Estimating the effects of transcription factors binding and histone modifications on gene expression levels in human cells

The risk model construction of the genes regulated by H3K36me3 and H3K79me2 in breast cancer

Abnormal histone modifications (HMs) can promote the occurrence of breast cancer. To elucidate the relationship between HMs and gene expression, we analyzed HM binding patterns and calculated their signal changes between breast tumor cells and normal cells. On this basis, the influences of HM signal changes on the expression changes of breast cancer-related genes were estimated by three different methods. The results showed that H3K79me2 and H3K36me3 may contribute more to gene expression changes. Subsequently, 2109 genes with differential H3K79me2 or H3K36me3 levels during cancerogenesis were identified by the Shannon entropy and submitted to perform functional enrichment analyses. Enrichment analyses displayed that these genes were involved in pathways in cancer, human papillomavirus infection, and viral carcinogenesis. Univariate Cox, LASSO, and multivariate Cox regression analyses were then adopted, and nine potential breast cancer-related driver genes were extracted from the genes with differential H3K79me2/H3K36me3 levels in the TCGA cohort. To facilitate the application, the expression levels of nine driver genes were transformed into a risk score model, and its robustness was tested via time-dependent receiver operating characteristic curves in the TCGA dataset and an independent GEO dataset. At last, the distribution levels of H3K79me2 and H3K36me3 in the nine driver genes were reanalyzed in the two cell lines and the regions with significant signal changes were located.

Recognition of driver genes with potential prognostic implications in lung adenocarcinoma based on H3K79me2

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The efficacy of H3K79me2 on gene expression regulation is affirmed in LUAD.

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An open-source algorithm for identifying LUAD-related driver genes is presented.

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12 H3K79me2-targeted driver genes with clinical values are verified by qPCR.

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The regions with obvious H3K79me2 signals changes on driver genes are pinpointed.

Lung adenocarcinoma is a malignancy with a low overall survival and a poor prognosis. Studies have shown that lung adenocarcinoma progression relates to locus-specific/global changes in histone modifications. To explore the relationship between histone modification and gene expression changes, we focused on 11 histone modifications and quantitatively analyzed their influences on gene expression. We found that, among the studied histone modifications, H3K79me2 displayed the greatest impact on gene expression regulation. Based on the Shannon entropy, 867 genes with differential H3K79me2 levels during tumorigenesis were identified. Enrichment analyses showed that these genes were involved in 16 common cancer pathways and 11 tumors and were target-regulated by trans-regulatory elements, such as Tp53 and WT1. Then, an open-source computational framework was presented (https://github.com/zlq-imu/Identification-of-potential-LUND-driver-genes). Twelve potential driver genes were extracted from the genes with differential H3K79me2 levels during tumorigenesis. The expression levels of these potential driver genes were significantly increased/decreased in tumor cells, as assayed by RT–qPCR. A risk score model comprising these driver genes was further constructed, and this model was strongly negatively associated with the overall survival of patients in different datasets. The proportional hazards assumption and outlier test indicated that this model could robustly distinguish patients with different survival rates. Immune analyses and responses to immunotherapeutic and chemotherapeutic agents showed that patients in the high and low-risk groups may have distinct tendencies for clinical selection. Finally, the regions with clear H3K79me2 signal changes on these driver genes were accurately identified. Our research may offer potential molecular biomarkers for lung adenocarcinoma treatment.

Genetic Biomarkers in Chronic Myeloid Leukemia: What Have We Learned So Far?

Chronic Myeloid Leukemia (CML) is a rare malignant proliferative disease of the hematopoietic system, whose molecular hallmark is the Philadelphia chromosome (Ph). The Ph chromosome originates an aberrant fusion gene with abnormal kinase activity, leading to the buildup of reactive oxygen species and genetic instability of relevance in disease progression. Several genetic abnormalities have been correlated with CML in the blast phase, including chromosomal aberrations and common altered genes. Some of these genes are involved in the regulation of cell apoptosis and proliferation, such as the epidermal growth factor receptor (EGFR), tumor protein p53 (TP53), or Schmidt-Ruppin A-2 proto-oncogene (SRC); cell adhesion, e.g., catenin beta 1 (CTNNB1); or genes associated to TGF-β, such as SKI like proto-oncogene (SKIL), transforming growth factor beta 1 (TGFB1) or transforming growth factor beta 2 (TGFB2); and TNF-α pathways, such as Tumor necrosis factor (TNFA) or Nuclear factor kappa B subunit 1 (NFKB1). The involvement of miRNAs in CML is also gaining momentum, where dysregulation of some critical miRNAs, such as miRNA-451 and miRNA-21, which have been associated to the molecular modulation of pathogenesis, progression of disease states, and response to therapeutics. In this review, the most relevant genomic alterations found in CML will be addressed.

