Identification and validation of a five-necroptosis-related lncRNAs signature for prognostic prediction in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is among the most prevalent digestive system malignancies and is associated with a poor prognosis. Necroptosis, a form of regulated death mediated by death receptors, exhibits characteristics of both necrosis and apoptosis. Long non-coding RNAs (lncRNAs) have been identified as crucial regulators in tumor necroptosis. This study aims to identify the necroptosis-related lncRNAs (np-lncRNA) in HCC and investigate their relationships with prognosis. Method: The RNA-sequencing data, along with clinicopathological and survival information of HCC patients were sourced from The Cancer Genome Atlas (TCGA) database. The np-lncRNAs were analyzed to assess their potential in predicting HCC prognosis. Prognostic signatures related to necroptosis were constructed using stepwise multivariate Cox regression analysis. The prognosis of patients was compared using Kaplan-Meier (KM) analysis. The accuracy of the prognostic signature was evaluated using Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Quantitative real-time polymerase chain reaction(qPCR) was employed to validate the lncRNAs expression levels of lncRNAs among samples from an independent cohort. Results: The np-lncRNAs ZFPM2-AS1, AC099850.3, BACE1-AS, KDM4A-AS1 and MKLN1-AS were identified as potential prognostic biomarkers. The prognostic signature constructed from these np-lncRNAs achieved an Area Under the Curve (AUC) of 0.773. Based on the risk score derived from the signature, patients were divided into two groups, with the high-risk group exhibiting poorer overall survival. Gene Set Enrichment Analysis (GSEA) revealed significantly different between the low risk and high risk groups in tumor-related pathways (such as mTOR, MAPK and p53 signaling pathways) and immune-related functions (like T cell receptor signaling pathway and natural killer cell mediated cytotoxicity). The increased expression of np-lncRNAs was confirmed in another independent HCC cohort. Conclusions: This signature offers a dependable method for forecasting the prognosis of HCC patients. Our findings indicate a subset of np-lncRNA biomarkers that could be utilized for prognosis prediction and personalized treatment strategies of HCC patients.

Co-regulatory mechanisms and potential markers of oxidative stress-related genes in vitiligo and alopecia areata.

BACKGROUND: The specific role of oxidative stress (OS) in vitiligo and alopecia areata (AA) remains unclear. The aim of this study was to analyze and identify the key markers of OS in vitiligo and AA by bioinformatics. METHODS: We obtained vitiligo and AA datasets from gene expression omnibus (GEO) database. The difference-expressed genes of vitiligo and AA were identified by differential analysis, and the functions of difference-expressed genes were identified by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) enrichment analysis. Subsequently, Veen package was used to obtain the intersection genes of OS-related genes with vitiligo and AA. Finally, we used CIBERSORT to assess the infiltration of immune cells in vitiligo and AA. RESULTS: Through enrichment analysis, we found that vitiligo and AA were mainly enriched in cell cycle and cell adhesion molecular channels. We identified KLB and EIF3C as key genes in OS regulation of vitiligo and AA, and found that KLB and EIF3C participate in disease progression by regulating T cells and neutrophils. CONCLUSIONS: According to our findings, KLB and EIF3C play a crucial role in the progression and development of vitiligo and AA, which have been identified as biomarkers and target for early diagnosis of patients.

Hub genes identification and validation of ferroptosis in SARS-CoV-2 induced ARDS: perspective from transcriptome analysis.

