About

Nuala J. Meyer, MD, MS (MSTR), is the William Maul Measey Professor of Medicine at the University of Pennsylvania's Perelman School of Medicine. She serves as the Director of the Center for Translational Lung Biology and is the Vice Chief for Research in the Pulmonary, Allergy, and Critical Care Division. Her clinical expertise includes critical care medicine, with a focus on acute respiratory distress syndrome (ARDS), septic shock, and pulmonary medicine. Her research identifies genetic and molecular risk factors for ARDS and organ dysfunction during sepsis to better understand the pathophysiology of these conditions and to develop personalized therapies. Her work involves large observational cohorts of critically ill patients, discovery and validation of genetic and molecular risk factors, and clinical trials for ARDS and sepsis, including active studies such as iSPY COVID and CHILL cooling to heal injured lungs.

Research topics

• Medicine
• Immunology
• Virology
• Biology
• Pathology
• Internal medicine
• Intensive care medicine
• Computer Science
• Genetics
• Emergency medicine
• Cell biology
• Surgery
• Computational biology
• Biochemistry
• Anesthesia
• Cardiology
• Nursing

Selected publications

• Whole blood transcriptomics reveals sepsis mortality-associated changes in neutrophil degranulation

  American Journal of Respiratory Cell and Molecular Biology · 2026-02-10

  articleOpen accessSenior author

  Transcriptomic analysis of blood cells can reveal key elements of the dysregulated host response in sepsis and spur biomarker and mechanism identification. We hypothesized that sepsis nonsurvivors exhibit a distinct transcriptional signature in whole blood that reflects insights to sepsis mortality. We conducted a prospective observational cohort study of 161 critically ill sepsis patients. Whole blood RNA was collected within 24 hours of intensive care unit admission. Gene expression levels were measured using microarrays and changes in gene levels were compared between 30-day nonsurvivors and survivors, adjusting for age, sex, and neutrophil count. Pathway overrepresentation analysis and weighted gene co-expression analysis were performed to identify biological pathways and gene co-expression groups, respectively, associated with sepsis mortality. Gene- and pathway-based results were compared to findings in an independent cohort of 479 sepsis patients with 28-day mortality data. Thirty-day mortality in the enrolled sepsis cohort was 37% (60 of 161 patients). We identified 1,106 differentially expressed genes in nonsurvivors (Benjamini-Hochberg-adjusted p-value <0.05), including several neutrophil-related genes (CEACAM8, ELANE, PRTN3, MPO, CEACAM6, DEFA4, MS4A3) with expression levels over 1.8 times higher in nonsurvivors despite adjusting for neutrophil counts. The neutrophil degranulation pathway was prominent based on its overrepresentation in 1) differentially expressed genes in both cohorts, 2) overrepresentation by gene set enrichment analysis, and 3) four of the six gene co-expression groups correlated with sepsis mortality. Our findings highlight the involvement of neutrophil degranulation genes in sepsis mortality, prompting further study to better understand whether they constitute a modifiable target.

  PublisherDOI

• What Is Sepsis, Who Gets It, How, and Why? The Keys to Unlocking Precision Medicine in Sepsis

  Critical Care Medicine · 2026-01-23

  article

  Due to its nonspecific clinical criteria, sepsis is clinically, microbiologically, pathophysiologically and immunologically highly heterogeneous. Consequently, despite hundreds of clinical trials, no host-targeted therapy has been shown to be ubiquitously efficacious, leading investigators to pursue more precision-based approaches for enriching sepsis populations through the identification of subgroups or phenotypes. Here, we review the myriad domains in which heterogeneity is observed in sepsis and the challenges and opportunities they offer to improve outcomes. We review current strategies used by investigators leveraging novel biological measurements and/or computational algorithms to identify more homogeneous subgroups either based on pathogen or host characteristics or both. Finally, we review some of the most promising recent advances that seek to bring these complex and innovative discoveries to the bedside to facilitate precision medicine in sepsis.

  PublisherDOI

• Integrative omics and multi-cohort identify &lt;i&gt;IRF1&lt;/i&gt; and biological targets related to sepsis-associated acute respiratory distress syndrome

