanke Biotechnology Co., Ltd.) used in this invention.

[0031] The present invention provides a method for simultaneous detection of host chromatin accessibility and in vivo microbiome based on transposase (Transposase-Accessible Chromatin and Microbiome sequencing, TACM-seq), which is a high-throughput sequencing technology that combines chromatin accessibility analysis and microbial detection. By optimizing the transposase reaction system and sample processing flow, it achieves highly sensitive simultaneous detection of open regions of host cell chromatin and invading microbial genomes.

[0032] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1: TACM-seq analysis of Salmonella-infected macrophages Experimental materials: Cell line: RAW264.7 macrophage cell line; Microorganism: Salmonella Typhimurium; Transposase and high-throughput sequencing library construction kit, mild detergent, cell culture related reagents, DNA extraction reagents, RNA extraction reagents, etc.

[0033] RAW264.7 macrophages were co-cultured with Salmonella Typhimurium at different infection ratios (MOI 10:1 to 1:10000) as samples for subsequent analysis.

[0034] (1) Sample processing Co-culture system: RAW264.7 cells were cultured at a density of 5 × 10⁶ cells per well. 5 Cells were seeded in 6-well plates and cultured for 24 hours in DMEM medium containing 10% FBS. Salmonella was activated with LB broth, washed with PBS, and resuspended in DMEM (containing 10% FBS) and incubated on ice for 30 minutes. Subsequently, Salmonella suspension was added according to specified multiplicity of infection gradients (10:1, 1:1, 1:10, 1:100, 1:1000, 1:1000, 1:10000); the control group received an equal volume of sterile PBS. The co-culture systems were incubated at 37°C with 5% CO2 for 30 minutes.

[0035] After co-culturing, the cells were washed three times with pre-warmed PBS to thoroughly remove any bacteria that had not yet invaded the cells. The medium was then replaced with complete medium containing 100 μg / mL gentamicin, and the cells were cultured for another hour to completely remove any remaining extracellular bacteria. Afterward, the cells were washed three more times with PBS, and the medium was replaced with maintenance medium containing 25 μg / mL gentamicin, and the cells were cultured for another 4 hours.

[0036] (2) Sample collection and sequencing TACM-seq sample processing: Approximately 60,000 post-infected cells were collected. The cell pellet was resuspended in 50 μL of pre-chilled nuclear extraction buffer (containing 0.1% NP-40, 0.1% Tween-20, and 0.01% Digitonin) and incubated on ice for 3 minutes. Then, lysis was terminated by adding 1 mL of pre-chilled RSB buffer containing 0.1% Tween-20. The pellet was centrifuged at 500g for 5 minutes at 4°C, and the nuclear pellet was collected. The nuclear pellet was resuspended in 50 μL of transposable reaction system (containing 25 μL 2× TD buffer, 1 μL L n5 transposase (with sequencing adapter loaded), 16.5 μL PBS, 0.5 μL 1% Digitonin, 0.5 μL 10% Tween-20, and 6.5 μL H2O) and incubated at 37°C for 30 minutes. After the transposition reaction, the transposition product was purified using a DNA purification kit (ZymoResearch, DNA Clean & Concentrator-5) and eluted with nuclease-free water. An Illumina sequencing library was constructed using the Chromatin Profile kit (nearshore protein: N248-01A).

[0037] WGS Sample Processing: Total genomic DNA (containing host and bacterial genomes) was extracted from approximately 60,000 infected cells using the QIAamp DNA Micro Kit (Qiagen: 56304). Subsequently, library construction was performed according to the standard Illumina library construction protocol, including DNA fragmentation, end repair, A-tailing, adapter ligation, and PCR amplification.

[0038] RNA-seq sample processing: Approximately 600,000 infected cells were collected, lysed using TRIzol reagent, and total RNA was extracted using the Direct-zol RNA Microprep kit (Zymo Research: R2062). mRNA was enriched by poly(A) selection, and sequencing libraries were constructed using the dUTP library construction protocol.

[0039] (3) Sequencing All samples were sequenced using the Illumina NovaSeq 6000 platform with paired ends at 150 bp to ensure a data volume of approximately 10 Gb per sample.

