Open Access

Identification of candidate genes for drought tolerance in coffee by high-throughput sequencing in the shoot apex of different Coffea arabica cultivars

  • Luciana Souto Mofatto1,
  • Fernanda de Araújo Carneiro2,
  • Natalia Gomes Vieira2,
  • Karoline Estefani Duarte2,
  • Ramon Oliveira Vidal1,
  • Jean Carlos Alekcevetch2,
  • Michelle Guitton Cotta2,
  • Jean-Luc Verdeil3,
  • Fabienne Lapeyre-Montes3,
  • Marc Lartaud3,
  • Thierry Leroy3,
  • Fabien De Bellis3,
  • David Pot3,
  • Gustavo Costa Rodrigues4,
  • Marcelo Falsarella Carazzolle1,
  • Gonçalo Amarante Guimarães Pereira1,
  • Alan Carvalho Andrade2, 5 and
  • Pierre Marraccini2, 3Email author
Contributed equally
BMC Plant BiologyBMC series – open, inclusive and trusted201616:94

https://doi.org/10.1186/s12870-016-0777-5

Received: 14 October 2015

Accepted: 13 April 2016

Published: 19 April 2016

Abstract

Background

Drought is a widespread limiting factor in coffee plants. It affects plant development, fruit production, bean development and consequently beverage quality. Genetic diversity for drought tolerance exists within the coffee genus. However, the molecular mechanisms underlying the adaptation of coffee plants to drought are largely unknown. In this study, we compared the molecular responses to drought in two commercial cultivars (IAPAR59, drought-tolerant and Rubi, drought-susceptible) of Coffea arabica grown in the field under control (irrigation) and drought conditions using the pyrosequencing of RNA extracted from shoot apices and analysing the expression of 38 candidate genes.

Results

Pyrosequencing from shoot apices generated a total of 34.7 Mbp and 535,544 reads enabling the identification of 43,087 clusters (41,512 contigs and 1,575 singletons). These data included 17,719 clusters (16,238 contigs and 1,575 singletons) exclusively from 454 sequencing reads, along with 25,368 hybrid clusters assembled with 454 sequences. The comparison of DNA libraries identified new candidate genes (n = 20) presenting differential expression between IAPAR59 and Rubi and/or drought conditions. Their expression was monitored in plagiotropic buds, together with those of other (n = 18) candidates genes. Under drought conditions, up-regulated expression was observed in IAPAR59 but not in Rubi for CaSTK1 (protein kinase), CaSAMT1 (SAM-dependent methyltransferase), CaSLP1 (plant development) and CaMAS1 (ABA biosynthesis). Interestingly, the expression of lipid-transfer protein (nsLTP) genes was also highly up-regulated under drought conditions in IAPAR59. This may have been related to the thicker cuticle observed on the abaxial leaf surface in IAPAR59 compared to Rubi.

Conclusions

The full transcriptome assembly of C. arabica, followed by functional annotation, enabled us to identify differentially expressed genes related to drought conditions. Using these data, candidate genes were selected and their differential expression profiles were confirmed by qPCR experiments in plagiotropic buds of IAPAR59 and Rubi under drought conditions. As regards the genes up-regulated under drought conditions, specifically in the drought-tolerant IAPAR59, several corresponded to orphan genes but also to genes coding proteins involved in signal transduction pathways, as well as ABA and lipid metabolism, for example. The identification of these genes should help advance our understanding of the genetic determinism of drought tolerance in coffee.

Keywords

Candidate gene Coffee Drought Differential gene expression RNA-Seq Real-time PCR (RT-qPCR)

Background

Coffee is the single most important tropical commodity traded worldwide and is a source of income for many developing countries in Tropics [1]. In the coffee genus, Coffea arabica accounts for approximately 70 % of total production worldwide, estimated at 8.5 million tons in 2015 [2]. Coffee production is subject to regular fluctuations mainly due to the natural biennial cycle but also caused by adverse climatic effects. Among them, drought is a widespread limiting factor and affects flowering and bean development, hence coffee yield [3]. Marked variations in rainfall also increase bean defects and modify the biochemical composition of beans, hence the final quality of the beverage [4]. Periods of drought may become more pronounced as a consequence of global climate change and geographical coffee growing regions may shift considerably, leading to environmental, economic and social problems [5]. In such a context, the creation of drought-tolerant coffee varieties has now become a priority for coffee research.

Genetic variability for drought tolerance exits in the coffee genus, particularly in Coffea canephora [6, 7] but also in C. arabica [8]. Although molecular mechanisms of drought tolerance have been widely studied in model plants [9], they are less well understood in Coffee sp. In a previous study analysing the effects of drought on gene expression, we recently identified a set of 30 genes differentially expressed in the leaves of drought-tolerant and drought-susceptible clones of C. canephora grown in the greenhouse under control (unstressed) and drought conditions [10, 11]. In that case, the expression of genes encoding glycine-rich proteins, heat shock proteins, dehydrins, ascorbate peroxidase, as well as trans-acting factors (such as DREB1D), for example, increased under drought conditions.

In Coffea sp., EST resources have been developed for various species and tissues including roots, leaves, and fruits [1216]. However, no genomic resources are available for shoot apices, which are considered as key organs for plant development by integrating several signals, such as environmental stimuli as well as hormones (abscisic acid [ABA], auxins, cytokinins) and transcription [17]. On the other hand, next-generation sequencing (NGS) provides new opportunities to study transcriptomic responses and to combine high-throughput sequencing with the functional annotation capacity of generated ESTs [18].

In order to identify candidate genes involved in drought tolerance in coffee plants, we collected the shoot apices from drought-tolerant IAPAR59 and drought-susceptible Rubi cultivars of C. arabica under control and drought conditions to generate libraries that were sequenced using the GS-FLX Titanium strategy. A reference full transcriptome was annotated and compared to pre-identify genes differentially expressed between cultivars and drought conditions. The transcription profiles of these genes were further analysed by qPCR in the plagiotropic buds of these plants.

Methods

Plant material

We compared two cultivars of Coffea arabica, the drought-susceptible (DS) Rubi MG1192 (also referred to hereafter as RUB) and the drought-tolerant (DT) IAPAR59 (also referred to hereafter as I59). Rubi did not undergo recent introgression with C. canephora genomic DNA, while IAPAR59 is the result of a cross between the Timor hybrid HT832/2 and the Villa Sarchi cultivar [19].

Field experiment

Seeds of these two commercial cultivars came from fruits harvested in May 2007 in the coffee experimental fields of the Institute for Research and Rural Assistance (Incaper, Vitoria, Espirito Santo, Brazil) and germinated (September 2007) in greenhouse of this institute. Five-month-old plantlets of the Rubi and IAPAR59 were then planted (January 2008) in a field experiment (0.7 m spacing between plants and 3 m spacing between rows) at the Cerrado Agricultural Research Center (Planaltina-DF, Brazil 15°35’44”S - 47°43’52”W) under full-sunlight conditions in two blocks of 30 plants for each cultivar. Under the conditions of the Cerrado climate [20], the rainfall pattern is divided into a dry season (from May to September) followed by a wet season (from October to April) that concentrates more than 80 % of annual precipitations. For each cultivar, one control (C) block was irrigated while the drought (D) block was not irrigated during the dry seasons. For the control condition, irrigation was supplied by sprinklers (1.5 m in height) set up in the field in such a way that irrigation was uniform. Soil water content was monitored using PR2 profile probes (Delta-T Devices Ltd), and irrigation was applied regularly so as to maintain a moisture content above 0.27 cm3 H2O.cm-1.

