ecophysiology intestinal

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Microbial Eco-Physiology of the HumanIntestinal Tract:

A Flow Cytometric Approach


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Promotor: Prof. dr. W. M. de VosHoogleraar Microbiologie

Wageningen Universiteit

Co-promotoren: Dr. T. AbeeUniversitair Hoofddocent bij de leerstoelgroep Levensmiddelen-microbiologie

Wageningen Universiteit

Dr. E. E. VaughanLead ScientistUnilever Research and Development, Vlaardingen

Promotiecommissie : Prof. dr. ir. M. H. ZwieteringWageningen Universiteit

Prof.dr. J. BindelsWageningen Universiteit

Dr. G. W. WellingUniversiteit Groningen

Dr. J. Dor

Institut National de Recherche AgronomiqueCentre de recherche de Jouy-en-Josas, France

Dit onderzoek is uitgevoerd binnen de onderzoekschool VLAG


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Microbial Eco-Physiology of the Human

Intestinal Tract:

A Flow Cytometric Approach

Kaouther Ben Amor

Proefschrift

Ter verkrijging van de graad van doctor

op gezag van de rector magnificus

van Wageningen Universiteit,

Prof. dr. ir. L. Speelman,in het openbaar te verdedigen

op vrijdag 10 september 2004

des namiddags te vier uur in de Aula


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This work was carried out at Wageningen University, Department of Agro-Technology andFood Sciences, Laboratory of Food Microbiology and Laboratory of Microbiology.

K. Ben Amor. Microbial Eco-physiology of the human intestinal tract: A flow cytometricapproach. PhD thesis. Wageningen University, The Netherlands, 2004. With summaries inDutch and English.

Key words: gastrointestinal tract, fecal microbiota, probiotics, 16S rRNA, Fluorescent In-SituHybridization (FISH), flow cytometry, cell sorting, fluorescent probes, viability, microbialphysiology, Bifidobacteria, Inflammatory Bowel Disease (IBD).

ISBN:90-8504-042-6


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TABLE OF CONTENTS

Chapter 1:

General introduction .............................................................................................................................. 1

Chapter 2:Application of flow cytometry in microbiology............................................................................... 21

Chapter 3:Quantification of uncultured Ruminococcus obeum-like bacteria in human fecal

samples with fluorescent in situhybridization and flow cytometry

using 16S ribosomal RNA targeted probes....................................................................................... 49

Chapter 4:

Mucosa-associated bacteria in the human gastrointestinal tract

are uniformly distributes along the colon and differ from

the community recovered from feces ................................................................................................ 67

Chapter 5:

Populations dynamics and diversity of fecal microbiota of patients

with ulcerative colitis participating in a probiotic trial .................................................................... 83

Chapter 6:

Multiparametric flow cytometry and cell sorting for the assessment of viable,

injured, and dead Bifidobacteriumcells during bile salt stress ......................................................... 105

Chapter 7:

Genetic diversity of live, injured and dead fecal bacteria assessed by fluorescence

activated cell sorting and 16S RRNA gene analysis ....................................................................... 123

Chapter 8:

Summary and concluding remarks.................................................................................................... 149

Samenvatting.........................................................................................................................................155

Acknowledgments ...............................................................................................................................161

About the author..................................................................................................................................163

List of publications ..............................................................................................................................164


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Chapter 1

GENERAL INTRODUCTION:

Ecology of the human intestinal microbiota

A modified version of this chapter has been accepted for publication as a chapter in:

Gastrointestinal Microbiology

(Edited by Arthur Ouwehand and Elaine E. Vaughan and published by Marcel Dekker)

Molecular tools to analyze the composition of intestinal microbiotaKaouther Ben Amor and Elaine E. Vaughan

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The human gastro-intestinal (GI) tract is the home of a huge microbial assemblage, the

vast extent of which is only being revealed. The number of microorganisms (microbiota) greatly

exceeds human cells, resulting in one of the most diverse and dynamic microbial ecosystems,

where relationships amongst the microbes and between those and the host have a profound

influence on all concerned (33). This microbiota play essential roles in a wide variety ofmetabolic and immunological processe and therefore significantly contribute to the well being

of the host (17). During the last decade, food-grade specific isolates, termed probiotics have been

extensively used in an attempt to modulate the composition and/or activity of the intestinal

microbiota so as to provide an advantage to the host. Despite certain haziness about the use of

probiotics as functional foods or as bio-therapeutic agents, today there is persuasive evidence

supporting their efficacy in the prevention or treatment of a number of intestinal disorders in

humans (54, 57). Nevertheless, in order to rationally use probiotics as functional foods or as

therapeutic agents, in-depth knowledge of the structure, dynamics and function of the bacterial

populations of the GI-tract microbiota is crucial.

Although the human intestinal microbiota have been extensively investigated by

culture-based methods more than any other natural ecosystem (19, 31, 46), our knowledge

about the culturable fraction of this community is limited (3, 75). The advent of molecular

techniques based on the 16S ribosomal RNA (rRNA) gene analysis is now allowing a more

complete assessment of this complex microbial ecosystem by unraveling the extent of the

diversity, abundance and population dynamics of this community. These techniques have

extended our view of those microorganisms that have proven difficult to culture and which

play an important role in the gut physiology. This huge intestinal microbial reservoir,

estimated to contain more than 1,000 bacterial species (82) and as much as 10 13 cells,

exhibits a highly diverse set of metabolic activities (17, 32). Hence, it is essential to identify

these microbes based upon their eco-physiological traits i.e. those that are functionally

active versus those that are effectively redundant and play little or no role at a particular

time or at a given site of the intestinal tract. It is therefore a major challenge to develop

approaches that monitor the activity of these microorganisms at the single cell level in their

natural habitat. This chapter will focus on these new insights, highlight newly developed

molecular methods to study the eco-physiology of the GI-tract, and culminates in anoverview of this thesis.

1.1 The uncultured GI-tract microbiota is identified by 16S rRNAgene sequencing and phylogenetic analysis

The comparative analysis of environmentally retrieved nucleic acid sequences, most

notably of rRNA molecules and the genes encoding them, has become a standard for

cultivation-independent assessment of bacterial diversity in environmental samples (3).

Ribosomal RNA gene fragments are today routinely retrieved without prior cultivation of the

microbes by constructing 16S ribosomal DNA (rDNA) libraries. Large databases of especially

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16S rRNA gene sequence information for described as well as uncultured microorganisms are

available, and thus provide a high-resolution platform for the assignment of those new

sequences obtained in 16S rDNA libraries (41). The procedure is based upon PCR-mediated

amplification of 16S rRNA genes or gene fragments, using rRNA or rDNA isolated from the

environmental sample, followed by segregation of individual gene copies by cloning intoEscherichia coli. In this way a library of community 16S rRNA genes is generated, the

composition of which can be estimated by screening clones, full or partial sequence analysis

and comparing them with adequate reference sequences to infer their phylogenetic affiliation.

