Research article Special Issues

Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations

  • Received: 01 April 2020 Accepted: 01 July 2020 Published: 16 July 2020
  • Glioblastomas (GBMs) are the most aggressive primary brain tumours and have no known cure. Each individual tumour comprises multiple sub-populations of genetically-distinct cells that may respond differently to targeted therapies and may contribute to disappointing clinical trial results. Image-localized biopsy techniques allow multiple biopsies to be taken during surgery and provide information that identifies regions where particular sub-populations occur within an individual GBM, thus providing insight into their regional genetic variability. These sub-populations may also interact with one another in a competitive or cooperative manner; it is important to ascertain the nature of these interactions, as they may have implications for responses to targeted therapies. We combine genetic information from biopsies with a mechanistic model of interacting GBM sub-populations to characterise the nature of interactions between two commonly occurring GBM sub-populations, those with EGFR and PDGFRA genes amplified. We study population levels found across image-localized biopsy data from a cohort of 25 patients and compare this to model outputs under competitive, cooperative and neutral interaction assumptions. We explore other factors affecting the observed simulated sub-populations, such as selection advantages and phylogenetic ordering of mutations, which may also contribute to the levels of EGFR and PDGFRA amplified populations observed in biopsy data.

    Citation: Bethan Morris, Lee Curtin, Andrea Hawkins-Daarud, Matthew E. Hubbard, Ruman Rahman, Stuart J. Smith, Dorothee Auer, Nhan L. Tran, Leland S. Hu, Jennifer M. Eschbacher, Kris A. Smith, Ashley Stokes, Kristin R. Swanson, Markus R. Owen. Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 4905-4941. doi: 10.3934/mbe.2020267

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  • Glioblastomas (GBMs) are the most aggressive primary brain tumours and have no known cure. Each individual tumour comprises multiple sub-populations of genetically-distinct cells that may respond differently to targeted therapies and may contribute to disappointing clinical trial results. Image-localized biopsy techniques allow multiple biopsies to be taken during surgery and provide information that identifies regions where particular sub-populations occur within an individual GBM, thus providing insight into their regional genetic variability. These sub-populations may also interact with one another in a competitive or cooperative manner; it is important to ascertain the nature of these interactions, as they may have implications for responses to targeted therapies. We combine genetic information from biopsies with a mechanistic model of interacting GBM sub-populations to characterise the nature of interactions between two commonly occurring GBM sub-populations, those with EGFR and PDGFRA genes amplified. We study population levels found across image-localized biopsy data from a cohort of 25 patients and compare this to model outputs under competitive, cooperative and neutral interaction assumptions. We explore other factors affecting the observed simulated sub-populations, such as selection advantages and phylogenetic ordering of mutations, which may also contribute to the levels of EGFR and PDGFRA amplified populations observed in biopsy data.


    The beetroot (Beta vulgaris L. ssp.) is a vegetable that is particularly popular in the central and eastern parts of Europe, which is due to its palatability and high levels of health-promoting compounds with antioxidant capabilities [1,2]. The highest activity in free radical capture is attributed to betanin [3,4,5]. Red beetroot betalain extract, consisting mostly of betanin (E162), which is widely used as a natural colorant in many dairy products, beverages, candies and cattle products. However, this compound can cause allergies and for this reason some of the restrictions concerning consumption by children have been introduced. Following a request from the European Commission, the European Food Safety Authority (EFSA) has submitted an opinion regarding the safety usage of beetroot red/betanin (E 162) in Foods for Special Medical Purposes (FSMP) for young children aged 1–3 years [6]. According to EFSA the safe content of betanin in FSMP is 20 mg L−1 of the final product. In the root vegetable group, the beetroot is among those exhibiting a high tendency to accumulate nitrates (Ⅴ) [7]. Nitrates associate negatively due to N-nitrosamines, which are formed indirectly from nitrates (Ⅴ) in the acidic environment of the stomach. However, they can perform important and beneficial functions in the human body, associated with the formation of nitric oxide (NO) [4]. This compound can reduce blood vessel tone, inhibit platelet agammaegation, and improve the physical performance of the organism [8,9]. Therefore, it is reasonable to look for an alternative to red beetroot. A possible solution could be the use of beetroot in food processing, free of allergenic betanin, and containing lower amounts of harmful compounds. Two beetroot cultivars with a distinctive white root colour are known: ‘Albina Vereduna’ originating from the Netherlands, and ‘Śnieżna Kula’ bred at Torseed SA in Toruń, Poland. The Polish cultivar ‘Śnieżna Kula’ is the world’s first non-GMO beetroot cultivar registered in the European Union. A characteristic feature of the beetroot with white-coloured flesh is the lack of betalain pigments found in the flesh of purple and red beetroot [10,11]. Beetroot is by far the best vegetable in terms of antioxidant compounds [12,13,14,15]. Moreover, the beetroot is a very good source of carbohydrates, protein, dietary fibre, organic acids (citric, oxalic, malic, and tartaric), folic acid and many minerals [1,14,16,17].

    It is known that the most important tool in shaping the properties and quality of a food product is the appropriate selection of raw material. In food processing, the whole raw material is usually used, however, to increase the health-promoting value of the final product, one should consider the use of its parts. Therefore, conducting research in this area is fully justified and needed.

    The aim of the study was to determine the physicochemical characteristics of the white beetroot cultivar ‘Śnieżna Kula’ in terms of selected nutrient and harmful component contents and its antioxidant capacity that determine its pro-health value. The characteristics were determined, depending on the size of the root and its parts.

