Metabolomics最新文献

{"title":"Navigating common pitfalls in metabolite identification and metabolomics bioinformatics.","authors":"Elva María Novoa-Del-Toro, Michael Witting","doi":"10.1007/s11306-024-02167-2","DOIUrl":"10.1007/s11306-024-02167-2","url":null,"abstract":"<p><strong>Background: </strong>Metabolomics, the systematic analysis of small molecules in a given biological system, emerged as a powerful tool for different research questions. Newer, better, and faster methods have increased the coverage of metabolites that can be detected and identified in a shorter amount of time, generating highly dense datasets. While technology for metabolomics is still advancing, another rapidly growing field is metabolomics data analysis including metabolite identification. Within the next years, there will be a high demand for bioinformaticians and data scientists capable of analyzing metabolomics data as well as chemists capable of using in-silico tools for metabolite identification. However, metabolomics is often not included in bioinformatics curricula, nor does analytical chemistry address the challenges associated with advanced in-silico tools.</p><p><strong>Aim of review: </strong>In this educational review, we briefly summarize some key concepts and pitfalls we have encountered in a collaboration between a bioinformatician (originally not trained for metabolomics) and an analytical chemist. We identified that many misunderstandings arise from differences in knowledge about metabolite annotation and identification, and the proper use of bioinformatics approaches for these tasks. We hope that this article helps other bioinformaticians (as well as other scientists) entering the field of metabolomics bioinformatics, especially for metabolite identification, to quickly learn the necessary concepts for a successful collaboration with analytical chemists.</p><p><strong>Key scientific concepts of review: </strong>We summarize important concepts related to LC-MS/MS based non-targeted metabolomics and compare them with other data types bioinformaticians are potentially familiar with. Drawing these parallels will help foster the learning of key aspects of metabolomics.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11416380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142290877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GC-MS metabolomics of French lettuce (Lactuca Sativa L. var capitata) leaves exposed to bisphenol A via the hydroponic media.","authors":"Jerónimo Cabrera-Peralta, Araceli Peña-Alvarez","doi":"10.1007/s11306-024-02168-1","DOIUrl":"10.1007/s11306-024-02168-1","url":null,"abstract":"<p><strong>Introduction: </strong>Bisphenol A (BPA), an organic compound used to produce polycarbonate plastics and epoxy resins, has become a ubiquitous contaminant due to its high-volume production and constant release to the environment. Plant metabolomics can trace the stress effects induced by environmental contaminants to the variation of specific metabolites, making it an alternative way to study pollutants toxicity to plants. Nevertheless, there is an important knowledge gap in metabolomics applications in this area.</p><p><strong>Objective: </strong>Evaluate the influence of BPA in French lettuce (Lactuca Sativa L. var capitata) leaves metabolic profile by gas chromatography coupled to mass spectrometry (GC-MS) using a hydroponic system.</p><p><strong>Methods: </strong>Lettuces were cultivated in the laboratory to minimize biological variation and were analyzed 55 days after sowing (considered the plant's adult stage). Hexanoic and methanolic extracts with and without derivatization were prepared for each sample and analyzed by GC-MS.</p><p><strong>Results: </strong>The highest number of metabolites was obtained from the hexanoic extract, followed by the derivatized methanolic extract. Although no physical differences were observed between control and contaminated lettuce leaves, the multivariate analysis determined a statistically significant difference between their metabolic profiles. Pathway analysis of the most affected metabolites showed that galactose metabolism, starch and fructose metabolism and steroid biosynthesis were significantly affected by BPA exposure.</p><p><strong>Conclusions: </strong>The preparation of different extracts from the same sample permitted the determination of metabolites with different physicochemical properties. BPA alters the leaves energy and membrane metabolism, plant growth could be affected at higher concentrations and exposition times.