Evidence map›Paper›PMID 40749790›Full record

ArticleJournal of advanced research2026

Drug-resistant Escherichia coli metabolomics via pseudo-targeted SWATH/IDA-MRM: Bridging high coverage and precision.

Xi Jia, Liang Zhao, Saiwa Liu, Jingjing Du, Zhinan Wang, Lirui Ge, Jian Xu, Kexin Cui, Yu Ga, Xiaowei Li and 2 more

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Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Xi JiaState Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing, China.
Liang ZhaoState Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing, China.
Saiwa LiuState Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing, China.
Jingjing DuState Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing, China.
Zhinan WangState Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing, China.
Lirui GeState Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing, China.
Jian XuState Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing, China.
Kexin CuiState Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing, China.
Yu GaState Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing, China.
Xiaowei LiState Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing, China.
Jianzhong ShenState Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing, China. Electronic address: sjz@cau.edu.cn.
Xi XiaState Key Laboratory of Veterinary Public Health and Safety, College of Veterinary Medicine, China Agricultural University, Beijing, China. Electronic address: xxia@cau.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMetabolic reprogramming plays a significant role in the emergence, progression, and response to antibiotic pressure in bacterial resistance. Current metabolomics approaches face significant limitations: untargeted methods lack quantitative precision, while targeted analyses suffer from limited coverage. These technical constraints hinder comprehensive evaluation of metabolic contributions to antibiotic activity and resistance evolution, creating a critical knowledge gap in understanding treatment outcomes for resistant bacteria.

objectiveTo establish a metabolomics method with comprehensive coverage, excellent reproducibility, high sensitivity, and wide dynamic range for elucidating the dynamic relationships between bacterial metabolic reprogramming, antibiotic activity, and resistance phenotype development.

methodsWe employed a complementary liquid chromatography system incorporating reverse-phase liquid chromatography, hydrophilic interaction liquid chromatography, and metal-sensitive liquid chromatography. Coupled with high-resolution mass spectrometry and utilizing three complementary data acquisition modes - full scan, information-dependent acquisition (IDA), and sequential window acquisition of all theoretical mass spectra (SWATH) - we developed a novel pseudo-targeted metabolomics approach based on triple quadrupole mass spectrometry, designated as SWATH/IDA-MRM. This optimized method was subsequently applied to investigate metabolic reprogramming in Escherichia coli strains harboring the resistance genes mcr-1, bla

resultsOur analytical platform successfully identified 3,529 metabolic features using six complementary chromatographic separation conditions, achieving broader metabolite coverage than conventional targeted metabolomics. Comparative evaluation against untargeted approaches revealed marked improvements in analytical performance, including enhanced linearity, reproducibility, detection sensitivity, and dynamic range, along with superior capacity for discriminating metabolic profiles between sample groups. Application to antibiotic-resistant E. coli strains revealed substantial metabolic flux alterations in resistant versus susceptible strains, with predominant perturbations in nucleotide metabolism, amino acid metabolism, energy metabolism, lipid metabolism, and redox balance pathways.

conclusionThe developed SWATH/IDA-MRM platform represents a significant methodological advancement for investigating the complex interplay between microbial metabolic adaptation and antimicrobial responses. This integrated analytical approach enables systematic characterization of resistance-associated metabolic reprogramming, thereby establishing a framework for developing targeted therapeutic strategies against pathogen-specific metabolic vulnerabilities.

Indexed as

Drug Resistance, BacterialEscherichia coliMetabolomicsAnti-Bacterial AgentsChromatography, LiquidMass SpectrometryMetabolomeReproducibility of ResultsAnti-Bacterial AgentsBacterial metabolismbla(NDM)mcr-1Pseudo-targeted metabolomics

Identifiers

PMID40749790
PMCPMC13131515

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.