    The beetroots were obtained from a cultivation carried out at a farm in Pędzewo near Bydgoszcz, and sown mechanically on 29 May 2016 on soil of the 3rd soil valuation class. Mineral fertilisation was applied (Korn-kali at 400 kg/ha, ammonium phosphate at 150 kg ha−1, and urea at 230 kg ha−1) as well as two herbicidal treatments: Betanal Maxx Pro 209OD (desmedifam 47 g L−1, etofumesat 75 g L−1, lenacyl 27 g L−1, fenmedifam 60 g L−1; Bayer SAS, France) at 1.2 l ha−1 and Goltix S 700 SC (metamitron 700 g L−1; ADAMA Sp. z o.o., Poland) at 1.0 l ha−1. After the harvest, representative samples were placed in a modern storage chamber with a constant temperature (+1 ℃) and 98% relative air humidity.

    Urea, acetic acid, sodium nitrate, sodium hydroxide and phosphate buffer were purchased from Merck KGaA, Darmstadt, Germany, and CA from Sigma–Aldrich Co., LLC, USA.

    Washed raw material was divided into four size groups: A—roots weighing < 400 g (an average of 279 g); B—roots weighing 401–600 g (an average of 488 g); C—roots weighing 601–900 g (an average of 737 g); and D—roots weighing > 901 g (an average of 1118 g). Within each group, the roots were cut transversely to the axis into four parts: the head with the upper hypocotyl part (G) and three hypocotyl parts—the upper (GCP), middle (ŚCP), and lower (DCP) (Figure 1) [18]. The G part accounted for 11.4% of the entire root, GCP for 39.9%, ŚCP for 39.4%, and DCP for 9.3%. The separated parts of the beetroots, as well as the entire roots, were then used to extract juice using a Zelmer ZJE 1200G (power—500W, speed—1200 rpm) juice extractor (ZELMER, Poland).

    Figure 1.  The allocation of red beet root into parts: G—head with the upper under leaf part, GCP—upper under leaf part, SCP—the central under leaf part, DCP—the bottom under leaf part [18].

    The plant material was purified manually (using for a knife) and foreign substances (which included soil and dust particles) were then removed under running cold water and plant tissue samples were reduced to a 0.5 to 1.0 cm size to ensure uniformity. Raw roots were then cut into 1-cm-thick slices, frozen in a Whirlpool AFG 6402 E-B freezer (Italy) to −22 ℃ and freeze-dried (CHRIST ALPHA 1–4 LSC, Germany) in order to achieve a permanent weight. The final moisture content of the material was below 2%. Operational parameters of the lyophiliser: Condenser temperature of −55 ℃, vacuum 4 kPa at 20 ℃. The drying was carried out for 24 hours. Freeze-dried samples were then ground into flour using an electric grinder (power-800W, speed-24000 rpm, time-30 s) (CHEMLAND, Type FW 177, Poland) and were then used for a chemical analysis. The obtained flour samples were then stored in sealed plastic bags at −20 ℃ before analysis.

    The determination of the total soluble solid content was performed using a PAL-1 digital camera (Atago, Japan) refractometer at 20 ℃.

    Betalain pigments were determined by the spectrophotometric method using a Shimadzu UV-1800 spectrophotometer (UV Spectrophotometer System, Japan) according to Nillson, which involved the determination of purple and yellow [19]. The purple pigment content was expressed as betanine, and the yellow pigment content as vulgaxanthin. Prior to the absorbance measurement, the sample was diluted with a phosphate buffer (0.2 N) with pH of 6.5 so that the absorbance value fell within the range of 0.2–0.8. The blank test was a phosphate buffer. Absorbance was measured at the layer thickness of 1 cm and at wavelengths of 476,538, and 600 nm.

    The assay was carried out using the method developed by Fang et al. [20]. The method involves the measurement of absorbance of the complex formed from the reaction between polyphenols and the tungsten and molybdenum reagent (Folin-Ciocalteu reagent). The assay was conducted as follows: 200 μL of a suitably diluted sample was transferred to a test tube and 800 μL of water was added. After thorough mixing, 5 mL of 0.2 N Folin-Ciocalteu reagent was added to the test tube and mixed again. After 3 minutes, 4 mL of sodium carbonate was added (75 g L−1). The prepared samples were then incubated in a dark room for two hours. Absorbance was measured at the layer thickness of 1 cm and at a wavelength of 735 nm. The polyphenol content was calculated based on the standard curve prepared for chlorogenic acid.

    The CA—chlorogenic acid content was determined colorimetrically by the method of Griffiths et al. [21]. Briefly, the diluted extract was vortexed with 2 mL of urea (0.17 M) and acetic acid (0.10 M). To this, 1 mL of sodium nitrite (0.14 M) was added, followed by 1 mL of sodium hydroxide (0.5 M) after incubation at room temperature for 2 min. The suspension was then centrifuged (Hettina Zentrifugen, Rotina 420 R, Germany) at 2250 g for 10 min. An aliquot of the supernatant was taken and the absorbance of the cherry red complex formed was read at 510 nm (UV-1800, UV Spectrophotometer System, Japan). A standard curve was prepared using different concentrations of CA and the results were expressed as mg of CA kg-1 of fresh beetroot.

    Reducing sugars were assayed using the Talburt and Smith [22] method. 2 g of freeze-dried sample was mixed with 150 mL of distilled water and shaken vigorously for 60 minutes. The flask was then made up with distilled water to 250 mL and mixed for 3 minutes. The mixture was then filtered through Whatman No.1 filter paper. 1 mL of the filtrate and 3 mL of the DNP reagent were transferred to a test tube and heated in a water bath at 95 ℃ for 6 minutes. After the heating, the samples were immediately cooled to room temperature. The absorbance of the mixture was measured at a wavelength of 600 nm using a Shimadzu UV-1800 (UV Spectrophotometer System, Japan). Reducing sugar content was then estimated using a glucose standard curve.

