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Quantitative proteomics for the comprehensive analysis of stress responses of Lactobacillus paracasei subsp. paracasei F19 Ann-Sophie Schott, Juergen Behr, Andreas J Geissler, Bernhard Kuster, Hannes Hahne, and Rudi F. Vogel J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00474 • Publication Date (Web): 01 Sep 2017 Downloaded from http://pubs.acs.org on September 2, 2017

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Journal of Proteome Research

Quantitative proteomics for the comprehensive analysis of stress responses of Lactobacillus paracasei subsp. paracasei F19 Ann-Sophie Schott1, Jürgen Behr1,2,*, Andreas J. Geißler1, Bernhard Kuster2,3,4, Hannes Hahne5, Rudi F. Vogel1

3

1

Chair of Technical Microbiology, Technische Universität München, Freising, Germany

2

Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Freising, Germany

Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany 4

Center for Integrated Protein Science Munich, Freising, Germany 5

OmicScouts GmbH, Freising, Germany

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ABSTRACT. Lactic acid bacteria are broadly employed as starter cultures in the manufacture of foods. Upon technological preparation, they are confronted with drying stress that amalgamates numerous stress conditions resulting in losses of fitness and survival. To better understand and differentiate physiological stress responses, discover general and specific markers for the investigated stress conditions and predict optimal preconditioning for starter cultures, we performed a comprehensive genomic and quantitative proteomic analysis of a commonly used model system, Lactobacillus paracasei subsp. paracasei TMW 1.1434 (isogenic with F19) under eleven typical stress conditions, including among others oxidative, osmotic, pH and pressure stress.

We identified and quantified >1900 proteins in triplicate analyses, representing 65 % of all genes encoded in the genome. The identified genes were thoroughly annotated in terms of subcellular localization prediction and biological functions, suggesting unbiased and comprehensive proteome coverage. In total, 427 proteins were significantly differentially expressed in at least one condition. Most notably, our analysis suggests that optimal preconditioning towards drying was predicted to be alkaline and high-pressure stress preconditioning. Taken together, we believe the presented strategy may serve as a prototypic example for the analysis and utility of employing quantitative mass spectrometry-based proteomics to study bacterial physiology.

KEYWORDS. Lactobacillus paracasei, stress response, differentially expressed protein, quantitative proteomics, genomics.


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INTRODUCTION In bioprocess engineering, lactic acid bacteria (LAB) play a major role since, due to their sensory qualities and potentially health-promoting properties, they are used as starter cultures in many commercial products

1-4

and as probiotics with potential health-promoting effects

5-9

.

Currently, the industrial standard allows the application of concentrated starter cultures as dried pellet which has to be reliable in terms of quality, performance and viability

10-12

.

Therefore, starter cultures are technologically produced in a drying process, whereby LAB are exposed to various unfavorable conditions affecting their cellular viability and performance. At this, LAB encounter different stress qualities, such as extreme values of aridity, temperature, pH and osmotic stress 10-12 resulting in a reduced bacterial survival 13-14. So far, approaches to improve starter culture preparation focused on alternative drying processes, which, in comparison to the generally applied freeze-drying process, resulted in a positively affected survival rate

15-16

. Bauer et al. reported that Lactobacillus (Lb.) paracasei

subsp. paracasei F19 exhibited higher stability and survival rates if the strain was subjected to low-temperature vacuum drying rather than freeze drying

17-20

. However, exploiting the

approach of stress preconditioning, we investigated an additional alternative that could enhance vitality and fitness before the technological preparation and thus may contribute to an increased survival rate afterwards. In general, bacteria exhibit fundamental mechanisms in order to survive stress conditions. They are equipped with a widely spread regulatory network of stress response mechanisms to maintain cellular viability

21

. Van de Guchte et al. and Papadimitriou et al. reported

specifically about stress responses in LAB that commonly increase stress resistance by the development of adaptive responses to an induced stress 22-26. Besides, adaptive responses often induce cross resistances to another stress leading to a significantly increased stress tolerance level and survival rate 12, 27. Various strategies are documented describing the effects of stress on LAB. As cellular functions are directly reflected at the protein level, the development of 3 ACS Paragon Plus Environment


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adaptive responses can be detected in the proteome of bacteria. To date, several reports describe the stress response of LAB as the expression of stress proteins, which are considered to be general or specific, depending on the species, on the strain and on the type of induced stress. This work focuses on the detection of stress responses based on protein expression analysis using mass spectrometry-based proteomics 28-32. In the past years, mass spectrometry has made great technological progress and is widely applied for the analysis of complex protein mixtures 33, particularly for the proteome analysis of human tissues 33-34. Accordingly, the quantitative analysis of proteomes moved in the center of interest in MS-based proteomics

35-38

. Gradually, the approach of protein expression

analysis by mass spectrometry using TMT is adapted and has recently been applied to the field of microbiology 34, 39-42. In this study, we designed an experimental strategy to characterize physiological responses induced by stress and identified differentially expressed proteins using quantitative proteomics. Further, we present a potential practical application by analyzing stress preconditioning responses in Lactobacillus paracasei subsp. paracasei TMW 1.1434 (isogenic with F19) providing the basis for the prediction of optimal preconditioning for starter culture preparation based on stress response similarities. EXPERIMENTAL SECTION BACTERIAL STRAIN AND GROWTH CONDITIONS. In this work Lb. paracasei subsp. paracasei TMW 1.1434 (isogenic with the commercial strain F19 from Chr. Hansen A/S, Hørsholm, Denmark) was used as model microorganism. In the text, we use the designation F19 to facilitate cross referencing to relevant literature. Cells were supplied as frozen concentrate with a titer of 108 cfu/ml and stored at – 80 °C in stocks with a final glycerol concentration of 45 % (Gerbu, Heidelberg, Germany). Standardized inocula, fermentation processes and stress treatments were carried out as described by Schott et al.

