Salt responsive physiological, photosynthetic and biochemical attributes at early seedling stage for screening soybean genotypes

Accepted Manuscript Salt responsive physiological, photosynthetic and biochemical attributes at early seedling stage for screening soybean genotypes D.B. Shelke, M. Pandey, G.C. Nikalje, P. Suprasanna, B.N. Zaware, T.D. Nikam PII:

S0981-9428(17)30235-8

DOI:

10.1016/j.plaphy.2017.07.013

Reference:

PLAPHY 4938

To appear in:

Plant Physiology and Biochemistry

Received Date: 14 February 2017 Revised Date:

8 June 2017

Accepted Date: 14 July 2017

Please cite this article as: D.B. Shelke, M. Pandey, G.C. Nikalje, P. Suprasanna, B.N. Zaware, T.D. Nikam, Salt responsive physiological, photosynthetic and biochemical attributes at early seedling stage for screening soybean genotypes, Plant Physiology et Biochemistry (2017), doi: 10.1016/ j.plaphy.2017.07.013. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Salt responsive physiological, photosynthetic and biochemical attributes at early

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seedling stage for screening soybean genotypes

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India.

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Shelke DB, 2Pandey M, 1, 2Nikalje GC, 2Suprasanna P, 3Zaware BN, 1*Nikam TD

Department of Botany, Savitribai Phule Pune University, Pune 411 007, MS, India.

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Nuclear Agriculture and Biotechnology Division, Bhabha Atomic Research Center, Trombay, Mumbai 400 085, MS, India.

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P.D.E.A.'s Anantrao Pawar College, Pirangut, Tal. Mulshi, Dist. Pune - 411 042, MS,

Department of Botany, Amruteshwar Art’s, Commerce and Science College, Vinzar,

Velha, Pune, 412213, MS, India.

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Correspondence:

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Email: *[email protected]

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Abstract

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Salt stress affects all the stages of plant growth however seed germination and early

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seedling growth phases are more sensitive and can be used for screening of crop

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germplasm. In this study, we aimed to find the most effective indicators of salt tolerance

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for screening ten genotypes of soybean (SL-295, Gujosoya-2, PS-1042, PK-1029, ADT-

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1, RKS-18, KDS-344, MAUS-47, Bragg and PK-416). The principal component

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analysis (PCA) resulted in the formation of three different clusters, salt sensitive (SL-

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295, Gujosoya-2, PS-1042 and ADT-1) salt tolerant (MAUS-47, Bragg and PK-416)

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and moderately tolerant/sensitive (RKS-18, PK-1029 and KDS-344) suggesting that

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there was considerable genetic variability for salt tolerance in the soybean genotypes.

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Subsequently, genotypes contrasting in salt tolerance were analyzed for their

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physiological traits, photosynthetic efficiency and mitochondrial respiration at seedling

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stage and early germination stage respectively. It was found that salt mediated increase

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in AOX-respiration, root and shoot K+/Na+ ratio, improved leaf area and water use

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efficiency were the key determinants of salinity tolerance, which could modulate the net

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photosynthesis (carbon assimilation) and growth parameters (carbon allocation). The

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results suggest that AOX respiration, root and shoot K+/Na+ ratio, improved leaf area

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and water use efficiency are useful biomarkers for screening soybean genotypes for salt

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tolerance.

Key words: Salt stress, Soybean, Principal Component Analysis, Alternate oxidase, K+/Na+ ratio, Gas exchange.

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Abbreviations:

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G%

Germination percentage

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RL

Root length

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SL

Shoot length

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SFW

Shoot fresh weight

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RFW

Root fresh weight

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SDW

Shoot dry weight

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RDW

Root dry weight

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STWC%

Shoot tissue water content percentage

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RTWC%

Root tissue water content percentage

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GFW

Gain in Fresh weight

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SR

Secondary root

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Chl A

Chlorophyll- A

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Chl B

Chlorophyll-B

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T Chl

Total Chlorophyll

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Car

Carotenoids

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R K+/Na+

Root Potassium/Sodium

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R Ca+

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S K+/Na+

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Root Calcium

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Shoot Potassium/Sodium

S Ca+

Shoot Calcium

LA

Leaf area

PN

Photosynthetic rate

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WUS

Water use efficiency

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gs

Stomatal conductance

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E

Transpiration rate

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1. Introduction

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Soil salinity is becoming a major threat to realizing crop yield of agricultural crops.

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About 20% cultivated land and 33% of irrigated agricultural land is affected worldwide

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by salinity and average yields for most major crop plants have dropped by more than

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50% (Gupta and Huang, 2014). Thus out of the 25% irrigated area, 17% area is salt

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affected in India and this is increasing every year (Parihar et al., 2015). Alternatively,

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plant based solutions have assumed significance for improving salt tolerance in crop

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germplasm. Therefore it is essential to screen for tolerant genotypes which can survive

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and provide higher yield in salt affected soils. In a given species, it is known that

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different genotypes show contrasting response to salinity (Ali et al., 2014; Sharma,

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2015; Kan et al., 2015) and it is thus desirable to, screen for salt tolerant genotypes

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which will be good candidates for cultivation in saline soils.

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It is well established that salinity imposes osmotic (physiological drought or

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water limitation) and ionic stress (Munns and Tester, 2008). In the early phase, salinity

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inhibits water uptake, cell elongation, root development, formation of new leaves while

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in the later phase, salt ions accumulates leading to premature senescence, disruption in

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enzyme functionality and inhibition of photosynthesis (Roy et al., 2014; Munns, 2005).

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It affects every developmental stage of plants from seed germination to reproductive

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stage. The seed germination and early seedling growth are the most susceptible stages of plant growth to salt stress and can be used to screen genotypes for their tolerance or sensitivity (Pandey and Suprasanna, 2016). Seed germination and early seedling stages

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are the important stages in the life cycle of a plant, as they regulate the seed vigor and

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consequently plant adaptation to salt stress (Carpici et al., 2009). Genotype screening at

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seedling and germination are most preferred because it is rapid, less laborious and

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inexpensive (Bafeel, 2014). Salt tolerant genotypes may have reduced ionic toxicity, adjust their osmotic

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pressure by the synthesis of compatible solutes (Munns and Tester, 2008), enhanced

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antioxidant mechanisms and maintenance of photosynthetic rate for efficient reactive

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oxygen species (ROS) scavenging (Miller et al., 2011). Development of biomarkers is

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important for screening a large number of genotypes. These include, antioxidant

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enzymes, osmolytes (proline, glycine betaine, and total soluble sugars), physiological

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markers like photosynthetic efficiency, relative water content, malondialdehyde content.

