جستجوی مقالات مرتبط با کلیدواژه "بیوانفورماتیک" در نشریات گروه "علوم دام"

In recent years, highly pathogenicity avian influenza (HPAI), especially H5N1, has emerged as a major global health concern due to its potential as a zoonotic disease and its devastating impact on poultry populations. Identifying the molecular mechanisms of response to HPAI infection is critical to control, treat, and prevent the risk of a potential pandemic. Microarray technology is becoming a standard technology used in research laboratories all across the world and it is considered as one of the centers of research in cellular processes related to the level and manner of gene expression, including gene function and cell differentiation mechanisms. By using microarray technology, it is possible to obtain a detailed view of the interaction function of genes while simultaneously studying how the genome is expressed. Using microarrays provides the analysis of gene expression in response to viral infections such as influenza, etc., the study of host-pathogen interactions, and also the identification of the effectiveness of drugs and vaccines. This study aimed to analyze the microarray data of H5N1 avian influenza to compare the gene network and analyze the functional pathway in chickens and ducks.

Data mining and searching of microarray data related to Highly Pathogenic Avian Influenza infection was done in the GEO gene expression database (https://www.ncbi.nlm.nih.gov/geo). The microarray data set with accession number GSE33389 based on the GPL3213 platform was selected which contained lung tissue samples challenged with H5N1 virus in chickens and ducks. Normalization of selected microarray data was done using R software, and samples were grouped to compare between infected and control samples. Limma, Biobase, and GEOquery software packages in R software were used to determine the expression level of genes and to investigate the differentially expressed genes (DEGs) between healthy and H5N1 influenza virus-infected lung tissue samples in chickens and ducks. The criterion for selecting significant DEGs was considered as |logFC|>2 and P<0.05. DAVID online tool (https://david.ncifcrf.gov) was used to investigate biological pathways, structural and functional characteristics of genes with different expressions, and functional interpretation of upregulated and downregulated DEGs. It was evaluated and visualized separately based on biological processes (BP), molecular functions (MF), and cellular components (CC). KEGG tool (http://www.genome.jp/kegg) was used to evaluate and study metabolic pathway enrichment. To reveal interactions between proteins and analyze them, STRING database and Cytoscape software were used. While using the Cytohubba plugin to identify and display key genes, the main modules affecting the interaction of genes and proteins were also identified by the MCODE plugin.

Gene expression analysis revealed 2062 and 565 differentially expressed genes between normal and infected tissue in chickens and ducks, respectively (P<0.05 and |logFC|>2). The results of bioinformatics analysis and protein-protein interaction network analysis showed BUB1, NDC80, CDC20, PLK1, PRC1, KIF11, and AURKA genes as hub genes in chicken and also COL6A3, COL3A1, COL4A3, COL18A1, PLOD2, PLOD1, and P4HA2 as highly effective genes in duck (P< 0.05). The results of the ontology comparison of DEGs proved that most of these genes in chickens are involved in the innate immune response and inflammatory resistance of the host, and the most effective genes in ducks play a role in lipid metabolism and energy production to meet the host's resistance to disease. The results of comparative gene network analysis between chickens and ducks are promising to increase our understanding of the host response to H5N1 influenza infection and the factors affecting virus pathogenesis in different avian species. Differentially expressed genes in response to H5N1 infection in chickens and ducks play critical roles in various biological processes, including immune response, inflammation, viral replication, and host-pathogen interactions.In general, gene network analysis showed that chickens and ducks use different genetic strategies to respond to avian influenza virus infection.

The present research was conducted to discover the response to H5N1 HPAI infection in chickens and ducks through comparative gene network analysis. It is important to note that the gene network analysis presented in this research is an initial step towards discovering the response mode of HPAI (H5N1) infection in chickens and ducks, and further functional studies, validation experiments, and integration with other omics data are needed to confirm the role of genes, pathways and hub genes in the host response to H5N1 virus. Therefore, the results of comparative gene network analysis in chickens and ducks obtained from this research can provide valuable insight into the underlying molecular mechanisms of host response to H5N1 influenza infection. Thus, by identifying differentially expressed genes, functional modules, and hub genes in this research, it can be stated that potential targets for future research have been highlighted to some extent. Undoubtedly, further studies in this field will improve our knowledge about the pathogenesis of avian influenza and will help to develop strategies for effective control and prevention of H5N1 influenza outbreaks.

