Salt Stress Induced Changes in the Exoproteome of the Halotolerant Bacterium Tistlia consotensis Deciphered by Proteogenomics

Carolina Rubiano-Labrador; Céline Bland; Guylaine Miotello; Jean Armengaud; Sandra Baena

doi:10.1371/journal.pone.0135065

Abstract

The ability of bacteria to adapt to external osmotic changes is fundamental for their survival. Halotolerant microorganisms, such as Tistlia consotensis, have to cope with continuous fluctuations in the salinity of their natural environments which require effective adaptation strategies against salt stress. Changes of extracellular protein profiles from Tistlia consotensis in conditions of low and high salinities were monitored by proteogenomics using a bacterial draft genome. At low salinity, we detected greater amounts of the HpnM protein which is involved in the biosynthesis of hopanoids. This may represent a novel, and previously unreported, strategy by halotolerant microorganisms to prevent the entry of water into the cell under conditions of low salinity. At high salinity, proteins associated with osmosensing, exclusion of Na⁺ and transport of compatible solutes, such as glycine betaine or proline are abundant. We also found that, probably in response to the high salt concentration, T. consotensis activated the synthesis of flagella and triggered a chemotactic response neither of which were observed at the salt concentration which is optimal for growth. Our study demonstrates that the exoproteome is an appropriate indicator of adaptive response of T. consotensis to changes in salinity because it allowed the identification of key proteins within its osmoadaptive mechanism that had not previously been detected in its cell proteome.

Citation: Rubiano-Labrador C, Bland C, Miotello G, Armengaud J, Baena S (2015) Salt Stress Induced Changes in the Exoproteome of the Halotolerant Bacterium Tistlia consotensis Deciphered by Proteogenomics. PLoS ONE 10(8): e0135065. https://doi.org/10.1371/journal.pone.0135065

Editor: Shao Jun Dai, Northeast Forestry University, CHINA

Received: November 10, 2014; Accepted: July 16, 2015; Published: August 19, 2015

Copyright: © 2015 Rubiano-Labrador et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: The Whole Genome Shotgun project has been deposited atDDBJ/EMBL/GenBank under the accession numbers CBKU010000001-CBKU010002377 and is available at the European Nucleotide Archive (http://www.ebi.ac.uk/ena/ data/view/CBKU010000001-CBKU010002377).

Funding: The authors thank the Commissariat àl’Energie Atomique et aux Energies Alternatives (C.E.A. Lab Biochim SystemPerturb, France), Pontificia Universidad Javeriana (Colombia), Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS, Colombia), Colombian Center for Genomics and Bioinformatics of Extreme Environments (GeBiX, Colombia), and the ECOS-NORD bilateralprogram for financial support. This work was carried out under MAVDT Research Permit Resolution No. 0168, 2009 and MAVDT Contract No. 56, 2013 for Access to Genetic Resources.

Competing interests: The authors have declared that no competing interests exist.

Introduction

All living microorganisms are exposed to a variety of environmental parameters that define their habitats. To survive they must be able to sense environmental changes and react to with various adaptive mechanisms [1]. One of the most important environmental factors for halotolerant organisms is the salt concentration of the surrounding water while osmotic stress is a major generator of selective pressure on cells. In habitats such as saline springs the halotolerant organisms must cope with variable salinity conditions because they may have varying salt concentrations due to rains and drought. For these organisms to live under such conditions, various physiological strategies have evolved [2]. A low salinity leads to an immediate influx of water which a cell counteracts by fast efflux of small solutes thus relieving physical stress. In contrast, a high salinity leads to water efflux which is counterbalanced by an increase of compatible solutes such as proline, glutamate, glycine betaine, ectoine and trehalose [3].

Tistlia consotensis is a halotolerant Alphaproteobacterium which forms a distant phylogenetic line of descent with members of the genus Thalassobaculum of the family Rhodospirillaceae which is comprised primarily of microorganisms inhabiting marine environments [4]. T. consotensis was isolated from a terrestrial saline spring with an NaCl content of 4.5% (w/v) and which is characterized by oscillating values of chlorides (10646–15100 mg·l^-1), electrical conductivity (29300–35150 μS·cm^-1) and total dissolved solids (24185–41859 mg·l^-1). This is probably related to rainfall and drought as reported previously [5]. This organism achieves optimal growth in the presence of 0.5% NaCl but can grow when salinity ranges between 0.0 and 4.0% NaCl [4]. Although T. consotensis presents a wide tolerance range of salinity, the particular characteristics of its habitat make it possible to suppose that this halotolerant bacterium is exposed to continuous changes in salt concentration. This suggests mechanisms that allow it to adapt to or to tolerate these changing conditions. Although it has been widely studied as extreme halophiles live in environments with high concentrations of salt, little is known about the mechanisms employed by halotolerant organisms to survive under changing salinity conditions.

Analysis of proteomes has been proven to be useful for prediction of structure and function of proteins, particularly those related to experimental growth conditions [6].Recently, the use of experimental proteomic data with proteogenomics has emerged as an interesting approach for improving genome annotation and for dealing with non-model organisms [7]. Interpretation of proteomic data requires a comprehensive protein sequence database. For a non-model organism, it is worthwhile to first establish the genome sequence that can then be translated into the six reading frames. The resulting protein database can be queried with experimental tandem mass spectrometry results in order to quickly establish the list of key cellular or extracellular players. Proteins may be quantified with either a labeling or non-labeling approach [6]. Although non-labeling approaches based on spectral counts are by far the most frequently used as they are simple to perform and reliable even for the analysis of extracellular components [8,9], labeling methodology is also adequate for this purpose [10]. In a previous study, we used a proteogenomic strategy to analyze changes which occurred in the cellular proteome of T. consotensis in response to different concentrations of salt [7]. This study revealed that T. consotensis combined different adaptive strategies to counteract the changes in the environmental salinity. At low salinities, the bacterium up-produces general stress proteins that contribute to proper folding and functioning of other proteins. In contrast, at high salinities T. consotensis up-produces proteins that are involved in signaling allowing it to efficiently detect changes in salinity. These proteins are also related to a large number of transport systems which are probably involved in transport and capture of compatible solutes such as glutamate [7]. Although this study identified proteins that play an important role in the osmoregulatory mechanisms of T. consotensis under hypo-osmotic and hyperosmotic conditions, information found regarding secreted proteins involved in salinity stress response was limited.

This limitation can be overcome through analysis of the using exoproteome. The exoproteome consists of all proteins secreted from extracellular and outer membranes. In the case of Gram-negative bacteria it also includes proteins from the periplasm. These proteins play roles in the organism’s survival in extreme habitats such as saline environments [8]. Even though there are few studies of exoproteome responses to salt stress, those that have been done demonstrate potential roles of altered exoproteomes in adaptive responses during salt stress. This has been demonstrated for Burkholderia pseudomallei which expresses metabolic enzymes, transcription/translation regulators, potential virulence factors, chaperones, phage capsid proteins, drug resistance proteins and solute transport regulator under conditions of high levels of salinity [9]. Exploring the extracellular proteome provides expanded coverage of the repertoire of proteins expressed under salt stress, and this is essential for understanding mechanisms of osmoadaptation.

Accordingly, this study aims to provide a better understanding of the adaptive response of T. consotensis to changes in salinity through analyzing the extracellular protein content when T. consotensis grows in conditions of low and high salinity. We used a proteogenomic approach to examine alterations in the exoproteome of T. consotensis under two conditions of salt stress: low salinity (0.0% NaCl, w/v) and high salinity (4.0% NaCl, w/v). We then compared these results to results obtained at optimal salinity (0.5% NaCl, w/v). Using this approach, we found that a number of secreted proteins were up-and-down-produced in response to salt stress.

Results and Discussion

Exoproteome of T. consotensis under salt stress

Extracellular protein expression can be seen as the physiological response of cells to a specific growth condition and also as indicators of how microorganisms interact with their environments [8]. Considering the importance of these proteins in the survival of organisms to changing environmental conditions, in this study, we analyzed the exoproteome of T. consotensis in order to complement information previously obtained from its cellular proteome and thus gain a more comprehensive view of salt stress responses in this bacterial model.

We performed a large tandem mass spectrometry survey of the exoproteomes of four independent biological replicates for three growth conditions: 0.0%, 0.5%, and 4.0% NaCl. The twelve resulting samples were extensively analyzed with a high resolution Orbitrap mass spectrometer as analytical duplicates were recorded. The analysis of variance of the replicates for each salinity evaluated allows a determination that smaller variations occur in the total spectral counts when the cells are in the optimal growth salinity, 0.5% NaCl, (12%), than when they are in salinities of 0.0% and 4.0% NaCl in which there were variations of 14% and 18%, respectively (S1 Table). A total of 30,130 MS/MS spectra were assigned to 2,321 tryptic peptides (S2 Table). This was done with a stop-to-stop ORF database that had been constructed by six frame translation of its chimeric genome in an earlier study [7]. These peptides allowed the identification of 249 non-redundant proteins in which at least two peptides were validated by tandem mass spectrometry (S3 Table). Fig 1 shows a Venn diagram of the extracellular proteins detected within the different growth conditions. A total of 196 proteins were identified at 0.0% NaCl, 175 proteins at 0.5% NaCl and 199 proteins at 4.0% NaCl. As indicated in Fig 1, 131 proteins were common to the three conditions, which were mostly related to central metabolism processes. On the other hand, 24 proteins were common only to 0.0% and 0.5% NaCl conditions while 15 proteins were common to only 4.0% and 0.5% NaCl conditions. In both cases the majority of the proteins were again related to central metabolism. In the case of proteins common to 0.0% and 4.0% NaCl, 20 of the proteins identified were mainly involved in translation processes.