[0040] (4) Data Analysis The raw data obtained from TACM-seq, WGS, and RNA-seq sequencing were filtered for quality control using FastP and then aligned to the mouse reference genome (mm39) using Bowtie2. The sequencing data were then divided into host-derived reads and non-host-derived reads.

[0041] Host chromatin accessibility analysis: For host-derived reads obtained from TACM-seq, mitochondrial-derived, low-quality, and PCR duplicate reads were removed. Chromatin accessibility peaks were identified using MACS2, and a sample-peak matrix was generated. Differential peak analysis was performed using DiffBind, with a significance threshold set at FDR < 0.05 and |log2(fold difference)| > 1.

[0042] Microbial identification and analysis: Samtools was used to extract reads from all sequencing methods that did not align to the host genome for microbial analysis. Kraken2 was used to analyze these reads for species classification and identification; its database is a reference database integrating mouse and Salmonella typhimurium reference genomes. To accurately calculate genome coverage, non-host reads were further aligned to the Salmonella typhimurium reference genome using Bowtie2, and reads consistent with the Kraken2 identification results were selected. Samtools was then used to calculate coverage. Furthermore, based on NCBI's genome annotation files, BEDTools was used to perform functional annotation (e.g., protein-coding genes, rRNA, etc.) on the genomic regions located by the microbial reads to analyze their functional distribution.

[0043] The number of Salmonella reads detected by TACM-seq, WGS, and RNA-seq at each MOI was counted, and relative abundance and genome coverage were calculated and plotted. By comparing the difference peaks between different MOIs and the control group in the TACM-seq results, the biological processes and signaling pathways of gene enrichment near the Salmonella-induced upregulation peaks were analyzed, and some immune-related genes with increased chromatin openness (such as...) were identified. Il33 , Crtam and Blk (See appendix for details) Figures 2-3 As shown.

[0044] From the appendix Figures 2-3 Data shows that TACM-seq detects 10-100 times more Salmonella reads than WGS and several thousand times more than RNA-seq, indicating that TACM-seq has a significant advantage in microbial detection sensitivity and can more effectively capture microbial information; in addition, TACM-seq also simultaneously reveals infection-related genes ( Il33 , Crtam and Blk The increased chromatin openness of microorganisms indicates that TACM-seq can not only detect microorganisms with high sensitivity, but also provide epigenetic regulatory information of host cells under infection conditions. This enables simultaneous analysis of microbial detection and host epigenetic response, providing key data for a deeper understanding of the host-microorganism interaction mechanism.

[0045] Example 2: Detection of synthetic microbial communities Sample: RAW264.7 macrophages mixed with synthetic communities of 21 bacteria / fungi (concentration 0.018-4,002 microbial cells / 60,000 host cells).

[0046] (1) Sample mixing and preparation A commercially available synthetic microbial community standard (ZymoBIOMICS® Gut Microbiome Standard) was mixed with RAW264.7 cells for experiments. This standard contained 21 bacterial, archaea, and fungal strains with logarithmic abundance, at a stock solution concentration of 3.94 × 10⁻⁶. 9 The stock solution was stored in DNA / RNA Shield solution at a concentration of 20,000 cells / μL before the experiment.

[0047] For TACM-seq and WGS sequencing, 60,000 RAW264.7 cells were mixed with 1 μL of diluted synthetic microbial community solution (an equal volume of ultrapure water was added to the control group). For RNA-seq analysis, 600,000 cells were mixed with 10 μL of diluted bacterial culture or control solution, then directly lysed with Trizol and stored at -80°C.

[0048] (2) Sequencing sample preparation TACM-seq sample processing: The cell pellet was resuspended in 50 μL of pre-chilled nuclear extraction buffer (containing 0.1% NP-40, 0.1% Tween-20, and 0.01% Digitonin) and incubated on ice for 3 minutes. Then, lysis was terminated by adding 1 mL of pre-chilled RSB buffer containing 0.1% Tween-20, and the pellet was collected by centrifugation at 500g for 5 minutes at 4°C. The nuclear pellet was resuspended in 50 μL of transposition reaction system (containing 25 μL 2× TD buffer, 1 μL Tn5 transposase (loaded with sequencing adapters), 16.5 μL PBS, 0.5 μL 1% Digitonin, 0.5 μL 10% Tween-20, and 6.5 μL H2O) and incubated at 37°C for 30 minutes. After the transposition reaction, the transposition products were purified using a DNA purification kit and eluted with nuclease-free water. Illumina sequencing libraries were constructed using the Chromatin Profile kit.

