Identification of biomarker genes from multiple studies for abiotic stress in maize through machine learning.

Abiotic stresses are major limiting factors for maize growth. Therefore, exploration of the mechanisms underlying the response to abiotic stress in maize is of great interest. Toward this end, we performed integration of the feature selection method into the meta-analysis of microarray gene expression. Following extraction of raw data, normalization, and batch effect removal, the data were merged into one expression profile. Differentially expressed genes (DEGs) between control and abiotic conditions were used for the feature selection algorithm to find the minimum features for high-performance classification. Feature selection was performed using a correlation-based feature selection (CFS) algorithm, considering features with a coefficient of 0.7 to 1. Different algorithms of Bayes, Functions, Lazy, Meta, Rules, and Trees were then tested in order to classify the samples and find the best performance classifier in each group. Moreover, the biological pathways and promoter motif analysis of selected genes were identified. The superior and overall performance of classification using all features (DEGs) were 98.86% (Multilayer Perceptron) and 81.25%, respectively. Classification based on feature selection resulted in an average accuracy of 94.69% and 93.56% with 33 and 12 features, respectively. Subsequently, gene ontology and promoter analysis were performed for the 12 selected biomarker genes. Five of them were downregulated and 7 were upregulated. ABRE, unnamed-1, G-box, and G-Box are motifs related to genes involved in several abiotic stress responses and are located upstream of at least nine probes in our study. This study revealed key genes associated with tolerance to abiotic stress in maize.

Transcriptional survey of abiotic stress response in maize (Zea mays) in the level of gene co-expression network and differential gene correlation analysis.

Abstract. Maize may be exposed to several abiotic stresses in the field. Therefore, identifying the tolerance mechanisms of natural field stress is mandatory. Gene expression data of maize upon abiotic stress were collected, and 560 differentially expressed genes (DEGs) were identified through meta-analysis. The most significant gene ontology terms in up-regulated genes were 'response to abiotic stress' and 'chitinase activity'. 'Phosphorelay signal transduction system' was the most significant enriched biological process in down-regulated DEGs. The co-expression analysis unveiled seven modules of DEGs, with a notable positive correlation between the modules and abiotic stress. Furthermore, the statistical significance was strikingly high for the turquoise, green and yellow modules. The turquoise group played a central role in orchestrating crucial adaptations in metabolic and stress response pathways in maize when exposed to abiotic stress. Within three up-regulated modules, Zm.7361.1.A1_at, Zm.10386.1.A1_a_at and Zm.10151.1.A1_at emerged as hub genes. These genes might introduce novel candidates implicated in stress tolerance mechanisms, warranting further comprehensive investigation and research. In parallel, the R package glmnet was applied to fit a logistic LASSO regression model on the DEGs profile to select candidate genes associated with abiotic responses in maize. The identified hub genes and LASSO regression genes were validated on an independent microarray dataset. Additionally, Differential Gene Correlation Analysis (DGCA) was performed on LASSO and hub genes to investigate the gene-gene regulatory relationship. The P value of DGCA of 16 pairwise gene comparisons was lower than 0.01, indicating a gene-gene significant change in correlation between control and abiotic stress. Integrated weighted gene correlation network analysis and logistic LASSO analysis revealed Zm.11185.1.S1_at, Zm.2331.1.S1_x_at and Zm.17003.1.S1_at. Notably, these 3 genes were identified in the 16 gene-pair comparisons. This finding highlights the notable significance of these genes in the abiotic stress response. Additional research into maize stress tolerance may focus on these three genes.

Deciphering the molecular basis of abiotic stress response in cucumber (Cucumis sativus L.) using RNA-Seq meta-analysis, systems biology, and machine learning approaches.

