GLOBAL GENE EXPRESSION CHANGES CAUSED BY NEUROTOXINS PACHIAPPAN ARJUNAN (B. Sc., B. Ed., M. Sc., M. Phil.) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF ANATOMY NATIONAL UNIVERSITY OF SINGAPORE 2006 ii iii ACKNOWLEDGEMENTS I am greatly indebted to my supervisor Professor P. Gopalakrishnakone for his continuous guidance, abundant support, encouragement, effort and a touch of philosophy which directed my line of doctoral research that is of immense importance to my academic career. I have learnt so much working with him and am very grateful that he has allowed me to come into my own as a researcher. I truly appreciate the fact that he shared his ideas with me and was always ready to hear mine. I also take this opportunity to thank Professor Ling Eng Ang, Head of the department for his strong support and provision of excellent working facilities extended towards my candidature with kind encouragement to carry out this research. I would also like to extend my sincere thanks to Professor G. Jayaraman, Madras University, India, for the generous gift of his suggestions and critical improvement of this thesis. My special debt of gratitude is always due to Dr. M. M. Thwin, (Research Fellow, Department of Anatomy) for his consecutive discussion, fruitful teamwork and insightful suggestions that have greatly improved the contents of this thesis as well as of my research publications. I also wish to thank Mr. J. Manikandan, Department of Physiology, for his great help on Gene Chip data analysis. I continue to be indebted to the academic, technical and administrative staff of the department of Anatomy, BFIG core lab (Clinical Research Center, Faculty of Medicine) and the protein and proteomics center (Department of Biological Sciences) for their assistance. It has indeed been my very good fortune to work with members of the Venom and Toxin Research Programme (VTRP), both present and past, Doctors S. Nirthanan, K. N. Srinivasan, K. Subramaniyan, R. Saminathan, Le Van Dong, Le Khac Quyen, R. Perumal Samy, Zhong Shuang and Ms. J. Hema, Mr. Feng Luo, Mrs. Ler Siok Ghee, Ms. Xiao Jun, from whose experience and expertise I have benefited. The friendly atmosphere they created has been and will always be unforgettable. They are the greatest bunch of colleagues and have helped me to maintain my sanity till today! I could not have come this far without the constant support and encouragement of all my friends, in particular Dr. V. Sivakumar, Dr. B. Susithra and Mr. R. Sriram. I am also thankful to Mr. Yick Tuck Yong and Mr. Gobalakrishnan, Multimedia Unit, and all those at the Operation Theater, Histology, and Neurobiology Laboratories, Anatomy department, for their technical assistance and continuous cooperation. Thanks are also due to the general office staff of the department of Anatomy for the support and co-operation given to me, especially during the final days of thesis preparation. iv Indebted and duty-bound as I am, I acknowledge with immense gratitiudem the support and co-operation extended to me by the staff of the department of Biological Sciences, especially to Professor R.M. Kini, and my colleagues Dr. R. Lakshminarayanan, Mr. N. Kishore, and Dr. Vivek. I would like to acknowledge the National University of Singapore for awarding me a research scholarship which made possible for me to complete my research work without much of the hurdles. Finally, I gratefully acknowledge the support and encouragement of my parents throughout the endeavor, and for their pivotal role in my progress. The completion of this thesis is really the culmination of years of sacrifice, unconditional love, quiet support and constant prayers. Pachiappan Arjunan. Singapore 2006. v TABLE OF CONTENTS Page Dedication ii Acknowledgements iii Table of contents v Abstract xi Summary xii List of publications xiv List of figures xvii List of tables xix Abbreviations xx Chapter 1: INTRODUCTION 1.1 Venomous snakes 1.2 Classification of venomous snakes and their distribution 1.3 Value of snake toxins in science and medicine 1.4 Snake venoms and components 1.5 Neurotoxinology 1.5.1 The categorization of neurotoxins 11 1.6 Three-finger neurotoxins 12 1.6a Structure of three-finger toxins 13 1.6b Non-conventional three-finger proteins 14 1.6c Three-finger toxins from other species 15 1.7 The Malayan krait (Bungarus candidus) 16 1.8. Pharmacological properties of Bungarus candidus 18 1.9. Biochemical and pharmacological properties of candoxin 19 1.10. The Structure of candoxin 20 1.11. The Structure of the nicotinic acetylcholine receptor 22 1.12 Nerve Agents 1.12.1 Introduction 24 1.12.2 Classification of nerve agents 25 1.12.2a Other types of chemical agents 27 vi 1.12.3 General mechanism of action of chemical nerve agents 28 1.12.4 Effects and symptoms of nerve agent poisoning 29 1.12.5 Effects of nerve agent poisoning on blood ChE and others 30 1.13 Properties and pathogenesis of sarin 1.13a Physical and chemical properties of sarin 33 1.13b Pathological changes caused by Sarin 34 1.13c Toxicodynamics of acetylcholinesterase inhibtion by sarin 35 1.14 The gene expression study using microarray technology 1.14a Overview of microarray 37 1.14b Principle of affyChip’s technology 39 1.14c Types of microarrays 40 1.14d Microarray data validation and statistical analysis 43 1.15 Real-time qRT-PCR detection methods and applications 46 1.16 Applications of gene expression studies on snake Venoms/Toxins and nerve agents 48 1.17 Use of toxicogenomics in toxinology 51 1.18 Recent advances in microarray technology 54 1.19 Aim and scope of the thesis 55 Chapter 2: MATERIALS AND METHODS 2.1 Candoxin isolation and purification 2.1a Materials and reagents 58 2.1b Purification and mass determination of candoxin 58 2.1c Q-TOF mass spectrometry and sequencing 59 2.1d Sodium dodecyl sulphate-polyacrylamide gel electrophoresis 60 2.1.1 Animals 61 2.1.2 Experimental groups and candoxin treatment 62 2.1.3 Light microscopy observation 2.1.3a Perfusion and fixation 63 2.1.3b Preparation of gelatinized slides 63 vii 2.1.4 TUNEL-Histochemistry assay 63 2.1.5 Measurement of brain edema 64 2.1.6 Antineurotoxic effects of CDX inhibitor (P-NT.II) 65 2.1.7 Study design for gene expression 66 2.1.8 Cell culture and candoxin treatment 66 2.1.8a Cell Line description 66 2.1.8b Culture techniques 66 2.1.8c Culture medium and chemicals 68 2.1.8d Sub-culture and propagation of Hs 683 68 2.1.8e. Hs 683 cells-stock preparation and preservation 69 2.1.8f Cell proliferation assay (XTT-Based) 69 2.1.8g Exposure of Hs 683 to candoxin 70 2.1.9 Total RNA Isolation and Quantification 2.1.9a Isolation of total RNA from Hs 683 cells 71 2.1.9b Quantification of total RNA 72 2.1.9c. Agarose gel electrophoresis 73 2.1.10 Gene expression studies using oligonucleotide microarrays 2.1.10.1 Eukaryotic target preparation 73 2.1.10.1a Reagents and materials 73 2.1.10.2 Synthesis of double-stranded cDNA 2.1.10.2a First strand cDNA synthesis 74 2.1.10.2b Second strand cDNA synthesis 74 2.1.9.3 Cleanup of double-stranded cDNA 2.1.9.3a Phase Lock Gels -Phenol/Chloroform extraction 76 2.1.9.3b Ethanol precipitation 76 2.1.10.4 Synthesis of biotin-labeled cRNA 77 2.1.10.5 Cleaning up and quantifying IVT products 2.1.10.5a In-Vitro Transcription (IVT) clean up 77 2.1.10.5b Quantifying the cRNA (IVT Product) 78 2.1.10.6 Fragmenting the cRNA for target preparation 78 viii 79 2.1.10.7 Eukaryotic target hybridization 2.1.11 Washing, Staining and Scanning of Probe Arrays 2.1.11a Reagents and materials 80 2.1.11b Experiment and fluidics station setup 81 2.1.11c Wash and stain: antibody amplification for target 81 2.1.11d Probe array scanning 82 2.1.12 Microarray Data Analysis 2.1.12a Absolute analysis 83 2.1.12b Comparison analysis 84 2.1.12c Categorization and criteria for gene selection 85 2.1.12d. Data Mining and clustering algorithm by software’s 85 2.1.13 Semi-Quantitative Reverse Transcription–PCR 87 2.1.14 Quantitative Real Time–PCR (LightCycler) 88 2.1.15 Statistical analysis 90 2.2 Neuropathogenesis of sarin 2.2.1 Cell culture and sarin exposure 90 2.2.1a Materials and Reagents 90 2.2.1b Cell Line description 91 2.2.1c Culture techniques 91 2.2.1d Culture medium and chemicals 91 2.2.1e Subculture, propagation and preservation of SH-SY5Y 92 2.2.1f Cell proliferation assay and EC50 determination 93 2.2.1g Exposure of SH-SY5Y to sarin 93 2.2.1h RNA extraction and Validation 94 2.2.1i Agilent bioanalysis 94 2.2.1j Cell lysate AChE assay 96 2.2.2 Microarray GeneChipTM Analysis and Clustering Algorithm 2.2.2a Categorization and Criteria for Gene Selection Using Software’s 96 2.2.2b Gene Expression Profiles (URL Links) and Clustering Methods 97 ix 97 2.2.3 Quantitative Real Time–PCR (qRT-PCR) 2.2.4 Protein Isolation 100 2.2.5 Western Blot Analysis 100 2.2.6 Analysis of immunoflurescence 101 2.2.7 Human Brain cDNA library 2.2.7a. Chemicals 102 2.2.7b. Polymerase Chain Reaction for Human Brain cDNA library 102 Chapter 3: RESULTS AND DISCUSSION–CDX 3.1 Results 3.1.1 Isolation and Purification of Candoxin 104 3.1.2 Determination of the Amino Acid Sequence of Candoxin 106 3.1.3 SDS-PAGE Profiles 108 3.1.4 Cell Propagation and EC50 Determination 