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Butyrylcholinesterase is an enzyme that may serve as a marker of metabolic …


Biology Articles » Biochemistry » Lipid Biochemistry » Serum butyrylcholinesterase in type 2 diabetes mellitus: a biochemical and bioinformatics approach » Materials and Methods

Materials and Methods
- Serum butyrylcholinesterase in type 2 diabetes mellitus: a biochemical and bioinformatics approach

We studied 30 individuals with clinical type 2 diabetes mellitus at our Centre in southern India (14 men, 16 women; age 51.9 years+/- 7.9 years, duration of diabetes 6.6 years+/-3.74 years). Butyrylcholinesterase was estimated colorimetrically using a commercially available kit (Randox Lab, UK). Serum insulin and C-peptide were measured by radioimmunoassay, and lipids (total cholesterol, triglycerides, HDL cholesterol) by colorimetry.

Results are expressed as mean+/- SD. Multiple correlation analysis was done by SPSS package (v10.5). A p value of < 0.05 was taken as significant.

Twenty five sequences of BchE gene were retrieved from National Centre for Biotechnology Information (NCBI): Rattus norvegicus (Accession no: NM_022942), Mus musculus (NM_009738), homo sapiens (NM_000055), homo sapiens (BC018141), homo sapiens (BC008396), Sus scrofa (AF222914), Gallus gallus (AJ306928), Panthera tigris tigris (AF053484), Felis catus (AF053483), Equus caballus (AF178685), Rattus norvegicus (AF244349), Oryctolagus cuniculus (X52092), Oryctolagus cuniculus (X52091), Oryctolagus cuniculus(X52090), homo sapiens (M16541), Oryctolagus cuniculus(U04814), homo sapiens (M16474), Canis Familiaris (M62411), Bos taurus (M62410), Macaca mulatta (M62777), Ovis aries (M62780), Oryctolagus cuniculus (M62779), Sus scrofa (M62778), Mus musculus (NM_009599), and homo sapiens (NM_000446).

Phylogenetic trees were constructed using two methods: distance method (Fitch and Margoliash method) and maximum parsimony method (MP) [3].

In the distance method (Fitch and Margoliash) the sequences were aligned by local pair-wise method and a distance score obtained for each pair of sequences (25 × 25 matrix). The scores were represented as a distance matrix; the most closely related sequences in the matrix were identified and represented as a tree/branch. The average distance between these sequences and each of the other sequences was calculated to obtain a new distance matrix. This process was repeated till all sequences were added to the tree.

In the maximum parsimony method sequences were aligned by global pairwise method, possible trees constructed, parsimony cost of each tree calculated and the one with minimum cost identified as the optimal tree, which was the selected output.


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