Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Exposure of Helicobacter pylori to clarithromycin in vitro resulting in the development of resistance and triggers metabolic reprogramming associated with virulence and pathogenicity

  • Naim Asyraf Rosli,

    Roles Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing

    Affiliation Faculty of Medicine, Department of Medical Microbiology, Universiti Malaya, Kuala Lumpur, Malaysia

    ⨯
  • Anis Rageh Al-Maleki ,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    anisrageh@um.edu.my, almaleki2004@gmail.com

    Affiliations Faculty of Medicine, Department of Medical Microbiology, Universiti Malaya, Kuala Lumpur, Malaysia, Faculty of Medicine and Health Sciences, Department of Medical Microbiology, Sana’a University, Sana’a, Yemen

    ⨯
  • Mun Fai Loke,

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Camtech Biomedical Pte Ltd, Singapore, Singapore

    ⨯
  • Sun Tee Tay,

    Roles Validation, Writing – review & editing

    Affiliation Faculty of Medicine, Department of Medical Microbiology, Universiti Malaya, Kuala Lumpur, Malaysia

    ⨯
  • Mohd Salleh Rofiee,

    Roles Methodology

    Affiliation Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA, Selangor, Malaysia

    ⨯
  • Lay Kek Teh,

    Roles Methodology

    Affiliation Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA, Selangor, Malaysia

    ⨯
  • Mohd Zaki Salleh,

    Roles Methodology

    Affiliation Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA, Selangor, Malaysia

    ⨯
  • Jamuna Vadivelu

    Roles Validation, Writing – review & editing

    Affiliation Faculty of Medicine, Medical Education Research and Development Unit (MERDU), Universiti Malaya, Kuala Lumpur, Malaysia

    ⨯

Abstract

In H. pylori infection, antibiotic-resistance is one of the most common causes of treatment failure. Bacterial metabolic activities, such as energy production, bacterial growth, cell wall construction, and cell-cell communication, all play important roles in antimicrobial resistance mechanisms. Identification of microbial metabolites may result in the discovery of novel antimicrobial therapeutic targets and treatments. The purpose of this work is to assess H. pylori metabolomic reprogramming in order to reveal the underlying mechanisms associated with the development of clarithromycin resistance. Previously, four H. pylori isolates were induced to become resistant to clarithromycin in vitro by incrementally increasing the concentrations of clarithromycin. Bacterial metabolites were extracted using the Bligh and Dyer technique and analyzed using metabolomic fingerprinting based on Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry (LC-Q-ToF-MS). The data was processed and analyzed using the MassHunter Qualitative Analysis and Mass Profiler Professional software. In parental sensitivity (S), breakpoint isolates (B), and induced resistance isolates (R) H. pylori isolates, 982 metabolites were found. Furthermore, based on accurate mass, isotope ratios, abundances, and spacing, 292 metabolites matched the metabolites in the Agilent METLIN precise Mass-Personal Metabolite Database and Library (AM-PCDL). Several metabolites associated with bacterial virulence, pathogenicity, survival, and proliferation (L-leucine, Pyridoxone [Vitamine B6], D-Mannitol, Sphingolipids, Indoleacrylic acid, Dulcitol, and D-Proline) were found to be elevated in generated resistant H. pylori isolates when compared to parental sensitive isolates. The elevated metabolites could be part of antibiotics resistance mechanisms. Understanding the fundamental metabolome changes in the course of progressing from clarithromycin-sensitive to breakpoint to resistant in H. pylori clinical isolates may be a promising strategy for discovering novel alternatives therapeutic targets.

Citation: Rosli NA, Al-Maleki AR, Loke MF, Tay ST, Rofiee MS, Teh LK, et al. (2024) Exposure of Helicobacter pylori to clarithromycin in vitro resulting in the development of resistance and triggers metabolic reprogramming associated with virulence and pathogenicity. PLoS ONE 19(3): e0298434. https://doi.org/10.1371/journal.pone.0298434

Editor: Bashir Sajo Mienda, Federal University Dutse, NIGERIA

Received: August 28, 2023; Accepted: January 23, 2024; Published: March 6, 2024

Copyright: © 2024 Rosli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This work is financially supported by the Fundamental Research Grant Scheme (FRGS) No. DP KPT FRGS/1/2020/SKK0/UM/02/20 provided by Malaysian Ministry of Higher Education (https://www.mohe.gov.my). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Metabolomic reprogramming refers to the ability of cells to alter their metabolome and phenotypic characteristics in response to environmental changes [1,2]. Metabolites are intermediate molecules and by-products of metabolic catalysis [3]. A key component of cellular regulatory system is metabolites produced by bacteria that interacts with the surrounding host microenvironment [4,5]. Several studies have shown that in Staphylococcus aureus and Vibrio alginolyticus, phenotypic changes, including antimicrobial resistance, can be associated with metabolomic reprogramming [6,7]. Antibiotic resistance has been demonstrated to correlate with alterations in bacterial metabolic pathway, such as folate biosynthesis pathway which is associated with changing bacterial susceptibility to antibiotics (phenotypic resistance) [8]. Moreover, the acquisition of resistance is shown in in Escherichia coli and S. aureus to impact bacterial physiology due to increased metabolomic burden and fitness cost [9]. These reports are supported by the other findings that acquiring phenotypic resistance requires the concerted activity of many components that are important in microbial physiology, including electron transport chain and metabolism of amino acids, fatty acids, or nucleotides [7,9–11].

Helicobacter pylori is a Gram-negative microaerophilic bacterium that causes gastric diseases, such as gastritis and peptic ulcers, and is associated with stomach cancer [12,13]. The ability of H. pylori to adapt and thrive in the harsh acidic environment of the stomach is one of the most remarkable elements of its biology [14]. H. pylori accomplishes this via producing a variety of metabolites, such as cholesterol-derived metabolites [15]. These compounds are critical in the bacterium’s capacity to colonize and maintain infection, avoid detection by the host immune system, and interact with the host stomach epithelium [15,16]. In addition, the metabolic status of bacterial cells exposed to antibiotics may directly or indirectly result in antimicrobial resistance (AMR) [17]. Reduced metabolic activity can lead to AMR by limiting antibiotic absorption [18,19]. In contrast, high metabolic activity is essential to sustain energy-demanding AMR processes, such cell-wall changes and efflux pump overexpression [9,20–22].

Clarithromycin-based triple treatment remains the therapy of choice for treating H. pylori infection [23]. Clarithromycin, a macrolide antibiotic, is still an effective antibiotic used in many H. pylori treatment regimens [24]. Clarithromycin interacts with 50S ribosomal subunit of H. pylori, leading to the suppressing of protein synthesis and resulting in a bacteriostatic effect [25]. However, metabolomic reprograming and metabolites associated with the development of clarithromycin-resistance in H. pylori is not well studied yet. Therefore, in this study, the H. pylori metabolomic reprogramming was assessed to elucidate the underlying mechanisms associated with in vitro induced development of clarithromycin-resistance by comparing the metabolomic profiles in the course of progressing from clarithromycin-sensitive to breakpoint to resistance H. pylori clinical isolates.