Abiotic stress in cucumber (Cucumis sativus L.) may trigger distinct transcriptome responses, resulting in significant yield loss. More insight into the molecular underpinnings of the stress response can be gained by combining RNA-Seq meta-analysis with systems biology and machine learning. This can help pinpoint possible targets for engineering abiotic tolerance by revealing functional modules and key genes essential for the stress response. Therefore, to investigate the regulatory mechanism and key genes, a combination of these approaches was utilized in cucumber subjected to various abiotic stresses. Three significant abiotic stress-related modules were identified by gene co-expression network analysis (WGCNA). Three hub genes (RPL18, δ-COP, and EXLA2), ten transcription factors (TFs), one transcription regulator, and 12 protein kinases (PKs) were introduced as key genes. The results suggest that the identified PKs probably govern the coordination of cellular responses to abiotic stress in cucumber. Moreover, the C2H2 TF family may play a significant role in cucumber response to abiotic stress. Several C2H2 TF target stress-related genes were identified through co-expression and promoter analyses. Evaluation of the key identified genes using Random Forest, with an area under the curve of ROC (AUC) of 0.974 and an accuracy rate of 88.5%, demonstrates their prominent contributions in the cucumber response to abiotic stresses. These findings provide novel insights into the regulatory mechanism underlying abiotic stress response in cucumber and pave the way for cucumber genetic engineering toward improving tolerance ability under abiotic stress.

Gene Expression Classification for Biomarker Identification in Maize Subjected to Various Biotic Stresses.

Various diseases severely affect maize, leading to a significant reduction in yield and crop quality. Therefore, the identification of genes responsible for tolerance to biotic stress is important in maize breeding programs. In the present study, a meta-analysis on microarray gene expression of maize imposed to various biotic stresses, induced by fungal pathogens or pests, was performed to identify key tolerant genes. Correlation-based Feature Selection (CFS) was performed to attain fewer DEGs discriminating control and stress conditions. As a result, 44 genes were selected and their performance was confirmed in the Bayes Net, MLP, SMO, KStar, Hoeffding Tree, and Random Forest models. Bayes Net outperformed the other algorithms representing an accuracy level of 97.1831%. Pathogen recognition genes, decision tree models, co-expression analysis, and functional enrichment were implemented on these selected genes. A robust co-expression was observed among 11 genes responsible for defense response, diterpene phytoalexin biosynthetic process, and diterpenoid biosynthetic process in terms of biological process. This study could provide new information on the genes responsible for resistance to biotic stress in maize to be implicated in biology or maize breeding.

Evaluation of grain yield stability of tritipyrum as a novel cereal in comparison with triticale lines and bread wheat varieties through univariate and multivariate parametric methods.

Salinity is a major abiotic stress affecting cereal production. Thus, tritipyrum (x. Tritipyrum), a potential novel salt-tolerant cereal, was introduced as an appropriate alternative for cereal production. The purposes of this study were to evaluate agronomic traits, yield, and yield stability of eight primary tritipyrum lines, five promising triticale lines, and four bread wheat varieties and to screen a stable yielding line. The experiments were conducted in randomized complete block designs with three replicates in three locations during four growing seasons. Analysis of variance in each environment and Bartlett's test for the variance homogeneity of experimental errors were made. Subsequently, separate experiments were analyzed as a combined experiment. The stability of grain yield was analyzed according to Eberhart and Russell's regression method, environmental variance, Wrick's ecovalance, Shokla's stability variance, AMMI, and Tai methods. Genotype × environment interactions (GEI) and environments were significant for the agronomic traits. Stability analysis revealed that combined primary tritipyrum line (Ka/b)(Cr/b)-5 and triticale 4115, 4108, and M45 lines had good adaptability in all environments. The results of the AMMI3 model and pattern analysis showed that the new cereal, tritipyrum, had the most stable response in various environments. The tritipyrum line (Ka/b)(Cr/b)-5 had the best yield performance and general adaptability. Based on Tai's method, the contribution of spike number to the stability of grain yield over different environments was higher than that of other yield components. Also, tritipyrum lines demonstrated higher stability compared with wheat and triticale. Totally, M45 triticale and tritipyrum (Ka/b)(Cr/b)-5 lines were the most stable genotypes with high grain yield. Complementary agronomic experiments may then release a new grain crop of triticale and a new pasture line of combined primary tritipyrum for grain and forage. Moreover, the combined tritipyrum line can be used in bread wheat breeding programs for producing salt-tolerant wheat cultivars.

