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The outbreak of coronavirus disease 2019 (COVID-19) has affected more than 200 countries and regions to date. mRNA m6A methylation has been reported as a new therapeutic target in COVID-19 patients, and has received widespread attention. However, the effects of m6A on COVID-19 clinical features as well as the molecular characterization of m6A in real COVID-19 patients are largely unclear. We collected RNA-seq data and clinical data from 100 COVID-19 patients, and sequenced m6A-seq from peripheral blood mononuclear cells (PBMCs) of two COVID-19 patients. Using bioinformatics analysis, we found that m6A is associated with several clinical features of COVID-19 patients. Furthermore, molecular characterization of m6A of COVID-19 patients in vivo rather than cell lines is demonstrated for the first time. Our findings will be valuable for the development of novel treatment strategies for COVID-19 patients.

Lymphoma is accompanied by the impairment of multiple immune functions. Cytokines play an important role in a variety of immune-related functions and affect the tumor microenvironment. However, the exact regulatory mechanisms between them remain unclear. This study aimed to explore the cytokines expression and function in Hodgkin's lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), and mantle cell lymphoma (MCL). We performed a transcriptome integration analysis of 14 lymphoma datasets including 240 Hodgkin's lymphoma, 891 diffuse large B-cell lymphoma, 216 mantle cell lymphoma, and 64 health samples. The results showed that multiple immune functions and signal pathway damage were shared by all three types of lymphoma, and these functions were related to cytokines. Furthermore, through co-expression network and functional interaction network analysis, we identified CXCL14 as a key regulator and it affects cell chemotaxis and migration functions. The functional experiment showed that CXCL14 knockdown inhibited cell migration in MCL cell lines. This study suggested that high expression of CXCL14 may aggravate MCL via promoting cell migration. Our findings provide novel insights into the biology of this disease and would be helpful for the pathogenesis study and drug discovery of lymphomas.

Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. In this study, an antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database. The results showed that both models have excellent prediction performance (mean accuracy0.9 and mean AUC0.9 for the two models). We used the predictor to screen potential antibacterial compounds from FDA-approved drugs in the DrugBank database. The screening results showed that 1087 small-molecule drugs have potential antibacterial activity and 154 of them are FDA-approved antibacterial drugs, which accounts for 76.2% of the approved antibacterial drugs collected in this study. Through molecular fingerprint similarity analysis and common substructure analysis, we screened 8 predicted antibacterial small-molecule compounds with novel structures compared with known antibacterial drugs, and 5 of them are widely used in the treatment of various tumors. This study provides a new insight for predicting antibacterial compounds by using approved drugs, the predicted compounds might be used to treat bacterial infections and extend lifespan.

Backgrounds:. Azithromycin mass drug administration (MDA) is a key part of the strategy for controlling trachoma. This systematic review aimed to comprehensively summarize the present studies of azithromycin MDA on trachoma; provide an overview of the impact of azithromycin MDA on trachoma in different districts; and explore the possible methods to enhance the effectiveness of azithromycin MDA in hyperendemic districts. Methods:. PubMed, Embase, the Cochrane Central Register of Controlled Trials, Web of Science, and ClinicalTrials.gov were searched up to February 2021 with no language restriction. Studies reporting the effect of azithromycin MDA on trachoma were included. Mathematical modeling studies, animal studies, case reports, and reviews were excluded. The trachomatous inflammation-follicular (TF) 30.0%), especially with baseline TF >50.0%, annual MDA was unable to achieve the TF 10.0% is not appropriate for all eligible districts.

A new bicyclic lactam derivatives penicilactam B (

Brain capillaries are crucial for cognitive functions by supplying oxygen and other nutrients to and removing metabolic wastes from the brain. Recent studies have demonstrated that constriction of brain capillaries is triggered by beta-amyloid (Aβ) oligomers via endothelin-1 (ET1)-mediated action on the ET1 receptor A (ETRA), potentially exacerbating Aβ plaque deposition, the primary pathophysiology of Alzheimer’s disease (AD). However, direct evidence is still lacking whether changes in brain capillaries are causally involved in the pathophysiology of AD. Using APP/PS1 mouse model of AD (AD mice) relative to age-matched negative littermates, we identified that reductions of density and diameter of hippocampal capillaries occurred from 4 to 7 months old while Aβ plaque deposition and spatial memory deficit developed at 7 months old. Notably, the injection of ET1 into the hippocampus induced early Aβ plaque deposition at 5 months old in AD mice. Conversely, treatment of ferulic acid against the ETRA to counteract the ET1-mediated vasoconstriction for 30 days prevented reductions of density and diameter of hippocampal capillaries as well as ameliorated Aβ plaque deposition and spatial memory deficit at 7 months old in AD mice. Thus, these data suggest that reductions of density and diameter of hippocampal capillaries are crucial for initiating Aβ plaque deposition and spatial memory deficit at the early stages, implicating the development of new therapies for halting or curing memory decline in AD. Supplementary Information The online version contains supplementary material available at 10.1007/s13311-021-01024-7.