The impact of gene-body H3K36me3 patterns on gene expression level changes in chronic myelogenous leukemia

Chronic myelogenous leukemia (CML) is a malignant clonal disease of hematopoietic stem cells. Researches have exhibited that the progression of CML is related to histone modifications. Here, we perform the systematic analyses of H3K36me3 patterns and gene expression level changes. We observe that the genes with higher gene-body H3K36me3 levels in normal cells show fewer expression changes during leukemogenesis, while the genes with lower gene-body H3K36me3 levels in normal cells yield obvious expression changes during leukemogenesis (ρ = -0.98, P = 9.30 × 10^-8). These findings are conserved in human lung/breast cancers and mouse CML, regardless of gene expression levels and gene lengths. Regulatory element analysis and Random Forest regression display that Hoxd13, Rara, Scl, Smad3, Smad4 and Tgif1 induce the up-regulation of genes with lower H3K36me3 levels (ρ = 0.97, P = 2.35 × 10^-56). Enrichment analysis shows that the differentially expressed genes with lower H3K36me3 levels are involved in leukemia-related pathways, such as leukocyte migration and regulation of leukocyte activation. Finally, six driver genes (Tp53, Wt1, Dnmt3a, Cacna1b, Phactr1 and Gbp4) with lower H3K36me3 levels are identified. Our analyses indicate that lower gene-body H3K36me3 levels may serve as a biomarker for the progression of CML.

Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources

Background

Transcriptional regulation is complex, requiring multiple cis (local) and trans acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from cis regulatory factors. However, local chromatin looping and trans-acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. In this study, we create a general model of transcription factor influence on gene expression by incorporating both cis and trans gene regulatory features.

Results

We describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both cis and trans acting mechanisms, and measures of the local chromatin context. These prediction models perform significantly better compared to models containing cis-regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight cis-regulatory rare variants for sequence kernel association test analyses of gene expression.

Conclusions

Our models generate refined effect estimates for the influence of individual transcription factors on gene expression, allowing characterization of their roles across the genome. This work also provides a framework for integrating multiple data types into a single model of transcriptional regulation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04126-3.

Identification of Key Histone Modifications and Their Regulatory Regions on Gene Expression Level Changes in Chronic Myelogenous Leukemia

Chronic myelogenous leukemia (CML) is a type of cancer with a series of characteristics that make it particularly suitable for observations on leukemogenesis. Research have exhibited that the occurrence and progression of CML are associated with the dynamic alterations of histone modification (HM) patterns. In this study, we analyze the distribution patterns of 11 HM signals and calculate the signal changes of these HMs in CML cell lines as compared with that in normal cell lines. Meanwhile, the impacts of HM signal changes on expression level changes of CML-related genes are investigated. Based on the alterations of HM signals between CML and normal cell lines, the up- and down-regulated genes are predicted by the random forest algorithm to identify the key HMs and their regulatory regions. Research show that H3K79me2, H3K36me3, and H3K27ac are key HMs to expression level changes of CML-related genes in leukemogenesis. Especially H3K79me2 and H3K36me3 perform their important functions in all 100 bins studied. Our research reveals that H3K79me2 and H3K36me3 may be the core HMs for the clinical treatment of CML.

The Regulation of Target Genes by Co-occupancy of Transcription Factors, c-Myc and Mxi1 with Max in the Mouse Cell Line

Background:

The regulatory function of transcription factors on genes is not only related to the location of binding genes and its related functions, but is also related to the methods of binding.

Objective:

It is necessary to study the regulation effects in different binding methods on target genes.

Methods:

In this study, we provided a reliable theoretical basis for studying gene expression regulation of co-binding transcription factors and further revealed the specific regulation of transcription factor co-binding in cancer cells.

Results:

Transcription factors tend to combine with other transcription factors in the regulatory region to form a competitive or synergistic relationship to regulate target genes accurately.

