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Do C, Jiang G, Cova G, Katsifis CC, Narducci DN, Yang J, Sakellaropoulos T, Vidal R, Lhoumaud P, Fara Regis F, Kakabadze N, Nora EP, Noyes M, Cheng X, Hansen AS, Skok JA. Brain and cancer associated binding domain mutations provide insight into CTCF's relationship with chromatin and its ability to act as a chromatin organizer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.11.575070. [PMID: 38370764 PMCID: PMC10871189 DOI: 10.1101/2024.01.11.575070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Although only a fraction of CTCF motifs are bound in any cell type, and few occupied sites overlap cohesin, the mechanisms underlying cell-type specific attachment and ability to function as a chromatin organizer remain unknown. To investigate the relationship between CTCF and chromatin we applied a combination of imaging, structural and molecular approaches, using a series of brain and cancer associated CTCF mutations that act as CTCF perturbations. We demonstrate that binding and the functional impact of WT and mutant CTCF depend not only on the unique binding properties of each protein, but also on the genomic context of bound sites and enrichment of motifs for expressed TFs abutting these sites. Our studies also highlight the reciprocal relationship between CTCF and chromatin, demonstrating that the unique binding properties of WT and mutant proteins have a distinct impact on accessibility, TF binding, cohesin overlap, chromatin interactivity and gene expression programs, providing insight into their cancer and brain related effects.
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Wortmann SB, Oud MM, Alders M, Coene KLM, van der Crabben SN, Feichtinger RG, Garanto A, Hoischen A, Langeveld M, Lefeber D, Mayr JA, Ockeloen CW, Prokisch H, Rodenburg R, Waterham HR, Wevers RA, van de Warrenburg BPC, Willemsen MAAP, Wolf NI, Vissers LELM, van Karnebeek CDM. How to proceed after "negative" exome: A review on genetic diagnostics, limitations, challenges, and emerging new multiomics techniques. J Inherit Metab Dis 2022; 45:663-681. [PMID: 35506430 PMCID: PMC9539960 DOI: 10.1002/jimd.12507] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/26/2022] [Accepted: 04/27/2022] [Indexed: 11/28/2022]
Abstract
Exome sequencing (ES) in the clinical setting of inborn metabolic diseases (IMDs) has created tremendous improvement in achieving an accurate and timely molecular diagnosis for a greater number of patients, but it still leaves the majority of patients without a diagnosis. In parallel, (personalized) treatment strategies are increasingly available, but this requires the availability of a molecular diagnosis. IMDs comprise an expanding field with the ongoing identification of novel disease genes and the recognition of multiple inheritance patterns, mosaicism, variable penetrance, and expressivity for known disease genes. The analysis of trio ES is preferred over singleton ES as information on the allelic origin (paternal, maternal, "de novo") reduces the number of variants that require interpretation. All ES data and interpretation strategies should be exploited including CNV and mitochondrial DNA analysis. The constant advancements in available techniques and knowledge necessitate the close exchange of clinicians and molecular geneticists about genotypes and phenotypes, as well as knowledge of the challenges and pitfalls of ES to initiate proper further diagnostic steps. Functional analyses (transcriptomics, proteomics, and metabolomics) can be applied to characterize and validate the impact of identified variants, or to guide the genomic search for a diagnosis in unsolved cases. Future diagnostic techniques (genome sequencing [GS], optical genome mapping, long-read sequencing, and epigenetic profiling) will further enhance the diagnostic yield. We provide an overview of the challenges and limitations inherent to ES followed by an outline of solutions and a clinical checklist, focused on establishing a diagnosis to eventually achieve (personalized) treatment.
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Affiliation(s)
- Saskia B. Wortmann
- Radboud Center for Mitochondrial and Metabolic Medicine, Department of PediatricsAmalia Children's Hospital, Radboud University Medical CenterNijmegenThe Netherlands
- University Children's Hospital, Paracelsus Medical UniversitySalzburgAustria
| | - Machteld M. Oud
- United for Metabolic DiseasesAmsterdamThe Netherlands
- Department of Human GeneticsDonders Institute for Brain, Cognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
| | - Mariëlle Alders
- Department of Human GeneticsAmsterdam UMC, University of Amsterdam, Amsterdam Reproduction and Development Research InstituteAmsterdamThe Netherlands
| | - Karlien L. M. Coene
- United for Metabolic DiseasesAmsterdamThe Netherlands
- Translational Metabolic Laboratory, Department of Laboratory MedicineRadboud University Medical CenterNijmegenThe Netherlands
| | - Saskia N. van der Crabben
- Department of Human GeneticsAmsterdam University Medical Centers, University of AmsterdamAmsterdamThe Netherlands
| | - René G. Feichtinger
- University Children's Hospital, Paracelsus Medical UniversitySalzburgAustria
| | - Alejandro Garanto
- Radboud Center for Mitochondrial and Metabolic Medicine, Department of PediatricsAmalia Children's Hospital, Radboud University Medical CenterNijmegenThe Netherlands
- Department of PediatricsAmalia Children's Hospital, Radboud Institute for Molecular LifesciencesNijmegenThe Netherlands
- Department of Human GeneticsRadboud Institute for Molecular LifesciencesNijmegenThe Netherlands
| | - Alex Hoischen
