Daily Respiratory Research Analysis
A bat merbecovirus (HKU5-CoV lineage 2) was shown to use human ACE2 with a distinct binding mode and infected human respiratory and enteric organoids, sharpening zoonotic risk assessments. An interpretable machine-learning framework using B/T-cell receptor repertoires across 593 individuals simultaneously detected infections (including SARS-CoV-2), autoimmunity, and disease severity. A multicenter ICU analysis found SpO2/FiO2 frequently misclassifies ARDS severity versus PaO2/FiO2, cautioning ag
Summary
A bat merbecovirus (HKU5-CoV lineage 2) was shown to use human ACE2 with a distinct binding mode and infected human respiratory and enteric organoids, sharpening zoonotic risk assessments. An interpretable machine-learning framework using B/T-cell receptor repertoires across 593 individuals simultaneously detected infections (including SARS-CoV-2), autoimmunity, and disease severity. A multicenter ICU analysis found SpO2/FiO2 frequently misclassifies ARDS severity versus PaO2/FiO2, cautioning against substituting pulse-oximetry ratios for severity staging.
Research Themes
- Zoonotic coronavirus receptor usage and structural virology
- Immune repertoire-based multi-disease diagnostics
- Critical care metrics and ARDS severity classification
Selected Articles
1. Bat-infecting merbecovirus HKU5-CoV lineage 2 can use human ACE2 as a cell entry receptor.
A bat HKU5-CoV lineage 2 virus was identified that efficiently uses human ACE2, with a receptor-binding footprint overlapping sarbecoviruses. Authentic virus infected hACE2 cell lines and human respiratory/enteric organoids, indicating broad tropism and enhanced adaptation versus lineage 1. These findings elevate the zoonotic risk profile of merbecoviruses.
Impact: First demonstration that an HKU5 merbecovirus lineage can use human ACE2 with a distinct structural mode and infect human organoids, directly informing pandemic preparedness.
Clinical Implications: Enhances surveillance priorities for merbecoviruses, supports functional receptor screening of animal coronaviruses, and guides countermeasure development (vaccines/antivirals) targeting ACE2-using emergent strains.
Key Findings
- HKU5-CoV lineage 2 efficiently utilizes human ACE2 as an entry receptor.
- Cryo-EM reveals a distinct RBD–ACE2 binding mode with a footprint overlapping ACE2-using sarbecoviruses and NL63.
- Authentic HKU5-CoV-2 infects hACE2-expressing cell lines and human respiratory and enteric organoids.
- Lineage 2 shows better adaptation to human ACE2 than lineage 1 HKU5-CoV.
Methodological Strengths
- Integrated structural biology (cryo-EM) with functional entry and authentic virus infection assays
- Use of human respiratory and enteric organoids to demonstrate tissue tropism
Limitations
- In vivo pathogenicity and transmissibility were not assessed
- Prevalence and ecological dynamics of HKU5-CoV-2 in bats remain undefined
Future Directions: Assess in vivo pathogenicity/transmission, map receptor usage across merbecoviruses, and evaluate cross-neutralization and antiviral susceptibility to inform countermeasures.
Merbecoviruses comprise four viral species with remarkable genetic diversity: MERS-related coronavirus, Tylonycteris bat coronavirus HKU4, Pipistrellus bat coronavirus HKU5, and Hedgehog coronavirus 1. However, the potential human spillover risk of animal merbecoviruses remains to be investigated. Here, we reported the discovery of HKU5-CoV lineage 2 (HKU5-CoV-2) in bats that efficiently utilize human angiotensin-converting enzyme 2 (ACE2) as a functional receptor and exhibits a broad host tropism. Cryo-EM analysis of HKU5-CoV-2 receptor-binding domain (RBD) and human ACE2 complex revealed an entirely distinct binding mode compared with other ACE2-utilizing merbecoviruses with RBD footprint largely shared with ACE2-using sarbecoviruses and NL63. Structural and functional analyses indicate that HKU5-CoV-2 has a better adaptation to human ACE2 than lineage 1 HKU5-CoV. Authentic HKU5-CoV-2 infected human ACE2-expressing cell lines and human respiratory and enteric organoids. This study reveals a distinct lineage of HKU5-CoVs in bats that efficiently use human ACE2 and underscores their potential zoonotic risk.
