Pub Date : 2024-07-25DOI: 10.1101/2024.07.25.605053
Solene Song, Paul Villoutreix
Development is a highly complex process consisting of coordinated cell proliferation, cell differentiation and spatial organization. Classically, two ways to specify cell types during development are hypothesized : mosaic and regulative modes. In mosaic development, a particular cell isolated from the rest of the embryo will nevertheless give rise to cells with a fate identical to the ones expected in normal development, thus relying on lineage-inherited factors. In regulative development, the fate of a cell depends on its interactions with its environment, and thus relies on space-dependant factors. Both modes often coexist in the development of a given animal. We propose a method to quantify their respective contributions. C. elegans development provides a unique opportunity to elaborate such a measure. Indeed, its invariant lineage development allows to combine spatial positions, lineage relationships and protein expression data. Using the single cell protein expression profile as a readout of the cell state, we relate the contributions of the mosaic and the regulative modes to the following measurable quantities. The contribution of the mosaic mode, or lineage-inherited contribution is quantified by the strength of the relationship between the cell-cell lineage distance and the cell-cell expression distance. Similarly, the contribution of the regulative mode, or context-dependent contribution is quantified by the strength of the relationship between the cell-cell context distance and the cell-cell expression distance. The cell-cell context distance measures the similarity between the spatial neighborhoods of two cells based on the gene expression profiles of their neighbours. We assess the significance of these contributions by comparing the empirical results obtained on C. elegans data to artificial models generated using simple rules. With these measures, we show the co-existence of mosaic and regulative modes in the development of C. elegans. The relative contribution of these two modes varies across the different tissues and in time. In particular, we see in the skin tissue that during early development, the mosaic mode dominates while at later stages, regulative mode dominates, suggesting a convergence of single cell trajectories. These measures are general and can be applied to other datasets that will be made available with the progress of spatial transcriptomics and lineage-tracing, paving the way for a quantitative, unbiased and perturbation-free study of fundamental concepts in developmental biology.
{"title":"Assessing the Relative Contributions of Mosaic and Regulatory Developmental Modes from Single-Cell Trajectories","authors":"Solene Song, Paul Villoutreix","doi":"10.1101/2024.07.25.605053","DOIUrl":"https://doi.org/10.1101/2024.07.25.605053","url":null,"abstract":"Development is a highly complex process consisting of coordinated cell proliferation, cell differentiation and spatial organization. Classically, two ways to specify cell types during development are hypothesized : mosaic and regulative modes. In mosaic development, a particular cell isolated from the rest of the embryo will nevertheless give rise to cells with a fate identical to the ones expected in normal development, thus relying on lineage-inherited factors. In regulative development, the fate of a cell depends on its interactions with its environment, and thus relies on space-dependant factors. Both modes often coexist in the development of a given animal. We propose a method to quantify their respective contributions. C. elegans development provides a unique opportunity to elaborate such a measure. Indeed, its invariant lineage development allows to combine spatial positions, lineage relationships and protein expression data. Using the single cell protein expression profile as a readout of the cell state, we relate the contributions of the mosaic and the regulative modes to the following measurable quantities. The contribution of the mosaic mode, or lineage-inherited contribution is quantified by the strength of the relationship between the cell-cell lineage distance and the cell-cell expression distance. Similarly, the contribution of the regulative mode, or context-dependent contribution is quantified by the strength of the relationship between the cell-cell context distance and the cell-cell expression distance. The cell-cell context distance measures the similarity between the spatial neighborhoods of two cells based on the gene expression profiles of their neighbours. We assess the significance of these contributions by comparing the empirical results obtained on C. elegans data to artificial models generated using simple rules. With these measures, we show the co-existence of mosaic and regulative modes in the development of C. elegans. The relative contribution of these two modes varies across the different tissues and in time. In particular, we see in the skin tissue that during early development, the mosaic mode dominates while at later stages, regulative mode dominates, suggesting a convergence of single cell trajectories. These measures are general and can be applied to other datasets that will be made available with the progress of spatial transcriptomics and lineage-tracing, paving the way for a quantitative, unbiased and perturbation-free study of fundamental concepts in developmental biology.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"430 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1101/2024.07.25.604999