Materials and methods

H. pylori parental and induced isogenic isolates

Four H. pylori isolates have been retrieved from the collection of the Helicobacter Research Laboratory (Marshall Centre), HIR at the Universiti Malaya. Ethics approval was not required since no human participants and/or medical data were involved in the investigation. Four clarithromycin-sensitive H. pylori isolates were induced by exposing them to sequentially increasing concentrations of clarithromycin ranging from 0.0156 g/mL to 32 g/mL incorporated in CA plates in our previous study [26]. The parental uninduced clinical isolates were labeled as Sensitive (S), the induced isolates collected one passage immediate to becoming resistant were labeled as Breakpoint (B), and the induced resistance isogenic isolates were labeled Resistant (R). Isolates that grew on plate containing >1 μg/mL clarithromycin were considered as resistant. The isolates were grouped based on their clarithromycin susceptibility into S isolates (UM171-S, UM626A1-S, and UM678A-S with MIC of 0.064 μg/mL and UM650B-S with MIC of <0.016 to 0.064 μg/mL, B isogenic isolates (UM171-B, UM UM626A1-B, UM650B-B, and UM678A-B with MIC of 0.125 μg/mL), and R isogenic isolates (UM171-R, UM626A1-R, UM650B-R, and UM678A-R with MIC of >64 μg/mL). Comparing B isolates to those of R and S isolates allows us to provide insight on metabolomic reprogramming which has previously received little attention. The MIC fold changes of B and R compared to the parental S isolates are shown in S1 Fig.

Microbiological conditions for culturing

All H. pylori isolates were grown on chocolate agar (CA) (Oxoid, UK) for three days in a humidified 10% CO2 incubator at 37 °C (Thermo Fisher Scientific, USA) [27]. For broth culture, Brain heart infusion (BHI) broth (Oxoid, UK) supplemented with 0.4% yeast extract (Oxoid, UK) and 1% β-Cyclodextrin (Sigma, USA) was used.

Preparation of metabolite samples

The H. pylori colonies from a non-selective CA plate were harvested and inoculated into BHI broth before being incubated at 37 °C in a 10% CO2 incubator for another three days to reach log growth phase. The bacterial cultures were then adjusted with BHI broth to an OD600 nm of 1.0. Subsequently, 500 μL of the adjusted bacterial suspensions were pelleted at 10,000 rpm for one minute and washed three times with fresh BHI broth. The metabolites were extracted from the bacterial pellet using the Bligh and Dyer extraction procedure [28]. In brief, 300 μL of 2:1 methanol-chloroform (v/v) was added to each bacterial pellet and vortexed for a minute before incubating at room temperature for an hour. Following that, 200 μL of 1:1 chloroform-water (v/v) obtained from the Milli-Q® Benchtop Lab Water Purification Systems (Sigma-Aldrich, USA) were added to the mixture. The extract was then incubated at room temperature for 10 minutes and centrifuged at 10,000 rpm for ten minutes at 4 °C. the upper aqueous and lower organic phases were harvested and dried at 4 °C using the Labconco Refrigerated Centrivap concentrator (Kansas City, MO, USA).

LC-Q-TOF analysis of metabolites

The analysis of metabolites was performed by the method described previously with modification [28,29]. Briefly, the dried samples were redissolved in 50 μl of acetonitrile and water. The samples were mass spectrometrically analyzed on a 1200 Infinity Quaternary Liquid Chromatography system outfitted with a 6520 Quadrupole Time-of-Flight mass spectrometer attached to a Dual Agilent Jet Stream Electrospray Ionization (Dual AJS ESI) ionization source (Agilent Technologies, CA, USA). The samples were separated using an Agilent Zorbax Eclipse plus C18 Rapid Resolution High Throughput separation column (2.1 100 mm 1.8 m), kept at 40 °C to 45 °C, and analyzed in positive mode. Water with 1% formic acid (A) and acetonitrile with 0.1% formic acid (B) were utilized as mobile phases. A linear gradient from 95% to 5% was set to run for 36 minutes, with 5% A held for 5 minutes and flushed for 7 minutes at 0.5 mL/min. In the event of the run, the injection volume was set to 2 μL, and three injections were performed in complete loop injection mode for each sample. For instrument control and data collection, Agilent MassHunter Workstation Data Acquisition software (Agilent Technologies, CA, USA) was utilized. The nebulizer gas pressure (nitrogen) was set to 160 kPa, the ion source temperature was set to 200°C, the dry gas flow was set to 7 mL/minute at source temperature, and the spectral rate was set to 3 Hz for MS1 and 10 Hz for MS2. Voltage 3.0 kV, gas temperature 300 °C, drying gas 8 L/min, nebulizer 35 psig, VCap 3500 V, fragmentor 175 V, and skimmer 65 V were the electrospray ionization (ESI) conditions. To ensure high-quality data in metabolite profiling, sample blanks were run after each sample to clean the column and prevent previous samples from being carried over.

Metabolomics data processing and statistical analysis

The metabolome analysis of S, B, and R groups of H. pylori isolates were performed as described previously [28]. For the qualification and quantification of metabolites, the MassHunter Qualitative Analysis (MQA) software (Qual; version R.06.00) (Agilent Technologies, CA, USA) removes convolution of the raw data into individual chemical peaks for Molecular Feature Extraction. Moreover, we identified features with a minimum absolute abundance of 1000 counts within a predetermined mass accuracy (+/-5 ppm). The produced data were then imported into the Agilent Mass Profiler Professional (MPP) software (version B.12.61) for binning, alignment, and consensus generation for each feature. The data was imported back into MPP for annotation, statistical analysis, and comparison of S, B, and R groups’ metabolites. The abundance for each feature was normalized to the median abundance across all samples using MPP’s baselining option. The inherent variation between the S, B, and R groups was visualized using unsupervised principal component analysis (PCA) with median centering and scaling. One-way ANOVA was used to identify the metabolites that vary significantly between any two groups at a fold change (FC) cutoff value of 2 at p-value <0.05 with the Benjamini and Hochberg False Discovery Rate (FDR) adjusted to 1% for multiple testing adjustments. Furthermore, t-test was also applied to calculate the significance and FC of the metabolites between two groups. The metabolites were identified using MPP’s IDBrowser function by comparing mined features based on accurate mass, isotope ratios, abundances, and spacing to metabolites in the Agilent METLIN Accurate Mass-Personal Metabolite Database and Library (AM-PCDL) database (version 5.0). Subsequently, the identified metabolites were further analyzed for their correlation to the development of antibiotic resistance in H. pylori by comparing the metabolites expressed in B and R isolates with the parental S isolates.

