Jun-Juan Zheng

Homocysteine Metabolism Gene Polymorphisms (MTHFR C677T, MTHFR A1298C, MTR A2756G and MTRR A66G) Jointly Elevate the Risk of Folate Deficiency

Folate deficiency is strongly associated with cardiovascular disease. We aimed to explore the joint effect of the methylenetetrahydrofolate reductase (MTHFR) C677T and A1298C, methionine synthase (MTR) A2756G, and methionine synthase reductase (MTRR) A66G polymorphisms on folate deficiency in a Chinese hypertensive population. A total of 480 subjects aged 28–75 were enrolled in this study from September 2005–December 2005 from six hospitals in different Chinese regions. Known genotypes were detected by PCR-RFLP methods and serum folate was measured by chemiluminescence immunoassay. Our results showed that MTHFR 677TT and MTR 2756AG + GG were independently associated with a higher risk of folate deficiency (TT vs. CC + CT, p < 0.001 and AG + GG vs. AA p = 0.030, respectively). However, the MTHFR A1298C mutation may confer protection by elevating the serum folate level (p = 0.025). Furthermore, patients carrying two or more risk genotypes showed higher odds of folate deficiency than null risk genotype carriers, especially those carrying four risk genotypes. These findings were verified by generalized multifactor dimensionality reduction (p = 0.0107) and a cumulative effects model (p = 0.001). The results of this study have shown that interactions among homocysteine metabolism gene polymorphisms lead to dramatic elevations in the folate deficiency risk.

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.

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.

Integrated analysis of ischemic stroke datasets revealed sex and age difference in anti-stroke targets

Ischemic stroke is a common neurological disorder and the burden in the world is growing. This study aims to explore the effect of sex and age difference on ischemic stroke using integrated microarray datasets. The results showed a dramatic difference in whole gene expression profiles and influenced pathways between males and females, and also in the old and young individuals. Furthermore, compared with old males, old female patients showed more serious biological function damage. However, females showed less affected pathways than males in young subjects. Functional interaction networks showed these differential expression genes were mostly related to immune and inflammation-related functions. In addition, we found ARG1 and MMP9 were up-regulated in total and all subgroups. Importantly, IL1A, ILAB, IL6 and TNF and other anti-stroke target genes were up-regulated in males. However, these anti-stroke target genes showed low expression in females. This study found huge sex and age differences in ischemic stroke especially the opposite expression of anti-stroke target genes. Future studies are needed to uncover these pathological mechanisms, and to take appropriate pre-prevention, treatment and rehabilitation measures.

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.

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

Scientific Reports 6: Article number: 25462; published online: 05 May 2016; updated: 10 October 2016 .

A strategy to find novel candidate anti-Alzheimer’s disease drugs by constructing interaction networks between drug targets and natural compounds in medical plants

Background Alzheimer’ disease (AD) is an ultimately fatal degenerative brain disorder that has an increasingly large burden on health and social care systems. There are only five drugs for AD on the market, and no new effective medicines have been discovered for many years. Chinese medicinal plants have been used to treat diseases for thousands of years, and screening herbal remedies is a way to develop new drugs. Methods We used molecular docking to screen 30,438 compounds from Traditional Chinese Medicine (TCM) against a comprehensive list of AD target proteins. TCM compounds in the top 0.5% of binding affinity scores for each target protein were selected as our research objects. Structural similarities between existing drugs from DrugBank database and selected TCM compounds as well as the druggability of our candidate compounds were studied. Finally, we searched the CNKI database to obtain studies on anti-AD Chinese plants from 2007 to 2017, and only clinical studies were included. Results A total of 1,476 compounds (top 0.5%) were selected as drug candidates. Most of these compounds are abundantly found in plants used for treating AD in China, especially the plants from two genera Panax and Morus. We classified the compounds by single target and multiple targets and analyzed the interactions between target proteins and compounds. Analysis of structural similarity revealed that 17 candidate anti-AD compounds were structurally identical to 14 existing approved drugs. Most of them have been reported to have a positive effect in AD. After filtering for compound druggability, we identified 11 anti-AD compounds with favorable properties, seven of which are found in anti-AD Chinese plants. Of 11 anti-AD compounds, four compounds 5,862, 5,863, 5,868, 5,869 have anti-inflammatory activity. The compound 28,814 mainly has immunoregulatory activity. The other six compounds have not yet been reported for any biology activity at present. Discussion Natural compounds from TCM provide a broad prospect for the screening of anti-AD drugs. In this work, we established networks to systematically study the connections among natural compounds, approved drugs, TCM plants and AD target proteins with the goal of identifying promising drug candidates. We hope that our study will facilitate in-depth research for the treatment of AD in Chinese medicine.

