experiment, the combined soil fertilisation with 50 kg Zn ha−1 and the foliar application of 8 kg Zn ha−1 + 10 g Se ha−1 increased on average the yield of grain, the main product of this crop, by almost 7%. Although less important in the global farm incomes, the straw yield also increased by around 26% after that combined application. Both increases, especially that of the grain yield, might give solid arguments able to persuade farmers to implement these programs, besides the increase in the Zn and Se concentration in their products which, although beneficial for society and stockbreeders, are still not compensated in their selling prices. Future trials should aim at facilitating and reducing costs to make agronomic biofortification with Se and Zn even more attractive for farmers. A good example is that reported by Wang et al. [54], who combined Zn application plus pesticide, showing it as a cost-effective ready-to-use strategy to fight human Zn deficiency in wheat-dominated regions around the world.

The rest of parameters analysed, such as straw fibres, lignin, or mineral status (Mg, Ca, Fe), resulted in either unaffected or very few affected by the biofortification with Zn and Se. Once again, this is a good result to try to successfully implement these programs among farmers, as its application might not be detrimental for the quality of their productions.

#### **4. Materials and Methods**

#### *4.1. Site, Experimental Design and Crop Management*

The field experiment was conducted in Badajoz, southern Spain (38◦54 N, 6◦44 W, 186 m above sea level), in a Xerofluvent soil, according to Soil Taxonomy, under rainfed Mediterranean conditions in 2017/2018 and 2018/2019 growing seasons. Weather-related parameters for this area for the concerned years, as well as for the average year obtained from a 30-year period, are shown in Figure 6. All climate data were taken from a weather station located at the study site. In the first study year, rainfall was like the average year but much higher (40%) than in 2018/2019. The months of March and April were exceptionally rainy, with ~175 and ~80 mm, respectively, which is much higher than in the average year. The second year was extraordinarily dry, with a very different seasonal distribution, being autumn the wettest season, to the extent that it caused a two-month delay in sowing compared to the first year, and with important drought periods between February and March and May and June.

The experiment was designed as a split-split plot arrangement with four replications randomly distributed. The main plots were the study year (2017/2018 and 2018/2019), subplots were Zn soil application (without any application [0SZn] and with a soil application of 50 kg ZnSO4-7H2O ha−1 [50SZn]) (equivalent to 11.4 kg of Zn ha−1) and sub-subplots were foliar application with four treatments (without any application [0F]; two foliar applications of 4 kg ZnSO4-7H2O ha−1 each (equivalent to 0.91 kg of Zn ha−1) at the start of flowering and two weeks later [8FZn]: a foliar application of 10 g Se ha−1 as Na2SeO4 (equivalent to 24 g ha−1 of Na2SeO4) at the start of flowering [10FSe]: a combination of 8FZn and 10FSe [8FZn + 10FSe]). The crop area for each treatment was 15 m2 (3 m × 5 m). Zinc soil treatment was only made at the beginning of the first season, in October 2017, before the sowing, sprayed as a solid in the soil surface and incorporated into the soil by tillage.

The foliar Zn application treatment consisted of two times of foliar Zn application at the start of flowering, and it was repeated two weeks later as described by Gomez-Coronado et al. [12]. At each time, 0.5% (*w*/*v*) of aqueous solution of ZnSO4·7H2O ha−1 with 800 L per hectare were sprayed until most of the leaves were covered at the very late afternoon to avoid burning in plants. For the Se treatment 10 g of Se applied as Na2SeO4 ha−1 was diluted in 800 L H2O ha−1 to obtain a 0.003% (*w*/*v*) solution, and applied as in the case of foliar Zn, as described by Poblaciones et al. [9].

The bread wheat (*Triticum aestivum* L.) cultivar used was "Antequera". Conventional tillage treatment was used to prepare a proper seedbed before sowing. The sowing was in late October in the first year (2017) and late December in 2018 (due to the intense rainfall in autumn in the second year). The sowing rate was of 350 seeds m2; each plot had six rows of 20 cm apart. A N-P-K fertiliser (15-15-15) was applied before sowing at a 200 kg ha−1 dose in all plots. Weed control was carried out by applying Trigonil (concentrated in suspension to 400 g L−1 of chlortoluron and 25 g L−1 of diflufenican) in the sowing.

#### *4.2. Soil Analysis*

To characterise the experimental soil, four representative soil samples of 30 cm depth were taken in September 2017 from the experimental site. Soil samples were air dried and sieved to <2 mm using a roller mil. Texture was clay loam, determined gravimetrically; soil pH was slightly acid, with 6.4 ± 0.2 (mean ± standard error) using a calibrated pH meter (ratio, 10-g soil:25-mL deionised H2O), soil organic matter was very low with 1.31 ± 0.09% determined by oxidation with potassium dichromate [55], total N was medium with 0.12 ± 0.007% [56], P Olsen with 4.9 ± 0.05 g P kg−1 was low, measured following the Olsen procedure, and assimilable K was low with 321 ± 8 mg kg −1, determined with ammonium acetate (1N) and quantified by atomic absorption spectrophotometry.

Soils contained low concentrations of Ca with 1248 ± 134 mg kg−1, medium of Mg with 1455 ± 145 mg kg−1. They were extracted according to the method of [57] by extraction with DTPA (diethylenetriamine pentaacetic acid) and measured by inductively coupled plasma optical emission spectroscopy (ICP-MS) (Agilent 7500ce, Agilent Technologies, Palo Alto, CA, USA). Extractable Se was very low (with 1.27 ± 0.01 μg Se kg−1) determined by using KH2PO4 (0.016 mmol L−1, pH 4.8) at a ratio of 10 g dry weight soil: 30 mL KH2PO4 (*w*/*v*) [58]. The Se concentration in the extracts was determined by ICP-MS, as described below. All the results were reported on a dry weight basis.

To evaluate the residual effect along the experiment of the Zn soil treatments, i.e., NoSZn, 50SZn + 0F and 50SZn + 8FZn, four sampling, as well as the initial sampling, was taken in January in both study years and before each harvest (therefore in September 2017, January 2018, May 2018, January 2019 and May 2019). Zinc-DTPA were determined in each moment.

