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Joan L Slonczewski

Publications and source records attributed to Joan L Slonczewski.

7 recordsLinked to original sources
PubMed2006

Oxygen limitation modulates pH regulation of catabolism and hydrogenases, multidrug transporters, and envelope composition in Escherichia coli K-12.

BACKGROUND: In Escherichia coli, pH regulates genes for amino-acid and sugar catabolism, electron transport, oxidative stress, periplasmic and envelope proteins. Many pH-dependent genes are co-regulated by anaerobiosis, but the overall intersection of pH stress and oxygen limitation has not been investigated. RESULTS: The pH dependence of gene expression was analyzed in oxygen-limited cultures of E. coli K-12 strain W3110. E. coli K-12 strain W3110 was cultured in closed tubes containing LBK broth buffered at pH 5.7, pH 7.0, and pH 8.5. Affymetrix array hybridization revealed pH-dependent expression of 1,384 genes and 610 intergenic regions. A core group of 251 genes showed pH responses similar to those in a previous study of cultures grown with aeration. The highly acid-induced gene yagU was shown to be required for extreme-acid resistance (survival at pH 2). Acid also up-regulated fimbriae (fimAC), periplasmic chaperones (hdeAB), cyclopropane fatty acid synthase (cfa), and the "constitutive" Na+/H+ antiporter (nhaB). Base up-regulated core genes for maltodextrin transport (lamB, mal), ATP synthase (atp), and DNA repair (recA, mutL). Other genes showed opposite pH responses with or without aeration, for example ETS components (cyo,nuo, sdh) and hydrogenases (hya, hyb, hyc, hyf, hyp). A hypF strain lacking all hydrogenase activity showed loss of extreme-acid resistance. Under oxygen limitation only, acid down-regulated ribosome synthesis (rpl,rpm, rps). Acid up-regulated the catabolism of sugar derivatives whose fermentation minimized acid production (gnd, gnt, srl), and also a cluster of 13 genes in the gadA region. Acid up-regulated drug transporters (mdtEF, mdtL), but down-regulated penicillin-binding proteins (dacACD, mreBC). Intergenic regions containing regulatory sRNAs were up-regulated by acid (ryeA, csrB, gadY, rybC). CONCLUSION: pH regulates a core set of genes independently of oxygen, including yagU, fimbriae, periplasmic chaperones, and nhaB. Under oxygen limitation, however, pH regulation is reversed for genes encoding electron transport components and hydrogenases. Extreme-acid resistance requires yagU and hydrogenase production. Ribosome synthesis is down-regulated at low pH under oxygen limitation, possibly due to the restricted energy yield of catabolism. Under oxygen limitation, pH regulates metabolism and transport so as to maximize alternative catabolic options while minimizing acidification or alkalinization of the cytoplasm.

Carrier Proteins↗
PubMed2006

Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution.

BACKGROUND: DNA microarrays are a powerful technology that can provide a wealth of gene expression data for disease studies, drug development, and a wide scope of other investigations. Because of the large volume and inherent variability of DNA microarray data, many new statistical methods have been developed for evaluating the significance of the observed differences in gene expression. However, until now little attention has been given to the characterization of dispersion of DNA microarray data. RESULTS: Here we examine the expression data obtained from 682 Affymetrix GeneChips with 22 different types and we demonstrate that the Gaussian (normal) frequency distribution is characteristic for the variability of gene expression values. However, typically 5 to 15% of the samples deviate from normality. Furthermore, it is shown that the frequency distributions of the difference of expression in subsets of ordered, consecutive pairs of genes (consecutive samples) in pair-wise comparisons of replicate experiments are also normal. We describe a consecutive sampling method, which is employed to calculate the characteristic function approximating standard deviation and show that the standard deviation derived from the consecutive samples is equivalent to the standard deviation obtained from individual genes. Finally, we determine the boundaries of probability intervals and demonstrate that the coefficients defining the intervals are independent of sample characteristics, variability of data, laboratory conditions and type of chips. These coefficients are very closely correlated with Student's t-distribution. CONCLUSION: In this study we ascertained that the non-systematic variations possess Gaussian distribution, determined the probability intervals and demonstrated that the K(alpha) coefficients defining these intervals are invariant; these coefficients offer a convenient universal measure of dispersion of data. The fact that the K(alpha) distributions are so close to t-distribution and independent of conditions and type of arrays suggests that the quantitative data provided by Affymetrix technology give "true" representation of physical processes, involved in measurement of RNA abundance. REVIEWERS: This article was reviewed by Yoav Gilad (nominated by Doron Lancet), Sach Mukherjee (nominated by Sandrine Dudoit) and Amir Niknejad and Shmuel Friedland (nominated by Neil Smalheiser).

