Bekzod Khakimov

UDP-Glycosyltransferases from the UGT73C Subfamily in Barbarea vulgaris Catalyze Sapogenin 3-O-Glucosylation in Saponin-Mediated Insect Resistance

Triterpenoid saponins are bioactive metabolites that have evolved recurrently in plants, presumably for defense. Their biosynthesis is poorly understood, as is the relationship between bioactivity and structure. Barbarea vulgaris is the only crucifer known to produce saponins. Hederagenin and oleanolic acid cellobioside make some B. vulgaris plants resistant to important insect pests, while other, susceptible plants produce different saponins. Resistance could be caused by glucosylation of the sapogenins. We identified four family 1 glycosyltransferases (UGTs) that catalyze 3-O-glucosylation of the sapogenins oleanolic acid and hederagenin. Among these, UGT73C10 and UGT73C11 show highest activity, substrate specificity and regiospecificity, and are under positive selection, while UGT73C12 and UGT73C13 show lower substrate specificity and regiospecificity and are under purifying selection. The expression of UGT73C10 and UGT73C11 in different B. vulgaris organs correlates with saponin abundance. Monoglucosylated hederagenin and oleanolic acid were produced in vitro and tested for effects on P. nemorum. 3-O-β-d-Glc hederagenin strongly deterred feeding, while 3-O-β-d-Glc oleanolic acid only had a minor effect, showing that hydroxylation of C23 is important for resistance to this herbivore. The closest homolog in Arabidopsis thaliana, UGT73C5, only showed weak activity toward sapogenins. This indicates that UGT73C10 and UGT73C11 have neofunctionalized to specifically glucosylate sapogenins at the C3 position and demonstrates that C3 monoglucosylation activates resistance. As the UGTs from both the resistant and susceptible types of B. vulgaris glucosylate sapogenins and are not located in the known quantitative trait loci for resistance, the difference between the susceptible and resistant plant types is determined at an earlier stage in saponin biosynthesis.

Plant metabolomics: Resolution and quantification of elusive peaks in liquid chromatography–mass spectrometry profiles of complex plant extracts using multi-way decomposition methods

Previous studies on LC-MS metabolomic profiling of 127 F2 Barbarea vulgaris plants derived from a cross of parental glabrous (G) and pubescent (P) type, revealed four triterpenoid saponins (hederagenin cellobioside, oleanolic acid cellobioside, epihederagenin cellobioside, and gypsogenin cellobioside) that correlated with resistance of plants against the insect herbivore, Phyllotreta nemorum. In this study, for the first time, we demonstrate the efficiency of the multi-way decomposition method PARAllel FACtor analysis 2 (PARAFAC2) for exploring complex LC-MS data. PARAFAC2 enabled automated resolution and quantification of several elusive chromatographic peaks (e.g. overlapped, elution time shifted and low s/n ratio), which could not be detected and quantified by conventional chromatographic data analysis. Raw LC-MS data of 127 F2 B. vulgaris plants were arranged in a three-way array (elution time point×mass spectra×samples), divided into 17 different chromatographic intervals and each interval were individually modeled by PARAFAC2. Three main outputs of the PARAFAC2 models described: (1) elution time profile, (2) relative abundance, and (3) pure mass spectra of the resolved peaks modeled from each interval of the chromatographic data. PARAFAC2 scores corresponding to relative abundances of the resolved peaks were extracted and further used for correlation and partial least squares (PLS) analysis. A total of 71 PARAFAC2 components (which correspond to actual peaks, baselines and tails of neighboring peaks) were modeled from 17 different chromatographic retention time intervals of the LC-MS data. In addition to four previously known saponins, correlation- and PLS-analysis resolved five unknown saponin-like compounds that were significantly correlated with insect resistance. The method also enabled a good separation between resistant and susceptible F2 plants. PARAFAC2 spectral loadings corresponding to the pure mass spectra of chromatographic peaks matched well with experimentally recorded mass spectra (correlation based similarity >95%). This enabled to extract pure mass spectra of highly overlapped and low s/n ratio peaks.

