Inborn diseases of steroid metabolism are detectable at birth and treatable with low-cost medicine. They are characterized by a gross increase of a specific steroid or set of steroids in the urine of affected infants. At present, however, there is no cost-effective method for screening for all such diseases. Even in developed countries, screening for only one subtype of only one such disease (congenital adrenal hyperplasia) is considered cost-effective.
In this CDI project, chemical sensor arrays are being developed that are capable of cheap, powerful, and reliable screening for diseases of steroid metabolism. The arrays use oligonucleotide-based receptors known as three-way junctions (TWJs). A systematic procedure for chemical sensor array design is used, covering the phases of sensor synthesis, feature (sensor) selection, training data collection, and classifier design and analysis. The TWJ acts as a scaffold for sensor design, allowing thousands of variations, each with a different selectivity for small molecules such as steroids. Comprehensive characterization of thousands of sensor responses is made possible with microchips that can synthesize up to 90,000 sensors at fixed locations. Wrapper-based feature selection approaches are used to find small, high-quality sensor subsets from these thousands.
Diagnostic decisions require detecting and quantifying gross increases in concentrations of particular indicative steroids. These concentration changes must be detected in the presence of small concentrations of other steroids, and, owing to differences in kidney filtrations, samples may occur over a range of overall dilutions. This requires mixed classification/regression inference algorithms capable of working over a range of input concentrations. TWJ sensors have non-linear responses to concentration, and non-additive signals for analyte mixtures, and this requires new approaches to chemical sensor array analysis and classifier design. Lastly, new wrapper-based concentration coverage procedures are being developed to ensure accurate representation of sensor response profiles in training data while minimizing the number of measurements needed.
The vast majority of newborns in developing countries are not screened for inborn illnesses of steroid metabolism; even in the US the coverage is not complete. Current methods are precise but disease-specific, expensive, and impractical outside a modern hospital. TWJ sensor arrays will be cheap, stable, and reliable; they are powerful enough to test for many steroid metabolic diseases simultaneously, and can identify new diseases via anomaly detection. They will have the potential to be deployed in the field, resulting in cost-effective screening of many rare diseases in developed countries, and, for the first time, cost-effective screening in the rest of the world as well.
The main focus of this grant has been on the development of new methods to generate a particular type of oligonucleotide-based receptors for small molecules (also known as aptamers). The unique aspect of the program is the requirement that a set of these aptamers, none of which would be specific for any particular biomarker, should be suitable for a classification of patient samples into "HEALTH" and "DISEASE" classes, that is, capable of preliminary indication of diagnosis. Thus, the focus of our research was on search for (selection of) optimal sensors that form an optimal set for such classifications. Two main results of our project are: 1. We developed an evolutionary protocol for selecting sensors based on their interactions with samples (mock patient samples). In this protocol a large collection of possible aptamers was treated with samples belonging to different classes (proof-of-concept for "HEALTH" and "DISEASE") and as a result two sets of sensors were generated. When combined, these sensors were able to tell us (i.e., "classify") to which of the two sets samples belong. The protocol was demonstrated on hydrophobic compounds, an example of which were steroids, the abundance and misbalance of which in urine is a sign of metabolic diseases. 2. In order to expand the use of this approach to other analytes, we developed a new type of aptameric sensors, based on ternary complexes between organometallic metal ion complexes, analytes, and oligonucleotides. These ternary sensors were demonstrated on amino acids, and we were able to isolate both highly specific and very non-specific sensors (the latter are used for classifications). This development allows us to expand very broadly our approach to diagnostic classifications of samples. Thus, the main intellectual merit of our work is in new approaches to novel sets of sensors that are suitable for health care applications. The most important broader impact will be in health care because our sensors will lead to fast and cheap point-of-care assay. Along these lines we demonstrated that a sensor for one of amino acids, phenylalanine, can be used for after-meal monitoring of this amino acid in blood of children with phenylketonuria.
