Metabolic characterization of Palatinate German white wines according to sensory attributes, varieties, and vintages using NMR spectroscopy and multivariate data analyses

1H NMR (nuclear magnetic resonance spectroscopy) has been used for metabolomic analysis of ‘Riesling’ and ‘Mueller-Thurgau’ white wines from the German Palatinate region. Diverse two-dimensional NMR techniques have been applied for the identification of metabolites, including phenolics. It is shown that sensory analysis correlates with NMR-based metabolic profiles of wine. 1H NMR data in combination with multivariate data analysis methods, like principal component analysis (PCA), partial least squares projections to latent structures (PLS), and bidirectional orthogonal projections to latent structures (O2PLS) analysis, were employed in an attempt to identify the metabolites responsible for the taste of wine, using a non-targeted approach. The high quality wines were characterized by elevated levels of compounds like proline, 2,3-butanediol, malate, quercetin, and catechin. Characterization of wine based on type and vintage was also done using orthogonal projections to latent structures (OPLS) analysis. ‘Riesling’ wines were characterized by higher levels of catechin, caftarate, valine, proline, malate, and citrate whereas compounds like quercetin, resveratrol, gallate, leucine, threonine, succinate, and lactate, were found discriminating for ‘Mueller-Thurgau’. The wines from 2006 vintage were dominated by leucine, phenylalanine, citrate, malate, and phenolics, while valine, proline, alanine, and succinate were predominantly present in the 2007 vintage. Based on these results, it can be postulated the NMR-based metabolomics offers an easy and comprehensive analysis of wine and in combination with multivariate data analyses can be used to investigate the source of the wines and to predict certain sensory aspects of wine

Regulatory network modelling of iron acquisition by a fungal pathogen in contact with epithelial cells

Peer reviewedPublisher PD

Multi-stability with ambiguous visual stimuli in <i>Drosophila</i> orientation behavior

<div><p>It is widely accepted for humans and higher animals that vision is an active process in which the organism interprets the stimulus. To find out whether this also holds for lower animals, we designed an ambiguous motion stimulus, which serves as something like a multi-stable perception paradigm in <i>Drosophila</i> behavior. Confronted with a uniform panoramic texture in a closed-loop situation in stationary flight, the flies adjust their yaw torque to stabilize their virtual self-rotation. To make the visual input ambiguous, we added a second texture. Both textures got a rotatory bias to move into opposite directions at a constant relative angular velocity. The results indicate that the fly now had three possible frames of reference for self-rotation: either of the two motion components as well as the integrated motion vector of the two. In this ambiguous stimulus situation, the flies generated a continuous sequence of behaviors, each one adjusted to one or another of the three references.</p></div

Monocular TPMP reduces SPS for regressively moving bias and MA behavior.

<p>(A), Monocular stimulation with regular dots patterns. (B), One pattern in closed loop (progressive or regressive bias, 20° per s), the other stationary (mean ± SEM, <i>n</i> = 20 flies per group, F(F(2, 57) = 0.4453, <i>p</i> = 0.0272, R² = 0.1188, ANOVA)). Pro bias was more extensively stabilized than Reg bias (<i>p</i> = 0.0203, Tukey’s multiple comparisons test). Neither performance value was significantly different from PS with binocular stimulation (grey) (p_reg = 0.3822, p_pro = 0.3289, Tukey’s multiple comparisons test). (C), In the TPMP, flies with monocular input stabilized the pro moving pattern significantly more than the reg moving one (mean ± SEM; <i>n</i> = 21 flies for binocular stimulus, <i>n</i> = 42 flies for monocular stimulus; H(3, 123) = 31.3, <i>p</i> < 0.0001, Kruskal-Wallis test; p_pro-reg = < 0.0001, p_bino1-bino2 > 0.9999; p_pro-bino1 = 0.2677, p_reg-bino2 = 0.3702, Dunn’s multiple comparisons test). With monocular stimulation (pro+reg), MA behavior was reduced compared to binocular stimulation (binocular)(U = 401.5, <i>p</i> = 0.57, Mann-Whitney test), while overall SPS was not different (U = 205, <i>p</i> = 0.0004, Mann-Whitney test). Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003113#pbio.2003113.s012" target="_blank">S1 Data</a> and <a href="https://doi.org/10.6084/m9.figshare.4668571.v1" target="_blank">https://doi.org/10.6084/m9.figshare.4668571.v1</a>. MA, motion average; Pro, progressive; PS, pattern stabilization; Reg, regressive; SPS, single pattern stabilization; TPMP, transparent panorama motion paradigm.</p

Evaluation, patterns, and bias settings used in the TPMP.

