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.

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.

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.

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.

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.