advanced(project(thesis(in(biology( 30hp( vt2013(675844/fulltext01.pdf · 2013. 12. 4. ·...

Advanced Project Thesis in Biology 30 hp VT2013

Lactobacilli suppress gene expression of key proteins involved in miRNA biogenesis in

HT29 and VK2/E6E7 cells

Annette Jacobsen [email protected]

Örebro University 2013

SCHOOL OF SCIENCE AND TECHNOLOGY

Bacterial modulation of RNAi machinery

1

Abstract

It has previously been demonstrated that lactic acid bacteria are able to influence the innate

immune response of host cells. One way this can be achieved is through modulation of

inflammatory cascades initiated by pattern recognition elements such as toll-like receptors.

Micro RNA can also have an effect on innate immunity, and has been shown to have an

influence in regulation of these pathways in immune responsive cells. However, it is yet to be

determined if the interaction between lactic acid bacteria and host cells involves regulation of

the RNA interference machinery involved in micro RNA biogenesis. Three of the key

proteins responsible for miRNA production and activation are Argonaute 2, Dicer and

Drosha. Together, these are responsible for the processing and activation of miRNA to enable

post-transcriptional gene regulation. In this study we have used quantitative PCR to evaluate

changes in gene expression of these enzymes in HT29 and VK2/E6E7 mucosal epithelial cells

after treatment with Lactobacillus and uropathogenic bacteria. We have found that bacterial

treatment downregulates gene expression of elements responsible for miRNA biogenesis, and

our results showed different responses dependent on the cell line. In addition to this we have

also determined stable reference genes for use in further studies involving this model. Our

findings indicate that modulation of the RNAi machinery might be an important element of

immune regulation by bacterial colonists.

Keywords: Argonaute, Dicer, Drosha


2

Background

RNA interference (RNAi) is a phenomenon that has only been identified in the last 25 years,

but during that time much progress has been made in characterization of the pathways

involved. RNAi involves the mediation of gene expression by endogenous or exogenous

double-stranded RNA (dsRNA) (Peer and Lieberman, 2011). This can result in inhibition of

translation, mRNA degradation, or have a direct influence on gene transcription through

methylation pathways. RNAi is not only important for internal gene regulation and

developmental processes but also has relevance for the interaction between the host and

parasitic organisms such as viruses (Siomi and Siomi, 2009).

Endogenous RNAi is mediated by a number of different classes of short RNA molecules. One

of the most abundant and well characterised of these classes is micro RNA (miRNA). Mature

miRNA are 20-22 base pair long molecules that must be processed by a number of different

enzymes before they are functional. Primary precursors of miRNA (pri-miRNA) begin as

single strand hairpin RNA produced by RNA polymerase II or III (Winter et al., 2009). This

pri-miRNA is then processed by drosha, ribonuclease type III (DROSHA) to form a pre-

mRNA before export from the nucleus. In the cytoplasm, the enzyme dicer 1, ribonuclease

type III (DICER1) cleaves the hairpin region to form the mature double-stranded miRNA (Ma

et al., 2010). The miRNA then associates with an Argonaute protein, which is the catalytic

element of the RNA-induced silencing complex (RISC). As a result of this, one strand of the

miRNA is then degraded, and the remaining strand acts as a guide to determine which mRNA

the RISC associates with (Winter et al., 2009).

Of these enzymes, it is the Argonaute proteins that are of particular interest, as the type of

Argonaute within the RISC influences the type of RNAi that will be carried out (Siomi and

Siomi, 2009). Humans possess eight Argonaute genes, of which four are Argonaute-like

proteins and four are PIWI-like proteins (Höck and Meister, 2008). Of the four Argonaute-

like proteins (AGO1 - 4), only argonaute RISC catalytic compound 2 (AGO2) is associated

with slicer activity (Rand et al., 2004). This involves the ability to act as an endonuclease

against mRNA that is targeted by the RISC, making it a key mediator of the mRNA

degradation pathway of RNAi (Okamura et al., 2004). Further to this, it has been shown that

Argonaute proteins, in some circumstances, are able to act independently from Dicer in

processing miRNA (Yang and Lai, 2010).


3

Regulation through RNAi is believed to have a significant role in gene expression, and

influences a number of different pathways. One pathway that is regulated by miRNA is innate

immunity, particularly through toll-like receptors (TLRs) (Gantier, 2010). Some miRNAs

that are important to this pathway are miR-146, miR-155, and miR-132, all of which are

recognised to be responsive to the lipopolysaccharide (LPS) found in the cell walls of gram-

negative bacteria (Schulte et al., 2013; Zhou et al., 2012). LPS is recognised by host cells

through TLR-4, which then initiates a pro-inflammatory signalling cascade through the

nuclear factor kappa B (NF-kB) signalling pathway (Han and Ulevitch, 2005). The LPS-

sensitive miRNAs in this pathway affect the stability of mRNAs downstream of TLR-4 and

are thought to be a mechanism of preventing excessive activation of TLR-4 and tempering the

immune response (Gantier, 2010).

It has also been demonstrated that some commensal bacteria have the ability to modulate the

innate immune response through interaction with circulating immune cells and epithelial cells

(Howarth and Wang, 2013). This has also been found to occur through interactions between

bacteria and innate immune receptors, including TLRs and nucleotide-binding

oligomerisation domain-containing proteins (NODs) (Clarke et al., 2010; Karlsson et al.,

2012b; O’Hara et al., 2006). Lactic acid bacteria make up a significant proportion of the

commensal flora, and a number of these have been shown to influence the innate immune

response in response to pathogens (Karlsson et al., 2012a; Vizoso Pinto et al., 2009; Wagner

and Johnson, 2012). Recently, miRNA activation has also been shown to be involved in this

response (Archambaud et al., 2012; Singh et al., 2012).

