MACC: a visual interactive knowledgebase of metabolite-associated cell communications.

Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported, no relevant database has become available so far. To address this gap, we here developed the first knowledgebase (MACC), to comprehensively describe human metabolite-associated cell communications through curation of experimental literatures. MACC currently contains: (a) 4206 carefully curated metabolite-associated cell communications pairs involving 244 human endogenous metabolites and reported biological effects in vivo and in vitro; (b) 226 comprehensive cell subtypes and 296 disease states, such as cancers, autoimmune diseases, and pathogenic infections; (c) 4508 metabolite-related enzymes and transporters, involving 542 pathways; (d) an interactive tool with user-friendly interface to visualize networks of multiple metabolite-cell interactions. (e) overall expression landscape of metabolite-associated gene sets derived from over 1500 single-cell expression profiles to infer metabolites variations across different cells in the sample. Also, MACC enables cross-links to well-known databases, such as HMDB, DrugBank, TTD and PubMed etc. In complement to ligand-receptor databases, MACC may give new perspectives of alternative communication between cells via metabolite secretion and adsorption, together with the resulting biological functions. MACC is publicly accessible at: http://macc.badd-cao.net/.

A comparative study on laparoscopic and open surgical approaches for perforated peptic ulcer repair: efficacy and outcomes analysis.

PURPOSE: This study aimed to compare the clinical outcomes of the clinical outcomes of laparoscopic and open sutures for peptic ulcer perforation (PPU). MATERIALS AND METHODS: PubMed, EMBASE, and Cochrane Library databases were searched for eligible studies from inception to March 31, 2023. Odds ratios (OR) and 95% confidence intervals (Cl) were also calculated. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the included studies. This study was performed using the Stata (V.16.0) software. RESULTS: A total of 29 studies involving 17,228 patients were included in this study. In terms of postoperative outcomes, the laparoscopic group had a shorter postoperative hospital stay (MD = -0.29, 95%CI = -0.44 to -0.13, P = 0.00), less blood loss (MD = -0.45, 95%CI = -0.82 to -0.08, P = 0.02), fewer wound infection (OR = 0.20, 95%CI = 0.17 to 0.24, P = 0.00), fewer pneumonia (OR = 0.59, 95%CI = 0.41 to 0.87, P = 0.01), fewer respiratory complications (OR = 0.26, 95%CI = 0.13 to 0.55, P = 0.00) and lower postoperative morbidity (OR = 0.51, 95%CI = 0.33 to 0.78, P = 0.00). The laparoscopic group had a lower mortality rate (OR = 0.36, 95%CI = 0.27 to 0.49, P = 0.00) than the open group. We also found that the laparoscopic group had a higher overall complication rate than the open group (OR = 0.45, 95%CI = 0.34 to 0.60, P = 0.00). CONCLUSION: Laparoscopic repair was associated with a lower risk of mortality than open repair in patients with PPU. Laparoscopic repair may be a better option in patients with PPU.

Impact of preoperative type 2 diabetes mellitus on the outcomes of patients with gastric cancer following gastrectomy: Analysis of 834 patients using propensity score matching.

The purpose of the current study was to compare the outcomes of patients with gastric cancer (GC) between the type 2 diabetes mellitus (T2DM) group and the non-T2DM group. The PubMed, Embase and Cochrane Library databases were searched from inception to March 8, 2022, to identify propensity score matching (PSM) studies that analyzed the effect of T2DM on the outcomes of patients with GC. Total complications, overall survival (OS), disease-free survival (DFS) and cancer-specific survival (CSS) were compared between the T2DM group and the non-T2DM group. A total of four PSM studies with 834 patients were included in the current study. There were 311 and 523 patients in the T2DM group and the non-T2DM group, respectively. Baseline characteristics of the two groups were adjusted with PSM in all the four studies, however, no significant difference was found in baseline characteristics (P>0.05). DFS was significantly worse in the T2DM group compared with that in the non-T2DM group [hazard ratio (HR), 1.45; 95% confidence interval (CI), 1.10-1.90; P=0.007)]. However, after pooling up the data, there was no significant difference between the T2DM group and the non-T2DM group in terms of OS (HR, 1.41; 95% CI, 0.92-2.16; P=0.11), CSS (HR, 1.29; 95% CI, 0.92-1.81; P=0.14) and total complications (odds ratio, 1.01; 95% CI, 0.64-1.60; P=0.95). Patients with GC and T2DM are associated with poor DFS. However, there were no significant differences between the T2DM group and the non-T2DM group in terms of OS, CSS and total complications.

