Genomic Characterization of Cronobacter spp. and Salmonella spp. Strains Isolated From Powdered Infant Formula in Chile

Abstract

This study characterized five Cronobacter spp. and six Salmonella spp. strains that had been isolated from 155 samples of powdered infant formula (PIF) sold in Chile and manufactured in Chile and Mexico in 2018–2020. Two strains of Cronobacter sakazakii sequence type (ST) ST1 and ST31 (serotypes O:1 and O:2) and one strain of Cronobacter malonaticus ST60 (O:1) were identified. All Salmonella strains were identified as Salmonella Typhimurium ST19 (serotype O:4) by average nucleotide identity, ribosomal multilocus sequence typing (rMLST), and core genome MLST (cgMLST). The C. sakazakii and C. malonaticus isolates were resistant to cephalothin, whereas the Salmonella isolates were resistant to oxacillin and ampicillin. Nineteen antibiotic resistance genes were detected in the C. sakazakii and C. malonaticus isolates; the most prevalent were mcr-9.1, bla_CSA, and bla_CMA. In Salmonella, 30 genes encoding for aminoglycoside and cephalosporin resistance were identified, including aac(6′)-Iaa, β-lactamases ampH, ampC1, and marA. In the Cronobacter isolates, 32 virulence-associated genes were detected by WGS and clustered as flagellar proteins, outer membrane proteins, chemotaxis, hemolysins, invasion, plasminogen activator, colonization, transcriptional regulator, survival in macrophages, use of sialic acid, and toxin-antitoxin genes. In the Salmonella strains, 120 virulence associated genes were detected, adherence, magnesium uptake, resistance to antimicrobial peptides, secretion system, stress protein, toxin, resistance to complement killing, and eight pathogenicity islands. The C. sakazakii and C. malonaticus strains harbored I-E and I-F CRISPR-Cas systems and carried Col(pHHAD28) and IncFIB(pCTU1) plasmids, respectively. The Salmonella strains harbored type I-E CRISPR-Cas systems and carried IncFII(S) plasmids. The presence of C. sakazakii and Salmonella in PIF is a health risk for infants aged less than 6 months. For this reason, sanitary practices should be reinforced for its production and retail surveillance.

Introduction

The need to ensure the safety of powdered infant formula (PIF) led the FAO/WHO to establish the microbiological or epidemiological relationship of microbial agents found in PIF with infant infection. They identified three categories of microorganisms based on evidence of a causal relationship between the presence of these microorganisms and the disease they cause. The first category of microorganisms with clear causality were identified as Cronobacter spp. and Salmonella enterica. The second consists of microorganisms for which causality is possible but has not yet been demonstrated. These were mainly from the Enterobacteriaceae family, but also included Acinetobacter. Finally, the third involves microorganisms for which causality is less likely or has not yet been shown and has not been identified in PIF. Based on this the FAO/WHO recommended the absence of Cronobacter spp. and Salmonella in PIF for target age less than 6 months (FAO/WHO, 2004, 2006; Forsythe, 2018).

Cronobacter is a genus of bacterial pathogens consisting of seven species: C. sakazakii, C. malonaticus, C. universalis, C. turicensis, C. muytjensii, C. dublinensis, and C. condimenti (Iversen et al., 2008; Joseph et al., 2012; Stephan et al., 2014). The species with the greatest clinical significance are C. sakazakii and C. malonaticus and have been reported in cases and outbreaks associated with PIF in infants (Forsythe, 2018; Parra-Flores et al., 2021a). At present in the United States, three cases of illness caused by PIF contaminated with C. sakazakii, resulting in one fatality, and one case of Salmonella Newport are being investigated; this has prompted an international voluntary recall of these PIFs by the manufacture (U.S FDA, 2022). The severity of the clinical condition has been associated with the presence of virulence factors encoded on plasmids (Shi et al., 2018; Aly et al., 2019), adherence and invasion traits (Cruz et al., 2011; Parra-Flores et al., 2018a; Holý et al., 2021), and various other genes such as aut, cpA, fliC, hly, ompA, sip, plas, and inv (Cruz et al., 2011; Franco et al., 2011; Aldubyan et al., 2017; Holý et al., 2019). Other factors are the use of sialic acid as a carbon source, capsule composition and the presence of its capsule, and endotoxin production (Ogrodzki and Forsythe, 2015). Another important aspect is the resistance to β-lactam antibiotics such as cephalothin, cefotaxime, ceftazidime, and ampicillin in addition to the presence of resistance genes such as marA, glpT, ampH, blaCSA, and mcr (Flores et al., 2011; Lee et al., 2012; Fei et al., 2017; Holý et al., 2021).

Salmonella enterica is a gram-negative, rod-shaped, facultative anaerobic genus. More than 2,600 serotypes belonging to S. enterica have been described worldwide que incluyen, which can cause diseases in humans and animals (Mezal et al., 2014). Salmonella is the most widely studied microbial pathogen, and can be isolated from a variety of foods, including PIF associated with disease outbreaks in infants (Angulo et al., 2008; Carrasco et al., 2012; Jourdan-da Silva et al., 2018). Gastroenteric Salmonella infections usual develop as self-limiting gastroenteritis, and antibiotic treatment is necessary only in severe cases more often associated with immunocompromised patients or those at the extremes of age such as infants (de Toro et al., 2014). Therefore, the emergence of strains that are resistant to β-lactams and cephalosporins is a relevant public health and food safety problem (Güerri et al., 2004; de Toro et al., 2011; Wang et al., 2019). In addition, Salmonella exhibits virulence factors that play a decisive role in systemic infections, such as pathogenicity islands (PAIs), invasion and adherence genes, and enterotoxin coding (Murugkar et al., 2003; Nayak et al., 2004; Huehn et al., 2010; Thung et al., 2018).

Salmonella and Cronobacter species are known persist in low-moisture foods such as milk powder and powdered infant formula for up to 2 years (Caubilla-Barron and Forsythe, 2007). Consequently, outbreaks due to the consumption of contaminated products have been reported (Forsythe, 2018; Jones et al., 2019). Cronobacter can survive spray-drying and persist in the manufacturing environment as biofilms. Genotyping has shown the persistence of specific Salmonella and Cronobacter strains within production facilities for many years (Craven et al., 2010; Jones et al., 2019). Contamination may occur post-pasteurization due to the addition of contaminated ingredients (FAO/WHO, 2008).

Whole-genome sequencing (WGS) has facilitated the in-depth study of pathogenic organisms by generating extensive information that helps to determine relationships and taxonomic differences between them (Leopold et al., 2014). It is not only used for isolate identification, but also extensive profiling and genotyping; such as conventional 7-loci multilocus sequence typing (MLST), core genome MLST (cgMLST) and/or single nucleotide polymorphism (SNP) analysis, molecular serotyping, CRISPR-Cas array profiling, and detection of genes associated with antibiotic resistance and virulence. Consequently, more precise epidemiological links can be established (Marraffini, 2013; Leopold et al., 2014; Uelze et al., 2020). Therefore, the analysis of the complete genomes and their comparison enables a more complete analysis of the pathogenesis process of C. sakazakii (Lehner et al., 2018).

In 2017, a recall of powdered formula samples contaminated with Cronobacter occurred in Chile (Parra-Flores et al., 2018b). This situation led to the incorporation of microbial criteria (n = 30; c = 0) for Cronobacter spp. in PIF intended for consumption by infants aged less than 12 months into the Chilean Food Sanitary Regulations (RSA; Parra-Flores et al., 2018b), given that the microbiological criteria in the RSA for Salmonella was already defined (n = 10; c = 0). This study considers the safety of PIF from 2018 to 2020 with the objective of performing a genomic characterization of five Cronobacter spp. and six Salmonella strains isolated from PIF sold in Chile. These PIF had been and manufactured in Chile and Mexico.

