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Relationship between rhizosphere soil properties and disease severity in highbush blueberry (Vaccinium corymbosum)
Applied Soil Ecology 137 (2019) 187–194
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Applied Soil Ecology journal homepage: www.elsevier.com/locate/apsoil
Relationship between rhizosphere soil properties and disease severity in highbush blueberry (Vaccinium corymbosum)
T
Sujuan Chen, Ye Zhu, Tianyun Shao, Xiaohua Long , Xiumei Gao, Zhaosheng Zhou ⁎
College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, PR China
ARTICLE INFO
ABSTRACT
Keywords: Blueberry Soil enzyme activity Microbial abundance Microbial diversity Nutrients Rhizosphere
In order to understand the effects of soil physical and chemical properties on the disease status and death of highbush blueberry (Vaccinium corymbosum L.) plants, the microbial diversity, enzyme activity, soil nutrient availability, pH, soil organic matter and element content of various parts of the poorly-performing and healthy plants were studied. The rhizosphere soil pHwater was higher in healthy than poor highbush blueberry plants. The content of available potassium was higher in the rhizosphere, and in contrast, that of exchangeable calcium and magnesium lower, in the rhizosphere than non-rhizosphere soil of both poor and healthy plants. The microbial diversity and the relative abundance were lower in the rhizosphere than non-rhizosphere soil, and were lowest in the rhizosphere soil of the poor plants. The catalase and acid phosphatase activities were higher in the rhizosphere than non-rhizosphere soil, but there was no difference in invertase activity. Redundancy analysis indicated that the relative abundance of Mizugakiibacter, Rhodanobacter, Gamma_proteobacterium_OR-113, Castellaniella, Acidobacterium, Bryobact, and Ottowia was positively correlated with soil acid phosphatase and invertase activities, available potassium and iron, and organic matter content, and was negatively correlated with pH, catalase activity and exchangeable calcium and magnesium. The content of Fe was lower in the dead compared with healthy roots. Therefore, it was known from the above experimental results that the poor growth of blueberry plants may be related to the acid phosphatase activity, the microbial diversity and relative abundance of rhizosphere soil.
1. Introduction
strong economic value and development prospects. Blueberries prefer acidic soils, but different varieties have different requirements. In general, the suitable soil pH range is 3.8–5.5 or 4.5–5.5 (Spiers and Braswell, 1992; Drummond et al., 2009). Most reports on the diseases affecting blueberries consider plants vs. pathogens or insects only. Austin (1994) reported that the inflorescences withered by Botrytis spp. were the main cause of significant loss of blueberry cultivation in the southeastern United States. Bryla and Linderman (2007) concluded that drip irrigation increased not only soil moisture near the base of the plant, but also the occurrence of root rot of Vaccinium corymbosum in northern China. In contrast, we could not find any studies relating the severity of blueberry poor growth to soil nutritional and microbial properties. Even though the link between soil pH and fertility and blueberry physiology was studied (e.g. Inostroza-Blancheteau et al., 2013; RojasLillo et al., 2014; Yanez-Mansilla et al., 2015), similar links regarding a disease burden and poor growth in blueberries could not be found in the literature. Hence, the main purpose of this study was to relate the soil pH, organic matter content, available nutrients, enzyme activities,
Blueberry (Vaccinium spp.) belongs to the family Ericaceae (Vasco et al., 2017). Common blueberry species include Vaccinium corymbosum L., Vaccinium angustifolium Aiton and Vaccinium ashei JM Reade. Although blueberries are native to North America, they are now grown in Australia, New Zealand and South America. Since the beginning of this century, blueberries have been successfully introduced and cultivated in China. Blueberries are considered functional food with rich nutritional value. There is increasing evidence of their health benefits and versatility in manufacturing popular consumer products (Hu et al., 2007). In view of their rich nutrient content, a series of studies on blueberries have shown high content of anthocyanins (Kazan et al., 2016) as well as polyphenols, flavonoids and various nutrients that improve the human immune system (Basu et al., 2010; Rendeiro et al., 2012). Blueberries have the highest antioxidant capacity among fruits and vegetables (Prior and Cao, 2000; Wu et al., 2004). Hence, blueberries are becoming increasingly popular with consumers, resulting in the
⁎
Corresponding author at: College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, PR China. E-mail address: [email protected] (X. Long).
https://doi.org/10.1016/j.apsoil.2019.02.015 Received 18 December 2018; Received in revised form 30 January 2019; Accepted 15 February 2019 Available online 26 February 2019 0929-1393/ © 2019 Elsevier B.V. All rights reserved.
Applied Soil Ecology 137 (2019) 187–194
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and the soil microbiome diversity and abundance to the severity of disease and poor growth in highbush blueberry to elucidate potential mechanisms contributing to poor performance and death of blueberry plants.
using AxyPrep DNA Gel Recovery Kit (AXYGEN). The PCR products were quantified by a QuantiFluorTM-ST Blue Fluorescence Quantification System (Promega), and then mixed according to the sequencing amount of each sample. The potassium permanganate titration method was used for soil catalase activity. Soil invertase activity was measured by the 3,5‑dinitrosalicylic acid colorimetric method, and soil acid phosphatase was determined by phosphorodisodium phosphate colorimetry. Soil was extracted by NH4OAc followed by measuring available potassium using flame photometry (Bao, 1988) and exchangeable calcium and magnesium using atomic absorption spectrometry (Jiang et al., 2011). Soil available iron was determined using DTPA extraction and atomic absorption spectrometry (Bao, 2000). The potassium, calcium, magnesium, and iron contents in plant samples were determined using sulfuric acid-hydrogen peroxide digestion and inductively-coupled plasma emission spectrometry (ICPOES).
2. Materials and methods The studied area is located at Shitouzhai Village, Baima Town, Lishui County, Nanjing City, Jiangsu Province (31°31′31.86″N, 119°08′27.09″E). The area is in the transition zone from the northern to the mid-subtropics, with four distinct seasons, abundant rainfall (average of 1087 mm/year) and sunshine (average of 2240 h/year), frost-free period of up to 237 days, and 15.4 °C average annual temperature. 2.1. Sample collection The main experimental species was highbush blueberry (Vaccinium corymbosum L.) cultivar Brigita (also widely grown elsewhere in the world, e.g. see Rojas-Lillo et al., 2014). The sampling of randomly selected dead and healthy blueberry bushes planted in the field soil was collected on June 14, 2017 when most fruits were mature. The rhizosphere (representing the soil still attached to the roots after gentle shaking) and non-rhizosphere soils (the soil shaken off roots) were collected separately, mixed well and packed into ziplock bags for soil biological and chemical analyses. Simultaneously, about 10 g of fresh soil was wrapped in tin foil and stored in liquid nitrogen for determination of microbial abundance and diversity. Plant samples (roots, stems, leaves and fruits) were collected at the same positions where soils had been sampled.
3. Data analysis The data analysis software used in the whole experiment included Excel 2007 and IBM SPSS Statistics 20. There were three replicates. One-way analysis of variance (ANOVA) was performed to compare the treatments, and the Pearson correlation method was used to analyze the correlation between the indicators. The significance was defined as the 0.05 level. 4. Results 4.1. Soil pH and organic matter content
2.2. Sample processing
Regardless of the plant status, the rhizosphere soil pH ranged from 4.0 to 4.8, and the non-rhizosphere soil exceeded 5.5 (Table 1). In the rhizosphere soil, the pH was significantly lower around poorly-performing than healthy plants, but there was no significant difference in the pH of non-rhizosphere soil regardless of the plant status. The organic matter content in all samples was between 19 and 21 g/ kg. There were no differences among the sample groups (Table 1).
The soil samples were air-dried, crushed and sieved through a 0.15 mm sieve, mixed and stored at room temperature. The fresh soilstored in liquid nitrogen was transferred to storage at −80 °C (Rodrigues et al., 2013). Samples of roots, stems, leaves, and developing (blue-green) and mature fruits (blue-violet) were first rinsed with tap water, then washed with distilled water, blotted dry with absorbent paper, and dried in an oven at 105 °C for 30 min to inactivate the enzymes, dried at 75 °C until constant weight, crushed and sieved through a 200 mesh sieve and stored at 4 °C in refrigerator.