43-44

. A standardized fermentation process of the strain was 4

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performed, while cells were grown anaerobically at 37 °C in modified MRS (Spicher broth) at pH 5.4 (control condition). The strain was grown to exponential phase in a final volume of 50 ml and stress treatments were applied for 60 min. Prior stress treatments, which are listed in Table 1, control sample was taken. The control (untreated) and stress (treated) samples were prepared in biological triplicates and applied to sample preparation for proteomic analysis. Table 1. Applied stress treatments employed on Lb. paracasei subsp. paracasei TMW 1.1434 (isogenic with F19) 45. Listed are stress quality, the corresponding ID and the stress condition, which is specified as the combination of stress intensity and application time. Stress quality

ID

Stress condition

Acid stress

pH4

pH 4, 60 min

Alkaline stress

pH9

pH 9, 60 min

Cold stress

15C

15 °C, 60 min

Heat stress

45C

45 °C, 60 min

Lack of glucose (Starvation stress)

glu10

10 % (w/w), 60 min

Osmotic stress - lactose

lac

0.32 M, 60 min

Osmotic stress - potassium chloride

KCl

1 M, 60 min

Osmotic stress - sodium chloride

NaCl

1 M, 60 min

Osmotic stress - sucrose

suc

1.15 M, 60 min

Oxidative stress - hydrogen peroxide

H2O2

1.4 mM, 60 min

High hydrostatic pressure stress*

HHP

350 MPa, 10 min, 60 min*

Drying stress°

D

RT, 60 min

*A recovery phase in Spicher broth is attached and is yet, for simplification, further discussed as stress quality. °Simulated drying process for starter culture preparation. ESTABLISHING A REFERENCE GENOME. In order to have a high quality, reference genome for in silico protein prediction and for the consequent mapping of MS data, a whole genome sequence was generated. Therefore, high molecular weight DNA of the strain was isolated using the Genomic-tip 100/G kit (Qiagen, Venlo, Netherlands) according to the 5 ACS Paragon Plus Environment


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manufacture with modifications regarding the lysis time (16 h). Quantity and quality of isolated DNA was checked using agarose gel electrophoresis and NanoDrop (Thermo Fisher Scientific). Isolated DNA was sequenced at GATC Biotech (Konstanz, Germany) via PacBio Single Molecule Real Time (SMRT) sequencing

46

. Employing P4-C2 chemistry, a single

library was prepared with an insert size of 8 to 12 kb, and more than 200 Mb of raw data were generated from a single SMRT cell. Raw data were assembled with SMRT Analysis (Version 2.2.0.p2), using the hierarchical genome assembly process (HGAP3) and manual curation as described

by

PacBio

(https://github.com/PacificBiosciences/Bioinformatics-

Training/wiki/Finishing-Bacterial-Genomes). Prior to annotation, contigs were circularized using minimus2 (AMOS, http://amos.sourceforge.net). The genome was further annotated using RAST (Rapid Annotations using Subsystems Technology), a SEED-based prokaryotic genome annotation service, and by the NCBI Prokaryotic Genome Annotation Pipeline (https://www.ncbi.nlm.nih.gov/genome/annotation_prok/).

Genome

sequences

were

submitted to GenBank and can be accessed with the accession numbers CP016355-CP016356 (Bioproject PRJNA327719, Biosample SAMN05356834) 47-48. PREDICTION OF SUBCELLULAR LOCALIZATION AND FUNCTIONAL ANALYSIS. Subcellular localization of proteins was predicted utilizing the tool PSORTb (Version 3.0.2, http://www.psort.org/psortb/)

49-50

. Functional analysis was accomplished using SEED

categorization based on RAST and the SEED subsystem analysis (Subsystem and FIGfams Technology) 51-53. The SEED subsystem analysis enables the assignment of predicted genes to a hierarchical three-level categorization system (category, subcategory, subsystem), whereby they can be assigned to several subsystems. CELL LYSIS, PROTEIN DIGESTION AND PEPTIDE PURIFICATION. Cell samples for proteomic analysis were washed twice in TBS buffer (50 mM Tris, 150 mM NaCl, pH 7.5), reconstituted in lysis buffer (8 M urea, 50 mM TEAB, 1x protease inhibitor SigmaFast) and mechanically disrupted by passing them three times through a French Press at 275 MPa (HTU 6 ACS Paragon Plus Environment

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DIGI-F-Press, Modell F-013, G. Heinemann Ultraschall- und Labortechnik, Schwäbisch Gmünd, Germany). Total protein concentration of the lysate was determined using the Bradford method (Bio-Rad Protein Assay, Bio-Rad Laboratories GmbH, Munich, Germany). 200 µg of protein from the lysate was used for in-solution digestion. Protein was reduced with 1 M DTT (1:100 v/v, final concentration 10 mM DTT) at 56 °C for 45 min and subsequently alkylated with iodoacetamide (1:10 v/v, final concentration 50 mM IAA) at room temperature for 60 min. Reduced protein was digested overnight at 37 °C using 2 µg trypsin (protease-toprotein ratio of 1:100). Digested peptides were desalted according to the manufacturer’s instructions by C18 solid phase extraction using stop and go extraction tips (C18-Stage Tips) 54-55

. Lately, the samples were frozen at -80 °C and dried completely in a speed vac.

