**Recovery of Wheat Heritage for Traditional Food: Genetic Variation for High Molecular Weight Glutenin Subunits in Neglected**/**Underutilized Wheat**

#### **Juan B. Alvarez \* and Carlos Guzmán**

Departamento de Genética, Escuela Técnica Superior de Ingeniería Agronómica y de Montes, Edificio Gregor Mendel, Campus de Rabanales, Universidad de Córdoba, CeiA3, ES-14071 Córdoba, Spain; carlos.guzman@uco.es

**\*** Correspondence: jb.alvarez@uco.es

Received: 2 October 2019; Accepted: 13 November 2019; Published: 14 November 2019

**Abstract:** Club wheat (*Triticum aestivum* L. ssp. *compactum* (Host) Mackey), macha wheat (*T. aestivum* L. ssp. *macha* (Dekapr. & A.M. Menabde) Mackey) and Indian dwarf wheat (*T. aestivum* L. ssp. *sphaerococcum* (Percival) Mackey) are three neglected or underutilized subspecies of hexaploid wheat. These materials were and are used to elaborate modern and traditional products, and they could be useful in the revival of traditional foods. Gluten proteins are the main grain components defining end-use quality. The high molecular weight glutenin subunit compositions of 55 accessions of club wheat, 29 accessions of macha wheat, and 26 accessions of Indian dwarf wheat were analyzed using SDS-PAGE. Three alleles for the *Glu-A1* locus, 15 for *Glu-B1* (four not previously described), and four for *Glu-D1* were detected. Their polymorphisms could be a source of genes for quality improvement in common wheat, which would permit both their recovery as new crops and development of modern cultivars with similar quality characteristics but better agronomic traits.

**Keywords:** electrophoresis; genetic resources; neglected hexaploid wheat; seed-storage proteins

#### **1. Introduction**

Wheat is an important crop that has been associated with human food for many centuries [1]. It is the basis for a diverse range of products, mainly bread, noodles, pasta, and beer, which are present in most diets worldwide. In some cases, the same wheat type is used for all four different products depending on the geographical or cultural area [2]. Up until the Industrial Revolution, all baking processes were carried out by hand, which permitted the use of wheat varieties with rheological properties greatly different to current wheat varieties. Nevertheless, the use of machinery in baking processes forced people to look for varieties with very specific qualities [3], neglecting the traditional wheats mainly because of their lower yields and, in many cases, their unsuitability for mechanized production.

In recent times, in many places throughout the world, the search for more balanced and healthier diets has strengthened the return to traditional products [4]. However, paradoxically, one of the main problems is the need to use the flour of modern cultivars to develop these old products, and this is not successful in all cases because the modern cultivars have characteristics adapted to new uses. In this context, recovery of the materials that were traditionally used to develop these products has proven to be key in this revival.

In addition, some studies have suggested that the wheat breeding programs centered on high-yield cultivars could have eroded the genetic variability from the quality traits among and within cultivars [5]. This has given great importance to the search for species that could be useful in contributing genes for wheat quality improvement [6]. Wheat relatives are considered to be interesting sources of new

alleles for these traits that could increase the crop's genetic basis [7]. Among these relatives, the wild relatives as well as the old varieties and landraces of the current or ancient wheats of all ploidic levels are included. Utilization of these latter materials as gene sources is advantageous, compared to the wild relatives, because they are easy to cross with modern wheat and there is little linkage drag of unwanted traits, which results from their high degree of domestication [8].

Wheat quality is associated with three main grain components: endosperm storage proteins related to gluten visco-elastic properties [9], starch synthases related to starch [10], and puroindolines related to grain hardness [11]. The endosperm storage proteins are divided in two main groups, gliadins and glutenins, according to their molecular characteristics [12]. Glutenins are also divided into high molecular weight (HMWGs) and low molecular weight (LMWGs) subunits [13,14]. HMWGs are coded at the *Glu-1* loci located on the long arm of group-1 homologous chromosomes, whereas the *Glu-3* loci that code for the LMWGs and the *Gli-1* loci that controls synthesis of ω-, γ-, and some β-gliadins are located on the short arm. On the short arm of group-6 homologous chromosomes, the *Gli-2* loci that code mainly for components present in the α region and some β-gliadins are located [15]. Among the endosperm storage proteins, the best studied are the HMWG subunits coded at the *Glu-A1*, *Glu-B1*, and *Glu-D1* loci on the long arm of group-1 homologous chromosomes in common wheat [12]. Each locus consists of two linked genes that code for two types of HMWG subunits, with different mobilities in SDS-PAGE, named *x*- and *y*-types [16].

Within the hexaploid species, over the last decade, our research group has conducted several studies on the genetic diversity for endosperm storage proteins, waxy proteins, and puroindolines in Spanish and Mexican landraces of common wheat (*Triticum aestivum* L. ssp. *aestivum*) [17,18] and Spanish spelt wheat (*T. aestivum* L. ssp. *spelta* (L.) Thell.) [19–22]. Recently, other neglected or underutilized wheat subspecies have been screened for genes related to quality traits, including club wheat (*T. aestivum* L. ssp. *compactum* (Host) Mackey) (important in the Pacific Northwest region in the USA but not in the rest of the world) and Indian dwarf wheat (*T. aestivum* L. ssp. *sphaerococcum* (Percival) Mackey), both included within the naked wheat group as common wheat, and macha wheat (*T. aestivum* L. ssp. *macha* (Dekapr. & A.M. Menabde) Mackey) from the same hulled wheat group as spelt wheat. Our data obtained with these species showed notable variability for waxy proteins (granule-bound starch synthase I, E.C. 2.4.11.11), detecting new allelic variants for starch synthase not previously described in common wheat [23]. However, variability studies on the endosperm storage proteins in these species have been scarce, showing low variation [24,25].

The main goal of this survey was to evaluate the polymorphisms of the seed storage proteins present in a collection of hexaploid wheats, club wheat, macha wheat, and Indian dwarf wheat, collected in their natural distribution areas. The variation of these wheats for endosperm storage proteins could be a good source of quality genes for common wheat breeding, increasing the wheat genetic background together with the development of new cultivars.

