10.1371/journal.pone.0202471
Dominik Langgartner
Dominik
Langgartner
Andrea M. Füchsl
Andrea
M. Füchsl
Lisa M. Kaiser
Lisa M.
Kaiser
Tatjana Meier
Tatjana
Meier
Sandra Foertsch
Sandra
Foertsch
Christian Buske
Christian
Buske
Stefan O. Reber
Stefan
O. Reber
Medhanie A. Mulaw
Medhanie A.
Mulaw
Biomarkers for classification and class prediction of stress in a murine model of chronic subordination stress
Public Library of Science
2018
body weight gain
tsp
SVM
non-stressed mice
subordination stress Selye
PCA
CSC
SHC
triad
support vector machines
analysis
GC
2018-09-05 17:33:19
Dataset
https://plos.figshare.com/articles/dataset/Biomarkers_for_classification_and_class_prediction_of_stress_in_a_murine_model_of_chronic_subordination_stress/7050230
<div><p>Selye defined stress as the nonspecific response of the body to any demand and thus an inherent element of all diseases. He reported that rats show adrenal hypertrophy, thymicolymphatic atrophy, and gastrointestinal ulceration, referred to as the stress triad, upon repeated exposure to nocuous agents. However, Selye’s stress triad as well as its extended version including reduced body weight gain, increased plasma glucocorticoid (GC) concentrations, and GC resistance of target cells do not represent reliable discriminatory biomarkers for chronic stress. To address this, we collected multivariate biological data from male mice exposed either to the preclinically validated chronic subordinate colony housing (CSC) paradigm or to single-housed control (SHC) condition. We then used principal component analysis (PCA), top scoring pairs (tsp) and support vector machines (SVM) analyses to identify markers that discriminate between chronically stressed and non-stressed mice. PCA segregated stressed and non-stressed mice, with high loading for some of Selye’s stress triad parameters. The tsp analysis, a simple and highly interpretable statistical approach, identified left adrenal weight and relative thymus weight as the pair with the highest discrimination score and prediction accuracy validated by a blinded dataset (92% p-value < 0.0001; SVM model = 83% accuracy and p-value < 0.0001). This finding clearly shows that simultaneous consideration of these two parameters can be used as a reliable biomarker of chronic stress status. Furthermore, our analysis highlights that the tsp approach is a very powerful method whose application extends beyond what has previously been reported.</p></div>