Biology subjects

Dunn, S.-J.

Publications and source records attributed to Dunn, S.-J..

7 recordsLinked to original sources

bioRxiv2023

DiscoGen: Learning to Discover Gene Regulatory Networks

Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inherently causal in nature. To accurately identify GRNs, perturbational data is required. However, most GRN discovery methods only operate on observational data. Recent advances in neural network-based causal discovery methods have significantly improved causal discovery, including handling interventional data, improvements in performance and scalability. However, applying state-of-the-art (SOTA) causal discovery methods in biology poses challenges, such as noisy data and a large number of samples. Thus, adapting the causal discovery methods is necessary to handle these challenges. In this paper, we introduce DiscoGen, a neural network-based GRN discovery method that can denoise gene expression measurements and handle interventional data. We demonstrate that our model outperforms SOTA neural network-based causal discovery methods.

genomics↗

bioRxiv2023

The Nucleosome Remodelling and Deacetylation complex coordinates the transcriptional response to lineage commitment in pluripotent cells

As cells exit the pluripotent state and begin to commit to a specific lineage they must activate genes appropriate for that lineage while silencing genes associated with pluripotency and preventing activation of lineage-inappropriate genes. The Nucleosome Remodelling and Deacetylation (NuRD) complex is essential for pluripotent cells to successfully undergo lineage commitment. NuRD controls nucleosome density at regulatory sequences to facilitate transcriptional responses, and also has been shown to prevent unscheduled transcription (transcriptional noise) in undifferentiated pluripotent cells. How these activities combine to ensure cells engage a gene expression program suitable for successful lineage commitment has not been determined. Here we show that while NuRD is not required to silence all genes, its activity is important to restrict expression of genes primed for activation upon exit from the pluripotent state, and that NuRD activity facilitates their subsequent transcriptional activation. We further show that NuRD coordinates gene expression changes, which acts to maintain a barrier between different stable states. Thus NuRD-mediated chromatin remodelling serves multiple functions, including reducing transcriptional noise, priming genes for activation and coordinating the transcriptional response to facilitate lineage commitment.

developmental biology↗

bioRxiv2022

Functional feature selection reveals the inner cell mass in human pre-implantation embryo single cell RNA sequencing data

A major challenge in single cell gene expression analysis is to discern meaningful cellular heterogeneity from technical or biological noise. To address this challenge, we present Entropy Sorting, a mathematical framework that distinguishes genes indicative of cell identity. ES achieves this in an unsupervised manner by quantifying if observed correlations between features are more likely to have occurred due to random chance versus a dependent relationship, without the need for any user defined significance threshold. On synthetic data we demonstrate the removal of noisy signals to reveal a higher resolution of gene expression patterns than commonly used feature selection methods. We then apply ES to human pre-implantation embryo scRNA-seq data. Previous studies failed to unambiguously identify early inner cell mass (ICM), suggesting that the human embryo may diverge from the mouse paradigm. In contrast, ES resolves the ICM and reveals sequential lineage bifurcations as in the classical model. Entropy sorting thus provides a powerful approach for maximising information extraction from high dimensional datasets such as scRNA-seq data.

bioinformatics↗

bioRxiv2018

Formal Analysis of Network Motifs

A recurring set of small sub-networks have been identified as the building blocks of biological networks across diverse organisms. These network motifs have been associated with certain dynamical behaviors and define key modules that are important for understanding complex biological programs. Besides studying the properties of motifs in isolation, existing algorithms often evaluate the occurrence frequency of a specific motif in a given biological network compared to that in random networks of similar structure. However, it remains challenging to relate the structure of motifs to the observed and expected behavior of the larger network. Indeed, even the precise structure of these biological networks remains largely unknown. Previously, we developed a formal reasoning approach enabling the synthesis of biological networks capable of reproducing some experimentally observed behavior. Here, we extend this approach to allow reasoning about the requirement for specific network motifs as a way of explaining how these behaviors arise. We illustrate the approach by analyzing the motifs involved in sign-sensitive delay and pulse generation. We demonstrate the scalability and biological relevance of the approach by revealing the requirement for certain motifs in the network governing stem cell pluripotency.

systems biology↗

bioRxiv2017

A common molecular logic determines embryonic stem cell self-renewal and reprogramming

During differentiation and reprogramming new cell identities are generated by reconfiguration of gene regulatory networks. Here we combined automated formal reasoning with experimentation to expose the logic of network activation during induction of naive pluripotency. We find that a Boolean network architecture defined for maintenance of naive state embryonic stem cells (ESC) also explains transcription factor behaviour and potency during resetting from primed pluripotency. Computationally identified gene activation trajectories were experimentally substantiated at single cell resolution. Contingency of factor availability explains the counterintuitive observation that Klf2, which is dispensable for ESC maintenance, is required during resetting. We tested 136 predictions formulated by the dynamic network, finding a predictive accuracy of 78.7%. Finally, we show that this network explains and predicts experimental observations of somatic cell reprogramming. We conclude that a common deterministic program of gene regulation is sufficient to govern maintenance and induction of naive pluripotency. The tools exemplified here could be broadly applied to delineate dynamic networks underlying cell fate transitions.

developmental biology↗

bioRxiv2017

A protein phosphatase network controls temporal and spatial dynamics of differentiation commitment in human epidermis

Epidermal homeostasis depends on a balance between stem cell renewal and terminal differentiation. The transition between the two cell states, termed commitment, is poorly understood. Here we characterise commitment by integrating transcriptomic and proteomic data from disaggregated primary human keratinocytes held in suspension for up to 12h to induce differentiation. We find that cell detachment induces a network of protein phosphatases. The pro-commitment phosphatases - including DUSP6, PPTC7, PTPN1, PTPN13 and PPP3CA - promote differentiation by negatively regulating ERK MAPK and positively regulating AP1 transcription factors. Their activity is antagonised by concomitant upregulation of DUSP10. Boolean network modelling of phosphatase interactions identifies commitment as an inherently unstable biological switch between the stem and differentiated cell states. Furthermore, phosphatase expression is spatially regulated both in vivo and in vitro. We conclude that an auto-regulatory phosphatase network maintains epidermal homeostasis by controlling the onset and duration of commitment.

developmental biology↗

bioRxiv2017

A protein phosphatase network controls the temporal and spatial dynamics of differentiation commitment in human epidermis

Epidermal homeostasis depends on a balance between stem cell renewal and terminal differentiation1,2. While progress has been made in characterising the stem and differentiated cell compartments3, the transition between the two cell states, termed commitment4, is poorly understood. Here we characterise commitment by integrating transcriptomic and proteomic data from disaggregated primary human keratinocytes held in suspension for up to 12h. We have previously shown that commitment begins at approximately 4h and differentiation is initiated by 8h5. We find that cell detachment induces a network of protein phosphatases. The pro-commitment phosphatases - including DUSP6, PPTC7, PTPN1, PTPN13 and PPP3CA - promote terminal differentiation by negatively regulating ERK MAPK and positively regulating key API transcription factors. Their activity is antagonised by concomitant upregulation of the anti-commitment phosphatase DUSP10. The phosphatases form a dynamic network of transient positive and negative interactions, with DUSP6 predominating at commitment. Boolean network modelling identifies a mandatory switch between two stable states (stem cell and differentiated cell) via an unstable (committed) state. In addition phosphatase expression is spatially regulated relative to the location of stem cells, both in vivo and in response to topographical cues in vitro. We conclude that an auto-regulatory phosphatase network maintains epidermal homeostasis by controlling the onset and duration of commitment.

cell biology↗