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Article ; Online: Uncovering Protein Networks in Cardiovascular Proteomics.

Hasman, Maria / Mayr, Manuel / Theofilatos, Konstantinos

2023 Volume 22, Issue 8, Page(s) 100607

Abstract: Biological networks have been widely used in many different diseases to identify potential biomarkers and design drug targets. In the present review, we describe the main computational techniques for reconstructing and analyzing different types of ... ...

Abstract	Biological networks have been widely used in many different diseases to identify potential biomarkers and design drug targets. In the present review, we describe the main computational techniques for reconstructing and analyzing different types of protein networks and summarize the previous applications of such techniques in cardiovascular diseases. Existing tools are critically compared, discussing when each method is preferred such as the use of co-expression networks for functional annotation of protein clusters and the use of directed networks for inferring regulatory associations. Finally, we are presenting examples of reconstructing protein networks of different types (regulatory, co-expression, and protein-protein interaction networks). We demonstrate the necessity to reconstruct networks separately for each cardiovascular tissue type and disease entity and provide illustrative examples of the importance of taking into consideration relevant post-translational modifications. Finally, we demonstrate and discuss how the findings of protein networks could be interpreted using single-cell RNA-sequencing data.
MeSH term(s)	Proteomics ; Gene Regulatory Networks ; Protein Interaction Maps ; Proteins ; Computational Biology/methods
Chemical Substances	Proteins
Language	English
Publishing date	2023-06-24
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	2075924-1
ISSN	1535-9484 ; 1535-9476
ISSN (online)	1535-9484
ISSN	1535-9476
DOI	10.1016/j.mcpro.2023.100607
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Article: Omics-CNN: A comprehensive pipeline for predictive analytics in quantitative omics using one-dimensional convolutional neural networks.

Zompola, Anastasia / Korfiati, Aigli / Theofilatos, Konstantinos / Mavroudi, Seferina

Heliyon

2023 Volume 9, Issue 11, Page(s) e21165

Abstract: Background and objective: The development of machine learning-based models that can be used for the prediction of severe diseases has been one of the main concerns of the scientific community. The current study seeks to expand a highly sophisticated ... ...

Abstract	Background and objective: The development of machine learning-based models that can be used for the prediction of severe diseases has been one of the main concerns of the scientific community. The current study seeks to expand a highly sophisticated tool, the Convolutional Neural Networks, making it applicable in multidimensional omics data classification problems and testing the newly introduced method on publicly available transcriptomics and proteomics data. Methods: In this study, we introduce Omics-CNN, a Convolutional Neural Network-based pipeline, which couples Convolutional Neural Networks with dimensionality reduction, preprocessing, clustering, and explainability techniques to make them suitable to build highly accurate and interpretable classification models from high-throughput omics data. The developed tool has the potential to classify patients depending on the expression of genetic and clinical factors and identify features that can act as diagnostic biomarkers. Regarding dimensionality reduction, univariate and multivariate techniques were explored and compared. Gradient Weighted Class Activation Mapping analysis was performed to determine the most important features in the classification of the samples after training the model. Results: The newly introduced pipeline was applied to one transcriptomics and one proteomics dataset for the identification of diagnostic models and biosignatures for Ischemic Stroke (IS) and COVID-19 infection, reporting highly accurate biosignatures with accuracies of 96 % and 95.41 %, respectively. Meanwhile, classification models based solely on a small part of attributes provided lower predictive accuracy, but identified compact transcript biosignature (KRT15, VPRBP, TNFRSF4, GORASP2) for Ischemic Stroke and protein biosignature (ADGRB3, VNN2, AGER, CIAPIN1) for Covid-19 infection diagnosis, respectively. Conclusions: Omics-CNN, overcame the inherent problems of applying Convolutional Neural Networks for the training diagnostic models with quantitative omics data, outperforming previous models of machine learning developed using the same datasets for Ischemic Stroke and Covid-19 infection diagnosis, determining the most contributing biomarkers for both diseases.
Language	English
Publishing date	2023-10-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e21165
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Article ; Online: MEvA-X: a hybrid multiobjective evolutionary tool using an XGBoost classifier for biomarkers discovery on biomedical datasets.

Panagiotopoulos, Konstantinos / Korfiati, Aigli / Theofilatos, Konstantinos / Hurwitz, Peter / Deriu, Marco Agostino / Mavroudi, Seferina

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 7

Abstract: Motivation: Biomarker discovery is one of the most frequent pursuits in bioinformatics and is crucial for precision medicine, disease prognosis, and drug discovery. A common challenge of biomarker discovery applications is the low ratio of samples over ... ...