2. Disease diagnostics using machine learning of B cell and T cell receptor sequences.
Using receptor repertoires from 593 individuals, the authors developed an interpretable ML framework (MAchine Learning for Immunological Diagnosis) that detects infections, autoimmune diseases, vaccine responses, and severity differences from BCR/TCR sequences. Model features recapitulated known immune responses to SARS-CoV-2, illustrating translational potential for respiratory and systemic diseases.
Impact: Introduces a generalizable, interpretable immunodiagnostic platform that can transform multi-disease screening, including respiratory infections such as COVID-19.
Clinical Implications: Potential for minimally invasive blood-based diagnostics to augment current testing for respiratory pathogens, stratify disease severity, and monitor vaccine responses.
Key Findings
- Developed an interpretable ML framework using BCR/TCR sequences from 593 individuals to screen multiple diseases or test a specific condition.
- Accurately detected specific infections, autoimmune disorders, vaccine responses, and disease severity differences.
- Model features recapitulated known immune responses to SARS-CoV-2, supporting relevance to respiratory infections.
Methodological Strengths
- Interpretable ML approach linking model features to known immunology
- Multi-condition, multi-cohort receptor repertoire analysis
Limitations
- Prospective clinical validation and real-world workflow integration are needed
- Cohort size and disease breadth, while broad, may require expansion for rare conditions
Future Directions: Prospective trials in clinical settings, expansion across pathogens and autoimmune spectra, and head-to-head comparisons with standard diagnostics.
Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndrome
3. Limitations of SpO2/FiO2 to classify ARDS severity.
Across 708 ARDS patients from three ICU databases, ARDS severity classification using SpO2/FiO2 frequently diverged from PaO2/FiO2-based staging. The findings caution against substituting pulse-oximetry ratios for PaO2/FiO2 when grading ARDS severity.
Impact: Directly affects bedside severity staging and trial enrollment criteria by demonstrating misclassification risk with SpO2/FiO2.
Clinical Implications: Prefer PaO2/FiO2 for ARDS severity grading when feasible; if using SpO2/FiO2, apply with caution and consider confirmatory arterial blood gases.
Key Findings
- In 708 ARDS patients from ICU Cockpit, MIMIC-IV, and SICdb, SpO2/FiO2-based severity classification frequently misaligned with PaO2/FiO2.
- Time-matched SpO2, PaO2, and FiO2 analyses demonstrate limitations of pulse-oximetry ratios for ARDS staging.
- Findings support cautious use of SpO2/FiO2 and continued reliance on PaO2/FiO2 where possible.
Methodological Strengths
- Multicenter analysis across three high-resolution ICU databases
- Time-matched pairing of SpO2, PaO2, and FiO2 measurements
Limitations
- Retrospective observational design limits causal inference
- Abstracted thresholds and detailed misclassification rates are not specified in the abstract
Future Directions: Define calibrated SpO2/FiO2 thresholds by saturation range; prospective validation and integration into ARDS definitions and trial criteria.
BACKGROUND: The ratio of pulse-oximetric peripheral oxygen saturation to fraction of inspired oxygen (SpO METHODS: Observational cohort study of ARDS patients from three high-resolution Intensive Care Unit databases, including our own database ICU Cockpit, MIMIC-IV (Version 3.0) and SICdb (Version 1.0.6). Patients with ARDS were identified based on the Berlin criteria or ICD 9/10-codes. Time-matched datapoints of SpO RESULTS: Overall, 708 ARDS patients were included in the analysis. ARDS severity was misclassified by SpO CONCLUSIONS: The use of SpO