4.2. Soil microbial analysis 4.2.1. Data quality control and OTU fundamental analysis Operational Taxonomic Units (OTUs) were defined on 97% similarity. A total of 1283 OTUs were generated, with the following order HNR > PNR > HR > PR. The PNR and PR combined had 496 OTUs, and PNR and HNR had a total of 942 OTUs. The PR and HR combined had 590 OTUs, and HR and HNR had a total of 781 OTUs (Fig. 1).
2.3. Analytical methods In soils, we analyzed pH, organic matter content, microbial diversity and abundance, enzyme activities (catalase, acid phosphatase and invertase), available potassium and iron, and exchangeable calcium and magnesium. In plants, we determined the content of potassium, calcium, magnesium and iron in different organs. Soil pH was measured in a 1:5 ratio of soil to water. Organic matter content was determined using low temperature external thermal dichromate oxidation colorimetry (Lu, 1999). DNA was extracted by MoBio PowerSoil® DNA Isolation Kit. DNA concentration and quality were measured using an ultramicroscope spectrophotometer. After extraction, the genomic DNA was detected by 1% w/w agarose gel electrophoresis. Sequencing of the V3-V4 region of the 16Sr RNA gene of bacteria and archaea were usd the Illumina MiSeq platform sequencing (Aoweisen Gene Technology Co., Ltd., Beijing, China). We used forward primer 338F (50-ACTCCTACGGGAGGCAG CAG-30) and the reverse primer 806R (50-GGACTACHVGGGTWTCTAAT-30). PCR used TransGen AP221-02: TransStart Fastpfu DNA Polymerase. Each sample was analyzed in three replicates. The PCR products of the same sample were mixed and checked by 2% w/w agarose gel electrophoresis, and the PCR products were recovered by cutting the gel
4.3. Alpha diversity analysis Alpha diversity analysis was conducted to assess the abundance and diversity of microbial communities. In all the four types of samples, the rhizosphere soil of poorly-performing plants had lower indices (Chao1, Table 1 pH and organic matter content in the rhizosphere and non-rhizosphere soil of poorly-performing and healthy blueberry plants. Soil sample
pH
PR PNR HR HNR
4.01 5.69 4.84 5.81
OM(g/kg) ± ± ± ±
0.16c 0.13a 0.47b 0.26a
20.7 19.0 19.3 18.7
± ± ± ±
1.43a 1.91a 1.88a 2.40a
Note: P = poor, H = healthy, R = rhizosphere, NR = non-rhizosphere. Different letters in the same column indicate significant (P ≤ 0.05) differences among the four groups of samples. Means of three replicates ± standard error. 188
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relatively more abundant in the rhizosphere of healthy plants than in the other three sample groups. The differences in relative abundance of other phyla among the four sample groups (even when significant) were relatively small (Fig. 3A). The relative abundance of Mizugakiibacter, Rhodanobacter, gamma_proteobacterium_OR_113 and Castellaniella was significantly higher in the rhizosphere of poor plants than in the other three sample groups (Fig. 3B), whereas the relative abundance of Acidothermus was higher in the rhizosphere soil of poor or healthy plants compared with the non-rhizosphere soil. There was no difference in the relative abundance of Bryobacter, Acidobacterium and Acidibacter in the four sample groups (Fig. 3B). 4.4.2. Soil enzyme activities The invertase activity did not differ significantly among the four sample groups (Table 3). The catalase activity was lower in the nonrhizosphere than rhizosphere soil (this difference was significant in case of poor plants, but occurred just as a non-significant trend in case of healthy plants). The same relationships were found for acid phosphatase activity, with the rhizosphere soil having significantly lower activity than the non-rhizosphere soil in either poor or healthy plants (Table 3).
Fig. 1. Venn diagram of OTUs distribution in the rhizosphere (PR and HR) and non-rhizosphere soil (PNR and HNR) of poor (PR and PNR) and healthy plants (HR and HNR).
4.5. Soil effective nutrient analysis There was no difference in the available soil Fe concentration in the four groups (Table 4). The content of available K was higher in the rhizosphere soil of poor plants than in the other three sample groups that did not differ among themselves. The exchangeable Ca concentration was significantly greater in the non-rhizosphere than rhizosphere soil of either poor or healthy plants. In the rhizosphere, there was no difference between poor and healthy plants, but the non-rhizosphere soil had higher exchangeable Ca in case of poor than healthy plants. The exchangeable Mg concentration followed the same relationships as exchangeable Ca (Table 4).
Table 2 Alpha diversity indices of rhizosphere and non-rhizosphere soils around poorlyperforming and healthy plants. Soil sample
Chao1 index
Shannon
Observed OTUs
PD-whole-tree
PR PNR HR HNR
476 933 733 943
4.66 8.11 7.12 7.12
323 816 603 815
34.3 72.6 56.6 71.6
± ± ± ±
207b 33a 114a 28a
± ± ± ±
1.59b 0.13a 0.41a 0.41a
± ± ± ±
150c 8a 107b 24a
± ± ± ±
12.5c 1.4a 8.9b 2.0a
Note: P = poor, H = healthy, R = rhizosphere, NR = non-rhizosphere. Different letters in the same column indicate significant (P ≤ 0.05) differences among the four groups of samples. Means of three replicates ± standard error.
4.6. Plant nutrient analysis Concentrations of K, Ca and Mg did not differ between poor and healthy plants for any plant part analyzed (Table 5). There was also no difference between unripe and mature fruits regardless of the plant status. In contrast, Fe concentration was significantly higher in roots of healthy compared with poor plants, but no difference between poor and healthy plants was noted regarding Fe concentration in other plant parts (Table 5).
Shannon, observed OTUs and PD-whole-tree) and thus lower microbial abundance and diversity than the other three sample groups (Table 2). Regardless of the plant status, the non-rhizosphere soils had the higher observed OTUs and PD-whole-tree indices (but similar Chao1 and Shannon indices) compared with the rhizosphere soils. 4.4. Species annotation/taxonomic analysis
4.7. Correlation analysis of various factors in soil
4.4.1. Analysis of species composition Fig. 2A and B showed the diversity of microorganisms in different soils. At the phylum level, Proteobacteria, Acidobacteria, Actinobacteria and Chloroflexi were the top four phyla in relative abundance, with Proteobacteria especially abundant. The relative abundance in Proteobacteria PR was (67%) > HNR (38%) > PNR (36%) > HR (34%); in Acidobacteria it was PNR (33%) > HNR (29%) > HR (21%) > PR (16%); in Actinobacteria HR (27%) > PR (9.2%) > HNR (6.3%) > PNR (3.9%); and Chloroflexi PNR (9.7%) > HNR (7.4%) > HR (5.9%) > PR (3%) (Fig. 2A). At the genus level, the most abundant bacteria were Unidentified, Mizugakiibacter and Rhodanobacter. For Unidentified the relative abundance was PNR (72%) > HNR (65%) > HR (37%) > PR (17%); Mizugakiibacter PR (17%) > HR (3.6%) > PNR = HNR (0.01%); and Rhodanobacter PR (16%) > HR (2.7%) > HNR (0.8%) > PNR (0.48%) (Fig. 2B). The relative abundance of Proteobacteria was greater in the rhizosphere soil of poor plants than in the other three sample groups, whereas Acidobacteria were more abundant in the non-rhizosphere than rhizosphere of poorly-performing plants (Fig. 3A). Actinobacteria were
Soil catalase was significantly negatively correlated with soil organic matter and available iron, and was significantly positively correlated with soil pH (Table 6). There was a significant negative correlation between soil organic matter and pH, and a significant positive correlation with available iron. Soil pH was significantly negatively correlated with available soil iron. There was a significant negative correlation between soil available iron and invertase activity. Exchangeable calcium was positively correlated with exchangeable magnesium and acid phosphatase activity. Exchangeable magnesium was also significantly positively correlated with acid phosphatase activity (Table 6). 4.8. RDA (redundancy analysis) The RDA analysis showed that the bacterial genera ranked in the top nine in terms of relative abundance: Mizugakiibacter, Rhodanobacter, gamma_proteobacterium_OR-113, Castellaniella, Acidobacterium, Bryobacter and Ottowia were positively correlated with soil acid 189
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Fig. 2. The relative abundance of bacteria at phylum (A) and genus levels (B) in the rhizosphere and non-rhizosphere soils of poorly-performing and healthy plants. Note: P = poor, H = healthy, R = rhizosphere soil, NR = non-rhizosphere.