[0049] WGS Sample Processing: Total genomic DNA (containing host and bacterial genomes) was extracted from the mixed cells using the QIAamp DNA Micro kit. Subsequently, library construction was performed according to the standard Illumina library construction procedure, including DNA fragmentation, end repair, A-tailing, adapter ligation, and PCR amplification.

[0050] RNA-seq sample processing: Total RNA was extracted from TRIzol-lysed cells using the Direct-zol RNA Microprep kit. mRNA was enriched by poly(A) selection, and sequencing libraries were constructed using the dUTP library preparation protocol.

[0051] (3) Sequencing All samples were sequenced using the Illumina NovaSeq 6000 platform with paired ends at 150 bp to ensure a data volume of approximately 10 Gb per sample.

[0052] (4) Data Analysis Raw data from TACM-seq, WGS, and RNA-seq sequencing were filtered for quality control using FASTP and then aligned to the mouse reference genome (mm39) using Bowtie2. Reads from all sequencing methods that did not align to the host genome were extracted using Samtools for microbial analysis. Species identification of non-host sequencing reads was performed using Kraken2. To this end, we constructed a customized reference database: this database integrates all complete RefSeq genomes of archaea, bacteria, and viruses, as well as fungal genomes and the UniVec_Core library, and adds the mouse reference genome (mm39) to exclude host sequences. To accurately calculate genome coverage, non-host reads were further aligned to specific microbial reference genomes using Bowtie2, and reads consistent with the Kraken2 identification results were selected, with coverage calculated using Samtools.

[0053] Comparison of Detection Performance: **Number of Microbial Reads Captured:** The number of reads belonging to the 21 added microorganisms detected by the three sequencing methods was counted, and the method capturing more microbial reads was compared. **Genome Coverage:** The coverage of the 21 microbial genomes by the three sequencing methods was calculated, i.e., the proportion of the genome region covered by the sequencing data to the total genome, and the method with higher genome coverage was compared. **Signal-to-Noise Ratio:** The ratio of real microbial signal to background noise in the three sequencing methods was analyzed to evaluate the superiority of the signal-to-noise ratio. This can be evaluated by counting the ratio of reads matching known microbial sequences to non-matching reads. Results are shown in the appendix. Figure 4 As shown.

[0054] From the appendix Figure 4 Data shows that the TACM-seq method of this invention captures more microbial reads compared to WGS and RNA-seq, achieving higher genome coverage and a superior signal-to-noise ratio. This result is attributed to the specific cleavage of open chromatin regions by transposases, which label only open regions comprising approximately 0.5% to 2% of the host genome. However, for microorganisms lacking nucleosome structures, their entire genome DNA is in an open state, thus being efficiently and almost randomly cleaved and labeled by transposases. The method of this invention, based on TACM-seq for microbial detection, offers advantages such as high sensitivity in effectively capturing low-abundance microorganisms, strong specificity in suppressing host background interference, and high accuracy with highly consistent quantitative results compared to theoretical values.

[0055] Example 3: Detection of Klebsiella pneumoniae in liver tissue of a mouse model of Klebsiella pneumoniae infection Sample: Klebsiella pneumoniae mouse gavage model (BALB / c mice, Klebsiella pneumoniae) Klebsiella pneumoniae Frozen Klebsiella pneumoniae was inoculated into LB liquid medium and cultured at 37°C with shaking until the logarithmic growth phase (OD200). 600 (≈0.8), after centrifugation to collect bacterial cells, resuspend in PBS and adjust the bacterial concentration to 10. 8 CFU / 100 µL was prepared for use. Mice were first pretreated with ampicillin (0.5 g / L, via drinking water) for 3 days, followed by oral gavage inoculation with 10... 8 CFU of Klebsiella pneumoniae (experimental group) or an equal volume of PBS (control group). Forty-eight hours after infection, mouse liver tissue was collected. A portion of the tissue was homogenized and spread onto LB agar plates for colony counting (CFU) to verify successful infection. Simultaneously, another portion of the tissue was used for nuclear extraction and Tn5 transposition reaction (37°C, 30 min) as described in this invention, followed by construction of a TACM-seq library and sequencing. Sequencing reads were aligned to the mouse genome, and unaligned reads were then aligned to a microbial database using Kraken2 to identify and quantify Klebsiella pneumoniae. Specific results are detailed in the appendix. Figure 5 As shown.

