Extensive Post-Transcriptional Regulation Revealed by

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Extensive post-transcriptional regulation revealed by transcriptomic and proteomic integrative analysis in cassava under drought Zehong Ding, lili Fu, Weiwei Tie, Yan Yan, Chunlai Wu, Wei Hu, and Jiaming Zhang J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.9b00014 • Publication Date (Web): 04 Mar 2019 Downloaded from http://pubs.acs.org on March 5, 2019

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Journal of Agricultural and Food Chemistry

Extensive post-transcriptional regulation revealed by transcriptomic and proteomic integrative analysis in cassava under drought Zehong Ding1*, Lili Fu1, Weiwei Tie1, Yan Yan1, Chunlai Wu1,2, Wei Hu1*, Jiaming Zhang1* 1Key

Laboratory of Biology and Genetic Resources of Tropical Crops, Institute of

Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Xueyuan Road 4, Haikou, Hainan, China; 2Genetic

Engineering International Cooperation Base of Chinese Ministry of Science

and Technology, Chinese National Center of Plant Gene Research (Wuhan) HUST Part, Key Laboratory of Molecular Biophysics of Chinese Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology (HUST), Wuhan, China; *Correspondence:

[email protected] (Z.D.); [email protected] (W.H.);

[email protected] (J.Z.) Emails: [email protected] (Z.D.); [email protected] (L.F.); [email protected] (W.T.); [email protected] (Y.Y.); [email protected] (C.W.) [email protected] (W.H.); [email protected] (J.Z.)

1

ACS Paragon Plus Environment


Abstract: Cassava is a major tropical/sub-tropical food crop and its yield is greatly restrained by drought, however, the mechanism underlying the drought stress remains largely unknown. In this study, totally 1242 and 715 differentially expressed genes (DEGs), together with 237 and 307 differentially expressed proteins (DEPs), were respectively identified in cassava leaf and root through RNA-seq and iTRAQ techniques. The majority of DEGs and DEPs were exclusively regulated at the mRNA and protein level, respectively, whereas only a few were commonly regulated, indicating the major involvement of post-transcriptional regulation under drought. Subsequently, the functions of these specifically or commonly regulated DEGs and DEPs were analyzed, and the post-transcriptional regulation of genes involved in heat shock protein, secondary metabolism biosynthesis, and hormone biosynthesis was extensively discussed. This is the first report on an integration of transcriptomic and proteomic analysis in cassava, and it provides new insights into the post-transcriptional regulation of cassava drought stress. Keywords: drought; cassava; RNA-seq; iTRAQ; post-transcriptional regulation; heat shock protein; secondary metabolism; hormone biosynthesis

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Introduction Drought is one of the most severe abiotic stresses that dramatically affect plant growth and crop yield worldwide. Being sessile in nature, plants have evolved complex regulatory mechanisms to respond to drought stress, resulting in kinds of physiological and biochemical changes 1. Under prolonged and severe drought condition, canopy photosynthesis and carbohydrate metabolism of plants are greatly reduced 2. To adapt to water deficiency condition, plants will rapidly close its stomata and decrease its leaf canopy to maintain high water use efficiency, or enhance its root length to have access to water in deep soil layers

3-4.

At the mean

time, various molecule compounds including soluble proteins, soluble sugars, and proline are increased to keep moisture content in cells

5-6.

Reactive oxygen species

(ROS), which causes harmful effects on the growth and development of plants, is also generated. Consequently, to minimize the oxidative damages, many enzymes including peroxidase (POD), catalase (CAT), and superoxide dismutase (SOD) are activated 7-8. In recent years, thousands of genes and many regulatory signaling pathways were identified in response to drought, of which, hormone genes are key regulators related to drought regulatory networks 9-10. As the best-known hormone messenger involved in drought signaling, abscisic acid (ABA) was rapidly and strongly accumulated upon drought condition, accompanied by dramatic expression alteration of ABA biosynthesis genes (e.g., NCED3) 11. However, this is not always the case since ABA biosynthesis can also be regulated via negative feedback regulation 12.

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In addition to ABA, other hormones such as jasmonate (JA) and brassinosteroid (BR) that can interact with ABA are also involved in drought stress

13-14.

Besides, genes

related to ROS-signaling, heat shock proteins (HSPs), secondary metabolism, and transcriptional factors also have significantly contributed to drought stress 11, 15-16. With the increasing transcriptional and proteomic studies regarding the drought responses at a global scale, more and more evidence has demonstrated that transcriptome

is

often

inconsistent

with

proteome,

indicating

that

post-transcriptional regulation plays a crucial role in gene expression under stress conditions

17-18.

Further investigation revealed that the genes participated in

post-transcriptional regulation of drought stress are at least related to HSPs. AtHSP17.6A, a small HSP in Arabidopsis, was greatly induced by PEG-induced drought stress, however, the accumulation of its protein was not detected, suggesting

drought-induced

post-transcriptional

regulation

of

AtHSP17.6A

expression 15. Pretreatment with 100 μM ABA significantly induced the expression of three HSPs (including HSP17.2, HSP17.4, and HSP26 that were involved in drought and heat stress) at the protein level but only mildly at the mRNA level, strongly suggested that ABA played a role in the post-transcriptional regulation of HSP17.2, HSP17.4, and HSP26 expression under drought

19.

In addition, the involvement of

genes related to flavonoid biosynthesis and hormones (such as JA and BR) in post-transcriptional regulation of light and drought stresses was also reported 20-21. Cassava (Manihot esculenta Crantz) is a major root crop grown in tropical and subtropical regions and provides nourishment for more than 750 million people in

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the world

22.

Because of its starch-rich storage root, cassava is considered as an

essential source for bio-fuel, starch production, and animal feed 3. Cassava is generally drought-tolerant, but severe and prolonged drought stress greatly restrains its growth and development and finally decreases its economic yield 3. With the growth of high-throughput sequencing technology and the availability of cassava reference genome

23-24,

RNA-seq and iTRAQ have become important tools to

investigate the expression change of genes and proteins, respectively, on a global level in cassava, and significant progress has been obtained recently in extensive identification and functional characterization of cassava candidates in response to drought stress

25-28.

However, currently no studies were performed concerning the

expression variation of genes and proteins simultaneously, e.g., by the combination of RNA-seq and iTRAQ, and therefore less information was available about the association (e.g., post-transcriptional regulation) between transcriptomic and proteomic profiles under drought condition in cassava. To better understand the molecular mechanism underlying cassava drought response and to identify the critical genes and pathways at the transcriptional and proteomic levels, in this study, RNA-seq and iTRAQ were performed in parallel to explore the differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) between drought treatment and the control in cassava leaf and root, respectively. Subsequently, the functions of these DEGs/DEPs specifically or commonly regulated at the transcriptional and proteomic levels were revealed. These findings expand our knowledge of post-transcriptional regulation participating

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in drought response of cassava and also provide a set of useful candidates for breeding of drought-improved cassava varieties.