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Alternate oxidase (AOX) is a key enzyme in the respiratory chain of plants. The

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differential behavior of AOX under salinity stress has shown potential to be used as an

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indicator of salt tolerance particularly at early germination stage (Pandey and

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Suprasanna, 2016). The mechanisms by which AOX is involved in the tolerance of

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plants to abiotic stress is by maintaining the redox balance in plant cells (Cvetkovska

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and Vanlerberghe, 2012; Feng et al., 2013b), repair the photosynthetic machinery

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(Gandin et al., 2012; Feng et al., 2013b) and balance carbon and nitrogen ratio by

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modulating carbon-use efficiency (Kornfeld et al., 2013; Feng et al., 2013b). Soybean

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(Glycine max (L.) Merr.) is the leading economic oil seed crop worldwide, and is also an

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attractive crop for biodiesel production and source of protein for human and animal diet. Soybean is also economically important because of its high oil content (20 %) and protein (40 %) (Amirjani, 2010). It is classified as a moderately salt-sensitive

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glycophyte (Munns and Tester, 2008) and exhibits high degree of variation in the salt

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response among genotypes. Therefore it is essential to select highly tolerant genotype

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for cultivation under saline soils. Different strategies have been applied to screen

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tolerant genotypes by using physiological (Hakeem et al., 2012; Wu et al., 2014)

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biochemical (Hakeem et al., 2012; Wu et al., 2014) and molecular attributes (Fan et al.,

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2013; Guan et al., 2014). Most of these strategies are time consuming and cost effective

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and hence there is a need for development of quick and reliable methods of screening

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genotypes for salt tolerance. Negrao et al., (2017) outlined that studies on effects of

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salinity on plants should be based not on whole-plant total plant biomass, but rather on

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traits that may contribute to salinity tolerance. The PCA has been suggested to provide

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simultaneous analysis of the most important traits contributing to salinity tolerance (Su

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et al., 2013). This method has also been shown as an accurate and easy tool to screen

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germplasm at germination and early seedling level inn melon landraces (Sarabi et al.,

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2016) and rice (Chunthaburee et al., 2016). There is a need for development of rapid

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and consistent means for screening of soybean genotypes. The present study was aimed

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to examine the physiological and biochemical indicators at germination and early

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seedling stage in soybean under salt stress, and to apply the principal component

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analysis of salt stress responsive traits to discriminate soybean genotypes for salinity

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tolerance.

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2. Materials and methods

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2.1 Plant material, growth and treatment conditions

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The study included seven soybean genotypes: MAUS-47, PK-416, ADT-1, SL-295, Gujosoya-2, PS-1042 and KDS-344

collected from the National Institute of Soybean

Research, Indore, MP, India and three genotypes: Bragg, PK-1029 and RKS-18 from

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Agharkar Research Institute, Pune, MS, India.

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The seeds of all genotypes were surface sterilized with 0.1 % mercuric chloride and

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subsequently washed five times with sterile distilled water. The seeds were further

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allowed to germinate in separate petri plates (90 mm), containing germination paper

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supplemented with 15 ml of 0, 50, 100, 150 and 200 mM NaCl solution.

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experiment was performed in triplicates and each replicate had 10 seeds. Seeds were

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incubated in dark at 25±2˚C temperature for 10 days. After 10 days, growth parameters,

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such as germination percentage (G %), shoot length (SL), root length (RL), number of

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secondary roots (SR), Fresh weight (FW), Dry weight (DW) and tissue water content

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(TWC) (Muchate et al., 2016) were measured.

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A hydroponic set up was maintained to assess the impact of ionic stress in salinity. In

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short, evenly germinated seeds (5 No.) were transferred to hydroponic system after 3

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days germination and allowed to grow in controlled condition for 12 more days (total 15

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days). The nutrient medium for plant growth was ½ strength Hoagland’s nutrient

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solution (Hoagland and Arnon, 1950). Seeds of MAUS-47 and Gujosoya-2 genotypes

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were grown under control (0), 50, 100 and 150 mM salt treatment and on the basis of

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lipid peroxidation rate (MDA content), 100 mM NaCl concentration was selected for

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further experimentation (Supplementary information Fig. S1). At the end of 15 days,

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fresh weight of the seedlings was recorded and salt treatment was given with 100 mM

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NaCl for a period of 10 days. After the completion of salt stress treatment, fresh weight

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of the seedlings was recorded and, relative gain in fresh weight (GFW) was calculated

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as per Xu et al., (2011). The effect of stress treatment over dry matter partitioning was also analyzed, by calculating DW of the sample. The seedlings were blot dried and then were kept at 60 °C until a constant weight was achieved. Other parameter like RL, SL,

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SR, were manually measured, while tissue water content (TWC) was assayed as per

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Muchate et al., (2016).

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2.2 Leaf area measurement

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After 10 days of salt treatment, fully expanded second pair of mature leaves was

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selected for leaf area measurement. The leaf area was calculated by a millimeter graph

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paper method (Pandey and Singh, 2011).

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2.3 Photosynthetic Pigments

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The second pair of leaves was used for estimation of total chlorophyll, Chl a, Chl b and

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carotenoids. The leaves of soybean (0.3 g) were crushed in 5 ml chilled 80 % acetone in

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pre cooled mortar and pestle in dark. After centrifugation, absorbance of the supernatant

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was taken at 645, 663 and 470 nm (Witham et al., 1971).