110 3.1.5 Distribution of TUNEL Positivity 111 3.1.6 Cytotoxic Brain Edema 113 3.1.7 Differentially expressed gene profiles 114 3.1.8 Toxicofunctional genomics gene expression of CDX 117 3.1.9 Genes involved in signal transduction and internalization 120 3.1.10 Metabolic Pathways 121 3.1.11 Gene-specific correlation between RT-PCR and microarray results 122 3.1.12 Selective Inhibition of CDX-induced neurotoxicity by P-NT.II 125 3.1.13 Suppression of gene expression by a selective Inhibitor of CDX 127 3.1.14 Molecular Pathway and Target Identification 128 3.2 Discussion 130 3.3 Conclusion 145 Chapter 4: RESULTS AND DISCUSSION–GB 4.1 Results 4.1.1 Cell Viability and EC50 Determination 146 4.1.2 Cell lysate AChE assay 148 4.1.3 Microarray Gene ChipTM Analysis and data mining 148 4.1.4 Tree view and clustering analysis 156 x 4.1.5 Principal component analysis (PCA) 158 4.1.6 Quantitative Real Time RT-PCR 159 4.1.7 Western Blot Analysis 161 4.1.8 Evaluation of confocal images of proteins 164 4.1.9 Comparison of Microarray data with qRT-PCR, Western blot and Immunoflurosence analysis 179 4.1.10 Summary of Molecular Pathways Involved 179 4.2 Discussion 180 4.3 Conclusion 201 Chapter 5: GENERAL CONCLUSION AND FUTURE DIRECTIONS 5.1 General Conclusion and Future Directions Bibliography Appendix 202 204 I 884 A. Pachiappan et al. / Toxicon 46 (2005) 883–899 1. Introduction Venoms of the Elapidae family of snakes (cobras, kraits, coral snakes and mambas) are abundant in neurotoxins that affect nervous system at various stages due to their high affinity binding to specific receptors (Dufton, 1993; Nirthanan et al., 2002; 2003). Snake venom a-neurotoxins bind selectively to nicotinic acetylcholine receptor (nAChR) with high affinity, and almost irreversibly block Torpedo (abgd) receptor and chick or rat neuronal a7-nAChR (Servent et al., 1997; Servent and Menez, 2001). These curaremimetic neurotoxins—a-bungarotoxin, a-cobratoxin and erabutoxin—belong to the family of three finger toxins (Kini, 2002). Candoxin (CDX), a novel three-finger toxin recently purified from the venom of Malayan krait, Bungarus candidus (Nirthanan et al., 2002), is a reversible antagonist of post-junctional nAChRs and an irreversible antagonist of neuronal a7-nAChRs. The cellular mechanism of CDX on human brain glial cell lines, however, is still poorly understood. Glial cells (astrocytes) are the most abundant cells in the central nervous system and are known to exhibit a wide variety of biological activities (Fields and Stevens-Graham, 2002). Recent evidence suggests that astrocytes play crucial roles in neuron-glia cross-talk through the modulatory effects on synaptic transmission (D’Ascenzo et al., 2004). How glial cells respond to CDX insult or injury in the CNS, and whether these responses promote or inhibit repair in the nervous system is yet to be investigated. Hence, in order to fully identify the biological action of CDX in glial cell function, a better understanding of the gene expression profiling and molecular pathways involved is deemed to be essential. The high-density oligonucleotide arrays have been applied extensively to assess the effect of snake venoms in culture (Gallagher et al., 2003), and detect mutations and single nucleotide polymorphisms (SNP) linked to peripheral inflammatory disorders, such as Alzheimer’s disease and several chronic degenerative diseases (McGeer and McGeer, 2001). From the therapeutic viewpoint, the inflammatory process is considered to be important as reduced inflammatory related events could ameliorate neurodegeneration (Yoshihara et al., 2002). In neuronal disorders, exogenous and endogenous neurotoxins have been suggested as pathogenic contributors (Yoo et al., 2003). Hence, an invaluable tool like oligonucleotide microarray might help characterize novel genes that are implicated in the pathogenesis of glial–degeneration or other pathways (glia–glia or glia–neuron networks) that lead to neurodegeneration after exposure to neurotoxins. Increasing evidence from human and animal studies has suggested that neuroinflammation is an important contributor to loss of glia and neurons in neuronal diseases (Wyss-Coray and Mucke, 2002). An inflammatory response by glia cells surrounding the respective brain lesions may participate in impairment of neuronal function and eventually cause neuronal cell death (Heneka et al., 2001). There is substantial evidence for the role of glial cellmediation in the inflammation process that leads to neurodegenerative diseases (Teismann et al., 2003; Miller et al., 2004). The expression, function and regulation of human brain glial cells after exposure to CDX, with respect to neurotransmission blockade and glial–neurodegeneration have yet to be reported at molecular level. The present work was therefore conducted to specify the molecular mechanism involved in the stimulatory effect of CDX on global gene expression in glial cell lines. Recent success in the development of short synthetic peptides that bind with high affinity to a-bungarotoxin (Kasher et al., 2001) has prompted us to look for similar low molecule peptide inhibitors that could potentially modulate the activity of CDX. On the basis of the primary structure of an endogenous anti-inflammatory protein (Thwin et al., 2000), we designed the lead peptide P-NT.II (Thwin et al., 2002) as possible pharmacological antagonists to CDX activity, and used it for identification of the critical genes specifically involved in the regulation of glial–neuroinflammation and degeneration by CDX. Using high-throughput high-density oligonucleotide microarray and CDX-treated Hs683-model, along with the subsequent bioinformatics analysis, we herein explored differential gene expression pattern as a function of time (12, 24, and 48 h), with a view to obtain information on the pathways activated by CDX during glial cell-mediated inflammation and mitochondrial dysfunction. CDXmediated DNA-damage (TUNEL) and glial–neurodegeneration related genes were further evaluated with specific and selective CDX-inhibitor P-NT.II and confirmed by quantitative real-time RT-PCR. The resulting data should contribute towards uncovering novel target genes involved after exposure of glial cells to candoxin. 2. Materials and methods 2.1. Material and reagents Human brain glial (Hs 683) cell lines were obtained from ATCC (USA). Male Swiss albino mice were from Laboratory Animals Centre (Sembawang, Singapore). Affymetrix Human GeneChipw (HG-U133A) was purchased from Santa Clara (CA 95051, USA). Lyophilized form of B. candidus venom was supplied by the Venom Supplies Pvt Ltd (Tanunda, Australia) and erabutoxin-b was from Latoxan (Valence, France). The pre-packed Superdex30 molecular-sieve chromatography (1.6!60 cm) and Jupiter C18 (250!4.60 mm) columns were obtained from Amersham Biosciences (Uppsala, Sweden) and Phenomenex, (Torrance, CA, USA), respectively. RNeasyw mini kit and RNase-free DNase-I were purchased from QIAGEN (USA). LightCycler FastStart DNA MasterPLUS SYBR A. Pachiappan et al. / Toxicon 46 (2005) 883–899 Green-I kit was obtained from Roche Diagnostics (Penzberg, Germany). 2.2. Candoxin purification and mass determination Candoxin was purified as described (Nirthanan et al., 2002) with minor modifications. In brief, the Malayan krait venom (0.2 g/2 ml) was fractionated on a Superdex-30 FPLC-column with Tris–HCL buffer (50 mM, pH 7.5) as an eluant, followed by further purification on a reversed-phase ¨ KTA explorer Workstation, Jupiter C18 column (A Amersham Pharmacia Biotech, Sweden), eluted with a linear gradient of 80% acetonitrile in 0.1% trifluoroacetic acid. Elution of proteins was monitored at 280 and 215 nm. The precise mass (G0.01%) and homogeneity of purified CDX was determined by using electrospray ionization (ESI) mass spectrometry (PerkinElmer Life Sciences Sciex API 300 triple quadrupole LC/MS/MS system) and MALDITOF on a Voyager DE-STR Biospectrometry workstation (Applied Biosystems CA, USA). 2.2.1. Quadruple-time of flight mass spectroscopy (Q-TOF) and sequencing Nano electrospray ionization tandem MS was performed to confirm the molecular mass of the CDX using a Q-TOF2 mass spectrometer (Micromass, UK) according to the manufacturer’s instructions. N-terminal sequencing of CDX was done by automated Edman degradation using Perkin-Elmer Life Sciences 494-pulsed liquid phase protein sequencer. 2.2.2. Experimental animals Experiments were conducted in Male Swiss albino mice (nZ12; three control and nine experimental), and handled according to the guidelines of the National Medical Ethics Committee (Singapore), which conform to the WHO International Guiding Principles for Animal Research, and approved by the CIOMS (Howard-Jones, 1985). The mice (20G2 g body weight) were maintained on an ad libitum fed with standard laboratory chow and water. Doses of CDX (15 mg/gbw) in normal saline (10 ml) were injected intracerebroventricularly into mice using a fine capillary Hamilton microsyringe and stereotaxics as described (Smith et al., 2004). All mice were anaesthetized intraperitoneally (i.p.) prior to removal of whole brain for the TUNEL-POD assay. 