Yeast Biological Networks Unfold the Interplay of Antioxidants, Genome and Phenotype, and Reveal a Novel Regulator of the Oxidative Stress Response

Background Identifying causative biological networks associated with relevant phenotypes is essential in the field of systems biology. We used ferulic acid (FA) as a model antioxidant to characterize the global expression programs triggered by this small molecule and decipher the transcriptional network controlling the phenotypic adaptation of the yeast Saccharomyces cerevisiae. Methodology/Principal Findings By employing a strict cut off value during gene expression data analysis, 106 genes were found to be involved in the cell response to FA, independent of aerobic or anaerobic conditions. Network analysis of the system guided us to a key target node, the FMP43 protein, that when deleted resulted in marked acceleration of cellular growth (~15% in both minimal and rich media). To extend our findings to human cells and identify proteins that could serve as drug targets, we replaced the yeast FMP43 protein with its human ortholog BRP44 in the genetic background of the yeast strain Δfmp43. The conservation of the two proteins was phenotypically evident, with BRP44 restoring the normal specific growth rate of the wild type. We also applied homology modeling to predict the 3D structure of the FMP43 and BRP44 proteins. The binding sites in the homology models of FMP43 and BRP44 were computationally predicted, and further docking studies were performed using FA as the ligand. The docking studies demonstrated the affinity of FA towards both FMP43 and BRP44. Conclusions This study proposes a hypothesis on the mechanisms yeast employs to respond to antioxidant molecules, while demonstrating how phenome and metabolome yeast data can serve as biomarkers for nutraceutical discovery and development. Additionally, we provide evidence for a putative therapeutic target, revealed by replacing the FMP43 protein with its human ortholog BRP44, a brain protein, and functionally characterizing the relevant mutant strain

Linking Genotype and Phenotype of Saccharomyces cerevisiae Strains Reveals Metabolic Engineering Targets and Leads to Triterpene Hyper-Producers

Background: Metabolic engineering is an attractive approach in order to improve the microbial production of drugs. Triterpenes is a chemically diverse class of compounds and many among them are of interest from a human health perspective. A systematic experimental or computational survey of all feasible gene modifications to determine the genotype yielding the optimal triterpene production phenotype is a laborious and time-consuming process. Methodology/Principal Findings: Based on the recent genome-wide sequencing of Saccharomyces cerevisiae CEN.PK 113-7D and its phenotypic differences with the S288C strain, we implemented a strategy for the construction of a beta-amyrin production platform. The genes Erg8, Erg9 and HFA1 contained non-silent SNPs that were computationally analyzed to evaluate the changes that cause in the respective protein structures. Subsequently, Erg8, Erg9 and HFA1 were correlated with the increased levels of ergosterol and fatty acids in CEN.PK 113-7D and single, double, and triple gene over-expression strains were constructed. Conclusions: The six out of seven gene over-expression constructs had a considerable impact on both ergosterol and beta-amyrin production. In the case of beta-amyrin formation the triple over-expression construct exhibited a nearly 500% increase over the control strain making our metabolic engineering strategy the most successful design of triterpene microbial producers

Common and Distant Structural Characteristics of Feruloyl Esterase Families from Aspergillus oryzae

Background: Feruloyl esterases (FAEs) are important biomass degrading accessory enzymes due to their capability of cleaving the ester links between hemicellulose and pectin to aromatic compounds of lignin, thus enhancing the accessibility of plant tissues to cellulolytic and hemicellulolytic enzymes. FAEs have gained increased attention in the area of biocatalytic transformations for the synthesis of value added compounds with medicinal and nutritional applications. Following the increasing attention on these enzymes, a novel descriptor based classification system has been proposed for FAEs resulting into 12 distinct families and pharmacophore models for three FAE sub-families have been developed. Methodology/Principal Findings: The feruloylome of Aspergillus oryzae contains 13 predicted FAEs belonging to six sub-families based on our recently developed descriptor-based classification system. The three-dimensional structures of the 13 FAEs were modeled for structural analysis of the feruloylome. The three genes coding for three enzymes, viz., A.O.2, A.O.8 and A.O.10 from the feruloylome of A. oryzae, representing sub-families with unknown functional features, were heterologously expressed in Pichia pastoris, characterized for substrate specificity and structural characterization through CD spectroscopy. Common feature-based pharamacophore models were developed according to substrate specificity characteristics of the three enzymes. The active site residues were identified for the three expressed FAEs by determining the titration curves of amino acid residues as a function of the pH by applying molecular simulations. Conclusions/Significance: Our findings on the structure-function relationships and substrate specificity of the FAEs of A. oryzae will be instrumental for further understanding of the FAE families in the novel classification system. The developed pharmacophore models could be applied for virtual screening of compound databases for short listing the putative substrates prior to docking studies or for post-processing docking results to remove false positives. Our study exemplifies how computational predictions can complement to the information obtained through experimental methods. © 2012 Udatha et al.published_or_final_versio

