Biology subjects

Pease, N. A.

Publications and source records attributed to Pease, N. A..

6 recordsLinked to original sources

bioRxiv2026

Interpretable machine learning coupled to gene regulatory networks uncovers subcircuits underlying cell fate decisions

Gene regulatory networks (GRNs) model causal linkages that control cell fate decisions and differentiation transitions. Prioritizing regulatory subnetworks underlying cell state differences is of critical importance, but current methods including those reliant on topological metrics introduce circularity as the metrics prioritizing TFs are computed from the same networks whose assumptions they inherit. Separately, interpretable machine learning methods can identify latent factors (LFs) that discriminate cellular states with formal statistical guarantees but do not model regulatory linkages. Here, we present FOCAL (Factor-Outcome Coupling for Assessment of Linkages), a paradigm to prioritize regulatory subnetworks by coupling state-specific and dynamic GRNs with outcome-supervised LFs learned using interpretable machine learning without reference to network topology. This shifts GRN focus from macroscopic TF nodes to state-specific and dynamic TF-gene linkages. In B and T cells, FOCAL identified GIFs (GRNs coupled to Interpretable latent Factors), prioritized regulatory subnetworks underlying established states as well as transient regulatory episodes preceding them. By coupling LFs learnt from perturbation experiments of lineage-defining TFs, FOCAL identified transcriptional predisposition to alternative fates within progenitor cell populations before overt differentiation. This uncovered a novel NFATC2-IRF8 interplay in activated B cells, that was validated by in-vitro and in-vivo genetic perturbations. The two transcription factors act cooperatively to restrain extrafollicular plasmablast differentiation and promote germinal center B cell fate.

systems biology↗

bioRxiv2025

PRDM1 shapes germinal center B-cell clonal diversity by gating chromatin accessibility during light-to-dark zone transition

Germinal-center (GC) B-cell responses are defined by many positive regulators of affinity maturation, but few components that restrain clonal dominance, notably Nr4a1, are known. We reveal an unsuspected role for PRDM1 (BLIMP1), a plasma-cell determinant, as a feedback regulator of affinity maturation. Single cell RNA-seq and BCR-seq showed that B- cell-specific Prdm1 loss drives an exaggerated GC reaction with larger clones, increased somatic hypermutation, and greater clonal dominance, independent of Nr4a1. Single cell chromatin profiling with base-resolution modelling indicated that PRDM1 represses expression of BCR-signaling genes and gates chromatin accessibility at ISRE, EICE, NF-{kappa}B, and POU (Oct) motifs. In the absence of PRDM1, enhanced engagement of signaling-inducible transcription factors promotes G1-S transition during light-zone (LZ) selection and fuels dark-zone (DZ) expansion. Thus, PRDM1 attenuates BCR signaling and constrains the LZ[->]DZ transition, fine-tuning clonal competition thereby maintaining repertoire diversity. The chromatin-encoded checkpoint could be leveraged to modulate vaccine responses.

immunology↗

bioRxiv2025

Expanding the DNA Motif Lexicon of the Transcriptional Regulatory Code

Transcriptional regulatory sequences in metazoans contain intricate combinations of transcription factor (TF) motifs. Stereospecific arrangements of simple motifs constitute composite elements (CEs) that enhance DNA-protein interaction specificity and enable combinatorial regulatory logic. Despite their importance, CEs remain underexplored. We advance CE discovery and functional characterization by developing an integrated framework that combines computational prediction, experimental testing and deep learning. The extended TF motif catalog comprises both synergistic and counteracting CEs, which are supported by evidence of TF binding in vivo and in vitro. A deep learning model GRACE trained on customized massively parallel reporter assays learns the lexicon of CEs at single-nucleotide resolution. Comparative analysis with a neural network model trained on chromatin accessibility demonstrates striking convergence and distinctions within the expanded regulatory lexicon, enabling joint predictions of motif contributions and the impact of variants on chromatin structure and transcriptional activity in diverse cellular contexts.

genomics↗

bioRxiv2025

Cell cycle-coupled transcriptional network orchestrates human B cell fate bifurcation

Bifurcation of activated human B cells into plasmablast (PB) and germinal center precursor (preGC) fates underlies protective and autoimmune antibody responses, yet gene regulatory networks (GRNs) governing the alternative trajectories remain poorly defined. Using temporal single-cell multiomics, we assembled state-specific human B cell GRNs spanning four scales: transcription factor (TF)-to-fate, TF-to-gene, cis-regulatory element (CRE)-to-gene and nucleotide-to-CRE. Applying the framework to in vitro differentiated and tonsil B cells revealed concordant regulatory architectures and impacts of in silico TF perturbations. CRISPR perturbations validated many TF-to-fate and TF-to-gene predictions and revealed a mutually repressive BATF-IRF4/PRDM1 network module. The GRNs were used to predict and interpret effects of autoimmune disease variants, uncovering partitioning of disease risk at distinct TF motifs and B cell states. Predicted functional variants were independently supported by chromatin and expression QTLs and reporter assays. A web application enables exploration of B cell regulatory determinants of vaccine responses and autoimmune diseases.

immunology↗

bioRxiv2020

Multiplexed single-cell profiling of chromatin states at genomic loci by expansion microscopy

Proper regulation of genome architecture and activity is essential for the development and function of multicellular organisms. Histone modifications, acting in combination, specify these activity states at individual genomic loci. However, the methods used to study these modifications often require either a large number of cells or are limited to targeting one histone mark at a time. Here, we developed a new method called Single Cell Evaluation of Post-TRanslational Epigenetic Encoding (SCEPTRE) that uses Expansion Microscopy (ExM) to visualize and quantify multiple histone modifications at non-repetitive genomic regions in single cells at a spatial resolution of ~75 nm. Using SCEPTRE, we distinguished multiple histone modifications at a single housekeeping gene, quantified histone modification levels at multiple developmentally-regulated genes in individual cells, and identified a relationship between histone H3K4 trimethylation and the loading of paused RNA polymerase II at individual loci. Thus, SCEPTRE enables multiplexed detection of combinatorial chromatin states at single genomic loci in single cells.

genomics↗

bioRxiv2019

Temporal scaling in developmental gene networks by epigenetic timing control

During development, progenitors follow defined temporal schedules for differentiation, to form organs and body plans with precise sizes and proportions. Across diverse contexts, these developmental schedules are encoded by autonomous timekeeping mechanisms in single cells. These autonomous timers not only operate robustly over many cell generations, but can also operate at different speeds in different species, enabling proportional scaling of temporal schedules and population sizes. By combining mathematical modeling with live-cell measurements, we elucidate the mechanism of a polycomb-based epigenetic timer, that delays activation of the T-cell commitment regulator Bcl11b to facilitate progenitor expansion. This mechanism generates activation delays that are independent of cell cycle duration, and are tunably controlled by transcription factors and epigenetic modifiers. When incorporated into regulatory gene networks, this epigenetic timer enables progenitors to set scalable temporal schedules for flexible size control. These findings illuminate how evolution may set and adjust developmental speed in multicellular organisms.

systems biology↗