    The total soluble carbohydrate content was determined following sugar hydrolysis. 40 mL of the filtrate was transferred to an Erlenmeyer flask and a few drops of concentrated HCl were added. The samples were heated for 30 minutes in a water bath, then cooled and neutralised with a few drops of concentrated NaOH. 1 mL of the filtrate was mixed with 3 mL of the DNP reagent, and the procedure for determining reducing sugar content was then followed [22].

    Nitrate and nitrite contents were determined using the ion-selective method [23]. For the assay, a multi-functional ELMETRON CX-721 device equipped with a nitrate electrode, a double junction reference electrode (fill outer chamber with 0.02 M (NH4)2SO4 solution; Merck, Germany), a specific ion meter and a pH millivolt−1 (mV) meter with a 0.1 mV readability was used. Nitrates (Ⅴ) were extracted using the KAl2(SO4)3 solution (Merck, Germany) and determined potentiometrically using the ion-selective electrode. The determination limit was established at 30 mg kg−1, and the measurement error was at a level of 15% (k = 2, norm.), depending on the sample matrix that was measured.

    The determination of the antioxidant capacity by the FRAP method was conducted using the method developed by Benzie and Strein [24]. Immediately prior to the assay, a FRAP working solution was prepared. 250 mL of acetate buffer with pH of 3.6, 25 mL of the TPTZ solution (2, 4, 6-Tri(2-pyridyl)-s-triazine (10 millimoles in 40 mmol HCl), and 25 mL of an iron(Ⅲ) chloride hexahydrate solution (20 mmol) were mixed. The solution was incubated at 37 ℃ and assays were then performed. 6 mL of the FRAP solution was taken, and 200 μL of the sample and 600 μL of H2O were added to it. After 4 minutes from the addition of the sample, absorbance was measured at a wavelength of 593 nm. Based on the conducted measurements, a curve of dependence of the absorbance value on the juice concentration was plotted. Based on the curve, the absorbance value was determined at a concentration equal to the mean of the dilutions used, and the antioxidant capacity was calculated at the same absorbance value based in the standard curve determined for Fe2+ iron ions. In order to remove solid parts, the samples prior to the assays were centrifuged for 5 minutes on a Rotina 420R centrifuge (Hettich, Germany) at 3,000 revs min−1. All assays were carried out in three laboratory replications.

    To determine the antioxidant capacity, the ABTS cation radical [2, 2’-azinobis(3-ethylbenzothiazoline-6-sulphonate] method was employed according to Re et al. [25]. A stock solution of the ABTS radical cationwas generated chemically by mixing 7 mM ABTS solution (Sigma Aldrich) and 2.45 mmol K2S2O8 solution (POCH Gliwice), mixed in a ratio of 1:0.5. This mixture was allowed to stand for 12 h in the dark. On the day of analysis 0.5 mL of ABTS radical cation stock solution was mixed with 2 mL of phosphate buffer (pH 7.4) in a cuvette and the absorbance at 734 nm wasmeasured. For the measurement, four different sample dilutions were prepared so that the reduction in cation radical absorbance fell within the range of 20–80%. The measurement was performed as follows: 50 µL of the sample was added to 5 mL of diluted cation radical, shaken and incubated in a water bath at 30 ℃ for 6 minutes. After that time, the absorbance was measured using a Shimadzu UV-1800 (UV Spectrophotometer System, Japan), at a wavelength of 734 nm, against phosphate buffer. The decrease in absorbance caused by theaddition of Trolox as the standard was measured by the same pro-cedure for each concentration of Trolox (1–15 mmol kg−1) and thecalibration curve for the decrease in absorbance. Trolox concentration was constructed by linearregression. The results were provided expressed as mmol Trolox kg−1, having taken into account the dilution of samples.

    The results were statistically processed using STATISTICA 13.0 software. An analysis of statistical differences was carried out using ANOVA variance analysis followed by Tukey’s test, at a significance level α = 0.05. The linear correlation coefficient between the beetroots quality characteristics was investigated at P < 0.01 and P < 0.05. The results were presented as arithmetic means with standard deviations (SD).

    The total soluble solid in the examined white beetroot cultivar ranged from 158 g kg−1 for DCP to 161 g kg−1 fresh weight (FW) for G, and was significantly dependent on the size group (Table 1). The lowest total soluble solid was noted for the roots of group D, and the highest for the roots of group A. This indicates the occurrence of a relationship between the total soluble solid and the size of the root. With an increase in the weight of the roots, the extract content decreased (Table 1). The differences between the values were statistically significant. Similar total soluble solids are noted for the red beetroot. As reported by Biegańska-Marecik et al. [26] the total soluble solid in red beetroot varies, and may range from 120 to 180 g kg−1 FW. For food processing, a higher extract content in the root is preferable, as it affects the density of beet juice.

    Table 1.  Content of total extract in the white beetroot cv. ‘Śnieżna Kula’ depending on the size and weight of beetroots.
    Weight group Average root weight [g] Total soluble solid [mg kg−1 FW] Betalain pigments [mg kg−1 FW]
    A 279 ± 42a 161.1 ± 0.2D 0.0
    B 488 ± 65b 160.1 ± 0.2C 0.0
    C 737 ± 92c 159.2 ± 0.3B 0.0
    D 1118 ± 56d 158.1 ± 0.1A 0.0
    Note: Means sharing the same letter in column are not significantly different from each other (Tukey’s significant difference test, P < 0.05). Data are the averages (n = 16). Means sharing the same letter in column are not significantly different from each other (P ≤ 0.05): a, b…—within the root portion; A, B...—within the mean size for a given group.

