Biology subjects

Schwartz, J.-M.

Publications and source records attributed to Schwartz, J.-M..

16 recordsLinked to original sources

bioRxiv2025

Human pluripotent stem cell model of multiple epiphyseal dysplasia with MATN3 mutation identifies altered Matrix organisation and upregulation of the cholesterol biosynthesis pathway

Multiple epiphyseal dysplasia (MED), caused by mutations in MATN3, is a chondrodysplasia affecting the cartilage growth plate and is characterised by delayed epiphyseal ossification, short stature, and early onset osteoarthritis. Here we generated an in vitro human pluripotent stem cell (hPSC) model of cartilage growth-plate development to identify pathogenic mechanisms underlying MED. hPSCs were differentiated to chondrocytes via a mesenchymal intermediate, followed by TGF{beta}3+BMP2 induced chondrogenic pellet culture. MATN3-mutant hPSCs were generated by reprogramming MED patient PBMCs or by CRISPR-Cas9 gene editing to introduce a MATN3 mutation in a hESC line. RNAseq was used to assess chondrogenesis and identify MED pathogenic mechanisms. Transmission electron microscopy (TEM) was used to assess extracellular matrix assembly. The resultant hPSC-derived cartilage pellets displayed a typical cartilage morphology and strongly expressed cartilage matrix markers, e.g., collagen II and matrilin-3. Matrilin-3 protein was detected within both the matrix and cells of heterozygous mutant hPSC-cartilage pellets. RNAseq of mutant hPSC-cartilage pellets revealed significant enrichment for ECM organisation and cholesterol biosynthesis pathway genes as well as sightly increased expression of some unfolded protein response (UPR) marker genes. MATN3 mutant hPSC-derived cartilage pellets displayed abnormal matrix assembly, distended ER, accumulation of lipid droplets, and increased cholesterol content. Our model revealed mutant matrilin-3 induces cholesterol biosynthesis pathway upregulation and abnormal matrix assembly during MED pathogenesis. This study provides new insights into the molecular mechanisms underlying MED and highlights potential therapeutic targets.

cell biology↗

bioRxiv2025

Transcriptome-Driven Constraint-Based Modelling Reveals Metabolic Targets for Ovarian Cancer

Constraint-based modelling (CBM) is a powerful computational approach that reconstructs cellular metabolism by integrating omics data with genome-scale metabolic models (GEMs), enabling in silico hypothesis generation and genetic engineering studies. Advances in high-throughput omics technologies and the complete mapping of the human genome have expanded the application of CBM to human systems. Given that altered metabolism is a hallmark of cancer, this disease represents an ideal context for developing and applying CBM workflows. Despite the presence of well-characterised metabolic signatures and vulnerabilities in ovarian cancer, this tumour type remains under-explored within the CBM field. Meanwhile, the limited efficacy of current therapies and the frequent emergence of chemoresistance underscore the need for novel, mechanism-based approaches to therapeutic discovery. In this study, we constructed ovarian cancer-specific metabolic models using an omics integration algorithm that incorporates transcriptomic data in a way that is directed by experimental growth measurements. Simulations identified multiple candidate molecules predicted to influence cancer cell proliferation. Among these, triosephosphate isomerase 1 (TPI1) was selected for experimental validation based on qualitative prioritisation criteria. Notably, model predictions were supported by RNA sequencing and proliferation assays, implicating TPI1 in ovarian cancer cell growth. Our results provide novel insights into the metabolic dependencies of ovarian cancer and demonstrate a multi-omics CBM workflow that may be broadly applicable for uncovering therapeutic vulnerabilities in other malignancies.

cancer biology↗

bioRxiv2023

Development of a genome scale metabolic model for the lager hybrid yeast S. pastorianus to understand evolution of metabolic pathways in industrial settings.

