Word-based GWAS harnesses the rich potential of genomic data for E. coli quinolone resistance

Quinolone resistance presents a growing global health threat. We employed word-based GWAS to explore genomic data, aiming to enhance our understanding of this phenomenon. Unlike traditional variant-based GWAS analyses, this approach simultaneously captures multiple genomic factors, including single and interacting resistance mutations and genes. Analyzing a dataset of 92 genomic E. coli samples from a wastewater treatment plant in Dresden, we identified 54 DNA unitigs significantly associated with quinolone resistance. Remarkably, our analysis not only validated known mutations in gyrA and parC genes and the results of our variant-based GWAS but also revealed new (mutated) genes such as mdfA, the AcrEF-TolC multidrug efflux system, ptrB, and hisI, implicated in antibiotic resistance. Furthermore, our study identified joint mutations in 14 genes including the known gyrA gene, providing insights into potential synergistic effects contributing to quinolone resistance. These findings showcase the exceptional capabilities of word-based GWAS in unraveling the intricate genomic foundations of quinolone resistance.

1 Introduction

1.1 Conventional genome-wide association studies may not fully explore the potential of genomic data

While conventional GWAS methods have provided valuable insights, they may not fully explore the intricate genomic landscape underlying microbial phenotypes (Power et al., 2017), particularly in terms of variant interactions. These methods predominantly focus on individual single nucleotide polymorphisms (SNPs), potentially overlooking the significant influence of variant interactions and gene presence/absence on microbial phenotypic traits, including antibiotic resistance. To enhance our understanding and further complement conventional GWAS, embracing alternative strategies capable of simultaneously capturing the effects of genes and variants, whether they act individually or interactively, is crucial.

1.2 K-mers have the potential to broaden the horizons of conventional GWAS

K-mers, substrings of length k within biological sequences like DNA, RNA, or proteins, have the potential to broaden the horizons of conventional GWAS. By incorporating them into genome analysis, we can expand beyond the scope of conventional GWAS, which typically centers on single nucleotide polymorphisms (SNPs) or genes as the unit of study. Unlike these traditional approaches, k-mers offer the advantage of capturing the combined effects of both SNPs and genes simultaneously, providing a more comprehensive view of the genomic landscape underlying diseases and traits. Moreover, k-mers enable the capture of cumulative effects from multiple genomic variants in a single analysis, including rare and structural variants. K-mer-based GWAS stands out prominently in studying microbial genomes, as exemplified by Sheppard et al.'s work on Campylobacter isolates using 30-bp DNA sequences (Sheppard et al., 2013; Power et al., 2017).

1.3 K-mer-based GWAS pose challenges that can be mitigated by adopting unitigs

While k-mer-based GWAS excels in identifying genomic variants undetectable by variant-based GWAS, interpreting results proves challenging due to mapping difficulties and high redundancy. Unitigs, as unique non-redundant genome sequences, mitigate these challenges (Chaguza et al., 2020, 2022a). Longer than the common k-mer size, unitigs cover more extensive genomic regions, providing additional context for identified genomic variants. Mapping unitigs back to the original genome is also simplified compared to k-mers, as each unitig represents a unique non-redundant region. Previous studies support the efficacy of unitig-based GWAS approaches, offering specific genomic information and facilitating functional annotation of associated loci across various bacterial genomes, including Mycobacterium (Jaillard et al., 2018; Hang et al., 2019; Yano et al., 2021), Staphylococcus (Jaillard et al., 2018; Chaguza et al., 2022b; Raineri et al., 2022), and E. coli (Denamur et al., 2022; Van Wonterghem et al., 2022).

1.4 Unitig-based GWAS can study quinolone resistance

Unitig-based GWAS can be applied to any phenotype, including quinolone resistance. Quinolones are a broad-spectrum family of antibiotics used to treat both gram-negative and gram-positive bacterial infections (Emmerson and Jones, 2003). Quinolone resistance is a significant concern as it can lead to urinary tract and intraabdominal infections. Therefore, a better understanding of quinolone resistance mechanisms is necessary to develop effective approaches to overcome this issue.

1.5 Quinolone resistance mainly results from chromosomal mutations in gyrA and parC

Quinolones primarily target bacterial DNA topoisomerase II and topoisomerase IV. These enzymes are crucial for DNA replication, transcription, and recombination, as well as for helping to under- and over-wind DNA (Naeem et al., 2016). Quinolones aim to inhibit DNA synthesis and cell growth by targeting these enzymes and inhibiting their activity. Topoisomerase II consists of GyrA and GyrB subunits, while topoisomerase IV comprises ParC and two ParE subunits. GyrA is homologous to ParC, and GyrB is homologous to ParE (Hooper and Jacoby, 2015). While known point mutations in these genes play a significant role, other biomarkers can also impact quinolone resistance.

