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Biomedical subjects

Richard G Wunderink

Publications and source records attributed to Richard G Wunderink.

2 recordsLinked to original sources

Longitudinal clinical proteomics reveals pneumonia type-specific protein biomarkers and autoantibodies.

Community-acquired pneumonia is a major cause of morbidity and mortality globally. Specific molecular endotypes are currently not well defined, and different viral or bacterial pathogens may trigger specific host responses and pathogenic mechanisms. We performed longitudinal proteomic profiling of bronchoalveolar lavage fluid and plasma from bacterial, influenza, and SARS-CoV-2-driven pneumonia. Our analysis revealed highly pneumonia type-specific proteomic signatures, including COVID-19-specific antibodies locally produced in the lung. These antibodies showed biased immunoglobulin V-domain usage, linked to a CD69/CD83 plasma cell state associated with disease severity and degree of autoimmunity. Using mass spectrometry-driven autoantibody profiling in 2 independent COVID-19 cohorts, we identified 177 putative autoantibodies targeting extracellular matrix, nuclear, and immune-related proteins. Of note, temporal changes in autoantibody profiles correlated with clinical markers of inflammation, organ dysfunction, and duration of hospitalization. These findings highlight the autoimmune aspects of COVID-19 and provide potential biomarkers and therapeutic targets to help improve patient outcomes.

Humans

Transitions in lung microbiota landscape associate with distinct patterns of pneumonia progression.

The precise microbial determinants driving clinical outcomes in severe pneumonia are unknown. Competing ecological forces produce dynamic microbiota states in health and disease, and a more thorough understanding of these states has the potential to improve pneumonia therapy. Here, we leverage a large collection of bronchoscopic samples from patients with suspected pneumonia to determine lung microbial ecosystem dynamics throughout the course of pneumonia. We combine 16S rRNA gene, metagenomic, and metatranscriptomic sequencing with bacterial-load quantification to reveal clinically relevant drivers of pneumonia progression. Microbiota states are predictive of pneumonia subtypes and exhibit differential stability and pneumonia therapy response. Disruptive forces, such as aspiration, are associated with cohesive changes in gene expression and microbial community structure. In summary, we show that host and microbiota landscapes change in unison with clinical phenotypes and that microbiota state dynamics reflect pneumonia progression. We suggest that distinct pathways of lung microbial community succession mediate pneumonia progression.

Humans