Publications

The accessory Sec system is a specialized transport system that exports serine-rich repeat (SRR) glycoproteins of Gram-positive bacteria. This system contains two homologues of the general secretory (Sec) pathway (SecA2 and SecY2) and several other essential proteins (Asp1 to Asp5) that share no homology to proteins of known function. In Streptococcus gordonii, Asp2 is required for the transport of the SRR adhesin GspB, but its role in export is unknown. Tertiary structure predictions suggest that the carboxyl terminus of Asp2 resembles the catalytic region of numerous enzymes that function through a Ser-Asp-His catalytic triad. Sequence alignment of all Asp2 homologues identified a highly conserved pentapeptide motif (Gly-X-Ser(362)-X-Gly) typical of most Ser-Asp-His catalytic triads, where Ser forms the reactive residue. Site-directed mutagenesis of residues comprising the predicted catalytic triad of Asp2 of S. gordonii had no effect upon GspB transport but did result in a marked change in the electrophoretic mobility of the protein. Lectin-binding studies and monosaccharide content analysis of this altered glycoform revealed an increase in glucosamine deposition. Random mutagenesis of the Asp2 region containing this catalytic domain also disrupted GspB transport. Collectively, our findings suggest that Asp2 is a bifunctional protein that is essential for both GspB transport and correct glycosylation. The catalytic domain may be responsible for controlling the glycosylation of GspB, while other surrounding regions are functionally required for glycoprotein transport.

Mobile phones and devices, with their constant presence, data connectivity, and multiple intrinsic sensors, can support around-the-clock chronic disease prevention and management that is integrated with daily life. These mobile health (mHealth) devices can produce tremendous amounts of location-rich, real-time, high-frequency data. Unfortunately, these data are often full of bias, noise, variability, and gaps. Robust tools and techniques have not yet been developed to make mHealth data more meaningful to patients and clinicians. To be most useful, health data should be sharable across multiple mHealth applications and connected to electronic health records. The lack of data sharing and dearth of tools and techniques for making sense of health data are critical bottlenecks limiting the impact of mHealth to improve health outcomes. We describe Open mHealth, a nonprofit organization that is building an open software architecture to address these data sharing and "sense-making" bottlenecks. Our architecture consists of open source software modules with well-defined interfaces using a minimal set of common metadata. An initial set of modules, called InfoVis, has been developed for data analysis and visualization. A second set of modules, our Personal Evidence Architecture, will support scientific inferences from mHealth data. These Personal Evidence Architecture modules will include standardized, validated clinical measures to support novel evaluation methods, such as n-of-1 studies. All of Open mHealth's modules are designed to be reusable across multiple applications, disease conditions, and user populations to maximize impact and flexibility. We are also building an open community of developers and health innovators, modeled after the open approach taken in the initial growth of the Internet, to foster meaningful cross-disciplinary collaboration around new tools and techniques. An open mHealth community and architecture will catalyze increased mHealth efficiency, effectiveness, and innovation.