Pub Date : 2022-11-09DOI: 10.3389/fsysb.2022.930396
M. Vardhan, John P. Gounley, S. J. Chen, Priya Nair, Wei Wei, L. Hegele, Jonathan Kusner, A. Kahn, D. Frakes, J. Leopold, A. Randles
Background and objective: Coronary artery disease (CAD) is highly prevalent and associated with adverse events. Challenges have emerged in the treatment of intermediate coronary artery stenoses. These lesions are often interrogated with fractional flow reserve (FFR) testing to determine if a stenosis is likely to be causative for ischemia in a cardiac territory. This invasive test requires insertion of a pressure wire into a coronary vessel. Recently computational fluid dynamics (CFD) has been used to noninvasively assess fractional flow reserve in vessels reconstructed from medical imaging data. However, many of these simulations are unable to provide additional information about intravascular hemodynamics, including velocity, endothelial shear stress (ESS), and vorticity. We hypothesized that vorticity, which has demonstrated utility in the assessment of ventricular and aortic diseases, would also be an important hemodynamic factor in CAD. Methods: Three-dimensional (3D), patient-specific coronary artery geometries that included all vessels >1 mm in diameter were created from angiography data obtained from 10 patients who underwent diagnostic angiography and FFR testing (n = 9). A massively parallel CFD solver (HARVEY) was used to calculate coronary hemodynamic parameters including pressure, velocity, ESS, and vorticity. These simulations were validated by comparing velocity flow fields from simulation to both velocities derived from in vitro particle image velocimetry and to invasively acquired pressure wire-based data from clinical testing. Results: There was strong agreement between findings from CFD simulations and particle image velocimetry experimental testing (p < 0.01). CFD-FFR was also highly correlated with invasively measured FFR (ρ = 0.77, p = 0.01) with an average error of 5.9 ± 0.1%. CFD-FFR also had a strong inverse correlation with the vorticity (ρ = -0.86, p = 0.001). Simulations to determine the effect of the coronary stenosis on intravascular hemodynamics demonstrated significant differences in velocity and vorticity (both p < 0.05). Further evaluation of an angiographically normal appearing non-FFR coronary vessel in patients with CAD also demonstrated differences in vorticity when compared with FFR vessels (p < 0.05). Conclusion: The use of highly accurate 3D CFD-derived intravascular hemodynamics provides additional information beyond pressure measurements that can be used to calculate FFR. Vorticity is one parameter that is modified by a coronary stenosis and appears to be abnormal in angiographically normal vessels in patients with CAD, highlighting a possible use-case in preventative screening for early coronary disease.
{"title":"Evaluation of intracoronary hemodynamics identifies perturbations in vorticity","authors":"M. Vardhan, John P. Gounley, S. J. Chen, Priya Nair, Wei Wei, L. Hegele, Jonathan Kusner, A. Kahn, D. Frakes, J. Leopold, A. Randles","doi":"10.3389/fsysb.2022.930396","DOIUrl":"https://doi.org/10.3389/fsysb.2022.930396","url":null,"abstract":"Background and objective: Coronary artery disease (CAD) is highly prevalent and associated with adverse events. Challenges have emerged in the treatment of intermediate coronary artery stenoses. These lesions are often interrogated with fractional flow reserve (FFR) testing to determine if a stenosis is likely to be causative for ischemia in a cardiac territory. This invasive test requires insertion of a pressure wire into a coronary vessel. Recently computational fluid dynamics (CFD) has been used to noninvasively assess fractional flow reserve in vessels reconstructed from medical imaging data. However, many of these simulations are unable to provide additional information about intravascular hemodynamics, including velocity, endothelial shear stress (ESS), and vorticity. We hypothesized that vorticity, which has demonstrated utility in the assessment of ventricular and aortic diseases, would also be an important hemodynamic factor in CAD. Methods: Three-dimensional (3D), patient-specific