Volume 16, number 2
 Views: (Visited 479 times, 1 visits today)    PDF Downloads: 837

Al-Sulami N, Atef A, Al-Matary M, Edris S, Al-Ghamdi K. M, Al-Zahrani H. S. Bahieldin A. Molecular Analysis of Enzymes and Metabolites Regulated Under Drought Stress in the Wild Plant Senna (Cassia Angustifolia). Biosci Biotech Res Asia 2019;16(2).
Manuscript received on : 25-April-2019
Manuscript accepted on : 22-May-2019
Published online on:  24-05-2019

Plagiarism Check: Yes

Reviewed by: Vikas Guleria

Second Review by: Kandiah Pakeerathan

How to Cite    |   Publication History    |   PlumX Article Matrix

Molecular Analysis of Enzymes and Metabolites Regulated Under Drought Stress in the Wild Plant Senna (Cassia Angustifolia)

Nadiah Al-Sulami1, Ahmed Atef1, Mohammed Al-Matary1, Sherif Edris1,2,3, Khalid M. Al-Ghamdi1, Hassan S. Al-Zahrani1 and Ahmed Bahieldin*1,2

1Department of Biological Sciences, Faculty of Science, King Abdulaziz University (KAU), P.O. Box 80141, Jeddah 21589, Saudi Arabia.

2Department of Genetics, Faculty of Agriculture, Ain Shams University, Cairo, Egypt.

3Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), Faculty of Medicine, King Abdulaziz University (KAU), Jeddah, Saudi Arabia.

Corresponding Author E-mail: abmahmed@kau.edu.sa

DOI : http://dx.doi.org/10.13005/bbra/2747

ABSTRACT: This study aimed at studying differential presence of enzymes metabolites via KEGG analysis of trasncriptomes of the wild plant species senna (Cassia angustifolia Vahl.) due to watering. Senna is a shrub of the family Caesalpiniaceae with important applications in pharmaceuticals. Firstly, RNA-Seq datasets were produced by next-generation sequencing (NGS) of Illumina Miseq of leaf (day 1) in order to detect the influence of watering at day 2. Samples were harvested at three time points (e.g., dawn, midday and dusk) of the two days. de novo assembled datasets and number of annotated genes exceeded 2000 genes. As cluster analysis of gene expression almost showed no discrete differences at the transcriptome level due to watering within time points of dawn and dusk, the study focused mainly on those of the midday across the two days. KEGG analysis for genes whose differential expression between the two days was ≥5 FC resulted in a number of enzymes that were found repressed due to watering, thus likely participate in the molecular mechanisms utilized by the organism to adapt to the long-lasting drought stress. The recovered regulated metabolites and enzymes included abscisic acid (ABA) receptor PYL4 and PYL9, auxin response factor (ARF) 5 and 15, ARF (or Aux/IAA) proteins IAA7 and IAA14, indole-3-pyruvate (or flavin) monooxygenase, phosphoinositide phosphatase SAC1 and SAC6, pre-mRNA splicing factors 8, 8A, 19, 40A and ISY1, and serine/arginine-rich splicing regultors SCL33, RS31 and RS34. The two pathways tryptophan metabolism and plant hormone signal transduction likely crosstalk in senna (C. angustifolia) towards the maintenance of normal growth under adverse condition. Many other regulated metabolites and proteins in senna (C. angustifolia) including brassinosteroid, heat shock protein 95s, ATPase, several protein kinases such as mitogen-activated protein kinase (MAPK) and cytochrome c oxidase. Other enzymes include phospholipase C2 and allene oxide cyclase as well as isochorismate pathway were also regulated in senna (C. angustifolia). In conclusion, we think that we have scoped the light on the possible regulated metabolites under drought stress that might confer drought stress tolerance in the wild plant senna (C. angustifolia).

KEYWORDS: C. angustifolia; Isochorismate; Monooxygenase; Phosphoinositide

Download this article as: 
Copy the following to cite this article:

Al-Sulami N, Atef A, Al-Matary M, Edris S, Al-Ghamdi K. M, Al-Zahrani H. S. Bahieldin A. Molecular Analysis of Enzymes and Metabolites Regulated Under Drought Stress in the Wild Plant Senna (Cassia Angustifolia). Biosci Biotech Res Asia 2019;16(2).

Copy the following to cite this URL:

Al-Sulami N, Atef A, Al-Matary M, Edris S, Al-Ghamdi K. M, Al-Zahrani H. S. Bahieldin A. Molecular Analysis of Enzymes and Metabolites Regulated Under Drought Stress in the Wild Plant Senna (Cassia Angustifolia). Biosci Biotech Res Asia 2019;16(2). Available from: https://bit.ly/2wfuvbP

Introduction

Caesalpiniaceae is a large family with several pharmaceutical applications as being mainly used as a laxative and to relieve constipation. Cassia angustifolia, formally Senna angustifolia (2n = 28) is known for its important applications in pharmaceuticals. C. angustifolia is a wild medicinal drought-tolerant shrub (Ayoub 1977, Khalid et al. 2012) with many cathartic properties (Lemli 1986, Folkard 1995, Hammouda et al. 2005). Drought stress-related genes have been studied at the trancriptomic level in senna (C. angustifolia) (Mehta et al. 2017) as well as in many other plant species such as parsley (Li et al. 2014a,b), bean (Hiz et al. 2014), chrysanthemum (Xu et al. 2013), tall fescue (Hu et al. 2014), and grapevine (Rocheta et al. 2014). The lucine-rich repeat kinase family was recently found in senna (C. angustifolia) as the most abundant group of protein kinases under drought stress in addition to several families of transcription factors (e.g., bHLH, and bZIP, etc.) (Mehta et al. 2017). Previous studies on senna (C. angustifolia) deciphered some physiological, morphological and molecular mechanisms allowed the plant to tolerate drought stress (Ratnayaka and Kincaid 2005, Mehta et al. 2017).

Availability of water is a major obstacle for agricultural productivity. Wild plants growing in severe arid climates provide tools for studying plant response to extreme drought conditions. Generally speaking, drought, salt and heat stresses have large impacts on plant growth and productivity. Other abiotic deleterious stresses nowadays include increased chemicals and pollutants. Besides, it is likely that plants are exposed to more than one type of stress at a time. In particular, drought stress is a major threat in at least 26 % of world’s arable land (Blum 1988). The effects of drought include delayed or stunted growth as well as impaired physiological processes such as photosynthesis, respiration, and mineral exchange (Do et al. 2013). Therefore, it is crucial to get a better understanding the molecular and physiological impacts of drought stress in order to find solutions to help the plant cope with or at least lower the influence of this stress for the sake of maintaining crop productivity and possibly cultivate more crops in arid lands to mitigate global food crisis.

Plants are adapted to drought either by avoidance or tolerance, as the two main strategies, by which a crop plant can maintain yield components and minimize the loss due to the stress. Avoidance mechanisms include the occurrence of several morphological changes, such as stomatal closure and reduced leaf area to reduce respiration, as well as enlarging root systems in order to gain more water with the same intensity of cultivation (Levitt 1980, Budak et al. 2013, Rama Reddy et al. 2014). Alternatively, drought tolerance is a subject of intense research as it mainly occurs due to several physiological and molecular mechanisms that help the plant to adapt with the osmotic pressure due to the shortage of water (Bartels and Sunkars 2005). Tolerance mechanisms were proven to be genetically-dependent as different plant species have different strategies to cope with the problem. These strategies are supported by complex metabolic pathways that should link together and cross-talk in order to produce osmolytes and protein chaperons to secure the cell from the stress and avoid denaturation or damage of important compounds in the cell (Yamaguchi-Shinozaki and Shinozaki 2006, Kantar et al. 2011, Shanker et al. 2014). There are several abiotic stress-related enzymes like glutathione reductase, catalase, superoxide dismutase considered as biomarkers for drought stress tolerance (Khammari et al. 2012).

The present study aim at studying drought-related dynamics of leaf transcriptome in senna (C. angustifolia) to detect the crosstalking pathways possibly associated with drought stress tolerance to add to our understanding of the molecular mechanisms underlying drought stress tolerance in wild plants.

