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REVIEW
Molecular microbiology methods for environmental diagnosis
T. Bouchez
1
•A. L. Blieux
2
•S. Dequiedt
3
•I. Domaizon
4
•A. Dufresne
5
•
S. Ferreira
6
•J. J. Godon
7
•J. Hellal
8
•C. Joulian
8
•A. Quaiser
5
•F. Martin-Laurent
3
•
A. Mauffret
8
•J. M. Monier
9
•P. Peyret
10
•P. Schmitt-Koplin
11
•O. Sibourg
9
•
E. D’oiron
12
•A. Bispo
13
•I. Deportes
13
•C. Grand
13
•P. Cuny
14
•P. A. Maron
3
•
L. Ranjard
3
Received: 7 September 2016 / Accepted: 9 September 2016
Springer International Publishing Switzerland 2016
Abstract To reduce the environmental footprint of human
activities, the quality of environmental media such as
water, soil and the atmosphere should be first assessed.
Microorganisms are well suited for a such assessment
because they respond fast to environmental changes, they
have a huge taxonomic and genetic diversity, and they are
actively involved in biogeochemical cycles. Here, we
review microbiological methods that provide sensitive and
robust indicators for environmental diagnosis. Methods
include genomics, transcriptomics, proteomics and meta-
bolomics to study the abundance, diversity, activity and
functional potentials of indigenous microbial communities
in various environmental matrices such as water, soil, air
and waste. We describe the advancement, technical limits
and sensitivity of each method. Examples of method
application to farming, industrial and urban impact are
presented. We rank the most advanced indicators according
to their level of operability in the different environmental
matrices based on a technology readiness level scale.
Keywords Molecular microbiology Environmental
diagnosis Bioindicator Environmental matrix
Introduction
The greatest biological diversity amongst all the organisms
living on Earth is to be found in the «infinitely small» i.e.
the ‘‘invisible’’ world. Due to their extraordinary capacity
for genetic adaptation to variations in their environment,
microorganisms have indeed colonized all ecosystems,
without exception, on our planet. This ubiquitous distri-
bution can be explained by the extraordinary plasticity and
genetic diversity that characterizes the microbial world.
Thus, no fewer than 1 million species of bacteria and
P. Cuny, P. A. Maron and L. Ranjard have contributed equally to the
coordination of this review.
&L. Ranjard
lionel.ranjard@dijon.inra.fr
1
Irstea, UR HBAN, 1 rue Pierre-Gilles de Gennes,
92761 Antony Cedex, France
2
Welience AgroEnvironnement-SATT Grand-Est, AgrOnov –
RD31, 21110 Bretenie
`re, France
3
Agroe
´cologie, AgroSup Dijon, INRA, University of
Bourgogne Franche-Comte
´, 21000 Dijon, France
4
INRA UMR CARRTEL, Thonon, France
5
UMR CNRS, Universite
´Rennes, ECOBIO, Rennes, France
6
Genoscreen, GENOSCREEN Campus Pasteur - 1 rue du
Professeur Calmette, 59000 Lille, France
7
LBE, INRA, 11100 Narbonne, France
8
Bureau des Ressources Ge
´ologiques et Minie
`res, BP 36009,
45060 Orle
´ans Cedex 1, France
9
ENOVEO, 69002 Lyon, France
10
Universite
´d’Auvergne, EA CIDAM 4678, CRBV,
63001 Clermont-Fd, France
11
Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany
12
Observatoire Franc¸ais des Sols Vivants, Domaine de Danne,
49500 St Martin du Bois, France
13
ADEME, BP 90406, 49004 Angers Cedex 01, France
14
Universite
´Aix- Marseille, Institut Phyte
´as, OCEAMED,
Luminy, 13009 Marseille, France
123
Environ Chem Lett
DOI 10.1007/s10311-016-0581-3
100,000 species of fungi can be found in a single gram of
soil, 10,000 bacterial species per ml of water and 100,000
bacterial species per m
3
of air. Apart from their genetic
diversity, these communities also represent a very large
proportion of the biomass inhabiting ecosystems. The soil
microbial biomass, for example, can account for 2–10 tons
of carbon per hectare, i.e. the equivalent of about ten cows
grazing on the same surface area!
This amazing richness confers microorganisms with a
special place at the biosphere level as a reservoir of
genetic resources i.e. a true ‘‘patrimony’’ of humanity.
The enormous diversity of microorganisms is also
apparent in their active involvement in ecosystem func-
tions and in the services ensured by the environmental
matrices. Thus, microbial communities provide «support
and regulation services», notably through their roles in
the biogeochemical cycling of major elements such as
carbon, nitrogen, phosphorus, sulphur. In the nitrogen
cycle, for example, the microbial component is respon-
sible for transformations such as atmospheric nitrogen
fixation, ammonification, nitrification and denitrification
(Hayatsu et al. 2008). Similarly, the mineralization of
organic matter, a core process in the functioning of ter-
restrial and aquatic ecosystems, is mainly carried out by
microorganisms, which transform complex organic
molecules into mineral elements. In aquatic ecosystems,
about 90 % of the organisms performing photosynthesis
are microorganisms. They are responsible for approxi-
mately half the primary production of organic matter in
the biosphere, i.e. about 56 Pg C year
-1
(Buitenhuis et al.
2013). As a result of their metabolic plasticity, microor-
ganisms are also involved in the degradation and immo-
bilization of pollutants (heavy metals, pesticides, etc.) in
the environment. Certain microorganisms greatly impact
the health and growth of plants by participating in sym-
bioses, for example, or by causing diseases (Barrios
2007).
Like all living beings, microbial communities are con-
stantly interacting with their environment. Their highly
sensitive responses to changes in environmental conditions
may be apparent as modifications of biomass, diversity and
activity (Pulleman et al. 2012; Sharma et al. 2011). The
development of new molecular approaches, based on the
extraction and characterization of DNA from the environ-
ment (water, soil, sediments), during the 1980s, revolu-
tionized the analysis of microbial communities in the
environment, which had previously been based on cultur-
ing techniques or microscopic observation. All these
developments resulted in the current era of «OMICs»
approaches which enable the genetic and functional
diversity of microorganisms to be characterized as a whole,
and without a priori, by the high-throughput analysis of
DNAs (genomics), RNAs (transcriptomics), proteins
(proteomics) or metabolites (metabolomics) (Maron et al.
2011).
To be used in defining a relevant diagnosis of environ-
mental quality, biological indicators need to take the func-
tioning of the environment into account and be sensitive to
modifications in environmental conditions (Rames et al.
2013; Pulleman et al. 2012). Microbial communities offer
great potential in this regard because, as stated above, (1)
they are present at high densities and diversities in all envi-
ronments, (2) they are actively involved in biological func-
tioning and in the services rendered by ecosystems, and (3)
they respond in a highly sensitive way, in terms of modifi-
cations in biomass, structure/diversity and activity, to
changes in environmental conditions. Nevertheless, such
indicators must also satisfy practical and economic criteria
(i.e. be quick and easy to use and interpret, reproducible,
inexpensive, and readily accessible to users). They must also
be associated with appropriate reference systems so that the
desired diagnosis can be obtained by positioning the mea-
sured values within a range (weak/normal/high) of opera-
tional variability (Ritz et al. 2009). Although most of the
methods developed over the past 30 years to characterize
microbial communities in situ were proposed as potential
indicators of environmental matrix quality, not all fulfil these
different criteria. The greatest limitation is the absence of
standardized procedures and (reference systems for these
indicators, due precisely to the enormous diversity of
microorganisms and the environments in which they live. A
review of these various microbiological indicators now
seems timely, especially, the potential of molecular micro-
biological tools to provide new indicators of ecosystem
quality which respond to all the above-listed prerequisites.
In this context, the first aim of this article is to describe the
different molecular microbiology techniques in terms of their
advancement, technical limits and sensitivity for studying the
abundance, diversity, activity and functional potentials of
indigenous microbial communities in various environmental
matrices (water, soil, air, waste substrates). Secondly the article
proposes examples of applications illustrating the actual uses of
these techniques to assess or remedy the impact of human
activities (farming, industrial, urban) on the different environ-
mental matrices. In fine, the last section lists the most efficient
microbial indicators for diagnosing the quality of different
environmental matrices, for operational use by various stake-
holders (farmers, industrials, site managers, land developers).
Microbial molecular techniques
A major challenge in environmental microbiology consists
of understanding the impact of environmental perturbations
on the activities of indigenous microbial communities and
determining the link between the genetic/functional
Environ Chem Lett
123
diversity of these communities and the integrated func-
tioning of ecosystems. It implies being able to analyse the
different components (density, diversity, function, activity,
interactions), which characterize the microbial community
and their regulation by environmental factors. At present,
there are potentially four different levels at which micro-
bial communities can be studied in situ (Fig. 1):
•Analysis of the DNA from indigenous communities,
designated environmental genomics. The techniques
used can access the density of microbial communi-
ties and their genetic and functional diversity;
•Analysis of the RNA from indigenous communities (all
gene sequences expressed), known as environmental
transcriptomics, and providing a means of identifying
populations or functions which are active under certain
environmental constraints;
•Analysis of proteins, or examination of the total
proteins synthesized at the scale of the microbial
community, termed environmental proteomics and
providing access to the functions of microorganisms.