Materials and Methods

Sampling

A total of 155 PIF samples from two commercial brands whose main ingredient was casein and whey were analyzed. Of these, 80 PIF samples were made in Chile and 75 PIF samples were made in Mexico. The experimental units, milk cans, were obtained monthly from supermarkets and pharmacies because these products are replenished monthly. In addition, this allowed obtaining greater variability in terms of the origin of the production batch.

Isolation and Identification

Cronobacter were isolated according to the method described by Iversen and Forsythe (2004). For each sample, 225 ml buffered peptone water (BPW) was added to 25 g PIF, homogenized in a stomacher at a mean velocity for 60 s, and incubated at 37°C. For Cronobacter spp., 10 ml of each sample was inoculated after incubation at 37°C for 24 h in 90 ml Enterobacteriaceae enrichment broth (BD Difco, Sparks, MD, United States). A loop was extracted from the culture suspension and striated in Brilliance Chromogenic Agar CM 1035 (OxoidTermo-Fisher, Hampshire, United Kingdom) at 37°C for 20 h. Five strains, presumed to be colonies of Cronobacter spp. (green or blue), were striated in trypticase soy agar (BD Difco, Sparks, MD, United States) to verify their purity prior to future analyses. The isolated strains were maintained in a strain collection and stored at −80°C.

The official method (Instituto Nacional de Normalización, 2002) NCh 2675- ISO 6572-2 rev 2017 for Salmonella in Chile was used. From the initial incubated sample of 25 g PIF with 225 ml BPW for 24 h, 0.1 ml was inoculated in 10 ml of Rappaport-Vassiliadis with soya broth (RVS, Oxoid, Hampshire, United Kingdom) and 1 ml in Muller-Kauffmann-Tetrathionate-Novobiocin broth (MKTTn, Merck, Darmstadt, Germany) incubated for 24 ± 3 h at 41.5°C and 37°C, respectively. The colonies were then isolated in Xylose Lysine Deoxycoholate (XLD, Merck, Darmstadt, Germany) and Salmonella chromogenic agar incubated at 37°C for 24 h. The typical colonies were confirmed by biochemical tests. Both pathogens were identified by Matrix-Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS; Bruker, Billerica, MA, United States) and with the MBT Compass IVD software 4.1.60 (Bruker) described by Lepuschitz et al. (2017).

Whole-Genome Sequencing

Before WGS, all the Cronobacter spp. and Salmonella spp. strains were cultured in Columbia blood agar plates (bioMérieux, Marcy-l’Étoile, France) at 37°C for 24 h. DNA was isolated from bacterial cultures with the MagAttract HMW DNA Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. The amount of DNA was quantified on a Lunatic instrument (Unchained Labs, Pleasanton, CA, United States). Nextera XT chemistry (Illumina Inc., San Diego, CA, United States) was used to prepare sequencing libraries for a 2 × 300 bp paired-end sequencing run on an Illumina MiSeq sequencer. Samples were sequenced to achieve a minimum of 80-fold coverage using standard protocols by Illumina. The resulting FASTQ files were quality trimmed and de novo assembled with the SPAdes version 3.9.0. Contigs were filtered for a minimum of 5-fold coverage and 200 bp minimum length with Ridom SeqSphere+ software v. 7.8.0 (Ridom, Münster, Germany; Jünemann et al., 2013).

Sequence Type and Core Genome Multilocus Sequence Typing of Cronobacter spp. and Salmonella spp.

A total of 3,678 targets were used to establish the core genome multilocus sequence typing (cgMLST) scheme of Cronobacter spp. using strain ATCC BAA-894 as a reference using Ridom SeqSphere+ software v. 7.8.0 (Ridom, Münster, Germany; Jünemann et al., 2013). For Salmonella, the cgMLST scheme was performed based on the profile of 2,969 S. enterica target gene loci task template of the Ridom SeqSphere+ software v. 7.8.0 (Ridom, Münster, Germany). According to the cgMLST scheme, isolates were visualized with a minimum spanning tree (MST) to establish their genotypic relationships (Lepuschitz et al., 2019). In addition, the sequences of the seven housekeeping genes of the conventional MLST for Cronobacter spp. and Salmonella were extracted and cross-checked against the Cronobacter MLST database¹ (Baldwin et al., 2009) and Salmonella MLST² (Achtman et al., 2012), respectively. The Cronobacter strains are ID 3409–3413 in the Cronobacter PubMLST database and Salmonella are ID RID389119–RID389124 in the cgMLST database.

Determination of Serotypes

The gnd and galF genes that are specific to the Cronobacter serotype O region was determined by WGS sequence analysis with the BIGSdb tool available in the PubMLST database³ and CroTrait WGS analysis (Wang L. et al., 2021). For Salmonella, the SeqSero 1.2 tool available at https://cge.cbs.dtu.dk/services/SeqSero/ was used (Zhang et al., 2015).

Antibiotic Resistance Profile

The disk diffusion method was used in accordance with the recommendations of the Clinical and Laboratory Standards Institute (CLSI, 2020). The commercial disks that were used consist of ampicillin (10 μg), amikacin (30 μg), cephalothin (30 μg), chloramphenicol (30 μg), ceftriaxone (30 μg), cefotaxime (30 μg), cefepime (30 μg), gentamicin (10 μg), levofloxacin (5 μg), netilmicin (30 μg), oxacillin (1 μg), and sulfamethoxazole-trimethoprim (1.25/23.75 μg). The characterization of the resistance/susceptibility profiles was determined according to the CLSI guidelines. The Escherichia coli ATCC 25922 and Pseudomonas aeruginosa ATCC 27853 strains were used as references.

Detection of Antibiotic Resistance and Virulence Genes

The existence of virulence genes was confirmed by applying the task template function in SeqSphere+ for the WGS data and the ResFinder tool from the Center of Genomic Epidemiology (CGE).⁴ Thresholds for the target scanning procedure were set with a required identity of ≥90% to the reference sequence and an aligned reference sequence ≥99%. The Comprehensive Antibiotic Resistance Database (CARD) with the “perfect” and “strict” default settings for sequence analysis (Jia et al., 2017), the Task Template AMRFinderPlus 3.2.3 available in Ridom SeqSphere+ v. 7.8.0 software using the EXACT method at 100%, and BLAST alignment for protein identification available in the AMRFinderPlus database were used for antimicrobial resistance genes.

Detection of Plasmids and Mobile Genetic Elements

The PlasmidFinder 2.1 and MobileElementFinder 1.0 tools were used to detect plasmids and mobile genetic elements (MGEs). The selected minimum identity was 95% and 90%, respectively (Carattoli et al., 2014; Johansson et al., 2021).⁴

Profiling of CRISPR-Cas Loci Profiling

The search and characterization of CRISPR arrays and their association with Cas proteins was determined with CRISPRDetect and CRISPRminer (available at http://crispr.otago.ac.nz/CRISPRDetect/predict_crispr_array.html and http://www.microbiome-bigdata.com/CRISPRminer; Biswas et al., 2016; Zhang et al., 2018). The following parameters were applied: 18–55 pb repeated sequence length, 25–60 pb spacer length, 0.6–2.5 spacer sequence size as a function of repeated sequence size, and 60% maximum percentage similarity between spacers. The PHASTER program (available at https://phaster.ca/) was used to identify sequences associated with prophages within the study genomes, and the phages associated with the spacer sequences were determined with the CRISPRminer program. The types of CRISPR systems were determined with the CRISPRmap program (Lange et al., 2013). The CRISPRTarget program was used to determine the PAM (protospacer adjacent motif) sequences associated with each of the repeated sequences of the identified arrays.

Results

Identification, Genotyping, and Antibiotic Resistance Profiles of Cronobacter and Salmonella Isolates

Overall positivity for the Cronobacter spp. samples was 6.25% (5/80) and 2.7% (2/75) for Salmonella. Of the five Cronobacter spp. presumptive strains, four were identified as C. sakazakii and one as C. malonaticus. All strains were isolated from different PIF batches from the same manufacturer and country (Chile). The six Salmonella strains were identified as Salmonella Typhimurium. These were from two different batches and tins but same manufacturer and country (Mexico; Table 1).

