Selection of Escherichia coli heat shock promoters toward
their application as stress probes
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Citation
Rodrigues, Joana L., Marta Sousa, Kristala L.J. Prather, Leon D.
Kluskens, and Ligia R. Rodrigues. “Selection of Escherichia Coli
Heat Shock Promoters Toward Their Application as Stress
Probes.” Journal of Biotechnology 188 (October 2014): 61–71.
As Published
http://dx.doi.org/10.1016/j.jbiotec.2014.08.005
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Author's final manuscript
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Wed May 25 21:14:23 EDT 2016
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Accepted Manuscript
Title: Selection of Escherichia coli heat shock promoters
towards their application as stress probes
Author: Joana L. Rodrigues Marta Sousa Kristala L.J. Prather
Leon D. Kluskens Ligia R. Rodrigues
PII:
DOI:
Reference:
S0168-1656(14)00804-9
http://dx.doi.org/doi:10.1016/j.jbiotec.2014.08.005
BIOTEC 6807
To appear in:
Journal of Biotechnology
Received date:
Revised date:
Accepted date:
21-3-2014
24-7-2014
5-8-2014
Please cite this article as: Rodrigues, J.L., Sousa, M., Prather, K.L.J., Kluskens,
L.D., Rodrigues, L.R.,Selection of Escherichia coli heat shock promoters
towards their application as stress probes, Journal of Biotechnology (2014),
http://dx.doi.org/10.1016/j.jbiotec.2014.08.005
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Selection of Escherichia coli heat shock promoters towards their
application as stress probes
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Joana L. Rodrigues1,3, Marta Sousa1, Kristala L. J. Prather2,3, Leon D. Kluskens1, Ligia
R. Rodrigues1,3*
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Centre of Biological Engineering, University of Minho, 4710 - 057 Braga, Portugal
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Department of Chemical Engineering, Synthetic Biology Engineering Research Center
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(SynBERC) Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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MIT-Portugal Program, Cambridge, MA and Lisbon, Portugal
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*Corresponding author: Lígia R. Rodrigues
Address: Centre of Biological Engineering, University of Minho, 4710 057 Braga, Portugal
Tel: (+351) 253 604 401
Fax: (+351) 253 604 429
E-mail address: lrmr@deb.uminho.pt
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Short running title: Selection of E. coli heat shock promoters
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Abstract
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The mechanism of heat shock response of Escherichia coli can be explored to program
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novel biological functions. In this study, the strongest heat shock promoters were
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identified by microarray experiments conducted at different temperatures (37ºC and
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45ºC, 5 min). The promoters of the genes ibpA, dnaK and fxsA were selected and
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validated by RT-qPCR. These promoters were used to construct and characterize stress
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probes using green fluorescence protein (GFP). Cellular stress levels were evaluated in
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experiments conducted at different shock temperatures during several exposure times. It
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was concluded that the strength of the promoter is not the only relevant factor in the
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construction of an efficient stress probe. Furthermore, it was found to be crucial to test
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and optimize the ribosome binding site (RBS) in order to obtain translational efficiency
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that balances the transcription levels previously verified by microarrays and RT-qPCR.
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These heat shock promoters can be used to trigger in situ gene expression of newly
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constructed biosynthetic pathways.
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Keywords: E. coli; heat shock promoters; microarrays; RT-qPCR; stress probes
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1. Introduction
The heat shock response (HSR) is an essential and universal mechanism to protect
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cells (Lindquist, 1986). This mechanism is induced by a variety of stress conditions
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including physicochemical factors such as heat shock, complex metabolic processes and
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metabolically harmful substances (Arsène et al., 2000; Zhao et al., 2005) like heavy
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metals (Lindquist and Craig, 1988). The stress factors increase the level of denatured
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proteins, resulting in a biological activity change that can be damaging. In Escherichia
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coli exposed to heat shock conditions, there is an increase of the σ32 transcription factor
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(Grossman et al., 1984) that allows RNA polymerase to recognize the heat shock gene
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promoters. Consequently, the expression of heat shock proteins (HSP) increases
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(Lindquist, 1986; Zhao et al., 2005). Most HSP are chaperones and proteases (Arsène et
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al., 2000). Chaperones aid misfolded and unfolded proteins to fold correctly. Proteases
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degrade the proteins that chaperones cannot fold correctly, thus ensuring that problems
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created by stress conditions are minimized (Madigan et al., 2009). The E. coli HSR
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mechanism is, as in other organisms, very complex. When temperature increases up to
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42ºC, a cascade of events with several key players is initiated. Despite several scientific
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efforts (El-Samad et al., 2005; Genevaux et al., 2007; Georgopoulos 2006; Guisbert et
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al., 2004; Ito and Akiyama, 2005; Janaszak et al., 2007; Janaszak et al., 2009; Yura and
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Nakanigashi, 1999), some steps of this process have still not been completely
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elucidated.
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In the past decade numerous studies have been carried out using DNA
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microarrays to unravel the HSR of E. coli (Nonaka et al., 2006; Rasouly et al., 2009;
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Richmond et al., 1999; Wade et al., 2006; Zhao et al, 2005). These studies evaluated
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different control and shock temperatures, exposure times and gene expression induction
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methods (temperature increase, σ32 transcription factor increase, IPTG) leading to the
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identification of several repressed and overexpressed heat shock related genes. Three of
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the most highly expressed heat shock genes are ibpB, ibpA and dnaK (Nonaka et al.,
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2006; Rasouly et al., 2009; Richmond et al., 1999; Wade et al., 2006; Zhao et al, 2005).
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However, gene expression and induction ratios obtained in these studies proved to be
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difficult to compare, mainly due to the different parameters tested.
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The heat shock promoters can be exploited for designing biological systems
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where a temperature shift can be used to trigger the expression of a particular gene or
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set of genes and to build new biological functions and complex artificial systems that do
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not occur in nature (Khalil and Collins, 2010; Rodrigues and Kluskens, 2011). Induction
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methods like temperature shift are safe, easy to implement and administer (culture
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handling and contamination risks are minimized) and less expensive (Valdez-Cruz et
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al., 2010). For instance, the HSR in E. coli has been studied with a special focus on its
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potential use as a biosensor (transcriptional biosensing). For that purpose, the HSP
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promoters (dnaK, grpE, lon, clpB, ibpAB, clpPX, fxsA) have been fused to several
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reporter genes: lux (Ben-Israel et al., 1998; Dyk et al., 1994; Sagi et al., 2003), gfp
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(Aertsen et al., 2004; Cha et al., 1999; Nemecek et al., 2008; Sagi et al., 2003) and luc
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(Kraft et al., 2007) that allowed detecting the activation of the HSR by the fast
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occurrence of light. These biosensors enabled the detection of pollution by metals,
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solvents and other chemicals. Furthermore, they also helped to identify HSP promoters
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activated by different types of stress and the overload exerted in E. coli by the
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production of recombinant proteins. The HSP promoters can further have a significant
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role in another important field of synthetic biology, namely in engineering bacteria and
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viruses for therapeutic applications (Shankar and Pillai, 2011), like vaccines (Garmory
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et al., 2003) and gene delivery vectors (Drabner and Guzman, 2001). Besides sensing
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environmental pollution, it is feasible to program bacteria to sense the tumor
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environment and selectively release a drug to kill cancer cells (Anderson et al., 2006;
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Shankar and Pillai, 2011).