Function of normal oral mucosa revealed by single-cell RNA sequencing.

The functions of oral mucosa include barrier, sensation, and secretion. The barrier protection function is particularly important, which includes physical barrier and immunological barrier. Few studies have revealed the function of oral mucosa by displaying the map of normal oral mucosal cells from the perspective of single cells. Here, single-cell transcriptome sequencing was used to bring a relatively comprehensive map of the normal oral mucosal cells. In total, 26,398 cells from three cases of normal oral mucosa were analyzed by single-cell RNA-sequencing and 14 distinct cell groups were defined, 7 of which were immune cells. We performed subgroup classification and heterogeneity analysis of epithelial cells, T cells, and macrophagocytes, which found a subpopulation of epithelial cells with high expression of major histocompatibility complex class II molecules, a subpopulation CD8+ GZMK+ T cells, and two kinds of active macrophagocytes. Meanwhile, we identified ligand-receptor pairs among the major cell types to explore the interactions and how they maintain the homeostasis of normal oral mucosa. Based on these results, the epithelial barrier function, immunological barrier function, and potential maintenance function of stromal cells in the oral mucosa were described at the single-cell level, which provides basic data resources for further studies of oral mucosal diseases.

Probing Synergistic Targets by Natural Compounds for Hepatocellular Carcinoma.

BACKGROUND: Designing combination drugs for malignant cancers has been restricted due to the scarcity of synergy-medicated targets, while some natural compounds have demonstrated potential to enhance anticancer effects. METHODS: We here explored the feasibility of probing synergy-mediated targets by Berberine (BER) and Evodiamine (EVO) in hepatocellular carcinoma (HCC). Using the genomics-derived HCC signaling networks of compound treatment, NF-κB and c-JUN were inferred as key responding elements with transcriptional activity coinhibited during the synergistic cytotoxicity induction in BEL-7402 cells. Then, selective coinhibitors of NF-κB and c-JUN were tested demonstrating similar synergistic antiproliferation activity. RESULTS: Consistent with in vivo experiments of zebrafish, coinhibitors were found to significantly reduce tumor growth by 79% and metastasis by 96% compared to blank control, accompanied by anti-angiogenic activity. In an analysis of 365 HCC individuals, the low expression group showed significantly lower malignancies and better prognosis, with the median survival time increased from 67 to 213%, compared to the rest of the groups. CONCLUSION: Together, NF-κB and c-JUN were identified as promising synergistic inducers in developing anti-HCC therapies. Also, our method may provide a feasible strategy to explore new targeting space from natural compounds, opening opportunities for the rational design of combinational formulations in combatting malignant cancers.

Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer.

Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two technologies, enabling freely blended data for consolidated analysis. Rank-In was rigorously validated via the public cell and tissue samples tested by both technologies. On the two reference samples of the SEQC project, Rank-In not only perfectly classified the 44 profiles but also achieved the best accuracy of 0.9 on predicting TaqMan-validated DEGs. More importantly, on 327 Glioblastoma (GBM) profiles and 248, 523 heterogeneous colon cancer profiles respectively, only Rank-In can successfully discriminate every single cancer profile from normal controls, while the others cannot. Further on different sizes of mixed seq-array GBM profiles, Rank-In can robustly reproduce a median range of DEG overlapping from 0.74 to 0.83 among top genes, whereas the others never exceed 0.72. Being the first effective method enabling mixed data of cross-technology analysis, Rank-In welcomes hybrid of array and seq profiles for integrative study on large/small, paired/unpaired and balanced/imbalanced samples, opening possibility to reduce sampling space of clinical cancer patients. Rank-In can be accessed at http://www.badd-cao.net/rank-in/index.html.

