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A More Precise Way to Knock Out Skin Rashes

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A man scratches a rash on his arm, an immune cell zooms from the rash. Single-cell RNA data leads to Diagnosis

The NIH is committed to building a new era in medicine in which the delivery of health care is tailored specifically to the individual person, not the hypothetical average patient as is now often the case. This new era of “precision medicine” will transform care for many life-threatening diseases, including cancer and chronic kidney disease. But what about non-life-threatening conditions, like the aggravating rash on your skin that just won’t go away?

Recently, researchers published a proof-of-principle paper in the journal Science Immunology demonstrating just how precision medicine for inflammatory skin rashes might work [1]. While more research is needed to build out and further refine the approach, the researchers show it’s now technologically possible to extract immune cells from a patient’s rash, read each cell’s exact inflammatory features, and relatively quickly match them online to the right anti-inflammatory treatment to stop the rash.

The work comes from a NIH-funded team led by Jeffrey Cheng and Raymond Cho, University of California, San Francisco. The researchers focused their attention on two inflammatory skin conditions: atopic dermatitis, the most common type of eczema, which flares up periodically to make skin red and itchy, and psoriasis vulgaris. Psoriasis causes skin cells to build up and form a scaly rash and dry, itchy patches. Together, atopic dermatitis and psoriasis vulgaris affect about 10 percent of U.S. adults.

While the rashes caused by the two conditions can sometimes look similar, they are driven by different sets of immune cells and underlying inflammatory responses. For that reason, distinct biologic therapies, based on antibodies and proteins made from living cells, are now available to target and modify the specific immune pathways underlying each condition.

While biologic therapies represent a major treatment advance for these and other inflammatory conditions, they can miss their targets. Indeed, up to half of patients don’t improve substantially on biologics. Part of the reason for that lack of improvement is because doctors don’t have the tools they need to make firm diagnoses based on what precisely is going on in the skin at the molecular and cellular levels.

To learn more in the new study, the researchers isolated immune cells, focusing primarily on T cells, from the skin of 31 volunteers. They then sequenced the RNA of each cell to provide a telltale portrait of its genomic features. This “single-cell analysis” allowed them to capture high-resolution portraits of 41 different immune cell types found in individual skin samples. That’s important because it offers a much more detailed understanding of changes in the behavior of various immune cells that might have been missed in studies focused on larger groupings of skin cells, representing mixtures of various cell types.

Of the 31 volunteers, seven had atopic dermatitis and eight had psoriasis vulgaris. Three others were diagnosed with other skin conditions, while six had an indeterminate rash with features of both atopic dermatitis and psoriasis vulgaris. Seven others were healthy controls.

The team produced molecular signatures of the immune cells. The researchers then compared the signatures from the hard-to-diagnose rashes to those of confirmed cases of atopic dermatitis and psoriasis. They wanted to see if the signatures could help to reach clearer diagnoses.

The signatures revealed common immunological features as well as underlying differences. Importantly, the researchers found that the signatures allowed them to move forward and classify the indeterminate rashes. The rashes also responded to biologic therapies corresponding to the individuals’ new diagnoses.

Already, the work has identified molecules that help to define major classes of human inflammatory skin diseases. The team has also developed computer tools to help classify rashes in many other cases where the diagnosis is otherwise uncertain.

In fact, the researchers have launched a pioneering website called RashX. It is enabling practicing dermatologists and researchers around the world to submit their single-cell RNA data from their difficult cases. Such analyses are now being done at a small, but growing, number of academic medical centers.

While precision medicine for skin rashes has a long way to go yet before reaching most clinics, the UCSF team is working diligently to ensure its arrival as soon as scientifically possible. Indeed, their new data represent the beginnings of an openly available inflammatory skin disease resource. They ultimately hope to generate a standardized framework to link molecular features to disease prognosis and drug response based on data collected from clinical centers worldwide. It’s a major effort, but one that promises to improve the diagnosis and treatment of many more unusual and long-lasting rashes, both now and into the future.


