S. pneumoniae strain EF3030 (serotype 19F) was cultured in CDM+ with sub-cultivation as described above. Pneumococci were harvested at three defined optical densities: early exponential phase (OD₆₀₀ 0.4), mid exponential phase (OD₆₀₀ 0.6), and late exponential phase (OD₆₀₀ 1.0). A total of 6 mL of culture was centrifuged at 3900 rpm for 10 minutes, and the supernatant was carefully collected and the tube was immediately frozen by immersion in ice-cold ethanol and maintained at -70°C. The bacterial pellet was washed once with PBS, and the optical density was adjusted to 1.0. Subsequently, 1 mL of the bacterial suspension was centrifuged again at 3900 rpm for 5 minutes, and the resulting bacterial pellet was stored at -70°C until sample preparation.

The collected samples of bacterial pellets were each resuspended in 50 µL of 20 mM HEPES buffer (pH 8) containing 1% SDS. Cell disruption was achieved through mechanical disruption using a bead mill at 3000 rpm for 3 minutes in liquid nitrogen. The resulting frozen powder was then dissolved in 150 µL of 20 mM HEPES buffer (pH 8) with 1% SDS. The solution was subjected to shaking at 1400 rpm at 95°C for 1 minute. After cooling, Pierce™ Universal Nuclease (Pierce, Thermo Fisher Scientific, MA, United States; 2.5 U, 4 mM MgCl2) was added, followed by an incubation in an ultrasonic bath for 5 minutes. Thereafter, all samples were centrifuged at 17000 g for 30 minutes, and the supernatant was carefully collected for subsequent analysis by mass spectrometry.

Proteins from the supernatant were precipitated using trichloroacetic acid (TCA). A volume of 1.5 mL of the supernatant was mixed with 225 µL of 100% TCA, resulting in a final concentration of 15% TCA. The solution was thoroughly mixed and incubated at 4°C for 48 hours. Following incubation, the solution was centrifuged at 17,000 g for 60 minutes at 4°C. The supernatant was carefully discarded, and the pellet was washed with 70% ice-cold ethanol by centrifugation at 17,000 g for 10 minutes at 4°C. This wash procedure was repeated three times with decreasing volumes of ethanol: 500 µL wash 1), 200 µL (wash 2), 180 µL (wash 3). After the final washing step, the ethanol was removed and the pellet containing the proteins was air-dried. The proteins were then resuspended in 15 µL of 20 mM HEPES buffer (pH 8) containing 1% SDS to solubilize the membrane proteins and incubated at 65°C for 2 minutes. Protein concentrations of protein samples were determined using the Thermo Micro BCA Kit employing the BCA assay analysis pipeline included in the MassSpecPreppy pipeline (Reder et al., 2024). Tryptic digestion of the pellet and supernatant-derived proteins was performed using the SP3 protocol (Reder et al., 2024). The resulting peptides were analyzed by mass spectrometry on an Orbitrap Exploris™ 480 mass spectrometer (Thermo Fisher Scientific), coupled to an Ultimate 3000 nano-LC system (Thermo Fisher Scientific). A data-independent acquisition (DIA) mode was utilized to acquire the data, for specific details see Supplementary Tables S3 and S4 . Data are available at ProteomeXchange consortium (Deutsch et al., 2023) via PRIDE repository with identifier PXD062379 (Perez-Riverol et al., 2024, 2016).

The mass spectrometric data were analyzed using the SpectronautTM Software (v19.1), via a spectral library-based approach. The search against a protein sequences database of S. pneumoniae EF3030 (NCBI RefSeq accession no. NZ_CP035897.1, alternatively: NZ_CP035897) included tryptic peptides with up to two missed cleavages, fixed carbamidomethylation of cysteine and oxidation of methionine and N-terminal acetylation as variable modifications. Further analysis was performed using R version 4.4.1 and the SpectroPipeR package 0.3.0 (Michalik et al., 2025). The PneumoWiki (https://pneumowiki.med.uni-greifswald.de) annotations and descriptions of S. pneumoniae EF3030 proteins were used to analyze the genome’s functional response to the minimal medium.

