Pub Date : 2024-07-24DOI: 10.3389/fsysb.2024.1394084
John Casey, Brian Bennion, Patrik D’haeseleer, Jeffrey Kimbrel, G. Marschmann, Ali Navid
Mechanistic, constraint-based models of microbial isolates or communities are a staple in the metabolic analysis toolbox, but predictions about microbe-microbe and microbe-environment interactions are only as good as the accuracy of transporter annotations. A number of hurdles stand in the way of comprehensive functional assignments for membrane transporters. These include general or non-specific substrate assignments, ambiguity in the localization, directionality and reversibility of a transporter, and the many-to-many mapping of substrates, transporters and genes. In this perspective, we summarize progress in both experimental and computational approaches used to determine the function of transporters and consider paths forward that integrate both. Investment in accurate, high-throughput functional characterization is needed to train the next-generation of predictive tools toward genome-scale metabolic network reconstructions that better predict phenotypes and interactions. More reliable predictions in this domain will benefit fields ranging from personalized medicine to metabolic engineering to microbial ecology.
{"title":"Transporter annotations are holding up progress in metabolic modeling","authors":"John Casey, Brian Bennion, Patrik D’haeseleer, Jeffrey Kimbrel, G. Marschmann, Ali Navid","doi":"10.3389/fsysb.2024.1394084","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1394084","url":null,"abstract":"Mechanistic, constraint-based models of microbial isolates or communities are a staple in the metabolic analysis toolbox, but predictions about microbe-microbe and microbe-environment interactions are only as good as the accuracy of transporter annotations. A number of hurdles stand in the way of comprehensive functional assignments for membrane transporters. These include general or non-specific substrate assignments, ambiguity in the localization, directionality and reversibility of a transporter, and the many-to-many mapping of substrates, transporters and genes. In this perspective, we summarize progress in both experimental and computational approaches used to determine the function of transporters and consider paths forward that integrate both. Investment in accurate, high-throughput functional characterization is needed to train the next-generation of predictive tools toward genome-scale metabolic network reconstructions that better predict phenotypes and interactions. More reliable predictions in this domain will benefit fields ranging from personalized medicine to metabolic engineering to microbial ecology.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-17DOI: 10.3389/fsysb.2024.1417800
Saúl Huitzil, Cristián Huepe
Modularity, the structuring of systems into discrete, interconnected units, is a fundamental organizing principle in biology across multiple scales. Recent progress in understanding the role of modularity as an evolutionary mechanism and a key driver of biological complexity has highlighted its importance in shaping the structure and function of living systems. Here, we propose a unifying framework that identifies the potential evolutionary advantages of modularity in systems ranging from molecular networks to ecologies, such as facilitating evolvability, enhancing robustness, improving information flows, and enabling the emergence of higher-level functions. Our analysis reveals the pervasiveness of modularity in living systems and highlights its crucial role in the evolution of multiscale hierarchies of increasing complexity.
{"title":"Life’s building blocks: the modular path to multiscale complexity","authors":"Saúl Huitzil, Cristián Huepe","doi":"10.3389/fsysb.2024.1417800","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1417800","url":null,"abstract":"Modularity, the structuring of systems into discrete, interconnected units, is a fundamental organizing principle in biology across multiple scales. Recent progress in understanding the role of modularity as an evolutionary mechanism and a key driver of biological complexity has highlighted its importance in shaping the structure and function of living systems. Here, we propose a unifying framework that identifies the potential evolutionary advantages of modularity in systems ranging from molecular networks to ecologies, such as facilitating evolvability, enhancing robustness, improving information flows, and enabling the emergence of higher-level functions. Our analysis reveals the pervasiveness of modularity in living systems and highlights its crucial role in the evolution of multiscale hierarchies of increasing complexity.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-12DOI: 10.3389/fsysb.2024.1380685
Núria Folguera-Blasco, Florencia A. T. Boshier, Aydar Uatay, C. Pichardo-Almarza, Massimo Lai, Jacopo Biasetti, Richard Dearden, Megan Gibbs, Holly Kimko
Quantitative Systems Pharmacology (QSP) has become a powerful tool in the drug development landscape. To facilitate its continued implementation and to further enhance its applicability, a symbiotic approach in which QSP is combined with artificial intelligence (AI) and machine learning (ML) seems key. This manuscript presents four case examples where the application of a symbiotic approach could unlock new insights from multidimensional data, including real-world data, potentially leading to breakthroughs in drug development. Besides the remarkable benefits (gAIns) that the symbiosis can offer, it does also carry potential challenges (pAIns) such as how to assess and quantify uncertainty, bias and error. Hence, to ensure a successful implementation, arising pAIns need to be acknowledged and carefully addressed. Successful implementation of the symbiotic QSP and ML/AI approach has the potential to serve as a catalyst, paving the way for a paradigm shift in drug development.