Sampling

For both cultivars and experiments, leaf predawn water potentials (Ψ pd) were measured once a week during the 2009 dry season (from May to October) of (23-month-old plants) and only once in 2011 (at the end of the dry season) (47-month-old plants) using a Scholander-type pressure chamber (Plant Water Status Console, Model 3000 F01, Soil Moisture Equipment Corp, Santa Barbara, CA USA) in fully expanded leaves (8–15 cm long) from the third pair from the apex of plagiotropic branches located in the upper third of the plant canopy. For 454 sequencing, between 30 and 50 shoot apices were collected (between 10:00 and 11:00 am) from three different plants at the end of the dry season from Rubi and IAPAR59 under the control and drought conditions, and further dissected to isolate the shoot apex (Fig. 1b). For microscopic analyses, leaves identical to those used for Ψ pd measurements were also collected from the same plants. At the end of the 2011 dry season, Ψ pd were measured once for Rubi and IAPAR59 plants under control and drought treatments, and shoot apices were collected (Fig. 1a) for gene expression analyses (qPCR).
Fig. 1

Tissue dissection of plagiotropic buds. a The plagiotropic buds (including small leaves) were collected from plants during the 2011 dry season and used to extract RNA for qPCR expression analysis. b Meristem and leaf primordium dissected from plagiotropic buds harvested during the 2009 dry season and used to extract RNA for pyrosequencing. The dotted circles show the position of meristem and leaf primordium. The same scale (white bar = 1 mm) is used for both documents

RNA isolation, DNA synthesis and 454-sequencing

The plagiotropic buds were incubated for 5 min in the washing buffer (66 % chloroform, 33 % methanol, 1 % HCl) [21] and further incubated twice for 30 min under a vacuum in the fixation buffer (25 % acetic acid, 75 % ethanol RNAse-free) then cooled to 4 °C. Samples were stored in 75 % RNAse-free ethanol. For the control and drought conditions, shoot apices (meristems and primordium leaves) of three different plants were separated from plagiotropic buds under a binocular microscope by dissection and then ground to powder in liquid nitrogen using a pestle and mortar. Total RNA was extracted using the Nucleospin RNA Plant kit (Macherey-Nagel), including a DNAse-I treatment. The quality and quantity of RNA were checked with a Bioanalyzer (2100, RNA Nano 6000 Agilent). The 1st strand cDNA synthesis was performed using 1 μg total RNA and the SMARTer™ PCR cDNA Synthesis Kit (Clontech). Double-stranded DNA was then produced for each library (I59-C, I59-D, RUB-C and RUB-D). For each sample, DNA (around 5 μg) was nebulized to a mean fragment size of 650 bp, ligated to an adapter using standard procedures [22] and then sequenced by performing two runs (1 library per DNA sample x 2) using GS-FLX Titanium (Beckman Coulter Genomics SA, Grenoble, France) which generated one million reads corresponding to more than 255 Mb.

Transcriptome assembly and automatic annotation

All 454-sequencing reads were inspected for low quality reads and 454 adapters that were identified by SSAHA2 software [23]. A reference full transcriptome was then built using C. arabica reads originating from the present project and from the Brazilian Coffee Genome Project (BCGP) available in the GenBank public database [14, 24]. The Sanger and 454 reads were submitted for a trimming pipeline using bdtrimmer software [25] that was used to exclude ribosomal, vector, low quality (regions with a PHRED score less than 20) and short sequences (less than 100 bp). All sequences (454 and Sanger reads) were assembled using MIRA software [26]. The contigs formed by only Sanger reads were discarded from the full transcriptome assembly. The reference full transcriptome was annotated by Blast2GO software version 2.8 [27] using Non-Redundant protein (NCBI/NR), InterPro and Gene Ontology (GO) databases. The same program was also used to group datasets in GO according to the biological process. Further details on the automatic annotation of all contigs are provided in Additional file 1: Table S1. The complete bioinformatic pipeline used for this work is described in Additional file 2: Figure S1.

Digital gene expression analysis

The reference full transcriptome was also used to count all 454 reads/libraries individually by parsing the ACE file generated by MIRA software. The number of sequences anchored in each contig (read counts) was subjected to differential expression analysis between the libraries using DEseq [28] and EdgeR [29] software in the R/Bioconductor package. A unigene was considered as differentially expressed when it was identified in at least one software considering fold-change ≥ 2 (or fold-change ≤ -2) and p-value ≤ 0.05. The libraries were compared based on (1) differentially expressed genes in IAPAR59 between C (control) and D (drought) conditions (with the calculation of fold-change based on the I59-D/I59-C ratio), (2) differentially expressed genes in Rubi between C and D conditions (RUB-D/RUB-C), (3) differentially expressed genes in the control library between Rubi and IAPAR59 (RUB-C/I59-C) and (4) differentially expressed genes in the drought library between Rubi and IAPAR59 (RUB-D/I59-D). Further information about differentially expressed genes in all the libraries is given in Additional file 3: Table S2.

Functional annotation of differentially expressed genes

The lists of differentially expressed genes in each analysis were separated into UP and DOWN regulated and subjected to GO enrichment analysis to identify significantly enriched GO slim terms (Plant GO slim) using Blast2GO software and a p-value ≤ 0.05.

Selection of candidate genes

The comparison of DNA libraries led to the identification of 80 (20 for each library) candidate genes (CGs) that were up- and down-regulated (see Additional file 3: Table S2). For each CG, primer pairs were designed using Primer Express software (Applied Biosystems) and tested of their specificity and efficiency against a mix of ss-DNAs of plagiotropic buds (data not shown). The best primer pairs (n = 20) were used to monitor the expression of corresponding CGs in plagiotropic buds of Rubi and IAPAR59 under control and drought conditions. These genes corresponded to CaAEP1, CaCAB2, CaCHI1, CaCHI2, CaCHI3, CaDLP1, CaELIP3, CaGAS2, CaGRP2, CaH2A, CaHSP3, CaIPS1, CaJAMT1, CaMAS1, CaPP2, CaPSBB, CaSAMT1, CaSDC1, CaSLP1 and CaSTK1 (Table 1). This list of CGs was increased by adding other genes such as 14 orphan genes (CaUNK2-CaUNK7, CaUNK9 and CaUNK11-CaUNK17 already described to present differential gene expression profiles in different organs of C. canephora [30]. This list was finally completed by including the CaUNK1, CaUNK8 and CaUNK10 orphan genes, and LTP genes that were already studied in C. canephora [10, 11, 31] and C. arabica [32], respectively.
Table 1

Candidate genes and corresponding primers used for qPCR experiments

Gene

Protein name

C. canephora

GB

ATP

SGN

Primer

Primer sequences

bp

CaUBQ10

Ubiquitin

Cc02_g31600

GW488515

32782

U637098

BUBI-F

BUBI-R

5’ AAGACAGCTTCAACAGAGTACAGCAT 3’

5’ GGCAGGACCTTGGCTGACTATA 3’

104

CaAEP1 a

Putative aldose 1-epimerase

Cc07_g03170

GT005185

716

U637659

716-1 F

716-1R

5' CGGTGATGTCCTCTCTGATGAG 3’

5’ GTTGGGATGAGCTGGTTGTTC 3’

75

CaCAB2 a

Chlorophyll a/b-binding protein

Cc09_g09030

GT003492

33540

U629601

48565-F

48565-R

5’ GTTCAAGGCTGGATCCCAAA 3’

5’ GCAAGCCCAGATAGCCAAGA 3’

100

CaCHI1 a

Class III chitinase

Cc11_g00410

GT012279

32745

U637166

50103-F

50103-R

5’ AATCAAGCGACCGTCCATTC 3’

5’ GTGTTTCCGCTGTGGATGTG 3’

70

CaCHI2 a

Putative chitinase

Cc00_g14300

GT011845

32737

U638035

53058-F

53058-R

5’ CCTGCTCGCGGTTTCTACAC 3’

5’ TTGTTCCAAAAGCCCCATTG 3’

70

CaCHI3 a

Chitinase-like protein

Cc03_g13720

GW491433

32875

U645893

23638-F

23638-R

5’ AAACGGCCCGTCCAGAA 3’

5’ GCTTTGTCCTGCTGGTCCAT 3’

130

CaDLP1 a

Dirigent-like protein

Cc00_g27410

GW477731

35149

nf

39577-F

39577-R

5’ TTGGTAGTCCGGCGAGAGAA 3’

5’ GCATATCCCCGAGCAAACCT 3’

70

CaELIP3 a

Early light-induced protein (ELIP)

Cc03_g04300

GR985685

32771

U631550

32771-F

32771-R

5’ TCGGTTGCCATGCAATCTT 3’

5’ GCAGATGAAGCCCACAGCTT 3’

100

CaGAS2 a

Glucosyltransferase arbutin synthase

Cc02_g39100

GT697284

3945

U632419

632419-F

632419-R

5’ GCTGACGACGTTAGGATTGAGA 3’