For comparative analysis of protein composition between the cellular and supernatant fractions, intensity-based absolute quantification (iBAQ) values were calculated using Spectronaut. The relative iBAQ values were expressed as a percentage of the total sum of iBAQ values for each sample condition (Schwanhäusser et al., 2011) ( Supplementary Figure S3 ). Protein localization were predicted using DeepLocPro v1.0 (Moreno et al., 2024) with ‘−−group positive’ Gene set enrichment analyses (GSEA; Subramanian et al., 2005) was performed using the fast gene set enrichment analysis (fgsea) R-package (Korotkevich et al., 2016), leveraging TIGRFAM (Haft et al., 2013) functional annotations of S. pneumoniae EF3030 proteins as retrieved from PneumoWiki and integrating orthologously mapped RegPrecise (Novichkov et al., 2013) based regulon information of S. pneumoniae TIGR4 ( Supplementary Figure S2 ). Hierarchical clustering of protein profiles of proteins identified with at least two peptides in both fractions was performed in R using the hclust function ( Supplementary Excel Table S3 ). As distance metric, spearman correlation was applied, and 1-r was used. For distance determination between clusters, the average method was used. Clusters cutting height was set to 40% of the maximal distance. Functional characterization of the clusters was performed by over representation analysis using the Fisher’s Exact test and employing TIGRFAM functional annotation and regulon information.

S. pneumoniae EF3030 was cultured in CDM+ and harvested at three growth phases, defined by optical density (OD_600nm) values of 0.4 (early exp), 0.6 (mid-exp), and 1.0 (late exp) ( Figure 3A ). Total protein was isolated from both the cytosolic and supernatant fractions and proteome analysis was performed using the PneumoWiki annotated genome of strain EF3030. NZ_CP035897.1 predicts 1862 protein-coding genes, we were able to successfully identify 1381 proteins in the cytosolic fraction and 1373 proteins in the supernatant fraction, with more than two peptides with ion q-values less than 0.001 ( Figure 3B ). These numbers represent 74.16% and 73.73% of the annotated theoretical proteome in the cytosolic and supernatant fractions, respectively. The high number of different proteins in the supernatant indicated partial cell lysis already at an early stage. A comparison revealed 1,344 proteins common to both fractions, with 37 and 29 proteins uniquely identified in the cytosol and supernatant, respectively ( Figure 3B ).

In the cytosolic fraction, 37 proteins were uniquely identified ( Figure 3C ), including key stress response and growth-regulatory proteins. ComEC, which is involved in DNA internalization related to competence and PtvR, important for fitness coping with antibiotic and stress, were detected in the cytosol (Pimentel and Zhang, 2018; Liu et al., 2017). Notably, the toxin-antitoxin system components YefM, RelE1, and HicB, which are implicated in bacterial persistence, were identified in this fraction only. Bacterial persistence is a crucial factor in the survival of S. pneumoniae within the host and during antibiotic treatment. HicB was of particular interest, as it exhibited a 1.7-fold increase during the mid-exponential phase and 1.5-fold during the late exponential phase ( Supplementary Excel Table S1 ).

The supernatant fraction contained 29 unique proteins ( Figure 3B , Supplementary Excel Table S2 ). Most of these proteins were truly secreted or surface associated, including CbpF, ComGC, ComB, ComEA, NanB, EndoD, MtsC, QueT, CpsI, and EabC ( Figure 3C ). However, we also detected some cytoplasmic proteins such as Mga2, a virulence factor transcriptional regulator, together with Paal, and YbhL, mainly due to a substantial lysis rate. Notably, a significant proportion of the detected proteins in the supernatant fraction contribute directly or indirectly to virulence, including NanB, CpsI, ComEA and EndoD. These proteins exhibited distinct abundance patterns during growth, with EndoD and ComEA increasing in later growth phases, whereas CpsI showed a decrease in abundance under the same conditions. Furthermore, EabC, associated with a domain that could be linked to host interaction and the competence factor ComB displayed significant increases in abundance during the late exponential phase. The choline-binding protein CbpF also displayed a notable 3.2-fold increase during the late log phase. The surface proteins, including CpsI and ComEA, demonstrated phase-dependent abundance fluctuations, reflecting the adaptive response of S. pneumoniae to changing growth conditions.

