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

Carol L Fischer; Amber M Bates; Emily A Lanzel; Janet M Guthmiller; Georgia K Johnson; Neeraj Kumar Singh; Ansu Kumar; Robinson Vidva; Taher Abbasi; Shireen Vali; Xian Jin Xie; Erliang Zeng; Kim A Brogden

doi:10.1038/s41598-019-47381-4

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

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

Carol L Fischer, Amber M Bates, Emily A Lanzel, Janet M Guthmiller, Georgia K Johnson, Neeraj Kumar Singh, Ansu Kumar, Robinson Vidva, Taher Abbasi, Shireen Vali, …

Scientific reports, Vol.9(1), pp.10877-13

07/26/2019

DOI: 10.1038/s41598-019-47381-4

PMCID: PMC6659691

PMID: 31350446

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Abstract

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.

Biomarkers - metabolism

Epithelium - pathology

Prognosis

Cytokines - metabolism

Humans

Computational Biology

Dendritic Cells - pathology

Inflammation - immunology

Neoplasms - diagnosis

Multiple Myeloma - metabolism

Keratinocytes - pathology

Multiple Myeloma - pathology

Gingiva - pathology

High-Throughput Screening Assays

Keratinocytes - metabolism

Neoplasms - immunology

Computer Simulation

Cell Line, Tumor

Inflammation - diagnosis

Dendritic Cells - metabolism

Details

Title: Subtitle: Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer
Creators: Carol L Fischer - Department of Biology, Waldorf University, Forest City, IA, 50436, USA
Amber M Bates - Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA
Emily A Lanzel - Department of Oral Pathology, Radiology and Medicine, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA
Janet M Guthmiller - College of Dentistry, University of Nebraska Medical Center, Lincoln, NE, 68583, USA
Georgia K Johnson - Department of Periodontics, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA
Neeraj Kumar Singh - Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India
Ansu Kumar - Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India
Robinson Vidva - Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India
Taher Abbasi - Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India
Shireen Vali - Cellworks Research India Pvt. Ltd (Wholly owned subsidiary of Cellworks Group Inc.), Bangalore, India
Xian Jin Xie - Division of Biostatistics and Computational Biology, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA
Erliang Zeng - Division of Biostatistics and Computational Biology, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA
Kim A Brogden - Iowa Institute for Oral Health Research, College of Dentistry, University of Iowa, Iowa City, IA, 52242, USA. kim-brogden@uiowa.edu
Resource Type: Journal article
Publication Details: Scientific reports, Vol.9(1), pp.10877-13
DOI: 10.1038/s41598-019-47381-4
PMID: 31350446
PMCID: PMC6659691
NLM abbreviation: Sci Rep
ISSN: 2045-2322
eISSN: 2045-2322
Publisher: England
Grant note: R01 DE014390 / NIDCR NIH HHS T90 DE023520 / NIDCR NIH HHS
Language: English
Date published: 07/26/2019
Academic Unit: Preventive and Community Dentistry; Roy J. Carver Department of Biomedical Engineering; Oral Pathology, Radiology and Medicine; Iowa Neuroscience Institute; Biostatistics; Dental Research; Periodontics
Record Identifier: 9984070988702771

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