Biology subjects

Cursons, J.

Publications and source records attributed to Cursons, J..

11 recordsLinked to original sources

bioRxiv2026

A general thermodynamic approach for model reduction of enzyme cycles and electrogenic transporters

Mathematical models of enzyme cycles form the basis of quantifying key features of metabolism and membrane transport. These models are often integrated into more comprehensive models such as whole-cell models to understand emergent behaviours between interacting components. However, it is currently computationally infeasible to simulate the full dynamical behaviour of every enzyme at a network scale. Model reduction is frequently used to improve computational efficiency, but in general, these approaches do not preserve physical and thermodynamic consistency. Here, we outline a general method for simplifying enzyme kinetics models while retaining mass, charge and energy balance. We base our approach on the bond graph, which is a general methodology for modelling biological systems from fundamental physical laws. This approach ensures that key physical constraints are enforced in every model, regardless of their complexity. Our thermodynamic model reduction framework is readily extended to electrogenic transporters through the coupling of chemical and electrical processes. Through the application of our approach to both hypothetical enzyme cycles and real data from the Na+/K+ ATPase, we show that it can rapidly screen for plausible network structures in circumstances where enzyme catalytic mechanisms may not be fully characterised, facilitating biological discovery and drug development.

systems biology↗

bioRxiv2021

High temporal resolution RNA-seq time course data reveals mammalian lncRNA activationmirrors neighbouring protein-coding genes

BackgroundThe advent of next-generation sequencing revealed extensive transcription beyond protein-coding genes, identifying tens of thousands of long non-coding RNAs (lncRNAs). Selected functional examples raised the possibility that lncRNAs, as a class, may maintain broad regulatory roles. Compellingly, lncRNA expression is strongly linked with adjacent protein-coding gene expression, suggesting a potential cis-regulatory function. Evidence for these regulatory roles may be obtained through careful examination of the precise timing of lncRNA expression relative to adjacent protein-coding genes. ResultsWhere causal cis-regulatory relationships exist, lncRNA activation is expected to precede changes in adjacent target gene expression. Using an RNA-seq time course of uniquely high temporal resolution, we profiled the expression dynamics of several thousand lncRNAs and protein-coding genes in synchronized, transitioning human cells. Our findings reveal lncRNAs are expressed synchronously with adjacent protein-coding genes. Analysis of lipopolysaccharide-activated mouse dendritic cells revealed the same temporal relationship observed in transitioning human cells. ConclusionOur findings suggest broad-scale cis-regulatory roles for lncRNAs are not common. The strong association between lncRNAs and adjacent genes may instead indicate an origin as transcriptional by-products from active protein-coding gene promoters and enhancers.

bioinformatics↗

bioRxiv2021

Modular assembly of dynamic models in systems biology

It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the development of software tools to enhance model reusability, there has been a relative lack of consideration for how underlying biophysical principles can be applied to this space. Bond graphs combine the aspects of both modularity and physics-based modelling. In this paper, we argue that bond graphs are compatible with recent developments in modularity and abstraction in systems biology, and are thus a desirable framework for constructing large-scale models. We use two examples to illustrate the utility of bond graphs in this context: a model of a mitogen-activated protein kinase (MAPK) cascade to illustrate the reusability of modules and a model of glycolysis to illustrate the ability to modify the model granularity. Author summaryThe biochemistry within a cell is complex, being composed of numerous biomolecules and reactions. In order to develop fully detailed mathematical models of cells, smaller submodels need to be constructed and connected together. Software and standards can assist in this endeavour, but challenges remain in ensuring submodels are both consistent with each other and consistent with the fundamental conservation laws of physics. In this paper, we propose a new approach using bond graphs from engineering. In this approach, connections between models are defined using physical conservation laws. We show that this approach is compatible with current software approaches in the field, and can therefore be readily used to incorporate physical consistency into existing model integration methodologies. We illustrate the utility of this approach in streamlining the development of models for a signalling network (the MAPK cascade) and a metabolic network (the glycolysis pathway). The advantage of this approach is that models can be developed in a scalable manner while also ensuring consistency with the laws of physics, enhancing the range of data available to train models. This approach can be used to quickly construct detailed and accurate models of cells, facilitating future advances in biotechnology and personalised medicine.