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Fig 1. Venn diagram showing the number of extracellular proteins identified in cells grown in the three salinities.

The numbers of proteins detected through identification of a least two peptides are indicated in each category.

https://doi.org/10.1371/journal.pone.0135065.g001

The distribution of the proteins detected in the three salinities based on functional categories according to the KEGG database is summarized in Fig 2. A total of 265 proteins out of the 315 proteins identified could be assigned to 40 pathways involved in T. consotensis metabolism. The proteins identified have been assigned to the following pathways: metabolism (36%), membrane transport (19%), protein synthesis and degradation (18%), signal transduction (7%), uncategorised (7%) and cell growth (1%). Hypothetical conserved proteins accounted for 12% of the total proteins identified in the exoproteome of T. consotensis.

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Fig 2. Metabolic categories for the exoproteome of T. consotensis.

Functional categorization of non-redundant proteins detected in the exoproteoma of T. consotensis in the three salinities evaluated.

https://doi.org/10.1371/journal.pone.0135065.g002

The contaminants of cytoplasmic origins that we detected were typically proteins with intracellular functions, such as GroEL, DnaK, EF-Tu or ribosomal proteins. Their identification may indicate that cell lysis contributes intracellular material to the extracellular milieu during culturing or during the supernatant collection process. To address this issue in future studies, it is recommended that an extra step such as filtration using a 0.22 μm pore polyvinylidene fluoride (PVDF) membrane be used during the supernatant collection procedure [10]. Taking this slight contamination into account, we used the NSAF ratio to compare abundance of proteins in the secretome with their abundance in the cellular proteome, previously analyzed [7]. Proteins with a ratio (%NSAF secretome/%NSAF cellular proteome) above 1.0 were considered to be secretome specific (S4 Table). We choose this threshold value and consider that it is stringent based on previous studies. A secreted protein is expected to be more abundant in the secretome than in the cellular proteome in terms of ratio, but this depends on the different components of the secretome. The resulting global contamination rates at 0.0, 0.5 and 4.0% NaCl are shown in S5 Table: 10, 4 and 16%at 0.0, 0.5 and 4.0% NaCl, respectively.

Signal peptide prediction of the most abundant proteins from T. consotensis

In Gram-negative bacteria, the general secretion system directs proteins to the periplasmic space, the outer membrane or the extracellular medium. The mechanisms by which proteins are secreted or exported to different locations inside and outside of the cell frequently involve signal peptides. These N-terminal signal peptides are short chains of mostly hydrophobic amino acids that are cleavable after translocation [11]. Using the MetaLocGramN predictor we predicted subcellular localizations and the presence of signal peptides for 52 proteins found primarily in the exoproteome rather than in the intracellular proteome. Ten proteins were predicted to be extracellular, 17 proteins were predicted to be periplasmatic and 25 were predicted to be cytoplasmatic. Table 1 presents the list of the signal peptide predictions of the most abundant proteins from the T. consotensis exoproteome together with their functional annotations.

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Table 1. Signal peptide prediction of the most abundant proteins from the T. consotensis exoproteome.

https://doi.org/10.1371/journal.pone.0135065.t001

We also used the PRED-TAT program for Sec and Tat signal peptide predictions. Most of the proteins in T. consotensis were predicted for Sec signal peptide. The Sec pathway is the main secretion system in Gram-negative bacteria. It is used for the translocation of unfolded secretory proteins and promotes the export of proteins to the periplasm [12]. Twelve proteins with Sec signal predictions were detected in the periplasmic space (Table 1). These proteins were probably translocated to periplasmatic space by the type II secretion system because proteins secreted via this pathway are synthesized with an N-terminal signal sequence which directs them to the Sec machinery in the inner membrane. After translocation across the inner membrane, the proteins appear in the periplasm where they fold into their native conformations [13]. These results indicate that the Sec pathway probably has a prominent role in protein export and secretion in T. consotensis.

Influence of salinity on the exoproteomes of T. consotensis

For T. consotensis cells cultivated at low salinity (0.0% NaCl) we assigned a total of 10,568 MS/MS spectra in the replicates. A total of 1,574 unique peptides corresponding to 219 proteins could be detected with this pool of MS/MS spectra. In this salinity three of the proteins detected had the most significant changes (absolute fold change >1.5, p value <0.05). Two of these were up-produced and one was down-produced. At high salinity (4.0% NaCl) we assigned 10,813 MS/MS spectra to the replicates. A total of 1,661 unique peptides corresponding to 237 proteins could be detected with this pool of MS/MS spectra. At 4.0% NaCl, 14 of the proteins detected had the most significant changes (absolute fold change >1.5, p value <0.05). Thirteen were up-produced and one was found in a small amount. Table 2 shows the proteins with major changes at low and high salinities. The proteins with the most significant changes were divided into four major groups: (i) protein quality control, (ii) membrane lipid alterations for low salinity, (iii) signal transduction and (iv) compatible solute transport for high salinity. The potential roles of these proteins in response to salt stress are discussed below.

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Table 2. Proteins with the most significant changes of T. consotensis at low and high salinity.

https://doi.org/10.1371/journal.pone.0135065.t002

Protein quality control.

Under stress situations, a protein’s functional integrity is ensured through protein quality control mechanisms that include the action of molecular chaperones, protein-folding catalysts, and ATP-dependent proteases [14]. We detected high amounts of the putative protein disulfide oxidoreductase (DsbA) (5.1-fold) at low salinity. DsbA protein catalyzes disulfide bond formation into newly synthesized proteins that are translocated to the periplasm [15]. This probably indicates that DsbA protein is involved in the periplasmic folding of the Sec-dependent proteins of T. consotensis, and is translocated by the type II secretion system for translocation across the outer membrane which is the subsequent step in the secretion process. This key function of DsbA protein in processing protein translocation across the membrane explains its abundance at low salinities since formation of disulfide bonds is crucial for functioning of numerous proteins in harsh extracellular environments.

Membrane lipid alterations.

Adaptive changes in response to salinity variations have been observed in the lipid compositions of several bacteria. These changes maintain membrane fluidity in order to preserve biological function [16]. Under conditions of low salinity, larger amounts of HpnM protein were detected (8.1-fold). HpnM protein is involved in the biosynthesis of hopanoid lipids and is member of a family of putative transporters known as "toluene tolerance protein Ttg2D”. They are in turn involved in changes in the compositions of fatty acids and phospholipids of the cell membranes of Pseudomonas putida which lead to increased cell membrane rigidity. These changes should be regarded as physical mechanisms that prevent solvent penetration [17].

The hopanoids are pentacyclic triterpenoids that maintain membrane stability by increasing the rigidity of the lipid matrix in a way that is similar to that of some sterols in eukaryotes and that contributes to decreasing diffusion of ions across the cell membrane [16,18]. Several studies concerning hopanoid function propose that their production may be linked to environmental and physiological changes. Some studies have proposed that hopanoids are necessary for coping with external stresses such as ethanol tolerance in Zymomonas mobilis [19], extreme-pH tolerance in Alicyclobacillus acidocaldarius [20] and Rhodopseudomonas palustris [21] and oxygen diffusion in Frankia sp. [22].

Previous studies have reported that hopanoids induce higher levels of packaging of lipids in the membrane which reduces diffusion of small molecules even including water. This has been demonstrated in Streptomyces coelicolor which synthesizes hopanoids to prevent water diffusion when under stress in aerial mycelium [23]. Based on our data, the detection of higher amounts of HpnM protein in T. consotensis at 0.0% NaCl probably indicates that hopanoids are involved in the alteration of the composition of the cell membrane to help minimize diffusion of water and protect the cell against the effects of the influx of water. Although to date there have been no reports about the presence of hopanoids in response to osmotic stress, the relative abundance of HpnM protein in T. consotensis could indicate a key role for hopanoids under hypo-osmotic conditions and may represent a mechanism of adaptation by T. consotensis in response to decreased in salinity which has not been previously reported. Nevertheless, additional studies are required to determine the role played by hopanoids in the cellular response of T. consotensis at low salinity.

Signal transduction.

The evolutionary success of prokaryotes is dependent on their ability to rapidly sense and respond to changes inside and outside their cells [24]. Microorganisms have different mechanisms for sensing and responding to environmental stress, that is, ways to monitor physical and chemical changes in the external space. Signal transduction pathways transform a physical or chemical stimulus into useful signals which regulate cell functions through protein activation in the short term and through genetic regulation in the long term [3]. Some studies have shown a connection between the total number of signal transduction proteins, genome complexity (number of potential protein-coding genes) and an organism’s lifestyle [25]. Microbes with complex lifestyles and complex genomes generally encode more sophisticated and diverse regulatory systems indicating that higher degrees of complexity require higher levels of control of gene expression and cellular activity [26].

