Differential Expression of RNAseq Imprinted Genes from Bovine Females Before and After Puberty.

The productivity of beef cows depends on early reproduction traits such as puberty and has an economic impact on the efficiency of production system. Imprinted genes modulate many important endocrine processes such as growth, the onset of puberty and maternal reproductive and behavior. The role of imprinted genes in puberty is a challenging subject since they show the reciprocal role of maternal and paternal genomes in progeny. Although, there are evidences of the involvement of imprint genes in puberty in human, the role of this type of genes in the onset of puberty in cattle has not been studied yet. Here we examined the expression of 27 imprinted genes in pre and post puberty in a bovine model to find differentially expressed imprinted genes in maternal-paternal purebreds and reciprocal crosses across eight tissues and discussed the task of these genes in this crucial process of development and in onset of puberty. DLK1 and MKRN3 that previously described as cause of the central precocious puberty (CPP) in human were differentially expressed in this study. Functional annotation analysis of differentially imprinted genes in different tissues showed significant biological processes of cellular response to growth factor stimulus, response to growth factor, response to parathyroid hormone, developmental growth and the importance of alternative splicing. The results of this study have implications in understanding the role of imprinted genes in the onset of puberty in cattle.

In silico characterization of fructosyl peptide oxidase properties from Eupenicillium terrenum.

Fructosyl peptide oxidase (FPOX) enzyme from Eupenicillium terrenum has a high potential to be applied as a diagnostic enzyme. The aim of the present study is the characterization of FPOX from E. terrenum using different bioinformatics tools. The computational prediction of the RNA and protein secondary structures of FPOX, solubility profile in Escherichia coli, stability, domains, and functional properties were performed. In the FPOX protein, six motifs were detected. The d-amino acid oxidase motif was found as the most important motif that is a FAD-dependent oxidoreductase. The cysteines including 97, 154, 234, 280, and 360 showed a lower score than -10 that have a low possibility for participitation in the formation of the SS bond. The 56.52% of FPOX amino acids are nonpolar. Random coils are dominant in the FPOX sequence, followed by alpha-helix and extended strand. The fpox gene is capable of generating a stable RNA secondary structure (-423.90 kcal/mol) in E. coli. FPOX has a large number of hydrophobic amino acids. FPOX showed a low solubility in E. coli which has several aggregation-prone sites in its 3-D structure. According to the scores, the best mutation candidate for increasing solubility was the conversion of methionine 302 to arginine. The melting temperature of FPOX based on its amino acid sequence was 55°C to 65°C. The amounts of thermodynamic parameters for the FPOX enzyme were -137.4 kcal/mol, -3.59 kcal/(mol K), and -6.8 kcal/mol for standard folding enthalpy, heat capacity, and folding free energy, respectively. In conclusion, the in silico study of proteins can provide a valuable method for better understanding the protein properties and functions for use in our purposes.

Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.

This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify the most suitable factors in assessing the survival of AML patients. Here, six data mining algorithms including Decision Tree, Random Forrest, Logistic Regression, Naive Bayes, W-Bayes Net, and Gradient Boosted Tree (GBT) are employed for the detection model and implemented using the common data mining tool RapidMiner and open-source R package. To improve the predictive ability of our model, a set of features were selected by employing multiple feature selection methods. The accuracy of classification was obtained using 10-fold cross-validation for the various combinations of the feature selection methods and machine learning algorithms. The performance of the models was assessed by various measurement indexes including accuracy, kappa, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC). Our results showed that GBT with an accuracy of 85.17%, AUC of 0.930, and the feature selection via the Relief algorithm has the best performance in predicting the survival rate of AML patients.

Heterologous expression, purification, and refolding of SRY protein: role of L-arginine as analyzed by simulation and practical study.

