Cold storage reveals distinct metabolic perturbations in ...4 1 Introduction 2 3 Potato (Solanum...

1

Title: Cold storage reveals distinct metabolic perturbations in processing and non-processing

cultivars of potato

Running title: Metabolomic analysis of cold-induced sweetening in potato

Sagar S Datir*,§,1,a, Saleem Yousf§,3, Shilpy Sharma1, Mohit Kochle1, Ameeta Ravikumar2, and

Jeetender Chugh*,3,4

The affiliations and addresses of the authors:

1Department of Biotechnology, Savitribai Phule Pune University, Pune – 411007, India

aPresent address: Biology Department, Biosciences Complex, Queen’s University, Kingston, ON,

CA K7L 3N6

2Institute of Bioinformatics and Biotechnology, Savitribai Phule Pune University, Pune – 411007,

India

3Department of Chemistry, and 4Department of Biology, Indian Institute of Science, Education and

Research, Pune – 411008, India

§Authors contributed equally

*Corresponding authors:

Sagar S Datir, Ph.D.

Address: Department of Biotechnology, Savitribai Phule Pune University, Pune – 411007, India

E-mail: [email protected]

Phone: +918412013810

ORCID: 000-0003-0065-498X

and

Jeetender Chugh, Ph.D.

Assistant Professor, Department of Chemistry & Biology,

C-115, Indian Institute of Science Education & Research, Dr. Homi Bhabha Road, Pashan, Pune –

411008, India

E-mail: [email protected]

Phone: +91-20-25908121, +91-8378979667

certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was notthis version posted June 6, 2019. . https://doi.org/10.1101/661611doi: bioRxiv preprint

mailto:[email protected]


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2

ORCID: 0000-0002-9996-5202

E-mail addresses of the authors

Saleem Yousuf: [email protected]

Shilpy Sharma: [email protected]; [email protected]

Mohit Kochle: [email protected]

Ameeta Ravikumar: [email protected]

The date of submission: June 4, 2019

The number of supplementary tables: 4

The number of figures: 6 (Colour in online)

The number of supplementary figures: 7

The word count: 6516

Highlight

Metabolomic profiling using 1D 1H-NMR and bioinformatics analysis of potato cultivars for the

identification of metabolites and genes controlling biochemical pathways in cold-stored potato

tubers

Abstract

Cold-induced sweetening (CIS) causes a great loss to the potato (Solanum tuberosum L.) processing

industry wherein selection of potato genotypes using biochemical information through marker-trait

associations has found to be advantageous. In the present study, we have performed nuclear

magnetic resonance (NMR) spectroscopy-based metabolite profiling on tubers from five potato

cultivars (Atlantic, Frito Lay-1533, Kufri Jyoti, Kufri Pukhraj, and PU1) differing in their CIS ability

and processing characteristics at harvest and after one month of cold storage at 4°C. A total of 39

water-soluble metabolites were detected using 1H NMR. Multivariate statistical analysis

indicated significant differences in metabolite profiles between processing and non-processing

potato cultivars. Further analysis revealed distinct metabolite perturbations as induced by cold

storage in both types of cultivars wherein significantly affected metabolites were categorized mainly

as sugars, sugar alcohols, amino acids, and organic acids. Significant metabolic perturbations were

used to carry out metabolic pathway analysis that in turn tracked 130 genes encoding enzymes






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(involved directly and/or indirectly) involved in CIS pathway using potato genome sequence

survey data. Based on the metabolite perturbations, the possible relevant metabolite biomarkers,

significantly affected metabolic pathways, and key candidate genes responsible for the observed

metabolite variation were identified. Overall, studies provided new insights in further manipulation

of specific metabolites playing a crucial role in determining the cold-induced ability and processing

quality of potato cultivars for improved quality traits.

Keywords: Biomarker, cold-induced sweetening, metabolite, metabolomics, Nuclear Magnetic

Resonance, potato, processing cultivars, reducing sugar


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Introduction 1

2

Potato (Solanum tuberosum L.) – an important staple non-grain vegetable food crop – is used 3

globally for both processing and table purposes. Cold storage of potato tubers after harvesting is 4

mandatory to reduce sprouting, prevent diseases, avoid losses due to shrinkage, extend post-5

harvest shelf life, and to ensure year-round supply of quality tubers for consumption (Bianchi et 6

al., 2014; Hou et al., 2017). During cold storage the potato tubers exhibit the phenomenon of cold-7

induced sweetening (CIS), wherein rapid degradation of starch and sucrose hydrolysis has been 8

associated with accumulation of reducing sugars (RS) such as glucose and fructose (Burton, 1969; 9

Dale and Bradshaw, 2003). During the frying process, these RS react with free amino acids in a 10

Maillard reaction to generate dark-pigmented products that are bitter and unsightly to consumers. 11

In addition to this, one of the products of the Maillard reaction is acrylamide – a potent neurotoxin 12

and carcinogen (Menéndez et al., 2002; Mottram et al., 2002; Hajirezaei et al., 2003). Hence, CIS 13

is considered as one of the critical parameters in potato production as well as in processing; and 14

therefore identification and development of potato tubers resistant to CIS has become a priority in 15

a number of potato breeding programs (Xiong et al., 2002; Hamernik et al., 2009; Colman et al., 16

2017). It is necessary to identify and develop potato cultivars with CIS resistance along with good 17

processing quality attributes to meet the challenges of a frequently-changing market, production 18

circumstances, and improving their economic condition (Kaur and Aggarwal, 2014). In this regard, 19

the metabolic stability of potato tubers during the cold storage period has been identified as one of 20

the prime traits to be investigated for breeding programs worldwide (Sowokinos, 2001; Ali and 21

Jansky, 2015), wherein selection of potato genotypes at early generations using biochemical 22

information through marker-trait associations has been found to be advantageous (Slater et al., 23

2014; Gupta, 2017). 24

25

The potato processing industry is becoming an emerging sector in India and therefore, the demand 26

for processed potato products such as chips, French fries, flakes, etc. is increasing continuously 27

(Rana and Pandey, 2007). Ideally, potato cultivars suitable for processing should possess high 28

specific gravity and dry matter (DM) content along with low RS content (Kaur et al., 2012; Kaur 29

and Aggarwal, 2014). In this regard, commercially grown processing (Atlantic and Frito Lay 1533) 30

and popular Indian non-processing (Kufri Jyoti and Kufri Pukhraj) potato cultivars (Kaur and 31


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Aggarwal, 2014) along with one locally grown cultivar (PU1) were used as model-systems to 32

identify bio-markers for CIS. While, Atlantic and Frito Lay-1533 have been rated as the best 33

varieties for processing purpose with good storability, Indian potato cultivars Kufri Pukhraj and 34

Kufri Jyoti are used for table purpose due to their medium and average/poor storability, but have 35

been found to be inferior for processing purposes due to high RS and low DM content (Kaur and 36

Aggarwal, 2014; Aggarwal et al., 2017; Kaur and Khurana, 2017; Raigond et al., 2018). 37

38

We carried out nuclear magnetic resonance (NMR)-based untargeted metabolic profiling of five 39

potato cultivars differing in their CIS abilities from freshly harvested potatoes and after one month 40

of cold storage (4°C). The key objective in this study was to examine the differences in metabolic 41

profiles of these cultivars (between processing, non-processing, and local) at harvest and after cold 42

storage to further advance the knowledge of biochemical mechanisms underpinning the CIS trait. 43