{"title":"Coupling quantitative systems pharmacology modelling to machine learning and artificial intelligence for drug development: its pAIns and gAIns","authors":"Núria Folguera-Blasco, Florencia A. T. Boshier, Aydar Uatay, C. Pichardo-Almarza, Massimo Lai, Jacopo Biasetti, Richard Dearden, Megan Gibbs, Holly Kimko","doi":"10.3389/fsysb.2024.1380685","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1380685","url":null,"abstract":"Quantitative Systems Pharmacology (QSP) has become a powerful tool in the drug development landscape. To facilitate its continued implementation and to further enhance its applicability, a symbiotic approach in which QSP is combined with artificial intelligence (AI) and machine learning (ML) seems key. This manuscript presents four case examples where the application of a symbiotic approach could unlock new insights from multidimensional data, including real-world data, potentially leading to breakthroughs in drug development. Besides the remarkable benefits (gAIns) that the symbiosis can offer, it does also carry potential challenges (pAIns) such as how to assess and quantify uncertainty, bias and error. Hence, to ensure a successful implementation, arising pAIns need to be acknowledged and carefully addressed. Successful implementation of the symbiotic QSP and ML/AI approach has the potential to serve as a catalyst, paving the way for a paradigm shift in drug development.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-04DOI: 10.3389/fsysb.2024.1327357
J. Clemmer, W. Pruett, Robert L. Hester
Chronic kidney disease (CKD) is associated with the progressive loss of functional nephrons and hypertension (HTN). Clinical studies demonstrate calcium channel blocker (CCB) therapy mitigates the decline in renal function in humans with essential HTN. However, there are few long-term clinical studies that determine the impact of CCBs in patients with hypertensive CKD. African Americans (AA) have a higher prevalence of CKD and a faster progression to total kidney failure as compared to the white population but the mechanisms are poorly understood. Both clinical evidence (the African American Study of Kidney Disease and Hypertension, or AASK trial) and experimental studies have demonstrated that CCB may expose glomerular capillaries to high systemic pressures and exacerbate CKD progression. Therefore, using a large physiological model, we set out to replicate the AASK trial findings, predict renal hemodynamic responses and the role of the renin-angiotensin system during CCB antihypertensive therapy in a virtual population, and hypothesize mechanisms underlying those findings. Our current mathematical model, HumMod, is comprised of integrated systems that play an integral role in long-term blood pressure (BP) control such as neural, endocrine, circulatory, and renal systems. Parameters (n = 341) that control these systems were randomly varied and resulted in 1,400 unique models that we define as a virtual population. We calibrated these models to individual patient level data from the AASK trial: BP and glomerular filtration rate (GFR) before and after 3 years of amlodipine (10 mg/day). After calibration, the new virtual population (n = 165) was associated with statistically similar BP and GFR before and after CCB. Baseline factors such as elevated single nephron GFR and low tubuloglomerular feedback were correlated with greater declines in renal function and increased glomerulosclerosis after 3 years of CCB. Blocking the renin-angiotensin system (RAS) in the virtual population decreased glomerular pressure, limited glomerular damage, and further decreased BP (−14 ± 8 mmHg) as compared to CCB alone (−11 ± 9 mmHg). Our simulations echo the potential risk of CCB monotherapy in AA CKD patients and support blockade of the renin angiotensin system as a valuable tool in renal disease treatment when combined with CCB therapy.