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

#### *2.1. Plant Material*

Fifty-five accessions of club wheat, 29 accessions of macha wheat, and 26 accessions of Indian dwarf wheat obtained from the National Small Grain Collections (Aberdeen, ID, USA) were analyzed in this study (Tables S1, S2 and S3). At least five grains for each accession were analyzed to detect the possible intra-accession variability.

The HMWGs alleles were designated according to Payne and Lawrence [26]. Several cultivars of durum (cv. *Lobeiro*: 1, 14 + 15) and common wheat (cv. *Chinese Spring*: null, 7 + 8, 2 + 12, cv. *Cheyenne*: 2\*, 7 + 9, 5 + 10, and cv. *Frondoso*: 2\*, 13 + 19, 2 + 12) were used as standards to compare and classify the detected subunits in the analyzed species.

#### *2.2. Protein Extraction and SDS-PAGE Electrophoretic Analysis*

Proteins were extracted from crushed endosperm. Before glutenin solubilization, the gliadins were extracted with a 1.5 M dimethylformamide aqueous solution following a double-wash with 50% (v/v) propan-1-ol at 60 ◦C for 30 min with agitation every 10 min. Glutenin was solubilized with 250 μL of buffer containing 50% (v/v) propan-1-ol, 80 mM Tris-HCl (pH 8.5), and 2% (w/v) dithiothreitol at 60 ◦C for 30 min. After centrifugation, 200 μL of the supernatant was transferred to a new tube, mixed with 3 μL of 4-vinylpyridine, and incubated for 30 min at 60 ◦C. The samples were precipitated with 1 ml of cold-acetone. The dried pellet was solubilized in buffer containing 625 mM Tris-HCl (pH 6.8), 2% (w/v) SDS, 10% (v/v) glycerol, 0.02% (w/v) bromophenol blue, and 2% (w/v) dithiothreitol in a 1:5 ratio (mg/ μL) to wholemeal.

Reduced and alkylated glutenin subunits were fractionated by electrophoresis in vertical SDS-PAGE slabs in a discontinuous Tris-HCl–SDS buffer system (pH: 6.8/8.8) at a polyacrylamide concentration of 8% (w/v, C: 1.28%). The Tris-HCl/glycine buffer system of Laemmli [27] was used. Electrophoresis was performed at a constant current of 30 mA/gel at 18 ◦C for 45 min after the tracking dye migrated off the gel. Gels were stained overnight with 12% (w/v) trichloroacetic acid solution containing 5% (v/v) ethanol and 0.05% (w/v) Coomassie Brilliant Blue R-250. De-staining was carried out with tap water.

#### *2.3. Statistical Analysis*

Allelic frequencies for the *Glu-A1*, *Glu-B1,* and *Glu-D1* loci were calculated for each subspecies. The classification of Marshall and Brown [28] was used for evaluating the distribution of alleles by their presence as frequent (≥5%), rare (≤5%), and very rare (≤1%). To assess potential genetic erosion, the number of alleles per locus (*A*), the effective number of alleles per locus (*Ne*), and Nei's diversity index (*He*) were measured [29,30].

#### **3. Results**

#### *3.1. Variation for HMWGs*

The HMWG compositions of all accessions of each subspecies (club, macha, and Indian dwarf wheat) were analyzed. A representative sample of the variability detected for the HMWGs in each subspecies is shown in Figure 1.

**Figure 1.** SDS-PAGE of representative variation for high molecular weight glutenin subunits (HMWGs) found in the collection of club (lanes 1, 3, 5–8, and 10–13), macha (lanes 2, 4, and 9), and Indian dwarf wheat (lanes 14 and 15).

Twenty-one allelic variants (3 at the *Glu-A1* locus, 15 at *Glu-B1,* and 3 at *Glu-D1*) were detected in the evaluated accessions (Table 1). Four out of the 15 for *Glu-B1* locus were novel. The distribution of these alleles in each subspecies was unequal.

In club wheat, three alleles were found for the *Glu-A1* locus, with the *Glu-A1a* allele being the least frequent (Table 1). The *Glu-B1* locus was more variable with 11 alleles, although only 3 of them showed frequencies above the average value that should have occurred if the distribution was random (mean value = 9.1%). The rest of the alleles were classified as rare according to the Marshall and Brown classification [28]. One of these rare alleles (null+15, Figure 1 lane 15) was also novel, detected only in accession PI 157920, and we propose to tentatively name this *Glu-B1ck* following the order of the Wheat Gene Catalogue [31]. For the *Glu-D1* locus, one allele (*Glu-D1a*, subunits 2 + 12) was clearly hegemonic, being present in 78.2% of the accessions evaluated.


**Table 1.** Allelic frequency for *Glu-A1*, *Glu-B1,* and *Glu-D1* loci of the evaluated accessions.

Similar to club wheat, macha wheat presented three alleles for the *Glu-A1* locus, although in this case, one (*Glu-A1c*) was three times more frequent that the other two (Table 1). For *Glu-B1*, more than half of the materials presented the *Glu-B1b* allele, whereas two were rare and found in only one accession. Therefore, two novel alleles were detected in this subspecies (subunits 14 + 8 and 6 + 8\*, Figure 1 lanes 9 and 2, respectively). The first allele (subunits 14 + 8) was present in three accessions (PI 272554, PI 278660, and PI 290507), whereas the second (6 + 8\*) was only found in accession PI 428177. We propose to tentatively name these alleles *Glu-B1cl* and *Glu-B1cm*, respectively, following the current order in the Wheat Gene Catalogue [31]. The variation for the *Glu-D1* locus was largest for the three subspecies, showing three alleles, one hegemonic (subunits 2 + 12) and the other two with similar frequencies.

The variation for the *Glu-A1* locus was low in Indian dwarf wheat, with only two alleles found and one representing more of 80% of the material (Table 1). The *Glu-B1* locus showed some variation (six alleles); one of them (subunit 17+null, Fig.1 lane 14) was novel, detected in three accessions (CItr 4531, PI 272581, and PI 282452) and we propose to name it *Glu-B1cn*. In contrast, the materials were homogenous at the *Glu-D1* locus. In this subspecies, only the *Glu-B1b* allele (subunits 7+8) can be considered rare.

When the three loci were evaluated together, the number of combinations was highly variable among the three subspecies analyzed (Table 2), with eight in Indian dwarf wheat and 26 in club wheat.