Material and Methods

Plant material sampling and watering regime

The field experiment of sample treatment and harvesting was conducted for senna (C. angustifolia) shrubs grown near Jeddah, Saudi Arabia. Three plants of equal size were selected in which leaf samples were harvested in two consecutive days at three time point of the day (1 h post-dawn, midday and 1 h post-dusk). At dawn of the day 2, plants were watered (25 liters dH2O) and leaf samples were harvest at the same three time points.

RNA-Seq and KEGG Analyses

Total RNA was extracted from three similar-sized (10 mm2) leaf discs per plant of Cassia angustifolia, then shipped to Beijing Genome Institute (BGI), China, for next-generation sequencing. Recovered RNA-Seq datasets were de novo assembled using the Trinity RNA-Seq Assembly package (r2013-02-25) with optimized parameters and K-mer size set to 25 (Zhang et al. 2015). Differential expression and cluster analysis were done by EdgeR (version 3.0.0, R version 2.1.5). All predicted CDS were annotated against protein database in order to assign putative function of the transcriptome after translation into protein. To identify the biological pathways with enzymes that differentiate at midday samples, the detected genes were mapped to reference canonical pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.ad.jp/kegg/). RNA-Seq datasets of Cassia angustifolia were validated via qRT-PCR (data provided upon request).

Results and Discussion

Preliminary Data Analysis

Sequence assembly resulted in a number of ˃5000 regulated genes due to watering and the number of annotated genes ˃2000 genes. The term enrichment in this study refers to the increase of a given enzyme or metabolite in the second day due to watering, while suppression indicates that the intensity of the enzyme or metabolite was reduced due to watering. In other words, enzyme or metabolite enrichment indicates that the encoding genes were highly expressed after watering, while repression indicates that the expression of the encoding drought-related genes is abolished as it is no longer required after watering. GO classification indicated that the subcategory “response to stimulus” is repressed as the stress in the second day is completely relieved. We expected that several biological processes of this subgroup can confer tolerance to drought stress.

KEGG Analysis

In order to study the enzymes in selected biological pathways of Cassia angustifolia whose genes encoding them are highly (≥5 FC) regulated due to watering, we mapped the detected genes to reference canonical pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.ad.jp/kegg/). Drought tolerance is a multigenic process with different metabolic pathways affecting plant growth (Mehta et al. 2017). Pathways that were either enriched or repressed due to watering were examined (Table S1). Of which, we selected the pathways that might have emphasis on plant response to abiotic stress (Table 1). In addition, we have investigated few other pathways whose enzymes were suppressed due to watering, while showed no enzymes that were enriched due to the stress. When applying the criteria of ≥5 FC gene regulation in KEGG analysis, a number of five groups of pathways including 14 subgroups with 22 pathways were selected. These groups are “metabolism”, “Genetic Information Processing”, “Environmental Information Processing”, “Cellular Processes” and “Organismal Systems”. Of which, 10 pathways showed no enrichment in their enzymes due to watering, rather they were repressed due to watering. This indicates that these pathways likely participate in the molecular mechanisms utilized by the organism to cope with the stress, whose enrichment is not required when water becomes available. These pathways are “Biosynthesis of siderophore group nonribosomal peptides”, “Spliceosome”, “VEGF signaling pathway”, “Jak-STAT signaling pathway”, “Autophagy – other”, “Tight junction”, “Longevity regulating pathway”, “Longevity regulating pathway – multiple species”, “Circadian rhythm” and “Thermogenesis” (Table 1). Enzymes or metabolites of the enriched and suppressed pathways are shown in Tables 2 and 3, respectively. The number of activated enzymes due to watering was 43 enzymes, while the number was 107 for the suppressed enzymes due to watering (Tables 2 and 3).

Table 1: Selected pathways of C. angustifolia with enzymes encoded by genes highly (≥5 FC) regulated due to the watering. Green box = pathway enriched due to watering, red box = pathway suppressed due to watering, blue box = regulated TOR-dependent pathway.

Group Subgroup Pathway ID Pathway Enriched suppressed
1. Metabolism 1.1 Carbohydrate metabolism 562 Inositol phosphate metabolism
1.3 Lipid metabolism 592 alpha-Linolenic acid metabolism
1.5 Amino acid metabolism 380 Tryptophan metabolism
400 Phenylalanine, tyrosine and tryptophan biosynthesis
1.9 Metabolism of terpenoids and polyketides 1053 Biosynthesis of siderophore group nonribosomal peptides
2. Genetic information Processing 2.1 Transcription 3040 Spliceosome
2.3 Folding, sorting and degradation 4141 Protein processing in endoplasmic reticulum
3. Environmental information processing 3.2 Signal transduction 4012 ErbB signaling pathway
4016 MAPK signaling pathway – plant
4072 Phospholipase D signaling pathway
4075 Plant hormone signal transduction
4150 mTOR signaling pathway
4151 PI3K-Akt signaling pathway
4370 VEGF signaling pathway
4630 Jak-STAT signaling pathway
4. Cellular processes 4.1 Transport and catabolism 4136 Autophagy – other
4.2 Cell growth and death 4218 Cellular senescence
4.3 Cellular community – eukaryotes 4530 Tight junction
5. Organismal systems 5.9 Aging 4211 Longevity regulating pathway
4213 Longevity regulating pathway – multiple species
5.10 Environmental adaptation 4710 Circadian rhythm
4714 Thermogenesis

Table 2: Selected pathways of C. angustifolia with enzymes encoded by genes highly (≥5 FC) enriched at midday due to the watering. N = before watering, NR = after watering.