#### *4.3. Plant Analysis*

Harvesting was done at maturity in early July. Straw and grain samples were thoroughly washed with tap water, and then with distilled water to avoid the eventual presence of residues from foliar applications. Afterwards, samples were dried at 70 ◦C until constant weight, and their dry matter yield was then recorded. Thousand grain weight and hectolitre weight were determined from the grain samples. Official procedures [59] were followed to determine neutral detergent fiber (NDF), acid detergent fiber (ADF) and acid detergent lignin (ADL) by means of a fiber analyser (ANKOM8–98, ANKOM Technology, Macedon, NY, USA). Total ash content was determined by ignition of the sample in a muffle furnace at 600 ◦C, as is indicated in the official procedure [59]. Total straw and grain mineral concentration (Ca, Fe, Mg, Se and Zn) were determined as follows: straw and grain were finely grounded (<0.45 mm) using an agate ball mill (Retch PM 400 mill); a 1 g was digested with ultra-pure concentrated nitric acid (2 mL) and 30% *w*/*v* hydrogen peroxide (2 mL) using a closed-vessel microwave digestion protocol (Mars X, CEM Corp, Matthews, NC, USA) and diluted to 25 mL with ultra-purified water [60]. Sample vessels were thoroughly washed with acid before use. For quality assurance, a blank and a standard reference material (tomato leaf, NIST 1573a) were included in each batch of samples. The digested was determined by ICP-MS. The studied mineral recovery was 95%, compared with certified reference material (CRM) values. To consider the dilution effect in Zn and Se caused by the different straw and grain yield between growing seasons, total nutrient uptake was calculated multiplying grain yield by the total Zn and Se concentration in grain. On the other hand, phytate concentration was estimated by means of phytic acid, whose determination is based on precipitation of ferric phytate and measurement of iron (Fe) remaining in the supernatant [61]. Phytate was extracted from about 0.2 g of ground straw or grain in 10 mL of 0.2 M HCl (pH 0.3) after shaking for 2 h. One ml of supernatant was treated with 2 mL of ferric solution (NH4Fe(SO4)2·12 H2O) in a boiling water bath for 30 min. After cooling,

samples were centrifuged, and 1 mL of supernatant was treated with 1.5 mL of 0.064 M bipyridine (2-pyridin-2-ylpyridine, C10H8N2) to measure Fe. After mixing, the solution was incubated for 10 min at room temperature, and the light absorbance was measured with a spectrophotometer at 419 nm. Finally, the molar ratio between phytate and Ca, Fe, Mg, Se and Zn was calculated to estimate the bioavailability of those nutrients.

#### *4.4. Statistical Analysis*

The evolution of the Zn soil treatments on the soil Zn-DTPA was evaluated by a split-plot analysis of variance (ANOVA). The main-plot factor was "sample time" (before starting, in January and harvest of 2018, and in January and harvest of 2019), the subplot factor "Zn application" (0SZn + 0F, 50SZn + 0F, and 50SZn + 8FZn), and its interaction in the model.

Data of mineral concentration (Ca, Fe, Mg, Se and Zn) and phytate/mineral molar ratios in straw and grain, as well as straw and grain yield, thousand grain weight and hectolitre weight, and nutritive value parameters of the straw were subjected to split-split-plot ANOVAs, including the main-plot factor 'study year' (2017/2018 and 2018/2019), the subplot factor 'soil Zn application' (0SZn and 50SZn), the sub-subplot factor 'foliar application' (0F, 8FZn, 10FSe and 8FZn + 10FSe), and their interactions in the model. When significant differences were found in ANOVA, means were compared using Fisher's protected least significant difference (LSD) test at *p* ≤ 0.05. All these analyses were performed with the Statistix v. 8.10 package (Analytical Software, Tallahessee, FL, USA). In order to normalise the variable distribution, as well as to stabilise the variance of residues, the transformation Ln(x + 1) was performed for the concentration of Se in grain, total Se content in grain and straw, the molar ratio phytate/Se and the ash content in the straw.

#### **5. Conclusions**

The results presented here showed bread-making wheat to be a very suitable crop to be used in biofortification programs with Zn and Se, as it was able to substantially accumulate the Zn and Se applied in combination in the edible parts, to alleviate their deficiency in people when used as staple food, or in livestock when using its straw. In fact, the combined foliar application of Zn and Se increased in straw, 12.3- in Zn and 2.7-fold in Se, and grain, 1.3- and 4.3-fold, respectively. However, the efficiency of the uptake and later accumulation was highly affected by the rainfall. Thus, in Mediterranean climates, characterised by irregularity in precipitations, the years with extensive drought periods could account for lower values, especially in the grain. In addition to the higher Zn and Se concentration in the edible parts, the application of 50 kg Zn ha−1 produced on average an increase of around 7% in the grain yield, and 26% in the straw yield, with the remaining productive and nutritive quality parameters almost unaffected.

**Author Contributions:** Conceptualisation, O.S. and M.J.P.; methodology, D.R.-M., A.R.-M. and M.J.P.; software, O.S.; validation, D.R.-M. and A.R.-M.; formal analysis, D.R.-M. and M.J.P.; investigation, A.R.-M. and M.J.P.; resources, M.J.P.; data curation, D.R.-M. and A.R.-M.; writing—original draft preparation, O.S. and M.J.P.; writing—review and editing, O.S., D.R.-M., A.R.-M. and M.J.P.; visualisation, D.R.-M. and M.J.P.; supervision, O.S. and M.J.P.; project administration, M.J.P.; and funding acquisition, O.S. and M.J.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Regional Government of Extremadura (Spain) and by the European Regional Development Fund (ERDF), grant number IB16093.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data sharing is not applicable to this article.

**Acknowledgments:** All authors would like to thank Teodoro Garcia White for his invaluable help with all the laboratory work.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

### *Article* **Lamina Cell Shape and Cell Wall Thickness Are Useful Indicators for Metal Tolerance—An Example in Bryophytes**

**Katharina Petschinger 1, Wolfram Adlassnig 1, Marko S. Sabovljevic 2 and Ingeborg Lang 3,\***

**Abstract:** Bryophytes are widely used to monitor air quality. Due to the lack of a cuticle, their cells can be compared to the roots of crop plants. This study aimed to test a hypothetical relation between metal tolerance and cell shape in biomonitoring mosses (*Hypnum cupressiforme*, *Pleurozium schreberi*, *Pseudoscleropodium purum*) and metal sensitive species (*Physcomitrium patens*, *Plagiomnium affine*). The tolerance experiments were conducted on leafy gametophytes exposed to solutions of ZnSO4, ZnCl2, and FeSO4 in graded concentrations of 1 M to 10−8 M. Plasmolysis in D-mannitol (0.8 M) was used as a viability measure. The selected species differed significantly in lamina cell shape, cell wall thickness, and metal tolerance. In those tested mosses, the lamina cell shape correlated significantly with the heavy metal tolerance, and we found differences for ZnSO4 and ZnCl2. Biomonitoring species with long and thin cells proved more tolerant than species with isodiametric cells. For the latter, "death zones" at intermediate metal concentrations were found upon exposure to ZnSO4. Species with a greater tolerance towards FeSO4 and ZnSO4 had thicker cell walls than less tolerant species. Hence, cell shape as a protoplast-to-wall ratio, in combination with cell wall thickness, could be a good marker for metal tolerance.