Although there has been progress in determining how RNAi pathways function and the genes

they influence, little is still known about regulation of the RNAi machinery responsible for

miRNA biogenesis. However there is some evidence that these proteins can also be regulated

through activation of TLRs and miRNA pathways (Mallory and Vaucheret, 2010; Massirer

and Pasquinelli, 2013; Wiesen and Tomasi, 2010). In addition to this, it has been found that

certain pathogenic plant bacteria induce an upregulation of Argonaute proteins resulting in the

secretion on antimicrobial peptides (Zhang et al., 2011). These things have led us to

hypothesise that bacterial colonisation of mucosal surfaces could influence the transcription

rate of genes related to human RNAi machinery.


4

To accurately determine changes to transcription using quantitative PCR (qPCR) it is

important to normalise for any differences incurred in the experimental process. One of the

most common methods of doing this is by using reference genes, or internal control genes, as

they are subject to the same experimental conditions as the gene of interest (Huggett et al.,

2005). In this way, background noise from the experimental process can be eliminated and

true biological change can be identified (Derveaux et al., 2010). Selection of reference genes

is important, as they should show very little variation under the experimental conditions in

question. Although a number of traditional reference genes have been used to normalise

qPCR studies, it has now been found that these can vary under certain test conditions or

disease states (de Jonge et al., 2007; Suzuki et al., 2000). As such, reference gene validation

studies should ideally be conducted where an internal control has not already been validated

for a particular model or experimental stress (Bustin et al., 2009; Udvardi et al., 2008).

For both segments of the study, human mucosal cell lines were challenged with both health-

beneficial and pathogenic bacteria. Two bacterial strains with known health-beneficial effects

are Lactobacillus rhamnosus GR-1 and Lactobacillus acidophilus NCFM. L. rhamnosus GR-

1 has been shown to have protective influence against pathogens in the urogenital tract,

whereas L. acidophilus NCFM has more documented effect in the gastrointestinal tract

(Karlsson et al., 2012a; Kim and Mylonakis, 2012; Ouwehand et al., 2009; Wagner, 2012). In

this study we have examined the effect of these two bacteria and heat-killed Escherichia coli

GR-12, a uropathogenic strain, on gene expression of key RNAi machinery elements in

mucosal epithelial cell lines. In doing this we have also determined optimal reference genes

for use in similarly designed studies. We have found that probiotic bacteria tend to

downregulate gene expression of key proteins involved in RNAi biogenesis pathways. This

transcriptional suppression may contribute to increasing understanding of the mechanisms

involved in immune modulation as a result of bacterial colonisation in humans, and how

changes to this balance could impact human immune responses.


5

Methods

Candidate reference gene selection

Eight reference genes - glyceraldehyde-3-phosphate dehydrogenase (GAPDH),

phosphoglycerate kinase 1(PGK1), peptidylprolyl isomerase A (PPIA), ribosomal protein

large P0 (RPLP0), defensin beta 1 (DEFB1), transmembrane protein 222 (TMEM222),

mevalonate kinase (MVK), and polymerase (RNA) II (DNA directed) polypeptide A, 220 kDa

(POLR2A) - were selected as previously described (Jacobsen, 2013). In addition actin, beta

(ACTB), another commonly used reference gene whose primary function involves

cytoskeletal structure, was also included. The final two candidates from the miRNA biogenesis

pathway, DICER1 and DROSHA, were included in this analysis after initial results indicated

relatively stable expression in the HT29 cell line.

Primer design and validation

Reference gene primers already assessed in earlier analyses (GAPDH, PPIA, hBD1,

TMEM222, POLR2A, MVK, RPLP0, and PGK) were designed and validated as previously

described (Jacobsen, 2013). New primers introduced for this study (ACTB, DICER1,

DROSHA, and AGO2) were designed and checked for specificity against human mRNA using

the NCBI/Primer-BLAST tool (http://www.ncbi.nlm.nih.gov/tools/primer-blast/). Beacon

Designer (http://www.premierbiosoft.com/qpcr/index.html, Premier Biosoft, California, USA)

was used to assess potential primer-dimer associations, and UNAFold

(http://eu.idtdna.com/UNAFold, Integrated DNA Technologies, Iowa, USA) was used to

determine the likelihood of amplicon secondary structures. Primers were built by Eurofins

MWG Operon (Ebersberg, Germany), and a complete list of their sequences and locations are

shown in Table 1.

Conventional PCR, followed by electrophoresis on 1.5% agarose gels, was used to establish

amplification of a product of the correct size. Eight ten-fold serial dilutions of PCR product

were assessed using quantitative PCR (qPCR), including melting curve analysis, to determine

linear range, efficiency and specificity as previously described (Jacobsen, 2013).


6

Bacterial strains and culture conditions

Lactobacillus strains

Lactobacillus strains were prepared as previously described (Jacobsen, 2013; Tewelde, 2011).

Both strains (Lactobacillus acidophilus NCFM and Lactobacillus rhamnosus GR-1) were

grown under anaerobic conditions in GasPakTM EZ Anaerobe Generating Pouch Systems

(BD, Franklin Lakes, USA) on de Man Rogosa Sharp (MRS) agar (Becton Dickinson, Sparks,

USA) at 37°C for 24 hours. Then, one colony was used to inoculate a 15 mL falcon tube

containing MRS media, and incubated upright without shaking for a further 24 hours at 37°C.

