scholarly journals Impact of cancer mutational signatures on transcription factor motifs in the human genome

2018 ◽  
Author(s):  
Calvin Wing Yiu Chan ◽  
Zuguang Gu ◽  
Matthias Bieg ◽  
Roland Eils ◽  
Carl Herrmann
Keyword(s):  
Transcription Factor ◽  
Information Content ◽  
Molecular Mechanisms ◽  
Reference Dataset ◽  
Mutational Signatures ◽  
Binding Motifs ◽  
Background Estimation ◽  
Cancer Genomes ◽  
Cancer Types ◽  
Tfbs Motif

ABSTRACTBackgroundSomatic mutations in cancer genomes occur through a variety of molecular mechanisms, which contribute to different mutational patterns. To summarize these, mutational signatures have been defined using a large number of cancer genomes, and related to distinct mutagenic processes. Each cancer genome can be compared to this reference dataset and its exposure to one or the other signature be determined. Given the very different mutational patterns of these signatures, we anticipate that they will have distinct impact on genomic elements, in particular motifs for transcription factor binding sites (TFBS).ResultsIn this work, we build the link between mutational signatures and TFBS motif alterations. We investigated and computed the theoretical impact of mutational signatures on 512 TFBS motifs, hence translating the trinucleotide mutation frequencies of the signatures into alteration frequencies of specific TFBS motifs, leading either to creation of disruption of these motifs. We further build a theoretical prediction of the alteration patterns for different cancer types based on the exposure of these cancer types to the mutation signatures. For certain motifs, a high correlation is observed between the TFBS motif creation and disruption events related to the information content of the motif.ConclusionOur results show that the mutational signatures have different impact on the binding motifs of transcription factors and that for certain high complexity motifs there is a strong correlation between creation and disruption, related to the information content of the motif. This study represents a background estimation of the alterations due purely to mutational signatures in the absence of additional contributions, e.g. from evolutionary processes.

scholarly journals Learning mutational signatures and their multidimensional genomic properties with TensorSignatures

2019 ◽  
Author(s):  
Harald Vöhringer ◽  
Arne van Hoeck ◽  
Edwin Cuppen ◽  
Moritz Gerstung
Keyword(s):  
Somatic Hypermutation ◽  
Translesion Synthesis ◽  
Strand Break ◽  
Signature Analysis ◽  
Transcription Start ◽  
Mutational Signatures ◽  
Transcription Start Sites ◽  
Mutational Signature ◽  
Cancer Genomes ◽  
Cancer Types

AbstractMutational signature analysis is an essential part of the cancer genome analysis toolkit. Conventionally, mutational signature analysis extracts patterns of different mutation types across many cancer genomes. Here we present TensorSignatures, an algorithm to learn mutational signatures jointly across all variant categories and their genomic context. The analysis of 2,778 primary and 3,824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that practically all signatures operate dynamically in response to various genomic and epigenomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis, which is detected in 7 different cancer types. The analysis also unmasks replication- and double strand break repair-driven APOBEC mutagenesis, which manifests with differential numbers and length of mutation clusters indicating a differential processivity of the two triggers. As a fourth example, TensorSignatures detects a signature of somatic hypermutation generating highly clustered variants around the transcription start sites of active genes in lymphoid leukaemia, distinct from a more general and less clustered signature of Polη-driven translesion synthesis found in a broad range of cancer types.Key findingsSimultaneous inference of mutational signatures across mutation types and genomic features refines signature spectra and defines their genomic determinants.Analysis of 6,602 cancer genomes reveals pervasive intra-genomic variation of mutational processes.Distinct mutational signatures found in quiescent and active regions of the genome reveal differential repair and mutagenicity of UV- and tobacco-induced DNA damage.APOBEC mutagenesis produces two signatures reflecting highly clustered, double strand break repair-initiated and lowly clustered replication-driven mutagenesis, respectively.Somatic hypermutation in lymphoid cancers produces a strongly clustered mutational signature localised to transcription start sites, which is distinct from a weakly clustered translesion synthesis signature found in multiple tumour types.

scholarly journals Recent progress on the role and molecular mechanism of chicken ovalbumin upstream promoter-transcription factor II in cancer

2020 ◽  
Vol 48 (4) ◽  
pp. 030006052091923
Author(s):  
Seong-Hoon Yun ◽  
Joo-In Park
Keyword(s):  
Transcription Factor ◽  
Recent Progress ◽  
Molecular Mechanisms ◽  
Orphan Receptor ◽  
Expression Of Genes ◽  
Factor Ii ◽  
Upstream Promoter ◽  
Cancer Types ◽  
Breast And Prostate Cancer ◽  
Chicken Ovalbumin

