If you love learning more about biology at a fundamental level, I have a great video for you! It simulates the 3D folding of RNA. RNA is a single stranded molecule, but it is still capable of forming internal loops that can be stabilized by base pairing, just like its famously double-stranded parent, DNA. Understanding more about RNA folding may be valuable in many different areas of biomedical research, including developing ways to help people with RNA-related diseases, such as certain cancers and neuromuscular disorders, and designing better mRNA vaccines against infectious disease threats (like COVID-19).
Because RNA folding starts even while an RNA is still being made in the cell, the process has proven hugely challenging to follow closely. An innovative solution, shown in this video, comes from the labs of NIH grantees Julius Lucks, Northwestern University, Evanston, IL, and Alan Chen, State University of New York at Albany. The team, led by graduate student Angela Yu and including several diehard Star Wars fans, realized that to visualize RNA folding they needed a technology platform that, like a Star Wars droid, is able to “see” things that others can’t. So, they created R2D2, which is short for Reconstructing RNA Dynamics from Data.
What’s so groundbreaking about the R2D2 approach, which was published recently in Molecular Cell, is that it combines experimental data on RNA folding at the nucleotide level with predictive algorithms at the atomic level to simulate RNA folding in ultra-slow motion . While other computer simulations have been available for decades, they have lacked much-needed experimental data of this complex folding process to confirm their mathematical modeling.
As a gene is transcribed into RNA one building block, or nucleotide, at a time, the elongating RNA strand folds immediately before the whole molecule is fully assembled. But such folding can create a problem: the new strand can tie itself up into a knot-like structure that’s incompatible with the shape it needs to function in a cell.
To slip this knot, the cell has evolved immediate corrective pathways, or countermoves. In this R2D2 video, you can see one countermove called a toehold-mediated strand displacement. In this example, the maneuver is performed by an ancient molecule called a single recognition particle (SRP) RNA. Though SRP RNAs are found in all forms of life, this one comes from the bacterium Escherichia coli and is made up of 114 nucleotides.
The colors in this video highlight different domains of the RNA molecule, all at different stages in the folding process. Some (orange, turquoise) have already folded properly, while another domain (dark purple) is temporarily knotted. For this knotted domain to slip its knot, about 5 seconds into the video, another newly forming region (fuchsia) wiggles down to gain a “toehold.” About 9 seconds in, the temporarily knotted domain untangles and unwinds, and, finally, at about 23 seconds, the strand starts to get reconfigured into the shape it needs to do its job in the cell.
Why would evolution favor such a seemingly inefficient folding process? Well, it might not be inefficient as it first appears. In fact, as Chen noted, some nanotechnologists previously invented toehold displacement as a design principle for generating synthetic DNA and RNA circuits. Little did they know that nature may have scooped them many millennia ago!
You might recall learning in biology class that the cells constantly replicating and dividing in our bodies all carry the same DNA, inherited in equal parts from each parent. But it’s become increasingly clear in recent years that even seemingly healthy tissues contain neighborhoods of cells bearing their own acquired genetic mutations. The question is: What do all those altered cells mean for our health?
With support from a 2018 NIH Director’s New Innovator Award, Po-Ru Loh, Harvard Medical School, Boston, is on a quest to find out, though without the need for sequencing lots of DNA in his own lab. Loh will instead develop ultrasensitive computational tools to pick up on those often-subtle alterations within the vast troves of genomic data already stored in databases around the world.
How is that possible? The math behind it might be complex, but the underlying idea is surprisingly simple. His algorithms look for spots in the genome where a slight imbalance exists in the quantity of DNA inherited from mom versus dad.
Actually, Loh can’t tell from the data which parent provided any snippet of chromosomal DNA. But looking at DNA sequenced from a mixture of many cells, he can infer which stretches of DNA were most likely inherited together from a single parent.
Any slight skew in those quantities point the way to genomic territory where a tiny portion of chromosomal DNA either went missing or became duplicated in some cells. This common occurrence, especially in older adults, leads to a condition called genetic mosaicism, meaning that, contrary to most biology textbooks, all cells aren’t exactly the same.
By detecting those subtle imbalances in the data, Loh can pinpoint small DNA alterations, even when they occur in 1 in 1,000 cells collected from a person’s bloodstream, saliva, or tissues. That’s the kind of sensitivity that most scientists would not have thought possible.
