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Decoding Coccidioides: An LLM AI Approach to Genomic, Phenotypic, and Clinical Characterization of 200 Clinical Isolates

Award Number: R21AI199162
ORGANIZATION: NATIONAL INSTITUTE OF ALLERGY & INFECTIOUS DISEASES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2026
PERIOD OF PERFORMANCE END DATE: 08/31/2028

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Decoding Coccidioides: An LLM AI Approach to Genomic, Phenotypic, and Clinical Characterization of 200 Clinical Isolates - Introduction: Valley Fever is a fungal disease estimated to cause > 200,000 infections yearly. The impact of genomic features on clinical disease is largely unexplored. During antifungal therapy, the disease only has one specific marker, antibody titers, which is only semi- quantitative and slowly responsive to clinical changes. Better prognostic markers are needed. Long-term Objective: The goal of the laboratory is to develop better diagnostic and prognostic markers of coccidioidomycosis. Data from this study will contribute significant information to the field of valley fever and open new areas for research that will improve human health. NOT-AI-23-031 Notice of Special Interest (NOSI): Advancing Research Needed to Develop a Coccidioidomycosis (Valley fever) Vaccine. This project directly supports the NIH NOSI for Coccidioides by increasing our understanding of clinical data and develop better prognostic markers using our Complex Longitudinal Abstraction Tool (CLAT) and our genomic data will inform the potential robustness of vaccine candidates by providing a broader understanding of the Coccidioides genomic space. Hypotheses: 1 Genomic differences in Coccidioides spp. are linked to clinical pathological categories (e.g., pulmonary, extrapulmonary, meningitis) and outcome (resolution, severe/chronic disease, death). 2. Deep annotation of longitudinal patient data (symptoms, lab results, and radiology, etc.) may reveal previously unknown links of clinical features (e.g., erythema nodosum, fatigue, etc.) to fungal strain. Specific Aims: Aim 1: Sequence the 200 clinical isolates of Coccidioides spp. and perform genome-wide association studies in context of clinical disease features. Aim 2: Using the CLAT, abstract the course of disease for the patients from which the 200 isolates grew. Aim 3: Analyze the data using a Large Language Model (LLM) with a Model Context Protocol (MCP). Expected outcomes and impact: We will complete preliminary analyses of the genomes and associations of certain regions/features to clinical disease. The genomes will be deposited into a public database. The association of genomes to highly detailed patient data may reveal how some genomic regions are associated with specific disease presentations. These associations may provide both diagnostic and prognostic insights that could inform pathogenic mechanisms and treatment approaches. The analysis using the LLM and MCP models will provide for deep and iterative inquiry into our complex dataset, maximizing the conclusions that can be drawn from the data.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $220,796 )
2026	2026	MAYO CLINIC ARIZONA	13400 E SHEA BLVD	SCOTTSDALE	AZ	85259	MARICOPA	USA	Allergy and Infectious Diseases Research	000	1	9/1/2026	NEW	$220,796
														Subtotal = $220,796

Grand Total All Awards = $220,796

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Decoding Coccidioides: An LLM AI Approach to Genomic, Phenotypic, and Clinical Characterization of 200 Clinical Isolates

Award Number: R21AI199162

ORGANIZATION: NATIONAL INSTITUTE OF ALLERGY & INFECTIOUS DISEASES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/01/2026

PERIOD OF PERFORMANCE END DATE: 08/31/2028

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