Identification and Characterization of Marine
Microorganisms by Tandem Mass Spectrometry
Proteotyping

Clément Lozano, Mélodie Kielbasa, Jean-Charles Gaillard, Guylaine Miotello, Olivier Pible, Jean Armengaud

► To cite this version:

Clément Lozano, Mélodie Kielbasa, Jean-Charles Gaillard, Guylaine Miotello, Olivier Pible, et al. Identification and Characterization of Marine Microorganisms by Tandem Mass Spectrometry Proteotyping. Microorganisms, 2022, 10 (4), pp.719. 10.3390/microorganisms10040719. hal-04461138

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# Identification and Characterization of Marine Microorganisms by Tandem Mass Spectrometry Proteotyping

Clément Lozano, Mélodie Kielbasa, Jean-Charles Gaillard, Guylaine Miotello, Olivier Pible, Jean Armengaud

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

To cite this version:

Clément Lozano, Mélodie Kielbasa, Jean-Charles Gaillard, Guylaine Miotello, Olivier Pible, et al. Identification and Characterization of Marine Microorganisms by Tandem Mass Spectrometry Proteotyping. Microorganisms, 2022, 10 (4), pp.719. 10.3390/microorganisms10040719. hal-04461138

HAL Id: hal-04461138

https://hal.inrae.fr/hal-04461138v1

Submitted on 16 Feb 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

Article

# Identification and Characterization of Marine Microorganisms by Tandem Mass Spectrometry Proteotyping

Clément Lozano, Mélodie Kielbasa, Jean-Charles Gaillard, Guylaine Miotello, Olivier Pible and Jean Armengaud *

<figure><img src="image_3.png" /></figure>

Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, SPI, F-30200 Bagnols-sur-Cèze, France; clement.lozano@cea.fr (C.L.); melodie.kielbasa@cea.fr (M.K.); jean-charles.gaillard@cea.fr (J.-C.G.); guylaine.miotello@cea.fr (G.M.); olivier.pible@cea.fr (O.P.)
* Correspondence: jean.armengaud@cea.fr; Tel.: +33-466-79-62-77

**Abstract:** The vast majority of marine microorganisms and their functions are yet to be explored. The considerable diversity they encompass is an endless source of knowledge and wealth that can be valued on an industrial scale, emphasizing the need to develop rapid and efficient identification and characterization techniques. In this study, we identified 26 microbial isolates from coastal water of the NW Mediterranean Sea, using phylopeptidomics, a cutting-edge tandem mass spectrometry proteotyping technique. Taxonomical identification at the species level was successfully conducted for all isolates. The presence of strains belonging to the newly described Balneolaeota phylum, yet uncharacterized at the proteomics scale, was noted. The very first proteomics-based investigation of a representative of the Balneolaeota phylum, *Balneola vulgaris*, is proposed, demonstrating the use of our proteotyping workflow for the rapid identification and in-depth molecular characterization, in a single MS/MS analytical run. Tandem mass spectrometry proteotyping is a valuable asset for culturomic programs as the methodology is able to quickly classify the most atypical isolates.

**Keywords:** phylopeptidomics; proteomics; microorganisms; tandem mass spectrometry; proteotyping; identification; molecular phenotyping

## 1. Introduction

Marine bacteria are the key to major environmental processes. Their role in biogeochemical cycles [1], symbiotic interactions [2], and bioremediation [3] has been documented. Their potential as a source of new therapeutics and biotechnological applications is considered to be highly promising and has given rise to blue biotechnology. In the last two decades, the emergence of proteomics allowed the understanding of the bacterial response toward environmental stressors [4], pollutants [5], and antibiotics [6], providing an insight into the bacterial functional response. Proteomics and metaproteomics are considered the best molecular approaches to explore the functioning of microorganisms, from pure culture to complex microbial communities [7]. Their recent advances in terms of methodology [8] and possible applications in the field of blue biotechnology [9] have been reviewed.

Proteotyping is a powerful tool to identify microorganisms from various samples. The emergence of mass spectrometry in the field of diagnostics, especially via MALDI-TOF, has revolutionized bacteriology, previously dominated by molecular techniques based on DNA amplification. Because MALDI-TOF-based identification requires a comprehensive spectral database in order to identify the microorganisms present in the tested samples, this technology is not suitable for environmental samples and mixtures of microorganisms. As tandem mass spectrometry delivers more information than MALDI-TOF, alternative proteotyping approaches have been developed based on the identification of discriminating peptides or peptidome similarities [10–12]. Such technology requires no a priori, and is amenable to the high-throughput level required for characterizing numerous isolates from culturomics [13].

<figure><img src="image_4.png" /></figure>

### check for updates

**Citation:** Lozano, C.; Kielbasa, M.; Gaillard, J.-C.; Miotello, G.; Pible, O.; Armengaud, J. Identification and Characterization of Marine Microorganisms by Tandem Mass Spectrometry Proteotyping. *Microorganisms* **2022**, *10*, 719.
https://doi.org/10.3390/microorganisms10040719

Academic Editors: Laurent Dufossé and Ramesh Chatragadda

Received: 20 February 2022

Accepted: 24 March 2022

Published: 26 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_5.png" /></figure>

**Copyright:** © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
<figure><img src="image_3.png" /></figure>

Article

# Identification and Characterization of Marine Microorganisms by Tandem Mass Spectrometry Proteotyping

Clément Lozano, Mélodie Kielbasa, Jean-Charles Gaillard, Guylaine Miotello, Olivier Pible and Jean Armengaud*

<figure><img src="image_4.png" /></figure>

check for
updates

**Citation:** Lozano, C.; Kielbasa, M.; Gaillard, J.-C.; Miotello, G.; Pible, O.; Armengaud, J. Identification and Characterization of Marine Microorganisms by Tandem Mass Spectrometry Proteotyping. *Microorganisms* **2022**, *10*, 719.
https://doi.org/10.3390/microorganisms10040719

Academic Editors: Laurent Dufossé and Ramesh Chatragadda

Received: 20 February 2022

Accepted: 24 March 2022

Published: 26 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_5.png" /></figure>

**Copyright:** © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, SPI, F-30200 Bagnols-sur-Cèze, France; clement.lozano@cea.fr (C.L.); melodie.kielbasa@cea.fr (M.K.); jean-charles.gaillard@cea.fr (J.-C.G.); guylaine.miotello@cea.fr (G.M.); olivier.pible@cea.fr (O.P.)
* Correspondence: jean.armengaud@cea.fr; Tel.: +33-466-79-62-77

**Abstract:** The vast majority of marine microorganisms and their functions are yet to be explored. The considerable diversity they encompass is an endless source of knowledge and wealth that can be valued on an industrial scale, emphasizing the need to develop rapid and efficient identification and characterization techniques. In this study, we identified 26 microbial isolates from coastal water of the NW Mediterranean Sea, using phylopeptidomics, a cutting-edge tandem mass spectrometry proteotyping technique. Taxonomical identification at the species level was successfully conducted for all isolates. The presence of strains belonging to the newly described Balneolaeota phylum, yet uncharacterized at the proteomics scale, was noted. The very first proteomics-based investigation of a representative of the Balneolaeota phylum, *Balneola vulgaris*, is proposed, demonstrating the use of our proteotyping workflow for the rapid identification and in-depth molecular characterization, in a single MS/MS analytical run. Tandem mass spectrometry proteotyping is a valuable asset for culturomic programs as the methodology is able to quickly classify the most atypical isolates.

**Keywords:** phylopeptidomics; proteomics; microorganisms; tandem mass spectrometry; proteotyping; identification; molecular phenotyping

## 1. Introduction

Marine bacteria are the key to major environmental processes. Their role in biogeochemical cycles [1], symbiotic interactions [2], and bioremediation [3] has been documented. Their potential as a source of new therapeutics and biotechnological applications is considered to be highly promising and has given rise to blue biotechnology. In the last two decades, the emergence of proteomics allowed the understanding of the bacterial response toward environmental stressors [4], pollutants [5], and antibiotics [6], providing an insight into the bacterial functional response. Proteomics and metaproteomics are considered the best molecular approaches to explore the functioning of microorganisms, from pure culture to complex microbial communities [7]. Their recent advances in terms of methodology [8] and possible applications in the field of blue biotechnology [9] have been reviewed.

Proteotyping is a powerful tool to identify microorganisms from various samples. The emergence of mass spectrometry in the field of diagnostics, especially via MALDI-TOF, has revolutionized bacteriology, previously dominated by molecular techniques based on DNA amplification. Because MALDI-TOF-based identification requires a comprehensive spectral database in order to identify the microorganisms present in the tested samples, this technology is not suitable for environmental samples and mixtures of microorganisms. As tandem mass spectrometry delivers more information than MALDI-TOF, alternative proteotyping approaches have been developed based on the identification of discriminating peptides or peptidome similarities [10–12]. Such technology requires no a priori, and is amenable to the high-throughput level required for characterizing numerous isolates from culturomics [13].
Phylopeptidomics, a novel proteotyping concept first described in Pible et al. [14], allows for the taxonomic identification and relative biomass quantification of microorganisms in a sample [15], based on the combination of common and taxa-specific peptides obtained via shotgun proteomics. This technique presents numerous advantages as it relies on public databases of annotated sequenced genomes and requires no experimental spectral database. If a closely related genome is present in the database, it allows—in a single run—the identification of any prokaryotic and eukaryotic species, unlike 16S targeted sequencing approaches.

In the present study, we conducted—via state-of-the-art mass UPLC-MS/MS—the phylopeptidomics-based proteotyping of 26 marine bacteria, using a validated experimental workflow already documented as being robust and reliable for bacterial identification [13]. Further proteomic functional analyses were conducted for two environmentally relevant bacteria to exemplify the wealth of information given by a single analytical run. To our knowledge, this is the first application of tandem mass spectrometry proteotyping for the identification of marine microbial isolates, and the characterization of their respective proteomes, using a workflow fitting a 48 h window.