The comparative analysis of the relative iBAQ values for cytosolic proteins and proteins present in the supernatant revealed a wide range of protein abundances, spanning approximately 6 log10 steps ( Supplementary Figure S3A ). Highly abundant proteins, including Gap (GAPDH), Tuf, enolase (Eno), EF3030_RS05545 (PtsH), EF3030_RS02365 (Pgk) and ribosomal proteins (e.g., RplX), were found in both fractions. Notably, when the total proteins detected in the late exponential phase were analyzed, LytN and SpxB were found among the top 10 highest abundant proteins. LytN, a murein hydrolase, plays a crucial role in cell division and cell wall integrity. Overexpression of LytN has been shown to cause cell lysis in other bacteria like S. aureus (Frankel et al., 2011). SpxB, a pyruvate oxidase, is key enzyme for H₂O₂ production (Bättig and Mühlemann, 2008). The protein composition in the supernatant fraction in general mirrored that of the cytosolic profile ( Supplementary Figure S3B ). Proteins that were at least 100-fold enriched in the supernatant fraction, including those involved in cell surface attachment and extracellular functions, such as PfbA, ZmpD, ZmpB, MapP, EF3030_RS10100, EF3030_RS00555, and PcsB. Conversely, we identified the ribosomal protein RpmC, EF3030_RS06250 (AroH), EF3030_RS02670, EF3030_RS02470, and EF3030_RS10365 as being at least 20-fold enriched in the cytosolic fraction.

In silico predictions of protein localization by DeepLocPro (Moreno et al., 2024) indicate that the supernatant fraction is substantially enriched in extracellular proteins and cell wall and surface proteins across all analyzed growth states ( Figure 4 ). Notably, the relative abundance of membrane and cytoplasmic proteins does not differ between the two fractions, a finding that is consistent with the high frequency of lysis of pneumococcal cells (Terrasse et al., 2015; Eldholm et al., 2009)

To investigate pneumococcal physiology in the newly developed minimal medium CDM+, pneumococcal cells and supernatants were collected at early, mid, and late exponential growth phases as shown in Figure 3A . Comparative analysis of the mid and late exponential phase samples versus early exponential phase samples allowed for the examination of changes in protein expression profiles, metabolic alterations, and bacterial cell adaptations across varying growth states and cell densities ( Figure 5 ). This information is crucial for the development of therapeutics targeting bacterial proteins, as it can provide insight into strategies to inhibit bacterial growth and progression of infection at different stages. The distinct growth phases correspond to different numbers of bacteria during colonization or dissemination in the blood or cerebrospinal fluid. Initially low bacterial number initiate adherence or cross host barriers and subsequent accumulation or biofilm formation contribute to the progression of infection (Hall-Stoodley et al., 2004).

To gain a general overview about the protein abundance changes along the growth states, the obtained proteome profiles of S. pneumoniae EF3030 were normalized to the protein abundance at the early exponential phase and then clustered. Hierarchical clustering was applied ( Supplementary Excel Table S3 for the data) and clusters were defined by cutting of 40% of the total tree distance. This resulted in eight general protein clusters representing the proteome dynamics along the growth states in the supernatant and cytoplasmic fraction ( Figure 5 ). Clusters C1 and C6 represent proteins associated with metabolic functions as identified using overrepresentation analysis of known regulons and functional TIGRFAM categories. Of note, proteins belonging to the fatty acid biosynthesis FabT regulon and the purine synthesis PurR regulon as well as amino acid biosynthesis proteins tend to accumulate along the growth state in the cytoplasmic and supernatant fraction, whereas proteins belonging to the central sugar metabolism CcpA regulon, the maltose utilization MalR regulon, sialic acid utilization NanR and RpiR regulons, fructooligosaccharides utilization RbsR regulon and the trehalose utilization TreR regulon decreased in abundance in the cytosolic and supernatant fraction during progression through growth phases ( Figure 5 ). In that sense, our data reveal a shift from sugar-based glycolysis to other metabolic aspects over time when growth in the newly optimized medium and metabolic dynamics also shape the pathophysiological potential of pneumococci. In general, the CcpA regulated central metabolism is closely linked to virulence (Iyer et al., 2005; Carvalho et al., 2011; Zhang et al., 2023) and CodY-driven metabolic aspects are essential in colonization and infection processes of pneumococci (Hendriksen et al., 2007; Johnston et al., 2015). Proteins of the clusters C3 and C5 could be quantified in higher abundance in the later phases of growth in the supernatant. These clusters comprised of the zinc acquisition regulator AdcR, manganese homeostasis MntR regulon, the N-acetylglucosamine utilization and the hyaluronate utilization, RegR regulon as well as protein’s function associated to the cell envelope, demonstrating the need of metal ions and accumulation of classic extracellular proteins during later growth phases. Proteins of the DNA metabolism, protein biosynthesis (cluster C4) and biosynthesis of cofactors (cluster C8) remained relatively stable during the exponential growth phase, highlighting their essential role in the bacterial physiology.

