Decoding the underlying mechanisms of Di-Tan-Decoction in treating intracerebral hemorrhage based on network pharmacology.

BACKGROUND: Chinese medicine usually acts as "multi-ingredients, multi-targets and multi-pathways" on complex diseases, and these action modes reflect the coordination and integrity of the treatment process with traditional Chinese medicine (TCM). System pharmacology is developed based on the cross-disciplines of directional pharmacology, system biology, and mathematics, has the characteristics of integrity and synergy in the treatment process of TCM. Therefore, it is suitable for analyzing the key ingredients and mechanisms of TCM in treating complex diseases. Intracerebral Hemorrhage (ICH) is one of the leading causes of death in China, with the characteristics of high mortality and disability rate. Bring a significant burden on people and society. An increasing number of studies have shown that Chinese medicine prescriptions have good advantages in the treatment of ICH, and Ditan Decoction (DTT) is one of the commonly used prescriptions in the treatment of ICH. Modern pharmacological studies have shown that DTT may play a therapeutic role in treating ICH by inhibiting brain inflammation, abnormal oxidative stress reaction and reducing neurological damage, but the specific key ingredients and mechanism are still unclear. METHODS: To solve this problem, we established PPI network based on the latest pathogenic gene data of ICH, and CT network based on ingredient and target data of DTT. Subsequently, we established optimization space based on PPI network and CT network, and constructed a new model for node importance calculation, and proposed a calculation method for PES score, thus calculating the functional core ingredients group (FCIG). These core functional groups may represent DTT therapy for ICH. RESULTS: Based on the strategy, 44 ingredients were predicted as FCIG, results showed that 80.44% of the FCIG targets enriched pathways were coincided with the enriched pathways of pathogenic genes. Both the literature and molecular docking results confirm the therapeutic effect of FCIG on ICH via targeting MAPK signaling pathway and PI3K-Akt signaling pathway. CONCLUSIONS: The FCIG obtained by our network pharmacology method can represent the effect of DTT in treating ICH. These results confirmed that our strategy of active ingredient group optimization and the mechanism inference could provide methodological reference for optimization and secondary development of TCM.

Decoding the Key Functional Combined Components Group and Uncovering the Molecular Mechanism of Longdan Xiegan Decoction in Treating Uveitis.

Objective: Longdan Xiegan Decoction (LXD) is a famous herbal formula in China. It has been proved that LXD has been shown to have a significant inhibitory effect on suppresses the inflammatory cells associated with uveitis. However, the key functional combination of component groups and their possible mechanisms remain unclear. Methods: The community detecting model of the network, the functional response space, and reverse prediction model were utilized to decode the key components group (KCG) and possible mechanism of LXD in treating uveitis. Finally, MTT assay, NO assay and ELISA assay were applied to verify the effectiveness of KCG and the accuracy of our strategy. Results: In the components-targets-pathogenic genes-disease (CTP) network, a combination of Huffman coding and random walk algorithm was used and eight foundational acting communities (FACs) were discovered with important functional significance. Verification has shown that FACs can represent the corresponding C-T network for treating uveitis. A novel node importance calculation method was designed to construct the functional response space and pick out 349 effective proteins. A total of 54 components were screened and defined as KCG. The pathway enrichment results showed that KCG and their targets enriched signal pathways of IL-17, Toll-like receptor, and T cell receptor played an important role in the pathogenesis of uveitis. Furthermore, experimental verification results showed that important KCG quercetin and sitosterol markedly inhibited the production of nitric oxide and significantly regulated the level of TNF-α and IFN-γ in Lipopolysaccharide-induced RAW264.7 cells. Discussion: In this research, we decoded the potential mechanism of the multi-components-genes-pathways of LXD's pharmacological action mode against uveitis based on an integrated pharmacology approach. The results provided a new perspective for the future studies of the anti-uveitis mechanism of traditional Chinese medicine.

Detecting Key Functional Components Group and Speculating the Potential Mechanism of Xiao-Xu-Ming Decoction in Treating Stroke.

