Characterization of Plasmodium knowlesi dihydrofolate reductase-thymidylate synthase and sensitivity to antifolates

Malaria caused by an infection of Plasmodium knowlesi can result in high parasitemia and deaths. Therefore, effective and prompt treatment is necessary to reduce morbidity and mortality. The study aims to characterize P. knowlesi dihydrofolate reductase-thymidylate synthase enzyme (PkDHFR-TS) and its sensitivity to antifolates. The putative Pkdhfr gene was PCR amplified from field isolates collected from the Southern Thailand. Molecular analysis showed 11 polymorphisms in the dhfr domain of the bifunctional dhfr-ts gene. Of these, 1 polymorphism was a non-synonymous substitution (R34L) that had previously been reported but not associated with antifolate resistance. The recombinant PkDHFR-TS enzyme was found to be sensitive to standard antifolates—pyrimethamine and cycloguanil—as well as P218, a registered candidate drug currently first in human clinical trial. Results suggest that antifolates class of compounds should be effective against P. knowlesi infection.     

Genome-scale analysis of the metabolic networks of oleaginous Zygomycete fungi

Microbial lipids are becoming an attractive option for the industrial production of foods and oleochemicals. To investigate the lipid physiology of the oleaginous microorganisms, at the system level, genome-scale metabolic networks of Mortierella alpina and Mucor circinelloides were constructed using bioinformatics and systems biology. As scaffolds for integrated data analysis focusing on lipid production, consensus metabolic routes governing fatty acid synthesis, and lipid storage and mobilisation were identified by comparative analysis of developed metabolic networks. Unique metabolic features were identified in individual fungi, particularly in NADPH metabolism and sterol biosynthesis, which might be related to differences in fungal lipid phenotypes. The frameworks detailing the metabolic relationship between M. alpina and M. circinelloides generated in this study is useful for further elucidation of the microbial oleaginicity, which might lead to the production improvement of microbial oils as alternative feedstocks for oleochemical industry.     

MetGEMs Toolbox: Metagenome-scale models as integrative toolbox for uncovering metabolic functions and routes of human gut microbiome

Investigating metabolic functional capability of a human gut microbiome enables the quantification of microbiome changes, which can cause a phenotypic change of host physiology and disease. One possible way to estimate the functional capability of a microbial community is through inferring metagenomic content from 16S rRNA gene sequences. Genome-scale models (GEMs) can be used as scaffold for functional estimation analysis at a systematic level, however up to date, there is no integrative toolbox based on GEMs for uncovering metabolic functions. Here, we developed the MetGEMs (metagenome-scale models) toolbox, an open-source application for inferring metabolic functions from 16S rRNA gene sequences to facilitate the study of the human gut microbiome by the wider scientific community. The developed toolbox was validated using shotgun metagenomic data and shown to be superior in predicting functional composition in human clinical samples compared to existing state-of-the-art tools. Therefore, the MetGEMs toolbox was subsequently applied for annotating putative enzyme functions and metabolic routes related in human disease using atopic dermatitis as a case study.     

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production.     

Acute Respiratory Failure and Active Bleeding Are the Important Fatality Predictive Factors for Severe Dengue Viral Infection

Objective

To determine the outcome of severe dengue viral infection (DVI) and the main dengue fatality risk factors.

Study design

The medical records of patients aged <15 years admitted to Songklanagarind Hospital in southern Thailand during 1989–2011 were reviewed. Patients who had dengue hemorrhagic fever (DHF) grades III–IV, organ failure (cardiovascular, respiratory, liver, renal or hematologic), impaired consciousness, or aspartate aminotransferase more than 1,000 units/L, were classified as having severe DVI. To determine the fatality risk factors of severe DVI, the classification trees were constructed based on manual recursive partitioning.

Results

Of the 238 children with severe DVI, 30 (12.6%) died. Compared to the non-fatal DVI cases, the fatal cases had higher rates of DHF grade IV (96.7% vs 24.5%), repeated shock (93.3% vs 27.9%), acute respiratory failure (ARF) (100% vs 6.7%), acute liver failure (ALF) (96.6% vs 6.3%), acute kidney injury (AKI) (79.3% vs 4.5%), and active bleeding requiring blood transfusion (93.3% vs 5.4%), all p<0.01. The combined risk factors of ARF and active bleeding considered together predicted fatal outcome with sensitivity, specificity, and negative and positive predictive values of 0.93 (0.78–0.99), 0.97 (0.93–0.99), 0.99 (0.97–1.00), and 0.82 (0.65–0.93), respectively. The likelihood ratios for a fatal outcome in the patients who had and did not have this risk combination were 32.4 (14.6–71.7) and 0.07 (0.02–0.26), respectively.

Conclusion

Severe DVI patients who have ARF and active bleeding are at a high risk of death, while patients without these things together should survive.     

Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis

With the development of next-generation sequencing (NGS) techniques, many software tools have emerged for the discovery of novel microRNAs (miRNAs) and for analyzing the miRNAs expression profiles. An overall evaluation of these diverse software tools is lacking. In this study, we evaluated eight software tools based on their common feature and key algorithms. Three deep-sequencing data sets were collected from different species and used to assess the computational time, sensitivity and accuracy of detecting known miRNAs as well as their capacity for predicting novel miRNAs. Our results provide useful information for researchers to facilitate their selection of the optimal software tools for miRNA analysis depending on their specific requirements, i.e. novel miRNAs discovery or miRNA expression profile analysis of sequencing data sets.     

Annotation and analysis of malic enzyme genes encoding for multiple isoforms in the fungus Mucor circinelloides CBS 277.49

Based on the newly-released genomic data of Mucor circinelloides CBS 277.49, we have annotated five genes encoding for malic enzyme: all code for proteins that contain conserved domains/motifs for malic acid binding, NAD⁺ binding and NAD(P)⁺ binding. Phylogenetic analysis for malic enzyme genes showed that genes ID 78524 and 11639 share ~80% amino acid identity and are grouped in cluster 1; genes ID 182779, 186772 and 116127 share ~66% amino acid identity are grouped in cluster 2. Genes ID 78524, 11639 and 166127 produce proteins that are localized in the mitochondrion, while the products from genes 182779 and 186772 are localized in the cytosol. Based on the comparative analysis published previously by Song et al. (Microbiology 147:1507–1515, 2001), we propose that malic enzyme genes ID 78524, 166127, 182779, 186772, 11639, respectively, represent protein isoforms I, II, III/IV, V, and VI.