PEPTIDE LABELING USING TANDEM MASS TAGS (TMT10). Lyophilized peptides (200 µg) were reconstituted in 50 mM TEAB to a concentration of 1.25 µg/µl peptide. Further, to allow accurate quantification of differences in abundance levels between different treatments, we pooled appropriate amounts of each peptide sample (including biological triplicates) to a peptide mix that served as internal standard (IS). Internal standard and peptide samples were chemically labeled with the TMT10plex™ Isobaric Label Reagent Set according to the instructions of the supplier (Thermo Fisher Scientific). The TMT labeling pattern is presented in Table 2. Table 2. TMT labeling pattern of all samples with respect to TMT tag and LC-MS/MS mixture for quantitative proteomics. Samples are named according to applied stress treatment, whereby stress IDs can be found in Table 1. Biological triplicates are displayed in consecutive numbering (_1 - _3). Further internal standards (IS) are presented. TMT-labeled samples are combined by row to five LC-MS/MS mixtures (#1-#5). TMT10™-Label Reagent 126

127N

127C

128N

128C

129N

129C

130N

130C

131


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LC-MS/MS mixture


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#1

IS

IS

contr_1

glu10_1

15C_1

45C_1

D_1

pH4_1

pH9_1

HHP_1

#2

IS

IS

H2O2_1

NaCl_1

KCl_1

lac_1

suc_1

contr_2

glu10_2

15C_2

#3

IS

IS

45C_2

D_2

pH4_2

pH9_2

HHP_2

H2O2_2

NaCl_2

KCl_2

#4

IS

IS

lac_2

suc_2

contr_3

glu10_3

15C_3

45C_3

D_3

pH4_3

#5

IS

IS

pH9_3

HHP_3

H2O2_3

NaCl_3

KCl_3

lac_3

suc_3

blank

For correcting potential sample loss and varying labeling efficiency, a labeling test was carried out by measuring defined mixtures by LC-MS/MS (LC-MMS/MS mixtures), as indicated in Table 2. Samples were then mixed in equimolar ratios according to TMT labeling pattern for proteomic analysis, again desalted using C18-Stage Tips and reconstituted in 50 µl 5 mM Tris-HCl (pH 8.5; hSAX solvent A) prior to fist dimension peptide separation. FIRST DIMENSION PEPTIDE SEPARATION (HSAX). First dimension peptide separation was performed at the institute of Proteomics and Bioanalytics (Technische Universität München, Freising, Germany) using offline liquid chromatography. For full proteome analysis, peptide separation based on hydrophilic strong anion exchange (hSAX) was performed using a Dionex Ultimate 3000 LC system (Thermo Scientific, Bremen, Germany) which was equipped with an IonPac AG24 guard column (2 mm I.D. x 50 mm, Thermo Fisher Scientific) and an IonPac AS24 SAX-column (2 mm I.D. x 250 mm, Thermo Fisher Scientific). The system was operating at 25 °C with a flow rate of 250 µl/min and an initial equilibration step with 100 % hSAX solvent A (5 mM Tris-HCl, pH 8.5) followed by elution with a linear 24 min gradient up to 25 % hSAX solvent B (5 mM TrisHCl pH 8.5, 1 M NaCl). hSAX solvent B was increased to 100 % in 13 min and held constant for 4 min. Subsequently, a switch to 100 % hSAX solvent A in 1 min was followed by column re-equilibration with 100 % hSAX solvent A for 10 min. 24 fractions were collected starting from 2 min to 40 min, desalted using C18-Stage Tips, and reconstituted in 20 µl 0.1 % formic acid (FA) prior liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. 8 ACS Paragon Plus Environment

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LIQUID CHROMATOGRAPHY AND MASS SPECTROMETRY. Detailed LC-MS/MS parameters can be found in the Supporting Information. Briefly, LC-MS/MS analysis was carried out by coupling an UltiMate 3000 nano LC system (Thermo Scientific, Bremen, Germany) to a Q Exactive HF (Thermo Scientific, Bremen, Germany). Online second dimension peptide separation was conducted applying nanoflow ion-pairing reversed-phase (RPIP) chromatography. Peptides were transported to a trap column (100 μm I.D. x 2 cm, ReproSil-Pur C18-AQ, 5 µm, Dr. Maisch, Ammerbuch, Germany) for 10 min at a flow rate of 5 µl/min in loading solvent (0.1 % FA in HPLC grade water). For separation, peptides were then transferred and loaded onto an analytical column (75 µm I.D. x 40 cm, ReproSil-Pur C18-AQ, 3 µm, Dr. Maisch, Ammerbuch, Germany) using a 120 min gradient at a flow rate of 300 nl/min (solvent A: 0.1 % FA, 5 % DMSO in HPLC grade water; solvent B: 0.1 % FA, 5 % DMSO in ACN) (0 min: 2 % B; 11 min: 4 % B; 112 min: 32 % B; 114 min: 80 % B; 118 min: 2 % B). The ionization of the resulting peptides was conducted at a capillary temperature of 275 °C using 2.2 kV ion spray voltage. The mass spectrometer operated in data dependent acquisition mode, automatically switching between MS1 and MS2. Full scan MS1 spectra (360-1300 m/z) were acquired in the Orbitrap for a maximum ion injection time of 50 ms at 60,000 resolution and an automatic gain control (AGC) target value of 3e6. Up to 25 precursor ions were allowed to be selected for fragmentation, whereby isolation window was set to 1.3 m/z, normalized collision energy (NCE) to 33 % and the underfill ratio to 1 % with a dynamic exclusion of 20 s. MS2 spectra (200-2000 m/z) were acquired in the Orbitrap mass analyzer at a resolution of 30,000 and an AGC target value of 2e5 with a maximum ion injection time of 57 ms. EXPERIMENTAL DESIGN. Proteomic experiments were performed in three biological replicates per experimental condition (control condition, stress condition), resulting in 39 peptide samples. Further, ten IS samples were added, resulting in overall 49 samples. This



