Delisheng, a Chinese medicinal compound, exerts anti-proliferative and pro-apoptotic effects on HepG2 cells through extrinsic and intrinsic pathways

The anti-proliferative, cytotoxic and apoptogenic activities of delisheng, a Chinese medicinal compound, has been investigated. In this study, the hepatocarcinoma cell line (HepG2) and the liver cell line (L-02) were exposed to delisheng (6.25, 50 and 100 μl/ml). Delisheng suppressed the proliferation and viability of normal liver L-02 cells slightly, but strongly inhibited the proliferation and viability of hepatocarcinoma HepG2 cells. The flow cytometric analysis of HepG2 cells demonstrated that delisheng primarily arrested the HepG2 cells at the G1 phase of the cell cycle. Annexin V-FITC/PI staining corroborates the apoptogenic nature of delisheng on HepG2 cells. The anti-proliferative and pro-apoptotic effect of delisheng in HepG2 cells was associated with changes in the Bcl-2/Bax ratio and the induction of caspase-mediated apoptosis. Upregulation of DR5 expression was observed in HepG2 cells after treatment with delisheng. The findings from the present study suggest that delisheng has selective cytotoxic activities against HepG2 cells. Delisheng triggered time- and dose-dependent apoptosis in HepG2 cells by activating the mitochondria-mediated and death receptor-mediated apoptotic pathways

Identification of hub genes for glaucoma: a study based on bioinformatics analysis and experimental verification

AIM: To explore hub genes for glaucoma based on bioinformatics analysis and an experimental model verification. METHODS: In the Gene Expression Omnibus (GEO) database, the GSE25812 and GSE26299 datasets were selected to analyze differentially expressed genes (DEGs) by the GEO2R tool. Through bioinformatics analysis, 9 hub genes were identified. Receiver operating characteristic (ROC) curves and principal component analysis (PCA) were performed to verify whether the hub gene can distinguish glaucoma from normal eyes. The mouse model of glaucoma was constructed, and the real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) assay was performed to detect the expression levels of hub genes in glaucoma. RESULTS: There were 128 overlapping DEGs in the GSE25812 and GSE26299 datasets, mainly involved in intracellular signalling, cell adhesion molecules and the Ras signalling pathway. A total of 9 hub genes were screened out, including GNAL, BGN, ETS2, FCGP4, MAPK10, MMP15, STAT1, TSPAN8, and VCAM1. The area under the curve (AUC) values of 9 hub genes were greater than 0.8. The PC1 axle could provide a 70.5% interpretation rate to distinguish glaucoma from normal eyes. In the ocular tissues of glaucoma in the mice model, the expression of BGN, ETS2, FCGR4, STAT1, TSPAN8, and VCAM1 was increased, while the expression of GNAL, MAPK10, and MMP15 was decreased. CONCLUSION: Nine hub genes in glaucoma are identified, which may provide new biomarkers and therapeutic targets for glaucoma

Roles of sulfate-reducing bacteria in sustaining the diversity and stability of marine bacterial community

Microbes play central roles in ocean food webs and global biogeochemical processes. Yet, the information available regarding the highly diverse bacterial communities in these systems is not comprehensive. Here we investigated the diversity, assembly process, and species coexistence frequency of bacterial communities in seawater and sediment across ∼600 km of the eastern Chinese marginal seas using 16S rRNA gene amplicon sequencing. Our analyses showed that compared with seawater, bacterial communities in sediment possessed higher diversity and experienced tight phylogenetic distribution. Neutral model analysis showed that the relative contribution of stochastic processes to the assembly process of bacterial communities in sediment was lower than that in seawater. Functional prediction results showed that sulfate-reducing bacteria (SRB) were enriched in the core bacterial sub-communities. The bacterial diversities of both sediment and seawater were positively associated with the relative abundance of SRB. Co-occurrence analysis showed that bacteria in seawater exhibited a more complex interaction network and closer co-occurrence relationships than those in sediment. The SRB of seawater were centrally located in the network and played an essential role in sustaining the complex network. In addition, further analysis indicated that the SRB of seawater helped maintain the high stability of the bacterial network. Overall, this study provided further comprehensive information regarding the characteristics of bacterial communities in the ocean, and provides new insights into keystone taxa and their roles in sustaining microbial diversity and stability in ocean