1.6 Known and new biomarkers from our previous variant-based GWAS serve as a baseline for this study

In our previous variant-based GWAS study of quinolone resistance (Malekian et al., 2021), we confirmed known mutations in gyrA and parC, and we also identified new mutations, mainly in valS and bdcA genes. Using these findings as our baseline, we explore the efficacy of unitig-based GWAS to discover additional single and joint resistance mutations for quinolone resistance using the same dataset.

1.7 Approach overview

This study comprehensively analyzes 92 Escherichia coli (E. coli) genomes from a wastewater treatment plant in Dresden, Germany, and their corresponding quinolone resistance data. The approach includes extracting unitigs from the genomic data of the samples, applying quality control measures, and investigating their association with quinolone resistance labels for levofloxacin, norfloxacin, ciprofloxacin, and nalidixic acid. Subsequently, the unitigs are mapped back to a reference genome to determine the genes and mutations they encompass. Finally, the identified genes and mutations are investigated for their potential role in conferring resistance. An overview of our study is shown in Figure 1.

FIGURE 1

Figure 1. Overview of our study. The genotype and phenotype of wastewater E. coli samples were associated using a word-based GWAS analysis. Here, the genotype refers to the unitigs (genome words), and the phenotype refers to the samples' quinolone resistance value (the diameters of inhibition zones in the agar disk diffusion method). The analysis resulted in a list of significant unitigs that may contain single or joint candidate resistance mutations.

2 Methods

2.1 Sequence data and resistance phenotype

The dataset comprised 92 E. coli genomes from a municipal wastewater treatment plant in Dresden, Germany, and their antibiotic resistance values (i.e., the diameters of inhibition zones in the agar disk diffusion method) against 20 widely prescribed antibiotics, including four quinolones (levofloxacin, norfloxacin, ciprofloxacin, and nalidixic acid). The genomic data can be accessed directly from NCBI's assembly database under the project identifier PRJNA380388 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA380388/). Antibiotic resistance data is available within the Biosample field of the same dataset. For further details, (see Mahfouz et al., 2018).

2.2 Identifying unitigs

To identify unitigs, we used the unitig-counter v1.1.0 software tool (Lees et al., 2020). The input for the unitig-counter was the genomic assembly of all our isolates in the FASTA format. The algorithm of this tool involves constructing a compressed de Bruijn graph from all the input genome sequences and identifying contiguous sequences of nucleotides, known as unitigs, from the resulting graph.

2.3 Association analysis

We used the fixed-effect generalized linear model (SEER) of the Pyseer tool v1.3.9 (Lees et al., 2018) to associate the presence/absence of unitigs with continuous antibiotic resistance values, similar to the approach used in our previous variant-based GWAS (Malekian et al., 2021). Population structure was controlled by adding covariates to the linear regression model. We utilized multidimensional scaling (MDS) on distances from the phylogenetic tree constructed from the VCF file using VCF-kit v0.1.6 (Cook and Andersen, 2017). Selected components from the MDS model, the number of components at the knee of a scree plot, served as covariates to control for population structure. Significant unitigs were retained after applying the Bonferroni correction. To calculate the Bonferroni-corrected threshold, we used the Pyseer tool, which divides the standard significance level of 0.05 by the number of distinct unitig patterns, resulting in a p-value threshold of 8.06 × 10⁻⁸.

2.4 Unitig annotation

We mapped significant unitigs to our reference genome (E. coli K-12 MG1665, accession NC_000913.3) using BWA v0.7.17-r1188 (Li, 2013) and performed variant calling with Samtools v1.7 and Bcftools v1.8 (Danecek et al., 2021). The variants were annotated using SnpEff v5.1 (Cingolani et al., 2012). For variant-free unitigs associated with antibiotic susceptibility instead of resistance, we searched for resistance mutations in the complementing isolate set. The complementing isolate set comprises unitigs covering the same genomic regions and associated with antibiotic resistance (p-value < 0.05). Our final set of resistance mutations included mutations in both the mutated unitigs and the complementing isolate set of unmutated unitigs.

2.5 Functional analysis of annotated unitigs

We investigated the function of genes and variants mapped to our significant unitigs using the UniProt knowledgebase (The UniProt Consortium, 2023) and the EcoCyc E. coli database (Keseler et al., 2010).

2.6 Network analysis of new genes

To investigate connections among new (mutated) genes not captured in our previous variant-based GWAS, we employed STRING-db (Szklarczyk et al., 2015). Default settings were utilized, which incorporate multiple sources of evidence such as text mining, experimental data, databases, co-expression, neighborhood, gene fusion, and co-occurrence. The edge thickness setting was adjusted to indicate the strength of data support.

2.7 Antibiotic resistance analysis of unitigs

To assess the association between the genes containing significant unitigs and antibiotic resistance, we employed the CARD database (McArthur et al., 2013) and relevant literature.

3 Results and discussion

This study examined correlations between unitigs and antibiotic resistance labels for four quinolones. The first objective was to validate the coverage of the positive controls, which consisted of known mutations in gyrA and parC, by the unitigs. The word-based GWAS was also expected to confirm previously identified mutations from the variant-based GWAS, particularly the mutations in the bdcA and valS genes. Moreover, the study aimed to explore the possibility of discovering new mutations or genes that the variant-based GWAS had not detected. Finally, but most importantly, the investigation aimed to uncover joint mutations that could potentially interact with each other.