## 2. Materials and Methods

### 2.1. *Sampling and Bacterial Isolation*

Seawater was collected in a clean plastic bottle in Saintes-Maries-De-La-Mer (North-western Mediterranean sea, France, 43°26′54.3″ N 4°25′03.4″ E). Samples were kept at room temperature in the dark for 12 months before isolation on agar plates using the dilution to extinction method. Bacterial isolates were cultured in marine broth 2216 (DIFCO) at 25 °C. Aliquots were kept at –80 °C in glycerol saline buffer containing 21% glycerol, 0.05 M MgSO₄, and 0.025 M Trizma base until further use.

### 2.2. *Bacterial Culture and Protein Extraction*

Bacteria were cultured in 7 mL of marine broth at 25 °C in 15 mL tubes until turbidity appeared (24 h to 48 h). Liquid cultures were centrifuged for 15 min at 8000× g. Supernatants were discarded and cell pellets were kept at –20 °C until use. Proteins from the cell pellets were extracted as described in Hayoun et al. [16]. Briefly, pellets were suspended in 200 µL of LDS buffer containing 26.5 mM Tris HCl, 35.25 mM Tris base, 0.5% LDS, 2.5% Glycerol, and 0.13 mM EDTA, supplemented with 5% beta-mercaptoethanol. Samples were incubated for 5 min at 99 °C in a thermomixer (Eppendorf, Hamburg, Germany) and sonicated for 5 min in an ultrasonic water bath (VWR ultrasonic cleaner). Samples were transferred into 2 mL Screw Cap microtubes (Sarstedt, Nümbrecht, Germany) containing 200 mg of beads. Bead beating was performed with a Precellys Evolution instrument (Bertin Technologies, Rockville, MD, USA) at 10,000 rpm for 10 cycles of 30 s, with 30 s of pause between each cycle. Samples were centrifuged at 16,000× g for 1 min and supernatants were transferred to new microcentrifuge tubes before incubation at 99 °C for 5 min.

### 2.3. *Single-Pot Solid-Phase-Enhanced Sample Preparation (SP3) Proteolysis*

SP3 digestion, first described by Hughes et al. [17], was performed in a 96-well plate as described in Hayoun et al. 2019 [13]. A 1/1 mix of hydrophilic (Ref. n°24152105050250) and hydrophobic (Ref. n°44152105050250) Sera-Mag™ Magnetic beads (Merck) at 50 mg/mL was prepared and stored at 4 °C until use. A total of 200 µg of beads (4 µL) was added to 20 µL of cell lysate. The mix was acidified by adding 12 µL of formic acid and beads were activated with 204 µL CH₃ CN (85% final concentration). Bead-protein complexes were trapped using MagnaBind (Thermo Scientific, Waltham, MA, USA). Supernatants were discarded and proteins were washed twice with 200 µL of 70% ethanol and once with 180 µL CH₃ CN. Proteins were digested at 37 °C for 30 min with 50 µL of digestion buffer containing 0.1 µg of Trypsin Gold (Promega, Madison, WI, USA) in 50 mM NH₄HCO₃, supplemented with 0.01% of ProteaseMAX surfactant (Promega). Beads were trapped as
Phylopeptidomics, a novel proteotyping concept first described in Pible et al. [14], allows for the taxonomic identification and relative biomass quantification of microorganisms in a sample [15], based on the combination of common and taxa-specific peptides obtained via shotgun proteomics. This technique presents numerous advantages as it relies on public databases of annotated sequenced genomes and requires no experimental spectral database. If a closely related genome is present in the database, it allows—in a single run—the identification of any prokaryotic and eukaryotic species, unlike 16S targeted sequencing approaches.

In the present study, we conducted—via state-of-the-art mass UPLC-MS/MS—the phylopeptidomics-based proteotyping of 26 marine bacteria, using a validated experimental workflow already documented as being robust and reliable for bacterial identification [13]. Further proteomic functional analyses were conducted for two environmentally relevant bacteria to exemplify the wealth of information given by a single analytical run. To our knowledge, this is the first application of tandem mass spectrometry proteotyping for the identification of marine microbial isolates, and the characterization of their respective proteomes, using a workflow fitting a 48 h window.

**2. Materials and Methods**

**2.1. Sampling and Bacterial Isolation**

Seawater was collected in a clean plastic bottle in Saintes-Maries-De-La-Mer (North-western Mediterranean sea, France, 43°26′54.3″ N 4°25′03.4″ E). Samples were kept at room temperature in the dark for 12 months before isolation on agar plates using the dilution to extinction method. Bacterial isolates were cultured in marine broth 2216 (DIFCO) at 25 °C. Aliquots were kept at −80 °C in glycerol saline buffer containing 21% glycerol, 0.05 M MgSO₄, and 0.025 M Trizma base until further use.

**2.2. Bacterial Culture and Protein Extraction**

Bacteria were cultured in 7 mL of marine broth at 25 °C in 15 mL tubes until turbidity appeared (24 h to 48 h). Liquid cultures were centrifuged for 15 min at 8000× g. Supernatants were discarded and cell pellets were kept at −20 °C until use. Proteins from the cell pellets were extracted as described in Hayoun et al. [16]. Briefly, pellets were suspended in 200 µL of LDS buffer containing 26.5 mM Tris HCl, 35.25 mM Tris base, 0.5% LDS, 2.5% Glycerol, and 0.13 mM EDTA, supplemented with 5% beta-mercaptoethanol. Samples were incubated for 5 min at 99 °C in a thermomixer (Eppendorf, Hamburg, Germany) and sonicated for 5 min in an ultrasonic water bath (VWR ultrasonic cleaner). Samples were transferred into 2 mL Screw Cap microtubes (Sarstedt, Nümbrecht, Germany) containing 200 mg of beads. Bead beating was performed with a Precellys Evolution instrument (Bertin Technologies, Rockville, MD, USA) at 10,000 rpm for 10 cycles of 30 s, with 30 s of pause between each cycle. Samples were centrifuged at 16,000× g for 1 min and supernatants were transferred to new microcentrifuge tubes before incubation at 99 °C for 5 min.

**2.3. Single-Pot Solid-Phase-Enhanced Sample Preparation (SP3) Proteolysis**

SP3 digestion, first described by Hughes et al. [17], was performed in a 96-well plate as described in Hayoun et al. 2019 [13]. A 1/1 mix of hydrophilic (Ref. n°24152105050250) and hydrophobic (Ref. n°44152105050250) Sera-Mag™ Magnetic beads (Merck) at 50 mg/mL was prepared and stored at 4 °C until use. A total of 200 µg of beads (4 µL) was added to 20 µL of cell lysate. The mix was acidified by adding 12 µL of formic acid and beads were activated with 204 µL CH₃ CN (85% final concentration). Bead-protein complexes were trapped using MagnaBind (Thermo Scientific, Waltham, MA, USA). Supernatants were discarded and proteins were washed twice with 200 µL of 70% ethanol and once with 180 µL CH₃ CN. Proteins were digested at 37 °C for 30 min with 50 µL of digestion buffer containing 0.1 µg of Trypsin Gold (Promega, Madison, WI, USA) in 50 mM NH₄HCO₃, supplemented with 0.01% of ProteaseMAX surfactant (Promega). Beads were trapped as
described above and the resulting peptides were acidified with trifluoroacetic acid (final concentration 0.5%) before LC MS/MS analysis.

## 2.4. UPLC-MS/MS

Peptides were analyzed with a Q-Exactive HF (Thermo Scientific) tandem mass spectrometer coupled to an ultimate 3000 nano-LC system (Thermo Scientific). Peptides were desalted on a reverse-phase PepMap 100 C18 µ-precolumn (5 mm, 100 Å, 300 mm i.d. × 5 mm, Thermo Scientific) and separated on a nanoscale PepMap 100 C18 nanoLC column (3 mm, 100 Å, 75 mm i.d. × 50 cm, Thermo Scientific) at a flow rate of 0.3 µL/min using a 30 min gradient (2.5% B from 0 to 1.5 min, 2.5–25% B from 1.5 to 26.5 min, and 25–40% B from 26.5 to 30 min) of mobile phase A (0.1% HCOOH/100% H₂O) and phase B (0.1% HCOOH/80% CH₃CN). The mass spectrometer operated in data-dependent acquisition mode with a Top20 strategy, i.e., the 20 most abundant precursor ions were serially selected for fragmentation. Full-scan mass spectra were acquired from 350 to 1800 m/z. Only peptides with 2 or 3 positive charges were selected for fragmentation with a dynamic exclusion time of 10 s and an isolation window of 1.6 m/z.

## 2.5. Proteotyping

Proteotyping-based identification was conducted using an in-house-developed procedure consisting of a cascade search as follows: (1) the 10,000 best spectra were selected to run a Mascot search against a subset of the NCBInr database containing one representative per species and including 94,176,939 protein sequence entries totaling 39,636,215,241 amino acids and corresponding to 50,995 organisms (494 Archaea, 2231 Eukaryota, 12,047 Bacteria, and 36,223 Viruses); (2) all spectra were used for a Mascot query against a database reduced to the genera previously identified during step 1, and all their descendants; (3) similarly, all spectra were searched for against a database reduced to the species identified during step 2. Peptides were validated using a *p*-value below 0.3, 0.15, and 0.05 for steps 1, 2, and 3, respectively. Mascot searches were set up as follows: 3 ppm peptide tolerance during step 1, and 5 ppm peptide tolerance during steps 2 and 3, 0.02 Da MS/MS fragment tolerance, 2+ or 3+ peptide charges, a maximum of two missed cleavages, carbamidomethylation of cysteine as fixed modification, oxidation of methionine as variable modification, and trypsin as a proteolytic enzyme.

## 2.6. Proteomics and Functional Characterization

Protein identification was performed using Mascot Daemon software version 2.6.1 (Matrix Science) with the same parameters as described above. Each dataset was queried using a dedicated genus-specific database [18] (taxid 662 for Vibrio and 455358 for Balneola). Proteins identified at a false-discovery rate below 1% were further considered and annotated using KEGG Orthology (KO) terms via GhostKOALA (Available online: https://www.kegg.jp/ghostkoala/ accessed on 18 February 2022) [19].

## 2.7. Mass Spectrometry Data

The mass spectrometry and proteomics dataset are available through the ProteomeXchange Consortium via the PRIDE partner repository (Available online: https://www.ebi.ac.uk/pride/ accessed on 18 February 2022), under dataset identifiers PXD031583 and 10.6019/PXD031583.

