Zerumbone induced apoptosis in liver cancer cells via modulation of Bax/Bcl-2 ratio

Background  

Zerumbone is a cytotoxic component isolated from Zingiber zerumbet Smith, a herbal plant which is also known as lempoyang. This new anticancer bioactive compound from Z. zerumbet was investigated for its activity and mechanism in human liver cancer cell lines.     

Pep2Path: Automated Mass Spectrometry-Guided Genome Mining of Peptidic Natural Products

Nonribosomally and ribosomally synthesized bioactive peptides constitute a source of molecules of great biomedical importance, including antibiotics such as penicillin, immunosuppressants such as cyclosporine, and cytostatics such as bleomycin. Recently, an innovative mass-spectrometry-based strategy, peptidogenomics, has been pioneered to effectively mine microbial strains for novel peptidic metabolites. Even though mass-spectrometric peptide detection can be performed quite fast, true high-throughput natural product discovery approaches have still been limited by the inability to rapidly match the identified tandem mass spectra to the gene clusters responsible for the biosynthesis of the corresponding compounds. With Pep2Path, we introduce a software package to fully automate the peptidogenomics approach through the rapid Bayesian probabilistic matching of mass spectra to their corresponding biosynthetic gene clusters. Detailed benchmarking of the method shows that the approach is powerful enough to correctly identify gene clusters even in data sets that consist of hundreds of genomes, which also makes it possible to match compounds from unsequenced organisms to closely related biosynthetic gene clusters in other genomes. Applying Pep2Path to a data set of compounds without known biosynthesis routes, we were able to identify candidate gene clusters for the biosynthesis of five important compounds. Notably, one of these clusters was detected in a genome from a different subphylum of Proteobacteria than that in which the molecule had first been identified. All in all, our approach paves the way towards high-throughput discovery of novel peptidic natural products. Pep2Path is freely available from http://pep2path.sourceforge.net/, implemented in Python, licensed under the GNU General Public License v3 and supported on MS Windows, Linux and Mac OS X.

This is a PLOS Computational Biology Software Article.

     

Metabolomics to unveil and understand phenotypic diversity between pathogen populations

Leishmaniasis is a debilitating disease caused by the parasite Leishmania. There is extensive clinical polymorphism, including variable responsiveness to treatment. We study Leishmania donovani parasites isolated from visceral leishmaniasis patients in Nepal that responded differently to antimonial treatment due to differing intrinsic drug sensitivity of the parasites. Here, we present a proof-of-principle study in which we applied a metabolomics pipeline specifically developed for L. donovani to characterize the global metabolic differences between antimonial-sensitive and antimonial-resistant L. donovani isolates. Clones of drug-sensitive and drug-resistant parasite isolates from clinical samples were cultured in vitro and harvested for metabolomics analysis. The relative abundance of 340 metabolites was determined by ZIC-HILIC chromatography coupled to LTQ-Orbitrap mass spectrometry. Our measurements cover approximately 20% of the predicted core metabolome of Leishmania and additionally detected a large number of lipids. Drug-sensitive and drug-resistant parasites showed distinct metabolic profiles, and unsupervised clustering and principal component analysis clearly distinguished the two phenotypes. For 100 metabolites, the detected intensity differed more than three-fold between the 2 phenotypes. Many of these were in specific areas of lipid metabolism, suggesting that the membrane composition of the drug-resistant parasites is extensively modified. Untargeted metabolomics has been applied on clinical Leishmania isolates to uncover major metabolic differences between drug-sensitive and drug-resistant isolates. The identified major differences provide novel insights into the mechanisms involved in resistance to antimonial drugs, and facilitate investigations using targeted approaches to unravel the key changes mediating drug resistance.     

Biological microarray interpretation: the rules of engagement

Gene expression microarrays are now established as a standard tool in biological and biochemical laboratories. Interpreting the masses of data generated by this technology poses a number of unusual new challenges. Over the past few years a consensus has begun to emerge concerning the most important pitfalls and the proper ways to avoid them. This review provides an overview of these ideas, beginning with relevant aspects of experimental design and normalization, but focusing in particular on the various tools and concepts that help to interpret microarray results. These new approaches make it much easier to extract biologically relevant and reliable hypotheses in an objective and reasonably unbiased fashion.     