A new falcon tube containing MRS broth was then inoculated with 1% of the overnight

culture and incubated for another 24hrs.

To quantify the bacteria a colony forming unit count was conducted. First, centrifugation of 1

mL of the bacterial suspension at 3000 x g for 10 minutes was performed, then the pellet was

washed twice and resuspended in 1 mL of phosphate buffer saline (PBS pH 7.4). Finally,

serial dilutions were plated on MRS agar and left to incubate overnight in anaerobic pouches

at 37°C.

Heat killed Escherichia coli GR-12

Heat killed (HK) E. coli GR-12 were prepared as previously described (Jacobsen, 2013;

Tewelde, 2011). In brief, bacteria were grown for 24 hours at 37°C on Luria Bertani agar

(LB; Sigma-Aldrich, St-Louis, USA), then one colony was used to inoculate 5 mL of LB

broth. This was shaken at 200 rpm overnight at 37°C, after which the colony was pelleted by

centrifugation for 10 minutes at 3000 x g. The bacteria were washed twice and resuspended in

500 µL sterile PBS, then heated in a water bath for 1 hour at 70°C. Heat-treated bacterial cells

were plated in LB agar and incubated overnight to confirm there were no viable cells.

Quantification of bacteria was achieved by plating of serial dilutions and counting of CFUs

alongside the heat-killing step.


7

Epithelial cell culture

Epithelial cell lines were maintained as previously described (Jacobsen, 2013; Tewelde,

2011). HT-29 human intestinal epithelial cells were grown in McCoy’s 5A media (Hyclone,

Utah, USA) with 10% foetal bovine serum (FBS; Hyclone, Utah, USA). VK2/E6E7 human

vaginal epithelial cells were cultured in Keratinocyte-Serum Free medium with additional

calcium chloride (end concentration 0.4 mM), 0.05 mg/mL bovine pituitary extract and 0.1

ng/mL human recombinant endothelial growth factor 1-53 (all from Life Technologies,

Stockholm, Sweden). Cells were subcultured at 70% confluence with 0.25% trypsin, 0.53mM

EDTA. Cells were incubated at 37°C in 5% CO2 and stock cultures were maintained in liquid

nitrogen in growth medium containing 5% DMSO.

Cell treatment

For treatment, cells were seeded into 6 well plates (BD, Franklin Lakes, USA) at a density of

3x105 cells/well, and allowed to grow to a density of 3x106 cells/well, or approximately 90%

confluence. For HT29, cells were treated as previously described, with an additional two

treatment groups being utilised for the reference gene analysis (Jacobsen, 2013; Tewelde,

2011). VK2/E6E7 cells were treated as per HT29 cells. In brief, cells were changed to fresh

media with either 10 ng/mL TNFα, HK E. coli GR-12 (equivalent to 3x107 CFU/mL), L.

rhamnosus GR-1 (108 CFU/mL), L. acidophilus NCFM (108 CFU/mL), or with a combination

of HK E. coli GR-12 and one of the Lactobacillus strains. Cells were then incubated for 3

hours at 37°C in 5% CO2. Experiments were repeated three times for the VK2/E6E7 cell line

(n = 3), and five times for the HT29 cell line (n=5).

RNA extraction and cDNA synthesis

RNA was extracted as previously described using a Nucleospin II kit (Macherey-Nagel,

Düren, Germany) as per manufacturers instructions (Jacobsen, 2013; Tewelde, 2011). A

NanoVue Spectrophotometer (GE Healthcare, Buckinghamshire, UK) was used to estimate

quantity and purity, and then agarose gel electrophoresis was used to assess RNA quality. A

qScript kit (Quanta Bioscience, Gaithersburg, USA) was used to synthesise cDNA from 1 µg

of the extracted RNA as per the manufacturer’s instructions, and then diluted ten times before

use in the qPCR reaction.


8

Conventional polymerase chain reaction

Conventional PCR was performed using a Hybaid PCR Sprint thermocycler (Thermo

Scientific, Massachusetts, USA) as previously described (Jacobsen, 2013). Maxima Hot Start

Taq Buffer (10X; Thermo Scientific, Massachusetts, USA) was used to prepare 25 µL

reactions, using 2.5 mM MgCl2, 0.3 µM of forward and reverse primers, 0.2 mM dNTP, 1 µM

Maxima Hot Start Taq DNA Polymerase, and 25 pg template cDNA. The reaction was begun

with denaturation at 95°C for 4 minutes, followed by 40 cycles of denaturation for 30 seconds

at 95°C, annealing for 30 seconds at 60°C, and extension for 30 seconds at 72°C. A final five

minute extension at 72°C concluded the reaction. Gel elctrophoresis on 1.5% agarose was

used to confirm amplification of a product of the correct size.