[0056] From the appendix Figure 5Data showed that with increasing infection doses (173–5000 CFU / mg), both RPM and genome coverage detected by TACM-seq significantly increased (p < 0.05), demonstrating that sequencing results accurately reflect the number of viable bacteria. The control group (Ctrl) had near-zero RPM and coverage, eliminating false positives. The increased genome coverage (coverage > 35% in the 5000 CFU group) indicates that TACM-seq can comprehensively capture microbial genetic information, rather than just local fragments. This experiment cross-validated CFU counting with TACM-seq sequencing, demonstrating the latter's sensitivity, specificity, and quantitative accuracy in detecting microorganisms in complex tissues. TACM-seq provides a high-precision tool for studying host-pathogen interactions, and is irreplaceable, especially in low biomass or high host interference scenarios.

[0057] Example 4: Retrospective analysis of TCGA tumor data.

[0058] Sample: ATAC-seq data from 410 cases of 23 cancer types collected from the TCGA database. The non-host reads (i.e., potential microbial DNA) in these data were reanalyzed using the method of this invention to explore the association between the tumor microbiome and host epigenetic changes.

[0059] Analysis steps: 1. Data preprocessing and microbial read extraction The BAM file aligned to the human reference genome (hg38) was downloaded from TCGA, and unaligned reads were extracted using Samtools as potential microbial sequences. To reduce interference from residual host sequences, after quality control using FASTP, the sequences were rigorously aligned to the hg38 genome again using Bowtie2, retaining only the reads that were not aligned in both attempts for subsequent microbial analysis.

[0060] 2. Identification and Quantification of Microbial Composition Kraken2 was used to classify non-host reads into species. Its database integrates complete RefSeq genomes of archaea, bacteria, viruses, and fungi, as well as the UniVec_Core library, and includes human genomes to exclude interference from host homologous sequences. Microbial relative abundance was normalized to RPM values, and microorganisms with total reads exceeding 1000 in each cancer type were selected for further analysis.

[0061] 3. Association analysis between microbial abundance grouping and chromatin accessibility For each target microorganism, samples were divided into three groups based on RPM values: high, medium, and low abundance. The high abundance group consisted of samples with an RPM z-score > 1. The low abundance group was determined using an adaptive strategy (initial threshold RPM < 0.2; if the number of samples was less than 5, the group was divided based on the highest RPM change rate among adjacent samples; if still insufficient, non-high abundance samples were included). The remaining samples were assigned to the medium abundance group. MACS2 was used to retrieve chromatin open peaks, and DiffBind was used to compare the regions of difference in openness between the high and low abundance groups (FDR < 0.05).

[0062] 4. Multi-omics integration and functional mechanism analysis In the colorectal cancer (COAD) cohort, matched RNA-seq data were further integrated, and after excluding confounding factors such as sex and age (FDR < 0.01, |log2FC| > 1), differentially expressed peaks related to Clostridium nucleatum were screened. Peaks were correlated to genes within ±50 kb using BEDTools, retaining peak-gene pairs where epigenetic and transcriptomic changes were consistent and significant (p < 0.05). GO / KEGG enrichment analysis was performed using Metascape, and transcription factor binding motifs in differentially expressed regions were identified using HOMER.

[0063] See appendix Figure 6 This invention successfully identified the tumor microbiome of 23 types of cancer from TCGA public data. Figure 6 (A), and revealed a specific association between microbial abundance and host chromatin openness ( Figure 6 B). For example, in colorectal cancer, samples with high abundance of Clostridium nucleatum show Wnt pathway repressor genes (such as...). DKK1 , VANGL2 The significantly reduced openness of the promoter region suggests that this microorganism may promote the abnormal activation of key tumor signaling pathways through epigenetic silencing mechanisms.

[0064] This experiment re-mined publicly available TCGA data using the TACM-seq pipeline, revealing for the first time the association between microbial abundance and host chromatin accessibility across multiple cancer types, particularly in colorectal cancer. Fusobacterium nucleatum This method may promote tumor progression by epigenetically silencing Wnt repressor genes. It provides an efficient and low-cost strategy for tumor microbiome research, driving innovation in the field of epigenetic-microbe interactions.

