Daily Respiratory Research Analysis
Three high-impact studies advanced respiratory science and preparedness: a Science study uncovers macrophage peroxisomes as key regulators of alveolar repair and post-viral lung sequelae; a PNAS study links variable DPP4 expression in human nasal multiciliated cells to MERS-CoV upper-airway tropism and superspreading; and a Nature Biomedical Engineering study uses deep mutational learning to select antibody combinations resilient to SARS-CoV-2 evolution.
Summary
Three high-impact studies advanced respiratory science and preparedness: a Science study uncovers macrophage peroxisomes as key regulators of alveolar repair and post-viral lung sequelae; a PNAS study links variable DPP4 expression in human nasal multiciliated cells to MERS-CoV upper-airway tropism and superspreading; and a Nature Biomedical Engineering study uses deep mutational learning to select antibody combinations resilient to SARS-CoV-2 evolution.
Research Themes
- Macrophage organelle biology driving lung repair and inflammation resolution
- Host receptor variability shaping coronavirus upper-airway tropism and transmission
- AI-guided antibody design resilient to viral evolution
Selected Articles
1. Macrophage peroxisomes guide alveolar regeneration and limit SARS-CoV-2 tissue sequelae.
This mechanistic study shows macrophage peroxisomes as regulators of inflammation resolution and alveolar regeneration during severe respiratory viral infection. Peroxisome integrity supports lipid metabolism, mitochondrial health, and restrains inflammasome/IL-1β, thereby limiting pathological alveolar transitional cell accumulation after SARS-CoV-2.
Impact: Identifies a previously underappreciated organelle axis in immune cells that can be targeted to enhance lung repair and reduce post-viral sequelae. It reframes peroxisomes as therapeutic nodes in respiratory viral disease.
Clinical Implications: Modulating peroxisome biogenesis/function (e.g., via PPAR agonists or peroxisome proliferators) could become a strategy to promote alveolar repair after severe viral pneumonia and mitigate long-COVID lung sequelae. It also cautions against interventions that exacerbate interferon-driven peroxisome loss.
Key Findings
- Excess interferon signaling remodels and depletes macrophage peroxisomes during severe respiratory viral infection.
- Peroxisomes modulate lipid metabolism and mitochondrial health in a macrophage-type–specific manner to support alveolar repair.
- Peroxisomes restrain inflammasome activation and IL-1β release, limiting accumulation of KRT8+ alveolar transitional cells after SARS-CoV-2.
Methodological Strengths
- Mechanistic dissection across macrophage subtypes with in vivo respiratory viral models
- Integration of organelle biology with immunometabolism and tissue repair readouts
Limitations
- Predominantly preclinical models; human causal evidence is indirect
- Abstract does not report sample sizes or code/data availability
Future Directions: Test peroxisome-targeted interventions (e.g., PPAR agonists, lipid remodeling strategies) in translational/clinical studies for ARDS and post-viral lung disease; map peroxisome dynamics in human macrophages during acute infection and recovery.
Peroxisomes are vital but often overlooked metabolic organelles. We found that excessive interferon signaling remodeled macrophage peroxisomes. This loss of peroxisomes impaired inflammation resolution and lung repair during severe respiratory viral infections. Peroxisomes were found to modulate lipid metabolism and mitochondrial health in a macrophage type-specific manner and enhanced alveolar macrophage-mediated tissue repair and alveolar regeneration after viral infection. Peroxisomes also prevented excessive macrophage inflammasome activation and IL-1β release, limiting accumulation of KRT8
2. Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2.
Using a deep mutational library of Omicron BA.1 RBD and ensemble deep-learning models, the authors predicted antibody binding/escape and identified complementary two-antibody combinations resilient to viral evolution. This strategy enables prospective selection of therapeutic antibodies robust to emerging SARS-CoV-2 variants.
Impact: Provides a generalizable AI-enabled framework for designing evolution-resilient antibody therapies, addressing a central failure mode of current COVID-19 biologics.
Clinical Implications: Supports development of durable antibody cocktails for prophylaxis/treatment, informing stockpiles and rapid response to new variants. It guides epitope complementarity to minimize escape.
Key Findings
- Constructed a high-mutational-distance Omicron BA.1 full-length RBD library and screened for ACE2 and antibody binding.
- Trained ensemble deep-learning models to predict binding/escape for eight therapeutic antibody candidates across diverse epitopes.
- In silico evolution across millions of sequences identified two-antibody combinations with complementary, enhanced resistance to viral evolution.
