Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process

Lactation, a physiologically complex process, takes place in mammary gland after parturition. The expression profile of the effective genes in lactation has not comprehensively been elucidated. Herein, meta-analysis, using publicly available microarray data, was conducted identify the differentially expressed genes (DEGs) between pre- and post-peak milk production. Three microarray datasets of Rat, Bos Taurus, and Tammar wallaby were used. Samples related to pre-peak (n = 85) and post-peak (n = 24) milk production were selected. Meta-analysis revealed 31 DEGs across the studied species. Interestingly, 10 genes, including MRPS18B, SF1, UQCRC1, NUCB1, RNF126, ADSL, TNNC1, FIS1, HES5 and THTPA, were not detected in original studies that highlights meta-analysis power in biosignature discovery. Common target and regulator analysis highlighted the high connectivity of CTNNB1, CDD4 and LPL as gene network hubs. As data originally came from three different species, to check the effects of heterogeneous data sources on DEGs, 10 attribute weighting (machine learning) algorithms were applied. Attribute weighting results showed that the type of organism had no or little effect on the selected gene list. Systems biology analysis suggested that these DEGs affect the milk production by improving the immune system performance and mammary cell growth. This is the first study employing both meta-analysis and machine learning approaches for comparative analysis of gene expression pattern of mammary glands in two important time points of lactation process. The finding may pave the way to use of publically available to elucidate the underlying molecular mechanisms of physiologically complex traits such as lactation in mammals

Production of stable GFP-expressing neural cells from P19 embryonal carcinoma stem cells

Murine P19 embryonal carcinoma (EC) cells are convenient to differentiate into all germ layer derivatives. One of the advantages of P19 cells is that the exogenous DNA can be easily inserted into them. Here, at the first part of this study, we generated stable GFP-expressing P19 cells (P19-GFP+). FACS and western-blot analysis confirmed stable expression of GFP in the cells. We previously demonstrated the efficient induction of neuronal differentiation from mouse ES and EC cells by application of a neuroprotective drug, selegiline In the second part of this study selegiline was used to induce differentiation of P19-GFP+ into stable GFP-expressing neuron-like cells. Cresyl violet staining confirmed neuronal morphology of the differentiated cells. Furthermore, real-time PCR and immunoflourescence approved the expression of neuron specific markers. P19-GFP+ cells were able to survive, migrate and integrated into host tissues when transplanted to developing chick embryo CNS. The obtained live GFP-expressing cells can be used as an abundant source of developmentally pluripotent material for transplantation studies, investigating the cellular and molecular aspects of early differentiation

Unified Transcriptomic Signature of Arbuscular Mycorrhiza Colonization in Roots of Medicago truncatula by Integration of Machine Learning, Promoter Analysis, and Direct Merging Meta-Analysis

Plant root symbiosis with Arbuscular mycorrhizal (AM) fungi improves uptake of water and mineral nutrients, improving plant development under stressful conditions. Unraveling the unified transcriptomic signature of a successful colonization provides a better understanding of symbiosis. We developed a framework for finding the transcriptomic signature of Arbuscular mycorrhiza colonization and its regulating transcription factors in roots of Medicago truncatula. Expression profiles of roots in response to AM species were collected from four separate studies and were combined by direct merging meta-analysis. Batch effect, the major concern in expression meta-analysis, was reduced by three normalization steps: Robust Multi-array Average algorithm, Z-standardization, and quartiling normalization. Then, expression profile of 33685 genes in 18 root samples of Medicago as numerical features, as well as study ID and Arbuscular mycorrhiza type as categorical features, were mined by seven models: RELIEF, UNCERTAINTY, GINI INDEX, Chi Squared, RULE, INFO GAIN, and INFO GAIN RATIO. In total, 73 genes selected by machine learning models were up-regulated in response to AM (Z-value difference > 0.5). Feature weighting models also documented that this signature is independent from study (batch) effect. The AM inoculation signature obtained was able to differentiate efficiently between AM inoculated and non-inoculated samples. The AP2 domain class transcription factor, GRAS family transcription factors, and cyclin-dependent kinase were among the highly expressed meta-genes identified in the signature. We found high correspondence between the AM colonization signature obtained in this study and independent RNA-seq experiments on AM colonization, validating the repeatability of the colonization signature. Promoter analysis of upregulated genes in the transcriptomic signature led to the key regulators of AM colonization, including the essential transcription factors for endosymbiosis establishment and development such as NF-YA factors. The approach developed in this study offers three distinct novel features: (I) it improves direct merging meta-analysis by integrating supervised machine learning models and normalization steps to reduce study-specific batch effects; (II) seven attribute weighting models assessed the suitability of each gene for the transcriptomic signature which contributes to robustness of the signature (III) the approach is justifiable, easy to apply, and useful in practice. Our integrative framework of meta-analysis, promoter analysis, and machine learning provides a foundation to reveal the transcriptomic signature and regulatory circuits governing Arbuscular mycorrhizal symbiosis and is transferable to the other biological settings