[1] Classification of human chronic inflammatory skin disease based on single-cell immune profiling. Liu Y, Wang H, Taylor M, Cook C, Martínez-Berdeja A, North JP, Harirchian P, Hailer AA, Zhao Z, Ghadially R, Ricardo-Gonzalez RR, Grekin RC, Mauro TM, Kim E, Choi J, Purdom E, Cho RJ, Cheng JB. Sci Immunol. 2022 Apr 15;7(70):eabl9165. {Epub ahead of publication]


The Promise of Precision Medicine (NIH)

Atopic Dermatitis (National Institute of Arthritis and Musculoskeletal and Skin Diseases /NIH)

Psoriasis (NIAMS/NIH)

RashX (University of California, San Francisco)

Raymond Cho (UCSF)

Jeffrey Cheng (UCSF)

NIH Support: National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Center for Advancing Translational Sciences

All of Us: Release of Nearly 100,000 Whole Genome Sequences Sets Stage for New Discoveries

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Diverse group of cartoon people with associated DNA

Nearly four years ago, NIH opened national enrollment for the All of Us Research Program. This historic program is building a vital research community within the United States of at least 1 million participant partners from all backgrounds. Its unifying goal is to advance precision medicine, an emerging form of health care tailored specifically to the individual, not the average patient as is now often the case. As part of this historic effort, many participants have offered DNA samples for whole genome sequencing, which provides information about almost all of an individual’s genetic makeup.

Earlier this month, the All of Us Research Program hit an important milestone. We released the first set of nearly 100,000 whole genome sequences from our participant partners. The sequences are stored in the All of Us Researcher Workbench, a powerful, cloud-based analytics platform that makes these data broadly accessible to registered researchers.

The All of Us Research Program and its many participant partners are leading the way toward more equitable representation in medical research. About half of this new genomic information comes from people who self-identify with a racial or ethnic minority group. That’s extremely important because, until now, over 90 percent of participants in large genomic studies were of European descent. This lack of diversity has had huge impacts—deepening health disparities and hindering scientific discovery from fully benefiting everyone.

The Researcher Workbench also contains information from many of the participants’ electronic health records, Fitbit devices, and survey responses. Another neat feature is that the platform links to data from the U.S. Census Bureau’s American Community Survey to provide more details about the communities where participants live.

This unique and comprehensive combination of data will be key in transforming our understanding of health and disease. For example, given the vast amount of data and diversity in the Researcher Workbench, new diseases are undoubtedly waiting to be uncovered and defined. Many new genetic variants are also waiting to be identified that may better predict disease risk and response to treatment.

To speed up the discovery process, these data are being made available, both widely and wisely. To protect participants’ privacy, the program has removed all direct identifiers from the data and upholds strict requirements for researchers seeking access. Already, more than 1,500 scientists across the United States have gained access to the Researcher Workbench through their institutions after completing training and agreeing to the program’s strict rules for responsible use. Some of these researchers are already making discoveries that promote precision medicine, such as finding ways to predict how to best to prevent vision loss in patients with glaucoma.

Beyond making genomic data available for research, All of Us participants have the opportunity to receive their personal DNA results, at no cost to them. So far, the program has offered genetic ancestry and trait results to more than 100,000 participants. Plans are underway to begin sharing health-related DNA results on hereditary disease risk and medication-gene interactions later this year.

This first release of genomic data is a huge milestone for the program and for health research more broadly, but it’s also just the start. The program’s genome centers continue to generate the genomic data and process about 5,000 additional participant DNA samples every week.

The ultimate goal is to gather health data from at least 1 million or more people living in the United States, and there’s plenty of time to join the effort. Whether you would like to contribute your own DNA and health information, engage in research, or support the All of Us Research Program as a partner, it’s easy to get involved. By taking part in this historic program, you can help to build a better and more equitable future for health research and precision medicine.

Note: Joshua Denny, M.D., M.S., is the Chief Executive Officer of NIH’s All of Us Research Program.


