Microbial Ecology - ITQB

Microbial Ecology Metaproteomics: A New Approach for Studying Functional Microbial Ecology Pierre-Alain Maron, Lionel Ranjard, Christophe Mougel and Philippe Lemanceau UMR Microbiologie et Ge´ochimie des Sols, INRA/Universite´ de Bourgogne, CMSE, BP 86510, 17 rue de Sully, 21065, Dijon Cedex, France Received: 17 November 2006 / Accepted: 26 November 2006 / Online publication: 13 March 2007

Abstract

In the postgenomic era, there is a clear recognition of the limitations of nucleic acid-based methods for getting information on functions expressed by microbial communities in situ. In this context, the large-scale study of proteins expressed by indigenous microbial communities (metaproteome) should provide information to gain insights into the functioning of the microbial component in ecosystems. Characterization of the metaproteome is expected to provide data linking genetic and functional diversity of microbial communities. Studies on the metaproteome together with those on the metagenome and the metatranscriptome will contribute to progress in our knowledge of microbial communities and their contribution in ecosystem functioning. Effectiveness of the metaproteomic approach will be improved as increasing metagenomic information is made available thanks to the environmental sequencing projects currently running. More specifically, analysis of metaproteome in contrasted environmental situations should allow (1) tracking new functional genes and metabolic pathways and (2) identifying proteins preferentially associated with specific stresses. These proteins considered as functional bioindicators should contribute, in the future, to help policy makers in defining strategies for sustainable management of our environment.

Past, Present, and Future Prospects

Microbial ecology is a scientific domain derived (40 years ago) from medicine and agronomy by the need to elucidate relationships between microbes and their natural habitats (soil, water, sediments, rhizosphere, alimentary canal...). Soil microbial ecology is known to Correspondence to: Philippe Lemanceau; E-mail: [email protected]

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DOI: 10.1007/s00248-006-9196-8

be an integrative science with strong interconnections between systematics, genetics, biochemistry, molecular biology, physiology, modeling, paleobiology, soil science, parasitology, epidemiology, etc., with important food, public health, and environmental implications. Microbial ecology can be considered apart from Bclassical^ ecology by the specifics of the organisms involved. The small size of microorganisms, the difficulty defining bacterial species and the huge genetic/metabolic diversity among them in the various environments they colonize [50] led to the development of specific concepts and methodological approaches for elucidating the role of microbes in ecosystem functioning. Analysis of historical and recent advances in microbial ecology shows a Bstep-by-step^ evolution managed by methodological developments ([8], Fig. 1). In the 1960s, the most comprehensive studies focused on monoxenic cultures lacking interactions between microorganisms and between microorganisms and their habitat. In the 1980s, one of the first advances was to take into consideration not only single organisms but density, diversity, and activity of microbial populations isolated from natural environments. This was initiated by Brock (1987) [5] who stated that the properties of an organism cultivated in the laboratory may not necessary reflect its activity and physiology in the environment where factors such as resource competition, environmental heterogeneity, predation, and other interactions are prevalent. In the 1990s, many studies were dedicated to this type of approach and provided the basis for understanding the microbial world and its role in ecosystem functioning. However, the statements made by Bakken (1985) [3] and Amann et al. (1995) [1] that more than 90% of the microorganisms in the environment are not cultivable highlighted the limit of culture-dependent approaches to describe natural diversity and that most of the microbial diversity remained unexplored. Faced with

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Figure 1. Historical and step-by-step evolution of microbial ecology.

