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Avances en Investigación Agropecuaria 2023. 27: 197-207

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https://creativecommons.org/licenses/by/4.0/

http://doi.org/10.53897/RevAIA.23.27.70

Halotolerant Sinorhizobium meliloti Strain

Confers Salinity Tolerance to Medicago sativa

Cepa de Sinorhizobium meliloti halotolerante conere

tolerancia a la salinidad a Medicago sativa L

Evelyn Ailen Gonzalez https://orcid.org/0000-0002-6296-8688

María Cecilia Pacheco Insausti https://orcid.org/000-0002-4308-2021

Martín Gonzalo Zapico https://orcid.org/0000-0002-0604-9943

Achiary Malena https://orcid.org/0009-0009-3518-4376

Hilda Elizabeth Pedranzani https://orcid.org/0000-0002-1697-6599

Laboratorio Fisiología Vegetal. PROICO 2-3318. Ciencia y Técnica.

Facultad de Química Bioquímica y Farmacia.

Universidad Nacional de San Luis, San Luis, Argentina.

*Autor de correspondencia: evelynailegonzalez@gmail.com

Recepción: 13 de mayo de 2023

Aceptado: 23 de octubre de 2023

Abstract

Objective. To comprehensively evaluate the

effect of salinity on CW 660 Medicago sativa

L. plants subjected to two treatments: nitrogen

fertilization and inoculation with a halotolerant

strain of Sinorhizobium meliloti. Materials

and methods. M. sativa L. plants were di-

vided into two groups: fertilized with nitrogen

but not inoculated with S. meliloti (FP) and

inoculated with S. meliloti but not fertilized

(IP). Salt stress was induced with Hoagland’s

solution and NaCl (50, 100, and 200 mM)

for FP, the same solution with limited nitrogen

for IP. Response variables length (L), fresh

weight (FW), and dry weight (DW) of roots

Resumen

Objetivo. Evaluar de manera integral el efecto

de la salinidad en plantas de la variedad CW

660 de Medicago sativa L., sometidas a dos

tratamientos: fertilización con nitrógeno e inoc-

ulación con una cepa halotolerante de Sinorhi-

zobium meliloti. Materiales y métodos. Las

plantas de M. sativa L. se dividieron en dos

grupos: fertilizadas con nitrógeno, pero no in-

oculadas con S. meliloti (PF) e inoculadas con

S. meliloti pero sin fertilización (PI). El estrés

salino se indujo con solución de Hoagland y

NaCl (50, 100 y 200 mM) para PF, misma

solución con nitrógeno limitado para PI. Las

variables repuestas evaluadas fueron longitud

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Introduction

Soil salinity poses a significant threat to agricultural sustainability, food production, and

food security, particularly in arid and semi-arid regions (Shah et al., 2022). The sca-

le of the problem is considerable, with over one billion hectares of land affected by soil

salinity and its spread continuing (Hopmans et al., 2021). The crisis is intensifying at

over two million hectares per year (Singh, 2018), and the consequences are evident in

different regions.

Globally, the impact of soil salinity is remarkable. In Argentina, 34% of the irrigated

area is affected, while the figures are 18% in South Africa and 33% in Egypt (Adejumobi

et al., 2016). On a larger scale, more than one-fifth of the world’s total irrigated land is

affected by salinity. Without intervention, the expansion of salt-affected land could exceed

50% within the next three decades (Wang et al., 2020).

The impact of salinity on agricultural productivity is evident in drylands, where salts

accumulate due to increased evaporation, leading to osmotic stress and reduced water

availability for plants (Ramos et al., 2020). This challenge is compounded by the complex

interplay between salinity, nitrogen fertilization, and symbiotic interactions.

Fertilization management under saline conditions is complex. While efficient nitrogen

nutrition can increase crop resilience to salinity stress by mitigating toxic effects, excessive

nitrogen fertilization leads to environmental problems and negatively impacts air and water

quality, biodiversity, and human health (Zhao et al., 2013). Finding the right balance

is critical.

and aerial parts, photosynthetic pigments (chlo-

rophylls a and b and carotenoids), and proline

concentration were measured after four weeks.

