Chung, W.-Y. | Search bioRxiv

bioRxiv2026

Supporting-like cells constitute an alternative steroidogenic lineage conserved in amniotes

In mammals, the production of sex hormones is widely considered to depend on the interstitial lineage of the gonad, which differentiates into Leydig cells in males or theca cells in females. However, certain mammalian species evidence gonadal steroidogenic activity prior to the specialization of these interstitial lineages, suggesting that alternative cell types may assume this function. Here we reveal a previously unrecognized role for supporting-like cells (SLCs), which can act as a major steroidogenic lineage during mammalian embryonic development. Through comparative single-cell transcriptomics, steroidomics and in toto organ imaging we find that in rabbits, SLCs not only contribute to the formation of gonadal rete structures, as described for other mammals, but also differentiate into a steroid-producing population. The steroidogenic program of SLCs is initially activated in both sexes but selectively maintained in ovaries, whereas in testes it is progressively replaced by that of interstitially derived Leydig cells. Evolutionary comparisons indicate that SLCs may represent an ancestral lineage that is homologous to the steroidogenic cells of non-mammalian species, which also derive from supporting precursors and share expression of cell fate regulators such as PAX2/8 and TBX1. Altogether, our findings redefine current models of gonadal lineages, revealing an unexpected plasticity in sex differentiation and exemplifying how distinct cell types can converge on analogous functions during evolution.

developmental biology↗

bioRxiv2026

DualLoc: Full-parameter fine-tuning of cascaded dual transformers for protein subcellular localization prediction

Accurate protein subcellular localization is essential for biological function, and mislocalization is linked to numerous diseases. While current methods like DeepLoc 2.0 employ lightweight fine-tuning of protein language models (PLMs), their ability to predict multi-compartment localization remains limited. To address this, we introduce DualLoc, a multi-label localization predictor for ten compartments. DualLoc leverages full-parameter fine-tuning of a cascaded dual-transformer architecture, built upon foundational PLMs and augmented with attention and dropout layers. We evaluated this framework using three foundational PLMs--ProtBERT, ESM-2, and ProtT5--as backbones. Cross-validation on Swiss-Prot and independent validation on the Human Protein Atlas demonstrate consistent superiority over state-of-the-art baselines. The best-performing variant, DualLoc-ProtT5, achieves 0.5872 accuracy, 0.8271 micro-F1, and 0.7811 macro-F1, with substantial gains in the Matthews correlation coefficient for the nucleus (+0.13), cell membrane (+0.13), and extracellular space (+0.07). Pointwise mutual information analysis of model outputs reveals biologically relevant compartment couplings, notably between the Golgi apparatus and endoplasmic reticulum (PMI = 0.25, P < 10-6), accurately reflecting secretory pathway coordination. DualLoc provides both a highly accurate predictive tool and a robust framework for investigating protein multi-localization mechanisms. Author summaryWhere a protein resides within a cell determines what it does. When proteins end up in the wrong location, normal cellular function breaks down--a misplacement linked to diseases like cancer and Alzheimers. While computational tools exist to predict these locations, accurately tracking proteins that multitask across multiple cellular compartments simultaneously remains a major challenge. We developed DualLoc, a new approach that predicts protein locations across ten different cellular compartments, from the nucleus to the cell membrane. By training an advanced artificial intelligence model on large protein sequence databases, our method more accurately identifies where proteins go, especially in complex, multi-location scenarios. Importantly, our analysis revealed meaningful biological patterns. We found strong predictive links between compartments that work closely together, such as the Golgi apparatus and the endoplasmic reticulum--two organelles that coordinate protein processing and transport. This suggests our model captures genuine cellular logic rather than simply memorizing data. By improving how we predict protein localization, DualLoc helps researchers better understand normal cellular function and disease mechanisms. Our method is freely available to the biomedical community.

bioinformatics↗