Sequencing of 16S rDNA clone libraries generated from various sites of the GI-tract

including terminal ileum, colon, mucosa and feces, obtained from healthy and diseased people

have confirmed that a relevant fraction of gut bacteria were derived from new, as yet

undescribed bacterial phylotypes (30, 53, 70, 80, 83, 85). Such studies revealed that the vast

majority of rDNA amplicons generated directly from fecal or biopsy samples were assigned tothree major phylogenetic lineages, namely the Clostridium coccoides, Clostridium leptum and

Bacteroidesgroups. Comprehensive phylogenetic analysis demonstrated that more than half of

the observed diversity was attributable to unknown dominant microorganisms within the

human gut. Additionally, Zoetendal et al. (85) demonstrated that the majority of predominant

bacterial species from an adult fecal sample did not correspond to known species, but that the

prominent bacteria were assigned to different Clostridium clusters namely, Ruminococcus obeum,

Eubacterium hallii and Fusobacterium prausnitzii. On the other hand, phylogenetic analysis of

16S rDNA clone libraries generated from mucosa-associated microbiota of patients with

inflammatory bowel disease (IBD), revealed a reduction in diversity due to a loss of normal

anaerobic bacteria especially those belonging to the Bacteroides, Eubacterium and Lactobacillus

species. Most of the sequenced clones retrieved from the biopsy samples (70%), obtained from

IBD patients, were assigned to known intestinal bacteria, but a significant number of the

cloned sequences were affiliated to normal residents of the oral mucosa such as Streptococcus

species (53). The authors suggested that alteration of the bacterial microbiota in mucosal

inflammation reflects a metabolic imbalance of the complex microbial ecosystem with

severe consequences for the mucosal barrier rather than disrupted defense to single

microorganisms (53).

Even though sequencing of cloned 16S rDNA amplicons provides relevant information

about the identity of uncultured bacteria, the data are not quantitative. Moreover, PCR and

cloning steps are not without biases (76), a recent comparative analysis of clone libraries from

a fecal sample pointed out that the number of PCR cycles may affect the diversity of the

amplified 16S rDNAs and thus should be minimized (8). More rapid culture-independent

options to the cloning procedures include examination of complex microbial populations using

a variety of fingerprinting methods.

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1.2 Fingerprinting techniques reveal the stability, uniqueness andcomplexity of the GI-tract microbiota

The most commonly applied fingerprinting methods used to study the GI-tract

microbiota are denaturing temperature (DGGE) and temperature gradient gel electrophoresis(TGGE) of PCR-amplified genes coding for 16S rRNA (75, 88). Other techniques such as

terminal restriction fragment length polymorphism (T-RFLP) and single strand conformation

polymorphism (SSCP) analysis have been applied but less frequently (50, 53). The common

principle of these methods is based on the separation of PCR-amplified segments of 16S rRNA

genes of the same length but with different sequence to visualize the diversity within the PCR

amplicons by a banding pattern. With DGGE/TGGE, separation is based on the decreased

electrophoretic mobility of partially melted double-stranded DNA molecules in

polyacrylamide gels containing a linear gradient of DNA denaturants (a mixture or formamide

and urea) or a linear temperature gradient, respectively. As a result mixed amplified PCRproducts will form a banding pattern after staining that reflects the different melting behaviors

of the various sequences (49, 62). Subsequent identification of specific bacterial groups or

species present in the sample can be achieved either by cloning and sequencing of the excised

bands or by hybridization of the profile using phylogenetic probes (48). Furthermore,

complementation of the fingerprinting results with statistical analysis provides additional

information of the observed diversity by highlighting some putative correlations between

different sets of variables (20).

Since its application to study the intestinal microbiota, PCR-DGGE/-TGGEfingerprinting has advanced our knowledge of the intestinal microbiota by unraveling the

complexity of this ecosystem and providing insight in the establishment and succession of the

bacterial community within the host (18, 85). In healthy adults, the predominant fecal

microbiota was shown to be host-specific, relatively stable in time and not significantly altered

following consumption of certain probiotic strains (72, 74, 84, 85). Furthermore, it revealed

that the predominant bacterial species associated with the colonic mucosa are uniformly

distributed along the colon, but significantly different from the predominant fecal community

(89). Under certain environmental circumstances and/or in genetically susceptible individuals,

there is persuasive evidence that the GI-tract microbiota may play a role in the pathogenesis

and aetiology of a number of inflammatory diseases such as ulcerative colitis (UC), and Crohns

disease (CD) (10, 66). Using DGGE, TTGE and SSCP fingerprinting analyses, it was

demonstrated that fecal and mucosa-associated microbiota of patients with UC and CD is

altered, less complex, and also unstable over time as compared to matched healthy people (53,

64 )(Chapter 4).

Although DGGE or TGGE were initially developed for total ecosystem communities,

the sensitivity of the method for detecting specific groups that are present in lower numbers in

the GI-tract such as bifidobacteria and especially lactobacilli has been considerably enhanced

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by using group- or genus-specific primers (29, 63, 72, 79). Consequently, it was possible to

monitor the effect of the administration of prebiotics and/or probiotics on the composition of

indigenous bifidobacterial species, and to track the probiotic strain itself (63). In the latter case,

DGGE profiles showed that the simultaneous administration of the prebiotic and probiotic

(symbiotic approach) did not improve the colonization of the probiotic strain in the gut of thetested individuals. In another study, the DGGE profiles generated from fecal samples of healthy

individuals fed a probiotic strain Lactobacillus paracaseiF19, allowed the tracing of the probiotic

and supported its presence as autochthonous within the intestinal community of a number of

individuals (29).

While the application of 16S rDNA-based fingerprinting are particularly well suited for

examining time series and population dynamics, a more quantitative approach is useful to

complement our knowledge about the composition and structure of this complex intestinal

ecosystem.

1.3 16S rRNA-targeted probes quantify the GI-tract microbiota

Hybridization with rRNA-targeted oligonucleotide probes has become the method of

choice for the direct cultivation-independent identification of individual bacterial cells in

natural samples. During the last decade, this technique has extended our view of bacterial

assemblage and population dynamics of complex microbial communities (3, 38, 47). The

most commonly used biomarker for hybridization techniques, either dot blot or fluorescent

in situ hybridization (FISH) is the 16S rRNA molecule because of its genetic stability, domainstructure with conserved and variable regions, and high copy number. Highly conserved

stretches may thus be used to design domain-specific probes such as EUB338/EUBII/EUBIII

which collectively target most of the bacteria, whereas specific probes for each taxonomic

level, between bacterial and archaeal, down to genus-specific and species-specific, can be

designed according to the highly variable regions of the 16S rRNA (3, 4, 43). The increasing

availability of 16S rRNA sequences contributed significantly to the development of the

hybridization methods and their application in different microbial ecosystems (41).

