ChIP-seq in studying epigenetic mechanisms of disease and promoting precision medicine: progresses and future directions

Embryonic Zebrafish as a Model for Investigating the Interaction between Environmental Pollutants and Neurodegenerative Disorders

Environmental pollutants have been linked to neurotoxicity and are proposed to contribute to neurodegenerative disorders. The zebrafish model provides a high-throughput platform for large-scale chemical screening and toxicity assessment and is widely accepted as an important animal model for the investigation of neurodegenerative disorders. Although recent studies explore the roles of environmental pollutants in neurodegenerative disorders in zebrafish models, current knowledge of the mechanisms of environmentally induced neurodegenerative disorders is relatively complex and overlapping. This review primarily discusses utilizing embryonic zebrafish as the model to investigate environmental pollutants-related neurodegenerative disease. We also review current applicable approaches and important biomarkers to unravel the underlying mechanism of environmentally related neurodegenerative disorders. We found embryonic zebrafish to be a powerful tool that provides a platform for evaluating neurotoxicity triggered by environmentally relevant concentrations of neurotoxic compounds. Additionally, using variable approaches to assess neurotoxicity in the embryonic zebrafish allows researchers to have insights into the complex interaction between environmental pollutants and neurodegenerative disorders and, ultimately, an understanding of the underlying mechanisms related to environmental toxicants.

GMean-a semi-supervised GRU and K-mean model for predicting the TF binding site

The transcription factor binding site is a deoxyribonucleic acid sequence that binds to transcription factors. Transcription factors are proteins that regulate the transcription gene. Abnormal turnover of transcription factors can lead to uncontrolled cell growth. Therefore, discovering the relationships between transcription factors and deoxyribonucleic acid sequences is an important component of bioinformatics research. Numerous deep learning and machine learning language models have been developed to accomplish these tasks. Our goal in this work is to propose a GMean model for predicting unlabelled deoxyribonucleic acid sequences. The GMean model is a hybrid model with a combination of gated recurrent unit and K-mean clustering. The GMean model is developed in three phases. The labelled and unlabelled data are processed based on k-mers and tokenization. The labelled data is used for training. The unlabelled data are used for testing and prediction. The experimental data consists of deoxyribonucleic acid experimental of GM12878, K562 and HepG2. The experimental results show that GMean is feasible and effective in predicting deoxyribonucleic acid sequences, as the highest accuracy is 91.85% in predicting K562 and HepG2. This is followed by the prediction of the sequence between GM12878 and K562 with an accuracy of 89.13%. The lowest accuracy is the prediction of the sequence between HepG2 and GM12828, which is 88.80%.

A comprehensive review on anticancer evaluation techniques

The development of effective anticancer strategies and the improvement of our understanding of cancer need analytical tools. Utilizing a variety of analytical approaches while investigating anti-cancer medicines gives us a thorough understanding of the traits and mechanisms concerned to cancer cells, which enables us to develop potent treatments to combat them. The importance of anticancer research may be attributed to various analytical techniques that contributes to the identification of therapeutic targets and the assessment of medication efficacy, which are crucial things in expanding our understanding of cancer biology. The study looks at methods that are often used in cancer research, including cell viability assays, clonogenic assay, flow cytometry, 2D electrophoresis, microarray, immunofluorescence, western blot caspase activation assay, bioinformatics, etc. The fundamentals, applications, and how each technique analytical advances our understanding of cancer are briefly reviewed.

Applications of bioinformatics in epigenetics

Epigenetic modifications such as DNA methylation, post-translational chromatin modifications and non-coding RNA-mediated mechanisms are responsible for epigenetic inheritance. Change in gene expression due to these epigenetic modifications are responsible for new traits in different organisms leading to various diseases including cancer, diabetic kidney disease (DKD), diabetic nephropathy (DN) and renal fibrosis. Bioinformatics is an effective approach for epigenomic profiling. These epigenomic data can be analyzed by a large number of bioinformatics tools and software. Many databases are available online, which comprises huge amount of information regarding these modifications. Recent methodologies include many sequencing and analytical techniques to extrapolate different types of epigenetic data. This data can be used to design drugs against diseases linked to epigenetic modifications. This chapter briefly highlights different epigenetics databases (MethDB, REBASE, Pubmeth, MethPrimerDB, Histone Database, ChromDB, MeInfoText database, EpimiR, Methylome DB, and dbHiMo), and tools (compEpiTools, CpGProD, MethBlAST, EpiExplorer, and BiQ analyzer), which are being utilized to retrieve the data and mechanistically analysis of epigenetics modifications.