Introduction: Acute Respiratory Distress Syndrome (ARDS) poses a significant health challenge due to its high incidence and mortality rates. The emergence of SARS-CoV-2 has added complexity, with evidence suggesting a correlation between COVID-19 induced ARDS and post-COVID symptoms. Understanding the underlying mechanisms of ARDS in COVID-19 patients is crucial for effective clinical treatment. Method: To investigate the potential role of ferroptosis in SARS-CoV-2 induced ARDS, we conducted a comprehensive analysis using bioinformatics methods. Datasets from the Gene Expression Omnibus (GEO) were utilized, focusing on COVID-19 patients with varying ARDS severity. We employed weighted gene co-expression network analysis (WGCNA), differential gene expression analysis, and single-cell sequencing to identify key genes associated with ferroptosis in ARDS. Hub genes were validated using additional GEO datasets and cell experiment. Result: The analysis discerned 916 differentially expressed genes in moderate/severe ARDS patients compared to non-critical individuals. Weighted Gene Co-expression Network Analysis (WGCNA) unveiled two modules that exhibited a positive correlation with ARDS, subsequently leading to the identification of 15 hub genes associated with ferroptosis. Among the noteworthy hub genes were MTF1, SAT1, and TXN. Protein-protein interaction analysis, and pathway analysis further elucidated their roles. Immune infiltrating analysis highlighted associations between hub genes and immune cells. Validation in additional datasets confirmed the upregulation of MTF1, SAT1, and TXN in SARS-CoV-2-induced ARDS. This was also demonstrated by qRT-PCR results in the BEAS-2B cells vitro model, suggesting their potential as diagnostic indicators. Discussion: This study identifies MTF1, SAT1, and TXN as hub genes associated with ferroptosis in SARS-CoV-2-induced ARDS. These findings provide novel insights into the molecular mechanisms underlying ARDS in COVID-19 patients and offer potential targets for immune therapy and targeted treatment. Further experimental validation is warranted to solidify these findings and explore therapeutic interventions for ARDS in the context of COVID-19.

Identification and validation of genes associated with prognosis of cisplatin-resistant ovarian cancer.

PURPOSE: To investigate the role of prognostic genes related to cisplatin resistance in ovarian cancer during disease progression. METHOD: The gene expression profile of the NCI-60 cell line was acquired through comprehensive analysis of the GEO database accession GSE116439. We performed a thorough analysis of gene expression differences in samples from seven individuals exposed to cisplatin concentrations of 0 nM compared to seven samples exposed to 15000 nM over a 24-h period. Key genes were initially identified through LASSO regression, followed by their enrichment through differential gene function analysis (GO) and pathway enrichment analysis (KEGG). Subsequently, a prognostic risk model was established for these key genes. The prognostic model's performance was assessed through K-M survival curves and ROC curves. To examine the variance in immune cell infiltration between the high and low-risk groups, CIBERSORTx analysis was employed. Finally, validation of prognostic gene expression in cisplatin-resistant ovarian cancer was carried out using clinical samples, employing RT-qPCR and Western Blot techniques. RESULTS: A total of 132 differential genes were found between cisplatin resistance and control group, and 8 key prognostic genes were selected by analysis, namely VPS13B, PLGRKT, CDKAL1, TBC1D22A, TAP1, PPP3CA, CUX1 and PPP1R15A. The efficacy of the risk assessment model derived from prognostic biomarkers, as indicated by favorable performance on both Kaplan-Meier survival curves and ROC curves. Significant variations in the abundance of Macrophages M1, T cells CD4 memory resting, T cells follicular helper, and T cells gamma delta were observed between the high and low-risk groups. To further validate our findings, RT-qPCR and Western Blot analyses were employed, confirming differential expression of the identified eight key genes between the two groups. CONCLUSION: VPS13B, TBC1D22A, PPP3CA, CUX1 and PPP1R15A were identified as poor prognostic genes of cisplatin resistance in ovarian cancer, while PLGRKT, CDKAL1 and TAP1 were identified as good prognostic genes. This offers a novel perspective for future advancements in ovarian cancer treatment, suggesting potential avenues for the development of new therapeutic targets.

Network pharmacology and experimental validation to explore the role and potential mechanism of Liuwei Dihuang Decoction in prostate cancer.