Green Mass Production of Pure Nanodrugs via an Ice-Template-Assisted Strategy

To make nanomedicine potentially applicable in a clinical setting, several methods have been developed to synthesize pure nanodrugs (PNDs) without using any additional inert carriers. In this work, we report a novel green, low-cost, and scalable ice-template-assisted approach which shows several unique characteristics. First, the whole process only requires adding a drug solution into an ice template and subsequent melting (or freeze-drying), allowing easy industrial mass production with low capital investment. Second, the production yield is much higher than that of the traditional reprecipitation approach. The yield of Curcumin (Cur) PNDs is over two orders (∼140 times) magnitude higher than that obtained in a typical reprecipitation preparation. By adjusting simple processing parameters, PNDs with different sizes (∼20–200 nm) can be controllably obtained. Finally, the present approach can be easily applicable for a wide range of hydrophobic therapeutic drugs without any structural modification

The LAMOST Complete Spectroscopic Survey of Pointing Area (LaCoSSPAr) in the Southern Galactic Cap I. The Spectroscopic Redshift Catalog

We present a spectroscopic redshift catalog from the LAMOST Complete Spectroscopic Survey of Pointing Area (LaCoSSPAr) in the Southern Galactic Cap (SGC), which is designed to observe all sources (Galactic and extra-galactic) by using repeating observations with a limiting magnitude of $r=18.1~mag$ in two $20~deg^2$ fields. The project is mainly focusing on the completeness of LAMOST ExtraGAlactic Surveys (LEGAS) in the SGC, the deficiencies of source selection methods and the basic performance parameters of LAMOST telescope. In both fields, more than 95% of galaxies have been observed. A post-processing has been applied to LAMOST 1D spectrum to remove the majority of remaining sky background residuals. More than 10,000 spectra have been visually inspected to measure the redshift by using combinations of different emission/absorption features with uncertainty of $\sigma_{z}/(1+z)<0.001$. In total, there are 1528 redshifts (623 absorption and 905 emission line galaxies) in Field A and 1570 redshifts (569 absorption and 1001 emission line galaxies) in Field B have been measured. The results show that it is possible to derive redshift from low SNR galaxies with our post-processing and visual inspection. Our analysis also indicates that up to 1/4 of the input targets for a typical extra-galactic spectroscopic survey might be unreliable. The multi-wavelength data analysis shows that the majority of mid-infrared-detected absorption (91.3%) and emission line galaxies (93.3%) can be well separated by an empirical criterion of $W2-W3=2.4$. Meanwhile, a fainter sequence paralleled to the main population of galaxies has been witnessed both in $M_r$/$W2-W3$ and $M_*$/$W2-W3$ diagrams, which could be the population of luminous dwarf galaxies but contaminated by the edge-on/highly inclined galaxies ($\sim30\%$).Comment: 19 pages, 14 figures, 2 MRT, accepted by ApJ

Do the mutations of C1GALT1C1 gene play important roles in the genetic susceptibility to Chinese IgA nephropathy?

Background: The deficiency of beta 1,3 galactose in hinge region of IgA1 molecule played a pivotal role in pathogenesis of IgA nephropathy (IgAN). Cosmc, encoded by C1GALT1C1 gene, was indispensable to beta 1,3 galactosylation of IgA1. We designed a serial study to investigate the relationship between the mutations of C1GALT1C1 gene and the genetic susceptibility to IgAN. Methods: Nine hundred and thirty-eight subjects, including 661 patients with IgAN and 277 healthy controls were enrolled in the study. Firstly, single nucleotide polymorphisms (SNPs) in the promoter region of C1GALT1C1 gene were screened. Then the c.-347-190G&gt; A was analyzed by PCR-restriction fragment length polymorphism (PCR-RFLP) for further case-control association analysis. Secondly the somatic mutations of DNAs from peripheral blood B lymphocytes were detected in 15 patients and 7 normal controls. Results: No significant association was observed between the different alleles or genotypes of c.347-190G&gt;A and IgAN. The patients with different genotypes of C1GALT1C1 gene did not significantly associate with clinical manifestations, including hematuria, proteinuria, and serum creatinine of patients with IgAN. There was no somatic mutation detected in total 202 clones of 22 individuals. Conclusion: The c.-347-190G&gt;A polymorphism and the somatic mutation of encoding region of C1GALT1C1 gene were not significantly related to the genetic susceptibility to IgAN in Northern Chinese population.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000271285100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Genetics &amp; HereditySCI(E)PubMed1ARTICLE1011

