In vitro microbiotic fermentation causes an extensive metabolite turnover of rye bran phytochemicals

The human gut hosts a microbial community which actively contributes to the host metabolism and has thus remarkable effect on our health. Intestinal microbiota is known to interact remarkably with the dietary constituents entering the colon, causing major metabolic conversions prior to absorption. To investigate the effect of microbial metabolism on the phytochemical pool of rye bran, we applied an in vitro simulated colonic fermentation where samples were collected with intervals and analyzed by LC-MS based non-targeted metabolite profiling. The analyses revealed extensive metabolic turnover on the phytochemical composition of the bran samples, and showed effects on all the metabolite classes detected. Furthermore, the majority of the metabolites, both the precursors and the conversion products, remained unidentified indicating that there are numerous yet unknown phytochemicals, which can potentially affect on our health. This underlines the importance of comprehensive profiling assays and subsequent detailed molecular investigations in order to clarify the effect of microbiota on phytochemicals present in our everyday diet.     

Expression of a bacterial feedback-insensitive 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase of the shikimate pathway in Arabidopsis elucidates potential metabolic bottlenecks between primary and secondary metabolism

The shikimate pathway of plants mediates the conversion of primary carbon metabolites via chorismate into the three aromatic amino acids and to numerous secondary metabolites derived from them. However, the regulation of the shikimate pathway is still far from being understood. We hypothesized that 3-deoxy-d-arabino-heptulosonate 7-phosphate synthase (DAHPS) is a key enzyme regulating flux through the shikimate pathway. To test this hypothesis, we expressed a mutant bacterial AroG gene encoding a feedback-insensitive DAHPS in transgenic Arabidopsis plants. The plants were subjected to detailed analysis of primary metabolism, using GC-MS, as well as secondary metabolism, using LC-MS. Our results exposed a major effect of bacterial AroG expression on the levels of shikimate intermediate metabolites, phenylalanine, tryptophan and broad classes of secondary metabolite, such as phenylpropanoids, glucosinolates, auxin and other hormone conjugates. We propose that DAHPS is a key regulatory enzyme of the shikimate pathway. Moreover, our results shed light on additional potential metabolic bottlenecks bridging plant primary and secondary metabolism.     

<Emphasis Type="Italic">pc8.1</Emphasis>, a major QTL for pigment content in pepper fruit,is associated with variation in plastid compartment size

Studies on the genetic control of pigment content in pepper fruit have focused mainly on monogenic mutations leading to changes in fruit color. In addition to the qualitative variation in fruit color, quantitative variation in pigment content and color intensity exists in pepper giving rise to a range of color intensities. However, the genetic basis for this variation is poorly understood, hindering the development of peppers that are rich in these beneficial compounds. In this paper, quantitative variation in pigment content was studied in a cross between a dark-green Capsicum annuum pepper and a light-green C. chinense pepper. Two major pigment content QTLs that control chlorophyll content were identified, pc8.1 and pc10.1. The major QTL pc8.1, also affected carotenoid content in the ripe fruit. However, additional analyses in subsequent generations did not reveal a consistent effect of this QTL on carotenoid content in ripe fruit. Confocal microscopy analyses of green immature fruits of the parents and of near-isogenic lines for pc8.1 indicated that the QTL exerts its effect via increasing chloroplast compartment size in the dark-green genotypes, predominantly in a fruit-specific manner. Metabolic analyses indicated that in addition to chlorophyll, chloroplast-associated tocopherols and carotenoids are also elevated. Future identification of the genes controlling pigment content QTLs in pepper will provide a better understanding of this important trait and new opportunities for breeding peppers and other Solanaceae species with enhanced nutritional value.     

The legwd mutant uncovers the role of starch phosphorylation in pollen development and germination in tomato

Starches extracted from most plant species are phosphorylated. α-Glucan water dikinase (GWD) is a key enzyme that controls the phosphate content of starch. In the absence of its activity starch degradation is impaired, leading to a starch excess phenotype in Arabidopsis and in potato leaves, and to reduced cold sweetening in potato tubers. Here, we characterized a transposon insertion ( legwd::Ds ) in the tomato GWD ( LeGWD ) gene that caused male gametophytic lethality. The mutant pollen had a starch excess phenotype that was associated with a reduction in pollen germination. SEM and TEM analyses indicated mild shrinking of the pollen grains and the accumulation of large starch granules inside the plastids. The level of soluble sugars was reduced by 1.8-fold in mutant pollen grains. Overall, the transmission of the mutant allele was only 0.4% in the male, whereas it was normal in the female. Additional mutant alleles, obtained through transposon excision, showed the same phenotypes as legwd::Ds . Moreover, pollen germination could be restored, and the starch excess phenotype could be abolished in lines expressing the potato GWD homolog ( StGWD ) under a pollen-specific promoter. In these lines, where fertility was restored, homozygous plants for legwd::Ds were isolated, and showed the starch excess phenotype in the leaves. Overall, our results demonstrate the importance of starch phosphorylation and breakdown for pollen germination, and open up the prospect for analyzing the role of starch metabolism in leaves and fruits.     

Computational identification of three-way junctions in folded RNAs: a case study in Arabidopsis

Three-way junctions in folded RNAs have been investigated both experimentally and computationally. The interest in their analysis stems from the fact that they have significantly been found to possess a functional role. In recent work, three-way junctions have been categorized into families depending on the relative lengths of the segments linking the three helices. Here, based on ideas originating from computational geometry, an algorithm is proposed for detecting three-way junctions in data sets of genes that are related to a metabolic pathway of interest. In its current implementation, the algorithm relies on a moving window that performs energy minimization folding predictions, and is demonstrated on a set of genes that are involved in purine metabolism in plants. The pattern matching algorithm can be extended to other organisms and other metabolic cycles of interest in which three-way junctions have been or will be discovered to play an important role. In the test case presented here with, the computational prediction of a three-way junction in Arabidopsis that was speculated to have an interesting functional role is verified experimentally.     

Comparison of classifier fusion methods for predicting response to anti HIV-1 therapy

Background

Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers.

Principal Findings

The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system.

Conclusion

The combined EuResist prediction engine is freely available at http://engine.euresist.org.