Biology subjects

Penfold, C. A.

Publications and source records attributed to Penfold, C. A..

7 recordsLinked to original sources

bioRxiv2025

Digital Reprogramming Decodes Epigenetic Barriers of Cell Fate Changes

The fates of differentiated cells in our body can be induced to change by nuclear reprogramming. In this way, cells valuable for therapeutic purposes and disease modeling can be produced. However, the efficiency of this process is low, partly due to properties of somatic donor nuclei which stabilize their differentiated fate but also act as barriers reprogramming-associated cell fate changes. The identity of these reprogramming barriers is not fully understood. Here, we developed an artificial intelligence-based approach to model nuclear reprogramming and used it to identify the chromatin modification H3K27ac as an epigenetic barrier to reprogramming-induced cell fate changes. Using reprogramming by nuclear transfer (NT) to eggs of Xenopus laevis as a model system, we profiled chromatin modifications in differentiated cell types alongside gene expression patterns before and after reprogramming. Our model integrated the data and by leveraging model predictions, we find that genes resisting inactivation during reprogramming display chromatin modification barcodes. This revealed H3K27ac as a novel candidate barrier to NT reprogramming. Reducing H3K27ac levels using p300/CBP inhibitors before reprogramming led to an improved downregulation of genes linked to H3K27ac-modified enhancers after reprogramming. Importantly, these effects were accompanied by improved embryonic development of the resulting nuclear transfer embryos. In summary, our study identified H3K27ac as a safeguarding mechanism of cellular identities and as a reprogramming barrier during NT. Hence, the here-developed Digital Reprogramming" approach is capable of modelling and improving current cell-fate reprogramming strategies.

genomics↗

bioRxiv2024

Somatic Cell Identity Affects Specific Cell Differentiation Programs Upon Reprogramming In Vivo

Nuclear reprogramming can change cellular fates. Yet, reprogramming efficiency is low and the resulting cell types are often not functional. Here, we used nuclear transfer to eggs to follow single cells during reprogramming in vivo. We show that the differentiation success of reprogrammed cells varies across cell types and depends on the expression of genes specific to the previous cellular identity. We find subsets of reprogramming resistant cells that fail to form functional cell types, undergo cell death, or disrupt normal body patterning. Reducing expression levels of genes specific to the cell-type of origin leads to better reprogramming and improved differentiation trajectories. Thus, our work demonstrates that failing to reprogram in vivo is cell-type specific and emphasises the necessity of minimising aberrant transcripts of the previous somatic identity for improving reprogramming.

developmental biology↗

bioRxiv2022

Origin and segregation of the human germline

Human germline-soma segregation occurs during weeks 2-3 in gastrulating embryos. While direct studies are hindered, here we investigate the dynamics of human primordial germ cell (PGCs) specification using in vitro models with temporally resolved single-cell transcriptomics and in-depth characterisation to in vivo datasets from human and non-human primates, including a 3D marmoset reference atlas. We elucidate the molecular signature for the transient gain of competence for germ cell fate during peri-implantation epiblast development. Further, we show that both the PGCs and amnion arise from transcriptionally similar TFAP2A positive progenitors at the posterior end of the embryo. Notably, genetic loss of function experiments show that TFAP2A is crucial for initiating the PGC fate without detectably affecting the amnion, and its subsequently replaced by TFAP2C as an essential component of the genetic network for PGC fate. Accordingly, amniotic cells continue to emerge from the progenitors in the posterior epiblast, but importantly, this is also a source of nascent PGCs.

developmental biology↗

bioRxiv2020

Time series transcriptomics reveals a BBX32-directed control of dynamic acclimation to high light in mature Arabidopsis leaves

