Daily Respiratory Research Analysis
Three papers stand out today: an individual patient data meta-analysis shows blood eosinophils and fractional exhaled nitric oxide (FeNO) independently predict severe asthma attacks; a Cell Reports study identifies SIRT2 as a suppressor of aging-associated cGAS activation protecting aged mice from severe COVID-19; and a Nature Communications study reveals how bortezomib activates the Mycobacterium tuberculosis Clp protease system, informing anti-TB drug design.
Summary
Three papers stand out today: an individual patient data meta-analysis shows blood eosinophils and fractional exhaled nitric oxide (FeNO) independently predict severe asthma attacks; a Cell Reports study identifies SIRT2 as a suppressor of aging-associated cGAS activation protecting aged mice from severe COVID-19; and a Nature Communications study reveals how bortezomib activates the Mycobacterium tuberculosis Clp protease system, informing anti-TB drug design.
Research Themes
- Asthma risk stratification using type 2 inflammation biomarkers
- Innate immunity, aging, and COVID-19 pathogenesis (SIRT2–cGAS axis)
- Structural biology of M. tuberculosis proteases guiding antimicrobial discovery
Selected Articles
1. Structural Insights into Bortezomib-Induced Activation of the Caseinolytic Chaperone-Protease System in Mycobacterium tuberculosis.
Cryo-EM structures reveal that sub-stoichiometric bortezomib binding activates M. tuberculosis ClpP1P2, drives chaperone recruitment (ClpC1/ClpX), and uncovers a substrate channel gating mechanism. These insights connect a clinically approved proteasome inhibitor to actionable regulation of a validated TB target.
Impact: Defines structural mechanisms for pharmacologic activation of the Mtb Clp system, informing rational design or repurposing strategies for anti-TB therapeutics.
Clinical Implications: While preclinical, the work prioritizes ClpP1P2/ClpC1-ClpX interfaces and activation states as druggable sites; it also cautions about bortezomib’s host toxicity, guiding the search for TB-selective analogs.
Key Findings
- Cryo-EM structures of Mtb ClpP1P2, ClpC1P1P2, and ClpXP1P2 bound to bortezomib in multiple conformations were solved.
- Sub-stoichiometric orthosteric binding of bortezomib activates ClpP1P2 and promotes recruitment of ClpC1 or ClpX to form holoenzymes.
- A specialized substrate channel gating mechanism involving the ClpX pore-2 loop and ClpP2 N-termini was identified.
Methodological Strengths
- High-resolution cryo-EM across multiple complexes and conformational states
- Concordant structural and biochemical activation evidence
Limitations
- Preclinical structural/biochemical work without in vivo efficacy data
- Potential translational toxicity issues with bortezomib require TB-selective derivatives
Future Directions: Design TB-selective Clp modulators guided by binding poses; test efficacy and safety in Mtb infection models; explore resistance liabilities and combination regimens.
The caseinolytic protease (Clp) system has recently emerged as a promising anti-tuberculosis target. The anti-cancer drug bortezomib exhibits potent anti-mycobacterial activity and binds to Mycobacterium tuberculosis (Mtb) Clp protease complexes. We determine cryo-EM structures of Mtb ClpP1P2, ClpC1P1P2 and ClpXP1P2 complexes bound to bortezomib in different conformations. Structural and biochemical data indicate that sub-stoichiometric binding by bortezomib to the protease active sites orthosterically activates the MtbClpP1P2 complex. Bortezomib activation of MtbClpP1P2 induces structural changes promoting the recruitment of the chaperone-unfoldases, MtbClpC1 or MtbClpX, facilitating holoenzyme formation. The structures of the MtbClpC1P1P2 holoenzyme indicate that MtbClpC1 motion, induced by ATP rebinding at the MtbClpC1 spiral seam, translocates the substrate. In the MtbClpXP1P2 holoenzyme structure, we identify a specialized substrate channel gating mechanism involving the MtbClpX pore-2 loop and MtbClpP2 N-terminal domains. Our results provide insights into the intricate regulation of the Mtb Clp system and suggest that bortezomib can disrupt this regulation by sub-stoichiometric binding at the Mtb Clp protease sites.
2. SIRT2 suppresses aging-associated cGAS activation and protects aged mice from severe COVID-19.
In aged hosts, SIRT2 limits cGAS activation and mitigates severe SARS-CoV-2 disease; aged SIRT2-deficient mice exhibit exacerbated COVID-19, highlighting a mechanistic link between aging, innate DNA sensing, and outcomes.
Impact: Identifies a tractable pathway (SIRT2–cGAS) that may be targeted to reduce severe COVID-19 in older adults.
Clinical Implications: Supports exploration of SIRT2 activation or cGAS–STING modulation as therapeutic strategies in elderly COVID-19, pending translational validation.
Key Findings
- Aged SIRT2-deficient mice develop more severe disease following SARS-CoV-2 infection.
