Serotype and antimicrobial resistance of Salmonella from poultry meats in 2021 in Shanghai, China

ABSTRACT

Salmonella is a major cause of food poisoning, and its infection and antimicrobial resistance vary regionally due to different sanitary standards and the use of antimicrobials. The aim of this study is to evaluate the microbiological contamination and antimicrobial resistance of Salmonella in Shanghai, providing references for supervision over food safety and clinical treatment. This study analysed the distribution of serotypes, the distribution of antimicrobial resistance in different poultry meats and serotype of Salmonella, the correlation between different antimicrobial resistance, and multiple drug resistance (MDR) of 139 strains of Salmonella from poultry meats. The results showed that the proportion of Salmonella detected in pigeons was high, which could cause severe diseases to humans. The most common resistance was observed to TET (about 60%), and resistance was even found to IPM. Resistance to two or more antimicrobials was accounted for 75.5%, indicating a wide spreading MDR.

KEYWORDS:

• Salmonella
• poultry meats
• serotype
• antimicrobial resistance

1. Introduction

Salmonella is an important pathogen for poultry meats as well as for human due to zoonotic importance (Sedeik et al., 2019). It is also one of the most common etiologies of foodborne illnesses worldwide (Rincón-Gamboa et al., 2021). There are over 2600 serovars of Salmonella (Ding et al., 2021). The World Health Organization (WHO) declared Salmonella a high-priority pathogen due to increased resistance to first-line antibiotics, fluoroquinolones, and third-generation cephalosporins (Tacconelli et al., 2018; Thames & Sukumaran, 2020). Antibiotic resistance has compromised the effectiveness of antibiotics as a treatment against infections (Garcia et al., 2008; Golkar et al., 2014; Wyatt et al., 1995).

The serotype and antimicrobial resistance of Salmonella vary regionally, and the antimicrobial resistance, in particular, is affected by the commonly used antimicrobials in local practices. Antimicrobial agents are used to alleviate the spread of salmonellosis, which results in the development of bacterial resistance (Nalbantsoy, 2013; Yu et al., 2022). Animal-origin Salmonella strains with a MDR phenotype have been increasingly observed worldwide (Lamas et al., 2016; Yang et al., 2019). Sohyun Cho analysed the Salmonella isolated from a mixed-use watershed in Georgia, showing that S. Muenchen was the predominant serotype (22.7%) (Cho et al., 2022). Thida Kong-Ngoen investigated the antimicrobial resistance and virulence of Salmonella isolated from retail food samples in Bangkok. From 252 raw food samples, 58 Salmonella strains were isolated, and concluded the MDR was seriously in raw food (Kong-Ngoen et al., 2022). Lopatek, M. analysed the prevalence and antimicrobial resistance of bacterial from raw bivalve molluscs in Poland during a Ten-Year Period, showed the local feature that raw bivalve molluscs from the Polish market are frequently contaminated (Lopatek et al., 2022). Qu Mei investigated the serotyping and antimicrobial resistance of Salmonella strains isolated from diarrheal outpatients in Beijing during 2018–2021, identifying some common serotypes and their antimicrobial resistance (Mei et al., 2022). Zhong Yan investigated the prevalence of Salmonella in poultry meats of East China during 2016–2018, in which 110 strains (47.21%) of S.Typhimurium, 82 strains (35.19%) of S.Pullorum, and 25 strains (10.73%) of S.Enteritidis were identified (Yan et al., 2020). Jean-Christophe Augustin analysed the situation in France, concluded that “the thorough implementation of good hygienic practices (GHPs) at the final food preparation step could potentially reduce the FBDB by (67–85%) (CI90)” (Augustin et al., 2020). These publications indicate that the Salmonella distribute with local features.

Poultry meat is one of the major sources of Salmonella transmission to humans (Green et al., 2018; MinFang et al., 2018). Contaminated poultry meat is one of the largest contributors to salmonellosis with some studies suggesting that poultry meat is associated with 25% of outbreaks caused by foodborne pathogens (Akil & Ahmad, 2019). The Salmonella serotypes that frequently infect poultry meats are S.Heidelberg, S.Montevideo, S.Enteritidis and S.Kentucky (Green et al., 2018; Guo, 2017). Aziman, N. also reported the antimicrobial resistance of Salmonella isolated from chicken meat (Aziman et al., 2021).

