Time-course analysis of Streptococcus sanguinis after manganese depletion reveals changes in glycolytic, nucleotide, and redox metabolites

Introduction Manganese is important for the endocarditis pathogen, Streptococcus sanguinis. Little is known about why manganese is required for virulence or how it impacts the metabolome of streptococci. Objectives We applied untargeted metabolomics to cells and media to understand temporal changes resulting from manganese depletion. Methods EDTA was added to a S. sanguinis manganese-transporter mutant in aerobic fermentor conditions. Cell and media samples were collected pre- and post-EDTA treatment. Metabolomics data were generated using positive and negative modes of data acquisition on an LC-MS/MS system. Data were subjected to statistical processing using MetaboAnalyst and time-course analysis using Short Time series Expression Miner (STEM). Results We observed quantitative changes in 534 and 422 metabolites in cells and media, respectively, after EDTA addition. The 173 cellular metabolites identified as significantly different indicated enrichment of purine and pyrimidine metabolism. Further multivariate analysis revealed that the top 15 cellular metabolites belonged primarily to lipids and redox metabolites. The STEM analysis revealed global changes in cells and media in comparable metabolic pathways. Products of glycolysis such as pyruvate and fructose-1,6-bisphosphate increased, suggesting that enzymes that act on them may require manganese for activity or expression. Nucleosides accumulated, possibly due to a blockage in conversion to nucleobases. Simultaneous accumulation of ortho-tyrosine and reduced glutathione suggests that cells were unable to utilize glutathione as a reductant. Conclusion Differential analysis of metabolites revealed the activation of a number of metabolic pathways in response to manganese depletion, many of which may be connected to carbon catabolite repression.


Introduction 43
Streptococcus sanguinis is a gram-positive bacterium known for its duplicity. As an early and 44 abundant colonizer of teeth, S. sanguinis is associated with oral health (Kreth et al., 2017;Kreth et 45 al., 2005). However, when it enters the bloodstream, whether through dental procedures or activities 46 as routine as eating, it is known to colonize the heart valves or other endocardial surfaces of persons 47 with particular pre-existing cardiac conditions, leading to infective endocarditis (IE) ( Beginning ~38 min post-EDTA addition, cell growth slowed, resulting in a steady drop in OD 127 ( Figure 1). 128

Global metabolomics of S. sanguinis cells and BHI media 129
Our goal was to understand the metabolic consequences of Mn depletion during growth of a S. 130 sanguinis Mn-transporter mutant in a rich medium (BHI), as well as to survey changes in the 131 conditioned media during the growth and treatment periods. Extensive global untargeted 132 metabolomics analysis revealed 534 metabolites in cells and 422 metabolites in conditioned media. 133 The raw metabolite abundance values alongside the identified metabolite IDs, super pathways and 134 sub-pathway names, average mass, and identifiers such as Chemical Abstracts Service (CAS), 135 PubChem, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Human Metabolome Database 136 (HMDB) IDs are provided for both cellular and media metabolites (Tables S1-S2). These datasets 137 were refined through normalization, transformation, and scaling, followed by imputation (Tables S3-138 S4). The 534 metabolites belong to 57 different KEGG metabolic pathways ( Table S5). The 422 139 metabolites quantified in the conditioned BHI media belonged to 50 different metabolic pathways 140 (Table S6), all of which overlap with the metabolic pathways found in the cells. 141 BHI has as its chief constituents bovine and porcine brain and heart extracts. Based on comparison 142 with the pre-inoculation media samples, we identified several metabolites that appear to originate 143 from BHI, and were excluded from further statistical processing as they were unique to the growth 144 media alone (Table S7). Any metabolite that occurred in fewer than 75% of the samples was also 145 excluded from the analysis, which resulted in the exclusion of 9 of the 534 metabolites detected in 146 cells (Table S7). 147

