Chronobiology of Viscum album L.: a time series of daily metabolomic fingerprints spanning 27 years

Abstract

Introduction: European mistletoe (Viscum album L.) has been gaining increasing interest in the field of oncology as a clinically relevant adjunctive treatment in many forms of cancer. In the field of phytopharmacology, harvesting time is pivotal. In the last century, a form of metabolomic fingerprinting based on pattern formation was proposed as a way to determine optimal harvesting times to ensure high quality of mistletoe as raw material for pharmaceutical use. In order to further evaluate the information obtained with this metabolomic fingerprinting method, we analysed a large time series of previously undigitised daily mistletoe chromatograms dating back to the 1950s. Methods: These chromatograms were scanned and evaluated using computerized image analysis, resulting in 12 descriptors for each individual chromatogram. We performed a statistical analysis of the data obtained, investigating statistical distributions, cross-correlations and time self-correlations. Results: The analysed dataset spanning about 27 years, contains 19,037 evaluable chromatograms in daily resolution. Based on the distribution and cross-correlation analyses, the 12 descriptors could be clustered into six independent groups describing different aspects of the chromatograms. One descriptor was found to mirror the annual rhythm being well correlated with temperature and a phase shift of 10 days. The time self-correlation analysis showed that most other descriptors had a characteristic self-correlation of ∼50 days, which points to further infradian rhythms (i.e., more than 24 h). Discussion: To our knowledge, this dataset is the largest of its type. The combination of this form of metabolomic fingerprinting with the proposed computer analysis seems to be a promising tool to characterise biological variations of mistletoe. Additional research is underway to further analyse the different rhythms present in this dataset.

Keywords: chronobiology; image analysis; medicinal plants; metabolomic fingerprinting; mistletoe; pattern formation.

FIGURE 1

Typical examples of chromatograms of 50% extracts of Viscum album L. with AuCl₃ as reagent, used as metabolomic fingerprints. (α) homogeneously risen chromatogram; (β) not risen chromatogram; (γ) heterogeneously risen chromatogram; δ) chromatogram with a wide purple band in the lower part; (ε) chromatogram with a typical distribution of yellow colour (top and central section); (ζ) chromatogram with a compressed aspect; (η) chromatogram with elongated aspect and yellow colour only at the top; (θ) chromatogram with a massive presence of yellow colour.

FIGURE 2

Flow diagram of data pipeline and inclusion/exclusion criteria of the chromatograms for the analysis. * Among the scans of chromatograms suitable for image analysis, if two or more daily replicates were available, corresponding data of the twelve descriptors were averaged. We thus obtained a single set of the twelve descriptors per day, ending up with 9,845 sets of daily descriptors.

FIGURE 3

Original (left) and processed outline (right) of a representative chromatogram included in the “Standard frame” [dotted frame at position (36, 428), size (1,533, 1,477)] and in the “Bounding box” (dashed frame different for each chromatogram). The arrow represents “Prominence” defined as the difference between the highest and lowest peak. “Purple box” [purple frame at position (0, 1,188), size (1,533, 289)] is the region of interest for purple colour analysis. The area above the “Purple box” contained within the “Bounding box” is the region of interest for yellow colour analysis [position (0,0), size (1,533, 1,170)].

FIGURE 4

Summary of the descriptors. Outline, Texture and Colour descriptors are listed by their definition to describe the shape or size of the chromatograms. Subscripts from A to F represent the distribution groups to which descriptors belong (see Figure 5).

FIGURE 5

The six basic groups of data distribution (A–F) of the chromatogram descriptors. Greek letters referring to the chromatograms of Figure 1 are superimposed on the histograms to exemplify the meaning of the chromatogram descriptors.

FIGURE 6

Cross-correlation matrix between descriptors. The coloured bar on the right gives grades of colours as reference for highest (1 = yellow) to no correlation (0 = deep blue).

FIGURE 7

Self-correlation plots (A) plot “Area% St-Frame” (representative for “Height”, “Entropy”, “Ang_2nd_Moment”, “Inv_Diff_Moment” and “Yellow_Y”) shows a high correlation within the first 50 days; (B) “Purple Area” self-correlation with yearly trend; (C) “Yellow_X” with no self-correlation as expected from the control. In the small box, a short frame of 5 years (1971–1976) is provided for each graph to show the behaviour of the descriptors in the course of time, in the case of “Purple Area” the yearly rhythm is visible.

FIGURE 8

Correlation coefficient between “Purple Area” (descriptor) and air temperature 2 m above ground as daily mean “tre200d0” (predictor) as a function of time (days) delay.