PDE4B inhibition by nerandomilast: Effects on lung fibrosis and transcriptome in fibrotic rats and on biomarkers in human lung epithelial cells.

BACKGROUND AND PURPOSE: The PDE4 family is considered a prime target for therapeutic intervention in several fibro-inflammatory diseases. We have investigated the molecular mechanisms of nerandomilast (BI 1015550), a preferential PDE4B inhibitor. EXPERIMENTAL APPROACH: In addition to clinically relevant parameters of idiopathic pulmonary fibrosis (IPF; lung function measurement/high-resolution computed tomography scan/AI-Ashcroft score), whole-lung homogenates from a therapeutic male Wistar rat model of pulmonary fibrosis were analysed by next-generation sequencing (NGS). Data were matched with public domain data derived from human IPF samples to investigate how well the rat model reflected human IPF. We scored the top counter-regulated genes following treatment with nerandomilast in human single cells and validated disease markers discovered in the rat model using a human disease-relevant in vitro assay of IPF. KEY RESULTS: Nerandomilast improved the decline of lung function parameters in bleomycin-treated animals. In the NGS study, most transcripts deregulated by bleomycin treatment were normalised by nerandomilast treatment. Most notably, a significant number of deregulated transcripts that were identified in human IPF disease were also found in the animal model and reversed by nerandomilast. Mapping to single-cell data revealed the strongest effects on mesenchymal, epithelial and endothelial cell populations. In a primary human epithelial cell culture system, several disease-related (bio)markers were inhibited by nerandomilast in a concentration-dependent manner. CONCLUSIONS AND IMPLICATIONS: This study further supports the available knowledge about the anti-inflammatory/antifibrotic mechanisms of nerandomilast and provides novel insights into the mode of action and signalling pathways influenced by nerandomilast treatment of lung fibrosis.

Development of Helicobacter pylori Whole-Proteome Arrays and Identification of Serologic Biomarkers for Noncardia Gastric Cancer in the MCC-Spain Study.

BACKGROUND: Helicobacter pylori (H. pylori) is a bacterial carcinogen and the leading risk factor for noncardia gastric cancer (NCGC). Detecting antibodies against specific H. pylori proteins in peripheral blood can be applied to characterize infection and determine disease associations. Most studies analyzing the association between H. pylori infection and gastric cancer have focused on previously identified antigens, predominantly the virulence factor cytotoxin-associated gene A (CagA). Selecting antigens in an unbiased approach may, however, allow the identification of novel biomarkers. METHODS: Using a combination of multiple spotting technique and cell-free, on-chip protein expression, we displayed the H. pylori genome (strain 26695) on high-density microarrays. Immunogenic proteins were identified by serum pool incubations and henceforth analyzed in individual samples. To test its applicability, we used sera from a multicase-control (MCC)-Spain study. Serologic responses between NCGC cases and controls were assessed by conditional logistic regression estimating ORs and 95% confidence intervals. RESULTS: We successfully expressed 93% of the 1,440 H. pylori open reading frames in situ. Of these, 231 (17%) were found to be immunogenic. By comparing 58 NCGC cases with 58 matched controls, we confirmed a higher seroprevalence of CagA among cases (66%) than controls (31%). We further identified a potential novel marker, the Helicobacter outer membrane protein A (HopA). CONCLUSIONS: In this study, we provide evidence that our H. pylori whole-proteome microarray offers a platform for unbiased de novo identification of serologic biomarkers. IMPACT: Given its versatile workflow, antibody responses against other H. pylori strains and possible associations with diverse H. pylori-related outcomes can be systematically analyzed.

In situ, Cell-free Protein Expression on Microarrays and Their Use for the Detection of Immune Responses.

Until recently, whole-proteome microarrays for comprehensive studies of protein interactions were mostly produced by individual cloning and cellular expression of very many open reading frames, followed by protein isolation and purification as well as array production. To overcome this cumbersome process, we have developed a method to generate microarrays representing entire proteomes by a combination of multiple spotting and on-chip, cell-free protein expression. Here, we describe the protocol for the production of bacterial protein microarrays. With slight adaptations, however, the procedure can be applied to the proteome of any organism. Expression constructs of each gene are generated by PCR on bacterial genomic DNA followed by a common secondary amplification that is adding relevant regulative elements to either end of the constructs. The unpurified PCR-products are spotted onto the microarray surface. Full-length proteins are directly expressed in situ in a cell-free manner and stay attached to the surface without further action. As an example of a typical application, we describe here the proteome-wide analysis of the immune response to a bacterial infectious agent by characterizing the binding profiles of the antibodies in patient sera.