A regression-based differential expression detection algorithm for microarray studies with ultra-low sample size

Prenatal psychological distress and 11β-HSD2 gene expression in human placentas: Systematic review and meta-analysis

BACKGROUND

The placenta acts as a buffer to regulate the degree of fetal exposure to maternal cortisol through the 11-Beta Hydroxysteroid Dehydrogenase isoenzyme type 2 (11-β HSD2) enzyme. We conducted a systematic review and meta-analysis to assess the effect of prenatal psychological distress (PPD) on placental 11-β HSD2 gene expression and explore the related mechanistic pathways involved in fetal neurodevelopment.

METHODS

We searched PubMed, Embase, Scopus, APA PsycInfo®, and ProQuest Dissertations for observational studies assessing the association between PPD and 11-β HSD2 expression in human placentas. Adjusted regression coefficients (β) and corresponding 95% confidence intervals (CIs) were pooled based on three contextual PPD exposure groups: prenatal depression, anxiety symptoms, and perceived stress.

RESULTS

Of 3159 retrieved records, sixteen longitudinal studies involving 1869 participants across seven countries were included. Overall, exposure to PPD disorders showed weak negative associations with the placental 11-β HSD2 gene expression as follows: prenatal depression (β -0.01, 95% CI 0.05-0.02, I2=0%), anxiety symptoms (β -0.02, 95% CI 0.06-0.01, I2=0%), and perceived stress (β -0.01 95% CI 0.06-0.04, I2=62.8%). Third-trimester PPD exposure was more frequently associated with lower placental 11-β HSD2 levels. PPD and placental 11-β HSD2 were associated with changes in cortisol reactivity and the development of adverse health outcomes in mothers and children. Female-offspring were more vulnerable to PPD exposures.

CONCLUSION

The study presents evidence of a modest role of prenatal psychological distress in regulating placental 11-β HSD2 gene expression. Future prospective cohorts utilizing larger sample sizes or advanced statistical methods to enhance the detection of small effect sizes should be planned. Additionally, controlling for key predictors such as the mother's ethnicity, trimester of PPD exposure, mode of delivery, and infant sex is crucial for valid exploration of PPD effects on fetal programming.

Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest

Introduction: With the advancement of RNA-seq technology and machine learning, training large-scale RNA-seq data from databases with machine learning models can generally identify genes with important regulatory roles that were previously missed by standard linear analytic methodologies. Finding tissue-specific genes could improve our comprehension of the relationship between tissues and genes. However, few machine learning models for transcriptome data have been deployed and compared to identify tissue-specific genes, particularly for plants. Methods: In this study, an expression matrix was processed with linear models (Limma), machine learning models (LightGBM), and deep learning models (CNN) with information gain and the SHAP strategy based on 1,548 maize multi-tissue RNA-seq data obtained from a public database to identify tissue-specific genes. In terms of validation, V-measure values were computed based on k-means clustering of the gene sets to evaluate their technical complementarity. Furthermore, GO analysis and literature retrieval were used to validate the functions and research status of these genes. Results: Based on clustering validation, the convolutional neural network outperformed others with higher V-measure values as 0.647, indicating that its gene set could cover as many specific properties of various tissues as possible, whereas LightGBM discovered key transcription factors. The combination of three gene sets produced 78 core tissue-specific genes that had previously been shown in the literature to be biologically significant. Discussion: Different tissue-specific gene sets were identified due to the distinct interpretation strategy for machine learning models and researchers may use multiple methodologies and strategies for tissue-specific gene sets based on their goals, types of data, and computational resources. This study provided comparative insight for large-scale data mining of transcriptome datasets, shedding light on resolving high dimensions and bias difficulties in bioinformatics data processing.

The tweety Gene Family: From Embryo to Disease

The tweety genes encode gated chloride channels that are found in animals, plants, and even simple eukaryotes, signifying their deep evolutionary origin. In vertebrates, the tweety gene family is highly conserved and consists of three members—ttyh1, ttyh2, and ttyh3—that are important for the regulation of cell volume. While research has elucidated potential physiological functions of ttyh1 in neural stem cell maintenance, proliferation, and filopodia formation during neural development, the roles of ttyh2 and ttyh3 are less characterized, though their expression patterns during embryonic and fetal development suggest potential roles in the development of a wide range of tissues including a role in the immune system in response to pathogen-associated molecules. Additionally, members of the tweety gene family have been implicated in various pathologies including cancers, particularly pediatric brain tumors, and neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. Here, we review the current state of research using information from published articles and open-source databases on the tweety gene family with regard to its structure, evolution, expression during development and adulthood, biochemical and cellular functions, and role in human disease. We also identify promising areas for further research to advance our understanding of this important, yet still understudied, family of genes.

