Serotonin accumulation in transgenic rice by over-expressing tryptophan decarboxlyase results in a dark brown phenotype and stunted growth

A mutant M47286 with a stunted growth, low fertility and dark-brown phenotype was identified from a T-DNA-tagged rice mutant library. This mutant contained a copy of the T-DNA tag inserted at the location where the expression of two putative tryptophan decarboxlyase genes, TDC-1 and TDC-3, were activated. Enzymatic assays of both recombinant proteins showed tryptophan decarboxlyase activities that converted tryptophan to tryptamine, which could be converted to serotonin by a constitutively expressed tryptamine 5' hydroxylase (T5H) in rice plants. Over-expression of TDC-1 and TDC-3 in transgenic rice recapitulated the stunted growth, dark-brown phenotype and resulted in a low fertility similar to M47286. The degree of stunted growth and dark-brown color was proportional to the expression levels of TDC-1 and TDC-3. The levels of tryptamine and serotonin accumulation in these transgenic rice lines were also directly correlated with the expression levels of TDC-1 and TDC-3. A mass spectrometry assay demonstrated that the dark-brown leaves and hulls in the TDC-overexpressing transgenic rice were caused by the accumulation of serotonin dimer and that the stunted growth and low fertility were also caused by the accumulation of serotonin and serotonin dimer, but not tryptamine. These results represent the first evidence that over-expression of TDC results in stunted growth, low fertility and the accumulation of serotonin, which when converted to serotonin dimer, leads to a dark brown plant color

