Digital Insights Into Nucleotide Metabolism and Antibiotic Treatment Failure

The Combination of Exercise and Konjac Glucomannan More Effectively Prevents Antibiotics-Induced Dysbiosis in Mice Compared with Singular Intervention

Our previous studies have demonstrated that konjac glucomannan (KGM) can prevent dysbiosis induced by antibiotics. While exercise may also impact the gut microbiome, there are limited studies reporting its protective effect on antibiotic-induced dysbiosis. Therefore, this study investigated the preventive and regulatory effects of a combination of 6-week exercise and KGM intervention on antibiotic-induced dysbiosis in C57BL/6J mice compared with a single intervention. The results showed that combined exercise and KGM intervention could restore the changes in the relative abundance of Bacteroides (3.73% with CTL versus 14.23% with ATBX versus 4.46% with EK) and Prevotellaceae_Prevotella (0.33% with CTL versus 0.00% with ATBX versus 0.30% with EK) induced by antibiotics (p < 0.05), and minimized the Bray-Curtis distance induced by antibiotics (0.55 with CTL versus 0.81 with ATBX versus 0.80 with EXC versus 0.83 with KGM versus 0.75 with EK). Compared with the combined intervention, exercise intervention also produced a certain level of recovery effects; the relative abundance of Rikenellaceae (1.96% with CTL versus 0.09% with ATBX versus 0.49% with EXC) was restored, while KGM supplementation showed the best preventive effect. In addition, the combination of exercise and KGM significantly enriched microbial purine metabolic pathways (p < 0.05). These findings indicate that combining exercise with KGM could be a promising approach to reducing the side effects of antibiotics on the gut microbiome.

Unraveling bacterial stress responses: implications for next-generation antimicrobial solutions

The accelerated spread of antimicrobial-resistant bacteria has caused a serious health problem and rendered antimicrobial treatments ineffective. Innovative approaches are crucial to overcome the health threat posed by resistant pathogens and prevent the emergence of untreatable infections. Triggering stress responses in bacteria can diminish susceptibility to various antimicrobials by inducing resistance mechanisms. Therefore, a thorough understanding of stress response control, especially in relation to antimicrobial resistance, offers valuable perspectives for innovative and efficient therapeutic approaches to combat antimicrobial resistance. The aim of this study was to evaluate the stress responses of 8 different bacteria by analyzing reporter metabolites, around which significant alterations were observed, using a pathway-driven computational approach. For this purpose, the transcriptomic data that the bacterial pathogens were grown under 11 different stress conditions mimicking the human host environments were integrated with the genome-scale metabolic models of 8 pathogenic species (Enterococcus faecalis OG1R, Escherichia coli EPEC O127:H6 E2348/69, Escherichia coli ETEC H10407, Escherichia coli UPEC 536, Klebsiella pneumoniae MGH 78578, Pseudomonas aeruginosa PAO1, Staphylococcus aureus MRSA252, and Staphylococcus aureus MSSA476). The resulting reporter metabolites were enriched in multiple metabolic pathways, with cofactor biosynthesis being the most important. The results of this study will serve as a guide for the development of antimicrobial agents as they provide a first insight into potential drug targets.

Providing insight into the mechanism of action of cationic lipidated oligomers using metabolomics