T lymphocyte reduction and immunosenescence frequently occur in severe and critical coronavirus disease 2019 (COVID-19) patients, which may cause immunothrombosis and numerous sequelae. This study integrated analyzed multi-omics data from healthy donors, pneumonia, COVID-19 patients (mild & moderate, severe, and critical), and convalescences, including clinical, laboratory test, PBMC bulk RNA-seq, PBMC scRNA-seq and TCR-seq, BAL scRNA-seq, and lung proteome. We revealed that there are certain associations among T lymphocyte reduction, CD8+ T cell senescence, Th17 immune activation, and immunothrombosis. A specific phenotype (S. P.) CD8+ T cells were identified in severe and critical COVID-19 patients in both PBMC and BAL scRNA-seq, which showed highly TCR homology with terminal effector CD8+ T cells and senescent CD8+ T cells. Pseudotime analysis showed that the S. P. CD8+ T cells were located in the transition trajectory from mild to severe disease. Which may be activated by terminal effector CD8+ T cells or senescent CD8+ T cells, thereby promoting Th17 cell differentiation. This phenomenon was absent in healthy donors, mild and moderate COVID-19 patients, or convalescences. Our findings are an important reference for avoiding the conversion of patients with mild to severe diseases and provide insight into the future prevention and control of COVID-19 and its variants.

Breast cancer is the most common type of cancer amongst women worldwide, and numerous microRNAs (miRNAs/miRs) are involved in the initiation and progression of breast cancer. The aim of the present study was to identify hub miRNAs and determine the underlying mechanisms regulated by these miRNAs in breast cancer. Breast invasive carcinoma transcriptome data (including mRNAs and miRNAs), and clinical data were acquired from The Cancer Genome Atlas database. Differential gene expression analysis, co‑expression network analysis, gene set enrichment analysis (GSEA) and prognosis analysis were used to screen the hub miRNAs and explore their functions. Functional experiments were used to determine the underlying mechanisms of the hub miRNAs in breast cancer cells. The results revealed that low miR150 expression predicted a more advanced disease stage, and was associated with a less favorable prognosis. Through the combined use of five miRNA‑target gene prediction tools, 31 potential miR150 target genes were identified. GSEA revealed that low miR150 expression was associated with the upregulation of several cancer‑associated signaling pathways, and the downregulation of several tumor suppressor genes. Furthermore, miR150 independently affected overall survival in patients, and interacted with its target genes to indirectly affect overall and disease‑free survival. Functional experiments demonstrated that miR150 positively regulated B and T lymphocyte attenuator (BTLA), and the downregulation of miR150 and BTLA combined promoted cell migration. In conclusion, the present study revealed that low miR150 expression was associated with less favorable clinical features, upregulation of several carcinogenic signaling pathways, and poor patient survival. Additionally, a miR150‑BTLA axis was suggested to regulate cell viability and migration.

This study was to screen out potential driver long non-coding RNAs (lncRNAs) in lung cancer in Xuanwei (LCXW) differently expressed mRNAs and lncRNAs were detected by gene expression microarrays in 23 paired lung adenocarcinoma and adjacent tissues. Combined bioinformatics analysis was performed to identify potential driver lncRNAs and their potential regulatory relationships. Transcriptome and clinical data in TCGA-LUAD were used as comparison and validation dataset. The comparison of LCXW and TCGA-LUAD revealed significant differences in expression of some genes, signaling pathways affected by differentially expressed genes, and the 5-year survival rate of patients. We identified 14 consistently deregulated mRNAs and 5 lncRNAs as candidate genes, which affected multiple cancer-related pathways and influenced patients' overall survival. By combined bioinformatics analysis, we further identified a potential driver lncRNA fetal-lethal non-coding developmental regulatory RNA (FENDRR) and proposed its possible regulation mechanism. The low expression of FENDRR was positively correlated with Kruppel-like factor4 (KLF4), KLF4 down-regulation may loss the activation function of cyclin-dependent kinase inhibitor 1A (CDKN1A) and cyclin-dependent kinase inhibitor 1C (CDKN1C) and the inhibition function of CyclinB1 (CCNB1), eventually cause excessive cell cycle activation and lead to lung cancer. This study revealed a potential FENDRR-KLF4-cell cycle regulation axis. These results lay an important foundation for further research on the pathogenesis of LCXW and identification of potential novel biomarkers or therapeutic targets.

Considerable evidence suggests that metabolic abnormalities are associated with neurodegenerative diseases. This study aimed to conduct a systematic metabolic analysis of Alzheimer’s disease (AD), Parkinson’s disease (PD) and Huntington’s disease (HD). Human and mouse model microarray datasets were downloaded from the Gene Expression Omnibus database. The metabolic genes and pathways were collected from the Recon 3D human metabolic model. Drug and target information was obtained from the DrugBank database. This study identified ATP1A1, ATP6V1G2, GOT1, HPRT1, MAP2K1, PCMT1 and PLK2 as key metabolic genes that were downregulated in AD, PD and HD. We screened 57 drugs that target these genes, such as digoxin, ouabain and diazoxide. This study constructed multigene diagnostic models for AD, PD and HD by using metabolic gene expression profiles in blood, all models showed high accuracy (AUC > 0.8) both in the experimental and validation sets. Furthermore, analysis of animal models showed that there was almost no consistency among the metabolic changes between mouse models and human diseases. This study systematically revealed the metabolic damage among AD, PD, and HD and uncovered the differences between animal models and human diseases. This information may be helpful for understanding the metabolic mechanisms and drug development for neurodegenerative diseases.

Chitosan-based nanogels are effective carriers for drug delivery due to their biocompatibility and biodegradability. However, the chemically cross-linked nanogels usually require complicated procedures or tough conditions. Herein, we report a simple approach to generate chitosan-based nanogels by photo-crosslinking of poor solvent-induced nanoaggregates without requiring any emulsifying agent, catalyst, or external crosslinker. O-nitrobenzyl alcohol-modified carboxymethyl chitosan was synthesized and self-crosslinked into the nanogels in a mixed solution of ethanol and water under 365 nm light irradiation due to UV-induced primary amine and o-nitrobenzyl alcohol cyclization. The nanogels (CMC-NBA NPs) and lactobionic acid-decorated nanogels (LACMC-NBA NPs) displayed a uniform diameter (~200 nm) and excellent stability under physiological conditions. Notably, the nanogels exhibited a high loading content (~28 %) due to π-π stacking and electrostatic interactions between doxorubicin (DOX) and the carriers. These DOX-loaded nanogels showed rapid drug release under slightly acidic conditions. The cell and animal experiments confirmed that LACMC-NBA NPs increased cellular uptake, improved cytotoxicity in tumor cells, and enhanced growth inhibition in vivo than CMC-NBA NPs. Thus, these photo-crosslinked nanogels possess great potential for DOX delivery.

