Esmaeil Ebrahimie

Protein attributes contribute to halo-stability, bioinformatics approach

Halophile proteins can tolerate high salt concentrations. Understanding halophilicity features is the first step toward engineering halostable crops. To this end, we examined protein features contributing to the halo-toleration of halophilic organisms. We compared more than 850 features for halophilic and non-halophilic proteins with various screening, clustering, decision tree, and generalized rule induction models to search for patterns that code for halo-toleration. Up to 251 protein attributes selected by various attribute weighting algorithms as important features contribute to halo-stability; from them 14 attributes selected by 90% of models and the count of hydrogen gained the highest value (1.0) in 70% of attribute weighting models, showing the importance of this attribute in feature selection modeling. The other attributes mostly were the frequencies of di-peptides. No changes were found in the numbers of groups when K-Means and TwoStep clustering modeling were performed on datasets with or without feature selection filtering. Although the depths of induced trees were not high, the accuracies of trees were higher than 94% and the frequency of hydrophobic residues pointed as the most important feature to build trees. The performance evaluation of decision tree models had the same values and the best correctness percentage recorded with the Exhaustive CHAID and CHAID models. We did not find any significant difference in the percent of correctness, performance evaluation, and mean correctness of various decision tree models with or without feature selection. For the first time, we analyzed the performance of different screening, clustering, and decision tree algorithms for discriminating halophilic and non-halophilic proteins and the results showed that amino acid composition can be used to discriminate between halo-tolerant and halo-sensitive proteins.

Using the zebrafish model for Alzheimer’s disease research

Rodent models have been extensively used to investigate the cause and mechanisms behind Alzheimer’s disease. Despite many years of intensive research using these models we still lack a detailed understanding of the molecular events that lead to neurodegeneration. Although zebrafish lack the complexity of advanced cognitive behaviors evident in rodent models they have proven to be a very informative model for the study of human diseases. In this review we give an overview of how the zebrafish has been used to study Alzheimer’s disease. Zebrafish possess genes orthologous to those mutated in familial Alzheimer’s disease and research using zebrafish has revealed unique characteristics of these genes that have been difficult to observe in rodent models. The zebrafish is becoming an increasingly popular model for the investigation of Alzheimer’s disease and will complement studies using other models to help complete our understanding of this disease.

Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology

Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods.

Comparative GO: a web application for comparative gene ontology and gene ontology-based gene selection in bacteria

The primary means of classifying new functions for genes and proteins relies on Gene Ontology (GO), which defines genes/proteins using a controlled vocabulary in terms of their Molecular Function, Biological Process and Cellular Component. The challenge is to present this information to researchers to compare and discover patterns in multiple datasets using visually comprehensible and user-friendly statistical reports. Importantly, while there are many GO resources available for eukaryotes, there are none suitable for simultaneous, graphical and statistical comparison between multiple datasets. In addition, none of them supports comprehensive resources for bacteria. By using Streptococcus pneumoniae as a model, we identified and collected GO resources including genes, proteins, taxonomy and GO relationships from NCBI, UniProt and GO organisations. Then, we designed database tables in PostgreSQL database server and developed a Java application to extract data from source files and loaded into database automatically. We developed a PHP web application based on Model-View-Control architecture, used a specific data structure as well as current and novel algorithms to estimate GO graphs parameters. We designed different navigation and visualization methods on the graphs and integrated these into graphical reports. This tool is particularly significant when comparing GO groups between multiple samples (including those of pathogenic bacteria) from different sources simultaneously. Comparing GO protein distribution among up- or down-regulated genes from different samples can improve understanding of biological pathways, and mechanism(s) of infection. It can also aid in the discovery of genes associated with specific function(s) for investigation as a novel vaccine or therapeutic targets. Availability http://turing.ersa.edu.au/BacteriaGO.

