Population-Specific gene expression profiles in prostate cancer: insights from Weighted Gene Co-expression Network Analysis (WGCNA).

This study investigates the genetic factors contributing to the disparity in prostate cancer incidence and progression among African American men (AAM) compared to European American men (EAM). The research focuses on employing Weighted Gene Co-expression Network Analysis (WGCNA) on public microarray data obtained from prostate cancer patients. The study employed WGCNA to identify clusters of genes with correlated expression patterns, which were then analyzed for their connection to population backgrounds. Additionally, pathway enrichment analysis was conducted to understand the significance of the identified gene modules in prostate cancer pathways. The Least Absolute Shrinkage and Selection Operator (LASSO) and Correlation-based Feature Selection (CFS) methods were utilized for selection of biomarker genes. The results revealed 353 differentially expressed genes (DEGs) between AAM and EAM. Six significant gene expression modules were identified through WGCNA, showing varying degrees of correlation with prostate cancer. LASSO and CFS methods pinpointed critical genes, as well as six common genes between both approaches, which are indicative of their vital role in the disease. The XGBoost classifier validated these findings, achieving satisfactory prediction accuracy. Genes such as APRT, CCL2, BEX2, MGC26963, and PLAU were identified as key genes significantly associated with cancer progression. In conclusion, the research underlines the importance of incorporating AAM and EAM population diversity in genomic studies, particularly in cancer research. In addition, the study highlights the effectiveness of integrating machine learning techniques with gene expression analysis as a robust methodology for identifying critical genes in cancer research.

Effect of temperature on growth, wheat head infection, and nivalenol production by Fusarium poae.

Fusarium poae is one of the Fusarium species commonly detected in wheat kernels affected by Fusarium Head Blight. Fusarium poae produces a wide range of mycotoxins including nivalenol (NIV). The effect of temperature on colony growth and NIV production was investigated in vitro at 5-40 °C with 5 °C intervals. When the data were fit to a Beta equation (R2 ≥ 0.97), the optimal temperature was estimated to be 24.7 °C for colony growth and 27.5 °C for NIV production. The effects of temperature on infection incidence, fungal biomass, and NIV contamination were investigated by inoculating potted durum wheat plants at full anthesis; inoculated heads were kept at 10-40 °C with 5 °C intervals for 3 days and then at ambient temperature until ripening. Temperature significantly affected the incidence of floret infection and fungal biomass (as indicated by DNA amount) in the affected heads but did not affect NIV content in the head tissue. Inoculation of potted plants with F. poae did not reduce yield.

The effect of testosterone gel on fertility outcomes in women with a poor response in in vitro fertilization cycles: A pilot randomized clinical trial.

BACKGROUND: In this study, the effect of testosterone gel administration during ovulation induction on the fertility rate was examined in women with a poor ovarian response in in vitro fertilization (IVF) cycles. MATERIALS AND METHODS: The current study is a single-blinded, randomized clinical trial. Patients who met inclusion (Bologna) criteria were placed in the antagonist cycle group. The patients were randomly divided into two groups each included 25 participants treated with a placebo (lubricant gel, the controls) and testosterone gel (intervention). Fertility outcomes were compared between two study groups. RESULTS: The mean ± standard deviation (SD) age of intervention (41.04 ± 3.77) versus control group (39.69 ± 3.29) was not statistically different. The two studied groups were not statistically different in terms of follicle-stimulating hormone; antral follicle count, IVF, anti-Mullerian hormone, and the duration of infertility. The mean ± SD of oocyte 2.48 ± 1.64 versus 1.17 ± 1.27 and embryo 1.60 ± 1.58 versus 0.39 ± 0.58 in intervention group was significantly higher than control group (P < 0.01). The rate of pregnancy 16% versus 0% and embryo of quality A-B was significantly higher in intervention group than control (60% versus 17.4%, P < 0.05). CONCLUSION: The results of the current study showed that the testosterone gel has a significant impact on the fertility rate in women with a poor response in the IVF cycles. Further, randomized clinical trials with larger sample sized are recommended.

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.

Uncovering waterlogging-responsive genes in cucumber through machine learning and differential gene correlation analysis.