H-RACS: a handy tool to rank anti-cancer synergistic drugs.

Though promising, identifying synergistic combinations from a large pool of candidate drugs remains challenging for cancer treatment. Due to unclear mechanism and limited confirmed cases, only a few computational algorithms are able to predict drug synergy. Yet they normally require the drug-cell treatment results as an essential input, thus exclude the possibility to pre-screen those unexplored drugs without cell treatment profiling. Based on the largest dataset of 33,574 combinational scenarios, we proposed a handy webserver, H-RACS, to overcome the above problems. Being loaded with chemical structures and target information, H-RACS can recommend potential synergistic pairs between candidate drugs on 928 cell lines of 24 prevalent cancer types. A high model performance was achieved with AUC of 0.89 on independent combinational scenarios. On the second independent validation of DREAM dataset, H-RACS obtained precision of 67% among its top 5% ranking list. When being tested on new combinations and new cell lines, H-RACS showed strong extendibility with AUC of 0.84 and 0.81 respectively. As the first online server freely accessible at http://www.badd-cao.net/h-racs, H-RACS may promote the pre-screening of synergistic combinations for new chemical drugs on unexplored cancers.

Detecting Prognosis Risk Biomarkers for Colon Cancer Through Multi-Omics-Based Prognostic Analysis and Target Regulation Simulation Modeling.

BACKGROUND: Colon cancer is one of the most common health threats for humans since its high morbidity and mortality. Detecting potential prognosis risk biomarkers (PRBs) is essential for the improvement of therapeutic strategies and drug development. Currently, although an integrated prognostic analysis of multi-omics for colon cancer is insufficient, it has been reported to be valuable for improving PRBs' detection in other cancer types. AIM: This study aims to detect potential PRBs for colon adenocarcinoma (COAD) samples through the cancer genome atlas (TCGA) by integrating muti-omics. MATERIALS AND METHODS: The multi-omics-based prognostic analysis (MPA) model was first constructed to systemically analyze the prognosis of colon cancer based on four-omics data of gene expression, exon expression, DNA methylation and somatic mutations on COAD samples. Then, the essential features related to prognosis were functionally annotated through protein-protein interaction (PPI) network and cancer-related pathways. Moreover, the significance of those essential prognostic features were further confirmed by the target regulation simulation (TRS) model. Finally, an independent testing dataset, as well as the single cell-based expression dataset were utilized to validate the generality and repeatability of PRBs detected in this study. RESULTS: By integrating the result of MPA modeling, as well the PPI network, integrated pathway and TRS modeling, essential features with gene symbols such as EPB41, PSMA1, FGFR3, MRAS, LEP, C7orf46, LOC285000, LBP, ZNF35, SLC30A3, LECT2, RNF7, and DYNC1I1 were identified as PRBs which provide high potential as drug targets for COAD treatment. Validation on the independent testing dataset demonstrated that these PRBs could be applied to distinguish the prognosis of COAD patients. Moreover, the prognosis of patients with different clinical conditions could also be distinguished by the above PRBs. CONCLUSIONS: The MPA and TRS models constructed in this paper, as well as the PPI network and integrated pathway analysis, could not only help detect PRBs as potential therapeutic targets for COAD patients but also make it a paradigm for the prognostic analysis of other cancers.

Reveal the Regulation Patterns of Prognosis-Related miRNAs and lncRNAs Across Solid Tumors in the Cancer Genome Atlas.

