Citrobacter

The HiSeq and MiSeq metagenomes were built using 20 sets of bacterial whole-genome shotgun reads. These reads were found either as part of the GAGE-B project [21 (link)] or in the NCBI Sequence Read Archive. Each metagenome contains sequences from ten genomes (Additional file 1: Table S1). For both the 10,000 and 10 million read samples of each of these metagenomes, 10% of their sequences were selected from each of the ten component genome data sets (i.e., each genome had equal sequence abundance). All sequences were trimmed to remove low quality bases and adapter sequences.
The composition of these two metagenomes poses certain challenges to our classifiers. For example, Pelosinus fermentans, found in our HiSeq metagenome, cannot be correctly identified at the genus level by Kraken (or any of the other previously described classifiers), because there are no Pelosinus genomes in the RefSeq complete genomes database; however, there are seven such genomes in Kraken-GB’s database, including six strains of P. fermentans. Similarly, in our MiSeq metagenome, Proteus vulgaris is often classified incorrectly at the genus level because the only Proteus genome in Kraken’s database is a single Proteus mirabilis genome. Five more Proteus genomes are present in Kraken-GB’s database, allowing Kraken-GB to classify reads better from that genus. In addition, the MiSeq metagenome contains five genomes from the Enterobacteriaceae family (Citrobacter, Enterobacter, Klebsiella, Proteus and Salmonella). The high sequence similarity between the genera in this family can make distinguishing between genera difficult for any classifier.
The simBA-5 metagenome was created by simulating reads from the set of complete bacterial and archaeal genomes in RefSeq. Replicons from those genomes were used if they were associated with a taxon that had an entry associated with the genus rank, resulting in a set of replicons from 607 genera. We then used the Mason read simulator [22 ] with its Illumina model to produce 10 million 100-bp reads from these genomes. First we created simulated genomes for each species, using a SNP rate of 0.1% and an indel rate of 0.1% (both default parameters), from which we generated the reads. For the simulated reads, we multiplied the default mismatch and indel rates by five, resulting in an average mismatch rate of 2% (ranging from 1% at the beginning of reads to 6% at the ends) and an indel rate of 1% (0.5% insertion probability and 0.5% deletion probability). For the simBA-5 metagenome, the 10,000 read set was generated from a random sample of the 10 million read set.

We have expanded the Reference Gene Catalog^{8 (link)} to include genetic elements related to stress response and virulence genes; these expansions can be visualized in the Reference Gene Catalog Browser (https://www.ncbi.nlm.nih.gov/pathogens/refgene/). One reason we expanded AMRFinderPlus is to understand the linkages between AMR genes and stress response and virulence genes in food-borne pathogens; thus, the stress response and virulence genes included in the Reference Gene Catalog are composed primarily of E. coli-related genes derived primarily from González-Escalona et al.^{23 (link)} as well as BacMet^{24 (link)}, but also have been supplemented by manual curation efforts for other taxa. Stx gene nomenclature adopts the system of Scheutz et al.^{25 (link)} and the intimin (eae) gene nomenclature uses existing designations in the literature^{26 (link),27 (link)}. Genes are incorporated only if there is literature supporting the function of that protein or closely related sequences that meet the identification criteria. As a major focus of our work is to improve NCBI’s Pathogen Detection system^{16 (link)}, we excluded genes that belonged to organisms not deemed clinically relevant. To remove ‘housekeeping’ proteins that were universally found in one or more taxa in the Pathogen Detection system, sequences were not included if they were found at a frequency of greater than 95% in a survey of 58,531 RefSeq bacterial assemblies belonging to any of the following species: Acinetobacter, Campylobacter, Citrobacter, Enterococcus, Enterobacter, Escherichia/Shigella, Klebsiella, Listeria, Salmonella, Staphylococcus, Pseudomonas, and Vibrio. If genes of particular interest in foodborne pathogens exceeded this threshold, they were excluded in the taxa where they appear to be nearly universal (see “Identifying genomic elements” below). In addition, genes with misidentified functions, such as copper-binding proteins that use copper as a co-factor yet do not confer resistance to copper, also were excluded. As we continue to expand the database, we use similar criteria when adding genes.