OBJECTIVE: To evaluate the anti-tumor effector of Liuwei Dihuang Decoction (LWDHD) in prostate cancer (PCa) and explore the potential mechanism using experimental validation, network pharmacology, bioinformatics analysis, and molecular docking. METHODS: CCK test, Clone formation assay and wound-healing assays were used to determine the effect of LWDHD on prostate cancer growth and metastasis. The active ingredients and targets of LWDHD were obtained from the TCMSP database, and the relevant targets were selected by GeneCards, OMIM and DisGeNET databases for PCa. The cross-targets of drugs and disease were imported into the STRING database to construct protein interactions. The network was also visualized using Cytoscape software and core targets are screened using the Network Analyzer plug-in. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed using R software. TCGA database was used to analyze the correlation of bioinformatics genes. AutoDock vina was used to predict the molecular docking and binding ability of active ingredients to key targets. Through WB and q-PCR experiments, the above gene targets were detected to verify the effect of LWDHD on PCa. RESULTS: CCK and scratch tests confirmed that LWDHD could inhibit the proliferation, invasion and migration of prostate cancer cells. Clone formation experiments showed that LWDHD inhibited the long-term proliferative capacity of PC3 cells. LWDHD and PCa had a total of 99 common targets, establishing a "drug-ingredient-common target" network. Through GO and KEGG enrichment analysis, PI3K/AKT, MAPK, TP53 pathway, MYC, TNF pathway and other signaling pathways were found. Bioinformatics analysis showed that MYC gene was highly expressed and CCND1 and MAPK1 were low expressed in prostate cancer tissues. In addition, TP53, AKT1, MYC, TNF and CCND1 were positively correlated with MAPK1, among which AKT1 and CCND1 were most closely correlated with MAPK1. Molecular docking results showed that quercetin, kaempferol, ß-sitosterol and other main active ingredients of LWDHD treatment for PCa were combined with core proteins MAPK1 and AKT1 well. WB and q-PCR results showed that LWDHD inhibited the expression of PI3K and AKT in PC3 cells. CONCLUSION: The mechanism of LWDHD therapy for PCa is a multi-target and multi-pathway complex process, which may be related to the biological processes mediated by MAPK1 and AKT1 pathways, such as cell proliferation and inhibition of metastasis, and the regulation of signaling pathways. The PI3K/AKT signaling pathway may be a central pathway of LWDHD to inhibit prostate cancer proliferation.

Comprehensive analysis of circRNA-miRNA-mRNA regulatory network and novel potential biomarkers in eutopic endometrium of adenomyosis.

Adenomyosis (ADS) is a common gynecological disorder, and its pathogenesis remains unclear. This study explores the functions of circRNAs in the eutopic endometrium of ADS and their diagnostic efficacy for ADS. High-throughput RNA sequencing was performed on 12 eutopic endometrial samples from ADS patients and 3 control endometrial samples. Additionally, circRNAs were analyzed in conjunction with clinical features. A competitive endogenous RNA network was established based on bioinformatics analysis, comprising 3 circRNAs, 1 miRNA, and 13 mRNAs. In the ADS group, the expression levels of hsa_circ_0008959 and SLC15A4 were significantly reduced, while hsa-miR-124-3p expression was increased. SLC15A4 was associated with cell proliferation and invasion. Decreased expression of hsa_circ_0008959 and SLC15A4, along with high VAS scores and elevated hsa-miR-124-3p levels, were identified as risk factors for ADS development. The combination of hsa_circ_0008959 and VAS scores demonstrated the highest diagnostic value for ADS.

Comprehensive analysis revealed P4Hs as new biomarkers for prognosis and immunotherapy in head and neck cancer.

Prolyl 4-hydroxylases (P4Hs) are a family of key modifying enzymes in collagen synthesis. P4Hs have been confirmed to be closely associated with tumor occurrence and development. However, the expression of P4Hs in head and neck cancer (HNSC) as well as its relationship with prognosis and tumor immunity infiltration has not yet been analyzed. We investigated the transcriptional expression, survival data, and immune infiltration of P4Hs in patients with HNSC from multiple databases. P4H1-3 expression was significantly higher in HNSC tumor tissues than in normal tissues. Moreover, P4HA1 and P4HA2 were associated with tumor stage, patient prognosis, and immune cell infiltration. P4HA3 was related to patient prognosis and immune cell infiltration. Correlation experiments confirmed that P4HA1 may serve as a prognosis biomarker and plays a role in the progression of nasopharyngeal carcinoma. These findings suggest that P4HA1-3 may be a novel biomarker for the prognosis and treatment of HNSC, which is expected to support the development of new therapies for patients with head and neck tumors and improve patient outcomes.

Track Thyself? The Value and Ethics of Self-knowledge Through Technology.