No. Pathway/Enzyme Time point
Before watering After watering
0380 Tryptophan metabolism N1 N2 N3 NR1 NR2 NR3
1 Probable acetyl-CoA acetyltransferase cytosolic 2 -0.24212 -0.24212 -0.24212 2.0615160 1.8123826 1.789422
2 Amidase 1 -0.64225 -0.66793 -0.67082 4.195597 2.802451 3.00827
3 Aldehyde dehydrogenase family 7 member A1 -0.50968 -0.64755 -0.38832 1.789422 2.655908 2.62422
0400 Phenylalanine, tyrosine and tryptophan biosynthesis (2) N1 N2 N3 NR1 NR2 NR3
4 Arogenate dehydratase/prephenate dehydratase 2- chloroplastic -0.48907 -0.48907 -0.48907 2.473292 2.290608 2.324863
0562 Inositol phosphate metabolism (3) N1 N2 N3 NR1 NR2 NR3
5 Inositol oxygenase 4 -0.97716 -1.25336 -1.25336 2.448629 3.041257 2.848035
6 Inositol-tetrakisphosphate 1-kinase 3 -0.09205 0.007279 -0.27748 3.901911 3.545024 3.259165
7 Triosephosphate isomerase cytosolic -0.50236 -0.14356 0.089004 3.068392 2.946959 3.529814
0592 alpha-Linolenic acid metabolism (1) N1 N2 N3 NR1 NR2 NR3
8 Glyoxysomal fatty acid beta-oxidation multifunctional protein MFP-a -0.08301 -0.29871 -0.4084 2.058619 2.266513 0.83222
4012 ErbB signaling pathway (2) N1 N2 N3 NR1 NR2 NR3
9 Shaggy-related protein kinase kappa -0.58381 -0.58381 -0.58236 2.858208 2.870764 3.025239
10 Serine/threonine-protein kinase TOR -0.62635 -0.70496 -0.48959 3.435905 3.497071 3.2715
4016 MAPK signaling pathway – plant (2) N1 N2 N3 NR1 NR2 NR3
11 Ethylene-insensitive protein 2 -0.43202 -0.43202 -0.43202 1.314289 2.52031 2.397217
12 Protein ETHYLENE INSENSITIVE 3 -0.60329 -1.40404 -1.40404 4.847172 4.881385 4.470712
4072 Phospholipase D signaling pathway (5) N1 N2 N3 NR1 NR2 NR3
13 Phospholipase D zeta 1 -0.17576 -0.17576 -0.17576 1.624987 1.187133 1.17576
14 Dynamin-2B -0.57567 -0.57279 -0.28525 1.046323 1.758612 2.629566
15 Serine/threonine-protein kinase TOR -0.60295 -0.60295 -0.60295 3.150976 3.324852 2.568417
16 ADP-ribosylation factor 1 -0.65602 -0.65602 -0.65602 3.214932 2.680402 3.423353
17 ADP-ribosylation factor 2 -1.07784 -1.07784 -1.07784 4.767723 4.261117 3.38493
18 Ankyrin repeat- PH and SEC7 domain containing protein secG -0.5132 -0.5132 -0.5132 1.569848 1.580703 0.971201
4075 Plant hormone signal transduction (5) N1 N2 N3 NR1 NR2 NR3
19 Auxin response factor 1 -0.51286 -0.51286 -0.51286 3.266296 1.894222 2.251403
20 Histidine-containing phosphotransfer protein 1 -0.37534 -0.37534 -0.37534 -0.37534 2.328646 1.703269
21 Gibberellin receptor GID1B -1.05549 -1.14079 -1.36132 3.321495 2.686399 2.662871
22 Ethylene-insensitive protein 2 -0.43202 -0.43202 -0.43202 1.314289 2.52031 2.397217
4141 Protein processing in endoplasmic reticulum (9) N1 N2 N3 NR1 NR2 NR3
23 Cullin-1 -0.34984 -0.24015 -0.34984 0.731499 1.660223 1.86925
24 Heat shock cognate protein 80 -0.29447 -0.23504 -0.15431 2.723259 2.478121 1.853568
25 Dolichyl-diphosphooligosaccharide–protein glycosyltransferase subunit STT3A -0.24265 -0.12895 -0.24265 1.843793 1.209944 1.24265
26 Calreticulin -0.86438 -0.86438 -0.86438 3.981055 4.049367 3.634441
27 Heat shock protein 90-5- chloroplastic -0.88796 -0.34992 -0.3892 4.341242 3.589975 2.973597
28 Alpha-mannosidase I MNS4 -0.54941 -0.54941 0.088435 1.76006 3.3388 2.170433
29 Protein transport protein Sec61 subunit alpha -0.10453 -0.3406 -0.45163 1.002018 1.709612 2.032249
30 Derlin-1 -0.91968 -0.5126 0.469884 3.480923 3.095565 2.875045
31 Plant UBX domain-containing protein 4 -0.44073 -0.25043 -0.44073 1.155248 2.939165 2.130943
4150 mTOR signaling pathway (4) N1 N2 N3 NR1 NR2 NR3
32 V-type proton ATPase catalytic subunit A -0.33156 -0.33156 -0.33156 0.813791 2.564324 1.53317
33 V-type proton ATPase subunit F -0.88151 -0.88151 -0.88151 4.827068 4.137752 4.25776
34 Shaggy-related protein kinase kappa -0.76969 -0.76969 0.29795 0.655233 1.469709 0.984771
35 Serine/threonine-protein kinase TOR -0.60295 -0.60295 -0.60295 3.150976 3.324852 2.568417
4151 PI3K-Akt signaling pathway (5) N1 N2 N3 NR1 NR2 NR3
36 Shaggy-related protein kinase kappa -0.76969 -0.76969 0.29795 0.655233 1.469709 0.984771
37 Serine/threonine-protein phosphatase regulatory subunit A -0.59834 -0.63208 -0.97463 2.142898 2.687689 2.046494
38 Heat shock cognate protein 80 -0.29447 -0.23504 -0.15431 2.723259 2.478121 1.853568
39 Serine/threonine-protein kinase TOR -0.60295 -0.60295 -0.60295 3.150976 3.324852 2.568417
40 Heat shock protein 90-5- chloroplastic -0.88796 -0.34992 -0.3892 4.341242 3.589975 2.973597
4213 Longevity regulating pathway – multiple species (2) N1 N2 N3 NR1 NR2 NR3
41 SNF1-related protein kinase regulatory subunit beta-2 -0.20111 -0.39645 -0.39645 -0.39645 2.435828 2.398065
42 Serine/threonine-protein kinase TOR -0.60295 -0.60295 -0.60295 3.150976 3.324852 2.568417
4218 Cellular senescence (1) N1 N2 N3 NR1 NR2 NR3
43 Serine/threonine-protein kinase TOR -0.60295 -0.60295 -0.60295 3.150976 3.324852 2.568417

Drought stress triggers several plant responses at the gene expression levels, and likely result in the accumulation of secondary metabolites or osmolytes that help the plant stand the stress (Ramchandra Reddy et al. 2004, Ergen et al. 2009). In the present study, a large number of enzymes were found repressed due to watering, thus likely participate in the molecular mechanisms utilized to adapt to the long-lasting drought stress. Enrichment of these enzymes is not required when water becomes available, while re-enriched when land became dry again. KEGG analysis indicated that several gene families are involved as a safeguard against drought stress. Of these gene families, abscisic acid (ABA) receptor PYL seems to be required under drought stress for ABA-mediated responses such as stomatal closure (Hao et al. 2011). Suppression of two types of this receptor, namely PYL4 and PYL9 in senna (C. angustifolia) (Table 3, Figure 1), indicates that they participate in morphological changes as a mechanism of avoidance tolerance. PYL4 was reported to improved ABA-dependent inhibition of PP2CA and can modulate inhibition even in the absence of ABA (Pizzio et al. 2013). PP2CA plays a critical role to regulate both seed and vegetative responses to ABA and regulates stomatal aperture through interaction with the anion channel SLOW ANION CHANNEL1 (SLAC1) and the kinase OPEN STOMATA1 (Kuhn et al. 2006, Yoshida et al. 2006, Lee et al. 2009). In addition, ABA receptor PYL9 was proven to promote drought resistance and leaf senescence in Arabidopsis and rice (Zhao et al. 2016).

Figure 1: Enrichment pattern of the abscisic acid (ABA) receptors PYL4 and PYL9 at midday before (N) and after (NR) watering. Figure 1: Enrichment pattern of the abscisic acid (ABA) receptors PYL4 and PYL9 at midday before (N) and after (NR) watering.

 

Click here to view figure

Auxin response factor (ARF) 5 and 15 are among the mechanisms of senna (C. angustifolia) to tolerate drought stress (Table 3, Figure 2). ARF are potential mediators of biotic and abiotic stress responses in plant (Bouzroud et al. 2018). ARF5 gene was proven to increase the contents of carotenoids and enhance the tolerance to both salt and drought when expressed in Arabidopsis (Kang et al. 2018), while ARF15 regulates cell division activity during early tomato fruit development, thus, provides important influence on plant growth under drought stress (DeJong et al. 2015). ARF5 gene also act as an activator of auxin-responsive gene expression in Arabidopsis (Remington et al. 2004). This enzyme allows the activation of ARF, derepresses downstream auxin responsive pathways, thus mediates plant growth and development (Gray et al. 2001). Also, two ARF (or Aux/IAA) proteins, namely IAA7 and IAA14, exist in stressed plant only (Table 3, Figure 3).

Table 3: Selected pathways of C. angustifolia with enzymes encoded by drought-related genes highly (≥5 FC) suppressed due to the watering. N = before watering, NR = after watering.

