Регуляция гомеостаза ионов меди у Halobacterium salinarum: компьютерное моделирование и экспериментальный анализ
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1 Регуляция гомеостаза ионов меди у Halobacterium salinarum: компьютерное моделирование и экспериментальный анализ Regulation of Cu homeostasis in H. salinarum: computational modeling and experimental analysis Александр Ратушный Alexander Ratushny ИЦиГ СО РАН, 5 июля 2012
2 Halobacterium salinarum Model Organism Bonneau et.al. Cell (2007); Baliga NS Science (2008)
3 Archaea Archaea are one of three distinct domains of life on Earth. They are adapted to almost all extreme environments from Methanogens in swamps, to Halophilic Archaea in Salt lakes, and to Thermophilic Archaea in deep ocean thermal vents or hot springs. Halobacterium salinarum Pyrococcus furiosis Methanococcus maripaludis Sulfolobus solfataricus
4 Archaea 20% of the total biomass Global Oceans 1.3x10 28 Archaea 3.1x10 28 Bacteria (Karner et al. 2001) Allers & Mevarech, 2005
5 Reasons why H. salinarum NRC-1 is an ideal model organism for systems biology research a. It is non-pathogenic and easy-to-culture to very high cell densities (>10 8 cells/ml). b. It has a relatively small genome (2.6Mbp) that is completely sequenced 25 c. Over 62% of all protein coding genes in H. salinarum NRC-1 have been characterized through extensive proteomic analysis 26. d. Key genetic information processing and DNA repair mechanisms are eukaryotic-like 27. e. It is easily lysed in low salt buffers facilitating high-throughput RNA and protein preparations 20. f. It is biochemically, genetically and genomically tractable with a range of molecular biology, computational and high-throughput functional genomic tools 19, g. A network with an exhaustive number of inferred interactions/associations among the H. salinarum NRC1 proteins and protein-dna interactions for general transcription factors has been constructed through evolutionary comparisons and ChIP-chip experiments 5. More importantly, all of this information has been processed along with gene expression profiling using machine learning algorithms 29,30 to infer a global predictive gene regulatory network 24. h. Complete genome sequences of several closely related halophiles 25,35-37 will facilitate comparative genomics for extracting detailed information. Baliga lab
6 Resources for systems analysis of H. salinarum NRC-1 We have crafted tools for comprehensive systems analysis of Halobacterium salinarum NRC-1 from high-throughput experimental analysis, to network inference, visualization, and exploration of complex data. Several of these tools have been previously published and include classical genetics (gene knockouts 20-23,34, overexpression and epitope tagging 5 etc.), microarrays 19,21,23, quantitative proteomics (using ICAT 19 and itraq 20,22 ), protein surveys 26, protein complex immunoprecipitation for determining protein-protein and protein-dna interactions 5, computational algorithms for localizing protein-dna interactions 38 and regulatory network inference 29,30 and software for simultaneous exploration of diverse databases in a framework that enables software interoperability 31,39 Baliga lab
7 Baliga lab
8 Wackett et. al, App. Env. Microbiol, 2004
9 A systems-level model for transition metal stress response of Halobacterium NRC-1 Kaur et al., Genome Res, 2006
10 Cu: essential and hazardous Crucial micronutrient Co-factor in electron transport Redox detoxification Misregulation linked to: Wilson s disease Menke s syndrome Alzheimer's Cu import / efflux disorders Hamza et.al, PNAS, 2001
11 Cu Efflux in H. salinarum BLAST Alignment Score: 26.6 E-value: gb AAG MPKTLSIDE MGCEGCEDIV ENALAGVA-A VSDVDADHES 38 gb AAG MTELTVTD MSCAHCEQTV TDALTDLD-G VTDATADADT 37 ref NP_ ATOX1 (Human) MPKHEFSVD- MTCGGCAEAV SRVLNKLG-G V-KYDIDLPN 37 emb CAA Atx1p (yeast) MAE IKHYQFNVV- MTCSGCSGAV NKVLTKLEPD VSKIDISLEK 42 sp A8Z3F9.1 CopZ (S. aureus) MSQEILNVEG MSCGHCKSAV ESALNNID-G VTSADVNLEN 39 ref NP_ CCS (Mouse) 1 MASKSGDGGT VCALEFAVQ- MSCQSCVDAV HKTLKGVA-G VQNVDVQLEN 48 Clustal Consensus * * * * * * Metal Binding Domain gb AAG GTVTVDGD-- -ATDDDLLRS VELAGYDAEL ADA gb AAG DTVSYEGT-- -ADTDAVVAA IDDAGYTVAP ref NP_ KKVCIE---S EHSMDTLLAT LKKTGKTVSY LGLE emb CAA QLVDVYTT-- -LPYDFILEK IKKTGKEVRS GKQL