This study analysed the serotype, antimicrobial resistance phenotype, and antimicrobial resistance gene of poultry-detected Salmonella in Shanghai in 2021, aiming to provide suggestions for food safety supervision and proper usage of antimicrobials.

2. Materials and methods

2.1 Source of samples

The samples in this study were Salmonella strains from food in 16 districts in Shanghai during 2021, which were submitted to Center for Disease Control and Prevention, Shanghai, for a double check. All strains had been examined using standard biochemical tests. Of the 189 Salmonella strains, 139 strains were from poultry meats, accounting for 73.5%. Therefore, this study focused on the analysis of the distribution of salmonella in poultry meats and related coping strategies.

2.2 Reagents

Columbia Blood Agar Plate (Guangdong Huankai Microbial Sci. & Tech. Co., Ltd), Salmonella Serotyping Serum Kit (Statens Serum Institute), antimicrobial susceptibility test plate for Gram-negative aerobic bacteria (Zhuhai Meihua Medical Technology Co., Ltd), and QIAamp DNA Mini KIT(QIAGEN).

2.3 Methods

2.3.1 Serotype

The strains were cultured overnight on Columbia Blood Agar Plate, and the serotype of Salmonella was determined based on the identification of Salmonella somatic (O) and flagellar (H) antigens referring to the Kauffman-White reference catalog.

2.3.2 Susceptibility testing

139 strains of Salmonella were tested for antimicrobial susceptibility using broth micro dilution method in a 96-well plate. The Salmonella isolates were tested against the following 15 antimicrobial agents: tetracycline(TET), chloramphenicol(CHL), compound sulfamethoxazole(SXT), ampicillin(AMP), ampicillin-sulbactam(AMS), cefotaxime (CTX), cefoxitin (CFX), ceftazidime (CAZ), cefazolin (CFZ), imipenem (IPM), gentamicin(GEN), nalidixic acid (NAL), ciprofloxacin(CIP), azithromycin (AZM), and Colistin(CT). Antimicrobial minimum inhibitory concentrations (MICs) were determined using the microbial identification drug sensitivity analysis system in accordance with manufacture (Zhuhai Meihua Medical Technology Co., Ltd)'s instructions. The susceptibility results were determined according to the guidelines recommended by the Clinical and Laboratory Standards Institute (CLSI) (Clinical and Laboratory Standards Institute, 2016). Escherichia coli strains ATCC 25922 were used as quality control microorganisms.

2.3.3 Whole genome sequencing and analysis of resistant genes

Genomic DNA was extracted using QIAamp DNA Mini Kit, and nucleic acid concentration was quantified with Qubit. The qualified samples (A260/A280 ratio ranging between 1.8–2.0 and genomic DNA concentration higher than 100 ng/ųl) were sequenced in Sangon Biotech (Shanghai) Co., Ltd. The single-nucleotide polymorphisms (SNPs) were extracted using Snippy (https://github.com/tseemann/snippy) to generate core genomic alignment.

3. Results

3.1 Distribution of Salmonella serotypes

A total of 31 different Salmonella serotypes were identified among the 139 Salmonella strains, the most frequent were S.Enteritidis, S.Corvallis, S.Indiana, S.Typhimurium, and S.Kentucky. As shown in Figure 1, 95 strains of the above 5 serotypes were detected, accounting for 68.3% of the total strains.

Figure 1. Distribution of different serotypes detected among the 139 Salmonella strains.

The detailed distribution of Salmonella serotypes is shown in Table 1.

Table 1. Distribution of Salmonella serotypes in poultry meats, Shanghai, 2021.

