RIscoper: a tool for RNA-RNA interaction extraction from the literature

RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature

RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.

A Novel Deep Learning Model for Drug-drug Interactions

INTRODUCTION

Drug-drug interactions (DDIs) can lead to adverse events and compromised treatment efficacy that emphasize the need for accurate prediction and understanding of these interactions.

METHODS

In this paper, we propose a novel approach for DDI prediction using two separate message-passing neural network (MPNN) models, each focused on one drug in a pair. By capturing the unique characteristics of each drug and their interactions, the proposed method aims to improve the accuracy of DDI prediction. The outputs of the individual MPNN models combine to integrate the information from both drugs and their molecular features. Evaluating the proposed method on a comprehensive dataset, we demonstrate its superior performance with an accuracy of 0.90, an area under the curve (AUC) of 0.99, and an F1-score of 0.80. These results highlight the effectiveness of the proposed approach in accurately identifying potential drugdrug interactions.

RESULTS

The use of two separate MPNN models offers a flexible framework for capturing drug characteristics and interactions, contributing to our understanding of DDIs. The findings of this study have significant implications for patient safety and personalized medicine, with the potential to optimize treatment outcomes by preventing adverse events.

CONCLUSION

Further research and validation on larger datasets and real-world scenarios are necessary to explore the generalizability and practicality of this approach.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning

Subcellular localizations of ncRNAs are associated with specific functions. Currently, an increasing number of biological researchers are focusing on computational approaches to identify subcellular localizations of ncRNAs. However, the performance of the existing computational methods is low and needs to be further studied. First, most prediction models are trained with outdated databases. Second, only a few predictors can identify multiple subcellular localizations simultaneously. In this work, we establish three human ncRNA subcellular datasets based on the latest RNALocate, including lncRNA, miRNA and snoRNA, and then we propose a novel multi-label classification model based on ensemble learning called ncRNALocate-EL to identify multi-label subcellular localizations of three ncRNAs. The results show that the ncRNALocate-EL outperforms previous methods. Our method achieved an average precision of 0.709,0.977 and 0.730 on three human ncRNA datasets. The web server of ncRNALocate-EL has been established, which can be accessed at https://bliulab.net/ncRNALocate-EL.

LncReader: identification of dual functional long noncoding RNAs using a multi-head self-attention mechanism

Long noncoding ribonucleic acids (RNAs; LncRNAs) endowed with both protein-coding and noncoding functions are referred to as 'dual functional lncRNAs'. Recently, dual functional lncRNAs have been intensively studied and identified as involved in various fundamental cellular processes. However, apart from time-consuming and cell-type-specific experiments, there is virtually no in silico method for predicting the identity of dual functional lncRNAs. Here, we developed a deep-learning model with a multi-head self-attention mechanism, LncReader, to identify dual functional lncRNAs. Our data demonstrated that LncReader showed multiple advantages compared to various classical machine learning methods using benchmark datasets from our previously reported cncRNAdb project. Moreover, to obtain independent in-house datasets for robust testing, mass spectrometry proteomics combined with RNA-seq and Ribo-seq were applied in four leukaemia cell lines, which further confirmed that LncReader achieved the best performance compared to other tools. Therefore, LncReader provides an accurate and practical tool that enables fast dual functional lncRNA identification.

Comprehensive Analysis of the Molecular Characteristics and Prognosis value of AT II-associated Genes in Non-small Cell Lung Cancer

Alveolar type II (AT II) is a key structure of the distal lung epithelium and essential to maintain normal lung homeostasis. Dedifferentiation of AT II cells is significantly correlated with lung tumor progression. However, the potential molecular mechanism and clinical significance of AT II-associated genes for lung cancer has not yet been fully elucidated. In this study, we comprehensively analyzed the gene expression, prognosis value, genetic alteration, and immune cell infiltration of eight AT II-associated genes (AQP4, SFTPB, SFTPC, SFTPD, CLDN18, FOXA2, NKX2-1, and PGC) in Nonsmall Cell Lung Cancer (NSCLC). The results have shown that the expression of eight genes were remarkably reduced in cancer tissues and observably relating to clinical cancer stages. Survival analysis of the eight genes revealed that low-expression of CLDN18, FOXA2, NKX2-1, PGC, SFTPB, SFTPC, and SFTPD were significantly related to a reduced progression-free survival (FP), and low CLDN18, FOXA2, and SFTPD mRNA expression led to a short postprogression survival (PPS). Meanwhile, the alteration of 8 AT II-associated genes covered 273 out of 1053 NSCLC samples (26%). Additionally, the expression level of eight genes were significantly correlated with the infiltration of diverse immune cells, including six types of CD4+T cells, macrophages, neutrophils, B cells, CD8+ T cells, and dendritic cells. In summary, this study provided clues of the values of eight AT II-associated genes as clinical biomarkers and therapeutic targets in NSCLC and might provide some new inspirations to assist the design of new immunotherapies.