coronary artery geometries that included all vessels >1 mm in diameter were created from angiography data obtained from 10 patients who underwent diagnostic angiography and FFR testing (n = 9). A massively parallel CFD solver (HARVEY) was used to calculate coronary hemodynamic parameters including pressure, velocity, ESS, and vorticity. These simulations were validated by comparing velocity flow fields from simulation to both velocities derived from in vitro particle image velocimetry and to invasively acquired pressure wire-based data from clinical testing. Results: There was strong agreement between findings from CFD simulations and particle image velocimetry experimental testing (p < 0.01). CFD-FFR was also highly correlated with invasively measured FFR (ρ = 0.77, p = 0.01) with an average error of 5.9 ± 0.1%. CFD-FFR also had a strong inverse correlation with the vorticity (ρ = -0.86, p = 0.001). Simulations to determine the effect of the coronary stenosis on intravascular hemodynamics demonstrated significant differences in velocity and vorticity (both p < 0.05). Further evaluation of an angiographically normal appearing non-FFR coronary vessel in patients with CAD also demonstrated differences in vorticity when compared with FFR vessels (p < 0.05). Conclusion: The use of highly accurate 3D CFD-derived intravascular hemodynamics provides additional information beyond pressure measurements that can be used to calculate FFR. Vorticity is one parameter that is modified by a coronary stenosis and appears to be abnormal in angiographically normal vessels in patients with CAD, highlighting a possible use-case in preventative screening for early coronary disease.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45062356","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 : 2022-11-02DOI: 10.3389/fsysb.2022.926923
D. Coombe, V. Rezania, J. Tuszynski
The liver is the body’s primary metabolic organ and its functions operate at multiple time and spatial scales. Here we employ multiscale modelling techniques to describe these functions consistently, based on methods originally developed to describe reactive fluid flow processes in naturally-fractured geological sediments. Using a fully discretized idealized lobule model for flow and metabolism, a dual continuum approach is developed in two steps: 1) Two interacting continua models for tissue and sinusoids properties, followed by 2) further upscaled dual continua models leading to an averaged lobule representation. Results (flows, pressures, concentrations, and reactions) from these two approaches are compared with our original model, indicating the equivalences and approximations obtained from this upscaling for flow, diffusion, and reaction parameters. Next, we have generated a gridded dual continuum model of the full liver utilizing an innovative technique, based on published liver outline and vasculature employing a vasculature generation algorithm. The inlet and outlet vasculature systems were grouped into five generations each based on radius size. With a chosen grid size of 1 mm3, our resulting discretized model contains 3,291,430 active grid cells. Of these cells, a fraction is occupied vasculature, while the dominant remaining fraction of grid cells approximates liver lobules. Here the largest generations of vasculature occupy multiple grid cells in cross section and length. The lobule grid cells are represented as a dual continuum of sinusoid vasculature and tissue. This represents the simplest gridded dual continuum representation of the full liver organ. With this basic model, numerous full liver drug metabolism simulations were run. A non-reactive PAC (paclitaxel) injection case including only convective transfer between vasculature and tissue was compared with including an additional diffusive transfer mechanism. These two cases were then rerun with tissue reaction, converting injected PAC to PAC-OH (6-hydroxypaclitaxel). There was little transfer of PAC from vasculature to tissue without the addition of diffusive transfer, and this had a significant observable effect on internal PAC distribution in the absence of reaction, and also on the distribution of PAC-OH for the reactive cases.