Novel technological devices, applications, and algorithms can provide us with a vast amount of personal information about ourselves. Given that we have ethical and practical reasons to pursue self-knowledge, should we use technology to increase our self-knowledge? And which ethical issues arise from the pursuit of technologically sourced self-knowledge? In this paper, I explore these questions in relation to bioinformation technologies (health and activity trackers, DTC genetic testing, and DTC neurotechnologies) and algorithmic profiling used for recommender systems, targeted advertising, and technologically supported decision-making. First, I distinguish between impersonal, critical, and relational self-knowledge. Relational self-knowledge is a so far neglected dimension of self-knowledge which is introduced in this paper. Next, I investigate the contribution of these technologies to the three types of self-knowledge and uncover the connected ethical concerns. Technology can provide a lot of impersonal self-knowledge, but we should focus on the quality of the information which tends to be particularly insufficient for marginalized groups. In terms of critical self-knowledge, the nature of technologically sourced personal information typically impedes critical engagement. The value of relational self-knowledge speaks in favour of transparency of information technology, notably for algorithms that are involved in decision-making about individuals. Moreover, bioinformation technologies and digital profiling shape the concepts and norms that define us. We should ensure they not only serve commercial interests but our identity and self-knowledge interests.

Revealing PPP1R12B and COL1A1 as piRNA pathway genes contributing to abdominal aortic aneurysm through integrated analysis and experimental validation.

BACKGROUND: Abdominal aortic aneurysm (AAA) is a permanent dilation of the abdominal aorta, with a high mortality rate when rupturing. Although lots of piRNA pathway genes (piRPGs) have recently been linked to both neoplastic and non-neoplastic illnesses, their role in AAA is still unknown. Utilizing integrative bioinformatics methods, this research discovered piRPGs as biomarkers for AAA and explore possible molecular mechanisms. METHODS: The datasets were obtained from the Gene Expression Omnibus and piRPGs were identified from the Genecards database. The "limma" and "clusterProfiler" R-packages were used to discover differentially expressed genes and perform enrichment analysis, respectively. Hub piRPGs were further filtered using least absolute shrinkage and selection operator regression, random forests, as well as receiver operating characteristic curve. Additionally, multi-factor logistic regression (MLR), extreme gradient boosting (XGboost), and artificial neural network (ANN) were employed to construct prediction models. The relationship between hub piRPGs and immune infiltrating cells and sgGSEA were further studied. The expression of hub piRPGs was verified by qRT-PCR, immunohistochemistry, and western blotting in AAA and normal vascular tissues and analyzed by scRNA-seq in mouse AAA model. SRAMP and cMAP database were utilized for the prediction of N6-methyladenosine (m6A) targets therapeutic drug. RESULTS: 34 differentially expressed piRPGs were identified in AAA and enriched in pathways of immune regulation and gene silence. Three piRPGs (PPP1R12B, LRP10, and COL1A1) were further screened as diagnostic genes and used to construct prediction model. Compared with MLR and ANN, Xgboost showed better predictive ability, and PPP1R12B might have the ability to distinguish small and large AAA. Furthermore, the expression levels of PPP1R12B and COL1A1 were consistent with the results of bioinformatics analysis, and PPP1R12B showed a downward trend that may be related to m6A. CONCLUSION: The results suggest that piRPGs might serve a significant role in AAA. PPP1R12B, COL1A1, and LRP10 had potential as diagnostic-specific biomarkers for AAA and performed better in XGboost model. The expression and localization of PPP1R12B and COL1A1 were experimentally verified. Besides, downregulation of PPP1R12B caused by m6A might contribute to the formation of AAA.

Investigating the ID3/SLC22A4 as immune-related signatures in ischemic stroke.