# 3. Results

## 3.1. Identification of Microbial Isolates

Seawater was sampled and kept at room temperature in the dark without agitation for 12 months in order to select under-represented marine taxa that could survive this condition and could be more challenging to identify than more abundant marine microorganisms. Isolation on marine medium agar plate resulted in 26 isolates. The bacterial identifications, obtained by tandem mass spectrometry, are shown in Table 1. MS/MS spectra number,
described above and the resulting peptides were acidified with trifluoroacetic acid (final concentration 0.5%) before LC MS/MS analysis.

## 2.4. UPLC-MS/MS

Peptides were analyzed with a Q-Exactive HF (Thermo Scientific) tandem mass spectrometer coupled to an ultimate 3000 nano-LC system (Thermo Scientific). Peptides were desalted on a reverse-phase PepMap 100 C18 µ-precolumn (5 mm, 100 Å, 300 mm i.d. × 5 mm, Thermo Scientific) and separated on a nanoscale PepMap 100 C18 nanoLC column (3 mm, 100 Å, 75 mm i.d. × 50 cm, Thermo Scientific) at a flow rate of 0.3 µL/min using a 30 min gradient (2.5% B from 0 to 1.5 min, 2.5–25% B from 1.5 to 26.5 min, and 25–40% B from 26.5 to 30 min) of mobile phase A (0.1% HCOOH/100% H₂O) and phase B (0.1% HCOOH/80% CH₃CN). The mass spectrometer operated in data-dependent acquisition mode with a Top20 strategy, i.e., the 20 most abundant precursor ions were serially selected for fragmentation. Full-scan mass spectra were acquired from 350 to 1800 m/z. Only peptides with 2 or 3 positive charges were selected for fragmentation with a dynamic exclusion time of 10 s and an isolation window of 1.6 m/z.

## 2.5. Proteotyping

Proteotyping-based identification was conducted using an in-house-developed procedure consisting of a cascade search as follows: (1) the 10,000 best spectra were selected to run a Mascot search against a subset of the NCBInr database containing one representative per species and including 94,176,939 protein sequence entries totaling 39,636,215,241 amino acids and corresponding to 50,995 organisms (494 Archaea, 2231 Eukaryota, 12,047 Bacteria, and 36,223 Viruses); (2) all spectra were used for a Mascot query against a database reduced to the genera previously identified during step 1, and all their descendants; (3) similarly, all spectra were searched for against a database reduced to the species identified during step 2. Peptides were validated using a *p*-value below 0.3, 0.15, and 0.05 for steps 1, 2, and 3, respectively. Mascot searches were set up as follows: 3 ppm peptide tolerance during step 1, and 5 ppm peptide tolerance during steps 2 and 3, 0.02 Da MS/MS fragment tolerance, 2+ or 3+ peptide charges, a maximum of two missed cleavages, carbamidomethylation of cysteine as fixed modification, oxidation of methionine as variable modification, and trypsin as a proteolytic enzyme.

## 2.6. Proteomics and Functional Characterization

Protein identification was performed using Mascot Daemon software version 2.6.1 (Matrix Science) with the same parameters as described above. Each dataset was queried using a dedicated genus-specific database [18] (taxid 662 for Vibrio and 455358 for Balneola). Proteins identified at a false-discovery rate below 1% were further considered and annotated using KEGG Orthology (KO) terms via GhostKOALA (Available online: https://www.kegg.jp/ghostkoala/ accessed on 18 February 2022) [19].

## 2.7. Mass Spectrometry Data

The mass spectrometry and proteomics dataset are available through the ProteomeXchange Consortium via the PRIDE partner repository (Available online: https://www.ebi.ac.uk/pride/ accessed on 18 February 2022), under dataset identifiers PXD031583 and 10.6019/PXD031583.

# 3. Results

## 3.1. Identification of Microbial Isolates

Seawater was sampled and kept at room temperature in the dark without agitation for 12 months in order to select under-represented marine taxa that could survive this condition and could be more challenging to identify than more abundant marine microorganisms. Isolation on marine medium agar plate resulted in 26 isolates. The bacterial identifications, obtained by tandem mass spectrometry, are shown in Table 1. MS/MS spectra number,
along with the percentage of these spectra assigned to a peptide sequence, i.e., the peptide-to-spectra-matches (PSMs), and the number of PSMs assigned to a taxon at the species level, are provided. On average, 19,527 ± 1310 MS/MS spectra were recorded within 30 min, and 56.9 ± 3.8% were attributed to peptide sequences. This constant and high assignment rate indicates a good quality of the MS/MS spectra recorded for all samples. The number of PSMs attributed to a taxon, known as taxon-to-spectra-matches (TSMs) as previously defined [14], ranged from 6610 to 11,871. This number may be influenced by the density of genome sequences available for each taxon and the coverage of the genome sequence diversity within this taxon and its closely related neighbors. For environmental samples, especially marine isolates, this coverage is rather low, with a few representatives per species. Here, we introduced a novel ratio that corresponds to the percentage of PSMs assigned to TSMs of the identified species. This ratio is between 78.5 and 99.5% at the species level for the present dataset with an average of 96.4 ± 5.3%. The ratio depends on the relationship between the isolate and the reference genomes present in the database. A lower value may indicate that the sample harbors a significant number of peptide sequences that are not present in the reference genome but present in other representatives, which may not have been sequenced and therefore absent from the database. The lowest TSMs/PSMs ratio values were obtained for the isolates 4A (78.5%) and 8 (82.9%), both assigned to *Thalassospira profundimaris*. Notably, the last round database comprises eight genomes uncovering eight different species in the *Thalassospira* genus: *Thalassospira profundimaris*, *Thalassospira lucentensis*, *Thalassospira lohafexi*, *Thalassospira australica*, *Thalassospira xiamenensis*, *Thalassospira alkalitolerans*, *Thalassospira marina*, and *Thalassospira mesophila*. At the genus level, the TSMs/PSMs ratio was 91.0% and 95.9% for the two samples, respectively. Altogether, these results suggest that these isolates most likely belong to unsequenced *Thalassospira* species that share a significant number of protein sequences between several species.

As illustrated in Figure 1, among the 26 identified bacteria, 15 belong to Gammaproteobacteria, nine are Alphaproteobacteria, and two are Balneolaeota. At the order level, Vibrionales dominate with eight isolates, followed by Rhodospirallales, Alteromonadales, Rhizobiales, Xanthomonadales, Balneolales, and Sphingomonadales with 5, 5, 4, 2, and 2 isolates, respectively (Figure 1). Although Firmicutes, Actinobacteria, and Planctomycetes are absent from this list due to the low sample number, these observations are environmentally relevant considering the Proteobacteria dominance in marine waters [20].

<figure><img src="image_6.png" /><figcaption>**Figure 1.** Diversity of bacterial isolates at the phylum and order level.</figcaption></figure>
along with the percentage of these spectra assigned to a peptide sequence, i.e., the peptide-to-spectra-matches (PSMs), and the number of PSMs assigned to a taxon at the species level, are provided. On average, 19,527 ± 1310 MS/MS spectra were recorded within 30 min, and 56.9 ± 3.8% were attributed to peptide sequences. This constant and high assignment rate indicates a good quality of the MS/MS spectra recorded for all samples. The number of PSMs attributed to a taxon, known as taxon-to-spectra-matches (TSMs) as previously defined [14], ranged from 6610 to 11,871. This number may be influenced by the density of genome sequences available for each taxon and the coverage of the genome sequence diversity within this taxon and its closely related neighbors. For environmental samples, especially marine isolates, this coverage is rather low, with a few representatives per species. Here, we introduced a novel ratio that corresponds to the percentage of PSMs assigned to TSMs of the identified species. This ratio is between 78.5 and 99.5% at the species level for the present dataset with an average of 96.4 ± 5.3%. The ratio depends on the relationship between the isolate and the reference genomes present in the database. A lower value may indicate that the sample harbors a significant number of peptide sequences that are not present in the reference genome but present in other representatives, which may not have been sequenced and therefore absent from the database. The lowest TSMs/PSMs ratio values were obtained for the isolates 4A (78.5%) and 8 (82.9%), both assigned to *Thalassospira profundimaris*. Notably, the last round database comprises eight genomes uncovering eight different species in the *Thalassospira* genus: *Thalassospira profundimaris*, *Thalassospira lucentensis*, *Thalassospira lohafexi*, *Thalassospira australica*, *Thalassospira xiamenensis*, *Thalassospira alkalitolerans*, *Thalassospira marina*, and *Thalassospira mesophila*. At the genus level, the TSMs/PSMs ratio was 91.0% and 95.9% for the two samples, respectively. Altogether, these results suggest that these isolates most likely belong to unsequenced *Thalassospira* species that share a significant number of protein sequences between several species.

As illustrated in Figure 1, among the 26 identified bacteria, 15 belong to Gammaproteobacteria, nine are Alphaproteobacteria, and two are Balneolaeota. At the order level, Vibrionales dominate with eight isolates, followed by Rhodospirallales, Alteromonadales, Rhizobiales, Xanthomonadales, Balneolales, and Sphingomonadales with 5, 5, 4, 2, and 2 isolates, respectively (Figure 1). Although Firmicutes, Actinobacteria, and Planctomycetes are absent from this list due to the low sample number, these observations are environmentally relevant considering the Proteobacteria dominance in marine waters [20].