Material and methods Material and drought treatment This experiment was conducted as previously described

25:

the stem of cassava

(Manihot esculenta Crantz) 'Ku50' was cut into ~15 cm in length with 2-3 buds and planted vertically in plastic pots (bottom diameter × upper diameter × height = 148 mm × 185 mm × 188 mm) with vermiculite and soil (1:1) in the glass house of the Chinese Academy of Tropical Agricultural Sciences, Haikou, China. Forty-five days later, cassava seedlings with a uniform growth were selected and treated by drought stress using 20% PEG 6000 solution that was widely applied to simulate water-deficit condition

29-30,

according to our previous study

25.

Leaf and root samples were

subsequently collected at 0 h and 24 h after PEG treatment, respectively, and frozen immediately in liquid nitrogen. Each sample was pooled from at least five plants. Subsequently, two replicates of these samples were selected for RNA-seq and iTRAQ assay, respectively. To evaluate the physiological changes of cassava under drought stress, five traits including malondialdehyde (MDA), proline content, POD activity, soluble sugar content, and soluble protein content were measured in leaves with triple replicates as previously described 25.

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RNA-seq library construction and transcriptome analysis The RNA-seq libraries preparation and RNA-seq sequencing were conducted by the Annoroad Gene Technology Corporation (Beijing, China). Briefly, the quality and quantity of RNA were assayed using an Agilent 2100 Bioanalyzer (Agilent, USA) and a Nanodrop ND-2000 spectrophotometer (Thermo Scientific Inc., USA). The transcriptome libraries were prepared using Illumina TruSeqTM RNA sample prep Kit (Illumina, San Diego, CA, USA) with Ribo-Zero Magnetic kit for rRNA depletion in accordance with the manufacturer's instructions, and subsequently sequenced on the Illumina Hiseq 4000 system to produce 150 bp paired-end reads. As previously described sequences

were

25, 27,

filtered

low-quality reads were removed and adapter

from

raw

reads

via

FASTX-toolkit

pipeline

(http://hannonlab.cshl.edu/fastx_toolkit/index.html). The quality of sequence was checked

by

FastQC

tool

(http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Clean reads were subsequently mapped to the cassava reference genome (version 6.1), which was downloaded

from

the

(https://phytozome.jgi.doe.gov/pz/portal.html),

Phytozome using

Tophat

database v2.0.10

31.

Subsequently, reference genome-based transcriptome assembly was conducted using Cufflinks pipeline v2.1.1 32. Differential expressed genes (DEGs) were identified by DESeq 33 setting false discovery rate (FDR) < 0.05 and log2|fold-change| > 1. The gene expression level was calculated as fragments per kilobase per million mapped reads (FPKM).

7



Protein preparation and isobaric labeling The experiments were performed with the assistance from Shanghai Luming Biological Technology Co., Ltd. Briefly, proteins of samples, corresponding to those used in RNA-seq sequencing, were extracted using the method as described previously 26. The concentration of protein extract was assayed with Bio-Rad protein assay kit including a bovine serum albumin (BSA) standard. Totally two independent protein extractions were performed. For each sample, totally 100 μg protein was digested with Trypsin Gold at 37 °C for 16 h setting a protein: trypsin ratio equal to 30: 1. After the digestion of trypsin, the peptide was dried via vacuum centrifugation, and then reconstituted in 0.5 M triethylammonium bicarbonate (TEAB) buffer and processed in accordance with the manufacturer’s instructions for 4-plex iTRAQ (Applied Biosystems). Briefly, 1 unit of iTRAQ reagent was dissolved and mixed with 70 μl isopropanol. Peptides were marked with different iTRAQ isobaric tags by incubation for 2 h at room temperature. Two replicates of leaf samples harvested at 0 h and 24 h after PEG treatment were labeled with molecular masses of 113/115 (for 0 h) and 114/116 (for 24 h) Da, respectively, in one 4-plex iTRAQ. Similarly, Two replicates of root samples were labeled as above in another 4-plex iTRAQ. Afterward, all samples were pooled and then dried via vacuum centrifugation.

LC-MS/MS analysis The LC-MS/MS was conducted as described previously

8


34.

Briefly, the sample was

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loaded onto C18 nanoLC trap column (100 µm × 30 mm, C18, 3 µm, 150 Å) and subsequently washed by Nano-RPLC buffer A (containing 0.1% FA and 2% ACN) at 2 μL/min for 10 min in total. An elution gradient with acetonitrile from 5% to 35% containing 0.1% formic acid in 70 min gradient was applied on an analytical ChromXP C18 column (75 μm x 150 mm, C18, 3 μm, 120 Å) with spray tip. Mass spectrometer data were acquired on a Triple TOF 5600 instrument (AB SCIEX, USA) which was equipped with an AB SCIEX Nanospray III source as well as a pulled quartz tip as the emitter (New Objective, USA). Data acquisition was carried out using a nebulizer gas of 5 PSI, a curtain gas of 30 PSI, an interface heater temperature of 150 °C, and an ion spray voltage of 2.5 kV. Survey scan was acquired in 250 ms and totally 35 product ion scans were obtained when exceeding a threshold of 150 counts/s with charge numbers from 2 to 5 in the information dependent acquisition (IDA) mode. Total cycle time was set to 2.5 s. For collision-induced dissociation (CID), a setting of rolling collision energy was used to all precursor ions. For dynamic exclusion, a setting of half of the peak width (18 s) was applied. Subsequently, the precursor was accordingly refreshed off the exclusion list 34.

Mass spectrometer data analysis The MS/MS data were processed by ProteinPilot Software v5.0 (AB SCIEX, USA) in which the Paragon algorithm was employed against Manihot esculenta database for protein identification. False discovery rate (FDR) was estimated using the PSPEP (Proteomics System Performance Evaluation Pipeline), which was integrated into the

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ProteinPilot Software based on a strategy of automatic decoy database search 35. To minimize the probability of false identification, only unique peptides with the confidence greater than 95% were retained in iTRAQ labeling quantification, and proteins with the unused value greater than 1.3 were kept for further analysis. For comparisons of difference in protein abundance, Student’s t-test was conducted then followed by Benjamini-Hochberg correction. Differentially expressed proteins (DEPs) were identified setting q-value < 0.05 and fold-change > 1.3 (or < 0.77) between the treatment and control.

Functional enrichment analysis To interpret the biological and functional properties of DEGs and DEPs, cassava genes were annotated and grouped into hierarchical categories according to the classification system of MapMan 36. The significantly enriched functional categories were determined according to the Fisher’s exact test

25, 27.

Correlation coefficients

between the expression levels of DEPs and their corresponding mRNAs were calculated by Pearson correlation test.

qRT-PCR analysis The qRT-PCR assay was carried out as previously described

25.

Total RNA was

extracted from each sample using RNAiso reagent (OMEGA), respectively, and cDNA was reversely transcribed using PrimeScriptTM RT reagent Kit containing gDNA Eraser (Takara, Dalian, China). To confirm the RNA-seq results, a total of 15 DEGs

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involved in ROS-scavenging, hormone metabolism, heat shock protein, and transcriptional factor were selected and validated by qRT-PCR method (Table. S1). The qRT-PCR was conducted using SYBR Premix Ex TaqTM (Takara, Dalian, China) on a Stratagene Mx3000P machine (Stratagene, CA, USA) with the following protocol: 30 s at 95°C; then followed by 40 cycles of 10 s at 95°C and 30 s at 60°C. Then, a thermal denaturing step was performed to generate the melt curves for amplification specificity verification. The actin gene was applied as an endogenous control

25.

