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Sensing Plant Physiology and Environmental Stress By bioRxiv preprint doi: https://doi.org/10.1101/362939; this version posted July 5, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Sensing Plant Physiology and Environmental Stress by 2 Automatically Tracking Fj and Fi Features in PSII Chlorophyll 3 Fluorescence Induction 4 5 Qian Xia1,2, Jinglu Tan3, Shengyang Cheng1, Yongnian Jiang4, and Ya Guo1,2,3* 6 1. Key Laboratory of Advanced Process Control for Light Industry, Ministry of 7 Education, Jiangnan University, Wuxi 214122, China 8 2. School of Internet of Things, Jiangnan University, Wuxi 214122, China 9 3. Department of Bioengineering, University of Missouri, Columbia, MO 65211, USA 10 4. Jiangsu Zhongnong IoT Technology Co., LTD, Yixing 214200, China 11 * Corresponding: [email protected]; [email protected] 12 Abstract 13 Following a step excitation, chlorophyll fluorescence (ChlF) from photosystem II of a 14 dark-adapted plant leaf exhibits the well-known OJIP pattern. The OJIP induction has 15 been widely applied in plant science, agriculture engineering, and environmental 16 engineering. While the J and I phases are related to transitions of photochemical 17 reaction redox states, characteristic fluorescence intensities for the two phases (Fj and 18 Fi) are often treated as fixed time points in routine measurement and thus do not 19 account for variations in plant and experimental conditions, which (1) neglects the 20 time differences, potentially useful information for characterizing plant status and 21 environmental factors, and (2) leads to errors in measured Fj and Fi values. In this 22 work, a method for consistent measurement of Fj and Fi was developed through 23 polynomial fitting and curvature analysis. The method measures the curvatures in the 24 OJIP curve and automatically tracks the characteristic transition points under variable 25 sample and experimental conditions. Experiments were carried out to demonstrate the 26 concept and classification capabilities of the developed method. This research 27 established a new framework to analyze ChlF and enhanced the applications of ChlF. 28 29 Keywords: Chlorophyll Fluorescence; Photosynthesis; Photosystem II; Polynomial 30 Fitting; Curvature Analysis 31 1. Introduction 32 Light absorbed by plant photosystem II (PSII) in the photosynthetic process has three 33 subsequent pathways: photochemical reactions, heat and chlorophyll fluorescence 34 (ChlF) (Goltsev et al., 2003; Krause and Weis, 1991; Lavergene and Trissl, 1995; 35 Stirbet et al., 1998; Vredenberg, 2004; Taiz and Zeiger, 2006). Since the emission of 36 ChlF competes for light energy with the other two pathways (Lubitz et al., 2008), 37 almost all the changes in photosynthesis can be reflected by PSII ChlF (Zhu et al., 38 2005; Rodriguez & Greenbaum, 2009). ChlF measurement is thus a reliable method to 39 study plant physiology and environment factors that influence photosynthesis 40 (Mohammed et al., 1995; Maxwell & Johnson, 2000; Coombs & Long, 2014). In 41 addition, ChlF is a fast, noninvasive, simple, and intuitive way to represent the 42 changes in photosynthetic activities (Kootenet al., 1990; Mathur et al., 2011). 1 bioRxiv preprint doi: https://doi.org/10.1101/362939; this version posted July 5, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 43 When a step illumination is applied, the intensity of PSII ChlF from a dark-adapted 44 plant leaf changes with time in a unique pattern of the commonly-labeled O-J-I-P 45 phases, which is referred to as the ChlF induction curve or OJIP curve. The OJIP 46 curve contains abundant information about the process of photosynthesis: the 47 absorption and conversion of light energy, the transfer and distribution of energy, the 48 state of the reaction centers, the activities of the PSII donors and the receptors, 49 plastoquinone (PQ) pool size and the activities, excess light energy and its dissipation, 50 photosynthetic light suppression, and light damage. ChlF induction has been 51 extensively used in the literature to analyze plant photosynthesis and physiological 52 conditions (Ogaya et al., 2011; Schansker et al., 2014; Guo & Tan, 2015; Guo et al., 53 2015). 