Posted on by Dr. Francis Collins
Most of our immune cells circulate throughout the bloodstream to serve as a roving security force against infection. But some immune cells don’t travel much at all and instead safeguard a specific organ or tissue. That’s what you are seeing in this electron micrograph of a type of scavenging macrophage, called a Kupffer cell (green), which resides exclusively in the liver (brown).
Normally, Kupffer cells appear in the liver during the early stages of mammalian development and stay put throughout life to protect liver cells, clean up old red blood cells, and regulate iron levels. But in their experimental system, Christopher Glass and his colleagues from University of California, San Diego, removed all original Kupffer cells from a young mouse to see if this would allow signals from the liver that encourage the development of new Kupffer cells.
The NIH-funded researchers succeeded in setting up the right conditions to spur a heavy influx of circulating precursor immune cells, called monocytes, into the liver, and then prompted those monocytes to turn into the replacement Kupffer cells. In a recent study in the journal Immunity, the team details the specific genomic changes required for the monocytes to differentiate into Kupffer cells . This information will help advance the study of Kupffer cells and their role in many liver diseases, including nonalcoholic steatohepatitis (NASH), which affects an estimated 3 to 12 percent of U.S. adults .
The new work also has broad implications for immunology research because it provides additional evidence that circulating monocytes contain genomic instructions that, when activated in the right way by nearby cells or other factors, can prompt the monocytes to develop into various, specialized types of scavenging macrophages. For example, in the mouse system, Glass’s team found that the endothelial cells lining the liver’s blood vessels, which is where Kupffer cells hang out, emit biochemical distress signals when their immune neighbors disappear.
While more details need to be worked out, this study is another excellent example of how basic research, including the ability to query single cells about their gene expression programs, is generating fundamental knowledge about the nature and behavior of living systems. Such knowledge is opening new possibilities to more precise ways of treating and preventing diseases all throughout the body, including those involving Kupffer cells and the liver.
 Liver-Derived Signals Sequentially Reprogram Myeloid Enhancers to Initiate and Maintain Kupffer Cell Identity. Sakai M, Troutman TD, Seidman JS, Ouyang Z, Spann NJ, Abe Y, Ego KM, Bruni CM, Deng Z, Schlachetzki JCM, Nott A, Bennett H, Chang J, Vu BT, Pasillas MP, Link VM, Texari L, Heinz S, Thompson BM, McDonald JG, Geissmann F3, Glass CK. Immunity. 2019 Oct 15;51(4):655-670.
 Recommendations for diagnosis, referral for liver biopsy, and treatment of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Spengler EK, Loomba R. Mayo Clinic Proceedings. 2015;90(9):1233–1246.
Liver Disease (National Institute of Diabetes and Digestive and Kidney Diseases/NIH)
Glass Laboratory (University of California, San Diego)
NIH Support: National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences; National Cancer Institute
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
The standard view of biology is that every normal cell copies its DNA instruction book with complete accuracy every time it divides. And thus, with a few exceptions like the immune system, cells in normal, healthy tissue continue to contain exactly the same genome sequence as was present in the initial single-cell embryo that gave rise to that individual. But new evidence suggests it may be time to revise that view.
By analyzing genetic information collected throughout the bodies of nearly 500 different individuals, researchers discovered that almost all had some seemingly healthy tissue that contained pockets of cells bearing particular genetic mutations. Some even harbored mutations in genes linked to cancer. The findings suggest that nearly all of us are walking around with genetic mutations within various parts of our bodies that, under certain circumstances, may have the potential to give rise to cancer or other health conditions.
Efforts such as NIH’s The Cancer Genome Atlas (TCGA) have extensively characterized the many molecular and genomic alterations underlying various types of cancer. But it has remained difficult to pinpoint the precise sequence of events that lead to cancer, and there are hints that so-called normal tissues, including blood and skin, might contain a surprising number of mutations —perhaps starting down a path that would eventually lead to trouble.
In the study published in Science, a team from the Broad Institute at MIT and Harvard, led by Gad Getz and postdoctoral fellow Keren Yizhak, along with colleagues from Massachusetts General Hospital, decided to take a closer look. They turned their attention to the NIH’s Genotype-Tissue Expression (GTEx) project.
The GTEx is a comprehensive public resource that shows how genes are expressed and controlled differently in various tissues throughout the body. To capture those important differences, GTEx researchers analyzed messenger RNA sequences within thousands of healthy tissue samples collected from people who died of causes other than cancer.
