A method for predicting drugs that can boost the efficacy of immune checkpoint blockade.

Combination therapy is a promising therapeutic strategy to enhance the efficacy of immune checkpoint blockade (ICB); however, predicting drugs for effective combination is challenging. Here we developed a general data-driven method called CM-Drug for screening compounds that can boost ICB treatment efficacy based on core and minor gene sets identified between responsive and nonresponsive samples in ICB therapy. The CM-Drug method was validated using melanoma and lung cancer mouse models, with combined therapeutic efficacy demonstrated in eight of nine predicted compounds. Among these compounds, taltirelin had the strongest synergistic effect. Mechanistic analysis and experimental verification demonstrated that taltirelin can stimulate CD8+ T cells and is mediated by the induction of thyroid-stimulating hormone. This study provides an effective and general method for predicting and evaluating drugs for combination therapy and identifies candidate compounds for future ICB combination therapy.

GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels.

Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify potential biological insights on the gene set level. However, platforms exploring cancer multi-omics data using GSA methods are lacking. In this study, we upgraded our GSCALite to GSCA (gene set cancer analysis, http://bioinfo.life.hust.edu.cn/GSCA) for cancer GSA at genomic, pharmacogenomic and immunogenomic levels. In this improved GSCA, we integrated expression, mutation, drug sensitivity and clinical data from four public data sources for 33 cancer types. We introduced useful features to GSCA, including associations between immune infiltration with gene expression and genomic variations, and associations between gene set expression/mutation and clinical outcomes. GSCA has four main functional modules for cancer GSA to explore, analyze and visualize expression, genomic variations, tumor immune infiltration, drug sensitivity and their associations with clinical outcomes. We used case studies of three gene sets: (i) seven cell cycle genes, (ii) tumor suppressor genes of PI3K pathway and (iii) oncogenes of PI3K pathway to prove the advantage of GSCA over single gene analysis. We found novel associations of gene set expression and mutation with clinical outcomes in different cancer types on gene set level, while on single gene analysis level, they are not significant associations. In conclusion, GSCA is a user-friendly web server and a useful resource for conducting hypothesis tests by using GSA methods at genomic, pharmacogenomic and immunogenomic levels.

lncRNASNP v3: an updated database for functional variants in long non-coding RNAs.

Long non-coding RNAs (lncRNAs) act as versatile regulators of many biological processes and play vital roles in various diseases. lncRNASNP is dedicated to providing a comprehensive repository of single nucleotide polymorphisms (SNPs) and somatic mutations in lncRNAs and their impacts on lncRNA structure and function. Since the last release in 2018, there has been a huge increase in the number of variants and lncRNAs. Thus, we updated the lncRNASNP to version 3 by expanding the species to eight eukaryotic species (human, chimpanzee, pig, mouse, rat, chicken, zebrafish, and fruitfly), updating the data and adding several new features. SNPs in lncRNASNP have increased from 11 181 387 to 67 513 785. The human mutations have increased from 1 174 768 to 2 387 685, including 1 031 639 TCGA mutations and 1 356 046 CosmicNCVs. Compared with the last release, updated and new features in lncRNASNP v3 include (i) SNPs in lncRNAs and their impacts on lncRNAs for eight species, (ii) SNP effects on miRNA-lncRNA interactions for eight species, (iii) lncRNA expression profiles for six species, (iv) disease & GWAS-associated lncRNAs and variants, (v) experimental & predicted lncRNAs and drug target associations and (vi) SNP effects on lncRNA expression (eQTL) across tumor & normal tissues. The lncRNASNP v3 is freely available at http://gong_lab.hzau.edu.cn/lncRNASNP3/.

EVAtlas: a comprehensive database for ncRNA expression in human extracellular vesicles.

Extracellular vesicles (EVs) packing various molecules play vital roles in intercellular communication. Non-coding RNAs (ncRNAs) are important functional molecules and biomarkers in EVs. A comprehensive investigation of ncRNAs expression in EVs under different conditions is a fundamental step for functional discovery and application of EVs. Here, we curated 2030 small RNA-seq datasets for human EVs (1506 sEV and 524 lEV) in 24 conditions and over 40 diseases. We performed a unified reads dynamic assignment algorithm (RDAA) considering mismatch and multi-mapping reads to quantify the expression profiles of seven ncRNA types (miRNA, snoRNA, piRNA, snRNA, rRNA, tRNA and Y RNA). We constructed EVAtlas (http://bioinfo.life.hust.edu.cn/EVAtlas), a comprehensive database for ncRNA expression in EVs with four functional modules: (i) browse and compare the distribution of ncRNAs in EVs from 24 conditions and eight sources (plasma, serum, saliva, urine, sperm, breast milk, primary cell and cell line); (ii) prioritize candidate ncRNAs in condition related tissues based on their expression; (iii) explore the specifically expressed ncRNAs in EVs from 24 conditions; (iv) investigate ncRNA functions, related drugs, target genes and EVs isolation methods. EVAtlas contains the most comprehensive ncRNA expression in EVs and will be a key resource in this field.

ImmuCellAI-mouse: a tool for comprehensive prediction of mouse immune cell abundance and immune microenvironment depiction.

MOTIVATION: Immune cells are important components of the immune system and are crucial for disease initiation, progression, prognosis and survival. Although several computational methods have been designed for predicting the abundance of immune cells, very few tools are applicable to mouse. Given that, mouse is the most widely used animal model in biomedical research, there is an urgent need to develop a precise algorithm for predicting mouse immune cells. RESULTS: We developed a tool named Immune Cell Abundance Identifier for mouse (ImmuCellAI-mouse), for estimating the abundance of 36 immune cell (sub)types from gene expression data in a hierarchical strategy of three layers. Reference expression profiles and robust marker gene sets of immune cell types were curated. The abundance of cells in three layers was predicted separately by calculating the ssGSEA enrichment score of the expression deviation profile per cell type. Benchmark results showed high accuracy of ImmuCellAI-mouse in predicting most immune cell types, with correlation coefficients between predicted value and real cell proportion of most cell types being larger than 0.8. We applied ImmuCellAI-mouse to a mouse breast tumor dataset and revealed the dynamic change of immune cell infiltration during treatment, which is consistent with the findings of the original study but with more details. We also constructed an online server for ImmuCellAI-mouse, on which users can upload expression matrices for analysis. ImmuCellAI-mouse will be a useful tool for studying the immune microenvironment, cancer immunology and immunotherapy in mouse models, providing an indispensable supplement for human disease studies. AVAILABILITY AND IMPLEMENTATION: Software is available at http://bioinfo.life.hust.edu.cn/ImmuCellAI-mouse/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

mTOR-dependent TFEB activation and TFEB overexpression enhance autophagy-lysosome pathway and ameliorate Alzheimer's disease-like pathology in diabetic encephalopathy.

BACKGROUND: Diabetic encephalopathy (DE) is a complication of type 2 diabetes mellitus (T2DM) that features Alzheimer's disease (AD)-like pathology, which can be degraded by the autophagy-lysosome pathway (ALP). Since transcription factor EB (TFEB) is a master regulator of ALP, TFEB-mediated ALP activation might have a therapeutic effect on DE, but this has yet to be investigated. METHODS: We established T2DM mouse models and cultured HT22 cells under high-glucose (HG) conditions to confirm the role of ALP in DE. To further investigate this, both mice and HT22 cells were treated with 3-methyladenine (3-MA). We also analyzed the content of TFEB in the nucleus and cytoplasm to evaluate its role in ALP. To confirm the effect of TFEB activation at the post-translational level in DE, we used rapamycin to inhibit the mechanistic target of rapamycin (mTOR). We transduced both mice and cells with TFEB vector to evaluate the therapeutic effect of TFEB overexpression on DE. Conversely, we conducted TFEB knockdown to verify its role in DE in another direction. RESULTS: We found that T2DM mice experienced compromised cognitive function, while HG-cultured HT22 cells exhibited increased cell apoptosis. Additionally, both T2DM mice and HG-cultured HT22 cells showed impaired ALP and heavier AD-like pathology. This pathology worsened after treatment with 3-MA. We also observed decreased TFEB nuclear translocation in both T2DM mice and HG-cultured HT22 cells. However, inhibiting mTOR with rapamycin or overexpressing TFEB increased TFEB nuclear translocation, enhancing the clearance of ALP-targeted AD-like pathology. This contributed to protection against neuronal apoptosis and alleviation of cognitive impairment. Conversely, TFEB knockdown lessened ALP-targeted AD-like pathology clearance and had a negative impact on DE. CONCLUSION: Our findings suggest that impaired ALP is responsible for the aggravation of AD-like pathology in T2DM. We propose that mTOR-dependent TFEB activation and TFEB overexpression are promising therapeutic strategies for DE, as they enhance the clearance of ALP-targeted AD-like pathology and alleviate neuronal apoptosis. Our study provides insight into the underlying mechanisms of DE and offers potential avenues for the development of new treatments for this debilitating complication of T2DM. Video abstract.

The inhibition of Aurora A kinase regulates phospholipid remodeling by upregulating LPCAT1 in glioblastoma.

Metabolic reprogramming is a common feature of glioblastoma (GBM) progression and metastasis. Altered lipid metabolism is one of the most prominent metabolic alterations in cancer. Understanding the links between phospholipid remodeling and GBM tumorigenesis may help develop new anticancer strategies and improve treatments to overcome drug resistance. We used metabolomic and transcriptomic analyses to systematically investigate metabolic and molecular changes in low-grade glioma (LGG) and GBM. We then re-established the reprogrammed metabolic flux and membrane lipid composition in GBM based on metabolomic and transcriptomic analyses. By inhibiting Aurora A kinase via RNA interference (RNAi) and inhibitor treatment, we investigated the effect of Aurora A kinase on phospholipid reprogramming LPCAT1 enzyme expression and GBM cell proliferation in vitro and in vivo. We found that GBM displayed aberrant glycerophospholipid and glycerolipid metabolism compared with LGG. Metabolic profiling indicated that fatty acid synthesis and uptake for phospholipid synthesis were significantly increased in GBM compared to LGG. The unsaturated phosphatidylcholine (PC) and phosphatidylethanolamine (PE) levels were significantly decreased in GBM compared to LGG. The expression level of LPCAT1, which is required for the synthesis of saturated PC and PE, was upregulated in GBM, and the expression of LPCAT4, which is required for the synthesis of unsaturated PC and PE, was downregulated in GBM. Notably, the inhibition of Aurora A kinase by shRNA knockdown and treatment with Aurora A kinase inhibitors such as Alisertib, AMG900, or AT9283 upregulated LPCAT1 mRNA and protein expression in vitro. In vivo, the inhibition of Aurora A kinase with Alisertib increased LPCAT1 protein expression. Phospholipid remodeling and a reduction in unsaturated membrane lipid components were found in GBM. Aurora A kinase inhibition increased LPCAT1 expression and suppressed GBM cell proliferation. The combination of Aurora kinase inhibition with LPCAT1 inhibition may exert promising synergistic effects on GBM.

Anxiety prevalence and its association with physical activity in patients with non-communicable diseases during COVID-19 lockdown: a cross-sectional study in Shanghai, China.

BACKGROUND: Quarantine due to the COVID-19 pandemic may have created great psychological stress among vulnerable populations. We aimed to investigate the prevalence of anxiety and explore the association between physical activities (PA) and anxiety risk in people with non-communicable diseases during the period of COVID-19 lockdown. METHODS: We conducted a cross-sectional telephone survey from February 25 to April 20, 2020, the period of COVID-19 lockdown in Shanghai. Up to 8000 patients with type 2 diabetes and/or hypertension were selected using multi-stage cluster random sampling. PA level was measured based on the International Physical Activity Questionnaire using Metabolic Equivalent for Task scores, while symptoms of anxiety were assessed by the 7-item Generalized Anxiety Disorder scale. Multiple logistic regression analyses were performed to evaluate the associations of type and level of PA with the risk of anxiety. RESULTS: Of a total 4877 eligible patients, 2602 (53.4%) reported with anxiety, and 2463 (50.5%), 123 (2.5%) and 16 (0.3%) reported with mild, moderate, and severe anxiety. The prevalence of anxiety was higher in the females, the elders, non-smokers, non-drinkers, and patients with diabetes, and the associations of anxiety with sex, age, smoking, drinking and diagnosis of diabetes were significant. A significant negative association was observed for housework activities (OR 0.53, 95%CI: [0.45, 0.63], p < 0.001) and trip activities (OR 0.55, 95%CI: [0.48, 0.63], p < 0.001) with anxiety, but no significant was found for exercise activities (OR 1.06, 95%CI: [0.94, 1.20], p = 0.321). Compared with patients with a low PA level, those with a moderate (OR 0.53, 95%CI: [0.44, 0.64], p < 0.001) or a high PA level (OR 0.51, 95%CI: [0.43, 0.51], p < 0.001) had a lower prevalence of anxiety. CONCLUSION: This study demonstrates a higher prevalence of anxiety in patients with hypertension, diabetes, or both during the COVID-19 lockdown. The negative associations of housework and trip activities with anxiety highlight the potential benefit of PA among patients with non-communicable diseases.

Plasma Aß as a biomarker for predicting Aß-PET status in Alzheimer's disease：a systematic review with meta-analysis.

OBJECTIVE: Amyloid-ß positron emission tomography (Aß-PET) scan has been proposed to detect amyloid-ß (Aß) deposition in the brain. However, this approach is costly and not ideal for the early diagnosis of Alzheimer's disease. Blood-based Aß measurement offers a scalable alternative to the costly or invasive biomarkers. The aim of this study was to statistically validate whether plasma Aß could predict Aß-PET status via meta-analysis. METHODS: We systematically searched for eligible studies from PubMed, Embase and Cochrane Library, which reported plasma Aß levels of amyloid-ß positron emission tomography-positive (PET (+)) and amyloid-ß positron emission tomography-negative (PET (-)) subjects. We generated pooled estimates using random effects meta-analyses. For any study that has significant heterogeneity, metaregression and subgroup analysis were further conducted. Publication bias was appraised by funnel plots and Egger's test. RESULTS: 16 studies with 3047 participants were included in the meta-analysis. Among all the enrolled studies, 10 studies reported plasma Aß40 values, while 9 studies reported plasma Aß42 values and 13 studies reported Aß42/Aß40 ratio. The pooled standardised mean difference (SMD) was 0.76 (95% CI -0.61 to 2.14, p=0.28) in the plasma Aß40 values group. Plasma Aß42 values group has a pooled SMD of -0.60 (95% CI -0.80 to -0.41, p<0.0001). In the plasma Aß42/Aß40 ratio group, the pooled SMD was -1.44 (95% CI -2.17 to -0.72, p<0.0001). CONCLUSION: Plasma Aß40 values might not distinguish between PET (+) and PET (-) people. However, plasma Aß42 values and plasma Aß42/Aß40 ratio could be served as independent biomarkers for predicting Aß-PET status.

Prognostic significance of visit-to-visit variability, and maximum and minimum LDL cholesterol in diabetes mellitus.

BACKGROUND: Current guidelines for dyslipidemia management recommend that the LDL-C goal be lower than 70 mg/dL. The present study investigated the prognostic significance of visit-to-visit variability in LDL-C, and minimum and maximum LDL-C during follow-up in diabetes mellitus. METHODS: The risk of outcomes in relation to visit-to-visit LDL-C variability was investigated in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Lipid trial. LDL-C variability indices were coefficient of variation (CV), variability independent of the mean (VIM), and average real variability (ARV). Multivariable Cox proportional hazards models were employed to estimate the adjusted hazard ratio (HR) and 95% confidence interval (CI). RESULTS: Compared with the placebo group (n=2667), the fenofibrate therapy group (n=2673) had a significantly (P<0.01) lower mean plasma triglyceride (152.5 vs. 178.6 mg/dL), and total cholesterol (158.3 vs.162.9 mg/dL) but a similar mean LDL-C during follow-up (88.2 vs. 88.6 mg/dL, P>0.05). All three variability indices were associated with primary outcome, total mortality and cardiovascular mortality both in the total population and in the fenofibrate therapy group but only with primary outcome in the placebo group. The minimum LDL-C but not the maximum during follow-up was significantly associated with various outcomes in the total population, fenofibrate therapy and placebo group. The minimum LDL-C during follow-up ≥70 mg/dL was associated with an increased risk for various outcomes. CONCLUSIONS: Visit-to-visit variability in LDL-C was a strong predictor of outcomes, independent of mean LDL-C. Patients with LDL-C controlled to less than 70 mg/dL during follow-up might have a benign prognosis. ClinicalTrials.gov number: NCT00000620.

SNP2APA: a database for evaluating effects of genetic variants on alternative polyadenylation in human cancers.

Alternative polyadenylation (APA) is an important post-transcriptional regulation that recognizes different polyadenylation signals (PASs), resulting in transcripts with different 3' untranslated regions, thereby influencing a series of biological processes and functions. Recent studies have revealed that some single nucleotide polymorphisms (SNPs) could contribute to tumorigenesis and development through dysregulating APA. However, the associations between SNPs and APA in human cancers remain largely unknown. Here, using genotype and APA data of 9082 samples from The Cancer Genome Atlas (TCGA) and The Cancer 3'UTR Altas (TC3A), we systematically identified SNPs affecting APA events across 32 cancer types and defined them as APA quantitative trait loci (apaQTLs). As a result, a total of 467 942 cis-apaQTLs and 30 721 trans-apaQTLs were identified. By integrating apaQTLs with survival and genome-wide association studies (GWAS) data, we further identified 2154 apaQTLs associated with patient survival time and 151 342 apaQTLs located in GWAS loci. In addition, we designed an online tool to predict the effects of SNPs on PASs by utilizing PAS motif prediction tool. Finally, we developed SNP2APA, a user-friendly and intuitive database (http://gong_lab.hzau.edu.cn/SNP2APA/) for data browsing, searching, and downloading. SNP2APA will significantly improve our understanding of genetic variants and APA in human cancers.

SEGreg: a database for human specifically expressed genes and their regulations in cancer and normal tissue.

Human specifically expressed genes (SEGs) usually serve as potential biomarkers for disease diagnosis and treatment. However, the regulation underlying their specific expression remains to be revealed. In this study, we constructed SEG regulation database (SEGreg; available at http://bioinfo.life.hust.edu.cn/SEGreg) for showing SEGs and their transcription factors (TFs) and microRNA (miRNA) regulations under different physiological conditions, which include normal tissue, cancer tissue and cell line. In total, SEGreg collected 6387, 1451, 4506 and 5320 SEGs from expression profiles of 34 cancer types and 55 tissues of The Cancer Genome Atlas, Cancer Cell Line Encyclopedia, Human Body Map and Genotype-Tissue Expression databases/projects, respectively. The cancer or tissue corresponding expressed miRNAs and TFs were identified from miRNA and gene expression profiles, and their targets were collected from several public resources. Then the regulatory networks of all SEGs were constructed and integrated into SEGreg. Through a user-friendly interface, users can browse and search SEGreg by gene name, data source, tissue, cancer type and regulators. In summary, SEGreg is a specialized resource to explore SEGs and their regulations, which provides clues to reveal the mechanisms of carcinogenesis and biological processes.

FFLtool: a web server for transcription factor and miRNA feed forward loop analysis in human.

SUMMARY: Transcription factors (TFs) and microRNAs (miRNAs) are two kinds of important regulators for transcriptional and post-transcriptional regulations. Understanding cross-talks between the two regulators and their targets is critical to reveal complex molecular regulatory mechanisms. Here, we developed FFLtool, a web server for detecting potential feed forward loop (FFL) of TF-miRNA-target regulation in human. In FFLtool, we integrated comprehensive regulations of TF-target and miRNA-target, and developed two functional modules: (i) The 'FFL Analysis' module can detect potential FFLs and internal regulatory networks in a user-defined gene set. FFLtool also provides three levels of evidence to illustrate the reliability for each FFL and enrichment functions for co-target genes of the same TF and miRNA; (ii) The 'Browse FFLs' module displays FFLs comprised of differentially or specifically expressed TFs and miRNAs and their target genes in cancers. FFLtool is a valuable resource for investigating gene expression regulation and mechanism study in biological processes and diseases. AVAILABILITY AND IMPLEMENTATION: FFLtool is available on http://bioinfo.life.hust.edu.cn/FFLtool/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Agrin promotes the proliferation, invasion and migration of rectal cancer cells via the WNT signaling pathway to contribute to rectal cancer progression.

Rectal cancer is the most common malignant tumor in the digestive system with rapidly metastasis and highly recurrence. Agrin (AGRN) is a proteoglycan involving in a large number of human cancers. However, how AGRN regulates the progression of rectal cancer remains largely unknown. We aimed to determine the biological role of AGRN and its mechanism in rectal cancer. AGRN expression in rectal cancer tissues was detected based on TCGA. The survival curve was plotted using the Kaplan-Meier method. qRT-PCR and western blot were utilized to examine the expression level of AGRN in cells. Cell proliferation, clonogenic ability, invasion, and migration of rectal cancer cells were analyzed by CCK-8, colony formation and transwell experiments. GSEA was employed for the analysis of the potential pathways-related with AGRN in rectal cancer. The activity of WNT pathway was determined by western blot. AGRN expression was dramatically increased in rectal cancer, and its up-regulation was associated with poorer prognosis of rectal cancer patients. AGRN expression was an independent factor for the prognosis of rectal cancer. AGRN inhibition suppressed rectal cancer cell growth, invasion, and migration, whereas AGRN overexpression facilitated these behaviors of rectal cancer cells in vitro. Mechanistically, WNT signaling pathway was enriched in high AGRN-expressing patients with rectal cancer. AGRN elevated the activity of WNT pathway through increasing Cyclin D1, C-Myc, p-GSK-3ß, and p-ß-catenin expression. Our present study indicated that AGRN might function as an oncogenic indicator in rectal cancer via activating the WNT pathway, which would help develop optimized therapeutic therapies for rectal cancer.

Indole Alkaloids from a Soil-Derived Clonostachys rosea.

Clonorosins A (1) and B (2), two novel indole alkaloids featuring unprecedented 6/5/6/6/5 and 6/5/5 cores, together with seven known indole-linked 2,5-diketopiperazine alkaloids (3-9), were isolated from the soil-derived fungus Clonostachys rosea YRS-06. The new structures were proposed through HR-MS, NMR, and ECD spectroscopic data. They were established by comparing the calculated NMR, ECD, and specific rotation data with the experimental. To assist in determining the absolute configuration of the chiral carbon in the side chain of 2,5-diketopiperazine derivatives, flexible analogues 3i-3iv were synthesized and analyzed. 1 was active against Fusarium oxysporum with an MIC value of 50 µg/mL. 7 and 8 showed excellent activity against human HeLa and HepG2 cells with IC50 values of 0.12-0.60 µM.

AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors.

The Animal Transcription Factor DataBase (AnimalTFDB) is a resource aimed to provide the most comprehensive and accurate information for animal transcription factors (TFs) and cofactors. The AnimalTFDB has been maintained and updated for seven years and we will continue to improve it. Recently, we updated the AnimalTFDB to version 3.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB/) with more data and functions to improve it. AnimalTFDB contains 125,135 TF genes and 80,060 transcription cofactor genes from 97 animal genomes. Besides the expansion in data quantity, some new features and functions have been added. These new features are: (i) more accurate TF family assignment rules; (ii) classification of transcription cofactors; (iii) TF binding sites information; (iv) the GWAS phenotype related information of human TFs; (v) TF expressions in 22 animal species; (vi) a TF binding site prediction tool to identify potential binding TFs for nucleotide sequences; (vii) a separate human TF database web interface (HumanTFDB) was designed for better utilizing the human TFs. The new version of AnimalTFDB provides a comprehensive annotation and classification of TFs and cofactors, and will be a useful resource for studies of TF and transcription regulation.

EVmiRNA: a database of miRNA profiling in extracellular vesicles.

Extracellular vesicles (EVs), such as exosomes and microvesicles, acted as cell-to-cell communication vectors and potential biomarkers for diseases. microRNAs (miRNAs) are the most well studied molecules in EVs, thus a comprehensive investigation of miRNA expression profiles in EVs will be helpful to explore their functions and biomarkers. We curated 462 small RNA sequencing samples of EVs from 17 sources/diseases and constructed the EVmiRNA database (http://bioinfo.life.hust.edu.cn/EVmiRNA) to show the miRNA expression profiles. We found >1000 miRNAs expressed in these EVs and detected specific miRNAs for EVs of each source/disease. EVmiRNA provides three functional modules: (i) the miRNA expression profiles and the sample information of EVs from different sources (such as blood, breast milk etc.); (ii) the specifically expressed miRNAs in different EVs that would be helpful for biomarker identification; (iii) the miRNA annotations including the miRNA expression in EVs and TCGA cancer types, miRNA pathway regulations as well as miRNA function and publications. EVmiRNA has a user-friendly web interface with powerful browse and search functions, as well as data downloading. It is the first database focusing on miRNA expression profiles in EVs and will be useful for the research and application community of EV biomarker, miRNA function and liquid biopsy.