The study also aimed to identify known biochemical pathways and to reveal underlying genes that 44

control metabolite accumulation after cold storage of potato tubers. Finally, we targeted to identify 45

key metabolite biomarkers and candidate genes (based on the metabolomics data and pathway 46

analysis) that can potentially be used in breeding programs for the development of new cultivars 47

for CIS resistance and improved processing attributes thereby enhancing the potato tuber quality. 48

49

Materials and Methods 50

Plant Material 51

Two potato cultivars Atlantic and Frito Lay-1533 (FL-1533) (Pepsi Foods Pvt. Ltd. Channo, 52

Sangrur) suitable for processing purpose and two Indian cultivars Kufri Jyoti and Kufri Pukhraj 53

(Central Potato Research Institute, Shimla) with non-processing characteristics differing in their 54

cold storage ability (Marwaha et al., 2005; Kaur and Aggarwal, 2014; Sharma et al., 2012) used 55

in the present study were obtained from BT Company and Jai Kisan Farm Products and Cold 56

Chains Pvt. Ltd, India, Pune. One locally grown potato cultivar (PU1) possessed high RS and poor 57

storability (Datir et al., 2019) was also included in the study (Supplementary Table S1). 58

59

Potato Plantation and Harvesting 60

Tubers of 5 cultivars, namely Atlantic, FL-1533, Kufri Pukhraj, Kufri Jyoti, and PU1, were planted 61

in triplicates in separate PB 5 Polythene bags containing (potting mix: 60% shredded pine bark, 62


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20% crusher dust, cow dung, 20% soil supplemented with sand and slow release fertiliser) on 25th 63

June 2018, at the Department of Biotechnology, Savitribai Phule Pune University, Pune, India. 64

Tubers from 15 bags were harvested in the second week of October 2018. Six tubers of each 65

cultivar (two tubers from each triplicate) were transferred to individual paper bags and divided 66

into two groups consisting of three tubers each (one from each triplicate) for sampling at two 67

treatments, (a) fresh harvest (FH), and (b) after one month of cold storage at 4°C (CS). While the 68

first group of three tubers (one from each triplicate) from each of the 5 cultivars – making a total 69

of 15 tubers for treatment (a) – were immediately processed for metabolite extraction, a second 70

group of three tubers (one from each triplicate) from each of the 5 cultivars – making a total of 15 71

tubers – were stored at 4°C for one month (treatment b). All the tubers were subjected to freeze-72

drying (Operon, FDB-5503, Korea) for one week before using for metabolite extraction. 73

74

Metabolite extraction 75

The freeze-dried potato samples (from treatments a and b of five cultivars) were ground to a fine 76

powder and were used for metabolite extraction. Briefly, approximately 200 mg of freeze-dried 77

potato powder was re-suspended in 200 µl Phosphate Buffer Saline (PBS) in 1.5 ml tubes and 78

vortexed for five minutes. To each tube, 400 µl ice cold methanol (Sigma, HPLC grade) was added, 79

followed by vortexing for another 5 min. Samples were then stored at -20°C for 12 h. Post-80

incubation, the samples were centrifuged at 16,000 g (Eppendorf centrifuge 5415C, Hamburg, 81

Germany) for 20 min at 4°C. The supernatants were transferred to fresh 1.5 ml Eppendorf tubes 82

and were subjected for lyophilization (Operon, FDB-5503, Korea). There were 3 replicates for 83

each of the 5 cultivars processed in two treatments making a total of 30 distinct samples. The 84

lyophilized extracts of all the samples were reconstituted into 580 µl 100% NMR buffer (20 mM 85

sodium phosphate, pH 7.4 in D2O containing 0.4 mM DSS (2,2-dimethyl-2-silapentane-5-sulfonic 86

acid). For making buffer containing a known concentration of DSS, 17.46 ± 0.01 mg of DSS was 87

weighed (Mol wt. 218.32 g/mol) and dissolved in 2000 µl ± 2 µl of phosphate buffer. This stock 88

solution was then diluted to 100 fold resulting in a final buffer solution containing 87.30 ± 0.16 89

mg/L of DSS in solution, which corresponds to 399.9 ± 0.7 µM of DSS in the buffer. The samples 90

were vortexed for 2 min at room temperature and centrifuged at 4000 g for 2 min. The supernatants 91

were transferred to respective 5 mm NMR tubes for NMR data measurements. 92

93


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NMR Spectroscopy 94

All the NMR data was measured on a Bruker AVANCE III HD Ascend NMR spectrometer 95

operating at 14.1 Tesla. This spectrometer has been equipped with pulsed-field gradients in x, y, 96

and z directions (operating at 54 Gauss/cm), and Bruker high-performance shim system with 36 97

orthogonal shim gradients and integrated real-time shim gradient for 3-axis shimming. A 98

cryogenically cooled quad-channel (1H/13C/15N/31P-2H) probe was used to pump radio frequencies 99

and detection. All the NMR data was measured at 298 K controlled by the Bruker VT unit. Water-100

suppression pulse sequence from Bruker library (noesygppr1d) was used to measure all the 1H-101

NMR data, where water suppression was achieved by pre-saturating water using continuous wave 102

irradiation at 5.56E-05 W during the inter-scan relaxation delay of 5 s, and employing spoiler 103

gradients (Smoothed square shape SMSQ10.100, where G1 was with 50% power and G2 was with 104

-10% power for 1 ms duration each). The data acquisition period of 6.95 s (including inter-scan 105

delay) was used, giving a spectral width of 7200 Hz resolved in 32k data points. Sixty-four scans 106

were used to average the signal recorded on each sample. 1H 90 pulse-width, receiver gain, and 107

water-suppression parameters were kept invariant from sample to sample to rule out intensity 108

variations while recording data on different samples. To help with assignment of metabolites, 1H-109

1H total correlation spectroscopy (TOCSY) experiment (using mlevesgppg pulse sequence from 110

Bruker library) was measured with a 6000 Hz of spectral width resolved in 2048 1024 data points 111

with 40 transients per increment. A Hartman-Hahn mixing time of 80 ms was employed for the 112

TOCSY spin-lock using composite blocks of 90-180-90 pulses with 90 pulse width of 25 s 113

at 2.29 W of power. TOCSY data was recorded in States-TPPI mode and Smoothed square shaped 114

(SMSQ10.100) gradients were used with 31% power (after the spin-lock period) and 11% power 115

(before refocusing) for a duration of 1 ms. 116

117

Metabolite Identification and Quantification 118

All of the NMR data were processed using Topspin (v3.5) software 119

(www.bruker.com/bruker/topspin). 1H NMR raw data was multiplied with exponential function 120

and zero-filled to 64K data points prior to Fourier transformation. All the spectra are manually 121

phased and the baseline is corrected before subjecting to further analysis. 1H chemical shift was 122

directly referenced to DSS resonance (=0 ppm at 25 C). 1H-1H TOCSY was processed with a 123

pure cosine function (SINE with SSB = 2) and zero-filled to 2048 and 1024 data points in F1 and 124


http://www.bruker.com/bruker/topspin

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F2 dimensions prior to subjecting the data to Fourier transformation. Multiple peak parameters 125

including, chemical shift values, J-coupling values, line shape, and multiplicity information, in 126

combination with BMRB and HMDB data bases were used to assign the peaks to respective 127

metabolites. Chenomx NMR suite 8.1 software was used to carry out the 1H resonance assignment 128

with a chemical shift tolerance of 0.05 ppm when comparing the data with BMRB/HMDB. 129