Most of the proteins up-produced by T. consotensis under conditions of high salinity were related to signal transduction proteins such as methyl-accepting chemotaxis proteins (MCPs) (11.6-fold, 7.8-fold, 7.4-fold, 6.0-fold, and 5.5-fold) and kinase CheA protein (5.0-fold). MCPs are a family of bacterial receptors that mediate chemotaxis to diverse signals by altering swimming behavior in response to changes in the concentration of attractants and repellents in the environment [27]. Kinase CheA protein is a sensor for specific extracellular environmental stimuli in bacterial two-component regulatory systems [28]. An abundance of proteins involved in signal transduction has been reported in species that are widely distributed in soils, sediments and marine and freshwater environments. Since this may be related to their ability to adapt to multiple and changing environments [29], the high number of signal transduction proteins detected at 4.0% NaCl could be related with the lifestyle of T. consotensis and could indicate an ability to efficiently sense chemical changes in external space which might allow it to adapt rapidly to changing conditions of salinity.

MCPs and kinase CheA protein which were detected at 4.0% NaCl are the two primary mechanisms by which microorganisms sense environmental signals and adopt a chemotactic response to these stimuli. Signal recognition by MCP produces a molecular stimulus that modulates CheA autophosphorylation and in turn transphosphorylation activity towards the response regulators CheY and CheB. When CheY is phosphorylated, it undergoes a conformational change which allows interaction with the flagellar motor. As a result of this interaction, the cell modulates the direction of flagellar rotation and migrates towards favorable surroundings [30].

In the case of T. consotensis under optimum growth conditions (0.5% NaCl and 30°C), Díaz-Cárdenas et al. (2010), using electron microscopy, failed to find flagella or axial filaments. However, in this study at 4.0% NaCl the flagellin protein was identified as being up-produced in the cellular proteome and was one of the most abundant proteins in the exoproteome of T. consotensis at this salinity [7]. Flagellin protein, a polymeric protein that is the principal substituent of bacterial flagella, forms helical chains around the hollow core of the flagellar filament [31].

In some halophilic organisms, such as H. halophilus, it has demonstrated that some important physiological processes are dependent on Cl^-. These processes include germination of endospores, activation of compatible solute transporters and flagellar motility. This indicates the function of this anion in the activation of genes or proteins [32]. Roeβler& Müller (2002) analyzed the effect of Cl^- in fliC gene transcription which encodes one of the components of the flagellum. They found that the expression of this gene is stimulated in the presence of Cl^- [33]. This evidence may suggest that formation of flagella and chemotactic behavior by T. consotensis are activated in response to increased concentrations of Cl^- in the environment. This would explain the absence of flagella at optimal salinity for growth.

Transport system.

The primary response of bacteria for counteracting high salinity involves the activation of a wide variety of specific and nonspecific transport systems. These are associated with transport of compatible solutes, accumulation of K⁺ and exclusion of Na⁺ all of which are constitutively expressed and all of which regulate the level of transcription [3]. At high salinity we detected up-production of the periplasmic component of the TRAP-type C4-dicarboxylate transport system (6.0-fold) which is involved in the exclusion of Na⁺ from the cytoplasm. The TRAP-type C4-dicarboxylate transport system is involved in the transport of C4-dicarboxylates and sugars using a gradient of Na⁺ and is important in regulation of pH homeostasis and in the sodium cycles of organisms such as R. capsulatus [34], ‘Spiribacter salinus’ [35] and H. elongata [36] that inhabit saline environments. The increased abundance of this type of transporter in the exoproteome of T. consotensis in response to high salinity indicates that it plays a role in the output of Na⁺ to counteract the input of Cl^-.

We also detected up-production of a periplasmic component of branched-chain amino acid ABC transporter (5.7-fold) in response to high salinity. This protein is related to the transport of branched chain amino acids such as leucine, isoleucine and valine. In the presence of 2-oxoglutarate and pyridoxal 5-phosphate, these branched chain amino acids are converted to L-glutamate by a branched chain amino acid aminotransferase [37]. ABC branched chain amino acid (BCAA) transporters are widely distributed in various marine organisms, including Oceanibacillus iheyensis [37] and several marine Salinispora, Bacillus and Roseobacter strains [38,39]. They also account for a significant proportion of the genes observed in marine metagenomes [40]. BCAA transporters are probably an important marine adaptation because accumulated glutamate may function as a counterion for K⁺ which balances the electrical state of the cytoplasm [39]. Previous studies have reported a regulatory relationship between K⁺ and glutamate accumulation in response to osmotic stress in enteric bacteria and in the haloalkalophilic archaeon Natronococcus occultus [41]. Detection of larger amounts of BCAA transporter at high salinity in T. consotensis indicates that this halotolerant bacterium probably uses accumulated glutamate to maintain electrical equilibrium within the cell in response to high salt concentrations rather than as compatible solute as had previously been suggested from the analysis of its cellular proteome [7].

In relation to compatible solute transport systems, we detected an increase in the abundance of glycine betaine/L-proline binding protein (ProX) (3.1-fold) in the exoproteome of T. consotensis at 4.0% NaCl. The ProX protein is part of the ProU high-affinity transport system. This is an efficient transporter of both glycine betaine and proline betaine [42]. The ProU system is one of the most intensively characterized osmoprotectant transporters found in E. coli and Salmonella typhimurium. It has been reported that expression of proVWX structural genes strongly increases under hyper-osmotic conditions to allow increased compatible solute uptake in high osmolality environments [43]. These results indicate that, in addition to having the ability to take in glutamate as reported in the analysis of its cellular proteome [7]. T. consotensis may use glycine betaine or proline betaine as compatible solutes to counteract osmotic stress at 4.0% NaCl. Taking into account that the expression of uptake systems varies with respect to substrate specificity, mode of energetic coupling, maximum activity and regulation in response to osmotic stress [44], further studies are necessary to determine which compatible solute is preferentially expressed by T. consotensis when it is cultivated at 4.0% NaCl.

Previous studies have demonstrated that H. halophilus accumulate glutamate only transiently as a first response to salt stress but that glutamate is replaced by proline during long-term adaptation. Once the glutamate reaches a high concentration within the cell, then pro gene expression is stimulated by the triggering of proline production [45]. Based on our findings, we suggest that T. consotensis accumulate glutamate until a given concentration within the cell is reached. This concentration then directly leads to stimulation of ProU system expression for uptake of glycine betaine or proline betaine.

This analysis of the exoproteome of this halotolerant bacterium provides an overview of the adaptation mechanisms it uses to cope with changing external salinities. In this paper, we have described the various responses of T. consotensis to changes of salinities in the culture medium that could not be observed by analyzing the proteome. The integration of both approaches, allowed us to summarize the adaptation mechanisms of T. consotensis. Fig 3 shows a summary of the adaptation mechanisms of T. consotensis found through detection of specific proteins under hypo- osmotic stress and hyper-osmotic stress by analysis of the exoproteome and cellular proteome of T. consotensis. General stress proteins such as chaperons, proteases and proteins associated with protection against oxidative stress are produced by T. consotensis under hypo-osmotic stress. These probably ensure proper functioning of other proteins under this condition. Also, T. consotensis secretes larger amounts of the protein HpnM which is probably involved in altering the composition of the cellular membrane to minimize the diffusion of water. In response to increased salinity, T. consotensis produces MCPs and CheA proteins responsible for efficiently sensing changes in the concentration of Cl^- ions. This generates a biphasic response within which the primary response is an increase in glutamate levels. This is followed by a secondary response in which uptake of compatible solutes such as glycine betaine and proline betaine increases their concentration in the cytoplasm to counteract the outflow of water under hyper-osmotic stress. This scenario shows the versatility of responses that a halotolerant organism can display and allows us to understand how its efficient responses to environmental fluctuations lead to an adaptive advantage for survival in these habitats. This study can be considered a benchmark for understanding the adaptation of organisms isolated from saline terrestrial springs.

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Fig 3. Model of the cellular processes involved in salt adaptation in T. consotensis.

The pink panel shows the cellular response of T. consotensis at low salinity and the blue panel shows the cellular response of T. consotensis at high salinity. For explanations, see text.

https://doi.org/10.1371/journal.pone.0135065.g003

Materials and Methods

Strain and growth conditions

Tistlia consotensis–USBA 355^T from our culture collection was cultured in basal medium (BM) (l^-1) 1 g NH₄Cl, 0.3 g K₂HPO₄, 0.3 g KH₂PO₄, 3 g MgCl₂^.6H₂O, 0.1 g CaCl₂^.2H₂O, 0.1 g KCl and 10 mL trace element solution (SL-10) supplemented with 2 g yeast extract, and 20 mM D-glucose as the sole carbon source. The pH of the medium was adjusted to 6.8 with 5 N NaOH [4]. T. consotensis cells were cultured at 30°C and 200 rpm in 250 ml flasks containing 100 ml of BM with 0.0, 0.5 and 4.0% (w/v) NaCl medium. For each NaCl concentration, four flasks independent (biological tetraplicates) were inoculated with T. consotensis cells previously grown on agar plate. The twelve cultures were harvested by centrifugation at 4000 rpm when the mid exponential phase (OD₆₀₀ = 0.4) was reached.