PCRs were optimized and validated using the following control strains: a KPC producing Klebsiella pneumonia, a VIM-2 producing Pseudomonas aeruginosa, an IMP-18 producing P. aeruginosa, an IMP-28 producing K. pneumoniae, a NDM-1 positive K. pneumoniae, and an OXA-48 positive K. pneumoniae isolate.
Testing of PCR specificity was carried out on the following strain collection. The test collection of 86 isolates, included 58 carbapenemase producing isolates, and 28 carbapenemase negative controls. The 58 carbapenemase positive isolates consisted of 45 K. pneumoniae, 4 E. coli, 3 Enterobacter species, 2 P. mirabilis, 2 Citrobacter species, and 2 P. aeruginoasa isolates producing the following carbapenemases: 20 KPC-2/3, 4 KPC plus VIM, 21 VIM, 4 NDM-1, 2 IMP, and 7 OXA-48. The 28 carbapenemase negative controls consisted of 10 K. pneumoniae, 2 E. coli and 16 Enterobacter isolates, producing either an ESBL (20 isolates) or an AmpC beta-lactamase (Additional file 2: Table S2). As the reference test for presence of beta-lactamases, PCR and sequencing was used [10 (link)].

This was a retrospective observational case series.The institutional review board of Tehran University of Medical Sciences confirmed that no ethics approval is needed. The clinical and microbiological records of all culture-proven cases of Citrobacter keratitis, who presented to Farabi Eye Hospital from January 2012 to September 2020 were retrospectively reviewed. After slit-lamp examination, all corneal ulcers were scraped using a sterile scalpel blade for Gram stains. Fresh scalpel blades were then used to inoculate in chocolate agar and Sabouraud’s dextrose agar. The culture plates were incubated at 35 °C in carbon dioxide. Citrobacter spp. was considered as a causative agent for keratitis if there were discrete colonies of Citrobacter on two solid media or confluent growth of micro-organism was observed along with the site of inoculation. Sulfite indole motility (SIM) medium was used to confirm the growth of Citrobacter spp.. Production of H2S and a positive test for motility on SIM agar were considered as evidences of Citrobacter spp. growth. Antibiotic susceptibility testing was performed using the disk diffusion method. The following data were collected from patients’ records: age, sex, local and systemic predisposing factors, presenting signs and symptoms including the size of corneal infiltration, size of the epithelial defect, presence of corneal thinning and hypopyon, antibiotic sensitivity, and mode of treatment. Size of corneal epithelial defect was measured by multiplying the longest diameter of the defect by the longest width perpendicular to it.
The patients were admitted if any of the following criteria were present: (1) severe corneal infection according to overall clinical impression (2) presence of corneal thinning or perforation (3) The inability of the patient to instill drops intensively. The initial antibiotic instillation protocol was 1 drop/hour for 24 h. The antibiotic regimen was then modified according to clinical response and antibiotic susceptibility. The indications for adjunctive procedures such as cyanoacrylate glue application or therapeutic penetrating keratoplasty (PKP) were determined by an experienced cornea specialist.

Two hundred and seventeen wild house mice were caught around the southwestern French town of Espelette, during a five-week field work in September 2013 (first described in^{26 (link)}). Animals were live-trapped in 34 randomly chosen farms and brought back to a common location where they were euthanized with CO₂ and dissected on site. The presence of farm animals and use of poison was recorded for each farm (Suppl. Table 1). The pairwise distance between farms was calculated from their GPS coordinates with the “haversine” formula (Suppl. Table 3). Families were defined as groups of farms within which all farms are at most 2 km apart from each other, as suggested in;^{27 (link)} “super families” were defined as groups of farms within which all farms are at most 3.5 km apart from each other (Suppl. Tables 1 & 3, Figure 1a). For each mouse, body and tail length, weight and gender were recorded. The body mass index (BMI) was calculated as BMI = W/L,^{2 (link)} where W is the weight in kilograms, and L is the body length in meters (Suppl. Table 2). For the purposes of microbial analysis and histology, we chose the cecum, as it is the location in the gastrointestinal tract that harbors the most bacteria in mice, and because we had prior knowledge that B4galnt2 genotype can influence both microbiota and susceptibility to Enterobacteriaceae such as Salmonella Typhimurium^{22 (link)} or Citrobacter rodentium^{50 (link)} at this location. Further, little to no ileal inflammation is observed for either of the two above mentioned pathogens. The cecum was accordingly sampled in four pieces transversally (Suppl. Figure 8) and used for microbial analysis and histology. The tip of the cecum, thereafter referred as Cecum4 was stored in AllProtect (Qiagen) at +4°C; Cecum3 was stored in 10% formalin at +4°C; Cecum2 was stored in pre-reduced brain-heart infusion (BHI) with 20% glycerol at −80°C; Cecum1 was store in RNAlater at +4°C for 24 h, before removing the stabilizing solution and long-term storage at −20°C. A piece of the right ear was stored at −20°C and used for genotyping.