3.1 A total of 54 highly-quality significant unitigs were identified

We extracted a total of 1,491,067 unitigs from our genomes. Initially, we excluded unitigs that appeared in over 99% of the dataset, resulting in a reduction to 1,209,817 unitigs, representing a 19% decrease. We then associated the presence/absence of these unitigs with resistance values for the four quinolones: levofloxacin, norfloxacin, ciprofloxacin, and nalidixic acid. Applying a Bonferroni-corrected threshold (p-value: 8.06 × 10⁻⁰⁸), we identified 100 highly significant unitigs: 33 for levofloxacin, 65 for norfloxacin, 17 for ciprofloxacin, and 1 for nalidixic acid. Some unitigs were significant for more than one antibiotic, resulting in a total count that differs from 100.

To understand the biological significance of the unitigs, we mapped them to the reference genome (E. coli K-12 substr. MG1655) to determine the associated genes and variants. Among the 100 highly significant unitigs, 54 possessed clear and high-quality annotations, constituting our final set of significant unitigs: 18 for levofloxacin, 36 for norfloxacin, 12 for ciprofloxacin, and 1 for nalidixic acid (see Supplementary material).

The 54 significant unitigs captured both some of the previously identified mutations and novel mutations in unexplored genes. Additionally, we observed joint mutations within unitigs, and their combinations exhibited significant correlations with quinolone resistance. A summary of our findings is presented in Table 1, indicating the potential utility of word-based GWAS in identifying genomic mutations that may be associated with antibiotic resistance. We will elaborate on each of our new findings in the following.

TABLE 1

Table 1. Summary results of word-based GWAS for quinolone resistance.

3.2 Significant unitigs captured known mutations

By mapping our genomes to the reference, E. coli K-12 substr. MG1655, we successfully identified significant unitigs harboring known biomarkers of quinolone resistance. Notably, these unitigs encompassed mutations in key genes such as gyrA and parC. For levofloxacin resistance, we observed mutations including parC S80I and gyrA S83L. Similarly, for norfloxacin resistance, we found parC S80I, parE L416F, gyrA S83L, and gyrA D87N. In the case of ciprofloxacin resistance, the identified known mutations were parC S80I, gyrA S83L, and gyrA D87N. Additionally, gyrA S83L was associated with resistance to nalidixic acid. Detailed information about these unitigs, including p-values, effect sizes, frequencies, and more, can be found in Supplementary material.

Additionally, our top significant unitigs were located within the quinolone resistance-determining regions (QRDRs) of GyrA. The QRDRs are amino acids between 67 and 106, which are conserved regions involved in DNA binding and are well-known to cause quinolone resistance when mutations occur (Varughese et al., 2018). The QRDR is near tyrosine 122, which binds to DNA during DNA double-strand breaks or DNA single-strand rejoins. Non-synonymous mutations (gyrA S83L, gyrA D87N) and synonymous mutations (gyrA R91R, gyrA V85V) were observed in this region. Among the unitigs in this region, those without any mutations were correlated with antibiotic susceptibility, while those with mutations were correlated with antibiotic resistance.

3.3 Significant unitigs captured some of the mutations previously identified in our variant-based GWAS study

Our previous variant-based GWAS analysis (Malekian et al., 2021) discovered significant correlations between specific variants in the valS and bdcA genes, which are involved in translation and biofilm formation, respectively, and quinolone resistance. Building upon this finding, our subsequent GWAS analysis at the unitig level unveiled additional insights. Specifically, we found that two unitigs within the valS gene were strongly associated with resistance to both levofloxacin and norfloxacin, while one unitig within the bdcA gene exhibited a significant correlation with norfloxacin susceptibility. These results further reinforce the importance of these genomic regions in developing quinolone resistance.

Within the unitigs associated with the valS gene, one unitig contained two synonymous mutations, namely valS R733R and valS A730A. Notably, the variant valS R733R had also been identified in our previous variant-based GWAS analysis. In contrast, the other unitig related to valS did not exhibit any variants, and thus, it displayed a correlation with antibiotic susceptibility rather than resistance. Similarly, the unitig linked to the bdcA gene did not include any variants and was associated with antibiotic susceptibility. It is intriguing to observe that this unitig encompassed the region harboring the G135S variant previously identified in bdcA among resistant isolates. These findings highlight the potential of word-based GWAS in identifying potential antibiotic resistance targets, expanding our understanding of the genomic landscape of quinolone resistance. For more comprehensive information on the significant unitigs in the bdcA and valS genes, including p-values, effect sizes, frequencies, and other details, please refer to Supplementary material.

3.4 Significant unitigs captured new mutations

Additionally, the word-based GWAS revealed mutations in new genes linked to quinolone resistance. The list of these new (mutated) genes can be found in Table 1. For more in-depth information regarding the unitigs containing these mutations, including p-values, effect sizes, frequencies, and other relevant details (see Supplementary material).