Gas chromatography – mass spectrometry data processing made easy

Evaluation of GC-MS data may be challenging due to the high complexity of data including overlapped, embedded, retention time shifted and low S/N ratio peaks. In this work, we demonstrate a new approach, PARAFAC2 based Deconvolution and Identification System (PARADISe), for processing raw GC-MS data. PARADISe is a computer platform independent freely available software incorporating a number of newly developed algorithms in a coherent framework. It offers a solution for analysts dealing with complex chromatographic data. It allows extraction of chemical/metabolite information directly from the raw data. Using PARADISe requires only few inputs from the analyst to process GC-MS data and subsequently converts raw netCDF data files into a compiled peak table. Furthermore, the method is generally robust towards minor variations in the input parameters. The method automatically performs peak identification based on deconvoluted mass spectra using integrated NIST search engine and generates an identification report. In this paper, we compare PARADISe with AMDIS and ChromaTOF in terms of peak quantification and show that PARADISe is more robust to user-defined settings and that these are easier (and much fewer) to set. PARADISe is based on non-proprietary scientifically evaluated approaches and we here show that PARADISe can handle more overlapping signals, lower signal-to-noise peaks and do so in a manner that requires only about an hours worth of work regardless of the number of samples. We also show that there are no non-detects in PARADISe, meaning that all compounds are detected in all samples.

New nordic diet versus average Danish diet: A randomized controlled trial revealed healthy long-term effects of the new nordic diet by GC-MS blood plasma metabolomics

A previous study has shown effects of the New Nordic Diet (NND) to stimulate weight loss and lower systolic and diastolic blood pressure in obese Danish women and men in a randomized, controlled dietary intervention study. This work demonstrates long-term metabolic effects of the NND as compared with an Average Danish Diet (ADD) in blood plasma and reveals associations between metabolic changes and health beneficial effects of the NND including weight loss. A total of 145 individuals completed the intervention and blood samples were taken along with clinical examinations before the intervention started (week 0) and after 12 and 26 weeks. The plasma metabolome was measured using GC-MS, and the final metabolite table contained 144 variables. Significant and novel metabolic effects of the diet, resulting weight loss, gender, and intervention study season were revealed using PLS-DA and ASCA. Several metabolites reflecting specific differences in the diets, especially intake of plant foods and seafood, and in energy metabolism related to ketone bodies and gluconeogenesis formed the predominant metabolite pattern discriminating the intervention groups. Among NND subjects, higher levels of vaccenic acid and 3-hydroxybutanoic acid were related to a higher weight loss, while higher concentrations of salicylic, lactic, and N-aspartic acids and 1,5-anhydro-d-sorbitol were related to a lower weight loss. Specific gender and seasonal differences were also observed. The study strongly indicates that healthy diets high in fish, vegetables, fruit, and whole grain facilitated weight loss and improved insulin sensitivity by increasing ketosis and gluconeogenesis in the fasting state.

Comprehensive and Comparative Metabolomic Profiling of Wheat, Barley, Oat and Rye Using Gas Chromatography-Mass Spectrometry and Advanced Chemometrics

Beyond the main bulk components of cereals such as the polysaccharides and proteins, lower concentration secondary metabolites largely contribute to the nutritional value. This paper outlines a comprehensive protocol for GC-MS metabolomic profiling of phenolics and organic acids in grains, the performance of which is demonstrated through a comparison of the metabolite profiles of the main northern European cereal crops: wheat, barley, oat and rye. Phenolics and organic acids were extracted using acidic hydrolysis, trimethylsilylated using a new method based on trimethylsilyl cyanide and analyzed by GC-MS. In order to extract pure metabolite peaks, the raw chromatographic data were processed by a multi-way decomposition method, Parallel Factor Analysis 2. This approach lead to the semi-quantitative detection of a total of 247 analytes, out of which 89 were identified based on RI and EI-MS library match. The cereal metabolome included 32 phenolics, 30 organic acids, 10 fatty acids, 11 carbohydrates and 6 sterols. The metabolome of the four cereals were compared in detail, including low concentration phenolics and organic acids. Rye and oat displayed higher total concentration of phenolic acids, but ferulic, caffeic and sinapinic acids and their esters were found to be the main phenolics in all four cereals. Compared to the previously reported methods, the outlined protocol provided an efficient and high throughput analysis of the cereal metabolome and the acidic hydrolysis improved the detection of conjugated phenolics.