systems biology↗

bioRxiv2021

Targeting of TP53-independent cell cycle checkpoints overcomes FOLFOX resistance in Metastatic Colorectal Cancer

Patients with colorectal cancer (CRC) frequently develop liver metastases during the course of their disease. A substantial proportion of them receive neoadjuvant FOLFOX (5-Fluorouracil, Oxaliplatin, Leucovorin) prior to surgery in an attempt to enable successful surgical removal of their metastases and to reduce the risk of recurrence. Yet, the majority of patients progress during treatment or recur following surgery, and molecular mechanisms that contribute to FOLFOX resistance remain poorly understood. Here, using a combination of phenotypic, transcriptomic and genomic analyses of both tumor samples derived from patients with metastatic CRC and matching patient-derived tumor organoids (PDTOs), we characterize a novel FOLFOX resistance mechanism and identify inhibitors that target this mechanism to resensitize metastatic organoids to FOLFOX. Resistant PDTOs, identified after in vitro exposure to FOLFOX, exhibited elevated expression of E2F pathway, S phase, G2/M and spindle assembly checkpoints (SAC) genes. Similar molecular features were detected in CRLM from patients with progressive disease while under neoadjuvant FOLFOX treatment, highlighting the relevance of this finding. FOLFOX resistant PDTOs displayed inactivating mutations of TP53 and exhibited transcriptional features of P53 pathway downregulation. We found that they accumulated in early S-phase and underwent significant DNA damage during FOLFOX exposure, thereafter arresting in G2/M while they repaired their DNA after FOLFOX withdrawal. In parallel, results of a large kinase inhibitor screen indicated that drugs targeting regulators of the DNA damage response, G2M checkpoint and SAC had cytotoxic effects on PDTOs generated from patients whose disease progressed during treatment with FOLFOX. Corroborating this finding, CHK1 and WEE1 inhibitors were found to synergize with FOLFOX and sensitize previously resistant PDTOs. Additionally, targeting the SAC master regulator MPS1 using empesertib after exposure to FOLFOX, when cells accumulate in G2M, was also very effective to kill FOLFOX-resistant PDTOs. Our results indicate that targeted and timely inhibition of specific cell cycle checkpoints shows great potential to improve response rates to FOLFOX in patients with metastatic CRC, for whom therapeutic alternatives remain extremely limited.

cancer biology↗

bioRxiv2020

The ratio of exhausted to resident infiltrating lymphocytes is prognostic for colorectal cancer patient outcome

Immunotherapy success in colorectal cancer (CRC) is mainly limited to patients whose tumours exhibit high microsatellite instability (MSI). However, there is variability in treatment outcomes within this group, which is in part driven by the frequency and characteristics of tumour infiltrating immune cells. Indeed, the presence of specific infiltrating immune cell subsets has been shown to correlate with immunotherapy responses and is in many cases prognostic of treatment outcome. Tumour-infiltrating lymphocytes (TILs) can undergo distinct differentiation programs, acquiring features of tissue-residency or exhaustion, a process during which T cells upregulate inhibitory receptors such as PD-1 and loose functionality. While residency and exhaustion programs of CD8 T cells are relatively well-studied, these programs have only recently been appreciated in CD4 T cells and remain largely unknown in tumour-infiltrating natural killer (NK) cells. In this study, we use single cell RNA-seq data to identify signatures of residency and exhaustion in CRC infiltrating lymphocytes, including CD8, CD4 and NK cells. We then test these signatures in independent single cell data from tumour and normal tissue infiltrating immune cells. Further, we use versions of these signatures designed for bulk RNA-seq data to explore tumour intrinsic mutations associated with residency and exhaustion from TCGA data. Finally, using two independent transcriptomic data sets from patients with colon adenocarcinoma, we show that combinations of these signatures, in particular combinations of NK activity signatures, together with tumour-associated signatures, such as TGF-{beta} signalling, are associated with distinct survival outcomes in colorectal cancer patients.