Resonance assignment of metabolites was confirmed using 1H-1H TOCSY (Supplementary Fig. 130

S1) cross peak pattern of individual metabolites containing coupled 1H spin systems via a semi-131

automated software, MetaboMiner. A sets of five resonances remained unassigned and have been 132

duly marked as U1-U5 (Supplementary Fig. S2 and Supplementary Table S2). 133

134

After identification of metabolites, respective peaks were manually picked, integrated using 135

Topspin v3.5, and converted to absolute concentrations of individual metabolites using Chenomx 136

NMR suite 8.1 by comparing with the peak integrals from an external reference compound DSS 137

of known concentration (400 µM). The absolute concentrations obtained above were then 138

normalized using the dry weight obtained from the tuber mass used for metabolite extraction. The 139

data matrix file was created using concentrations of metabolites as obtained above from 30 distinct 140

samples. The lower limit of quantification achieved using above-mentioned NMR parameters was 141

0.25 M for the methyl peak of DSS at a s/n ratio of 10. 142

143

Metabolic pathway analysis, Blast similarity searching, gene identification, notation and 144

location on potato chromosomes 145

Metabolic pathway analysis depicting significantly affected metabolites in cold-stored potato 146

tubers was performed by comparing the primary metabolites based on KEGG and the reference 147

pathway (Sowokinos, 2001; Malone et al., 2006) using MetaboAnylst web tool 148

(https://www.metaboanalyst.ca/). BLAST similarity searching, gene identification and location in 149

the potato genome annotated to encode enzymes of biochemical pathways was retrieved from 150

Potato Genome Sequencing Consortium (PGSC) 151

(http://solanaceae.plantbiology.msu.edu/pgsc_download.shtml), National Center for 152

Biotechnology Information (NCBI) database (http://www.ncbi.nlm.nih.gov/), Sol Genomics 153

Network (https://solgenomics.net/), Phytozome version 12.1 154

(https://phytozome.jgi.doe.gov/pz/portal.html) and KEGG (https://www.genome.jp/kegg/) using 155


https://www.metaboanalyst.ca/

http://solanaceae.plantbiology.msu.edu/pgsc_download.shtml

http://www.ncbi.nlm.nih.gov/

https://solgenomics.net/

https://phytozome.jgi.doe.gov/pz/portal.html

https://www.genome.jp/kegg/

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key word searches. The gene IDs have been taken from PGSC. In the event where gene sequences 156

were not identified from PGSC, NCBI IDs have been provided. 157

158

Statistical analysis 159

Due to high dimensionality and large complexity (5 cultivars in triplicates in 2 processing 160

conditions with each NMR sample having ~1000 1H signals) of the metabolomics data, 161

multivariate statistical analysis was performed. To predict the differences in nature and 162

concentrations of metabolite in various cultivars in triplicates with treatments a and b, principal 163

component analysis (PCA) was carried out using normalized metabolite concentration as input in 164

the MetaboAnalyst web tool (www.metaboanalyst.ca). The input data table was normalized using 165

the Pareto-scaling approach available in the MetaboAnalyst. Correlation between first two 166

principal components was drawn as the scores plot for all the samples and clusters of normal 167

distribution were marked using ellipses showing 95% confidence limits for each group in PCA 168

analysis. Next, pair-wise analysis of all five cultivars in FH and CS treatments was achieved using 169

Volcano plot analysis, where metabolites were selected based on dual criteria, 1) the significance 170

(false discovery rate (FDR) corrected p-value < 0.05), and fold-change in concentration (cut-off 171

for fold change was set to 1.5 fold increase or decrease). In addition to this, the VIP score plot 172

obtained by PCA identified the key metabolites responsible for the clustering of various groups. 173

Metabolites with a VIP score of ≥1.0 are generally considered to be statistically significant (Ma et 174

al., 2016; Wu et al., 2018). A union set of significant metabolites (those identified from volcano 175

plot analysis, and from VIP score following the above-mentioned criteria) were taken for 176

generating Box and Whisker plots to highlight the variation of a particular metabolite across 177

replicates, different cultivars, and in different treatment conditions. Metabolites, e.g. ascorbate, 178

having low signal-to-noise (s/n < 15) in NMR spectra, although identified with confidence, were 179

not included in box and whisker plot analysis as they might be prone to over- or under-estimation 180

of concentrations. Further, correlation plots were drawn to identify all the correlated metabolites 181

in FH and CS treatments for all five cultivars. The significantly affected pathways were then 182

identified using significantly perturbed metabolites as input in MetaboAnalyst tool and KEGG 183

pathway database (www.genome.jp/kegg/pathway.html). 184

185

Results and discussion 186


http://www.metaboanalyst.ca/

http://www.genome.jp/kegg/pathway.html

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187

Global profiling of metabolites in different potato cultivars – Processing versus non-188

processing cultivars 189

Global profiling of metabolites obtained from the methanolic extracts of the FH and CS tubers 190

obtained from five different potato cultivars, differing in their cold storage behaviour and 191

processing characteristics (Supplementary Table S1), identified a total of 39 abundant water-192

soluble metabolites using 1D 1H-NMR (Supplementary Fig. S2); and confirmed using 2D 1H-1H 193

TOCSY (Supplementary Fig. S1) and BMRB database. Identified metabolites have been marked 194

on 1D 1H-NMR (Supplementary Fig. S2), listed in supplementary table S2, and were quantified. 195

Water-insoluble metabolites in the organic phase gave broad and overlapping signals in 1D 1H-196

NMR and thus were excluded from the analysis. A range of distinct metabolites was detected that 197

could be characterized mainly as sugars, sugar alcohols, amino acids, and organic acids 198

(Supplementary Table S2). The unsupervised PCA analysis showed a divergent separation on the 199

scores plot of PC1 and PC2, accounting for a 55.4% and 22.2% variation in the metabolites 200

extracted from the FH and CS treated tubers of the different cultivars used in the study (Fig. 1). 201

Interestingly, the clustering of single points in the principal component space (marked by ellipses 202

showing 95% confidence limits of a normal distribution) for metabolites from the FH tubers of 203

Atlantic and FL-1533 (processing cultivars) clustered together while FH tubers from Kufri Jyoti 204

and Kufri Pukhraj cultivars and the local PU1 were more similar to each other (Fig. 1). Further, 205

the ellipses marking the principal component space for the metabolites of the cold storage tubers 206

were also found to be different in processing, non-processing, and locally grown cultivars (Fig. 1). 207

These differences in the metabolite content in the different cultivars at the two time-points could 208

be attributed to the genetic make-up of each cultivar used in the present study. In fact, previous 209

studies have also reported such variability in the metabolite content from different potato cultivars 210

differing in their genetic background (Defernez et al., 2004; Uri et al., 2014). 211

212

Pair-wise analysis of metabolic changes upon cold storage in processing, non-processing, and 213

local cultivars 214

The variations in metabolite profiles of potato cultivars differing in their genetic constitution offer 215

a potential tool to develop CIS resistant potatoes with genotypes encoding improved processing 216

characteristics. However, studies investigating the metabolic diversity from cold-stored potato 217