Each sample was measured with three replicates, and the relative gene

expression was computed by the 2-ΔΔCt method 25.

Results Physiological responses of cassava under drought stress Compared with the control (0 h), leaves of cassava seedlings were slightly wilted at 3 h but badly wilted at 24 h under PEG-simulated drought stress (Fig. S1). Physiological investigation revealed that proline content, MDA, soluble protein content, soluble sugar content, and POD activity from leaves were significantly increased at 24 h after PEG treatment whereas most of them were only mildly changed at 3 h (Fig. 1), in accord with our previous study 25, which also demonstrated that thousands of genes were dramatically changed at the transcriptional level in leaf and root at 24 h of PEG treatment. As an extended research, similar PEG treatment was performed in this study but here we mainly focused on the drought responsive mechanisms revealed by

11



integrative proteomic and transcriptomic analyses.

Transcriptome profiling of cassava leaf and root under drought After discarding low-quality and contaminated reads, totally 499 million clean reads of 150-bp in length were obtained from eight libraries (four samples × two replicates) by paired-end sequencing with Illumina HiSeq 4000 platform, and ~80.1% of them were mapped to the reference genome of cassava for further analysis. The total length of all the mapped reads was over 59.6 gigabases (Gb), representing about 102-fold coverage of the cassava genome. To reduce the false positive identification of expressed genes, a threshold value of FPKM > 1 was arbitrarily selected as the cutoff to identify genes expressed among samples. Totally 24,276 expressed genes (including 20,531 and 22,508 from leaf and root, respectively), equal to about 74% of the genes annotated in the cassava genome (Phytozome Mesculenta version 6.1), were identified across all eight samples. To confirm the expression results of RNA-seq data, a total of fifteen genes, which were involved in ROS-scavenging, hormone metabolism, heat shock protein, and transcriptional factor, were tested by qRT-PCR method. Overall, high correlation coefficients (R = 0.71-0.98) were revealed between these two independent methods (Table. S1), indicating that the gene expression profiles detected by RNA-seq are reliable.

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DEGs identified in cassava leaf and root In total, 1,242 and 715 DEGs were identified in drought-stressed leaf and root, respectively, compared with the control. It worthy to note that the majority of DEGs were exclusively found in leaf and root while only a few DEGs were commonly identified (Fig. 2A), indicating that different functional pathways were influenced respectively in leaf and root under drought. To reveal the functional pathways affected by PEG treatment, these DEGs were classified into two groups of up- and down-regulated, and then functional category enrichment assay was conducted for each group, respectively. There were 503 up-regulated and 739 down-regulated DEGs in leaf. These up-regulated DEGs were significantly enriched in ABA metabolism, raffinose metabolism, protein folding and synthesis, and abiotic stress. In contrast, those down-regulated DEGs were significantly enriched in synthesis of amino acid (AA) metabolism, cellulose synthesis, glycolysis, jasmonate metabolism, lipid metabolism, synthesis of major CHO metabolism, trehalose metabolism, nitrate metabolism, secondary metabolism, calvin cycle, light signaling, and transport (Fig. S2). There were 178 up-regulated and 537 down-regulated DEGs in root. The enriched categories of these up-regulated genes included gluconeogenesis/glyoxylate cycle and abiotic stress, whereas the enriched categories of those down-regulated genes included cell organization, cell wall, hormone metabolisms such as JA and BR, RNA regulation of transcription, secondary metabolism of flavonoids, calcium signaling, and receptor kinases signaling (Fig. S2).

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Together, these results provide a general overview of the functional pathways influenced by drought stress in cassava at the transcriptional level.

Proteome profiling of cassava leaf and root under drought In parallel, a comparative proteome assay was conducted for above samples by iTRAQ approach to complement the transcriptome study. In total, 141,595 and 210,123 mass spectra were generated for leaf and root samples, respectively. After excluding low-scoring spectra and searching against cassava proteins, a total of 53,650 and 87,104 unique peptides, and 5,900 and 9,440 proteins were detected for leaf and root samples, respectively. By setting the criteria of unused protein score > 1.3 and peptides ≥ 1, a total of 6,152 non-redundant proteins (which accounted for ~18.7% of the genome annotated proteins) were confidently identified and quantified. Of which, 2,290 (37.2%) were commonly identified while 938 and 2,924 were exclusively identified in leaf and root, respectively. Subsequently, these 6,152 confidently identified proteins were used for further analysis.

DEPs identified in cassava leaf and root In total, 138 up-regulated and 99 down-regulated DEPs were significantly identified in leaf (Fig. 2B). Functional enrichment analysis revealed that these up-regulated proteins were significantly enriched in glycolysis, photosynthesis (including calvin cycle, light reaction, and photorespiration), redox, and abiotic stress, whereas these down-regulated proteins were significantly enriched in protein targeting, redox, and

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secondary metabolism of isoprenoids (Fig. S2). There were 162 up-regulated and 145 down-regulated DEPs in root. Similar to the observation from transcriptome analysis, very a few number of DEPs were commonly identified between leaf and root samples (Fig. 2B), supporting the influences of drought stress on different functional pathways in leaf and root. This conclusion was also confirmed by functional enrichment analysis, since most enriched categories in root were distinct with that in leaf. In root, these up-regulated DEPs were significantly enriched in AA metabolism, secondary metabolism of flavonoids, signaling of 14-3-3 proteins, abiotic stress, and TCA/org transformation, whereas these down-regulated DEPs were significantly enriched in cell vesicle transport, protein targeting, and secondary metabolism of phenylpropanoids (Fig. S2). Together, these results provide a general overview of the functional pathways influenced by drought stress in cassava at the proteomic level.

Correlation of mRNA and protein profiles A correlation analysis was performed between the transcriptomic and iTRAQ proteomic data. Among the 6,152 quantified proteins, 5,763 were detected (FPKM > 5) in the transcriptomic profiles. Correlation analysis revealed low correlation coefficients in leaf (R = 0.363, P = 1.3e−94) and root (R = 0.214, P = 4.1e−55), respectively, between the expression levels of all quantified proteins and their corresponding mRNAs. However, about two-fold higher correlations were indicated

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between the DEPs and their corresponding mRNAs in leaf (R = 0.647, P = 9.6e−27) and root (R = 0.454, P = 9.6e−15), respectively (Fig. 3), indicating a certain degree of biological relevance of protein and mRNA changes in response to drought. Overall, 1,242 and 715 DEGs, as well as 237 and 307 DEPs, were identified in leaf and root, respectively. Among these DEGs and DEPs, only 61 and 42 were commonly regulated in leaf and root, respectively, both at the mRNA and protein levels in response to drought. Of which, 57 and 31 were respectively changed with the same trend, indicating that the expression alterations of these proteins are mainly controlled by transcriptional changes. However, there were a few genes, including glutathione peroxidase 4 (GPX4) involved in redox metabolism, chalcone isomerase (CHI), dihydroflavonol 4-reductase (DFR), leucoanthocyanidin dioxygenase (LDOX), and anthocyanidin reductase (BAN) related to secondary metabolism of flavonoids, were regulated with the opposite trend at the mRNA and protein levels (Table 1 and Table 2), indicating the involvement of post-transcriptional regulation for these genes/proteins. Therefore, proteomic and transcriptomic integrative analysis would provide more information regarding the genes involved in drought response in cassava.