The photosynthetic capacity of mature leaves increases after several days exposure to constant or intermittent episodes of high light (HL) and is manifested primarily as changes in chloroplast physiology. This is termed dynamic acclimation but how it is initiated and controlled is unknown. From fully expanded Arabidopsis leaves, we determined HL-dependent changes in transcript abundance of 3844 genes in a 0-6h time-series transcriptomics experiment. It was hypothesised that among such genes were those that contribute to the initiation of dynamic acclimation. By focussing on HL differentially expressed transcription (co-)factor (TF) genes and applying dynamic statistical modelling to the temporal transcriptomics data, a gene regulatory network (GRN) of 47 predominantly photoreceptor-regulated (co)-TF genes was inferred. The most connected gene in this network was B-BOX DOMAIN CONTAINING PROTEIN32 (BBX32). Plants over-expressing BBX32 were strongly impaired in dynamic acclimation and displayed perturbed expression of genes involved in its initiation. These observations led to demonstrating that as well as regulation of dynamic acclimation by BBX32, CRYPTOCHROME1, LONG HYPOCOTYL5, CONSTITUTIVELY PHOTOMORPHOGENIC1 and SUPPRESSOR OF PHYA-105 are also important regulators of this process. Additionally, the BBX32-centric GRN provides a view of the transcriptional control of dynamic acclimation distinct from other photoreceptor-regulated processes, such as seedling photomorphogenesis.

plant biology↗

bioRxiv2018

Bayesian parameter estimation in non-stationary semiflexible polymers from ensembles of trajectories

During the cell-cycle and meiosis, during development, or in response to stress, chromosomes undertake dramatic programs of reorganisation, which can result in major changes to genomic architecture, as well as local changes to chromatin structure via chromatin remodelling and epigenetic modification. The biophysical properties of the genome may therefore vary significantly over time, from region to region, and from cell to cell.\n\nSemifleixble polymer models are frequently used to decipher the spatial and temporal aspects of chromosome organisation. Such models allow for parameter estimation from experimental observations (Bystricky et al., 2004, Ding et al., 2006, Koszul et al., 2008, Arbona et al., 2017), and so provide a concise quantification of the state of the system in terms of meaningful biophysical parameters, such as the compaction factor and bending-modulus. Simulation studies using appropriately parameterised models may also provide novel insights, and allow for predictions without confounding pleiotropic effects (Penfold et al., 2012), thus guiding future studies.\n\nMost semifleixble polymer models do not explicitly consider the spatial non-stationarity of chromosomes and chromatin. Furthermore, recent advances in chromosome conformation capture (3C)-based allow chromosome organisation to be (indirectly) measured in single cells (Belton et al., 2012, Nagano et al., 2013, 2016). The increasing availability of ensembles of trajectories sampled from potentially heterogeneous populations of cells means it is of interest to develop polymer statistic models that can capture both the spatial nonstationarity of the biophysical parameters, and the statistical relationships that exist within the population. Here we outline a statistical framework for non-stationary semiflexible polymers, and demonstrate how inference can be performed using ensembles of trajectories. For cells belonging to a homogenous population where the biophysical parameters are approximately identical in all cells, a (transformed) Gaussian process prior is assigned to the bending-modulus, and Markov chain Monte Carlo (MCMC) used to infer the posterior distribution of free parameters. For heterogeneous populations of cells, a transformed hierarchical GP (HGP) prior is assigned to the biophysical parameters, which naturally captures the statistical dependency of the parameters that exist across the population. Simulation studies demonstrate the accuracy of the model for homogenous and heterogeneous populations, while applications to yeast chromosome data demonstrates an improved ability to recapitulate trajectories of held out loci compared to related stationary models.\n\nCode used for these analyses, and a graphical user interface (GUI) for simulating non-stationary semifleixble polymer trajectories, have been implemented in MATLAB, and are available to download from: https://github.com/cap76/3MC.

bioinformatics↗

bioRxiv2018

Branch-recombinant Gaussian processes for analysis of perturbations in biological time series

