Stöger, T. | Search bioRxiv

bioRxiv2026

Lifespan Analysis of the Lung Epithelium Reveals Inflammatory Reprogramming and Regenerative Decline

Aging is a major risk factor for chronic lung diseases, associated with chronic low-grade inflammation (inflammaging) and impaired epithelial regeneration. How epithelial-intrinsic aging intersects with inflammaging across the lifespan remains poorly understood. Here, we systematically analyzed lung epithelial cells from neonatal, young adult, and aged mice to define age-dependent changes in regenerative capacity. RNA sequencing revealed lifespan-associated shifts characterized by early repression of developmental and WNT/{beta}-catenin programs and progressive activation of DNA damage, inflammation, and senescence signatures. Functionally, neonatal epithelial cells exhibited markedly enhanced organoid-forming capacity compared with young and aged cells. Aged organoids maintained a pro-inflammatory secretory profile indicative of cell-intrinsic inflammaging, and transfer of the aged secretome or TNF- to young cultures significantly impaired regeneration. Comparison of freshly isolated cells and long-term organoid cultures revealed sustained repression of regenerative pathways with age, consistent with stable epigenetic imprinting. Pharmacological inhibition of DNA methylation and WNT signalling partially restored regenerative capacity in adult organoids. Together, these findings identify epigenetic reprogramming and epithelial-intrinsic inflammaging as key determinants of age-dependent regenerative decline.

cell biology↗

bioRxiv2026

Report on pre-validation of an animal-free alternative method (NAM) for regulatory safety testing: InfiniteLungDT, an in-vitro-learned digital twin for the prediction of material-triggered chronic neutrophilic lung inflammation

Until now, there has been no animal-free alternative method for predicting chronic inflammation and delivering the associated dose responses, the timing of onset, and the duration of inflammation, as required by regulatory agencies. We present the results of pre-validation of an in-vitro-learned digital twin (InFiniteLungDT) capable of predicting chronic neutrophilic lung inflammation for regulatory use. The method is based on measuring the dynamics of early biological effects in vitro induced by respirable materials or their mixtures, without the need to know their intrinsic properties. We constructed the digital twin(s) for each of the material, for which we have in vivo exposure data. The instillation data set, comprising 49 different nanomaterials, was used as the primary anchor to calibrate the model. Inhalation data set, comprising 7 different nanomaterials, compliant with OECD TG 412, was used to show the general applicability of the method across species and for different exposure scenaria. In total, about 3094 single mouse exposures and 364 rat exposures (and approx. 775/225 non-exposed mouse/rat controls) were used to predict concentration-dependent time-evolved neutrophil influx into the lung. The accuracy (predictive capacity) of LOAEL determination is 93% for instillation and 84% for inhalation exposure. Taking into account the time-to-deliver-result being less than 1 week, this proves that the effect of inhaled material from acute to chronic conditions can be assessed orders of magnitude faster and cheaper than in a reference animal study.

pharmacology and toxicology↗