The increasing resistance of clinically relevant microbes against current commercially available antimicrobials underpins the urgent need for alternative and novel treatment strategies. Cationic lipidated oligomers (CLOs) are innovative alternatives to antimicrobial peptides and have reported antimicrobial potential. An understanding of their antimicrobial mechanism of action is required to rationally design future treatment strategies for CLOs, either in monotherapy or synergistic combinations. In the present study, metabolomics was used to investigate the potential metabolic pathways involved in the mechanisms of antibacterial activity of one CLO, C12-o-(BG-D)-10, which we have previously shown to be effective against methicillin-resistant Staphylococcus aureus (MRSA) ATCC 43300. The metabolomes of MRSA ATCC 43300 at 1, 3, and 6 h following treatment with C12-o-(BG-D)-10 (48 µg/mL, i.e., 3× MIC) were compared to those of the untreated controls. Our findings reveal that the studied CLO, C12-o-(BG-D)-10, disorganized the bacterial membrane as the first step toward its antimicrobial effect, as evidenced by marked perturbations in the bacterial membrane lipids and peptidoglycan biosynthesis observed at early time points, i.e., 1 and 3 h. Central carbon metabolism and the biosynthesis of DNA, RNA, and arginine were also vigorously perturbed, mainly at early time points. Moreover, bacterial cells were under osmotic and oxidative stress across all time points, as evident by perturbations of trehalose biosynthesis and pentose phosphate shunt. Overall, this metabolomics study has, for the first time, revealed that the antimicrobial action of C12-o-(BG-D)-10 may potentially stem from the dysregulation of multiple metabolic pathways.IMPORTANCEAntimicrobial resistance poses a significant challenge to healthcare systems worldwide. Novel anti-infective therapeutics are urgently needed to combat drug-resistant microorganisms. Cationic lipidated oligomers (CLOs) show promise as new antibacterial agents against Gram-positive pathogens like methicillin-resistant Staphylococcus aureus (MRSA). Understanding their molecular mechanism(s) of antimicrobial action may help design synergistic CLO treatments along with monotherapy. Here, we describe the first metabolomics study to investigate the killing mechanism(s) of CLOs against MRSA. The results of our study indicate that the CLO, C12-o-(BG-D)-10, had a notable impact on the biosynthesis and organization of the bacterial cell envelope. C12-o-(BG-D)-10 also inhibits arginine, histidine, central carbon metabolism, and trehalose production, adding to its antibacterial characteristics. This work illuminates the unique mechanism of action of C12-o-(BG-D)-10 and opens an avenue to design innovative antibacterial oligomers/polymers for future clinical applications.

Purine and pyrimidine synthesis differently affect the strength of the inoculum effect for aminoglycoside and β-lactam antibiotics

The inoculum effect has been observed for nearly all antibiotics and bacterial species. However, explanations accounting for its occurrence and strength are lacking. We previously found that growth productivity, which captures the relationship between [ATP] and growth, can account for the strength of the inoculum effect for bactericidal antibiotics. However, the molecular pathway(s) underlying this relationship, and therefore determining the inoculum effect, remain undiscovered. We show that nucleotide synthesis can determine the relationship between [ATP] and growth, and thus the strength of inoculum effect in an antibiotic class-dependent manner. Specifically, and separate from activity through the tricarboxylic acid cycle, we find that transcriptional activity of genes involved in purine and pyrimidine synthesis can predict the strength of the inoculum effect for β-lactam and aminoglycosides antibiotics, respectively. Our work highlights the antibiotic class-specific effect of purine and pyrimidine synthesis on the severity of the inoculum effect and paves the way for intervention strategies to reduce the inoculum effect in the clinic.

Sub-inhibitory antibiotic treatment selects for enhanced metabolic efficiency

Bacterial growth and metabolic rates are often closely related. However, under antibiotic selection, a paradox in this relationship arises: antibiotic efficacy decreases when bacteria are metabolically dormant, yet antibiotics select for resistant cells that grow fastest during treatment. That is, antibiotic selection counterintuitively favors bacteria with fast growth but slow metabolism. Despite this apparent contradiction, antibiotic resistant cells have historically been characterized primarily in the context of growth, whereas the extent of analogous changes in metabolism is comparatively unknown. Here, we observed that previously evolved antibiotic-resistant strains exhibited a unique relationship between growth and metabolism whereby nutrient utilization became more efficient, regardless of the growth rate. To better understand this unexpected phenomenon, we used a simplified model to simulate bacterial populations adapting to sub-inhibitory antibiotic selection through successive bottlenecking events. Simulations predicted that sub-inhibitory bactericidal antibiotic concentrations could select for enhanced metabolic efficiency, defined based on nutrient utilization: drug-adapted cells are able to achieve the same biomass while utilizing less substrate, even in the absence of treatment. Moreover, simulations predicted that restoring metabolic efficiency would re-sensitize resistant bacteria exhibiting metabolic-dependent resistance; we confirmed this result using adaptive laboratory evolutions of Escherichia coli under carbenicillin treatment. Overall, these results indicate that metabolic efficiency is under direct selective pressure during antibiotic treatment and that differences in evolutionary context may determine both the efficacy of different antibiotics and corresponding re-sensitization approaches.IMPORTANCEThe sustained emergence of antibiotic-resistant pathogens combined with the stalled drug discovery pipelines highlights the critical need to better understand the underlying evolution mechanisms of antibiotic resistance. To this end, bacterial growth and metabolic rates are often closely related, and resistant cells have historically been characterized exclusively in the context of growth. However, under antibiotic selection, antibiotics counterintuitively favor cells with fast growth, and slow metabolism. Through an integrated approach of mathematical modeling and experiments, this study thereby addresses the significant knowledge gap of whether antibiotic selection drives changes in metabolism that complement, and/or act independently, of antibiotic resistance phenotypes.