phosphatase, invertase, available potassium, organic matter and available iron (Fig. 4). In contrast, these bacteria were negatively correlated with pH, catalase, and exchangeable calcium and magnesium. However, there was no correlation between the relative abundance of Bradyrhizobium and Acidothermus and environmental factors tested (Fig. 4).
enough to affect plant growth and fruit quality. The rhizosphere soil of poor plants had the lowest microbial diversity (Table 2). The main bacterial phylum was Proteobacteria, with relative abundance of 67% (Fig. 2A). The phylum Proteobacteria has a major advantage over Acidobacteria in ocean, fresh water, wastewater, spa mats, oral cavity. (Lapara and Alleman, 1999; Layton et al., 2000; Sievert et al., 2000; Smit et al., 2001; Paster Jr et al., 2002; Martiny et al., 2003; Polymenakou et al., 2009; Penn et al., 2006). This was consistent with the study presented here. Lee et al. (2008) showed that Acidobacteria may be predominant in quantity and metabolically active in the rhizosphere soil because of their involvement in the biogeochemical cycling. They may have a positive effect on the ecosystems dynamics because of their abundance in soil (Quaiser et al., 2003; Eichorst et al., 2007) as well genetic and metabolic diversity (Hugenholtz et al., 1998). In the current research, the lowest relative abundance of Acidobacteria was in the rhizosphere soil of poor plants (Fig. 3A), potentially contributing to low microbial diversity in that rhizosphere. Although Actinobacteria was the third most dominant group at the phylum classification level (Fig. 3A), their relative abundance in the rhizosphere soil of poor plants was only about 10%. A possible reason for such low abundance was the low content of soil organic matter.
5. Discussion Under the same fertilization conditions, highbush blueberry grew better in the soil at pH 4.5 than 6 (Liu et al., 2014), and often grew poorly when soil pH was > 5.5 (Almutairi et al., 2017). In the present study, the pH was higher than 5.5 in the non-rhizosphere soil, but more importantly it was 4.0–4.8 (Table 1) in the rhizosphere soil regardless of the plant status, suggesting that pH around roots was suitable and was unrelated to poor plant growth. Also, at soil pH 6, blueberries can grow healthily if soils have high organic matter content (Sciarappa et al., 2008). In many studies, however, there was no specific standard (or minimum required content) for organic matter specified, and most studies suggested that blueberry grow well when the content of organic matter was > 30 g/kg (Ahn et al., 2013). However, in the study presented here, the content of organic matter in the rhizosphere or nonrhizosphere soil was 19–21 g/kg (Table 1), which might have been low 190
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Fig. 3. The relative abundance of the top nine phyla (A) and genera (B) in the rhizosphere and non-rhizosphere soil or poorly-performing and healthy plants. Note: P = poor, H = healthy, R = rhizosphere, NR = non-rhizosphere. Different letters in the same column indicate significant (P ≤ 0.05) differences among the four groups of samples.
are unfavorable for the growth of blueberries (Pang et al., 2007; Farooque et al., 2011). It is important to bear in mind the sample collection in the present study was conducted in the local rainy season that could have resulted in hypoxic conditions in soil. Different fertilization methods, including application of manure and urea, can change the soil denitrification potential and the structure of denitrification microbial communities (Hallin et al., 2009; Enwall et al., 2005; Deiglmayr et al., 2004). According to the redundancy analysis (Fig. 4), at the genus classification level, relatively high relative abundance of bacteria was significantly negatively correlated with soil pH, and most of these bacteria would be involved in denitrification under anaerobic conditions. The microbial diversity in rhizosphere and non-rhizosphere soils in this experiment indicated that denitrification was relatively strong in the soils tested. Rhizosphere microorganisms not only inhibit soil pathogens through nutrient competition, antagonism, and induction of systemic resistance (Bakker et al., 2013), but also may cause plant death through pathogenesis (Santhanam et al., 2015; Shao et al., 2018). In the present study, the rhizosphere soil of poorly-performing plants had poor microbial diversity and was significantly different from the other three groups, It indicated that the microorganisms in this group of samples has a poor effect on the soil, which affected the normal growth of the plants due to poor soil biological properties. However, Agrios (1997) suggested that the main pathogens were pathogenic fungi and
Table 3 The activities of invertase, catalase and acid phosphatase in the rhizosphere and non-rhizosphere soils of poorly-performing and healthy blueberry plants. Soil sample
PR PNR HR HNR
Invertase activity (mg/g per day) 0.98 1.25 1.17 0.67
± ± ± ±
0.40a 0.68a 0.10a 0.30a
Catalase activity (mg/g per 20 min) 1.27 ± 0.12b 1.48 ± 0.02a 1.41 ± 0.09ab 1.51 ± 0.04a
Acid phosphatase activity (μmol/g per day) 1.97 6.43 2.03 4.85
± ± ± ±
1.20b 0.65a 1.20b 0.61a
Note: P = poor, H = healthy, R = rhizosphere, NR = non-rhizosphere. Different letters in a column indicate significant differences (P ≤ 0.05). Means ± standard error, n = 3.
Mizugakiibacter and Rhodanobacter were ferrous oxide nitrate-reducing bacterial genera present in sludge (Wang et al., 2017). Rhodanobacter was commonly found in iron ore fields (Hong et al., 2015; Blöthe and Roden, 2009). Castellaniella was a genus of gram-negative, facultatively anaerobic, motile bacteria from the family Alcaligenaceae (Dumler et al., 2015) that can also reduce nitrate (Pang and Liu, 2007). Therefore, in the present study the relatively abundant bacterial genera were mainly related to reduction of nitrate to nitrite, which occurs in hypoxic partly anaerobic conditions. However, anaerobic conditions 191
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Table 4 Effective nutrient concentrations (available Fe and K and exchangeable Ca and Mg) in the rhizosphere and non-rhizosphere soils of poorly-performing and healthy blueberry plants. Soil sample PR PNR HR HNR
AFe (mg·kg−1)
AK (mg·kg−1)
E-Ca (mg·kg−1)
E-Mg (mg·kg−1)
53 ± 10a 41 ± 6.2a 45 ± 4.6a 52 ± 6.4a
607 ± 24a 317 ± 30b 286 ± 115b 315 ± 28b
84 ± 22c 177 ± 22a 70 ± 26c 128 ± 14b
20.5 ± 4.3bc 42.0 ± 3.5a 17.6 ± 6.7c 28.1 ± 1.9b
Note: P = poor, H = healthy, R = rhizosphere, NR = non-rhizosphere. Different letters in a column indicate significant differences (P ≤ 0.05). Means ± standard error, n = 3. Table 5 Nutrient concentrations in different parts of poor and healthy blueberry plants. Plant type
K (g·kg−1)
Ca (g·kg−1)
Mg (g·kg−1)
Fe (g·kg−1)
H P
Roots 7 ± 2bc 4.77 ± 0.85de
Roots 44 ± 11ab 35.7 ± 5.56abcde
Roots 1.79 ± 0.40a 1.38 ± 0.45ab
Roots 3.72 ± 0.63a 2.17 ± 0.17b
H P
Stems 2.74 ± 0.70f 2.96 ± 0.72ef
Stems 31 ± 9bcdef 38 ± 5abcd
Stems 0.38 ± 0.01d 0.40 ± 0.08d
Stems 0.46 ± 0.16c 0.57 ± 0.12c
H P
Leaves 4.49 ± 0.26ef 3.81 ± 0.72ef
Leaves 44 ± 13abc 56 ± 14a
Leaves 1.63 ± 0.65ab 1.06 ± 0.32bc
Leaves 0.60 ± 0.23c 0.69 ± 0.12c
H P
Mature fruits 7.56 ± 0.26bc 8.68 ± 0.38ab
Mature fruits 11.3 ± 0.20f 12.9 ± 2.85ef
Mature fruits 0.36 ± 0.01d 0.50 ± 0.04 cd
Mature fruits 0.37 ± 0.09c 0.38 ± 0.10c
H P
Unripe fruits 6.28 ± 2.12 cd 9.86 ± 0.32a
Unripe fruits 15.1 ± 1.63def 18.0 ± 2.91cdef
Unripe fruits 0.34 ± 0.04d 0.34 ± 0.04 cd
Unripe fruits 0.62 ± 0.15c 0.55 ± 0.12c
Note: P = poor; H = healthy. Different letters in a column indicate significant differences (P ≤ 0.05). Means ± standard error, n = 3.
