Biology subjects

Forrow, A.

Publications and source records attributed to Forrow, A..

5 recordsLinked to original sources

bioRxiv2026

Phase-variable restriction modification systems dynamically regulate plasmid transmission in Neisseria gonorrhoeae

Horizontal gene transfer (HGT) in bacteria is shaped by restriction-modification systems (RMSs) that define genetic barriers between species and lineages. Neisseria gonorrhoeae harbours numerous RMS, most of which are part of its core genome. Here, we show that phase variation in these RMSs can limit interspecies and intraspecies plasmid exchange. While NlaIV likely influenced the acquisition of pConj from the meningococcus, phase variation of the NgoAV RMS produces epigenetically heterogeneous populations that present variable bottlenecks to within-species plasmid transmission, influencing the spread of anti-microbial-resistance plasmids, pConj and pbla. A LEAT sequence extends or retracts a molecular ruler within the HsdS specificity subunit, altering spacing of recognition motifs, while truncation of the protein leads to a switch from non-palindromic to palindromic recognition motifs. Thus, while the repertoire of gonococcal RMSs is largely conserved, phase-variation dynamically modulates gene flow, shaping plasmid transmission, and the evolution and spread of antimicrobial resistance.

microbiology↗

bioRxiv2023

Time- and lineage-resolved transcriptional profiling uncovers gene expression programs and clonal relationships that underlie human T lineage specification

T cells develop from multi-potent hematopoietic progenitors in the thymus and provide adaptive protection against pathogens and cancer. However, the emergence of human T cell-competent blood progenitors, and their subsequent specification to the T lineage, has been challenging to capture in real time. Here, we leveraged a pluripotent stem cell differentiation system to understand the transcriptional dynamics and cell fate restriction events that underlie this critical developmental process. Time-resolved single cell RNA sequencing revealed that cell-cycle exit, downregulation of the multipotent hematopoietic program, and upregulation of >90 lineage-associated transcription factors all occur within a highly co-ordinated and narrow developmental window. Computational gene-regulatory network inference elucidated the transcriptional logic of T lineage specification, uncovering an important role for YBX1. We mapped the differentiation cell fate hierarchy using transcribed lineage barcoding and mathematical trajectory inference and discovered that mast and myeloid potential bifurcate from each other early in haematopoiesis, upstream of T lineage restriction. Collectively, our analyses provide a quantitative, time-resolved model of human T cell specification with relevance for regenerative medicine and developmental immunology.

immunology↗

bioRxiv2022

Lymphatic endothelium arises directly from specialized angioblasts

The lymphatic vasculature is essential for tissue fluid homeostasis, immune cell surveillance and dietary lipid absorption, and has emerged as a key regulator of organ growth and repair1. Despite significant advances in our understanding of lymphatic function, the precise developmental origin of lymphatic endothelial cells (LECs) has remained a point of debate for over a century2-5. It is currently widely accepted that most LECs are derived from venous endothelium4,6, although other sources have been described, including mesenchymal cells3, hemogenic endothelium7 and musculoendothelial progenitors8,9. Here we show that the initial expansion of mammalian LECs is driven primarily by the in situ differentiation of specialized angioblasts and not migration from venous endothelium. Single-cell RNA sequencing and genetic lineage tracing experiments in mouse revealed a population of Etv2+Prox1+ lymphangioblasts that arise directly from paraxial mesoderm-derived progenitors. Conditional lineage labelling and morphological analyses showed that these specialized angioblasts emerge within a tight spatiotemporal window, and give rise to LECs in numerous tissues. Analysis of early LEC proliferation and migration supported these findings, suggesting that emergence of LECs from venous endothelium is limited. Collectively, our data reconcile discrepancies between previous studies and indicate that LECs form through both de novo specification from lymphangioblasts and transdifferentiation from venous endothelium.

developmental biology↗

bioRxiv2020

A Unified Framework for Lineage Tracing and Trajectory Inference

Understanding the genetic and epigenetic programs that control differentiation during development is a fundamental challenge, with broad impacts across biology and medicine. New measurement technologies like single-cell RNA-sequencing and CRISPR-based lineage tracing have opened new windows on these processes, through computational trajectory inference and lineage reconstruction. While these two mathematical problems are deeply related, they been approached from separate directions: methods for trajectory inference are not typically designed to leverage information from lineage tracing and vice versa. We present a novel, unified framework for lineage tracing and trajectory inference. Specifically, we develop a method for reconstructing developmental trajectories from time courses with snapshots of both cell states and lineages, leveraging mathematical tools from graphical models and optimal transport. We find that lineage data helps disentangle complex state transitions with increased accuracy using fewer measured time points. Moreover, integrating lineage tracing with trajectory inference in this way enables accurate reconstruction of developmental pathways that are impossible to recover with state-based methods alone.

developmental biology↗

bioRxiv2018

Learning dynamical information from static protein and sequencing data

Many complex processes, from protein folding and virus evolution to brain activity and neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. While efficient algorithms for cluster detection and data completion in high-dimensional spaces have been developed and applied over the last two decades, considerably less is known about the reliable inference of state transition dynamics in such settings. Here, we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions. Our approach combines Gaussian mixture approximations and self-consistent dimensionality reduction with minimal-energy path estimation and multi-dimensional transition-state theory. We demonstrate the practical potential of the inference scheme by reconstructing the network dynamics for several protein folding transitions, gene regulatory network motifs and HIV evolution pathways. The predicted network topologies and relative transition time scales agree well with direct estimates from time-dependent molecular dynamics data, stochastic simulations and phylogenetic trees, respectively. The underlying numerical protocol thus allows the recovery of relevant dynamical information from instantaneous ensemble measurements, effectively alleviating the need for time-dependent data in many situations. Owing to its generic structure, the framework introduced here will be applicable to high-throughput RNA and protein sequencing datasets and future cryo-electron-microscopy data, and can guide the design of new experimental approaches towards studying complex multiphase phenomena.

bioinformatics↗