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BackgroundThe active metabolite of benzo[a]pyrene (BaP), 7,8-dihydroxy-9,10-epoxybenzo[a]pyrene (BPDE), can form adducts with DNA, but the spectrum of BPDE-DNA adducts is unclear. ObjectiveTo identify the distribution of BPDE adduct sites and associated genes at the whole-genome level by chromatin immunoprecipitation followed by sequencing (ChIP-Seq), and serve as a basis for further exploring the toxicological mechanisms of BaP. MethodsHuman bronchial epithelial-like cells (16HBE) were cultured to the fourth generation inthe logarithmic growth phase. Cells were harvested and added to chromatin immunoprecipitation lysis buffer. The lysate was divided into experimental and control groups. The experimental group received a final concentration of 20 μmol·L−1 BPDE solution, while the control group received an equivalent volume of dimethyl sulfoxide solution. The cells were then incubated at 37 °C for 24 h. Chromatin fragments of 100-500 bp were obtained through sonication. BPDE-specific antibody (anti-BPDE 8E11) was used to enrich DNA fragments with BPDE adducts. High-throughput sequencing was conducted to detect BPDE adduct sites. The top 1000 peak sequences were subjected to motif analysis using MEME and DREME software. BPDE adduct target genes at the whole-genome level were annotated, and Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of BPDE adduct target genes were conducted using bioinformatics techniques. ResultsThe high-throughput sequencing detected a total of 842 BPDE binding sites, distributed across various chromosomes. BPDE covalently bound to both coding and non-coding regions of genes, with 73.9% binding sites located in intergenic regions, 19.6% in intronic regions, and smaller proportions in upstream 2 kilobase, exonic, downstream 2 kilobase, and 5' untranslated regions. Regarding the top 1000 peak sequences, four reliable motifs were identified, revealing that sites rich in adenine (A) and guanine (G) were prone to binding. Through the enrichment analysis of binding sites, a total of 199 BPDE-adduct target genes were identified, with the majority located on chromosomes 1, 5, 7, 12, 17, and X. The GO analysis indicated that these target genes were mainly enriched in nucleic acid and protein binding, participating in the regulation of catalytic activity, transport activity, translation elongation factor activity, and playing important roles in cell division, differentiation, motility, substance transport, and information transfer. The KEGG analysis revealed that these target genes were primarily enriched in pathways related to cardiovascular diseases, cancer, and immune-inflammatory responses. ConclusionUsing ChIP-Seq, 199 BPDE adduct target genes at genome-wide level are identified, impacting biological functions such as cell division, differentiation, motility, substance transport, and information transfer. These genes are closely associated with cardiovascular diseases, tumors, and immune-inflammatory responses.

A universal method for rapid identifying super-enhancers which are large domains of multiple closely-spaced enhancers is proposed. The method applies configurable cloud virtual machines (cVMs) and the rank-ordering of super-enhancers (ROSE) algorithm. To identify super-enhancers a сVM-based analysis of the ChIP-seq binding patterns of the active enhancer-associated mark is employed. The use of the proposed method is described step-by-step: configuration of cVM; ChIP-seq data alignment; peak calling; ROSE algorithm; interpretation of the results on a client machine. The method was validated for the search of super-enhancers using the H3K27ac mark in the sample datasets of a cell line (human MCF-7), mouse tissue (heart), and human tissue (adrenal gland). The total analysis cycle time of raw ChIP-seq data ranges from 15 to 48 min, depending on the number of initial short reads. Depending on the data processing step and availability of multi-threading, a cVM can be scaled up to a multi-CPU configuration with large amount of RAM. An important feature of the method is that it can run on a client machine that has low-performance with virtually any OS. The proposed method allows for simultaneous and independent processing of different sample datasets on multiple clones of a single cVM. • Cloud VMs were used for rapid processing of ChIP-seq data to identify super-enhancers. • The method can use a low-performance computer with virtually any OS on it. • It can be scaled up for parallel processing of individual sample datasets on their own VMs for rapid high-throughput processing.

Cancer cells within a tumor exhibit phenotypic plasticity that allows adaptation and survival in hostile tumor microenvironments. Reprogramming of epigenetic landscapes can support tumor progression within a specific microenvironment by influencing chromatin accessibility and modulating cell identity. The profiling of epigenetic landscapes within various tumor cell populations has significantly improved our understanding of tumor progression and plasticity. This protocol describes an integrated approach using chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) optimized to profile genome-wide post-translational modifications of histone tails in tumors. Essential tools amenable to ChIP-seq to isolate tumor cell populations of interest from the tumor microenvironment are also presented to provide a comprehensive approach to perform heterogeneous epigenetic landscape profiling of the tumor microenvironment., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

A universal method for rapid identifying super-enhancers which are large domains of multiple closely-spaced enhancers is proposed. The method applies configurable cloud virtual machines (cVMs) and the rank-ordering of super-enhancers (ROSE) algorithm. To identify super-enhancers a сVM-based analysis of the ChIP-seq binding patterns of the active enhancer-associated mark is employed. The use of the proposed method is described step-by-step: configuration of cVM; ChIP-seq data alignment; peak calling; ROSE algorithm; interpretation of the results on a client machine. The method was validated for the search of super-enhancers using the H3K27ac mark in the sample datasets of a cell line (human MCF-7), mouse tissue (heart), and human tissue (adrenal gland). The total analysis cycle time of raw ChIP-seq data ranges from 15 to 48 min, depending on the number of initial short reads. Depending on the data processing step and availability of multi-threading, a cVM can be scaled up to a multi-CPU configuration with large amount of RAM. An important feature of the method is that it can run on a client machine that has low-performance with virtually any OS. The proposed method allows for simultaneous and independent processing of different sample datasets on multiple clones of a single cVM.•Cloud VMs were used for rapid processing of ChIP-seq data to identify super-enhancers.•The method can use a low-performance computer with virtually any OS on it.•It can be scaled up for parallel processing of individual sample datasets on their own VMs for rapid high-throughput processing., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Authors. Published by Elsevier B.V.)