This study aims to identify the strongest heat shock promoters in E. coli to be used
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in synthetic biology approaches to reprogram organisms and trigger the expression of
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novel biomolecular components, networks and pathways. The promoter’s strength was
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evaluated using microarray data and was further validated by quantitative RT-PCR (RT-
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qPCR) and by the construction and characterization of stress probes.
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2. Materials and methods
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2.1. Media and strains
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E. coli K-12 MG1655 (The Coli Genetic Stock Center - CGSC#: 6300, New Haven,
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CT, USA) was grown in LB medium (tryptone 1% (w/v), yeast extract 0.5% (w/v),
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sodium chloride 1% (w/v)) at 37ºC/30 ºC and 200 rpm in Erlenmeyer flasks and in a 2 L
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stirred tank bioreactor (Autoclavable Benchtop Fermenter Type RALF, Bioengineering,
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AG, Wald, Switzerland) Bioengineering, Type R’ALF) (section 2.3). Pre-inocula were
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prepared from frozen glycerol stocks and incubated overnight (~12 h) in LB at 37
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ºC/30ºC and 200 rpm. Both Erlenmeyer flasks and bioreactor were inoculated with 1%
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of inoculum. Biomass was monitored in a spectrophotometer at OD600nm. The cell
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concentrations at which the heat shock should be applied were determined using
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biomass calibration curves. Samples were collected over time for RNA extraction or
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fluorescence measurements.
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2.2. Heat shock probes construction
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GFP (mut3b) was amplified by PCR from pSB1A2-GFP (Cormack et al., 1996). The
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promoters were amplified from E. coli gDNA extracted using the Wizard® Genomic
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DNA Purification Kit (Promega, Madison, WI, USA). Q5® Hot Start High-Fidelity
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DNA Polymerase (NEB, Ipswich, MA, USA) was used to amplify GFP and all the
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promoters used. The heat shock promoter sequences were obtained from the NCBI (The
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National Center for Biotechnology Information) database. The RBS was calculated
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using the software RBS Calculator v1.1 (Salis, 2011). GFP and all the promoters were
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cloned using restriction enzymes and DNA T4 ligase (NEB, Ipswich, MA, USA). GFP
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was cloned in MCS1 of pETduet (Novagen, Darmstadt, Germany), between the BamHI
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and AscI site. After cloning GFP, the T7 promoter was replaced by each of the three
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heat shock promoters (selected after microarrays data treatment) that were cloned
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between the SphI and BamHI site. The SacI site was added downstream of the SphI site
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to aid colony screening. E. coli ElectroMAXTM DH10B competent cells (F- mcrA
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∆(mrr-hsdRMS-mcrBC) φ80lacZ∆M15 ∆lacX74 recA1 endA1 araD139
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leu)7697 galU galK λ- rpsL nupG) (Invitrogen/Life Technologies, Carlsbad, CA, USA)
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(Invitrogen Life Technologies) were transformed with the ligation mixture. Colonies
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were screened by plasmid digestion and E. coli K12 MG1655 DE3 competent cells
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were transformed with the plasmid containing the gene or promoter of interest. Primers
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(Sigma-Aldrich, St. Louis, MO, USA) and restriction enzyme sites are listed in Table 1.
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2.3. Heat shock experiments
Erlenmeyer flasks were placed in an incubator at 37ºC and 200 rpm. Bacterial
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growth was monitored by optical density (OD600
nm).
Heat shock was applied at a cell
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concentration of 5 to 6 × 108 cells per mL, a concentration obtained after approximately
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7.5 h of growth. Control flasks were kept at 37ºC, while the ones corresponding to the
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heat shock assay were immersed in a water bath at 45ºC for 5 min. Samples were taken
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before heat shock and after 5 min of heat shock. These experiments were conducted six-
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fold (two for microarray analysis and four for RT-qPCR analysis).
In order to fully characterize the transcriptional profiles of the heat shock genes,
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two experiments were conducted in a 2 L bioreactor (1 L of LB medium) at 200 rpm,
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30% of dissolved oxygen, and at different control (30 ºC and 37 ºC) and heat shock
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temperatures (45ºC and 50ºC) during different exposure times and different heating
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rates (Fig. S1.). A very high heat shock temperature (50ºC) was chosen to observe the
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differences during and after heat shock under more lethal conditions.
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The experiments with the heat shock probes containing GFP were also performed
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in Erlenmeyer flasks and the heat shock was applied in a water bath at 45 and 50ºC for 5
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or 10 min. In each experiment 5 flasks were permanently held at 30 or 37ºC (control)
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and 5 flasks were subjected to the corresponding heat shock temperature for 5-10 min.
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Each culture flask contained one of the five following plasmids: T7promoter_pETduet
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(empty),
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dnaKpromoter_pETduet_GFPmut3b,
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plasmids with a T7 promoter, 0.1 mM IPTG was added at induction time zero. Each
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experiment was conducted three times at different experimental conditions (heat shock
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temperature and/or duration).
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T7promoter_pETduet_GFPmut3b,
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ibpApromoter_pETduet_GFPmut3b,
fxsApromoter_pETduet_GFPmut3b.
For
the
2.4. RNA extraction and DNase treatment
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All samples collected for microarrays and RT-qPCR analysis were immediately
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stabilized with the RNAprotect Bacteria Reagent (Qiagen, Germantown, MD, USA) and
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the pellets were maintained at -80ºC.
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RNA extraction for microarray analysis was conducted using the RNeasy®
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Protect Bacteria Mini Kit (Qiagen, Germantown, MD, USA), following the
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manufacturer’s guidelines. The Qiagen RNase-free DNase Set (Qiagen, Germantown,
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MD, USA) was used to eliminate any DNA contamination from the samples.
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RNA extraction for RT-qPCR analysis was performed by the phenol-chloroform
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method followed by ethanol precipitation, and then the addition of DEPC
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(Diethylpyrocarbonate) treated water (Khodursky et al., 2003). To eliminate any DNA
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contamination from these samples, the Turbo DNA-freeTM Kit (Ambion/Applied
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BioSystems/Life Technologies, Carlsbad, CA, USA) was used according to the
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manufacturer’s guidelines.
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determined using a NanoDrop
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spectrophotometer (ND-1000, Thermo Scientific, Wilmington, DE, USA). RNA purity
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levels were estimated by the A260/A280 and A260/A230 ratios. Furthermore, RNA integrity
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was evaluated by running a 1.5% (w/v) agarose gel electrophoresis loaded with 5 μL of
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RNA sample.
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2.5. Microarrays
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Genetic expression levels of samples grown in Erlenmeyer flasks were analyzed by
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microarray experiments (two control samples – CA and CB - and two heat shock
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samples – TA and TB). Total RNA samples were sent to an Affymetrix core facility
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(Instituto Gulbenkian de Ciência, Oeiras, Portugal) where quality-control analysis was
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carried out using the Bioanalyzer 2100 (Agilent Technologies, Waldbronn, Germany).