A rapid and efficient method for regeneration of plantlets from embryo explants of cumin (Cuminum cyminum)

A new, simple and efficient method was developed for multiple shoot regeneration of cumin from imbibed embryo cultures. This method yielded a large number of shoots within short period of time (30–50 days) without any subculturing. The effects of different media, different embryo explants and various combinations of plant growth regulators (PGRs) on callus formation and shoot regeneration in cumin were investigated. Simultaneous callus formation and shoot regeneration was obtained. The best response for multiple shoot regeneration was observed on B5 medium containing 1.0 mg l−1 BAP, 0.2 mg l−1 NAA and 0.4 mg l−1 IAA, with an average of 140 shoots per explant.

Underlying functional genomics of fat deposition in adipose tissue

The objective of this study was to gain insight into the underlying mechanisms of fat deposition. Two sheep breeds with large fat-tail (Lori-Bakhtiari) and with thin-tail (Zel) were used as models. To determine important and key candidate lipid metabolism related genes, comparative genomic approaches were employed. Gene expression profiles of adipose tissues were analyzed in human, pig, and cattle by express sequence tag (EST) analysis. EST analysis determined 65, 102 and 125 transcripts in human, pig and cattle respectively with at least 10 fold over-expression in the adipose tissue. Based on our comparative functional genomic analysis, seven genes were more abundant and common in investigated mammalian adipose tissues promising a conserved novel gene network in mammalian lipid metabolism. The candidate genes including fatty acid binding protein 4 (FABP4), fatty acid synthase (FASN), Stearoyl-CoA desaturase (SCD) and Lipoprotein lipase (LPL) were selected for further gene expression investigation within two sheep breeds. The real time PCR results showed that among the genes tested, FABP4 was expressed at higher levels than the others. The expression of FABP4 was significantly higher in the fat-tail of Lori-Bakhtiari than in the fat-tail and visceral adipose tissues of Zel (P<0.05). The findings suggest that the FABP4 gene expression in the fat-tail is an important index of fat deposition.

Prediction of thermostability from amino acid attributes by combination of clustering with attribute weighting: a new vista in engineering enzymes.

The engineering of thermostable enzymes is receiving increased attention. The paper, detergent, and biofuel industries, in particular, seek to use environmentally friendly enzymes instead of toxic chlorine chemicals. Enzymes typically function at temperatures below 60°C and denature if exposed to higher temperatures. In contrast, a small portion of enzymes can withstand higher temperatures as a result of various structural adaptations. Understanding the protein attributes that are involved in this adaptation is the first step toward engineering thermostable enzymes. We employed various supervised and unsupervised machine learning algorithms as well as attribute weighting approaches to find amino acid composition attributes that contribute to enzyme thermostability. Specifically, we compared two groups of enzymes: mesostable and thermostable enzymes. Furthermore, a combination of attribute weighting with supervised and unsupervised clustering algorithms was used for prediction and modelling of protein thermostability from amino acid composition properties. Mining a large number of protein sequences (2090) through a variety of machine learning algorithms, which were based on the analysis of more than 800 amino acid attributes, increased the accuracy of this study. Moreover, these models were successful in predicting thermostability from the primary structure of proteins. The results showed that expectation maximization clustering in combination with uncertainly and correlation attribute weighting algorithms can effectively (100%) classify thermostable and mesostable proteins. Seventy per cent of the weighting methods selected Gln content and frequency of hydrophilic residues as the most important protein attributes. On the dipeptide level, the frequency of Asn-Glu was the key factor in distinguishing mesostable from thermostable enzymes. This study demonstrates the feasibility of predicting thermostability irrespective of sequence similarity and will serve as a basis for engineering thermostable enzymes in the laboratory.

