bims-ectoca Biomed News
on Epigenetic control of Tolerance in Cancer
Issue of 2021‒07‒18
twenty-two papers selected by
Ankita Daiya
BITS Pilani
  1. Bayesian information sharing enhances detection of regulatory associations in rare cell types.
  2. Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC.
  3. Inferring single cell expression profiles from overlapped pooling sequencing data with compressed sensing strategy.
  4. Epigenetic regulation of autophagy: A key modification in cancer cells and cancer stem cells.
  5. scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling.
  6. An active learning approach for clustering single-cell RNA-seq data.
  7. Comprehensive analysis of partial methylation domains in colorectal cancer based on single-cell methylation profiles.
  8. Single-cell and spatial analyses of cancer cells: toward elucidating the molecular mechanisms of clonal evolution and drug resistance acquisition.
  9. Single-cell microRNA sequencing method comparison and application to cell lines and circulating lung tumor cells.
  10. Functional genomics approaches to improve pre-clinical drug screening and biomarker discovery.
  11. Single-cell analyses reveal diverse mechanisms of resistance to EGFR tyrosine kinase inhibitors in lung cancer.
  12. Genome-wide analyses of XRN1-sensitive targets in osteosarcoma cells identifies disease-relevant transcripts containing G-rich motifs.
  13. CHK Methylation Is Elevated in Colon Cancer Cells and Contributes to the Oncogenic Properties.
  14. Exosomal noncoding RNAs: key players in glioblastoma drug resistance.
  15. 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.
  16. Long reads capture simultaneous enhancer-promoter methylation status for cell-type deconvolution.
  17. Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data.
  18. Nuclear speckles: dynamic hubs of gene expression regulation.
  19. Recent advances in the epigenetics of bone metabolism.
  20. Cell Models in Autophagy Research.
  21. The Epigenetic Factors that Drive Cancer Drug Resistance.
  22. Bioinformatics Technologies in Autophagy Research.
  1. Bioinformatics. 2021 07 12. 37(Suppl_1): i349-i357
    Bayesian information sharing enhances detection of regulatory associations in rare cell types.
    Alexander P Wu, Jian Peng, Bonnie Berger, Hyunghoon Cho.
      MOTIVATION: Recent advances in single-cell RNA-sequencing (scRNA-seq) technologies promise to enable the study of gene regulatory associations at unprecedented resolution in diverse cellular contexts. However, identifying unique regulatory associations observed only in specific cell types or conditions remains a key challenge; this is particularly so for rare transcriptional states whose sample sizes are too small for existing gene regulatory network inference methods to be effective.RESULTS: We present ShareNet, a Bayesian framework for boosting the accuracy of cell type-specific gene regulatory networks by propagating information across related cell types via an information sharing structure that is adaptively optimized for a given single-cell dataset. The techniques we introduce can be used with a range of general network inference algorithms to enhance the output for each cell type. We demonstrate the enhanced accuracy of our approach on three benchmark scRNA-seq datasets. We find that our inferred cell type-specific networks also uncover key changes in gene associations that underpin the complex rewiring of regulatory networks across cell types, tissues and dynamic biological processes. Our work presents a path toward extracting deeper insights about cell type-specific gene regulation in the rapidly growing compendium of scRNA-seq datasets.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    AVAILABILITY AND IMPLEMENTATION: The code for ShareNet is available at http://sharenet.csail.mit.edu and https://github.com/alexw16/sharenet.
    DOI:  https://doi.org/10.1093/bioinformatics/btab269
  2. Methods Mol Biol. 2021 ;2328 171-182
    Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC.
    Nilesh Kumar, Bharat Mishra, Mohammad Athar, Shahid Mukhtar.