Some of these new (mutated) genes, such as mdfA, the AcrEF-TolC multidrug efflux system, ptrB, and hisI have been previously associated with antibiotic or quinolone resistance in the literature.

The mdfA gene encodes a multidrug efflux pump that, when upregulated, is strongly linked to resistance to several antibiotics, including quinolones (Yasufuku et al., 2011; Gu et al., 2021; Li and Ge, 2023). In this study, we found a region within the mdfA gene that displayed a strong association with norfloxacin susceptibility. This region was mostly unmutated in susceptible samples but mutated in resistant samples, with a synonymous variant, A42A.

The AcrEF-TolC multidrug efflux system is a homolog of the well-known AcrAB-TolC multidrug efflux system and is composed of three genes, acrF, acrE, and tolC. Prior research has demonstrated that overexpression of the acrF and acrE genes results in antibiotic resistance, including fluoroquinolones (Ma et al., 1993; Okusu et al., 1996; Lau and Zgurskaya, 2005). Our study found one unitig in the acrE gene and one unitig in the acrF gene strongly associated with norfloxacin susceptibility.

Overexpression of the hisI and ptrB genes has been shown to increase ciprofloxacin resistance in Escherichia coli (Ranjith et al., 2017) and Pseudomonas aeruginosa (Sun et al., 2014), respectively. This study found a non-synonymous mutation, L46I, and a synonymous mutation, T52T, in the hisI gene correlated with ciprofloxacin resistance. However, for the ptrB gene, we found a non-synonymous mutation, V629I, was linked to norfloxacin resistance, which is in the relative vicinity of the predicted active sites at positions 617 and 652; refer to UniProt ID P24555.

Furthermore, while the direct association of the remaining new (mutated) genes with antibiotic resistance was not explored in the literature, we identified evidence indicating potential links for 19 out of the 25 new (mutated) genes (refer to Table 2). As the table shows, these genes are actively involved in crucial functions such as multidrug efflux, bacterial tolerance, bacterial persistence, fitness to stress, and biofilm formation. Thus, they present plausible candidates that could potentially be linked to antibiotic resistance.

TABLE 2

Table 2. The (potential) role of new (mutated) genes in antibiotic resistance (AR).

Moreover, the involvement of new (mutated) genes in antibiotic resistance through interactions with other genes is plausible, as demonstrated in Figure 2. The interaction network in this figure shows the potential collaborative relationships among these genes. For instance, genes like gnd, dut, and mutM, which lacked evidence of a direct link to antibiotic resistance, might still confer resistance when cooperating with the hisI and nrdD genes, possibly aiding in biofilm formation. Similarly, the sgbU gene could contribute to antibiotic resistance in partnership with the yiaM and yiaN genes, potentially through biofilm formation mechanisms.

FIGURE 2

Figure 2. Network analysis of new (mutated) genes discovered by word-based GWAS, using STRING database (Szklarczyk et al., 2015). The edge thickness indicates the strength of data support. The network analysis shows evidence of interactions between four groups of genes: (1) the gnd, hisI, nrdD, dut, and mutM genes, (2) the cheA and cheZ genes, (3) sgbU, yiaN, and yiaM, and (4) the acrE, yhdV, acrF, and mdfA genes. However, there is no evidence of interactions among other genes.

3.5 Significant unitigs captured joint mutations

Significant unitigs captured joint mutations that could interact with each other to drive quinolone resistance. Unlike conventional variant-based GWAS, which only considers individual mutations, word-based GWAS analyzes larger genome portions, enabling the detection of mutation interactions. The word-based GWAS identified highly significant unitigs for quinolone resistance that contain multiple variants, suggesting that the interaction between these mutations contributes to the development of antibiotic resistance. Some of these variants were found in well-known targets of quinolone resistance, such as gyrA and parC, while others were located in new genes, including galF, cheA, yiaM, and cheZ. Notably, these variations were in crucial positions, such as the quinolone-resistance determining regions for gyrA (Varughese et al., 2018), the substrate binding site neighborhood for gnd (UniProt ID P00350), or an essential catalytic domain for the cheA (UniProt ID P07363), yiaM (UniProt ID P37674), and cheZ (UniProt ID P0A9H9) genes. For a full list of genes that contain joint mutations, refer to Table 1. For further details on unitigs that contain such mutations, such as p-value, effect size, frequency, etc., (see Supplementary material).

3.6 General discussion

3.6.1 Word-based GWAS using unitigs yielded significant quinolone resistance findings

The word-based GWAS using unitigs was highly effective for analyzing quinolone resistance. Comparing it to k-mer level analyses (results not provided here), we found unitig analysis superior in terms of interoperability and significant findings. We identified 54 unitigs containing regions covering known mutations in gyrA and parC genes, as well as previously identified mutations using our variant-based GWAS in bdcA and valS.

