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Spectroscopy on plant leafs

Spectroscopy on plant leafs



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I'm trying to do spectroscopy readings on plants where I shoot electromagnetic waves and look at their signature/profile. I want to be able to do this at a distance, in the field and on live plants so that I can modify my rig later to automatically do this on multiple plants daily.

I have no background in spectroscopy, but I heard that waves directed through leaves can then be received on the other end using something like waveguide spectroscopy. I do not know the frequency range I should be looking at and was wondering how can I identify the range of interest for my plants. Let's say I want to approximate the chlorophyll content in leaves and see if the leaf is healthy or not and look at its change daily. Some references to learn about this would be very helpful. Thanks!


Analytical electron microscopy as a powerful tool in plant cell biology: examples using electron energy loss spectroscopy and X-ray microanalysis

Energy filtering transmission electron microscopy in combination with energy dispersive X-ray analysis (EDX) and quantumchemical calculations opens new possibilities for elemental and bone analysis at the ultrastructural level. The possibilities and limitations of these methods, applied to botanical samples, are discussed and some examples are given. Ca-oxalate crystals in plant cell vacuoles show a specific C K-edge in the electron energy loss spectrum (EELS), which allows a more reliable identification than light microscopical or cytochemical methods. In some dicots crystalline inclusions can be observed in different cell compartments, which are identified as silicon dioxide or calcium silicate by the fine structure of the Si L2,3-edge. Their formation is discussed on the basis of EEL-spectra and quantumchemical calculations. Examples concerning heavy metal detoxification are given for some tolerant plants. In Minuartia Zn is bound as Zn-silicate in cell walls Armeria accumulates Cu in leaf idioblasts by chelation with phenolic compounds and Cd is precipitated as CdS/phytochelatin-complexes in tomato.


Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy ¶

*To whom correspondence should be addressed at: School of Natural Resource Sciences, University of Nebraska—Lincoln, 113 Nebraska Hall, Lincoln, NE 68588-0517, USA. Fax: 402-472-4608 [email protected] Search for more papers by this author

Department of Geological and Environmental Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel

Department of Cell Physiology and Immunology, Faculty of Biology, Moscow State University, Moscow, Russia

Department of Cell Physiology and Immunology, Faculty of Biology, Moscow State University, Moscow, Russia

School of Natural Resource Sciences, University of Nebraska—Lincoln, Lincoln, NE

Department of Geological and Environmental Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel

*To whom correspondence should be addressed at: School of Natural Resource Sciences, University of Nebraska—Lincoln, 113 Nebraska Hall, Lincoln, NE 68588-0517, USA. Fax: 402-472-4608 gitel[email protected] Search for more papers by this author

Department of Geological and Environmental Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel

Department of Cell Physiology and Immunology, Faculty of Biology, Moscow State University, Moscow, Russia

Department of Cell Physiology and Immunology, Faculty of Biology, Moscow State University, Moscow, Russia

Posted on the web site on December 31, 2001.

ABSTRACT

Spectral reflectance of maple, chestnut and beech leaves in a wide range of pigment content and composition was investigated to devise a nondestructive technique for total carotenoid (Car) content estimation in higher plant leaves. Reciprocal reflectance in the range 510 to 550 nm was found to be closely related to the total pigment content in leaves. The sensitivity of reciprocal reflectance to Car content was maximal in a spectral range around 510 nm however, chlorophylls (Chl) also affect reflectance in this spectral range. To remove the Chl effect on the reciprocal reflectance at 510 nm, a reciprocal reflectance at either 550 or 700 nm was used, which was linearly proportional to the Chl content. Indices for nondestructive estimation of Car content in leaves were devised and validated. Reflectances in three spectral bands, 510 ± 5 nm, either 550 ± 15 nm or 700 ± 7.5 nm and the near infrared range above 750 nm are sufficient to estimate total Car content in plant leaves nondestructively with a root mean square error of less than 1.75 nmol/cm 2 .


Results

Spectral analysis of tomato fruit Development

Tomato fruit development and ripening were split into two distinct processes, as shown visually in Fig. 1. Spectra were acquired from each developmental timepoint including ripening. Figure 2 shows the class mean raw and pre-processed fingerprint spectra for the development (Fig. 2a and b) and ripening (Fig. 2c and d) processes. Figure 2 clearly shows that most sharp absorbance peaks are evident within the fingerprint region between 1800 and 900 cm − 1 . This region holds most of the biochemical information pertaining to the samples and was therefore the focus of the investigation.


