Pub Date : 2024-08-22DOI: 10.1101/2024.08.21.608986
Huixia Ren, Yanjun Li, Beichen Xie, Weiran Qian, Yi Yu, Tianyi Chang, Xiaojing Yang, kim sneppen, Liangyi Chen, Chao Tang
Glucose-induced pancreatic islet hormone release is tightly coupled with oscillations in cytoplasmic free Ca2+ concentration of islet cells, which is regulated by a complex interplay between intercellular and intracellular signaling. Delta cells, which entangle with alpha cells located at the islet periphery, are known to be important paracrine regulators. However, the role of delta cells in regulating Ca2+ oscillation pattern remains unclear. Here we show that delta-alpha cell-to-cell interactions are the source of variability in glucose-induced Ca2+ oscillation pattern. Somatostatin secreted from delta cells prolonged the islet's oscillation period in an alpha cell mass-dependent manner. Pharmacological and optogenetic perturbations of delta-alpha interactions led islets to switch between fast and slow Ca2+ oscillations. Continuous adjustment of delta-alpha coupling strength caused the fast oscillating islets to transition to mixed and slow oscillations. We developed a mathematical model, demonstrating that the fast-mixed-slow oscillation transition is a Hopf bifurcation. Our findings provide a comprehensive understanding of how delta cells modulate islet Ca2+ dynamics and reveal the intrinsic heterogeneity of islets due to the structural composition of different cell types.
{"title":"δ-α cell-to-cell interactions modulate pancreatic islet Ca2+ oscillation modes","authors":"Huixia Ren, Yanjun Li, Beichen Xie, Weiran Qian, Yi Yu, Tianyi Chang, Xiaojing Yang, kim sneppen, Liangyi Chen, Chao Tang","doi":"10.1101/2024.08.21.608986","DOIUrl":"https://doi.org/10.1101/2024.08.21.608986","url":null,"abstract":"Glucose-induced pancreatic islet hormone release is tightly coupled with oscillations in cytoplasmic free Ca2+ concentration of islet cells, which is regulated by a complex interplay between intercellular and intracellular signaling. Delta cells, which entangle with alpha cells located at the islet periphery, are known to be important paracrine regulators. However, the role of delta cells in regulating Ca2+ oscillation pattern remains unclear. Here we show that delta-alpha cell-to-cell interactions are the source of variability in glucose-induced Ca2+ oscillation pattern. Somatostatin secreted from delta cells prolonged the islet's oscillation period in an alpha cell mass-dependent manner. Pharmacological and optogenetic perturbations of delta-alpha interactions led islets to switch between fast and slow Ca2+ oscillations. Continuous adjustment of delta-alpha coupling strength caused the fast oscillating islets to transition to mixed and slow oscillations. We developed a mathematical model, demonstrating that the fast-mixed-slow oscillation transition is a Hopf bifurcation. Our findings provide a comprehensive understanding of how delta cells modulate islet Ca2+ dynamics and reveal the intrinsic heterogeneity of islets due to the structural composition of different cell types.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"734 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202768","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 : 2024-08-21DOI: 10.1101/2024.08.20.608830
Brian M Westwood, Mark C Chappell
As the Vitamin C, Thiamine, and Steroids in Sepsis (VICTAS) Trial was not designed to measure Renin-Angiotensin-Aldosterone System components, this hub model was developed to limit eight components to a system with one entrance and two exits to facilitate the calculation of angiotensinogen levels. Bootstrapping the bioactive peptide egress rate constant using control subjects and incorporating the previously established renin relationship, the model was used to develop a contingency test to index and classify component relationships.