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All microarray steps (cDNA synthesis with appropriate random primers (Invitrogen/Life
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Technologies, P/N 48190-011, Carlsbad, CA, USA), cDNA fragmentation and labeling
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through incorporation
of biotinylated
ribonucleotide
analogs (ddUTP-biotin),
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hybridization to the GeneChip® E. coli Genome 2.0 Array (Affymetrix, Santa Clara,
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CA, USA), staining with streptavidin- phycoerythrin and scanning) were done
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according to Affymetrix protocols. The GeneChip® includes approximately 10,000
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probe sets for all 20,366 genes present in four strains of E. coli, including the K12
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MG1655 used in this study.
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2.6. RT-qPCR
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The primers used for specific amplification (Invitrogen) are described in Table 2
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and were designed using the Primer 3 program (Rozen and Skaletsky, 2000). The size
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of all amplicons was between 100 and 105 base pairs. Reverse Transcriptase (RT)
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reaction was performed in a MyCycler™ Personal Thermal Cycler (BioRad, Hercules,
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CA, USA) and the iScriptTM Select cDNA Synthesis kit (BioRad, Hercules, CA, USA)
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was used according to the manufacturer’s instructions, except that the reaction volume
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used was 10 μL. The final RNA template concentration in the cDNA synthesis reaction
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was 0.05 μg/μL, and was calculated using the RNA concentrations as determined by
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NanoDrop. The reverse primers used in the RT reaction are also described in Table 2.
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qPCR was used to evaluate the transcription levels of the heat shock genes that
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were selected from the microarray experiments. qPCR was carried out in a CFX96 Real-
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Time PCR Detection System (BioRad, Hercules, CA, USA) using SsoFastTM
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EvaGreen® Supermix (BioRad, Hercules, CA, USA) as specified by the manufacturer,
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except that the reaction volume used was 10 μL. Primer efficiency and correlation
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values (R2) were determined by the CFX Manager Software (Biorad, Hercules, CA,
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USA). The cDNA dilution used in the qPCR experiments was 1/80. No-reverse
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transcriptase controls, in which RNA instead of cDNA was used, were included to
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evaluate the presence of any genomic DNA. Three and two technical replicates were
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performed for each biological replicate from flask and bioreactor heat shock
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experiments, respectively.
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2.7. Microarrays Data Treatment
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Analysis of the microarray data was conducted using three different software
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programs (Expression Console®, dChip and SAM - Significance Analysis of
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Microarrays). The Expression Console® software (Affymetrix, Santa Clara, CA, USA)
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was used to analyze the raw data from the four arrays (CA, CB, TA and TB) and assess
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the need for data pre-treatment (background correction, normalization and
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summarization). For data pre-treatment three different algorithms (RMA - Robust
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Multichip Average, PLIER - Probe Logarithmic Intensity Error Estimation and MAS5 -
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MicroArray Suite 5) were used. dChip software (Li and Wong, 2003) was used for data
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pre-treatment and to filter the genes in order to obtain the most expressed ones during
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heat shock conditions. In this case, the algorithm used for data pre-treatment was MBEI
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(Model Based Expression Index). For background correction the MM (MisMatch)
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probes where used to correct PM (Perfect Match) probes (PM/MM difference), and
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normalization was carried out using the invariant set method. Subsequently, data were
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filtered according to the name "MG1655," since the GeneChip® also includes three
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other pathogenic E. coli strains and several intergenic regions that are not relevant for
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the purpose of the current work. Fig. 1A. illustrates the roadmap of the microarrays data
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treatment.
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The genes were also filtered according to their dispersion coefficient in various
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samples (σ/ ) and to the percentage of presence of the gene in the four above-mentioned
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arrays. The ratio of standard deviation and mean of gene expression values across all
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samples should be greater than a certain threshold (0.5 < σ/ <10). The more variable a
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gene is across samples, the larger this ratio will be. Nevertheless, if a gene is mostly
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absent across samples, this ratio can be large due to a small mean. For these reasons, the
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gene should be present in more than 40% of the arrays (i.e. at least in two arrays).
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Following these procedures, 703 genes were found to verify the filtering criteria.
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The filtered genes were used to compare the different samples and to identify the
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differentially expressed genes. These genes were selected according to their induction
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ratio
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difference
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samples
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t-test for paired samples was performed for each gene identified and a p-value lower
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than 0.05 was assumed to obtain significant results (Cui and Churchill, 2003). The
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differentially expressed genes were grouped by hierarchical clustering strategies to
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evaluate the different expression profiles.
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the
.
The
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The genes identified by the dChip program as differentially expressed (189
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genes) were subsequently tested in the SAM (Significance Analysis of Microarrays)
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program (S-test) (http://statweb.stanford.edu/~tibs/SAM/). The "two-class paired" test
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(two groups, and one-to-one pairing between a member of group A and a corresponding
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member of group B) was chosen with the following parameters: number of
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permutations = 5000 (at least 1000 permutations are recommended for accurate
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estimates) and Δ = 1 (delta parameter is used to adjust the false positive rate, that can be
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decreased at the cost of missing true positives, or increased at the cost of obtaining more
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false negatives. The number of significant genes and the number of false significant
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genes will decrease with increasing delta values) (Dziuda, 2010).
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2.8. RT-qPCR Data Treatment
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RT-qPCR data were analyzed using the 2-ΔΔCT method described by Livak and
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Schmittgen (2001). In this method, the amount of mRNA for a target gene is normalized
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to an endogenous control and relative to a calibrator or arbitrary control condition. The
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housekeeping rrsA gene, encoding the 16S RNA, was used as an endogenous control to
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normalize the differences in total RNA quantity (Dong et al., 2008; Kobayashi et al.,
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2006). RT-qPCR values were obtained for each gene for both control and heat shock
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conditions. Afterwards, data were normalized with the 16S CT value and subsequently
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the expression value of each gene under the control conditions (Livak and Schmittgen,
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2001):
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gene-CT,16S) Heat Shock
– (CT,heat-shock
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Equation 1: ΔΔCT= (CT,heat-shock
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Control.
gene-CT,16S)
Finally, the expression level for each heat shock gene (E-ΔCT) and the induction
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ratio (E-ΔΔCT) were calculated. E represents the real calculated efficiency value for each
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set of primers, which was very close to 100 % (97.8%-100.8%).
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2.9. Fluorescence measurements and data treatment
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The GFP assay was performed by measuring fluorescence intensity with a
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fluorescence spectrometer (Infinite 200® PRO, Tecan) at an excitation wavelength of
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485 nm and an emission wavelength of 535 nm. The measurements were performed in
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Nunc-Immuno™ MicroWell™ 96
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Scientific, Rochester, NY, USA) with 300 uL of culture sample resuspended in PBS.
