The Disease Maps Project (https://disease-maps.org) focuses on the development of disease-specific comprehensive structured knowledge repositories supporting translational medicine research. These disease maps require continuous interdisciplinary collaboration, and they should be reusable and interoperable. Adhering to the Findable, Accessible, Interoperable and Reusable (FAIR) principles enhances the utility of such digital assets. We used the RDA FAIR Data Maturity Model and assessed the FAIRness of the Molecular Interaction NEtwoRk VisuAlization (MINERVA) Platform. MINERVA is a standalone webserver that allows users to manage, explore and analyze disease maps and their related data manually or programmatically. We exemplify the FAIR assessment on the Parkinson's Disease Map (PD map) and the COVID-19 Disease Map, which are large-scale projects under the umbrella of the Disease Maps Project, aiming to investigate molecular mechanisms of the Parkinson's disease and SARS-CoV-2 infection, respectively. We discuss the FAIR features supported by the MINERVA Platform and we outline steps to further improve the MINERVA FAIRness and to better connect this resource to other ongoing scientific initiatives supporting FAIR in computational systems biomedicine.
{"title":"FAIR assessment of MINERVA as an opportunity to foster open science and scientific crowdsourcing in systems biomedicine","authors":"Irina Balaur, Danielle Welter, Adrien Rougny, Esther Thea Inau, Alexander Mazein, Soumyabrata Ghosh, Reinhard Schneider, Dagmar Waltemath, Marek Ostaszewski, Venkata Satagopam","doi":"10.1101/2024.08.28.610042","DOIUrl":"https://doi.org/10.1101/2024.08.28.610042","url":null,"abstract":"The Disease Maps Project (https://disease-maps.org) focuses on the development of disease-specific comprehensive structured knowledge repositories supporting translational medicine research. These disease maps require continuous interdisciplinary collaboration, and they should be reusable and interoperable. Adhering to the Findable, Accessible, Interoperable and Reusable (FAIR) principles enhances the utility of such digital assets. We used the RDA FAIR Data Maturity Model and assessed the FAIRness of the Molecular Interaction NEtwoRk VisuAlization (MINERVA) Platform. MINERVA is a standalone webserver that allows users to manage, explore and analyze disease maps and their related data manually or programmatically. We exemplify the FAIR assessment on the Parkinson's Disease Map (PD map) and the COVID-19 Disease Map, which are large-scale projects under the umbrella of the Disease Maps Project, aiming to investigate molecular mechanisms of the Parkinson's disease and SARS-CoV-2 infection, respectively. We discuss the FAIR features supported by the MINERVA Platform and we outline steps to further improve the MINERVA FAIRness and to better connect this resource to other ongoing scientific initiatives supporting FAIR in computational systems biomedicine.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202757","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 : 2024-08-28DOI: 10.1101/2024.08.27.609825
Jiahang Li, martin brenner, iro pierides, barbara wessner, bernhard franzke, eva maria strasser, Steffen Waldherr, karl heinz wagner, Wolfram Weckwerth
Physical inactivity and a weak fitness status have become a global health concern. Metabolomics, as an integrative systematic approach, might link to individual fitness at the molecular level. In this study, we performed blood samples metabolomics analysis of a cohort of elderly people with different treatments. By defining two groups of fitness and corresponding metabolites profiles, we tested several machine learning classification approaches to identify key metabolite biomarkers, which showed robustly aspartate as a dominant negative marker of fitness. Following, the metabolomics data of the two groups were analyzed by a novel approach for metabolic network interaction termed COVRECON. Where we identified the enzyme AST as the most important metabolic regulation between the fit and the less fit groups. Routine blood tests in these two cohorts validated significant differences in AST and ALT. In summary, we combine machine learning classification and COVRECON to identify metabolomics biomarkers and causal processes for fitness of elderly people.