{"title":"Predicting chronic responses to calcium channel blockade with a virtual population of African Americans with hypertensive chronic kidney disease","authors":"J. Clemmer, W. Pruett, Robert L. Hester","doi":"10.3389/fsysb.2024.1327357","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1327357","url":null,"abstract":"Chronic kidney disease (CKD) is associated with the progressive loss of functional nephrons and hypertension (HTN). Clinical studies demonstrate calcium channel blocker (CCB) therapy mitigates the decline in renal function in humans with essential HTN. However, there are few long-term clinical studies that determine the impact of CCBs in patients with hypertensive CKD. African Americans (AA) have a higher prevalence of CKD and a faster progression to total kidney failure as compared to the white population but the mechanisms are poorly understood. Both clinical evidence (the African American Study of Kidney Disease and Hypertension, or AASK trial) and experimental studies have demonstrated that CCB may expose glomerular capillaries to high systemic pressures and exacerbate CKD progression. Therefore, using a large physiological model, we set out to replicate the AASK trial findings, predict renal hemodynamic responses and the role of the renin-angiotensin system during CCB antihypertensive therapy in a virtual population, and hypothesize mechanisms underlying those findings. Our current mathematical model, HumMod, is comprised of integrated systems that play an integral role in long-term blood pressure (BP) control such as neural, endocrine, circulatory, and renal systems. Parameters (n = 341) that control these systems were randomly varied and resulted in 1,400 unique models that we define as a virtual population. We calibrated these models to individual patient level data from the AASK trial: BP and glomerular filtration rate (GFR) before and after 3 years of amlodipine (10 mg/day). After calibration, the new virtual population (n = 165) was associated with statistically similar BP and GFR before and after CCB. Baseline factors such as elevated single nephron GFR and low tubuloglomerular feedback were correlated with greater declines in renal function and increased glomerulosclerosis after 3 years of CCB. Blocking the renin-angiotensin system (RAS) in the virtual population decreased glomerular pressure, limited glomerular damage, and further decreased BP (−14 ± 8 mmHg) as compared to CCB alone (−11 ± 9 mmHg). Our simulations echo the potential risk of CCB monotherapy in AA CKD patients and support blockade of the renin angiotensin system as a valuable tool in renal disease treatment when combined with CCB therapy.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141679000","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 : 2024-06-07DOI: 10.3389/fsysb.2024.1384481
P. Nymark, Laure-Alix Clerbaux, Maria-João Amorim, Christos Andronis, Francesca de Bernardi, Gillina F. G. Bezemer, Sandra Coecke, Felicity N. E. Gavins, Daniel Jacobson, E. Lekka, Luigi Margiotta-Casaluci, Marvin Martens, S. Mayasich, Holly M. Mortensen, Young Jun Kim, M. Sachana, Shihori Tanabe, V. Virvilis, Steve W. Edwards, Sabina Halappanavar
The COVID-19 pandemic generated large amounts of data on the disease pathogenesis leading to a need for organizing the vast knowledge in a succinct manner. Between April 2020 and February 2023, the CIAO consortium exploited the Adverse Outcome Pathway (AOP) framework to comprehensively gather and systematically organize published scientific literature on COVID-19 pathology. The project considered 24 pathways relevant for COVID-19 by identifying essential key events (KEs) leading to 19 adverse outcomes observed in patients. While an individual AOP defines causally linked perturbed KEs towards an outcome, building an AOP network visually reflect the interrelatedness of the various pathways and outcomes. In this study, 17 of those COVID-19 AOPs were selected based on quality criteria to computationally derive an AOP network. This primary network highlighted the need to consider tissue specificity and helped to identify missing or redundant elements which were then manually implemented in the final network. Such a network enabled visualization of the complex interactions of the KEs leading to the various outcomes of the multifaceted COVID-19 and confirmed the central role of the inflammatory response in the disease. In addition, this study disclosed the importance of terminology harmonization and of tissue/organ specificity for network building. Furthermore the unequal completeness and quality of information contained in the AOPs highlighted the need for tighter implementation of the FAIR principles to improve AOP findability, accessibility, interoperability and re-usability. Finally, the study underlined that describing KEs specific to SARS-CoV-2 replication and discriminating physiological from pathological inflammation is necessary but requires adaptations to the framework. Hence, based on the challenges encountered, we proposed recommendations relevant for ongoing and future AOP-aligned consortia aiming to build computationally biologically meaningful AOP networks in the context of, but not limited to, viral diseases.