Single base-pair resolution analysis of DNA binding motif with MoMotif reveals an oncogenic function of CTCF zinc-finger 1 mutation

Defining the impact of missense mutations on the recognition of DNA motifs is highly dependent on bioinformatic tools that define DNA binding elements. However, classical motif analysis tools remain limited in their capacity to identify subtle changes in complex binding motifs between distinct conditions. To overcome this limitation, we developed a new tool, MoMotif, that facilitates a sensitive identification, at the single base-pair resolution, of complex, or subtle, alterations to core binding motifs, discerned from ChIP-seq data. We employed MoMotif to define the previously uncharacterized recognition motif of CTCF zinc-finger 1 (ZF1), and to further define the impact of CTCF ZF1 mutation on its association with chromatin. Mutations of CTCF ZF1 are exclusive to breast cancer and are associated with metastasis and therapeutic resistance, but the underlying mechanisms are unclear. Using MoMotif, we identified an extension of the CTCF core binding motif, necessitating a functional ZF1 to bind appropriately. Using a combination of ChIP-Seq and RNA-Seq, we discover that the inability to bind this extended motif drives an altered transcriptional program associated with the oncogenic phenotypes observed clinically. Our study demonstrates that MoMotif is a powerful new tool for comparative ChIP-seq analysis and characterising DNA-protein contacts.

Bibliometric review of ATAC-Seq and its application in gene expression

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Analysis of H3K4me3-ChIP-Seq and RNA-Seq data to understand the putative role of miRNAs and their target genes in breast cancer cell lines

Breast cancer is one of the leading causes of cancer in women all over the world and accounts for ~25% of newly observed cancers in women. Epigenetic modifications influence differential expression of genes through non-coding RNA and play a crucial role in cancer regulation. In the present study, epigenetic regulation of gene expression by in-silico analysis of histone modifications using chromatin immunoprecipitation sequencing (ChIP-Seq) has been carried out. Histone modification data of H3K4me3 from one normal-like and four breast cancer cell lines were used to predict miRNA expression at the promoter level. Predicted miRNA promoters (based on ChIP-Seq) were used as a probe to identify gene targets. Five triple-negative breast cancer (TNBC)‒specific miRNAs (miR153-1, miR4767, miR4487, miR6720, and miR-LET7I) were identified and corresponding 13 gene targets were predicted. Eight miRNA promoter peaks were predicted to be differentially expressed in at least three breast cancer cell lines (miR4512, miR6791, miR330, miR3180-3, miR6080, miR5787, miR6733, and miR3613). A total of 44 gene targets were identified based on the 3′-untranslated regions of downregulated mRNA genes that contain putative binding targets to these eight miRNAs. These include 17 and 15 genes in luminal-A type and TNBC respectively, that have been reported to be associated with breast cancer regulation. Of the remaining 12 genes, seven (A4GALT, C2ORF74, HRCT1, ZC4H2, ZNF512, ZNF655, and ZNF608) show similar relative expression profiles in large patient samples and other breast cancer cell lines thereby giving insight into predicted role of H3K4me3 mediated gene regulation via the miRNA-mRNA axis.

The Key Characteristics of Carcinogens: Relationship to the Hallmarks of Cancer, Relevant Biomarkers, and Assays to Measure Them

The key characteristics (KC) of human carcinogens provide a uniform approach to evaluating mechanistic evidence in cancer hazard identification. Refinements to the approach were requested by organizations and individuals applying the KCs. We assembled an expert committee with knowledge of carcinogenesis and experience in applying the KCs in cancer hazard identification. We leveraged this expertise and examined the literature to more clearly describe each KC, identify current and emerging assays and in vivo biomarkers that can be used to measure them, and make recommendations for future assay development. We found that the KCs are clearly distinct from the Hallmarks of Cancer, that interrelationships among the KCs can be leveraged to strengthen the KC approach (and an understanding of environmental carcinogenesis), and that the KC approach is applicable to the systematic evaluation of a broad range of potential cancer hazards in vivo and in vitro We identified gaps in coverage of the KCs by current assays. Future efforts should expand the breadth, specificity, and sensitivity of validated assays and biomarkers that can measure the 10 KCs. Refinement of the KC approach will enhance and accelerate carcinogen identification, a first step in cancer prevention.See all articles in this CEBP Focus section, "Environmental Carcinogenesis: Pathways to Prevention."