缺乏运动和体能状况不佳已成为全球关注的健康问题。代谢组学作为一种综合性的系统方法，可能在分子水平上与个人体质有关。在这项研究中，我们对一组接受不同治疗的老年人进行了血液样本代谢组学分析。通过定义两组体质和相应的代谢物特征，我们测试了几种机器学习分类方法，以确定关键的代谢物生物标志物，结果显示天门冬氨酸是体质的主要负标志物。随后，我们采用一种名为 COVRECON 的代谢网络交互新方法对两组的代谢组学数据进行了分析。在此基础上，我们发现氨基转移酶（AST）是调节体能良好组和体能较差组之间代谢的最重要因素。这两个组群的常规血液检测验证了 AST 和 ALT 的显著差异。总之，我们将机器学习分类和 COVRECON 结合起来，确定了老年人体能的代谢组学生物标志物和因果过程。
{"title":"Machine learning and data-driven inverse modeling of metabolomics unveil key process of active aging","authors":"Jiahang Li, martin brenner, iro pierides, barbara wessner, bernhard franzke, eva maria strasser, Steffen Waldherr, karl heinz wagner, Wolfram Weckwerth","doi":"10.1101/2024.08.27.609825","DOIUrl":"https://doi.org/10.1101/2024.08.27.609825","url":null,"abstract":"Physical inactivity and a weak fitness status have become a global health concern. Metabolomics, as an integrative systematic approach, might link to individual fitness at the molecular level. In this study, we performed blood samples metabolomics analysis of a cohort of elderly people with different treatments. By defining two groups of fitness and corresponding metabolites profiles, we tested several machine learning classification approaches to identify key metabolite biomarkers, which showed robustly aspartate as a dominant negative marker of fitness. Following, the metabolomics data of the two groups were analyzed by a novel approach for metabolic network interaction termed COVRECON. Where we identified the enzyme AST as the most important metabolic regulation between the fit and the less fit groups. Routine blood tests in these two cohorts validated significant differences in AST and ALT. In summary, we combine machine learning classification and COVRECON to identify metabolomics biomarkers and causal processes for fitness of elderly people.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202761","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 : 2024-08-28DOI: 10.1101/2024.08.28.609099
Laura Baumgartner, Sandra Witta, Jerome Noailly
Background: Intervertebral disc (IVD) degeneration is characterized by a disruption of the balance between anabolic and catabolic cellular processes. Within the Nucleus Pulposus (NP), this involves increased levels of the pro-inflammatory cytokines Interleukin 1beta (IL1B) and Tumor Necrosis Factor (TNF) and an upregulation of the protease families MMP and ADAMTS. Primary inhibitors of those proteases are the tissue inhibitors of matrix metalloproteinases (TIMP). This work aims at contributing to a better understanding of the dynamics among proteases, TIMP and proinflammatory cytokines within the complex, multifactorial environment of the NP. Methods: The Parallel Network (PN)-Methodology was used to estimate relative mRNA expressions of TIMP1-3, MMP3 and ADAMTS4 for five simulated human activities; walking, sitting, jogging, hiking with 20 kg extra weight, and exposure to high vibration. Simulations were executed for nutrient conditions in non- and early-degenerated IVD approximations. To estimate the impact of cytokines, the PN-Methodology inferred relative protein levels for IL1B and TNF, re-integrated as secondary stimuli into the network. Results: TIMP1 and TIMP2 expression were found to be overall lower than TIMP3 exp. In absence of pro-inflammatory cytokines, MMP3 and/or ADAMTS4 expression were strongly downregulated in all conditions but vibration and hiking with extra weight. Pro-inflammatory cytokine exposure resulted in an impaired inhibition of MMP3, rather than of ADAMTS4, progressively rising with increasing nutrient deprivation. TNF mRNA was less expressed than IL1B. However, at the protein level, TNF was mainly responsible for the catabolic shift in the simulated pro-inflammatory environment. Overall, results agreed with previous experimental findings. Conclusions: The PN-Methodology successfully allowed the exploration of the relative dynamics of TIMP and protease regulations in different mechanical, nutritional, and inflammatory environments, in the NP. It shall stand for a comprehensive tool to integrate in vitro model results in IVD research and approximate NP cell activities in complex multifactorial environments.