The interest of the proteome, in relation to the
transcriptome, is to target the enzymes actually respon-
sible for the activity of the community under specific
conditions;
•Analysis of metabolites: study of the metabolites
synthesized by the microbial communities is termed
environmental metabolomics and can be used to
identify the final or intermediate products of their
activity.
Altogether, these technics can be used to characterize a
single organism or a community of organisms (Fig. 1). In
this latter case, the prefix ‘‘meta’’ must be used.
Environmental genomics and transcriptomics
DNA (Deoxyribonucleic Acid) constitutes the physical
support for genetic information in all living organisms.
This genetic information as a whole constitutes the gen-
ome, which groups together all the genes (genes coding for
ribosomal RNA, proteins). DNA is a very large molecule
formed by the association of numerous nucleotides of four
different types (A, T, C and G), and each gene is charac-
terized by a specific nucleotide sequence. This information
can be decoded by reading the sequence of the gene or
genome. It is thus possible not only to determine the
functions of genes and the corresponding proteins but also
to decipher the parental relationships between organisms
and thereby to reconstruct «an evolutionary history of life’’.
RNA (Ribonucleic Acid) is present in all organisms and
represents the product of gene expression. Like DNA, RNA
consists of a succession of 4 nucleotides (A, U, G and C).
The RNA molecule, however, due to differences in struc-
ture, is far less stable than DNA and can easily be degra-
ded. RNA molecules are synthesized during transcription
using DNA as a model.
DNA or RNA analysis is currently the preferred way to
describe the repertoire of genes and functions associated
with microorganisms (Fig. 2).
DNA analysis can be used to identify the microorgan-
isms present in an environmental sample, their proportions
within the community, and even their physiological and
metabolic potential. The study of genes and genomes also
provides information about «family relationships» between
microorganisms and permits reconstruction of their evo-
lutionary history. However, it cannot be used to identify
the genes being expressed at a given moment. Thus, it
Fig. 1 Different levels of
integration (DNA, RNA, protein
and metabolite) of molecular
microbiology techniques for
studying single microbial
organism or communities
(ADEME)
Environ Chem Lett
123
cannot determine the functions, which are being performed
in relation to the state of the environment or its fluctua-
tions. Nor are the most active micro-organisms in a com-
munity necessarily the most abundant. Also, a large
fraction of the extracted DNA has been shown to corre-
spond to extracellular DNA, actively excreted by
microorganisms or passively released after cell death
(Nielsen et al. 2007). Thus, the detection of DNA in a
sample does not signify that the organisms from which this
DNA originates are viable or that the associated genes are
functional.
Gene transcripts (RNA) analysis can be used to establish
a more direct link between the presence of an organism and
its activity. However, it has to be remembered that the
different regulatory mechanisms involved in RNA degra-
dation or protein synthesis can strongly modify the links
between genes expression and protein activity. For this
reason, the nature of the relationship between gene
expression, the amount of corresponding proteins and
actual activity has still not been deciphered. RNA analysis
enables the taxonomic diversity and composition to be
characterized as well as the functions carried out by active
or at least viable microorganisms. All this information can
provide answers to numerous microbial ecology questions,
e.g., which microorganisms are involved in the transfor-
mation of a chemical element or in the degradation of a
pollutant? How do microorganisms perceive and adapt to
perturbations in their environment? How do they interact
with each other?
How DNA and RNA are extracted from environmental
matrices
Over the past thirty years, numerous methodological
approaches have been developed for the extraction of DNA
and RNA from different environmental matrices (Terrat
et al. 2015). These approaches are based on in situ cell lysis
(thermic and/or chemical and/or mechanical and/or enzy-
matic) followed by separation/purification of the DNA
from the rest of the environmental matrix (mineral and
organic). Because of the extreme microbial diversity and
the different matrices being explored, complete extraction
of the entire DNA present in a sample is currently
impossible. Relatively comprehensive information about
the composition of a community could probably be
obtained by applying several different extraction protocols
(Delmont et al. 2011). However, this approach cannot be
considered in many cases, in view of the numerous samples
to be treated or insufficient quantities of sample available.
Ways to standardize this extraction step have therefore
been initiated to permit meaningful comparisons of sam-
ples (Terrat et al. 2015). As regards the soil matrix, this
standardization effort has resulted in an ISO standard (ISO
11063:2012) for the direct extraction of DNA from
samples.
Compared with DNA, RNA is less stable and more
sensitive to the perturbations caused by taking samples, and
partial degradation of the RNA from environmental sam-
ples is often observed. RNA extraction and manipulation is
Fig. 2 Tools for DNA and RNA analysis and types of information obtained (ADEME)
Environ Chem Lett
123
therefore carried out under much more stringent conditions
(clean room, ribonuclease treatment of equipment) than for
DNA. The extracted RNA can be converted into comple-
mentary DNA (cDNA) by reverse transcription and, as
cDNA possesses the same degree of stability as DNA, it
can then be analysed with the same molecular tools
(Fig. 2).
The different techniques for characterizing DNA and RNA
Tools for analysing the nucleic acids from microorganisms
can be divided into targeted and non-targeted approaches,
depending on whether they are based on the analysis of
previously selected genes (i.e. genetic markers) or all the
genes without a priori (Fig. 2).
Two types of markers, i.e. markers of identity (phylo-
genetic genes) or markers of functions (functional genes),
can be used in the targeted approach. The phylogenetic
biomarker most often employed in microbial ecology is the
gene expressing the small ribosomic RNA subunit (16 S
rRNA in prokaryotes and 18S rRNA in eukaryotes). The
functional biomarkers correspond to the genes coding for
proteins. Their sequences are generally more variable than
those of ribosomal RNA. These genes also have a much
less ubiquitous distribution and are strongly influenced by
horizontal transfers of genetic information. They generally
provide information about the potential presence of a
function within a sample (presence of the gene in the DNA
sample) or about the expression of this function (detection
of the gene transcript in the RNA sample). Whichever
technique is adopted, analysis of these markers initially
involves the use of oligonucleotides (small fragment of
single strand DNA) synthesized chemically. Complemen-
tarity between the nucleic acid bases of the oligonu-
cleotides and those of the targeted genetic markers enables
the latter to be isolated and characterized. PCR (poly-
merase chain reaction) is used to specifically amplify a
small portion of sequence within the marker.
The PCR-amplified DNA fragments are then character-
ized according to their sequences variability i.e. their
‘‘genetic fingerprints’’. DNA chip approaches and gene
capture through hybridization operate on the same princi-
ple of base pairing and permit specific analysis of a large
set of phylogenetic or functional markers. All these
approaches require the extraction of nucleic acids from
cells. In contrast, in situ hybridization (FISH: fluorescent
in situ hybridization) allows the presence of a gene to be
directly detected inside the cell.
Given the immense diversity of microorganisms in the
various environmental matrices, non-targeted, so-called
holistic strategies have been developed to accompany these
targeted approaches. The main one is based on direct
analysis of the DNA and RNA extracted from natural
matrices, without a priori. From a technical point of view,
this holistic approach is based on high-throughput
sequencing of the metagenomes or metatranscriptomes of
the microbial communities. The sequences can then be
subjected to bioinformatics analysis to establish «who is
there?», «in what proportion?» and «who does what?».
This approach also offers the immense advantage of being
able to identify new taxons or new functions. Determina-
tion of the link between community structure (diversity,
abundance) and the different functions performed consti-
tutes a major challenge in microbial ecology. At present,
this non-targeted approach is the one most often used to
investigate the diversity and activity of microbial com-
munities. However, due to the cost of sequencing and the
complexity of analysing the resulting data, it has not been
widely developed for use as a diagnostic tool. High-
throughput sequencing of metagenomes or metatranscrip-
tomes generates extremely large and highly complex vol-
umes of data, which, in general, require access to
computerized equipment with high storage and calculating
capacities. It has to be emphasized that the computerized
analysis of high-throughput sequencing data is particularly
time consuming. This particular step often takes much
longer, in fact, than all the previous steps between sample
collection and sequencing.
These metagenomic approaches have been used to
demonstrate the different microbial organisms and mech-
anisms involved in depollution functions (Bertin et al.
2011; Pelletier et al. 2008), industrial applications (Mao
et al. 2014), adaptation to changing adjacent conditions in
different environmental matrices (Ng et al. 2010; Mondav
et al. 2014; Tseng and Tang 2014) and in biogeochemical
cycles (Ghai et al. 2014).