{"title":"Dual continuum upscaling of liver lobule flow and metabolism to the full organ scale","authors":"D. Coombe, V. Rezania, J. Tuszynski","doi":"10.3389/fsysb.2022.926923","DOIUrl":"https://doi.org/10.3389/fsysb.2022.926923","url":null,"abstract":"The liver is the body’s primary metabolic organ and its functions operate at multiple time and spatial scales. Here we employ multiscale modelling techniques to describe these functions consistently, based on methods originally developed to describe reactive fluid flow processes in naturally-fractured geological sediments. Using a fully discretized idealized lobule model for flow and metabolism, a dual continuum approach is developed in two steps: 1) Two interacting continua models for tissue and sinusoids properties, followed by 2) further upscaled dual continua models leading to an averaged lobule representation. Results (flows, pressures, concentrations, and reactions) from these two approaches are compared with our original model, indicating the equivalences and approximations obtained from this upscaling for flow, diffusion, and reaction parameters. Next, we have generated a gridded dual continuum model of the full liver utilizing an innovative technique, based on published liver outline and vasculature employing a vasculature generation algorithm. The inlet and outlet vasculature systems were grouped into five generations each based on radius size. With a chosen grid size of 1 mm3, our resulting discretized model contains 3,291,430 active grid cells. Of these cells, a fraction is occupied vasculature, while the dominant remaining fraction of grid cells approximates liver lobules. Here the largest generations of vasculature occupy multiple grid cells in cross section and length. The lobule grid cells are represented as a dual continuum of sinusoid vasculature and tissue. This represents the simplest gridded dual continuum representation of the full liver organ. With this basic model, numerous full liver drug metabolism simulations were run. A non-reactive PAC (paclitaxel) injection case including only convective transfer between vasculature and tissue was compared with including an additional diffusive transfer mechanism. These two cases were then rerun with tissue reaction, converting injected PAC to PAC-OH (6-hydroxypaclitaxel). There was little transfer of PAC from vasculature to tissue without the addition of diffusive transfer, and this had a significant observable effect on internal PAC distribution in the absence of reaction, and also on the distribution of PAC-OH for the reactive cases.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42982610","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 : 2022-10-28DOI: 10.3389/fsysb.2022.962790
K. Kaouri, Neophytos Christodoulou, A. Chakraborty, Paul E. Méndez, P. Skourides, R. Ruiz-Baier
Embryonic epithelial cells exhibit strong coupling of mechanical responses to chemical signals and most notably to calcium. Recent experiments have shown that the disruption of calcium signals during neurulation strongly correlates with the appearance of neural tube defects. We, thus, develop a multi-dimensional mechanochemical model and use it to reproduce important experimental findings that describe anterior neural plate morphogenetic behaviour during neural tube closure. The governing equations consist of an advection-diffusion-reaction system for calcium concentration which is coupled to a force balance equation for the tissue. The tissue is modelled as a linear viscoelastic material that includes a calcium-dependent contraction stress. We implement a random distribution of calcium sparks that is compatible with experimental findings. A finite element method is employed to generate numerical solutions of the model for an appropriately chosen range of parameter values. We analyse the behaviour of the model as three parameters vary: the level of IP3 concentration, the strength of the stretch-sensitive activation and the maximum magnitude of the calcium-dependent contraction stress. Importantly, the simulations reproduce important experimental features, such as the spatio-temporal correlation between calcium transients and tissue deformation, the monotonic reduction of the apical surface area and the constant constriction rate, as time progresses. The model could also be employed to gain insights into other biological processes where the coupling of calcium signalling and mechanics is important, such as carcinogenesis and wound healing.
{"title":"A new mechanochemical model for apical constriction: Coupling calcium signalling and viscoelasticity","authors":"K. Kaouri, Neophytos Christodoulou, A. Chakraborty, Paul E. Méndez, P. Skourides, R. Ruiz-Baier","doi":"10.3389/fsysb.2022.962790","DOIUrl":"https://doi.org/10.3389/fsysb.2022.962790","url":null,"abstract":"Embryonic epithelial cells exhibit strong coupling of mechanical responses to chemical signals and most notably to calcium. Recent experiments have shown that the disruption of calcium signals during neurulation strongly correlates with the appearance of neural tube defects. We, thus, develop a multi-dimensional mechanochemical model and use it to reproduce important experimental findings that describe anterior neural plate morphogenetic behaviour during neural tube closure. The governing equations consist of an advection-diffusion-reaction system for calcium concentration which is coupled to a force balance equation for the tissue. The tissue is modelled as a linear viscoelastic material that includes a calcium-dependent contraction stress. We implement a random distribution of calcium sparks that is compatible with experimental findings. A finite element method is employed to generate numerical solutions of the model for an appropriately chosen range of parameter values. We analyse the behaviour of the model as three parameters vary: the level of IP3 concentration, the strength of the stretch-sensitive activation and the maximum magnitude of the calcium-dependent contraction stress. Importantly, the simulations reproduce important experimental features, such as the spatio-temporal correlation between calcium transients and tissue deformation, the monotonic reduction of the apical surface area and the constant constriction rate, as time progresses. The model could also be employed to gain insights into other biological processes where the coupling of calcium signalling and mechanics is important, such as carcinogenesis and wound healing.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44239298","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 : 2022-10-26DOI: 10.3389/fsysb.2022.1021897