**Keywords:** bioindication; bryophytes; moss; zinc; iron; cell shape; particulate matter

#### **1. Introduction**

Bryophytes, especially mosses, are widely used for biomonitoring in different environmental studies [1–3]. Similar to primary roots in seed plants, most bryophytes do not possess a cuticle. Their leaflets consist of a monolayer of cells. Thus, bryophytes indiscriminately collect nutrients and other substances from atmospheric, mainly wet deposits. Therefore, they are perfectly suitable organisms to monitor the overall exposure at a given locality over a prolonged time span. Additionally, most bryophyte species are physiologically active over the winter and continue to adsorb deposited elements all year long.

A common method to analyze the air quality is to measure particulate matter (PM) values [4]. PM may include solid particles and liquid droplets found in the air. PM2.5 are fine inhalable particles with a diameter of up to 2.5 μM, and PM10 includes inhalable particles with a diameter of 10 μM and lower. These particles may contain hundreds of different chemicals [5,6], some of which may seriously affect the human and animal respiratory system [6], resulting in a need for constant PM surveillance.

In Austria, iron and zinc hold the largest proportions of all heavy metals in the PM10 and PM2.5 range [4,7]. Therefore, the focus is on these two metals as they also play an important political and environmental role regarding air quality monitoring by the Austrian government to ensure policy compliances by the European Union. Furthermore,

**Citation:** Petschinger, K.; Adlassnig, W.; Sabovljevic, M.S.; Lang, I. Lamina Cell Shape and Cell Wall Thickness Are Useful Indicators for Metal Tolerance—An Example in Bryophytes. *Plants* **2021**, *10*, 274. https://doi.org/10.3390/ plants10020274

Academic Editor: Gokhan Hacisalihoglu Received: 28 November 2020 Accepted: 25 January 2021 Published: 31 January 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

mosses may exhibit differences in metal uptake behavior and tolerance with respect to the element [8,9]. However, biomonitoring usually considers widely distributed species within the geographic region of interest and neglects possible differences of the species in terms of tolerance levels to elements or compounds.

The over 12,000 moss species are representing a broad morphological diversity. Furthermore, each species manifests itself as protonema, leafy or thallous gametophore, or sporophyte [10]. Here, we focus on the leaflets (lamina, [11]) of the gametophyte since these represent most of the total surface. In spite of a multitude of different lamina shapes, most moss leaflets are composed of a single cell layer. The leaflets may consist of quadrate, rectangular, oblong, fusiform, rhomboidal, hexagonal, linear, flexuous, or vermicular shaped cells of extremely different size, sometimes supplemented by a costa ("midrib"), aberrant cells at the base of the leaf, or by lamellae, papillae and mamillae increasing the leaf surface [11,12]. Usually, lamina cells are classified as parenchymatic (roundish, rectangular, or hexagonal) or prosencymatic (elongated and interleaved; [13]). In this approach, we used a simplified determination of lamina cell form comparing roundish or hexagonal shapes with rectangular or elongated rectangular ones.

Although habitat or life forms have been frequently discussed as predictors of metal tolerance in mosses [8,14,15], we are not aware of studies considering moss morphology or anatomy as related to heavy metal pollutions. The focus is not on molecular differences in cell wall composition across kingdoms, as this has been thoroughly discussed by Sarkar et al. [16] or Fangel et al. [17]. Here, the hypothesis is tested that cell size and/or cell shape is related to tolerance of certain metals in selected moss species. Species commonly used in biomonitoring and species that are not considered as suitable were selected. Comparison of the metal tolerance, therefore, contributes to quality assurance in the field of biomonitoring of heavy metals.

#### **2. Results**

#### *2.1. Lamina Cell Measurements*

The five different moss species (*Physcomitrium patens*, *Plagiomnium affine*, *Hypnum cupressiforme*, *Pleurozium schreberi*, and *Pseudoscleropodium purum*) have distinct leaflets and differ significantly in the size and shape of lamina cells (Figure 1, Table 1). Lamina cells showed a roughly rectangular shape for *P. patens* and a hexagonal shape for *P. affine*. *H. cupressiforme*, *P. schreberi*, and *P. purum* had elongated rectangular or linear lamina cells. No significant difference in cell shape was found within the same species.

**Table 1.** Five moss species (*Plagiomnium affine*, *Physcomitrium patens*, *Pseudoscleropodium purum*, *Hypnum cupressiforme*, and *Pleurozium schreberi*) were compared by mean cell length (μm), cell width (μm), length to width ratio, shape, and mean cell area (μM2) of mid lamina cells (*n* = 40). SD = Standard deviation; μ = mean value; *p* = 0.0001 (Kruskal–Wallis test comparing all five species).

*P. schreberi* had the greatest average cell length (94 μM) followed by *H. cupressiforme* with 72 μM. The latter had the smallest cell width (3 μM), the biggest ratio of cell length to cell width (25), and the smallest cell area with only 220 μM2 (always respective mean values). The largest average cell width was measured in the moss *P. affine* (37 μM), but the largest cell area was found for *P. patens* (1979 μM2). With a value of 1, *P. affine* had the smallest ratio of cell length to cell width (Table 1).

**Figure 1.** Lamina and cell shapes of five selected bryophyte species (**A**) *Physcomitrium patens*, (**B**) *Plagiomnium affine*, (**C**) *Hypnum cupressiforme*, (**D**) *Pleurozium schreberi*, and (**E**) *Pseudoscleropodium purum.* Bar: 250 μM for leaflet (overview); 25 μM for the respective cell shape (middle panel); 10 μM for close up (right panel).

Cell wall thickness differed significantly between the five species (Dunn's test: *p* < 0.05) except for *H. cupressiforme and P. schreberi* (*p* = 0.354). The thinnest cell walls were found in *P. patens* (μ = 0.26 μM). Wall thickness increased from *P. patens* < *P. purum* < *P. affine* < *H. cupressiforme* to *P. schreberi* with a mean thickness of almost 0.9 μM (Figure 2A). Thus, the biomonitor species *P. schreberi* and *H. cupressiforme* form thick cell walls compared to, e.g., *P. patens*. The tested species showed significant differences in the ratio of cell length to cell wall thickness (Figure 2B; Dunn's text: *p* < 0.05) except for *P. affine* and *H. cupressiforme* (*p* = 0.3418) that both had a similar ratio of lengths to thick walls. This ratio increased from *P. affine* = *H. cupressiforme* < *P. schreberi* < *P. purum* < *P. patens* that had the short cells (μ = 63 μM) and thinnest walls (Figure 2B). Also, the ratio of cell width to cell wall thickness was significantly different in all tested species (Figure 2C; Dunn's test: *p* < 0.05) and increased from *H. cupressiforme* < *P. schreberi* < *P. purum* < *P. affine* < *P. patens*. The thin cells of *H. cupressiforme* (μ = 3 μM) had a width to cell wall thickness ratio of four whereas *P. patens* with its wide cells (μ = 31 μM) had a ratio more than 100 times higher of cell width to cell wall thickness (Figure 2C). The ratio of the cell area to cell wall thickness was similar to the ratio of the width to wall thickness with the same increasing order of species (Figure 2D). The ratio of the cell area to cell wall thickness was 30 times higher in *P. patens* as compared to *H. cupressiforme*.