<figure><img src="image_6.png" /><figcaption>**Figure 1.** Diversity of bacterial isolates at the phylum and order level.</figcaption></figure>
<table><caption>**Table 1.** Proteotyping results for the 26 bacterial isolates ranked by alphabetical order, including the species taxonomy and MS/MS spectra assigned characteristics.</caption><thead><tr><th>Sample ID</th><th>MS/MS Spectra</th><th>% of MS/MS Spectra Resulting in PSMs</th><th>Species</th><th>Species-Assigned TSMs</th><th>TSMs/PSMs Ratio (%)</th></tr></thead><tbody><tr><td>4D</td><td>13508</td><td>50.6</td><td>Balneola vulgaris</td><td>6610</td><td>96.7</td></tr><tr><td>5</td><td>19600</td><td>59.7</td><td>Balneola vulgaris</td><td>11578</td><td>98.9</td></tr><tr><td>12</td><td>18645</td><td>58.1</td><td>Marinobacter salarius</td><td>10713</td><td>98.8</td></tr><tr><td>6B</td><td>20211</td><td>62.6</td><td>Marinobacter salarius</td><td>12553</td><td>99.2</td></tr><tr><td>7</td><td>19994</td><td>57.9</td><td>Marinobacter salarius</td><td>11441</td><td>98.8</td></tr><tr><td>1B</td><td>20109</td><td>50.5</td><td>Martelella mediterranea</td><td>9208</td><td>90.6</td></tr><tr><td>19</td><td>19612</td><td>59.4</td><td>Ochrobactrum pseudogrignonense</td><td>10402</td><td>89.3</td></tr><tr><td>13</td><td>20115</td><td>59.2</td><td>Ochrobactrum pseudogrignonense</td><td>11786</td><td>98.9</td></tr><tr><td>15B</td><td>19502</td><td>61.7</td><td>Ochrobactrum pseudogrignonense</td><td>11871</td><td>98.7</td></tr><tr><td>17</td><td>18259</td><td>58</td><td>Pseudoalteromonas piscicida</td><td>10377</td><td>97.9</td></tr><tr><td>21</td><td>19533</td><td>55.6</td><td>Pseudoalteromonas piscicida</td><td>10694</td><td>98.5</td></tr><tr><td>14</td><td>19475</td><td>58.5</td><td>Stenotrophomonas maltophilia</td><td>11331</td><td>99.5</td></tr><tr><td>18B</td><td>19882</td><td>57.4</td><td>Stenotrophomonas maltophilia</td><td>11347</td><td>99.4</td></tr><tr><td>10</td><td>20355</td><td>56.4</td><td>Thalassospira lucentensis</td><td>10961</td><td>95.5</td></tr><tr><td>8</td><td>20249</td><td>47</td><td>Thalassospira profundimaris</td><td>7894</td><td>82.9</td></tr><tr><td>4A</td><td>20053</td><td>48.6</td><td>Thalassospira profundimaris</td><td>7650</td><td>78.5</td></tr><tr><td>3</td><td>19955</td><td>59.6</td><td>Thalassospira xiamenensis</td><td>11755</td><td>98.8</td></tr><tr><td>1A</td><td>19681</td><td>60.8</td><td>Thalassospira xiamenensis</td><td>11542</td><td>96.4</td></tr><tr><td>16</td><td>19769</td><td>57.2</td><td>Vibrio alginolyticus</td><td>11136</td><td>98.5</td></tr><tr><td>20</td><td>20090</td><td>57.9</td><td>Vibrio alginolyticus</td><td>11454</td><td>98.5</td></tr><tr><td>22</td><td>19780</td><td>57.4</td><td>Vibrio alginolyticus</td><td>11201</td><td>98.6</td></tr><tr><td>23</td><td>19748</td><td>56.7</td><td>Vibrio alginolyticus</td><td>11039</td><td>98.6</td></tr><tr><td>24</td><td>20011</td><td>57.4</td><td>Vibrio alginolyticus</td><td>11321</td><td>98.6</td></tr><tr><td>18A</td><td>19990</td><td>56.8</td><td>Vibrio alginolyticus</td><td>11160</td><td>98.3</td></tr><tr><td>23A</td><td>19632</td><td>56.8</td><td>Vibrio alginolyticus</td><td>11006</td><td>98.7</td></tr><tr><td>25B</td><td>19945</td><td>56.8</td><td>Vibrio alginolyticus</td><td>11112</td><td>98.2</td></tr></tbody></table>

## 3.2. Examples of Phylopeptidomic Signatures

Phylopeptidomic signatures of *V. alginolyticus* (sample 25B) and *Balneola vulgaris* (sample 5) are shown in Figure 2A,B, respectively. These signatures represent the mathematic fit explaining the number of TSMs as a function of the phylogenetic distance to the identified taxon. Although TSMs contributing to the signature (d < 0.5) can be assigned to 5180 sequenced organisms phylogenetically distant from *V. alginolyticus* (Figure 2A), only six organisms were found in the database for *B. vulgaris* (Figure 2B). This discrepancy, due to a high number of fully sequenced Vibrionales compared to the newly described Balneolales [21], reminds us that proteotyping relies on public databases. Hence, identification accuracy depends on the taxonomic depth of the queried database and its annotation quality [22,23].

Despite the low density of sequenced Balneolaeota in the database (two genomes), we noted a remarkably high level of TSMs assigned to *B. vulgaris* [24], indicating close phylogenetic proximity between the isolate 5 and the sequenced *B. vulgaris* strain present in the queried database. In future, increasing efforts at sequencing additional Balneolaeota representatives may improve the fit presented in Figure 2B.
<table><caption>**Table 1.** Proteotyping results for the 26 bacterial isolates ranked by alphabetical order, including the species taxonomy and MS/MS spectra assigned characteristics.</caption><thead><tr><th>Sample ID</th><th>MS/MS Spectra</th><th>% of MS/MS Spectra Resulting in PSMs</th><th>Species</th><th>Species-Assigned TSMs</th><th>TSMs/PSMs Ratio (%)</th></tr></thead><tbody><tr><td>4D</td><td>13508</td><td>50.6</td><td>Balneola vulgaris</td><td>6610</td><td>96.7</td></tr><tr><td>5</td><td>19600</td><td>59.7</td><td>Balneola vulgaris</td><td>11578</td><td>98.9</td></tr><tr><td>12</td><td>18645</td><td>58.1</td><td>Marinobacter salarius</td><td>10713</td><td>98.8</td></tr><tr><td>6B</td><td>20211</td><td>62.6</td><td>Marinobacter salarius</td><td>12553</td><td>99.2</td></tr><tr><td>7</td><td>19994</td><td>57.9</td><td>Marinobacter salarius</td><td>11441</td><td>98.8</td></tr><tr><td>1B</td><td>20109</td><td>50.5</td><td>Martelella mediterranea</td><td>9208</td><td>90.6</td></tr><tr><td>19</td><td>19612</td><td>59.4</td><td>Ochrobactrum pseudogrignonense</td><td>10402</td><td>89.3</td></tr><tr><td>13</td><td>20115</td><td>59.2</td><td>Ochrobactrum pseudogrignonense</td><td>11786</td><td>98.9</td></tr><tr><td>15B</td><td>19502</td><td>61.7</td><td>Ochrobactrum pseudogrignonense</td><td>11871</td><td>98.7</td></tr><tr><td>17</td><td>18259</td><td>58</td><td>Pseudoalteromonas piscicida</td><td>10377</td><td>97.9</td></tr><tr><td>21</td><td>19533</td><td>55.6</td><td>Pseudoalteromonas piscicida</td><td>10694</td><td>98.5</td></tr><tr><td>14</td><td>19475</td><td>58.5</td><td>Stenotrophomonas maltophilia</td><td>11331</td><td>99.5</td></tr><tr><td>18B</td><td>19882</td><td>57.4</td><td>Stenotrophomonas maltophilia</td><td>11347</td><td>99.4</td></tr><tr><td>10</td><td>20355</td><td>56.4</td><td>Thalassospira lucentensis</td><td>10961</td><td>95.5</td></tr><tr><td>8</td><td>20249</td><td>47</td><td>Thalassospira profundimaris</td><td>7894</td><td>82.9</td></tr><tr><td>4A</td><td>20053</td><td>48.6</td><td>Thalassospira profundimaris</td><td>7650</td><td>78.5</td></tr><tr><td>3</td><td>19955</td><td>59.6</td><td>Thalassospira xiamenensis</td><td>11755</td><td>98.8</td></tr><tr><td>1A</td><td>19681</td><td>60.8</td><td>Thalassospira xiamenensis</td><td>11542</td><td>96.4</td></tr><tr><td>16</td><td>19769</td><td>57.2</td><td>Vibrio alginolyticus</td><td>11136</td><td>98.5</td></tr><tr><td>20</td><td>20090</td><td>57.9</td><td>Vibrio alginolyticus</td><td>11454</td><td>98.5</td></tr><tr><td>22</td><td>19780</td><td>57.4</td><td>Vibrio alginolyticus</td><td>11201</td><td>98.6</td></tr><tr><td>23</td><td>19748</td><td>56.7</td><td>Vibrio alginolyticus</td><td>11039</td><td>98.6</td></tr><tr><td>24</td><td>20011</td><td>57.4</td><td>Vibrio alginolyticus</td><td>11321</td><td>98.6</td></tr><tr><td>18A</td><td>19990</td><td>56.8</td><td>Vibrio alginolyticus</td><td>11160</td><td>98.3</td></tr><tr><td>23A</td><td>19632</td><td>56.8</td><td>Vibrio alginolyticus</td><td>11006</td><td>98.7</td></tr><tr><td>25B</td><td>19945</td><td>56.8</td><td>Vibrio alginolyticus</td><td>11112</td><td>98.2</td></tr></tbody></table>

## 3.2. Examples of Phylopeptidomic Signatures

Phylopeptidomic signatures of *V. alginolyticus* (sample 25B) and *Balneola vulgaris* (sample 5) are shown in Figure 2A,B, respectively. These signatures represent the mathematic fit explaining the number of TSMs as a function of the phylogenetic distance to the identified taxon. Although TSMs contributing to the signature (d < 0.5) can be assigned to 5180 sequenced organisms phylogenetically distant from *V. alginolyticus* (Figure 2A), only six organisms were found in the database for *B. vulgaris* (Figure 2B). This discrepancy, due to a high number of fully sequenced Vibrionales compared to the newly described Balneolales [21], reminds us that proteotyping relies on public databases. Hence, identification accuracy depends on the taxonomic depth of the queried database and its annotation quality [22,23].