5’ AACTTGGCGGTGTCAACCAA 3’

101

CaGRP2 a

Glycin-rich protein

Cc00_g16260

GW430980

32799

U635030

53139-1 F

53139-1R

5’ CACATATGCTGGTGAGCCAAA 3’

5’ AGGCATTTAAGCGCCATGAT 3’

100

CaH2A a

Putative histone H2A

Cc01_g12440

GT723387

33557

U630412

53417-F

53417-R

5’ GCACTGGAGCTCCGGTCTAC 3’

5’ AGCAGCATTTCCAGCCAATT 3’

80

CaHSP3 a

Heat schock protein (HSP) 70 kDa

Cc02_g08040

GR982512

33197

U636531

33197-1 F

33197-1R

5’ GGCGTCTGGCAACACGAT 3’

5’ CGATGAGACGCTCGGTGTCT 3’

100

CaIPS1 a

Myo-inositol 1-phosphate synthase

Cc07_g15530

GT003538

10496

U632517

10496-1 F

10496-1R

5’ AAGCAACCTGAATTTGGCTGAT 3’

5’ GAGAGGGACCATGGATTCCA 3’

100

CaJAMT1 a

Jasmonate O-methyltransferase

Cc03_g07330

GR989151

33008

U631389

47327-F

47327-R

5’ CTGTGGCTGAACCCTTGCTT 3’

5’ TCTTTGGACATGCGATCAGAAA 3’

100

CaMAS1 a

Momilactone-A synthase

Cc00_g13640

GW479615

33413

nf

33413-F

33413-R

5’ GGGCAGAGGCACGAAAAA 3’

5’ GGTACCCTGCCGCAACTATG 3’

60

CaPP2 a

Putative phloem protein 2 (PP2)

Cc03_g13000

GR995691

33207

U633544

33207-F

33207-R

5’ GGTGTTGGCGATGTCGAGAT 3’

5’ TTCCTTGGGTCGAAGCTCAA 3’

90

CaPSBB a

Photosystem II CP47 (psbB)-like protein

nf

GW447378

22102

U630312

55586-F

55586-R

5’ ATCGGAAATAATCCGGCAAA 3’

5’ AACCATCCAATCGCTATTCCA 3’

80

CaSAMT1 a

S-adenosyl-methionine-methyltransferase

Cc03_g05630

DV672716

754

U629783

34318-F

34318-R

5’ AACGTTTGGGTGATGAATGTTG 3’

5’ GTGCCAATAAGCCCTCTATCGT 3’

80

CaSDC1 a

S-adenosyl-L-methionine decarboxylase

Cc11_g11130

GT002431

8508

U629687

8508-1 F

8508-1R

5’ CTCGATTCCTCCCATCCTGAA 3’

5’ TGACTGTGCCCCAGGGAATA 3’

100

CaSLP1 a

Subtilisin-like protein

Cc00_g19100

GW430663

1620

nf

7961-F

7961-R

5’ CCATCGTTCTCGGTGGTCTT 3’

5’ GCATTGCTCCCCACATTCTT 3’

80

CaSTK1 a

Hypothetical S/T protein kinase

Cc00_g18670

GT687049

6301

U631794

6301-1 F

6301-1R

5’ CCACCCACAAGCTGTATTCTCA 3’

5’ GACCCAATGGGATGTCATCAC 3’

80

CaUNK1 c

Unknown protein 1

Cc03_g08880

DV689820

33062

U614843

182052-F

182052-R

5’ TATAGTGTTTATGGTGTGGCTTTCAGT 3’

5’ GTACCACCGTAGGGAGACGTATG 3’

79

CaUNK2 b

Unknown protein 2

Cc07_g01940

DV708962

31492

U637447

33353-F

33353-R

5’ GAACTTACAAACGCGCGTAACC 3’

5’ CATGGTCGAATCCAGATTTCATT 3’

80

CaUNK3 b

Unknown protein 3

nf

nf

22823

nf

22823-F

22823-R

5’ GGAAGCATGCACACAGAAAATAGA 3’

5’ TTCCTGTTTACGTCTTTTTCAATTGA 3’

80

CaUNK4 b

Unknown protein 4

Cc06_g11210

GW465088

39984

nf

55677-F

55677-R

5’ GCTGTGGTTTTAAAGTTTTGATGGA 3’

5’ TGCAAAATTAAGGTCCCAACAGT 3’

81

CaUNK5 b

Unknown protein 5

Cc08_g09510

GW474926

4578

nf

4578-F

4578-R

5’ GGAGTTCCTGTCCGAAGTTGTT 3’

5’ GGCATGCTGTCACCTGAAAA 3’

80

CaUNK6 b

Unknown protein 6

Cc03_g06850

GT002178

34993

U632634

34993-F

34993-R

5’ AAGCCAATGCCGATCGATT 3’

5’ CGCCGCCGAAGATCTCTAG 3’

100

CaUNK7 b

Unknown protein 7

Cc03_g00560

GW444736

33613

U631416

25639-F

25639-R

5’ CGAGGAAGCTGAAGGAAAGGA 3’

5’ TCCGACTGGCCTAACAAGGT 3’

61

CaUNK8 b

Unknown protein 8

Cc00_g04970

DV695331

33190

U640780

LP18101-F

LP18100-R

5’ CTCGCGTGGCCGAGATC 3’

5’ CCCTCACATTTCCACGTGAAT 3’

100

CaUNK9 b

Unknown protein 9

Cc03_g08920

GT649500

32762

U636808

30926-F

30926-R

5’ CGGAGGAGGCCATGGAGGT 3’

5’ CCGTGTCCATAACCACCATGT 3’

123

CaUNK10 c

Unknown protein 10

nf

GT648004

14813

U645073

D18240-F

D18240-R

5’ TAGCCTTGTTCTTTTAGGGAGTCTTATC 3’

5’ AGAGCTTCGTCCAGGAAGAAGA 3’

134

CaUNK11 b

Unknown protein 11

Cc03_g14330

GR991912

8598

U637116

32792-F

32792-R

5’ GCTGGGAAAGCTACAGAAACCA 3’

5’ GAACTCCAACGCCAAGCATT 3’

100

CaUNK12 b

Unknown protein 12

Cc10_g12840

nf

53029

nf

53029-F

53029-R

5’ CTTCACACCATTCAGACAATCGA 3’

5’ GACCGTAATTGGGCGTCAAT 3’

100

CaUNK13 b

Unknown protein 13

Cc00_g17760

GT673421

14198

U639484

33980-F

33980-R

5’ ATTGCCCTGTTTGCATGCAT 3’

5’ CTGCATGGTGATTGTCCTCAGT 3’

100

CaUNK14 b

Unknown protein 14

Cc00_g16260

GT672564

48325

U635030

11524-F

11524-R

5’ GGCGGTTGTCATGGATACG 3’

5’ TTTGGCTCACCAGCATATGTG 3’

119

CaUNK15 b

Unknown protein 15

Cc00_g04970

GR983286

33190

U636790

05517-F

05517-R

5’ AAAATTTCACCACGGCAAGCT 3’

5’ TTGCCTCCCTCACATTTCCA 3’

72

CaUNK16 b

Unknown protein 16

nf

GW464209

9761

U639049

18112-F

18112-R

5’ TGTGAACTGCCATCCCAAGA 3’

5’ AAGACTACCATGTCCAACAACTTCAG 3’

88

CaUNK17 b

Unknown protein 17

Cc03_g08920

GT685623

32762

U636800

42747-F

42747-R

5’ AGGTGGCTGCCAAGTCAGTT 3’

5’ ATGGTACTTGGCTTCTCCTTCCTC 3’

71

CaLTP1 d

CaLTP2 d

Non-specific lipid transfer protein (nsLTP)

Cc11_g09700

HG008739

HG008740

46897

U632702

LTP-R2

LTP-FT

5’ CACCATTACATGGGAACGTTGC 3’

5’ CTGTGGTCTGAAATGGCCAACT 3’

120

CaLTP3 d

Non-specific lipid transfer protein (nsLTP)

Cc04_g06890

HG008741

33368

U632702

LTP-R1

LTP-FT

5’ ATTCAACACCATTACTAGTTTTCGAGC 3’

5’ CTGTGGTCTGAAATGGCCAACT 3’

