Abstract Cotton is an economically important crop, essential for the agriculture and textile industries. Through integrating transcriptomic data, we discovered that multi-dimensional co-expression network analysis was powerful for predicting cotton gene functions and functional modules. Here, the recently available transcriptomic data on Gossypium arboreum , including data on multiple growth stages of tissues and stress treatment samples were applied to construct a co-expression network exploring multi-dimensional expression (development and stress) through multi-layered approaches. Based on differential gene expression and network analysis, a fibre development regulatory module of the gene GaKNL1 was found to regulate the second cell wall through repressing the activity of REVOLUTA , and a tissue-selective module of GaJAZ1a was examined in response to water stress. Moreover, comparative genomics analysis of the JAZ1 -related regulatory module revealed high conservation across plant species. In addition, 1155 functional modules were identified through integrating the co-expression network, module classification and function enrichment tools, which cover functions such as metabolism, stress responses, and transcriptional regulation. In the end, an online platform was built for network analysis ( http://structuralbiology.cau.edu.cn/arboreum ), which could help to refine the annotation of cotton gene function and establish a data mining system to identify functional genes or modules with important agronomic traits.
Tomato (Solanum lycopersicum), belonging to the Solanaceae family, holds the distinction of being the second most important vegetable crop on a global scale. As a model plant renowned for its insights into fruit ripening and disease resistance, the collaborative analysis of multi-omics data takes on an indispensable role in advancing the flavor and genetic traits of this vital crop. In our endeavor, we have seamlessly integrated a staggering 343 transcriptome data sets to create a co-expression network. This network encompasses a global and conditional perspective, offering a multi-dimensional insight into gene expression patterns. Simultaneously, we harnessed the power of 136 epigenomic datasets to define 35 distinct chromatin states, employing the sophisticated ChromHMM algorithm. Our pursuit of holistic understanding culminated in the fusion of multi-omics data, encompassing the genome, transcriptome, and epigenome. This comprehensive approach extends to functional identification, offering invaluable insights into the intricate web of biological interactions. Our offering goes beyond mere data analysis; it presents a platform for comparative network exploration, enabling users to draw meaningful comparisons between two networks. Additionally, we have thoughtfully included extensive annotation for gene sets, encompassing GO terms, KEGG pathways, plantCyc, gene families, literature references, miRNA target information, and functional modules. The culmination of our efforts is the Tomato multi-omics data Analysis Platform (TomAP, http://bioinformatics.cau.edu.cn/TomAP/). The co-expression network and the defined chromatin states open up a realm of possibilities, not only for investigating the commonalities and variations among co-expressed genes in the context of chromatin states but also for comparative functional assessments of orthologs across species. Our aspiration is that TomAP will become an invaluable resource for the research community, enabling the identification of functional genes or modules that underpin critical tomato agronomic traits.
Abstract Background ND630 is believed to be a new therapy pharmacologic molecule in targeting the expression of ACACA and regulating the lipid metabolism. However, the function of ND630 in prostate cancer remains unknown. KIF18B, as an oncogene, plays a vital role in prostate cancer progression. circKIF18B_003 was derived from oncogene KIF18B and was markedly overexpressed in prostate cancer tissues. We speculated that oncoprotein KIF18B-derived circRNA circKIF18B_003 might have roles in prostate cancer promotion. The aim of this study was to validate whether ND630 could control ACACA and lipid reprogramming in prostate cancer by regulating the expression of circKIF18B_003. Methods RT-qPCR was used to analyze the expression of circKIF18B_003 in prostate cancer cell lines and prostate cancer samples. circKIF18B_003 expression was modulated in prostate cancer cells using circKIF18B_003 interference or overexpression plasmid. We examined the function and effects of circKIF18B_003 in prostate cancer cells using CCK-8, colony formation, wound healing, and Transwell invasion assays and xenograft models. Fluorescence in situ hybridization (FISH) was performed to evaluate the localization of circKIF18B_003. RNA immunoprecipitation (RIP), RNA pull down, and luciferase reporter assay were performed to explore the potential mechanism of circKIF18B_003. Results The function of ND630 was determined in this study. circKIF18B_003 was overexpressed in prostate cancer tissues, and overexpression of circKIF18B_003 was associated with poor survival outcome of prostate cancer patients. The proliferation, migration, and invasion of prostate cancer cells were enhanced after up-regulation of circKIF18B_003. circKIF18B_003 is mainly located in the cytoplasm of prostate cancer cells, and the RIP and RNA pull down assays confirmed that circKIF18B_003 could act as a sponge for miR-370-3p. Further study demonstrated that up-regulation of circKIF18B_003 increased the expression of ACACA by sponging miR-370-3p. The malignant ability of prostate cancer cells enhanced by overexpression of circKIF18B_003 was reversed by the down-regulation of ACACA. We found that overexpression of circKIF18B_003 was associated with lipid metabolism, and a combination of ND-630 and docetaxel markedly attenuated tumor growth. Conclusion ND630 could control ACACA and lipid reprogramming in prostate cancer by regulating the expression of circKIF18B_003. ND630 and circKIF18B_003 may represent a novel target for prostate cancer.