**Table 2.** Frequencies of the HMW glutenin subunit compositions found among accessions analyzed.

In each subspecies, the most frequent combination also differed, and in some cases the most frequent one in one subspecies was the least in another. In club wheat, although there was a great number of combinations, any of them can be considered hegemonic, and the most frequent combination was 2\*, 7 + 8, and 2 + 12, which appeared in 9 of the 55 accessions. This combination was only found in three accessions of macha wheat, but it was absent in Indian dwarf wheat. A similar situation occurred with the most frequent combination in Indian dwarf wheat (null, 17 + 18, and 2 + 12), which was only detected in two accessions of club wheat and one of macha wheat.

The *Glu-1* quality score [32] for this last combination was associated with low gluten quality (score = 6), while the first combination (2\*, 7 + 8, and 2 + 12) had a higher value and was associated with medium gluten quality (score = 8). Only one club wheat accession showed the highest score (10 for 1, 7 + 8, and 5 + 10) according to the scale of Payne et al. [32] developed for use in modern breeding programs targeting industrial bread-making processes.

#### *3.2. Genetic Diversity*

Some genetic parameters measured in each subspecies are shown in Table 3. These parameters detected important genetic erosion both in club wheat and macha wheat. The *Ne* values were especially significant for the *Glu-B1* locus in both subspecies, with values lower than 43% of the allelic variation detected (*A*). However, the genetic diversity (*He*) of this locus was high, possibly related to the fact that no hegemonic allele was detected in these subspecies. Nevertheless, the low frequency of most of the alleles suggested that these could easily be missed due to genetic drift effects.


**Table 3.** Genetic parameters for the *Glu-1* loci in the evaluated subspecies.

*A*: number of alleles; *Ne*: effective number of alleles; and *He*: genetic diversity.

These differences were slightly lower in Indian dwarf wheat, for which only the *Glu-B1* locus showed certain variability. In contrast, the *Glu-D1* locus was similar for all accessions evaluated (*Glu-D1a* allele = 2 + 12).

#### **4. Discussion**

Since the mid-twentieth century, the development of high-yield wheat cultivars has led to the replacement of local varieties, old varieties, or ancient wheat by modern cultivars [5]. Many of these materials remain stored in gene banks, being used in many cases as resources to generate new cultivars. However, new food movements have led to some of these neglected or underutilized crops beginning to be used as sources in traditional products. Unfortunately, for years, because of the lack of appropriate flours, these 'traditional' products have been made with modern flours that must be conditioned for these uses; consequently, whether these old materials would be better for making these traditional products remains unknown. For this reason, their analysis is necessary to determine the quality traits associated with optimal adaptation for this traditional food. This would permit both their recovery as new crops and the development of modern cultivars with similar quality characteristics but better agronomic traits. In any case, it is important to indicate that these ancient or neglected wheats cannot substitute for modern wheat; both types are clearly complementary within a more varied diet. Among these neglected wheats, the three subspecies evaluated in this study are options to independently explore, and they can be used as sources of agronomic traits. In this respect, Indian dwarf wheat has been evaluated as a potential source of stripe rust resistance [33] and salt tolerance [34]. However, the aspects related to flour quality have been seldom studied, mainly because most studies aimed to obtain new wheat cultivars with strong gluten that could be used for flour enrichment. In this context, these old wheats are not the best candidates. The interest in these wheat types has its origin in the recovery of traditional products performed with old techniques [35]. For this reason, comparisons with modern standards should be made cautiously.

Numerous studies carried out with wide collections of wheat have shown the high variation for HMWGs in this crop [9]. The high level of polymorphisms in these genes is a consequence of their physiological role. During the germination process, the seed storage proteins are a source of amino acid residues in the synthesis of new proteins needed for plant development [36]. Apparently, these proteins have no catalytic function, which has meant that changes in their amino acid composition or size has had no effect on plant viability and permits that the mutations can be easily fixed, generating wide polymorphisms. Numerous variants of these proteins have been detected in wheat, although many appear at very low frequencies, which implies a high risk of loss due to genetic drift processes.

However, the relationship of these proteins with food products has resulted in some alleles being fixed and some discarded, as the flour of these genotypes has shown better adaptation to a specific product or use. For this reason, variations are lower in modern than in ancient wheat. Although our knowledge of these proteins and their roles in flour quality is relatively recent [9], it is obvious that some of these alleles were empirically fixed by the farmers and bakers over time. They selected flours adapted to traditional uses, and, consequently, the HMWG combinations that better suited these quality properties were fixed, while the rest were gradually discarded. In this respect, the Indian dwarf wheat evaluated here is a wheat type endemic to the north of India and Pakistan associated with the elaboration of flat breads such as chapati [2], which requires flour with a medium gluten strength (score = 4–6) and high extensibility. Club wheat was also traditionally used to make cookies, for which weak flour is required; now, this wheat has some commercial importance in the Pacific Northwest of the USA because of this use [37].

Many other examples about the preferred use of ancient wheat and old wheat landraces for the elaboration of traditional wheat products are found in the literature. In a recent study in Turkey, Morgounov et al. [38] showed that, in different regions across the country, farmers have access to modern cultivars but still kept growing their landraces. Their main reason to do this is because they were happy with the grain quality and its suitability for homemade products (mainly typical loaves and thin types with bread wheat, and bulgur with durum wheat). In the same study, only 30% of the farmers rated the yield of the landraces as good, which clearly indicates that, despite their higher yields, modern wheat varieties do not satisfy them because their end-use quality is inadequate. This is in agreement with Bardsley et al. [39], who explained that the landrace Kirik is retained in Northeast Anatolia villages primarily because the baking qualities in the flour are appropriate for the local bread, lavash. Further studies are necessary to determine the grain quality components and properties that led to this preference, something that has already started in that country [40]. In other cases, such as the bread named "Pane Nero di Castelvetrano" from Sicily (Italy), the association between its end-use quality and the use of a specific landrace to elaborate it has been established [41]. Castelvetrano black bread is characterized by the intense brownness of its crumb. This bread is elaborated with durum wheat, and at least 20% of the flour blend should be from the autochthonous durum cultivar Timilia [42]. In fact, this landrace has high concentration of phenolic compounds that, when coupled with its extremely high polyphenol oxidase (PPO) activity, leads to the intense brownness of crumb [43]. On the other hand, modern durum cultivars are characterized for their low PPO activity, which makes them unsuitable for the preparation of this bread type. Other Italian traditional breads such as "Pane di Altamura" or "Pagnotta del Dittaino" (which have the European mark of Protected Designation of Origin, PDO) need to also be manufactured, by law, with durum semolina or re-milled semolina of specific cultivars (Appulo, Arcangelo, Duilio, and Simeto) to confer these breads their distinctive sensory properties and longer shelf-life [42,44]. These cultivars were developed in different times of the twentieth century, but they all have in their pedigree Cappelli, an old Italian cultivar derived from the Tunisian landrace Jennah Khetifa [45].