Stroke is a cerebrovascular event with cerebral blood flow interruption which is caused by occlusion or bursting of cerebral vessels. At present, the main methods in treating stroke are surgical treatment, statins, and recombinant tissue-type plasminogen activator (rt-PA). Relatively, traditional Chinese medicine (TCM) has widely been used at clinical level in China and some countries in Asia. Xiao-Xu-Ming decoction (XXMD) is a classical and widely used prescription in treating stroke in China. However, the material basis of effect and the action principle of XXMD are still not clear. To solve this issue, we designed a new system pharmacology strategy that combined targets of XXMD and the pathogenetic genes of stroke to construct a functional response space (FRS). The effective proteins from this space were determined by using a novel node importance calculation method, and then the key functional components group (KFCG) that could mediate the effective proteins was selected based on the dynamic programming strategy. The results showed that enriched pathways of effective proteins selected from FRS could cover 99.10% of enriched pathways of reference targets, which were defined by overlapping of component targets and pathogenetic genes. Targets of optimized KFCG with 56 components can be enriched into 166 pathways that covered 80.43% of 138 pathways of 1,012 pathogenetic genes. A component potential effect score (PES) calculation model was constructed to calculate the comprehensive effective score of components in the components-targets-pathways (C-T-P) network of KFCGs, and showed that ferulic acid, zingerone, and vanillic acid had the highest PESs. Prediction and docking simulations show that these components can affect stroke synergistically through genes such as MEK, NFκB, and PI3K in PI3K-Akt, cAMP, and MAPK cascade signals. Finally, ferulic acid, zingerone, and vanillic acid were tested to be protective for PC12 cells and HT22 cells in increasing cell viabilities after oxygen and glucose deprivation (OGD). Our proposed strategy could improve the accuracy on decoding KFCGs of XXMD and provide a methodologic reference for the optimization, mechanism analysis, and secondary development of the formula in TCM.

Uncovering the potential mechanism of Xue Fu Zhu Yu Decoction in the treatment of intracerebral hemorrhage.

BACKGROUND: Chinese herbal medicine (CHM) is characterized by "multi- compounds, multi-targets and multi-pathway", which has advanced benefits for preventing and treating complex diseases, but there still exists unsolved issues, mainly include unclear material basis and underlying mechanism of prescription. Integrated pharmacology is a hot cross research area based on system biology, mathematics and poly-pharmacology. It can systematically and comprehensively investigate the therapeutic reaction of compounds or drugs on pathogenic genes network, and is especially suitable for the study of complex CHM systems. Intracerebral Hemorrhage (ICH) is one of the main causes of death among Chinese residents, which is characterized with high mortality and high disability rate. In recent years, the treatment of ICH by CHM has been deeply researched. Xue Fu Zhu Yu Decoction (XFZYD), one of the commonly used prescriptions in treating ICH at clinic level, has not been clear about its mechanism. METHODS: Here, we established a strategy, which based on compounds-targets, pathogenetic genes, network analysis and node importance calculation. Using this strategy, the core compounds group (CCG) of XFZYD was predicted and validated by in vitro experiments. The molecular mechanism of XFZYD in treating ICH was deduced based on CCG and their targets. RESULTS: The results show that the CCG with 43 compounds predicted by this model is highly consistent with the corresponding Compound-Target (C-T) network in terms of gene coverage, enriched pathway coverage and accumulated contribution of key nodes at 89.49%, 88.72% and 90.11%, respectively, which confirmed the reliability and accuracy of the effective compound group optimization and mechanism speculation strategy proposed by us. CONCLUSIONS: Our strategy of optimizing the effective compound groups and inferring the mechanism provides a strategic reference for explaining the optimization and inferring the molecular mechanism of prescriptions in treating complex diseases of CHM.

Elucidating the Synergistic Effect of Multiple Chinese Herbal Prescriptions in the Treatment of Post-stroke Neurological Damage.

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

Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods.

Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. In this study, an antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database. The results showed that both models have excellent prediction performance (mean accuracy >0.9 and mean AUC >0.9 for the two models). We used the predictor to screen potential antibacterial compounds from FDA-approved drugs in the DrugBank database. The screening results showed that 1087 small-molecule drugs have potential antibacterial activity and 154 of them are FDA-approved antibacterial drugs, which accounts for 76.2% of the approved antibacterial drugs collected in this study. Through molecular fingerprint similarity analysis and common substructure analysis, we screened 8 predicted antibacterial small-molecule compounds with novel structures compared with known antibacterial drugs, and 5 of them are widely used in the treatment of various tumors. This study provides a new insight for predicting antibacterial compounds by using approved drugs, the predicted compounds might be used to treat bacterial infections and extend lifespan.

