@article{chhabra_shockley_conners_scott_wolfinger_kelly_2003, title={Carbohydrate-induced differential gene expression patterns in the hyperthermophilic bacterium Thermotoga maritima}, volume={278}, ISSN={["1083-351X"]}, DOI={10.1074/jbc.M211748200}, abstractNote={The hyperthermophilic bacteriumThermotoga maritima MSB8 was grown on a variety of carbohydrates to determine the influence of carbon and energy source on differential gene expression. Despite the fact that T. maritima has been phylogenetically characterized as a primitive microorganism from an evolutionary perspective, results here suggest that it has versatile and discriminating mechanisms for regulating and effecting complex carbohydrate utilization. Growth ofT. maritima on monosaccharides was found to be slower than growth on polysaccharides, although growth to cell densities of 108 to 109 cells/ml was observed on all carbohydrates tested. Differential expression of genes encoding carbohydrate-active proteins encoded in the T. maritimagenome was followed using a targeted cDNA microarray in conjunction with mixed model statistical analysis. Coordinated regulation of genes responding to specific carbohydrates was noted. Although glucose generally repressed expression of all glycoside hydrolase genes, other sugars induced or repressed these genes to varying extents. Expression profiles of most endo-acting glycoside hydrolase genes correlated well with their reported biochemical properties, although exo-acting glycoside hydrolase genes displayed less specific expression patterns. Genes encoding selected putative ABC sugar transporters were found to respond to specific carbohydrates, and in some cases putative oligopeptide transporter genes were also found to respond to specific sugar substrates. Several genes encoding putative transcriptional regulators were expressed during growth on specific sugars, thus suggesting functional assignments. The transcriptional response ofT. maritima to specific carbohydrate growth substrates indicated that sugar backbone- and linkage-specific regulatory networks are operational in this organism during the uptake and utilization of carbohydrate substrates. Furthermore, the wide ranging collection of such networks in T. maritima suggests that this organism is capable of adapting to a variety of growth environments containing carbohydrate growth substrates. The hyperthermophilic bacteriumThermotoga maritima MSB8 was grown on a variety of carbohydrates to determine the influence of carbon and energy source on differential gene expression. Despite the fact that T. maritima has been phylogenetically characterized as a primitive microorganism from an evolutionary perspective, results here suggest that it has versatile and discriminating mechanisms for regulating and effecting complex carbohydrate utilization. Growth ofT. maritima on monosaccharides was found to be slower than growth on polysaccharides, although growth to cell densities of 108 to 109 cells/ml was observed on all carbohydrates tested. Differential expression of genes encoding carbohydrate-active proteins encoded in the T. maritimagenome was followed using a targeted cDNA microarray in conjunction with mixed model statistical analysis. Coordinated regulation of genes responding to specific carbohydrates was noted. Although glucose generally repressed expression of all glycoside hydrolase genes, other sugars induced or repressed these genes to varying extents. Expression profiles of most endo-acting glycoside hydrolase genes correlated well with their reported biochemical properties, although exo-acting glycoside hydrolase genes displayed less specific expression patterns. Genes encoding selected putative ABC sugar transporters were found to respond to specific carbohydrates, and in some cases putative oligopeptide transporter genes were also found to respond to specific sugar substrates. Several genes encoding putative transcriptional regulators were expressed during growth on specific sugars, thus suggesting functional assignments. The transcriptional response ofT. maritima to specific carbohydrate growth substrates indicated that sugar backbone- and linkage-specific regulatory networks are operational in this organism during the uptake and utilization of carbohydrate substrates. Furthermore, the wide ranging collection of such networks in T. maritima suggests that this organism is capable of adapting to a variety of growth environments containing carbohydrate growth substrates. analysis of variance carboxymethylcellulose phosphotransferase system carbon catabolite repression Saccharolytic microorganisms employ a range of proteins to hydrolyze, transport, and utilize complex carbohydrates that serve as carbon and energy sources (1de Vos W.M. Kengen S.W.M. Voorhorst W.G.B. van der Oost J. Extremophiles. 