Integrated proteomic and transcriptomic analysis in leaf By comparing the DEGs with the DEPs, there were 49 up-regulated and 8 down-regulated genes changed with the same trend in leaf both at the mRNA and protein levels (Table 1). These up-regulated genes were enriched in protein folding,

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calvin cycle, and abiotic stress (Fig. 4). It's worthy to note that 61.2% (30/49) genes were belonged to HSPs, strongly indicated the involvement of HSPs in drought responses in cassava. Consistently, RCA, CPN60A, and CPNB2 involved in calvin cycle, together with PSBR involved in PSII of light reaction and ELIP1 involved in light signaling were greatly up-regulated. Besides, three genes, ascorbate peroxidase 2 (APX2), galactinol synthase 1 (GolS1), and glutathione S-transferase TAU 19 (GSTU19) involved in ROS scavenging, were also significantly up-regulated (Table 1). These down-regulated genes were enriched in minor CHO metabolism, for example, SIP2 and MIPS3, respectively involved in raffinose synthase and inositol-1-phosphate synthase, were included (Fig. 4 and Table 1). Since only a few genes were significantly changed both at the transcriptomic and proteomic levels, therefore, similar functional enrichment results were observed for the up- and down-regulated genes with or without removing those significantly changed at the proteomic level, and also for the up- and down-regulated proteins with or without removing those significantly changed at the transcriptomic level (Fig. 4 and Fig. S2). In the next, we mainly focused on the genes/proteins changed only at the transcriptomic or proteomic level in specific pathways. In leaf, there were 454 up-regulated and 731 down-regulated DEGs whose corresponding proteins were not differentially expressed (Fig. 2C). Consistent with the functional enrichment results, drought greatly induced the expression of genes referred to raffinose metabolism. For examples, galactinol and raffinose synthase genes, GolS1 and SIP1, which catalyze the first two reactions of RFOs (raffinose

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family of oligosaccharides) biosynthesis, were dramatically induced by drought. Drought also greatly induced the expression of genes associated with ABA metabolism, including ABA inducible genes (HVA22A and HAI3) and an ABA responsive elements-binding factor (ABF2). Drought significantly induced the expression of abiotic stress genes as well. Most of these genes were associated with heat stress, including HSPs (e.g., HSP17.4, HSP18.2, HSP22.0, HSP70, and HSP90.1) and heat shock transcription factors (e.g., HSF4 and HSFA2). In addition, a few genes referred to water deprivation, LTI65 and ERD15, were also included (Table. S2). In contrast, drought severely inhibited the expression of genes related to calvin cycle. For examples, rubisco activase (RCA), glyceraldehyde 3-phosphate dehydrogenase A subunit

(GAPA),

fructose-bisphosphate

aldolase

2

(FBA2),

and

fructose

1,6-bisphosphate phosphatase 1 (FBP1) were dramatically depressed by drought. Accordingly, light signaling related genes, including red/far-red light receptor phytochrome B (PHYB), blue light receptor PAS/LOV protein B (PLPB), and phototropic-responsive NPH3 family proteins, were also greatly inhibited. Consistent with

calvin

cycle,

starch

biosynthesis

genes

including

ADP-glucose

pyrophosphorylase (AGPase) and starch synthase (SS), as well as sucrose biosynthesis

genes

including

sucrose-phosphate

synthase

(SPS)

and

sucrose-6F-phosphate phosphohydrolase (SPP) were greatly suppressed by drought. Three trehalose-6-phosphate phosphatases (TPPs) and one trehalose-6-phosphate synthase (TPS) that involved in the trehalose biosynthesis pathway were also greatly suppressed. Consistent to major CHO metabolism, genes participated in glycolysis

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were significantly suppressed by drought as well, including phospho-enol-pyruvate carboxylase (PEPC), PEPC kinase (PEPCK), phosphoglucomutase (PGM), and pyruvate kinase (PK). Similarly, many genes involved in lipid metabolism were also severely suppressed. Drought also greatly inhibited the expression of genes related to N-metabolism, including ammonium transporter 1;1 (AMT1;1), nitrate transporter 1.1 (NRT1.1), nitrite reductase 1 (NIR1), nitrate reductase 1 (NIA1), and NIA2. Consistently, those genes referred to AA synthesis metabolism were significantly depressed. In addition, drought significantly suppressed the expression of genes related to secondary metabolism, including flavonoid pathway related genes like chalcone synthase (CHS) and flavonoid 3'-hydroxylase (F3'H), and phenylpropanoid pathway related genes (with emphasis on lignin biosynthesis) such as phenylalanine ammonia-lyase (PAL), cinnamate-4-hydroxylase (C4H), 4-hydroxycinnamoyl-CoA ligase (4CL), hydroxycinnamoyltransferase (HCT), cinnamoyl-CoA reductase (CCR), and cinnamyl-alcohol dehydrogenase (CAD). Interestingly, several genes referred to JA biosynthesis and signaling transduction, including allene oxide cyclase (AOC), lipoxygenase (LOX), and jasmonate-zim-domain proteins (e.g, JAZ1 and JAZ3), were also greatly suppressed by drought (Table. S2). In leaf, there were 89 up-regulated and 91 down-regulated DEPs whose corresponding genes were not differentially expressed (Fig. 2C). As expected, drought significantly induced proteins involved in photosynthesis, including photosystem I subunit PSAD, PSAE and PSAF, photosystem II subunit LHCB4.2 and LHCB5, chlorophyll A/B binding protein CAB1, and glyceraldehyde 3-phosphate

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dehydrogenase A subunit (GAPA) and GAPB. Drought also significantly induced the proteins involved in glycolysis such as triosephosphate isomerase (TPI) and glyceraldehyde-3-phosphate dehydrogenase C subunit 1 (GAPC1) participated in the ROS signaling. Accordingly, monodehydroascorbate reductase 1 (MDAR1) involved in toxic H2O2 removal and glutathione peroxidase 6 (GPX6) responded to oxidative stress were greatly induced. In addition, drought significantly induced the expression of HSPs such as HSP22, HSP70, and HSC70-2. On the contrary, drought dramatically suppressed the expression of redox-related proteins including glutathione peroxidase 4 (GPX4), oxoprolinase 1 (OXP1), and copper/zinc superoxide dismutase 1 (CSD1) which can detoxify superoxide radicals.