  Journal of Biomedical Research · 2025-05-26

  articleOpen access

  Interferon-related genes are involved in antiviral responses, inflammation, and immunity, which are closely related to sepsis-associated acute respiratory distress syndrome (ARDS). We analyzed 1972 participants with genotype data and 681 with gene expression data from the Molecular Epidemiology of ARDS (MEARDS), the Molecular Epidemiology of Sepsis in the ICU (MESSI), and the Molecular Diagnosis and Risk Stratification of Sepsis (MARS) cohorts in a three-step study focusing on sepsis-associated ARDS and sepsis-only controls. First, we identified and validated interferon-related genes associated with sepsis-associated ARDS risk using genetically regulated gene expression (GReX). Second, we examined the association of the confirmed gene (interferon regulatory factor 1, <italic>IRF1</italic>) with ARDS risk and survival and conducted a mediation analysis. Through discovery and validation, we found that the GReX of <italic>IRF1</italic> was associated with ARDS risk (OR_MEARDS = 0.84, <italic>P</italic> = 0.008; OR_MESSI = 0.83, <italic>P</italic> = 0.034). Furthermore, individual-level measured <italic>IRF1</italic> expression was associated with reduced ARDS risk (OR = 0.58, <italic>P</italic> = 8.67×10⁻⁴), and improved overall survival in ARDS patients (HR_28-day = 0.49, <italic>P</italic> = 0.009) and sepsis patients (HR_28-day = 0.76, <italic>P</italic> = 0.008). Mediation analysis revealed that <italic>IRF1</italic> may activate immune functions by regulating the major histocompatibility complex, including <italic>HLA-F</italic>, which mediated over 70% of the protective effects of <italic>IRF1</italic> on ARDS. The findings were validated by <italic>in vitro</italic> biological experiments involving time-series infection dynamics, overexpression, knockout, and chromatin immunoprecipitation sequencing. Early prophylactic interventions to activate <italic>IRF1</italic> in sepsis patients, thereby regulating <italic>HLA-F</italic>, might reduce the risk of ARDS development and mortality, especially in severely illness patients.

  PublisherOA PDFDOI

• The Effects of Cyproheptadine on Severe COVID-19 From the I-SPY COVID Adaptive Platform Trial

  CHEST Critical Care · 2025-08-06

  articleOpen access

  <h3>Background</h3> Severe COVID-19 has been associated with hypercoagulability and platelet activation, which is known to result in excessive accumulation of serotonin. However, few studies have evaluated whether serotonergic blockade may improve clinical outcomes among those with COVID-19. <h3>Research Question</h3> Does cyproheptadine, an antiserotonergic drug most commonly used to treat serotonin syndrome, improve time to clinical recovery in patients with severe COVID-19? <h3>Study Design and Methods</h3> The Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging and Molecular Analysis in COVID (I-SPY COVID) study is a phase 2, multicenter, adaptive, open-label randomized controlled trial designed to screen potential therapeutic agents rapidly to identify those with a high probability of improving outcomes for hospitalized critically ill patients with COVID-19. For this report, participants randomized to receive cyproheptadine 8 mg enterally every 8 hours for 10 days or until hospital discharge were compared with concurrently enrolled control patients who were treated with the standard of care regimen of dexamethasone and remdesivir and did not receive an investigational agent. Bayesian survival regression models were used to model the hazard functions for the 2 events of interest: (1) recovery (treating death as a competing event) and (2) overall death as a function of randomization arm and baseline COVID-19 level. <h3>Results</h3> From September through December 2021, 35 participants were randomized and consented to receive cyproheptadine and 61 concurrent control patients were eligible for analysis. At that point, futility criteria were met and the data monitoring committee halted further enrollment into the cyproheptadine arm. The posterior probability that cyproheptadine would increase the rate of recovery was 0.1% and the posterior probability that cyproheptadine would improve survival was 16.3%. <h3>Interpretation</h3> In an open-label phase 2 trial of adults critically ill with severe COVID-19, cyproheptadine did not improve recovery or survival compared with the standard of care. <h3>Clinical Trial Registration</h3> ClinicalTrials.gov; No.: NCT04488081; URL: www.clinicaltrials.gov

  PublisherDOI

• Use of a Lung Microphysiological System to Test TLR9 Inhibition for Sepsis Plasma-induced Endothelial Dysfunction