BACKGROUND: The dysregulation of non-coding RNAs (ncRNAs) such as miRNAs and lncRNAs are associated with the pathogenesis and progression in multiple cancers including solid tumors. Comprehensive investigations of prognosis-related ncRNA markers could promote the development of therapeutic strategies for solid tumors, but rarely reported. METHODS: By taking advantage of The Cancer Genome Atlas (TCGA), pan-cancer prognosis analysis (PCPA) models were firstly constructed based on miRNA and lncRNA expression profiles of 8,450 samples in 19 solid tumors. Further, the co-occurrence and exclusivity among ncRNA markers were systematically analyzed for different cancers. RESULTS: In identified ncRNA makers, 71% of the miRNA markers were shared in multiple cancers, whereas 96% of the lncRNA markers were cancer-specific. Moreover, to analyze the regulation patterns of prognosis-related ncRNAs at the pan-cancer level, miRNA markers were further annotated into eight carcinogenic pathways. Results represented that approximately 86% of these miRNA markers could regulate the PI3K-Akt signaling pathway, while only 48% for the Notch signaling pathway. Finally, among 126 common genes that participated in eight carcinogenic pathways, BCL2, CSNK2A1, EGFR, PDGFRA, and VEGFA were proposed as potential drug targets for multiple cancers. CONCLUSION: The prognosis analysis and regulation characteristics of ncRNAs presented in this study may help to facilitate the discovery of anti-cancer drugs for multiple solid tumors.

Exploring the Mechanism of Flavonoids Through Systematic Bioinformatics Analysis.

Flavonoids are the largest class of plant polyphenols, with common structure of diphenylpropanes, consisting of two aromatic rings linked through three carbons and are abundant in both daily diets and medicinal plants. Fueled by the recognition of consuming flavonoids to get better health, researchers became interested in deciphering how flavonoids alter the functions of human body. Here, systematic studies were performed on 679 flavonoid compounds and 481 corresponding targets through bioinformatics analysis. Multiple human diseases related pathways including cancers, neuro-disease, diabetes, and infectious diseases were significantly regulated by flavonoids. Specific functions of each flavonoid subclass were further analyzed in both target and pathway level. Flavones and isoflavones were significantly enriched in multi-cancer related pathways, flavan-3-ols were found focusing on cellular processing and lymphocyte regulation, flavones preferred to act on cardiovascular related activities and isoflavones were closely related with cell multisystem disorders. Relationship between chemical constitution fragment and biological effects indicated that different side chain could significantly affect the biological functions of flavonoids subclasses. Results will highlight the common and preference functions of flavonoids and their subclasses, which concerning their pharmacological and biological properties.

Diagnostic accuracy of transient elastography (FibroScan) in detection of esophageal varices in patients with cirrhosis: A meta-analysis.

AIM: To investigate the diagnostic accuracy of FibroScan (FS) in detecting esophageal varices (EV) in cirrhotic patients. METHODS: Through a systemic literature search of multiple databases, we reviewed 15 studies using endoscopy as a reference standard, with the data necessary to calculate pooled sensitivity (SEN) and specificity (SPE), positive and negative LR, diagnostic odds ratio (DOR) and area under receiver operating characteristics (AUROC). The quality of the studies was rated by the Quality Assessment of Diagnostic Accuracy studies-2 tool. Clinical utility of FS for EV was evaluated by a Fagan plot. Heterogeneity was explored using meta-regression and subgroup analysis. All statistical analyses were conducted via Stata12.0, MetaDisc1.4 and RevMan5. RESULTS: In 15 studies (n = 2697), FS detected the presence of EV with the summary sensitivities of 84% (95%CI: 81.0%-86.0%), specificities of 62% (95%CI: 58.0%- 66.0%), a positive LR of 2.3 (95%CI: 1.81-2.94), a negative LR of 0.26 (95%CI: 0.19-0.35), a DOR of 9.33 (95%CI: 5.84-14.92) and an AUROC of 0.8262. FS diagnosed the presence of large EV with the pooled SEN of 0.78 (95%CI: 75.0%-81.0%), SPE of 0.76 (95%CI: 73.0%-78.0%), a positive and negative LR of 3.03 (95%CI: 2.38-3.86) and 0.30 (95%CI: 0.23-0.39) respectively, a summary diagnostic OR of 10.69 (95%CI: 6.81-16.78), and an AUROC of 0.8321. A meta-regression and subgroup analysis indicated different etiology could serve as a potential source of heterogeneity in the diagnosis of the presence of EV group. A Deek's funnel plot suggested a low probability for publication bias. CONCLUSION: Using FS to measure liver stiffness cannot provide high accuracy for the size of EV due to the various cutoff and different etiologies. These limitations preclude widespread use in clinical practice at this time; therefore, the results should be interpreted cautiously given its SEN and SPE.