BACKGROUND: Ischemic stroke (IS) is a fearful disease that can cause a variety of immune events. Nevertheless, precise immune-related mechanisms have yet to be systematically elucidated. This study aimed to identify immune-related signatures using machine learning and to validate them with animal experiments and single cell analysis. METHODS: In this study, we screened 24 differentially expressed genes (DEGs) while identifying immune-related signatures that may play a key role in IS development through a comprehensive strategy between least absolute shrinkage and selection operation (LASSO) regression, support vector machine (SVM) and immune-related genes. In addition, we explored immune infiltration using the CIBERSORT algorithm. Finally, we performed validation in mouse brain tissue and single cell analysis. RESULTS: We identified 24 DEGs for follow-up analysis. ID3 and SLC22A4 were finally identified as the better immune-related signatures through a comprehensive strategy among DEGs, LASSO, SVM and immune-related genes. RT-qPCR, western blot, and immunofluorescence revealed a significant decrease in ID3 and a significant increase in SLC22A4 in the middle cerebral artery occlusion group. Single cell analysis revealed that ID3 was mainly concentrated in endothelial_2 cells and SLC22A4 in astrocytes in the MCAO group. A CIBERSORT finds significantly altered levels of immune infiltration in IS patients. CONCLUSIONS: This study focused on immune-related signatures after stroke and ID3 and SLC22A4 may be new therapeutic targets to promote functional recovery after stroke. Furthermore, the association of ID3 and SLC22A4 with immune cells may be a new direction for post-stroke immunotherapy.

Structural Study of the Exocyst Subunit Human Sec6.

In eukaryotic cells, vesicular transport plays a crucial role in the docking and fusion of secretory vesicles with their respective target membranes. This intricate process is dependent on a complex network of multiple molecules. One of the important processes is tethering. The exocyst complex facilitates the tethering of secretory vesicles to the plasma membrane during exocytosis. The Sec6 subunit in yeast interacts with other exocyst subunits and may regulate SNARE assembly, which is crucial for understanding the assembly mechanism of exocyst and its interaction with SNARE. In this study, we designed two truncated forms of HuSec6, HuSec6 121-734 and HuSec6 121-745, based on results of bioinformatics analysis. We expressed and purified the proteins in E. coli, obtaining a protein purity of over 95% and protein crystals. X-ray diffraction results showed a resolution of approximately 9 Å for the crystals, providing a solid foundation for the crystal structure analysis of HuSec6.

Mechanism investigation of Duhuo Jisheng pill against rheumatoid arthritis based on a strategy for the integration of network pharmacology, molecular docking and in vivo experimental verification.

CONTEXT: Duhuo Jisheng pill (DHJS) is a classic traditional Chinese medicine (TCM) formula for rheumatoid arthritis (RA). The effective components and therapeutic mechanisms of DHJS for treating RA are still unclear. OBJECTIVE: To explore the potential mechanism of DHJS against RA by means of network pharmacology and experimental verification. MATERIALS AND METHODS: A network pharmacology and molecular docking analysis based on phytochemistry was used to elucidate the mechanism of DHJS against RA. The targets of DHJS anti-RA active ingredient were obtained by searching TCMSP, ETCM and TCMSID. The RA model induced by collagen was established in Wistar rats. The rats in the DHJS group were administered doses of 0.5, 1.0 and 2.0 g/kg for a period of 10 d. The expression of targets was measured with Western blot. RESULTS: Network pharmacology analysis showed that the anti-RA effect of DHJS was mediated by targets involved in immunity, inflammation and apoptosis, as well as PI3K-Akt and NF-κB signalling pathways. Of 2.0 g/kg DHJS significantly alleviated the ankle inflammation (IL-6: 62.73 ± 8.39 pg/mL, IL-1ß: 50.49 ± 11.47 pg/mL, TNF-α: 16.88 ± 3.05 pg/mL, IL-17A: 12.55 ± 1.87 pg/mL, IL-10: 16.24 ± 3.00 pg/mL), comparing with the model group (IL-6: 92.02 ± 13.25 pg/mL, IL-1ß: 71.85 ± 4.12 pg/mL, TNF-α: 25.64 ± 3.69 pg/mL, IL-17A: 22.14 ± 4.56 pg/mL, IL-10: 9.51 ± 3.03 pg/mL) (p < 0.05). Moreover, the protein expression of p-PI3K, p-AKT and p-p65 significantly decreased after DHJS administration. CONCLUSIONS: DHJS could alleviate the collagen-induced arthritis (CIA) by the PI3K/AKT/NF-κB signalling pathway.