Results

Global metabolome profile

Unsupervised PCA was used for quality control of samples. PCA is most frequently used to compress, with the least amount of information loss, the information present in a large number of original variables into a smaller set of new composite dimensions [30]. The first two principal components of the PCA are represented by x axis (principal component 1, PC1) and y axis (principal component 2, PC2). PC1 reveals the maximum variation among data points, while PC2 reveals the second most variation. The values on each axis show the proportion of variance that each component accounts for. For global metabolomic profiles (i.e., all molecular characteristics without filtering using Euclidean distance matrix), the B isolates (blue) formed a closely clustered group on the PCA plot. UM626A1-S and UM678A-S (green) clustered closely with the B isolates while UM650B-S and UM171S formed a separate cluster, which is close to the B cluster along PC1. While UM171-R, UM626A1-R, and UM650B-R formed a distinctly separate cluster along PC1 away from the S and B cluster, UM678A-R clustered together with UM650B-S and UM171-S (Fig 1). Thus, each group has a distinct global metabolomic profile that could be examined further in the study.

thumbnail
Download:
Fig 1. PCA in two dimensions of 12 H. pylori isolates created using MPP.

12 S isolates (4 isolates x 3 triplicate = 12); dark green color squares, 12 B isolates (4 isolates x 3 triplicate = 12); blue color squares, and 12 R isolates (4 isolates x 3 triplicate = 12) red color squares. H. pylori isolates formed distinct clusters based on metabolites acquired in positive ionization mode using components 1 (71.87%) and 2 (12.2%).

https://doi.org/10.1371/journal.pone.0298434.g001

A total of 982 metabolites were identified in this study. Notably, using one-way ANOVA, 593 metabolites were significantly (p <0.05) upregulated in R compared to B. However, 3 metabolites were not significantly changed in these two groups. Moreover, 585 metabolites were significantly (p <0.05) upregulated in R isolates against S isolates. However, 5 metabolites were not significantly changed in these two groups. Interestingly, 568 metabolites were not significantly changed in B and S groups. In contrast, only 249 metabolites were significantly (p <0.05) upregulated in B isolates against S isolates. Thus, there were more metabolites that were significantly upregulated as the H. pylori isolates progressed from sensitive to breakpoint and eventually to resistant.

Furthermore, the metabolites were matched with metabolites in the AM-PCDL database based on accurate mass, isotope ratios, abundances and spacing to identify the metabolites. Among the 982 metabolites identified, 292 metabolites matched metabolites in the database (S1 Table). Interestingly, among the 292 metabolites, 3 metabolites were significantly upregulated (p < 0.05, FC > 2) in R against B and S isolates (Fig 2A), and significantly upregulated (p < 0.05, FC > 2) in B isolates against S isolates. Moreover, there were 171 metabolites significantly upregulated (p < 0.05, FC > 2) in R isolates against B and S, however there were no significant difference between B isolates and S isolates (Fig 2B). Nonetheless, 14 metabolites were significantly downregulated (p < 0.05, FC < -2) in R isolates against B and S isolates, however there were no significant difference between those metabolites in B and S isolates (Fig 3A). Notably, 1 metabolite (11,12-dihydroxy arachidic acid) was significantly downregulated (p < 0.05, FC < -2) in B and R isolates against S, however, there were no significant difference between those metabolites in B and R isolates (Fig 3B). Additionally, it is interesting to note that 12 metabolites were significantly downregulated (p < 0.05, FC < -2) in R isolates against B and S isolates, and significantly downregulated (p < 0.05, FC < -2) in B isolates against S isolates (Fig 3C). Fig 4 depicts the metabolites regulation among the 12 individual isolates with a significant of p <0.05 and fold change of 2 as a cutoff.

thumbnail
Download:
Fig 2. The regulation of metabolites across S, B, and R isolates.

A) The metabolites were significantly upregulated (p < 0.05, FC > 2) in R isolates against B and S isolates, and significantly upregulated (p < 0.05, FC > 2) in B isolates against S isolates. B) 171 metabolites (S1 Table) were significantly upregulated (p < 0.05, FC > 2) in R isolates against B and S isolates, however there were no significant difference between those metabolites in B and S isolates.

https://doi.org/10.1371/journal.pone.0298434.g002

thumbnail
Download:
Fig 3. The regulation of metabolites across S, B, and R isolates.

A) The metabolites were significantly downregulated (p < 0.05, FC < -2) in R isolates against B and S isolates, however there were no significant difference between those metabolites in B and S isolates. B) The metabolites were significantly downregulated (p < 0.05, FC < -2) in B and R isolates against S, however, there were no significant difference between those metabolites in B and R isolates. C) The metabolites were significantly downregulated (p < 0.05, FC < -2) in R isolates against B and S isolates, and significantly downregulated (p < 0.05, FC < -2) in B isolates against S isolates.

https://doi.org/10.1371/journal.pone.0298434.g003

thumbnail
Download:
Fig 4. Heat map and clustering presenting the significant metabolome profiles of the 12 individual (S, B, and R) H. pylori isolates based on identified metabolites.

The heat maps and clustering based on Euclidean distance were generated using MetaboAnalyst version 5. The metabolites regulation was significant with p <0.05 and fold change of 2 as a cutoff.

https://doi.org/10.1371/journal.pone.0298434.g004

Metabolic pathway profiles

The identified metabolites were mapped to metabolic pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Notably, metabolites up-regulated in R isolates against S isolates were mapped to 53 metabolic pathways, while only 20 metabolic pathways were mapped by the down-regulated metabolites (S2 Fig). Similarly, metabolites up-regulated in R isolates against B isolates were mapped to 58 metabolic pathways, whereas down-regulated metabolites were mapped to only 20 metabolic pathways (S4 Fig). Additionally, up-regulated metabolites in B isolates against S isolates were mapped to 20 metabolic pathways, whereas 36 metabolic pathways were mapped by the down-regulated metabolites (S3 Fig). The metabolic pathway was further analyzed with MetaboAnalyst 5.0. The up-regulated metabolites among R isolates against B and S isolates were mapped to 10 metabolic pathways (Table 1 and Fig 5). The results of metabolic pathway analysis demonstrate that the up-regulated metabolites that associated with clarithromycin resistance among R isolates against B and S isolates are primarily related to the fructose and mannose metabolism, vitamin B6 metabolism, purine metabolism, valine, leucine, and isoleucine biosynthesis, phenylalanine metabolism, galactose metabolism, cysteine and methionine metabolism and aminoacyl-tRNA biosynthesis.

thumbnail
Download:
Fig 5. Overview of the pathway analysis which may be associated with the development of antibiotic resistance in H. pylori using MetaboAnalyst 5.0.

