We developed a whole-cell surface plasmon resonance (SPR) sensor based on a leucine auxotroph of Escherichia coli displaying a gold-binding protein (GBP) in response to cell growth and applied this sensor to the diagnosis of maple syrup urine disease, which is represented by the elevated leucine level in blood. The leucine auxotroph was genetically engineered to grow displaying GBP in a proportion to the concentration of target amino acid leucine. The GBP expressed on the surface of the auxotrophs directly bound to the golden surface of an SPR chip without the need for any additional treatment or reagents, which consequently produced SPR signals used to determine leucine levels in a test sample. Gold nanoparticles (GNPs) were further applied to the SPR system, which significantly enhanced the signal intensity up to 10-fold by specifically binding to GBP expressed on the cell surface. Finally, the diagnostic utility of our system was demonstrated by its employment in reliably determining different statuses of maple syrup urine disease based on a known cutoff level of leucine. This new approach based on an amino acid-auxotrophic E. coli strain expressing a GBP that binds to an SPR sensor holds great promise for detection of other metabolic diseases of newborn babies including homocystinuria and phenylketonuria, which are also associated with abnormal levels of amino acids.
To determine whether each coefficient is equal to zero or not, usual t-tests are a popular choice (among others) in linear regression to practitioners because all statistical packages provide the statistics and their corresponding p-values. Under smaller samples (especially with non-normal errors) the tests often fail to correctly detect statistical significance. We propose a permutation approach by adopting a sufficient dimension reduction methodology to overcome this deficit. Numerical studies confirm that the proposed method has potential advantages over the t-tests. In addition, data analysis is also presented.
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