Over the past ten years, metatranscriptome sequencing
has enabled microbial diversity and the functions
expressedbymicro-organismstobecharacterizedina
large number of environments, such as soil (Bailly et al.
2007;Tveitetal.2013;Geisenetal.2015); sediments
(Dumont et al. 2013); the euphotic layer in oceans (Frias-
Lopez et al. 2008;Gilbertetal.2008; Gifford et al. 2014;
Poretsky et al. 2005,2009) and in deep ocean (Les-
niewskietal.2012); lakes (Vila-Costa et al. 2013);
microbial mats (Quaiser et al. 2014) and the rhizosphere
(Turner et al. 2013; Chaparro et al. 2014). The results
have contributed to the identification of new key players
notably in the nitrogen cycle in soils (Urich et al. 2008)
and deep oceans (Baker et al. 2013); the discovery of
mechanisms coordinating gene expression in different
species within a community in response to variations in
environmental conditions over time (Ottesen et al. 2013)
and in describing the temporal dynamics of communities
involved in the recycling of organic matter (McCarren
et al. 2010).
Environ Chem Lett
123
Environmental proteomics
This approach targets the analysis of proteins at the scale
of the microbial organism or community and provides a
functional complement to studies based on the analysis of
DNA or even RNA. It is indeed the functional proteins,
not the genes, which ‘‘act’’. It is the functional proteins,
which catalyse the chemical reactions and control the
mechanisms underlying the activity of organisms and
their interactions with the environment. They are the
instantaneous indicators of the functional state of organ-
isms, whereas the genes provide information about their
metabolic predispositions. The proteome designates the
set of products of functional genes (i.e. the proteins) of an
organism at a given instant. Proteomics consists of the
comprehensive analysis of such proteins. The metapro-
teome, by analogy to the metagenome, is defined as the
complete set of proteins synthesized by the entire
microbial community at a given time. The analysis of the
metaproteome is described as metaproteomics (Maron
et al. 2007).
Due to the great sensitivity of proteomics, phenotypic
modifications can be detected at the cell level, even before
their appearance at a macroscopic level. This property
makes proteomics particularly suitable to evaluate the
impact of perturbations on microorganisms and also to
monitor their functional role in the environment.
The principal challenge in environmental proteomics
concerns mapping the proteins extracted from the micro-
bial communities colonizing different environmental
matrices and identifying, in a non-targeted way, new
metabolic pathways and their associated coding genes.
Analysing the metaproteome of microbial communities in
the environment involves different technical stages, start-
ing with the extraction of microbial proteins from the
environmental matrix and ending with their separation and
identification (Fig. 3). Protein analysis is most often based
on a combination of three methodologies: two-dimensional
electrophoresis (2DE) which separates the proteins
according to their isoelectric point (pI) and molecular
weight (MW), mass spectrometry (MS) and then bioin-
formatics for their identification.
As in studies based on nucleic acids analysis, the most
crucial step in studies of the metaproteome is extraction. It
must result in the recovery of a set of proteins (1) repre-
sentative of the sample, and (2) of sufficiently high quality
and quantity to be analysed with the available molecular
tools (Leary et al. 2013). The choice of extraction proce-
dure will vary as a function of the type of proteins targeted
(for example prokaryote/eukaryote; extracellular/cellular)
and the analytical methods used (comparative analysis of
protein maps, measure/detection of specific protein or
enzyme activities).
Once the proteins have been extracted, different bio-
chemical methods can be applied to analyse the metapro-
teome, depending on the type of information and the level
of resolution required. A non-targeted ‘‘protein map’’ of the
community can be obtained by separating the proteins by
one- or two-dimensional gel electrophoresis (1DE) or
(2DE). Two-dimensional gel electrophoresis is usually
preferred as it ensures better protein separation and facili-
tates their subsequent identification. The proteins of inter-
est can then be extracted from the gels and identified by
mass spectrometry (MS). If the genome of the organism or
community under study has already been sequenced, MS
analyses of MALDI-TOF type (‘‘matrix-assisted laser
Fig. 3 Procedure for analysing
the metaproteome of microbial
communities in their
environment (ADEME)
Environ Chem Lett
123
desorption/ionization–time of flight’’) can be used to
compare the experimentally obtained mass spectra with
theoretical values in the protein databases and usually
provide valid identifications (‘‘Peptide Mass Fingerprint-
ing’’ technique). In the absence of identification or if the
organisms have not already been sequenced, which is the
case in the great majority of environmental proteomics
studies, it is better to generate fragmentation spectra by
tandem mass spectrometry (MS/MS) and to carry out de
novo sequencing of one or several peptides in the protein.
These spectra can then be interpreted in silico to detect
homologies with data in the genomic or proteomic data-
bases available online.
The applications for environmental proteomics in stud-
ies of microbial communities functioning, in situ and
without a priori, are enormous. Analyses of the metapro-
teome are currently being used in a great variety of envi-
ronmental matrices such as soil, water (fresh or marine),
sediments, deep aquifers, mining effluents or sludge from
sewage treatment plants. Whatever the environment con-
sidered, all studies show that the metaproteome is highly
complex, dynamic and sensitive to environmental varia-
tions. They reveal the potential capacity of environmental
proteomics to reflect the biological state of the
environment.
However, despite their tremendous evolution, the tech-
nologies used to characterize the metaproteome are still
limited in relation to those used for nucleic acids (DNA and
RNA). Thus, protein extraction still poses considerable
methodological problems (Leary et al. 2013; Keiblinger
et al. 2012) and continues to prevent routine application of
this approach. Similarly, protein separation by 2DE, which
is still the most frequently used method, remains limited by
the major difficulties associated with analysing poorly
abundant, very hydrophobic, very acidic or very basic
proteins. For these reasons, two-dimensional gel elec-
trophoresis has been labelled the ‘‘Achilles heel of pro-
teomics’’ (Figeys 2000). Due to the limits associated with
protein extraction and separation, it is estimated that only
1 % of the total metaproteome can be characterized by
current methods (Wilmes and Bond 2006). Because of this,
considerable efforts have been made to develop alternative
processes to «2DE», such as separation by chromatogra-
phy/capillary electrophoresis (Yates et al. 1993), and pro-
tein chips (Ramachandran et al. 2004). These developments
should eventually lead to high-throughput analyses of
complex mixtures of proteins and extend the possible
applications of proteomics in environmental studies.
Environmental metabolomics
Metabolomics is the most recent of the so-called «omic»
technologies, which also include genomics, transcriptomics
and environmental proteomics. This approach does not
involve molecular microbiology techniques sensu stricto
and has not yet resulted in the development of techniques
for routine use in natural matrices. It must, nevertheless, be
mentioned due to the great potential medium-term appli-
cations in environmental diagnosis based on the develop-
ment of bioindicators of microbial quality or the functional
potentials of different ecosystems. Metabolomics consists
of analysing all the molecules of low molecular weight
(metabolic intermediates, hormones…) synthesized by an
organism. At the scale of an organism, analysis of the
metabolome provides a realistic picture of the metabolic
pathways activated and their importance (Fiehn et al. 2000;
London and Houck 2004; Nicholson et al. 1999) and can
therefore be used to study response mechanisms to differ-
ent stresses (temperature, water, UV, nutritional limita-
tions, pollution due to a chemical contaminant). This
concept was initially introduced into Life Sciences by
Holmes, Lindon and Nicholson at the end of the 1990s and
developed rapidly thereafter (Nicholson et al. 1999). At
present, metabolomics is applied in different domains,
including fundamental biological and medical science and,
more recently, environmental sciences.
In environmental metabolomics, this approach is used to
study the interactions occurring between organisms and
their environment (Lankadurai et al. 2013). It can be used,
for example, (1) to study the regulation and evolution of
metabolic pathways as a function of environmental con-
ditions, (2) to identify synthesized products of high bio
added value and markers of exposure to a stress, and (3) to
study the mechanisms of adaptation to environmental
perturbations such as chemical pollution or climatic
change. This approach can be applied not only to an
organism but also to study the metabolome of a whole
community of organisms.
The different technical steps involved in studying the
metabolome of an organism or group of organisms are
shown in Fig. 4. The choice of sample is particularly
critical in environmental metabolomics as markers of stress
are often identified by comparing the metabolomes
expressed by organisms subjected or not to the environ-
mental stress under test. This is the case in medical
research when the metabolomes of healthy and sick indi-
viduals are compared. Once the samples have been selec-
ted, they have to be made compatible with the analytical
method. It is thus generally necessary to use several dif-
ferent procedures to extract metabolites (Mushtag et al.
2013) e.g. liquid–liquid extraction, micro-wave assisted
extraction, supercritical phase extraction, solid phase
extraction (SPE), or solid phase micro-extraction (SPME).
During this step, the choice of extraction conditions (type
of solvent, solid phases used) will depend on the analytical
procedure envisaged.