A new bicyclic lactam derivatives penicilactam B (1) and a new monocyclic amide penicillamide D (2), along with four known compounds (3-6), were isolated from the fermentation broth of the derived fungus Penicillium rubens PQJ-2. Their structures and stereochemistry were elucidated by comprehensive spectroscopic analyses and quantum ECD calculations. All the compounds were evaluated for their antibacterial activities against Staphylococcus aureus subsp, Candida albicans, Escherichia coli and insecticidal activity against Helicoverpa armigera Hubner. Compounds 1-3 exhibited modest insecticidal activity against H. armigera Hubner.

Background: Due to the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. The aim of this study is to construct an antibacterial compound predictor using machine learning methods to screen potential antibacterial drugs.Methods: Active and inactive antibacterial compounds were acquired from the ChEMBL and PubChem database, which were used to construct benchmark datasets. The antibacterial compound predictor is constructed using the support vector machine (SVM), random forest (RF), and multi-layer perception (MLP) methods. We predicted the antibacterial activity of the Food and Drug Administration (FDA) approved drugs in the DrugBank database and screened novel antibacterial drugs through structural similarity analysis.Results: In the initial screen process, the results suggested that the benchmark dataset based on FP2 molecular fingerprints, along with the SVM, RF, and MLP methods showed excellent prediction performance (mean AUC > 0.9 for all models). Using the combination of these three models, a total of 957 potential antibacterial drugs were predicted. Most of the predicted drugs showed low structural similarities compared with the FDA approved antibacterial drugs. We finally screened 9 predicted antibacterial drugs with novel structures including 2 anti-tumor drugs (cyclophosphamide and ifosfamide), 2 ophthalmic drugs (apraclonidine and echothiophate) and 5 anesthetics (desflurane, enflurane, isoflurane, methoxyflurane, and sevoflurane).Conclusions: This study provides a new insight for predicting antibacterial compounds with novel structures by using FDA approved drugs. The predicted compounds with novel structures are worthy of further experimental verification in the future.

Background: Traditional Chinese medicine (TCM) has been widely used in the treatment of human diseases. However, the synergistic effects of multiple TCM prescriptions in the treatment of stroke have not been thoroughly studied.Objective of the study: This study aimed to reveal the mechanisms underlying the synergistic effects of these TCM prescriptions in stroke treatment and identify the active compounds.Methods: Herbs and compounds in the Di-Tan Decoction (DTD), Xue-Fu Zhu-Yu Decoction (XFZYD), and Xiao-Xu-Ming Decoction (XXMD) were acquired from the TCMSP database. SEA, HitPick, and TargetNet web servers were used for target prediction. The compound-target (C-T) networks of three prescriptions were constructed and then filtered using the collaborative filtering algorithm. We combined KEGG enrichment analysis, molecular docking, and network analysis approaches to identify active compounds, followed by verification of these compounds with an oxygen-glucose deprivation and reoxygenation (OGD/R) model.Results: The filtered DTD network contained 39 compounds and 534 targets, the filtered XFZYD network contained 40 compounds and 508 targets, and the filtered XXMD network contained 55 compounds and 599 targets. The filtered C-T networks retained approximately 80% of the biological functions of the original networks. Based on the enriched pathways, molecular docking, and network analysis results, we constructed a complex network containing 3 prescriptions, 14 botanical drugs, 26 compounds, 13 targets, and 5 pathways. By calculating the synergy score, we identified the top 5 candidate compounds. The experimental results showed that quercetin, baicalin, and ginsenoside Rg1 independently and synergistically increased cell viability.Conclusion: By integrating pharmacological and chemoinformatic approaches, our study provides a new method for identifying the effective synergistic compounds of TCM prescriptions. The filtered compounds and their synergistic effects on stroke require further research.

Mouse Nsmce1 gene is the homolog of non-structural maintenance of chromosomes element 1 (NSE1) that is mainly involved in maintenance of genome integrity, DNA damage response, and DNA repair. Defective DNA repair may cause neurological disorders such as Alzheimer's disease (AD). So far, there is no direct evidence for the correlation between Nsmce1 and AD. In order to explore the function of Nsmce1 in the regulation of nervous system, we have overexpressed or knocked down Nsmce1 in the mouse hippocampal neuronal cells (MHNCs) HT-22 and detected its regulation of AD marker genes as well as cell proliferation. The results showed that the expression of App, Bace2, and Mapt could be inhibited by Nsmce1 overexpression and activated by the knockdown of Nsmce1. Moreover, the HT-22 cell proliferation ability could be promoted by Nsmce1 overexpression and inhibited by knockdown of Nsmce1. Furthermore, we performed a transcriptomics study by RNA sequencing (RNA-seq) to evaluate and quantify the gene expression profiles in response to the overexpression of Nsmce1 in HT-22 cells. As a result, 224 significantly dysregulated genes including 83 upregulated and 141 downregulated genes were identified by the comparison of Nsmce1 /+ to WT cells, which were significantly enriched in several Gene Ontology (GO) terms and pathways. In addition, the complexity of the alternative splicing (AS) landscape was increased by Nsmce1 overexpression in HT-22 cells. Thousands of AS events were identified to be mainly involved in the pathway of ubiquitin-mediated proteolysis (UMP) as well as 3 neurodegenerative diseases including AD. The protein-protein interaction network was reconstructed to show top 10 essential genes regulated by Nsmce1. Our sequencing data is available in the Gene Expression Omnibus (GEO) database with accession number as GSE113436. These results may provide some evidence of molecular and cellular functions of Nsmce1 in MHNCs.