Differential gene expression and potential regulatory network of fatty acid biosynthesis during fruit and leaf development in yellowhorn (Xanthoceras sorbifolium), an oil-producing tree with significant deployment values

Xanthoceras sorbifolium (yellowhorn) is a woody oil plant with super stress resistance and excellent oil characteristics. The yellowhorn oil can be used as biofuel and edible oil with high nutritional and medicinal value. However, genetic studies on yellowhorn are just in the beginning, and fundamental biological questions regarding its very long-chain fatty acid (VLCFA) biosynthesis pathway remain largely unknown. In this study, we reconstructed the VLCFA biosynthesis pathway and annotated 137 genes encoding relevant enzymes. We identified four oleosin genes that package triacylglycerols (TAGs) and are specifically expressed in fruits, likely playing key roles in yellowhorn oil production. Especially, by examining time-ordered gene co-expression network (TO-GCN) constructed from fruit and leaf developments, we identified key enzymatic genes and potential regulatory transcription factors involved in VLCFA synthesis. In fruits, we further inferred a hierarchical regulatory network with MYB-related (XS03G0296800) and B3 (XS02G0057600) transcription factors as top-tier regulators, providing clues into factors controlling carbon flux into fatty acids. Our results offer new insights into key genes and transcriptional regulators governing fatty acid production in yellowhorn, laying the foundation for efforts to optimize oil content and fatty acid composition. Moreover, the gene expression patterns and putative regulatory relationships identified here will inform metabolic engineering and molecular breeding approaches tailored to meet biofuel and bioproduct demands

Analysis and Prediction of Translation Rate Based on Sequence and Functional Features of the mRNA

Protein concentrations depend not only on the mRNA level, but also on the translation rate and the degradation rate. Prediction of mRNA's translation rate would provide valuable information for in-depth understanding of the translation mechanism and dynamic proteome. In this study, we developed a new computational model to predict the translation rate, featured by (1) integrating various sequence-derived and functional features, (2) applying the maximum relevance & minimum redundancy method and incremental feature selection to select features to optimize the prediction model, and (3) being able to predict the translation rate of RNA into high or low translation rate category. The prediction accuracies under rich and starvation condition were 68.8% and 70.0%, respectively, evaluated by jackknife cross-validation. It was found that the following features were correlated with translation rate: codon usage frequency, some gene ontology enrichment scores, number of RNA binding proteins known to bind its mRNA product, coding sequence length, protein abundance and 5′UTR free energy. These findings might provide useful information for understanding the mechanisms of translation and dynamic proteome. Our translation rate prediction model might become a high throughput tool for annotating the translation rate of mRNAs in large-scale

High-quality genome assembly enables prediction of allele-specific gene expression in hybrid poplar