  American Journal of Respiratory and Critical Care Medicine · 2025-05-01

  article

  Abstract Rationale: Endothelial dysfunction may propagate systemic injury to the lung during sepsis. Biomarkers of endothelial dysfunction are increased in hyperinflammatory sepsis subphenotype patients. Therapies targeting endothelial dysfunction may be more effective in enriched populations such as the hyperinflammatory subphenotype. We developed a 3D human primary lung endothelial cell (EC) microphysiological system (MPS) to model sepsis plasma-induced endothelial dysfunction and test endothelial-targeted therapies. We sought to determine how inhibiting a classic DAMP sensor, TLR9, affects endothelial cell (EC) responses to sepsis plasma in our MPS, hypothesizing that TLR9 inhibitor E6446 would decrease EC dysfunction biomarkers.Methods: Using plasma from 21 sepsis patients, we measured baseline EC dysfunction markers (“EC biomarkers”) IL-1B, IL-6, IL-8, IL-18, TNF-a, TNF-R1, PAI-1, VEGF-R1, VCAM-1, and ICAM-1 by Luminex panel and stratified hyper- and hypoinflammatory subphenotypes using the parsimonious model (serum bicarbonate, sTNF-R1 and IL-8). We incubated plasma in the MPS for 16 hours +/- TLR9 inhibitor E6446 dihydrochloride. We quantified EC biomarkers in post-incubation plasma and measured EC permeability using dextran diffusion. We compared EC biomarkers in untreated vs E6446-treated post-incubation plasma using paired t-testing. We applied principal component analysis (PCA) to differences in untreated vs E6446-treated EC biomarkers to identify similar response clusters. Using linear regression, we tested for association of E6446 responses with log-transformed baseline plasma EC biomarkers to identify potential predictors of response.Findings: E6446 treatment did not affect EC biomarkers in the overall cohort. Stratification by subphenotype revealed 10 hyperinflammatory and 11 hypoinflammatory patients. E6446 decreased IL-1B concentration in hyperinflammatory post-incubation plasma (31.23 pg/mL vs 21.12 pg/mL, p=0.029). PCA revealed 3 response clusters (RCs) representing similar vectors of EC biomarker responses to E6446 in post-incubation plasma (Fig1): inflammatory (similar IL-6, IL-18, TNF-R1, and IL-1B responses, “RC1”), vascular activation (PAI-1, VEGF-R1, and TNF-a, “RC2”), and permeability (IL-8, VCAM-1, and measured permeability, “RC3”). RC3 was comprised of only hypoinflammatory patients (7/11 patients, p=0.034). Baseline plasma IL-1B concentrations were significantly associated with IL-8, IL-18, and TNF-R1 responses to E6446 (p=0.007, 0.0025, 0.044, respectively), while baseline TNF-R1 concentrations were associated with IL-18, TNF-R1, and TNF-a responses (p=0.005, 0.017, 0.026, respectively). Conclusion: EC biomarker responses to TLR9 inhibition in the MPS did not differ overall or by subphenotype. However, baseline plasma IL-1B and TNF-R1 concentrations were significantly associated with decreased EC biomarkers after E6446 treatment. Post incubation plasma responses to E6446 could be separated into distinct clusters of inflammation, vascular activation and permeability.

  PublisherDOI

• 37: DYSREGULATED STAT3 SIGNALING DEFINES A TARGETABLE, HIGH-MORTALITY SUBPHENOTYPE OF PEDIATRIC SEPSIS

  Critical Care Medicine · 2025-01-01

  article
  PublisherDOI

• Trial Analysis and Interpretation in Critical Care Using the Evidential (Likelihood) Approach: Rationale and Practical Considerations

  American Journal of Respiratory and Critical Care Medicine · 2025-07-01 · 3 citations

  articleOpen access

  Abstract Selecting the optimal methodological framework for evidence synthesis presents a fundamental challenge in contemporary clinical research. In critical care, in which many interventions yield inconclusive results under traditional P value–based analyses, complementary analytical approaches can enhance our understanding of trial data. Although frequentist statistics remain predominant and Bayesian methods have recently experienced a resurgence of interest, the evidential (or likelihood) framework offers a methodological perspective that potentially bridges these two inferential paradigms. In this Concise Translational Review, we introduce readers to the evidential approach. To present the evidential approach as an analytical tool for critical care trials, we demonstrate its application using data from two mechanical ventilation trials (ART [the Alveolar Recruitment Trial], N = 1,010; and STAMINA [Strategy for Community Acquired Pneumonia Trial], N = 214) and one trial evaluating balanced solutions (BaSICS [Balanced Solutions in Intensive Care Study], N = 10,520). We focus on how concepts and terminology translate across paradigms, the framework’s measures of effect (i.e., likelihood ratios, support values, and support intervals), proposals for its use in sequential analysis and trial monitoring, and how to report results from this framework in research articles. We propose that the evidential framework provides a clinically intuitive approach to trial interpretation by focusing on the relative evidence between competing hypotheses, thereby offering additional and complementary insights that align with clinical reasoning processes. To facilitate implementation by the scientific community, we have developed an interactive Shiny (open-source web-based) application (https://fzampier.shinyapps.io/Likelihood_Shiny/).