Unquestionably, the success of the implementation of 16S rRNA hybridization strategies

depends on different factors, among them a rational design and validation of newly designedrRNA-targeted probes.

Probe design and validation

When designing new probes, one must consider specificity, sensitivity and accessibility

to the target sequence. Nucleic acid probes can be designed to specifically target taxonomic

groups at different levels of specificity (from species to domain) by virtue of variable

evolutionary conservation of the ribosomal rRNA molecules. Appropriate software such as the

ARB software package (40) and availability of large databases (http://rdp.cme.msu.edu/html/)or the online resource for oligonucleotide probes Probe Base (39) are useful tools for a rapid

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probe design and in silico specificity profiling. Additional experimental evaluation of the probes

with target and non-target microorganisms is necessary to ensure the specificity and the

sensitivity of the newly designed probe. It is important to notice that the validation of a newly

designed probe requires different procedures for the dot blot (15) and FISH format (12).

Moreover, the hybridization and washing conditions (temperature, salt concentration anddetergent) are also crucial for obtaining a detectable probe signal (69). The accessibility of the

probe to its target site is another factor to be considered when designing new probes. The

accessibility of probe target sites on the 16S and 23S rRNA of Escherichia colihas been mapped

systematically by flow cytomety (FCM) and FISH and it was shown that probe-conferred

signal intensities vary greatly among different targets sites (23, 24). More recently, it was

demonstrated that accessibility patterns of 16S rRNAs are more similar for phylogenetically

related organisms; these findings may be the first description of consensus probe accessibility

maps for prokaryotes (5).

Hybridization techniques

Nucleic acid probing of complex communities comprises two major techniques: dot

blot hybridization and fluorescent in situ hybridization (FISH). In the dot blot format, total

DNA or RNA is extracted from the sample and is immobilized on a membrane together with

a series of RNA from reference strains. Subsequently, the membrane is hybridized with a

radioactively labeled probe and after a stringent washing step the amount of target rRNA is

quantified. The membrane can be rehybridized with a general bacterial probe and the amount

of population-specific rRNA detected with the specific probe is expressed as a fraction of thetotal bacterial RNA. Quantification of the absolute and relative (as compared to total rRNA)

amounts of a specific rRNA reflects the abundance of the target population and thus do not

represent a direct measure of cell number since cellular rRNA content varies with the current

environmental conditions and the physiological activity of the cells at the time of sampling

(45). Dot blot hybridization has been successfully used to quantify rRNA from human fecal

and cecal samples (44, 65). It was found that strict anaerobic bacterial populations represented

by the Bacteroides, Clostridium leptum and Clostridium coccoidesgroups were significantly lower

in the cecum (right colon) than in the feces, while the Lactobacillus group was significantly

higher in the feces than in the cecum (44).

In contrast to dot blot hybridization, FISH is applied to morphologically intact

cells and thus provides a quantitative measure of the target organism without the limitation

of culture-dependent methods (2, 3). Following fixation, bacteria from any given sample can

be hybridized with an appropriate probe or set of probes. The fixation allows permeabilization

of the cell membrane and thus facilitates the accessibility of the fluorescent probes to the

target sequence. For some Gram-positive bacteria, especially lactobacilli, additional pre-

treatments including the use of cell wall lytic enzymes e.g. lysozyme, mutanolysin, protease K

or a mixture is needed (6, 28). Prior to hybridization, the cells can be either immobilized on

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gelatine-treated glass slides or simply kept in suspension when analyzed by FCM. The

stringency, i.e. conditions of hybridization that increase the specificity of binding between the

probe and its target sequence, can be adjusted by varying either the hybridization temperature

or formamide concentration. Under highly stringent conditions oligonucleotide probes can

discriminate closely related target sites. Post-hybridization stringency can be achieved bylowering the salt concentration in the washing buffer in order to remove unbound probe and

avoid unspecific binding.

Quantification of FISH signals

Over the past years, significant methodological improvements of the probe

fluorescent-conferred signal have been reported. These include the use of (i) brighter

fluorochromes i.e. Cy3 and Cy5 (25, 68), (ii) unlabeled helper oligonucleotide probes (22)

(iii) signal amplification with reporter enzymes (CARD-FISH) (55), and (iv) the use ofpeptide nucleic acid (PNA) probes (52, 56). Commonly epifluorescence microscopy is the

standard method by which fluorescent-stained cells are enumerated, however the method is

time consuming and subjective (38, 47). Recently, this technique has been improved by

development of automated image acquisition and analysis software allowing accurate

microscopic enumeration of fecal bacteria cells (34). Alternatively, FCM offers a potential

platform for high-resolution, high throughput identification and enumeration of

microorganisms using fluorescent rRNA-targeted oligonucleotides with the possibility of cell

sorting (60, 77, 78, 87).

A FCM method for direct detection of the anaerobic bacteria in human feces

was first described by Van der Waaij et al. (73). They used a membrane-impermeant

nucleic acid dye propidium iodide (PI) in combination with the intrinsic scatter parameters of

the cells to discriminate the fecal cells from large particles. Coupling FCM results and image

analysis, the authors showed that most of the particles detected with a large forward scatter

value corresponded to aggregates most likely representing mucus fragments and indigested

dietary compounds. They confirmed by means of cell sorting that the PI-stained cells (fecal

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thus used as an internal standard to calibrate the measured volume and to determine the

absolute count of the probe-detected cells (Chapter 5). In addition to the determination of

the absolute cell counts, the fluorescence intensity signal can also be quantified using

fluorescent beads with known fluorescent intensities (67). This is of major importance for

determining optimal hybridization conditions for newly designed probes (16, 61). Definitely,FCM will become the method of choice for high-resolution, high throughput identification

of microorganisms using fluorescent rRNA-targeted oligonucleotides.

Application of FISH to study the GI-tract ecosystem

During the last five years, hybridizations with rRNA-targeted probes have provided a

significant knowledge about the structure of the gut microbiota. A large panel of

oligonucleotide probes specific for various genera predominant in the GI-tract have been

designed and validated. These include Clostridium, Bacteroides, Eubacterium, Ruminococcus,Bifidobacterium, Lactobacillus, Streptococcus, Fusobacterium, Collinsella, Atopobium and

Veillonella specific probes (Table 1) and which have been used intensively to study the

composition and structure of the intestinal microbiota. .