Discovery of EST-SSRs in Lung Cancer: Tagged ESTs with SSRs Lead to Differential Amino Acid and Protein Expression Patterns in Cancerous Tissues

Tandem repeats are found in both coding and non-coding sequences of higher organisms. These sequences can be used in cancer genetics and diagnosis to unravel the genetic basis of tumor formation and progression. In this study, a possible relationship between SSR distributions and lung cancer was studied by comparative analysis of EST-SSRs in normal and lung cancerous tissues. While the EST-SSR distribution was similar between tumorous tissues, this distribution was different between normal and tumorous tissues. Trinucleotides tandem repeats were highly different; the number of trinucleotides in ESTs of lung cancer was 3 times higher than normal tissue. Significant negative correlation between normal and cancerous tissue showed that cancerous tissue generates different types of trinucleotides. GGC and CGC were the more frequent expressed trinucleotides in cancerous tissue, but these SSRs were not expressed in normal tissue. Similar to the EST level, the expression pattern of EST-SSRs-derived amino acids was significantly different between normal and cancerous tissues. Arg, Pro, Ser, Gly, and Lys were the most abundant amino acids in cancerous tissues, and Leu, Cys, Phe, and His were significantly more abundant in normal tissues than in cancerous tissues. Next, the putative functions of triplet SSR-containing genes were analyzed. In cancerous tissue, EST-SSRs produce different types of proteins. Chromodomain helicase DNA binding proteins were one of the major protein products of EST-SSRs in the cancerous library, while these proteins were not produced from EST-SSRs in normal tissue. For the first time, the findings of this study confirmed that EST-SSRs in normal lung tissues are different than in unhealthy tissues, and tagged ESTs with SSRs cause remarkable differences in amino acid and protein expression patterns in cancerous tissue. We suggest that EST-SSRs and EST-SSRs differentially expressed in cancerous tissue may be suitable candidate markers for lung cancer diagnosis and prediction

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

Phenotypic evaluation of feed efficiency, growth and carcass traits in native turkeys

Improving feed efficiency decreases feed intake, total cost, and the environmental emission of poultry production. This study aimed to investigate different feed efficiency, growth and carcass traits between high and low feed efficiency birds in Iranian native turkeys. Growth and carcass characteristics of native turkeys were recorded. Four different feed efficiency traits, including feed conversion ratio (FCR), residual feed intake (RFI), residual body weight gain (RG), and residual intake and body weight gain (RIG) were calculated. The phenotypic correlations were calculated among feed efficiency measurements and different growth traits. High and low feed efficiency birds based on FCR were compared for growth and carcass traits. The phenotypic correlation between FCR and RFI was 0.5 and FCR was strongly correlated with RG and RIG. Breast muscle weight of high feed efficiency birds based of FCR was significantly higher than low feed efficiency birds. The results showed that phenotypic selection based on each of the feed efficiency traits will automatically progress the others, however, using FCR can be more straightforward in local farms and results in producing more beneficial turkeys with better growth and carcass features

Crosstalk between short- and long-term calorie restriction transcriptomic signatures with anxiety-like behavior, aging, and neurodegeneration: implications for drug repurposing