Despite the low density of sequenced Balneolaeota in the database (two genomes), we noted a remarkably high level of TSMs assigned to *B. vulgaris* [24], indicating close phylogenetic proximity between the isolate 5 and the sequenced *B. vulgaris* strain present in the queried database. In future, increasing efforts at sequencing additional Balneolaeota representatives may improve the fit presented in Figure 2B.
<figure><img src="image_7.png" /><figcaption>**Figure 2.** Phylopeptidomic signatures and TSM values assigned to each taxon in the database as a function of the phylogenetic distance from *Vibrio alginolyticus* (A) and *Balneola vulgaris* (B). Orange dots represent experimentally obtained TSMs by querying the full NCBIr database while the black curve represents the modelized phylopeptidomic signature, i.e., a theoretical exponential distribution of TSMs relative to the phylogenetic distance separating organisms present in the queried database.</figcaption></figure>

3.3. *Proteome Characterization of Two Environmentally Relevant Marine Bacteria*

Two bacterial species were used as an example to illustrate the ability of our workflow to provide a comprehensive proteomic analysis for each bacterium. The acquired MS/MS spectra dataset was exploited by means of a shotgun pan-proteomics workflow [18]. The complete proteomes of *V. alginolyticus* (sample 25B, Table S1) and *B. vulgaris* (sample 5, Table S2) comprise a total of 1173 and 1138 identified proteins, uncovering 25.8% and 46.8% of the theoretical proteomes, respectively. Notably, proteome coverages could be optimized by multiplying culture conditions and bacterial physiological states [4,25]. The list of proteins was validated at a false discovery rate set to 1% and the abundance of each protein was assessed by their respective spectral counts. Protein annotation was conducted using KO terms, although ontology-based annotation is described to be perfectible, especially for prokaryotic species [26]. Figure 3 reports the percentage of proteins identified for each main functional category for both isolates (left panel) and the protein biomass for each of these categories (right panel). For the latter estimation, protein abundances were weighted using normalized spectral abundance factor (NSAF) as defined [27]. Although "carbohydrate metabolism" was the dominating function, representing 17.7% for *V. alginolyticus*, it held third place and accounted for 14.6% for *B. vulgaris*, for which the greatest function was "amino acid metabolism", with 17.0%. Function weighting using NSAF provides a more representative picture of the protein abundance by function. "Genetic information processing" becomes the most abundant KO term, followed by "carbohydrate metabolism", for both species. The percentages of molecules devoted to "amino acid metabolism" in both species differ drastically: 12.7% of the identified proteins are involved in this process in *B. vulgaris* whereas only 8.4% are involved in *V. alginolyticus*. Major differences are also observed for proteins involved in "environmental information processing", with 8.2% of the protein biomass for *V. alginolyticus* but only 3.9% for *B. vulgaris*. These discrepancies may be related to fundamental differences in their adaptation to their natural environment.

The comparison of the number of identified proteins belonging to particular functions between *V. alginolyticus* (isolate 25B) and *B. vulgaris* (isolate 5) revealed major differences between the two species (Table 2). Particularly interesting for preliminary functional molecular characterization, the functions Beta-lactam resistance and Biofilm formation were selected as they may be valuable in clinical microbiology, whereas Flagellar assembly, Bacterial chemotaxis, and Quorum sensing are at the core of recent environmental microbiology studies [28,29]. The proteome of the former harbored 40 proteins involved in Biofilm formation, 23 in Quorum sensing, 16 in Chemotaxis, seven in Flagellar assembly and 10 in Beta-lactam resistance. On the other hand, *B. vulgaris* held only six proteins involved in biofilm formation, 15 in quorum sensing and none in chemotaxis, suggesting that this bacterium is non-motile and non-biofilm forming in our culture condition.
<figure><img src="image_7.png" /><figcaption>**Figure 2.** Phylopeptidomic signatures and TSM values assigned to each taxon in the database as a function of the phylogenetic distance from *Vibrio alginolyticus* (A) and *Balneola vulgaris* (B). Orange dots represent experimentally obtained TSMs by querying the full NCBIr database while the black curve represents the modelized phylopeptidomic signature, i.e., a theoretical exponential distribution of TSMs relative to the phylogenetic distance separating organisms present in the queried database.</figcaption></figure>

3.3. *Proteome Characterization of Two Environmentally Relevant Marine Bacteria*

Two bacterial species were used as an example to illustrate the ability of our workflow to provide a comprehensive proteomic analysis for each bacterium. The acquired MS/MS spectra dataset was exploited by means of a shotgun pan-proteomics workflow [18]. The complete proteomes of *V. alginolyticus* (sample 25B, Table S1) and *B. vulgaris* (sample 5, Table S2) comprise a total of 1173 and 1138 identified proteins, uncovering 25.8% and 46.8% of the theoretical proteomes, respectively. Notably, proteome coverages could be optimized by multiplying culture conditions and bacterial physiological states [4,25]. The list of proteins was validated at a false discovery rate set to 1% and the abundance of each protein was assessed by their respective spectral counts. Protein annotation was conducted using KO terms, although ontology-based annotation is described to be perfectible, especially for prokaryotic species [26]. Figure 3 reports the percentage of proteins identified for each main functional category for both isolates (left panel) and the protein biomass for each of these categories (right panel). For the latter estimation, protein abundances were weighted using normalized spectral abundance factor (NSAF) as defined [27]. Although "carbohydrate metabolism" was the dominating function, representing 17.7% for *V. alginolyticus*, it held third place and accounted for 14.6% for *B. vulgaris*, for which the greatest function was "amino acid metabolism", with 17.0%. Function weighting using NSAF provides a more representative picture of the protein abundance by function. "Genetic information processing" becomes the most abundant KO term, followed by "carbohydrate metabolism", for both species. The percentages of molecules devoted to "amino acid metabolism" in both species differ drastically: 12.7% of the identified proteins are involved in this process in *B. vulgaris* whereas only 8.4% are involved in *V. alginolyticus*. Major differences are also observed for proteins involved in "environmental information processing", with 8.2% of the protein biomass for *V. alginolyticus* but only 3.9% for *B. vulgaris*. These discrepancies may be related to fundamental differences in their adaptation to their natural environment.

The comparison of the number of identified proteins belonging to particular functions between *V. alginolyticus* (isolate 25B) and *B. vulgaris* (isolate 5) revealed major differences between the two species (Table 2). Particularly interesting for preliminary functional molecular characterization, the functions Beta-lactam resistance and Biofilm formation were selected as they may be valuable in clinical microbiology, whereas Flagellar assembly, Bacterial chemotaxis, and Quorum sensing are at the core of recent environmental microbiology studies [28,29]. The proteome of the former harbored 40 proteins involved in Biofilm formation, 23 in Quorum sensing, 16 in Chemotaxis, seven in Flagellar assembly and 10 in Beta-lactam resistance. On the other hand, *B. vulgaris* held only six proteins involved in biofilm formation, 15 in quorum sensing and none in chemotaxis, suggesting that this bacterium is non-motile and non-biofilm forming in our culture condition.
<figure><img src="image_8.png" /><figcaption>**Figure 3.** KEGG functional characterization of the proteomes of *Vibrio alginolyticus* and *Balneola vulgaris* using protein functional attribution (top panel) and functional attribution weighted by the NSAF of each protein (bottom panel).</figcaption></figure>

<table><caption>**Table 2.** Comparison of the numbers of identified proteins belonging to major functions between *V. alginolyticus* and *B. vulgaris*.</caption><thead><tr><th rowspan="2">Functions</th><th colspan="2">Number of Identified Proteins</th></tr><tr><th><i>V. alginolyticus</i><br>(Isolate 25B)</th><th><i>B. vulgaris</i><br>(Isolate 5)</th></tr></thead><tbody><tr><td>Beta-lactam resistance</td><td>10</td><td>7</td></tr><tr><td>Quorum sensing</td><td>23</td><td>15</td></tr><tr><td>Bacterial chemotaxis</td><td>16</td><td>0</td></tr><tr><td>Biofilm formation</td><td>40</td><td>6</td></tr><tr><td>Flagellar assembly</td><td>7</td><td>1</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>**Figure 3.** KEGG functional characterization of the proteomes of *Vibrio alginolyticus* and *Balneola vulgaris* using protein functional attribution (top panel) and functional attribution weighted by the NSAF of each protein (bottom panel).</figcaption></figure>

<table><caption>**Table 2.** Comparison of the numbers of identified proteins belonging to major functions between *V. alginolyticus* and *B. vulgaris*.</caption><thead><tr><th rowspan="2">Functions</th><th colspan="2">Number of Identified Proteins</th></tr><tr><th><i>V. alginolyticus</i><br>(Isolate 25B)</th><th><i>B. vulgaris</i><br>(Isolate 5)</th></tr></thead><tbody><tr><td>Beta-lactam resistance</td><td>10</td><td>7</td></tr><tr><td>Quorum sensing</td><td>23</td><td>15</td></tr><tr><td>Bacterial chemotaxis</td><td>16</td><td>0</td></tr><tr><td>Biofilm formation</td><td>40</td><td>6</td></tr><tr><td>Flagellar assembly</td><td>7</td><td>1</td></tr></tbody></table>
**4. Discussion**

Cost-effective, fast, and reliable identification of microorganisms constitutes a chal-
lenging task, especially for environmental strains, the majority of which are unsequenced
and poorly studied. As previously documented, the phylopeptidomics approach used in
this study permits the identification at the species level with no a priori data required for
any isolate regardless of its eukaryotic or prokaryotic nature [14,30].

We successfully identified 26 environmental marine isolates (Table 1), most of them be-
longing to the Gammaproteobacteria, followed by Alphaproteobacteria. We also identified
two isolates belonging to Balneolaeota (Figure 1), a new phylum recently separated from
the Bacteroidetes [21], which have been poorly investigated to date. Here, we identified
*Balneola vulgaris*, a species first isolated from the bay of Banyuls-sur-Mer in the north-
western Mediterranean Sea and described in 2006 [24]. The present new isolate was from
sampling undertaken by the seaside 200 km away and obtained with the same culture
conditions. Information on this species is scarce and limited to morphological and pheno-
typic characterizations [31,32]. We provided its first description at the molecular level, its
genome sequence being available since 2013 through a program performed by the DOE
Joint Genome Institute but until now not explored. Proteomic characterization suggests
that *V. alginolyticus* is capable of quorum sensing, biofilm formation, and motility, which
is supported by the literature [33–35]. Our data suggest that *B. vulgaris* is non-motile and
does not harbor a flagellum, as described in a prior study [24]. When taking into account
the NSAF, we observed that the percentage attributed to each function changed signifi-
cantly (Figure 3, bottom panel), providing a better picture of the proteome of our isolates
in the present culture conditions. Interestingly, we observed for both strains an increase
in the attribution percentage of proteins involved in Genetic information, Carbohydrate
metabolism, and Energy metabolism, which is consistent considering that both bacteria
were cultured in a rich medium. A total of 10 and 7 proteins were attributed to Beta-lactam
resistance for *V. alginolyticus* and *B. vulgaris*, respectively. Among these proteins, *B. vulgaris*
harbored an efflux RND transporter periplasmic adaptor subunit (WP_157464801.1), a
penicillin-binding protein 1A (WP_018126279.1), and the outer membrane protein TolC
(WP_018126232.1), documented to be involved in antibiotic export and resistance to antimi-
crobial peptides [36–38].