Hepatocellular carcinoma (HCC) is a major type of primary liver cancer. HCC is influenced by sex and multiple metabolic abnormalities. The present study aimed to compare the overall metabolic changes between male and female HCC patients and identify key metabolic genes. Metabolic genes and pathways were identified based on analyses of publicly available data. Differential expression analysis, gene set enrichment analysis, survival analysis and transcriptional regulation analysis were employed to explore sex differences and identify key metabolic genes in HCC. The results suggested that female patients had more severe metabolic gene expression abnormalities and pathway deregulation than male patients. This study identified 9 key metabolic genes, and only upregulated ALDH1A2 independently increased overall survival risk in patients. Bioinformatic analyses suggest that upregulated GATA3 and TAL1 activate ALDH1A2 and then disrupt amino acid and carbohydrate metabolism, which may increase the risk of HCC. This study identified a novel contribution of upregulated ALDH1A2 to HCC. Future studies are needed to elucidate the potential metabolic mechanism of the role of ALDH1A2 in HCC.

BACKGROUND Worldwide, hepatocellular carcinoma (HCC) accounts for 80-90% of all cases of primary liver cancer, and is one of the ten most common malignancies. This study used bioinformatics analysis to identify genes associated with patient outcome in stages I-IV HCC and the gene pathways that distinguished between normal liver and liver cells and HCC and human HCC cell lines. MATERIAL AND METHODS Target genes were defined as those that had marketed drugs or drugs under development targeting a specific gene and acquired from the Clarivate Analytics Integrity Database. Differential expression gene analysis, co-expression network analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, survival analysis and receiver operating characteristic (ROC) curve analysis were used to explore the similarities and differences in gene expression profiles, functional associations, and survival in stage I-IV HCC. Normal liver cells (HL-7702) and HCC cell lines (HepaRG, HepG2, SK-Hep1, and Huh7) were studied using Western blot and quantitative reverse transcription PCR (RT-qPCR). RESULTS Hierarchical gene clustering identified target genes that distinguished between HCC and normal liver tissue. For stages I-IV HCC, there were seven commonly upregulated target genes EPHB1, LTK, NTRK2, PTK7, TBK1, TIE1, and TLR3, which were mainly involved in immune and signaling transduction pathways. PTK7 was highly expressed in stage I-IV HCC and was an independent prognostic marker for reduced overall survival (OS). CONCLUSIONS Bioinformatics analysis, combined with patient survival analysis, identified PTK7 gene expression as a potential therapeutic target and prognostic biomarker for all stages of HCC.

Although many of the genetic loci associated with breast cancer risk have been reported, there is a lack of systematic analysis of regulatory networks composed of different miRNAs and mRNAs on survival analysis in breast cancer. To reconstruct the microRNAs-genes regulatory network in breast cancer, we employed the expression data from The Cancer Genome Atlas (TCGA) related to five essential miRNAs including miR-21, miR-22, miR-210, miR-221, and miR-222, and their associated functional genomics data from the GEO database. Then, we performed an integration analysis to identify the essential target factors and interactions for the next survival analysis in breast cancer. Based on the results of our integrated analysis, we have identified significant common regulatory signatures including differentially expressed genes, enriched pathways, and transcriptional regulation such as interferon regulatory factors (IRFs) and signal transducer and activator of transcription 1 (STAT1). Finally, a reconstructed regulatory network of five miRNAs and 34 target factors was established and then applied to survival analysis in breast cancer. When we used expression data for individual miRNAs, only miR-21 and miR-22 were significantly associated with a survival change. However, we identified 45 significant miRNA-gene pairs that predict overall survival in breast cancer out of 170 one-on-one interactions in our reconstructed network covering all of five miRNAs, and several essential factors such as PSMB9, HLA-C, RARRES3, UBE2L6, and NMI. In our study, we reconstructed regulatory network of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets. These results may provide new insights into regulatory network-based precision medicine for breast cancer.

Aging is a complex life process that human organs and tissues steadily and continuously decline. Aging has huge heterogeneity, which shows different aging rates among different individuals and in different tissues of the same individual. Many studies of aging are often contradictory and show little common signature. The integrated analysis of these transcriptome datasets will provide an unbiased global view of the aging process. Here, we integrated 8 transcriptome datasets including 757 samples from healthy human blood to study aging from three aspects of gene expression, mutations, and alternative splicing. Surprisingly, we found that transcriptome changes in blood are relatively independent of the chronological age. Further pseudotime analysis revealed two different aging paths (AgingPath1 and AgingPath2) in human blood. The differentially expressed genes (DEGs) along the two paths showed a limited overlap and are enriched in different biological processes. The mutations of DEGs in AgingPath1 are significantly increased in the aging process, while the opposite trend was observed in AgingPath2. Expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) analysis identified 304 important mutations that can affect both gene expression and alternative splicing during aging. Finally, by comparison between aging and Alzheimer's disease, we identified 37 common DEGs in AgingPath1, AgingPath2 and Alzheimer's disease. These genes may contribute to the shift from aging state to Alzheimer's disease. In summary, this study revealed the two aging paths and the related genes and mutations, which provides a new insight into aging and aging-related disease.