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11416399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142290895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stool and blood metabolomics in the metabolic syndrome: a cross-sectional study.","authors":"Mariana Ponce-de-Leon, Rui Wang-Sattler, Annette Peters, Wolfgang Rathmann, Harald Grallert, Anna Artati, Cornelia Prehn, Jerzy Adamski, Christa Meisinger, Jakob Linseisen","doi":"10.1007/s11306-024-02166-3","DOIUrl":"10.1007/s11306-024-02166-3","url":null,"abstract":"<p><strong>Introduction/objectives: </strong>Changes in the stool metabolome have been poorly studied in the metabolic syndrome (MetS). Moreover, few studies have explored the relationship of stool metabolites with circulating metabolites. Here, we investigated the associations between stool and blood metabolites, the MetS and systemic inflammation.</p><p><strong>Methods: </strong>We analyzed data from 1,370 participants of the KORA FF4 study (Germany). Metabolites were measured by Metabolon, Inc. (untargeted) in stool, and using the AbsoluteIDQ^® p180 kit (targeted) in blood. Multiple linear regression models, adjusted for dietary pattern, age, sex, physical activity, smoking status and alcohol intake, were used to estimate the associations of metabolites with the MetS, its components and high-sensitivity C-reactive protein (hsCRP) levels. Partial correlation and Multi-Omics Factor Analysis (MOFA) were used to investigate the relationship between stool and blood metabolites.</p><p><strong>Results: </strong>The MetS was significantly associated with 170 stool and 82 blood metabolites. The MetS components with the highest number of associations were triglyceride levels (stool) and HDL levels (blood). Additionally, 107 and 27 MetS-associated metabolites (in stool and blood, respectively) showed significant associations with hsCRP levels. We found low partial correlation coefficients between stool and blood metabolites. MOFA did not detect shared variation across the two datasets.</p><p><strong>Conclusions: </strong>The MetS, particularly dyslipidemia, is associated with multiple stool and blood metabolites that are also associated with systemic inflammation. Further studies are necessary to validate our findings and to characterize metabolic alterations in the MetS. Although our analyses point to weak correlations between stool and blood metabolites, additional studies using integrative approaches are warranted.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11416374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142290878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolomic profiling of dengue infection: unraveling molecular signatures by LC-MS/MS and machine learning models.","authors":"Jhansi Venkata Nagamani Josyula, Aashika Raagavi JeanPierre, Sachin B Jorvekar, Deepthi Adla, Vignesh Mariappan, Sai Sharanya Pulimamidi, Siva Ranganathan Green, Agieshkumar Balakrishna Pillai, Roshan M Borkar, Srinivasa Rao Mutheneni","doi":"10.1007/s11306-024-02169-0","DOIUrl":"10.1007/s11306-024-02169-0","url":null,"abstract":"<p><strong>Background & objective: </strong>The progression of dengue fever to severe dengue (SD) is a major public health concern that impairs the capacity of the medical system to predict and treat dengue patients. Hence, the present study used a metabolomic approach integrated with machine models to identify differentially expressed metabolites in patients with SD compared to nonsevere patients and healthy controls.</p><p><strong>Methods: </strong>Comprehensively, the plasma was collected at different clinical phases during dengue without warning signs (DWOW, N = 10), dengue with warning signs (DWW, N = 10), and SD (N = 10) at different stages [i.e., day of admission (DOA), day of defervescence (DOD), and day of convalescent (DOC)] in comparison to healthy control (HC). The samples were subjected to LC‒ESI‒MS/MS to identify metabolites. Statistical and machine learning analyses were performed using R and Python language. Further, biomarker, pathway and correlation analysis was performed to identify potential predictors of dengue.</p><p><strong>Results & conclusion: </strong>A total of 423 metabolites were identified in all the study groups. Paired and unpaired t-tests revealed 14 highly differentially expressed metabolites between and across the dengue groups, with four metabolites (shikimic acid, ureidosuccinic acid, propionyl carnitine, and alpha-tocopherol) showing significant differences compared to HC. Furthermore, biomarker (ROC) analysis revealed 11 potential molecules with a significant AUC value of 1 that could serve as potential biomarkers for identifying different dengue clinical stages that are beneficial for predicting dengue disease outcomes. The logistic regression model revealed that S-adenosylhomocysteine, hypotaurine, and shikimic acid metabolites could be beneficial indicators for predicting severe dengue, with an accuracy and AUC of 0.75. The data showed that dengue infection is related to lipid metabolism, oxidative stress, inflammation, metabolomic adaptation, and virus manipulation. Moreover, the biomarkers had a significant correlation with biochemical parameters like platelet count, and hematocrit. These results shed some light on host-derived small-molecule biomarkers that are associated with dengue severity and novel insights into metabolomics mechanisms interlinked with disease severity.