{"title":"Parallel networks to predict TIMP and protease cell activity of Nucleus Pulposus cells exposed and not exposed to pro-inflammatory cytokines","authors":"Laura Baumgartner, Sandra Witta, Jerome Noailly","doi":"10.1101/2024.08.28.609099","DOIUrl":"https://doi.org/10.1101/2024.08.28.609099","url":null,"abstract":"Background: Intervertebral disc (IVD) degeneration is characterized by a disruption of the balance between anabolic and catabolic cellular processes. Within the Nucleus Pulposus (NP), this involves increased levels of the pro-inflammatory cytokines Interleukin 1beta (IL1B) and Tumor Necrosis Factor (TNF) and an upregulation of the protease families MMP and ADAMTS. Primary inhibitors of those proteases are the tissue inhibitors of matrix metalloproteinases (TIMP). This work aims at contributing to a better understanding of the dynamics among proteases, TIMP and proinflammatory cytokines within the complex, multifactorial environment of the NP. Methods: The Parallel Network (PN)-Methodology was used to estimate relative mRNA expressions of TIMP1-3, MMP3 and ADAMTS4 for five simulated human activities; walking, sitting, jogging, hiking with 20 kg extra weight, and exposure to high vibration. Simulations were executed for nutrient conditions in non- and early-degenerated IVD approximations. To estimate the impact of cytokines, the PN-Methodology inferred relative protein levels for IL1B and TNF, re-integrated as secondary stimuli into the network. Results: TIMP1 and TIMP2 expression were found to be overall lower than TIMP3 exp. In absence of pro-inflammatory cytokines, MMP3 and/or ADAMTS4 expression were strongly downregulated in all conditions but vibration and hiking with extra weight. Pro-inflammatory cytokine exposure resulted in an impaired inhibition of MMP3, rather than of ADAMTS4, progressively rising with increasing nutrient deprivation. TNF mRNA was less expressed than IL1B. However, at the protein level, TNF was mainly responsible for the catabolic shift in the simulated pro-inflammatory environment. Overall, results agreed with previous experimental findings. Conclusions: The PN-Methodology successfully allowed the exploration of the relative dynamics of TIMP and protease regulations in different mechanical, nutritional, and inflammatory environments, in the NP. It shall stand for a comprehensive tool to integrate in vitro model results in IVD research and approximate NP cell activities in complex multifactorial environments.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202760","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}
Recent advancements in machine learning-based data processing techniques have facilitated the inference of gene regulatory interactions and the identification of key genes from multidimensional gene expression data. In this study, we applied a stepwise Bayesian framework to uncover a novel regulatory component involved in differentiation of specific neural and neuronal cells. We treated naive neural precursor cells with Sonic Hedgehog (Shh) at various concentrations and time points, generating comprehensive whole-genome sequencing data that captured dynamic gene expression profiles during differentiation. The genes were categorized into 224 subsets based on their expression profiles, and the relationships between these subsets were extrapolated. To accurately predict gene regulation among subsets, known networks were used as a core model and subsets to be added were tested stepwise. This approach led to the identification of a novel component involved in neural tube patterning within gene regulatory networks (GRNs), which was experimentally validated. Our study highlights the effectiveness of in silico modeling for extrapolating GRNs during neural development.