Protein sample preparation

Proteins were precipitated and purified from the supernatants by the addition of trichloroacetic acid (TCA) following the protocol described by [46]. Briefly, cells were removed by centrifugation at 4000 rpm for 20 min and discarded. The supernatants collected (40 ml) were precipitated by treating with 50% (v/v) TCA (Sigma- Aldrich) in ice for 10 min. After, the precipitate was collected by centrifugation at 4000 rpm for 15 min at 4°C. The resulting pellets were resuspended in 30 μl of lithium dodecyl sulphate-β-mercaptoethanol protein gel sample buffer (LDS sample buffer) (Invitrogen) and incubated at 99°C for 5 min prior to SDS-PAGE.

Trypsin in-gel proteolysis and nanoLC-MS/MS analysis

The samples of exoproteome (20 μl) were resolved by denaturing dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) on 4% to 12% gradient NuPAGE (Invitrogen) gels with a migration of 5 min at 200 V. MES (Invitrogen) was used as running buffer. Gels were stained with Simply Blue Safe Stain (Invitrogen). Each gel lane was divided into two fractions and cut. Polyacryamide gel bands were processed for in-gel proteolysis with trypsin (Roche) following the ProteaseMax protocol (Promega) as previously described [47].

NanoLC-MS/MS analysis

Each biological replicates was analyzed by duplicate resulting 24NanoLC-MS/MS experiments (experimental replicates). The 24 resulting peptide mixtures were performed using an LTQ-Orbitrap XL hybrid mass spectrometer (ThermoFisher) coupled to an UltiMate 3000 LC system (Dionex-LC Packings) in conditions similar to those previously described [48,49]. Peptide mixtures (10 μl) were loaded and desalted on-line in a reverse phase precolumn (Acclaim PepMap 100 C18, 5μm bead size, 100 Å pore size, 5mm x 300μm) from LC Packings at a flow rate of 0.3 μl/min with a gradient of CH₃CN/0.1% formic acid prior to injection into the ion trap mass spectrometer. Peptides were resolved using a 90 min gradient from 5% to 60% of solvent B (0.1% HCOOH/80% CH₃CN). Solvent A was 0.1% HCOOH/100% H₂O. Full-scan mass spectra were measured from m/z 300 to 1,700 with the LTQ-Orbitrap XL mass spectrometer in data-dependent mode using the TOP7 strategy. In brief, a scan cycle was initiated with a full scan of high mass accuracy in the Orbitrap. This was followed by MS/MS scans in the linear ion trap of the seven most abundant precursor ions, with dynamic exclusion of previously selected ions.

Genome sequencing of T. consotensis and 6-frame ORF database

Genomic DNA extraction and sequencing of T. consotensis cells was performed as described by Rubiano-Labrador et al. (2014). Briefly, the DNA was fragmented and a 454-library with an average 8-kb insert size was constructed and sequenced on the Roche 454 GS FLX+ System (Titanium XL+ sequencing chemistry) by the GATC company. A total of 171,055 sequencing reads were recorded which resulted in 59,754,518 bases read. De novo assembling with the 454 Newbler assembler tool resulted in 2377 contigs (5,701,113 nt, 161,098 reads used) for the T. consotensis genome (10.4× coverage). This Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession numbers CBKU010000001-CBKU010002377 and is available at the European Nucleotide Archive (http://www.ebi.ac.uk/ena/data/view/CBKU010000001-CBKU010002377). The 2377 contigs sequences were concatenated end to end (from the largest to the shortest) to create a virtual continuous genome sequence. This artificial chimeric sequence was translated into the six reading frames using Bioedit version 7.1.3 software which resulted in ORF polypeptide sequences (from stop to stop). The resulting ORF database comprised 52,246 putative polypeptide sequences, totaling 9,292,134 amino acids with an average of 178 amino acids per putative polypeptide. Proteomic data and proteogenomic procedures were then used to discriminate between false, correct but undetected, and correct and detected protein sequences as described earlier [48].

MS/MS database search

The recorded MS/MS spectra of one fraction of each replicate were merged before a further search against our home-made ORF database was done. First, peak lists were generated with MASCOT DAEMON version 2.3.2 from Matrix Science using the extract_msn.exe data import filter from the Xcalibur FT package (version 2.0.7) proposed by ThermoFisher. Data import filter options were set as previously described at 400 (minimum mass), 5,000 (maximum mass), 0 (grouping tolerance), 0 (intermediate scans), and 1,000 (threshold) [50]. We performed a search for the MS/MS spectra in the ORF database with the following parameters: tryptic peptides with a maximum of 2 missed cleavages during proteolytic digestion, a mass tolerance of 5 ppm on the parent ion, and MS/MS of 0.5 Da; fixed modification for carbamidomethylated Cys (+57.0215); and variable modification for oxidized Met (+15.9949). All peptide matches with the ORF database with a peptide score above the query threshold of p< 0.05 and rank 1 were parsed using the IRMa 1.28.0 [51]. Using a decoy database the false-positive rate for peptide identification with these parameters was estimated at below 0.5%. MS/MS spectra assigned to several loci were systematically removed. A total of 80,542 MS/MS spectra were recorded during the 24 nanoLC-MS/MS runs. Four biological repeats were done for each of the three growth media and the two analytical replicates. A protein was considered validated when at least two different peptides of that protein were detected in the same experiment. Using a reverse decoy database false-positive identification of proteins with these parameters was estimated at below 0.1%. The six reading frame ORF database from genome sequencing of T. consotensis previously constructed was directly used for assigning tandem mass spectrometry spectra.

Comparison of protein abundance with PatternLab

The number of MS/MS spectra per protein (spectral counts) was determined for the different replicates under each growth condition. A unique biological replicate, 0.5% NaCl, was considered to be an outlier because of its significantly low number of assigned spectra. The salinity of 0.5%NaCl was used as a control condition for the comparative analysis of the abundance of the detected proteins. Protein abundances were first compared between 0.0% NaCl medium (8 replicates) and the reference medium of 0.5% NaCl (6 replicates), and between 4.0% NaCl medium (8 replicates) and the reference medium of 0.5% NaCl (6 replicates). A list of non-redundant proteins detected among the corresponding datasets was established for each of these two comparisons. The total spectral count of each polypeptide was used to rank the proteins detected from highest to lowest. The statistical variation of proteins among the replicates under the two specific growth conditions compared was calculated using the T-Fold option of PatternLab 2.0 [52]. This module allows normalization of spectral count datasets, calculating average fold changes with the t-test, and estimating the resulting theoretical false discovery rate. Stringent parameters were used for this analysis: minimum fold change of 1.5, minimum p-value of 0.05 and BH-FDR Alfa of 0.15. The normalized spectral abundance factor (NSAF) for each protein was calculated as described [50].

Protein and nucleic sequence analysis

A BLASTp search was performed at the NCBI website facilities (www.blast.ncbi.nlm.nih.gov) using the non-redundant protein sequences and default parameters. The functional annotations of all proteins identified in this study were further confirmed using the KEGG database (www.genome.jp/kegg/). The genomic contexts of specific loci were inspected using the GenoMapper tool [53]. For the predictions of signal peptides, proteins identified exclusively in the exoproteome of T. consotensis, i.e. which were not present in the cellular proteome were manually re-annotated in terms of sequence structure through inspection of the corresponding stop-to-stop open reading frame and blast results. The sequences obtained were exported into a FASTA file for evaluation by the MetaLocGramN facilities (http://genesilico.pl/MetaLocGramN/) [54], SecretomeP 2.0 with settings for Gram-negative bacteria (http://www.cbs.dtu.dk/services/SecretomeP/) [55]and PRED-TAT (http://www.compgen.org/tools/PRED-TAT) [56]. On the basis of the NSAF percentage, a ratio was calculated for each protein identified in the proteomic dataset. This was used to compare their abundance in the secretome with their abundance in the previously analyzed global cellular proteome [7]. Proteins with a ratio (%NSAF secretome/%NSAF cellular proteome) above 1.0 were considered to be secretome specific.

Supporting Information

S1 Table. Analysis of variation among the replicates in each salinity.

https://doi.org/10.1371/journal.pone.0135065.s001

(XLSX)

S2 Table. List of MS/MS spectra assigned to peptides from Tistlia consotensis exoproteomes.

https://doi.org/10.1371/journal.pone.0135065.s002

(XLSX)

S3 Table. List of proteins from Tistlia consotensis exoproteome.

https://doi.org/10.1371/journal.pone.0135065.s003

(XLSX)

S4 Table. NSAF ratio for all proteins detected.

https://doi.org/10.1371/journal.pone.0135065.s004

(XLSX)

S5 Table. Rate of contamination with intracellular proteins.

https://doi.org/10.1371/journal.pone.0135065.s005

(XLSX)

Acknowledgments

C. Rubiano-Labrador was supported by a fellowship from COLCIENCIAS (Departamento Administrativo de Ciencia, Tecnología e Innovación) and the Colombian Center for Genomics and Bioinformatics of Extreme Environments–GeBiX (Colombia). C. Bland was supported by a fellowship from the Commissariat à l’Energie Atomique et aux Energies Alternatives (France) and the Région Languedoc-Roussillon (France). We thank the Commissariat à l’Energie Atomique et aux Energies Alternatives (France), Pontificia Universidad Javeriana (Colombia), and the ECOS-NORD bilateral program for financial support. This work was carried out under MAVDT Research Permit Resolution No. 0168, 2009 and MAVDT Contract No. 56, 2013 for Access to Genetic Resources.