Abstract	Motivation: Biomarker discovery is one of the most frequent pursuits in bioinformatics and is crucial for precision medicine, disease prognosis, and drug discovery. A common challenge of biomarker discovery applications is the low ratio of samples over features for the selection of a reliable not-redundant subset of features, but despite the development of efficient tree-based classification methods, such as the extreme gradient boosting (XGBoost), this limitation is still relevant. Moreover, existing approaches for optimizing XGBoost do not deal effectively with the class imbalance nature of the biomarker discovery problems, and the presence of multiple conflicting objectives, since they focus on the training of a single-objective model. In the current work, we introduce MEvA-X, a novel hybrid ensemble for feature selection (FS) and classification, combining a niche-based multiobjective evolutionary algorithm (EA) with the XGBoost classifier. MEvA-X deploys a multiobjective EA to optimize the hyperparameters of the classifier and perform FS, identifying a set of Pareto-optimal solutions and optimizing multiple objectives, including classification and model simplicity metrics. Results: The performance of the MEvA-X tool was benchmarked using one omics dataset coming from a microarray gene expression experiment, and one clinical questionnaire-based dataset combined with demographic information. MEvA-X tool outperformed the state-of-the-art methods in the balanced categorization of classes, creating multiple low-complexity models and identifying important nonredundant biomarkers. The best-performing run of MEvA-X for the prediction of weight loss using gene expression data yields a small set of blood circulatory markers which are sufficient for this precision nutrition application but need further validation. Availability and implementation: https://github.com/PanKonstantinos/MEvA-X.
MeSH term(s)	Tool Use Behavior ; Algorithms ; Biomarkers ; Computational Biology
Chemical Substances	Biomarkers
Language	English
Publishing date	2023-06-15
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad384
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Article: Optimisation Models for Pathway Activity Inference in Cancer.

Chen, Yongnan / Liu, Songsong / Papageorgiou, Lazaros G / Theofilatos, Konstantinos / Tsoka, Sophia

Cancers

2023 Volume 15, Issue 6

Abstract: Background: With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large ... ...

Abstract	Background: With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models. Methodology: A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation. Results: The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction.
Language	English
Publishing date	2023-03-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers15061787
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Article ; Online: Serum metabolomics improves risk stratification for incident heart failure.

Oexner, Rafael R / Ahn, Hyunchan / Theofilatos, Konstantinos / Shah, Ravi A / Schmitt, Robin / Chowienczyk, Philip / Zoccarato, Anna / Shah, Ajay M

European journal of heart failure

2024

Abstract: Aims: Prediction and early detection of heart failure (HF) is crucial to mitigate its impact on quality of life, survival, and healthcare expenditure. Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton ... ...

Abstract	Aims: Prediction and early detection of heart failure (HF) is crucial to mitigate its impact on quality of life, survival, and healthcare expenditure. Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance [ Methods and results: Leveraging data of 68 311 individuals and >0.8 million person-years of follow-up from the UK Biobank cohort, we (i) fitted per-metabolite Cox proportional hazards models to assess individual metabolite associations, and (ii) trained and validated elastic net models to predict incident HF using the serum metabolome. We benchmarked discriminative performance against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF [PCP-HF]). During a median follow-up of ≈12.3 years, several metabolites showed independent association with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters (≈80% feature reduction). Adding metabolomics to PCP-HF improved predictive performance (Harrel's C: 0.768 vs. 0.755, ΔC = 0.013, [95% confidence interval [CI] 0.004-0.022], continuous net reclassification improvement [NRI]: 0.287 [95% CI 0.200-0.367], relative integrated discrimination improvement [IDI]: 17.47% [95% CI 9.463-27.825]). Models including age, sex and metabolomics performed almost as well as PCP-HF (Harrel's C: 0.745 vs. 0.755, ΔC = 0.010 [95% CI -0.004 to 0.027], continuous NRI: 0.097 [95% CI -0.025 to 0.217], relative IDI: 13.445% [95% CI -10.608 to 41.454]). Risk and survival stratification was improved by integrating metabolomics. Conclusion: Serum metabolomics improves incident HF risk prediction over PCP-HF. Scores based on age, sex and metabolomics exhibit similar predictive power to clinically-based models, potentially offering a cost-effective, standardizable, and scalable single-domain alternative.
Language	English
Publishing date	2024-04-16
Publishing country	England
Document type	Journal Article
ZDB-ID	1483672-5
ISSN	1879-0844 ; 1388-9842
ISSN (online)	1879-0844
ISSN	1388-9842
DOI	10.1002/ejhf.3226
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Article ; Online: Systems biology in cardiovascular disease: a multiomics approach.