No. Pathway/Enzyme Time point
Before watering After watering
00380 Tryptophan metabolism (2) N1 N2 N3 NR1 NR2 NR3
1 Aldehyde dehydrogenase family 3 member F1 1.587468 1.287719 -0.50763 -1.456226318 -1.456226318 -1.456226318
2 Probable indole-3-pyruvate monooxygenase YUCCA10 2.227227 2.164615 1.962111 -4.308717014 -4.308717014 -4.308717014
00400 Phenylalanine, tyrosine and tryptophan biosynthesis (4) N1 N2 N3 NR1 NR2 NR3
3 Probable aminotransferase TAT2 0.34544 0.16178 0.060001 -0.918195045 -0.918195045 -0.918195045
4 Anthranilate synthase alpha subunit 2- chloroplastic 0.685204 0.618131 0.077341 -1.623098806 -1.623098806 -1.623098806
5 3-dehydroquinate synthase 0.059773 0.131979 0.080449 -0.258688436 -0.258688436 -0.258688436
6 Bifunctional 3-dehydroquinate dehydratase/shikimate dehydrogenase- chloroplastic 0.339229 0.373643 -0.09642 -2.534977735 -2.534977735 -2.534977735
00562 Inositol phosphate metabolism (6) N1 N2 N3 NR1 NR2 NR3
7 Putative 1-phosphatidylinositol-3-phosphate 5-kinase FAB1C -0.18249 0.261804 -0.48236 -0.696482365 -0.696482365 -0.696482365
8 Inositol-3-phosphate synthase -1.33722 -1.07768 -2.64815 -2.648146652 -2.648146652 -2.648146652
9 Phosphoinositide phospholipase C 2 0.044158 0.002828 -0.23448 -3.234123788 -3.234123788 -2.779947895
10 SAL1 phosphatase -0.21719 0.201112 -0.62427 -0.624266235 -0.624266235 -0.624266235
11 Phosphoinositide phosphatase SAC6 -0.22023 0.207125 0.361932 -1.00264063 -1.00264063 -1.00264063
12 Phosphoinositide phosphatase SAC1 -0.28007 -0.00855 -0.60254 -0.689320217 -0.689320217 -0.689320217
00592 alpha-Linolenic acid metabolism (4) N1 N2 N3 NR1 NR2 NR3
13 Peroxisomal acyl-coenzyme A oxidase 1 0.420476 0.332021 0.490144 -0.580245492 -0.580245492 -0.580245492
14 3-ketoacyl-CoA thiolase 2- peroxisomal 2.497536 0.943436 0.033103 -1.311157654 -1.223016057 -1.160597977
15 Allene oxide cyclase- chloroplastic 0.982792 0.939056 0.797635 -4.8274607 -4.747141535 -4.75890803
16 Fatty acid hydroperoxide lyase- chloroplastic 0.794242 0.693469 0.69914 -3.482149218 -4.223724066 -4.223724066
01053 Biosynthesis of siderophore group nonribosomal peptides (1) N1 N2 N3 NR1 NR2 NR3
17 Isochorismate synthase- chloroplastic 0.246862 0.101321 0.281063 -0.416710774 -0.416710774 -0.416710774
03040 Spliceosome (21) N1 N2 N3 NR1 NR2 NR3
18 Pre-mRNA-processing factor 19 homolog 2 0.60534 1.088448 1.057733 -1.29615449 -1.29615449 -1.29615449
19 Small nuclear ribonucleoprotein SmD1b 1.055236 0.646652 -0.28834 -1.809891101 -1.809891101 -1.809891101
20 U1 small nuclear ribonucleoprotein 70 kDa 0.810862 1.303077 -1.39203 -1.119405074 -1.392025529 -1.392025529
21 Probable small nuclear ribonucleoprotein F 1.16508 0.276073 -0.15363 -2.104102705 -2.104102705 -2.104102705
22 Sm-like protein LSM7 1.206399 0.436801 0.56956 -2.583434918 -2.583434918 -2.583434918
23 DEAD-box ATP-dependent RNA helicase 42 0.343033 0.826515 0.910273 -3.419209265 -3.419209265 -3.419209265
24 Pre-mRNA-splicing factor ATP-dependent RNA helicase DEAH1 0.070534 0.243749 -0.15351 -0.153506306 -0.153506306 -0.153506306
25 Pre-mRNA-processing protein 40A 0.82283 1.068422 0.835349 -2.682934189 -2.682934189 -2.682934189
26 RNA-binding protein 25 0.240599 0.642963 0.572124 -3.238113765 -3.238113765 -3.238113765
27 Splicing factor 3B subunit 2 0.270645 1.218281 -0.54197 -0.773399755 -0.657034999 -0.773399755
28 Splicing factor U2af small subunit B 0.454521 0.634509 -0.44233 -1.463101427 -1.463101427 -1.463101427
29 Splicing factor U2AF 65 kDa subunit 0.11815 0.929108 0.938241 -0.780631873 -0.79641487 -0.79641487
30 Protein RRC1 0.532613 0.082866 -0.39497 -1.366562985 -1.366562985 -1.366562985
31 Pre-mRNA-splicing factor 8 homolog 0.570287 0.259443 -0.02415 -3.086094827 -3.086094827 -3.086094827
32 Pre-mRNA-processing-splicing factor 8A -0.11281 0.637654 0.642344 -2.907325618 -2.907325618 -2.907325618
33 Pre-mRNA-splicing factor ISY1 homolog 0.395342 0.524189 -0.37189 -0.371889402 -0.371889402 -0.371889402
34 THO complex subunit 1 0.093394 0.446651 -0.47707 -0.477068528 -0.477068528 -0.477068528
35 Serine/arginine-rich-splicing factor SR34 -0.2751 0.380628 0.087468 -1.088216087 -1.088216087 -1.088216087
36 Serine/arginine-rich splicing factor SC35 0.733706 0.937683 0.670312 -2.421557464 -2.421557464 -2.421557464
37 Serine/arginine-rich splicing factor RS31 0.58339 0.093735 0.63455 -0.452233488 -0.452233488 -0.452233488
38 Serine/arginine-rich SC35-like splicing factor SCL33 -0.1577 0.030388 0.045774 -0.454007978 -0.454007978 -0.454007978
04012 ErbB signaling pathway (1) N1 N2 N3 NR1 NR2 NR3
39 Serine/threonine-protein kinase TOR -0.13965 0.024261 0.222411 -0.467335376 -0.467335376 -0.467335376
04016 MAPK signaling pathway – plant (8) N1 N2 N3 NR1 NR2 NR3
40 BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1 -0.39322 0.158405 0.151183 -0.981064495 -0.981064495 -0.981064495
41 Transcription factor bHLH14 0.548965 0.884457 -0.22924 -0.229243769 -0.229243769 -0.229243769
42 Transcription factor MYC2 0.463226 0.388223 0.246732 -3.542892865 -3.542892865 -3.542892865
43 Abscisic acid receptor PYL4 1.852722 2.016651 1.774202 -2.549995805 -3.405985503 -2.273079691
44 Abscisic acid receptor PYL9 -0.04695 0.02713 0.14755 -0.851006972 -0.851006972 -0.851006972
45 Serine/threonine-protein kinase SAPK10 0.085518 0.408981 0.024526 -0.672358315 -0.672358315 -0.672358315
46 Serine/threonine-protein kinase SRK2I 0.352227 0.265327 0.300548 -0.464986643 -0.464986643 -0.464986643
47 Ethylene-insensitive protein 2 0.625007 0.49946 0.634267 -2.388455563 -2.388455563 -2.388455563
48 ETHYLENE INSENSITIVE 3-like 3 protein 0.006315 0.249024 0.482061 -0.302861221 -0.302861221 -0.302861221
49 Mitogen-activated protein kinase 3 -0.32703 -0.24247 -0.65804 -2.104820666 -2.104820666 -2.104820666
50 Mitogen-activated protein kinase kinase kinase NPK1 -0.10581 0.342507 -0.21778 -0.406812599 -0.406812599 -0.406812599
04072 Phospholipase D signaling pathway (3) N1 N2 N3 NR1 NR2 NR3
51 Dynamin-2B 0.036532 0.427132 -0.81227 -0.812266541 -0.812266541 -0.812266541
52 Serine/threonine-protein kinase TOR -0.13965 0.024261 0.222411 -0.467335376 -0.467335376 -0.467335376
53 Ankyrin repeat- PH and SEC7 domain containing protein secG 0.054366 0.039542 0.172577 -0.679422307 -0.679422307 -0.679422307
04075 Plant hormone signal transduction (12) N1 N2 N3 NR1 NR2 NR3
54 BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1 -0.39322 0.158405 0.151183 -0.981064495 -0.981064495 -0.981064495