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    The results of the current study demonstrated that the roots of white-coloured beetroot contain no pigment, unlike the red beetroot which contains both purple and yellow pigments (Table 1). According Bean et al. [27] white beets make betalains but accumulate very low levels of betalain pigmentation compared to red beets. Their amounts vary greatly from 400 to 2100 mg kg−1 FW for purple pigments, and from 200 to 1400 mg kg−1 FW for yellow pigments [19], and is significantly determined by the size, shape, and part of the root [28,29,30,31].

    The total sugar and reducing sugar contents in the white beetroot averaged 32.3 and 1.7 mg kg−1 FW, respectively. In the red beetroot, the contents of these compounds are found at a significantly higher level, and are mainly determined by the cultivar. For the conventionally cultivated ‘Czerwona Kula’ cultivar, the total sugar content amounted to 40.0 mg kg−1 FW, reducing sugar content to 19.7 mg kg−1 FW, and for the ‘Regulski’ cultivar, it was 49.3 and 16.8 mg kg−1 FW, respectively [14]. Jabłońska-Ceglarek and Rosa [30] determined the total sugar and reducing sugar contents to be 164 and 7.4 mg kg−1 FW, respectively, for the ‘Opolski’ cultivar. On the other hand, Wruss et al. [31], having tested seven cultivars, obtained a total sugar content ranging from 62.0 mg kg−1 FW for the ‘Egyptische Plattrunde’ cultivar to 92.0 mg kg−1 FW for the ‘Forono’ cultivar and reducing sugar content from 1.64 mg kg−1 FW for the ‘Redval’ cultivar to 5.89 mg kg−1 FW for the ‘Egyptische Plattrunde’ cultivar. The sugar content of the beet roots is also determined by the cultivation system [32]. The authors obtained a total sugar content ranging from 126 mg kg−1 FW in conventional cultivation to 143 mg kg−1 FW in organic cultivation. As it results from the difference in the content of total sugars and monosaccharides (Table 1), the dominant compound in all 4 parts of the beet root is sucrose. Glucose and fructose were present in smaller amounts, because theroot is the storage organ of plants and energy in beetroots is storedin the form of sucrose [33]. Sucrose is delivered by the phloem to the mostdistant root tips and, en route to the tip, is used by the different root tissues formetabolism and storage [34]. The study demonstrated a relationship between the total sugar and simple sugar contents and the size of the root (Table 2). For total sugars, significant differences were demonstrated between the size groups B and D, and their content ranged from 30.7 mg kg−1 FW for group D to 33.5 mg kg−1 FW for group B. On the other hand, reducing sugar content was the lowest for group B with an average of 1.2 mg kg−1, and the highest for group A, (2.3 mg kg−1). No effect of the part of the root on the total sugar content was proven in any of the size groups (Table 1). On the other hand, for reducing sugars, significantly higher contents were noted for the parts G and DCP in each size group (Figure 2a), earmarking them to be used in the production of special-purpose food.

    Table 2.  Content of sugars and nitrogen compounds in root the white beetroot cv. ‘Śnieżna Kula’ depending on the size and part of the roots.
    Weight group [g] Part of the root Reducing sugar [mg kg−1 FW] Total sugar [mg kg−1 FW] Nitrate (Ⅲ) [mg kg−1 FW] Nitrate (Ⅴ) [mg kg−1 FW]
    A < 400 G 4.66 ± 0.90hi 32.4 ± 3.6bcd 13.8 ± 0.9abcd 2375 ± 36e
    GCP 0.80 ± 0.60abc 34.1 ± 6.6ad 15.3 ± 0.4bcdef 3777 ± 29k
    ŚCP 0.60 ± 0.36ab 29.5 ± 6.3ab 14.6 ± 1.0abcdef 3692 ± 33k
    DCP 5.02 ± 0.42i 33.7 ± 2.4cd 13.4 ± 1.2abc 3244 ± 25hi
    Total root* 2.25 ± 0.59B 32.2 ± 2.3AB 14.7 ± 0.6A 3534 ± 24D
    B 401–600 G 4.01 ± 0.54h 33.5 ± 3.3cd 12.6 ± 0.8ab 1825 ± 40b
    GCP 0.60 ± 0.60ab 33.8 ± 1.8cd 13.6 ± 0.4abcd 3476 ± 25j
    ŚCP 0.44 ± 0.42a 33.5 ± 2.4cd 16.8 ± 0.7ef 3550 ± 19j
    DCP 3.05 ± 1.35fg 32.1 ± 3.0bcd 12.5 ± 1.2a 3308 ± 21i
    Total root 1.15 ± 0.52A 33.5 ± 1.5B 14.6 ± 0.8A 3301 ± 31C
    C 601–900 G 3.17 ± 0.99g 32.7 ± 3.3bcd 14.4 ± 1.0abcde 1746 ± 14b
    GCP 0.79 ± 0.87abc 33.4 ± 1.5cd 16.9 ± 1.7ef 2328 ± 18de
    ŚCP 1.39 ± 0.72cd 32.9 ± 2.1bcd 17.1 ± 0.6ef 2605 ± 23f
    DCP 1.78 ± 0.93de 30.5 ± 1.2abc 12.6 ± 0.7ab 1460 ± 35a
    Total root 1.39 ± 0.83A 32.9 ± 1.9AB 16.3 ± 1.1AB 2290 ± 21A
    D > 901 G 2.37 ± 0.51ef 33.2 ± 3.9cd 16.2 ± 0.6def 2027 ± 30C
    GCP 1.30 ± 0.60bcd 28.3 ± 3.0a 16.0 ± 0.5cdef 3188 ± 34h
    ŚCP 1.81 ± 0.42de 32.0 ± 1.8bcd 17.2 ± 0.9f 2845 ± 37g
    DCP 1.97 ± 0.48de 33.0 ± 2.1cd 13.3 ± 0.8abc 2245 ±33d
    Total root 1.69 ± 0.51A 30.7 ± 1.2A 16.2 ± 0.7B 2833 ± 35B
    Note: The results for the total root were presented as a weighted average, taking into account the average percentage share of the individual parts in the total root. Means sharing the same letter in column are not significantly different from each other (P ≤ 0.05): a, b…—within the root portion; A, B...—within the mean size for a given group. Means sharing the same letter in column are not significantly different from each other (Tukey’s significant difference test, P < 0.05). Data are the averages (n = 16). G—head with the upper under leaf part, GCP—upper under leaf part, SCP—the central under leaf part, DCP—the bottom under leaf part (Figure 1).