54 55 There are four important points on an OJIP curve. O reflects the initial fluorescence 56 when a leaf is exposed to light after dark adaptation. J indicates accumulation of - 57 plastoquinone A (QA ) (Strasser et al., 1995). I is related to the heterogeneity of the PQ 58 pool (Strasser et al., 1995; Jee, 1995). P shows the maximum value of fluorescence. Fj 59 and Fi represent the ChlF intensity during the J and I phases of the ChlF kinetics, 60 respectively. Obviously, these time points depend on the photochemical reaction 61 kinetics, which implies that differences in plant physiological and experimental 62 conditions may lead to different times of occurrence for these phases. J and I are 63 generally defined as the first and the second inflection points or intermediary peaks on 64 the ChlF induction curve, respectively (see Figure 1). Different plant species, light 65 intensity, temperature, salinity, and drought may affect the plant physiological status 66 (D’Ambrosio et al., 2006; Koyro, 2006; Ruban & Belgio, 2014; Guo & Tan, 2015) 67 and thus the times of occurrence of these transitions. 68 69 The direct ChlF induction parameters include Fo, Fj, Fi, and Fm. Many other ChlF 70 parameters can be computed from these four. Fo is defined as the initial fluorescence 71 and thus it can be measured at a fixed time after the initiation of excitation. Fm 72 represents the maximum fluorescence in the entire ChlF induction curve and thus the 73 exact time of Fm measurement is not critical. J and I, however, are transient phases 74 between O and P. They may occur at different times depending on the plant species, 75 illumination, and growth environments. Currently, all commercial ChlF instruments 76 measured Fj and Fi at a fixed predefined time, although it is manually adjustable. The 77 instruments cannot automatically adjust the characteristic time and track the Fj and Fi 78 points. Although users may configure the predefined time through operating the 79 instrument menu, but it is boring and time consuming to set it for each measurement 80 and ChlF features were usually read according to a fixed time. This has several 81 obvious drawbacks: (1) It neglects the time differences, which may be useful 82 information for characterizing plant status and environmental factors because these 83 times of occurrence reflect the reaction rates, and (2) It leads to errors in measured Fj 84 and Fi values because a fixed time is inappropriate for all plant and experimental 85 conditions. This may limit the ChlF usefulness and results in discrepancies in 86 interpretation of ChlF kinetics. 2 bioRxiv preprint doi: https://doi.org/10.1101/362939; this version posted July 5, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 87 This work was aimed at developing a method to determine the J and I characteristic 88 times adaptively and consistently when plant and experimental conditions vary. Based 89 on the common interpretations of the ChlF induction kinetics, these times were 90 determined according to the curvature changes on the OJIP curve. Least-squares 91 polynomial fitting was used to filter experimental data. Comparisons were made 92 between the proposed method and the traditional method. Applications were used to 93 validate the usefulness of the proposed method. 94 95 2. Method Development 96 97 A typical OJIP induction curve is shown in Figure 1. Since J and I are inflection 98 points in ChlF intensity resulting from changes in forward (downstream) reactions 99 (Strasser et al., 1995; Stirbet and Govindjee, 1992), they are simply points of 100 curvature changes in the induction curve. Consequently, they can be conveniently and 101 consistently located by finding the local curvature maxima on the induction curve. 102 Because the induction curve is commonly observed in semi-log scale, curvature is 103 computed based on logarithm of time. 104 105 Fig. 1. A typical ChlF induction or OJIP curve 106 107 The curvature of a curve can be easily found by computing numerical differences, but 108 numerical differences of a measured curve easily suffer from noise, resulting in 109 difficulty in finding the true local maxima of curvature. To eliminate the influence of 110 noise, the induction curve may be first fitted with a spline or polynomial of the form. N 111 = n ∑ n xay (1) n=0 112 where y is ChlF, x is the logarithm of time (as the induction curve is usually presented 113 in semi-log scale to reveal J and I), N is the order of the fitted polynomial, an (n = 114 0 … N) are coefficients, and n is an integer. 115 116 From Eq. (1), the first derivative of y with respect to x can be computed as: N 117 & = n−1 ∑ ay nnx (2) n=1 118 and the second order derivative y&& is: N 119 && = − n−2 ∑ n )1( xnnay (3) n=2 120 Curvature k is computed as: y&& 121 k = (4) + y& )1( 32 3 bioRxiv preprint doi: https://doi.org/10.1101/362939; this version posted July 5, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 122 In this work, N was experimentally determined. When N increases, the fitting error for 123 Eq. (1) will decrease. The N-value where the fitting error starts to level off was 124 selected as the desired value and it was 15 in this work.
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