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tubers differing in their processing and non-processing characteristics have been limiting. In order 218

to highlight the differences in the FH and the CS condition from the processing, the non-219

processing, and the local potato cultivars used in the study, pair-wise analysis was done (Fig. 2). 220

In addition to this, volcano plot analysis (Fig. 3) and VIP score plot analysis (Fig. 4) was also 221

performed under these conditions to identify the significantly affected metabolites in cold storage. 222

223

a. Metabolic changes in processing cultivars in CS conditions 224

The chemometric analysis was performed to assess the metabolic perturbations of potato tubers 225

upon CS in all 5 cultivars. PCA analysis of metabolites obtained from the processing cultivars, 226

Atlantic and FL-1533 showed 84.1% variation in PC1 and 11.2% variation in PC2 (Fig. 2A); and 227

84.3% variation in PC1 and 9.7% variation in PC2 (Fig. 2B), respectively. While in the Atlantic 228

cultivar, fumarate and glutamate were found to be significantly downregulated upon CS when 229

compared with FH; fructose, glucose, galactose, methanol, sucrose, and asparagine were 230

significantly upregulated upon CS (Fig. 3A and Fig. 4A). Similarly, in the FL-1533 cultivar, CS 231

treatment significantly increased the levels of fructose, glucose, sucrose, galactose, fumarate, 232

trigonelline, citrate, aspartate, glutamate, and glutamine (Fig. 3B and Fig. 4B). On the other hand, 233

the levels of mannose and 3-hydroxyisobutyrate were significantly reduced upon CS in this 234

cultivar. 235

236

b. Metabolic changes in non-processing and the local cultivars in cold storage conditions 237

PCA analysis of metabolites obtained from the non-processing cultivars, Kufri Jyoti and Kufri 238

Pukhraj, showed 92.8% variation in PC1 and 4% variation in PC2 (Fig. 2C); and 90.4% variation 239

in PC1 and 6.1% variation in PC2 (Fig. 2D), respectively upon CS. The levels of glucose, fructose, 240

mannose, galactose, aspartate, malate, fumarate, leucine, proline, trigonelline, asparagine, and 241

serine were increased in the Kufri Jyoti cultivar upon CS, while the levels of sucrose and alanine 242

were reduced (Fig. 3C and Fig. 4C). Similarly, CS treatment of the Kufri Pukhraj cultivar was 243

associated with significant increase in the levels of fructose, glucose, 3-hydroxyisobutyrate, 244

mannose, malate, leucine, aspartate, serine, proline, isoleucine, adenosine, arginine, asparagine, 245

and methanol on one hand; it significantly decreased the levels of chlorogenate and formate upon 246

CS (Fig. 3D and Fig. 4D). In the local PU1 cultivar, levels of formate, tryptophan, and sucrose 247

were significantly decreased, while 3-hydroxyisobutyrate, methanol, fructose, glucose, proline, 4-248


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aminobutyrate, trigonelline, myo-inositol, arginine, aspartate, uridine, and sn-glycero-3-249

phosphocholine showed significant increase upon cold storage treatment (Fig. 3E and Fig. 4E). 250

251

Metabolomics approach has been previously used to assess the effect of storage conditions on a 252

variety of potato cultivars. For example, metabolic profiles in different life cycle stages of potato 253

tubers were characterized to link temporal changes in metabolites related to trait development 254

(Shepherd et al., 2010). In a recent study, comprehensive metabolomics and ionomics analysis on 255

raw and cooked potato tubers of 60 unique genotypes were performed to understand the chemical 256

variation and nutritional values in different varieties (Chaparro et al., 2018). In another study, 257

storage of commercial cultivars at 20-22 C in the dark suggested a significant decrease in sucrose 258

and fructose (Uri et al., 2014). Here, we have reported that the storage of potato tubers at 4°C 259

significantly increased the levels of sucrose, particularly in Atlantic and Frito Lay 1533, while it 260

was significantly decreased in Kufri Jyoti and PU1, and remained invariant in Kufri Pukhraj (Fig. 261

5). On the other hand, we found that the increase in RS was more pronounced in the non-processing 262

cultivars Kufri Pukhraj, Kufri Jyoti, and PU1 as compared to the processing cultivars, Atlantic and 263

Frito Lay 1533 (Fig. 3, Fig. 4, and Fig. 5). These results are in agreement with other studies that 264

observed an increase in RS after cold storage of potato tubers (Kaur and Aggarwal, 2014; 265

Aggarwal et al., 2017; Datir et al., 2019), which has been attributed to the enhanced activity of the 266

vacuolar invertase (Lin et al., 2013). The effect of silencing of vacuolar invertase, which converts 267

sucrose into glucose and fructose, on sugar metabolism pathways has previously been studied to 268

find suitable targets for further genetic manipulation to improve the tuber quality (Wiberley-269

Bradford et al., 2014). Brummell et al., (2011) analysed the RS along with the expression of 270

invertase and invertase inhibitors in cold-stored potato tubers obtained from cold-sweetening 271

susceptible and cold-sweetening resistant cultivars. They demonstrated that the levels of RS 272

decreased after one month of cold storage and this was accompanied by an increase in expression 273

of the vacuolar invertase inhibitor mRNA accumulation in processing cultivars. Therefore, a 274

relatively lower increase of RS after cold storage in Atlantic and Frito Lay 1533 cultivars (when 275

compared with non-processing and local cultivar) used in this study could be attributed to 276

increased levels of vacuolar invertase inhibitor. We recently studied the allelic variations in the 277

vacuolar invertase inhibitor gene from Atlantic, Frito Lay 1533, Kufri Jyoti, Kufri Pukhraj, and 278

PU1 cultivars and proposed that the SNPs in the vacuolar invertase inhibitor gene could be 279


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associated with the variation in RS levels in these cultivars (Datir et al., 2019). However, these 280

results need to be further validated using a qRT-PCR expression of vacuolar invertase inhibitor 281

gene before and after cold storage in these cultivars. 282

283

Although CS resulted in several significant metabolic perturbations, it is important to highlight the 284

significance of some metabolites that can be related to the CIS status of the potato cultivars. For 285

example, it is noteworthy that FL-1533 exhibited significantly higher citrate levels as compared to 286

rest of the cultivars after CS (Fig. 5), which might be associated with CIS resistance along with 287

chips with an acceptable colour. This is particularly important as citric acid is known as a popular 288

anti-browning agent, mainly because it not only inhibits the polyphenol oxidase by reducing pH 289

but also chelates copper at the enzyme-active site (McCord and Kilara 1983). Likewise, the 290

changes in the levels of total amino acids, specifically the levels of asparagine, and the ratio of free 291

asparagine to RS during cold storage were found to be significantly varied among different potato 292

cultivars upon CS (Fig. 5). These factors, therefore, can further influence the processing quality of 293

potato tubers. It is interesting to note that, among all the cultivars, PU1 cultivar in particular 294

showed significantly higher levels of methanol after CS (Fig. 5). The amount of methanol released 295

on saponification is the measure of the degree of pectin methylation and was found to be associated 296

indirectly with the potato tuber texture properties (Ross et al., 2010b). It can also be presumed that 297

some of these significantly affected metabolites might have acted as osmolytes such as proline, 298

trigonelline, 4-aminobutyrate (GABA), etc. (Evers et al., 2010) (Fig. 5, Fig. 6) as an acclimation 299

response under CS treatment. However, not much research has been focused on understanding the 300

function of various metabolites in the CIS process of potato tubers. Therefore, these uniquely 301

observed metabolite variations provide new insights into identifying and developing CIS resistant 302

potato genotypes. 303

304

Metabolic correlation network analysis 305

Person’s correlation coefficient analysis was used to analyze the metabolite-metabolite 306

correlation among identified metabolites in all five cultivars at both the time-points 307

