Mathematics enables scientists to understand the organization within the cell nucleus

Nature Communications (2022). DOI: 10.1038 / s41467-022-32980-z” width=”800″ height=”488″/>

Categories of copy sets. In the self-sustaining transcriptome assembly, there are both TF and TF-containing gene coding for. The intra- and intra-chromosomal examples in (a) and (b), respectively, illustrate this phenomenon where we see in a TF of interest (orange triangle) circulating in the cluster, the splicing subject is located on the chromatin (orange part), and the corresponding gene expressed (rectangle orange on chromosome 6). Gray shapes represent additional TFs with binding motifs (the gray portion of chromatin) in the cluster. The black rectangles on chromosomes 3, 9 and 19 represent additional genes present in the cluster. c A class of analog-independent transcript clusters in which we observe association of TF (red box) in a transcriptome set (red block) and its corresponding gene expressed in a separate transcript set (grey block), but not in the same set. d A class of analog-independent transcription groups in which we observe association of TF (green circle) in a transcriptome set (green set) and its corresponding gene being expressed but not within a transcriptome set. e Genome-wide cell type-specific self-sustaining transcriptomes extracted from omnidirectional contact data and decomposed into Hi-C contact arrays at 100 kb resolution. The communication frequencies are converted to logarithmic for better visualization. Frequencies along the diameter indicate the interaction between two or more unique multidirectional loci that lie within the same 100 kb bin. The axis labels are non-contiguous 100 kb coordinates in a chromosomal arrangement. The omnidirectional contacts that make up the self-sustaining transcriptome assemblies are enforced. The multidirectional contacts with loci in green represent the ‘core’ transcript clusters – the transcriptome assemblies that contain a master regulator and the genetic analog. An example read-level connection map of the self-sustaining transcriptome assembly between FOXO3 chromosomes is indicated by the highlighted orange box in the adult fibroblast contact matrix and a read-level connection map for the ZNF320 self-sustaining transcriptome set within chromosomes is indicated by the blue highlighted box. The values ​​along the left axis of these read-level contact matrices are the base pair positions of the contact sites in the genome. attributed to him: Nature Communications (2022). DOI: 10.1038 / s41467-022-32980-z

The Third Law of science fiction writer Arthur C. Clarke says that “any sufficiently advanced technology is indistinguishable from magic.”

Indika Rajapakse, Ph.D., believer. The engineer and mathematician now finds himself a biologist. He believes that the beauty of blending these three disciplines is crucial to revealing how cells function.

His latest development is a new mathematical technique to begin to understand how the cell nucleus is organized. The technique, which Rajapaks and colleagues tested on several cell types, revealed what the researchers called self-sustaining transcriptome clusters, a subset of proteins that play a key role in maintaining cell identity.

They hope this understanding will reveal vulnerabilities that can be targeted to reprogram a cell to stop cancer or other diseases.

Rajapaks, assistant professor of computer science, said: “More and more cancer biologists believe that genome regulation plays a huge role in understanding the uncontrollable cell division and whether we can reprogram a cancer cell. This means we need to understand more details. about what happens in the nucleus. Medicine, bioinformatics, mathematics and Biomedical engineering at the University of Michigan. He is also a member of the UM Rogel Cancer Center.

Rajapakse is lead author on the paper published in Nature Communications. The project was led by three graduate students with a multidisciplinary team of researchers.

The team has improved an ancient chromatin-screening technique, called Hi-C, which maps which parts of the genome are close together. It can identify chromosomal translocations, such as those that occur in some types of cancer. However, its limitation is that it only sees these adjacent genomic regions.

The new technology, called Pore-C, uses more data to visualize how all the pieces interact within the cell nucleus. The researchers used a mathematical technique called hypergraphs. Think: a 3D Venn diagram. It allows researchers to see not only the pairs of genomic regions that interact but the entirety of the complex and overlapping genome-wide relationships within cells.

“This is a multidimensional relationship that we can understand unambiguously. It gives us a more detailed way to understand the organizational principles within the nucleus. If you understand that, you can also understand where these organizational principles veer, as in cancer“This is like putting three worlds together – technology, mathematics and biology – to study more detail inside the core,” Rajapakse said.

The researchers tested their approach on newborn fibroblasts, adult fibroblasts obtained from biopsy and B lymphocytes. They identified the regulators of transcriptomes for each cell type. They also found what they called self-sustaining transcriptional assemblies, which act as key transcriptional signatures for a cell type.

Rajapaksa describes this as the first step into a bigger picture.

“My goal is to build this kind of picture of the cell cycle to understand how the cell goes through different stages. Cancer is uncontrollable cell division,” Rajapakse said. “If we understand how a normal cell changes over time, we can begin to examine controlled and uncontrolled systems and find ways to reprogram that system.”


Unraveling the mysteries of the genome structure in the human cell nucleus using 3D computational simulation


more information:
Gabriel A. Dotson et al., Deciphering multidirectional interactions in the human genome, Nature Communications (2022). DOI: 10.1038 / s41467-022-32980-z

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University of Michigan


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