Using the NIAID Data Ecosystem Discovery Portal to Search Across Data Repositories

Data Science Dispatch |

a doctor pointing to science based icons on a screen
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NIAID has developed a platform to help researchers find data related to infectious and immune-mediated disease (IID) across multiple data repositories. The NIAID Data Ecosystem Discovery Portal is a centralized hub cataloging millions of datasets from over 50 sources.

Researchers can use the Discovery Portal to find data, resources, and computational tools from different repositories. This can save them time otherwise spent combing through multiple sources and help them find datasets they weren’t aware of previously.

The Discovery Portal includes resources from IID and generalist repositories. Representative resources include NIAID-sponsored repositories such as AccessClinicalData@NIAID, ImmPort, and VDJServer, as well as repositories funded outside of NIAID but relevant to IID research. Resources in the Discovery Portal include a diverse array of data types spanning multiple domains of IID research, including -omics data, clinical data, epidemiological data, pathogen-host interaction data, flow cytometry, imaging, and other experimental data.

The Discovery Portal supports NIAID objectives of maximizing the impact of scientific data, reducing duplication of efforts in research, and promoting data reuse, data transparency and compliance with data-sharing policies. The portal aligns with many of the principles of findable, accessible, interoperable, and reusable (FAIR) data practices by making data easier to find and access.

Using metadata to drive discovery

The NIAID Data Ecosystem Discovery Portal does not contain data itself. Instead, it contains detailed information about IID datasets and resources drawn from metadata. Users can then access the resources through external links.

The portal uses metadata to support several key features:

  • Search and Discovery: Users can rapidly search millions of datasets across both IID and generalist repositories using the Search or Advanced Search options. Metadata categories such as funding source, repository, and conditions of access help filter search results and identify relevant research data.
  • Metadata Compatibility: Each individual dataset in the Discovery Portal has a “metadata compatibility score,” which displays specific metadata elements collected for a given resource.  Additionally, the Discovery Portal has metadata compatibility visualizations which capture the breadth of metadata at the repository level. This information can help researchers and data contributors quickly understand a repository’s metadata structure, aiding in decisions about where to deposit or retrieve resources.
  • Downloadable Metadata: The portal has buttons that allow users to download metadata to perform meta-analyses.

The Discovery Portal is working to fill missing or incomplete metadata fields (such as Pathogen Species, Health Condition, and Host Species) by augmenting and standardizing metadata fields to provide more of this necessary information for users.

New Program Collection tool and other features

One of the new features of the NIAID Data Ecosystem Discovery Portal is the “Program Collection” filter. These are groups of datasets contributed by specialized NIAID research programs and initiatives. The Discovery Portal displays the Program Collection filter on the search page, and current efforts are focused on expanding Program Collection data.

The Program Collection filter allows researchers to discover high-quality, program-specific data relevant to their area of interest and find collections that align with the broader objectives of NIAID’s strategic research efforts. The feature also amplifies the scientific contributions of participating networks and increases the likelihood of researchers using these datasets. 

Using the Sources page of the Discovery Portal can also help researchers and data providers make informed decisions about different repositories where they can deposit their data.

The Discovery Portal is now connected to National Center for Biotechnology Information (NCBI) databases through NCBI LinkOut. When NCBI database content is linked to data described in the Portal, a link to the related Portal entry can be found on the NCBI page.

Learn more by visiting the Discovery Portal, reviewing the Getting Started page, and exploring the Knowledge Center. 

Immune Tolerance Processes in Autoimmune Disease

Understanding Metadata: A Key to Data Sharing and Reuse

Data Science Dispatch |

A lab scientist documents research data
Credit: National Eye Institute, National Institutes of Health

Metadata plays a crucial role in sharing and reusing scientific data. Understanding what metadata is and how it is used can accelerate your research and increase the visibility of your work. It can also help to advance the field of infectious and immune-mediated disease (IID) research.

What is metadata?

Metadata is data about data. It provides additional information to help people understand the data, such as its origin, structure, and context. 

For example, for a genome sequence, the data is the actual sequence of nucleotides. The metadata is the author of the data, the date the data was collected, the measurement techniques used, the health condition at the focus of dataset (like asthma or autoimmune diseases), and more. You can see another example of data versus metadata in the video on the right (data management and sharing webinar from the National Institute of Diabetes and Digestive and Kidney Diseases, 4:22-6:28).

Examples of common metadata elements that describe IID research data are available at the NIAID Data Ecosystem’s list of common fundamental and recommended metadata elements. 