Although the variation for seed storage proteins of these subspecies was previously evaluated by Rayfuse and Jones [24] and Xu et al. [25], notable differences between our data and these previous findings were found. The detection of alleles that they did not find, together with changes in the frequencies of numerous common alleles, were especially significant. One possible cause of these differences is the use of different polyacrylamide concentrations in separation gels. Some of these subunits (e.g., subunit 2\* vs. subunit 2) are difficult to detect in SDS-PAGE gel with 10% polyacrylamide concentration (C: 2.67%). For this reason, in the current study, the subunits were separated in SDS-PAGE gels using different concentrations (T: 8%, C: 1.28%) that our previous studies had confirmed as more

adequate for separation of these proteins [17–19]. In total, 3 alleles were found for *Glu-A1*, 15 for *Glu-B1* (4 of them novel), and 3 for *Glu-D1.*

Probably because of their endemic condition and local use, the observed variation was especially low in macha and Indian dwarf wheat. This high homogeneity is very notable in all materials that have been cyclically used and neglected, as the narrowing of the genetic base, and subsequent reduced selection pressure, results in the loss of spontaneous variants and fixing of the most common ones by genetic drift. Compared with other hexaploid wheat subspecies, such as spelt, the variation was similar for the *Glu-A1* and *Glu-B1* loci, whereas variation for the *Glu-D1* locus detected here was slightly lower than that found in a wide collection of this hulled wheat of Spanish origin, where up to nine alleles were detected for this locus [19]. This could be a consequence of the wider geographical area where it is grown and its more diverse uses [46].

#### **5. Conclusions**

The neglected/underutilized wheat subspecies evaluated here showed wide polymorphisms for HMWGs, including novel alleles not previously described. Although the effects of these new allelic variants on technological properties should be further evaluated, this information may be of interest to wheat breeders for choosing parents to obtain recombinant lines with different gluten properties. Nevertheless, in the context of healthier and sustainable food, and as sources of genes for quality improvement in common wheat, these subspecies could be used to develop new/old crops with good agronomic traits and optimal flour characteristics for new and traditional products.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2073-4395/9/11/755/s1, Table S1: Allelic composition for the *Glu-1* loci in club wheat, Table S2: Allelic composition for the *Glu-1* loci in macha wheat, Table S3: Allelic composition for the *Glu-1* loci in Indian dwarf wheat.

**Author Contributions:** J.B.A. conceived and designed the study. J.B.A. and C.G. performed the experiments, analyzed the data, and wrote the paper. Both authors read and approved the final manuscript.

**Funding:** This research was supported by grant RTI2018-093367-B-I00 from the Spanish State Research Agency (Ministry of Science, Innovation and Universities), co-financed by the European Regional Development Fund (FEDER) from the European Union. Carlos Guzman gratefully acknowledges the European Social Fund and the Spanish State Research Agency (Ministry of Science, Innovation and Universities) for financial funding through the Ramon y Cajal Program (RYC-2017-21891).

**Acknowledgments:**We thank theNational Small Grain Collection (Aberdeen, USA) for supplying the analyzedmaterial.

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

#### **References**


© 2019 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 (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Long-Term E**ff**ects of Biochar-Based Organic Amendments on Soil Microbial Parameters**

#### **Martin Brtnicky 1,2, Tereza Dokulilova 1, Jiri Holatko 1, Vaclav Pecina 1, Antonin Kintl 3, Oldrich Latal 4, Tomas Vyhnanek 5, Jitka Prichystalova <sup>2</sup> and Rahul Datta 1,\***


Received: 8 October 2019; Accepted: 9 November 2019; Published: 12 November 2019

**Abstract:** Biochar application to the soil has been recommended as a carbon (C) management approach to sequester C and improve soil quality. Three-year experiments were conducted to investigate the interactive effects of three types of amendments on microbial biomass carbon, soil dehydrogenase activity and soil microbial community abundance in luvisols of arable land in the Czech Republic. Four different treatments were studied, which were, only NPK as a control, NPK + cattle manure, NPK + biochar and NPK + combination of manure with biochar. The results demonstrate that all amendments were effective in increasing the fungal and bacterial biomass, as is evident from the increased values of bacterial and fungal phospholipid fatty acid analysis. The ammonia-oxidizing bacteria population increases with the application of biochar, and it reaches its maximum value when biochar is applied in combination with manure. The overall results suggest that co-application of biochar with manure changes soil properties in favor of increased microbial biomass. It was confirmed that the application of biochar might increase or decrease soil activity, but its addition, along with manure, always promotes microbial abundance and their activity. The obtained results can be used in the planning and execution of the biochar-based soil amendments.

**Keywords:** biomass; biochar; soil; BPLFA; FPLFA; DHA; ammonia-oxidizing bacteria

#### **1. Introduction**

Fertilizers used in agricultural management influence soil quality and health [1,2]. The recent rise in concerns about environmental problems caused by the excessive use of chemical fertilizers necessitates detailed studies on alternate strategies to address such hazards. Long-term use of organic amendments to the soil helps in improving several soil parameters like organic carbon, aggregate stability and crop yield, in contrast to the application of chemical fertilizers [3–5]. Organic amendments also increase soil carbon sequestration and play a decisive role in mitigating the adverse effect of climate change [3,6,7]. Independent of the type and nature of the applied organic amendment, different changes in soil properties and fertility have been observed in a broad time horizon under different pedoclimatic conditions [8]. The positive effect on total soil carbon, soil nitrogen, soil microbial biomass carbon (MBC) and dehydrogenase activity (DHA) has been observed in soils treated with manure [9,10].