1998; 2: 201-205Crossref PubMed Scopus (38) Google Scholar). In some cases, these proteins are very specific to particular carbohydrates, whereas in other situations they mediate the processing of a broader range of glycosides. For simple sugars, such as glucose, binding and transport proteins alone mediate substrate entry into specific intracellular anabolic and catabolic pathways (2Galperin M.Y. Noll K.M. Romano A.H. Appl. Environ. Microbiol. 1996; 62: 2915-2918PubMed Google Scholar). However, for complex carbohydrates, a series of glycoside hydrolases must first process the polysaccharide so that its backbone and side chain glycosidic linkages are hydrolyzed to the extent needed for binding, transport, and intracellular utilization. How specific organisms develop the capacity to utilize complex carbohydrates is not known, but this probably involves evolutionary pressures in addition to acquisition of this genetic potential through horizontal gene transfer events. In any case, a microorganism's capacity to utilize carbohydrates presumably reflects the availability of such substrates in its habitat. Therefore, insights into the repertoire of carbohydrate-active proteins in a given organism and how the expression of these proteins is regulated would reveal much about particular metabolic features in addition to how it interacts within a given ecosystem. Thermotoga maritima is an obligately anaerobic, heterotrophic, hyperthermophilic bacterium originally isolated from geothermal features associated with Vulcano Island, Italy (3Huber R. Langworthy T.A. Konig H. Thomm M. Woese C.R. Sleytr U.B. Stetter K.O. Arch. Microbiol. 1986; 144: 324-333Crossref Scopus (623) Google Scholar). Its capacity to utilize a wide range of simple and complex carbohydrates was confirmed by the inventory of glycoside hydrolases encoded in its genome (4Nelson K.E. Clayton R.A. Gill S.R. Gwinn M.L. Dodson R.J. Haft D.H. Hickey E.K. Peterson J.D. Nelson W.C. Ketchum K.A. McDonald L. Utterback T.R. Malek J.A. Linher K.D. Garrett M.M. Stewart A.M. Cotton M.D. Pratt M.S. Phillips C.A. Richardson D. Heidelberg J. Sutton G.G. Fleischmann R.D. Eisen J.A. Fraser C.M. et al.Nature. 1999; 399: 323-329Crossref PubMed Scopus (1206) Google Scholar). In fact, the T. maritima genome, despite its relatively small size, encodes the largest number of glycoside hydrolases of any bacterial or archaeal genome sequenced to date (see Fig. 1). From growth experiments and characterization of specific glycoside hydrolases (5Chhabra S.R. Shockley K.R. Ward D.E. Kelly R.M. Appl. Environ. Microbiol. 2002; 68: 545-554Crossref PubMed Scopus (91) Google Scholar), T. maritima is known to metabolize both polysaccharides and simple sugars, including carboxymethylcellulose, barley glucan, starch, galactomannan (5Chhabra S.R. Shockley K.R. Ward D.E. Kelly R.M. Appl. Environ. Microbiol. 2002; 68: 545-554Crossref PubMed Scopus (91) Google Scholar), xylan (6Bronnenmeier K. Kern A. Liebl W. Staudenbauer W.L. Appl. Environ. Microbiol. 1995; 61: 1399-1407Crossref PubMed Google Scholar), pectin, 1L. D. Kluskens, personal communication. 1L. D. Kluskens, personal communication. mannose, xylose, and glucose (2Galperin M.Y. Noll K.M. Romano A.H. Appl. Environ. Microbiol. 1996; 62: 2915-2918PubMed Google Scholar). In some cases, the proteins involved in the processing, transport, and utilization of these glycosides can be inferred from their apparent organization into operons in the T. maritimagenome sequence (4Nelson K.E. Clayton R.A. Gill S.R. Gwinn M.L. Dodson R.J. Haft D.H. Hickey E.K. Peterson J.D. Nelson W.C. Ketchum K.A. McDonald L. Utterback T.R. Malek J.A. Linher K.D. Garrett M.M. Stewart A.M. Cotton M.D. Pratt M.S. Phillips C.A. Richardson D. Heidelberg J. Sutton G.G. Fleischmann R.D. Eisen J.A. Fraser C.M. et al.Nature. 