113

LTP d

nf

 

-

U632702

LTP-F100

LTP-R100

5’ TGCAATTTTATCAAAGATCCAGC 3’

5’ AGTTGGCCATTTCAGACCACA 3’

93

Gene names were assigned based on the best BLAST hit obtained by comparing the coffee ESTs with public databases. C. canephora means coffee sequences that aligned with the candidate genes using BLASTx searches against NR/NCBI and filtration (http://coffee-genome.org [59]). GenBank (GB: http://blast.ncbi.nlm.nih.gov/Blast.cgi), ATP (http://www.lge.ibi.unicamp.br/cirad/) and SGN (Sol Genomics Network, http://solgenomics.net/) accession numbers of coffee ESTs are also given, as well as the length of base pairs (bp) of amplicons. nf: no-hits found (SGN: tools/blast/SGN Clusters [current version] / Coffee species Clusters, GB: BLASTn/Nucleotide collection [nr/nt]). The size of amplicons is based on the unigene. (a): candidate genes (n = 20) identified during this study. (b): orphan genes (n = 14) previously described [35] and analysed in this study. (c): orphan genes (n = 3) with expression already been studied in leaves of DT and DS clones of C. canephora conilon [10, 11, 36]. (d): LTP-encoding genes were previously described [37]

Real-time quantitative PCR assays

For qPCR experiments, plagiotropic buds containing shoot apices and small leaves (Fig. 1a) were immediately frozen in liquid nitrogen after collection, and stored at -80 °C before being extracted and converted into single-strand cDNA as previously described [33]. Real-time qPCR assays were carried out using the protocol recommended for the use of 7500 Fast Real-Time PCR Systems (Applied Biosystems, Foster City, CA, USA). DNA preparations were diluted (1/50) and tested by qPCR using CG primer pairs (Table 1). RT-qPCR was performed with 1 μl of diluted ss-DNA and 0.2 μM (final concentration) of each primer in a final volume of 10 μl with SYBR green fluorochrome (SYBRGreen qPCR Mix-UDG/ROX, Invitrogen). The reaction mixture was incubated for 2 min at 50 °C (Uracil DNA-Glycosilase treatment), then for 5 min at 95 °C (inactivation of UDGase), followed by 40 amplification cycles of 3 sec at 95 °C and finally for 30 sec at 60 °C. Data were analysed using SDS 2.1 software (Applied Biosystems) to determine cycle threshold (Ct) values. The specificity of the PCR products generated for each set of primers was verified by analysing the Tm (dissociation) of amplified products. PCR efficiency (E) was estimated using absolute fluorescence data captured during the exponential phase of amplification of each reaction with the equation E (in %) = (10(-1/slope) -1) x 100 [34]. Efficiency values were taken into account in all subsequent calculations. Gene expression levels were normalized to expression levels of CaUBQ10 as a constitutive reference. Relative expression was quantified by applying the formula (1 + E)−ΔΔCt where ΔCt target = Ct target gene – Ct reference gene and ΔΔCt = ΔCt target – ΔCt internal calibrator, with the internal reference always being the Rubi-control (RUB-C) sample with relative expression equal to 1.

Leaf histological analysis of cuticle

Mature leaves of the IAPAR59 and Rubi genotypes were fixed for 48 h in 100 mM phosphate buffer at pH 7.2, supplemented with 1 % (v/v) glutaraldehyde, 2 % (v/v) paraformaldehyde, and 1 % (w/v) caffeine, at room temperature [35]. The samples were dehydrated and embedded in Technovit 7100 resin (Heraeus Kulzer) according to the manufacturer’s recommendations. Three-micrometer semi-thin sections were cut with glass knives on a Leica RM2065 Microtome. The resulting sections were double stained according to Buffard-Morel et al. [36]. Briefly, polysaccharides were stained dark pink with periodic acid Schiff (PAS) and soluble proteins were stained blue with naphthol blue-black (NBB) [37]. Sections were then mounted in Mowiol. The slides were observed with a Leica DM6000 microscope (Leica, Germany) under bright field or epifluorescent light (A4 filter). Pictures were taken with a Retiga 2000R camera (QImaging Co.) and the images were processed with Volocity 4.0.1 (Improvision, Lexington, MA, USA). Cuticle thickness was measured with the freeware Image J software (http://imagej.nih.gov/ij/). Experiments were conducted on the “Plate-Forme d’Histocytologie et Imagerie Cellulaire Végétale (PHIV platform)” (http://phiv.cirad.fr/) using microscopes belonging to the Montpellier Rio Imaging platform (www.mri.cnrs.fr). The results are expressed as means (μm) of 11 measured values. The data were statistically processed using (1) an analysis of variance computer program (Statistica, StatSoft, Inc.), and (2) the Student-Newman-Keuls (SNK) mean comparison test [38] when the effect of the factor tested was found to be statistically significant. A probability level of P ≤ 0.05 was considered significant for all the statistical analyses.

Results

Monitoring drought under field conditions

In 2009, leaf predawn water potential (Ψ pd) values were similar in the leaves of irrigated Rubi and IAPAR59 plants, ranging from -0.06 to -0.16 MPa (Fig. 2a). This confirmed the unstressed status of these plants which were considered as the control in our experiment. At the same time, the Ψ pd values decreased gradually during the dry season in the leaves of Rubi and IAPAR59 under drought conditions reaching the lowest values at the end of the dry season (Fig. 2a). At that time, the less negative Ψ pd values in IAPAR59 indicated that it had better access to soil water. The first rains then occurred and the Ψ pd values of drought-stressed plants increased almost to those measured in irrigated plants, illustrating the complete recovery of stressed plants. In 2011, Ψ pd was measured at the peak of the drought (end of dry season). Under drought conditions, both Rubi and IAPAR59 had similar Ψ pd values that were more negative than those measured in 2009, indicating more severe drought stress in 2011 (Fig. 2b).
Fig. 2

Predawn leaf water potentials (Ψ pd) measured in plants of C. arabica. Rubi (RUB, triangle) and IAPAR59 (I59, square) cultivars were grown under control (C, open symbols) and drought (D, black symbols) conditions. Ψ pd values (expressed in mega-Pascal, MPa) were measured once a week during the 2009 dry season (23-month-old plants) (a). The time scale is in days and months (dd/mm, from 20/05 to 02/10). Vertical bars are standard deviations (n = 9 leaves) and the dashed vertical line (20/08) represents the harvest point of plagiotropic buds for RNA extraction for 454 sequencing and leaves for microscopic analyses. b Ψ pd of Rubi and IAPAR59 plants (47-month-old plants) measured at the end of the 2011dry season. In this case, Ψ pd values ranged from -0.1 to -0.2 MPa for the control conditions, but were below (< -4.0 MPa = severe drought) the range of use of a Scholander-type pressure chamber for drought conditions

Sequencing, assembly and annotation of the Coffee shoot apex transcriptome

The final reference assembly generated a total of 34,743,872 bp (34.7 Mbp) with coverage of 6.5x and 43,087 clusters, corresponding to 41,512 contigs and 1,575 singletons. These data are composed of: (1) 17,719 clusters (16,238 contigs and 1,575 singletons) from 454 sequences, exclusively; and (2) 25,368 hybrid clusters that contain 454 reads, and at least one contig from Sanger sequencing (public database). The contigs formed by only Sanger reads were discarded from the full transcriptome assembly. On average, 22.4 % and 55.6 % of the total raw data were discarded from Sanger and 454, respectively, due to low quality. After removing the adapters, these reads had a size of 379.2 bp (on average). The statistical data for the Sanger and 454 reads are listed in Table 2.
Table 2

Characteristics of reads used in this work

Libraries

Total reads

Trimmed reads

Average length of reads

Public Sanger database

195,110

151,403

518

I59-C

135,304

66,641

325

I59-D

282,213

112,518

351

RUB-C

230,064

101,394

360

RUB-D

345,751

153,572

342

Total

1,188,442

585,528

379.2

Statistics of all reads used in this work: public Sanger reads and 454 sequenced reads from two cultivars under two conditions. Cultivars (RUB: Rubi and I59: IAPAR59) of C. arabica and treatments (C control and D drought) are indicated. The number of total reads, trimmed reads and average read length (in bp) are indicated

Transcriptome annotation by Blast2GO using Non-Redundant protein (NCBI/NR) and InterPro databases resulted in 36,965 transcriptome clusters (85.8 %) with a known protein function, 1,824 conserved proteins of unknown function (4.2 %), 1,515 proteins identified by InterPro only (3.5 %) and 2,783 unidentified proteins (6.5 % no-hits found).