{"title":"Stepwise Bayesian Machine Learning Uncovers a Novel Gene Regulatory Network Component in Neural Tube Development","authors":"Chen Xing, Yuichi Sakumura, Toshiya Kokaji, Katsuyuki Kunida, Noriaki Sasai","doi":"10.1101/2024.08.25.609396","DOIUrl":"https://doi.org/10.1101/2024.08.25.609396","url":null,"abstract":"Recent advancements in machine learning-based data processing techniques have facilitated the inference of gene regulatory interactions and the identification of key genes from multidimensional gene expression data. In this study, we applied a stepwise Bayesian framework to uncover a novel regulatory component involved in differentiation of specific neural and neuronal cells. We treated naive neural precursor cells with Sonic Hedgehog (Shh) at various concentrations and time points, generating comprehensive whole-genome sequencing data that captured dynamic gene expression profiles during differentiation. The genes were categorized into 224 subsets based on their expression profiles, and the relationships between these subsets were extrapolated. To accurately predict gene regulation among subsets, known networks were used as a core model and subsets to be added were tested stepwise. This approach led to the identification of a novel component involved in neural tube patterning within gene regulatory networks (GRNs), which was experimentally validated. Our study highlights the effectiveness of in silico modeling for extrapolating GRNs during neural development.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202762","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 : 2024-08-26DOI: 10.1101/2024.08.26.609695
Ekaterina Vinogradov, Lior Ravkaie, Bar Edri, Juman Jubran, Anat Ben-Zvi, Esti Yeger-Lotem
Protein clearance is fundamental to proteome health. In eukaryotes, it is carried by two highly conserved proteolytic systems, the ubiquitin-proteasome system (UPS) and the autophagy-lysosome pathway (ALP). Despite their pivotal role, the basal organization of the human protein clearance systems across tissues and cell types remains uncharacterized. Here, we interrogated this organization using diverse omics datasets. Relative to other protein-coding genes, UPS and ALP genes were more widely expressed, encoded more housekeeping proteins, and were more essential for growth, in accordance with their fundamental roles. Most of the UPS and ALP genes were nevertheless differentially expressed across tissues, and their tissue-specific upregulation was associated with tissue-specific functions, phenotypes, and disease susceptibility. The small subset of UPS and ALP genes that was stably expressed across tissues was more highly and widely expressed and more essential for growth than other UPS and ALP genes, suggesting that it acts as a core. Lastly, we compared protein clearance to other branches of the proteostasis network. Protein clearance and folding were closely coordinated across tissues, yet both were less pivotal than protein synthesis. Taken together, we propose that the proteostasis network is organized hierarchically and is tailored to the proteome needs. This organization could contribute to and illuminate tissue-selective phenotypes.
蛋白质清除是蛋白质组健康的基础。在真核生物中，蛋白质清除由两个高度保守的蛋白水解系统进行，即泛素-蛋白酶体系统（UPS）和自噬-溶酶体途径（ALP）。尽管它们发挥着关键作用，但人类蛋白质清除系统在不同组织和细胞类型中的基本组织结构仍未得到表征。在这里，我们利用不同的全息数据集研究了这种组织结构。与其他蛋白编码基因相比，UPS 和 ALP 基因表达更广泛，编码的管家蛋白更多，对生长更重要，这与它们的基本作用相符。然而，大多数 UPS 和 ALP 基因在不同组织中的表达存在差异，它们在组织中的特异性上调与组织的特异性功能、表型和疾病易感性有关。与其他 UPS 和 ALP 基因相比，在不同组织间稳定表达的一小部分 UPS 和 ALP 基因的表达量更高、范围更广，而且对生长更为重要，这表明它们起着核心作用。最后，我们将蛋白质清除与蛋白稳态网络的其他分支进行了比较。蛋白质清除和折叠在不同组织间密切协调，但两者的关键作用都不如蛋白质合成。综上所述，我们认为蛋白质稳定网络是分层组织的，是根据蛋白质组的需要定制的。这种组织结构可能导致并阐明组织选择性表型。
{"title":"The landscape of cellular clearance systems across human tissues and cell types is shaped by tissue-specific proteome needs","authors":"Ekaterina Vinogradov, Lior Ravkaie, Bar Edri, Juman Jubran, Anat Ben-Zvi, Esti Yeger-Lotem","doi":"10.1101/2024.08.26.609695","DOIUrl":"https://doi.org/10.1101/2024.08.26.609695","url":null,"abstract":"Protein clearance is fundamental to proteome health. In eukaryotes, it is carried by two highly conserved proteolytic systems, the ubiquitin-proteasome system (UPS) and the autophagy-lysosome pathway (ALP). Despite their pivotal role, the basal organization of the human protein clearance systems across tissues and cell types remains uncharacterized. Here, we interrogated this organization using diverse omics datasets. Relative to other protein-coding genes, UPS and ALP genes were more widely expressed, encoded more housekeeping proteins, and were more essential for growth, in accordance with their fundamental roles. Most of the UPS and ALP genes were nevertheless differentially expressed across tissues, and their tissue-specific upregulation was associated with tissue-specific functions, phenotypes, and disease susceptibility. The small subset of UPS and ALP genes that was stably expressed across tissues was more highly and widely expressed and more essential for growth than other UPS and ALP genes, suggesting that it acts as a core. Lastly, we compared protein clearance to other branches of the proteostasis network. Protein clearance and folding were closely coordinated across tissues, yet both were less pivotal than protein synthesis. Taken together, we propose that the proteostasis network is organized hierarchically and is tailored to the proteome needs. This organization could contribute to and illuminate tissue-selective phenotypes.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202759","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 : 2024-08-26DOI: 10.1101/2024.08.23.609433
