Dr. Francis Collins
Posted on by Dr. Francis Collins
August is here, and many folks have plans to enjoy a well-deserved vacation this month. I thought you might enjoy taking a closer look during August at the wonder and beauty of the brain here on my blog, even while giving your own brains a rest from some of the usual work and deadlines.
Some of the best imagery—and best science—comes from the NIH-led Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative, a pioneering project aimed at revolutionizing our understanding of the human brain. Recently, the BRAIN Initiative held a “Show Us Your Brain Contest!”, which invited researchers involved in the effort to submit their coolest images. So, throughout this month, I’ve decided to showcase a few of these award-winning visuals.
Let’s start with the first-place winner in the still-image category. What you see above is an artistic rendering of deep brain stimulation (DBS), an approach now under clinical investigation to treat cognitive impairment that can arise after a traumatic brain injury and other conditions.
The vertical lines represent wire leads with a single electrode that has been inserted deep within the brain to reach a region involved in cognition, the central thalamus. The leads are connected to a pacemaker-like device that has been implanted in a patient’s chest (not shown). When prompted by the pacemaker, the leads’ electrode emits electrical impulses that stimulate a network of neuronal fibers (blue-white streaks) involved in arousal, which is an essential component of human consciousness. The hope is that DBS will improve attention and reduce fatigue in people with serious brain injuries that are not treatable by other means.
Andrew Janson, who is a graduate student in Christopher Butson’s NIH-supported lab at the Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, composed this image using a software program called Blender. It’s an open-source, 3D computer graphics program often used to create animated films or video games, but not typically used in biomedical research. That didn’t stop Janson.
With the consent of a woman preparing to undergo experimental DBS treatment for a serious brain injury suffered years before in a car accident, Janson used Blender to transform her clinical brain scans into a 3D representation of her brain and the neurostimulation process. Then, he used a virtual “camera” within Blender to capture the 2D rendering you see here. Janson plans to use such imagery, along with other patient-specific modeling and bioelectric fields simulations, to develop a virtual brain stimulation surgery to predict the activation of specific fiber pathways, depending upon lead location and stimulation settings.
DBS has been used for many years to relieve motor symptoms of certain movement disorders, including Parkinson’s disease and essential tremor. More recent experimental applications include this one for traumatic brain injury, and others for depression, addiction, Alzheimer’s disease, and chronic pain. As the BRAIN Initiative continues to map out the brain’s complex workings in unprecedented detail, it will be exciting to see how such information can lead to even more effective applications of to DBS to help people living with a wide range of neurological conditions.
Deep Brain Stimulation for Movement Disorders (National Institute of Neurological Disorders and Stroke/NIH)
Video: Deep Brain Stimulation (University of Utah, Salt Lake City)
Butson Lab (University of Utah)
Show Us Your Brain! (BRAIN Initiative/NIH)
NIH Support: National Institute of Neurological Disorders and Stroke
Posted on by Dr. Francis Collins
When I volunteered to serve as a physician at a hospital in rural Nigeria more than 25 years ago, I expected to treat a lot of folks with infectious diseases, such as malaria and tuberculosis. And that certainly happened. What I didn’t expect was how many people needed care for type 2 diabetes (T2D) and the health problems it causes. Surprisingly, these individuals were generally not overweight, and the course of their illness seemed different than in the West.
The experience inspired me to join with other colleagues at Howard University, Washington, DC, to help found the Africa America Diabetes Mellitus (AADM) study. It aims to uncover genomic risk factors for T2D in Africa and, using that information, improve understanding of the condition around the world.
So, I’m pleased to report that, using genomic data from more than 5,000 volunteers, our AADM team recently discovered a new gene, called ZRANB3, that harbors a variant associated with T2D in sub-Saharan Africa . Using sophisticated laboratory models, the team showed that a malfunctioning ZRANB3 gene impairs insulin production to control glucose levels in the bloodstream.
Since my first trip to Nigeria, the number of people with T2D has continued to rise. It’s now estimated that about 8 to 10 percent of Nigerians have some form of diabetes . In Africa, diabetes affects more than 7 percent of the population, more than twice the incidence in 1980 .