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allows the analysis of five single TMT10-plex experiments (LC-MS/MS mixtures) that were combined as displayed in Table 2. PEPTIDE AND PROTEIN IDENTIFICATION AND QUANTIFICATION. For protein identification and quantification, raw files (MS/MS data) were analyzed at the Bavarian Center for Biomolecular Mass Spectrometry (Technische Universität München, Freising, Germany)

using

MaxQuant

(Version

1.5.5.2,

http://www.coxdocs.org/doku.php?id=:maxquant:start) and the integrated search engine Andromeda. Raw files of full proteome fractions were searched against the strain-specific database composed of the in silico proteome of Lb. paracasei susp. paracasei F19 (2938 entries) and common contaminant proteins. Carbamidomethylated cysteine was set as fixed modifications, and oxidation of methionine was allowed as variable modification with maximal 5 modifications per peptide. Proteolytic enzyme was set to trypsin/P with cleavage after proline to account for quantification, whereby two missed cleavage sites were allowed. Minimum peptide length was set to seven amino acids. Quantification was performed on reporter ion MS2 using the isobaric label set TMT10plex with a reporter mass tolerance of 0.01 Da. The mass tolerance was set to 4.5 ppm for precursor ions and to 20 ppm for fragment ions. For peptide and protein identification, peptide spectrum matches (PSM) FDR, protein False Discovery Rate (FDR) and site decoy fraction filtering were set to 1%, 5 % and 1 %, respectively. The minimum number of total peptides a protein group should have to be considered as identified is adjusted to one. Further, second peptides, Match-between-runs (MBR) with a match time window of 0.7 min and an alignment time window of 20 min and dependent peptides with FDR of 1 % and mass tolerance (mass bin size) of 0.0065 Da were enabled. For protein quantification, minimum ratio count for protein quantification was set to one, unique and razor peptides were used for quantification, whereby unmodified and modified peptides (oxidized methionine) were used for quantification.


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Data were further processed using Microsoft Excel and the R statistical programming environment (see below). STATISTICAL ANALYSES AND VISUALIZATION. Statistical analyses were carried out using R software (Version 3.3.2, http://www.r-project.org/) and Excel (Microsoft). Quality control (QC) and quality analysis of MaxQuant output was conducted using the automated R-based QC pipeline Proteomics Quality Control (‘PTXQC’ package) 56. PTXQC analyzes measurement bias, consistency and error by creating a QC report containing a set of QC metrics that are augmented with automated scoring functions. The automated scores are summarized in an overview heatmap providing a quality rating of the MaxQuant output ranging from “fail” over “under performing” to “best”. PTXQC was executed, whereby PTXQC settings can be found in the Supporting Information. Proteomic data, on the basis of identified protein groups and respective reporter intensities (RI), were filtered, whereby contaminants, reverse and only identified by site proteins were excluded. Further, proteins with at least seven missing RI values across all samples were excluded from analysis. To reduce technical and experimental variations, normalization to the internal standard was conducted across all TMT channels and across all TMT experiments using Microsoft Excel, followed by normalization using the function ‘CountDataSet’ (‘DESeq’ package)

57

. Subsequently, proteomic data was log2-transformed. Since the data

were log- normally distributed, we used MANOVA for checking dependent variable significance of quantified proteins. Tukey Honest Significance Difference (HSD) correction was performed to proof statistical significance over the groups (p-value < 0.05) followed by the calculation of Log2FoldChange (FC) of proteins in any condition. Proteins displaying large magnitude changes within a defined cutoff (allocated in the 5 % bottom and top edge of the extreme) were classified as differentially expressed proteins and visualized in volcano plots (‘gplot’ package).



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Fisher’s exact test, which uses the analysis of contingency table, was performed applying the function ‘fisher.test’ (‘stats’ package) in order to assess the influence of stress on protein expression. Thereby, the null hypothesis of independence is tested for two nominal variables (expressed and differentially expressed proteins). Resulting p-values < 0.05 indicate the rejection of the null hypothesis and thus indicate significant difference. Hierarchical cluster analysis was performed using the function ‘heatmap.2’ (‘gplot’ package) in order to detect stress response similarities. Thereby, a heat map with dendrogram was created, first calculating the Minkowski distance of a matrix and then using the function ‘hclust’ combined with the cluster method ‘average’ for plotting. Protein expression analysis was computed based on the calculation of clustered heat maps using the function ‘pheatmap’ (‘pheatmap’ package). First, cluster analysis was performed calculating the Euclidean distance and then using the function ‘hclust’ combined with the cluster method ‘complete’ for plotting. Proteomic distribution of differentially expressed proteins was visualized using BLAST ring image generator (BRIG) 58. Further, visualization of shared differentially expressed proteins was achieved in venn diagrams using VennMaster (Version 0.38.2, http://sysbio.uniulm.de/?Software:VennMaster). RESTULTS AND DISCUSSION QUANTITATIVE PROTEOMICS FOR THE ANALYSIS OF BACTERIAL STRESS RESPONSES. To enable the efficient analysis of bacterial stress responses, we developed a straightforward experimental strategy based on genomics and quantitative proteomics using chemical labeling. We tested this strategy for the investigation of bacterial stress responses of the strain Lactobacillus paracasei subsp. paracasei F19 accompanied by the identification of differentially expressed (DE) proteins (Figure 1). Stress treatments were prepared in biological triplicates based on identified stress conditions 59, cell lysis was carried out and whole-cell protein extracts were subjected to proteolytic digestion with trypsin. Peptide 12 ACS Paragon Plus Environment

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samples were labeled with isobaric tandem mass tags (TMT), and fractionated using off-line hSAX liquid chromatography to reduce sample complexity and alleviate TMT ratio distortion 46

. Full in-depth proteome analysis was performed with LC-MS/MS. For protein identification

and quantification, tandem mass spectra were processed with MaxQuant, searched against a protein sequence database based on the whole genome sequence of Lb. paracasei subsp. paracasei F19, and statistically analyzed in R. Thereby, an overall quality of MaxQuant output of “best” was calculated and differentially expressed proteins identified. For generating the whole genome sequence, high molecular DNA was isolated and whole genome sequenced via PacBio SMRT sequencing, genome was assembled via SMRT analysis and annotated using RAST and NCBI Prokaryotic Genome Annotation Pipeline. Genome sequence was submitted to GenBank.