      With the advent of recent next-generation sequencing (NGS) technologies in genomics, transcriptomics, and epigenomics, profiling single-cell sequencing became possible. The single-cell RNA sequencing (scRNA-seq) is widely used to characterize diverse cell populations and ascertain cell type-specific regulatory mechanisms. The gene regulatory network (GRN) mainly consists of genes and their regulators-transcription factors (TF). Here, we describe the lightning-fast Python implementation of the SCENIC (Single-Cell reEgulatory Network Inference and Clustering) pipeline called pySCENIC. Using single-cell RNA-seq data, it maps TFs onto gene regulatory networks and integrates various cell types to infer cell-specific GRNs. There are two fast and efficient GRN inference algorithms, GRNBoost2 and GENIE3, optionally available with pySCENIC. The pipeline has three steps: (1) identification of potential TF targets based on co-expression; (2) TF-motif enrichment analysis to identify the direct targets (regulons); and (3) scoring the activity of regulons (or other gene sets) on single cell types.
    Keywords:  Gene co-expression network; Gene regulatory network; RNA-Seq count data; scRNA-seq
    DOI:  https://doi.org/10.1007/978-1-0716-1534-8_10
  3. Nucleic Acids Res. 2021 Jul 09. pii: gkab581. [Epub ahead of print]
    Inferring single cell expression profiles from overlapped pooling sequencing data with compressed sensing strategy.
    Mengting Huang, Yixuan Yang, Xingzhao Wen, Weiqiang Xu, Na Lu, Xiao Sun, Jing Tu, Zuhong Lu.
      Though single cell RNA sequencing (scRNA-seq) technologies have been well developed, the acquisition of large-scale single cell expression data may still lead to high costs. Single cell expression profile has its inherent sparse properties, which makes it compressible, thus providing opportunities for solutions. Here, by computational simulation as well as experiment of 54 single cells, we propose that expression profiles can be compressed from the dimension of samples by overlapped assigning each cell into plenty of pools. And we prove that expression profiles can be inferred from these pool expression data with overlapped pooling design and compressed sensing strategy. We also show that by combining this approach with plate-based scRNA-seq measurement, it can maintain its superiorities in gene detection sensitivity and individual identity and recover the expression profile with high precision, while saving about half of the library cost. This method can inspire novel conceptions on the measurement, storage or computation improvements for other compressible signals in many biological areas.
    DOI:  https://doi.org/10.1093/nar/gkab581
  4. World J Stem Cells. 2021 Jun 26. 13(6): 542-567
    Epigenetic regulation of autophagy: A key modification in cancer cells and cancer stem cells.
    Harpreet K Mandhair, Urban Novak, Ramin Radpour.
      Aberrant epigenetic alterations play a decisive role in cancer initiation and propagation via the regulation of key tumor suppressor genes and oncogenes or by modulation of essential signaling pathways. Autophagy is a highly regulated mechanism required for the recycling and degradation of surplus and damaged cytoplasmic constituents in a lysosome dependent manner. In cancer, autophagy has a divergent role. For instance, autophagy elicits tumor promoting functions by facilitating metabolic adaption and plasticity in cancer stem cells (CSCs) and cancer cells. Moreover, autophagy exerts pro-survival mechanisms to these cancerous cells by influencing survival, dormancy, immunosurveillance, invasion, metastasis, and resistance to anti-cancer therapies. In addition, recent studies have demonstrated that various tumor suppressor genes and oncogenes involved in autophagy, are tightly regulated via different epigenetic modifications, such as DNA methylation, histone modifications and non-coding RNAs. The impact of epigenetic regulation of autophagy in cancer cells and CSCs is not well-understood. Therefore, uncovering the complex mechanism of epigenetic regulation of autophagy provides an opportunity to improve and discover novel cancer therapeutics. Subsequently, this would aid in improving clinical outcome for cancer patients. In this review, we provide a comprehensive overview of the existing knowledge available on epigenetic regulation of autophagy and its importance in the maintenance and homeostasis of CSCs and cancer cells.
    Keywords:  Autophagy; Cancer cells; Cancer stem cells; DNA methylation; Epigenetics; Histone remodeling; Non-coding RNA
    DOI:  https://doi.org/10.4252/wjsc.v13.i6.542
  5. Bioinformatics. 2021 07 12. 37(Suppl_1): i358-i366
    scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling.
    Dongyuan Song, Kexin Li, Zachary Hemminger, Roy Wollman, Jingyi Jessica Li.
      MOTIVATION: Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data.RESULTS: Here, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and the cell-type annotation on targeted gene profiling data.