The results of the digital gene expression analysis (Table 3) showed more differentially expressed genes (DEG) in the cultivars Rubi (RUB) and IAPAR59 (I59) cultivars under drought (D) conditions (RUB-D/I59-D), totalling 490 clusters (1.14 % of the total), with 320 clusters classified as up-regulated. Under the control (C) conditions, a few DEG were found (RUB-C/I59-C), corresponding to 184 clusters (0.43 % of total clusters). The comparison between control and drought conditions showed a prevalence of up-regulated genes (165 clusters) and a total of 226 DEG in IAPAR59 (I59-D/I59-C) with 0.52 % of total clusters, and 343 clusters in Rubi (RUB-D/RUB-C) with 0.80 % of total clusters.
Table 3

Reads showing differential expression between cultivars and/or treatments

Libraries

EdgeR DEG (% of total clusters)

DEseq DEG (% of total clusters)

Total DEG (% of total clusters)

Up-regulated clusters (% of total clusters)

Down-regulated clusters (% of total clusters)

I59-D/I59-C

209 (0.49 %)

176 (0.41 %)

226 (0.52 %)

165 (0.38 %)

61 (0.14 %)

RUB-D/RUB-C

323 (0.75 %)

306 (0.71 %)

343 (0.80 %)

251 (0.58 %)

92 (0.21 %)

RUB-C/I59-C

173 (0.40 %)

169 (0.39 %)

184 (0.43 %)

104 (0.24 %)

80 (0.19 %)

RUB-D/I59-D

392 (0.91 %)

433 (1.00 %)

490 (1.14 %)

320 (0.74 %)

170 (0.39 %)

Differentially expressed genes (DEG) were obtained with the R/Bioconductor packages DEseq and EdgeR. Total DEG values mean the union of DEseq and EdgeR results. The calculation of percentage was based on total of clusters (43,087 clusters). Cultivars (RUB Rubi and I59: IAPAR59) of C. arabica and treatments (C control and D drought) are indicated

The results of the gene ontology (GO) enrichment analysis are shown in Fig. 3 and all GO enrichment data are listed in Additional file 1: Tables S1 and Additional file 3: Table S2. For IAPAR59, the comparison of drought and control conditions (I59-D/I59-C) identified over-represented GO terms characterized by up-regulated genes involved in expression (gALL_c3501) and translation (gALL_c2033, gALL_c4461, gALL_c6492) processes and in the generation of precursor metabolites and energy (gALL_c921, gALL_c4013, gALL_c4540). For Rubi, a comparison of the RUB-D/RUB-C libraries revealed an over-representation of the following GO terms which were up-regulated: protein metabolic process (gALL_c2021, gALL_c3355), response to stress (gALL_rep_c33197/CaHSP3) and response to abiotic stimulus (gALL_rep_c32771/CaELIP3, gALL_c2829, gALL_rep_c32766). When comparing both cultivars under drought conditions (RUB-D/I59-D), GO terms were identified related to increased enrichment of tropism for up-regulated genes (gALL_c1270, gALL_c1524, gALL_c1864) and photosynthesis for down-regulated genes (gALL_c27215, gALL_rep_c34074, gALL_rep_c34746). Under the control conditions (RUB-C/I59-C), proteins of translational machinery were identified for up-regulated genes (gALL_c3061, gALL_c16674, gALL_c19094) and photosynthesis for down-regulated genes (gALL_rep_c34074, gALL_rep_c37283, gALL_rep_c50892).
Fig. 3

Gene ontology (GO) enrichment analysis on a list of differentially expressed genes up- and down-regulated under four conditions. The calculation of fold change was based on the ratio of: (a) I59-D/I59-C; (b) RUB-D/RUB-C; (c) RUB-C/I59-C; and (d) RUB-D/I59-D. The Y axis indicates the number of genes normalized by the total number of genes used in each comparison from each library. Cultivars (RUB: Rubi and I59: IAPAR59) of C. arabica and treatments (C: control and D: drought) are indicated

Expression profiles of candidate genes

Among the candidate genes (CGs) identified in silico as presenting up- and down-regulation, expression profiles from 20 of them were analysed by qPCR together with the expression of 17 orphan genes (3 of them already studied in C. canephora [10, 11, 30, 31]) and LTP genes [32]. For all these genes, expression profiles were analysed in plagiotropic buds of Rubi and IAPAR59 under control and drought conditions. These results are presented in separate sections below, according to the observed expression patterns.

Genes with induced expression under drought conditions

Twenty-five genes showing up-regulated expression profiles under drought conditions, mainly in IAPAR59 and to a lesser extent in Rubi, were identified (Fig. 4). This was observed for CaSTK1 which encodes a putative oxidative stress response serine/threonine protein kinase with 87 % identity with a predicted protein of Populus trichocarpa (XP_002299433). In that case, expression of this gene was highly induced by drought in the DT cultivar IAPAR59. Similar profiles were also observed for the CaSAMT1 gene encoding a putative S-adenosyl-L-methionine-dependent methyltransferase and the orphan genes CaUNK2 and CaUNK3. The latter gene had no open reading frame but presented high identity (e-value 2E-45) with the SGN-U637447 contig and also with various coffee ESTs mainly found in C. canephora cherries at early developmental stages (data not shown).
Fig. 4

Expression profiles of genes up-regulated under drought conditions. Gene expression was analysed in plagiotropic buds of Rubi (RUB) and IAPAR59 (I59) cultivars of C. arabica grown under control (white isobars) and drought (black isobars) conditions. The gene names are indicated in the histograms. Transcript abundances were normalized using the expression of the CaUBQ10 gene as the endogenous control. Results are expressed using RUB-C as the reference sample (Relative expression = 1). Values of three technical replications are presented as mean ± SD (bar)

Expression of the CaSLP1 gene encoding a putative protein homologous (65 % identity, 74 % similarity) to a protein of Nicotiana benthamiana containing a peptidase S8/subtilisin-related domain, was also higher in IAPAR59 than in Rubi under drought conditions. A similar situation was observed for the CaMAS1gene encoding a protein of 311 amino acid residues sharing similarities (e-value 2E-121, 66 % identity, 82 %, similarity) with momilactone A synthase-like protein from Vitis vinifera (XP_002275768) that contains a secoisolariciresinol dehydrogenase conserved domain.

Similar expression profiles, characterized by high up-regulation under drought conditions particularly in IAPAR59, were observed for the orphan genes CaUNK1, CaUNK4, CaUNK5, CaUNK8, and for CaPSBB (similar to the gene of C. arabica chloroplast genome encoding the photosystem II CP47 chlorophyll apoprotein) and CaSDC1 encoding a putative protein related (81 % identity, 88 %, similarity) to the adenosylmethionine decarboxylase proenzyme of Catharanthus roseus). Expression of the CaUNK6 gene was also induced under drought conditions but without significant difference in expression between the two cultivars.

Interestingly, the expression profiles of orphan genes CaUNK7, CaUNK9, CaUNK10, CaUNK15, CaUNK16 and CaUNK17 were similar to that of HSP-encoding gene CaHSP3 in the sense that gene expression was highly up-regulated under drought conditions in both cultivars. In the case of CaUNK10, it is worth noting that expression increased 145- and 88-fold under drought conditions in Rubi and IAPAR59, respectively.

Under drought conditions, expression of the CaGAS2 gene encoding a putative protein homologous (73 % identity, 86 % similarity) to the arbutin synthase from Rauvolfia serpentina (AJ310148), was slightly increased in IAPAR59 but reduced in Rubi. The CaCAB2, CaCHI1 and CaELIP3 genes encoding a photosystem II light harvesting chlorophyll A/B binding protein of Gardenia jasminoides (ACN41907), a class III chitinase of C. arabica (ADH10372) and an early light-induced protein (ELIP) of Glycine max (NP_001235754), respectively, showed similar profiles but with lower expression in Rubi than in IAPAR59, under control and drought conditions. Lastly, expression of the CaPP2 gene encoding a putative phloem protein 2 (PP2) of Vitis vinifera (XP_002279245) increased under drought conditions in Rubi but was quite stable in IAPAR59 under both conditions.