Conclusion:

We found that up-regulated genes in cancer cells were involved in the regulation of their own immune system related to the normal cells.

Cell-free Chromatin Immunoprecipitation (cfChIP) from blood plasma can determine gene-expression in tumors from non-small-cell lung cancer patients

OBJECTIVES

Lung cancer is the leading cause of cancer related death worldwide. Accurate molecular diagnostics from a tumor biopsy is paramount for correct diagnosis, treatment strategy, and prediction of outcome. However, a tumor biopsy can be misleading due to tumor heterogeneity and consecutive biopsies are rarely achievable. Importantly, tumor-specific genetic information concerning mutations and translocations, can also be obtained from liquid biopsies, e.g. blood plasma, containing cell-free DNA (cfDNA) with both systemic and tumor origin. Tumor-specific gene-expression information can also be determined from liquid biopsies using cfDNA methylation and cell-free RNA analyses. However, supplementary methodologies that can determine gene-expression patterns in lung tumors from liquid biopsies could also have diagnostic impact.

MATERIALS AND METHODS

We here present the method cell-free chromatin Immunoprecipitation (cfChIP), which for genes having high expression specifically in the tumor, can determine such gene-expression from blood plasma. In cfChIP cell-free nucleosomes modified with histone H3 lysine 36 tri-methylation (H3K36me3), a mark quantitatively correlated with the transcription of the underlying gene, are isolated, and associated cfDNA quantified.

RESULTS

We demonstrate that cfChIP from lung cancer patient blood plasma can successfully quantify the level of H3K36me3 associated with circulating cell-free nucleosomes and thereby quantify the transcriptional level of genes associated with these nucleosomes. Moreover, as a proof-of-principle we show that in blood plasma from 14 lung cancer patients, H3K36me3 cfChIP can replicate the expected higher expression of KRT6 in lung squamous cell carcinoma relative to adenocarcinoma.

CONCLUSION

This work shows that for genes with a high expression specifically in tumor, cfChIP can determine this gene-expression pattern from blood plasma. cfChIP is a method that determine gene-expression at the transcriptional level and accordingly can supplement cfDNA methylation and cell-free RNA analyses.

MTTFsite: cross-cell type TF binding site prediction by using multi-task learning

Motivation

The prediction of transcription factor binding sites (TFBSs) is crucial for gene expression analysis. Supervised learning approaches for TFBS predictions require large amounts of labeled data. However, many TFs of certain cell types either do not have sufficient labeled data or do not have any labeled data.

Results

In this paper, a multi-task learning framework (called MTTFsite) is proposed to address the lack of labeled data problem by leveraging on labeled data available in cross-cell types. The proposed MTTFsite contains a shared CNN to learn common features for all cell types and a private CNN for each cell type to learn private features. The common features are aimed to help predicting TFBSs for all cell types especially those cell types that lack labeled data. MTTFsite is evaluated on 241 cell type TF pairs and compared with a baseline method without using any multi-task learning model and a fully shared multi-task model that uses only a shared CNN and do not use private CNNs. For cell types with insufficient labeled data, results show that MTTFsite performs better than the baseline method and the fully shared model on more than 89% pairs. For cell types without any labeled data, MTTFsite outperforms the baseline method and the fully shared model by more than 80 and 93% pairs, respectively. A novel gene expression prediction method (called TFChrome) using both MTTFsite and histone modification features is also presented. Results show that TFBSs predicted by MTTFsite alone can achieve good performance. When MTTFsite is combined with histone modification features, a significant 5.7% performance improvement is obtained.

Availability and implementation

The resource and executable code are freely available at http://hlt.hitsz.edu.cn/MTTFsite/ and http://www.hitsz-hlt.com:8080/MTTFsite/.

Supplementary information

Supplementary data are available at Bioinformatics online.

JMJD2C promotes colorectal cancer metastasis via regulating histone methylation of MALAT1 promoter and enhancing β-catenin signaling pathway

Background

Our previous work demonstrated that lncRNA-MALAT1 was overexpressed in recurrent colorectal cancer (CRC) and metastatic sites in post-surgical patients. However, the upstream regulatory mechanism of MALAT1 is not well-defined. Histone demethylase JMJD2C holds great potential of epigenetic regulating mechanism in tumor diseases, especially the moderating effect on the promoter activity of targeted genes associated closely with tumor development. Therefore, we herein investigated whether JMJD2C could epigeneticly regulate the promoter activity of MALAT1 and the downstream β-catenin signaling pathway, thereby affecting the metastatic abilities of CRC cells.