MotivationA common class of behaviour encountered in the biological sciences involves branching and recombination. During branching, a statistical process bifurcates resulting in two or more potentially correlated processes that may under-go further branching; the contrary is true during recombination, where two or more statistical processes converge into one. A key objective is to identify the time of this bifurcation (branch time) from time series measurements e.g., comparing a control time series with a perturbed time series. Whilst statistical treatments for the two branch (control versus treatment) case exists, the ability to infer more complex branching structure from time series data remains open. Gaussian processes (GPs) represents an ideal framework for such analysis, allowing for nonlinear regression that includes a rigorous treatment of uncertainty. Currently, however, GP models only exist for two-branch systems. Here we highlight how arbitrarily complex branching processes can be built using the correct composition of covariance functions within a GP framework, thus outlining a general framework for the treatment of branching and recombination in the form of branch-recombinant Gaussian processes (B-RGPs). We first demonstrate the performance of B-RGPs compared to a variety of existing regression approaches, and demonstrate robustness to model misspecification. B-RGPs are then used to investigate the branching patterns of Arabidopsis thaliana gene expression following inoculation with the hemibotrophic bacteria, Pseudomonas syringae DC3000, and a disarmed mutant strain, hrpA. By grouping genes according to the number of branches, we could naturally separate out genes involved in basal immune response from those subverted by the virulent strain, and show enrichment for targets of pathogen protein effectors. Finally, we identify two early branching genes WRKY11 and WRKY17, and showed that groups of genes that branched at similar times to WRKY11/17 were enriched for W-box binding motifs, and overrepresented for genes differentially expressed in WRKY11/17 knockouts, suggesting that branch time could be used for identifying direct and indirect binding targets of key transcription factors. Software is available from: https://github.com/cap76/BranchingGPs.

bioinformatics↗

bioRxiv2017

Nonparametric Bayesian inference of transcriptional branching and recombination identifies regulators of early human germ cell development

During embryonic development, cells undertake a series of fate decisions to form a complete organism comprised of various cell types, epitomising a branching process. A striking example of branching occurs in humans around the time of implantation, when primordial germ cells (PGCs), precursors of sperm and eggs, and somatic lineages are specified. Due to inaccessibility of human embryos at this stage of development, understanding the mechanisms of PGC specification remains difficult. The integrative modelling of single cell transcriptomics data from embryos and appropriate in vitro models should prove to be a useful resource for investigating this system, provided that the cells can be suitably ordered over a developmental axis. Unfortunately, most methods for inferring cell ordering were not designed with structured (time series) data in mind. Although some probabilistic approaches address these limitations by incorporating prior information about the developmental stage (capture time) of the cell, they do not allow the ordering of cells over processes with more than one terminal cell fate. To investigate the mechanisms of PGC specification, we develop a probabilistic pseudotime approach, branch-recombinant Gaussian process latent variable models (B-RGPLVMs), that use an explicit model of transcriptional branching in individual marker genes, allowing the ordering of cells over developmental trajectories with arbitrary numbers of branches. We use first demonstrate the advantage of our approach over existing pseudotime algorithms and subsequently use it to investigate early human development, as primordial germ cells (PGCs) and somatic cells diverge. We identify known master regulators of human PGCs, and predict roles for a variety of signalling pathways, transcription factors, and epigenetic modifiers. By concentrating on the earliest branched signalling events, we identified an antagonistic role for FGF receptor (FGFR) signalling pathway in the acquisition of competence for human PGC fate, and identify putative roles for PRC1 and PRC2 in PGC specification. We experimentally validate our predictions using pharmacological blocking of FGFR or its downstream effectors (MEK, PI3K and JAK), and demonstrate enhanced competency for PGC fate in vitro, whilst small molecule inhibition of the enzymatic component of PRC1/PRC2 reveals reduced capacity of cells to form PGCs in vitro. Thus, B-RGPLVMs represent a powerful and flexible data-driven approach for dissecting the temporal dynamics of cell fate decisions, providing unique insights into the mechanisms of early embryogenesis. Scripts relating to this analysis are available from: https://github.com/cap76/PGCPseudotime

developmental biology↗