Damage sensing through TLR9 Promotes Viral Clearance and Recovery During Influenza Infection.

Host response aimed at eliminating the infecting pathogen, as well as the pathogen itself, can cause tissue injury. Tissue injury leads to the release of a myriad of cellular components including mitochondrial DNA, which the host senses through pattern recognition receptors. How the sensing of tissue injury by the host shapes the anti-pathogen response remains poorly understood. In this study, we utilized mice that are deficient in toll-like receptor-9 (TLR9), which binds to unmethylated CpG DNA sequences such as those present in bacterial and mitochondrial DNA. To avoid direct pathogen sensing by TLR9, we utilized the influenza virus, which lacks ligands for TLR9, to determine how damage sensing by TLR9 contributes to anti-influenza immunity. Our data show that TLR9-mediated sensing of tissue damage promotes an inflammatory response during early infection, driven by the myeloid cells and associated cytokine responses. Along with the diminished inflammatory response, the absence of damage sensing through TLR9 led to impaired viral clearance manifested as a higher and prolonged influenza burden in the lung. The absence of TLR9 led to extensive infection of myeloid cells including monocytes and macrophages rendering them highly inflammatory, despite having a low initial inflammatory response. The persistent inflammation driven by infected myeloid cells led to persistent lung injury and impaired recovery in influenza-infected TLR9-/- mice. Further, we show elevated circulating TLR9 ligands in the plasma samples of patients with influenza, demonstrating its clinical relevance. Overall, over data show an essential role of damage sensing through TLR9 in promoting anti-influenza immunity.

An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model.

Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R2 = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources.

A dual role for CRTH2 in acute lung injury.

Acute respiratory distress syndrome (ARDS) is a life-threatening clinical condition defined by rapid onset respiratory failure following acute lung injury (ALI). Its increased incidence due to COVID-19 and high mortality rate (∻40%) make the study of ARDS pathogenesis a crucial research priority. CRTH2 is a G protein-coupled receptor with established roles in type 2 immunity and well-characterized inhibitors. Prior studies have shown it also promotes neutrophilic inflammation, indicating that CRTH2 inhibition may be a potential therapeutic strategy for ARDS. To test this hypothesis, we first examined the expression pattern of CRTH2 on murine neutrophils. We found it is expressed on neutrophils, but only after extravasation into the lung. Next, we showed that extravasated lung neutrophils generate inflammatory responses upon stimulation with the CRTH2-specific agonist DK-PGD2, as demonstrated by reactive oxygen species (ROS) production. This response was abrogated in CRTH2 KO neutrophils. Inhibition of CRTH2 with fevipiprant suppressed baseline ROS production, indicating an autocrine PGD2-CRTH2 signaling loop. We then evaluated the role of CRTH2 in vivo using a murine model of LPS-induced ALI. Despite the pro-inflammatory effects of CRTH2 on neutrophils in vitro, we observed worsening of lung injury in CRTH2-deficient mice in terms of neutrophilic inflammation, vascular leak, and survival. Bulk RNAseq of lung tissue indicated an impairment in type 2 immune signaling; qPCR and ELISA confirmed downregulation of the key type 2 effector cytokine, IL-4. Thus, CRTH2 appears to play a dual role in ALI, directly promoting neutrophil effector responses, but indirectly suppressing lung injury and neutrophilic inflammation through type 2 immunity. These findings reveal a novel protective function for CRTH2 during lung injury and argue against the use of CRTH2 inhibitors in ARDS.

Single-cell characterization of a model of poly I:C-stimulated peripheral blood mononuclear cells in severe asthma.

BACKGROUND: Asthma has been associated with impaired interferon response. Multiple cell types have been implicated in such response impairment and may be responsible for asthma immunopathology. However, existing models to study the immune response in asthma are limited by bulk profiling of cells. Our objective was to Characterize a model of peripheral blood mononuclear cells (PBMCs) of patients with severe asthma (SA) and its response to the TLR3 agonist Poly I:C using two single-cell methods. METHODS: Two complementary single-cell methods, DropSeq for single-cell RNA sequencing (scRNA-Seq) and mass cytometry (CyTOF), were used to profile PBMCs of SA patients and healthy controls (HC). Poly I:C-stimulated and unstimulated cells were analyzed in this study. RESULTS: PBMCs (n = 9414) from five SA (n = 6099) and three HC (n = 3315) were profiled using scRNA-Seq. Six main cell subsets, namely CD4 + T cells, CD8 + T cells, natural killer (NK) cells, B cells, dendritic cells (DCs), and monocytes, were identified. CD4 + T cells were the main cell type in SA and demonstrated a pro-inflammatory profile characterized by increased JAK1 expression. Following Poly I:C stimulation, PBMCs from SA had a robust induction of interferon pathways compared with HC. CyTOF profiling of Poly I:C stimulated and unstimulated PBMCs (n = 160,000) from the same individuals (SA = 5; HC = 3) demonstrated higher CD8 + and CD8 + effector T cells in SA at baseline, followed by a decrease of CD8 + effector T cells after poly I:C stimulation. CONCLUSIONS: Single-cell profiling of an in vitro model using PBMCs in patients with SA identified activation of pro-inflammatory pathways at baseline and strong response to Poly I:C, as well as quantitative changes in CD8 + effector cells. Thus, transcriptomic and cell quantitative changes are associated with immune cell heterogeneity in this model to evaluate interferon responses in severe asthma.

