RNA-Seq results, de novo assembly, and functional description of contigs
The RNA-Seq produced a total number of short reads between 44.7 and 58.1 million for each library with two exceptions (Table S4) totaling 608,041,012 raw reads. The de novo assembly of the reference transcriptome of T. pratense produced 44,643 contigs, of which 41,505 contigs were annotated and 29,781 contigs were identified as plant specific. The minimal length of the contigs was 124 bp, the maximal length 15,551 bp (Table S5). After the de novo assembly of the T. pratense transcriptome, each library was mapped back against the reference transcriptome to determine the overall alignment rate, which was between 77.85 % and 90.32 % (Table S6).
63 % of the 44,643 contigs could be mapped to a known locus of the T. pratense genome annotation [12, 35], 32 % could be mapped to an unknown locus of the T. pratense genome and 5 % could not be mapped to the T. pratense genome (Fig. S2). All plant-specific contigs were annotated with several databases (Table S3). To further verify the quality of our replicates, we identified the transcripts shared by the two replicates. We calculated TPM values for each transcript and discarded transcripts with TPM values < 1. The percentage of transcripts shared between the two replicates was between 90 % and 94 % for all treatments/localities, suggesting that the RNA-Seq data are highly reproducible (Table S8).
Differentially expressed gene analysis reveals diverse subsets of genes involved in regrowth influenced by location and environmental conditions
To identify gene expression responses underlying the regrowth response after mowing, a digital gene expression analysis was performed comparing field A mown vs. field A non-mown (FaM vs. FaNM); field B mown vs. field B non-mown (FbM vs. FbNM); greenhouse mown vs. greenhouse non-mown (GM vs. GNM) to identify DEGs (Table S9) from mown plants. Interestingly, using the |log2foldchange| ≥ 2, the number of differentially expressed genes (DEGs) is rather similar in all comparisons, ranging from 119 (GM vs. GNM) to 142 (FaM vs. FaNM) (Table 1).
Table 1: The numbers of differentially expressed transcripts (contigs) between libraries with changes equal or above |log2foldchange| 2. Upregulation for each comparison is shown.
Analysis
|
total DEGs
|
Number of upregulated transcripts in mown libraries
|
Number of upregulated transcripts in unmown libraries
|
GM vs. GNM
|
119
|
54
|
65
|
FaM vs. FaNM
|
142
|
49
|
93
|
FbM vs. FbNM
|
122
|
59
|
63
|
We were then interested to identify transcriptional changes in developmental processes required for the regrowth process. Thus, the results of the DEG analysis were restructured such that the DEGs were grouped in 16 descriptive classes defined by database and literature mining (Tables S3 and S10). Those classes (abiotic stress, abiotic/biotic stress, biotic stress, development, general cell function, growth, metabolism, photosynthesis, phytohormone, secondary metabolite biosynthesis, senescence, signaling, symbiosis, transcription, transposon, no available annotation) describe major functional groups and serve to broadly categorize the potential role of a gene (Table S10).
We compared the top 20 DEGs of mown vs. not mown plants and observed that the greenhouse plants displayed more DEGs (classes growth, transcription, and phytohormone) involved in regrowth processes (Fig. 1C, Table S3). Three DEGs involved in growth, two phytohormone genes, and two transcriptional regulators are among the top 20 DEGs, while ten DEGs are related to biotic and abiotic stress in the greenhouse (Table S3). The top 20 DEGs of field A grown plants include four growth related, three development related and five stress-related DEGs. The top 20 DEGs of field B grown plants included only two growth related and six stress-related transcripts. Taken together, the greenhouse grown plants showed most DEGs related to growth, transcription, and phytohormone actions indicative of a regrowth reaction, as they grew under less stressful conditions than the field grown plants, for which stress related DEGs were more dominant (Fig. 1 A-C /Table 2-4).
