Synergistic effects of arbuscular mycorrhizal fungi and plant growth-promoting bacteria benefit maize growth under increasing soil salinity

Journal of Environmental Management 257 (2020) 109982

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Research article

Synergistic effects of arbuscular mycorrhizal fungi and plant growth-promoting bacteria benefit maize growth under increasing soil salinity Helena Moreira *, 1, Sofia I.A. Pereira 1, Alberto Vega, Paula M.L. Castro, Ana P.G.C. Marques Universidade Cat� olica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina – Laborat� orio Associado, Escola Superior de Biotecnologia, Rua Diogo Botelho 1327, 4169-005, Porto, Portugal

A R T I C L E I N F O

A B S T R A C T

Keywords: PGPB AMF Salt stress GLM Bioinocula

Salt-affected soils are a major problem worldwide for crop production. Bioinocula such as plant growthpromoting bacteria (PGPB) and arbuscular mycorrhizal fungi (AMF) can help plants to thrive in these areas but interactions between them and with soil conditions can modulate the effects on their host. To test potential synergistic effects of bioinoculants with intrinsically different functional relationships with their host in buffering the effect of saline stress, maize plants were grown under increasing soil salinity (0–5 g NaCl kg -1 soil) and inoculated with two PGPB strains (Pseudomonas reactans EDP28, and Pantoea alli ZS 3-6), one AMF (Rhizoglomus irregulare), and with the combination of both. We then modelled biomass, ion and nutrient content in maize plants in response to increasing salt concentration and microbial inoculant treatments using generalized linear models. The impacts of the different treatments on the rhizosphere bacterial communities were also analyzed. Microbial inoculants tended to mitigate ion imbalances in plants across the gradient of NaCl, promoting maize growth and nutritional status. These effects were mostly prominent in the treatments comprising the dual inoculation (AMF and PGPB), occurring throughout the gradient of salinity in the soil. The composition of bacterial communities of the soil was not affected by microbial treatments and were mainly driven by salt exposure. The tested bioinocula are most efficient for maize growth and health when co-inoculated, increasing the content of Kþ accompanied by an effective decrease of Naþ in plant tissues. Moreover, synergistic effects potentially contribute to expanding crop production to otherwise unproductive soils. Results suggest that the combination of AMF and PGPB leads to interactions that may have a potential role in alleviating the stress and improve crop productivity in salt-affected soils.

1. Introduction Soil salinization is one of the most injurious factors for crop devel­ opment (Machado and Serralheiro, 2017). Regions with low precipita­ tion, high surface evaporation and/or defective drainage fallouts are particularly prone to high levels of salinity in soils (AbdElgawad et al., 2016; Arora et al., 2018). Moreover, the successive application of fer­ tilizers and amendments with high salt content (Shrivastava and Kumar, 2015), and irrigation with low-quality water is scaling-up the problem to other cultivated areas (Enebe and Babalola, 2018). Salt-affected soils contain high levels of soluble salts, predominantly NaCl, which de­ creases plant growth by inducing osmotic stress, nutrient uptake

imbalances and modifications in plants’ metabolic processes (Deinlein et al., 2014). Soil microbial communities and their activities are also disturbed by high NaCl concentrations (Rath and Rousk, 2015), with detrimental effects in nutrient cycling and organic matter decomposi­ tion (Yan et al., 2015). The limited availability of new areas that can be converted to agri­ cultural land is forcing a shift in the paradigm of soil exploitation, leading to the rehabilitation of low-productive soils, such as those affected by high levels of salt (Ladeiro, 2012). Rehabilitation approaches such as leaching or the application of organic/inorganic amendments are frequently employed to reclaim salt-affect soils, but these treatments raise environmental concerns by requiring high quality water and hav­ ing elevated financial costs (Laudicina et al., 2009). Research on plant

* Corresponding author. E-mail addresses: [email protected] (H. Moreira), [email protected] (S.I.A. Pereira), [email protected] (A. Vega), [email protected] (P.M.L. Castro), [email protected] (A.P.G.C. Marques). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.jenvman.2019.109982 Received 23 September 2019; Received in revised form 8 December 2019; Accepted 9 December 2019 Available online 23 December 2019 0301-4797/© 2019 Elsevier Ltd. All rights reserved.

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Journal of Environmental Management 257 (2020) 109982

In this study we conducted a microcosms experiment, which aimed at understanding the effect of PGPB and/or AMF on the biomass, ion uptake and soil bacterial community structure of maize plants grown under increasing salt concentrations in soil. We hypothesized that a) bioinoculants increase plant biomass, but their beneficial effects change with increasing soil salt concentrations; b) co-inoculation of PGPB and AMF outperforms single inoculation; c) bioinoculation alters the bac­ terial community in the soil.

Abbreviation list AMF – AIC – AICc –

arbuscular mycorrhizal fungi aikaike information criterion aikaike information criterion with small sample correction B– rhizobacteria Pseudomonas reactans EDP28 C– control DGGE – denaturing gradient gel electrophoresis E endophytic bacteria Pantoea alli ZS 3-6 F– fungus Rhizoglomus irregulare GLM – generalized linear models H’ – Shannon-Wiener index HCN hydrogen cyanide IAA – indole acetic acid ICP-OES - inductively coupled plasma optical emission spectrometry M– mixture of bioinocula (B þ E þ F) PCA – principal component analysis PCR – polymerase chain reaction PGPB – plant growth-promoting bacteria S– richness SRM – standard reference material TSB – tryptic soy broth