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2.4 Determination of ion content

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Inorganic ions such as sodium, potassium and calcium content were measured using

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acid digestion method. The dried root and leaf samples (50 mg DW) were incubated in 5

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ml concentrated nitric acid (HNO3) for overnight digestion. The digested sample was

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boiled on hot dry bath at 100 oC (DBK Temp. Controller DBK 5097/1) (Singh et al.,

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2015). The digested samples were then dissolved in 10 ml deionized distilled water. Ion

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content was analyzed by microcontroller flame photometer (Labtronics, model no- LT-

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671, India). A five point calibration was done using known concentration (100 ppm, 80

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ppm, 40 ppm, 20 ppm and 10 ppm) of KCl and NaCl solutions prepared in milli-Q

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water. The ionic content of the acid digested sample were calculated and represented as mg g-1 DW of plant tissue. 2.5 Gas Exchange Parameters Measurement

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The second pair of leaves was selected for analysis of Photosynthetic rate (PN), stomata

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conductance (gS), transpiration rate (E) and water use efficiency (WUE) using a portable

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infrared gas analyzer (LI-6400XT, Li-Cor Inc., USA). The parameters were measured

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under a constant flow of 300 µmol mol-1 CO2 and 1000 µmol m–2 s–1 PAR, 3.9 kPa

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vapor pressure deficits, and 25 ◦C block temperature. The CO2 and PAR value was

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decided after optimization of CO2 and PAR curve. The measurement of the all the gas

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exchange parameters like PN, gS, and E were carried out with 2 cm2 of total leaf area.

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The long term effect of salinity over photosynthesis and water use efficiency was

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assayed by time dependent change in these activities under NaCl treatment condition.

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The leaf position i.e 2nd fully expanded leaf and the measuring time (9 am to 11:30 am)

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were kept fixed for all the time points. The water use efficiency (WUE) was the ratio of

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Photosynthesis rate and Transpiration rate (Rabhi et al., 2012).

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2.6 Mitochondrial respiration

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After surface sterilization, seeds of all genotypes were incubated in distilled water for

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overnight soaking. The mitochondrial respiration in the imbibed seeds was assayed as

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per Pandey and Suprasanna (2016), Briefly five seeds per treatments were taken in 1

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mL of medium containing 10 mmol L–1 HEPES buffer (pH 7.2), 0.45 mol L–1 mannitol,

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5 mmol L–1 MgCl2, 10 mmol L–1 KCl, 0.1 % (w/v) BSA, 5 mmol L–1 of succinate, 1.5

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mmol L–1 of NADH and 0.2 mmol L–1 ADP and incubated separately for 5 min at 25 oC.

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The equilibrated seeds were transferred to fresh reaction mixture with or without

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inhibitors KCN and SHAM (1 mmol L-1). All the total eight reactions were performed in

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technical duplicates. The respiration rate was measured using Clark-type O2 electrode (Model DW2, Hansatech Ltd., King’s Lynn, UK). 3. Statistical analysis

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All the experiments were performed in completely random design (CRD). Data from

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each of the experiments and treatments with three replicates were subjected to Analysis

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of Variance and Principal Component Analysis was done using Statistical Package

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PAST (Hammer et al., 2001). Principle Component Analysis (PCA) was performed to

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visualize the differences among the genotypes for various stress- related physiological

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and biochemical parameters. SAS software was used to calculate Pearson correlation

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coefficients (r) among eleven parameters. Test of significance was performed at *P<

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0.05 and **P< 0.01. Under: under H0: Rho=0. The data from AOX and COX

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experiments and treatments with two replicates were subjected to One way analysis of

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Variance (ANOVA) by using SPSS version16 software. Means differing significantly

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were compared using Duncan’s (1955) Multiple Range Test (DMRT) at significance

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level P≤ 0.05.

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4. Results

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4.1 PCA analysis of soybean genotypes at the seed germination and growth

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PCA analysis was performed on different growth parameters like radicle emergence G

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%, SR, FW, DW and TWC of root and shoot tissue at early germination stage among the

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ten soybean genotypes. The Fig.1 of the PCA shows the relative distribution of the

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genotypes for their salt response. The biplot analysis of the genotypes under controlled

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condition revealed the existence of two groups: group 1 (MAUS-47, RKS-18, and PK-

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1029) and group 2 (ADT-1, PK-416, KDS-344 and PS-1042) (Fig.1). It suggested that

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group 1 and group 2 genotypes were placed in close proximity () while Bragg, SL-295

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and Gujosoya-2 were different from the bulk group and formed the extreme end from the bulk group. Results of PCA revealed that the first component accounted for 82.65 % of variance and the second component for 12.42 % variance. The biplot (Fig.1a) showed

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that PC1 gave RL as the dominant variables appearing to be positive PC1 Value and SR

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as the dominant variable for second component. The group-1 genotypes (MAUS-47,

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PK-1029 and RKS-18) showed better root architecture in terms of improved RL and SR.

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Genotypes SL-295 and Gujosoya-2 were distantly apart from group-1 and considered to

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have poor root architecture, while Bragg showed improved SR. On the other hand,

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group-2 comprising (PK-416, KDS-344, ADT-1 and PS-1042) had intermediated values.

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The correlation study (supplementary table; S2 A) between the parameters of the PCA

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showed a positive association between RL, SFW and DW (r=0.99**), SDW (r=0.98**)

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and RTWC (r= 0.97**). The SR root also positively correlated to that of SFW (r=

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0.73**) and SDW (r= 0.78**).

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In order to investigate the principal component(s) involved in attributing to salt

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tolerance character, PCA analysis was performed under a range of salt treatments (0

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mM, 50 mM, 100 mM, 150 mM and 200 mM NaCl) for different growth parameters

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during early germination. The principal component of first axis (PCA1) was identified

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as distribution among ten soybean genotypes as per their salt responsiveness. The

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second principal component (PCA2) was considered as the variation with different

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attributes at germination stage under salinity stress. The components revealed significant

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variation between the NaCl treatments; five different loading plots were generated for

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each of the treatment conditions i.e. control (Fig. 1a ), 50 mM NaCl (Fig. 1b ), 100 mM

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NaCl (Fig. 1c ), 150 mM NaCl (Fig. 1d ) and 200 mM NaCl (Fig. 1e).