The figure displays pathways that correspond to annotated metabolites and are organized according to the p-value (y-axis), which represents pathway enrichment analysis, and the pathway impact values (x-axis), which represents pathway topology analysis. The metabolic pathways are shown as bubbles based on their scores from enrichment as seen in the y axis, and topology (pathway impact) values as seen in x axis. The p-value determines the node color of each pathway (red = lowest p-value and highest statistical significance) and indicates that metabolite changes in the relevant pathway are more significant. The size of the bubble correlates to the pathway impact score (biggest indicating the highest impact) and is related to the centrality of the metabolites involved. Specify pathway analysis parameters include Relative-betweenness Centrality for topology analysis and Hypergeometric Test for enrichment method. The KEGG pathway has been selected as the library pathway. A) Pathway analysis of metabolites expressed in R isolates against S isolates, B) Pathway analysis of metabolites expressed in R isolates against B isolates.

https://doi.org/10.1371/journal.pone.0298434.g005

thumbnail
Download:
Table 1. The metabolic pathways analyzed with MetaboAnalyst 5.0 among up-regulated metabolites.

https://doi.org/10.1371/journal.pone.0298434.t001

Antibiotic resistance associated metabolites

Unique and significant metabolites that may be linked to antibiotic resistance, virulence, pathogenicity, and growth were significantly expressed in R isolates as compared to S isolates and B isolates (Table 2). These include L-Leucine, Pyridoxine (Vitamin B6), Indoleacrylic acid, D-Mannitol, Sphingolipids (Sphinganine, C16 Sphinganine, and C17, Sphinganine,), Dulcitol, and D-Proline. Nonetheless, 11,12-dihydroxy arachidic acid was common in B and R isolates (downregulated) which may be associated with the development of clarithromycin resistance in H. pylori. Interestingly, L-leucine (KEGG C00123) was involved in several metabolic pathways in the R isolates which includes valine, leucine, and isoleucine biosynthesis (map00290) and degradation (map00280), and aminoacyl-tRNA biosynthesis pathways (map00980). Furthermore, Pyridoxine (KEGG C00314) was present in the vitamin B6 metabolism pathway (map00750), and D-Mannitol (KEGG C00392) was present in the fructose and mannose metabolism pathway (map00051) (Fig 5). Additionally, few metabolites were downregulated in the R isolates and were upregulated in S and B isolates shown to have a role in diminishing the survival of bacteria [31,32]. Vitamin D metabolites [(25R)-1?,25,26-trihydroxy-22-oxavitamin D3, 23,24-didehydro-25-hydroxyvitamin D3, and 26,26,26,27,27,27-hexafluoro-1?a-hydroxyvitamin D3] were found to be downregulated in R isolates against S and B isolates (Table 2).

thumbnail
Download:
Table 2. The regulation of metabolites that correlate with bacterial survival and may constitute a potential antibiotic mechanism.

https://doi.org/10.1371/journal.pone.0298434.t002

Discussion

Using the metabolomics approach, it is possible to identify chemicals or molecular targets that encourage antibiotic resistance and can be used in designing drugs against clinical isolates that are the focus of antibiotic treatment [9,33]. Untargeted metabolomics analyses, which enables the high-throughput characterization of a large scale of small molecule metabolites, is a useful discovery tool can provide novel and unbiased insights into the variations between drug-resistant and drug-sensitive strains [34]. Results from untargeted metabolomics can be very important in providing the basis for targeted analysis of specific metabolites or metabolic flux analysis of specific metabolic pathways [35].

In this study, a comparative global metabolome profiles of B and R isolates with parental the S isolates were investigated. The finding showed that more metabolites were expressed in B and R group compared to their parental S group. Nonetheless, B and S group expressed the most similar metabolites. Therefore, it could be suggested that R isolates may have produced more metabolites to enhance antibiotics resistance. This is in line with other finding that show the number of metabolites found in resistant mutants is threefold greater than the number of metabolites identified wild type [36]. This has also been demonstrated in E. coli, Pseudomonas aeruginosa, and S. aureus, which showed that drug resistant bacteria produce more metabolites when compared to drug sensitive strains [37–39]. It is crucial to note that the metabolic burden on a resistant pathogen relies highly on the adaptations to bacterial metabolism [8]. Moreover, bacteria producing more diverse metabolites can influence the antibiotic susceptibility of bacteria [40].

The involvement of bacterial metabolism in antibiotic resistance mechanisms and clinically relevant bacterial infections have been revealed by the cutting-edge metabolomics approaches. Amino acid metabolism, virulence, stress response, and cell-to-cell contacts in biofilms are examples of key cellular changes linked with antibiotic resistance in which metabolism plays a crucial role [9,17,41]. Interestingly, L-leucine, Pyridoxine (Vitamin B6), D-Mannitol, Sphingolipids, Indoleacrylic acid, Dulcitol, D-Proline were found to be significantly up-regulated in R isolates compared to S and B isolates. These metabolites may be associated with the development of antibiotic resistance in H. pylori.

In terms of essential nutrients for bacterial physiology, branched-chain amino acids such as Leucine (Leu) play a variety of roles, from promoting protein synthesis to signaling and support not only proliferation during infection, but also the evasion of host defenses [42–44]. It is also reported that Leu is essential for survival and bacterial growth [45]. Chemotactic responses of H. pylori to Leu were enhanced absolute requirement for growth [46]. Moreover, the biosynthesis of vitamin B6 has been shown to boost virulence in H. pylori, Mycobacterium tuberculosis, and Streptococcus pneumoniae [47]. Grubman et al. [48] demonstrated that mutant H. pylori deficient an enzyme PdxA, vital in vitamin B6 production were incapable to generate motility appendages and to establish chronic colonization in mice. This observation states that over-expression of vitamin B6 enhances virulence in bacteria which was also observed in the R isolates of this study.

D-mannitol is said to be a crucial component of the suitable solute system in osmoprotection in bacteria such as S. aureus [49]. In reaction to osmotic stress, mannitol-1-phosphate dehydrogenase (M1DPH) alters fructose-6-phosphate (F6P) to mannitol through the mannitol metabolism cycle [50]. M1DPH pathway was suggested as a target by Nguyen et al. [51] to establish an alternative treatment for Methicillin-resistant S. aureus (MRSA) infections. Furthermore, sphingolipids are found in the host cell membrane and act as building elements and signaling molecules which were reported to play an important role in the adhesion and invasion into the host cell for pathogenic Neisseria [52,53]. Although H. pylori has not been known to synthesize sphingolipid, other gut bacteria, such as Bacteroides and Prevotella spp., are known to produce sphingolipid to confer bacterial fitness in the gut [54,55]. Several studies have highlighted the role of sphingolipids as signaling molecules in the growth and pathogenicity of microbial pathogens including bacteria [56–59]. H. pylori have developed different strategies to manipulate and use host sphingolipids to promote their pathogenicity. It was reported that H. pylori uses glycosphingolipids as a receptor for adhesion and uptake [60]. This study showed that in response to clarithromycin stress, H. pylori generated sphinganine, therefore, its role in H. pylori needs to be further explored.

The functional role of indoleacrylic acid and dulcitol in H. pylori has not yet been revealed. However, its role in drug resistance and virulence in other Gram-negative bacteria has previously been reported. Indoleacrylic acid is a derivative of indole, an intercellular signaling chemical, which were reported be vital in the regulation of xenobiotic exporter genes, mdtEF and acrD, which increases drug resistance in E. coli [61]. Additionally, Nikaido et al. [62] proposed that indole increases drug resistance in Salmonella enterica by activating the efflux pump system via the transcriptional regulator. Additionally, the dulcitol fermentation is considered as a virulence factor [63]. A link was discovered between dulcitol fermentation and E. coli strain pathogenicity [64]. In mice, it was discovered that dulcitol-fermenting uroisolates were more nephropathogenic (but not deadly) than nondulcitol fermenting strains [65]. As a result, we postulated that both indoleacrylic acid and dulcitol might be involved or play a comparable role in drug resistance or virulence in H. pylori.