2.2.3. TUNEL-histochemistry To detect the astrocyte (glial) damage showing DNA fragmentation (DNA-damage), mice sacrificed at 12, 24 and 48 h intervals after injection with CDX or saline were dissected. The brains were removed, fixed with 4% paraformaldehyde and were kept in 0.1 M phosphate buffer containing 15% sucrose overnight at 8C. Serial coronal sections of the brains (100 mm) were cut using vibrotome and rinsed with phosphate buffer-Triton X-100 for 885 permeabilization. The sections were again fixed with 4% paraformaldehyde for 20 and incubated with the TUNEL reaction mixture (in situ cell-death detection kit) for h before being examined in a light microscope to detect brain damage and cell death. In addition, TUNEL-assay was also carried out in Hs 683 cell lines according to the manufacturer’s instructions. TUNEL-POD results were analyzed and quantified as described (Ananth et al., 2001). 2.2.4. Cell culture and CDX treatment Human brain glial cell lines were propagated in 72 cm2 flasks at a density of w1!107 cells/12 ml in DMEM culture medium supplemented with 10% fetal bovine serum (FBS), 100 units mlK1 penicillin, 100 mg mlK1 streptomycin. The cells were allowed to adhere to the bottom of the flask for overnight at 37 8C in a humidified atmosphere of 5% CO2 and 95% air. The culture medium was changed three times a week. The fibroblast-like cell lines were established from temporal lobe of glioma patient and cultivated as monolayer with a suggested oligodendroglial phenotype, i.e. Hs 683 (HTB-138; ATCC). To minimize the cell density in studying the effect of CDX on the differential gene expression, Hs 683 cells were grown to 40% confluence for 24 h before treatment with CDX at predetermined dose (EC50w1 mM) and time intervals (12, 24, and 48 h). To minimize between-sample (inter-individual flask) variation, 36 independent flasks were established overall for 12 biological samples; three flasks were pooled for each experiment for a total of three replicates for each time points or untreated control. The cells were harvested under extreme RNase free condition for RNA extraction. 2.2.5. Cell viability assay (XTT-based cytotoxicity assay) To analyze the initial events of CDX-mediated cell viability, the toxin was applied to cultured glial cell lines at different concentrations (0.01–100 mM) and varied time intervals (6, 12, 24, 48, and 72 h). The cell viability was measured using tetrazolium salts (XTT), Cell Proliferation Kit II (Roche Applied Science, Singapore). Cells were seeded at the concentration of w1!104 cells/well (96 wells plate) in 100 ml culture medium containing mg/ml XTT labeling reagent without phenol red and 1.25 mM PMS in phosphate buffer, and were incubated at 37 8C for mh according to the manufacturer’s instructions. Cell proliferation was spectrophotometrically quantified using an ELISA plate reader at 490 nm. A decrease in optical density, compared with control cells, provides a quantitative assessment of cell death in a concentration (EC50w1 mM) and time-dependent manner. All assays were prepared in triplicates and repeated thrice. 2.2.6. RNA isolation and quantification Total RNA was isolated from CDX-treated (nZ3 flasks for each time point) and untreated control (nZ3) cells using RNeasyw mini kit. The RNA sample was subsequently treated with RNase-free Dnase-I at room temperature for 886 A. Pachiappan et al. / Toxicon 46 (2005) 883–899 20 and stored at K80 8C until use. The quality and quantity of extracted RNA was determined by spectrophotometry (Bio-Rad, USA). All RNA samples used for microarray hybridization and QRT-PCR experiments were of highest purity with A260/A280 ratios of 1.9–2.1. The integrity and relative contamination with residual genomic DNA was assessed by 1% denaturing agarose gel electrophoresis. 2.2.7. Microarray gene chipe analysis and clustering algorithm The preparation and processing of labeled, fragmented cRNA for oligonucleotide microarray hybridization were performed according to the Affymetrix protocols described in the technical manual (Affymetrix, Santa Clara, CA). The quality of labeled target cRNA was initially assessed using GeneChip Test3 arrays. Following this, hybridization on the HG-U133A GeneChipe expression arrays was for 16 h at 45 8C with 60 rpm. Three chips were used for each experiment according to MIAME guidelines (Brazma et al., 2001). Expression values for each gene were calculated using the Affymetrix Microarray Suite (MAS 5.0) software. Using the 50th percentile of each chip’s (per chip normalization) intensity range, expression values (200–44,913.8) were normalized across the sample set by scaling the average of the intensities of all genes to constant target intensity of 800.The resultant data were put into GeneSpring v7.0 (Silicon Genetics, CA, USA) for temporal sequence analyses by parametric test based on cross-gene error model (PCGEM). Relative expression data for each probe set were generated by normalization over the median of the entire experiment set (per gene normalization). Compared with control, all genes showing a change of R3-fold in two out of three chips were included in subsequent analysis. Differentially expressed genes were analyzed by One-way ANOVA with P levels of !0.01 taken as statistically significant. Agglomerative average-linkage hierarchical clustering of the four different experimental conditions was obtained for selected genes with GeneSpring software using standard correlation as similarity matrix. Cluster members were categorized according to their biological functions as described in the NetAffx database (Affymetrix). Besides, we used Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.ad.jp/kegg) and Gene MicroArray Pathway Profiler (GenMAPP; http://www.genmapp. org/) databases to characterize the responsive genes on molecular interaction networks in metabolic and regulatory pathways. The data discussed in this article have been deposited in NCBIs Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series accession number GSE1682. 2.2.8. Quantitative real-time RT-PCR (LightCycler) Exposure of human glial cells to CDX and RNA extraction were performed as described above. Quantitative fluorescence-based real-time PCR was carried out according to the manufacturer’s instructions using the LightCycler system with LightCycler FastStart DNA MasterPLUS SYBR Green I kit (Roche Diagnostics, Germany) as an independent method for assessing relative gene expression and validation of microarray data. Genes for QRT-PCR were selected based on transcripts that were significantly (P!0.01) and consistently expressed. A set of 105 genes identified by multiple statistical approaches, and the genes in the six functional clusters identified by gene ontology were included in the selection. Reverse Transcription (RT) of RNA and QRT-PCR were performed as follows: single stranded cDNA was generated from mg of total RNA, 200 mM nucleotides, 500 units Superscript II reverse transcriptase (Invitrogen) and 1.5 mM oligo(dT)15 primers in 50 ml reactions. Reverse transcription was stopped after h by heating to 95 8C for min. The sense and antisense primers used for specific amplification and conditions for PCR cycles are shown in Table 1. The primers were designed and subsequently checked for specificity using BLAST:http://www.ncbi.nlm.nih.gov/genome/seq/ HsBlast.html. All primers were synthesized by 1st BASE Pvt Ltd. The expression of GAPDH was used as an internal calibrator for equal RNA loading and to normalize relative expression data for all other genes analyzed. The copy ratio of each analyzed cDNA was determined as the mean of three experiments. The real-time quantitative RT-PCR data were quantified using relative quantification (2KDDCT) method as described (Livak and Schmittgen, 2001). 2.2.9. Antineurotoxic effects of CDX inhibitor (P-NT.II) To determine if an inhibitor of CDX could alter gene expression in human brain glial cell lines exposed to CDX, a potent and selective inhibitor of CDX (6.6 mM P-NT.II) was first assayed in vitro and in vivo for its ability to inhibit neurotoxic activity of CDX, and was later used in cultures of CDX-exposed Hs 683 cell lines. In vivo inhibitory effect of the low molecule weight peptide (P-NT.II) against CDX toxicity was studied by injecting 2LD50 doses (i.p.) of 3-finger neurotoxins (20 mg candoxin, 12 mg erabutoxin-b or cobratoxin) into male Swiss albino mice (20G2 g; nZ3) in the presence and absence of an inhibitor P-NT.II at 1:30 molar ratio of toxin to inhibitor. The in vitro effect of PNT.II on the blockade of nerve-evoked twitch responses produced by CDX (20 mg/ml) or erabutoxin-b (2 mg/ml) in the mouse hemidiaphragm (MHD) was also studied. Either of the two neurotoxins was incubated with the respective concentrations of P-NT.II (1.6, 3.3 or 6.6 mM) for 30 prior to its addition to the organ bath. The MHD with the associated phrenic nerve was isolated from male Swiss Albino mice (20G2 g) as described (Bulbring, 1946), and mounted in a ml organ bath. For indirect stimulation, the phrenic nerve was electrically stimulated at a frequency of 0.2 Hz in rectangular pulses of 0.2 ms duration and supramaximal voltage (7–10 V). 