2. Material and methods 2.1. Bacterial plant growth-promoting (PGP) traits The strains Pseudomonas reactans EDP28 (B - rhizobacteria) and Pantoea alli ZS 3-6 (E endophytic bacteria) were isolated from sedi­ ments of a stream (Pires et al., 2017) and from tissues of maize plants growing in a contaminated agricultural soil (Pereira and Castro, 2014), respectively, near the Industrial Complex of Estarreja, Portugal. Both bacteria were tested for their growth-promoting traits under increasing NaCl concentrations (0, 2.5, 5, 10 g L 1). Analyses were performed for indole acetic acid (IAA), siderophores, ammonia and hydrogen cyanide (HCN) production, and phosphate solubilization. Experiments were repeated three times, and determinations were made in triplicate. The estimation of IAA production by bacterial cultures was deter­ mined colorimetrically following the method of Gordon and Weber (1951). For this purpose, bacteria were grown overnight in trypticase soy broth (TSB) and then aseptically collected by centrifugation at 9000 rpm for 10 min. The bacterial pellet was then incubated at 30 � C for 48 h with 3 mL of phosphate buffer (pH 7.5) with glucose (1%) and amended with NaCl and 2 mL of L-tryptophan (1%). After the incubation period, 2 mL of 5% trichloroacetic acid and 1 mL of 0.5 M CaCl2 were added. The solution was centrifuged at 9000 rpm and the supernatant (500 μL) was mixed with 300 μL of Salper solution (2 mL of 0.5 M FeCl3 and 98 mL of 35% (v/v) perchloric acid) and allowed to stand for 30 min for color development. The absorbance of pink color was read at 535 nm with a Shimadzu UV-1603 spectrophotometer. Isolates under test were checked for ammonia production after growth in test tubes with 10 mL peptone water supplemented with the different NaCl concentrations and incubated for 48 h at 30 � C. The accumulation of ammonia was detected by the addition of 0.5 mL of Nessler’s reagent to each tube. The development of yellow to brown color was considered as a positive result for ammonia production (Cappuccino and Sherman, 1992). The screening of HCN production by the bacterial isolates was made by amending nutrient agar with 4.4 g glycine L 1 and with the different NaCl concentrations. A Whatman No.1 filter paper soaked in a 2% so­ dium carbonate solution in 0.5% picric acid solution was placed on top of each plate and plates were sealed and incubated at 30 � C for 4 d. Hydrogen cyanide production was indicated by change in color of the filter paper from yellow to light brown or reddish brown (Ahmad et al., 2008). The bacterial siderophores production was assayed qualitatively by inoculating bacterial isolates on chrome-azurol-S agar medium supple­ mented with NaCl concentrations. The development of a yellow to or­ ange halo around the bacterial growth after incubation at 30 � C for 4872 h indicated a positive result for siderophore production (Schwyn and Neilands, 1987). For all the abovementioned tests, sterile nutrient broth or agar were used as a control for bacterial growth. No growth was observed on control samples.

breeding (Ismail and Horie, 2017), genetic engineering for the devel­ opment of transgenic plants (Wang et al., 2016) and the use of microbial inoculants to assist crop growth in salt-affected soils (Nadeem et al., 2014) have increased during the last decade. The latter approach overcomes the other two by i) requiring less financial investment; ii) being less time-consuming and labor-intensive; iii) not deploying modified genes into the environment; and iv) by recognizing the importance of plant-microbe interactions and ecological networks. Bioinoculants include plant growth-promoting bacteria (PGPB) and arbuscular mycorrhizal fungi (AMF), which can help plants’ growth and development under environmental stressful conditions by producing growth-promoting substances, enhancing nutrient uptake and regu­ lating the ion uptake (Nadeem et al., 2014). PGPB include rhizobacteria that dwell in the rhizosphere as well as bacterial endophytes that colo­ nize the inner tissues of plants (Vessey, 2003). Most studies employing bioinoculants are conducted using either PGPB or AMF and overlook the potential synergistic effects of co-inoculation. The synergistic in­ teractions between both types of microorganisms can be helpful in enhancing plant growth and tolerance under stressful environments (Moreira et al., 2016; Pereira et al., 2016) as well as in decreasing the negative effect of stress in microbial functions (Nadeem et al., 2014). Few studies have explored the potential of co-inoculating both micro­ organisms in the context of crop production in salt-affected soils. With a worldwide production of ca. 1135 Mt in 2017, maize (Zea mays L.) is one of the most important cereals for humans (http://faostat 3.fao.org) and boosting maize production in salt-affected soils can have significant importance. Maize is a salt-sensitive plant, therefore, ensuring its successful growth under stressful environments via bio­ inoculation could provide a sustainable approach to meet the UN Sus­ tainable Development Goals. Optimizing the use of bioinoculants requires quantifying the isolated effect of potential microorganisms (e.g. PGPB or AMF), but also their cooperative effects. Modelling approaches, such as regression equations, can help describing the behavior of dependent variables (e.g. plant biomass) as a response to specific predictors (e.g. salt concentration and bioinoculants). These tools can be of invaluable importance for opti­ mizing sustainable agricultural practices under climate change.

2.2. Microcosm experiment The experiment was conducted using soil collected from an agri­ cultural land of Northern Portugal (41� 100 N, 8� 330 W; NW Portugal) at 2

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Journal of Environmental Management 257 (2020) 109982

a depth of 0–15 cm. The soil was air-dried and sieved through a 2 mm mesh-sieve. Initial soil properties were as follows (methods shown be­ tween parentheses): pH 6.7 � 0.1 (potentiometric); organic matter 3.1 � 0.2% (Walkley-Black); cation exchange capacity 13.6 � 0.1 meq 100 g 1 (Mehlich); electrical conductivity 50 � 3 μS cm 1 (potentiometric); total nitrogen 1736 � 50 mg kg 1 (Kjeldahl); total phosphorus 2600 � 23 mg kg 1 (colorimetric-ascorbic acid); total sodium 101 � 0.5 mg kg 1 (acid digestion - inductively coupled plasma optical emission spectrometry (ICP-OES); extractable potassium 106 � 12 mg kg 1 (Egn�er-Riehm); extractable magnesium 109 � 6 mg kg 1 (ammonium acetate); extractable calcium 1629 � 33 mg kg 1 (ammonium acetate). The soil was supplemented with NaCl solutions in order to achieve final concentrations of 2.5 and 5 g NaCl kg 1 of air-dried soil, which can be found among salt-affected areas and affect the growth of maize plants. The treatment without addition of salt was considered as 0 g NaCl kg 1. The experiment followed a 5.3 factorial randomized block design, and included five microbial treatments: i) C – control (without microbial inoculant); ii) B – rhizobacteria P. reactans EDP28; iii) E endophytic bacteria P. alli ZS3-6; iv) F - AMF Rhizoglomus irregulare; v) M – all three microbial inoculants; and three levels of salinized soils: i) 0 (no addition of NaCl), ii) 2.5 g NaCl kg 1 soil, iii) 5 g NaCl kg 1 soil. Each treatment was replicated 5 times. A commercially AMF product of R. irregulare purchased from INOQ, GmbH (Germany) was selected for this study and mixed in the soils (100 mL kg 1) one day before seedling transference and bacterial (B) inoc­ ulation. Bacterial inoculants and maize seeds (var. AVELINE, purchased from Lusosem, Portugal) were prepared as described in Moreira et al. (2016). Seeds were germinated in water-agar for seven days prior to sowing in the pots. For the treatment with the bacterial endophyte (E), surface sterilized seeds were dipped in a bacterial suspension (ca. 108 CFU mL 1) or sterile phosphate buffer (control) for 2 h with agitation (120 rpm) before transference to agar plates, as described in Pereira and Castro (2014). Ten seedlings were moved to plastic pots with about 500 g of soil. For the rhizobacteria inoculation (B), 10 mL of bacterial so­ lution (ca. 108 CFU mL 1) was inoculated one day after the transference of maize seedlings to the pots. To pots with no rhizobacterial inoculation 10 mL of sterilized phosphate buffer was added. One week after trans­ plantation seedlings were reduced to 6 per pot. The plants were main­ tained in a controlled growth room (12 h photoperiod, 450 μmol m 2 s 1 photosynthetically active radiation, 18–21 � C temperature range, 50–60% relative humidity range). The soil was moistened to 60% water-holding capacity. No fertilizer was added to pots and harvest occurred 10 weeks after the beginning of the experiment.