Phenotypic Analysis and Molecular Characterization of Enlarged Cell Size Mutant in Nannochloropsis oceanica.

The cell cycle is the fundamental cellular process of eukaryotes. Although cell-cycle-related genes have been identified in microalgae, their cell cycle progression differs from species to species. Cell enlargement in microalgae is an essential biological trait. At the same time, there are various causes of cell enlargement, such as environmental factors, especially gene mutations. In this study, we first determined the phenotypic and biochemical characteristics of a previously obtained enlarged-cell-size mutant of Nannochloropsis oceanica, which was designated ECS. Whole-genome sequencing analysis of the insertion sites of ECS indicated that the insertion fragment is integrated inside the 5'-UTR of U/P-type cyclin CYCU;1 and significantly decreases the gene expression of this cyclin. In addition, the transcriptome showed that CYCU;1 is a highly expressed cyclin. Furthermore, cell cycle analysis and RT-qPCR of cell-cycle-related genes showed that ECS maintains a high proportion of 4C cells and a low proportion of 1C cells, and the expression level of CYCU;1 in wild-type (WT) cells is significantly increased at the end of the light phase and the beginning of the dark phase. This means that CYCU;1 is involved in cell division in the dark phase. Our results explain the reason for the larger ECS size. Mutation of CYCU;1 leads to the failure of ECS to fully complete cell division in the dark phase, resulting in an enlargement of the cell size and a decrease in cell density, which is helpful to understand the function of CYCU;1 in the Nannochloropsis cell cycle.

Tumor purity-associated genes influence hepatocellular carcinoma prognosis and tumor microenvironment.

Introduction: Tumor purity takes on critical significance to the progression of solid tumors. The aim of this study was at exploring potential prognostic genes correlated with tumor purity in hepatocellular carcinoma (HCC) by bioinformatics analysis. Methods: The ESTIMATE algorithm was applied for determining the tumor purity of HCC samples from The Cancer Genome Atlas (TCGA). The tumor purity-associated genes with differential expression (DEGs) were identified based on overlap analysis, weighted gene co-expression network analysis (WGCNA), and differential expression analysis. The prognostic genes were identified in terms of the prognostic model construction based on the Kaplan-Meier (K-M) survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses. The expression of the above-described genes was further validated by the GSE105130 dataset from the Gene Expression Omnibus (GEO) database. We also characterized the clinical and immunophenotypes of prognostic genes. Gene set enrichment analysis (GSEA) was carried out for exploring the biological signaling pathway. Results: A total of 26 tumor purity-associated DEGs were identified, which were involved in biological processes such as immune/inflammatory responses and fatty acid elongation. Ultimately, we identified ADCK3, HK3, and PPT1 as the prognostic genes for HCC. Moreover, HCC patients exhibiting higher ADCK3 expression and lower HK3 and PPT1 expressions had a better prognosis. Furthermore, high HK3 and PPT1 expressions and low ADCK3 expression resulted in high tumor purity, high immune score, high stromal score, and high ESTIMATE score. GSEA showed that the abovementioned prognostic genes showed a significant correlation with immune-inflammatory response, tumor growth, and fatty acid production/degradation. Discussion: In conclusion, this study identified novel predictive biomarkers (ADCK3, HK3, and PPT1) and studied the underlying molecular mechanisms of HCC pathology initially.

Construction of circRNA-miRNA-mRNA Network Reveal Functional circRNAs and Key Genes in Acute Myeloid Leukemia.

