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Schmitz MGJ, Aarts JGM, Burroughs L, Sudarsanam P, Kuijpers TJM, Riool M, de Boer L, Xue X, Bosnacki D, Zaat SAJ, de Boer J, Alexander MR, Dankers PYW. Merging Modular Molecular Design with High Throughput Screening of Cell Adhesion on Antimicrobial Supramolecular Biomaterials. Macromol Rapid Commun 2024:e2300638. [PMID: 38530968 DOI: 10.1002/marc.202300638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 03/05/2024] [Indexed: 03/28/2024]
Abstract
A polymer microarray based on the supramolecular ureido-pyrimidinone (UPy) moiety is fabricated to screen antimicrobial materials for their ability to support cell adhesion. UPy-functionalized additives, either cell-adhesive, antimicrobial or control peptides, are used, and investigated in different combinations at different concentrations, resulting in a library of 194 spots. These are characterized on composition and morphology to evaluate the microarray fabrication. Normal human dermal fibroblasts are cultured on the microarrays and cell adhesion to the spots is systematically analyzed. Results demonstrate enhanced cell adhesion on spots with combinations including the antimicrobial peptides. This study clearly proves the power of the high throughput approach in combination with supramolecular molecules, to screen additive libraries for desired biological response.
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Affiliation(s)
- Moniek G J Schmitz
- Department of Biomedical Engineering, Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, PO Box 513, Eindhoven, 5600 MB, The Netherlands
| | - Jasper G M Aarts
- Department of Biomedical Engineering, Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, PO Box 513, Eindhoven, 5600 MB, The Netherlands
| | - Laurence Burroughs
- School of Pharmacy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Phanikrishna Sudarsanam
- Department of Biomedical Engineering, Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, PO Box 513, Eindhoven, 5600 MB, The Netherlands
| | - Tim J M Kuijpers
- Department of Biomedical Engineering, Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, PO Box 513, Eindhoven, 5600 MB, The Netherlands
| | - Martijn Riool
- Department of Medical Microbiology and Infection Prevention, Amsterdam institute for Infection and Immunity, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, 1105 AZ, The Netherlands
| | - Leonie de Boer
- Department of Medical Microbiology and Infection Prevention, Amsterdam institute for Infection and Immunity, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, 1105 AZ, The Netherlands
| | - Xuan Xue
- School of Pharmacy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Dragan Bosnacki
- Department of Biomedical Engineering, Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, PO Box 513, Eindhoven, 5600 MB, The Netherlands
| | - Sebastian A J Zaat
- Department of Medical Microbiology and Infection Prevention, Amsterdam institute for Infection and Immunity, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, 1105 AZ, The Netherlands
| | - Jan de Boer
- Department of Biomedical Engineering, Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, PO Box 513, Eindhoven, 5600 MB, The Netherlands
| | - Morgan R Alexander
- School of Pharmacy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Patricia Y W Dankers
- Department of Biomedical Engineering, Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, PO Box 513, Eindhoven, 5600 MB, The Netherlands
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Kuijpers TJM, Kleinjans JCS, Jennen DGJ. From multi-omics integration towards novel genomic interaction networks to identify key cancer cell line characteristics. Sci Rep 2021; 11:10542. [PMID: 34006939 PMCID: PMC8131752 DOI: 10.1038/s41598-021-90047-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 04/26/2021] [Indexed: 11/09/2022] Open
Abstract
Cancer is a complex disease where cancer cells express epigenetic and transcriptomic mechanisms to promote tumor initiation, progression, and survival. To extract relevant features from the 2019 Cancer Cell Line Encyclopedia (CCLE), a multi-layer nonnegative matrix factorization approach is used. We used relevant feature genes and DNA promoter regions to construct genomic interaction network to study gene-gene and gene-DNA promoter methylation relationships. Here, we identified a set of gene transcripts and methylated DNA promoter regions for different clusters, including one homogeneous lymphoid neoplasms cluster. In this cluster, we found different methylated transcription factors that affect transcriptional activation of EGFR and downstream interactions. Furthermore, the hippo-signaling pathway might not function properly because of DNA hypermethylation and low gene expression of both LATS2 and YAP1. Finally, we could identify a potential dysregulation of the CD28-CD86-CTLA4 axis. Characterizing the interaction of the epigenome and the transcriptome is vital for our understanding of cancer cell line behavior, not only for deepening insights into cancer-related processes but also for future disease treatment and drug development. Here we have identified potential candidates that characterize cancer cell lines, which give insight into the development and progression of cancers.
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Affiliation(s)
- T J M Kuijpers
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands.
| | - J C S Kleinjans
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
| | - D G J Jennen
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
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Kuijpers TJM, Wolters JEJ, Kleinjans JCS, Jennen DGJ. DynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks. BMC Bioinformatics 2019; 20:417. [PMID: 31409281 PMCID: PMC6693283 DOI: 10.1186/s12859-019-2995-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 07/16/2019] [Indexed: 01/11/2023] Open
Abstract
Background The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need for bioinformatics tools that address this problem. Results Here we present DynOVis, a network visualization tool that can capture dynamic dose-over-time effects in biological networks. DynOVis is an integrated work frame of R packages and JavaScript libraries and offers a force-directed graph network style, involving multiple network analysis methods such as degree threshold, but more importantly, it allows for node expression animations as well as a frame-by-frame view of the dynamic exposure. Valuable biological information can be highlighted on the nodes in the network, by the integration of various databases within DynOVis. This information includes pathway-to-gene associations from ConsensusPathDB, disease-to-gene associations from the Comparative Toxicogenomics databases, as well as Entrez gene ID, gene symbol, gene synonyms and gene type from the NCBI database. Conclusions DynOVis could be a useful tool to analyse biological networks which have a dynamic nature. It can visualize the dynamic perturbations in biological networks and allows the user to investigate the changes over time. The integrated data from various online databases makes it easy to identify the biological relevance of nodes in the network. With DynOVis we offer a service that is easy to use and does not require any bioinformatics skills to visualize a network.
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Affiliation(s)
- T J M Kuijpers
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands.
| | - J E J Wolters
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands.,Present Address: School for Mental Health and Neuroscience (MHeNS), University Eye clinic Maastricht, Maastricht University Medical Centre + (MUMC+), P.O. Box 5800, Maastricht, 6229 HX, The Netherlands
| | - J C S Kleinjans
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands
| | - D G J Jennen
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands
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