Gong-Hua Li

CDRUG: a web server for predicting anticancer activity of chemical compounds

Cancer is the leading cause of death worldwide. Screening anticancer candidates from tens of millions of chemical compounds is expensive and time-consuming. A rapid and user-friendly web server, known as CDRUG, is described here to predict the anticancer activity of chemical compounds. In CDRUG, a hybrid score was developed to measure the similarity of different compounds. The performance analysis shows that CDRUG has the area under curve of 0.878, indicating that CDRUG is effective to distinguish active and inactive compounds.

In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database.

There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents.

Inferring therapeutic targets from heterogeneous data: HKDC1 is a novel potential therapeutic target for cancer.

MOTIVATION The discovery of therapeutic targets is important for cancer treatment. Although dozens of targets have been used in cancer therapies, cancer remains a serious disease with a high mortality rate. Owing to the expansion of cancer-related data, we now have the opportunity to infer therapeutic targets using computational biology methods. RESULTS Here, we describe a method, termed anticancer activity enrichment analysis, used to determine genes that could be used as therapeutic targets. The results show that these genes have high likelihoods of being developed into clinical targets (>60%). Combined with gene expression data, we predicted 50 candidate targets for lung cancer, of which 19 of the top 20 genes are targeted by approved drugs or drugs used in clinical trials. A hexokinase family member, hexokinase domain-containing protein 1 (HKDC1), is the only one of the top 20 genes that has not been targeted by either an approved drug or one being used in clinical trials. Further investigations indicate that HKDC1 is a novel potential therapeutic target for lung cancer. CONCLUSION We developed a protocol to identify potential therapeutic targets from heterogeneous data. We suggest that HKDC1 is a novel potential therapeutic target for lung cancer. CONTACT huangjf@mail.kiz.ac.cn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Integrated Analysis of Residue Coevolution and Protein Structures Capture Key Protein Sectors in HIV-1 Proteins

HIV type 1 (HIV-1) is characterized by its rapid genetic evolution, leading to challenges in anti-HIV therapy. However, the sequence variations in HIV-1 proteins are not randomly distributed due to a combination of functional constraints and genetic drift. In this study, we examined patterns of sequence variability for evidence of linked sequence changes (termed as coevolution or covariation) in 15 HIV-1 proteins. It shows that the percentage of charged residues in the coevolving residues is significantly higher than that in all the HIV-1 proteins. Most of the coevolving residues are spatially proximal in the protein structures and tend to form relatively compact and independent units in the tertiary structures, termed as “protein sectors”. These protein sectors are closely associated with anti-HIV drug resistance, T cell epitopes, and antibody binding sites. Finally, we explored candidate peptide inhibitors based on the protein sectors. Our results can establish an association between the coevolving residues and molecular functions of HIV-1 proteins, and then provide us with valuable knowledge of pathology of HIV-1 and therapeutics development.

MOfinder: a novel algorithm for detecting overlapping modules from protein-protein interaction network.

Since organism development and many critical cell biology processes are organized in modular patterns, many algorithms have been proposed to detect modules. In this study, a new method, MOfinder, was developed to detect overlapping modules in a protein-protein interaction (PPI) network. We demonstrate that our method is more accurate than other 5 methods. Then, we applied MOfinder to yeast and human PPI network and explored the overlapping information. Using the overlapping modules of human PPI network, we constructed the module-module communication network. Functional annotation showed that the immune-related and cancer-related proteins were always together and present in the same modules, which offer some clues for immune therapy for cancer. Our study around overlapping modules suggests a new perspective on the analysis of PPI network and improves our understanding of disease.

Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets

Breast cancer is the most commonly diagnosed malignancy in women. Several key genes and pathways have been proven to correlate with breast cancer pathology. This study sought to explore the differences in key transcription factors (TFs), transcriptional regulation networks and dysregulated pathways in different tissues in breast cancer. We employed 14 breast cancer datasets from NCBI-GEO and performed an integrated analysis in three different tissues including breast, blood and saliva. The results showed that there were eight genes (CEBPD, EGR1, EGR2, EGR3, FOS, FOSB, ID1 and NFIL3) down-regulated in breast tissue but up-regulated in blood tissue. Furthermore, we identified several unreported tissue-specific TFs that may contribute to breast cancer, including ATOH8, DMRT2, TBX15 and ZNF367. The dysregulation of these TFs damaged lipid metabolism, development, cell adhesion, proliferation, differentiation and metastasis processes. Among these pathways, the breast tissue showed the most serious impairment and the blood tissue showed a relatively moderate damage, whereas the saliva tissue was almost unaffected. This study could be helpful for future biomarker discovery, drug design, and therapeutic and predictive applications in breast cancers.