A parallelized, automated platform enabling individual or sequential ChIP of histone marks and transcription factors

Despite its popularity, chromatin immunoprecipitation followed by sequencing (ChIP-seq) remains a tedious (>2 d), manually intensive, low-sensitivity and low-throughput approach. Here, we combine principles of microengineering, surface chemistry, and molecular biology to address the major limitations of standard ChIP-seq. The resulting technology, FloChIP, automates and miniaturizes ChIP in a beadless fashion while facilitating the downstream library preparation process through on-chip chromatin tagmentation. FloChIP is fast (<2 h), has a wide dynamic range (from 10⁶ to 500 cells), is scalable and parallelized, and supports antibody- or sample-multiplexed ChIP on both histone marks and transcription factors. In addition, FloChIP's interconnected design allows for straightforward chromatin reimmunoprecipitation, which allows this technology to also act as a microfluidic sequential ChIP-seq system. Finally, we ran FloChIP for the transcription factor MEF2A in 32 distinct human lymphoblastoid cell lines, providing insights into the main factors driving collaborative DNA binding of MEF2A and into its role in B cell-specific gene regulation. Together, our results validate FloChIP as a flexible and reproducible automated solution for individual or sequential ChIP-seq.

Advances in Diagnostic Procedures and Their Applications in the Era of Cancer Immunotherapy

Diagnostic procedures play critical roles in cancer immunotherapy. In this chapter, we briefly discuss three major diagnostic procedures widely used in immunotherapy: immunohistochemistry, next-generation sequencing, and flow cytometry. We also describe the uses of other diagnostic procedures and preclinical animal models in cancer immunotherapy translational research.

Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine

To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core discipline of precision medicine, and currently, the clinical application of cutting-edge genomic medicine aimed at improving the prevention, diagnosis and treatment of a wide range of diseases is promoted. However, although the Human Genome Project was completed in 2003 and large-scale genetic analyses have since been accomplished worldwide with the development of next-generation sequencing (NGS), explaining the mechanism of disease onset only using genetic variation has been recognized as difficult. Meanwhile, the importance of epigenetics, which describes inheritance by mechanisms other than the genomic DNA sequence, has recently attracted attention, and, in particular, many studies have reported the involvement of epigenetic deregulation in human cancer. So far, given that genetic and epigenetic studies tend to be accomplished independently, physiological relationships between genetics and epigenetics in diseases remain almost unknown. Since this situation may be a disadvantage to developing precision medicine, the integrated understanding of genetic variation and epigenetic deregulation appears to be now critical. Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. In this regard, it is important to develop a platform that can conduct multimodal analysis of medical big data using AI as this may accelerate the realization of precision medicine. In this review, we discuss the importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine.

Genomics of benign adrenocortical tumors

Benign adrenocortical adenomas and hyperplasia are relatively common and include a spectrum of distinct entities, which diagnosis depends on the macroscopic aspect and the secretion profile. Recent advances in genomics have proposed high-throughput molecular characterization of adrenal tumors, thereby improving our knowledge on the pathophysiology and tumorigenesis of these tumors. Genomic (exome and chromosome alteration profiles), epigenomic (micro-RNAs expression and methylation profiles) and transcriptomic (gene expression profiles) studies highlighted the major roles of intracellular calcium signaling in aldosterone-producing adenomas (APA), of protein kinase A (PKA)/cAMP pathway in cortisol-producing tumors, and of Wnt/beta-catenin pathway in non-secreting tumors. Exome sequencing revealed new major drivers in all tumor types, including KCNJ5, ATP1A1, ATP2B3, CACNA1D and CACNA1H mutations in APA, PRKACA mutations in cortisol-producing adenomas (CPA) and ARMC5 mutations in primary macronodular adrenocortical hyperplasia (PMAH). The clinical impact of these findings is just starting to evolve. The identification of genetic syndromes, such as germline ARMC5 mutations in PMAH, has allowed genetic counseling. Key molecular alterations could serve as a basis for the development of targeted medical treatments for benign adrenal tumors. The recent developments in genomics, including single-cell technologies, and in proteomics and metabolomics will probably offer new perspectives for characterizing benign adrenal tumorigenesis.