Objective To explore novel long non-coding RNA (lncRNA) molecular markers related to bladder cancer prognosis and to construct a prognostic prediction model for bladder cancer patients. Methods LncRNA expression data of patients with bladder cancer were downloaded from TCGA database. Univariate Cox regression and likelihood-based survival analysis were used to discover prognosis related lncRNAs. Functional studies of prognosis related lncRNAs were conducted by co-expression analysis and pathway enrichment analysis. Multivariate Cox regression analysis was used to establish risk score model, and Receiver Operating Characteristic analysis was used to determine the optimal cut-off point of the model. The risk score model was validated through Kaplan Meier estimation method and log-rank test. Results Seven prognosis related lncRNAs (OCIAD1-AS1, RP11-111J6.2, AC079354.3, RP11-553A21.3, RP11-598F7.3, CYP4F35P and RP11-113K21.4) which can predict survival of bladder cancer patient were discovered. Co-expression analysis and pathway analysis of these novel lncRNA signature and their target genes further revealed that these lncRNAs play important roles in the occurrence and development of bladder cancer. Additionally, a seven-lncRNA signature based risk score model for prognostic prediction of bladder cancer patients was established and validated. Notably, we identified the potential significance of two tumor-related antisense lncRNAs (OCIAD1-AS1 and RP11-553A21.3) in the prognosis of bladder cancer. Conclusion Our results suggest that these lncRNA markers may serve as potential prognosis predictors for bladder cancer and deserve further functional verification studies.

Transcriptome differences between Hodgkin's lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), and mantle cell lymphoma (MCL), which are all derived from B cell, remained unclear. This study aimed to construct lymphoma-specific diagnostic models by screening lymphoma marker genes. Transcriptome data of HL, DLBCL, and MCL were obtained from public databases. Lymphoma marker genes were screened by comparing cases and controls as well as the intergroup differences among lymphomas. A total of 9 HL marker genes, 7 DLBCL marker genes, and 4 MCL marker genes were screened in this study. Most HL marker genes were upregulated, whereas DLBCL and MCL marker genes were downregulated compared to controls. The optimal HL-specific diagnostic model contains one marker gene (MYH2) with an AUC of 0.901. The optimal DLBCL-specific diagnostic model contains 7 marker genes (LIPF, CCDC144B, PRO2964, PHF1, SFTPA2, NTS, and HP) with an AUC of 0.951. The optimal MCL-specific diagnostic model contains 3 marker genes (IGLV3-19, IGKV4-1, and PRB3) with an AUC of 0.843. The present study reveals the transcriptome data-based differences between HL, DLBCL, and MCL, when combined with other clinical markers, may help the clinical diagnosis and prognosis.

An amendment to this paper has been published and can be accessed via the original article.

Immune cells have an uncertain function during the progression of extranodal natural killer/T-cell lymphoma (ENKTL). The present study determined the distribution, phenotype, and clinical significance of B lymphocytes in ENKTL. Immunohistochemistry indicated high infiltration of CD20+ B lymphocytes in the tumour tissues of 40% of the patients, and that a high infiltration correlated with better overall survival. Moreover, B lymphocytes had an active mature phenotype in situ and suppressed the proliferation of ENKTL cells in vitro. These results suggest that tumour infiltration of CD20+ B lymphocytes may be a new prognostic indicator for patients with ENKTL.

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BackgroundEvidence from multiple studies suggests metabolic abnormalities play an important role in lung cancer. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. The present study aimed to explore differences in the global metabolic response between male and female patients in LUAD and to identify the metabolic genes associated with lung cancer susceptibility.MethodsTranscriptome and clinical LUAD data were acquired from The Cancer Genome Atlas (TCGA) database. Information on metabolic genes and metabolic subsystems were collected from the Recon3D human metabolic model. Two validation datasets (GSE68465 and GSE72094) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis, gene set enrichment analysis and protein-protein interaction networks were used to identified key metabolic pathways and genes. Functional experiments were used to verify the effects of genes on proliferation, migration, and invasion in lung cancer cells in vitro.ResultsSamples of tumors and adjacent non-tumor tissue from both male and female patients exhibited distinct global patterns of gene expression. In addition, we found large differences in methionine and cysteine metabolism, pyruvate metabolism, cholesterol metabolism, nicotinamide adenine dinucleotide (NAD) metabolism, and nuclear transport between male and female LUAD patients. We identified 34 metabolic genes associated with lung cancer susceptibility in males and 15 in females. Most of the metabolic cancer-susceptibility genes had high prediction accuracy for lung cancer (AUC > 0.9). Furthermore, both bioinformatics analysis and experimental results showed that TAOK2 was down-regulated and ASAH1 was up-regulated in male tumor tissue and female tumor tissue in LUAD. Functional experiments showed that inhibiting ASAH1 suppressed the proliferation, migration, and invasion of lung cancer cells.ConclusionsMetabolic cancer-susceptibility genes may be used alone or in combination as diagnostic markers for LUAD. Further studies are required to elucidate the functions of these genes in LUAD.

