A Strategy based on Bioinformatics and Machine Learning Algorithms Reveals Potential Mechanisms of Shelian Capsule against Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent and life-threatening form of cancer, with Shelian Capsule (SLC), a traditional Chinese medicine (TCM) formulation, being recommended for clinical treatment. However, the mechanisms underlying its efficacy remain elusive. This study sought to uncover the potential mechanisms of SLC in HCC treatment using bioinformatics methods. METHODS: Bioactive components of SLC were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and HCC-related microarray chip data were sourced from the Gene Expression Omnibus (GEO) database. The selection criteria for components included OB ≧ 30% and DL ≧ 0.18. By integrating the results of differential expression analysis and weighted gene co-expression network analysis (WGCNA), disease-related genes were identified. Therapeutic targets were determined as shared items between candidate targets and disease genes. Protein-protein interaction (PPI) network analysis was conducted for concatenated genes, with core protein clusters identified using the MCODE plugin. Machine learning algorithms were applied to identify signature genes within therapeutic targets. Subsequently, immune cell infiltration analysis, single-cell RNA sequencing (sc-RNA seq) analysis, molecular docking, and ADME analysis were performed for the screened genes. RESULTS: A total of 153 SLC ingredients and 170 candidate targets were identified, along with 494 HCCrelated disease genes. Overlapping items between disease genes and drug candidates represented therapeutic genes, and PPI network analysis was conducted using concatenated genes. MCODE1 and MCODE2 cluster genes underwent Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Four signature genes (TOP2A, CYP1A2, CYP2B6, and IGFBP3) were identified from 28 therapeutic genes using 3 machine learning algorithms, with ROC curves plotted. Molecular docking validated the interaction modes and binding abilities between signature genes and corresponding compounds, with free binding energy all <-7 kcal/mol. Finally, ADME analysis revealed similarities between certain SLC components and the clinical drugs Sorafenib and Lenvatinib. CONCLUSION: In summary, our study revealed that the mechanism underlying the anti-HCC effects of SLC involves interactions at three levels: components (quercetin, beta-sitosterol, kaempferol, baicalein, stigmasterol, and luteolin), pathways (PI3K-Akt signaling pathway, TNF signaling pathway, and IL-17 signaling pathway), and targets (TOP2A, CYP1A2, CYP2B6, and IGFBP3). This study provides preliminary insights into the potential pharmacological mechanisms of SLC in HCC treatment, aiming to support its clinical application and serve as a reference for future laboratory investigations.

Combining single-cell RNA sequencing data and transcriptomic data to unravel potential mechanisms and signature genes of the progression of idiopathic pulmonary fibrosis to lung adenocarcinoma and predict therapeutic agents.

Patients with idiopathic pulmonary fibrosis (IPF) have a significantly higher prevalence of lung adenocarcinoma (LUAD) than normal subjects, although the underlying association is unclear. The raw data involved were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis were used to screen for differentially expressed genes (DEGs) and modular signature genes (MSGs). Genes intersecting DEGs and MSGs were considered hub genes for IPF and LUAD. Machine learning algorithms were applied to capture epithelial cell-derived signature genes (EDSGs) shared. External cohort data were exploited to validate the robustness of EDSGs. Immunohistochemical staining and K-M plots were used to denote the prognostic value of EDSGs in LUAD. Based on EDSGs, we constructed a TF-gene-miRNA regulatory network. Molecular docking can validate the strength of action between candidate drugs and EDSGs. Epithelial cells, 650 DEGs, and 1773 MSGs were shared by IPF and LUAD. As for 379 hub genes, we performed pathway and functional enrichment analysis. By analyzing sc-RNA seq data, we identified 1234 marker genes of IPF epithelial cell-derived and 1481 of LUAD. And these genes shared 8 items with 379 hub genes. Through the machine learning algorithms, we further fished TRIM2, S100A14, CYP4B1, LMO7, and SFN. The ROC curves emphasized the significance of EDSGs in predicting the onset of LUAD and IPF. The TF-gene-miRNA network revealed regulatory relationships behind EDSGs. Finally, we predicted appropriate therapeutic agents. Our study preliminarily identified potential mechanisms between IPF and LUAD, which will inform subsequent studies.