Why is metadata important? When you share scientific data, metadata provides the context that allows others to understand, trust, reproduce, or reuse data. This is particularly important in studies or secondary analyses where data is integrated from multiple sources; comprehensive metadata enables a scientist to combine data from different sources.

Using metadata effectively can also help your data get discovered, reused, and cited—thereby maximizing the value and impact of your research.

Collecting rich metadata during research

Effective metadata use starts with collecting rich metadata throughout the research process. “Rich” metadata is detailed and structured, making it easier for people to quickly learn about your data. 

Including standardized formats and schemas makes it clear which metadata components are present and where they can be found. Using common terminologies, ontologies, and data formats takes this a step further by defining specific metadata elements for both people and computers. Machine-readable metadata allows users to learn about and use data using code, helping them quickly learn about many data files.

Some common examples of collecting metadata in a structured way include defining standardized date and time formats and using ORCID IDs for authors to ensure precise identification.

Biomedical researchers can follow some basic steps to ensure that they are collecting comprehensive and standardized metadata. 

1. Determine necessary metadata content and formats

Collecting data in the format you intend to share it in is more efficient than reformatting everything at the end. Here are some questions to help you determine data and metadata formats:

  • Who will use these data and how will they use it? What information do they need to understand the data?
  • Many research areas have standardized metadata formats that researchers can follow. What metadata standards or schemas do other researchers in your field use? Would using these standards and schemas help researchers understand and reuse these data?
  • Does the target repository or scientific journal have any specific metadata or formatting requirements? If the repository where you plan to share your data has specific guidance, follow that guidance from the start of your research.

2. Create metadata throughout the data lifecycle

Before data collection, collect protocol documentation and set up systems for data and metadata collection. These systems can collect information using the standards, formats, vocabularies, and ontologies selected, and will save you time when preparing data and metadata for publication.

During the data collection phase, document anything that fits into the target metadata fields. These may include the dates data was collected, variables measured, the units of measurement, the instruments used, and the conditions under which the data was collected.

After data collection, add any remaining metadata elements from your plan. These elements may focus more on describing data processing steps, versioning, authors, or related topics. 

3. Prepare to share data and metadata 

Verify that metadata meets requirements for where you would like to share your data, and add any elements that you may finalize late in the data lifecycle, like associated publications, license for reuse, or a data author list prior to sharing.

Throughout the process, you can seek guidance from your program officer or the repositories where you intend to share data to ensure that metadata is collected and shared effectively.

Sharing data and metadata

The NIH Data Management and Sharing Policy encourages sharing metadata that describes or supports your scientific data. NIH recommends data management and sharing practices consistent with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and it strongly encourages the use of established data repositories for preserving and sharing data. 

In some instances, the full scientific data cannot be shared easily. This may be due to large file sizes — particularly with imaging-related research — or data privacy regulations. However, even if the actual scientific data cannot be shared, sharing metadata is still valuable. This practice ensures that there is a public record of the data's existence and provides important background information that can be used by other researchers.

Metadata is also a powerful tool for finding scientific data in repositories. Researchers can use metadata to search for data sets that match specific criteria. One tool that can help researchers find relevant data is the NIAID Data Ecosystem Discovery Portal, which uses metadata present in data stored in repositories to search across over 50 different IID repositories and data sources. 

Learn more about developing a data management and sharing plan and compliance with relevant NIH data sharing policies by reviewing the Data Policy and Guidance page. 

Chung Park, M.S., Ph.D.

Section or Unit Name
B-Cell Molecular Immunology Section
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NIH Main Campus, Bethesda, MD
This Researcher/Clinician’s Person Page
Chung Park, M.S., Ph.D.
Parent Lab/Program
Laboratory of Immunoregulation
Program Description

Philosophy - Advancing Human Health Through Immunological Research:

  • Enhance understanding of immune system regulation in health and disease
  • Provide mechanistic insights into disease pathology to inform therapeutic strategies
  • Support translational research to develop targeted treatments for immune-related disorders

Secondary Lymphoid Organ Remodeling and Pathogen-Immune cell Interactions:

  • Investigate structural remodeling of lymph nodes in immune responses
  • Examine chemokine receptor sensitivity modulation by RGS proteins
  • Characterize cellular networks facilitating virus envelope protein transfer

Extracellular Signaling, GPCR Signal Transduction and Immune Modulation:

