Biology subjects

Kirsten, H.

Publications and source records attributed to Kirsten, H..

3 recordsLinked to original sources

bioRxiv2024

Neutrophil-chemoattractant CXCL5 induces lung barrier permeability in acute lung injury

RationaleAcute lung injury is frequently caused by pneumonia, and severity correlates with inflammatory cell recruitment and lung barrier failure. Deciphering drug-targetable pathways is of clinical importance. ObjectivesInvestigation of paracrine and autocrine effects of epithelial-derived chemokine CXCL5 in acute lung injury. MethodsWe assessed the role of CXCL5 and related chemokines in patients with severe pneumonia and primary cells or cell lines challenged with Streptococcus pneumoniae or exposed to mechanical stretch. Furthermore, we evaluated the role of CXCL5 in in vivo models of acute lung injury through use of Cxcl5-deficient mice and in vitro human lung barrier models. ResultsPneumococcal infection and mechanical ventilation were associated with CXCL5 production in human subjects and in mice. CXCL5 was produced by bronchial and alveolar epithelial cells. The alveolar-epithelial barrier was protected in acute lung injury models in Cxcl5-deficient mice, independent of alveolar neutrophil recruitment. Single-cell transcriptomics revealed enhanced cell junctional transcripts in epithelial, but not endothelial cells in Cxcl5-deficient mice. Accordingly, CXCL5 exposure disrupted the barrier function of TNF-primed human primary alveolar epithelial cells, but not pulmonary microvascular endothelial cells. ConclusionsWe describe a novel function of CXCL5 in acute lung injury. Besides its recognized role in recruiting highly inflammatory cells such as neutrophils, CXCL5 increases alveolar-epithelial barrier permeability. Thus, in severe bacterial pneumonia, targeting of CXCL5 as adjunctive therapy to antibiotics may aid in reducing overshooting inflammation as well as stabilizing lung barrier function.

immunology↗

bioRxiv2024

Neural Network-Assisted Humanization of COVID-19 Hamster scRNAseq Data Reveals Matching Severity States in Human Disease

Translating findings from animal models to human disease is essential for dissecting disease mechanisms, developing and testing precise therapeutic strategies. The coronavirus disease 2019 (COVID-19) pandemic has highlighted this need, particularly for models showing disease severity-dependent immune responses. Single-cell transcriptomics (scRNAseq) is well poised to reveal similarities and differences between species at the molecular and cellular level with unprecedented resolution. However, computational methods enabling detailed matching are still scarce. Here, we provide a structured scRNAseq-based approach that we applied to scRNAseq from blood leukocytes originating from humans and hamsters affected with moderate or severe COVID-19. Integration of COVID-19 patient data with two hamster models that develop moderate (Syrian hamster, Mesocricetus auratus) or severe (Roborovski hamster, Phodopus roborovskii) disease revealed that most cellular states are shared across species. A neural network-based analysis using variational autoencoders quantified the overall transcriptomic similarity across species and severity levels, showing highest similarity between neutrophils of Roborovski hamsters and severe COVID-19 patients, while Syrian hamsters better matched patients with moderate disease, particularly in classical monocytes. We further used transcriptome-wide differential expression analysis to identify which disease stages and cell types display strongest transcriptional changes. Consistently, hamsters response to COVID-19 was most similar to humans in monocytes and neutrophils. Disease-linked pathways found in all species specifically related to interferon response or inhibition of viral replication. Analysis of candidate genes and signatures supported the results. Our structured neural network-supported workflow could be applied to other diseases, allowing better identification of suitable animal models with similar pathomechanisms across species. Key PointsO_LINeural networks can successfully match disease states between animal models and humans using single-cell data as shown for COVID-19 C_LIO_LIModerately diseased patients best matched Syrian hamster cells; severely diseased patients best matched Roborovski hamster neutrophils C_LI

immunology↗

bioRxiv2018

Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis

SummaryWhile many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear.\n\nTo identify these effects, we performed cis- and trans-expression quantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium.\n\nWe observed that cis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to use cis-eQTLs to pinpoint causal genes within susceptibility loci.\n\nIn contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology.\n\nWe observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.

genomics↗