Getz, Yizhak, and colleagues wanted to use that extensive RNA data in another way: to detect mutations that had arisen in the DNA genomes of cells within those tissues. To do it, they devised a method for comparing those tissue-derived RNA samples to the matched normal DNA. They call the new method RNA-MuTect.
All told, the researchers analyzed RNA sequences from 29 tissues, including heart, stomach, pancreas, and fat, and matched DNA from 488 individuals in the GTEx database. Those analyses showed that the vast majority of people—a whopping 95 percent—had one or more tissues with pockets of cells carrying new genetic mutations.
While many of those genetic mutations are most likely harmless, some have known links to cancer. The data show that genetic mutations arise most often in the skin, esophagus, and lung tissues. This suggests that exposure to environmental elements—such as air pollution in the lung, carcinogenic dietary substances in the esophagus, or the ultraviolet radiation in sunlight that hits the skin—may play important roles in causing genetic mutations in different parts of the body.
The findings clearly show that, even within normal tissues, the DNA in the cells of our bodies isn’t perfectly identical. Rather, mutations constantly arise, and that makes our cells more of a mosaic of different mutational events. Sometimes those altered cells may have a subtle growth advantage, and thus continue dividing to form larger groups of cells with slightly changed genomic profiles. In other cases, those altered cells may remain in small numbers or perhaps even disappear.
It’s not yet clear to what extent such pockets of altered cells may put people at greater risk for developing cancer down the road. But the presence of these genetic mutations does have potentially important implications for early cancer detection. For instance, it may be difficult to distinguish mutations that are truly red flags for cancer from those that are harmless and part of a new idea of what’s “normal.”
To further explore such questions, it will be useful to study the evolution of normal mutations in healthy human tissues over time. It’s worth noting that so far, the researchers have only detected these mutations in large populations of cells. As the technology advances, it will be interesting to explore such questions at the higher resolution of single cells.
Getz’s team will continue to pursue such questions, in part via participation in the recently launched NIH Pre-Cancer Atlas. It is designed to explore and characterize pre-malignant human tumors comprehensively. While considerable progress has been made in studying cancer and other chronic diseases, it’s clear we still have much to learn about the origins and development of illness to build better tools for early detection and control.
 RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues. Yizhak K, Aguet F, Kim J, Hess JM, Kübler K, Grimsby J, Frazer R, Zhang H, Haradhvala NJ, Rosebrock D, Livitz D, Li X, Arich-Landkof E, Shoresh N, Stewart C, Segrè AV, Branton PA, Polak P, Ardlie KG, Getz G. Science. 2019 Jun 7;364(6444).
The Cancer Genome Atlas (National Cancer Institute/NIH)
Pre-Cancer Atlas (National Cancer Institute/NIH)
Getz Lab (Broad Institute, Cambridge, MA)
NIH Support: Common Fund; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; National Institute of Mental Health; National Cancer Institute; National Library of Medicine; National Institute on Drug Abuse; National Institute of Neurological Diseases and Stroke
Posted on by Dr. Francis Collins
Back in April 2003, when the international Human Genome Project successfully completed the first reference sequence of the human DNA blueprint, we were thrilled to have achieved that feat in just 13 years. Sure, the U.S. contribution to that first human reference sequence cost an estimated $400 million, but we knew (or at least we hoped) that the costs would come down quickly, and the speed would accelerate. How far we’ve come since then! A new study shows that whole genome sequencing—combined with artificial intelligence (AI)—can now be used to diagnose genetic diseases in seriously ill babies in less than 24 hours.
Take a moment to absorb this. I would submit that there is no other technology in the history of planet Earth that has experienced this degree of progress in speed and affordability. And, at the same time, DNA sequence technology has achieved spectacularly high levels of accuracy. The time-honored adage that you can only get two out of three for “faster, better, and cheaper” has been broken—all three have been dramatically enhanced by the advances of the last 16 years.
Rapid diagnosis is critical for infants born with mysterious conditions because it enables them to receive potentially life-saving interventions as soon as possible after birth. In a study in Science Translational Medicine, NIH-funded researchers describe development of a highly automated, genome-sequencing pipeline that’s capable of routinely delivering a diagnosis to anxious parents and health-care professionals dramatically earlier than typically has been possible .