Author Contributions

Conceived and designed the experiments: CRL JA SB. Performed the experiments: CRL GM. Analyzed the data: CRL CB JA SB. Contributed reagents/materials/analysis tools: JA SB. Wrote the paper: CRL JA SB.

References

1. Harrison JP, Gheeraert N, Tsigelnitskiy D, Cockell CS (2013) The limits for life under multiple extremes. Trends in Microbiology 21: 204–212. pmid:23453124
- View Article
- PubMed/NCBI
- Google Scholar
2. Tsuzuki M, Moskvin OV, Kuribayashi M, Sato K, Retamal S, Abo M, et al. (2011) Salt stress-induced Changes in the transcriptome, compatible solutes, and membrane lipids in the facultatively phototrophic bacterium Rhodobacter sphaeroides. Applied and Environmental Microbiology 77: 7551–7559. pmid:21908636
- View Article
- PubMed/NCBI
- Google Scholar
3. Krämer R (2010) Bacterial stimulus perception and signal transduction: Response to osmotic stress. The Chemical Record 10: 217–229. pmid:20607761
- View Article
- PubMed/NCBI
- Google Scholar
4. Díaz-Cárdenas C, Patel BKC, Baena S (2010) Tistlia consotensis gen. nov., sp. nov., an aerobic, chemoheterotrophic, free-living, nitrogen-fixing alphaproteobacterium, isolated from a Colombian saline spring. International Journal of Systematic and Evolutionary Microbiology 60: 1437–1443. pmid:19671725
- View Article
- PubMed/NCBI
- Google Scholar
5. Díaz-Cárdenas C (2011) Estudio de la composición de la comunidad bacteriana en manantiales salinos de los departamentos de Risaralda y Boyaca. [Tesis doctoral]: Pontificia Universidad Javeriana. 289 p.
6. Armengaud J (2013) Microbiology and proteomics, getting the best of both worlds! Environmental Microbiology 15: 12–23. pmid:22708953
- View Article
- PubMed/NCBI
- Google Scholar
7. Rubiano-Labrador C, Bland C, Miotello G, Guérin P, Pible O, Baena S, et al. (2014) Proteogenomic insights into salt tolerance by a halotolerant alpha-proteobacterium isolated from an Andean saline spring. Journal of Proteomics 97: 36–47. pmid:23727365
- View Article
- PubMed/NCBI
- Google Scholar
8. Armengaud J, Christie-Oleza JA, Clair G, Malard V, Duport C (2012) Exoproteomics: exploring the world around biological systems. Expert Review of Proteomics 9: 561–575. pmid:23194272
- View Article
- PubMed/NCBI
- Google Scholar
9. Pumirat P, Saetun P, Sinchaikul S, Chen S-T, Korbsrisate S, Thongboonkerd V (2009) Altered secretome of Burkholderia pseudomallei induced by salt stress. Biochimica et Biophysica Acta (BBA)—Proteins and Proteomics 1794: 898–904.
- View Article
- Google Scholar
10. Shinya R, Morisaka H, Kikuchi T, Takeuchi Y, Ueda M, Futai K (2013) Secretome analysis of the pine wood nematode Bursaphelenchus xylophilus reveals the tangled roots of parasitism and its potential for molecular mimicry. PLoS ONE 8: e67377. pmid:23805310
- View Article
- PubMed/NCBI
- Google Scholar
11. Muddiman D, Andrews G, Lewis D, Notey J, Kelly R (2010) Part I: characterization of the extracellular proteome of the extreme thermophile Caldicellulosiruptor saccharolyticus by GeLC-MS2. Analytical and Bioanalytical Chemistry 398: 377–389. pmid:20623222
- View Article
- PubMed/NCBI
- Google Scholar
12. Dalbey RE, Kuhn A (2012) Protein traffic in Gram-negative bacteria–how exported and secreted proteins find their way. FEMS Microbiology Reviews 36: 1023–1045. pmid:22250915
- View Article
- PubMed/NCBI
- Google Scholar
13. Koster M, Bitter W, Tommassen J (2000) Protein secretion mechanisms in Gram-negative bacteria. International Journal of Medical Microbiology 290: 325–331. pmid:11111906
- View Article
- PubMed/NCBI
- Google Scholar
14. Hahne H, Mäder U, Otto A, Bonn F, Steil L, Bremer E, et al. (2010) A comprehensive proteomics and transcriptomics analysis of Bacillus subtilis salt stress adaptation. Journal of Bacteriology 192: 870–882. pmid:19948795
- View Article
- PubMed/NCBI
- Google Scholar
15. Heras B, Shouldice SR, Totsika M, Scanlon MJ, Schembri MA, Martin J L. (2009) DSB proteins and bacterial pathogenicity. Nat Rev Microbiol 7: 215–225. pmid:19198617
- View Article
- PubMed/NCBI
- Google Scholar
16. De Carvalho CCCR, Fernandes P (2010) Production of metabolites as bacterial responses to the marine environment. Marine Drugs 8: 705–727. pmid:20411122
- View Article
- PubMed/NCBI
- Google Scholar
17. Kim K, Lee S, Lee K, Lim D (1998) Isolation and characterization of toluene-sensitive mutants from the toluene-resistant bacterium Pseudomonas putida GM73. Journal of Bacteriology 180: 3692–3696. pmid:9658016
- View Article
- PubMed/NCBI
- Google Scholar
18. Schmerk CL, Bernards MA, Valvano MA (2011) Hopanoid production is required for low-pH tolerance, antimicrobial resistance, and motility in Burkholderia cenocepacia. Journal of Bacteriology 193: 6712–6723. pmid:21965564
- View Article
- PubMed/NCBI
- Google Scholar
19. Horbach S, Neuss B, Sahm H (1991) Effect of azasqualene on hopanoid biosynthesis and ethanol tolerance of Zymomonas mobilis. FEMS Microbiology Letters 79: 347–350.
- View Article
- Google Scholar
20. Poralla K, Härtner T, Kannenberg E (1984) Effect of temperature and pH on the hopanoid content of Bacillus acidocaldarius. FEMS Microbiology Letters 23: 253–256.
- View Article
- Google Scholar
21. Welander PV, Hunter RC, Zhang L, Sessions AL, Summons RE, Newman DK. (2009) Hopanoids play a role in membrane integrity and pH homeostasis in Rhodopseudomonas palustris TIE-1. Journal of Bacteriology 191: 6145–6156. pmid:19592593
- View Article
- PubMed/NCBI
- Google Scholar
22. Berry AM, Harriott OT, Moreau RA, Osman SF, Benson DR, Jones AD (1993) Hopanoid lipids compose the Frankia vesicle envelope, presumptive barrier of oxygen diffusion to nitrogenase. Proceedings of the National Academy of Sciences 90: 6091–6094.
- View Article
- Google Scholar
23. Poralla K, Muth G, Härtner T (2000) Hopanoids are formed during transition from substrate to aerial hyphae in Streptomyces coelicolor A3(2). FEMS Microbiol Lett 189: 93–95. pmid:10913872
- View Article
- PubMed/NCBI
- Google Scholar
24. Wuichet K, Cantwell BJ, Zhulin IB (2010) Evolution and phyletic distribution of two-component signal transduction systems. Current Opinion in Microbiology 13: 219–225. pmid:20133179
- View Article
- PubMed/NCBI
- Google Scholar
25. Galperin MY, Higdon R, Kolker E (2010) Interplay of heritage and habitat in the distribution of bacterial signal transduction systems. Molecular BioSystems 6: 721–728. pmid:20237650
- View Article
- PubMed/NCBI
- Google Scholar
26. Ashby MK (2004) Survey of the number of two-component response regulator genes in the complete and annotated genome sequences of prokaryotes. FEMS Microbiology Letters 231: 277–281. pmid:14987775
- View Article
- PubMed/NCBI
- Google Scholar
27. Derr P, Boder E, Goulian M (2006) Changing the specificity of a bacterial chemoreceptor. Journal of Molecular Biology 355: 923–932. pmid:16359703
- View Article
- PubMed/NCBI
- Google Scholar
28. Jung K, Fried L, Behr S, Heermann R (2012) Histidine kinases and response regulators in networks. Current Opinion in Microbiology 15: 118–124. pmid:22172627
- View Article
- PubMed/NCBI
- Google Scholar
29. Lacal J, García-Fontana C, Muñoz-Martínez F, Ramos J-L, Krell T (2010) Sensing of environmental signals: classification of chemoreceptors according to the size of their ligand binding regions. Environmental Microbiology 12: 2873–2884. pmid:20738376
- View Article
- PubMed/NCBI
- Google Scholar
30. Krell T, Lacal J, Muñoz-Martínez F, Reyes-Darias JA, Cadirci BH, García-Fontana C, et al. (2011) Diversity at its best: bacterial taxis. Environmental Microbiology 13: 1115–1124. pmid:21087385
- View Article
- PubMed/NCBI
- Google Scholar
31. Zhang W-J, Santini C-L, Bernadac A, Ruan J, Zhang S-D, Kato T, et al. (2012) Complex spatial organization and flagellin composition of flagellar propeller from marine magnetotactic ovoid strain MO-1. Journal of Molecular Biology 416: 558–570. pmid:22245577