Arnab Mutsuddy, Jonah R Huggins, Aurore Amrit, Cemal Erdem, Jon C Calhoun, Marc R Birtwistle
Data from cell viability assays, which measure cumulative division and death events in a population and reflect substantial cellular heterogeneity, are widely available. However, interpreting such data with mechanistic computational models is hindered because direct model/data comparison is often muddled. We developed an algorithm that tracks simulated division and death events in mechanistically detailed single-cell lineages to enable such a model/data comparison and suggest causes of cell-cell drug response variability. Using our previously developed model of mammalian single-cell proliferation and death signaling, we simulated drug dose response experiments for four targeted anti-cancer drugs (alpelisib, neratinib, trametinib and palbociclib) and compared them to experimental data. Simulations are consistent with data for strong growth inhibition by trametinib (MEK inhibitor) and overall lack of efficacy for alpelisib (PI-3K inhibitor), but are inconsistent with data for palbociclib (CDK4/6 inhibitor) and neratinib (EGFR inhibitor). Model/data inconsistencies suggest (i) the importance of CDK4/6 for driving the cell cycle may be overestimated, and (ii) that the cellular balance between basal (tonic) and ligand-induced signaling is a critical determinant of receptor inhibitor response. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions. Variations in mother cells prior to drug treatment all impinging on ERK pathway activity are associated with the rapidly dividing phenotype and trametinib resistance. This work lays a foundation for the application of mechanistic modeling to large-scale cell viability assay datasets and better understanding determinants of cellular heterogeneity in drug response.
{"title":"Mechanistic modeling of cell viability assays with in silico lineage tracing","authors":"Arnab Mutsuddy, Jonah R Huggins, Aurore Amrit, Cemal Erdem, Jon C Calhoun, Marc R Birtwistle","doi":"10.1101/2024.08.23.609433","DOIUrl":"https://doi.org/10.1101/2024.08.23.609433","url":null,"abstract":"Data from cell viability assays, which measure cumulative division and death events in a population and reflect substantial cellular heterogeneity, are widely available. However, interpreting such data with mechanistic computational models is hindered because direct model/data comparison is often muddled. We developed an algorithm that tracks simulated division and death events in mechanistically detailed single-cell lineages to enable such a model/data comparison and suggest causes of cell-cell drug response variability. Using our previously developed model of mammalian single-cell proliferation and death signaling, we simulated drug dose response experiments for four targeted anti-cancer drugs (alpelisib, neratinib, trametinib and palbociclib) and compared them to experimental data. Simulations are consistent with data for strong growth inhibition by trametinib (MEK inhibitor) and overall lack of efficacy for alpelisib (PI-3K inhibitor), but are inconsistent with data for palbociclib (CDK4/6 inhibitor) and neratinib (EGFR inhibitor). Model/data inconsistencies suggest (i) the importance of CDK4/6 for driving the cell cycle may be overestimated, and (ii) that the cellular balance between basal (tonic) and ligand-induced signaling is a critical determinant of receptor inhibitor response. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions. Variations in mother cells prior to drug treatment all impinging on ERK pathway activity are associated with the rapidly dividing phenotype and trametinib resistance. This work lays a foundation for the application of mechanistic modeling to large-scale cell viability assay datasets and better understanding determinants of cellular heterogeneity in drug response.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202763","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 : 2024-08-26DOI: 10.1101/2024.08.23.609415
Mathura A Thevandavakkam, Natalie E Long, Brianna M Roel, Kristin A Kantautas, Shiri Zakin, Van Duesterberg, Ethan O Perlstein
The development of therapies for rare diseases, particularly inherited metabolic disorders (IMDs), faces significant challenges due to the high cost and lengthy timelines involved. This study presents a yeast-based platform for drug repurposing that capitalizes on the remarkable similarity between yeast and human cellular pathways. This platform enables rapid, cost-effective screening of potential therapeutic compounds for rare diseases, offering a quick turnaround compared to traditional drug development processes. Utilizing a TargetMol library of comprising ~50% nutraceuticals, our pipeline accelerates translation of promising drug repurposing hits into patient observational studies in as little as 6 months. We demonstrate the efficacy of this platform through three case studies in the context of IMDs, showcasing its potential to uncover novel treatments and reduce the time and expense associated with bringing therapies to patients with rare diseases.
