Arthropod-Plant Interactions (2014) 8:163–169 DOI 10.1007/s11829-014-9298-z
ORIGINAL PAPER
EPG-Calc: a PHP-based script to calculate electrical penetration graph (EPG) parameters Philippe Giordanengo
Received: 28 May 2013 / Accepted: 11 February 2014 / Published online: 26 February 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract Electrical penetration graph (EPG) technique is a powerful tool to investigate the hidden feeding behavior of piercing–sucking insects allowing to link recorded EPG waveforms to stylet penetration and complex behaviors related to feeding activities occurring within plant tissue. Calculating the numerous EPG parameters necessary to unravel the complex insect–plant interactions is very time consuming, and few tools have been developed to automate it. EPG-Calc is a rich internet application intended to fill this gap, providing a fast and user-friendly web-based interface that uses analysis files from dedicated software (STYLET?) or database-compatible CSV text files containing waveform codes and cumulative time as input, and produces output files in database-compatible CSV text or Microsoft ExcelÒ XLS format that are directly usable by different statistical analysis softwares. EPG-Calc greatly reduces the time needed for EPG parameters calculation and allows to calculate more than 100 different parameters based on standardized definitions and calculus methods in such a way that avoid confusion between all kinds of definitions and calculations by individual authors.
EPG-Calc can be accessed online at http://www2.sophia.inra.fr/ID/ epg.php. Handling Editor: Henryk Czosnek. P. Giordanengo (&) Universite´ de Picardie Jules Verne, 80039 Amiens Cedex 1, France e-mail:
[email protected] P. Giordanengo Sophia Agrobiotech Institute, CNRS 7254, INRA 1355, Universite´ de Nice Sophia Antipolis, 06903 Sophia Antipolis, France
Keywords Hemiptera Aphid Feeding behavior Insect–plant interaction Automated software
Introduction Phytophagous hemiptera cause serious economic losses to cultivated crops damaging plants directly, by taking their liquid diet thus altering plant development, and indirectly, by vectoring numerous pathogens. These piercing–sucking pests withdraw their nutrients through stylet (i.e., mouthparts modified in a long and flexible needle containing a food canal and a saliva canal) insertion within plant tissue. Therefore, conversely with chewing insects, their feeding features are hidden activities. The electrical penetration graph (EPG) technique is a powerful tool to investigate such hidden feeding behaviors. Introduced by McLean and Kinsey (1984) then developed by Tjallingii (1978, 1985a, b), the direct current electrical penetration graph (DC-EPG) technique is used to monitor probing behavior by piercing– sucking insects including an insect and its host plant in an electrical circuit. Recorded electrical waveforms and their relation to stylet penetration, cell puncture, ingestion of xylem sap, phloem sap or mesophyll fluids, and salivation bouts have been extensively described in the literature for various piercing–sucking insects including aphids (Tjallingii 1985a, b; Tjallingii and Hogen Esch 1993; Prado and Tjallingii 1994; Pointeau et al. 2012), whiteflies (Janssen et al. 1989; Jiang et al. 1999; Johnson and Walker 1999), psyllids (Ullman and McLean 1988; Bonani et al. 2010; Civolani et al. 2011), coccoids (Calatayud et al. 1994; Huang et al. 2012), planthoppers (Spiller 1990; Backus et al. 2005; Dugravot et al. 2008), and leafhoppers (Kimmins and Bosque-Perez 1996; Lett et al. 2001; Carpane et al. 2011; Jin et al. 2012). This technique has been used to
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study all aspects of host–plant interactions such as plant resistance (Le Roux et al. 2008; Brunissen et al. 2010; Pompon et al. 2010a, 2010b) and insect-induced plant resistance (Dugravot et al. 2007; Prado and Tjallingii 2007; Brunissen et al. 2009), pathogen transmission and acquisition (Powell 2005; Fereres and Moreno 2009; Boquel et al. 2011), and phytopharmaceutical mode of action (Ameline et al. 2010; He et al. 2011; Jacobson and Kennedy 2013). Analysis of recorded waveforms is conducted with a dedicated software, STYLET? (Tjallingii 2013, www.epg systems.eu) for Windows platforms. However, calculating EPG parameters related to the duration and the number of the different waveform occurrences (non-sequential parameters) and the numerous sequential parameters related to specific sequences of these waveforms is tedious and remains one of the main limitations of the EPG technique. A few automatic parameter calculation macro workbooks in MS Excel have been developed in addition to full manual Excel data processing. The EMIF workbook (Van Giessen and Jackson 1998) allows to calculate a limited number of EPG parameters. Alternatives to calculating a large number of parameters useful to decipher the complexity of insect–plant interactions, are the workbooks, developed by Sarria et al. (2009) and Schliephacke (Julius Ku¨hn-Institut, Quedlinburg, Germany, unpublished), which can generate output sheets for further statistics. However, these different tools present various limitations such as restricted number of calculated parameters, discrepancies in the calculation methods, or limited number of analyzed files that can be processed once. To address the need for rapidly providing EPG parameters in a reliable way on the basis of standardized parameter definitions and calculus methods, we developed EPG-Calc, a PHP script-based web browser to automatically analyze EPG analysis files.