Abstract

Equations for non-destructive determination of chlorophyll b : a ratios in grasses were developed from reflectance spectra of intact leaves of barley (Hordeum vulgare L.) and two barley mutants: clorina f2, which lacks chlorophyll b and clorina f104, which has a low chlorophyll b content. These plants enabled separation of effects of chlorophyll composition on reflectance spectra due to differential light absorption by chlorophylls a and b and to measure the effects of chlorophyll b on the contribution of fluorescence emitted by chlorophyll a to the reflectance spectra. Indices developed from these data were then tested on growth chamber-grown leaves from six C3 and 17 C4 grass species (7 NAD-ME and 10 NADP-ME subtypes). We used the chlorophyll b : a ratio because the data were less skewed than the chlorophyll a : b ratio. The best index for determination of the chlorophyll b : a ratio utilised wavelengths affected by chlorophyll absorbance: [R626 – 0.5 (R603 + R647)]/[R552– R626]. The chlorophyll b : a ratio was significantly lower in the C4 than C3 grasses, but was not sufficient in itself to separate these two functional groups. However, because of differences in fluorescence characteristics, C3 and C4 species could be distinguished by an index based on wavelengths affected by chlorophyll fluorescence: [R696 to 709/R545 to 567].

Additional keywords: C3-photosynthesis, C4-photosynthesis, leaf pigments, remote sensing.


Annual Review of Plant Biology

2020 Release of Journal Citation Reports

The 2020 Edition of the Journal Citation Reports® (JCR) published by Clarivate Analytics provides a combination of impact and influence metrics from 2019 Web of Science source data. This measure provides a ratio of citations to a journal in a given year to the citable items in the prior two years.

Download Annual Reviews 2020 Edition JCR Rankings in Excel format.