  • Investigate chemokine receptor-mediated signaling in immune cell regulation
  • Examine heterotrimeric G-protein activation in lymphocyte function
  • Study molecular mechanisms of G-protein-coupled receptor (GPCR) signaling
  • Analyze how GPCR signaling orchestrates immune responses and cell dynamics

Experimental Approaches:

  • Utilize genetically engineered murine models
  • Employ intravital two-photon laser scanning microscopy (TP-LSM) and high-throughput flow cytometry
Selected Publications

Park C, Hwang IY, Yan SL, Vimonpatranon S, Wei D, Van Ryk D, Girard A, Cicala C, Arthos J, Kehrl JH. Murine alveolar macrophages rapidly accumulate intranasally administered SARS-CoV-2 Spike protein leading to neutrophil recruitment and damage. Elife. 2024 Mar 20;12:RP86764.

Park C, Kehrl JH. An integrin/MFG-E8 shuttle loads HIV-1 viral-like particles onto follicular dendritic cells in mouse lymph node. Elife. 2019 Dec 6;8:e47776.

Guzzo C, Ichikawa D, Park C, Phillips D, Liu Q, Zhang P, Kwon A, Miao H, Lu J, Rehm C, Arthos J, Cicala C, Cohen MS, Fauci AS, Kehrl JH, Lusso P. Virion incorporation of integrin α4β7 facilitates HIV-1 infection and intestinal homing. Sci Immunol. 2017 May 12;2(11):eaam7341.

Park C, Arthos J, Cicala C, Kehrl JH. The HIV-1 envelope protein gp120 is captured and displayed for B cell recognition by SIGN-R1(+) lymph node macrophages. Elife. 2015 Aug 10;4:e06467.

Park C, Hwang IY, Sinha RK, Kamenyeva O, Davis MD, Kehrl JH. Lymph node B lymphocyte trafficking is constrained by anatomy and highly dependent upon chemoattractant desensitization. Blood. 2012 Jan 26;119(4):978-89.

Sinha RK*, Park C*, Hwang IY, Davis MD and Kehrl JH. B lymphocytes Exit Lymph Nodes through Cortical Lymphatic Sinosoids Near to Lymph Nodes Follicles by a Mechanism Independent of S1P-Mediated Chemotaxis. Immunity. 2009 Feb 18. [Epub ahead of print] (*Co-first publication)

Visit PubMed for a complete publication listing.

Major Areas of Research
  • Lymphocyte trafficking and cellular migration dynamics from homeostasis to pathological conditions
  • B-cell signaling, G-protein signaling pathways, and the regulatory role of RGS proteins  
  • Mechanisms underlying complex cellular immune responses induced by diverse antigens and pathogens 

Fengkai Zhang, M.D., M.Math.

Section or Unit Name
Computational Systems Biology Section
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NIH Main Campus, Bethesda, MD
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Fengkai Zhang, M.D., M.Math.
Parent Lab/Program
Laboratory of Immune System Biology
Program Description

A range of methodologies is utilized in systems biology to simulate biological pathways, uncovering the intricate ways molecules function within living organisms and enabling hypotheses about potential interactions and reaction rate ranges without relying on traditional experimental measurements. Among these, rule-based modeling abstracts similar reactions into rules that summarize the characteristics and binding states of individual molecular components within large molecular complexes. This approach offers a distinct advantage by simplifying the representation of molecular reactions, organizing entities within hierarchical structures that align with biological formats at both the molecular and sub-molecular levels, such as domains and binding sites. Furthermore, the rule-based modeling approach can address the combinatorial complexity inherent in biological systems.

Simmune is a software suite that uses rule-based modeling to simulate biological pathways. It constructs models of biological reactions through its unique icon-based modeling language from single reactions and provides a flexible, high-level network view for model inspection. Simmune can simulate models in well-stirred environments, efficiently explore large parameter spaces, and help users identify the most relevant parameters, offering insights into the relationships between different parameters. Additionally, Simmune supports the simulation of spatially resolved models in discrete grid morphologies and 3D environments, with the ability to analyze and visualize results at fine-grained subcellular levels.

Simmune supports the Multistate, Multicomponent, and Multicompartment Species Package for SBML Level 3 (SBML-Multi) to exchange rule-based models. This standard is part of an initiative by the COmputational Modeling in BIology NEtwork (COMBINE) community standards and formats for computational models. Our group leads the effort for the SBML-Multi standard.