Deciphering the underlying mechanisms of Sanleng-Ezhu for the treatment of idiopathic pulmonary fibrosis based on network pharmacology and single-cell RNA sequencing data.

AIMS: To decipher the underlying mechanisms of Sanleng-Ezhu for the treatment of idiopathic pulmonary fibrosis based on network pharmacology and single-cell RNA sequencing data. BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is the most common type of interstitial lung disease. Although the combination of herbs Sanleng (SL) and Ezhu (EZ) has shown reliable efficacy in the management of IPF, its underlying mechanisms remain unknown. OBJECTIVE: To decipher the pathogenesis of IPF and achieve personalized clinical management of IPF patients Method: Based on LC-MS/MS analysis and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, we identified the bioactive components of SL-EZ. After obtaining the IPF-related dataset GSE53845 from the Gene Expression Omnibus (GEO) database, we performed the differential expression analysis and the weighted gene co-expression network analysis (WGCNA), respectively. We obtained lowly and highly expressed IPF subtype gene sets by comparing differentially expressed genes (DEGs) with the most significantly negatively and positively related IPF modules in WGCNA. Subsequently, we performed Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on IPF subtype gene sets. The low- and high-expression MCODE subgroup feature genes were identified by the MCODE plug-in and were adopted for Disease Ontology (DO), GO, and KEGG enrichment analyses. Next, we performed the immune cell infiltration analysis of the MCODE subgroup feature genes. Single-cell RNA sequencing analysis demonstrated the cell types which expressed different MCODE subgroup feature genes. Molecular docking and animal experiments validated the effectiveness of SL-EZ in delaying the progression of pulmonary fibrosis. RESULT: We obtained 5 bioactive components of SL-EZ as well as their corresponding 66 candidate targets. After normalizing the samples of the GSE53845 dataset from the GEO database source, we obtained 1907 DEGs of IPF. Next, we performed a WGCNA analysis on the dataset and got 11 modules. Notably, we obtained 2 IPF subgroups by contrasting the most significantly up- and down-regulated modular genes in IPF with DEGs, respectively. The different IPF subgroups were compared with drug-candidate targets to obtain direct targets of action. After constructing the protein interaction networks between IPF subgroup genes and drug candidate targets, we applied the MCODE plug-in to filter the highest-scoring MCODE components. DO, GO, and KEGG enrichment analyses were applied to drug targets, IPF subgroup genes, and MCODE component signature genes. In addition, we downloaded the single-cell dataset GSE157376 from the GEO database. By performing quality control and dimensionality reduction, we clustered the scattered primary sample cells into 11 clusters and annotated them into 2 cell subtypes. Drug sensitivity analysis suggested that SL-EZ acts on different cell subtypes in IPF subgroups. Molecular docking revealed the mode of interaction between targets and their corresponding components. Animal experiments confirmed the efficacy of SL-EZ. CONCLUSION: We found SL-EZ acted on epithelial cells mainly through the calcium signaling pathway in the lowly-expressed IPF subtype, while in the highly-expressed IPF subtype, SL-EZ acted on smooth muscle cells mainly through the viral infection, apoptosis, and p53 signaling pathway.

The improved assembly of 7DL chromosome provides insight into the structure and evolution of bread wheat.

Wheat is one of the most important staple crops worldwide and also an excellent model species for crop evolution and polyploidization studies. The breakthrough of sequencing the bread wheat genome and progenitor genomes lays the foundation to decipher the complexity of wheat origin and evolutionary process as well as the genetic consequences of polyploidization. In this study, we sequenced 3286 BACs from chromosome 7DL of bread wheat cv. Chinese Spring and integrated the unmapped contigs from IWGSC v1 and available PacBio sequences to close gaps present in the 7DL assembly. In total, 8043 out of 12 825 gaps, representing 3 491 264 bp, were closed. We then used the improved assembly of 7DL to perform comparative genomic analysis of bread wheat (Ta7DL) and its D donor, Aegilops tauschii (At7DL), to identify domestication signatures. Results showed a strong syntenic relationship between Ta7DL and At7DL, although some small rearrangements were detected at the distal regions. A total of 53 genes appear to be lost genes during wheat polyploidization, with 23% (12 genes) as RGA (disease resistance gene analogue). Furthermore, 86 positively selected genes (PSGs) were identified, considered to be domestication-related candidates. Finally, overlapping of QTLs obtained from GWAS analysis and PSGs indicated that TraesCS7D02G321000 may be one of the domestication genes involved in grain morphology. This study provides comparative information on the sequence, structure and organization between bread wheat and Ae. tauschii from the perspective of the 7DL chromosome, which contribute to better understanding of the evolution of wheat, and supports wheat crop improvement.