Additionally, we discovered new variants in previously unexplored genes, some of which have been linked to antibiotic resistance. However, further investigation is required to confirm these associations. Notably, these new genes were missed by both our previous variant-based GWAS (Malekian et al., 2021) and the positive selection (Malekian et al., 2022) analysis of E. coli for antibiotic resistance. Therefore, our unitig-based GWAS allowed for a comprehensive analysis of quinolone resistance, showcasing the value of this approach in identifying genomic factors associated with antibiotic resistance.

3.6.2 Word-based and variant-based GWAS do not fully overlap in their detection of single mutations

Word-based GWAS and variant-based GWAS did not identify exactly the same list of single mutations. The reason behind this relies on the way that unitigs are built. In the context of variant-based GWAS, cases consist of isolates harboring particular mutations, while controls encompass isolates devoid of these mutations. Conversely, in word-based GWAS, the landscape is more complex, encompassing unitigs that contain a specific single mutation, unitigs devoid of the mutation, and unitigs with the specific mutation alongside other mutations. Consequently, when focusing on the detection of single mutations, the results from variant-based GWAS are considered more reliable.

3.6.3 A significant portion of the identified resistance mutations are synonymous

The list of significant resistant mutations contain many synonymous mutations. Unlike non-synonymous mutations that directly impact the protein product, structure, and function, synonymous mutations exert their influence indirectly by affecting processes such as splicing, RNA stability, RNA folding, translation, and cotranslational protein folding (Sharma et al., 2019). As a result, these synonymous mutations play an indirect yet vital role in shaping the phenotype of interest.

3.6.4 Word-based GWAS suggests specific mutations and their interactions drive quinolone resistance

Our study emphasizes the significance of mutations and their interactions over individual resistance gene presence in quinolone resistance. However, the factors influencing resistance may vary depending on the antibiotic under study. We detected joint variants in the known target gyrA and new genes (galF, cheA, yiaM, and cheZ), all situated in critical regions. These mutations occupy essential positions, such as quinolone-resistance determining regions for gyrA, substrate binding site neighborhood for gnd, and catalytic domains for cheA, yiaM, and cheZ.

3.6.5 While word-based GWAS demonstrates computational power, biological experimental validation remains essential to confirm findings

We conducted our analysis on an E. coli dataset of 92 samples from a wastewater treatment plant, which might initially appear modest in size. However, the dataset's capability to affirm the presence of positive controls, notably the gyrA and parC genes, instilled confidence in its reliability for conducting thorough statistical analyses and evaluating the efficacy of the word-based GWAS approach. This robust dataset, anchored in unbiased sequencing and antibiotic resistance measurement methods (Mahfouz et al., 2018), underscores the comprehensiveness of our study. Word-based GWAS in bacterial studies can unveil genomic sequence patterns associated with bacterial phenotypes beyond single-nucleotide variations, providing efficient phenotype predictions and valuable functional insights. However, experimental validation is necessary to affirm the biological findings.

4 Conclusion

This study utilized a word-based GWAS to overcome the limitations of conventional variant-based GWAS analyses for genomic data. By examining genome words in 92 wastewater E. coli genomes, the study identified 54 significant words strongly associated with quinolone resistance. Positive controls, including known mutations in gyrA and parC, were validated, along with previously identified mutations in bdcA and valS from variant-based GWAS. Additionally, novel (mutated) genes such as mdfA, the acrEF-TolC multidrug efflux system, ptrB, and hisI were discovered, which are known to contribute to antibiotic resistance. Notably, the study revealed potentially interacting mutations in 14 genes, one of them being the well-known quinolone target gyrA. These mutations are located in critical sites, including quinolone-resistance determining regions in gyrA, the neighborhood of the substrate binding site in gnd, and the catalytic domains of cheA, yiaM, and cheZ. This finding suggests that quinolone resistance may not only result from individual mutations identified by variant-based GWAS but also from potential interactions between mutations.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA380388/.

Author contributions

NM: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing—original draft, Writing—review & editing. SS: Data curation, Formal analysis, Investigation, Validation, Writing—original draft, Writing—review & editing. AA-F: Writing—review & editing, Formal analysis. MS: Formal analysis, Writing—review & editing, Project administration, Supervision.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by ACRAS-R and SNRT projects. The funder of both project is Bundesministeriums für Bildung und Forschung (BMBF).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1276332/full#supplementary-material

References

Alcock, B. P., Raphenya, A. R., Lau, T. T., Tsang, K. K., Bouchard, M., Edalatmand, A., et al. (2020). Card 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucl. Acids Res. 48, D517–D525. doi: 10.1093/nar/gkz935

PubMed Abstract | CrossRef Full Text | Google Scholar

Chaguza, C., Jamrozy, D., Bijlsma, M. W., Kuijpers, T. W., van de Beek, D., van der Ende, A., et al. (2022a). Population genomics of group b streptococcus reveals the genetics of neonatal disease onset and meningeal invasion. Nat. Commun. 13, 4215. doi: 10.1038/s41467-022-31858-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Chaguza, C., Smith, J. T., Bruce, S. A., Gibson, R., Martin, I. W., and Andam, C. P. (2022b). Prophage-encoded immune evasion factors are critical for staphylococcus aureus host infection, switching, and adaptation. Cell Genom. 2, 100194. doi: 10.1016/j.xgen.2022.100194

