Parodi, P. | Search bioRxiv

bioRxiv2026

Ancestry-Linked IL-10 Signaling and Macrophage Activation Modulate Fibroblast Responses to Oxidative Stress in a PEG-Based Microphysiological System

Ancestry-associated immune differences influence fibrosis risk, however, how fibrosis-associated pathways vary across individuals remains poorly understood. Fibroblasts are a main cell type involved in fibrosis. The fibroblast response is shaped by cytokine signaling and macrophage activation. The extent to which these pathways vary across individuals, and how ancestry-associated immune differences influence fibrosis risk, remains poorly understood. Here, a poly(ethylene glycol) (PEG)-based hydrogel microphysiological system was leveraged to model fibroblast-macrophage interactions following oxidative stress and to integrate donor-specific immune signals using matched macrophages and serum. Individuals of self-reported African ancestry exhibited higher monocyte expression of CCL4, lower monocyte expression of OXER1, and increased serum IL-10, compared to individuals of European ancestry. Within the hydrogel, oxidative stress reduced fibroblast prevalence while inducing Ki67 and p16. Exogenous TGF-{beta}1 increased fibroblast prevalence and collagen 3 production but did not independently increase -SMA. Incorporating donor-specific macrophages and serum revealed that cultures from individuals of European ancestry demonstrated higher fibroblast -SMA and p16 expression. Pharmacologic inhibition of IL-10 further increased -SMA, particularly in African ancestry-derived cultures, identifying IL-10 as a key protective signal limiting fibroblast activation. This hydrogel system provides a platform for dissecting inter-individual immune variation and identifying mechanisms underlying ancestry-associated fibrosis risk.

bioengineering↗

bioRxiv2026

Deep-Plant: a supervised foundation model for plant regulatory genomics

Large-scale sequence-to-function deep learning models have demonstrated unparalleled ability to model biological sequences and have revolutionized the field of regulatory genomics. However, the majority of such efforts have centered on human and mammalian systems, leaving plant regulatory genomics comparatively underexplored. To address this gap, we introduce DO_SCPLOWEEPC_SCPLOW-PO_SCPLOWLANTC_SCPLOW, a supervised foundation model trained to predict chromatin state directly from genomic sequence. In contrast to large language models, which are trained in a selfsupervised manner using sequence alone, our model is trained to predict chromatin state across tissues and conditions. Training the model on a large collection of genome-wide experiments including DNA accessibility, transcription factor binding, and histone modifications, provides it with added biological context beyond the sequence itself. We demonstrate that the resulting model is an effective platform for developing accurate models of regulatory activity relevant to gene expression and active enhancers, exhibiting large improvements in speed, accuracy, and interpretability over the complementary approach of fine-tuning DNA language models. DO_SCPLOWEEPC_SCPLOW-PO_SCPLOWLANTC_SCPLOW models are available in Arabidopsis and rice, and work well as a building block for sequence modeling in related species such as corn. Together, these results establish supervised, chromatin-informed foundation models as a practical and effective paradigm for regulatory sequence modeling in plants.

genomics↗