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On the other hand, the PCA analysis among the genotypes under 50 mM NaCl showed

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98.52 % variance by PC1 and 0.927 % by PC2 (Fig. 1b). The G % and RL were the dominant variables of PC1, while STWC % was the dominant variable of PC2. The change in G % and STWC % together led to formation of a separate group 1 (MAUS-

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47, Bragg, PK-1029, RKS-18 and PK-416) and group 2 (KDS-344, ADT-1, SL-295,

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Gujosoya-2, and PS-1042). The group one members showed an improved G % and

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STWC % and therefore were considered as tolerant group as compared to group 2. The

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data revealed a strong positive correlation (supplementary table; S2 B) between the G %

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and root architecture i.e. RL (r = 0.89**) and SR (r= 0.75**). The root architecture was

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also positively correlated with that of shoot phenotypic traits like SL (r= 0.77**) and

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SFW (r = 0.81**) and SDW (r = 0.85**).

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When the magnitude of the salinity stress was increased to 100 mM NaCl,

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further refinement was achieved in the screening response of the soybean genotypes.

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The PCA analysis showed the existence of three separate groups: group-1 (MAUS-47

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and Bragg), group-2 (ADT-1, SL-295, Gujosoya-2, PS-1042) and group-3 (PK-1029,

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RKS-18, PK-416, KDS-344). The variance was explained by PC1 (86.20 %) and PC2

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(12.57 %) (Fig.1c). The STWC % was the major determinant for grouping under 100

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mM NaCl suggesting that retention of STWC % is a prerequisite for salinity tolerance

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trait under salt stress, as compared to maintenance of germination efficiency under mild

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stress (50 mM). The correspondence analysis of the data (supplementary table; S2C)

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also revealed that an increase in TWC in the shoot of seedlings subjected to salt stress

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was directly corresponded to increase in the SR (r= 0.94**) and shoot phenotype. The

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group-1 members showed an improved G % and STWC % and therefore were

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considered as tolerant group compared to group 2 which were salt sensitive and group-3

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were moderately salt tolerant/sensitive.

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The PCA analysis of the seedlings under 150 mM (Fig. 1d) showed PC1 (97.87

%) and PC2 (2.05 %). The analysis revealed that at higher salt concentration both the G % and RTWC % were the major determinants for the grouping of genotypes. Tolerant

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group-1 consisting of MAUS-47, PK-416, Bragg, PK-1029 and RKS-18 showed

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improvement in both the parameters, whereas moderately tolerant/sensitive group-2

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(KDS-344) showed only one of the parameters improved, and the sensitive group-3

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(ADT-1, Gujosoya-2, SL-295 and PS-1042)

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improved under 150 mM salt stress.

showed neither of the parameters as

The PCA analysis of the very high salt (200 mM; Fig 1e) stressed seedlings

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further discriminated the moderately sensitive and tolerant genotypes. The analysis

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revealed that at higher salt concentrations, both the G % and RTWC % were the major

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determinants for the grouping of genotypes. Tolerant group-1 consisting of MAUS-47,

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PK-416 and Bragg showed improvement in both the parameters, while moderately

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tolerant/sensitive group-2 (PK-1029 and RKS-18) showed one of the parameters

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improved and the sensitive group-3 (KDS-344, ADT-1, Gujosoya-2, SL-295 and PS-

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1042) showed neither of the parameters improved under high salt stress. A very strong

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positive correlation was observed (supplementary table; S2 E) between G % and other

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parameters like RL (r = 0.93**), RFW (r = 0.98**), RDW (r = 0.98**) and RTWC (r =

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0.98**).

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In conclusion, the PCA analysis of the genotypes under increasing concentration

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of salt discriminated the genotypes into sensitive and tolerant types. At low magnitude

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of salt stress (50 mM NaCl), a wide spectrum of random distribution of groups was

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observed, whereas the genotypes clustered into more specified groups at 150 mM NaCl

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and beyond this salt concentration, complexity of grouping the genotypes for salt stress

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response was reduced. The comparative results of 150 mM and 200 mM NaCl clearly indicated that among the genotypes screened MAUS-47, Bragg and PK-416 were the most tolerant genotypes, while SL-295, Gujosoya-2, PS-1042, KDS-344 and ADT-1

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represented the sensitive genotypes and PK-1029 and RKS-18 were placed in between

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these groups. The improved growth traits particularly G %, RL, SR, STWC % and

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RTWC % were the deterministic feature of tolerant soybean genotypes.

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4.2 PCA analysis of Soybean genotypes at the seedling stage

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To further reveal the salt responses at the seedling stage, the PCA analysis was

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performed on the data obtained from hydroponically grown 15 day soybean seedlings

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subjected to 100 mM NaCl for 10 days. The genotypes were categorized into different

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groups on the basis of early germination data under various magnitudes of salt

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concentrations ranging from 50 mM to 200 mM NaCl. The results showed Gujosoya-2

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and Bragg as the most sensitive and tolerant genotype, respectively. Further to analyze

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the effect of salinity at seedling stage, three different salt concentrations i.e. 50, 100 and

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150 mM were given to 15 d old hydroponically grown seedlings of representative

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tolerant (Bragg) and sensitive genotype (Gujosoya-2). The optimum concentration was

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found to be 100 mM NaCl on the basis of MDA content. PCA analysis was conducted

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on different growth parameters like RL, SL, FW, DW, TWC, SR, GFW, LA and

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biochemical parameters like total chlorophyll content, ion content (K+/Na+ ratio, Ca2+

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content in root and shoot tissue) . The results showed relative distribution of the

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genotypes for their salt response at the seedling stage (Fig. 2a) and principal component

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of first axis (PCA 1) as the variation distribution in the tested genotypes. The second

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principal component (PCA 2) accounted for the distribution of growth/ biochemical

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parameters attributed to stress response under salinity. Two different loading plots were

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generated for treatment conditions i.e. control and 100 mM NaCl. The result of the PCA analysis revealed the existence of three separate groups under control condition. The first component accounted for 60.89 % of variance and the second one for 17.92 %

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variance. The biplot (Fig 2a) depicted the shoot K+/Na+, SL and SR as the dominant

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variables. The biplot analysis of the genotypes under controlled condition, revealed the

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existence of four groups: group 1 (ADT-1, PK-416, PK-1029), group 2 (MAUS-47,

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RKS-18, KDS-344 and Bragg) group 3 (PS-1042 and SL-295) and Gujosoya-2 lie

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separately. The seedling growth in controlled condition was found to be correlated with

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that of RL and SR. A positive correlation (supplementary table; S4 A) was observed for

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RL and FW (r = 0.70*) and DW (r = 0.88**). The number of SR influenced the seedling

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vigor as a direct correlation was observed between SR and FW (r = 0.86**) and DW (r

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= 0.89**). A strong positive correlation was also observed for total chlorophyll content

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and carotenoid content of the leaf sample (r = 0.95**).

