Patricia L. Dolan

Towards De Novo Design of Deoxyribozyme Biosensors for GMO Detection

Hybrid systems that provide a seamless interface between nanoscale molecular events and microsystem technologies enable the development of complex biological sensor systems that not only detect biomolecular threats, but also are able to determine and execute a programmed response to such threats. The challenge is to move beyond the current paradigm of compartmentalizing detection, analysis, and interpretation into separate steps. We present methods that will enable the de novo design and development of customizable biosensors that can exploit deoxyribozyme computing (Stojanovic and Stefanovic, 2003) to concurrently perform in vitro target detection, genetically modified organism detection, and classification.

Rational Redesign of Glucose Oxidase for Improved Catalytic Function and Stability

Glucose oxidase (GOx) is an enzymatic workhorse used in the food and wine industries to combat microbial contamination, to produce wines with lowered alcohol content, as the recognition element in amperometric glucose sensors, and as an anodic catalyst in biofuel cells. It is naturally produced by several species of fungi, and genetic variants are known to differ considerably in both stability and activity. Two of the more widely studied glucose oxidases come from the species Aspergillus niger (A. niger) and Penicillium amagasakiense (P. amag.), which have both had their respective genes isolated and sequenced. GOx from A. niger is known to be more stable than GOx from P. amag., while GOx from P. amag. has a six-fold superior substrate affinity (K M) and nearly four-fold greater catalytic rate (k cat). Here we sought to combine genetic elements from these two varieties to produce an enzyme displaying both superior catalytic capacity and stability. A comparison of the genes from the two organisms revealed 17 residues that differ between their active sites and cofactor binding regions. Fifteen of these residues in a parental A. niger GOx were altered to either mirror the corresponding residues in P. amag. GOx, or mutated into all possible amino acids via saturation mutagenesis. Ultimately, four mutants were identified with significantly improved catalytic activity. A single point mutation from threonine to serine at amino acid 132 (mutant T132S, numbering includes leader peptide) led to a three-fold improvement in k cat at the expense of a 3% loss of substrate affinity (increase in apparent K M for glucose) resulting in a specify constant (k cat/K M) of 23.8 (mM−1 · s−1) compared to 8.39 for the parental (A. niger) GOx and 170 for the P. amag. GOx. Three other mutant enzymes were also identified that had improvements in overall catalysis: V42Y, and the double mutants T132S/T56V and T132S/V42Y, with specificity constants of 31.5, 32.2, and 31.8 mM−1 · s−1, respectively. The thermal stability of these mutants was also measured and showed moderate improvement over the parental strain.

Bio micro fuel cell grand challenge final report.

Christopher Apblett, Kent Schubert, Bruce Kelley, Andrew Walker, Blake Simmons, Ted Borek, Stephen Meserole, Todd Alam, Brian Cherry, Greg Roberts, Jim Novak, Jim Hudgens, Dave Peterson, Jason Podgorski, Susan Brozik, Jeb Flemming, Stan Kravitz, David Ingersoll, Steve Eisenbies, Randy Shul, Sarah Rich, Carrie Schmidt, Mike Beggans, Jeanne Sergeant, Chris Cornelius, Cy Fujimoto, Micheal Hickner, Swapnil Chabra, Suzanne Ma, Joanne Volponi, Micheal Kelly, Kevin Zavadil, Chad Staiger, Patricia Dolan, Monica Manginell, Jason Harper, Dan Doughty, Steve Casalnuovo

Syndrome-Based Discrimination of Single Nucleotide Polymorphism

The ability to discriminate nucleic acid sequences is necessary for a wide variety of applications: high throughput screening, distinguishing genetically modified organisms (GMOs), molecular computing, differentiating biological markers, fingerprinting a specific sensor response for complex systems, etc. Hybridization-based target recognition and discrimination is central to the operation of nucleic acid sensor systems. Therefore developing a quantitative correlation between mishybridization events and sensor out put is critical to the accurate interpretation of results. In this work, using experimental data produced by introducing single mutations (single nucleotide polymorphisms, SNPs) in the probe sequence of computational catalytic molecular beacons (deoxyribozyme gates) [1], we investigate coding theory algorithms for uniquely categorizing SNPs based on the calculation of syndromes