A new method of identifying glioblastoma subtypes and creation of corresponding animal models

Glioblastoma (GBM) accounts for up to 50% of brain parenchymal tumors. It is the most malignant type of brain cancer with very poor survival and limited remedies. Cancer subtyping is important for cancer research and therapy. Here, we report a new subtyping method for GBM based on the genetic alterations of CDKN2A and TP53 genes. CDKN2A and TP53 are the most frequently mutated genes with mutation rates of 60 and 30%, respectively. We found that patients with deletion of CDKN2A possess worse survival than those with TP53 mutation. Interestingly, survival of patients with both TP53 mutation and CDKN2A deletion is no worse than for those with only one of these genetic alterations, but similar to those with TP53 mutation alone. Next, we investigated differences in the gene expression profile between TP53 and CDKN2A samples. Consistent with the survival data, the samples with both TP53 mutation and CDKN2A deletion showed a gene expression profile similar to those samples with TP53 mutation alone. Finally, we found that activation of RAS pathway plus Cdkn2a/b silencing can induce GBM, in a similar way to tumor induction by RAS activation plus TP53 silencing. In conclusion, we show that the genetic alterations of CDKN2A and TP53 may be used to stratify GBM, and the new animal models matching this stratification method were generated.

Efficient repositioning of approved drugs as anti-HIV agents using Anti-HIV-Predictor

Treatment of AIDS still faces multiple challenges such as drug resistance and HIV eradication. Development of new, effective and affordable drugs against HIV is urgently needed. In this study, we developed a world’s first web server called Anti-HIV-Predictor (http://bsb.kiz.ac.cn:70/hivpre) for predicting anti-HIV activity of given compounds. This machine learning based web server is rapid and accurate (accuracy >93% and AUC > 0.958), which enables us to screen tens of millions of compounds and discover new anti-HIV agents. We firstly applied the server to screen 1835 approved drugs for anti-HIV therapy. Then the predicted new anti-HIV compounds were experimentally evaluated. Finally, we repurposed 7 approved drugs (cetrorelix, dalbavancin, daunorubicin, doxorubicin, epirubicin, idarubicin and valrubicin) as new anti-HIV agents. The original indication of these drugs is involved in a variety of diseases such as female infertility, acute bacterial infections, leukemia and other cancers. Anti-HIV-Predictor and the 7 repurposed anti-HIV agents provided here demonstrate the efficacy of this strategy for discovery of new anti-HIV agents. This strategy and the server should significantly advance current anti-HIV research.

Anti-HIV activity prediction and enrichment identify novel human targets for Anti-HIV therapy

Human immunodeficiency virus (HIV) relies heavily on the host proteins to facilitate its entry and replication. Currently, more than 4000 human proteins are recorded to be involved in the HIV-1 life cycle. Identifying appropriate anti-HIV targets from so many host proteins is crucial to anti-HIV drug development, but a challenging work. Here we combined anti-HIV activity prediction and enrichment analysis to identify novel human targets for anti-HIV therapy. We firstly developed an accurate prediction tool named Anti-HIV-Predictor (AUC>0.96) to predict the anti-HIV activity of given compounds. Using this tool, we predicted 10488 anti-HIV compounds from ChEMBLdb. Then, based on this result and relationships of targets and compounds, we inferred 73 anti-HIV targets that enriched with anti-HIV compounds. The functional annotation and network analysis revealed that they directly or indirectly interact with 20 HIV proteins through neuropeptide signaling, GPCR signaling, cell surface signaling pathway, and so on. Nearly half of these targets overlap with the NCBI HIV dataset. However, the percentage of known therapeutic targets in these targets is significantly higher than that in the NCBI HIV dataset. After a series of feature analysis, we identified 13 novel human targets with high potential as anti-HIV targets, the inhibitors of which have experimentally confirmed anti-HIV activity. It is noteworthy that the inhibitors of REN and CALCA have better anti-HIV activity than CCR5 inhibitors. Taken together, our findings provide novel human targets for the host-oriented anti-HIV drug development and should significantly advance current anti-HIV research.