Methods

JMJD2C expressions in human CRC samples were detected by real-time PCR and immunohistochemistry staining. Gene silencing and overexpressing efficiencies of JMJD2C were confirmed by real-time PCR and western blot. The migration of CRC cells in vitro were tested by transwell and wound healing assays. The protein expression and cellular localization of JMJD2C and β-catenin were characterized by immunofluorescence staining and western blot. The histone methylation level of MALAT1 promoter region (H3K9me3 and H3K36me3) was tested by ChIP-PCR assays. The promoter activity of MALAT1 was detected by luciferase reporter assay. The expressions of MALAT1 and the downstream β-catenin signaling pathway related genes in CRC cells were detected by real-time PCR and western blot, respectively. The nude mice tail vein metastasis model was established to observe the effect of JMJD2C on the lung metastasis of CRC cells in vivo.

Results

Our present results indicated that histone demethylase JMJD2C was overexpressed in matched CRC tumor tissues of primary and metastatic foci, and CRC patients with lower JMJD2C expression in primary tumors had better prognosis with longer OS (Overall Survival). The following biological function observation suggested that JMJD2C promoted CRC metastasis in vitro and in vivo. Further molecular mechanism investigation demonstrated that JMJD2C protein translocated into the nuclear, lowered the histone methylation level of MALAT1 promoter in the sites of H3K9me3 and H3K36me3, up-regulated the expression of MALAT1, and enhanced the β-catenin signaling pathway in CRC cells.

Conclusion

Our data demonstrated that JMJD2C could enhance the metastatic abilities of CRC cells in vitro and in vivo by regulating the histone methylation level of MALAT1 promoter, thereby up-regulating the expression of MALAT1 and enhancing the activity of β-catenin signaling pathway, providing that JMJD2C might be a novel therapeutic target for CRC metastasis.

Relationship Between DNA Methylation in Key Region and the Differential Expressions of Genes in Human Breast Tumor Tissue

Breast cancer has a high mortality rate for females. Aberrant DNA methylation plays a crucial role in the occurrence and progression of breast carcinoma. By comparing DNA methylation differences between tumor breast tissue and normal breast tissue, we calculate and analyze the distributions of the hyper- and hypomethylation sites in different function regions. Results indicate that enhancer regions are often hypomethylated in breast cancer. CpG islands (CGIs) are mainly hypermethylated, while the flanking CGI (shores and shelves) is more easily hypomethylated. The hypomethylation in gene body region is related to the upregulation of gene expression, and the hypomethylation of enhancer regions is closely associated with gene expression upregulation in breast cancer. Some key hypomethylation sites in enhancer regions and key hypermethylation sites in CGIs for regulating key genes are, respectively, found, such as oncogenes ESR1 and ERBB2 and tumor suppressor genes FBLN2, CEBPA, and FAT4. This suggests that the recognizing methylation status of these genes will be useful for the diagnosis of breast cancer.

Histone demethylase JMJD2C: epigenetic regulators in tumors

Histone methylation is one of the major epigenetic modifications, and various histone methylases and demethylases participate in the epigenetic regulating. JMJD2C has been recently identified as one of the histone lysine demethylases. As one member of the Jumonji-C histone demethylase family, JMJD2C has the ability to demethylate tri- or di-methylated histone 3 and 2 in either K9 (lysine residue 9) or K36 (lysine residue 36) sites by an oxidative reaction, thereby affecting heterochromatin formation, genomic imprinting, X-chromosome inactivation, and transcriptional regulation of genes. JMJD2C was firstly found to involve in embryonic development and stem cell regulation. Afterwards, aberrant status of JMJD2C histone methylation was observed during the formation and development of various tumors, and it has been reported to play crucial roles in the progression of breast cancer, prostate carcinomas, osteosarcoma, blood neoplasms and so on, indicating that JMJD2C represents a promising anti-cancer target. In this review, we will focus on the research progress and prospect of JMJD2C in tumors, and provide abundant evidence for the functional application and therapeutic potential of targeting JMJD2C in tumors.