Table 1.

Identification of Cronobacter spp. and Salmonella spp. strains isolated from powdered infant formula by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) and whole-genome sequencing (WGS).

Sample ID (MLST database)	Country	MALDI-TOF MS	WGS	ST	CC	Serotype
510197-19 (*3409)	Chile	C. sakazakii	C. sakazakii	1	1	O-1
510199-19 (*3410)	Chile	C. sakazakii	C. sakazakii	1	1	O-1
510290-19 (*3411)	Chile	C. sakazakii	C. sakazakii	1	1	O-1
510556-19 (*3412)	Chile	C. sakazakii	C. sakazakii	31	31	O-2
510557-19 (*3413)	Chile	C. malonaticus	C. malonaticus	60	60	O-1
510535-21 (**RD389119)	Mexico	S. Typhimurium	S. Typhimurium	19	19	O-4:-
510536-21 (**RD389120)	Mexico	S. Typhimurium	S. Typhimurium	19	19	O-4:-:-
510537-21 (**RD389121)	Mexico	S. Typhimurium	S. Typhimurium	19	19	O-4:i:1,2
510538-21 (**RD389122)	Mexico	S. Typhimurium	S. Typhimurium	19	19	O:4:i:1,2
510539-21 (**RD389123)	Mexico	S. Typhimurium	S. Typhimurium	19	19	*O-4:i:-
510540-21 (**RD389124)	Mexico	S. Typhimurium	S. Typhimurium	19	19	*O-4:i:-

ST, sequence type and CC, clonal complex.

^*MLST database ID.

^**cgMLST database ID. * Potential monophasic variant of S. Typhimurium.

Three strains of C. sakazakii ST1 (CC1) and ST31, CC31 (serotypes Csak: O:1 and O:2, respectively), and one strain of C. malonaticus ST60, CC60 (O:1) were identified by average nucleotide identity, rMLST, and cgMLST (Figure 1; Table 1).

Figure 1.

Minimum spanning tree (MST) of five strains of Cronobacter sakazakii and one of Cronobacter malonaticus from powdered infant formula manufactured in Chile, complemented with strains of C. sakazakii and C. malonaticus ST1, ST31, and ST60 of clinical and food origin. Calculation of the MST was based on the defined cgMLST scheme comprising 3,678 target genes for C. sakazakii and C. malonaticus. Isolates are represented as colored circles according to the classical MLST. Black numbers according to the allelic differences between isolates. Isolates with closely related genotypes are marked as Cluster.

All Salmonella strains were identified as S. Typhimurium ST19 (CC19; serotype O:4). Two Salmonella strains (510539-21 and 510540-21) were identified as potential monophasic variants of S. Typhimurium according to SeqSero analysis (Figure 2; Table 1).

Figure 2.

Minimum spanning tree of six Salmonella Typhimurium strains from powdered infant formula manufactured in Mexico and supplemented with other S. Typhimurium ST19 strains of clinical and food origin. Calculation of the MST was based on the defined cgMLST scheme comprising 3,002 target genes for Salmonella. Isolates are represented as colored circles according to the classical MLST. Black numbers according to the allelic differences between isolates. Isolates with closely related genotypes are marked as Cluster.

All the C. sakazakii and C. malonaticus strains were susceptible to 10 of the 12 evaluated antibiotics. However, 100% of the Cronobacter strains were resistant to cephalothin and 40% to ampicillin. Meanwhile, 100% of the Salmonella isolates were resistant to oxacillin, 83% to ampicillin, 66.6% to cephalothin, and 16.6% to gentamicin (Table 2).

Table 2.

Antibiotic resistance profile of Cronobacter spp. and Salmonella spp. strains.

	Strain ID	Species	Antibiotics
			AM (10 μg)	AK (30 μg)	CL (30 μg)	CRO (30 μg)	CTX (30 μg)	FEP (30 μg)	GE (10 μg)	KF (30 μg)	LEV (5 μg)	NET (30 μg)	OX (1 μg)	SXT (25 μg)
Cronobacter spp.	510197-19	C. sakazakii	R	S	S	S	S	S	S	R	S	S	S	S
	510199-19	C. sakazakii	S	S	S	S	S	S	S	R	S	S	S	S
	510290-19	C. sakazakii	S	S	S	S	S	S	S	R	S	S	S	S
	510556-19	C. sakazakii	I	S	S	S	S	S	S	R	S	S	S	S
	510557-19	C. malonaticus	R	S	S	S	S	S	S	R	S	S	S	S
Salmonella spp.	510535-21	S. Typhimurium	I	S	S	S	S	S	I	S	S	S	R	S
	510536-21	S. Typhimurium	R	S	S	S	I	S	R	S	S	S	R	S
	510537-21	S. Typhimurium	R	S	S	S	S	S	S	R	S	S	R	S
	510538-21	S. Typhimurium	R	S	S	S	S	S	S	R	S	S	R	S
	510539-21	S. Typhimurium	R	S	S	S	S	S	S	R	S	S	R	S
	510540-21	S. Typhimurium	R	S	S	S	S	S	S	R	S	S	R	S

AM, ampicillin; AK, amikacin; CL, chloramphenicol; CRO, ceftriaxone; CTX, cefotaxime; FEP, cefepime; GE, gentamicin; KF, cephalothin; LEV, levofloxacin; NET, netilmicin; OX, oxacillin; SXT, trimethoprim/sulfamethoxazole; R, resistance; and S, susceptibility; I, intermediate. The values in parentheses in bold correspond to the concentrations of antibiotics.

Detection of Antibiotic Resistance and Virulence Genes

A total of 19 antibiotic resistance genes were detected in the C. sakazakii and C. malonaticus isolates. All the C. sakazakii exhibited bla_CSA-1 and the C. malonaticus strain showed bla_CMA-1, conferring resistance to cephalosporins. Both C. sakazakii ST1 strains harbored the mcr-9.1 gene, conferring resistance to colistin. All the C. sakazakii and C. malonaticus strains exhibited the same efflux genes (adeF, H-NS, msbA, marA, kpnF, kpnE, emrR, emrB, rsmA, and CRP), antibiotic inactivation gene (ampH), and four antibiotic target alteration genes (pBP3, glpT, eF-Tu, and marR; Table 3).

Table 3.

Antibiotic-resistant genes of Cronobacter spp. strains identified by Comprehensive Antibiotic Resistance Database (CARD).

Best hits antibiotic resistance ontology (ARO)	Drug class	Resistance mechanism	510197-19 (ST1)	510199-19 (ST1)	510290-18 (ST1)	510556-19 (ST31)	510557-19 (ST60)
MCR-9.1	Peptide antibiotic	Antibiotic target alteration	+	+	+	−	−
CSA-1	Cephalosporin	Antibiotic inactivation	+	+	+	+	−
CMA-1	Cephalosporin	Antibiotic inactivation	−	−	−	−	+
pBP3	Cephalosporin, cephamycin, and penam	Antibiotic target alteration	+	+	+	+	+
glpT	Fosfomycin	Antibiotic target alteration	+	+	+	+	+
eF-Tu	Elfamycin antibiotic	Antibiotic target alteration	+	+	+	+	+
marR	Fluoroquinolone antibiotic, triclosan, rifamycin antibiotic, penam, phenicol antibiotic, glycylcycline, tetracycline antibiotic, and cephalosporin	Antibiotic target alteration	+	+	+	+	+
adeF	Fluoroquinolone antibiotic and tetracycline antibiotic	Antibiotic efflux	+	+	+	+	+
H-NS	Macrolide antibiotic, fluoroquinolone antibiotic, cephalosporin, cephamycin, penam, and tetracycline antibiotic	Antibiotic efflux	+	+	+	+	+
msbA	Nitroimidazole antibiotic	Antibiotic efflux	+	+	+	+	+
marA	Fluoroquinolone antibiotic, monobactam, carbapenem, cephalosporin, glycylcycline, cephamycin, penam, tetracycline antibiotic, rifamycin antibiotic, phenicol antibiotic, triclosan, and penem	Antibiotic efflux	+	+	+	+	+
pnF	Macrolide antibiotic, aminoglycoside antibiotic, cephalosporin, tetracycline antibiotic, peptide antibiotic, and rifamycin antibiotic	Antibiotic efflux	+	+	+	+	+
kpnE	Macrolide antibiotic, aminoglycoside antibiotic, cephalosporin, tetracycline antibiotic, peptide antibiotic, and rifamycin antibiotic	Antibiotic efflux	+	+	+	+	+
emrR	Fluoroquinolone antibiotic	Antibiotic efflux	+	+	+	+	+
emrB	Fluoroquinolone antibiotic	Antibiotic efflux	+	+	+	+	+
rsmA	Fluoroquinolone antibiotic, diaminopyrimidine antibiotic, and phenicol antibiotic	Antibiotic efflux	+	+	+	+	+
cRP	Fluoroquinolone antibiotic, macrolide antibiotic, and penam	Antibiotic efflux	+	+	+	+	+
kpnH	Macrolide antibiotic, fluoroquinolone antibiotic, aminoglycoside antibiotic, carbapenem, cephalosporin, penam, peptide antibiotic, and penem	Antibiotic efflux	−	−	−	−	−
ampH ampC-type β-lactamase	Cephalosporin and penam	Antibiotic inactivation	+	+	+	+	+