Jacob D. Davis, Daniel V. Olivença, S. Brown, E. Voit
The Lotka-Volterra (LV) model was introduced in the early 20th Century to describe predator-prey systems. Since then, the model has been expanded to capture the dynamics of numerous types of interacting populations and to include the effects of external factors from the environment. Despite many simplifying assumptions, the LV approach has proven to be a very valuable tool for gaining insights into the dynamics of diverse biological interaction systems. In particular, recognizing the critical importance of microbiomes for human and environmental heath, LV systems have become effective tools of analysis and, indeed, the default for quantitatively assessing interactions within these large microbial communities. Here we present an overview of parameter inference methods for LV systems, specifically addressing individuals entering the field of biomathematical modeling, who have a modest background in linear algebra and calculus. The methods include traditional local and global strategies, as well as a recently developed inference method based strictly on linear algebra. We compare the different strategies using both lab-acquired and synthetic time series data. We also address a recent debate within the scientific community of whether it is legitimate to compose large models from information inferred for the dynamics of subpopulations. In addition to parameter estimation methods, the overview includes preparatory aspects of the inference process, including data cleaning, smoothing, and the choice of an adequate loss function. Our comparisons demonstrate that traditional fitting strategies, such as gradient descent optimization and differential evolution, tend to yield low residuals but sometimes overfit noisy data and incur high computation costs. The linear-algebra-based method produces a satisfactory solution much faster, generally without overfitting, but requires the user to estimate slopes from the time series, which can introduce undue error. The results also suggest that composing large models from information regarding sub-models can be problematic. Overall, there is no clear “always-best method” for inferring parameters from data, and prudent combinations may be the best strategy.
{"title":"Methods of quantifying interactions among populations using Lotka-Volterra models","authors":"Jacob D. Davis, Daniel V. Olivença, S. Brown, E. Voit","doi":"10.3389/fsysb.2022.1021897","DOIUrl":"https://doi.org/10.3389/fsysb.2022.1021897","url":null,"abstract":"The Lotka-Volterra (LV) model was introduced in the early 20th Century to describe predator-prey systems. Since then, the model has been expanded to capture the dynamics of numerous types of interacting populations and to include the effects of external factors from the environment. Despite many simplifying assumptions, the LV approach has proven to be a very valuable tool for gaining insights into the dynamics of diverse biological interaction systems. In particular, recognizing the critical importance of microbiomes for human and environmental heath, LV systems have become effective tools of analysis and, indeed, the default for quantitatively assessing interactions within these large microbial communities. Here we present an overview of parameter inference methods for LV systems, specifically addressing individuals entering the field of biomathematical modeling, who have a modest background in linear algebra and calculus. The methods include traditional local and global strategies, as well as a recently developed inference method based strictly on linear algebra. We compare the different strategies using both lab-acquired and synthetic time series data. We also address a recent debate within the scientific community of whether it is legitimate to compose large models from information inferred for the dynamics of subpopulations. In addition to parameter estimation methods, the overview includes preparatory aspects of the inference process, including data cleaning, smoothing, and the choice of an adequate loss function. Our comparisons demonstrate that traditional fitting strategies, such as gradient descent optimization and differential evolution, tend to yield low residuals but sometimes overfit noisy data and incur high computation costs. The linear-algebra-based method produces a satisfactory solution much faster, generally without overfitting, but requires the user to estimate slopes from the time series, which can introduce undue error. The results also suggest that composing large models from information regarding sub-models can be problematic. Overall, there is no clear “always-best method” for inferring parameters from data, and prudent combinations may be the best strategy.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46029130","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 : 2022-10-26DOI: 10.3389/fsysb.2022.1022486
Frederick St. Peter, Srinivas Mukund Vadrev, O. Soufan