We then performed a GO enrichment analysis with the DEGs of each group to obtain a differential view on the transcriptional changes occurring in relation to regrowth (Table 2, 3, 4). The results revealed that GO terms involved mainly in general metabolic processes and pathways, as well as general reactions are enriched in non-mown plants including i.e. the GO terms “protein metabolic process”, “metabolic process”, “cellular process”, “catabolic process”, "biosynthetic process" (Fig. S3 and Table S11). Within mown samples we found the following GO terms enriched: “nucleic acid binding “(GM); GO terms related to photosynthesis ("photosynthesis", "thylakoid"), cell components and protein transport ("Golgi apparatus", "cytoplasm") and related to regrowth and stress response ("generation of precursor metabolites and energy", "cell growth", "cell communication") (Fig. S3 and Table S11). Within the GO term “cell growth” the contigs GIBBERELLIN-REGULATED PROTEIN 1 which is involved in cell elongation and ROOT HAIR DEFECTIVE 3, a protein involved in root hair growth are present. For FbM we found the GO term related to metabolic processes ("metabolic process", "lipid metabolic process"), cell related ("cytoplasm", "extracellular space"), enzymatic and catabolic processes ("enzyme regulator activity", "catalytic activity") and the GO term "binding", which included a contig encoding for “V”, a protein involved in the ethylene biosynthesis.
Interestingly, most functional groups differ between the field and greenhouse location (Fig. 1A-C), for example, more genes related to growth are upregulated in the non-mown Fa location but in the Fb and greenhouse location, they more genes are upregulated in the mown plants. Only genes related to biotic stress processes were upregulated in all unmown locations and more transposon-related genes are upregulated in mown plants (Fig. 1 A-C).
The photosynthesis- and phytohormone-related genes of field A show a similar pattern to the field B plants as do the phytohormone- and signaling related genes. Genes related to development, general cell functions and transcription are also similar between field A and the greenhouse grown plants, such that more transcription- and development-related genes are upregulated in mown plants. And unexpectedly, senescence-related genes are upregulated in mown plants of field B. However, as our analysis cannot discriminate between activating and repressing factors of senescence, we cannot conclude from our data weather the mown plants have activated or repressed their senescence program.
The largest group of differentially expressed genes is the one related to biotic stress with up to 38% differentially expressed genes in one location (field B, Fig. 1 B). This suggests that biotic stresses play a prominent role in non-mown plants. A similar phenomenon can be observed for growth related processes, where up to 24% genes were upregulated in the mown and unmown plants indicating that different growth programs are active in mown vs. unmown plants.
Taken together we can state that mown plants in all locations change their transcriptional programs upon mowing suggesting that they massively change their metabolism and signaling processes. However, the molecular answer to substantial biomass loss differed between all three locations.
To find similarly regulated genes between the treatments and/or locations, Venn diagrams were generated to compare the number of shared DEGs within the mown samples and the non- mown samples (Fig. 1 D-E, Table S12). Within the mown samples we detected no overlap between the groups with the exception of four upregulated DEGs in the two field transcriptomes (FbM and FaM (Fig. 1 D). Within the non-mown samples, also four genes were shared between the field transcriptomes (FbNM and FaNM)) and one was shared between the field B and the greenhouse (Fig. 1 E). No genes were shared between all three samples, neither in the mown treatment, nor in the non-mown treatment.
We were interested in the contribution of individual phytohormones to the regrowth reaction in T. pratense, as they are known to play a major role in the regulation of development and stress response. We identified DEGs related to phytohormone synthesis, homeostasis, transport, and signaling for all major classes of phytohormones in the datasets. The four phytohormones with the most associated DEGs were: abscisic acid (8 DEGs), gibberellin (8 DEGs), salicylic acid (6 DEGs), and auxin (5 DEGs) (Fig. 1 F). Abscisic acid and salicylic acid are well-known to be involved in response to drought and abiotic/biotic stress, respectively. Auxin is the major phytohormone required for growth and cell division regulation and thus, we expected DEGs related to these phytohormones to be abundant in our analysis. However, unexpectedly, eight DEGs with gibberellin association were found. As gibberellins regulate growth in response to stresses but have so far not been associated with regrowth after biomass loss, we suggest gibberellin as a novel candidate phytohormone to influence the regrowth response.