Mixed model-based eQTL analysis reveals lncRNAs associated with regulation of genes involved in sex determination and spermatogenesis: The key to understanding human gender imbalance

BACKGROUND

An imbalance in the prenatal sex ratio in humans may be due to several factors affecting sperm physiology, including genetic features. In this study, we conducted a transcriptome-wide analysis of expression quantitative trait loci (eQTLs) to identify target genes associated with previously described QTLs associated with gender imbalance.

METHODS

A mixed model explaining polygenic effects by genomic covariance among individuals was used to identify the eQTLs using gene expression and genotype data from 462 European/African individuals.

RESULTS

Eight eGenes were associated with four QTLs (P < 4.00 × 10^-5), with strong associations found (P < 4.00 × 10^-8) between rs2485007 and eGenes ANKRD26P3 (P = 3.40 × 10^-9) and LINC00421 (P = 1.35 × 10^-9). ANKRD26P3 and LINC00421 are both lncRNAs associated with the control of testis-dominant genes PELP1, TAF15, NANOG, TEX14, TCF3, ZNF433, ZNF555, TEX37, FATE1, TCP11, and CYLC2 and Y-linked genes SRY and ZFY, as well as several genes with roles in spermatogenesis (ODF1, SPATC1, SPATA3, SPATA31E1, SPERT, SPATA16, MOSPD1, SPATA24, and SPO11) and sex determination (SOX family genes).

CONCLUSIONS

The above eGenes contribute directly or indirectly to gene regulation for sex determination and spermatogenesis, thereby serving as important functional clues for gender-biased selection.

Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions

Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

Exploring the Potential Key IncRNAs with Endometriosis by Construction of a ceRNA Network

Purpose

The etiology and pathophysiology of endometriosis remain unclear. Current research indicates long noncoding RNA (lncRNA) may play an important role in the pathogenesis and development of endometriosis. However, the molecular mechanism of lncRNA in endometriosis is far from clear.

Patients and Methods

The lncRNA and mRNA expression of 8 patients with ovarian endometriosis were determined by high-throughput RNA sequencing (8 ectopic endometria samples vs 8 eutopic endometria samples), and miRNA expression profiles were obtained from our previous study. Then a lncRNA-associated competing endogenous RNA (ceRNA) network was constructed by combining the regulatory interaction and negative co-expression interaction between the differentially expressed lncRNAs/mRNAs and miRNAs by different rules.

Results

The constructed lncRNA-related ceRNA network was composed of two separate networks, network 1 including 14,137 dysregulated lncRNA–mRNA interactions, referring to 242 lncRNAs, 55 miRNAs and 1600 mRNAs, network 2 including 4459 dysregulated lncRNA–mRNA interactions, referring to 111 lncRNAs, 39 miRNAs and 1151 mRNAs. The top six hub lncRNAs (LINC01140, MSC-AS1, HAGLR, CKMT2-AS1, JAKMIP2-AS1, AL365361.1) in the significant ternary relationship of mRNA–miRNA–lncRNA in network 1, and the top six hub lncRNAs (PAX8-AS1, MIR17HC, PART1, HOXA-AS3, PLAC4, LINC00511) in the significant ternary relationship of mRNA–miRNA–lncRNA in network 2 were selected. Functional enrichment analysis of these lncRNA-related mRNAs indicated that the lncRNAs in network 1 mainly take part in positive regulation of phagocytosis, myeloid leukocyte activation, and tissue remodeling, while the lncRNAs in network 2 mainly take part in negative regulation of cell proliferation, blood vessel development and regulation of epithelial cell differentiation, which is consistent with the results obtained from the different rules to construct the networks.

