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Design, Assessment, and in vivo Evaluation of a Computational Model Illustrating the Role of CAV1 in CD4+ T-lymphocytes

Overview of attention for article published in Frontiers in immunology, December 2014
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Title
Design, Assessment, and in vivo Evaluation of a Computational Model Illustrating the Role of CAV1 in CD4+ T-lymphocytes
Published in
Frontiers in immunology, December 2014
DOI 10.3389/fimmu.2014.00599
Pubmed ID
Authors

Brittany D. Conroy, Tyler A. Herek, Timothy D. Shew, Matthew Latner, Joshua J. Larson, Laura Allen, Paul H. Davis, Tomáš Helikar, Christine E. Cutucache

Abstract

Caveolin-1 (CAV1) is a vital scaffold protein heterogeneously expressed in both healthy and malignant tissue. We focus on the role of CAV1 when overexpressed in T-cell leukemia. Previously, we have shown that CAV1 is involved in cell-to-cell communication, cellular proliferation, and immune synapse formation; however, the molecular mechanisms have not been elucidated. We hypothesize that the role of CAV1 in immune synapse formation contributes to immune regulation during leukemic progression, thereby warranting studies of the role of CAV1 in CD4(+) T-cells in relation to antigen-presenting cells. To address this need, we developed a computational model of a CD4(+) immune effector T-cell to mimic cellular dynamics and molecular signaling under healthy and immunocompromised conditions (i.e., leukemic conditions). Using the Cell Collective computational modeling software, the CD4(+) T-cell model was constructed and simulated under CAV1 (+/+), CAV1 (+/-), and CAV1 (-/-) conditions to produce a hypothetical immune response. This model allowed us to predict and examine the heterogeneous effects and mechanisms of CAV1 in silico. Experimental results indicate a signature of molecules involved in cellular proliferation, cell survival, and cytoskeletal rearrangement that were highly affected by CAV1 knock out. With this comprehensive model of a CD4(+) T-cell, we then validated in vivo protein expression levels. Based on this study, we modeled a CD4(+) T-cell, manipulated gene expression in immunocompromised versus competent settings, validated these manipulations in an in vivo murine model, and corroborated acute T-cell leukemia gene expression profiles in human beings. Moreover, we can model an immunocompetent versus an immunocompromised microenvironment to better understand how signaling is regulated in patients with leukemia.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 19%
Student > Master 4 19%
Researcher 3 14%
Student > Doctoral Student 2 10%
Professor 1 5%
Other 3 14%
Unknown 4 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 29%
Medicine and Dentistry 3 14%
Immunology and Microbiology 2 10%
Psychology 1 5%
Biochemistry, Genetics and Molecular Biology 1 5%
Other 2 10%
Unknown 6 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 December 2014.
All research outputs
#20,674,485
of 25,394,764 outputs
Outputs from Frontiers in immunology
#24,770
of 31,554 outputs
Outputs of similar age
#272,942
of 367,215 outputs
Outputs of similar age from Frontiers in immunology
#131
of 172 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 31,554 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 367,215 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 172 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.