1999; 399: 323-329Crossref PubMed Scopus (1206) Google Scholar), whereas in other cases such classification is not clear. Regulation of genes encoding specific carbohydrate-active proteins in T. maritima has only been studied to a limited extent thus far (5Chhabra S.R. Shockley K.R. Ward D.E. Kelly R.M. Appl. Environ. Microbiol. 2002; 68: 545-554Crossref PubMed Scopus (91) Google Scholar, 7Nguyen T.N. Borges K.M. Romano A.H. Noll K.M. FEMS Microbiol. Lett. 2001; 195: 79-83Crossref PubMed Google Scholar), and the coordinated regulation of related genes involved in polysaccharide utilization has not been examined. Here, a targeted cDNA microarray, based on carbohydrate-active proteins from T. maritima, was used in conjunction with mixed model analysis (8Jin W. Riley R.M. Wolfinger R.D. White K.P. Passador-Gurgel G. Gibson G. Nat. Genet. 2001; 29: 389-395Crossref PubMed Scopus (522) Google Scholar, 9Wolfinger R.D. Gibson G. Wolfinger E.D. Bennett L. Hamadeh H. Bushel P. Afshari C. Paules R.S. J. Comput. Biol. 2001; 8: 625-637Crossref PubMed Scopus (856) Google Scholar) to explore issues related to saccharide utilization by this organism. Despite the fact thatT. maritima has been phylogenetically characterized as a primitive microorganism from an evolutionary perspective (10Achenbach-Richter L. Gupta R. Stetter K.O. Woese C.R. Syst. Appl. Microbiol. 1987; 9: 34-39Crossref PubMed Scopus (180) Google Scholar), results here support that it has versatile and discriminating mechanisms for regulating and effecting complex carbohydrate utilization. The relative importance of evolutionary processes and horizontal gene transfer (4Nelson K.E. Clayton R.A. Gill S.R. Gwinn M.L. Dodson R.J. Haft D.H. Hickey E.K. Peterson J.D. Nelson W.C. Ketchum K.A. McDonald L. Utterback T.R. Malek J.A. Linher K.D. Garrett M.M. Stewart A.M. Cotton M.D. Pratt M.S. Phillips C.A. Richardson D. Heidelberg J. Sutton G.G. Fleischmann R.D. Eisen J.A. Fraser C.M. et al.Nature. 1999; 399: 323-329Crossref PubMed Scopus (1206) Google Scholar) in developing its carbohydrate utilization capacity is not known, butT. maritima's ability to respond to various substrates in its growth environment underlies its ubiquity in global geothermal settings (11Nesbo C.L. Nelson K.E. Doolittle W.F. J. Bacteriol. 2002; 184: 4475-4488Crossref PubMed Scopus (55) Google Scholar). Open reading frames (total of 269) of known and putative genes related to sugar processing and other related metabolic functions were identified through BLAST (12Altschul S.F. Gish W. Miller W. Myers E.W. Lipman D.J. J. Mol. Biol. 1990; 215: 403-410Crossref PubMed Scopus (69678) Google Scholar) comparisons of protein sequences from the T. maritima MSB8 genome available on the World Wide Web at www.tigr.org/ tigrscripts/CMR2/GenomePage3.spl?database=btm. DNA primers were designed with similar annealing temperatures and minimal hairpin formation using Vector NTI 7.0 (Informax, Bethesda, MD). The selected probes were PCR-amplified in a PTC-100 Thermocycler (MJ Research, Inc., Waltham, MA) using Taq polymerase (Roche Molecular Biochemicals) and T. maritima genomic DNA, isolated as described previously (5Chhabra S.R. Shockley K.R. Ward D.E. Kelly R.M. Appl. Environ. Microbiol. 2002; 68: 545-554Crossref PubMed Scopus (91) Google Scholar). The integrity and concentration of the PCR products were verified on 1% agarose gels. PCR products were purified to 100 ng/μl using 96-well QIAquick PCR purification kits (Qiagen, Valencia, CA), resuspended in 50% Me2SO, and printed onto CMT-GAPS aminosilane-coated microscope slides (Corning Glass) using a 417 Arrayer (Affymetrix, Santa Clara, CA) in the North Carolina State University Genome Research Laboratory (Raleigh, NC). Eight replicates of each gene fragment were printed onto each slide. The DNA was then attached to the slides by UV cross-linking using a GS GeneLinker UV Chamber (Bio-Rad) set at 250 mJ and baked at 75 °C for 2 h. Growth ofT. maritima MSB8 cultures in artificial sea water was followed using optical density measurements and epifluorescence microscopic cell density enumeration, as described previously (5Chhabra S.R. Shockley K.R. Ward D.E. Kelly R.M. Appl. Environ. Microbiol. 