Integrated proteomic and transcriptomic analysis in root Likely, there were 25 up-regulated and 6 down-regulated genes significantly changed with the same trend in root both at the transcriptomic and proteomic levels (Fig. 2D and Table 2). These up-regulated genes were notably enriched in AA synthesis metabolism, gluconeogenesis/glyoxylate cycle, and abiotic stress. Notably, eight (32%) out of these up-regulated genes belonged to HSPs. Interestingly, four HSPs, together with two ROS-scavenging genes (APX2 and GSTU19), were commonly up-regulated both in leaf and root (Table 1 and Table 2), indicating their crucial roles in drought response in cassava. Totally four AA metabolism genes, of which EDA9, PSAT, and CysC1 for synthesis and TAT1 for degradation, were up-regulated. Besides, one each gene related to glyoxylate cycle (AAE7), ABA stimulus (GLP5), lipid

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metabolism (MFP2), myo-inositol-1-phosphate synthase (MIPS2), and raffinose synthase (SIP1) was also up-regulated. On the contrary, these down-regulated genes were marginally significantly enriched in cellulose synthesis (e.g., CSLA09) and peroxidase metabolism (e.g., PRXR1) (Table 2). There were 153 up-regulated and 531 down-regulated DEGs significantly changed only at the transcriptomic level. Although no significantly enriched category was observed for the up-regulated genes, drought dramatically suppressed the expression of genes referred to JA signaling (e.g., JAZ1) and JA biosynthesis including LOX1, LOX3, AOS, AOC3, and OPR3, which covered all enzymes located on the JA biosynthesis pathway. Several genes related to BR biosynthesis, including SMT2, CPD, and CYP51, were greatly depressed by drought. In addition, a few genes referred to ABA biosynthesis, including ABA2 and NCED9, were also significantly suppressed. Accordingly, receptor kinases signaling genes associated with these hormones, e.g., THE1 regulated by BR and CRK29 involved in ABA stimulus, were greatly suppressed. Calcium signaling related genes, e.g., calmodulin like 23 (CML23) and calcium ATPase 2 (ACA2), were suppressed as well. Drought also dramatically suppressed the expression of cell wall related genes, including XTH5, XTH6, XTH8, XTH9, and XTH15 related to cell wall modification and CESA3, CESA6, CSLD3, CSLD4, and CSLD5 involved in cellulose biosynthesis. Interestingly, we found that four enzymes including CHS, F3H, F3'H, and FLS that involved in the flavonoid biosynthesis pathways were greatly suppressed. Drought significantly depressed the expression of tens of transcription factors, of which the most abundant TF families were WRKY

21



(11), MYB (7), and AP2/EREBP (6). A few TFs related to drought (CBF4), cold (CBF3), salt (MYB102), lateral root development (IAA14), root patterning (MGP), and iron ion response (FRU) were included (Table. S3). There were 137 up-regulated and 139 down-regulated DEPs whose corresponding genes were not differentially expressed (Fig. 2D). Strikingly, 39 out of 41 DEPs related to abiotic stress were significantly induced, and the majority (92%) of them were HSPs, including HSP17.4, HSP18.2, HSP21, HSP70, HSP90, and HSP101, strongly suggesting the activation of HSPs in response to drought at the protein level. In accordance with the functional enrichment result, proteins involved in signaling of 14-3-3 proteins (GRF1 and GRF2) and TCA/org transformation (NADP-ME3 and IDH-III) were dramatically induced. In addition, ABA DEFICIENT 2 (ABA2), which converted xanthoxin to ABA-aldehyde in ABA biosynthesis, was also dramatically induced. In contrast, drought dramatically suppressed the expression of proteins related to nitrate transport (NRT2.4) and phosphate transport (PHT1;5 and PHT1;7). Drought also significantly suppressed the proteins related to cell division (FIS1B and CLASP), cell organization (OBE2), and cell vesicle transport (VTI1B and SYP32). Besides, several proteins responded to abiotic stress (e.g., ANNAT3, TUB6, YLS5, and PIP2) and ROS scavenging (CSD1) were also greatly depressed. Finally, these pathways significantly influenced by drought stress through RNA-seq and iTRAQ integrative analysis were summarized in Fig. 5.

Discussion

22


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Page 23 of 52


Genes shared by DEGs and DEPs of cassava in response to drought RNA-seq and iTRAQ approaches have widely been used to identify DEGs and DEPs involved in plant development and abiotic stresses. In the past decades, many RNA-seq studies have been extensively performed to investigate the transcriptional responses of cassava under drought stress

25, 27-28,

but the application of

iTRAQ-based proteomic analysis was relatively rare and so far, no report was available to explore cassava drought response by using these two approaches simultaneously. To better understand the molecular mechanism underlying the drought response and to identify the critical genes and pathways at the transcriptional and proteomic levels, in this work, RNA-Seq and iTRAQ were performed in parallel to explore the DEGs and DEPs between drought treatment and the control in cassava leaf and root, respectively. In total, 24,276 genes and 6,152 proteins were quantified, however, poor correlations (R < 0.36) were revealed between these quantified proteins and their corresponding mRNAs, in accord with the results of previous studies comparing protein and mRNA expression levels

37-38.

Interestingly, the correlation values were

about two-fold higher between the expression levels of DEGs and DEPs (Fig. 3), indicating the contribution of consistency changes of DEGs and DEPs to drought stress in cassava. After comparison of DEGs and DEPs, however, only a few genes were commonly regulated at the transcriptomic and proteomic levels, indicated that the proteins exhibited notable expression changes did not always have a corresponding change at the transcript level. The lack of overlap between DEGs and

23



Page 24 of 52

DEPs also suggested that post-transcriptional regulation plays a crucial role in the cassava response to drought. Of these overlapped DEGs and DEPs, the categories of most abundant members were related to HSPs, then followed by secondary metabolism, redox, and protein folding, suggesting their involvement in drought response of cassava.

Roles of HSPs in drought stress HSPs play an important role not only in heat stress but also in other stresses including drought, salt, cold, high light, and oxidative stress

39.

Moreover, their

expression levels are mediated both at the transcriptional and proteomic levels 39-40. Consistently, through RNA-seq and iTRAQ analyses, in total 92 DEGs and 64 DEPs associated with abiotic stress were identified in this study, of which 84% (77/92) and 80% (51/64) were annotated as HSPs according to Mapman annotation, indicating the important roles of HSPs upon PEG-simulated drought stress in cassava. The involvement of HSPs in cassava drought stress was also confirmed by other independent experiments. By comparing our results with previous studies

25-26,

a

total of 58 and 21 HSPs were commonly identified and differentially expressed under drought at the transcriptional and proteomic levels, respectively. However, those previously reported studies did not investigate the gene expression at the transcriptional and proteomic levels in parallel, and thus could not provide any information about the association between transcriptomic and proteomic profiles (e.g., post-transcriptional regulation). In our study, lots of HSPs (including HSP17.4

24


Page 25 of 52


and HSP70) were exclusively identified at the transcriptional level but not at the proteomic level, in accord with the involvement of post-transcriptional regulation of HSPs in drought stress in other species

15, 19.

In addition, several HSPs were

consistently changed both at the mRNA and protein levels, together with tens of HSPs exclusively altered at the proteomic level (Fig. S3), also suggesting the involvement of HSPs in drought stress via transcriptional and translational regulation. HSPs usually act as molecular chaperones to protect cells against multiple stresses through signaling, protein targeting and degradation HSPs can also function via protein folding

41.

39.

In addition, it seems that

This hypothesis can be confirmed

through gene co-expression network analysis, based on which genes of similar expression changes usually participated in similar functional pathways

42.