Genome-wide maps of chromatin states have become a powerful representation of genome annotation and regulatory activity. We collected public and in-house plant epigenomic data sets and applied a Hidden Markov Model to define chromatin states, which included 290 553 (36 chromatin states), 831 235 (38 chromatin states) and 3 936 844 (26 chromatin states) segments across the whole genome of Arabidopsis thaliana, Oryza sativa and Zea mays, respectively. We constructed a Plant Chromatin State Database (PCSD, http://systemsbiology.cau.edu.cn/chromstates) to integrate detailed information about chromatin states, including the features and distribution of states, segments in states and related genes with segments. The self-organization mapping (SOM) results for these different chromatin signatures and UCSC Genome Browser for visualization were also integrated into the PCSD database. We further provided differential SOM maps between two epigenetic marks for chromatin state comparison and custom tools for new data analysis. The segments and related genes in SOM maps can be searched and used for motif and GO analysis, respectively. In addition, multi-species integration can be used to discover conserved features at the epigenomic level. In summary, our PCSD database integrated the identified chromatin states with epigenetic features and may be beneficial for communities to discover causal functions hidden in plant chromatin.
The cultivation of Panax notoginseng has been plagued by a multitude of challenges, including recurrent diseases, suboptimal value, inadequate quality, and environmental degradation resulting from improper water and fertilizer management. To address these issues and improve the yield of P. notoginseng and its saponin content, this study endeavors to identify the optimal irrigation and fertilization levels in shaded environments in Yunnan Province in Southwest China. In this field experiment, three-year-old plants were tested to evaluate the effects of water, soluble organic fertilizers, and their combinations on plant growth, physiological parameters, yield, and saponin content. The experiment included 12 treatments with three types of irrigation (10 (W1), 15 (W2), and 20 (W3) mm), totaling 440, 660, and 880 mm, and four levels of the total amount of fertilization (F1 (60, total N 12.6, total P 5.5, and total K 10.5 kg ha−1), F2 (90, total N 18.9, total P 8.3, and total K 15.7 kg ha−1), F3 (120, total N 25.2, total P 11.0, and total K 20.9 kg ha−1), F4 (150, total N 31.5, total P 13.8, and total K 26.1 kg ha−1)). The randomized complete block design was used, with 36 plots in total and 3 replications. The study utilized the TOPSIS method to determine the most effective water and fertilizer management strategy for the growth and production of P. notoginseng. The assessment of yield, water and fertilizer productivity, and saponin content across all treatments revealed that the W3F3 treatment resulted in significant increases in the plant’s height, stem diameter, and net photosynthetic rate. Meanwhile, the W2F3 treatment exhibited the best root morphological traits. The W3F4 treatment effectively increased dry matter and transpiration. The combination of water and fertilization had a coupling effect that not only increased yield to 1400 kg ha−1 but also improved water–fertilizer productivity. The application of the W2F3 treatment resulted in a significant increase in the accumulation of active components, leading to a total P. notoginseng saponin (PNS) content of 24.94%. Moreover, the comprehensive index obtained through the TOPSIS model indicated that the W2F3 treatment outperformed other treatments. Therefore, this treatment can be considered a promising water and fertilizer model for P. notoginseng cultivation, which can enhance its yield, quality, and productivity while promoting sustainable green development.
Extensive research has indicated that tumor stemness promotes tumor progression. However, the underlying role of stemness-related genes (SRGs) in esophageal cancer (ESCA) remains unclear.This study identified differentially expressed stemness-related (DESR) messenger RNAs (mRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) in ESCA, and correlated them with the clinical features of patients with ESCA to develop a prognostic risk assessment model. Functional analysis, protein-protein interaction (PPI) analysis, competing endogenous RNA (ceRNA) networks, and tumor-infiltrating immune cell analyses were performed to corroborate the results obtained from the model.Correlation analysis of the stemness enrichment scores revealed 1,106 DESR genes (DESRGs), 84 DESRmiRNAs, and 320 DESRlncRNAs were identified from The Cancer Genome Atlas Esophageal Carcinoma (TCGA-ESCA) dataset. Network clustering was performed and the top 20 connection points were identified, including CDC20 that connects to 136 adjacent nodes. A ceRNA network was constructed, including 17 DESRmiRNAs, 44 DESRlncRNAs, and 55 DESRGs.NCAPG [log2fold change (FC) =1.81; q value =2.68×10-11] was significantly upregulated in ESCA and positively correlated with resting natural killer (NK) cells, suggesting that human NK cells rest via the overexpression of NCAPG in ESCA. hsa-miR-1269a is significantly upregulated in ESCA patients with poor prognostic features. CD4+ resting memory T cells (P<0.01) were significantly negatively correlated with hsa-miR-1269a. The insights presented in this study will contribute to the development of innovative therapeutics for the treatment of patients with ESCA.
Based on the analysis of the Ecole Des Beaux-Arts and Bauhaus, this paper points out some defects of the conventional architectural education concept and system in China and tries to conceive the modern architectural education conception and teaching method.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)