Quantitative polymerase chain reaction

The qPCR reactions were performed as previously described (Jacobsen, 2013). An ABI

Prism® 7900HT Sequence Detection System (Applied Biosystems, California, USA) was used

for the qPCR reaction. A Maxima SYBR Green qPCR Master Mix (x2) (Thermo Scientific,

Massachusetts, USA) was used to prepare 15 µL reactions with 267nM final concentration of

ROX, 25 pg template cDNA, and a 0.2 µM end concentration of each of the forward and

reverse primers (except for ACTB). For ACTB, primer concentrations needed to be optimised

and were used at 0.2 µM of forward primer and 0.07 µM of reverse primer, with all other

elements of the reaction staying the same. The reaction was initiated with 10 minutes of

denaturation at 95°C, then 40 cycles of denaturation at 95°C for 15 seconds and

annealing/extension at 60°C for 60 seconds. A dissociation step was performed at the

conclusion of all reactions so as to collect melting curve data, and both no template and no

reverse transcriptase controls were performed. For experiments related to assessing gene

expression of miRNA biomachinery, PGK1 was used to normalise the HT29 data set and

RPLP0 was used to normalise the VK2/E6E7 data set.

Data analysis

Real-time PCR Miner (http://www.miner.ewindup.info/; Version 4.0) was used to generate

primer efficiency and Cq values of the candidate reference genes, using the raw fluorescence


9

data generated during the qPCR reactions. Assessment of candidate gene performance was

performed as previously described (Jacobsen, 2013). In brief, we used four different

algorithms: geNorm Plus, BestKeeper, Normfinder and the comparative ΔCq method.

Corrections for efficiency using the values generated by PCR Miner were performed prior to

analysis. Overall ranking of reference gene candidates was determined using the geometric

mean of the rankings generated from the individual algorithms.

In addition to the previous study, we included more reference gene candidates (ACTB,

DICER1, and DROSHA) and more test conditions (L. acidophilus NCFM with HK E. coli

GR-12 and L. rhamnosus GR-1 with HK E. coli GR-12) so as to best represent the conditions

of our current model. This included all treatments performed, excepting with TNFα. The data

sets were also separated so we could compare the effects of different Lactobacilli on our

model. After analysis these data sets were compared to assess potential differences between

both cell lines and treatment groups.

Statistical analysis

For analysis of gene expression in the RNAi pathway comparison of means was performed

using ANOVA combined with Tukey’s range test. All data sets had n = 3 except the

HT29/AGO2 data set, which had n = 5. HT29 expression was normalised against PGK1 and

VK2/E6E7 expression was normalised against RPLP0. Data is presented as mean ± s.d. and

statistical significance was taken as p < 0.05.


10

Results

Reference gene selection is cell line specific, and is not greatly influenced by the algorithm

used

Our analysis found PGK1 to be the best selection for the HT29 data set and RPLP0 to be

more appropriate for the VK2/E6E7 data set. All four algorithms used were in agreement with

the best and worst candidates in both data sets (Tables 2,3). Although there were some

differences in the rankings between the algorithms, all correlated well in elimination of the

less suitable reference genes from analysis. Overall, we found a strong comparison in

rankings between the GenormPLUS and BestKeeper results, and likewise between the

Normfinder and comparative ΔCq method.

Reference gene selection was more strongly influenced by the cell line than by the species of

Lactobacillus used in treatment

To fully determine reference gene stability we subdivided our data set to determine if there

was any difference in reference stability dependent on the species of Lactobacillus we were

using. We found there to be very little difference in reference gene suitability as a result of the

species used for treatment in both cell lines (Figs. 1A,B). The only notable exception was

PPIA in the VK2/E6E7 data set, which ranked as the best choice for L. rhamnosus, but only

7th best for L. acidophilus. When the full data set for this cell line was analysed PPIA was

placed in 4th position overall.

To further address the question of cell line specificity versus treatment specificity, we used

radar graphs to illustrate the differences between the comprehensive and sub-divided rankings

of both our cell lines. This showed clear differences between the cell lines assessed (Fig. 1C).

ACTB and TMEM222 ranked highly for the VK2 data set, but flagged as poor choices for the

HT29 cell line and, conversely, DICER1 was indicated as a good selection for HT29 but was

one of the lowest ranked for VK2/E6E7 cells.


11

Treatment with Lactobacillus is associated with decreased AGO2 mRNA expression

We found significant changes to expression of AGO2 in response to treatment with both

Lactobacillus strains (Fig. 2). For HT29, this was only seen in response to treatment with

Lactobacillus alone (from control: NCFM, p = 0.040; GR-1, p = 0.002). In the VK2/E6E7 cell

line, reduced expression of AGO2 was found whether live Lactobacillus or HK E. coli were

used (from control: NCFM, p = 0.009; GR-1, p = 0.006; GR-12, p = 0.001; NCFM/GR-12, p

< 0.001; GR-1/GR-12 = 0.004). Further to this, we also found an increased expression of

AGO2 in response to TNFα in the HT29 cell line (p = 0.046), which is consistent with

previous findings out of this laboratory.

mRNA expression of DICER1 is reduced after treatment with Lactobacillus in VK2/E6E7 cells

We found significant reductions of DICER1 expression in VK2/E6E7 cells in all bacterial

treatments involving Lactobacilli (Fig. 3B; NCFM, p = 0.004; GR-1, p = 0.0203; NCFM/GR-

12, p = 0.001; GR-1/GR-12, p = 0.0048). Interestingly, the most statistically significant effect

was seen after treatment with HK E. coli (GR-12, p < 0.001). Further to this, the HK E. coli

treatment was also the only one to significantly affect DICER1 transcription in the HT29 cells

(Fig. 3A; GR-12, p = 0.048).