色胺酸脫羧酶大量表現之轉殖水稻中血清素之累積造成深褐色外表性狀及發育遲緩

A mutant M47286 with a stunted growth, low fertility and dark-brown phenotype was identified from a T-DNA-tagged rice mutant library, the Taiwan Rice Insertional Mutant (TRIM). This mutant contained a copy of T-DNA tag inserted at the location where the expression of two putative tryptophan decarboxylase genes, TDC-1 and TDC-3, were activated. Enzymatic assays of both recombinant proteins showed tryptophan decarboxylase activities that converted tryptophan to tryptamine, which would be converted to serotonin by a constitutively expressed tryptamine 5' hydroxylase (T5H) in rice plants. Over-expression of TDC-1 or TDC-3 in transgenic rice recapitulated the stunted growth, dark-brown phenotype and resulted in a low fertility similar to M47286. The degree of stunted growth and dark-brown colored phenotypes was proportional to the expression levels of transgenes TDC-1 or TDC-3. The level of tryptamine and serotonin accumulation in these transgenic rice lines were also directly correlated with the expression levels of TDC-1 and TDC-3. A mass spectrometry assay demonstrated that the dark-brown leaves and hulls in the TDC over-expressing transgenic rice were resulted by the accumulation of serotonin dimer and that the stunted growth and low fertility were also caused by the accumulation of serotonin and serotonin dimer, but not tryptamine. These results represent the first evidence that over-expression of TDC results in stunted growth, low fertility and the accumulation of serotonin, which when converted to serotonin dimer, leads to a dark brown plant color.水稻T-DNA插入突變株M47286具有發育遲緩、低稔實與深褐色外表性狀。此突變株帶有單一T-DNA標誌，其插入位置造成兩個色胺酸脫羧酶 (tryptophan decarboxylase) 基因TDC-1與TDC-3的活化。TDC重組蛋白質之酵素活性分析結果顯示，二者皆具有色胺酸脫羧酶活性，可將色胺酸 (tryptophan) 轉化成色胺 (tryptamine) 。色胺進一步被水稻中的色胺酸-5-羥化酶 (tryptamine 5’hydroxylase , T5H) 轉化成血清素 (serotonin)。大量表現TDC-1與TDC-3之轉殖水稻亦呈現與M47286相似之農藝性狀，如發育遲緩、深褐色外表性狀、低稔實等，其影響程度取決於轉殖水稻中TDC-1及TDC-3之表現量。轉殖水稻中之色胺與血清素的累積亦發現與TDC-1及TDC-3之表現量有正相關。質譜分析結果證實，TDC轉殖水稻之深褐色葉片與穀殼是由於血清素二聚體 (serotonin dimer) 之累積所造成的現象；發育遲緩與低稔實是因為血清素與血清素二聚體的累積所引起，而非色胺所導致的結果。本研究結果提供了初步的證據顯示，大量表現TDC之轉殖水稻具有發育遲緩與低稔實之農藝性狀，而其植株內所累積的血清素及血清素二聚體則會導致植株呈現深褐色的外表性狀。中文摘要 ……………………………………………………………………………………i Abstract ……………………………………………………………………………………ii List of Tables .…………………………………………………………………………….vi List of Figures ……………………………………………………………………...……vi List of Appendices ……………………………………………………………………..viii Abbreviations ……………………………………………………………...…………….ix Introduction ………………………………………………………………..…………….1 Materials and Methods 1. Plant materials and growth conditions……………………………...………..…..6 2. Bioinformatics analysis…………………………………………………….…….6 3. DNA extraction………………………………………………………..……..…..7 4. Plasmid Rescue…………………………………………………………...…..….7 5. Genotyping of the progenies of mutant M47286……………………………...…8 6. Southern hybridization………………………………………………..………….9 6.1 Genomic DNA digestion and blotting in nitrocellulose membrane ……9 6.2 Probe preparation……………………………………....................…….9 6.3 Hybridization of membrane and Autoradiography…………….……10 7. RNA extraction and RT-PCR………………………………….………………..10 8. Antibody preparation……………………………………………………………11 9. Western-Blot Analysis………………………..………………...………………12 9.1 Extraction of total rice protein……………………………..………….12 9.2 SDS PAGE………………………………………….……………..….12 9.3 Western blot…………………………………………………………...13 10. Construction of TDC overexpression vectors and plant transformation………14 11. Enzyme activity and an assay for the substrate-specificity of Tryptophan decarboxylase………………………………………………………………….14 12. Measurement of tryptamine and serotonin…………………………………….14 13. UV catalyzed photo chemical reaction and identification of serotonin dimer structure….………………….…………………………………...…….15 14. Pigment extraction, Liquid Chromatography and Mass Spectrometry………..16 15. Analysis of the growth effects of the tryptamine, serotonin or serotonin dimer treatments………………………………………………...…...………...16 16. Pollen Viability Assays……………………………………………....………..17 Results 1. Isolation and identification of mutant M47286……………...……..…...………18 2. Activation of TDC genes and the accumulation of tryptamine and serotonin in various genotypes of M47286…………………………………………………..20 3. TDC-3 has tryptophan decarboxylase activity………………………………….22 4. Expression profile of TDC-1 and TDC-3 genes in rice………….…...…………23 5. Over-expressing TDCs in transgenic rice recapitulate the mutant phenotype….25 6. Polymerization of serotonin dimer caused the dark-brown colored phenotype in rice plants……………………………………….…...……………26 7. Over-expressing TDCs reduced pollen viability and affected fertilization in female gametophyte…………………………………………………….………28 8. Ectopic expression of rice TDC genes in Arabidopsis affected growth and produce brown and sterile flower buds…….………….………………………..29 9. Exogenous serotonin and serotonin dimer affect plant growth and flower development……………………………………...……………………………..29 Discussions 1. Serotonin accumulation in TDC-overexpressing transgenic rice affects plant growth……………………………………………..……………………...31 2. High levels of serotonin accumulation in rice plants are necessary but not sufficient for the development of the dark-brown phenotype………….………33 3. Serotonin dimer involved in brown pigmentation in TDC-overexpressing transgenic and mutant rice and at pathogen infection sites…………………….35 Tables……………………………………………………………………………..……….37 Figures……………………………………………………………………………..…...…43 References…………………………………………………………………………...……73 Appendices..........................................................................................................................8