<p>(A), SPS led to bimodal distribution of MA of yaw torque. Mean histogram of MA of yaw torque over 2 s with random dots patterns at 37% pattern contrast. Colored areas indicate the ranges where cw (blue) and ccw (green) SPS as well as MA behavior (yellow) were detected; the dotted lines indicate the respective exact stabilization and MA values. (<i>n</i> = 20 flies). (B), Stabilization of a single random dots pattern with the second one stationary at the same contrast led to unimodal yaw torque distribution with the peak at the stabilization value. Mean histogram of moving average over 2 s (<i>n</i> = 20 flies). (C), With the feedback in the TPMP switched off (open loop), flies showed a broader, multi-modal yaw torque distribution (red; cw: 20° per s, ccw: 20° per s) than in open loop without any motion stimuli (black; cw: 0° per s, ccw: 0° per s), but with the patterns still present (<i>n</i> = 11 flies). (D), Overlays of the different patterns used in the TPMP. (E), Different panorama patterns gave similar results. Only between Reg. dots and bars were significant differences found (mean ± SEM; <i>n</i> = 20 flies per group, F_SPS[F(2,57) = 3.341, <i>p</i> = 0.0425, R² = 0.1049, ANOVA] with Tukey’s multiple comparisons test [<i>p</i> = 0.0384], F_MA[F(2,57) = 3.988, <i>p</i> = 0.0239, R² = 0.1227, ANOVA] with Tukey’s multiple comparisons test [<i>p</i> = 0.0238]). (F), Asymmetric bias settings caused preferential SPS with the less biased pattern. Flies performing in the TPMP with one bias set to 0° per s (SPS 1 hatched) and the other set to 40° per s (SPS 2 hatched) showed a significant preference for the pattern without a bias (<i>t</i>(19) = 4.340, <i>p</i> = 0.0004, paired <i>t</i> test). SPS 1 and SPS 2 values with a symmetric bias of 20° per s (solid) were not different (<i>t</i>(19) = 0.2384, <i>p</i> = 0.8142, paired <i>t</i> test). Also, for overall SPS (<i>t</i>(38) = 1.654, <i>p</i> = 0.1063, <i>t</i> test) and MA behavior (<i>t</i>(38) = 1.117, <i>p</i> = 0.2708, <i>t</i> test), values did not differ significantly between the two bias settings (<i>n</i> = 20 flies per group). Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003113#pbio.2003113.s012" target="_blank">S1 Data</a> and <a href="https://doi.org/10.6084/m9.figshare.5786922" target="_blank">https://doi.org/10.6084/m9.figshare.5786922.v1</a>. ccw, counterclockwise; cw, clockwise; MA, motion average; ns, not significant; Ran, random; Reg., regular; SPS, single pattern stabilization; TPMP, transparent panorama motion paradigm.</p

Object response may contribute to MA behavior.

<p>(A), With a low number of bars, SPS decreased when their number increased, and with many bars SPS went up again with increasing numbers. MA behavior developed inversely (mean ± SEM; <i>n</i> = 20 flies per number of bars). (B), Increased contrast had no effect on ratio of SPS and MA behavior with low number of bars, but with 20 bars the effect was highly significant (mean ± SEM; <i>n</i> = 20 flies per number of bars and contrast condition; data of the 37% contrast condition are the same as in (A); SPS 1 Bar: t(38) = 0.0686, <i>p</i> = 0.946, <i>t</i> test; MA 1 Bar: U = 188.5, <i>p</i> = 0.764, Mann-Whitney test; SPS 2 Bars: t(38) = 0.4210, <i>p</i> = 0.676, <i>t</i> test; MA 2 Bars: t(38) = 0.521, p = 0.606, t-test; SPS 20 Bars: U = 26, <i>p</i> < 0.0001, Mann-Whitney test; MA 20 Bars: U = 15, <i>p</i> < 0.0001, Mann-Whitney test). (C), With one vertical bar per pattern (width = 6°; contrast: 37%), the flies preferentially stabilized the bars in the frontal visual field on the side where their bias drove them progressively. Position histograms of the one bar per pattern experiment of (A). Green: bias ccw; blue: bias cw. Horizontal dotted line indicates chance level. (D), With 20 evenly spaced bars per pattern, no 18° modulation of the position histograms is apparent. Horizontal dotted line: chance value as in (C). (E), Power spectra of position histograms of orientation in closed loop with a single pattern of 6, 8, 10, 12, or 20 vertical bars (<i>n</i> = 20 flies per number of bars). Fourier transform showed fixation of bars for the 6- and 8-bar patterns but not for those with 10, 12, and 20 bars. The color code indicates the number of bars in the respective experiment. (F), A selected 9 s flight episode with one bar per pattern, in which the fly switched from fixating the cw bar to fixating the ccw bar after the bars cross each other. Grey area indicates the time during which average yaw torque (light red) was in the MA range. (Compare to <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003113#pbio.2003113.g002" target="_blank">Fig 2</a>). (G), A selected 9 s flight episode in which the fly stabilized the ccw bar shortly interrupted this behavior in favor of MA behavior (grey area) after the bars cross, then returned to stabilizing the ccw bar. (H) Flies in the 1–3 bar/pattern experiment in (A) showed significantly more MA behavior with diverging bars. Bar chart of the fraction of time of MA behavior when the two bars div and conv (1 bar: t(19) = 5.082, <i>p</i> < 0.0001, ratio paired <i>t</i> test; 2 bars: t(19) = 5.177, <i>p</i> < 0.0001, ratio paired <i>t</i> test; 3 bars: t(19) = 10.14, <i>p</i> < 0.0001, ratio paired <i>t</i> test; 4 bars: W = −68, <i>p</i> = 0.2162; Wilcoxon matched-pairs signed rank tests). Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003113#pbio.2003113.s012" target="_blank">S1 Data</a>, <a href="https://doi.org/10.6084/m9.figshare.4668559.v1" target="_blank">https://doi.org/10.6084/m9.figshare.4668559.v1</a> and <a href="https://doi.org/10.6084/m9.figshare.4668565.v1" target="_blank">https://doi.org/10.6084/m9.figshare.4668565.v1</a>. ccw, counterclockwise; cw, clockwise; conv, converging; div, diverging; MA, motion average; SPS, single pattern stabilization.</p