Conclusion

lncRNA-related ceRNA network analysis recognized key lncRNAs related to the development of endometriosis.

Exploring the Protective Effect of ShenQi Compound on Skeletal Muscle in Diabetic Macrovasculopathy Mice

OBJECTIVE

ShenQi compound (SQC) is a traditional herbal formula, which has been used to treat Type 2 diabetes mellitus (T2DM) and complications for years. The aim of this study was to explore the preventive and protective effects of SQC recipe on the skeletal muscle of diabetic macrovasculopathy mice, which provides a theoretical basis for the clinical use of this formula.

METHODS

We evaluated the effect of SQC in a diabetic vasculopathy mouse model by detecting a series of blood indicators (blood glucose, lipids and insulin) and performing histological observations. Meanwhile, we explored the molecular mechanism of SQC treatment on skeletal muscle by genome expression profiles.

RESULTS

The results indicated that SQC could effectively improve blood glucose, serum lipids (total cholesterol (TC), Triglyceride (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C)) and insulin (INS) levels in diabetic vasculopathy mice, as well as alleviating skeletal muscle tissue damage for diabetic macrovasculopathy. Meanwhile, compared with rosiglitazone, SQC showed a better effect on blood glucose fluctuation. Moreover, the gene microarray analysis indicated that SQC might improve T2DM by affecting biological functions related to cell death and cell adhesion. Moreover, 7 genes (Celsr2, Rilpl1, Dlx6as, 2010004M13Rik, Anapc13, Gm6097, Ddx39b) might be potential therapeutic targets of SQC.

CONCLUSION

All these results indicate that SQC is an effective preventive and protective drug for skeletal muscle in diabetic macrovasculopathy, and could alleviate skeletal muscle tissue damage through affecting biological functions related to cell death and cell adhesion.

Mutations in cis that affect mRNA synthesis, processing and translation

Genetic mutations that cause hereditary diseases usually affect the composition of the transcribed mRNA and its encoded protein, leading to instability of the mRNA and/or the protein. Sometimes, however, such mutations affect the synthesis, the processing or the translation of the mRNA, with similar disastrous effects. We here present an overview of mRNA synthesis, its posttranscriptional modification and its translation into protein. We then indicate which elements in these processes are known to be affected by pathogenic mutations, but we restrict our review to mutations in cis, in the DNA of the gene that encodes the affected protein. These mutations can be in enhancer or promoter regions of the gene, which act as binding sites for transcription factors involved in pre-mRNA synthesis. We also describe mutations in polyadenylation sequences and in splice site regions, exonic and intronic, involved in intron removal. Finally, we include mutations in the Kozak sequence in mRNA, which is involved in protein synthesis. We provide examples of genetic diseases caused by mutations in these DNA regions and refer to databases to help identify these regions. The over-all knowledge of mRNA synthesis, processing and translation is essential for improvement of the diagnosis of patients with genetic diseases.

Cellinker: a platform of ligand-receptor interactions for intercellular communication analysis

MOTIVATION

Ligand-receptor (L-R) interactions mediate cell adhesion, recognition and communication and play essential roles in physiological and pathological signaling. With the rapid development of single-cell RNA sequencing (scRNA-seq) technologies, systematically decoding the intercellular communication network involving L-R interactions has become a focus of research. Therefore, construction of a comprehensive, high-confidence and well-organized resource to retrieve L-R interactions in order to study the functional effects of cell-cell communications would be of great value.

RESULTS

In this study, we developed Cellinker, a manually curated resource of literature-supported L-R interactions that play roles in cell-cell communication. We aimed to provide a useful platform for studies on cell-cell communication mediated by L-R interactions. The current version of Cellinker documents over 3,700 human and 3,200 mouse L-R protein-protein interactions (PPIs) and embeds a practical and convenient webserver with which researchers can decode intercellular communications based on scRNA-seq data. And over 400 endogenous small molecule (sMOL) related L-R interactions were collected as well. Moreover, to help with research on coronavirus (CoV) infection, Cellinker collects information on 16 L-R PPIs involved in CoV-human interactions (including 12 L-R PPIs involved in SARS-CoV-2 infection). In summary, Cellinker provides a user-friendly interface for querying, browsing and visualizing L-R interactions as well as a practical and convenient web tool for inferring intercellular communications based on scRNA-seq data. We believe this platform could promote intercellular communication research and accelerate the development of related algorithms for scRNA-seq studies.