In this

work, it is clearly observed that the majority (76%) of HSPs were dramatically increased upon PEG treatment at mRNA or both mRNA and protein levels in leaf (Fig. S3). Moreover, categories of protein folding as well as abiotic stress were significantly enriched in the two groups containing these HSPs (Fig. 4), supporting the roles of HSPs in protein folding. It has been suggested that HSPs are mediated by heat stress transcription factors (HSFs) and each HSF plays its roles in expression regulation in plants 40. In total, three HSFs associated DEGs were identified in this study. Of which, HSFA6B, which participated in ABA-mediated response of heat, salt, and drought stress 43, exhibited the similar expression changes with these HSPs dramatically up-regulated in leaf

25



(Table. S2), suggesting that HSFA6B is a critical regulator in cassava under drought stress. In addition, HSPs can also interact with ROS-scavenging genes to protect cells against abiotic stresses

44.

Consistently, to eliminate ROS accumulated by drought

stress, tens of HSPs together with three ROS-scavenging genes (including APX2, GolS1, and GSTU19) were commonly up-regulated at the mRNA and protein levels in leaf, indicating the interactions between HSPs and ROS-scavenging genes in drought stress of cassava. Taken together, these findings strongly indicate that HSPs, along with HSFs and genes involved in protein folding and ROS scavenging, play crucial roles in drought stress of cassava via transcriptional, post-transcriptional, and translational regulation.

Roles of flavonoid and phenylpropanoid biosynthesis genes in drought Secondary metabolites such as phenylpropanoids, flavonoids, and anthocyanins are important compounds essential for plant acclimation and survival to various environmental conditions 45. For examples, shade stress dramatically suppressed the genes related to anthocyanins biosynthesis

46,

while water deficit significantly

changed the expression of genes related to phenylpropanoid and flavonoid pathways 16. In this work, a total of 10 and 18 genes respectively covered the entire flavonoid and phenylpropanoid biosynthesis pathways were identified (Fig. 6A and Fig. 6B). Although the majority of members were significantly down-regulated in either leaf or root at the mRNA level, they exhibited inconsistent expression changes

26


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Page 27 of 52


between the mRNA and protein levels, suggesting that post-transcriptional regulation is the main cause for the expression regulation of flavonoid and phenylpropanoid biosynthesis genes. These results also suggested that integrative transcriptomic and proteomic analysis is very critical for expression investigation of flavonoid and phenylpropanoid biosynthesis genes at least under drought condition in cassava. On the contrary, two genes, LDOX and CCoAOMT, showed consistent expression changes at mRNA and protein levels in leaf and root, respectively, indicating that these two genes were modulated mainly by transcriptional regulation. Together, these results suggested that flavonoid and phenylpropanoid biosynthesis genes were involved in cassava drought stress via transcriptional and post-transcriptional regulation, consistent with several recently reported studies which demonstrated that flavonoid biosynthesis genes were governed by the transcriptional and post-transcriptional regulation in response to light and environmental stimuli 20, 47.

Roles of hormone biosynthesis genes in drought Plant hormones are key players involved in drought stress signaling

10, 48.

Typically,

abscisic acid (ABA), as the well-known hormone participating drought signaling in plants, was strongly accumulated under drought condition with dramatic expression alterations (either depressed or induced) of many genes 10. Likely, in this work, we observed that ABA levels were significantly induced in both leaf and root of cassava in response to drought (Fig. S4). However, the transcripts of several ABA biosynthesis

27



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genes (e.g., NCED9 and ABA2) were greatly suppressed, indicating a possible feedback regulation of ABA biosynthesis genes under drought

12.

AREB/ABFs were

important bZIP TFs involved in ABA signaling transduction and expression regulation of ABA-responsive genes

49.

Over-expression of AREB1/ABF2 in Arabidopsis and

soybean exhibited improved drought tolerance as well as ABA hypersensitivity

50-51.

In addition, TFs such as NAC (e.g, RD26) and WRKY were also participated in ABA-mediated drought stress response, e.g., RD26 was drought-inducible and trans-genetic plants over-expressing RD26 were hypersensitive to ABA

52.

In this

work, the gene expression levels of ABF2, RD26, and a few WRKYs (e.g., WRKY21) were significantly up-regulated in leaf. PP2Cs, which act as important players in core ABA signaling pathways 53, were also greatly up-regulated. It's noteworthy that the expression changes of these ABA signaling genes were consistent with the increase of ABA levels, strongly supporting their involvement in ABA-dependent drought response in cassava. In addition to ABA, it is noteworthy that other hormones such as BR and JA are also involved in drought stress response

13, 54.

Besides, these

hormones can interact with each other in regulating drought tolerance and related signaling pathways in plants 13-14. Amazingly, in this work, we found that all enzymes associated with JA and BR biosynthesis were dramatically suppressed either in leaf or root of cassava, but their protein levels were not significantly altered (Fig. 6C and Fig. 6D), strongly indicating the involvement of JA and BR in drought stress response via post-transcriptional regulation, in accord with recently reported studies

21, 55.

Compared with these three hormones, however, only a few genes associated with

28


Page 29 of 52


other hormones including auxin, ethylene, GA, and SA were identified. Together, these findings implied that ABA, JA, and BR are the main hormones involved in the drought stress response of cassava via post-transcriptional regulation. In conclusion, our work provided a comprehensive transcriptomic and proteomic analysis of cassava under drought stress. To our knowledge, this is the first work to investigate the drought response of cassava simultaneously at the mRNA and protein levels. Our findings not only provide new insights into the post-transcriptional regulation of drought but also highlighted a set of genes/proteins that can be applied for genetic improvement of drought resistance in cassava.

Abbreviations used 4CL: 4-hydroxycinnamoyl-CoA ligase; AA: amino acid; ABA: abscisic acid; ABA2: ABA deficient 2; ABF: ABA responsive elements-binding factor; AGPase: ADP-glucose pyrophosphorylase; AMT1;1: ammonium transporter 1;1; AOC: allene oxide cyclase; APX2: ascorbate peroxidase 2; BR: brassinosteroid; BSA: bovine serum albumin; C4H: cinnamate-4-hydroxylase; CAD: cinnamyl-alcohol dehydrogenase; CAT: catalase; CCR: cinnamoyl-CoA reductase; CHI: chalcone isomerase; CHS: chalcone synthase; CID: collision-induced

dissociation;

DEGs:

differentially

expressed

genes;

DEPs:

differentially expressed proteins; DFR: dihydroflavonol 4-reductase; F3'H: flavonoid 3'-hydroxylase;

FBA2:

fructose-bisphosphate

aldolase

2;

FBP1:

fructose

1,6-bisphosphate phosphatase 1; FDR: false discovery rate; FPKM: fragments per

29



kilobase

per

million

mapped

reads;