[Application of Bishop-Koop stoma in refractory congenital intestinal atresia].

OBJECTIVE: To explore the feasibility and safety of Bishop-Koop stoma procedure in the treatment of neonates with refractory congenital intestinal atresia. METHODS: Clinical and follow-up data of 25 neonates with refractory congenital intestinal atresia undergoing Bishop-Koop stoma procedure in our center from January 2011 to December 2014 were retrospectively analyzed. Of 25 neonates, 13 (52%) were male, 12(48%) were female, the birth weight was 1600-3800 g (mean 2920 g), the age of admission was 10 hours to 20 days, and the age of operation was 1-58 d (mean 7 d). Diameter ratio of proximal atresia intestine to distal atresia intestine was all greater than 4. Eleven cases(44%) were high jejunal atresia, 3 cases(12%) type III( b, 7 cases(28%) type IIII(, 14 cases(56%) were identified as complex meconium peritonitis, and 3 cases (12%) received reoperation. RESULTS: All the cases completed their Bishop-Koop stoma operations successfully with median operative time of 3 (1.2-4.5) hours and median intra-operative blood loss of 3.5(1-18) ml. The postoperative complication rate was 20%(5/25), including 3 cases of cholestasis, 1 case of ileus, and 1 case of neonatal necrotizing enterocolitis with septicemia who died 6 days after operation resulting in the mortality of 4%. Besides, 1 case gave up treatment because of economic reason. For the rest 23 neonates, the median first feeding time was 11 days and mean time was 11(5 to 20) days; the median time of postoperative total parenteral nutrition (TPN) was 15 days and mean time was 21 (5 to 68) days; the median hospital stay was 33 days and mean hospital stay was 25(12 to 81) days, respectively. Two-stage stoma closure operations were performed in all the 23 cases afterwards and no postoperative associated complications were found. When discharge after Bishop-Koop stoma operations, Z score of body weight was normal in 3 cases(13.0%) and lower than normal in 20 cases(87.0%), while in hospitalization for stoma closure, Z score of body weight was normal in 19 cases(82.6%) and lower than normal in 4 cases (17.4%). Of 23 cases, serum albumin level was normal in 9 cases(39.1%) before operation, in 3 cases (13.0%) when discharge and in 22 cases(95.7%) in hospitalization for stoma closure. CONCLUSION: Bishop-Koop stoma procedure is safe and feasible in the treatment of neonates with refractory congenital intestinal atresia, and can obviously improve the nutritional status.

Covariation Analysis of Serumal and Urinary Metabolites Suggests Aberrant Glycine and Fatty Acid Metabolism in Chronic Hepatitis B.

BACKGROUND: Chronic hepatitis b (CHB) is one of the most serious viral diseases threatening human health by putting patients at lifelong risk of cirrhosis and hepatocellular carcinoma (HCC). Although some proofs of altered metabolites in CHB were accumulated, its metabolic mechanism remains poorly understood. Analyzing covariations between metabolites may provide new hints toward underlying metabolic pathogenesis in CHB patients. METHODS: The present study collected paired urine and serum samples from the same subjects including 145 CHB and 23 healthy controls. A large-scale analysis of metabolites' covariation within and across biofluids was systematically done to explore the underlying biological evidences for reprogrammed metabolism in CHB. Randomization and relative ranking difference were introduced to reduce bias caused by different sample size. More importantly, functional indication was interpreted by mapping differentially changed covariations to known metabolic pathways. RESULTS: Our results suggested reprogrammed pathways related to glycine metabolism, fatty acids metabolism and TCA cycle in CHB patients. With further improvement, the covariation analysis combined with network association study would pave new alternative way to interpret functional clues in clinical multi-omics data.