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On the other hand, the PCA of the seedlings subjected to 100 mM NaCl was

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found to account for 88.04 % variance by PC1 and 6.61 % by PC2 (Fig 2b). Among the

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various parameters studied, LA, SR and root Ca2+ emerged as the dominant variable of

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PC2. The root Ca2+ negatively correlated (supplementary table; S4 B) with that of shoot

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K+/Na+ ratio (r = - 0.84**). Further the shoot K+/Na+ ratio was strongly correlated (r =

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0.98**) to that of root. The improved Shoot K+/Na+ ratio was also found to be

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positively correlated with that of LA (r = 0.87**) and photosynthetic pigment content

15

including total chlorophyll (r = 0.93**) and carotenoid (r = 0.96**). The TWC % was

16

also positively correlated with that of LA (r = 0.94**). A decrease in the root Ca2+

17

under 100 mM NaCl treatment led to shifting of MAUS-47 and Bragg from group 2 to

18

group 1 including PK-1029 and PK-416, suggesting this group as tolerant. A slight

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increase in the root Ca2+ observed for KDS-344 and RKS-18 together constituted a group with moderate tolerance/ susceptible behavior. The rest of the genotypes together constituted a third group of susceptible genotypes which included ADT-1, SL-295, PS1042 and Gujosoya-2.

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4.3 Gas exchange parameters

2

Further the PCA analysis was performed on all the genotypes and stomatal conductance

3

parameters like PN, WUE, E at 0 DAS (days after stress imposition), 5 DAS and 10

4

DAS. A comparative assessment of the effect of salinity among the genotypes was made

5

with the aid of two biplot analysis (under control conditions and under 100 mM NaCl

6

stress). The biplot analysis of the genotypes under controlled condition (Fig. 3a) showed

7

the inherent variance in the growth parameters between the genotypes. The WUE being

8

the key detriment particularly at 5 d and 10 d under control condition favored the

9

MAUS-47, Bragg, KDS-334 and ADT-1, while higher transpiration rate in PK-1029 and

10

Gujosoya-2 limited its water use and photosynthetic efficiencies. On the other hand PK-

11

416, SL-295 and PS-1042 clustered together to form a separate group, predicted to

12

respond differently from rest of the two groups.

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The salinity imposition and its impact over stomatal conductance, revealed the

14

physiological basis of salt response among various genotypes. Results of PCA revealed

15

the existence of three separate groups under salt stress (Fig. 3b). The first component

16

accounted for 75.2 % of variance and the second one to 19.07 % variance. Although a

17

positive correlation existed for WUE and PN under control conditions, a declining trend

18

was observed with the correlation coefficients ranging from 0.93**, 0.83**, 0.50* for 0

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d, 5d and 10 d respectively (supplementary table; S4 C). On the other hand, the E under salinity decreased which in turn increased the WUE and PN showing the correlation coefficients ranging from 0.95**, 0.94** and 0.80** for 0d, 5d and 10 d after stress

22

respectively (supplementary table; S4 D). The WUE and PN at 5 DAS were the major

23

determinants for the salt response among the genotypes. The outcome of the PCA

24

analysis revealed the existence of four separate groups under 100 mM NaCl; the group-1

ACCEPTED MANUSCRIPT 17

(MAUS-47, Bragg) was shown to have better PN and WUE as compared to Group-3

2

(PS-1042, SL-295 and ADT-1) which was distant from group-1 and considered to have

3

poor PN and WUE. Group-4 (PK-416, KDS-334) and group-2 (PK-1029, RKS-18 and

4

Gujosoya-2) were placed in between the two extreme groups i.e. group-1 and group-3

5

suggesting the response into moderately tolerant/sensitive genotypes. Depending on the

6

extent of deviation from the major determinants, group-4 tended towards moderately

7

tolerant as compared to group-2 (moderately sensitive).

8

4.3 Mitochondrial respiration response in germinating seeds under salinity stress

9

The mitochondrial respiration was measured to investigate the involvement of the

10

cytochrome C-oxidase (COX) and alternate oxidase (AOX) pathways of mitochondrial

11

respiration in germinating seeds under salinity stress. An intense increase (> 70%) in

12

mitochondrial respiration was observed for genotypes ADT-1 (94 %), RKS-18 (82 %),

13

KDS-344 (82 %) while moderate increase (>25%) was observed for SL-295 (59 %), PS-

14

1042 (50 %) and Gujosoya-2 (28 %) under 100 mM NaCl. On the other hand, a slow

15

increase in respiration rate (< 25%) was observed for MAUS-47 (22%), Bragg (0 %),

16

PK-1029 (6 %) and PK-416 (20 %) under NaCl treatment as compared to respective

17

controls (Table 1).

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Further the respiration under NaCl was analyzed via Alternative oxidase (AOX)

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and cytochrome C oxidase (COX) pathways. The genotypes studied differed in their ability to adopt the different mitochondrial pathway under NaCl stress. Blocking of COX pathway facilitated the respiration by alternative pathway, and formation of two

22

separate groups; group 1 where more than 70% respiration was maintained via AOX

23

port which includes PK-416 (99%), RKS-18 (92%), Bragg (90 %) and MAUS-47

24

(70%). On the other hand group 2 constitute a fairly low contribution of AOX under

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salinity treatment ranging from PK-1029 (63%), KDS-344 (60 %), SL-295 (56%), ADT-

2

1 (54%), Gujosoya-2 (43%) and PS-1042 (35%). The same trend was observed in the COX respiration pathway, where a large

4

proportion of the mitochondrial respiration was maintained under AOX blocked

5

condition ranging up to 90%, 83% and 81% for PK-416, Bragg and MAUS-47

6

respectively. Further the mitochondrial COX pathway contribution for various

7

genotypes under salinity and AOX blockage condition came as KDS-344 (74 %), RKS-

8

18 (69%), PK-1029 (60%), Gujosoya-2 (56 %), SL-295 (48%), PS-1042 (47%) and

9

ADT-1 (28%).