{"title":"ANGIOTENSIN CONVERTING ENZYME HUB CAPACITY - A SIMPLE MODEL OF RELATING PEPTIDE FLUX AND RENIN INFLUENCE","authors":"Brian M Westwood, Mark C Chappell","doi":"10.1101/2024.08.20.608830","DOIUrl":"https://doi.org/10.1101/2024.08.20.608830","url":null,"abstract":"As the Vitamin C, Thiamine, and Steroids in Sepsis (VICTAS) Trial was not designed to measure Renin-Angiotensin-Aldosterone System components, this hub model was developed to limit eight components to a system with one entrance and two exits to facilitate the calculation of angiotensinogen levels. Bootstrapping the bioactive peptide egress rate constant using control subjects and incorporating the previously established renin relationship, the model was used to develop a contingency test to index and classify component relationships.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202788","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 : 2024-08-21DOI: 10.1101/2024.08.15.608061
Zachary Levine, Guy Lutsker, Anastasia Godneva, Adina Weinberger, Maya Lotan-Pompan, Yeela Talmor-Barkan, Yotam Reisner, Hagai Rossman, Eran Segal
Background The genetic underpinnings of cardiovascular disease remain elusive. Contrastive learning algorithms have recently shown cutting-edge performance in extracting representations from electrocardiogram (ECG) signals that characterize cross-temporal cardiovascular state. However, there is currently no connection between these representations and genetics. Methods We designed a new metric, denoted as Delta ECG, which measures temporal shifts in patients' cardiovascular state, and inherently adjusts for inter-patient differences at baseline. We extracted this measure for 4,782 patients in the Human Phenotype Project using a novel self-supervised learning model, and quantified the associated genetic signals with Genome-Wide-Association Studies (GWAS). We predicted the expression of thousands of genes extracted from Peripheral Blood Mononuclear Cells (PBMCs). Downstream, we ran enrichment and overrepresentation analysis of genes we identified as significantly predicted from ECG. Findings In a Genome-Wide Association Study (GWAS) of Delta ECG, we identified five associations that achieved genome-wide significance. From baseline embeddings, our models significantly predict the expression of 57 genes in men and 9 in women. Enrichment analysis showed that these genes were predominantly associated with the electron transport chain and the same immune pathways as identified in our GWAS. Conclusions We validate a novel method integrating self-supervised learning in the medical domain and simple linear models in genetics. Our results indicate that the processes underlying temporal changes in cardiovascular health share a genetic basis with CVD, its major risk factors, and its known correlates. Moreover, our functional analysis confirms the importance of leukocytes, specifically eosinophils and mast cells with respect to cardiac structure and function.
{"title":"Genetic underpinnings of predicted changes in cardiovascular function using self supervised learning","authors":"Zachary Levine, Guy Lutsker, Anastasia Godneva, Adina Weinberger, Maya Lotan-Pompan, Yeela Talmor-Barkan, Yotam Reisner, Hagai Rossman, Eran Segal","doi":"10.1101/2024.08.15.608061","DOIUrl":"https://doi.org/10.1101/2024.08.15.608061","url":null,"abstract":"Background\u0000The genetic underpinnings of cardiovascular disease remain elusive. Contrastive learning algorithms have recently shown cutting-edge performance in extracting representations from electrocardiogram (ECG) signals that characterize cross-temporal cardiovascular state. However, there is currently no connection between these representations and genetics.\u0000Methods\u0000We designed a new metric, denoted as Delta ECG, which measures temporal shifts in patients' cardiovascular state, and inherently adjusts for inter-patient differences at baseline. We extracted this measure for 4,782 patients in the Human Phenotype Project using a novel self-supervised learning model, and quantified the associated genetic signals with Genome-Wide-Association Studies (GWAS). We predicted the expression of thousands of genes extracted from Peripheral Blood Mononuclear Cells (PBMCs). Downstream, we ran enrichment and overrepresentation analysis of genes we identified as significantly predicted from ECG.\u0000Findings\u0000In a Genome-Wide Association Study (GWAS) of Delta ECG, we identified five associations that achieved genome-wide significance. From baseline embeddings, our models significantly predict the expression of 57 genes in men and 9 in women. Enrichment analysis showed that these genes were predominantly associated with the electron transport chain and the same immune pathways as identified in our GWAS.\u0000Conclusions\u0000We validate a novel method integrating self-supervised learning in the medical domain and simple linear models in genetics. Our results indicate that the processes underlying temporal changes in cardiovascular health share a genetic basis with CVD, its major risk factors, and its known correlates. Moreover, our functional analysis confirms the importance of leukocytes, specifically eosinophils and mast cells with respect to cardiac structure and function.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"167 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202769","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}
The COVID-19 pandemic significantly transformed the field of mathematical modeling in immunology. International collaboration among numerous research groups yielded a substantial amount of experimental data, which greatly facilitated model validation and led to the development of new mathematical models. The aim of the study is an improvement of system understanding of the immune response to SARS-CoV-2 infection based on the development of a modular mathematical model which provides a foundation for further research on host-pathogen interactions. We utilized the open-source BioUML platform to develop a model using ordinary, delay and stochastic differential equations. The model was validated using experimental data from middle-aged individuals with moderate COVID-19 progression, including measurements of viral load, antibodies, CD4+ and CD8+ T cells, and interleukin-6 levels. Parameter optimization and sensitivity analysis were conducted to refine the model`s accuracy. The model effectively reproduces moderate, severe, and critical COVID-19 progressions, consistent with experimental observations. We investigated the efficiency and contributions of innate and adaptive immunity in response to SARS-CoV-2 infection and assessed immune system behavior during co-infection with HIV and organ transplantation. Additionally, we studied therapy methods, such as interferon administration. The developed model can be employed as a framework for simulating other infectious diseases taking into account follow-up immune response.
