Kailin Tang

HIT: linking herbal active ingredients to targets

The information of protein targets and small molecule has been highly valued by biomedical and pharmaceutical research. Several protein target databases are available online for FDA-approved drugs as well as the promising precursors that have largely facilitated the mechanistic study and subsequent research for drug discovery. However, those related resources regarding to herbal active ingredients, although being unusually valued as a precious resource for new drug development, is rarely found. In this article, a comprehensive and fully curated database for Herb Ingredients’ Targets (HIT, http://lifecenter.sgst.cn/hit/) has been constructed to complement above resources. Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for users reference. Derived from more than 3250 literatures, it currently contains 5208 entries about 1301 known protein targets (221 of them are described as direct targets) affected by 586 herbal compounds from more than 1300 reputable Chinese herbs, overlapping with 280 therapeutic targets from Therapeutic Targets Database (TTD), and 445 protein targets from DrugBank corresponding to 1488 drug agents. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.

Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data

BackgroundRecent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.ResultsWe propose a method based on statistical moments to reduce feature dimensions. After refining and t-testing, SELDI-TOF data are divided into several intervals. Four statistical moments (mean, variance, skewness and kurtosis) are calculated for each interval and are used as representative variables. The high dimensionality of the data can thus be rapidly reduced. To improve efficiency and classification performance, the data are further used in kernel PLS models. The method achieved average sensitivity of 0.9950, specificity of 0.9916, accuracy of 0.9935 and a correlation coefficient of 0.9869 for 100 five-fold cross validations. Furthermore, only one control was misclassified in leave-one-out cross validation.ConclusionThe proposed method is suitable for analyzing high-throughput proteomics data.

Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer

The identification of synergistic chemotherapeutic agents from a large pool of candidates is highly challenging. Here, we present a Ranking-system of Anti-Cancer Synergy (RACS) that combines features of targeting networks and transcriptomic profiles, and validate it on three types of cancer. Using data on human β-cell lymphoma from the Dialogue for Reverse Engineering Assessments and Methods consortium we show a probability concordance of 0.78 compared with 0.61 obtained with the previous best algorithm. We confirm 63.6% of our breast cancer predictions through experiment and literature, including four strong synergistic pairs. Further in vivo screening in a zebrafish MCF7 xenograft model confirms one prediction with strong synergy and low toxicity. Validation using A549 lung cancer cells shows similar results. Thus, RACS can significantly improve drug synergy prediction and markedly reduce the experimental prescreening of existing drugs for repurposing to cancer treatment, although the molecular mechanism underlying particular interactions remains unknown.

SEPPA 2.0—more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen

Spatial Epitope Prediction server for Protein Antigens (SEPPA) has received lots of feedback since being published in 2009. In this improved version, relative ASA preference of unit patch and consolidated amino acid index were added as further classification parameters in addition to unit-triangle propensity and clustering coefficient which were previously reported. Then logistic regression model was adopted instead of the previous simple additive one. Most importantly, subcellular localization of protein antigen and species of immune host were fully taken account to improve prediction. The result shows that AUC of 0.745 (5-fold cross-validation) is almost the baseline performance with no differentiation like all the other tools. Specifying subcellular localization of protein antigen and species of immune host will generally push the AUC up. Secretory protein immunized to mouse can push AUC to 0.823. In this version, the false positive rate has been largely decreased as well. As the first method which has considered the subcellular localization of protein antigen and species of immune host, SEPPA 2.0 shows obvious advantages over the other popular servers like SEPPA, PEPITO, DiscoTope-2, B-pred, Bpredictor and Epitopia in supporting more specific biological needs. SEPPA 2.0 can be accessed at http://badd.tongji.edu.cn/seppa/. Batch query is also supported.

Towards a bioinformatics analysis of anti-Alzheimer’s herbal medicines from a target network perspective

With the growth of aging population all over the world, a rising incidence of Alzheimers disease (AD) has been recently observed. In contrast to FDA-approved western drugs, herbal medicines, featured as abundant ingredients and multi-targeting, have been acknowledged with notable anti-AD effects although the mechanism of action (MOA) is unknown. Investigating the possible MOA for these herbs can not only refresh but also extend the current knowledge of AD pathogenesis. In this study, clinically tested anti-AD herbs, their ingredients as well as their corresponding target proteins were systematically reviewed together with applicable bioinformatics resources and methodologies. Based on above information and resources, we present a systematically target network analysis framework to explore the mechanism of anti-AD herb ingredients. Our results indicated that, in addition to the binding of those symptom-relieving targets as the FDA-approved drugs usually do, ingredients of anti-AD herbs also interact closely with a variety of successful therapeutic targets related to other diseases, such as inflammation, cancer and diabetes, suggesting the possible cross-talks between these complicated diseases. Furthermore, pathways of Ca(2+) equilibrium maintaining upstream of cell proliferation and inflammation were densely targeted by the anti-AD herbal ingredients with rigorous statistic evaluation. In addition to the holistic understanding of the pathogenesis of AD, the integrated network analysis on the MOA of herbal ingredients may also suggest new clues for the future disease modifying strategies.

