Unraveling The Mystery Of Our Scrunched-Up DNA

A group of researchers at the University of Pennsylvania led by NYSCF — Robertson Investigator Jennifer Phillips-Cremins, PhD, have developed a tool to examine why the physical position of DNA within our cells is an important factor for health and disease.

The human genome is 2 meters long. If you were to take all the DNA out of one of your cells and stretch it out from floor to ceiling, it would be about as tall as Michael Jordan. But the size of a cell’s nucleus (where the DNA is held) is just 10 micrometers—about the same as the width of a single strand of hair. So how does all our DNA fit into that tiny nucleus? It simply crumples together and folds.

It may seem like the position of DNA doesn’t matter—that it would just need to fit inside the nucleus to be functional. But the way our DNA folds isn’t random, it dictates how a cell works and survives. If DNA folds incorrectly, it can lead to disease.

When a group of folded-up DNA segments appear to interact with each other a lot, scientists refer to that group as a topologically associating domain (TAD). And when certain segments of DNA within that TAD interact with each other a lot, we call those segments a sub-TAD. The role of these TADs and sub-TADs in biological function, however, is poorly understood, in part because they’re hard to detect and study.

One way scientists can identify TADs and sub-TADs is by using networks. Networks are graphs consisting of groups (or nodes) that connect to each other and represent information systems (in this case, biological information pertaining to our DNA). Existing versions of models that analyze these networks often struggle to identify the complexities of TADs and sub-TADs, so Dr. Phillips-Cremins and her team sought to develop a better version.

Published in Nature Methods, their model, called 3DNetMod, helps identify these tiny DNA groups by maximizing something called network modularity. Modularity is a measure of network segmentation, or how networks are broken into their individual groups. Networks representing DNA folding tend to be very complex and hierarchical, and by maximizing their modularity (as 3DNetMod does), it becomes easier to observe characteristics within them.

When testing their model against two existing models, the researchers found that 3DNetMod was better at identifying TADs and sub-TADs in a network of simulated biological data as well as one of actual data from human cortical plate tissue.

The researchers hope that this model will help us study the structure of our genome and identify when changes in that structure lead to disease.

Read the paper in Nature Methods

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