The Infectious Disease Genomics team is focused on understanding the dynamic between host and pathogen to discover and develop host-response markers that can diagnose and predict health and disease. This approach to diagnosing illness has been termed a “paradigm shift” and has the potential to significantly impact individual as well as public health, considering the rise of antibiotic resistance.
Synergy between multi-disciplinary experts is crucial to tackle the threats posed by infectious diseases and the rise in antimicrobial resistance. We have successfully assembled a team of clinical scientists, data scientists, laboratorians, clinical research coordinators, among many others. Potential collaborators are encouraged to contact us.
With any potential infectious disease diagnosis, it is difficult, if not impossible, to determine the underlying cause of illness at the time of presentation. For example, acute respiratory illness is among the most frequent reasons for patients to seek care. These symptoms, such as cough, sore throat, and fever may be due to a bacterial infection, viral infection, both or a non-infectious condition such as asthma or allergies. Given the difficulties in making a diagnosis, most patients are inappropriately treated with antibacterials. However, each of these etiologies (bacteria, virus or neither) leaves a fingerprint embedded in the host gene expression response. We are very interested in finding those fingerprints and exploiting them to generate new approaches to disease diagnosis.
These principles also apply to sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Just as with acute respiratory illness, it is often difficult to identify whether infection is responsible for a patient’s critical illness. We have embarked on a number of research programs that aim to better identify sepsis; define sepsis subtypes that can be used to guide future clinical research; and to better predict sepsis outcomes. These efforts have focused on many systems biology modalities including transcriptomics, miRNA, metabolomics, and proteomics. Consequently, our Data Science team has utilized these highly complex data to develop new statistical methods, furthering both the clinical and statistical research communities.
While these two areas, bacterial/viral discrimination and sepsis, epitomize the nature of our work, they are but a part of our overall Applied ID Genomics program. Some additional examples are represented below. New opportunities continue to develop, and we remain eager to forge new collaborations and partnerships.
Our research conducted to date in this area has demonstrated the utility of biomarker analyses to classify subjects into clinically meaningful groups to make inferences about outcomes. Our initial work during the Predicting Health and Disease (PHD) Program with the Defense Advanced Research Projects Agency (DARPA) was focused on controlled infection challenge studies and community acquired infections to identifying novel host-based biomarkers of symptomatic infection. We then applied these methods to infections in the clinical setting as observed in acute care units and the community. We demonstrated that at the time of hospital admission, the profile of metabolites and proteins in the circulation differed markedly in patients that would ultimately die from those of patients who would survive. Furthermore, an algorithm derived from clinical features, combined with measurements of five metabolites, improved the prediction of patient survival.
Our research into biomarkers capable of predicting and diagnosing health and disease states such as sepsis and antibiotic resistant infections (ARIs) is supported by funds from the Austere environments Consortium for Enhanced Sepsis Outcomes (ACESO), National Institutes of Health Antibiotic Resistance Leadership Group (ARLG), Henry M. Jackson Foundation for the advancement of Military Medicine, Defense Threat Reduction Agency and Bill and Melinda Gates Foundation.
- ETEC: Enterotoxigenic Escherichia coli (ETEC) is a globally prevalent cause of diarrhea. Though usually self-limited, it can be severe and debilitating. Little is known about the host transcriptional response to infection. In collaboration with the Johns Hopkins Bayview Center for Immunization Research, we challenged healthy adult volunteers with ETEC. Notably, some became ill with diarrheal illness while others remained asymptomatic. An analysis of gene expression over time identified statistically significant and biologically plausible differences in host gene expression induced by ETEC infection. Differential baseline expression of some genes may indicate resilience to infection. Drug repositioning analysis also identified several drug classes with potential utility in augmenting immune response or mitigating symptoms.
- Pneumoccocal pneumonia challenge: Pneumococcal pneumonia is a leading cause of bacterial infection and death worldwide. Current diagnostic tests for detecting Streptococcus pneumoniae can be unreliable and can mislead clinical decision-making and treatment. To address this concern, we developed a preclinical model of pneumococcal pneumonia in nonhuman primates useful for identifying novel biomarkers, diagnostic tests, and therapies for human S. pneumoniae infection (Bill and Melinda Gates Foundation). This baboon model was successfully able to model the key aspects of human bacterial pneumonia. It also provided an opportunity to model the host response to bacterial pneumonia as identified in the transcriptome, circulating cytokines, as well as nasopharyngeal lavage cytokines. Furthermore, the model has been adapted to study septic shock and evaluate novel sepsis therapeutics developed under the DARPA Dialysis Like Therapeutics program.