(Supplementary Fig. S3-S7). A total of 100, 84, and 45 significant correlations (p-value < 0.5) 308

were obtained at FH among processing group (Atlantic and FL-1533), non-processing group 309

(Kufri Pukhraj and Jyoti), and local (PU1) cultivars, respectively (upper-right half of the plot 310


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marked with white triangle in supplementary Fig. S3-S7). After CS, the number of significant 311

correlations changed to 108, 105 and 31 (lower-left half of the plot marked with blue triangle 312

in supplementary Fig. S3-S7) in the processing group, non-processing group, and local cultivar, 313

respectively. The number of positive vs. negative correlations also varied depending on the 314

variety (Supplementary Table S3). Remarkably, among all the metabolites, amino acids 315

dominated the significant metabolite correlations. In general, the metabolite-metabolite 316

correlations detected in the present work were highly dependent on the type of the cultivar 317

considered; however, some particular behaviours of the metabolic network after CS are worth 318

mentioning. For instance, a positive correlation of fructose with phenylalanine, valine, 4-319

aminobutyrate, glutamine, choline, and glutamate was evident in Atlantic (Supplementary Fig. 320

S3) upon CS. In the case of FL-1533, pyroglutamate was found to be positively correlated with 321

valine, alanine, arginine, and 4-aminobutyrate after CS (Supplementary Fig. S4). Whereas 4-322

amiobutyrate was positively correlated with trigonelline, sucrose, allantoin, and arginine, citrate 323

and malate displayed significantly positive correlations with several other metabolites in Kufri 324

Jyoti after CS (Supplementary Fig. S5) which were not evident in the other cultivars. However, 325

several negative correlations were exclusively observed in PU1 cultivar after CS 326

(Supplementary Fig. S7). No correlation between metabolites that are close in a metabolic 327

pathway was observed after CS. For instance, glutamate and glutamine are metabolic neighbours 328

in the glutamine synthase pathway and are found to be uncorrelated in the non-processing and 329

local cultivars (Supplementary Fig. S5-S7) in FH as well as CS treatments, while are found to be 330

correlated in processing cultivars (Supplementary Fig. S3 and S4). On the other hand, several other 331

metabolite correlations were noted even if they are not metabolic neighbours. Significant 332

correlations among various potato cultivars might help to predict the CIS status of the particular 333

potato genotype based on FH and CS tuber profiling. However, the reason for these strong 334

correlations remains unclear as no direct link has been reported so far and further investigation 335

is needed. Metabolite correlations of potato groups differing in the genetic background have 336

been previously reported (Dobson et al., 2010; Chaparro et al., 2018). Significant metabolite 337

variation and metabolite-metabolite correlations were detected from a collection of 60 unique 338

potato genotypes that span 5 different market classes such as russet, red, yellow, chip, and 339

speciality (Chaparro et al., 2018), where authors concluded that metabolite diversity and 340

correlations data can support the potential to breed new cultivars for improved health traits. 341


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342

Metabolite biomarkers for the identification of CIS resistant and susceptible genotypes 343

CIS is a multigenic complex trait involving multiple intricate metabolic pathways which clearly 344

indicates that it is unlikely to be controlled by a single metabolite; thus, multiple metabolites would 345

come-up as plausible biomarkers for CIS in potatoes. Previous studies have suggested that various 346

primary metabolites in potato tubers can be utilized as biomarkers in breeding programs for 347

predicting agronomically important traits such as black spot bruising and chip quality (Steinfath 348

et al., 2010; Instroza-Blancheteau et al., 2018). We would like to point out that in addition to the 349

amount of RS, the total and individual amino acid content, the asparagine content, levels of organic 350

acids, and other metabolites could be considered as important processing parameters. Breeders aim 351

for the identification and development of processing potato cultivars with low free-asparagine and 352

RS as desirable characteristics for processing purpose. In the current study, a unique metabolite 353

combination was observed for the processing cultivar, FL-1533, which was represented by the 354

lowest amount of RS and asparagine compared to rest of the cultivars CS (Fig. 3, Fig. 4, and Fig. 355

5). The levels of RS and asparagine have been used as markers for potato trait development 356

(Shepherd et al., 2010). 357

358

Amongst the different TCA cycle metabolites, the levels of citrate were found to be significantly 359

higher in the processing cultivar, FL-1533, whereas Kufri Jyoti and Kufri Pukhraj showed 360

significantly higher levels of malate after CS (Fig. 6). Citrate and malate are critical in 361

determination of non-enzymic browning reactions, after cooking darkening, physiological age/ 362

stages of development in the storage of potato tubers (Wichrowska et al., 2009; Reust and Aerny, 363

1985). In addition, they indirectly influence the texture of cooked and fried potato products 364

(Heisler et al., 1964; Thomas et al., 1979; Lynch and Kaldy, 1985). Hence, it is necessary to 365

develop the indicators of tuber browning and physiological age mainly because both the growers 366

and seed companies can optimize the storage conditions for individual cultivars. Moreover, such 367

indicators will be extremely important in the determination of the suitability of potato tubers for 368

culinary use and industrial processing (Reust and Aerny, 1985). 369

370

The texture of potato tubers is a key determinant of the quality of processing as well as cooked 371

potato as has been shown to greatly influence the consumer’s preference (Shomer and Kaaber, 372


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16

2006; Thybo et al., 2006; McGregor, 2007). It is mainly determined by the breakdown of the cell 373

wall middle lamella during cooking, and the correlation between pectin methylesterase activity 374

and the degree of methylation of cell wall pectin (reviewed in Taylor et al., 2007; Ross et al., 375

2010b). The amount of methanol released on saponification is the measure of the degree of pectin 376

methylation and is indirectly associated with the potato tuber texture properties (Ross et al., 377

2010b). Significantly highest levels of methanol were exclusively recorded in the PU1 cultivar 378

after CS (Fig. 5). Therefore, the amount of methanol present in potato tubers can be used as a 379

potential marker for screening of potato cultivars for texture properties. 380

381

Importantly, several other metabolites such as fumarate, adenosine, sn-glycero-3-phosphocholine, 382

4-aminobutyrate, 3-hydroxyisobutyrate, trigonelline, and chlorogenate were significantly varied 383

upon CS (Fig. 3, Fig. 4, and Fig. 6) indicating that these metabolites might have a role in the CIS 384

process, as well as the determination of processing quality of these cultivars. In order to improve 385

potato (Solanum tuberosum L.) genotypes through selection or breeding, it is helpful to determine 386

the chemical composition of tubers (Pal et al., 2008). Maintaining the quality of potato tubers 387

during storage is a major challenge. Therefore, the information on the response of potato cultivars 388

to cold storage and metabolite accumulation can be useful for the development of biomarkers 389

predicting severity of CIS of different potato genotypes. Such biomarkers (supplementary table 390

