New method analyzes long-range interactions in the interactome
Researchers at the Josep Carreras Leukaemia Research Institute have developed a method to analyze long-range interactions in the DNA -the interactome- from a very low amount of starting material. The method, named liCHi-C, opens the door to study the interactome of patient-derived samples instead of in vitro models for the first time and help us understand how alterations in regulatory regions affect the inner working of cancer cells.
For a long time, cancer genetics and epigenetics have been focusing on alterations in genes, proteins and its activity to understand what makes a cell go wrong and become malignant. However, the inner working of a cell implies the crosstalk between highly complex gene networks, controlled by small dispersed regulatory elements physically contacting DNA regions.
Many cancers show alterations in this crosstalk due to, for instance, DNA gains, loses or rearrangements. While genes are perfectly fine, being in a different position in the nucleus disrupts their ability to contact with its regulatory elements. The study of this network of interactions, known as the interactome, is one of the most challenging and unknown fields in cancer biology.
Analyzing the interactome is not an easy task. After carefully extracting the DNA of a cancer cell, taking all possible precautions to avoid disrupting those parts of the genome that are "talking to each other", scientists stick them together using a few biochemical tricks and reveal which genes and regulatory elements are in physical contact. By comparing healthy from cancer cells, they hope to understand the aberrant "discourse" of malignancies.
Up until now, this methodology needed a fair amount of DNA to be successful and was, therefore, far from the clinical management due to the limited sample size obtained in a biopsy. This may change thanks to liCHi-C, a new method developed by the 3D Chromatin Organization Lab led by Dr. Biola M. Javierre at the Josep Carreras Leukaemia Research Institute, a public facility belonging to the Generalitat de Catalunya's CERCA network.
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In a recent publication at Nature Communications, first authored by Laureano Tomás-Daza and Llorenç Rovirosa, the interactome of a particular tumor can be analyzed directly from patient samples instead of in vitro models, a large leap forward. This feat relies on the fact that liCHi-C, an improvement of the previous Promoter Capture Hi-C (PCHi-C) method, works with as low as 50 thousand cells instead of the millions needed for other methods. This substantial reduction in sample size is possible thanks to the combination of a streamlined experimental protocol with new bioinformatic tools.
Together with colleagues from the Josep Carreras Institute, the Barcelona Supercomputing Center, Wellcome-MRC Cambridge Stem Cell Institute, Sant Joan de Déu, IDIBAPS and the Hospital Clinic among others, the researchers have been able to increase the resolution of the interactome maps in developing hematopoietic stem cells from healthy donors and cancer patients, identifying altered networks occurring during leukemia. Also, they show how liCHi-C can be used to identify large DNA rearrangements with higher precision and to understand the role of non-coding alteration in cancer development.
Understanding the aberrant crosstalk in cancer cells may help develop new therapies, aimed at disrupting the "toxic chatter" inside them. We are still far from the clinic, but liCHi-C is the first step towards a wider and richer description of what happens inside a cancer cell, one of the hallmarks of biomedical research.
Josep Carreras Leukaemia Research Institute
Tomás-Daza, L., et al. (2023) Low input capture Hi-C (liCHi-C) identifies promoter-enhancer interactions at high-resolution. Nature Communications. doi.org/10.1038/s41467-023-35911-8.
Posted in: Molecular & Structural Biology | Cell Biology
Tags: Biopsy, Cancer, Cell, Chromatin, DNA, Epigenetics, Gene, Genes, Genetics, Genome, Hospital, in vitro, Interactome, Leukemia, Malignant, Promoter, Research, Stem Cells, Tumor
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