In addition, H. pylori uses proline as a carbon and energy source, and it flourishes in proline-rich microenvironments [66,67]. Studies has shown proline is a key role in H. pylori infection as stomach colonization requires proline utilization P (puP) [68,69]. It has also been shown that proline utilization A (PutA) proline metabolism increases oxidative stress tolerance in E. coli, indicating that proline metabolism needs to be further examined in the physiology and pathophysiology of H. pylori [70].

Therefore, the up-regulation of these metabolites (L-leucine, Pyridoxine (Vitamin B6), D-Mannitol, Sphingolipids, Indoleacrylic acid, Dulcitol, and D-Proline), that are linked to the development of antibiotic resistance, in R comparing to B and S may suggest a role or an association of those metabolites in enhancing the clarithromycin resistance to facilitate the H. pylori persistence [71]. Future research could explore more about the exact role of these metabolites in resistance mechanisms and its involvement in the microbial pathogenesis and virulence, that may offer a novel class of antimicrobial therapies to combat the ever-growing problem of antibiotic resistance [72]. Nevertheless, the downregulation of specific metabolites may assist bacteria in dealing with antibiotic stress [73]. Several vitamin D metabolites were shown to be downregulated in R isolates compared to S and B isolates, which have been shown to be significant in reducing bacterial overgrowth in the gut [31,32]. Therefore, it might be suggested that reduction of vitamin D production may enhance survivability of R isolates.

H. pylori infection plays a crucial role in the development of gastric cancer [4]. H. pylori can cause stomach epithelial cells to become inflamed, which can lead to the development of gastric cancer [71]. H. pylori can also directly affect epithelial cells, which can result in protein modification and DNA mutation [74]. However, metabolites produced by H. pylori have yet to be studied for their correlation with cancer development. The metabolites that were expressed in the R isolates were proven to play a role in cancer development and growth. For example, Leucine was shown to be a substrate for the development of cancer because its metabolism can affect a variety of biological functions, including protein synthesis and epigenetic control [75]. Additionally, sphingolipids, particularly sphingosine 1-phosphate (S1P), have become bio-effector molecules that promote angiogenesis, cancer cell development, and transformation [76]. Therefore, it is crucial to further study these metabolites and their correlation with cancer development, which will lead to the development of novel therapeutic strategies against human cancers [77].

Conclusions

The study suggested that H. pylori may have undergone metabolomic reprogramming in the development of antibiotic resistance as B and R isolates produced more metabolites than S isolates. Moreover, we also suggest that several metabolites which include L-leucine, Pyridoxine (Vitamin B6), D-Mannitol, Sphingolipids (Sphinganine, C16 Sphinganine, and C17 Sphinganine), Indoleacrylic acid, Dulcitol, and D-Proline may have an association with the development of antibiotic resistant in H. pylori. The findings of this study could lead to a new strategy to discover new therapy to combat antibiotic resistance and could prevent antibiotic resistance development in H. pylori.

Supporting information

S1 Fig. Fold change of B and R compared to the parental S isolate.

https://doi.org/10.1371/journal.pone.0298434.s001

(TIF)

S2 Fig. Pathway analysis of R isolates against S isolates.

A) The up-regulated metabolites which were mapped to the metabolic pathways in the KEGG database. B) The down-regulated metabolites which were mapped to the metabolic pathways in the KEGG database.

https://doi.org/10.1371/journal.pone.0298434.s002

(TIF)

S3 Fig. Pathway analysis of B isolates against S isolates.

A) The up-regulated metabolites which were mapped to the metabolic pathways in the KEGG database. B) The down-regulated metabolites which were mapped to the metabolic pathways in the KEGG database.

https://doi.org/10.1371/journal.pone.0298434.s003

(TIF)

S4 Fig. Pathway analysis of R isolates against B isolates.

A) The up-regulated metabolites which were mapped to the metabolic pathways in the KEGG database. B) The down-regulated metabolites which were mapped to the metabolic pathways in the KEGG database.

https://doi.org/10.1371/journal.pone.0298434.s004

(TIF)

S1 Table. The regulations of the compounds expressed by S, B, and R Helicobacter pylori isolates.

https://doi.org/10.1371/journal.pone.0298434.s005

(XLSX)

Acknowledgments

The authors would like to express their utmost gratitude and appreciation to the late Emeritus Professor Dato’ Dr. Khean Lee Goh for his help and support in setting up the Helicobacter Research Laboratory (UM Marshall Centre) at the Universiti Malaya and establishing the centre’s H. pylori culture collection. In addition, the authors would like to thank collaborators, Department of Medical Microbiology staff, and students from Universiti Malaya for their contributions to this study.