{"title":"Building an Adverse Outcome Pathway network for COVID-19","authors":"P. Nymark, Laure-Alix Clerbaux, Maria-João Amorim, Christos Andronis, Francesca de Bernardi, Gillina F. G. Bezemer, Sandra Coecke, Felicity N. E. Gavins, Daniel Jacobson, E. Lekka, Luigi Margiotta-Casaluci, Marvin Martens, S. Mayasich, Holly M. Mortensen, Young Jun Kim, M. Sachana, Shihori Tanabe, V. Virvilis, Steve W. Edwards, Sabina Halappanavar","doi":"10.3389/fsysb.2024.1384481","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1384481","url":null,"abstract":"The COVID-19 pandemic generated large amounts of data on the disease pathogenesis leading to a need for organizing the vast knowledge in a succinct manner. Between April 2020 and February 2023, the CIAO consortium exploited the Adverse Outcome Pathway (AOP) framework to comprehensively gather and systematically organize published scientific literature on COVID-19 pathology. The project considered 24 pathways relevant for COVID-19 by identifying essential key events (KEs) leading to 19 adverse outcomes observed in patients. While an individual AOP defines causally linked perturbed KEs towards an outcome, building an AOP network visually reflect the interrelatedness of the various pathways and outcomes. In this study, 17 of those COVID-19 AOPs were selected based on quality criteria to computationally derive an AOP network. This primary network highlighted the need to consider tissue specificity and helped to identify missing or redundant elements which were then manually implemented in the final network. Such a network enabled visualization of the complex interactions of the KEs leading to the various outcomes of the multifaceted COVID-19 and confirmed the central role of the inflammatory response in the disease. In addition, this study disclosed the importance of terminology harmonization and of tissue/organ specificity for network building. Furthermore the unequal completeness and quality of information contained in the AOPs highlighted the need for tighter implementation of the FAIR principles to improve AOP findability, accessibility, interoperability and re-usability. Finally, the study underlined that describing KEs specific to SARS-CoV-2 replication and discriminating physiological from pathological inflammation is necessary but requires adaptations to the framework. Hence, based on the challenges encountered, we proposed recommendations relevant for ongoing and future AOP-aligned consortia aiming to build computationally biologically meaningful AOP networks in the context of, but not limited to, viral diseases.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141373398","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 : 2024-06-03DOI: 10.3389/fsysb.2024.1402664
Jiaxin Yang, Sikta Das Adhikari, Hao Wang, Binbin Huang, Wenjie Qi, Yuehua Cui, Jianrong Wang
Deciphering the functional effects of noncoding genetic variants stands as a fundamental challenge in human genetics. Traditional approaches, such as Genome-Wide Association Studies (GWAS), Transcriptome-Wide Association Studies (TWAS), and Quantitative Trait Loci (QTL) studies, are constrained by obscured the underlying molecular-level mechanisms, making it challenging to unravel the genetic basis of complex traits. The advent of Next-Generation Sequencing (NGS) technologies has enabled context-specific genome-wide measurements, encompassing gene expression, chromatin accessibility, epigenetic marks, and transcription factor binding sites, to be obtained across diverse cell types and tissues, paving the way for decoding genetic variation effects directly from DNA sequences only. The de novo predictions of functional effects are pivotal for enhancing our comprehension of transcriptional regulation and its disruptions caused by the plethora of noncoding genetic variants linked to human diseases and traits. This review provides a systematic overview of the state-of-the-art models and algorithms for genetic variant effect predictions, including traditional sequence-based models, Deep Learning models, and the cutting-edge Foundation Models. It delves into the ongoing challenges and prospective directions, presenting an in-depth perspective on contemporary developments in this domain.
