Frontiers in systems biology最新文献

{"title":"Exploring the disconnect: mechanisms underpinning the absence of physical function improvement with SGLT2 inhibitors.","authors":"Cian Sutcliffe, Jack A Sargeant, Thomas Yates, Melanie J Davies, Luke A Baker","doi":"10.3389/fsysb.2025.1593229","DOIUrl":"10.3389/fsysb.2025.1593229","url":null,"abstract":"<p><p>Current evidence suggests sodium-glucose cotransporter 2 inhibitors (SGLT2i) do not consistently improve patient physical function, despite improvements in clinical symptoms and reductions in both adiposity and body weight. We highlight heterogenous methodologies in SGLT2i physical function trials. We then provide context to these findings by collating new data which describes how reduced glycaemia with SGLT2i alters numerous physiological processes and discuss how these alterations may diminish or prevent expected functional improvements. Alterations include changes to energy homeostasis, pancreatic hormones, muscle metabolism, physical activity, and appetite regulation. Current evidence in humans is limited and the mechanistic interaction between SGLT2i, skeletal muscle, and physical function remains incompletely understood. Future investigations must embed comprehensive molecular techniques within suitably designed clinical trials to determine how skeletal muscle health and patient mobility is influenced by acute and long term SGLT2i prescription.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1593229"},"PeriodicalIF":2.3,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical modeling of pharmacokinetics and pharmacodynamics of losartan in relation to <i>CYP2C9</i> allele variants.","authors":"Dmitry Babaev, Elena Kutumova, Fedor Kolpakov","doi":"10.3389/fsysb.2025.1504077","DOIUrl":"10.3389/fsysb.2025.1504077","url":null,"abstract":"<p><p>Losartan is a selective angiotensin II AT1-receptor antagonist for the treatment of arterial hypertension and heart failure. It is converted to a pharmacologically active metabolite carboxylosartan (E-3174) in the liver mainly by CYP2C9 enzyme, a member of the cytochrome P450 superfamily. The gene encoding this protein is highly polymorphic: numerous single nucleotide polymorphisms that alter the enzyme function have been described in the literature. The most widespread <i>CYP2C9</i> alleles are <i>CYP2C9*1</i> (wild-type), <i>CYP2C9*2</i>, and <i>CYP2C9*3</i>. Here we performed mathematical modeling of the metabolism of orally administered losartan to E-3174 taking into account combinations of the most common <i>CYP2C9</i> alleles. Next, using the previously created model of the human cardiovascular and renal systems, we demonstrated that the blood pressure response to losartan therapy in a cohort of virtual hypertensive patients depended on <i>CYP2C9</i> allelic variants. Individuals with the <i>CYP2C9*1/CYP2C9*1</i> genotype responded better to treatment than patients carrying <i>CYP2C9*2</i> or <i>CYP2C9*3</i> alleles. The results of the modeling can potentially be used for personalization of drug therapy for arterial hypertension.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1504077"},"PeriodicalIF":2.3,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biotechnology systems engineering: preparing the next generation of bioengineers.","authors":"Sebastián Espinel-Ríos","doi":"10.3389/fsysb.2025.1583534","DOIUrl":"10.3389/fsysb.2025.1583534","url":null,"abstract":"","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1583534"},"PeriodicalIF":2.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341963/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of probiotics, prebiotics, and synbiotics in the treatment of inflammatory bowel diseases: an overview of recent clinical trials.","authors":"Fayez Yassine, Adam Najm, Melhem Bilen","doi":"10.3389/fsysb.2025.1561047","DOIUrl":"10.3389/fsysb.2025.1561047","url":null,"abstract":"<p><strong>Background: </strong>The increasing incidence of inflammatory bowel diseases (IBD) over the last two decades has prompted the need to create new types of therapeutic interventions. The gut microbiome has emerged as a key component in the prognosis and pathophysiology of IBDs. The alteration or dysbiosis of the gut microbiome has been shown to exacerbate IBDs. The bacterial composition of the gut microbiome can be modulated through the usage of probiotics, prebiotics, and synbiotics. These interventions induce the growth of beneficial bacteria. Additionally, these interventions could be used to maintain gut homeostasis, reduce the inflammation seen in these morbidities, and strengthen the gut epithelial barrier.</p><p><strong>Methods: </strong>The literature review was conducted in October 2024 using PubMed, Scopus, and Google Scholar screening for recent clinical trials in addition to reviews relevant to the topic.</p><p><strong>Aims: </strong>This review aims to summarize the recent clinical trials of probiotics, prebiotics, and synbiotics in IBD patients highlighting their potential benefits in alleviating symptoms and enhancing the quality of life.</p><p><strong>Conclusion: </strong>Certain probiotic formulations such as single strain ones consisting of <i>Lactobacillus,</i> or mixed-strain combinations of <i>Lactobacillus</i> and <i>Bifidobacterium</i>, prebiotic compounds such as fructooligosaccharides, and synbiotic combinations of both have proven effective in improving the clinical, immunological, and symptomatic aspects of the disease course. While promising, these findings remain inconclusive due to inconsistent study designs, small sample sizes, and varying patient responses. This emphasizes the need for larger, well-controlled trials to determine their clinical efficacy.