All of Us Research Program (NIH)

All of Us Research Hub

Join All of Us (NIH)

Seeking Consensus on the Use of Population Descriptors in Genomics

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Laptop with research article on Ethnicity and Race. Printer printing a page of cartoon faces
Credit: Ernesto del Aguila III, National Human Genome Research Institute, NIH

Cataloging and characterizing the thousands of genomic variants—differences in DNA sequences among individuals—across human populations is a foundational component of genomics. Scientists from various disciplinary fields compare the variation that occurs within and between the genomes of individuals and groups. Such efforts include attributing descriptors to population groups, which have historically included the use of social constructs such as race, ethnicity, ancestry, and political geographic location. Like any descriptors, these words do not fully account for the scope and diversity of the human species.

The use of race, ethnicity, and ancestry as descriptors of population groups in biomedical and genomics research has been a topic of consistent and rigorous debate within the scientific community. Human health, disease, and ancestry are all tied to how we define and explain human diversity. For centuries, scientists have incorrectly inferred that people of different races reflect discrete biological groups, which has led to deep-rooted health inequities and reinforced scientific racism.

In recent decades, genomics research has revealed the complexity of human genomic variation and the limitations of these socially derived population descriptors. The scientific community has long worked to move beyond the use of the social construct of race as a population descriptor and provide guidance about agreed-upon descriptors of human populations. Such a need has escalated with the growing numbers of large population-scale genomics studies being launched around the world, including in the United States.

To answer this call, NIH is sponsoring a National Academies of Sciences, Engineering, and Medicine (NASEM) study that aims to develop best practices in the use of race, ethnicity, and genetic ancestry in genomics research. The NASEM study is sponsored by 14 NIH institutes, centers, offices, and programs, and the resulting report will be released in February 2023.

Experts from various fields—including genomics, medicine, and social sciences—are conducting the study. Much of the effort will revolve around reviewing and assessing existing methodologies, benefits, and challenges in the use of race and ethnicity and other population descriptors in genomics research. The ad hoc committee will host three public meetings to obtain input. Look for more information regarding the committee’s next public session planned for April 2022 on the NASEM “Race, Ethnicity, and Ancestry as Population Descriptors in Genomics Research” website.

To further underscore the need for the NASEM study, an NIH study published in December 2021 revealed that the descriptors for human populations used in the genetics literature have evolved over the last 70 years [1]. For example, the use of the word “race” has substantially decreased, while the uses of “ancestry” and “ethnicity” have increased. The study provided additional evidence that population descriptors often reflect fluid, social constructs whose intention is to describe groups with common genetic ancestry. These findings reinforce the timeliness of the NASEM study, with the clear need for experts to provide guidance for establishing more stable and meaningful population descriptors for use in future genomics studies.

The full promise of genomics, including its application to medicine, depends on improving how we explain human genomic variation. The words that we use to describe participants in research studies and populations must be transparent, thoughtful, and consistent—in addition to avoiding the perpetuation of structural racism. The best and most fruitful genomics research demands a better approach.


[1] Evolving use of ancestry, ethnicity, and race in genetics research—A survey spanning seven decades. Byeon YJJ, Islamaj R, Yeganova L, Wilbur WJ, Lu Z, Brody LC, Bonham VL. Am J Hum Genet. 2021 Dec 2;108(12):2215-2223.

Use of Race, Ethnicity, and Ancestry as Population Descriptors in Genomics Research (National Academies of Sciences, Engineering, and Medicine)

Language used by researchers to describe human populations has evolved over the last 70 years.” (National Human Genome Research Institute/NIH)

Genomic Variation Program (NHGRI)

[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s institutes and centers to contribute occasional guest posts to the blog as a way to highlight some of the cool science that they support and conduct. This is the third in the series of NIH institute and center guest posts that will run until a new permanent NIH director is in place.]

New Technology Opens Evolutionary Window into Brain Development

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DNA runs across the top and bottom. Skulls show the evolution of man from left to right

One of the great mysteries in biology is how we humans ended up with such large, complex brains. In search of clues, researchers have spent years studying the protein-coding genes activated during neurodevelopment. But some answers may also be hiding in non-coding regions of the human genome, where sequences called regulatory elements increase or decrease the activity of genes.