In silico tools such as genome-scale metabolic models (GSMM) have shown to be powerful for metabolic engineering of microorganisms. Here, we created the iSP_1513 GSMM for the aneuploid hybrid S. pastorianus CBS1513 to allow top-down computational approaches to predict the evolution of metabolic pathways and to aid strain optimisation and media engineering in production processes. The iSP_1513 comprises 4062 reactions, 1808 alleles and 2747 metabolites, and takes into account the functional redundancy in the gene-protein-reaction rule caused by the presence of orthologous genes. Moreover, a universal algorithm to constrain GSMM reactions using transcriptome data was developed as a python library and enabled the integration of temperature as parameter. Essentiality datasets, growth data on various carbohydrates and volatile metabolites secretion were used to validate the model. Overall, the iSP_1513 GSMM represent an important step towards understanding the metabolic capabilities, evolutionary trajectories and adaptation potential of S. pastorianus in different industrial settings.

systems biology↗

bioRxiv2023

Constraint-based modelling predicts metabolic signatures of low- and high-grade serous ovarian cancer

Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways link to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modeling (CBM) represents a powerful technology, inviting the integration of omics data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies, thus highlighting a niche in disease research. Here, a novel method for constraint-based modeling has been developed, employing the genome-scale model Human1 and flux balance analysis (FBA), enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer as a case study, subtype-specific metabolic differences have been predicted, which have been supported with CRISPR-Cas9 data and an extensive literature review. Metabolic drivers of aggressive phenotypes, as well as pathways responsible for increased proliferation and chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate fatty acid biosynthesis and the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.

bioinformatics↗

bioRxiv2023

A subset of CD4+ effector memory T cells limit immunity to pulmonary viral infection and prevent tissue pathology via activation of latent TGFβ

A rapid immune response to pathogen re-exposure underpins immunological memory, with protection against divergent pathogens such as heterologous or novel viral strains requiring cross-reactive memory T cells. Understanding the pathways that control memory T cell function is therefore important for the rational design of viral vaccines and will aid the discovery of therapies to boost anti-viral immunity. Here, we identify a sub-population of memory T cells that limit secondary immune responses to viral re-infection, which is crucial in preventing host tissue damage. We show that a population of CD4+ effector memory T (TEM) cells activate the important immunoregulatory cytokine TGF{beta}, via expression of an integrin, v{beta}8. Integrin v{beta}8 expression marks a transcriptionally distinct sub-population of CD4+ TEM, enriched for anti-inflammatory pathways. Loss of integrin v{beta}8 on murine CD4+ TEM, but not Foxp3+ regulatory T cells (TREG), led to exacerbated virus-specific CD8+ T cell responses following secondary influenza A virus (IAV) infection, which was associated with enhanced viral clearance. However, although accelerating clearance, loss of integrin v{beta}8 expression on CD4+ TEM resulted in enhanced lung pathology following secondary IAV infection, which was completely reversed by adoptive transfer of v{beta}8+ CD4+ TEM cells. These data highlight a new pathway by which a distinct CD4+ memory T cell subset restrains anti-viral immunity to prevent host tissue damage during secondary viral infection. Such pathways could be targeted therapeutically to either boost memory T-cell-mediated immunity or restrain host tissue damage during viral infection.

immunology↗

bioRxiv2021

Spatially Resolved Phosphoproteomics Reveals Fibroblast Growth Factor Receptor Recycling-driven Regulation of Autophagy and Survival

Receptor Tyrosine Kinase (RTK) endocytosis-dependent signalling drives cell proliferation and motility during development and adult homeostasis, but is dysregulated in diseases, including cancer. The recruitment of RTK signalling partners during endocytosis, specifically during recycling to the plasma membrane, is still unknown. Focusing on Fibroblast Growth Factor Receptor 2b (FGFR2b) recycling, we revealed FGFR signalling partners proximal to recycling endosomes (REs) by developing a Spatially Resolved Phosphoproteomics (SRP) approach based on APEX2-driven biotinylation followed by phosphopeptide enrichment. Combining this with traditional phosphoproteomics, bioinformatics, and targeted assays, we uncovered that FGFR2b stimulated by its recycling ligand FGF10 activates mTOR-dependent signalling and ULK1 at the REs, leading to autophagy suppression and cell survival. This adds to the growing importance of RTK recycling in orchestrating cell fate and suggests a therapeutically targetable vulnerability in ligand-responsive cancer cells. Integrating SRP with other systems biology approaches provides a powerful tool to spatially resolve celllar signalling.