破解非编码基因变异的功能效应是人类遗传学面临的一项基本挑战。传统的方法，如全基因组关联研究（GWAS）、全转录组关联研究（TWAS）和定量性状位点研究（QTL），受制于模糊的分子水平机制，使得揭示复杂性状的遗传基础具有挑战性。下一代测序（NGS）技术的出现使人们能够在不同的细胞类型和组织中获得特定的全基因组测量结果，包括基因表达、染色质可及性、表观遗传标记和转录因子结合位点，为仅从 DNA 序列直接解码遗传变异效应铺平了道路。对功能效应的全新预测，对于提高我们对转录调控及其由与人类疾病和性状相关的大量非编码基因变异引起的破坏的理解至关重要。本综述系统地概述了用于遗传变异效应预测的最先进模型和算法，包括传统的基于序列的模型、深度学习模型和最先进的基础模型。它深入探讨了当前面临的挑战和未来的发展方向，对该领域的当代发展提出了深入的看法。
{"title":"De novo prediction of functional effects of genetic variants from DNA sequences based on context-specific molecular information","authors":"Jiaxin Yang, Sikta Das Adhikari, Hao Wang, Binbin Huang, Wenjie Qi, Yuehua Cui, Jianrong Wang","doi":"10.3389/fsysb.2024.1402664","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1402664","url":null,"abstract":"Deciphering the functional effects of noncoding genetic variants stands as a fundamental challenge in human genetics. Traditional approaches, such as Genome-Wide Association Studies (GWAS), Transcriptome-Wide Association Studies (TWAS), and Quantitative Trait Loci (QTL) studies, are constrained by obscured the underlying molecular-level mechanisms, making it challenging to unravel the genetic basis of complex traits. The advent of Next-Generation Sequencing (NGS) technologies has enabled context-specific genome-wide measurements, encompassing gene expression, chromatin accessibility, epigenetic marks, and transcription factor binding sites, to be obtained across diverse cell types and tissues, paving the way for decoding genetic variation effects directly from DNA sequences only. The de novo predictions of functional effects are pivotal for enhancing our comprehension of transcriptional regulation and its disruptions caused by the plethora of noncoding genetic variants linked to human diseases and traits. This review provides a systematic overview of the state-of-the-art models and algorithms for genetic variant effect predictions, including traditional sequence-based models, Deep Learning models, and the cutting-edge Foundation Models. It delves into the ongoing challenges and prospective directions, presenting an in-depth perspective on contemporary developments in this domain.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141272489","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 : 2024-04-25DOI: 10.3389/fsysb.2024.1293265
B. Kurowski, A. Treble-Barna, Valentina Pilipenko, Lisa J. Martin, Anil G. Jegga, Aimee E Miley, Nanhua Zhang, Anthony Fabio, Ranjit S. Chima, Anna-Lynne R. Adlam, Kenneth Kaufman, Michael J Bell, Sue R Beers, Stephen R. Wisniewski, Shari L. Wade
Introduction: There is significant unexplained variability in behavioral and executive functioning after pediatric traumatic brain injury (TBI). Prior research indicates that there are likely genetic contributions; however, current research is limited. The purpose of this study is to use a systems biology informed approach to characterize the genomic signature related to behavioral and executive functioning ∼12 months after moderate through severe TBI in children.Methods: Participants were from two prospective cohorts of children with severe TBI (Cohort #1) and moderate-severe TBI and an orthopedic injury (OI) group (Cohort #2). Participants included 196 children (n = 72 and n = 124 total from each respective cohort), ranging in age between 0–17 years at the time of injury. In total, 86 children had severe TBI, 49 had moderate TBI, and 61 had an OI. Global behavioral functioning assessed via the Child Behavior Checklist and executive function assessed via the Behavioral Rating Inventory of Executive Function at ∼ 12 months post injury served as outcomes. To test for a genomic signature, we compared the number of nominally significant (p < 0.05) polymorphisms associated with the outcomes in our systems biology identified genes to a set 10,000 permutations using control genes (e.g., not implicated by systems biology). We used the ToppFun application from Toppgene Suite to identify enriched biologic processes likely to be associated with behavioral and executive function outcomes.Results: At 12 months post injury, injury type (TBI vs OI) by polymorphism interaction was significantly enriched in systems biology selected genes for behavioral and executive function outcomes, suggesting these genes form a genomic signature. Effect sizes of the associations from our genes of interest ranged from .2–.5 for the top 5% of variants. Systems biology analysis of the variants associated with the top 5% effect sizes indicated enrichment in several specific biologic processes and systems.Discussion: Findings indicate that a genomic signature may explain heterogeneity of behavioral and executive outcomes after moderate and severe TBI. This work provides the foundation for constructing genomic signatures and integrating systems biology and genetic information into future recovery, prognostic, and treatment algorithms.