Serotype	Chicken	Duck	Goose	Pigeon	Total	Ratio
Enteritidis	25	6	1	0	32	23%
Corvallis	11	5	3	5	24	17.3%
Indiana	8	2	0	5	15	10.8%
Typhimurium	3	2	1	8	14	10.1%
Kentucky	6	1	2	2	11	7.9%
Agona	0	2	2	2	6	4.3%
Derby	3	0	0	0	3	2.2%
Muenster	0	2	0	1	3	2.2%
Mbandaka	0	1	1	1	3	2.2%
Thompson	2	1	0	0	3	2.2%
Aarhus	1	1	0	0	2	1.4%
Braenderup	0	2	0	0	2	1.4%
London	1	1	0	0	2	1.4%
Newport	1	1	0	0	2	1.4%
Senftenberg	1	0	0	0	1	0.7%
Ball	1	0	0	0	1	0.7%
Entebbe	0	0	0	1	1	0.7%
Havana	0	1	0	0	1	0.7%
Heidelberg	1	0	0	0	1	0.7%
Paratyphi A	0	1	0	0	1	0.7%
Rissen	1	0	0	0	1	0.7%
Kedougou	1	0	0	0	1	0.7%
Koessen	1	0	0	0	1	0.7%
Kottbus	1	0	0	0	1	0.7%
Liverpool	1	0	0	0	1	0.7%
Saintpaul	0	1	0	0	1	0.7%
Wandsworth	0	1	0	0	1	0.7%
Schwarzengrund	1	0	0	0	1	0.7%
Anatum	0	0	1	0	1	0.7%
Idikan	1	0	0	0	1	0.7%
Paratyphi B	1	0	0	0	1	0.7%
Total	72	31	11	25	139
Ratio	51.8%	22.3%	7.9%	18.0%

We can get the following results and conclusions from Table 1.

1. Most detected Salmonella (51.8%) were found in Chicken, the most common poultry meats. Such a significant proportion is due to a large amount of consumption, as chicken meat, chicken wings, and chicken offal in China are processed into food and consumed in diverse ways; therefore, strengthening the supervision over the safety of chicken production is an important task for food safety. This result is consistent with Ref (Ehuwa et al., 2021; Sun et al., 2021; Wessels et al., 2021). The most frequently detected Salmonella serotypes were S.Enteritidis, S.Corvallis, and S.Indiana, three of which combined account for 61.9% of the total. S.Enteritidis and S.Indiana still remained predominant serotypes in poultry meats compared to the distribution of Salmonella serotypes in Shanghai in 2016 (Yue et al., 2018).

   S.Corvallis become an emerging predominant serotype. S.Corvallis was first isolated by Dr. Dickinson in the USA in 1949 from the cecum of chicks suffering from enteritis (Edwards & Hermann, 1949). There have been several cases of food poisoning caused by S.Corvallis worldwide, including the outbreak of food poisoning in Sant'Ilario d'Enza, Italy, in 1985 (Nastasi et al., 1987), and in a hospital in Hyogo Prefecture, Japan, in 2021(Hamada & Tsuji, 2001). China’s first S.Corvallis was isolated from a healthy population in Guangdong Province by Li Yingxia in June 2006 (Yingxia et al., 2010). The first strain isolated during the process of broiler breeding, slaughter and processing in China was reported in 2012 in Henan Province, a major province for farming and animal husbandry (Jie et al., 2013). A possible explanation for S.Corvallis to emerge as a predominant serotype in Shanghai during 2021 was the spread of different serotypes of Salmonella caused either by the frequent introduction and trade between poultry farms in different regions, or the diversified poultry sources. It is worth noting that while the MDR rate of S.Corvallis is lower than other serotypes in Shanghai, it presents an upward trend in antimicrobial resistance.

2. Of note, as an uncommon food, pigeons accounted for 18% of the detected Salmonella. S. Typhimurium accounted for 36% of the Salmonella detected in pigeons. S.Indiana and S.Corvallis were also major serotypes, the three together accounting for 72%. It indicated that pigeons are more risky as a food than chicken, goose and duck. Birds especially pigeons have been mentioned as one of the most important carriers of Salmonella serovars (Madadgar et al., 2009; Pasmans et al., 2008). The ratio and distribution of serotypes of Salmonella is still surprising.

3. S. Enteritidis was the most commonly detected serotype in this study, accounting for 23%. It was also the most frequently reported Salmonella serotype in human infections, revealing that poultry meat, as a major carrier of Salmonella, plays an important role in the transmission of animal infections and human infections. Other Salmonella serotypes commonly recovered from the samples were S.Corvallis and S.Indiana, accounting for 17.3% and 10.8%, respectively.