Materials and methods Calculus methods Analysis of the EPG waveforms provides two types of parameters (Table 1). Sequential parameters are related to durations and specific biological sequences of events during the probing behavior, and quantitatively describe EPG data. They include the transition period from the start of the experiment to the first event of a waveform (e.g., t-rec[1G, recording time to first G), the interval, average time or minimum time between the occurence of different waveforms event (e.g., t[1E, time to first E from first probe), and the number of events of a waveform before or after another waveform occurence (e.g., n_Pr\1sE2, number of
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probes after first sustained E2). In case a waveform is not observed in an analysis file, the total recording time or the time from the first probe is used for the first event of this waveform so that the bias is reduced and the number of replicates is not affected (e.g., t[1E2, time to first E2). In such a case, the other parameters are discarded and appear as ‘‘-’’ in the output file (e.g., n_bPr[1E, number of brief probes before first E). Non-sequential parameters comprise number, average, median, sum, maximum, and minimum durations of the events of each waveform in whole recording time and are independent of any sequence during stylet penetration. It is noteworthy that when a waveform is artificially ended by the recording, average, median and minimum durations are affected, which may lead to artifactual differences between experiments. Experimental data A set of data from an EPG experiment using two potato cultivars with Myzus persicae, 32 replicates on each cultivar was used. EPG parameters were calculated using both EPG-Calc 6.1 (vs updated August 2013) and a successful tool, the Microsoft ExcelÒ workbook for automatic parameter calculation of EPG data Ó 4.4.2 (vs. updated September 2011) (Sarria et al. 2009).
Results and discussion Using EPG-Calc, parameter calculation took 1 min and 30 s including ZIP archive creation (30 s) for each experimental class (aphids fed on the one or the other cultivar) with a dedicated software. Only the uploading and reception time of the files emailed by the PHP script may vary according to the internet connection speed. Thus, calculating EPG parameters for two experimental series composed of 32 individuals (i.e., analysis files) each spent \1 min. Calculating EPG parameters with the same dataset using the workbook developed by Sarria et al. (2009) was much more time consuming as the whole process took more than 20 min. Studying insect–plant relationships through the variations of the different behavioral steps involved in piercing–sucking insects feeding usually involves numerous hours of recordings. For instance, characterizing plant resistance requires a large screening to select among many ecotypes the most and least resistant lines (Gabrys and Pawluk 1999; Le Roux et al. 2008; Pompon et al. 2010a, b). Analyzing an unlimited number of files and managing several experimental classes thus undoubtedly constitute a great advantage. EPG-Calc allows to calculate parameters over a shorter time than used for recording (e.g., only first hour) without manual modification of each analysis file, just by keying
Electrical penetration graph parameters
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Table 1 List of calculated variables Class Non-probing
Probes
C waveform
F waveform
G waveform
Phloemian waveforms
#
Acronym
Definition
1
n_NP
Number of NP
2
a_NP
Average duration of a NP
3
m_NP
Median duration of NP
4
s_NP
Total duration of NP
5
n_Pr
Number of Pr
6
n_bPr
Number of brief Pr (\3 min)
7
a_Pr
Average duration of Pr
8
m_Pr
Median duration of Pr
9
s_Pr
Total duration of Pr
10
t[1Pr
Time to 1st Pr
11
d_1Pr
Duration of 1st Pr
12
n_C
Number of C
13
a_C
Average duration of C
14
m_C
Median duration of C
15
s_C
Total duration of C
Calculation
If no Pr, discardeda
16
n_F
Number of F
17
a_F
Average duration of F
18
m_F
Median duration of F
19
s_F
Total duration of F
20
n_G
Number of G
21
a_G
Average duration of G
22
m_G
Median duration of G
23
s_G
Total duration of G
24
t-rec[1G
Recording time to 1st G
If no G, TRT
25
t[1G
Time to 1st G from 1st probe
If no G, TRT—(t[1Pr)
26
n_Pr[1G
Number of probes before 1st G
If no G, discarded
27
n_E1e
Number of E1e (extracellular E1)
28
a_E1e
Average duration of E1e
29
m_E1e
Median duration of E1e
30
s_E1e
Total duration of E1e
31
n_sgE1
Number of sgE1 (E1 non-followed by E2)
32
a_sgE1
Average duration of sgE1
33
m_sgE1
Median duration of sgE1
34
s_sgE1
Total duration of sgE1
35
mx_sgE1
Maximum duration of sgE1
36
n_frE1
Number of frE1 (E1 followed by E2)
37
a_frE1
Average duration of frE1
38
m_frE1
Median duration of frE1
39
s_frE1
Total duration of frE1
40
mx_frE1
Maximum duration of frE1
41
n_E1
Number of E1 (sgE1 ? frE1)
42
a_E1
Average duration of E1
43
m_E1
Median duration of E1
44
s_E1
Total duration of E1
45
mx_E1
Maximum duration of E1
46
n_E12
Number of any sequence of E1–E2 etc.