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Figure 1. Experimental strategy for the efficient analysis of bacterial stress responses based on genomics and quantitative proteomics using chemical labeling. Note, the analysis of


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bacterial stress responses is accompanied by the identification of differentially expressed proteins. Overall, we identified 2159 proteins with at least one unique peptide (False Positive Rate, FPR, 5.4 % on protein level) and 2005 proteins with at least two unique peptides (FPR, 3.1%) in biological triplicates. The identified proteins covered 73 % and 68 % of the in silico proteome, respectively. Moreover, the protein identifications were based on 21,871 and 21,717 unique peptide identifications (FPR, 0.9 % and 0.9 % on peptide level, respectively) with an average of 10.1 unique peptides per protein identification. 1917 proteins were quantified, of which 427 proteins were differentially expressed (Figure 2A). This dataset represents one of the largest collections of identified, quantified and DE microbial proteins in a single experiment. To our knowledge, there is no other proteomic study of bacterial stress responses that describes an equally large proteome coverage.



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Figure 2. Global proteome characterization. (A) Comparison of in silico proteome with identified, quantified, expressed and differentially expressed proteins. (B) SEED subsystem proteome coverage. (C) Predicted subcellular localization (SCL) of in silico proteome, identified, quantified, expressed and differentially expressed (DE) proteins. Displayed are percentage (C.1) and absolute number (N) (C.2). The complete list of predicted SCL is provided (Table S3). Applying the analysis of subcellular localization (SCL) prediction of proteins, we were able to identify extracellular, cytoplasmic, cytoplasmic membrane (cell membrane) and cell wall proteins (Figure 2C). To assess the influence of stress, SCL of expressed proteins were compared to SCL of differentially expressed proteins using Fisher’s exact test. Thereby, evidence was found that stress significantly (p-value = 0.002) influences the expression of proteins of different SCL. Cells responded to stress with the visual relative enhancement of cell wall and membrane proteins, which was accompanied by the relative reduction of cytoplasmic proteins. This differentially expression of proteins related to the cell wall and membrane triggered by stress may be consistent with the common notion that changes in its chemical composition, structure and functionality improves cell survival. Cell wall and membrane are the physical barriers separating the cell from its environment and, as such, the first line of defense against detrimental environmental conditions. Maintaining the integrity under changing environmental conditions, especially of the membrane, is a matter of life or death for bacteria

60

. Overall, the protein subcellular localization analysis displayed full

coverage of the subcellular compartments including proteins of the cytoplasmic membrane and cell wall, and thus demonstrated that cellular fractionation of the protein extract during sample preparation is not required to achieve comprehensive proteome coverage. A broad range of biological functions based on the SEED subsystem analysis was detected covering 40 % of the in silico proteome of F19 (Figure 3A). Our data comprises a wealth of 16 ACS Paragon Plus Environment

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biologically interesting proteins that are often not identified in global proteome profiling studies. Most abundant proteins were assigned to carbohydrate (23 %) and protein metabolism (12 %), followed by proteins associated with cell wall and capsule biosynthesis, amino acids and derivatives biosynthesis and cofactors/vitamins/prosthetic group biosynthesis (7 %, respectively) (Figure 3A). Within these categories, we found proteins related to general metabolic and regulatory functions, e.g. di- and oligosaccharide metabolism, protein biosynthesis,

capsular

and

extracellular

polysaccharide

biosynthesis,

lysine/threonine/methionine/cysteine metabolism, and folate and pterines biosynthesis. However, for graphical illustration, categories with a constant proportion less than 5 % were summarized

as

“Other

SEED

categories”

(Respiration;

Secondary

Metabolism;

Phages/Prophages/Transposable elements/Plasmids; Iron Acquisition and Metabolism; Cell Division and Cell Cycle; Nitrogen Metabolism; Potassium Metabolism; Regulation and Cell signaling; Metabolism of Aromatic Compounds; Miscellaneous Metabolism; Sulfur Metabolism; Phosphorus Metabolism; Motility and Chemotaxis; Dormancy and Sporulation).



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Figure 3. Global analysis of biological functions. (A) Biological functions based on SEED categories. Illustrated are percentage (A.1) and absolute number (N) (A.2) of in silico proteome, identified, quantified, expressed and differentially expressed proteins. (B) Estimated relative protein investment of the cell in biological functions upon stress (DE proteins) based on the calculation of the relative protein mass on a proteome-wide scale (% protein mass of the total dry mass). The complete list of predicted SEED category is provided (Table S4). To evaluate the influence of stress on biological functions, SEED categories of expressed and DE proteins were compared applying Fisher’s exact test. Thereby, we found evidence that stress significantly influences (p-value = 0.001) the expression of proteins of different 18 ACS Paragon Plus Environment