GAPA:

glyceraldehyde

Page 30 of 52

3-phosphate

dehydrogenase A subunit; GAPC1: glyceraldehyde-3-phosphate dehydrogenase C subunit 1; Gb: gigabases; GolS1: galactinol synthase 1; GPX4: glutathione peroxidase 4; GPX6: glutathione peroxidase 6; GSTU19: glutathione S-transferase TAU 19; HCT: hydroxycinnamoyltransferase; HSFs: heat stress transcription factors; HSPs: heat shock proteins; IDA: information dependent acquisition; JA: jasmonate; LC: liquid chromatography; LDOX: leucoanthocyanidin dioxygenase; LOX: lipoxygenase; MDA: malondialdehyde; MDAR1: monodehydroascorbate reductase 1; NIA1: nitrate reductase 1; NIR1: nitrite reductase 1; NRT: nitrate transporter; OXP1: oxoprolinase 1; PAL: phenylalanine ammonia-lyase; PEPC: phospho-enol-pyruvate carboxylase; PEPCK: PEPC kinase; PGM: phosphoglucomutase; PK: pyruvate kinase; POD: peroxidase; PSPEP: proteomics system performance evaluation pipeline; RCA: rubisco activase; RFOs: raffinose family of oligosaccharides; ROS: reactive oxygen species; SCX: strong cationic exchange; SOD: superoxide dismutase; SPP: sucrose-6F-phosphate phosphohydrolase; SPS: sucrose-phosphate synthase; SS: starch synthase; TEAB: triethylammonium bicarbonate; TPI: triosephosphate isomerase; TPPs: trehalose-6-phosphate phosphatases; TPS: trehalose-6-phosphate synthase Authors' contributions JZ, WH, and ZD conceived the idea and designed the experiments. ZD, LF, WT, CW, and YY performed the experiments. ZD and LF analyzed the data. ZD wrote the draft. JZ, WH, and ZD finalized the manuscript. All authors read and approved the final

30


Page 31 of 52


manuscript. Acknowledgements This research was funded by the National Natural Science Foundation of P. R. China (31600198), the Supporting Scheme for Returned Overseas Chinese Students' Entrepreneurial Start-Ups (Innovation sub-project) from MOHRSS to Z.D., the Central Public-Interest Scientific Institution Basal Research Fund for Innovative Research Team Program of CATAS (1630052017021, 1630052017017, 17CXTD-28), and the Central Public-Interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences (1630052019023, 1630052016005, 1630052016006). Ethics approval and consent to participate Not applicable. Availability of data and material The datasets generated and analyzed in our study are available in the NCBI Sequence Read Archive under the accession number of SRP162280. Competing interests The authors declare that they have no competing interests.

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Legends of Tables and Figures Table 1. The overlap of DEGs and DEPs in leaf. No.

Gene ID

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Manes.14G036000 Manes.07G123200 Manes.01G042200 Manes.06G134700 Manes.02G124800 Manes.10G020600* Manes.04G138400 Manes.02G124600 Manes.04G113600* Manes.16G083600 Manes.07G031300 Manes.12G017300 Manes.14G022300 Manes.07G114200 Manes.03G090500* Manes.15G154400* Manes.02G051600 Manes.13G013400 Manes.01G237700 Manes.05G012000 Manes.08G171800 Manes.17G070300

Gene symbol / HSP18.2 HSP21 HSP23.6 / / HSP17.4 / / HSP17.6 HSP22 HSP21 HSP90.1 HSC70-1 GSTU19 / CLPB3 HSP21 PAP29 GolS1 LTP4 /

FClg

FClp

Functional annotation

4.29 3.74 5.98 3.98 4.79 4.77 4.78 3.34 5.07 5.64 6.66 6.71 3.52 1.94 2.83 6.34 3.19 2.39 1.71 4.17 3.02 2.03

2.56 2.52 2.37 2.29 2.28 2.24 2.09 1.90 1.81 1.61 1.58 1.48 1.48 1.47 1.35 1.27 1.11 1.06 1.01 0.98 0.97 0.97

heat shock protein heat shock protein heat shock protein heat shock protein heat shock protein heat shock protein heat shock protein heat shock protein heat shock protein heat shock protein heat shock protein heat shock protein heat shock protein heat shock protein glutathione S transferases heat shock protein heat shock protein heat shock protein purple acid phosphatase galactinol synthase lipid metabolism SGS domain-containing

35



23 24 25 26 27 28 29

Manes.06G085600 Manes.11G014800 Manes.15G189500* Manes.09G095000 Manes.02G046700 Manes.01G119000 Manes.14G149700

HSP101 / HSP22 RCA FTSH6 / HSP18.2

1.94 3.16 6.03 2.43 3.35 3.01 5.99

0.96 0.96 0.93 0.93 0.92 0.91 0.90

30

Manes.10G087000

EGY3

3.01

0.84

31 32 33 34 35 36 37 38 39 40 41 42 43 44

Manes.15G110300 Manes.14G149400 Manes.15G033100 Manes.14G091600 Manes.01G053300 Manes.11G149300 Manes.12G050700 Manes.11G067500 Manes.04G063000 Manes.08G002700* Manes.03G082900 Manes.01G074500 Manes.16G091000 Manes.10G094100

CPNB2 HSP18.2 ELIP1 CR88 BiP CPN60A / HSP70b ATJ3 APX2 PSBR HIP1 CPN20 HSC70-5

2.15 2.54 1.65 2.35 1.94 2.28 2.02 4.97 1.16 6.11 1.33 1.57 2.32 1.57

0.63 0.63 0.62 0.62 0.58 0.58 0.57 0.54 0.52 0.52 0.48 0.47 0.47 0.46

45

Manes.01G259500

/

1.53

0.44

46 47 48 49 50 51

Manes.10G062500 Manes.03G086700 Manes.15G048600 Manes.16G016600 Manes.09G188200 Manes.14G002800

/ CPNB2 HSP60 HSA32 / /

1.83 1.57 2.52 2.67 -1.00 -1.06

0.43 0.41 0.41 0.40 0.38 0.40

1 2 3 4 5 6

Manes.09G161200 Manes.01G070200* Manes.01G124200 Manes.14G113100 Manes.09G072200 Manes.04G053900

MEE32 LDOX PSY scpl29 SIP2 PME61

-2.24 -4.18 -1.24 -1.12 -1.34 -3.94

-0.66 -0.59 -0.54 -0.52 -0.50 -0.49

7

Manes.12G137800

MIPS3

-1.48

-0.43

8 9 10

Manes.03G081600 Manes.01G209700 Manes.03G103900

/ GPX4 HGO

-1.16 1.73 1.87

-0.41 -1.28 -0.51

protein heat shock protein heat shock protein heat shock protein photosynthesis photosynthesis heat shock protein heat shock protein response to light, H2O2, and heat photosynthesis heat shock protein light signalling heat shock protein heat shock protein photosynthesis involved in protein folding heat shock protein heat shock protein redox metabolism photosynthesis HSP70-interacting protein involved in protein folding heat shock protein RNA regulation of transcription function unknown photosynthesis heat shock protein heat shock protein glycine-tRNA ligase activity chaperonin-like RbcX protein amino acid metabolism secondary metabolism secondary metabolism protein degradation raffinose synthases cell wall myo-inositol-1-phosphate synthase function unknown redox metabolism amino acid metabolism

36


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Note: FClg and FClp indicate the fold-change in abundance of mRNA and protein, respectively, in leaf. Symbols '*' indicate the mRNAs/proteins that were also differentially expressed in root.

Table 2. The overlap of DEGs and DEPs in root. No.

