Long non-coding RNAs (lncRNAs) perform distinct functions in various biological processes in mammals, including sex differentiation. However, the roles of lncRNAs in other vertebrates, especially in the Chinese soft-shell turtle (Pelodiscus sinensis), remain to be clarified. In this study, we performed genome-wide analysis of the lncRNA expression profiles in gonad tissues and screened numerous sex-specific lncRNAs in the Chinese soft-shell turtle. Of the 363,310,650 clean reads obtained, 5,994 sequences were typed as lncRNAs, of which 4,463 were novel. A selection of sex-specific lncRNAs (♀ 932, ♂ 449) from female ovaries and male testis were shown to act on target genes in cis and in trans, and most were involved in gonad differentiation based on Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Furthermore, interactions among the differentially expressed lncRNA-mRNAs and protein coding genes were identified by construction of correlation networks. Overall, our systematic analysis of lncRNA expression profiles in gonad tissues revealed numerous sex-specific lncRNAs in P. sinensis. Thereby, these findings provide new insights into the function of lncRNAs in sex differentiation and highlight a group of candidate lncRNAs for future studies.
The turtle carapace is composed of severely deformed fused dorsal vertebrae, ribs, and bone plates. In particular, the lateral growth in the superficial layer of turtle ribs in the dorsal trunk causes an encapsulation of the scapula and pelvis. The recent study suggested that the carapacial ridge (CR) is a new model of epithelial–mesenchymal transition which is essential for the arrangement of the ribs. Therefore, it is necessary to explore the regulatory mechanism of carapacial ridge development to analyze the formation of the turtle shell. However, the current understanding of the regulatory network underlying turtle carapacial ridge development is poor due to the lack of both systematic gene screening at different carapacial ridge development stages and gene function verification studies. In this study, we obtained genome-wide gene transcription and gene translation profiles using RNA sequencing and ribosome nascent-chain complex mRNA sequencing from carapacial ridge tissues of Chinese soft-shell turtle at different development stages. A correlation analysis of the transcriptome and translatome revealed that there were 129, 670, and 135 codifferentially expressed genes, including homodirection and opposite-direction differentially expressed genes, among three comparison groups, respectively. The pathway enrichment analysis of codifferentially expressed genes from the Kyoto Encyclopedia of Genes and Genomes showed dynamic changes in signaling pathways involved in carapacial ridge development. Especially, the results revealed that the Wnt signaling pathway and MAPK signaling pathway may play important roles in turtle carapacial ridge development. In addition, Wnt and Fgf were expressed during the carapacial ridge development. Furthermore, we discovered that Wnt5a regulated carapacial ridge development through the Wnt5a/JNK pathway. Therefore, our studies uncover that the morphogenesis of the turtle carapace might function through the co-operation between conserved WNT and FGF signaling pathways. Consequently, our findings revealed the dynamic signaling pathways acting on the carapacial ridge development of Chinese soft-shell turtle and provided new insights into uncover the molecular mechanism underlying turtle shell morphogenesis.
Annelids such as earthworms are considered to have central pattern generators (CPGs) that generate rhythms in neural circuits to coordinate the deformation of body segments for effective locomotion. At present, the states of earthworm‐like robot segments are often assigned holistically and artificially by mimicking the earthworms’ retrograde peristalsis wave, which is unable to adapt their gaits for variable environments and tasks. This motivates the authors to extend the bioinspired research from morphology to neurobiology by mimicking the CPG to build a versatile framework for spontaneous motion control. Here, the spatiotemporal dynamics is exploited from the coupled Hopf oscillators to not only unify the two existing gait generators for restoring temporal‐symmetric phase‐coordinated gaits and discrete gaits but also generate novel temporal‐asymmetric phase‐coordinated gaits. Theoretical and experimental tests consistently confirm that the introduction of temporal asymmetry improves the robot's locomotion performance. The CPG‐based controller also enables seamless online switching of locomotion gaits to avoid abrupt changes, sharp stops, and starts, thus improving the robot's adaptability in variable working scenarios.
Matching 16S rRNA gene sequencing data to a metabolic reference database is a meaningful way to predict the metabolic function of bacteria and archaea, bringing greater insight to the working of the microbial community. However, some operational taxonomy units (OTUs) cannot be functionally profiled, especially for microbial communities from non-human samples cultured in defective media. Therefore, we herein report the development of Hierarchical micrObial functions Prediction by graph aggregated Embedding (HOPE), which utilizes co-occurring patterns and nucleotide sequences to predict microbial functions. HOPE integrates topological structures of microbial co-occurrence networks with k-mer compositions of OTU sequences and embeds them into a lower-dimensional continuous latent space, while maximally preserving topological relationships among OTUs. The high imbalance among KEGG Orthology (KO) functions of microbes is recognized in our framework that usually yields poor performance. A hierarchical multitask learning module is used in HOPE to alleviate the challenge brought by the long-tailed distribution among classes. To test the performance of HOPE, we compare it with HOPE-one, HOPE-seq, and GraphSAGE, respectively, in three microbial metagenomic 16s rRNA sequencing datasets, including abalone gut, human gut, and gut of Penaeus monodon. Experiments demonstrate that HOPE outperforms baselines on almost all indexes in all experiments. Furthermore, HOPE reveals significant generalization ability. HOPE's basic idea is suitable for other related scenarios, such as the prediction of gene function based on gene co-expression networks. The source code of HOPE is freely available at https://github.com/adrift00/HOPE.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.