Journal Article↗
PubMed2005

Polyamine stress at high pH in Escherichia coli K-12.

BACKGROUND: Polyamines such as spermine and spermidine are required for growth of Escherichia coli; they interact with nucleic acids, and they bind to ribosomes. Polyamines block porins and decrease membrane permeability, activities that may protect cells in acid. At high concentrations, however, polyamines impair growth. They impair growth more severely at high pH, probably due to their increased uptake as membrane-permeant weak bases. The role of pH is critical in understanding polyamine stress. RESULTS: The effect of polyamines was tested on survival of Escherichia coli K-12 W3110 in extreme acid or base (pH conditions outside the growth range). At pH 2, 10 mM spermine increased survival by 2-fold, and putrescine increased survival by 30%. At pH 9.8, however, E. coli survival was decreased 100-fold by 10 mM spermine, putrescine, cadaverine, or spermidine. At pH 8.5, spermine decreased the growth rate substantially, whereas little effect was seen at pH 5.5. Spermidine required ten-fold higher concentrations to impair growth. On proteomic 2-D gels, spermine and spermidine caused differential expression of 31 different proteins. During log-phase growth at pH 7.0, 1 mM spermine induced eight proteins, including PykF, GlpK, SerS, DeaD, OmpC and OmpF. Proteins repressed included acetate-inducible enzymes (YfiD, Pta, Lpd) as well as RapA (HepA), and FabB. At pH 8.5, spermine induced additional proteins: TnaA, OmpA, YrdA and NanA (YhcJ) and also repressed 17 proteins. Four of the proteins that spermine induced (GlpK, OmpA, OmpF, TnaA) and five that were repressed (Lpd, Pta, SucB, TpiA, YfiD) show similar induction or repression, respectively, in base compared to acid. Most of these base stress proteins were also regulated by spermidine, but only at ten-fold higher concentration (10 mM) at high pH (pH 8.5). CONCLUSION: Polyamines increase survival in extreme acid, but decrease E. coli survival in extreme base. Growth inhibition by spermine and spermidine requires neutral or higher pH. At or above pH 7, spermine and spermidine regulate specific proteins, many of which are known to be regulated by base stress. High pH amplifies polyamine stress; and naturally occurring polyamines may play an important role in base stress.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2005

pH regulates genes for flagellar motility, catabolism, and oxidative stress in Escherichia coli K-12.

Gene expression profiles of Escherichia coli K-12 W3110 were compared as a function of steady-state external pH. Cultures were grown to an optical density at 600 nm of 0.3 in potassium-modified Luria-Bertani medium buffered at pH 5.0, 7.0, and 8.7. For each of the three pH conditions, cDNA from RNA of five independent cultures was hybridized to Affymetrix E. coli arrays. Analysis of variance with an alpha level of 0.001 resulted in 98% power to detect genes showing a twofold difference in expression. Normalized expression indices were calculated for each gene and intergenic region (IG). Differential expression among the three pH classes was observed for 763 genes and 353 IGs. Hierarchical clustering yielded six well-defined clusters of pH profiles, designated Acid High (highest expression at pH 5.0), Acid Low (lowest expression at pH 5.0), Base High (highest at pH 8.7), Base Low (lowest at pH 8.7), Neutral High (highest at pH 7.0, lower in acid or base), and Neutral Low (lowest at pH 7.0, higher at both pH extremes). Flagellar and chemotaxis genes were repressed at pH 8.7 (Base Low cluster), where the cell's transmembrane proton potential is diminished by the maintenance of an inverted pH gradient. High pH also repressed the proton pumps cytochrome o (cyo) and NADH dehydrogenases I and II. By contrast, the proton-importing ATP synthase F1Fo and the microaerophilic cytochrome d (cyd), which minimizes proton export, were induced at pH 8.7. These observations are consistent with a model in which high pH represses synthesis of flagella, which expend proton motive force, while stepping up electron transport and ATPase components that keep protons inside the cell. Acid-induced genes, on the other hand, were coinduced by conditions associated with increased metabolic rate, such as oxidative stress. All six pH-dependent clusters included envelope and periplasmic proteins, which directly experience external pH. Overall, this study showed that (i) low pH accelerates acid consumption and proton export, while coinducing oxidative stress and heat shock regulons; (ii) high pH accelerates proton import, while repressing the energy-expensive flagellar and chemotaxis regulons; and (iii) pH differentially regulates a large number of periplasmic and envelope proteins.

