extfieldsofstudy:
Environmental Science
Physics

text:
Metabologically active hydrolytical microbial communities of soil ecosystems under influence of soil physical factors

Application of molecular biological techniques in environmental studies provides a more complete information concerning the taxonomic diversity and potential hydrolytic activity of soil microbial complexes that exist in a wide range of environmental factors. Among the key environmental parameters that determine the functional activity of the hydrolytic complex of soil layer, the most significant one is moisture. Moisture levels providing maximum activity of a hydrolytic microbial complex depend on the soil type. At high levels of moisture and temperature, the role of prokaryotic organisms, mainly actinomycetes, in the microbial complex significantly increases. It was discovered a new functional activity of actinomycetes in the hydrolytic prokaryotic complex: their controlling influence on the respiratory level of the complex in a wide range of parameters (moisture, organic matter, successional time).At the optimum for the life of most microorganisms levels of moisture (60% of field capacity) and temperature (27°C), representatives of Firmicutes and Actinobacteria phylums stand out among chitinolytic and pectinolytic dominants of the studied soils within the Bacteria domain. With increasing moisture and decreasing temperature the proportion of Proteobacteria increases. With decreasing moisture and increasing temperature, there is an increase in the amount of unicellular actinobacteria.

Introduction
The rate of the microbial hydrolytic complex development and the efficiency of microbial destruction of biopolymers, polysaccharides specifically, are different in soils of different climatic zones [1,2]. The intensity ofmicroorganisms-destroyers strongly depends on environmental factors, including temperature, moisture, redox conditions, osmotic pressure of soil solutions, etc. [3]. Identification of parameters of stability and functional activity of hydrolytic prokaryotic communities in a wide range of key environmental factors is essential for solving the problems of soil fertility and addressing the issues of biotechnology related to processing of renewable raw materials.Polysaccharides of different structures entering the ecosystem are extremely widespread in nature, and are an important source of organic matter. Such polysaccharides as chitin, pectin, cellulose, constantly present in litter, make up a renewable pool of organic carbon and nitrogen [4]. Content of polysaccharides in different types of soils varies from 0.06% to 3.0% of the mass of soil, and from 1% to 14% of the mass of organic matter in soils. Soils with plant residue receive from 2 to 14 t/ha of polysaccharides, a considerable part of which are mineralised or participate as structural elements in the formation of humic acids [5]. The inclusion of elements of feeding into the biological cycle requires the degradation of polysaccharides by respective enzyme complexes of microorganisms. Both in Russia and abroad, this area of research is still largely unexplored. However, numerous scientific and technological publications have covered the issues of synthesis of microbial hydrolases in laboratories and industrial sites, and of their application in various industries, medicine and agriculture. World scientific literature on the issue of the present thesis contain, as a rule, the results of research of microbial depolymerase (chitinase, cellulase, ligninase etc.) not in natural systems, but derived from cultures of microorganisms incubated on artificial growth media. Processes of microbial destruction of polysaccharides, and the activity of chitinases specifically, can become the basis for development of effective methods of soil bioremediation and detoxication. However, the problem of microbial degradation of polysaccharides in natural systems, especially soils is hitherto little studied, despite its immense relevance. The study of structural and functional diversity of microbial hydrolytic complexes localized in soil will provide a more complete picture of gradual destruction of biopolymers in terrestrial biogeocenoses, and help assess the role of different groups of organisms at different stages of the process.

The objects of study
The objects of study were samples of soils, which are: the upper humus horizons of Voronic Chernozems(N 51°1'41", E 40°43'31"), Haplic Arenic Kashtanozem (N 49.39936º, E 46.81083º), Haplic Luvisols (Abruptic) (N 53°58'23", E 37°10'34"). Samples of substrates were repeatedly selected, sufficiently so to account for the spatial and temporal variations in the structure of hydrolytic microbial communities.Special attention is paid to the analysis of dynamics of structural and functional parameters in succession of hydrolytic (chitinolytic and pectinolytic) microbial communities of terrestrial ecosystems. To determine the structure of hydrolytic microbial complexes in the samples of terrestrial ecosystems, we used the method of microcosms with microbial succession initiation through moisturising and introduction of purified polysaccharides (chitin («ICN Biomedicals», Germany), pectin («Sigma», Germany), or cellulose («Sigma», Germany)). The mass of added substrates in all cases did not exceed tenths of a percent of the mass of the sample. Incubation of soil microcosms was carried out in a wide range of values of soil moisture pressure matrix, P (0 to -808 kPa), redox potential and temperature (5, 27 and 50°C).

2.2.Evaluation of the abundance and taxonomy for the dominant groups of hydrolytic prokaryotic complexes microorganismswas performed by molecular-biological methods of FISH (fluorescence in situ hybridisation)
Evaluation of the amount of bacteria, the length of the mycelium of actinomycetes and fungi was performed using the method of fluorescence microscopy of preparations stained with a number of different fluorochromes (microscope «Axioskop2 +»). Using the method of fluorescent hybridisation in situ (FISH) [6], that we had modified to work with the soil, we revealed the specifics of prokaryotic metabolically active community destroying polysaccharides. We applied a range of probes specific for members of Archaea and Bacteria domains, as well as certain phylogenetic Bacteria groups. Total biomass and the proportion of metabolically active microorganisms were determined by calculation [7].

2.3.Emissions of greenhouse gases
Emissions of greenhouse gases(carbon dioxide, nitrous oxide and methane) from samples of litter and soil tiers with and without the introduction of polysaccharides were evaluated by gas chromatography [8].Statistical analysis was conducted by multivariate methods of mathematical statistics using STATISTICA-6 and Statgraphics Plus software.

3.1.The functional activity of microbial soil hydrolytic complexes under influence of soil moisture
The study of the influence of soil moisture on the emission of carbon dioxide from soils with the introduction of various polysaccharides has shown that the pressure of soil moisture affects the dynamics  3 and intensity of carbon dioxide emissions to a greater extent with the introduction of chitin than of other tested substrates (pectin, glucose and cellulose). The mass of introduced substrates in all samples contained the equal number of moles of carbon. As the soil moisture pressure increases from -800 kPa to -0.5 kPa, CO2 emission intensity with the introduction of chitin in microcosms with chernozem samples increases more than by two times (from 4 to 18 µg C-CO2/g of soil per hour), which is not observed with the introduction of other substrates. Similar results were observed for the microcosms of all studied soils. Since polysaccharides occur in soils in both aerobic and anaerobic microzones, we studied their transformation in microaerobic conditions. As an indicator of transformation intensity we used methane emissions from soil samples with and without the introduction of chitin. When moisture level is close to full capacity, intense emission of methane was observed for all tested samples of soil, which indicates the presence of strictly anaerobic microzones in all these soils. The difference in methane emissions between soil samples with the introduction of chitin and without it (control) was apparent at the early stages of soil incubation, which proves that it was being used by microorganisms already during the first day of the experiment. The highest methane emission values and the differences in the values of emission between the experimental and control variants were recorded on 20-30 th days of the experiment, which is clearly connected with the development of methanogens in hydrolytic (chitinolytic) metabolism products. It should be noted that the emission of methane from soils was observed at all tested levels of matrix soil moisture pressure: from -800 to -0.5 kPa. It was found out that the anaerobic transformation of chitin occurs in soils with different intensity at the studied levels of moisture and the matrix potential. Intervals of moisture values for anaerobic transformation of chitin in different soils are different. Thus, in the weakly sandy soil the most active work of hydrolytic anaerobes was registered in a fairly narrow range of moisture content: from 15% of the soil mass (matrix pressure -1.16 kPa) to 25% of the soil mass (full moisture capacity 0 kPa), whereas in chernozem that interval increased from 36% of the soil mass (matrix pressure -32.6 kPa) to 64% of the soil mass (full moisture capacity 0 kPa). In soils with a high content of clay fractions (EPCH <1 mm -28%) the transformation ratio of chitin increased in areas with high moisture values, and for the sandy soil ─ with lower values (EPCH <1 mm -5%). There is a correlation between the rate of transformation of chitin in anaerobic conditions and the radius of pores occupied by water. With the radius of pores (water-filled) is less than 6 micrometers, the activity of anaerobic chitinolytic microbial complex, determined by the values of transformation ratios of chitin, was minimal, indicating impediments in development of anaerobic microorganisms due to the small pore diameter. Thus, the upper moisture limit for the studied process is determined by the full moisture capacity of soils, and bottom limit -by the size of pores, where the bacteria growth is still possible.

3.2.The functional activity of soil hydrolytic microbes under influence of temperature
The study of the respiratory activity in soil microcosms with the introduction of chitin and pectin showed that CO2 emission in these cases exceeded emission in control, on average, by 2-6 times for all the soils. The rate of CO2 production in the experimental variants with the introduction of polysaccharides varied depending on incubation temperature. At the minimum temperature (5 °C), the peak of activity was observed at the end of the third week of the experiment, at 27°C -by the end of the first week, at 50°C -on the first day of the experiment. In the control variants such activity was not observed, indicating a high functional ability of microbial complexes to hydrolyse polysaccharides.
We used emission of carbon dioxide as a functional indicator of microbial chitinolytic complex in the experiments based on mathematical methods of experimental design in order to optimise conditions (optimal values of factors), providing microbial decomposition of chitin in soils. The experiment was planned according to the scheme of the central composite design "2 2 +star." It has been revealed that the optimal values of factors differ for different types of soils. For chernozem, the maximum of respiration is detected approximately on the 20 th day at 25 °C; for the sandy soil -on the 10 th day at 30 °C. Thus, thanks to experimental design we demonstrate the principal possibility of solving an optimisation problem by defining the optimal values of factors (temperature and successional time) for the functional indicator (respiration) of microbial hydrolytic complex evolving in soils of different types.