Corrigendum: Detecting key functional components group and speculating the potential mechanism of Xiao-Xu-Ming decoction in treating stroke.

[This corrects the article DOI: 10.3389/fcell.2022.753425.].

Detecting Critical Functional Ingredients Group and Mechanism of Xuebijing Injection in Treating Sepsis.

Sepsis is a systemic inflammatory reaction caused by various infectious or noninfectious factors, which can lead to shock, multiple organ dysfunction syndrome, and death. It is one of the common complications and a main cause of death in critically ill patients. At present, the treatments of sepsis are mainly focused on the controlling of inflammatory response and reduction of various organ function damage, including anti-infection, hormones, mechanical ventilation, nutritional support, and traditional Chinese medicine (TCM). Among them, Xuebijing injection (XBJI) is an important derivative of TCM, which is widely used in clinical research. However, the molecular mechanism of XBJI on sepsis is still not clear. The mechanism of treatment of "bacteria, poison and inflammation" and the effects of multi-ingredient, multi-target, and multi-pathway have still not been clarified. For solving this issue, we designed a new systems pharmacology strategy which combines target genes of XBJI and the pathogenetic genes of sepsis to construct functional response space (FRS). The key response proteins in the FRS were determined by using a novel node importance calculation method and were condensed by a dynamic programming strategy to conduct the critical functional ingredients group (CFIG). The results showed that enriched pathways of key response proteins selected from FRS could cover 95.83% of the enriched pathways of reference targets, which were defined as the intersections of ingredient targets and pathogenetic genes. The targets of the optimized CFIG with 60 ingredients could be enriched into 182 pathways which covered 81.58% of 152 pathways of 1,606 pathogenetic genes. The prediction of CFIG targets showed that the CFIG of XBJI could affect sepsis synergistically through genes such as TAK1, TNF-α, IL-1ß, and MEK1 in the pathways of MAPK, NF-κB, PI3K-AKT, Toll-like receptor, and tumor necrosis factor signaling. Finally, the effects of apigenin, baicalein, and luteolin were evaluated by in vitro experiments and were proved to be effective in reducing the production of intracellular reactive oxygen species in lipopolysaccharide-stimulated RAW264.7 cells, significantly. These results indicate that the novel integrative model can promote reliability and accuracy on depicting the CFIGs in XBJI and figure out a methodological coordinate for simplicity, mechanism analysis, and secondary development of formulas in TCM.

A metabolic data-driven systems pharmacology strategy for decoding and validating the mechanism of Compound Kushen Injection against HCC.

ETHNOPHARMACOLOGICAL RELEVANCE: Compound Kushen Injection (CKI) is a widely used TCM formula for treatment of carcinomatous pain and tumors of digestive system including hepatocellular carcinoma (HCC). However, the potential mechanisms of CKI for treatment of HCC have not been systematically and deeply studied. AIM OF STUDY: A metabolic data-driven systems pharmacology approach was utilized to investigate the potential mechanisms of CKI for treatment of HCC. MATERIALS AND METHODS: Based on phenotypic data generated by metabolomics and genotypic data of drug targets, a propagation model based on Dijkstra program was proposed to decode the effective network of key genotype-phenotype of CKI in treating HCC. The pivotal pathway was predicted by target propagation mode of our proposed model, and was validated in SMMC-7721 cells and diethylnitrosamine-induced rats. RESULTS: Metabolomics results indicated that 12 differential metabolites, and 5 metabolic pathways might be involved in the anti-HCC effect of CKI. A total of 86 metabolic related genes that affected by CKI were obtained. The results calculated by propagation model showed that 6475 shortest distance chains might be involved in the anti-HCC effect of CKI. According to the results of propagation mode, EGFR was identified as the core target of CKI for the anti-HCC effect. Finally, EGFR and its related pathway EGFR-STAT3 signaling pathway were validated in vivo and in vitro. CONCLUSION: The proposed method provides a methodological reference for explaining the underlying mechanism of TCM in treating HCC.

A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus.