55 Transcription factor bHLH14 0.548965 0.884457 -0.22924 -0.229243769 -0.229243769 -0.229243769
56 Transcription factor MYC2 0.463226 0.388223 0.246732 -3.542892865 -3.542892865 -3.542892865
57 ABSCISIC ACID-INSENSITIVE 5-like protein 2 0.147921 0.100752 0.205535 -0.336475436 -0.336475436 -0.336475436
58 Ubiquitin C-terminal hydrolase 22 0.85731 0.59537 -1.46606 -1.466060125 -1.466060125 -1.466060125
59 Auxin-responsive protein IAA14 1.958978 1.720019 1.690397 -2.897808345 -2.897808345 -1.762930291
60 Auxin-responsive protein IAA7 -1.12006 -1.0914 -1.24864 -2.252966449 -2.250083941 -2.251524475
61 Protein TRANSPORT INHIBITOR RESPONSE 1 (TIR1) 1.184285 1.267837 1.534964 -2.01593676 -2.01593676 -2.01593676
62 Auxin response factor 5 -0.20576 0.035701 -0.49736 -0.497363523 -0.497363523 -0.497363523
63 Auxin response factor 15 -0.20652 -0.18762 -0.10993 -2.070066629 -2.070066629 -2.070066629
64 Two-component response regulator ARR3 0.66018 0.893755 0.665409 -0.801348332 -0.801348332 -0.801348332
65 Abscisic acid receptor PYL4 1.852722 2.016651 1.774202 -2.549995805 -3.405985503 -2.273079691
66 Abscisic acid receptor PYL9 -0.04695 0.02713 0.14755 -0.851006972 -0.851006972 -0.851006972
67 Serine/threonine-protein kinase SAPK10 0.182149 0.394438 0.649098 -0.879973008 -0.879973008 -0.879973008
68 Serine/threonine-protein kinase SRK2I 0.352227 0.265327 0.300548 -0.464986643 -0.464986643 -0.464986643
69 Methyltransferase-like protein 2 0.088466 0.244608 0.328877 -0.652245369 -0.652245369 -0.652245369
70 Ethylene-insensitive protein 2 0.625007 0.49946 0.634267 -2.388455563 -2.388455563 -2.388455563
71 ETHYLENE INSENSITIVE 3-like 3 protein 0.006315 0.249024 0.482061 -0.302861221 -0.302861221 -0.302861221
04136 Autophagy – other (3) N1 N2 N3 NR1 NR2 NR3
72 Serine/threonine-protein kinase TOR -0.13965 0.024261 0.222411 -0.467335376 -0.467335376 -0.467335376
73 Autophagy-related protein 8C 0.034356 0.272842 -1.83552 -1.835515633 -1.831194027 -1.58334222
04141 Protein processing in endoplasmic reticulum (9) N1 N2 N3 NR1 NR2 NR3
74 Protein transport protein Sec61 subunit beta -0.41036 -0.01659 -1.17928 -1.815265804 -1.815265804 -1.815265804
75 Heat shock protein 90-5- chloroplastic 1.960161 2.058363 1.856076 -4.675500173 -4.675500173 -4.675500173
76 Ubiquitin recognition factor in ER-associated degradation protein 1 0.284782 0.397391 -0.11243 -1.516790405 -1.516790405 -1.516790405
04150 mTOR signaling pathway (4) N1 N2 N3 NR1 NR2 NR3
77 CBL-interacting protein kinase 1 0.411691 0.2668 0.148233 -1.62111525 -1.62111525 -1.62111525
78 Serine/threonine-protein kinase TOR -0.13965 0.024261 0.222411 -0.467335376 -0.467335376 -0.467335376
79 Serine/threonine-protein kinase ATG1c -0.08368 -0.07452 0.311552 -0.581421762 -0.581421762 -0.581421762
80 Calcium-binding protein 39-like 0.015711 0.085252 0.051792 -1.643756869 -1.643756869 -1.520752915
04151 PI3K-Akt signaling pathway (5) N1 N2 N3 NR1 NR2 NR3
81 Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A beta isoform 0.810633 0.662534 -0.48803 -0.488025561 -0.488025561 -0.488025561
82 CBL-interacting protein kinase 1 0.411691 0.2668 0.148233 -1.62111525 -1.62111525 -1.62111525
83 Serine/threonine-protein kinase TOR -0.13965 0.024261 0.222411 -0.467335376 -0.467335376 -0.467335376
84 Myb-related protein A 0.451647 0.311785 0.130007 -2.191054894 -2.191054894 -2.191054894
85 Heat shock protein 90-5- chloroplastic 1.960161 2.058363 1.856076 -4.675500173 -4.675500173 -4.675500173
04211 Longevity regulating pathway (2) N1 N2 N3 NR1 NR2 NR3
86 CBL-interacting protein kinase 1 0.411691 0.2668 0.148233 -1.62111525 -1.62111525 -1.62111525
87 Serine/threonine-protein kinase TOR -0.13965 0.024261 0.222411 -0.467335376 -0.467335376 -0.467335376
04213 Longevity regulating pathway – multiple species (2) N1 N2 N3 NR1 NR2 NR3
88 CBL-interacting protein kinase 1 0.411691 0.2668 0.148233 -1.62111525 -1.62111525 -1.62111525
89 Serine/threonine-protein kinase TOR -0.13965 0.024261 0.222411 -0.467335376 -0.467335376 -0.467335376
04218 Cellular senescence (3) N1 N2 N3 NR1 NR2 NR3
90 Calcineurin B-like protein 3 1.070011 1.306436 1.147796 -1.734456047 -1.734456047 -1.734456047
91 Serine/threonine-protein kinase TOR -0.13965 0.024261 0.222411 -0.467335376 -0.467335376 -0.467335376
92 Mitochondrial outer membrane protein porin 2 0.659578 0.668525 0.995054 -0.288275387 -0.288275387 -0.288275387
04370 VEGF signaling pathway (1) N1 N2 N3 NR1 NR2 NR3
93 Calcineurin B-like protein 3 1.070011 1.306436 1.147796 -1.734456047 -1.734456047 -1.734456047
04530 Tight junction (3) N1 N2 N3 NR1 NR2 NR3
94 Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A beta isoform 0.810633 0.662534 -0.48803 -0.488025561 -0.488025561 -0.488025561
95 CBL-interacting protein kinase 1 0.411691 0.2668 0.148233 -1.62111525 -1.62111525 -1.62111525
96 Tubulin alpha-5 chain 0.591109 -0.00636 0.338417 -2.790866288 -2.790866288 -2.790866288
04630 JAK-STAT signaling pathway (1) N1 N2 N3 NR1 NR2 NR3
97 Serine/threonine-protein kinase TOR -0.13965 0.024261 0.222411 -0.467335376 -0.467335376 -0.467335376
04710 Circadian rhythm (1) N1 N2 N3 NR1 NR2 NR3
98 CBL-interacting protein kinase 1 0.411691 0.2668 0.148233 -1.62111525 -1.62111525 -1.62111525
04714 Thermogenesis (9) N1 N2 N3 NR1 NR2 NR3
99 Succinate dehydrogenase [ubiquinone] flavoprotein subunit 1- mitochondrial 1.353688 0.91732 1.373706 -1.736656898 -1.736656898 -1.736656898
100 Cytochrome c oxidase assembly protein COX11- mitochondrial 1.118139 0.44765 0.259696 -0.848660867 -0.848660867 -0.848660867
101 Cytochrome c oxidase assembly protein COX15 1.006468 0.719522 0.640444 -0.994613871 -0.994613871 -0.994613871
102 NADH dehydrogenase [ubiquinone] flavoprotein 1- mitochondrial -0.18283 0.628526 -0.4885 -0.488504281 -0.488504281 -0.488504281
103 CBL-interacting protein kinase 1 0.411691 0.2668 0.148233 -1.62111525 -1.62111525 -1.62111525
104 Serine/threonine-protein kinase TOR -0.13965 0.024261 0.222411 -0.467335376 -0.467335376 -0.467335376
105 SWI/SNF complex subunit SWI3C 0.082451 0.391757 -0.14529 -0.145290342 -0.145290342 -0.145290342
106 PsbP domain-containing protein 7- chloroplastic 0.099498 0.212171 0.147611 -0.243057157 -0.243057157 -0.243057157
107 Protein arginine methyltransferase NDUFAF7 homolog- mitochondrial {ECO:0000250|UniProtKB:Q7L592} 0.684605 0.685377 0.493703 -2.538045118 -2.006975625 -2.538045118