2002; 68: 545-554Crossref PubMed Scopus (91) Google Scholar). Growth substrates glucose, mannose, xylose, β-xylan (birchwood), laminarin (Laminaria digitata), and starch (potato) were obtained from Sigma. Galactomannan (carob), glucomannan (konjac), carboxymethylcellulose, and β-glucan (barley) were obtained from Megazyme (Wicklow, Ireland). Growth substrates were prepared as described previously (5Chhabra S.R. Shockley K.R. Ward D.E. Kelly R.M. Appl. Environ. Microbiol. 2002; 68: 545-554Crossref PubMed Scopus (91) Google Scholar) and included in the medium at a final concentration of 0.25% (w/v). Substrate purities as provided by the manufacturers varied from 95 to 99%. To ensure minimum carryover between substrates, cells were grown for at least 10 passes on each carbon source using a 0.5% (v/v) starting innoculum before obtaining the growth curves. Specific growth rates on mono- and polysaccharide substrates were determined from the slopes of semilog plots of exponential cell growth versus time. Isolation of total RNA from T. maritima was performed on cells that were grown until early- to mid-exponential phase on the various growth substrates, as described in detail previously (5Chhabra S.R. Shockley K.R. Ward D.E. Kelly R.M. Appl. Environ. Microbiol. 2002; 68: 545-554Crossref PubMed Scopus (91) Google Scholar). First-strand cDNA was prepared from T. maritima total RNA using Stratascript (Stratagene, La Jolla, CA) and random hexamer primers (Invitrogen) by the incorporation of 5-[3-aminoallyl]-2′-deoxyuridine-5′-triphosphate (Sigma) as described elsewhere (13Hasseman J. TIGR Microarray Protocols. 2001; (http://www.tigr.org/tdb/microarray/protocolsTIGR.shtml)Google Scholar). The slides were scanned using a Scanarray 4000 scanner (GSI Lumonics and Billerica) in the North Carolina State University Genome Research Laboratory. Signal intensity data were obtained using Quantarray (GSI Lumonics). A loop design was constructed (see Fig. 2) to ensure reciprocal labeling for all 10 different experimental conditions. Replication of treatments, arrays, dyes, and cDNA spots allowed the use of analysis of variance (ANOVA)2 models for data analysis. ANOVAs are especially appropriate for loop designs in which a large number of conditions are compared with one another, eliminating uninteresting reference samples and allowing for the collection of more information on experimental conditions (14Kerr M.K. Churchill G.A. Genet. Res. 2001; 77: 123-128Crossref PubMed Scopus (465) Google Scholar). Mixed ANOVA models, in which some effects are considered fixed and others are considered random, have been used to re-examine published microarray data sets (9Wolfinger R.D. Gibson G. Wolfinger E.D. Bennett L. Hamadeh H. Bushel P. Afshari C. Paules R.S. J. Comput. Biol. 2001; 8: 625-637Crossref PubMed Scopus (856) Google Scholar) and examine the effects of sex, genotype, and age on transcription inDrosophila melanogaster (8Jin W. Riley R.M. Wolfinger R.D. White K.P. Passador-Gurgel G. Gibson G. Nat. Genet. 2001; 29: 389-395Crossref PubMed Scopus (522) Google Scholar). Using existing SAS procedures and customized Perl code, an automated data import system was developed to merge Quantarray intensity measurements, coordinate files generated by the array printer, and corresponding T. maritima locus numbers in a SAS data set (SAS Institute, Cary, NC). The data import system was verified through independent calculations in Excel (Microsoft, Seattle, WA). A linear normalization ANOVA model (9Wolfinger R.D. Gibson G. Wolfinger E.D. Bennett L. Hamadeh H. Bushel P. Afshari C. Paules R.S. J. Comput. Biol. 2001; 8: 625-637Crossref PubMed Scopus (856) Google Scholar) of log base 2 intensities was used to estimate global variation in the form of fixed (dye, treatment) and random (array, pin within array, pin spot within array) effects and random error using the following model: log2(y ijklmn) =m + Dj + T k +A i + A i(P1) +A i(S m P l) + εijklmn. The estimated effects calculated from this model were used to predict an expected intensity for each value, and then a residual was calculated as the difference between a replicate's observed and predicted intensity and then used as data to capture variation attributable to gene-specific effects after