{"title":"Industrializing yeast as a drug repurposing platform for inherited metabolic diseases","authors":"Mathura A Thevandavakkam, Natalie E Long, Brianna M Roel, Kristin A Kantautas, Shiri Zakin, Van Duesterberg, Ethan O Perlstein","doi":"10.1101/2024.08.23.609415","DOIUrl":"https://doi.org/10.1101/2024.08.23.609415","url":null,"abstract":"The development of therapies for rare diseases, particularly inherited metabolic disorders (IMDs), faces significant challenges due to the high cost and lengthy timelines involved. This study presents a yeast-based platform for drug repurposing that capitalizes on the remarkable similarity between yeast and human cellular pathways. This platform enables rapid, cost-effective screening of potential therapeutic compounds for rare diseases, offering a quick turnaround compared to traditional drug development processes. Utilizing a TargetMol library of comprising ~50% nutraceuticals, our pipeline accelerates translation of promising drug repurposing hits into patient observational studies in as little as 6 months. We demonstrate the efficacy of this platform through three case studies in the context of IMDs, showcasing its potential to uncover novel treatments and reduce the time and expense associated with bringing therapies to patients with rare diseases.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202764","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 : 2024-08-26DOI: 10.1101/2024.08.23.608747
Alberto Pérez-Posada, Helena García-Castro, Elena Emili, Virginia Vanni, Cirenia Arias-Baldrich, Siebren Frölich, Simon J. van Heeringen, Nathan J. Kenny, Jordi Solana
Cell type identity is determined by gene regulatory networks (GRNs), comprising the expression of specific transcription factors (TFs) regulating target genes (TGs) via binding to open chromatin regions (OCRs). The regulatory logic of differentiation includes factors specific to one or multiple cell types, functioning in a combinatorial fashion. Classic approaches of GRN discovery used perturbational data to elucidate TF-TG links, but are laborious and not scalable across the tree of life. Single cell transcriptomics has emerged as a revolutionary approach to study gene expression with cell type resolution, but incorporating perturbational data is challenging. Planarians, with their pluripotent neoblast stem cells continuously giving rise to all cell types, offer an ideal model to attempt this integration. Despite extensive single cell transcriptomic studies, the transcriptional and chromatin regulation at the cell type level remains unexplored. Here, we investigate the regulatory logic of planarian stem cell differentiation by obtaining an organism-level integration of single cell transcriptomics and single cell accessibility data. We identify specific open chromatin profiles for major differentiated cell types and analyse their transcriptomic landscape, revealing distinct gene modules expressed in individual types and combinations of them. Integrated analysis unveils gene networks reflecting known TF interactions in each type and identifies TFs potentially driving differentiation across multiple cell types. To validate our predictions, we combined TF knockdown RNAi experiments with single cell transcriptomics. We focus on hnf4, a TF known to be expressed in gut phagocytes, and confirm its influence on other types, including parenchymal cells. Our results demonstrate high overlap between predicted targets and experimentally-validated differentially-regulated genes. Overall, our study integrates TFs, TGs and OCRs to reveal the regulatory logic of planarian stem cell differentiation, showcasing that the combination of single cell methods and perturbational studies will be key for characterising GRNs widely.