The causes of T2D involve a complex interplay of genetic, environmental, and lifestyle factors. I was particularly interested in finding out whether the genetic factors for T2D might be different in sub-Saharan Africa than in the West. But at the time, there was a dearth of genomic information about T2D in Africa, the cradle of humanity. To understand complex diseases like T2D fully, we need all peoples and continents represented in the research.
To begin to fill this research gap, the AADM team got underway and hasn’t looked back. In the latest study, led by Charles Rotimi at NIH’s National Human Genome Research Institute, in partnership with multiple African diabetes experts, the AADM team enlisted 5,231 volunteers from Nigeria, Ghana, and Kenya. About half of the study’s participants had T2D and half did not.
As reported in Nature Communications, their genome-wide search for T2D gene variants turned up three interesting finds. Two were in genes previously linked to T2D risk in other human populations. The third involved a gene that codes for ZRANB3, an enzyme associated with DNA replication and repair that had never been reported in association with T2D.
To understand how ZRANB3 might influence a person’s risk for developing T2D, the researchers turned to zebrafish (Danio rerio), an excellent vertebrate model for its rapid development. The researchers found that the ZRANB3 gene is active in insulin-producing beta cells of the pancreas. That was important to know because people with T2D frequently have reduced numbers of beta cells, which compromises their ability to produce enough insulin.
The team next used CRISPR/Cas9 gene-editing tools either to “knock out” or reduce the expression of ZRANB3 in young zebrafish. In both cases, it led to increased loss of beta cells.
Additional study in the beta cells of mice provided more details. While normal beta cells released insulin in response to high levels of glucose, those with suppressed ZRANB3 activity couldn’t. Together, the findings show that ZRANB3 is important for beta cells to survive and function normally. It stands to reason, then, that people with a lower functioning variant of ZRANB3 would be more susceptible to T2D.
In many cases, T2D can be managed with some combination of diet, exercise, and oral medications. But some people require insulin to manage the disease. The new findings suggest, particularly for people of African ancestry, that the variant of the ZRANB3 gene that one inherits might help to explain those differences. People carrying particular variants of this gene also may benefit from beginning insulin treatment earlier, before their beta cells have been depleted.
So why wasn’t ZRANB3 discovered in the many studies on T2D carried out in the United States, Europe, and Asia? It turns out that the variant that predisposes Africans to this disease is extremely rare in these other populations. Only by studying Africans could this insight be uncovered.
More than 20 years ago, I helped to start the AADM project to learn more about the genetic factors driving T2D in sub-Saharan Africa. Other dedicated AADM leaders have continued to build the research project, taking advantage of new technologies as they came along. It’s profoundly gratifying that this project has uncovered such an impressive new lead, revealing important aspects of human biology that otherwise would have been missed. The AADM team continues to enroll volunteers, and the coming years should bring even more discoveries about the genetic factors that contribute to T2D.
 ZRANB3 is an African-specific type 2 diabetes locus associated with beta-cell mass and insulin response. Adeyemo AA, Zaghloul NA, Chen G, Doumatey AP, Leitch CC, Hostelley TL, Nesmith JE, Zhou J, Bentley AR, Shriner D, Fasanmade O, Okafor G, Eghan B Jr, Agyenim-Boateng K, Chandrasekharappa S, Adeleye J, Balogun W, Owusu S, Amoah A, Acheampong J, Johnson T, Oli J, Adebamowo C; South Africa Zulu Type 2 Diabetes Case-Control Study, Collins F, Dunston G, Rotimi CN. Nat Commun. 2019 Jul 19;10(1):3195.
 Diabetes mellitus in Nigeria: The past, present and future. Ogbera AO, Ekpebegh C. World J Diabetes. 2014 Dec 15;5(6):905-911.
 Global report on diabetes. Geneva: World Health Organization, 2016. World Health Organization.