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    Figure 2.  The significant relationship between the date of a) sugar reducing, sugar total, b) nitrate (Ⅴ), nitrate (Ⅲ), c) polyphenols, FRAP d) chlorogenic acid, ABTS and part of root.

    The nitrate (Ⅴ) content of the beetroots of the ‘Śnieżna Kula’ cultivar varied and ranged from 2.29 mg kg−1 FW of the juice from the roots of the size group C to 3.53 mg kg−1 FW of the juice from the roots of size group A (Table 2).

    Vasconcellos et al. [35] obtained nitrate (Ⅴ) content of up to 12253 mg kg−1 in red beet juice. Other studies found a large variation in the nitrate (Ⅲ) and (Ⅴ) contents for the same vegetable species. The nitrate (Ⅴ) content in beetroots originating from Estonia was 1446 mg kg−1 [36], 3.046 mg kg−1 in beetroots from Iran [37] and 4900 mg kg−1 [38] and 1306 mg kg−1 for beetroots from Poland [39].

    Ziarati et al. [37] observed a reversed relationship between the vegetable size and the nitrate (Ⅴ) content. The authors demonstrated higher nitrate (Ⅴ) contents in smaller roots of the beetroot, which is in line with the authors’ own study results R = −0.474 (Figure 3b).

    Figure 3.  The significant relationship between the date of a) sugar reducing, sugar total, b) nitrate (Ⅴ), nitrate (Ⅲ), c) polyphenols, chlorogenic acid, FRAP, ABTS and weight group.

    For the nitrate (Ⅲ) content, a reverse tendency was observed, as the roots with a greater weight, i.e. those of groups C and D contained their greater amount (16.3 and 16.2 mg kg−1, respectively). Ziarati et al. [37] observed a reversed relationship between the vegetable size and the nitrate (Ⅲ) content. Different tendencies were observed for nitrates (Ⅲ), for which higher values were noted for the roots with a higher weight, i.e. for the size groups C and D (16.3 and 16.2 mg kg−1, respectively) (Table 2).

    Nitrate (Ⅴ) and nitrate (Ⅲ) values were higher for the inner parts of the roots (GCP and ŚCP) than for the outer parts (G and DCP) (Figure 2b). This sugests that the outer parts of the root are more useful for processing health-promoting food. Similar results were obtained by Czapski et al. [28] for the red beetroot. This is due to the fact that the vascular tissue contains more NO3 than the parenchymal tissue, since NO3- in the form of mineral salts are transported from the soil with water via the vascular tissue [36]. A low (P≤0.05) but significant correlation was observed between reducing sugars and total polyphenols and nitrates (Ⅲ) and (Ⅴ) (Table 4).

    Table 3.  Antioxidant capacity and polyphenols and chlorogenic acid content in root the white beetroot cv. ‘Śnieżna Kula’ depending on the size and part of the roots.
    Weight group [g] Part of the root Polyphenols [mg kg−1 FW] Chlorogenic acid [mg kg−1 FW] FRAP[mmol Fe2+ kg−1 FW] ABTS [mmol Trolox kg−1 FW]
    A < 400 G 665 ± 13f 542 ± 20i 17.5 ± 1.2d 8.50 ± 0.33e
    GCP 189 ± 11a 145 ± 12c 13.7 ± 0.6ab 6.55 ± 0.16ab
    ŚCP 394 ± 10d 200 ± 14de 14.5 ± 0.8abc 6.70 ± 0.27ab
    DCP 519 ± 20e 297 ± 14g 16.3 ± 1.3bcd 8.40 ± 0.16de
    Total root 415 ± 16B 279 ± 13C 14.7 ± 0.8B 7.00 ± 0.22A
    B 401–600 G 529 ± 17g 204 ± 9e 15.3 ± 0.0abcd 7.14 ± 0.37abc
    GCP 188 ± 20a 148 ± 12c 14.6 ± 0.6abc 6.76 ± 0.29ab
    ŚCP 183 ± 14a 139 ± 14bc 13.6 ± 1.1ab 6.44 ± 0.48ab
    DCP 391 ± 11d 265 ± 12fg 15.8 ± 1.0abcd 7.25 ± 0.36bcd
    Total root 243 ± 12A 199 ± 14B 14.4 ± 0.2B 6.68 ± 0.38AB
    C 601–900 G 355 ± 12d 269 ± 14fg 14.6 ± 1.2abc 6.45 ± 0.58ab
    GCP 195 ± 8ab 103 ± 9ab 13.3 ± 0.4a 6.22 ± 0.48ab
    ŚCP 238 ± 8c 100 ± 10a 14.4 ± 0.5abc 6.39 ± 0.37ab
    DCP 485 ± 14e 165 ± 14cd 16.8 ± 1.3cd 8.33 ± 0.25de
    Total root 257 ± 9A 136 ± 10A 14.2 ± 0.6A 6.51 ± 0.43A
    D > 901 G 625 ± 19e 348 ± 13h 16.0 ± 1.4abcd 8.05 ± 0.74cde
    GCP 178 ± 7a 94 ± 10a 13.6 ± 1.1ab 6.34 ± 0.17ab
    ŚCP 214 ± 5abc 94 ± 6a 14.0 ± 0.4abc 6.28 ± 0.36ab
    DCP 235 ± 11bc 234 ± 7ef 14.9 ± 0.8abcd 6.11 ± 0.25a
    Total root 248 ± 8A 136 ± 9A 14.2 ± 0.8A 6.53 ± 0.32A
    Note: The results for the total root were presented as a weighted average, taking into account the average percentage share of the individual parts in the total root. Means sharing the same letter in column are not significantly different from each other (P ≤ 0.05): a, b…—within the root portion; A, B...—within the mean size for a given group. Means sharing the same letter in column are not significantly different from each other (Tukey’s significant difference test, P < 0.05). Data are the averages (n = 16). G—head with the upper under leaf part, GCP—upper under leaf part, SCP—the central under leaf part, DCP—the bottom under leaf part (Figure 1).