Annual Review of: Rank Category Name Ranked Journals in Category Impact Factor Cited Half-Life Immediacy Index
Analytical Chemistry 6 Chemistry, Analytical 86 7.023 7.1 2.042
Analytical Chemistry3Spectroscopy427.0237.12.042
Animal Biosciences2Zoology1686.0914.13.125
Animal Biosciences17Biotechnology and Applied Microbiology1566.0914.13.125
Animal Biosciences1Agriculture, Dairy, and Animal Sciences636.0914.13.125
Animal Biosciences2Veterinary Science1426.0914.13.125
Anthropology6Anthropology903.17515.60.240
Astronomy and Astrophysics1Astronomy and Astrophysics6832.96310.85.133
Biochemistry3Biochemistry and Molecular Biology29725.78712.34.933
Biomedical Engineering2Biomedical Engineering8715.5419.01.524
Biophysics3Biophysics7111.6856.63.130
Cancer Biology53Oncology2445.4132.02.826
Cell and Developmental Biology13Cell Biology19514.66710.50.552
Cell and Developmental Biology1Developmental Biology4114.66710.50.552
Chemical and Biomolecular Engineering1Chemistry, Applied719.5615.60.941
Chemical and Biomolecular Engineering5Engineering, Chemical1439.5615.60.941
Clinical Psychology1Psychology, Clinical (Social Sciences)13113.6927.93.304
Clinical Psychology4Psychology (Science)7713.6927.93.304
Condensed Matter Physics6Physics, Condensed Matter6914.8334.97.273
Criminology1Criminology & Penology696.3481.40.955
Earth and Planetary Sciences4Geosciences, Multidisciplinary2009.08914.22.727
Earth and Planetary Sciences5Astronomy and Astrophysics689.08914.22.727
Ecology, Evolution, and Systematics2Evolutionary Biology5014.04117.40.440
Ecology, Evolution, and Systematics2Ecology16814.04117.40.440
Economics39Economics3713.5916.40.686
Entomology1Entomology10113.79614.34.762
Environment and Resources5Environmental Studies (Social Science)1238.0659.60.563
Environment and Resources14Environmental Sciences (Science)2658.0659.60.563
Financial Economics36Business, Finance1082.0577.00.167
Financial Economics107Economics3712.0577.00.167
Fluid Mechanics1Physics, Fluids and Plasmas3416.30615.49.190
Fluid Mechanics1Mechanics13616.30615.49.190
Food Science and Technology3Food Science & Technology1398.9605.22.615
Genetics5Genetics & Heredity17711.14610.80.500
Genomics and Human Genetics15Genetics & Heredity1777.2439.10.955
Immunology4Immunology15819.90010.75.875
Law and Social Science18Law1542.5887.70.233
Law and Social Science20Sociology1502.5887.70.233
Linguistics23Linguistics1872.0263.31.000
Marine Science2Geochemistry & Geophysics8516.3596.67.050
Marine Science1Marine & Freshwater Biology10616.3596.67.050
Marine Science1Oceanography6616.3596.67.050
Materials Research19Materials Science, Multidisciplinary31412.53110.62.267
Medicine6Medicine, Research & Experimental1389.7168.63.829
Microbiology9Microbiology13511.00013.70.967
Neuroscience9Neurosciences27112.54713.62.130
Nuclear and Particle Science2Physics, Nuclear198.7789.81.000
Nuclear and Particle Science3Physics, Particles and Fields298.7789.81.000
Nutrition2Nutrition & Dietetics8910.89714.20.714
Organizational Psychology and Organizational Behavior2Psychology, Applied8410.9234.41.222
Organizational Psychology and Organizational Behavior2Management22610.9234.41.222
Pathology: Mechanisms of Disease1Pathology7816.7507.26.500
Pharmacology and Toxicology1Toxicology9211.25011.45.793
Pharmacology and Toxicology5Pharmacology & Pharmacy27011.25011.45.793
Physical Chemistry19Chemistry, Physical15910.63812.13.667
Physiology2Physiology8119.55611.14.769
Phytopathology4Plant Sciences23412.62312.70.478
Plant Biology1Plant Sciences23419.54013.04.586
Political Science8Political Science1804.00011.30.750
Psychology2Psychology (Science)7718.15612.36.367
Psychology3Psychology, Multidisciplinary (Social Science)13818.15612.36.367
Public Health2Public, Environmental & Occup. Health (Social Science)17016.4639.53.880
Public Health3Public, Environmental & Occup. Health (Science)19316.4639.53.880
Resource Economics70Economics3712.7455.80.167
Resource Economics48Environmental Studies (Social Science)1162.7455.80.167
Resource Economics4Agricultural Economics and Policy (Science)212.7455.80.167
Sociology 1Sociology1506.40017.70.767
Statistics and Its Application4Mathematics, Interdisciplinary Applications1065.0953.21.350
Statistics and Its Application2Statistics and Probability1245.0953.21.350
Virology2Virology378.0213.61.172
Vision Science34Neurosciences2715.8973.40.391
Vision Science5Ophthalmology605.8973.40.391

AIMS AND SCOPE OF JOURNAL: The Annual Review of Plant Biology, in publication since 1950, covers the significant developments in the field of plant biology, including biochemistry and biosynthesis, genetics, genomics and molecular biology, cell differentiation, tissue, organ and whole plant events, acclimation and adaptation, and methods and model organisms.


Combining Near-Infrared Spectroscopy and Chemometrics for Rapid Recognition of an Hg-Contaminated Plant

The feasibility of rapid recognition of an Hg-contaminated plant as a soil pollution indicator was investigated using near-infrared spectroscopy (NIRS) and chemometrics. The stem and leave of a native plant, Miscanthus floridulus (Labill.) Warb. (MFLW), were collected from Hg-contaminated areas (

) as well as from regular areas (

). The samples were dried and crushed and the powders were sieved through an 80-mesh sieve. Reference analysis of Hg levels was performed using inductively coupled plasma-atomic emission spectrometry (ICP-AES). The actual Hg contents of contaminated and normal samples were 16.2–30.5 and 0.0–0.1 mg/Kg, respectively. The NIRS measurements of impacted sample powders were collected in the mode of reflectance. The DUPLEX algorithm was utilized to split the NIRS data into representative training and test sets. Different spectral preprocessing methods were performed to remove the unwanted and noncomposition-correlated spectral variations. Classification models were developed using partial least squares discrimination analysis (PLSDA) based on the raw, smoothed, second-order derivative (D2), and standard normal variate (SNV) data, respectively. The prediction accuracy obtained by PLSDA with each data preprocessing option was 100%, indicating pattern recognition of Hg-contaminated MFLW samples using NIRS data was in perfect consistence with the ICP-AES results. NIRS combined with chemometrics will provide a tool to screen the Hg-contaminated MFLW, which can be potentially used as an indicator of soil pollution.