this major limitation, Pace et al. (1985) [34] introduced a cultivation-independent approach based on the extraction, amplification, cloning, and characterization of rDNA genes directly from natural environments. Beginning with these early works, many efforts have been dedicated to developing molecular methods to characterize microbial information contained in the nucleic acids extracted from environmental samples [1, 20]. These developments enabled the characterization of variations of the microbial community structure and diversity in multiple situations allowing the identification of populations preferentially associated with environmental perturbations (for review, see [44]). Further methodological progress allowed the cloning and sequencing of large genome fragments (about 40 kb) from microbial communities of a planktonic marine archaeon [49]. This work provided the first glimpse into content and diversity of marine archae but was also the first example of the feasibility of metagenome characterization [collective genome from all (micro-) organisms present in an ecosystem] [46]. Recent advances in high-throughput screening and sequencing facilitate this type of study and have provided the majority of DNA sequences now found in databases. Metagenomic approaches provide new insights into genetic diversity and

evolution of uncultured microorganisms. However, indications of genetic potential does not contribute to the elucidation of the functionality (level of expression of the genetic potential) (Fig. 2) of microbial communities in ecosystems [8, 45, 50, 52]. Different methods have been developed to discriminate active populations from quiescent ones in natural habitats by incorporation of labeled markers in microbial biomass such as 13C (DNA-Stable isotope probing methods [41]), or BrdU [4]. However, these approaches only provide limited information on the populations associated with a specific process rather than a complete description of their functional role within a Bcommunity^. Bioinformatic data acquired over the last 20 years on putative functional genes allows the design of primers and probes to target specific functional communities in complex environments. The design of such primers enables (1) the quantification of gene copy number [real time polymerase chain reaction (PCR)] and (2) the characterization of polymorphism(s) of functional gene(s). These studies performed at the DNA level can be further linked to measurement of the corresponding activities [27]. However, this integrated approach is restricted to a limited number of functions (denitrification, nitrification,

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Figure 2. Schematic representation of the FMeta_ levels in the ecology of microbial communities.

and methane oxidation) for which genes involved in each step of the metabolic pathway are known and sufficiently conserved to allow the design of consensus primers. Furthermore, the presence of a gene within populations of a given Bfunctional community^ does not necessarily mean that it is expressed in the habitat. In the postgenomic era, a major challenge is to elucidate the functional role of the metagenome by linking genetic structure and diversity of microbial communities with their functions. To fulfill this challenge, advances in the understanding of the evolution of microorganisms must be achieved by developing new approaches (Fig. 1). Databases also include putative functional gene sequences from microbial groups which have yet to be cultivated in isolation in the laboratory. Until we manage to grow them, progress in our knowledge of their functions in complex environments can only be achieved by untargeted culture-independent characterization of their functionality. As shown in Fig. 2, microbial functionality can be characterized either by the analysis of transcripts (metranscriptome: collective RNA from all microorganisms present in an ecosystem) and/or proteins (metaproteome: collective proteins from all microorganisms present in an ecosystem [45]). So far, major limitations related to the short half-life of RNA, difficulty in eliminating humic acids during the extraction process, differential transcription kinetics of similar genes in different populations, low correlation between RNA levels and synthesis of the corresponding proteins have hampered the study of the metatranscriptome of indigenous microbial communities [17, 58]. These limitations, together with progress in protein analysis (see below), have stimulated interest in metaproteome characterization. The term Bmetaproteomics^ was first proposed by Wilmes and Bond (2004) [55] as Bthe large-scale characterization of the entire protein complement of environmental microbiota at a given point in time^. As proteins, and more precisely enzymes, are involved in biotransformation processes, metaproteome analysis constitutes a suitable way to characterize the dynamics of microbial function in a holistic way,

which represents the last and crucial step toward our understanding of metabolome regulation (collective metabolites from all microorganisms present in an ecosystem [12]) (Fig. 2). The aim of this position paper is to stress the relevance of metaproteome analysis in (1) describing new functional genes and (2) relating genetic and taxonomic diversity to the functionality of microbial communities in complex environments.

On the Track of Metaproteomics?