Expression of type II nsLTP genes

The expression of Type II nsLTP-encoding genes was also monitored using the primer pairs LTP-FT/LTP-R1 (specific to the CaLTP1 and CaLTP2 genes from the C. eugenioides sub-genome of C. arabica, hereafter referred to as CaCe), LTP-FT/LTP-R2 (specific to CaLTP3 genes from the C. canephora of C. arabica, hereafter CaCc) and LTP-F100/LTP-R100 recognizing all homologous genes [32]. No expression of nsLTP genes was detected under the control conditions in both cultivars (Fig. 5). However, expression of nsLTP genes was highly up-regulated in IAPAR59 but not in Rubi under drought conditions. It is worth noting that the CaLTP1-CaLTP2 and CaLTP3 genes were co-expressed in IAPAR59, and that the expression of CaCc genes was slightly higher than that of CaCe genes.
Fig. 5

Expression of nsLTP genes. Expression of CaLTP1-CaLTP2 (CaCe: white isobars), CaLTP3 (CaCc: grey isobars) and all (CaLTP1, CaLTP2 and CaLTP3: black isobars) genes was analysed by qPCR in plagiotropic buds of Rubi (RUB) and IAPAR59 (I59) cultivars of C. arabica grown under control (C) and drought (D) conditions, using the LTP-FT/LTP-R2, LTP-FT/LTP-R1 and LTP-F100/LTP-R100 primer pairs, respectively [37]. Expression levels are expressed in arbitrary units (AU) of nsLTP genes using the expression of the CaUBQ10 gene as the endogenous control and RUB-C (with LTP100 primers) as the reference sample (Relative expression = 1). Values of three technical replications are presented as mean ± SD (bar)

Drought influences leaf cuticle thickness

Leaf anatomical analyses were also performed, revealing that the abaxial epidermis of IAPAR59 had a thicker cuticle than Rubi under drought conditions (Fig. 6). There was also a strong interaction between genotype and drought conditions (F1, 40 = 16,2). For example, in the DT cultivar IAPAR59, the abaxial epidermis cuticle thickness greatly increased under drought conditions compared with the control treatment (Table 4). However, no significant variation in abaxial epidermis cuticle thickness could be observed between the control and drought treatments for Rubi leaves.
Fig. 6

Comparative analysis of leaf histological cross sections of IAPAR59 (a and b) and Rubi (c and d) cultivars of C. arabica under control (irrigation: a and c) and drought (b and d) conditions. Samples were double stained with Schiff and NBB and observed under wide field (at the bottom left of each image) and fluorescent microscopy (A4 filter). LE = Lower (abaxial) epidermis. The white arrows indicate the fluorescent cuticle. Values of leaf cuticle thickness are given in Table 4. Bars = 20 μm

Table 4

Influence of drought on leaf cuticle thickness

 

Cuticle thickness (μm)

Treatment

IAPAR59

Rubi

Control

1.49 ± 0.19(a)

1.75 ± 0.15(b)

Drought

1.98 ± 0.19(c)

1.73 ± 0.28(b)

Leaves of IAPAR59 and Rubi cultivars of C. arabica grown under control (irrigation) and drought conditions were analysed to measure the cuticle thickness of the abaxial faces. Values (in μm) correspond to the average calculated from 11 independent measurements. Those marked with different letters are significantly different (Student-Newman-Keuls mean comparison test, P < 0.05)

Genes with reduced expression under drought conditions

The qPCR experiments led to the identification of several genes whose expression was reduced under drought conditions (Fig. 7). In both cultivars, expression of the orphan genes CaUNK11 and CaUNK12, and of the CaDLP1 gene encoding a putative protein containing a dirigent-like protein domain homologous to the hypothetical protein (CAN61316) of Vitis vinifera, was greatly reduced under drought conditions. Expression of the CaCHI2 gene encoding a protein homologous to the putative chitinase of Catharanthus roseus (ADK98562), was 5-fold higher in IAPAR59 than in Rubi under the control conditions but decreased under drought conditions. However, the expression level of the CaCHI2 gene was similar in IAPAR59 and Rubi under drought conditions. For the genes CaCHI3 (putative protein related to chitinase-like protein Artemisia annua [ABJ74186]), CaUNK13 and CaJAMT1 (putative protein containing a methyltransferase domain [pfam03492] found in enzymes acting on salicylic acid, jasmonic acid and 7-methylxanthine), similar expression profiles were found. In these cases, drought reduced gene expression in both cultivars but expression levels were always higher in IAPAR59 than in Rubi, particularly for CaJAMT1.
Fig. 7

Expression profiles of genes down-regulated under drought conditions. Gene expression was analysed in plagiotropic buds of Rubi (RUB) and IAPAR59 (I59) cultivars of C. arabica grown under control (white isobars) and drought (black isobars) conditions. The gene names are indicated in the histograms. Transcript abundances were normalized using the expression of the CaUBQ10 gene as the endogenous control. Results are expressed using RUB-C as the reference sample (Relative expression = 1). Values of three technical replications are presented as mean ± SD (bar)

Gene expression levels of the CaH2A (H2A histone protein), CaGRP2 (putative glycin-rich protein) and CaUNK14 genes, were similar in Rubi and IAPAR59. For the CaAEP1 (putative aldose 1-epimerase) and CaIPS1 (myo-inositol 1-phosphate synthase) genes, gene expression remained high in IAPAR59 under both control and drought conditions, but decreased drastically in Rubi under drought conditions.

Discussion

In this study, we obtained 34.7 Mbp (coverage 6.5x) of sequences with longer reads (mean of 379.2 bp) from plagiotropic shoot apices enriched in meristems and primordium leaves of two cultivars of C. arabica under control (irrigation) and drought conditions. These sequences were assembled giving 43,087 clusters (17,719 contigs exclusively from 454-sequencing and 25,368 hybrid contigs formed by 454 and Sanger sequences) with a mean size ≥ 300 bp each. These RNAseq data, which complement those already available in public databases for coffee ESTs (407 million ESTs: dbEST release June 2015), can be considered as innovative and relevant in the sense that they were produced from C. arabica tissues (meristems) that have never previously been studied [39].

The transcriptome annotation by Blast2GO provided information based on the nomenclature and organism of origin of genes in the NCBI/NR database, the enzyme family, a functional analysis of proteins from the InterPro database, and metabolic functions, biological processes and cellular location from gene ontology. Our results showed that a large percentage of transcriptome alignment had 36,965 hits with known function (85.8 %), 1,824 genes with unknown function (4.2 %) both in the NCBI/NR database, and only 1,515 hits in the Interpro database (3.5 %), thereby enabling the identification of most genes. With this analysis, we identified 34,857 genes related to Coffea sp. (80.9 % of the total). We also found 1,383 genes from Solanum sp., 573 genes from Populus trichocarpa, 482 genes from Vitis vinifera and 156 genes from Arabidopsis sp. Thus, the transcriptome was aligned with several genes from different plant species and these genes may be conserved among these species, including Coffea sp. On the other hand, our results also included 2,783 “no-hit” genes (6.5 %), perhaps indicating the presence of unannotated or new genes.

The comparisons of DNA libraries undertaken during this work led to the identification of 1,243 genes (Table 3: ∑ Total DEG %) with differential expression profiles in silico between the drought-susceptible (Rubi) and drought-tolerant (IAPAR59) cultivars of C. arabica with drought conditions. The expression profiles of these genes, as well as those of other previously identified genes [10, 11, 3032], were analysed by qPCR in plagiotropic buds (containing meristems and small leaves) taken from control and drought-stressed plants of Rubi and IAPAR59. For most of the CGs identified during this work, in vivo gene expression profiles confirmed those deduced from in silico comparisons of DNA libraries. For example, this was the case for the CaHSP3 (heat shock protein) gene whose up-regulated expression under drought conditions can be considered as a “molecular control” of stress applied to the plants during this study and confirmed by leaf water potential (Ψ pd) measurements. Many ESTs encoding putative HSPs were also found in leaf cDNA libraries of C. arabica (SH2) and C. canephora (SH3) plants grown under drought conditions [31], heat stress [40], leaf infection by Hemileia vastatrix [15, 16] and also during bean development [14].