Simmune is built with technologies including C/C++, Qt, VTK, CMake, Python, SQL, MongoDB, Boost, version control, Sundials, and parallel computing, providing a powerful, flexible modeling tool that remains user-friendly for biological researchers with limited computing expertise. Our group collaborates with researchers in immunology, proteomics, computational modeling, and systems biology.

Selected Publications

Xu X, Quan W, Zhang F, Jin T. A systems approach to investigate GPCR-mediated Ras signaling network in chemoattractant sensing. Mol Biol Cell. 2022 Mar 1;33(3):ar23.

Zhang F, Smith LP, Blinov ML, Faeder J, Hlavacek WS, Juan Tapia J, Keating SM, Rodriguez N, Dräger A, Harris LA, Finney A, Hu B, Hucka M, Meier-Schellersheim M. Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2. J Integr Bioinform. 2020 Jul 6;17(2-3):20200015.

Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, Bergmann FT, Finney A, Gillespie CS, Helikar T, Hoops S, Malik-Sheriff RS, Moodie SL, Moraru II, Myers CJ, Naldi A, Olivier BG, Sahle S, Schaff JC, Smith LP, Swat MJ, Thieffry D, Watanabe L, Wilkinson DJ, Blinov ML, Begley K, Faeder JR, Gómez HF, Hamm TM, Inagaki Y, Liebermeister W, Lister AL, Lucio D, Mjolsness E, Proctor CJ, Raman K, Rodriguez N, Shaffer CA, Shapiro BE, Stelling J, Swainston N, Tanimura N, Wagner J, Meier-Schellersheim M, Sauro HM, Palsson B, Bolouri H, Kitano H, Funahashi A, Hermjakob H, Doyle JC, Hucka M; SBML Level 3 Community members. SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol. 2020 Aug;16(8):e9110.

Cheng HC, Angermann BR, Zhang F, Meier-Schellersheim M. NetworkViewer: visualizing biochemical reaction networks with embedded rendering of molecular interaction rules. BMC Syst Biol. 2014 Jun 16;8:70.

Zhang F, Angermann BR, Meier-Schellersheim M. The Simmune Modeler visual interface for creating signaling networks based on bi-molecular interactions. Bioinformatics. 2013 May 1;29(9):1229-30.

Angermann BR, Klauschen F, Garcia AD, Prustel T, Zhang F, Germain RN, Meier-Schellersheim M. Computational modeling of cellular signaling processes embedded into dynamic spatial contexts. Nat Methods. 2012 Jan 29;9(3):283-9.

Additional Information

Tools and Resources

Simmune Project

Research Networks

Systems Biology Markup Language (SBML)
COmputational Modeling in BIology NEtwork (COMBINE)

Major Areas of Research
  • Design and development of systems biology simulation applications
  • Development of a standard of the Multistate, Multicomponent and Multicompartment Species Package for SBML Level 3 to exchange rule-based models
  • Simulating and analyzing well-stirred and spatially resolved computational models for signaling processes

Rahul K. Suryawanshi, Ph.D.

Section or Unit Name
Neurovirology Unit
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Hamilton, MT
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Rahul K. Suryawanshi, Ph.D.
Parent Lab/Program
Laboratory of Neurological Infections and Immunity
Program Description

The Neurovirology Unit conducts research on the acute and long-term complications associated with human alphaherpesvirus infections and pulmonary infections caused by coronaviruses and influenza.

Using transgenic animal models and integrating approaches from molecular virology, neurobiology, and immunology, we investigate the mechanisms underlying viral pathogenesis in the central nervous system, which particularly involves analyzing roles of immunomodulatory host factors to understand their roles in pathogenesis, neuroprotection, and potentiating antiviral immunity. While studying different aspects of antiviral immunity, we also focus on understanding the neurological regulation of antiviral immunity, neuroinflammation, and the long-term manifestations of viral infection, such as neurodegeneration and cognitive decline using machine learning-based behavioral approaches.

Additionally, the Neurovirology Unit explores the interactions between viral proteins, host factors, and immune responses that drive differential disease severity observed in humans, paving the way for innovative therapeutic strategies. We are also committed to advancing human brain and lung organoid models to recapitulate disease phenotypes in humans and thereby enhance our understanding of viral disease mechanisms.