 

Figure 2: Enrichment pattern of auxin response factor (ARF) 5 and 15 at midday before (N) and after (NR) watering. Figure 2: Enrichment pattern of auxin response factor (ARF) 5 and 15 at midday before (N) and after (NR) watering.

 

Click here to view figure

 

Figure 3: Enrichment pattern of AUX/IAA factors IAA14 and IAA7 at midday before (N) and after (NR) watering. Figure 3: Enrichment pattern of AUX/IAA factors IAA14 and IAA7 at midday before (N) and after (NR) watering.

 

Click here to view figure

Aux/IAA proteins orchestrate several biological and physiological processes such as embryogenesis, leaf expansion and senescence, lateral root development and fruit development by regulating the expression of auxin response genes (Wilmoth et al. 2005, Sagar et al. 2013). IAA7 controls the morphological responses induced by light, e.g., promoting leaf development under adverse condition, while IAA14 acts in controlling lateral root formation by interacting with two ARF proteins (Luo et al. 2018). These two Aux/IAAs are produced due to the accumulated IAA generated is a result of tryptophan metabolism pathway and specifically oriented towards the induction of plant hormone signal transduction pathway to help the plant maintains normal performance inder drought stress. As the highly conserved domain II of the other Aux/IAA proteins is a target for degradation process promoted by auxin, this action allows the participation of the auxin-induced Protein TRANSPORT INHIBITOR RESPONSE 1 (TIR1). Consequently, TIR1 activates its target factors, e.g., ARFs, thus, allowing the auxin-responsive downstream genes, AUX/LAX, LBD and SAUR, to function and promote plant growth under adverse conditions. This Protein TRANSPORT INHIBITOR RESPONSE 1 (TIR1) also shown to be a major player under drought stress in senna as it is enriched only under the stress (Table 3, Figure 4). BRASSINOSTEROID INSENSITIVE 1 (BRI1)-associated receptor kinase 1 (or BAK1) belongs to a large group of plant transmembrane proteins known as the leucine-rich repeat receptor kinases (Belkhadir and Jaillais 2015). BAK1 has important role in brassinosteroid signaling (Li et al. 2002). Brassinosteroid is a plant hormone with important roles in cell proliferation and growth (Belkhadir and Jaillais 2015). BAK1 particularly acts in repressing the development of unneeded stomata in plant leaves, thus maintain water turgor under drought stress (Smakowska-Luzan et al. 2018). BAK1 also shown to be a major player under drought stress in senna as it is enriched only under the stress (Table 3, Figure 5).

Figure 4: Enrichment pattern of Protein Transport Inhibitor Response 1 (TIR1) at midday before (N) and after (NR) watering. Details of enrichment pattern of this protein is shown in Table 3. Figure 4: Enrichment pattern of Protein Transport Inhibitor Response 1 (TIR1) at midday before (N) and after (NR) watering. Details of enrichment pattern of this protein is shown in Table 3.

 

Click here to view figure

 

Figure 5: Enrichment pattern of BRI1-associated receptor kinase 1 (BAK1) at midday before (N) and after (NR) watering. Details of enrichment pattern of this enzyme is shown in Table 3. Figure 5: Enrichment pattern of BRI1-associated receptor kinase 1 (BAK1) at midday before (N) and after (NR) watering. Details of enrichment pattern of this enzyme is shown in Table 3.

 

Click here to view figure

In senna, it is evident that PYL and BAK1 are induced by ABA and brassinosteroid towards the occurrence of stomatal closure and in cell elongation and division. These roles represent two bottle-necks in the plant hormone signal transduction pathway. Over and above, indole-3-pyruvate (or flavin) monooxygenase, encoded by YUC2 gene, also participates in tryptophan metabolism pathway (Watanabe and Lam 2006) towards the biosynthesis of IAA that, as mentioned above, is required for triggering the plant hormone signal transduction pathway. Accordingly, this enzyme likely has a role in conferring drought stress tolerance in senna (C. angustifolia) (Table 3, Figure 6). The two pathways likely crosstalk in senna (C. angustifolia) towards the maintenance of normal growth under adverse condition (Figures 7 and 8).

Figure 6: Enrichment pattern of indole-3-pyruvate (or flavin) monooxygenase at midday before (N) and after (NR) watering. Details of enrichment pattern of this enzyme is shown in Table 3. Figure 6: Enrichment pattern of indole-3-pyruvate (or flavin) monooxygenase at midday before (N) and after (NR) watering. Details of enrichment pattern of this enzyme is shown in Table 3.

 

Click here to view figure

 

Figure 7: Tryptophan metabolism pathway indicating two important enzymes Aldehyde dehydrogenase family 3 member F1 (EC: 1.2.1.3) and indole-3-pyruvate monooxygenase (EC: 11.41.31.68) that were enriched under drought stress, while suppressed due to watering. Details of enrichment pattern of these two enzymes are shown in Table 3. Figure 7: Tryptophan metabolism pathway indicating two important enzymes Aldehyde dehydrogenase family 3 member F1 (EC: 1.2.1.3) and indole-3-pyruvate monooxygenase (EC: 11.41.31.68) that were enriched under drought stress, while suppressed due to watering. Details of enrichment pattern of these two enzymes are shown in Table 3.

 

Click here to view figure

 

Figure 8: Plant hormone signal transduction pathway indicating the auxin response factor (ARF) and the Aux/IAA that were enriched under drought stress, while suppressed due to watering. Details of enrichment pattern of these two enzymes are shown in Table 3. Figure 8: Plant hormone signal transduction pathway indicating the auxin response factor  (ARF) and the Aux/IAA that were enriched under drought stress, while suppressed due to watering. Details of enrichment pattern of these two enzymes are shown in Table 3.

 

Click here to view figure

Phosphoinositide phosphatase SAC1 and SAC6 can also be among the molecular mechanisms to cope with the stress in senna (C. angustifolia) (Table 3, Figure 9). The domains of SAC protein possess phosphoinositide phosphatase activities. SAC1 was previously shown to affect diverse cellular functions such as normal cell morphogenesis, Golgi function, and maintenance of vacuole morphology (Zhong et al. 2005), while SAC6 gene was predominantly expressed in flowers and proven to be highly induced by salinity in Arabidopsis (Zhong and Ye 2003).

Figure 9: Enrichment pattern of SAC1 and SAC6 at midday before (N) and after (NR) watering. Details of enrichment pattern of these proteins are shown in Table 3. Figure 9: Enrichment pattern of SAC1 and SAC6 at midday before (N) and after (NR) watering. Details of enrichment pattern of these proteins are shown in Table 3.

 

Click here to view figure

A number of five pre-mRNA splicing factors as well as three serine/arginine-rich (SR) regulator factors were shown to participate in drought stress tolerance in senna (C. angustifolia) (Table 3, Figure 10). The pre-mRNA factors included splicing factors (SF) 8, 8A, 19, 40A and ISY1, while SRs included factors SCL33, RS31 and RS34. No particular function is assigned to any of these splicing factors and regulators except that each one might act on a certain protein or protein type. This conclusion warrant further experimentation and analysis. Pre-mRNAs usually contain introns that requires splicing or alternative splicing (AS) to produce structurally and functionally different proteins from the same gene (Palusa et al. 2007). Pre-mRNAs-related genes encode serine/arginine-rich (SR), which is a conserved protein family of splicing regulators in plant. Splicing of SR genes has proven to be developmental- and tissue-specific. Prior research indicated that abiotic stresses regulate the splicing of the pre-mRNAs of SR genes to produce different protein isoforms, thus different functions. We speculate that the altered splicing in senna seems like the action of the immune systems as to produce a specific antibodies for a specific antigen. Here, we think that alternative splicing of SR under stress produce the proper isoform of the proteins that can hold their structures and avoid denaturation or unfolding as a response to stimuli.

Figure 10: Enrichment pattern of the five Pre-mRNA splicing factors SF19, SF A40, SF 8, SF 8A and SF ISY1 as well as the three serine/arginine-rich SR34, SC35 and SR31 regulator factors at midday before (N) and after (NR) watering. Details of enrichment pattern of these proteins are shown in Table 3. Figure 10: Enrichment pattern of the five Pre-mRNA splicing factors SF19, SF A40, SF 8, SF 8A and SF ISY1 as well as the three serine/arginine-rich SR34, SC35 and SR31 regulator factors at midday before (N) and after (NR) watering. Details of enrichment pattern of these proteins are shown in Table 3.