**Figure 2.** Box plots comparing the five investigated moss species. (**A**) cell wall thickness, (**B**) lamina cell length to cell wall thickness, (**C**) lamina cell width to cell wall thickness, and (**D**) lamina cell area to cell wall thickness. Pa: *Plagiomnium affine*, Ppa: *Physcomitrium patens*, Ppu: *Pseudoscleropodium purum*, Hc: *Hypnum cupressiforme*, and Ps: *Pleurozium schreberi.*

#### *2.2. Metal Tolerance*

Metal tolerance was determined by viability tests using plasmolysis in 0.8 M mannitol (Figure 3). Living cells are able to undertake plasmolysis, whereas dead cells have lost semipermeable membrane function and therefore cannot plasmolyze [18]. The "no observed effective concentration" (NOEC) and "lowest observed effective concentration" (LOEC) for the three tested heavy metal solutions (ZnCl2, ZnSO4, and FeSO4) were assessed to achieve a numeric variable of tolerance data (Table 2).

**Figure 3.** Percentage of dead lamina cells (0%, 25%, 50%, and 100%) and respective dose-response-curves for five moss species (*P. patens*, *P. affine*, *H. cupressiforme*, *P. schreberi*, *P. purum*) in tenfold dilution series of (**A**) ZnCl2, (**B**) ZnSO4 and (**C**) FeSO4. The arrows in B point to possible "death zones"; blue arrow: *P. affine*; red arrow: *P. patens*.

**Table 2.** Median of no effect concentration (NOEC) and lowest observed effect concentration (LOEC) of tested substances (ZnCl2, ZnSO4, FeSO4 in Mol) for five moss species. *n* = 40–80 cells.

For each effect concentration, the median of the tolerance experiments was calculated (*n* = 40–80 cells per species). Since *P. affine* showed a death zone, the NOEC was formed by an average of three concentrations (10−7 M, 10−6 M, 10−5 M).

For ZnCl2, we observed a decreasing tolerance of moss species *P. purum* > *P. patens* > *P. schreberi* and *H. cupressiforme* > *P. affine*, whereby *P. affine* was the at least tolerant moss of the investigated species. *H. cupressiforme* and *P. schreberi* showed the same LOEC (10−4 M ZnCl2). *P. purum* could tolerate the highest observed concentration of 10−2 M ZnCl2 (Figure 3A). Apart from *H. cupressiforme* and *P. schreberi,* the tested species differ significantly with regard to their tolerance to ZnCl2 (*p* < 0.05).

The tolerance of moss species to ZnSO4 dropped from *H. cupressiforme* > *P. patens* > *P. schreberi* > *P. purum* > *P. affine* (concentration range: 10−8–1 M) with *H. cupressiforme* showing the highest NOEC of 10−3 M (Figure 3B). There was a significant difference between all tested species to tolerate ZnSO4 (*p* < 0.05). Interestingly, death zones emerged in the case of *P. affine* showing 100 % viable cells at a concentration of 10−5 M ZnSO4 and only 50 % of viable cells at a lower concentration of 10−6 M ZnSO4. Additionally, a death zone is likely in *P. patens* between a concentration of >10−1 M ZnSO4: at 1 M ZnSO4, the tolerance tests showed only 50 % of dead cells, whereas, at a lower concentration (10−1 to 10−2 M ZnSO4), 100 % of cells died (Figure 3B, arrow).

Thus, visible effects of ZnCl2 (Figure 3A) could be observed at lower concentrations compared to ZnSO4 in *P. affine* and *H. cupressiforme* (Figure 3B) but not in *P. patens*, *P. schreberi*, and *P. purum*. Apparently, some mosses are more sensitive to ZnCl2 than to ZnSO4. *P. patens* and *P. schreberi* had the same LOEC for ZnCl2 and ZnSO4, although the latter species with a slower transition and both tolerated a 10-fold higher concentration of ZnCl2 and ZnSO4 according to the percentage of viability. In contrast, *P. purum* survived a 104 higher concentration of ZnCl2 than ZnSO4.

The overall tolerance to iron was greater than to zinc (Figure 3). In the concentration range of 10−8–1 M FeSO4, the tolerance of the tested species decreased as: *P. schreberi* = *P. purum* > *H. cupressiforme* > *P. patens* > *P. affine*. The species used for biomonitoring (*P. schreberi*, *P. purum* and *H. cupressiforme)* had the same LOEC of 10−1 FeSO4, whereas *P. patens* and *P. affine* had a LOEC of 10−4 M FeSO4. The transition from living to dead *P. affine* and *H. cupressiforme* cells was sudden when compared to the other species (Figure 3C).

#### *2.3. Correlations between Cell Shape and Metal Tolerance*

The NOEC and LOEC for the three tested heavy metal solutions (ZnCl2, ZnSO4, FeSO4, respectively) were assessed to achieve a numeric variable of tolerance data (Table 2) and to correlate them to cell shape. The results are shown in Table 3.

**Table 3.** Spearman correlation between morphometric parameters and maximum no-effect concentrations, shown as *ρ* (*p*). Strong (*ρ* ≥ 0.8) and highly significant (*p* < 0.01) correlations are highlighted (bold). *n* = 200 cells.

A strong, negative correlation occurred between the cell width and the tolerance to FeSO4 (*ρ* = −0.85, *n* = 200, *p* ≤ 0.001, Table 3), and also the correlation between the ratio of the cell width to the cell wall thickness was highly negative (*ρ* = −0.85, *n* = 200, *p* ≤ 0.001). A strong, positive correlation between the ratio of the cell length to the cell width was also highly significant (*ρ* = 0.85, *n* = 200, *p* ≤ 0.001). The same trend, albeit with less strong correlation, was found for both zinc treatments. These data show an increased metal tolerance in species with elongated cells and thick walls.

#### **3. Discussion**

Many bryologists are aware of tolerance differences among selected species, their physiological state, life form or even genotypes when comparing or interpreting the results obtained. However, the mechanisms of resistance/tolerance to pollution substances remain obscured. Moreover, with such a huge variation of species, in addition to environmental, physiological, and morphological factors used in biomonitoring studies blur the pattern of

pollutant tolerance. Therefore, we aimed to find a "simple" commodity like lamina cell shape that could be linked to metal tolerance.

In general, the monitoring of airborne heavy metal pollutants is a very difficult process [15]. Field receptor measurements are highly expensive, but they supply precise and reliable distribution-estimations about the airborne pollutants. However, they lack information on the effects of these pollutants on biological systems [19]. In this case, other methods were more appropriate, like the biomonitoring of heavy metal pollutants using bryophytes [1,2,15].