Introduction: CircRNA is closely correlated with a wide variety of processes of acute myeloid leukemia (AML), whereas the novel circRNAs, their molecular mechanism and the specific function they played in AML should be explored in depth. Methods: The microarray chip data of AML patients and normal samples in the Gene Expression Omnibus (GEO) database were selected to differentially expressed (DE) circRNA, miRNA, and mRNA genes. The miRNA gene was the intersection of the circRNA target gene predicted using CSCD and the miRNA gene screened from AML patients, while the mRNA gene was the intersection of the target gene mRNA of miRNA predicted using miRanda and miRTarBase software and the mRNA gene screened from AML patients. The hub mRNAs related to survival were further screened through Cox proportional hazard regression. CircRNA/miRNA/mRNA interaction network was constructed by using Cytoscape software.10 circRNAs and 6 miRNAs in bone marrow mononuclear cells (BMMNCs) of AML patients (n=43) and healthy controls (n=35) were determined by RT-qPCR. Correlations between them were analyzed by Pearson correlation coefficient. Results: 10 circRNAs, 6 miRNAs, and 33 mRNAs were identified. Subsequently, the network of circRNAs, miRNAs, and hub genes was built using Cystoscope. Four key circRNAs, seven hub genes and their regulatory pathways were identified. The result of RT-qPCRs showed that hsa_circ_0009581 and hsa_circ_0005273 were significantly upregulated in AML patients while hsa_circ_0000497 and hsa_circ_0001947 were significantly downregulated. Hsa-miR-150-5p was significantly downregulated; hsa-miR-454-3p was upregulated in AML patients. Hsa_circ_0009581 and hsa-miR-150-5p; hsa_ circ_0001947 and hsa-miR-454-3p were inversely correlated using Pearson's correlation coefficient. Conclusion: This study suggests that differentially expressed circRNAs take on a critical significance to AML development and may be the effective therapeutic targets. We suppose that hsa_circ_0009581 promotes leukemia development through hsa-miR-150-5p and hsa_circ_0001947 through hsa-miR-454-3p. hsa_circ_0001947 and hsa_circ_0009581 may provide new directions in the pathogenesis of AML.

Using a stacked ensemble learning framework to predict modulators of protein-protein interactions.

Identifying small molecule protein-protein interaction modulators (PPIMs) is a highly promising and meaningful research direction for drug discovery, cancer treatment, and other fields. In this study, we developed a stacking ensemble computational framework, SELPPI, based on a genetic algorithm and tree-based machine learning method for effectively predicting new modulators targeting protein-protein interactions. More specifically, extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), random forest (RF), cascade forest, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost) were used as basic learners. Seven types of chemical descriptors were taken as the input characteristic parameters. Primary predictions were obtained with each basic learner-descriptor pair. Then, the 6 methods mentioned above were used as meta learners and trained on the primary prediction in turn. The most efficient method was utilized as the meta learner. Finally, the genetic algorithm was used to select the optimal primary prediction output as the input of the meta learner for secondary prediction to obtain the final result. We systematically evaluated our model on the pdCSM-PPI datasets. To our knowledge, our model outperformed all existing models, which demonstrates its great power.

Genome-Wide Analysis of the Rad21/REC8 Gene Family in Cotton (Gossypium spp.).

Cohesin is a ring-shaped protein complex and plays a critical role in sister chromosome cohesion, which is a key event during mitosis and meiosis. Meiotic recombination protein REC8 is one of the subunits of the cohesion complex. Although REC8 genes have been characterized in some plant species, little is known about them in Gossypium. In this study, 89 REC8 genes were identified and analyzed in 16 plant species (including 4 Gossypium species); 12 REC8 genes were identified in Gossypium. hirsutum, 11 in Gossypium. barbadense, 7 in Gossypium. raimondii, and 5 in Gossypium. arboreum. In a phylogenetic analysis, the 89 RCE8 genes clustered into 6 subfamilies (I-VI). The chromosome location, exon-intron structure, and motifs of the REC8 genes in the Gossypium species were also analyzed. Expression patterns of GhREC8 genes in various tissues and under abiotic stress treatments were analyzed based on public RNA-seq data, which indicated that GhREC8 genes might have different functions in growth and development. Additionally, qRT-PCR analysis showed that MeJA, GA, SA, and ABA treatments could induce the expression of GhREC8 genes. In general, the genes of the REC8 gene family of cotton were systematically analyzed, and their potential function in cotton mitosis, meiosis, and in response to abiotic stress and hormones were preliminary predicted, which provided an important basis for further research on cotton development and resistance to abiotic stress.

Genome-wide identification and analyses of cotton high-affinity nitrate transporter 2 family genes and their responses to stress.