The uniqueness and complexity of the human gut microbiota revealed

by fingerprinting techniques was supported by results of analysis using nucleic-acid

probes based methods. Results of such studies revealed that the majority of fecal bacteria

belong to the Bacteroides-Prevotella, C. coccoides, C. leptum group, Atopobium group and

bifidobacteria (21, 26, 35, 60). These investigations showed that the genus Bacteroides andmembers of C. coccoides and C. leptum constitute more than half of the fecal microbiota.

Among members of the C. coccoidesgroup which equates to Clostridium rRNA cluster XIVa

(11), Ruminococcus, Eubacterium hallii, Lachnospira and Eubacterium cylindroides related

bacteria were found to be dominant members of the microbiota. However, Enterobacteriaceae,

Lactobacillus-Enterococcusgroup, Phascolarctobacterium and relatives, and Veillonellawere less

dominant (26). However, differences in the occurrence of these bacterial groups have been

reported by different research groups. These deviations may be due the different methods or

probes used but it is also likely that the observed variance is due to the differences in the

genetic background, lifestyle, and diet in the human populations studied (60). The results oftwo extensive studies, where an extensive array of oligonucleotide probes targeting the major

bacterial groups in the GI-tract was used, showed that 62-75% of the fecal bacteria could be

detected and identified (26, 36). The remainder (~ 30%) could either belong to members of

the Archaea, Eukarya or most likely to yet unknown bacteria. Furthermore, FISH-FCM

analysis of fecal microbiota of patients with ulcerative colitis revealed substantial temporal

variations in the major bacterial groups studied (i.e. Bacteroides, C. coccoides, Atopobium,

bifidobacteria and lactobacilli) (Chapter 5).

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Table 1: Major FISH probes used to study the GI-tract microbiota.

Probe Probe Sequence (5-3) Target organism % Formamide Reference

Eub338 GCTGCCTCCCGTAGGAGT Most bacteria 0 80 (4)

EubII GCAGCCACCCGTAGGTGT Planctomycetes 0- 60 (12)

EubIII GCTGCCACCCGTAGGTGT Verrucomicrobia 0- 60 (12)

Bac303 CCAATGTGGGGGACCTT Bacteroides/Prevotella 0 (43)

Erec482 GCTTCTTAGTCAR*GTACCG Clostridium coccoides cluster 0 (21)

Elgc01 GGGACGTTGTTTCTGAGT Clostridium leptum cluster 0 (21)

Fprau645 CCTCTGCACTACTCAAGAAAA Fusobacterium prausnitzii 15 (71)

Bif164 CATCCGGCATTACCACCC Bifidobacteria 0 (35)

Ato291 GGTCGGTCTCTCAACCC Atopobium group 0 (27)

Veil223 AGACGCAATCCCCTCCTT Veillonella 0 (26)

Ecyl1387 CGCGGCATTGCTGCTTCA Eubacterium cylindroides 20 (26)

Rbro729 AAAGCCCAGTAAGCCGCCRuminococcus group 20 (26)

Rfla730 TAAAGCCCAGY*AGGCCGC

Lach571 GCCACCTACACTCCCTTT Lachonospira group 40 (26)

Ehal1469 CCAGTTACCGGCTCCACC Eubacterium hallii group 20 (26)

Phasco741 TCAGCGTCAGACACAGTC Phascolarctobacterium group 0 (26)

Bdis656 CCGCCTGCCTCAAACATA Bacteroides distasonis 0 (21)

Bfra602 GAGCCGCAAACTTTCACAA Bacteroides fragilis 30 (21)

Bvulg1017 AGATGCCTTGCGGCTTACGGC Bacteroides vulgatus 30 (61)

Bfrag998 GTTTCCACATCATTCCACTG Bacteroides fragilis 30 (61)

Bdist1025 CGCAAACGGCTATTGGTAG Bacteroides distasonis 30 (61)

Lab158 GGTATTAGCAY*CTGT TTCCA Lactobacillus/Enterococcus 0 (28)

Urobe63a AATAAAGTAATTCCCGTTCG Uncultured Ruminococcus 20 (87)

Urobeb63b AATRAARTATTTCCCGTTCG obeum-like bacteria

Non338 ACATCCTACGGGAGGC Negative control (77)

* R and Y are the International Union of Pure and Applied Chemistry codes for ambiguous bases.

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1.4 Inferring structure to metabolic activity

The aforementioned molecular techniques have greatly contributed to our

fundamental understanding of the biodiversity, establishment, succession and structure of the

intestinal microbiota; yet little is known about the in situ association between the microbialdiversity and the metabolic activity of a phylogenetic affiliated group. It is well recognized that

this highly diverse microbiota plays a significant role in the processing of undigested food to

the benefit of the host and contributes to the host defense by limiting colonization of the GI-

tract by pathogens (17, 32). For instance the generation of short chain fatty acids is a common

feature of the climax community, although many of the specific species responsible remain

undefined. It is therefore a major challenge to develop methods that allow monitoring of

microorganisms according to their eco-physiological traits in situ.

During the last years several innovative methods have been developed to resolve thelinkage between structure, activity and function in microbial communities. These include

methods where molecular techniques are coupled with substrate labeling such as stable isotope

probing (SIP) (58, 59), microautoradiography and FISH (MAR-FISH) (37, 51) or labeling

with fluorescent functional probes followed by flow cytometry and cell sorting analysis (7, 81,

Chapter 7). MAR-FISH allows monitoring of the radiolabeled substrate uptake patterns of

the probe-identified organisms under different environmental conditions (13, 37). This

method has been applied with high throughput DNA microarray analysis to study the

complex activated sludge ecosystem (1). In stable isotope probing (SIP), either lipid

biomarkers (9), DNA (58) or RNA (42) are extracted from microbial communities incubatedwith 13C-labeled substrates. If cells grow on the added compounds, their pool of

macromolecules will be isotopically enriched (heavy) compared to those of inactive organisms.

For DNA- or RNA-SIP, identification of the metabolically active organisms (heavy) is

achieved by separation of community DNA/RNA according to their buoyant density by

means of equilibrium density-gradient centrifugation, followed by PCR-amplification of 16S

rRNA genes in the isotopically heavy DNA/RNA pool, cloning and sequencing. The use of

RNA was proposed as a more responsive biomarker as its turnover is much higher than that

of DNA (42). Phospholipid fatty acids are also used as biomarker for 13C enrichments, but

their resolution for diversity analysis is less powerful than for sequence analysis. On the other

hand, FCM has been viewed as a powerful technique to monitor the metabolic activity of

stressed and starved bacteria and identifies microorganisms in their natural habitat, with

potential for automation (Chapter 2). One major advantage of FCM is that it allows

monitoring of bacterial heterogeneity at the single cell level and provides a mean to sort sub-

populations of interest for further molecular analysis (14). This approach has been ultimately

applied to fecal microbiota, the results provided relevant ecological information related to the

diversity and activity of different affiliated phylogenetic groups and highlighted the

physiological heterogeneity of this complex ecosystem (Chapter 7). The application of

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cytometric protocols using fluorescent probes in combination with molecular techniques

opens the potential for examining key microbial processes and community function in

complex microbial ecosystems.

