MIPHENO: data normalization for high throughput metabolite analysis

BioHackathon 2015: Semantics of data for life sciences and reproducible research

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

A High-Throughput Method for the Quantitative Determination of Free Amino Acids in Saccharomyces cerevisiae by Hydrophilic Interaction Chromatography-Tandem Mass Spectrometry

Amino acids are the building blocks for protein synthesis and the precursors for many biomolecules, such as glutathione and S-adenosylmethionine. Their intracellular concentrations provide valuable information about the overall metabolic state of the cell, as they are closely connected to carbon and nitrogen metabolism and are tightly regulated to meet cellular demands in ever-changing environments. Here, we describe a fast and simple method enabling metabolic profiling for free amino acids for large numbers of yeast strains. Metabolites are extracted with boiling ethanol and, without further conditioning, analyzed by hydrophilic interaction chromatography-tandem mass spectrometry (HILIC-MS/MS). Several hundred samples can be prepared in a single day with an analytical runtime of 3.25 min. This method is valuable for functional characterization, identification of metabolic regulators and processes, or monitoring of biotechnological processes.

Utility and Limitations of Using Gene Expression Data to Identify Functional Associations

Gene co-expression has been widely used to hypothesize gene function through guilt-by association. However, it is not clear to what degree co-expression is informative, whether it can be applied to genes involved in different biological processes, and how the type of dataset impacts inferences about gene functions. Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes. By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation, we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset, annotation quality, gene function, expression similarity measure, and clustering approach significantly impacts the ability to recover functional associations between genes using Arabidopsis thaliana as an example. Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better. In addition, to recover the maximum number of known pathways and identify candidate genes with similar functions, it is important to explore rather exhaustively multiple dataset combinations, similarity measures, clustering algorithms and parameters. Finally, we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants. This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets.

There remain genes with no known function even in the most well studied, model species. One common way to hypothesize gene function is based on the assumption that genes with similar expression profiles tend to have similar functions. However, using datasets and biological pathway information from the model plant Arabidopsis thaliana as an example, we discovered that, although genes in the same pathways are functionally related, genes in only a subset of the pathways have highly similar expression patterns. In addition, our ability to hypothesize gene functions based on expression is significantly impacted by how the dataset is processed and combined as well as the methodology used to identify genes with similar expression. Therefore, multiple datasets and methods should be tested to maximize the functional information that we can get based on similarity in gene expression.

Functional Metabolomics Describes the Yeast Biosynthetic Regulome

Genome-metabolism interactions enable cell growth. To probe the extent of these interactions and delineate their functional contributions, we quantified the Saccharomyces amino acid metabolome and its response to systematic gene deletion. Over one-third of coding genes, in particular those important for chromatin dynamics, translation, and transport, contribute to biosynthetic metabolism. Specific amino acid signatures characterize genes of similar function. This enabled us to exploit functional metabolomics to connect metabolic regulators to their effectors, as exemplified by TORC1, whose inhibition in exponentially growing cells is shown to match an interruption in endomembrane transport. Providing orthogonal information compared to physical and genetic interaction networks, metabolomic signatures cluster more than half of the so far uncharacterized yeast genes and provide functional annotation for them. A major part of coding genes is therefore participating in gene-metabolism interactions that expose the metabolism regulatory network and enable access to an underexplored space in gene function.

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One-third of coding genes significantly impact yeast biosynthetic metabolism

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The amino acid metabolome is most sensitive to chromatin and transport proteins

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TORC1 affects biosynthetic amino acid metabolism via vesicle-mediated transport

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Metabolic signatures are gene specific and cluster 3,923 genes according to function

Analysis of Loss-of-Function Mutants in Aspartate Kinase and Homoserine Dehydrogenase Genes Points to Complexity in the Regulation of Aspartate-Derived Amino Acid Contents