Hepatocellular carcinoma (HCC) is one of the most lethal and malignant types of cancer that affects global human health. The present study aimed to investigate the effect of pyruvate kinase muscle isozyme M2 (PKM2) expression on the clinical features and prognosis of HCC. The present study employed univariate logistic regression to investigate the correlation between PKM2 expression and clinical features. Univariate and multivariate Cox regression analyses were performed to estimate the independent effect of PKM2 expression on survival status. The results revealed that patients in the high PKM2 group (≥11.25) exhibited significantly lower creatinine levels (P=0.043), higher fetoprotein levels (P

Glioblastoma (GBM) accounts for up to 50% of brain parenchymal tumors. It is the most malignant type of brain cancer with very poor survival and limited remedies. Cancer subtyping is important for cancer research and therapy. Here, we report a new subtyping method for GBM based on the genetic alterations of CDKN2A and TP53 genes. CDKN2A and TP53 are the most frequently mutated genes with mutation rates of 60 and 30%, respectively. We found that patients with deletion of CDKN2A possess worse survival than those with TP53 mutation. Interestingly, survival of patients with both TP53 mutation and CDKN2A deletion is no worse than for those with only one of these genetic alterations, but similar to those with TP53 mutation alone. Next, we investigated differences in the gene expression profile between TP53 and CDKN2A samples. Consistent with the survival data, the samples with both TP53 mutation and CDKN2A deletion showed a gene expression profile similar to those samples with TP53 mutation alone. Finally, we found that activation of RAS pathway plus Cdkn2a/b silencing can induce GBM, in a similar way to tumor induction by RAS activation plus TP53 silencing. In conclusion, we show that the genetic alterations of CDKN2A and TP53 may be used to stratify GBM, and the new animal models matching this stratification method were generated.

Background Coronary heart disease (CHD) is a common cardiovascular disease with high morbidity and mortality in China. The CHD risk prediction model has a great value in early prevention and diagnosis. Methods In this study, CHD risk prediction models among rural residents in Xinxiang County were constructed using Random Forest (RF), Support Vector Machine (SVM), and the least absolute shrinkage and selection operator (LASSO) regression algorithms with identified 16 influencing factors. Results Results demonstrated that the CHD model using the RF classifier performed best both on the training set and test set, with the highest area under the curve (AUC = 1 and 0.9711), accuracy (one and 0.9389), sensitivity (one and 0.8725), specificity (one and 0.9771), precision (one and 0.9563), F1-score (one and 0.9125), and Matthews correlation coefficient (MCC = one and 0.8678), followed by the SVM (AUC = 0.9860 and 0.9589) and the LASSO classifier (AUC = 0.9733 and 0.9587). Besides, the RF model also had an increase in the net reclassification index (NRI) and integrated discrimination improvement (IDI) values, and achieved a greater net benefit in the decision curve analysis (DCA) compared with the SVM and LASSO models. Conclusion The CHD risk prediction model constructed by the RF algorithm in this study is conducive to the early diagnosis of CHD in rural residents of Xinxiang County, Henan Province.

The lack of early diagnostic markers and novel therapeutic targets for clear cell renal cell carcinoma (ccRCC) negatively affects patient prognosis. Cancer metabolism is an attractive area for the understanding of the molecular mechanism of carcinogenesis. The present study attempted to identify metabolic changes from the view of the expression of metabolism-associated genes between control samples and those of ccRCC at different disease stages. Data concerning ccRCC gene expression obtained by RNA-sequencing was obtained from The Cancer Genome Atlas and data on metabolism-associated genes were extracted using the Recon2 model. Following analysis of differential gene expression, multiple differentially expressed metabolic genes at each tumor-node-metastasis disease stage were identified, compared with control non-disease samples: Metabolic genes (305) were differentially expressed in stage I disease, 323 in stage II disease, 355 in stage III disease and 363 in stage IV disease. Following enrichment analysis for differential metabolic genes, 22 metabolic pathways were identified to be dysregulated in multiple stages of ccRCC. Abnormalities in hormone, vitamin, glucose and lipid metabolism were present in the early stages of the disease, with dysregulation to reactive oxygen species detoxification and amino acid metabolism pathways occurring with advanced disease stages, particularly to valine, leucine, and isoleucine metabolism, which was substantially dysregulated in stage IV disease. The xenobiotic metabolism pathway, associated with multiple cytochrome P450 family genes, was dysregulated in each stage of the disease. This pathway is worthy of substantial attention since it may aid understanding of drug resistance in ccRCC. The results of the present study offer information to aid further research into early diagnostic biomarkers and therapeutic targets of ccRCC.

To study the cellular and molecular function of peroxisome proliferator-activated receptor γ (PPARγ) in skeletal muscle differentiation, we have generated inducible gain-of-function to overexpress PPARγ in C2C12 myoblasts. In order to identify PPARγ targets, RNA sequencing (RNA-seq) was used to evaluate and quantify the transcriptomes and expression patterns during myogenic differentiation under the overexpression of PPARγ. The formation of myotubes and the expression of muscle-specific myogenic genes such as MyoD and MyoG may be inhibited by PPARγ overexpression. Multiple genes and pathways were significantly involved in this process, including 11 genes such as Fndc9 and Slc14a1 with fundamental change of regulation modes, 9 genes of which were validated by the data of qRT-PCR. Our studies demonstrate that PPARγ would play critical roles on myoblasts differentiation, mediating crosstalk among several pathways and transcription factors. Our data is available in the Gene Expression Omnibus (GEO) database with the accession number as GSE99399.

The reactivation of HIV latency cell will be necessary to curing HIV infection. Although many latency-reversal agents (LRAs) have proven effective to reactivate the latency cell, there is a lack of any systematic analysis of the molecular targets of these LRAs and related pathways in the context of transcriptome. In this study, we performed an integrated analysis of the target profile of bryostatin and transcriptome of the reactivated CD4+ T cells after exposing to bryostatin. The result showed a distinct gene expression profile between latency cells and bryostatin reactivated cells. We found bryostatin can target multiple types of protein other than only protein kinase C. Functional network analysis of the target profile and differential expressed genes suggested that bryostatin may activate a few novel pathways such as pyrimidine metabolism, purine metabolism and p53 signaling pathway, besides commonly known pathways DNA replication, cell cycle and so on. The results suggest that bryostatin may reactivate the HIV-latent cells through up-regulation of pyrimidine and purine metabolism or through starting the cell-cycle arrest and apoptosis induced by up-regulation of p53 signaling pathway. Our study provides some novel insights into the role of bryostatin and its affected pathways in controlling HIV latency and reactivation.

