Efficient degradation of hexabromocyclododecane using montmorillonite supported nano-zero-valent iron and Citrobacter sp. Y3.

The coupling of modified nanoscale zero-valent iron (nZVI) with organohalide-degrading bacteria provides a promising solution for the remediation of hexabromocyclododecane (HBCD)-contaminated environments. However, the interactions between modified nZVI and dehalogenase bacteria are intricate, and the mechanisms of synergistic action and electron transfer are not clear, and requires further specific investigation. In this study, HBCD was used as a model pollutant, and stable isotope analysis revealed that organic montmorillonite (OMt)-supported nZVI coupled with the degrading bacterial strain Citrobacter sp. Y3 (nZVI/OMt-Y3) can use [13C]HBCD as the sole carbon source and degrade or even mineralise it into 13CO2 with a maximum conversion rate of 100% within approximately 5 days. Analysis of the intermediates showed that the degradation of HBCD mainly involves three different pathways: dehydrobromination, hydroxylation, and debromination. The proteomics results showed that nZVI introduction promoted the transport of electrons and debromination. Combining the results from XPS, FTIR, and Raman spectroscopy with the analysis results of proteinomics and biodegradation products, we verified the process of electron transport and proposed a metabolic mechanism of HBCD degradation by the nZVI/OMt-Y3. Moreover, this study provides insightful avenues and models for the further remediation of HBCD and other similar pollutants in the environment.

Dimorphism of Candida tropicalis and its effect on nitrogen and phosphorus removal and sludge settleability.

Candida tropicalis PNY, a novel dimorphic strain with the capacity of simultaneous carbon, nitrogen and phosphorus removal in anaerobic and aerobic conditions, was isolated from activated sludge. Dimorphism of C. tropicalis PNY had effect on removing nitrogen and phosphorous and slightly affected COD removal under aerobic condition. Sample with high hypha formation rate (40 ± 5%) had more removal efficiencies of NH4+-N (50 mg/L) and PO43--P (10 mg/L), which could achieve 82.19% and 97.53%, respectively. High hypha cells dosage exhibited good settleability and filamentous overgrowth was not observed. According to label-free quantitative proteomics assays. Up-regulated proteins involved in the mitogen-activated protein kinase (MAPK) pathway indicated the active growth and metabolism process of sample with high hypha formation rate (40 ± 5%). And proteins concerning about glutamate synthetase and SPX domain-contain protein explain for the nutrient removal mechanism including assimilation of ammonia and polyphosphates synthesis.

System-level metabolic modeling facilitates unveiling metabolic signature in exceptional longevity.

Although it is well known that metabolic control plays a crucial role in regulating the health span and life span of various organisms, little is known for the systems metabolic profile of centenarians, the paradigm of human healthy aging and longevity. Meanwhile, how to well characterize the system-level metabolic states in an organism of interest remains to be a major challenge in systems metabolism research. To address this challenge and better understand the metabolic mechanisms of healthy aging, we developed a method of genome-wide precision metabolic modeling (GPMM) which is able to quantitatively integrate transcriptome, proteome and kinetome data in predictive modeling of metabolic networks. Benchmarking analysis showed that GPMM successfully characterized metabolic reprogramming in the NCI-60 cancer cell lines; it dramatically improved the performance of the modeling with an R2 of 0.86 between the predicted and experimental measurements over the performance of existing methods. Using this approach, we examined the metabolic networks of a Chinese centenarian cohort and identified the elevated fatty acid oxidation (FAO) as the most significant metabolic feature in these long-lived individuals. Evidence from serum metabolomics supports this observation. Given that FAO declines with normal aging and is impaired in many age-related diseases, our study suggests that the elevated FAO has potential to be a novel signature of healthy aging of humans.

Transcriptome integration analysis and specific diagnosis model construction for Hodgkin's lymphoma, diffuse large B-cell lymphoma, and mantle cell lymphoma.