cell biology↗

bioRxiv2021

SubcellulaRVis: an app to visualize subcellular compartment enrichment

High-throughput omics methods result in lists of differentially regulated or expressed genes or proteins, whose function is generally studied through statistical methods such as enrichment analyses. One aspect of protein regulation is subcellular localization, which is crucial for their correct processing and function and can change in response to various cellular stimuli. Enrichment of proteins for subcellular compartments is often based on Gene Ontology Cellular Compartment annotations. Results of enrichment are typically visualized using bar-charts, however enrichment analyses can result in a long list of significant annotations which are highly specific, preventing researchers from gaining a broad understanding of the subcellular compartments their proteins of interest may be located in. Schematic visualization of known subcellular locations has become increasingly available for single proteins via the UniProt and COMPARTMENTS platforms. However, it is not currently available for a list of proteins (e.g. from the same experiment) or for visualizing the results of enrichment analyses. To generate an easy-to-interpret visualization of protein subcellular localization after enrichment we developed the SubcellulaRVis web app, which visualizes the enrichment of subcellular locations of gene lists in an easy and impactful manner. SubcellulaRVis projects the results of enrichment analysis on a graphical representation of a eukaryotic cell. Implemented as a web app and an R package, this tool is user-friendly, provides exportable results in different formats, and can be used for gene lists derived from multiple organisms. Here, we show the power of SubcellulaRVis to assign proteins to the correct subcellular compartment using gene list enriched in previously published spatial proteomics datasets. We envision SubcellulaRVis will be useful for cell biologists with limited bioinformatics expertise wanting to perform precise and quick enrichment analysis and immediate visualization of gene lists. Author SummaryCells contain intracellular compartments, such as membrane-bound organelles and the nucleus, and are surrounded by a plasma membrane. Proteins can be found in different cellular compartments; depending on the subcellular compartment they are localized to, they can be differentially regulated or perform location-specific functions. High-throughput omics experiments result in a list of proteins or genes of interest; one way in which their functional role can be understood is through their subcellular localization, as deduced through statistical enrichment for Gene Ontology Cellular Component (GOCC) annotations or similar. We have designed a bioinformatic tool, named SubcellulaRVis, that compellingly visualizes the results of protein localization after enrichment and simplifies the results for a quick interpretation. We demonstrate that SubcellulaRVis precisely describes the subcellular localization of gene lists whose locations have been previously ascertained in publications. SubcellulaRVis can be accessed via the web or as a stand-alone app. SubcellulaRVis will be useful for experimental biologists with limited bioinformatics expertise analyzing data related to cellular signalling, protein (re)localization and regulation, and location-specific functional modules within the cell.

bioinformatics↗

bioRxiv2021

Cytosolic fumarase acts as a metabolic fail-safe for both high and low temperature acclimation of Arabidopsis thaliana

Plants acclimate their photosynthetic capacity in response to changing environmental conditions. In Arabidopsis thaliana, photosynthetic acclimation to cold requires the accumulation of the organic acid fumarate, catalysed by a cytosolic fumarase FUM2. However, the role of this accumulation is currently unknown. In this study, we use an integrated experimental and modelling approach to examine the role of FUM2 and fumarate across the physiological temperature range. Using physiological and biochemical analyses, we demonstrate that FUM2 is necessary for high as well as low temperature acclimation. We have adapted a reliability engineering technique, Failure Mode and Effect Analysis (FMEA), to formalize a rigorous approach for ranking metabolites according to the potential risk that they pose to the metabolic system. FMEA identifies fumarate as a low-risk metabolite, while its precursor, malate, is shown to be high-risk and liable to cause system instability. We conclude that the role of cytosolic fumarase, FUM2, is to provide a fail-safe, controlling malate concentration, maintaining system stability in a changing environment. We argue that FMEA is a technique that is not only useful in understanding plant metabolism but can also be used to study reliability in other systems and synthetic pathways.