1.5 Outline of the thesisThroughout this thesis, the potential of flow cytometry (FCM) and fluorescence

activated cell sorting (FACS) for the analysis of the complex intestinal microbiota will be

demonstrated, with the ultimate aim to provide insight into the biodiversity of the intestinal

ecosystem coupled with the global in situ activity of these microbes.

Chapter 2 reviews the potential of FCM and FACS as an analytical and preparative tool

to analyze microorganisms in different environmental settings.

Chapters 3 describes the application of FCM in combination with FISH (FISH-FCM)to identify and enumerate an uncultured group of fecal bacteria, which have only been detected

by PCR-based approaches.

Chapter 4 describes the distribution of the predominant and Lactobacillusgroup bacterial

community along different sites of the colon of different individuals some of which are diagnosed

with ulcerative colitis or polyposis. The results demonstrate the ability of FCM for studying not

only fecal bacteria (suspended cells) but also mucosa-associated microbiota (attached cells).

Chapter 5 describes the application of FISH-FCM and denaturing gradient gel

electrophoresis (DGGE) as a high throughput platform to evaluate the effect of two probiotic

strains on the population dynamics of fecal microbiota of patients with ulcerative colitis during

a probiotic trial.

Chapter 6 describes a new approach based on the use of functional probes to assess the

viability of Bifidobacterium adolescentis and Bifidobacterium lactisduring bile salt stress and

highlights the importance of multiparametric FCM as a powerful technique to monitor

physiological heterogeneity including live, dead and injured cells within stressed populations at

the single cell level.

Chapter 7 illustrates a novel approach where functional probes and FACS are combined

with 16S rRNA gene analyses to get insight into the genetic diversity of live, dead and injured

fecal bacteria.

The summary and concluding remarks are presented in Chapter 8.

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1.6 References

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2. Amann, R., B. M. Fuchs, and S. Behrens. 2001. The identification of microorganisms

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Chapter 2

FLOW

CYTOMETRIC

ANALYSIS OF

MICROOGRANISMS

Kaouther Ben Amor, Willem M. de Vos and Tjakko Abee

Abstract: Flow cytometry analysis of fluorescently-labeled microorganisms has a

wide range of applications including detection, identification, viability assessment,

analysis of cellular function, as well as heterogeneity assessment of cell

populations. This chapter seeks to review the recent applications of flow cytometry

and fluorescent probes in microbial ecology and physiology and highlights the

progress made in developing new strategies for use in microbiological

investigations

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2.1 Introduction

The continuous improvement of the sensitivity and the performance of flow

cytometric instruments has resulted on a wide range of applications to characterize bacteria,

yeast, fungi and even viruses (76, 89, 113). Until the late 1970s, applications of flowcytometry (FCM) in the field of microbiology were rather limited due to the fact that most

of the flow cytometers available at that time were not suited for measurement of bacteria due

to their small size compared to that of mammalian cells. The first applications of flow

cytometry in the field of microbiology were published by Paau et al. (99) and Bailey et al. (11)

who studied the cell cycle of three bacterial species with different growth rates (Escherichia

coli, Rhizobium melilotiand Rhizobium japonicum) using a combination of light scattering and

ethidium bromide fluorescence signals. Hutter et al. (59) published a series of pioneering flow

cytometric studies demonstrating the suitability of the technique to determine the DNA and

protein content of several types of microorganisms and to discriminate live and dead cells onthe basis of their light scattering behaviour. However, Steen and co-workers (120, 121) were

the first to design a flow cytometer well suited for the analysis of bacteria and this led to a

breakthrough in the field of microbial FCM. The power of FCM stems from the ability to

perform multiparametric analysis at the single cell level, the high throughput capacity, and the

option of cell sorting. In this chapter we will discuss the principle of FCM and highlight a

number of applications in microbial ecology and physiology.

2.2 How does it work?

Flow cytometry is a mean of measuring specific physical and chemical characteristics

of cells or particles as they flow single-filed in a liquid stream through the focus of a laser

beam(s). At the sensing or measuring point, the stained cells will scatter light in different

directions and emit fluorescence. These light pulses are then collected by an array of detectors,

which in turn translate these signals into electrical pulses (voltage) (Fig. 1). The voltage level

of each detector, either a photo multiplier tube (PMT) or photodiode, can be adjusted to

optimize signal amplification. Logarithmic amplification is used to provide a wide dynamic

range so that both weak and strong signals can be recorded in the same scale. The analog

signal is then converted to a digital value, which is stored in a list mode data files where each

event (i.e. presence of a microbial cell) with the corresponding data for each parameter is

recorded sequentially. One of the main aims in analyzing flow cytometric data is to distinguish

individual target cells among the total population. This is accomplished in a first step by

setting an electronic threshold or by using electronic gating in order to minimize the

background noise or exclude non-targeted cells, respectively. Statistical analysis is then used to

generate representing cell counts including, the median, the mean, the standard deviation and

the coefficient of variance of the measured parameters. For visualization purposes, data are

displayed either as a frequency distribution where the magnitude of the parameter measuredis expressed as a function of number of cells, or two- or three-parameter dot plots or density

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plots (Fig. 2). For multiparametric analysis, more advanced multivariate statistical methods

such as principal component analysis, cluster analysis or neural networks can be used in order

to extract useful information from the large data sets (38). For a more detailed discussion on

the principles of FCM and data analysis the monograph by Shapiro (113) is recommended.

Figure 1: Standard optical detector array of a FACSCalibur cytometer equipped with a dual- laser (a blue

and red-diode lasers emitting at 488nm diode 623 nm, respectively). A cell is intercepted at the focused

laser beam (s) within the sensing region of the flow cell. Light is scattered by the cell in the forward angle

and detected by a photodiode (FSC). Light scattered at right angles to the cell passes through a dichroic

filter than splits the light wavelengths > 560 nm and < 560 nm. Fluorescence > 560 nm is subsequently

split again with a 640 nm long pass filter. The wave lengths > 650 nm are detected by the red

fluorescence PMT (FL3) and the range of wavelengths within approximately 560-545 nm is detected by

the orange fluorescence (FL2) PMT. Fluorescence within the range 515-545 nm is collected by the green

fluorescence (FL1) PMT. (With permission from Beckton and Dickinson, The Benelux).