Selected Publications

Suryawanshi RK, Chen IP, Ma T, Syed AM, Brazer N, Saldhi P, Simoneau CR, Ciling A, Khalid MM, Sreekumar B, Chen PY, Kumar GR, Montano M, Gascon R, Tsou CL, Garcia-Knight MA, Sotomayor-Gonzalez A, Servellita V, Gliwa A, Nguyen J, Silva I, Milbes B, Kojima N, Hess V, Shacreaw M, Lopez L, Brobeck M, Turner F, Soveg FW, George AF, Fang X, Maishan M, Matthay M, Morris MK, Wadford D, Hanson C, Greene WC, Andino R, Spraggon L, Roan NR, Chiu CY, Doudna JA, Ott M. Limited cross-variant immunity from SARS-CoV-2 Omicron without vaccination. Nature. 2022 Jul;607(7918):351-355.

Ryu JK, Yan Z, Montano M, Sozmen EG, Dixit K, Suryawanshi RK, Matsui Y, Helmy E, Kaushal P, Makanani SK, Deerinck TJ, Meyer-Franke A, Rios Coronado PE, Trevino TN, Shin MG, Tognatta R, Liu Y, Schuck R, Le L, Miyajima H, Mendiola AS, Arun N, Guo B, Taha TY, Agrawal A, MacDonald E, Aries O, Yan A, Weaver O, Petersen MA, Meza Acevedo R, Alzamora MDPS, Thomas R, Traglia M, Kouznetsova VL, Tsigelny IF, Pico AR, Red-Horse K, Ellisman MH, Krogan NJ, Bouhaddou M, Ott M, Greene WC, Akassoglou K. Fibrin drives thromboinflammation and neuropathology in COVID-19. Nature. 2024 Sep;633(8031):905-913.

Suryawanshi RK, Patil CD, Agelidis A, Koganti R, Ames JM, Koujah L, Yadavalli T, Madavaraju K, Shantz LM, Shukla D. mTORC2 confers neuroprotection and potentiates immunity during virus infection. Nat Commun. 2021 Oct 14;12(1):6020.

Suryawanshi RK, Patil CD, Agelidis A, Koganti R, Yadavalli T, Ames JM, Borase H, Shukla D. Pathophysiology of reinfection by exogenous HSV-1 is driven by heparanase dysfunction. Sci Adv. 2023 Apr 28;9(17):eadf3977.

Suryawanshi RK, Jaishankar P, Correy GJ, Rachman MM, O'Leary PC, Taha TY, Zapatero-Belinchón FJ, McCavittMalvido M, Doruk YU, Stevens MGV, Diolaiti ME, Jogalekar MP, Richards AL, Montano M, Rosecrans J, Matthay M, Togo T, Gonciarz RL, Gopalkrishnan S, Neitz RJ, Krogan NJ, Swaney DL, Shoichet BK, Ott M, Renslo AR, Ashworth A, Fraser JS. The Mac1 ADP-ribosylhydrolase is a Therapeutic Target for SARS-CoV-2. eLife14:RP103484.

Suryawanshi R, Ott M. SARS-CoV-2 hybrid immunity: silver bullet or silver lining?. Nat Rev Immunol. 2022 Oct;22(10):591-592.

Major Areas of Research
  • Acute and post-acute neuropathies of virus infections
  • Impact of genetics on disease severity
  • Host-virus interactions and its effect on antiviral immunity
  • Human brain and lung organoid models to study virus infection

Fabiano Oliveira, M.D., Ph.D.

Section or Unit Name
Vector Molecular Biology Section

Highlight

New Tool Identifies Aedes Mosquito Exposure in People

May 1, 2024

NIAID-developed test could be used to find hot spots for disease-spreading mosquitoes.

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Rockville, MD
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Fabiano Oliveira, M.D., Ph.D.
Parent Lab/Program
Laboratory of Malaria and Vector Research
Program Description

Our research focuses on the complex interactions between the human immune system and insect-derived molecules, and how these interactions can influence the outcomes of vector-borne diseases such as dengue, Zika, Chikungunya, and leishmaniasis. When an insect bites, it injects hundreds of arthropod molecules into the host's skin, alerting our immune system to these foreign agents. If the insect is infected with a pathogen, the microorganism is delivered along with these insect-derived molecules. Our immune response to these molecules over time can either help or hinder pathogen establishment, ultimately affecting the disease outcome.

Our work is conducted at two primary locations: the Laboratory of Malaria and Vector Research (LMVR) in Rockville, which is equipped with cutting-edge technologies, and the NIAID International Center of Excellence in Research (ICER) in Cambodia, where we conduct field observations and studies.

