Chinae Thammarongtham

The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: A scaffold to query lipid metabolism

BackgroundUp to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, iIN800 that includes a more rigorous and detailed description of lipid metabolism.ResultsThe reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of iIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets.ConclusionPerforming integrated analyses using iIN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states.

Alternative routes of acetyl-CoA synthesis identified by comparative genomic analysis: involvement in the lipid production of oleaginous yeast and fungi

For a bio-based economy, microbial lipids offer a potential solution as alternative feedstocks in the oleochemical industry. The existing genome data for the promising strains, oleaginous yeasts and fungi, allowed us to investigate candidate orthologous sequences that participate in their oleaginicity. Comparative genome analysis of the non-oleaginous (Saccharomyces cerevisiae, Candida albicans and Ashbya gossypii) and oleaginous strains (Yarrowia lipolytica, Rhizopus oryzae, Aspergillus oryzae and Mucor circinelloides) showed that 209 orthologous protein sequences of the oleaginous microbes were distributed over several processes of the cells. Based on the 41 sequences categorized by metabolism, putative routes potentially involved in the generation of precursors for fatty acid and lipid synthesis, particularly acetyl-CoA, were then identified that were not present in the non-oleaginous strains. We found a set of the orthologous oleaginous proteins that was responsible for the biosynthesis of this key two-carbon metabolite through citrate catabolism, fatty acid β-oxidation, leucine metabolism and lysine degradation. Our findings suggest a relationship between carbohydrate, lipid and amino acid metabolism in the biosynthesis of acetyl-CoA, which contributes to the lipid production of oleaginous microbes.

Heterogeneous ensemble approach with discriminative features and modified-SMOTEbagging for pre-miRNA classification

An ensemble classifier approach for microRNA precursor (pre-miRNA) classification was proposed based upon combining a set of heterogeneous algorithms including support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), then aggregating their prediction through a voting system. Additionally, the proposed algorithm, the classification performance was also improved using discriminative features, self-containment and its derivatives, which have shown unique structural robustness characteristics of pre-miRNAs. These are applicable across different species. By applying preprocessing methods—both a correlation-based feature selection (CFS) with genetic algorithm (GA) search method and a modified-Synthetic Minority Oversampling Technique (SMOTE) bagging rebalancing method—improvement in the performance of this ensemble was observed. The overall prediction accuracies obtained via 10 runs of 5-fold cross validation (CV) was 96.54%, with sensitivity of 94.8% and specificity of 98.3%—this is better in trade-off sensitivity and specificity values than those of other state-of-the-art methods. The ensemble model was applied to animal, plant and virus pre-miRNA and achieved high accuracy, >93%. Exploiting the discriminative set of selected features also suggests that pre-miRNAs possess high intrinsic structural robustness as compared with other stem loops. Our heterogeneous ensemble method gave a relatively more reliable prediction than those using single classifiers. Our program is available at http://ncrna-pred.com/premiRNA.html.

Draft genome sequence of Arthrospira platensis C1 (PCC9438)

Arthrospira platensis is a cyanobacterium that is extensively cultivated outdoors on a large commercial scale for consumption as a food for humans and animals. It can be grown in monoculture under highly alkaline conditions, making it attractive for industrial production. Here we describe the complete genome sequence of A. platensis C1 strain and its annotation. The A. platensis C1 genome contains 6,089,210 bp including 6,108 protein-coding genes and 45 RNA genes, and no plasmids. The genome information has been used for further comparative analysis, particularly of metabolic pathways, photosynthetic efficiency and barriers to gene transfer.

Identification of non-coding RNAs with a new composite feature in the Hybrid Random Forest Ensemble algorithm

To identify non-coding RNA (ncRNA) signals within genomic regions, a classification tool was developed based on a hybrid random forest (RF) with a logistic regression model to efficiently discriminate short ncRNA sequences as well as long complex ncRNA sequences. This RF-based classifier was trained on a well-balanced dataset with a discriminative set of features and achieved an accuracy, sensitivity and specificity of 92.11%, 90.7% and 93.5%, respectively. The selected feature set includes a new proposed feature, SCORE. This feature is generated based on a logistic regression function that combines five significant features—structure, sequence, modularity, structural robustness and coding potential—to enable improved characterization of long ncRNA (lncRNA) elements. The use of SCORE improved the performance of the RF-based classifier in the identification of Rfam lncRNA families. A genome-wide ncRNA classification framework was applied to a wide variety of organisms, with an emphasis on those of economic, social, public health, environmental and agricultural significance, such as various bacteria genomes, the Arthrospira (Spirulina) genome, and rice and human genomic regions. Our framework was able to identify known ncRNAs with sensitivities of greater than 90% and 77.7% for prokaryotic and eukaryotic sequences, respectively. Our classifier is available at http://ncrna-pred.com/HLRF.htm.