887 A. Pachiappan et al. / Toxicon 46 (2005) 883–899 Table Primer sequences used in quantitative real-time PCR analysis of glial cell lines for amplification Gene name Sense primer sequence (5 –3 ) Up-regulated GAAD45A GAA CGG TGA TGG CAT CTG A IL13RA2 CTT TGG GAC CTA TTC CAG CA TNFRSF12A CAA GCT CCT CCA ACC ACA AG CD44 TTC ATC CTC GGG TGT GCT AT IL7R GAC GCC CCT ATT CTC TCC TC UBE2E3 GCC AGA CAG TGG ACC AAG AG THBS1 GCT GAC TGG CGT TAG CTG AT GABRA2 TGCAGACAGAAAGCACTCCA IFI44 AGC CTG TGA GGT CCA AGC TA CASP1 ATC CCA CAA TGG GCT CTG T Down-regulated LPL TGA CCC AGG GTG CAT TAA CT IGFBP4 GTT GTT GCA CCA TCT GCT TG ASS ATA CCG GTT TAC GGC CTA GC CPS1 GCT CAC TGC AAT TTG CGT CT AQP3 TGG GAA GTA GGG TGG ATG TG PTGS1 CCT GGC TGA TGA TCC AGA AC DDX17 CTA TGG GGC AGC TGC TTA TG DAF CAC CAC ACC AAA TGC TCA AG OSMR GGT GGC TGT GGT CCT AGA AGT CHRNE GCA GGG AGC CCT CAC TCT C House keeping gene GAPDH AGG GGT CTA CAT GGC AAC TG Antisense primer sequence (5 –3 ) Annealing temp. (8C) Product size (bp) CTT CCT GCA TGG TTC TTT G CTT CAC CTT CCC AGC ATT GT CCC AGG TCC TAA GGA AGG AG GTA CAC CCC AAC CTC AGT GG TAA GAA TGG GCT GAC CCT GA GAG TCT CCC AGG CTC CAA AG AGC CAG CAC TGC CTT ACA CT ACCAAAAGGGTCCACAAAGG ATC TGC AGC CCA TAG CAT TC CTC TTT CAG TGG TGG GCA TC 60 55 59 58 56 59 59 56 58 60 273 222 219 200 184 177 171 162 158 150 TTG GCT CTG TGA GAC CAT CA GTG ACC ACA AAT GGG GTA GG CAG CCT GAG GGA ATT GAT GT ATT CCA AAA CTG GGG GAG AG ATC CCG GAT CCC TAA GAC TG AGG CAC AGA TTC AGG GAA TG GGC TGT GCA AAC TGT TGT GA TGG TTA CTA GCG TCC CAA GC GGC CTC AAA CAC CTG TGA GT CCA AGG CCA CTA CAT TGA GG 56 57 59 56 61 57 57 61 64 59 250 237 233 221 220 154 183 170 170 111 CGA CCA CTT TGT CAA GCT CA 58 230 2.2.10. Measurement of brain edema (wet-dry brain weight ratios) Whole brains were removed from CDX treated (nZ3) and control (nZ3) mice, and weighed to obtain the ratio (wet weight/dry weight) as described (Sun et al., 2003), with some modification. Briefly, brain samples were lyophilized to remove the water and weighed again after days to obtain dry weights. 2.2.11. Statistical analysis Statistical analyses were performed using paired Student’s t-test, with the Statistical Package for Social Sciences (SPSS v11.5). One-way ANOVA (Benjamini and Hochberg False Discovery Rate multiple testing) was then used for array data to compare differences between the means of the various treatment groups, and results represented as meansGSEM. P levels of !0.01 was considered statistically significant. Graphs, EC50 determination and statistical analysis were performed using GraphPad Prism version 4.01 for Windows, GraphPad Software. 3. Results The full names and GeneBank accession numbers of the altered genes in Hs 683 after CDX treatment are given in Tables and 3. To avoid redundancy, only abbreviations are used in Results and Discussion. 3.1. Purification of candoxin Candoxin was purified to homogeneity by consecutive gel filtration and reverse-phase HPLC as described above. The purified CDX was subjected to ESI-mass spectrometry, MALDI-TOF mass spectrometry, Q-TOF and found to be homogenous. CDX has a molecular mass of 7334.83G0.45 as determined by ESI/MS, MALDI-TOF (7335.47G0.36) and also reconfirmed by Q-TOF (7334.4004) as shown in Fig. 1. 3.2. Cell propagation and EC50 determination Results of XTT-assay indicate that cell survival decreases with increasing concentrations of CDX with the EC50 calculated as w1 mM. CDX did not significantly affect the cell viability at 300 nM concentrations (Fig. 2). Morphological changes of the cell lines indicate that the cells remain intact with moderate swelling but without any membrane disruption or lysis at 300 nM, whereas significant cell death is evident at higher (1–100 mM) concentrations of CDX in a time dependent manner. Although the growth kinetics of brain cells were not affected particularly at the optimal dose of CDX (300 nM) tested in cell culture media, 888 A. Pachiappan et al. / Toxicon 46 (2005) 883–899 Table The differentially expressed genes in signal transduction and ubiquitin-inflammation linking with neuroglial pathogenesis in glial response to CDX Seq. derived from Gene description (A) Up-regulated genes NM_003246 Thrombospondin NM_002185 Interleukin receptor NM_003139 Signal recognition particle receptor (‘docking protein’) NM_002053 Guanylate binding protein 1, interferon-inducible, 67 kDa NM_000610 CD44 antigen NM_003873 Neuropilin AL567376 Putative G protein-coupled receptor GPR39 NM_016639 Tumor necrosis factor receptor superfamily, member 12A NM_000640 Interleukin 13 receptor, alpha NM_005345 Heat shock 70 kDa protein 1A NM_002852 Pentaxin-related gene, rapidly induced by IL-1 beta AB005043.1 Suppressor of cytokine signaling NM_001924 Growth arrest and DNA-damage-inducible, alpha AB040875 Solute carrier family 7, (cystine glutamate exchanger) member 11 NM_003768 Phosphoprotein enriched in astrocytes 15 AB007458 TP53 target gene AL554008 G protein-coupled receptor 56 AK025298 Autism susceptibility candidate NM_000366 Tropomyosin (alpha) NM_000820 Growth arrest-specific BF448062 Ubiquitin-conjugating enzyme E2D3 U13698 Caspase 1, nterleukin-1b convertase (IL-1bC) NM_013943 Chloride intracellular channel NM_006394 Regulated in glioma NM_001792 Cadherin 2, type 1, N-cadherin (neuronal) NM_001348 Death-associated protein kinase N30649 Sequestosome NM_000807 Gamma-aminobutyric acid (GABA) A receptor, alpha AB017644 Ubiquitin-conjugating enzyme E2E3 NM_001839 Calponin 3, acidic NM_021159 RAP1, GTP-GDP dissociation stimulator BC002446 Similar to MRJ gene for a member of the DNAJ protein family NM_006417 Interferon-induced protein 44 (B) Down-regulated genes NM_000425 L1 cell adhesion molecule (MASA) NM_001206 Basic transcription element binding protein NM_000284 Pyruvate dehydrogenase–mitogen activated protein kinase k k NM_001293 Chloride channel, nucleotide-sensitive, 1A NM_006598 Solute carrier family 12 (potassium/chloride transporters) member NM_001845 Collagen, type IV, alpha AL157418 Misshapen/NIK-related kinase NM_001321 Cysteine and glycine-rich protein M15329 Interleukin 1, alpha NM_007308 Synuclein, alpha (non-A4 component of amyloid precursor) Gene symbol 12 h CDX 24 h CDX 48 h CDX THBS1 IL7R SRPR C12.18G1.44 C10.04G1.65 C6.03G0.20 C5.77G0.61 C6.70G1.84 C4.15G0.31 C4.36G1.46 C4.78G1.25 C3.50G0.84 GBP1 C5.97G0.87 C6.29G1.56 C4.44G1.24 CD44 NRP1 GP39 TNFRSF12A IL13RA2 HSPA1A PTX3 C5.91G1.07 C5.75G0.57 C5.48G0.24 C5.38G0.24 C5.29G1.05 C5.69G0.79 C7.48G0.31 C4.48G0.31 C4.38G1.32 C4.35G0.98 C5.06G0.59 C4.06G0.59 C5.31G0.44 C5.21G0.11 C5.01G0.77 C4.18G0.66 C3.14G0.45 C5.36G2.67 C5.49G1.37 C5.39G1.41 C6.27G3.42 SSI-1 GADD45A SLC7A11 C4.99G0.47 C4.73G0.62 C4.70G0.61 C4.46G0.19 C3.83G1.57 C3.81G0.39 C4.12G0.35 C7.01G1.40 C5.82G2.20 PEA15 TP53TG1 GPR56 AUTS2 TPM1 GAS6 UBE2D3 CASP1 CLIC4 RIG CDH2 DAPK3 SQSTM1 GABRA2 C4.69G0.61 C4.44G0.52 C4.34G0.22 C4.05G0.47 C3.88G0.38 C3.84G0.53 C3.81G0.15 C3.79G0.13 C3.78G0.17 C3.54G0.43 C3.39G0.32 C3.38G0.12 C3.35G0.28 C3.27G0.56 C4.33G0.29 C4.26G0.61 C5.43G0.35 C4.12G0.37 C3.26G0.36 C3.51G0.49 C3.27G0.16 C4.21G0.40 C3.84G0.36 C3.72G0.24 C3.44G0.35 C3.42G0.21 C5.05G0.23 C3.71G0.42 C3.47G0.70 C3.86G0.29 C3.39G0.31 C3.75G1.07 C3.34G0.58 C3.10G0.64 C2.41G0.21 C3.07G0.21 C3.21G0.61 C4.85G1.28 C1.92G0.44 C3.42G0.19 C6.61G2.24 C5.10G0.43 UBE2E3 CNN3 RAP1GDS1 DNAJB6 C3.22G0.38 C3.13G0.30 C3.08G0.26 C3.07G0.76 C3.07G0.63 C3.12G0.38 C3.11G0.16 C3.28G0.48 C3.64G2.16 C2.92G0.22 C2.73G0.21 C3.00G0.78 IFI44 C3.06G0.31 C3.48G0.55 C3.37G0.30 L1CAM BTEB1 MAPKKK5 K3.13G0.12 K3.20G0.11 K3.23G0.12 K5.55G0.18 K4.03G0.17 K5.00G0.15 K4.17G0.20 K2.85G0.21 K3.03G0.23 CLNS1A SLC12A7 K3.23G0.12 K3.33G0.13 K3.13G0.15 K3.23G0.16 K2.86G0.18 K2.56G0.18 COL4A1 MINK CSRP2 IL1A SNCA K3.38G0.12 K3.45G0.14 K3.45G0.14 K3.48G0.57 K3.70G0.14 K3.45G0.15 K3.85G0.15 K2.78G0.18 K3.78G0.43 K3.45G0.22 K4.35G0.21 K3.33G0.18 K4.00G0.20 K3.21G0.98 K3.47G0.19 (continued on next page) 889 A. Pachiappan et al. / Toxicon 46 (2005) 883–899 Table (continued) Seq. derived from Gene description Gene symbol NM_017753 NM_003589 NM_003851 NM_006317 Plasticity related gene Cullin 4A Cellular repressor of E1A-stimulated genes Brain abundant, membrane attached signal protein Interferon induced transmembrane protein Interferon induced transmembrane protein Jun B proto-oncogene Ras homolog gene family, member B Dual specificity phosphatase Protein tyrosine phosphatase, receptor type, M Phosphodiesterase 5A, cGMP-specific Eukaryotic translation elongation factor alpha Protein tyrosine phosphatase type IVA, member Fibrillin Cyclin G2 Hypoxia-inducible protein Insulin-like growth factor receptor DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 21 Tissue inhibitor of metalloproteinase (pseudoinflammatory) Cholinergic receptor, nicotinic, epsilon polypeptide HIV-1 Tat interactive protein V-myc myelocytomatosis viral oncogene homolog Stratifin Golgin-67 transcript variant Chromosome 20 open reading frame 35 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 17, 72 kDa Oncostatin M receptor (similar to IL-6 family) Nuclear receptor subfamily 4, group A, member N-myc downstream regulated gene Interferon induced transmembrane