Charles S. Dela Cruz

Single-cell transcriptomic profiling of chronic obstructive pulmonary disease (COPD) lungs identified QKI, an RNA-binding protein, as a candidate emphysema-associated gene, but its epithelial role in COPD pathobiology remains unclear. We show that QKI expression is reduced in human COPD lungs and that alveolar type 2 epithelial (AT2) cell QKI protein levels correlate strongly with spirometric indices and diffusing capacity (DL _CO ). Lung epithelium-specific QKI knockout mice (QKI ^Δ/Δ ) developed spontaneous airspace enlargement with emphysema-like mechanics, and QKI-deficient AT2 cells showed impaired spheroid colony formation and increased apoptosis. Integrated transcriptomic and proteomic analyses of primary AT2 cells revealed a selective reduction in functional mitochondrial (respiratory-chain and metabolic) protein abundance despite relatively preserved transcript levels, consistent with mitochondrial transcriptome-proteome discordance. QKI loss increased mtDNA abundance and TOMM20 staining but decreased ATP5A, indicating accumulation of structurally increased but functionally dysfunctional mitochondria. In human epithelial cells, CRISPR-mediated QKI deficiency reduced oxidative respiration, increased glycolytic reliance, elevated mitochondrial ROS and membrane potential, and increased apoptosis; these phenotypes were partially rescued by QKI re-expression. These findings identify epithelial QKI as a regulator of mitochondrial integrity and stress tolerance in COPD.

Research using lower respiratory tract (LRT) sampling may lead to improved understanding and management of patients with acute respiratory failure (ARF). Research bronchoscopy is a valuable tool for sampling the LRT during ARF. However, bronchoscopy may be limited by challenges with repeated sampling, the inability to sample the most severely ill patients, and increased resource utilization. “Noninvasive” sampling strategies, such as nonbronchoscopic bronchoalveolar lavage, endotracheal aspirate collection, and heat moisture exchange filter fluid collection, may expand the opportunity to collect LRT samples for research. Upper respiratory tract and sputum sampling may enable sampling in nonintubated patients with ARF, for whom there is no direct noninvasive access to the LRT. We convened a workshop with 32 experts from diverse continents, scientific backgrounds, and healthcare professions, all with experience in clinical or research practice during ARF. The Workshop goals were to review existing noninvasive sampling methods, evaluate their respective potential benefits and limitations, and identify future research priorities aimed at improving standardization and integration of these approaches into practice. Findings were generated by review of literature, expert presentations, meeting discussions, and electronic surveys using a modified Delphi approach. Participants agreed that each sampling method provides biologically meaningful data during ARF while also acknowledging that each method has benefits and limitations. Various potential advantages to noninvasive methods include reduced cost, enhanced speed, and ease of adoption compared with standard fiberoptic bronchoscopy. Despite the potential advantages, rigorous head-to-head comparisons between noninvasive methods and/or standard bronchoscopy are limited, which is a priority for future research. Additional research priorities include examination of the feasibility and benefit of serial sampling of the LRT and investigation of how LRT biomarkers are related to lung pathophysiology and patient-centered clinical outcomes during ARF. This Workshop Report provides guidance for investigators to integrate LRT sampling into their research and highlights key priorities, such as rigorous head-to-head comparison of sampling methods for research, to improve the standardization of noninvasive sampling methods and use of LRT sampling in research and clinical practice.

The global incidence of respiratory infectious diseases caused by bacteria continues to increase, with acute lower respiratory tract infections contributing to significant morbidity and mortality. Preclinical models designed to investigate such respiratory bacterial diseases are of utmost importance to decipher their pathogenesis and develop novel targets for intervention and treatment. Animal models offer the powerful ability to investigate different pneumonia types at varying stages of infection and disease. However, the same models can promote important variations in outcome, potentially confounding scientific understanding in the field. Therefore, an expert panel was convened to deliberate best practices in animal models of bacterial pneumonia to identify validated methodologies and acknowledge limitations in the use of animal and non-animal models in this field of study. Herein, we summarize this American Thoracic Society workshop on animal models of bacterial pneumonia. This workshop further includes review of non-animal complementary or alternative models for studying bacterial pneumonia. Emphasis was placed on discussion of bacterial pathogens that frequently cause community- and hospital-acquired pneumonia, highlighting key aspects in modeling infection. Animal models discussed included small and large animals, based on their strengths. Finally and most importantly, the ethical considerations in the use of animal modeling for the study of bacterial lung infections was discussed. This workshop report is intended to provide insights to investigators in the field and may serve as a starting point for formal recommendations in the future.

Continued improvements in the treatment of pulmonary infections have paradoxically resulted in a growing challenge of individuals with postinfectious pulmonary complications (PIPCs). PIPCs have been long recognized after tuberculosis, but recent experiences such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic have underscored the importance of PIPCs following other lower respiratory tract infections. Independent of the causative pathogen, most available studies of pulmonary infections focus on short-term outcomes rather than long-term morbidity among survivors. In this document, we establish a conceptual scope for PIPCs with discussion of globally significant pulmonary pathogens and an examination of how these pathogens can damage different components of the lung, resulting in a spectrum of PIPCs. We also review potential mechanisms for the transition from acute infection to PIPC, including the interplay between pathogen-mediated injury and aberrant host responses, which together result in PIPCs. Finally, we identify cross-cutting research priorities for the field to facilitate future studies to establish the incidence of PIPCs, define common mechanisms, identify therapeutic strategies, and ultimately reduce the burden of morbidity in survivors of pulmonary infections.