Complex disease is a cascade process which is associated with functional abnormalities in multiple proteins and protein-protein interaction (PPI) networks. One drug one target has not been able to perfectly intervene complex diseases. Increasing evidences show that Chinese herb formula usually treats complex diseases in the form of multi-components and multi-targets. The key step to elucidate the underlying mechanism of formula in traditional Chinese medicine (TCM) is to optimize and capture the important components in the formula. At present, there are several formula optimization models based on network pharmacology has been proposed. Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. How to select the key group of effective components (KGEC) from the formula of TCM based on the optimal space which link pathogenic genes and drug targets is a bottleneck problem in network pharmacology. To address this issue, we designed a novel network pharmacological model, which takes Lang Chuang Wan (LCW) treatment of systemic lupus erythematosus (SLE) as the case. We used the weighted gene regulatory network and active components targets network to construct disease-targets-components network, after filtering through the network attribute degree, the optimization space and effective proteins were obtained. And then the KGEC was selected by using contribution index (CI) model based on knapsack algorithm. The results show that the enriched pathways of effective proteins we selected can cover 96% of the pathogenetic genes enriched pathways. After reverse analysis of effective proteins and optimization with CI index model, KGEC with 82 components were obtained, and 105 enriched pathways of KGEC targets were consistent with enriched pathways of pathogenic genes (80.15%). Finally, the key components in KGEC of LCW were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the KGEC in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.

Uncovering the Complexity Mechanism of Different Formulas Treatment for Rheumatoid Arthritis Based on a Novel Network Pharmacology Model.

Traditional Chinese medicine (TCM) with the characteristics of "multi-component-multi-target-multi-pathway" has obvious advantages in the prevention and treatment of complex diseases, especially in the aspects of "treating the same disease with different treatments". However, there are still some problems such as unclear substance basis and molecular mechanism of the effectiveness of formula. Network pharmacology is a new strategy based on system biology and poly-pharmacology, which could observe the intervention of drugs on disease networks at systematical and comprehensive level, and especially suitable for study of complex TCM systems. Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease, causing articular and extra articular dysfunctions among patients, it could lead to irreversible joint damage or disability if left untreated. TCM formulas, Danggui-Sini-decoction (DSD), Guizhi-Fuzi-decoction (GFD), and Huangqi-Guizhi-Wuwu-Decoction (HGWD), et al., have been found successful in controlling RA in clinical applications. Here, a network pharmacology-based approach was established. With this model, key gene network motif with significant (KNMS) of three formulas were predicted, and the molecular mechanism of different formula in the treatment of rheumatoid arthritis (RA) was inferred based on these KNMSs. The results show that the KNMSs predicted by the model kept a high consistency with the corresponding C-T network in coverage of RA pathogenic genes, coverage of functional pathways and cumulative contribution of key nodes, which confirmed the reliability and accuracy of our proposed KNMS prediction strategy. All validated KNMSs of each RA therapy-related formula were employed to decode the mechanisms of different formulas treat the same disease. Finally, the key components in KNMSs of each formula were evaluated by in vitro experiments. Our proposed KNMS prediction and validation strategy provides methodological reference for interpreting the optimization of core components group and inference of molecular mechanism of formula in the treatment of complex diseases in TCM.

A Novel Strategy for Decoding and Validating the Combination Principles of Huanglian Jiedu Decoction From Multi-Scale Perspective.

Traditional Chinese medicine (TCM) formulas treat complex diseases through combined botanical drugs which follow specific compatibility rules to reduce toxicity and increase efficiency. "Jun, Chen, Zuo and Shi" is one of most used compatibility rules in the combination of botanical drugs. However, due to the deficiency of traditional research methods, the quantified theoretical basis of herbal compatibility including principles of "Jun, Chen, Zuo and Shi" are still unclear. Network pharmacology is a new strategy based on system biology and multi-disciplines, which can systematically and comprehensively observe the intervention of drugs on disease networks, and is especially suitable for the research of TCM in the treatment of complex diseases. In this study, we systematically decoded the "Jun, Chen, Zuo and Shi" rules of Huanglian Jiedu Decoction (HJD) in the treatment of diseases for the first time. This interpretation method considered three levels of data. The data in the first level mainly depicts the characteristics of each component in single botanical drug of HJD, include the physical and chemical properties of component, ADME properties and functional enrichment analysis of component targets. The second level data is the characterization of component-target-protein (C-T-P) network in the whole protein-protein interaction (PPI) network, mainly include the characterization of degree and key communities in C-T-P network. The third level data is the characterization of intervention propagation properties of HJD in the treatment of different complex diseases, mainly include target coverage of pathogenic genes and propagation coefficient of intervention effect between target proteins and pathogenic genes. Finally, our method was validated by metabolic data, which could be used to detect the components absorbed into blood. This research shows the scientific basis of "Jun-Chen-Zuo-Shi" from a multi-dimensional perspective, and provides a good methodological reference for the subsequent interpretation of key components and speculation mechanism of the formula.