S4) can then be tested on a wide range of potato genotypes differing in CIS response and easily 391

integrated into the existing potato storage management and breeding methods. Moreover, such 392

predictive biomarkers can be used in selection for potato breeding and for tailoring storage 393

conditions for each lot of harvested tubers (Neilson et al., 2017). Furthermore, biomarkers can be 394

utilized for the manipulation of a specific metabolite pathway for developing potato genotypes 395

with improved processing characteristics. 396

397

Metabolic Pathway Analysis 398

We performed the pathway analysis depicting significantly affected metabolites in cold-stored 399

potato tubers by comparing the primary metabolites based on KEGG and the reference pathway 400

(Fig. 6) (Sowokinos, 2001; Malone et al., 2006). Cold temperature induces starch degradation in 401

potato tubers to principal sugars including sucrose, glucose, and fructose, thereby leading to an 402

imbalance between starch degradation and sucrose metabolism in tubers. So far, CIS studies have 403


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17

mainly concentrated on the activity of enzymes involved in the conversion of starch and sugars 404

(Jansky and Fajardo, 2014). However, potato tubers displayed diverse biochemical mechanisms 405

during CIS and the amount of sugar in potato tubers is influenced by several candidate genes 406

operating in glycolysis, hexogenesis, and mitochondrial respiration (Sowokinos, 2001). The 407

metabolic pathway analysis presented in this study suggests that several metabolites were affected 408

during cold storage and mainly resulted from the alanine, aspartate, and glutamate metabolism; 409

valine, leucine, and isoleucine biosynthesis; arginine and proline metabolism; glycine, serine, 410

and threonine metabolism; the TCA cycle, fructose and mannose metabolism, galactose 411

metabolism, nicotinate and nicotinamide metabolism, glycolysis; and sucrose metabolism along 412

with several other metabolites (Fig. 6). Also, the levels of metabolites were found to be 413

specifically different depending on potato cultivars (Fig. 3, Fig. 4, and Fig. 5) indicating that the 414

specific metabolites might play a crucial role in determining the cold-induced ability of potato 415

cultivars. Also, the molecular events controlling such metabolic perturbations in potato tubers after 416

cold storage are still puzzling. Among various metabolic processes, carbohydrates, amino acids 417

and organic acids were identified as the main players in the CIS process and were either decreased 418

or increased under cold storage condition. In the amino acid metabolism pathways, the distinct 419

significantly affected pathways include metabolism of 11 amino acids: isoleucine, glutamate, 420

glutamine, leucine, alanine, arginine, proline, tryptophan, aspartate, asparagine and serine 421

metabolism. In the TCA cycle, the levels of citrate, malate, and fumarate were significantly 422

affected by CS. Particularly, citrate and fumarate synthesis was up-regulated in FL-1533 423

cultivar (Fig. 6). Several other metabolites such as 3-hydroxyisobutyrate, trigonelline, 424

galactose, mannose, etc. were either up-regulated or down-regulated in response to cold storage 425

(Fig. 6). On the other hand, methanol production was significantly enhanced in Atlantic, Kufri 426

Pukhraj, and PU1 cultivars although the extent of this increase was significantly higher in PU1 427

(Fig. 6). The GABA shunt pathway was significantly enhanced as seen by the increased levels of 428

4-aminobutyarte in PU1 upon cold storage, whereas it was not significantly affected in any other 429

potato cultivar. The convergence and divergence of various pathways involved in CIS revealed a 430

complex metabolic network. However, the roles of these metabolites and their accumulation 431

pattern in response to cold storage in different potato cultivars remains to be further investigated. 432

A possible approach to achieve this goal is to identify the genes which are putatively involved in 433

the formation of enzymes involved in the biosynthesis of these metabolites. 434


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435

Putative genes controlling CIS process of potato tubers 436

Taking leads from the identified metabolites and the major metabolic pathways affected during 437

CIS, key-word searches for the gene name were conducted on multiple databases including PGSC, 438

NCBI, Sol Genome Network, and Phytozome to identify candidate genes likely to be involved in 439

the observed metabolic variation (Table 1). Based on the metabolic pathway analysis, a total of 29 440

significantly affected metabolites (Fig. 6) encompassing 130 genes that are likely to participate in 441

CIS mechanism were identified (Table 1). Although, candidate genes involved in starch and sugar 442

metabolism linked to the quantitative trait loci (QTL) for sugar and starch contents have been 443

reported earlier (Chen et al., 2001), information about several other enzymes controlling various 444

metabolites in CIS is still lagging, which lays an obstacle for metabolic engineering of potato. 445

Fischer et al., (2013) reported that besides starch-sugar interconversion and membrane 446

composition, the adaptation of tubers to cold storage might include other pathways. In the current 447

study, in addition to sucrose metabolism, majority of the enzymes involved in controlling amino 448

acid metabolism, and organic acids metabolism were identified (Table 1). In several cases, more 449

than one isoform was identified indicating most of the enzymes were encoded by multigene 450

families. Interestingly, tryptophan synthase, sucrose phosphate phosphatase, sucrose synthase, 451

malate dehydrogenase, glutamine synthetase related to tryptophan, sucrose biosynthesis, malate, 452

and glutamine metabolism represented by a larger multigene families consisting of 5 copies of 453

genes located on various chromosomes. Also, several genes annotated with different loci were 454

observed (Table 1). Metabolic perturbations in response to cold storage (Fig. 3, Fig. 4, and Fig. 5) 455

could be attributed to either the natural allelic variations of genes or changes in transcript levels. 456

Natural variation in candidate genes such as invertase and invertase inhibitors revealed that the 457

genetic polymorphism raises the possibility that SNPs in alleles of these genes may contribute to 458

the phenotypic variation in response to CIS among the potato genotypes (Menéndez et al., 2002; 459

Baldwin et al., 2011; Datir et al., 2012; Datir et al., 2019). 460

461

Considerable variation in the levels of citrate and malate were observed in the present investigation 462

(Fig. 5). Citrate synthase and malate dehydrogenase (Table 1) were mapped on potato 463

chromosomes (Chen et al., 2001). However, the natural allelic variations present in these genes 464

controlling citrate and malate levels have not been documented. It is noticeable that the levels of 465


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glutamine were exclusively significantly higher in FL-1533 and Kufri Pukhraj after CS as 466

compared to rest of the potato cultivars (Fig. 5, Fig. 6) probably due to an elevated transcription 467

of glutamine synthetase (Roessner-Tunali et al., 2003). Enzymes branched-chain amino acid 468

aminotransferase and glutamine synthetase (Table 1) involved in glutamine biosynthesis were 469

found to be potentially involved in potato tuber quality traits (Ducreux et al., 2008). Significantly 470

increased asparagine levels were observed in Atlantic, Kufri Jyoti, Kufri Pukhraj, and PU1 after 471

CS (Fig. 3, Fig. 4, and Fig. 5). Silencing of vacuolar invertase and asparagine synthetase (AS1 and 472

AS2) genes demonstrated that the transcript levels of these genes were correlated with RS and 473

asparagine content in transgenic (Zhu et al., 2016). Therefore, metabolite variations (Fig. 3, Fig. 474

4, and Fig. 5) and various correlations (Supplementary Fig. S3-S7) obtained especially after cold 475

storage raises the possibility to test the function of genes or the combination of transgenes using 476

genetic engineering approaches to further validate the role of other candidate genes identified in 477

this study. Also, the future challenge will be to perform the qRT-PCR assays to ascertain and 478

discover the expression of genes involved in CIS and finding their precise role in controlling 479

metabolite accumulation. This approach will allow the identification of new candidate genes 480

involved in CIS processes and can be used in further genetic improvement of potato tuber quality. 481

