<figure><img src="image_1.png" /></figure>

Environmental Microbiology (2020) 22(8), 2996–3004

doi:10.1111/1462-2920.14975

Minireview

Quick microbial molecular phenotyping by differential
shotgun proteomics

Duarte Gouveia ID, Lucia Grenga ID, Olivier Pible and Jean Armengaud ID*

Laboratoire Innovations technologiques pour la
Détection et le Diagnostic (Li2D), Service de
Pharmacologie et Immunoanalyse (SPI), CEA, INRAE,
F-30207 Bagnols-sur-Cèze, France.

Summary

Differential shotgun proteomics identifies proteins that discriminate between sets of samples based on differences in abundance. This methodology can be easily applied to study (i) specific microorganisms subjected to a variety of growth or stress conditions or (ii) different microorganisms sampled in the same condition. In microbiology, this comparison is particularly successful because differing microorganism phenotypes are explained by clearly altered abundances of key protein players. The extensive description and quantification of proteins from any given microorganism can be routinely obtained for several conditions within a few days by tandem mass spectrometry. Such protein-centred microbial molecular phenotyping is rich in information. However, well-designed experimental strategies, carefully parameterized analytical pipelines, and sound statistical approaches must be applied if the shotgun proteomic data are to be correctly interpreted. This minireview describes these key items for a quick molecular phenotyping based on label-free quantification shotgun proteomics.

Introduction

As part of an investigation into how the organism func-
tions, molecular phenotyping of a microorganism consists
in the extensive description of its biochemical character-
istics, which are shaped by both genetic makeup and

environmental influences. Approaches to holistic molecu-
lar investigation of microorganisms have changed drasti-
cally in recent years. Gene sequencing and annotation of
new isolates are now routine in many microbiology labo-
ratories. In addition, large-scale molecular phenotyping
tools such as metabolomics and proteomics are gaining
momentum, allowing us to more intimately observe how
microbial cells function (Bochner, 2009; Nichols et al.,
2011). Metabolomic or proteomic results can be exploited
to generate novel hypothesis on the metabolic, cellular
and biological processes affected upon stimulus or condi-
tion change and can be ideally complemented with fur-
ther assays to validate these hypotheses. With the
shotgun proteomic approach, a global cellular metabolic
view can be obtained, thus surpassing traditional bio-
chemical assays or western blot detection focused on
only one specific protein. Today, a deep proteomics anal-
ysis can detect and monitor the abundances of thou-
sands of proteins under different conditions (Armengaud,
2013) and is thus an ideal complement to other
phenomics methodologies. Remarkably, the sample
required for performing such study is usually easy to
obtain (5–10 mg of pelleted cells that should be immedi-
ately frozen in liquid nitrogen, biological triplicates) and
does not necessitate any additional costly reagent as
proteins are stable if kept at –80°C contrary to other
techniques such as transcriptomics.

However, for many microbiologists, proteomics may
still seem quite difficult to grasp. These difficulties stem in
part from the high level of expertise and specific instru-
mentation required, but also from the wide variety of
strategies developed to meet the experimental objectives
of different experiments. Indeed, several facets of proteo-
mics can be explained by the various characteristics of
proteins. For example, multiple proteoforms may be pro-
duced from a single bacterial gene (Bland et al., 2014).
Their primary structure can be evaluated in terms of
translational initiation, peptide signal processing, and pro-
tease maturation. In addition, proteins may be post-
translationally modified, dramatically increasing the com-
plexity of the proteome (Dai et al., 2017). The nature of

Received 1 February, 2020; revised 29 February, 2020; accepted 2
March, 2020. *For correspondence. E-mail jean.armengaud@cea.fr;
Tel. (+00) 33 4 66 79 62 77; Fax: (+00) 33 4 66 79 64 60.
any post-translational modifications and corresponding amino acid locations on the polypeptide can be determined by tandem mass spectrometry. The identity of transient protein partners (interactome), information relating to quaternary structure, topology, thermal stability, production, or even their half-life can all be established by applying specific proteomic strategies. Localization of distinct proteoforms at the subcellular level, both in the periplasm, or the exoproteome for secreted proteins, can be also assessed. It is thus no wonder that researchers from other fields may feel overwhelmed.