Viral Vectors for Gene Modification of Plants as Chem/Bio Sensors

Chemical or biological sensors that are specific, sensitive, and robust allowing intelligence gathering for verification of nuclear non-proliferation treaty compliance and detouring production of weapons of mass destruction are sorely needed. Although much progress has been made in the area of biosensors, improvements in sensor lifetime, robustness, and device packaging are required before these devices become widely used. Current chemical and biological detection and identification techniques require less-than-covert sample collection followed by transport to a laboratory for analysis. In addition to being expensive and time consuming, results can often be inconclusive due to compromised sample integrity during collection and transport. We report here a demonstration of a plant based sensor technology which utilizes mature and seedling plants as chemical sensors. One can envision genetically modifying native plants at a site of interest that can report the presence of specific toxins or chemicals. In this one year project we used a developed inducible expression system to show the feasibility of plant sensors. The vector was designed as a safe, non-infectious vector which could be used to invade, replicate, and introduce foreign genes into mature host plants that then allow the plant to sense chem/bio agents. The genes introduced through the vector included a reporter gene that encodes for green fluorescent protein (GFP) and a gene that encodes for a mammalian receptor that recognizes a chemical agent. Specifically, GFP was induced by the presence of 17-{beta}-Estradiol (estrogen). Detection of fluorescence indicated the presence of the target chemical agent. Since the sensor is a plant, costly device packaging development or manufacturing of the sensor were not required. Additionally, the biological recognition and reporting elements are maintained in a living, natural environment and therefore do not suffer from lifetime disadvantages typical of most biosensing platforms. Detection of the chem/bio agent reporter (GFP) can be detected only at a specific wavelength.

Quantitative assessment of SNP discrimination for computational molecular beacons

The ability to discriminate nucleic acid sequences is necessary for a wide variety of applications: high throughput screening, distinguishing genetically modified organisms (GMOs), molecular computing, differentiating biological markers, fingerprinting a specific sensor response for complex systems, etc. Hybridization-based target recognition and discrimination is central to the operation of nucleic acid microsensor systems. Therefore developing a quantitative correlation between mishybridization events and sensor output is critical to the accurate interpretation of results. Additionally, knowledge of such correlation can be used to design intelligent sensor systems that incorporate mishybridization noise into system design. Using experimental data produced by introducing single mutations (single nucleotide polymorphisms, SNPs) in the probe sequence of computational catalytic molecular beacons (deoxyribozyme gates) [Stojanovic & Stefanovic, 2003], we investigate correlations between free energy of the target-probe complex and the measured fluorescence of the deoxyribozyme gate. Experimental data for forty-five SNP-containing probe sequences are compiled and compared against the true probe sequence to determine the relationship between position, type of mutation, and the fluorescence level of the molecular beacon. The sequence set accounts for every possible SNP for a fifteen-base probe. Experiments are conducted using a 55 mul detection volume containing a modified YESiA(E6) deoxyribozyme molecular beacon (100 nM) [Stojanovic et al., 2001], TAMRA substrate (1 muM) and input sequences (2 muM). Using free energy as a first-approximation of the energetic interactions that occur during target-probe recognition, we generate empirical data for each target-probe pair using a nucleic acid hybridization thermodynamics server called HyTher (http://ozone2.chem.wayne.edu/). HyTher uses empirical fits of experimentally measured data to generate hybridization thermodynamic predictions for nucleic acid sequence pairs. Empirical data for all target-probe combinations are correlated with experimental fluorescence measurements to determine a quantitative link between target-probe hybridization free energy and molecular beacon fluorescence for each SNP-containing probe. We investigate Bayesian-based classification approaches as well as combinatorial design based methods for identifying and classifying mismatch patterns that produce similar fluorescence levels