Metabolomics reveals the mechanism of action of meropenem and amikacin combined in the treatment of Pseudomonas aeruginosa

The treatment of Pseudomonas aeruginosa infection often involves the combined use of β-lactam and aminoglycoside antibiotics. In this study, we employed metabolomic analysis to investigate the mechanism responsible for the synergistic activities of meropenem/amikacin combination therapy against multidrug-resistant P. aeruginosa strains harboring OXA-50 and PAO genes. Antibiotic concentrations for meropenem (2 mg/L) monotherapy, amikacin (16 mg/L) monotherapy, and meropenem/amikacin (2/16 mg/L) combination therapy were selected based on clinical breakpoint considerations. Metabolomic analysis revealed significant alterations in relevant metabolites involved in bacterial cell membrane and cell wall synthesis within 15 min of combined drug administration. These alterations encompassed various metabolic pathways, including fatty acid metabolism, peptidoglycan synthesis, and lipopolysaccharide metabolism. Furthermore, at 1 h and 4 h, the combination therapy exhibited significant interference with amino acid metabolism, nucleotide metabolism, and central carbon metabolism pathways, including the tricarboxylic acid cycle and pentose phosphate pathway. In contrast, the substances affected by single drug administration at 1 h and 4 h demonstrated a noticeable reduction. Meropenem/amikacin combination resulted in notable perturbations of metabolic pathways essential for survival of P. aeruginosa, whereas monotherapies had comparatively diminished impacts.

Antibiotic-induced gut microbiota dysbiosis has a functional impact on purine metabolism

BACKGROUND

Dysbiosis of the gut microbiota is closely linked to hyperuricemia. However, the effect of the microbiome on uric acid (UA) metabolism remains unclear. This study aimed to explore the mechanisms through which microbiomes affect UA metabolism with the hypothesis that modifying the intestinal microbiota influences the development of hyperuricemia.

RESULTS

We proposed combining an antibiotic strategy with protein-protein interaction analysis to test this hypothesis. The data demonstrated that antibiotics altered the composition of gut microbiota as UA increased, and that the spectrum of the antibiotic was connected to the purine salvage pathway. The antibiotic-elevated UA concentration was dependent on the increase in microbiomes that code for the proteins involved in purine metabolism, and was paralleled by the depletion of bacteria-coding enzymes required for the purine salvage pathway. On the contrary, the microbiota with abundant purine salvage proteins decreased hyperuricemia. We also found that the antibiotic-increased microbiota coincided with a higher relative abundance of bacteria in hyperuricemia mice.

CONCLUSIONS

An antibiotic strategy combined with the prediction of microbiome bacterial function presents a feasible method for defining the key bacteria involved in hyperuricemia. Our investigations discovered that the core microbiomes of hyperuricemia may be related to the gut microbiota that enriches purine metabolism related-proteins. However, the bacteria that enrich the purine salvage-proteins may be a probiotic for decreasing urate, and are more likely to be killed by antibiotics. Therefore, the purine salvage pathway may be a potential target for the treatment of both hyperuricemia and antibiotic resistance.

Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology

Antimicrobial peptides (AMPs) have been investigated for their potential use as an alternative to antibiotics due to the increased demand for new antimicrobial agents. AMPs, widely found in nature and obtained from microorganisms, have a broad range of antimicrobial protection, allowing them to be applied in the treatment of infections caused by various pathogenic microorganisms. Since these peptides are primarily cationic, they prefer anionic bacterial membranes due to electrostatic interactions. However, the applications of AMPs are currently limited owing to their hemolytic activity, poor bioavailability, degradation from proteolytic enzymes, and high-cost production. To overcome these limitations, nanotechnology has been used to improve AMP bioavailability, permeation across barriers, and/or protection against degradation. In addition, machine learning has been investigated due to its time-saving and cost-effective algorithms to predict AMPs. There are numerous databases available to train machine learning models. In this review, we focus on nanotechnology approaches for AMP delivery and advances in AMP design via machine learning. The AMP sources, classification, structures, antimicrobial mechanisms, their role in diseases, peptide engineering technologies, currently available databases, and machine learning techniques used to predict AMPs with minimal toxicity are discussed in detail.

