Caulk, A. W. | bioRxiv Science

bioRxiv2019

Genetic Background Dominates Fibrotic Aortic Remodeling During Angiotensin-Induced Hypertension in Mice

Many genetic mutations affect aortic structure and function in mice, but little is known about the influence of background strain. We phenotyped aortas from C57BL/6J and 129SvEv mice before and after continuous infusion of angiotensin II (AngII) for two weeks, which elevated blood pressure similarly in both strains (1.34-fold vs. 1.32-fold, systolic). Excised thoracic aortas were characterized functionally using isobaric vasoactive and cyclic passive stiffness tests whereas immunohistological studies quantified altered medial and adventitial composition as well as the infiltration of pan-inflammatory CD45+ cells. Baseline aortic geometry, composition, and biomechanical properties were similar across strains, consistent with mechanical homeostasis. Yet, aortic remodeling in response to AngII-induced hypertension differed dramatically between strains, with gross maladaptive remodeling in C57BL/6J but not in 129SvEv mice. CD45+ cell density was markedly higher in C57BL/6J than 129SvEv aortas while vasoconstrictive responses to AngII were greater in 129SvEv than C57BL/6J both before and after hypertension; importantly, smooth muscle mediated vasoconstriction reduces pressure-induced wall stress. Bulk RNA sequencing, layer-specific biomechanical modeling, and growth and remodeling simulations support the emergent hypothesis that mechanical stress-mediated immune processes promote maladaptive remodeling while smooth muscle contractile processes reduce wall stress and thereby protect against fibrosis. Differentially expressed mechano-sensitive genes thus play key roles in the distinct hypertensive aortic remodeling in C57BL/6J and 129SvEv mice and must be considered when comparing studies in different background strains, particularly mixed strains that are often used to generate mice with targeted mutations. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/727800v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@e9f1d9org.highwire.dtl.DTLVardef@104e221org.highwire.dtl.DTLVardef@19a00bforg.highwire.dtl.DTLVardef@1a70cd5_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2019

Robust latent-variable interpretation of in vivo regression models by nested resampling

Simple multilinear methods, such as partial least squares regression (PLSR), are effective at interrelating dynamic, multivariate datasets of cell-molecular biology through high-dimensional arrays. However, data collected in vivo are more difficult, because animal-to-animal variability is often high, and each time-point measured is usually a terminal endpoint for that animal. Observations are further complicated by the nesting of cells within tissues or tissue sections, which themselves are nested within animals. Here, we introduce principled resampling strategies that preserve the tissue-animal hierarchy of individual replicates and compute the uncertainty of multidimensional decompositions applied to global averages. Using molecular-phenotypic data from the mouse aorta and colon, we find that interpretation of decomposed latent variables (LVs) changes when PLSR models are resampled. Lagging LVs, which statistically improve global-average models, are unstable in resampled iterations that preserve nesting relationships, arguing that these LVs should not be mined for biological insight. Interestingly, resampling is less discriminatory for multidimensional regressions of in vitro data, suggesting it is unnecessary when replicate-to-replicate variance is low. Our work illustrates the challenges and opportunities in translating systems-biology approaches from cultured cells to living organisms. Nested resampling adds a straightforward quality-control step aiding the interpretability of in vivo regression models.

systems biology↗