nematodes, which were not measured in the present study. Therefore, the unfavorable structure of the bacterial communities, and potentially a presence of bacteria that caused disease death in the rhizosphere soil of poor plants were suggested, but it was not possible to rule out pathogens that have previously caused differences in health and poor growth. Rhizosphere soil and plant roots are interconnected. Enzyme activity in the rhizosphere soil could affect plant growth. Significant improvement in P nutrition and growth in cowpea was associated with increased acid phosphatase activity in rhizosphere soil (Makoi et al., 2010). In the present study, the acid phosphatase activity was significantly lower in the rhizosphere soil than non-rhizosphere soil. Moreover, the acid phosphatase activity in the rhizosphere soil was lowest in poorly-performing plants (Table 3), suggesting that poorly growing blueberry plants were related to acid phosphatase activity in rhizosphere soil. Soil invertase can be used as a good biological indicator of soil quality and fertility (Ge et al., 2012). There was no significant difference in soil invertase activity in the present study in which blueberry plants were in the fruiting stage. At that stage, poor blueberry growth was associated with low catalase activity in the
rhizosphere soil, which was significantly different from the rhizosphere and non-rhizosphere soils in the healthy blueberry plants. In the present study, the contents of K, Ca, Mg, and Fe were higher (albeit non-significantly) in leaves of poor than healthy plants. This may be related to nutrient availability in soil, as well as to the mechanisms by which blueberries absorb nutrients. Eck (1988) pointed out that healthy blueberry shrubs usually had 3–8 g·Ca kg−1 in leaves. However, in the study presented here there was no shortage of Ca in blueberry organs, with up to 40 g·Ca kg−1 in leaves. The critical concentrations of K, Ca, Mg and Fe in healthy blueberry fruits are 7.7–11.4 g·kg−1, 0.6–0.9 g·kg−1, 0.6–0.9 g·kg−1 and 0.03–0.04 g·kg−1, respectively (Hu et al., 2007). In our data, concentrations of K, Ca, Mg and Fe in fruits were 6.3–7.5 g·kg−1, 11.3–15.1 g·kg−1, 0.34–0.36 g·kg−1 and 0.37–0.62 g·kg−1, respectively, regardless of the plant health status. The content of Mg and Fe was highest, and that of Ca and K the lowest, in roots. Iron and Ca had the highest contents in roots and leaves, respectively. According to the correlation analysis, there was a significant negative correlation between pH and effective soil Fe content (Table 6).
Table 6 Pearson correlation coefficients between various chemical and biochemical properties of soil. SOM Catalase SOM pH Available potassium Available iron Exchangeable calcium Exchangeable magnesium Invertase ⁎ ⁎⁎
−0.99
pH ⁎⁎
0.99 −0.96⁎ ⁎
Available potassium
Available iron
Exchangeable calcium
Exchangeable magnesium
Invertase
Acid phosphatase
−0.88 0.93 0.81
−0.96 0.96⁎ −0.96⁎ 0.87
0.65 −0.58 0.76 −0.35 −0.77
0.60 −0.53 0.70 −0.32 −0.74 0.99⁎⁎
0.17 0.10 −0.18 −0.13 −0.96⁎ 0.09 0.21
0.77 −0.72 0.86 −0.48 −0.86 0.98⁎ 0.96⁎ 0.01
⁎
Significant at 5% level. Significant at 1% level. 192
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Kazan, A., Sevimli-Gur, C., Yesil-Celiktas, O., Dunford, N.T., 2016. In vitro tumor suppression properties of blueberry extracts in liquid and encapsulated forms. Eur. Food Res. Technol. 243 (6), 1–7. Lapara, T.M., Alleman, J.E., 1999. Thermophilic aerobic biological wastewater treatment. Water Res. 33 (4), 895–908. Layton, A.C., Karanth, P.N., Lajoie, C.A., Meyers, A.J., Gregory, I.R., Stapleton, R.D., Taylor, D.E., Sayler, G.S., 2000. Quantification of Hyphomicrobium populations in activated sludge from an industrial wastewater treatment system as determined by 16S rRNA analysis. Appl. Environ. Microbiol. 66 (3), 1167–1174. Lee, S.H., Ka, J.O., Cho, J.C., 2008. Members of the phylum Acidobacteria are dominant and metabolically active in rhizosphere soil. FEMS Microbiol. Lett. 285 (2), 263–269. Liu, X., Jiang, Y., Wei, J., Liu, M.H., Yu, H., Zeng, Q.L., 2014. Effect of ammonium/nitrate ratio on growth of southern highbush blueberry in soil with different pH levels. Journal of plant resources. Environment. 23 (2), 60–64. Lu, R.k., 1999. Analytical Method of Soil Agricultural Chemistry. China Agricultural Science and Technology. Makoi, J.H.J.R., Chimphango, S.B.M., Dakora, F.D., 2010. Elevated levels of acid and alkaline phosphatase activity in roots and rhizosphere of cowpea (Vigna unguiculata L. Walp.) genotypes grown in mixed culture and at different densities with sorghum (Sorghum bicolor L.). Crop Pasture Sci. 61 (4), 279–286. Martiny, A.C., Jørgensen, T.M., Albrechtsen, H., Arvin, E., Molin, S., 2003. Long-term succession of structure and diversity of a biofilm formed in a model drinking water distribution system. Appl. Environ. Microbiol. 69 (11), 6899–6907. Pang, C.M., Liu, W.T., 2007. Community structure analysis of reverse osmosis membrane biofilms and the significance of Rhizobiales bacteria in biofouling. Environ. Sci. Technol. 41 (13), 4728–4734. Pang, J., Cuin, T., Shabala, L., Zhou, M., Mendham, N., Shabala, S., 2007. Effect of secondary metabolites associated with anaerobic soil conditions on ion fluxes and
Fig. 4. RDA (redundancy analysis) for correlation analysis between microbial community structure at genus level and soil chemical and biochemical properties.
6. Conclusions The poor growth of blueberry was related to relatively high pH of non-rhizosphere soil, and was closely related to soil organic matter content, microbial abundance and diversity and the catalase and acid phosphatase activities. Microbial diversity was lowest in the rhizosphere soil of poorly-performing plants. Proteobacteria, Acidobacteria, Actinobacteria and Chloroflexi were the dominant bacterial phyla in the rhizosphere soil of poor plants. Mizugakiibacter, Rhodanobacter, gamma_proteobacterium_OR-113, and Castellaniella were the most abundant in the rhizosphere soil of poor plants. According to the ecological characteristics and functions of these genera, it can be inferred that denitrification process was intense. Acknowledgements The authors are grateful for the financial support of Jiangsu Agricultural Science and Technology Innovation Fund Project [CX(18) 2013], the National Key Research and Development Program of China (2016YFC0501207), the National Key Project of Scientific and Technical Supporting Programs funded by Science and Technology Department of Jiangsu Province (Nos. BE2017310-2, BE2018387 and BN2016145), and the Fundamental Research Funds for the Central Universities (KYZ201623, YZ2016-1 and KYYJ201703). Conflict of interest statement The authors declare no competing financial interests. References Agrios, G.N., 1997. Plant Pathology, 4th ed. . Ahn, I., Kim, S.H., Maeng, W.Y., Lee, I.E., Chang, K.W., Lee, J.J., 2013. Effects of soil acidity and organic matter by application of organic materials and soil mulching with pine needles for soil surface management in blueberry eco-friendly farming. J. Korean Soil Fertilizer Soc. 46 (12), 556–562. Almutairi, K.F., Rui, M.A.M., Bryla, D.R., Strik, B.C., 2017. Chemigation with micronized sulfur rapidly reduces soil pH in a new planting of northern highbush blueberry. Hortscience 52 (10), 1413–1418.