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    Table 4.  The correlation coefficients (R) between the studied characters beetroot cv. ‘Śnieżna Kula’.
    Sugar reducing Sugar total Nitrate (Ⅲ) Nitrate (Ⅴ) Polyphenols Chlorogenic acid FRAP ABTS
    Sugar reducing 0.119 −0.397 −0.506 0.648 0.726 0.697 0.459
    Sugar total 0.211 0.095 0.034 0.221 0.219 −0.218
    Nitrate (Ⅲ) 0.234 −0.389 −0.480 −0.384 −0.590
    Nitrate (Ⅴ) −0.461 −0.243 −0.321 −0.214
    Polyphenols 0.791 0.809 0.662
    Chlorogenic acid 0.728 0.555
    Note: Bold indicates that the correlation is significant at the 0.01 probability level.

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    The total polyphenol content varied greatly (from 178 to 665 mg kg−1 FW) and was determined by the size and part of the root (Table 3). The high total polyphenol content was also determined by Vasconcellos et al. [35] in red beet juice. On the other hand, no effect of the increase in the root weight on the content of these compounds was noted (Figure 3c). In size groups A and D, the highest content was noted for the part G (665 and 625 mg kg−1 FW) and in the size group C for the part DCP (485 mg kg−1 FW) (Figure 2c).

    As regards chlorogenic acid, which is classified as a phenolic compound, the average content for the white beetroot amounted to 188 mg kg−1 FW. In studies by Kazimierczak et al. [14], chlorogenic acid content in the beetroots amounted, on average for the cultivars, to 60 mg kg−1 FW, and on average for various cultivation systems, to 52 mg kg−1 FW. Therefore, the chlorogenic acid content in the analysed beetroot of the ‘Śnieżna Kula’ cultivar was three times higher than in the red beetroots. Carrillo et al. [40] claim that the chlorogenic acid content of red beets ranges from 27.9 to 279.8 mg kg−1 FW. It was also demonstrated that the roots belonging to group A had the highest chlorogenic acid content. In each size group, chlorogenic acid was present in the greatest amounts in the outer parts of the roots (G and DCP) (Figure 2d). The literature on the subject shows that in the roots of the red beetroot, the highest polyphenol content is found in the skin and the part adjacent to the skin; the deeper into the root, the lower the polyphenol content [15]. Own study results confirm this relationship, as the skin-to-flesh ratio is the highest in the head and the lower hypocotyl part.

    For the white beetroot, the antioxidant capacity averages 14.4 mmol Fe2+ kg−1 FW (FRAP) i 6.7 mmol Trolox kg−1 FW (ABTS) (Table 3). As demonstrated by Czapski et al. [3] and Wruss et al. [31], the antioxidant capacity is determined by the purple and yellow pigment contents of the roots. For the ‘Śnieżna Kula’ cultivar, the absence of betalain pigments resulted in significantly lower FRAP values, which were determined by the content of phenolic compounds (R = 0.809, P≤0.05), including chlorogenic acid (R = 0.728, P≤0.05). At the same time, a highly positive correlation was demonstrated between the total phenolic compound content and the chlorogenic acid content (0.791) (Table 4). As reported by Czapski et al. [37] and Carrillo et al. [40], the antioxidant capacity of the red beetroot is cultivar-specific. In those studies, the antioxidant capacity, measured using the FRAP and ABTS methods, averaged on the level from 63.7 to 276 mmol Fe2+ L−1 and from 21.1 to 146.0 mmol Trolox L−1 juice, respectively.

    The conducted study demonstrated that the FRAP and ABTS ranged respectively from 14.2 mmol Fe2+ kg−1 and 6.5 mmol Trolox kg−1 for the juice extracted from the size groups C and D to 14.7 mmol Fe2+ kg−1 and 7.0 mmol Trolox kg−1 for the juice extracted from the size group A (Table 3). However, based on statistical analysis, it was found that the antioxidant capacity was not significantly correlated with the root size R = −0.209 (FRAP) R = −0.315 (ABTS) (Figure 3c). Reverse results were obtained for the red beetroot of the ‘Wodan’ cultivar, for which the antioxidant capacity significantly decreased with an increase in the weight of the root; it ranged from 226 mmol Fe2+ kg−1 for the roots with an average weight of 220 g to 314 mmol Fe2+ kg−1 of the juice from the roots with an average weight of 170 g [28]. It should be noted, however, that red beetroot cultivars have a considerably lower average weight of the roots than the white beetroot, and the values provided by Czapski et al. [28] concern the root weight included in group A for the white beetroot.