+, presence and −, absence.

In Salmonella, 30 genes that encode for aminoglycoside and cephalosporin resistance were identified, including aac(6′)-Iaa, ampH, ampC1, and marA. All the strains exhibited the same efflux genes (acrAB, golS, mdsA, adeF, marA, kpnF, kpnE, emrRB, rsmA, baeR, H-NS, sdiA, mdfA, mdtK, and kdpE), three antibiotic resistance genes (aac(6′)-Iaa, β-lactamase ampH, and ampC1), and nine antibiotic target alteration genes (bacA, pmrF, uhpT, glpT, PBP3, EF-Tu, soxS, soxR, and marR; Table 4).

Table 4.

Antibiotic-resistant genes of S. Typhimurium strains identified by CARD.

Best hits antibiotic resistance ontology (ARO)	Drug class	Resistance mechanism	510535-21	510536-21	510537-21	510538-21	510539-21	510540-21
AAC(6′)-Iaa	Aminoglycoside antibiotic	Antibiotic inactivation	+	+	+	+	+	+
ampH β-lactamase	Cephalosporin and penam	Antibiotic inactivation	+	+	+	+	+	+
ampC1 β-lactamase	Cephalosporin and penam	Antibiotic inactivation	+	+	+	+	+	+
bacA	Peptide antibiotic	Antibiotic target alteration	+	+	+	+	+	+
pmrF	Peptide antibiotic	Antibiotic target alteration	+	+	+	+	+	+
uhpT	Fosfomycin	Antibiotic target alteration	+	+	+	+	+	+
glpT	Fosfomycin	Antibiotic target alteration	+	+	+	+	+	+
PBP3	Cephalosporin, cephamycin, and penam	Antibiotic target alteration	+	+	+	+	+	+
EF-Tu	Elfamycin antibiotic	Antibiotic target alteration, antibiotic efflux	+	+	+	+	+	+
soxS	Fluoroquinolone antibiotic, monobactam, carbapenem, cephalosporin, glycylcycline, cephamycin, penam, tetracycline antibiotic, rifamycin antibiotic, phenicol antibiotic, triclosan, and penem	Antibiotic target alteration, antibiotic efflux, reduced permeability to antibiotic	+	+	+	+	+	+
soxR	Fluoroquinolone antibiotic, cephalosporin, glycylcycline, penam, tetracycline antibiotic, rifamycin antibiotic, phenicol antibiotic, and triclosan	Antibiotic target alteration, antibiotic efflux, and reduced permeability to antibiotic	+	+	+	+	+	+
marR	Fluoroquinolone antibiotic, cephalosporin, glycylcycline, penam, tetracycline antibiotic, rifamycin antibiotic, phenicol antibiotic, and triclosan	Antibiotic target alteration, antibiotic efflux	+	+	+	+	+	+
acrA	Fluoroquinolone antibiotic, cephalosporin, glycylcycline, penam, tetracycline antibiotic, rifamycin antibiotic, phenicol antibiotic, and triclosan	Antibiotic efflux	+	+	+	+	+	+
AcrB	Fluoroquinolone antibiotic, cephalosporin, glycylcycline, penam, tetracycline antibiotic, rifamycin antibiotic, phenicol antibiotic, and triclosan	Antibiotic efflux	+	+	+	+	+	+
golS	Monobactam, carbapenem, cephalosporin, cephamycin, penam, phenicol antibiotic, and penem	Antibiotic efflux	+	+	+	+	+	+
MdsA	Monobactam, carbapenem, cephalosporin, cephamycin, penam, phenicol antibiotic, and penem	Antibiotic efflux	+	+	+	+	+	+
adeF	Fluoroquinolone antibiotic, and tetracycline antibiotic	Antibiotic efflux	+	+	+	+	+	+
marA	Fluoroquinolone antibiotic, monobactam, carbapenem, cephalosporin, glycylcycline, cephamycin, penam, tetracycline antibiotic, rifamycin antibiotic, phenicol antibiotic, triclosan, and penem	Antibiotic efflux, reduced permeability to antibiotic	+	+	+	+	+	+
kpnE	Macrolide antibiotic, aminoglycoside antibiotic, cephalosporin, tetracycline antibiotic, peptide antibiotic, and rifamycin antibiotic	Antibiotic efflux	+	+	+	+	+	+
kpnF	Macrolide antibiotic, aminoglycoside antibiotic, cephalosporin, tetracycline antibiotic, peptide antibiotic, and rifamycin antibiotic	Antibiotic efflux	+	+	+	+	+	+
emrR	Fluoroquinolone antibiotic	Antibiotic efflux	+	−	+	+	+	+
emrB	Fluoroquinolone antibiotic	Antibiotic efflux	+	+	+	+	+	+
rsmA	Fluoroquinolone antibiotic, diaminopyrimidine antibiotic, and phenicol antibiotic	Antibiotic efflux	+	+	+	+	+	+
baeR	Aminoglycoside antibiotic and aminocoumarin antibiotic	Antibiotic efflux	+	+	+	+	+	+
H-NS	Macrolide antibiotic, fluoroquinolone antibiotic, cephalosporin, cephamycin, penam, and tetracycline antibiotic	Antibiotic efflux	+	+	+	+	+	+
sdiA	Fluoroquinolone antibiotic, cephalosporin, glycylcycline, penam, tetracycline antibiotic, rifamycin antibiotic, phenicol antibiotic, and triclosan	Antibiotic efflux	+	+	+	+	+	+
mdfA	Fluoroquinolone antibiotic, macrolide antibiotic, and penam	Antibiotic efflux	+	+	+	+	+	+
MdtK	Fluoroquinolone antibiotic	Antibiotic efflux	+	+	+	+	+	+
CRP	Macrolide antibiotic, fluoroquinolone antibiotic, and penam	Antibiotic efflux	−	+	+	+	+	+
kdpE	Aminoglycoside antibiotic	Antibiotic efflux	+	+	+	+	+	+

+, presence and −, absence.

Cronobacter sakazakii isolates showed 32 virulence genes that were detected by WGS and clustered as flagellar proteins, outer membrane proteins, chemotaxis, hemolysins, invasion, plasminogen activator (cpa), colonization, transcriptional regulator, survival in macrophages, utilization of sialic acid (nanA,K,T), desiccation tolerance (cheB, wzzB), and toxin-antitoxin genes (fic, relB). In the C. malonaticus strain, the same virulence genes were detected as found in C. sakazakii, except for the cpa and nanAK,T genes (Table 5).

Table 5.