To gain detailed insights into the growth state dependent changes in protein levels in both the cytosolic and supernatant fraction, pairwise comparisons were performed for the samples harvested in the late exponential phase to samples harvested in early exponential phase using the ROPECA approach (Suomi and Elo, 2017) ( Figure 6 , Supplementary Excel Tables S1 and S2 ). Only proteins with an absolute fold change of 1.5-fold or greater and an adjusted p-value less than 0.05 were considered as significantly altered and were analyzed ( Supplementary Table S4 ). 34 proteins showed increased abundance in both fractions, the cytosol and supernatant. In contrast, 65 proteins were significantly lower in abundance in the cytosol fraction and 11 proteins were significantly lower in the supernatant fraction ( Figure 6A ). We then classified these differentially expressed proteins into functional groups of interest: transporter proteins (29 in cytosol and 10 in supernatant), gene regulation proteins (7 in cytosol and 1 identified in supernatant), metabolic proteins (31 in cytosol and 6 in supernatant), virulence and competence factors (9 in cytosol and 15 in supernatant), proteins with others functions (11 in cytosol and 10 in supernatant) and proteins of unknown function (7 in cytosol and 5 in supernatant) ( Figure 6B ). Detailed description along with the fold change is shown in the Supplementary Tables S4A, B .

To determine the overlap of the proteins shown in the volcano plot ( Figure 6A ) in both the cytosolic and the supernatant fraction, a Venn diagram ( Figure 6C ) was used. Sixteen proteins (the Pht family proteins, the Adc system proteins involved in Zinc homeostasis, ComGA, sortase SrtA, YdcP_2, BguA, Mip, Nfr1, Nfr2) were found to significantly increased in both the cytosol and the supernatant, while three proteins (FruA, TreP and GadW) were significantly reduced in both fractions ( Supplementary Tables S4A, B , proteins marked with “*”).

During the late-log phase (OD₆₀₀ 1.0), increased abundance of various transporter proteins was measured in both the cytosolic and supernatant fractions ( Figure 6B , Supplementary Table S4A ). Notably, PTS and ABC transporters involved in sugar uptake, such as BguB, BguC, and BguD, were more abundant in the cytosol. Moreover, zinc and metal transporters, such as AdcA, AdcAII, AdcC, and CtpA, were elevated in both fractions, indicating the bacterial adaptation to metal ion availability during growth, as indicated by overrepresentation of the AdcR regulon in cluster C3 ( Figure 5 ). In contrast, several sugar transporters, including MalC, BrnQ, GadW, FruA, and TreP exhibited reduced abundance during the late-exponential phase ( Supplementary Table S4B ). This is in line with the observation that the general sugar metabolism decreases in later growth states (cluster C6; Figure 5 ).

Several transcriptional regulators, including the Zn-dependent regulator AdcR showed increased abundance in both the cytosolic fraction and the supernatant fraction during the late-exponential phase ( Figure 6A , Supplementary Table S4A ). Other regulators like GntR and CopY were significantly increased in the cytosolic fraction during the later growth phase ( Supplementary Excel Table S1 ). In contrast, other regulators such as NmlR and FruR, exhibited a significant reduction in the cytosol and were also reduced in the supernatant during later growth phases suggesting growth-phase-specific regulation of bacterial adaptation and stress response ( Supplementary Table S4B , Excel Tables S1, S2 ).