- View Article
- PubMed/NCBI
- Google Scholar
32. Averhoff B, Müller V (2010) Exploring research frontiers in microbiology: recent advances in halophilic and thermophilic extremophiles. Research in Microbiology 161: 506–514. pmid:20594981
- View Article
- PubMed/NCBI
- Google Scholar
33. Roeβler M, Müller V (2002) Chloride, a new environmental signal molecule involved in gene regulation in a moderately halophilic bacterium, Halobacillus halophilus. Journal of Bacteriology 184: 6207–6215. pmid:12399491
- View Article
- PubMed/NCBI
- Google Scholar
34. Walmsley AR, Shaw JG, Kelly DJ (1992) The mechanism of ligand binding to the periplasmic C4-dicarboxylate binding protein (DctP) from Rhodobacter capsulatus. Journal of Biological Chemistry 267: 8064–8072. pmid:1569065
- View Article
- PubMed/NCBI
- Google Scholar
35. López-Pérez M, Ghai R, Leon M, Rodríguez-Olmos Á, Copa-Patiño J, Soliveri J, et al. (2013) Genomes of “Spiribacter”, a streamlined, successful halophilic bacterium. BMC Genomics 14: 1–12. pmid:23323973
- View Article
- PubMed/NCBI
- Google Scholar
36. Mulligan C, Fischer M, Thomas GH (2011) Tripartite ATP-independent periplasmic (TRAP) transporters in bacteria and archaea. FEMS Microbiology Reviews 35: 68–86. pmid:20584082
- View Article
- PubMed/NCBI
- Google Scholar
37. Takami H, Takaki Y, Uchiyama I (2002) Genome sequence of Oceanobacillus iheyensis isolated from the Iheya Ridge and its unexpected adaptive capabilities to extreme environments. Nucleic Acids Research 30: 3927–3935. pmid:12235376
- View Article
- PubMed/NCBI
- Google Scholar
38. Moran MA, Belas R, Schell MA, González JM, Sun F, Sun S, et al. (2007) Ecological genomics of marine Roseobacters. Applied and Environmental Microbiology 73: 4559–4569. pmid:17526795
- View Article
- PubMed/NCBI
- Google Scholar
39. Penn K, Jensen P (2012) Comparative genomics reveals evidence of marine adaptation in Salinispora species. BMC Genomics 13: 86. pmid:22401625
- View Article
- PubMed/NCBI
- Google Scholar
40. Morris RM, Nunn BL, Frazar C, Goodlett DR, Ting YS, Rocap G (2010) Comparative metaproteomics reveals ocean-scale shifts in microbial nutrient utilization and energy transduction. ISME Journal: Multidisciplinary Journal of Microbial Ecology 4: 673–685.
- View Article
- Google Scholar
41. Kokoeva MV, Storch KF, Klein C, Oesterhelt D (2002) A novel mode of sensory transduction in archaea: binding protein mediated chemotaxis towards osmoprotectants and amino acids. The EMBO Journal 21: 2312–2322. pmid:12006484
- View Article
- PubMed/NCBI
- Google Scholar
42. Gul N, Poolman B (2013) Functional reconstitution and osmoregulatory properties of the ProU ABC transporter from Escherichia coli. Molecular Membrane Biology 30: 138–148. pmid:23249124
- View Article
- PubMed/NCBI
- Google Scholar
43. Breed J, Kneip S, Gade J, Welte W, Bremer E (2001) Purification, crystallization and preliminary crystallographic analysis of the periplasmic binding protein ProX from Escherichia coli. Acta Crystallogr D Biol Crystallogr 57: 448–450. pmid:11223528
- View Article
- PubMed/NCBI
- Google Scholar
44. Krämer R (2009) Osmosensing and osmosignaling in Corynebacterium glutamicum. Amino Acids 37: 487–497. pmid:19308662
- View Article
- PubMed/NCBI
- Google Scholar
45. Saum SH, Müller V (2007) Salinity-dependent switching of osmolyte strategies in a moderately halophilic bacterium: glutamate induces proline biosynthesis in Halobacillus halophilus. Journal of Bacteriology 189: 6968–6975. pmid:17660292
- View Article
- PubMed/NCBI
- Google Scholar
46. Christie-Oleza JA, Armengaud J (2010) In-depth analysis of exoproteomes from marine bacteria by shotgun liquid chromatography-tandem mass spectrometry: the Ruegeria pomeroyi DSS-3 case-study. Marine Drugs 8: 2223–2239. pmid:20948905
- View Article
- PubMed/NCBI
- Google Scholar
47. Clair G, Roussi S, Armengaud J, Duport C (2010) Expanding the known repertoire of virulence factors produced by Bacillus cereus through early secretome profiling in three redox conditions. Molecular & Cellular Proteomics.
- View Article
- Google Scholar
48. de Groot A, Dulermo R, Ortet P, Blanchard L, Guérin P, Fernandez B, et al. (2009) Alliance of Proteomics and Genomics to Unravel the Specificities of Sahara Bacterium Deinococcus deserti. PLoS Genet 5: e1000434. pmid:19370165
- View Article
- PubMed/NCBI
- Google Scholar
49. Dedieu A, Gaillard J-C, Pourcher T, Darrouzet E, Armengaud J (2011) Revisiting iodination sites in thyroglobulin with an organ-oriented shotgun strategy. Journal of Biological Chemistry 286: 259–269. pmid:20978121
- View Article
- PubMed/NCBI
- Google Scholar
50. Christie-Oleza JA, Fernandez B, Nogales B, Bosch R, Armengaud J (2012) Proteomic insights into the lifestyle of an environmentally relevant marine bacterium. ISME Journal: Multidisciplinary Journal of Microbial Ecology 6: 124–135.
- View Article
- Google Scholar
51. Dupierris V, Masselon C, Court M, Kieffer-Jaquinod S, Bruley C (2009) A toolbox for validation of mass spectrometry peptides identification and generation of database: IRMa. Bioinformatics 25: 1980–1981. pmid:19420053
- View Article
- PubMed/NCBI
- Google Scholar
52. Carvalho P, Fischer J, Chen E, Yates J, Barbosa V (2008) PatternLab for proteomics: a tool for differential shotgun proteomics. BMC Bioinformatics 9: 316. pmid:18644148
- View Article
- PubMed/NCBI
- Google Scholar
53. Zivanovic Y, Armengaud J, Lagorce A, Leplat C, Guerin P, Dutertre M, et al. (2009) Genome analysis and genome-wide proteomics of Thermococcus gammatolerans, the most radioresistant organism known amongst the Archaea. Genome Biology 10: R70. pmid:19558674
- View Article
- PubMed/NCBI
- Google Scholar
54. Magnus M, Pawlowski M, Bujnicki JM (2012) MetaLocGramN: A meta-predictor of protein subcellular localization for Gram-negative bacteria. Biochimica et Biophysica Acta (BBA)—Proteins and Proteomics 1824: 1425–1433.
- View Article
- Google Scholar
55. Bendtsen J, Kiemer L, Fausboll A, Brunak S (2005) Non-classical protein secretion in bacteria. BMC Microbiology 5: 58. pmid:16212653
- View Article
- PubMed/NCBI
- Google Scholar
56. Bagos PG, Tsirigos KD, Liakopoulos TD, Hamodrakas SJ (2008) Prediction of lipoprotein signal peptides in Gram-positive bacteria with a hidden markov model. Journal of Proteome Research 7: 5082–5093. pmid:19367716
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Harrison JP, Gheeraert N, Tsigelnitskiy D, Cockell CS (2013) The limits for life under multiple extremes. Trends in Microbiology 21: 204–212. pmid:23453124
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Tsuzuki M, Moskvin OV, Kuribayashi M, Sato K, Retamal S, Abo M, et al. (2011) Salt stress-induced Changes in the transcriptome, compatible solutes, and membrane lipids in the facultatively phototrophic bacterium Rhodobacter sphaeroides. Applied and Environmental Microbiology 77: 7551–7559. pmid:21908636
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Krämer R (2010) Bacterial stimulus perception and signal transduction: Response to osmotic stress. The Chemical Record 10: 217–229. pmid:20607761
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Díaz-Cárdenas C, Patel BKC, Baena S (2010) Tistlia consotensis gen. nov., sp. nov., an aerobic, chemoheterotrophic, free-living, nitrogen-fixing alphaproteobacterium, isolated from a Colombian saline spring. International Journal of Systematic and Evolutionary Microbiology 60: 1437–1443. pmid:19671725
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Díaz-Cárdenas C (2011) Estudio de la composición de la comunidad bacteriana en manantiales salinos de los departamentos de Risaralda y Boyaca. [Tesis doctoral]: Pontificia Universidad Javeriana. 289 p.