The latest major advances in transcriptomics technologies, especially next-generation sequencing technologies and advanced bioinformatics tools, allows deeper exploration of messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs), including miRNAs. These technologies have offered important chance for a deeper study of miRNA association in farm animal diseases, as well as livestock productivity and welfare. Since the discovery of lin‑4 and let‑7, many microRNAs have been identified in farm animal species and deposited in miRNA databases. miRNA can be used as biomarkers in the context of farm animal disease diagnostics, prediction, and therapeutic purposes, for the management of livestock diseases. By the sequencing of Bos Taurus (cattle) genome, we have an opportunity to discover novel miRNAs in this species. However, the experimental determination of miRNA sequence and structure is both expensive and time-consuming, therefore, computational and machine learning-based approaches have been adopted to predict novel microRNAs in the Bos Taurus (cattle) genome.

Finding an accurate method for Identification of miRNA molecules can help for understanding of regulatory processes. Currently, computational methods based on learning algorithms have been extensively applied for miRNA prediction.Inspired by the work of predecessors, we proposed an improved computational model based on random forest (RF) for identifying real miRNA precursor sequences (pre-miRNAs). First, the occurrence frequencies of the dinucleotide of pre-miRNAs genes, and the percentage of G+C content were calculated. The observed dinucleotide composition was calculated as the structural features of the sequence composition for each miRNA gene. A total of cattle (Bos Taurus) dinucleotide compositions with their genomic G+C contents for 1064 genes encoding miRNA and non-miRNA sequences were calculated. In the next step two classification models based on machine learning approach were trained to identify real and pseudo bovine pre-miRNAs. One set of 17 optimized features related to sequence structures were used to train the models. These models were trained and validated with 10-fold cross validation method.

Our goal was to investigate the predictive performance of RNA features in distinguishing pre-miRNAs from pseudo hairpins. Our model achieved 99% precision, and 97.9% MCC using Bos Taurus datasets.

Computational methods of Artificial intelligence can detect novel potential miRNAs in the bovine genome, some of which to have previously undetected in this genome. As a result, it seems necessary to use computational methods to identify these regulatory RNAs in livestock for breeding purposes. Our discoveries support that dinucleotide features will be beneficial to achieve the highest accuracies for miRNA sequences prediction.

 Obviously, the recent decades strategy in cancer therapy, anticancer drug discovery and drug improvement is to characterize, distinguish and validate the most promising cancer-related molecular targets to which new drugs can be designed. The unique features of the pancreatic RNase (RNase A) such as high activity, stability, lack of a cofactor, and small molecular size have made it the most popular enzyme in the ribonuclease family. Specifically, RNase A is involved in endonucleolytic cleavage of 3'-phosphomononucleotides and 3'- phosphooligonucleotides ending in C-P or U-P with 2', 3’- cyclic phosphate intermediates. RNase A was purified from the Bovidae family including bovine, ovine, bison, eland, goat and gnu. Although phylogenetic analyses of RNase A revealed high similarity among members of the Bovidae family, some functional mutations were also found.Several studies showed that the RNA hydrolyzing action of ribonuclease is able to induce apoptosis and cell death in cancer cells, independently. This effect could be enhanced thousands of times when ribonuclease is linked to antibodies. These enzymes show potent cytotoxic activity on cell internalization but do not show sensible immunogenicity or non-specific toxicity toward normal cells. Ovine pancreatic ribonuclease enzyme is a member of super family RNase A, it can be a good candidate as a toxin for designing new drugs. The objective of this study was to produce ovine pancreatic ribonuclease enzyme in E. coli and characterize its activity.

All structures needed for this study were downloaded from the Protein Data Bank (PDB) website (http://www.rcsb.org/). PDB files (accession numbers: 10.2210 /pdb1YV6/pdb and 10.2210/pdb3SNF/pdb) related to natural Bovine Pancreatic RNase were selected. Gene synthesis and production of recombinant protein were conducted by using the pelB signal sequence at the beginning of the structure for periplasmic protein production. The native ovine RNase and bovine RNase A were optimized for E. coli host by GenRay codon optimization service and sent to GenRay Biotechnology (Shanghai, China) for synthesis. The target genes in pGH vector were sub-cloned in pUC19 and then cloned into the pET21b (+) vector between XbaI and HindIII sites. After transformation, E. coli cells containing recombinant pET21b (+) were cultivated in LB broth medium containing ampicillin. To extract the proteins, osmotic shock methods were applied. After that Q-sepharose chromatography was used to extract the target protein. Finally, Bradford analysis was used to determine the protein concentration. The ribonucleolytic activity of the recombinant native ovine RNase A was compared with native bovine RNase A following Tripathy et al (2013) method. To investigate the antitumor activity of recombinant proteins, HeLa cells were prepared for seeding in a 96-well flask and incubated at 37°C in 5% CO2 for 72 h.

