NIAID-led scientists’ discovery of a hidden gene variant that causes some cases of a devastating inherited disease will enable earlier diagnosis of the disorder in people with the variant, facilitating earlier medical care that may prolong their lives. The researchers are working on a treatment for this unusual form of the rare autoimmune disease, known as APECED, and have traced its evolutionary origins. The findings are published in the journal Science Translational Medicine. 

APECED—short for autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy—causes multi-organ dysfunction, usually beginning in childhood, and can kill up to 30% of people with the syndrome. If diagnosed early and treated by a multidisciplinary healthcare team, however, people with APECED can survive into adulthood. Scientists in NIAID’s Laboratory of Clinical Immunology and Microbiology (LCIM) have developed a world-class APECED diagnostic and treatment program, currently caring for more than 100 patients as part of an observational study and serving as a resource for clinicians across the globe.

APECED is caused by mutations in a gene called AIRE, which provides instructions for making a protein that keeps the immune system’s T cells from attacking the body’s tissues and organs. These genetic mutations reduce or eliminate the protein’s normal function, leading to autoimmunity. 

Most people with APECED are diagnosed based on their clinical signs and symptoms as well as on genetic testing that confirms they have a disease-causing mutation in the AIRE gene. However, as the LCIM team studied people who came to NIH with APECED, they found 17 study participants with clinical signs and symptoms of the disease but no detectable mutations in AIRE. These participants shared two notable characteristics. The families of 15 of the 17 participants were wholly or partly from Puerto Rico, a relatively small, self-contained geographic area, suggesting that the individuals’ disease might have the same genetic cause. In addition, all 17 participants had the same harmless mutation to a single building block, or nucleotide, in both copies of their AIRE gene (one inherited from each parent). This suggested they all might have a similar stretch of genetic material in or around AIRE. These clues led the researchers to start hunting for a unique genetic mechanism that could be causing APECED in the group. 

The Quest for a Genetic Cause

Using technologies called whole-exome sequencing and whole-genome sequencing, the scientists determined the order of all the nucleotides in the DNA of each study participant. By examining and comparing these genetic sequences, the researchers discovered that the 17 participants had the same mutation to a single nucleotide located in a different part of the AIRE gene than the mutations commonly known to cause APECED. APECED-causing mutations usually occur in parts of the AIRE gene called “exons,” which contain the DNA code for the protein. The mutations also sometimes occur at either end of the large, non-coding sections of AIRE called “introns,” which are located in-between the exons. The newly discovered mutation was in the middle of an AIRE intron rather than at either end, so how it caused disease was initially unclear.

To solve this puzzle, the researchers examined what happens when the version of AIRE with this mid-intron mutation gets transcribed into mature messenger RNA (mRNA), the protein precursor. Normally, a molecule called a spliceosome detects the boundaries between introns and exons, cuts out the exons, and “pastes” them together in order. The scientists discovered that the mid-intron AIRE mutation fools the spliceosome into “thinking” that part of the intron is an exon, leading it to cut and paste part of the intron—extraneous genetic material—into the mature mRNA. This gives cells instructions to make an AIRE protein with an incorrect amino-acid sequence at one end. The researchers predicted and then showed that this protein can’t function normally, confirming that the mid-intron AIRE mutation causes APECED in the 17 study participants who previously lacked a genetic diagnosis. 

The scientists anticipate that the newly discovered AIRE variant will be added to genetic screening panels given to people who doctors suspect have APECED or who have a family history of the disease. This could facilitate earlier diagnosis and treatment of people with the mid-intron AIRE mutation, potentially prolonging their lives. It will also enable these individuals to receive genetic counseling to inform their family planning decisions. According to the researchers, the new findings also suggest that there may be other undiscovered, mid-intron mutations that cause APECED or other inherited diseases.

A Potential Treatment in the Making

Now NIAID LCIM scientists are working on a treatment for APECED caused by the mid-intron mutation. They engineered five different strings of nucleic acids, known as antisense oligonucleotides (ASOs), designed to hide the mutation from the spliceosome. Laboratory testing in cells with the mid-intron AIRE mutation showed that one ASO worked. Unable to “see” the mutation, the spliceosome cut out the correct AIRE exons and pasted them together to make mature mRNA that could be translated into a normal AIRE protein. Next, the researchers will test this mutation-masking tool in a mouse model of APECED with this specific mid-intron mutation. They expect results in two to three years. 