Environ Chem Lett
123
Thus, if the aim is to be as comprehensive as possible,
which is often the case in metabolomics, solvents or solid
phases that allow the extraction of a large range of com-
pounds will be selected. Conversely, more selective con-
ditions will be chosen if the goal is to focus on specific
families of compounds. The resulting extracts will then be
analysed by nuclear magnetic resonance (NMR) and/or
mass spectrometry, which are the two best adapted ana-
lytical techniques. The generated data are often presented
as a spectrum (profile of the more or less large peaks) and
needs pre-treatment (alignment of the spectra, subtraction
of blanks and background noise, etc.) to ensure that the
metabolomes can be compared with each other without
analytical artefacts. Then comes the critical phase of sta-
tistically analysing the data by chemometric methods
[principal component analysis (PCA), partial least squares
(PLS), multi-tables analysis, etc.]. These methods allow
those metabolites with different levels of expression
between samples to be identified (Lucio 2009). This
approach, known as non-targeted metabolomics, is used to
elucidate metabolism without an initial hypothesis. The
objective is to detect the largest possible number of com-
pounds in order to identify the metabolic pathways
involved, together with those compounds which can be
considered as biomarkers of exposure to environmental
stresses.
More and more applications of this technique to envi-
ronmental matrices (soils, water, wastes) are being
described (Lankadurai et al. 2013). However, it is clear that
no single metabolomic procedure can cover the entire
metabolome of an organism or community of organisms.
Exploration of the metabolome in a given ecosystem
therefore requires the use of several extraction techniques
coupled with high-resolution analytical approaches.
Potential applications of microbiology techniques
as bioindicators for environmental diagnosis
The above-described molecular microbiology techniques
have made it possible not only to better explore, understand
and even predict microbial ecosystems in terms of abun-
dance, diversity and activity but also to develop new,
sensitive and robust bioindicators to assess the impact of
natural perturbations or those resulting from human activ-
ities. These different bioindicators are:
•microbial molecular biomass;
•detection and counting in situ of certain microbial
organisms or functional genes;
•taxonomic microbial diversity;
•functional microbial diversity;
•taxonomic composition of the microbial communities.
Microbial molecular biomass
As DNA is present in all microorganisms (bacteria, fungi,
viruses, etc.), the microbial biomass (total quantity of live
microorganisms) in an environmental matrix can be
determined from the amount of DNA extracted. The bio-
mass represents the capacity of an ecosystem to harbour a
more or less large quantity of microorganisms (bacteria,
fungi, viruses, etc.). It is crucial to the biological quality of
ecosystems due to its role in nutrients regulation and
transformation. It is also sensitive to the perturbations of
ecosystems resulting from human activities (farming,
industry, urbanization). The estimated biomass, obtained
from microbial DNA measurements, can therefore be
considered as a sensitive and robust indicator of the bio-
logical state of an environmental matrix, despite the exis-
tence of an extraction bias.
This indicator is interpreted by comparing the obtained
values with a local reference baseline or from its position
within a local or broad scale reference system (Fig. 5).
Microbial molecular biomass measurements for the ‘‘soil’’
matrix were obtained from the 2200 samples in the
‘‘ R e
´seau de Mesure de la Qualite
´du Sol’’ (RMQS). This
gave rise to a national map and to the first reference system
associated with this measurement (Dequiedt et al. 2011;
Horrigue et al. 2016).
Fig. 4 The different technical steps in a metabolomic approach
(ADEME)
Environ Chem Lett
123
In situ detection and/or counting of microbial
organisms or microbial functions
Detection and accurate counting of microorganisms or
microbial functions is essential to understand the ecologi-
cal functioning of an ecosystem or to assess the microbi-
ological quality of an environment. Different approaches
can be applied in the various environmental matrices
(water, soils, atmosphere, wastes, etc.) such as quantitative
PCR (qPCR), fluorescent in situ hybridization (FISH) and
DNA biochips.
Detection and Counting by real-time quantitative PCR
(qPCR)
Real-time quantitative PCR (qPCR) is a molecular
approach that allows the quantification of specific markers
(i.e. genes) in a DNA sample extracted from an environ-
mental matrix. The target to be quantified can be a taxo-
nomic or functional marker represented by a fragment of
DNA or complementary DNA derived from mRNA. This
molecular method provides an alternative technique for the
detection and quantification of microorganisms and their
activities in the environment to culture-based microbio-
logical methods. The principle in PCR is to use a ther-
mostable DNA polymerase to amplify target DNA
fragments in the nucleic acids extracts. The particularity of
qPCR is to determine the initial quantity of target DNA by
measuring the amount of DNA produced in a reaction at
the end of each cycle, which can thus be used to estimate
the number of taxonomic sequences of a given microbe or
functional gene within a complex community.
qPCR allows a sensitive, robust determination of the
abundance of genetic markers within the microbial
community of an environmental matrix. This technique can
generate different indicators of matrix quality, depending
on the markers targeted.
–Taxonomic markers (i.e. ribosomal genes) can be used
to determine the abundance of microorganisms.
Depending on the specificity of the primers used for
PCR, this quantification can be carried out at different
levels, ranging from the community as a whole (e.g. all
the bacteria or fungi) to determination of the abundance
of families, species or specific strains (e.g. pathogens,
symbiotes). An example of application for the detection
of cyanobacteria in lake has been described in Fig. 6.
–Functional markers (i.e. functional genes coding for the
proteins involved in the functions of interest, i.e.
degradation of pollutants, pathogenic responses, path-
ways of organic matter transformation associated with
biogeochemical cycles). The measurements of these
markers provide an estimation of the potential expres-
sion of the targeted function within the community
(example of application in Fig. 7).
Detection and counting by FISH (fluorescent in situ
hybridization)
Fluorescent in situ hybridization labelling can be used to
identify and count the cells of micro-organisms belonging
to a relatively large taxonomic group (‘‘family’’ of organ-
isms). The principle of FISH is based on the use of small
DNA probes, which are able to directly recognize and
adhere (by hybridization) to specific regions (ribosomal or
functional genes), within the cells of micro-organisms.
These probes consist of synthetic oligonucleotides and
carry a fluorescent molecule. After hybridization, the
Fig. 5 Variations in microbial molecular biomass on the national
scale (left) and as a function of land use (right). The mean microbial
molecular biomass of French soils is 61 lg DNA per gram of soil.
The soils under grassland have the highest microbial biomass
followed by those under forests. The soils under arable crops have
a lower microbial biomass than the national mean value. The soils
under vineyards and orchards have the lowest microbial biomass with,
on average, only 27 lg DNA per gram of soil (Dequiedt et al. 2011;
Horrigue et al. 2016)(ADEME)
Environ Chem Lett
123
labelled cells are fluorescent and can be counted by epi-
fluorescence microscopy.
The sensitivity and specificity of FISH labelling make it
a particularly effective tool for detecting the presence of a
microbial taxon in all sorts of environmental matrices (soil,
water, sediment, air). It is the only method able to directly
quantify cell abundance. These indicators (presence and
abundance) are very useful for determining the microbio-
logical quality of an environment, detecting the presence of
a taxon or a functional group of microorganisms and for
assessing the effects of environmental perturbations on
microbial communities (example of application on waste
water in Fig. 8).
These analyses allowed evaluating bacterial floc mor-
phology and the location of filamentous bacterial cells.
More precisely, it has been observed the internalization of
filamentous bacteria belonging to Microthrix parvicella but
also of phylum Chloroflexi inside the bacterial floc, which
increase their compaction and decantation (measured by
sludge index) (ADEME).
DNA biochips
A DNA biochip consists of a group of DNA molecules,
called probes, fixed to a small glass, silicon or plastic
surface (Fig. 9). Thanks to new miniaturization technolo-
gies, hundreds of thousands, even millions of different
probes can be attached to a single DNA chip. Several
thousand genes can therefore be identified in a single
experiment.
The general principle of DNA biochips resides in
specific recognition, due to pairing of the immobilized
probes with their targets (generally nucleic acids such as
the products of PCR, DNA or RNAs), during the
Fig. 6 Detection and quantification by qPCR of the species of
cyanobacteria Planktothrix rubescens and its gene for cyanotoxin
production in peri-alpine lakes in France as a function of their
mesotrophic or eutrophic conditions (Savichtcheva et al. 2014) for the
period 1939–2008. This analysis was able to demonstrate the
historical presence of a single genotype of Planktothrix rubescens
present throughout the last century, which systematically carried the
gene coding for the production of cyanotoxins, specifically respon-
sible for the massive blooms occurring during the mesotrophic period
(Lake Bourget) (ADEME)
Fig. 7 Metabolic pathway and genes involved in the degradation of
ethylene chlorides in a contaminated soil (left) (adapted from
Freedman and Gossett 1989), quantification of the genes involved
(right) (Monier, unpublished). The quantification of biomarkers by
qPCR allows the presence/absence of microorganisms or the
functions of biodegradation of interest to be established. The
generated data serve as a support in decision-making for implemen-
tation of a treatment. The raw data obtained under laboratory
conditions need to be contextualized in order to assess the best
depollution procedures to set up on-site (ADEME)
Environ Chem Lett
123
hybridization step (Fig. 10). This recognition is possible
because of the property of the bases (adenine, thymine,
cytosine and guanine) constituting a single strand of DNA
to spontaneously reform a double helix in the presence of a
strand of complementary DNA (Joux et al. 2011).