Differential expression of seven conserved microRNAs in response to abiotic stress and their regulatory network in Helianthus annuus

Biotic and abiotic stresses affect plant development and production through alternation of the gene expression pattern. Gene expression itself is under the control of different regulators such as miRNAs and transcription factors (TFs). MiRNAs are known to play important roles in regulation of stress responses via interacting with their target mRNAs. Here, for the first time, seven conserved miRNAs, associated with drought, heat, salt and cadmium stresses were characterized in sunflower. The expression profiles of miRNAs and their targets were comparatively analyzed between leaves and roots of plants grown under the mentioned stress conditions. Gene ontology analysis of target genes revealed that they are involved in several important pathways such as auxin and ethylene signaling, RNA mediated silencing and DNA methylation processes. Gene regulatory network highlighted the existence of cross-talks between these stress-responsive miRNAs and the other stress responsive genes in sunflower. Based on network analysis, we suggest that some of these miRNAs in sunflower such as miR172 and miR403 may play critical roles in epigenetic responses to stress. It seems that depending on the stress type, theses miRNAs target several pathways and cellular processes to help sunflower to cope with drought, heat, salt and cadmium stress conditions in a tissue-associated manner.

DIRECT SHOOT REGENERATION FROM MATURE EMBRYO AS A RAPID AND GENOTYPE-INDEPENDENT PATHWAY IN TISSUE CULTURE OF HETEROGENEOUS DIVERSE SETS OF CUMIN (CUMINUM CYMINUM L.) GENOTYPES

SummaryA rapid and one-step protocol for direct regeneration of shoots from cumin embryo explants has been developed. Embryo explants with shoot meristems were cultured on shoot regeneration medium for 15–22 d. After embryo culture, shoots were regenerated from the area adjacent to the region between the cotyledons and embryo axis within 2 wk, without any intermediate callus phase. Shoot proliferation and elongation were achieved on shoot regeneration medium without subculture. Among the different combinations of 6-benzylaminopurine, α-naphthaleneacetic acid (NAA), and indole-3-acetic acid (IAA) tested, 0.8 mgl−1 (4.3 μM) NAA in combination with 0.3 mgl−1 (1.71 μM) IAA in the B5 medium resulted in the most efficient direct shoot regeneration. No significant difference was detected for the number of regenerated explants when different heterogeneous endemic varieties were compared. This plant regeneration procedure was applicable to different cumin genotypes and regenerated plants were phenotypically normal.

Efficient and Simple Production of Insulin-Producing Cells from Embryonal Carcinoma Stem Cells Using Mouse Neonate Pancreas Extract, As a Natural Inducer

An attractive approach to replace the destroyed insulin-producing cells (IPCs) is the generation of functional β cells from stem cells. Embryonal carcinoma (EC) stem cells are pluripotent cells which can differentiate into all cell types. The present study was carried out to establish a simple nonselective inductive culture system for generation of IPCs from P19 EC cells by 1–2 weeks old mouse pancreas extract (MPE). Since, mouse pancreatic islets undergo further remodeling and maturation for 2–3 weeks after birth, we hypothesized that the mouse neonatal MPE contains essential factors to induce in vitro differentiation of pancreatic lineages. Pluripotency of P19 cells were first confirmed by expression analysis of stem cell markers, Oct3/4, Sox-2 and Nanog. In order to induce differentiation, the cells were cultured in a medium supplemented by different concentrations of MPE (50, 100, 200 and 300 µg/ml). The results showed that P19 cells could differentiate into IPCs and form dithizone-positive cell clusters. The generated P19-derived IPCs were immunoreactive to proinsulin, insulin and insulin receptor beta. The expression of pancreatic β cell genes including, PDX-1, INS1 and INS2 were also confirmed. The peak response at the 100 µg/ml MPE used for investigation of EP300 and CREB1 gene expression. When stimulated with glucose, these cells synthesized and secreted insulin. Network analysis of the key transcription factors (PDX-1, EP300, CREB1) during the generation of IPCs resulted in introduction of novel regulatory candidates such as MIR17, and VEZF1 transcription factors, as well as MORN1, DKFZp761P0212, and WAC proteins. Altogether, we demonstrated the possibility of generating IPCs from undifferentiated EC cells, with the characteristics of pancreatic β cells. The derivation of pancreatic cells from EC cells which are ES cell siblings would provide a valuable experimental tool in study of pancreatic development and function as well as rapid production of IPCs for transplantation.