protein Transcription factor (represses interleukin expression) Insulin receptor substrate Decay accelerating factor for complement (CD55) Eukaryotic translation initiation factor 5A Tumor protein D52 Insulin-like growth factor binding protein Aquaporin PRG-3 CUL4A CREG BASP1 K3.76G0.15 K3.85G0.12 K3.85G0.13 K4.00G0.12 K3.63G0.21 K4.55G0.17 K3.85G0.15 K3.85G0.17 K3.36G0.20 K4.55G0.18 K2.70G0.20 K3.57G0.27 IFITM2 IFITM3 JUNB ARHB DUSP9 PTPRM PDE3B EEF1A2 PTP4A1 FBN2 CCNG2 HIG2 IGF1R DDX21 K4.17G0.12 K4.17G0.16 K4.17G0.14 K4.35G0.12 K4.55G0.17 K4.55G0.12 K4.71G0.13 K4.76G0.12 K4.76G0.13 K5.00G0.16 K5.00G0.15 K5.26G0.13 K5.26G0.12 K5.26G0.11 K3.85G0.16 K3.70G0.15 K3.45G0.16 K7.14G0.20 K4.35G0.18 K4.76G0.17 K3.94G0.20 K5.00G0.15 K5.88G0.17 K5.56G0.18 K3.57G0.15 K4.55G0.15 K7.14G0.16 K5.56G0.15 K3.57G0.19 K3.33G0.18 K3.03G0.22 K1.11G0.25 K5.88G0.20 K3.70G0.18 K3.58G0.22 K5.26G0.18 K6.27G0.18 K4.00G0.19 K2.64G0.22 K6.67G0.19 K5.56G0.27 K5.56G0.22 TIMP3 K5.45G0.41 K5.57G0.72 K4.92G0.67 CHRNE K5.56G0.18 K5.00G0.20 K5.00G0.18 HTATIP2 MYC K6.67G0.12 K6.67G0.12 K8.33G0.20 K7.14G0.20 K6.67G0.26 K12.50G0.25 SFN GOLGIN-67 C20orf35 DDX17 K7.14G0.11 K7.69G0.14 K8.33G0.13 K8.33G0.13 K6.67G0.15 K6.25G0.22 K5.00G0.17 K7.69G0.16 K7.14G0.18 K6.25G0.24 K5.88G0.19 K10.00G0.23 OSMR NR4A2 NDRG1 IFITM1 TCF8 K10.00G0.13 K12.50G0.12 K14.70G0.13 K17.24G0.15 K17.54G0.12 K10.00G0.17 K9.09G0.16 K11.23G0.17 K25.00G0.15 K20.83G0.20 K12.56G0.35 K7.14G0.24 K10.75G0.18 K7.14G0.19 K27.03G0.17 IRS2 DAF K15.87G0.11 K15.87G0.12 K19.60G0.15 K17.54G0.16 K12.19G0.19 K10.52G0.26 EIF5A TPD52 IGFBP4 AQP3 K27.77G0.13 K29.49G0.29 K31.63G0.14 K35.70G0.13 K17.87G0.16 K13.51G0.17 K31.25G0.15 K47.75G0.17 K20.40G0.23 K11.76G0.18 K40.98G0.19 K29.57G0.20 NM_006435 BF338947 NM_002229 NM_004040 NM_001395 NM_002845 NM_000753 NM_001958 NM_003463 NM_001999 NM_004354 NM_013332 NM_000875 NM_004728 NM_000362 AI859060 BC002439 NM_002467 X57348 NM_015003 NM_018478 NM_030881 NM_003999 NM_006186 NM_006096 AA749101 NM_030751 AF073310 NM_000574 NM_001970 NM_005079 NM_001552 N74607 12 h CDX 24 h CDX 48 h CDX The fold changes are meanGSD of 12 chips cross-comparisons between nine candoxin (CDX)(treated chips and three control chips. All the fold changes reported are statistically significant (P!0.01) compared with control. the pairwise comparison analysis showed that the cells might have gene alterations in the presence of CDX. Thus, for the gene expression studies, CDX was treated at the optimum concentration of 300 nM (as measured by XTTassay) to see the differential gene expression in human brain glial cell lines. 3.3. Distribution of TUNEL positivity To substantiate the CDX-induced neuroglial DNAdamage, we performed a TUNEL-POD assay, an in situ cell death that detects brain cell damage. Following administration of CDX at 12, 24 and 48 h, TUNEL-positive 890 A. Pachiappan et al. / Toxicon 46 (2005) 883–899 Table The genes belonging to different metabolic and regulatory pathways inter-relation according to KEGG and GenMAPP pathway databases. Average fold change of each gene is also shown along with direction in which they have altered in all three time points Gene bank ID Gene description (gene symbol) Avg. fold change Mitochondrial/glutamate metabolism NM_003201 Transcription factor A, mitochondrial (TFAM) NM_003355 Uncoupling protein (UCP2, mitochondrial, proton carrier) AI339331 Glutamate dehydrogenase (GLUD1) NM_001698 AU RNA-binding proteinenoyl-Coenzyme A hydratase (AUH) AF116273 BCL2-associated athanogene (BAG1) NM_005165 Aldolase C, fructose-bisphosphate (ALDOC) NM_003979 Retinoic acid induced (RAI3, Class C metabotropic glutamate) NM_004052 BCL2 interacting nuclear gene encoding mitochondrial protein (BNIP3) NM_013410 Adenylate kinase (AK3, nuclear gene encoding mitochondrial protein) NM_001216 Carbonic anhydrase IX (CA9) NM_001875 Carbamyl-phosphate synthetase 1, mitochondrial (CPS1) W80357 Carbamyl-phosphate synthetase 1, mitochondrial (CPS1) Amino acid/protein metabolism AI339331 Glutamate dehydrogenase (GLUD1) NM_015989 Cysteine sulfinic acid decarboxylase (CSAD) NM_012423 Ribosomal protein L13a (RPL13A) AW024383 Ribosomal protein S21 (RPS21) NM_000666 Aminoacylase (ACY1) NM_001823 Creatine kinase, brain (CKB) W80357 Carbamyl-phosphate synthetase 1, mitochondrial (CPS1) NM_000050 Argininosuccinate synthetase (ASS) Carbohydrate metabolism NM_001500 GDP-mannose 4,6-dehydratase GMDS) NM_005165 Aldolase C, fructose-bisphosphate (ALDOC) NM_004481 UDP-N-acetyl-alpha-D-galactosaminyltransferase (GALNT2) NM_003749 Insulin receptor substrate (IRS2) Metabolism of complex lipids NM_002543 Oxidised low density lipoprotein (lectin-like) receptor (OLR1) AF010316 Prostaglandin E synthase (PTGES) M68874 Phospholipase A2, group IVA (PLA2G4A, cytosolic, calcium-dependent) NM_006227 Phospholipid transfer protein (PLTP) NM_000962 Prostaglandin-endoperoxide synthase (PTGS1, cyclooxygenase) NM_000963 Prostaglandin-endoperoxide synthase (PTGS2, cyclooxygenase) NM_000237 Lipoprotein lipase (LPL) neurons were observed almost in the whole region (hippocampus, frontal cortex, and temporal regions) of the mouse brain, but not in the controls. We also used the in vivo glial abundant temporal region of mice brain model to reconfirm the results obtained with glial ex vivo model (Fig. 3A–D). Time dependent cell death was observed in glial cell lines, as compared to the control (Fig. 3E–H). These results taken together show the significant delayed cell death both in vitro and in vivo in a time dependent manner. Pronounced glial and neuronal damage was observed particularly, at 24 h time points (Fig. 3C and G). 3.4. Brain edema To quantitatively assess the brain edema in adult mice injected intracerebroventricularly (i.c.v) with CDX, whole brain wet-to-dry weight ratios were determined. Quantitative measurement of brain edema showed that the wet–dry K3.26G0.17 K3.34G0.15 K3.41G0.14 K3.62G0.16 K4.13G0.16 K4.53G0.19 K5.06G0.19 K5.07G0.18 K10.08G0.17 K18.07G0.16 K21.01G0.18 K54.97G0.18 K3.41G0.14 K3.48G0.17 K3.55G0.13 K4.40G0.15 K5.00G0.18 K5.79G0.16 K54.97G0.18 K59.41G0.15 K3.30G0.19 K4.53G0.19 K5.30G0.17 K16.39G0.14 K6.03G0.19 K6.48G0.18 K7.21G0.16 K7.81G0.21 K11.43G0.16 K14.82G0.22 K18.09G0.17 ratio increased significantly (P!0.05) at 12 h (158G1.7%) and 24 h (169G1%), respectively, against saline treated controls (data not shown). 3.5. Toxicogenomics of differentially expressed gene profiles Each gene chips in the HG-U133A GeneChipe Array set represents w22, 238 genes and expressed sequence tags. The unsupervised cluster analysis shows the overall expression pattern and the biological correlation of replicates (12 chips of transcripts) in cellular response to CDX treatment over three time points (Fig. 4A). To obtain a global view of differential gene expression, we focused on genes that were either consistently increased or decreased in all sets of experiment with respect to time intervals. All probe sets designated ‘absent’ by the GeneSpring software were removed, and only those genes whose expressions changed by 3-fold or greater in at least two pairwise A. Pachiappan et al. / Toxicon 46 (2005) 883–899 891 Fig. 1. Q-TOF mass spectrum of candoxin (CDX). Quadruple-time of flight mass spectroscopy (Q-TOF) mass spectrum of CDX. The peaks delineating m/z 7334.40 and 3667.20 represent (MCH)C and (MC2H)2C ionization states of CDX, respectively. comparisons were taken as significant. The highly and tightly clustered patterns were identified with those genes whose expression was changed after CDX-treatment followed by a dramatic increase (Fig. 4B, red) or decrease (Fig. 4B, blue). In total, 238 genes (P!0.05) were assayed for fold change levels for up and down regulation, as induced by CDX (Fig. 4B). Using a cutoff value of RG3fold change in transcript abundance in all the experiments, a total of 105 genes were identified as relatively responsive to CDX (Fig. 4C, bar diagram). CDX responsive genes were divided into functional classes based on gene ontology (http://www.godatabase.org/cgi-bin/go.cgi) including, biological function, cellular component, and molecular function in all sets of experiment (data not shown). The majority of genes altered by CDX are in the biological process gene ontology classes of cell communication (signal transduction and inflammation, 45.71%) and metabolism (28.57%). The Venn diagram (Fig. 4D, see the legend for filter criteria) shows the total number of genes determined to be present as per Affymetrix data for the experimental conditions, which were absent in the control. Of these, 33 genes were increased, and 72 genes decreased significantly (P%0.01) in their expression (Tables and 3). Other genes that failed to meet this criterion (P%0.01) have significant changes in gene expression when measured at the level of P!0.05. 