Emerging infectious diseases present a profound threat to global health, economic development, and political stability, and therefore represent a significant national security concern for the United States. The increased prevalence of international travel and globalized trade further amplify the threat of infectious disease outbreaks of catastrophic effect. The key to containing and eradicating an outbreak before it goes global is rapid identification of index cases and initial clusters of affected individuals. This depends upon establishment of a biosurveillance network that effectively reaches infectious disease hotspots in even the most remote regions of the world and provides a network-integrated, location-appropriate diagnostic capability. At present, there are two critical needs which must be addressed in order to extend biosurveillance activities beyond centralized laboratory facilities: 1) A simple, reliable, and safe method for immediate stabilization of clinical specimens in the field; and 2) A flexible sample processing platform that enables in-field preparation of clinical specimens for rapid, on-site analysis using a variety of diagnostic assay platforms. These needs are not necessarily mutually exclusive; in fact, we propose that they are most efficiently addressed by a deployable sample processing platform that immediately stabilizes the information content of clinical specimens through transformation of the inherently unstable analytes of interest into stable equivalents that are appropriately formatted for downstream analysis. In order to address this problem, we have developed a sample processing pipeline and microfluidics-based platform modules enabling: 1) Extraction of total RNA from finger-stick quantities of human whole blood; and 2) Microscale synthesis of appropriately-formatted cDNA products that capture the information content of blood RNA in a stable form that supports pathogen detection and/or characterization via PCR and/or Second Generation Sequencing (SGS). Through this research we have discovered new, effective solutions for problems that thus far have hindered use of digital microfluidics (DMF) in biomedical applications. Our work reveals a clear path forward to fieldable, automated sample processing systems that will enable rapid, on-site identification of usual-suspect and novel pathogens in clinical specimens for improved biosurveillance.
We have implemented a ligand-alignment algorithm into our developed computational pipeline for identifying specificity-determining features (SDFs) in protein-ligand complexes. Given a set of protein-ligand complex structures, the algorithm aligns the complexes by ligand rather than by the C -RMSD or standard approach, providing a single reference frame for extracting SDFs. We anticipate that this ligand-alignment capability will be highly useful for protein function prediction. We already have a database containing > 20 K ligand-protein complex crystal structures taken from the Protein Data Bank. By aligning these proteins to single reference frames using ligand alignment, we can submit the complexes to our pipeline for SDF extraction. The SDFs derived from this training procedure can be used as thumbprints that are hallmarks of individual enzyme classes. These SDF thumbprints may then serve as guides to the prediction of function of new unknown proteins.
Sandia's scientific and engineering expertise in the fields of computational biology, high-performance prosthetic limbs, biodetection, and bioinformatics has been applied to specific problems at the forefront of cancer research. Molecular modeling was employed to design stable mutations of the enzyme L-asparaginase with improved selectivity for asparagine over other amino acids with the potential for improved cancer chemotherapy. New electrospun polymer composites with improved electrical conductivity and mechanical compliance have been demonstrated with the promise of direct interfacing between the peripheral nervous system and the control electronics of advanced prosthetics. The capture of rare circulating tumor cells has been demonstrated on a microfluidic chip produced with a versatile fabrication processes capable of integration with existing lab-on-a-chip and biosensor technology. And software tools have been developed to increase the calculation speed of clustered heat maps for the display of relationships in large arrays of protein data. All these projects were carried out in collaboration with researchers at the University of Texas M. D. Anderson Cancer Center in Houston, TX.
We have developed a novel modular automated processing system (MAPS) that enables reliable, high-throughput analysis as well as sample-customized processing. This system is comprised of a set of independent modules that carry out individual sample processing functions: cell lysis, protein concentration (based on hydrophobic, ion-exchange and affinity interactions), interferent depletion, buffer exchange, and enzymatic digestion of proteins of interest. Taking advantage of its unique capacity for enclosed processing of intact bioparticulates (viruses, spores) and complex serum samples, we have used MAPS for analysis of BSL1 and BSL2 samples to identify specific protein markers through integration with the portable microChemLab{trademark} and MALDI.
