bims-ectoca 2021-11-07 papers

bims-ectoca

Biomed News

on Epigenetic control of tolerance in cancer

Issue of 2021‒11‒07
six papers selected by
Ankita Daiya
BITS Pilani

Tau Stabilizes Chromatin Compaction.
Superenhancer-transcription factor regulatory network in malignant tumors.
Model of Drug Delivery to Populations Composed of Two Cell Types.
A Novel Overall Survival Prediction Signature Based on Cancer Stem Cell-Related Genes in Osteosarcoma.
The histone chaperone FACT facilitates heterochromatin spreading by regulating histone turnover and H3K9 methylation states.
Machine Learning from Omics Data.

Front Cell Dev Biol. 2021 ;9 740550

Tau Stabilizes Chromatin Compaction.

Thomas Rico, Melissa Gilles, Alban Chauderlier, Thomas Comptdaer, Romain Magnez, Maggy Chwastyniak, Herve Drobecq, Florence Pinet, Xavier Thuru, Luc Buée, Marie-Christine Galas, Bruno Lefebvre.

  An extensive body of literature suggested a possible role of the microtubule-associated protein Tau in chromatin functions and/or organization in neuronal, non-neuronal, and cancer cells. How Tau functions in these processes remains elusive. Here we report that Tau expression in breast cancer cell lines causes resistance to the anti-cancer effects of histone deacetylase inhibitors, by preventing histone deacetylase inhibitor-inducible gene expression and remodeling of chromatin structure. We identify Tau as a protein recognizing and binding to core histone when H3 and H4 are devoid of any post-translational modifications or acetylated H4 that increases the Tau's affinity. Consistent with chromatin structure alterations in neurons found in frontotemporal lobar degeneration, Tau mutations did not prevent histone deacetylase-inhibitor-induced higher chromatin structure remodeling by suppressing Tau binding to histones. In addition, we demonstrate that the interaction between Tau and histones prevents further histone H3 post-translational modifications induced by histone deacetylase-inhibitor treatment by maintaining a more compact chromatin structure. Altogether, these results highlight a new cellular role for Tau as a chromatin reader, which opens new therapeutic avenues to exploit Tau biology in neuronal and cancer cells.

Keywords:  Tau protein (Tau); cancer biology; chromatin regulation; chromatin remodeling; histone (de)acetylation; histone deacetylase inhibitor (HDAC inhibitor); histone modification and chromatin structure

DOI:  https://doi.org/10.3389/fcell.2021.740550
Open Med (Wars). 2021 ;16(1): 1564-1582

Superenhancer-transcription factor regulatory network in malignant tumors.

Yuan Liang, Linlin Li, Tian Xin, Binru Li, Dalin Zhang.

  Objective: This study aims to identify superenhancer (SE)-transcriptional factor (TF) regulatory network related to eight common malignant tumors based on ChIP-seq data modified by histone H3K27ac in the enhancer region of the SRA database.Methods: H3K27ac ChIP-seq data of eight common malignant tumor samples were downloaded from the SRA database and subjected to comparison with the human reference genome hg19. TFs regulated by SEs were screened with HOMER software. Core regulatory circuitry (CRC) in malignant tumor samples was defined through CRCmapper software and validated by RNA-seq data in TCGA. The findings were substantiated in bladder cancer cell experiments.
Results: Different malignant tumors could be distinguished through the H3K27ac signal. After SE identification in eight common malignant tumor samples, 35 SE-regulated genes were defined as malignant tumor-specific. SE-regulated specific TFs effectively distinguished the types of malignant tumors. Finally, we obtained 60 CRC TFs, and SMAD3 exhibited a strong H3K27ac signal in eight common malignant tumor samples. In vitro experimental data verified the presence of a SE-TF regulatory network in bladder cancer, and SE-TF regulatory network enhanced the malignant phenotype of bladder cancer cells.
Conclusion: The SE-TF regulatory network with SMAD3 as the core TF may participate in the carcinogenesis of malignant tumors.

Keywords:  ChIP-seq; RNA-seq; core regulatory circuitry; malignant tumors; super enhancers; superenhancer–transcription factor regulatory network; transcription factors

DOI:  https://doi.org/10.1515/med-2021-0326
J Theor Biol. 2021 Oct 27. pii: S0022-5193(21)00366-0. [Epub ahead of print] 110947

Model of Drug Delivery to Populations Composed of Two Cell Types.

Sid Becker, Andrey V Kuznetsov, Dan Zhao, Filippo de Monte, Giuseppe Pontrelli.

  The rate of drug delivery to cells and the subsequent rate of drug metabolism are dependent on the cell membrane permeability to the drug. In some cases, tissue may be composed of different types of cells that exhibit order of magnitude differences in their membrane permeabilities. This paper presents a brief review of the components of the tissue scale three-compartment pharmacokinetic model of drug delivery to single-cell-type populations. The existing model is extended to consider tissue composed of two different cell types. A case study is presented of infusion mediated delivery of doxorubicin to a tumor that is composed a drug reactive cell type and of a drug resistive cell type. The membrane permeabilities of the two cell types differ by an order of magnitude. A parametric investigation of the population composition is conducted and it is shown that the drug metabolism of the low permeability cells are negatively influenced by the fraction of the tissue composed of the permeable drug reactive cells. This is because when the population is composed mostly of drug permeable cells, the extracellular space is rapidly depleted of the drug. This has two compounding effects: (i) locally there is simply less drug available to the neighboring drug resistant cells, and (ii) the depletion of the drug from the extracellular space near the vessel-tissue interface leaves less drug to be transported to booth cell types farther away from the vessel.