PubMed Abstract | CrossRef Full Text | Google Scholar

Chaguza, C., Yang, M., Cornick, J. E., Du Plessis, M., Gladstone, R. A., Kwambana-Adams, B. A., et al. (2020). Bacterial genome-wide association study of hyper-virulent pneumococcal serotype 1 identifies genetic variation associated with neurotropism. Commun. Biol. 3, 559. doi: 10.1038/s42003-020-01290-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Cingolani, P., Platts, A., Wang, L. L., Coon, M., Nguyen, T., Wang, L., et al. (2012). A program for annotating and predicting the effects of single nucleotide polymorphisms, snpeff: SNPS in the genome of drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92. doi: 10.4161/fly.19695

PubMed Abstract | CrossRef Full Text | Google Scholar

Cook, D. E., and Andersen, E. C. (2017). Vcf-kit: assorted utilities for the variant call format. Bioinformatics 33, 1581–1582. doi: 10.1093/bioinformatics/btx011

PubMed Abstract | CrossRef Full Text | Google Scholar

Culler, H. F., Couto, S. C., Higa, J. S., Ruiz, R. M. J, Yang, M., et al. (2018). Role of sdia on biofilm formation by atypical enteropathogenic Escherichia coli. Genes 9, 253. doi: 10.3390/genes9050253

PubMed Abstract | CrossRef Full Text | Google Scholar

Danecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V., Pollard, M. O., et al. (2021). Twelve years of samtools and bcftools. Gigascience, 10, giab008. doi: 10.1093/gigascience/giab008

PubMed Abstract | CrossRef Full Text | Google Scholar

Denamur, E., Condamine, B., Esposito-Farèse, M., Royer, G., Clermont, O., Laouenan, C., et al. (2022). Genome wide association study of Escherichia coli bloodstream infection isolates identifies genetic determinants for the portal of entry but not fatal outcome. PLoS Genet. 18, e1010112. doi: 10.1371/journal.pgen.1010112

PubMed Abstract | CrossRef Full Text | Google Scholar

Edgar, R., and Bibi, E. (1997). Mdfa, an Escherichia coli multidrug resistance protein with an extraordinarily broad spectrum of drug recognition. J. Bacteriol. 179, 2274–2280. doi: 10.1128/jb.179.7.2274-2280.1997

PubMed Abstract | CrossRef Full Text | Google Scholar

Emmerson, A., and Jones, A. (2003). The quinolones: decades of development and use. J. Antimicr. Chemother. 51, 13–20. doi: 10.1093/jac/dkg208

PubMed Abstract | CrossRef Full Text | Google Scholar

Fortier, L.-C., and Sekulovic, O. (2013). Importance of prophages to evolution and virulence of bacterial pathogens. Virulence 4, 354–365. doi: 10.4161/viru.24498

PubMed Abstract | CrossRef Full Text | Google Scholar

Gu, Y., Huang, L., Wu, C., Huang, J., Hao, H., Yuan, Z., et al. (2021). The evolution of fluoroquinolone resistance in salmonella under exposure to sub-inhibitory concentration of enrofloxacin. Int. J. Molec. Sci. 22, 12218. doi: 10.3390/ijms222212218

PubMed Abstract | CrossRef Full Text | Google Scholar

Hang, N. T. L., Hijikata, M., Maeda, S., Thuong, P. H., Ohashi, J., Van Huan, H., et al. (2019). Whole genome sequencing, analyses of drug resistance-conferring mutations, and correlation with transmission of mycobacterium tuberculosis carrying katg-s315t in hanoi, vietnam. Scient. Rep. 9, 1–14. doi: 10.1038/s41598-019-51812-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Hooper, D. C., and Jacoby, G. A. (2015). Mechanisms of drug resistance: quinolone resistance. Ann. NY. Acad. Sci. 1354, 12. doi: 10.1111/nyas.12830

CrossRef Full Text | Google Scholar

Jaillard, M., Lima, L., Tournoud, M., Mahé, P., Van Belkum, A., Lacroix, V., et al. (2018). A fast and agnostic method for bacterial genome-wide association studies: bridging the gap between k-mers and genetic events. PLoS Genet. 14, e1007758. doi: 10.1371/journal.pgen.1007758

PubMed Abstract | CrossRef Full Text | Google Scholar

Kawai, Y., Matsumoto, S., Ling, Y., Okuda, S., and Tsuneda, S. (2018). ALDB controls persister formation in Escherichia coli depending on environmental stress. Microbiol. Immunol. 62, 299–309. doi: 10.1111/1348-0421.12587

