過去微生物學家認為Haloarcula marismortui 不會泳動，然而近年的研究證明了H. marismortui 鞭毛的存在，顯示其具有泳動能力。本研究觀察H. marismortui 的活菌泳動現象，並以ImageJ 追蹤其泳動軌跡 (swimming trajectory)。由於其軌跡相當雜噪 (noisy)，因此難以適用於前人所建立的泳動分析演算法 (motion analysis algorithm)。在此我提出 “window vector” 的概念，以Microscope Excel-VBA 程式語言建立一個微生物泳動分析演算法 (microbial motion analysis algorithm)，具有以下功能：(1) 不需以實驗結果個別化估算分析參數 (empirical parameter customization)，而用冪次率關係 (power-law relationship) 自動分辨細胞為泳動或非泳動；(2) 減低布朗運動對泳動軌跡造成的雜訊 (noise)，因此提升泳動折返 (swim reversal) 判斷的準確性。藉此演算法，本研究證明了HmSRI 和HmSRII 這兩個近來被確認的感光視紫質 (sensory rhodopsin) 的生理功能，分別是趨光性 (photoattractant) 和避光性 (photorepellent) 反應。H. marismortui 是除了 Halobacterium salinarum 外，唯一被確認具有光趨性 (phototaxis) 的古生菌。

Haloarcula marismortui has been described to be nonmotile prior to the recent identification of flagellar filaments, suggesting the motile nature of H. marismortui. Here we observed the locomotion of freshly cultured H. marismortui cells and tracked the swimming trajectories via ImageJ. Trajectories of H. marismortui are intrinsically noisy, posing difficulties in motion analysis with previously established algorithms. By introducing the concept of ‘‘window vector,’’ a Microsoft Excel-VBA-implemented microbial motion analysis algorithm reported here was able to (1) discriminate nonswimming objects from swimming cells without empirical customization by applying a power-law relationship and (2) reduce the noise caused by Brownian motion, thus enhancing the accuracy of swim reversal identification. Based on this motion analysis algorithm, two recently identified sensory rhodopsins, HmSRI and HmSRII, were shown to mediate photoattractant and photorepellent responses, respectively, revealing the phototactic activity of H. marismortui, the only archaeon showing such phenomenon other than Halobacterium salinarum.

口試委員會審定書…i
謝誌…ii
Contents…iii
List of Figures …vi
List of Tables…viii
中文摘要…ix
Abstract…x
Chapter 1 Introduction…1
1.1 Haloarcula marismortui, a halophilic archaeon from the Dead Sea…1
1.2 Genome sequencing of H. marismortui implied a phototactic nature…4
1.3 Mechanism of swim-reversal motility – a biased random walk…7
1.4 Brief introduction of microbial rhodopsins…9
1.5 Biophysical properties of the six rhodopsins in H. marismortui…14
1.6 Predictions for the physiologic functions of the three sensory rhodopsins,
HmSRI, HmSRII and HmSRM…17
1.7 Main questions and framework of this study…18
Chapter 2 Materials and Methods…22
2.1 Motile cell culture and sample preparation…22
2.1.1 Preparation of Halomedium…22
2.1.2 Preparation of swarm plate…22
2.1.3 Motile cell culture…22
2.2 Flagella stain…23
2.3 Hardware…25
2.4 Software…28
2.4.1 Image processing with ImageJ…29
2.4.2 Particle tracking…35
2.4.3 The concept of “window vector”…38
2.4.4 Trajectory discrimination using the power law…42
2.4.5 Mathematical descriptions of the power law…45
2.4.6 User manual of the Excel-VBA program…49
2.4.7 An overview of the motion analysis software system…53
2.5 Verification of the motion analysis system…56
Chapter 3 Results…57
3.1 The swimming pattern and autonomous reversal rate of freshly cultured
H. marismortui cells in the dark…57
3.2 Pulse illumination experiments demonstrate HmSRII mediates photorepellent
responses…60
3.3 Continuous illumination experiments demonstrate HmSRI and HmSRII
mediate photoattractant and photorepellent responses, respectively…62
3.4 The photorepellent signal of HmSRII was antagonized by HmSRI…63
3.5 HmSRM does not mediate phototactic responses…65
3.5.1 Activation of HmSRM did not elicit distinct phototactic effect…66
3.5.2 Simultaneous activation of HmSRM and HmSRI / HmSRII did not
elicit distinct phototactic effect…69
3.5.3 The signaling kinetics of HmSRII was not affected by HmSRM…71
3.6 Conclusions…73
Chapter 4 Discussion…74
4.1 Advantages of the concept of window vector in noise reduction…74
4.2 Estimation of the optimal window width…74
4.3 Advantage of using power law for trajectory discrimination…75
4.4 Prerequisites for accurate trajectory discrimination…76
4.5 Potential methods to reveal the physiologic function of HmSRM…77
4.6 Future perspectives…77
Reference…79
Appendices…85
Appendix 1. Thesis defense PowerPoint slides…85
Appendix 2. Thesis defense questions and answers…97
Appendix 3. Poster…105

1.http://en.wikipedia.org/wiki/Dead_sea Dead Sea - Wikipedia, the free encyclopedia (2010).