plant biology↗

bioRxiv2021

Transcriptional network of the industrial hybrid Saccharomyces pastorianus reveals temperature-dependent allele expression bias and preferential orthologous protein assemblies

Saccharomyces pastorianus is an industrial natural yeast evolved from different hybridisation events between the mesophilic S. cerevisiae and the cold-tolerant S. eubayanus. This complex aneuploid hybrid carries multiple copies of the parental alleles alongside specific hybrid genes and encodes for multiple protein isoforms which impart novel phenotypes, such as the strong ability to ferment at low temperature. These characteristics lead to agonistic or antagonistic competition for substrates and a plethora of biochemical activities, resulting in a unique cellular metabolism. Here, we investigated the transcriptional signature of the different orthologous alleles in S. pastorianus during temperature shifts. We identified temperature-dependent media-independent genes and showed that 35% have their regulation dependent on extracellular leucine uptake, suggesting an interplay between leucine metabolism and temperature response. The analysis of the expression of ortholog parental alleles unveiled that the majority of the genes express preferentially one parental allele over the other, and that S. eubayanus-like alleles are significantly over-represented among the genes involved in cold acclimatisation. The presence of functionally redundant parental alleles may impact on the nature of protein complexes established in the hybrid, where both parental alleles are competing. Our expression data indicate that the majority of the protein complexes in the hybrid are likely to be either exclusively chimeric or uni-specific, and that the redundancy is discouraged, a scenario which fits well with the stoichiometric balance-hypothesis. This study offers the first overview of the transcriptional pattern of S. pastorianus and provide a rationalisation for its unique industrial traits at expression level.

genomics↗

bioRxiv2020

Using Multilayer Heterogeneous Networks to Infer Functions of Phosphorylated Sites

1Mass spectrometry-based quantitative phosphoproteomics has become an essential approach in the study of cellular processes such as signaling. Commonly used methods to analyze phosphoproteomics datasets depend on generic, gene-centric annotations such as Gene Ontology terms which do not account for the function of a protein in a particular phosphorylation state. Analysis of phosphoproteomics data is hampered by a lack of phosphorylated site-specific annotations. We propose a method that combines shotgun phosphoproteomics data, protein-protein interactions, and functional annotations into a heterogeneous multilayer network. Phosphorylation sites are associated to potential functions using a random walk on heterogeneous network (RWHN) algorithm. We validated our approach against a model of the MAPK/ERK pathway and functional annotations from PhosphoSite Plus and were able to associate differentially regulated sites on the same proteins to their previously described specific functions. We further tested the algorithm on three previously published datasets and were able to reproduce their experimentally validated conclusions and to associate phosphorylation sites with known functions based on their regulatory patterns. Our approach provides a refinement of commonly used analysis methods and accurately predicts context-specific functions for sites with similar phosphorylation profiles. For table of contents onlyWe confirm that the eTOC figure contains original material drawn by the authors. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC="FIGDIR/small/266072v2_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@f11983org.highwire.dtl.DTLVardef@c21d48org.highwire.dtl.DTLVardef@1078ecorg.highwire.dtl.DTLVardef@1730d96_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

A Failure Mode and Effect Analysis of plant metabolism reveals why cytosolic fumarase is required for temperature acclimation in Arabidopsis