    AVAILABILITY AND IMPLEMENTATION: The R package is open-access and available at https://github.com/JSB-UCLA/scPNMF. The data used in this work are available at Zenodo: https://doi.org/10.5281/zenodo.4797997.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btab273
  6. Lab Invest. 2021 Jul 09.
    An active learning approach for clustering single-cell RNA-seq data.
    Xiang Lin, Haoran Liu, Zhi Wei, Senjuti Basu Roy, Nan Gao.
      Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover undiscovered cell types. Most methods for clustering scRNA-seq data use an unsupervised learning strategy. Since the clustering step is separated from the cell annotation and labeling step, it is not uncommon for a totally exotic clustering with poor biological interpretability to be generated-a result generally undesired by biologists. To solve this problem, we proposed an active learning (AL) framework for clustering scRNA-seq data. The AL model employed a learning algorithm that can actively query biologists for labels, and this manual labeling is expected to be applied to only a subset of cells. To develop an optimal active learning approach, we explored several key parameters of the AL model in the experiments with four real scRNA-seq datasets. We demonstrate that the proposed AL model outperformed state-of-the-art unsupervised clustering methods with less than 1000 labeled cells. Therefore, we conclude that AL model is a promising tool for clustering scRNA-seq data that allows us to achieve a superior performance effectively and efficiently.
    DOI:  https://doi.org/10.1038/s41374-021-00639-w
  7. Brief Bioinform. 2021 Jul 13. pii: bbab267. [Epub ahead of print]
    Comprehensive analysis of partial methylation domains in colorectal cancer based on single-cell methylation profiles.
    Yan Huang, Pingping Wang, Wenyang Zhou, Meng Luo, Zhaochun Xu, Rui Cheng, Chang Xu, Xiyun Jin, Yiqun Li, Qinghua Jiang.
      Epigenetic aberrations have played a significant role in affecting the pathophysiological state of colorectal cancer, and global DNA hypomethylation mainly occurs in partial methylation domains (PMDs). However, the distribution of PMDs in individual cells and the heterogeneity between cells are still unclear. In this study, the DNA methylation profiles of colorectal cancer detected by WGBS and scBS-seq were used to depict PMDs in individual cells for the first time. We found that more than half of the entire genome is covered by PMDs. Three subclasses of PMDS have distinct characteristics, and Gain-PMDs cover a higher proportion of protein coding genes. Gain-PMDs have extensive epigenetic heterogeneity between different cells of the same tumor, and the DNA methylation in cells is affected by the tumor microenvironment. In addition, abnormally elevated promoter methylation in Gain-PMDs may further promote the growth, proliferation and metastasis of tumor cells through silent transcription. The PMDs detected in this study have the potential as epigenetic biomarkers and provide a new insight for colorectal cancer research based on single-cell methylation data.
    Keywords:  heterogeneity; partial methylation domains; single cell
    DOI:  https://doi.org/10.1093/bib/bbab267
  8. Inflamm Regen. 2021 Jul 16. 41(1): 22
    Single-cell and spatial analyses of cancer cells: toward elucidating the molecular mechanisms of clonal evolution and drug resistance acquisition.
    Satoi Nagasawa, Yukie Kashima, Ayako Suzuki, Yutaka Suzuki.