2.Oren, A. & Ventosa, A. Benjamin Elazari Volcani (1915-1999): sixty-three years of studies of the microbiology of the Dead Sea. Int Microbiol 2, 195-8 (1999).
3.Oren, A., Ginzburg, M., Ginzburg, B.Z., Hochstein, L.I. & Volcani, B.E. Haloarcula marismortui (Volcani) sp. nov., nom. rev., an extremely halophilic bacterium from the Dead Sea. Int. J. Syst. Bacteriol. 40, 209-10 (1990).
4.Javor, B., Requadt, C. & Stoeckenius, W. Box-shaped halophilic bacteria. J Bacteriol 151, 1532-42 (1982).
5.Mevarech, M., Frolow, F. & Gloss, L.M. Halophilic enzymes: proteins with a grain of salt. Biophys Chem 86, 155-64 (2000).
6.Ban, N., Nissen, P., Hansen, J., Moore, P.B. & Steitz, T.A. The complete atomic structure of the large ribosomal subunit at 2.4 A resolution. Science 289, 905-20 (2000).
7.Martinez, S.E. & Smith, J.L. Crystallization and preliminary characterization of mitogillin, a ribosomal ribonuclease from Aspergillus restrictus. J Mol Biol 218, 489-92 (1991).
8.Baliga, N.S. et al. Genome sequence of Haloarcula marismortui: a halophilic archaeon from the Dead Sea. Genome. Res. 14, 2221-34 (2004).
9.Ng, W.V. et al. Genome sequence of Halobacterium species NRC-1. Proc. Natl. Acad. Sci. U S A 97, 12176-81 (2000).
10.Pyatibratov, M.G. et al. Alternative flagellar filament types in the haloarchaeon Haloarcula marismortui. Can. J. Microbiol. 54, 835-44 (2008).
11.Berg, H.C. The rotary motor of bacterial flagella. Annu Rev Biochem 72, 19-54 (2003).
12.Mauriello, E.M., Mignot, T., Yang, Z. & Zusman, D.R. Gliding motility revisited: how do the myxobacteria move without flagella? Microbiol Mol Biol Rev 74, 229-49.
13.Sundberg, S.A., Alam, M. & Spudich, J.L. Excitation signal processing times in Halobacterium halobium phototaxis. Biophys. J. 50, 895-900 (1986).
14.Amsler, C.D. Use of computer-assisted motion analysis for quantitative measurements of swimming behavior in peritrichously flagellated bacteria. Anal. Biochem. 235, 20-5 (1996).
15.Mitchell, J.G. & Kogure, K. Bacterial motility: links to the environment and a driving force for microbial physics. FEMS Microbiol. Ecol. 55, 3-16 (2006).
16.Wadhams, G.H. & Armitage, J.P. Making sense of it all: bacterial chemotaxis. Nat Rev Mol Cell Biol 5, 1024-37 (2004).
17.Spudich, J.L., Yang, C.S., Jung, K.H. & Spudich, E.N. Retinylidene proteins: structures and functions from archaea to humans. Annu Rev Cell Dev Biol 16, 365-92 (2000).
18.Briggs, W.R. & Spudich, J.L. Handbook of photosensory receptors (Wiley-VCH, Weinheim, 2005).
19.Oesterhelt, D. & Stoeckenius, W. Functions of a new photoreceptor membrane. Proc. Natl. Acad. Sci. U S A 70, 2853-7 (1973).