Different tolerance levels of the tested moss species to ZnCl2 and ZnSO4 were found but also to FeSO4 (Figure 3). Interspecific differences in the sensitivity were also reported by Tyler [20] as tested by net photosynthesis. We also used alternative tools and found different tolerance levels according to bryophyte species, life forms, or metal applied [9,18,21–23].

The tolerance experiments conducted with the biomonitoring mosses and the cultured mosses represent the tolerance of the species without considering their own background concentration of trace metals. In monitoring surveys, this background concentration of heavy metals in the mosses is usually not determined. However, if a pre-disposition with metals exits in the field samples, the tolerance levels would be lower, which leads to more conservative pollution estimations. The collection sites of the biomonitoring species used here are in the Viennese forest, away from civilization. No particular metal contamination is assumed. Furthermore, it should be noted that mosses have a relatively high, intrinsic concentration of zinc (about 20 μg/g), which is not due to emissions [24]. The same can be assumed for the tested iron samples. In *H. cupressiforme*, the comparison of field samples and tissue culture probes did not result in significant tolerance differences (A. Khan, unpublished data).

Death zones as found in this study for *P. patens* and *P. affine* exposed to ZnSO4 are known from the literature [25–28]. Biebl [25] reported death zones for certain bryophyte species, whereby a low and a high metal concentration resulted in little mortality, but intermediate metal concentrations were lethal. Biebl's observations fit with the results of our study as a high (>1 M) concentration of ZnSO4 caused rather little mortality of lamina cells of *P. patens* (Figure 3). However, further studies are necessary to show if lamina cells of *P. patens* are completely viable at concentrations above 1 M ZnSO4. *P. affine* had a more distinct death zone at a lower concentration of 10−6 M ZnSO4. Url [28] also observed death zones in *Nardia scalaris* (a liverwort) for copper and vanadium. The physiological reasons for the death zones are still unclear and would need further investigations.

Metals are positively charged and become first adsorbed to cation exchange sites at the cell wall [29,30]. Sequential elution studies also found most metal retention in the cell wall [31,32]. Hence, metals are deposited to the apoplast, where they have little impact on the living cytoplasm. In mosses with thick cell walls, the apoplast contributes more to the total surface of the leaf compared to mosses with thinner walls. The same is true for species with elongated cells, compared to species with cells of the same volume but more globular or cube-shaped cells. If the cell wall plays a major role in metal retention [21], moss species with such cells or thick walls are therefore more tolerant.

In the present study, cell wall thickness was determined by light microscopy, but even at the highest possible resolution, the edges of cell walls may appear blurry (see Figure 2). To lower this statistical variance and conceive reliable results, a high number of measurements was performed (*n* = 40–80). Certainly, the molecular composition of the cell wall is also relevant as it has become adapted during evolution and in conquering various ecological niches [16,17]. However, bryophyte species used in biomonitoring appear to have a higher percentage of cell wall within the lamina. This renders them more tolerant, and therefore, they can adsorb more metals. The application of these species in biomonitoring thus results in higher metal amounts measured because other species, mosses or vascular plants, have thinner cell walls with less adsorption capacity. However, for the estimation of toxic pollution levels, we prefer a conservative approach that rather

over-estimates the metal levels. This way, the present study confirms that the commonly used species are well suitable for biomonitoring.

#### **4. Material and Methods**

#### *4.1. Plant Species*

Five species of mosses were selected to study the lamina cell shape in combination with heavy metal tolerance. The chosen species are from five different families and four different orders. *Pleurozium schreberi* (Will. ex Brid.) Mitt. (Hylocomiaceae), *Hypnum cupressiforme* Hedw. (Hypnaceae), and *Pseudoscleropodium purum* (Hedw.) M. Fleisch. (Brachytheciaceae) are commonly used in environmental and biomonitoring studies and were collected in the forest near Vienna, Austria (14 March and 13 April, 2018). Additionally, we selected two species with very different cell shapes and sizes, *Physcomitrium* (*Physcomitrella) patens* (Hedw.) Bruch and Schimp. (Funariaceae) and *Plagiomnium affine* (Blandow ex Funck) T.J. Kop. (Mniaceae). They were cultured in a growth cabinet (Conviron) at 20 ◦C with a 14/10 h light/dark cycle. Sterile cultures of *P. patens* were propagated according to [33]. The widely used model species *P. patens* has a shorter life span than the bigger *P. affine*, and they also differ in life forms. None of the species shows a particular preference for metal contaminated sites, although *P. schreberi* has been found at the periphery of mine tailings [34,35]. Table 4 shows further details on the plant material and its cultivation.

**Table 4.** Taxonomy, origin, and culture of the plant material.

#### *4.2. Tolerance Tests*

Two to three young but fully expanded leaflets of each moss species were placed in 96-well plates filled with serial dilutions (1 M to 10 nM) of ZnCl2 (Carl Roth, Karlsruhe, Germany), ZnSO4 (Merck), and FeSO4 (Merck), respectively. After 48 h, cell viability was tested via plasmolysis [18]; Figure 4B). In brief, after metal exposure, the leaflets were transferred into 0.8 M mannitol (Carl Roth, Germany) solution for 20 to 30 min. Then, the samples were placed in a droplet of the 0.8 M mannitol on a microscope slide, covered with a coverslip, and imaged in the light microscope (see below). The high sugar concentration of the mannitol solution causes osmotic water loss from the cell, mainly the vacuole. The water efflux from the cell eventually leads to a detachment of the protoplast from the cell wall as the vacuole diminishes in size (plasmolysis; Figure 4B). This process works by intact, semipermeable membranes only; damaged or dead cells, e. g. by high metal concentrations, do not plasmolyze. Hence, the plasmolytic viability tests allowed the determination of effect concentrations for the respective metal as well as the generation of dose-response curves. A minimum of 40 lamina cells were assessed per leaf. The leaflet was divided into four quarters; cells at the edges or midrib were not counted. In each quarter, the cells were analyzed individually using higher magnification, and the values summarized into 0%, 25%, 50%, or 100% dead cells, respectively, per quarter. Aiming to evaluate the significance of species differences in tolerance, the "no effect concentration" (NOEC) was analyzed in R Studio using Kruskal–Wallis rank sum test and Dunn's test (see statistical analysis below). For the interpretation of the tolerance data, "the lowest observed effect concentration" (LOEC) was also used.

**Figure 4.** (**A**) measurements of cell length and widths in rectangular cells using the "extended focus" function (Nikon NIS -ElementsBR) of the microscope; bar: 50 μM; and (**B**) hexagonal cell type of *P. affine*, plasmolyzed in 0.8 M mannitol for 20 min; the protoplasts are detached from the cell wall; bar: 25 μM. (**C**) schematic and formula for area calculation of hexagonal cells (changed after Hnilica and Kohout 2018).