System Pharmacology-Based Strategy to Decode the Synergistic Mechanism of Zhi-zhu Wan for Functional Dyspepsia.

Functional dyspepsia (FD) is a widely prevalent gastrointestinal disorder throughout the world, whereas the efficacy of current treatment in the Western countries is limited. As the symptom is equivalent to the traditional Chinese medicine (TCM) term "stuffiness and fullness," FD can be treated with Zhi-zhu Wan (ZZW) which is a kind of Chinese patent medicine. However, the "multi-component" and "multi-target" feature of Chinese patent medicine makes it challenge to elucidate the potential therapeutic mechanisms of ZZW on FD. Presently, a novel system pharmacology model including pharmacokinetic parameters, pharmacological data, and component contribution score (CS) is constructed to decipher the potential therapeutic mechanism of ZZW on FD. Finally, 61 components with favorable pharmacokinetic profiles and biological activities were obtained through ADME (absorption, distribution, metabolism, and excretion) screening in silico. The related targets of these components are identified by component targeting process followed by GO analysis and pathway enrichment analysis. And systematic analysis found that through acting on the target related to inflammation, gastrointestinal peristalsis, and mental disorder, ZZW plays a synergistic and complementary effect on FD at the pathway level. Furthermore, the component CS showed that 29 components contributed 90.18% of the total CS values of ZZW for the FD treatment, which suggested that the effective therapeutic effects of ZZW for FD are derived from all active components, not a few components. This study proposes the system pharmacology method and discovers the potent combination therapeutic mechanisms of ZZW for FD. This strategy will provide a reference method for other TCM mechanism research.

Network Intervention, a Method to Address Complex Therapeutic Strategies.

Objective: Network-based approaches emerged as powerful tools for studying complex diseases. Our intention in this article was to raise awareness of the benefits of new therapeutic strategy in biological networks context and provide an introduction to this topic. Methods: This article will discuss the rational for network intervention, and outline some of the important aspects of deciphering targets activities in the network and future embodiments of network intervention. We also present examples of network intervention based on the strategies these approaches use. Results: Network intervention seeks for target combinations to perturb a specific subset of nodes in disease networks to inhibit the bypass mechanisms at systems level. Experimental results derived from our studies are discussed, with conclusions that lead to future research directions. A simple diagram is designed to give a way to find the minimum number of external input required for a network intervention based on the graph theory and get the analytical value of the least input. Conclusion: Creating network intervention that addresses blindness and unthinking action in this way could, therefore, provide more benefit than multi-target therapy. We hope that this article will give readers an appreciation for a new therapeutic strategy that has been proposed for improving clinical benefit by adopting network-based approaches as well as insight into their properties.

A contig-based strategy for the genome-wide discovery of microRNAs without complete genome resources.

MicroRNAs (miRNAs) are important regulators of many cellular processes and exist in a wide range of eukaryotes. High-throughput sequencing is a mainstream method of miRNA identification through which it is possible to obtain the complete small RNA profile of an organism. Currently, most approaches to miRNA identification rely on a reference genome for the prediction of hairpin structures. However, many species of economic and phylogenetic importance are non-model organisms without complete genome sequences, and this limits miRNA discovery. Here, to overcome this limitation, we have developed a contig-based miRNA identification strategy. We applied this method to a triploid species of edible banana (GCTCV-119, Musa spp. AAA group) and identified 180 pre-miRNAs and 314 mature miRNAs, which is three times more than those were predicted by the available dataset-based methods (represented by EST+GSS). Based on the recently published miRNA data set of Musa acuminate, the recall rate and precision of our strategy are estimated to be 70.6% and 92.2%, respectively, significantly better than those of EST+GSS-based strategy (10.2% and 50.0%, respectively). Our novel, efficient and cost-effective strategy facilitates the study of the functional and evolutionary role of miRNAs, as well as miRNA-based molecular breeding, in non-model species of economic or evolutionary interest.