Calorie restriction (CR) is considered an effective intervention for anxiety, aging, and obesity. We investigated the effects of short- and long-term CR on behavior as well as transcriptome profiles in the hypothalamus, amygdala, prefrontal cortex, pituitary, and adrenal glands of Hooded Wistar and Long Evans male rats. A reduction in anxiety-like behavior, as assessed via the elevated plus maze, was observed in both short- and long-term CR. Despite this, short- and long-term CR regulated different sets of genes, leading to distinct transcriptomic signatures. The employed models were able to simultaneously analyze categorical and numerical variables, evaluating the effect of tissue type along with expression data. In all tissues, transcription factors, zinc finger protein 45-like and zinc finger BTB domain-containing two, were the top selected genes by the models in short and long-term CR treatments, respectively. Text mining identified associations between genes of the short-term CR signature and neurodegeneration, stress, and obesity and between genes of the long-term signature and the nervous system. Literature mining-based drug repurposing showed that alongside known CR mimetics such as resveratrol and rapamycin, candidates not typically associated with CR mimetics may be repurposed based on their interaction with transcriptomic signatures of CR. This study goes some way to unravelling the global effects of CR and opens new avenues for treatment for emotional disorders, neurodegeneration, and obesity

Expression of Serine Biosynthesis Pathway Genes in Breast Muscles of Iranian Native Turkeys with Divergent Feed Efficiency

Introduction: Feed is the main cost part of poultry production. High feed efficiency poultry produce less feed and less excrement per unit weight gain. Therefore, a comprehensive understanding of the biological mechanisms that control feed efficiency is crucial for the development of optimal breeding and selection strategies. The serine biosynthesis pathway is one of the most important pathways in animals with high feed efficiency. The aim of this study was to investigate the expression of PHGDH, PSAT1 and PSPH genes by real-time PCR in Iranian native turkeys with high and low feed efficiencies.Materials and Methods: Iranian native male turkeys (n=500) were reared up to 20 weeks of age under standard production guidelines. Then 75 turkeys were randomly selected and placed in separate cages with free access to water and feed from 20 to 24 weeks. Turkeys were ranked based on feed conversion ratio (FCR) and three turkeys with the highest and three turkeys with lowest feed efficiency were selected as high feed efficiency (HFE) and low feed efficiency (LFE) birds, respectively. After slaughter of turkeys, RNA was extracted from breast tissue. Quantity and purity of the extracted RNAs were determined using a nanodrop device and its quality was evaluated using 1% agarose gel electrophoresis. Sequences of PSPH, PHGDH, PSAT1 and RSP7 genes were collected from the NCBI database. The primer was designed using Primer Premier version 5 software. All primers were synthesized by Sinaclon (Iran). In this study, RSP7 gene was used as a reference gene. Then, cDNA synthesis was performed. The best amplification temperature for simultaneous amplification of target and reference genes was determined. Samples were amplified for each gene with 3 replications using real-time PCR reaction. Significance level between treatments for each gene was determined separately using t-test in SAS software version 9.2 (P<0.05).Results and Discussion: Results of ultraviolet light absorption measurements at 260 and 280 nm by the nanodrop device showed that the quantity and quality of RNA extracted from the breast muscle samples were of high purity and not contaminated. The range of RNA concentration of the extracted samples was between 480 to 962 ng/μl and the ratio of absorption at 260 and 280 wavelength was about 2.1, which indicates the good quality of the extracted RNAs. The most suitable temperature was selected for specific binding of primers and simultaneous amplification of target genes and temperature control of 58 °C. To investigate and confirm the specificity of replication, melting curves were created to ensure the specificity of the amplified products, the absence of non-specific bands and secondary structures such as hairpin and primer-dimer structures. The results showed that there was only one narrow peak for each gene. The results of studying the expression of serine biosynthesis pathway genes (PSPH, PHGDH and PSAT1) showed that the expression level of these genes in HFE male turkeys was significantly higher than LFE male. Higher expression of PSPH, PHGDH and PSAT1 genes in HFE animals than in LFE animals indicates activation of the serine amino acid biosynthesis pathway, which itself can provide precursors for the Krebs cycle and purine biosynthesis. Glucose is the main source of metabolic energy in the body. When glucose enters the cell, glycolysis begins in the cytoplasm. The pathway of glycolysis and Glutamine catabolism produces an intermediate metabolite called 3-phosphoglycerate, which is gradually catalyzed to serine by PHGDH, PSAT1, and PSPH. Eventually serine is converted to glycine. Activation of this pathway indicates the higher ability of HFE animals to make better use of energy sources such as glucose, which increases protein production in breast muscle tissue and enhances volume and weight of muscle tissue in HFE turkeys.Conclusion: The results of this study showed that the expression of serine biosynthesis pathway genes (PSPH, PHGDH and PSAT1) was significantly higher in high feed efficiency turkeys than in low feed efficiency turkeys. In fact, these results at the level of molecular biology show that turkeys with higher feed efficiency cultivate better use of energy received from feed. Activation of this pathway increases the biosynthesis of various amino acids and thus increases protein and muscle mass in birds. The results of this study can be a promising window to introduce genes that affect feed efficiency in order to further investigate the population and larger flocks of birds

