Jarnuczak, A. | Search bioRxiv

bioRxiv2025

Spatio-Temporal Multi-Omics Profiling of Mechanisms and Biomarkers in Inhaled Drug-Induced Lung Toxicity

Comprehensive understanding and early detection of drug-induced lung toxicity remain critical challenges in respiratory drug development. In this study, we propose a multi-omics framework that integrates spatial and temporal tissue-specific transcriptomic signatures with proteomics from minimally invasive biofluids to understand mechanisms and identify safety biomarkers associated with lung toxicity. Using this framework, we identified a panel of candidate biomarkers in bronchoalveolar lavage fluid and plasma, including LCN2/NGAL, RETNLA, SP-D, SPP1/osteopontin, and MMP7, that correlate with histopathological features (e.g., inflammation and epithelial remodeling). We confirmed that these molecular biomarkers were consistently dysregulated across a range of inhaled lung toxicants, human disease (IPF), and environmental exposures (smoke, Alternaria), demonstrating broad applicability across different toxic exposures and translatability. Collectively, this study establishes a robust workflow for mechanism-guided biomarker discovery and proposes a panel of candidates for monitoring drug-induced lung injury in humans.

systems biology↗

bioRxiv2018

Quantifying the impact of public omics data

The amount of omics data in the public domain is increasing every year [1, 2]. Public availability of datasets is growing in all disciplines, because it is considered to be a good scientific practice (e.g. to enable reproducibility), and/or it is mandated by funding agencies, scientific journals, etc. Science is now a data intensive discipline and therefore, new and innovative ways for data management, data sharing, and for discovering novel datasets are increasingly required [3, 4]. However, as data volumes grow, quantifying its impact becomes more and more important. In this context, the FAIR (Findable, Accessible, Interoperable, Reusable) principles have been developed to promote good scientific practises for scientific data and data resources [5]. In fact, recently, several resources [1, 2, 6] have been created to facilitate the Findability (F) and Accessibility (A) of biomedical datasets. These principles put a specific emphasis on enhancing the ability of both individuals and software to discover and re-use digital objects in an automated fashion throughout their entire life cycle [5]. While data resources typically assign an equal relevance to all datasets (e.g. as results of a query), the usage patterns of the data can vary enormously, similarly to other \"research products\" such as publications. How do we know which datasets are getting more attention? More generally, how can we quantify the scientific impact of datasets?

bioinformatics↗