A fascinating example involves a type of regulatory element called a human accelerated region (HAR). Although “human” is part of this element’s name, it turns out that the genomes of all vertebrates—not just humans—contain the DNA segments now designated as HARs.

In most organisms, HARs show a relatively low rate of mutation, which means these regulatory elements have been highly conserved across species throughout evolutionary time [1]. The big exception is Homo sapiens, in which HARs have exhibited a much higher rate of mutations.

The accelerated rate of HARs mutations observed in humans suggest that, over the course of very long periods of time, these genomic changes might have provided our species with some sort of evolutionary advantage. What might that be? Many have speculated the advantage might involve the brain because HARs are often associated with genes involved in neurodevelopment. Now, in a paper published in the journal Neuron, an NIH-supported team confirms that’s indeed the case [2].

In the new work, researchers found that about half of the HARs in the human genome influence the activity, or expression, of protein-coding genes in neural cells and tissues during the brain’s development [3]. The researchers say their study—the most comprehensive to date of the 3,171 HARs in the human genome—firmly establishes that this type of regulatory element helps to drive patterns of neurodevelopmental gene activity specific to humans.

Yet to be determined is precisely how HARs affect the development of the human brain. The quest to uncover these details will no doubt shed new light on fundamental questions about the brain, its billions of neurons, and their trillions of interconnections. For example, why does human neural development span decades, longer than the life spans of most primates and other mammals? Answering such questions could also reveal new clues into a range of cognitive and behavioral disorders. In fact, early research has already made tentative links between HARs and neurodevelopmental conditions such as autism spectrum disorder and schizophrenia [3].

The latest work was led by Kelly Girskis, Andrew Stergachis, and Ellen DeGennaro, all of whom were in the lab of Christopher Walsh while working on the project. An NIH grantee, Walsh is director of the Allen Discovery Center for Brain Evolution at Boston Children’s Hospital and Harvard Medical School, which is supported by the Paul G. Allen Foundation Frontiers Group, and is an Investigator of the Howard Hughes Medical Institute.

Though HARs have been studied since 2006, one of the big challenges in systematically assessing them has been technological. The average length of a HAR is about 269 bases of DNA, but current technologies for assessing function can only easily analyze DNA molecules that span 150 bases or less.

Ryan Doan, who was then in the Walsh Lab, and his colleagues solved the problem by creating a new machine called CaptureMPRA. (MPRA is short for “massively parallel reporter assays.”) This technological advance cleverly barcodes HARs and, more importantly, makes it possible to analyze HARs up to about 500 bases in length.

Using CaptureMPRA technology in tandem with cell culture studies, researchers rolled up their sleeves and conducted comprehensive, full-sequence analyses of more than 3,000 HARs. In their initial studies, primarily in neural cells, they found nearly half of human HARs are active to drive gene expression in cell culture. Of those, 42 percent proved to have increased ability to enhance gene expression compared to their orthologues, or counterparts, in chimpanzees.

Next, the team integrated these data with an existing epigenetic dataset derived from developing human brain cells, as well as additional datasets generated from sorted brain cell types. They found that many HARs appeared to have the ability to increase the activity of protein-coding genes, while a smaller—but very significant—subset of the HARs appeared to be enhancing gene expression specifically in neural progenitor cells, which are responsible for making various neural cell types.

The data suggest that as the human HAR sequences mutated and diverged from other mammals, they increased their ability to enhance or sometimes suppress the activity of certain genes in neural cells. To illustrate this point, the researchers focused on two HARs that appear to interact specifically with a gene referred to as R17. This gene can have highly variable gene expression patterns not only in different human cell types, but also in cells from other vertebrates and non-vertebrates.

In the human cerebral cortex, the outermost part of the brain that’s responsible for complex behaviors, R17 is expressed only in neural progenitor cells and only at specific time points. The researchers found that R17 slows the progression of neural progenitor cells through the cell cycle. That might seem strange, given the billions of neurons that need to be made in the cortex. But it’s consistent with the biology. In the human, it takes more than 130 days for the cortex to complete development, compared to about seven days in the mouse.