3.3.Correlation of biomass accumulation and activity of chitinolytic and pectinolytic microbial complexes of soils developed at different parameters of temperature and soil moisture pressure
Determining the amount of microorganisms and the biomass of hydrolytic microbial complex we registered their increase for all groups of microorganisms in cases of the complex developing with the introduction of polysaccharides, as compared to control options. Experiments on the dynamics of abundance and biomass of different groups of microorganisms during microbial succession in soil samples incubated at different temperatures, generally, confirmed the data obtained as a result of the study of respiratory activity dynamics in all soils. Thus, the activity of chitinase in wet chernozem in situ even without the introduction of chitin at 27°C reached its maximum on the 8 th day of the experiment and was six times higher than the activity of chitinase in the same soil, incubated at 5°C.Evaluation of the ratio of biomass of individual groups of microorganisms (prokaryotes, fungi) in the samples with and without the introduction of the substrate showed that regardless of the type of the substrate and the type of soil, fungi (unlike prokaryotes) tend to decompose polysaccharides more actively at low temperatures. The activity of prokaryotic complex was largely related to the type of soil. The activity of actinomycetes at increased temperatures was especially great in the chernozem with the introduction of chitin. Thus, the phylogenetic structure of the natural hydrolytic microbial complex depends on climatic conditions and soil properties.

3.4.Analysis of structural and functional parameters of the hydrolytic microbial complex development
We have obtained some new and interesting results during the analysis of basic information concerning the dynamics of structural (biomass of fungi, bacteria and actinomycetes) and functional (carbon dioxide emissions) indicators of the hydrolytic complex development conducted by methods of multivariate mathematical statistics. Stepwise multiple regression to determine the relationship between functional and structural indicators reveals that minor biomass components -the filamentous and unicellular bacteria, especially actinomycetes, play a significant role in the functioning of soil microbial complex regardless of the temperature. The resulting regression model based on two variables has a high statistical significance (explains up to 90% of the variance of the functional value). Dependence is described by the following equations for the Chernozem soil (equation1) and Haplic Luvisols (equation 2): = 52.87 * + 89.27 * , (1) = 37.77 * + 555.49 * , (2) where R-СО2 emission (µg С-CO2/g•h), А -actinomycetes biomass (mg/g), В -bacteria biomass (mg/g).
Thus we have established a high importance of minor biomass components of the microbial complex in the hydrolysis of polysaccharides, regardless of the temperature of the process; these componentsfilamentous and unicellular prokaryotes -have been shown to contribute significantly to the control of microbial complex respiration in a wide range of environmental parameters (temperature, moisture, organic matter, successional time). In the process of chitin degradation a particularly significant role is played by actinomycetes. Stepwise multivariate regression analysis of the data obtained for a variety of microbial successions (with the introduction of chitin and pectin and without the introduction of polymers) in a wide range of soil moisture pressure, allowed to identify and describe certain connection with the regression model equation 5: = 399.03 * + 2.37 * , (5) where R -СО2 emission (µg С-CO2/g•h), А -actinomycetes biomass (mg/g), F -fungi biomass (mg/g).
Analysis of variance indicates the high quality of the model (p <0,01, the level of 99%), which generally has enough information capacity at the coefficient of determination of 69%.The connection between respiration and fungal biomass was revealed to be as expected and quite traditional, but what turned out to be very new and unexpected was the determining influence of the complex of actinomycetes on this functional parameter. The degree of influence of actinomycetes on respiration, according to analysis of variance, is 87%.
Thus, we demonstrate that the role of a minor component of the biomass (actinomycetes) in the functioning of soil microbial complex is essential. Respiration as a key functional indicator of natural microbial complexes can be substantially controlled by actinomycetes, whose role seems to be determined not so much by their direct contribution to the stoichiometry of the processes of organic matter decomposition, but rather by their contribution to the regulation of the functioning of the microbial complex as a whole. Regulatory role of actinomycetes, apparently, is provided for by the mechanisms based on the production of biologically active substances, which is typical of actinomycetes. It is essential, that this result was obtained under conditions simulating a wide range of environmental situations (moisture, organic matter, successional time), which may occur in situ.
It was also revealed that the level of soil moisture in a given range of certain structural parameters may be a more important factor than those essential for microbial complex factors, such as nutrient supply and time. Moisture has a more significant effect on the activity of chitinolytic complexes than on pectinolytic ones.
In the process of degradation of polysaccharides, with the increase of soil moisture in the microbial complex, the role of prokaryotic microorganisms perceptibly increases, too, especially that of actinomycetes. Respiration in a wide range of conditions (moisture, organic matter, successional time) can be significantly controlled by actinomycetes, whose role seems to be determined by their contribution to the regulatory control of the microbial community functioning.

3.5.Molecular and biological analysis (method FISH) of component composition of hydrolytic prokaryotic chitinolytic and pectinolytic complexes of soils developed at different values of moisture and temperature
The abundance of metabolically active cells of prokaryotes in microbial hydrolytic complexes of the studied soils was estimated by the method of in situ-hybridisation with rRNA-specific fluorescentlabeled oligonucleotide probes (FISH-method) in the time frames of the experiment corresponding to the maximum accumulation of microbial biomass, as measured by luminescence microscopy, and the greatest respiration rate in the process of soil incubation at 5°C, 27°C and 50°C.
The experiments showed that the total accumulated amount of cells in the soil samples (for members of Bacteria and Archaea domains) comprised from 26% (for Chernozem) to 90% (for Haplic Luvisols) of the total number of cells detected by hybridisation with universal probes and identified by acridine orange staining. A particularly low amount of cells was registered by FISH in the control variants in chernozem samples incubated at 5°C. It should be noted that during the development of the microbial complex of this sample at all temperatures, and especially at 5°C, most of the bacteria were represented by unidentified cells of small sizes. These data suggest that a significant proportion of prokaryotic organisms of chernozem occur in the form of dormant or metabolically inactive cells. The variants of different types of soils incubated at all studies temperatures with chitin and pectin, as compared to control variants, showed the increase (by 1.5-3 times) in the amount of metabolically active members of BacteriaandArchaeadomains: their number was from 4 to 20,5•10 8 cells/g of soil for Bacteria and from 9 to 29•10 7 cells/g of soil for Archaea. Participation of archaea in the mineralization of nitrogencontaining organic matter in soils is discussed in many papers [9,10].
In chitinolytic and pectinolytic complexes of chernozem the total amount of metabolically active forms of Bacteria varied slightly at different temperatures, which is apparenly related to the high buffer capacity of chernozem and a high content of organic matter in the soil.
Component analysis of the composition of cells within the Bacteria domain in the hydrolytic complex of typical chernozem samples revealed differences in the response of different groups of bacteria to the soil incubation temperature change, which determined the number of dominants.
It has been shown that members of phylogenetic groups Actinobacteria, FirmicutesandBacteroidetes responded with significant increase in the number of metabolically active forms to the introduction of chitin and pectin in all the studied soils regardless of temperature, indicating the active participation of the representatives of the mentioned phylogenetic groups in the decomposition of polysaccharides in a wide temperature range. These groups of organisms are most frequently mentioned among the main agents of destruction of organic matter in natural ecosystems [1]. When the temperature was decreased to 5°C, the hydrolytic microbial soil complex showed the increase in the number of Firmicutes andBetaproteobacteria and the decrease of gram-positive bacteria belonging to the phylogenetic group of Actinobacteria, with high content of G + C pairs in the DNA, as compared to their amount in soils at 50 °C. The number of representatives of the Actinobacteria phylogenetic group increased considerably in the experimental variants and made up to 35% of the identified unicellular units within the Bacteria domain in the hydrolytic complex of soils. It should be noted that the amount of mycelial forms of actinobacteria increased, too; their biomass in the desert-steppe soil and chernozem with the introduction of chitin increased by almost an order. Also, in variants with polysaccharides and incubation at 50°C (in certain soils at 27°C, as well), the number of metabolically active cells of Gammaproteobacteria was bigger than in the control samples. The increase in the number of Verrucomicrobia in variants with pectin indicates their possible involvement in the process of degradation of this substrate. Nowadays, among the described and legitimized representatives of Verrucomicrobia, there have been identified bacteria capable of growing on cellulose, pectin, starch [11]. The result of the next set of experiments was identification of characteristics of the phylogenetic structure of pectinolytic and chitinolytic complexes in soil samples of varying moisture degrees. With the development of pectinolytic and chitinolytic complexes within the Bacteria domain, under the average soil moisture pressure (-3.2 kPa) the dominants are the representatives of Firmicutesand Actinobacteria phylogenetic groups, which was registered for all the studied soils. With the increase of moisture, the number of gram-negative forms (proteobacteria) showed increase, as well. With the decrease of matrix pressure to the values close to the wilting point, the number of gamma-and alpha-proteobacteria and unicellular actinobacteria in the hydrolytic complex increases.
Thus, the application of FISH method, modified by us for the purposes of soil microbiology, enabled us to identify certain characteristics in the structure of microbial hydrolytic complex of the studied soils under various environmental conditions (temperature and moisture). Within the Bacteria domain, with an average moisture content (60% of full capacity) and temperature (27°C), the representatives of Firmicutes and Actinobacteria phylogenetic groups dominate over pectinolytic and chitinolytic complexes. The increase of moister and the decrease of temperature lead to the increase in the number of gram-negative alpha-and beta-proteobacteria, whereas the decrease of moisture and the increase of temperature result in the increase in the number of actinobacteria in the studied prokaryotic microbial complexes, which deserves special attention.