4. S.Enteritidis, the most frequently detected serotype in this study, was mostly found in chicken, accounting for 80.6%, while pigeons accounted for 57.1% of S.Typhimurium, which could cause severe diseases to humans.

5. The distribution of Salmonella in ducks and geese was comparatively balanced, with no obvious predominant serotypes.

6. It is also worth noting that among the 17 serotypes that were detected in only one sample, 10 were observed on chickens and five on ducks. Such a large proportion indicated that chickens and ducks were prone to carry uncommon Salmonella.

7. 40 frozen and chilled samples tested positive, accounting for 28.8%, much lower than the ambient samples, which suggested that freezing and chilling can effectively reduce Salmonella infection. The fluctuations in temperature may result in the growth of Salmonella and the consequent health problems (Ling et al., 2016).

8. With 31 different serotypes, the serotypes identified in poultry meats in Shanghai in 2021 were more diversified than serotypes detected in poultry meats in East China (in which Shanghai locates) from 2016 to 2018 (Yan et al., 2020).

3.2 Antimicrobial resistance

The antimicrobial resistance in poultry meat samples in Shanghai, 2021, is shown in the Figure 2.

Figure 2. Antimicrobial resistance of 139 Salmonella isolates from poultry meats to selected antimicrobial.

(TET, tetracycline; CHL, chloramphenicol; SXT, compound sulfamethoxazole; AMP, ampicillin; AMS, ampicillin–sulbactam; CTX, cefotaxime; CFX, cefoxitin; CAZ, ceftazidime; CFZ, cefazolin; IPM, imipenem; GEN, gentamicin; NAL, nalidixic acid; CIP, ciprofloxacin; AZM, azithromycin; CT, Colistin)

The current recommendations for antimicrobial therapy for Salmonella infections include extended-spectrum cephalosporins and fluoroquinolones (Alonso-Hernando et al., 2013). In this study, 83 (59.7%) of the 139 poultry meat samples were resistant to TET, which was followed by NAL, 72 (51.8%), and CHL, 67 (48%). This finding was similar to the results of Shen Yuehua, who tested antimicrobial resistance of Salmonella detected from clinical diarrhea patients (Yuehua et al., 2022), suggesting that animal is vital to estimate the potential risks to humans.

Of note, the samples were resistant to every selected antimicrobial, indicating that antimicrobials are commonly used in practices. The least resistance rate was found to IPM (0.72%), with only 1 sample showing resistance, 1 intermediate, and the remaining 137 sensitivity. Less resistance rates were observed in CFX (3.6%), with 5 samples showing resistance, 10 samples intermediate, and 124 samples sensitivity, AZM (10.8%), with 15 samples showing resistance, 124 sensitivity, and CT (14.4%). The samples were also less resistant to CAZ, GEN, CFZ, CTX, etc.

31 (22.3%) samples displayed intermediate resistance to AMS, which ranked the highest among the total samples. It was followed by CIP, with 15 samples (10.8%), and CFX, with 10 (7.2%). The rising number of intermediate resistances indicated that the level of resistance is increasing rapidly. The number of other resistance genes displaying intermediate was relatively small. There are 7 antimicrobials displaying 0 intermediate resistance, which are NAL, CTX, AMP, CFZ, AZM, SXT, and CT, indicating that there was a clear boundary whether the samples were resistant to them or not.

3.3 Multidrug resistance

The resistance of Salmonella samples to 15 antimicrobials is shown in Figure 3.

Figure 3. Distribution of antimicrobial resistance of Salmonella in poultry meats.

The following results can be seen from Figure 3.

1. Among the 139 Salmonella samples in poultry meats, 22 samples (15.8%) were resistant to CHL and TET, which accounted for the highest rate. 12 samples were not resistant to any antimicrobial, accounting for 8.6%.

2. There were three samples that were resistant to 13 of the 15 selected antimicrobials, all of which were from ambient chicken meat. Such a high rate of resistance should be caused by the antimicrobial contamination that occurred along the links of the food industrial chain.

3. The samples were frequently resistant to TET, CHL, and NAL. Carbapenems remain one of the last resorts for the treatment of severe Salmonella infections (Yue et al., 2022). However, in this study, one sample was resistant to IPM, to which Carbapenems belong.