3.6. Metabolic pathways CDX responsive genes, categorized according to KEGG and GenMAPP-Finder (Table 3) indicate that 28 genes which were consistently and highly down-regulated (RK3fold) in all experiments, appear to be involved in mitochondrial/glutamate, carbohydrate, lipid, amino acid, and nucleotide metabolisms. Six genes (CPS1, ASS, PTGS1, CA9, LPL, and GLUD1) that inter-contribute to energy/glutamate, lipid and amino acid metabolisms were found to be highly down-regulated (Table 3), while those Fig. 2. Cell viability assay. (A) XTT values for effective concentration (EC50) of CDX relative to untreated control. Doseand time-dependent induction of glial neuron cell death by CDX. Following incubation of cultured human brain glial cells for 6, 12, 24, 48, and 72 h with increasing concentrations (0.01–100 mM) of CDX, mitochondrial activity of viable cells was quantified using the XTT assay. The results from the XTT-assay are expressed relative to the untreated control. The EC50 value for CDX toxicity was w1 mM. Values represent average of three independent experiments with triplicate measurements; error bars indicate GSEM. The Hs 683 cells were seeded on untreated culture flask for 24 h at the same time and the same number as those on CDX-treated flask. Significant cell death was observed only at higher concentration of CDX as compared to the control, but not at the optimum dose (300 nM) used in the present experiments. 892 A. Pachiappan et al. / Toxicon 46 (2005) 883–899 Fig. 3. TUNEL-histochemistry. The TUNEL-assay shows the whole brain section of mice (A–D), and the inner panels show the glial abundant TUNEL-positive temporal region at 12, 24 and 48 h (B–D) as compared to saline treated control (A). The adjacent panels (F–H) show the significant cell death induced by CDX in Hs 683 cells after 12, 24 and 48 h incubation compared to untreated control (E). Note the timedependent increase in cell death, with significant cell death observed particularly at 24 h. Scale bars: 200 mm (A–D); 50 mm (E–H) and inner panels. genes (HSPA1A, GADD45A, TP53TG1, and DNAJB6), that are usually involved in oxidative stress response, DNAdamage and mitochondrial energy metabolisms, were upregulated (Table 2). Interestingly, ASS (K59.41 folds), CPS1 (K54.97 folds) and AQP3 (K37.41 folds) were most drastically down-regulated in response to CDX-treatment as compared with the control (Fig. 4C). 3.7. Gene-specific correlation between RT-PCR and microarray results The initial gene expression changes obtained from microarray studies were further verified and confirmed by quantitative RT-PCR for selected genes. The thermalcyclerPCR for GAPDH and other genes showed a single (peak) A. Pachiappan et al. / Toxicon 46 (2005) 883–899 893 Fig. 4. Hierarchical clustering algorithm and differential gene expression of human glial cells to CDX-treatment (filter criteria). (A) The hierarchical dendrogram presents the clustered samples in column and the genes in rows. Three chips were used for each group, and the individual expression signal of each transcript (w22, 285) in each of the 12 chips was clustered using GeneSpring software. Based on the arrangement according to the clustering algorithm, the transcripts with most similar expression blueprints were placed adjacent to each other. Pseudo colored representation of gene expression is shown according to the scale at the top. (B) The highly up and down regulated genes are tightly clustered and are interrelated to neuroglial degeneration and brain edema (see the text for details). (C) The Bar chart depicting the number of genes (105, P!0.01) with a cut-off level of three-fold increase or decrease in all of the experiments for three time points. (D) The Venn diagram summarizing the distribution of genes regulated commonly in each experiment in time dependent manner as compared to control which passed all the filters. First filter: signal algorithm genesR3-fold (log2RG1.5) changes of up or down regulated genes in all the experiment. Second filter: change algorithm genes having a change value as increased, decreased, marginally increased or marginally decreased in any two of the experiments for each time point. Third filter: detection algorithm genes having detection value of present or marginal in any two of the experiments (chip) for each time points where considered. The genes designated as internal controls (b-actin, GAPDH) by affymetrix were removed. Forth filter: ANOVA (P!0.01) a total of 105 genes that passed all those filters. product indicating that no false amplification occurred. The expressions of 20 genes (Fig. 5) were quantified for their respective abundance. The amount of each gene was normalized to the housekeeping gene, GAPDH. In most cases, the relative expression determined by real-time RTPCR quantitation was fully concordant with the expression determined by gene array analysis (Fig. 5). While a pronounced increase in the expression level of IL7R was detected almost at all time points, significant (P!0.01) increase (IL13RA2, TNFRSF12A, GADD45A, CD44, UBE2E3, IFI44, CASP1, THBS1 and GABRA2) or decrease (ASS, CPS1, AQP3 and CHRNE) in the expression levels was detected only at 24 h time point of CDX exposure. With gene array analysis (Fig. 5, inner panel), expression of all selected genes were altered (RG4-fold) at all time points, whereas with RT-PCR, significant gene alterations (P!0.01) were noted only at 12 and 24 h time points of CDX treatment. No significant change was however, observed in the expression level of THBS1, IGFBP4, LPL, OSMR, DDX17 and PTGS1 genes at 48 h time point. Moreover, the Pearson correlation between quantitative real-time RT-PCR and microarray analysis among the twenty genes was 75% (P!0.01) for the IL7R, THPS1, IL13RA2, TNFRSF12A, GADD45A, CD44, UBE2E3, IFI44, CASP1, GABRA2, ASS, CPS1, AQP3, PTGS1, CHRNE and 45% (P!0.05) for the remaining genes such as IGFBP4, LPL, DAF, DDX17, and OSMR (Fig. 5). 3.8. Selective inhibition of CDX-induced inhibition by P-NT.II A small molecule peptide inhibitor P-NT.II produced a significant inhibition (P!0.05) of the twitch blockade induced by CDX at a lower peptide concentrations (6.7 mg/ml or 3.33 mM), while it is weakly inhibited the erabutoxin-b induced twitch blockade only at a relatively higher concentration (13.4 mg/ml or 6.66 mM). P-NT.II was also found to neutralize the in vivo toxicity of CDX when pre-incubated with the peptide prior to intraperitoneal 894 A. Pachiappan et al. / Toxicon 46 (2005) 883–899 Fig. 5. Comparisons of microarray and quantitative real-time RT-PCR after exposure of CDX to glial cells. Results of chronological QRT-PCR analysis of relative gene expression for selected genes. The amount of each mRNA was obtained by quantitative RT-PCR. Each PCR generate only one product (peak) of the predicted size, indicating the specificity of primers used. The relative levels of gene expression were normalized to the housekeeping gene (GAPDH). The results are representative of nine experiments consisting of 300 nM CDX vs. three time points (12, 24 and 48 h) of brain cells in triplicates for each time point. Values are expressed as GSE. *P!0.05; **P!0.01. The inset line graph delineates the overall results of QRT-PCR relative expression levels of 20 genes in comparison with microarray data. In most cases, qualitative and quantitative expression levels detected by QRT-PCR were consistent and comparable to the microarray expression of all the genes tested (see the text for details). injection into albino mice. In contrast, P.NT.II failed to neutralize the toxicity of other potent a-neurotoxins such as erabutoxin-b, a-bungarotoxin, or a-cobratoxin, under similar experimental conditions. These results indicate that P-NT.II is a potent and selective inhibitor of candoxin. alone did not show any significant changes in those gene expression levels as compared to controls. 3.9. Suppression of gene expression by a selective inhibitor of CDX Gene expression profiling using microarrays is a novel approach in identifying gene alterations and the molecular pathways (gene– gene interactions) involved after exposure to toxins. The present study is the first report involving gene expression profiles using microarray analysis in CDXstimulated glial cells. The glial cells are the major component (w80%) cell of the brain, and play crucial roles in glia-neuron cross-talk through the modulating effects on synaptic transmission (D’Ascenzo et al., 2004). From this study, we identified 105 genes whose expressions were significantly alerted (O3-fold) by CDX treatment (Tables and 3). The data revealed that decreased nAChR, and increased inhibitory-GABRA2 mediated signals could alter the gene expression effectively in different In order to confirm the potential candidate genes related to glial–degeneration in the regulation of neurotransmission blockade by CDX, we used QRT-PCR for assessing relative mRNA expression levels of CDX induced genes in the presence and absence of a potent and selective CDX inhibitor P-NT.II. Coaddition of P-NT.II with CDX resulted in a significant suppression of those genes—IL7R, CD44, TNFRSF12, IFI44, IL13RA2 and CASP1 (P!0.01); UBE2E3, GAAD45A, GABRA2 and THBS1 (P!0.05)— that were highly expressed after exposure of glial cell lines to CDX alone (Fig. 6). Treatment of the cells with P-NT.II 4. Discussion A. Pachiappan et al. / Toxicon 46 (2005) 883–899 895 Fig. 6. Changes of gene expression by a selective inhibitor (P-NT.II) of CDX. The bar diagram represents the candidate genes related to