 

Click here to view figure

Many other regulated metabolites and proteins in senna (C. angustifolia) including protective proteins, such as heat shock protein 95s were previously reported to play functional roles in drought tolerance in plants (Hu and Xiong 2014, Umezawa et al. 2006). Senna might also developed efficient signal transduction cascades to cope with drought stress as it proved to regulate ATPase, which is a major signaling factor involved in drought stress signaling (Akpinar et al. 2012, 2013). Protein kinases participate in developmental and environmental signal transduction in plants (Rodriguez et al. 2010, Liu et al. 2016) and play a key role in activating transcription factors and drought-responsive proteins under drought tolerance. Among them, mitogen-activated protein kinase (MAPK) and cytochrome c oxidase that were regulated in senna (C. angustifolia) (Table 3).

Other enzymes include phospholipase C2 and allene oxide cyclase as well as isochorismate pathway were regulated in senna (C. angustifolia). The two enzymes play a role in JA pathway, while isochorismate pathway results in the production of salicylic acid (SA) (Kawano et al. 2004, Mustafa et al. 2009). The latter pathways have important applications in plant production.

In conclusion, we speculate that we have expanded our understanding of the molecular mechanism underlying drought stress tolerance in senna (C. angustifolia). This information will be valuable resource for accelerated genomics-assisted genetic breeding programs targeting the improvement of drought tolerance in economically important crop plants.