Radiation’s harmful effects on biological organisms have long been studied through mainly evaluating pathological changes in cells, tissues, or organs. Recently, there have been more accessible gene expression datasets relating to radiation exposure studies. This provides an opportunity to analyze responses at the molecular level toward revealing phenotypic differences. Biomarkers in toxicogenomics have been suggested as indicators of radiation exposure and seem to react differently to various dosages of radiation. This study proposes a predictive gene signature specific to radiation exposure and can be used in automatically diagnosing the exposure dose. In searching for a reliable gene set that will correctly identify the exposure dose, consideration needs to be given to the size of the set. For this reason, we experimented with the number of genes used for training and testing. Gene set sizes of 28, 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1,000 were tested to find the size that provided the best accuracy across three datasets. Models were then trained and tested using multiple datasets in various ways, including an external validation. The dissimilarities between these datasets provide an analogy to real-world conditions where data from multiple sources are likely to have variances in format, settings, time parameters, participants, processes, and machine tolerances, so a robust training dataset from many heterogeneous samples should provide better predictability. All three datasets showed positive results with the correct classification of the radiation exposure dose. The average accuracy of all three models was 88% for gene sets of both 400 and 1,000 genes. R400 provided the best results when testing the three datasets used in this study. A literature validation of top selected genes shows high relevance of perturbations to adverse effects reported during cancer radiotherapy.
{"title":"R400: A novel gene signature for dose prediction in radiation exposure studies in humans","authors":"Frederick St. Peter, Srinivas Mukund Vadrev, O. Soufan","doi":"10.3389/fsysb.2022.1022486","DOIUrl":"https://doi.org/10.3389/fsysb.2022.1022486","url":null,"abstract":"Radiation’s harmful effects on biological organisms have long been studied through mainly evaluating pathological changes in cells, tissues, or organs. Recently, there have been more accessible gene expression datasets relating to radiation exposure studies. This provides an opportunity to analyze responses at the molecular level toward revealing phenotypic differences. Biomarkers in toxicogenomics have been suggested as indicators of radiation exposure and seem to react differently to various dosages of radiation. This study proposes a predictive gene signature specific to radiation exposure and can be used in automatically diagnosing the exposure dose. In searching for a reliable gene set that will correctly identify the exposure dose, consideration needs to be given to the size of the set. For this reason, we experimented with the number of genes used for training and testing. Gene set sizes of 28, 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1,000 were tested to find the size that provided the best accuracy across three datasets. Models were then trained and tested using multiple datasets in various ways, including an external validation. The dissimilarities between these datasets provide an analogy to real-world conditions where data from multiple sources are likely to have variances in format, settings, time parameters, participants, processes, and machine tolerances, so a robust training dataset from many heterogeneous samples should provide better predictability. All three datasets showed positive results with the correct classification of the radiation exposure dose. The average accuracy of all three models was 88% for gene sets of both 400 and 1,000 genes. R400 provided the best results when testing the three datasets used in this study. A literature validation of top selected genes shows high relevance of perturbations to adverse effects reported during cancer radiotherapy.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43218200","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 : 2022-10-24DOI: 10.3389/fsysb.2022.1026686
Congmin Xu, Huyun Lu, Peng Qiu
When analyzing scRNA-seq data with clustering algorithms, annotating the clusters with cell types is an essential step toward biological interpretation of the data. Annotations can be performed manually using known cell type marker genes. Annotations can also be automated using knowledge-driven or data-driven machine learning algorithms. Majority of cell type annotation algorithms are designed to predict cell types for individual cells in a new dataset. Since biological interpretation of scRNA-seq data is often made on cell clusters rather than individual cells, several algorithms have been developed to annotate cell clusters. In this study, we compared five cell type annotation algorithms, Azimuth, SingleR, Garnett, scCATCH, and SCSA, which cover the spectrum of knowledge-driven and data-driven approaches to annotate either individual cells or cell clusters. We applied these five algorithms to two scRNA-seq datasets of peripheral blood mononuclear cells (PBMC) samples from COVID-19 patients and healthy controls, and evaluated their annotation performance. From this comparison, we observed that methods for annotating individual cells outperformed methods for annotation cell clusters. We applied the cell-based annotation algorithm Azimuth to the two scRNA-seq datasets to examine the immune response during COVID-19 infection. Both datasets presented significant depletion of plasmacytoid dendritic cells (pDCs), where differential expression in this cell type and pathway analysis revealed strong activation of type I interferon signaling pathway in response to the infection.
