Biology subjects

Oehler, S.

Publications and source records attributed to Oehler, S..

2 recordsLinked to original sources

bioRxiv2025

Longitudinal multi-omic evaluation of biomarkers of health and ageing over smoking cessation intervention

Smoking is one of the single most important preventable risk factors for cancer and other adverse health outcomes 1,2. Smoking cessation represents a key public health intervention with the potential to reduce its negative health outcomes 2-4. While epidemiological, cross-sectional, and individual longitudinal omic or biomarker studies have evaluated the impact of smoking cessation, no study to date has systematically profiled molecular and clinical changes in several organ systems or tissues longitudinally over the course of smoking cessation that could allow for more detailed assessment of response biomarkers and the identification of interindividual differences in the recovery of physiological functions. Here, we report the first human longitudinal multi-omic study of smoking cessation, evaluating 2,501 unique single or composite features from 1,094 longitudinal samples. Our comprehensive analysis, leveraging over half a million longitudinal data points, revealed a profound effect of smoking cessation on epigenetic biomarkers and microbiome features across multiple organ systems within 6 months of smoking cessation, alongside shifts in the immune and blood oxygenation system. Moreover, our multi-omic analysis provided unprecedented granularity that allows for identification of new cross-ome associations for mechanistic discovery. We anticipate that data and an interactive app from the Tyrol Lifestyle Atlas (eutops.github.io/lifestyle-atlas), comprising the current study and a parallel study arm evaluating the impact of diet on biomarkers of health and disease, will provide the basis for future discovery, biomarker benchmarking in their responsiveness to health-promoting interventions, and study of individualised response group, representing a major advance for personalised health monitoring using biomarkers.

systems biology↗

bioRxiv2023

A deep learning approach for the discovery of tumor-targeting small organic ligands from DNA-Encoded Chemical Libraries

DNA-Encoded Chemical Libraries (DELs) emerged as efficient and cost-effective ligand discovery tools, which enable the generation of protein-ligand interaction data of unprecedented size. In this article, we present an approach that combines DEL screening and instance-level deep learning modeling to identify tumor-targeting ligands against Carbonic Anhydrase IX (CAIX), a clinically validated marker of hypoxia and clear cell Renal Cell Carcinoma. We present a new ligand identification and HIT-to-LEAD strategy driven by Machine Learning (ML) models trained on DELs, which expand the scope of DEL-derived chemical motifs. CAIX screening datasets obtained from three different DELs were used to train ML models for generating novel HITs, dissimilar to elements present in the original DELs. Out of the 152 novel potential HITs that were identified with our approach and screened in an in vitro enzymatic inhibition assay, 70% displayed submicromolar activities (IC50 < 1 M). Based on the first HIT set, the model was further used to prioritize and generate LEAD compounds with nanomolar affinity for in vivo tumor-targeting applications. Three LEAD candidates showed accumulation on the surface of CAIX-expressing tumor cells in cellular binding assays. The best compound displayed in vitro KD of 5.7 nM and selectively targeted tumors in mice bearing human Renal Cell Carcinoma lesions. Our results demonstrate the synergy between DEL and machine learning for the identification of novel HITs and for the successful translation of LEAD candidates for in vivo targeting applications. Graphical Abstracts O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/525453v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@105913corg.highwire.dtl.DTLVardef@823522org.highwire.dtl.DTLVardef@6e85e9org.highwire.dtl.DTLVardef@19a611_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