Fluoroquinolones

We conducted a systematic review of published literature between 1990 and 2018 following the PRISMA guidelines (Additional file 1: Table S1) [22 (link)]. The protocol was registered with the international prospective register of systematic reviews (CRD42018029432). The search strategy was devised by an academic librarian (EH). MEDLINE, Ovid Embase, Global Health, Cochrane Library, Scopus, Web of Science-Core Collection and LILACS were searched using a syntax that combined Medical Subject Headings (MeSH) and free text terms for the pathogens of interest (e.g. S. Typhi, S. Paratyphi A, enteric fever) with terms for antimicrobial resistance (e.g. resistan*, suscept*, surveil*) (Additional file 1: Table S2). The extended search was conducted in October 2017 and updated in March 2019. The search was limited to publications from 1990 onwards; no restrictions on language or filters (e.g. humans) were implemented.
Included studies were required to report quantifiable in vitro antimicrobial susceptibility data for S. Typhi and/or S. Paratyphi A isolated from blood culture, examining at least 10 representative organisms and indicating the study location. Reports from travellers being diagnosed in high-income countries were excluded. Studies with pooled S. Typhi and S. Paratyphi A susceptibility data, studies reporting on isolates from stool culture and duplicate isolates were also excluded.
Prospective and retrospective hospital-, laboratory- and community-based studies were included, if they met the specified inclusion criteria. Review articles were scanned for relevant references. Studies were screened at title, abstract and full-text stage by one author (CD) and reviewed by a second author (AB). Data were extracted into a predefined database by AB and reviewed by BKH and JL. Additionally, 20% of the extracted studies were checked by a third reviewer (CD). Disagreements were resolved by discussion. Susceptibility data for antimicrobials recommended for the treatment of enteric fever by WHO, i.e. ampicillin/amoxicillin, chloramphenicol, trimethoprim-sulphamethoxazole (co-trimoxazole), fluoroquinolones (e.g. ciprofloxacin and ofloxacin), third-generation cephalosporins (e.g. ceftriaxone and cefixime) and azithromycin, were extracted [11 ]. Furthermore, multidrug resistance (MDR; defined as resistance to ampicillin/amoxicillin, chloramphenicol and co-trimoxazole) and nalidixic acid resistance, as a proxy marker for reduced ciprofloxacin susceptibility, were recorded [18 (link)].
Variables extracted included the study start and end dates, patients’ characteristics (age range, mean age, percentage of males, inpatients or outpatients), study design, number of patients screened, number of patients with positive blood culture, antimicrobial susceptibility testing (AST) method and the number (or percentage) of resistant, intermediate and susceptible isolates out of the total number of isolates tested against each antimicrobial. We also recorded case fatalities and clinical outcomes when available. Additionally, the testing standard (e.g. Clinical and Laboratory Standards Institute (CLSI)) and interpretive criteria (including version or year) used to determine resistance, use of internal quality controls and participation in external quality assessments schemes were recorded. The study setting, precise study location, country and GBD study region were recorded for each study. Data were disaggregated by serovar and study location.
We aimed to control for bias and allow for comparison across studies by adhering to the predefined inclusion and exclusion criteria. We expected that there would be differences in the quality of the AST and interpretation of results, reflecting the reality in many LMICs. We adapted a descriptive tool for quality assessment used by Arndt, based on sample size and microbiological testing methodology [23 (link)]. We reviewed the complete description of susceptibility testing methods, which included testing standard, version and/or year (i.e. breakpoints), internal quality controls and external quality assessment. No study was excluded based on this assessment, due to the lack of standardised reporting guidelines for microbiological studies.

