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

Background: Up 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, ilN800 that includes a more rigorous and detailed descrition of lipid metabolism. Results: The 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 ilN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of ilN800, 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. Conclusions: Performing integrated analyses using ilN800 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

Genetic aberration analysis in thai colorectal adenoma and early-stage adenocarcinoma patients by whole-exome sequencing

Colorectal adenomas are precursor lesions of colorectal adenocarcinoma. The transition from adenoma to carcinoma in patients with colorectal cancer (CRC) has been associated with an accumulation of genetic aberrations. However, criteria that can screen adenoma progression to adenocarcinoma are still lacking. This present study is the first attempt to identify genetic aberrations, such as the somatic mutations, copy number variations (CNVs), and high-frequency mutated genes, found in Thai patients. In this study, we identified the genomic abnormality of two sample groups. In the first group, five cases matched normal-colorectal adenoma-colorectal adenocarcinoma. In the second group, six cases matched normal-colorectal adenomas. For both groups, whole-exome sequencing was performed. We compared the genetic aberration of the two sample groups. In both normal tissues compared with colorectal adenoma and colorectal adenocarcinoma analyses, somatic mutations were observed in the tumor suppressor gene APC (Adenomatous polyposis coli) in eight out of ten patients. In the group of normal tissue comparison with colorectal adenoma tissue, somatic mutations were also detected in Catenin Beta 1 (CTNNB1), Family With Sequence Similarity 123B (FAM123B), F-Box And WD Repeat Domain Containing 7 (FBXW7), Sex-Determining Region Y-Box 9 (SOX9), Low-Density Lipoprotein Receptor-Related Protein 5 (LRP5), Frizzled Class Receptor 10 (FZD10), and AT-Rich Interaction Domain 1A (ARID1A) genes, which are involved in the Wingless-related integration site (Wnt) signaling pathway. In the normal tissue comparison with colorectal adenocarcinoma tissue, Kirsten retrovirus-associated DNA sequences (KRAS), Tumor Protein 53 (TP53), and Ataxia-Telangiectasia Mutated (ATM) genes are found in the receptor tyrosine kinase-RAS (RTK–RAS) signaling pathway and p53 signaling pathway, respectively. These results suggest that APC and TP53 may act as a potential screening marker for colorectal adenoma and early-stage CRC. This preliminary study may help identify patients with adenoma and early-stage CRC and may aid in establishing prevention and surveillance strategies to reduce the incidence of CRC

Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs

Antimicrobial peptides (AMPs) are natural peptides possessing antimicrobial activities. These peptides are important components of the innate immune system. They are found in various organisms. AMP screening and identification by experimental techniques are laborious and time-consuming tasks. Alternatively, computational methods based on machine learning have been developed to screen potential AMP candidates prior to experimental verification. Although various AMP prediction programs are available, there is still a need for improvement to reduce false positives (FPs) and to increase the predictive accuracy. In this work, several well-known single and ensemble machine learning approaches have been explored and evaluated based on balanced training datasets and two large testing datasets. We have demonstrated that the developed program with various predictive models has high performance in differentiating between AMPs and non-AMPs. Thus, we describe the development of a program for the prediction and recognition of AMPs using MaxProbVote, which is an ensemble model. Moreover, to increase prediction efficiency, the ensemble model was integrated with a new hybrid feature based on logistic regression. The ensemble model integrated with the hybrid feature can effectively increase the prediction sensitivity of the developed program called Ensemble-AMPPred, resulting in overall improvements in terms of both sensitivity and specificity compared to those of currently available programs

RNA secondary structure prediction using conditional random fields model

Non-coding RNAs (ncRNAs) have important biological functions in living cells dependent on their conserved secondary structures. Here, we focus on computational RNA secondary structure prediction by exploring primary sequences and complementary base pair interactions using the Conditional Random Fields (CRFs) model, which treats RNA prediction as a sequence labelling problem. Proposing suitable feature extraction from known RNA secondary structures, we developed a feature extraction based on natural RNA's loop and stem characteristics. Our CRFs models can predict the secondary structures of the test RNAs with optimal F-score prediction between 56.61 and 98.20% for different RNA families

Genome Characterization of Oleaginous Aspergillus oryzae BCC7051: A Potential Fungal-Based Platform for Lipid Production

The selected robust fungus, Aspergillus oryzae strain BCC7051 is of interest for biotechnological production of lipid-derived products due to its capability to accumulate high amount of intracellular lipids using various sugars and agro-industrial substrates. Here, we report the genome sequence of the oleaginous A. oryzae BCC7051. The obtained reads were de novo assembled into 25 scaffolds spanning of 38,550,958\ua0bps with predicted 11,456 protein-coding genes. By synteny mapping, a large rearrangement was found in two scaffolds of A. oryzae BCC7051 as compared to the reference RIB40 strain. The genetic relationship between BCC7051 and other strains of A. oryzae in terms of aflatoxin production was investigated, indicating that the A. oryzae BCC7051 was categorized into group 2 nonaflatoxin-producing strain. Moreover, a comparative analysis of the structural genes focusing on the involvement in lipid metabolism among oleaginous yeast and fungi revealed the presence of multiple isoforms of metabolic enzymes responsible for fatty acid synthesis in BCC7051. The alternative routes of acetyl-CoA generation as oleaginous features and malate/citrate/pyruvate shuttle were also identified in this A. oryzae strain. The genome sequence generated in this work is a dedicated resource for expanding genome-wide study of microbial lipids at systems level, and developing the fungal-based platform for production of diversified lipids with commercial relevance