Although proteins may be analysed based on their total molecular masses, most proteomic strategies involve the analysis of protein fragments by tandem mass spectrometry (Swanson and Washburn, 2005). Shotgun proteomics consists in proteolyzing the proteins extracted from a biological sample and analysing the resulting peptides with a high-resolution tandem mass spectrometer coupled to a reverse-phase chromatography system. For these types of assays, trypsin is the most commonly used protease, because it generates peptides with an average of 10–12 residues, ending with a basic residue (arginine or lysine). Consequently, these peptides are easily observable by mass spectrometry. In a first MS scan, the mass spectrometer records the mass/charge (m/z) ratios of the peptide ions eluting from the chromatography at a given time point. Then, the mass spectrometer selects the most abundant ion and triggers its fragmentation. The mass/charge ratios of the secondary ions are then recorded, generating a specific MS/MS spectrum representative of the amino acid sequence of the peptide. Other peptide ions can be selected for fragmentation and MS/MS measurement before a new cycle of MS is launched. The tandem mass spectrometer provides three elements of information: the molecular mass of each peptide, the molecular mass of their chemical fragments—which is dictated by the amino acid sequence of the corresponding peptide—and the abundance of each peptide. Large-scale analysis of these peptides can be used to identify and quantify the original proteins, while also establishing their sequence coverage.

Differential shotgun proteomics compares the proteome of a biological entity subjected to varying conditions, or those of multiple closely related microorganisms (e.g. wild type versus mutant) in the same conditions. This approach aims to identify proteins that are present at different levels across these samples. Today, a single nanoLC-MS/MS run on a high-resolution high-throughput tandem mass spectrometer can monitor the abundances of more than a 1000 proteins. Therefore, this methodology is increasingly used by microbiologists working with proteomics platforms to perform tandem mass spectrometry measurements. In this minireview, we propose guidelines for a winning strategy for differential shotgun

proteomics analyses, specifically to help microbiologists when designing their experiments. We also describe some statistical approaches for use when interpreting shotgun proteomics data.

*Spectral counting, a simple strategy to rapidly assess protein abundances*

In shotgun proteomics, several strategies have been devised to monitor the abundances of proteins. The most elaborate strategy involves labelling proteins/peptides with specific chemical reagents, mixing the samples, and performing a single nanoLC-MS/MS run in data-dependent acquisition mode (DDA), where each peptide is isolated by the mass spectrometer before its fragmentation. The mass spectrometry data from the different samples are distinguished using the specific fragmentation signal corresponding to their respective labels. Although interesting on paper, this approach requires larger amounts of initial biological material, fractionation of the samples to allow a comprehensive survey, and repeated measurements (Choi et al., 2008). In addition, unfortunately, the reagents required for labelling are also quite expensive.

Another strategy relies on the comparison of signals from nanoLC-MS/MS runs performed systematically for each sample (Neilson et al., 2011). Because of its robust performances, this ‘label-free’ strategy is currently the most used and can be applied to any sample. The abundance of each peptide is directly related to the intensity of the *m/z* peak measured in MS mode (extracted ion chromatogram, XIC), but this signal depends on the ability of ionization of the compound, or can be deduced from the number of MS/MS spectra assigned to it (spectral counts). This last feature was established as a robust proxy for protein abundance, which is particularly appropriate for use in complex samples where the MS signal may be distorted due to the presence of overlapping ion signals (Liu et al., 2004). While peak intensities for quantitation is currently becoming popular, we warn the reader to compare XIC and spectral count approaches for specific set of samples as in microbiology drastic proteomic changes may occur triggering a novel variety of signals in one condition that may interfere with the XIC measurements.