Temporal dynamics and behavioral stability over time in the TPMP.

<p>(A), SPS did not change over time. Mean SPS values per minute measured with random dots patterns at 8% contrast (mean ± SEM; <i>n</i> = 18 flies; F(3.84, 65.21) = 0.94, <i>p</i> = 0.446, R² = 0.052, rm-ANOVA). (B), Number of SPS phases did not change over time. Mean number of SPS phases per minute (Q(5) = 7.45, <i>p</i> = 0.189, Friedman test). (C), Mean number of ISPs per minute. One ISP was detected as onset of SPS 1 when the last SPS was SPS 2 and vice versa (Q(5) = 5.502, <i>p</i> = 0.357, Friedman test). (D), Mean duration of SPS phases differed among flies. Duration of one SPS phase was calculated as t(SPS1_offset)-t(SPS1_onset) or t(SPS2_offset)-t(SPS2_onset), respectively. Tukey-Boxplot. (E), Mean duration of ISPs differed strongly among flies. Duration of one ISP was calculated as t(SPS2_onset)-t(SPS1_onset) or t(SPS1_onset)-t(SPS2_onset). Tukey-Boxplot. (F), Probability distribution of normalized SPS phase duration fit gamma distribution (R² = 0.84). Individual SPS durations were normalized to the mean SPS phase duration of the respective fly. A replicates test for lack of fit showed no lack of fit (F = 0.24, <i>p</i> = 0.999). (G), Probability distribution of normalized ISP duration fit gamma distribution (R² = 0.55). Single ISP durations were normalized to the mean ISP phase duration of the respective fly. A replicates test for lack of fit showed no lack of fit (F = 0.13, <i>p</i> = 1). Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003113#pbio.2003113.s012" target="_blank">S1 Data</a> and <a href="https://doi.org/10.6084/m9.figshare.4668376.v1" target="_blank">https://doi.org/10.6084/m9.figshare.4668376.v1</a>. ISP, inter-switch-phase; SPS, single pattern stabilization; TPMP, transparent panorama motion paradigm.</p

Flight simulator setup and the three flight control behaviors in the TPMP with a pattern contrast of 37%.

<p>(A), Example of fly showing SPS_ccw. Yaw torque (light red, red is moving average) in the green area (solid green line at T = −4 × 1⁻¹⁰ Nm shows stabilization value) compensated ccw bias and led to almost stationary pattern orientation (dotted green line). (B), Same as in (A) but for cw bias. Pattern was stabilized with yaw torque in blue area (SPS_cw). (C), With yaw torque in yellow area around T = 0 Nm (solid black line), the fly stabilized the mean of the two bias values (MA behavior). (D), Virtual flight trajectories of a single 3 min flight in the TPMP in relation to the three references for straight flight, the two patterns (green, blue) and the MA (yellow), assuming a constant flight velocity (i.e., constant thrust). Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003113#pbio.2003113.s012" target="_blank">S1 Data</a>. arb, arbitrary; ccw, counterclockwise; cw, clockwise; MA, motion average; SPS_ccw, counterclockwise single pattern stabilization; SPS_cw, clockwise single pattern stabilization; TPMP, transparent panorama motion paradigm.</p