Proteomics, Bthe large-scale study of proteins expressed by an organism^ [54], truly emerged in the middle of the 1970s, when scientists started to map protein expression using the newly developed two-dimensional (2-D) gel electrophoresis [29]. By applying the 2-D technique, it became possible to separate proteins from complex mixtures of cellular extracts into individual polypeptides, thus, allowing the analysis of bacterial response to various growth conditions [38]. However, protein identification was time consuming and tedious due to the lack of sensitive and fast sequencing technologies for protein analysis. From the 1990s, proteomics has been made advanced, thanks to the development of highefficiency peptide ionization methods in mass spectrometry (MS), allowing rapid and highly sensitive protein identification [36, 56]. In parallel, progress was made in bioinformatic tools and in their adaptation to the analysis of information from 2-D gels and MS, making possible (1) the identification of proteins by database searching with MS information [35], (2) the characterization of the corresponding genes by reverse genetics [22], and (3) the determination of protein posttranslational modifications [2]. Over the last decade, the advances in proteomic technologies, together with the sequencing of an increasing number of complete genomes of different microorganisms, provide the opportunity to link phylogeny with the function of microorganisms. In this context, the proteomic characterization of model organisms currently being sequenced further facilitates the descrip-

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tion of those physiological pathways involved in various functions and interactions within and between organisms such as symbiosis [14], pathogenicity [28], antibiotic resistance [6], and adaptation to stresses [16, 51]. These studies stress the relevance of proteome analysis for investigating global modifications in genome expression of prokaryotic organisms and to progress in our knowledge of those processes that regulate gene expression. However, these investigations have largely been performed under laboratory conditions, at the organism level, without taking into account the biotic (among microorganisms) and abiotic (microorganisms with their natural habitat) interactions governing the ecology of microbial communities in situ. Therefore, new approaches are needed for in situ characterization of the global protein expression at the population, or more widely, at the community level. The main challenge of environmental proteomics is to map proteins (proteotyping) extracted from indigenous microbial communities and identify in an untargeted way new physiological pathways and their associated coding genes.

How to Do It?

Metaproteome analysis of soil microbial communities implies the development of different technical steps, from the extraction of microbial proteins from the

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environmental matrix to the resolution of their diversity and their identification (Fig. 3). As with in situ nucleic acid-based studies, the most crucial step in the metaproteome analysis is ensuring that the quality and quantity of the proteins extracted are representative of the sample. This is particularly true for environmental proteomic studies because of the complexity of indigenous microbial communities, the heterogeneity of natural environments, especially soil, and the presence of interfering compounds (phenolic compounds, humic acids...) making difficult the extraction of a suitable protein fraction for analysis. The extraction strategy varies according to the targeted protein fraction (i.e., procaryota/eucaryota, extracellular/cell associated) and by the subsequent methods of protein analysis (i.e., 2-D comparative protein maps or measurement/ detection of specific polypeptides or enzymatic activities). Recently, Schulze et al. (2004) [47] characterized extracellular proteins isolated from dissolved organic matter in different environments and showed that the relative proportion of the proteins originating from bacteria varied from 78% in lake water to less than 50% in a forest soil solution. For an exhaustive recovery of environmental proteins (cellular + extracellular), organisms may be lyzed directly in the environmental matrix before purification, quantification, and analysis [30–32, 48, 55]. This strategy, based on direct lysis, was applied by several authors

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Figure 3. Experimental strategy and expected outcomes of the metaproteome characterization.