Putative virulence and distribution of other genes in seven strains of Cronobacter spp. by WGS.

Virulence gene	Function	510197-19 (ST1)	510199-19 (ST1)	510290-18 (ST1)	510556-19 (ST31)	510557-19 (ST60)	C. sakazakii BAA-894 (ST1)	C. malonaticus LMG23826T (ST7)	ES15 control (ST125)
flgB	Motility	+	+	+	+	+	+	+	+
flgK	Flagellar hook-associated protein 1	+	+	+	+	+	+	+	+
flgL	Flagellar hook-associated protein 3	+	+	+	+	+	+	+	−
flgM	Negative regulator of flagellin synthesis	+	+	+	+	+	+	+	+
flgN	Flagellar synthesis FlgN protein	+	+	+	+	+	+	+	+
flhD	Flagellar hook-associated protein 2	+	+	+	+	+	+	+	+
fliA	Flagellar operon FliA	+	+	+	+	+	+	+	+
fliC	Flagellin	+	+	+	+	+	−	+	−
fliD	Flagellar hook-associated protein 2	+	+	+	+	+	+	+	+
fliR	Flagellar biosynthetic FliR protein	+	+	+	+	+	+	+	+
fliT	Flagellar FliT protein	+	+	+	+	+	+	+	+
fliZ	FliZ protein	+	+	+	+	+	+	+	+
lolA	Outer membrane lipoprotein carrier protein	+	+	+	+	+	+	+	+
motB	Chemotaxis MotA protein	+	+	+	+	+	+	+	+
sdiA	LuxR family transcriptional regulator	+	+	+	+	+	+	+	+
slyB	Outer membrane lipoprotein SlyB	+	+	+	+	+	+	+	+
tolC	Outer membrane channel protein	+	+	+	+	+	+	+	+
msbA	Survival in macrophage	+	+	+	+	+	+	−	+
mviN	Protective immunity and colonization	+	+	+	+	+	+	+	+
cpa	Plasminogen activator	+	+	+	+	−	+	−	−
hly	Hemolysin	+	+	+	+	+	+	−	+
ompA	Adhesion cell, biofilm formation	+	+	+	+	+	+	+	+
ompX	Adhesion cell	+	+	+	+	+	+	+	+
cheR	Chemotaxis protein methyltransferase	+	+	−	+	+	−	+	−
cheY	Response regulator of chemotaxis family	+	+	+	+	+	+	+	+
cheB	Desiccation tolerance	+	+	+	+	+	+	+	+
lpxA	Epithelial cell invasion and lipid A production	+	+	+	+	+	+	+	+
nanA,K,T	Exogenous sialic acid utilization	+	+	+	+	−	+	−	+
ibpA	Small heat shock protein	+	+	+	+	+	+	+	+
wzzB	Desiccation tolerance	+	+	+	+	+	+	+	+
fic	Cell filamentation protein	+	+	+	+	+	+	+	+
relB	RelE antitoxin	+	+	+	+	+	+	−	+

+, presence and −, absence.

In the Salmonella strains, 120 virulence genes and eight pathogenicity islands were detected. The virulence genes clustered as adherence, magnesium uptake, resistance to antimicrobial peptides, secretion system, stress protein, toxin, resistance to complement killing. The shdA gene associated with persistence of the bacteria in the intestine was only present in the 510535-21 strain. The gogB, pipB, ssaCTU, ssel/srfH, sseL, sspH2, shdA, sopD2, and sseK1,2 genes were not found in the 510536-21, 510537-21, 510538-21, 510539-21, and 510540-21 strains associated with the secretion system, effector proteins, adherence, and host survival (Table 6).

Table 6.

Putative virulence and distribution of other genes in six strains of S. Typhimurium by WGS.

	Genes	510535	510536	510537	510538	510539	510540
Virulence	avrAbcfAbcfBbcfCbcfDbcfEbcfFbcfGcsgAcsgBcsgCcsgDcsgEcsgFcsgGfimCfimDfimFfimHfimIgrvAinvAinvBinvCinvEinvFinvGinvHinvIinvJlpfAlpfBlpfClpfDlpfEmgtBmgtCmig-14misLorgAorgBorgCpefApefBpefCpefDpipB2prgHprgIprgJprgKratBrcksicAsicPsifAsifBsinHsipA/sspAsipB/sspBsipC/sspCsipDslrPsodCIsopAsopB/sigDsopDsopE2spaOspaPspaQspaRspaSspiC/ssaBsptPspvBspvCspvRssaDssaEssaGssaHssaIssaJssaKssaLssaMssaNssaOssaPssaQssaRssaSssaVsscAsscBsseAsseBsseCsseDsseEsseFsseGsseJsseLsspH1steAsteBsteC	+	+	+	+	+	+
	gogBpipBssaCTUssel/srfHsseK2sseLsspH2	+	−	+	+	+	+
	shdA	+	−	−	+	−	−
	sopD2	+	−	−	+	+	+
	sseK1	+	−	+	+	−	+
	TTSS(Type III secretion system)	+	+	+	+	+	+
Pathogenicity islands	SPI-1	+	+	+	+	+	+
	SPI-2	+	+	+	+	+	+
	SPI-3	+	+	+	+	+	+
	SPI-4	+	+	−	+	+	−
	SPI-5	+	+	+	+	+	+
	SPI-9	+	+	+	+	+	+
	SPI-13	+	+	+	+	+	+
	SPI-14	+	+	+	+	+	+

+, presence and −, absence.

Detection of Plasmids and Mobile Genetics Elements

The Col(pHHAD28) plasmids and seven MGEs (IS903, IS26, ISEsa2, IS5075, ISEsa1, ISPpu12, and IS102) were detected in only three C. sakazakii strains. The IncFIB(pCTU1) plasmid and one MGE (IS481) were detected in the C. malonaticus strain.

All the Salmonella strains exhibited the IncFII(S) plasmids and five similar MGEs (ISSen7, ISSty2, ISEcI10, MITEEcl, and ISSen1; Table 7).

Table 7.

Plasmids and mobile genetic elements of Cronobacter spp. and S. Typhimurium.

Bacteria	ID strain	Plasmid	Plasmids accession number	Mobile genetic elements
Cronobacter sakazakii	510197-19	Col(pHDA28)	KU674895	IS903, IS26, ISEsa2, IS5075, ISEsa1, ISPpu12, IS102
	510199-19	Col(pHDA28)	KU674895	IS903, IS26. ISEsa2, IS5075, ISEsa1, ISPpu12, IS102
	510290-18	Col(pHDA28)	KU674895	ISEsa2, IS5075, ISEsa1, ISPpu12, IS102
	510556	----		ISEsa1
Cronobacter malonaticus	510557-19	IncFIB(pCTU1)	FN543094	IS481
Salmonella Typhimurium	510535-21	IncFII(S)	FN543094	ISSen7, ISSty2,ISEcI10, MITEEcl, ISSen1
	510536-21	IncFIB(S) INCFII(S)	FN432031 CP000858	ISSen7, ISSen1, MITEEc1, ISEcl10
	510537-21	IncFII(S)	FN543094	ISSen7, MITEEcl, ISSen1, ISEcI10, ISSty2
	510538-21	IncFII(S)	FN543094	ISSen7, MITEEcl, ISEcI10, ISSen1
	510539-21	IncFII(S)	FN543094	ISSen7, MITEEcl, ISSen1, ISSty2, ISEcI10
	510540-21	IncFII(S)	FN543094	ISSen7, MITEEcl, ISSen1, ISEcI10

CRISPR-Cas Loci Profiling

Genome analysis showed CRISPR-Cas systems in all of the genomes. In Cronobacter spp., 80% (n = 4/5) of the genomes revealed the presence of up to three arrays, which were characterized by the same repeated sequences but at different positions in the genome (Table 8). In the case of Salmonella spp. isolates, 100% (n = 6/6) of the genomes showed two arrays associated with the CRISPR-Cas systems in different positions but characterized by the same number of repeated sequences and spacers, with up to 28 repeated sequences and 27 spacers.