ASOs are an emerging form of treatment for rare genetic diseases, sometimes custom-made for just one person.

Origins of the Mutation

Through genetic and statistical analyses, the researchers estimated that the mid-intron mutation first occurred about 450 years ago. This timing coincides with when the first Europeans colonized Puerto Rico, hailing from the Cádiz province of Spain. Notably, one of the two study participants who did not have Puerto Rican ancestry also was from Cádiz and had the same set of DNA variants on one of his chromosomes as the participants with Puerto Rican ancestry. According to the researchers, these findings suggest that one or a few early Spanish colonizers of Puerto Rico carried the mid-intron AIRE mutation, and it eventually became a major cause of APECED in the Puerto Rican population. Further studies are needed to determine the prevalence of this cause of APECED among Puerto Ricans and other populations with Spanish ancestry.    

By contrast, one member of the study cohort had no known Puerto Rican or Spanish ancestry and did not share the same set of DNA variants as the other 16 participants. The investigators say this suggests that the mid-intron AIRE mutation also emerged independently in North America and will likely be found in additional Americans with APECED who do not have Puerto Rican or Spanish ancestry.

Note: APECED is also known as APS-1, short for autoimmune polyglandular syndrome type 1. 

References 

S Ochoa et al. A deep intronic splice-altering AIRE variant causes APECED syndrome through antisense oligonucleotide-targetable pseudoexon inclusion. Science Translational Medicine DOI: 10.1126/scitranslmed.adk0845 (2024).

D Karishma et al. Antisense oligonucleotides: an emerging area in drug discovery and development. Journal of Clinical Medicine DOI: 10.3390/jcm9062004 (2020).

F Collins. One little girl’s story highlights the promise of precision medicine. NIH Director’s Blog. https://directorsblog.nih.gov/tag/milasen/ Oct. 23, 2019. Accessed Oct. 30, 2024.

Findings From NIH-Funded Study Could Facilitate Early Treatment of Neonatal Sepsis

Scientists have identified a four-gene signature detectable in newborns’ blood at birth that predicts before symptom onset whether a baby will develop sepsis during the first week of life, according to a study co-funded by the National Institutes of Health’s National Institute of Allergy and Infectious Diseases (NIAID). Sepsis is a potentially life-threatening condition that arises when the body's response to infection injures its own tissues and organs. Using the newly discovered genetic signature to identify newborns who will develop sepsis could facilitate early treatment and obviate the need to give antibiotics to all newborns with suspected sepsis but lacking a definitive diagnosis. The findings were published today in the journal eBioMedicine.

Two to 3% of newborns globally develop sepsis, and 17.6% of those babies die. The signs of sepsis in newborns—such as irritability, poor feeding and respiratory distress—are common to many illnesses. Consequently, clinicians sometimes misdiagnose newborn sepsis or suspect it too late, leading to death. If a clinician does suspect that a newborn has sepsis, they give the baby antibiotics pending confirmatory laboratory diagnosis of infection. The most common diagnostic technique takes several days, however, and is often inconclusive. As a result, clinicians often must decide between stopping antibiotics early and risking under-treatment, or giving antibiotics based only on a clinical diagnosis and risking serious side effects and development of antimicrobial resistance.

The NIAID-supported study aimed to find a way to accurately predict sepsis in newborns so it can be diagnosed and treated early while avoiding unnecessary antibiotic use. The researchers conducted their study in a subset of 720 initially healthy, full-term newborns who were enrolled in a larger clinical trial at two community health centers in The Gambia, West Africa. Blood was collected from all babies at birth.

Thirty-three infants were hospitalized within the first month of life for clinical signs suggestive of sepsis. Of those, 21 babies were diagnosed with sepsis, including 15 within the first week of life, which is considered early-onset sepsis. Twelve babies were diagnosed with non-septic localized infections. The researchers matched these 33 babies with 33 healthy controls and analyzed their blood to identify genes that were comparatively more active or less active at birth in each of the four groups. Using machine learning methods, the researchers detected four genes that were comparatively more active at birth only in those newborns who developed early-onset sepsis. The four-gene signature was 92.5% accurate at predicting at birth which of the 66 infants would develop early-onset sepsis.