Specific transcriptional regulator families are differentially expressed during the regrowth process
As the regulation of stress response, growth and development depends on differential activity of transcription factors, we aimed to identify transcriptional relevant to the biological processes occurring two weeks after mowing by mapping the transcriptome to the PlnTFDB [36]. All members of a specific transcriptional regulator family (TRF) were identified in silico and their expression was compared between mown and unmown plants (Table S13). Fig. 2 shows TRFs with significantly differential expression between mown and unmown conditions in at least 10% of their members, Fig. S2 and table S13 includes also those TRFs with 5% of their members regulated differentially upon mowing.
17 TRFs were identified of which at least 10% of the members showed differential expression in mown versus unmown comparisons (Fig. 2): ABI3VP1, AP2-EREBP, C2C2-Dof, C2C2-GATA, GRAS, HSF, LOB, MADS, mTERF, MYB, NAC, PHD, SBP, SNF2, TCP, TRAF, WRKY.
On field A, the AP2-EREBP, LOB, MADS, MYB, NAC, PHD, SBP, TCP and WRKY TRFs were more prominent in unmown plants, and only the HSF TRFs were more prominent upon mowing. On field B, ABI3VP1, C2C2-Dof, GRAS, HSF, LOB, MADS, mTERF, SNF2, TRAF, and WRKY TRFs were reduced upon mowing. In the greenhouse-grown plants, members of ABI3VP1, C2C2-Dof, C2C2-GATA, and GRAS, show increased numbers in response to mowing. In addition, ARF, C2H2, homeobox, MYB, NAC, and TRAF TRFs show changes in expression in all locations, albeit with only between 5 – 10 % of the members being differentially expressed (Table S13).
Two TRFs showed a repression of expression upon mowing: 10 % of the WRKY transcripts were less abundant in mown plants regardless of the provenance. In addition, MADS-box transcripts were found upregulated as well, but only in the field-derived transcriptomes. Generally, only four of the 17 TRFs analyzed here showed significant changes in expression towards mowing in the greenhouse-derived plants, suggesting that they react less strongly towards mowing than the field-derived plants. Six TRFs (AP2-EREBP, MYB, NAC, PHD, SBP, and TCP) showed transcriptional changes in reaction to mowing only in field A while only three TRFs (mTERF, SNF2, TRAF) showed this only in field B, suggesting that the combination of biotic and abiotic factors with mowing differed between the two field locations, and, in a similar way, between the field locations and the greenhouse.
Notably, only the C2C2-GATA TRF showed transcriptional changes in at least 10% of its members towards mowing under greenhouse but not under field-conditions, indicating that transcriptional changes in reaction to other biotic and abiotic factors may overlay the regrowth reaction. Taken together, the TRF analysis showed that the reaction towards mowing induces transcriptional changes in only a subset of TRFs, suggesting that those play a major role in relieving the stress of biomass loss and regrowth.
Table 2: Twenty most strongly differentially expressed genes of the GM vs. GNM analysis. Shown are the transcript name, |log2foldchange| ≥ 2 of the corresponding transcript, the library in which the transcript is upregulated (pattern), gene name based on T. pratense genome annotation, corresponding Phytozome description, gene name and species name of the next homologs and A. thaliana gene name, and locus name based on information available on Tair.