The use of DNA biochips requires initial extraction of
the nucleic acids (DNA or RNA) from the matrix under
examination (soil, water, atmosphere, wastes). Two types
of DNA biochips are most often used in microbial ecology
studies (Dugat-Bony et al. 2012): phylogenetic biochips (in
which the probes target taxonomic marker genes) to iden-
tify micro-organisms, and functional biochips (in which the
probes target genes coding for the proteins involved in the
functions of interest) to reveal metabolic functions.
The PhyloChip (Brodie et al. 2006) carries approxi-
mately 500,000 probes, each 25 bases in length, and cov-
ering almost all the prokaryotic community diversity in the
databases. It has been used in numerous environmental
studies. However, due to our still incomplete knowledge of
environmental microorganisms, the obtained information
reflects only a portion of the diversity present in complex
environments (Fig. 11). Even so, DNA biochips due to
their low cost, their capacity to specifically target a huge
number of genetic markers, both from a qualitative and
quantitative point of view, together with their ease of use
and analysis, still remain competitive when compared with
mass sequencing (Zhou et al. 2015).
The diversity and taxonomic and functional
composition of microbial communities
Even though the quality of natural ecosystems is dependent
on the abundance of indigenous microorganisms, it is even
more affected by the diversity of organisms and the func-
tions that they harbour. Microbiological diversity, in terms
of the number of taxon or different functions, is in fact
directly linked to the biological functions that an ecosystem
can support (such as fertility, degradation of pollutants,
plant production, barrier effect to invasive species, etc.)
(Maron et al. 2011). The taxonomic diversity and microbial
composition of an environmental matrix can be assessed by
using approaches such as DNA chips (see above para-
graph), mass sequencing of amplicons or global
sequencing.
Mass sequencing of amplicons
The objective in sequencing amplicons is to determine the
sequence of DNA fragments amplified by PCR. It involves
several technical steps followed by an analysis as sum-
marized in Fig. 12.
First, the DNA must be extracted from the matrix being
investigated (soil, water, atmosphere, waste). This DNA is
then used to generate amplicons by amplifying (by PCR)
the DNA sequence of interest. At the end of amplification,
the PCR product consists of a mixture of amplicons rep-
resentative of the abundance and diversity of the targeted
gene in the microbial community under study. These
amplicons are then subjected to a mass sequencing tech-
nique using new generation automatic sequencers (py-
rosequencer Roche, MiSeq Illumina, or other). Between
several thousand and several tens of thousands, sometimes
even more, sequences of the targeted gene can thus be
obtained from a single sample. This massive set of
sequencing data then needs to be analysed with appropriate
Fig. 8 Detection and
quantification by FISH
technique of two species of
filamentous bacteria responsible
for biological dysfunction in
sewage treatment plants.
aThiothrix sp., bMicrothrix
parvicella (red)—Photo by:
N. Durban—Irstea
(Durban et al. 2016)
Fig. 9 Glass slide with zones in the centre for attaching DNA
molecules (probes) (ADEME)
Environ Chem Lett
123
tools arranged in a bioinformatic pipeline. For this, a
number of pipelines (Mothur, Qiime, Pangea, GnSPipe,
etc.) are freely available online which enable the obtained
sequences to be filtered, sorted, classified, grouped together
and affiliated by comparison with sequences in the inter-
national databases.
In the context of microbial ecology, the sequencing of
DNA amplicons derived from environmental matrices has
been applied to determine the variability in gene compo-
sition between populations carrying the genes of interest,
within a given community. Depending on the targeted
genes, this technique can be used to generate different
indicators of the quality of an environmental matrix. Two
types of genes are most often used:
•ribosomal genes are taxonomic markers and their
sequencing can be used to determine the taxonomic
diversity and composition of microbial communities.
Microbial diversity is a key factor affecting the
biological quality of ecosystems due to its role in
nutrients recycling, the degradation of pollutants and,
indeed, the very stability of ecosystems. This indicator
is sensitive to perturbations within an ecosystem
(changes in farming practices on a soil, pollution of a
water course or sediments, alteration of the organic
status of a lake, stage of maturity of a compost, etc.).
The example of application shown in Fig. 13 described
the characterization of soil bacterial diversity on the
scale of French national territory;
Fig. 10 Principle of DNA biochips. The targets (DNA or RNA) labelled with a fluorochrome specifically hybridize with the probes
complementary to them. Analysis of the subsequent image permits determination of the genes present or expressed in the sample (ADEME)
Fig. 11 Response of genes involved in pathways for the degradation
of chlorinated solvents during bioremediation of a polluted water
table. The signal obtained for each gene is shown by the intensity of
shading, depending on the well (P1, P2, P3 or P4), and the date of
sampling (T number of days). PCE perchloroethylene, TCE
trichlorethylene, DCE dichloroethylene, VC vinyl chloride, ETH
ethylene). Analysis of the results obtained with the DNA chip
confirmed the biological origin of the degradation process and
demonstrated the involvement of a single metabolic pathway:
anaerobic reductive dechlorination. Several microorganisms were
identified which ensured complete degradation of the chemical
contaminant into an inoffensive molecule, ethylene. Bioremediation
is therefore efficient and leads to the production of a less toxic
molecule than vinyl chloride (Dugat-Bony et al. 2012)(ADEME)
Environ Chem Lett
123
•functional genes code for the proteins involved in the
functions of interest (e.g. degradation of pollutants,
pathogenesis, transformations of biogeochemical
cycles, etc.). By sequencing such genes, the structure
and functional diversity of the communities involved in
these activities can be assessed. These measurements
give a good indication not only of the state of such
communities in the environment but also of their
evolution in response to anthropogenic or natural
perturbations. They provide information about the
functional potential of the targeted communities.
Global sequencing of DNA or RNA
Global sequencing of the DNA extracted from an envi-
ronmental matrix sample is used to access the genes of all
the microorganisms present without involving a PCR
Fig. 12 Principal technical
stages involved in the
sequencing of amplicons
(ADEME)
Fig. 13 Variation in the bacterial diversity of soil (in number of
taxons per gram of soil) on the scale of France (left) and as a function
of land use (right) (Terrat et al. personal communication). This map
shows the large variations in diversity with zones where the number
of taxons is high (in red on the map) and others where it is very low
(in blue on the map). At this scale, no influence of the climate or
geomorphology (presence of mountain, river, coastline) is shown.
Statistical analysis show that these variations in bacterial diversity
observed at the national scale are influenced by the type de soil (in
terms of pH, texture and C/N ratio) but also as a function of land use
(farming, grassland, forest). Thus, the soils under grassland and
forests show lower levels of diversity (1291 and 1136 taxons,
respectively) in relation to farm soils or vineyards (1363 and 1409,
respectively). This can be explained by the ecological concept of
«intermediate perturbation» which predicts that the biodiversity of an
ecosystem is maximum when this latter is subjected to a perturbation
of intermediate intensity (neither too severe nor too weak) and
minimum when it is subjected to a weak perturbation (phenomenon of
competitive exclusion of species) or strong (selection of species).
Thus, the soils under forests and grassland are ecosystems, which
undergo weak perturbations for the bacterial communities due to the
quasi absence of human activities and therefore contain a weak
bacterial diversity. Conversely, the arable and vineyard soils which
are generally subjected to a multitude of interventions are more
perturbed systems (but not excessively) and therefore exhibit a higher
bacterial diversity (ADEME)
Environ Chem Lett
123
amplification step. The resulting information can be used to
identify the microorganisms and their functions. The same
global sequencing approach can be used to sequence the
RNA of a microbial community extracted from an envi-
ronmental sample and to determine which microorganisms
are active and the genes that are being expressed (Fig. 14).
From a technical point of view, global sequencing is
based on two main steps: splitting the DNA or RNA
molecules into numerous tiny, partially overlapping, and
more or less redundant, fragments and comparing the
sequences of these fragments in order to reassemble and
reconstitute the sequence(s) present in the analysed sample.
Global sequencing, unlike amplicon sequencing, does
not target any one gene in particular. Nor does this
approach require a priori knowledge of the microorganisms
being studied. It can therefore be applied to any microbial
community and to any environmental matrix and can also
be used to sequence the genome or transcriptome of
microorganisms cultured in the laboratory. The data
obtained contain both the sequences of phylogenetic
biomarkers (gene and ribosomal RNA transcripts) and
functional biomarkers (genes coding for proteins and
messenger RNA). An example using this approach to
analyse the response of microbial communities to a pol-
lution of marine sediments by an oil spill is presented
below in Fig. 15.