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S. Chen, et al. electrophysiology in barley roots. Plant Physiol. 145 (1), 266–276. Paster Jr., B.J., W, F.J., Enwonwu, C.O., Idigbe, E.O., Savage, K.O., Levanos, V.A., Tamer, M.A., Ericson, R.L., Lau, C.N., Dewhirst, F.E., 2002. Prevalent bacterial species and novel phylotypes in advanced noma lesions. J. Clin. Microbiol. 40 (6), 2187–2191. Penn, K., Wu, D., Eisen, J.A., Ward, N., 2006. Characterization of bacterial communities associated with deep-sea corals on gulf of Alaska seamounts. Appl. Environ. Microbiol. 72 (2), 1680–1683. Polymenakou, P.N., Lampadariou, N., Mandalakis, M., Tselepides, A., 2009. Phylogenetic diversity of sediment bacteria from the southern Cretan margin, eastern Mediterranean sea. Syst. Appl. Microbiol. 32 (1), 17–26. Prior, R.L., Cao, G., 2000. Analysis of botanicals and dietary supplements for antioxidant capacity: a review. J. AOAC Int. 83 (4), 950–956. Quaiser, A., Ochsenreiter, T., Lanz, C., Schuster, S.C., Treusch, A.H., Eck, J., Schleper, C., 2003. Acidobacteria form a coherent but highly diverse group within the bacterial domain: evidence from environmental genomics. Mol. Microbiol. 50 (2), 563–575. Rendeiro, C., Vauzour, D., Kean, R.J., Butler, L.T., Rattray, M., Spencer, J.P., Williams, C.M., 2012. Blueberry supplementation induces spatial memory improvements and region-specific regulation of hippocampal bdnf mrna expression in young rats. Psychopharmacology. 223 (3), 319–330. Rodrigues, J.L.M., Pellizari, V.H., Mueller, R., Beak, K., Jesus, E.D., Paula, F.S., Mirza, B., Hamaoul, G.S., Tsai, S.M., Feigl, B., Tidje, J.M., Bohannan, B.J.M., Nusslein, K., 2013. Conversion of the Amazon rainforest to agriculture results in biotic homogenization of soil bacterial communities. Proc. Natl. Acad. Sci. U. S. A. 110 (3), 988. Rojas-Lillo, Y., Alberdi, M., Acevedo, P., Inostroza-Blancheteau, C., Rengel, Z., Mora, M.D., Reyes-Diaz, M., 2014. Manganese toxicity and UV-B radiation differentially influence the physiology and biochemistry of highbush blueberry (Vaccinium corymbosum) cultivars. Funct. Plant Biol. 41 (2), 156. Santhanam, R., Luu, V.T., Weinhold, A., Goldbeng, J., Oh, Y., Baldwin, I.T., 2015. Native root-associated bacteria rescue a plant from a sudden-wilt disease that emerged
during continuous cropping. Hortscience 112 (36), 5013. Sciarappa, W., Polavarapu, S., Barry, J., Oudemans, P., Ehlenfeldt, M., Pavlis, G., Polk, D., Holdcraft, R., 2008. Developing an organic production system for highbush blueberry. Hortscience 43 (1), 51–57. Shao, T., Zhao, J., Zhu, T., et al., 2018. Relationship between rhizosphere soil properties and blossom-end rot of tomatoes in coastal saline-alkali land. Appl. Soil Ecol. 127, 96–101 S0929139317313707. Sievert, S.M., Kuever, J., Muyzer, G., 2000. Identification of 16S ribosomal DNA-defined bacterial populations at a shallow submarine hydrothermal vent near Milos Island (Greece). Appl. Environ. Microbiol. 66 (7), 3102–3109. Smit, E., Leeflang, P., Gommans, S., Van, D.B.J., Van, M.S., Wernars, K., 2001. Diversity and seasonal fluctuations of the dominant members of the bacterial soil community in a wheat field as determined by cultivation and molecular methods. Appl. Environ. Microbiol. 67 (5), 2284–2291. Spiers, J.M., Braswell, J.H., 1992. Soil-applied sulfur affects elemental leaf content and growth of ‘Tifblue’ rabbiteye blueberry. J. Am. Soc. Hortic. Sci. 117 (2), 230–233. Vasco, V.R.L., Leopizzi, M., Maio, V.D., Raimo, T.D., Cesa, S., Masci, A., Rocca, C.D., 2017. LPS, oleuropein and blueberry extracts affect the survival, morphology and phosphoinositide signalling in stimulated human endothelial cells. J. Cell Commun. Signal. 11 (10), 1–11. Wang, R., Yang, C., Zhang, M., Xu, S.Y., Dai, C.L., Liang, L.Y., Zhao, H.P., Zheng, p., 2017. Chemoautotrophic denitrification based on ferrous iron oxidation: reactor performance and sludge characteristics. Chem. Eng. J. 313, 693–701. Wu, X., Beecher, G.R., Holden, J.M., Haytowitz, D.B., Gebhardt, S.E., Prior, R.L., 2004. Lipophilic and hydrophilic antioxidant capacities of common foods in the United States. J. Agric. Food Chem. 52 (12), 4026–4037. Yanez-Mansilla, E., Cartes, P., Reyesdíaz, M., Ribera-Fonseca, A., Rengel, Z., Lobos, W., Alberdi, M., 2015. Leaf nitrogen thresholds ensuring high antioxidant features of Vaccinium corymbosum cultivars. J. Soil Sci. Plant Nutr. 15 (3), 545–546.
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Relationship between rhizosphere soil properties and disease severity in highbush blueberry (Vaccinium corymbosum)
T
Sujuan Chen, Ye Zhu, Tianyun Shao, Xiaohua Long , Xiumei Gao, Zhaosheng Zhou ⁎
College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, PR China
ARTICLE INFO
ABSTRACT
Keywords: Blueberry Soil enzyme activity Microbial abundance Microbial diversity Nutrients Rhizosphere
In order to understand the effects of soil physical and chemical properties on the disease status and death of highbush blueberry (Vaccinium corymbosum L.) plants, the microbial diversity, enzyme activity, soil nutrient availability, pH, soil organic matter and element content of various parts of the poorly-performing and healthy plants were studied. The rhizosphere soil pHwater was higher in healthy than poor highbush blueberry plants. The content of available potassium was higher in the rhizosphere, and in contrast, that of exchangeable calcium and magnesium lower, in the rhizosphere than non-rhizosphere soil of both poor and healthy plants. The microbial diversity and the relative abundance were lower in the rhizosphere than non-rhizosphere soil, and were lowest in the rhizosphere soil of the poor plants. The catalase and acid phosphatase activities were higher in the rhizosphere than non-rhizosphere soil, but there was no difference in invertase activity. Redundancy analysis indicated that the relative abundance of Mizugakiibacter, Rhodanobacter, Gamma_proteobacterium_OR-113, Castellaniella, Acidobacterium, Bryobact, and Ottowia was positively correlated with soil acid phosphatase and invertase activities, available potassium and iron, and organic matter content, and was negatively correlated with pH, catalase activity and exchangeable calcium and magnesium. The content of Fe was lower in the dead compared with healthy roots. Therefore, it was known from the above experimental results that the poor growth of blueberry plants may be related to the acid phosphatase activity, the microbial diversity and relative abundance of rhizosphere soil.
1. Introduction
strong economic value and development prospects. Blueberries prefer acidic soils, but different varieties have different requirements. In general, the suitable soil pH range is 3.8–5.5 or 4.5–5.5 (Spiers and Braswell, 1992; Drummond et al., 2009). Most reports on the diseases affecting blueberries consider plants vs. pathogens or insects only. Austin (1994) reported that the inflorescences withered by Botrytis spp. were the main cause of significant loss of blueberry cultivation in the southeastern United States. Bryla and Linderman (2007) concluded that drip irrigation increased not only soil moisture near the base of the plant, but also the occurrence of root rot of Vaccinium corymbosum in northern China. In contrast, we could not find any studies relating the severity of blueberry poor growth to soil nutritional and microbial properties. Even though the link between soil pH and fertility and blueberry physiology was studied (e.g. Inostroza-Blancheteau et al., 2013; RojasLillo et al., 2014; Yanez-Mansilla et al., 2015), similar links regarding a disease burden and poor growth in blueberries could not be found in the literature. Hence, the main purpose of this study was to relate the soil pH, organic matter content, available nutrients, enzyme activities,
Blueberry (Vaccinium spp.) belongs to the family Ericaceae (Vasco et al., 2017). Common blueberry species include Vaccinium corymbosum L., Vaccinium angustifolium Aiton and Vaccinium ashei JM Reade. Although blueberries are native to North America, they are now grown in Australia, New Zealand and South America. Since the beginning of this century, blueberries have been successfully introduced and cultivated in China. Blueberries are considered functional food with rich nutritional value. There is increasing evidence of their health benefits and versatility in manufacturing popular consumer products (Hu et al., 2007). In view of their rich nutrient content, a series of studies on blueberries have shown high content of anthocyanins (Kazan et al., 2016) as well as polyphenols, flavonoids and various nutrients that improve the human immune system (Basu et al., 2010; Rendeiro et al., 2012). Blueberries have the highest antioxidant capacity among fruits and vegetables (Prior and Cao, 2000; Wu et al., 2004). Hence, blueberries are becoming increasingly popular with consumers, resulting in the
⁎
Corresponding author at: College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, PR China. E-mail address: [email protected] (X. Long).
https://doi.org/10.1016/j.apsoil.2019.02.015 Received 18 December 2018; Received in revised form 30 January 2019; Accepted 15 February 2019 Available online 26 February 2019 0929-1393/ © 2019 Elsevier B.V. All rights reserved.