Expression of DROSHA mRNA is reduced after bacterial treatment

We found small but significant decreases in DROSHA mRNA after treatments in both cell

lines (Fig. 4). In the HT29 data set, this included all bacterial treatments except the L.

acidophilus group (GR-1, p = 0.004; GR-12, p = 0.039; NCFM/GR-12, p = 0.008; GR-1/GR-

12, p = 0.005), and in the VK2/E6E7 cells this included all except the L. rhamnosus group

(NCFM; p = 0.023; GR-12, p = 0.004; NCFM/GR-12, p = 0.007; GR-1/GR-12, p = 0.029).

Both these groups, however, had relatively large standard deviations.

In the HT29 data set, we also found a small but significant reduction in DROSHA expression

after treatment with TNFα (p = 0.017).


12

Discussion

Both miRNA pathways and the interaction between commensal bacteria and the host are now

recognised as important aspects in the control of innate immunity, but only recently have

these two pathways been linked together (Archambaud et al., 2012; Singh et al., 2012). It has

also now been determined that the regulation of proteins involved in miRNA biogenesis can

influence the effect of RNAi (Betancur and Tomari, 2012; Cheloufi et al., 2010). In this study,

we have used qualitative PCR analysis to investigate whether gene expression of key proteins

involved in miRNA biogenesis in human mucosal cells is affected by bacterial treatment. Our

results indicate a transcriptional repression of the miRNA machinery in two human epithelial

cell lines after treatment with different strains of Lactobacillus. Further to this, we have

observed a downregulation of these genes after treatment with HK E. coli GR-12, particularly

in the VK2/E6E7 cell line. Also, we have been able to identify stable reference genes for use

in further studies involving this model.

Our findings also indicate cell-specific differences in the nature of the transcriptional

repression. In the VK2/E6E7 cell line, all three enzymes involved in the canonical pathway of

miRNA processing showed significant downregulation after bacterial treatment. We were

unable to find a significant downregulation of DROSHA after treatment with L. rhamnosus

GR-1, however due to the larger standard deviation seen here it is difficult to draw any

conclusion. For the HT29 data set we found a significant downregulation of AGO2 only

where the Lactobacillus alone were used. There was no significant effect on DICER1 as a

result of treatment involving either of the lactic acid bacteria, however a small but significant

response was found in response to HK E. coli GR-12. A significant downregulation of

DROSHA was observed as a result of most bacterial treatments, however it should be noted

that the magnitude of these changes was small. Further studies will need to be performed to

determine if there is any biological significance as a result of this.

These different effects in gene regulation seen as a result of our treatments are most likely due

to differences in the cell lines used as models. As they are representative of different

anatomical locations (colon and vagina) it is quite possible that it is simply a result of the

different signalling pathways and receptors that are necessary for their individual

physiological roles. However it should not be discounted that the method of immortalisation

of these cell lines may also have an impact on the responsiveness of the cells to treatment.


13

HT29 cells are colon carcinoma cells whereas the VK2/E6E7 cell line has been transformed

using the Human papillomavirus (HPV), and both these things may affect normal cell

physiology (Futscher, 2013; Kohlhof et al., 2009; Romilda et al., 2012).

The different affects on transcription of our targets may also indicate differences in the way

miRNA biogenesis is affected in these cell lines. Although sequential processing of miRNA

through DROSHA, DICER1 and AGO2 is the most well known mechanism of miRNA

biogenesis, alternate pathways have now been identified. One example of this that is

particularly relevant to the HT29 model involves biogenesis of miR-451. Processing of this

miRNA involves a DICER-independent mechanism, being processed directly by AGO2 after

being exported from the nucleus (Cheloufi et al., 2010). miR-451 is involved in a number of

different biological pathways, one of which involves regulation of P-glycoprotein 1, an ATP-

binding cassette transporter protein that is extensively distributed in the gastrointestinal

epithelium, and another being direct regulation of macrophage migration inhibitory factor

(Bandres et al., 2009; Bitarte et al., 2011). Both these factors can have an influence in the

innate immune response. This relationship may provide further information on location

specific colonisation mechanisms of Lactobacilli, and provides an interesting focus of

investigation for further study in this model.

We also noted cell-specific differences in the gene expression regulation of the RNAi

machinery after treatment with the HK E. coli. As E. coli GR-12 is a uropathogenic strain,

this response may provide more information about their ability to cause infection in the

genitourinary tract. Although we were not using viable bacteria, elements able to regulate an

immune response are still present after the heat-killing process. Most commonly, this is used

as a model for LPS/Lipid A stimulation of TLR4, but elements such as flagellum, fimbriae

and pili may also activate innate immunity through activating TLR5 or TLR2 (Kawai and

Akira, 2010). This may also provide an interesting avenue for further investigation.

In both cell lines, further studies are required before any strong conclusions are made. Our

initial studies have focused on changes to mRNA levels only, and this does not always

provide an accurate picture of what is occurring at a functional level. This is particularly

relevant to our data, as although statistical significance was found in a number of cases, often

this only represented small absolute changes. A first step to determine the biological

significance of our results would be analysis of protein expression in response to our


14

treatments. Further to this, it would be worthwhile conducting a time point analysis of both

protein and mRNA to determine where maximal effect of treatment occurred. If these data

provide evidence of a biological significance, further indication could be done to investigate

the specific pathways involved. This could include screening of miRNA expression, or the

use of inhibitors to investigate the activation of specific TLRs.