AVAILABILITY

Cellinker is available at http://www.rna-society.org/cellinker/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation

Many studies have indicated that non-coding RNA (ncRNA) dysfunction is closely related to numerous diseases. Recently, accumulated ncRNA-disease associations have made related databases insufficient to meet the demands of biomedical research. The constant updating of ncRNA-disease resources has become essential. Here, we have updated the mammal ncRNA-disease repository (MNDR, http://www.rna-society.org/mndr/) to version 3.0, containing more than one million entries, four-fold increment in data compared to the previous version. Experimental and predicted circRNA-disease associations have been integrated, increasing the number of categories of ncRNAs to five, and the number of mammalian species to 11. Moreover, ncRNA-disease related drug annotations and associations, as well as ncRNA subcellular localizations and interactions, were added. In addition, three ncRNA-disease (miRNA/lncRNA/circRNA) prediction tools were provided, and the website was also optimized, making it more practical and user-friendly. In summary, MNDR v3.0 will be a valuable resource for the investigation of disease mechanisms and clinical treatment strategies.

cncRNAdb: a manually curated resource of experimentally supported RNAs with both protein-coding and noncoding function

RNA endowed with both protein-coding and noncoding functions is referred to as 'dual-function RNA', 'binary functional RNA (bifunctional RNA)' or 'cncRNA (coding and noncoding RNA)'. Recently, an increasing number of cncRNAs have been identified, including both translated ncRNAs (ncRNAs with coding functions) and untranslated mRNAs (mRNAs with noncoding functions). However, an appropriate database for storing and organizing cncRNAs is still lacking. Here, we developed cncRNAdb, a manually curated database of experimentally supported cncRNAs, which aims to provide a resource for efficient manipulation, browsing and analysis of cncRNAs. The current version of cncRNAdb documents about 2600 manually curated entries of cncRNA functions with experimental evidence, involving more than 2,000 RNAs (including over 1300 translated ncRNAs and over 600 untranslated mRNAs) across over 20 species. In summary, we believe that cncRNAdb will help elucidate the functions and mechanisms of cncRNAs and develop new prediction methods. The database is available at http://www.rna-society.org/cncrnadb/.

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.

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).

A susceptibility biomarker identification strategy based on significantly differentially expressed ceRNA triplets for ischemic cardiomyopathy

Ischemic cardiomyopathy (ICM) is a common human heart disease that causes death. No effective biomarkers for ICM could be found in existing databases, which is detrimental to the in-depth study of this disease. In the present study, ICM susceptibility biomarkers were identified using a proposed strategy based on RNA-Seq and miRNA-Seq data of ICM and normal samples. Significantly differentially expressed competing endogenous RNA (ceRNA) triplets were constructed using permutation tests and differentially expressed mRNAs, miRNAs and lncRNAs. Candidate ICM susceptible genes were screened out as differentially expressed genes in significantly differentially expressed ceRNA triplets enriched in ICM-related functional classes. Finally, eight ICM susceptibility genes and their significantly correlated lncRNAs with high classification accuracy were identified as ICM susceptibility biomarkers. These biomarkers would contribute to the diagnosis and treatment of ICM. The proposed strategy could be extended to other complex diseases without disease biomarkers in public databases.

RNAInter in 2020: RNA interactome repository with increased coverage and annotation

Research on RNA-associated interactions has exploded in recent years, and increasing numbers of studies are not limited to RNA-RNA and RNA-protein interactions but also include RNA-DNA/compound interactions. To facilitate the development of the interactome and promote understanding of the biological functions and molecular mechanisms of RNA, we updated RAID v2.0 to RNAInter (RNA Interactome Database), a repository for RNA-associated interactions that is freely accessible at http://www.rna-society.org/rnainter/ or http://www.rna-society.org/raid/. Compared to RAID v2.0, new features in RNAInter include (i) 8-fold more interaction data and 94 additional species; (ii) more definite annotations organized, including RNA editing/localization/modification/structure and homology interaction; (iii) advanced functions including fuzzy/batch search, interaction network and RNA dynamic expression and (iv) four embedded RNA interactome tools: RIscoper, IntaRNA, PRIdictor and DeepBind. Consequently, RNAInter contains >41 million RNA-associated interaction entries, involving more than 450 thousand unique molecules, including RNA, protein, DNA and compound. Overall, RNAInter provides a comprehensive RNA interactome resource for researchers and paves the way to investigate the regulatory landscape of cellular RNAs.