Our results also identified several genes differentially expressed in plagiotropic buds of IAPAR59 and Rubi, as for the CaSTK1 gene encoding a putative serine/threonine protein kinase containing a conserved domain (cd06610) of mitogen-activated protein kinases (MAPKs). These kinases are known to have a central role in the transduction of extra- and intracellular signals in plants, including cell division and differentiation, as well as in responses to various types of stress [41]. In Pisum sativum, there is evidence that the MAPK cascade is involved in ABA-regulated stomatal activity as well as ABA-induced gene expression in the epidermal peels [42]. In a recent study, Shen et al. [43] showed that the phosphorylation of OsWRKY30 protein by MAPKs is a key step in conferring drought tolerance in transgenic rice. According to our results, higher CaSTK1 expression under drought conditions in IAPAR59 than in Rubi could enhance the MAPK cascade and therefore be involved in the drought tolerance of IAPAR59. In this cultivar, the over-expression of CaSAMT1 under drought conditions is also particularly interesting because this sequence encodes a putative S-adenosyl-L-methionine-dependent methyltransferase related to the TUMOROUS SHOOT DEVELOPMENT2 (TSD2) gene. In Arabidopsis thaliana, tsd2 is a pleiotropic mutation that affects leaf, root and shoot meristem development [44]. Expression of a TSD2:: GUS reporter gene has mainly been detected in meristems where this gene is essential for cell adhesion and coordinated plant development. The weaker expression of CaSAMT1 in Rubi than in IAPAR59 under drought conditions, points to the existence of major developmental differences between these two cultivars. The differential expression in Rubi and IAPAR59 of the CaSLP1 gene encoding a putative subtilisin-like protein is also worth noting. In Arabidopsis, the subtilisin-like serine-protease SDD1 (stomatal density and distribution) gene was shown to be strongly expressed in stomatal precursor cells (meristemoids and guard mother cells) [45]. In addition, sdd1 mutation increased leaf stomatal density (SD) while SDD1 over-expression led to the opposite phenotype with decreased SD. In C. arabica, maximum and minimum average stomatal densities were observed in full sunlight and shaded conditions respectively, providing evidence for the existence of plasticity for this characteristic in this coffee species [46, 47]. Even though no SD were observed between Rubi and IAPAR59 under moderate drought conditions [48], the CaSLP1 expression profiles presented here do not preclude the involvement of this gene in the genetic determinism of drought tolerance in coffee.

Another interesting response concerned the differential expression of the CaMAS1 gene encoding a putative protein containing the conserved domain [cd05326]. This domain is also found in secoisolariciresinol dehydrogenase-like proteins catalyzing the NAD-dependent conversion of (-)-secoisolariciresinol to (-)-matairesinol, like the Arabidopsis ABA2 protein considered to be one of the key regulators of ABA biosynthesis [49]. Based on the CaMAS1 expression profiles presented here, it is possible that ABA synthesis was enhanced by drought in plagiotropic buds of IAPAR59 but not (or to a lesser extent) in those of Rubi. This hypothesis is also reinforced by the fact that higher CaJAMT1 expression was observed in IAPAR59 than in Rubi buds. Indeed, in addition to well-known functions of jasmonates in plant defence mechanisms in response to biotic stress [50], recent studies also demonstrated that methyl jasmonate stimulates ABA biosynthesis under drought conditions in panicles of Oryza sativa [51].

Higher expression of CaSDC1 (encoding a protein sharing 89 % similarity with the S-adenosyl-L-methionine decarboxylase from Catharanthus roseus) under drought conditions in IAPAR59 than in Rubi is also worth noting because this enzyme catalyzes the synthesis of polyamines (e.g. spermine, spermidine and putrescine) involved in stress tolerance in higher plants [52]. In Theobroma cacao, ABA and drought induced the expression of TcSAMDC increasing spermine and spermidine leaf contents correlated with changes in stomatal conductance [53]. More recently, SAMDC over-expression in transgenic rice was also shown to facilitate drought tolerance [54]. Investigation of polyamine levels in plagiotropic buds and leaves of IAPAR59 and Rubi would be of particular interest to see if these compounds are involved in drought tolerance in coffee.

In mature plants, nuclear-encoded early-light inducible proteins (ELIPs) accumulate in response to various stress conditions including ABA or desiccation [55]. These proteins are presumed to protect the chloroplast apparatus from photo-oxidation occurring after stomatal limitation of photosynthesis [56]. In a recent study, transgenic plants of Medicago truncatula over-expressing the Dsp22 gene from Craterostigma plantagineum were shown to be able to recover from water deprivation better than wild type plants, thereby reinforcing the idea of using ELIP-encoding genes to improve abiotic stress resistance in crops [57]. Our results clearly highlight the increased expression of the CaELIP3 (ELIP-like), CaPSBB (CP47-like) and CaCAB2 (PSII Cab proteins) genes, respectively, under drought conditions. Interestingly, the expression levels of all these genes were always higher in IAPAR59 than in Rubi. These results are also in accordance with electronic Northern experiments which showed high accumulation of ELIP and Cab-encoding ESTs in cDNA libraries of C. arabica and C. canephora subjected to drought [58].

Another surprising result concerned the CaPSBB gene that was reverse-transcribed and detected during our qPCR experiments despite the fact that it corresponds to a chloroplast gene [59]. However, preliminary analyses of a whole genome sequence of C. canephora revealed the presence of a CP47/like nuclear gene [60]. Interestingly, photosystem II CP47 chlorophyll apoproteins encoding ESTs have also been reported to be expressed in C. arabica beans [61], leaves infected by Hemileia vastatrix [62] and also in the cDNA libraries (SH2 and SH3) of drought-stressed coffee plants [14, 24, 31], demonstrating increased expression of this gene under biotic and abiotic stress. As CP47 and ELIP proteins are essential for the activity and protection of the photosynthetic apparatus [55], the expression profiles reported here probably reflect a better photosynthetic and physiological status of IAPAR59 compared to Rubi.

Differential expression was also observed for the chitinase-encoding gene CaCHI1, with higher expression in IAPAR59 than in Rubi. An opposite situation was observed with respect to the chitinase-encoding genes CaCHI2 and CaCHI3, whose expression was reduced under drought conditions. It is worth noting that the expression of these genes under drought conditions was always higher in IAPAR59 than in Rubi. These results also show that coffee chitinase-encoding genes responded in different ways to drought. A large number of chitinase-encoding ESTs were identified in the BCGP project [24], mainly in the SH2 cDNA library of drought-stressed plants of C. arabica var. Catuai [58], but also in the leaves of C. arabica infected by leaf rust [62]. Even though chitinases are defence-related enzymes induced by abiotic stress, some evidence also indicates their participation in tolerance to abiotic stress [63]. Even though the roles of pathogenesis-related proteins in abiotic stress are still not fully understood, DT transgenic plants over-expressing chitinase genes have been obtained [64]. In that sense, the high level of expression for CaCHI1 in plagiotropic buds of IAPAR59 under both control and drought conditions could have an important function in drought tolerance.

Arbutin is a phenolic glucoside (4-hydroxyphenyl-β-D-glucopyranoside) abundant in the leaves of many freezing- or desiccation-tolerant plants [65] and also present in coffee fruits [66]. In a previous study, down-regulation of the CcGAS1gene encoding arbutin synthase was reported in leaves of C. canephora under drought conditions [10]. The results presented here clearly demonstrated differential expression profiles for CaGAS2 between the two cultivars of C. arabica. Gene expression increased under drought conditions in IAPAR59 while the opposite was observed in Rubi. Even though the presence of arbutin in coffee leaves has never been demonstrated, further analyses of this metabolite should be performed to investigate the role of this glucoside (and of other phenolic compounds) in preventing cell damage in coffee subject to abiotic stresses.