47
a_E12
Average duration of E12
48
m_E12
Median duration of E12
49
s_E12
Total duration of E12
50
mx_E12
Maximum duration of a E12
51
n_E2
Number of E2
–b
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Table 1 continued Class
11 and 12 waveformsc
Potential dropsd
123
#
Acronym
Definition
Calculation
52
a_E2
Average duration of E2
53
m_E2
Median duration of E2
54
s_E2
Total duration of E2
55
mx_E2
Maximum duration of E2
56
n_sE2
Number of sustained E2 (sE2[10 min)
57
a_sE2
Average duration of sE2
58
m_sE2
Median duration of sE2
59
s_sE2 s
Total duration of sE2
60
n_E
Number of E (any E sequence)
61
s_E
Total duration of E
62
t-rec[1E
Recording time to 1st E
If no E, TRT
63
t[1E
Time to 1stE from 1st Pr
If no E, TRT—(t[1Pr)
64
tC[1E/1Pr
Time C to 1st E in 1st Pr with E (no Pr with F or G)
If no E, discarded
65
a_tC[1E/Pr
Average time C to 1st E in Pr with E
If no E, discarded
66
mn_tC[1E/Pr
Minimum time C to 1st E in Pr with E
If no E, discarded
67
n_bPr[1E
Number of brief Pr before 1st E
If no E, discarded
68
n_Pr[1E
Number of Pr before 1st E
If no E, discarded
69
t[1E12
Time to 1st E12
If no E12, TRT—(t[1Pr)
70
t[1E2
Time to 1st E2
If no E2, TRT—(t[1Pr)
71
t[1sE2
Time to 1st sE2
If no sE2, TRT—(t[1Pr)
72
n_Pr1E2
Number of Pr before 1st E2
If no E2, discarded
73
n_Pr[1sE2
Number of Pr before 1st sE2
If no sE2, discarded
74
n_Pr\1sE2
Number of Pr after 1st sE2
If no sE2, discarded
75
n_E2[1sE2
Number of E2 before 1st sE2
If no E2 or sE2, discarded
76
%_E2/C
E2/C ratio (phloem index)
If no E2, discarded
77
%_E1/allE
E1/E ratio
If no E, discarded
78
n_frE1/n_E12
E fraction index
If no E, discarded
79
%_E2\1E2
% of E2 after 1st E2
If no E2, discarded
80
n_id11
Number of id11
81
a_id11
Average duration of id11
82
m_id11
Median duration of id11
83
s_id11
Total duration of id11
84
t-rec[1id11
Recording time to 1st id11
If no id11, TRT
85
t[1id11
Time to 1st id11 from 1st Pr
If no id11, TRT—(t[1Pr)
86
n_Pr[1id11
Number of Pr before 1st id11
If no id11, discarded
87
n_id12
Number of id12
88
a_id12
Average duration of id12
89
m_id12
Median duration of id12
90
s_id12
Total duration of id12
91
t-rec[1id12
Recording time to 1st id12
If no id12, TRT
92
t[1id12
Time to 1st id12 from 1st Pr
If no id12, TRT—(t[1Pr)
93
n_Pr[1id12
Number of Pr before 1st id12
If no id 12, discarded
94
n_pd
Number of pd
95
a_pd
Average duration of pd
96
m_pd
Median duration of pd
97
s_pd
Total duration of pd
98
a_pdII-1
Average duration of pd subphase II.1
99
m_pdII-1
Median duration of pd subphase II.1
100
s_pdII-1
Total duration of pd subphase II.1
101
a_pdII-2
Average duration of pd subphase II.2
102
m_pdII-2
Median duration of pd subphase II.2
103
s_pdII-2
Total duration of pd subphase II.2
Electrical penetration graph parameters
167
Table 1 continued Class
Population index
a
#
Acronym
Definition
104
a_pdII-3
Average duration of pd subphase II.3
105
m_pdII-3
Median duration of pd subphase II.3
106
s_pdII-3
Total duration of pd subphase II.3
107
t-rec[1pd
Recording time to 1st pd
108
t[1pd
Time to 1st pd
109
t[1pd/Pr
Time to 1st pd in 1st Pr with pd
110
a_t[1pd/Pr
Average time to 1st pd for all Pr
111
m_t[1pd/Pr
Median time to 1st pd for all Pr
112
mn_t[1pd/Pr
Minimum time to 1st pd for all Pr
113
n_pd/minC
Number of pd per minute C
114
n_pd/1Pr
Number of pd in 1st Pr
115
n_Pr [ 1pd
Number of Pr before 1st pd
116
d_1pd
Duration of 1st pd
117
d_2pd
Duration of 2nd pd
118
a_pd/1-5pd
Average duration of pd 1 to 5
119
s_pdII-3/1-5pd
Total duration of pd subphase II.3 in pd 1 to 5
120
%_E2/Tr
% aphids with E2 (per treatment, not per individual)
121
%_sE2/Tr
