Integrated Analysis of Hub Genes and MicroRNAs in Human Placental Tissues from In Vitro Fertilization-Embryo Transfer

OBJECTIVE

Supraphysiological hormone exposure, in vitro culture and embryo transfer throughout the in vitro fertilization-embryo transfer (IVF-ET) procedures may affect placental development. The present study aimed to identify differences in genomic expression profiles between IVF-ET and naturally conceived placentals and to use this as a basis for understanding the underlying effects of IVF-ET on placental function.

METHODS

Full-term human placental tissues were subjected to next-generation sequencing to determine differentially expressed miRNAs (DEmiRs) and genes (DEGs) between uncomplicated IVF-ET assisted and naturally conceived pregnancies. Gene ontology (GO) enrichment analysis and transcription factor enrichment analysis were used for DEmiRs. MiRNA-mRNA interaction and protein-protein interaction (PPI) networks were constructed. In addition, hub genes were obtained by using the STRING database and Cytoscape. DEGs were analyzed using GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Differentially expressed miRNAs were validated through qRT-PCR.

RESULTS

Compared against natural pregnancies, 12 DEmiRs and 258 DEGs were identified in IVF-ET placental tissues. In a validation cohort, it was confirmed that hsa-miR-204-5p, hsa-miR-1269a, and hsa-miR-941 were downregulation, while hsa-miR-4286, hsa-miR-31-5p and hsa-miR-125b-5p were upregulation in IVF-ET placentas. Functional analysis suggested that these differentially expressed genes were significantly enriched in angiogenesis, pregnancy, PI3K-Akt and Ras signaling pathways. The miRNA-mRNA regulatory network revealed the contribution of 10 miRNAs and 109 mRNAs while EGFR was the most highly connected gene among ten hub genes in the PPI network.

CONCLUSION

Even in uncomplicated IVF-ET pregnancies, differences exist in the placental transcriptome relative to natural pregnancies. Many of the differentially expressed genes in IVF-ET are involved in essential placental functions, and moreover, they provide a ready resource of molecular markers to assess the association between placental function and safety in IVF-ET offspring.

Integrated analysis of transcriptome, small RNA, and degradome sequencing provides insights into mango anthracnose resistance

Mango anthracnose is a major biotic stress that limits mango production worldwide. An integrated transcriptome, small RNA (sRNA), and degradome sequencing analysis was conducted to determine the complex molecular mechanisms of mango anthracnose resistance. The transcriptome sequencing of 12 samples from a mango anthracnose resistant genotype under control and stress conditions on the 3rd and 5th day post inoculation identified 3,462 and 8,341 differentially expressed genes (DEGs), respectively, which were mainly involved in transcription factor activity, defense response, an obsolete oxidation - reduction process, and flavonoid biosynthetic processes. The sRNA sequencing of the samples identified 372 known and 104 novel miRNAs. A total of 81 differentially expressed miRNAs were identified, of which three were differentially expressed at both the 3rd and 5th day post-inoculation (dpi), including ath-MIR166e-p5_1ss15AC, mtr-miR156e, and csi-miR3954_L + 1_2ss17CG21CT. According to degradome sequencing, 2,274 targets were predicted for 341 miRNAs. The combined analysis of transcriptome and sRNA sequencing identified 257 miRNA-mRNA interaction pairs. In these pairs, csi-miR3954_L + 1_2ss17CG21CT was up-regulated at both the 3rd and 5th dpi under stress, which could cleave multiple sites of an NAC gene (LOC123212502) that was down-regulated under stress. Overall, these miRNAs and genes provide a molecular foundation for the miRNA-mediated response to mango anthracnose stress and can be regarded as promising candidates for mango improvement.

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer

Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics. The Gra-CRC-miRTar web server can be found at: http://gra-crc-mirtar.com/.