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and the soil microbiome diversity and abundance to the severity of disease and poor growth in highbush blueberry to elucidate potential mechanisms contributing to poor performance and death of blueberry plants.
using AxyPrep DNA Gel Recovery Kit (AXYGEN). The PCR products were quantified by a QuantiFluorTM-ST Blue Fluorescence Quantification System (Promega), and then mixed according to the sequencing amount of each sample. The potassium permanganate titration method was used for soil catalase activity. Soil invertase activity was measured by the 3,5‑dinitrosalicylic acid colorimetric method, and soil acid phosphatase was determined by phosphorodisodium phosphate colorimetry. Soil was extracted by NH4OAc followed by measuring available potassium using flame photometry (Bao, 1988) and exchangeable calcium and magnesium using atomic absorption spectrometry (Jiang et al., 2011). Soil available iron was determined using DTPA extraction and atomic absorption spectrometry (Bao, 2000). The potassium, calcium, magnesium, and iron contents in plant samples were determined using sulfuric acid-hydrogen peroxide digestion and inductively-coupled plasma emission spectrometry (ICPOES).
2. Materials and methods The studied area is located at Shitouzhai Village, Baima Town, Lishui County, Nanjing City, Jiangsu Province (31°31′31.86″N, 119°08′27.09″E). The area is in the transition zone from the northern to the mid-subtropics, with four distinct seasons, abundant rainfall (average of 1087 mm/year) and sunshine (average of 2240 h/year), frost-free period of up to 237 days, and 15.4 °C average annual temperature. 2.1. Sample collection The main experimental species was highbush blueberry (Vaccinium corymbosum L.) cultivar Brigita (also widely grown elsewhere in the world, e.g. see Rojas-Lillo et al., 2014). The sampling of randomly selected dead and healthy blueberry bushes planted in the field soil was collected on June 14, 2017 when most fruits were mature. The rhizosphere (representing the soil still attached to the roots after gentle shaking) and non-rhizosphere soils (the soil shaken off roots) were collected separately, mixed well and packed into ziplock bags for soil biological and chemical analyses. Simultaneously, about 10 g of fresh soil was wrapped in tin foil and stored in liquid nitrogen for determination of microbial abundance and diversity. Plant samples (roots, stems, leaves and fruits) were collected at the same positions where soils had been sampled.
3. Data analysis The data analysis software used in the whole experiment included Excel 2007 and IBM SPSS Statistics 20. There were three replicates. One-way analysis of variance (ANOVA) was performed to compare the treatments, and the Pearson correlation method was used to analyze the correlation between the indicators. The significance was defined as the 0.05 level. 4. Results 4.1. Soil pH and organic matter content
2.2. Sample processing
Regardless of the plant status, the rhizosphere soil pH ranged from 4.0 to 4.8, and the non-rhizosphere soil exceeded 5.5 (Table 1). In the rhizosphere soil, the pH was significantly lower around poorly-performing than healthy plants, but there was no significant difference in the pH of non-rhizosphere soil regardless of the plant status. The organic matter content in all samples was between 19 and 21 g/ kg. There were no differences among the sample groups (Table 1).
The soil samples were air-dried, crushed and sieved through a 0.15 mm sieve, mixed and stored at room temperature. The fresh soilstored in liquid nitrogen was transferred to storage at −80 °C (Rodrigues et al., 2013). Samples of roots, stems, leaves, and developing (blue-green) and mature fruits (blue-violet) were first rinsed with tap water, then washed with distilled water, blotted dry with absorbent paper, and dried in an oven at 105 °C for 30 min to inactivate the enzymes, dried at 75 °C until constant weight, crushed and sieved through a 200 mesh sieve and stored at 4 °C in refrigerator.
4.2. Soil microbial analysis 4.2.1. Data quality control and OTU fundamental analysis Operational Taxonomic Units (OTUs) were defined on 97% similarity. A total of 1283 OTUs were generated, with the following order HNR > PNR > HR > PR. The PNR and PR combined had 496 OTUs, and PNR and HNR had a total of 942 OTUs. The PR and HR combined had 590 OTUs, and HR and HNR had a total of 781 OTUs (Fig. 1).
2.3. Analytical methods In soils, we analyzed pH, organic matter content, microbial diversity and abundance, enzyme activities (catalase, acid phosphatase and invertase), available potassium and iron, and exchangeable calcium and magnesium. In plants, we determined the content of potassium, calcium, magnesium and iron in different organs. Soil pH was measured in a 1:5 ratio of soil to water. Organic matter content was determined using low temperature external thermal dichromate oxidation colorimetry (Lu, 1999). DNA was extracted by MoBio PowerSoil® DNA Isolation Kit. DNA concentration and quality were measured using an ultramicroscope spectrophotometer. After extraction, the genomic DNA was detected by 1% w/w agarose gel electrophoresis. Sequencing of the V3-V4 region of the 16Sr RNA gene of bacteria and archaea were usd the Illumina MiSeq platform sequencing (Aoweisen Gene Technology Co., Ltd., Beijing, China). We used forward primer 338F (50-ACTCCTACGGGAGGCAG CAG-30) and the reverse primer 806R (50-GGACTACHVGGGTWTCTAAT-30). PCR used TransGen AP221-02: TransStart Fastpfu DNA Polymerase. Each sample was analyzed in three replicates. The PCR products of the same sample were mixed and checked by 2% w/w agarose gel electrophoresis, and the PCR products were recovered by cutting the gel
4.3. Alpha diversity analysis Alpha diversity analysis was conducted to assess the abundance and diversity of microbial communities. In all the four types of samples, the rhizosphere soil of poorly-performing plants had lower indices (Chao1, Table 1 pH and organic matter content in the rhizosphere and non-rhizosphere soil of poorly-performing and healthy blueberry plants. Soil sample
pH
PR PNR HR HNR
4.01 5.69 4.84 5.81
OM(g/kg) ± ± ± ±
0.16c 0.13a 0.47b 0.26a
20.7 19.0 19.3 18.7
± ± ± ±
1.43a 1.91a 1.88a 2.40a
Note: P = poor, H = healthy, R = rhizosphere, NR = non-rhizosphere. Different letters in the same column indicate significant (P ≤ 0.05) differences among the four groups of samples. Means of three replicates ± standard error. 188
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relatively more abundant in the rhizosphere of healthy plants than in the other three sample groups. The differences in relative abundance of other phyla among the four sample groups (even when significant) were relatively small (Fig. 3A). The relative abundance of Mizugakiibacter, Rhodanobacter, gamma_proteobacterium_OR_113 and Castellaniella was significantly higher in the rhizosphere of poor plants than in the other three sample groups (Fig. 3B), whereas the relative abundance of Acidothermus was higher in the rhizosphere soil of poor or healthy plants compared with the non-rhizosphere soil. There was no difference in the relative abundance of Bryobacter, Acidobacterium and Acidibacter in the four sample groups (Fig. 3B). 4.4.2. Soil enzyme activities The invertase activity did not differ significantly among the four sample groups (Table 3). The catalase activity was lower in the nonrhizosphere than rhizosphere soil (this difference was significant in case of poor plants, but occurred just as a non-significant trend in case of healthy plants). The same relationships were found for acid phosphatase activity, with the rhizosphere soil having significantly lower activity than the non-rhizosphere soil in either poor or healthy plants (Table 3).
