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The Center for Microbial Ecology Michigan State University 540 Plant and Soil Science Building East Lansing, MI 48824-1325 (517) 353-9021. Chip 1 20 PM controls 20 MM controls ~3700 ORFs. Chip 2 20 PM controls 20 MM controls ~3700 ORFs. Chip 3 20 PM controls 20 MM controls
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The Center for Microbial Ecology Michigan State University 540 Plant and Soil Science Building East Lansing, MI 48824-1325 (517) 353-9021 Chip 1 20 PM controls 20 MM controls ~3700 ORFs Chip 2 20 PM controls 20 MM controls ~3700 ORFs Chip 3 20 PM controls 20 MM controls ~2100 ORFs + 1600 ORFs Chip 1 Burkholderia sp. novum LB400 Genomic microarray (9534 ORFs) * succinate aa-dUTP + Cy dye labeling RNA Extr. * B. sp. LB400 biphenyl * Growth phase: mid-log * aa-dUTP + Cy dye labeling RNA Extr. * biphenyl succinate biphenyl succinate biphenyl Log Ratio Chip 3 Exp1: LB400 chip 1 and 3 Log Ratio Chip 1 Exp2: LB400 chip 1 Exp2: LB400 chip 3 Biomass succinate biphenyl succinate biphenyl Biphenyl Benzoate metabolism Biphenyl Biphenyl Biphenyl metabolism RNA Closest match: Benzoyl-CoA ligase (Rhodopseudomonas palustris) Biological Replicate 1 ASM Q-076 ? TCA benzoate Labeled cDNA All previously annotated bph pathway genes Rep1: LB400 chip 1,2,3 Rep2: LB400 chip 1,2,3 Succinate Succinate Succinate Biological Replicate 2 Outlining Genome-Wide Carbon Source Specific Metabolic Networks In Burkholderia sp. nov. LB400. ASM Q-076 V.J. Denef1,2, J. Park2, T.V. Tsoi2, J.-M. Rouillard4, H. Zhang5, X. Wang5, J.A. Wibbenmeyer5, X. Gao5, S.A. Hashsham3,2, J.M. Tiedje2 1Laboratory of Microbial Ecology and Technology, Ghent, Belgium; 2Center for Microbial Ecology and 3Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI, 48824; 4Chemical Engineering Department, University of Michigan, Ann Arbor, MI 48109; 5Xeotron Corporation, Houston, TX 77054. ABSTRACT RESULTS CONCLUSION We developed and implemented the first microarray for an organism capable of bio-degrading recalcitrant organic pollutants. A new microarray platform (XeoChipsTM) was succesfully adapted for prokaryotic usage and is characterized by a high technical reproducibility, increasing the reliability of our results. Our hybridization data was in general correspondance with a subset of genes tested by Q-RT-PCR. Only 1.4 % of the 9534 ORFs or Burkholderia sp. novum LB400 were more than 2x differentially expressed in biphenyl-grown cells vs. succinate-grwon cells. All genes previously identified as being directly involved in biphenyl degradation, were up-regulated when grown on biphenyl in comparison to succinate-grown cells. Our data strongly indicated the usage of a novel aerobic benzoate pathway through CoA activation, previously described in Azoarcus evansii, instead of the traditional catechol pathway. We have designed and successfully implemented the use of in-situ synthesized 45-mer oligonucleotide DNA microarrays (XeoChipsTM) for genome-wide expression profiling of Burkholderia sp. nov. LB400, arguably the best aerobic PCB-degrader so far known. We outlined carbon source-dependent metabolic networks of the LB400 genome by conducting differential gene expression profiling of LB400 grown on biphenyl vs. succinate as sole carbon sources. Our results indicated that 0.6% out of the 9534 annotated ORFs were at least 2-fold up-regulated, while 0.8% were at least 2-fold down-regulated when cells were grown on biphenyl compared to growth on succinate. Compared to these genome-wide differences in expression, a higher fraction of the genes involved in information storage and processing were down-regulated, while no general trend was observed in the type of genes up-regulated in biphenyl-grown cells. As was expected, biphenyl and benzoate pathway genes were up-regulated by growth on biphenyl. Our data strongly indicated the usage of a novel aerobic benzoate pathway, as previously characterized in Azoarcus evansii, instead of the traditional catechol pathway. Fig.1: Experimental design of thedifferential gene expression proficiling between cells grown with 10mM succinate or 5mM biphenyl. Succinate-grown cells were harvested at an OD of ~0.4, while biphenyl-grown