accounting for global variation. Gene-specific ANOVA models were then used to partition variation into gene-specific treatment effects, dye effects, and the same hierarchy of random effects described previously. Specifically, the model r ijklmn =m+ D i + T k +A i + A i(P1) +A i(S m P1) + εijklmn was fit separately to the residuals for each gene, and the resulting parameter estimates and S.E. values were then used for statistical inference. Volcano plots were used to visualize interesting contrasts or comparisons between two treatments or two groups of treatments (9Wolfinger R.D. Gibson G. Wolfinger E.D. Bennett L. Hamadeh H. Bushel P. Afshari C. Paules R.S. J. Comput. Biol. 2001; 8: 625-637Crossref PubMed Scopus (856) Google Scholar). A Bonferroni correction was utilized to adjust for the expected increase in false positives due to multiple comparisons (9Wolfinger R.D. Gibson G. Wolfinger E.D. Bennett L. Hamadeh H. Bushel P. Afshari C. Paules R.S. J. Comput. Biol. 2001; 8: 625-637Crossref PubMed Scopus (856) Google Scholar). Genes meeting the Bonferroni significance criteria were selected for further study, ensuring that genes with inconsistent fold changes would be eliminated from further analysis. Two complementary approaches were utilized to cluster data from T. maritima growth on 10 saccharides. To visualize the relative expression levels of all genes withina treatment, hierarchical clustering was performed on least squares means calculated from the linear models for each sugar (Fig. 3). To visualize the expression pattern of each single gene acrosstreatments, the least squares mean estimates were standardized using the mean and S.D. of the 10 least squares means estimates for a given gene. Each of the 10 least squares means estimates were standardized accordingly with the formula Y i = (X i − μ)/ς, where Y i = the standardized least squares means variable, μ = ΣX i/n, and ς = (Σ(X i − μ)2) 12. The standardized variable was then utilized for clustering (Fig. 3). For complete information on signal intensity, significance of expression changes, -fold changes, pairwise volcano plots, and hierarchical clustering for all of the genes included on the array, see the Supplemental Material. A targeted cDNA microarray for T. maritima was constructed that included 269 known and putative genes or about 15% of the total open reading frames in the T. maritima genome. This included the known set of genes related to glycoside utilization and modification (65 genes), proteolysis (40 genes), stress response, and proteolytic fermentation. Genes related to sugar transport (21 genes) or transcriptional regulation (69 genes) and 66 other genes of interest were also included. Genes apparently related to glycoside utilization and modification in T. maritima include 41 glycoside hydrolases, 17 glycosyl transferases, 6 carbohydrate esters, and 1 polysaccharide lyase. The corresponding encoded proteins have been classified into several families, based on amino acid sequence homology (15Henrissat B. Bairoch A. Biochem. J. 1996; 316: 695-696Crossref PubMed Scopus (1179) Google Scholar) (available on the World Wide Web at afmb.cnrs-mrs.fr/CAZY). There are over 130T. maritima proteins with sufficient BLAST homology to be classified into transcriptional regulatory or signal transduction COG categories (16Tatusov R.L. Natale D.A. Garkavtsev I.V. Tatusova T.A. Shankavaram U.T. Rao B.S. Kiryutin B. Galperin M.Y. Fedorova N.D. Koonin E.V. Nucleic Acids Res. 2001; 29: 22-28Crossref PubMed Scopus (1539) Google Scholar). These regulatory proteins have been assigned to families based on sequence homology; however, different proteins in the same families may have different DNA and substrate-binding specificities (17Mirny L.A. Gelfand M.S. J. Mol. Biol. 2002; 321: 7-20Crossref PubMed Scopus (116) Google Scholar). Also, proteins placed in different families may share the same name because of their regulon composition, as in the case of the Escherichia coli and Bacillus subtilis xylR protein (18Song S. Park C. J. Bacteriol. 1997; 179: 7025-7032Crossref PubMed Scopus (120) Google Scholar, 19Kreuzer P. Gartner D. Allmansberger R. Hillen W. J. Bacteriol. 