Evaluation of the Different Methods to Detect Salmonella in Poultry Feces Samples

doi:10.21203/rs.3.rs-894517/v1

Salmonella is one of the most common causes of food-borne outbreaks and infection worldwide. The gold standard detection method of Salmonella is cultivation. With time-consuming cultivation, there is a need to investigate rapid and accurate processes. The study evaluated different approaches to detect Salmonella in poultry feces samples. Poultry farm feces samples from 21 cities in Iran were collected from January 2016 to December 2019. Microbiological cultures, serological assays, and multiplex PCR (m-PCR) were used to detect and characterize Salmonella spp. isolates. Serological assays and m-PCR were used to determine the serogroups A, B, C1, C2, D1, E, H, and FliC. The m-PCR was used for the detection of seven Salmonella serovars and a chi-square test was performed to compare the discriminatory power of the methods. Out of 2300 poultry feces samples, 173 (7.5%) and 166 (7.2%) samples were detected as Salmonella spp. by cultivation and m-PCR, respectively. The sensitivity of the molecular method was equal to cultivation at 0.96 (CI = 95%). Assessment of H antigenic subgroups showed the same for both m-PCR and serological tests. Therefore, the matching rate of the two methods for detection of all H antigenic subgroups was 100%. Thus, the relationship between the results obtained from both methods was significant in the contingency table test (P < 0.01). The PCR-based approach confirmed the detection of Salmonella in a shorter period (24–36 hours) compared to the conventional microbiological approach (3–8 days).

General Microbiology

Molecular Genetics

Molecular Biology

Salmonella

Poultry. Feces

Multiplex PCR

Culture

Salmonella are gram-negative, rod-shaped bacteria in the family Enterobacteriaceae. Salmonella is the most common bacterial pathogen associated with food-borne disease in the United States (Gu, Strawn, Zheng, Reed, & Rideout, 2019). Poultry products, including meat and eggs, have been a significant source of Salmonella infections (J. Wang, Li, Liu, Cheng, & Su, 2020). Salmonella infections account for 93.8 million cases and 155,000 deaths per year worldwide with several serotypes involved such as Salmonella enterica serovar Enteritidis, S. enterica serovar Senftenberg, S. enterica serovar Hadar, S. enterica serovar Agona, and S. enterica serovar Typhimurian (Gantois et al., 2008). S. enterica serovar Enteritidis is the most reported in human outbreaks during the last two decades (Ghazalibina et al., 2019). Therefore, Salmonella spp. infection prevention is crucial for poultry health and food processing industries.

Accordingly, the diagnosis and serotyping of Salmonella spp. are critical subjects. Conventional Salmonella detection methods includes culturing in a selective medium, followed by colony characterization using biochemical and serological tests (Kasturi, 2020). Most laboratories use serotyping as the main phenotyping method for subspecies Salmonella spp. typing and approximately 2600 serotypes has been described according to the Kauffman–White-LeMinor scheme. Included is the somatic antigen (O) determining the group, flagellar antigen (H) determining the serotype, and capsular antigen (K) (Kariuki, Gordon, Feasey, & Parry, 2015).

Conventional detection methods are laborious and time-consuming. Serotyping methods are often ineffective as epidemiological tools, because of their low discriminatory capacity for strains with the same serotype or similar biochemical characteristics (Barrow & Neto, 2011). Therefore, it not often possible for research laboratories to detect Salmonella in-house and isolates are sent to commercial or expert laboratories, which may delay the results (Diep et al., 2019). Consequently, a rapid and sensitive method to detect Salmonella spp. and their serovars is required.