[ref6] 6. Armengaud J (2013) Microbiology and proteomics, getting the best of both worlds! Environmental Microbiology 15: 12–23. pmid:22708953
View Article
PubMed/NCBI
Google Scholar

[19] View Article

[20] PubMed/NCBI

[21] Google Scholar

[ref7] 7. Rubiano-Labrador C, Bland C, Miotello G, Guérin P, Pible O, Baena S, et al. (2014) Proteogenomic insights into salt tolerance by a halotolerant alpha-proteobacterium isolated from an Andean saline spring. Journal of Proteomics 97: 36–47. pmid:23727365
View Article
PubMed/NCBI
Google Scholar

[23] View Article

[24] PubMed/NCBI

[25] Google Scholar

[ref8] 8. Armengaud J, Christie-Oleza JA, Clair G, Malard V, Duport C (2012) Exoproteomics: exploring the world around biological systems. Expert Review of Proteomics 9: 561–575. pmid:23194272
View Article
PubMed/NCBI
Google Scholar

[27] View Article

[28] PubMed/NCBI

[29] Google Scholar

[ref9] 9. Pumirat P, Saetun P, Sinchaikul S, Chen S-T, Korbsrisate S, Thongboonkerd V (2009) Altered secretome of Burkholderia pseudomallei induced by salt stress. Biochimica et Biophysica Acta (BBA)—Proteins and Proteomics 1794: 898–904.
View Article
Google Scholar

[31] View Article

[32] Google Scholar

[ref10] 10. Shinya R, Morisaka H, Kikuchi T, Takeuchi Y, Ueda M, Futai K (2013) Secretome analysis of the pine wood nematode Bursaphelenchus xylophilus reveals the tangled roots of parasitism and its potential for molecular mimicry. PLoS ONE 8: e67377. pmid:23805310
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref11] 11. Muddiman D, Andrews G, Lewis D, Notey J, Kelly R (2010) Part I: characterization of the extracellular proteome of the extreme thermophile Caldicellulosiruptor saccharolyticus by GeLC-MS2. Analytical and Bioanalytical Chemistry 398: 377–389. pmid:20623222
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref12] 12. Dalbey RE, Kuhn A (2012) Protein traffic in Gram-negative bacteria–how exported and secreted proteins find their way. FEMS Microbiology Reviews 36: 1023–1045. pmid:22250915
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref13] 13. Koster M, Bitter W, Tommassen J (2000) Protein secretion mechanisms in Gram-negative bacteria. International Journal of Medical Microbiology 290: 325–331. pmid:11111906
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref14] 14. Hahne H, Mäder U, Otto A, Bonn F, Steil L, Bremer E, et al. (2010) A comprehensive proteomics and transcriptomics analysis of Bacillus subtilis salt stress adaptation. Journal of Bacteriology 192: 870–882. pmid:19948795
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref15] 15. Heras B, Shouldice SR, Totsika M, Scanlon MJ, Schembri MA, Martin J L. (2009) DSB proteins and bacterial pathogenicity. Nat Rev Microbiol 7: 215–225. pmid:19198617
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref16] 16. De Carvalho CCCR, Fernandes P (2010) Production of metabolites as bacterial responses to the marine environment. Marine Drugs 8: 705–727. pmid:20411122
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref17] 17. Kim K, Lee S, Lee K, Lim D (1998) Isolation and characterization of toluene-sensitive mutants from the toluene-resistant bacterium Pseudomonas putida GM73. Journal of Bacteriology 180: 3692–3696. pmid:9658016
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref18] 18. Schmerk CL, Bernards MA, Valvano MA (2011) Hopanoid production is required for low-pH tolerance, antimicrobial resistance, and motility in Burkholderia cenocepacia. Journal of Bacteriology 193: 6712–6723. pmid:21965564
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref19] 19. Horbach S, Neuss B, Sahm H (1991) Effect of azasqualene on hopanoid biosynthesis and ethanol tolerance of Zymomonas mobilis. FEMS Microbiology Letters 79: 347–350.
View Article
Google Scholar

[70] View Article

[71] Google Scholar

[ref20] 20. Poralla K, Härtner T, Kannenberg E (1984) Effect of temperature and pH on the hopanoid content of Bacillus acidocaldarius. FEMS Microbiology Letters 23: 253–256.
View Article
Google Scholar

[73] View Article

[74] Google Scholar

[ref21] 21. Welander PV, Hunter RC, Zhang L, Sessions AL, Summons RE, Newman DK. (2009) Hopanoids play a role in membrane integrity and pH homeostasis in Rhodopseudomonas palustris TIE-1. Journal of Bacteriology 191: 6145–6156. pmid:19592593
View Article
PubMed/NCBI
Google Scholar

[76] View Article

[77] PubMed/NCBI

[78] Google Scholar

[ref22] 22. Berry AM, Harriott OT, Moreau RA, Osman SF, Benson DR, Jones AD (1993) Hopanoid lipids compose the Frankia vesicle envelope, presumptive barrier of oxygen diffusion to nitrogenase. Proceedings of the National Academy of Sciences 90: 6091–6094.
View Article
Google Scholar

[80] View Article

[81] Google Scholar

[ref23] 23. Poralla K, Muth G, Härtner T (2000) Hopanoids are formed during transition from substrate to aerial hyphae in Streptomyces coelicolor A3(2). FEMS Microbiol Lett 189: 93–95. pmid:10913872
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref24] 24. Wuichet K, Cantwell BJ, Zhulin IB (2010) Evolution and phyletic distribution of two-component signal transduction systems. Current Opinion in Microbiology 13: 219–225. pmid:20133179
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref25] 25. Galperin MY, Higdon R, Kolker E (2010) Interplay of heritage and habitat in the distribution of bacterial signal transduction systems. Molecular BioSystems 6: 721–728. pmid:20237650
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref26] 26. Ashby MK (2004) Survey of the number of two-component response regulator genes in the complete and annotated genome sequences of prokaryotes. FEMS Microbiology Letters 231: 277–281. pmid:14987775
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref27] 27. Derr P, Boder E, Goulian M (2006) Changing the specificity of a bacterial chemoreceptor. Journal of Molecular Biology 355: 923–932. pmid:16359703
View Article
PubMed/NCBI
Google Scholar

[99] View Article

[100] PubMed/NCBI

[101] Google Scholar

[ref28] 28. Jung K, Fried L, Behr S, Heermann R (2012) Histidine kinases and response regulators in networks. Current Opinion in Microbiology 15: 118–124. pmid:22172627
View Article
PubMed/NCBI
Google Scholar

[103] View Article

[104] PubMed/NCBI

[105] Google Scholar

[ref29] 29. Lacal J, García-Fontana C, Muñoz-Martínez F, Ramos J-L, Krell T (2010) Sensing of environmental signals: classification of chemoreceptors according to the size of their ligand binding regions. Environmental Microbiology 12: 2873–2884. pmid:20738376
View Article
PubMed/NCBI
Google Scholar

[107] View Article

[108] PubMed/NCBI

[109] Google Scholar

[ref30] 30. Krell T, Lacal J, Muñoz-Martínez F, Reyes-Darias JA, Cadirci BH, García-Fontana C, et al. (2011) Diversity at its best: bacterial taxis. Environmental Microbiology 13: 1115–1124. pmid:21087385
View Article
PubMed/NCBI
Google Scholar

[111] View Article

[112] PubMed/NCBI

[113] Google Scholar

[ref31] 31. Zhang W-J, Santini C-L, Bernadac A, Ruan J, Zhang S-D, Kato T, et al. (2012) Complex spatial organization and flagellin composition of flagellar propeller from marine magnetotactic ovoid strain MO-1. Journal of Molecular Biology 416: 558–570. pmid:22245577
View Article
PubMed/NCBI
Google Scholar

[115] View Article

[116] PubMed/NCBI

[117] Google Scholar

[ref32] 32. Averhoff B, Müller V (2010) Exploring research frontiers in microbiology: recent advances in halophilic and thermophilic extremophiles. Research in Microbiology 161: 506–514. pmid:20594981
View Article
PubMed/NCBI
Google Scholar

[119] View Article

[120] PubMed/NCBI

[121] Google Scholar

[ref33] 33. Roeβler M, Müller V (2002) Chloride, a new environmental signal molecule involved in gene regulation in a moderately halophilic bacterium, Halobacillus halophilus. Journal of Bacteriology 184: 6207–6215. pmid:12399491
View Article
PubMed/NCBI
Google Scholar

[123] View Article

[124] PubMed/NCBI

[125] Google Scholar

[ref34] 34. Walmsley AR, Shaw JG, Kelly DJ (1992) The mechanism of ligand binding to the periplasmic C4-dicarboxylate binding protein (DctP) from Rhodobacter capsulatus. Journal of Biological Chemistry 267: 8064–8072. pmid:1569065
View Article
PubMed/NCBI
Google Scholar