Joshi, Abhishek / Rienks, Marieke / Theofilatos, Konstantinos / Mayr, Manuel

Nature reviews. Cardiology

2020 Volume 18, Issue 5, Page(s) 313–330

Abstract: Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for ... ...

Abstract	Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.
MeSH term(s)	Cardiovascular Diseases ; Computational Biology ; Gene Regulatory Networks ; Genomics ; Humans ; Lipidomics ; Machine Learning ; Metabolomics ; Neural Networks, Computer ; Proteomics ; Systems Biology
Language	English
Publishing date	2020-12-18
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2490375-9
ISSN	1759-5010 ; 1759-5002
ISSN (online)	1759-5010
ISSN	1759-5002
DOI	10.1038/s41569-020-00477-1
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Article ; Online: Precision Medicine Approach for Cardiometabolic Risk Factors in Therapeutic Apheresis.

Yin, X / Takov, K / Straube, R / Voit-Bak, K / Graessler, J / Julius, U / Tselmin, S / Rodionov, Roman N / Barbir, M / Walls, M / Theofilatos, K / Mayr, M / Bornstein, S R

Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme

2022 Volume 54, Issue 4, Page(s) 238–249

Abstract: Lipoprotein apheresis (LA) is currently the most powerful intervention possible to reach a maximal reduction of lipids in patients with familial hypercholesterolemia and lipoprotein(a) hyperlipidemia. Although LA is an invasive method, it has few side ... ...

Abstract	Lipoprotein apheresis (LA) is currently the most powerful intervention possible to reach a maximal reduction of lipids in patients with familial hypercholesterolemia and lipoprotein(a) hyperlipidemia. Although LA is an invasive method, it has few side effects and the best results in preventing further major cardiovascular events. It has been suggested that the highly significant reduction of cardiovascular complications in patients with severe lipid disorders achieved by LA is mediated not only by the potent reduction of lipid levels but also by the removal of other proinflammatory and proatherogenic factors. Here we performed a comprehensive proteomic analysis of patients on LA treatment using intra-individually a set of differently sized apheresis filters with the INUSpheresis system. This study revealed that proteomic analysis correlates well with routine clinical chemistry in these patients. The method is eminently suited to discover new biomarkers and risk factors for cardiovascular disease in these patients. Different filters achieve reduction and removal of proatherogenic proteins in different quantities. This includes not only apolipoproteins, C-reactive protein, fibrinogen, and plasminogen but also proteins like complement factor B (CFAB), protein AMBP, afamin, and the low affinity immunoglobulin gamma Fc region receptor III-A (FcγRIIIa) among others that have been described as atherosclerosis and metabolic vascular diseases promoting factors. We therefore conclude that future trials should be designed to develop an individualized therapy approach for patients on LA based on their metabolic and vascular risk profile. Furthermore, the power of such cascade filter treatment protocols may improve the prevention of cardiometabolic disease and its complications.
MeSH term(s)	Blood Component Removal/adverse effects ; Blood Component Removal/methods ; Cardiometabolic Risk Factors ; Cardiovascular Diseases/etiology ; Cardiovascular Diseases/prevention & control ; Cholesterol, LDL ; Humans ; Lipoprotein(a) ; Precision Medicine/adverse effects ; Proteomics ; Risk Factors ; Treatment Outcome
Chemical Substances	Cholesterol, LDL ; Lipoprotein(a)
Language	English
Publishing date	2022-04-12
Publishing country	Germany
Document type	Journal Article
ZDB-ID	80125-2
ISSN	1439-4286 ; 0018-5043
ISSN (online)	1439-4286
ISSN	0018-5043
DOI	10.1055/a-1776-7943
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Article ; Online: Correction: Precision Medicine Approach for Cardiometabolic Risk Factors in Therapeutic Apheresis.