Comparative genomics and transcriptomics of Chrysolophus provide insights into the evolution of complex plumage coloration.

Background: As one of the most recognizable characteristics in birds, plumage color has a high impact on understanding the evolution and mechanisms of coloration. Feather and skin are ideal tissues to explore the genomics and complexity of color patterns in vertebrates. Two species of the genus Chrysolophus, golden pheasant (Chrysolophus pictus) and Lady Amherst's pheasant (Chrysolophus amherstiae), exhibit brilliant colors in their plumage, but with extreme phenotypic differences, making these two species great models to investigate plumage coloration mechanisms in birds. Results: We sequenced and assembled a genome of golden pheasant with high coverage and annotated 15,552 protein-coding genes. The genome of Lady Amherst's pheasant is sequenced with low coverage. Based on the feather pigment identification, a series of genomic and transcriptomic comparisons were conducted to investigate the complex features of plumage coloration. By identifying the lineage-specific sequence variations in Chrysolophus and golden pheasant against different backgrounds, we found that four melanogenesis biosynthesis genes and some lipid-related genes might be candidate genomic factors for the evolution of melanin and carotenoid pigmentation, respectively. In addition, a study among 47 birds showed some candidate genes related to carotenoid coloration in a broad range of birds. The transcriptome data further reveal important regulators of the two colorations, particularly one splicing transcript of the microphthalmia-associated transcription factor gene for pheomelanin synthesis. Conclusions: Analysis of the golden pheasant and its sister pheasant genomes, as well as comparison with other avian genomes, are helpful to reveal the underlying regulation of their plumage coloration. The present study provides important genomic information and insights for further studies of avian plumage evolution and diversity.

Chromosome-level reference genome and alternative splicing atlas of moso bamboo (Phyllostachys edulis).

Background: Bamboo is one of the most important nontimber forestry products worldwide. However, a chromosome-level reference genome is lacking, and an evolutionary view of alternative splicing (AS) in bamboo remains unclear despite emerging omics data and improved technologies. Results: Here, we provide a chromosome-level de novo genome assembly of moso bamboo (Phyllostachys edulis) using additional abundance sequencing data and a Hi-C scaffolding strategy. The significantly improved genome is a scaffold N50 of 79.90 Mb, approximately 243 times longer than the previous version. A total of 51,074 high-quality protein-coding loci with intact structures were identified using single-molecule real-time sequencing and manual verification. Moreover, we provide a comprehensive AS profile based on the identification of 266,711 unique AS events in 25,225 AS genes by large-scale transcriptomic sequencing of 26 representative bamboo tissues using both the Illumina and Pacific Biosciences sequencing platforms. Through comparisons with orthologous genes in related plant species, we observed that the AS genes are concentrated among more conserved genes that tend to accumulate higher transcript levels and share less tissue specificity. Furthermore, gene family expansion, abundant AS, and positive selection were identified in crucial genes involved in the lignin biosynthetic pathway of moso bamboo. Conclusions: These fundamental studies provide useful information for future in-depth analyses of comparative genome and AS features. Additionally, our results highlight a global perspective of AS during evolution and diversification in bamboo.

Genome-Wide Identification of Destruxin A-Responsive Immunity-Related MicroRNAs in Diamondback Moth, Plutella xylostella.