      Even within a single type of cancer, cells of various types exist and play interrelated roles. Each of the individual cells resides in a distinct microenvironment and behaves differently. Such heterogeneity is the most cumbersome nature of cancers, which is occasionally uncountable when effective prevention or total elimination of cancers is attempted. To understand the heterogeneous nature of each cell, the use of conventional methods for the analysis of "bulk" cells is insufficient. Although some methods are high-throughput and compressive regarding the genes being detected, the obtained data would be from the cell mass, and the average of a large number of the component cells would no longer be measured. Single-cell analysis, which has developed rapidly in recent years, is causing a drastic change. Genome, transcriptome, and epigenome analyses at single-cell resolution currently target cancer cells, cancer-associated fibroblasts, endothelial cells of vessels, and circulating and infiltrating immune cells. In fact, surprisingly diverse features of clonal evolution of cancer cells, during the development of cancer or acquisition of drug resistance, accompanied by corresponding gene expression changes in the circumstantial stromal cells, appeared in recent single-cell analyses. Based on the obtained novel insights, better optimal drug selection and new drug administration sequences were started. Even a remaining concern of the single cell analyses is being addressed. Until very recently, it was impossible to obtain positional information of cells in cancer via single-cell analysis because such information is lost during preparation of single-cell suspensions. A new method, collectively called spatial transcriptome (ST) analysis, has been developed and rapidly applied to various clinical specimens. In this review, we first outline the recent achievements of single-cell cancer analysis in analyzing the molecular basis underlying the acquisition of drug resistance, particularly focusing on the latest anti-epidermal growth factor receptor tyrosine kinase inhibitor, osimertinib. Further, we review the currently available ST analysis methods and introduce our recent attempts regarding the respective topics.
    Keywords:  Anticancer drug resistance; Single-cell RNA-seq; Single-cell multiome analysis; Spatial transcriptome analysis
    DOI:  https://doi.org/10.1186/s41232-021-00170-x
  9. Nat Commun. 2021 07 14. 12(1): 4316
    Single-cell microRNA sequencing method comparison and application to cell lines and circulating lung tumor cells.
    Sarah M Hücker, Tobias Fehlmann, Christian Werno, Kathrin Weidele, Florian Lüke, Anke Schlenska-Lange, Christoph A Klein, Andreas Keller, Stefan Kirsch.
      Molecular single cell analyses provide insights into physiological and pathological processes. Here, in a stepwise approach, we first evaluate 19 protocols for single cell small RNA sequencing on MCF7 cells spiked with 1 pg of 1,006 miRNAs. Second, we analyze MCF7 single cell equivalents of the eight best protocols. Third, we sequence single cells from eight different cell lines and 67 circulating tumor cells (CTCs) from seven SCLC patients. Altogether, we analyze 244 different samples. We observe high reproducibility within protocols and reads covered a broad spectrum of RNAs. For the 67 CTCs, we detect a median of 68 miRNAs, with 10 miRNAs being expressed in 90% of tested cells. Enrichment analysis suggested the lung as the most likely organ of origin and enrichment of cancer-related categories. Even the identification of non-annotated candidate miRNAs was feasible, underlining the potential of single cell small RNA sequencing.
    DOI:  https://doi.org/10.1038/s41467-021-24611-w
  10. EMBO Mol Med. 2021 Jul 13. e13189
    Functional genomics approaches to improve pre-clinical drug screening and biomarker discovery.
    Long V Nguyen, Carlos Caldas.
      Advances in sequencing technology have enabled the genomic and transcriptomic characterization of human malignancies with unprecedented detail. However, this wealth of information has been slow to translate into clinically meaningful outcomes. Different models to study human cancers have been established and extensively characterized. Using these models, functional genomic screens and pre-clinical drug screening platforms have identified genetic dependencies that can be exploited with drug therapy. These genetic dependencies can also be used as biomarkers to predict response to treatment. For many cancers, the identification of such biomarkers remains elusive. In this review, we discuss the development and characterization of models used to study human cancers, RNA interference and CRISPR screens to identify genetic dependencies, large-scale pharmacogenomics studies and drug screening approaches to improve pre-clinical drug screening and biomarker discovery.
    Keywords:  biomarker discovery; cancer models; drug screening; pharmacogenomics; single-cell sequencing
    DOI:  https://doi.org/10.15252/emmm.202013189
  11. Cancer Res. 2021 Jul 09. pii: canres.2811.2020. [Epub ahead of print]
    Single-cell analyses reveal diverse mechanisms of resistance to EGFR tyrosine kinase inhibitors in lung cancer.
    Yukie Kashima, Daisuke Shibahara, Ayako Suzuki, Kyoko Muto, Ikei S Kobayashi, David Plotnick, Hibiki Udagawa, Hiroki Izumi, Yuji Shibata, Kosuke Tanaka, Masanori Fujii, Akihiro Ohashi, Masahide Seki, Koichi Goto, Katsuya Tuchihara, Yutaka Suzuki, Susumu S Kobayashi.
