Researchers Develop Algorithm, Assay to Assess Genome Structure

Researchers from Weill Cornell Medicine and the New York Genome Center have developed a new assay and corresponding algorithm that allows them to study the three-dimensional structure of the human genome, or how the genome folds, which may impact cell function and gene expression.

Previous work to analyze the human genome has shown that that there is a relationship between genome structure and cellular identity, but previous methods to assess how the genome folds have offered incomplete insights.

“Knowing the three-dimensional genome structure will help researchers better understand how the genome functions, and particularly how it encodes different cell identities,” said senior author Marcin Imieliński, MD, PhD, associate professor of pathology and laboratory medicine and computational genomics in computational biomedicine at Weill Cornell Medicine, in the press release. “The ways that we’ve had to study genome structure have given us amazing insights, but there have also been key limitations.”

One such limitation is that previous methods only allowed researchers to observe how frequently two loci, or physical locations on the genome, interact with each other. These pairs of loci, known as enhancers and promoters, interact with one another to influence gene expression. While these pairs offer valuable information, they do not provide a complete picture of genome functions.

Scientists have theorized that folding patterns within the genome are linked to how the genome codes for a specific cell type or identity, but this is difficult to determine without assessing groups of loci, rather than just pairs. To overcome this, the researchers sought to develop a genome-wide assay and algorithm that would allow them to study groups of loci and investigate how cell types are encoded, particularly in the structure of the DNA.

The researchers developed their assay by adapting a traditional method called chromatin conformation capture (Hi-C), which is designed to assess a mixture of DNA and protein. This enables the analysis of the three-dimensional genome structure with nanopore sequencing, which is the high-throughput sequencing of long, continuous strands of DNA molecules. The resulting assay, Pore-C, allows the researchers to observe tens of millions of locus groupings.

They also created a corresponding algorithm to determine which locus groupings were important based on whether they interacted cooperatively to influence gene expression. The researchers noted that not all interactions of the genome are important. The algorithm helps prioritize the interactions that are likely to matter to genome function.

The researchers found that the most significant cooperative groupings of DNA elements occurred around genes associated with cell identity. According to the study, the new assay and algorithm may help researchers to understand how stem cells differentiate into different cell types or how cancer cell genomes drive cancer growth and spread.

In the future, they will explore which specific groupings of genomic components are essential for various aspects of cell identity, the researchers stated.