Prediction of chronic liver diseases on the basis of the N-acetyltransferase 2 phenotype

Role of N-acetyltransferase 2 gene polymorphism in the human pathology

Nowadays multiple heterogeneous chemicals affect the human body. They include drugs, household chemicals, dyes, food supplements and others. The human organism can modify, inactivate, and eliminate the chemicals by biotransformation enzymes. But it is well known that biotransformation can lead to toxification phenomenon. Individuals differ from each other by the rate of chemical modification that promotes accumulation of toxins and carcinogens in some patients. An N-acetyltransferase 2 enzyme participates in the aromatic amines second phase metabolism. This work reviews the acetyltransferase gene polymorphism possible role in diseases development including drug-induced organs damage.

Gene of acetyltransferase has polymorphisms associated with two haplotypes of  fast and slow substrate acetylation. Gene alleles combine in three genotypes: fast, intermediate, and slow acetylators. Acetylation rate plays a significant role in side effects development during tuberculosis treatment and cancer pathogenesis. Recently, new data described the role of enzyme in development of non-infectious diseases in the human. Scientists consider that slow acetylation genotype in combination with high xenobiotic load result in accumulation of toxic substances able to damage cells.

Therefore, acetyltransferase genotyping helps to reveal risk groups of cancer and non-infectious disease development and to prescribe more effective and safe doses of drugs. 

Prediction of gene phenotypes based on GO and KEGG pathway enrichment scores

Observing what phenotype the overexpression or knockdown of gene can cause is the basic method of investigating gene functions. Many advanced biotechnologies, such as RNAi, were developed to study the gene phenotype. But there are still many limitations. Besides the time and cost, the knockdown of some gene may be lethal which makes the observation of other phenotypes impossible. Due to ethical and technological reasons, the knockdown of genes in complex species, such as mammal, is extremely difficult. Thus, we proposed a new sequence-based computational method called kNNA-based method for gene phenotypes prediction. Different to the traditional sequence-based computational method, our method regards the multiphenotype as a whole network which can rank the possible phenotypes associated with the query protein and shows a more comprehensive view of the protein's biological effects. According to the prediction result of yeast, we also find some more related features, including GO and KEGG information, which are making more contributions in identifying protein phenotypes. This method can be applied in gene phenotype prediction in other species.

Predicting protein phenotypes based on protein-protein interaction network

Background

Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins.

Methodology/Principal Findings

Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked acording to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%.

Conclusions/Significance

The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms.