New triterpenoids from the aerial parts of the Uygur medicine Salvia deserta.

Phytochemical investigation on the aerial parts of Salvia deserta led to the isolation of eight new pentacyclic triterpenoids including three oleanane- (1 - 3) and five ursane-type (4 - 8) triterpenoids, whose structures were elucidated based on extensive spectroscopic analysis and quantum chemical calculation. Weak immunosuppressive potency was observed for compounds 1, 2, and 4 - 8 via inhibiting the secretion of cytokines TNF-α and IL-6 in LPS-induced macrophages RAW264.7 at 20 µM. In addition, compounds 1, 2, and 4 - 6 exhibited moderate protective activity on t-BHP-induced oxidative injury in HepG2 cells.

Clerodane diterpenoids from the Uygur medicine Salvia deserta with immunosuppressive activity.

Six undescribed clerodane diterpenoids along with five known ones were isolated from the aerial parts of Salvia deserta, a traditional Uygur medicine. Their chemical structures including absolute configurations were elucidated by extensive spectroscopic analysis (including 1D and 2D NMR, HRESIMS, and IR), combined with calculated ECD method and single-crystal X-ray diffraction analysis. All the compounds possessed a terminal α,ß-unsaturated-γ-lactone moiety, and were assayed for their immunosuppressive activity via inhibiting the secretion of cytokines TNF-α and IL-6 in macrophages RAW264.7. Among them, (5R,8R,9S,10R)-18-nor-cleroda-2,13-dien-16,15-olide-4-one obviously suppressed the secretion of TNF-α and IL-6 with IC50 values of 8.55 and 13.65 µM, respectively.

Artificial intelligence and its applications in digital hematopathology.

The advent of whole-slide imaging, faster image data generation, and cheaper forms of data storage have made it easier for pathologists to manipulate digital slide images and interpret more detailed biological processes in conjunction with clinical samples. In parallel, with continuous breakthroughs in object detection, image feature extraction, image classification and image segmentation, artificial intelligence (AI) is becoming the most beneficial technology for high-throughput analysis of image data in various biomedical imaging disciplines. Integrating digital images into biological workflows, advanced algorithms, and computer vision techniques expands the biologist's horizons beyond the microscope slide. Here, we introduce recent developments in AI applied to microscopy in hematopathology. We give an overview of its concepts and present its applications in normal or abnormal hematopoietic cells identification. We discuss how AI shows great potential to push the limits of microscopy and enhance the resolution, signal and information content of acquired data. Its shortcomings are discussed, as well as future directions for the field.

The Clinical Application Value of the Prognostic Nutritional Index for the Overall Survival Prognosis of Patients with Esophageal Cancer: A Robust Real-World Observational Study in China.

Esophageal cancer is a kind of cancer with high morbidity and mortality, which is accompanied by a profound poor prognosis. A prognostic nutritional index, based on serum albumin levels and peripheral lymphocyte count, has been confirmed to be significantly associated with various cancers. This study was aimed at exploring the prognostic significance of PNI in the overall survival prognosis of patients with esophageal cancer. As a real-world study based on the big database, clinical data of 2661 patients with esophageal cancer were evaluated retrospectively, and the individuals were randomly divided into training and testing cohorts. In these two cohorts, patients are classified into a high-risk group (PNI < 49) and a low-risk group (PNI ≥ 49). Univariate and multivariate analyses were performed to analyze the independent risk factors for the prognosis of esophageal cancer patients by using the Cox proportional hazards regression model. In this study, whether in the training cohort or the testing cohort, according to the univariate analysis, gender, tumor size, tumor grade, T stage, N stage, M stage, TNM stage, and PNI were significantly correlated with overall survival. Furthermore, the multivariate analysis showed that gender, T stage, N stage, M stage, TNM stage, and PNI were independent prognostic risk factors for esophageal cancer. PNI can be regarded as an independent prognostic factor combined with gender, T stage, N stage, M stage, and TNM stage, and it might be a novel reliable biomarker for esophageal cancer.

ViRBase v3.0: a virus and host ncRNA-associated interaction repository with increased coverage and annotation.

As a means to aid in the investigation of viral infection mechanisms and identification of more effective antivirus targets, the availability of a source which continually collects and updates information on the virus and host ncRNA-associated interaction resources is essential. Here, we update the ViRBase database to version 3.0 (http://www.virbase.org/ or http://www.rna-society.org/virbase/). This update represents a major revision: (i) the total number of interaction entries is now greater than 820,000, an approximately 70-fold increment, involving 116 virus and 36 host organisms, (ii) it supplements and provides more details on RNA annotations (including RNA editing, RNA localization and RNA modification), ncRNA SNP and ncRNA-drug related information and (iii) it provides two additional tools for predicting binding sites (IntaRNA and PRIdictor), a visual plug-in to display interactions and a website which is optimized for more practical and user-friendly operation. Overall, ViRBase v3.0 provides a more comprehensive resource for virus and host ncRNA-associated interactions enabling researchers a more effective means for investigation of viral infections.

RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility.

Establishing an RNA-associated interaction repository facilitates the system-level understanding of RNA functions. However, as these interactions are distributed throughout various resources, an essential prerequisite for effectively applying these data requires that they are deposited together and annotated with confidence scores. Hence, we have updated the RNA-associated interaction database RNAInter (RNA Interactome Database) to version 4.0, which is freely accessible at http://www.rnainter.org or http://www.rna-society.org/rnainter/. Compared with previous versions, the current RNAInter not only contains an enlarged data set, but also an updated confidence scoring system. The merits of this 4.0 version can be summarized in the following points: (i) a redefined confidence scoring system as achieved by integrating the trust of experimental evidence, the trust of the scientific community and the types of tissues/cells, (ii) a redesigned fully functional database that enables for a more rapid retrieval and browsing of interactions via an upgraded user-friendly interface and (iii) an update of entries to >47 million by manually mining the literature and integrating six database resources with evidence from experimental and computational sources. Overall, RNAInter will provide a more comprehensive and readily accessible RNA interactome platform to investigate the regulatory landscape of cellular RNAs.

mRNALocater: Enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy.

The functions of mRNAs are closely correlated with their locations in cells. Knowledge about the subcellular locations of mRNA is helpful to understand their biological functions. In recent years, it has become a hot topic to develop effective computational models to predict eukaryotic mRNA subcellular localizations. However, existing state-of-the-art models still have certain deficiencies in terms of prediction accuracy and generalization ability. Therefore, it is urgent to develop novel methods to accurately predict mRNA subcellular localizations. In this study, a novel method called mRNALocater was proposed to detect the subcellular localization of eukaryotic mRNA by adopting the model fusion strategy. To fully extract information from mRNA sequences, the electron-ion interaction pseudopotential and pseudo k-tuple nucleotide composition were used to encode the sequences. Moreover, the correlation coefficient filtering algorithm and feature forward search technology were used to mine hidden feature information, which guarantees that mRNALocater can be more effectively applied to new sequences. The results based on the independent dataset tests demonstrate that mRNALocater yields promising performances for predicting eukaryotic mRNA subcellular localizations and is a powerful tool in practical applications. A freely available online web server for mRNALocater has been established at http://bio-bigdata.cn/mRNALocater.

Systematic Analysis of Competing Endogenous RNA Networks in Diffuse Large B-Cell Lymphoma and Hodgkin's Lymphoma.

Lymphoma is a systemic malignancy, originating from the lymphatic system, which accounts for 3 to 4% of all tumors. There are two major subtypes of lymphoma, namely, diffuse large B-cell lymphoma (DLBCL) and Hodgkin's lymphoma (HL). Elucidation of the pathogenesis of these two lymphoma types is crucial for the identification of potential therapeutic targets. Compared with the corresponding knowledge of other diseases, the understanding of the regulatory networks involved in DLBCL and HL is relatively deficient. To address this, we comprehensively analyzed the mRNAs, lncRNAs, and miRNAs that were differentially expressed between normal and tumor samples of DLBCL and HL. In addition, functional enrichment analysis of the differentially expressed mRNAs was performed. We constructed two specific ceRNA networks of DLBCL and HL. The pathways enriched by dysregulated mRNAs in DLBCL and HL were mainly involved in immune responses, transcription process, and metabolism process. The ceRNA network analysis revealed that 45 ceRNAs were shared between the two ceRNA networks, including five pivotal lncRNAs (MALAT1, CTBP1-AS, THUMPD3-AS, PSMA3-AS1, and NUTM2A-AS1). In addition, we proposed a DLBCL survival risk model based on a DLBCL-specific network constructed by Lasso regression analysis. The model, which is based on eight mRNAs, exhibited excellent performance in regard to predicting outcomes in DLBCL patients, with a p value of 0.0017 and AUC of 0.9783. In summary, although the molecular mechanisms underlying tumorigenesis in DLBCL and HL were quite different, the same pivotal lncRNAs acted as key regulators. Our findings identify novel potential prognostic and therapeutic targets for DLBCL and HL.

ncPro-ML: An integrated computational tool for identifying non-coding RNA promoters in multiple species.

The promoter is located near the transcription start sites and regulates transcription initiation of the gene. Accurate identification of promoters is essential for understanding the mechanism of gene regulation. Since experimental methods are costly and ineffective, developing efficient and accurate computational tools to identify promoters are necessary. Although a series of methods have been proposed for identifying promoters, none of them is able to identify the promoters of non-coding RNA (ncRNA). In the present work, a new method called ncPro-ML was proposed to identify the promoter of ncRNA in Homo sapiens and Mus musculus, in which different kinds of sequence encoding schemes were used to convert DNA sequences into feature vectors. To test the length effect, for each species, datasets including sequences with different lengths were built. The results demonstrated that ncPro-ML achieved the best performance based on the dataset with the sequence length of 221 nucleotides for human and mouse. The performances of ncPro-ML were also satisfying from both independent dataset test and cross-species test. The results indicate that the proposed predictor can server as a powerful tool for the discovery of ncRNA promoters. In addition, a web-server for ncPro-ML was developed, which can be freely accessed at http://www.bio-bigdata.cn/ncPro-ML/.

MicroRNA-124 Overexpression in Schwann Cells Promotes Schwann Cell-Astrocyte Integration and Inhibits Glial Scar Formation Ability.

Schwann cell (SC) transplantation is a promising approach for the treatment of spinal cord injury (SCI); however, SC grafts show a low migratory capacity within the astrocytic environment, which inevitably hampers their therapeutic efficacy. The purpose of this study was to explore mechanisms to modify the characteristics of SCs and astrocytes (ASs), as well as to adjust the SC-AS interface to break the SC-AS boundary, thus improving the benefits of SCI treatment. We observed that the expression levels of miR-124 in SCs and ASs were significantly lower than those in the normal spinal cord. Furthermore, overexpressing miR-124 in SCs (miR-124-SCs) significantly inhibited gene and protein expression levels of SC-specific markers, such as GFAP and Krox20. The expression of neurotrophic factors, Bdnf and Nt-3, was up-regulated in miR-124-SCs without affecting their proliferation. Further, the boundary assay showed an increased number of miR-124-SCs that had actively migrated and entered the astrocytic region to intermingle with ASs, compared with normal SCs. In addition, although Krox20 protein expression was down-regulated in miR-124-SCs, the luciferase assay showed that Krox20 is not a direct target of miR-124. RNA sequencing of miR-124-SCs revealed seven upregulated and eleven downregulated genes involved in cell migration and motility. Based on KEGG pathway and KOG functional analyses, changes in these genes corresponded to the activation of Hippo, FoxO, and TGF-beta signaling pathways, cytokine-cytokine receptor interactions, and the cell cycle. Finally, co-culturing of miR-124-SCs and ASs in a transwell system revealed that GFAP and p-STAT3 protein expression in ASs was significantly reduced. Collectively, these results show that overexpression of miR-124 in SCs promotes SC-AS integration in vitro and may attenuate the capacity of ASs to form glial scars. Thus, this study provides novel insights into modifying SCs by overexpressing miR-124 to improve their therapeutic potential in SCI.

Lactate Promotes Reactive Astrogliosis and Confers Axon Guidance Potential to Astrocytes under Oxygen-Glucose Deprivation.

During cerebral ischemia, brain lactate concentration increases, and astrogliosis is triggered. Herein, we investigated lactate's role in astrogliosis and explored the functions of lactate-activated astrocytes in vitro. In rat models of cerebral ischemia, we observed increased glial fibrillary acidic protein (GFAP) expression, reflecting astrogliosis, and increased lactate levels in the ischemic brain region. Lactate upregulated GFAP and SRY-box transcription factor 9 (SOX9) expression and activated Akt and signal transducer and activator of transcription 3 (STAT3) signaling pathways in astrocytes cultured under oxygen-glucose deprivation (OGD); these effects were abrogated upon monocarboxylate transporter 1 (MCT1) knockdown. RNA-Seq analysis revealed 221 differentially expressed genes (DEGs) between lactate-treated and untreated astrocytes. Genes upregulated by lactate treatment included those regulating astrogliosis and axon guidance. Consistently, lactate-treated astrocytes induced neuronal outgrowth upon coculture. Our results suggest that lactate promotes reactive astrogliosis and confers axon guidance potential to astrocytes under OGD.

SSH: A Tool for Predicting Hydrophobic Interaction of Monoclonal Antibodies Using Sequences.

Therapeutic antibodies are one of the most important parts of the pharmaceutical industry. They are widely used in treating various diseases such as autoimmune diseases, cancer, inflammation, and infectious diseases. Their development process however is often brought to a standstill or takes a longer time and is then more expensive due to their hydrophobicity problems. Hydrophobic interactions can cause problems on half-life, drug administration, and immunogenicity at all stages of antibody drug development. Some of the most widely accepted and used technologies for determining the hydrophobic interactions of antibodies include standup monolayer adsorption chromatography (SMAC), salt-gradient affinity-capture self-interaction nanoparticle spectroscopy (SGAC-SINS), and hydrophobic interaction chromatography (HIC). However, to measure SMAC, SGAC-SINS, and HIC for hundreds of antibody drug candidates is time-consuming and costly. To save time and money, a predictor called SSH is developed. Based on the antibody's sequence only, it can predict the hydrophobic interactions of monoclonal antibodies (mAbs). Using the leave-one-out crossvalidation, SSH achieved 91.226% accuracy, 96.396% sensitivity or recall, 84.196% specificity, 87.754% precision, 0.828 Mathew correlation coefficient (MCC), 0.919 f-score, and 0.961 area under the receiver operating characteristic (ROC) curve (AUC).

DNA4mC-LIP: a linear integration method to identify N4-methylcytosine site in multiple species.

MOTIVATION: DNA N4-methylcytosine (4mC) is a crucial epigenetic modification. However, the knowledge about its biological functions is limited. Effective and accurate identification of 4mC sites will be helpful to reveal its biological functions and mechanisms. Since experimental methods are cost and ineffective, a number of machine learning-based approaches have been proposed to detect 4mC sites. Although these methods yielded acceptable accuracy, there is still room for the improvement of the prediction performance and the stability of existing methods in practical applications. RESULTS: In this work, we first systematically assessed the existing methods based on an independent dataset. And then, we proposed DNA4mC-LIP, a linear integration method by combining existing predictors to identify 4mC sites in multiple species. The results obtained from independent dataset demonstrated that DNA4mC-LIP outperformed existing methods for identifying 4mC sites. To facilitate the scientific community, a web server for DNA4mC-LIP was developed. We anticipated that DNA4mC-LIP could serve as a powerful computational technique for identifying 4mC sites and facilitate the interpretation of 4mC mechanism. AVAILABILITY AND IMPLEMENTATION: http://i.uestc.edu.cn/DNA4mC-LIP/. CONTACT: hlin@uestc.edu.cn or hj@uestc.edu.cn or chenweiimu@gmail.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NeuroCS: A Tool to Predict Cleavage Sites of Neuropeptide Precursors.

BACKGROUND: Neuropeptides are a class of bioactive peptides produced from neuropeptide precursors through a series of extremely complex processes, mediating neuronal regulations in many aspects. Accurate identification of cleavage sites of neuropeptide precursors is of great significance for the development of neuroscience and brain science. OBJECTIVE: With the explosive growth of neuropeptide precursor data, it is pretty much needed to develop bioinformatics methods for predicting neuropeptide precursors' cleavage sites quickly and efficiently. METHODS: We started with processing the neuropeptide precursor data from SwissProt and NueoPedia into two sets of data, training dataset and testing dataset. Subsequently, six feature extraction schemes were applied to generate different feature sets and then feature selection methods were used to find the optimal feature subset of each. Thereafter the support vector machine was utilized to build models for different feature types. Finally, the performance of models were evaluated with the independent testing dataset. RESULTS: Six models are built through support vector machine. Among them the enhanced amino acid composition-based model reaches the highest accuracy of 91.60% in the 5-fold cross validation. When evaluated with independent testing dataset, it also showed an excellent performance with a high accuracy of 90.37% and Area under Receiver Operating Characteristic curve up to 0.9576. CONCLUSION: The performance of the developed model was decent. Moreover, for users' convenience, an online web server called NeuroCS is built, which is freely available at http://i.uestc.edu.cn/NeuroCS/dist/index.html#/. NeuroCS can be used to predict neuropeptide precursors' cleavage sites effectively.

AGONOTES: A Robot Annotator for Argonaute Proteins.