Differential accumulation patterns of metabolites over time course and EDTA treatment 148
We used a false discovery rate (FDR)-corrected ANOVA to determine metabolites that were 149 significantly different in abundance between the different time-points. ANOVA revealed 173 and 13 150 metabolites that were significantly different in cells and media, respectively (Tables S8-9). To 151 investigate whether these differential metabolites would map to metabolic pathways, we mapped the 152 set of metabolites using the Streptococcus pyogenes M1 476 KEGG database within MetaboAnalyst 153 by implementing overrepresentation analysis with Fisher's exact test and pathway topology analysis 154 using relative-betweenness centrality (Jewison et al., 2014). Pathway enrichment analysis of the 173 155 cellular metabolites that were differential along the time course of EDTA treatment identified only 156 purine and pyrimidine metabolism (nominal P-value < 0.05) ( Figure S1a; Table S10). Surprisingly 157 pathway enrichment analysis of the 13 media metabolites that were differential along the time course 158 identified purine and pyrimidine metabolism as above, but also glyoxylate and dicarboxylate 159 metabolism, and alanine, aspartate, and glutamate metabolism (nominal P-value < 0.05) ( Figure  160 S1b; Table S11). When metabolite abundances were compared for the two post-EDTA time points 161 vs T -20 , it was revealed that one, five, 13, and 30 metabolites were increased in T 25 and T 50 in media 162 and T 25 and T 50 in cells, respectively ( Figure S1c). Of these, only 2'-deoxyadenosine increased in 163 both cells and media at T 50 (Tables S12-13). The 30 metabolites increased in T 50 in cells were mostly 164 lipids, energy metabolites, nucleotide phosphates, and dinucleotides (Table S12). When significantly 165 decreased metabolites were compared, it was revealed that 1, 1, 13, and 30 metabolites were 166 decreased in T 25 and T 50 in media and T 25 and T 50 in cells, respectively (Figure S1d). Only 167 glutamine levels decreased in both media samples ( Table S13). The five metabolites that decreased 168 in cells at T 25 included cCMP and cUMP, while the 18 metabolites that decreased at T 50 in the cells 169 included IMP and XMP (Table S12). 170

Multivariate and hierarchical clustering analysis 171
To define the metabolomic changes caused by Mn depletion, we used multivariate analysis and 172 HCA. Using an unsupervised multivariate analysis, PCA, we observed that metabolite abundances 173 alone were able to discriminate between the samples and explain 58.

Metabolomic analysis of BHI spent media reveals metabolic interactions of S. sanguinis 224
with the extracellular environment 225 Our purpose in conducting this study was to examine the role of Mn in S. sanguinis metabolism, 226 particularly in relation to IE. While the perfect medium for such a study would have been serum or 227 plasma, this would not have been feasible, and so we instead used another complex yet commercially 228 accessible medium-BHI. As with plasma, BHI has glucose as its most abundant sugar (0.2% w/v in 229 BHI and ~0.1% w/v in plasma). Although serum and plasma have been the subject of many 230 metabolomic studies, we are not aware of any previous metabolomic analysis of BHI. Thus, the 231 analysis of the pre-inoculated BHI ( Table S2) may be of interest to the many investigators who use 232 this medium. Likewise, the comparison of the pre-inoculated and T -20 media samples tells us much 233 concerning the metabolic and transport capabilities of S. sanguinis under Mn-replete conditions 234 (Table S13). 235 As expected, we observed a significant decrease of glucose in spent media (Figure 3a), indicating its 236 utilization as carbon source. Levels of fructose and mannose significantly decreased as well ( Figure  237 (Table S11). One potential explanation for the increase in pyruvate levels is that fewer 259 pyruvate molecules were oxidized by pyruvate oxidase (SpxB) into H 2 O 2 and acetyl phosphate, 260 consistent with our finding of a significant decrease in H 2 O 2 levels after Mn depletion (Figure 1) 261 . 262 There was a significant accumulation of hexose diphosphates in cells at T 50 and a slight increase in 263 spent media as well (Figure 3b & d). Since levels of other glycolytic intermediates such as glucose-264 6-phosphate, glycerone, and glyceraldehyde-3-phosphate could not be measured using our platform 265 (Tables S1-2), we are unable to assess the impact on this pathway using metabolomics alone. We 266 hypothesize that the hexose diphosphate is primarily fructose-1,6-bisphosphate and its accumulation 267 results from the reduced activity of two potentially Mn-cofactored fructose-1,6-bisphosphate-268 consuming enzymes in the glycolytic pathway: fructose-1,6-bisphosphatase (Fbp; SSA_1056) and depletion results in many changes in the CcpA regulon, which may explain the repression of this 296 operon at T 50 . Thus, this may be but one example of a non-carbon catabolite pathway impacted by 297 Mn depletion through its effect on CCR. 298 Similar to the purines, the pyrimidine nucleosides appear to be taken up from the media and the 299 nucleobases were likely generated by cells (Figures 4 and S5). Mean uridine levels in cells 300 decreased slightly in cells after Mn depletion, whereas UMP (Figure 4) and uracil (Figures 4 and  301 S5h) levels dropped significantly. Uracil levels in cells likely decreased due to lower UMP 302 production. Interestingly, orotidine levels increased in cells (Figure 4), indicating a potential 303 blockage in the conversion to UMP, although the explanation for this remains elusive as no PyrF 304 enzyme is known to utilize a Mn cofactor (https://www.brenda-enzymes.org/). 305 (Figures 4 and S5d & h). which 306 corresponds to a decrease in expression of pdp (pyrimidine nucleoside phosphorylase; SSA_1035; 307 thymidine to thymine conversion) . Oddly, thymidine levels decreased as well, 308 although this may be explained by the increase in dTDP-rhamnose levels at T 50 (Table S10), 309