If these findings are supported by functional analyses and become biologically significant,

these results could help to increase our understanding of the mechanisms involved in bacterial

regulation of host gene expression, and its impact on immune modulation. This could prove

important when considering using health-beneficial bacteria as prophylactic or active

treatment for disease, or in understanding changes that may occur when commensal

populations are disrupted, such as after antibiotic use (Clemente et al., 2012; Wolvers et al.,

2010). A general suppression of immune responses by colonising bacteria is not unusual in

itself, but identification of new pathways involved in this process creates novel targets for

drugs designed to increase the body’s own immune response (Hancock et al., 2012; Round et

al., 2010). This could become particularly relevant if there is found to be significant

differences in the way pathogenic bacteria affect miRNA biogenesis, but could also provide

new avenues of treatment for a range of diseases that are believed to occur as a result of

disruption of the interaction between the host and commensal bacteria.

As our hypothesis focussed on the changes in gene expression, we felt it important that the

study was normalised with an appropriate reference gene. There are a number of common

reference genes used for such studies, including glyceraldehyde-3-phosphate dehydrogenase

(GAPDH) and actin, beta (ACTB), but we were unable to locate evidence of thorough

validation studies being performed specific to our model (Bahrami et al., 2011; Libby et al.,

2008; Węglarz et al., 2006). As commonly used reference genes such as these have now been

found to be unsuitable for a number of different studies, this led us to question whether they

were the most suitable choice for our model (de Jonge et al., 2007; Huggett et al., 2005). To

determine this we performed an in-depth validation study, using four different algorithms, to

compare expression pattern of these two genes against nine other reference gene candidates.

This was an extension on a previous reference gene study in which eight reference gene

candidates were assessed (Jacobsen, 2013). After doing this we did note some changes in the

overall rankings of our reference gene candidates compared to the previous study, which may

be as a result of either the newly introduced genes (ACTB, DICER1, and DROSHA) or the


15

newly introduced test conditions (L. acidophilus NCFM with HK E. coli GR-12 and L.

rhamnosus GR-1 with HK E. coli GR-12). However, most of the changes were minor, with

the exception of RPLP0 moving from a poor ranking to a middle ranking in the HT29 data

set, and GAPDH improving from last ranked to poor in the VK2/E6E7 data set, and from well

ranked to middle ranked in the HT29 results. Also, the additional cDNA synthesis performed

to complete the analysis may have a minor influence on the overall ranking, despite

calibration. In general, we found increasing the data set for the reference gene analysis tended

to make results more comparable across the different algorithms. In both data sets the best and

worst candidates were the same in all four methods applied. For the HT29 data set PGK1 was

found to be most suitable and RPLP0 was found to be the best choice for the VK2/E6E7 cells.

Two of the genes examined in the miRNA processing study were included into the reference

gene study, DICER1 and DROSHA, as they were found to show minimal expression changes

as assessed in an unnormalised pilot study, particularly in relation to the HT-29 data set.

Inclusion of these genes in the reference gene analysis served two purposes. Firstly, if our

hypothesis in regards to gene regulation of the miRNA processing machinery was wrong and

these genes were stable expressed, it could have provided novel reference gene candidates for

further studies involving this model. Secondly, if our hypothesis was correct, and there was

some change to gene expression, a second method of analysing gene stability could help to

clarify and strengthen these results. This was especially pertinent when changes to gene

expression levels were small. We found DROSHA to be a poor choice as a reference gene

candidate for both data sets, and DICER1 to be a poor choice for the VK2/E6E7 data set. This

is in line with the final normalised expression analysis, which showed significant changes to

these genes under most experimental conditions. Interestingly, DICER1 was found to be the

second best candidate for the HT29 data set. This is supported by the normalised expression

analysis, in which only treatment with E. coli GR-12 showed a significant expression change.

It is worth noting the fold change here is small (-0.2±0.12 fold) and only just statistically

significant (p = 0.048).

In addition to assessing overall stability, we also performed an additional two analyses using

all four algorithms for each data set where only one Lactobacillus strain was involved (e.g.

control, NCFM, GR-12, and NCFM/GR-12 combined compared to comtrol, GR-1, GR-12,

and GR-1/GR-12 combined). This was done to try and determine whether the strain of

Lactobacillus of the cell type being investigated had a larger impact on reference gene


16

suitability. Overall, our results also indicated that the type of cell line had a greater effect than

the species of Lactobacillus we were investigating. This suggests that these genes may also

prove suitable for normalising other studies in these cell lines where different lactic acid

bacteria are used as the challenge. Depending on the sensitivity required, a second reference

gene could be added to increase the robustness of the normalisation.

In conclusion, our studies indicate a general suppression of transcription of key proteins

involved in miRNA transcription. Although further studies are required to substantiate these

findings, this may help in better characterising the pathways involved in immune modulation

and colonisation strategies of commensal and pathogenic bacteria in specific mucosal cell

lines.


17

Acknowledgements

Sincere thanks to my supervisor, Dr Nikolai Scherbak (School of Life Science and

Technology, Örebro Life Science Centre), for his support and encouragement during my time

at Örebro University, and for giving me the opportunity to be involved in such an interesting

project. Thanks also to Bisrat Tewelde, who was responsible for cell treatments and sample

preparation in relation to this study.