Yet another strategy, data-independent acquisition mode (DIA), was recently proposed (Ludwig et al., 2018), but up to now has mainly been used for human proteomics projects. In DIA, all peptide ions are fragmented simultaneously for a certain *m/z* range established a priori; it thus generates complex data and requires strong computing skills to perform iterative searches to identify the less abundant proteins.
Due to the difficulty and expense of the alternatives, most current differential proteomic studies are based on estimating the abundances of proteins by spectral counts. Although this strategy is simple to perform, it requires some care to ensure the necessary depth of the MS/MS analysis: in the setting of the instrument parameters, when selecting the number of replicates, the quality of the protein sequence database to be used for interpreting MS/MS spectra, and the strategy used to infer proteins from peptide sequences. For spectral-count-based label-free quantitation of proteins, the exclusion time for MS/MS acquisition is also an important parameter. Although this time window is frequently increased to maximize the number of peptides to monitor and thus identify low-abundance proteins, a high value (more than 60 s) may be detrimental to accurate spectral counting of the most abundant proteins, leading to under-evaluation due to signal saturation. Common good practices in shotgun proteomics (Matthiesen et al., 2011; Bereman, 2015) include monitoring LC-MS/MS performance using specific standards, randomization of samples prior to injection to avoid batch effects (Cuklina et al., 2020), prioritizing biological replicates over technical or analytical replicates, and verifying reproducibility of replicates. When selecting the database against which the recorded MS/MS spectra will be matched, a well-annotated genome of the microorganism present in the sample would be the best option, but if not available a draft genome sequence can be a valid alternative (Rubiano-Labrador et al., 2014). Alternatively, the annotated genome from the closest phylogenetic relative of the microorganism, or several such genomes (pan-proteomics) could also be useful when investigating the proteomes of phylogenetically similar bacterial strains (Broadbent et al., 2016). In studies comparing several strains, a common database aggregating individual genome-derived databases can be fruitfully used to reduce the impact of the individual databases on differential results. Computational tools are essential when interpreting proteomics data and are thus continuously being improved to be more efficient, easily accessible, and user-friendly. Recent benchmarking studies and reviews should be consulted to select the most appropriate ones. The readers addressed here may not require these programmes as the proteomics platform may give the microbiologist the results in terms of protein identification and label-free quantification. We have therefore focused this minireview on the statistical assessment of data from label-free protein measurements.

### Pipeline for robust, rapid molecular phenotyping

Figure 1 shows the seven steps involved in a quick molecular phenotyping by proteomics. Bacterial cultures

or direct sampling are performed in the multiple conditions to be compared. For sure, these conditions should be well documented by preliminary physiological tests (growth curves, biochemistry assays, etc.) to back the proteomics conclusions. A minimum of three biological replicates is recommended, but a higher number should be considered whenever slight differences (typically an increase or decrease of less than 50% of the abundance of key proteins) are expected between conditions or when the biological replicates are found to be highly variable (more than 25% variation for the majority of proteins). Fortunately, in most cases, phenotypic differences in microbiology stem from strong differences in the abundances of the key protein players (Gallois et al., 2018). After cell lysis, proteins may be subjected to a short SDS-PAGE migration to remove any reagent incompatible with mass spectrometry and then proteolyzed in-gel with trypsin (Hartmann et al., 2014). A faster alternative is to perform in-solution proteolysis directly on the released proteins or after SP3-purification (Hughes et al., 2014; Hayoun et al., 2019). However, when using these protocols, particular attention must be paid to detergents included in the lysis buffer, or complex matrices from the initial sample, as they may adversely affect chromatography or tandem mass spectrometry.

Whatever the digestion protocol applied, the resulting peptides are then subjected to LC-MS/MS and identified. The peptides are then used to establish the list of proteins detected in the whole experiment, i.e. confidently validated in at least one of the samples. Subsequently, the abundance of each protein can be estimated for each replicate and condition based on the intensity of the signals measured for its specific peptides; peptides shared between proteins may also be considered. These data must generally be pre-processed before performing statistical analysis by means of uni- or multi-variate approaches.

The final and most important step is the interpretation of the results. This step should highlight (i) the proteins for which the abundances vary significantly in a given set of conditions, and if applicable (ii) the aggregated molecular characteristics of proteins, such as metabolic pathways or molecular functional groups, that are modified.

We will use two experiments taken from two recently published microbiological studies to illustrate the statistical tests presented in Figs. 2 and 3. First, Shah et al. (Shah et al., 2019) described the comparative proteomic analysis of a marine sulfur-oxidizing isolate, 'Candidatus Thioglobus autotrophicus', grown under oxic versus anoxic conditions with varying levels of sulfur availability. The authors analysed biological triplicates and identified peptides using a Q-Exactive HF tandem mass spectrometer following a 145-min reverse-phase gradient. MS/MS spectra were acquired with a Top10 strategy, i.e. 10 most
<figure><img src="image_2.png" /><figcaption>Fig. 1. Schematic pipeline for quick but robust molecular phenotyping.</figcaption></figure>