Clearly, to learn more about how the human brain evolved, researchers will need to look for clues in many parts of the genome at once, including its non-coding regions. To help researchers navigate this challenging terrain, the Walsh team has created an online resource displaying their comprehensive HAR data. It will appear soon, under the name HAR Hub, on the University of California Santa Cruz Genome Browser.


[1] An RNA gene expressed during cortical development evolved rapidly in humans. Pollard KS, Salama SR, Lambert N, Lambot MA, Coppens S, Pedersen JS, Katzman S, King B, Onodera C, Siepel A, Kern AD, Dehay C, Igel H, Ares M Jr, Vanderhaeghen P, Haussler D. Nature. 2006 Sep 14;443(7108):167-72.

[2] Rewiring of human neurodevelopmental gene regulatory programs by human accelerated regions. Girskis KM, Stergachis AB, DeGennaro EM, Doan RN, Qian X, Johnson MB, Wang PP, Sejourne GM, Nagy MA, Pollina EA, Sousa AMM, Shin T, Kenny CJ, Scotellaro JL, Debo BM, Gonzalez DM, Rento LM, Yeh RC, Song JHT, Beaudin M, Fan J, Kharchenko PV, Sestan N, Greenberg ME, Walsh CA. Neuron. 2021 Aug 25:S0896-6273(21)00580-8.

[3] Mutations in human accelerated regions disrupt cognition and social behavior. Doan RN, Bae BI, Cubelos B, Chang C, Hossain AA, Al-Saad S, Mukaddes NM, Oner O, Al-Saffar M, Balkhy S, Gascon GG; Homozygosity Mapping Consortium for Autism, Nieto M, Walsh CA. Cell. 2016 Oct 6;167(2):341-354.


Christopher Walsh Laboratory (Boston Children’s Hospital and Harvard Medical School)

The Paul G. Allen Foundation Frontiers Group (Seattle)

NIH Support: National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institute of General Medical Sciences; National Cancer Institute

Engineering a Better Way to Deliver Therapeutic Genes to Muscles

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Green adenovirus delivers therapeutic genes to muscles which glow green

Amid all the progress toward ending the COVID-19 pandemic, it’s worth remembering that researchers here and around the world continue to make important advances in tackling many other serious health conditions. As an inspiring NIH-supported example, I’d like to share an advance on the use of gene therapy for treating genetic diseases that progressively degenerate muscle, such as Duchenne muscular dystrophy (DMD).

As published recently in the journal Cell, researchers have developed a promising approach to deliver therapeutic genes and gene editing tools to muscle more efficiently, thus requiring lower doses [1]. In animal studies, the new approach has targeted muscle far more effectively than existing strategies. It offers an exciting way forward to reduce unwanted side effects from off-target delivery, which has hampered the development of gene therapy for many conditions.

In boys born with DMD (it’s an X-linked disease and therefore affects males), skeletal and heart muscles progressively weaken due to mutations in a gene encoding a critical muscle protein called dystrophin. By age 10, most boys require a wheelchair. Sadly, their life expectancy remains less than 30 years.

The hope is gene therapies will one day treat or even cure DMD and allow people with the disease to live longer, high-quality lives. Unfortunately, the benign adeno-associated viruses (AAVs) traditionally used to deliver the healthy intact dystrophin gene into cells mostly end up in the liver—not in muscles. It’s also the case for gene therapy of many other muscle-wasting genetic diseases.

The heavy dose of viral vector to the liver is not without concern. Recently and tragically, there have been deaths in a high-dose AAV gene therapy trial for X-linked myotubular myopathy (XLMTM), a different disorder of skeletal muscle in which there may already be underlying liver disease, potentially increasing susceptibility to toxicity.

To correct this concerning routing error, researchers led by Mohammadsharif Tabebordbar in the lab of Pardis Sabeti, Broad Institute of MIT and Harvard and Harvard University, Cambridge, MA, have now assembled an optimized collection of AAVs. They have been refined to be about 10 times better at reaching muscle fibers than those now used in laboratory studies and clinical trials. In fact, researchers call them myotube AAVs, or MyoAAVs.