At LMVR-Rockville, we use advanced technologies and methodologies to explore the molecular and immunological mechanisms underlying the human response to arthropod bites and the pathogens they transmit. In Cambodia, at the NIAID ICER, we engage in extensive fieldwork to gather critical data and observations directly from affected populations. By integrating field data with laboratory findings, we aim to develop robust hypotheses that can lead to effective strategies for disease mitigation and control.

Our multidisciplinary approach allows us to bridge the gap between laboratory research and field applications. By understanding how the human immune system responds to arthropod molecules, we can identify potential targets for vaccines, therapeutics, and diagnostic tools. Additionally, our research contributes to the development of innovative vector control strategies that can reduce the incidence of these debilitating diseases.

Through collaboration with local communities, healthcare providers, and international partners, we strive to translate our scientific discoveries into practical solutions that can improve public health outcomes. Our ultimate goal is to reduce the burden of vector-borne diseases and enhance the quality of life for people living in endemic regions.

Our research aims to improve dengue prevention and treatment strategies for U.S. travelers, personnel in endemic areas, and regions with reported dengue cases, such as Hawaii, Florida, Texas, Puerto Rico, the U.S. Virgin Islands, and Guam. Enhanced predictive, management, diagnostic, and preventive measures for dengue outbreaks are particularly crucial for these at-risk regions. The development and use of prophylactic therapeutics targeting specific immune responses to mosquito bites could reduce the transmission of arboviruses, including eastern equine encephalitis, Jamestown Canyon, La Crosse, Powassan, St. Louis encephalitis, and West Nile viruses. Improved diagnostic capabilities for vector-borne diseases and emerging infections will lead to better patient outcomes. 

Clinical Studies
Selected Publications

Manning JE, Chea S, Parker DM, Bohl JA, Lay S, Mateja A, Man S, Nhek S, Ponce A, Sreng S, Kong D, Kimsan S, Meneses C, Fay MP, Suon S, Huy R, Lon C, Leang R, Oliveira F. Development of Inapparent Dengue Associated With Increased Antibody Levels to Aedes aegypti Salivary Proteins: A Longitudinal Dengue Cohort in Cambodia. J Infect Dis. 2022 Oct 17;226(8):1327-1337.

Guerrero D, Vo HTM, Lon C, Bohl JA, Nhik S, Chea S, Man S, Sreng S, Pacheco AR, Ly S, Sath R, Lay S, Missé D, Huy R, Leang R, Kry H, Valenzuela JG, Oliveira F, Cantaert T, Manning JE. Evaluation of cutaneous immune response in a controlled human in vivo model of mosquito bites. Nat Commun. 2022 Nov 17;13(1):7036.

Chea S, Willen L, Nhek S, Ly P, Tang K, Oristian J, Salas-Carrillo R, Ponce A, Leon PCV, Kong D, Ly S, Sath R, Lon C, Leang R, Huy R, Yek C, Valenzuela JG, Calvo E, Manning JE, Oliveira F. Antibodies to Aedes aegypti D7L salivary proteins as a new serological tool to estimate human exposure to Aedes mosquitoes. Front Immunol. 2024 May 1;15:1368066.

Guimaraes-Costa AB, Shannon JP, Waclawiak I, Oliveira J, Meneses C, de Castro W, Wen X, Brzostowski J, Serafim TD, Andersen JF, Hickman HD, Kamhawi S, Valenzuela JG, Oliveira F. A sand fly salivary protein acts as a neutrophil chemoattractant. Nat Commun. 2021 May 28;12(1):3213.

Oliveira F, Rowton E, Aslan H, Gomes R, Castrovinci PA, Alvarenga PH, Abdeladhim M, Teixeira C, Meneses C, Kleeman LT, Guimarães-Costa AB, Rowland TE, Gilmore D, Doumbia S, Reed SG, Lawyer PG, Andersen JF, Kamhawi S, Valenzuela JG. A sand fly salivary protein vaccine shows efficacy against vector-transmitted cutaneous leishmaniasis in nonhuman primates. Sci Transl Med. 2015 Jun 3;7(290):290ra90.

Manning JE, Oliveira F, Coutinho-Abreu IV, Herbert S, Meneses C, Kamhawi S, Baus HA, Han A, Czajkowski L, Rosas LA, Cervantes-Medina A, Athota R, Reed S, Mateja A, Hunsberger S, James E, Pleguezuelos O, Stoloff G, Valenzuela JG, Memoli MJ. Safety and immunogenicity of a mosquito saliva peptide-based vaccine: a randomised, placebo-controlled, double-blind, phase 1 trial. Lancet. 2020 Jun 27;395(10242):1998-2007.