abundant precursor ions were sequentially fragmented before performing another full scan, and with a 20-s dynamic exclusion. The data were deposited on the PRIDE repository under data set identifier PXD013243. They can thus be freely downloaded and exploited. Shah *et al.* showed that the isolate exhibits an unsuspected metabolic flexibility resulting in the production of more organic carbon in the ocean than would have been estimated based solely on their anaerobic phenotype. The second example we use here was published by Negretti *et al.* (Negretti *et al.*, 2019), who demonstrated that the *Campylobacter jejuni* bacterium produces the same virulence factors following contact with two different human cell lines. In this case, the authors performed the analysis under five conditions with biological triplicates. The peptides from each sample were monitored with a

Q-Exactive HF tandem mass spectrometer after separation along a 200-min gradient. The data were also deposited onto the PRIDE repository, under data set identifier PXD009817. The dynamic exclusion was set to 45 s to promote the identification of more peptides from low-abundance proteins.

## Statistical approaches for spectral count protein quantitation

In a shotgun proteomics experiment, each sample is described by the abundance values of an extensive list of proteins. When specific cellular proteolytic events are suspected, peptide rather than protein intensities may be compared. Whatever the case, data must be pre-processed if statistical comparisons are to be reliable. A
<figure><img src="image_3.png" /><figcaption>**Fig. 3.** Multivariate analysis of PXD009817 shotgun proteomics data set. A. Sample PLS-DA plot with 95% confidence ellipses on components 1 and 2. B. Sample PLS-DA plot with 95% confidence ellipses on components 3 and 4. C. Non-supervised hierarchical clustering of samples based on the features selected from the sPLS-DA analysis (subset of 424 proteins). D. Loading plot showing 10 most discriminating proteins on component 1, with colour indicating the condition with a maximal mean abundance value for each protein.</figcaption></figure>
normalization based on the systematic addition of a
pseudo spectral count per protein and per sample is rec-
ommended (Carvalho et al., 2008), avoiding values at
zero. When the total numbers of spectral counts are very
different between samples, an additional normalization
step may be applied (Carvalho et al., 2008). In this case,
abundances are scaled relative to the sample with the
highest spectral count, and the total number of spectral
counts detected for each sample is used as a normalizing
factor. Alternatively, zero values can be considered as
missing values. Several algorithms have been developed
to statistically impute missing values in a specific sample
based on the existing experimental values of replicates
or by comparison to the relative abundance levels of
related proteins. The imputation method should be care-
fully selected (Lazar et al., 2016). Following pre-
processing, the spectral count data must be analysed by
applying suitable statistical methods dictated by the bio-
logical question to be answered. When comparing two
conditions (e.g. test condition versus control, mutant ver-
sus wild-type, strain A versus strain B), the comparative
analysis consists in calculating a ratio of abundance
between the two conditions for each protein. This ratio
may be presented as a fold-change which corresponds to
the abundance under condition 1 divided by that mea-
sured under condition 2 or as a more sophisticated ratio
(Carvalho et al., 2008). The statistical significance of this
comparison is typically assessed by applying univariate
tests to identify the proteins for which differential abun-
dances are detected between the two conditions. Univari-
ate tests examine proteins individually by applying
parametric or non-parametric tests (Student's t test,
ANOVA, Wilcoxon-Mann-Whitney test). These tests are
hypothesis-driven and based on rejecting or accepting
the null hypothesis within a certain confidence level (a P-
value threshold). The confidence level is often set to 0.01
or 0.05, but should be adjusted, and most importantly
interpreted, in conjunction with the design and context of
the study (Betensky, 2019). T test and ANOVAs are com-
monly used to reveal differences between samples. To
be reliable, these tests require that protein abundance for
each sample follow a normal distribution, and that sample
variances are homogeneous. These aspects can easily
be evaluated by measuring the distribution symmetry
(skewness) and whether the data are peaked or flat rela-
tive to a normal distribution (kurtosis). In proteomic data
sets based on spectral counts or XIC area, logarithmic
transformations are often required to produce a reason-
able normality compatible with statistical rigour. The para-
metric Student's t test is the approach most frequently
used to identify proteins presenting statistically significant
differences in levels between two samples. It can be
based on a minimum of three replicates. The more robust
Wilcoxon-Mann-Whitney non-parametric test is more