Fig. 14 Global sequencing of a (meta)genome or (meta)transcrip-
tome process. The grey spiral represents the general process starting
with the sample and ending with data analysis. The principal
molecular biology steps are shown in red and the results of these
steps in orange. The steps involved in the bioinformatics analyses are
indicated in green and the corresponding results in blue. The black
arrows represent the multiplicity of bioinformatics analyses possible.
Adapted from Raes and Bork (2008)(ADEME)
Fig. 15 Venn diagram showing the number of genes under- or over-
expressed in oil-contaminated coastal marine sediments compared
with control sediments (t). Three treatments were compared with the
control sediments: addition of 5000 ppm of Rebco crude oil (HC),
addition of 1000 ind m
-2
of the marine annelid Hediste (Nereis)
diversicolor (N), and the addition of oil and H. diversicolor (HCN).
The numbers in the intersections of the circles correspond to genes for
which the expression in the two treatments is similarly modified. For
example, 1949 genes are under- or over-expressed in comparison with
the control sediments (t) in the HC and HCN treatments («oil effect»).
245 genes are specifically modified in relation to the control when
marine worms are added to the sediments and improve bioturbation of
the sediments (aeration and remobilization of the contaminants).
(Militon et al. 2016)(ADEME)
Environ Chem Lett
123
Global sequencing of the genome or transcriptome is
currently the most efficient means for determining the
taxonomic position of a microorganism, (i.e. its place in the
tree of life) and above all, for understanding its metabolism
and way of life. In the case of a microbial community,
global sequencing of the metagenome or metatranscrip-
tome offers the most comprehensive approach for studying
the diversity and role of microorganisms in ecosystems.
The global approach is technically relatively simple to
carry out. However, the cost of the required equipment and
the complexity of the resulting data make it unsuitable for
large-scale use as a diagnostic tool. Global sequencing is
nevertheless useful as an initial approach to get a detailed
picture of the microbial communities in an ecosystem. The
generated data can then be examined to find new indicators
for use in a targeted approach.
Operational dashboard of microbial indicators
for environmental diagnosis
The major technological advances achieved over the past
10 years have led to an exponential increase in the scien-
tific and technological knowledge associated with molec-
ular microbiology, which has, in turn, encouraged the
development of new techniques and new molecular mark-
ers, and opened up new horizons for their application in
environmental diagnosis (studies of impact, bioremediation
of polluted sites and soils, evaluation of farming practices,
optimization of bioprocesses for treating waste products,
etc.). The demand from industry and farming to use these
research-derived technologies is, in fact, ever increasing.
Even so, it is important to understand that the notion of
«proven technique» differs from the notion of «operational
bioindicator». Certain standardized techniques are destined
to remain in the research domain and never have final
applications as indicators due to their cost, excessive
technicality or because the results can only be interpreted
by specially trained personnel.
In the context of Environmental Molecular Microbiol-
ogy, a bioindicator therefore corresponds to a combined
technique and genetic marker (taxonomic or functional,
more or less specific). It can be used to detect, quantify, or
characterize a community, population, species, function or
functional group, in a given matrix or context. It is possible,
with a suitably chosen bioindicator, to qualify or quantify the
microbiological state of an environment and to provide
information, for example, on the impact of a perturbation of
the biological system being examined or assessed.
An efficient bioindicator needs to be:
•robust, reliable, accurate and specific: its interpretation
must remain stable and coherent over time;
•sensitive: reflect even weak variations in the environ-
ment or system under study;
•easy to understand, simple and readily applicable by all
the agents involved;
•appropriate in view of the fixed objective;
•acceptable in terms of price in relation to the informa-
tion provided;
•relevant so as to truly facilitate decision-making;
•and finally, must allow a diagnosis either by compar-
ative analysis (e.g. evolution over time or space in
relation to a reference situation) or by comparison with
a reference system (database).
The TRL (Technology Readiness Level) scale can be
applied to assess the level of maturity of new technologies.
The scale begins with the observation and description of
base principles (level 1—concept) and terminates with the
actual operational application (outside the experimental
context) of a technology (level 9). This scale can be sim-
plified by considering just three levels of technological
maturity:
•A: Discovery of a marker gene of interest and
identification of the most suitable technique for its
study—TRL 1–3: fundamental research.
•B: Use of the bioindicator in an experimental context—
TRL 4–6: development—industrialization.
•C: Use of the bioindicator in an actual context,
responding to a market demand, and that suppliers
can propose for routine use—TRL 7–9: industrial
applications.
Based on this TRL scale, the different indicators
described in ‘‘Potential applications of microbiology tech-
niques as bioindicators for environmental diagnosis’’ sec-
tion can be positioned according to their level of
operability (Table 1). The properties of environmental
matrices show considerable variability between soil, sedi-
ments, water, wastes and the atmosphere (spatial variabil-
ity, heterogeneity, cell density, etc.). This means that the
«importance» of the technical constraint, as regards the
development of molecular tools on which the measure-
ments of the indicators will be based, will depend on the
matrix considered. Apart from this ‘‘technical’’ dimension,
it is important to remember that the notion of «proven
technology» differs from the notion of «operable bioindi-
cator». The definition of operability is, indeed, not only
based on the technological maturity of the selected
molecular method, but also on other essential criteria such
as cost, ease of use, existence of reference systems, etc.,
which will also depend on the matrix under study (Fig. 16).
The level of operability of an indicator cannot therefore be
defined in absolute terms but only in relation to a given
environmental matrix (Table 1).
Environ Chem Lett
123
The indexing of operability for the indicators presented
in the above table is obviously not fixed for a long term but
simply provides a snapshot defined at the date of publica-
tion. The rapid evolution of molecular tools, inquiry and
understanding of the different environmental matrices will
undoubtedly lead to the evolution of these indicators
towards better operability and eventually permit their use
in diagnosis of the quality of our environment and the
different matrices of which it is made.
Conclusion
The analysis of microbial communities was initially depen-
dent on so-called «Pasteur techniques» or culturing
approaches which are relatively time consuming and there-
fore permit the study and characterization of only a limited
number of samples. We also only know how to culture a tiny
percentage of the microorganisms present in environments.
Despite their interest, notably for the detection of pathogens
Table 1 Level of operability of
molecular microbial indicators
according to each
environmental matrix and
determined based on the TRL
scale presented in the Fig. 16
Environmental matrix Soil Sediments Water Wastes/
bioprocessing
Atmosphere
Molecular biomass TRL C TRL B TRL B TRL B NO
Detection/abundance of organisms or functions
Quantitative PCR TRL C TRL C TRL C TRL C TRL C
FISH TRL B TRL B TRL B TRL B NO
DNA chip TRL C TRL B TRL C TRL C TRL A
Taxonomic and functional diversity
Mass sequencing of amplicons TRL C TRLB TRLB TRLB TRL B
Global sequencing TRL A TRL A TRL A TRL A TRL A
NO non-operable for this matrix
Fig. 16 Flow chart of the
operational value of a
bioindicator (ADEME)
Environ Chem Lett
123
in food, or water used for bathing, and the enrichment of
databases with the genomes of organisms sequenced after
isolation, the use of such approaches to discover ‘‘efficient
indicators’’ remains limited.
This paper highlights how, thanks to the advent of
molecular biology in the 1990s and the more recent «omics»
revolution, several molecular techniques can now be used to
detect, quantify or characterize a microbial community,
population, species, function or functional group in a precise
and robust manner. Although several limits to their appli-
cation persist, (e.g., absence of reference systems for certain
matrices that remain unexamined or insufficiently investi-
gated, such as the oceanic ecosystems, the volume and
complexity of the data generated for certain techniques,
extraction bias, etc.), molecular techniques offer the possi-
bility of monitoring and finely characterizing the microbi-
ological patrimony and even the functional state of different
environmental matrices. Several bioindicators such as
microbial molecular biomass, the detection and counting
in situ of certain microbial organisms or functional genes,
microbial taxonomic diversity and even microbial func-
tional diversity can thus be defined. Due to the major role
played by microorganisms, application of bioindicators for
environmental diagnosis not only helps to determine the
‘‘good condition’’ of a biological system but also to monitor
its evolution over time after a perturbation and/or rehabili-
tation, and even to assess the efficacy of bioprocesses. Even
so, the potential of molecular microbiology remains little
exploited in the domain of bioindication and ecotoxicology.
However, there is no doubt that environmental diagnosis,
based on the use of increasingly efficient microbial
bioindicators, will develop considerably in the years to
come. In the short term, and apart from diagnosis, these
bioindicator will result in the setting up of a real advisory
system, indispensable to better ecosystem management and
sustainability.
Acknowledgments The authors would like to thank the scientific
experts P. Amato (CNRS), T. Heulin (CNRS), B. Balloy (Chambre
d’agriculture de France), S. Courtois (SUEZ), J.Y. Richard (SUEZ),
P. Bonin (Universite
´Aix Marseille), J. M. Baudoin (ONEMA), A.
