Metallothioneins (MTs) are proteins originally identified in horse renal cortex tissue.1
MTs function in metal ion transport, maintain oxidative reduction conditions, and regulate gene expression. They are also implicated as a player in the cancer cell cycle.2 The field of metalloproteomics is rapidly gaining popularity, with bioinformatics strategies used to interpret data.MTs have a low molecular mass and contain a large amount of cystein residues. These unique structural characteristics make detection and quantification difficult.
In the past, MTs have successfully been detected by targeting bonded metal ions or thiol moieties, testing the protein mobility in an electric field, interactions with sorbent, or ELISA. The differential pulse voltammetry Brdicka reaction is the most accurate method for determining the presence of MTS.3
Over The Brdicka reaction4 is a method for polarographic determination of proteins containing SH-groups in an ammonia-buffered cobalt (III) solution. This electrochemical method of quantifying MTs in blood and tissue samples was believed to be of great interest to future developments in cancer research; however, molecular biology techniques have become the standard for current studies.
One major limitation of the Brdicka reaction is the enormous amount of data generated. For each sample studied, the Brdicka reaction generates tens to hundreds of values based on the composition of the sample.
In their publication, Sobrova et al.3 proposed a method of evaluating data produced by the Brdicka reaction with software designed to compress the data and generate data curves that correctly identify tissues types with over 95% confidence.
Liver, kidney, spleen, heart, brain, eye, gonads, blood, and femoral muscle tissue samples were obtained from 28-day-old male laboratory rats. The tissue samples were homogenized and denatured to remove proteins with high molecular weights. Electrochemical measurements were performed and repeated five times for each tissue type.
The shape of the Brdicka curve was unique depending on the tissue type, the number and height of peaks, and the overall shape. Using a mathematical, bioinformatics approach, a decision tree was constructed, allowing unknown rat tissue to be classified based on the electrochemical analyses of MTs present in the tissue.
1. Margoshes, M. and Vallee, B.L. (1957) ‘Metallothionein: A cadmium protein from equine kidney cortex‘, Journal of the American Chemical Society, 79 (17), (pp. 4813-4814)
2. Coyle, P. (2002) ‘Metallothionein: The multipurpose protein‘, Cellular and Molecular Life Sciences, 59 (4), (pp. 627-647)
3. Sobrova, P. (2012) ‘Tissue specific electrochemical fingerprinting‘, PLoS One, 7 (11), doi: 10.1371/journal.pone.0049654
4. Brdicka, R. (1933) ‘Polarographic studies with the Dropping Mercury Kathode. Part XXXII. -Activation of Hydrogen in Sulphydryl Group of Some Thio-Acids in Cobalt Salts Solutions’, Collection of Czechoslovak Chemical Communications, 5, (pp. 148-164)