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142290876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-quality identification of volatile organic compounds (VOCs) originating from breath.","authors":"Wisenave Arulvasan, Hsuan Chou, Julia Greenwood, Madeleine L Ball, Owen Birch, Simon Coplowe, Patrick Gordon, Andreea Ratiu, Elizabeth Lam, Ace Hatch, Monika Szkatulska, Steven Levett, Ella Mead, Chloe Charlton-Peel, Louise Nicholson-Scott, Shane Swann, Frederik-Jan van Schooten, Billy Boyle, Max Allsworth","doi":"10.1007/s11306-024-02163-6","DOIUrl":"10.1007/s11306-024-02163-6","url":null,"abstract":"<p><strong>Introduction: </strong>Volatile organic compounds (VOCs) can arise from underlying metabolism and are detectable in exhaled breath, therefore offer a promising route to non-invasive diagnostics. Robust, precise, and repeatable breath measurement platforms able to identify VOCs in breath distinguishable from background contaminants are needed for the confident discovery of breath-based biomarkers.</p><p><strong>Objectives: </strong>To build a reliable breath collection and analysis method that can produce a comprehensive list of known VOCs in the breath of a heterogeneous human population.</p><p><strong>Methods: </strong>The analysis cohort consisted of 90 pairs of breath and background samples collected from a heterogenous population. Owlstone Medical's Breath Biopsy^® OMNI^® platform, consisting of sample collection, TD-GC-MS analysis and feature extraction was utilized. VOCs were determined to be \"on-breath\" if they met at least one of three pre-defined metrics compared to paired background samples. On-breath VOCs were identified via comparison against purified chemical standards, using retention indexing and high-resolution accurate mass spectral matching.</p><p><strong>Results: </strong>1471 VOCs were present in > 80% of samples (breath and background), and 585 were on-breath by at least one metric. Of these, 148 have been identified covering a broad range of chemical classes.</p><p><strong>Conclusions: </strong>A robust breath collection and relative-quantitative analysis method has been developed, producing a list of 148 on-breath VOCs, identified using purified chemical standards in a heterogenous population. Providing confirmed VOC identities that are genuinely breath-borne will facilitate future biomarker discovery and subsequent biomarker validation in clinical studies. Additionally, this list of VOCs can be used to facilitate cross-study data comparisons for improved standardization.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11379754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142145960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolomic profiling of elite female soccer players: urinary biomarkers over a championship season.","authors":"Maria Mariana Sabino Gouveia, Maria Beatriz Augusto do Nascimento, Alessandre Carmo Crispim, Edmilson Rodrigues da Rocha, Maryssa Pontes Pinto Dos Santos, Edson de Souza Bento, Thiago Mendonça De Aquino, Pedro Balikian, Natália Almeida Rodrigues, Thays Ataide-Silva, Gustavo Gomes de Araujo, Filipe Antonio de Barros Sousa","doi":"10.1007/s11306-024-02164-5","DOIUrl":"10.1007/s11306-024-02164-5","url":null,"abstract":"<p><strong>Introduction: </strong>In soccer, most studies evaluate metabolic profile changes in male athletes, often using data from a single match. Given the current landscape of women's soccer and the effects of biological sex on the physiological response and adaptation to exercise, more studies targeting female athletes and analyzing pre- and post-game moments throughout the season are necessary.</p><p><strong>Objectives: </strong>To describe the metabolomics profile of female soccer athletes from an elite team in Brazil. The study observed the separation of groups in three pre- and post-game moments and identified the discriminating metabolites.</p><p><strong>Methods: </strong>The study included 14 female soccer athletes. Urine samples were collected and analyzed using Nuclear Magnetic Resonance in pre-game and immediate post-game moments over three national championship games. The metabolomics data were then used to generate OPLS-DA and VIP plots.