Meta-Analysis of Parkinson’s Disease and Alzheimer’s Disease Revealed Commonly Impaired Pathways and Dysregulation of NRF2-Dependent Genes

Many lines of evidence suggest that Parkinsons disease (PD) and Alzheimers disease (AD) have common characteristics, such as mitochondrial dysfunction and oxidative stress. As the underlying molecular mechanisms are unclear, we perform a meta-analysis with 9 microarray datasets of PD studies and 7 of AD studies to explore it. Functional enrichment analysis revealed that PD and AD both showed dysfunction in the synaptic vesicle cycle, GABAergic synapses, phagosomes, oxidative phosphorylation, and TCA cycle pathways, and AD had more enriched genes. Comparing the differentially expressed genes between AD and PD, we identified 54 common genes shared by more than six tissues. Among them, 31 downregulated genes contained the antioxidant response element (ARE) consensus sequence bound by NRF2. NRF2 is a transcription factor, which protects cells against oxidative stress through coordinated upregulation of ARE-driven genes. To our surprise, although NRF2 was upregulated, its target genes were all downregulated. Further exploration found that MAFF was upregulated in all tissues and significantly negatively correlated with the 31 NRF2-dependent genes in diseased conditions. Previous studies have demonstrated over-expressed small MAFs can form homodimers and act as transcriptional repressors. Therefore, MAFF might play an important role in dysfunction of NRF2 regulatory network in PD and AD.

Folate Deficiency and Gene Polymorphisms of MTHFR, MTR and MTRR Elevate the Hyperhomocysteinemia Risk.

BACKGROUND Hyperhomocysteinemia (HHcy) is an independent risk factor for cardiovascular diseases (CVDs). We aimed to investigate the joint effect of homocysteine metabolism gene polymorphisms, as well as the folate deficiency on the risk of HHcy in a Chinese hypertensive population. METHODS This study enrolled 480 hypertensive patients aged 28 - 75 from six hospitals in different Chinese regions from 9/2005 - 12/2005. Known genotypes of methylenetetrahydrofolate reductase (MTHFR) C677T and A1298C, methionine synthase (MTR) A2756G, and methionine synthase reductase (MTRR) A66G were detected by PCRRFLP methods. Serum Hcy was measured by high-performance liquid chromatography and serum folate was measured by chemiluminescent immunoassay. RESULTS MTHFR C677T and MTR A2756G can independently elevate the risk of HHcy (TT vs. CC + CT, p < 0.001 and AG + GG vs. AA, p = 0.026, respectively), whereas MTHFR A1298C decreased HHcy risk (AC + CC vs. AA, p < 0.001) and showed a protective effect against HHcy risk. Importantly, the joint effect of these risk genotypes showed significantly higher odds of HHcy than non-risk genotypes, especially the patients with four risk genotypes. It is noteworthy that this deleterious effect was aggravated by folate deficiency. These findings were verified by generalized multifactor dimensionality reduction model (p = 0.001) and a cumulative effects model (p < 0.001). CONCLUSIONS We have first demonstrated that the joint effect of homocysteine metabolism gene polymorphisms and folate deficiency lead to dramatic elevations in the HHcy risk.

Integrated Analysis of Alzheimer's Disease and Schizophrenia Dataset Revealed Different Expression Pattern in Learning and Memory.

Alzheimers disease (AD) and schizophrenia (SZ) are both accompanied by impaired learning and memory functions. This study aims to explore the expression profiles of learning or memory genes between AD and SZ. We downloaded 10 AD and 10 SZ datasets from GEO-NCBI for integrated analysis. These datasets were processed using RMA algorithm and a global renormalization for all studies. Then Empirical Bayes algorithm was used to find the differentially expressed genes between patients and controls. The results showed that most of the differentially expressed genes were related to AD whereas the gene expression profile was little affected in the SZ. Furthermore, in the aspects of the number of differentially expressed genes, the fold change and the brain region, there was a great difference in the expression of learning or memory related genes between AD and SZ. In AD, the CALB1, GABRA5, and TAC1 were significantly downregulated in whole brain, frontal lobe, temporal lobe, and hippocampus. However, in SZ, only two genes CRHBP and CX3CR1 were downregulated in hippocampus, and other brain regions were not affected. The effect of these genes on learning or memory impairment has been widely studied. It was suggested that these genes may play a crucial role in AD or SZ pathogenesis. The different gene expression patterns between AD and SZ on learning and memory functions in different brain regions revealed in our study may help to understand the different mechanism between two diseases.

The identification and molecular mechanism of anti-stroke traditional Chinese medicinal compounds.

Stroke is a worldwide epidemic disease with high morbidity and mortality. The continuously exploration of anti-stroke medicines and molecular mechanism has a long way to go. In this study, in order to screen candidate anti-stroke compounds, more than 60000 compounds from traditional Chinese medicine (TCM) database were computationally analyzed then docked to the 15 known anti-stroke targets. 192 anti-stroke plants for clinical therapy and 51 current anti-stroke drugs were used to validate docking results. Totally 2355 candidate anti-stroke compounds were obtained. Among these compounds, 19 compounds are structurally identical with 16 existing drugs in which part of them have been used for anti-stroke treatment. Furthermore, these candidate compounds were significantly enriched in anti-stroke plants. Based on the above results, the compound-target-plant network was constructed. The network reveals the potential molecular mechanism of anti-stroke for these compounds. Most of candidate compounds and anti-stroke plants are tended to interact with target NOS3, PSD-95 and PDE5A. Finally, using ADMET filter, we identified 35 anti-stroke compounds with favorable properties. The 35 candidate anti-stroke compounds offer an opportunity to develop new anti-stroke drugs and will improve the research on molecular mechanism of anti-stroke.