MyoAAVs can deliver therapeutic genes to muscle at much lower doses—up to 250 times lower than what’s needed with traditional AAVs. While this approach hasn’t yet been tried in people, animal studies show that MyoAAVs also largely avoid the liver, raising the prospect for more effective gene therapies without the risk of liver damage and other serious side effects.

In the Cell paper, the researchers demonstrate how they generated MyoAAVs, starting out with the commonly used AAV9. Their goal was to modify the outer protein shell, or capsid, to create an AAV that would be much better at specifically targeting muscle. To do so, they turned to their capsid engineering platform known as, appropriately enough, DELIVER. It’s short for Directed Evolution of AAV capsids Leveraging In Vivo Expression of transgene RNA.

Here’s how DELIVER works. The researchers generate millions of different AAV capsids by adding random strings of amino acids to the portion of the AAV9 capsid that binds to cells. They inject those modified AAVs into mice and then sequence the RNA from cells in muscle tissue throughout the body. The researchers want to identify AAVs that not only enter muscle cells but that also successfully deliver therapeutic genes into the nucleus to compensate for the damaged version of the gene.

This search delivered not just one AAV—it produced several related ones, all bearing a unique surface structure that enabled them specifically to target muscle cells. Then, in collaboration with Amy Wagers, Harvard University, Cambridge, MA, the team tested their MyoAAV toolset in animal studies.

The first cargo, however, wasn’t a gene. It was the gene-editing system CRISPR-Cas9. The team found the MyoAAVs correctly delivered the gene-editing system to muscle cells and also repaired dysfunctional copies of the dystrophin gene better than the CRISPR cargo carried by conventional AAVs. Importantly, the muscles of MyoAAV-treated animals also showed greater strength and function.

Next, the researchers teamed up with Alan Beggs, Boston Children’s Hospital, and found that MyoAAV was effective in treating mouse models of XLMTM. This is the very condition mentioned above, in which very high dose gene therapy with a current AAV vector has led to tragic outcomes. XLMTM mice normally die in 10 weeks. But, after receiving MyoAAV carrying a corrective gene, all six mice had a normal lifespan. By comparison, mice treated in the same way with traditional AAV lived only up to 21 weeks of age. What’s more, the researchers used MyoAAV at a dose 100 times lower than that currently used in clinical trials.

While further study is needed before this approach can be tested in people, MyoAAV was also used to successfully introduce therapeutic genes into human cells in the lab. This suggests that the early success in animals might hold up in people. The approach also has promise for developing AAVs with potential for targeting other organs, thereby possibly providing treatment for a wide range of genetic conditions.

The new findings are the result of a decade of work from Tabebordbar, the study’s first author. His tireless work is also personal. His father has a rare genetic muscle disease that has put him in a wheelchair. With this latest advance, the hope is that the next generation of promising gene therapies might soon make its way to the clinic to help Tabebordbar’s father and so many other people.


[1] Directed evolution of a family of AAV capsid variants enabling potent muscle-directed gene delivery across species. Tabebordbar M, Lagerborg KA, Stanton A, King EM, Ye S, Tellez L, Krunnfusz A, Tavakoli S, Widrick JJ, Messemer KA, Troiano EC, Moghadaszadeh B, Peacker BL, Leacock KA, Horwitz N, Beggs AH, Wagers AJ, Sabeti PC. Cell. 2021 Sep 4:S0092-8674(21)01002-3.


Muscular Dystrophy Information Page (National Institute of Neurological Disorders and Stroke/NIH)

X-linked myotubular myopathy (Genetic and Rare Diseases Information Center/National Center for Advancing Translational Sciences/NIH)

Somatic Cell Genome Editing (Common Fund/NIH)

Mohammadsharif Tabebordbar (Broad Institute of MIT and Harvard and Harvard University, Cambridge, MA)

Sabeti Lab (Broad Institute of MIT and Harvard and Harvard University)

NIH Support: Eunice Kennedy Shriver National Institute of Child Health and Human Development; Common Fund

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