References

  1. 1. Bueno J. Metabolomics in Antimicrobial Drug Discovery: The Success of the Chemical Diversity. J Microb Biochem Technol. 2015;7:380–3.
  2. 2. Cazzaniga M, Bonanni B. Relationship Between Metabolic Reprogramming and Mitochondrial Activity in Cancer Cells. Understanding The Anticancer Effect of Metformin and Its Clinical Implications. Anticancer Res. 2015;35(11):5789–96. pmid:26503999.
  3. 3. Nagata M, Toyonaga K, Ishikawa E, Haji S, Okahashi N, Takahashi M, et al. Helicobacter pylori Metabolites Exacerbate Gastritis Through C-type Lectin Receptors. J Exp Med. 2021;218(1). pmid:32991669; PubMed Central PMCID: PMC7527975.
  4. 4. Zhang Y, Chen R, Zhang D, Qi S, Liu Y. Metabolite interactions between host and microbiota during health and disease: Which feeds the other? Biomedicine & Pharmacotherapy. 2023;160:114295. pmid:36709600
  5. 5. Al-Maleki AR, Mariappan V, Vellasamy KM, Tay ST, Vadivelu J. Altered Proteome of Burkholderia pseudomallei Colony Variants Induced by Exposure to Human Lung Epithelial Cells. PLOS ONE. 2015;10(5):e0127398.
  6. 6. Huemer M, Mairpady Shambat S, Bergada-Pijuan J, Söderholm S, Boumasmoud M, Vulin C, et al. Molecular Reprogramming and Phenotype Switching in Staphylococcus aureus Lead to High Antibiotic Persistence and Affect Therapy Success. Proc Natl Acad Sci U S A. 2021;118(7). pmid:33574060; PubMed Central PMCID: PMC7896289.
  7. 7. Skliros D, Kalatzis PG, Kalloniati C, Komaitis F, Papathanasiou S, Kouri ED, et al. The Development of Bacteriophage Resistance in Vibrio alginolyticus Depends on a Complex Metabolic Adaptation Strategy. Viruses. 2021;13(4). Epub 2021/04/10. pmid:33920240; PubMed Central PMCID: PMC8069663.
  8. 8. Reygaert WC. An Overview of the Antimicrobial Resistance Mechanisms of Bacteria. AIMS Microbiol. 2018;4(3):482–501. Epub 2018/06/26. pmid:31294229; PubMed Central PMCID: PMC6604941.
  9. 9. Kok M, Maton L, van der Peet M, Hankemeier T, van Hasselt JGC. Unraveling Antimicrobial Resistance Using Metabolomics. Drug Discov Today. 2022;27(6):1774–83. pmid:35341988
  10. 10. Peterson E, Kaur P. Antibiotic Resistance Mechanisms in Bacteria: Relationships Between Resistance Determinants of Antibiotic Producers, Environmental Bacteria, and Clinical Pathogens. Front Microbiol. 2018;9. pmid:30555448
  11. 11. Al-Maleki AR, Vellasamy KM, Mariappan V, Venkatraman G, Tay ST, Vadivelu J. Transcriptome analysis of Burkholderia pseudomallei SCV reveals an association with virulence, stress resistance and intracellular persistence. Genomics. 2020;112(1):501–12. doi: https://doi.org/https://doi.org/10.1016/j.ygeno.2019.04.002.
  12. 12. Sun Y, Zhang J. Helicobacter pylori Recrudescence and its Influencing Factors. J Cell Mol Med. 2019;23(12):7919–25. Epub 2019/09/19. pmid:31536675; PubMed Central PMCID: PMC6850920.
  13. 13. Hussein RA, Al-Ouqaili MTS, Majeed YH. Association between alcohol consumption, cigarette smoking, and Helicobacter pylori infection in Iraqi patients submitted to gastrointestinal endoscopy. Journal of Emergency Medicine, Trauma and Acute Care. 2022;2022(6 The Anbar 2nd International Medical Conference (AIMCO 2022)). doi: https://doi.org/https://doi.org/10.5339/jemtac.2022.aimco.12.
  14. 14. Ansari S, Yamaoka Y. Survival of Helicobacter pylori in gastric acidic territory. Helicobacter. 2017;22(4). Epub 20170412. pmid:28402047; PubMed Central PMCID: PMC5851894.
  15. 15. Jan H-M, Chen Y-C, Shih Y-Y, Huang Y-C, Tu Z, Ingle AB, et al. Metabolic labelling of cholesteryl glucosides in Helicobacter pylori reveals how the uptake of human lipids enhances bacterial virulence. Chemical Science. 2016;7(9):6208–16.
  16. 16. Wunder C, Churin Y, Winau F, Warnecke D, Vieth M, Lindner B, et al. Cholesterol glucosylation promotes immune evasion by Helicobacter pylori. Nature Medicine. 2006;12(9):1030–8.
  17. 17. Martínez JL, Rojo F. Metabolic regulation of antibiotic resistance. FEMS Microbiol Rev. 2011;35(5):768–89. Epub 20110628. pmid:21645016.
  18. 18. Dwyer DJ, Belenky PA, Yang JH, MacDonald IC, Martell JD, Takahashi N, et al. Antibiotics induce redox-related physiological alterations as part of their lethality. Proceedings of the National Academy of Sciences. 2014;111(20):E2100–E9. pmid:24803433
  19. 19. Brynildsen MP, Winkler JA, Spina CS, MacDonald IC, Collins JJ. Potentiating antibacterial activity by predictably enhancing endogenous microbial ROS production. Nature Biotechnology. 2013;31(2):160–5. pmid:23292609
  20. 20. Olivares Pacheco J, Alvarez-Ortega C, Alcalde Rico M, Martínez JL. Metabolic Compensation of Fitness Costs Is a General Outcome for Antibiotic-Resistant Pseudomonas aeruginosa Mutants Overexpressing Efflux Pumps. mBio. 2017;8(4). Epub 20170725. pmid:28743808; PubMed Central PMCID: PMC5527304.
  21. 21. Wagner S, Baars L, Ytterberg AJ, Klussmeier A, Wagner CS, Nord O, et al. Consequences of membrane protein overexpression in Escherichia coli. Mol Cell Proteomics. 2007;6(9):1527–50. Epub 20070419. pmid:17446557.
  22. 22. Parsons JB, Rock CO. Bacterial lipids: metabolism and membrane homeostasis. Prog Lipid Res. 2013;52(3):249–76. Epub 20130314. pmid:23500459; PubMed Central PMCID: PMC3665635.
  23. 23. Kocsmár É, Buzás GM, Szirtes I, Kocsmár I, Kramer Z, Szijártó A, et al. Primary and Secondary Clarithromycin Resistance in Helicobacter pylori and Mathematical Modeling of the Role of Macrolides. Nat Comm. 2021;12(1):2255.
  24. 24. Vieira RR, Fontes LES, Pacheco RL, Fernandes MAP, Malta PP, Riera R. Proton pump inhibitor‐ and clarithromycin‐based triple therapies for Helicobacter pylori eradication. Cochrane Database of Systematic Reviews. 2020;(9). doi: https://doi.org/10.1002/14651858.CD013734 PubMed PMID: CD013734.
  25. 25. Francesco VD, Zullo A, Hassan C, Giorgio F, Rosania R, Ierardi E. Mechanisms of Helicobacter pylori antibiotic resistance: An updated appraisal. World J Gastrointest Pathophysiol. 2011;2(3):35–41. pmid:21860834; PubMed Central PMCID: PMC3158889.
  26. 26. Rosli NA, Al-Maleki AR, Loke MF, Chua EG, Alhoot MA, Vadivelu J. Polymorphism of virulence genes and biofilm associated with in vitro induced resistance to clarithromycin in Helicobacter pylori. Gut Pathog. 2023;15(1):52. Epub 20231028. pmid:37898785; PubMed Central PMCID: PMC10613384.