Table 8.

CRISPR-Cas systems identified in the Cronobacter spp. and Salmonella spp. genomes.

Strains	Operon structure type	Position	Maximum number of spacers per strain	Number of CRISPR arrays per strain	Repeat consensus	cas genes
510197-Cronobacter spp.	Type I-F CAS	77362-76641	12	13	GTTCACTGCCGTACAGGCAGCTTAGAAA	DEDDh,cas3,cas8e,cse2gr11,cas7,cas5,cas6e,cas1,cas2
	Type I-E CAS	171199-172847	27	28	CTGTTCCCCGCGCGAGCGGGGATAAACCG
		199092-200862	29	30	GTGTTCCCCGCGCGAGCGGGGATAAACCG
510199-Cronobacter spp.	Type I-E CAS	482009-482670	11	12	GTTCACTGCCGTACAGGCAGCTTAGAAA	cas2,cas1,cas6e,cas5,cas7,cse2gr11,cas8e,cas3,DEDDh
		77461-79109	27	28	CGGTTTATCCCCGCTCGCGCGGGGAA
		105476-107002	25	26	CGGTTTATCCCCGCTCGCGCGGGGAACAG
510290-Cronobacter spp.	Type I-F CAS	480714-481435	12	13	GTTCACTGCCGTACAGGCAGCTTAGAAA	DEDDh, csa3,cas3,cas8e,cse2gr11,cas7,DinG,cas6,cas1
	-Type I-E CAS	173384-175032	27	28	CTGTTCCCCGCGCGAGCGGGGATAAACCG
		201277-203047	29	30	GTGTTCCCCGCGCGAGCGGGGATAAACCG
510556-Cronobacter spp.	Type I-F CAS	161191-162091	9	10	CTGTTCCCCGCGCGAGCGGGGATAAACCG	cas2, csa3,DinG,cas3,DEDDh,csa3,cas2,cas1,cas6e,cas5,cas7,cse2gr11,cas8e
	-Type I-E CAS	7728-8277	15	16	GTGTTCCCCGCGCGAGCGGGGATAAACCG
		199129-200044	15	16	GTTCACTGCCGTACAGGCAGCTTAGAAA
510557-Cronobacter spp.	-Type I-E CAS	6074-7051	17	18	GTGTTCCCCGCGCGAGCGGGGATAAACCG	cas8e,cas3,cse2gr11,cas7,cas5,cas6e,cas1,cas2,csa3,WYL,csa3,DEDDh, DinG
		165212-166676	25	26	CTGTTCCCCGCGCGAGCGGGGATAAACCG
510535-Salmonella spp.	Type I-E CAS	241410-242857	24	25	GTGTTCCCCGCGCCAGCGGGGATAAACCG	DinG,c2c9_V-U4,cas3,cas14j,cas3,PD-DExK,cas2,cas1,cas6e,cas5,cas7,cse2gr11,cas8e
		259017-260604
			27	28	GTGTTCCCCGCGCCAGCGGGGATAAACCG
510536-Salmonella spp.	Type I-E CAS	7220-8807	27	28	GTGTTCCCCGCGCCAGCGGGGATAAACCG	c2c9_V-U4,cas14j,csa3 DinG,DEDDh,cas3,cas2,cas1,cas6e,cas5,cas7,cse2gr11,cas8e,PD-DExK
		27933-29380	24	25	GTGTTCCCCGCGCCAGCGGGGATAAACCG
510537-Salmonella spp.	Type I-E CAS	5320-6907	27	28	GTGTTCCCCGCGCCAGCGGGGATAAACCG	c2c9_V-U4,cas14j,DinG,c2c9_V-U4,DEDDh,cas3,DEDDh,DinG,cas3,cas8e,cse2gr11,cas7,cas5,cas6e,cas1,cas2,cas14j
		23067-24514	24	25	GTGTTCCCCGCGCCAGCGGGGATAAACCG
510538-Salmonella spp.	Type I-E CAS	166775-165328	24	25	GTGTTCCCCGCGCCAGCGGGGATAAACCG	c2c9_V-U4,cas14j,c2c9_V-U4,DinG,DEDDh,cas3,PD-DExK,cas2,cas1,cas6e,cas5,cas7,cse2gr11,cas8e
		184522-182935	27	28	GTGTTCCCCGCGCCAGCGGGGATAAACCG
510539-Salmonella spp.	Type I-E CAS	5479-7066	24	25	GTGTTCCCCGCGCCAGCGGGGATAAACCG	c2c9_V-U4,c2c9_V-U4,csa3,DinG,cas14j,cas3,cas8e,DnG,cse2gr11,cas7,cas5,cas6e,cas1,cas2,DEDDh,cas3
		8963-10410	27	28	GTGTTCCCCGCGCCAGCGGGGATAAACCG
510540-Salmonella spp.	-Type I-E CAS	161008-162455	24	25	GTGTTCCCCGCGCCAGCGGGGATAAACCG	c2c9_V-U4,c2c9_V-U4,DinG,DEDDh,cas3,DEDDh,cas14j,cas3,PD-DExK,cas2,cas1,cas6e,cas5,cas7,cse2gr11,cas8e,cas3
		178615-180202	27	28	GTGTTCCCCGCGCCAGCGGGGATAAACCG

Characteristic repeated sequences of the identified CRISPR arrays and their position in the genome.

Using the CRISPRmap program, the repeated sequences and associated cas genes allowed us to determine that the CRISPR systems identified in Cronobacter spp. genomes belonged to type I-E and I-F in which 60% (n = 3/5) were characterized by the presence of both types of CRISPR-Cas systems. However, the opposite was observed in the Salmonella genomes, which were associated with the presence of type I-E systems. As for the associated cas genes, type I-E systems of the Salmonella genomes showed a larger number of genes associated with the CRISPR arrays (Figure 3).

Figure 3.

CRISPR-Cas systems identified in Cronobacter spp. and Salmonella spp. genomes. The identified systems belong to the I-E and I-F CRISPR-Cas systems.

The analysis of the spacers with CRISPRminer revealed bacteriophage sequences with bacteriophages that are characteristic of Salmonella and Enterobacteriaceae in three Cronobacter spp. genomes (Supplementary Table 1). However, when searching for the PAM sequences, the spacers of the CRISPR arrays identified in the Cronobacter genomes were also characterized by phages associated with the Klebsiella, Streptococcus, and Acinetobacter genera. The spacers of the Salmonella spp. genomes were associated with the crAss and Acanthamoeba polyphaga moumouvirus phages and bacteriophages that are characteristic of Salmonella and E. coli (Supplementary Table 2). These results are correlated with the phages that were identified in the study genomes (Supplementary Table 3).

Discussion

WGS enables the molecular typing of bacterial strains on a routine basis for use in epidemiology and real-time infection control. Its usefulness has also been demonstrated in identifying antimicrobial resistance markers, virulence, and the genetic prediction of antibiotic susceptibility test results (Lepuschitz et al., 2020).

In the present study, we identified and characterized C. sakazakii ST1 and ST31, C. malonaticus ST60, and S. Typhimurium ST19 strains from commercially available PIF. The three strains of the pathovar C. sakazakii ST1 were isolated from different batches of PIF which came same manufacturer. This may reflect either the widespread occurrence of this sequence type in PIF manufacturing environment or that a common ingredient was contaminated with the same strain (Sonbol et al., 2013). Cronobacter sakazakii ST1 has frequently been found in PIF commercialized in different countries, in the PIF processing environment, and in invasive clinical cases such as fatal meningitis and septicemia (Joseph and Forsythe, 2012; Sonbol et al., 2013; Fei et al., 2015; Csorba et al., 2021; Holý et al., 2021; Parra-Flores et al., 2021b). A survey of Cronobacter in the Americas showed the majority of reports isolations were from North America (57.4%, n = 465) and Brazil (42.6%, n = 465). There were a total of 75 sequence types, with the most frequently reported being the C. sakazakii pathovars ST4 (CC4) and ST1 (CC1; Costa et al., 2021).