Calmodulin as a Potential Target by Which Berberine Induces Cell Cycle Arrest in Human Hepatoma Bel7402 Cells

Berberine is an isoquinoline alkaloid that has drawn extensive attention because it possesses various biological activities. Several mechanisms have been proposed to interpret the anticancer activity of berberine. However, these explanations are mostly based on its downstream‐regulated genes or proteins; information on the direct target proteins that mediate the antiproliferative action of berberine remains unclear. In this study, a computational pipeline based on a ligand–protein inverse docking program and mining of the ‘Connectivity MAP’ data was adopted to explore the potential target proteins for berberine. The results showed that four proteins, that is calmodulin, cytochrome P450 3A4, sex hormone‐binding globulin, and carbonic anhydrase II, were suggested to be the potential targets of berberine. The anticalmodulin property of berberine was demonstrated with an in vitro phosphodiesterase activity assay. Flow cytometric analysis found that G1 cell cycle arrest induced by berberine in Bel7402 cells was enhanced by cotreatment with calmodulin inhibitors. Western blotting results indicated that berberine treatment decreased phosphorylation of calmodulin kinase II and blocked subsequent MEK1 activation as well as p27 protein degradation. These results suggested that calmodulin might play crucial roles in berberine‐induced cell cycle arrest in cancer cells.

iPEAP: integrating multiple omics and genetic data for pathway enrichment analysis

UNLABELLED A challenge in biodata analysis is to understand the underlying phenomena among many interactions in signaling pathways. Such study is formulated as the pathway enrichment analysis, which identifies relevant pathways functional enriched in high-throughput data. The question faced here is how to analyze different data types in a unified and integrative way by characterizing pathways that these data simultaneously reveal. To this end, we developed integrative Pathway Enrichment Analysis Platform, iPEAP, which handles transcriptomics, proteomics, metabolomics and GWAS data under a unified aggregation schema. iPEAP emphasizes on the ability to aggregate various pathway enrichment results generated in different high-throughput experiments, as well as the quantitative measurements of different ranking results, thus providing the first benchmark platform for integration, comparison and evaluation of multiple types of data and enrichment methods. AVAILABILITY AND IMPLEMENTATION iPEAP is freely available at http://www.tongji.edu.cn/∼qiliu/ipeap.html.

Local combinational variables: an approach used in DNA-binding helix-turn-helix motif prediction with sequence information

Sequence-based approach for motif prediction is of great interest and remains a challenge. In this work, we develop a local combinational variable approach for sequence-based helix-turn-helix (HTH) motif prediction. First we choose a sequence data set for 88 proteins of 22 amino acids in length to launch an optimized traversal for extracting local combinational segments (LCS) from the data set. Then after LCS refinement, local combinational variables (LCV) are generated to construct prediction models for HTH motifs. Prediction ability of LCV sets at different thresholds is calculated to settle a moderate threshold. The large data set we used comprises 13 HTH families, with 17 455 sequences in total. Our approach predicts HTH motifs more precisely using only primary protein sequence information, with 93.29% accuracy, 93.93% sensitivity and 92.66% specificity. Prediction results of newly reported HTH-containing proteins compared with other prediction web service presents a good prediction model derived from the LCV approach. Comparisons with profile-HMM models from the Pfam protein families database show that the LCV approach maintains a good balance while dealing with HTH-containing proteins and non-HTH proteins at the same time. The LCV approach is to some extent a complementary to the profile-HMM models for its better identification of false-positive data. Furthermore, genome-wide predictions detect new HTH proteins in both Homo sapiens and Escherichia coli organisms, which enlarge applications of the LCV approach. Software for mining LCVs from sequence data set can be obtained from anonymous ftp site ftp://cheminfo.tongji.edu.cn/LCV/freely.

Cancer classification from the gene expression profiles by Discriminant Kernel-PLS.

Cancer diagnosis depending on microarray technology has drawn more and more attention in the past few years. Accurate and fast diagnosis results make gene expression profiling produced from microarray widely used by a large range of researchers. Much research work highlights the importance of gene selection and gains good results. However, the minimum sets of genes derived from different methods are seldom overlapping and often inconsistent even for the same set of data, partially because of the complexity of cancer disease. In this paper, cancer classification was attempted in an alternative way of the whole gene expression profile for all samples instead of partial gene sets. Here, the three common sets of data were tested by NIPALS-KPLS method for acute leukemia, prostate cancer and lung cancer respectively. Compared to other conventional methods, the results showed wide improvement in classification accuracy. This paper indicates that sample profile of gene expression may be explored as a better indicator for cancer classification, which deserves further investigation.

Potential metabolic mechanism of girls' central precocious puberty: a network analysis on urine metabonomics data

BackgroundCentral precocious puberty (CPP) is a common pediatric endocrine disease caused by early activation of hypothalamic-putuitary-gonadal (HPG) axis, yet the exact mechanism was poorly understood. Although there were some proofs that an altered metabolic profile was involved in CPP, interpreting the biological implications at a systematic level is still in pressing need. To gain a systematic understanding of the biological implications, this paper analyzed the CPP differential urine metabolites from a network point of view.ResultsIn this study, differential urine metabolites between CPP girls and age-matched normal ones were identified by LC-MS. Their basic topological parameters were calculated in the background network. The network decomposition suggested that CPP differential urine metabolites were most relevant to amino acid metabolism. Further proximity analysis of CPP differential urine metabolites and neuro-endocrine metabolites showed a close relationship between CPP metabolism and neuro-endocrine system. Then the core metabolic network of CPP was successfully constructed among all these differential urine metabolites. As can be demonstrated in the core network, abnormal aromatic amino acid metabolism might influence the activity of HPG and hypothalamic pituitary adrenal (HPA) axis. Several adjustments to the early activation of puberty in CPP girls could also be revealed by urine metabonomics.ConclusionsThe present article demonstrated the ability of urine metabonomics to provide several potential metabolic clues for CPPs mechanism. It was revealed that abnormal metabolism of amino acid, especially aromatic amino acid, might have a close correlation with CPPs pathogenesis by activating HPG axis and suppressing HPA axis. Such a method of network-based analysis could also be applied to other metabonomics analysis to provide an overall perspective at a systematic level.