Jingxin Li, Pavithran T. Ravindran, Aoife O'Farrell, Gianna T. Busch, Ryan H. Boe, Zijian Niu, Sean Woo, Maraget C. Dunagin, Naveen Jain, Yogesh Goyal, Kavitha Sarma, Meenhard Herlyn, Arjun Raj
Cellular responses to environmental stimuli are typically thought to be governed by genetically encoded programs. We demonstrate that melanoma cells can form and maintain cellular memories during the acquisition of therapy resistance that exhibit characteristics of cellular learning and are dependent on the transcription factor AP-1. We show that cells exposed to a low dose of therapy adapt to become resistant to a high dose, demonstrating that resistance was not purely selective. The application of therapy itself results in the encoding of transient gene expression into cellular memory and that this encoding occurs for both transiently induced and probabilistically arising expression. Chromatin accessibility showed concomitant persistence. A two-color AP-1 reporter system showed that these memories are encoded in cis, constituting an example of activating cis epigenetics. Our findings establish the formation and maintenance of cellular memories as a critical aspect of gene regulation during the development of therapy resistance.
{"title":"AP-1 Mediates Cellular Adaptation and Memory Formation During Therapy Resistance","authors":"Jingxin Li, Pavithran T. Ravindran, Aoife O'Farrell, Gianna T. Busch, Ryan H. Boe, Zijian Niu, Sean Woo, Maraget C. Dunagin, Naveen Jain, Yogesh Goyal, Kavitha Sarma, Meenhard Herlyn, Arjun Raj","doi":"10.1101/2024.07.25.604999","DOIUrl":"https://doi.org/10.1101/2024.07.25.604999","url":null,"abstract":"Cellular responses to environmental stimuli are typically thought to be governed by genetically encoded programs. We demonstrate that melanoma cells can form and maintain cellular memories during the acquisition of therapy resistance that exhibit characteristics of cellular learning and are dependent on the transcription factor AP-1. We show that cells exposed to a low dose of therapy adapt to become resistant to a high dose, demonstrating that resistance was not purely selective. The application of therapy itself results in the encoding of transient gene expression into cellular memory and that this encoding occurs for both transiently induced and probabilistically arising expression. Chromatin accessibility showed concomitant persistence. A two-color AP-1 reporter system showed that these memories are encoded in cis, constituting an example of activating cis epigenetics. Our findings establish the formation and maintenance of cellular memories as a critical aspect of gene regulation during the development of therapy resistance.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prader-Willi syndrome (PWS) is a multigenic disorder caused by the loss of seven contiguous paternally expressed genes. Mouse models with inactivation of all PWS genes are lethal. Knockout (KO) mouse models for each candidate gene were generated, but they lack the functional interactions between PWS genes. Here, we revealed an interplay between Necdin and Magel2 PWS genes and generated a novel mouse model (named Madin) with a deletion including both genes. A subset of Madin KO mice showed neonatal lethality. Behaviorally, surviving mutant mice exhibited sensory delays during infancy and alterations in social exploration at adulthood. Madin KO mice had a lower body weight before weaning, persisting after weaning in males only, with reduced fat mass and improved glucose tolerance. Delayed sexual maturation and altered timing of puberty onset were observed in mutant mice. Adult Madin KO mice displayed increased ventilation and a persistent increase in apneas following a hypercapnic challenge. Transcriptomics analyses revealed a dysregulation of key circadian genes and alterations of genes associated with axonal function that were also found in the hypothalamus of patients with PWS. At neuroanatomical levels, we report an impaired maturation of oxytocin neurons and a disrupted development of melanocortin circuits. Together, these data indicate that the Madin KO mouse is a reliable and more genetically relevant model for the study of PWS.