{"title":"The Regulatory Logic of Planarian Stem Cell Differentiation","authors":"Alberto Pérez-Posada, Helena García-Castro, Elena Emili, Virginia Vanni, Cirenia Arias-Baldrich, Siebren Frölich, Simon J. van Heeringen, Nathan J. Kenny, Jordi Solana","doi":"10.1101/2024.08.23.608747","DOIUrl":"https://doi.org/10.1101/2024.08.23.608747","url":null,"abstract":"Cell type identity is determined by gene regulatory networks (GRNs), comprising the expression of specific transcription factors (TFs) regulating target genes (TGs) via binding to open chromatin regions (OCRs). The regulatory logic of differentiation includes factors specific to one or multiple cell types, functioning in a combinatorial fashion. Classic approaches of GRN discovery used perturbational data to elucidate TF-TG links, but are laborious and not scalable across the tree of life. Single cell transcriptomics has emerged as a revolutionary approach to study gene expression with cell type resolution, but incorporating perturbational data is challenging. Planarians, with their pluripotent neoblast stem cells continuously giving rise to all cell types, offer an ideal model to attempt this integration. Despite extensive single cell transcriptomic studies, the transcriptional and chromatin regulation at the cell type level remains unexplored. Here, we investigate the regulatory logic of planarian stem cell differentiation by obtaining an organism-level integration of single cell transcriptomics and single cell accessibility data. We identify specific open chromatin profiles for major differentiated cell types and analyse their transcriptomic landscape, revealing distinct gene modules expressed in individual types and combinations of them. Integrated analysis unveils gene networks reflecting known TF interactions in each type and identifies TFs potentially driving differentiation across multiple cell types. To validate our predictions, we combined TF knockdown RNAi experiments with single cell transcriptomics. We focus on hnf4, a TF known to be expressed in gut phagocytes, and confirm its influence on other types, including parenchymal cells. Our results demonstrate high overlap between predicted targets and experimentally-validated differentially-regulated genes. Overall, our study integrates TFs, TGs and OCRs to reveal the regulatory logic of planarian stem cell differentiation, showcasing that the combination of single cell methods and perturbational studies will be key for characterising GRNs widely.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202765","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 : 2024-08-22DOI: 10.1101/2024.08.21.609027
Jose Carracedo-Gonzalez, Fausto Arellano-Carbajal, Etzel Garrido, Roberto Alvarez-Martinez
Systems biology is a helpful approach to study complex processes such as aging. Indeed, integrating experimental data with mathematical models and bioinformatics can help us to better understand the aging process.The long-lived mutants of C. elegans have generated extensive data about the molecular and cellular mechanisms involved in aging. Among these mutants, clk-1 is a well-studied gene that encodes for a ubiquitin precursor and exhibits a pleiotropic phenotype during aging, characterized by slow rate behaviors, high levels of mitochondrial ROS, autophagy induction, and metabolic changes. However, further elucidation is required to disentangle the relationship between these molecular changes and the phenotype (lifespan extension and changes in pharyngeal pumping, swimming, and defecation). We combined experimental data and modeling tools to represent the genetic interactions with a boolean network. We then inferred the differential equations for each node , following the boolean rules, to achieve a continuous approach. The results show that aak-2 (AMPK) is a critical gene for the long lifespan of clk-1, given its essential role in the induction of a stress response observed in the network attractors and the health condition and lifespan. To define the health condition of the strains (N2, clk-1, aak-2, and clk-1;aak-2), we propose a novel health index estimation based on the attrition of neuromuscular behaviors. We found that the attractor properties in the clk-1 mutant widely depend on a cyclic regulation for the stress response. From our findings, we infer that while stress responses can increase lifespan, health primarily relies on the amount of damage.