Methodological Strengths
- Integration of deep mutational scanning with ensemble deep learning and in silico evolution at scale
- Epitope-diverse antibody panel enabling complementary resistance analysis
Limitations
- Computational predictions require continuous experimental validation against emerging variants
- Focus on RBD-targeting antibodies; non-RBD epitopes were not explored
Future Directions: Prospective clinical translation of AI-selected antibody cocktails; expand to non-RBD epitopes and polyclonal mixtures; open benchmarks for model generalization across viral families.
Most antibodies for treating COVID-19 rely on binding the receptor-binding domain (RBD) of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). However, Omicron and its sub-lineages, as well as other heavily mutated variants, have rendered many neutralizing antibodies ineffective. Here we show that antibodies with enhanced resistance to the evolution of SARS-CoV-2 can be identified via deep mutational learning. We constructed a library of full-length RBDs of Omicron BA.1 with high mutational distance and screened it for binding to the angiotensin-converting-enzyme-2 receptor and to neutralizing antibodies. After deep-sequencing the library, we used the data to train ensemble deep-learning models for the prediction of the binding and escape of a panel of eight therapeutic antibody candidates targeting a diverse range of RBD epitopes. By using in silico evolution to assess antibody breadth via the prediction of the binding and escape of the antibodies to millions of Omicron sequences, we found combinations of two antibodies with enhanced and complementary resistance to viral evolution. Deep learning may enable the development of therapeutic antibodies that remain effective against future SARS-CoV-2 variants.
3. Variable DPP4 expression in multiciliated cells of the human nasal epithelium as a determinant for MERS-CoV tropism.
Using human airway organoid-derived cultures and single-cell analyses, MERS-CoV was shown to infect multiciliated cells in both nasal and pulmonary epithelia, causing ciliary loss. Donor-to-donor variability in replication correlated with focal, variable DPP4 expression in the human nose, offering a mechanistic explanation for sporadic superspreading.
Impact: Mechanistically links host receptor heterogeneity in the upper airway to coronavirus transmission patterns, informing risk assessment and countermeasures for MERS and future zoonoses.
Clinical Implications: Suggests that assessing nasal DPP4 expression or ciliated cell differentiation states could identify high-risk transmitters; reinforces the value of targeting the upper airway for interventions (vaccines, antivirals, barrier methods).
Key Findings
- MERS-CoV replicated to high titers in both pulmonary and nasal airway organoid-derived cultures.
- Single-cell mRNA-seq and histology showed preferential infection of multiciliated cells with loss of ciliary coverage.
- Replication efficiency varied widely between donors and correlated with focal, variable DPP4 expression in human nasal tissues.
Methodological Strengths
- Use of well-differentiated human airway organoid-derived cultures from nasal and pulmonary epithelia
- Convergent single-cell transcriptomics, immunofluorescence, and immunohistochemistry
Limitations
- In vitro organoid systems may not capture full in vivo mucosal immunity and aerosol dynamics
- Donor sample sizes per experiment are not detailed in the abstract
Future Directions: Quantify DPP4 variability in population-scale nasal biopsies; evaluate whether nasal DPP4 levels predict shedding/transmission; explore modulation of ciliated cell differentiation to reduce upper-airway tropism.
Transmissibility of respiratory viruses is a complex viral trait that is intricately linked to tropism. Several highly transmissible viruses, including severe acute respiratory syndrome coronavirus 2 and Influenza viruses, specifically target multiciliated cells in the upper respiratory tract to facilitate efficient human-to-human transmission. In contrast, the zoonotic Middle East respiratory syndrome coronavirus (MERS-CoV) generally transmits poorly between humans, which is largely attributed to the absence of its receptor dipeptidyl peptidase 4 (DPP4) in the upper respiratory tract. At the same time, MERS-CoV epidemiology is characterized by occasional superspreading events, suggesting that some individuals can disseminate this virus effectively. Here, we utilized well-differentiated human pulmonary and nasal airway organoid-derived cultures to further delineate the respiratory tropism of MERS-CoV. We find that MERS-CoV replicated to high titers in both pulmonary and nasal airway cultures. Using single-cell messenger-RNA sequencing, immunofluorescence, and immunohistochemistry, we show that MERS-CoV preferentially targeted multiciliated cells, leading to loss of ciliary coverage. MERS-CoV cellular tropism was dependent on the differentiation of the organoid-derived cultures, and replication efficiency varied considerably between donors. Similarly, variable and focal expression of DPP4 was revealed in human nose tissues. This study indicates that the upper respiratory tract tropism of MERS-CoV may vary between individuals due to differences in DPP4 expression, providing an explanation for the unpredictable transmission pattern of MERS-CoV.