Transcriptome differences between Hodgkin's lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), and mantle cell lymphoma (MCL), which are all derived from B cell, remained unclear. This study aimed to construct lymphoma-specific diagnostic models by screening lymphoma marker genes. Transcriptome data of HL, DLBCL, and MCL were obtained from public databases. Lymphoma marker genes were screened by comparing cases and controls as well as the intergroup differences among lymphomas. A total of 9 HL marker genes, 7 DLBCL marker genes, and 4 MCL marker genes were screened in this study. Most HL marker genes were upregulated, whereas DLBCL and MCL marker genes were downregulated compared to controls. The optimal HL-specific diagnostic model contains one marker gene (MYH2) with an AUC of 0.901. The optimal DLBCL-specific diagnostic model contains 7 marker genes (LIPF, CCDC144B, PRO2964, PHF1, SFTPA2, NTS, and HP) with an AUC of 0.951. The optimal MCL-specific diagnostic model contains 3 marker genes (IGLV3-19, IGKV4-1, and PRB3) with an AUC of 0.843. The present study reveals the transcriptome data-based differences between HL, DLBCL, and MCL, when combined with other clinical markers, may help the clinical diagnosis and prognosis.

Dietary antioxidant vitamin C influences the evolutionary path of insecticide resistance in Drosophila melanogaster.

Herbivorous insects encounter a variety of toxic environmental substances ranging from ingested plant defensive compounds to human-introduced insecticidal agents. Dietary antioxidants are known to reduce the negative physiological impacts of toxins in mammalian systems through amelioration of reactive oxygen-related cellular damage. The analogous impacts to insects caused by multigenerational exposure to pesticides and the effects on adaptive responses within insect populations, however, are currently unknown. To address these research gaps, we used Drosophila as a model system to explore adaptive phenotypic responses to acute dichlorodiphenyltrichloroethane (DDT) exposure in the presence of the dietary antioxidant vitamin C and to examine the structural genomic consequences of this exposure. DDT resistance increased significantly among four replicates exposed to a low concentration of DDT for 10 generations. In contrast, dietary intake of vitamin C significantly reduced DDT resistance after mutigenerational exposure to the same concentration of DDT. As to the genomic consequences, no significant differences were predicted in overall nucleotide substitution rates across the genome between any of the treatments. Despite this, replicates exposed to a low concentration of DDT without vitamin C showed the highest number of synonymous and non-synonymous variants (3196 in total), followed by the DDT plus vitamin C (1174 in total), and vitamin C alone (728 in total) treatments. This study demonstrates the potential role of diet (specifically, antioxidant intake) on adaptive genome responses, and thus on the evolution of pesticide resistance within insect populations.

FastMM: an efficient toolbox for personalized constraint-based metabolic modeling.

BACKGROUND: Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. RESULTS: Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). CONCLUSION: FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

Regulatory network reconstruction of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets.

Although many of the genetic loci associated with breast cancer risk have been reported, there is a lack of systematic analysis of regulatory networks composed of different miRNAs and mRNAs on survival analysis in breast cancer. To reconstruct the microRNAs-genes regulatory network in breast cancer, we employed the expression data from The Cancer Genome Atlas (TCGA) related to five essential miRNAs including miR-21, miR-22, miR-210, miR-221, and miR-222, and their associated functional genomics data from the GEO database. Then, we performed an integration analysis to identify the essential target factors and interactions for the next survival analysis in breast cancer. Based on the results of our integrated analysis, we have identified significant common regulatory signatures including differentially expressed genes, enriched pathways, and transcriptional regulation such as interferon regulatory factors (IRFs) and signal transducer and activator of transcription 1 (STAT1). Finally, a reconstructed regulatory network of five miRNAs and 34 target factors was established and then applied to survival analysis in breast cancer. When we used expression data for individual miRNAs, only miR-21 and miR-22 were significantly associated with a survival change. However, we identified 45 significant miRNA-gene pairs that predict overall survival in breast cancer out of 170 one-on-one interactions in our reconstructed network covering all of five miRNAs, and several essential factors such as PSMB9, HLA-C, RARRES3, UBE2L6, and NMI. In our study, we reconstructed regulatory network of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets. These results may provide new insights into regulatory network-based precision medicine for breast cancer.