% aphids with sE2 (per treatment, not per individual)
Calculation
Discarded recordings appear as ‘‘-’’ in the output file
b
Average, median, sum, maximum and minimum duration of an event are discarded when the waveform concerned has not been shown
c
User defined parameters, only appear in the output file if coded during the analysis
d
Potential drops only appear in the output file if coded during the analysis
TRT total recording time
online the selected analysis time. Such feature is particularly convenient for analyzing intracellular punctures (i.e., potential drops) in mesophyll tissue performed during stylet transit to the vascular bundles which are key behaviors in non-persistent virus transmission (Powell 2005; Fereres and Moreno 2009; Boquel et al. 2011). The annotation of waveforms corresponding to the three different subphases which compose these intracellular punctures is indeed dramatically time consuming and analyzing only the first hour of recording gives extensive and reliable information to assess the aphid transmission ability. In addition, while other tools only allow us to analyze the feeding behavior from the start of the recording time, parameter calculation can be realized from the start of stylet insertion within plant tissue (e.g., start of the pathway phase) allowing to compare identical periods of probing as time to initiate the first probe could be highly variable. EPG-Calc also integrates the management of the userdefined indexes (i.e., 11 and 12) introduced in the last version of STYLET? which are useful to code a specific activity event or as a marker, and the 121-calculated parameters (Table 1) include all the standardized EPG variables referenced with the same acronyms as used in the standardized list by Tjallingii (www.epgsystems.eu). Coding and duplication errors in the expected sequence of waveforms are checked automatically, and an alert
message is displayed identifying the row and the waveform code or time column in the file where an error occurs. In addition, EPG-Calc automatically marks the end of the recording time in case it has not been added manually in a file during the analysis with STYLET?. However, nonsheath-feeding hemipteran generally perform non-stereotypical waveform sequences (Backus et al. 2007). In such a case, an option allows to restrict the checking to coding and duplication errors. EPG-Calc is thus suitable to calculate parameters for both sheath- and non-sheath-feeding hemiptera. Finally, in addition to greatly reducing the time (90 %) needed for EPG parameters calculation, EPG-Calc is a flexible and user-friendly tool as: (1) the reporting of coding and duplication errors and the automatic calculation of the parameters prevents hand made errors, (2) an unlimited number of files can be analyzed once, (3) it allows the automatic management of multiple experimental classes, (4) multiple data files from the same experimental class can be uploaded once since in a ZIP archive, and (5) it is based on standardized parameter definitions and working out methods to calculate EPG parameters avoiding confusion between all kinds of definitions and calculations developed by individual authors. EPG-Calc is under continuous development and suggestions of new functionalities are welcome.
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168 Acknowledgments We are grateful to Dr Se´bastien Dugravot and Dr Julien Saguez for their help in testing the script. Many thanks to Dr Freddy Tjallingii for his invaluable help in calculation methods.
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