The 15 selected antimicrobials can be divided into 9 classes. The number of samples resistant to 1, 2, and more than 3 classes of antimicrobials was 23 (16.5%), 43 (30.9%), and 61 (43.9%), respectively. A multiple drug resistance (MDR) is defined as resistance to three or more antibiotics belonging to different antibiotic classes (Keshmiri, 2022). According to the definition, there are 43.9% samples belong to MDR. 12 samples (8.6%) were resistant to 7 classes of antimicrobials, and 1 sample was resistant to 8 classes. As shown in Figure 4, there were 32 patterns of antimicrobial resistance in total.

Figure 4. Distribution of resistance to class of antimicrobials.

There were three patterns among the 23 samples resistant to 1 class of antimicrobials: resistance to Quinolones,10 strains (43.5%); resistance to Tetracycline 9 strains (39.1%); and resistance to Phenicol, 4 strains (17.4%). 43 samples were resistant to 2 classes of antimicrobials, presenting 6 patterns.

MDR strains, which is carried by poultry meats, can be transmitted to humans along with food chain through consumption of contaminated foods (Vaez et al., 2020). MDR regulation is urgently needed in the regulation across the entire food chain. Figure 5 presents the 22 patterns of multidrug resistance, with 61 samples, which were resistant to three or more classes of antimicrobials.

Figure 5. Profile of multidrug resistance.

3.4 Correlations between antimicrobial resistance

The following figure shows the correlations between the resistance to the 15 selected antimicrobials. Correlation refers to the rate of being resistant or non-resistant at the same time.

We can get the following results from Figure 6.

1. The value of the correlation coefficient between CFZ and CTX reached 0.98. Differed in only 1 out of 139 samples, samples were always resistant to or not resistant to them at the same time. The high value of the correlation coefficient may be caused by a similar amount of usage in clinical practice.

2. The value of the correlation coefficient between CTX and CAZ was also high, reaching 0.72, which hints they were used in similar situations.

3. The values of the correlation coefficient between GEN and CAZ, GEN and CFZ both reached 0.67.

Figure 6. Correlation of antimicrobial resistance gene.

Two antimicrobials were likely to have some deep links if the value of the correlation coefficient between them was higher than 0.5.

3.5 Distribution of antimicrobial resistance in different poultry meats

The following table shows the distribution of antimicrobial resistance in different poultry meats.

It can be seen from Table 2 that samples collected from each different poultry meat showed predominant resistance to certain antimicrobials. For example, 56.5% of CIP-resistance was found in chicken meat; 47.8% of CHL-resistance was found in chicken meat.

Table 2. Distribution of antimicrobial resistance in different poultry meats.

Antimicrobial agent	Chicken	Duck	Goose	Pigeon	Total
TET	40	18	7	18	83
CHL	32	13	6	16	67
SXT	21	7	3	4	35
AMP	38	14	4	7	63
AMS	13	5	2	3	23
CTX	16	10	2	4	32
CFX	5	0	0	0	5
CAZ	11	7	2	3	23
CFZ	15	10	2	4	31
IPM	1	0	0	0	1
GEN	13	5	2	4	24
NAL	40	13	5	14	72
CIP	13	3	2	5	23
AZM	10	1	2	2	15
CT	15	4	1	0	20
Total	283	110	40	84	517

3.6 Distribution of antimicrobial resistance among serotypes

The average number of antimicrobial resistances by serotype is shown in the figure below.

As seen in the Figure 7, S.Paratyphi B was resistant to 13 antimicrobials, which had the highest rate of resistance to antimicrobials. S.Muenster was resistant to 11.5 anti- microbials on average, which is the second highest. S.Kentucky was resistant to 10.4 antimicrobials on average. S.Kedougou and S.Schwarzengrund were also resistant to 10 antimicrobials. Other serotypes resistant to more than 5 antimicrobials were S.Ball, S.Paratyphi A, S.Saintpaul, S.Idikan, and S.Indiana.

Figure 7. The average number of antimicrobial resistance of the serotypes.

The following table shows the distribution of antimicrobial resistance in the different serotypes Table 3.

