Circadian disruption dysregulates lung gene expression associated with inflammatory lung injury.

Rationale: Circadian systems drive the expression of multiple genes in nearly all cells and coordinate cellular-, tissue-, and system-level processes that are critical to innate immunity regulation. Objective: We examined the effects of circadian rhythm disorganization, produced by light shift exposure, on innate immunity-mediated inflammatory lung responses including vascular permeability and gene expression in a C57BL/6J murine model of inflammatory lung injury. Methods: A total of 32 C57BL/6J mice were assigned to circadian phase shifting (CPS) with intratracheal phosphate-buffered saline (PBS), CPS with intratracheal lipopolysaccharide (LPS), control (normal lighting) condition with intratracheal PBS, and control condition with intratracheal LPS. Bronchoalveolar lavage (BAL) protein, cell counts, tissue immunostaining, and differentially expressed genes (DEGs) were measured in lung tissues at 2 and 10 weeks. Measurements and results: In mice exposed to both CPS and intratracheal LPS, both BAL protein and cell counts were increased at both 2 and 10 weeks compared to mice exposed to LPS alone. Multiple DEGs were identified in CPS-LPS-exposed lung tissues compared to LPS alone and were involved in transcriptional pathways associated with circadian rhythm disruption, regulation of lung permeability, inflammation with Rap1 signaling, and regulation of actin cytoskeleton. The most dysregulated pathways included myosin light chain kinase, MAP kinase, profilin 2, fibroblast growth factor receptor, integrin b4, and p21-activated kinase. Conclusion: Circadian rhythm disruption results in exacerbated immune response and dysregulated expression of cytoskeletal genes involved in the regulation of epithelial and vascular barrier integrity-the mechanistic underpinnings of acute lung injury. Further studies need to explore circadian disorganization as a druggable target.

Genetic and Epigenetic Regulation of Cortactin (CTTN) by Inflammatory Factors and Mechanical Stress in Human Lung Endothelial Cells.

RATIONALE: Cortactin, an actin-binding cytoskeletal protein, plays a crucial role in maintaining endothelial cell (EC) barrier integrity and regulating vascular permeability. The gene encoding cortactin, CTTN, is implicated in various lung inflammatory disorders. Despite this, the transcriptional regulation of CTTN by inflammatory stimuli and promoter SNPs remains unexplored. METHODS: We transfected human lung ECs with a full-length CTTN promoters linked to a luciferase reporter to measure promoter activity. SNP-containing CTTN promoter was created via site-directed mutagenesis. Transfected ECs were exposed to LPS (PAMP), TNF-α (cytokine), cyclic stretch (CS), FG-4592 (HIF-inducer), NRF2 (anti-oxidant modulator), FTY-(S)-phosphate (endothelial barrier enhancer) and 5'-Aza (demethylation inducer). Immunohistochemistry was used to assess cortactin expression in mouse lungs exposed to LPS. RESULTS: LPS, TNF-α, and 18%CS significantly increased CTTN promoter activities in a time-dependent manner (p<0.05). The variant rs34612166 (-212T/C) markedly enhanced LPS- and 18%CS- induced CTTN promoter activities (p<0.05). FG-4592 significantly boosted CTTN promoter activities (p<0.01), which were partially inhibited by HIF1a (KC7F2) and HIF2a (PT2385) inhibitors (p<0.05). NRF2 activator Bixin increased CTTN promoter activities, whereas NRF2 inhibitor Brusatol reduced them (p<0.05). 5'-Aza increased CTTN promoter activities by 2.9-fold (p<0.05). NF-kB response element mutations significantly reduced CTTN promoter activities response to LPS and TNF-α. FTY-(S)-phosphate significantly increased CTTN promoter activities in 24hrs. In vivo, cortactin levels were significantly elevated in inflammatory mouse lungs exposed to LPS for 18hrs. CONCLUSION: CTTN transcriptional is significantly influenced by inflammatory factors and promoter variants. Cortactin, essential in mitigating inflammatory edema, presents a promising therapeutic target to alleviate severe inflammatory disorders.

