Tahereh Deihimi; Esmaeil Ebrahimie; Ali Niazi; Mansour Ebrahimi; Shahab Ayatollahi; Ahmad Tahmasebi; Touraj Rahimi; Moein Jahanbani Veshareh
Abstract
Applying microorganism in oil recovery has attracted attentions recently. Surfactin produced by Bacillus subtilis is widely used industrially in a range of industrial applications in pharmecutical and environmental sectors. Little information about molecular mechanism of suffactin compound is available. ...
Read More
Applying microorganism in oil recovery has attracted attentions recently. Surfactin produced by Bacillus subtilis is widely used industrially in a range of industrial applications in pharmecutical and environmental sectors. Little information about molecular mechanism of suffactin compound is available. In this study, we performed promoter and network analysis of surfactin production genes in Bacillus subtilis subsp. MJ01 (isolated from oil contaminated soil in South of Iran), spizizenii and 168. Our analysis revealed that comQ and comX are the genes with sequence alterations among these three strains of Bacillus subtilis and are involved in surfactin production. Promoter analysis indicated that lrp, argR, rpoD, purr and ihf are overrepresented and have the highest number of transcription factor binding sites (TFBs) on the key surfactin production genes in all 3 strains. Also the pattern of TFBs among these three strains was completely different. Interestingly, there is distinct difference between 168, spizizenii and MJ01 in their frequency of TFs that activate genes involve in surfactin production. Attribute weighting algorithms and decision tree analysis revealed ihf, rpoD and flHCD as the most important TF among surfactin production. Network analysis identified two significant network modules. The first one consists of key genes involved in surfactin production and the second module includes key TFs, involved in regulation of surfactin production. Our findings enhance understanding the molecular mechanism of surfactin production through systems biology analysis.
Nassim Rahmani; Esmaeil Ebrahimie; Ali Niazi; Najaf Allahyari Fard; Bijan Bambai; Zarrin Minuchehr; Mansour Ebrahimi
Abstract
Allergens are proteins or glycoproteins which make widespread disorders that can lead to a systemic anaphylactic shock and even death within a short period of time. Understanding the protein features that are involved in allergenicity is important in developing future treatments as well as engineering ...
Read More
Allergens are proteins or glycoproteins which make widespread disorders that can lead to a systemic anaphylactic shock and even death within a short period of time. Understanding the protein features that are involved in allergenicity is important in developing future treatments as well as engineering proteins in genetic transformation projects. A big dataset of 1439 protein features from 761 plant allergens and 7815 non-allergen proteins was constructed. Thereafter, 10 different attribute weighting algorithms were utilized to find the key characteristics differentiating allergens and non-allergen proteins. The frequency of Leu, Arg and Gln selected by different attribute weighting algorithms with more than 50% confidence, including attribute weighting by Weight_Info Gain, Weight Chi Squared, Weight_Gini Index and Weight_Relief. High amount of Gln and low percentage of Leu and Arg discriminate plant allergens from non-allergens