Plants acclimate their photosynthetic capacity in response to changing environmental conditions. In Arabidopsis thaliana, photosynthetic acclimation to cold requires the accumulation of the organic acid fumarate, catalysed by a cytosolic fumarase FUM2, however the role of this is currently unclear. In this study, we use an integrated experimental and modelling approach to examine the role of FUM2 and fumarate across the physiological temperature range. Using physiological and biochemical analyses, we demonstrate that FUM2 is necessary for high as well as low temperature acclimation. To understand the role of FUM2 activity, we have adapted a reliability engineering technique, Failure Mode and Effect Analysis (FMEA), to formalize a rigorous approach for ranking metabolites according to the potential risk that they pose to the metabolic system. FMEA identifies fumarate as a low-risk metabolite. Its precursor, malate, is shown to be high-risk and liable to cause system instability. We conclude that the role of cytosolic fumarase, FUM2, is to provide a fail-safe, maintaining system stability under changing environmental conditions. We argue that FMEA is a technique which is not only useful in understanding plant metabolism, it can also be used to study reliability in other systems and aid the design of synthetic pathways.

plant biology↗

bioRxiv2020

Combinatorial mathematical modelling approaches to interrogate rear retraction dynamics in 3D cell migration

Cell migration in 3D micro-environments is a complex process which depends on the coordinated activity of leading edge protrusive force and rear retraction in a push-pull mechanism. While the potentiation of protrusions has been widely studied, the precise signalling and mechanical events that lead to forward movement of the cell rear are much less well understood, particularly in physiological 3D extra-cellular matrix (ECM). We previously discovered that rear retraction in fast moving cells is a highly dynamic process involving the precise spatiotemporal interplay of mechanosensing by caveolae and signalling through RhoA. To further interrogate the dynamics of rear retraction, we have adopted three distinct mathematical modelling approaches here based on (i) Boolean logic, (ii) deterministic kinetic ordinary differential equations (ODEs) and (iii) stochastic simulations. The aims of this multi-faceted approach are twofold: firstly to derive new biological insight into cell rear dynamics via generation of testable hypotheses and predictions; and secondly to compare and contrast the distinct modelling approaches when used to describe the same, relatively under-studied system. Whilst Boolean logic was not able to fully recapitulate the complexity of rear retraction signalling completely, our ODE model could make plausible population level predictions. Stochastic simulations added a further level of complexity by accurately mimicking previous experimental findings and acting as a single cell simulator. Our approach has also highlighted the unanticipated potential of targeting CDK1 to abrogate cell movement, a prediction we confirmed experimentally. Moreover, we have made a novel prediction regarding the potential existence of a set point in local stiffness gradients that promotes polarisation and rapid rear retraction. Overall, our modelling approaches complement each other, suggesting that such a multi-faceted approach is more informative than methods based on a single modelling technique to interrogate biological systems.

cell biology↗

bioRxiv2020

HybridMine: pipeline for allele inheritance and gene copy number prediction in industrial yeast hybrids

Saccharomyces pastorianus is an allopolyploid sterile yeast hybrid used in brewing to produce lager-style beers. The development of new yeast strains with valuable industrial traits such as improved maltose utilization or balanced flavour profiles are now a major ambition and challenge in craft brewing and distilling industries. Genome-scale computational approaches are opening opportunities to model and predict favourable combination of traits for strain development. However, mining the genome of these complex hybrids is not currently an easy task, due to the high level of redundancy and presence of homologous. Moreover, no genome annotation for these industrial strains have been published. Here, we developed HybridMine, a new user-friendly, open-source tool for functional annotation of hybrid aneuploid genomes of any species by predicting parental alleles including paralogs. As proof of principle, we carried out a comprehensive structural and functional annotation of complex yeast hybrids to enable system biology prediction studies. HybridMine is developed in Python, Perl and Bash programming languages and is available at https://github.com/Sookie-S/HybridMine.

genomics↗

bioRxiv2019

Modelling the role of dual specificity phosphatases in Herceptin resistant breast cancer cell lines