Results. Generalized additive models (GAM)

were used to evaluate the effect of salinity and

inoculation on the response variables. The ino-

culated plants showed significant improvements

in aerial and radical length, and chlorophylls

a and b under salinity stress compared to the

fertilized plants FP. Proline concentration was

decreased in IP. Nodulation decreased due to

salt stress, but inoculation promoted active no-

dules. Conclusion. Inoculation with halotole-

rant S. meliloti improved salt stress resistance

and growth in plant.

Keywords

Saline soils, bacterial inoculation, sustainable

agriculture.

(L), peso fresco (PF) y peso seco (PS) de

raíces y partes aéreas, pigmentos fotosintéticos

(clorofilas a, b y carotenoides) y concentración

de prolina tras cuatro semanas de tratamiento.

Resultados. Mediante modelos aditivos gen-

eralizados (GAM, por sus siglas en inglés), se

evaluó el efecto de salinidad e inoculación sobre

las variables repuestas. Las PI mostraron mejo-

ras significativas en la longitud aérea y radical, y

en las clorofilas a y b bajo estrés salino, en com-

paración con PF. La concentración de prolina

bajó en PI. La nodulación disminuyó debido

al estrés salino, pero la inoculación promovió

nódulos activos. Conclusión. La inoculación

con S. meliloti halotolerante mejoró la resisten-

cia al estrés salino, impulsando el crecimiento

de las plantas.

Palabras clave

Suelos salinos, inoculación bacteriana, agricul-

tura sostenible.

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Medicago sativa L., commonly known as alfalfa, stands out as an important forage

legume due to its high protein content and nitrogen fixation capacity (Rokebul-Anower et

al., 2017). Its symbiotic association with Sinorhizobium meliloti is crucial, as this interaction

leads to atmospheric nitrogen fixation, benefiting both the plant and the bacteria.

However, alfalfa is considered moderately salt tolerant and can tolerate 20 mM

NaCl equivalent (Bertrand et al., 2015). Rhizobia associated with alfalfa roots play a

role in enhancing salt tolerance through mechanisms such as maintaining ion balance and

producing osmo-protective compounds (Bertrand et al., 2020).

This study focuses on how the interplay of salinity, nitrogen fertilization, and S.

meliloti inoculation affects M. sativa L. plants. This research aims to provide important

insights for improving salt stress tolerance in different agricultural management contexts.

Materials and methods

Experimental design

Eight treatments were compared under a randomized complete block design with five

replicates. Treatments came from the combination of two factors: (1) inoculated and non-

inoculated (fertilized) plants with S. meliloti (2) four growing conditions non-stressed

(control), and any of three stresses: 50mM; 100 mM and 200 mM NaCl. The experi-

mental unit was a pot.

Germination and inoculation of seeds

M. sativa L. seeds of CW 660 variety were washed with water for 5 min, 70% alcohol

for 1 min, 10% sodium hypochlorite for 10 min, and three rinses with sterile water bet-

ween each step. They were then sown in Petri dishes with double-moistened filter paper

and kept for 48 hours in the dark at a temperature of 25 ºC. After germination, the seeds

were divided into two groups. One group of seeds was inoculated with S. meliloti, while

the other group of seeds was not inoculated (fertilized) with S. meliloti.

To inoculate the seeds, a halotolerant strain of S. meliloti was previously selected,

which was isolated from the saline soil of the Villa Mercedes Experimental Station

(SL) of INTA (Pacheco et al., 2019), with a (Minimum Inhibitory Concentration,

MIC) of 600 mM NaCl (the minimum concentration of a substance that fully inhibits

microbial growth) (Nonnoi et al., 2012). The rhizobia grew in Tryptone-Yeast Extract

TY culture medium (Vincent, 1970) for 24 h at 28 ºC with constant agitation until it

reached its exponential growth phase. This was verified with a spectrophotometer at a

wavelength of 600 nm (0.8 λ). Before sowing, seeds were inoculated by immersion for

1 h in bacterial inoculum. Seeds were then transferred to 100 mL pots filled with sterile

vermiculite. Plants were re-inoculated with 1 ml of a 108 cfu/mL (Colony Forming Units

per milliliter) suspension of the corresponding bacterial strain, whereas 1 ml of water was

added to non-inoculated plants.

