Integrating metabolomics and transcriptomics data to discover a biocatalyst that can generate the amine precursors for alkamide biosynthesis.

The Echinacea genus is exemplary of over 30 plant families that produce a set of bioactive amides, called alkamides. The Echinacea alkamides may be assembled from two distinct moieties, a branched-chain amine that is acylated with a novel polyunsaturated fatty acid. In this study we identified the potential enzymological source of the amine moiety as a pyridoxal phosphate-dependent decarboxylating enzyme that uses branched-chain amino acids as substrate. This identification was based on a correlative analysis of the transcriptomes and metabolomes of 36 different E. purpurea tissues and organs, which expressed distinct alkamide profiles. Although no correlation was found between the accumulation patterns of the alkamides and their putative metabolic precursors (i.e., fatty acids and branched-chain amino acids), isotope labeling analyses supported the transformation of valine and isoleucine to isobutylamine and 2-methylbutylamine as reactions of alkamide biosynthesis. Sequence homology identified the pyridoxal phosphate-dependent decarboxylase-like proteins in the translated proteome of E. purpurea. These sequences were prioritized for direct characterization by correlating their transcript levels with alkamide accumulation patterns in different organs and tissues, and this multi-pronged approach led to the identification and characterization of a branched-chain amino acid decarboxylase, which would appear to be responsible for generating the amine moieties of naturally occurring alkamides.

A global approach to analysis and interpretation of metabolic data for plant natural product discovery.

Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publicly available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these datasets with transcriptomic data to create hypotheses concerning specialized metabolisms that generate the diversity in natural product chemistry. We discuss the importance of close collaborations among biologists, chemists, computer scientists and statisticians throughout the development of such integrated metabolism-centric databases and software.

Butyrate producing microbiota are reduced in chronic kidney diseases.

Chronic kidney disease is a major public health concern that affects millions of people globally. Alterations in gut microbiota composition have been observed in patients with chronic kidney disease. Nevertheless, the correlation between the gut microbiota and disease severity has not been investigated. In this study, we performed shot-gun metagenomics sequencing and identified several taxonomic and functional signatures associated with disease severity in patients with chronic kidney disease. We noted that 19 microbial genera were significantly associated with the severity of chronic kidney disease. The butyrate-producing bacteria were reduced in patients with advanced stages of chronic kidney diseases. In addition, functional metagenomics showed that two-component systems, metabolic activity and regulation of co-factor were significantly associated with the disease severity. Our study provides valuable information for the development of microbiota-oriented therapeutic strategies for chronic kidney disease.

Combined use of cutinase and high-resolution mass-spectrometry to query the molecular architecture of cutin.

BACKGROUND: Cutin is a complex, highly cross-linked polyester consisting of hydroxylated and epoxidated acyl lipid monomers. Because of the complexity of the polymer it has been difficult to define the chemical architecture of the polymer, which has further limited the ability to identify the catalytic components that assemble the polymer. Analogous to methods that define the structure of oligosaccharides, we demonstrate a strategy that utilizes cutinase to generate cutin subfragments consisting of up to four monomeric units, whose structure and spatial distribution in the polymer is revealed by high-resolution mass spectrometry. Moreover, the application of mass-spectrometric fragmentation and labelling of the end of the oligomers, one is able to define the order of monomers in the oligomer. The systematic application of this strategy can greatly facilitate understanding the chemical architecture of this complex polymer. RESULTS: The chemical architecture of plant cutin is dissected by coupling an enzymatic system that deconstructs the polymer into subfragments consisting of dimers, trimers and tetramers of cutin monomers, with group-specific labeling and mass spectrometry. These subfragments can be generated with one of over 1200 of cutinases identified from diverse biological sources. The parallel chemical labeling of the polymer with dansyl, alkyl or p-dimethylaminophenacyl reagents can identify the chemical distribution of non-esterified hydroxyl- and carboxyl-groups among the monomers. This combined strategy is applied to cutin isolated from with apple fruit skins, and a combination of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-quadrupole time-of-flight (Q-TOF) MS is used to determine the order of the monomers in the cutinase-generated subfragments. Finally, we demonstrate the use of matrix-assisted laser desorption-ionization-MS to determine the spatial distribution of the cutinase-generated subfragments. CONCLUSION: Our experimental results demonstrate an advancement to overcome the current limitations in identifying cutin oligomeric structure and allows one to more efficiently address new biological questions about cutin biosynthesis. We submit that the systematic application of these methods will enable the construction of more accurate architectural models of cutin, which is a prerequisite to identifying cutin-biosynthetic components.

A gene selection method for classifying cancer samples using 1D discrete wavelet transform.

Selecting a set of discriminant genes for biological samples is an important task for designing highly efficient classifiers using DNA microarray data. The wavelet transform is a very common tool in signal-processing applications, but its potential in the analysis of microarray gene expression data is yet to be explored fully. In this paper, we present a wavelet-based feature selection method that assigns scores to genes for differentiating samples between two classes. The gene expression signal is decomposed using several levels of the wavelet transform. The genes with the highest scores are selected to form a feature set for sample classification. In this study, the feature sets were coupled with k-nearest neighbour (kNN) classifiers. The classification accuracies were assessed using several real data sets. Their performances were compared with several commonly used feature selection methods. The results demonstrate that 1D wavelet analysis is a valuable tool for studying gene expression patterns.