  27. 27. Al-Maleki AR, Loke MF, Lui SY, Ramli NSK, Khosravi Y, Ng CG, et al. Helicobacter pylori outer inflammatory protein A (OipA) suppresses apoptosis of AGS gastric cells in vitro. Cell Microbiol. 2017;19(12). Epub 20170825. pmid:28776327.
  28. 28. Wong EHJ, Ng CG, Goh KL, Vadivelu J, Ho B, Loke MF. Metabolomic Analysis of Low and High Biofilm-forming Helicobacter pylori Strains. Sci Rep. 2018;8(1):1409.
  29. 29. Al-Maleki AR, Mariappan V, Vellasamy KM, Shankar EM, Tay ST, Vadivelu J. Enhanced intracellular survival and epithelial cell adherence abilities of Burkholderia pseudomallei morphotypes are dependent on differential expression of virulence-associated proteins during mid-logarithmic growth phase. J Proteomics. 2014;106:205–20. Epub 20140415. pmid:24742602.
  30. 30. Yi S, He Z, Jing XY, Li Y, Cheung YM, Nie F. Adaptive Weighted Sparse Principal Component Analysis for Robust Unsupervised Feature Selection. IEEE Trans Neural Netw Learn Syst. 2020;31(6):2153–63. Epub 20190828. pmid:31478875.
  31. 31. Akimbekov NS, Digel I, Sherelkhan DK, Lutfor AB, Razzaque MS. Vitamin D and the Host-Gut Microbiome: A Brief Overview. Acta Histochem Cytochem. 2020;53(3):33–42. Epub 20200616. pmid:32624628; PubMed Central PMCID: PMC7322162.
  32. 32. Yamamoto EA, Jørgensen TN. Relationships Between Vitamin D, Gut Microbiome, and Systemic Autoimmunity. Frontiers in Immunology. 2020;10. pmid:32038645
  33. 33. Schneider YK. Bacterial Natural Product Drug Discovery for New Antibiotics: Strategies for Tackling the Problem of Antibiotic Resistance by Efficient Bioprospecting. Antibiotics (Basel). 2021;10(7). Epub 20210710. pmid:34356763; PubMed Central PMCID: PMC8300778.
  34. 34. Vincent IM, Ehmann DE, Mills SD, Perros M, Barrett MP. Untargeted Metabolomics To Ascertain Antibiotic Modes of Action. Antimicrob Agents Chemother. 2016;60(4):2281–91. Epub 20160325. pmid:26833150; PubMed Central PMCID: PMC4808186.
  35. 35. Clish CB. Metabolomics: an emerging but powerful tool for precision medicine. Cold Spring Harb Mol Case Stud. 2015;1(1):a000588. pmid:27148576; PubMed Central PMCID: PMC4850886.
  36. 36. Lin Y, Li W, Sun L, Lin Z, Jiang Y, Ling Y, et al. Comparative Metabolomics Shows the Metabolic Profiles Fluctuate in Multi-drug Resistant Escherichia coli Strains. J Proteomics. 2019;207:103468. Epub 2019/07/30. pmid:31374362.
  37. 37. Gabryszewski SJ, Wong Fok Lung T, Annavajhala MK, Tomlinson KL, Riquelme SA, Khan IN, et al. Metabolic Adaptation in Methicillin-Resistant Staphylococcus aureus Pneumonia. Am J Respir Cell Mol Biol. 2019;61(2):185–97. pmid:30742488; PubMed Central PMCID: PMC6670030.
  38. 38. Mielko KA, Jabłoński SJ, Pruss Ł, Milczewska J, Sands D, Łukaszewicz M, et al. Metabolomics Comparison of Drug-Resistant and Drug-Susceptible Pseudomonas aeruginosa Strain (Intra- and Extracellular Analysis). Int J Mol Sci. 2021;22(19). Epub 2021/10/06. pmid:34639158; PubMed Central PMCID: PMC8509183.
  39. 39. Zampieri M, Enke T, Chubukov V, Ricci V, Piddock L, Sauer U. Metabolic Constraints on the Evolution of Antibiotic Resistance. Mol Syst Biol. 2017;13(3):917. Epub 2017/03/06. pmid:28265005; PubMed Central PMCID: PMC5371735.
  40. 40. Bhargava P, Collins James J. Boosting Bacterial Metabolism to Combat Antibiotic Resistance. Cell Metab. 2015;21(2):154–5. pmid:25651168
  41. 41. Schroeder M, Brooks BD, Brooks AE. The Complex Relationship between Virulence and Antibiotic Resistance. Genes (Basel). 2017;8(1). Epub 20170118. pmid:28106797; PubMed Central PMCID: PMC5295033.
  42. 42. Kaiser JC, Heinrichs DE. Branching Out: Alterations in Bacterial Physiology and Virulence Due to Branched-Chain Amino Acid Deprivation. mBio. 2018;9(5). Epub 2018/09/04. pmid:30181248; PubMed Central PMCID: PMC6123439.
  43. 43. Liu Y-K, Kuo H-C, Lai C-H, Chou C-C. Single Amino Acid Utilization for Bacterial Categorization. Sci Rep. 2020;10(1):12686. pmid:32728059
  44. 44. Hao H, Li F, Han J, Foley SL, Dai M, Wang X, et al. Cj1199 Affect the Development of Erythromycin Resistance in Campylobacter jejuni through Regulation of Leucine Biosynthesis. Front Microbiol. 2017;8.
  45. 45. Kaiser JC, King AN, Grigg JC, Sheldon JR, Edgell DR, Murphy MEP, et al. Repression of Branched-chain Amino Acid Synthesis in Staphylococcus aureus is Mediated by Isoleucine via CodY, and by a Leucine-rich Attenuator Peptide. PLoS Genet. 2018;14(1):e1007159. Epub 2018/01/22. pmid:29357354; PubMed Central PMCID: PMC5794164.
  46. 46. Abdollahi H, Tadjrobehkar O. The Role of Different Sugars, Amino Acids and Few Other Substances in Chemotaxis Directed Motility of Helicobacter pylori. Iran J Basic Med Sci. 2012;15(3):787–94. pmid:23492866; PubMed Central PMCID: PMC3586890.
  47. 47. Barra ALC, Dantas LOC, Morão LG, Gutierrez RF, Polikarpov I, Wrenger C, et al. Essential Metabolic Routes as a Way to ESKAPE From Antibiotic Resistance. Front Public Health. 2020;8:26. Epub 2020/02/28. pmid:32257985; PubMed Central PMCID: PMC7093009.
  48. 48. Grubman A, Phillips A, Thibonnier M, Kaparakis-Liaskos M, Johnson C, Thiberge JM, et al. Vitamin B6 is Required for Full Motility and Virulence in Helicobacter pylori. mBio. 2010;1(3). Epub 2010/08/17. pmid:21151756; PubMed Central PMCID: PMC3000542.
  49. 49. Edwards KG, Blumenthal HJ, Khan M, Slodki ME. Intracellular Mannitol, A Product of Glucose Metabolism in Staphylococci. J Bacteriol. 1981;146(3):1020–9. pmid:6787009; PubMed Central PMCID: PMC216956.
  50. 50. Ruijter GJ, Bax M, Patel H, Flitter SJ, van de Vondervoort PJ, de Vries RP, et al. Mannitol is Required for Stress Tolerance in Aspergillus niger conidiospores. Eukaryot Cell. 2003;2(4):690–8. pmid:12912888; PubMed Central PMCID: PMC178341.
  51. 51. Nguyen T, Kim T, Ta HM, Yeo WS, Choi J, Mizar P, et al. Targeting Mannitol Metabolism as an Alternative Antimicrobial Strategy Based on the Structure-Function Study of Mannitol-1-Phosphate Dehydrogenase in Staphylococcus aureus. mBio. 2019;10(4). Epub 20190709. pmid:31289190; PubMed Central PMCID: PMC6623548.