{"title":"Elucidating a genomic signature associated with behavioral and executive function after moderate to severe pediatric TBI: a systems biology informed approach","authors":"B. Kurowski, A. Treble-Barna, Valentina Pilipenko, Lisa J. Martin, Anil G. Jegga, Aimee E Miley, Nanhua Zhang, Anthony Fabio, Ranjit S. Chima, Anna-Lynne R. Adlam, Kenneth Kaufman, Michael J Bell, Sue R Beers, Stephen R. Wisniewski, Shari L. Wade","doi":"10.3389/fsysb.2024.1293265","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1293265","url":null,"abstract":"Introduction: There is significant unexplained variability in behavioral and executive functioning after pediatric traumatic brain injury (TBI). Prior research indicates that there are likely genetic contributions; however, current research is limited. The purpose of this study is to use a systems biology informed approach to characterize the genomic signature related to behavioral and executive functioning ∼12 months after moderate through severe TBI in children.Methods: Participants were from two prospective cohorts of children with severe TBI (Cohort #1) and moderate-severe TBI and an orthopedic injury (OI) group (Cohort #2). Participants included 196 children (n = 72 and n = 124 total from each respective cohort), ranging in age between 0–17 years at the time of injury. In total, 86 children had severe TBI, 49 had moderate TBI, and 61 had an OI. Global behavioral functioning assessed via the Child Behavior Checklist and executive function assessed via the Behavioral Rating Inventory of Executive Function at ∼ 12 months post injury served as outcomes. To test for a genomic signature, we compared the number of nominally significant (p < 0.05) polymorphisms associated with the outcomes in our systems biology identified genes to a set 10,000 permutations using control genes (e.g., not implicated by systems biology). We used the ToppFun application from Toppgene Suite to identify enriched biologic processes likely to be associated with behavioral and executive function outcomes.Results: At 12 months post injury, injury type (TBI vs OI) by polymorphism interaction was significantly enriched in systems biology selected genes for behavioral and executive function outcomes, suggesting these genes form a genomic signature. Effect sizes of the associations from our genes of interest ranged from .2–.5 for the top 5% of variants. Systems biology analysis of the variants associated with the top 5% effect sizes indicated enrichment in several specific biologic processes and systems.Discussion: Findings indicate that a genomic signature may explain heterogeneity of behavioral and executive outcomes after moderate and severe TBI. This work provides the foundation for constructing genomic signatures and integrating systems biology and genetic information into future recovery, prognostic, and treatment algorithms.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140658194","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 : 2024-04-09DOI: 10.3389/fsysb.2024.1368555
Carley V. Cook, Ariel M. Lighty, Brenda J. Smith, Ashlee N. Ford Versypt
Bone remodeling is an essential, delicately balanced physiological process of coordinated activity of bone cells that remove and deposit new bone tissue in the adult skeleton. Due to the complex nature of this process, many mathematical models of bone remodeling have been developed. Each of these models has unique features, but they have underlying patterns. In this review, the authors highlight the important aspects frequently found in mathematical models for bone remodeling and discuss how and why these aspects are included when considering the physiology of the bone basic multicellular unit, which is the term used for the collection of cells responsible for bone remodeling. The review also emphasizes the view of bone remodeling from a systems biology perspective. Understanding the systemic mechanisms involved in remodeling will help provide information on bone pathology associated with aging, endocrine disorders, cancers, and inflammatory conditions and enhance systems pharmacology. Furthermore, some features of the bone remodeling cycle and interactions with other organ systems that have not yet been modeled mathematically are discussed as promising future directions in the field.