  PublisherDOI

• Two Physiologic Latent Classes of Acute Hypoxemic Respiratory Failure in Sepsis Are Distinguished by Lung Mechanics and Gas Exchange

  Critical Care Explorations · 2025-09-22 · 1 citations

  articleOpen access

  OBJECTIVES: Physiologic subtypes of acute hypoxemic respiratory failure (AHRF) may confer a differential response to treatments, particularly therapeutic strategies that are specific to pulmonary organ failure. We sought to identify physiologic latent classes of sepsis-associated AHRF defined by respiratory mechanics, oxygenation, ventilation, and radiographic patterns of lung injury, and to determine the association between class membership and 30-day mortality. DESIGN: We performed latent class analysis of patients with AHRF newly requiring mechanical ventilation enrolled in a prospective cohort of patients with sepsis from 2011 to 2020. We used logistic regression adjusted for Acute Physiology and Chronic Health Evaluation to determine the association between class membership and 30-day mortality and examined the distribution of patients classified as "hyperinflammatory" by previously described biomarker-based subphenotyping paradigms. SETTING: Philadelphia, Pennsylvania, United States. PATIENTS: Eight hundred eighty-two patients. MEASUREMENTS AND MAIN RESULTS: We identified two physiologic latent classes. Class 1 (n = 390) was characterized by low static compliance and impaired ventilation when compared with class 2 (n = 432). Mortality at 30 days was higher in the more physiologically severe class 1 when compared with class 2 (adjusted risk difference 0.12, p < 0.001) despite a similar severity of sepsis. Class 1 also contained a higher proportion of female patients and patients with obesity. CONCLUSIONS: We identified two physiologic latent classes of sepsis-associated AHRF. Relative to class 2, class 1 was distinguished by low compliance, impaired ventilation, and higher 30-day mortality independent of the severity of sepsis. The higher percentage of female patients and patients with obesity in class 1 suggests a potential role for body composition in class determination. Physiologic classes were not primarily determined by qualification for acute respiratory distress syndrome or previously described biomarker-based subphenotypes, suggesting a distinct physiologic "axis" of heterogeneity.

  PublisherDOI

• Knowledge Transfer in the 21st Century: The Continuing Evolution of Critical Care Medicine

  Critical Care Medicine · 2025-01-01 · 1 citations

  article
  PublisherDOI

• cyMAEv2: learning robust cell representations in mass cytometry without supervision 4582

  The Journal of Immunology · 2025-11-01

  articleOpen access

  Abstract Description Batch effects in cytometry lead to inconsistent protein expression measurements and prevent researchers from integrating data from the most well-controlled studies. To address these challenges, we present cyMAEv2, an enhanced self-supervised learning model to generate robust cell representations in mass cytometry data. Compared to existing approaches, cyMAEv2 introduces 1) Enhanced Data Diversity and 2) Learning Cell Population Distributions. The model learns both protein expression distributions and their percentile distributions of eight distinct cohorts without cell type label. Validation was performed using two testing sets from 8 cohorts 1) a healthy donor set (HD), containing data with similar biological variations but different technical variations and; 2) an all-cohort set (ALL), which includes both healthy and diseased samples, reflecting differing biological variations. We used simplified single-cell integration benchmarking (scIB) metrics, to quantify conservation of biological variance (AvgBIO) and the removal of batch effects (AvgBATCH). For the HD set, cyMAEv2 achieved AvgBIO=0.833 and AvgBATCH=0.780, showing gains of + 0.066 AvgBIO and +0.175 AvgBATCH compared to using the original data. For the ALL set, cyMAEv2 achieved AvgBATCH=0.685, showing +0.071 gain from original data. These results demonstrate its ability to preserve biological integrity while mitigating batch effects. We believe this study marks a significant step forward for cytometry data integration. Funding Sources Supported by NIH AI082630, QuantumLeap Healthcare Collaborative fund, the University of Pennsylvania Perelman School of Medicine COVID Fund. Topic Categories Computational and Systems Immunology (COMP)

  PublisherOA PDFDOI

Recent grants

• NIH Grant F32HL08885801

  NIH · $57k · 2008

• Reconsidering the IL-1 axis in sepsis-associated ARDS

  NIH · $2.1M · 2017–2022

• Mentoring Patient-Oriented Research to Promote ARDS Precision Medicine

  NIH · $124k · 2021–2021

• NIH Grant R56HL122474

  NIH · $614k · 2017

• TRAINING IN PULMONARY IMMUNOLOGY

  NIH · $10.8M · 1985–2026

Frequent coauthors

• Jason D. Christie

  University of Pennsylvania

  163 shared

• John P. Reilly

  107 shared

• Michael G. S. Shashaty

  University of Pennsylvania

  101 shared

• E. John Wherry

  University of Pennsylvania

  90 shared

• C.A.G. Ittner

  University of Pennsylvania

  68 shared

• Rui Feng

  University of Pennsylvania

  61 shared

• Tiffanie K. Jones

  University of Pennsylvania

  51 shared

• Nilam S. Mangalmurti

  47 shared

Education

• M.S. , Institute of Translational Medicine and Therapeutics

  University of Pennsylvania

  2011

• M.D.

  University of Chicago Pritzker School of Medicine

  2000

• B.S.

  Yale College

  1996

Awards & honors

• William Maul Measey Professor of Medicine
• Director, Center for Translational Lung Biology, Department…
• Vice Chief for Research, Pulmonary, Allergy, and Critical Ca…