  52. 52. Kunz TC, Kozjak-Pavlovic V. Diverse Facets of Sphingolipid Involvement in Bacterial Infections. Front Cell Dev Biol. 2019;7:203. Epub 2019/09/19. pmid:31608278; PubMed Central PMCID: PMC6761390.
  53. 53. Harvey HA, Swords WE, Apicella MA. The Mimicry of Human Glycolipids and Glycosphingolipids by the Lipooligosaccharides of Pathogenic Neisseria and Haemophilus. J Autoimmun. 2001;16(3):257–62. pmid:11334490.
  54. 54. Lee MT, Le HH, Johnson EL. Dietary sphinganine is selectively assimilated by members of the mammalian gut microbiome. J Lipid Res. 2021;62:100034. Epub 20210206. pmid:32646940; PubMed Central PMCID: PMC7910519.
  55. 55. Geiger O, González-Silva N, López-Lara IM, Sohlenkamp C. Amino acid-containing membrane lipids in bacteria. Prog Lipid Res. 2010;49(1):46–60. Epub 20090822. pmid:19703488.
  56. 56. Brown EM, Clardy J, Xavier RJ. Gut microbiome lipid metabolism and its impact on host physiology. Cell Host & Microbe. 2023;31(2):173–86. pmid:36758518
  57. 57. An D, Na C, Bielawski J, Hannun YA, Kasper DL. Membrane sphingolipids as essential molecular signals for Bacteroides survival in the intestine. Proc Natl Acad Sci U S A. 2011;108 Suppl 1(Suppl 1):4666–71. Epub 20100920. pmid:20855611; PubMed Central PMCID: PMC3063596.
  58. 58. Tourkochristou E, Mouzaki A, Triantos C. Unveiling the biological role of sphingosine-1-phosphate receptor modulators in inflammatory bowel diseases. World J Gastroenterol. 2023;29(1):110–25. pmid:36683721; PubMed Central PMCID: PMC9850947.
  59. 59. Marciniak A, Camp SM, Garcia JGN, Polt R. An update on sphingosine-1-phosphate receptor 1 modulators. Bioorg Med Chem Lett. 2018;28(23–24):3585–91. Epub 20181026. pmid:30409535; PubMed Central PMCID: PMC6430570.
  60. 60. Hanada K. Sphingolipids in Infectious Diseases. Jpn J Infect Dis. 2005;58(3):131–48. pmid:15973004.
  61. 61. Hirakawa H, Inazumi Y, Masaki T, Hirata T, Yamaguchi A. Indole Induces the Expression of Multidrug Exporter Genes in Escherichia coli. Mol Microbiol. 2005;55(4):1113–26. pmid:15686558.
  62. 62. Nikaido E, Yamaguchi A, Nishino K. AcrAB Multidrug Efflux Pump Regulation in Salmonella enterica serovar Typhimurium by RamA in Response to Environmental Signals. J Biol Chem. 2008;283(35):24245–53. Epub 2008/06/24. pmid:18577510; PubMed Central PMCID: PMC2527123.
  63. 63. Nakamura K, Seto K, Lee K, Ooka T, Gotoh Y, Taniguchi I, et al. Global Population Structure, Genomic Diversity and Carbohydrate Fermentation Characteristics of Clonal Complex 119 (CC119), an Understudied Shiga Toxin-producing E. coli (STEC) Lineage Including O165:H25 and O172:H25. Microb Genom. 2023;9(3). pmid:36951916.
  64. 64. Oliveira RA, Ng KM, Correia MB, Cabral V, Shi H, Sonnenburg JL, et al. Klebsiella michiganensis Transmission Enhances Resistance to Enterobacteriaceae Gut Invasion by Nutrition Competition. Nat Microbiol. 2020;5(4):630–41. Epub 2020/01/20. pmid:31959968.
  65. 65. Kostadinova S, Ivanov A. Biochemical Characteristics and some Virulence Factors of Escherichia Coli Strains Isolated from Urinary Infections. Biotechnol Biotechnol Equip. 1998;12(1):68–74.
  66. 66. Nagata K, Nagata Y, Sato T, Fujino MA, Nakajima K, Tamura T. L-Serine, D- and L-proline and Alanine as Respiratory Substrates of Helicobacter pylori: Correlation Between In Vitro and In Vivo Amino Acid Levels. Microbiology (Reading). 2003;149(Pt 8):2023–30. pmid:12904542.
  67. 67. Lee WC, Goh KL, Loke MF, Vadivelu J. Elucidation of the Metabolic Network of Helicobacter pylori J99 and Malaysian Clinical Strains by Phenotype Microarray. Helicobacter. 2017;22(1). Epub 2016/06/03. pmid:27258354; PubMed Central PMCID: PMC5248604.
  68. 68. Kavermann H, Burns BP, Angermuller K, Odenbreit S, Fischer W, Melchers K, et al. Identification and Characterization of Helicobacter pylori Genes Essential for Gastric Colonization. J Exp Med. 2003;197(7):813–22. Epub 2003/03/31. pmid:12668646; PubMed Central PMCID: PMC2193887.
  69. 69. Rivera-Ordaz A, Bracher S, Sarrach S, Li Z, Shi L, Quick M, et al. The Sodium/Proline Transporter PutP of Helicobacter pylori. PLoS One. 2013;8(12):e83576. Epub 2013/12/17. pmid:24358297; PubMed Central PMCID: PMC3866251.
  70. 70. Zhang L, Alfano JR, Becker DF. Proline Metabolism Increases katG Expression and Oxidative Stress Resistance in Escherichia coli. J Bacteriol. 2015;197(3):431–40. Epub 2014/11/10. pmid:25384482; PubMed Central PMCID: PMC4285992.
  71. 71. Roszczenko-Jasińska P, Wojtyś MI, Jagusztyn-Krynicka EK. Helicobacter pylori Treatment in the Post-antibiotics Era-searching for New Drug Targets. Appl Microbiol Biotechnol. 2020;104(23):9891–905. Epub 2020/10/14. pmid:33052519; PubMed Central PMCID: PMC7666284.
  72. 72. Wang J, Chen YL, Li YK, Chen DK, He JF, Yao N. Functions of Sphingolipids in Pathogenesis During Host-Pathogen Interactions. Front Microbiol. 2021;12:701041. Epub 2021/08/02. pmid:34408731; PubMed Central PMCID: PMC8366399.
  73. 73. Stokes JM, Lopatkin AJ, Lobritz MA, Collins JJ. Bacterial Metabolism and Antibiotic Efficacy. Cell Metab. 2019;30(2):251–9. Epub 20190703. pmid:31279676; PubMed Central PMCID: PMC6990394.
  74. 74. Salvatori S, Marafini I, Laudisi F, Monteleone G, Stolfi C. Helicobacter pylori and Gastric Cancer: Pathogenetic Mechanisms. Int J Mol Sci. 2023;24(3). Epub 2023/02/02. pmid:36769214; PubMed Central PMCID: PMC9917787.
  75. 75. Schiliro C, Firestein BL. Mechanisms of Metabolic Reprogramming in Cancer Cells Supporting Enhanced Growth and Proliferation. Cells. 2021;10(5). Epub 2021/04/29. pmid:33946927; PubMed Central PMCID: PMC8146072.
  76. 76. Ponnusamy S, Meyers-Needham M, Senkal CE, Saddoughi SA, Sentelle D, Selvam SP, et al. Sphingolipids and Cancer: Ceramide and Sphingosine-1-phosphate in the Regulation of Cell Death and Drug Resistance. Future Oncol. 2010;6(10):1603–24. pmid:21062159; PubMed Central PMCID: PMC3071292.
  77. 77. Fu Y, Zou T, Shen X, Nelson PJ, Li J, Wu C, et al. Lipid Metabolism in Cancer Progression and Therapeutic Strategies. MedComm (2020). 2021;2(1):27–59. Epub 2020/12/24. pmid:34766135; PubMed Central PMCID: PMC8491217.