We demonstrated that an MS/MS proteotyping method such as phylopeptidomics
allows, in a single run, the identification at the species level and the proteomic characteriza-
tion of environmental isolates, corroborating a considerable advantage over conventional
techniques such as MALDI-TOF or PCR. Notably, phylopeptidomics applies well to cultur-
able isolates as these microorganisms have currently more chances to be genome sequenced.
However, metagenome-assembled genomes can also be used to define new species and
will quickly populate genome and taxonomy databases [39], and phylopeptidomics can
theoretically be applied to more direct analysis of seawater without the need for cultivation.
In addition, we highlighted the capability of the technique for the detection of proteins
involved in antibiotic resistance, which is an asset for clinical and environmental sciences.
Thus, phylopeptidomics may be a valuable asset for culturomic programs because the
methodology is able to quickly classify the most atypical isolates. We provided the first
proteomic characterization of a bacterial species belonging to the Balnoleata phylum and
emphasized a gap in the literature that may inspire future sequencing efforts.

**Supplementary Materials:** The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms10040719/s1, Table S1: Identified proteins of *V. alginolyticus* (sample 25b) and their spectral counts; Table S2. Identified proteins of *B. vulgaris* (sample 5) and their spectral counts.
**4. Discussion**

Cost-effective, fast, and reliable identification of microorganisms constitutes a chal-
lenging task, especially for environmental strains, the majority of which are unsequenced
and poorly studied. As previously documented, the phylopeptidomics approach used in
this study permits the identification at the species level with no a priori data required for
any isolate regardless of its eukaryotic or prokaryotic nature [14,30].

We successfully identified 26 environmental marine isolates (Table 1), most of them be-
longing to the Gammaproteobacteria, followed by Alphaproteobacteria. We also identified
two isolates belonging to Balneolaeota (Figure 1), a new phylum recently separated from
the Bacteroidetes [21], which have been poorly investigated to date. Here, we identified
*Balneola vulgaris*, a species first isolated from the bay of Banyuls-sur-Mer in the north-
western Mediterranean Sea and described in 2006 [24]. The present new isolate was from
sampling undertaken by the seaside 200 km away and obtained with the same culture
conditions. Information on this species is scarce and limited to morphological and pheno-
typic characterizations [31,32]. We provided its first description at the molecular level, its
genome sequence being available since 2013 through a program performed by the DOE
Joint Genome Institute but until now not explored. Proteomic characterization suggests
that *V. alginolyticus* is capable of quorum sensing, biofilm formation, and motility, which
is supported by the literature [33–35]. Our data suggest that *B. vulgaris* is non-motile and
does not harbor a flagellum, as described in a prior study [24]. When taking into account
the NSAF, we observed that the percentage attributed to each function changed signifi-
cantly (Figure 3, bottom panel), providing a better picture of the proteome of our isolates
in the present culture conditions. Interestingly, we observed for both strains an increase
in the attribution percentage of proteins involved in Genetic information, Carbohydrate
metabolism, and Energy metabolism, which is consistent considering that both bacteria
were cultured in a rich medium. A total of 10 and 7 proteins were attributed to Beta-lactam
resistance for *V. alginolyticus* and *B. vulgaris*, respectively. Among these proteins, *B. vulgaris*
harbored an efflux RND transporter periplasmic adaptor subunit (WP_157464801.1), a
penicillin-binding protein 1A (WP_018126279.1), and the outer membrane protein TolC
(WP_018126232.1), documented to be involved in antibiotic export and resistance to antimi-
crobial peptides [36–38].

We demonstrated that an MS/MS proteotyping method such as phylopeptidomics
allows, in a single run, the identification at the species level and the proteomic characteriza-
tion of environmental isolates, corroborating a considerable advantage over conventional
techniques such as MALDI-TOF or PCR. Notably, phylopeptidomics applies well to cultur-
able isolates as these microorganisms have currently more chances to be genome sequenced.
However, metagenome-assembled genomes can also be used to define new species and
will quickly populate genome and taxonomy databases [39], and phylopeptidomics can
theoretically be applied to more direct analysis of seawater without the need for cultivation.
In addition, we highlighted the capability of the technique for the detection of proteins
involved in antibiotic resistance, which is an asset for clinical and environmental sciences.
Thus, phylopeptidomics may be a valuable asset for culturomic programs because the
methodology is able to quickly classify the most atypical isolates. We provided the first
proteomic characterization of a bacterial species belonging to the Balnoleata phylum and
emphasized a gap in the literature that may inspire future sequencing efforts.

**Supplementary Materials:** The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms10040719/s1, Table S1: Identified proteins of *V. alginolyticus* (sample 25b) and their spectral counts; Table S2. Identified proteins of *B. vulgaris* (sample 5) and their spectral counts.
**Author Contributions:** Conceptualization, C.L., M.K., G.M. and J.A.; methodology, C.L., M.K., G.M. and J.-C.G.; software, O.P.; data analysis, C.L. and J.-C.G.; validation, J.A.; formal analysis, C.L. and O.P.; investigation, C.L.; resources, J.A.; data curation, O.P.; writing—original draft preparation, C.L. and J.A.; writing—review and editing, O.P. and J.A.; supervision, J.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was not financially supported by specific grant.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors thank all their previous colleagues and mentors specialized in marine microbiology.

**Conflicts of Interest:** The authors declare no conflict of interest.

## References

1. Cotner, J.B.; Biddanda, B.A. Small players, large role: Microbial influence on biogeochemical processes in pelagic aquatic ecosystems. *Ecosystems* **2002**, *5*, 105–121. [CrossRef]

2. Christie-Oleza, J.A.; Sousoni, D.; Lloyd, M.; Armengaud, J.; Scanlan, D.J. Nutrient recycling facilitates long-term stability of marine microbial phototroph-heterotroph interactions. *Nat. Microbiol.* **2017**, *2*, 17100. [CrossRef] [PubMed]

3. De, J.; Ramaiah, N.; Vardanyan, L. Detoxification of toxic heavy metals by marine bacteria highly resistant to mercury. *Mar. Biotechnol.* **2008**, *10*, 471–477. [CrossRef] [PubMed]

4. Christie-Oleza, J.A.; Fernandez, B.; Nogales, B.; Bosch, R.; Armengaud, J. Proteomic insights into the lifestyle of an environmentally relevant marine bacterium. *ISME J.* **2011**, *6*, 124–135. [CrossRef] [PubMed]

5. Lozano, C.; Lee, C.; Wattiez, R.; Lebaron, P.; Matallana-Surget, S. Unraveling the molecular effects of oxybenzone on the proteome of an environmentally relevant marine bacterium. *Sci. Total Environ.* **2021**, *793*, 148431. [CrossRef]

6. Hashemi, M.M.; Holden, B.S.; Coburn, J.; Taylor, M.F.; Weber, S.; Hilton, B.; Zaugg, A.L.; McEwan, C.; Carson, R.; Andersen, J.L.; et al. Proteomic analysis of resistance of gram-negative bacteria to chlorhexidine and impacts on susceptibility to colistin, antimicrobial peptides, and ceragenins. *Front. Microbiol.* **2019**, *10*, 210. [CrossRef]

7. Van Den Bossche, T.; Arntzen, M.; Becher, D.; Benndorf, D.; Eijsink, V.G.H.; Henry, C.; Jagtap, P.D.; Jehmlich, N.; Juste, C.; Kunath, B.J.; et al. The Metaproteomics Initiative: A coordinated approach for propelling the functional characterization of microbiomes. *Microbiome* **2021**, *9*, 243. [CrossRef]

8. Gouveia, D.; Grenga, L.; Pible, O.; Armengaud, J. Quick microbial molecular phenotyping by differential shotgun proteomics. *Environ. Microbiol.* **2020**, *22*, 2996–3004. [CrossRef]

9. Hartmann, E.M.; Durighello, E.; Pible, O.; Nogales, B.; Beltrametti, F.; Bosch, R.; Christie-Oleza, J.A.; Armengaud, J. Proteomics meets blue biotechnology: A wealth of novelties and opportunities. *Mar. Genom.* **2014**, *17*, 35–42. [CrossRef]

10. Kuhring, M.; Kuhring, M.; Kuhring, M.; Kuhring, M.; Doellinger, J.; Doellinger, J.; Nitsche, A.; Muth, T.; Muth, T.; Renard, B.Y.; et al. TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples. *J. Proteome Res.* **2020**, *19*, 2501–2510. [CrossRef]

11. Boulund, F.; Karlsson, R.; Gonzales-Siles, L.; Johnning, A.; Karami, N.; AL-Bayati, O.; Ahrén, C.; Moore, E.R.B.; Kristiansson, E. Typing and characterization of bacteria using bottom-up tandem mass spectrometry proteomics. *Mol. Cell. Proteom.* **2017**, *16*, 1052–1063. [CrossRef] [PubMed]

12. Alves, G.; Wang, G.; Ogurtsov, A.Y.; Drake, S.K.; Gucek, M.; Sacks, D.B.; Yu, Y.K. Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry. *J. Am. Soc. Mass Spectrom.* **2018**, *29*, 1721–1737. [CrossRef] [PubMed]

13. Hayoun, K.; Gaillard, J.; Pible, O.; Alpha-bazin, B.; Armengaud, J. High-throughput proteotyping of bacterial isolates by double barrel chromatography-tandem mass spectrometry based on microplate paramagnetic beads and phylopeptidomics. *J. Proteom.* **2020**, *226*, 103887. [CrossRef] [PubMed]