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Figure 2: FCM data display. The data obtained from the FCM analysis can be displayed in different ways:

the one-parameter or frequency histogram (A), the two-parameter dot plot (B), and a three dimensionalrepresentation of the data (C), generated from the analysis of Bifidobacterium adolescentisduring pH, heat

and bile salt stress exposure, respectively. The plots were generated with WinMDI software available at

http//:facs.Scripps.edu/software.html.

2.3 What does it Measure?

A flow cytometer measures the light scattered by and the fluorescence emitted from

particles or cells upon excitation with a light source (laser) as they pass in liquid fluid. The lightscatter parameters measured by the FCM known as forward scatter (FSC) and side scatter

(SSC) provide information about the intrinsic cell properties. As a general rule, FSC is used to

estimate cell size and volume while the SSC parameter is a rough estimate of the internal cell

structure and granularity (1, 37, 39, 112, 113). Taken together, FSC and SSC can distinguish

cells in a mixed sample according to their morphological fingerprints, thus allowing exclusion

of aggregates or debris from the cell of interest. A FCM method for direct detection of

anaerobic bacteria in human feces and colon biopsies was described using propidium iodide

(PI), a nucleic acid dye, and scatter parameters to discriminate fecal and mucosa-associated

bacteria from non-bacterial aggregates (127, 128, 144). Combining cell sorting and image

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analysis, van der Waaij et al. (128) showed that particles with high value in the FSC represented

aggregated particles presumably food particles or mucus fragments. These parameters were also

used to determine bacterial cell biomass, size and volume (145). Sincock et al. (116)

discriminated populations of closely related Gram-positive spores based on their scattering

profiles.

Figure 3: Different cellular target sites for physiological and taxonomic fluorescent probes (See text for

further explanation).

While measurements of FSC/SSC parameters can be useful to characterize

bacterial cells and exclude background, it is the capability of the flow cytometer to

measure particle-associated fluorescence that makes the technique extremely attractive.

Commonly, the fluorescence emitted from the stained cell represents the expression

of an intracellular marker or a reporter molecule attached to an oligonucleotide or to an

antibody. The advance in fluorescent technology allowed the development of a wide range

of fluorescent probes (Table 1) targeting a large array of cellular site parameters (Fig. 3)

and well suited for FCM (53, 112). These include stains that have a high specificity for nucleic

acids, total proteins, or lipids. Indicators that reflect enzyme activity such as esterases,galactosidases or dehydrogenases are also available and have been used for a wide range

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26

Chapter 2

Table

1:Commonlyusedfluorescent

probestostudymicroorganism

sbyFCM(39,53).

Fluore

scentprobe

Ex(max)a(nm)

Em(

max)(nm)

Ligandorsubstrate

Applications

Propid

iumIodide(PI)

536

617

DNA,RNA

Viability,DNAcellcyc

le

TOTO

-1

514

533

DNA,RNA

Detection,enumeration

SYTO

13

488

509

DNA,RNA

Viability,Gramstainin

g

SYBR

GreenI

494

521

DNA,RNA

DNAquantification,V

iability

TOPR

O-3

642

661

DNA,RNA

Viability

DAPI

358

461

DNA/RNA

Detection,enumeration

Hoesh

st33258/22342

340

450

DNA(GCpairs)

Determinationof%G

Ccontent,

Hexidiumiodide(HI)

518

600

DNA,RNA

Gramstaining

FITC

495

525

Protein

Detection,Size

cFDA-SE

519

542

Protein

Celltracking

NileR

ed

551

636

Lipids

Poly--hydroxybutyra

teproduction

Indo-1

330-3

50

390-485

Ca2+

Calciumconcentration

Fluo-3

506

526

Ca2+

Calciumconcentration

Rhoda

mine123

510

580

Membranepotential

ViabilityofGram+bac

teria,

Oxonol[DiBAC4(3)]

488

525

Membranepotential

Viability,Antibibioticsusceptibility

BCEC

F

460-5

10

520-610

pH(6,9)

CellinternalpH,Viability

SNAR

F-1

490-5

40

587-635

pH

CellinternalpH

CFDA

492

517

Esterases

Viability

Calcein

494

517

Esterases

Viability

FDG

491

514

-Galactosidase

Reportergeneexpression,

CTC

530-5

50

varies

Dehydrogenases

Respiratoryactivity,viability

Fun-1

508

525-590

Yeastvacuolarenzymeactivity

Yeastmetabolicactivity

Calcofluor

347

436

Chitinandothercarbohydrate

Fungaldetection

Cy3

550

570

Nucleotidesequence,Antibodies

Identification

Cy5

651

674

Nucleotidesequence,Antibodies

Identification

aEx(m

ax)andEm

(max)arethew

avelengthsofmaximalexcitation

andemission,respectively.

Itshould

benoted

thatthese

parametersare

depend

enttoavariabledegreeonthecon

ditionsusedformeasurement.


32/171

of applications. Other fluorochromes are useful because their properties change as a function

of pH or because they are accumulated or extruded as a response to cell energization. The use

of fluorescently-labeled oligonucleotide probes, the use of fusions to fluorescent reporter

proteins such as GFP and the detection of molecular interaction by Fluorescent Resonance

Energy Transfer (FRET) offer other means to study microorganisms and their components byFCM (36, 98, 132, 139, 143).

2.4 What makes flow cytometry a powerful technology?

2.4.1 Multiparametric measurements

Flow cytometry is the most effective single-cell analysis technology available today,

since each cell can be characterized with 5-10 parameters allowing a multiparametric

characterization of a given cell population (38, 39, 112). Indeed, combinations of scatter

parameters with marker-conferred fluorescence are used to simultaneously characterize two or

more cellular properties i.e. DNA content, protein content, enzyme activity or simply to

identify target cells using oligonucleotides or antibodies (39). The combination of functional

probes and FCM has been applied to study the stress response of a number of lactic acid

bacteria and bifidoabcteria during heat, acid, ethanol and bile salt stress (15, 25, 34). The

multiparametric assay allowed to discriminate subpopulations with different physiological

status: live, dead and injured-damaged cells (Fig. 4). With the advent of FCM, it became

increasingly clear that even clonal populations derived from a single cell are far from

homogeneous. The development of complementary technologies using multiplexed bead-based arrays will offer even more possibilities for studying microbial eco-physiology (62, 96,

118, 119).

2.4.2 High throughput analysis.

The utility of FCM as a high throughput approach stems from the ability to perform

quantitative assays on a large number of cells, with single cell resolution. Analyses are

commonly performed at a flow rate of 10-100 l/minute thus enabling tens of thousands of

cells to be collected from one sample in less than one minute and statistics are then generated

in real-time. Recent development such as serial-sample delivery using micro-well plates will

extend the ability of FCM resulting in higher throughput and automation analysis of a large

number of samples (48, 72).