</p><p><strong>Results: </strong>Forty-three metabolites were identified in the samples. OPLS-DA analyses demonstrated a progressive separation between pre-post conditions, as supported by an increasing Q² value (0.534, 0.625, and 0.899 for games 1, 2 and 3, respectively) and the first component value (20.2% and 19.1% in games 1 and 2 vs. 29.9% in game 3). Eight out of the fifteen most discriminating metabolites appeared consistently across the three games: glycine, formate, citrate, 3-hydroxyvalerate, glycolic acid, trimethylamine, urea, and dimethylglycine.</p><p><strong>Conclusion: </strong>The main difference between the three games was the increasing separation between groups throughout the championship. Since the higher VIP-scores metabolites are linked to energy and protein metabolism, this separation may be attributed several factors, one being the accumulation of fatigue.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142133180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"White adipose tissue remodeling in Little Brown Myotis (Myotis lucifugus) with white-nose syndrome.","authors":"Evan L Pannkuk, Marianne S Moore, Shivani Bansal, Kamendra Kumar, Shubhankar Suman, Daryl Howell, Joseph A Kath, Allen Kurta, DeeAnn M Reeder, Kenneth A Field","doi":"10.1007/s11306-024-02165-4","DOIUrl":"10.1007/s11306-024-02165-4","url":null,"abstract":"<p><p>White-nose syndrome (WNS) is a fungal wildlife disease of bats that has caused precipitous declines in certain Nearctic bat species. A key driver of mortality is premature exhaustion of fat reserves, primarily white adipose tissue (WAT), that bats rely on to meet their metabolic needs during winter. However, the pathophysiological and metabolic effects of WNS have remained ill-defined. To elucidate metabolic mechanisms associated with WNS mortality, we infected a WNS susceptible species, the Little Brown Myotis (Myotis lucifugus), with Pseudogymnoascus destructans (Pd) and collected WAT biopsies for histology and targeted lipidomics. These results were compared to the WNS-resistant Big Brown Bat (Eptesicus fuscus). A similar distribution in broad lipid class was observed in both species, with total WAT primarily consisting of triacylglycerides. Baseline differences in WAT chemical composition between species showed that higher glycerophospholipids (GPs) levels in E. fuscus were dominated by unsaturated or monounsaturated moieties and n-6 (18:2, 20:2, 20:3, 20:4) fatty acids. Conversely, higher GP levels in M. lucifugus WAT were primarily compounds containing n-3 (20:5 and 22:5) fatty acids. Following Pd-infection, we found that perturbation to WAT reserves occurs in M. lucifugus, but not in the resistant E. fuscus. A total of 66 GPs (primarily glycerophosphocholines and glycerophosphoethanolamines) were higher in Pd-infected M. lucifugus, indicating perturbation to the WAT structural component. In addition to changes in lipid chemistry, smaller adipocyte sizes and increased extracellular matrix deposition was observed in Pd-infected M. lucifugus. This is the first study to describe WAT GP composition of bats with different susceptibilities to WNS and highlights that recovery from WNS may require repair from adipose remodeling in addition to replenishing depot fat during spring emergence.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142073231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomics and metabolomics analyses of urine for investigation of gallstone disease in a high-altitude area.","authors":"Ying Ma, Xiaofeng Jing, Defu Li, Tiecheng Zhang, Haiqi Xiang, Yonghong Xia, Fan Xu","doi":"10.1007/s11306-024-02162-7","DOIUrl":"10.1007/s11306-024-02162-7","url":null,"abstract":"<p><strong>Background: </strong>The incidence of gallstones is high in Qinghai Province. However, the molecular mechanisms underlying the development of gallstones remain unclear.</p><p><strong>Methods: </strong>In this study, we collected urine samples from 30 patients with gallstones and 30 healthy controls. The urine samples were analysed using multi-omics platforms. Proteomics analysis was conducted using data-independent acquisition, whereas metabolomics analysis was performed using liquid chromatography-mass spectrometry (LC-MS).</p><p><strong>Results: </strong>Among the patients with gallstones, we identified 49 down-regulated and 185 up-regulated differentially expressed proteins as well as 195 up-regulated and 189 down-regulated differentially expressed metabolites. Six pathways were significantly enriched: glycosaminoglycan degradation, arginine and proline metabolism, histidine metabolism, pantothenate and coenzyme A biosynthesis, drug metabolism-other enzymes, and the pentose phosphate pathway. Notably, 10 differentially expressed proteins and metabolites showed excellent predictive performance and were selected as potential biomarkers.</p><p><strong>Conclusion: </strong>The findings of our metabolomics and proteomics analyses provide new insights into novel biomarkers for patients with cholelithiasis in high-altitude areas.