{"title":"Investigation of a Novel Mouse Model of Prader-Willi Syndrome with Invalidation of Necdin and Magel2","authors":"Pierre-Yves Barelle, Alicia Sicardi, Fabienne Schaller, Julie Buron, Denis Becquet, Felix Omnes, Francoise Watrin, Catarina Santos, Clement Menuet, Anne-Marie Francois-Bellan, Emilie Caron, Jessica Klucznik, Vincent Prevot, Sebastien G Bouret, Francoise Muscatelli","doi":"10.1101/2024.07.24.604909","DOIUrl":"https://doi.org/10.1101/2024.07.24.604909","url":null,"abstract":"Prader-Willi syndrome (PWS) is a multigenic disorder caused by the loss of seven contiguous paternally expressed genes. Mouse models with inactivation of all PWS genes are lethal. Knockout (KO) mouse models for each candidate gene were generated, but they lack the functional interactions between PWS genes. Here, we revealed an interplay between Necdin and Magel2 PWS genes and generated a novel mouse model (named Madin) with a deletion including both genes. A subset of Madin KO mice showed neonatal lethality. Behaviorally, surviving mutant mice exhibited sensory delays during infancy and alterations in social exploration at adulthood. Madin KO mice had a lower body weight before weaning, persisting after weaning in males only, with reduced fat mass and improved glucose tolerance. Delayed sexual maturation and altered timing of puberty onset were observed in mutant mice. Adult Madin KO mice displayed increased ventilation and a persistent increase in apneas following a hypercapnic challenge. Transcriptomics analyses revealed a dysregulation of key circadian genes and alterations of genes associated with axonal function that were also found in the hypothalamus of patients with PWS. At neuroanatomical levels, we report an impaired maturation of oxytocin neurons and a disrupted development of melanocortin circuits. Together, these data indicate that the Madin KO mouse is a reliable and more genetically relevant model for the study of PWS.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-24DOI: 10.1101/2024.07.23.604780
Chon Lok Lei, Alexander P Clark, Michael Clerx, Siyu Wei, Meye Bloothooft, Teun P de Boer, David J Christini, Trine Krogh-Madsen, Gary R Mirams
Cellular electrophysiology is the foundation of many fields, from basic science in neurology, cardiology, oncology to safety critical applications for drug safety testing, clinical phenotyping, etc. Patch-clamp voltage clamp is the gold standard technique for studying cellular electrophysiology. Yet, the quality of these experiments is not always transparent, which may lead to erroneous conclusions for studies and applications. Here, we have developed a new computational approach that allows us to explain and predict the experimental artefacts in voltage-clamp experiments. The computational model captures the experimental procedure and its inadequacies, including: voltage offset, series resistance, membrane capacitance and (imperfect) amplifier compensations, such as series resistance compensation and supercharging. The computational model was validated through a series of electrical model cell experiments. Using this computational approach, the artefacts in voltage-clamp experiments of cardiac fast sodium current, one of the most challenging currents to voltage clamp, were able to be resolved and explained through coupling the observed current and the simulated membrane voltage, including some typically observed shifts and delays in the recorded currents. We further demonstrated that the typical way of averaging data for current-voltage relationships would lead to biases in the peak current and shifts in the peak voltage, and such biases can be in the same order of magnitude as those differences reported for disease-causing mutations. Therefore, the presented new computational pipeline will provide a new standard of assessing the voltage-clamp experiments and interpreting the experimental data, which may be able to rectify and provide a better understanding of ion channel mutations and other related applications.