Visit PubMed for a complete publication listing.

Additional Information

Articles

A tropical parasite, passed through the bite of a sand fly, is causing skin infections in the US

Major Areas of Research
  • Characterization of human immune response to ticks, mosquito, and sand fly saliva in the context of medically significant vector-borne diseases (Lyme disease, Powassan, dengue, malaria, and leishmaniasis)
  • Clinical and field epidemiology of the impact of mosquito saliva immunity on the outcome of dengue, Zika, and other diseases carried by mosquitos
  • Strategies to block vector-borne diseases by targeting the arthropod vector and interruption transmission to the human host

Michael S. Abers, M.D.

Section or Unit Name
Opportunistic Bacterial Pathogenesis Unit (OBPU)
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NIH Main Campus, Bethesda, MD
This Researcher/Clinician’s Person Page
Michael S. Abers, M.D.
Parent Lab/Program
Laboratory of Clinical Immunology and Microbiology
Program Description

The Opportunistic Bacterial Pathogenesis Unit is interested in Nocardia infections. Our bench-to-bedside research program incorporates immunology, microbiology, genetics, and bioinformatics to investigate the pathogenesis of nocardiosis. A major goal of our research is to identify the key immunological mechanisms that control Nocardia. Insights from this work will inform future efforts to develop host-directed therapies for nocardiosis. Another area of interest is host- and pathogen-specific factors that determine patterns of dissemination and patient outcomes.

Selected Publications

Visit PubMed for a complete publication list.

Additional Information

Training Programs

Infectious Disease Training Program

Major Areas of Research
  • Inherited and acquired susceptibility to Nocardia infections (nocardiosis)
  • Host defense mechanisms that protect against nocardiosis
  • Development of host-directed therapies for Nocardia infection

Measuring Innovation: Laboratory Infrastructure to Deliver Essential HIV Clinical Trial Results

NIAID Now |

Rows of identical tubes with spherical openings pointing up.

A box of pipette tips.

Credit: NIAID

This blog is the fifth in a series about the future of NIAID's HIV clinical research enterprise. For more information, please visit the HIV Clinical Research Enterprise page.

The outcomes of HIV clinical trials are often determined by precisely and accurately measuring how specific interventions work biologically in people. Whether tracking immune responses to a preventive vaccine candidate, monitoring changes to the amount of virus in the body, or screening for certain adverse events after administering a novel therapeutic, study teams routinely interact with clinical trial participants to safely obtain, store, transport, and analyze tissue and bodily fluid samples to answer important scientific questions about the impact of an HIV intervention in a laboratory. High quality, reliable laboratory infrastructure is critical to the accuracy and validity of clinical trial results. 

More than 150 NIAID-supported laboratories in 20 countries are addressing the diverse scientific programs of the four clinical trials networks in the Institute’s HIV clinical research enterprise. Since the start of HIV clinical research, laboratory capacities have grown in scope to support an increasing number of global clinical trials, emerging complexities in study protocol design and laboratory testing demands and evolving regulatory requirements for research and licensure.

NIAID is engaging research partners, community representatives, and other public health stakeholders in a multidisciplinary evaluation of its HIV clinical trials networks’ progress toward short- and long-term scientific goals. This process assesses knowledge gained since the networks were last awarded in 2020 to identify an essential path forward based on the latest laboratory and clinical evidence. Future NIAID HIV clinical research investments build on the conclusions of these discussions. 

In the next iteration of HIV clinical trials networks, laboratory functions will continue to evolve to align with scientific priorities and research approaches. Networks will support small early-phase trials, large registrational trials and implementation science research to examine preventive vaccine candidates and non-vaccine prevention interventions, antiviral treatments, HIV curative strategies, and therapies to improve the clinical outcomes of people affected by and living with HIV. Selected studies also will rely on high quality laboratory resources to examine interventions for tuberculosis, hepatitis, mpox and other infectious diseases. Clinical trial networks will need to employ a variety of laboratory types to achieve these objectives.  To increase flexibility and ensure the timeliness and the high quality standards the HIV field relies on for evidence that informs science, licensure and equitable practice, NIAID will have the ultimate authority for laboratory selection and approval.