GC-MS metabolite profiling of extreme southern Pinot Noir wines: Effects of vintage, barrel maturation, and fermentation dominate over vineyard site and clone selection

Wine is an extremely complex beverage that contains a multitude of volatile and nonvolatile compounds. This study investiged the effect of vineyard site and grapevine clone on the volatile profiles of commercially produced Pinot noir wines from central Otago, New Zealand. Volatile metabolites in Pinot noir wines produced from five grapevine clones grown on six vineyard sites in close proximity, over two consecutive vintages, were surveyed using gas chromatography-mass spectrometry (GC-MS). The raw GC-MS data were processed using parallel factor analysis (PARAFAC2), and final metabolite data were analyzed by principal component analysis (PCA). Winemaking conditions, vintage, and barrel maturation were found to be the most dominant factors. The effects of vineyard site and clone were mostly vintage dependent. Although four compounds including β-citronellol, homovanillyl alcohol, N-(3-methylbutyl)acetamide, and N-(2-phenylethyl)acetamide discriminated the vineyard sites independent of vintage, Pinot noir wines from different clones were only partially discriminated by PCA, and marker compound selection remained challenging.

Lepidopteran defence droplets - a composite physical and chemical weapon against potential predators

Insects often release noxious substances for their defence. Larvae of Zygaena filipendulae (Lepidoptera) secrete viscous and cyanogenic glucoside-containing droplets, whose effectiveness was associated with their physical and chemical properties. The droplets glued mandibles and legs of potential predators together and immobilised them. Droplets were characterised by a matrix of an aqueous solution of glycine-rich peptides (H-WG11-NH2) with significant amounts of proteins and glucose. Among the proteins, defensive proteins such as protease inhibitors, proteases and oxidases were abundant. The neurotoxin β-cyanoalanine was also found in the droplets. Despite the presence of cyanogenic glucosides, which release toxic hydrogen cyanide after hydrolysis by a specific β-glucosidase, the only β-glucosidase identified in the droplets (ZfBGD1) was inactive against cyanogenic glucosides. Accordingly, droplets did not release hydrogen cyanide, unless they were mixed with specific β-glucosidases present in the Zygaena haemolymph. Droplets secreted onto the cuticle hardened and formed sharp crystalline-like precipitates that may act as mandible abrasives to chewing predators. Hardening followed water evaporation and formation of antiparallel β-sheets of the peptide oligomers. Consequently, after mild irritation, Zygaena larvae deter predators by viscous and hardening droplets that contain defence proteins and β-cyanoalanine. After severe injury, droplets may mix with exuding haemolymph to release hydrogen cyanide.

Resveratrol in the foodomics era: 1:25,000

Resveratrol is probably the most investigated plant secondary metabolite ever. An epidemiological study known as the French paradox showed a correlation between red wine intake and low mortality due to coronary heart diseases, and the red wine substance resveratrol was claimed to play a key role. Since then, several hundred resveratrol studies have been conducted to demonstrate its antioxidant and other beneficial properties. In the foodomics era, considering a complex foodome including over 25,000 substances that make up the human diet, it appears to be outdated to pursue the hunt for biological activities one function/compound at a time. First, nature is multivariate, and the effect of any one molecule will have to be modulated by its carrying matrix, its bioavailability, and synergies with other molecules. Second, a large number of targeted studies have the tendency to become biased, as they tend to retain only the data that the researchers think are relevant and thus increase the chances of spurious correlations. In this concise review, we retrace the research toward a more inductive, holistic, and multivariate path.