2.4. Denaturing gradient gel electrophoresis (DGGE) analysis of rhizospheric bacterial community To assess DGGE profiles of bacterial 16S rRNA genes, composite samples of rhizosphere soil of each treatment were collected at the end of the experiment and used to extract total DNA using Power Soil DNA Isolation Kit (MOBIO Laboratories, Inc., USA). In order to increase the sensitivity of DGGE, a nested PCR technique was applied as described in Pereira and Castro (2014; detailed description in SM). DGGE was performed using a Bio-Rad Dcode™ Universal Mutation Detection System (Bio-Rad, USA) following the method described in Moreira et al. (2014). The gels were stained in a 10x GelGreen Nucleic Acid Stain solution (Biotium Inc., USA) in 0.1 M NaCl. The DGGE images were acquired using a Safe Imager™ Blue-Light Transilluminator (Invitrogen™, USA) and a MicroDOC™ gel documentation system (Cleaver Scientific Ltd, UK). Analysis of bacterial community profiles was performed with the software Bionumerics® (Applied Maths NV, version 7.6) as described in Pereira and Castro (2014; full description in SM). DGGE sample profiles were compared using Pearson correlation similarity and clustered ac­ cording to the Ward method. The presence and absence of the bands in the DGGE profiles were converted to binary data. Shannon-Wiener (H0 ) index was calculated using the following formula: H’ ¼

S X

ðNi = NÞ ln ðNi = NÞ i¼1

where Ni is the peak density of the i band, N is the total peak density of all bands in a lane, and S (Richness) is the total band number in a lane. 2.5. Statistical analysis Generalized linear models (GLMs) were used to evaluate the effect of the two treatments (soil salinity and type of inoculum) on specific plant traits, regarded as response variables – biomass, and the concentration of Naþ, Kþ, Mg2þ, Ca2þ, N, and P –, using the ‘lme4’ package (Bates et al., 2015) in software R (R Core Team, 2018). Fourteen model sets (one for each response variable) were constructed, each comprising all covariate combinations, including the interaction between salt level and inoculation treatment, and a null (intercept-only) model (Burnham and Anderson, 2002). Models were fitted using a Gaussian and inverse Gaussian error structure with an identity link function, to allow for the relaxation of the homoscedasticity assumption and non-linear response (Crawley, 2013). Models were ranked using the ‘MuMIn’ R package � , 2017) according to aikake information criterion (AIC) values (Barton corrected for small sample sizes (AICc) to find most parsimonious one, and all within a ΔAICc < 2 units from the top-supported model were considered as having substantial support for being the best (Burnham and Anderson, 2002). Principal Component Analysis (PCA) was performed using the package ‘factoextra’ (Kassambara and Mundt, 2017) in software R, and for the visualization of results the packages ‘ggpot2’ (Wickham, 2009) and ‘gridExtra’ (Auguie, 2017) were used. Differences in parameters of all tested treatments were statistically analyzed by two-way and one-way ANOVA. Significant differences be­ tween the means were determined by Duncan’s post-hoc test in software R (R Core Team, 2018).

2.3. Plant biomass and nutrient content At harvest, plants were washed with tap water, followed by deion­ ized water. The biomass of the plants was determined after roots and shoots were oven dried at 70 � C for 48 h. Plants were ground and digested at high temperature in a PerkinElmer MicroWave using a H2SO4:H2O2 mixture (1:1). Phosphorous, Kþ, Naþ, Mg2þ, and Ca2þ content was assessed using ICP-OES in an Optima 7000 DV spectro­ photometer (PerkinElmer, USA). Nitrogen content was determined by adding two reagents to the digests: 50 mM disodium hydrogen phos­ phate (Na2HPO4) buffer (pH ¼ 12.3) and a 4% solution of sodium hy­ pochlorite (NaOCl) (reagent 1) and 1 M sodium salicylate (C7H5NaO3) solution, 1 mM sodium nitroprusside (Na2[Fe(CN)5NO]) solution and 3 mM ethylenediamine tetraacetic acid (EDTA, C10H16N2O8) solution (reagent 2). On the resulting solutions, N concentration was determined on an UNICAM HELIOS® spectrophotometer (Waltham, USA) at 660 nm. The biological standard reference material (SRM) 1573a tomato leaves, provided by the National Institute of Standards and Technology, was analyzed using the same methods as for the other samples. Values obtained confirmed the accuracy and precision of methods by compar­ ison with certified values of each element.