Mining transcriptomic data to identify Saccharomyces cerevisiae signatures related to improved and repressed ethanol production under fermentation.

Saccharomyces cerevisiae is known for its outstanding ability to produce ethanol in industry. Underlying the dynamics of gene expression in S. cerevisiae in response to fermentation could provide informative results, required for the establishment of any ethanol production improvement program. Thus, representing a new approach, this study was conducted to identify the discriminative genes between improved and repressed ethanol production as well as clarifying the molecular responses to this process through mining the transcriptomic data. The significant differential expression probe sets were extracted from available microarray datasets related to yeast fermentation performance. To identify the most effective probe sets contributing to discriminate ethanol content, 11 machine learning algorithms from RapidMiner were employed. Further analysis including pathway enrichment and regulatory analysis were performed on discriminative probe sets. Besides, the decision tree models were constructed, the performance of each model was evaluated and the roots were identified. Based on the results, 171 probe sets were identified by at least 5 attribute weighting algorithms (AWAs) and 17 roots were recognized with 100% performance Some of the top ranked presets were found to be involved in carbohydrate metabolism, oxidative phosphorylation, and ethanol fermentation. Principal component analysis (PCA) and heatmap clustering validated the top-ranked selective probe sets. In addition, the top-ranked genes were validated based on GSE78759 and GSE5185 dataset. From all discriminative probe sets, OLI1 and CYC3 were identified as the roots with the best performance, demonstrated by the most weighting algorithms and linked to top two significant enriched pathways including porphyrin biosynthesis and oxidative phosphorylation. ADH5 and PDA1 were also recognized as differential top-ranked genes that contribute to ethanol production. According to the regulatory clustering analysis, Tup1 has a significant effect on the top-ranked target genes CYC3 and ADH5 genes. This study provides a basic understanding of the S. cerevisiae cell molecular mechanism and responses to two different medium conditions (Mg2+ and Cu2+) during the fermentation process.

Integrating expression data and genomic sequences to investigate transcriptional regulation in barley in response to abiotic stress.

Abiotic stress responses are regulated critically at the transcriptional level. Clarifying the intricate mechanisms that regulate gene expression in response to abiotic stress is crucial and challenging. For this purpose, the factors that regulate gene expression and their binding sites in DNA should be determined. By using bioinformatics tools, the differentially expressed probe sets were studied. A meta-analysis of transcriptomic responses to several abiotic stresses in barley was performed. Motif enrichments revealed that AP2/ERF (APETALA2/Ethylene-Responsive Factor) has the most frequent binding sites. We found that the bHLH transcription factor family has the highest number of transcription factor members. Moreover, network construction revealed that AP2 has the highest number of connections with other genes, which indicates its critical role in abiotic stress responses. The present research further predicted 49 miRNAs belonging to 23 miRNA families. This study identified the probable conserved and enriched motifs, which might have a role in the regulation of differentially expressed genes under abiotic stresses. In addition to shedding light on gene expression regulation, a toolbox of available promoters for genetic engineering of crop plants under such abiotic stresses was developed.

Computational approaches for classification and prediction of P-type ATPase substrate specificity in Arabidopsis.