extfieldsofstudy: Medicine Biology

provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2019.jsonl.gz.gz

text:
Genetic Mapping and Prediction Analysis of FHB Resistance in a Hard Red Spring Wheat Breeding Population

Fusarium head blight (FHB) is one of the most destructive diseases in wheat worldwide. Breeding for FHB resistance is hampered by its complex genetic architecture, large genotype by environment interaction, and high cost of phenotype screening. Genomic selection (GS) is a powerful tool to enhance improvement of complex traits such as FHB resistance. The objectives of this study were to (1) investigate the genetic architecture of FHB resistance in a North Dakota State University (NDSU) hard red spring wheat breeding population, (2) test if the major QTL Fhb1 and Fhb5 play an important role in this breeding population; and (3) assess the potential of GS to enhance breeding efficiency of FHB resistance. A total of 439 elite spring wheat breeding lines from six breeding cycles were genotyped using genotyping-by-sequencing (GBS) and 102,147 SNP markers were obtained. Evaluation of FHB severity was conducted in 10 unbalanced field trials across multiple years and locations. One QTL for FHB resistance was identified and located on chromosome arm 1AL, explaining 5.3% of total phenotypic variation. The major type II resistance QTL Fhb1 only explained 3.1% of total phenotypic variation and the QTL Fhb5 was not significantly associated with FHB resistance in this breeding population. Our results suggest that integration of many genes with medium/minor effects in this breeding population should provide stable FHB resistance. Genomic prediction accuracies of 0.22–0.44 were obtained when predicting over breeding cycles in this study, indicating the potential of GS to enhance the improvement of FHB resistance.

Fusarium head blight (FHB) is one of the most destructive diseases in wheat worldwide. Breeding for FHB resistance is hampered by its complex genetic architecture, large genotype by environment interaction, and high cost of phenotype screening. Genomic selection (GS) is a powerful tool to enhance improvement of complex traits such as FHB resistance. The objectives of this study were to (1) investigate the genetic architecture of FHB resistance in a North Dakota State University (NDSU) hard red spring wheat breeding population, (2) test if the major QTL Fhb1 and Fhb5 play an important role in this breeding population; and (3) assess the potential of GS to enhance breeding efficiency of FHB resistance. A total of 439 elite spring wheat breeding lines from six breeding cycles were genotyped using genotyping-by-sequencing (GBS) and 102,147 SNP markers were obtained. Evaluation of FHB severity was conducted in 10 unbalanced field trials across multiple years and locations. One QTL for FHB resistance was identified and located on chromosome arm 1AL, explaining 5.3% of total phenotypic variation. The major type II resistance QTL Fhb1 only explained 3.1% of total phenotypic variation and the QTL Fhb5 was not significantly associated with FHB resistance in this breeding population. Our results suggest that integration of many genes with medium/minor effects in this breeding population should provide stable FHB resistance. Genomic prediction accuracies of 0.22-0.44 were obtained when predicting over breeding cycles in this study, indicating the potential of GS to enhance the improvement of FHB resistance.

INTRODUCTION
Fusarium head blight (FHB) is one of the most destructive diseases of wheat worldwide. The disease is caused by Fusarium species and can lead to severe grain yield and quality loss. HRSW is a major class of wheat and is used for the finest baked goods owing to its high protein content and superior gluten quality. In the United States, HRSW is annually planted on over 13 million acres, primarily in the Northern Great Plains. FHB epidemics in HRSW producing areas of the US in the early 1990s resulted in devastating economic losses (Wilcoxson et al., 1992;McMullen et al., 1997) and the disease still frequently threatens wheat production in this region. Controlling FHB through agronomic practices such as crop rotation is challenging due to the wide host range of the Fusarium species. Fungicidal control is effective only in a narrow operative window. Therefore, developing FHB resistant cultivars is crucial to prevent the destruction caused by this disease.
Fusarium head blight resistance exhibits quantitative variation, suggesting complex genetic architecture. Most public wheat breeding programs in the US rely on phenotypic selection for FHB resistance using field nurseries to evaluate breeding materials. However, disease infection and development is highly dependent on environmental conditions at flowering and early kernel development stages, making effective phenotypic evaluation and selection difficult. In addition, phenotypic evaluation of FHB resistance is time-consuming and demands extensive labor and resources in multiple environment trials, limiting the number of breeding lines that can be evaluated in each breeding cycle. MAS of major QTL could enhance selection efficiency for FHB resistance (Anderson, 2007;Buerstmayr et al., 2009). From previous bi-parental QTL mapping studies, hundreds of QTL for FHB resistance have been identified on all 21 wheat chromosomes (Buerstmayr et al., 2009;Löffler et al., 2009). In previous studies, few QTL were identified for type I resistance (against initial infection of the pathogen) as most previous studies focused on type II resistance (against spread of the pathogen) (Buerstmayr et al., 2009). Many QTL were commonly identified from multiple populations Löffler et al., 2009), of which major QTL like Fhb1 (Bai et al., 1999;Waldron et al., 1999;Anderson et al., 2001;Liu et al., 2006;Rawat et al., 2016;Su et al., 2018), Fhb2 (Cuthbert et al., 2007;Jia et al., 2018), Fhb4 (Xue et al., 2010;Jia et al., 2018), and Fhb5 (Xue et al., 2011;Jia et al., 2018) were finely mapped and/or characterized. The most stable type II resistance QTL Fhb1 can reduce disease severity by 20-25% on average, depending on the genetic background (Pumphrey et al., 2007), and has been widely integrated into wheat breeding populations through MAS (Anderson, 2007;Buerstmayr et al., 2009;Steiner et al., 2017). However, utilization of other QTL for MAS in wheat breeding has been scarce, due to lack of diagnostic markers or pending validation.
Genome wide association studies (GWAS) enable genetic mapping to be performed in a breeding population, and the identified markers can be used for MAS directly (Pozniak et al., 2012;Wang et al., 2012). Advances in genome sequencing and high-throughput genotyping have facilitated GWAS in wheat, a polyploid species of large genome size (Poland et al., 2012;Wang et al., 2014;Allen et al., 2017;International Wheat Genome Sequencing Consortium [IWGSC], 2018). Numerous GWAS have been conducted for FHB resistance in wheat and confirm its complex genetic architecture (Mirdita et al., 2015b;Arruda et al., 2016a;Wang et al., 2017). Arruda et al. (2016a) performed GWAS for FHB resistance using 273 winter wheat breeding lines from the Midwest and Eastern US, and found that Fhb1 explained only 8% of phenotypic variation. GWAS using a large panel of elite inbred lines from central European winter wheat found no QTL with large effect, despite broad genetic variation (Mirdita et al., 2015b). Taken together, these observations confirm that many genes with medium or small effects contribute to FHB resistance along with the few well-characterized major QTL.
A simulation study suggested that GS was more efficient for the improvement of complex traits compared to MAS (Meuwissen et al., 2001). In GS, genome wide markers are used to predict breeding values, which are consequently used for selection. Empirical experiments found that prediction ability of FHB resistance using genome wide markers was better than using statistically significant markers only (Jiang et al., 2015;Mirdita et al., 2015a;Arruda et al., 2016b). Several GS studies suggested that GS is promising to enhance FHB resistance improvement in wheat (Rutkoski et al., 2012;Mirdita et al., 2015a;Jiang et al., 2017;Herter et al., 2019a,b).
Diverse resistance donors like Sumai 3, Frontana, etc. have been used for the improvement of FHB resistance in wheat breeding programs including the NDSU HRSW breeding program. Well-characterized major QTL like Fhb1 and Fhb5 have been integrated into the breeding pools. The objectives of this study are to (1) investigate genetic architecture of FHB resistance in this breeding population; (2) test if the major QTL Fhb1 and Fhb5 play an important role in this population; and (3) assess potential of GS for FHB resistance enhancement in the NDSU HRSW breeding program.