glial– degeneration in the regulation of neurotransmission blockade by CDX. QRT-PCR was used for assessing relative mRNA expression levels of CDX-induced up-regulated genes in the presence and absence of a CDX selective inhibitor P-NT.II at 24 h. Results showed that co-treatment with P-NT.II significantly inhibited the CDX-induced up-regulation of (IL7R, CD44, TNFRSF12, IFI44, IL13RA2 and IL-1b)** and (GABRA2, UBE2E3, GAAD45A, and THBS1)* genes. Values are expressed as GSE. *P!0.05; **P!0.01. The QRT-PCR gel analysis shows the expression levels of each gene in cultured glial cells after exposed to CDX, CDXCP-NT.II and P-NT.II compared with control (Ctrl: control, CDX: candoxin, CDXCP-NT.II: candoxinCpeptide, P-NT.II: peptide). The sizes of the DNA ladder (100 bp) are depicted on the left, right and middle of the panels. GPCR-MAPK pathways. Since the selected genes, as confirmed by QRT-PCR analysis (Fig. 5), are involved in activation and alteration of the MAPK-signaling pathway, it seems likely that the effect of CDX on gene expression is probably through this signaling pathway linking with glial– inflammation and mitochondrial dysfunction (Fig. 7). Accumulated evidence attributes that SOCS1 directly inhibits the autophosphorylation of JAK-family (Krebs and Hilton, 2001) and hinders signaling by recruiting the proteasomal machinery to signaling complexes, resulting in the ubiquitination and subsequent degradation of associated signaling proteins (Endo et al., 1997). Both in vitro and in vivo studies have shown that neuronal nicotinic-ligands mediate neuroprotective activity, and the phosphorylation of JAK2 in the presence of nicotine is completely inhibited by a-Bungarotoxin, an antagonist to a7-nAChR (Shaw et al., 2002). Treatment of cells with CDX could inhibit the activation of JAK2-cascade by over-expression of SSI-1 and GABRA2 (Fig. 7), thus resulting in complete prevention of a7nAChR-mediated neuroprotective effects, thereby affecting the synaptic-neurotransmission in vitro and in vivo (Fig. 3). This indicates that an interaction of genes induced by CDX deactivates the MAPK-signal transduction cascade via cellular second messengers on inhibitory-GABRA2, and the internalization may lead to glial–neurodegenerative process. Hence, sequestration of GPCRs is thought to contribute to functional resensitization of CDX responsiveness in downstream of signal-transduction via glial– degenerative pathways. Subsequent to deactivation of MAPK-pathway, caspase-1 (IL-1b) activation may follow, thus resulting in glial–neuronal–degeneration (Fig. 7). Gene alteration can change the function of gene products in a way that has a detrimental effect on the cells. The increase in the expression of IL-1b induces neuroglial cell death, while its inhibition slows progression of neuronal disease (Ona et al., 1999; Ando et al., 2003). Several lines of evidence suggest that caspases have a role in glial– neurodegeneration (Friedlander, 2003), and can enhance the inflammatory response (Yoshihara et al., 2002). Recent study shows that CD44 expression is also highly and persistently up-regulated by glial cells involved in numerous inflammatory and demyelinating conditions (Tuohy et al., 2004). Besides, several genes including IL13RA, TNFRSF12 and IL7R, are identified as involved during the process of inflammation (Joshi et al., 2000; Kim et al., 2001; Yamazaki et al., 2003). The general increase in IL-1b, IL7R, IL13RA2, CD44, TNFRSF12 and IFI44 genes induced by CDX, may further confirm the concept of inflammation in glial-driven neurodegeneration (Fig. 5). Although the inflammatory process has been considered a secondary event following glial–degeneration, it can enhance neuronal injury through the expression of certain genes. Our results demonstrate that a wide variety of inflammation and apoptosis-related genes are differentially expressed in the process of glial–degeneration (Table 2). Exposure of human glial cells to CDX alters the gene expression and implicates in the regulation of nicotinic receptor-mediated neuronal pathology in the pathogenesis of the disease. It is possible that neurodegenerative lesions are linked with the abnormal accumulation of ubiquitinated proteins in neuroglial inclusions (Figueiredo-Pereira and Rockwell, 2001) and also with signs of inflammation (Miller et al., 2004). These neuroglial inclusions, which are hallmarks of neurodegeneration, might themselves lead to neuronal defect in neurotransmission (Li et al., 2003). Genes related to inflammation are known to potently produce 896 A. Pachiappan et al. / Toxicon 46 (2005) 883–899 Fig. 7. Schematic pathway. Candoxin alters the expression of genes during glial–inflammation and mitochondrial dysfunction in Hs 683 cells. A schematic diagram, summarizing the possible pathway involved in CDX-induced nAChR-mediated neuroglial injury, links inflammation, DNA-damage with mitochondrial dysfunction pathways in human brain Hs 683 cells as revealed by microarray analysis and quantitative realtime RT-PCR. neurotoxic cytokines, chemokines, proteases, and small reactive cytotoxic molecules associated with the accumulation of ubiquitinated proteins. Ubiquitin may play a variety of roles in regulation of gene expression and intermediate filament inclusion bodies in neuropathologic conditions, including Parkinson, Pick, Alzheimer diseases and Rosenthal fibres within astrocytes (Lowe et al., 1988). In contrast to nicotine, which reduces mRNA levels of UBE2D3 and UBE2E3 (Dunckley and Lukas, 2003), CDX elicits an increase in mRNA levels of those genes in glial cells. The loss of glia and neurons in brain regions, repression of genes encoding SNCA, UCH-L1 (Shimura et al., 2000), and increased cytoplasmic levels of a-synuclein and ubiquitin in glial cells (Miller et al., 2004; Ikonomovic et al., 2004), which are known to be associated with neuronal diseases, might function in a dominant-negative manner due to decreasing normal function of a-synuclein (Abeliovich et al., 2000). The present microarray data also show that CDXtreatment either up- or down-regulates those genes that are interrelated to neuroglial pathogenesis and DNA-damage. The induction of GADD45A, GAS6, TP53TG1 and DAPK3 are well known to induce DNA-damage, stress and arrest of cell growth. The expression of GADD45 is commonly associated with a corresponding increase in p53 (Stokes et al., 2002). Increased expression of TP53TG1, found in our study may imply p53-dependent induction of GADD45A, which may function in response to oxidative stress and cellular damage upon exposure to CDX. The TUNEL results provide significant evidence for DNA-damage both in vitro and in vivo in a time-dependent manner (Fig. 3). The mitochondrial dysfunction, exogenous/endogenous neurotoxins and genetic factors all play a key role in neuronal injury (Yoo et al., 2003; Holtz and O’Malley, 2003). Potent endogenous neurotoxins have been widely used to create animal models of neuronal diseases (Blum et al., 2001). Upon exposure of the cultured Hs 683 cells to CDX, which is an endogenous ligand for neuronal nAChRs (Nirthanan et al., 2002; 2003), mitochondrial–metabolism related genes—CPS1, TFAM, ALDOC, AK3, CA9, and UCP2—were significantly down-regulated (Table 3), thus implying that those genes might be involved in mitochondrial dysfunction. Argininosuccinate synthetase (ASS) is down-regulated by induction of CDX in vitro (Fig. 4c). It is the rate limiting enzyme in the metabolic pathway leading from L-citruline to L-arginine, the physiological substrate of A. Pachiappan et al. / Toxicon 46 (2005) 883–899 all isoforms of nitric oxide syntheses (NOS). Under proinflammatory conditions, ASS expression is up-regulated in glioma cells as well as in mixed glial and pure astroglial cultures and a functional role in the recycling of L-citruline for generation of NO has been demonstrated (Heneka et al., 2001). Besides, imbalance of astrocyte-mediated glutamate transport is common to both forms of disease and implies a central role of glial cells in neurodegeneration (Miller et al., 2004). Several studies have indicated that lipoprotein lipase (LPL) may play important roles in the lipid homeostasis of the brain, and mutant forms of LPL may increase the risk of developing neuronal damage (Paradis et al., 2003). Hence, CDX exposure decreases the levels of ASS, CPS1, RAI3, LPL and GLUD1, that might increase the concentration of ammonia contributing to glutamate excitotoxicity, impaired lipid-homeostasis, neuroglial cell death and cerebral edema (Table 3). Moreover, gene changes in hydrostatic pressure (AQP3) and ion channels (SLC12A7 and CLNS1A) homeostasis cause accumulation of intracellular fluid and brain edema following CDX stimulation (Fig. 7). The post-synaptic abnormalities are known to be associated with blocking of ACh-binding site, mutation, reduction or deficiency of functional nAChR (Slater et al., 1997) and lack of 3-subunits in nAChRs (Croxen et al., 2001). Besides, suppression of DAF causes post-junctionaldamage in AChR as well as muscle weakness (Lin et al., 2002). Vulnerability of post-synaptic damage causing genes—CHRNE, MINK, DAF, PLCL2 and PLA2G4A— supports