Understanding Impaired Immunity to Bacterial Pathogens Post-Bone Marrow Transplantation

Respiratory tract infections (RTI) are major causes of morbidity and mortality, with lower respiratory infections alone being the fourth major cause of death in the world, taking the life of over 3 million individuals in 2016 (World Health Organization (WHO), 2018). Bacterial infections are a major cause of RTI. In this dissertation we focused on a subject group who is at high risk of developing severe illness due to opportunistic bacterial infections, hematopoietic stem cell transplant (HSCT) patients. HSCT utilizes stem cells derived from bone marrow, umbilical cord blood, or peripheral blood of patients themselves (autologous) or matched donors (allogeneic) to treat or cure a variety of hematological and inherited disorders. This procedure has become standard of care with more than 18,000 HSCTs performed every year in the United States alone. Unfortunately, patients that undergo HSCT (both autologous and allogeneic) are immunosuppressed and remain so even after stem cell engraftment, making them susceptible to infections by a wide array of opportunistic pathogens. Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium that can cause life-threatening complications in HSCT patients and has recently been identified by the WHO as a critical pathogen for which new therapeutic strategies are needed. In the lung of immunosuppressed individuals, P. aeruginosa can cause lethal organ injury mainly by stimulating alveolar macrophages to secrete high levels of Interleukin-1β (IL-1β). IL-1β is a potent pro-inflammatory cytokine that is mainly activated by the serine protease caspase-1, but can be activated by caspase-8. Here, we aimed to understand the reasons behind the success of P. aeruginosa infections in HSCT subjects with the use of murine bone marrow transplantation (BMT) model. We identified that high levels of prostaglandin E2 (PGE2), a cyclooxygenase (COX) lipid metabolite with hormone-like characteristics and found at elevated levels in HSCT patients, induces exacerbated levels of IL-1β in HSCT subjects leading to severe lung injury post-P. aeruginosa infection. We identified that the PGE2-mediated increase in IL-1β is dependent on adenyl cyclase (AC) activation by EP2 and/or EP4 receptor stimulation which leads to activation of the transcription factor CREB. We hypothesized that reducing the levels of PGE2 in BMT mice can reduce IL-1β-mediated acute lung injury and improve outcome. In accordance with our hypothesis, we were able to decrease IL-1β levels, improve bacterial killing, and reduce lung injury by treating HSCT mice with indomethacin, a non-selective inhibitor of the two isoforms of COX (COX1 & COX2), post-P. aeruginosa infection. Additionally, we showed how PGE2 production impaired neutrophil extracellular trap (NETs), an important antimicrobial pathway, release post-HSCT in human and mouse neutrophils which could implicate immunosuppression to multiple microbes including bacterial pathogens. Our findings suggest new therapeutic strategies aimed at blocking PGE2 production or signaling may have positive impacts against bacterial infections in HSCT subjects. The appendix of this dissertation contains an additional chapter looking at influenza-induced immunosuppression of lung innate immunity and describes a novel role for toll like receptor 9 signaling in regulating secondary bacterial infection post-influenza.PHDImmunologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147671/1/martingj_1.pd

SARS-CoV-2 RNAemia predicts clinical deterioration and extrapulmonary complications from COVID-19

BackgroundThe determinants of coronavirus disease 2019 (COVID-19) disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterized relationships between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNAemia and disease severity, clinical deterioration, and specific EPCs.MethodsWe used quantitative and digital polymerase chain reaction (qPCR and dPCR) to quantify SARS-CoV-2 RNA from plasma in 191 patients presenting to the emergency department with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterized the role of RNAemia in predicting clinical severity and EPCs using elastic net regression.ResultsOf SARS-CoV-2-positive patients, 23.0% (44 of 191) had viral RNA detected in plasma by dPCR, compared with 1.4% (2 of 147) by qPCR. Most patients with serial measurements had undetectable RNAemia within 10 days of symptom onset, reached maximum clinical severity within 16 days, and symptom resolution within 33 days. Initially RNAemic patients were more likely to manifest severe disease (odds ratio, 6.72 [95% confidence interval, 2.45-19.79]), worsening of disease severity (2.43 [1.07-5.38]), and EPCs (2.81 [1.26-6.36]). RNA loads were correlated with maximum severity (r = 0.47 [95% confidence interval, .20-.67]).ConclusionsdPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Because many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate

Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department

Clinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial coinfection, and determining illness severity since current practices require separate workflows. Here, we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and bacterial coinfections and predicting clinical severity of COVID-19. A total of 161 patients with PCR-confirmed COVID-19 (52.2% female; median age, 50.0 years; 51% hospitalized; 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene blood RNA), and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter. The IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrollment, and the remaining patients oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial coinfection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e., Clostridioides difficile colitis (n = 1), urinary tract infection (n = 1), and clinically diagnosed bacterial infections (n = 3), for a specificity of 99.4%. Two of 101 (2.8%) patients in the IMX-SEV-3 "Low" severity classification and 7/60 (11.7%) in the "Moderate" severity classification died within 30 days of enrollment. IMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19 and bacterial coinfections and predicted patients' risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management, including more accurate treatment decisions and optimized resource utilization. IMPORTANCE We assay the utility of the single-test IMX-BVN-3/IMX-SEV-3 classifiers that require just 2.5 mL of patient blood in concurrently detecting viral and bacterial infections as well as predicting the severity and 30-day outcome from the infection. A point-of-care device, in development, will circumvent the need for blood culturing and drastically reduce the time needed to detect an infection. This will negate the need for empirical use of broad-spectrum antibiotics and allow for antibiotic use stewardship. Additionally, accurate classification of the severity of infection and the prediction of 30-day severe outcomes will allow for appropriate allocation of hospital resources