Fig. 1. Venn diagram of OTUs distribution in the rhizosphere (PR and HR) and non-rhizosphere soil (PNR and HNR) of poor (PR and PNR) and healthy plants (HR and HNR).
4.5. Soil effective nutrient analysis There was no difference in the available soil Fe concentration in the four groups (Table 4). The content of available K was higher in the rhizosphere soil of poor plants than in the other three sample groups that did not differ among themselves. The exchangeable Ca concentration was significantly greater in the non-rhizosphere than rhizosphere soil of either poor or healthy plants. In the rhizosphere, there was no difference between poor and healthy plants, but the non-rhizosphere soil had higher exchangeable Ca in case of poor than healthy plants. The exchangeable Mg concentration followed the same relationships as exchangeable Ca (Table 4).
Table 2 Alpha diversity indices of rhizosphere and non-rhizosphere soils around poorlyperforming and healthy plants. Soil sample
Chao1 index
Shannon
Observed OTUs
PD-whole-tree
PR PNR HR HNR
476 933 733 943
4.66 8.11 7.12 7.12
323 816 603 815
34.3 72.6 56.6 71.6
± ± ± ±
207b 33a 114a 28a
± ± ± ±
1.59b 0.13a 0.41a 0.41a
± ± ± ±
150c 8a 107b 24a
± ± ± ±
12.5c 1.4a 8.9b 2.0a
Note: P = poor, H = healthy, R = rhizosphere, NR = non-rhizosphere. Different letters in the same column indicate significant (P ≤ 0.05) differences among the four groups of samples. Means of three replicates ± standard error.
4.6. Plant nutrient analysis Concentrations of K, Ca and Mg did not differ between poor and healthy plants for any plant part analyzed (Table 5). There was also no difference between unripe and mature fruits regardless of the plant status. In contrast, Fe concentration was significantly higher in roots of healthy compared with poor plants, but no difference between poor and healthy plants was noted regarding Fe concentration in other plant parts (Table 5).
Shannon, observed OTUs and PD-whole-tree) and thus lower microbial abundance and diversity than the other three sample groups (Table 2). Regardless of the plant status, the non-rhizosphere soils had the higher observed OTUs and PD-whole-tree indices (but similar Chao1 and Shannon indices) compared with the rhizosphere soils. 4.4. Species annotation/taxonomic analysis
4.7. Correlation analysis of various factors in soil
4.4.1. Analysis of species composition Fig. 2A and B showed the diversity of microorganisms in different soils. At the phylum level, Proteobacteria, Acidobacteria, Actinobacteria and Chloroflexi were the top four phyla in relative abundance, with Proteobacteria especially abundant. The relative abundance in Proteobacteria PR was (67%) > HNR (38%) > PNR (36%) > HR (34%); in Acidobacteria it was PNR (33%) > HNR (29%) > HR (21%) > PR (16%); in Actinobacteria HR (27%) > PR (9.2%) > HNR (6.3%) > PNR (3.9%); and Chloroflexi PNR (9.7%) > HNR (7.4%) > HR (5.9%) > PR (3%) (Fig. 2A). At the genus level, the most abundant bacteria were Unidentified, Mizugakiibacter and Rhodanobacter. For Unidentified the relative abundance was PNR (72%) > HNR (65%) > HR (37%) > PR (17%); Mizugakiibacter PR (17%) > HR (3.6%) > PNR = HNR (0.01%); and Rhodanobacter PR (16%) > HR (2.7%) > HNR (0.8%) > PNR (0.48%) (Fig. 2B). The relative abundance of Proteobacteria was greater in the rhizosphere soil of poor plants than in the other three sample groups, whereas Acidobacteria were more abundant in the non-rhizosphere than rhizosphere of poorly-performing plants (Fig. 3A). Actinobacteria were
Soil catalase was significantly negatively correlated with soil organic matter and available iron, and was significantly positively correlated with soil pH (Table 6). There was a significant negative correlation between soil organic matter and pH, and a significant positive correlation with available iron. Soil pH was significantly negatively correlated with available soil iron. There was a significant negative correlation between soil available iron and invertase activity. Exchangeable calcium was positively correlated with exchangeable magnesium and acid phosphatase activity. Exchangeable magnesium was also significantly positively correlated with acid phosphatase activity (Table 6). 4.8. RDA (redundancy analysis) The RDA analysis showed that the bacterial genera ranked in the top nine in terms of relative abundance: Mizugakiibacter, Rhodanobacter, gamma_proteobacterium_OR-113, Castellaniella, Acidobacterium, Bryobacter and Ottowia were positively correlated with soil acid 189
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Fig. 2. The relative abundance of bacteria at phylum (A) and genus levels (B) in the rhizosphere and non-rhizosphere soils of poorly-performing and healthy plants. Note: P = poor, H = healthy, R = rhizosphere soil, NR = non-rhizosphere.
phosphatase, invertase, available potassium, organic matter and available iron (Fig. 4). In contrast, these bacteria were negatively correlated with pH, catalase, and exchangeable calcium and magnesium. However, there was no correlation between the relative abundance of Bradyrhizobium and Acidothermus and environmental factors tested (Fig. 4).
enough to affect plant growth and fruit quality. The rhizosphere soil of poor plants had the lowest microbial diversity (Table 2). The main bacterial phylum was Proteobacteria, with relative abundance of 67% (Fig. 2A). The phylum Proteobacteria has a major advantage over Acidobacteria in ocean, fresh water, wastewater, spa mats, oral cavity. (Lapara and Alleman, 1999; Layton et al., 2000; Sievert et al., 2000; Smit et al., 2001; Paster Jr et al., 2002; Martiny et al., 2003; Polymenakou et al., 2009; Penn et al., 2006). This was consistent with the study presented here. Lee et al. (2008) showed that Acidobacteria may be predominant in quantity and metabolically active in the rhizosphere soil because of their involvement in the biogeochemical cycling. They may have a positive effect on the ecosystems dynamics because of their abundance in soil (Quaiser et al., 2003; Eichorst et al., 2007) as well genetic and metabolic diversity (Hugenholtz et al., 1998). In the current research, the lowest relative abundance of Acidobacteria was in the rhizosphere soil of poor plants (Fig. 3A), potentially contributing to low microbial diversity in that rhizosphere. Although Actinobacteria was the third most dominant group at the phylum classification level (Fig. 3A), their relative abundance in the rhizosphere soil of poor plants was only about 10%. A possible reason for such low abundance was the low content of soil organic matter.
5. Discussion Under the same fertilization conditions, highbush blueberry grew better in the soil at pH 4.5 than 6 (Liu et al., 2014), and often grew poorly when soil pH was > 5.5 (Almutairi et al., 2017). In the present study, the pH was higher than 5.5 in the non-rhizosphere soil, but more importantly it was 4.0–4.8 (Table 1) in the rhizosphere soil regardless of the plant status, suggesting that pH around roots was suitable and was unrelated to poor plant growth. Also, at soil pH 6, blueberries can grow healthily if soils have high organic matter content (Sciarappa et al., 2008). In many studies, however, there was no specific standard (or minimum required content) for organic matter specified, and most studies suggested that blueberry grow well when the content of organic matter was > 30 g/kg (Ahn et al., 2013). However, in the study presented here, the content of organic matter in the rhizosphere or nonrhizosphere soil was 19–21 g/kg (Table 1), which might have been low 190
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Fig. 3. The relative abundance of the top nine phyla (A) and genera (B) in the rhizosphere and non-rhizosphere soil or poorly-performing and healthy plants. Note: P = poor, H = healthy, R = rhizosphere, NR = non-rhizosphere. Different letters in the same column indicate significant (P ≤ 0.05) differences among the four groups of samples.
