easyMF: A Web Platform for Matrix Factorization-Based Gene Discovery from Large-scale Transcriptome Data

Elucidating the impact of Y chromosome microdeletions and altered gene expression on male fertility in assisted reproduction

BACKGROUND

Genetic abnormalities like Y chromosome microdeletions are implicated in male infertility. This study investigated the association of azoospermia factor (AZF) region microdeletions with unsuccessful assisted reproductive techniques (ART), including in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI).

METHODS

This cross-sectional analysis study examined 80 Iranian oligospermic men (mean age 34 years) with prior failed ICSI and IVF cycles (IR.IAU.TNB.REC.1401.041). Semen analysis evaluated quantity/quality parameters based on World Health Organization guidelines. Participants were stratified by sperm DNA fragmentation (SDF) levels into: control (SDF < 15%, n = 20), mild elevation (15% ≤ SDF ≤ 30%, n = 60), and high (SDF > 30%, n = 20). Multiplex PCR mapped AZF microdeletions in the high SDF group. The AZF-associated genes were selected by RNA Seq analysis, and the candidate genes were checked for expression level by real-time PCR.

RESULTS

High SDF individuals exhibited poorer semen metrics, including 69% lower sperm concentration (P = 0.04) than those without SDF. Of this subset, 45% (9/20 men) harboured predominately AZF microdeletions. Men with AZF microdeletions showed higher SDF (32% vs 21%, P = 0.02) and altered AZF-associated genes expression. As USP9Y 3-fold, UTY 1.3-fold, and BPY2 1-fold revealed up-regulation, while IQCF1 8-fold, CDY 6.5-fold, DAZ 6-fold, and DDX3Y 1-fold underwent down-regulation. The PAWP gene was also down-regulated (5.7-fold, P = 0.029) in the IVF/ICSI failure group.

CONCLUSION

AZF microdeletions significantly impact male infertility and ART outcomes. High SDF individuals exhibited poorer semen metrics, with 45% AZF microdeletions. These microdeletions altered AZF-associated genes expression, affecting fertility mediator PAWP independently. Dual AZF and SDF screening enables personalized management in severe male infertility, potentially explaining IVF/ICSI failures.

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

HetFCM: functional co-module discovery by heterogeneous network co-clustering

Functional molecular module (i.e., gene-miRNA co-modules and gene-miRNA-lncRNA triple-layer modules) analysis can dissect complex regulations underlying etiology or phenotypes. However, current module detection methods lack an appropriate usage and effective model of multi-omics data and cross-layer regulations of heterogeneous molecules, causing the loss of critical genetic information and corrupting the detection performance. In this study, we propose a heterogeneous network co-clustering framework (HetFCM) to detect functional co-modules. HetFCM introduces an attributed heterogeneous network to jointly model interplays and multi-type attributes of different molecules, and applies multiple variational graph autoencoders on the network to generate cross-layer association matrices, then it performs adaptive weighted co-clustering on association matrices and attribute data to identify co-modules of heterogeneous molecules. Empirical study on Human and Maize datasets reveals that HetFCM can find out co-modules characterized with denser topology and more significant functions, which are associated with human breast cancer (subtypes) and maize phenotypes (i.e., lipid storage, drought tolerance and oil content). HetFCM is a useful tool to detect co-modules and can be applied to multi-layer functional modules, yielding novel insights for analyzing molecular mechanisms. We also developed a user-friendly module detection and analysis tool and shared it at http://www.sdu-idea.cn/FMDTool.

Machine learning bridges omics sciences and plant breeding

Some of the biological knowledge obtained from fundamental research will be implemented in applied plant breeding. To bridge basic research and breeding practice, machine learning (ML) holds great promise to translate biological knowledge and omics data into precision-designed plant breeding. Here, we review ML for multi-omics analysis in plants, including data dimensionality reduction, inference of gene-regulation networks, and gene discovery and prioritization. These applications will facilitate understanding trait regulation mechanisms and identifying target genes potentially applicable to knowledge-driven molecular design breeding. We also highlight applications of deep learning in plant phenomics and ML in genomic selection-assisted breeding, such as various ML algorithms that model the correlations among genotypes (genes), phenotypes (traits), and environments, to ultimately achieve data-driven genomic design breeding.