Biology subjects

Nikolenyi, G.

Publications and source records attributed to Nikolenyi, G..

3 recordsLinked to original sources

bioRxiv2025

Protein-protein interactions drive differences in the spatiotemporal dynamics of transcription factors NANOG and SOX2 in naïve pluripotent cells

Maintenance of naive pluripotency requires core transcription factors (TFs) like SOX2 and auxiliary TFs like NANOG, yet molecular mechanisms governing their intra-nuclear dynamics and DNA binding interactions remain unclear. Here, using high-density 3D single-molecule light-field microscopy combined with novel spatiotemporal analysis pipelines, we track SOX2 and NANOG dynamics in live cells. Despite lower protein abundance, NANOG displays a similar chromatin-bound fraction to SOX2. This arises partially because, while both TFs undergo frequent transient non-specific binding interactions ([~]0.5-0.7s), NANOG exhibits more stable specific binding ([~]25s vs [~]16s). Both TFs also assemble into phase-separated domains of [~]400 nm containing both freely diffusing and chromatin-bound proteins, which further influences their dynamics. Strikingly, NANOGs protein-protein interaction domain markedly increases chromatin residence time (>5-fold) and the size of these phase-separated domains. Our work uncovers how NANOG and SOX2 stabilise gene regulatory networks that maintain naive pluripotency while providing quantitative pipelines for dissecting spatiotemporal TF dynamics.

cell biology↗

bioRxiv2025

Predicting specificity of TCR-pMHC interactions using machine learning and biophysical models

Understanding the mechanism of T-cell activation and T-cell receptor (TCR) discrimination of MHC-presented epitope peptides (pMHCs) remains an open problem. Machine learning (ML)-based prediction of TCR specificity has gained considerable recent attention. However, the capacity of current models to generalize to peptides unseen during training is currently unknown. Here, we use a proprietary cancer-patient data set that profiles TCR binding to novel regions of peptide space to show that peptide generalization remains an unsolved problem. Specifically, we show that while ML methods have demonstrable utility in predicting TCR specificity for known peptides, they fail to generalize to novel peptides. We also show that physics-based methods utilizing classical energy functions outperform ML methods when predicting TCR binding to novel peptides but underperform them on known peptides. In light of these observations, we develop a new ML model that leverages general knowledge acquired by protein foundation models to achieve better or comparable performance than either ML or biophysical methods on both in- and out-of-distribution TCR-pMHC specificity prediction. We furthermore analyze model performance as a function of distance of TCR sequence specificity between the training and test sets to quantitatively characterize the generalization potential of any given TCR-pMHC model. Our analysis sheds light on the status of modeling TCR-pMHC interactions and suggests new paths forward for continued method development and data acquisition.

immunology↗

bioRxiv2023

Massively parallel base editing screens to map variant effects on anti-tumor hallmarks of primary human T cells

Base editing enables generation of single nucleotide variants, but large-scale screening in primary human T cells is limited due to low editing efficiency, among other challenges1. Here, we developed a high-throughput approach for high-efficiency and massively parallel adenine and cytosine base-editor screening in primary human T cells. We performed multiple large-scale screens editing 102 genes with central functions in T cells and full-length tiling mutagenesis of selected genes, and read out variant effects on hallmarks of T cell anti-tumor immunity, including activation, proliferation, and cytokine production. We discovered a broad landscape of gain- and loss-of-function mutations, including in PIK3CD and its regulatory subunit encoded by PIK3R1, LCK, AKT1, CTLA-4 and JAK1. We identified variants that affected several (e.g., PIK3CD C416R) or only selected (e.g. LCK Y505C) hallmarks of T cell activity, and functionally validated several hits by probing downstream signaling nodes and testing their impact on T cell polyfunctionality and proliferation. Using primary human T cells in which we engineered a T cell receptor (TCR) specific to a commonly presented tumor testis antigen as a model for cellular immunotherapy, we demonstrate that base edits identified in our screens can tune specific or broad T cell functions and ultimately improve tumor elimination while exerting minimal off-target activity. In summary, we present the first large-scale base editing screen in primary human T cells and provide a framework for scalable and targeted base editing at high efficiency. Coupled with multi-modal phenotypic mapping, we accurately nominate variants that produce a desirable T cell state and leverage these synthetic proteins to improve models of cellular cancer immunotherapies.

immunology↗