Table 3. The distribution of antimicrobial resistance in the different serotypes.

Antimicrobial agent	Enteritidis	Corvallis	Kentucky	Typhimurium	Indiana	Others	Total
TET	6	18	11	8	15	25	83
CHL	1	13	10	4	14	25	67
SXT	3	0	10	1	6	15	35
AMP	18	0	10	6	7	22	63
AMS	3	0	5	2	4	9	23
CTX	0	0	9	0	6	17	32
CFX	2	0	1	0	1	1	5
CAZ	2	0	9	0	2	10	23
CFZ	0	0	9	0	5	17	31
IPM	0	0	1	0	0	0	1
GEN	0	0	9	0	4	11	24
NAL	29	5	10	12	4	12	72
CIP	1	0	11	3	4	4	23
AZM	0	0	9	0	3	3	15
CT	20	0	0	0	0	0	20

The above table shows that S.Enteritidis is predominantly resistant to NAL, CT and AMP, accounting for 78.8% of the resistance of the three antimicrobials. S.Corvallis was mainly resistant to TET and CHL, accounting for 86.1% of the resistance of the two antimicrobials. S.Kentucky was resistant to more antimicrobials, mainly including TET, CIP, AMP,CHL, NAL, and SXT. S.Typhimurium was mainly resistant to NAL, accounting for 33.3% of resistance to NAL. S.Indiana frequently displayed resistance to CHL and TET, which accounted for 38.7%. The distribution of antimicrobial resistance among serotypes can provide preferences for clinical treatment.

3.8 Distribution and analysis of antimicrobial resistance genes

Antimicrobial resistance genes (ARGs), first recognized as emerging contaminants in 2006 (Pruden et al., 2006), could be spread through vertical gene transfer (VGT) and horizontal gene transfer (HGT), with HGT considered to be the primary one (Soucy et al., 2015). This study randomly selected 72 of the 139 samples for whole genome sequencing. A total of 9 kinds of genes associated with phenotypic resistance in antimicrobial-resistant Salmonella isolates were selected. They were tested for resistance genes of tetracycline (tetA), sulphonamides (sul1, sul2, sul3, dfrA14), β-lactams (blaCTX-M-55, blaTEM-1B), aminoglycoside (aac(3)-Id, aph(3'‘)-Ib, aac(6’)-Iaa, aph(6)-Id), quinolone (qnrS1), phenicol (floR,cmlA1), macrolide mph(A), lnu (F), Rifampicin (ARR-2), and Fosfomycin (fosA3). The results of resistance genes and resistance phenotypes in this study are shown in Table 4.

Table 4. Concordance between resistance genes and resistance phenotypes.

The class of antimicrobials	Resistant, resistance genes present	Non-resistant, resistance genes absent	Total number of consistent samples	Resistant, resistance genes absent	Non-resistant, resistance genes present	Total number of inconsistent samples	Consistency rate
Quinolones	11	19	30	31	11	42	42%
Phenicol	28	36	64	8	0	8	89%
Aminoglycosides	12	0	12	0	60	60	17%
Tetracycline	41	23	64	4	4	8	89%
β-lactams	26	2	28	7	37	44	39%
Carbapenems	0	71	71	1	0	1	99%
Macrolides	8	57	65	1	6	7	90%
Sulphonamides	17	35	52	1	19	20	72%
Colistin	0	62	62	10	0	10	86%

It can be seen from Table 4 that the concordance between resistance genes and resistance phenotypes for Aminoglycoside was only 17%, with 60 samples containing resistance genes but showing no resistance to it. This number was followed by that of β-lactams, 39%. There are 44 inconsistent samples, compared to 28 consistent samples. Concordance for other antimicrobials was relatively higher; 86% concordance was observed for Colistin; there was only one sample that displayed resistance to Carbapenems but lacked resistance genes.

3.9 Phylogenomic analysis

Phylogenomic analysis is a meaningful way to analyse the evolution of creatures (Zhang et al., 2022). The brief description is as follows: (I) single-nucleotide polymorphisms (SNPs) were extracted using Snippy (https://github.com/tseemann/snippy) to generate core genomic alignment. (II) Gubbins (Croucher et al., 2015) was then used to remove recombination regions. (III) The core SNP alignment was used to generate a maximum-likelihood phylogeny using RAxML v8.1.23 (Stamatakis, 2014) with the GTR nucleotide substitution model. Furthermore, 100 random bootstrap replicates were conducted to assess the node support. (IV) The phylogenetic tree was visualized together with metadata using Microreact v5.99.0 (Argimón et al., 2016).