#### *4.3. Cell Measurements*

Cell lengths were counted parallel to the longitudinal axis of the leaf, and cell width was defined as normally oriented to the longitudinal axis of the leaf (Figure 4). In all species, random measurements were done in fully developed leaves, i.e., leaf four and five from the top of the plantlets. Mid lamina cells are defined as those cells situated in the middle of the leaflets but not next to the margins and not next to the costa ("midrib"). This was done to reflect the cells that were covering the biggest leaf surface in each species and to avoid artifacts of different cell types present in some leaves. For cell wall measurements, at least two typical midleaf cells per lamina were randomly chosen, and at least 40 measurements per cell were performed towards all cell neighbors to reduce possible variabilities of wall thickness.

#### *4.4. Microscopy*

An upright light microscope (Nikon Eclipse Ni-U) was used in bright field and interference contrast mode. The instrument was equipped with the objectives Plan Fluor 4× (NA 0.13), Plan Apo 10× (NA 0.45), Plan Apo 20× (NA 0.75), Plan Fluor 40× (NA 0.75), Plan Fluor 60× oil immersion (NA 0.50–1.25), Plan Fluor 100x oil immersion (NA 1.30) and an attached camera (Nikon DS-Ri2). For picture processing, the software NIS-Element BR (Nikon), including an "extended focus" tool, was used. The calibrated measurements were directly exported to Excel (Microsoft Office 365).

The cell areas of *P. patens*, *H. cupressiforme*, *P. schreberi*, and *P. purum* were calculated using the formula of a rectangle. For *P. affine*, we used the area of a hexagon [36] as it fitted best to the cell shape of this species (Figure 4C).

#### *4.5. Statistical Analysis*

Statistical analyses were conducted in STATA, version 14.2 (StataCorp LLC, College Station, TX, USA), and documented by archival of files containing all commands. Cell size was characterized by arithmetic mean (μ), standard deviation (SD), and sample size (n). Since samples differed significantly from a normal distribution, differences between the species were tested for significance by using the Kruskal–Wallis test with the post hoc Dunn's test for pairwise comparison. Possible correlations between cell size and metal resistance were characterized by Spearman's Rho (*ρ*). *ρ* > 0.8 was regarded as a strong, *ρ* > 0.5 as a moderate, and *ρ* > 0.2 as a weak correlation. *p* < 0.05 was regarded as significant

throughout the study. Insignificant correlations were considered as meaningless, regardless of *ρ*. Figures were generated in R Studio, version 1.1.456 (RStudio, Boston, MA, USA).

#### **5. Conclusions**

In bryophytes, metal tolerance is species-specific, but the reasons for the different tolerance levels are not clear. Our data confirm a hypothetic relation of lamina cell shape and metal tolerance in the tested mosses. Those species with long and thin lamina cells cope better with high levels of metal than species with isodiametric cells. In the tested species, this correlation is particularly strong for iron, but a similar trend is shown for zinc. Apart from the cell shape, the thickness of the cell wall plays an important role in metal tolerance, most likely due to its adsorption capacity for positively charged ions. Although more bryophyte species should be tested in the future, plant cell anatomy, as in the case of lamina cells described here, is a helpful tool to indicate the metal tolerance of a moss.

**Author Contributions:** I.L. designed and supervised the work; K.P. conducted the experiments during her master thesis; W.A. was responsible for statistical data and analyses; M.S.S. supervised the work and contributed to the collection of field samples; all authors discussed the data and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work was supported by a bilateral grant for Scientific and Technological Cooperation between Austria and Serbia (OeAD; SRB 15/2018) to M.S.S. and I.L. This article was funded by Open Access Publishing Funds of the University of Vienna.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data are available upon request.

**Acknowledgments:** We thank Helmuth Goldammer for his tireless ambition to always aim for the best picture. This article was funded by Open Access Publishing Funds of the University of Vienna.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

### *Article* **Flax and Sorghum: Multi-Element Contents and Nutritional Values within 210 Varieties and Potential Selection for Future Climates to Sustain Food Security**

**Gokhan Hacisalihoglu 1,\* and Paul R. Armstrong 2**

**Abstract:** The Dietary Guidelines for Americans recommends giving priority to nutrient-dense foods while decreasing energy-dense foods. Although both flax (*Linum usitatissimum*) and sorghum (*Sorghum bicolor*) are rich in various essential minerals, their ionomes have yet to be investigated. Furthermore, previous studies have shown that elevated CO2 levels could reduce key nutrients in crops. In this study, we analyzed 102 flax and 108 sorghum varieties to investigate their ionomic variations (N, P, K, Ca, Mg, S, B, Zn, Mn, Fe, Cu, and Mo), elemental level interactions, and nutritional value. The results showed substantial genetic variations and elemental correlations in flax and sorghum. While a serving size of 28 g of flax delivers 37% daily value (DV) of Cu, 31% of Mn, 28% of Mg, and 19% of Zn, sorghum delivers 24% of Mn, 16% of Cu, 11% of Mg, and 10% of Zn of the recommended daily value (DV). We identified a set of promising flax and sorghum varieties with superior seed mineral composition that could complement breeding programs for improving the nutritional quality of flax and sorghum. Overall, we demonstrate additional minerals data and their corresponding health and food security benefits within flax and sorghum that could be considered by consumers and breeding programs to facilitate improving seed nutritional content and to help mitigate human malnutrition as well as the effects of rising CO2 stress.

**Keywords:** food security; nutrient dense; superfood; multi minerals; health benefits; zinc; iron; gluten free; percent daily value; elevated CO2

#### **1. Introduction**

There are several diet-related chronic diseases (e.g., diabetics, heart disease, obesity, and cancer), and therefore, specifically plant-based nutrition is expected to be increasingly important worldwide for prevention and control of these diseases [1]. Therefore, one of the utmost research areas of plant biology has been plant nutritional values for the human diet.

Due to the growing popularity and demand for plant nutrition, there is an increasing need for research on the improvement of yield and quality of crop plants. Flax (*Linum usitatissimum*) is an annual crop and an important source of alpha linolenic acid (ALA) omega-3 fats as well as protein with all nine essential amino acids, except lysine (Figure 1) [2]. Flax is grown in cool climates including Canada, China, Russia, and the United States (North Dakota and Minnesota) [1]. Sorghum (*Sorghum bicolor*) is one of the Poaceae family cereal crops with important antidiabetic, anticholesterol, and low glycemic index (GI) features and is grown in the United States, India, Mexico, and China (Figure 1) [3]. Flax and sorghum seeds both contain a diverse set of mineral nutrients together with protein, oil, and carbohydrate (Figure 1). Ionomic profiling of the accumulated elements in living organisms has been successfully applied to study leaves, roots, whole plants, and seeds [4]. Furthermore, mineral and trace elements have been successfully determined in many other crop species including common beans, peas, soybeans, and maize [5–13].