 The production of native ovine and bovine RNase A was confirmed by SDS PAGE. Protein purification was successfully performed using osmotic shock in the Q-Sepharose column. Although our findings confirmed protein expression, no detectable proteins other than RNase A was observed in the LB medium, indicating that almost all the proteins were expressed either inside the bacterial cell or secreted into the periplasmic area. According to the Bradford analysis, the concentration of the recombinant proteins extracted was 4.78 ovine RNase A. Based on our results, it was shown that the ribonucleolytic activity of native ovine RNase A was 558±22. The results showed that RNase A exhibited resistance to pepsin degradation during the whole incubation process (22 h), in the course of which 90% of RNase A remained undamaged. To determine the cytotoxic effect of ovine RNase on the HeLa cell line, MTT assay was done following incubation with ovine RNase A. The commercial RNase A was used as control. The results showed that ovine RNase A and bovine RNase A had no cytotoxic effect on HeLa cells. When RNase treatment was done by the lipofectamine 3000 (Thermo, USA), the cytotoxicity effect was observed. Several studies have shown that some ribonucleases such as onconase, bovine seminal ribonuclease and bovine pancreatic ribonuclease have a great promise as cancer immunotherapeutic agents and cause a significant reduction in the protein synthesis of tumor cells after internalization into cytosol. 

 Our findings demonstrate that ovine RNase similar to bovine RNase has a great potential for use in drug design industry. We revealed that the native ovine RNase A was more stable than the native bovine RNase. In future work, we intend to fuse the engineered ovine-RNase A to dedicated recombinant antibodies for cancer therapy and investigation of engineered immuno-ribonuclease potency and cell killing effects as a fusion protein.

Information from RNAs, because they are the link between genotype and phenotype, helps to understand the biological aspects of physiological pathways. MicroRNAs are molecules with 22 nucleotides in length and single strand which can specifically affect the function and expression of genes, hence many studies have been done on the role of these molecules in various biological processes, such as milk production. The milk production trait is one of the most important traits in the dairy cattle industry. This trait is a polygenic trait in farm animals and controlled by a large number of genes and each gene may be involved in various biological pathways and mutually any biological pathway can include a large number of genes. These relationships form the complex network which indicates the association of a large number of biological pathways with lots of genes. Signal molecules that can act as morphogens control the pattern of the gene network of tissue structure throughout the lifespan of the growing embryo stage to the adult organism. The morphogens depend on the amount of secretion and the destination of secretion source can produce different cellular responses. Therefore, the aim of the present study was to investigate target genes of differentially expressed microRNAs in bovine mammary tissue to identify a number of biological pathways involved in the milk production process.

In order to identify and investigate the biological pathways involved in milk production, microRNA sequenced data were used which were extracted from ArrayExpress database with E-GEOD-61227 access number. In present study, all stages of standardization of data, differences between expression of microRNA and significance determination were performed by GEO2R software. The criteria for the selection of microRNA in this study were corrected P-value

The results of this study showed that there are 23 microRNAs with different expressions that affect many genes. These genes are involved in the signaling pathways of TGF-β, WNT, MAPK, mTOR, PI3k-Akt, insulin, estrogen and prolactin. Most of these signaling pathways are involved in cell growth and proliferation, mammary gland development, and epithelial cell activity. Since these identified pathways are associated with milk production, the genes identified in these signaling pathways can be used to improve milk production trait.

MAPK1, MAPK8, FASLG and PTEN genes are activate in most biological pathways and they are associated with various genes, accordingly these genes are the major genes in the process of milk production.