      Tumor heterogeneity underlies resistance to tyrosine kinase inhibitors (TKI) in lung cancers harboring epidermal growth factor receptor (EGFR) mutations. Previous evidence suggested that subsets of preexisting resistant cells are selected by EGFR-TKI treatment, or alternatively, that diverse acquired resistance mechanisms emerge from drug-tolerant persister (DTP) cells. Many studies have used bulk tumor specimens or subcloned resistant cell lines to identify resistance mechanism. However, intratumoral heterogeneity can result in divergent responses to therapies, requiring additional approaches to reveal the complete spectrum of resistance mechanisms. Using EGFR-TKI-resistant cell models and clinical specimens, we performed single-cell RNA-seq and single-cell ATAC-seq analyses to define the transcriptional and epigenetic landscape of parental cells, DTPs, and tumor cells in a fully resistant state. In addition to AURKA, VIM, and AXL, which are all known to induce EGFR-TKI resistance, CD74 was identified as a novel gene that plays a critical role in the drug-tolerant state. In vitro and in vivo experiments demonstrated that CD74 upregulation confers resistance to the EGFR-TKI osimertinib and blocks apoptosis, enabling tumor regrowth. Overall, this study provides new insight into the mechanisms underlying resistance to EGFR-TKIs.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-20-2811
  12. RNA. 2021 Jul 15. pii: rna.078872.121. [Epub ahead of print]
    Genome-wide analyses of XRN1-sensitive targets in osteosarcoma cells identifies disease-relevant transcripts containing G-rich motifs.
    Amy L Pashler, Benjamin P Towler, Christopher I Jones, Hope J Haime, Tom Burgess, Sarah F Newbury.
      XRN1 is a highly conserved exoribonuclease which degrades uncapped RNAs in a 5'-3' direction. Degradation of RNAs by XRN1 is important in many cellular and developmental processes and is relevant to human disease. Studies in D. melanogaster demonstrate that XRN1 can target specific RNAs, which have important consequences for developmental pathways. Osteosarcoma is a malignancy of the bone and accounts for 2% of all paediatric cancers worldwide. 5 -year survival of patients has remained static since the 1970s and therefore furthering our molecular understanding of this disease is crucial. Previous work has shown a downregulation of XRN1 in osteosarcoma cells, however the transcripts regulated by XRN1 which might promote osteosarcoma remain elusive. Here, we confirm reduced levels of XRN1 in osteosarcoma cell lines and patient samples and identify XRN1-sensitive transcripts in human osteosarcoma cells. Using RNA-seq in XRN1-knockdown SAOS-2 cells, we show that 1178 genes are differentially regulated. Using a novel bioinformatic approach, we demonstrate that 134 transcripts show characteristics of direct post-transcriptional regulation by XRN1. Long non-coding RNAs (lncRNAs) are enriched in this group suggesting that XRN1 normally plays an important role in controlling lncRNA expression in these cells. Among potential lncRNAs targeted by XRN1 is HOTAIR, which is known to be upregulated in osteosarcoma and contribute to disease progression. We have also identified G-rich and GU motifs in post-transcriptionally regulated transcripts which appear to sensitise them to XRN1 degradation. Our results therefore provide significant insights into the specificity of XRN1 in human cells which is relevant to disease.
    Keywords:  Ewing Sarcoma; RNA degradation; RNA-seq; XRN1; lncRNAs
    DOI:  https://doi.org/10.1261/rna.078872.121
  13. Front Cell Dev Biol. 2021 ;9 708038
    CHK Methylation Is Elevated in Colon Cancer Cells and Contributes to the Oncogenic Properties.
    Shudong Zhu, Yan Zhu, Qiuwen Wang, Yi Zhang, Xialing Guo.