Organic amendment to the soil a has long term impact on soil restoration. Miller and Miller (2000) showed that long term application of manure has greater impact on soil properties as compared to short term application [11]. Long-term application of manure causes enhanced soil physical, chemical and biological properties [12]. In another study Shindo et al. (2006) [13] reported that continuous long term application of manure to a field drastically increases fulvic, humic acids and total humus content in the soil. On the contrary, the absence of organic fertilizer input to soil results in non-complex light weight humus [14]. A literature survey on the long-term application of manure pointed out that the use of manure with a mineral fertilizer (NPK) enhances soil properties and crop yield as compared to organic amendment alone [15,16].

Biochar is a carbon-rich material produced by pyrolysis reaction under limited or no oxygen environments, often used for soil amendment and carbon sequestration [17]. Biochar amendment improves soil physicochemical and biochemical properties. It increases the soil pH and cation exchange capacity (CEC) [18], improves soil structure [19], alters soil microbial populations [20] and enhances nutrient retention [21–24]. Although biochar is recalcitrant in nature, its ability to interact with soil properties makes it a good investment in soil [25]. Long term application of biochar brings change in the physio chemical properties of the soil. That leads to an alternation in the soil bacterial community.

Many studies have reported a synergistic effect of biochar and organic fertilizer in (a) improving plant growth by nitrate-capture in co-composted biochar [23,26], (b) promoting carbon stabilization through the formation of organo-mineral complexes [27] and (c) affecting soil nutrient cycles [28]. Organic fertilizer may form a coating inside and outside the biochar particle and increase hydrophilicity, thus raising the nutrient retention [29]. However, the antagonistic or neutral effect of manure and biochar interaction has been reported in studies [30–32].

The positive effect of soil treatment with manure on total soil carbon, nitrogen, microbial biomass carbon and dehydrogenase activity in the surface soil (0–5 cm) has generally been observed [10]. However, the combined effect of manure and the other soil amendment (e.g., biochar) has not been widely studied. In a few studies in which manure-derived biochar was applied to soils, a mostly positive effect was seen [33,34], but also a converse [34–36] effect on plant growth and soil microbial diversity was observed.

This study aimed to determine and compare the long-term effect of the biochar-based organic amendments on selected soil properties (MBC, DHA and soil microbial community abundance) on the agricultural land of the temperate climate zone of Central Europe. Only a few studies have been done so far on the agricultural area of temperate soils [37–39]. Prior studies suggested that manure addition to biochar as a soil amendment might have a synergistic or antagonistic effect. We hypothesized that the supplement of manure with biochar will represent a valid strategy to further enhance the soil microbial biomass and related soil quality indicators.

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

#### *2.1. Study Site and Rationale behind the Field Scale Experiment*

Field-scale experiments were designed to evaluate the potential of biochar and its combination with manure in improving the studied soil properties in on-site conditions. Experiments were carried out at arable land (luvisols) during the cropping season from the year 2014 to 2017. Experimental plots were located in Rapotín locality, the Czech Republic, at an altitude of about 345 m a.s.l., and it comes under the temperate continental climate zone, with a mean annual temperature of 7 ◦C. The mean annual precipitation is about 705 mm in the area. The rainfall pattern is 400–450 mm in the vegetation season and 250–300 mm during the winter period. The experiment consisted of the application of four soil treatments that were only NPK (mineral fertilizer) as a control, NPK + cattle manure (50 t/ha), NPK + biochar (15 t/ha) and NPK + combination of manure (50 t/ha) with biochar (15 t/ha) (MB). Biochar was added at the start of the experiment, while manure was added every year. Dosage of cattle manure 50 t/ha is added as recommended by (Singh et al., 2011) [40].

Dosage of biochar 15 t/ha was chosen close to the maximum amount of biochar allowed to be amended to the arable soil on the field (Pereira et al., 2011) [41]. Variant 4 is a combination with the same dosage of both amendments i.e., cattle manure 50 t/ha and biochar 15 t/ha. The experimental area was divided as follows: three small-scale-plots (10 × 10 m) per each of four variants of soil amendment (12 small-scale-plots overall).

#### *2.2. Soil Sampling and Preparation*

At the end of third crop season, the samples for the final analyses of the application of amendments were collected in October 2017. Three spatially-independent mixed soil subsamples from each experimental variant were collected in the following way: A portion of topsoil from a depth 0–15 cm was taken by a soil drill at five spots of an experimental field and mixed in a plastic sampling bag. The samples (app. 500 g) were immediately cooled down and transported to the laboratory at 0–4 ◦C and homogenized by sieving the soil through a 2 mm mesh under sterile conditions [42]. Samples for the enzyme activity assays were stored at 4 ◦C until analyzed (within one week). Samples for qPCR and phospholipid fatty acids (PLFA) analysis were freeze-dried.

#### *2.3. Dehydrogenase Activities*

Triphenyl tetrazolium chloride-dehydrogenase activity (TTC-DHA) was used to determine microbial activity in the soil. The methodology was modified according to Tabatabi (1994) [43], based on (Casida et al., 1964) [44]: 3-gram soil sample was mixed with MgO and sealed with the standard solution (triphenyl tetrazolium chloride + distilled water). The samples were incubated in the thermostat at 37 ◦C for 24 h. Afterwards, triphenylformazan (TPF) was extracted from the samples using methyl alcohol, resulting in the color change of the solution. The spectrophotometer (DR 3900, Hach Lang, Duesseldorf Germany) was used to measure the color intensity at a wavelength of 485 nm. DHA was calculated according to the calibration curve and expressed in <sup>μ</sup>g TPF·g−1·h<sup>−</sup>1.

#### *2.4. Quantification of Microbial Biomass*

The samples for PLFA analysis were extracted from the mixture of chloroform-methanol-phosphate buffer (1:2:0.8) [45]. Phospholipids were separated using solid-phase extraction cartridges (LiChrolut Si 40, Merck, Bellefonte, PA, USA). The samples were then subjected to mild alkaline methanolysis and extracted to hexane as a final solvent [46]. The free methyl esters of phospholipid fatty acids were analyzed using gas chromatography-mass spectrometry (Agilent 7890A with FID detector, Agilent Technologies, USA). The gas chromatography instrument was equipped with a split/splitless injector, and a CP Sil 88 column was used for separation (100 m, 0.25 mm i.d., 0.2 μm film thickness). The temperature program started at 80 ◦C and was held for 1 min in splitless mode. Then the splitter was opened, and the oven was heated to 160 ◦C at a rate of 20 ◦C·min<sup>−</sup>1. The second temperature ramp was up to 225 ◦C at a rate of 5 ◦C·min<sup>−</sup>1; this temperature was maintained for 12 min.