ID
|
Pattern
|
Contig ID
|
|log2foldchange| ≥ 2
|
Class (basis of classifiation)
|
Gene name T. pratense
|
Next homolog gene name
|
Next homolog species name
|
A. thaliana gene name
|
A. thaliana locus name
|
1
|
GHNM
|
tdn_99733
|
-9.5
|
Growth (M. truncatula)
|
Tp57577_TGAC_v2_mRNA4544.v2
|
Medtr4g029550.1
|
M. truncatula
|
-
|
-
|
2
|
GHNM
|
k41_54584
|
-6.3
|
Biotic stress (T.pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA28349.v2
|
Medtr5g073620.1
|
M. truncatula
|
ATEXO70B1
|
AT5G58430
|
3
|
GHNM
|
tdn_92791
|
-5.5
|
Abiotic/biotic stress (T.pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA20498.v2
|
Medtr1g041150.1
|
M. truncatula
|
ATCPK1
|
AT5G04870
|
4
|
GHNM
|
k41_130218
|
-5.5
|
-
|
-
|
-
|
-
|
-
|
-
|
5
|
GHNM
|
tdn_53091
|
-4.8
|
Phytohormone (M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA39912.v2
|
Medtr4g010250.1
|
M. truncatula
|
-
|
AT5G20190
|
6
|
GHNM
|
tgg_43136
|
-4.4
|
Transcription (M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA29629.v2
|
Medtr4g098630.1
|
M. truncatula
|
ANAC071
|
AT4G17980
|
7
|
GHNM
|
tdn_141837
|
-4.3
|
Abiotic stress (T.pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA760.v2
|
Medtr2g022700.1
|
M. truncatula
|
ATGPT2
|
AT1G61800
|
8
|
GHNM
|
tdn_40997
|
-4.2
|
Abiotic stress (T.pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA25718.v2
|
Medtr4g130540.1
|
M. truncatula
|
HSP70B
|
AT1G16030
|
9
|
GHNM
|
k71_5292
|
-4.1
|
Biotic stress (T.pratense, M. truncatula)
|
Tp57577_TGAC_v2_mRNA23166.v2
|
Medtr0163s0020.1
|
M. truncatula
|
LECRK-IX.1
|
AT5G10530
|
10
|
GHNM
|
k59_6358
|
-3.9
|
Growth (T.pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA12337.v2
|
Medtr3g435430.1
|
M. truncatula
|
ATEXP15
|
AT2G03090
|
11
|
GHM
|
tdn_86219
|
8.0
|
Biotic stress (T.pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA29036.v2
|
Medtr4g066210.1
|
M. truncatula
|
BGLU12
|
AT5G42260
|
12
|
GHM
|
k23_115785
|
8.0
|
Abiotic stress (T.pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA22071.v2
|
Glyma.01G001000.1
|
G. max
|
-
|
AT5G58110
|
13
|
GHM
|
tdn_91159
|
8.1
|
Biotic stress (T.pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA7745.v2
|
Medtr4g035870.1
|
M. truncatula
|
-
|
AT5G62360
|
14
|
GHM
|
k65_43517
|
8.3
|
Phytohormone (T.pratense, A. thaliana)
|
Tp57577_TGAC_v2_mRNA6281.v2
|
Medtr1g082750.1
|
M. truncatula
|
ATAMI1
|
AT1G08980
|
15
|
GHM
|
tgg_18067
|
8.4
|
-
|
Tp57577_TGAC_v2_mRNA32019.v2
|
-
|
-
|
-
|
-
|
16
|
GHM
|
k61_38813
|
9.0
|
-
|
-
|
-
|
-
|
-
|
-
|
17
|
GHM
|
k49_82496
|
9.0
|
Abiotic/biotic stress (G. max, A. thaliana)
|
Tp57577_TGAC_v2_mRNA37976.v2
|
Glyma.06G268800.1
|
G. max
|
-
|
AT4G04790
|
18
|
GHM
|
k67_38815
|
9.1
|
Biotic stress (T.pratense)
|
Tp57577_TGAC_v2_mRNA41666.v2
|
Medtr0062s0020.1
|
M. truncatula
|
-
|
-
|
19
|
GHM
|
k45_11164
|
9.6
|
Transcription (T.pratense)
|
Tp57577_TGAC_v2_mRNA29953.v2
|
Medtr3g092510.1
|
M. truncatula
|
ATRBP37
|
AT4G10610
|
20
|
GHM
|
tdn_25484
|
9.6
|
Growth (Phaseolus vulgaris)
|
Tp57577_TGAC_v2_mRNA13093.v2
|
Phvul.006G033800.1
|
Phaseolus vulgaris
|
-
|
-
|
Table 3: Twenty most strongly differentially expressed genes of the FaM vs. FaNM analysis. Shown are the transcript name, |log2foldchange| ≥ 2 of the corresponding transcript, the library in which the transcript is upregulated (pattern), gene name based on T. pratense genome annotation, corresponding Phytozome description, gene name and species name of the next homologs and A. thaliana gene name, and locus name based on information available on Tair.