**Citation:** Hacisalihoglu, G.; Armstrong, P.R. Flax and Sorghum: Multi-Element Contents and Nutritional Values within 210 Varieties and Potential Selection for Future Climates to Sustain Food Security. *Plants* **2022**, *11*, 451. https://doi.org/10.3390/ plants11030451

Academic Editor: Dimitris L. Bouranis

Received: 4 January 2022 Accepted: 2 February 2022 Published: 6 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Atmospheric carbon dioxide (CO2) levels have increased from 278 μmol/mol to 417 μmol/mol (present, 2021) and are expected to reach 550 μmol/mol and 800+ μmol/mol by 2050 and 2100, respectively, with the current average increase rate of 2.5 μmol/mol [14]. A continuous rise in the levels of atmospheric CO2 is expected to potentially affect plant life negatively. A study with soybean found that elevated CO2 levels influenced seed nutritional levels by decreasing most mineral content including the concentrations of K, Mg, Fe, and B [15]. Furthermore, a reduction in seed nitrogen (N) has been reported, and therefore, protein levels under elevated CO2 levels [16].

**Figure 1.** Potential health and food security benefits of flax and sorghum seeds.

One way to mitigate future climate effects and maintain food sustainability is to screen and identify top varieties that can naturally offer superior mineral concentrations. The importance of seeds in maintaining human health and diet could be determined by their nutritional content through a recommended percent daily value (% DV, how much it contributes to a daily 2000 calorie diet) [6]. Therefore, measurement of mineral element contents can provide valuable information for consumers and crop breeders.

Among several studies that have been conducted on seed ionome, most of them have been carried out in major staple crop plants. The specific aims of this study were: (1) to determine the variability of macronutrient and trace-element concentrations among 102 flax and 108 sorghum varieties; (2) to analyze the elemental interrelationships and % DV of nutrients; (3) to identify superior varieties that could be used to improve the nutritional value potential in flax and sorghum.

#### **2. Materials and Methods**

#### *2.1. Flax and Sorghum Seeds*

A total collection of diverse global varieties including 102 flax (*Linum usitatissimum*) and 108 sorghum (*Sorghum bicolor*) varieties were selected based on their maximum geographic diversity obtained from the USDA National Germplasm Center. All seeds used in this study were field grown with standard agronomical practices (Tables 1 and 2).

#### *2.2. Multi-Elemental Seed Analysis*

Elemental concentrations of macro- and micronutrients were quantified by Waters Agricultural Labs Inc. (Camilla, GA, USA). Minerals were analyzed by open vessel wet digestion using an inductively coupled argon plasma spectrometer (ICAP, DigiBlock 3000 ICP-MS). Briefly, seeds were dried at 80 ◦C in an oven overnight, and then ground in a Wiley mill. A 0.5 g dried sample was mixed with 5 mL concentrated nitric acid and incubated at 95 ◦C for 90 min. Then, 4 mL of 30% H2O2 was added to each tube and incubated at 95 ◦C for 20 min. Samples were cooled for 2 min, brought to 50 mL with distilled H2O, and mixed. Samples were transferred to ICP tubes for analysis, in accordance with manufacturer's specifications. The ICP-MS was calibrated using distilled H2O as a blank and two plant standards.

Total nitrogen (N) determination was performed by U.S. Department of Agriculture (Manhattan, KS, USA) using combustion gas analysis (LECO FP-628, St. Joseph, MI, USA), following the manufacturer's instructions as previously described in Hacisalihoglu et al. [13].

#### *2.3. Estimating Nutritional Value (% DV)*

The nutritional values of flax and sorghum seeds were estimated using a 28 g dry weight serving portion. The U.S. recommended daily value indices (2000 calorie diet for adults) were as follows: Mn (2 mg), P (1000 mg), Cu (2 mg), Fe (18 mg), Mg (400 mg), Zn (15 mg), and Ca (1000 mg) [17,18]. Percent daily values (% DV) were estimated from a 28 g of seeds serving (dry weight basis) by using the following formula:

% DV = (amount of nutrient mg / recommended DV mg)∗100 (1)

%DV; Percent daily values; mg; milligrams.

#### *2.4. Data Analysis*

All lab analyses were completed with three replications. Elemental statistical correlation analysis was performed using SigmaPlot (SPSS Inc., Chicago, IL, USA), as described previously [9]. Descriptive statistics for each macro- and micronutrients and varieties were determined using the average of the ICP-MS results from the three biological replications. Graphs were made with SigmaPlot software (SPSS Inc., Chicago, IL, USA).

#### **3. Results**

#### *3.1. Variations in Flax and Sorghum Multi-Element Contents*

The 102 flax varieties showed a wide variation in seed multi-element contents (Tables 1 and 2 and Figure 2). There was a 5.7-fold range of copper (Cu) content, 4.5-fold range of iron (Fe) content, 4.2-fold range of boron (B) content, 3.3-fold range of zinc (Zn) content, 2.6-fold range of manganese (Mn) content, 2.1-fold range of calcium (Ca) content, and 2-fold range of potassium (K) and molybdenum (Mo) contents (Table 2).

The 108 flax varieties showed a wide variation in seed multi-element contents (Table 2 and Figure 2). There was a 46-fold range of Fe content, 12-fold range of Cu content, 6.6-fold range of B content, 6.3-fold range of Mn content, 9.7-fold range of Zn content, 4.5-fold range of Mo content, 5-fold range of Ca, 2.7-fold range of P, and 2.6-fold range of potassium (K) (Table 2).

**Figure 2.** Average concentration of three replicates: (**A**) Flax Zn; (**B**) flax P; (**C**) flax Mg; (**D**) sorghum Zn; (**E**) sorghum P; and (**F**) sorghum Mg. (see Table 1 for all others).

**Table 1.** Mean elemental concentrations of 102 flax varieties as % (N, P, K, Mg, Ca, and S) and μg/g (Zn, Mn, Fe, Cu, and Mo) obtained from ICP-MS.

**Table 1.** *Cont.*

**Table 1.** *Cont.*

**Table 2.** Mean elemental concentrations of 108 sorghum varieties as % (N, P, K, Mg, Ca, and S) and μg/g (Zn, Mn, Fe, Cu, and Mo) obtained from ICP-MS.

**Table 2.** *Cont.*

**Table 2.** *Cont.*

#### *3.2. Elemental Correlations among Nutrients in Seeds*

There was a positive relationship between flax seed P and Mg (Figure 3A) and a good positive correlation between P and K (Figure 3B). There was a strong positive relationship between sorghum seed P and Mg (Figure 3C) and S and Mn (Figure 3D,E). A weak relationship was observed between sorghum seed Zn and Cu (Figure 3F,G) when excluding the very high singular point. Sorghum seed P and K had a reasonable positive correlation (Figure 3H).