- VTEU, TRAP-LRTI: “Targeted Reduction of Antibiotics Using Procalcitonin in a Multi-center, Randomized, Double-Blinded, Placebo-Controlled Non-Inferiority Study of Azithromycin Treatment in Outpatient Adults with Suspect Lower Respiratory Tract Infection (LRTI) and a Procalcitonin(PCT)Level of <0.25ng/mL.” This study is being conducted through the Vaccine and Treatment Evaluation Unit, supported by NIH. Duke and the Durham Veterans Affairs Medical Center (VAMC) are enrollment sites, while Duke also serves as the coordinating center. This trial will compare the efficacy of azithromycin versus placebo in subjects with LRTI and a low procalcitonin level, which has previously been used to help discriminate patients with bacterial and viral infection.
- Community Acquired Pneumonia and Sepsis Outcome Diagnostic study (CAPSOD): In patients with suspected sepsis or early community-acquired pneumonia (CAP), rapid identification of patients who will develop severe sepsis or CAP is critical for effective management and positive outcome. The CAPSOD study is designed to identify novel tests for early diagnosis of severe sepsis and CAP. When performed in patients at the earliest stages of disease, these tests will have prognostic value, rapidly identifying those who will have poor outcomes or complicated courses.
- CAPSOD prospectively enrolled patients with sepsis and CAP at Duke University Medical Center, Henry Ford Hospital, and the Durham VAMC. The study has developed and used advanced bioinformatic, metabolomic, proteomic and mRNA sequencing technologies to identify specific changes, or biomarkers, in patient blood samples that predict outcome in sepsis and CAP. Development of biomarker-based tests will permit patient selection for appropriate disposition, such as the intensive care unit, and use of intensive medical therapies, thereby reducing mortality and increasing effectiveness of resource allocation.
- Austere Environments Consortium for Enhanced Sepsis Outcomes-MTF: An Observational Study to Identify Biomarkers and Pathways Predictive of Sever Sepsis and Septic Mortality (ACESO): In collaboration with the Walter Reed National Military Medical Center, this program aims to identify prognostic, diagnostic, and therapeutic indices (isolated biomarkers, panels of biomarkers, and panels of multidimensional data) of sepsis (stratified by severity) by analysis of individual and integrated physiological, biochemical, immunological, transcriptomic, proteomic, phosphoproteomic, metabolomics, and microbiological data. In support of this program, Duke has enrolled patients with sepsis locally and has also led the data analysis aspects of the biomarker discovery and validation for patients enrolled at multiple clinical sites around the globe.
- Respiratory Viral DREAM Challenge: Sponsored by DARPA, and led by Sage Bionetworks, this program utilized publicly and privately held data from human viral challenge experiments to discover dynamic molecular signatures in response to virus exposure. The aggregated data was presented to the scientific community in a competitive as well as collaborative framework to foster innovative solutions. Specifically, this program strived to identify intrinsic host gene expression determinants of infectious disease susceptibility and resilience. Another goal of this program was to utilize publicly and privately held sepsis data to identify determinants of resilience vs. susceptibility as defined by 30-day survival.
PROMETHEUS: This program seeks to discover a minimal set of molecular biomarkers that would indicate, less than 24 hours after exposure to a pathogen, whether an individual will become contagious. That window is narrow enough to allow for early treatment or the initiation of other mitigating steps before a person begins infecting others. As part of that effort, we will strive to develop a fundamental understanding of the biological responses occurring in a recently infected person. Duke is among several performers and will specifically perform human influenza challenge experiments in collaboration with Professor Chris Chiu (Imperial College of London), conduct all metabolomics discovery experiments, and participate in the predictive modeling aspects in collaboration with Professor Al Hero (University of Michigan).
The overarching goal of our current DARPA Biochronicity program is to develop biomolecular classifiers of the baseline ‘state of health’ (B-HEALTH) for a given individual that enables an accurate prediction of future health versus disease states for that person.
The biomarker discoveries made in these various research programs offer insights into host-pathogen interactions. However, their greatest impact may be when these biomarkers develop into clinical assays. Among the most robust and discriminating diagnostic and prognostic strategies we have identified resides with host gene expression signatures. However, there is currently no available test platform that measures host gene expression signatures in the timeframe necessary to impact clinical decision making for acutely ill patients. Therefore, we have sought out technology partners where the development of a simple, rapid test that quantifies the host response is within reach. Supported by DARPA and the Antibacterial Resistance Leadership Group (ARLG), point-of-care host response assays are now possible.
Antibiotic resistant bacteria infect at least 2 million people each year in the United States and result in over 23,000 deaths.
Vector-borne and zoonotic diseases