The researchers tested the predictive accuracy of this gene signature in a different group of 12 infants whose blood had been collected soon after birth. Half had developed early-onset sepsis, while the other half had remained healthy. The four-gene signature predicted sepsis with 83% accuracy in this group. Further research is needed to determine how well the gene signature predicts early-onset sepsis in much larger groups of newborns.

The study was led by Robert E. W. Hancock, Ph.D.; Tobias R. Kollmann, M.D., Ph.D.; Beate Kampmann, M.D., Ph.D.; and Amy H. Lee, Ph.D. NIAID co-funded the study through its Human Immunology Project Consortium (HIPC) and Immune Development in Early Life program.

The larger study that enrolled the 720 newborns was called Systems Biology to Identify Biomarkers of Neonatal Vaccine Immunogenicity, sponsored by Boston Children's Hospital and funded by NIAID through the HIPC. More information is available in ClinicalTrials.gov at study identifier NCT03246230.

Reference: An et al. Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation. eBioMedicine DOI: 10.1016/j.ebiom.2024.105411 (2024).

NIAID Scientists Detail First Structure of a Natural Mammalian Prion

The near-atomic structure of a chronic wasting disease (CWD) prion should help scientists explain how CWD prions spread and become the most naturally infectious of the many mammalian protein aggregation diseases. NIAID scientists revealed the structure in a new study in Acta Neuropathologica. Such detailed knowledge could guide the rational design of vaccines and therapeutics, as well as identify mechanisms that protect humans from CWD pathogens in deer, elk, moose, and reindeer.

Many brain diseases of humans and other mammals involve specific proteins (e.g., prion protein or PrP) gathering into abnormal thread-like structures that grow by sticking to normal versions of the same protein. These threads can also fragment and spread throughout the nervous system and accumulate to deadly levels. For unknown reasons, CWD prions are more naturally contagious than most other protein aggregates and are spreading rampantly among cervid species in North America, Korea and northern Europe. Recalling the bovine spongiform encephalopathy (BSE) or “mad cow disease” epidemic of the mid-1980s and mid-1990s, there are concerns that CWD might similarly be transmissible to humans.

To date, no CWD transmission to humans has been substantiated, and the new CWD structure suggests preliminarily why we might be protected. The structure also reveals multiple differences between CWD and previously determined structures of highly infectious, but experimentally rodent-adapted, PrP-based prions. Differences are even more profound when compared to largely non-transmissible PrP filaments isolated from humans with Gerstmann-Sträussler-Scheinker syndrome, a genetic prion disorder.

PrP-based prion diseases are degenerative, untreatable, and fatal diseases of the central nervous system that occur in people and other mammals. These diseases primarily involve the brain, but also can affect the eyes and other organs. CWD-infected animals shed infectious prions in their feces, urine, and other fluids and body components while alive, and from their carcasses after dying. The prions can remain infectious in the environment for years.

Scientists at NIAID’s Rocky Mountain Laboratories in Hamilton, Montana, determined the CWD structure from the brain tissue from a naturally infected white-tailed deer. They isolated the prions and froze them in glass-like ice. Then, using electron microscopy techniques, they developed a 3-D electron density map that indicated the detailed shapes of the protein molecules within the prion structure. This involved taking nearly 80,000 video clips of the sample, magnified 105,000 times the original size, at various orientations. They marked prion filaments in the video clips and collected more than 500,000 overlapping sub-images. They isolated about 7,300 of the highest quality sub-images and then used supercomputers to generate a 3-D density map and a molecular model to fit the map.

Vaccine development is among the many research areas where scientists could use high-resolution prion structures to advance their work. The study authors note that previous attempts to develop vaccines against CWD in cervids failed to be protective, and, at least in one case, had the opposite effect. They speculate that one explanation for adverse vaccine effects could be that antibody binding to the sides, rather than the ends of prion fibril surfaces, promotes fragmentation – creating infectious particles rather destroying them. Thus, a strategy to explore with vaccines and small-molecule inhibitors, they say, is to target the tips of prion structures where binding and conversion of prion protein molecules occurs.

The research team is planning to solve other naturally occurring prion structures, hoping to advance its understanding of the molecular basis of prion transmission and disease.