COVID-19 大流行极大地改变了免疫学数学建模领域。众多研究小组之间的国际合作产生了大量的实验数据，这极大地促进了模型的验证，并导致了新数学模型的开发。本研究的目的是在建立模块化数学模型的基础上，加深系统对 SARS-CoV-2 感染免疫反应的理解，为进一步研究宿主与病原体之间的相互作用奠定基础。我们利用开源的 BioUML 平台开发了一个使用常微分方程、延迟微分方程和随机微分方程的模型。该模型利用中度 COVID-19 进展期中年人的实验数据进行了验证，包括病毒载量、抗体、CD4+ 和 CD8+ T 细胞以及白细胞介素-6 水平的测量数据。对模型进行了参数优化和敏感性分析，以提高模型的准确性。该模型有效地再现了 COVID-19 的中度、重度和临界进展，与实验观察结果一致。我们研究了先天性免疫和适应性免疫在应对 SARS-CoV-2 感染时的效率和贡献，并评估了与 HIV 共同感染和器官移植期间的免疫系统行为。此外，我们还研究了干扰素等治疗方法。所开发的模型可用作模拟其他传染病的框架，同时考虑后续免疫反应。
{"title":"A modular model of immune response as a computational platform to investigate a pathogenesis of infection disease","authors":"Maxim Miroshnichenko, Fedor Anatolyevich Kolpakov, Ilya Rinatovich Akberdin","doi":"10.1101/2024.08.19.608570","DOIUrl":"https://doi.org/10.1101/2024.08.19.608570","url":null,"abstract":"The COVID-19 pandemic significantly transformed the field of mathematical modeling in immunology. International collaboration among numerous research groups yielded a substantial amount of experimental data, which greatly facilitated model validation and led to the development of new mathematical models. The aim of the study is an improvement of system understanding of the immune response to SARS-CoV-2 infection based on the development of a modular mathematical model which provides a foundation for further research on host-pathogen interactions. We utilized the open-source BioUML platform to develop a model using ordinary, delay and stochastic differential equations. The model was validated using experimental data from middle-aged individuals with moderate COVID-19 progression, including measurements of viral load, antibodies, CD4+ and CD8+ T cells, and interleukin-6 levels. Parameter optimization and sensitivity analysis were conducted to refine the model`s accuracy. The model effectively reproduces moderate, severe, and critical COVID-19 progressions, consistent with experimental observations. We investigated the efficiency and contributions of innate and adaptive immunity in response to SARS-CoV-2 infection and assessed immune system behavior during co-infection with HIV and organ transplantation. Additionally, we studied therapy methods, such as interferon administration. The developed model can be employed as a framework for simulating other infectious diseases taking into account follow-up immune response.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202784","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 : 2024-08-19DOI: 10.1101/2024.08.15.608169
Javier Villela-Castrejon, Herbert Levine, José Nelson Onuchic, Jason T George, Dongya Jia
Abnormal metabolism is a hallmark of cancer. Initially recognized through the observation of aerobic glycolysis in cancer nearly a century ago. Also, we now know that mitochondrial respiration is also used by cancer for progression and metastasis. However, it remains largely unclear the mechanisms by which cancer cells mix and match different metabolic modalities (oxidative/reductive) and leverage various metabolic ingredients (glucose, fatty acids, glutamine) to meet their bioenergetic and biosynthetic needs. Here, we formulate a phenotypic model for cancer metabolism by coupling master gene regulators (AMPK, HIF-1, Myc) with key metabolic substrates (glucose, fatty acid, and glutamine). The model predicts that cancer cells can acquire four metabolic phenotypes: a catabolic phenotype characterized by vigorous oxidative processes - O, an anabolic phenotype characterized by pronounced reductive activities - W, and two complementary hybrid metabolic states - one exhibiting both high catabolic and high anabolic activity - W/O, and the other relying mainly on glutamine oxidation - Q. Using this framework, we quantified gene and metabolic pathway activity respectively by developing scoring metrics based on gene expression. We validated the model-predicted gene-metabolic pathway association and the characterization of the four metabolic phenotypes by analyzing RNA-seq data of tumor samples from TCGA. Strikingly, carcinoma samples exhibiting hybrid metabolic phenotypes are often associated with the worst survival outcomes relative to other metabolic phenotypes. Our mathematical model and scoring metrics serve as a platform to quantify cancer metabolism and study how cancer cells adapt their metabolism upon perturbations, which ultimately could facilitate an effective treatment targeting cancer metabolic plasticity.