10

5. Discussion

11

5.1 Salt stress and soybean genotypes categorization at the seed germination stage

12

Salt stress affects plant growth and development through osmotic and ionic imbalances

13

and disruption of cellular and metabolic machinery (Gupta and Huang, 2014; Muchate

14

et al. 2016). Soybean is a moderately salt-tolerant crop; however soil salinity exceeding

15

5 dS/m severely affects seed germination and other developmental processes (Phang et

16

al., 2008). Seed germination and early seedling phases are the sensitive phases in the life

17

cycle of a plant (Munns and Tester, 2008) and hence are used in screening for genetic

18

variation in salt stress responses. In the present work, we have analyzed the salt

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responses in 10 Soybean genotypes by using various biochemical and physiological parameters at germination and early seedling stages and applied data clustering approach (PCA analysis) for the identification of stress tolerance related traits and

22

genotypes. The soybean genotypes MAUS-47, Bragg, PK-1029, RKS-18 and PK-416

23

were found to maintain their germination potential even at 150 mM and 200 mM NaCl

24

while, rest of the genotypes were unable to germinate under these conditions suggesting

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seed germination as one of the key determinants of salt tolerance. Salinity affects the

2

seed imbibition and thereby germination rate through decrease in water potential and

3

imbalance in nutrient uptake due to Na toxicity (Devkota and Jha, 2010; Pandey and

4

Suprasanna, 2016). Salt stress induced toxicity and differential germination response of

5

genotypes has also been observed in Wheat (Sairam et al., 2002), Niger (Patil et al.,

6

2010), Sorghum (Jogeswar et al., 2006), Tomato (Raza et al., 2017). Salt stress impacts

7

seedling traits and hence have become useful parameters in screening for salt tolerance.

8

In the present study, various seedling growth parameters such as FW, DW, STWC %,

9

RTWC %, SR, SL and RL were found to be affected by salt stress. Based on these

10

parameters, the genotypes were grouped as tolerant (MAUS-47, Bragg and PK-416),

11

moderately sensitive/tolerant (KDS-334 and RKS-18) and sensitive (SL-295, Gujosoya-

12

2, PS-1042, PK-1029 and ADT-1) to salt stress. The data showed that the G %, TWC

13

and root architecture, particularly RL and SR, were the deterministic feature in soybean

14

genotypes. The RL and SR together affected the vitality of the seedlings under salt stress

15

(Fig-1) suggesting that salt stress response of the genotypes is governed by the

16

physiological adaption of root architecture at the early seedling stage. Being the first

17

organ to sense osmotic stress, plants respond by redirecting the growth and dry matter

18

partitioning into roots (Koevoets et al., 2016). It has been suggested that the root system

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architecture can be explored to improve plant vigor by maximizing water and nutrient use efficiency under non favorable conditions (Li et al., 2015). It is well established that multivariate analysis of physiological parameters is

22

valuable for the categorization of genotypes for salt tolerance or sensitivity (Negrao et

23

al., 2017). Studies have shown the potential of multivariate techniques for the

24

identification of salt tolerant Rice (Chaum et al., 2009), Tomato (Juan et al., 2005),

ACCEPTED MANUSCRIPT 20

Sugarcane (Chaum et al., 2012), and Peanut (Liu et al., 2012). PCS enables

2

simultaneous analysis of multiple physiological and biochemical attributes to

3

discriminate crop germplasm for salt tolerance (Su et al., 2013). Moreover,

4

identification is also possible of probable groupings and establishment of relationships

5

among individuals and variables (Martinez-Calvo et al., 2008; Sarabi et al., 2016). Our

6

results showed that under low magnitude of salt stress (50 mM NaCl), a random

7

distribution of groups was observed, which became more specified at higher salt

8

concentration (150 mM NaCl and beyond). The germination parameters and salinity

9

magnitude were the important factors which together served as a basis for categorization

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of soybean genotypes into tolerant and sensitive types.

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5.2 Salt stress and soybean genotypes categorization at the seedling stage

12

Further, in order to investigate the physiological and biochemical determinants of salt

13

stress responses at post germination stage, salt stress responses were studied in

14

hydroponically grown seedlings. In general, there was a strong positive correlation for

15

root growth parameters (RL, SR), plant growth parameters (FW, DW) and

16

photosynthetic pigments (chlorophyll and carotenoid). Salinity reduces biophysical

17

restraints to cell-wall expansion which in turn inhibit root, shoot growth and thus can

18

limits biomass accumulation (Shahid et al., 2012). The upper aerial part is more

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susceptible to severe salinity stress with decreased SFW and reduced LA. The reduction in SFW decreases the transpiration rate, thereby increasing the water retention capability to be effectively used for other metabolic processes (Singh et al., 2015). The limitation

22

in leaf expansion in turn could inhibit plant growth by decreased photoassimilate

23

production and dry matter partitioning (Zhang et al., 2016).The salt tolerant genotypes

24

were able to sustain their growth by minimizing the salinity mediated decrease in SL

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and LA. Based on these parameters, the PCA analysis of our datasets categorized the

2

genotypes as tolerant (MAUS-47, Bragg, RKS-18 and KDS-344) and sensitive (ADT-1,

3

SL-295, Gujosoya-2, PS-1042 and PK-1029). The PCA analysis also validated this as

4

the improved shoot K+/Na+ ratio was positively correlated to LA, total chlorophyll and

5

carotenoid content.

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Plant pigments such as chlorophylls and carotenoids constitute the antioxidative

7

system against salt induced oxidative stress (Ashraf and Harris, 2013). Chlorophyll and

8

carotenoids have been used as biomarkers for screening Brassica germplasm for salt

9

tolerance (Pandey and Suprasanna 2016). Higher reduction in chl- a, chl- b, total

10

chlorophyll and carotenoid content was observed in ADT-1, SL-295, Gujosoya-2 and

11

PS-1042 genotypes under salt treatment while MAUS-47, Bragg and PK-416 stably

12

maintained these parameters.

13

contents declined in the sensitive group but increased in the tolerant group suggesting

14

differential mechanism of defense. These parameters could be useful reference indices

15

for distinguishing tolerant from sensitive soybean genotypes.