References:

P Alam, F Hoyt, E Artikis, et al. Cryo-EM structure of a natural prion: chronic wasting disease fibrils from deer. Acta Neuropathologica DOI: 10.1007/s00401-024-02813-y (2024).

A Kraus et al. High-resolution structure and strain comparison of infectious mammalian prions. Molecular Cell. DOI: 10.1016/j.molcel.2021.08.011. (2021).

Country-led genetic analysis of samples collected through the Republic of Congo (RoC) epidemiologic surveillance system in early 2024 showed that mpox was affecting people in parts of the country where it has not been historically reported, and point to increases in human-to-human transmission across the border with the neighboring Democratic Republic of the Congo (DRC), where a large outbreak was declared a public health emergency of international concern in August of the same year. The analysis was conducted by the RoC Laboratoire National de Santé Publique (LNSP) in Brazzaville with support and scientific partnership from NIAID and was published in The Lancet. 

There are two known types or “clades” of monkeypox virus (MPXV), which causes mpox clinical disease. Clade I is endemic in Central Africa and can cause severe illness. Clade II, endemic in West Africa, caused the global mpox outbreak that began in 2022 and tends to result in milder illness. Each clade has two known subtypes referred to as “a” and “b.” Clade Ia has been identified in RoC and DRC intermittently for decades and Clade Ib was first identified during the active DRC outbreak. Mpox is a zoonotic disease, meaning it can be spread between animals and people. MPXV has been detected in rodents that live in areas historically affected by mpox. 

Genetic sequencing of MPXV can help determine the transmission dynamics and guide the public health response to mpox, but until recently most sequencing of MPXV was done outside of affected countries like RoC, requiring costly sample transport and delaying decision-making by local health authorities. 

To better understand whether mpox in RoC was driven by spillover from local animal hosts or cross-border human-to-human transmission from DRC, a team led by the RoC LNSP analyzed 31 samples of laboratory-confirmed MPXV collected through the country’s routine epidemiologic surveillance system between January and April of 2024. Using new in-country sequencing technology, the team determined that there were diverse circulating strains of MPXV in the country, all of the Clade Ia subtype, and some showed up to 99.9% genetic similarity to MPXV sequenced from the DRC. Moreover, MPXV samples came from provinces without historical reports of mpox. 

According to the authors, the diversity of identified stains suggest MPXV has been introduced to the human population in RoC through multiple distinct events, which could be a combination of direct zoonotic transmission from local animals as well as human-to-human transmission within and across the country’s borders. They state that current epidemiological data are insufficient to definitively confirm the directionality of MPXV transmission and that further epidemiological research is needed to understand local transmission patterns and inform the public health response in RoC. Finally, they highlight that while only 31 samples met criteria for analysis in the study, it is likely these cases represent only a fraction of the RoC mpox burden at the time of collection.

This research informed the RoC’s decision to declare a national mpox epidemic in April 2024. It is part of a longstanding scientific collaboration between NIAID’s Rocky Mountain Laboratories and the Congolese government. The U.S. Embassy in RoC, the U.S. Agency for International Development, the U.S. Centers for Disease Control and Prevention, and the World Health Organization also provided technical and laboratory support for this study. 

Learn more about NIAID’s mpox research priorities. Play a video of NIAID Director Jeanne Marrazzo discussing these priorities. 

Reference:

CK Yinda, et al. Genetic sequencing analysis of monkeypox virus clade I in Republic of the Congo: a cross-sectional, descriptive study. The Lancet DOI: 10.1016/S0140-6736(24)02188-3 (2024)

NIAID Funds Cutting-Edge Genomics and Bioinformatics Programs

NIAID has announced six awards to continue the Genomics Centers for Infectious Diseases (GCIDs) and Bioinformatics Resource Centers (BRCs) for Infectious Diseases, both important data science networks offering critical resources for the scientific community. NIAID expects to commit approximately $19.1 million per year to fund the five-year programs. The awards mark the 20^th anniversaries of the GCID and BRC programs and extend NIAID's history of investing in cutting-edge pathogen genomics and bioinformatics research – the relatively new field of using patient gene sequences and computer analysis to identify, predict and prevent disease.