PubMed Abstract | CrossRef Full Text | Google Scholar

Keseler, I. M., Collado-Vides, J., Santos-Zavaleta, A., Peralta-Gil, M., Gama-Castro, S., Mu niz-Rascado, L., et al. (2010). ECOCYC: a comprehensive database of Escherichia coli biology. Nucl. Acids Res. 39, D583–D590. doi: 10.1093/nar/gkq1143

PubMed Abstract | CrossRef Full Text | Google Scholar

Lau, S. Y., and Zgurskaya, H. I. (2005). Cell division defects in Escherichia coli deficient in the multidrug efflux transporter acref-tolc. J. Bacteriol. 187, 7815–7825. doi: 10.1128/JB.187.22.7815-7825.2005

PubMed Abstract | CrossRef Full Text | Google Scholar

Lees, J. A., Galardini, M., Bentley, S. D., Weiser, J. N., and Corander, J. (2018). PYSEER: a comprehensive tool for microbial pangenome-wide association studies. Bioinformatics 34, 4310–4312. doi: 10.1093/bioinformatics/bty539

PubMed Abstract | CrossRef Full Text | Google Scholar

Lees, J. A., Mai, T. T., Galardini, M., Wheeler, N. E., Horsfield, S. T., Parkhill, J., et al. (2020). Improved prediction of bacterial genotype-phenotype associations using interpretable pangenome-spanning regressions. MBio 11, e01344–e01320. doi: 10.1128/mBio.01344-20

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, H. (2013). Aligning sequence reads, clone sequences and assembly contigs with BWA-mem. arXiv preprint arXiv:1303.3997.

Google Scholar

Li, Y., and Ge, X. (2023). Enhanced internal ionic interaction of MFS efflux pump MDFA contributes to its elevated antibiotic export. Phys. Chem. Chem. Phys. 25, 788–795. doi: 10.1039/D2CP05059E

PubMed Abstract | CrossRef Full Text | Google Scholar

Ma, D., Cook, D., Alberti, M., Pon, N., Nikaido, H., and Hearst, J. (1993). Molecular cloning and characterization of acra and acre genes of Escherichia coli. J. Bacteriol. 175, 6299–6313. doi: 10.1128/jb.175.19.6299-6313.1993

PubMed Abstract | CrossRef Full Text | Google Scholar

Mahfouz, N., Caucci, S., Achatz, E., Semmler, T., Guenther, S., Berendonk, T. U., et al. (2018). High genomic diversity of multi-drug resistant wastewater Escherichia coli. Scient. Rep. 8, 8928. doi: 10.1038/s41598-018-27292-6

CrossRef Full Text | Google Scholar

Malekian, N., Agrawal, A. A., Berendonk, T. U., Al-Fatlawi, A., and Schroeder, M. (2022). A genome-wide scan of wastewater E. coli for genes under positive selection: focusing on mechanisms of antibiotic resistance. Scient. Rep. 12, 8037. doi: 10.1038/s41598-022-11432-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Malekian, N., Al-Fatlawi, A., Berendonk, T. U., and Schroeder, M. (2021). Mutations in BDCA and VALS correlate with quinolone resistance in wastewater Escherichia coli. Int. J. Molec. Sci. 22, 6063. doi: 10.3390/ijms22116063

CrossRef Full Text | Google Scholar

McArthur, A. G., Waglechner, N., Nizam, F., Yan, A., Azad, M. A., Baylay, A. J., et al. (2013). The comprehensive antibiotic resistance database. Antimicr. Agents Chemother. 57, 3348–3357. doi: 10.1128/AAC.00419-13

CrossRef Full Text | Google Scholar

Mehta, P., Casjens, S., and Krishnaswamy, S. (2004). Analysis of the Lambdoid prophage element e14 in the E. coli k-12 genome. BMC Microbiol. 4, 1–13. doi: 10.1186/1471-2180-4-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Merchel Piovesan Pereira, B., Wang, X., and Tagkopoulos, I. (2020). Short-and long-term transcriptomic responses of Escherichia coli to biocides: a systems analysis. Appl. Environ. Microbiol. 86, e00708–e00720. doi: 10.1128/AEM.00708-20

PubMed Abstract | CrossRef Full Text | Google Scholar

Naeem, A., Badshah, S. L., Muska, M., Ahmad, N., and Khan, K. (2016). The current case of quinolones: synthetic approaches and antibacterial activity. Molecules 21, 268. doi: 10.3390/molecules21040268

PubMed Abstract | CrossRef Full Text | Google Scholar

Okusu, H., Ma, D., and Nikaido, H. (1996). Acrab efflux pump plays a major role in the antibiotic resistance phenotype of Escherichia coli multiple-antibiotic-resistance (mar) mutants. J. Bacteriol. 178, 306–308. doi: 10.1128/jb.178.1.306-308.1996