{"title":"Resolving artefacts in voltage-clamp experiments with computational modelling: an application to fast sodium current recordings","authors":"Chon Lok Lei, Alexander P Clark, Michael Clerx, Siyu Wei, Meye Bloothooft, Teun P de Boer, David J Christini, Trine Krogh-Madsen, Gary R Mirams","doi":"10.1101/2024.07.23.604780","DOIUrl":"https://doi.org/10.1101/2024.07.23.604780","url":null,"abstract":"Cellular electrophysiology is the foundation of many fields, from basic science in neurology, cardiology, oncology to safety critical applications for drug safety testing, clinical phenotyping, etc. Patch-clamp voltage clamp is the gold standard technique for studying cellular electrophysiology. Yet, the quality of these experiments is not always transparent, which may lead to erroneous conclusions for studies and applications. Here, we have developed a new computational approach that allows us to explain and predict the experimental artefacts in voltage-clamp experiments. The computational model captures the experimental procedure and its inadequacies, including: voltage offset, series resistance, membrane capacitance and (imperfect) amplifier compensations, such as series resistance compensation and supercharging. The computational model was validated through a series of electrical model cell experiments. Using this computational approach, the artefacts in voltage-clamp experiments of cardiac fast sodium current, one of the most challenging currents to voltage clamp, were able to be resolved and explained through coupling the observed current and the simulated membrane voltage, including some typically observed shifts and delays in the recorded currents. We further demonstrated that the typical way of averaging data for current-voltage relationships would lead to biases in the peak current and shifts in the peak voltage, and such biases can be in the same order of magnitude as those differences reported for disease-causing mutations. Therefore, the presented new computational pipeline will provide a new standard of assessing the voltage-clamp experiments and interpreting the experimental data, which may be able to rectify and provide a better understanding of ion channel mutations and other related applications.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-24DOI: 10.1101/2024.07.24.604914
Maria Pires Pacheco, Deborah Gerard, Riley J. Mangan, Alec R. Chapman, Dennis Hecker, Manolis Kellis, Marcel H. Schulz, Lasse Sinkkonen, Thomas Sauter
Constraint-based network modelling is a powerful tool for analysing cellular metabolism at genomic scale. Here, we conducted an integrative analysis of metabolic networks reconstructed from RNA-seq data with paired epigenomic data from the EpiATLAS resource of the International Human Epigenome Consortium (IHEC). Applying a state-of-the-art contextualisation algorithm, we reconstructed metabolic networks across 1,555 samples corresponding to 58 tissues and cell types. Analysis of these networks revealed the distribution of metabolic functionalities across human cell types and provides a compendium of human metabolic activity. This integrative approach allowed us to define, across tissues and cell types, i) reactions that fulfil the basic metabolic processes (core metabolism), and ii) cell type-specific functions (unique metabolism), that shape the metabolic identity of a cell or a tissue. Integration with EpiATLAS-derived cell type-specific gene-level chromatin states and enhancer-gene interactions identified enhancers, transcription factors, and key nodes controlling core and unique metabolism. Transport and first reactions of pathways were enriched for high expression, active chromatin state, and Polycomb-mediated repression in cell types where pathways are inactive, suggesting that key nodes are targets of repression. This integrative analysis forms the basis for identifying regulation points that control metabolic identity in human cells.