1989; 171: 3840-3845Crossref PubMed Google Scholar). Of the 69 transcription/transduction genes on the array, six share similarity with the ROK (receptor, open reading frame,kinase) family of transcriptional regulators, which include glucokinases, B. subtilis XylR, and E. coli NagC (COG1940) (20Titgemeyer F. J. Cell. Biochem. 1993; 51: 69-74Crossref PubMed Scopus (19) Google Scholar). Six members of the PurR/LacI superfamily (COG1609) were included (21Mirny L.A. Gelfand M.S. Nucleic Acids Res. 2002; 30: 1704-1711Crossref PubMed Scopus (58) Google Scholar) along with the T. maritima IclR transcriptional regulator, whose structure was recently solved (22Zhang R.G. Kim Y. Skarina T. Beasley S. Laskowski R. Arrowsmith C. Edwards A. Joachimiak A. Savchenko A. J. Biol. Chem. 2002; 277: 19183-19190Abstract Full Text Full Text PDF PubMed Scopus (59) Google Scholar). Several pairs of sensor histidine kinases and response regulators of putative two-component regulatory systems were included, as were regulators from the MarR (23Cohen S.P. Hachler H. Levy S.B. J. Bacteriol. 1993; 175: 1484-1492Crossref PubMed Scopus (274) Google Scholar), AraC (24Martin R.G. Rosner J.L. Curr. Opin. Microbiol. 2001; 4: 132-137Crossref PubMed Scopus (182) Google Scholar), TroR (25Hardham J.M. Stamm L.V. Porcella S.F. Frye J.G. Barnes N.Y. Howell J.K. Mueller S.L. Radolf J.D. Weinstock G.M. Norris S.J. Gene (Amst.). 1997; 197: 47-64Crossref PubMed Scopus (62) Google Scholar), LytR (26Nikolskaya A.N. Galperin M.Y. Nucleic Acids Res. 2002; 30: 2453-2459Crossref PubMed Scopus (147) Google Scholar), ArsR (27Diorio C. Cai J. Marmor J. Shinder R. DuBow M.S. J. Bacteriol. 1995; 177: 2050-2056Crossref PubMed Google Scholar), and CspC (28Phadtare S. Alsina J. Inouye M. Curr. Opin. Microbiol. 1999; 2: 175-180Crossref PubMed Scopus (271) Google Scholar) families. The T. maritima genome contains ∼120 genes involved in oligopeptide/sugar transport. In the targeted microarray used here, 21 genes related to sugar transport were included on the basis of their proximity to the genes involved in glycoside utilization. This targeted microarray was used to examine the differential response of T. maritima grown on a range of mono- and polysaccharides at its optimal growth temperature of 80 °C. Growth conditions were analyzed based on an incomplete loop design (Fig.2). Treatments in the loop design were balanced with respect to dyes so that treatment effects were not confounded with dye effects. T. maritima cultures were grown on a variety of saccharides, including the monosaccharides glucose, mannose, and xylose. The polysaccharides investigated differed in backbone sugar type (glucose, mannose, and xylose), backbone linkage type (β-1,3; β-1,4; or α-1,4), and side chain residue type (galactose, glucuronic acid, or glucose) (see TableI). Included in these were a mixed backbone (konjac glucomannan: glucose/mannose) and a mixed linkage (barley glucan: β-1,4/1,3) polysaccharide. Final cell densities were in the range of 108 to 109cells/ml in all cases. Doubling times (min) for galactomannan (carob), β-glucan (barley), laminarin (L. digitata), β-xylan (birchwood), starch (potato), glucomannan (konjac), and carboxymethylcellulose were estimated to be 85, 72, 143, 61, 117, 74, and 78, respectively. On the monosaccharides, the doubling times (min) were 162, 253, and 188, for glucose, mannose, and xylose, respectively. Under identical conditions, the average doubling time for growth on monosaccharides (201 min) was observed to be substantially higher than that on the corresponding polysaccharide substrates (90 min).Table ICarbon sources used in this studyPoly/monosaccharideSourceBackbone structureSide chainMassDaGlucoseNAaNA, not available.Glc180MannoseNAMan180XyloseNAXylbXyl, xylose.150GalactomannanCarob(Man β1→4 Man)nGal (α1→6)NAGlucomannanKonjac(Glc β1→4 Man)n100,000Carboxymethyl celluloseNA(Glc β1→4 Glc)n90,000β-1,3/1,4-GlucanBarley(Glc β1→3,4 Glc)n250,000LaminarinL. digitata(Glc β1→3 Glc)n5,000StarchPotato(Glc α1→4 Glc)nGlc (α→16)nNAβ-XylanBirchwood(Xyl β1→4 Xyl)nGlr (α1→6)cGlr, glucuronic acid.NAa NA, not available.b Xyl, xylose.c Glr, glucuronic acid. Open table in a new tab Two hierarchical clusters are shown in Fig.3 to summarize the expression patterns of 269 T. maritima genes during growth on 10 saccharides. The first cluster is based on least squares means