20.Lanyi, J.K. & Luecke, H. Bacteriorhodopsin. Curr. Opin. Struct. Biol. 11, 415-9 (2001).
21.Jin, Y. et al. Bacteriorhodopsin as an electronic conduction medium for biomolecular electronics. Chem Soc Rev 37, 2422-32 (2008).
22.Varo, G. Analogies between halorhodopsin and bacteriorhodopsin. Biochim Biophys Acta 1460, 220-9 (2000).
23.傅煦媛. 表現 Haloarcula marismortui 之六個光感受體揭露其獨特的感光特性. 國立台灣大學微生物與生化學研究所碩士論文 (2008).
24.劉康正. Haloarcula marismortui中HmBRI及HmBRII蛋白質特性及功能研究. 國立台灣大學微生物與生化學研究所碩士論文 (2009).
25.Hulko, M. et al. The HAMP domain structure implies helix rotation in transmembrane signaling. Cell 126, 929-40 (2006).
26.Robb, F.T. Archaea : a laboratory manual (Cold Spring Harbor Laboratory Press, Plainview, N.Y., 1995).
27.Sager, B.M., Sekelsky, J.J., Matsumura, P. & Adler, J. Use of a computer to assay motility in bacteria. Anal. Biochem. 173, 271-7 (1988).
28.Hader, D.-P. Image analysis in biology (CRC Press, Boca Raton, Fla., 1992).
29.Khan, S., Amoyaw, K., Spudich, J.L., Reid, G.P. & Trentham, D.R. Bacterial chemoreceptor signaling probed by flash photorelease of a caged serine. Biophys. J. 62, 67-8 (1992).
30.Khan, S. et al. Excitatory signaling in bacterial probed by caged chemoeffectors. Biophys. J. 65, 2368-82 (1993).
31.Alon, U. et al. Response regulator output in bacterial chemotaxis. EMBO J. 17, 4238-48 (1998).
32.Streif, S., Staudinger, W.F., Oesterhelt, D. & Marwan, W. Quantitative analysis of signal transduction in motile and phototactic cells by computerized light stimulation and model based tracking. Rev. Sci. Instrum. 80, 023709 (2009).
33.Sbalzarini, I.F. & Koumoutsakos, P. Feature point tracking and trajectory analysis for video imaging in cell biology. J. Struct. Biol. 151, 182-95 (2005).
34.http://faculty.washington.edu/gmilne/tracker.htm The Particle Tracking Resource Page @ The University of Washington (2010).
35.Mittal, N., Budrene, E.O., Brenner, M.P. & Van Oudenaarden, A. Motility of Escherichia coli cells in clusters formed by chemotactic aggregation. Proc. Natl. Acad. Sci. U S A 100, 13259-63 (2003).
36.Polin, M., Tuval, I., Drescher, K., Gollub, J.P. & Goldstein, R.E. Chlamydomonas swims with two "gears" in a eukaryotic version of run-and-tumble locomotion. Science 325, 487-90 (2009).
37.Berg, H.C. Random walks in biology (Princeton University Press, Princeton, N.J., 1993).
38.Allen, L.J.S. An introduction to stochastic processes with applications to biology (Pearson/Prentice Hall, Upper Saddle River, N.J., 2003).
39.http://en.wikipedia.org/wiki/Rice_distribution Rice distribution - Wikipedia, the free encyclopedia (2010).
40.Rice, S.O. Mathematical Analysis of Random Noise. Bell Syst. Tech. J. 23, 282-332 (1944).
41.Karlsen, O.T., Verhagen, R. & Bovee, W.M. Parameter estimation from Rician-distributed data sets using a maximum likelihood estimator: application to T1 and perfusion measurements. Magn. Reson. Med. 41, 614-23 (1999).
42.Sasaki, J. et al. Different dark conformations function in color-sensitive photosignaling by the sensory rhodopsin I-HtrI complex. Biophys J 92, 4045-53 (2007).
43.Spudich, J.L. The multitalented microbial sensory rhodopsins. Trends Microbiol. 14, 480-7 (2006).
44.Lin, Y.C., Fu, H.Y. & Yang, C.S. Phototaxis of Haloarcula marismortui Revealed Through a Novel Microbial Motion Analysis Algorithm. Photochem Photobiol (2010).
45.Barabasi, A.L. Scale-free networks: a decade and beyond. Science 325, 412-3 (2009).