1. Introduction

The growing development of agricultural, industrial, and urban activities has largely increased the release of toxic substances such as heavy metals and organic compounds to environmental systems [1–3]. In particular, toxic heavy metals in air, water, soil, and plants have caused severe public environmental concern because of their severe adverse influences on human health [4–6]. It is well known that soil is a major sink for heavy metal pollutants, which can be accumulated and transferred to water, air, plants, and animals. It was estimated that 20% of the total farmland in China had been contaminated, which directly threatens the safety of food production [7].

Numerous research efforts have been devoted to evaluation of the level of soil contamination with heavy metals caused by human activities, including electroplating industry, mining, smelting, coal-fired power stations, steel and iron manufacturing, waste incineration, leather industry, and cement production [8–12]. Most of these researches focused on direct determination of heavy metal levels in the soil. Various analytical methods have been developed and used to quantify the levels of heavy metals in soil, plants, and animals [13, 14], including inductively coupled plasma-atomic emission spectroscopy (ICP-AES), inductively coupled mass spectroscopy (ICP-MS), atomic fluorescence spectrometer (AFS), X-ray fluorescence spectrometer (XRF), neutron activation analysis (NAA), DC argon plasma multielement atomic emission spectrometer (DCP-MAES), atomic absorption spectrometer (AAS), and scanning electron microscopy with energy dispersive X-ray (SEM–EDX). Although accurate evaluation of heavy metals can be obtained, most of these techniques generally require laborious preconcentration of analytes and sample pretreatment, which have made the analysis time-consuming.

It is well known that excessive adsorption and accumulation of certain pollutants can influence the growth and metabolism of native plants [15, 16]. A traditional method to recognize and evaluate soil pollution is by examining the morphological variations of plant indicators caused by soil pollution, which are sensitive to the presence of certain pollutants. Although the level of soil pollution could only be qualitatively evaluated using plant indicators, it is more convenient and economic compared with direct methods by analysis of pollutants in soil. However, the use of plant indicators for soil pollution can be limited for some reasons. Firstly, because the plant species in an area can be influenced by many factors, such as geographical and climatic conditions, usually it is not ready to have a well-studied and suitable plant indicator in certain areas. Secondly, in some seriously polluted areas, the plants sensitive to soil pollution would perish and be gradually replaced with the dominant species, which have adapted to the pollution and whose morphological changes are not significant enough to be exactly recognized by the naked eye. Therefore, rather than examining the plants by the naked eye, it is more reasonable and reliable to characterize the changes in chemical compositions of polluted plants using instrumental techniques.

Near-infrared spectroscopy (NIRS) has been widely applied to analysis of various food and agricultural products [17–22]. The feasibility of using NIRS for quantitative analysis of heavy metals in environmental samples has been extensively evaluated [23–26]. Although in some cases NIRS demonstrates potential for quantitative analysis of heavy metals, in many cases, the sensitivity is lower than by using other methods and time-consuming sample preparation is required to obtain reliable results. Some studies also indicate NIRS is very useful for qualitative analysis of heavy metals [27]. NIRS can provide a powerful tool to simultaneously characterize the multicompounds in a complex system, which could be combined with pattern recognition methods [28, 29] to perform rapid classifications of different types of samples.

Therefore, the objective of this paper was to investigate the feasibility of rapid recognition of a native Hg-contaminated plant Miscanthus floridulus (Labill.) Warb. (MFLW) from normal MFLW using NIRS and chemometrics. Special attention was made on the experimental design and data collection to avoid obtaining artifacts caused by factors other than Hg-contamination.

2. Materials and Methods

2.1. Collection and Preparation of MFLW Samples

MFLW samples were collected with leaves cut off from the upper end with a length of 25 cm. The Hg-contaminated MFLW samples ( ) were collected around a mercury mining factory in Huashi, Tongren, China, within a range of 3 Kilometers normal samples ( ) were collected from an area about 10 Kilometers away (Chuandong, Tongren, China). All the MFLW samples were cleaned with water and kept in a cool, dry, and ventilated place away from direct sunlight to remove the moisture. Each sample (leaves and stalks) was crushed by a disintegrator and then the powders were sieved through an 80-mesh sieve. The dried, crushed, and filtered samples were kept with integrate packaging. An ultraviolet lamp was used to dry each sample for 10 minutes before NIR analysis and Hg reference analysis. The flowchart of sample preparation is shown in Figure 1.