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The software used in the analysis was the Tecan i-ControlTM version 1.9.17.0 (Tecan,
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Morrisville, NC, USA). The specific fluorescence intensity (SFI) is calculated by
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dividing the raw fluorescence intensity by the optical density at 600 nm and allows
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comparison of samples with different optical density at time zero.
well
polystyrene
black
plates
(Nunc/Thermo
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3. Results and discussion
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3.1. Differential expression of HSP genes and identification of the strongest HSP
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promoters using microarrays
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From t The microarray results of the heat shock experiments conducted in flasks at
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different temperatures (held constant at 37ºC relative to incubation at 45ºC for 5 min)
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we were able allowed to identify the strongest heat shock promoters (i.e. the most
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highly expressed genes). After the heat shock, the percentage of transcripts considered
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present increases slightly (≈ 2%) and the transcripts considered absent decrease
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proportionally. This means that the heat shock led to an increase of the expression of
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some genes, some of which are possibly not transcribed under normal growth
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conditions.
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These heat shock promoters were identified after the data pre-treatment
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(background correction and normalization, see section 2.7). E. coli K-12 MG1655 genes
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were selected from all the genes present in the Affymetrix array and were further
320
filtered and compared to the criteria mentioned before (Fig. 1A). These criteria allowed
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to only select genes with low variation between the replicates and that are differentially
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expressed comparing to the control sample. Based on the different criteria (Fig. 1A.),
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189 genes were selected and classified as differentially expressed. These differentially
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expressed genes were then hierarchically clustered. As shown in Fig. 1B., the tree is divided in
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two main branches, displaying underexpressed (upper branch) and overexpressed genes (lower
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branch). All replicates (CA and CB, TA and TB) demonstrate a great affinity with each other
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(Fig. 1B.) with a Spearman coefficient near 1. When comparing the two conditions (control and
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heat shock) the coefficient is near -1, demonstrating that there is a significant difference in gene
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expression between the samples.
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Afterwards, these 189 genes were subjected to the t-test and from the 189 genes
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only 56 were considered as being significantly transcribed. However, the t-test is not
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satisfactory when used in the analysis of data from microarrays with few replicates (as
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is the case of this work) since it is difficult to estimate the variance (Cui and Churchill,
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2003). Thus, an adapted t-test, for example, the S- test, should be used However, since
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the t-test is not satisfactory in the analysis of data from microarrays with few replicates
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as is the case for this work, the S-test was chosen because it prevents the selection of
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genes with small changes as significant genes and it uses the values of the replicates to
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estimate the percentage of genes identified by chance (FDR - False Discovery Rate).
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Using the S-test, from the 189 genes identified as differentially expressed by dChip
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program, only 128 were considered significant with an FDR equal to 0%. The q-value,
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which is the lowest FDR at which a gene is described as significantly regulated, was
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also 0.
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After comparing the samples, the 189 differentially expressed genes were then
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hierarchically clustered. As shown in Fig. 1B., the tree is divided in two main branches,
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displaying underexpressed (upper branch) and overexpressed genes (lower branch). All
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replicates (CA and CB, TA and TB) demonstrate a great affinity with each other (Fig.
347
1B.) with a Spearman coefficient near 1. When comparing the two conditions (control
348
and heat shock) the coefficient is near -1, demonstrating that there is a significant
349
difference in gene expression between the samples.
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343
Table 3 shows the 34 genes that were found to be the most expressed due to the
351
heat treatment (45ºC, 5 min) and which simultaneously met verified the comparison
352
criteria. In Fig. 1B., these genes can be found in the branch called "TA Expression ≈ TB
353
Expression." The microarray results obtained in the current work are consistent with
14
Page 14 of 44
354
others reported in the literature (Nonaka et al., 2006; Rasouly et al., 2009; Richmond et
355
al., 2009; Wade et al., 2006; Zhao et al., 2005).
356
3.1.2. Selection of the appropriate heat shock promoters to trigger gene expression
358
In metabolic engineering it is very important to choose the right promoter(s) since many
359
rate-limiting steps may exist in the pathways and accumulation of intermediate
360
metabolites may occur. Therefore, after constructing a novel pathway(s), the expression
361
levels of all the components should be orchestrated to allow fine-tuning of expression
362
levels and optimization of metabolic fluxes (Khalil and Collins 2010; Li et al., 2012).
363
For this reason, it is essential to construct libraries of engineered tandem promoters with
364
varying strengths (Alper et al., 2005; Ellis et al.,2009; Jensen and Hammer, 1998; Li et
365
al., 2012). Depending on the envisaged purpose for the use of HSP promoters, several
366
options can be considered to make an appropriate choice. For example, if building
367
engineered bacteria to overexpress a given compound is foreseen, the promoter of
368
choice will depend essentially on the product concentration needed and its toxicity to
369
the cell, since a low concentration may not be sufficient for the intended purpose, but
370
too high a concentration can damage the cells. Table 3 illustrates three main situations
371
when considering the genes placed directly downstream of the promoters (*): high
372
induction rate and high expression level during heat shock (ibpA, ycjX, ldhA); high
373
induction rate but low expression during heat shock (mutM, mlc, fxsA) when compared
374
to the expression levels of other genes; and medium induction rate and high expression
375
level (clpB, dnaK, groES, lon).
376
3.1.2.1. High induction rate and high expression level
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377
If the aim is to achieve a very high concentration of a given compound, the chosen
378
promoter has to allow a rapid induction of gene expression, but also a high expression.
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Page 15 of 44
In this study (Table 3) and in several of the microarray-based studies to identify E. coli
380
heat shock genes, it was found that ibpA and ibpB were the two most induced genes,
381
with a consistently higher induction rate for ibpB (Caspeta et al., 2009; Richmond et al.,
382
1999; Zhao et al., 2005). These genes, along with yidE gene, belong to the same
383
transcription unit and the σ32 promoter-dependent is upstream of the ibpA gene.
384
Moreover, ibpA expression is higher than ibpB expression, both under normal growth
385
conditions and after heat shock treatment. This suggests that the high induction rate of
386
ibpB is not due to a higher expression compared to the gene ibpA, but to its low
387
expression at 37°C (control conditions). Therefore, at 37ºC the ibpB gene is poorly
388
expressed, possibly due to the short half-life of the σ32 factor, while under heat shock
389
conditions, due to an increase in denatured proteins, the half-life of σ32 increases and the
390
ibpB gene is transcribed similarly to the ibpA gene. Hence, despite the high induction
391
rate of the ibpB gene, one can conclude that the promoter upstream of the ibpA gene is
392
by far the one that allows a more pronounced expression of the HSP genes (Caspeta et
393
al., 2009). As can be seen in Table 3, the strongest promoter following the ibpA gene
394
promoter is the promoter upstream the operon comprising ycjX, ycjF and tyrR. These
395
three genes have been reported to be σ32 dependent in all microarray studies conducted
396
so far. The ldhA gene, encoding the enzyme D-lactate dehydrogenase, was also
397
identified by several authors as a heat shock gene (Richmond et al., 1999; Wade et al.,
398
2006; Zhao et al., 2005). The difference in hslJ gene expression, present in the same
399
transcription unit, is not so evident in the current results but remains significant.