2.4.3 Cell sorting.

The most attractive feature of FCM is that cells of interest can be physically separated for

subsequent molecular analysis (16, 133) or functional assays (56, 93), the so-called fluorescence

activated cell sorting (FACS). The cells of interest can be separated from a complex mixture even

though it may be a minor subpopulation and thus allowing for physical enrichments. Amorphologically conspicuous bacterium that accounted for less than 0.01% of the total microbial

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community of activated sludge was physically sorted using the fluorescent signal conferred by a

specific oligonucleotide probe and forward scatter as sorting criteria. Cell sorting resulted in a 30%

enrichment of the target bacteria while traditional microbiological enrichment had failed (117).

Multiparameter FCM combined with cell sorting unquestionably adds an extra dimension to the

information one gains from the results of the post-sorting assays (36, 126).

Figure 4: Dual parameter dot plot of B. adolescentisDSM 20083 representing carboxyfluorescein (cF)

versus propidium iodide (PI) fluorescence. Cultures were exposed for 10 min to different concentrations

of deconjugated bile salts (0.1 %, left and 0.25 %, right) and subsequently stained with cFDA and PI in

order to monitor the esterase activity and membrane permeability, respectively. Three main subpopulations

can be readily differentiated, corresponding to viable cF-stained cells, injured cells double stained with PI

and cF and dead PI-stained cells. The number of colony forming units corresponded to the number of cF-

stained cells, while the injured cells were not detected by the plate count method.

2.5 Applications of FCM in microbiology

Excellent reviews describing applications of FCM in the field of general (21, 39, 116,137), clinical (3), food (8, 116) and environmental microbiology (65, 104) have been published.

In this section we will focus more specifically on viability assessment, detection and identification

of microorganisms, and the use of gene reporter systems in combination with FCM.

2.5.1 Assessment of bacterial viability and metabolic activity

The most important application of FCM in the field of microbiology is the assessment

of cell viability. Although viability is one of the most basic properties of a cell, its definition still

remains a topic of discussion among microbiologists (13, 19, 68). The gold standard method

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Membrane potential

Membrane potential plays a critical role in bacterial physiology. As a component of the

proton motive force, it is intimately involved in the generation of ATP, but it has also been

implicated in various energy-requiring processes, such as nutrient transport, chemotaxis,

survival at low pH, and bacterial autolysis. Membrane potential analysis is based on the

selective permeability and active transport of charged molecules through intact membranes.

Cells with a trans-membrane potential actively take up lipophilic cationic dyes such as

Rhodamine 123 and 3, 3-dihexyloxacarbocyanine (DiOC6(3)) or actively exclude lipophilic

anionic dyes such as the negatively charged bis-(1,3-dibutylbarbutiric acid trimethine oxonol)

DiBAC4(3) known as oxonol (Fig. 3). The specific accumulation of Rhodamine 123, which is

taken up by polarized cells, has been used together with FCM to assess the viability of starved

M. luteus(67), to monitor the survival of starved Escherichia coliand Salmonellapopulations

in seawater (77), and to assess mitochondrial membrane potential inZygosaccharomyces bailiiand Saccharomyces cerevisiae (81). However, the limited applicability of Rhodamine 123

especially for Gram-negative bacteria, which need to be permeabilized prior to the staining,

has led to the extensive use of oxonol for rapid assessment of the microbial response to

antibiotics (28, 64, 85, 122, 123) as well as for cell viability (9, 54, 77, 78, 86). Correcting

the fluorescence conferred by oxonol for bacterial cell size, a better discrimination was

obtained between viable and depolarized/dead cells in bile salt-stressed bifidobacteria (15).

Enzyme activity

Monitoring the cellular esterase activity is another approach to determine the viability

of cell populations by means of FCM. Assessment of the esterase activity is determined using

a lipophylic, uncharged and non-fluorescent precursor. A number of fluorochromes such as 5,

(and-6)-carboxyfluorescein diacetate (cFDA), calcein-AM, 5, (and-6)-carboxyfluorescein

succinimidyl ester cFDASE; 2,7bis (2-carboxyethl)-5-(and-6)-caboxyfluorescein acetoxy

methyl ester (BCECF-AM) have been extensively used to monitor the viability of a wide range

of microorganisms (Table 1) (9, 15, 22, 25, 29, 44, 84, 105). These fluorogenic molecules can

diffuse across the membrane of viable cells where they are cleaved by non-specific esterases and

converted to polar fluorescent products such as fluorescein or fluorescein derivatives (Fig. 3).The staining capacity and subsequent retention of the fluorescent substrate by intact cells are

good indicators for metabolic activity and membrane integrity of the cell. However, the major

limitations encountered with the fluorogenic esterases are related to a poor dye uptake

especially by Gram-negative bacteria and active dye-extrusion resulting in non-stained viable

cells (23). On the other hand, since cFDA-cleaving reactions are typically not energy

dependent, some authors argue that cFDA alone cannot be used as a viability indicator. Da

Silveira et al. (34) demonstrated that in addition to cF-labeling, the subsequent extrusion of

the probe provided a high sensitive indicator of stress response in ethanol-stressed and

ethanol-adapted Oenococcus oeni cells during malolactic fermentation. The combination of

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cFDA with other viability indicators such as PI, TOTO-1 or oxonol provided more solid

information to assess the viability of stressed bacteria (9, 15, 24, 34, 141).

The respiration activity in aerobic bacteria can be detected using the substrate 5-cyano-

2, 3-ditolyl-tetrazolium chloride (CTC). CTC acts as an electron acceptor in the electron

transport system and can be reduced by a variety of dehydrogenases to an insoluble fluorescent

formazan, which accumulates inside the cell (Fig. 3). Since electron transport is directly related

to cellular energy metabolism in respiring cells, the ability of cells to reduce tetrazolium

compounds can be considered an indicator of bacterial activity. The CTC assay in combination

with FCM has been extensively used as a measure of cellular metabolic activity of different

bacterial species (66, 77, 108, 122, 123). Furthermore, its applicability to a number of anaerobic

bacteria including the sulphate reducers and methanogens has been demonstrated (18). The

redox CTC dye has gained plenty of applications in the study of microbial activity in different

ecosystems including drinking water (111), seawater (16, 114), activated sludge (94), soil (136),food matrices (51, 138) and biofilms (7, 83). However, the universality of CTC to detect

respiring cells in natural samples remains arguable, due to its possible toxic effect on bacterial

cells (129).

