Chondrocyte Adhesion to RGD-bonded Alginate: Effect on Mechanotransduction and Matrix Metabolism: a Dissertation

The mechanism of mechanotransduction in chondrocyte matrix metabolism is not well understood, in part because of the density of cartilage and in part because of limitations in in vitroculture systems. Using alginate covalently modified to include the integrin adhesion ligand R-G-D (arginine-glycine-aspartate) represents a unique approach to studying mechanotransduction in that it allows for exploration of the role of integrin adhesion in mediating changes to chondrocyte behavior. The hypothesis of this research was that chondrocytes will form a cytoskeletal adhesion to RGD-alginate mediated integrins, that this attachment will enable chondrocyte sensation of mechanical signals, and this signaling will alter chondrocyte matrix metabolism. The first aim of this research was to characterize chondrocyte attachment to RGD-alginate, and assess the role of substrate mechanics on chondrocyte attachment kinetics and morphology. Secondly, the effect of chondrocyte attachment to RGD-alginate in 3D culture on matrix biosynthesis was assessed, as were changes in substrate mechanics. Finally, this research aimed to determine the metabolic response of chondrocytes to changes in intrinsic and extrinsic mechanics. It was found that the RGD ligand functionalized the alginate scaffold, enabling chondrocytes to sense the mechanical environment. Attachment kinetics, morphology, and proteoglycan metabolism were found to adapt to hydrogel matrix stiffness when an integrin adhesion was present. Externally applied compression was transmitted through this integrin attachment, causing changes in proteoglycan synthesis. Components of media serum were found to modulate the effects of integrin mechanotransduction. These results were obtained by analyzing a novel approach with established techniques, such as the DMB dye assay for sulfated GAG content. The conclusions conform to diverse data from cartilage explant loading and monolayer culture studies, yet were accomplished using one versatile system in a straightforward manner. The potential of this system extends further, into identification of intracellular signaling pathways and extracellular modulation of matrix components. Seeded RGD-alginate is well suited for studying consequences of integrin attachment

Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations.

Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies of asthma in 5,416 individuals with asthma (cases) including individuals of European American, African American or African Caribbean, and Latino ancestry, with replication in an additional 12,649 individuals from the same ethnic groups. We identified five susceptibility loci. Four were at previously reported loci on 17q21, near IL1RL1, TSLP and IL33, but we report for the first time, to our knowledge, that these loci are associated with asthma risk in three ethnic groups. In addition, we identified a new asthma susceptibility locus at PYHIN1, with the association being specific to individuals of African descent (P = 3.9 × 10(-9)). These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and that ancestry-specific associations also contribute to the complex genetic architecture of asthma

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Effect of substrate mechanics on chondrocyte adhesion to modified alginate surfaces

This study characterized the attachment of chondrocytes to RGD-functionalized alginate by examining the effect of substrate stiffness on cell attachment and morphology. Bovine chondrocytes were added to wells coated with 2% alginate or RGD-alginate. The alginate was crosslinked with divalent cations ranging from 1.25 to 62.5 mmol/g alginate. Attachment to RGD-alginate was 10-20 times higher than attachment to unmodified alginate and was significantly inhibited by antibodies to integrin subunits alpha3l and beta1, cytochalasin-D, and soluble RGD peptide. The equilibrium level and rate of attachment increased with crosslink density and substrate stiffness. Substrate stiffness also regulated chondrocyte morphology, which changed from a rounded shape with nebulous actin on weaker substrates to a predominantly flat morphology with actin stress fibers on stiffer substrates. The dependence of attachment on integrins and substrate stiffness suggests that chondrocyte integrins may play a role in sensing the mechanical properties of the matrices to which they are attached

METHODS FOR CLUSTERING TIME SERIES DATA ACQUIRED FROM MOBILE HEALTH APPS

In our recent Asthma Mobile Health Study (AMHS), thousands of asthma patients across the country contributed medical data through the iPhone Asthma Health App on a daily basis for an extended period of time. The collected data included daily self-reported asthma symptoms, symptom triggers, and real time geographic location information. The AMHS is just one of many studies occurring in the context of now many thousands of mobile health apps aimed at improving wellness and better managing chronic disease conditions, leveraging the passive and active collection of data from mobile, handheld smart devices. The ability to identify patient groups or patterns of symptoms that might predict adverse outcomes such as asthma exacerbations or hospitalizations from these types of large, prospectively collected data sets, would be of significant general interest. However, conventional clustering methods cannot be applied to these types of longitudinally collected data, especially survey data actively collected from app users, given heterogeneous patterns of missing values due to: 1) varying survey response rates among different users, 2) varying survey response rates over time of each user, and 3) non-overlapping periods of enrollment among different users. To handle such complicated missing data structure, we proposed a probability imputation model to infer missing data. We also employed a consensus clustering strategy in tandem with the multiple imputation procedure. Through simulation studies under a range of scenarios reflecting real data conditions, we identified favorable performance of the proposed method over other strategies that impute the missing value through low-rank matrix completion. When applying the proposed new method to study asthma triggers and symptoms collected as part of the AMHS, we identified several patient groups with distinct phenotype patterns. Further validation of the methods described in this paper might be used to identify clinically important patterns in large data sets with complicated missing data structure, improving the ability to use such data sets to identify at-risk populations for potential intervention