18

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Table 1. Primer sequences used for qPCR analysis. Target Gene Accession

Number Primer Sequence (5'-3') Tm *

(°C) Location

(exon) Product Size (bp)

Design Method

POLR2A* NM_000937 F: AAGTTCAACCAAGCCATTGCG R: GACACACCAGCATAGTGGAAGG

62.00 62.69

19-20 20

116 QuantPrime

TMEM222* NM_032125 F: TCTACGGGAAGTACGTCAGC R: CCATCACCGGAGGTTAAAGACC

60.63 62.44

2-3 3

110 QuantPrime

MVK* NM_000431 F: AAGGTAGCACTGGCTGTATCC R: CCAATGTTGGGTAAGCTGAGG

61.28 60.66

6-8 8

95 QuantPrime

DEFB1* NM_005218

F: GTTCCTGAAATCCTGGGTGTTG R: CTGTGAGAAAGTTACCACCTGAG

61.20 60.44

1 1-2

114 QuantPrime

PPIA* NM_021130 F: GCTTGCTGGCAGTTAGATGTC R: AGAGGTCTGTTAAGGTGGGC

61.32 60.79

5 5

73 Primer-BLAST

GAPDH* NM_002046 F: ATTTGGCTACAGCAACAGGG R: TCAAGGGGTCTACATGGCA

60.93 61.17

10 10

192 Primer-BLAST

RPLP0* NM_001002 F: ACAATGGCAGCATCTACA R: GTAATCCGTCTCCACAGA

55.50 54.61

6 7

191 Sigma-Aldrich**

PGK1* NM_000291 F: GAGATGATTATTGGTGGTGGAA R: AGTCAACAGGCAAGGTAATC

57.62 57.09

7 8

160 Sigma-Aldrich**

AGO2 NM_012154 F: CATGGACGCCCACCCCAATCG R: CCACTTTTCCCAACCCGCTCGT

67.15 66.71

15 16-17

333 Primer-BLAST

DICER1 NM_177438 F: GTACGACTACCACAAGTACTTC R: ATAGTACACCTGCCAGACTGT

57.13 59.36

24 25

253 Primer-BLAST

DROSHA NM_013235 F; GCAGCGCAAAGGCAAGACGC R: AGGCGGGGAGACTGTGATCCG

65.00 66.67

10 11

131 Primer-BLAST

ACTB NM_001101.3 F: CACACAGGGGAGGTGATAGC R: GACCAAAAGCCTTCATACATCTCA

59.82 59.06

6 6

169 Primer-BLAST

POLR2A - polymerase (RNA) II (DNA directed) polypeptide A, TMEM222 - transmembrane protein 222, MVK - mevalonate kinase, DEFB1 - defensin beta 1, PPIA - peptidylprolyl isomerase, GAPDH - glyceraldehyde-3-phosphate dehydrogenase, RPLP0 - ribosomal protein large P0, PGK1 - phosphoglycerate kinase 1, AGO2 - argonaute RISC catalytic compound 2, DICER1 - dicer 1, ribonuclease type III, DROSHA - drosha, ribonuclease type III, ACTB – actin, beta, F - forward, R - reverse * sequences taken from previous study (Jacobsen, 2013) ** Tm values for primers generated using primer-BLAST for specific conditions of the qPCR assay in this study *** Sigma-Aldrich primers not designed by this laboratory


23

Table 2. Comprehensive ranking of reference gene candidates by calculation of a geometric mean for HT-29 data set. Genorm+ BestKeeper Normfinder ΔCq Previous

ranking1 New

ranking2 Geometric

mean

PGK1 PGK1 PGK1 PGK1 PGK1 PGK1 1.000

RPLP0 PPIA DICER DICER GAPDH DICER* 2.632

PPIA DICER PPIA PPIA PPIA PPIA 2.711

DICER POLR2A POLR2A GAPDH POLR2A RPLP0 4.356

GAPDH RPLP0 MVK POLR2A TMEM222 POLR2A 5.030

DROSHA MVK RPLP0 RPLP0 MVK GAPDH 5.595

MVK GAPDH GAPDH MVK RPLP0 DROSHA* 5.856

POLR2A DROSHA DROSHA DROSHA DEFB1 MVK 6.192

TMEM222 TMEM222 TMEM222 TMEM222 TMEM222 9.000

ACTB ACTB ACTB ACTB ACTB* 10.00

DEFB1 DEFB1 DEFB1 DEFB1 DEFB1 11.00 Genes are ranked with the most stable positioned first (top) and the least stable positioned last (bottom). * newly introduced candidate for this study 1 Rankings generated from previous study (Jacobsen, 2013) 2 Rankings genrated from the current study


24

Table 3 . Comprehensive ranking of reference gene candidates by calculation of a geometric mean for VK2/E6E7 data set.

Genorm+ BestKeeper Normfinder ΔCq Previous ranking1

New ranking2

Geometric mean

RPLP0 RPLP0 RPLP0 RPLP0 TMEM222 RPLP0 1.000

ACTB ACTB TMEM222 TMEM222 PPIA TMEM222 3.440

GAPDH GAPDH PPIA PPIA RPLP0 ACTB* 3.600

PGK1 PGK1 POL2RA POLR2A PGK1 PPIA 4.054

TMEM222 PPIA PGK1 PGK1 DEFB1 GAPDH 4.409

PPIA POLR2A GAPDH ACTB POLR2A PGK1 4.472

POLR2A TMEM222 ACTB GAPDH MVK POLR2A 5.091

DEFB1 DEFB1 DICER DEFB1 GAPDH DEFB1 8.239

MVK MVK DEFB1 DICER DICER* 9.212

DICER DICER MVK MVK MVK 9.487

DROSHA DROSHA DROSHA DROSHA DROSHA* 11.00 Genes are ranked with the most stable positioned first (top) and the least stable positioned last (bottom). * newly introduced candidate for this study 1 Rankings generated from previous study (Jacobsen, 2013) 2 Rankings genrated from the current study


25

Figure Legends

Figure 1 Comparison of reference gene rankings based on cell type and bacterial

challenge: Gene names are around the outside of the wheel. The further away from the centre

the line is the better the reference gene. Where lines are close together along the spoke, a

better correlation between analyses is indicated. A. HT29 data set. Blue: combined data set.