cancer biology↗

bioRxiv2020

Stable gene expression for normalisation and single-sample scoring

BackgroundTranscriptomic signatures are useful in defining the molecular phenotypes of cells, tissues, and patient samples. Their most successful and widespread clinical application is the stratification of breast cancer patients into molecular (PAM50) subtypes. In most cases, gene expression signatures are developed using transcriptome-wide measurements, thus methods that match signatures to samples typically require a similar degree of measurements. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical applications, and accordingly thousands of existing gene signatures are unexplored in a clinical context. ResultsGenes in a molecular signature can provide information about molecular phenotypes and their underlying transcriptional programs from tissue samples, however determining the transcriptional state of these genes typically requires the measurement of all genes across multiple samples to allow for comparison. An efficient assay and scoring method should quantify the relative abundance of signature genes with a minimal number of additional measurements. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across large numbers (thousands) of samples, allowing signature scoring, and supporting general data normalisation for transcriptomic data. Based on singscore, we have developed a new method, stingscore, which quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these "stably-expressed genes". ConclusionWe show that our proposed list of stable genes has better stability across cancer and normal tissue data than previously proposed stable or housekeeping genes. Additionally, we show that signature scores computed from whole-transcriptome data are comparable to those calculated using only values for signature genes and our panel of stable genes. This new approach to gene expression signature analysis may facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

bioinformatics↗

bioRxiv2018

Therapeutic blockade of Activin-A improves NK cell function and anti-tumor immunity

Natural killer (NK) cells are innate lymphocytes that play a major role in immunosurveillance against tumor initiation and metastasis spread. Signals and checkpoints that regulate NK cell fitness and function in the tumor microenvironment are not well defined. Transforming grow factor (TGF)-{beta} is a recognized suppressor of NK cells that inhibits IL-15 dependent signaling events and induces cellular transdifferentiation, however the role of other SMAD signaling pathways in NK cells is unknown. In this report, we show that NK cells express the type I Activin receptor, ALK4, which upon binding its ligand Activin-A, phosphorylates SMAD2/3 to efficiently suppress IL-15-mediated NK cell metabolism. Activin-A impairs human and mouse NK cell proliferation and downregulates intracellular granzyme B levels to impair tumor killing. Similar to TGF-{beta}, Activin-A also induced SMAD2/3 phosphorylation and drove NK cells to upregulate several ILC1-like surface markers including CD69, TRAIL and CD49a. Activin-A also induced these changes on TGF-{beta} receptor deficient NK cells, highlighting that Activin-A and TGF-{beta} are independent pathways that drive SMAD2/3-mediated NK cell suppression. Finally, therapeutic inhibition of Activin-A by Follistatin significantly slowed orthotopic melanoma growth in mice. These data highlight independent SMAD2/3 pathways target NK cell fitness and function and identify a novel therapeutic axis to promote tumor immunity.\n\nOne Sentence Summary: Activin-A can directly inhibit NK cell effector functions, promote NK cells transdifferentiation into ILC1-like cells and suppress anti-melanoma immunity.

immunology↗

bioRxiv2018

A natural killer cell gene signature predicts melanoma patient survival

Animal models have demonstrated that natural killer (NK) cells can limit the metastatic dissemination of tumors, however their ability to combat established human tumors has been difficult to investigate.\n\nA number of computational methods have been developed for the deconvolution of immune cell types within solid tumors. We have taken the NK cell gene signatures from several tools, then curated and expanded this list using recent reports from the literature. Using a gene set scoring method to investigate RNA-seq data from The Cancer Genome Atlas (TCGA) we show that patients with metastatic cutaneous melanoma have an improved survival rate if their tumor shows evidence of greater NK cell infiltration. Furthermore, these survival effects are enhanced in tumors which have a higher expression of NK cell stimuli such as IL-15, suggesting NK cells are part of a coordinated immune response within these patients. Using this signature we then examine transcriptomic data to identify tumor and stromal components which may influence the penetrance of NK cells into solid tumors.\n\nThese data support a role for NK cells in the regulation of human tumors and highlight potential survival effects associated with increased NK cell activity. Furthermore, our computational analysis identifies a number of potential targets which may help to unleash the anti-tumor potential of NK cells as we enter the age of immunotherapy.