Nitrate transporters (NRTs) are crucial for the uptake, use, and storage of nitrogen by plants. In this study, 42 members of the GhNRT2 (Nitrate Transporter 2 family) were found in the four different cotton species. The conserved domains, phylogenetic relationships, physicochemical properties, subcellular localization, conserved motifs, gene structure, cis-acting elements, and promoter region expression patterns of these 42 members were analyzed. The findings confirmed that members of the NRT2 family behaved typically, and subcellular localization tests confirmed that they were hydrophobic proteins that were mostly located on the cytoplasmic membrane. The NRT2 family of genes with A.thaliana and rice underwent phylogenetic analysis, and the results revealed that GhNRT2 could be divided into three groups. The same taxa also shared similar gene structure and motif distribution. The composition of cis-acting elements suggests that most of the expression of GhNRT2 may be related to plant hormones, abiotic stress, and photoreactions. The GhNRT2 gene was highly expressed, mainly in roots. Drought, salt, and extreme temperature stress showed that GhNRT2 gene expression was significantly up-regulated or down-regulated, indicating that it may be involved in the stress response of cotton. In general, the genes of the NRT2 family of cotton were comprehensively analyzed, and their potential nitrogen uptake and utilization functions in cotton were preliminarily predicted. Additionally, we provide an experimental basis for the adverse stress conditions in which they may function.

The Potential Mechanism of Cannabidiol (CBD) Treatment of Epilepsy in Pentetrazol (PTZ) Kindling Mice Uncovered by Multi-Omics Analysis.

Cannabidiol (CBD) is the main active ingredient in the cannabis plant used for treating epilepsy and related diseases. However, how CBD ameliorates epilepsy and its effect on the hippocampus remains unknown. Herein, we evaluated how CBD ameliorates seizure degree in pentylenetetrazol (PTZ) induced epilepsy mice after being exposed to CBD (10 mg/kg p.o). In addition, transcriptome and metabolomic analysis were performed on the hippocampus. Our results suggested that CBD could alleviate PTZ-induced seizure, of which the NPTX2, Gprc5c, Lipg, and Stc2 genes were significantly down-regulated in mice after being exposed to PTZ. Transcriptome analysis showed 97 differently expressed genes (CBD) and the PTZ groups. Metabonomic analysis revealed that compared with the PTZ group, 41 up-regulated and 67 down-regulated metabolites were identified in the hippocampus of epileptic mice exposed to CBD. The correlation analysis for transcriptome and metabolome showed that (±) 15-HETE and carnitine C6:0 were at the core of the network and were involved in the positive or negative regulation of the related genes after being treated with CBD. In conclusion, CBD ameliorates epilepsy by acting on the metabolism, calcium signaling pathway, and tuberculosis pathways in the hippocampus. Our study provided a practical basis for the therapeutic potential of treating epilepsy using CBD.

Quantitative evaluations of variations using the population mean as a baseline for bioinformatics interpretation.

Objective: The purpose of this study were to establish a model of quantitative evaluation that uses the population mean as a baseline of variations and describe variations derived from different types and systems using new concepts. Methods: The observed datasets, including measurement data and relative data, were transformed to 0-1.0 using the population mean. Datasets derived from different types (same category of dataset, different categories of datasets, and datasets with the same baseline) were transformed using different methods. The 'middle compared index' (MCI) was used to describe the change in magnitude as follows: [a/(a+b)+(1-b)/(2-a-b)-1]1.7, where 'a' represents the number after the magnitude change and 'b' represents the number before the magnitude change. Actual data were used to observe the MCI's ability to evaluate variations quantitatively. Results: When the value before the magnitude change was equal to that after the magnitude change, the MCI was equal to 0; when the value before the magnitude change was equal to 0 and that after the magnitude change was equal to 1, the MCI was equal to 1. This implies the MCI is valid. When the value before the magnitude change was 0 and that after the magnitude change was 0.5, or when the value before the magnitude change was 0.5 and that after the magnitude change was 1.0, each MCI was approximately equal to 0.5. The values derived from the absolute, ratio, and MCI methods were different, indicating that the MCI is an independent index. Conclusion: The MCI perfectly performs as an evaluation model using the population mean as the baseline, and it may be more a reasonable index than the ratio or absolute methods. The MCI increases our understanding of quantitative variations in evaluation measures of association using new concepts.