{"title":"A review of mathematical modeling of bone remodeling from a systems biology perspective","authors":"Carley V. Cook, Ariel M. Lighty, Brenda J. Smith, Ashlee N. Ford Versypt","doi":"10.3389/fsysb.2024.1368555","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1368555","url":null,"abstract":"Bone remodeling is an essential, delicately balanced physiological process of coordinated activity of bone cells that remove and deposit new bone tissue in the adult skeleton. Due to the complex nature of this process, many mathematical models of bone remodeling have been developed. Each of these models has unique features, but they have underlying patterns. In this review, the authors highlight the important aspects frequently found in mathematical models for bone remodeling and discuss how and why these aspects are included when considering the physiology of the bone basic multicellular unit, which is the term used for the collection of cells responsible for bone remodeling. The review also emphasizes the view of bone remodeling from a systems biology perspective. Understanding the systemic mechanisms involved in remodeling will help provide information on bone pathology associated with aging, endocrine disorders, cancers, and inflammatory conditions and enhance systems pharmacology. Furthermore, some features of the bone remodeling cycle and interactions with other organ systems that have not yet been modeled mathematically are discussed as promising future directions in the field.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140722853","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 : 2024-04-09DOI: 10.3389/fsysb.2024.1284668
Samuel King, Xinyi E. Chen, Sarah W. S. Ng, Kimia Rostin, Samuel V. Hahn, Tylo Roberts, Janella C. Schwab, Parneet Sekhon, Madina Kagieva, Taylor Reilly, Ruo Chen Qi, Paarsa Salman, Ryan J. Hong, Eric J. Ma, Steven J. Hallam
The emergence of SARS-CoV-2 variants during the COVID-19 pandemic caused frequent global outbreaks that confounded public health efforts across many jurisdictions, highlighting the need for better understanding and prediction of viral evolution. Predictive models have been shown to support disease prevention efforts, such as with the seasonal influenza vaccine, but they require abundant data. For emerging viruses of concern, such models should ideally function with relatively sparse data typically encountered at the early stages of a viral outbreak. Conventional discrete approaches have proven difficult to develop due to the spurious and reversible nature of amino acid mutations and the overwhelming number of possible protein sequences adding computational complexity. We hypothesized that these challenges could be addressed by encoding discrete protein sequences into continuous numbers, effectively reducing the data size while enhancing the resolution of evolutionarily relevant differences. To this end, we developed a viral protein evolution prediction model (VPRE), which reduces amino acid sequences into continuous numbers by using an artificial neural network called a variational autoencoder (VAE) and models their most statistically likely evolutionary trajectories over time using Gaussian process (GP) regression. To demonstrate VPRE, we used a small amount of early SARS-CoV-2 spike protein sequences. We show that the VAE can be trained on a synthetic dataset based on this data. To recapitulate evolution along a phylogenetic path, we used only 104 spike protein sequences and trained the GP regression with the numerical variables to project evolution up to 5 months into the future. Our predictions contained novel variants and the most frequent prediction mapped primarily to a sequence that differed by only a single amino acid from the most reported spike protein within the prediction timeframe. Novel variants in the spike receptor binding domain (RBD) were capable of binding human angiotensin-converting enzyme 2 (ACE2) in silico, with comparable or better binding than previously resolved RBD-ACE2 complexes. Together, these results indicate the utility and tractability of combining deep learning and regression to model viral protein evolution with relatively sparse datasets, toward developing more effective medical interventions.