Some molecular techniques are widely used to detect Salmonella spp. and serovars. Molecular techniques are used instead of conventional methods because of their reduced time for diagnosis with similar or higher efficiency, increased discriminatory power, simplicity, better standardization, reproducibility, and higher sensitivity and specificity (Khaledi & Meskini, 2020; Malorny, Huehn, Dieckmann, Krämer, & Helmuth, 2009). Molecular techniques based on the amplification of DNA, such as multiplex polymerase chain reaction (m-PCR), have been used to detect Salmonella serotypes (Du et al., 2020). The m-PCR uses pairs of primers that allow the simultaneous detection and identification of different specific DNA sequences in the same reaction (Maciorowski, Pillai, Jones, & Ricke, 2005). To the best of our knowledge, the comparison of traditional versus m-PCR techniques to detect Salmonella genus, serogroups, and serovars in Iran has not been conducted. Therefore, this study compared the discriminatory power of several methods such as cultivation, serological, and m-PCR to detect Salmonella genus, serogroups and serovar in farm poultry feces samples.

1.1. Collection of Samples and Isolation of Salmonella

Poultry feces samples from five different areas in each poultry farm were collected. Farms were located in Semnan, Fars, Qazvin, Qom, Yazd, Khorasan Razavi, Mazandaran, Kerman, Alborz, South Khorasan, Kurdistan, Markazi, Isfahan, Kohkiluyeh, Boyerahmad, West Azerbaijan, Golestan, Zanjan, Hamedan, Kermanshah, Khuzestan, and Lorestan, of Iran. Samples were collected in sterile zipper bags from January 2016 to December 2019 using sterile spoons. All samples were transported immediately to the Department of Molecular Microbiology, Pasteur Institute of Iran, while maintaining sterile and cold chain conditions.

A 25 g of each sample was added to 225 mL 0.1% peptone water, and the mixture was incubated overnight at 37°C. After incubation, 0.1 mL of each sample was transferred to 10 mL Rappaport-Vassiliadis Soy Peptone (RVS) Broth (Merck, Germany) and incubated overnight at 41.5°C. Following the incubation, samples were cultured on Xylose Lysine Desoxycholate (XLD) agar (Merck, Germany) and incubated overnight at 37°C. Red colonies with a black center were subcultured in nutrient agar (NA) (Merck, Germany) to perform Gram staining and biochemical tests (Sobur et al., 2019). Gram staining identified morphological characteristics and biochemical identification included sugar fermentation, Voges Proskauer (VP) test, indole, and methyl red (MR) test (Alam et al., 2020). Following the identification of the isolates, serotyping was performed by O (polyvalent), A, B, C1, C2, D1, and E antisera (MAST, Germany).

1.2. Dna Extraction And Molecular Detection Of Salmonella Genus

Genomic DNA was extracted using a High Pure PCR Template Preparation Kit (Roche, Germany) (Meskini & Esmaeili, 2018; Meskini, Ghorbani, Bahadoran, & Esmaeili, 2020). Following the extraction, genomic DNA was subjected to polymerized chain reaction (PCR) amplification of the invA gene using oligonucleotide primers, Fw 5´-AAA CGT TGA AAA ACT GAG GA-3' and Rv 5´-TCG TCA TTC CAT TAC CTA CC-3' (MWG, Germany) (Hoorfar, Ahrens, & Rådström, 2000). The 25 µL of PCR reaction mix final volume contained 15.2 µL distilled water, 2.5 µL buffer 10× (with 15 mM MgCl₂), 1 µL dNTP (10mM), 1 µL MgCl₂ (25 mM), 10 µM of each of the primers, 0.3 µL Hot start Taq DNA polymerase (5U/µL) (QIAGEN, Germany), and 3 µL DNA template. Thermal cycling condition consisted of one cycle of initial denaturation (95°C, 10 min), 35 cycles of denaturation (94°C at 60 s), annealing (62°C at 90 s) extension step (72°C at 60 s), and a final extension cycle (72°C at 10 min). The PCR products were run on a 2% (w/v) agarose gel containing 1 µg/mL ethidium bromide. A 100 bp ladder) was used. The PCR primers, GeneAmp™ PCR Core Kit, and DNA molecular marker were procured from MWG (Germany), Perkin Elmer Cetus (Norwalk CT), and Bethesda Research Laboratories (Inc. Burlington, Ontario), respectively.