14. Pible, O.; Allain, F.; Jouffret, V.; Culotta, K.; Miotello, G.; Armengaud, J. Estimating relative biomasses of organisms in microbiota using "phylopeptidomics". *Microbiome* **2020**, *8*, 30. [CrossRef]

15. Hayoun, K.; Pible, O.; Petit, P.; Allain, F.; Jouffret, V.; Culotta, K.; Rivasseau, C.; Armengaud, J.; Alpha-Bazin, B. Proteotyping environmental microorganisms by phylopeptidomics: Case study screening water from a radioactive material storage pool. *Microorganisms* **2020**, *8*, 1525. [CrossRef]

16. Hayoun, K.; Gouveia, D.; Grenga, L.; Pible, O.; Armengaud, J.; Alpha-bazin, B. Evaluation of Sample Preparation Methods for Fast Proteotyping of Microorganisms by Tandem Mass Spectrometry. *Front. Microbiol.* **2019**, *10*, 1985. [CrossRef]

17. Hughes, C.S.; Moggridge, S.; Müller, T.; Sorensen, P.H.; Morin, G.B.; Krijgsveld, J. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. *Nat. Protoc.* **2019**, *14*, 68–85. [CrossRef]
**Author Contributions:** Conceptualization, C.L., M.K., G.M. and J.A.; methodology, C.L., M.K., G.M. and J.-C.G.; software, O.P.; data analysis, C.L. and J.-C.G.; validation, J.A.; formal analysis, C.L. and O.P.; investigation, C.L.; resources, J.A.; data curation, O.P.; writing—original draft preparation, C.L. and J.A.; writing—review and editing, O.P. and J.A.; supervision, J.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was not financially supported by specific grant.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors thank all their previous colleagues and mentors specialized in marine microbiology.

**Conflicts of Interest:** The authors declare no conflict of interest.

## References

1. Cotner, J.B.; Biddanda, B.A. Small players, large role: Microbial influence on biogeochemical processes in pelagic aquatic ecosystems. *Ecosystems* **2002**, *5*, 105–121. [CrossRef]

2. Christie-Oleza, J.A.; Sousoni, D.; Lloyd, M.; Armengaud, J.; Scanlan, D.J. Nutrient recycling facilitates long-term stability of marine microbial phototroph-heterotroph interactions. *Nat. Microbiol.* **2017**, *2*, 17100. [CrossRef] [PubMed]

3. De, J.; Ramaiah, N.; Vardanyan, L. Detoxification of toxic heavy metals by marine bacteria highly resistant to mercury. *Mar. Biotechnol.* **2008**, *10*, 471–477. [CrossRef] [PubMed]

4. Christie-Oleza, J.A.; Fernandez, B.; Nogales, B.; Bosch, R.; Armengaud, J. Proteomic insights into the lifestyle of an environmentally relevant marine bacterium. *ISME J.* **2011**, *6*, 124–135. [CrossRef] [PubMed]

5. Lozano, C.; Lee, C.; Wattiez, R.; Lebaron, P.; Matallana-Surget, S. Unraveling the molecular effects of oxybenzone on the proteome of an environmentally relevant marine bacterium. *Sci. Total Environ.* **2021**, *793*, 148431. [CrossRef]

6. Hashemi, M.M.; Holden, B.S.; Coburn, J.; Taylor, M.F.; Weber, S.; Hilton, B.; Zaugg, A.L.; McEwan, C.; Carson, R.; Andersen, J.L.; et al. Proteomic analysis of resistance of gram-negative bacteria to chlorhexidine and impacts on susceptibility to colistin, antimicrobial peptides, and ceragenins. *Front. Microbiol.* **2019**, *10*, 210. [CrossRef]

7. Van Den Bossche, T.; Arntzen, M.; Becher, D.; Benndorf, D.; Eijsink, V.G.H.; Henry, C.; Jagtap, P.D.; Jehmlich, N.; Juste, C.; Kunath, B.J.; et al. The Metaproteomics Initiative: A coordinated approach for propelling the functional characterization of microbiomes. *Microbiome* **2021**, *9*, 243. [CrossRef]

8. Gouveia, D.; Grenga, L.; Pible, O.; Armengaud, J. Quick microbial molecular phenotyping by differential shotgun proteomics. *Environ. Microbiol.* **2020**, *22*, 2996–3004. [CrossRef]

9. Hartmann, E.M.; Durighello, E.; Pible, O.; Nogales, B.; Beltrametti, F.; Bosch, R.; Christie-Oleza, J.A.; Armengaud, J. Proteomics meets blue biotechnology: A wealth of novelties and opportunities. *Mar. Genom.* **2014**, *17*, 35–42. [CrossRef]

10. Kuhring, M.; Kuhring, M.; Kuhring, M.; Kuhring, M.; Doellinger, J.; Doellinger, J.; Nitsche, A.; Muth, T.; Muth, T.; Renard, B.Y.; et al. TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples. *J. Proteome Res.* **2020**, *19*, 2501–2510. [CrossRef]

11. Boulund, F.; Karlsson, R.; Gonzales-Siles, L.; Johnning, A.; Karami, N.; AL-Bayati, O.; Ahrén, C.; Moore, E.R.B.; Kristiansson, E. Typing and characterization of bacteria using bottom-up tandem mass spectrometry proteomics. *Mol. Cell. Proteom.* **2017**, *16*, 1052–1063. [CrossRef] [PubMed]

12. Alves, G.; Wang, G.; Ogurtsov, A.Y.; Drake, S.K.; Gucek, M.; Sacks, D.B.; Yu, Y.K. Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry. *J. Am. Soc. Mass Spectrom.* **2018**, *29*, 1721–1737. [CrossRef] [PubMed]

13. Hayoun, K.; Gaillard, J.; Pible, O.; Alpha-bazin, B.; Armengaud, J. High-throughput proteotyping of bacterial isolates by double barrel chromatography-tandem mass spectrometry based on microplate paramagnetic beads and phylopeptidomics. *J. Proteom.* **2020**, *226*, 103887. [CrossRef] [PubMed]

14. Pible, O.; Allain, F.; Jouffret, V.; Culotta, K.; Miotello, G.; Armengaud, J. Estimating relative biomasses of organisms in microbiota using "phylopeptidomics". *Microbiome* **2020**, *8*, 30. [CrossRef]

15. Hayoun, K.; Pible, O.; Petit, P.; Allain, F.; Jouffret, V.; Culotta, K.; Rivasseau, C.; Armengaud, J.; Alpha-Bazin, B. Proteotyping environmental microorganisms by phylopeptidomics: Case study screening water from a radioactive material storage pool. *Microorganisms* **2020**, *8*, 1525. [CrossRef]

16. Hayoun, K.; Gouveia, D.; Grenga, L.; Pible, O.; Armengaud, J.; Alpha-bazin, B. Evaluation of Sample Preparation Methods for Fast Proteotyping of Microorganisms by Tandem Mass Spectrometry. *Front. Microbiol.* **2019**, *10*, 1985. [CrossRef]

17. Hughes, C.S.; Moggridge, S.; Müller, T.; Sorensen, P.H.; Morin, G.B.; Krijgsveld, J. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. *Nat. Protoc.* **2019**, *14*, 68–85. [CrossRef]
18. Broadbent, J.A.; Broszczak, D.A.; Tennakoon, I.U.K.; Huygens, F. Pan-proteomics, a concept for unifying quantitative proteome measurements when comparing closely-related bacterial strains. *Expert Rev. Proteom.* **2016**, *13*, 355–365. [CrossRef]

19. Kanehisa, M.; Sato, Y.; Morishima, K. BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences. *J. Mol. Biol.* **2016**, *428*, 726–731. [CrossRef]

20. Mestre, M.; Höfer, J.; Gasol, J.M.; Jeffrey, W.H. Seasonal Variation of Bacterial Diversity Along the Marine Particulate Matter Continuum. *Front. Microbiol.* **2020**, *11*, 1590. [CrossRef]

21. Hahnke, R.L.; Meier-Kolthoff, J.P.; García-López, M.; Mukherjee, S.; Huntemann, M.; Ivanova, N.N.; Woyke, T.; Kyrpides, N.C.; Klenk, H.P.; Göker, M. Genome-based taxonomic classification of Bacteroidetes. *Front. Microbiol.* **2016**, *7*, 2003. [CrossRef] [PubMed]

22. Machado, K.C.T.; Fortuin, S.; Tomazella, G.G.; Fonseca, A.F.; Warren, R.M.; Wiker, H.G.; De Souza, S.J.; De Souza, G.A. On the impact of the pangenome and annotation discrepancies while building protein sequence databases for bacteria proteogenomics. *Front. Microbiol.* **2019**, *10*, 1410. [CrossRef] [PubMed]

23. Pible, O.; Armengaud, J. Improving the quality of genome, protein sequence, and taxonomy databases: A prerequisite for microbiome meta-omics 2.0. *Proteomics* **2015**, *15*, 3418–3423. [CrossRef] [PubMed]

24. Urios, L.; Agogué, H.; Lesongeur, F.; Stackebrandt, E.; Lebaron, P. Balneola vulgaris gen. nov., sp. nov., a member of the phylum Bacteroidetes from the north-western Mediterranean Sea. *Int. J. Syst. Evol. Microbiol.* **2006**, *56*, 1883–1887. [CrossRef] [PubMed]

25. Surget, S.M. Proteogenomic analysis of Epibacterium mobile BBCC367, a relevant marine bacterium isolated from the South Pacific Ocean. *Front. Microbiol.* **2018**, *9*, 1222. [CrossRef]

26. Pearson, W.R. Protein Function Prediction: Problems and Pitfalls. *Curr. Protoc. Bioinform.* **2015**, *51*, 4.12.1–4.12.8. [CrossRef]

27. McIlwain, S.; Mathews, M.; Bereman, M.S.; Rubel, E.W.; MacCoss, M.J.; Noble, W.S. Estimating relative abundances of proteins from shotgun proteomics data. *BMC Bioinform.* **2012**, *13*, 308. [CrossRef]

28. Blanchet, E.; Prado, S.; Stien, D.; da Silva, J.O.; Ferandin, Y.; Batailler, N.; Intertaglia, L.; Escargueil, A.; Lami, R. Quorum sensing and quorum quenching in the Mediterranean Seagrass Posidonia Oceanica microbiota. *Front. Mar. Sci.* **2017**, *4*, 218. [CrossRef]