While the cost of rapid DNA sequencing continues to fall, challenges remain in utilizing this valuable tool to make quick diagnostic decisions. In most clinical settings, the wait for whole-genome sequencing results still runs more than two weeks. Attempts to obtain faster results also have been labor intensive, requiring dedicated teams of experts to sift through the data, one sample at a time.
In the new study, a research team led by Stephen Kingsmore, Rady Children’s Institute for Genomic Medicine, San Diego, CA, describes a streamlined approach that accelerates every step in the process, making it possible to obtain whole-genome test results in a median time of about 20 hours and with much less manual labor. They propose that the system could deliver answers for 30 patients per week using a single genome sequencing instrument.
Here’s how it works: Instead of manually preparing blood samples, his team used special microbeads to isolate DNA much more rapidly with very little labor. The approach reduced the time for sample preparation from 10 hours to less than three. Then, using a state-of-the-art DNA sequencer, they sequence those samples to obtain good quality whole genome data in just 15.5 hours.
The next potentially time-consuming challenge is making sense of all that data. To speed up the analysis, Kingsmore’s team took advantage of a machine-learning system called MOON. The automated platform sifts through all the data using artificial intelligence to search for potentially disease-causing variants.
The researchers paired MOON with a clinical language processing system, which allowed them to extract relevant information from the child’s electronic health records within seconds. Teaming that patient-specific information with data on more than 13,000 known genetic diseases in the scientific literature, the machine-learning system could pick out a likely disease-causing mutation out of 4.5 million potential variants in an impressive 5 minutes or less!
To put the system to the test, the researchers first evaluated its ability to reach a correct diagnosis in a sample of 101 children with 105 previously diagnosed genetic diseases. In nearly every case, the automated diagnosis matched the opinions reached previously via the more lengthy and laborious manual interpretation of experts.
Next, the researchers tested the automated system in assisting diagnosis of seven seriously ill infants in the intensive care unit, and three previously diagnosed infants. They showed that their automated system could reach a diagnosis in less than 20 hours. That’s compared to the fastest manual approach, which typically took about 48 hours. The automated system also required about 90 percent less manpower.
The system nailed a rapid diagnosis for 3 of 7 infants without returning any false-positive results. Those diagnoses were made with an average time savings of more than 22 hours. In each case, the early diagnosis immediately influenced the treatment those children received. That’s key given that, for young children suffering from serious and unexplained symptoms such as seizures, metabolic abnormalities, or immunodeficiencies, time is of the essence.
Of course, artificial intelligence may never replace doctors and other healthcare providers. Kingsmore notes that 106 years after the invention of the autopilot, two pilots are still required to fly a commercial aircraft. Likewise, health care decisions based on genome interpretation also will continue to require the expertise of skilled physicians.
Still, such a rapid automated system will prove incredibly useful. For instance, this system can provide immediate provisional diagnosis, allowing the experts to focus their attention on more difficult unsolved cases or other needs. It may also prove useful in re-evaluating the evidence in the many cases in which manual interpretation by experts fails to provide an answer.
The automated system may also be useful for periodically reanalyzing data in the many cases that remain unsolved. Keeping up with such reanalysis is a particular challenge considering that researchers continue to discover hundreds of disease-associated genes and thousands of variants each and every year. The hope is that in the years ahead, the combination of whole genome sequencing, artificial intelligence, and expert care will make all the difference in the lives of many more seriously ill babies and their families.
 Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation. Clark MM, Hildreth A, Batalov S, Ding Y, Chowdhury S, Watkins K, Ellsworth K, Camp B, Kint CI, Yacoubian C, Farnaes L, Bainbridge MN, Beebe C, Braun JJA, Bray M, Carroll J, Cakici JA, Caylor SA, Clarke C, Creed MP, Friedman J, Frith A, Gain R, Gaughran M, George S, Gilmer S, Gleeson J, Gore J, Grunenwald H, Hovey RL, Janes ML, Lin K, McDonagh PD, McBride K, Mulrooney P, Nahas S, Oh D, Oriol A, Puckett L, Rady Z, Reese MG, Ryu J, Salz L, Sanford E, Stewart L, Sweeney N, Tokita M, Van Der Kraan L, White S, Wigby K, Williams B, Wong T, Wright MS, Yamada C, Schols P, Reynders J, Hall K, Dimmock D, Veeraraghavan N, Defay T, Kingsmore SF. Sci Transl Med. 2019 Apr 24;11(489).