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biological functions. We noted numerous SEED categories with different relative proportions when exposed to stress. Among the top 5 ranked, a visual relative enhancement based on the associated proteins was revealed for SEED categories carbohydrate metabolism, nucleoside and nucleotide biosynthesis and membrane transport, whereas a relative reduction was observed for DNA metabolism and cell wall and capsule biosynthesis. The downregulation of proteins associated with the cell wall and capsule biosynthesis seems to contradict the previous findings of the enhancement of cell wall and membrane proteins (see SCL analysis). Here, it is important to note that not all proteins, which are located in the cell wall or cell membrane, are actually involved in its biosynthesis, instead they can have diverse biological functions. Interestingly, we noticed an upregulation of cell wall and membrane proteins that are involved in stress response, which confirms that the applied stress condition indeed caused stress responses in F19, as well as in fatty acid, lipid and isoprenoid biosynthesis. Besides visual relative differences of numerous SEED categories, the relative protein mass on a proteome-wide scale (% protein mass of the total dry mass) was calculated in order to estimate the relative protein investment of the cell in biological functions. Upon stress, cells invest mostly in proteins of carbohydrate metabolism, followed by nucleosides and nucleotides metabolism and membrane transport (Figure 3B), which is in concordance to the visual relative enhancement of these biological functions/SEED categories. Summing up the results, the application of the developed strategy enabled the quantification of approximately 65 % of the in silico proteome, followed by the identification of > 400 differentially expressed proteins. Further, we were able to investigate stress induced effects based on the prediction of SCL of proteins and the analysis of biological functions using SEED subsystems. In terms of protein subcellular localization analysis, there was no strong bias between the SCL of the in silico proteome and the SCL of the quantified proteins. Further coverage of all subcellular compartments including proteins of the cell wall and membrane was observed, and thus revealed that cellular fractionation of the protein extract 19 ACS Paragon Plus Environment


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during sample preparation is not required. The SEED subsystem analysis reflected the large variety of biological functions, enabled the evaluation of the applied stimuli and represented a first insight in the regulation/protein investment of the cell in biological functions upon stress. According to that, proteomics is the method of choice for the analysis of physiological responses. Although transcriptomics has emerged as a powerful tool in this field, protein expression levels are still a better proxy for protein activity than mRNA levels 61. Moreover, proteomics enable the dynamic reflection of both genes and environment at any cell stage as it can be used to identify biological active proteins

62-63

. And thus, in terms of changing

environmental conditions, to which organisms respond with changes in its phenotype, proteomics can be the preferable approach for the analysis of phenotypic plasticity. Nonetheless, there are various proteomic procedures for the analysis of physiological responses. Besides the global proteome profiling, we present a straightforward strategy which facilitate the analysis of bacterial stress responses in a comprehensive and multiplexed manner resulting in resource and time savings. Considering these merits, this strategy has major advantages opposite other proteomic procedures such as 2D gel electrophoresis, which is on the one hand resource and time consuming, and on the other hand limited in the analysis of membrane and dynamic proteins or in the analysis depth itself

64

. Taken together, the

straightforward experimental strategy based on quantitative proteomics is the state of the art technique for the analysis of physiological responses. PREDICTION OF OPTIMAL PRECONDITIONING FOR STARTER CULTURE PREPARATION. Starter culture preparation is based on a drying process of high-cell density LAB cultures. Upon preparation, LAB are confronted with drying stress which combines various different stresses such as extreme temperatures, which can affect membrane fluidity, compromise cellular integrity and basic cellular processes, such as ribosomal function, protein folding, and enzymatic activity

65

. Besides temperature stress, cells are also exposed to

osmotic and oxidative stress, carbon-source and amino-acid limitations, as well as a decreased 20 ACS Paragon Plus Environment

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water availability. In order to improve the fitness and vitality of LAB before preparation, we exploited the procedure of stress preconditioning, a defined application of stress. Stress preconditioning effects were analyzed and optimal preconditioning predicted based on the analysis of stress response similarities towards drying. First, stress preconditioning was conducted in Lactobacillus paracasei subsp. paracasei F19, stress responses on a proteome-wide scale were investigated and differentially expressed proteins in reference to control conditions identified. Analogously, the same was done for drying stress. During stress preconditioning and drying stress, in total 188 proteins were differentially expressed covering approximately 6 % of the in silico proteome. Further, DE proteins were distributed over the whole genome including chromosome and plasmid (Figure 4A). Among these proteins, 132 were uniquely DE indicating to be stress specifically induced, which match the widespread conception of stress specific mechanisms 66. We noted quite a high number of proteins (56 proteins) that were differentially expressed in multiple stress conditions (Figure 4B). Keeping this in mind, we suggest that stress specific responses are less in common than it has so far been assumed. We assume that general cellular mechanisms are more frequently used in counteracting different stress qualities/conditions and thus can play key roles as general stress responses, which has also been reported by Hecker et al. 27. But clearly, further work is required to comprehensively analyze bacterial stress responses to environmental changes as well as their precise characterization.



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Figure 4. Differentially expressed proteins in reference to control condition of Lactobacillus paracasei subsp. paracasei F19. (A) BLAST ring image of proteomic properties. All rings are described from the inside to the outside: ring 1 (black) represents the total genome sequence of F19 as reference with bp coordinates; ring 2 (black) shows the GC content; ring 3 (blue) represents the different contigs of F19; ring 4 (purple) shows the coding density, illustrating the in silico annotated proteome; ring 5 (orange) represents all quantified proteins; ring 6 (red) shows all differentially expressed (DE) proteins; ring 7(aqua) illustrates differentially expressed (DE) proteins in reference to control condition (vs contr). (B) Venn diagram depicting the overlap of DE proteins vs contr of all applied stress conditions. Upon drying, 58 proteins were differentially expressed, of which 35 and 23 proteins were up and down regulated, respectively (Figure 5A, Table S5). Further, the SCL prediction revealed the differentially expression of cytoplasmic and membrane proteins (Figure 5B). Besides SCL, numerous biological functions were influenced by drying stress, whereby most DE proteins were assigned to categories related to nucleoside and nucleotide biosynthesis, carbohydrates, virulence/disease/defense and to protein metabolism. Interestingly, proteins associated to nucleoside and nucleotide biosynthesis or carbohydrate metabolism were up regulated, whereas proteins related to protein metabolism were down regulated (Figure 5C).