Plutella xylostella, a global key pest, is one of the major lepidopteran pests of cruciferous vegetables owing to its strong ability of resistance development to a wide range of insecticides. Destruxin A, a mycotoxin of the entomopathogenic fungus, Metarhizium anisopliae, has broad-spectrum insecticidal effects and has been used as an alternative control strategy to reduce harmful effects of insecticides. However, microRNA (miRNA)-regulated reactions against destruxin A have not been elucidated yet. Therefore, here, to identify immunity-related miRNAs, we constructed four small RNA libraries from destruxin A-injected larvae of P. xylostella at three different time courses (2, 4, and 6 h) with a control, and sequenced by Illumina. Our results showed that totally 187 known and 44 novel miRNAs were identified in four libraries by bioinformatic analysis. Interestingly, among differentially expressed known miRNAs, some conserved miRNAs, such as miR-263, miR-279, miR-306, miR-2a, and miR-308, predicted to be involved in regulating immunity-related genes, were also identified. Worthy to mention, miR-306 and miR-279 were also listed as common abundantly expressed miRNA in all treatments. The Kyoto Encyclopedia of Genes and Genomes pathway analysis also indicated that differentially expressed miRNAs were involved in several immunity-related signaling pathways, including toll signaling pathway, IMD signaling pathway, JAK-STAT signaling pathway, and cell adhesion molecules signaling pathway. To the best of our knowledge, this is the first comprehensive report of destruxin A-responsive immunity-related miRNAs in P. xylostella. Our findings will improve in understanding the role of destruxin A-responsive miRNAs in the host immune system and would be useful to develop biological control strategies for controlling P. xylostella.

Identification of immunity-related genes in Plutella xylostella in response to fungal peptide destruxin A: RNA-Seq and DGE analysis.

Plutella xylostella has become the major lepidopteran pest of Brassica owing to its strong ability of resistance development to a wide range of insecticides. Destruxin A, a mycotoxin of entomopathogenic fungus, Metarhizium anisopliae, has broad-spectrum insecticidal effects. However, the interaction mechanism of destruxin A with the immune system of P. xylostella at genomic level is still not well understood. Here, we identified 129 immunity-related genes, including pattern recognition receptors, signal modulators, few members of main immune pathways (Toll, Imd, and JAK/STAT), and immune effectors in P. xylostella in response to destruxin A at three different time courses (2 h, 4 h, and 6 h). It is worthy to mention that the immunity-related differentially expressed genes (DEGs) analysis exhibited 30, 78, and 72 up-regulated and 17, 13, and 6 down-regulated genes in P. xylostella after destruxin A injection at 2 h, 4 h, and 6 h, respectively, compared to control. Interestingly, our results revealed that the expression of antimicrobial peptides that play a vital role in insect immune system was up-regulated after the injection of destruxin A. Our findings provide a detailed information on immunity-related DEGs and reveal the potential of P. xylostella to limit the infection of fungal peptide destruxin A by increasing the activity of antimicrobial peptides.

The Entomopathogenic Fungi Isaria fumosorosea Plays a Vital Role in Suppressing the Immune System of Plutella xylostella: RNA-Seq and DGE Analysis of Immunity-Related Genes.

Most, if not all, entomopathogenic fungi have been used as alternative control agents to decrease the insect resistance and harmful effects of the insecticides on the environment. Among them, Isaria fumosorosea has also shown great potential to control different insect pests. In the present study, we explored the immune response of P. xylostella to the infection of I. fumosorosea at different time points by using RNA-Sequencing and differential gene expression technology at the genomic level. To gain insight into the host-pathogen interaction at the genomic level, five libraries of P. xylostella larvae at 12, 18, 24, and 36 h post-infection and a control were constructed. In total, 161 immunity-related genes were identified and grouped into four categories; immune recognition families, toll and Imd pathway, melanization, and antimicrobial peptides (AMPs). The results of differentially expressed immunity-related genes depicted that 15, 13, 53, and 14 up-regulated and 38, 51, 56, and 49 were down-regulated in P. xylostella at 12, 18, 24, and 36 h post-treatment, respectively. RNA-Seq results of immunity-related genes revealed that the expression of AMPs was reduced after treatment with I. fumosorosea. To validate RNA-Seq results by RT-qPCR, 22 immunity-related genes were randomly selected. In conclusion, our results demonstrate that I. fumosorosea has the potential to suppress the immune response of P. xylostella and can become a potential biopesticide for controlling P. xylostella.

Corrigendum: Transcriptome Analysis of Hamelia patens (Rubiaceae) Anthers Reveals Candidate Genes for Tapetum and Pollen Wall Development.

[This corrects the article on p. 1991 in vol. 7, PMID: 28119704.].