Escherichia coli is a widely-used cell factory for recombinant protein production, nevertheless, high amount of produced protein is seen in aggregated form. The purpose of this study was to improve the solubility of recombinant bovine sex-determining region Y protein (rbSRY) by exploring the effect of temperature, inducer, and water-arginine mixed solvent. Codon-optimized rbSRY expressed in Rosetta-gami B (DE3) pLysS and purified by NI-NTA His-select affinity chromatography in the native and denaturing conditions. A three-dimensional model of SRY was built and studied through molecular dynamics simulations in water and in the presence of L-arginine as co-solvent. Results indicated the significant effects of temperature and IPTG concentration (P < 0.001) on the solubility of rbSRY. The binding activity of native, inclusion bodies and refolded fractions to anti-rbSRY monoclonal antibody were concentration-dependent (P < 0.001). Based on molecular modeling results, the propensity of fragments in the N-terminal domain to form ß-sheet and the relative instability of α-helices in terminal domains are the probable reasons for the high aggregation potential of SRY, which are mitigated in the presence of L-arginine. Altogether, our rbSRY protein was properly produced and applying appropriate culture conditions could help enhance its solubility, refold inclusion bodies, and improve its activity upon refolding.

Genome imprinting in stem cells: A mini-review.

Genomic imprinting is an epigenetic process result in silencing of one of the two alleles (maternal or paternal) based on the parent of origin. Dysregulation of imprinted genes results in detectable developmental and differential abnormalities. Epigenetics erasure is required for resetting the cell identity to a ground state during the production of induced pluripotent stem (iPS) cells from somatic cells. There are some contradictory reports regarding the status of the imprinting marks in the genome of iPS cells. Additionally, many studies highlighted the existence of subtle differences in the imprinting loci between different types of iPS cells and embryonic stem (ES) cells. These observations could ultimately undermine the use of patient-derived iPS cells for regenerative medicine.

Characterization of bovine (Bos taurus) imprinted genes from genomic to amino acid attributes by data mining approaches.

Genomic imprinting results in monoallelic expression of genes in mammals and flowering plants. Understanding the function of imprinted genes improves our knowledge of the regulatory processes in the genome. In this study, we have employed classification and clustering algorithms with attribute weighting to specify the unique attributes of both imprinted (monoallelic) and biallelic expressed genes. We have obtained characteristics of 22 known monoallelically expressed (imprinted) and 8 biallelic expressed genes that have been experimentally validated alongside 208 randomly selected genes in bovine (Bos taurus). Attribute weighting methods and various supervised and unsupervised algorithms in machine learning were applied. Unique characteristics were discovered and used to distinguish mono and biallelic expressed genes from each other in bovine. To obtain the accuracy of classification, 10-fold cross-validation with concerning each combination of attribute weighting (feature selection) and machine learning algorithms, was used. Our approach was able to accurately predict mono and biallelic genes using the genomics and proteomics attributes.

Attribute selection and model evaluation for the maternal and paternal imprinted genes in bovine (Bos Taurus) using supervised machine learning algorithms.

Imprinted genes display biased expression of paternal and maternal alleles in mammals. They are marked through epigenetic process during gametogenesis. Characterization of imprinted genes has expanded our understanding of the regulation and function of genes. In the current study, 22 experimentally validated imprinted genes in bovine (Bos Taurus) were analysed. Several supervised machine learning algorithms and attribute weighting methods were used to find characteristics of different types of imprinted genes and suggest a classification method for finding maternally and paternally expressed genes in bovine. For assessing the best model and comparing attributes in other organisms, we have also conducted a comparative analysis for human and sheep imprinted genes. According to the results of the present study, GC contents 10 and 100 kb upstream, Gly and Gln amino acids, Ile/ATC codon usage, LINE and SINE in 100kbup and length of first intron were significantly different between the maternal and paternal genes in cattle. Considering all species together, we found that GC content 100 kb up, LINE 100 kb up and the frequency of amino acids like Gly, Gln and Met were the most important attributes for identifying the paternal and maternal imprinted genes. These findings could imply conservation pattern in the attributes among these species.