      Src is an important oncogene that plays key roles in multiple signal transduction pathways. Csk-homologous kinase (CHK) is a kinase whose molecular roles are largely uncharacterized. We previously reported expression of CHK in normal human colon cells, and decreased levels of CHK protein in colon cancer cells leads to the activation of Src (Zhu et al., 2008). However, how CHK protein expression is downregulated in colon cancer cells has been unknown. We report herein that CHK mRNA was decreased in colon cancer cells as compared to normal colon cells, and similarly in human tissues of normal colon and colon cancer. Increased levels of DNA methylation at promotor CpG islands of CHK gene were observed in colon cancer cells and human colon cancer tissues as compared to their normal healthy counterparts. Increased levels of DNA methyltransferases (DNMTs) were also observed in colon cancer cells and tissues. DNA methylation and decreased expression of CHK mRNA were inhibited by DNMT inhibitor 5-Aza-CdR. Cell proliferation, colony growth, wound healing, and Matrigel invasion were all decreased in the presence of 5-Aza-CdR. These results suggest that increased levels of DNA methylation, possibly induced by enhanced levels of DNMT, leads to decreased expression of CHK mRNA and CHK protein, promoting increased oncogenic properties in colon cancer cells.
    Keywords:  CHK; DNA methylation; FHC; colon cancer; drug resistance
    DOI:  https://doi.org/10.3389/fcell.2021.708038
  14. Mol Cell Biochem. 2021 Jul 17.
    Exosomal noncoding RNAs: key players in glioblastoma drug resistance.
    Ahmad Movahedpour, Seyyed Hossein Khatami, Marjan Khorsand, Mahsa Salehi, Amir Savardashtaki, Seyedeh Habibeh Mirmajidi, Babak Negahdari, Nezhat Khanjani, Parisa Naeli, Omid Vakili, Mortaza Taheri-Anganeh.
      Glioma, as one of the most severe human malignancies, is defined as the Central Nervous System's (CNS) tumors. Glioblastoma (GBM) in this regard, is the most malignant type of gliomas. There are multiple therapeutic strategies to cure GBM, for which chemotherapy is often the first-line treatment. Still, various cellular processes, such as uncontrolled proliferation, invasion and metastasis, may disturb the treatment efficacy. Drug resistance is another process in this way, which can also cause undesirable effects. Thereupon, identifying the mechanisms, involved in developing drug resistance and the relevant mechanisms can be very helpful in GBM management. The discovery of exosomal non-coding RNAs (ncRNAs), RNA molecules that can be transferred between the cells and different tissues using the exosomes, was a milestone in this regard. It has been revealed that the key exosomal ncRNAs, including circular RNAs, microRNAs, and long ncRNAs, are able to modulate GBM drug resistance through different signaling pathways or by affecting regulatory proteins and their corresponding genes. Nowadays, researchers are trying to overcome the limitations of chemotherapy by targeting these RNA molecules. Accordingly, this review aims to clarify the substantial roles of exosomal ncRNAs in GBM drug resistance and involved mechanisms.
    Keywords:  Drug resistance; Exosomes; Glioblastoma; Noncoding RNAs
    DOI:  https://doi.org/10.1007/s11010-021-04221-2
  15. Genomics Inform. 2021 Jun;19(2): e17
    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.
    Aneesh Kotipalli, Ruma Banerjee, Sunitha Manjari Kasibhatla, Rajendra Joshi.
      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.
    Keywords:  ChIP-Seq; RNA-Seq; breast neoplasms; luminal-A/triple-negative; miRNA
    DOI:  https://doi.org/10.5808/gi.21020
  16. Bioinformatics. 2021 07 12. 37(Suppl_1): i327-i333
    Long reads capture simultaneous enhancer-promoter methylation status for cell-type deconvolution.
    Sapir Margalit, Yotam Abramson, Hila Sharim, Zohar Manber, Surajit Bhattacharya, Yi-Wen Chen, Eric Vilain, Hayk Barseghyan, Ran Elkon, Roded Sharan, Yuval Ebenstein.