Drastic expression change of transposon-derived piRNA-like RNAs and microRNAs in early stages of chicken embryos implies a role in gastrulation.

Recent studies have shown that endogenous small RNAs regulate a variety of biological processes during vertebrate development; however, little is known about the role of small RNAs in regulating developmental signaling pathways during early embryogenesis. In this study, we applied Illumina sequencing to characterize an unexpected endogenous small RNA catalog and demonstrated a dramatic transition from transposon-derived piRNA-like small RNAs (pilRNAs) to microRNAs (miRNAs) in pre- and post-gastrula chicken embryos. The comprehensive expression profile of chicken miRNAs at the pre- and post-gastrula stages revealed that most known and new miRNAs were dynamically regulated during development. In addition to embryonic stem cell-related miRNAs, Gene Ontology (GO) analysis showed that miRNAs enriched in early stage chicken embryos targeted multiple signal transduction pathways associated with the reproductive process and embryogenesis, including Wnt and TGF-ß, which specifies the neural fate of blastodermal cells. Intriguingly, a large cohort of pilRNAs primarily derived from the active and most abundant transposable elements (TEs) were enriched in chicken stage X blastoderms. Within stage X blastoderms, pilRNAs were specifically localized to the primordial germ cells (PGCs), indicating their post-zygotic origin. Together, these findings imply a role for small RNAs in gastrulation in early stage chicken embryos.

mirExplorer: detecting microRNAs from genome and next generation sequencing data using the AdaBoost method with transition probability matrix and combined features.

microRNAs (miRNAs) represent an abundant group of small regulatory non-coding RNAs in eukaryotes. The emergence of Next-generation sequencing (NGS) technologies has allowed the systematic detection of small RNAs (sRNAs) and de novo sequencing of genomes quickly and with low cost. As a result, there is an increased need to develop fast miRNA prediction tools to annotate miRNAs from various organisms with a high level of accuracy, using the genome sequence or the NGS data. Several miRNA predictors have been proposed to achieve this purpose. However, the accuracy and fitness for multiple species of existing predictors needed to be improved. Here, we present a novel prediction tool called mirExplorer, which is based on an integrated adaptive boosting method and contains two modules. The first module named mirExplorer-genome was designed to de novo predict pre-miRNAs from genome, and the second module named mirExplorer-NGS was used to discover miRNAs from NGS data. A set of novel features of pre-miRNA secondary structure and miRNA biogenesis has been extracted to distinguish real pre-miRNAs from pseudo ones. We used outer-ten-fold cross-validation to verify the mirExplorer-genome computation, which obtained a specificity of 95.03% and a sensitivity of 93.71% on human data. This computation was made on test data from 16 species, and it achieved an overall accuracy of 95.53%. Systematic outer-ten-fold cross-validation of the mirExplorer-NGS model achieved a specificity of 98.3% and a sensitivity of 97.72%. We found that the good performance of the mirExplorer-NGS model was upheld across species from vertebrates to plants in test datasets. The mirExplorer is available as both web server and software package at http://biocenter.sysu.edu.cn/mir/.

ncRNAimprint: a comprehensive database of mammalian imprinted noncoding RNAs.

Imprinted noncoding RNAs (ncRNAs) are expressed mono-allelically in a parent-of-origin-dependent manner, which is mainly evident in mammals. Lying at a crossroad between imprinted genes and ncRNAs, imprinted ncRNAs show distinct features. They are likely to function in nontraditional ways compared to non-imprinted ncRNAs, and are much more responsible for the mechanism of genomic imprinting compared to imprinted protein-coding genes. An increasing number of human diseases have been shown to be related to abnormalities in imprinted ncRNAs. Due to their functional importance, many studies focusing on imprinted ncRNAs have been published in recent years; however, there is no systematic collection or description of imprinted ncRNAs and the rapidly growing knowledge is scattered in various places. Here, we describe a new database, ncRNAimprint, which serves as a comprehensive resource center for mammalian imprinted ncRNAs. A catalog of imprinted ncRNAs, including snoRNAs, microRNAs, piRNAs, siRNAs, antisense ncRNAs, and mRNA-like ncRNAs, was annotated in detail using information extracted from relevant literature and databases. Comprehensive collections of imprinted ncRNA-related diseases, imprinting control regions (ICRs), and imprinted regions were manually compiled to provide resources for current research focusing on imprinted ncRNAs. Small RNA deep sequencing reads that fully matched within imprinted regions were also included to offer useful evidence in detecting novel imprinted ncRNAs and to aid in analyzing expression patterns of known imprinted ncRNAs. A search page including four effective search forms and two graphical browsers was created for rapid retrieval and visualization of these data. The imprinted ncRNA database is freely accessible at http://rnaqueen.sysu.edu.cn/ncRNAimprint.