Metabolic proteins involved in amino acid and sugar metabolism, such as galactokinase (GalK), LacG, and arginine deaminase system (ArcA, ArgF (ArcB), ArcC), exhibited decreased abundance during the late-log phase in the cytosol ( Supplementary Figure S2 , Supplementary Table S4B ). In contrast, some metabolic enzymes, including GuaA2, PEP phosphomutase, YdpC_2, and BguA, showed increased abundance in the cytosol, indicating changes in metabolic activity during different growth phases ( Supplementary Table S4A ). This metabolic adaptation is also reflected by the identification of the two general metabolic clusters C1 and C6 ( Figure 5 ). In general, sugar metabolism proteins tend to decrease during the late exponential phase in both the cytosolic and supernatant fractions, whereas the abundance of proteins involved in amino acid and purine and pyrimidine biosynthesis increased.

This shift in metabolic regulation is further supported by gene set enrichment analysis (GSEA), as depicted in Supplementary Figure S2 . The plot illustrates functional annotation and regulon-based enrichment across different growth phases (OD 0.6/0.4 and OD 1.0/0.4). The color of the dots represents the direction of normalized enrichment scores (NES), with red indicating positive enrichment and blue indicating negative enrichment. The size of each dot corresponds to the absolute NES value, highlighting the magnitude of enrichment. Notably, during the transition from mid to late exponential phase, cellular processes and nucleotide metabolism were positively enriched, while energy metabolism showed a negative enrichment. Additionally, the CcpA regulon, known for its role in carbon metabolism, exhibited significant negative enrichment, reinforcing the observed decline in sugar metabolism proteins ( Supplementary Figure S2 ). These findings collectively suggest a metabolic shift favoring nucleotide and amino acid biosynthesis over sugar metabolism during later growth stages.

Remarkably, we observed a more than 2-fold increase of the competence type IV pilus ATPase ComGA in both pneumococcal fractions during the late exponential phase ( Figure 6A , Supplementary Table S4A ). In addition, ComB, a component of the competence system, was found to be 6.1-fold increase in the supernatant at the late exponential phase ( Figure 6A , Supplementary Table S4A ). Competence requires a complex and well-timed interplay of several regulons (such as ComE, ComX, BlpR) (Winkler and Morrison, 2019; Weyder et al., 2018) We found that the ComX1 regulon has a general peak in the mid exponential phase while protein levels decrease in the supernatant fraction during the late exponential phase (cluster C7; Figure 5 )

Additionally, several cytoplasmic proteins involved in DNA processing and repair showed increased abundance over time ( Figure 6A , Supplementary Table S4A ). The single-strand DNA-binding protein, SsbB (EF3030_RS09295) stabilizes single-stranded DNA during replication, recombination, and repair. DprA facilitates natural transformation by binding and guiding incoming DNA into the chromosome. Sortase A, although primarily responsible for anchoring surface proteins, may support competence by attaching transformation-related proteins to the cell surface. SsbB and DprA are directly involved in DNA uptake and processing during natural transformation, a process that enhances genetic adaptability. Sortase A might contribute to this process by supporting the display of competence-associated factors on the cell surface. In the supernatant fraction, bacteriocin (EF3030_RS00555) or bacteriocin-like protein BlpU, bacteriocin secretion accessory protein (EF3030_RS02515), serine hydrolase (EF3030_RS00045), a glycoside hydrolase (EF3030_RS10670), and a surface protein (EF3030_RS05660) increased significantly at late exponential phase ( Supplementary Table S4A ). The secreted proteins (bacteriocins, hydrolases, and surface proteins) indicate enhanced bacterial competition, environmental adaptation, and potential host interactions. In contrast, out of the 11 significantly reduced proteins in the supernatant ( Figure 6C ), 8 proteins were exclusively found in the supernatant including YqeH, RsgA, ParB partition protein, methyltransferase domain containing protein, and the DEAD box helicase, whereas 3 were also found in the cytosol ( Figure 6C , Supplementary Table S4B ).

