Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer.

Autor: Fischer CL; Department of Biology, Waldorf University, Forest City, IA, 50436, USA., Bates AM; Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA., Lanzel EA; Department of Oral Pathology, Radiology and Medicine, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA., Guthmiller JM; College of Dentistry, University of Nebraska Medical Center, Lincoln, NE, 68583, USA., Johnson GK; Department of Periodontics, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA., Singh NK; Cellworks Group Inc., San Jose, CA, 95110, USA.; Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India., Kumar A; Cellworks Group Inc., San Jose, CA, 95110, USA.; Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India., Vidva R; Cellworks Group Inc., San Jose, CA, 95110, USA.; Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India., Abbasi T; Cellworks Group Inc., San Jose, CA, 95110, USA.; Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India., Vali S; Cellworks Group Inc., San Jose, CA, 95110, USA.; Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India., Xie XJ; Division of Biostatistics and Computational Biology, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA., Zeng E; Division of Biostatistics and Computational Biology, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA., Brogden KA; Department of Periodontics, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA. kim-brogden@uiowa.edu.; Iowa Institute for Oral Health Research, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA. kim-brogden@uiowa.edu.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2019 Jul 26; Vol. 9 (1), pp. 10877. Date of Electronic Publication: 2019 Jul 26.
DOI: 10.1038/s41598-019-47381-4
Abstrakt: Individual computational models of single myeloid, lymphoid, epithelial, and cancer cells were created and combined into multi-cell computational models and used to predict the collective chemokine, cytokine, and cellular biomarker profiles often seen in inflamed or cancerous tissues. Predicted chemokine and cytokine output profiles from multi-cell computational models of gingival epithelial keratinocytes (GE KER), dendritic cells (DC), and helper T lymphocytes (HTL) exposed to lipopolysaccharide (LPS) or synthetic triacylated lipopeptide (Pam3CSK4) as well as multi-cell computational models of multiple myeloma (MM) and DC were validated using the observed chemokine and cytokine responses from the same cell type combinations grown in laboratory multi-cell cultures with accuracy. Predicted and observed chemokine and cytokine responses of GE KER + DC + HTL exposed to LPS and Pam3CSK4 matched 75% (15/20, p = 0.02069) and 80% (16/20, P = 0.005909), respectively. Multi-cell computational models became 'personalized' when cell line-specific genomic data were included into simulations, again validated with the same cell lines grown in laboratory multi-cell cultures. Here, predicted and observed chemokine and cytokine responses of MM cells lines MM.1S and U266B1 matched 75% (3/4) and MM.1S and U266B1 inhibition of DC marker expression in co-culture matched 100% (6/6). Multi-cell computational models have the potential to identify approaches altering the predicted disease-associated output profiles, particularly as high throughput screening tools for anti-inflammatory or immuno-oncology treatments of inflamed multi-cellular tissues and the tumor microenvironment.
Databáze: MEDLINE
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