{"title":"Computational modeling of cancer cell metabolism along the catabolic-anabolic axes","authors":"Javier Villela-Castrejon, Herbert Levine, José Nelson Onuchic, Jason T George, Dongya Jia","doi":"10.1101/2024.08.15.608169","DOIUrl":"https://doi.org/10.1101/2024.08.15.608169","url":null,"abstract":"Abnormal metabolism is a hallmark of cancer. Initially recognized through the observation of aerobic glycolysis in cancer nearly a century ago. Also, we now know that mitochondrial respiration is also used by cancer for progression and metastasis. However, it remains largely unclear the mechanisms by which cancer cells mix and match different metabolic modalities (oxidative/reductive) and leverage various metabolic ingredients (glucose, fatty acids, glutamine) to meet their bioenergetic and biosynthetic needs. Here, we formulate a phenotypic model for cancer metabolism by coupling master gene regulators (AMPK, HIF-1, Myc) with key metabolic substrates (glucose, fatty acid, and glutamine). The model predicts that cancer cells can acquire four metabolic phenotypes: a catabolic phenotype characterized by vigorous oxidative processes - O, an anabolic phenotype characterized by pronounced reductive activities - W, and two complementary hybrid metabolic states - one exhibiting both high catabolic and high anabolic activity - W/O, and the other relying mainly on glutamine oxidation - Q. Using this framework, we quantified gene and metabolic pathway activity respectively by developing scoring metrics based on gene expression. We validated the model-predicted gene-metabolic pathway association and the characterization of the four metabolic phenotypes by analyzing RNA-seq data of tumor samples from TCGA. Strikingly, carcinoma samples exhibiting hybrid metabolic phenotypes are often associated with the worst survival outcomes relative to other metabolic phenotypes. Our mathematical model and scoring metrics serve as a platform to quantify cancer metabolism and study how cancer cells adapt their metabolism upon perturbations, which ultimately could facilitate an effective treatment targeting cancer metabolic plasticity.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"106 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202789","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 : 2024-08-19DOI: 10.1101/2024.08.18.608300
Doris Kaltenecker, Izabela Horvath, Rami Al-Maskari, Zeynep Ilgin Kolabas, Ying Chen, Luciano Hoeher, Mihail Todorov, Saketh Kapoor, Mayar Ali, Florian Kofler, Pauline Morigny, Julia Geppert, Denise Jeridi, Carolina Cigankova, Victor Miro Kolenic, Nilsu Gur, Chenchen Pan, Marie Piraud, Daniel Rueckert, Maria Rohm, Farida Hellal, Markus Elsner, Harsharan Singh Bhatia, Bjorn H Menze, Stephan Herzig, Johannes Christian Paetzold, Mauricio Berriel Diaz, Ali Erturk
Many diseases, such as obesity, have systemic effects that impact multiple organ systems throughout the body. However, tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking. Here, we developed a suite of deep learning-based image analysis algorithms (MouseMapper) and integrated it with tissue clearing and light-sheet microscopy to enable a comprehensive analysis of diseases impacting diverse systems across the mouse body. This approach enables the quantitative analysis of cellular and structural changes across the entire mouse body at unprecedented resolution and scale, including tracking nerves over several centimeters in whole animal bodies. To demonstrate its power, we applied MouseMapper to study nervous and immune systems in high-fat diet induced obesity. We uncovered widespread changes in both immune cell distribution and nerve structures, including alterations in the trigeminal nerve characterized by a reduced number of nerve endings in obese mice. These structural abnormalities were associated with functional deficits of whisker sensing and proteomic changes in the trigeminal ganglion, primarily affecting pathways related to axon growth and the complement system. Additionally, we found heterogeneity in obesity-induced whole-body inflammation across different tissues and organs. Our study demonstrates MouseMapper's capability to discover and quantify pathological alterations at the whole-body level, offering a powerful approach for investigating the systemic impacts of various diseases.