recommended but normally requires a higher number of
replicates. In large proteomics data sets, because many
proteins are quantitated, it is also important to pay atten-
tion to the multiplicity of tests performed. Indeed, the rep-
etition of individual tests on thousands of proteins
increases the occurrence of type I errors, i.e. false posi-
tive results (chance rejection of the null hypothesis when
it was true). The false discovery rate (FDR) associated
with the repetition of such tests can be evaluated. Multi-
ple testing corrections include the ultra-conservative
Bonferroni procedure or the less conservative Benjamini–
Hochberg calculation. The latter is the most popular
approach since it limits type I errors without being too
conservative, thus also limiting type II errors (false nega-
tives). This calculation aims to control the FDR, i.e. the
expected proportion of errors among the rejected hypoth-
eses. By adjusting the list of raw P-values to correspond
to an FDR of 0.01, a maximum of 1% of the resulting dis-
coveries are expected to correspond to false positive
results. As an example, Fig. 2 shows a volcano plot
resulting from a univariate analysis on the PXD013243
data set. We wished to discover the proteins displaying
the greatest differential abundance between the two con-
ditions and selected a threshold of at least 50% change
(fold of 1.5). The plot shows the distribution of the pro-
teins identified according to log₂(fold-change) on the
abscissa (x) and statistical confidence on the ordinate
(y). The data set contains three biological replicates from
two conditions (anaerobic versus aerobic). The mean
normalized protein abundance values for each condition
were compared by applying multiple *t* tests. The resulting
raw P-values were adjusted to an FDR of 0.05 using the
Benjamini-Hochberg method.

Using these parameters, a total of 152 proteins are sig-
nificantly detected in this data set when comparing the
two conditions. The proteins of interest are indicated by
the blue dots in the volcano plot (Fig. 2). Among these,
40 are more abundant in the anaerobic condition and
112 are more abundant in the aerobic condition. As indi-
cated in the plot, the most extensive significant changes
are for the alanine-glyoxylate aminotransferase (more
abundant in the aerobic condition) and the nitrate reduc-
tase (more abundant in the anaerobic condition). We also
highlighted the presence of two isoforms of the large sub-
unit of ribulose 1,5-bisphophoate carboxylase (Rubisco),
each being specific of a condition.

For more complex experimental designs, e.g. including
a larger number of conditions, classic univariate tests will
be more difficult to interpret. The high dimensionality of
the data imposes a need for simplification to maximize
the relevant features and minimize redundancy. In these
cases, multivariate tests that consider whole groups of
features together and present a view of the overall struc-
ture of the data, are essential to reduce data
dimensionality through the selection or extraction of rele-
vant subsets of features – here, proteins (Meng et al.,
2016). In proteomics, the most commonly used multivari-
ate methods are principal component analysis (PCA), lin-
ear discriminant analysis (LDA), partial least squares
discriminant analysis (PLS-DA), and hierarchical cluster-
ing (HC).

PCA and HC are both non-supervised exploratory
methods. These methods are non-supervised as they
only take as input the abundance values of proteins per
sample, with no need to provide information about the
nature of the samples. PCA and HC can be used to clas-
sify samples into groups and thus verify the overall
homogeneity level between replicates. PCA reduces the
dimensionality of the data set by projecting features onto
a lower dimensional space, clearly revealing the variabil-
ity in the data set. HC aims to cluster similar entities
together into the same class, through the iterative con-
struction of a hierarchical tree known as a dendrogram.

In contrast, LDA and PLS-DA are supervised multivari-
ate methodsthe input data are labelled with information
about the samples. The two methods are conceptually
similar, but PLS-DA can readily handle multiple depen-
dent categorical variables and is compatible with very
noisy data sets, whereas multicollinearity issues may
weaken LDA application (Noes & Mevik, 2001). In PLS-
DA, new components maximize the covariance between
features (proteins). By using the information provided
about the nature of the samples, the new components
can specifically be used to discriminate between the
sample groups.