Exploration of the Hepatoprotective Effect and Mechanism of Swertia mussotii Franch in an Acute Liver Injury Rat Model

Background and Objective:

Swertia mussotii Franch, also known as “Zangyinchen”, is one of a Tibetan traditional herb used for treatment of liver diseases over thousands of years at Qinghai-Tibet Plateau, has been confirmed to be hepatoprotective. However, the underlying mechanism is largely unknown.

Materials and Method:

In this study, we evaluated the effect of S. mussotii treatment in a carbon tetrachloride-induced acute liver injury rat model by examining the serum alanine aminotransferase, aspartate aminotransferase, total bilirubin levels and performing histological observations of the liver tissues. Meanwhile, the metabolomics analysis was used to explore the molecular mechanism of S. mussotii treatment by high performance liquid chromatography tandem mass spectrometry.

Results:

The results showed that S. mussotii treatment could effectively improve the serum alanine aminotransferase, aspartate aminotransferase, total bilirubin in acute liver injury rat model. Histological observation showed that S. mussotii treatment could effectively alleviate liver injury. Moreover, the metabolomics analysis showed that S. mussotii treatment could normalize the levels of many fatty acid metabolism related metabolites. And the results of pathway analysis showed that these metabolites significantly enriched in fatty acid biosynthesis pathway (myristic acid, dodecanoic acid and capric acid) and linoleic acid metabolism pathway (13-OxoODE).

Conclusion:

The results indicated that S. mussotii treatment could significantly improve acute liver injury through affecting the pathways related to lipid metabolism.

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.

Exploration of invasive mechanisms via global ncRNA-associated virus-host crosstalk

Viral infection is a complex pathogenesis and the underlying molecular mechanisms remain poorly understood. In this study, an integrated multiple resources analysis was performed and showed that the cellular ncRNAs and proteins targeted by viruses were primarily "hubs" and "bottlenecks" in the human ncRNA/protein-protein interaction. The common proteins targeted by both viral ncRNAs and proteins tended to skew toward higher degrees and betweenness compared with other proteins, showed significant enrichment in the cell death process. Specifically, >800 pairs of human cellular ncRNAs and viral ncRNAs that exhibited a high degree of functional homology were identified, representing potential ncRNA-mediated co-regulation patterns of viral invasion. Additionally, clustering analysis further revealed several distinct viral clusters with obvious functional divergence. Overall, this is the first attempt to systematically explore the invasive mechanism via global ncRNA-associated virus-host crosstalk. Our results provide useful information in comprehensively understanding the viral invasive mechanism.

CISI: A Tool for Predicting Cross-interaction or Self-interaction of Monoclonal Antibodies Using Sequences

Monoclonal antibodies (mAbs) are one of the robust classes of therapeutic proteins. Their stability, specificity, and high solubility allow the successful development and commercialization of antibody-based drugs. Though with these characteristics, mAbs projects are often suspended due to self- or cross-interaction of monoclonal antibodies. This is one of the main reasons which causes the development of mAbs into drugs taking forever and expensive. CISI is short for cross-interaction or self-interaction of mAbs. It can be quantified by several assays. The assays such as poly-specificity reagent and cross-interaction chromatography can measure cross-interaction of mAbs. Self-interaction can be assayed through clone self-interaction by biolayer interferometry and affinity-capture self-interaction nanoparticle spectroscopy. To save time and money, we developed a model called CISI which can predict cross-interaction or self-interaction based on tripeptide composition. It showed 88.20% accuracy, 90.22% sensitivity, 86.05% specificity, 0.78 Mathew correlation coefficient, and 0.96 area under the receiver operating characteristic (ROC) curve (AUC) in the leave-one-out cross-validation. CISI is freely available at http://i.uestc.edu.cn/eli/cgi-bin/cisi.pl.