Methylated fatty acids were identified according to their mass spectra and using a mixture of chemical standards obtained from Sigma Aldrich (Merck, USA)/Matreya LLC (USA). Fungal biomass was quantified based on the 18:2ω6,9 content (FPLFA), and bacterial biomass was quantified as a sum of i14:0, i15:0, a15:0, 16:1ω7t, 16:1ω9, 16:1ω7, 10Me-16:0, i17:0, a17:0, cy17:0, 17:0, 10Me-17:0, 10Me-18:0 and cy19:0 (BPLFA). The fatty acids found in both bacteria and fungi, 15:0, 16:0 and 18:1ω7, were excluded from the analysis. The relative content of individual PLFA molecules was also calculated. The total content of all PLFA molecules (PLFAT) was used as an indicator of total microbial biomass.

#### *2.5. Microbial Biomass Carbon*

Soil Microbial Biomass Carbon (MBC) was characterized and determined by the fumigation extraction method [47], based on the lysis of microbial cells upon contact with chloroform (24 h). Sample sets were duplicated, and only one set was subjected to fumigation, followed by the extraction of K2SO4 and comparison of fumigated and non-fumigated samples.

#### *2.6. DNA Extraction and Real-Time qPCR*

DNA was extracted from 0.5 g of lyophilized soil with the help of a DNeasy PowerSoil Kit (Qiagen, Valencia, CA, United States). Real-time PCR was performed to quantify partial bacterial (16S rDNA) and fungal (18S rDNA) rDNA gene in soil DNA extracts. Each sample was spiked with the DNA of plasmid vector derived from pUC18 serving as an internal standard for valuation of yield efficiency and contamination with PCR inhibitors. Isolated DNA was quantified using Picodrop. SYBR-green assays were performed in a CFX96 Real-Time PCR Detection System (Bio-Rad Laboratories). The primers used were 1108F (5 ATGGYTGTCGTCAGCTCGTG 3 ) and 1132R (5 GGGTTGCGCTCGTTGC 3 ) for bacteria and FF390 (5 AICCATTCAATCGGTAIT 3 ) and FR1 (5 AICCATTCAATCGGTAIT 3 ) for fungi [48]. Combination of primer was used for the quantification of ammonia-oxidizing bacteria (AOB) 16S rDNA, CTO189FA/B (5 GGAGRAAAGCAGGGGATCG 3 ), CTO189FC (5 GGAGGAAAGTAGGGGATCG 3 ), and RT1R (5 CGTCCTCTCAGACCARCTACTG 3 ) primers at a 2:1:2 ratio [49]. DNA of pUC18-derivate (internal standard) was quantified by qPCR using SQP (5 GTTTTCCCAGTCACGAC 3 ) and SQPR2 (5 CTCGTATGTTGTGTGGAA 3 ) primers.

#### *2.7. Statistics Analysis*

Comparison of individual data sets was made by one-way analysis of variance (ANOVA) and comparison methods. Duncan's multiple range test was used to compare treatments means, and a (*p* < 0.05) was considered statistically significant. Two way (ANOVA) was used to find the interaction between manure and biochar on measured soil properties (MBC, DHA, microbial community abundance). All the data were analyzed by Statistica ver. 13.4.0.14 software package.

#### **3. Results and Discussion**

#### *3.1. Dehydrogenase*

Dehydrogenase enzyme catalyzes organic matter decomposition in soil by transferring H<sup>+</sup> from the organic substrate with the help of coenzyme such as NAD+/NADP<sup>+</sup> [50]. It is located inside living soil bacteria (e.g., genus *Pseudomonas*) and acts as an extracellular enzyme. It also implies that the enzyme cannot be deposited extra-cellularly in the soil in its active form [51]. Therefore, a high DHA activity suggests a higher number of the bacterial community present in the soil [52].

There was a significant decrease in DHA activity for the soil samples treated with biochar as compared to the control (Figure 1A). Data concerning the effect of different types (sources and pyrolytic temperature) of biochar on the dehydrogenase activity are still limited and contradictory. Different research groups reported different effects of biochar amendments on BPLFA values i.e., positive [53,54], neutral [55], and negative [56] effects. At least one study reported that DHA activity (and C mineralization) was lower in the biochar amended soil, however-glucosidase activity and the extracted PLFA concentration was not affected by biochar treatment [57]. Mechanisms for these different responses remain unclear [58]. Biochar effect on dehydrogenase activity in soil depends on the extent of the interaction between the substrate, enzyme and biochar (e.g., sorption and desorption of substrates on the biochar surface) [59]. The substrate and enzyme are attracted toward functional groups present on the biochar surface. Sorption of the enzyme to biochar blocks the active site present on the enzyme; resulting in the reduction in dehydrogenase activity [60]. The application of biochar produced at high temperature (approx. 400 ◦C or more) decreases soil enzyme activity and affects the soil nutrition dynamics, as it nonselectively sorbs the enzyme as well as a substrate due to its high absorptivity [61]. This high absorptivity can be attributed to high surface area and porosity of biochar created due to high temperature.

**Figure 1.** Geographical picture of study area.

MB amendment to biochar shows a significant increase in DHA activity as compared to biochar treatment (Figure 2A). The coapplication of manure caused a decrease in absorptive surface characteristics of biochar (sorptive sites occupancy) and hence neutralizing the negative effect of biochar. This agreed with the concept that net negative surface charge on biochar sorbs positive charge nutrients (NH4 <sup>+</sup>, K+, Ca+, Fe++, Cu++) from manure [62]. Additionally, The sustainable and slow release of immobilized nutrient from biochar for an extended period is another reason for the increase in DHA activity [63–67]. The sorption of nutrient onto the biochar is reported to be a reversible phenomenon which favors the retention, and they are delay released into the soil [68–70].

**Figure 2.** (**A**) DHA activity in soil amended with manure, biochar and MB. (**B**) Interaction graph of biochar and manure for DHA activity.