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1561047"},"PeriodicalIF":2.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scientia machina: a proposed conceptual framework for a technology-accelerated system of biomedical science.","authors":"Sean T Manion","doi":"10.3389/fsysb.2025.1576989","DOIUrl":"10.3389/fsysb.2025.1576989","url":null,"abstract":"","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1576989"},"PeriodicalIF":2.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-omics strategy to understand PASC through the RECOVER cohorts: a paradigm for a systems biology approach to the study of chronic conditions.","authors":"Jun Sun, Masanori Aikawa, Hassan Ashktorab, Noam D Beckmann, Michael L Enger, Joaquin M Espinosa, Xiaowu Gai, Benjamin D Horne, Paul Keim, Jessica Lasky-Su, Rebecca Letts, Cheryl L Maier, Meisha Mandal, Lauren Nichols, Nadia R Roan, Mark W Russell, Jacqueline Rutter, George R Saade, Kumar Sharma, Stephanie Shiau, Stephen N Thibodeau, Samuel Yang, Lucio Miele","doi":"10.3389/fsysb.2024.1422384","DOIUrl":"10.3389/fsysb.2024.1422384","url":null,"abstract":"<p><p>Post-Acute Sequelae of SARS-CoV-2 infection (PASC or \"Long COVID\"), includes numerous chronic conditions associated with widespread morbidity and rising healthcare costs. PASC has highly variable clinical presentations, and likely includes multiple molecular subtypes, but it remains poorly understood from a molecular and mechanistic standpoint. This hampers the development of rationally targeted therapeutic strategies. The NIH-sponsored \"Researching COVID to Enhance Recovery\" (RECOVER) initiative includes several retrospective/prospective observational cohort studies enrolling adult, pregnant adult and pediatric patients respectively. RECOVER formed an \"OMICS\" multidisciplinary task force, including clinicians, pathologists, laboratory scientists and data scientists, charged with developing recommendations to apply cutting-edge system biology technologies to achieve the goals of RECOVER. The task force met biweekly over 14 months, to evaluate published evidence, examine the possible contribution of each \"omics\" technique to the study of PASC and develop study design recommendations. The OMICS task force recommended an integrated, longitudinal, simultaneous systems biology study of participant biospecimens on the entire RECOVER cohorts through centralized laboratories, as opposed to multiple smaller studies using one or few analytical techniques. The resulting multi-dimensional molecular dataset should be correlated with the deep clinical phenotyping performed through RECOVER, as well as with information on demographics, comorbidities, social determinants of health, the exposome and lifestyle factors that may contribute to the clinical presentations of PASC. This approach will minimize lab-to-lab technical variability, maximize sample size for class discovery, and enable the incorporation of as many relevant variables as possible into statistical models. Many of our recommendations have already been considered by the NIH through the peer-review process, resulting in the creation of a systems biology panel that is currently designing the studies we proposed. This system biology strategy, coupled with modern data science approaches, will dramatically improve our prospects for accurate disease subtype identification, biomarker discovery and therapeutic target identification for precision treatment. The resulting dataset should be made available to the scientific community for secondary analyses. Analogous system biology approaches should be built into the study designs of large observational studies whenever possible.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"4 ","pages":"1422384"},"PeriodicalIF":2.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data.","authors":"Andrea Angarita-Rodríguez, Nicolás Mendoza-Mejía, Janneth González, Jason Papin, Andrés Felipe Aristizábal, Andrés Pinzón","doi":"10.3389/fsysb.2024.1500710","DOIUrl":"10.3389/fsysb.2024.1500710","url":null,"abstract":"<p><strong>Introduction: </strong>The availability of large-scale multi-omic data has revolution-ized the study of cellular machinery, enabling a systematic understanding of biological processes. However, the integration of these datasets into Genome-Scale Models of Metabolism (GEMs) re-mains underexplored. Existing methods often link transcriptome and proteome data independently to reaction boundaries, providing models with estimated maximum reaction rates based on individual datasets. This independent approach, however, introduces uncertainties and inaccuracies.</p><p><strong>Methods: </strong>To address these challenges, we applied a principal component analysis (PCA)-based approach to integrate transcriptome and proteome data. This method facilitates the reconstruction of context-specific models grounded in multi-omics data, enhancing their biological relevance and predictive capacity.</p><p><strong>Results: </strong>Using this approach, we successfully reconstructed an astrocyte GEM with improved prediction capabilities compared to state-of-the-art models available in the literature.</p><p><strong>Discussion: </strong>These advancements underscore the potential of multi-omic inte-gration to refine metabolic modeling and its critical role in studying neurodegeneration and developing effective therapies.