The cgMLST scheme analysis clustered the three C. sakazakii ST1 strains closely to the C. sakazakii ST1 strains isolated from the food alert in Chile on 2017 with one to three alleles differences (Parra-Flores et al., 2018b). When analyzing the isolated strains of human infections in a European multicenter study using cgMLST, eight C. sakazakii ST1 isolates were found among all the C. sakazakii strains. Of these eight ST1 isolates, two strains were analyzed from an outbreak affecting two newborns suffering from necrotizing enterocolitis in Austria in 2009, which differed by only one allele. In addition, three ST1 isolates from Austria and one ST1 from Denmark differed by 203 alleles with the ATCC BAA-894 strain, which was isolated from PIF associated with a fatal case of an infant in the United States in 2001. Cronobacter sakazakii ST31 has also been isolated from clinical cases with fatal outcomes; however, it has been less prevalent in PIF and the environment (Sonbol et al., 2013; Fei et al., 2015; Ogrodzki and Forsythe, 2015; Lepuschitz et al., 2019). In this context, C. sakazakii ST1, ST8, and especially ST4 are the STs with the highest risk of causing disease in infants (Joseph and Forsythe, 2011; Forsythe, 2018; Lachowska et al., 2021). Cronobacter malonaticus ST60 has also been isolated in powdered milk, food, and the environment, but with less significant clinical cases than C. malonaticus ST7 and C. sakazakii ST1 (Forsythe, 2018; Costa et al., 2021).

Salmonella enterica is the most frequently identified cause of food poisoning in the European Union; serotype Typhimurium ST19 is most often associated with disease and death (Carroll et al., 2017; de Frutos et al., 2018) and commonly identified in human clinical samples, animals, food, and the environment (Panzenhagen et al., 2018; Monte et al., 2020). The cgMLST scheme analysis revealed a cluster of six S. Typhimurium ST19 with one to two allele differences. In this context, the present study is the first to identify S. Typhimurium ST19 in PIF, whereas recent reports of Salmonella outbreaks in PIF have involved S. Agona (Brouard et al., 2007; Jourdan-da Silva et al., 2018). We also presumptively identified two Salmonella monophasic variant strains. Monophasic Salmonella has been identified in different human cases in the United States, Spain, Brazil, and Thailand and characterization of these strains revealed resistance to multiple antibiotics (Mossong et al., 2007).

All five C. sakazakii strains analyzed in the present study were resistant to cephalothin and only two strains to ampicillin. The resistance of C. sakazakii to cephalothin and ampicillin has been reported in several studies, also suggesting an almost intrinsic resistance of the Cronobacter spp. genus to cephalothin (Kim et al., 2008; Molloy et al., 2009; Flores et al., 2011; Chon et al., 2012). Parra-Flores et al. (2020) found that 100% of the C. sakazakii strains isolated in powdered infant formula distributed in Latin America were resistant to cefotaxime and ampicillin, 60% to cefepime, 40% to amikacin, and 20% to cephalothin. Furthermore, one of the strains was resistant to six of the 12 evaluated antibiotics and another strain was resistant to five antibiotics. Multiple drug resistance (MDR) is a cause for concern; in a case of neonatal meningitis caused by C. sakazakii in China, the isolated strains were resistant to eight antibiotics (Zeng et al., 2018).

For the antibiotic resistance genes, all C. sakazakii strains and the C. malonaticus strain exhibited the same efflux, antibiotic inactivation, and antibiotic target alteration genes that confer antibiotic resistance to β-lactams, fluoroquinolones, aminoglycosides, imidazoles, and disinfectants such as triclosan. The marA gene, whose transcription function regulates multidrug efflux, modulates membrane permeability and activates the transcription of the AcrAB-TolC efflux pump that plays an important role in antibiotic resistance (Wang X. et al., 2021). Several authors have detected the msbA, emrR, H-NS, emrB, marA, CRP, PBP3, H-NS, and msrB genes that are associated with antibiotic resistance by using efflux pumps, regulatory systems, and antibiotic target protection genes (Aly et al., 2019; Lepuschitz et al., 2019; Parra-Flores et al., 2021b). All strains exhibited the glpT gene that encodes fosfomycin resistance. This is relevant because fosfomycin is considered effective in patients with MDR bacterial infections (Falagas et al., 2019). We also identified the mcr-9.1 gene that confers resistance to colistin and the bla_CSA and bla_CMA genes that confer resistance to cephalothin. The mcr-9.1 gene can generate resistance to colistin in several enteropathogens and can silently circulate undetected unless induced by colistin (Carroll et al., 2019; Kieffer et al., 2019). The presence of mobile genes resistant to colistin (mcr) is a worldwide public concern because colistin is considered as a last resort to treat infections caused by multidrug-resistant Enterobacteriaceae (Borowiak et al., 2020). The bla_CSA and bla_CMA genes were first described in 2014 and are associated with β-lactamase class C resistance; they are not inducible and are regarded as cephalosporinases (Müller et al., 2014). Jang et al. (2020) found variants of class C bla resistance genes identified as CSA-2 or CSA-1 and CMA in all the analyzed strains. Holý et al. (2021) encountered bla_CSA genes in all the C. sakazakii strains isolated in powdered milk produced in the Czech Republic between 2010 and 2014.

In the present study, all six S. Typhimurium ST19 strains were resistant to oxacillin, five to ampicillin, four to cephalothin, and one to gentamicin. S. Typhimurium ST19 has shown extensive resistance to a variety of critically important antimicrobials (Monte et al., 2020). Jain et al. (2018) reported that all the evaluated S. Typhimurium ST19 strains were resistant to 7 of the 15 tested antibiotics and encountered only strains that were susceptible to ampicillin and gentamicin; this contrasts with our study in which strains were resistant to these two antibiotics.

The Salmonella strains carried many genes such as aac(6′)-Iaa that encodes resistance to aminoglycosides (e.g., gentamicin), which have been found in multidrug-resistant S. Typhimurium strains and caused a foodborne outbreak at a banquet in China in 2017 (Xiang et al., 2020). Wei et al. (2019) reported the presence of β-lactamase ampC genes the same as in our study; in addition to finding β-lactamase ampC-1, we found that it co-harbored with β-lactamase ampH. The β-lactamase ampC is chromosomally encoded in several gram-negative bacteria, including Enterobacter spp., Citrobacter freundii, or Serratia marcescens. Furthermore, plasmid-encoded ampC genes can be horizontally transferred to other Enterobacteriaceae without the presence of chromosomally encoded ampC, such as Klebsiella and Salmonella, resulting in a highly effective and dynamic dissemination mechanism (Ingram et al., 2011). We also found the uhpT and glpT genes that encode fosfomycin resistance. The UhpT and GlpT transporters facilitate fosfomycin incorporation into bacterial cells (Silver, 2017). The reduced expression or introduction of glpT or uhpT mutations and the efflux pump can decrease fosfomycin uptake and thus lower antibiotic susceptibility (Garallah and Al-Jubori, 2020). Several studies have now shown that the overuse of antibiotics in the food chain and the presence of several antibiotic resistance operons promote multiresistance to these drugs (Ferri et al., 2017).

Among the 30 virulence genes detected in C. sakazakii, we identified C. sakazakii ompA and ompX, which encode proteins for basolateral adherence in cell lines and a possible involvement in the blood–brain barrier penetration (Mange et al., 2006; Kim et al., 2010). The Cpa protein is related to C. sakazakii serum resistance and invasion. Recent studies have suggested that the cpa locus could be considered specific for C. sakazakii and C. universalis but is absent in C. malonaticus (Franco et al., 2011). Jang et al. (2020) noted that highly virulent C. sakazakii ST8 clinical strains that carry the pESA3 plasmid do not possess the cpa gene; this indicates that the disease can be associated with other virulence factors. Hemolysins (Hly) are outer membrane proteins occurring in various pathogens belonging to the Enterobacteriaceae family that have hemolytic ability, such as E. coli, Klebsiella, and some gram-positive pathogens (Mare et al., 2020; Mazzantini et al., 2020). Other important genes of the C. sakazakii strains isolated from PIF are nanAKT that encode for exogenous sialic acid utilization. Sialic acid is naturally present in breast milk and supplemented in PIF because of its association with brain development (Forsythe, 2018). Sialic acid also regulates the expression of enzymes, such as sialidase and adhesins, or inhibits transcription factors of the fimB gene that mediates epithelial cell adherence and invasion. Escherichia coli K1 use this compound to modify their cell surface (Severi et al., 2007; Sohanpal et al., 2007).