cells were harvested at an OD of ~0.35. Biological replication with dye-swapping was performed. Fig.4: Reproducibility of Log Ratio (Cy5-Succ / Cy3-Biph) of chip-to-chip (Experiment 1 in RED) and technical replications (Experiment 2 in BLUE). Data is filtered by removal of low intensity data, i.e. data with Log[(Cy5*Cy3)1/2] < 6. Fig.2: Hybridization results of 1 biological replicate. Since each XeoChipTM contains a maximum of 4000 probes, on genomic microarray consists of three chips. Chip three contains 1600 probes also present on chip 1 and were used for chip-to-chip replication evaluation (Fig. 4) ACKNOWLEDGMENTS • We would like to thank Xiaoyun Qiu (CME, MSU) for useful discussions and her methodological advice. • Special thanks to Wouter Donckerwolcke for digital photography • Vincent Denef is an aspirant of the Fund for Scientific Research - Flanders, Belgium. • This work was supported by NIEHS Grant P42 ES 04911-12 C A B Fig.5: A high reproducibility was observed between dye-swapped biological replicates. Based on the high technical reproducibility shown in Fig.4, it can be concluded that variation between biological replicates originated predominantly from biological sources. Microarrays have been established as a useful tool in studying the expression patterns of all the genes in a sequenced organism when grown under different conditions. A novel type of microarrays, XeoChipsTM (Xeotron Corporation), produced by light-directed in-situ synthesis of long oligonucleotide probes in a microfluidic system, promises to solve some of the variability issues of traditional, spotted array technology. Polychlorinated biphenyls (PCBs) are chlorinated aromatic compounds with a biphenyl core, substituted with a varying number of chlorines at varying positions. The toxicity, persistence and carcinogenic potential of PCBs and their tendency to accumulate in food chains make them problematic environmental pollutants, forming a threat to the global ecosystem One of the most potent aerobic PCB-degraders known is Burkholderia sp. novum LB400. We investigated the metabolism of this bacterial species, which has an exceptionally large genome (~9 Mbp / 9534 ORFs), when grown on biphenyl (5mM), the core structure of PCBs and a reference carbon source, succinate (10mM), a central metabolism metabolite (TCA cycle). Information storage and processing J = Translation, ribos.l structure, biogenesis K = Transcription L = DNA Replication, recombination, repair Cellular processes D = Cell division and chromosome partitioning M = Cell envelope biogenesis, outer membrane N = Cell motility and secretion O = Posttranslat. Mod., protein turnover P = Inorganic ion transport and metabolism T = Signal transduction mechanisms Metabolism C = Energy production and conversion E = Amino acid transport and metabolism F = Nucleotide transport and metabolism G = Carbohydrate transport and metabolism H = Coenzyme transport and metabolism I = Lipid metabolism Poorly characterized proteins R = General function prediction only S = Function unknown No = No match with any prev. seq. ORF D F New aerobic benzoate metabolism as characterized in Azoarcus evansii E Fig.6: Evaluation of hybridization results by Q-RT-PCR (SYBR-Green, Molecular Probes) of a subset of 20 genes. Q-RT-PCR results were normalized on amount of cDNA template used. General correspondence was observed. Inconsistencies occurred when hybridization signals were low in one or both channels. bphK ORF0 ORF1 Cluster 1 Cluster 2 INTRODUCTION Fig.3: Data was analyzed using GeneSpring (SiliconGenetics). Expression profile of (a) all 9534 ORFs (b) all bph pathway genes (c) all ORFs with annotation involved in benzoate metabolism. We mapped the genes out for (e) biphenyl metabolism through the bph pathway and (f) benzoate metabolism through a novel aerobic benzoate pathway as characterized in Azoarcus evansii (LB400 has two gene clusters with high similarity to those in A. evansii)(d)Differentially expressed genes were organized based on COGs classification (Clusters of Orthologous Groups of proteins) and the percentage differentially expressed ORFs of each COG is presented. Gescher, J. et al., 2002 (J. Bacteriol., 184, 6301-6315) (KEGG Metabolic Pathways)