{"title":"Deep Learning and 3D Imaging Reveal Whole-Body Alterations in Obesity","authors":"Doris Kaltenecker, Izabela Horvath, Rami Al-Maskari, Zeynep Ilgin Kolabas, Ying Chen, Luciano Hoeher, Mihail Todorov, Saketh Kapoor, Mayar Ali, Florian Kofler, Pauline Morigny, Julia Geppert, Denise Jeridi, Carolina Cigankova, Victor Miro Kolenic, Nilsu Gur, Chenchen Pan, Marie Piraud, Daniel Rueckert, Maria Rohm, Farida Hellal, Markus Elsner, Harsharan Singh Bhatia, Bjorn H Menze, Stephan Herzig, Johannes Christian Paetzold, Mauricio Berriel Diaz, Ali Erturk","doi":"10.1101/2024.08.18.608300","DOIUrl":"https://doi.org/10.1101/2024.08.18.608300","url":null,"abstract":"Many diseases, such as obesity, have systemic effects that impact multiple organ systems throughout the body. However, tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking. Here, we developed a suite of deep learning-based image analysis algorithms (MouseMapper) and integrated it with tissue clearing and light-sheet microscopy to enable a comprehensive analysis of diseases impacting diverse systems across the mouse body. This approach enables the quantitative analysis of cellular and structural changes across the entire mouse body at unprecedented resolution and scale, including tracking nerves over several centimeters in whole animal bodies. To demonstrate its power, we applied MouseMapper to study nervous and immune systems in high-fat diet induced obesity. We uncovered widespread changes in both immune cell distribution and nerve structures, including alterations in the trigeminal nerve characterized by a reduced number of nerve endings in obese mice. These structural abnormalities were associated with functional deficits of whisker sensing and proteomic changes in the trigeminal ganglion, primarily affecting pathways related to axon growth and the complement system. Additionally, we found heterogeneity in obesity-induced whole-body inflammation across different tissues and organs. Our study demonstrates MouseMapper's capability to discover and quantify pathological alterations at the whole-body level, offering a powerful approach for investigating the systemic impacts of various diseases.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"402 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202786","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 : 2024-08-19DOI: 10.1101/2024.08.18.608377
Saburo Tsuru, Chikara Furusawa
Gene expression responds to various perturbations, such as mutations, environmental changes, and stochastic perturbations. The variability in gene expression levels differs among genes, influencing the availability of adaptive variants or mutants and thereby affecting nongenetic and genetic adaptations. Different types of variability are interdependent, suggesting global canalization/decanalization against different perturbations and a common underlying mechanism. Despite this, the relationship between plasticity (variability in response to environmental changes) and noise (variability among cells under the same conditions) in gene expression remains debatable. Previous studies reported a positive correlation between plasticity and noise, but these variabilities are often measured at different levels: plasticity at the mRNA level and noise at the protein level. This methodological discrepancy complicates the understanding of their relationship. We investigated this by measuring protein expression levels of essential and nonessential genes in Escherichia coli. Using flow cytometry, we quantified noise and plasticity from the same dataset. Essential genes exhibited lower noise and plasticity than nonessential genes. Nonessential genes showed a positive correlation between noise and plasticity, while essential genes did not. This study provides empirical evidence of essentiality-dependent coupling between noise and plasticity in protein expression, highlighting the organization of different types of variabilities.