Precision mapping of the metabolome

The global study of the structure and dynamics of metabolic networks has been hindered by a lack of techniques that identify metabolites and their biochemical relationship in complex mixtures. The recent application of Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) to metabolomic analysis suggests a way to tackle the problem. A lower-cost alternative to high-field FTICR-MS, the Orbitrap mass analyzer, promises accelerated activity in this area. Here, we show how the ultra-high mass accuracy and resolution provided by this new generation of mass spectrometers can help to identify metabolites and connect them into metabolic networks. Data from perturbation studies and isotope-tracking experiments can complement this information to create metabolic maps de novo and chart unexplored areas of metabolism.     

Metabolic adaptations of Leishmania donovani in relation to differentiation,drug resistance,and drug pressure

Antimonial (sodium stibogluconate, SSG) resistance and differentiation have been shown to be closely linked in Leishmania donovani, with SSG‐resistant strains showing an increased capacity to generate infectious (metacyclic) forms. This is the first untargeted LC‐MS metabolomics study which integrated both phenomena in one experimental design and provided insights into metabolic differences between three clinical L. donovani strains with a similar genetic background but different SSG‐susceptibilities. We performed this analysis at different stages during promastigote growth and in the absence or presence of drug pressure. When comparing SSG‐resistant and SSG‐sensitive strains, a number of metabolic changes appeared to be constitutively present in all growth stages, pointing towards a clear link with SSG‐resistance, whereas most metabolic changes were only detected in the stationary stage. These changes reflect the close intertwinement between SSG‐resistance and an increased metacyclogenesis in resistant parasites. The metabolic changes suggest that SSG‐resistant parasites have (i) an increased capacity for protection against oxidative stress; (ii) a higher fluidity of the plasma membrane; and (iii) a metabolic survival kit to better endure infection. These changes were even more pronounced in a resistant strain kept under Sb^III drug pressure.     

Tobacco-smoking-related differential DNA methylation: 27K discovery and replication

Tobacco smoking is responsible for substantial morbidity and mortality worldwide, in particular through cardiovascular, pulmonary, and malignant pathology. CpG methylation might plausibly play a role in a variety of smoking-related phenomena, as suggested by candidate gene promoter or global methylation studies. Arrays allowing hypothesis-free searches on a scale resembling genome-wide studies of SNPs have become available only very recently. Methylation extents in peripheral-blood DNA were assessed at 27,578 sites in more than 14,000 gene promoter regions in 177 current smokers, former smokers, and those who had never smoked, with the use of the Illumina HumanMethylation 27K BeadChip. This revealed a single locus, cg03636183, located in F2RL3, with genome-wide significance for lower methylation in smokers (p = 2.68 × 10(-31)). This was similarly significant in 316 independent replication samples analyzed by mass spectrometry and Sequenom EpiTyper (p = 6.33 × 10(-34)). Our results, which were based on a rigorous replication approach, show that the gene coding for a potential drug target of cardiovascular importance features altered methylation patterns in smokers. To date, this gene had not attracted attention in the literature on smoking.     

Predicting host tropism of influenza A virus proteins using random forest

Background

Majority of influenza A viruses reside and circulate among animal populations, seldom infecting humans due to host range restriction. Yet when some avian strains do acquire the ability to overcome species barrier, they might become adapted to humans, replicating efficiently and causing diseases, leading to potential pandemic. With the huge influenza A virus reservoir in wild birds, it is a cause for concern when a new influenza strain emerges with the ability to cross host species barrier, as shown in light of the recent H7N9 outbreak in China. Several influenza proteins have been shown to be major determinants in host tropism. Further understanding and determining host tropism would be important in identifying zoonotic influenza virus strains capable of crossing species barrier and infecting humans.

Results

In this study, computational models for 11 influenza proteins have been constructed using the machine learning algorithm random forest for prediction of host tropism. The prediction models were trained on influenza protein sequences isolated from both avian and human samples, which were transformed into amino acid physicochemical properties feature vectors. The results were highly accurate prediction models (ACC>96.57; AUC>0.980; MCC>0.916) capable of determining host tropism of individual influenza proteins. In addition, features from all 11 proteins were used to construct a combined model to predict host tropism of influenza virus strains. This would help assess a novel influenza strain's host range capability.

Conclusions

From the prediction models constructed, all achieved high prediction performance, indicating clear distinctions in both avian and human proteins. When used together as a host tropism prediction system, zoonotic strains could potentially be identified based on different protein prediction results. Understanding and predicting host tropism of influenza proteins lay an important foundation for future work in constructing computation models capable of directly predicting interspecies transmission of influenza viruses. The models are available for prediction at http://fluleap.bic.nus.edu.sg.