Rationale: Changes in peripheral blood cell populations have been observed, but not detailed, at single-cell resolution in idiopathic pulmonary fibrosis (IPF). Objectives: We sought to provide an atlas of the changes in the peripheral immune system in stable and progressive IPF. Methods: Peripheral blood mononuclear cells (PBMCs) from patients with IPF and control subjects were profiled using 10× chromium 5′ single-cell RNA sequencing. Flow cytometry was used for validation. Protein concentrations of regulatory T cells (Tregs) and monocyte chemoattractants were measured in plasma and lung homogenates from patients with IPF and control subjects. Measurements and Main Results: Thirty-eight PBMC samples from 25 patients with IPF and 13 matched control subjects yielded 149,564 cells that segregated into 23 subpopulations. Classical monocytes were increased in patients with progressive and stable IPF compared with control subjects (32.1%, 25.2%, and 17.9%, respectively; P < 0.05). Total lymphocytes were decreased in patients with IPF versus control subjects and in progressive versus stable IPF (52.6% vs. 62.6%, P = 0.035). Tregs were increased in progressive versus stable IPF (1.8% vs. 1.1% of all PBMCs, P = 0.007), although not different than controls, and may be associated with decreased survival (P = 0.009 in Kaplan-Meier analysis; and P = 0.069 after adjusting for age, sex, and baseline FVC). Flow cytometry analysis confirmed this finding in an independent cohort of patients with IPF. The fraction of Tregs out of all T cells was also increased in two cohorts of lung single-cell RNA sequencing. CCL22 and CCL18, ligands for CCR4 and CCR8 Treg chemotaxis receptors, were increased in IPF. Conclusions: The single-cell atlas of the peripheral immune system in IPF reveals an outcome-predictive increase in classical monocytes and Tregs, as well as evidence for a lung–blood immune recruitment axis involving CCL7 (for classical monocytes) and CCL18/CCL22 (for Tregs).

Post-acute infection syndromes may develop after acute viral disease¹. Infection with SARS-CoV-2 can result in the development of a post-acute infection syndrome known as long COVID. Individuals with long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions^2–4. However, the biological processes that are associated with the development and persistence of these symptoms are unclear. Here 275 individuals with or without long COVID were enrolled in a cross-sectional study that included multidimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with long COVID. Marked differences were noted in circulating myeloid and lymphocyte populations relative to the matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with long COVID. Furthermore, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with long COVID, particularly Epstein–Barr virus. Levels of soluble immune mediators and hormones varied among groups, with cortisol levels being lower among participants with long COVID. Integration of immune phenotyping data into unbiased machine learning models identified the key features that are most strongly associated with long COVID status. Collectively, these findings may help to guide future studies into the pathobiology of long COVID and help with developing relevant biomarkers.

Objectives: The aim of this study was to evaluate the severity of COVID-19 patients' disease by comparing a multiclass lung lesion model to a single-class lung lesion model and radiologists' assessments in chest computed tomography scans. Materials and Methods: The proposed method, AssessNet-19, was developed in 2 stages in this retrospective study. Four COVID-19–induced tissue lesions were manually segmented to train a 2D-U-Net network for a multiclass segmentation task followed by extensive extraction of radiomic features from the lung lesions. LASSO regression was used to reduce the feature set, and the XGBoost algorithm was trained to classify disease severity based on the World Health Organization Clinical Progression Scale. The model was evaluated using 2 multicenter cohorts: a development cohort of 145 COVID-19–positive patients from 3 centers to train and test the severity prediction model using manually segmented lung lesions. In addition, an evaluation set of 90 COVID-19–positive patients was collected from 2 centers to evaluate AssessNet-19 in a fully automated fashion. Results: AssessNet-19 achieved an F1-score of 0.76 ± 0.02 for severity classification in the evaluation set, which was superior to the 3 expert thoracic radiologists (F1 = 0.63 ± 0.02) and the single-class lesion segmentation model (F1 = 0.64 ± 0.02). In addition, AssessNet-19 automated multiclass lesion segmentation obtained a mean Dice score of 0.70 for ground-glass opacity, 0.68 for consolidation, 0.65 for pleural effusion, and 0.30 for band-like structures compared with ground truth. Moreover, it achieved a high agreement with radiologists for quantifying disease extent with Cohen κ of 0.94, 0.92, and 0.95. Conclusions: A novel artificial intelligence multiclass radiomics model including 4 lung lesions to assess disease severity based on the World Health Organization Clinical Progression Scale more accurately determines the severity of COVID-19 patients than a single-class model and radiologists' assessment.

Lysosomes are key organelles that contribute to homeostatic functions such as autophagy-mediated recycling of cellular components and innate immune response through phagocytosis-mediated pathogen killing during infections. Viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has developed unique adaptation to not only avoid lysosome-mediated destruction but also actively utilize lysosomal machinery to both enter and exit cells. To survive the highly hostile lysosomal environment, coronaviruses deacidify the lysosomes, potentially by manipulating H+ ion exchange across the lysosomal lumen, ensuring coronavirus survival. At the same time, this deacidification not only impairs cellular homeostatic functions such as autophagy but also renders the host susceptible to secondary bacterial infections. Furthermore, lysosomal enzymes promote extensive cell death and tissue damage during secondary bacterial infections. Thus, targeting lysosomal pathways provide a great opportunity to limit both viral replication and subsequent negative impact on host immunity against secondary bacterial infections.