The GCIDs and BRCs provide public access to high-quality genomic data and data analytics technologies, tools, and training to facilitate discoveries by researchers studying viruses, bacteria, fungi, parasites, other eukaryotic pathogens, and vectors. In addition, in the event of an infectious disease outbreak, the GCID and BRC programs offer network expertise and resources and provide a coordinated research response.

For example, the GCIDs use innovative, large-scale genomics technology and bioinformatics tools to find specific genetic sequences to explain how pathogens cause disease and whether pathogens are resistant to available treatments. GCID studies can enhance understanding of infection mechanisms, track pathogen transmission dynamics, and improve detection – all leading to better diagnostics, prevention, treatment, and pathogen elimination strategies.

For more information, visit the GCID program website.

The BRCs are publicly accessible online resources that include data on pathogens, vectors, and hosts. The newly funded BRCs will have four primary objectives:

1. To provide integrated data and bioinformatics resources for infectious diseases.
2. To develop advanced innovative bioinformatics technologies, software, and tools to accelerate basic and applied human infectious diseases research.
3. To offer state-of-the-art bioinformatics trainings, educational materials, and other community outreach activities for the infectious diseases research community in the United States and globally.
4. To offer cutting-edge bioinformatics resources and analytics in response to emerging needs, outbreaks, and public health emergencies consistent with NIAID’s mission.

The newly funded BRCs will align with the NIH Strategic Plan for Data Science and incorporate globally distributed repositories and analytical capabilities that will be strengthened by a program-wide commitment to FAIR data principles and collaborative work. All three funded centers will conduct activities and advance research across all four programmatic objectives and will become operational soon after the awards are made. Two centers, the Bioinformatics Resource Analytics Center (BRC.analytics) and the Pathogen Data Network will address all pathogen types relevant to the NIAID mission and will continue to make available bioinformatics data compiled during previous funding periods from eukaryotic pathogens and vectors, and from bacteria and viruses. Both centers will have a specific focus on advancing the knowledge base and tools for bioinformatics analysis of eukaryotic genomes but will also advance technologies for bacterial and viral bioinformatics. The Bacterial and Viral Bioinformatics Resource Center (BV-BRC) will continue its focus on bacterial and viral pathogens, and bioinformatics data compiled for bacteria and viruses during previous funding periods will be found on its site.

Bioinformatics infrastructure advances anticipated include: providing uniform and easy access to numerous pathogen-relevant external resources; integrating infectious diseases data with additional human and clinical data; and providing large-scale automated bioinformatics workflows and dataset management.

The BRC program is expected to enhance NIAID’s outbreak and pandemic preparedness response by offering accessible platforms that integrate public health, pathogen and other data. For more information, visit the BRC program website.

GCID award recipients are:

The Center for Advancing Genomic, Transcriptomic and Functional Approaches to Combat Globally Important and Emerging Pathogens

• Principal Investigator/Director: Daniel Neafsey, Ph.D.
• Institute: Broad Institute, Boston, Massachusetts

The Center for Integrated Genomics of Mucosal Infections

• Principal Investigator/Director: Joseph Petrosino, Ph.D.
• Institute: Baylor College of Medicine, Houston, Texas

The Michigan Infectious Disease Genomics (MIDGE) Center

• Principal Investigator/Director: Adam Lauring, M.D., Ph.D.
• Institute: The University of Michigan, Ann Arbor, Michigan

BRC award recipients are:

The Bacterial and Viral Bioinformatics Resource Center (BV-BRC)

• Principal Investigator/Director: Rick Stevens, Ph.D.
• Institute: University of Chicago, Chicago, Illinois
• Bacterial and Viral Bioinformatics Resource Center

The Bioinformatics Resource Analytics Center (BRC.analytics)

• Principal Investigator/Director: Anton Nekrutenko, Ph.D.
• Institute: Pennsylvania State University, University Park, Pennsylvania
• BRC Analytics

The Pathogen Data Network

• Principal Investigator/Director: Aitana Neves, Ph.D.
• Institute: Swiss Institute of Bioinformatics, Lausanne, Switzerland
• Pathogen Data Network

The immune system senses and responds to changes in physiologic health, and a new tool called the immune health metric (IHM) can measure and even predict some of these changes, an NIAID study has found. If doctors could use the IHM to detect health problems long before symptoms appear, they could potentially act early to prevent disease, the investigators suggest. Their findings are published in the journal Nature Medicine. 