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Cultivation of plants and saline treatments

Cultivation was carried out using vermiculite sterilized in an oven at 80 °C for 48 h, dis-

tributed in 100 ml pots in which the previously germinated and inoculated seeds were

placed. Plants were grown in a culture chamber at 25 °C with a photoperiod of 16 h light

and 8 h dark for 5 weeks. The saline treatment was initiated one week after sowing: (1)

fertilized plants without inoculum (FP) were irrigated with Hoagland’s solution + 50,

100, and 200 mM NaCl, and (2) inoculated plants (IP) were irrigated with the same

nitrogen-limiting solution + 50, 100, and 200 mM NaCl, control plants were only irri-

gated with Hoagland’s solution.

Nodule production

After 5 weeks, roots were extracted from plants, washed, and nodules were counted, and

activity was assessed by cross sections and coloration (Chmelíková and Hejcman, 2012).

Active nodules were defined as those with pink coloration inside, and inactive nodules

were defined as those with white coloration.

Biomass, plant growth, and determination of photosynthetic pigments

At the end of the treatments, length (L, cm), fresh weight (FW, g.), and dry weight (DW,

g.) of roots and aerial parts were measured in quintuplicate. To calculate the DW, the

samples were placed in an oven at a temperature of 30 ºC for 7 days.

The determination of chlorophylls a, b, and carotenoids was carried out according to

Porra (2002). One hundred mg of fresh aerial material (leaves) were collected, crushed

in a mortar with 10 ml of 80% (v/v) acetone, and filtered. The extract was kept at 4 °C

until the spectrophotometer reading. For the quantification of chlorophylls, the absorbance

was measured at a wavelength of 646.6 nm (chlorophyll a) and 663.6 nm (chlorophyll b)

and 470 nm for carotenoids, using 80% (v/v) acetone as a blank, expressing the results

in µg/ml.

Proline determination

The Bates method (1973) was used for proline determination. We crushed 0.5 g of fresh

aerial material (leaves) with a mortar and pestle with 10 ml of 3% aqueous sulfosalicylic

acid solution. The homogenate was filtered and mixed with 2 ml of acid ninhydrin and 2

ml of glacial acetic acid. The mixture was boiled in a water bath at 100 ºC for one hour

at a constant temperature, and the reaction was stopped by immersing the tube in cold

water. For proline quantification, 0.5 ml of the samples were taken, and the absorbance

of each sample was determined at a wavelength of 520 nm.

Statistical analysis

To examine the effects of stress and fertilization-inoculation on alfalfa plants, we used

generalized additive models (GAMs), a robust statistical modeling technique. GAMs

use smoothed functions of predictor variables, including factors such as salinity stress and

inoculation, to identify their relationships with response variables (Hastie, 1986). These

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smooth functions, often represented as splines, skillfully model and capture nonlinearities

inherent in the data. The effect of salinity stress on nodule production was assessed using

ANOVA. Data preprocessing and analysis were performed using RStudio version 4.1.

Results

Ten GAMs were created, each model using the two predictor variables or factors (sali-

nity stress and inoculation) to predict different response variables and select the models

with the lowest AIC and best R2.

Parameters morphology

Generalized additive models (GAM) with gamma distribution (logarithmic modality)

were used to evaluate the effect of salinity and inoculation on aerial length (AL) and root

length (RL). Regarding the effect of salinity stress, the associated coefficient µ was esti-

mated to be -0.003 for AL and -0.001 for RL. This suggests that with each increase in

salinity, there is an expected reduction in both aerial length (AL) and root length (RL).

However, when plants are inoculated, the values of AL and RL show an approximate

increase of 0.364 and 0.151, respectively. Significant effects of both salinity and inocu-

lation were observed for both response variables.

To accurately capture the underlying response distribution of ADF, ADW, RFW, and

RDW, we used GAM models fitted with an exponential distribution. The coefficient µ

associated with salinity stress was -0.002, -0.002, -0.002, and -0.0005 for the response

variables ADF, ADW, RFW, and RDW, respectively. Conversely, the coefficient µ

associated with inoculation for these response variables increased by approximately 0.336,

0.439, 0.224, and 0.095, respectively. However, similar to the salinity results, these results

did not reach statistical significance (table 1).

The quality of the models can be assessed by considering the adjusted R² values for

the observed (response) and modeled (adjusted) values and the AIC value (table 2).