Xenopus embryos show a compensatory response following perturbation of the Notch signaling pathway

As an essential feature of development, robustness ensures that embryos attain a consistent phenotype despite genetic and environmental variation. The growing number of examples demonstrating that embryos can mount a compensatory response to germline mutations in key developmental genes has heightened interest in the phenomenon of embryonic robustness. While considerable progress has been made in elucidating genetic compensation in response to germline mutations, the diversity, mechanisms, and limitations of embryonic robustness remain unclear. In this work, we have examined whether Xenopus laevis embryos are able to compensate for perturbations of the Notch signaling pathway induced by RNA injection constructs that either upregulate or inhibit this signaling pathway. Consistent with earlier studies, we found that at neurula stages, hyperactivation of the Notch pathway inhibited neural differentiation while inhibition of Notch signaling increases premature differentiation as assayed by neural beta tubulin expression. However, surprisingly, by hatching stages, embryos begin to compensate for these perturbations, and by swimming tadpole stages most embryos exhibited normal neuronal gene expression. Using cell proliferation and TUNEL assays, we show that the compensatory response is, in part, mediated by modulating levels of cell proliferation and apoptosis. This work provides an additional model for addressing the mechanisms of embryonic robustness and of genetic compensation.

Genomic signature of parity in the breast of premenopausal women

Background

Full-term pregnancy (FTP) at an early age confers long-term protection against breast cancer. Previously, we reported that a FTP imprints a specific gene expression profile in the breast of postmenopausal women. Herein, we evaluated gene expression changes induced by parity in the breast of premenopausal women.

Methods

Gene expression profiling of normal breast tissue from 30 nulliparous (NP) and 79 parous (P) premenopausal volunteers was performed using Affymetrix microarrays. In addition to a discovery/validation analysis, we conducted an analysis of gene expression differences in P vs. NP women as a function of time since last FTP. Finally, a laser capture microdissection substudy was performed to compare the gene expression profile in the whole breast biopsy with that in the epithelial and stromal tissues.

Results

Discovery/validation analysis identified 43 differentially expressed genes in P vs. NP breast. Analysis of expression as a function of time since FTP revealed 286 differentially expressed genes (238 up- and 48 downregulated) comparing all P vs. all NP, and/or P women whose last FTP was less than 5 years before biopsy vs. all NP women. The upregulated genes showed three expression patterns: (1) transient: genes upregulated after FTP but whose expression levels returned to NP levels. These genes were mainly related to immune response, specifically activation of T cells. (2) Long-term changing: genes upregulated following FTP, whose expression levels decreased with increasing time since FTP but did not return to NP levels. These were related to immune response and development. (3) Long-term constant: genes that remained upregulated in parous compared to nulliparous breast, independently of time since FTP. These were mainly involved in development/cell differentiation processes, and also chromatin remodeling. Lastly, we found that the gene expression in whole tissue was a weighted average of the expression in epithelial and stromal tissues.

Conclusions

Genes transiently activated by FTP may have a role in protecting the mammary gland against neoplastically transformed cells through activation of T cells. Furthermore, chromatin remodeling and cell differentiation, represented by the genes that are maintained upregulated long after the FTP, may be responsible for the lasting preventive effect against breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1128-x) contains supplementary material, which is available to authorized users.

Automated Classification of Benign and Malignant Proliferative Breast Lesions

Misclassification of breast lesions can result in either cancer progression or unnecessary chemotherapy. Automated classification tools are seen as promising second opinion providers in reducing such errors. We have developed predictive algorithms that automate the categorization of breast lesions as either benign usual ductal hyperplasia (UDH) or malignant ductal carcinoma in situ (DCIS). From diagnosed breast biopsy images from two hospitals, we obtained 392 biomarkers using Dong et al.'s (2014) computational tools for nuclei identification and feature extraction. We implemented six machine learning models and enhanced them by reducing prediction variance, extracting active features, and combining multiple algorithms. We used the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for performance evaluation. Our top-performing model, a Combined model with Active Feature Extraction (CAFE) consisting of two logistic regression algorithms, obtained an AUC of 0.918 when trained on data from one hospital and tested on samples of the other, a statistically significant improvement over Dong et al.'s AUC of 0.858. Pathologists can substantially improve their diagnoses by using it as an unbiased validator. In the future, our work can also serve as a valuable methodology for differentiating between low-grade and high-grade DCIS.