As an economically important crop, apple is one of the most cultivated fruit trees in temperate regions worldwide. Recently, a large number of high-quality transcriptomic and epigenomic datasets for apple were made available to the public, which could be helpful in inferring gene regulatory relationships and thus predicting gene function at the genome level. Through integration of the available apple genomic, transcriptomic, and epigenomic datasets, we constructed co-expression networks, identified functional modules, and predicted chromatin states. A total of 112 RNA-seq datasets were integrated to construct a global network and a conditional network (tissue-preferential network). Furthermore, a total of 1,076 functional modules with closely related gene sets were identified to assess the modularity of biological networks and further subjected to functional enrichment analysis. The results showed that the function of many modules was related to development, secondary metabolism, hormone response, and transcriptional regulation. Transcriptional regulation is closely related to epigenetic marks on chromatin. A total of 20 epigenomic datasets, which included ChIP-seq, DNase-seq, and DNA methylation analysis datasets, were integrated and used to classify chromatin states. Based on the ChromHMM algorithm, the genome was divided into 620,122 fragments, which were classified into 24 states according to the combination of epigenetic marks and enriched-feature regions. Finally, through the collaborative analysis of different omics datasets, the online database AppleMDO () was established for cross-referencing and the exploration of possible novel functions of apple genes. In addition, gene annotation information and functional support toolkits were also provided. Our database might be convenient for researchers to develop insights into the function of genes related to important agronomic traits and might serve as a reference for other fruit trees.

Catharanthus roseus is a medicinal plant, which can produce monoterpene indole alkaloid (MIA) metabolites with biological activity and is rich in vinblastine and vincristine. With release of the scaffolded genome sequence of C. roseus , it is necessary to annotate gene functions on the whole-genome level. Recently, 53 RNA-seq datasets are available in public with different tissues (flower, root, leaf, seedling, and shoot) and different treatments (MeJA, PnWB infection and yeast elicitor). We used in-house data process pipeline with the combination of PCC and MR algorithms to construct a co-expression network exploring multi-dimensional gene expression (global, tissue preferential, and treat response) through multi-layered approaches. In the meanwhile, we added miRNA-target pairs, predicted PPI pairs into the network and provided several tools such as gene set enrichment analysis, functional module enrichment analysis, and motif analysis for functional prediction of the co-expression genes. Finally, we have constructed an online croFGD database ( http://bioinformatics.cau.edu.cn/croFGD/ ). We hope croFGD can help the communities to study the C. roseus functional genomics and make novel discoveries about key genes involved in some important biological processes.

Endomembrane remodeling to form a viral replication complex (VRC) is crucial for a virus to establish infection in a host. Although the composition and function of VRCs have been intensively studied, host factors involved in the assembly of VRCs for plant RNA viruses have not been fully explored. TurboID-based proximity labeling (PL) has emerged as a robust tool for probing molecular interactions in planta. However, few studies have employed the TurboID-based PL technique for investigating plant virus replication. Here, we used Beet black scorch virus (BBSV), an endoplasmic reticulum (ER)-replicating virus, as a model, and systematically investigated the composition of BBSV VRCs in Nicotiana benthamiana by fusing the TurboID enzyme to viral replication protein p23. Among the 185 identified p23-proximal proteins, the reticulon family of proteins showed high reproducibility in the mass spectrometry datasets. We focused on RETICULON-LIKE PROTEIN B2 (RTNLB2) and demonstrated its pro-viral functions in BBSV replication. We showed that RTNLB2 binds to p23, induces ER membrane curvature, and constricts ER tubules to facilitate the assembly of BBSV VRCs. Our comprehensive proximal interactome analysis of BBSV VRCs provides a resource for understanding plant viral replication and offers additional insights into the formation of membrane scaffolds for viral RNA synthesis.

Analysis Reveals Dynamic Modules Regulating the Growth and Development of Cirri in the Rattans (Calamus simplicifolius and Daemonorops jenkinsiana).

Endometriosis (EM), an estrogen-dependent inflammatory disease with unknown etiology, affects thousands of childbearing-age couples, and its early diagnosis is still very difficult. With the rapid development of sequencing technology in recent years, the accumulation of many sequencing data makes it possible to screen important diagnostic biomarkers from some EM-related genes. In this study, we utilized public datasets in the Gene Expression Omnibus (GEO) and Array-Express database and identified seven important differentially expressed genes (DEGs) (COMT, NAA16, CCDC22, EIF3E, AHI1, DMXL2, and CISD3) through the random forest classifier. Among these DEGs, AHI1, DMXL2, and CISD3 have never been reported to be associated with the pathogenesis of EMs. Our study indicated that these three genes might participate in the pathogenesis of EMs through oxidative stress, epithelial–mesenchymal transition (EMT) with the activation of the Notch signaling pathway, and mitochondrial homeostasis, respectively. Then, we put these seven DEGs into an artificial neural network to construct a novel diagnostic model for EMs and verified its diagnostic efficacy in two public datasets. Furthermore, these seven DEGs were included in 15 hub genes identified from the constructed protein–protein interaction (PPI) network, which confirmed the reliability of the diagnostic model. We hope the diagnostic model can provide novel sights into the understanding of the pathogenesis of EMs and contribute to the clinical diagnosis and treatment of EMs.