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Figure 5. Characterization of drying stress induced differentially expressed proteins in reference to control condition (DE: D vs contr) of Lactobacillus paracasei subsp. paracasei F19. (A) Differentially expressed proteins and respective Log2FoldChanges (Log2FC). Where applicable, SEED category of respective protein is marked. Color scheme can be obtained from C. (B) Predicted subcellular localization (SCL) in percentage with the 23 ACS Paragon Plus Environment


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corresponding absolute number is illustrated. (C) Biological functions based on SEED categories in percentage is illustrated. Displayed are differentially expressed proteins (DE: D vs contr), along with the Log2FoldChange. Proteins with a positive Log2FoldChange (up regulated) and proteins with a negative Log2FoldChange (down regulated) are illustrated. The complete lists of differentially expressed proteins, predicted SCL and SEED category are provided, respectively (Table S5, Table S3 and Table S4). Regarding the SEED category nucleoside and nucleotide biosynthesis, up regulated proteins were involved in the first steps of purine and pyrimidine metabolism. These results suggested potential increased levels of the intermediate products 5-phosphoribosyl-a-1-pyrophosphate (PRPP), IMP and UMP. The potential increase in the PRPP pool in Lb. paracasei subsp. paracasei F19 can probably contribute to enhanced levels of NAD+ and NADP+ and thus to oxidative resistance mechanism using NADH oxidase and NADH peroxidase

26, 67-68

. In particularly, PRPP is a

common intermediate in both pyrimidine and purine de novo pathways and is used for the biosynthesis of nicotinamide coenzymes (NAD+ and NADP+). NAD+ and NADP+ are linked to the redox potential, as NAD+/NADP+ and NADH/NADPH are used as electron acceptor and donor, respectively

69

. Maintaining a low intracellular redox potential is essential for

keeping proteins in their reduced active form. However, upon oxidative stress, which can also be caused by drying, the redox potential of the cell is influenced by reactive oxygen species (ROS), which have oxidizing potential, affecting many enzymatic reactions. ROS are highly reactive and toxic, therefore LAB possess several mechanisms for their detoxification. One of the most conserved oxidative resistance mechanism to counteract the effects of oxidative stress results from the coupling of NADH oxidase and NADH peroxidase 70. At first, oxygen is used for the oxidation of NADH to NAD+ via the NADH oxidase. Thereby, H2O2 is produced which is subsequently reduced to water by the NADH peroxidase. Considering this, 24 ACS Paragon Plus Environment

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up regulated proteins that are involved in the first steps of purine and pyrimidine metabolism may indicate the activation of this oxidative stress resistance mechanism for counteracting drying stress in Lb. paracasei subsp. paracasei F19. The probable enhancement of IMP and UMP in Lb. paracasei subsp. paracasei F19 is ambiguous and since no other proteins related to nucleotide biosynthesis of purines and pyrimidines are differentially expressed, the role of IMP and UMP enhancement can only be speculated. In the past years, changes in the purine nucleotide pool of guanine have been described in the stringent response (SR), a conserved bacterial stress response. The SR induces large-scale transcriptional alterations, such as the decrease in stable RNAs, resulting in a physiological shift to a nongrowth state 69. In the presence of extreme stress, bacterial cells accumulate the protein alarmone (p)ppGpp that triggers the SR

71-75

. However, the

detailed role of (p)ppGpp in the modulation of cell physiology in LAB remains to be established. In Bacillus (B.) subtilis, the induction of (p)ppGpp affects the transcription of rRNA genes by reducing the availability of the initiating nucleotide GTP 71, whereby GTP is a growth limiting factor 76. In this study, in Lb. paracasei subsp. paracasei F19, two genes code for a protein similar to alarmone (p)ppGpp: the GTP pyrophosphokinase. Interestingly, the adjustment of intracellular growth processes was confirmed by a decrease in protein metabolism, particularly by the downregulation of ribosomal proteins belonging to the small and large ribosomal subunit. Besides their roles in the assembly and the structural constituent of the small and large ribosomal subunit, the down regulated 30S ribosomal protein S16 is essential in terms of protein synthesis. Its’ 3’ end, containing the anti-Shine-Dalgarno sequence, binds upstream to the AUG start codon on the mRNA and is thus responsible for translational initiation. Although the detailed role of IMP generation in LAB physiology is unrevealed, we speculate that the probable increase in the IMP pool, which is caused by up regulated proteins that are involved in purine biosynthesis, is connected to the accumulation of (p)ppGpp. Further, there is some evidence assuming that nucleotide pools may be involved 25 ACS Paragon Plus Environment


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in the modulation of (p)ppGpp production 77 and thus to the general stress response caused by the SR. Besides transcriptional adaptation based on the SR, it seems likely that bacteria that are subjected to extreme stresses, such as drying stress, may benefit from mechanisms which rapidly adjust the rates of protein and DNA synthesis. We further suggest that the probable upregulation of the intracellular IMP and UMP nucleotide pools contribute to translational and replicational adaptations. Overall, we suppose that large-scale alterations in the nucleotide pool may play a general role in multiple stress resistance to drying and to other stresses. The adaptation of Lb. paracasei subsp. paracasei F19 to drying stress is also accompanied by activating known stress response mechanisms and prokaryotic defense systems. In particular, oxidative stress response mechanism is affected as well as two toxin-antitoxin systems (Type II: YoeB-YefM, MazF-MazE) that can result in global translational and replicational inhibition, followed by growth arrest and cell death 78. In order to predict optimal preconditioning for F19, hierarchical cluster analysis was performed for the comparison of the drying stress response with stress preconditioning responses. We identified a high similarity between drying and alkaline (pH9) or high-pressure (HHP) stress response (Figure 6A). For visualizing protein expression similarities of drying induced stress proteins among stress conditions, protein expression analysis based on the calculation of clustered heat maps was performed. Again, a close relationship for drying and alkaline or high-pressure stress preconditioning was identified (Figure 6C). Further, shared proteins of these stress conditions were identified using a Venn diagram (Figure 6B). Five proteins were shared with alkaline stress preconditioning and six proteins were shared with high-pressure stress preconditioning, whereby shared proteins displayed the same expression profile. 26 ACS Paragon Plus Environment