The researchers developed the IHM starting with extensive analyses of biological samples from nearly 230 people who have one of 22 rare, severe immune disorders caused by a mutation in just one gene. The scientists also included samples from 42 healthy people matched to the others by age and sex. The analyses involved many elements, including gene transcripts in immune cells, blood-based proteins, and the frequency of various blood cells, all related to the immune system. The initial goal was to learn if there were immune-system similarities among people with the diverse array of diseases.

To the researchers’ surprise, the disparate diseases had many similar features when viewed through the lens of the immune system as a whole, rather than only the mutated gene and its effects. The primary source of immune variation came from aspects of the individual, irrespective of their disease or the medication they were taking. 

To explore this observation further, the scientists fed their gene-transcript and blood-based protein data into artificial intelligence (AI) tools. The first tool assessed differences among the people in the study without knowing their disease or symptoms. This analysis yielded a numeric measurement called jPC1 that was based on a specific combination of key gene transcripts and proteins. jPC1 correlated negatively with inflammation and related markers, and positively with parameters not linked to inflammation. This suggested that jPC1 could be used to measure immune health. Further supporting this finding, the group of healthy participants had a significantly higher mean jPC1 score than people grouped by severe immune disorder. 

The second AI tool the researchers tested is a machine-learning model that they taught to distinguish between healthy people and those with severe immune disorders. The investigators did this using the gene transcripts, blood-based proteins, and blood cells from the original biological samples. The scientists used their model to compute the probability that a person belonged to the immunologically healthy group. Each person received a score based on that probability. The researchers called this scoring system the immune health metric, or IHM. The IHM scores correlated highly with the jPC1 scores, suggesting that the gene transcripts and proteins key to jPC1 drive immune health differences among individuals.

When the scientists applied the IHM to the healthy people in their study and data from an independent study of healthy aging conducted by NIH’s National Institute on Aging, the one variable the metric correlated with was age. There was an inverse relationship between IHM score and age, with ages ranging from 22 to 93. This indicated that aging, like disease, distances people from optimal immune health. 

The authors validated the IHM by showing it could reflect immune health status and treatment outcomes and even predict some health outcomes when applied to gene transcript data, blood-based protein data, or both from studies previously conducted by other scientists. For instance, IHM and jPC1 scores accurately distinguished people with common autoimmune and inflammatory diseases from healthy people. IHM scores also reflected variability in disease activity among people with lupus, an autoimmune disease, during periods with symptoms of differing severity and periods without symptoms. Among people with rheumatoid arthritis, IHM scores reflected differences in the immune health of people whose symptoms responded to treatment compared to those whose symptoms did not. In vaccine studies and a heart failure study, people with higher baseline IHM scores had better antibody responses to vaccines and better future heart health than people with lower baseline scores. Finally, there was an inverse relationship between IHM score and body-mass index (BMI) in a study of sedentary adults, even after controlling for age, sex, race, and levels of C-reactive protein, which the liver releases in response to inflammation. 

While there are many tools available to measure physiologic and organ-system function and health, few tools measure immune-system health. The IHM could help fill this gap. The investigators hope that clinicians will one day be able to use the predictive capacity of the IHM to detect diseases early enough for preventive medicine to halt disease progression and preserve health.

John S. Tsang, Ph.D., and Rachel Sparks, M.D., M.P.H., led the study. Dr. Tsang was co-director of the NIH Center for Human Immunology at NIAID and chief of the Multiscale Systems Biology Section in the NIAID Laboratory of Immune System Biology when most of the research was conducted. He is now the founding director of the Yale Center for Systems and Engineering Immunology, a professor of immunobiology and biomedical engineering at Yale University, and an adjunct investigator in the NIAID Laboratory of Immune System Biology. 

Dr. Sparks was an assistant clinical investigator in the NIAID Laboratory of Immune System Biology and an attending physician at the NIH Clinical Center when she conducted the research. She is now an experimental medicine physician at Astra Zeneca in Gaithersburg, Maryland, and a special volunteer in the NIAID Laboratory of Immune System Biology. 

Reference: R Sparks et al. A unified metric of human immune health. Nature Medicine DOI: 10.1038/s41591-024-03092-6 (2024).