18 research outputs found
Insight into trichomonas vaginalis genome evolution through metabolic pathways comparison
Trichomonas vaginalis causes the trichomoniasis, in women and urethritis and prostate cancer in men. Its genome draft published by
TIGR in 2007 presents many unusual genomic and biochemical features like, exceptionally large genome size, the presence of
hydrogenosome, gene duplication, lateral gene transfer mechanism and the presence of miRNA. To understand some of genomic
features we have performed a comparative analysis of metabolic pathways of the T. vaginalis with other 22 significant common
organisms. Enzymes from the biochemical pathways of T. vaginalis and other selected organisms were retrieved from the KEGG
metabolic pathway database. The metabolic pathways of T. vaginalis common in other selected organisms were identified. Total 101
enzymes present in different metabolic pathways of T. vaginalis were found to be orthologous by using BLASTP program against
the selected organisms. Except two enzymes all identified orthologous enzymes were also identified as paralogous enzymes.
Seventy-five of identified enzymes were also identified as essential for the survival of T. vaginalis, while 26 as non-essential. The
identified essential enzymes also represent as good candidate for novel drug targets. Interestingly, some of the identified
orthologous and paralogous enzymes were found playing significant role in the key metabolic activities while others were found
playing active role in the process of pathogenesis. The N-acetylneuraminate lyase was analyzed as the candidate of lateral genes
transfer. These findings clearly suggest the active participation of lateral gene transfer and gene duplication during evolution of
T. vaginalis from the enteric to the pathogenic urogenital environment
<span style="font-size:11.0pt;font-family: "Times New Roman";mso-fareast-font-family:"Times New Roman";mso-bidi-font-family: Mangal;mso-ansi-language:EN-GB;mso-fareast-language:EN-US;mso-bidi-language: HI" lang="EN-GB">Analyzing time course microarray data of <i style="mso-bidi-font-style: normal">Toxoplasma gondii</i> and study the impact on host transcript levels using Bioconductor</span>
46-51Toxoplasma gondii is an obligate, intracellular,
apicomplexan parasite that can infect a wide range of warm-blooded animals
including humans. In humans and other intermediate hosts, Toxoplasma develops into
chronic infection that cannot be eliminated by host’s immune response or by
currently used drugs. The ability of the parasite to convert to the bradyzoite
stage and to live inside slow-growing cysts that can go unnoticed by the host
immune system allows for the persistence of parasite throughout the life of the
infected host. Little is known, however, about how bradyzoites manipulate their
host cell. Large scale microarray experiments are becoming increasingly
routine, particularly those which track a number of different cell lines through
time. This time course information provides valuable insight into dynamics of
various biological processes. The proper statistical analysis, however,
requires the use of more sophisticated tools and complex statistical models. In
the current study, the open-source R programming environment in conjunction
with the open-source Bioconductor software was used to analyze microarray data
of T. gondii. Several statistical analysis procedures like (log) fold
changes in conjunction with ordinary and moderated t-statistics were used to
determine differentially expressed genes. The differentially expressed genes
were subjected to cluster analysis, followed by the annotation of the up and
down regulated genes based on the gene ontology. The findings in the present study
suggest the overall effect of the gene expression changes is to modulate the
key metabolic pathways leading to compromised host immune response, enhancement
in programmed cell death, depression in cell proliferation process and
induction of various diseases
Statistical analysis of differential gene expression profile for colon cancer
396-403To analyze
differentially expressed genes in colon cancer, we compared expression profiles
of colorectal cancer cells from normal colonic cells using data of DNA
microarray consisting of 6584 human genes. Each probe set on the array consisted
of EST (expressed sequence tag) sequence of 20 feature pairs of 25 bp sequence.
The data set comprised of 61 samples, divided into two groups of 40 samples for
tumor cells (Group 1) and 21 samples for normal cells (Group 2). In order to do background
adjustments for the negative expression values, the data was transformed into
log base 2 and estimation of missing values was performed by K-nearest neighbor
method, followed by normalization using ‘minimum mean ratio’ among arrays. The basic statistics used for the significance analysis was J5
test, which was computed for each probe and for each contrast with a threshold
value of 4.0 and mean as the measure of central tendency. The differentially
expressed genes were expressed at high frequency in tumour samples. The Naive
Bayes Classifier Algorithm was used to test defined classification of samples
of genes. Correlation distance was measured with the help of Pearson’s
correlation distance. On the basis of J5 test scores, top 5 upregulated genes, viz., vasopressin-neurophysin 2-copeptin
preproprotein, cytochrome, P450 2A7 isoform, major centromere autoantigen B,
myelin associated glycoprotein and bone morphogenetic protein 1 isoform 3
precursor, were selected for further analysis. The above said genes have not
yet been reported to be differentially overexpressed in colon cancer cells,
while their overexpression was reported in other cancers, such as, lung and
breast cancer, etc. These genes can be used for prediction and analyses of the
gene products, which will help in designing new diagnostic and treatment
strategies for the colon cancer
MOLECULAR MODELING AND DRUG DISCOVERY OF POTENTIAL INHIBITORS FOR ANTI CANCER TARGET GENE PLK- POLO LIKE KINASE1
published quarterly. The aim of IJPBS is to publish. peer reviewed research and review articles rapidly without delay in the developing field of pharmaceutical and biological science
Computer-Aided Drug Design for cancer-causing H-Ras p21 mutant protein
Abstract: GTP-bound mutant form H-Ras (Harvey-Ras) proteins are found in 30% of human tumors. Activation of H-Ras is due to point mutation at positions 12, 13, 59 and/or 61 codon. Mutant form of H-Ras proteins is continuously involved in signal transduction for cell growth and proliferation through interaction of downstream-regulated protein Raf. In this paper, we have reported the virtual screening of lead compounds for H-Ras P 21 mutant protein from ChemBank and DrugBank databases using LigandFit and DrugBank-BLAST. The analysis resulted in 13 hits which were docked and scored to identify structurally active leads that make similar interaction to those of bound complex of H-Ras P 21 mutantRaf. This approach produced two different leads, 3-Aminopropanesulphonic acid (docked energy -3.014 kcal/mol) and Hydroxyurea (docked energy -0.009 kcal/mol) with finest Lipinski's rule-of-five. Their docked energy scores were better than the complex structure of H-Ras P 21 mutant protein bound with Raf (1.18 kcal/mol). All the leads were docked into effector region forming interaction with ILE36, GLU37, ASP38 and SER39