400
3.1.2.2. High induction rate but moderate expression during heat shock
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401
If a fast expression induction and, at the same time, a more moderate expression is
402
required after heat shock, then mutM, mlc, fxsA and prlC promoters can be used. These
403
genes have been reported in several studies as heat shock genes, their functions are
16
Page 16 of 44
well-known and their promoters have been properly identified (Nonaka et al., 2006;
405
Shin et al., 2001; Tsui et al., 1996; Zhao et al., 2005). However, in the current work the
406
expression values obtained for mutM and mlc gene duplicates are very different and not
407
conclusive, which can be clearly seen by the t- and S-test results (Table 3). The same
408
occurs with the yibA gene which was only been identified as heat shock gene by Zhao et
409
al. (2005) and Wade et al. (2006), although the sequence of its promoter has not been
410
defined. Thus, if a rapid induction and a lower gene expression are required, then the
411
fxsA and prlC promoters would be a preferable choice.
412
3.1.2.3. Medium induction rate and high expression level
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The promoters associated with other genes with a relatively high induction ratio
414
and expression, such as htpG, ybbN, htpX, yceP, clpB, yrfH, hslV, dnaK, groES, lon,
415
yhdN and sdaA, can also be considered as strong promoters and therefore may be used
416
in the construction of engineered bacteria. However, it is necessary to take into account
417
that the lower induction rate in these cases is primarily due to a high expression of
418
genes in control samples. Thus, it is necessary to evaluate any implications that the
419
possible expression of these genes could have on the applications envisaged before the
420
heat shock treatment occurs. If the production/expression of the compound/gene before
421
heat shock induction leads to misleading/undesirable results, then these promoters
422
should not be used. As can be observed in Table 3, dnaK has the highest expression at
423
45 ºC, followed very closely by ibpA. Although they have similar expression levels
424
under heat shock conditions, the dnaK induction rate is much smaller than that of ibpA.
425
DnaK is one of the most reported chaperones and belongs to the DnaK system, together
426
with the DnaJ (known to assist the DnaK chaperone) and GrpE proteins (nucleotide
427
exchanger factor). In the absence of stress, these proteins have an important role in the
428
regulation of the stability of the σ32 transcription factor, and in the maintenance and
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413
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Page 17 of 44
restoration of the homeostatic balance. As for the ybbN, htpX, yceP, yrfH, hslV, yhdN
430
and sdaA genes, it is known that all of them are also associated with σ32 controlled
431
promoters (Chuang et al., 1993; Cowing et al., 1985; Kornitzer et al., 1991; Nonaka et
432
al., 2006; Zhao et al., 2005). The two htpG genes and clpB, groES and lon genes have
433
σ32-dependent promoters but they are also controlled by σ54 and σ70 transcription factors.
434
Therefore, the possibility that the high ratio and expression levels in this study are due
435
to the higher affinity of the other factors to the promoter cannot be ruled out.
436
Nevertheless, since the binding sites of the two factors are superimposed, the use of
437
these promoters with possibly more affinity for other transcription factors is not
438
necessarily a disadvantage, since both factors can hypothetically bind during heat shock
439
induction, which in turn could lead to a high gene expression.
440
3.2. Validation by RT-qPCR of the differential expression of HSP genes
441
Validation by quantitative RT-PCR (RT-qPCR) of the overexpression of some of these
442
HSP genes identified by microarrays, for example ibpA, fxsA and dnaK, is required
443
before using their promoters to trigger a gene or a biosynthetic pathway by heat. For
444
RT-qPCR validation, flask experiments were conducted twice in duplicate using the
445
same temperatures and exposure time as for the microarray studies (Fig. 2.).
446
Furthermore, several experiments were conducted in a bioreactor at different heat shock
447
temperatures, during different exposure times and at different heating rates in order to
448
fully characterize the transcriptional profiles of the selected heat shock genes (Fig. 3.).
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449
Fig. 2A. shows the relative expression of the three selected genes (ibpA, dnaK, fxsA)
450
with respect to the rrsA gene in the two sample types (control and heat shock). A
451
different expression of the heat shock genes could be observed when comparing the
452
control with the heat shock sample. In the case of the heat shock genes it was found that
18
Page 18 of 44
the gene expression is lower in the control, which was foreseen since the heat shock
454
transcripts are lower at 37ºC. Comparison of the three heat shock genes shows that the
455
dnaK gene possessed the highest expression in heat shock conditions. However, this
456
gene also showed the highest expression in normal growth conditions, which in turn
457
contributes to a lower induction ratio (Fig. 2B.). The ibpA gene showed the highest
458
induction ratio, since it has a high expression in heat shock conditions but a low
459
expression at 37ºC. The fxsA gene showed a moderate expression under heat shock
460
conditions when compared to the other two heat shock genes, as expected.
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As can be observed in Fig. 3A. and Fig. 3B., the expression profiles of the three
462
heat shock genes obtained in the bioreactor are very similar. The expression increases
463
when temperature up-shift starts and maximum expression occurs when the culture
464
medium reaches 45ºC (Point D, Fig. 3A.) and 50ºC (Point O, Fig. 3B.). After reaching
465
the maximum temperature, the gene expression starts to decrease until a new stationary
466
state is reached due to bacterial adaptation to heat. Also, following the temperature
467
down-shift to the original temperatures, 37ºC (Point I, Fig. 3A.) and 30ºC (Point T, Fig.
468
3B.), the heat shock gene expression almost returns to its original basal level. During
469
adaptation, to establish the homeostatic balance, σ32 transcription factor is degraded by
470
proteases, such as FtsH. This occurs due to the chaperones negative modulation
471
mechanism, in which they regulate their own synthesis by controlling the activity,
472
stability and synthesis of the σ32 transcription factor. The HSR at 45ºC was found to be
473
in accordance with previous studies conducted at 42ºC (Nagai et al., 1991; Rasouly et
474
al., 2009; Tilly et al., 1989). In contrast, the heat shock response at 50ºC was more
475
prolonged, which was expected since it is more lethal to the cells and it is more difficult
476
for the cells to adapt.
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From the analysis of the expression profiles represented in Fig. 3A. and Fig. 3B.,
478
we can conclude that the ipbA gene had a higher fold change, since in normal growth
479
conditions (37 ºC) it is less expressed than dnaK gene, and at heat shock temperatures it
480
reaches levels similar to the ones observed for the dnaK gene. As expected, the fxsA
481
gene showed a moderate expression. These results are in accordance with the ones
482
obtained in the flasks experiments. Fig. 3C. and Fig. 3D. show the genes fold change for
483
the two experiments after normalization of the data. The gene expression is higher in the
484
experiment conducted at the lethal temperature (50ºC) as for this temperature, the heat
485
shock genes’ transcription is controlled by the transcription level of the rpoH gene. The
486
rpoH gene encodes the σ32 transcription factor. The rpoH gene transcription is
487
controlled by different transcription factors depending on the temperature. At 30ºC or
488
37ºC, the rpoH gene transcription is controlled by three σ70 transcription factor
489
dependent promoters (Erickson et al., 1987). In heat shock conditions around 42-45ºC,
490
the transcription of the rpoH gene does not increase significantly. In these cases, the
491
increase of σ32, and consequently the expression of the heat shock genes, is due to the
492
de-repression of rpoH gene translation. However, in lethal heat shock conditions
493
(greater than 45ºC) the HSR is also controlled at the rpoH transcription level. When the
494
cells are maintained at these elevated temperatures a balanced growth is no longer
495
possible, the HSP are essentially the only proteins to be synthesized (Raina et al., 1995)
496
and the HSR remains elevated until the cells begin to die, unless the temperature
497
decreases (Erickson et al., 1987; Guisbert et al., 2004; Yamamori et al., 1978; Nagai et
498
al., 1991).