DNA Sequencing Fact Sheet (National Human Genome Research Institute/NIH)
Genomics and Medicine (NHGRI/NIH)
Genetic and Rare Disease Information Center (National Center for Advancing Translational Sciences/NIH)
Stephen Kingsmore (Rady Children’s Institute for Genomic Medicine, San Diego, CA)
NIH Support: National Institute of Child Health and Human Development; National Human Genome Research Institute; National Center for Advancing Translational Sciences
Posted on by Dr. Francis Collins
Predicting whether someone will get Alzheimer’s disease (AD) late in life, and how to use that information for prevention, has been an intense focus of biomedical research. The goal of this work is to learn not only about the genes involved in AD, but how they work together and with other complex biological, environmental, and lifestyle factors to drive this devastating neurological disease.
It’s good news to be able to report that an international team of researchers, partly funded by NIH, has made more progress in explaining the genetic component of AD. Their analysis, involving data from more than 35,000 individuals with late-onset AD, has identified variants in five new genes that put people at greater risk of AD . It also points to molecular pathways involved in AD as possible avenues for prevention, and offers further confirmation of 20 other genes that had been implicated previously in AD.
The results of this largest-ever genomic study of AD suggests key roles for genes involved in the processing of beta-amyloid peptides, which form plaques in the brain recognized as an important early indicator of AD. They also offer the first evidence for a genetic link to proteins that bind tau, the protein responsible for telltale tangles in the AD brain that track closely with a person’s cognitive decline.
The new findings are the latest from the International Genomics of Alzheimer’s Project (IGAP) consortium, led by a large, collaborative team including Brian Kunkle and Margaret Pericak-Vance, University of Miami Miller School of Medicine, Miami, FL. The effort, spanning four consortia focused on AD in the United States and Europe, was launched in 2011 with the aim of discovering and mapping all the genes that contribute to AD.
An earlier IGAP study including about 25,500 people with late-onset AD identified 20 common gene variants that influence a person’s risk for developing AD late in life . While that was terrific progress to be sure, the analysis also showed that those gene variants could explain only a third of the genetic component of AD. It was clear more genes with ties to AD were yet to be found.
So, in the study reported in Nature Genetics, the researchers expanded the search. While so-called genome-wide association studies (GWAS) are generally useful in identifying gene variants that turn up often in association with particular diseases or other traits, the ones that arise more rarely require much larger sample sizes to find.
To increase their odds of finding additional variants, the researchers analyzed genomic data for more than 94,000 individuals, including more than 35,000 with a diagnosis of late-onset AD and another 60,000 older people without AD. Their search led them to variants in five additional genes, named IQCK, ACE, ADAM10, ADAMTS1, and WWOX, associated with late-onset AD that hadn’t turned up in the previous study.
Further analysis of those genes supports a view of AD in which groups of genes work together to influence risk and disease progression. In addition to some genes influencing the processing of beta-amyloid peptides and accumulation of tau proteins, others appear to contribute to AD via certain aspects of the immune system and lipid metabolism.
Each of these newly discovered variants contributes only a small amount of increased risk, and therefore probably have limited value in predicting an average person’s risk of developing AD later in life. But they are invaluable when it comes to advancing our understanding of AD’s biological underpinnings and pointing the way to potentially new treatment approaches. For instance, these new data highlight intriguing similarities between early-onset and late-onset AD, suggesting that treatments developed for people with the early-onset form also might prove beneficial for people with the more common late-onset disease.
It’s worth noting that the new findings continue to suggest that the search is not yet over—many more as-yet undiscovered rare variants likely play a role in AD. The search for answers to AD and so many other complex health conditions—assisted through collaborative data sharing efforts such as this one—continues at an accelerating pace.
 Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, et. al. Nat Genet. 2019 Mar;51(3):414-430.
 Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, et al. Nat Genet. 2013 Dec;45(12):1452-8.
Alzheimer’s Disease Genetics Fact Sheet (National Institute on Aging/NIH)
Margaret Pericak-Vance (University of Miami Health System, FL)
NIH Support: National Institute on Aging; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; National Institute of Allergy and Infectious Diseases; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Diabetes and Digestive and Kidney Disease; National Institute of Neurological Disorders and Stroke