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.

Identification of metabolism-associated genes and pathways involved in different stages of clear cell renal cell carcinoma.

The lack of early diagnostic markers and novel therapeutic targets for clear cell renal cell carcinoma (ccRCC) negatively affects patient prognosis. Cancer metabolism is an attractive area for the understanding of the molecular mechanism of carcinogenesis. The present study attempted to identify metabolic changes from the view of the expression of metabolism-associated genes between control samples and those of ccRCC at different disease stages. Data concerning ccRCC gene expression obtained by RNA-sequencing was obtained from The Cancer Genome Atlas and data on metabolism-associated genes were extracted using the Recon2 model. Following analysis of differential gene expression, multiple differentially expressed metabolic genes at each tumor-node-metastasis disease stage were identified, compared with control non-disease samples: Metabolic genes (305) were differentially expressed in stage I disease, 323 in stage II disease, 355 in stage III disease and 363 in stage IV disease. Following enrichment analysis for differential metabolic genes, 22 metabolic pathways were identified to be dysregulated in multiple stages of ccRCC. Abnormalities in hormone, vitamin, glucose and lipid metabolism were present in the early stages of the disease, with dysregulation to reactive oxygen species detoxification and amino acid metabolism pathways occurring with advanced disease stages, particularly to valine, leucine, and isoleucine metabolism, which was substantially dysregulated in stage IV disease. The xenobiotic metabolism pathway, associated with multiple cytochrome P450 family genes, was dysregulated in each stage of the disease. This pathway is worthy of substantial attention since it may aid understanding of drug resistance in ccRCC. The results of the present study offer information to aid further research into early diagnostic biomarkers and therapeutic targets of ccRCC.

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.

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.

Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin.

Besides its anti-inflammatory, analgesic and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin likely involve several molecular targets and pathways rather than a single target. Therefore, systematic identification of these targets of aspirin can help us understand the underlying mechanisms of the activities. In this study, we identified 23 putative targets of aspirin in the human proteome by using binding pocket similarity detecting tool combination with molecular docking, free energy calculation and pathway analysis. These targets have diverse folds and are derived from different protein family. However, they have similar aspirin-binding pockets. The binding free energy with aspirin for newly identified targets is comparable to that for the primary targets. Pathway analysis revealed that the targets were enriched in several pathways such as vascular endothelial growth factor (VEGF) signaling, Fc epsilon RI signaling and arachidonic acid metabolism, which are strongly involved in inflammation, cardiovascular disease and cancer. Therefore, the predicted target profile of aspirin suggests a new explanation for the disease prevention ability of aspirin. Our findings provide a new insight of aspirin and its efficacy of disease prevention in a systematic and global view.

Iron Overload Coordinately Promotes Ferritin Expression and Fat Accumulation in Caenorhabditis elegans.

The trace element iron is crucial for living organisms, since it plays essential roles in numerous cellular functions. Systemic iron overload and the elevated level of ferritin, a ubiquitous intracellular protein that stores and releases iron to maintain the iron homeostasis in cells, has long been epidemiologically associated with obesity and obesity-related diseases. However, the underlying mechanisms of this association remain unclear. Here, using Caenorhabditis elegans, we show that iron overload induces the expression of sgk-1, encoding the serum and glucocorticoid-inducible kinase, to promote the level of ferritin and fat accumulation. Mutation of cyp-23A1, encoding a homolog of human cytochrome P450 CYP7B1 that is related to neonatal hemochromatosis, further enhances the elevated expression of ftn-1, sgk-1, and fat accumulation. sgk-1 positively regulates the expression of acs-20 and vit-2, genes encoding homologs of the mammalian FATP1/4 fatty acid transport proteins and yolk lipoproteins, respectively, to facilitate lipid uptake and translocation for storage under iron overload. This study reveals a completely novel pathway in which sgk-1 plays a central role to synergistically regulate iron and lipid homeostasis, offering not only experimental evidence supporting a previously unverified link between iron and obesity, but also novel insights into the pathogenesis of iron and obesity-related human metabolic diseases.