    The beetroot of the ‘Śnieżna Kula” cultivar is characterised by high contents of phenolic compounds, including chlorogenic acid. It also exhibits a high antioxidant capacity (FRAP). It was found that the roots of the white beetroot with a lower weight have significantly higher sugar and total polyphenol (including chlorogenic acid) contents. At the same time, as for the red beetroot, attention should be paid to sustainable cultivation, including the application of nitrogen fertilisation to prevent excessively high nitrate (Ⅲ) and (Ⅴ) contents. Based on the conducted study, it was found that the beetroot of the ‘Śnieżna Kula’ cultivar, despite the absence of betalain pigments typical of red beetroots, is a vegetable that should be recommended for direct consumption and food processing.

    All authors declare no conflict of interest in this paper.



    [1] R. Stupp, W. P. Mason, M. J. Van Den Bent, M. Weller, B. Fisher, M. J. B. Taphoorn, et al., Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma, N. Engl. J. Med., 352 (2005), 987-996.
    [2] R. Stupp, M. E. Hegi, W. P. Mason, M. J. Van Den Bent, M. J. B. Taphoorn, R. C. Janzer, et al., Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase iii study: 5-year analysis of the eortc-ncic trial, Lancet Oncol., 10 (2009), 459-466.
    [3] R. Bonavia, W. K. Cavenee, F. B. Furnari, Heterogeneity maintenance in glioblastoma: a social network, Cancer Res., 71 (2011), 4055-4060.
    [4] Z. An, O. Aksoy, T. Zheng, Q.-W. Fan, W. A. Weiss, Epidermal growth factor receptor and egfrviii in glioblastoma: signaling pathways and targeted therapies, Oncogene, 37 (2018), 1561-1575.
    [5] M. Nakada, D. Kita, T. Watanabe, Y. Hayashi, J.-i. Hamada, The mechanism of chemoresistance against tyrosine kinase inhibitors in malignant glioma, Brain Tumor Pathol., 31 (2014), 198-207.
    [6] N. J. Szerlip, A. Pedraza, D. Chakravarty, M. Azim, J. McGuire, Y. Fang, et al., Intratumoral heterogeneity of receptor tyrosine kinases egfr and pdgfra amplification in glioblastoma defines subpopulations with distinct growth factor response, Proc. Natl. Acad. Sci. U.S.A., 109 (2012), 3041-3046. doi: 10.1073/pnas.1114033109
    [7] L. S. Hu, S. Ning, J. M. Eschbacher, L. C. Baxter, N. Gaw, S. Ranjbar, et al., Radiogenomics to characterize regional genetic heterogeneity in glioblastoma, Neuro-oncology, 19 (2017), 128-137.
    [8] S. J. Smith, M. Diksin, S. Chhaya, S. Sairam, M. A. Estevez-Cebrero, R. Rahman, The invasive region of glioblastoma defined by 5ala guided surgery has an altered cancer stem cell marker profile compared to central tumour, Int.J. Mol. Sci., 18 (2017), 2452.
    [9] A. Sottoriva, I. Spiteri, S. G. Piccirillo, A. Touloumis, V. P. Collins, J. C. Marioni, et al., Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics, Proc. Natl. Acad. Sci. U.S.A., 110 (2013), 4009-4014.
    [10] J. G. Lyons, E. Lobo, A. M. Martorana, M. R. Myerscough, Clonal diversity in carcinomas: its implications for tumour progression and the contribution made to it by epithelial-mesenchymal transitions, Clin. Exp. Metastasis, 25 (2008), 665-677.
    [11] C. Lopez-Gines, R. Gil-Benso, R. Ferrer-Luna, R. Benito, E. Serna, J. Gonzalez-Darder, et al., New pattern of egfr amplification in glioblastoma and the relationship of gene copy number with gene expression profile, Mod. Pathol., 23 (2010), 856-865. doi: 10.1038/modpathol.2010.62
    [12] F. B. Furnari, T. F. Cloughesy, W. K. Cavenee, P. S. Mischel, Heterogeneity of epidermal growth factor receptor signalling networks in glioblastoma, Nat. Rev. Cancer, 15 (2015), 302.
    [13] B. R. Voldborg, L. Damstrup, M. Spang-Thomsen, H. S. Poulsen, Epidermal growth factor receptor (egfr) and egfr mutations, function and possible role in clinical trials, Ann. Oncol., 8 (1997), 1197-1206.
    [14] J. J. Parker, K. R. Dionne, R. Massarwa, M. Klaassen, N. K. Foreman, L. Niswander, et al., Gefitinib selectively inhibits tumor cell migration in egfr-amplified human glioblastoma, Neurooncology, 15 (2013), 1048-1057.
    [15] K. M. Talasila, A. Soentgerath, P. Euskirchen, G. V. Rosland, J. Wang, P. C. Huszthy, et al., Egfr wild-type amplification and activation promote invasion and development of glioblastoma independent of angiogenesis, Acta Neuropathol., 125 (2013), 683-698.
    [16] N. Shinojima, K. Tada, S. Shiraishi, T. Kamiryo, M. Kochi, H. Nakamura, et al., Prognostic value of epidermal growth factor receptor in patients with glioblastoma multiforme, Cancer Res., 63 (2003), 6962-6970.
    [17] A. Alentorn, Y. Marie, C. Carpentier, B. Boisselier, M. Giry, M. Labussiere, et al., Prevalence, clinico-pathological value, and co-occurrence of pdgfra abnormalities in diffuse gliomas, Neurooncology, 14 (2012), 1393-1403.
    [18] P. Blume-Jensen, T. Hunter, Oncogenic kinase signalling, Nature, 411 (2001), 355
    [19] Cancer Genome Atlas Research Network, Comprehensive genomic characterization defines human glioblastoma genes and core pathways, Nature, 455 (2008), 1061.