PCA, LDA and PLS-DA can all be used to extract the
features (proteins) that contribute to distinguishing sam-
ple groups, such as, for example, strains with different
molecular profiles and consequently different pheno-
types, or conditions producing the most contrasted
effects. Figure 3 shows an example of multivariate analy-
sis applied to the data from the PX009817 data set,
which correspond to the results of three biological repli-
cates from five different conditions. When merging the
results of these 15 samples, a total of 1333 proteins (feature)
s were validated and quantified, resulting in a table
with 19 995 measured values. Because of this high
dimensionality, a PLS-DA was performed to reveal the
protein subsets discriminating between the different con-
ditions (Fig. 3). The two first main components explain
32% and 9% of the variability. Components 1 and
2 clearly discriminate conditions MH_20h and MH_4h
from the other three conditions (Fig. 3A). Components
3 and 4 are more discrete (5% each) but allow the three
other conditions to be distinguished (MEM_4h on compo-
nent 3, Caco2_4h on component 4, INT407 in both com-
ponents), as visible in Fig. 3B. The subset of proteins
that best discriminates the different conditions was then

extracted from the four components using a sparse ver-
sion of PLS-DA analysis, sPLS-DA (Le Cao et al., 2008),
available in the mixOmics R package (Rohart et al.,
2017). To validate that this subset discriminates the five
groups of samples, these 424 proteins were used to build
a dendrogram based on correlation-base distances.
Briefly, the distance between samples was determined
by the Spearman correlations of their overall protein
abundance (i.e. the rankings of the proteins listed as a
function of their abundances for all samples were com-
pared). If the features of two samples were highly
correlated—if the proteins had almost similar abundance
ratios and were ranked similarly—samples would cluster
together. As shown in Fig. 3C, five clusters corres-
ponding to the five study conditions were obtained. This
subset of proteins can be further explored to verify which
proteins correlated with which group of samples, provid-
ing invaluable information on the specific bacterial func-
tionalities in each condition. Figure 3D, for example,
shows 10 proteins that contribute the most to group dis-
crimination when examining component one, and in
which condition they are more abundant. Six of these
proteins are more abundant in the MH_20h condition,
providing information about the functionalities that dis-
criminate this condition from the others.

This statistical exploration of label-free quantification
data can be performed with a myriad of different tools
that cover different levels of expertise of the end-user
(Tsiamis et al., 2019). Packages such as RforProteomics
(Gatto and Christoforou, 2014) or mixOmics (Rohart
et al., 2017) are available in R for deep data exploration,
statistics and visualization. Non-expert users can rely on
more user-friendly platforms such as OmicsPlayground
(Akhmedov et al., 2019), PatternLab (Carvalho et al.,
2016), Perseus (Tyanova et al., 2016), or LFQ-Analyst
(Shah et al., 2020) for evaluating differential protein
abundances and generating publication-quality data
plots. Functional enrichment of related proteins may be
evaluated by clustering based on KEGG features – using
GhostKOALA (Kanehisa et al., 2016) – or Gene Ontology
features. These features can be statistically assessed,
compared with the observed phenotypes, and further vali-
dated during a new round of directed experiments (see
Clair et al., 2012 as an example). Thus, the great amount
of data generated by shotgun proteomics approaches
can be straightforwardly mined using modern statistical
tools to extract key information and therefore focus on
the notable differences between conditions.

Concluding remarks

The label-free shotgun proteomics approach briefly pres-
ented here is a robust and rapid procedure compatible
with molecular phenotyping of microorganisms in a range
of conditions. Although not as accurate as the most
exquisite targeted quantitation strategies, spectral cou-
ting allows a simple and robust estimation of protein
abundance changes when comparing conditions for
microbiological samples. In terms of time-frame, the anal-
ysis of the whole proteome from a microorganism grown
under three different conditions with four biological repli-
cates per condition can be performed in routine mode
with the above-described methodology within a week: a
single working day would be necessary for protein extrac-
tion from the 12 samples and trypsin proteolysis, a whole
day for the 12 LC-MS/MS runs, interpretation of the raw
MS/MS results would also take 1 day, and 2 additional
days would be needed for data verification and statistical
analysis. The methodology described here is thus easily
applicable to tens of conditions and to numerous biologi-
cal replicates if subtle changes are sought. With results
for more than a 1000 proteins in hand, the most important
challenge becomes the bibliographic work to be done on
the proteins, to determine the enriched functions and
pathways revealed by the differential proteomics
approach. This information must then be integrated within
the specific biological context of the experimental set-up,
and the most interesting discoveries validated by appro-
priate experiments.