{"title":"Epigenetic control of metabolic identity across cell types","authors":"Maria Pires Pacheco, Deborah Gerard, Riley J. Mangan, Alec R. Chapman, Dennis Hecker, Manolis Kellis, Marcel H. Schulz, Lasse Sinkkonen, Thomas Sauter","doi":"10.1101/2024.07.24.604914","DOIUrl":"https://doi.org/10.1101/2024.07.24.604914","url":null,"abstract":"Constraint-based network modelling is a powerful tool for analysing cellular metabolism at genomic scale. Here, we conducted an integrative analysis of metabolic networks reconstructed from RNA-seq data with paired epigenomic data from the EpiATLAS resource of the International Human Epigenome Consortium (IHEC). Applying a state-of-the-art contextualisation algorithm, we reconstructed metabolic networks across 1,555 samples corresponding to 58 tissues and cell types. Analysis of these networks revealed the distribution of metabolic functionalities across human cell types and provides a compendium of human metabolic activity. This integrative approach allowed us to define, across tissues and cell types, i) reactions that fulfil the basic metabolic processes (core metabolism), and ii) cell type-specific functions (unique metabolism), that shape the metabolic identity of a cell or a tissue. Integration with EpiATLAS-derived cell type-specific gene-level chromatin states and enhancer-gene interactions identified enhancers, transcription factors, and key nodes controlling core and unique metabolism. Transport and first reactions of pathways were enriched for high expression, active chromatin state, and Polycomb-mediated repression in cell types where pathways are inactive, suggesting that key nodes are targets of repression. This integrative analysis forms the basis for identifying regulation points that control metabolic identity in human cells.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141786330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1101/2024.07.22.604713
Peng Ken Lim, Ruoxi Wang, Jenet Princy Antony Velankanni, Marek Mutwil
Gene co-expression networks (GCNs) generated from public transcriptomic datasets can elucidate the co-regulatory and co-functional relationships between genes, making GCNs an important tool to predict gene functions. However, current GCN construction methods are sensitive to the quality of the data, and the interpretability of the identified relationships between genes is still difficult. To address this, we present a novel method: Two-Tier Ensemble Aggregation (TEA-) GCN. TEA-GCN utilizes unsupervised partitioning of big transcriptomic datasets and three correlation coefficients to generate ensemble GCNs in a two-step aggregation process. We show that TEA-GCN outperforms in finding correct functional relationships between genes over the current state-of-the-art across three model species, and is able to not only capture condition/tissue-specific gene co-expression but explain them through the use of natural language processing (NLP). In addition, we found TEA-GCN to be especially performant in identifying relationships between transcription factors and their activation targets, making it effective in inferring gene regulatory networks. TEA-GCN is available at https://github.com/pengkenlim/TEA-GCN.
{"title":"Constructing Ensemble Gene Functional Networks Capturing Tissue/condition-specific Co-expression from Unlabled Transcriptomic Data with TEA-GCN","authors":"Peng Ken Lim, Ruoxi Wang, Jenet Princy Antony Velankanni, Marek Mutwil","doi":"10.1101/2024.07.22.604713","DOIUrl":"https://doi.org/10.1101/2024.07.22.604713","url":null,"abstract":"Gene co-expression networks (GCNs) generated from public transcriptomic datasets can elucidate the co-regulatory and co-functional relationships between genes, making GCNs an important tool to predict gene functions. However, current GCN construction methods are sensitive to the quality of the data, and the interpretability of the identified relationships between genes is still difficult. To address this, we present a novel method: Two-Tier Ensemble Aggregation (TEA-) GCN. TEA-GCN utilizes unsupervised partitioning of big transcriptomic datasets and three correlation coefficients to generate ensemble GCNs in a two-step aggregation process. We show that TEA-GCN outperforms in finding correct functional relationships between genes over the current state-of-the-art across three model species, and is able to not only capture condition/tissue-specific gene co-expression but explain them through the use of natural language processing (NLP). In addition, we found TEA-GCN to be especially performant in identifying relationships between transcription factors and their activation targets, making it effective in inferring gene regulatory networks. TEA-GCN is available at https://github.com/pengkenlim/TEA-GCN.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1101/2024.07.18.604203