Levels of thymine decreased in cells after Mn depletion
indicating that thymidine may have been shuttled to sugar metabolism. Mean cytosine and cytidine 310 levels increased slightly in cells after Mn depletion (Figures 4 and S5f & h), which is the opposite 311 trend from the other pyrimidines. Levels of downstream products 3'-CMP and 2', 3'-cyclic CMP 312 increased as well (Figure 4). The discrepancy may be explained by decreased conversion to uridine 313 as its levels dropped after Mn depletion (Figures 4 and S5f). This is supported by a decrease in 314 expression of cdd (cytidine deaminase; SSA_1037) after Mn depletion  and Cdd 315 may be Mn-cofactored (Hosono and Kuno, 1973). 316

Oxidized and reduced glutathione levels in Mn-depleted S. sanguinis cells 317
Glutathione (γ-glutamyl-cysteinylglycine) is a nonprotein thiol produced by cells to  constant (Figure 5b). Since the air flow was kept constant 325 throughout the experiment, we expected that GSH would have been utilized by redox enzymes for 326 ROS remediation. While ROS levels were not measured directly by the metabolomics analysis, 327 levels of ortho-tyrosine increased (Figure 5c)

5.
Conclusions 345 In this study, we showed system-wide metabolomic changes induced in S. sanguinis Mn-transporter 346 mutant cells and spent media in response to EDTA treatment over time. This study captured the Mn-347 responsive metabolic processes, such as dysregulations in carbohydrate, nucleotide, and redox 348 metabolism, many of which may contribute to the reduction in bacterial growth rate and virulence. 349 The decrease in available Mn led to the accumulation of fructose-1,6-bisphosphate, which likely 350 resulted in induction of carbon catabolite repression. This has widespread consequences, such as the 351 blockage of nucleobases conversion into nucleosides and accumulation of reduced glutathione. In 352 addition, we provide insights into the metabolic composition of BHI and the components streptococci 353 may utilize from this undefined medium. with Enveda Therapeutics; however, he has no conflict of interest with this study. 364

Ethics approval 365
This article does not contain any studies with human participants or animals performed by any of the 366 authors. 367

Consent to participate 368
Not applicable 369

Consent for publication 370
All authors have read, approved and have provided consent for this publication. 371

Availability of data and material 372
The datasets generated and analyzed during the current study are available as Supplementary Tables 373 S1 and S2 as provided by Metabolon, Inc. 374