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Potassium is an essential cation for plant growth and development while high

17

sodium accumulation is toxic by disrupting cellular processes, and interferes with K+

18

uptake and homeostasis, since both cations compete for the same transport systems

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(Adams and Shin, 2014). Maintenance of an appropriate K+/Na+ ratio under salt stress has been used as a potential biomarker in screening of genotypes for salt tolerance (Rahman et al., 2016). The Higher correlation of K+/Na+ ratio with Na+ than with K+

22

concentration in shoot suggests that this ratio is mainly driven by Na+ uptake and

23

translocation to shoots (Pires et al., 2015). The ability of the tolerant genotypes to

24

restrict increased accumulation of Na+ in their roots is an indication of higher tissue

ACCEPTED MANUSCRIPT 22

tolerance. Our results corroborate this and in our study, tolerant genotypes were found to

2

maintain their K+/Na+ ratio and photosynthetic pigments under salinity stress. K+/Na+

3

homeostasis might increase root osmotic potential and enhance water uptake, while

4

protecting the photosynthetic and actively growing tissues (Ma et al., 2011).

5

5.3 Gas exchange parameters for soybean genotypes categorization under salt

6

stress

7

The CO2-exchange characteristics of plants reflect the photosynthetic efficiency and can

8

be used as important indicator of tolerance under salt stress (Ashraf and Harris, 2013).

9

Oxidative stress causes early senescence, loss of chlorophyll, and decline in membrane

10

permeability which leads to a progressive reduction in photosynthetic efficiency

11

(Sedigheh et al., 2011). It has been proposed that the primary limiting factor for net

12

photosynthesis upon exposure to salinity is stomatal closure (Tavakkoli et al., 2011).

13

High correlation between leaf gas-exchange parameters and leaf sodium content

14

suggests that the toxic effect of the accumulated ions could be involved in reduction of

15

PN, gs, E and WUE in the tested soybean genotypes. It can be concluded therefore that

16

the decrease in the PN reported in our study was not only due to biochemical limitations,

17

but also due to stomatal components. The increase in the PN and WUE are the key

18

determinants for plant growth under salinity. The result of our study showed that

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MAUS-47 and Bragg genotypes were found to be better adapted to salinity as they were able to utilize their water (WUE) and nutrient resources (photosynthesis) more efficiently as compared to other genotypes. In Quinoa, salinity induced inhibition of

22

photosynthesis process was coincided with a strong decrease in the transpiration rate (E)

23

which contributes to a positive water balance (Eisa et al., 2012). In salt tolerant

24

plants, such water upkeep has been shown under saline conditions (Debez et al., 2006).

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Under stress conditions, drop in stomatal conductance could have defensive effects by

2

allowing plant water economy and improving plant water-use efficiency (Chaves et al.,

3

2009).

4

5.4 Mitochondrial respiration in soybean genotypes under salt stress

5

Alternative oxidase (AOX) is a central component of the non-phosphorylating

6

alternative respiratory pathway in plants and is important for mitochondrial function

7

under environmental stress (Kreps et al., 2002).

8

mitochondrial respiration via the AOX pathway has been demonstrated in several plant

9

species undergoing salt stress (Marti et al., 2011; Andronis and Roubelakis-Angelakis,

10

2010). The result of our study showed a general increase in the mitochondrial respiration

11

under salt stress (Table 1). Both the respiration pathways (AOX and COX) were found

12

to be activated under salt stress. The genotypes that maintained the COX along with

13

increase in AOX were considered to be tolerant on the basis of energy supply (COX

14

mediated ATP synthesis) and decreased ROS load to the mitochondrial ETC (AOX

15

mediated energy dissipation) (Vishwakarma et al., 2015). The genotypes PK-416,

16

MAUS-47 and Bragg were able to maintain the mitochondrial respiration under salt

17

treatment and thereby behaved as tolerant genotypes. These genotypes were also

18

validated using the biochemical and physiological analyses (Fig. 1 and 2). The

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The active involvement of

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mitochondrial respiration in these genotypes showed some kind of plasticity where disruption in one respiration pathway can be easily carried over by the other pathway. The flexibility in the mitochondrial respiration could equip the mitochondrial ETC such

22

as that it can efficiently use both the respiration port for its growth maintenance and salt

23

tolerance (Dinakar et al., 2016).

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The relative high respiration by AOX (92 %) over COX (69 %) in case of RKS-

2

18 can be attributed to growth penalty under salt stress, rendering it a moderately salt

3

tolerant genotype. The same was true for KDS-344 and PK-1029 maintaining the COX

4

and AOX up to 60- 75 % of their respective respiration under NaCl condition. On the

5

other hand, genotypes like ADT-1, SL-295, Gujosoya-2 and PS-1042 showed a

6

significant decrease in respiration rate through AOX and COX pathways under salt

7

stress (Table-1), suggesting the higher growth penalty and vulnerability to salt stress.

8

The results showed that AOX operates efficiently in the tolerant genotypes to cope up

9

with salinity and it offers as an easy tool to screen genotypes at seed imbibition stage.

10

The dynamic and robust behavior of AOX under salinity makes it a suitable candidate

11

for studying the salt response in the imbibed seeds (Marti et al., 2011; Pandey and

12

Suprasanna, 2016).

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6. Conclusions

15

The differential responses to salinity stress at early seed germination stage among the

16

tested soybean genotypes could be useful in screening for salt tolerant genotypes. The

17

PCA results indicated that the seed germination and seed vigor at early germination

18

stage and, WUE at seedling stage are key biomarkers for predicting the salt response.

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The principal component analysis (PCA) is valuable and accurate method for screening soybean for salt tolerance however; a large number of genotypes will be needed to further validate the method. The differential behavior of AOX in soybean plants under

22

salt stress is suggested as a potentially useful tool to screen plant germplasm at seed

23

imbibition stage for salt tolerance.