To examine the potential analytical advantage of whole genome sequencing comparison was made with three commercial tests: (1) the Xpert MTB/RIF (Cepheid Inc., USA) which targets the rpoB gene for RMP resistance; (2) the LPA MTBDRplus for MDR-TB (Hain Lifescience, Germany) which targets rpoB, katG and inhA for resistance to RMP and INH; and (3) the LPA MTBDRsl (Hain Lifescience, Germany) which targets gyrA, rrs and embB for resistance to the fluoroquinolones (FLQ), aminoglycosides and ethambutol, respectively. In silico versions were developed based on the polymorphisms used by these assays and their performance compared to the whole genome mutation library. In particular, in silico analysis of the six datasets was performed and analytical sensitivities and specificities of the inferred resistance relative to the reported phenotype were compared (Figure 2, Additional file 1: Figures S3 and S4). KvarQ [35 (link)], a new tool that directly scans fastq files of bacterial genome sequences for known genetic polymorphisms, was run across all 792 samples using the MTBC test suite and default parameters. Sensitivity and specificity achieved by this method using phenotypic DST results as the reference standard were calculated.Figure 2

Inferred analytical accuracies of the whole genome mutation library and three commercial molecular tests for resistance. In silico analysis of published sequence data using mutation libraries derived from XpertMTB/RIF (Cepheid Inc., USA) (purple), MTBDRsl (red) and MTBDRplus (orange) (Hain Life Sciences, Germany), and the curated whole genome library (blue). For each library in silico inferred resistance phenotypes were compared to reported phenotypes obtained from conventional drug susceptibility testing. Errors bars correspond to 95% confidence intervals. Abbreviations: AMK, amikacin; CAP, capreomycin; EMB, ethambutol; ETH, ethionamide; INH, Isoniazid; KAN, kanamycin; MDR, multi-drug resistance; MOX, moxifloxacin; OFX, ofloxacin; PZA, pyrazinamide; RMP, rifampicin; STR, streptomycin; XDR, extensive drug resistance.

When AMR detection is switched on, Kleborate screens for known acquired AMR determinants using a curated version of the CARD AMR nucleotide database (v3.0.8 downloaded February 2020; see doi.org/10.6084/m9.figshare.13256759.v1 for full details on curation). Genes are identified using nucleotide BLAST (and amino acid search with tBLASTx if no exact nucleotide match is found). Gene truncations and spurious hits are identified as described above for virulence genes. Unlike the acquired forms, the intrinsic variants of oqxAB, chromosomal fosA and ampH are not associated with clinical resistance in KpSC and are therefore not reported. However, SHV, LEN or OKP β-lactamase alleles intrinsic to KpSC species are known to confer clinical resistance to penicillins and are reported in the Bla_chr column. Acquired SHV variants, and individual SHV sequence mutations known to confer resistance to extended-spectrum β-lactams or β-lactamase inhibitors, are reported separately (see Supplementary Note 3, Supplementary Data 11 and 12 for details).
Chromosomally encoded mutations and gene loss or truncations known to be associated with AMR are reported for genomes identified as KpSC species. These include fluoroquinolone resistance mutations in GyrA (codons 83 and 87) and ParC (codons 80 and 84)^{76 (link)}, and colistin resistance from truncation or loss of MgrB and PmrB^{57 –59 (link)} (defined as <90% amino acid sequence coverage). Mutations in the OmpK35 and OmpK36 osmoporins reportedly associated with reduced susceptibility to β-lactamases^{41 (link),42 (link)} are also screened and reported for KpSC genomes, and include truncation or loss of these genes and OmpK36GD and OmpK36TD transmembrane β-strand loop insertions^{41 (link)}. SHV β-lactamase, GyrA, ParC and OmpK mutations are identified by alignment of the translated amino acid sequences against a reference using BioPython, followed by an interrogation of the alignment positions of interest (see Supplementary Note 3, Supplementary Data 11 and 12 for a list of relevant positions).
AMR genes and mutations are reported by drug class, with β-lactamases further categorized by enzyme activity (β-lactamase, ESBL or carbapenemase, with/without resistance to β-lactamase inhibitors). Horizontally acquired AMR genes are reported separately from mutational resistance and contribute to the AMR gene count; these plus chromosomal mutations count towards the number of acquired resistance classes (intrinsic SHV alleles, reported in Bla_chr column, are not included in either count). Resistance scores are calculated as follows: 0 = no ESBL or carbapenemase, 1 = ESBL without carbapenemase (regardless of colistin resistance); 2 = carbapenamase without colistin resistance (regardless of ESBL); 3 = carbapenemase with colistin resistance (regardless of ESBL).