Lysine is one of the essential amino acids not synthesized biologically in the human body and mammals so should be supplied through diets. Among the industrially important amino acids, L-lysine is in 1st position, which is used in pharmaceuticals, animals, human feeds and precursors for the production of peptides or agrochemicals. As L-lysine has large applications, the demand for it is increasing constantly year by year. To minimize the gap between increasing demand and production of L-lysine, it has to be produced in large scale. Corynebacterium species especially Corynebacterium glutamicum is widely used for the industrial production of amino acids especially L-glutamate and L- lysine. The C. glutamicum from long period has been used for the industrial production of various amino acids, primary metabolites and nucleotides. This organism is an aerobic gram positive, rod shaped and non-sporulating bacteria which used for the industrial production of amino acids of L-lysine and L-glutamate. This bacterium uses a variety of carbohydrates, alcohols and organic acids as single sources of carbon and energy for growth and also for the amino acid production. The quantity of lysine production by wild (natural) type of Corynebacterium glutamycum is very low, and its cultivation and propagation cannot provide the amino acid required by markets, therefor the wild type of this bacterium is not suitable and cost-effective for industrial purposes. To minimize the gap between increasing demand and production of L-lysine, it has to be produced in large scale. Aspartate kinase (AK) is one of the key enzymes in the biosynthesis of aspartate-derived amino acids such as lysine. This enzyme catalyzes the transfer of the C-phosphate group of ATP to aspartic acid. In most bacteria, the reaction is the first step of branched biosynthetic pathway for lysine, threonine, isoleucine and methionine and is known to be regulated by the end metabolites through feedback inhibition. For example, aspartate kinase from Corynebacterium glutamicum is concertedly inhibited by lysine and threonine, while aspartate kinase I and III from Escherichia coli is inhibited by threonine and lysine, respectively. Due to industrial production of lysine amino acid by using Corynebacterium species, a lot of researches have been done to improve the genetic modification of these microorganisms. Today, bioinformatics tools are available as online access through web-based databases and software, which can be used to study best structures of aspartate kinase enzyme with the least cost and time.  Also due to high laboratory costs, the use of bioinformatics methods will be important in obtaining the final result. The aim of this study was to investigate the bioinformatics structure of aspartate kinase enzyme in different species of Corynebacterium by authenticated bioinformatics databases to suggest best bioinformatics structure of the aspartate kinase enzyme for applying in laboratory cloning and production of lysine amino acid for industrial purposes.

The amino acid sequences of the aspartic kinase enzyme from 33 species of the Corynebacterium was obtained from the NCBI (https://www.ncbi.nlm.nih.gov/protein) and stored as FASTA. In order to study the genetic distances and similarities in 33 species of Corynebacterium a phylogenetic tree was constructed using the Neighbor Joining method using the Mega software (MEGA 6). (A bootstrap check with 1000 replications was also conducted to obtain a confidence level for the branches) ClC main work bench5 software was used to investigate genetic similarities using protein sequences. The Evolutionary properties, physiological and physicochemical properties of aspartate kinase enzyme were studied and investigated in 33 different species of Corynebacterium through valid databases and software of NCBI, MEGA, ProtScale and ProtParam. In order to predict the second structure, two proteins selected from the psipred server were used (http://bioinf.cs.ucl.ac.uk/psipred). For this purpose, the protein sequences of aspartate kinase enzyme in Corynebacterium glutamicum with access numbers of CAO00530.1 and SJM57548.1 introduced into the psipred and their second structure was mapped. Afterward, three-dimensional structure of mentioned protein was modeled using Swiss-model server (https://swissmodel.expasy.org) Then the quality of the two predicted models evaluated by the Rampage server (http://mordred.bioc.cam.ac.uk/ ~ rapper / rampage2.php), and in the next step its ramachandran plot mapped.

The results of evolutionary tree analysis in Corynebacterium species showed that derivation time of aspartate kinase protein in these 33 species is very close. The results of the ProtScale and ProtParam databases showed that the aspartatekinase enzyme of Corynebacterium glutamicum with the access number of CAO00530.1 and SJM57548.1 have the best physicochemical and maximum stability among 33 different species of study. Afterward, with further in silico investigation by the Swiss-Model server and Rampage tool, it was found that the two access numbers of CAO00530.1 and SJM57548.1 had the best three-dimensional structure. From the results of in silico analyses, it can be inferred that the aspartate kinase enzyme with the two access numbers of CAO00530.1 and SJM57548.1 have the best physicochemical properties, the most stable and also the best three-dimensional structure and therefore could be offered for laboratory cloning and production of lysine amino acid for industrial purposes.