The argonaute protein (Ago) exists in almost all organisms. In eukaryotes, it functions as a regulatory system for gene expression. In prokaryotes, it is a type of defense system against foreign invasive genomes. The Ago system has been engineered for gene silencing and genome editing and plays an important role in biological studies. With an increasing number of genomes and proteomes of various microbes becoming available, computational tools for identifying and annotating argonaute proteins are urgently needed. We introduce AGONOTES (Argonaute Notes). It is a web service especially designed for identifying and annotating Ago. AGONOTES uses the BLASTP similarity search algorithm to categorize all submitted proteins into three groups: prokaryotic argonaute protein (pAgo), eukaryotic argonaute protein (eAgo), and non-argonaute protein (non-Ago). Argonaute proteins can then be aligned to the corresponding standard set of Ago sequences using the multiple sequence alignment program MUSCLE. All functional domains of Ago can further be curated from the alignment results and visualized easily through Bio::Graphic modules in the BioPerl bundle. Compared with existing tools such as CD-Search and available databases such as UniProt and AGONOTES showed a much better performance on domain annotations, which is fundamental in studying the new Ago. AGONOTES can be freely accessed at http://i.uestc.edu.cn/agonotes/. AGONOTES is a friendly tool for annotating Ago domains from a proteome or a series of protein sequences.

NeuroPP: A Tool for the Prediction of Neuropeptide Precursors Based on Optimal Sequence Composition.

Neuropeptides (NPs) are short secreted peptides produced mainly in the nervous system and digestive system. They activate signaling cascades to control a wide range of biological functions, such as metabolism, sensation, and behavior. NPs are typically produced from a larger NP precursor (NPP) which includes a signal peptide sequence, one or more NP sequences, and other sequences. With the drastic growth of unknown protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for identifying NPP rapidly and efficiently. In this article, we developed a predictor for NPPs based on optimized sequence composition of single amino acid, dipeptide, and tripeptide. Evaluated with independent data set, the predictor showed excellent performance that achieved an accuracy of 88.65% with AUC of 0.95. The corresponding web server was developed, which is freely available at http://i.uestc.edu.cn/neuropeptide/neuropp/home.html . It can help relevant researchers to screen candidate NP precursor, shorten experimental cycle, and reduce costs.

Comprehensive exploration of the enzymes catalysing oxygen-involved reactions and COGs relevant to bacterial oxygen utilization.

To better understand the mechanisms of bacterial adaptation in oxygen environments, we explored the aerobic living-associated genes in bacteria by comparing Clusters of Orthologous Groups of proteins' (COGs) frequencies and gene expression analyses and 38 COGs were detected at significantly higher frequencies (p-value less than 1e-6) in aerobes than in anaerobes. Differential expression analyses between two conditions further narrowed the prediction to 27 aerobe-specific COGs. Then, we annotated the enzymes associated with these COGs. Literature review revealed that 14 COGs contained enzymes catalysing oxygen-involved reactions or products involved in aerobic pathways, suggesting their important roles for survival in aerobic environments. Additionally, protein-protein interaction analyses and step length comparisons of metabolic networks suggested that the other 13 COGs may function relevantly with the 14 enzymes-corresponding COGs, indicating that these genes may be highly associated with oxygen utilization. Phylogenetic and evolutionary analyses showed that the 27 COGs did not have similar trees, and all suffered purifying selection pressures. The divergent times of species containing or lacking aerobic COGs validated that the appearing time of oxygen-utilizing gene was approximately 2.80 Gyr ago. In addition to help better understand oxygen adaption, our method may be extended to identify genes relevant to other living environments.

Biopanning data bank 2018: hugging next generation phage display.

Database URL: The BDB database is available at http://immunet.cn/bdb.

PSBinder: A Web Service for Predicting Polystyrene Surface-Binding Peptides.

Polystyrene surface-binding peptides (PSBPs) are useful as affinity tags to build a highly effective ELISA system. However, they are also a quite common type of target-unrelated peptides (TUPs) in the panning of phage-displayed random peptide library. As TUP, PSBP will mislead the analysis of panning results if not identified. Therefore, it is necessary to find a way to quickly and easily foretell if a peptide is likely to be a PSBP or not. In this paper, we describe PSBinder, a predictor based on SVM. To our knowledge, it is the first web server for predicting PSBP. The SVM model was built with the feature of optimized dipeptide composition and 87.02% (MCC = 0.74; AUC = 0.91) of peptides were correctly classified by fivefold cross-validation. PSBinder can be used to exclude highly possible PSBP from biopanning results or to find novel candidates for polystyrene affinity tags. Either way, it is valuable for biotechnology community.