cancer biology↗

bioRxiv2018

Inferring Edge Function in Protein-Protein Interaction Networks

Motivation: Post-translational modifications (PTMs) regulate many key cellular processes. Numerous studies have linked the topology of protein-protein interaction (PPI) networks to many biological phenomena such as key regulatory processes and disease. However, these methods fail to give insight in the functional nature of these interactions. On the other hand, pathways are commonly used to gain biological insight into the function of PPIs in the context of cascading interactions, sacrificing the coverage of networks for rich functional annotations on each PPI. We present a machine learning approach that uses Gene Ontology, InterPro and Pfam annotations to infer the edge functions in PPI networks, allowing us to combine the high coverage of networks with the information richness of pathways.\n\nResults: An ensemble method with a combination Logistic Regression and Random Forest classifiers trained on a high-quality set of annotated interactions, with a total of 18 unique labels, achieves high a average F1 score 0.88 despite not taking advantage of multi-label dependencies. When applied to the human interactome, our method confidently classifies 62% of interactions at a probability of 0.7 or higher.\n\nAvailability: Software and data are available at https://github.com/DavisLaboratory/pyPPI\n\nContact: davis.m@wehi.edu.au\n\nSupplementary information: Supplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

Defining a landscape of molecular phenotypes using a simple single sample scoring method

BackgroundGene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition across a data set (e.g. varying numbers of samples for different cancer subtypes). To address these issues we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore.\n\nResultsWe have developed a rank-based single sample scoring method, implemented as a Bioconductor package. We use multiple cancer data sets to compare it against widely-used scoring methods, including GSVA, z-scores, PLAGE, and ssGSEA. Our approach does not depend upon background samples and thus the scores are stable regardless of the composition and number of samples in the gene expression data set. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (nsamples < 25). We show that the computational time for singscore is faster than current implementations of GSVA and ssGSEA, and is comparable with that of z-score and PLAGE. The singscore package also produces visualisations and interactive plots that enable exploration of molecular phenotypes.\n\nConclusionsThe single sample scoring method described here is independent of sample composition in gene expression data and thus it provides stable scores that are less likely to be influenced by unwanted variation across samples. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

bioinformatics↗

bioRxiv2017

Post-Transcriptional Control Of EMT Is Coordinated Through Combinatorial Targeting By Multiple microRNAs

Epithelial-mesenchymal transition (EMT) is a process whereby cells undergo reversible phenotypic change, losing epithelial characteristics and acquiring mesenchymal attributes. While EMT underlies normal, physiological programs in embryonic tissue development and adult wound healing, it also contributes to cancer progression by facilitating metastasis and altering drug sensitivity. Using a cell model of EMT (human mammary epithelial (HMLE) cells), we show that miRNAs act as an additional regulatory layer over and above the activity of the transcription factors with which they are closely associated. In this context, miRNAs serve to both enhance expression changes for genes with EMT function, whilst simultaneously reducing transcriptional noise in non-EMT genes. We find that members of the polycistronic miR-200c~141 and miR-183~182 clusters (which are decreased during HMLE cell EMT and are associated with epithelial gene expression in breast cancer patients) co-regulate common targets and pathways to enforce an epithelial phenotype. We demonstrate their combinatorial effects are apparent much closer to endogenous expression levels (and orders of magnitude lower than used in most studies). Importantly, the low levels of combinatorial miRNAs that are required to exert biological function ameliorate the \"off-target\" effects on gene expression that are a characteristic of supra-physiologic miRNA manipulation. We argue that high levels of over-expression characteristic of many miRNA functional studies have led to an over-estimation of the effect of many miRNAs in EMT regulation, with over 130 individual miRNAs directly implicated as drivers of EMT. We propose that the functional effects of co-regulated miRNAs that we demonstrate here more-accurately reflects the endogenous post-transcriptional regulation of pathways, networks and processes, and illustrates that the post-transcriptional miRNA regulatory network is fundamentally cooperative.

molecular biology↗