Plant Materials and Evaluation of FHB Severity
In total, 427 F 9 breeding lines from 2011-16 advanced yield trials (AYTs) of the NDSU HRSW breeding program, representing six breeding cycles, and 15 check cultivars were evaluated for FHB resistance from 2011 to 2016 (Supplementary Table S1).
Each year, a subset of breeding lines from one breeding cycle was evaluated at two locations, Langdon and Prosper, ND. However, the trials from 2013 Prosper and 2016 Langdon were not used because of poor quality of the phenotypic data. Therefore, the 427 breeding lines were actually tested in an unbalanced experiment including 10 trials across 6 years (Supplementary Table S1). The 15 check cultivars were ND2398, ND2710, Barlow, Bolles, Elgin-ND, Faller, Glenn, Howard, ND817, Prosper, RB07, Steele-ND, SY Soren, and Velva. The checks ND2398, ND2710, Barlow, Faller, Glenn, and Prosper were evaluated at all 10 trials at the two locations from 2011 to 2016. In each trial, the experimental design was a randomized complete block design with two replicates. In the field experiment, each line was planted in a hill plot with 15 seeds. Spawn corn kernels were prepared as inoculum. To prepare inoculum, autoclaved corn kernels were infected with a mixture of spores produced separately from 20 F. graminearum strains, including ten 3ADON (3-acetyl deoxynivalenol) producers and ten 15ADON (15-acetyl deoxynivalenol) producers, collected from fields in North Dakota (Puri and Zhong, 2010), according to the procedure described by Zhang et al. (2008). The spawn corn kernels were applied to the FHB nurseries at a rate of approximately 0.20 kg/m 2 starting at the boot stage, and repeated every 2 weeks until all wheat accessions completed anthesis. The nurseries were overhead misted for 5 min in 15min intervals for 12 h daily (4:00 p.m. to 4:00 a.m.), until 14 days after anthesis of the latest lines. Eight to twenty spikes from each hill plot were visually scored at 21 days post anthesis using a visual scale (0,7,14,21,33,50,66,75,90, and 100, all in percentage) (Stack and McMullen, 1995). Disease severity was calculated as the average percentage of infected spikelets.

Molecular Marker Genotyping
The 427 elite breeding lines and 12 of the 15 check cultivars were genotyped using GBS. DNA was isolated with the Wizard Genomic DNA Purification Kit (A1125; Promega) per the manufacturer's instructions and quantified with a Quant-iT PicoGreen dsDNA assay kit (P7589; Thermo Fisher Scientific). GBS libraries were constructed based on the protocol of Poland et al. (2012) with minor modifications. Briefly, 200 ng of DNA for each line was digested with PstI and MseI, and then ligated to a common adapter and a barcoded adapter unique to each sample. Equal volumes of the ligated products were pooled and purified with the QIAquick PCR purification kit (28104; QIAGEN) for PCR amplification. For the PCR amplification, 50 ng of template DNA was mixed with NEB 2X Taq Master Mix and two primers (5 nmol each) in 200 µL total reaction volume and PCR amplified for 18 cycles with 10 s of denaturation at 98 • C, followed by 30 s of annealing at 65 • C, and finally 30 s extension at 72 • C. The PCR product was cleaned using a QIAquick PCR purification kit. The library was sequenced on an Illumina HiSeq 2500 to generate single-end, 100-bp reads at the Genomic Sequencing and Analysis Facility at the University of Texas Southwestern Medical Center at Dallas, Texas. All sequences were submitted to the National Center for Biotechnology Information Short Read Archive (experiment #SRP144046). SNP discovery and genotype calling was performed using the TASSEL-GBS pipeline (Glaubitz et al., 2014) with the Triticum aestivum IWGSC RefSeq v1.0 as the reference genome (International Wheat Genome Sequencing Consortium [IWGSC], 2018). SNP markers were filtered with an individual read depth greater than 1, MAF greater than 0.05, and missing data less than 50%. Filtering SNPs yielded 102,147 SNP markers (Supplementary Table S1). Missing values were imputed with LD-KNNi method (Money et al., 2015) implemented in TASSEL v.5 (Bradbury et al., 2007).
Each SNP was assayed using the KBiosciences Competitive Allele-Specific PCR genotyping system (KASP). Briefly, genotyping reactions were performed in 384-well plates on a Procycle thermocycler (Life) in 4 µL reactions containing two allele-specific primers, one common reverse primer, 1 x KASPaR v4.0 SNP Mastermix (LGC Genomics), and 36 ng of gDNA. After an initial denaturation step of 15 min at 94 • C, PCR amplification reactions consisted of 10 cycles of 20 s denaturation at 94 • C, followed by 60 s at a touchdown annealing/elongation at 65-57 • C (decreasing 0.8 • C each cycle), then 29 cycles at 20 s denaturation at 94 • C, followed by annealing/elongation 60 s at 57 • C. Fluorescence of PCR products was measured on a Roche Light Cycler R 480 and accompanying software (v 1.51) was used to distinguish clusters and call genotypes.

Phenotypic Data Analysis
The number of breeding lines per trial ranged from 31 to 112 (Supplementary Table S1). Two-stage analysis of phenotypic data was performed. In the first stage, best linear unbiased estimators (BLUEs) were estimated for all breeding lines within each individual trial using R package lme4 (Bates et al., 2014;R Development Core Team, 2018). The model was where y is the vector of unadjusted phenotypes, µ is the overall mean, g i is the fixed effect of the i th genotype and r j is the random effect of the j th block. To identify outlier trial, we estimated broadsense heritability (H 2 ) for each individual trial. All factors were considered as random. The variance components of error (σ 2 ) and genotype (σ 2 g ) were estimated. The broad-sense heritability was estimated as σ 2 /(σ 2 + σ 2 g ) and was used to eliminate trials with a H 2 score < 0.1.
In the second analysis stage, we estimated BLUEs of breeding lines across all 10 trials using the R package lme4 (Bates et al., 2014). The model was where y * represents the estimated BLUEs of breeding lines within each individual trial calculated in the first stage, µ is the overall mean, g i is the random effect of the i th genotype and t j is the random effect of the j th trial. The estimated BLUEs were further used in GWAS to identify major QTL and in genomic prediction analysis. A relationship matrix was calculated using the 102,147 SNP markers with the R package rrBLUP (Endelman, 2011). Then, additive variance components (Va) and error variance component (Ve) were calculated with the mixed.solve function in the package rrBLUP (Endelman, 2011). Genomic heritability (h 2 ) was calculated as Va/(Va + Ve).

Marker-Trait Association Analysis
Principal component analysis (PCA) was conducted with the 102,147 SNPs to assess population structure using TASSEL v.5 (Bradbury et al., 2007). SNP-based GWAS was performed using TASSEL v.5 (Bradbury et al., 2007). Based on the Scree plot (Supplementary Figure S1), the first three PCs were chosen as covariates to capture population structure in association analysis. A centered kinship (K) matrix was calculated based on the 102,147 SNPs using TASSEL. A linear mixed model including population structure and kinship matrix was used to test marker-trait association. False discovery rate (FDR) was calculated from p-values using the R function p.adjust (method = fdr) (Benjamini and Hochberg, 1995). Significance of marker-trait association is defined by FDR as a q value smaller than 0.1.

Development and Validation of Genomic Prediction Models
Genomic prediction was evaluated with the statistical model rrBLUP. The rrBLUP model was constructed using R package rrBLUP (Endelman, 2011). Prediction accuracies were first validated using five-fold cross-validation, in which 80% of individuals were randomly selected as the training population and the remaining 20% of individuals were used to validate the genomic prediction accuracy. Genomic prediction accuracy was estimated as the Pearson correlation (r) between genomic estimated breeding values (GEBVs) and BLUPs of phenotypic values. Random sampling of training and validation sets was repeated 100 times and the mean of r divided by the square root of the estimated heritability was defined as the genomic prediction accuracy.
To test prediction accuracy across breeding cycles, we developed prediction models using a training population that includes five breeding cycles to predict breeding lines from the remaining breeding cycles, e.g., to predict 2012 breeding lines, the five breeding cycles tested in the other 5 years (2011,(2013)(2014)(2015)(2016) were used as training population.
Because genetic relationship between training population and validation population can affect prediction accuracy, we calculated genetic relationships between training population and validation population based on identity-by-state similarity (proportion of shared alleles) between all individuals using R package "snpRelate" (Zheng et al., 2012).
To evaluate the effect of marker number on prediction accuracy, we randomly sampled 100, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, and 5000 markers to develop prediction models and validate their prediction accuracies. Random sampling for each subset was repeated 500 times and the mean of the Pearson correlations between GEBVs and BLUPs was defined as the genomic prediction accuracy.

RESULTS
Genetic Diversity of the Elite Spring Wheat Lines and Their Reaction to FHB Broad-sense heritability was estimated for each individual trial and ranged from 0.21 to 0.67. No outlier trials were identified at threshold of H 2 < 0.1. BLUEs of FHB severity were estimated for the 427 elite spring wheat lines and 15 check cultivars using the unbalanced phenotypic data collected from all 10 field trials at two locations across 6 years. Among the 15 checks, the resistant check variety ND2710 showed the lowest FHB severity (15.9%) while the susceptible check variety ND2398 had the highest disease severity (75.8%). Among the 427 breeding lines, FHB severity ranged from 6.0% to 83.1% and broad variation was found in all six breeding cycles ( Table 1). In total, 102,147 markers were obtained with missing values less than 50%. MAF of the 102,147 markers averaged 0.19, ranging from 0.05 to 0.5 (Supplementary Figure S2). PCA was performed and the PC1 and PC2 explained 6.9 and 6.0% of total variation, respectively. Scatter plots of PC1 against PC2 for the 427 lines indicated there was no clear population structure and breeding lines from different breeding cycles were intermixed (Supplementary Figure S3).