Acknowledgements

The authors thank the Commissariat à l'Energie Atomique et
aux Energies Alternatives (France) and the Agence
Nationale de la Recherche program 'Phylopeptidomics'
(ANR-17-CE18-0023-01) for financial support.

**References**

Akhmedov, M., Martinelli, A., Geiger, R., and Kwee, I. (2019) Omics playground: a comprehensive self-service platform for visualization, analytics and exploration of big omics data. *NAR Genom Bioinformatics* **2**: lqz019. https://academic.oup.com/nargab/article/2/1/lqz019/5661104.

Armengaud, J. (2013) Microbiology and proteomics, getting the best of both worlds! *Environ Microbiol* **15**: 12–23.

Bereman, M.S. (2015) Tools for monitoring system suitability in LC MS/MS centric proteomic experiments. *Proteomics* **15**: 891–902.

Betensky, R.A. (2019) The p-value requires context, not a threshold. *The American Statistician* **73**(sup1): 115–117.

Bland, C., Hartmann, E.M., Christie-Oleza, J.A., Fernandez, B., and Armengaud, J. (2014) N-Terminal-oriented proteogenomics of the marine bacterium roseobacter denitrificans Och114 using (N-Sucinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl) phosphonium bromide (TMPP) labeling and diagonal chromatography. *Mol Cell Proteomics* **13**: 1369–1381.

Bochner, B.R. (2009) Global phenotypic characterization of bacteria. *FEMS Microbiol Rev* **33**: 191–205.

Broadbent, J.A., Broszczak, D.A., Tennakoon, I.U., and Huygens, F. (2016) Pan-proteomics, a concept for unifying quantitative proteome measurements when comparing closely-related bacterial strains. *Expert Rev Proteomics* **13**: 355–365.

Carvalho, P.C., Fischer, J.S., Chen, E.I., Yates, J.R., 3rd, and Barbosa, V.C. (2008) PatternLab for proteomics: a tool for differential shotgun proteomics. *BMC Bioinformatics* **9**: 316.

Carvalho, P.C., Lima, D.B., Leprevost, F.V., Santos, M.D., Fischer, J.S., Aquino, P.F., et al. (2016) Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0. *Nat Protoc* **11**: 102–117.

Choi, H., Fermin, D., and Nesvizhskii, A.I. (2008) Significance analysis of spectral count data in label-free shotgun proteomics. *Mol Cell Proteomics* **7**: 2373–2385.

Clair, G., Armengaud, J., and Duport, C. (2012) Restricting fermentative potential by proteome remodeling: an adaptive strategy evidenced in *Bacillus cereus*. *Mol Cell Proteomics* **11**: 013102.

Cuklina, J., Pedrioli, P.G.A., and Aebersold, R. (2020) Review of batch effects prevention, diagnostics, and correction approaches. *Methods Mol Biol* **2051**: 373–387.

Dai, Y., Shortreed, M.R., Scalf, M., Frey, B.L., Cesnik, A.J., Solntsev, S., et al. (2017) Elucidating *Escherichia coli* proteoform families using intact-mass proteomics and a global PTM discovery database. *J Proteome Res* **16**: 4156–4165.

Gallois, N., Alpha-Bazin, B., Ortet, P., Barakat, M., Piette, L., Long, J., et al. (2018) Proteogenomic insights into uranium tolerance of a Chernobyl's microbacterium bacterial isolate. *J Proteomics* **177**: 148–157.

Gatto, L., and Christoforou, A. (2014) Using R and Bioconductor for proteomics data analysis. *Biochim Biophys Acta* **1844**: 42–51.

Hartmann, E.M., Allain, F., Gaillard, J.C., Pible, O., and Armengaud, J. (2014) Taking the shortcut for high-throughput shotgun proteomic analysis of bacteria. *Methods Mol Biol* **1197**: 275–285.

Hayoun, K., Gouveia, D., Grenga, L., Pible, O., Armengaud, J., and Alpha-Bazin, B. (2019) Evaluation of sample preparation methods for fast proteotyping of microorganisms by tandem mass spectrometry. *Front Microbiol* **10**: 1985.