{"title":"Stress responses and dynamic equilibrium: Key determinants of aging in the C. elegans clk-1 mutant","authors":"Jose Carracedo-Gonzalez, Fausto Arellano-Carbajal, Etzel Garrido, Roberto Alvarez-Martinez","doi":"10.1101/2024.08.21.609027","DOIUrl":"https://doi.org/10.1101/2024.08.21.609027","url":null,"abstract":"Systems biology is a helpful approach to study complex processes such as aging. Indeed, integrating experimental data with mathematical models and bioinformatics can help us to better understand the aging process.The long-lived mutants of C. elegans have generated extensive data about the molecular and cellular mechanisms involved in aging. Among these mutants, clk-1 is a well-studied gene that encodes for a ubiquitin precursor and exhibits a pleiotropic phenotype during aging, characterized by slow rate behaviors, high levels of mitochondrial ROS, autophagy induction, and metabolic changes. However, further elucidation is required to disentangle the relationship between these molecular changes and the phenotype (lifespan extension and changes in pharyngeal pumping, swimming, and defecation). We combined experimental data and modeling tools to represent the genetic interactions with a boolean network. We then inferred the differential equations for each node , following the boolean rules, to achieve a continuous approach. The results show that aak-2 (AMPK) is a critical gene for the long lifespan of clk-1, given its essential role in the induction of a stress response observed in the network attractors and the health condition and lifespan. To define the health condition of the strains (N2, clk-1, aak-2, and clk-1;aak-2), we propose a novel health index estimation based on the attrition of neuromuscular behaviors. We found that the attractor properties in the clk-1 mutant widely depend on a cyclic regulation for the stress response. From our findings, we infer that while stress responses can increase lifespan, health primarily relies on the amount of damage.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202766","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 : 2024-08-22DOI: 10.1101/2024.08.20.608739
Halima Hannah Schede, Leila Haj Abdullah Alieh, Laurel Rohde, Antonio Hererra, Anjalie Schlaeppi, Guillaume Valentin, Alireza Gargoori Motlagh, Albert Hannah Dominguez Mantes, Chloe Jollivet, Jonathan Paz Montoya, Laura Capolupo, Irina Khven, Andrew C Oates, Giovanni D'Angelo, Gioele La Manno
Embryo development entails the formation of anatomical structures with distinct biochemical compositions. Compared with the wealth of knowledge on gene regulation, our understanding of metabolic programs operating during embryogenesis is limited. Mass spectrometry imaging (MSI) has the potential to map the distribution of metabolites across embryo development. Here, we established an analytical framework for the joint analysis of large MSI datasets that allows for the construction of multidimensional metabolomic atlases. Employing this framework, we mapped the 4D distribution of over a hundred lipids at quasi-single-cell resolution in Danio rerio embryos. We discovered metabolic trajectories that unfold in concert with morphogenesis and revealed spatially organized biochemical coordination overlooked by bulk measurements. Interestingly, lipid mapping revealed unexpected distributions of sphingolipid and triglyceride species, suggesting their involvement in pattern establishment and organ development. Our approach empowers a new generation of whole-organism metabolomic atlases and enables the discovery of spatially organized metabolic circuits.
{"title":"Unified Mass Imaging Maps the Lipidome of Vertebrate Development","authors":"Halima Hannah Schede, Leila Haj Abdullah Alieh, Laurel Rohde, Antonio Hererra, Anjalie Schlaeppi, Guillaume Valentin, Alireza Gargoori Motlagh, Albert Hannah Dominguez Mantes, Chloe Jollivet, Jonathan Paz Montoya, Laura Capolupo, Irina Khven, Andrew C Oates, Giovanni D'Angelo, Gioele La Manno","doi":"10.1101/2024.08.20.608739","DOIUrl":"https://doi.org/10.1101/2024.08.20.608739","url":null,"abstract":"Embryo development entails the formation of anatomical structures with distinct biochemical compositions. Compared with the wealth of knowledge on gene regulation, our understanding of metabolic programs operating during embryogenesis is limited. Mass spectrometry imaging (MSI) has the potential to map the distribution of metabolites across embryo development. Here, we established an analytical framework for the joint analysis of large MSI datasets that allows for the construction of multidimensional metabolomic atlases. Employing this framework, we mapped the 4D distribution of over a hundred lipids at quasi-single-cell resolution in Danio rerio embryos. We discovered metabolic trajectories that unfold in concert with morphogenesis and revealed spatially organized biochemical coordination overlooked by bulk measurements. Interestingly, lipid mapping revealed unexpected distributions of sphingolipid and triglyceride species, suggesting their involvement in pattern establishment and organ development. Our approach empowers a new generation of whole-organism metabolomic atlases and enables the discovery of spatially organized metabolic circuits.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202767","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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