Marker-Trait Association
Marker-trait association was conducted with a linear mixed model including population structure and kinship matrix. Significant marker-trait association was determined by FDR as a q value smaller than 0.1. The Manhattan plot is shown in Figure 1. In total, six SNPs significantly associated with FHB resistance were identified on chromosome arms 1AL (Figure 1 and Table 2). The most significant markers (S1A_477852878 and S1A_477852881) explained 5.3% of the total phenotypic variation ( Table 2). Over 81% of the 439 tested lines had the favorable allele for the two most significant markers at this locus   ( Table 2). The favorable allele alone could reduce 7.5% of FHB severity ( Table 2).

Frequencies and Effects of the Molecular Markers Linked to Fhb1 and Fhb5
We genotyped one SNP marker linked to the major QTL Fhb1 and one SNP marker linked to the major QTL Fhb5. The marker linked to Fhb1, IFA-FM227, was significantly associated with FHB severity at p-value less than 0.001 and explained 3.1% of total phenotypic variation ( Table 2). In total, 140 (33%) of the 429 lines contained the favorable allele of the major QTL Fhb1. The marker linked to Fhb5, barc186-80018, was segregated in this population with the favorable allele frequency at 26% (Table 2). However, this marker was not significantly associated with FHB severity at p value smaller than 0.05 ( Table 2).

Genomic Prediction
Based on the marker and phenotypic data, genomic heritability (h 2 ) for the FHB severity was estimated to be 0.43 in this HRSW breeding population. We evaluated genomic prediction for FHB severity with statistical model rrBLUP. Prediction accuracy using fivefold cross validation was 0.35. One potential application of GS is to predict breeding lines from a new breeding cycle and perform pre-selection. To test such prediction ability, we used breeding lines from five of the six breeding cycles as training population to predict breeding lines from the remaining breeding cycle. The prediction accuracies ranged from 0.22 when predicting 2012 breeding lines to 0.44 for predicting 2015 breeding lines (Figure 2). The identity-by-state similarity between all pairs of breeding lines were calculated using the 102,147 markers. The genetic relationships between one breeding cycle and other breeding cycles were calculated based on the identity-by-state similarities between pairs of breeding lines. We found that the genetic relationships between the breeding cycles were similar to the average genetic relationship of all pairs of breeding lines (Supplementary Figure S4) and pairs of breeding lines within a breeding cycle (data not shown). Therefore, genetic relationships between training and validation population did not impact the prediction accuracies, which varied between different breeding cycles. We also evaluated effect of marker number on prediction accuracy. The prediction accuracies increased from FIGURE 2 | Prediction accuracies for FHB severity across different breeding cycles using varied number of markers. E.g., to predict 2012 breeding lines, the breeding lines evaluated in the other five breeding cycles (2011,(2013)(2014)(2015)(2016) were used as training population. The x-axis shows the number of markers in each subset (100, 500, 1000, 1500, 2000, 2500, 3000, 4000, 5000, and 102147 markers). 100 markers to 1000 markers but attained a plateau after 1000 markers (Figure 2).

Genetic Architecture of FHB Resistance in the NDSU HRSW Population
The major QTL Fhb1 is the most stable type II resistant QTL identified, with a large genetic effect consistently presented in previous bi-parental mapping studies (Anderson et al., 2001;Pumphrey et al., 2007;Buerstmayr et al., 2009;Zhao et al., 2018b). In this study, we evaluated FHB reactions of 427 breeding lines from the NDSU HRSW breeding program, but the marker linked to Fhb1 showed a relatively small effect, explaining approximately 3.1% of the total phenotypic variation. The varied effects of Fhb1 have to do with differences in genetic background, tested environments, and/or resistance types evaluated. For example, Bokore et al. (2017) evaluated Sumai 3-derived North American spring wheat breeding lines at two field nurseries in Canada and found that Fhb1 was more effective at one location than the other and that Fhb1 was more effective in some families than others. In this study, FHB resistance -measured as severity -was conducted in field nurseries, where FHB resistance assessment is confounded by natural infection in addition to manual inoculation. Different from single-spikelet inoculation, high levels of natural Fusarium inoculum can increase infection incidences, mitigating the effectiveness of Fhb1 and the type II resistance it confers. Zhao et al. (2018b) studied type II FHB resistance using a population derived from a cross between ND2710 and Bobwhite with single-spikelet inoculation, and detected Fhb1 as major QTL in both greenhouse and field evaluations in North Dakota, however, with Fhb1 explaining much less phenotypic variation under field conditions. Failing to detect the QTL Fhb1 for FHB severity under field inoculation was also reported in winter wheat (Herter et al., 2019b). Some studies found that the QTL Fhb5, providing type I resistance, was of higher effect than Fhb1 under field inoculation (Von der Ohe et al., 2010;Miedaner et al., 2019). However, the QTL Fhb5 was not significantly associated with FHB severity in this study. The discrepancy between this study and the other two studies (Von der Ohe et al., 2010;Miedaner et al., 2019) remains unclear.
We identified one QTL on chromosome arm 1AL in this breeding population through association mapping. A few previous mapping studies also detected FHB resistance QTL on chromosome arms 1AL (Lin et al., 2004;Petersen et al., 2017;Yi et al., 2018). Petersen et al. (2017) identified a QTL related to Type I resistance between 90K-SNP markers IWB29758 and IWB73950 on chromosome arm 1AL using a double haploid population derived from two US soft red winter wheat cultivars. The physical locations of the two markers were 539895992-541895688 bp, determined by BLASTn searches of the primer sequences against the Triticum aestivum IWGSC RefSeq v1.0 (International Wheat Genome Sequencing Consortium [IWGSC], 2018). Yi et al. (2018) found a QTL related to FHB severity on chromosome arm 1AL, where the closest 90K-SNP markers were RAC875_c6338_1887 under singlespikelet inoculation and wsnp_CAP12_c2438_1180601 under spay inoculation, respectively. The two markers were located at 350007881 and 39649561 bp on chromosome arm 1AL. The QTL found from our study are distant from those identified from previous studies, suggesting it is a possible novel locus for FHB resistance.
Glenn is a HRSW cultivar with good grain yield and high protein content (Mergoum et al., 2006). Glenn showed high resistance to FHB despite of lacking the favorable allele of Fhb1 (Mergoum et al., 2006;Bokore et al., 2017); our results confirmed this observation (data not shown). The cultivar Glenn and its derived lines have been widely used as parents in the NDSU HRSW breeding program. A Chinese landrace Haiyanzhong has shown high level of resistance to FHB, and a mapping study using a population derived from Haiyanzhong found that it carried Fhb2, Fhb4, Fhb5, and four other minor QTL (Cai et al., 2016). The accumulation of QTL with medium/minor effects conferring high FHB resistance have been identified in other wheat lines such as PI277012 (Chu et al., 2011;Zhao et al., 2018a), Wanshuibai (Lin et al., 2006), and Frontana (Steiner et al., 2004). Therefore, the high level of FHB resistance observed in Glenn and other breeding lines in this HRSW breeding population is likely a result of numerous genes. The identified QTL explained 5.3% of total phenotypic variation and the genomic heritability estimated with genome wide markers was 0.43, suggesting that there are unidentified genes with small additive effects that also contribute to the FHB resistance. Integrating QTL with major, medium, and minor effects for both type I and type II resistance seems crucial to developing stable FHB resistance under field conditions.