DATA AVAILABILITY : The whole genome sequencing raw data, genome assemblies, and annotations have been deposited in the Genome Sequence Archive in National Genomics Data Center (https://ngdc.cncb.ac.cn/gwh) under the accession number GWHBJXC00000000 (Bio-Project ID: PRJCA010836). The genome assembly and annotation data for subgenomes A and G have also been deposited in the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/) under Biological Project accession numbers PRJNA1025943 and PRJNA1025942, respectively. Scripts used for centromere identification are publicly available at: https://github.com/ShuaiNIEgithub/Centromics. Codes and data for allele identification, allele-specific gene expression, and XGBoost model construction are available on Git-hub (https://github.com/shitianle77/84K_genome) and figshare (https://figshare.com/articles/dataset/Gap-free_genome_assembly_of_hybrid_poplar_84K_/24279211). A computational pipeline for allele identification and allele-specific gene expression with haplotype-resolved diploid genome assembly is available at: https://github.com/shitianle77/Allele_auto.SUPPLEMENTARY DATA : SUPPLEMENTARY FIGURE S1. Images of the sequenced individual (the F1 hybrid poplar “84K”). SUPPLEMENTARY FIGURE S2. The schematic diagram illustrates the overall process of the assembly of the poplar “84K” genome and the data required for the assembly process. SUPPLEMENTARY FIGURE S3. Putative centromeres (green boxes) are determined based on the distribution of the tandem repeat with the highest frequency. SUPPLEMENTARY FIGURE S4. Telomere sequences assembled in each chromosome. SUPPLEMENTARY FIGURE S5. Positions of the two gaps located on chromosome 9A (chr09A). SUPPLEMENTARY FIGURE S6. Genome-wide analysis of chromatin interactions in the genome based on Hi-C data. SUPPLEMENTARY FIGURE S7.K-mer frequency distribution estimated from (A) Illumina, (B) HiFi, and (C) ONT sequences after filtering and correction at K-mer size of 17. SUPPLEMENTARY FIGURE S8. Collinearity of 2 haplotype genomes of the poplar clone “84K” with that of P. trichocarpa. SUPPLEMENTARY FIGURE S9. Collinearity of 2 haplotype genomes of the current (this study) with published genomes of “84K” (Qiu et al. 2019). SUPPLEMENTARY FIGURE S10. Distribution of rDNA on chromosomes. SUPPLEMENTARY FIGURE S11. Distribution of rDNA on chromosomes of Salicaceae species. SUPPLEMENTARY FIGURE S12. Gene family evolution and collinearity analyses among Salicaceae species. SUPPLEMENTARY FIGURE S13. Length of structural variation and local sequence differences between the subgenomes A and G (subgenome G for the assembly of P. tremula var. glandulosa and subgenome A for the assembly of P. alba). SUPPLEMENTARY FIGURE S14. Statistics on overlaps between the inversion regions and different TE types (left panel) and between breakpoint region of inversion and different TE types (right panel) in the 2 subgenomes (G for the assembly of P. tremula var. glandulosa and A for the assembly of P. alba). SUPPLEMENTARY FIGURE S15. DNA methylation patterns. SUPPLEMENTARY FIGURE S16. Collinearity of a pair of alleles on 2 parental genomes. SUPPLEMENTARY FIGURE S17. Absolute TPM expression abundance for Diff00, Diff0, Diff2, and Diff8. SUPPLEMENTARY FIGURE S18. GO enrichment analysis of 5 categories of allelic expression bias. SUPPLEMENTARY FIGURE S19. Importance ranking and ROC curves of Model 0 (with 46 predictors/features). SUPPLEMENTARY FIGURE S20. Pair-wise correlation among 46 predictors (features) used in modeling (Model 0). SUPPLEMENTARY FIGURE S21. Ranking of the 15 features in the XGBoost model (Model 2) and the model assessment. SUPPLEMENTARY FIGURE S22. Ranking of the 15 features in the XGBoost model (Model 3) and the model assessment. SUPPLEMENTARY TABLE S1. Statistics of whole genome sequencing data. SUPPLEMENTARY TABLE S2. Summary of the Illumina reads for the genome assembly of “84K”. SUPPLEMENTARY TABLE S3. Statistics of the different versions of genome assembly. SUPPLEMENTARY TABLE S4. Statistics of the genome quality for the final assembly. SUPPLEMENTARY TABLE S5. Mapping rates of Illumina reads, HiFi reads, and ONT reads to the present genome assembly of “84K”. SUPPLEMENTARY TABLE S6. Summary of BUSCO evaluation for genome assembly and gene prediction. SUPPLEMENTARY TABLE S7. Summary statistics of the gene annotation of the “84K” genome. SUPPLEMENTARY TABLE S8. Summary of functional annotation of predicted genes. SUPPLEMENTARY TABLE S9. Summary of the annotated RNA genes. SUPPLEMENTARY TABLE S10. Summary of the repeat elements annotated in the “84K.” SUPPLEMENTARY TABLE S11. Annotated TF gene families in the “84K” genome. SUPPLEMENTARY TABLE S12. Summary of gene family expansion and contraction in the “84K” genome. SUPPLEMENTARY TABLE S13. Summary of identified SVs between 2 parental genomes. SUPPLEMENTARY TABLE S14. Summary of the percentage of methylation sites of CG, CHG, and CHH in DNA methylation. SUPPLEMENTARY TABLE S15. Categories and number of allelic expression biases between 2 parental genomes. SUPPLEMENTARY TABLE S16. 46 features used in the XGBoost machine-learning modeling of ASE. SUPPLEMENTARY TABLE S17. Ranking of the 46 features in the XGBoost model (Model 0). SUPPLEMENTARY TABLE S18. Ranking of the 15 features in the XGBoost models (Model 1, Model 2, and Model 3). SUPPLEMENTARY TABLE S19. Evaluation of the classification XGBoost models (Model 0, Model 1, and Model 2). SUPPLEMENTARY TABLE S20. Evaluation of the regression XGBoost model (Model 3). SUPPLEMENTARY TABLE S21. Statistics of transcriptome assembly by different methods. SUPPLEMENTARY NOTE S1. 46 features used in the XGBoost machine-learning modeling of ASE. SUPPLEMENTARY NOTE S2. Library construction and sequencing. SUPPLEMENTARY NOTE S3. Genome assembly and quality assessment. SUPPLEMENTARY NOTE S4. Gene prediction and functional annotation. SUPPLEMENTARY NOTE S5. Phylogenetics and gene collinearity in the Salicaceae. SUPPLEMENTARY NOTE S6. Variation between the 2 parental genomes. SUPPLEMENTARY NOTE S7. RNA-seq data and allelic gene expression. SUPPLEMENTARY NOTE S8. DNA methylation quantification from ONT long reads. SUPPLEMENTARY NOTE S9. Feature extraction for machine-learning modeling. SUPPLEMENTARY NOTE S10. Model construction.SUPPLEMENTARY DATA SET 1. RNA-Seq data used for gene expression analysis. SUPPLEMENTARY DATA SET 2. Statistics of mRNA sequencing data for gene annotation. SUPPLEMENTARY DATA SET 3. Summary of the amount of rDNA on different chromosomes among the Salicaceae species and 2 subgenomes (Subgenomes A and G). SUPPLEMENTARY DATA SET 4. GO enrichment of the significantly expanded gene families in 1 parental genome (P. alba genome, the subgenome A). SUPPLEMENTARY DATA SET 5. GO enrichment of the significantly expanded gene families in one parental genome (the P. tremula var. glandulosa genome, the subgenome G).Poplar (Populus) is a well-established model system for tree genomics and molecular breeding, and hybrid poplar is widely used in forest plantations. However, distinguishing its diploid homologous chromosomes is difficult, complicating advanced functional studies on specific alleles. In this study, we applied a trio-binning design and PacBio high-fidelity long-read sequencing to obtain haplotype-phased telomere-to-telomere genome assemblies for the 2 parents of the well-studied F1 hybrid “84K” (Populus alba × Populus tremula var. glandulosa). Almost all chromosomes, including the telomeres and centromeres, were completely assembled for each haplotype subgenome apart from 2 small gaps on one chromosome. By incorporating information from these haplotype assemblies and extensive RNA-seq data, we analyzed gene expression patterns between the 2 subgenomes and alleles. Transcription bias at the subgenome level was not uncovered, but extensive-expression differences were detected between alleles. We developed machine-learning (ML) models to predict allele-specific expression (ASE) with high accuracy and identified underlying genome features most highly influencing ASE. One of our models with 15 predictor variables achieved 77% accuracy on the training set and 74% accuracy on the testing set. ML models identified gene body CHG methylation, sequence divergence, and transposon occupancy both upstream and downstream of alleles as important factors for ASE. Our haplotype-phased genome assemblies and ML strategy highlight an avenue for functional studies in Populus and provide additional tools for studying ASE and heterosis in hybrids.The National Key R&D Program of China and National Natural Science Foundation of China.https://academic.oup.com/plphyshj2024BiochemistryGeneticsMicrobiology and Plant PathologySDG-15:Life on lan