Unraveling the transcriptional complexity of compactness in sistan grape cluster

© 2018 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license:http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (Feb 2018) in accordance with the publisher’s archiving policyYaghooti grape of Sistan is the earliest ripening grape in Iran, harvested every May annually. It is adapted to dry conditions in Sistan region and its water requirement is less than the other grape cultivars. The transcriptional complexity of this grape was studied in three stages of cluster development. Totally, 24121 genes were expressed in different cluster development steps (step 1: cluster formation, step 2: berry formation, step 3: final size of cluster) of which 3040 genes in the first stage, 2381 genes in the second stage and 2400 genes in the third stage showed a significant increase in expression. GO analysis showed that when the clusters are ripening, the activity of the nucleus, cytoplasmic, cytosol, membrane and chloroplast genes in the cluster architecture cells decreases. In contrast, the activity of the endoplasmic reticulum, vacuole and extracellular region genes enhances. When Yaghooti grape is growing and developing, some of metabolic pathways were activated in the response to biotic and abiotic stresses. Gene co-expression network reconstruction showed that AGAMOUS is a key gene in compactness of Sistan grape cluster, because it influences on expression of GA gene which leads to increase cluster length and berries size

Tissue-specific transcriptional biomarkers in medicinal plants: Application of large-scale meta-analysis and computational systems biology

© 2019 Elsevier BV. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 12 month embargo from date of publication (January 2019) in accordance with the publisher’s archiving policyBiosynthesis of secondary metabolites in plant is a complex process, regulated by many genes and influenced by several factors. In recent years, the next-generation sequencing (NGS) technology and advanced statistical analysis such as meta-analysis and computational systems biology have provided novel opportunities to overcome biological complexity. Here, we performed a meta-analysis on publicly available transcriptome datasets of twelve economically significant medicinal plants to identify differentially expressed genes (DEGs) between shoot and root tissues and to find the key molecular features which may be effective in the biosynthesis of secondary metabolites. Meta-analysis identified a total of 880 genes with differential expression between two tissues. Functional enrichment and KEGG pathway analysis indicated that the functions of those DEGs are highly associated with the developmental process, starch metabolic process, response to stimulus, porphyrin and chlorophyll metabolism, biosynthesis of secondary metabolites and phenylalanine metabolism. In addition, systems biology analysis of the DEGs was applied to find protein–protein interaction network and discovery of significant modules. The detected modules were associated with hormone signal transduction, transcription repressor activity, response to light stimulus and epigenetic processes. Finally, analysis was extended to search for putative miRNAs that are associated with DEGs. A total of 31 miRNAs were detected which belonged to 16 conserved families. The present study provides a comprehensive view to better understand the tissue-specific expression of genes and mechanisms involved in secondary metabolites synthesis and may provide candidate genes for future researches to improve yield of secondary metabolites