2.2. NIRS Measurements

Impacted MFLW powders were analyzed in a quartz sample cup using an Antaris II Fourier transform-NIR spectrometer (Thermo Electron Co., Waltham, Massachusetts, USA) using the RESTLT 3.0 software in the reflectance mode. The spectra were measured using a PbS detector with an internal gold background as the reference. The working range of spectrometer was 4000−10000 cm −1 . Each sample was measured triply while being stirred and impacted before each measurement and the average spectra were obtained. The number of scans for each measurement was 32. The instrumental resolution was 8 cm −1 with a scanning interval of 3.857 cm −1 , so each raw spectrum had 1557 wavelengths. The temperature was kept at around 25°C and the humidity was kept at a stable level during analysis. In order to avoid artificial spectral variations between different types of samples, the order of analysis for all the samples was permuted randomly.

2.3. Reference Analysis of Hg Using Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES)

The total Hg contents in MFLW were analyzed according to the national standard (GB5009.17-2014). The MFLW powders were digested using the CEM Mars 5 Microwave Accelerated Reaction System (CEM Corp., Matthews, USA). About 0.4 g of homogenized samples was digested in Teflon vessels with 8.0 mL of nitric acid (HNO3) (V/V, 10%) overnight and kept at 150°C for 5 h. The programmed digestion conditions are summarized in Table 1. Hg contents were analyzed using an Agilent 725 ICP-AES system (Agilent, Victoria, Australia). The precision of ICP-AES analysis was verified by triplication of the samples. Pearson’s

of the standard curve was over 0.9999. The average relative standard deviation (RSD) was less than 5.0% and the recovery rate was 96.1

104.5%. The limit of detection (LOD) was calculated to be 0.0025 mg/Kg according to the IUPAC method, where the signal of 3σ of 11 blank solutions was calibrated using the standard curve.

2.4. Chemometrics Analysis

The data analysis was performed on MATLAB 7.0.1 (Mathworks, Sherborn, MA). In order to remove the unwanted variation in NIRS data, smoothing [30], taking second-order derivative (D2) [30], and standard normal variate (SNV) [31] were performed on the raw data. The DUPLEX algorithm [32] was used to divide the measured samples into a representative training set and test set.

Partial least squares discriminant analysis (PLSDA) [33] was used to develop classification models to distinguish the Hg-contaminated from the regular samples. For PLSDA, a dummy response vector was constructed using +1 and −1 to represent the regular and Hg-contaminated samples, respectively. The number of PLSDA components was estimated using Monte Carlo cross validation (MCCV) [34]. The number of PLS components was determined as to obtain the lowest error rate of MCCV (ERMCCV):

is the number of MCCV data splitting and and are the numbers of misclassified and leave-out samples, respectively. For prediction, a cutoff value of zero was used to assign a new sample to one of the two classes.

For prediction, the overall accuracy (ACCU) was computed to evaluate the performance of classification models:

where TP, TN, FN, and FP represent the numbers of true positives, true negatives, false negatives, and false positives, respectively. In this work, regular and Hg-contaminated MFLW samples were seen as “positives” and “negatives,” respectively. Another two usually used indices, sensitivity (SENS) and specificity (SPEC), were also adopted to evaluate the classification performance:

SENS and SPEC describe the model ability to correctly accept the “positives” and to correctly reject the “negatives,” respectively.

3. Results and Discussion

According to the analytical results of ICP-AES, the Hg contents of regular and contaminated MFLW objects ranged from 0.0 to 0.1 mg/Kg and 16.2 to 30.5 mg/Kg, respectively, indicating an obvious Hg-contamination of soil surrounding the mercury mining areas. The NIR spectra of regular and Hg-contaminated MFLW samples are shown in Figure 2. Seen from Figure 2, the raw spectra of regular and Hg-contaminated MFLW samples have verysimilar absorbance peaks in the range of 4000–10000 cm −1 . The peaks can be mainly assigned as follows [35]: 8377 cm −1 (the second overtones of C–H stretching), 6823 cm −1 (overlapping of the first overtone of O–H stretching and N–H stretching), 5662 cm −1 (the first overtones of C–H stretching), 5184 cm −1 (the combination of the baseband of O–H stretching and the first overtone of C–O deformation), and 4748 cm −1 (combination of N–H stretching and deformation of peptide groups). Some bands (8377 cm −1 , 5662 cm −1 , and 4748 cm −1 ) are very weak and the peak resolution is very low. Figure 2 also demonstrates the NIRS data preprocessed by smoothing and taking D2 and SNV transformation. Even with data preprocessing, the spectral difference between regular and Hg-contaminated MFLW samples is still very subtle and is difficult to be distinguished by the naked eye. Therefore, it is necessary to develop chemometric models to extract the relevant information for classification of regular and Hg-contaminated MFLW samples.