Uracil restores susceptibility of methicillin-resistant Staphylococcus aureus to aminoglycosides through metabolic reprogramming

Background: Methicillin-resistant Staphylococcus aureus (MRSA) has now become a major nosocomial pathogen bacteria and resistant to many antibiotics. Therefore, Development of novel approaches to combat the disease is especially important. The present study aimed to provide a novel approach involving the use of nucleotide-mediated metabolic reprogramming to tackle intractable methicillin-resistant S. aureus (MRSA) infections. Objective: This study aims to explore the bacterial effects and mechanism of uracil and gentamicin in S. aureus. Methods: Antibiotic bactericidal assays was used to determine the synergistic bactericidal effect of uracil and gentamicin. How did uracil regulate bacterial metabolism including the tricarboxylic acid (TCA) cycle by GC-MS-based metabolomics. Next, genes and activity of key enzymes in the TCA cycle, PMF, and intracellular aminoglycosides were measured. Finally, bacterial respiration, reactive oxygen species (ROS), and ATP levels were also assayed in this study. Results: In the present study, we found that uracil could synergize with aminoglycosides to kill MRSA (USA300) by 400-fold. Reprogramming metabolomics displayed uracil reprogrammed bacterial metabolism, especially enhanced the TCA cycle to elevate NADH production and proton motive force, thereby promoting the uptake of antibiotics. Furthermore, uracil increased cellular respiration and ATP production, resulting the generation of ROS. Thus, the combined activity of uracil and antibiotics induced bacterial death. Inhibition of the TCA cycle or ROS production could attenuate bactericidal efficiency. Moreover, uracil exhibited bactericidal activity in cooperation with aminoglycosides against other pathogenic bacteria. In a mouse mode of MRSA infection, the combination of gentamicin and uracil increased the survival rate of infected mice. Conclusion: Our results suggest that uracil enhances the activity of bactericidal antibiotics to kill Gram-positive bacteria by modulating bacterial metabolism.

Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria

The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate anaerobes which thrives on sulfate reduction for its energy requirements. The present study used Oleidesulfovibrio alaskensis G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein-protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the sat gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under "persistent," inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under "shell." Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies.

RNA-Seq-based transcriptome analysis of methicillin-resistant Staphylococcus aureus growth inhibition by propionate

Staphylococcus aureus is a pathogen that causes a variety of infectious diseases such as pneumonia, endocarditis, and septic shock. Methicillin-resistant S. aureus (MRSA) evades virtually all available treatments, creating the need for an alternative control strategy. Although we previously demonstrated the inhibitory effect of sodium propionate (NaP) on MRSA, the regulatory mechanism of this effect remains unclear. In this study, we investigated the regulatory mechanism responsible for the inhibitory effect of NaP on MRSA using RNA-Seq analysis. Total RNAs were isolated from non-treated and 50 mM NaP-treated S. aureus USA300 for 3 h and transcriptional profiling was conducted by RNA-Seq analysis. A total of 171 differentially expressed genes (DEGs) with log₂ fold change ≥2 and p < 0.05 was identified in the NaP treatment group compared with the control group. Among the 171 genes, 131 were up-regulated and 40 were down-regulated. Upon gene ontology (GO) annotation analysis, total 26 specific GO terms in "Biological process," "Molecular function," and "Cellular component" were identified in MRSA treated with NaP for 3 h. "Purine metabolism"; "riboflavin metabolism"; and "glycine, serine, and threonine metabolism" were identified as major altered metabolic pathways among the eight significantly enriched KEGG pathways in MRSA treated with NaP. Furthermore, the MRSA strains deficient in purF, ilvA, ribE, or ribA, which were the up-regulated DEGs in the metabolic pathways, were more susceptible to NaP than wild-type MRSA. Collectively, these results demonstrate that NaP attenuates MRSA growth by altering its metabolic pathways, suggesting that NaP can be used as a potential bacteriostatic agent for prevention of MRSA infection.