{"title":"Comparison of cell type annotation algorithms for revealing immune response of COVID-19","authors":"Congmin Xu, Huyun Lu, Peng Qiu","doi":"10.3389/fsysb.2022.1026686","DOIUrl":"https://doi.org/10.3389/fsysb.2022.1026686","url":null,"abstract":"When analyzing scRNA-seq data with clustering algorithms, annotating the clusters with cell types is an essential step toward biological interpretation of the data. Annotations can be performed manually using known cell type marker genes. Annotations can also be automated using knowledge-driven or data-driven machine learning algorithms. Majority of cell type annotation algorithms are designed to predict cell types for individual cells in a new dataset. Since biological interpretation of scRNA-seq data is often made on cell clusters rather than individual cells, several algorithms have been developed to annotate cell clusters. In this study, we compared five cell type annotation algorithms, Azimuth, SingleR, Garnett, scCATCH, and SCSA, which cover the spectrum of knowledge-driven and data-driven approaches to annotate either individual cells or cell clusters. We applied these five algorithms to two scRNA-seq datasets of peripheral blood mononuclear cells (PBMC) samples from COVID-19 patients and healthy controls, and evaluated their annotation performance. From this comparison, we observed that methods for annotating individual cells outperformed methods for annotation cell clusters. We applied the cell-based annotation algorithm Azimuth to the two scRNA-seq datasets to examine the immune response during COVID-19 infection. Both datasets presented significant depletion of plasmacytoid dendritic cells (pDCs), where differential expression in this cell type and pathway analysis revealed strong activation of type I interferon signaling pathway in response to the infection.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47117100","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 : 2022-10-18DOI: 10.3389/fsysb.2022.1026424
Abraham Román-Figueroa, Luis Tenorio-Hernández, M. Furlan-Magaril
The circadian clock synchronizes the temporal activity of physiological processes with geophysical time. At the molecular level circadian rhythms arise from negative feedback loops between activator and repressor transcription factors whose opposite and rhythmic activity at gene promoters sustains cyclic transcription. Additional epigenetic mechanisms driving rhythmic transcription involve dynamic remodeling of the proximal and distal chromatin environment of cyclic genes around the day. In this context, previous studies reported that thousands of enhancer elements display rhythmic activity throughout the 24 h and more recently, 3C-based technologies have shown that circadian genes establish static and rhythmic contacts with enhancers. However, the precise mechanisms by which the clock modulates gene topology are yet to be fully characterized and at the frontier of chronobiology. Here we review evidence of the proximal and long-distance epigenetic mechanisms controlling circadian transcription in health and disease.