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141982699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Endometabolic profiling of pigmented glacier ice algae: the impact of sample processing.","authors":"Elisa K Peter, Carsten Jaeger, Jan Lisec, R Sven Peters, Rey Mourot, Pamela E Rossel, Martyn Tranter, Alexandre M Anesio, Liane G Benning","doi":"10.1007/s11306-024-02147-6","DOIUrl":"10.1007/s11306-024-02147-6","url":null,"abstract":"<p><strong>Introduction: </strong>Glacier ice algae, mainly Ancylonema alaskanum and Ancylonema nordenskiöldi, bloom on Greenland Ice Sheet bare ice surfaces. They significantly decrease surface albedo due to their purple-brown pigmentation, thus increasing melt. Little is known about their metabolic adaptation and factors controlling algal growth dynamics and pigment formation. A challenge in obtaining such data is the necessity of melting samples, which delays preservation and introduces bias to metabolomic analysis. There is a need to evaluate the physiological response of algae to melting and establish consistent sample processing strategies for metabolomics of ice microbial communities.</p><p><strong>Objectives: </strong>To address the impact of sample melting procedure on metabolic characterization and establish a processing and analytical workflow for endometabolic profiling of glacier ice algae.</p><p><strong>Methods: </strong>We employed untargeted, high-resolution mass spectrometry and tested the effect of sample melt temperature (10, 15, 20 °C) and processing delay (up to 49 h) on the metabolome and lipidome, and complemented this approach with cell counts (FlowCam), photophysiological analysis (PAM) and diversity characterization.</p><p><strong>Results and conclusion: </strong>We putatively identified 804 metabolites, with glycerolipids, glycerophospholipids and fatty acyls being the most prominent superclasses (> 50% of identified metabolites). Among the polar metabolome, carbohydrates and amino acid-derivatives were the most abundant. We show that 8% of the metabolome is affected by melt duration, with a pronounced decrease in betaine membrane lipids and pigment precursors, and an increase in phospholipids. Controlled fast melting at 10 °C resulted in the highest consistency, and is our recommendation for future supraglacial metabolomics studies.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11315761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141913286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kiphynet: an online network simulation tool connecting cellular kinetics and physiological transport.","authors":"M Deepa Maheshvare, Rohit Charaborty, Subhraneel Haldar, Soumyendu Raha, Debnath Pal","doi":"10.1007/s11306-024-02151-w","DOIUrl":"10.1007/s11306-024-02151-w","url":null,"abstract":"<p><strong>Introduction: </strong>Human metabolism is sustained by functional networks that operate at diverse scales. Capturing local and global dynamics in the human body by hierarchically bridging multi-scale functional networks is a major challenge in physiological modeling.</p><p><strong>Objectives: </strong>To develop an interactive, user-friendly web application that facilitates the simulation and visualization of advection-dispersion transport in three-dimensional (3D) microvascular networks, biochemical exchange, and metabolic reactions in the tissue layer surrounding the vasculature.</p><p><strong>Methods: </strong>To help modelers combine and simulate biochemical processes occurring at multiple scales, KiPhyNet deploys our discrete graph-based modeling framework that bridges functional networks existing at diverse scales. KiPhyNet is implemented in Python based on Apache web server using MATLAB as the simulator engine. KiPhyNet provides the functionality to assimilate multi-omics data from clinical and experimental studies as well as vascular data from imaging studies to investigate the role of structural changes in vascular topology on the functional response of the tissue.</p><p><strong>Results: </strong>With the network topology, its biophysical attributes, values of initial and boundary conditions, parameterized kinetic constants, biochemical species-specific transport properties such as diffusivity as inputs, a user can use our application to simulate and view the simulation results. The results of steady-state velocity and pressure fields and dynamic concentration fields can be interactively examined.</p><p><strong>Conclusion: </strong>KiPhyNet provides barrier-free access to perform time-course simulation experiments by building multi-scale models of microvascular networks in physiology, using a discrete modeling framework. KiPhyNet is freely accessible at   http://pallab.cds.iisc.ac.in/kiphynet/ and the documentation is available at   https://deepamahm.github.io/kiphynet_docs/ .</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}