BackgroundBreast cancer remains the most lethal type of cancer for women. A significant proportion of breast cancer cases are characterised by overexpression of the human epidermal growth factor receptor 2 protein (HER2). These cancers are commonly treated by Herceptin (Trastuzumab), but resistance to drug treatment frequently develops in tumour cells. Dual-specificity phosphatases (DUSPs) are thought to play a role in the mechanism of resistance, since some of them were reported to be overexpressed in tumours resistant to Herceptin. ResultsWe used a systems biology approach to investigate how DUSP overexpression could favour cell proliferation and to predict how this mechanism could be reversed by targeted inhibition of selected DUSPs. We measured the expression of 20 DUSP genes in two breast cancer cell lines following long-term (6 months) exposure to Herceptin, after confirming that these cells had become resistant to the drug. We constructed several Boolean models including specific substrates of each DUSP, and showed that our models correctly account for resistance when overexpressed DUSPs were kept activated. We then simulated inhibition of both individual and combinations of DUSPs, and determined conditions under which the resistance could be reversed. ConclusionsThese results show how a combination of experimental analysis and modelling help to understand cell survival mechanisms in breast cancer tumours, and crucially enable us to generate testable predictions potentially leading to new treatments of resistant tumours.

systems biology↗

bioRxiv2018

Using set theory to reduce redundancy in pathway sets

1.1.01 BackgroundThe consolidation of pathway databases, such as KEGG[1], Reactome[2]and ConsensusPathDB[3], has generated widespread biological interest, however the issue of pathway redundancy impedes the use of these consolidated datasets. Attempts to reduce this redundancy have focused on visualizing pathway overlap or merging pathways, but the resulting pathways may be of heterogeneous sizes and cover multiple biological functions. Efforts have also been made to deal with redundancy in pathway data by consolidating enriched pathways into a number of clusters or concepts. We present an alternative approach, which generates pathway subsets capable of covering all of genes presented within either pathway databases or enrichment results, generating substantial reductions in redundancy.\n\n1.02 ResultsWe propose a method that uses set cover to reduce pathway redundancy, without merging pathways. The proposed approach considers three objectives: removal of pathway redundancy, controlling pathway size and coverage of the gene set. By applying set cover to the ConsensusPathDB dataset we were able to produce a reduced set of pathways, representing 100% of the genes in the original data set with 74% less redundancy, or 95% of the genes with 88% less redundancy. We also developed an algorithm to simplify enrichment data and applied it to a set of enriched osteoarthritis pathways, revealing that within the top ten pathways, five were redundant subsets of more enriched pathways. Applying set cover to the enrichment results removed these redundant pathways allowing more informative pathways to take their place.\n\n1.03 ConclusionOur method provides an alternative approach for handling pathway redundancy, while ensuring that the pathways are of homogeneous size and gene coverage is maximised. Pathways are not altered from their original form, allowing biological knowledge regarding the data set to be directly applicable. We demonstrate the ability of the algorithms to prioritise redundancy reduction, pathway size control or gene set coverage. The application of set cover to pathway enrichment results produces an optimised summary of the pathways that best represent the differentially regulated gene set.

bioinformatics↗

bioRxiv2018

Network controllability: viruses are driver agents in dynamic molecular systems

In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However in an intra-cellular network it is unclear what control means. To address this limitation we use viral infection, specifically HIV-1 and HCV, as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two dynamic states: normal/uninfected and infected. Our network controllability analysis demonstrates how a virus efficiently brings the dynamic host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The driver nodes used by the virus are distributed throughout the pathways in specific locations enabling effective control of the cell via the high control centrality of the viral and targeted host nodes. Furthermore, this viral infection of the human system permits discrimination between available network-control models, and demonstrates the minimum-dominating set (MDS) method better accounts for how biological information and signals are transferred than the maximum matching (MM) method as it identified most of the HIV-1 proteins as critical driver nodes and goes beyond identifying receptors as the only critical driver nodes. This is because MDS, unlike MM, accounts for the inherent non-linearity of signalling pathways. Our results demonstrate control-theory gives a more complete and dynamic understanding of the viral hijack mechanism when compared with previous analyses limited to static single-state networks.

systems biology↗