- SIRT2 suppresses aging-associated cGAS activation, linking sirtuin biology to innate antiviral responses.
- Data support SIRT2 as a protective factor in severe COVID-19 pathogenesis in aged hosts.
Methodological Strengths
- Use of aged mouse models with genetic SIRT2 loss in an authentic SARS-CoV-2 infection setting
- Mechanistic focus on the cGAS pathway connecting aging and innate immunity
Limitations
- Preclinical animal data; human translational relevance needs confirmation
- Abstract details are limited in the provided text; specific effect sizes are not reported here
Future Directions: Test pharmacologic SIRT2 activation or cGAS modulation in aged animal models and early-phase clinical studies; map cell-type specificity and safety profile.
Aging-associated vulnerability to coronavirus disease 2019 (COVID-19) remains poorly understood. Here, we show that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected aged mice lacking SIRT2, a cytosolic NAD
3. Inflammatory and clinical risk factors for asthma attacks (ORACLE2): a patient-level meta-analysis of control groups of 22 randomised trials.
Across 6513 patients from 22 RCT control groups, both blood eosinophils and FeNO independently increased severe asthma attack risk (RR 1.48 and 1.44 per 10-fold increase). Attack history and greater disease severity added prognostic value, supporting biomarker-driven risk stratification.
Impact: Provides high-certainty, IPD-based quantification of type 2 biomarkers’ incremental prognostic value for asthma attacks, directly informing guideline risk assessment.
Clinical Implications: Incorporating blood eosinophils and FeNO with attack history and severity can refine risk stratification to guide inhaled corticosteroid optimization and biologic therapy selection.
Key Findings
- Higher baseline blood eosinophil count and FeNO independently increased severe attack risk (per 10-fold increase: RR 1.48 and 1.44).
- Attack history (RR 1.94) and severe vs moderate disease (RR 1.57) further stratified risk.
- IPD from 22 RCTs (n=6513) with negative binomial models and GRADE assessment underpin high-certainty evidence.
Methodological Strengths
- Individual patient data meta-analysis across 22 RCT control groups
- Pre-registered protocol (PROSPERO) with GRADE certainty assessment and adjusted models
Limitations
- Findings derive from RCT control arms rather than population-based cohorts
- Potential residual heterogeneity and limited applicability to mild asthma not well represented
Future Directions: Develop and validate pragmatic risk calculators integrating eosinophils, FeNO, and clinical factors; test biomarker-guided treatment strategies in prospective trials.
BACKGROUND: Clinical risk factors for severe asthma attacks have been identified, but their incremental prognostic values are unclear. Additionally, the incremental contribution of type 2 inflammation, a common, treatable process, is undetermined. We aimed to quantify the prognostic value of baseline characteristics and type 2 inflammatory biomarkers, specifically blood eosinophil count and fractional exhaled nitric oxide (FeNO), to predict asthma attacks. METHODS: In this systematic review and meta-analysis of randomised controlled trials (RCTs), Oxford Asthma Attack Risk Scale 2 (ORACLE2), we searched MEDLINE from Jan 1, 1993, to April 1, 2021, for trials investigating fixed treatment regimen effects on asthma attack rates for at least 6 months with baseline blood eosinophil count and FeNO. Eligible participants were aged 12 years or older with asthma (any severity) who had been randomly assigned to the control group of an RCT. Relevant trials were manually retrieved and reviewed by two independent reviewers (SC and IDP). Disagreements were discussed with five reviewers. Individual patient data (IPD) for meta-analysis were requested from study authors. We investigated the rate of severe asthma attacks (≥3 days of systemic corticosteroids) for at least 6 months and prognostic effects of baseline blood eosinophil count and FeNO in control group participants. Rate ratios (RRs) with 95% CIs were derived for annualised asthma attack rates from negative binomial models adjusted for key variables, including blood eosinophil count and FeNO, and interactions between these type 2 inflammatory biomarkers were explored. Certainty of evidence was assessed using GRADE. The heterogeneity of the included studies and potential for ecological bias were quantified by the concordance statistic (C-statistic). This study was registered with PROSPERO, CRD42021245337. FINDINGS: We identified 976 potentially eligible studies. After automated screening, we manually reviewed 219 full-text articles. Of these, 19 publications comprising 23 RCTs were eligible. 6513 participants (4140 [64%] female; 2370 [36%] male; three missing) spanning 22 RCTs were included for data analysis. 5972 (92%) of 6513 patients had moderate-to-severe asthma. 4615 asthma attacks occurred during 5482 person-years of follow-up (annualised rate 0·84 per person-year). Higher blood eosinophil count or FeNO was linked to higher asthma attack risk (per 10-fold increase, RR 1·48 [95% CI 1·30-1·68] for blood eosinophil count and 1·44 [1·26-1·65] for FeNO; high-certainty evidence). Other prognostic factors were attack history (yes vs no, RR 1·94 [1·61-2·32]); disease severity (severe vs moderate, RR 1·57 [1·22-2·03]); FEV