3. Results 3.1. Plant growth-promoting traits of bacterial strains Both PGPB (B and E) produced HCN and ammonia and revealed to be very good producers of siderophores in the absence of NaCl in the growth media (Table 1). 3

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Journal of Environmental Management 257 (2020) 109982

They were also able to produce IAA, with P. alli (E) producing over two-fold the levels of P. reactans (B). The increase of NaCl concentrations reduced the production of HCN and siderophores by both strains, and decreased IAA production by B at the highest salt concentration tested (10 g L 1, Table 1). Nonetheless, the production of IAA was not affected in E, which maintained twice the capacity of B to produce this hormone. The production of ammonia remained unchanged in both strains. 3.2. Maize biomass All bioinoculants significantly increased roots and shoots biomass in non-saline conditions (Fig. 1; Froot ¼ 21.32, p < 0.001; Fshoot ¼ 12.12, p < 0.001), with plants inoculated with the AMF (F) and the mixture of inoculants (M) presenting the highest biomass. The increase of soil NaCl concentration negatively affected plants’ biomass, but inoculated plants tended to present higher root and shoot growth (Fig. 1; Table 2; see model selection table in Supplementary Table 1 – ST1; Supplementary Material – SM). According to our data, the increase of salt in soils affected the effect of bioinocula on roots’ biomass: the fungus reduces up 10% plants’ biomass (5 g NaCl kg 1 soil), whereas the mixture M steps up by 35% plants’ biomass at the highest NaCl concentration (Table 2). However, P. alli E ceased its positive impact in root growth as salinity increased in the soil, while P. reactans B maintained its effect independently of increasing salt concentrations. Globally, the outcomes of bioinoculants in roots’ biomass across soils’ increasing salinity can be coded by the expression M > F > B > E ¼ C. For shoots, the beneficial effect of bioinoculants in the absence of salt in soils was maintained under saline conditions. All treatments signifi­ cantly increased biomass, with M producing the highest overall benefit as shown by the highest β estimate (Fig. 1; Table 2). Contrarily to roots, all bioinoculants maintained their positive effect constant throughout the different levels of salinity as the models including the treatments and salt level interaction effect were not supported by the data (Table 2). The expression that better suits the inocula effect in shoots is M > F > B ¼ E > C.

Fig. 1. Maize dry biomass (g pot 1) at increasing NaCl concentrations in soil. Dots represent mean and respective range of measured values; lines represent predicted biomass according to the best-performing model.

table in ST1, SM). All microbial treatments increased their effect across the gradient of salinity, but the mixture of inoculants M exhibited the most pronounced increase with an efficiency in Naþ reduction in root tissues of ca. 34% at 5 g NaCl kg 1 soil. Likewise, Naþ concentration decreased in shoot tissues of inoculated plants. As for roots, the buffering effect of bioinoculants also augmented across soils’ salinity, with F and M (Fig. 2; Table 3) producing the highest Naþ decrease in maize’s shoots. These bioinocula were able to reduce up 60% of Naþ concen­ tration at 5 g NaCl kg 1 soil. Results also showed that plants tended to respond to the increasing soil salinity by enhancing the translocation of Naþ from roots to shoot tissues, reaching equal concentrations at 4 g NaCl kg 1 soil (Fig. 3; Table 4; see ST2 for model selection, SM). However, the mixture M and the fungus F considerably reduced this translocation, independently of the amount of salt in soil, and the 1:1 threshold was not exceeded in plants exposed to these treatments. No effect was observed in the bac­ terial treatments B and E in the translocation of Naþ to shoot tissues (Table 4).

3.3. Effect of microbial inoculants on Naþ, Kþ, Mg2þ and Ca2þ concentration in plant tissues 3.3.1. Naþ No effect of bioinocula in Naþ concentration was observed in root and shoot tissues of plants grown in soils without any salt amendment (Fig. 2; Froot ¼ 7.785; p < 0.001; Fshoot ¼ 13.395, p < 0.001). However, the increase of salt levels in the soil solution led to a concomitant in­ crease of Naþ in both plant tissues (Fig. 2; Table 3; see model selection

3.3.2. Kþ Potassium levels in maize’s roots and shoots were significantly affected by bioinocula (Fig. 2; Froot ¼ 207.384, p < 0.001; Fshoot ¼ 20.631, p < 0.001) in non-amended soils. The most relevant results were obtained for the fungal and mixture treatments F and M, which signif­ icantly enhanced the accumulation of Kþ in plant tissues. The increase of soils’ salinity induced a nonlinear decline of Kþ concentration in root tissues, which was steeper at lower salt levels and tended to level off with increasing salinity (Fig. 2). Following the pattern observed in non-saline conditions, inoculating maize with F and M allowed the plants to retain higher levels of Kþ in their roots, indepen­ dently of the concentration of salt in the soil as shown by the β estimates of the top-supported model (Fig. 2; Table 3; see model selection table in ST1, SM). The same treatments also significantly increased the Kþ content in shoot tissues, effect enhanced with increasing soil salt con­ centrations. Moreover, these bioinocula were able to induce plants to accumulate Kþ levels of 22 and 26 g kg 1 plant, independently of soil salt concentrations. The bacterial treatments B and E had no significant effect in Kþ concentrations neither in roots nor in shoots (Table 3).

Table 1 In vitro plant growth-promoting traits of bacterial strains P. reactans (B) and P. alli (E) at different levels of NaCl exposure. Bacteria

NaCl g L (n ¼ 4)

B

0 2.5 5 10 0 2.5 5 10

E

1

HCN (n ¼ 4)

NH3 (n ¼ 4)

Siderophores (n ¼ 4)

IAA (mg L 1) (n ¼ 9)

þþ þ þ þ þþ þþ þ þ

þ þ þ þ þ þ þ þ

þþþ þþþ þþ þþ þþþ þþ þþ þþ

13 14 12 10 28 21 19 22

� 2a � 1a � 4ab � 1b � 8a � 2a � 4a � 6a

- negative; þ positive production; þþ good production; þþþ very good pro­ duction. Results are expressed as mean � SE. A one-way ANOVA was performed to determine the influence of each tested concentration on IAA production by B and E. Means for the same bacterial strain with different letters are significantly different from each other according to the post-hoc Duncan test. The F values are F ¼ 18.234 (p < 0.05) and F ¼ 1.112 (p > 0.05) for B and E respectively. 4

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Table 2 Parameters estimates (β) and respective standard errors (SE), and significance levels of the best AICc-ranked Generalized Linear Model explaining the variability of biomass of root and shoot (response variables). Response variable Biomass