Yin, X / Takov, K / Straube, R / Voit-Bak, K / Graessler, J / Julius, U / Tselmin, S / Rodionov, Roman N / Barbir, M / Walls, M / Theofilatos, K / Mayr, M / Bornstein, S R

Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme

2022 Volume 54, Issue 4, Page(s) e3

Language	English
Publishing date	2022-05-11
Publishing country	Germany
Document type	Journal Article ; Published Erratum
ZDB-ID	80125-2
ISSN	1439-4286 ; 0018-5043
ISSN (online)	1439-4286
ISSN	0018-5043
DOI	10.1055/a-1840-6523
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Article ; Online: VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites.

Pallante, Lorenzo / Cannariato, Marco / Androutsos, Lampros / Zizzi, Eric A / Bompotas, Agorakis / Hada, Xhesika / Grasso, Gianvito / Kalogeras, Athanasios / Mavroudi, Seferina / Di Benedetto, Giacomo / Theofilatos, Konstantinos / Deriu, Marco A

Scientific reports

2024 Volume 14, Issue 1, Page(s) 6296

Abstract: Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their ...

Abstract	Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.
MeSH term(s)	Humans ; Ligands ; Binding Sites ; Proteins/metabolism ; Taste ; Taste Buds/metabolism ; Protein Binding
Chemical Substances	Ligands ; Proteins
Language	English
Publishing date	2024-03-15
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-56893-7
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Article ; Online: Sox9 Accelerates Vascular Aging by Regulating Extracellular Matrix Composition and Stiffness.

Faleeva, Maria / Ahmad, Sadia / Theofilatos, Konstantinos / Lynham, Steven / Watson, Gabriel / Whitehead, Meredith / Marhuenda, Emilie / Iskratsch, Thomas / Cox, Susan / Shanahan, Catherine M

Circulation research

2024 Volume 134, Issue 3, Page(s) 307–324

Abstract: Background: Vascular calcification and increased extracellular matrix (ECM) stiffness are hallmarks of vascular aging. Sox9 (SRY-box transcription factor 9) has been implicated in vascular smooth muscle cell (VSMC) osteo/chondrogenic conversion; however, ...

Abstract	Background: Vascular calcification and increased extracellular matrix (ECM) stiffness are hallmarks of vascular aging. Sox9 (SRY-box transcription factor 9) has been implicated in vascular smooth muscle cell (VSMC) osteo/chondrogenic conversion; however, its relationship with aging and calcification has not been studied. Methods: Immunohistochemistry was performed on human aortic samples from young and aged patients. Young and senescent primary human VSMCs were induced to produce ECM, and Sox9 expression was manipulated using adenoviral overexpression and depletion. ECM properties were characterized using atomic force microscopy and proteomics, and VSMC phenotype on hydrogels and the ECM were examined using confocal microscopy. Results: In vivo, Sox9 was not spatially associated with vascular calcification but correlated with the senescence marker p16 (cyclin-dependent kinase inhibitor 2A). In vitro Sox9 showed mechanosensitive responses with increased expression and nuclear translocation in senescent cells and on stiff matrices. Sox9 was found to regulate ECM stiffness and organization by orchestrating changes in collagen (Col) expression and reducing VSMC contractility, leading to the formation of an ECM that mirrored that of senescent cells. These ECM changes promoted phenotypic modulation of VSMCs, whereby senescent cells plated on ECM synthesized from cells depleted of Sox9 returned to a proliferative state, while proliferating cells on a matrix produced by Sox9 expressing cells showed reduced proliferation and increased DNA damage, reiterating features of senescent cells. LH3 (procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3) was identified as an Sox9 target and key regulator of ECM stiffness. LH3 is packaged into extracellular vesicles and Sox9 promotes extracellular vesicle secretion, leading to increased LH3 deposition within the ECM. Conclusions: These findings highlight the crucial role of ECM structure and composition in regulating VSMC phenotype. We identify a positive feedback cycle, whereby cellular senescence and increased ECM stiffening promote Sox9 expression, which, in turn, drives further ECM modifications to further accelerate stiffening and senescence.
MeSH term(s)	Aged ; Humans ; Aging ; Cells, Cultured ; Extracellular Matrix/metabolism ; Muscle, Smooth, Vascular/metabolism ; Myocytes, Smooth Muscle/metabolism ; Vascular Calcification/genetics
Chemical Substances	SOX9 protein, human
Language	English
Publishing date	2024-01-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80100-8
ISSN	1524-4571 ; 0009-7330 ; 0931-6876
ISSN (online)	1524-4571
ISSN	0009-7330 ; 0931-6876
DOI	10.1161/CIRCRESAHA.123.323365
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