      MOTIVATION: While promoter methylation is associated with reinforcing fundamental tissue identities, the methylation status of distant enhancers was shown by genome-wide association studies to be a powerful determinant of cell-state and cancer. With recent availability of long reads that report on the methylation status of enhancer-promoter pairs on the same molecule, we hypothesized that probing these pairs on the single-molecule level may serve the basis for detection of rare cancerous transformations in a given cell population. We explore various analysis approaches for deconvolving cell-type mixtures based on their genome-wide enhancer-promoter methylation profiles.RESULTS: To evaluate our hypothesis we examine long-read optical methylome data for the GM12878 cell line and myoblast cell lines from two donors. We identified over 100 000 enhancer-promoter pairs that co-exist on at least 30 individual DNA molecules. We developed a detailed methodology for mixture deconvolution and applied it to estimate the proportional cell compositions in synthetic mixtures. Analysis of promoter methylation, as well as enhancer-promoter pairwise methylation, resulted in very accurate estimates. In addition, we show that pairwise methylation analysis can be generalized from deconvolving different cell types to subtle scenarios where one wishes to resolve different cell populations of the same cell-type.
    AVAILABILITY AND IMPLEMENTATION: The code used in this work to analyze single-molecule Bionano Genomics optical maps is available via the GitHub repository https://github.com/ebensteinLab/Single_molecule_methylation_in_EP.
    DOI:  https://doi.org/10.1093/bioinformatics/btab306
  17. Bioinformatics. 2021 07 12. 37(Suppl_1): i222-i230
    Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data.
    Daiyun Huang, Bowen Song, Jingjue Wei, Jionglong Su, Frans Coenen, Jia Meng.
      MOTIVATION: Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for understanding the regulatory mechanisms of RNAs. To date, many computational approaches for predicting RNA modifications have been developed, most of which were based on strong supervision enabled by base-resolution epitranscriptome data. However, high-resolution data may not be available.RESULTS: We propose WeakRM, the first weakly supervised learning framework for predicting RNA modifications from low-resolution epitranscriptome datasets, such as those generated from acRIP-seq and hMeRIP-seq. Evaluations on three independent datasets (corresponding to three different RNA modification types and their respective sequencing technologies) demonstrated the effectiveness of our approach in predicting RNA modifications from low-resolution data. WeakRM outperformed state-of-the-art multi-instance learning methods for genomic sequences, such as WSCNN, which was originally designed for transcription factor binding site prediction. Additionally, our approach captured motifs that are consistent with existing knowledge, and visualization of the predicted modification-containing regions unveiled the potentials of detecting RNA modifications with improved resolution.
    AVAILABILITY IMPLEMENTATION: The source code for the WeakRM algorithm, along with the datasets used, are freely accessible at: https://github.com/daiyun02211/WeakRM.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btab278
  18. FEBS J. 2021 Jul 10.
    Nuclear speckles: dynamic hubs of gene expression regulation.
    İbrahim Avşar Ilık, Tuğçe Aktaş.
      Complex, multi-step biochemical reactions that routinely take place in our cells require high concentrations of enzymes, substrates and other structural components to proceed efficiently, and typically require chemical environments that can inhibit other reactions in their immediate vicinity. Eukaryotic cells solve these problems by restricting such reactions into diffusion-restricted compartments within the cell called organelles that can be separated from their environment by a lipid membrane, or into membrane-less compartments that form through liquid-liquid phase separation (LLPS). One of the most easily noticeable, and the earliest discovered organelle is the nucleus, which harbors the genetic material in cells where transcription by RNA polymerases produce most of the messenger RNAs and a plethora of noncoding RNAs, which in turn are required for translation of mRNAs in the cytoplasm. The interior of the nucleus is not a uniform soup of biomolecules, and rather consists of a variety of membraneless bodies, such as the nucleolus, nuclear speckles (NS), paraspeckles, Cajal bodies, histone locus bodies and more. In this review, we will focus on NS with an emphasis on recent developments including our own findings about the formation of NS by two large IDR-rich proteins SON and SRRM2.
    Keywords:  Nuclear speckles; Phase separation; SON; SRRM2; Splicing; Transcription
    DOI:  https://doi.org/10.1111/febs.16117
  19. J Bone Miner Metab. 2021 Jul 11.
    Recent advances in the epigenetics of bone metabolism.
    Yuexin Xu, Jing Ma, Guohua Xu, Duan Ma.