GS for FHB Resistance Improvement
MAS of major QTL like Fhb1 has contributed to the development of FHB resistant cultivars in several wheat breeding programs (Anderson, 2007;Steiner et al., 2017). However, phenotypic selection is still routinely utilized in wheat breeding programs due to the complex genetic architecture of FHB resistance. Phenotypic selection of FHB resistance is generally conducted in later generations with replicates because of strong genotype by environment interactions (Anderson, 2007). The 427 breeding lines used in this study were F 9 breeding lines from 2011 to 2016 AYTs, which is a 3-year experiment. Most of the breeding lines had been evaluated for FHB resistance in at least two prior years using F 7 or F 8 generations. Some breeding lines with good grain yield and/or quality traits could have been selected into AYTs even if possessing only moderate FHB resistance. Additionally, it is likely that many F 5 or F 6 breeding lines with good FHB resistance and grain yield potential were missed during generational selection due to limited resources for FHB and grain yield evaluation.
In GS, selection is based on the predicted breeding values from genome wide markers. One potential application of GS is to select promising lines from a large number of newly developed early generation breeding lines (i.e., F 5 or F 6 ) into expensive yield trials and therefore improve selection efficiency. For example, GS pre-selection of end-use quality traits in wheat has successfully reduced the number of poor performing lines advanced to expensive yield trials (Guzman et al., 2016). In this study, we found medium prediction accuracy of 0.35 using cross-validation within population. Higher prediction accuracies were observed for FHB resistance from some previous studies (Mirdita et al., 2015a;Arruda et al., 2016b;Galiano-Carneiro et al., 2019). In most of the previous studies, balanced phenotypic data was used where all lines were phenotyped at same set of environments. In this study, phenotypic data for the 439 breeding lines collected from different years and locations were unbalanced, and the estimated BLUEs were adjusted by common checks. Because genotype by environment interaction could not be integrated in the statistics models, bias of the estimated BLUEs was inevitable, which could further lower the prediction accuracy in this study. Large training population and/or high heritability of FHB resistance derived from multiple locations' phenotypic evaluation could also contribute to the high prediction accuracy observed from previous studies (Mirdita et al., 2015a).
Prediction accuracy of between-crosses or families is generally low because of distant relationship between training population and validation population (Crossa et al., 2014;Herter et al., 2019b). In this study, we found that accuracies of 0.22-0.44 for predicting FHB severity over breeding cycles was not dramatically decreased compared to cross-validation. This could be thanks to the high level of genetic relationships between different breeding cycles, where some elite cultivars and their derived lines were commonly used as parents for crossing blocks. This study aimed to develop an initial prediction model, and will frequently update it by adding newly phenotyped and genotyped breeding lines. In the NDSU HRSW breeding program during the period of this study, over 1,300 inbred breeding lines were evaluated for FHB resistance each year in the FHB field nurseries at two locations in hill plots. This includes around 750 F 7 lines being tested for the first time, 350 second year F 8 lines, 150 F 9 advanced lines, and 75 F 10 elite lines. Yield tests, quality screening, and selection for other economically important diseases were conducted in each generation. It was reported that genomic assisted selection by combining phenotypic data and predicted values from GS model provided higher selection accuracy than phenotypic selection alone for grain yield and protein content at preliminary yield trial, where phenotypic data is collected from few environments with few or no replications . We plan to perform genomic assisted selection for FHB resistance at F 7 generation, where FHB severity will be estimated using both phenotypic data and predicted values from the initial GS model. GS model will be updated by adding the F 7 lines in the training population. Implementing GS pre-selection of FHB resistance on untested early generation lines like F 6 lines can be carried out once high prediction accuracy is obtained from the updated prediction model.
One limiting factor to utilize GS in pre-selection of promising lines is high genotyping cost for a large number of selection lines. Similar to other GS studies in wheat breeding populations (Jiang et al., 2015;Haile et al., 2018), we also found that a relatively small number of markers (e.g., 1000 markers) can achieve high prediction accuracy, owing to the high level of linkage disequilibrium. Simulation and empirical studies proved that a panel of low-density markers (50 to 100s of markers) genotyped for inbred progenies and imputed to high-density markers could maintain high prediction accuracies (Hickey et al., 2015;Jacobson et al., 2015;Gorjanc et al., 2017). Such a panel of low-density markers could be genotyped with a SNP array or amplicon GBS at a low cost, potentially enabling the utility of GS on larger populations (Campbell et al., 2015;Pembleton et al., 2016). Together with advances in low-cost genotyping and imputation methods, the high prediction accuracy found in this work pave the way to implement GS for FHB resistance in the NDSU HRSW breeding program.
The 427 breeding lines from the NDSU HRSW breeding program represent the current breeding pool. Few of the new breeding lines showed better FHB resistance than ND2710, one of the first experimental lines released by the NDSU HRSW breeding program from efforts to introduce FHB resistance from Sumai 3 into elite wheat lines adapted to the Northern Great Plains (Frohberg et al., 2004). Multiple target traits including grain yield, end-use quality traits, disease resistance, etc. are selected simultaneously in the NDSU HRSW breeding program, which can partially explain the relatively static FHB resistance development observed in these new breeding lines. The potential to utilize GS pre-selection for multiple traits like FHB resistance, grain yield, and other end-use quality traits at early generations further enhances the advantage conferred by GS compared to traditional phenotypic selection in HRSW variety development.

DATA AVAILABILITY
All datasets for this study are included in the manuscript and the Supplementary Files.

AUTHOR CONTRIBUTIONS
SZ and XL conceived and designed the study. YL, ES, JF, JH, AG, MM, SZ, and XL generated and analyzed the data. All authors wrote and approved the manuscript.

FUNDING
This work was supported by the NDSU Agricultural Experiment Station FHB funding to XL (FARG003502) and the US Wheat and Barley Scab Initiative to SZ (Agreement No. 59-0200-3-004).

ACKNOWLEDGMENTS
The authors would like to thank Joe Mullins and Yueqiang Leng for their contributions to the field plot maintenance and phenotypic evaluation. The authors would also like to thank Shiaoman Chao for the genotyping of candidate gene markers IFA-FM227 and barc186-80018.

SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2019.01007/ full#supplementary-material FIGURE S1 | Scree plot of the first 30 PCs derived from a principle component analysis.


extfieldsofstudy: Biology Business

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Agricultural And Food Sciences
Medicine

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The Swedish example of food animal production without extensive use of antibiotics – or “healthy animals do not need antibiotics”

Objective To describe how stakeholders at different levels in food animal production in Sweden work to contain antibiotic resistance, with a special focus on poultry production. The stakeholders’ perceptions of antibiotic resistance and awareness of the One Health concept were also studied. Methods This is an interview study with thirteen informants. They represent policymakers, trade organisations, and veterinarians and farmers in the poultry industry. Interview transcripts were analysed using content analysis. The analysis continued until a latent theme emerged, and then the content was rearranged in four domains. Findings A latent theme “Working in unison” emerged, based on the consistency expressed by the informants when they discussed antibiotic resistance, use of antibiotics and food animal production methods. The theme was built on four domains, representing the content of the interviews: Knowledge and engagement; Cooperation; Animal health concept; and Development in balance with economic prerequisites. The work for healthy animals started in Sweden already in the 1920-ies and continued step by step in cooperation and with support from the government. In 1986 Sweden became the first country to ban antibiotics for growth promotion. Veterinarians were considered important drivers of processes by spreading knowledge and working close to the farmers. Farmers felt involved in the development of production methods. The One Health concept was well known among stakeholders working at national level but not among veterinarians in production or farmers. Conclusions Sweden has come far in work to contain antibiotic resistance in the animal sector by practicing restrictive use of antibiotics in food animal production. This practise is based on a long tradition of cooperation among stakeholders, from policymakers to farmers, and with a primary focus on animal health and welfare.

To describe how stakeholders at different levels in food animal production in Sweden work to contain 23 antibiotic resistance, with a special focus on poultry production. The stakeholders' perceptions of 24 antibiotic resistance and awareness of the One Health concept were also studied.

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This is an interview study with thirteen informants. They represent policymakers, trade organisations,

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and veterinarians and farmers in the poultry industry. Interview transcripts were analysed using 28 content analysis. The analysis continued until a latent theme emerged, and then the content was 29 rearranged in four domains.

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A latent theme "Working in unison" emerged, based on the consistency expressed by the informants 32 when they discussed antibiotic resistance, use of antibiotics and food animal production methods. The a "One Health" approach (6). This approach was taken since resistant bacteria can be transmitted 55 between humans, animals, food and the environment, and across international borders. This action 56 plan emphasises a need for coordination among international sectors and actors including human and 57 veterinary medicine, agriculture, environment, finance and consumers (6).

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Efforts to contain antibiotic resistance started early in Sweden. In the human health sector "Strama", 59 the Swedish strategic programme against antibiotic resistance, was formed in 1995 (7). Even earlier, in 60 1986, the use of antibiotics for growth promotion in food producing animals was banned (8). Today

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Sweden has low levels of antibiotic use, and one of the lowest levels of antibiotic resistance compared 62 to most countries in the world (9). The Swedish government strategy for containing antibiotic 63 resistance from 2016 takes a One Health approach (10) with the overall goal to preserve the possibility 64 of effective treatment of bacterial infections in both humans and animals. The strategy was up-dated in one from human sector (human sector study is presented elsewhere). This pilot test did not change the 98 interview guide and these informants were included in the studies.

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The interviews were performed at a place convenient for the informant, often at their workplace. The

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informants could associate and speak freely from the main questions, and the interviewer followed the 101 conversation and asked probing questions. All interviews were audio-recorded and transcribed 102 verbatim by an external transcriber. Before the analysis started, the interviews were listened through 103 and transcripts checked by author IB. The results of the content analysis are presented in Table 3, which shows the relation between theme,   125 domains and categories, followed by a description of each domain and its categories. The descriptions 126 in general represent the whole informant group, and focus primarily on poultry production. When a 127 statement is related to a specific category of informants, the category is given, e.g. the category 128 "veterinarian/s" is used when veterinarians from different categories express a similar perception.

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Descriptions are then followed by table 4, which presents quotes from the informants in the domains.
130 Table 3. The categories are sorted in the domains that were identified together with the latent theme.

Theme Domain Category
Perceptions of antibiotics and antibiotic resistance We must do what we can to contain antibiotic resistance The use of antibiotics must be reduced antibiotics in need of more resources, and that this is a political issue.