    [20] M. M. Lino, A. Merlo, Pi3kinase signaling in glioblastoma, J. Neurooncol., 103 (2011), 417-427.
    [21] M. Snuderl, L. Fazlollahi, L. P. Le, M. Nitta, B. H. Zhelyazkova, C. J. Davidson, et al., Mosaic amplification of multiple receptor tyrosine kinase genes in glioblastoma, Cancer Cell, 20 (2011), 810-817.
    [22] F. Chen, L. Ding, Co-survival of the fittest few: mosaic amplification of receptor tyrosine kinases in glioblastoma, Genome Biol., 13 (2012), 141.
    [23] M. J. Borad, M. D. Champion, J. B. Egan, W. S. Liang, R. Fonseca, A. H. Bryce, et al., Integrated genomic characterization reveals novel, therapeutically relevant drug targets in fgfr and egfr pathways in sporadic intrahepatic cholangiocarcinoma, PLoS Genet., 10, e1004135.
    [24] D. W. Craig, J. A. O'Shaughnessy, J. A. Kiefer, J. Aldrich, S. Sinari, T. M. Moses, et al., Genome and transcriptome sequencing in prospective metastatic triple-negative breast cancer uncovers therapeutic vulnerabilities, Mol. Cancer Ther., 12 (2013), 104-116.
    [25] R. Mehrian-Shai, M. Yalon, I. Moshe, I. Barshack, D. Nass, J. Jacob, et al., Identification of genomic aberrations in hemangioblastoma by droplet digital pcr and snp microarray highlights novel candidate genes and pathways for pathogenesis, BMC Genom., 17 (2016), 56.
    [26] J. C. L. Alfonso, K. Talkenberger, M. Seifert, B. Klink, A. Hawkins-Daarud, K. R. Swanson, et al., The biology and mathematical modelling of glioma invasion: a review, J. R. Soc. Interface, 14 (2017), 20170490.
    [27] K. R. Swanson, H. L. P. Harpold, D. L. Peacock, R. Rockne, C. Pennington, L. Kilbride, et al., Velocity of radial expansion of contrast-enhancing gliomas and the effectiveness of radiotherapy in individual patients: a proof of principle, Clin. Oncol., 20 (2008), 301-308. doi: 10.1016/j.clon.2008.01.006
    [28] K. R. Swanson, R. C. Rostomily, E. C. Alvord Jr, Confirmation of a theoretical model describing the relative contributions of net growth and dispersal in individual infiltrating gliomas, Can. J. Neurol. Sci., 30 (2003), 407-434.
    [29] K. R. Swanson, R. C. Rostomily, E. C. Alvord Jr, A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle, Br. J. Cancer, 98 (2008), 113-119.
    [30] K. R. Swanson, E. C. Alvord, J. D. Murray, Virtual brain tumours (gliomas) enhance the reality of medical imaging and highlight inadequacies of current therapy, Br. J. Cancer, 86 (2002), 14-18.
    [31] K. R. Swanson, E. C. Alvord Jr, J. D. Murray, A quantitative model for differential motility of gliomas in grey and white matter, Cell Prolif., 33 (2000), 317-329.
    [32] K. R. Swanson, C. Bridge, J. D. Murray, E. C. Alvord Jr, Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion, J. Neurosci., 216 (2003), 1-10.
    [33] A. L. Baldock, S. Ahn, R. Rockne, S. Johnston, M. Neal, D. Corwin, et al., Patient-specific metrics of invasiveness reveal significant prognostic benefit of resection in a predictable subset of gliomas, PLoS One, 9, e99057.
    [34] P. R. Jackson, J. Juliano, A. Hawkins-Daarud, R. C. Rockne, K. R. Swanson, Patient-specific mathematical neuro-oncology: using a simple proliferation and invasion tumor model to inform clinical practice, Bull. Math. Biol., 77 (2015), 846-856.
    [35] C. H. Wang, J. K. Rockhill, M. Mrugala, D. L. Peacock, A. Lai, K. Jusenius, et al., Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model, Cancer Res., 69 (2009), 9133-9140.
    [36] K. J. Painter, T. Hillen, Volume-filling and quorum-sensing in models for chemosensitive movement, Can. Appl. Math. Quart, 10 (2002), 501-543.
    [37] P. Gerlee, S. Nelander, The impact of phenotypic switching on glioblastoma growth and invasion, PLoS Comput. Biol., 8, e1002556.
    [38] K. R. Swanson, R. C. Rockne, J. Claridge, M. A. Chaplain, E. C. Alvord, A. R. A. Anderson, Quantifying the role of angiogenesis in malignant progression of gliomas: in silico modeling integrates imaging and histology, Cancer Res., 71 (2011), 7366-7375.
    [39] S. M. Blower, H. Dowlatabadi, Sensitivity and uncertainty analysis of complex models of disease transmission: an hiv model, as an example, International Statistical Review/Revue Internationale de Statistique, 229-243.
    [40] A. Hawkins-Daarud, S. K. Johnston, K. R. Swanson, Quantifying uncertainty and robustness in a biomathematical model-based patient-specific response metric for glioblastoma, JCO Clin. Cancer Inform., 3 (2019), 1-8.
    [41] S. Marino, I. B. Hogue, C. J. Ray, D. E. Kirschner, A methodology for performing global uncertainty and sensitivity analysis in systems biology, J. Theor. Biol., 254 (2008), 178-196.
    [42] S. C. Massey, J. C. Urcuyo, B. M. Marin, J. N. Sarkaria, K. R. Swanson, Quantifying glioblastoma drug response dynamics incorporating resistance and blood brain barrier penetrance from experimental data, Front. Physiol., In Press.
    [43] C. A. Smith, C. A. Yates, The auxiliary region method: a hybrid method for coupling pde-and brownian-based dynamics for reaction-diffusion systems, Royal Soc. Open Sci., 5 (2018), 180920.
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