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to characterize the metaproteome in different environments such as a natural microbial biofilm [42], water [18, 30–32], sediment [30], soil [30, 48], and activated sludge [55]. From these studies, the protein complement of the metagenome appears to be very complex and vary according to the target environment [30] and with the surrounding local conditions [31, 48]. Thus, in situ lysis allows an exhaustive protein recovery from indigenous bacteria, fungi, protozoa, and multicellular organisms; this mixture being likely to introduce difficulties in the taxonomic delineation of the detected proteins. Furthermore, a direct lysis strategy is technically difficult to apply to natural environments as protein extracts are widely contaminated with interfering compounds, making protein characterization difficult with the biochemical methods available. Another option is to follow an indirect lysis strategy in which proteins are extracted, purified, and separated from organisms that have been previously extracted from the environmental matrix [9, 23, 24]. The relevance of this indirect approach for genetic diversity analysis based on nucleic acid characterization was recently shown [7]. This strategy allows the precise targeting of the bacterial fraction and to obtain a cellular fraction only slightly contaminated by soil compounds. However, the efficiency of bacterial extraction is dependent on the physicochemical characteristics of the environmental matrix, which may introduce further bias in the quantitative and qualitative recovery of environmental bacterial proteins [25]. By using this approach, Ehlers and Clote (1999) [9] demonstrated the similarity of the functional structure of 21 different activated sludge systems which differed in design and phosphorous removal; similarly, we have shown that water pollution with mercury and cadmium leads to a modification of the metaproteome of freshwater when compared to the unpolluted control [26]. Once the protein samples are obtained, different biochemical methods can be applied for metaproteome analysis according to the type of information and the level of resolution required. To get a Bproteofingerprint^ of the bacterial community in an untargeted way, environmental proteins can be separated by one-dimensional or two-dimensional gel electrophoresis before being stained (Fig. 3). 2-D gel electrophoresis is preferred to obtain better protein separation, facilitating the further identification of polypeptides by database searches with MS information. However, this technique remains limited by major problems such as the impossibility of reliably monitoring low abundant [15], very hydrophobic, very acidic or very basic proteins [19]. For these reasons, 2-D gel electrophoresis has also been designated as Bthe Achilles_ heel of proteomics^ [11]. Others consider that proteomics is still Btechnologydriven and technology-limited discovery science^ [19]. In this context, there is a need for alternative separation

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methods. Considerable efforts have already been dedicated in this direction with the development of various technologies such as: chromatographic/capillary electrophoresis separation [57] and protein microarray developments [10, 43]. This last methodology should enable high throughput analysis of complex protein mixtures and increase the possible applications of proteomics in environmental studies. Despite the technical limitations of 2-D gel electrophoresis, this technique still provides a vast amount of new data and remains the traditional technique used to separate proteins for proteomic studies. Recently, Wilmes and Bond (2004) [55] analyzed the diversity of the protein pool of microbial communities in activated sludges by combining 2-D polyacrylamide gel electrophoresis with MS and demonstrated that it is possible to isolate and identify microbial proteins from a complex environment. Schulze et al. (2004) [47] also characterized protein pools, from complex environments such as lake water, soil solutions, and soil particles by electrophoresis coupled with MS. Within a protein pool, proteins synthesized in response to a stress can be detected by isotopic labeling [13, 31] and visualized by autoradiography after separation on acrylamide gels [31]. Specific proteins can also be detected and quantified in complex environments such as soil using immunological methods [23, 24]. Their quantification gives access to functionalities (i.e., level of expression of the genetic potential at a given point in time) of the microbial community. However, the application of such strategy is limited by the specificity of the antibodies targeting a class of functional proteins (such as the dissimilative nitrate reductase) being produced by a wide diversity of populations belonging to different species [24, 39]. The strategy is also limited by the small number of enzymes catalyzing biological functions that have been identified (i.e., enzymes involved in nitrogen cycle). Alternatively, specific polypeptides can be detected on the basis of their metabolic activities by blotting of environmental protein samples and applying staining techniques [21, 32, 33]. This strategy requires the preservation of the catabolic potential of the extracted enzymes implying the use of gentle and nondenaturing protein extraction procedures that may preclude the exhaustive recovery and accurate separation of proteins from environmental samples. The step after the identification of proteins associated with specific environments or those produced after imposed stress is to relate these proteins to the corresponding gene sequences (Fig. 3). One of the goals of the metaproteomic approach is to link biological functions to gene sequences. Technologies (extraction and separation) for characterizing the protein complement of genome are still limited compared to those used for nucleic acids and need to be optimized. However, to

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meet the major scientific challenge of this postgenomic area, proteomic analysis is presently one of the fastest developing areas of biological research [47].