PubMed Abstract | CrossRef Full Text | Google Scholar

Plantinga, T. H., van der Does, C., Tomkiewicz, D., van Keulen, G., Konings, W. N., and Driessen, A. J. (2005). Deletion of the yiamno transporter genes affects the growth characteristics of Escherichia coli k-12. Microbiology 151, 1683–1689. doi: 10.1099/mic.0.27851-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Power, R. A., Parkhill, J., and de Oliveira, T. (2017). Microbial genome-wide association studies: lessons from human GWAS. Nat. Rev. Genet. 18, 41. doi: 10.1038/nrg.2016.132

PubMed Abstract | CrossRef Full Text | Google Scholar

Qin, Y., He, Y., She, Q., Larese-Casanova, P., Li, P., and Chai, Y. (2019). Heterogeneity in respiratory electron transfer and adaptive iron utilization in a bacterial biofilm. Nat. Commun. 10, 3702. doi: 10.1038/s41467-019-11681-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Raineri, E. J., Maaß, S., Wang, M., Brushett, S., Palma Medina, L. M., Sampol Escandell, N., et al. (2022). Staphylococcus aureus populations from the gut and the blood are not distinguished by virulence traits–a critical role of host barrier integrity. Microbiome 10, 239. doi: 10.1186/s40168-022-01419-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Ranjith, K., Arunasri, K., Reddy, G. S., Adicherla, H., Sharma, S., and Shivaji, S. (2017). Global gene expression in Escherichia coli, isolated from the diseased ocular surface of the human eye with a potential to form biofilm. Gut Pathog. 9, 1–15. doi: 10.1186/s13099-017-0164-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Répérant, M., Porcheron, G., Rouquet, G., and Gilot, P. (2011). The YICJI metabolic operon of Escherichia coli is involved in bacterial fitness. FEMS Microbiol. Lett. 319, 180–186. doi: 10.1111/j.1574-6968.2011.02281.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Sharma, Y., Miladi, M., Dukare, S., Boulay, K., Caudron-Herger, M., Groß, M., et al. (2019). A pan-cancer analysis of synonymous mutations. Nat. Commun. 10, 1–14. doi: 10.1038/s41467-019-10489-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Sheppard, S. K., Didelot, X., Meric, G., Torralbo, A., Jolley, K. A., Kelly, D. J., et al. (2013). Genome-wide association study identifies vitamin B5 biosynthesis as a host specificity factor in campylobacter. Proc. Natl. Acad. Sci. 110, 11923–11927. doi: 10.1073/pnas.1305559110

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, Z., Shi, J., Liu, C., Jin, Y., Li, K., Chen, R., et al. (2014). PRTR homeostasis contributes to pseudomonas aeruginosa pathogenesis and resistance against ciprofloxacin. Infect. Immun. 82, 1638–1647. doi: 10.1128/IAI.01388-13

PubMed Abstract | CrossRef Full Text | Google Scholar

Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., et al. (2015). String v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452. doi: 10.1093/nar/gku1003

PubMed Abstract | CrossRef Full Text | Google Scholar

The UniProt Consortium (2023). Uniprot: the universal protein knowledgebase in 2023. Nucl. Acids Res. 51, D523–D531. doi: 10.1093/nar/gkac1052

CrossRef Full Text | Google Scholar

Tirumalai, M. R., Karouia, F., Tran, Q., Stepanov, V. G., Bruce, R. J., Ott, C. M., et al. (2019). Evaluation of acquired antibiotic resistance in Escherichia coli exposed to long-term low-shear modeled microgravity and background antibiotic exposure. Mbio 10, 10–1128. doi: 10.1128/mBio.02637-18

PubMed Abstract | CrossRef Full Text | Google Scholar

Van Wonterghem, L., De Chiara, M., Liti, G., Warringer, J., Farewell, A., Verstraeten, N., et al. (2022). Genome-wide association study reveals host factors affecting conjugation in Escherichia coli. Microorganisms 10, 608. doi: 10.3390/microorganisms10030608

CrossRef Full Text | Google Scholar

Varughese, L. R., Rajpoot, M., Goyal, S., Mehra, R., Chhokar, V., and Beniwal, V. (2018). Analytical profiling of mutations in quinolone resistance determining region of GYRA gene among UPEC. PLoS ONE 13, e0190729. doi: 10.1371/journal.pone.0190729

PubMed Abstract | CrossRef Full Text | Google Scholar

Yano, H., Nishiuchi, Y., Arikawa, K., Ota, A., Miki, M., Maruyama, F., et al. (2021). Genome-wide association study reveals putative bacterial risk factors for cavitary mycobacterium avium complex lung disease. bioRxiv 2021–07. doi: 10.1101/2021.07.06.451401

CrossRef Full Text | Google Scholar

Yasufuku, T., Shigemura, K., Shirakawa, T., Matsumoto, M., Nakano, Y., Tanaka, K., et al. (2011). Correlation of overexpression of efflux pump genes with antibiotic resistance in Escherichia coli strains clinically isolated from urinary tract infection patients. J. Clin. Microbiol. 49, 189–194. doi: 10.1128/JCM.00827-10

PubMed Abstract | CrossRef Full Text | Google Scholar