Chicken ovalbumin upstream promoter-transcription factor II (COUP-TFII) is an orphan receptor that regulates the expression of genes involved in development and homeostasis. COUP-TFII is also dysregulated in cancer, where it plays important roles in oncogenesis and malignant progression. Recent studies have also investigated altered microRNA-mediated regulation of COUP-TFII in cancer. Although many investigators have studied the expression and clinical significance of COUP-TFII in several cancer types, there remain many controversies regarding its role in these diseases. In this review, we will describe the functions and underlying molecular mechanisms of COUP-TFII in several cancers, especially colorectal, gastric, breast, and prostate cancer; additionally, we will briefly summarize what is known about microRNA-mediated regulation of COUP-TFII.

scholarly journals MoRAine - A web server for fast computational transcription factor binding motif re-annotation

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Jan Baumbach ◽  
Tobias Wittkop ◽  
Jochen Weile ◽  
Thomas Kohl ◽  
Sven Rahmann
Keyword(s):  
Transcription Factor ◽  
Information Content ◽  
Web Server ◽  
Transcription Factor Binding ◽  
Classification Performance ◽  
Binding Motif ◽  
Binding Motifs ◽  
Factor Binding ◽  
Sequence Logos ◽  
Transcription Factor Binding Motifs

SummaryBackground: A precise experimental identification of transcription factor binding motifs (TFBMs), accurate to a single base pair, is time-consuming and difficult. For several databases, TFBM annotations are extracted from the literature and stored 5ʹ → 3ʹ relative to the target gene. Mixing the two possible orientations of a motif results in poor information content of subsequently computed position frequency matrices (PFMs) and sequence logos. Since these PFMs are used to predict further TFBMs, we address the question if the TFBMs underlying a PFM can be re-annotated automatically to improve both the information content of the PFM and subsequent classification performance.Results: We present MoRAine, an algorithm that re-annotates transcription factor binding motifs. Each motif with experimental evidence underlying a PFM is compared against each other such motif. The goal is to re-annotate TFBMs by possibly switching their strands and shifting them a few positions in order to maximize the information content of the resulting adjusted PFM. We present two heuristic strategies to perform this optimization and subsequently show that MoRAine significantly improves the corresponding sequence logos. Furthermore, we justify the method by evaluating specificity, sensitivity, true positive, and false positive rates of PFM-based TFBM predictions for E. coli using the original database motifs and the MoRAine-adjusted motifs. The classification performance is considerably increased if MoRAine is used as a preprocessing step.Conclusions: MoRAine is integrated into a publicly available web server and can be used online or downloaded as a stand-alone version from http://moraine.cebitec.uni-bielefeld.de.

Transcription factor/DNA interactions visualized by electron spectroscopic imaging

1992 ◽  
Vol 50 (1) ◽  
pp. 450-451
Author(s):  
David P. Bazett-Jones ◽  
Mark L. Brown
Keyword(s):  
Transcription Factor ◽  
Dna Sequences ◽  
Transcription Initiation ◽  
Molecular Mechanisms ◽  
Phosphorus Content ◽  
Spectroscopic Imaging ◽  
Electron Spectroscopic Imaging ◽  
Dna Backbone ◽  
Two Parameters ◽  
High Level

A multisubunit RNA polymerase enzyme is ultimately responsible for transcription initiation and elongation of RNA, but recognition of the proper start site by the enzyme is regulated by general, temporal and gene-specific trans-factors interacting at promoter and enhancer DNA sequences. To understand the molecular mechanisms which precisely regulate the transcription initiation event, it is crucial to elucidate the structure of the transcription factor/DNA complexes involved. Electron spectroscopic imaging (ESI) provides the opportunity to visualize individual DNA molecules. Enhancement of DNA contrast with ESI is accomplished by imaging with electrons that have interacted with inner shell electrons of phosphorus in the DNA backbone. Phosphorus detection at this intermediately high level of resolution (≈lnm) permits selective imaging of the DNA, to determine whether the protein factors compact, bend or wrap the DNA. Simultaneously, mass analysis and phosphorus content can be measured quantitatively, using adjacent DNA or tobacco mosaic virus (TMV) as mass and phosphorus standards. These two parameters provide stoichiometric information relating the ratios of protein:DNA content.

scholarly journals Molecular regulation of Snai2 in development and disease

2019 ◽  
Vol 132 (23) ◽  
Author(s):  
Wenhui Zhou ◽  
Kayla M. Gross ◽  
Charlotte Kuperwasser
Keyword(s):  
Transcription Factor ◽  
Cell Biology ◽  
Molecular Mechanisms ◽  
Epithelial To Mesenchymal Transition ◽  
Zinc Finger Protein ◽  
Main Role ◽  
Biological Processes ◽  
Developmental Defects ◽  
Mesenchymal Transition ◽  
Damage Repair

