Stevens, H. P. | Search bioRxiv

bioRxiv2025

Conserved Master Regulators Orchestrate Cellular Reprogramming-Induced Rejuvenation

Partial somatic cell reprogramming has been proposed as a rejuvenation strategy, yet the regulatory architecture orchestrating age reversal remains unclear. Here, we performed gene regulatory network reconstruction across several independent systems to identify master regulators that coordinate reprogramming-induced rejuvenation (RIR). In mouse mesenchymal stem cells, mouse adipocytes, and human fibroblasts undergoing partial reprogramming, we identified genes showing opposite expression dynamics during aging and reprogramming. This approach revealed regulators governing rejuvenation rather than developmental programs. Despite divergent overall network architectures, nine transcription factors converged as master regulators across all three systems, including Ezh2, Parp1, and Brca1. These regulators undergo coordinated reorganization during reprogramming, characterized by broader target engagement and enhanced regulatory coherence. We further demonstrated that direct perturbation of Ezh2 bidirectionally modulates transcriptomic age. Notably, overexpression of a catalytically inactive Ezh2 mutant achieved rejuvenation, suggesting mechanisms distinct from canonical H3K27me3-mediated regulation are involved in RIR. Our findings reveal that cellular rejuvenation is orchestrated by conserved master regulators whose network coordination can be targeted independently of the reprogramming process.

systems biology↗

bioRxiv2023

Identifying images in the biology literature that are problematic for people with a color-vision deficiency

To help maximize the impact of scientific journal articles, authors must ensure that article figures are accessible to people with color-vision deficiencies (CVDs), which affect up to 8% of males and 0.5% of females. We evaluated images published in biology-and medicine-oriented research articles between 2012 and 2022. Most included at least one color contrast that could be problematic for people with deuteranopia ("deuteranopes"), the most common form of CVD. However, spatial distances and within-image labels frequently mitigated potential problems. Initially, we reviewed 4,964 images from eLife, comparing each against a simulated version that approximated how it might appear to deuteranopes. We identified 636 (12.8%) images that we determined would be difficult for deuteranopes to interpret. Our findings suggest that the frequency of this problem has decreased over time and that articles from cell-oriented disciplines were most often problematic. We used machine learning to automate the identification of problematic images. For hold-out test sets from eLife (n = 879) and PubMed Central (n = 1,191), a convolutional neural network classified the images with areas under the precision-recall curve of 0.75 and 0.38, respectively. We created a Web application (https://bioapps.byu.edu/colorblind_image_tester); users can upload images, view simulated versions, and obtain predictions. Our findings shed new light on the frequency and nature of scientific images that may be problematic for deuteranopes and motivate additional efforts to increase accessibility.

bioinformatics↗