Red: Lactobacillus acidophilus NCFM used as the commensal strain. Green: Lactobacillus

rhamnosus GR-1 used as the commensal strain. B. VK2/E6E7 data set. Blue: combined data

set. Red: L. acidophilus NCFM used as the commensal strain. Green: L. rhamnosus GR-1

used as the commensal strain. C. Comparison of combined data sets of the HT29 and

VK2/E6E7 cell lines. Blue: HT29. Red: VK2/E6E7.

Figure 2 Changes to mRNA expression of AGO2: Data is shown as mean±s.d. All

significance indicated is relative to control. C = control, TNF = TNFα, N = Lactobacillus

acidophilus NCFM, Gr1 = Lactobacillus rhamnosus GR-1, and Gr12 = heat-killed

Escherichia coli GR-12 A. In HT29 cells we found a significant decrease in mRNA

expression of AGO2 after treatment with L. acidophilus NCFM and L. rhamnosus GR-1. We

also noted a significant increase after treatment with TNFα. (control -> TNF, p = 0.0459;

control -> N, p = 0.0399; control -> Gr1, p = 0.0017; Gr-1 -> Gr12, p = 0.0441; n = 5; * p <

0.05, ** p < 0.01)

B. . In VK2/E6E7 cells we found a significant decrease in mRNA expression of AGO2 after

all bacterial treatments (control -> NCFM, p = 0.0089; control -> Gr-1, p = 0.0058; control ->

Gr12 = 0.0011; control -> NCFM/Gr12, p = 0.0008; control -> Gr-1/Gr12, p = 0.0041; n = 3;

** p < 0.01, *** p < 0.001)

Figure 3 Changes to mRNA expression of DICER1: Data is shown as mean±s.d. All




expression of DICER1 only after treatment with heat-killed E. coli GR-12 (control -> Gr12, p


26

= 0.0478; n = 3; * p < 0.05) B. In VK2/E6E7 cells we found a significant decrease in mRNA

expression of DICER1 after all bacterial treatments (control -> NCFM, p = 0.0039; control ->

Gr-1, p = 0.0203; control -> Gr12, p = 0.0002; control -> NCFM/Gr12, p = 0.0014; control ->

Gr-1/Gr12, p = 0.0048; n = 3; * p < 0.05, ** p < 0.01, *** p < 0.001)

Figure 4 Changes to mRNA expression of DROSHA: Data is shown as mean±s.d. All




expression of DROSHA only after all bacterial treatments, except L. acidophilus NCFM. We

also noted a significant decrease in DROSHA mRNA after treatment with TNFα (control ->

TNF, p = 0.0172; control -> Gr-1, p = 0.0038; control -> Gr12, p = 0.0388; control ->

NCFM/Gr12, p = 0.0077; control -> Gr-1/Gr12, p = 0.0047; n = 3; * p < 0.05. ** p < 0.01) B.

In VK2/E6E7 cells we found a significant decrease in mRNA expression of DICER1 after all

bacterial treatments except L. rhamnosus GR-1 (control -> NCFM, p = 0.0232; control ->

Gr12, p = 0.0044; control -> NCFM/Gr12, p = 0.0065; control -> Gr-1/Gr12, p = 0.0294; n =

3; * p < 0.05. ** p < 0.01)


27

Figure 1 A. HT29

B. VK2

C. HT29 vs VK2

0 2 4 6 8 10 12

PGK1

DICER

PPIA

RPLP0

POLR2A

GAPDH DROSHA

MVK

TMEM222

ACTB

DEFB1

All

NCFM

Gr-‐1

0 2 4 6 8 10 12

PGK1

DICER

PPIA

RPLP0

POLR2A

GAPDH DROSHA

MVK

TMEM222

ACTB

DEFB1

All

NCFM

Gr-‐1

0 2 4 6 8 10 12

PGK1

DICER

PPIA

RPLP0

POLR2A

GAPDH DROSHA

MVK

TMEM222

ACTB

DEFB1

HT29

VK2


28

Figure 2 A. HT29

B. VK2/E6E7

0 0.2 0.4 0.6 0.8 1

1.2 1.4 1.6 1.8 2

C TNF N Gr1 Gr12 N/Gr12 Gr1/Gr12

Fold Change -‐ AGO

2

Treatment

**

0 0.2 0.4 0.6 0.8 1

1.2 1.4 1.6 1.8 2


Fold Change -‐ AGO

2

Treatment

** ** ** ***

**

*

*


29

Figure 3 A. HT29

B. VK2/E6E7

0 0.2 0.4 0.6 0.8 1

1.2 1.4 1.6 1.8 2


Fold Change -‐ DICER1

Treatment

*

0 0.2 0.4 0.6 0.8 1

1.2 1.4 1.6 1.8 2


Fold Change -‐ DICER1

Treatment

** *

*** ** **


30

Figure 4 A. HT29

B. VK2/E6E7

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2


Fold Change -‐ DRO

SHA

Treatment

* **

* ** **

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2


Fold Change -‐ DRO

SHA

Treatment

* ** ** *

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