Natural variation in wild tomato trichomes; selecting metabolites that contribute to insect resistance using a random forest approach

BACKGROUND

Plant-produced specialised metabolites are a powerful part of a plant's first line of defence against herbivorous insects, bacteria and fungi. Wild ancestors of present-day cultivated tomato produce a plethora of acylsugars in their type-I/IV trichomes and volatiles in their type-VI trichomes that have a potential role in plant resistance against insects. However, metabolic profiles are often complex mixtures making identification of the functionally interesting metabolites challenging. Here, we aimed to identify specialised metabolites from a wide range of wild tomato genotypes that could explain resistance to vector insects whitefly (Bemisia tabaci) and Western flower thrips (Frankliniella occidentalis). We evaluated plant resistance, determined trichome density and obtained metabolite profiles of the glandular trichomes by LC-MS (acylsugars) and GC-MS (volatiles). Using a customised Random Forest learning algorithm, we determined the contribution of specific specialised metabolites to the resistance phenotypes observed.

RESULTS

The selected wild tomato accessions showed different levels of resistance to both whiteflies and thrips. Accessions resistant to one insect can be susceptible to another. Glandular trichome density is not necessarily a good predictor for plant resistance although the density of type-I/IV trichomes, related to the production of acylsugars, appears to correlate with whitefly resistance. For type VI-trichomes, however, it seems resistance is determined by the specific content of the glands. There is a strong qualitative and quantitative variation in the metabolite profiles between different accessions, even when they are from the same species. Out of 76 acylsugars found, the random forest algorithm linked two acylsugars (S3:15 and S3:21) to whitefly resistance, but none to thrips resistance. Out of 86 volatiles detected, the sesquiterpene α-humulene was linked to whitefly susceptible accessions instead. The algorithm did not link any specific metabolite to resistance against thrips, but monoterpenes α-phellandrene, α-terpinene and β-phellandrene/D-limonene were significantly associated with susceptible tomato accessions.

CONCLUSIONS

Whiteflies and thrips are distinctly targeted by certain specialised metabolites found in wild tomatoes. The machine learning approach presented helped to identify features with efficacy toward the insect species studied. These acylsugar metabolites can be targets for breeding efforts towards the selection of insect-resistant cultivars.

Adult GAMT deficiency: A literature review and report of two siblings

Guanidinoacetate methyltransferase (GAMT) deficiency is a creatine deficiency disorder and an inborn error of metabolism presenting with progressive intellectual and neurological deterioration. As most cases are identified and treated in early childhood, adult phenotypes that can help in understanding the natural history of the disorder are rare. We describe two adult cases of GAMT deficiency from a consanguineous family in Pakistan that presented with a history of global developmental delay, cognitive impairments, excessive drooling, behavioral abnormalities, contractures and apparent bone deformities initially presumed to be the reason for abnormal gait. Exome sequencing identified a homozygous nonsense variant in GAMT: NM_000156.5:c.134G>A (p.Trp45*). We also performed a literature review and compiled the genetic and clinical characteristics of all adult cases of GAMT deficiency reported to date. When compared to the adult cases previously reported, the musculoskeletal phenotype and the rapidly progressive nature of neurological and motor decline seen in our patients is striking. This study presents an opportunity to gain insights into the adult presentation of GAMT deficiency and highlights the need for in-depth evaluation and reporting of clinical features to expand our understanding of the phenotypic spectrum.

Increased comparability between RNA-Seq and microarray data by utilization of gene sets

The field of transcriptomics uses and measures mRNA as a proxy of gene expression. There are currently two major platforms in use for quantifying mRNA, microarray and RNA-Seq. Many comparative studies have shown that their results are not always consistent. In this study we aim to find a robust method to increase comparability of both platforms enabling data analysis of merged data from both platforms. We transformed high dimensional transcriptomics data from two different platforms into a lower dimensional, and biologically relevant dataset by calculating enrichment scores based on gene set collections for all samples. We compared the similarity between data from both platforms based on the raw data and on the enrichment scores. We show that the performed data transforms the data in a biologically relevant way and filters out noise which leads to increased platform concordance. We validate the procedure using predictive models built with microarray based enrichment scores to predict subtypes of breast cancer using enrichment scores based on sequenced data. Although microarray and RNA-Seq expression levels might appear different, transforming them into biologically relevant gene set enrichment scores significantly increases their correlation, which is a step forward in data integration of the two platforms. The gene set collections were shown to contain biologically relevant gene sets. More in-depth investigation on the effect of the composition, size, and number of gene sets that are used for the transformation is suggested for future research.