Severe SARS-CoV-2 infection in humans is defined by a shift in the serum lipidome resulting in dysregulation of eicosanoid immune mediators.

The COVID-19 pandemic has affected more than 10 million people worldwide with mortality exceeding half a million patients. Risk factors associated with severe disease and mortality include advanced age, hypertension, diabetes, and obesity.1 Clear mechanistic understanding of how these comorbidities converge to enable severe infection is lacking. Notably each of these risk factors pathologically disrupts the lipidome and this disruption may be a unifying feature of severe COVID-19.1-7 Here we provide the first in depth interrogation of lipidomic changes, including structural-lipids as well as the eicosanoids and docosanoids lipid mediators (LMs), that mark COVID-19 disease severity. Our data reveal that progression from moderate to severe disease is marked by a loss of specific immune regulatory LMs and increased pro-inflammatory species. Given the important immune regulatory role of LMs, these data provide mechanistic insight into the immune balance in COVID-19 and potential targets for therapy with currently approved pharmaceuticals.8.

Clinical Characteristics and Outcomes for 7,995 Patients with SARS-CoV-2 Infection.

OBJECTIVE: Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2. DESIGN: This was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository. SETTING: Yale New Haven Health (YNHH) is a five-hospital academic health system serving a diverse patient population with community and teaching facilities in both urban and suburban areas. POPULATIONS: The study included adult patients who had SARS-CoV-2 testing at YNHH between March 1 and April 30, 2020. MAIN OUTCOME AND PERFORMANCE MEASURES: Primary outcomes were admission and in-hospital mortality for patients with SARS-CoV-2 infection as determined by RT-PCR testing. We also assessed features associated with the need for respiratory support. RESULTS: Of the 28605 patients tested for SARS-CoV-2, 7995 patients (27.9%) had an infection (median age 52.3 years) and 2154 (26.9%) of these had an associated admission (median age 66.2 years). Of admitted patients, 2152 (99.9%) had a discharge disposition at the end of the study period. Of these, 329 (15.3%) required invasive mechanical ventilation and 305 (14.2%) expired. Increased age and male sex were positively associated with admission and in-hospital mortality (median age 80.7 years), while comorbidities had a much weaker association with the risk of admission or mortality. Black race (OR 1.43, 95%CI 1.14-1.78) and Hispanic ethnicity (OR 1.81, 95%CI 1.50-2.18) were identified as risk factors for admission, but, among discharged patients, age-adjusted in-hospital mortality was not significantly different among racial and ethnic groups. CONCLUSIONS: This observational study identified, among people testing positive for SARSCoV-2 infection, older age and male sex as the most strongly associated risks for admission and in-hospital mortality in patients with SARS-CoV-2 infection. While minority racial and ethnic groups had increased burden of disease and risk of admission, age-adjusted in-hospital mortality for discharged patients was not significantly different among racial and ethnic groups. Ongoing studies will be needed to continue to evaluate these risks, particularly in the setting of evolving treatment guidelines.

Circulating Markers of Angiogenesis and Endotheliopathy in COVID-19.

Despite over 9.3 million infected and 479,000 deaths, the pathophysiological factors that determine the wide spectrum of clinical outcomes in COVID-19 remain inadequately defined. Importantly, patients with underlying cardiovascular disease have been found to have worse clinical outcomes,1 and autopsy findings of endotheliopathy as well as angiogenesis in COVID-19 have accumulated.2,3 Nonetheless, circulating vascular markers associated with disease severity and mortality have not been reliably established. To address this limitation and better understand COVID-19 pathogenesis, we report plasma profiling of factors related to the vascular system from a series of patients admitted to Yale-New Haven Hospital with confirmed diagnosis of COVID-19 via PCR, which demonstrate significant increase in markers of angiogenesis and endotheliopathy in patients hospitalized with COVID-19.

Severe SARS-CoV-2 infection in humans is defined by a shift in the serum lipidome resulting in dysregulation of eicosanoid immune mediators.

The COVID-19 pandemic has affected more than 10 million people worldwide with mortality exceeding half a million patients. Risk factors associated with severe disease and mortality include advanced age,hypertension, diabetes, and obesity. Clear mechanistic understanding of how these comorbidities converge to enable severe infection is lacking. Notably each of these risk factors pathologically disrupts the lipidome and this disruption may be a unifying feature of severe COVID-19. Here we provide the first in depth interrogation of lipidomic changes, including structural-lipids as well as the eicosanoids and docosanoids lipid mediators (LMs), that mark COVID-19 disease severity. Our data reveal that progression from moderate to severe disease is marked by a loss of specific immune regulatory LMs and increased pro-inflammatory species. Given the important immune regulatory role of LMs, these data provide mechanistic insight into the immune balance in COVID-19 and potential targets for therapy with currently approved pharmaceuticals.

Author Correction: Mechanosensation of cyclical force by PIEZO1 is essential for innate immunity.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.