Gene ID

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Manes.03G105500 Manes.15G181900 Manes.10G127300 Manes.04G113600* Manes.15G154400* Manes.11G128200 Manes.08G103000 Manes.01G148900 Manes.03G090500* Manes.09G108600 Manes.04G138300 Manes.10G020600* Manes.08G002700* Manes.08G079400 Manes.02G112800

Gene symbol TAT1 FDH HSP22 / / EP3 GLP5 / GSTU19 / HSP18.2 HSP17.4 APX2 / PSAT

16

Manes.13G091100

17 18 19 20 21 22 23

FCrg

FCrp

Functional annotation

1.37 1.49 1.93 1.52 1.29 3.42 1.46 2.07 1.55 2.79 1.89 1.25 1.53 1.45 2.12

1.20 1.17 1.04 1.02 0.99 0.93 0.90 0.90 0.78 0.76 0.72 0.71 0.71 0.71 0.70

MIPS2

1.90

0.67

Manes.10G105900 Manes.07G069600 Manes.03G051500 Manes.05G033000 Manes.15G189500* Manes.15G006100 Manes.03G197400

HSP22 EDA9 / CysC1 HSP22 AAE7 MFP2

1.34 2.46 1.72 1.55 1.42 1.94 1.39

0.66 0.66 0.60 0.52 0.46 0.45 0.44

24

Manes.06G108300

SIP1

1.40

0.41

25 26 27 28 29 30

Manes.15G028300 Manes.07G140900 Manes.07G114800 Manes.16G016400 Manes.18G022300 Manes.01G070200*

HSP70T-2 CHIL HSC70-1 BAN DFR LDOX

1.33 -2.64 -1.83 -1.81 -2.12 -2.89

0.40 0.78 0.69 0.64 0.61 0.49

amino acid metabolism formate dehydrogenase heat shock protein heat shock protein heat shock protein chitinase activity ABA metabolism metal ion transport glutathione S transferases PEBP family protein heat shock protein heat shock protein redox metabolism involved in oxidation reduction amino acid metabolism myo-inositol-1-phosphate synthase heat shock protein amino acid metabolism function unknown amino acid metabolism heat shock protein glyoxylate cycle lipid metabolism raffinose synthase family protein heat shock protein secondary metabolism heat shock protein secondary metabolism secondary metabolism secondary metabolism

37



31 32 33 34 35

Manes.07G107200 Manes.14G066800 Manes.16G109900 Manes.06G013200 Manes.10G014200

CHI PAO1 NADP-ME3 SBT1.8 /

-1.82 -1.57 -1.46 -1.42 -2.01

0.45 0.42 0.40 0.39 0.39

secondary metabolism involved in oxidation reduction malate dehydrogenase activity protein degradation function unknown

1 2 3 4 5 6 7

Manes.17G085000 Manes.03G030500 Manes.04G116100 Manes.09G059800 Manes.16G007300 Manes.13G020800 Manes.05G091400

/ CCOAOMT7 / CSLA09 PRXR1 / EMB2004

-2.94 -4.69 -2.70 -1.63 -2.40 -2.38 1.57

-0.68 -0.66 -0.63 -0.46 -0.45 -0.38 -0.39

cytochrome P450 secondary metabolism function unknown cell wall redox metabolism cellulase protein development

Note: FCrg and FCrp indicate the fold-change in abundance of mRNA and protein, respectively, in root. Symbols '*' indicate the mRNAs/proteins that were also differentially expressed in leaf.

Fig. 1 Physiological investigation of cassava leaves in response to PEG treatments. Proline content (A), soluble sugar content (B), POD activity (C), soluble protein content (D), and MDA content (E). Data were shown as mean ± standard deviation 38


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derived from three biological replicates, and values with different letters were significant (P < 0.05) based on Duncan's multiple range tests.

Fig. 2 Venn diagrams showing the numbers of DEGs and DEPs. Brown and blue arrowheads represent up- and down-regulated DEGs/DEPs, respectively. (A) the overlap of DEGs between leaf and root; (B) the overlap of DEPs between leaf and root; (C) the overlap of DEGs and DEPs in leaf; (D) the overlap of DEGs and DEPs in root.

39



Fig. 3 Correlation between the expression of proteins and mRNAs in leaf (A) and root (B). R1: correlation coefficient of all quantified proteins and their corresponding mRNAs; R2: correlation coefficient of DEPs and their corresponding mRNAs. Blue and black dots represent significant and non-significant differentially expressed proteins, respectively.

40


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Fig. 4 Functional category enrichment of DEGs/DEPs based on MapMan annotation. DEGs/DEPs were classified into a total of 12 groups according to RNA-seq and iTRAQ integrative analysis in leaf and root, respectively. Each group was indicated by tissues (leaf and root) followed by expression level ('go' for gene only, 'po' for

41



protein only, and 'gp' for both gene and protein level, respectively) and regulatory direction ('dn' for down-regulated and 'up' for up-regulated, respectively).

Fig. 5 Representative pathways significantly influenced by drought stress through RNA-seq and iTRAQ integrative analysis in leaf and root, respectively. Up- and down-regulated DEGs/DEPs were indicated by red and blue colors, respectively. 42


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Fig. 6 Heatmap of DEGs and DEPs related to flavonoid biosynthesis (A), phenylpropanoid biosynthesis (B), JA biosynthesis (C), and BR biosynthesis (D). Upand down-regulated DEGs/DEPs were indicated by brown and blue colors, respectively. Non-significant genes/proteins were indicated by grey color. The cells (from left to right) in heatmap represent the expression change of gene in leaf (leaf.g), protein in leaf (leaf.p), gene in root (root.g), and protein in root (root.p), respectively.

43



TOC graphic

44


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Supporting information description Fig. S1 Phenotypes of cassava planted under PEG-simulated drought stress. After 24 h of treatment, cassava leaves were badly wilted as indicated by a red box. Fig. S2 Functional category enrichment of DEGs/DEPs based on MapMan annotation. DEGs/DEPs were classified into two groups of up-regulated or down-regulated in leaf and root, respectively, without DEGs and DEPs comparison. Each group was indicated by tissues (leaf and root) followed by expression level ('g' for gene and 'p' for protein level, respectively) and regulatory direction ('dn' for down-regulated and 'up' for up-regulated, respectively). Fig. S3 Heatmap of DEGs and DEPs related to HSPs in leaf and root. Up- and down-regulated DEGs/DEPs were indicated by brown and blue colors, respectively. Non-significant genes/proteins were indicated by grey color. The cells (from left to right) in heatmap represent the expression change of gene in leaf (leaf.g), protein in leaf (leaf.p), gene in root (root.g), and protein in root (root.p), respectively. Fig. S4 ABA content determined in leaf and root under drought stress. Data were shown as mean ± standard deviation derived from three biological replicates, and p-values were determined by Student's t-test. Table. S1 Primers of genes used for qRT-PCR and the expression correlation between RNA-seq and qRT-PCR. Table. S2 Summary of DEGs and DEPs identified in leaf. Table. S3 Summary of DEGs and DEPs identified in root.

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Fig. 1 78x52mm (300 x 300 DPI)


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Fig. 2 69x48mm (300 x 300 DPI)



Fig. 3 79x38mm (300 x 300 DPI)


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Fig. 4 381x585mm (300 x 300 DPI)



Fig. 5 74x94mm (300 x 300 DPI)


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Fig. 6 95x57mm (300 x 300 DPI)



TOC graphic 107x60mm (300 x 300 DPI)


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Extensive Post-Transcriptional Regulation Revealed by

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