Biosynthesis of aspartate (Asp)-derived amino acids lysine (Lys), methionine (Met), threonine (Thr), and isoleucine involves monofunctional Asp kinases (AKs) and dual-functional Asp kinase-homoserine dehydrogenases (AK-HSDHs). Four-week-old loss-of-function Arabidopsis (Arabidopsis thaliana) mutants in the AK-HSDH2 gene had increased amounts of Asp and Asp-derived amino acids, especially Thr, in leaves. To explore mechanisms behind this phenotype, we obtained single mutants for other AK and AK-HSDH genes, generated double mutants from ak-hsdh2 and ak mutants, and performed free and protein-bound amino acid profiling, transcript abundance, and activity assays. The increases of Asp, Lys, and Met in ak-hsdh2 were also observed in ak1-1, ak2-1, ak3-1, and ak-hsdh1-1. However, the Thr increase in ak-hsdh2 was observed in ak-hsdh1-1 but not in ak1-1, ak2-1, or ak3-1. Activity assays showed that AK2 and AK-HSDH1 are the major contributors to overall AK and HSDH activities, respectively. Pairwise correlation analysis revealed positive correlations between the amount of AK transcripts and Lys-sensitive AK activity and between the amount of AK-HSDH transcripts and both Thr-sensitive AK activity and total HSDH activity. In addition, the ratio of total AK activity to total HSDH activity negatively correlates with the ratio of Lys to the total amount of Met, Thr, and isoleucine. These data led to the hypothesis that the balance between Lys-sensitive AKs and Thr-sensitive AK-HSDHs is important for maintaining the amounts and ratios of Asp-derived amino acids.

Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis

Despite recent intensive research efforts in functional genomics, the functions of only a limited number of Arabidopsis (Arabidopsis thaliana) genes have been determined experimentally, and improving gene annotation remains a major challenge in plant science. As metabolite profiling can characterize the metabolomic phenotype of a genetic perturbation in the plant metabolism, it provides clues to the function(s) of genes of interest. We chose 50 Arabidopsis mutants, including a set of characterized and uncharacterized mutants, that resemble wild-type plants. We performed metabolite profiling of the plants using gas chromatography-mass spectrometry. To make the data set available as an efficient public functional genomics tool for hypothesis generation, we developed the Metabolite Profiling Database for Knock-Out Mutants in Arabidopsis (MeKO). It allows the evaluation of whether a mutation affects metabolism during normal plant growth and contains images of mutants, data on differences in metabolite accumulation, and interactive analysis tools. Nonprocessed data, including chromatograms, mass spectra, and experimental metadata, follow the guidelines set by the Metabolomics Standards Initiative and are freely downloadable. Proof-of-concept analysis suggests that MeKO is highly useful for the generation of hypotheses for genes of interest and for improving gene annotation. MeKO is publicly available at http://prime.psc.riken.jp/meko/.

Adaptive laboratory evolution -- principles and applications for biotechnology

Adaptive laboratory evolution is a frequent method in biological studies to gain insights into the basic mechanisms of molecular evolution and adaptive changes that accumulate in microbial populations during long term selection under specified growth conditions. Although regularly performed for more than 25 years, the advent of transcript and cheap next-generation sequencing technologies has resulted in many recent studies, which successfully applied this technique in order to engineer microbial cells for biotechnological applications. Adaptive laboratory evolution has some major benefits as compared with classical genetic engineering but also some inherent limitations. However, recent studies show how some of the limitations may be overcome in order to successfully incorporate adaptive laboratory evolution in microbial cell factory design. Over the last two decades important insights into nutrient and stress metabolism of relevant model species were acquired, whereas some other aspects such as niche-specific differences of non-conventional cell factories are not completely understood. Altogether the current status and its future perspectives highlight the importance and potential of adaptive laboratory evolution as approach in biotechnological engineering.

Integrated LC-MS/MS system for plant metabolomics

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is highly sensitive, selective, and enables extensive detection of metabolites within a sample. The result allows us to characterize comprehensive metabolite accumulation patterns without dependence on authentic standard compounds and isolation of the individual metabolites. A reference database search is essential for the structural assignment process of un-targeted MS and MS/MS data. Moreover, the characterization of unknown metabolites is challenging, since these cannot be assigned a candidate structure by using a reference database. In this case study, integrated LC-MS/MS based plant metabolomics allows us to detect several hundred metabolites in a sample; and integrated omics analyses, e.g., large-scale reverse genetics, linkage mapping, and association mapping, provides a powerful tool for candidate structure selection or rejection. We also examine emerging technology and applications for LC-MS/MS-based un-targeted plant metabolomics. These activities promote the characterization of massive extended detectable metabolites.