Due to wide applications and importance of lysine production in our country and also the necessity of selecting appropriate strain of Corynebacterium for genetic engineering and industrial production, this bioinformatics study was done to predict best structure of aspartate kinase enzyme and best strain of Corynebacterium. Based on the results of our in silico analysis, it is suggested that corynebacterium glutamicum has the best protein structure of aspartate kinase enzyme and may be beneficial to increase Industrial lysine amino acid production.

MicroRNAs (miRNAs) are small noncoding RNA molecules that are found in plants, animals and some viruses and play important role in regulation of transcription (3). Recently, importance of miRNA roles in biology of living organisms has been discovered, thus miRNAs identification became more important (1). Experimental detection of miRNAs can be obtained using different miRNA profiling methods, such as quantitative real-time PCR, microarray, and high-throughput RNA sequencing technologies. Since the identification and verification of miRNA by laboratory
methods is time-consuming and costly (4), for improving miRNA identification and lowering costs, it is more reasonable that miRNA loci predicted by reliable bioinformatic approaches then experimental methods used for confirmation. This may decrease false positive results. Recently, several hundreds of miRNAs reported in variety of species including human and mouse, as well as domestic animals such as cattle, pig, chicken and goat, however there are relatively less information about sheep miRNAs. In this study, we developed a method for prediction and in silico
validation of miRNAs located on ovine chromosome twenty.

 In this study, an ab initio approach was used. The sequence of ovine chromosome 20 was used as input for EMBOSS and Sequence-Structure Motif Base: Pre-miRNA Prediction Webservers applications, then all predicted stem and loop structures were entered into mfold software. Pre-miRNA features for them were calculated and sequences that had these features were selected. Since the probability of miRNA presence in the coding region is very low, miRNAs that were predicted in the coding regions were removed. To confirm the prediction of miRNAs, selected sequences were homology searched within all registered miRNAs in miRBase. In order to evaluate the in silico expression of miRNAs, predicted miRNAs were BLASTed against expression data from Sequence Read Archive (SRA) of ovine muscle tissue (Accession: PRJNA223213), liver tissue (Accession: GSM1366318) and mixture of tissues including heart, kidney, brain, liver, ovary, lung, skin, and adipose (Accession: GSE56643). In order to evaluate the accuracy of this method, a positive control region including a cluster of validated miRNAs from ovine chromosome 18 were analyzed by the same method and sensitivity and selectivity of this method were calculated based on this region from chromosome 18.

After prediction by softwares and investigation of pre-miRNAs features by mfold, 400 stem and loop structures that had pre-miRNA features were chosen. Fifty miRNAs from those miRNAs contained conserved mature miRNAs sequence and 350 of them were recognized as novel miRNAs based on registered miRNAs in the mirBase. None of the novel miRNAs were located in the coding regions. In silico validation of these novel miRNAs in SRA data was indicated that 81 miRNAs are expressed in different ovine tissues including 33 in muscle and 10 in liver.
Results on the positive control data showed that 40 miRNAs were predicted which majority of them (36 miRNAs) were already validated by experiments. This indicates a high reliability for this method. With putting in sensitivity and selectivity formulas, both of two factors were calculated and it was observed that the sensitivity and selectivity values for our method were 67% and 90%, respectively. Fewer studies accomplished in detection of ovine miRNAs in compare to other farm animals. In previous studies to identify miRNAs in ovine species, mostly laboratory-based or comparative methods were used. This was the first study that used SRA database to check miRNA expression in RNAseq data in order to decrease the discovery of false positive results. Comparing this method with others including CID-miRNA (19), miRPara (20), VMir (6) and miRNAFold (18) methods, we may conclude that this method’s sensitivity is less than
CID-miRNA, miRPara, miRNAFold and srnaloop. Although selectivity for this method is higher than all above methods because the false positive in this method is less than other method. This method showed high selectivity and low FP that due to improved prediction method for identify miRNAs.

In the current study, predicted ovine miRNAs were validated by an in silico method using SRA database that resulted in a higher reliability than other ab initio approaches. Although this method is not very fast and fully automated. With running this method on ovine chromosome 20, 81 novel miRNA were predicted which were expressed in different tissues of sheep. This method could be applied on other ovine chromosomes as well as other mammalian species, although future validation by experimental approaches is required.