{"title":"Forecasting SARS-CoV-2 spike protein evolution from small data by deep learning and regression","authors":"Samuel King, Xinyi E. Chen, Sarah W. S. Ng, Kimia Rostin, Samuel V. Hahn, Tylo Roberts, Janella C. Schwab, Parneet Sekhon, Madina Kagieva, Taylor Reilly, Ruo Chen Qi, Paarsa Salman, Ryan J. Hong, Eric J. Ma, Steven J. Hallam","doi":"10.3389/fsysb.2024.1284668","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1284668","url":null,"abstract":"The emergence of SARS-CoV-2 variants during the COVID-19 pandemic caused frequent global outbreaks that confounded public health efforts across many jurisdictions, highlighting the need for better understanding and prediction of viral evolution. Predictive models have been shown to support disease prevention efforts, such as with the seasonal influenza vaccine, but they require abundant data. For emerging viruses of concern, such models should ideally function with relatively sparse data typically encountered at the early stages of a viral outbreak. Conventional discrete approaches have proven difficult to develop due to the spurious and reversible nature of amino acid mutations and the overwhelming number of possible protein sequences adding computational complexity. We hypothesized that these challenges could be addressed by encoding discrete protein sequences into continuous numbers, effectively reducing the data size while enhancing the resolution of evolutionarily relevant differences. To this end, we developed a viral protein evolution prediction model (VPRE), which reduces amino acid sequences into continuous numbers by using an artificial neural network called a variational autoencoder (VAE) and models their most statistically likely evolutionary trajectories over time using Gaussian process (GP) regression. To demonstrate VPRE, we used a small amount of early SARS-CoV-2 spike protein sequences. We show that the VAE can be trained on a synthetic dataset based on this data. To recapitulate evolution along a phylogenetic path, we used only 104 spike protein sequences and trained the GP regression with the numerical variables to project evolution up to 5 months into the future. Our predictions contained novel variants and the most frequent prediction mapped primarily to a sequence that differed by only a single amino acid from the most reported spike protein within the prediction timeframe. Novel variants in the spike receptor binding domain (RBD) were capable of binding human angiotensin-converting enzyme 2 (ACE2) in silico, with comparable or better binding than previously resolved RBD-ACE2 complexes. Together, these results indicate the utility and tractability of combining deep learning and regression to model viral protein evolution with relatively sparse datasets, toward developing more effective medical interventions.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140723569","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 : 2024-04-03DOI: 10.3389/fsysb.2024.1385112
Glenn D. R. Watson, Stefano Meletti, Anil K. Mahavadi, Pierre Besson, S. Bandt, Jared B. Smith
The function of the claustrum and its role in neurological disorders remains a subject of interest in the field of neurology. Given the claustrum’s susceptibility to seizure-induced damage, there is speculation that it could serve as a node in a dysfunctional epileptic network. This perspective article aims to address a pivotal question: Does the claustrum play a role in epilepsy? Building upon existing literature, we propose the following hypotheses for the involvement of the claustrum in epilepsy: (1) Bilateral T2/FLAIR magnetic resonance imaging (MRI) hyperintensity of the claustrum after status epilepticus represents a radiological phenomenon that signifies inflammation-related epileptogenesis; (2) The ventral claustrum is synonymous with a brain area known as ‘area tempestas,’ an established epileptogenic center; (3) The ventral subsector of the claustrum facilitates seizure generalization/propagation through its connections with limbic and motor-related brain structures; (4) Disruption of claustrum connections during seizures might contribute to the loss of consciousness observed in impaired awareness seizures; (5) Targeting the claustrum therapeutically could be advantageous in seizures that arise from limbic foci. Together, evidence from both clinical case reports and animal studies identify a significant role for the ventral claustrum in the generation, propagation, and intractable nature of seizures in a subset of epilepsy syndromes.
{"title":"Is there room in epilepsy for the claustrum?","authors":"Glenn D. R. Watson, Stefano Meletti, Anil K. Mahavadi, Pierre Besson, S. Bandt, Jared B. Smith","doi":"10.3389/fsysb.2024.1385112","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1385112","url":null,"abstract":"The function of the claustrum and its role in neurological disorders remains a subject of interest in the field of neurology. Given the claustrum’s susceptibility to seizure-induced damage, there is speculation that it could serve as a node in a dysfunctional epileptic network. This perspective article aims to address a pivotal question: Does the claustrum play a role in epilepsy? Building upon existing literature, we propose the following hypotheses for the involvement of the claustrum in epilepsy: (1) Bilateral T2/FLAIR magnetic resonance imaging (MRI) hyperintensity of the claustrum after status epilepticus represents a radiological phenomenon that signifies inflammation-related epileptogenesis; (2) The ventral claustrum is synonymous with a brain area known as ‘area tempestas,’ an established epileptogenic center; (3) The ventral subsector of the claustrum facilitates seizure generalization/propagation through its connections with limbic and motor-related brain structures; (4) Disruption of claustrum connections during seizures might contribute to the loss of consciousness observed in impaired awareness seizures; (5) Targeting the claustrum therapeutically could be advantageous in seizures that arise from limbic foci. Together, evidence from both clinical case reports and animal studies identify a significant role for the ventral claustrum in the generation, propagation, and intractable nature of seizures in a subset of epilepsy syndromes.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748799","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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