{"title":"Distal and proximal control of rhythmic gene transcription","authors":"Abraham Román-Figueroa, Luis Tenorio-Hernández, M. Furlan-Magaril","doi":"10.3389/fsysb.2022.1026424","DOIUrl":"https://doi.org/10.3389/fsysb.2022.1026424","url":null,"abstract":"The circadian clock synchronizes the temporal activity of physiological processes with geophysical time. At the molecular level circadian rhythms arise from negative feedback loops between activator and repressor transcription factors whose opposite and rhythmic activity at gene promoters sustains cyclic transcription. Additional epigenetic mechanisms driving rhythmic transcription involve dynamic remodeling of the proximal and distal chromatin environment of cyclic genes around the day. In this context, previous studies reported that thousands of enhancer elements display rhythmic activity throughout the 24 h and more recently, 3C-based technologies have shown that circadian genes establish static and rhythmic contacts with enhancers. However, the precise mechanisms by which the clock modulates gene topology are yet to be fully characterized and at the frontier of chronobiology. Here we review evidence of the proximal and long-distance epigenetic mechanisms controlling circadian transcription in health and disease.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48419524","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 : 2022-10-13DOI: 10.3389/fsysb.2022.903512
Pantea Pooladvand, Peter S. Kim
Solid tumours develop much like a fortress, acquiring characteristics that protect them against invasion. A common trait observed in solid tumours is the synthesis of excess collagen which traps therapeutic agents, resulting in a lack of dispersion of treatment within the tumour mass. In most tumours, this results in only a localised treatment. Often the tumour quickly recovers and continues to invade surrounding regions. Anti-tumour viral therapy is no exception to this rule. Experimental results show collagen density affects virus diffusion and inhibits cell infection; therefore, accurately modelling virus dispersion is an important aspect of modelling virotherapy. To understand the underlying dynamics of viral diffusion in collagen, we derive a novel non-Fickian diffusion term from first principles. We demonstrate that this diffusion term captures experimentally observed virus dispersion in cancer-associated collagen, unlike the standard diffusion term, commonly used in virotherapy models. Then, using a system of partial differential equations, we explore virotherapy in relation to collagen density. We show that our model can predict therapy outcome in relation to collagen density. The results also suggest that modifications in virus performance, such as increased virus infectivity, is not effective in dense collagen; therefore, reducing collagen, might be the best approach when dealing with collagen-rich tumours. We also investigate virotherapy in relation to collagen structures and find that size of collagen deposits are as important to outcome as collagen density. Together, these results demonstrate that understanding virus diffusion in oncolytic virotherapy is a crucial step in capturing tumour response to treatment.
{"title":"Modelling oncolytic virus diffusion in collagen-dense tumours","authors":"Pantea Pooladvand, Peter S. Kim","doi":"10.3389/fsysb.2022.903512","DOIUrl":"https://doi.org/10.3389/fsysb.2022.903512","url":null,"abstract":"Solid tumours develop much like a fortress, acquiring characteristics that protect them against invasion. A common trait observed in solid tumours is the synthesis of excess collagen which traps therapeutic agents, resulting in a lack of dispersion of treatment within the tumour mass. In most tumours, this results in only a localised treatment. Often the tumour quickly recovers and continues to invade surrounding regions. Anti-tumour viral therapy is no exception to this rule. Experimental results show collagen density affects virus diffusion and inhibits cell infection; therefore, accurately modelling virus dispersion is an important aspect of modelling virotherapy. To understand the underlying dynamics of viral diffusion in collagen, we derive a novel non-Fickian diffusion term from first principles. We demonstrate that this diffusion term captures experimentally observed virus dispersion in cancer-associated collagen, unlike the standard diffusion term, commonly used in virotherapy models. Then, using a system of partial differential equations, we explore virotherapy in relation to collagen density. We show that our model can predict therapy outcome in relation to collagen density. The results also suggest that modifications in virus performance, such as increased virus infectivity, is not effective in dense collagen; therefore, reducing collagen, might be the best approach when dealing with collagen-rich tumours. We also investigate virotherapy in relation to collagen structures and find that size of collagen deposits are as important to outcome as collagen density. Together, these results demonstrate that understanding virus diffusion in oncolytic virotherapy is a crucial step in capturing tumour response to treatment.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43945169","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 : 2022-10-10DOI: 10.3389/fsysb.2022.928159
S. Freiesleben, M. Unverricht-Yeboah, Lea Gütebier, Dagmar Waltemath, R. Kriehuber, O. Wolkenhauer
MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are involved in the modulation of the DNA-damage response (DDR) and upon exposure to ionizing radiation (IR), their expression fluctuates. In this study, we propose a workflow that enables the creation of regulatory networks by integrating transcriptomics data as well as regulatory data in order to better understand the interplay between genes, transcription factors (TFs), miRNAs, and lncRNAs in the cellular response to IR. We preprocessed and analyzed publicly available gene expression profiles and then applied our consensus and integration approach using open source data and tools. To exemplify the benefits of our proposed workflow, we identified a total of 32 differentially expressed transcripts corresponding to 20 unique differentially expressed genes (DEGs) and using these DEGs, we constructed a regulatory network consisting of 106 interactions and 100 nodes (11 DEGs, 78 miRNAs, 1 DEG acting as a TF, and 10 lncRNAs). Overrepresentation analyses (ORAs) furthermore linked our DEGs and miRNAs to annotations pertaining to the DDR and to IR. Our results show that MDM2 and E2F7 function as network hubs, and E2F7, miR-25-3p, let-7a-5p, and miR-497-5p are the four nodes with the highest betweenness centrality. In brief, our workflow, that is based on open source data and tools, and that generates a regulatory network, provides novel insights into the regulatory mechanisms involving miRNAs and lncRNAs in the cellular response to IR.