[Rapid discovery of tyrosinase inhibitors from Sophora flavescens:significance for comprehensive utilization of its non-medicinal resources].

Sophorae Flavescentis Radix,derived from the root of Sophora flavescens in the Leguminosae family,has been widely used in the medicine,agriculture,animal husbandry,and daily chemical industry. A pharmacophore model-based method for rapid discovery of tyrosinase inhibitors( TIs) from S. flavescens was established by molecular docking under Lipinski rules,and verified by enzyme assays. Briefly,the chemical constituent database of S. flavescens( CDSF) was established based on the previous papers. Theoptimal pharmacophore model( OPM) was constructed by DS 2019 on the basis of known active TIs. Eighty-three hits predominated by flavonoids having higher fitting scores with OPM than the positive control were screened out,and subjected to molecular docking based on the three-dimensional structure of tyrosinase crystal protein. The potential TIs such as kurarinone and nor-kurarinone were rapidly discovered from the compounds with higher docking scores than the positive control under the Lipinski rules. The results were verified by the in vitro enzyme assays. The inhibition activities of tyrosinase from non-medicinal parts of S. flavescens were also tested to explore the relationship between the inhibition activity and chemical compositions. This study is expected to provide data support for the comprehensive application and development of S. flavescens and also a new method for the rapid discovery of active substances or functional constituents in the complex systems.

Crystal structure of ClA1, a type of chlorinase from soil bacteria.

Halogenated compounds are widely discovered in nature, and many of them exhibit biological activities, such as an important chlorinated natural product salinosporamide A serving as a potential anticancer agent. Compared with bromination, iodination and fluorination, chlorination is the mainly important modification. To shed light on the mechanism of SAM-dependent chlorinases, a recombinant chlorinase ClA1 was expressed in Escherichia coli and further purified for crystallization and X-ray diffraction experiments. The flake crystals of ClA1 were able to diffract to a resolution of 1.85 Å. The crystals belonged to space group R3, with unit-cell parameters α = ß = 90.0°, γ = 120.0°. By determining the structure of ClA1, it is revealed that the side chain of Arg242 in ClA1 may have contacts with the L-Met. However, in SalL the equivalent Arg243's side chain is far from L-Met. Considering the ClA1 and SalL are from different environments and their enzyme kinetics are quite different, it is suggested that the side chain conformation differences of the conserved arginine are possibly related with the enzyme activity differences of the two chlorinases.

lncRNASNP2: an updated database of functional SNPs and mutations in human and mouse lncRNAs.

Long non-coding RNAs (lncRNAs) are emerging as important regulators in different biological processes through various ways. Because the related data, especially mutations in cancers, increased sharply, we updated the lncRNASNP to version 2 (http://bioinfo.life.hust.edu.cn/lncRNASNP2). lncRNASNP2 provides comprehensive information of SNPs and mutations in lncRNAs, as well as their impacts on lncRNA structure and function. lncRNASNP2 contains 7260238 SNPs on 141353 human lncRNA transcripts and 3921448 SNPs on 117405 mouse lncRNA transcripts. Besides the SNP information in the first version, the following new features were developed to improve the lncRNASNP2. (i) noncoding variants from COSMIC cancer data (859534) in lncRNAs and their effects on lncRNA structure and function; (ii) TCGA cancer mutations (315234) in lncRNAs and their impacts; (iii) lncRNA expression profiling of 20 cancer types in both tumor and its adjacent samples; (iv) expanded lncRNA-associated diseases; (v) optimized the results about lncRNAs structure change induced by variants; (vi) reduced false positives in miRNA and lncRNA interaction results. Furthermore, we developed online tools for users to analyze new variants in lncRNA. We aim to maintain the lncRNASNP as a useful resource for lncRNAs and their variants.