and compares the normalized expression levels of all genes within each treatment condition. The second cluster is based on standardized least squares means for a single gene across all 10 treatments to show the effect of different treatments on the relative expression of a particular gene. The hierarchical clustering based on standardized least squares means revealed many cases of apparent co-regulation of genes within potential operons (29McGuire A.M. Hughes J.D. Church G.M. Genome Res. 2000; 10: 744-757Crossref PubMed Scopus (160) Google Scholar). Several sets of spatially distant gene strings were observed to cluster with similar expression profiles, suggesting the presence of regulons in the T. maritima genome. Representative clusters are displayed in Fig.4. Overall expression levels of a number of genes remained consistently high or low regardless of the growth condition. These included constitutively expressed genes like TM0017 (pyruvate ferredoxin oxidoreductase) and TM0688 (glyceraldehyde-3-phosphate dehydrogenase) (30Blamey J.M. Adams M.W. Biochemistry. 1994; 33: 1000-1007Crossref PubMed Scopus (92) Google Scholar) as well as genes related to proteolytic activity. Both sets of genes with the corresponding known or putative functions are displayed in Fig. 5. Individual genes with high overall expression levels on only a single carbon source are indicated in Table II. Least squares means for all genes included in this study for all growth conditions are shown in Supplemental Table IV, along with the corresponding standardized values in Supplemental Table V. Below, gene regulation patterns within each functional category are examined for each monosaccharide and corresponding polysaccharide growth substrate.Figure 4Substrate-dependent regulation. Sample Clusters constructed using standardized least squares means. Known or putative functions as reported in the genome sequence are indicated.View Large Image Figure ViewerDownload (PPT)Figure 4Substrate-dependent regulation. Sample Clusters constructed using standardized least squares means. Known or putative functions as reported in the genome sequence are indicated.View Large Image Figure ViewerDownload (PPT)Figure 5Genes with overall high or low expression levels for all growth substrates. Clusters constructed using least squares means. Known or putative functions as reported in the genome sequence are indicated.View Large Image Figure ViewerDownload (PPT)Table IIGenes with high overall expression levels (log2R ≥ 0.6) on indicated growth substrateGrowth substrateLocusFunctionCarboxymethylcelluloseTM0963Oligoendopeptidase, putativeMannoseTM1755Phosphate butyryltransferaseTM1754Butyrate kinase, putativeTM1756Branched chain fatty acid kinase, putativeLaminarinTM0024LaminarinaseTM0032Transcriptional regulator, XylR-relatedStarchTM1835Cyclomaltodextrinase, putativeTM1840α-AmylaseTM1845PullulanaseXylanTM0055α-GlucuronidaseTM0065Transcriptional regulator, IclR familyXyloseTM0949Transcriptional regulator, LacI family Open table in a new tab Backbone- and linkage-specific gene regulation was observed in the case of endoglycoside hydrolase genes for growth on α- and β-specific glucans. Growth on carboxymethylcellulose (CMC) (see cluster 4.1), a β-1,4-linked glucose polymer, induced genes encoding extracellular endoglucanases TM1525 (cel12B) and TM0305 (cel74), as well as the intracellular endoglucanase TM1524 (cel12A) and the intracellular cellobiosyl phosphorylase, TM1848. Examination of cluster I (Fig. 3) reveals that expression levels of cel74 were substantially lower than those ofcel12A on glucan polysaccharides. Although the presence of a β-1,4-glucosidase gene (bglA) (accession number CAA52276) in T. maritima MSB8 has been reported (31Liebl W. Methods Enzymol. 2001; 330: 290-300Crossref PubMed Scopus (16) Google Scholar), the corresponding protein sequence does not show homology to deduced sequences identified in the T. maritima MSB8 genome (4Nelson K.E. Clayton R.A. Gill S.R. Gwinn M.L. Dodson R.J. Haft D.H. Hickey E.K. Peterson J.D. Nelson W.C. Ketchum K.A. McDonald L. Utterback T.R. Malek J.A. Linher K.D. Garrett M.M. Stewart}, number={9}, journal={JOURNAL OF BIOLOGICAL CHEMISTRY}, author={Chhabra, SR and Shockley, KR and Conners, SB and Scott, KL and Wolfinger, RD and Kelly, RM}, year={2003}, month={Feb}, pages={7540–7552} }