Keywords:  Michaelis-Menten reaction; binding model; chemotherapy; continuum; drug delivery; macroscale; pharmacokinetic; porous; three compartment; transport

DOI:  https://doi.org/10.1016/j.jtbi.2021.110947
Front Cell Dev Biol. 2021 ;9 753414

A Novel Overall Survival Prediction Signature Based on Cancer Stem Cell-Related Genes in Osteosarcoma.

Bo Xiao, Liyan Liu, Zhuoyuan Chen, Aoyu Li, Yu Xia, Pingxiao Wang, Cheng Xiang, Yi Zeng, Hui Li.

  Background: Osteosarcoma is the most general bone malignancy that mostly affects children and adolescents. Numerous stem cell-related genes have been founded in distinct forms of cancer. This study aimed at identifying a stem cell-related gene model for the expected assessment of the prognosis of osteosarcoma patients. Methods: We obtained the genes expression data and relevant clinical materials from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We identified differentially expressed genes (DEGs) from the GEO dataset, whereas prognostic stem cell-related genes were obtained from the TARGET database. Subsequently, univariate, LASSO and multivariate Cox regression analyses were applied to establish the stem cell-related signature. Finally, the prognostic value of the signature was validated in the GEO dataset. Results: Twenty-five genes were prognostic ferroptosis-related DEGs. Consequently, we identified eight stem cell-related genes as a signature of prognosis of osteosarcoma patients. Then, the Kaplan-Meier (K-M) curve, the AUC value of ROC, and Cox regression analysis verified that the eight stem cell-related gene model were a new and substantial prognostic marker independent of other clinical traits. Moreover, the nomogram on the foundation of risk score and other clinical traits was established for predicting the survival rate of osteosarcoma patients. Biological function analyses displayed that tumor related pathways were affluent. Conclusion: The expression level of stem cell-related genes offers novel prognostic markers as well as underlying therapeutic targets for the therapy and prevention of osteosarcoma.

Keywords:  gene; osteosarcoma; overall survival; prognosis; signature; stem cells

DOI:  https://doi.org/10.3389/fcell.2021.753414
Cell Rep. 2021 Nov 02. pii: S2211-1247(21)01417-0. [Epub ahead of print]37(5): 109944

The histone chaperone FACT facilitates heterochromatin spreading by regulating histone turnover and H3K9 methylation states.

Magdalena Murawska, R A Greenstein, Tamas Schauer, Karl C F Olsen, Henry Ng, Andreas G Ladurner, Bassem Al-Sady, Sigurd Braun.

  Heterochromatin formation requires three distinct steps: nucleation, self-propagation (spreading) along the chromosome, and faithful maintenance after each replication cycle. Impeding any of those steps induces heterochromatin defects and improper gene expression. The essential histone chaperone FACT (facilitates chromatin transcription) has been implicated in heterochromatin silencing, but the mechanisms by which FACT engages in this process remain opaque. Here, we pinpoint its function to the heterochromatin spreading process in fission yeast. FACT impairment reduces nucleation-distal H3K9me3 and HP1/Swi6 accumulation at subtelomeres and derepresses genes in the vicinity of heterochromatin boundaries. FACT promotes spreading by repressing heterochromatic histone turnover, which is crucial for the H3K9me2 to me3 transition that enables spreading. FACT mutant spreading defects are suppressed by removal of the H3K9 methylation antagonist Epe1. Together, our study identifies FACT as a histone chaperone that promotes heterochromatin spreading and lends support to the model that regulated histone turnover controls the propagation of repressive methylation marks.

Keywords:  Epe1; FACT; heterochromatin spreading; histone chaperone; histone turnover

DOI:  https://doi.org/10.1016/j.celrep.2021.109944
Methods Mol Biol. 2022 ;2390 421-431

Machine Learning from Omics Data.

René Rex.

  Machine learning (ML) already accelerates discoveries in many scientific fields and is the driver behind several new products. Recently, growing sample sizes enabled the use of ML approaches in larger omics studies. This work provides a guide through a typical analysis of an omics dataset using ML. As an example, this chapter demonstrates how to build a model predicting Drug-Induced Liver Injury based on transcriptomics data contained in the LINCS L1000 dataset. Each section covers best practices and pitfalls starting from data exploration and model training including hyperparameter search to validation and analysis of the final model. The code to reproduce the results is available at https://github.com/Evotec-Bioinformatics/ml-from-omics .

Keywords:  Artificial intelligence; DILI; Drug discovery; Drug-Induced Liver Injury; Machine learning; SVM; Support vector machine; Transcriptomics

DOI:  https://doi.org/10.1007/978-1-0716-1787-8_18