In order to obtain representative data sets for developing and validating classification models, the DUPLEX algorithm was adopted to divide the collected samples into training and prediction objects. Considering the regular and Hg-contaminated MFLW samples have different distributions, the DUPLEX algorithm was performed separately on the two classes. The 116 regular samples were split into 80 training and 36 test samples the 125 Hg-contaminated objects were split into 85 training and 40 test samples. For model building, the training and test samples from the two classes were combined to form the final training and test sets, so 165 (

) test samples were obtained.

PLSDA models were developed with the raw and preprocessed spectra. With different numbers of PLSDA components, ERMCCV was computed and the model complexity was determined as to minimize the ERMCCV value. The number of MCCV data splitting was set to be 100 in this work. Considering the size of training set is moderate, in each MCCV data splitting, 30% of the training set was randomly left out for prediction and the other 70% training samples were used for model development. Based on different data preprocessing options, the model parameters and prediction performance are shown in Table 2. It can be seen that, with each data preprocessing option and even without data preprocessing, PLSDA could obtain perfect classification of regular and Hg-contaminated samples and the accuracy, sensitivity, and specificity were all 1, indicating data preprocessing was not necessary to develop an accurate model. Moreover, all the PLSDA models had 2 latent variables and the low model complexity means that the models would provide good generalization performance. The prediction results by PLSDA with different data preprocessing are shown in Figure 3, indicating distinct classification of regular and Hg-contaminated MFLW samples by PLSDA despite the kind of data preprocessing. By examining and comparing the predicted responses by PLSDA models with different preprocessing methods, the results by PLSDA with raw data and smoothed spectra were very similar, which were obviously different from those obtained by PLSDA with D2 and SNV spectra. Moreover, the prediction errors (with references to the dummy response vector of +1 and −1) obtained by PLSDA with D2 and SNV spectra were much lower than those obtained by PLSDA with the raw and smoothed spectra. Although all the four PLSDA models could achieve a classification accuracy of 1, D2 and SNV were still necessary to remove some unwanted spectral variations to ensure the generalization performance of PLSDA when predicting new samples.


Activity Details

  • Subjects:SCIENCE, ENGINEERING, TECHNOLOGY
  • Types:CLASSROOM ACTIVITY
  • Grade Levels:6 - 11
  • Primary Topic:LIGHT AND OPTICS
  • Additional Topics:
    EARTH
    PHYSICAL SCIENCES
  • Time Required: Longer than 2 hrs
  • Next Generation Science Standards (Website)

Develop and use a model to describe that waves are reflected, absorbed, or transmitted through various materials

Analyze and interpret data to determine scale properties of objects in the solar system

Use mathematical representations to support a claim regarding relationships among the frequency, wavelength, and speed of waves traveling in various media

Communicate technical information about how some technological devices use the principles of wave behavior and wave interactions with matter to transmit and capture information and energy

Construct an explanation of the Big Bang theory based on astronomical evidence of light spectra, motion of distant galaxies, and composition of matter in the universe


A plant gene may have helped whiteflies become a major pest

Whiteflies (one pictured) are aphidlike insects that feed on the leaves of hundreds of plants. A gene acquired from a plant at least 35 million years ago allows the pests to neutralize a common chemical defense employed by many plants.

Jixing Xia and Zhaojiang Guo

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At some point between 35 million and 80 million years ago, a whitefly landed on a leaf and started sucking its sweet sap. That fateful meal provided more than sugar. Somehow, a gene from the plant wound its way into the whitefly’s genome, a new study suggests, and may have helped its ancestors become one of the most notorious agricultural pests today.

