Ristic, N. | Search bioRxiv

bioRxiv2022

A dual role for CRTH2 in acute lung injury

Acute respiratory distress syndrome (ARDS) is a life-threatening clinical condition defined by rapid-onset respiratory failure following acute lung injury (ALI). The high mortality rate and rising incidence of ARDS due to COVID-19 make it an important research priority. Here we sought to investigate the role of chemoattractant receptor-homologous molecule expressed on Th2 cells (CRTH2) in ARDS. CRTH2 is a G protein-coupled receptor best studied in the context of type 2 immunity, but it also exerts effects on neutrophilic inflammation. To evaluate its role in mouse models of ARDS, we first examined its expression pattern on murine neutrophils. We found it is expressed on neutrophils, but only after extravasation into the lung. Next, we showed that CRTH2 expression on extravasated lung neutrophils promotes cell survival, as genetic deletion of CRTH2 and pharmacologic inhibition of CRTH2 using fevipiprant both led to increased apoptosis in vitro. We then evaluated the role of CRTH2 in vivo using a murine model of LPS-induced ALI. In line with the pro-inflammatory effects of CRTH2 in vitro, we observed improvement of lung injury in CRTH2-deficient mice in terms of vascular leak, weight loss and survival after LPS administration. However, neutrophilic inflammation was elevated, not suppressed in the CRTH2 KO. This finding indicated a second mechanism offsetting the pro-survival effect of CRTH2 on neutrophils. Bulk RNAseq of lung tissue indicated impairments in type 2 immune signaling in the CRTH2 KO, and qPCR and ELISA confirmed downregulation of IL-4, which is known to suppress neutrophilic inflammation. Thus, CRTH2 may play a dual role in ALI, directly promoting neutrophil cell survival, but indirectly suppressing neutrophil effector function via IL-4.

immunology↗

bioRxiv2021

ProteinPrompt: a webserver for predictingprotein-protein interactions

MotivationProtein-protein interactions play an essential role in a great variety of cellular processes and are therefore of significant interest for the design of new therapeutic compounds as well as the identification of side-effects due to unexpected binding. Here, we present ProteinPrompt, a webserver that uses machine-learning algorithms to calculate specific, currently unknown protein-protein interactions. Our tool is designed to quickly and reliably predict contacts based on an input sequence in order to scan large sequence libraries for potential binding partners, with the goal to accelerate and assure the quality of the laborious process of drug target identification. MethodsWe collected and thoroughly filtered a comprehensive database of known contacts from several sources, which is available as download. ProteinPrompt provides two complementary search methods of similar accuracy for comparison and consensus building. The default method is a random forest algorithm that uses the auto-correlations of seven amino acid scales. Alternatively, a graph neural network implementation can be selected. Additionally, a consensus prediction is available. For each query sequence, potential binding partners are identified from a protein sequence database. The proteom of several organisms are available and can be searched for contacts. ResultsTo evaluate the predictive power of the algorithms, we prepared a test dataset that was rigorously filtered for redundancy. No sequence pairs similar to the ones used for training were included in this dataset. With this challenging dataset, the random forest method achieved an accuracy rate of 0.88 and an area under curve of 0.95. The graph neural network achieved an accuracy rate of 0.86 using the same dataset. Since the underlying learning approaches are unrelated, comparing the results of random forest and graph neural networks reduces the likelihood of errors. The consensus reached an accuracy of 0.89. ProteinPrompt is available online at: http://proteinformatics.org/ProteinPrompt The server makes it possible to scan the human proteome for potential binding partners of an input sequence within minutes. For local offline usage, we furthermore created a ProteinPrompt Docker image which allows for batch submission: https://gitlab.hzdr.de/Proteinprompt/ProteinPrompt. In conclusion, we offer a fast, accurate, easy-to-use online service for predicting binding partners from an input sequence.

bioinformatics↗