Jordan M Kraaijenhof, Marije Kerkvliet, Nick S. Nurmohamed, Aldo Grefhorst, Jeffrey Kroon, Nicholas J. Wareham, G. Kees Hovingh, Erik SG Stroes, S. Matthijs Boekholdt, Laurens F. Reeskamp
Background: Both plasma levels of remnant cholesterol and low-density lipoprotein cholesterol (LDL-C) levels are independent risk factors for atherosclerotic cardiovascular disease. However, only remnant cholesterol has consistently been associated with systemic inflammation. The extent to which inflammation mediates the effect of remnant cholesterol on major adverse cardiovascular events (MACE) remains unclear. Methods and Results: This study included 16,445 participants without prior atherosclerotic cardiovascular disease from the EPIC-Norfolk cohort, with a mean age of 58.8 +/- 9.1 years, of which 9,357 (56.9%) were women. Every 1 mmol/L higher remnant cholesterol was associated with 29.5% higher hsCRP levels (95% Confidence Interval (CI): 22.1, 37.4, p<0.001), whereas LDL-C was not significantly associated with hsCRP levels in the fully adjusted model. Additionally, each 1 mmol/L higher remnant cholesterol was associated with a hazard ratio (HR) of 1.31 (95% CI: 1.14, 1.50, p<0.001) for MACE, compared to a HR of 1.21 (95% CI: 1.13, 1.31, p<0.001) for LDL-C. Mediation analysis showed that hsCRP mediated 5.9% (95% CI: 1.2, 10.6%, p<0.001) of the effect of remnant cholesterol on MACE, whereas hsCRP did not mediate the effect of LDL-C. Conclusions: Plasma remnant cholesterol levels are independently associated with systemic inflammation and cardiovascular events. Inflammation, as measured with hsCRP, contributed minorly to the association between remnant cholesterol and MACE. This underscores the need to address both remnant cholesterol and systemic inflammation separately in the clinical management of cardiovascular disease.
{"title":"Systemic inflammation is a minor contributor to remnant cholesterol atherogenicity","authors":"Jordan M Kraaijenhof, Marije Kerkvliet, Nick S. Nurmohamed, Aldo Grefhorst, Jeffrey Kroon, Nicholas J. Wareham, G. Kees Hovingh, Erik SG Stroes, S. Matthijs Boekholdt, Laurens F. Reeskamp","doi":"10.1101/2024.07.18.604203","DOIUrl":"https://doi.org/10.1101/2024.07.18.604203","url":null,"abstract":"Background:\u0000Both plasma levels of remnant cholesterol and low-density lipoprotein cholesterol (LDL-C) levels are independent risk factors for atherosclerotic cardiovascular disease. However, only remnant cholesterol has consistently been associated with systemic inflammation. The extent to which inflammation mediates the effect of remnant cholesterol on major adverse cardiovascular events (MACE) remains unclear.\u0000Methods and Results:\u0000This study included 16,445 participants without prior atherosclerotic cardiovascular disease from the EPIC-Norfolk cohort, with a mean age of 58.8 +/- 9.1 years, of which 9,357 (56.9%) were women. Every 1 mmol/L higher remnant cholesterol was associated with 29.5% higher hsCRP levels (95% Confidence Interval (CI): 22.1, 37.4, p<0.001), whereas LDL-C was not significantly associated with hsCRP levels in the fully adjusted model. Additionally, each 1 mmol/L higher remnant cholesterol was associated with a hazard ratio (HR) of 1.31 (95% CI: 1.14, 1.50, p<0.001) for MACE, compared to a HR of 1.21 (95% CI: 1.13, 1.31, p<0.001) for LDL-C. Mediation analysis showed that hsCRP mediated 5.9% (95% CI: 1.2, 10.6%, p<0.001) of the effect of remnant cholesterol on MACE, whereas hsCRP did not mediate the effect of LDL-C.\u0000Conclusions:\u0000Plasma remnant cholesterol levels are independently associated with systemic inflammation and cardiovascular events. Inflammation, as measured with hsCRP, contributed minorly to the association between remnant cholesterol and MACE. This underscores the need to address both remnant cholesterol and systemic inflammation separately in the clinical management of cardiovascular disease.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1101/2024.07.19.604351
Dietmar Kültz, Alison M. Gardell, Anthony DeTomaso, Greg Stoney, Baruch Rinkevich, Andy Qarri, Jens Hamar