1.3. Multiplex PCR to Serogroup Typing Salmonella

The m-PCR was performed with a final volume of 25 µL in a gradient thermal cycler (Eppendorf, Germany). The optimized PCR mixture for A$D group, OriC, and Vi strains consisted of 1.5 µL F- prt (10 µM), 1.5 µL R-prt, 1.5 µL P1 (10 µM), 1.5 µL P2, 1.5 µL F-vi (10 µM), and 1.5µL R-vi, 2.5 µL buffer 10X, 1 µL MgCl₂ (25 mM), 1 µL dNTP (10 mM), 0.3 µL Taq (5 U/µl) (Hot start PCR Taq plus DNA polymerase, QIAGEN, Germany), 5 µL sample (300–500 ng/µL), and 3.2 µL distilled water. The optimized cycling parameters of the m-PCR consisted of pre-denaturation at 95°C for 10 min, followed by 35 cycles of 94°C for 60 s, 56°C for 90 s, 72°C for 60 s, and a final extension at 72°C for 10 min. Inside, the P1-P2 primer pair targeting the oriC gene was included as an internal control in all m-PCR reactions.

The optimized PCR mixture for B, C1, C2, D, and E strains consisted of 1.5 µL F-rfbj (10 µM), 1.5 µL R-rfbj, 1.5 µL F-tyv (10 µM), 1.5 µL R-tyv, 1.5 µL F-wzxC1 (10 µM), 1.5 µL R-wzxC1, 1.5 µL F-wzxE1 (10 µM), 1.5 µL R-wzxE1, 1.5 µL F-wzxC2 (10 µM), and 1.5 µL R-wzxC2, 2.5 µL buffer 10X, 1 µL MgCl₂ (25 mM), 1 µL dNTP (10 mM), 0.3 µL Taq (5 U/µL) (Hot start PCR Taq plus DNA polymerase, QUIAGEN, Germany), 5 µL sample (300–500 ng/µL), and 3.2 µL DW. The optimized cycling parameters of the m-PCR consisted of pre-denaturation at 95°C for 10 min, followed by 35 cycles of 94°C for 60 s, 59°C for 90 s, 72°C for 60 s and a final extension at 72°C for 10 min .

The optimized PCR mixture for Ha, Hb, Hd, Hj, and oriC strains consisted of 1 µL F-H (10 µM), 1 µL R-Ha, 1 µL R-Hb (10 µM), 1 µL R-Hd, 1 µL P1, 1 µL P2, 2.5 µL buffer 10X, 1 µL MgCl₂ (25mM), 1 µL dNTP (10 mM), 0.3 µL Taq (5 U/µl) (Hot start PCR Taq plus DNA polymerase, QIAGEN, Germany), 5 µL of the sample (300–500 ng/µL), and 9.2 µL DW. The optimized cycling parameters of the m-PCR consisted of pre-denaturation at 95°C for 10 min, followed by 35 cycles 94°C for 60 s, 56°C for 90 s, 72°C for 60 s, and a final extension at 72°C for 10 min.

The PCR was performed in a thermocycler (Eppendorf Thermomixer comfort, Germany). The oligonucleotide sequences used in this study, annealing temperature, and the expected band size are listed in Table 1. The PCR product fragments were analyzed in 2% (w/v) agarose gel by electrophoresis using a 1X TAE buffer. Fragment size was determined by comparison with Gene-Ruler 100 bp DNA ladder (Fermentas, EU).