As climate change intensifies, the frequency and severity of waterlogging are expected to increase, necessitating a deeper understanding of the cucumber response to this stress. In this study, three public RNA-seq datasets (PRJNA799460, PRJNA844418, and PRJNA678740) comprising 36 samples were analyzed. Various feature selection algorithms including Uncertainty, Relief, SVM (Support Vector Machine), Correlation, and logistic least absolute shrinkage, and selection operator (LASSO) were performed to identify the most significant genes related to the waterlogging stress response. These feature selection techniques, which have different characteristics, were used to reduce the complexity of the data and thereby identify the most significant genes related to the waterlogging stress response. Uncertainty, Relief, SVM, Correlation, and LASSO identified 4, 4, 10, 21, and 13 genes, respectively. Differential gene correlation analysis (DGCA) focusing on the 36 selected genes identified changes in correlation patterns between the selected genes under waterlogged versus control conditions, providing deeper insights into the regulatory networks and interactions among the selected genes. DGCA revealed significant changes in the correlation of 13 genes between control and waterlogging conditions. Finally, we validated 13 genes using the Random Forest (RF) classifier, which achieved 100% accuracy and a 1.0 Area Under the Curve (AUC) score. The SHapley Additive exPlanations (SHAP) values clearly showed the significant impact of LOC101209599, LOC101217277, and LOC101216320 on the model's predictive power. In addition, we employed the Boruta as a wrapper feature selection method to further validate our gene selection strategy. Eight of the 13 genes were common across the four feature weighting algorithms, LASSO, DGCA, and Boruta, underscoring the robustness and reliability of our gene selection strategy. Notably, the genes LOC101209599, LOC101217277, and LOC101216320 were among genes identified by multiple feature selection methods from different categories (filtering, wrapper, and embedded). Pathways associated with these specific genes play a pivotal role in regulating stress tolerance, root development, nutrient absorption, sugar metabolism, gene expression, protein degradation, and calcium signaling. These intricate regulatory mechanisms are crucial for cucumbers to adapt effectively to waterlogging conditions. These findings provide valuable insights for uncovering targets in breeding new cucumber varieties with enhanced stress tolerance.

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.

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.

Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress.

Biotic stress imposed by pathogens, including fungal, bacterial, and viral, can cause heavy damage leading to yield reduction in maize. Therefore, the identification of resistant genes paves the way to the development of disease-resistant cultivars and is essential for reliable production in maize. Identifying different gene expression patterns can deepen our perception of maize resistance to disease. This study includes machine learning and deep learning-based application for classifying genes expressed under normal and biotic stress in maize. Machine learning algorithms used are Naive Bayes (NB), K-Nearest Neighbor (KNN), Ensemble, Support Vector Machine (SVM), and Decision Tree (DT). A Bidirectional Long Short Term Memory (BiLSTM) based network with Recurrent Neural Network (RNN) architecture is proposed for gene classification with deep learning. To increase the performance of these algorithms, feature selection is made from the raw gene features through the Relief feature selection algorithm. The obtained finding indicated the efficacy of BiLSTM over other machine learning algorithms. Some top genes ((S)-beta-macrocarpene synthase, zealexin A1 synthase, polyphenol oxidase I, chloroplastic, pathogenesis-related protein 10, CHY1, chitinase chem 5, barwin, and uncharacterized LOC100273479 were proved to be differentially upregulated under biotic stress condition.

A non-linear model for temperature-dependent sporulation and T-2 and HT-2 production of Fusarium langsethiae and Fusarium sporotrichioides.

This research has produced new quantitative data on the sporulation and T-2+HT-2 toxin production that could be further integrated to develop a comprehensive disease or toxin prediction model for Fusarium langsethiae and Fusarium sporotrichioides. Experiments were conducted to determine the effect of temperature or incubation time on sporulation and the effect of temperature on T-2+HT-2 toxin production of strains of the two species. F. sporotrichioides demonstrated a preference for higher temperatures than F. langsethiae during sporulation; the optimum temperature was 24.5 ± 0.7 °C for F. langsethiae and 32.3 ± 2.1 °C for F. sporotrichioides, according to the Beta equation fitted to the data. The dynamics of sporulation over different incubation times were fitted by a Gompertz function. The maximum spore production was estimated to be after 18 and 8 d incubation at optimum temperatures for F. langsethiae and F. sporotrichioides, respectively. F. sporotrichioides produced more T-2+HT-2 than F. langsethiae. The best fit of the effect of temperature on T-2+HT-2 production in wheat grains was obtained with a Beta equation showing an optimum temperature of 14.7 ± 0.8 °C for F. langsethiae and 12.1 ± 0.2 °C for F. sporotrichioides. The optimum temperature for mycotoxin production was lower than for sporulation.