482

Conclusions 483

A number of commercial potato cultivars used for processing and table purpose are currently 484

available. However, the information on physiological, biochemical, and molecular mechanisms 485

underlying the CIS status of various potato cultivars is very scanty. So far much attention has been 486

given towards understanding the role of RS and asparagine in the CIS process and processing 487

attributes of potato tubers. Here, we have presented the differences in natural variation in several 488

other tuber metabolite contents such as amino acids, citrate, malate, methanol, etc. especially after 489

cold storage which indicated that these metabolites can be used to distinguish potato cultivars 490

differing in their CIS response and processing quality attributes. Selection and development of 491

potato cultivars for long term storage along with good processing attributes using traditional 492

breeding techniques may be cumbersome. Hence, the knowledge of appropriate parents using 493

metabolite diversity is needed. Also, the presence or absence of specific metabolites cannot be the 494

only concluding answer for the prediction of CIS behaviour, therefore, the relative amount of the 495

specific metabolite present can also play a contribution in CIS status of potato cultivar. Therefore, 496


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the knowledge and information of various metabolites along with candidate genes are necessary 497

for the detailed understanding of various biochemical mechanisms underlying metabolite 498

variations in different potato cultivars differencing in their CIS response. Information obtained 499

based on such observations is of major interest to potato breeders and the processing industry for 500

further utilization of metabolite marker in the selection of CIS resistant potato genotypes. 501

502

Acknowledgements 503

The research was supported by the Department Research and Development Program (DRDP), 504

Department of Biotechnology, Savitribai Phule Pune University. The authors are also grateful to 505

BT Company; and Jai Kisan Farm Products and Cold Chains Pvt. Ltd, India, Pune for generously 506

providing the potato cultivars. The authors acknowledge HF-NMR facility at IISER-Pune (co-507

funded by DST-FIST and IISER Pune). SY is thankful for the financial assistance from UGC-JRF, 508

Government of India. JC acknowledges the funding from IISER Pune, Government of India; 509

extramural funding from the Science and Engineering Research Board (SERB), Govt. of India 510

(EMR/2015/001966), and from Department of Biotechnology (DBT), Govt. of India 511

(BT/PR24185/BRB/10/1605/2017). SS acknowledges the funding from Ramalingaswami 512

fellowship (BT/RLF/Re-entry/11/2012; Department of Biotechnology - DBT, Government of 513

India); and University Grants Commission (UGC, Government of India F.4-5(18-FRP) (IV-514

Cycle)/2017(BSR)). MK acknowledges DBT, GOI for his Masters in Biotechnology fellowship. 515

516

Conflict of interest 517

Authors declare no potential conflict of interest. 518


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Table 1: Summary of potato gene sequences with annotation and amino acid identity to enzymes

producing significant metabolites identified in the present study.