Efficiency and Versatility 

Laboratory assays for HIV clinical trials continue to expand in quantity and complexity and require proportionate technical expertise and management. Future clinical research needs will include immunologic, microbiologic, and molecular testing, as well as standard chemistries and hematologic assays, with fluctuating volumes across a global collection of research sites. Balancing capacity, efficiency, scalability, and cost will require a mixed methods approach. These may include centralized laboratory testing where feasible and advantageous for protocol-specified tests; standardized processes for rapid assessment and approval of new network laboratories; and validated third-party outsourcing of routine assays to ensure timely turnaround when demands surge. 

Quality and Standardization

Ensuring consistent laboratory operations and high quality laboratory data will require continued compliance with the NIAID Division of AIDS Good Clinical Laboratory Practices and other applicable regulatory guidelines, ongoing external quality assurance monitoring, strong inventory management, importation and exportation expertise, and data and specimen management.

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Some People with Advanced HIV Have Anti-CD4 Autoantibodies Associated with Dampened Immune Recovery

NIAID Now |

blue and green human H9 T cell infected with yellow HIV virus particles.

Scanning electron micrograph of a human H9 T cell (blue/green) infected with HIV virus particles (yellow).

Credit: NIAID

More than one quarter of people with advanced HIV who had never taken antiretroviral therapy (ART) harbored antibodies that target the body’s own immune cells, and the presence of those antibodies was associated with slower immune system recovery once they initiated ART, according to an analysis of NIAID-sponsored studies. The antibodies, called anti-CD4 autoantibodies, target CD4+ T cells—a type of white blood cell essential for maintaining the body’s immune system—which are also the target of HIV. Typically, initiating ART helps to restore the body’s CD4+ T-cell count to a typical range. However, the analysis found that people with advanced HIV and anti-CD4 autoantibodies experienced limited CD4+ T-cell reconstitution through up to four years of observation after ART initiation, highlighting a potential immune effect of long-term unsuppressed HIV. The findings were published in Clinical Infectious Diseases.

The analysis included 210 people with advanced HIV—defined as having CD4+ T-cell counts of less than or equal to 100 cells per microliter (μL) of blood—who had never taken ART and were enrolled in one of two clinical studies examining the effects of HIV and ART on the immune system between December 2006 and June 2019. Study participants initiated ART and were clinically assessed for a median of 192 weeks after ART initiation at the NIH Clinical Center. 

Anti-CD4 autoantibodies were identified in the blood samples of 29% of participants with advanced HIV. The prevalence of anti-CD4 autoantibodies was four times higher in female participants compared to male participants. After initiating ART, the pace and extent of CD4+ T-cell recovery was lower in participants with anti-CD4 autoantibodies, who had a median CD4+ T cell count of 268 cells/µL after 192 weeks after ART, compared to 355 cells/µL in those without anti-CD4 autoantibodies. In a sub analysis, the investigators found that participants with anti-CD4 autoantibodies who were also incidentally taking clinically indicated immunosuppressive therapy such as corticosteroids experienced a significantly higher rate of CD4+ T-cell recovery and higher median CD4+ T-cell counts at week 192 than participants with autoantibodies and no immunosuppressive therapy. 

Researchers also examined blood samples from other study populations without advanced HIV, such as people with untreated HIV and CD4+ T-cell counts above 200 cells/μL, people who met criteria for designation as long-term non-progressors, people with autoimmune lymphoproliferative disease, people with idiopathic CD4 lymphocytopenia and healthy controls without HIV. Anti-CD4 autoantibodies were found in 9% of long-term non-progressors and 26% of people with untreated HIV and CD4+ T-cell counts above 200 cells/μL. Yet, the autoantibodies were absent in the other study groups, showing the strength of association between untreated HIV and the development of anti-CD4 autoantibodies. 

Overall, the findings show that untreated HIV is associated with the presence of anti-CD4 autoantibodies that could negatively impact CD4+ T-cell recovery in advanced disease. According to the authors, larger cohort studies are necessary to validate these findings, and further studies are needed to support the potential association seen with improved CD4+ T cell recovery in those with anti-CD4 autoantibodies who received immunosuppressive therapy. Authors also suggest large cohort studies can support the investigation of how sex disparities in anti-CD4 autoantibody prevalence relate to other sex-specific immunological mechanisms that predispose women to autoimmunity. 

Reference:

B Epling et al. Impact of Anti-CD4 Autoantibodies on Immune Reconstitution in People With Advanced Human Immunodeficiency Virus. Clinical Infectious Diseases DOI: 10.1093/cid/ciae562 (2024)

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