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Except for one of the farmers, the informants were very concerned about antibiotic resistance, and 142 described it as a very serious threat. They understood that antibiotic resistance means that we cannot 143 treat diseases, not perform surgery safely, as well as increased mortality. A common perception was 144 that antibiotic resistance already exists, but that the real threat is a future problem. One policymaker 145 informant pointed out the economic consequences of antibiotic resistance and referred to estimates 146 from the World Bank Group. A farmer thought that soon people will hesitate to travel abroad, due to 147 the risk of bringing back antibiotic resistance. Some of the veterinarians compared the antibiotic 148 resistance issue with the issues of environment and climate -creeping threats, and issues which bring 149 out the need for behaviour change in humans. Furthermore, climate change and antibiotic resistance 150 were said to be connected, and climate change can increase the problem of antibiotic resistance.

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Informants perceived antibiotic resistance as caused by too extensive consumption of antibiotics in the 152 human sector, but the animal sector also contributes to the development of antibiotic resistance. They 153 also felt that antibiotic resistance mainly developed abroad and then imported to Sweden.
Veterinarians had comprehensive knowledge and could explain how antibiotic resistance develops.

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Some veterinarians explained that resistant bacteria we bring home when travelling disappear after a 156 while. One policymaker informant asked for more knowledge about how chemicals as heavy metals 157 and biocides can stress bacteria into developing resistance.

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We must do everything possible to contain antibiotic resistance 159 All veterinarians emphasized that we must do what we can to contain antibiotic resistance. In their

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The use of ABs must be reduced

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The veterinarians explained that eradicating antibiotic resistance is difficult but that reducing antibiotic 173 use is possible. The purpose is to reduce selection pressure. The use or non-use of antibiotics in food 174 producing animals is a matter of production methods, veterinarians said.

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Methods to reduce antibiotics in food producing animals were similar to methods in humans, e.g.

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finding alternatives to antibiotics, refraining from antibiotics when treatment is not necessary, and 188

Awareness, knowledge and information is needed
189 The informants expressed that awareness, knowledge and understanding was necessary among

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Cooperation in Sweden between animal and human sectors at policy level has a longstanding history,

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in Strama since the 90s, and later at the Swedish cooperative platform. Here animal and human sectors 216 agree to reduce the antibiotic use. A rather new discussion was cost sharing, meaning that costs were 217 to be shared by both sectors when actions were taken in the animal sector for the sake of public health.

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One hindering factor expressed by the veterinarians was the 'blame game'. This meant blaming other 219 professionals, sectors or other countries for doing less, a belief that others must change but that we are 220 doing enough. This could happen between animal and human sectors, both locally and internationally,

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or when statistics on antibiotic use were presented and countries were compared. Such attitudes could 222 hinder the will to cooperate and stop efforts to reduce antibiotic use. Two policymaker informants knew that antibiotic resistant bacteria can be found in the environment,

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and that the meaning of this is not yet known. They explained that we need more knowledge to 233 understand the meaning and how the environmental sector shall be involved in the One Health work.

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The other informants had no knowledge of antibiotic resistance in the environment but reflected on the 235 issue. 236

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Use of antibiotics to animals in Sweden is low

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All informants talked about how little antibiotics are used in food producing animals in Sweden.

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Antibiotic use was especially low in chicken farming and was not used at all in egg production. Areas

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The informants did not see antibiotic resistance as a problem in food animal production in Sweden.

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The farmers said it did not affect their work. However, all informants talked about the fact that in 2010

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Swedish chickens were infected by ESBL from breeding animals. Despite hard work Swedish 246 chickens may still carry ESBL. As one veterinarian in production explained, E coli infections in 247 chickens are not treated with antibiotics so as not to promote spread of ESBL resistance.

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All informants perceived antibiotics to be extensively used in food producing animals abroad. One  supply. Veterinarians and farmers stated keeping animals' health was a daily never-ending process.

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Chicken production in Sweden was described by the informants as industrial, large-scale and well 292 controlled. Globally, the chicken industry was described as a pyramid, with a few breeding companies 293 on top producing grandparents to all chickens in the world. Sweden buys from two of these breeders.

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There are two levels in Sweden above the chicken farmers, breeders and hatcheries. The hatcheries 295 deliver chickens to the farmers, which in turn are connected to a slaughterhouse. The slaughterhouse 296 does a planning based on peoples' demand of chicken and calculates the number of chickens to be 297 ordered from the hatchery, and when they need to be delivered. After delivery the chickens live 298 indoors until slaughter. Biosecurity was prioritized, and locally produced food was recommended 299 before ecologic production, which was regarded riskier for chickens and too costly for many 300 consumers. This was an overall perception among the informants, except one farmer who had small-301 scale ecological egg production.

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Economy rules food production in Sweden

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Economy rules the chicken production and the production methods. A farmer explained the economic 331 interest to follow all control programs very carefully, especially when you have a large production it 332 will be very costly if something goes wrong. A veterinarian in production reflected that it is not laws 333 that rule production methods, it is profitability. One veterinarian believed that in Sweden, agreements and guidelines had been more important than legislation, while another believed that governmental 335 financing had controlled the development of food production methods and there had been both 336 legislation and voluntary actions. The government has contributed financially to eradication of 337 diseases.

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All informants felt that farmers must be able to live on their production. If they cannot sell their goods, 339 production will cease. Buying Swedish meat supports a production that uses less antibiotics, and not 340 buying Swedish products means moving the antibiotic resistance problem somewhere else. However, 341 production without antibiotics was said to be more expensive and informants suggested that the 342 Swedish government could support Swedish production by explaining to consumers why Swedish 343 meat is more expensive. There was a belief that many Swedish consumers trust the Swedish 344 production of meat.

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Veterinarians expressed that we must safeguard the production we have, and governmental politicians 346 must know this. Threats to the Swedish production were highlighted, for instance too strict 347 regulations, lack of understanding of the factors that can undermine the food industry, and Swedish 348 animal-rights organisations which work hard to eliminate Swedish animal food production.

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Politicians have a responsibility for the antibiotic resistance issue. Regarding this, policymaker 350 informants seemed to think globally and the other informants nationally or at EU level. Politicians 351 need to allocate resources for research, monitoring and education. One farmer was sceptical and felt 352 that politicians think too short-term. Containing antibiotic resistance is politically charged, one trade 353 organisation informant said, reflecting on selling meat in the common market.

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The informants thought it was difficult to assess whether enough resources were allocated to 355 containing antibiotic resistance. Some policymaker informants mentioned that authorities needed more  Table 4. Quotes from the informants sorted in domains. All the informants have contributed to the 370 following quotes.

Domain Quotes
We have two possible tools we can work with -wise antibiotic use, and we can work with preventing infections. And then it is not only the spread of resistant bacteria, but all kinds of infections.
[…] As it looks today, we can't afford not to work with both tools, and I don't believe, I don't believe it will be as effective if we don't work with both.
Policymaker informant 1 So we try, oh, oh…yes…to keep discussions alive during the whole year, both about disease control but above all the use of antibiotics in this area. between different stakeholders in Sweden. The latent theme "Working in unison" was based on the 377 consistency expressed among the informants when they discussed antibiotic resistance, use of 378 antibiotics and production methods, with a special focus on poultry.

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The WHO guidelines for antibiotic use in food-producing animals include complete restriction of 380 antibiotic use for growth promotion and disease prevention in healthy animals, and restrictive use of 381 antibiotics identified as critically important for humans (13). Recommendations are based on evidence

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of decreased presence of bacterial antibiotic resistance in animals, and also humans, after interventions 383 to reduce antibiotic usage (14). According to our findings, the WHO guidelines are followed in

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Sweden. Studies have shown that stakeholders in food production may believe they use less antibiotics 385 than others (15). This could also be the case here. However, statistics on antibiotic use in food 386 producing animals show that antibiotic usage in Sweden is low, only Norway and Island use less 387 antibiotics (9). 388

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Regulations and action plans at global and national levels recommend restrictive antibiotic use in order 390 to contain antibiotic resistance. To make a change, theory needs to be transformed into practice, and 391 actors need to believe in the message. Some actors contest the link between agricultural antibiotic use 392 and antibiotic resistance, but studies report compelling supporting scientific evidence for the need to journey towards lower antibiotic use, it can probably help with a One Health approach. Sweden has 501 worked for 20-30 years to get where it is today. That time does not exist for countries about to start 502 their work now. Hopefully, a strategy based on One Health will help and be more effective. Also, it 503 will be interesting to see how the One Health approach will influence antibiotic resistance containing 504 measurements in Sweden.

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Limitations and strengths 506 Trustworthiness is of major importance in all research. In this study we used the criteria developed by 507 Lincoln and Guba to ensure high quality (37,38). To meet the criteria of credibility we recruited 508 stakeholders with different experience to gain a broad view of perceptions. Furthermore, the analysis 509 was well structured and carefully performed. Quotations from the text are used to demonstrate 510 confirmability. Transferability must be judged by the readers themselves and to make this possible we 511 described how the data were collected and analysed and gave background information about the 512 participants. Due to practical and financial reasons the number of informants was limited. The study 513 included a small number of Swedish stakeholders, and practitioners had experience from poultry 514 sector. The poultry farmers were recruited via the veterinarians, and it is possible that they had more 515 knowledge of antibiotics and were more motivated to work according to guidelines than farmers in 516 general. However, we recruited one egg farmer separately and used this interview to get a wider 517 picture. This informant never used antibiotics on the farm, and awareness of antibiotic resistance was 518 low. Like the other farmers the informant primarily described the daily work to help the animals to 519 stay as healthy as possible. A strength of our study was the choice of personal interviews, which often 520 give richer material, instead of by telephone, which might have produced more interviews.