Two-factor ANOVA (with replication) was conducted to test the interaction effect of manure and biochar on DHA activity. The result shows that manure and biochar have a significant main effect on DHA activity (Figure 2B), whereas MB treatment shows a significant ordinal interaction on DHA activity (Figure 2B).

#### *3.2. Soil Phospholipid Fatty Acid Analysis*

PLFA, a rapid and sensitive method, was used to detect changes in the microbial community in soil. PLFAT, FPLFA and BPLFA can be viewed as indicators of total microbial biomass, fungal biomass, and bacterial biomass, respectively. Soil PLFAs analysis is a widely accepted method based on the rapid degradation of PLFAs after cell death [71].

Soil microbial community biomass represented by FPLFA was significantly higher in the sample treated with biochar, as compared to the control (Figure 3A). The highest value of FPLFA was recorded for soil treated with MB (Figure 3A). Generally, biochar is considered recalcitrant in nature [72], and microorganisms use it rarely, while mediating it in the rhizosphere as a source of nutrient for plants [73]. However, some studies suggest the existence of a labile-carbon fraction in it [31,41,74]. This labile fraction is available for the microorganism as a carbon source and supports microbial growth. This labile fraction may contain lipids, (up to 4.5%) with strong domination of glycolipids and phospholipids [75]. Additionally, biochar has been suggested to stimulate the dormant soil microorganism growth, thereby increasing the microbial biomass [76–79]. Saprotrophic fungi were shown to efficiently colonize biochar in association with decomposing fibrous organic matter [80], and the higher nutrient (namely P) content of the biochar increases its fungal colonization [81]. The unexpectedly higher FPLFA value in the sample treated with biochar (compared to manure treatment) can be explained due to the combined effect of initial biochar-derived phospholipids and the P-enhanced fungal colonization of biochar particles. This is also documented in agreement with high 18S rDNA content in the biochar-treated soil sample (Figure 5A). The synergy in co-application of manure and biochar on PLFA can be explained by their direct interaction and stimulation of microbial growth [82,83].

**Figure 3.** (**A**) Amount of FPLFA (nmol·g<sup>−</sup>1) in soil amended with manure, biochar and MB. (**B**) Interaction graph of biochar and manure for FPLFA.

Two-factor ANOVA (with replication) was done to test the interaction effect of manure and biochar on the FPLFA value. The result shows that manure and biochar have a significant main effect on this FPLFA value (Figure 3B), whereas biochar and manure show significant ordinal interaction (Figure 3B).

BPLFA shows a similar response to biochar amendment (Figure 4) as FPLFA. There was a significant increase in BPLFA value following the addition of biochar. The noticeable increase in BPLFA concentration was observed when the mixture of manure with biochar is applied to the soil. BPLFA value for MB treatment was even higher than manure itself (Figure 4A).

Two-factor ANOVA (with replication) was conducted to test the interaction effect of manure and biochar on BPLFA values. The result shows that manure and biochar have a significant main effect on BPLFA values (Figure 4B), and a significant ordinal interaction was found between biochar and manure (Figure 4B).

**Figure 4.** (**A**) Amount of BPLFA (nmol·g<sup>−</sup>1) in soil amended with manure biochar and MB. (**B**) Interaction graph of biochar and manure for BPLFA.

#### *3.3. Microbial Biomass Carbon*

MBC is the main characteristic of soil organic carbon activity, so it is primarily used for the evaluation of soil quality [84]. It plays an essential role in biogeochemical cycles and is a major driver of ecosystem functioning [85,86]. Our experiment result shows a decline in the MBC value for the soil sample treated with biochar, and was lowest among all treatment (Figure 5A). Decrease in MBC upon biochar treatment has also been reported in the past [87,88]. We speculate that decline in the MBC value was either due to immobilization of carbon [89–91]. Or it could be due to the fact that biochar reduced the concentrations of nutrients through sorption and sequestration. Nutrients are protected from the microbes by their adsorption on the biochar surface. A decrease in the nutrient arability as a result reduces the microbial abundance. According to Dempster et al. (2012) [87] decrease in decomposition of SOM could be the main reason for the reduction in soil microbial biomass, upon biochar treatment. Our results appear to support this view. Several other studies show a positive effect of biochar on soil microbes [92,93]. Highest values for MBC were recorded when manure was applied in combination with biochar (Figure 5A). MBC values for biochar-only treatments are contradictory by PFLA values.

**Figure 5.** (**A**) MBC in soil amended with manure biochar and MB. (**B**) Interaction graph of biochar and manure for MBC.

These results indicate that chloroform fumigation used to determine microbial biomass and the decline in active cell numbers, does not always accompany with any decrease in microbial biomass. Hence, PLFAs, as a constant composition of living cell membranes, may also be unchanged when fumigation-sensitive microbial biomass decreases.

Two-factor ANOVA (with replication) was conducted to test the interaction effect of manure and biochar on MBC value. The result shows that biochar does not have any significant main effect on MBC (Figure 5B), whereas significant ordinal interaction was found between biochar and manure (Figure 5B).

#### *3.4. DNA Extraction and Real-time qPCR*

The effect of biochar, manure, and MB on the microbial community of treated soils is represented by the quantities of bacterial and fungal biomass, estimated as total 18S rDNA (fungal DNA) and 16S rDNA (bacterial DNA) content in the soil.

#### 3.4.1. 18S rDNA

One-way analysis of variance shows a significant increase in log 18S rDNA copies relative to control on an application of biochar. The possible reason for an increase in 18S rDNA is that biochar can act as a habitat for many fungi [94–97]. Even though the extent of the hyphal colonization of biochar in soil is reported to be weak, extensive hyphal colonization of the surface of the biochar occurs, however it contrasts with a limited hyphal colonization of pores within the biochar [98]. Available nutrients like P, K and Ca on the biochar surface were possible reasons for fungal colonization [80,81], while the role of the labile-carbon fraction in the biochar was considered, similarly to the explanation of the observed FPLFA values (Topic 3.2). Small condo in biochar protects the microorganism from a natural soil predator such as mites, *Collembola* and larger (>16 μm in diameter) protozoans and nematodes [95,97,99–101]. The soil samples treated with MB showed a significant increase in log 18S rDNA copies, as compared to all treatments, including control (Figure 6). The increases in log 18S rDNA value followed the order: control < manure < biochar < MB (Figure 6A).