The CaPP2 gene (encoding a putative phloem protein 2, PP2) also showed differential expression profiles, with higher expression in IAPAR59 than in Rubi. In higher plants, PP2s are sieve elements (SE) very abundant in the phloem sap. These proteins are believed to play an important role in the establishment of phloem-based defence mechanisms induced by insect attacks and feeding stress [67], but also by wounding and oxidative conditions [68]. The functions of PP2 proteins are still not clear but they could act by forming high molecular weight polymers to close (“SE plugging”) the sieve pores caused by external injuries mainly due to biotic stress [69]. When Arabidopsis was treated with HrpNEa (a proteinaceous elicitor of plant defences produced by gram-negative plant pathogenic bacteria), the suppression of phloem-feeding activities by aphids was attributed to over-expression of the PP2-encoding gene AtPP2-A1 [70]. Other studies showed that HrpN activated ABA signalling, thereby inducing drought tolerance in Arabidopsis thaliana [71]. Based on these results, the involvement of PP2 proteins in plant response mechanisms to abiotic stress can be hypothesized, for example by maintaining (or protecting) the integrity of vessels under drought conditions by forming sieve plate filaments upon oxidation [72]. In that case, higher synthesis of CaPP2 which would be expected to occur in IAPAR59 plagiotropic buds under drought conditions could play a role in drought-tolerance by reducing sap-flow in young leaves and consequently increasing the water use efficiency of this cultivar [48].

Other interesting results concerned the gene expression stability of the CaAEP1 (putative aldose 1-epimerase) and CaIPS1 (myo-inositol 1-phosphate synthase) genes observed in IAPAR59 under control and drought conditions, whereas expression of both genes decreased under drought conditions in Rubi. Plant cells use myo-inositol to synthesize a variety of low molecular weight compounds and sugar alcohols such as the galactinol, a key element in the formation of raffinose family oligosaccharides. Nishizawa et al. [73] found that plants with high galactinol and raffinose contents were less susceptible to oxidative stress. In C. arabica, up-regulation of CaGolS genes involved in galactinol biosynthesis was reported in leaves of plants subjected to severe drought [74]. In addition, drought up-regulated the expression of mannose 6-phosphate reductase (involved in mannitol biosynthesis) in leaves of C. canephora [10, 11] and C. arabica [75, 76]. Even though little is known about the biochemical mechanisms of drought tolerance in coffee, the accumulation of carbohydrates expected in leaves of drought-stressed plants as a consequence of the up-regulated expression of these genes, could play an important role in the genetic determinism of this phenotype in coffee [77].

In addition to the previously described genes, our results also identified several orphan genes that presented differential expression profiles between the cultivars and treatments, such as CaUNK2, CaUNK3 and CaUNK4 whose expression was highly induced under drought conditions in IAPAR59 and to a lesser extent in Rubi. Orphan genes are also expected to interact specifically with the environment as a consequence of lineage-specific adaptations to that environment [78].

Interestingly, the expression profiles of the CaUNK2 and CaUNK3 orphan genes were very similar to those of Type II nsLTP-encoding genes, with high expression mainly detected under drought conditions in plagiotropic buds of IAPAR59 but not in those of Rubi. Up-regulation of LTP genes under drought conditions is well documented in higher plants [7981]. Lipid transfer proteins (LTPs) are thought to be involved in the transfer of lipids through the extracellular matrix for the formation of cuticular wax [82]. In fact, together with the lipophilic cutin polymer matrix, waxes enter in the composition of cuticle, which forms the first barrier between plants and environmental stresses by limiting non-stomatal water loss and gas exchanges, hence mitigating the effects of drought by controlling water loss associated with epidermal conductance [83]. In Nicotiana glauca, LTP genes are predominantly expressed in the guard and epidermal cells and are induced under drought conditions [84], providing evidence that LTP play an important role in the development of drought tolerance. Even though the up-regulation of CaLTP genes observed under drought in plagiotropic buds of IAPAR59 cannot explain directly the greater thickness of leaf cuticle observed in this cultivar than in Rubi, these results strongly suggested that lipid metabolism plays a major role in coffee drought tolerance.

As reported in other higher plants, our study also highlighted the differential expression of many genes encoding proteins known to be over-expressed under biotic stress (e.g. chitinases and PP2), by drought. The fact that our experiment was conducted with drought-stressed plants grown under uncontrolled (field) conditions, could explain such a situation. However, it is also probable that these results reflect a biological reality since it is well known that crosstalk exists in higher plants between signalling pathways for biotic and abiotic stress responses [85].

Conclusions

During this work, we produced some new transcriptomic information for C. arabica with a total of 34.7 Mbp of sequences assembled into 43,087 clusters (41,512 contigs and 1,575 singletons) from genes expressed in plagiotropic shoot apices enriched in meristems and primordium leaves in DT (IAPAR59) and DS (Rubi) cultivars grown under control and drought conditions. Major differences between these plants concerned their phenotypic behaviour (e.g. predawn leaf water potential, Ψ pd) and transcriptome expression profiles. Differences between these plants affected genes of specific pathways such as those involved in abscisic acid biosynthesis, perception and transduction of drought stress, plant development and lipid metabolism. In that sense, the present study increased the number of CGs potentially involved in the genetic determinism of drought tolerance firstly identified in C. canephora. Because C. arabica is an amphidiploid species (originating from a natural hybridization event between C. canephora and C. eugenioides), its transcriptome is a mixture of homologous genes expressed from these two sub-genomes in which C. eugenioides is assumed to express genes mainly for proteins involved in basal biological processes (e.g. photosynthesis), while the C. canephora sub-genome is assumed to regulate Arabica gene expression by expressing genes for regulatory proteins and adaptation processes [86]. In this genetic context, it is possible that the characteristics of IAPAR59 that enable it to better withstand drought stress than Rubi, really originated from the specific expression of C. canephora genes recently introgressed (through the Timor hybrid HT832/2 [19]) in this cultivar of C. arabica [33]. Even though this study provides further indications about the way in which different coffee cultivars activate their transcriptomes, additional work is still required to understand how epigenetics and epistasis regulate gene expression in the different coffee sub-genomes (CaCe and CaCc) in C. arabica under drought conditions.

Source of the plant materials and permissions

This work was carried out as part of the scientific cooperation project entitled “Study of genetic determinism of drought tolerance in coffee” (2006–2010) approved between Embrapa and CIRAD. It complied with all institutional, national, or international guidelines. In the frame of this project, field experiments were conducted at the Cerrado Agricultural Research Center (Planaltina-DF, Brazil) with all permissions of partners and in accordance with local legislation.

Ethics approval and consent to participate

Not applicable.

Consent to publish

Not applicable.

Availability of supporting data

The reads were submitted to GenBank and to the BioProject/NCBI database under the accession number PRJNA282394.

Abbreviations

EST: 

expressed sequence tag

qPCR: 

quantitative polymerase chain reaction

Declarations

Acknowledgments

The authors would like to thank Dr Aymbiré Francisco Almeida da Fonseca from the INCAPER Institute for kindly providing seeds of the Rubi and IAPAR59 commercial cultivars of C. arabica. The authors are also very grateful to Drs Antonio Fernando Guerra and Omar Cruz Rocha (Embrapa Cerrados) for their assistance during the field trial experiments. The authors wish also to thank Daphne Goodfellow, as well as Peter Biggins and Cécile Fovet-Rabot (CIRAD-DGDRS) for English revision of the manuscript.

Funding

PM acknowledges financial support from CIRAD (Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Montpellier, France) and the CIRAD ATP project “Analysis of phenotypic plasticity in response to water constraints in perennial plants growing under different field conditions”. ACA acknowledges financial support from the Brazilian Coffee R&D Consortium, FINEP and INCT-café (CNPq/FAPEMIG). MFC and GAGP acknowledge financial support from the Center for Computational Engineering and Sciences - FAPESP/Cepid (2013/08293-7). The authors acknowledge the scholarships from the Brazilian agencies CAPES (KED, NGV), Brazilian Consortium of Coffee Research (FAC, JCA) and CAPES-COFECUB Project Sv738-12 (MGC).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Laboratório de Genômica e Expressão (LGE), Departamento de Genética e Evolução, Instituto de Biologia/UNICAMP, Cidade Universitária Zeferino Vaz
(2)
Embrapa Recursos Genéticos e Biotecnologia (LGM-NTBio), Parque Estação Biológica, CP 02372, 70770-917
(3)
CIRAD UMR AGAP
(4)
Embrapa Informática Agropecuária, UNICAMP
(5)
present address: Embrapa Café, INOVACAFÉ, Campus UFLA

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