Table 1

P-values for morphological variables GAMs as predictors for salinity and inoculation

Factors

Parameters

AL RL AFW RFW ADW RDW

Salinity 2.0*10-11 1.9*10-7 0.25 0.28 0.34 0.79

Inoculation 5.1*10-08 5.2*10-4 0.29 0.17 0.48 0.76

AL: aerial length, RL: root length, AFW: aerial fresh weight, RFW: root fresh weight, ADW: aerial dry

weight, RDW: root dry weight. P-values ≤0.1 are in bold.

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Table 2

AIC and R2 values for six different GAMs used for morphological parameters

Parameters

AL RL AFW RFW ADW RDW

AIC 182.5 213.5 -11.6 -21.5 -154.7 -206.3

R20.88 0.74 0.69 0.77 0.67 0.48

AL: aerial length, RL: root length, AFW: aerial fresh weight, RFW: root fresh weight, ADW: aerial dry

weight, RDW: root dry weight. The lowest AIC value is in bold, and the highest R2 values are in bold.

Nodulation

Figure 1a shows a significant decrease in the number of active nodules from 50 mM on-

wards concerning the control. Figure 1b shows the roots of M. sativa L. plants with acti-

ve nodules, with pink coloration, the plants without inoculation did not present nodules.

Figure 1

(a) Number of M. sativa L. var CW 660 active nodules inoculated with S. meliloti at

different NaCl concentrations. b) Root of M. sativa L. var. CW 660 with active S.

meliloti nodules

(a) (b)

Different letters represent significant differences in the treatments (NaCl) about the control determined by

the analysis of variance (ANOVA) according to Tukey’s test (p≤0, 05).

Determination of photosynthetic pigments and proline content in SF and SI

GAMs fitted to a normal distribution (NO) were used to assess chlorophyll a, b, caro-

tenoids, and proline concentrations to ensure adequate capture of the underlying distri-

bution of responses.

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For chlorophylls “a” and “b”, the coefficient µ associated with salinity stress was

-0.0001 and -0.0004, respectively. This suggests that a decrease in these parameters can

be expected with each increase in salinity, but these results were not significant (Table 3).

The coefficient µ associated with the inoculation for chlorophylls “a” and “b” increased

by approximately 0.613 and 0.783, respectively. These results were significant.

The µ coefficients associated with salinity stress -0.002 and inoculation -0.652 for

carotenoids showed negative values. This means that as salinity increases, the values are

likely to decrease, and inoculation is likely to have a negative effect on carotenoids. The

results were statistically significant for both salinity and inoculation (table 3).

Table 3

P-values for pigments. GAMs as predictor variables for salinity and inoculation

Pigments

Factors Chlorophylls a Chlorophylls b Carotenoids

Salinity 0.77 0.01 0.01

Inoculación 1.6*10-5 2.3*10-5 0.0001

P values ≤0.1 are in bold.

The adjusted R² values for the observed (response) and modeled (adjusted) values

and the AIC value were used to assess the quality of the models (table 4).

Table 4

AIC and R2 values for four different GAMs used for the pigments

Pigments

Chlorophylls a Chlorophylls b Carotenoids

AIC 120.6 109.4 63.3

R20.83 0.79 0.78

The lowest AIC value is indicated in bold, and the highest R2 values are indicated in bold.

In the case of proline, the µ coefficient associated with salinity stress was 0.010. This

indicates that with each increase in salinity, an increase in proline can be expected (figure

2a). However, the µ coefficient associated with inoculation decreases by approximately

-1.187 (figure 2b). In both cases, these results were significant (figure 2a y 2b).

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Figure 2

a) Smooth function showing the effect of salinity on proline concentration. The marks

on the x-axis are different treatment salinities. b) Effect of proline in fertilized and

inoculated plants

The quality of the model can be assessed by considering the confidence interval (gray shaded area) and sig-

nificance (p-values) of each smoothed term for the predictor variable used (Figure 2a y 2b). The adjusted R²

values for the observed (response) and modeled (adjusted) 0.976. The AIC value corresponds to 329.2136.

Discussion

In this study, we used generalized additive models (GAMs) to investigate the effects of

salinity stress and inoculation with the halotolerant strain of Sinorhizobium meliloti on

the Medicago sativa L variety CW 660. During the early stages of development, our ob-

servations revealed a significant interaction between these factors and their influences on

various physiological and morphological parameters of plants.