{"title":"A workflow for the creation of regulatory networks integrating miRNAs and lncRNAs associated with exposure to ionizing radiation using open source data and tools","authors":"S. Freiesleben, M. Unverricht-Yeboah, Lea Gütebier, Dagmar Waltemath, R. Kriehuber, O. Wolkenhauer","doi":"10.3389/fsysb.2022.928159","DOIUrl":"https://doi.org/10.3389/fsysb.2022.928159","url":null,"abstract":"MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are involved in the modulation of the DNA-damage response (DDR) and upon exposure to ionizing radiation (IR), their expression fluctuates. In this study, we propose a workflow that enables the creation of regulatory networks by integrating transcriptomics data as well as regulatory data in order to better understand the interplay between genes, transcription factors (TFs), miRNAs, and lncRNAs in the cellular response to IR. We preprocessed and analyzed publicly available gene expression profiles and then applied our consensus and integration approach using open source data and tools. To exemplify the benefits of our proposed workflow, we identified a total of 32 differentially expressed transcripts corresponding to 20 unique differentially expressed genes (DEGs) and using these DEGs, we constructed a regulatory network consisting of 106 interactions and 100 nodes (11 DEGs, 78 miRNAs, 1 DEG acting as a TF, and 10 lncRNAs). Overrepresentation analyses (ORAs) furthermore linked our DEGs and miRNAs to annotations pertaining to the DDR and to IR. Our results show that MDM2 and E2F7 function as network hubs, and E2F7, miR-25-3p, let-7a-5p, and miR-497-5p are the four nodes with the highest betweenness centrality. In brief, our workflow, that is based on open source data and tools, and that generates a regulatory network, provides novel insights into the regulatory mechanisms involving miRNAs and lncRNAs in the cellular response to IR.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43691445","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 : 2022-10-10DOI: 10.3389/fsysb.2022.998048
R. P. van Rosmalen, V. M. D. Martins dos Santos, M. Suárez-Diez
Model-driven design has shown great promise for shortening the development time of cell factories by complementing and guiding metabolic engineering efforts. Still, implementation of the prized cycle of model predictions followed by experimental validation remains elusive. The development of modelling frameworks that can lead to actionable knowledge and subsequent integration of experimental efforts requires a conscious effort. In this review, we will explore some of the pitfalls that might derail this process and the critical role of achieving alignment between the selected modelling framework, the available data, and the ultimate purpose of the research. Using recent examples of studies successfully using modelling or other methods of data integration, we will then review the various types of data that can support different modelling formalisms, and in which scenarios these different models are at their most useful.
{"title":"Questions, data and models underpinning metabolic engineering","authors":"R. P. van Rosmalen, V. M. D. Martins dos Santos, M. Suárez-Diez","doi":"10.3389/fsysb.2022.998048","DOIUrl":"https://doi.org/10.3389/fsysb.2022.998048","url":null,"abstract":"Model-driven design has shown great promise for shortening the development time of cell factories by complementing and guiding metabolic engineering efforts. Still, implementation of the prized cycle of model predictions followed by experimental validation remains elusive. The development of modelling frameworks that can lead to actionable knowledge and subsequent integration of experimental efforts requires a conscious effort. In this review, we will explore some of the pitfalls that might derail this process and the critical role of achieving alignment between the selected modelling framework, the available data, and the ultimate purpose of the research. Using recent examples of studies successfully using modelling or other methods of data integration, we will then review the various types of data that can support different modelling formalisms, and in which scenarios these different models are at their most useful.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45561635","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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