Hughes, C.S., Foehr, S., Garfield, D.A., Furlong, E.E., Steinmetz, L.M., and Krijgsveld, J. (2014) Ultrasensitive proteome analysis using paramagnetic bead technology. *Mol Syst Biol* **10**: 757.

Kanehisa, M., Sato, Y., and Morishima, K. (2016) BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. *J Mol Biol* **428**: 726–731.

Lazar, C., Gatto, L., Ferro, M., Bruley, C., and Burger, T. (2016) Accounting for the multiple natures of missing values in label-free quantitative proteomics data sets to compare imputation strategies. *J Proteome Res* **15**: 1116–1125.

Le Cao, K.A., Rossouw, D., Robert-Granie, C., and Besse, P. (2008) A sparse PLS for variable selection when integrating omics data. *Stat Appl Genet Mol Biol* **7**: Article 35.
Liu, H., Sadygov, R.G., and Yates, J.R., 3rd. (2004) A model for random sampling and estimation of relative protein abundance in shotgun proteomics. *Anal Chem* **76**: 4193–4201.

Ludwig, C., Gillet, L., Rosenberger, G., Amon, S., Collins, B. C., and Aebersold, R. (2018) Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. *Mol Syst Biol* **14**: e8126.

Matthiesen, R., Azevedo, L., Amorim, A., and Carvalho, A.S. (2011) Discussion on common data analysis strategies used in MS-based proteomics. *Proteomics* **11**: 604–619.

Meng, C., Zeleznik, O.A., Thallinger, G.G., Kuster, B., Gholami, A.M., and Culhane, A.C. (2016) Dimension reduction techniques for the integrative analysis of multi-omics data. *Brief Bioinform* **17**: 628–641.

Negretti, N.M., Clair, G., Talukdar, P.K., Gourley, C.R., Huynh, S., Adkins, J.N., *et al*. (2019) Campylobacter jejuni demonstrates conserved proteomic and transcriptomic responses when co-cultured with human INT 407 and Caco-2 epithelial cells. *Front Microbiol* **10**: 755.

Neilson, K.A., Ali, N.A., Muralidharan, S., Mirzaei, M., Mariani, M., Assadourian, G., et al. (2011) Less label, more free: approaches in label-free quantitative mass spectrometry. *Proteomics* **11**: 535–553.

Nichols, R.J., Sen, S., Choo, Y.J., Beltrao, P., Zietek, M., Chaba, R., et al. (2011) Phenotypic landscape of a bacterial cell. *Cell* **144**: 143–156.

Noes, T., and Mevik, B.-H. (2001) Understanding the collinearity problem in regression and discriminant analysis. *Journal of Chemometrics* **15**: 413–426.

Rohart, F., Gautier, B., Singh, A., and Le Cao, K.A. (2017) mixOmics: an R package for 'omics feature selection and multiple data integration. *PLoS Comput Biol* **13**: e1005752.

Rubiano-Labrador, C., Bland, C., Miotello, G., Guerin, P., Pible, O., Baena, S., and Armengaud, J. (2014) Proteogenomic insights into salt tolerance by a halotolerant alpha-proteobacterium isolated from an Andean saline spring. *J Proteomics* **97**: 36–47.

Shah, A.D., Goode, R.J.A., Huang, C., Powell, D.R., and Schittenhelm, R.B. (2020) LFQ-analyst: an easy-to-use interactive web platform to analyze and visualize label-free proteomics data preprocessed with MaxQuant. *J Proteome Res* **19**: 204–211.

Shah, V., Zhao, X., Lundeen, R.A., Ingalls, A.E., Nicastro, D., and Morris, R.M. (2019) Morphological plasticity in a sulfur-oxidizing marine bacterium from the SUP05 clade enhances dark carbon fixation. *mBio* **10**: e00216–19.

Swanson, S.K., and Washburn, M.P. (2005) The continuing evolution of shotgun proteomics. *Drug Discov Today* **10**: 719–725.

Tsiamis, V., Ienasescu, H.I., Gabrielaitis, D., Palmblad, M., Schwammle, V., and Ison, J. (2019) One thousand and one software for proteomics: tales of the toolmakers of science. *J Proteome Res* **18**: 3580–3585.

Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M.Y., Geiger, T., et al. (2016) The Perseus computational platform for comprehensive analysis of (prote)omics data. *Nat Methods* **13**: 731–740.