References

  1. Akpinar BA, Avsar B, Lucas SJ, Budak H (2012) Plant abiotic stress signalling. Plant Signal Behav 7(11):1450–1455.
    CrossRef
  2. Akpinar BA, Lucas SJ, Budak H (2013) Genomics approaches for crop improvement against abiotic stress. Sci World J 15:361921.
    CrossRef
  3. Ayoub AT (1977) Some primary features of salt tolerance in senna (Cassia angastifolia). J Exp Bot 28:484–492.
    CrossRef
  4. Bartels D, Sunkar R (2005) Drought and salt tolerance in plants. Cr Rev Plant Sci 24(1):23–58.
    CrossRef
  5. Belkhadir Y, Jaillais Y (2015). The molecular circuitry of brassinosteroid signaling”. The New Phytologist 206): 522–40.
    CrossRef
  6. Blum A (1988) Plant breeding for stress environments. CRC Press, Inc., Boca Raton.
  7. Bolger AM, LohseM, Usadel B (2014) Trimmomatic: a flexible trimmer for illumina sequence data. Bioinformatics 30:2114–2120.
    CrossRef
  8. Bouzroud S, Gouiaa S, Hu N, Bernadac A, Mila I, Bendaou N, et al. (2018). Auxin Response Factors (ARFs) are potential mediators of auxin action in tomato response to biotic and abiotic stress (Solanum lycopersicum). PLoS ONE 13(2): e0193517.
    CrossRef
  9. Bowman MJ, Park W, Bauer PJ, Udall JA, Page JT, Raney J, Scheffler BE, Jones DC, Campbell BT (2013) RNA-Seq transcriptome profiling of upland cotton (Gossypium hirsutum L.) root tissue under water-deficit stress. PLoS One 8(12):e82634.
    CrossRef
  10. Budak H, Kantar M, Kurtoglu KY (2013) Drought tolerance in modern and wild wheat. Sci World J 15:548246.
    CrossRef
  11. DeJong M, Wolters-Arts M, Schimmel BC, Stultiens CL., de Groot PF, et al. (2015). Solanum lycopersicum AUXIN RESPONSE FACTOR 9 regulates cell division activity during early tomato fruit development. J. Exp. Bot.66, 3405–3416.
    CrossRef
  12. Do PT, Degenkolbe T, Erban A, Heyer AG, Kopka J, Köhl KI, Hincha DK, Zuther E (2013) Dissecting rice polyamine metabolism under controlled long-term drought stress. PLoS One 8(4):e60325.
    CrossRef
  13. Dong Y, Fan G, Deng M, Xu E, Zhao Z (2014) Genome-wide expression profiling of the transcriptomes of four Paulownia tomentosa accessions in response to drought. Genomics 104(4):295–305.
    CrossRef
  14. Ergen NZ, Thimmapuram, Bohnert J, Hans J, Budak H (2009) Transcriptome pathways unique to dehydration tolerant relatives of modern wheat. Funct Integr Genomics 9(3):377–396.
    CrossRef
  15. Folkard C (1995) Encyclopedia of herbs and their uses. Herb Society of America, Dorling Kindersley Publishing Inc., New York.
  16. Gray WM, Kepinski S, Rouse D, Leyser O, Estelle M (2001). Auxin regulates SCFTIR1-dependent degradation of AUX/IAA proteins. Nature 414: 271-276.
    CrossRef
  17. Hammouda FM, Ismail SI, Abdel-Azim NS, Shams KA (2005) A guide to medicinal plants in North Africa. In: Batanouny KH (eds) IUCN Centre for Mediterranean Cooperation, Malaga, Andalusia, Spain, pp 217–218.
  18. Hao Q., Yin P., Li W., Wang L., Yan C., et al. (2011). The molecular basis of ABA-independent inhibition of PP2Cs by a subclass of PYL proteins. Mol. Cell 42: 662-672.
    CrossRef
  19. Hirayama T, Shinozaki K (2010) Research on plant abiotic stress responses in the postgenome era: past, present and future. Plant J 61(6):1041–1052.
    CrossRef
  20. Hiz MC, Canher B, Niron H, Turet M (2014) Transcriptome analysis of salt tolerant common bean (Phaseolus vulgaris L.) under saline conditions. PLoS One 9(3):e92598.
    CrossRef
  21. Hu H, Xiong L (2014) Genetic engineering and breeding of droughtresistant crops. Annu Rev Plant Biol 65:715–741.
    CrossRef
  22. Hu T, Sun X, Zhang X, Nevo E, Fu J (2014) An RNA sequencing transcriptome analysis of the high-temperature stressed tall fescue reveals novel insights into plant thermotolerance. BMC Genomics 15:1147.
    CrossRef
  23. Kang C, He S, Zhai H, Li R, Zhao N and Liu Q (2018) A Sweetpotato Auxin Response Factor Gene (IbARF5) Is Involved in Carotenoid Biosynthesis and Salt and Drought Tolerance in Transgenic Arabidopsis. Front. Plant Sci. 9: 1307.
    CrossRef
  24. Kantar M, Lucas SJ, Budak H (2011) Drought stress: molecular genetics and genomics approaches. Adv Bot Res 57:445–493.
    CrossRef
  25. Kawano T, Furuichi T, Muso S (2004) Controlled free salicylic acid levels and corresponding signaling mechanisms in plants. Plant Biotechnol 21:319–335.
    CrossRef
  26. Khalid H, Abdalla WE, Abdelgadir H, Opatz T, Effert T (2012) Gems from traditional north-African medicine: medicinal and aromatic plants from Sudan. Nat Prod Bioprospect 2(3):92–103.
    CrossRef
  27. Khammari I, Galavi M, Ghanbari A, Solouki M, Poorchaman MRA (2012) The effect of drought stress and nitrogen levels on antioxidant enzymes, proline and yield of Indian Senna (Cassia angustifolia L.). J Med Plants Res 6(11):2125–2130.
    CrossRef
  28. Kuhn JM, Boisson-Dernier A, Dizon MB, Maktabi MH, Schroeder JI (2006). The protein phosphatase AtPP2CA negatively regulates abscisic acid signal transduction in Arabidopsis, and effects of abh1 on AtPP2CA mRNA. Plant Physiol. 140(1):127-39.
    CrossRef
  29. Lee SC, Lan W, Buchanan BB, Luan S (2009). A protein kinase-phosphatase pair interacts with an ion channel to regulate ABA signaling in plant guard cells. Proc Natl Acad Sci USA. 106(50):21419-24.
    CrossRef
  30. Lemli J (1986) The chemistry of senna. Fitoterapia 57:33–40.
  31. Levitt J (1980) Responses of plants to environmental stress: chilling, freezing and high temperature stresses, vol 1, 2nd edn. Academic, New York.
    CrossRef
  32. Li J, Wen J, Lease KA, Doke JT, Tax FE, et al. (2002). BAK1, an Arabidopsis LRR receptor-like protein kinase, interacts with BRI1 and modulates brassinosteroid signaling. Cell 110: 213–22.
    CrossRef
  33. Li MY, Tan HW, Wang F, Jiang Q, Xu ZS, et al. (2014a) De novo transcriptome sequence assembly and identification of AP2/ERF transcription factor related to abiotic stress in parsley (Petroselinum crispum). PLoS One 9(9):e108977.
    CrossRef
  34. Li PS, Yu TF, He GH, Chen M, Zhou YB, et al. (2014b) Genome-wide analysis of the Hsf family in soybean and functional identification of GmHsf-34 involvement in drought and heat stresses. BMC Genom 15:1009.
    CrossRef
  35. Li WX, Oono Y, Zhu J, He XJ, Wu JM, et al. (2008) The Arabidopsis NFYA5 transcription factor is regulated transcriptionally and post-transcriptionally to promote drought resistance. Plant Cell 20(8):2238–2251.
    CrossRef
  36. Liu H, Che Z, Zeng X, Zhou X, Sitoe HM, et al. (2016) Genome-wide analysis of calcium-dependent protein kinases and their expression patterns in response to herbivore and wounding stresses in soybean. Funct Integr Genom 16(5):481–493.
    CrossRef
  37. Luo J, Jing-Jing Zhou J-J, Zhang J-Z (2018). Aux/IAA Gene Family in Plants: Molecular Structure, Regulation, and Function. Int J Mol Sci. 19(1): 259.
    CrossRef
  38. Mehta RH, Ponnuchamy M, Kumar J, et al. (2017). Exploring drought stress-regulated genes in senna (Cassia angustifolia Vahl.): a transcriptomic approach. Funct Integr Genomics (2017) 17:1–25.
    CrossRef
  39. Mustafa NR, Kim HK, Choi YH, Erkelens C, Lefeber AW, Spijksma G, van der Heijden R,Verpoorte R (2009) Biosynthesis of salicylic acid in fungus elicited Catharanthus roseus cells. Phytochemistry 70(4): 532–539.
    CrossRef
  40. Palusa SG, Ali GS, Reddy AS (2007). Alternative splicing of pre-mRNAs of Arabidopsis serine/arginine-rich proteins: regulation by hormones and stresses. Plant J. 49(6):1091-107.
    CrossRef
  41. Pizzio GA, Rodriguez L, Antoni R, Gonzalez-Guzman M, Yunta C, et al. (2013). The PYL4 A194T Mutant Uncovers a Key Role of PYR1-LIKE4/PROTEIN PHOSPHATASE 2CA Interaction for Abscisic Acid Signaling and Plant Drought Resistance. Plant Physiol. 163(1): 441–455.
    CrossRef
  42. Rama Reddy NR, Ragimasalawada M, Sabbavarapu MM, Nadoor S, Patil JV (2014) Detection and validation of stay-green QTL in post-rainy sorghum involving widely adapted cultivar, M35-1 and a popular stay-green genotype B35. BMC Genom 18(15):909.
    CrossRef
  43. Ramchandra Reddy A, Chaitanya KV, Vivekanandan M (2004) Drought induced responses of photosynthesis and antioxidant metabolism in higher plants. J Plant Physiol 161(11):1189–1202.
    CrossRef
  44. Remington DL, Vision TJ, Guilfoyle TJ, Reed JW (2004). Contrasting modes of diversification in the Aux/IAA and ARF gene families. Plant Physiol 135: 1738-1752
    CrossRef
  45. Ratnayaka HH, Kincaid D (2005) Gas exchange and leaf ultrastructure of tinnevelly senna, cassia angustifolia, under drought and nitrogen stress. Crop Sci 45(3):840–847.
    CrossRef
  46. Rocheta M, Becker JD, Coito JL, Carvalho L, Amâncio S (2014) Heat and water stress induce unique transcriptional signatures of heatshock proteins and transcription factors in grapevine. Funct Integr Genomics 14(1):135–148.
    CrossRef
  47. Rodriguez MC, Petersen M, Mundy J (2010) Mitogen-activated protein kinase signaling in plants. Annu Rev Plant Biol 61:621–649.
    CrossRef
  48. Sagar M, Chervin C, Mila I, Hao Y, Roustan JP, et al. (2013). SlARF4, an auxin response factor involved in the control of sugar metabolism during tomato fruit development. Plant Physiol. 161(3):1362-74.
    CrossRef
  49. Shanker AK, Maheswari M, Yadav SK, Desai S, Bhanu D, Attal NB, Venkateswarlu B (2014) Drought stress responses in crops. Funct Integr Genomics 14(1):11–22.
    CrossRef
  50. Smakowska-Luzan E, Mott GA, Parys K, Stegmann M, Howton TC, et al. (2018). An extracellular network of Arabidopsis leucine-rich repeat receptor kinases. Nature 553: 342–346.
    CrossRef
  51. Thumma BR, Sharma N, Southerton SG (2012) Transcriptome sequencing of Eucalyptus camaldulensis seedlings subjected to water stress reveals functional single nucleotide polymorphisms and genes under selection. BMC Genomics 13:364.
    CrossRef
  52. Umezawa T, Fujita M, Fujita Y, Yamaguchi-Shinozaki K, Shinozaki K (2006) Engineering drought tolerance in plants: discovering and tailoring genes to unlock the future. Curr Opin Biotechnol 17(2): 113–122.
    CrossRef
  53. Watanabe N, Lam E (2006). Arabidopsis Bax inhibitor-1 functions as an attenuator of biotic and abiotic types of cell death. The Plant Journal. 45, 884-894.
    CrossRef
  54. Wilmoth JC, Wang S, Tiwari SB, Joshi AD, Hagen G, et al. (2005). NPH4/ARF7 and ARF19 promote leaf expansion and auxin-induced lateral root formation. Plant J. 43(1):118-30.
    CrossRef
  55. Xu Y, Gao S, Yang Y, Huang M, Cheng L,Wei Q, Fei Z, Gao J, Hong B (2013) Transcriptome sequencing and whole genome expression profiling of chrysanthemum under dehydration stress. BMC Genomics 14:662.
    CrossRef
  56. Yamaguchi-Shinozaki K, Shinozaki K (2006) Transcriptional regulatory networks in cellular response and tolerance to dehydration and cold stresses. Annu Rev Plant Biol 57:781–803.
    CrossRef
  57. Yoshida T, Nishimura N, Kitahata N, Kuromori T, Ito T, et al. (2006). ABA-hypersensitive germination3 encodes a protein phosphatase 2C (AtPP2CA) that strongly regulates abscisic acid signaling during germination among Arabidopsis protein phosphatase 2Cs. Plant Physiol. 140(1):115-26.
    CrossRef
  58. Zhang J, Ruhlman TA, Mower JP, Jansen RK (2013b) Comparative analyses of two Geraniaceae transcriptomes using next-generation sequencing. BMC Plant Biology 13: 228.
    CrossRef
  59. Zhang X, Allan AC, Li C,Wang Y, Yao Q (2015) De novo assembly and characterization of the transcriptome of the Chinese medicinal herb, Gentiana rigescens. Int J Mol Sci 16(5):11550–11573.
    CrossRef
  60. Zhao Y, Chan Z, Gao J, Xing L, Cao M, et al. (2016). ABA receptor PYL9 promotes drought resistance and leaf senescence. PNAS 113(7):1949-1954.
    CrossRef
  61. Zhong R, Burk DH, Nairn CJ, Wood-Jones A, Morrison WH, et al. (2005). Mutation of SAC1, an Arabidopsis SAC domain phosphoinositide phosphatase, causes alterations in cell morphogenesis, cell wall synthesis, and actin organization. Plant Cell 17:1449-1466.
    CrossRef
  62. Zhong R, Ye Z-H (2003). The SAC Domain-Containing Protein Gene Family in Arabidopsis. Plant Physiol. 132(2): 544–555.
    CrossRef
  63. Zhou X, Li L, Xiang J, Gao G, Xu F, Liu A, Zhang X, Peng Y, Chen X, Wan X (2015) OsGL1-3 is involved in cuticular wax biosynthesis and tolerance to water deficit in rice. PLoS One 10(1):e116676.
    CrossRef
  64. Zhu H, Dardick CD, Beers EP, Callanhan AM, Xia R, Yuan R (2011) Transcriptomics of shading-induced and NAA-induced abscission in apple (Malus domestica) reveals a shared pathway involving reduced photosynthesis, alterations in carbohydrate transport and signaling and hormone crosstalk. BMC Plant Biol 11:138.
    CrossRef
(Visited 479 times, 1 visits today)

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.