29. Urvoy, M.; Lami, R.; Dreanno, C.; Daudé, D.; Rodrigues, A.M.S.; Gourmelon, M.; L'Helguen, S.; Labry, C. Quorum sensing disruption regulates hydrolytic enzyme and biofilm production in estuarine bacteria. *Environ. Microbiol.* **2021**, *23*, 7183–7200. [CrossRef]

30. Grenga, L.; Pible, O.; Armengaud, J. Pathogen proteotyping: A rapidly developing application of mass spectrometry to address clinical concerns. *Clin. Mass Spectrom.* **2019**, *14* Pt A, 9–17. [CrossRef]

31. Xia, J.; Ling, S.K.; Wang, X.Q.; Chen, G.J.; Du, Z.J. Aliifodinibius halophilus sp. nov., a moderately halophilic member of the genus Aliifodinibius, and proposal of Balneolaceae fam. nov. *Int. J. Syst. Evol. Microbiol.* **2016**, *66*, 2225–2233. [CrossRef] [PubMed]

32. Joung, Y.; Park, M.; Jang, H.-J.; Jung, I.; Cho, J.-C. A report of 22 unrecorded bacterial species in Korea, isolated from the North Han River basin in 2017. *J. Species Res.* **2018**, *7*, 193–201. [CrossRef]

33. Ye, J.; Ma, Y.; Liu, Q.; Zhao, D.L.; Wang, Q.Y.; Zhang, Y.X. Regulation of Vibrio alginolyticus virulence by the LuxS quorum-sensing system. *J. Fish Dis.* **2008**, *31*, 161–169. [CrossRef] [PubMed]

34. Snoussi, M.; Noumi, E.; Cheriaa, J.; Usai, D.; Sechi, L.A.; Zanetti, S.; Bakhrouf, A. Adhesive properties of environmental Vibrio alginolyticus strains to biotic and abiotic surfaces. *New Microbiol.* **2008**, *31*, 489–500.

35. Gu, D.; Liu, H.; Yang, Z.; Zhang, Y.; Wang, Q. Chromatin immunoprecipitation sequencing technology reveals global regulatory roles of low-cell-density quorum-sensing regulator AphA in the pathogen Vibrio alginolyticus. *J. Bacteriol.* **2016**, *198*, 2985–2999. [CrossRef]

36. Hamilton, A.; Popham, D.L.; Carl, D.J.; Lauth, X.; Nizet, V.; Jones, A.L. Penicillin-binding protein 1a promotes resistance of group B streptococcus to antimicrobial peptides. *Infect. Immun.* **2006**, *74*, 6179–6187. [CrossRef]

37. Anes, J.; McCusker, M.P.; Fanning, S.; Martins, M. The ins and outs of RND efflux pumps in Escherichia coli. *Front. Microbiol.* **2015**, *6*, 587. [CrossRef]

38. Zgurskaya, H.I.; Krishnamoorthy, G.; Ntreh, A.; Lu, S. Mechanism and function of the outer membrane channel TolC in multidrug resistance and physiology of enterobacteria. *Front. Microbiol.* **2011**, *2*, 189. [CrossRef]

39. Murray, A.E.; Freudenstein, J.; Gribaldo, S.; Hatzenpichler, R.; Hugenholtz, P.; Kämpfer, P.; Konstantinidis, K.T.; Lane, C.E.; Papke, R.T.; Parks, D.H.; et al. Roadmap for naming uncultivated Archaea and Bacteria. *Nat. Microbiol.* **2020**, *5*, 987–994. [CrossRef]
18. Broadbent, J.A.; Broszczak, D.A.; Tennakoon, I.U.K.; Huygens, F. Pan-proteomics, a concept for unifying quantitative proteome measurements when comparing closely-related bacterial strains. *Expert Rev. Proteom.* **2016**, *13*, 355–365. [CrossRef]

19. Kanehisa, M.; Sato, Y.; Morishima, K. BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences. *J. Mol. Biol.* **2016**, *428*, 726–731. [CrossRef]

20. Mestre, M.; Höfer, J.; Gasol, J.M.; Jeffrey, W.H. Seasonal Variation of Bacterial Diversity Along the Marine Particulate Matter Continuum. *Front. Microbiol.* **2020**, *11*, 1590. [CrossRef]

21. Hahnke, R.L.; Meier-Kolthoff, J.P.; García-López, M.; Mukherjee, S.; Huntemann, M.; Ivanova, N.N.; Woyke, T.; Kyrpides, N.C.; Klenk, H.P.; Göker, M. Genome-based taxonomic classification of Bacteroidetes. *Front. Microbiol.* **2016**, *7*, 2003. [CrossRef] [PubMed]

22. Machado, K.C.T.; Fortuin, S.; Tomazella, G.G.; Fonseca, A.F.; Warren, R.M.; Wiker, H.G.; De Souza, S.J.; De Souza, G.A. On the impact of the pangenome and annotation discrepancies while building protein sequence databases for bacteria proteogenomics. *Front. Microbiol.* **2019**, *10*, 1410. [CrossRef] [PubMed]

23. Pible, O.; Armengaud, J. Improving the quality of genome, protein sequence, and taxonomy databases: A prerequisite for microbiome meta-omics 2.0. *Proteomics* **2015**, *15*, 3418–3423. [CrossRef] [PubMed]

24. Urios, L.; Agogué, H.; Lesongeur, F.; Stackebrandt, E.; Lebaron, P. Balneola vulgaris gen. nov., sp. nov., a member of the phylum Bacteroidetes from the north-western Mediterranean Sea. *Int. J. Syst. Evol. Microbiol.* **2006**, *56*, 1883–1887. [CrossRef] [PubMed]

25. Surget, S.M. Proteogenomic analysis of Epibacterium mobile BBCC367, a relevant marine bacterium isolated from the South Pacific Ocean. *Front. Microbiol.* **2018**, *9*, 1222. [CrossRef]

26. Pearson, W.R. Protein Function Prediction: Problems and Pitfalls. *Curr. Protoc. Bioinform.* **2015**, *51*, 4.12.1–4.12.8. [CrossRef]

27. McIlwain, S.; Mathews, M.; Bereman, M.S.; Rubel, E.W.; MacCoss, M.J.; Noble, W.S. Estimating relative abundances of proteins from shotgun proteomics data. *BMC Bioinform.* **2012**, *13*, 308. [CrossRef]

28. Blanchet, E.; Prado, S.; Stien, D.; da Silva, J.O.; Ferandin, Y.; Batailler, N.; Intertaglia, L.; Escargueil, A.; Lami, R. Quorum sensing and quorum quenching in the Mediterranean Seagrass Posidonia Oceanica microbiota. *Front. Mar. Sci.* **2017**, *4*, 218. [CrossRef]

29. Urvoy, M.; Lami, R.; Dreanno, C.; Daudé, D.; Rodrigues, A.M.S.; Gourmelon, M.; L'Helguen, S.; Labry, C. Quorum sensing disruption regulates hydrolytic enzyme and biofilm production in estuarine bacteria. *Environ. Microbiol.* **2021**, *23*, 7183–7200. [CrossRef]

30. Grenga, L.; Pible, O.; Armengaud, J. Pathogen proteotyping: A rapidly developing application of mass spectrometry to address clinical concerns. *Clin. Mass Spectrom.* **2019**, *14* Pt A, 9–17. [CrossRef]

31. Xia, J.; Ling, S.K.; Wang, X.Q.; Chen, G.J.; Du, Z.J. Aliifodinibius halophilus sp. nov., a moderately halophilic member of the genus Aliifodinibius, and proposal of Balneolaceae fam. nov. *Int. J. Syst. Evol. Microbiol.* **2016**, *66*, 2225–2233. [CrossRef] [PubMed]

32. Joung, Y.; Park, M.; Jang, H.-J.; Jung, I.; Cho, J.-C. A report of 22 unrecorded bacterial species in Korea, isolated from the North Han River basin in 2017. *J. Species Res.* **2018**, *7*, 193–201. [CrossRef]

33. Ye, J.; Ma, Y.; Liu, Q.; Zhao, D.L.; Wang, Q.Y.; Zhang, Y.X. Regulation of Vibrio alginolyticus virulence by the LuxS quorum-sensing system. *J. Fish Dis.* **2008**, *31*, 161–169. [CrossRef] [PubMed]

34. Snoussi, M.; Noumi, E.; Cheriaa, J.; Usai, D.; Sechi, L.A.; Zanetti, S.; Bakhrouf, A. Adhesive properties of environmental Vibrio alginolyticus strains to biotic and abiotic surfaces. *New Microbiol.* **2008**, *31*, 489–500.

35. Gu, D.; Liu, H.; Yang, Z.; Zhang, Y.; Wang, Q. Chromatin immunoprecipitation sequencing technology reveals global regulatory roles of low-cell-density quorum-sensing regulator AphA in the pathogen Vibrio alginolyticus. *J. Bacteriol.* **2016**, *198*, 2985–2999. [CrossRef]

36. Hamilton, A.; Popham, D.L.; Carl, D.J.; Lauth, X.; Nizet, V.; Jones, A.L. Penicillin-binding protein 1a promotes resistance of group B streptococcus to antimicrobial peptides. *Infect. Immun.* **2006**, *74*, 6179–6187. [CrossRef]

37. Anes, J.; McCusker, M.P.; Fanning, S.; Martins, M. The ins and outs of RND efflux pumps in Escherichia coli. *Front. Microbiol.* **2015**, *6*, 587. [CrossRef]

38. Zgurskaya, H.I.; Krishnamoorthy, G.; Ntreh, A.; Lu, S. Mechanism and function of the outer membrane channel TolC in multidrug resistance and physiology of enterobacteria. *Front. Microbiol.* **2011**, *2*, 189. [CrossRef]

39. Murray, A.E.; Freudenstein, J.; Gribaldo, S.; Hatzenpichler, R.; Hugenholtz, P.; Kämpfer, P.; Konstantinidis, K.T.; Lane, C.E.; Papke, R.T.; Parks, D.H.; et al. Roadmap for naming uncultivated Archaea and Bacteria. *Nat. Microbiol.* **2020**, *5*, 987–994. [CrossRef]