Toward Reliable Lipoprotein Particle Predictions from NMR Spectra of Human Blood: An Interlaboratory Ring Test

Lipoprotein profiling of human blood by 1H nuclear magnetic resonance (NMR) spectroscopy is a rapid and promising approach to monitor health and disease states in medicine and nutrition. However, lack of standardization of measurement protocols has prevented the use of NMR-based lipoprotein profiling in metastudies. In this study, a standardized NMR measurement protocol was applied in a ring test performed across three different laboratories in Europe on plasma and serum samples from 28 individuals. Data was evaluated in terms of (i) spectral differences, (ii) differences in LPD predictions obtained using an existing prediction model, and (iii) agreement of predictions with cholesterol concentrations in high- and low-density lipoproteins (HDL and LDL) particles measured by standardized clinical assays. ANOVA-simultaneous component analysis (ASCA) of the ring test spectral ensemble that contains methylene and methyl peaks (1.4–0.6 ppm) showed that 97.99% of the variance in the data is related to subject, 1.62% to sample type (serum or plasma), and 0.39% to laboratory. This interlaboratory variation is in fact smaller than the maximum acceptable intralaboratory variation on quality control samples. It is also shown that the reproducibility between laboratories is good enough for the LPD predictions to be exchangeable when the standardized NMR measurement protocol is followed. With the successful implementation of this protocol, which results in reproducible prediction of lipoprotein distributions across laboratories, a step is taken toward bringing NMR more into scope of prognostic and diagnostic biomarkers, reducing the need for less efficient methods such as ultracentrifugation or high-performance liquid chromatography (HPLC).

CoMiniGut—a small volume in vitro colon model for the screening of gut microbial fermentation processes

Driven by the growing recognition of the influence of the gut microbiota (GM) on human health and disease, there is a rapidly increasing interest in understanding how dietary components, pharmaceuticals and pre- and probiotics influence GM. In vitro colon models represent an attractive tool for this purpose. With the dual objective of facilitating the investigation of rare and expensive compounds, as well as an increased throughput, we have developed a prototype in vitro parallel gut microbial fermentation screening tool with a working volume of only 5 ml consisting of five parallel reactor units that can be expanded with multiples of five to increase throughput. This allows e.g., the investigation of interpersonal variations in gut microbial dynamics and the acquisition of larger data sets with enhanced statistical inference. The functionality of the in vitro colon model, Copenhagen MiniGut (CoMiniGut) was first demonstrated in experiments with two common prebiotics using the oligosaccharide inulin and the disaccharide lactulose at 1% (w/v). We then investigated fermentation of the scarce and expensive human milk oligosaccharides (HMOs) 3-Fucosyllactose, 3-Sialyllactose, 6-Sialyllactose and the more common Fructooligosaccharide in fermentations with infant gut microbial communities. Investigations of microbial community composition dynamics in the CoMiniGut reactors by MiSeq-based 16S rRNA gene amplicon high throughput sequencing showed excellent experimental reproducibility and allowed us to extract significant differences in gut microbial composition after 24 h of fermentation for all investigated substrates and fecal donors. Furthermore, short chain fatty acids (SCFAs) were quantified for all treatments and donors. Fermentations with inulin and lactulose showed that inulin leads to a microbiota dominated by obligate anaerobes, with high relative abundance of Bacteroidetes, while the more easily fermented lactulose leads to higher relative abundance of Proteobacteria. The subsequent study on the influence of HMOs on two infant GM communities, revealed the strongest bifidogenic effect for 3′SL for both infants. Inter-individual differences of infant GM, especially with regards to the occurrence of Bacteroidetes and differences in bifidobacterial species composition, correlated with varying degrees of HMO utilization foremost of 6′SL and 3′FL, indicating species and strain related differences in HMO utilization which was also reflected in SCFAs concentrations, with 3′SL and 6′SL resulting in significantly higher butyrate production compared to 3′FL. In conclusion, the increased throughput of CoMiniGut strengthens experimental conclusions through elimination of statistical interferences originating from low number of repetitions. Its small working volume moreover allows the investigation of rare and expensive bioactives.