M. Pourcher (IRSTEA), A. Henry (VEOLIA), for their comments and
review of this article. This review was granted by ADEME (French
National Agency for Energy and Environment).
References
Bailly J, Fraissinet-Tachet L, Verner M-C, Debaud J-C, Lemaire M,
Wesolowski-Louvel M et al (2007) Soil eukaryotic functional
diversity, a metatranscriptomic approach. ISME J 1(7):632–642.
doi:10.1038/ismej.2007.68
Baker BJ, Sheik CS, Taylor CA, Jain S, Bhasi A, Cavalcoli JD, Dick
GJ (2013) Community transcriptomic assembly reveals microbes
that contribute to deep-sea carbon and nitrogen cycling. ISME J
7(10):1962–1973. doi:10.1038/ismej.2013.85
Barrios E (2007) Soil biota, ecosystem services and land productivity.
Ecol Econ 64(2):269–285. doi:10.1016/j.ecolecon.2007.03.004
Bertin PN, Heinrich-Salmeron A, Pelletier E, Goulhen-Chollet F,
Arse
`ne-Ploetze F, Gallien S et al (2011) Metabolic diversity
among main microorganisms inside an arsenic-rich ecosystem
revealed by meta- and proteo-genomics. ISME J 5(11):
1735–1747. doi:10.1038/ismej.2011.51
Brodie EL, DeSantis TZ, Joyner DC, Baek SM, Larsen JT, Andersen
GL, Hazen TC, Richardson PM, Herman DJ, Tokunaga TK, Wan
JM, Firestone MK (2006) Application of a high-density
oligonucleotide microarray approach to study bacterial popula-
tion dynamics during uranium reduction and reoxidation. Appl
Environ Microbiol 72(9):6288–6298. doi:10.1128/AEM.00246-
06
Buitenhuis ET, Hashioka T, Le Que
´re
´C (2013) Combined constraints
on global ocean primary production using observations and
models. Glob Biogeochem Cycles 27:847–858. doi:10.1002/gbc.
20074
Chaparro JM, Badri DV, Vivanco JM (2014) Rhizosphere micro-
biome assemblage is affected by plant development. ISME J
8(4):790–803. doi:10.1038/ismej.2013.196
Delmont TO, Robe P, Clark I, Simonet P, Vogel T (2011)
Metagenomic comparison of direct and indirect soil DNA
extraction approaches. J Microbiol Methods 86(3):397–400.
doi:10.1016/j.mimet.2011.06.013
Dequiedt S, Saby NPA, Lelievre M, Jolivet C, Thioulouse J, Toutain
B, Arrouays D, Bispo A, Lemanceau P, Ranjard L (2011)
Biogeographical patterns of soil molecular microbial biomass as
influenced by soil characteristics and management. Glob Ecol
Biogeogr 20(4):641–652. doi:10.1111/j.1466-8238.2010.00628.x
Dugat-Bony E, Peyretaillade E, Parisot N, Biderre-Petit C, Jaziri F,
Hill D, Rimour S, Peyret P (2012) Detecting unknown sequences
with DNA microarrays: explorative probe design strategies.
Environ Microbiol 14(2):356–371. doi:10.1111/j.1462-2920.
2011.02559.x
Dumont MG, Pommerenke B, Casper P (2013) Using stable isotope
probing to obtain a targeted metatranscriptome of aerobic
methanotrophs in lake sediment. Environ Microbiol Rep
5:757–764. doi:10.1111/1758-2229.12078
Durban N, Juzan L, Krier J, Gillot S (2016) Control of Microthrix
parvicella by aluminium salts addition. Water Sci Technol
73(2):414–422. doi:10.2166/wst.2015.456
Fiehn O, Kopka J, Do
¨rmann P, Altmann T, Trethewey RN,
Willmitzer L (2000) Metabolite profiling for plant functional
genomics. Nat Biotechnol 18(11):1157–1161. doi:10.1038/
81137
Figeys D (2000) The Achilles’ heel of proteomics. Trends Biotechnol
18(12):483. doi:10.1016/S0167-7799(00)01507-9
Freedman DL, Gossett JM (1989) Biological reductive dechlorination
of tetrachloroethylene and trichloroethylene to ethylene under
methanogenic conditions. Appl Environ Microbiol 55(9):
2144–2151
Frias-Lopez J, Shi Y, Tyson GW, Coleman ML, Schuster SC,
Chisholm SW, Delong EF (2008) Microbial community gene
expression in ocean surface waters. Proc Natl Acad Sci USA
105(10):3805–3810. doi:10.1073/pnas.0708897105
Geisen S, Tveit A, Clark I, Richter A, Svenning MM, Bonkowski M,
Urich T (2015) Metatranscriptomic census of active protists in
soils. ISME J 9(10):2178–2190. doi:10.1038/ismej.2015.30
Ghai R, Mizunon CM, Picazo A, Camacho A, Rodriguez-Valera F
(2014) Key roles for freshwater Actinobacteria revealed by deep
metagenomic sequencing. Mol Ecol 23(24):6073–6090. doi:10.
1111/mec.12985
Gifford SM, Sharma S, Moran MA (2014) Linking activity and
function to ecosystem dynamics in a coastal bacterioplankton
Environ Chem Lett
123
community. Front Microbiol 5:185. doi:10.3389/fmicb.2014.
00185
Gilbert J, Field D, Huang Y, Edwards R, Li W, Gilna P, Joint I (2008)
Detection of large numbers of novel sequences in the metatran-
scriptomes of complex marine microbial communities. PLoS
One 3(8):e3042. doi:10.1371/journal.pone.0003042
Hayatsu M, Tago K, Saito M (2008) Various players in the nitrogen
cycle: diversity and functions of the microorganisms involved in
nitrification and denitrification. Soil Sci Plant Nutr Soil
54(1):33–45. doi:10.1111/j.1747-0765.2007.00195.x
Horrigue W, Dequiedt S, Chemidlin Pre
´vost-Boure
´N, Jolivet C, Saby
N, Arrouays D, Bispo A, Maron PA, Ranjard L (2016) Predictive
model of soil molecular microbial biomass. Ecol Indic
64:203–211. doi:10.1016/j.ecolind.2015.12.004
Joux F, Bertrand JC, De Wit R, Grossi V, Intertaglia L, Lebaron P,
Michotey V, Normand P, Peyret P, Raimbault P, Tamburini C,
Urios L (2011) Les biopuces en e
´cologie microbienne. Me
´thodes
d’e
´tudes des micro-organismes dans l’environnement. Chapitre
17. In: Bertrand JC, Caumette P, Lebaron P, Matheron R,
Normand P (eds) Ecologie Microbienne. Microbiologie des
milieux naturels et anthropise
´s. Presses Universitaires de Pau et
des Pays de l’Adour, Pau, pp 799–876
Keiblinger KM, Wilhartitz IC, Schneider T, Roschitzki B, Schmid E,
Eberl L, Riedel K, Zechmeister-Boltenstern S (2012) Soil
metaproteomics—comparative evaluation of protein extraction
protocols. Soil Biol Biochem 54:14–24. doi:10.1016/j.soilbio.
2012.05.014
Lankadurai BP, Nagato EG, Simpson MJ (2013) Environmental
metabolomics: an emerging approach to study organism responses
to environmental stressors. Environ Rev 21(13):180–205. doi:10.
1139/er-2013-0011
Leary DH, Hervey WJ IV, Deschamps JR, Kusterbeck AW, Vora GJ
(2013) Which metaproteome? The impact of protein extraction
bias on metaproteomic analyses. Mol Cell Probes
27(5–6):193–199. doi:10.1016/j.mcp.2013.06.003
Lesniewski RA, Jain S, Anantharaman K, Schloss PD, Dick GJ
(2012) The metatranscriptome of a deep-sea hydrothermal plume
is dominated by water column methanotrophs and lithotrophs.