For comparative analysis, we classified various proteins based on their surface anchoring mechanisms ( Figure 7 ) (Lane et al., 2022; Bergmann and Hammerschmidt, 2006; Pérez-Dorado et al., 2012). The data revealed a significant increase in the signal log ratio of several lipoproteins, choline-binding proteins, sortase-anchored proteins, and non-classical surface proteins (NCSP) in both the cytosolic and supernatant fractions comparing the mid and late log phase to the early log phase ( Figure 7 , Supplementary Excel Tables S1, S2 ). Choline-binding proteins were detected in both the cytoplasmic and supernatant fractions, with the most striking increase observed for the murein hydrolase CbpD, which exhibited a 54.7-fold increase in abundance in the supernatant during the late log phase ( Figure 7 , Supplementary Table S4A ). A 3.2-fold increase in abundance was also detected for CbpF, which was exclusively found in the supernatant ( Figure 3 ). In contrast, while all choline-binding proteins were present in the cytoplasmic fraction at low levels, their abundance remained relatively unchanged throughout growth. We observed PhtD to be the most significantly elevated protein detected in both the cytoplasmic and supernatant fractions during the later stages of log growth ( Figure 6A ). PhtD and PhtE are members of the histidine triad protein (Pht) family, characterized by four to six histidine motifs that bind divalent metal cations, such as Zn²⁺ (Bersch et al., 2013).

Among the lipoproteins, AdcA and AdcAII were the prominent proteins with higher abundance, found in the late log phase in both the cytosolic fraction and extracellular fraction. A significant increase of ABC_SBP_8 was observed at the late exponential phase in the extracellular fraction, whereas other ABC-SBP (-SBP_1 and –SBP_7) components remained unchanged throughout growth. Furthermore, MsmE, ArtP_1, RS04305, and AliB as subunit of the Ami oligopeptide ABC transporter showed higher abundance in the late log phase. AliA, another subunit of the Ami transporter system, did not exhibit significant changes in abundance during growth. Notably, MsrAB, as part of the detoxification system for oxidized proteins, was consistently detected in higher abundance across all growth conditions compared to the early exponential phase. Additionally, several other lipoproteins like LdcB or SdbB remained stable during growth ( Figure 7 ).

Significant differences in the abundance of sortase-anchored proteins compared to the early exponential phase were observed in the supernatant fraction. A notable increase in the abundance of EndoD, which contains an Ig-like domain, was found in the extracellular fraction. Additionally, increases were also observed for Eng, ZmpB, and PfbA. In contrast, the neuraminidase NanA level remained unchanged during growth, and similarly, SpnHL SpuA, StrH and PspC_2 did not exhibit alterations. A decreased abundance was detected for the adhesion PavB in the late exponential growth phase. Furthermore, decreases in NanA and StrH levels were observed in the cytoplasmic fraction during exponential growth phases ( Figure 7 ).

The non-classical surface proteins (NCSP) like enolase (Eno) and GAPDH (Gap) were found in abundance in both the cytosol and the supernatant ( Supplementary Table S4A ). They were observed in the early exponential phase and significantly increased during the mid-exponential phase ( Figure 7 ). Notably, PavA (pneumococcal adherence and virulence factor) lacking a secretion signal and domains for cell surface anchoring increased significantly during the late exponential phase in the supernatant fraction.

Proteases and peptidases are essential for multiple physiological processes in bacteria. Most of the proteases and peptidases discussed in this study ( Figure 8 ) maintains important cellular functions attributing to survival, adaptability and pathogenesis (Marquart, 2021). The typical Clp protease components (ClpP, ClpX, ClpC and ClpE) and the chaperone ClpL, were found in both fractions. The ClpP ratio decreased in the cytoplasmatic fraction during the late exponential phase compared to the early growth phase, however there is no significant change in either fraction during the exponential growth. Interestingly, out of the three extracellular serine proteases present in the colonizing strain, EF3030, the secreted serine proteases PrtA and HtrA were identified in both the fractions. No significant change of HtrA was observed during the exponential growth. In the cytosol, PrtA significantly reduced more than 1.5-fold during the late exponential phase, however it increased significantly in the supernatant during the exponential growth ( Supplementary Table S4A ). CbpG, the third serine protease was not detected under our experimental conditions. YdcP_2, a member of the U32 family peptidases, showed an increased abundance during growth in both fractions.

Among the pneumococcal peptidases, PepX and PepO exhibited increased abundance during late exponential phase in the cytoplasmic fraction compared to the early exponential phase, whereas PepS and AlbF showed a decrease in abundance during growth. Most of these peptidases had a decreased abundance during exponential growth in the extracellular fraction. AlbF was an exception, with a small increase in abundance observed at the late exponential phase.