Table 1

The m-PCR primers sequence, target gene, target serogroup detected, and the expected band size
Serogroups	Target gene	Primer name	Oligonucleotide sequences (5to3)	Annealing temperature (°C)	PCR product size (bp)	Reference
B	rfbj	F-rfbj	CCAGCACCAGTTCCAACTTGATAC	59	662	(Lim et al., 2003)
B	rfbj	R-rfbj	GGCTTCCGGCTTTATTGGTAAGCA	59	662	(Lim et al., 2003)
D	tyv	F-tyv	GAGGAAGGGAAATGAAGCTTTT	59	614	(Hirose et al., 2002)
D	tyv	R-tyv	TAGCAAACTGTCTCCCACCATAC	59	614
C1	wzxC1	F-wzxC1	CAGTAGTCCGTAAAATACAGGGTGG	59	483	(Herrera-León et al., 2007)
C1	wzxC1	R-wzxC1	GGGGCTATAAATACTGTGTTAAATTCC	59	483	(Herrera-León et al., 2007)
E	wzxE1	F-wzxE1	TAAAGTATATGGTGCTGATTTAACC	59	345	(Herrera-León et al., 2007)
E	wzxE1	R-wzxE1	GTTAAAATGACAGATTGAGCAGCAAG	59	345	(Herrera-León et al., 2007)
C2	wzxC2	F-wzxC2	ACTGAAGGTGGTATTTCATGGG	59	154	(Herrera-León et al., 2007)
C2	wzxC2	R-wzxC2	AAGACATCCCTAACTGCCCTGC	59	154	(Herrera-León et al., 2007)
A	A$D group	F-prt	CTTGCTATGGAAGACATAACGAACC	55	256	(Hirose et al., 2002)
A	A$D group	R-prt	CGTCTCCATCAAAAGCTCCATAGA	55	256	(Hirose et al., 2002)
OriC	OriC	P1	TTATTAGGATCGCGCCAGGC	55	163	(Widjojoatmodjo, Fluit, Torensma, Keller, & Verhoef, 1991)
OriC	OriC	P2	AAAGAATAACCGTTGTTCAC	55	163	(Widjojoatmodjo, Fluit, Torensma, Keller, & Verhoef, 1991)
Vi	Vi strain	F-vi	GTTATTCAGCATAAGGAG	55	439	(Hirose et al., 2002)
Vi	Vi strain	R-vi	CTTCCATACCACTTTCCG	55		(Hirose et al., 2002)
H	Ha	F-H	ACTCAGGCTTCCCGTAACGC	55	423	(Levy et al., 2008)
	Hb	R-Ha	GAGGCCAGCACCATCAAGTGC		551
	Hd	R-Hb	GCTTCATACAGACCATCTTTAGTTG		763
	Hj	R-Hd	GGCTAGTATTGTCCTTATCGG		502

1.4. Multiplex PCR to Serovar Typing Salmonella

The m-PCR was performed for invA (Styinva-JHO-2), sdf (S. enterica serovar Enteritidis), STM4492 (S. enterica serovar Typhimurium), IE-1 (S. enterica serovar Enteritidis), Flic-C (S. enterica serovar Typhimurium), 878–897 (S. enterica serovar Infantis), had (Salmonella serogroup C2), Cholerae-Suis Flin C, (Heidelberg) heli (predicted helicase), and (S. enterica serovar Kentucky) gly (putative membrane protein) genes in a final volume of 25 µL in a gradient thermal cycler (Eppendorf™, Mastercycler Pro, Germany). The optimized PCR mixture and cycling parameters consisted of pre-denaturation, denaturation, annealing, extension, and the final extension for the mentioned genes (Tables 2 and 3). The PCR product fragments were analyzed in 2% (w/v) agarose gel by electrophoresis using a 1X TAE buffer. Fragment size was determined by comparison with Gene-Ruler 100 bp DNA ladder (Fermentas, EU). Type strain S. enterica serovar Enteritidis (ATCC 13076), S. enterica serovar Typhimurium (ATCC 14028), S enterica serovar Typhimurium (ATCC1730), S. enterica serovar Infantis (ATCC BAA-1675), S. enterica serovar Hadar (ATCC 51956), S. enterica serovar Dublin (ATCC 15480), Enterococcus faecalis (ATCC ® 51299™), Citrobacter freundii (ATCC 8090), Escherichia coli (ATCC 25952), Klebsiella pneumoniae (ATCC 13883), Acinetobacter lwoffii ATCC-type strain 1, and Acinetobacter baumanii (ATCC 19606:1113) were used as m-PCR control.