An Inserted α/β Subdomain Shapes the Catalytic Pocket of Lactobacillus johnsonii Cinnamoyl Esterase

Microbial enzymes produced in the gastrointestinal tract are primarily responsible for the release and biochemical transformation of absorbable bioactive monophenols. In the present work we described the crystal structure of LJ0536, a serine cinnamoyl esterase produced by the probiotic bacterium Lactobacillus johnsonii N6.2.We crystallized LJ0536 in the apo form and in three substrate-bound complexes. The structure showed a canonical α/β fold characteristic of esterases, and the enzyme is dimeric. Two classical serine esterase motifs (GlyXSerXGly) can be recognized from the amino acid sequence, and the structure revealed that the catalytic triad of the enzyme is formed by Ser(106), His(225), and Asp(197), while the other motif is non-functional. In all substrate-bound complexes, the aromatic acyl group of the ester compound was bound in the deepest part of the catalytic pocket. The binding pocket also contained an unoccupied area that could accommodate larger ligands. The structure revealed a prominent inserted α/β subdomain of 54 amino acids, from which multiple contacts to the aromatic acyl groups of the substrates are made. Inserts of this size are seen in other esterases, but the secondary structure topology of this subdomain of LJ0536 is unique to this enzyme and its closest homolog (Est1E) in the Protein Databank.The binding mechanism characterized (involving the inserted α/β subdomain) clearly differentiates LJ0536 from enzymes with similar activity of a fungal origin. The structural features herein described together with the activity profile of LJ0536 suggest that this enzyme should be clustered in a new group of bacterial cinnamoyl esterases

How well do the substrates KISS the enzyme? Molecular docking program selection for feruloyl esterases

Molecular docking is the most commonly used technique in the modern drug discovery process where computational approaches involving docking algorithms are used to dock small molecules into macromolecular target structures. Over the recent years several evaluation studies have been reported by independent scientists comparing the performance of the docking programs by using default 'black boxg' protocols supplied by the software companies. Such studies have to be considered carefully as the docking programs can be tweaked towards optimum performance by selecting the parameters suitable for the target of interest. In this study we address the problem of selecting an appropriate docking and scoring function combination (88 docking algorithm-scoring functions) for substrate specificity predictions for feruloyl esterases, an industrially relevant enzyme family. We also propose the 'Key Interaction Score Systemg' (KISS), a more biochemically meaningful measure for evaluation of docking programs based on pose prediction accuracy.published_or_final_versio

Targeted Metabolic Engineering Guided by Computational Analysis of Single-Nucleotide Polymorphisms (SNPs)

The non-synonymous SNPs, the so-called non-silent SNPs, which are single-nucleotide variations in the coding regions that give “birth” to amino acid mutations, are often involved in the modulation of protein function. Understanding the effect of individual amino acid mutations on a protein/enzyme function or stability is useful for altering its properties for a wide variety of engineering studies. Since measuring the effects of amino acid mutations experimentally is a laborious process, a variety of computational methods have been discussed here that aid to extract direct genotype to phenotype information.link_to_subscribed_fulltex

pH dependent relative activity profiles for the three expressed FAEs.

<p>Values are expressed relative to the hydrolytic efficiency of respective FAE for methyl ferulate. (A) Effect of pH on A.O.2 activity. (B) Effect of pH on A.O.8 activity. (C) Effect of pH on A.O.10 activity.</p

Feruloylome members of <i>Aspergillus oryzae.</i>

1<p>For convenience the ID designations were used throughout the article.</p>2<p>According to descriptor based FAE classification system <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039473#pone.0039473-Udatha1" target="_blank">[4]</a>.</p

Regions of similar SSEs among the FEF family members shown as ribbon structures.

<p>Structurally similar regions are colored whereas dissimilar regions are depicted in grey. (A) Structural similarity in the secondary structure elements of FEF 4 family members. (B) Structural similarity in the secondary structure elements of FEF 6 family members. (C) Structural similarity in the secondary structure elements of FEF 7 family members. (D) Structural similarity in the secondary structure elements of FEF 12 family members.</p