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
• Website: https://www.bv-brc.org/

The Bioinformatics Resource Analytics Center (BRC.analytics)  

• Principal Investigator/Director: Anton Nekrutenko, Ph.D.
• Institute: Pennsylvania State University, University Park, Pennsylvania 
• Website: https://brc-analytics.org/

The Pathogen Data Network 

• Principal Investigator/Director: Aitana Neves, Ph.D.
• Institute: Swiss Institute of Bioinformatics, Lausanne, Switzerland
• Website: https://pathogendatanetwork.org/

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).