ABSTRACT The transcription factor Snai2, encoded by the SNAI2 gene, is an evolutionarily conserved C2H2 zinc finger protein that orchestrates biological processes critical to tissue development and tumorigenesis. Initially characterized as a prototypical epithelial-to-mesenchymal transition (EMT) transcription factor, Snai2 has been shown more recently to participate in a wider variety of biological processes, including tumor metastasis, stem and/or progenitor cell biology, cellular differentiation, vascular remodeling and DNA damage repair. The main role of Snai2 in controlling such processes involves facilitating the epigenetic regulation of transcriptional programs, and, as such, its dysregulation manifests in developmental defects, disruption of tissue homeostasis, and other disease conditions. Here, we discuss our current understanding of the molecular mechanisms regulating Snai2 expression, abundance and activity. In addition, we outline how these mechanisms contribute to disease phenotypes or how they may impact rational therapeutic targeting of Snai2 dysregulation in human disease.

scholarly journals Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marleen M. Nieboer ◽  
Luan Nguyen ◽  
Jeroen de Ridder
Keyword(s):  
Multiple Instance Learning ◽  
Cancer Diagnostics ◽  
Common Mechanism ◽  
Open Chromatin ◽  
Driver Genes ◽  
3D Genome ◽  
Whole Genomes ◽  
Cancer Genomes ◽  
Cancer Types ◽  
The Impact

AbstractOver the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. Here, we introduce svMIL2, an improved version of our Multiple Instance Learning-based method to study the effect of somatic non-coding SVs disrupting boundaries of TADs and CTCF loops in 1646 cancer genomes. We demonstrate that svMIL2 predicts pathogenic non-coding SVs with an average AUC of 0.86 across 12 cancer types, and identifies non-coding SVs affecting well-known driver genes. The disruption of active (super) enhancers in open chromatin regions appears to be a common mechanism by which non-coding SVs exert their pathogenicity. Finally, our results reveal that the contribution of pathogenic non-coding SVs as opposed to driver SNVs may highly vary between cancers, with notably high numbers of genes being disrupted by pathogenic non-coding SVs in ovarian and pancreatic cancer. Taken together, our machine learning method offers a potent way to prioritize putatively pathogenic non-coding SVs and leverage non-coding SVs to identify driver genes. Moreover, our analysis of 1646 cancer genomes demonstrates the importance of including non-coding SVs in cancer diagnostics.

scholarly journals Learning mutational signatures and their multidimensional genomic properties with TensorSignatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Harald Vöhringer ◽  
Arne Van Hoeck ◽  
Edwin Cuppen ◽  
Moritz Gerstung
Keyword(s):  
Somatic Hypermutation ◽  
Excision Repair ◽  
Metastatic Cancer ◽  
Strand Break ◽  
Lymphoid Leukaemia ◽  
Mutational Signatures ◽  
Transcription Start Sites ◽  
Nucleotide Excision ◽  
Cancer Types ◽  
Underlying Processes

AbstractWe present TensorSignatures, an algorithm to learn mutational signatures jointly across different variant categories and their genomic localisation and properties. The analysis of 2778 primary and 3824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that all signatures operate dynamically in response to genomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis in 7 different cancer types. The algorithm also extracts distinct signatures of replication- and double strand break repair-driven mutagenesis by APOBEC3A and 3B with differential numbers and length of mutation clusters. Finally, TensorSignatures reproduces a signature of somatic hypermutation generating highly clustered variants at transcription start sites of active genes in lymphoid leukaemia, distinct from a general and less clustered signature of Polη-driven translesion synthesis found in a broad range of cancer types. In summary, TensorSignatures elucidates complex mutational footprints by characterising their underlying processes with respect to a multitude of genomic variables.

scholarly journals The AP-1 Transcription Factor Fosl-2 Regulates Autophagy in Cardiac Fibroblasts during Myocardial Fibrogenesis

2021 ◽  
Vol 22 (4) ◽  
pp. 1861
Author(s):  
Jemima Seidenberg ◽  
Mara Stellato ◽  
Amela Hukara ◽  
Burkhard Ludewig ◽  
Karin Klingel ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Molecular Mechanisms ◽  
Cardiac Fibrosis ◽  
Type I Collagen ◽  
Transforming Growth Factor ◽  
Cardiac Fibroblasts ◽  
Smooth Muscle Actin ◽  
Beclin 1 ◽  
Type I ◽  
Alpha Smooth Muscle Actin