應用於農業環境自動車引導控制系統之研究

本研究證明穩定一個穩定及可靠的引導控制系可用於自動車行徑在農業室外環境裡。我們使用一種座標演算法的新技術並結合差分GPS（DGPS）和雙電子羅盤（DEC），可使系統能正確地判定車輛在果園的坐標，經由利用立方樣條函數，系統建立一個事先行駛在道路的人工引導路徑。因此，藉由使用我們新的控制演算法，系統能自動控制車輛即時朝向準確目標的路線在相同引導路徑。此外，本系統經由測試在自動噴霧透過實際驗證實驗。本新開發的系統可令人滿意地應用於農業的環境。This work demonstrates a stable and reliable guiding control system for the autonomous vehicle working in Agricultural outdoor environment. Using a new technique of position algorithm, combination of differential GPS (DGPS) and double electric compasses (DEC), our system is able to properly decide the coordinates of the vehicle moving in the orchard. Through utilizing the cubic spline function, the system creates an artificial guiding line from previous driven motion path. As a result, by using the new control algorithm, the system can automatically control the vehicle in the same route along the guiding line in the real-time toward the destination with acceptable accuracy. In addition, this system is verified through real experiments by testing on an autonomous sprayer. This newly developed system was found to work satisfactorily under agricultural environment.Acknowledgment ........................................................................................... I Abstract ........................................................................................................... II Contents ............................................................................................... IV List of Tables .................................................................................................. VI List of Figures ................................................................................................ VII List of Appendices ......................................................................................... IX Abbreviations ................................................................................................. X Chapter 1 INTRODUCTION ............................................................................ 1 1.1 Objective ………………………………………………………………… 1 Chapter 2 LITERATURE REVIEW ………………………………………………… 2 Chapter 3 THEORY AND PRINCIPLE CONCEPT …………………………….. 6 3.1 The Differential Global Positioning System (DGPS) ........................ 6 3.2 Differential GPS (DGPS) with Beacon Correction or Land Based Augmentation System (LBAS) ............................................................................ 7 3.3 Differential GPS (DGPS) with Satellite Based Augmentation System (SBAS) and the Wide Area Augmentation System (WAAS) ............................... 8 3.4 Vehicle heading sensor ...................................................................... 14 3.5 Development of double electric compass ........................................... 15 3.6 The one-turn rotation (OTR) scheme ................................................. 16 3.7 The compensation of the interferences caused by the slope and tilt ... 18 3.8 Linear potentiometer .......................................................................... 21 3.9 Vehicle platform ................................................................................. 22 3.10 Vehicle hydraulics control system .................................................... 24 3.11 Combining a differential global positioning system and double electric compass to improve multi-path error correction for a high-precision agricultural robotic vehicle................................................... 24 3.12 Positioning system ........................................................................... 24 3.13 Coordinate conversion ..................................................................... 25 3.14 Position predictive algorithm ............................................................ 27 3.15 Guiding line algorithm ....................................................................... 28 3.16 The Guiding algorithm of autonomous vehicle.................................... 30 3.17 Electric circuits system …………………………………………………. 33 3.18 Software ........................................................................................... 34 CHAPTER 4 RESULTS AND DISCUSSION ……………………………………... 38 4.1 The calibration and confirm the reliability of the new heading sensor.. 38 4.2 Vehicle position estimated by the positioning system ....................... 41 4.3 Confirm the reliability of the autonomous control system .................. 44 CHAPTER 5 CONCLUSION AND SUGGESTION ………………………………. 47 References ........................................................................................................ 49 Appendices ....................................................................................................... 52 LIST OF TABLES Table 1. NMEA messages ................................................................................ 13 Table 2. OTR result and coefficient of Compasses 1 and 2 ............................ 39 Table 3. The results on position system measured of distance error................... 44 Table 4. The results on the automatic control of distance error ........................ 46 LIST OF FIGURES Figure1. (a) Route test inside the campus, (b) lack of data and interrupted positioning data due to inadequate satellites in site ........................... 4 Figure 2. The Land Based Augmentation System (LBAS) ................................ 8 Figure 3. The Based Augmentation System (SBAS) ....................................... 9 Figure 4. The coverage of future SBAS systems around the world .................. 10 Figure 5. Trimble DSM 232-DGPS devices, DSM 232 receiver and antenna-GPS/Beacon DSM132 ....................................................................... 12 Figure 6. V2xe magnetic field sensors .............................................................. 14 Figure 7. Double electric compass .................................................................... 16 Figure 8. Magnetic circle shifting generated by a vehicle body ........................ 17 Figure 9. Magnetic field distortion generated by slope and tilt …………………. 19 Figure 10. The predictive calibration algorithm for slope and tilt ....................... 20 Figure 11. The vehicle system, Illustration: vehicle hydraulics control system (a), heading sensors and receiver board (b), DGPS receiver (c), PC (d), DGPS antenna (e) and linear potentiometer........................................................................... 23 Figure 12. The flow diagram of positioning system ............................................. 25 Figure 13. Predictive platform box diagram ......................................................... 28 Figure 14.The illustrated of spline cubic curve and target point …………………... 30 Figure 15. The determination of guiding point for the vehicle ............................. 31 Figure 16. The electric circuit system ……………………………………………..... 34 Figure 17. Features of the guiding control software …………………………….... 35 Figure 18. The overview of guiding control system of an autonomous vehicle ... 36 Figure 19. Measurement results, angle measurement of external interferences field and calibration function (a), heading (b), and heading angle error on path (c) ............................................................................................ 40 Figure 20. Difference between the position on the actual path and that measured by the DGPS and our system ............................................ 41 Figure 21. Plots of coordinate positions on the actual path (red circles) and those by DGPS (Green squares) and our system on the x-axis (a) and the y-axis (b), and the number of satellites used (grey diamonds) and the HDOP value (pink diamonds) (c) .......................................... 42 Figure 22. Field testing, the first movement by the driver ................................... 45 Figure 23. Vehicle testing results in autonomous mode .................................... 46 LIST OF APPENDICES Appendix A1. The format of raw data output are obtained from the sensors System ....................................................................................... 53 Appendix A2. DGPS receiver circuit ................................................................. 54 Appendix A3. Electric compass circuit (V2xe) .................................................. 55 Appendix A4. Linear potentiometer circuit ........................................................ 56 Appendix A5. PC interface circuit ..................................................................... 57 Appendix A6. IO control circuits ....................................................................... 58 Appendix A7. IO driver circuit ........................................................................... 59 Appendix B1. Main flow chart diagram ............................................................. 60 Appendix B2. Sub flow of angle ........................................................................ 61 Appendix B3. Sub flow of atan2 ....................................................................... 64 Appendix B4. Sub flow of auto-mark ................................................................ 65 Appendix B5. Sub flow of data receives ........................................................... 66 Appendix B6. Sub flow of DGPS ...................................................................... 69 Appendix B7. Sub flow of interpolatecatmullrom2D ......................................... 71 Appendix B8. Sub flow of mark enabled ........................................................... 72 Appendix B9. Sub flow of parameter ................................................................ 73 Appendix B10. Sub flow of plot-mark ................................................................. 74 Appendix B11. Sub flow of vrr ............................................................................ 75 Appendix B12. Sub flow of read data ................................................................. 76 Appendix B13. Sub flow of repeat deletes mark ................................................. 77 Appendix B14. Sub flow of predictive ................................................................. 78 Appendix B15. Sub flow of plot path ................................................................... 7