Metabolite Enzyme EC

Numbe

r

PGSC Gene ID Chromoso

me

Sucrose

Phosphoglucomutase

UDP-glucose

pyrophosphorylase/ UTP-

-glucose-1-phosphate

uridylyltransferase

Sucrose phosphate

synthase

Sucrose-phosphate

phosphatase

Sucrose synthase

5.4.2.2

2.7.7.9

2.4.1.14

3.1.3.24

2.1.3.13

PGSC0003DMG400001912

PGSC0003DMG401031123

PGSC0003DMG401013333

PGSC0003DMG400029892

PGSC0003DMG400027936

PGSC0003DMG400028134

PGSC0003DMG400016730

PGSC0003DMG400031046

PGSC0003DMG400013546

PGSC0003DMG400013547

PGSC0003DMG400002895

PGSC0003DMG400016730

PGSC0003DMG400031046

PGSC0003DMG400013546

PGSC0003DMG400006672

PGSC0003DMG400002895

VIII

I

XII

VIII

VII

X

II

III

VII

VII

XII

II

III

VII

IX

XII

Glucose

Vacuolar invertase

Vacuolar invertase

inhibitor

3.2.1.26

--

PGSC0003DMG400013856

PGSC0003DMG400004616

III

XII

Fructose

Vacuolar invertase

3.2.1.26

PGSC0003DMG400013856

III


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Vacuolar invertase

inhibitor

-- PGSC0003DMG400004616

XII

Mannose

Phosphomannose

isomerase/ Mannose-6-

phosphate isomerase

Phosphomannomutase

GDP-mannose

pyrophosphorylase

5.3.1.8

5.4.2.8

2.7.7.13

PGSC0003DMG400010399

PGSC0003DMG400026392

PGSC0003DMG400011772

PGSC0003DMG400021636

PGSC0003DMG400013702

PGSC0003DMG400005806

PGSC0003DMG400015098

PGSC0003DMG400015645

II

II

VI

V

V

VIII

III

VI

Galactose

GDP-L-galactose

phosphorylase

2.7.7.69 PGSC0003DMG400027012 VI

Isolucine

Threonine dehydratase

Acetolactate synthase

Ketol-acid

reductoisomerase

Dihydroxyacid

dehydratase

Branched-chain-amino-

acid aminotransferase

4.1.1.19

2.2.1.6

1.1.1.86

4.2.1.9

2.6.1.42

PGSC0003DMG400012987

PGSC0003DMG400016242

PGSC0003DMG400034102

PGSC0003DMG400013027

PGSC0003DMG400007078

PGSC0003DMG400020446

PGSC0003DMG400007859

PGSC0003DMG400019163

PGSC0003DMG400017100

PGSC0003DMG400004951

PGSC0003DMG402011540

IX

XI

III

VI

VII

VII

XII

V

III

IV

XII

Leucine

Acetolactate synthase

2.2.1.6

PGSC0003DMG400016242

PGSC0003DMG400034102

XI

III


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Ketol-acid

reductoisomerase

Dihydroxyacid

dehydratase

2-isopropylmalate

synthase

3-isopropylmalate

dehydratase

3-isopropylmalate

dehydrogenase

Branched-chain-amino-

acid aminotransferase

1.1.1.86

4.2.1.9

2.3.3.13

4.2.1.33

1.1.1.85

2.6.1.42

PGSC0003DMG400013027

PGSC0003DMG400007078

PGSC0003DMG400020446

PGSC0003DMG400007859

PGSC0003DMG400019163

PGSC0003DMG400016337

PGSC0003DMG400006730

PGSC0003DMG400023230

PGSC0003DMG400014622

PGSC0003DMG400013459

PGSC0003DMG401001552

PGSC0003DMG400030577

PGSC0003DMG400024154

PGSC0003DMG400017100

PGSC0003DMG400004951

PGSC0003DMG402011540

VI

VII

VII

XII

V

VI

VIII

VIII

VIII

III

IX

V

VI

III

IV

XII

Alanine Alanine aminotransferase 2.6.1.2 PGSC0003DMG400004899 VI

Arginine

Ornithine

carbamoyltransferase

Argininosuccinate

synthase

Argininosuccinate lyase

2.1.3.3

6.3.4.5

4.3.2.1

PGSC0003DMG400003741

PGSC0003DMG400015463

PGSC0003DMG400028317

PGSC0003DMG400009343

IX

XII

V

IV


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Trypotophan

Tryptophan synthase 4.2.1.20 PGSC0003DMG400029136

PGSC0003DMG400001048

PGSC0003DMG401014396

PGSC0003DMG400011282

PGSC0003DMG400029380

VI

VII

X

X

XII

Aspartate

L-asparaginase 3.5.1.1 PGSC0003DMG400024526

PGSC0003DMG400008000

PGSC0003DMG400004063

III

IV

VI

Proline

Ornithine

carbamoyltransferase

Ornithine

aminotransferase

pyrroline-5-carboxylate

reductase

2.1.3.3

2.6.1.13

1.5.1.2

PGSC0003DMG400003741

PGSC0003DMG400015463

PGSC0003DMG400029872

PGSC0003DMG400010441

IX

XII

VIII

II

Serine

Phosphoglycerate

dehydrogenase

Phosphoserine

aminotransferase

3-phosphoserine

phosphatase

1.1.1.95

2.6.1.52

3.1.3.3

PGSC0003DMG400009159

PGSC0003DMG400018130

PGSC0003DMG400023264

PGSC0003DMG400027624

PGSC0003DMG400001524

PGSC0003DMG400030337

III

III

X

XI

II

VI

Glutamate

NADH-dependent

glutamate synthase

1.4.1.14 XM_006350500

XM_015310142

XM_015310141

Unknown

Glutamine

Glutamine synthetase 6.3.1.2 PGSC0003DMG400004355

PGSC0003DMG400023620

I

IV


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PGSC0003DMG400014592

PGSC0003DMG400013235

PGSC0003DMG400014454

V

XI

XII

Asparagine Asparagine synthase 6.3.1.1 PGSC0003DMG400004170 VI

Citrate

Citrate synthase 2.3.3.1 PGSC0003DMG400028982

PGSC0003DMG400017338

PGSC0003DMG400007797

I

VII

XII

Malate

Malate dehydrogenase

NAD-malate

dehydrogenase

1.1.1.38

1.1.1.37

PGSC0003DMG400026029

PGSC0003DMG400010386

PGSC0003DMG400012395

PGSC0003DMG400017170

PGSC0003DMG400031063

PGSC0003DMG400019511

PGSC0003DMG400011570

I

II

VII

IX

XI

III

IX

Fumarate

Aconitase

Isocitrate dehydrogenase

2-oxoglutarate

dehydrogenase

4.2.1.3

1.1.1.42

1.2.4.2

PGSC0003DMG400028951

PGSC0003DMG400008740

PGSC0003DMG400032124

PGSC0003DMG400000481

PGSC0003DMG400013332

PGSC0003DMG400023519

PGSC0003DMG400027739

VII

XII

I

II

XI

V

IX

4-

Aminobutyrate

Glutamate decarboxylase 4.1.1.15 PGSC0003DMG400022764

PGSC0003DMG400031042

PGSC0003DMG400013331

I

III

XI

Uridine

Uridine kinase 2.7.1.48 PGSC0003DMG400006962

PGSC0003DMG400014372

II

X


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Adenosine

5'-nucleotidase/ Cytosolic

5’-nucleotidase

3.1.3.5 PGSC0003DMG400011823

PGSC0003DMG400001988

VI

XI

3-

hydroxyisobuty

rate

3-hydroxybutyrate

dehydrogenase

1.1.1.30 PGSC0003DMG400031113

I

Sn-glycero-3-

phosphocholine

1-

acylglycerophosphocholi

ne O-acyltransferase

2.3.1.23 PGSC0003DMG401000007

I

Chlorogenate

hydroxycinnamoyl d-

glucose: quinate

hydroxycinnamoyl

transferase

p-coumarate 3'-

hydroxylase

hydroxycinnamoyl CoA

quinate

hydroxycinnamoyl

transferase

2.3.1.99

1.14.14.

96

Unknown

PGSC0003DMG400003289

PGSC0003DMG400011189

Unknown

I

VII

Formate

Formate C-

acetyltransferase

2.3.1.54 Unknown Unknown

Methanol methanol dehydrogenase

methanol dehydrogenase

(cytochrome c/ methanol

dehydrogenase

alcohol oxidase/ethanol

oxidase/alcohol:oxygen

oxidoreductase

1.1.1.24

4

1.1.2.7

1.1.3.13

Unknown

Unknown

Unknown

Unknown

Unknown

Unknown


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Myo-inositol Myo-inositol oxygenase

1.13.99.

1

PGSC0003DMG400004872

PGSC0003DMG400001976

PGSC0003DMG401000287

VI

XI

XII

Trigonelline nicotinate N-

methyltransferase/Trigon

elline synthase

Nicotinamidase

2.1.1.7

3.5.1.19

Unknown

PGSC0003DMG400033583

PGSC0003DMG400016131

Unknown

I

XI

The chromosomal locations are based in identity with DNA sequences in the survey sequence from Potato Genome Sequencing Consortium

(PGSC), National Centre for Biotechnology Information (NCBI), Phytozome 12.1 and Sol Genomics Network.


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Figure legends:

Fig. 1: Scores plot as obtained by PCA utility of MetaboAnalyst software for the different potato

cultivars (Atlantic, Frito Lay-1533, Kufri Pukhraj, Kufri Jyoti, and PU1) at fresh harvest (FH) and

one month cold storage at 4C (CS). Three replicates were used for each cultivar and at each

condition (as described in Materials and Methods). Ellipses showing 95% confidence limits of a

normal distribution for each group of the samples have been marked in respective colours for each

cultivar. Color legends have been mentioned in the figure.

Fig. 2: PCA score plots for pair-wise analysis of metabolites obtained from the different cultivars

of potatoes at fresh harvest (Red) and cold storage at 4C for 1 month (Green). A) Atlantic, B)

Frito Lay-1533, C) Kufri Jyoti, D) Kufri Pukhraj, and E) PU1. Ellipses showing 95% confidence

limits of a normal distribution for each group of the samples have been marked in respective

colours (as mentioned above).

Fig. 3: Volcano plots, where log10(FDR-corrected p-value) is plotted against log2(fold-change in

concentration), depicting the changes in the metabolite concentration from freshly harvested potato

tubers and tubers stored at 4 C for one month. The different cultivars used for the study have been

depicted as A) Atlantic, B) Frito Lay-1533, C) Kufri Jyoti, D) Kufri Pukhraj, and E) PU1. The

significantly down-regulated metabolites upon cold storage have been marked in red and the ones

up-regulated have been marked in green.

Fig. 4: VIP scores obtained after pair-wise PCA analysis for A) Atlantic, B) Frito Lay-1533, C)

Kufri Jyoti, D) Kufri Pukhraj, and E) PU1. A VIP score of ≥1.0 is considered significant.

Fig. 5: Box-Whisker plot for the significantly different metabolites (p-value < 0.05, and with VIP

score ≥1.0) for the different potato cultivars. The significantly different metabolites obtained from

ANOVA and post-hoc analysis were selected individually and the relative concentrations of each

of these were plotted against the two time-points, i.e., fresh harvest and one month cold storage

for the 5 cultivars used in the study. FH – fresh harvest and CS – cold storage at 4°C.


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Fig. 6: Pictorial representation of metabolic pathways affected during cold-induced sweetening in

the different cultivars of potato. The significantly different metabolites under cold storage have

been marked with arrows wherein an indicates upregulated metabolites and the arrow indicates

down regulated metabolites. TCA – tricarboxylic acid.


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Fig. 1


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Fig. 2


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Fig. 3


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Fig. 4


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Fig. 5


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Fig. 6


https://doi.org/10.1101/661611

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