ISME J 6(12):2257–2268. doi:10.1038/ismej.2012.63
London RE, Houck DR (2004) Introduction to metabolomics and
metabolic profiling. In: Hamadeh HK, Afshari CA (eds)
Toxicogenomics: principles and applications. Wiley, New York,
pp 299–340
Lucio M (2009) Datamining metabolomics: the convergence point of
non-target approach and statistical investigation. Thesis, Tech-
nical university of Munchen, 189p. http://nbn-resolving.de/urn:
nbn:de:bvb:91-diss-20080916-673608-1-4
Mao Y, Xia Y, Wang Z, Zhang T (2014) Reconstructing a Thauera
genome from a hydrogenotrophic-denitrifying consortium using
metagenomic sequence data. Appl Microbiol Biotechnol
98(15):6885–6895. doi:10.1007/s00253-014-5756-x
Maron PA, Ranjard L, Mougel C, Lemanceau P (2007) Metapro-
teomics: a new approach for studying functional microbial
ecology. Microbial Ecol 53(3):486–493. doi:10.1007/s00248-
006-9196-8
Maron PA, Mougel C, Ranjard L (2011) Soil microbial diversity:
spatial overview, driving factors and functional interest. CRAS
Biol II 334(5–6):403–411. doi:10.1016/j.crvi.2010.12.003
McCarren J, Becker JW, Repeta DJ, Shi Y, Young CR, Malmstrom
RR, Chisholm SW, DeLong EF (2010) Microbial community
transcriptomes reveal microbes and metabolic pathways associ-
ated with dissolved organic matter turnover in the sea. Proc Natl
Acad Sci 107(38):16420–16427. doi:10.1073/pnas.1010732107
Militon C, Atkinson A, Michotey V, Jeziorski C, Cravo-Laureau C,
Durand R, Bonin P, Cuny P (2016) Metatranscriptomes of
marine coastal sediment affected by oil addition and/or by the
bioturbating activity of the marine polychaete Hediste diversi-
color: who are the microbial players? Mar Genom. doi:10.1016/
j.margen.2016.06.006
Mondav R, Woodcroft BJ, Kim EH, McCalley CK, Hodgkins SB,
Crill PM, Chanton J, Hurst GB, VerBerkmoes NC, Saleska SR,
Hugenholtz P, Rich VI, Tyson GW (2014) Discovery of a novel
methanogen prevalent in thawing permafrost. Nat Commun
5:3212. doi:10.1038/ncomms4212
Mushtag MY, Choi YH, Verpoorte R, Wilson EG (2013) Extraction
for metabolomics: access to the metabolome. Phytochem Anal
25(4):291–306. doi:10.1002/pca.2505
Ng C, DeMaere MZ, Williams TJ, Lauro FM, Raftery M, Gibson
JAE, Andrews-Pfannkoch C, Lewis M, Hoffman JM, Thomas T,
Cavicchioli R (2010) Metaproteogenomic analysis of a dominant
green sulfur bacterium from Ace Lake, Antarctica. ISME J
4(8):1002–1019. doi:10.1038/ismej.2010.28
Nicholson JK, Lindon JC, Holmes E (1999) ‘Metabonomics’: under-
standing the metabolic responses of living systems to pathophys-
iological stimuli via multivariate statistical analysis of biological
NMR spectroscopic data. Xenobiotica 29(11):1181–1189. doi:10.
1080/004982599238047
Nielsen KM, Johnsen PJ, Bensasson D, Daffonchio D (2007) Release
and persistence of extracellular DNA in the environment.
Environ Biosaf Res 6(1–2):37–53. doi:10.1051/ebr:2007031
Ottesen E, Young CR, Eppley JM, Ryan JP, Chavez FP, Scholin CA,
DeLonga EF (2013) Pattern and synchrony of gene expression
among sympatric marine microbial populations. Proc Natl Acad
Sci USA 110(6):E488–E497. doi:10.1073/pnas.1222099110
Pelletier E, Kreimeyer A, Bocs S, Rouy Z, Gyapay G, Chouari R et al
(2008) ‘‘Candidatus Cloacamonas acidaminovorans’’: genome
sequence reconstruction provides a first glimpse of a new
bacterial division. J Bacteriol 190(7):2572–2579. doi:10.1128/
JB.01248-07
Poretsky RS, Bano N, Buchan A, LeCleir G, Kleikemper J, Pickering
M, Pate WM, Moran MA, Hollibaugh JT (2005) Analysis of
microbial gene transcripts in environmental samples. Appl
Environ Microbiol 71(7):4121–4126. doi:10.1128/AEM.71.7.
4121-4126.2005
Poretsky RS, Hewson I, Sun S, Allen AE, Zehr JP, Moran MA (2009)
Comparative day/night metatranscriptomic analysis of microbial
communities in the North Pacific subtropical gyre. Environ
Microbiol 11(6):1358–1375. doi:10.1111/j.1462-2920.2008.
01863.x
Pulleman M, Creamer R, Hamer U, Helder J, Pelosi C, Pe
´re
`sG,
Rutgers M (2012) Soil biodiversity, biological indicators and soil
ecosystem services—an overview of European approaches. Curr
Opin Environ Sustain 4(5):529–538. doi:10.1016/j.cosust.2012.
10.009
Quaiser A, Bodi X, Dufresne A, Naquin D, Francez AJ, Dheilly A,
Coudouel S, Pe
´drot M, Vandenkoornhuyse P (2014) Unraveling
the stratification of an iron-oxidizing microbial mat by meta-
transcriptomics. PLoS One 9(7):e102561. doi:10.1371/journal.
pone.0102561
Raes J, Bork P (2008) Molecular eco-systems biology: towards an
understanding of community function. Nat Rev Microbiol
6(9):693–699. doi:10.1038/nrmicro1935
Ramachandran N, Hainsworth E, Bhullar B, Eisenstein S, Rosen B, Lau
AY, Walter JC, LaBaer J (2004) Self-assembling protein microar-
rays. Science 305(5680):86–90. doi:10.1126/science.1097639
Rames EK, Smith MK, Hamill SD, De Faveri J (2013) Microbial
indictors related to yield and disease and changes in soil
microbial community structure with ginger farm management
practices. Aust Plant Pathol 42(6):685–692. doi:10.1007/s13313-
013-0231-1
Ritz K, Black HIJ, Campbell CD, Harris JA, Wood C (2009)
Selecting indicators for monitoring soils: a framework for
Environ Chem Lett
123
balancing scientific and technical opinion to assist policy
development. Ecol Ind 9(6):1212–1221. doi:10.1016/j.ecolind.
2009.02.009
Savichtcheva O, Debroas D, Perga ME, Arnaud F, Lyautey E,
Kirkham A, Chardon C, Alric B, Domaizon I (2014) Effects of
nutrients and warming on Planktothrix dynamics and diversity: a
palaeolimnological view based on sedimentary DNA and RNA.
Freshw Biol 60:31–49
Sharma SK, Ramesh A, Sharma MP, OmJoshi OP, Govaerts B,
Steenwerth KL, Karlen DL (2011) Microbial community struc-
ture and diversity as indicators for evaluating soil quality. In:
Lichtfouse E (ed) Biodiversity, biofuels, agroforestry and
conservation agriculture. Springer, Berlin, pp 317–358. doi:10.
1007/978-90-481-9513-8_11
Terrat S, Plassart P, Bourgeois E, Ferreira S, Dequiedt S, Adele-Dit-
De-Renseville A, Lemanceau P, Bispo A, Chabbi A, Maron PA,
Ranjard L (2015) Meta-barcoded evaluation of the ISO standard
11063 DNA extraction procedure to characterize soil bacterial
and fungal community diversity and composition. Microbial
Biotechnol 8(1):131–142. doi:10.1111/1751-7915.12162
Tseng CH, Tang SL (2014) Marine microbial metagenomics: from
individual to the environment. Int J Mol Sci 15(5):8878–8892.
doi:10.3390/ijms15058878
Turner TR, Ramakrishnan K, Walshaw J, Heavens D, Alston M,
Swarbreck D, Osbourn A, Grant A, Poole PS (2013) Compar-
ative metatranscriptomics reveals kingdom level changes in the
rhizosphere microbiome of plants. ISME J 7(12):2248–2258.
doi:10.1038/ismej.2013.119
Tveit A, Schwacke R, Svenning MM, Urich T (2013) Organic carbon
transformations in high-Arctic peat soils: key functions and
microorganisms. ISME J 7(2):299–311. doi:10.1038/ismej.2012.
99
Urich T, Lanze
´n A, Qi J, Huson DH, Schleper C, Schuster SC (2008)
Simultaneous assessment of soil microbial community structure
and function through analysis of the meta-transcriptome. PLoS
One 3(6):e2527. doi:10.1371/journal.pone.0002527
Vila-Costa M, Sharma S, Moran MA, Casamayor EO (2013) Diel
gene expression profiles of a phosphorus limited mountain lake
using metatranscriptomics. Environ Microbiol 15(4):1190–1203.
doi:10.1111/1462-2920.12033
Wilmes P, Bond PL (2006) Metaproteomics: studying functional gene
expression in microbial ecosystems. Trends Microbiol
14(2):92–97. doi:10.1016/j.tim.2005.12.006
Yates JR 3rd, Speicher S, Griffin PR, Hunkapiller T (1993) Peptide
mass maps: a highly informative approach to protein identifica-
tion. Anal Biochem 214(2):397–408. doi:10.1006/abio.1993.
1514
Zhou J, He Z, Yang Y, Deng Y, Tringe SG, Alvarez-Cohen L (2015)
High-throughput metagenomic technologies for complex micro-
bial community analysis: open and closed formats. MBio
6(1):e02288-14. doi:10.1128/mBio.02288-14
Environ Chem Lett
123