Expected Outcomes Development of Functional Bioindicators. Nowadays, specific attention is given to the impact of anthropogenic activities, and more generally, of global change on the quality of the environment. In this context, there is a strong demand for bioindicators that will enable characterization of the dynamics and sustainability of environmental quality. The metaproteomic approach appears to be a relevant option for the identification of functional bioindicators. This was demonstrated by a global approach based on the quantification of the total soil proteins by Singleton et al. (2003) [48] who showed that contamination of a soil with cadmium leads to a significant decrease in its protein content. It can be assumed that the qualitative analysis of the metaproteome will provide more sensitive and specific bioindicators of stress. As indicated above, the total protein pool of indigenous microbial communities can be resolved by gel electrophoresis, providing a proteofingerprint (Fig. 3). Possible shifts of the proteofingerprint in relation to environmental stresses would be related to changes in the functional structure of the microbial communities. This type of relation can be compared to that established between variations of DNA fingerprints of microbial communities and shifts in their genetic structure after being submitted to stresses. Specific proteins identified as being induced or repressed by a given perturbation may be considered as functional bioindicators, after validation of their sensitivity, specificity (of the perturbation), and ubiquity in different environments. These proteins could be applied to design easy, fast, and sensitive tests that allow the evaluation of the impact of different stresses on ecosystem function by immunological approaches and/or protein chips technologies that still need development (for review, see [37]). Tracking New Functional Genes and Complex Metaproteomics is expected to Metabolic Pathways.

allow the identification of new functions involved in complex biological pathways. Most of the functional genes identified, so far, code for enzymes catalyzing simple reactions such as those involved in nitrogen cycle. These genes are used as genetic markers to characterize the structure of specific functional communities. However, this strategy, based on a limited number of functional genes, does not allow one to study complex biochemical pathways involving numerous and diverse genes. This is especially true for complex biogeochem-

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ical cycles such as that of carbon. Indeed, little information is available on the dynamics of biochemical transformations involved in the degradation of complex carbon molecules from plant residues or exudates. Furthermore, the role of the dynamic succession of populations, belonging to fungi, bacteria and fauna, expressing complementary functions is poorly documented. In that context, metaproteomics presents the advantage of allowing the identification of proteins involved in a biochemical process in an untargeted way. Sequences of the corresponding coding genes can then be deduced from amino acid sequences by reverse genetics and ultimately used to develop tool probes for DNA/RNA analysis (see Fig. 3). Combination of molecular and biochemical tools, targeting functional genes, and corresponding proteins allows the dynamics of the functions and the associated communities in complex environments to be followed. Revisiting Microbial Ecology Concepts with a The stability of ecosystems Functional Point of View.

depends on (1) their resistance (the magnitude of change caused by a disturbance), (2) their resilience (the speed with which they return to their predisturbance level) [40], and (3) their functional redundancy (same function achieved by different populations) [53]. These parameters, well identified for higher organisms, have so far been poorly addressed in microbial ecology. This limitation is related to the huge taxonomic and functional diversity within microbial communities, together with the difficulty of studying them in their natural habitats, which hamper the characterization of population dynamics and definition of functional groups. Up to now, the three parameters (resistance, resilience, and functional redundancy) are only documented by diversity data based on DNA analysis that addresses taxonomic and genetic diversity. Metaproteomics should provide complementary functional insights supporting the application of these parameters to microbial ecology. Resistance and resilience of ecosystems could be evaluated by the magnitude of modifications occurring in the functional structure of microbial communities similar to the assessment currently made for the genetic structure using DNA fingerprinting. Specific functional markers deduced from these modifications would then be used to define functional groups involved in the community response to perturbations. Further diversity analysis of these reactive functional groups would give information on their level of functional redundancy and on the contribution of this redundancy to ecosystem stability. Functional insights provided by metaproteomics should help the scientific community to address major questions in microbial ecology related to (1) the link between genetic and functional diversity in microbial communities and (2) the relative contribution of

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taxonomic diversity and functional diversity on the stability of ecosystems.

Acknowledgements

The authors are grateful to K. Klein for helpful comments and correcting the English text.

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