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Additionally, our findings are in line with the perceived opinion in this field in Sweden and the 522 consistency in responses means we feel that our findings give a good picture of knowledge, attitudes 523 and practices in this sector.

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Sweden has come far in the work to contain antibiotic resistance in the animal sector by practicing 526 restrictive use of antibiotics in food animal production. This practise is based on a long tradition of


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Performance of Parents and F1 Hybrids for Quality Parameters in Tomato (Solanum lycopersicum L.)

Tomato (Solanum lycopersicum L.) is an important vegetable crop and particularly now a commercial crop widely grown all over tropical, sub-tropical and temperate regions of the world for both fresh and processing purpose. It ranks second only after potato (Bose et al., 2002). The optimum temperature for tomato growth and development is 20–24 °C. In India it occupies an area of about 0.78 million hectares with the production of 19.37 million tons and an average productivity of 28.10 tonnes per ha. Karnataka, occupies an area of 0.63 lakh hectares with a production of 21.38 lakh tons with an average productivity of 33.55 tonnes per ha (Anon., 2017). Tomato stands unique among vegetables because of its high nutritive values and myriad uses. Tomato is a gregarious vegetable, which fits comfortability with almost all the culinary items of the kitchen.

Introduction
Tomato (Solanum lycopersicum L.) is an important vegetable crop and particularly now a commercial crop widely grown all over tropical, sub-tropical and temperate regions of the world for both fresh and processing purpose. It ranks second only after potato (Bose et al., 2002). The optimum temperature for tomato growth and development is 20-24 °C. In India it occupies an area of about 0.78 million hectares with the production of 19.37 million tons and an average productivity of 28.10 tonnes per ha. Karnataka, occupies an area of 0.63 lakh hectares with a production of 21.38 lakh tons with an average productivity of 33.55 tonnes per ha (Anon., 2017). Tomato stands unique among vegetables because of its high nutritive values and myriad uses. Tomato is a gregarious vegetable, which fits comfortability with almost all the culinary items of the kitchen. attract more and more consumers because of its versatility of uses in domestic cooking and also in commercial production of processed products.
Three major processed products are: (i) tomato preserves (e.g. whole peeled tomatoes, tomato juice, tomato pulp, tomato puree, tomato paste and pickled tomatoes); (ii) dried tomatoes (tomato powder, tomato flakes and dried tomato fruits); and (iii) tomato-based foods (e.g. tomato soup, tomato sauces and ketchup). Thus, today tomato is one of the important raw materials for multimillion food industries.
Therefore development of tomatoes for fresh market and for processing purpose need improvement of quality traits like colour, fruit pH, acidity, total soluble solids and viscosity which helps in value addition and prevention of post-harvest losses. Therefore, there is a need for breeding tomato to improve traits pertaining to fresh and processed forms with high nutritive value.

Results and Discussion
The mean performance of the parents and hybrids for various characters under investigation are presented in Table 1a and  Table 1b, respectively and the results are presented as fallows.
Fruits with higher firmness can withstand physical and mechanical shock during shipping and remain healthy and nonperishable for more number of days as compared to fruits with lesser firmness. The firmness of fruits was determined by using penetrometer. The present study revealed that tomato had a wide and significant variation in fruit firmness (kg/cm 2 ) among the different parents and hybrids. Fruit firmness for parents ranged from 2.81 to 5.90 kg per cm 2 (Table  1a). The maximum fruit firmness was recorded line L4 (5.90 kg per cm 2 ) followed by the line L6 (5.79 kg per cm 2 ). Among the crosses, Maximum fruit firmness was recorded in the cross L4 X T3 (6.17 kg per cm 2 ) followed by L4 X T4 (6.12 kg per cm 2 ) as compared to general mean value of all the crosses (4.26 kg per cm 2 ). Similar results were supported by the Renuka et al., (2014) and Bharathkumar et al., (2017).  The maximum number of locules per fruit was found in line L4 (7.33) followed by L12 (5.67). Among crosses, the number of locules per fruit was found high in L4 X T3 and L4 X T4 (6.67 each). The increase in number of locules per fruit was reported by Ahmad et al., (2011) and Farzane et al., (2012).
Among the different genotypes significant differences were observed with respect to the polar diameter of fruits. It ranged of 3.24 to 5.64 cm (Table 1a). The maximum fruit length of the fruit was recorded in line L6 (5.64 cm) followed by L8 (5.07 cm). Among the crosses, the maximum fruit length was observed in the crosses, L4 X T1 (4.67 cm) closely followed by L2 X T1 (4.66 cm) in comparison to general mean (4.07 cm). Among 48 crosses studied ten have significantly more polar diameter than standard checks Arka Rakshak and Arka Samrat (6.17 cm and 4.30 cm, respectively). The significantly higher fruit diameter of the fruit was recorded in line L5 (6.10 cm) followed by L6 (5.82 cm. In the crosses, L4 X T1 (6.20 cm) followed by L4 X T4 (6.18 cm) have higher fruit diameter as compared to general mean (4.48 cm). Only eight crosses's fruit diameter was on par with the standard checks, Arka Rakshak and Arka Samrat (5.17 cm and 5.17 cm, respectively). The mean fruit size for parents ranged from 12.25 cm 2 (L11) to 32.83 cm 2 (L6). The line L6 (32.83 cm 2 ) followed by line L5 (25.49 cm 2 ) which had larger fruit size (Table 1b). Among F 1 s the mean performance was found in the range of 11.51 to 28.99 cm 2 . The maximum fruit size was observed in the cross L4 X T1 (28.99 cm 2 ) followed by L4 X T4 (28.28 cm 2 ). The general mean for fruit size of all crosses was 18.40 cm 2 . Only six crosses have shown significance in fruit size compared to commercial check Arka Samrat. These results are suggested by Chattopadhyay et al., 2013 andBharathkumar et al., 2017 in tomato crop.
The genotypes were noticed to have significant differences with respect to pH of fruit juice (Table 1a). Among parents L4 (5.27) followed by L6 (5.14) have shown higher level of pH of fruit juice. Out of 48 F 1 hybrids evaluated, the cross, L5 X T2 (5.17) followed by L3 X T2 (5.15) have shown highest pH of juice compared to general mean (4.59). The highest TSS was noted in line L5 (5.45 0 B) fallowed by L4 (5.43) and tester, T2 (5.37 0 B). Among crosses, the cross L5 X T3 (5.46 0 B) recorded the highest TSS. Among the 48 crosses, seven were noted to have higher TSS than commercial check, Arka Samrat (5.03 0 B) but none of the crosses have surpassed the commercial check, Arka Rakshak (5.58 0 B). Higher TSS content was in conformity with the earlier results of Singh et al., (2008), Kumari and Sharma, (2011) and Droka et al., (2012).
The highest titrable acidity of fruits was noticed in the cross L12 X T3 (0.72 %). Out of 48 crosses, 43 have recorded the more acidity than the standard check, Arka Rakshak (0.36 %) and 27 crosses have registered more acidity than Araka Samrat (0.48 %). High acidity of fruits is important for processing purposes, while the fruits with low acidity are preferred for fresh table purpose (Table 1b). Hence, the fruits of both kinds have commercial value in the market. TSS to acidity ratio of fruits is important in maintaining balanced taste for fresh table use as well as for processing. In the present study, higher TSS to acidity ratio was registered in the cross L1 X T2 (15.54). High acidity of fruits was also revealed by Kanthaswamy and Balkrishnan (1989), Kurian and Peter (1995) and Droka et al., (2012). The ascorbic acid content of tomato fruits varied between 12.83 to 28.41 mg per 100 g (Table 1b). In lines, the highest value for ascorbic acid content was observed for L9 (28.52 mg/100 g). Among the crosses, the highest amount of ascorbic acid was exhibited in L12 X T1 (34.98 mg/100 g) and most of the cross combinations have recorded higher ascorbic acid content over the standard checks, Arka Rakshak and Arka Samrat (19.99 mg/100 g and 25.82 mg/100 g). Ascorbic acid is much important from nutrition point of view. The findings are suggested by Kumari and Sharma, (2011) and Droka et al., (2012), Renuka et al., 2014 andBharathkumar et al., 2017 in tomato crop.
Lycopene content of the fruit varied significantly among all the parents and F 1 hybrids. The maximum lycopene content was observed in L12 (7.00 mg/100 g) and the lowest in L7 (3.03 mg/100 g). The present findings are acknowledged by the results supported by in Renuka et al., (2014) and Bharathkumar et al., (2017). Colour value L* content was found highest in line L4 (36.56) and was least in the line L11 (47.13). For color value a* the line L8 (41.76) was found high (Table 1a). In the same way for color value b* it was high for the line L11 (37.82) and least for line L4 (26.63). For character pH of the fruit juice, the highest value was registered in the cross L5 X T2 (5.17) fallowed by L3 X T2 (5.12). In general, mean performance of crosses for all the characters was higher than that of the parents suggesting the presence of overall heterosis. These findings are supported by Nagamani, (2017) in tomato.