Examination of eQTL Polymorphisms Associated with Increased Risk of Progressive Complicated Sarcoidosis in European and African Descent Subjects.

Background: A limited pool of SNPs are linked to the development and severity of sarcoidosis, a systemic granulomatous inflammatory disease. By integrating genome-wide association studies (GWAS) data and expression quantitative trait loci (eQTL) single nuclear polymorphisms (SNPs), we aimed to identify novel sarcoidosis SNPs potentially influencing the development of complicated sarcoidosis. Methods: A GWAS (Affymetrix 6.0) involving 209 African-American (AA) and 193 European-American (EA, 75 and 51 complicated cases respectively) and publicly-available GWAS controls (GAIN) was utilized. Annotation of multi-tissue eQTL SNPs present on the GWAS created a pool of ~46,000 eQTL SNPs examined for association with sarcoidosis risk and severity (Logistic Model, Plink). The most significant EA/AA eQTL SNPs were genotyped in a sarcoidosis validation cohort (n=1034) and cross-validated in two independent GWAS cohorts. Results: No single GWAS SNP achieved significance (p<1x10-8), however, analysis of the eQTL/GWAS SNP pool yielded 621 eQTL SNPs (p<10-4) associated with 730 genes that highlighted innate immunity, MHC Class II, and allograft rejection pathways with multiple SNPs validated in an independent sarcoidosis cohort (105 SNPs analyzed) (NOTCH4, IL27RA, BTNL2, ANXA11, HLA-DRB1). These studies confirm significant association of eQTL/GWAS SNPs in EAs and AAs with sarcoidosis risk and severity (complicated sarcoidosis) involving HLA region and innate immunity. Conclusion: Despite the challenge of deciphering the genetic basis for sarcoidosis risk/severity, these results suggest that integrated eQTL/GWAS approaches may identify novel variants/genes and support the contribution of dysregulated innate immune responses to sarcoidosis severity.

A Least Absolute Shrinkage and Selection Operator-Derived Predictive Model for Postoperative Respiratory Failure in a Heterogeneous Adult Elective Surgery Patient Population.

BACKGROUND: Postoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients. RESEARCH QUESTION: Can a predictive model be developed to accurately identify patients at high risk of PRF? STUDY DESIGN AND METHODS: In this single-site proof-of-concept study, we used structured query language to extract, transform, and load electronic health record data from 23,999 consecutive adult patients admitted for elective surgery (2014-2021). Our primary outcome was PRF, defined as mechanical ventilation after surgery of > 48 h. Predictors of interest included demographics, comorbidities, and intraoperative factors. We used logistic regression to build a predictive model and the least absolute shrinkage and selection operator procedure to select variables and to estimate model coefficients. We evaluated model performance using optimism-corrected area under the receiver operating curve and area under the precision-recall curve and calculated sensitivity, specificity, positive and negative predictive values, and Brier scores. RESULTS: Two hundred twenty-five patients (0.94%) demonstrated PRF. The 18-variable predictive model included: operations on the cardiovascular, nervous, digestive, urinary, or musculoskeletal system; surgical specialty orthopedic (nonspine); Medicare or Medicaid (as the primary payer); race unknown; American Society of Anesthesiologists class ≥ III; BMI of 30 to 34.9 kg/m2; anesthesia duration (per hour); net fluid at end of the operation (per liter); median intraoperative FIO2, end title CO2, heart rate, and tidal volume; and intraoperative vasopressor medications. The optimism-corrected area under the receiver operating curve was 0.835 (95% CI,0.808-0.862) and the area under the precision-recall curve was 0.156 (95% CI, 0.105-0.203). INTERPRETATION: This single-center proof-of-concept study demonstrated that a structured query language extract, transform, and load process, based on readily available patient and intraoperative variables, can be used to develop a prediction model for PRF. This PRF prediction model is scalable for multicenter research. Clinical applications include decision support to guide postoperative level of care admission and treatment decisions.