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"4 ","pages":"1500710"},"PeriodicalIF":2.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341997/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning Gaussian Graphical Models from Correlated Data.","authors":"Zeyuan Song, Sophia Gunn, Stefano Monti, Gina Marie Peloso, Ching-Ti Liu, Kathryn Lunetta, Paola Sebastiani","doi":"10.3389/fsysb.2025.1589079","DOIUrl":"10.3389/fsysb.2025.1589079","url":null,"abstract":"<p><p>Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. The advantage of using partial correlation is to show the relation between two variables after \"adjusting\" for the effects of other variables and leads to more parsimonious and interpretable models. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data that result in correlated observations and ignoring this correlation among observations can lead to inflated Type I error. In this paper, we propose a cluster-based bootstrap algorithm to infer GGMs from correlated data. We use extensive simulations of correlated data from family-based studies to show that the proposed bootstrap method does not inflate the Type I error while retaining statistical power compared to alternative solutions when there are sufficient number of clusters. We apply our method to learn the GGM that represents complex relations between 47 Polygenic Risk Scores generated using genome-wide genotype data from the Long Life Family Study. By comparing it to the conventional methods that ignore within-cluster correlation, we show that our method controls the Type I error well without power loss.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12323441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144790888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immune disease dialogue of chemokine-based cell communications as revealed by single-cell RNA sequencing meta-analysis.","authors":"Mouly F Rahman, Andre H Kurlovs, Munender Vodnala, Elamaran Meibalan, Terry K Means, Nima Nouri, Emanuele de Rinaldis, Virginia Savova","doi":"10.3389/fsysb.2024.1466368","DOIUrl":"10.3389/fsysb.2024.1466368","url":null,"abstract":"<p><p>Immune-mediated diseases are characterized by aberrant immune responses, posing significant challenges to global health. In both inflammatory and autoimmune diseases, dysregulated immune reactions mediated by tissue-residing immune and non-immune cells precipitate chronic inflammation and tissue damage that is amplified by peripheral immune cell extravasation into the tissue. Chemokine receptors are pivotal in orchestrating immune cell migration, yet deciphering the signaling code across cell types, diseases and tissues remains an open challenge. To delineate disease-specific cell-cell communications involved in immune cell migration, we conducted a meta-analysis of publicly available single-cell RNA sequencing (scRNA-seq) data across diverse immune diseases and tissues. Our comprehensive analysis spanned multiple immune disorders affecting major organs: atopic dermatitis and psoriasis (skin), chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis (lung), ulcerative colitis (colon), IgA nephropathy and lupus nephritis (kidney). By interrogating ligand-receptor (L-R) interactions, alterations in cell proportions, and differential gene expression, we unveiled disease-specific and common cell-cell communications involved in chemotaxis and extravasation to shed light on shared immune responses across tissues and diseases. Further, we performed experimental validation of two understudied cell-cell communications. Insights gleaned from this meta-analysis hold promise for the development of targeted therapeutics aimed at modulating immune cell migration to mitigate inflammation and tissue damage. This nuanced understanding of immune cell dynamics at the single-cell resolution opens avenues for precision medicine in immune disease management.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"4 ","pages":"1466368"},"PeriodicalIF":2.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An exploration of testing genetic associations using goodness-of-fit statistics based on deep ReLU neural networks.","authors":"Xiaoxi Shen, Xiaoming Wang","doi":"10.3389/fsysb.2024.1460369","DOIUrl":"10.3389/fsysb.2024.1460369","url":null,"abstract":"<p><p>As a driving force of the fourth industrial revolution, deep neural networks are now widely used in various areas of science and technology. Despite the success of deep neural networks in making accurate predictions, their interpretability remains a mystery to researchers. From a statistical point of view, how to conduct statistical inference (e.g., hypothesis testing) based on deep neural networks is still unknown. In this paper, goodness-of-fit statistics are proposed based on commonly used ReLU neural networks, and their potential to test significant input features is explored. A simulation study demonstrates that the proposed test statistic has higher power compared to the commonly used t-test in linear regression when the underlying signal is nonlinear, while controlling the type I error at the desired level. The testing procedure is also applied to gene expression data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"4 ","pages":"1460369"},"PeriodicalIF":2.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341992/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}