Sparse feature selection methods identify unexpected global cellular response to strontium-containing materials

Abstract

Despite the increasing sophistication of biomaterials design and functional characterization studies, little is known regarding cells' global response to biomaterials. Here, we combined nontargeted holistic biological and physical science techniques to evaluate how simple strontium ion incorporation within the well-described biomaterial 45S5 bioactive glass (BG) influences the global response of human mesenchymal stem cells. Our objective analyses of whole gene-expression profiles, confirmed by standard molecular biology techniques, revealed that strontium-substituted BG up-regulated the isoprenoid pathway, suggesting an influence on both sterol metabolite synthesis and protein prenylation processes. This upregulation was accompanied by increases in cellular and membrane cholesterol and lipid raft contents as determined by Raman spectroscopy mapping and total internal reflection fluorescence microscopy analyses and by an increase in cellular content of phosphorylated myosin II light chain. Our unexpected findings of this strong metabolic pathway regulation as a response to biomaterial composition highlight the benefits of discovery-driven nonreductionist approaches to gain a deeper understanding of global cellmaterial interactions and suggest alternative research routes for evaluating biomaterials to improve their design.strontium-releasing biomaterials | human mesenchymal stem cells | microarray analysis | sparse feature selection analysis | mevalonate pathway A n important aim of regenerative medicine is to design smart biomaterials to trigger specific biological responses and enable complex tissue repair (1). Standard in vitro and in vivo testing of such materials usually focuses on assessing the anticipated cell response, often stem cell differentiation to a particular lineage and/or appropriate tissue formation. Although this strategy allows the characterization of specific outcomes, the global cell responses to most biomaterials remain relatively unknown and their mechanisms of action largely unidentified. In comparison with this standard approach, the pharmacology and molecular biology communities have revolutionized their respective fields by taking advantage of unsupervised "-omic" technologies that allow the global biological response to be examined without the inherent bias introduced by predicting particular outcomes. The adoption of comparable hypothesis-generating holistic approaches in the biomaterials communities could stimulate a similar paradigm shift, allowing prospective, rational material design instead of retrospective material evaluation.With more than 2.2 million bone-grafting procedures carried out annually worldwide, the market for smart biomaterials that can be used as functional alternatives to current autogenic and allogenic grafts is significant (2). One biomaterial-based regenerative approach involves the incorporation of biologically active moieties into biomaterials to enhance their bone regeneration properties (3). Strontium ranelate (SrRan) reduces vertebral and nonvertebral fr...

Therapeutic Ion-Releasing Bioactive Glass Ionomer Cements with Improved Mechanical Strength and Radiopacity

“…Furthermore, strontium ions are known to enhance osteoblastic bone formation and reduce osteoclastic bone resorption (Marie et al, 2001), and they are used for the treatment of osteoporosis (Marie, 2005). Strontium-containing bioactive glasses (BG) have been shown to release strontium ions when in contact with aqueous solutions (Fredholm et al, 2012), and they have been suggested to combine the benefits of strontium ions with those of BG (bioactivity, apatite formation, controlled release of therapeutic ions, and delivery versatility) (Gentleman et al, 2010;Autefage et al, 2015).…”

Relevance Vector Machines: Sparse Classification Methods for QSAR

“…9 These machine-learning methods have been successfully applied to QSAR modeling of diverse data sets. 1,2,5,[15][16][17][18][19][20][21][22] They can be further improved by using a sparsity-inducing Laplacian prior ∑ ห‫ݓ‬ ห ே ೢ ୀଵ (denoted as Bayesian regularized artificial neural networks with a Laplacian prior, BRANNLP) 8,9 which enables the irrelevant weights in feature space to be set to zero, leaving the remainder to define the model. In practical terms this means that both the less relevant descriptors and the number of effective weights in the neural network model are pruned to the optimal level.…”

Heartbreak hotel: a convergence in cardiac regeneration

“…Nano-analytical electron microscopy is also being applied to gain insights into cardiovascular tissue calcification (Bertazzo et al, 2013;Autefage et al, 2015), while porous silicon nanoneedles have been devised for sensing intracellular pH changes and enzyme activities, and for delivering drugs, nanoparticles and nucleic acids (Chiappini et al, 2015a,b).…”