Diabetes (National Institute of Diabetes ad Digestive and Kidney Diseases/NIH)
Diabetes and African Americans (Department of Health and Human Services)
Why Use Zebrafish to Study Human Diseases (Intramural Research Program/NIH)
Charles Rotimi (National Human Genome Research Institute/NIH)
NIH Support: National Human Genome Research Institute; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute on Minority Health and Health Disparities
Posted on by Dr. Francis Collins
Courtesy of the Chen and Macosko labs
A few years ago, I highlighted a really cool technology called Drop-seq for simultaneously analyzing the gene expression activity inside thousands of individual cells. Today, one of its creators, Evan Macosko, reports significant progress in developing even better tools for single-cell analysis—with support from an NIH Director’s New Innovator Award.
In a paper in the journal Science, Macosko, Fei Chen, and colleagues at the Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, recently unveiled another exciting creation called Slide-seq . This technology acts as a GPS-like system for mapping the exact location of each of the thousands of individual cells undergoing genomic analysis in a tissue sample.
This 3D video shows the exquisite precision of this new cellular form of GPS, which was used to generate a high-resolution map of the different cell types found in a tiny cube of mouse brain tissue. Specifically, it provides locations of the cell types and gene expression in the hippocampal regions called CA1 (green), CA2/3 (blue), and dentate gyrus (red).
Because using Slide-seq in the lab requires no specialized imaging equipment or skills, it should prove valuable to researchers across many different biomedical disciplines who want to look at cellular relationships or study gene activity in tissues, organs, or even whole organisms.
How does Slide-seq work? Macosko says one of the main innovations is an inexpensive rubber-coated glass slide nicknamed a puck. About 3 millimeters in diameter, pucks are studded with tens of thousands of 10 micron-sized beads, each one decorated with a random snippet of genetic material—an RNA barcode—that serves as its unique identifier of the bead.
The barcodes are sequenced en masse, and the exact location of each barcoded bead is indexed using innovative software developed by a team led by Chen, who is an NIH Director’s Early Independence awardee.
Then, the researchers place a sample of fresh-frozen tissue (typically, 10 micrometers, or 0.00039 inches, thick) on the puck and dissolve the tissue, lysing the cells and releasing their messenger RNA (mRNA). That leaves only the barcoded beads binding the mRNA transcripts expressed by the cells in the tissue—a biological record of the genes that were turned on at the time the sample was frozen.
The barcoded mRNA is then sequenced. The spatial position of each mRNA molecule can be inferred, using the reference index on the puck. This gives researchers a great deal of biological information about the cells in the tissue, often including their cell type and their gene expression pattern. All the data can then be mapped out in ways similar to those seen in this video, which was created using data from 66 pucks.
Slide-seq has been tested on a range of tissues from both mouse and human, replicating results from similar maps created using existing approaches, but also uncovering new biology. For example, in the mouse cerebellum, Slide-seq allowed the researchers to detect bands of variable gene activity across the tissues. This intriguing finding suggests that there may be subpopulations of cells in this part of the brain that have gene activity influenced by their physical locations.
Such results demonstrate the value of combining cell location with genomic information. In fact, Macosko now hopes to use Slide-seq to study the response of brain cells that are located near the buildup of damaged amyloid protein associated with the early-stage Alzheimer’s disease. Meanwhile, Chen is interested in pursuing cell lineage studies in a variety of tissues to see how and where changes in the molecular dynamics of tissues can lead to disease.
These are just a few examples of how Slide-seq will add to the investigative power of single-cell analysis in the years ahead. In meantime, the Macosko and Chen labs are working hard to develop even more innovative approaches to this rapidly emerging areas of biomedical research, so who knows what “seq” we will be talking about next?
 Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E, Vanderburg CR, Welch J, Chen LM, Chen F, Macosko EZ. Science. 2019 Mar 29;363(6434):1463-1467.
Single Cell Analysis (NIH)
Macosko Lab (Broad Institute of Harvard and MIT, Cambridge)
Chen Lab (Broad Institute)
NIH Support: National Institute on Aging; Common Fund
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