The field of transcriptomics uses and measures mRNA as a proxy of gene expression. There are currently two major platforms in use for quantifying mRNA, microarray and RNA-Seq. Many comparative studies have shown that their results are not always consistent. In this study we aim to find a robust method to increase comparability of both platforms enabling data analysis of merged data from both platforms. We transformed the high dimensional transcriptomics data from the two different platforms into lower dimensional, and biologically relevant gene set scores. These gene sets were defined a-priori as specific combination of genes (e.g. up-regulated in a certain pathway). We observed that although microarray and RNA-Seq expression levels might appear different, using these gene sets to transform the data significantly increases their correlation. This is a step forward in data integration of the two platforms. More in-depth investigation on the effect of the composition, size, and number of gene sets that are used for the transformation is suggested for future research.

Separating common from distinctive variation

Background

Joint and individual variation explained (JIVE), distinct and common simultaneous component analysis (DISCO) and O2-PLS, a two-block (X-Y) latent variable regression method with an integral OSC filter can all be used for the integrated analysis of multiple data sets and decompose them in three terms: a low(er)-rank approximation capturing common variation across data sets, low(er)-rank approximations for structured variation distinctive for each data set, and residual noise. In this paper these three methods are compared with respect to their mathematical properties and their respective ways of defining common and distinctive variation.

Results

The methods are all applied on simulated data and mRNA and miRNA data-sets from GlioBlastoma Multiform (GBM) brain tumors to examine their overlap and differences. When the common variation is abundant, all methods are able to find the correct solution. With real data however, complexities in the data are treated differently by the three methods.

Conclusions

All three methods have their own approach to estimate common and distinctive variation with their specific strength and weaknesses. Due to their orthogonality properties and their used algorithms their view on the data is slightly different. By assuming orthogonality between common and distinctive, true natural or biological phenomena that may not be orthogonal at all might be misinterpreted.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1037-2) contains supplementary material, which is available to authorized users.

Nutrikinetic modeling reveals order of genistein phase II metabolites appearance in human plasma

SCOPE

Genistein from foods or supplements is metabolized by the gut microbiota and the human body, thereby releasing many different metabolites into systemic circulation. The order of their appearance in plasma and the possible influence of food format are still unknown. This study compared the nutrikinetic profiles of genistein metabolites.

METHODS AND RESULTS

In a randomized cross-over trial, 12 healthy young volunteers were administered a single dose of 30 mg genistein provided as a genistein tablet, a genistein tablet in low fat milk, and soy milk containing genistein glycosides. A high mass resolution LC-LTQ-Orbitrap FTMS platform detected and quantified in human plasma: free genistein, seven of its phase-II metabolites and 15 gut-derived metabolites. Interestingly, a novel metabolite, genistein-4'-glucuronide-7-sulfate (G-4'G-7S) was identified. Nutrikinetic analysis using population-based modeling revealed the order of appearance of five genistein phase II metabolites in plasma: (1) genistein-4',7-diglucuronide, (2) genistein-7-sulfate, (3) genistein-4'-sulfate-7-glucuronide, (4) genistein-4'-glucuronide, and (5) genistein-7-glucuronide, independent of the food matrix.

CONCLUSION

The conjugated genistein metabolites appear in a distinct order in human plasma. The specific early appearance of G-4',7-diG suggests a multistep formation process for the mono and hetero genistein conjugates, involving one or two deglucuronidation steps.

Semi-automated non-target processing in GC × GC-MS metabolomics analysis: applicability for biomedical studies

Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC-MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC-MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC-MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five quality control (QC) samples prepared from the study samples) were measured with GC × GC-MS and GC-MS to study the development and progression of insulin resistance, a primary characteristic of diabetes type 2. A total of 170 and 691 peaks were quantified in, respectively, the GC-MS and GC × GC-MS data for all study and QC samples. The quantitative results for the QC samples were compared to assess the quality of semi-automated GC × GC-MS processing compared to targeted GC-MS processing which involved time-consuming manual correction of all wrongly integrated metabolites and was considered as golden standard. The relative standard deviations (RSDs) obtained with GC × GC-MS were somewhat higher than with GC-MS, due to less accurate processing. Still, the biological information in the study samples was preserved and the added value of GC × GC-MS was demonstrated; many additional candidate biomarkers were found with GC × GC-MS compared to GC-MS. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-010-0219-6) contains supplementary material, which is available to authorized users.

Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping

Analytical errors caused by suboptimal performance of the chosen platform for a number of metabolites and instrumental drift are a major issue in large-scale metabolomics studies. Especially for MS-based methods, which are gaining common ground within metabolomics, it is difficult to control the analytical data quality without the availability of suitable labeled internal standards and calibration standards even within one laboratory. In this paper, we suggest a workflow for significant reduction of the analytical error using pooled calibration samples and multiple internal standard strategy. Between and within batch calibration techniques are applied and the analytical error is reduced significantly (increase of 25% of peaks with RSD lower than 20%) and does not hamper or interfere with statistical analysis of the final data.