[127] View Article

[128] PubMed/NCBI

[129] Google Scholar

[ref35] 35. López-Pérez M, Ghai R, Leon M, Rodríguez-Olmos Á, Copa-Patiño J, Soliveri J, et al. (2013) Genomes of “Spiribacter”, a streamlined, successful halophilic bacterium. BMC Genomics 14: 1–12. pmid:23323973
View Article
PubMed/NCBI
Google Scholar

[131] View Article

[132] PubMed/NCBI

[133] Google Scholar

[ref36] 36. Mulligan C, Fischer M, Thomas GH (2011) Tripartite ATP-independent periplasmic (TRAP) transporters in bacteria and archaea. FEMS Microbiology Reviews 35: 68–86. pmid:20584082
View Article
PubMed/NCBI
Google Scholar

[135] View Article

[136] PubMed/NCBI

[137] Google Scholar

[ref37] 37. Takami H, Takaki Y, Uchiyama I (2002) Genome sequence of Oceanobacillus iheyensis isolated from the Iheya Ridge and its unexpected adaptive capabilities to extreme environments. Nucleic Acids Research 30: 3927–3935. pmid:12235376
View Article
PubMed/NCBI
Google Scholar

[139] View Article

[140] PubMed/NCBI

[141] Google Scholar

[ref38] 38. Moran MA, Belas R, Schell MA, González JM, Sun F, Sun S, et al. (2007) Ecological genomics of marine Roseobacters. Applied and Environmental Microbiology 73: 4559–4569. pmid:17526795
View Article
PubMed/NCBI
Google Scholar

[143] View Article

[144] PubMed/NCBI

[145] Google Scholar

[ref39] 39. Penn K, Jensen P (2012) Comparative genomics reveals evidence of marine adaptation in Salinispora species. BMC Genomics 13: 86. pmid:22401625
View Article
PubMed/NCBI
Google Scholar

[147] View Article

[148] PubMed/NCBI

[149] Google Scholar

[ref40] 40. Morris RM, Nunn BL, Frazar C, Goodlett DR, Ting YS, Rocap G (2010) Comparative metaproteomics reveals ocean-scale shifts in microbial nutrient utilization and energy transduction. ISME Journal: Multidisciplinary Journal of Microbial Ecology 4: 673–685.
View Article
Google Scholar

[151] View Article

[152] Google Scholar

[ref41] 41. Kokoeva MV, Storch KF, Klein C, Oesterhelt D (2002) A novel mode of sensory transduction in archaea: binding protein mediated chemotaxis towards osmoprotectants and amino acids. The EMBO Journal 21: 2312–2322. pmid:12006484
View Article
PubMed/NCBI
Google Scholar

[154] View Article

[155] PubMed/NCBI

[156] Google Scholar

[ref42] 42. Gul N, Poolman B (2013) Functional reconstitution and osmoregulatory properties of the ProU ABC transporter from Escherichia coli. Molecular Membrane Biology 30: 138–148. pmid:23249124
View Article
PubMed/NCBI
Google Scholar

[158] View Article

[159] PubMed/NCBI

[160] Google Scholar

[ref43] 43. Breed J, Kneip S, Gade J, Welte W, Bremer E (2001) Purification, crystallization and preliminary crystallographic analysis of the periplasmic binding protein ProX from Escherichia coli. Acta Crystallogr D Biol Crystallogr 57: 448–450. pmid:11223528
View Article
PubMed/NCBI
Google Scholar

[162] View Article

[163] PubMed/NCBI

[164] Google Scholar

[ref44] 44. Krämer R (2009) Osmosensing and osmosignaling in Corynebacterium glutamicum. Amino Acids 37: 487–497. pmid:19308662
View Article
PubMed/NCBI
Google Scholar

[166] View Article

[167] PubMed/NCBI

[168] Google Scholar

[ref45] 45. Saum SH, Müller V (2007) Salinity-dependent switching of osmolyte strategies in a moderately halophilic bacterium: glutamate induces proline biosynthesis in Halobacillus halophilus. Journal of Bacteriology 189: 6968–6975. pmid:17660292
View Article
PubMed/NCBI
Google Scholar

[170] View Article

[171] PubMed/NCBI

[172] Google Scholar

[ref46] 46. Christie-Oleza JA, Armengaud J (2010) In-depth analysis of exoproteomes from marine bacteria by shotgun liquid chromatography-tandem mass spectrometry: the Ruegeria pomeroyi DSS-3 case-study. Marine Drugs 8: 2223–2239. pmid:20948905
View Article
PubMed/NCBI
Google Scholar

[174] View Article

[175] PubMed/NCBI

[176] Google Scholar

[ref47] 47. Clair G, Roussi S, Armengaud J, Duport C (2010) Expanding the known repertoire of virulence factors produced by Bacillus cereus through early secretome profiling in three redox conditions. Molecular & Cellular Proteomics.
View Article
Google Scholar

[178] View Article

[179] Google Scholar

[ref48] 48. de Groot A, Dulermo R, Ortet P, Blanchard L, Guérin P, Fernandez B, et al. (2009) Alliance of Proteomics and Genomics to Unravel the Specificities of Sahara Bacterium Deinococcus deserti. PLoS Genet 5: e1000434. pmid:19370165
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref49] 49. Dedieu A, Gaillard J-C, Pourcher T, Darrouzet E, Armengaud J (2011) Revisiting iodination sites in thyroglobulin with an organ-oriented shotgun strategy. Journal of Biological Chemistry 286: 259–269. pmid:20978121
View Article
PubMed/NCBI
Google Scholar

[185] View Article

[186] PubMed/NCBI

[187] Google Scholar

[ref50] 50. Christie-Oleza JA, Fernandez B, Nogales B, Bosch R, Armengaud J (2012) Proteomic insights into the lifestyle of an environmentally relevant marine bacterium. ISME Journal: Multidisciplinary Journal of Microbial Ecology 6: 124–135.
View Article
Google Scholar

[189] View Article

[190] Google Scholar

[ref51] 51. Dupierris V, Masselon C, Court M, Kieffer-Jaquinod S, Bruley C (2009) A toolbox for validation of mass spectrometry peptides identification and generation of database: IRMa. Bioinformatics 25: 1980–1981. pmid:19420053
View Article
PubMed/NCBI
Google Scholar

[192] View Article

[193] PubMed/NCBI

[194] Google Scholar

[ref52] 52. Carvalho P, Fischer J, Chen E, Yates J, Barbosa V (2008) PatternLab for proteomics: a tool for differential shotgun proteomics. BMC Bioinformatics 9: 316. pmid:18644148
View Article
PubMed/NCBI
Google Scholar

[196] View Article

[197] PubMed/NCBI

[198] Google Scholar

[ref53] 53. Zivanovic Y, Armengaud J, Lagorce A, Leplat C, Guerin P, Dutertre M, et al. (2009) Genome analysis and genome-wide proteomics of Thermococcus gammatolerans, the most radioresistant organism known amongst the Archaea. Genome Biology 10: R70. pmid:19558674
View Article
PubMed/NCBI
Google Scholar

[200] View Article

[201] PubMed/NCBI

[202] Google Scholar

[ref54] 54. Magnus M, Pawlowski M, Bujnicki JM (2012) MetaLocGramN: A meta-predictor of protein subcellular localization for Gram-negative bacteria. Biochimica et Biophysica Acta (BBA)—Proteins and Proteomics 1824: 1425–1433.
View Article
Google Scholar

[204] View Article

[205] Google Scholar

[ref55] 55. Bendtsen J, Kiemer L, Fausboll A, Brunak S (2005) Non-classical protein secretion in bacteria. BMC Microbiology 5: 58. pmid:16212653
View Article
PubMed/NCBI
Google Scholar

[207] View Article

[208] PubMed/NCBI

[209] Google Scholar

[ref56] 56. Bagos PG, Tsirigos KD, Liakopoulos TD, Hamodrakas SJ (2008) Prediction of lipoprotein signal peptides in Gram-positive bacteria with a hidden markov model. Journal of Proteome Research 7: 5082–5093. pmid:19367716
View Article
PubMed/NCBI
Google Scholar

[211] View Article

[212] PubMed/NCBI

[213] Google Scholar

Figures

Abstract

Introduction

Results and Discussion

Exoproteome of T. consotensis under salt stress

Signal peptide prediction of the most abundant proteins from T. consotensis

Influence of salinity on the exoproteomes of T. consotensis

Protein quality control.

Membrane lipid alterations.

Signal transduction.

Transport system.

Materials and Methods

Strain and growth conditions

Protein sample preparation

Trypsin in-gel proteolysis and nanoLC-MS/MS analysis

NanoLC-MS/MS analysis

Genome sequencing of T. consotensis and 6-frame ORF database

MS/MS database search

Comparison of protein abundance with PatternLab

Protein and nucleic sequence analysis

Supporting Information

S1 Table. Analysis of variation among the replicates in each salinity.

S2 Table. List of MS/MS spectra assigned to peptides from Tistlia consotensis exoproteomes.

S3 Table. List of proteins from Tistlia consotensis exoproteome.

S4 Table. NSAF ratio for all proteins detected.

S5 Table. Rate of contamination with intracellular proteins.

Acknowledgments

Author Contributions

References