- Department of Human Genetics, Department of Internal Medicine and Radboud Center for Infectious DiseasesRadboud Institute of Medical Life Sciences, Radboud University Medical CenterNijmegenthe Netherlands
| | - Mirjam Langeveld
- Department of Endocrinology and MetabolismAmsterdam University Medical Centers, location AMC, University of AmsterdamAmsterdamThe Netherlands
| | - Dirk Lefeber
- United for Metabolic DiseasesAmsterdamThe Netherlands
- Translational Metabolic Laboratory, Department of Laboratory MedicineRadboud University Medical CenterNijmegenThe Netherlands
- Department of Neurology, Donders Institute for BrainCognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
| | - Johannes A. Mayr
- University Children's Hospital, Paracelsus Medical UniversitySalzburgAustria
| | - Charlotte W. Ockeloen
- Department of Human GeneticsRadboud Institute for Molecular LifesciencesNijmegenThe Netherlands
| | - Holger Prokisch
- School of MedicineInstitute of Human Genetics, Technical University Munich and Institute of NeurogenomicsNeuherbergGermany
| | - Richard Rodenburg
- Radboud Center for Mitochondrial and Metabolic MedicineTranslational Metabolic Laboratory, Department of Pediatrics, Radboud University Medical CenterNijmegenThe Netherlands
| | - Hans R. Waterham
- United for Metabolic DiseasesAmsterdamThe Netherlands
- Laboratory Genetic Metabolic Diseases, Department of Clinical ChemistryAmsterdam University Medical Centers, location AMC, University of AmsterdamAmsterdamThe Netherlands
| | - Ron A. Wevers
- United for Metabolic DiseasesAmsterdamThe Netherlands
- Translational Metabolic Laboratory, Department of Laboratory MedicineRadboud University Medical CenterNijmegenThe Netherlands
| | - Bart P. C. van de Warrenburg
- Department of Neurology, Donders Institute for BrainCognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
| | - Michel A. A. P. Willemsen
- Departments of Pediatric Neurology and PediatricsAmalia Children's Hospital, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical CenterNijmegenThe Netherlands
| | - Nicole I. Wolf
- Amsterdam Leukodystrophy Center, Department of Child NeurologyEmma Children's Hospital, Amsterdam University Medical Centers, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Lisenka E. L. M. Vissers
- Department of Human GeneticsDonders Institute for Brain, Cognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
| | - Clara D. M. van Karnebeek
- Radboud Center for Mitochondrial and Metabolic Medicine, Department of PediatricsAmalia Children's Hospital, Radboud University Medical CenterNijmegenThe Netherlands
- United for Metabolic DiseasesAmsterdamThe Netherlands
- Department of Human GeneticsAmsterdam UMC, University of Amsterdam, Amsterdam Reproduction and Development Research InstituteAmsterdamThe Netherlands
- Department of Pediatrics, Emma Center for Personalized MedicineAmsterdam University Medical Centers, Amsterdam, Amsterdam Genetics Endocrinology Metabolism Research Institute, University of AmsterdamAmsterdamThe Netherlands
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Biswas A, Narlikar L. Resolving diverse protein-DNA footprints from exonuclease-based ChIP experiments. Bioinformatics 2021; 37:i367-i375. [PMID: 34252930 PMCID: PMC8275329 DOI: 10.1093/bioinformatics/btab274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
MOTIVATION High-throughput chromatin immunoprecipitation (ChIP) sequencing-based assays capture genomic regions associated with the profiled transcription factor (TF). ChIP-exo is a modified protocol, which uses lambda exonuclease to digest DNA close to the TF-DNA complex, in order to improve on the positional resolution of the TF-DNA contact. Because the digestion occurs in the 5'-3' orientation, the protocol produces directional footprints close to the complex, on both sides of the double stranded DNA. Like all ChIP-based methods, ChIP-exo reports a mixture of different regions associated with the TF: those bound directly to the TF as well as via intermediaries. However, the distribution of footprints are likely to be indicative of the complex forming at the DNA. RESULTS We present ExoDiversity, which uses a model-based framework to learn a joint distribution over footprints and motifs, thus resolving the mixture of ChIP-exo footprints into diverse binding modes. It uses no prior motif or TF information and automatically learns the number of different modes from the data. We show its application on a wide range of TFs and organisms/cell-types. Because its goal is to explain the complete set of reported regions, it is able to identify co-factor TF motifs that appear in a small fraction of the dataset. Further, ExoDiversity discovers small nucleotide variations within and outside canonical motifs, which co-occur with variations in footprints, suggesting that the TF-DNA structural configuration at those regions is likely to be different. Finally, we show that detected modes have specific DNA shape features and conservation signals, giving insights into the structure and function of the putative TF-DNA complexes. AVAILABILITY AND IMPLEMENTATION The code for ExoDiversity is available on https://github.com/NarlikarLab/exoDIVERSITY. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Anushua Biswas
- Department of Chemical Engineering, CSIR-National Chemical Laboratory, Pune 411008, India.,Academy of Scientific and Innovative Research, Ghaziabad 201002, India
| | - Leelavati Narlikar
- Department of Chemical Engineering, CSIR-National Chemical Laboratory, Pune 411008, India.,Academy of Scientific and Innovative Research, Ghaziabad 201002, India
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