[0065] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for simultaneous detection of host chromatin openness and in vivo microbiome based on transposase, characterized in that, The method comprises the following steps: Step 1: performing a cell nucleus extraction treatment on a mixed sample containing host cells and microorganisms to obtain a treated mixed sample; Step 2: co-incubating the treated mixed sample with a transposase mixture to selectively insert and fragment the open chromatin regions of the host to obtain DNA fragments treated by the transposase; Step 3: constructing a high-throughput sequencing library for the DNA fragments treated by the transposase, performing double-end sequencing, and obtaining mixed sequencing data of the host and the microorganisms; Step 4: based on the mixed sequencing data, aligning the mixed sequencing data with a host reference genome, and dividing the host reference genome into host-derived reads and non-host-derived reads; performing chromatin openness analysis on the host-derived reads; aligning the non-host-derived reads with a microorganism genome database to identify the species of the microorganisms and quantify the relative abundance of the microorganisms; correlating the abundance information of the microorganisms with the chromatin openness changes of the host to identify differential open regions and correlate host gene functions, and obtaining the regulation mechanism of the microorganisms on the epigenetics of the host.

2. The method according to claim 1, wherein, The transposase is Tn5 transposase, and the transposase reaction temperature is 36-38 DEG C for 20-40 min.

3. The method according to claim 1, wherein the method is characterized by, The cell nucleus extraction treatment comprises: lysing the cell membrane of the host cells by using a mild detergent or homogenizing the tissue sample by using a mechanical homogenizer, and avoiding density gradient centrifugation.

4. The method according to claim 1, wherein, In step 4, a sequence alignment tool is used to align the sequencing reads with the host reference genome; based on the reads aligned to the host genome, a peak identification algorithm is used to identify the chromatin openness regions, and a statistical analysis method is used for differential openness analysis; a species classification algorithm is used to identify the microorganisms for the reads not aligned to the host genome.

5. A DNA sequencing library for synchronous detection, characterized in that, The library is constructed by the method of any one of claims 1-4, and comprises: DNA fragments derived from the open chromatin regions of the host; DNA fragments derived from the whole genomes of the microorganisms; sequencing adapter sequences supporting double-end sequencing of a high-throughput sequencing platform.

6. A system for implementing the method of simultaneously detecting openness of host chromatin and in vivo microbiome based on transposase according to any one of claims 1-4, characterized in that, It comprises: a sample processing module: integrating cell lysis, homogenization and transposase reaction units; a sequencing module: used for high-throughput sequencing of the open chromatin DNA and the microorganism DNA of the mixed sample; a data processing module: comprising a data splitting unit for separating host and microorganism sequencing reads; a microbiome analysis unit for performing species classification and abundance quantification; a host analysis unit for detecting chromatin openness region differences, correlating gene functions and transcription factor motifs; an association integration module: based on the microorganism abundance and the host epigenetic changes, a host-microorganism interaction network model is constructed.

7. The system of claim 6, wherein, The data analysis module preinstalls a standardized process to support raw data quality control, host contamination filtering, microorganism identification and quantification, and microorganism function annotation.

8. The system of claim 6, wherein, The association integration module contains a statistical modeling tool for correlation analysis or regulation mechanism analysis.

9. The system of claim 6, wherein, The association integration module contains a visualization interface to output a microorganism species and abundance column chart, a host gene promoter chromatin openness change and a corresponding microorganism abundance heat map.

10. The use of the method for simultaneous detection of the openness of host chromatin and the composition of microorganisms in vivo according to any one of claims 1-4, or the DNA sequencing library for simultaneous detection according to claim 5, or the system according to any one of claims 6-9, characterized in that, It comprises: infectious disease research: analyzing the regulation mechanism of pathogens on the chromatin accessibility of the host; and / or gut microbiome studies: revealing the identity and abundance of symbiotic communities and their influence on host gene expression and epigenetics; and / or cancer microenvironment analysis: exploring the association of tumor-associated microbes with host epigenetic responses; and / or public database reanalysis: mining chromatin openness sequencing data in the Cancer Genome Atlas database for potential tumor-associated microbial signals.

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