Identification of Toxic Pyrrolizidine Alkaloids and Their Common Hepatotoxicity Mechanism.

Pyrrolizidine Alkaloids (PAs) are currently one of the most important botanical hepatotoxic ingredients. Glutathion (GSH) metabolism is the most reported pathway involved in hepatotoxicity mechanism of PAs. We speculate that, for different PAs, there should be a common mechanism underlying their hepatotoxicity in GSH metabolism. Computational methods were adopted to test our hypothesis in consideration of the limitations of current experimental approaches. Firstly, the potential targets of 22 PAs (from three major PA types) in GSH metabolism were identified by reverse docking; Secondly, glutathione S-transferase A1 (GSTA1) and glutathione peroxidase 1 (GPX1) targets pattern was found to be a special characteristic of toxic PAs with stepwise multiple linear regressions; Furthermore, the molecular mechanism underlying the interactions within toxic PAs and these two targets was demonstrated with the ligand-protein interaction analysis; Finally, GSTA1 and GPX1 were proved to be significant nodes in GSH metabolism. Overall, toxic PAs could be identified by GSTA1 and GPX1 targets pattern, which suggests their common hepatotoxicity mechanism: the interfering of detoxication in GSH metabolism. In addition, all the strategies developed here could be extended to studies on toxicity mechanism of other toxins.

Largescale Transcriptomics Analysis Suggests Over-Expression of BGH3, MMP9 and PDIA3 in Oral Squamous Cell Carcinoma.

Oral squamous cell carcinoma (OSCC) has been reported as the most prevalent cancer of the head and neck region, while early diagnosis remains challenging. Here we took a comprehensive bioinformatics study on microarray data of 326 OSCC clinical samples with control of 165 normal tissues. The cell interaction pathways of ECM-receptor interaction and focal adhesion were found to be significantly regulated in OSCC samples. Further analysis of the topological properties and expression consistency identified that three hub genes in the gene interaction network, MMP9, PDIA3 and BGH3, were consistently up-expressed in OSCC samples. When being validated on additional microarray datasets of 41 OSCC samples, the validation rate of over-expressed BGH3, MMP9, and PDIA3 reached 90%, 90% and 84% respectively. At last, immuno-histochemical assays were done to test the protein expression of the three genes on newly collected clinical samples of 35 OSCC, 20 samples of pre-OSCC stage, and 12 normal oral mucosa specimens. Their protein expression levels were also found to progressively increase from normal mucosa to pre-OSCC stage and further to OSCC (ANOVA p = 0.000), suggesting their key roles in OSCC pathogenesis. Based on above solid validation, we propose BGH3, MMP9 and PDIA3 might be further explored as potential biomarkers to aid OSCC diagnosis.

Functional Cross-Talking between Differentially Expressed and Alternatively Spliced Genes in Human Liver Cancer Cells Treated with Berberine.

Berberine has been identified with anti-proliferative effects on various cancer cells. Many researchers have been trying to elucidate the anti-cancer mechanisms of berberine based on differentially expressed genes. However, differentially alternative splicing genes induced by berberine might also contribute to its pharmacological actions and have not been reported yet. Moreover, the potential functional cross-talking between the two sets of genes deserves further exploration. In this study, RNA-seq technology was used to detect the differentially expressed genes and differentially alternative spliced genes in BEL-7402 cancer cells induced by berberine. Functional enrichment analysis indicated that these genes were mainly enriched in the p53 and cell cycle signalling pathway. In addition, it was statistically proven that the two sets of genes were locally co-enriched along chromosomes, closely connected to each other based on protein-protein interaction and functionally similar on Gene Ontology tree. These results suggested that the two sets of genes regulated by berberine might be functionally cross-talked and jointly contribute to its cell cycle arresting effect. It has provided new clues for further researches on the pharmacological mechanisms of berberine as well as the other botanical drugs.

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 ß-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.

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.

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.

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 Alzheimer's 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.