{"title":"Congruence between noise and plasticity in protein expression","authors":"Saburo Tsuru, Chikara Furusawa","doi":"10.1101/2024.08.18.608377","DOIUrl":"https://doi.org/10.1101/2024.08.18.608377","url":null,"abstract":"Gene expression responds to various perturbations, such as mutations, environmental changes, and stochastic perturbations. The variability in gene expression levels differs among genes, influencing the availability of adaptive variants or mutants and thereby affecting nongenetic and genetic adaptations. Different types of variability are interdependent, suggesting global canalization/decanalization against different perturbations and a common underlying mechanism. Despite this, the relationship between plasticity (variability in response to environmental changes) and noise (variability among cells under the same conditions) in gene expression remains debatable. Previous studies reported a positive correlation between plasticity and noise, but these variabilities are often measured at different levels: plasticity at the mRNA level and noise at the protein level. This methodological discrepancy complicates the understanding of their relationship. We investigated this by measuring protein expression levels of essential and nonessential genes in Escherichia coli. Using flow cytometry, we quantified noise and plasticity from the same dataset. Essential genes exhibited lower noise and plasticity than nonessential genes. Nonessential genes showed a positive correlation between noise and plasticity, while essential genes did not. This study provides empirical evidence of essentiality-dependent coupling between noise and plasticity in protein expression, highlighting the organization of different types of variabilities.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"62 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202785","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 : 2024-08-19DOI: 10.1101/2024.08.17.608427
Maximilian Gehri, Lukas Stelzl, Heinz Koeppl
Biological signal processing typically requires energy, leading us to hypothesize that a cell's information processing capacity is constrained by its energy dissipation. Signals and their processing mechanisms are often modeled using Markovian chemical reaction networks (CRNs). To enable rigorous analysis, we review and reformulate stochastic thermodynamics for open CRNs, utilizing Kurtz's process-based formulation. In particular, we revisit the identification of the energy dissipation rate with the entropy production rate (EPR) at the non-equilibrium steady state (NESS). We also highlight potential inconsistencies in traditional formulations for generic Markov processes when applied to open CRNs, which may lead to erroneous conclusions about equilibrium, reversibility, and the EPR. Additionally, we review the concepts of mutual information (MI) and directed information (DI) between continuous-time trajectories of CRNs, which capture the transmission of spatiotemporal patterns. We generalize existing expressions for the MI, originally accounting for transmission between two species, to now include transmission between arbitrary subnetworks. A rigorous derivation of the DI between subnetworks is presented. Based on channel coding theorems for continuous-time channels with feedback, we argue that directed information is the appropriate metric for quantifying information throughput in cellular signal processing. To support our initial hypothesis within the context of gene regulation, we present two case studies involving small promoter models: a two-state nonequilibrium promoter and a three-state promoter featuring two activation levels. We provide analytical expressions of the directed information rate (DIR) and maximize them subject to an upper bound on the EPR. The maximum is shown to increase with the EPR.
{"title":"Entropy production constrains information throughput in gene regulation","authors":"Maximilian Gehri, Lukas Stelzl, Heinz Koeppl","doi":"10.1101/2024.08.17.608427","DOIUrl":"https://doi.org/10.1101/2024.08.17.608427","url":null,"abstract":"Biological signal processing typically requires energy, leading us to hypothesize that a cell's information processing capacity is constrained by its energy dissipation. Signals and their processing mechanisms are often modeled using Markovian chemical reaction networks (CRNs). To enable rigorous analysis, we review and reformulate stochastic thermodynamics for open CRNs, utilizing Kurtz's process-based formulation. In particular, we revisit the identification of the energy dissipation rate with the entropy production rate (EPR) at the non-equilibrium steady state (NESS). We also highlight potential inconsistencies in traditional formulations for generic Markov processes when applied to open CRNs, which may lead to erroneous conclusions about equilibrium, reversibility, and the EPR. Additionally, we review the concepts of mutual information (MI) and directed information (DI) between continuous-time trajectories of CRNs, which capture the transmission of spatiotemporal patterns. We generalize existing expressions for the MI, originally accounting