**Figure 3.** *Cont*.

**Figure 3.** Selected correlations among seed elemental concentrations: (**A**) Flax P and Mg; (**B**) flax P and K; (**C**) sorghum P and Mg; (**D**) sorghum S and Mn; (**E**) sorghum large subset S and Mn; (**F**) sorghum Zn and Cu; (**G**) sorghum large subset Zn and Cu; (**H**) sorghum P and K. \* and \*\*, significant at the *p* < 0.05 and *p* < 0.01, respectively. NS, not significant, as determined using linear regression; R2, linear regression coefficient squared.

#### *3.3. Nutritional Value of Flax and Sorghum*

Eight minerals were analyzed based on the United States Department of Agriculture (USDA) [16,17] recommended percentage daily value (% DV), which is a calculation of nutritional content in a 28 g (1 oz) serving of food and contribution to a 2000 calorie daily diet (USDA, 2020) [15]. As shown in Figure 4, daily consumption of 28 g of flax seeds could provide 37% DV of Cu, 31% DV of Mn, 28% DV of Mg, 19% DV of Zn, 19% DV of Zn, 18% DV of P, 11% DV of Fe, and 5% DV of Ca and K.

Our analysis of sorghum showed that daily consumption of 28 g seeds could provide 24% DV of Mn, 16% DV of Cu, 11% DV of Mg, 10% DV of Zn, 9% DV of P, 7% DV of Fe, 4% DV of Ca, and 3% DV of K (Figure 4).

**Figure 4.** Daily value (% DV) provided by the 28 g serving size of flax and sorghum.

#### *3.4. Identification of Promising Top Flax and Sorghum Varieties*

Based on the results of screening the 102 flax varieties, six varieties were chosen for their superior mineral content performance: Omega, Clli1374, Clli1418, Clli1821, Clli643, and Clli2033 (Table 3). Similarly for screening of 108 sorghum varieties, six varieties were chosen for their superior mineral content performance: PI529799, PI365024, PI185574, PI266958, PI534144, and PI550850 (Table 3).

**Table 3.** A set of top six superior varieties of flax and sorghum with superior seed mineral concentration. Highest, the highest mean for that specific element; High, considerably higher mean than other varieties.

#### **4. Discussion**

The expected rising carbon dioxide (CO2) levels (from 416 ppm in 2021 to 550 ppm in 2050) could cause stress to food crop plants. Furthermore, this could cause a reduction in nutritional quality or fewer nutritious crops, and therefore, trigger malnutrition [15,16]. One approach to minimize this issue is to identify food crop varieties with higher natural nutrient composition potential.

In this study, we analyzed ionomic data from 210 flax and sorghum varieties collected from around the world. Our results demonstrated substantial genetic variation in both crop species (Figure 2 and Table 4). This is consistent and follows a number of recent findings in peas, soybean and common beans, pearl millet, and sweet potato [7,9,13,19,20].

A better understanding of the relationships among various mineral nutrients is also critically important. A small number of correlated element pairs were detected in the current study. The selected correlations in Figure 3 were the best examples of elemental pair correlations in both flax and sorghum. The results of our analysis of macronutrients (N, P, K, Ca, Mg, and S) and micronutrients (Zn, Fe, Cu, B, Mn, and Mo) are summarized in Figure 3. In particular, the correlation analysis showed that seed P had a positive correlation with K and Mg in both crop species, therefore, their accumulation may be related. Furthermore, sorghum seed Zn had a positive correlation with Cu, which suggests that accumulation of these trace elements is related. Furthermore, the positive correlations between element pairs suggest potential shared transport systems in flax and sorghum systems. Similar positive associations between Zn and Cu have been reported in previous studies in soybean and common beans [7,9]. These findings are also consistent with previous studies in sweet potatoes that showed medium to high correlations among minerals such as Fe, Zn, Ca, and Mg [20]. This may suggest that elemental correlations may simplify selection for future breeding efforts. Furthermore, this is consistent with studies in pearl millet that have reported good elemental correlation and the possibility of simultaneous improvement of those nutrients [19].

Overall, among the 210 total varieties, six unique flax varieties and six unique sorghum varieties were identified with superior seed nutrient composition (Table 3). In flax, the highest P and Mo were observed in variety Omega, while the highest Cu and B were observed in variety Clli1821. In sorghum, the highest K, Mn, Fe, and B were observed in variety PI365024, while the highest Mg, Ca, S, and Ni were observed in variety PI185574. In addition, four more flax and sorghum varieties were identified as superior varieties (Table 3). These sets of a few selected superior varieties show significantly higher mineral concentration, which suggests that there is a potential of further improving mineral nutrient content in both flax and sorghum. Similarly, Gorindaraj et al. [19] explored the genetic variability of pearl millet for seed nutritional traits and reported the top 10 pearl millet accessions that could be used to develop nutritionally superior cultivars.

 Descriptive statistics in seed ionomic concentrations of 102 diverse flax genotypes and 108 sorghum genotypes. SD, standard deviation. Each value is the mean of three replicates.

#### **Micronutrients (Trace elements) Zn Fe Cu B Mn Mo** μg/g μg/g μg/g μg/g μg/g μg/g **Flax Avg.** 73.40 70.10 11.80 18.80 25.50 2.98 **SD** 16.90 16.50 3.45 5.36 5.11 0.35 **Min** 38.00 43.70 4.21 9.61 15.50 2.27 **Max** 127.00 196.00 24.20 40.50 41.00 4.58 **Sorghum Avg.** 40.80 48.10 5.25 3.89 20.10 2.35 **SD** 20.1 69.10 2.96 1.79 7.98 0.73 **Min** 21.80 14.00 2.19 1.96 9.70 1.15 **Max** 211 643 27.0 13.0 61.1 5.12

#### **5. Conclusions**

In this study, the multi-element contents and nutritional values of 102 flax and 108 sorghum varieties were evaluated. Our results revealed that there is substantial genetic variation of seed mineral nutrient traits both in flax and in sorghum. We elaborated on the six superior flax varieties and the corresponding six superior sorghum varieties that seem to hold promise for mitigating rising CO2 stress as well as malnutrition. This study also provides an opportunity for future genetic studies to further efforts in biofortification efforts of flax and sorghum.

**Author Contributions:** Conceptualization, G.H.; methodology, G.H. and P.R.A.; formal analysis, G.H. and P.R.A.; writing—original draft preparation, G.H.; writing—review and editing, G.H. and P.R.A.; funding acquisition, G.H. and P.R.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the United States Department of Agriculture (USDA-ARS) 1890 Research Sabbatical grant to G.H. (FAMU) in collaboration with P.A. (USDA).

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors thank the USDA National Plant Germplasm Center for providing.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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