Loh has already begun putting his new computational approach to work, as reported in Nature last year . In DNA data from blood samples of more than 150,000 participants in the United Kingdom Biobank, his method uncovered well over 8,000 mosaic chromosomal alterations.
The study showed that some of those alterations were associated with an increased risk of developing blood cancers. However, it’s important to note that most people with evidence of mosaicism won’t go on to develop cancer. The researchers also made the unexpected discovery that some individuals carried genetic variants that made them more prone than others to pick up new mutations in their blood cells.
What’s especially exciting is Loh’s computational tools now make it possible to search for signs of mosaicism within all the genetic data that’s ever been generated. Even more importantly, these tools will allow Loh and other researchers to ask and answer important questions about the consequences of mosaicism for a wide range of diseases.
Caption: New computational tool determines whether a gut microbe is the source of a hospital-acquired bloodstream infection Credit: Fiona Tamburini, Stanford University, Palo Alto, CA
While being cared for in the hospital, a disturbingly large number of people develop potentially life-threatening bloodstream infections. It’s been thought that most of the blame lies with microbes lurking on medical equipment, health-care professionals, or other patients and visitors. And certainly that is often true. But now an NIH-funded team has discovered that a significant fraction of these “hospital-acquired” infections may actually stem from a quite different source: the patient’s own body.
In a study of 30 bone-marrow transplant patients suffering from bloodstream infections, researchers used a newly developed computational tool called StrainSifter to match microbial DNA from close to one-third of the infections to bugs already living in the patients’ large intestines . In contrast, the researchers found little DNA evidence to support the notion that such microbes were being passed around among patients.
Not so long ago, Hilary Finucane was a talented young mathematician about to complete a master’s degree in theoretical computer science. As much as she enjoyed exploring pure mathematics, Finucane had begun having second thoughts about her career choice. She wanted to use her gift for numbers in a way that would have more real-world impact.
The solution to her dilemma was, literally, standing right by her side. Her husband Yakir Reshef, also a mathematician, was developing a new algorithm at the Broad Institute of MIT and Harvard, Cambridge, MA, to improve detection of unexpected associations in large data sets. So, Finucane helped the Broad team with modeling biomedical topics ranging from the gut microbiome to global health. That work led to her co-authoring a paper in the journal Science , providing a strong start to what’s shaping up to be a rewarding career in computational biology.
Caption: Networks of neurons in the mouse retina. Green cells form a special electrically coupled network; red cells express a distinctive fluorescent marker to distinguish them from other cells; blue cells are tagged with an antibody against an enzyme that makes nitric oxide, important in retinal signaling. Such images help to identify retinal cell types, their signaling molecules, and their patterns of connectivity. Credit: Jason Jacoby and Gregory Schwartz, Northwestern University
For Gregory Schwartz, working in total darkness has its benefits. Only in the pitch black can Schwartz isolate resting neurons from the eye’s retina and stimulate them with their natural input—light—to get them to fire electrical signals. Such signals not only provide a readout of the intrinsic properties of each neuron, but information that enables the vision researcher to deduce how it functions and forges connections with other neurons.
The retina is the light-sensitive neural tissue that lines the back of the eye. Although only about the size of a postage stamp, each of our retinas contains an estimated 130 million cells and more than 100 distinct cell types. These cells are organized into multiple information-processing layers that work together to absorb light and translate it into electrical signals that stream via the optic nerve to the appropriate visual center in the brain. Like other parts of the eye, the retina can break down, and retinal diseases, including age-related macular degeneration, retinitis pigmentosa, and diabetic retinopathy, continue to be leading causes of vision loss and blindness worldwide.
In his lab at Northwestern University’s Feinberg School of Medicine, Chicago, Schwartz performs basic research that is part of a much larger effort among vision researchers to assemble a parts list that accounts for all of the cell types needed to make a retina. Once Schwartz and others get closer to wrapping up this list, the next step will be to work out the details of the internal wiring of the retina to understand better how it generates visual signals. It’s the kind of information that holds the key for detecting retinal diseases earlier and more precisely, fixing miswired circuits that affect vision, and perhaps even one day creating an improved prosthetic retina.