Background Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. Results Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). Conclusion FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

There are various types of bioactive substances in human breast milk, such as active proteins, growth factors, cytokines, oligosaccharides, probiotics and cells. Many studies have shown that these bioactive substances in breast milk have important protective effects on infant growth and development, including anti-bacterial and anti-viral effects and the promotion of infant growth and development and immunologic maturation. They can also reduce the incidence rate of infectious diseases in infants, improve neural development in preterm infants, and reduce the risk of obesity and diabetes in future. However, there is still no clinical evidence for the effects of several active substances in breast milk, and their immunoregulatory mechanism remains unclear. Therefore, further studies are needed for clarification.

OBJECTIVE: To study the effect and mechanism of action of irisin on hypoxic-ischemic brain damage in neonatal rats. METHODS: A total of 248 7-day-old Sprague-Dawley rats were randomly divided into a sham-operation group, a model group, and low-and high-dose irisin intervention groups (n=62 each). The rats in the model and irisin intervention groups were given hypoxic treatment after right common carotid artery ligation to establish a model of hypoxic-ischemic brain damage. Those in the sham-operation group were given the separation of the right common carotid artery without ligation or hypoxic treatment. The rats in the high-and low-dose irisin intervention groups were given intracerebroventricular injection of recombinant irisin polypeptide at a dose of 0.30 μg and 0.15 μg respectively. Those in the model and sham-operation groups were given the injection of an equal volume of PBS. The water maze test was used to compare neurological behaviors between groups. TTC staining, hematoxylin-eosin staining and TUNEL staining were used to observe histopathological changes of the brain. Western blot was used to measure the expression of the apoptosis-related molecules cleaved-caspase-3 (CC3), BCL-2 and BAX. RESULTS: Compared with the sham-operation group, the model group had a significant increase in latency time and a significant reduction in the number of platform crossings (P < 0.05). Compared with the model group, the high-dose irisin intervention group had a significant reduction in latency time and a significant increase in the number of platform crossings (P < 0.05). Compared with the sham-operation group, the model group had massive infarction in the right hemisphere, with significant increases in karyopyknosis and karyorrhexis. Compared with the model group, the high-dose irisin intervention group had a smaller infarct area of the right hemisphere, with reductions in karyopyknosis and karyorrhexis. The model group had a significantly higher apoptosis rate of cells in the right cerebral cortex and the hippocampus than the sham-operation group. The high-dose irisin intervention group had a significantly lower apoptosis rate than the model group (P < 0.05). At 24 and 48 hours after modeling, the sham-operation group had a significantly lower level of CC3 than the model group (P < 0.05). Compared with the model group, the high-dose irisin intervention group had a significantly lower level of CC3 and a significantly higher BCL-2/BAX ratio (P < 0.05). The low-dose irisin intervention group had similar laboratory markers and histopathological changes of the brain to the model group. CONCLUSIONS: Irisin can alleviate hypoxic-ischemic brain damage in neonatal rats in a dose-dependent manner, possibly by reducing cell apoptosis in the cerebral cortex and the hippocampus.

Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor with poor prognosis. Currently, its mechanism of pathogenesis remains elusive. In this study, we employed integrative -omics approaches (RNA-seq, ChIP-seq, and MNase-seq) to interrogate the epigenome in ICC. The results of ChIP-seq, differential gene expression, and MNase-seq analyses suggested that ICC cell lines exhibit less histone modification of cancer-related genes compared with a normal cell line. The region of different histone modification patterns is enrichment in sites of AP-1 motif. Subsequent analysis showed that ICC had different nucleosome occupancy in differentially expressed genes compared to a normal cell line. Furthermore, we found that AP-1 plays a key role in ICC and regulates ICC-related genes through its AP-1 binding site. This study is the first report showing the global features of histone modification, transcript, and nucleosome profiles in ICC; we also show that the transcription factor AP-1 might be a key target gene in ICC. Funding Statement: This work was supported by granted from the National Natural Science Foundation of China (No. 81071990, No. 81641110); Science and Technology Program of Guangzhou, China (No. 201707010305); Youth Science Foundation of Guangdong Second Provincial General Hospital (No. YQ2016-001). Declaration of Interests: The authors declare that they have no conflicts of interest.

BackgroundVarious apolipoproteins widely distributed among vertebrata play key roles in lipid metabolism and have a direct correlation with human diseases as diagnostic markers. However, the evolutionary progress of apolipoproteins in species remains unclear. Nine human apolipoproteins and well-annotated genome data of 30 species were used to identify 210 apolipoprotein family members distributed among species from fish to humans. Our study focused on the evolution of nine exchangeable apolipoproteins (ApoA-I/II/IV/V, ApoC-I~IV and ApoE) fromChondrichthyes,Holostei,Teleostei,Amphibia,Sauria (includingAves),Prototheria,MarsupialiaandEutheria.ResultsIn this study, we reported the overall distribution and the frequent gain and loss evolutionary events of apolipoprotein family members in vertebrata. Phylogenetic trees of orthologous apolipoproteins indicated evident divergence between species evolution and apolipoprotein phylogeny. Successive gain and loss events were found by evaluating the presence and absence of apolipoproteins in the context of species evolution. For example, only ApoA-I and ApoA-IV occurred in cartilaginous fish as ancient apolipoproteins. ApoA-II, ApoE, and ApoC-I/ApoC-II were found inHolostei, Coelacanthiformes, andTeleostei,respectively, but the latter three apolipoproteins were absent fromAves. ApoC-I was also absent fromCetartiodactyla. The apolipoprotein ApoC-III emerged in terrestrial animals, and ApoC-IV first arose inEutheria. The results indicate that the order of the emergence of apolipoproteins is most likely ApoA-I/ApoA-IV, ApoE, ApoA-II, ApoC-I/ApoC-II, ApoA-V, ApoC-III, and ApoC-IV.ConclusionsThis study reveals not only the phylogeny of apolipoprotein family members in species fromChondrichthyestoEutheriabut also the occurrence and origin of new apolipoproteins. The broad perspective of gain and loss events and the evolutionary scenario of apolipoproteins across vertebrata provide a significant reference for the research of apolipoprotein function and related diseases.