Azvudine, a novel nucleoside reverse transcriptase inhibitor showed good drug combination features and better inhibition on drug-resistant strains than lamivudine in vitro.

Azvudine is a novel nucleoside reverse transcriptase inhibitor with antiviral activity on human immunodeficiency virus, hepatitis B virus and hepatitis C virus. Here we reported the in vitro activity of azvudine against HIV-1 and HIV-2 when used alone or in combination with other antiretroviral drugs and its drug resistance features. Azvudine exerted highly potent inhibition on HIV-1 (EC(50)s ranging from 0.03 to 6.92 nM) and HIV-2 (EC(50)s ranging from 0.018 to 0.025 nM). It also showed synergism in combination with six approved anti-HIV drugs on both C8166 and PBMC. In combination assay, the concentrations of azvudine used were 1000 or 500 fold lower than other drugs. Azvudine also showed potent inhibition on NRTI-resistant strains (L74V and T69N). Although M184V caused 250 fold reduction in susceptibility, azvudine remained active at nanomolar range. In in vitro induced resistant assay, the frequency of M184I mutation increased with induction time which suggests M184I as the key mutation in azvudine treatment. As control, lamivudine treatment resulted in a higher frequency of M184I/V given the same induction time and higher occurrence of M184V was found. Molecular modeling analysis suggests that steric hindrance is more pronounced in mutant M184I than M184V due to the azido group of azvudine. The present data demonstrates the potential of azvudine as a complementary drug to current anti-HIV drugs. M184I should be the key mutation, however, azvudine still remains active on HIV-1LAI-M184V at nanomolar range.

The characterization and comparison of amyloidogenic segments and non-amyloidogenic segments shed light on amyloid formation.

Amyloid fibrillar aggregates of proteins or peptides are involved in the etiology of several neurodegenerative diseases and represent a major problem in healthcare. Short regions in the protein trigger this aggregation. It is important to understand the basis of such short regions aggregation and amyloidosis for therapeutic intervention. In this study, we describe specific physico-chemical properties of amyloidogenic segments and compare them with non-amyloidogenic segments. First, amyloidogenic segments are characterized by lower values for average net charge, electrostatic potential, solvent accessible surface area and B-factor when compared to the non-amyloidogenic segments of the same proteins. Second, they are enriched in hydrophobic residues and have a tendency to form hydrogen bonds. Thus, amyloidogenic segments have distinct physico-chemical properties that are different from those of non-amyloidogenic segments. Third, and quite unexpectedly, our dynamic simulation studies support the hypothesis that amyloidogenic segments have lower average flexibility than non-amyloidogenic segments. Furthermore, the presence of amyloidogenic segments in disordered proteins does not contradict the observation that amyloidogenic segments are less flexible.

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.

Molecular dynamics simulations and statistical coupling analysis reveal functional coevolution network of oncogenic mutations in the CDKN2A-CDK6 complex.

Coevolution between proteins is crucial for understanding protein-protein interaction. Simultaneous changes allow a protein complex to maintain its overall structural-functional integrity. In this study, we combined statistical coupling analysis (SCA) and molecular dynamics simulations on the CDK6-CDKN2A protein complex to evaluate coevolution between proteins. We reconstructed an inter-protein residue coevolution network, consisting of 37 residues and 37 interactions. It shows that most of the coevolved residue pairs are spatially proximal. When the mutations happened, the stable local structures were broken up and thus the protein interaction was decreased or inhibited, with a following increased risk of melanoma. The identification of inter-protein coevolved residues in the CDK6-CDKN2A complex can be helpful for designing protein engineering experiments.

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.

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.