**Figure 6.** (**A**) 18S rDNA log (copies·g<sup>−</sup>1) in soil amended with Biochar and MB. (**B**) Interaction graph of biochar and manure for 18S rDNA log (copies·g<sup>−</sup>1).

The increase in log 18S rDNA copies upon MB treatment was putatively associated with a favorable environment for microbial proliferation [17]. Moreover, the functional group present on the biochar surface helps the sorption of dissolved organic carbon, decomposable organic compounds, and the chemisorption of the ammonium ion (NH4+), and makes it a perfect microbial habitat [64]. Previous research showed that manure supplies macro- and micronutrients to be sorbed on the biochar surface and provide a suitable environment for fungal and other microbial growth and proliferation [27,63,102].

Two-factor ANOVA (two-way ANOVA with replication) is conducted to test the interaction effect of manure and biochar on log 18S rDNA copies. The result shows that manure and biochar have a significant main effect on log 18S rDNA copies (Figure 6B). Also, manure and biochar show significant ordinal interaction (Figure 6B).

#### 3.4.2. 16S rDNA

Similarly, observations of an increase in log 16S rDNA copies relative to control was observed for biochar treatment and the soil treated with biochar in combination with manure, which shows the highest value of log 16S rDNA copies, even higher than the only manure treatment (Figure 7). Some authors also reported a significant increase in bacterial 16S rDNA genes abundance in samples coupled with biochar poultry-manure [94,103].

**Figure 7.** (**A**) 16S rDNA in soil amended with manure biochar and MB. (**B**) Interaction graph of biochar and manure for 16S, log (copies·g<sup>−</sup>1).

Two-factor ANOVA (with replication) was conducted to test the interaction effect of manure with biochar on 16S rDNA gene copy. The result shows that manure and biochar have a significant main effect on the log-transformed 16S rDNA value (Figure 7B), whereas there was also a significant ordinal interaction found between biochar and manure (Figure 7B) with an increase in the 16S rDNA gene abundance, which was also observed in earlier studies [21].

The results obtained by estimation of 16S and 18S rDNA were supported by quantification of microbial biomass via phospholipidic fatty acids (PLFA). Two-factor ANOVA results of both bacterial BPLFA and fungal FPLFA show the statistical significance interactions between the amendments of biochar and manure.

#### 3.4.3. 16S rDNA (AOB)

The effects of biochar and their combination with manure on 16S rDNA AOB copies are presented in Figure 8A. There was a significant increase in log 16S rDNA AOB copies for all the treatments compared to the control (Figure 8A). The 16S rDNA AOB value is an indicator of microbial activity in the process of nitrogen mineralization. It has been observed that the addition of organic fertilizers (e.g., compost) positively affects the microbial activity and utilization of nitrogen in contrast to the addition of the NPK fertilizer [104,105]. Among all the treatments, manure amendment with biochar shows the highest log 16S rDNA AOB copies.

**Figure 8.** (**A**) 16S rDNA AOB rRNA in soil amended with biochar and combination with manure. (**B**) Interaction graph of biochar and manure for log 16S rDNA AOB copies.

Adding biochar into soils changes the soil structure [99,100] and alters soil microbial populations [79]. It is well known from the previous studies that adding biochar to soil, especially in combination with manure, can potentially alter the nitrification process in soil by affecting ammonia- and nitrite-oxidizing bacteria [106], decreasing N2O emission [107,108] and increasing NH4 <sup>+</sup> storage [107].

Recently, several studies found that phenolic compounds (PHCs) and polycyclic aromatic hydrocarbons (PAHs) are retained in the biochar during the pyrolysis, and are really responsible for the inhibition of microbial activity, soil AOB and soil NO3 − [107,109]. Previously published experimental outcomes proved that biochar addition to the soil retards the microbial nitrification mainly due to the toxicity of PHCs to AOB [110]. However, in our experiments, 16S rDNA AOB copies increase in response to the application of either biochar or its combination with manure. It was either due to lesser or no PHCs in the biochar, or increased availability of NH4 <sup>+</sup> sorbed on the biochar surface [62].

Two-factor ANOVA (with replication) was conducted to test the interaction effect of manure and biochar on log-transformed 16S rDNA AOB value (Figure 8B). Manure and biochar have a significant main effect on log 16S rRNA copies (Figure 8B), whereas significant ordinal interaction was found between biochar and manure (Figure 8B).

#### **4. Conclusions**

The results of this study demonstrate that the application of biochar with or without manure positively affect the fungal and bacterial biomass, as evident from the increased quantity of phospholipid fatty acid (BPLFA and FPLFA) and the DNA copy number (16S r DNA and 18S rDNA). Soil MBC and DHA activity decrease with the incorporation of biochar, but the maximum value is recorded for co-application with manure. These two properties were most affected by sorptive characteristics that might be directly dependent on the pyrolytic temperature used in biochar preparation. The results also revealed that the AOB population increased with the application of biochar and reached its maximum value when biochar is applied in combination with manure. However, further detailed studies are required to investigate the influence of biochars on nitrification and AOB community.

It can be concluded that the amendment of biochar in combination with manure changed the soil properties in three years of soil experiment in favor of increased microbial biomass. It was also confirmed that the application of biochar solely might increase or decrease soil activity, but their addition, along with manure, always promotes microbial abundance and their activity. However, quantity and sorption characteristics must be taken into account while planning the biochar-based soil amendments.

**Author Contributions:** Conceptualization, M.B. and J.H.; methodology, M.B., J.H.; software, R.D.; validation, M.B., J.H. and A.K.; formal analysis, T.D. and V.P.; investigation, M.B., J.H., J.P., T.V.; resources, M.B., J.H., O.L.; data curation, T.D., V.P., A.K.; writing—original draft preparation, R.D., J.H.; writing—review and editing, R.D.; visualization, J.H.; supervision, J.H., O.L., T.V.; project administration, M.B.; funding acquisition, M.B.

**Funding:** This research received no external funding.

**Acknowledgments:** This research was funded by Technology Agency of the Czech Republic project TH02030169: "Effect of biologically transformed organic matter and biochar application on the stability of productive soil properties and reduction of environmental risks" and by Technology Agency of the Czech Republic project TH03030319: "Promoting the functional diversity of soil organisms by applying classical and modified stable organic matter while preserving the soil's production properties" and the APC was funded by these projects.

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

#### **References**


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*Article*