Repertoire of malic enzymes in yeast and fungi: insight into their evolutionary functional and structural significance

Malic enzyme (ME) is one of the important enzymes for furnishing the cofactor NAD(P)H for the biosynthesis of fatty acids and sterols. Due to the existence of multiple ME isoforms in a range of oleaginous microbes, a molecular basis for the evolutionary relationships amongst the enzymes in oleaginous fungi was investigated using sequence analysis and structural modelling. Evolutionary distance and structural characteristics were used to discriminate the MEs of yeasts and fungi into several groups. Interestingly, the NADP(+)-dependent MEs of Mucoromycotina had an unusual insertion region (FLxxPG) that was not found in other fungi. However, the subcellular compartment of the Mucoromycotina enzyme could not be clearly identified by an analysis of signal peptide sequences. A constructed structural model of the ME of Mucor circinelloides suggested that the insertion region is located at the N-terminus of the enzyme (aa 159-163). In addition, it is presumably part of the dimer interface region of the enzyme, which might provide a continuously positively charged pocket for the efficient binding of negatively charged effector molecules. The discovery of the unique structure of the Mucoromycotina ME suggests the insertion region could be involved in particular kinetics of this enzyme, which may indicate its involvement in the lipogenesis of industrially important oleaginous microbes.

Genome mining of fungal lipid-degrading enzymes for industrial applications.

Lipases are interesting enzymes, which contribute important roles in maintaining lipid homeostasis and cellular metabolisms. Using available genome data, seven lipase families of oleaginous and non-oleaginous yeast and fungi were categorized based on the similarity of their amino acid sequences and conserved structural domains. Of them, triacylglycerol lipase (patatin-domain-containing protein) and steryl ester hydrolase (abhydro_lipase-domain-containing protein) families were ubiquitous enzymes found in all species studied. The two essential lipases rendered signature characteristics of integral membrane proteins that might be targeted to lipid monolayer particles. At least one of the extracellular lipase families existed in each species of yeast and fungi. We found that the diversity of lipase families and the number of genes in individual families of oleaginous strains were greater than those identified in non-oleaginous species, which might play a role in nutrient acquisition from surrounding hydrophobic substrates and attribute to their obese phenotype. The gene/enzyme catalogue and relevant informative data of the lipases provided by this study are not only valuable toolboxes for investigation of the biological role of these lipases, but also convey potential in various industrial applications.

Integrative computational approach for genome-based study of microbial lipid-degrading enzymes

Lipid-degrading or lipolytic enzymes have gained enormous attention in academic and industrial sectors. Several efforts are underway to discover new lipase enzymes from a variety of microorganisms with particular catalytic properties to be used for extensive applications. In addition, various tools and strategies have been implemented to unravel the functional relevance of the versatile lipid-degrading enzymes for special purposes. This review highlights the study of microbial lipid-degrading enzymes through an integrative computational approach. The identification of putative lipase genes from microbial genomes and metagenomic libraries using homology-based mining is discussed, with an emphasis on sequence analysis of conserved motifs and enzyme topology. Molecular modelling of three-dimensional structure on the basis of sequence similarity is shown to be a potential approach for exploring the structural and functional relationships of candidate lipase enzymes. The perspectives on a discriminative framework of cutting-edge tools and technologies, including bioinformatics, computational biology, functional genomics and functional proteomics, intended to facilitate rapid progress in understanding lipolysis mechanism and to discover novel lipid-degrading enzymes of microorganisms are discussed.

Prediction of Non-coding RNA and Their Targets in Spirulina platensis Genome

Non-coding RNAs (ncRNAs), transcripts that have function without being translated to protein, have a number of roles in the cell including important regulatory roles. Efforts to identify the whole set of ncRNAs and then to elucidate their functions would gain better biological understanding. Although ncRNA is another type of genome constituent, most of the genes for ncRNA are overlooked by standard genome annotation of genome sequencing projects. This also happens in Spirulina platensis genome sequencing project. It is because gene finding tools generally are able to identify only protein-coding genes but not non-protein-coding ones. In this study, S. platensis ncRNAs were detected by comparative genomics approach using computational tools, together with RNA secondary structure prediction. It was found that more than 100 predicted ncRNA loci matched with known ncRNAs for example cobalamin riboswitch, RNaseP, Signal Recognition Particle RNA, Group II intron RNA and Yfr1. It has been reported that Yfr1 has been found in most cyanobacterial genomes sequenced. The result showed that more than 70 putative loci were similar to Group II intron RNAs. In addition, approximately 100 predicted ncRNA loci were not matched with any known ncRNAs. The predicted targets for some putative ncRNAs are also proposed.

Networking Omic Data to Envisage Systems Biological Regulation

To understand how biological processes work, it is necessary to explore the systematic regulation governing the behaviour of the processes. Not only driving the normal behavior of organisms, the systematic regulation evidently underlies the temporal responses to surrounding environments (dynamics) and long-term phenotypic adaptation (evolution). The systematic regulation is, in effect, formulated from the regulatory components which collaboratively work together as a network. In the drive to decipher such a code of lives, a spectrum of technologies has continuously been developed in the post-genomic era. With current advances, high-throughput sequencing technologies are tremendously powerful for facilitating genomics and systems biology studies in the attempt to understand system regulation inside the cells. The ability to explore relevant regulatory components which infer transcriptional and signaling regulation, driving core cellular processes, is thus enhanced. This chapter reviews high-throughput sequencing technologies, including second and third generation sequencing technologies, which support the investigation of genomics and transcriptomics data. Utilization of this high-throughput data to form the virtual network of systems regulation is explained, particularly transcriptional regulatory networks. Analysis of the resulting regulatory networks could lead to an understanding of cellular systems regulation at the mechanistic and dynamics levels. The great contribution of the biological networking approach to envisage systems regulation is finally demonstrated by a broad range of examples.