ID
|
Pattern
|
Contig ID
|
|log2foldchange| ≥ 2
|
Class (basis of classifiation)
|
Gene name T. pratense
|
Next homolog gen name
|
Next homolog species name
|
A. thaliana gene name
|
A. thaliana locus name
|
1
|
TPNM2
|
k33_17052
|
-9,0
|
Biotic stress (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA21474.v2
|
Medtr4g079440.1
|
M. truncatula
|
na
|
AT1G06260
|
2
|
TPNM2
|
k43_111792
|
-8,8
|
Biotic stress (M. truncatula)
|
Tp57577_TGAC_v2_mRNA26333.v2
|
Medtr8g101900.1
|
M. truncatula
|
CCOAOMT7
|
AT4G26220
|
3
|
TPNM2
|
tdn_34568
|
-8,6
|
-
|
Tp57577_TGAC_v2_mRNA9104.v2
|
Glyma.13G061800.1
|
G. max
|
-
|
AT5G39530
|
4
|
TPNM2
|
tdn_49640
|
-8,6
|
-
|
-
|
-
|
-
|
-
|
-
|
5
|
TPNM2
|
tdn_58745
|
-8,5
|
Biotic stress (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA20190.v2
|
Medtr8g075200.1
|
M. truncatula
|
-
|
AT1G75900
|
6
|
TPNM2
|
tdn_47209
|
-8,5
|
Growth (M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA10703.v2
|
Medtr1g053315.1
|
M. truncatula
|
-
|
AT1G03390
|
7
|
TPNM2
|
tdn_48478
|
-8,4
|
Biotic stress (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA19516.v2
|
Medtr2g099020.1
|
M. truncatula
|
-
|
AT3G59510
|
8
|
TPNM2
|
k41_17597
|
-8,4
|
Growth stress (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA8526.v2
|
Medtr1g036490.1
|
M. truncatula
|
ATCOMT, ATOMT1
|
AT5G54160
|
9
|
TPNM2
|
k51_82581
|
-8,2
|
Growth (T. pratense)
|
Tp57577_TGAC_v2_mRNA23127.v2
|
Medtr2g436480.1
|
M. truncatula
|
KCS21
|
AT5G49070
|
10
|
TPNM2
|
tdn_82424
|
-8,1
|
Growth (T. pratense)
|
Tp57577_TGAC_v2_mRNA17103.v2
|
Medtr2g013740.1
|
M. truncatula
|
KCS10
|
AT2G26250
|
11
|
TPM2
|
k49_380
|
7,5
|
Development (A. thaliana)
|
Tp57577_TGAC_v2_mRNA37185.v2
|
SapurV1A.0885s0040.1
|
Salix purpurea
|
DAYSLEEPER
|
AT3G42170
|
12
|
TPM2
|
tdn_49869
|
7,6
|
-
|
-
|
-
|
-
|
-
|
-
|
13
|
TPM2
|
tdn_54983
|
7,7
|
-
|
-
|
-
|
-
|
-
|
-
|
14
|
TPM2
|
k37_9029
|
7,8
|
-
|
-
|
-
|
-
|
-
|
-
|
15
|
TPM2
|
k45_6120
|
8,4
|
-
|
Tp57577_TGAC_v2_mRNA2166.v2
|
Medtr2g007510.1
|
M. truncatula
|
-
|
-
|
16
|
TPM2
|
k71_23808
|
8,4
|
Development (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA14131.v2
|
Medtr1g021320.1
|
M. truncatula
|
-
|
AT4G33280
|
17
|
TPM2
|
k59_3541
|
8,4
|
Development (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA34193.v2
|
Medtr4g089030.1
|
M. truncatula
|
CYP71A26
|
AT3G48270
|
18
|
TPM2
|
k59_360
|
8,6
|
Metabolism (Linum usitatissimum, T. pratense)
|
Tp57577_TGAC_v2_mRNA21875.v2
|
Lus10012445
|
Linum usitatissimum
|
-
|
AT1G50020
|
19
|
TPM2
|
k53_38903
|
9,0
|
Abiotic stress (A. thaliana)
|
Tp57577_TGAC_v2_mRNA37328.v2
|
Medtr8g063190.1
|
M. truncatula
|
PRIN2
|
AT1G10522
|
20
|
TPM2
|
tdn_129978
|
9,6
|
-
|
Tp57577_TGAC_v2_mRNA9318.v2
|
Medtr7g062280.1