In our study, we found the fic toxin encoding gene and the relB gene that encodes for the relE antitoxin. The fic gene encodes for the toxin component of the toxin-antitoxin bicistronic operon. The toxin-antitoxin (TA) systems are small genetic elements found in plasmids, phage genomes, and in the chromosomes of different bacterial species. The TA genes play a fundamental role in the physiology of bacterial stress response, such as in stabilizing horizontally acquired mobile genetic elements and participating in a persistence phenotype in some species, including E. coli and Salmonella (Deter et al., 2017; Walling and Butler, 2019). Finkelstein et al. (2019) noted in preliminary studies with C. sakazakii isolates that 2 typical toxin genes, fic and hipA, followed the evolutionary lines of the species and that C. sakazakii ST1 strains were the only strains containing the 22 TA homologs.

In the Salmonella strains, 110 of the 121 detected virulence genes were similar in all the strains, including the invA, ssaAR ssrAB, sipAC, sopBE, spvBC, and rck genes. Genes such as invA, sipA, sopB, and sopE are associated with adherence in epithelial cells and phagocyte invasion. Salmonella pathogenicity islands (SPI) are virulence gene clusters acquired by horizontal transfer that promote virulence in Salmonella spp. The SPI-2 gene encodes genes such as ssaR and ssrA that promote survival and replication within the macrophages and their dissemination in the host (Barilli et al., 2018). The spvC gene enables the survival and rapid growth of Salmonella in the host (Hu et al., 2021), while the spvB gene is known as an intracellular toxin that encodes an enzyme that ADP-ribosylates actin and destabilizes the cytoskeleton of eukaryotic cells (Lesnick et al., 2001).

The rck gene is defined as an invasin that generates a colonization niche or as a cyclomodulin with genotoxic activity (Mambu et al., 2020). To date, 24 SPIs have been identified in Salmonella of which SPI-1 and SPI-2 are the most important for virulence traits. SPI-1 is involved in the entire Salmonella infection process, including invasion, macrophage proliferation, and host responses. Both SPI-1 and SPI-2 encode different secretion systems, such as T3SS, that transport effector proteins to the host cells (Cheng et al., 2019; Lou et al., 2019).

Only the C. sakazakii ST1 strains carried the COL(pHHAD28) plasmids, which are associated with antibiotic resistance genes; these have previously been detected in C. sakazakii strains isolated from dairy products in Chile (Ramsamy et al., 2020; Khezri et al., 2021; Parra-Flores et al., 2021b). The C. malonaticus strain harbored a plasmid homologous to pESA3 called IncFIB (pCTU1), which encodes iron acquisition virulence genes necessary for pathogen survival, but not an external protease known as plasminogen activator (cpa) that enhances pathogen propagation in the host (Franco et al., 2011). Meanwhile, all the Salmonella strains showed IncFII(S) plasmid incompatibility, which is associated with several antibiotic resistance genes, such as aac(6′)-Iaa and mcr-9, and β-lactamase, such as ampC (Cha et al., 2020; Richter et al., 2020).

Bacterial CRISPR-Cas systems are considered as mechanisms of acquired immunity because they provide them with the ability to avoid bacteriophage infection and the acquisition of mobile genetic material from plasmids. This immunity is due to the information stored in the matrices of these systems, specifically in the spacer sequences. It was possible to determine that repeated sequences and cas genes in Cronobacter spp. were associated with two types of systems previously identified as I-E and I-F (Ogrodzki and Forsythe, 2016; Zeng et al., 2017), including cases in which the same strain showed both systems. The opposite was observed in Salmonella spp. genomes that were characterized only by the presence of type I-E (Louwen et al., 2014). Although both genera carry systems of the same type, there are differences between the associated cas gene operons because the Salmonella spp. systems show a larger number of genes. However, these systems are characterized by the presence of the cas1 and cas2 genes, which are involved in integrating and processing the information that is integrated into functional CRISPR arrays. Salmonella enterica and E. coli are closely related and harbor the IE-type CRISPR-Cas system in a similar manner (Makarova et al., 2015). The array sizes of the Cronobacter spp. genomes are smaller. However, when showing more than two arrays, they tend to have a larger number of spacer, suggesting that they have been exposed to a larger number of invasive elements than S. Typhimurium. Although it was not possible to determine that all the spacers of the systems are similar to bacteriophage sequences, there are cases in which they are specific to these genera. Of particular interest, some of the S. Typhimurium spacer were associated with CrAssphages and other phages associated with the human intestinal microbiota (Guerin et al., 2018). It is known that Salmonella is recognized as an intestinal pathogen; therefore, this is an ecosystem where bacteria can acquire external genetic material and integrate it into their genome by various horizontal transfer mechanisms and by other means such as CRISPR-Cas systems (Louwen et al., 2014). The cas1 and cas2 genes are indispensable for integrating and processing the information acquired by the bacteria; in their absence, the system loses the ability to acquire information (Parra-Flores et al., 2021b). The search for prophages in the studied genomes showed that they may have few intact phages. Cronobacter spp. tend to show a larger number of intact and incomplete prophages, which are not only characteristic of this genus but also of Salmonella. Therefore, our data suggest that these genomes carry the necessary information to prevent these bacteriophages from infecting both Cronobacter spp. and Salmonella strains. In addition, this information can be useful in the future when using gene therapy as a therapeutic option for infections caused by these pathogens that are difficult to treat (Gordillo Altamirano and Barr, 2019).

In the present study, WGS allowed us to determine multiple virulence and antibiotic resistance genes in bacterial pathogens isolated from PIF intended for consumption by infants aged less than 6 months and distributed throughout Latin America. In our opinion, the identification of C. sakazakii and S. Typhimurium in PIFs not only violates current health regulations but also endanger the health of infants consuming these products. It is therefore necessary that health authorities conduct more preventive control activities related to these products and carry out campaigns that emphasize the use of rehydration water at 70°C for infant formula. This recommendation by the World Health Organization highlights that the 70°C temperature has a proven effect in significantly decreasing the risk of disease by pathogens in reconstituted PIF (FAO/WHO, 2004, 2006). Control of Salmonella and Cronobacter during the production of milk powder and PIF is through microbiological sampling according to Codex Alimentarius Commission (2007) of finished product, as well as ingredients and intermediate products (International Standards for Organization (ISO), 2017; Podolak and Black, 2017). The use of zoning production facilities to focus environmental sampling according to risk is used (Cordier, 2008; FDA, 2019).

Conclusion

C. sakazakii, C. malonaticus, and S. Typhimurium strains isolated from PIF exhibit antibiotic resistance and various virulence genes and resistance to β-lactam antibiotics. Continuous monitoring of these powdered infant formulas is necessary due to the risk associated with pathogen contamination of the product and consumption by the immunologically vulnerable child population.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Author Contributions

JP-F, OH, SA, SL, AC-C, JM-R, AC, and SF conceived the experiments and prepared the manuscript. JP-F, AC-F, PC-S, AC-C, AP, SL, and WR conducted the laboratory work. JP-F, OH, SA, AC-C, JX-C, JM-R, AC, WR, and SF drafted the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This research was funded by the Research Directorate of the Universidad del Bío-Bío, Projects 191520 4/R and GI 195420/EF.

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

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Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2022.884721/full#supplementary-material