24

ACCEPTED MANUSCRIPT 25

Acknowledgement

2

The authors gratefully acknowledge Agharkar Research Institute, Pune and National

3

Institute of soybean Research, Indore, MP, India for source of seeds of soybean

4

genotypes; BCUD, Savitribai Phule Pune University, Pune for stipend to Mr. DB Shelke

5

and research funds under Departmental Research and Development program ; DST-

6

PURSE and FIST Program and UGC DSA-I program of Government of India for

7

research facility development grant and Bhaba Atomic Research Centre, Mumbai for

8

experimental facility.

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FIGURE LEGENDS:

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Fig 1: Biplots of principal component analysis for early germination parameters under

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1a) Control 1b) 50 mM 1c) 100 mM 1d) 150 mM 1e) 200 mM NaCl salt. The variance

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analysis was done using the mean of the triplicates.

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Fig 2: Biplots of principal component analysis for early seedling parameters under a)

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control and b) 100 mM NaCl salt condition. The variance analysis was done using the

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mean of triplicate.

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Fig 3: Biplots of principal component analysis of gas exchange parameters of early

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seedling under a) control and b) 100 mM salt condition in 0 day, 5th day and 10th day. The variance analysis was done using the mean of the triplicates. Supplementary fig. S1

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Estimation of malondialdehyde content under 0, 50, 100, 150 mM NaCl at seedling

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stage in soybean.

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TABLE LEGENDS:

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Table 1: Mitochondrial respiration activity of different Soybean genotypes under salt

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stress. Each value is the mean (± SE) of three replicates (Duncan’s test, P ≤ 0.05) and

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different letters for each factor in each column indicate significant difference.

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Supplementary Tables:

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Table S1: Results of various growth parameters like root length (cm), shoot length

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(cm), shoot fresh weight (gm ), root fresh weight (gm ), shoot dry weight (gm), root dry

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weight (gm), shoot tissue water content (% ) root tissue water content (%) and number

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of secondary roots in germination of Glycine max seed under control, 50, 100, 150, and

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200 mM NaCl conditions. The data was presented in the form of mean of biological

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triplicates ± S.D.

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Table S2: Pearson Correlation Coefficients table for the physiological parameters under

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germination level analysed by Principal component analysis for Glycine max under

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control (A) 50 (B) 100 (C) 150 (D) and 200 (E) mM of NaCl stress. The treatment

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conditions Pearson Correlation Coefficient (r) was used for each of the parameter using

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SAS software. Significance at *P< 0.05 and ** P< 0.01.

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Table S3. Results of various growth parameters like root length (cm ), shoot length

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(cm), fresh weight (gm), dry weight (gm), tissue water content (% ), number of

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secondary roots, gain in fresh weight (gm), leaf area (cm2), chlorophyll content (mg/g FW ), carotenoid content (mg/g FW ), ratio of K+/Na+ and Ca2+ in root/shoot in

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hydroponically grown Glycine max seedlings under control and

100 mM NaCl

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conditions (A). Results of various photosynthesis parameters like Photosynthetic rate

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(µmol CO2 m-2 s-1), Water use efficiency (µmol CO2 mmol-1H2O), Stomatal

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conductance (mol H2O m-2 s-1), Transpiration rate (mmol H2O m-2 s-1) in 0 days, 5th

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day and 10th day after treatment of Glycine max seedlings leaves under control and 100

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mM NaCl conditions (B). The data was presented in the form of mean of biological

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triplicates ± S.D.

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Table S4. Pearson Correlation Coefficients table for the biochemical parameters

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analysed by Principal component analysis for Glycine max under control (A) and 100

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mM (B) of NaCl stress. The Pearson Correlation Coefficients table for the effect of

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NaCl over photosynthesis parameters were also represented for control (C) and 100 mM

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NaCl (D) treatment conditions Pearson Correlation Coefficient (r) was used for each of

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the parameter using SAS software. Significance at *P< 0.05 and ** P< 0.01.

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Table 1: Mitochondrial respiration activity of different Soybean genotypes under salt stress. Each value is the mean (± SE) of three replicates (Duncan’s test, P ≤ 0.05) and different letters for each factor in each column indicate significant difference.

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Control 13.5±0.2bc 13.4±0bc 22.9±1a 12.4±0.9bc 11.6±0c 11±0c 13.1±2.3bc 12.8±2.2bc 15.7±0b 14±0bc

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Genotypes MAUS 47 Bragg PK1029 RKS18 PK416 KDS 344 ADT1 SL-295 Gujosoya-2 PS1042

nMoles of O2 consumed s-1 NaCl AOX d 16.5±0 11.5±0.3bcd 13.4±0.2e 12±1.6bcd 24.3±0a 15.2±0bc 22.5±1.1b 20.7±0a 14±0.3e 13.9±1.3b 19.9±0.3c 11.9±2bcd 25.4±0a 13.7±0b 20.3±0.3c 11.3±0cd 20.1±0c 8.7±1.4de 21±0c 7.4±1.6e

COX 13.3±0.4bc 11.1±0de 14.7±0ab 15.5±0.1a 12.6±1.3cd 14.8±0.1ab 7.2±1.1f 9.8±0.5e 11.3±0de 9.9±0.4e

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Fig: 1b

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Fig 1: Biplots of principal component analysis for early germination parameters under 1a) Control 1b) 50 mM 1c) 100 mM 1d) 150 mM 1e) 200 mM NaCl salt. The variance analysis was done using the mean of the triplicates.

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Fig 2: Biplots of principal component analysis for early seedling parameters under a) control and b) 100 mM NaCl salt condition. The variance analysis was done using the mean of triplicate.

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Fig 3: Biplots of principal component analysis of gas exchange parameters of early seedling under a) control and b) 100 mM salt condition in 0 day, 5th day and 10th day. The variance analysis was done using the mean of the triplicates.

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Fig: 3a

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Highlights:

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1. The principal component analysis of physiological and biochemical parameters resulted in three clusters; salt sensitive, salt tolerant and moderately tolerant/sensitive soybean genotypes. 2. AOX-respiration, root and shoot K+/Na+ ratio, improved leaf area and water use efficiency were the key determinant for salt tolerant soybean genotypes.

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Author contribution Prof. T. D. Nikam, Dr. P. Suprasanna and Dr. B. N. Zaware have designed and supervised the research project and written manuscript. D. B. Shelke, M. Pandey and G. C. Nikalje have contributed to lab experiments, data collection, and analysis and to write manuscript.