Covariates (Intercept) Salt level Treatment (B) Treatment (E) Treatment (F) Treatment (M) Salt level: Treatment (B) Salt level: Treatment (E) Salt level: Treatment (F) Salt level: Treatment (M)

Root

Shoot

β

SE

t-value

p-value

β -Estimate

SE

t-value

p-value

1.82 1.18 1.05 0.57 1.21 1.05 0.29 0.04 0.7 0.9

0.27 0.15 0.30 0.34 0.29 0.30 0.18 0.20 0.17 0.16

6.79 7.89 3.46 1.69 4.1 3.47 1.59 0.19 4.23 5.55

*** *** *** NS *** *** NS NS *** ***

2.11 0.24 0.45 0.43 0.63 0.87 2.11 0.24 0.45 0.43

0.06 0.01 0.08 0.08 0.08 0.08 0.06 0.01 0.08 0.08

33.21 19.4 5.75 5.42 8 11.04 33.21 19.4 5.75 5.42

*** *** *** *** *** *** *** *** *** ***

NS – non-significant, p > 0.05; *p < 0.05; **p < 0.01; ***p < 0.001.

Fig. 2. Ion (Naþ, Kþ, Mg2þ and Ca2þ) concentrations (g kg 1) in roots and shoots of maize plants at increasing NaCl concentrations in soil. Dots represent mean and respective range of measured values; lines represent predicted biomass according to the best-performing model.

Across the range of soils’ salt concentrations, plants showed a slight tendency to increase the translocation of Kþ. Although not inducing a significant effect on Kþ at low soil salinity values, M and F gradually increased this ratio especially at higher concentrations of salt in the soil (Fig. 3; Table 4; see ST2 for model selection, SM). As for Naþ, no effect in Kþ translocation from roots to shoots was detected in plants inoculated with B and E.

in the roots, which was boosted as salinity increased. The strongest ef­ fects were observed in plants inoculated with B and M (Fig. 2; Table 3; see model selection table in ST1, SM). While the trend of Mg2þ in shoots was the opposite of that observed in shoots (i.e. decreased with increasing soil salinity), the effects of the bioinoculants where analo­ gous. All but the bacteria P. reactans B induced a significant reduction in Mg2þ concentration in shoot tissues, the highest of which was observed in plants inoculated with M (β salt.level:shoot ¼ 0.25 � 0.05; Fig. 2; Table 3).

3.3.3. Mg2þ A slight decrease of Mg2þ content in both root and shoot tissues was observed in inoculated plants grown in control soils (Fig. 2; Froot ¼ 15.899, p < 0.001; Fshoot ¼ 6.051, p < 0.01). The increase of NaCl in the soil led to a significant increase of Mg2þ in roots, and conversely a significant decrease in shoots. All bio­ inoculants revealed a significant effect in reducing Mg2þ concentration

3.3.4. Ca2þ While in shoots of maize grown in non-amended soils microbial treatments tended to decrease the content of Ca2þ, in roots they affected its concentration distinctly: E produced an increase, B and M produced a decrease, and F had no influence on Ca2þ content (Fig. 2; Froot ¼ 99.501, p < 0.001; Fshoot ¼ 51.417, p < 0.001). With increasing salt 5

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Table 3 Parameters estimates (β) with respective standard errors (SE), and significance levels of the best AICc-ranked Generalized Linear Model explaining the variability of ion (Naþ, Kþ, Mg2þ and Ca2þ) concentrations in roots and shoots of maize plants (response variables). Response variable Naþ

Kþ

Mg2þ

Ca2þ

Root

Shoot

Covariates

β

SE

t-value

p-value

β

SE

t-value

p-value

(Intercept) Salt level Treatment (B) Treatment (E) Treatment (F) Treatment (M) Salt level: Treatment (B) Salt level: Treatment (E) Salt level: Treatment (F) Salt level: Treatment (M) (Intercept) Salt level Treatment (B) Treatment (E) Treatment (F) Treatment (M) Salt level: Treatment (B) Salt level: Treatment (E) Salt level: Treatment (F) Salt level: Treatment (M) (Intercept) Salt level Treatment (B) Treatment (E) Treatment (F) Treatment (M) Salt level: Treatment (B) Salt level: Treatment (E) Salt level: Treatment (F) Salt level: Treatment (M) (Intercept) Salt level Treatment (B) Treatment (E) Treatment (F) Treatment (M) Salt level:Treatment (B) Salt level:Treatment (E) Salt level:Treatment (F) Salt level:Treatment (M)

1.59 2.07 0.06 0.38 0.55 0.37 0.27 0.46 0.23 ¡0.74 0.05 0.04 0.00 0.03 ¡0.03 ¡0.03 0.01 0.03 0.01 0.02 2.17 ¡0.39 3.59 1.18 1.94 2.93 ¡0.67 ¡0.24 ¡0.39 ¡0.37 1.39 ¡0.18 ¡0.80 0.60 0.23 ¡0.73 0.16 ¡0.20 0.04 0.23

0.48 0.15 0.68 0.68 0.68 0.68 0.21 0.21 0.21 0.21 0.01 0.01 0.02 0.02 0.02 0.02 0.01 0.02 0.01 0.01 0.25 0.05 0.57 0.42 0.46 0.55 0.12 0.09 0.10 0.12 0.08 0.02 0.11 0.11 0.11 0.11 0.03 0.03 0.03 0.03

3.31 13.88 0.08 0.56 0.82 0.55 1.30 2.21 1.11 ¡3.50 3.23 4.15 0.05 1.11 ¡2.22 ¡2.10 1.01 1.83 0.68 1.59 8.79 ¡7.48 6.34 2.81 4.19 5.36 ¡5.71 ¡2.79 ¡4.12 ¡3.02 18.05 ¡7.65 ¡7.34 5.51 2.10 ¡6.72 4.87 ¡5.97 1.24 6.93

** *** NS NS NS NS NS * NS *** ** *** NS NS * * NS NS NS NS *** *** *** ** *** *** *** ** *** ** *** *** *** *** * *** *** *** NS ***