are unfavorable for the growth of blueberries (Pang et al., 2007; Farooque et al., 2011). It is important to bear in mind the sample collection in the present study was conducted in the local rainy season that could have resulted in hypoxic conditions in soil. Different fertilization methods, including application of manure and urea, can change the soil denitrification potential and the structure of denitrification microbial communities (Hallin et al., 2009; Enwall et al., 2005; Deiglmayr et al., 2004). According to the redundancy analysis (Fig. 4), at the genus classification level, relatively high relative abundance of bacteria was significantly negatively correlated with soil pH, and most of these bacteria would be involved in denitrification under anaerobic conditions. The microbial diversity in rhizosphere and non-rhizosphere soils in this experiment indicated that denitrification was relatively strong in the soils tested. Rhizosphere microorganisms not only inhibit soil pathogens through nutrient competition, antagonism, and induction of systemic resistance (Bakker et al., 2013), but also may cause plant death through pathogenesis (Santhanam et al., 2015; Shao et al., 2018). In the present study, the rhizosphere soil of poorly-performing plants had poor microbial diversity and was significantly different from the other three groups, It indicated that the microorganisms in this group of samples has a poor effect on the soil, which affected the normal growth of the plants due to poor soil biological properties. However, Agrios (1997) suggested that the main pathogens were pathogenic fungi and
Table 3 The activities of invertase, catalase and acid phosphatase in the rhizosphere and non-rhizosphere soils of poorly-performing and healthy blueberry plants. Soil sample
PR PNR HR HNR
Invertase activity (mg/g per day) 0.98 1.25 1.17 0.67
± ± ± ±
0.40a 0.68a 0.10a 0.30a
Catalase activity (mg/g per 20 min) 1.27 ± 0.12b 1.48 ± 0.02a 1.41 ± 0.09ab 1.51 ± 0.04a
Acid phosphatase activity (μmol/g per day) 1.97 6.43 2.03 4.85
± ± ± ±
1.20b 0.65a 1.20b 0.61a
Note: P = poor, H = healthy, R = rhizosphere, NR = non-rhizosphere. Different letters in a column indicate significant differences (P ≤ 0.05). Means ± standard error, n = 3.
Mizugakiibacter and Rhodanobacter were ferrous oxide nitrate-reducing bacterial genera present in sludge (Wang et al., 2017). Rhodanobacter was commonly found in iron ore fields (Hong et al., 2015; Blöthe and Roden, 2009). Castellaniella was a genus of gram-negative, facultatively anaerobic, motile bacteria from the family Alcaligenaceae (Dumler et al., 2015) that can also reduce nitrate (Pang and Liu, 2007). Therefore, in the present study the relatively abundant bacterial genera were mainly related to reduction of nitrate to nitrite, which occurs in hypoxic partly anaerobic conditions. However, anaerobic conditions 191
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Table 4 Effective nutrient concentrations (available Fe and K and exchangeable Ca and Mg) in the rhizosphere and non-rhizosphere soils of poorly-performing and healthy blueberry plants. Soil sample PR PNR HR HNR
AFe (mg·kg−1)
AK (mg·kg−1)
E-Ca (mg·kg−1)
E-Mg (mg·kg−1)
53 ± 10a 41 ± 6.2a 45 ± 4.6a 52 ± 6.4a
607 ± 24a 317 ± 30b 286 ± 115b 315 ± 28b
84 ± 22c 177 ± 22a 70 ± 26c 128 ± 14b
20.5 ± 4.3bc 42.0 ± 3.5a 17.6 ± 6.7c 28.1 ± 1.9b
Note: P = poor, H = healthy, R = rhizosphere, NR = non-rhizosphere. Different letters in a column indicate significant differences (P ≤ 0.05). Means ± standard error, n = 3. Table 5 Nutrient concentrations in different parts of poor and healthy blueberry plants. Plant type
K (g·kg−1)
Ca (g·kg−1)
Mg (g·kg−1)
Fe (g·kg−1)
H P
Roots 7 ± 2bc 4.77 ± 0.85de
Roots 44 ± 11ab 35.7 ± 5.56abcde
Roots 1.79 ± 0.40a 1.38 ± 0.45ab
Roots 3.72 ± 0.63a 2.17 ± 0.17b
H P
Stems 2.74 ± 0.70f 2.96 ± 0.72ef
Stems 31 ± 9bcdef 38 ± 5abcd
Stems 0.38 ± 0.01d 0.40 ± 0.08d
Stems 0.46 ± 0.16c 0.57 ± 0.12c
H P
Leaves 4.49 ± 0.26ef 3.81 ± 0.72ef
Leaves 44 ± 13abc 56 ± 14a
Leaves 1.63 ± 0.65ab 1.06 ± 0.32bc
Leaves 0.60 ± 0.23c 0.69 ± 0.12c
H P
Mature fruits 7.56 ± 0.26bc 8.68 ± 0.38ab
Mature fruits 11.3 ± 0.20f 12.9 ± 2.85ef
Mature fruits 0.36 ± 0.01d 0.50 ± 0.04 cd
Mature fruits 0.37 ± 0.09c 0.38 ± 0.10c
H P
Unripe fruits 6.28 ± 2.12 cd 9.86 ± 0.32a
Unripe fruits 15.1 ± 1.63def 18.0 ± 2.91cdef
Unripe fruits 0.34 ± 0.04d 0.34 ± 0.04 cd
Unripe fruits 0.62 ± 0.15c 0.55 ± 0.12c
Note: P = poor; H = healthy. Different letters in a column indicate significant differences (P ≤ 0.05). Means ± standard error, n = 3.
nematodes, which were not measured in the present study. Therefore, the unfavorable structure of the bacterial communities, and potentially a presence of bacteria that caused disease death in the rhizosphere soil of poor plants were suggested, but it was not possible to rule out pathogens that have previously caused differences in health and poor growth. Rhizosphere soil and plant roots are interconnected. Enzyme activity in the rhizosphere soil could affect plant growth. Significant improvement in P nutrition and growth in cowpea was associated with increased acid phosphatase activity in rhizosphere soil (Makoi et al., 2010). In the present study, the acid phosphatase activity was significantly lower in the rhizosphere soil than non-rhizosphere soil. Moreover, the acid phosphatase activity in the rhizosphere soil was lowest in poorly-performing plants (Table 3), suggesting that poorly growing blueberry plants were related to acid phosphatase activity in rhizosphere soil. Soil invertase can be used as a good biological indicator of soil quality and fertility (Ge et al., 2012). There was no significant difference in soil invertase activity in the present study in which blueberry plants were in the fruiting stage. At that stage, poor blueberry growth was associated with low catalase activity in the
rhizosphere soil, which was significantly different from the rhizosphere and non-rhizosphere soils in the healthy blueberry plants. In the present study, the contents of K, Ca, Mg, and Fe were higher (albeit non-significantly) in leaves of poor than healthy plants. This may be related to nutrient availability in soil, as well as to the mechanisms by which blueberries absorb nutrients. Eck (1988) pointed out that healthy blueberry shrubs usually had 3–8 g·Ca kg−1 in leaves. However, in the study presented here there was no shortage of Ca in blueberry organs, with up to 40 g·Ca kg−1 in leaves. The critical concentrations of K, Ca, Mg and Fe in healthy blueberry fruits are 7.7–11.4 g·kg−1, 0.6–0.9 g·kg−1, 0.6–0.9 g·kg−1 and 0.03–0.04 g·kg−1, respectively (Hu et al., 2007). In our data, concentrations of K, Ca, Mg and Fe in fruits were 6.3–7.5 g·kg−1, 11.3–15.1 g·kg−1, 0.34–0.36 g·kg−1 and 0.37–0.62 g·kg−1, respectively, regardless of the plant health status. The content of Mg and Fe was highest, and that of Ca and K the lowest, in roots. Iron and Ca had the highest contents in roots and leaves, respectively. According to the correlation analysis, there was a significant negative correlation between pH and effective soil Fe content (Table 6).
Table 6 Pearson correlation coefficients between various chemical and biochemical properties of soil. SOM Catalase SOM pH Available potassium Available iron Exchangeable calcium Exchangeable magnesium Invertase ⁎ ⁎⁎
−0.99
pH ⁎⁎
0.99 −0.96⁎ ⁎
Available potassium
Available iron
Exchangeable calcium
Exchangeable magnesium
Invertase
Acid phosphatase
−0.88 0.93 0.81
−0.96 0.96⁎ −0.96⁎ 0.87
0.65 −0.58 0.76 −0.35 −0.77
0.60 −0.53 0.70 −0.32 −0.74 0.99⁎⁎
0.17 0.10 −0.18 −0.13 −0.96⁎ 0.09 0.21
0.77 −0.72 0.86 −0.48 −0.86 0.98⁎ 0.96⁎ 0.01
⁎
Significant at 5% level. Significant at 1% level. 192
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Fig. 4. RDA (redundancy analysis) for correlation analysis between microbial community structure at genus level and soil chemical and biochemical properties.
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