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Figure 6. Prediction of optimal preconditioning towards drying for starter culture preparation. (A) Analysis of stress response similarities based on hierarchical cluster analysis of quantified (A.1) and differentially expressed proteins (A.2). (B) Venn diagram depicting shared differentially expressed proteins in reference to control (vs contr) of drying stress (D, blue) with alkaline (pH9, green) and high-pressure (HHP, yellow) stress preconditioning. (C) Protein expression analysis of drying stress induced differentially expressed proteins in reference to control (DE: D vs contr) in all stress conditions. Log2FoldChange ranging from 3 (red) to + 3 (blue) is illustrated and color-coded. Shared proteins of D, pH9 and HHP are marked: shared proteins of D with pH9 and HHP (blue), shared proteins of D with HHP (yellow), shared proteins of D with pH9 (green). 27 ACS Paragon Plus Environment


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For alkaline stress preconditioning shared proteins, we noted predominantly cytoplasmic proteins that are either related in nucleoside and nucleotide biosynthesis or involved as transporters in the pyruvate metabolism (primary active transport, down regulated) and in the starch and sucrose metabolism (PTS system, up regulated). In terms of high-pressure stress preconditioning shared proteins, we noted the downregulation of ribosomal proteins, which again indicate the SR. Besides, we predominantly detected cell wall and cytoplasmic membrane proteins. As cell wall and membrane are the first line of defense against detrimental environmental conditions 79, they are probably among the major site of pressure damage. In order to survive these changing conditions, it is essential for bacteria to maintain a functional cell membrane

61

. However, upon pressure, the fluidity of the bacterial cell

membrane decreases, leading to the loss of membrane integrity and thus functionality. By increasing the ratio of saturated to unsaturated fatty acids in the cell membrane, bacteria maintain their membrane integrity 61. Analogously, these effects have also been described for bacterial cells exposed to lateral temperature and drying, where the decreasing water content results in a reduced cell membrane fluidity and thus to membrane phase transitions from the liquid crystalline to gel phase

80-82

. Accordingly, we presume that pressure stress reduces

membrane fluidity, resulting as well in membrane phase transitions. Further, we assume that, in order to maintain membrane integrity caused by any stress, bacteria use global stress mechanisms. CONCLUSION The strategy we employed for the study of physiological stress responses of Lb. paracasei subsp. paracasei F19 provides a comprehensive genomic analysis, and examines in parallel and in a time-resolved manner the quantitative changes of the proteome. This proteome-wide approach yields further an extensive proteome coverage. In this study, our data presented a comprehensive picture of the dynamic changes in gene expression and the accompanying differentially expression of proteins that occur in Lb. 28 ACS Paragon Plus Environment

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paracasei subsp. paracasei F19 cells exposed to eleven different stress conditions. Further, our data provide an unprecedented rich basis for further in-depth investigation of the physiological and genetic responses of Lb. paracasei subsp. paracasei F19 to changing environmental conditions. We believe that the developed experimental strategy has great potential for the analysis of bacterial physiology, particularly can be the state of the art technique for the analysis of physiological stress responses and can thus be the preferable approach for the study of phenotypic plasticity. ASSOCIATED CONTENT Supporting Information. Additional information is available as noted in text. The following files are available free of charge. PTXQC settings; MS parameters; Supplementary Table Legends (PDF). Table S1. Complete spectra search output and unprocessed MaxQuant output (XLXS). Table S2. Sample description (sample ID, run, condition) with respect to unprocessed MaxQuant output (ID raw) (XLXS). Table S3. Subcellular localization prediction of total proteins/protein datasets (XLXS). Table S4. SEED subsystem analysis of total proteins/protein datasets (XLXS). Table S5. Differentially expressed (DE) proteins with respect to condition, Log2FoldChange, p-value and metadata (XLXS). Table S6. In silico proteome with metadata (XLXS). Table S7. Terminology of frequently used terms (XLXS). 29 ACS Paragon Plus Environment


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AUTHOR INFORMATION Corresponding Author *E-mail: [email protected]. Phone: +49 8161 71 6129 Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

Funding Sources Part of this work was funded by the German Federal Office of Agriculture and Food in project BLE2817400111 (www.ble.de). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Notes H.H. is shareholder and employee of OmicScouts GmbH, a company focused on contract research in proteomics. B.K. is cofounder and shareholder of OmicScouts GmbH.

The authors declare no competing financial interest.

ACKNOWLEDGMENT The authors gratefully acknowledge the support of the Chair of Proteomics and Bioanalytics, and the Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS) both at the Technische Universität München. ABBREVIATIONS 1D, first dimension; 2D, second dimension; DE, differentially expressed; hSAX, hydrophilic strong anion exchange separation; LAB, lactic acid bacteria; LC, liquid chromatography; LCMS/MS, liquid chromatography tandem mass spectrometry; MANOVA, multivariate analysis of variance; MS, mass spectrometer; MS, mass spectrometry; MS/MS, tandem mass spectrometry; MS1, precursor mass spectrum; MS2, fragment mass spectrum; nESI, nano 30 ACS Paragon Plus Environment

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electro-spray ionization; RPIP, reversed phase ion ppairing; TMT, tandem mass tags; TMW, Technische Mikrobiologie Weihenstephan. REFERENCES 1.

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