499
3.3. Construction of heat shock inducible stress probes using GFP
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477
500
Since we aimed to identify the strongest heat shock promoters that can trigger a
501
biosynthetic pathway as a result of a temperature increase, besides the transcription
20
Page 20 of 44
levels study, it is also important to study and optimize the protein translation to obtain
503
high protein expression. To study the protein translation, the HSP promoter elements
504
were fused to the gfp reporter gene. This strategy has been applied in the past by Cha et
505
al. (1999), where the heat shock response of E. coli resting cells was quantified by
506
dnaK, clpB, and rpoH GFP promoter probes. In their experiment they used chemical
507
(addition of IPTG, acetic acid, ethanol, phenol, antifoam or salt) and physical stresses
508
(heat shock or nutrient limitation) to induce GFP expression. Gill et al. (1998) and Seo
509
et al. (2003) also fused GFP to rpoH, clpB and dnaK promoters to monitor the toxic
510
effect of dithiothreitol addition and compare the cellular stress levels of four strains
511
caused by temperature up-shift, respectively. Lu et al. (2005a,b) and Messaoudi et al.
512
(2013) constructed oxidative stress probes by fusing the promoters of several oxidative
513
stress gene promoters with GFP. Other stress probe strategies have also been used. Van
514
Dyk et al., (1994), Bianchi and Baneyx (1999a,b), Lesley et al. (2002), Pérez et al.
515
(2007) Kraft et al. (2007) and Kotova et al (2010) fused heat shock promoters to β-
516
galactosidase (lacZ) and to bioluminescent luciferase bioreporters (lux and luc) to
517
investigate the toxicity of heterologous protein expression and other compounds. In our
518
study we used the gfp reporter gene because of its small size, minimal toxicity, high
519
stability and ease of use (March et al., 2003).
cr
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520
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502
The use of stress probes with the HSP promoters fused to gfp helped to study how
521
efficient the promoter sequences and the original heat shock RBS are in the translation
522
of a gene. Different experiments were done with different controls (30 ºC and 37 ºC)
523
and heat shock (45 ºC and 50 ºC) temperatures and different heat shock duration (5 and
524
10 min). Fig. 4. shows the fluorescence results obtained for the above mentioned
525
experiments. As observed before, the dnaK promoter allows the highest expression after
526
heat shock treatment, but the induction ratio is not as high as for ibpA since the dnaK
21
Page 21 of 44
expression at 37ºC is already very high and in most cases even higher than ibpA
528
expression after heat shock conditions. The fxsA promoter showed less GFP expression,
529
corroborating the microarrays and RT-qPCR results. Comparing the two heat shock
530
temperatures tested (45 and 50ºC) it can be concluded that the highest temperature
531
allows a higher promoter response and consequently, a higher GFP expression.
532
However, the fluorescence level at 50ºC depends on the initial temperature (30 and
533
37ºC). In general, an initial lower temperature (30ºC) that corresponds to less basal
534
fluorescence also corresponds to lower specific fluorescence intensity at 50ºC. The SFI
535
obtained with the original T7 promoter (data not shown) was around 33000 and the
536
fluorescence reached the steady state in about 2–3 h (37-30ºC experiments) and
537
remained stable until the end of the experiment. As expected, the T7 promoter is a better
538
option as the expression obtained is higher and more stable. However, the use of IPTG
539
has some disadvantages, such as cost and toxicity, and limited utility for in situ gene
540
expression. The lower stability of the mRNA when using heat shock promoters can be
541
overcome by adding sequences to form stem-loop at its 5’ and 3’ untranslated regions
542
(UTRs) (Chen et al., 1991; Higgins et al, 1993). This prolongs the half-life of a
543
normally unstable mRNA by presumably interfering with RNase binding.
cr
us
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d
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Ac
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544
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t
527
These results (Fig. 4.) were obtained after studying four different promoter
545
sequences (supplementary material, Table S1 and S2). The first heat shock promoter
546
sequences used to replace the T7 promoter were obtained from gDNA amplification of
547
the -50 region until the start codon (ATG) of the heat shock gene. The TATA box
548
regions, transcription initiation site, RBS and translation initiation site of the native
549
proteins were used for GFP regulation. The only promoter that showed a significant
550
increase in GFP fluorescence after heat shock was dnaK. In order to improve the
551
expression of GFP, the promoter sequence from the -150 region to the heat shock gene
22
Page 22 of 44
ATG was amplified, towards the inclusion of some possible existing binding sites for
553
transcription activator proteins present in the region upstream of the core promoter,
554
referred as the upstream (UP) element (Ross et al., 1993). The new sequences did not
555
result in an effective improvement of the GFP expression level. Therefore, the promoter
556
sequence from the -150 region to the +21 nucleotides from the three heat shock genes
557
was amplified. Several authors (Bianchi and Baneyx, 1999a, b; Pérez et al., 2007) have
558
reported good results when including the first 21 nucleotides of the ibpA open reading
559
frame in a lacZ gene fusion. However, fluorescence did not improve and therefore the
560
experiments were repeated and samples were analyzed by RT-qPCR. The preliminary
561
results showed a significant increase of gfp transcription after heat shock which allowed
562
us to conclude that the lack of fluorescence after heat shock induction was not related to
563
the promoter strength but to the translational efficiency. To overcome this issue, the
564
RBS Calculator v1.1 (Salis, 2011; Salis et al., 2009) software was used. Twenty N
565
nucleotides (any base calculated by the software) were added to the RBS constraints,
566
followed by GGATCC, which corresponds to the BamHI site. The target translation
567
initiation rate was set to 20000 for the three cases. With these new constructions, we
568
were able to observe an increase of fluorescence in the fxsA and ibpA stress probes.
569
However, the fluorescence for the fxsA probe was higher than for ibpA, which was not
570
expected according to the previously observed mRNA levels. The translation initiation
571
rates determined using the RBS calculator for the heat shock gene promoters in the E.
572
coli genome presented the following relation:
573
obtain GFP expression results more similar to the ones of the heat shock genes in the
574
genome, the RBS Calculator was used. After running the software for 8 h, a relation
575
closer to the translation initiation rates in the genome was found, namely
576
, where fxsA construction possessed the same
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. To
23
Page 23 of 44
577
translation initiation rate as the one used in the previous constructions (19000). These
578
newly determined RBS were then used to obtain the results gathered in Fig. 4.