0.28 2.63 0.30 0.61 0.48 0.80 0.42 ¡0.80 ¡1.69 ¡1.85 21.01 ¡1.89 0.35 1.19 5.15 5.40 0.13 0.36 1.52 1.18 0.17 0.02 0.06 0.12 0.11 0.25

0.61 0.19 0.86 0.86 0.86 0.86 0.27 0.27 0.27 0.27 0.68 0.21 0.97 0.97 0.97 0.97 0.30 0.30 0.30 0.30 0.03 0.01 0.04 0.04 0.04 0.05

0.47 13.93 0.35 0.71 0.56 0.93 1.57 ¡2.99 ¡6.35 ¡6.92 30.78 ¡8.95 0.37 1.23 5.33 5.59 0.43 1.22 5.08 3.94 6.64 3.05 1.72 3.11 2.91 5.30

NS *** NS NS NS NS NS ** *** *** *** *** NS NS *** *** NS NS *** *** *** ** NS ** ** ***

3.18 ¡0.14 ¡0.63 ¡0.92 ¡1.89 ¡2.24 0.05 0.09 0.13 0.34

0.12 0.04 0.17 0.17 0.17 0.17 0.05 0.05 0.05 0.05

26.48 ¡3.70 ¡3.73 ¡5.44 ¡11.10 ¡13.21 0.99 1.78 2.43 6.49

*** *** *** *** *** *** NS NS * ***

NS – non-significant, p > 0.05; *p < 0.05; **p < 0.01; ***p < 0.001.

concentrations, maize plants tended to reduce the amount of Ca2þ in both root and shoot tissues at equivalent rates (β salt.level:root ¼ - 0.18 � 0.02; β salt.level:shoot ¼ - 0.14 � 0.04; Table 3; see model selection table in SM). A distinct trend in Ca2þ accumulation was observed in plants for each of the microbial treatments (Fig. 2). The effects of bioinocula application in Ca2þ concentration in roots were as follows: E and F led to a significant increase, while B and M produced a significant decrease. However, these effects interacted with soil salinity, leading to different outcomes in plants inoculated with E, B and M. F was the only bio­ inoculum that retained a constant effect regardless of soil salinity. All bioinoculants led to a significant decreased in Ca2þ concentration in shoot tissues, which was particularly evident in plants inoculated with F and M at higher salt concentrations (Table 3).

selection table in ST1, SM). All but the P. reactans treatment (B) signif­ icantly contributed to rise N levels in roots, effect that was maintained regardless of the level of salt in soil. Plants inoculated with the mixture of microorganisms (M) outperformed the other treatments in roots (βM (root) ¼ - 0.37 � 0.68), while P. alli (E) outperformed in shoot (βsalt:E(shoot) ¼ - 0.73 � 0.19). The tested inoculants did not have any meaningful effect in P con­ centration in roots nor in shoots, and in neither non-saline conditions nor across the tested gradient of salinity (Fig. 4; Froot ¼ 73.45, p < 0.001; Fshoot ¼ 86.29, p < 0.001). Increasing salt concentrations in the soil had a marginal contribution for P accumulation in roots and significantly decreased P levels in shoots (Fig. 4; ST1, SM).

3.4. Effect of microbial inoculants on N and P content

3.5. PCA of total biomass and nutrient content of maize plants

Neither of the singly inoculated microorganisms (B, E and F) pro­ duced any significant changes in the concentration of N in roots nor in shoots under non-saline conditions. However, mixed inoculation (M) significantly increased N values in the shoots (Fig. 4; Froot ¼ 1.996, p > 0.05; Fshoot ¼ 5.509, p < 0.01). Increasing salt levels in the soil significantly decreased the N con­ centration in maize’s roots and shoots (Fig. 4; Table 5; see model

PCA was performed with total plant biomass and total concentration of nutrients and ions in maize tissues in order to establish clustering patterns of the different treatments (Fig. 5). The first two components (PC1 and PC2) explained 74.5% of the total variability in the original data. PC1 accounts for 56.9% of the variability and has a strong positive contribution of biomass and P, which were negatively correlated to Naþ content. Ca2þ, Mg2þ and P are the best discriminating parameters for 6

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Journal of Environmental Management 257 (2020) 109982

Fig. 3. Shoot: root ratio of Naþ and Kþ in maize plants at increasing NaCl concentrations in soil. Dots represent mean and respective range of measured values; lines represent predicted biomass according to the best-performing model.

Table 4 Parameters estimates (β) and respective standard errors (SE), and significance levels of the best AICc-ranked Generalized Linear Model explaining the variability of shoot: root ratio of Naþ and Kþ (response variables). Response variable

Covariates

Shoot: root

(Intercept) Salt level Treatment (B) Treatment (E) Treatment (F) Treatment (M) Salt level: Treatment (B) Salt level: Treatment (E) Salt level: Treatment (F) Salt level: Treatment (M)

Naþ

Kþ

β

SE

t-value

p-value

β -Estimate

SE

t-value

p-value

0.3 0.17 0.02 0.06 0.3 0.23

0.11 0.02 0.14 0.14 0.14 0.14

2.68 7.81 0.15 0.41 2.12 1.64

** *** NS NS * NS

0.29 0.26 1.18 0.98 0.9 0.24 0.25 0.95 0.52 0.29

0.19 0.89 0.89 0.89 0.89 0.27 0.27 0.27 0.27 0.19

1.48 0.29 1.34 1.11 1.02 0.87 0.91 3.48 1.9 1.48

NS NS NS NS NS NS NS NS *** NS

NS – non-significant, p > 0.05; E1.

PC2, which explains 17.6% of the variability in the data. The grouping patterns along the components PC1 and PC2 reveals the presence of three main clusters, discriminated by salt treatments (Fig. 5a). In nonsaline conditions, plants tend to have higher concentrations of Ca2þ, Kþ, N and P and higher biomass. Sodium concentration in the plant increases with the increase of soil salinity, along with a reduction in the amount of the abovementioned nutrients, although with a high vari­ ability in the patterns of Ca2þ and Mg2þ at intermediate salt levels. Bioinoculation contributed to different nutrient uptake patterns within the salt-level clusters (Fig. 5b). Plants inoculated with the fungus (F) and the mixture (M) had higher biomass and decreased Naþ content irre­ spectively of soil salt concentrations, although producing a concurrent reduction in the overall concentrations of Mg2þ and Ca2þ. Bacterial inoculation tended to produce intermediate effects between noninoculated and the other inoculated plants (M, F). However, at the highest salt concentration, P. reactans (B) appears not to have any meaningful effect.