The rapid evolution of flagellar ion selectivity in experimental populations of E. coli

Determining which cellular processes facilitate adaptation requires a tractable experimental model where an environmental cue can generate variants that rescue function. The bacterial flagellar motor (BFM) is an excellent candidate-an ancient and highly conserved molecular complex for bacterial propulsion toward favorable environments. Motor rotation is often powered by H⁺ or Na⁺ ion transit through the torque-generating stator subunit of the motor complex, and ion selectivity has adapted over evolutionary time scales. Here, we used CRISPR engineering to replace the native Escherichia coli H⁺-powered stator with Na⁺-powered stator genes and report the spontaneous reversion of our edit in a low-sodium environment. We followed the evolution of the stators during their reversion to H⁺-powered motility and used both whole-genome and RNA sequencing to identify genes involved in the cell's adaptation. Our transplant of an unfit protein and the cells' rapid response to this edit demonstrate the adaptability of the stator subunit and highlight the hierarchical modularity of the flagellar motor.

Hematophagy and tick-borne Rickettsial pathogen shape the microbial community structure and predicted functions within the tick vector, Amblyomma maculatum

Background

Ticks are the primary vectors of emerging and resurging pathogens of public health significance worldwide. Analyzing tick bacterial composition, diversity, and functionality across developmental stages and tissues is crucial for designing new strategies to control ticks and prevent tick-borne diseases.

Materials and methods

Here, we explored the microbial communities across the developmental timeline and in different tissues of the Gulf-Coast ticks (Amblyomma maculatum). Using a high-throughput sequencing approach, the influence of blood meal and Rickettsia parkeri, a spotted fever group rickettsiae infection in driving changes in microbiome composition, diversity, and functionality was determined.

Results

This study shows that the core microbiome of Am. maculatum comprises ten core bacterial genera. The genus Rickettsia, Francisella, and Candidatus_Midichloria are the key players, with positive interactions within each developmental stage and adult tick organ tested. Blood meal and Rickettsia parkeri led to an increase in the bacterial abundance in the tissues. According to functional analysis, the increase in bacterial numbers is positively correlated to highly abundant energy metabolism orthologs with blood meal. Correlation analysis identified an increase in OTUs identified as Candidatus Midichloria and a subsequent decrease in Francisella OTUs in Rickettsia parkeri infected tick stages and tissues. Results demonstrate the abundance of Rickettsia and Francisella predominate in the core microbiome of Am. maculatum, whereas Candidatus_Midichloria and Cutibacterium prevalence increase with R. parkeri-infection. Network analysis and functional annotation suggest that R. parkeri interacts positively with Candidatus_Midichloria and negatively with Francisella.

Conclusion

We conclude that tick-transmitted pathogens, such as R. parkeri establishes infection by interacting with the core microbiome of the tick vector.

Integrated Profiling of Gram-Positive and Gram-Negative Probiotic Genomes, Proteomes and Metabolomes Revealed Small Molecules with Differential Growth Inhibition of Antimicrobial-Resistant Pathogens

Probiotics produce small molecules that may serve as alternatives to conventional antibiotics by suppressing growth of antimicrobial resistant (AMR) pathogens. The objective of this study was to identify and examine antimicrobials produced and secreted by probiotics using 'omics' profiling with computer-based metabolic flux analyses. The cell-free supernatant of Gram-positive Lacticaseibacillus rhamnosus GG (LGG) and Gram-negative Escherichia coli Nissle (ECN) probiotics inhibited growth of AMR Salmonella Typhimurium, Escherichia coli, and Klebsiella oxytoca ranging between 28.85 - 41.20% (LGG) and 11.48 - 29.45% (ECN). A dose dependent analysis of probiotic supernatants showed LGG was 6.27% to 20.55% more effective at reducing AMR pathogen growth when compared to ECN. Principal component analysis showed clear separation of ECN and LGG cell free supernatant metabolomes. Among 667 metabolites in the supernatant, 304 were differentially abundant between LGG and ECN probiotics. Proteomics identified 87 proteins, whereby 67 (ECN) and 14 (LGG) showed differential expression as enzymes related to carbohydrate and energy metabolic pathways. The whole genomes and metabolomes were next used for in-silico metabolic network analysis. The model predicted the production of 166 metabolites by LGG and ECN probiotics across amino acid, carbohydrate/energy, and nucleotide metabolism with antimicrobial functions. The predictive accuracy of the metabolic flux analysis highlights the novel utility for profiling probiotic supplements as dietary-based antimicrobial alternatives in the control of AMR pathogen growth.