579
Synthetic biology finds applications in several fields, such as biosensing (pollution
detection),
(drug
discovery/target
identification,
therapeutic
581
delivery/treatment), and production of biofuels, pharmaceuticals and novel biomaterials
582
(construction/optimization of biosynthetic pathways, programming novel functionality
583
and materials). For these applications, new biological devices and systems to regulate
584
gene expression and metabolite pathways are required. Furthermore, external control
585
and triggers for such devices and systems can be designed using adequate gene
586
promoter strategies. Most promoters commonly used in heterologous protein expression
587
are inducible promoters. Chemical inducers can be expensive at laboratory or pilot-plant
588
scales and are often toxic, thus their presence in either the final product or in waste
589
effluents is highly undesirable and requires their disposal/elimination. These restrictions
590
are particularly important in food, pharmaceutical-grade proteins and other products
591
intended for human use, such as therapeutic products. Induction methods having a
592
thermo-regulated expression system with heat shock promoters are even more important
593
for in vivo applications which rely on an external stimulus to the body. The heat shock
594
promoters identified in this study can be used in the construction of new therapeutic
595
bacteria, in which the biosynthetic pathway of a therapeutic agent will be inserted and
596
whose expression will be triggered in situ by heat, emitted by laser therapy or
597
ultrasound. The expression obtained with these heat shock promoters will most likely
598
not reach the same level as the one obtained with other stronger promoters such as T7;
599
however, in situ delivery will likely not require expression levels as high as those
600
necessary in bioreactors. In addition, the promoter sequences can be optimized as
601
demonstrated by the approach herein reported using RBS optimization to improve the
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therapeutics
ip
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580
24
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602
translation efficiency and to reach the desired response and obtain the expected results
603
in the production of novel genes or pathways. Overall, a heat shock approach would be
604
easier to implement and administer and will be safer since no chemicals are involved.
ip
t
605
Acknowledgments
607
This work was supported by FEDER funds through the COMPETE and ON2 program
608
and through National funds of FCT in the scope of the project FCOMP-01-0124-
609
FEDER-027462, PEst-OE/EQB/LA0023/2013, NORTE-07-0124-FEDER-000028 and
610
NORTE-07-0124-FEDER-000027. Financial support for this work was provided by
611
FCT grant SFRH / BD / 51187 / 2010 and SYNBIOBACTHER project (PTDC/EBB-
612
BIO/102863/2008). We acknowledge Nuno Cerca (IBB), Jörg Becker (IGC) and Rui
613
Pereira (CEB-UM) for critical discussions relative to RT-qPCR, microarrays, and heat
614
shock probes results, respectively.
617
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802
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803
Yamamori, T., Ito, K., Nakamura, Y., Yura, T., 1978. Transient regulation of protein
804
synthesis in Escherichia coli upon shift-up of growth temperature. J. Bacteriol. 134,
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1133-1140.
806
Yura, T., Nakanigashi, K., 1999. Regulation of the heat-shock response. Curr. Opin.
807
Microbiol. 2, 153-158.
808
Zhao, K., Liu, M., Burgess, R.R., 2005. The global transcriptional response of
809
Escherichia coli to induced σ32 protein involves σ32 regulon activation followed by
810
inactivation and degradation of σ32 in vivo. J. Biol. Chem. 280, 17758-17768.
812
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813
814
Figure Captions
816
Fig. 1. Roadmap of the microarrays data treatment (A) and the Spearman correlation
817
matrix and hierarchical clustering of 189 genes whose expression is significantly altered
818
following heat shock induction (B). The red colour in the hierarchical clustering
819
represents expression levels above the average of gene expression in all samples, the
820
white colour represents an average expression, and the blue colour represents expression
821
levels below average.
822
Fig. 2. Gene expression profiles (A) and fold change (B) during the temperature up-shift
823
from 37 to 45ºC in flask.
824
Fig. 3. Gene expression profiles (A, B) and fold changes (C, D) during up-shift from 37
825
to 45 ºC (A, C) and 30 to 50ºC (B, D) and subsequent down-shift in bioreactor.
826
Fig. 4. Specific fluorescence intensity after heat shock in E. coli MG1655 DE3
827
transformed with pETduet_GFPmut3b plasmids with different promoters: ibpA
828
promoter (), dnaK promoter () and fxsA promoter () (control – 37 or 30ºC), ibpA
829
promoter (■), dnaK promoter (●) and fxsA promoter (▲) (heat shock – 45 or 50ºC).
830
The three biological replicates for each experiment were performed in different days.
831
The fluorescence values in the figures on the right correspond to the maximum values
832
obtained for each case (1 h after heat shock induction).
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834
Table 1. Set of primers for PCR amplification used in GFP and heat shock promoters
835
for stress probe construction (forward and reverse primers – fw and rev)
ip
t
Primer sequence (5’ > 3’)
AAAAAGGATCCAATGCGTAAAGGAGAAGAACT
AAAAAGGCGCGCCTTATTATTTGTATAGTTCATCCATGCC
AAAAAGCATGCGAGCTCAAAATAACATCATCATTACGTCG
AAAAAGGATCCTCCTTAATAGTTCGCTCGTACGATGTAAAAATGGGTCTG
AAAAAGCATGCGAGCTCATGCCTTGGCTGCGATT
AAAAAGGATCCTCCTCGTCTATTCCGACTCCTATTCGGTCATCATGTGGT
AAAAAGCATGCGAGCTCGAGGATTTCTACCGTAATCTG
AAAAAGGATCCTCCCGATCCGTCGTGCTTTCTTTAGCTTATATTGTGGTCATTAG
cr
Primer Name
GFPmut3b.fw
GFPmut3b.rev
ibpA_P_fw
ibpA_P_rev
dnaK_P_fw
dnaK_P_rev
fxsA_P_fw
fxsA_P_rev
836
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837
Table 2. Set of primers used in RT (reverse primers - rev) and qPCR amplification
838
(forward and reverse primers – fw and rev)
ibpA
fxsA
rrsA - 16S
Primer Sequence (5’ > 3’)
AATCGAACTGTCTTCCGCTCTTCGCACGAGTCACTTTGAT
ACAAAAAGAGCGCACCTATCT
CAGGTTAGCACCACGAACA
CGGCGGAGATGATTAAAAGT
CGGCGGCAATAAAAGTAGA
TAGCGGTGAAATGCGTAGAG
TAATCCTGTTTGCTCCCCAC
ip
t
dnaK
Primer name
dnaK.fw
dnaK.rev
ibpA.fw
ibpA.rev
fxsA.fw
fxsA.rev
16S.fw
16S.rev
cr
Gene name
us
839
840
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Page 37 of 44
Table 3. Heat shock genes induction ratio, expression values (average of two biological
replicates) and t and S test results. Genes marked with "*" represent the ones that are
directly downstream of the σ32 factor dependent promoters. The list presented is ordered
according to the average rate of induction (Test/Control) of the genes. q-value was 0 for
all the genes. In bold it is possible to observe the promoters chosen for posterior
validation.