Identification of factors associated with duplicate rate in ChIP-seq data

Chromatin immunoprecipitation and sequencing (ChIP-seq) has been widely used to map DNA-binding proteins, histone proteins and their modifications. ChIP-seq data contains redundant reads termed duplicates, referring to those mapping to the same genomic location and strand. There are two main sources of duplicates: polymerase chain reaction (PCR) duplicates and natural duplicates. Unlike natural duplicates that represent true signals from sequencing of independent DNA templates, PCR duplicates are artifacts originating from sequencing of identical copies amplified from the same DNA template. In analysis, duplicates are removed from peak calling and signal quantification. Nevertheless, a significant portion of the duplicates is believed to represent true signals. Obviously, removing all duplicates will underestimate the signal level in peaks and impact the identification of signal changes across samples. Therefore, an in-depth evaluation of the impact from duplicate removal is needed. Using eight public ChIP-seq datasets from three narrow-peak and two broad-peak marks, we tried to understand the distribution of duplicates in the genome, the extent by which duplicate removal impacts peak calling and signal estimation, and the factors associated with duplicate level in peaks. The three PCR-free histone H3 lysine 4 trimethylation (H3K4me3) ChIP-seq data had about 40% duplicates and 97% of them were within peaks. For the other datasets generated with PCR amplification of ChIP DNA, as expected, the narrow-peak marks have a much higher proportion of duplicates than the broad-peak marks. We found that duplicates are enriched in peaks and largely represent true signals, more conspicuous in those with high confidence. Furthermore, duplicate level in peaks is strongly correlated with the target enrichment level estimated using nonredundant reads, which provides the basis to properly allocate duplicates between noise and signal. Our analysis supports the feasibility of retaining the portion of signal duplicates into downstream analysis, thus alleviating the limitation of complete deduplication.

Enhanced and controlled chromatin extraction from FFPE tissues and the application to ChIP-seq

Background

Epigenetic dysregulation is involved in the etiology and progression of various human diseases. Formalin-fixed paraffin-embedded (FFPE) samples represent the gold standard for archiving pathology samples, and thus FFPE samples are a major resource of samples in clinical research. However, chromatin-based epigenetic assays in the clinical settings are limited to fresh or frozen samples, and are hampered by low chromatin yield in FFPE samples due to the lack of a reliable and efficient chromatin preparation method. Here, we introduce a new chromatin extraction method from FFPE tissues (Chrom-EX PE) for chromatin-based epigenetic assays.

Results

During rehydration of FFPE tissues, applying a tissue-level cross-link reversal into the deparaffinized tissue at 65 °C dramatically increased chromatin yield in the soluble fraction. The resulting chromatin is compatible with targeted ChIP-qPCR and genome-wide ChIP-seq approaches. The chromatin prepared by Chrom-EX PE showed a gradual fragmentation pattern with varying incubation temperature. At temperatures below 37 °C, the majority of soluble chromatin is over 1 kb. The soluble chromatin prepared in the range of 45–60 °C showed a typical nucleosomal pattern. And the majority of chromatin prepared at 65 °C is close to mononucleosomal size. These observations indicate that chromatin preparation from FFPE samples can be controlled for downstream chromatin-based epigenetic assays.

Conclusions

This study provided a new method that achieves efficient extraction of high-quality chromatin suitable for chromatin-based epigenetic assays with less damage on chromatin. This approach may provide a way to circumvent the over-fixed nature of FFPE tissues for future technology development.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5639-8) contains supplementary material, which is available to authorized users.

Identification of Transcription Factor-Binding Sites in the Mouse FOXO1 Promoter

One critical determinant of levels of gene expression is binding of transcription factors to cognate DNA sequences in promoter and enhancer regions of target genes. Transcription factors are DNA-binding proteins to which transcriptional co-regulators are bound, ultimately resulting in histone modifications that change chromatin structure to regulate transcription. Examples of transcription factors include hormone-activated transcription factors such as the glucocorticoid receptor, transcription factors regulated by cell surface receptors such as FOXO1 and Smad2/Smad3, and many others. Promoter regions typically contain multiple, diverse transcription factor-binding sites. Binding sites for cell-type-specific transcription factors involved in cell fate determination such as Runx2, MyoD, or myogenin are frequently observed. Promoter regions are located within ~2 kb upstream of the transcriptional start site, whereas enhancers may be located at some distance from promoter sequences and exert long-range effects. Here, we will discuss classical and emerging technologies by which one can understand the role of binding of specific transcription factors in regulation of transcription of FOXO genes.