the view that inhibition of nAChR affects the expression of these genes. Our results indicate that CDX antagonistically and robustly down-regulated the nAChRinteracting genes by more than 4-fold of intensity (Table 2). Impaired neurogenesis in terms of growth and differentiation, especially of neurons and glial cells, was reported to be associated with significant reduction of DEAD-box protein levels in fetal brain (Kircher et al., 2002). A significant down-regulation of the gene encoding DDX17 and DDX21 after CDX-treatment, as evident in the present study, might possibly lead to deficient growth and impaired neurogenesis in brain cells (Table 2). Significant polymorphism in inflammatory agents (TNFA, IL-1a, and IL-6) has been reported to increase the risk of neuropathology (McGeer and McGeer, 2001; Court et al., 2001). In response to CDX, the inflammation-related genes (IL-1a, OLR1, NPR3, TIMP3, and OSMR-IL-6) were significantly downregulated and are likely to be important regulators of the inflammatory response associated with glial–neurodegeneration. In addition, genes (PTGS1 and CR3) that are related to prostaglandins and leukotrienes, which are both agonists and antagonists of the same pathological process (Cher et al., 2003), were differentially expressed by CDX, thus implying their interrelationship to neuroglial-inflammation (Table 3). CDX is a novel ligand for neuronal (7-nAChRs as well as being a uniquely reversible blocker of the muscle nAChRs (Nirthanan et al., 2002). Using structural information 897 combined with electrophysiological data, we have designed (Thwin et al., 2002) a novel 17-mer specific CDX-binding peptide P-NT.II, and used it to explore any significant alterations on gene expression in human Hs 683 cells exposed to CDX. When the CDX-induced glial cells from the human brain were treated with 18.16 mg/ml (9 mmol) of P-NT.II peptide, the level of the (IL7R, CD44, TNFRSF12A, IL-1b, IFI44, and IL13RA2) gene transcripts was significantly suppressed at 24 h time point (Fig. 6). However, the control peptide, which was nonspecific to CDX, did not represent any detectable alteration of the expression of those genes. These results indicate that the peptide treatment on the CDX-induced glial cells specifically inhibit the expression of those genes that are involved in the inflammatory pathway, while those genes that are implicated in JAK-pathway are not affected at all by the peptide treatment (Fig. 7). During inflammation, several genes, including, IL13RA2, TNFRSF12A, IL7R, TNF-a, IL-6, IL-1a, IL-1b, COX-2 and CD44, are identified as involved in the process (Joshi et al., 2000; Kim et al., 2001; Cosenza et al., 2002; Yamazaki et al., 2003; Cher et al., 2003; Tuohy et al., 2004). In our attempt to find out whether the expression of those genes that are affected by CDX treatment could be significantly inhibited or reduced by the specific inhibitor of CDX, we identified IL7R, CD44, IL-1b, IFI44, IL13RA2 and TNFRSF12A, genes as the ones that were reduced by P-NT.II treatment at a highly significant level (P!0.01) (Fig. 6). The other genes (THBS1, UBE2E3, GADD45A and GABRA2) that were also suppressed at a lower level of significance (P!0.05) by the peptide may also be implicated after exposure of glial cells to candoxin. The possibility that inhibition of those gene expression by PNT.II treatment could be due to other non-specific inhibition was excluded by the observation that treatment with the unrelated control peptide showed no detectable inhibition on the expression of those genes. Hence, inhibition of the gene expression at the mRNA level in the peptide-treated glial cells is related to the reduced CDX activity. Consequently, this implies that those genes are specifically involved in the molecular pathways after exposure to CDX. These results demonstrate that a multidisciplinary approach involving microarray expression study and QRTPCR analyses combined with specific inhibitors is a very efficient way for uncovering novel target genes involved after exposure of glial cells to neurotoxins, and has implied potential for dissecting other signaling pathways. In conclusion, glia–glia and glia–neuron network related genes could be promising therapeutic targets for inhibition of glial-driven neuronal damage in DNA repair mechanisms and/or cell death machinery. Acknowledgements We are grateful to Dr S. Nirthanan (Department of Neurobiology, Harvard Medical School, Boston, USA) and 898 A. Pachiappan et al. / Toxicon 46 (2005) 883–899 Dr K.N. 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Differential Gene Expression of Human Glial Cells to CDX-treatment 3.14 Functional Classification of Candoxin Inducible Genes 3.15 Semi-Quantitative (qualitative) RT-PCR for up-regulated genes 3.16 Comparisons of Microarray and qRT-PCR Results 3.17 Inhibition of CDX (20µg/ml) and Erabutoxin-b (2 µg/ml) – induced Neuromuscular Blockade in the Mouse Hemidiaphragm by P-NT.II 3.18 Changes of Gene Expression by a... inflammation and neurodegeneration induced by candoxin, a novel neurotoxin from Bungarus candidus venom: Global gene expression analysis using microarray Toxicon, 46(8): 883-899 2 Kellathur N Srinivasan, Pachiappan Arjunan, Mer Lin DH, Lim Jim Jim, Wei-Yi Ong, Loke Weng Keong, Ho Diana SC, Tang Jing Ping, Raghavendra Prasad HS, Lee Fook Kay & Gopalakrishnakone Ponnampalam (2006) Global Gene Expression Profile,... Identifying potential biomarker genes specific for neurotoxins based on gene expression profiles Life Sciences in Singapore: Integrating Multidimensional perspectives; Fourth combined scientific meeting (Incorporating the Second Singapore microarray meeting) Singapore (January 2003) 5 A Pachiappan, K N Srinivasan, S Nirthanan, H S Raghavendra Prasad, and P Gopalakrishnakone Global gene expression Study of Human... expressed genes in signal transduction and ubiquitininflammation linking with neuro-glial pathogenesis in glial response to CDX 3.2 Genes belonging to different metabolic and regulatory pathways inter-relation according to KEGG and GenMAPP pathway databases 122 Chapter 3 4.1 Differentially expressed genes involved in in neurodegenration and neuropathogenesis of human neuronal cells induced by sarin... down-regulation of genes and a range of anticholinesterase effects In contrast, repeated doses produced persistent irreversible down-regulation of genes related to neurodegenerative mechanism at 48h Quantitative Real-time PCR, western blot and confocal microscopic analysis confirmed the increased expression of apolipoproteinE (ApoE), V-ets erythroblastosis-2 (Ets-2), and reduced expression of presenilin1... Pachiappan, C Ananth and Z Qi Global gene expression profiling of Human Genome following exposure to toxins-emerging field of TOXINOGENOMICS 6th Asia-pacific congress on animal, plant and microbial toxins and 11th Annual scientific meeting of the Australasian College of tropical medicine, Cairns, Australia (July 2002) 2 P Gopalakrishnakone, K N Srinivasan, S Zhong and A Pachiappan Gene Expression Studies of... China (April 2002) 3 A Pachiappan and P Gopalakrishnakone Brain edema and neuro-glial damage induced by a neurotoxin candoxin, differential gene expression analysis using microarray International Biomedical Science Conference, Kunming, China (December 2004) 4 Gopalakrishnakone P and Pachiappan A Gene expression profiling and fingerprinting of Human Genome following exposure to toxins/nerve agents –... Fook Kay and Gopalakrishnakone Ponnampalam Gene expression profiles of human neuroblastoma (SH-SY5Y) cell lines exposed to a single and repeated lowdose of nerve-agent ‘sarin’ “Symposium on chemical, biological, nuclear and radiological threats” Finland, (June 18-21, NBC 2006) 7 Pachiappan A, Gopalakrishnakone P, Thwin M.M, Weng Keong L, and Lee F.K GLOBAL GENE EXPRESSION PROFILING OF HUMAN GENOME FOLLOWING... into the mechanisms of neuro-degeneration after exposure to animal and/or chemical neurotoxins Besides providing an in-vitro experimental model for studies on the neuropathophysiology of brain cells, this investigation further yields the molecular mechanism of glial-driven neuro-degeneration and provides a clue by which nerve agents such as sarin could mediate neuro-degeneration xiii PUBLICATIONS FULL... activity METHODS: Gene expression profiles of cultured human neuronal cells exposed to candoxin (CDX) and sarin (GB), respectively were studied using Affymetrix GeneChips (HG-U133A) A single dose of CDX was used on Hs 683 glial cell line, while a single (3h-acute or 24h-intermediate) as well as repeated (48h-delayed) doses of sarin (5ppm) were used on SH-SY5Y cells Genes altered by 3-fold (105 for . GLOBAL GENE EXPRESSION CHANGES CAUSED BY NEUROTOXINS PACHIAPPAN ARJUNAN (B. Sc., B. Ed., M. Sc., M. Phil.). and pathogenesis of sarin 1.13a Physical and chemical properties of sarin 33 1.13b Pathological changes caused by Sarin 34 1.13c Toxicodynamics of acetylcholinesterase inhibtion by sarin 35. expressed gene profiles 114 3.1.8 Toxicofunctional genomics gene expression of CDX 117 3.1.9 Genes involved in signal transduction and internalization 120 3.1.10 Metabolic Pathways 121 3.1.11 Gene- specific