The gene helps plants neutralize and safely store certain toxic molecules they use to deter herbivores. In whiteflies (Bemisia tabaci), it allows the insects to feed on flora, undeterred by one of the plant world’s best chemical weapons, researchers report March 25 in Cell. This plant-to-insect gene swap is the second ever documented, and the clearest example of an insect effectively commandeering the genetic toolkit of their “prey” to use it against them.

“Ten or 20 years ago no one thought that this kind of gene transfer was possible,” says Roy Kirsch, a chemical ecologist at the Max Planck Institute for Chemical Ecology in Jena, Germany, who wasn’t involved in the study. “There are so many barriers a gene must overcome to move from a plant to an insect, but this study clearly shows that it happened, and that the gene provides a benefit to whiteflies.”

Gene swapping is common among bacteria (SN: 10/31/11), and occasionally happens between gut microbes and their animal hosts. Known as horizontal gene transfer, this process allows organisms to bypass the plodding nature of parent-to-offspring inheritance and instantly acquire genes shaped by generations of natural selection. But a genetic jump from plants to insects, lineages separated by at least a billion years of evolution, has been documented only once before, also in whiteflies.

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Whiteflies are aphidlike insects that feed on over 600 different plants around the globe. The pernicious pests’ wide-ranging diet stems in part from their ability to evade many common plant defenses (SN: 4/4/19). While looking for genes that underlie this ability, researchers in China stumbled upon something strange in three closely related whitefly species — a gene, called BtPMaT1, not known to exist outside of plants.

Two scenarios could explain such a pattern. Either the gene arose in the common ancestor of plants and insects and was subsequently snuffed out on all intervening branches of the tree of life, or whiteflies somehow acquired the gene from plants. Because plants and insects are so distantly related, the latter scenario is “much, much more likely,” says Kirsch. These three whitefly species split some 35 million years ago, suggesting they got the gene before then. But close relatives that diverged 80 million years ago lack the gene, suggesting the transfer happened within that window.

The gene allows plants to stow a common class of defensive chemicals called phenolic glycosides by neutralizing the toxins until herbivores start munching. “Phenolic glycosides are very toxic to insects,” says study coauthor Ted Turlings, a chemical ecologist and entomologist at the University of Neuchâtel in Switzerland. The possibility that whiteflies might use a plant detoxification gene to tolerate plant toxins tantalized Turlings’ colleagues in China.

The researchers inserted a bit of RNA into tomato plants in the lab. Once ingested by whiteflies, the RNA was designed to disable their BtPMaT1 gene. Then, the team let whiteflies loose. After a week of feeding on five genetically altered plants, nearly all of the approximately 2,500 whiteflies were dead, compared with only about 20 percent of those that fed on unaltered plants. Such a drastic effect suggests this gene plays an important role in helping whiteflies bypass plant defenses, Turlings says.

How exactly a plant BtPMaT1 wound up in whiteflies remains a mystery. Viruses can accidentally shuttle bits of DNA between hosts, and Turlings suspects this likely happened here. “This is an extremely rare event, but when you’re talking about billions of insects and plants interacting over millions of years, it becomes more possible,” he says. Horizontal gene transfer might possibly be “an important mechanism for pests to gain abilities to deal with plant defenses.”

The first documented plant-to-insect gene swap, reported September 23 in Scientific Reports, also occurred in whiteflies, though the function of the gene in that swap is less clear. It may not be a coincidence, though, that the two known examples of such an event occurred in the same herbivorous insect.

“The lives of whiteflies and their plant hosts are closely intertwined,” says Shannon Soucy, an evolutionary microbiologist at Dartmouth College who wasn’t involved in the research. That consistent exposure primes the system to be ready for this kind of event, she says, which ultimately allowed whiteflies to use this plant defense gene against its maker.

Questions or comments on this article? E-mail us at [email protected]

Editor's Note:

This story was updated on April 1, 2021, to correct that nearly all (not all) of the whiteflies that ate the genetically altered plants in the experiment died.

A version of this article appears in the April 24, 2021 issue of Science News.

Citations

W.J. Lapadula, M.L. Mascotti and M.J. Ayub. Whitefly genomes contain ribotoxin coding genes acquired from plants. Scientific Reports. Vol. 10, September 23, 2020. doi: 10.1038/s41598-020-72267-1.

About Jonathan Lambert

Jonathan Lambert is the staff writer for biological sciences, covering everything from the origin of species to microbial ecology. He has a master’s degree in evolutionary biology from Cornell University.


Science News

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