Background Lymphoma is accompanied by multiple immune functions impairment. Cytokines play an important role in a variety of immune-related functions and affect the tumor microenvironment. However, the exact regulatory mechanisms between them remains unclear. This study aimed to explore the cytokines expression and function in Hodgkin's lymphoma (HL), diffuse large B-cell lymphoma (DLBCL) and mantle cell lymphoma (MCL). Results We performed a transcriptome integration analysis of 14 lymphoma datasets including 240 Hodgkin's lymphoma, 891 diffuse large B-cell lymphoma, 216 mantle cell lymphoma, and 64 health samples. The results showed that multiple immune function and signal pathway damage were shared by all the three types of lymphoma, and these functions were related to cytokines. Furthermore, through co-expression network and functional interaction network analysis, we identified CXCL14 as a key regulator and it affects cell chemotaxis and migration functions. Functional experiment showed that CXCL14 knockdown inhibited cell migration in MCL cell lines. Conclusions High expression of CXCL14 may aggravate MCL via promote cell migration. Our findings provide novel insights into the biology of MCL and would be helpful for the pathogenesis study and drug discovery of lymphomas.

From the perspectives of humanity, ethics and ecology, an analysis is made in terms of the text, theme, and moral of the novel The Lord of Flies. It is argued that the dual confrontation between the good and the evil, and that between civilization and brutality contains a profound thesis that the evil of the humanity is the very source of its own destruction. Meanwhile, “Natural Selection” is not the whole of the natural and human history. One cannot abuse the natural resources regardless the interests of others, ignoring the natural laws. Human beings’ fighting each other, resorting to war will greatly accelerate the process of their destruction. The novel is mirroring the real world of the time and predicting the future. As a responsible writer, Golding puts forward his alert: our environment and the survival of mankind are seriously threatened.

Burkitt’s lymphoma (BURK), diffuse large B-cell lymphoma (DLBCL) and mantle cell lymphoma (MCL) are three main types of B-cell lymphomas. This study aimed to compare the differences of affected biological functions and pathways, as well as to explore the possible regulatory mechanisms and the potential therapeutic targets in BURK, DLBCL and MCL. We performed an integrated analysis of 10 lymphoma datasets including 352 BURK patients, 880 DLBCL patients, 216 MCL patients, and 33 controls. Our results showed that signaling pathways, amino acid metabolism and several lipid metabolism pathways varies considerably among these three types of lymphoma. Furthermore, we identified several key transcription factors (TFs) and their target genes that may promote these diseases by influencing multiple carcinogenic pathways. Among these TFs, we reported first that E2F8 displayed the most significant effects in BURK and MCL. Our results demonstrate that over-expression of E2F8 activates target genes that may promote cell cycle, mitosis, immune and other cancer related functions in BURK and MCL. Therefore, we suggest that E2F8 could be used as a biomarker and potential therapeutic target for BURK and MCL. These findings would be helpful in the study of pathogenesis, and drug discovery and also in the prognosis of B cell lymphomas.

We had previously identified that the co-expression of transmembrane CXCL16 (TM-CXCL16) and its receptor CXCR6 is an independent risk factor for poor survival in patients with diffuse large B-cell lymphoma (DLBCL). However, the impact of the soluble form of CXCL16 (sCXCL16) on the pathogenesis of DLBCL remains unknown. In the present study, the synergistic effect of sCXCL16 and tumor necrosis factor α (TNF-α) on apoptosis in DLBCL cell lines (OCI-LY8 and OCI-LY10) was investigated in vitro. sCXCL16 reinforced TNF-α-mediated inhibition of DLBCL cell proliferation, as determined by the cell counting kit-8 assay. The results of annexin V staining showed that sCXCL16 enhanced TNF-α-induced apoptosis in OCI-LY8 and OCI-LY10 cells through a death receptor-caspase signaling pathway. The results of gene microarray suggested a significant upregulation of differentially expressed genes in the TNF signaling pathway. sCXCL16 increased the concentration of extracellular TNF-α by binding to CXCR6 to activate the nuclear factor-κB (NF-κB) signaling pathway. TNF-α also induced the secretion of sCXCL16 by increasing the expression of ADAM10, which is known to cleave TM-CXCL16 to yield sCXCL16. Moreover, bioinformatics analysis revealed that elevated TNF-α and ADAM10 expression levels in tumor tissues predicted better survival in patients with DLBCL. Thus, our study suggests that sCXCL16 enhances TNF-α-induced apoptosis of DLBCL cells, which may involve a positive feedback loop consisting of TNF-α, ADAM10, sCXCL16, and members of the NF-κB pathway. sCXCL16 and TNF-α may be used as prognostic markers in the clinic, and their combinational use is a promising approach in the context of DLBCL therapy.