Combining a differential global positioning system and double electric compass to improve multi-path error correction for a high-precision agricultural robotic vehicle

This work has addressed the improving of the multi-path error in positioning systems by coupling a differential global positioning system (DGPS) and double electric compass (DEC) in the navigation system of an orchard robotic vehicle. A novel corrective algorithm model was applied to predicting the positioning coordinates during vehicle movement. The model manipulates a combination of data from both the DEC and the DGPS when the DGPS receiver is in problematic conditions in which the horizontal dilution of precision (HDOP) is higher than three and the number of satellites is fewer than six. The constructed corrective algorithm model, the DEC and the DGPS together form a combined DGPS-DEC system that is inexpensive and of high-precision fitting for a vehicle-guiding instrument. In a field test in an outdoor environment with sections of tree shade in the guiding path, the combined DGPS-DEC positioning system effectively improved the reliability of positioning by correcting the DGPS multi-path error precisely to within 20 cm. By applying a mini-sprayer, further agricultural applications were feasible. In summary, the combined DGPS-DEC positioning system can obtain the correct position of a vehicle in real time for agricultural applications

Expression and characterization of a thermostable l-aminoacylase in transgenic rice

The gene encoding a thermostable L-aminoacylase (LAA) from Deinococcus radiodurans BCRC12827 was isolated and expressed in transgenic rice under the control of a rice actin gene promoter or a seed-specific promoter, Ose705. The recombinant LAA in the transgenic line Ose705:LAA was specifically detected in rice grains, but not in leaves, and its identity was confirmed by a LC/MS/MS assay. Furthermore, was efficiently purified via affinity chromatography using a nickel column. Enzymatic activity of this rice-produced LAA was determined by HPLC and a maximum activity at pH 8.0 and 45 °C in a phosphate buffer supplemented with the divalent metal ion Co2+ using NAc-L-HPA as a substrate was obtained, similar to its host counterpart. This rice-produced LAA maintained approximately 50% enzyme activity after 48 h of incubation under 45 °C and maintained approximately 90% activity compared to a freshly prepared sample after being stored in rice seeds for 4 years. The present study indicated that seed-specific protein production in transgenic rice is a good and safe source for mass production of LAA, and this system can be useful for the production of other biomedical proteins as well