3.6. Bacterial community analysis Coherently to what was observed in the plants’ PCA analysis, bac­ terial community analysis of DGGE band patterns showed that samples clustered in three main groups according to the level of NaCl exposure (Fig. 6a). This clustering pattern suggests that differences between the com­ munities were mostly driven by the salt exposure than by the inocula applied. Nonetheless, M and F samples showed high similarity in all soils’ salt concentrations, and B and E are closely related in 2.5 and 5 g NaCl kg 1 amended soils. Shannon-Wiener index (H0 ) showed in average higher values (H0 � 1.48) in samples collected from soils exposed to 2.5 g kg 1 NaCl, and lower bacterial diversity (H0 � 1.28) in soils with 5 g kg 1 salt (Fig. 6b), when compared to control samples (H0 � 1.34).

7

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Journal of Environmental Management 257 (2020) 109982

Fig. 4. Nitrogen and Phosphorous concentrations (g kg 1) in maize plants at increasing NaCl concentrations in soil, according to each respective best model. Dots represent mean and respective range of measured values; lines represent predicted biomass according to the best-performing model.

Table 5 Parameters estimates (β) and respective standard errors (SE), and significance levels of the best AICc-ranked Generalized Linear Model explaining the variability N and P (response variables). Response variable N

P

Covariates (Intercept) Salt level Treatment (B) Treatment (E) Treatment (F) Treatment (M) Salt level: Treatment Salt level: Treatment Salt level: Treatment Salt level: Treatment (Intercept) Salt level

Root

(B) (E) (F) (M)

Shoot

β

SE

1.87E 06 5.27E 07 -4.27E 07 6.46E 07 6.20E 07 1.04E 06

2.46E 4.77E 3.11E 2.99E 3.01E 2.79E

0.27

07 08 07 07 07 07

0.01

t-value

p-value

β -Estimate

SE

7.62 11.04 1.37 2.16 2.06 3.73

*** *** NS * * ***

2.52E 06 8.65E 07 2.57E 07 5.30E 07 1.96E 07 5.45E 07 3.09E 07 7.26E 07 4.75E 07 4.74E 07 3.79 0.16

3.64E 1.63E 5.30E 4.67E 5.23E 4.70E 2.23E 1.91E 2.15E 2.01E 0.08 0.03

28.34

***

07 07 07 07 07 07 07 07 07 07

t-value

p-value

6.92 5.30 0.49 1.14 0.37 1.16 1.39 3.79 2.21 2.36 46.18 6.32

*** *** NS NS NS NS NS *** * * *** ***

NS – non-significant, p > 0.05; *p < 0.05; **p < 0.01; ***p < 0.001.

4. Discussion

Smith, 2017). In fact, an increase of Naþ concentration in roots and shoots of maize plants under rising soil salt concentrations was detected, regardless the microbial treatment, indicating maize does not have the ability to prevent the absorption of this ion to noxious concentrations. Consequently, biomass decreased likely due to toxic levels of Naþ in maize tissues across the gradient of salinity tested, as supported by the biomass decrease alongside a concurrent increase in roots’ Naþ. High concentrations of Naþ may disturb nutrient uptake (Deinlein et al., 2014) by competing with important ions, such as Kþ (Porcel et al., 2012). Our results support this hypothesis as revealed by the concurrent decrease in the overall Kþ content in plant tissue.

Microbial inoculants, especially when including the AMF R. irregulare (F and M), were able to alleviate ion imbalances in maize plants across gradients of NaCl in soil, promoting their growth and nutritional status particularly at high salinity levels. As a salt-sensitive plant, maize presents a stress symptomatology towards salt-affected soils that includes reduced growth and yield (Bano and Fatima, 2009; Shrivastava and Kumar, 2015). This response was also observed in our study and is known as a consequence of osmotic and ionic imbalances imposed by salt (Marschner, 1995; Ilangumaran and 8

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Journal of Environmental Management 257 (2020) 109982

Fig. 5. Principal Component Analysis (PCA) biplot showing the multivariate variation of biometric parameters, ion and nutrient content among maize plants under different soil salt and microbial treatments. The first two principal components explained 74.5% of the variance. Vectors indicate the direction and strength of each variable to the overall distribution. Samples are grouped by salt level in soil (a), and microbial treatments (b).

Fig. 6. Dendrogram constructed using Pearson correlation similarity and clustered according to the Ward method from DGGE profiles of bacterial communities of rhizospheres’ soil samples (a) microbial diversity indices for the rhizospheres’ soil samples (b) S – Species richness; H’ – Shannon-Wiener diversity index; C - control; B– P. reactans EDP28; E P. alli ZS 3-6; F – R. irregulare; M – B þ E þ F. 9

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Journal of Environmental Management 257 (2020) 109982