      Osteoporosis is a common form of metabolic bone disease that is costly to treat and is primarily diagnosed on the basis of bone mineral density. As the influences of genetic lesions and environmental factors are increasingly studied in the pathological development of osteoporosis, regulated epigenetics are emerging as the important pathogenesis mechanisms in osteoporosis. Recently, osteoporosis genome-wide association studies and multi-omics technologies have revealed that susceptibility loci and the misregulation of epigenetic modifiers are key factors in osteoporosis. Over the past decade, extensive studies have demonstrated epigenetic mechanisms, such as DNA methylation, histone/chromatin modifications, and non-coding RNAs, as potential contributing factors in osteoporosis that affect disease initiation and progression. Herein, we review recent advances in epigenetics in osteoporosis, with a focus on exploring the underlying mechanisms and potential diagnostic/prognostic biomarker applications for osteoporosis.
    Keywords:  DNA methylation; Epigenetics; Histone modification; NcRNA; Osteoporosis
    DOI:  https://doi.org/10.1007/s00774-021-01249-8
  20. Adv Exp Med Biol. 2021 ;1208 311-332
    Cell Models in Autophagy Research.
    Rui Huang, Shuyan Wu.
      Autophagy is highly conserved in organisms ranging from yeast to humans. C. elegans, D. melanogaster, zebrafish, and mice have been extensively used to study autophagy, though each of them has shortcomings. Suitable cell models are very important, and there is considerable potential for them to help advance autophagy research. Cell models have advantages in speed, stability, economy, etc. Moreover, experimental conditions are more easily controlled in cell models than in animal models. More than 40 ATG genes have been found in budding yeast and other fungi since 1992. As a model organism, yeast has a unique place in autophagy research and has become the most widely used cell model. It is almost equal to E. coli in terms of rapid proliferation, ease of culture, and handling. Yeast is also a good host for eukaryotic gene expression and can be used for screens that help clarify the function of unknown genes. However, as a lower unicellular organism, it is unable to show tissue-specific regulation of autophagy. Cells from higher organisms, such as humans or other animals, are indispensable. Deeper and more extensive study of autophagy using cell models such as nervous tissue-derived cell models, epithelial tissue-derived cell models, muscle tissue-derived cell models, blood cell, and immune cell models has made significant progress.
    DOI:  https://doi.org/10.1007/978-981-16-2830-6_14
  21. Curr Cancer Drug Targets. 2021 ;21(4): 269-273
    The Epigenetic Factors that Drive Cancer Drug Resistance.
    Manny D Bacolod.
      
    DOI:  https://doi.org/10.2174/156800962104210527150438
  22. Adv Exp Med Biol. 2021 ;1208 387-453
    Bioinformatics Technologies in Autophagy Research.
    Yu Xue, Dong Wang, Di Peng.
      Autophagy is an important and dynamic biological process, and provides an ideal application scenario for bioinformatics to develop new data resources, algorithms, tools and computational or mathematic models for a better understanding of complex regulatory mechanisms in cells. In the past decade, great efforts have been taken on the development of numerous bioinformatics technologies in autophagy research, and a comprehensive summarization of these important studies will provide a timely reference for both biologists and bioinformaticians who are working in the field of autophagy. In this book chapter, we first introduce bioinformatics technologies that allow sequence analysis of autophagy genes. We briefly summarize the mainstream algorithms in sequence alignment for the identification of homologous autophagy genes and emphasize the computational identification of potential orthologs and paralogs, as well as the evolutionary analysis of autophagy gene families. Three methods for the recognition of autophagy-related sequence motifs are introduced: regular expression, position-specific scoring matrix (PSSM) and group-based prediction system (GPS). Second, we carefully summarize recent progress in the analysis of autophagy-related omics data. We discuss how two major types of computational methods, enrichment analysis and network analysis can be used to analyze omics data, including transcriptomics, non-coding RNAomics, epigenomics, proteomics, phosphoproteomics and protein lysine modification (PLM) omics data. Finally, we summarize several important autophagy-related data resources, including both autophagy gene databases and autophagy-related RNA databases. We anticipate that more useful bioinformatics technologies will be developed and play an ever-more-important role in the analysis of autophagy.
    DOI:  https://doi.org/10.1007/978-981-16-2830-6_18
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