Background: Pathological activation of cardiac fibroblasts is a key step in development and progression of cardiac fibrosis and heart failure. This process has been associated with enhanced autophagocytosis, but molecular mechanisms remain largely unknown. Methods and Results: Immunohistochemical analysis of endomyocardial biopsies showed increased activation of autophagy in fibrotic hearts of patients with inflammatory cardiomyopathy. In vitro experiments using mouse and human cardiac fibroblasts confirmed that blockade of autophagy with Bafilomycin A1 inhibited fibroblast-to-myofibroblast transition induced by transforming growth factor (TGF)-β. Next, we observed that cardiac fibroblasts obtained from mice overexpressing transcription factor Fos-related antigen 2 (Fosl-2tg) expressed elevated protein levels of autophagy markers: the lipid modified form of microtubule-associated protein 1A/1B-light chain 3B (LC3BII), Beclin-1 and autophagy related 5 (Atg5). In complementary experiments, silencing of Fosl-2 with antisense GapmeR oligonucleotides suppressed production of type I collagen, myofibroblast marker alpha smooth muscle actin and autophagy marker Beclin-1 in cardiac fibroblasts. On the other hand, silencing of either LC3B or Beclin-1 reduced Fosl-2 levels in TGF-β-activated, but not in unstimulated cells. Using a cardiac hypertrophy model induced by continuous infusion of angiotensin II with osmotic minipumps, we confirmed that mice lacking either Fosl-2 (Ccl19CreFosl2flox/flox) or Atg5 (Ccl19CreAtg5flox/flox) in stromal cells were protected from cardiac fibrosis. Conclusion: Our findings demonstrate that Fosl-2 regulates autophagocytosis and the TGF-β-Fosl-2-autophagy axis controls differentiation of cardiac fibroblasts. These data provide a new insight for the development of pharmaceutical targets in cardiac fibrosis.

scholarly journals The apple C2H2-type zinc finger transcription factor MdZAT10 positively regulates JA-induced leaf senescence by interacting with MdBT2

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Kuo Yang ◽  
Jian-Ping An ◽  
Chong-Yang Li ◽  
Xue-Na Shen ◽  
Ya-Jing Liu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Transcription Factor ◽  
Liquid Chromatography ◽  
Zinc Finger ◽  
Leaf Senescence ◽  
Transcriptional Activity ◽  
Molecular Mechanisms ◽  
Liquid Chromatography Mass Spectrometry ◽  
Zinc Finger Transcription Factor ◽  
Insight Into

AbstractJasmonic acid (JA) plays an important role in regulating leaf senescence. However, the molecular mechanisms of leaf senescence in apple (Malus domestica) remain elusive. In this study, we found that MdZAT10, a C2H2-type zinc finger transcription factor (TF) in apple, markedly accelerates leaf senescence and increases the expression of senescence-related genes. To explore how MdZAT10 promotes leaf senescence, we carried out liquid chromatography/mass spectrometry screening. We found that MdABI5 physically interacts with MdZAT10. MdABI5, an important positive regulator of leaf senescence, significantly accelerated leaf senescence in apple. MdZAT10 was found to enhance the transcriptional activity of MdABI5 for MdNYC1 and MdNYE1, thus accelerating leaf senescence. In addition, we found that MdZAT10 expression was induced by methyl jasmonate (MeJA), which accelerated JA-induced leaf senescence. We also found that the JA-responsive protein MdBT2 directly interacts with MdZAT10 and reduces its protein stability through ubiquitination and degradation, thereby delaying MdZAT10-mediated leaf senescence. Taken together, our results provide new insight into the mechanisms by which MdZAT10 positively regulates JA-induced leaf senescence in apple.

scholarly journals Hyperprogressive Disease: Main Features and Key Controversies

2021 ◽  
Vol 22 (7) ◽  
pp. 3736
Author(s):  
Hugo Arasanz ◽  
Miren Zuazo ◽  
Ana Bocanegra ◽  
Luisa Chocarro ◽  
Ester Blanco ◽  
...  
Keyword(s):  
Tumor Growth ◽  
Associated Factors ◽  
Molecular Mechanisms ◽  
Natural Course ◽  
Preferential Treatment ◽  
Radiological Criteria ◽  
Tumor Histology ◽  
Fast Progression ◽  
Cancer Types ◽  
Hyperprogressive Disease

Along with the positioning of immunotherapy as a preferential treatment for a wide variety of neoplasms, a new pattern of response consisting in a sudden acceleration of tumor growth has been described. This phenomenon has received the name of “hyperprogressive disease”, and several definitions have been proposed for its identification, most of them relying on radiological criteria. However, due to the fact that the cellular and molecular mechanisms have not been elucidated yet, there is still some debate regarding whether this fast progression is induced by immunotherapy or only reflects the natural course of some highly aggressive neoplasms. Moreover, contradictory results of trials including patients with different cancer types suggest that both the incidence, the associated factors and the implications regarding prognosis might differ depending on tumor histology. This article intends to review the main publications regarding this matter and critically approach the most controversial aspects.

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