SnoRNAs from the filamentous fungus Neurospora crassa: structural, functional and evolutionary insights.

BACKGROUND: SnoRNAs represent an excellent model for studying the structural and functional evolution of small non-coding RNAs involved in the post-transcriptional modification machinery for rRNAs and snRNAs in eukaryotic cells. Identification of snoRNAs from Neurospora crassa, an important model organism playing key roles in the development of modern genetics, biochemistry and molecular biology will provide insights into the evolution of snoRNA genes in the fungus kingdom. RESULTS: Fifty five box C/D snoRNAs were identified and predicted to guide 71 2'-O-methylated sites including four sites on snRNAs and three sites on tRNAs. Additionally, twenty box H/ACA snoRNAs, which potentially guide 17 pseudouridylations on rRNAs, were also identified. Although not exhaustive, the study provides the first comprehensive list of two major families of snoRNAs from the filamentous fungus N. crassa. The independently transcribed strategy dominates in the expression of box H/ACA snoRNA genes, whereas most of the box C/D snoRNA genes are intron-encoded. This shows that different genomic organizations and expression modes have been adopted by the two major classes of snoRNA genes in N. crassa . Remarkably, five gene clusters represent an outstanding organization of box C/D snoRNA genes, which are well conserved among yeasts and multicellular fungi, implying their functional importance for the fungus cells. Interestingly, alternative splicing events were found in the expression of two polycistronic snoRNA gene hosts that resemble the UHG-like genes in mammals. Phylogenetic analysis further revealed that the extensive separation and recombination of two functional elements of snoRNA genes has occurred during fungus evolution. CONCLUSION: This is the first genome-wide analysis of the filamentous fungus N. crassa snoRNAs that aids in understanding the differences between unicellular fungi and multicellular fungi. As compared with two yeasts, a more complex pattern of methylation guided by box C/D snoRNAs in multicellular fungus than in unicellular yeasts was revealed, indicating the high diversity of post-transcriptional modification guided by snoRNAs in the fungus kingdom.

Genome-wide analysis of chicken snoRNAs provides unique implications for the evolution of vertebrate snoRNAs.

BACKGROUND: Small nucleolar RNAs (snoRNAs) represent one of the largest groups of functionally diverse trans-acting non-protein-coding (npc) RNAs currently known in eukaryotic cells. Chicken snoRNAs have been very poorly characterized when compared to other vertebrate snoRNAs. A genome-wide analysis of chicken snoRNAs is therefore of great importance to further understand the functional evolution of snoRNAs in vertebrates. RESULTS: Two hundred and one gene variants encoding 93 box C/D and 62 box H/ACA snoRNAs were identified in the chicken genome and are predicted to guide 86 2'-O-ribose methylations and 69 pseudouridylations of rRNAs and spliceosomal RNAs. Forty-four snoRNA clusters were grouped into four categories based on synteny characteristics of the clustered snoRNAs between chicken and human. Comparative analyses of chicken snoRNAs revealed extensive recombination and separation of guiding function, with cooperative evolution between the guiding duplexes and modification sites. The gas5-like snoRNA host gene appears to be a hotspot of snoRNA gene expansion in vertebrates. Our results suggest that the chicken is a good model for the prediction of functional snoRNAs, and that intragenic duplication and divergence might be the major driving forces responsible for expansion of novel snoRNA genes in the chicken genome. CONCLUSION: We have provided a detailed catalog of chicken snoRNAs that aids in understanding snoRNA gene repertoire differences between avians and other vertebrates. Our genome-wide analysis of chicken snoRNAs improves annotation of the 'darkness matter' in the npcRNA world and provides a unique perspective into snoRNA evolution in vertebrates.