for transmission between two species, to now include transmission between arbitrary subnetworks. A rigorous derivation of the DI between subnetworks is presented. Based on channel coding theorems for continuous-time channels with feedback, we argue that directed information is the appropriate metric for quantifying information throughput in cellular signal processing. To support our initial hypothesis within the context of gene regulation, we present two case studies involving small promoter models: a two-state nonequilibrium promoter and a three-state promoter featuring two activation levels. We provide analytical expressions of the directed information rate (DIR) and maximize them subject to an upper bound on the EPR. The maximum is shown to increase with the EPR.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"100 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202787","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 : 2024-08-17DOI: 10.1101/2024.08.15.607975
Yana Demyanenko, Andrew M. Giltrap, Benjamin G. Davis, Shabaz Mohammed
N-Hydroxysuccinimide (NHS) ester chemistry is used extensively across proteomics sample preparation. One of its increasingly prevalent applications is in isobaric reagent-based quantitation such as the iTRAQ (isobaric tags for relative and absolute quantitation) and TMT (tandem mass tag) approaches. In these methods, labelling on the primary amines of lysine residues and N-termini of tryptic peptides via amide formation (N-derivatives) from corresponding NHS ester reagents is the intended reactive outcome. However, the role of NHS esters as activated carboxyls can also drive the formation of serine-, tyrosine-, and threonine- derived esters (O-derivatives). These O-derivative peptides are typically classed as over-labelled and are disregarded for quantification, leading to loss of information and hence potential sensitivity. Their presence also unnecessarily increases sample complexity, which reduces the overall identification rates. One common approach for removing these unwanted labelling events has involved a quench with hydroxylamine. We show here that this approach is not fully efficient and can still leave substantial levels of unwanted over-labelled peptides. Through systematic screening of nucleophilic aminolysis reagents and reaction conditions, we have now developed a robust method to efficiently remove over-labelled peptides. The new method reduces the proportion of over-labelled peptides in the sample to less than 1% without affecting the labelling rate or introducing other modifications, leading to superior identification rates and quantitation precision.
N-羟基琥珀酰亚胺（NHS）酯化学被广泛应用于蛋白质组学样品制备。其日益普遍的应用之一是基于等压试剂的定量，如 iTRAQ（用于相对和绝对定量的等压标记）和 TMT（串联质量标记）方法。在这些方法中，通过相应的 NHS 酯试剂形成的酰胺（N-衍生物）对赖氨酸残基的伯胺和胰蛋白酶肽的 N 端进行标记是预期的反应结果。不过，NHS 酯作为活化羧基的作用也能推动丝氨酸、酪氨酸和苏氨酸衍生酯（O-衍生物）的形成。这些 O 衍生物肽通常被归类为过度标记，在定量时会被忽略，从而导致信息丢失，进而影响潜在的灵敏度。它们的存在还会不必要地增加样品的复杂性，从而降低总体鉴定率。去除这些不需要的标记事件的一种常见方法是使用羟胺淬火。我们在此表明，这种方法并不完全有效，仍然会留下大量不需要的过度标记肽。通过系统地筛选亲核氨解试剂和反应条件，我们现在已经开发出了一种高效去除过标记肽的可靠方法。新方法可将样品中的过标记肽比例降至 1%以下，同时不影响标记率或引入其他修饰，从而实现更高的鉴定率和定量精度。
{"title":"Removal of NHS-labelling by-products in Proteomic Samples","authors":"Yana Demyanenko, Andrew M. Giltrap, Benjamin G. Davis, Shabaz Mohammed","doi":"10.1101/2024.08.15.607975","DOIUrl":"https://doi.org/10.1101/2024.08.15.607975","url":null,"abstract":"N-Hydroxysuccinimide (NHS) ester chemistry is used extensively across proteomics sample preparation. One of its increasingly prevalent applications is in isobaric reagent-based quantitation such as the iTRAQ (isobaric tags for relative and absolute quantitation) and TMT (tandem mass tag) approaches. In these methods, labelling on the primary amines of lysine residues and N-termini of tryptic peptides via amide formation (N-derivatives) from corresponding NHS ester reagents is the intended reactive outcome. However, the role of NHS esters as activated carboxyls can also drive the formation of serine-, tyrosine-, and threonine- derived esters (O-derivatives). These O-derivative peptides are typically classed as over-labelled and are disregarded for quantification, leading to loss of information and hence potential sensitivity. Their presence also unnecessarily increases sample complexity, which reduces the overall identification rates. One common approach for removing these unwanted labelling events has involved a quench with hydroxylamine. We show here that this approach is not fully efficient and can still leave substantial levels of unwanted over-labelled peptides. Through systematic screening of nucleophilic aminolysis reagents and reaction conditions, we have now developed a robust method to efficiently remove over-labelled peptides. The new method reduces the proportion of over-labelled peptides in the sample to less than 1% without affecting the labelling rate or introducing other modifications, leading to superior identification rates and quantitation precision.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202790","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 : 2024-08-12DOI: 10.1101/2024.08.12.607546
Jessica S Yu, Blair Lyons, Susanne M Rafelski, Julie A Theriot, Neda Bagheri, Graham Johnson