The in vitro susceptibility of all confirmed Campylobacter strains was determined by using the disc diffusion method on Mueller–Hinton agar (Oxoid, UK) according to the guidelines of the Clinical and Laboratory Standards Institute (CLSI) [47 ]. Antimicrobial agent selection was based on the importance for both human and veterinary fields in addition to their antimicrobial mechanisms. Nine antibiotics belonging to five classes were selected. They included penicillin (AX; 20 μg and AM; 10 μg), macrolides (E; 15 μg), aminoglycosides (S; 10 μg and AK; 30 μg), tetracyclines (TE; 30 μg and DO; 30 μg), fluoroquinolones (NOR; 10 µg and CIP; 5 μg). All antimicrobial agents used in this study were purchased from Oxoid (England). C. jejuni ATCC 33,560 and C. coli ATCC 33,559 were used as control strains. MDR strains of Campylobacter are those that are resistant to three or more different classes of antimicrobials. Additionally, MARI for all Campylobacter isolates was calculated using the formula a/b (where "a" represents the number of antimicrobials to which an isolate was resistant and "b" represents the overall number of antimicrobials to which the isolate was exposed) [48 ].

Positive blood cultures were identified in the database and the sample date, age, sex and microbiological findings were retrieved. For species with clinically used names that were changed during the study period (such as Cutibacterium acnes previously called Propionibacterium acnes) we have consistently aimed to use the valid names as of 2022 according to the International Code of Nomenclature of Prokaryotes [12 (link)]. Antimicrobial susceptibility was described for Enterobacterales only, for fluoroquinolones (ciprofloxacin), third generation cephalosporins (cefotaxime) and aminoglycosides (gentamicin). Due to a database update in 2010, susceptibility data were available from 2011 to 2019 only. In addition, zone diameters were incomplete in > 50% of records. Therefore, clinical classification into susceptible (S), increased exposure (I) and resistant (R) was used to describe susceptibility, using breakpoints as per the original microbiology reports [13 (link)]. For cases with zone data, susceptibility testing according to the 2022 EUCAST breakpoints was compared with the original SIR classification in a sensitivity analysis [11 ]. Negative blood cultures were retrieved on an aggregate level (only the total number of blood culture sets per year was available). Population data were retrieved from Statistics Sweden [14 ].

The quality-controlled, decontaminated forward and reverse paired sequences from the 127 leukemia and lymphoma samples were mapped to the pediatric-oncology-ARG-database created using bowtie2 (Langmead and Salzberg, 2012 (link)). Counts of sequence reads that mapped to each ARG in the database were obtained for each sample using samtools “sort”, “index” and “idxstat”. Mapped read counts were corrected by the number of sequence reads in each sample. While reads were mapped to all ARG sequences identified, only those ≥60% sequence identity were used in downstream analyses. Antibiotic classes were assigned to each ARGs using the CARD database designation, with two exceptions, 1) genes that occurred in an antibiotic class connected with β-lactam drugs were coded as β-lactam antibiotic class genes (i.e., carbapenem, penam, etc.), 2) genes that occurred in multiple antibiotic classes (i.e., penam, fluoroquinolone, glycopeptide), were coded as “multidrug” antibiotic class genes. Counts within samples assigned to the same gene were summed for downstream analysis. Only genes present in ≥5% of samples were used. Genes in four antibiotic classes were selected for closer analysis: β-lactam antibiotic class, glycopeptide antibiotic class, peptide antibiotic class, and multidrug antibiotic class. These classes were specifically selected as the β-lactam antibiotic class and multidrug antibiotic class potentially contains genes for resistance to β-lactam antibiotics, and the glycopeptide antibiotic class, peptide antibiotic class (a parent class to glycopeptide antibiotics), and multidrug antibiotic class potentially contains genes for resistance to vancomycin. All analyses were carried out on gene sequence data, no allele or SNP information was used.