|
M. truncatula
|
-
|
AT5G01140
|
Table 4: Twenty most strongly differentially expressed genes of the FbM vs. FbNM analysis. Shown are the transcript name, |log2foldchange| ≥ 2 of the corresponding transcript, the library in which the transcript is upregulated (pattern), gene name based on T. pratense genome annotation, corresponding Phytozome description, gene name and species name of the next homologs and A. thaliana gene name, and locus name based on information available on Tair.
ID
|
Pattern
|
Contig ID
|
|log2foldchange| ≥ 2
|
Class (basis of classifiation)
|
Gene name T. pratense
|
Next homolog gen name
|
next homolog species name
|
A. thaliana gene name
|
A. thaliana locus name
|
1
|
TPNM3
|
tdn_100726
|
-9,4
|
Biotic stress (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA24659.v2
|
Medtr4g094772.1
|
M. truncatula
|
CYP81D
|
AT4G37340
|
2
|
TPNM3
|
tgg_49631
|
-8,0
|
Biotic stress (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA37846.v2
|
Medtr6g034470.1
|
M. truncatula
|
-
|
AT2G34930
|
3
|
TPNM3
|
tdn_152262
|
-7,9
|
-
|
-
|
-
|
-
|
-
|
-
|
4
|
TPNM3
|
tdn_56712
|
-7,9
|
Biotic stress (T. pratense, M. truncatula)
|
Tp57577_TGAC_v2_mRNA30556.v2
|
Medtr8g027540.1
|
M. truncatula
|
-
|
-
|
5
|
TPNM3
|
tdn_87762
|
-7,9
|
Biotic stress (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA10533.v2
|
Medtr7g451400.1
|
M. truncatula
|
ATMCP1B, ATMCPB1
|
AT1G02170
|
6
|
TPNM3
|
tdn_86129
|
-7,1
|
General cell functions (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA10207.v2
|
Glyma.11G154500.1
|
G. max
|
RPB5E
|
AT3G54490
|
7
|
TPNM3
|
k55_46241
|
-6,9
|
Growth (T.pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA31452.v2
|
Medtr4g128150.1
|
M. truncatula
|
histone 4
|
AT2G28740
|
8
|
TPNM3
|
tdn_55533
|
-6,2
|
Abiotic stress (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA39263.v2
|
Medtr5g007790.1
|
M. truncatula
|
ATCRM1, ATXPO1
|
AT5G17020
|
9
|
TPNM3
|
tgg_51443
|
-4,7
|
Growth (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA37076.v2
|
Medtr5g019580.2
|
M. truncatula
|
UGT72E2
|
AT5G66690
|
10
|
TPNM3
|
tdn_136706
|
-4,7
|
-
|
-
|
-
|
-
|
-
|
-
|
11
|
TPM1
|
tdn_140636
|
8,8
|
General cell functions (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA28209.v2
|
Medtr8g005980.1
|
M. truncatula
|
C-NAD-MDH2
|
AT5G43330
|
12
|
TPM1
|
tdn_154158
|
8,9
|
General cell functions (T. pratense, M. truncatula)
|
Tp57577_TGAC_v2_mRNA39482.v2
|
Medtr3g114970.2
|
M. truncatula
|
-
|
AT5G55150
|
13
|
TPM1
|
tdn_65187
|
9,1
|
Transposon (T. pratense, Prunus persica A. thaliana)
|
Tp57577_TGAC_v2_mRNA30115.v2
|
Prupe.4G011200.1
|
Prunus persica
|
-
|
AT4G29090
|
14
|
TPM1
|
tdn_100956
|
9,2
|
Metabolism (T. pratense, Capsella rubella, A. thaliana)