Iterating between data-driven research and generative computational models is a powerful approach for emulating biological systems, testing hypotheses, and gaining a deeper understanding of these systems. We developed a hybrid agent-based model (ABM) that integrates a Cellular Potts Model (CPM) designed to investigate cell shape and colony dynamics in human induced pluripotent stem cell (hiPS cell) colonies. This model aimed to first mimic and then explore the dynamics observed in real-world hiPS cell cultures. Initial outputs showed great potential, seeming to mimic small colony behaviors relatively well. However, longer simulations and quantitative comparisons revealed limitations, particularly with the CPM component, which lacked long-range interactions that might be necessary for accurate simulations. This challenge led us to thoroughly examine the hybrid model's potential and limitations, providing insights and recommendations for systems where cell-wide mechanics play significant roles. The CPM supports 2D and 3D cell shapes using a Monte Carlo algorithm to prevent cell fragmentation. Basic "out of the box" CPM Hamiltonian terms of volume and adhesion were insufficient to match live cell imaging of hiPS cell cultures. Adding substrate adhesion resulted in flatter colonies, highlighting the need to consider environmental context in modeling. High-throughput parameter sweeps identified regimes that produced consistent simulated shapes and demonstrated the impact of specific model decisions on emergent dynamics. Full-scale simulations showed that while certain agent rules could form a hiPS cell monolayer in 3D, they could not maintain it over time. Our study underscores that "out of the box" 3D CPMs, which do not natively incorporate long-range cell mechanics like elasticity, may be insufficient for accurately simulating hiPS cell and colony dynamics. To address this limitation, future work could add mechanical constraints to the CPM Hamiltonian or integrate global agent rules. Alternatively, replacing the CPM with a methodology that directly represents cell mechanics might be necessary. Documenting and sharing our model development process fosters open team science and supports the broader research community in developing computational models of complex biological systems.
{"title":"4D hybrid model interrogates agent-level rules and parameters driving hiPS cell colony dynamics","authors":"Jessica S Yu, Blair Lyons, Susanne M Rafelski, Julie A Theriot, Neda Bagheri, Graham Johnson","doi":"10.1101/2024.08.12.607546","DOIUrl":"https://doi.org/10.1101/2024.08.12.607546","url":null,"abstract":"Iterating between data-driven research and generative computational models is a powerful approach for emulating biological systems, testing hypotheses, and gaining a deeper understanding of these systems. We developed a hybrid agent-based model (ABM) that integrates a Cellular Potts Model (CPM) designed to investigate cell shape and colony dynamics in human induced pluripotent stem cell (hiPS cell) colonies. This model aimed to first mimic and then explore the dynamics observed in real-world hiPS cell cultures. Initial outputs showed great potential, seeming to mimic small colony behaviors relatively well. However, longer simulations and quantitative comparisons revealed limitations, particularly with the CPM component, which lacked long-range interactions that might be necessary for accurate simulations. This challenge led us to thoroughly examine the hybrid model's potential and limitations, providing insights and recommendations for systems where cell-wide mechanics play significant roles. The CPM supports 2D and 3D cell shapes using a Monte Carlo algorithm to prevent cell fragmentation. Basic \"out of the box\" CPM Hamiltonian terms of volume and adhesion were insufficient to match live cell imaging of hiPS cell cultures. Adding substrate adhesion resulted in flatter colonies, highlighting the need to consider environmental context in modeling. High-throughput parameter sweeps identified regimes that produced consistent simulated shapes and demonstrated the impact of specific model decisions on emergent dynamics. Full-scale simulations showed that while certain agent rules could form a hiPS cell monolayer in 3D, they could not maintain it over time. Our study underscores that \"out of the box\" 3D CPMs, which do not natively incorporate long-range cell mechanics like elasticity, may be insufficient for accurately simulating hiPS cell and colony dynamics. To address this limitation, future work could add mechanical constraints to the CPM Hamiltonian or integrate global agent rules. Alternatively, replacing the CPM with a methodology that directly represents cell mechanics might be necessary. Documenting and sharing our model development process fosters open team science and supports the broader research community in developing computational models of complex biological systems.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945430","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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