Mechanisms Underlying Synergistic Killing of Polymyxin B in Combination with Cannabidiol against Acinetobacter baumannii: A Metabolomic Study

Polymyxins have resurged as the last-resort antibiotics against multidrug-resistant Acinetobacter baumannii. As reports of polymyxin resistance in A. baumannii with monotherapy have become increasingly common, combination therapy is usually the only remaining treatment option. A novel and effective strategy is to combine polymyxins with non-antibiotic drugs. This study aimed to investigate, using untargeted metabolomics, the mechanisms of antibacterial killing synergy of the combination of polymyxin B with a synthetic cannabidiol against A. baumannii ATCC 19606. The antibacterial synergy of the combination against a panel of Gram-negative pathogens (Acinetobacter baumannii, Klebsiella pneumoniae and Pseudomonas aeruginosa) was also explored using checkerboard and static time-kill assays. The polymyxin B–cannabidiol combination showed synergistic antibacterial activity in checkerboard and static time-kill assays against both polymyxin-susceptible and polymyxin-resistant isolates. The metabolomics study at 1 h demonstrated that polymyxin B monotherapy and the combination (to the greatest extent) significantly perturbed the complex interrelated metabolic pathways involved in the bacterial cell envelope biogenesis (amino sugar and nucleotide sugar metabolism, peptidoglycan, and lipopolysaccharide (LPS) biosynthesis), nucleotides (purine and pyrimidine metabolism) and peptide metabolism; notably, these pathways are key regulators of bacterial DNA and RNA biosynthesis. Intriguingly, the combination caused a major perturbation in bacterial membrane lipids (glycerophospholipids and fatty acids) compared to very minimal changes induced by monotherapies. At 4 h, polymyxin B–cannabidiol induced more pronounced effects on the abovementioned pathways compared to the minimal impact of monotherapies. This metabolomics study for the first time showed that in disorganization of the bacterial envelope formation, the DNA and RNA biosynthetic pathways were the most likely molecular mechanisms for the synergy of the combination. The study suggests the possibility of cannabidiol repositioning, in combination with polymyxins, for treatment of MDR polymyxin-resistant Gram-negative infections.

The Serum and Fecal Metabolomic Profiles of Growing Kittens Treated with Amoxicillin/Clavulanic Acid or Doxycycline

Simple Summary

This study investigated the impact of antibiotic treatment οn the serum and fecal metabolome (the collection of all small molecules produced by the gut bacteria and the host) of young cats. Thirty 2-month-old cats with an upper respiratory tract infection were treated with either amoxicillin/clavulanic acid for 20 days or doxycycline for 28 days. In addition, another 15 control cats that did not receive antibiotics were included. Blood was collected on days 0 (before treatment), 20/28 (last day of treatment), and 300 (10 months after the end of treatment), while feces were collected on days 0, 20/28, 60, 120, and 300. Seven serum and fecal metabolites differed between cats treated with antibiotics and control cats at the end of treatment period. Ten months after treatment, no metabolites differed from healthy cats, suggesting that amoxicillin/clavulanic acid or doxycycline treatment only temporarily affects the abundance of the serum and fecal metabolome.

Abstract

The long-term impact of antibiotics on the serum and fecal metabolome of kittens has not yet been investigated. Therefore, the objective of this study was to evaluate the serum and fecal metabolome of kittens with an upper respiratory tract infection (URTI) before, during, and after antibiotic treatment and compare it with that of healthy control cats. Thirty 2-month-old cats with a URTI were randomly assigned to receive either amoxicillin/clavulanic acid for 20 days or doxycycline for 28 days, and 15 cats of similar age were enrolled as controls. Fecal samples were collected on days 0, 20/28, 60, 120, and 300, while serum was collected on days 0, 20/28, and 300. Untargeted and targeted metabolomic analyses were performed on both serum and fecal samples. Seven metabolites differed significantly in antibiotic-treated cats compared to controls on day 20/28, with two differing on day 60, and two on day 120. Alterations in the pattern of serum amino acids, antioxidants, purines, and pyrimidines, as well as fecal bile acids, sterols, and fatty acids, were observed in antibiotic-treated groups that were not observed in control cats. However, the alterations caused by either amoxicillin/clavulanic acid or doxycycline of the fecal and serum metabolome were only temporary and were resolved by 10 months after their withdrawal.