Despite the negative impact of soils’ salinity on plants growth, the bioinoculants tested were able to mitigate these effects. The increase of biomass in inoculated plants seems to be mainly related to an effective decrease of Naþ concentration in both plant tissues, induced especially by bioinocula-containing AMF (F and M). According to Giri et al. (2007), AMF have the ability to retain this cation in roots, probably in intra­ radical hyphae, which decreases its availability to plants. Furthermore, a significant reduction in the translocation of Naþ from roots to shoots across soil salt concentrations was observed in inoculated plants, which may explain the highest plants’ development. Nevertheless, the inocu­ lation of the mixture of beneficial microorganisms (M) elicited the better performance, i.e., the effect of AMF seemed to have been intensified when coupled with bacterial strains (B and E). Similar results were observed by Pereira et al. (2016) in sunflower plants irrigated with sa­ line water. AMF and PGPB dual inoculation also enhanced alfalfa yield under salinity conditions through the increase of fungi colonization, nodulation and nutrient uptake (Ashrafi et al., 2014). This suggests an added value of using these PGPB associated with the AMF R. irregulare. PGPB are known to assist rhizospheric fungi in colonizing the roots of their host plants through the production of metabolites that increase cell permeability and stimulated its hyphal growth by enhancing root exudation rates (Jeffries et al., 2003). This synergy may have led to higher colonization rates by the AMF, improving the plants’ ability to manage salt stress (Aroca et al., 2013; Krishnamoorthy et al., 2016), which may have contributed to the outstanding performance of the M treatment. The additive effects of PGPB and AMF can explain the distinctive effect of this consortium at the highest levels of soil salinity, when the plants are exposed to higher stress. Besides decreasing Naþ in plant tissues, mycorrhized plants had considerably higher Kþ levels across the range of salinity tested, allowing salt-stressed plants to benefit from a balanced Kþ/Naþ ratio, and therefore preventing the disruption of several enzymatic processes and the inhibition of protein synthesis (Deinlein et al., 2014). PCA ordination supports that the increase of Kþ content induced by F and M, was accompanied by an effective decrease of Naþ in plant tissues and a concomitant increase in plant biomass, suggesting that the relative balance between these two ions played a key role in improving maize development. An increase in the concentration of Kþ has also been previously reported in maize plants inoculated with AMF (Pereira et al., 2016), and an improvement of this ion content through the co-inoculation of AMF and PGPB has also been reported by Lee et al. (2015). When singly inoculated, the rhizobacteria (B) and the endophyte (E) had a limited impact in Kþ and Naþ uptake. However, they contributed to an effective increase of the translocation of Kþ from roots to shoots, increasing shoots biomass. This suggests that bacterial bioinoculants can alleviate the harmful effects of salt stress in plants through other mechanisms rather than the decrease of Naþ. For this may have contributed the production of growth-promoting substances, namely IAA, which is known to decrease in plants under salt-stress situations (Dunlap and Binzel, 1996), prompting cell growth and proliferation (Ilangumaran and Smith, 2017). Additionally, these PGPB also showed to be siderophore producers that could have overcome a decrease of iron uptake that often occurs in salt-affected soils, by chelating iron and delivering it to plants (Farooq et al., 2015). We generally observed a steady decrease of Ca2þ in plants across the range of salinity tested. Naþ can interact with this ion, decreasing its uptake (Hu et al., 2007). Nonetheless, our results showed distinct pat­ terns in Ca2þ concentration in inoculated plants, suggesting there might be different interactive mechanisms induced by microbial inoculants in ion transport to plants and/or in osmotic adjustment. Similar findings are described by other studies, which report variable responses in the accumulation of this ion induced by bioinocula (Pereira et al., 2016; Rojas-Tapias et al., 2012). The amount of Mg2þ in plant tissues has also been reported to decrease in plants grown in salt-affected soils (Parihar et al., 2015).

Magnesium is a key component of the chlorophyll molecule, therefore Mg2þ deficient plants are expected to have limited growth capacity. Although this is coherent with our results for shoots, we detected the opposite pattern in roots, which was particularly evident at high salinity levels. Such patterns could be explained by the lower availability of Ca2þ in the soil at these concentrations. Although Ca2þ has higher affinity for roots’ membrane binding sites than Mg2þ (Marschner, 1995), high Naþ concentration can disturb that relation, and consequently decrease the competition between Ca2þ and Mg2þ. This may have improved the up­ take of the latter in the root at high NaCl concentrations. Coherently with the results of Evelin et al. (2009, 2012), we observed that soil-increasing salinity also induced a decrease of N concentration in plants. However, microbial treatments were able to heighten N levels in roots throughout the gradient of salinity tested, which should be related to the higher biomass values of inoculated plants. AMF could have increased N through the assimilation of nitrate in the extraradical mycelium and the enhancement of the production of enzymes that controls the primary nitrogen fixation in the extraradical mycelia (Evelin et al., 2009, 2012). Also, PGPB have the ability to produce ammonia, which could have contributed to this increase. A correlation between ammonia production by PGPB and maize biomass was already reported by Marques et al. (2010). P uptake is usually reduced in plants under saline stress (Bano and Fatima, 2009). This was also observed in our study, and neither PGPB nor AMF had any meaningful effect in P accumulation in plants. Although AMF has been reported to increase P concentration in plants grown in salt-affected soils (Shokri and Maadi, 2009), the high total concentration of this nutrient in the soil used in our study could have limit AMFs ability to mobilize it to plants. Phosphate ions may have precipitated with, e.g., Ca2þ, becoming unavailable to AMF and to plants (Evelin et al., 2009). Regarding soil bacterial communities, our study showed that NaCl concentration in the soils was the main factor in shaping their structure. Intermediate salinity levels seem to have thriven bacterial diversity when compared to control soils, suggesting that moderate concentra­ tions of NaCl and/or the interaction with maize roots exudates released could have favor the presence of more diverse communities. Nonethe­ less, this diversity decreased at the highest salt concentration, probably due to adverse osmotic conditions induced by salt (Rietz and Haynes, 2003). In order to restore cellular homeostasis, microorganisms have to integrate stress adaptive metabolic changes such as the accumulation of osmolytes (e.g. proline and glycine betaine) in the cytoplasm (Empadi­ nhas and da Costa, 2008), which requires high amounts of energy that can disturb reproduction and survival. We showed that modelling approaches are useful for describing the functional response of plants (e.g. biomass or nutrient content) to spe­ cific independent covariates, proving a valuable tool to extend the traditional control vs. treatment analysis towards a quantitative assess­ ment of the relative influence of each predictor in shaping a variable’s response. Fitting GLM to our data allowed us to describe the quantitative changes in each of the plants’ traits in response to bioinoculation and soil salinity, and to assess the interactive effects of these treatments on maize plants. The abnormal physiological functions of salt-affected plants can be buffered with microbial interaction, which can contribute to an overall stress reduction of plants. 5. Conclusions Effective microbe-assisted growth and establishment of plants in saline soils depend on the selection of the most suitable microorganisms, which can withstand increases in soil salinity levels without loss of function. Amongst the applied treatments in this study, it seems that inoculation with the mixed inocula (R. irregularis, P. reactans EDP28 and P. alli ZS 3-6) was the most efficient in promoting plant growth across the increasing soil NaCl concentrations. Further research is needed to elucidate the physiological 10

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mechanisms of plant-bacterial interactions to promote salt tolerance in plants and on the interaction between nutrient and osmotic adjustment.

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