12 Experimental Methods Assay for YvgX expression Transcriptionally fused GFP expression Assay for intracellular Cu Inductively coupled plasma mass spectrometry (ICP-MS) Compare: Deletion of chaperones Constitutive overexpression of chaperones
13 Cu Efflux has pulse dynamics Efflux pump Chaperones 0702 / mM Cu mrna profiles show pulse responses for efflux pump and metallochaperones
14 Metallochaperone Function Wild-type Cu 1179 a / b YvgX a / b = 0702 / 2581 Chaperones are a functionally important element in Cu efflux Trafficks Cu between sensory and efflux components Mediates negative feedback
15 A simple model for Cu Efflux
16 Transcriptional dynamics of YvgX and associated consequences on intracellular Cu levels t = 300min P yvgx -> GFP t = 300min Chaperone deletion increases sensitivity to Cu Chaperone overexpression quantitatively similar to wt Chaperone Deletion Chaperone Overexpression 16
17 Unique chaperone roles Two Chaperone Model 1 17 Parameter Default Model 0 Model 1.1 Model 1.2 M k.d2581.p1179.cu.binding k.d2581.p1179.cu.dissociation k.m2581.degradation k.m2581.transcription k.oe2581.transcription k.p0700.cu.by.p2581.f k.p0700.cu.by.p2581.r k.p0700.cu.by.p2581.f k.p0702.cu.bind 0.01 k.p0702.cu.by.p1179.f k.p0702.cu.by.q.f k.p0702.cu.by.p2581.f k.p0702.cu.by.p2581.r k.p0702.degradation 0 k.p2581.cu.bind k.p2581.cu.by.p1179.f k.p2581.cu.by.p0702.f k.p2581.cu.by.p0702.r k.p2581.cu.by.q.f k.p2581.cu.debind k.p2581.cu.degradation 0 0 k.p2581.degradation 0 0 k.p2581.translation k.p1179.cu.by.p0702.f k.p1179.cu.by.p0702.r k.p1179.cu.by.p0702.f k.p1179.cu.by.p2581.f k.p1179.cu.by.p2581.r k.p1179.cu.by.p2581.f k.q.cu.by.p0702.f
18 Experiment t = 300min Simulation 18
19 Unique chaperone roles Two Chaperone Model 2 19 Parameter Default Model 0 Model 1.1 Model 1.2 M k.d2581.p1179.cu.binding k.d2581.p1179.cu.dissociation k.m2581.degradation k.m2581.transcription k.oe2581.transcription k.p0700.cu.by.p2581.f k.p0700.cu.by.p2581.r k.p0700.cu.by.p2581.f k.p0702.cu.bind 0.01 k.p0702.cu.by.p1179.f k.p0702.cu.by.q.f k.p0702.cu.by.p2581.f k.p0702.cu.by.p2581.r k.p0702.degradation 0 k.p2581.cu.bind k.p2581.cu.by.p1179.f k.p2581.cu.by.p0702.f k.p2581.cu.by.p0702.r k.p2581.cu.by.q.f k.p2581.cu.debind k.p2581.cu.degradation 0 0 k.p2581.degradation 0 0 k.p2581.translation k.p1179.cu.by.p0702.f k.p1179.cu.by.p0702.r k.p1179.cu.by.p0702.f k.p1179.cu.by.p2581.f k.p1179.cu.by.p2581.r k.p1179.cu.by.p2581.f k.q.cu.by.p0702.f
20 Experiment t = 300min Simulation 20
21 Steady-state model for intracellular Cu Cu in c2 k0 k21 c2 c1 1 f ( c2, k20, k22) f ( c2,0, k23) k k where f ( x, k, k 1 2 ) x k1 k2 x 1 k 2 c 1 and c 2 are concentrations of 0702 and 2581 metallochaperones, respectively Experiment In silico simulated levels of intracellular Cu for different genetic backgrounds
22 Sensitivity analysis of the steady-state GHF model
23 GHF model Improvement of the portrait model Added basal Cu affinity of 0702 basal Cu trafficking of 2581 to YvgX degradation (on the order of cell growth) to the chaperones. Specifically, 2581 (MC2) is degraded slightly faster than 0702 (MC1). Modified the equilibrium for Cu mediated dimers that are formed during the Cu 'handoff' - made their prevalence lower. the activation rate of 1179 by 2581 Experiment Portrait model simulation results
24 Sensitivity analysis of the portrait model Reducing the activity of VNG0702H results in an increase in intracellular Cu. This can occur in several ways: the binding affinity of VNG0702H can be reduced (decreasing the equilibrium ratio between apo-/cubound VNG0702H, or apo- VNG0702H / Cu-VNG2581H). trafficking efficiency of VNG0702H to VNG0700G can be reduced. the lifetime of VNG0702H proteins can be reduced via increased specific degradation.
25 Generalized Hill function method
26 Acknowledgements Baliga and Aitchison Groups, ISB Lee Pang Amardeep Kaur Min Pan Aaron Brooks Danielle (Miller) Durudas Nitin Baliga John Aitchison Adams Group, UGa Aleksandar Cvetkovic Sunil Kumar Michael W. W. Adams Funding Sources NIH Department of Energy MAGGIE Enigma Center for Systems Biology ENIGMA, LBL: Adam Arkin
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