|
Tp57577_TGAC_v2_mRNA9542.v2
|
Carubv10008027m
|
Capsella rubella
|
AHA2
|
AT4G30190
|
15
|
TPM1
|
k63_21505
|
9,3
|
Biotic stress (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA19467.v2
|
Medtr3g022400.1
|
M. truncatula
|
-
|
AT3G14470
|
16
|
TPM1
|
tdn_142681
|
9,3
|
Secondary metabolite biosynthesis (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA15473.v2
|
Medtr8g074550.1
|
M. truncatula
|
-
|
AT2G18570
|
17
|
TPM1
|
k45_6120
|
9,6
|
-
|
Tp57577_TGAC_v2_mRNA2166.v2
|
Medtr2g007510.1
|
M. truncatula
|
-
|
-
|
18
|
TPM1
|
tdn_52922
|
10,1
|
-
|
Tp57577_TGAC_v2_mRNA41271.v2
|
mrna20290.1-v1.0-hybrid
|
Fragaria vesca
|
-
|
AT1G21280
|
19
|
TPM1
|
tdn_65185
|
10,9
|
-
|
-
|
-
|
-
|
-
|
-
|
20
|
TPM1
|
tdn_109277
|
11,7
|
Transcription (T. pratense, M. truncatula, A. thaliana)
|
Tp57577_TGAC_v2_mRNA29560.v2
|
Medtr5g028610.1
|
M. truncatula
|
-
|
AT3G14460
|
Gibberellins are also important regulators after mowing in red clover
We have shown previously (Fig. 1G) that genes related to gibberellins are also differentially expressed, even though GA is not well-known to regulate biological processes related to loss of biomass. We then wanted to know if GA is relevant for the regulation of regrowth and treated red clover plants exogenously with GA3.
A weekly gibberellin application during the regrowth process led to significant and specific changes in morphology (Fig. 3). Previous work suggested that regrowing plants produce smaller and rounder leaflets with shorter petioles than non-mown plants [24]. Thus, number of leaves, shoots and inflorescences, leaf area, and the roundness of leaflets were measured in this experiment (Fig. 3, Fig. S4). The first visible effects of gibberellin treatment were recognized after 1.5 weeks, showing a significant higher leaflet area of gibberellin treated. Later it was observed that the petioles of treated plants were on average twice as long as petioles of untreated plants (16.7 ± 1.9 cm and 8 ± 1.2 cm, respectively). Leaflets of gibberellin treated plants had with 4.7 ± 0.9 cm² almost double the size when compared with those of untreated plants (2.4 ± 0.6 cm²). However, gibberellin treated plants produced only 30% more total leaf area than control plants. Other morphological traits such as number of inflorescences, leaves, and shoots remained unaffected by the gibberellin treatment (Fig. S4). In summary, mown plants normally produce leaves with shorter petioles, restrict their leaflet area and their leaves become rounder. Gibberellin treatment partially alleviated these developmental changes such that the mown, gibberellin treated plants produced larger leaves with longer petioles while the leaf shape was unaffected by gibberellin treatment.