The colonial ascidian Boytryllus schlosseri is an invasive marine chordate that thrives under conditions of anthropogenic climate change. We show that the B. schlosseri expressed proteome contains unusually high levels of proteins that are adducted with 4-hydroxy-2-nonenal (HNE). HNE represents a prominent posttranslational modification resulting from oxidative stress. Although numerous studies have assessed oxidative stress in marine organisms HNE protein modification has not previously been determined in any marine species. LC/MS proteomics was used to identify 1052 HNE adducted proteins in B. schlosseri field and laboratory populations. Adducted amino acid residues were ascertained for 1849 modified sites, of which 1195 had a maximum amino acid localization score. Most HNE modifications were at less reactive lysines (rather than more reactive cysteines). HNE prevelance on most sites was high. These observations suggest that B. schlosseri experiences and tolerates high intracellular reactive oxygen species levels, resulting in substantial lipid peroxidation. HNE adducted B. schlosseri proteins show enrichment in mitochondrial, proteostasis, and cytoskeletal functions. Based on these results we propose that redox signaling contributes to regulating energy metabolism, the blastogenic cycle, oxidative burst defenses, and cytoskeleton dynamics during B. schlosseri development and physiology. A DIA assay library was constructed to quantify HNE adduction at 72 sites across 60 proteins that represent a holistic network of functionally discernable oxidative stress bioindicators. We conclude that the vast amount of HNE protein adduction in this circumpolar tunicate is indicative of high oxidative stress tolerance contributing to its range expansion into diverse environments.
{"title":"Proteome-wide 4-hydroxy-2-nonenal signature of oxidative stress in the marine invasive tunicate Botryllus schlosseri","authors":"Dietmar Kültz, Alison M. Gardell, Anthony DeTomaso, Greg Stoney, Baruch Rinkevich, Andy Qarri, Jens Hamar","doi":"10.1101/2024.07.19.604351","DOIUrl":"https://doi.org/10.1101/2024.07.19.604351","url":null,"abstract":"The colonial ascidian <em>Boytryllus schlosseri</em> is an invasive marine chordate that thrives under conditions of anthropogenic climate change. We show that the <em>B. schlosseri</em> expressed proteome contains unusually high levels of proteins that are adducted with 4-hydroxy-2-nonenal (HNE). HNE represents a prominent posttranslational modification resulting from oxidative stress. Although numerous studies have assessed oxidative stress in marine organisms HNE protein modification has not previously been determined in any marine species. LC/MS proteomics was used to identify 1052 HNE adducted proteins in <em>B. schlosseri</em> field and laboratory populations. Adducted amino acid residues were ascertained for 1849 modified sites, of which 1195 had a maximum amino acid localization score. Most HNE modifications were at less reactive lysines (rather than more reactive cysteines). HNE prevelance on most sites was high. These observations suggest that <em>B. schlosseri</em> experiences and tolerates high intracellular reactive oxygen species levels, resulting in substantial lipid peroxidation. HNE adducted B. schlosseri proteins show enrichment in mitochondrial, proteostasis, and cytoskeletal functions. Based on these results we propose that redox signaling contributes to regulating energy metabolism, the blastogenic cycle, oxidative burst defenses, and cytoskeleton dynamics during <em>B. schlosseri</em> development and physiology. A DIA assay library was constructed to quantify HNE adduction at 72 sites across 60 proteins that represent a holistic network of functionally discernable oxidative stress bioindicators. We conclude that the vast amount of HNE protein adduction in this circumpolar tunicate is indicative of high oxidative stress tolerance contributing to its range expansion into diverse environments.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1101/2024.07.19.604229
Pavol Bokes, Abhyudai Singh
Bacterial cell persistence, crucial for survival under adverse conditions like antibiotic exposure, is intrinsically linked to stochastic fluctuations in gene expression. Certain genes, while inhibiting growth under normal circumstances, confer tolerance to antibiotics at elevated expression levels. The occurrence of antibiotic events lead to instantaneous cellular responses with varied survival probabilities correlated with gene expression levels. Notably, cells with lower protein concentrations face higher mortality rates. This study aims to elucidate an optimal strategy for protein expression conducive to cellular survival. Through comprehensive mathematical analysis, we determine the optimal burst size and frequency that maximise cell proliferation. Furthermore, we explore how the optimal expression distribution changes as the cost of protein expression to growth escalates. Our model reveals a hysteresis phenomenon, characterised by discontinuous transitions between deterministic and stochastic optima. Intriguingly, stochastic optima possess a noise floor, representing the minimal level of fluctuations essential for optimal cellular resilience.