As an extended gamut of integral membrane (extrinsic) proteins, and based on their transporting specificities, P-type ATPases include five subfamilies in Arabidopsis, inter alia, P4ATPases (phospholipid-transporting ATPase), P3AATPases (plasma membrane H(+) pumps), P2A and P2BATPases (Ca(2+) pumps) and P1B ATPases (heavy metal pumps). Although, many different computational methods have been developed to predict substrate specificity of unknown proteins, further investigation needs to improve the efficiency and performance of the predicators. In this study, various attribute weighting and supervised clustering algorithms were employed to identify the main amino acid composition attributes, which can influence the substrate specificity of ATPase pumps, classify protein pumps and predict the substrate specificity of uncharacterized ATPase pumps. The results of this study indicate that both non-reduced coefficients pertaining to absorption and Cys extinction within 280 nm, the frequencies of hydrogen, Ala, Val, carbon, hydrophilic residues, the counts of Val, Asn, Ser, Arg, Phe, Tyr, hydrophilic residues, Phe-Phe, Ala-Ile, Phe-Leu, Val-Ala and length are specified as the most important amino acid attributes through applying the whole attribute weighting models. Here, learning algorithms engineered in a predictive machine (Naive Bays) is proposed to foresee the Q9LVV1 and O22180 substrate specificities (P-type ATPase like proteins) with 100 % prediction confidence. For the first time, our analysis demonstrated promising application of bioinformatics algorithms in classifying ATPases pumps. Moreover, we suggest the predictive systems that can assist towards the prediction of the substrate specificity of any new ATPase pumps with the maximum possible prediction confidence.

In silico identification of miRNAs and their target genes and analysis of gene co-expression network in saffron (Crocus sativus L.) stigma.

As an aromatic and colorful plant of substantive taste, saffron (Crocus sativus L.) owes such properties of matter to growing class of the secondary metabolites derived from the carotenoids, apocarotenoids. Regarding the critical role of microRNAs in secondary metabolic synthesis and the limited number of identified miRNAs in C. sativus, on the other hand, one may see the point how the characterization of miRNAs along with the corresponding target genes in C. sativus might expand our perspectives on the roles of miRNAs in carotenoid/apocarotenoid biosynthetic pathway. A computational analysis was used to identify miRNAs and their targets using EST (Expressed Sequence Tag) library from mature saffron stigmas. Then, a gene co- expression network was constructed to identify genes which are potentially involved in carotenoid/apocarotenoid biosynthetic pathways. EST analysis led to the identification of two putative miRNAs (miR414 and miR837-5p) along with the corresponding stem- looped precursors. To our knowledge, this is the first report on miR414 and miR837-5p in C. sativus. Co-expression network analysis indicated that miR414 and miR837-5p may play roles in C. sativus metabolic pathways and led to identification of candidate genes including six transcription factors and one protein kinase probably involved in carotenoid/apocarotenoid biosynthetic pathway. Presence of transcription factors, miRNAs and protein kinase in the network indicated multiple layers of regulation in saffron stigma. The candidate genes from this study may help unraveling regulatory networks underlying the carotenoid/apocarotenoid biosynthesis in saffron and designing metabolic engineering for enhanced secondary metabolites.

New layers in understanding and predicting α-linolenic acid content in plants using amino acid characteristics of omega-3 fatty acid desaturase.

α-linolenic acid (ALA) is the most frequent omega-3 in plants. The content of ALA is highly variable, ranging from 0 to 1% in rice and corn to >50% in perilla and flax. ALA production is strongly correlated with the enzymatic activity of omega-3 fatty acid desaturase. To unravel the underlying mechanisms of omega-3 diversity, 895 protein features of omega-3 fatty acid desaturase were compared between plants with high and low omega-3. Attribute weighting showed that this enzyme in plants with high omega-3 content has higher amounts of Lys, Lys-Phe, and Pro-Asn but lower Aliphatic index, Gly-His, and Pro-Leu. The Random Forest model with Accuracy criterion when run on the dataset pre-filtered with Info Gain algorithm was the best model in distinguishing high omega-3 content based on the frequency of Lys-Lys in the structure of fatty acid desaturase. Interestingly, the discriminant function algorithm could predict the level of omega-3 only based on the six important selected attributes (out of 895 protein attributes) of fatty acid desaturase with 75% accuracy. We developed "Plant omega3 predictor" to predict the content of α-linolenic acid based on structural features of omega-3 fatty acid desaturase. The software calculates the 6 key structural protein features from imported Fasta sequence of omega-3 fatty acid desaturase or utilizes the imported features and predicts the ALA content using discriminant function formula. This work unravels an underpinning mechanism of omega-3 diversity via discovery of the key protein attributes in the structure of omega-3 desaturase offering a new approach to obtain higher omega-3 content.