2.5.2 Reporter gene expression systems

FCM has been used to monitor gene expression by reporter genes in yeasts (10, 32),

bacteria (2, 31, 126) and to study microbe-host interaction (126). The LacZ gene encoding

-galactosidase and the green fluorescent protein (GFP) from the jellyfish (Aequorea victoria) are

particularly useful for such studies because they enable gene expression in individual cells to be

examined non-destructively and in real time (137)

The sensitive fluorogenic substrate fluorescein di--galactopyranoside (FDG) has been

proven effective for monitoring -galactosidase expression levels in single cells of bacteria and

yeast using FCM. The non-fluorescent FDG substrate is hydrolyzed by cellular -galactosidases

first to fluorescein monogalactosidase and then to the highly fluorescent fluorescein (Fig. 3).

A ready to use kit is available and widely used for mammalian cells, however its application for

bacteria is somewhat limited due the poor substrate uptake and retention of the fluorescent

product in active cells (53, 103). Different approaches have been used to overcome

these problems such as the development of lipophylic derivatives of FDG, use of hypertonic

shock and encapsulation of single cells in agarose microbeads. Nir et al. (95) showed that

encapsulation of E. coli and Candida pseudotropicalis in agar beads allowed the diffusion of

FDG into the microcolonies without permeabilization, and viable cells were then analyzed and

sorted by FACS on the basis of their native -galactosidase activity (110). In another study

it was shown that exposure to osmotic shock resulted in the uptake of FDG by Myxococcus

xanthus strains containing transcriptional fusions to lacZ and allowed sorting of

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subpopulations according to their level of -galactosidase expression. A lipophilic derivative of

FDG (C8-FDG), which yields a product that is better retained by cells, has been used to study

the gene expression in sporulating cultures of Bacillus subtilus(31, 52).

Advances in engineering of the green fluorescent protein (GFP) fromAequorea victoria

into mutants with improved properties and altered colours have provided the basic tools that

allow the investigation of complex processes in live cells (101, 124, 125, 140). A major

advantage of using GFP is that it can be used to stain living cells and monitor them in real time.

GFP in combination with FCM has been used to detect and enumerate starved bacteria (30,

79), to investigate the heterogeneity of stress gene expression in S. cerevisiae during heat

treatment (10) and to track Campylobacter jejuni, a food-borne pathogen, in a mouse model

(90). Gunasekera et al. (50) used GFP production and PI uptake to monitor the gene expression

and viability of Pseudomonas putida, a psychotrophic milk spoilage bacterium, following

pasteurization. Their results showed that a substantial fraction of cells that were incapable offorming colonies as a result of the heat stress, were metabolically active since they were able to

transcribe and translates genes, which in turn may affect milk quality and safety. The interaction

between bacteria and host cells has been also studied in vivo and ex-vivo using GFP in

conjunction with fluorescence activated cell sorting (46, 126).

A pioneering assay using -lactamase as reporter system has recently been reported by

Zlokarnik et al. (142). The novelty resides on the design of a fluorogenic substrate, derivative

of cephalosporins (coumarin cephalosporin fluorescein (CCF2)) that is well retained in active

cells. The nonfluorescent, esterified substrate CCF2/AM is relatively nonpolar and canpassively diffuses across the cell membrane. Once inside the cell, the ester groups are

hydrolyzed by nonspecific esterases to release and retain within the cells the substrate for

-lactamase. CCF2 is composed of a fluorescein and coumarin linked by a cephalosporin

bridge, which is cleaved in the presence of -lactamase. If the cells are not expressing the

-lactamase reporter enzyme, the intact CCF2 fluoresces green under ultraviolet excitation, as

a result of the fluorescence resonance energy transfer (FRET) between coumarin and the

green-emitting fluorescein. In the presence of -lactamase, substrate cleavage allows the

coumarin to emit blue fluorescence while the fluorescein is quenched. Using the ratio of

intensities at the two wavelengths (blue/green) allows more accurate signal quantification

since it improves signal-to-noise due to cell size variation. The -lactamase reporter system

(CCF2) was demonstrated to exhibit higher sensitivity and specificity than the -galactosidase

FDG, and it is well adapted for flow cytometric analysis (27, 70). Although this method was

designed to measure gene expression in mammalian cells, the principle of FRET and trapping

the substrate would also be applicable in analysis of microbial gene expression. The method

could be used without modification for the rapid analysis of cells expressing native

-lactamase activity and for screening for inhibitors (137).

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2.5.3 Identification of microorganisms

Flow cytometry offers numerous possibilities for identification and enumeration of

microorganisms in their natural habitat. Hence it provides an extensive toolbox for

microbiologists to detect, isolate and enumerate microorganisms of interest in natural samplesin an accurate and rapid way.

Nucleic acid dyes: non-specific detection

A large number of fluorescent dyes with high affinity binding to DNA and /or RNA are

used to discriminate the target cells from the background. The combination of scatter

parameters and the nucleic acid dye fluorescence is becoming the method of choice to

discriminate target cells from the background sample and is widely employed to detect and

enumerate bacteria in their natural environments (17, 20, 51, 65, 74, 109). A FCM method for

direct detection of the anaerobic bacteria in human feces and biopsies was described using PI for

discrimination the fecal cells and excluding large particles by FSC and SSC (128, 143, 144).

The range of fluorescent stains, with different spectral characteristics and high

quantum yield, appropriate for flow cytometry analysis is continuously expanding. One

further development in this area is the availability of some commercial kits well suited for

rapid detecting and counting of total and viable bacterial cells (53). The BacLight viability kit

has been used to monitor the viability of microorganisms in different environmental settings

such as in drinking water (20), seawater (45) and food products such as cheese (26) or milk

(51). Gram staining kits for unfixed and viable microorganisms have been developed (53) and

have recently been applied to study the viability of bacteria in sewage water (41). Using

hexidium iodide (HI), and SYTO13, Mason et al. (87) could correctly predict the Gram status

of 45 strains of clinically relevant organisms, including several known to be Gram variable.

Recently, a FCM-based Gram staining protocol was used to monitor milk contamined with

Staphylococcus aureusand E. colidemonstrating the suitability of this technique for detecting

bacteria in a food matrix by culture-independent means (55).

Antibodies: ImmunodetectionFlow cytometry in conjunction with fluorescent antibodies has been used to

detect surface antigens in a number of bacteria including Salmonella(33, 88), Legionella(61)

and E. coli (138). Antibodies can be made fluorescent by covalently attaching them to

fluorescent organic compounds such as fluorescein isothiocyanate (FITC),

tetramethylrhodamine isothiocyanate (TRITC), texa red, phycobiliproteins, the cyanine dyes

such as indocarbocyanine dyes (Cy3, Cy5 and Cy7) (113) and the ALEXA dyes

ecophysiology intestinal

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