Chemotaxis↗
PubMed2004

pH-dependent catabolic protein expression during anaerobic growth of Escherichia coli K-12.

During aerobic growth of Escherichia coli, expression of catabolic enzymes and envelope and periplasmic proteins is regulated by pH. Additional modes of pH regulation were revealed under anaerobiosis. E. coli K-12 strain W3110 was cultured anaerobically in broth medium buffered at pH 5.5 or 8.5 for protein identification on proteomic two-dimensional gels. A total of 32 proteins from anaerobic cultures show pH-dependent expression, and only four of these proteins (DsbA, TnaA, GatY, and HdeA) showed pH regulation in aerated cultures. The levels of 19 proteins were elevated at the high pH; these proteins included metabolic enzymes (DhaKLM, GapA, TnaA, HisC, and HisD), periplasmic proteins (ProX, OppA, DegQ, MalB, and MglB), and stress proteins (DsbA, Tig, and UspA). High-pH induction of the glycolytic enzymes DhaKLM and GapA suggested that there was increased fermentation to acids, which helped neutralize alkalinity. Reporter lac fusion constructs showed base induction of sdaA encoding serine deaminase under anaerobiosis; in addition, the glutamate decarboxylase genes gadA and gadB were induced at the high pH anaerobically but not with aeration. This result is consistent with the hypothesis that there is a connection between the gad system and GabT metabolism of 4-aminobutanoate. On the other hand, 13 other proteins were induced by acid; these proteins included metabolic enzymes (GatY and AckA), periplasmic proteins (TolC, HdeA, and OmpA), and redox enzymes (GuaB, HmpA, and Lpd). The acid induction of NikA (nickel transporter) is of interest because E. coli requires nickel for anaerobic fermentation. The position of the NikA spot coincided with the position of a small unidentified spot whose induction in aerobic cultures was reported previously; thus, NikA appeared to be induced slightly by acid during aeration but showed stronger induction under anaerobic conditions. Overall, anaerobic growth revealed several more pH-regulated proteins; in particular, anaerobiosis enabled induction of several additional catabolic enzymes and sugar transporters at the high pH, at which production of fermentation acids may be advantageous for the cell.

Anaerobiosis↗
PubMed2002

pH-dependent expression of periplasmic proteins and amino acid catabolism in Escherichia coli.

Escherichia coli grows over a wide range of pHs (pH 4.4 to 9.2), and its own metabolism shifts the external pH toward either extreme, depending on available nutrients and electron acceptors. Responses to pH values across the growth range were examined through two-dimensional electrophoresis (2-D gels) of the proteome and through lac gene fusions. Strain W3110 was grown to early log phase in complex broth buffered at pH 4.9, 6.0, 8.0, or 9.1. 2-D gel analysis revealed the pH dependence of 19 proteins not previously known to be pH dependent. At low pH, several acetate-induced proteins were elevated (LuxS, Tpx, and YfiD), whereas acetate-repressed proteins were lowered (Pta, TnaA, DksA, AroK, and MalE). These responses could be mediated by the reuptake of acetate driven by changes in pH. The amplified proton gradient could also be responsible for the acid induction of the tricarboxylic acid (TCA) enzymes SucB and SucC. In addition to the autoinducer LuxS, low pH induced another potential autoinducer component, the LuxH homolog RibB. pH modulated the expression of several periplasmic and outer membrane proteins: acid induced YcdO and YdiY; base induced OmpA, MalE, and YceI; and either acid or base induced OmpX relative to pH 7. Two pH-dependent periplasmic proteins were redox modulators: Tpx (acid-induced) and DsbA (base-induced). The locus alx, induced in extreme base, was identified as ygjT, whose product is a putative membrane-bound redox modulator. The cytoplasmic superoxide stress protein SodB was induced by acid, possibly in response to increased iron solubility. High pH induced amino acid metabolic enzymes (TnaA and CysK) as well as lac fusions to the genes encoding AstD and GabT. These enzymes participate in arginine and glutamate catabolic pathways that channel carbon into acids instead of producing alkaline amines. Overall, these data are consistent with a model in which E. coli modulates multiple transporters and pathways of amino acid consumption so as to minimize the shift of its external pH toward either acidic or alkaline extreme.

Amino Acids↗