Induction Ratios
Gene
Expression values
t-test - dChip
S-test SAM
1762978_s_at b3686
ibpB
224.76
22.55
5071.54
1769060_s_at b3687
ibpA*
55.68
106.34
5917.71
ip
t
Gene code
Blattner
1765041_s_at b1322
ycjF
23.82
62.22
1768270_s_at b1321
ycjX*
21.77
102.70
1766244_s_at b3635
mutM*
21.1
42.90
907.10
1762193_s_at b3685
yidE
21.09
21.95
463.43
1765416_s_at b1380
ldhA*
20.12
113.35
1760394_s_at b1594
mlc*
18.78
1763446_s_at b4140
fxsA*
1761819_s_at b0473
Number
Test/Control
Control
Test
t
p- value
S
0.08
8.39
8.95
0.07
9.05
1473.74
14.38
0.04
13.55
2230.95
24.86
0.03
24.53
5.10
0.12
4.92
2.29
0.26
2.23
2282.35
15.42
0.04
14.83
70.46
1324.72
3.83
0.16
3.76
16.89
104.78
1742.74
95.73
0.01
134.67
htpG*
13.02
347.16
4469.62
27.21
0.02
28.35
1766513_s_at b0492
ybbN*
11.15
215.08
2397.06
7.33
0.09
7.18
1761806_s_at b3401
hsp33
9.89
138.25
1365.18
8.22
0.08
7.93
1760965_s_at b1829
htpX*
9.82
379.64
3742.23
16.26
0.04
15.99
1763516_s_at b1060
yceP*
9.44
224.83
2117.17
6.75
0.09
6.60
1764871_s_at b2592
clpB*
9.17
548.94
4955.68
48.22
0.01
679.74
1761621_s_at b3498
prlC*
9.17
176.66
1609.55
10.99
0.06
10.54
1763737_s_at b3594
yibA*
9.01
41.95
376.25
3.78
0.16
3.54
1761422_s_at b3400
yrfH*
8.66
292.95
2517.09
10.39
0.06
10.14
1768615_s_at b3497
yhiQ
8.29
135.89
1127.29
5.65
0.11
5.46
1764204_s_at b3292
zntR
8.24
187.82
1541.54
72.42
0.01
155.46
1766949_s_at b3932
hslV*
8.07
416.46
3351.77
44.71
0.01
46.65
1768518_s_at b4143
groEL
7.90
743.13
5831.04
37.87
0.02
55.53
1763357_s_at b0014
dnaK*
7.59
814.09
6120.95
24.91
0.03
28.07
1769019_s_at b0015
dnaJ
7.08
559.61
3946.50
23.26
0.03
23.91
1767479_s_at b1593
ynfK
7.08
35.13
246.56
4.01
0.16
3.60
1764894_s_at b4142
groES*
6.79
772.53
5188.85
25.73
0.03
148.96
1767623_s_at b0439
lon*
6.76
604.83
4079.96
18.37
0.04
19.04
1762240_s_at b3931
hslU
6.58
518.63
3370.51
24.98
0.03
30.41
1764710_s_at b3293
yhdN*
6.42
369.45
2377.17
38.28
0.02
40.39
1767508_s_at b1814
sdaA*
6.37
536.85
3429.90
7.25
0.09
7.13
1764062_s_at b1379
hslJ
6.30
162.79
1016.68
21.90
0.03
19.99
1768300_s_at b0016
yi81_1
5.74
374.36
2133.52
23.63
0.03
22.85
1768989_s_at b1280
yciM
5.64
217.00
1218.50
18.14
0.04
17.24
1759242_s_at b0630
lipB
5.61
220.41
1235.10
64.04
0.01
70.13
te
d
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us
cr
8.25
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p
841
842
843
844
845
846
38
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d
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Ac
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p
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
Highlights
889
890
891

Heat shock promoters from Escherichia coli were identified and validated by microarrays and
RT-qPCR, respectively
39
Page 39 of 44

893
promoter strengths

895
896
expression

897
898
899
Stress probes designed with those promoters suggest their potential for controlling gene
It is crucial to test and optimize each promoter to obtain translational efficiency that balances
ip
t
894
ibpA, dnaK and fxsA were found to be the strongest heat shock promoters possessing distinct
transcription levels

Escherichia coli heat shock promoters can be explored as synthetic biology tools to trigger gene
cr
892
expression
us
900
an
901
902
Ac
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903
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Page 40 of 44
Figure 1
(B)
(A)
4070 genes
cr
Higher
expression
in TA
us
Filtering Criteria
703 genes
TB expression
higher or equal
Ac
ce
p
128 genes
te
Selection of
significant genes
by S-test
TB expression
much higher to
TA expression
d
M
189 genes
56 genes
TA ~ TB
expression
an
Overexpressed genes
Comparison
Criteria
Selection of
significant genes
by t- test
ip
t
Underexpressed genes
Selection of
E. coli K-12
MG1655 strain
genes
Page 41 of 44
Figure 2
(B)
100
dnaK
ibpA
fxsA
ip
t
10000
1000
100
10
us
1000
dnaK
ibpA
fxsA
Fold Change
107
10000
Expression Level
100000
cr
(A)
10
1
1
Heat Shock Gene
an
Heat Shock - 45 ºC
Ac
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Control - 37 ºC
Page 42 of 44
Figure 3
(B)
100
10
1000
100
10
1
1
M N O P Q R S T U V W
A B C D E F G H I J K L
(D)
M
100
te
d
10
0
1000
A B C D E F G H I J K L
dnaK
ibpA
fxsA
100
10
1
M N O P Q R S T U V W
Ac
ce
p
Fold Change
1000
10000
an
dnaK
ibpA
fxsA
10000
Fold Change
(C)
1
ip
t
1000
10000
us
Expression level x 107
10000
dnaK
ibpA
fxsA
100000
cr
dnaK
ibpA
fxsA
100000
Expression level x 107
(A)
Page 43 of 44
Figure 4
dnaK promoter
37 ºC - 45 ºC, 5 min
25
14
12
20
10
Control
Heat Shock
15
10
4
5
ip
t
6
2
0
0
37 ºC 45 ºC 37 ºC 50 ºC 30 ºC 50 ºC
2
4
Time after induction (h)
6
Heat Shock Experiments
cr
0
ibpA promoter
us
37ºC - 50 ºC, 10 min
6
25
Control
Heat Shock
5
20
an
4
15
3
10
5
0
2
4
Time after induction (h)
10
8
6
4
0,5
0,3
0,2
0,1
0
2
4
Time after induction (h)
Control
Heat Shock
0,4
2
0
37 ºC 45 ºC 37 ºC 50 ºC 30 ºC 50 ºC
Heat Shock Experiments
0,6
ce
pt
12
0
fxsA promoter
30 ºC - 50ºC, 10 min
14
1
6
ed
0
M
2
Ac
Specific Fluorescence Intensity (Fluorescence Intensity OD-1) * 10-3
8
0,0
6
37 ºC 45 ºC 37 ºC 50 ºC 30 ºC 50 ºC
Heat shock Experiments
Page 44 of 44