{"title":"Hysteresis and noise floor in gene expression optimised for persistence against lethal events","authors":"Pavol Bokes, Abhyudai Singh","doi":"10.1101/2024.07.19.604229","DOIUrl":"https://doi.org/10.1101/2024.07.19.604229","url":null,"abstract":"Bacterial cell persistence, crucial for survival under adverse conditions like antibiotic exposure, is intrinsically linked to stochastic fluctuations in gene expression. Certain genes, while inhibiting growth under normal circumstances, confer tolerance to antibiotics at elevated expression levels. The occurrence of antibiotic events lead to instantaneous cellular responses with varied survival probabilities correlated with gene expression levels. Notably, cells with lower protein concentrations face higher mortality rates. This study aims to elucidate an optimal strategy for protein expression conducive to cellular survival. Through comprehensive mathematical analysis, we determine the optimal burst size and frequency that maximise cell proliferation. Furthermore, we explore how the optimal expression distribution changes as the cost of protein expression to growth escalates. Our model reveals a hysteresis phenomenon, characterised by discontinuous transitions between deterministic and stochastic optima. Intriguingly, stochastic optima possess a noise floor, representing the minimal level of fluctuations essential for optimal cellular resilience.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141786278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1101/2024.07.21.604448
Haomiao Luo, Casey Hansen, Cheryl A Telmer, Difei Tang, Niloofar Arazkhani, Gaoxiang Zhou, Peter Spirtes, Natasa Miskov-Zivanov
Computational modeling seeks to construct and simulate intracellular signaling networks to understand health and disease. The scientific literature contains descriptions of experimental results that can be interpreted by machines using NLP or LLMs to itemize molecular interactions. This machine readable output can then be used to assess, update or improve existing biological models if there is a tool for comparing the existing model with the information extracted from the papers. Here we describe VIOLIN a tool for classifying machine outputs of molecular interactions with respect to a biological model. VIOLIN classifies interactions as corroborations, contradictions, flagged or extensions with subcategories of each class. This paper analyzes 2 different models, 9 reading sets, 2 NLP and 2 LLM tools to test VIOLIN's capabilities. The results show that VIOLIN successfully classifies interaction types and can be combined with automated filtering to provide a versatile tool for use by the systems biology community.
{"title":"Context-driven interaction retrieval and classification for modeling, curation, and reuse","authors":"Haomiao Luo, Casey Hansen, Cheryl A Telmer, Difei Tang, Niloofar Arazkhani, Gaoxiang Zhou, Peter Spirtes, Natasa Miskov-Zivanov","doi":"10.1101/2024.07.21.604448","DOIUrl":"https://doi.org/10.1101/2024.07.21.604448","url":null,"abstract":"Computational modeling seeks to construct and simulate intracellular signaling networks to understand health and disease. The scientific literature contains descriptions of experimental results that can be interpreted by machines using NLP or LLMs to itemize molecular interactions. This machine readable output can then be used to assess, update or improve existing biological models if there is a tool for comparing the existing model with the information extracted from the papers. Here we describe VIOLIN a tool for classifying machine outputs of molecular interactions with respect to a biological model. VIOLIN classifies interactions as corroborations, contradictions, flagged or extensions with subcategories of each class. This paper analyzes 2 different models, 9 reading sets, 2 NLP and 2 LLM tools to test VIOLIN's capabilities. The results show that VIOLIN successfully classifies interaction types and can be combined with automated filtering to provide a versatile tool for use by the systems biology community.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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