Spatiotemporal expression profile of embryonic and adult ankyrin repeat and EF-hand domain containing protein 1-encoding genes ankef1a and ankef1b in zebrafish

MicroRNAs bta-novel-miR-117, bta-novel-miR-234 and bta-novel-miR-417 have adverse effects on blastocyst formation

In a previous study we found that the levels of the novel microRNAs (miRNAs) bta-novel-miR-117 bta-novel-miR-234 and bta-novel-miR-417 (P < 0.001) are significantly up-regulated in extracellular vesicles (EVs) in the culture medium of degenerating embryos compared to blastocysts. Because the functions of these novel miRNAs are still unknown, we investigated their regulatory roles during bovine blastocyst development by adding their mimics and inhibitors to the culture medium. The addition of mimics for bta-novel-miR-117, bta-novel-miR-234 and bta-novel-miR-417 resulted in a decreased blastocyst rate, and supplementation of bta-novel-miR-234 inhibitors increased the cleavage rate significantly (P < 0.001). Low-input transcriptome analysis and RT-qPCR results revealed that bta-novel-miR-117, bta-novel-miR-234 and bta-novel-miR-417 co-target genes such as ANKEF1, HAND2 and SLC2A2, downregulated their expression significantly (P < 0.001). These genes associated with glucose transmembrane transport and plasma membrane raft metabolism play crucial roles in embryonic development. The results suggest that overexpressing of these three novel miRNAs impairs embryonic development, and they might serve as biomarkers to detect failing bovine embryos.

Unraveling the Molecular Puzzle: Exploring Gene Networks across Diverse EMT Status of Cell Lines

Understanding complex disease mechanisms requires a comprehensive understanding of the gene regulatory networks, as complex diseases are often characterized by the dysregulation and dysfunction of molecular networks, rather than abnormalities in single genes. Specifically, the exploration of cell line-specific gene networks can provide essential clues for precision medicine, as this methodology can uncover molecular interplays specific to particular cell line statuses, such as drug sensitivity, cancer progression, etc. In this article, we provide a comprehensive review of computational strategies for cell line-specific gene network analysis: (1) cell line-specific gene regulatory network estimation and analysis of gene networks under varying epithelial-mesenchymal transition (EMT) statuses of cell lines; and (2) an explainable artificial intelligence approach for interpreting the estimated massive multiple EMT-status-specific gene networks. The objective of this review is to help readers grasp the concept of computational network biology, which holds significant implications for precision medicine by offering crucial clues.

Regionalized Protein Localization Domains in the Zebrafish Hair Cell Kinocilium

Sensory hair cells are the receptors for auditory, vestibular, and lateral line sensory organs in vertebrates. These cells are distinguished by "hair"-like projections from their apical surface collectively known as the hair bundle. Along with the staircase arrangement of the actin-filled stereocilia, the hair bundle features a single, non-motile, true cilium called the kinocilium. The kinocilium plays an important role in bundle development and the mechanics of sensory detection. To understand more about kinocilial development and structure, we performed a transcriptomic analysis of zebrafish hair cells to identify cilia-associated genes that have yet to be characterized in hair cells. In this study, we focused on three such genes-ankef1a, odf3l2a, and saxo2-because human or mouse orthologs are either associated with sensorineural hearing loss or are located near uncharacterized deafness loci. We made transgenic fish that express fluorescently tagged versions of their proteins, demonstrating their localization to the kinocilia of zebrafish hair cells. Furthermore, we found that Ankef1a, Odf3l2a, and Saxo2 exhibit distinct localization patterns along the length of the kinocilium and within the cell body. Lastly, we have reported a novel overexpression phenotype of Saxo2. Overall, these results suggest that the hair cell kinocilium in zebrafish is regionalized along its proximal-distal axis and set the groundwork to understand more about the roles of these kinocilial proteins in hair cells.

Differential venom gland gene expression analysis of juvenile and adult scorpions Androctonus crassicauda

BACKGROUND

The Androctonus crassicauda, belonging to the genus Androctonus of the family Buthidae, is the most venomous scorpion in Middle East countries. However, the venom gland transcriptome profile of A. crassicauda scorpion has not yet been studied. In this study, we elucidated and compared the venom gland gene expression profiles of adult and juvenile male scorpion A. crassicauda using high-throughput transcriptome sequencing. This is the first report of transcriptional analysis of the venom glands of scorpions in different growth stages, with insights into the identification of the key genes during venom gland development.

RESULTS

A total of 209,951 mRNA transcripts were identified from total RNA-seq data, of which 963 transcripts were differentially expressed (DE) in adult and juvenile scorpions (p < 0.01). Overall, we identified 558 up-regulated and 405 down-regulated transcripts in the adult compared to the juvenile scorpions, of which 397 and 269 unique unigenes were annotated, respectively. GO and KEGG enrichment analyses indicated that the metabolic, thermogenesis, cytoskeleton, estrogen signaling, GnRH signaling, growth hormone signaling, and melanogenesis pathways were affected by two different growth conditions and the results suggested that the DE genes related to those pathways are important genes associated with scorpion venom gland development, in which they may be important in future studies, including Chs, Elovl, MYH, RDX, ACTN, VCL, PIP5K, PP1C, FGFR, GNAS, EGFR, CREB, CoA, PLCB, CALM, CACNA, PKA and CAMK genes.

CONCLUSIONS

These findings broadened our knowledge of the differences between adult and juvenile scorpion venom and opened new perspectives on the application of comparative transcriptome analysis to identify the special key genes.

Global gene network exploration based on explainable artificial intelligence approach

In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statistical and machine learning approaches have been developed and applied to reveal sample-specific regulatory pathways, integrative understanding of the massive multilayer networks remains a challenge. To resolve this problem, we propose a novel artificial intelligence (AI) strategy for comprehensive gene regulatory network analysis. In our strategy, personalized gene networks corresponding specific clinical characteristic are constructed and the constructed network is considered as a second-order tensor. Then, an explainable AI method based on deep learning is applied to decompose the multilayer networks, thus we can reveal all-encompassing gene regulatory systems characterized by clinical features of patients. To evaluate the proposed methodology, we apply our method to the multilayer gene networks under varying conditions of an epithelial–mesenchymal transition (EMT) process. From the comprehensive analysis of multilayer networks, we identified novel markers, and the biological mechanisms of the identified genes and their reciprocal mechanisms are verified through the literature. Although any biological knowledge about the identified genes was not incorporated in our analysis, our data-driven approach based on AI approach provides biologically reliable results. Furthermore, the results provide crucial evidences to reveal biological mechanism related to various diseases, e.g., keratinocyte proliferation. The use of explainable AI method based on the tensor decomposition enables us to reveal global and novel mechanisms of gene regulatory system from the massive multiple networks, which cannot be demonstrated by existing methods. We expect that the proposed method provides a new insight into network biology and it will be a useful tool to integrative gene network analysis related complex architectures of diseases.