The calculated coefficient (µ) associated with salinity stress showed that with each

incremental increase in salinity, a decrease in both aerial length (AL) and root length

(RL) is expected. Interestingly, our results showed a distinct pattern when plants were

inoculated. This highlights the potential mitigating effect of inoculation against the negative

effects of salinity on plant growth.

The derived coefficients (µ) associated with salinity stress indicated that these response

variable (ADF, ADW, RFW, RDW) all experienced a reduction with increasing salinity.

This result is in line with expectations, as saline conditions are known to inhibit plant

biomass production and development.

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In general, IP showed better performance in both aerial and root morphological

parameters compared to FP. This improvement in growth and salinity stress in IP with

salt-tolerant bacteria may involve different mechanisms such as antioxidant enzymes,

phosphate solubilization, siderophores, and secretion of different phytohormones (Alizadeh

and Parsaeimehr 2011, Chakraborty et al., 2011, Nabti et al., 2015).

However, high salinity can negatively affect nodulation ability by inhibiting the initial

steps of rhizobium-legume symbiosis establishment (Zahran, 1999). When analyzing

the number of active nodules, it is observed that it decreases significantly with increasing

salinity concentrations; these results are similar to those obtained for Gliricidia sepium

(Clavero and Razz, 2002).

Pigment content provides information on the effect of abiotic stress caused by

NaCl on the photosynthetic apparatus, since photosynthetic tissues are very sensitive to

environmental conditions (Esteban et al., 2015), making its use as a biological indicator

of stress useful.

When analyzing the coefficient (µ) associated with salinity stress, it was observed

that the concentration of chlorophyll a and b with each increase in salinity decreased

significantly. These results are consistent with those reported for sunflowers, where

salinity stress causes pigment degradation and reduces the activities of enzymes related

to chlorophyll biosynthesis, consequently affecting chlorophyll fluorescence and net

photosynthesis (Santos, 2004).

On the other hand, when analyzing the coefficient (µ) associated with inoculation, it

was observed that IP significantly alleviated salt stress by increasing chlorophyll a and b

levels compared to FP plants. These results are in agreement with those of Irshad et al.,

(2021), who showed that active nodulation increased chlorophyll a and b by 37.18%

and 44.51%, respectively, in Medicago truncatula inoculated with Rhizobium meliloti

and exposed to salt stress.

The µ coefficients related to salinity stress and inoculation on carotenoids showed

negative values, indicating higher concentrations of FP than in IP. This is because salinity

stress induces abscisic acid (ABA) biosynthesis from carotenoids through the mevalonate

pathway, which is responsible for regulating plant development in response to water

tolerance (Lim et al., 2012). Therefore, the accumulation of carotenoids in FP compared

to IP could be the result of stimulating the mevalonate pathway to induce ABA.

Proline acts as an osmoprotectant, can also act as a protein stabilizer, acts as a scavenger

of hydroxyl radicals, stabilizes cell membranes by interacting with phospholipids, and

serves as a source of carbon and nitrogen (Kishor et al., 2005). It is believed that plants

accumulate high levels of proline when exposed to salt stress to maintain cell turgor and

chlorophyll levels, allowing for efficient protection of photosynthetic activity (Silva-Ortega

et al., 2008). When analyzing the coefficients (µ) for proline, it was observed that these

concentrations increased with increasing salinity; this increase was lower in IP, indicating a

higher tolerance to salinity. These results are similar to those reported for Triticum durum

inoculated with rhizobacteria (Silini et al., 2016, Cherif-Silini et al., 2019).

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Conclusion

While the growth of M. sativa L. is indeed affected by salinity, the results of this study

offer a promising avenue for improving both the growth and yield of alfalfa crops in sa-

line soils. In addition, these results shed light on the physiological importance of haloto-

lerant bacteria in saline soil ecosystems. This research not only contributes to our basic

understanding of plant responses to salinity stress, but also has practical implications for

sustainable agriculture in regions facing salinity problems.

By demonstrating the potential of plant-bacterial symbiosis in the face of nitrogen

fertilization, this study lays the groundwork for future research and agricultural advances

aimed at improving plant resilience under adverse growing conditions.

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