 @article{ioannou_spector_scott_rockey_2002, title={Prospective evaluation of a clinical guideline for the diagnosis and management of iron deficiency anemia}, volume={113}, ISSN={["0002-9343"]}, DOI={10.1016/S0002-9343(02)01226-3}, abstractNote={We examined the effect of introducing an evidence-based clinical guideline on the diagnosis and evaluation of iron deficiency anemia.The guideline recommended measurement of serum ferritin levels for all anemic patients with a mean corpuscular volume (MCV) </=95 fL and endoscopic evaluation for those with a serum ferritin level less than 45 ng/mL. Physicians practicing in the general medicine inpatient and outpatient services of two university-affiliated hospitals were informed about the guideline and the data supporting it. Clinical evaluations during the 9 months before ("control period," n = 3341 patients) and the 9 months after ("intervention period," n = 3173 patients) the introduction of the guideline were compared.There was a 30% increase (95% confidence interval [CI]: 22% to 39%) in the proportion of anemic patients who underwent serum ferritin evaluation in the intervention period (41% [n = 1284]) compared with in the control period (31% [n = 1040]), and a 24% increase (95% CI: 4% to 48%) in the proportion with a serum ferritin level <45 ng/mL (8.1% [n = 256] vs. 6.5% [n = 217]). The proportion of anemic patients who underwent endoscopic evaluation within 4 months of measurement of low serum ferritin level in the intervention period (3.3% [n = 106]) was 67% higher (95% CI: 23% to 125%) than in the control period (2.0% [n = 67]), and the proportion with serious gastrointestinal lesions found as a result of endoscopy was 62% (95% CI: 8% to 145%) higher (1.8% [n = 57] vs. 1.1% [n = 37]).Introduction of a guideline describing appropriate evaluation of iron deficiency anemia led to an increase in the proportions of patients evaluated for iron deficiency anemia and found to have serious gastrointestinal lesions.}, number={4}, journal={AMERICAN JOURNAL OF MEDICINE}, author={Ioannou, GN and Spector, J and Scott, K and Rockey, DC}, year={2002}, month={Sep}, pages={281–287} }
 @article{wogalter_magurno_dietrich_scott_1999, title={Enhancing information acquisition for over-the-counter medications by making better use of container surface space}, volume={25}, ISSN={["0361-073X"]}, DOI={10.1080/036107399244129}, abstractNote={Most over-the-counter (OTC) pharmaceutical container labels are printed in very small type. Consequently, people with visual impairments (e.g., presbyopia in older adults) have difficult reading the material. Some OTC drugs are packaged in containers with easy-open caps. This design increases the surface area that could be used to enhance the labeling. In Experiment 1, older adults (M = 75.1 years, SD = 8.1) evaluated six container label variants for an actual OTC product. Besides having a multipanel main label, four containers had labels attached to the cap that displayed the most important information in large print but differed in color. Two control containers lacked a cap label; one had only a four-panel main label, and the other had only the front label. Participants ranked the containers on six dimensions (e.g., label noticeability, willingness to read). Results showed greater preference for containers with the cap labels. Experiment 2 again examined preferences but also measured information-acquisition performance after participants (M = 79 years, SD = 5.8) were briefly exposed to a realistic-appearing, but fictitious, OTC medication. Results showed greater knowledge and preference for containers with the cap labels. Experiment 2 showed that one of the cap colors (yellow) that was different from the main label was preferred over the white and orange (the same colors as on the main label), but color distinctiveness as an explanation was not fully supported because the green cap was not significantly different from the other cap labels. Implications for communicating information about OTC drugs using expanded labels are discussed.}, number={1}, journal={EXPERIMENTAL AGING RESEARCH}, author={Wogalter, MS and Magurno, AB and Dietrich, DA and Scott, KL}, year={1999}, pages={27–48} }