Figure 8. SNP analysis of the antimicrobial-resistant Salmonella isolates from 72 Salmonella samples.

In Figure 8, black represents resistance to the antimicrobial drugs or the presence of plasmid replicon types or resistance genes. Grey represents susceptibility to the antimicrobial drugs or the absence of plasmid replicon types or resistance genes. To further determine the phylogenetic characteristics of Salmonella isolates, phylogenomic analysis, shown in Figure 8, was performed on 72 genomes in this study, and a total of 229, 527 core SNPs were extracted, which were used to construct a maximum likelihood tree. Clustering effect was associated with serotypes. Salmonella Enteritidis isolates from different districts in Shanghai shared ≤20 SNPs, implying that clone spread occurred. Similar results were also observed in Salmonella Typhimurium, Salmonella Indiana, Salmonella Corvallis, and Salmonella Kentucky. Furthermore, these isolates were recovered from poultry meats, which indicated that poultry meats could be an important spread vehicle of Salmonella. Therefore, the government should strengthen the regulations and publicity on poultry meats to ensure food safety.

4 Discussion

For each antimicrobial, there are samples resistant, indicating that antimicrobials are widely used in practices, which is a key problem for the food safety in Shanghai, even China.

According to the definition, there are 43.9% samples belong to MDR. 12 samples (8.6%) were resistant to 7 classes of antimicrobials, and 1 sample was resistant to 8 classes. MDR regulation is urgently needed in the regulation across the entire food chain.

The correlations coefficient between antimicrobial resistance show the simultaneously or exclusive usage of antimicrobial. It is interesting and necessary to explore why they are correlated, to reduce MDR.

Although the serotype and antimicrobial resistance of Salmonella vary regionally, and the antimicrobial resistance, in particular, is affected by the commonly used antimicrobials in local practices, the results can reflect the situation of a country or more widely. Also, the analysis and methods can provide reference for other areas.

5 Conclusions and recommendations

In this study, chicken accounted for the highest percentage of Salmonella-positive samples, reaching 51.8%. Therefore, it is necessary to strengthen the test of chickens to prevent Salmonella pathogenesis. Pigeons also accounted for a high rate of positive Salmonella, and the frequently detected serotype is S.Typhimurium, which can be transmitted to humans and cause disease. Increasing the surveillance of foods processed from pigeons with warnings about the risk of consumption is recommended. When making pigeon foods, raw and cooked should be strictly separated.

S.Corvallis, emerging from a rare serotype to a predominant one, is mainly found in chicken. Preventing infection with S.Corvallis in chicken breeding remains an issue to be addressed by the livestock industry.

The results of this study demonstrated a severe situation triggered by the antimicrobial resistance of Salmonella in Shanghai. Resistance to two or more antimicrobials was observed in 75.5% of the samples; resistance to more than 5 antimicrobials was observed in 28% of the samples; some samples were resistant to more than 10 antimicrobials. The high concordance between resistance genes and resistance phenotypes for antimicrobials indicated the vertical gene transfer, while the low concordance rate for Aminoglycoside might be caused by the horizontal gene transfer, including conjugation, transformation, and transduction. According to the results, it is suggest to fine the producer of poultry with multi-resistance, and restrict the usage of antibiotic drug combination, to reduce multi-resistance.

Acknowledgements

Author Contributions: Data collecting: HZ Zhang; writing – original draft preparation, Q Xiao, writing – review and editing, Q Xiao, Y Yu, BY Xu, ZX Fang, WJ Chen, J Feng, YQ Zhu, Y Liu, QF Gu, JY Luo, X Song, ZF Zhang, M Chen; supervision: HZ Zhang. All authors have read and agreed to the published version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study is supported by a grant from the Shanghai Municipal Natural Science Foundation (no. 19ZR1451100 to Hongzhi Zhang, 2019)