Table 3

The m-PCR primers sequence, target gene, target serovar detected, and the band size
Target Gene (serovar)	Primer name	Oligonucleotide sequences (5to3)	Annealing temperature (°C)	PCR product size (bp)	Reference
invA (Styinva-JHO-2)	invA -f	AAA CGT TGA AAA ACT GAG GA	62	199	(Barrow & Neto, 2011)
invA (Styinva-JHO-2)	invA -r	TCG TCA TTC CAT TAC CTA CC	62	199	(Barrow & Neto, 2011)
Sdf (S. Enteritidis)	Sdf-f	AAA TGT GTT TTA TCT GAT GCA AGA GG	62	299	(O'Regan et al., 2008)
Sdf (S. Enteritidis)	Sdf-r	GTT CGT TCT GGT ACT TAC GAT GAC	62	299	(O'Regan et al., 2008)
STM4492 (S. Typhimurium)	STM4492-f	ACA GCT TGG CCT ACG CGA G	62	759	(McCarthy et al., 2009)
STM4492 (S. Typhimurium)	STM4492-r	AGC AAC CGT TCG GCC TGA C	62	759	(McCarthy et al., 2009)
IE-1 (S. Enteritidis)	IE-1-for	AGT GCC ATA CTT TTA ATG AC	58	316	(S. J. Wang & Yeh, 2002)
IE-1 (S. Enteritidis)	IE-1-rev	ACT ATG TCG ATA CGG TGG G	58	316	(S. J. Wang & Yeh, 2002)
Flic-C (S. Typhimurium)	Flic-C-for	CCC GCT TAC AGG TGG ACT AC	58	432	(Paião et al., 2013)
Flic-C (S. Typhimurium)	Flic-C-fev	AGC GGG TTT TCG GTG GTT GT	58	432	(Paião et al., 2013)
S. Infantis 878–897	878for	TTG CTT CAG ATG CTA AG	56	413	(Kardos, Farkas, Antal, Nogrady, & Kiss, 2007)
S. Infantis 878–897	1275rev	TTG CTT CAG ATG CTA AG	56	413	(Kardos, Farkas, Antal, Nogrady, & Kiss, 2007)
S. Hadar (Salmonella serogroup C2)	HAD-For	ACC GAG CCA ACG ATT ATC AA	57	502	(Ahmed et al., 2009)
S. Hadar (Salmonella serogroup C2)	HAD-rev	AAT AGG CCG AAA CAA CAT CG	57	502	(Ahmed et al., 2009)
Cholerae-Suis Flin C	Flin C-F	AAG GAA AAG ATC ATG GCA CAA	53	956	(Chiu, Pang, Hwang, & Tsen, 2005)
Cholerae-Suis Flin C	Flin C-R	GAA CCC ACC ATC AAT AAC TTT G	53	956	(Chiu, Pang, Hwang, & Tsen, 2005)
heli (Heidelberg) ORF (predicted helicase)	heli-F	ACAGCCCGCTGTTTAATGGTG	56	782	(Zhu et al., 2015)
heli (Heidelberg) ORF (predicted helicase)	heli-R	CGCGTAATCGAGTAGTTGCC	56	782	(Zhu et al., 2015)
gly (Kentucky) ORF (putative membrane protein)	gly-F	TTCCAATTGAAACGAGTGCGG	56	170	(Mahmud, Bari, & Hossain, 2011)
gly (Kentucky) ORF (putative membrane protein)	gly-R	ACTAACCGCTTGGGTTGTTGCTGT	56	170	(Mahmud, Bari, & Hossain, 2011)

1.5. Statistical Analysis:

The statistical analysis of data was conducted using IBM SPSS version 16.00 (SPSS Inc., Chicago, IL, USA). The chi-square test was used to compare different methods and P < 0.05 was considered statistically significant.

2.1. Salmonella Genus And Serogroup Detection

A total of 2300 poultry feces samples from farms located in 21 cities of Iran were collected from January 2016 to December 2019. The percentage of abundance of different Salmonella serogroups are given in Fig. 1. Among them, 173 (7.5%) samples were detected as Salmonella by cultivation, and 166 (7.2%) samples were detected as Salmonella by amplification of invA gene. Thus, the sensitivity of molecular detection and microbiological cultivation was equal to 0.96 (CI = 95%). Molecular serotyping gave the same results as the antisera approach with A (prt), C1 (wzxC1), and E (wzxE1) serogroups. The concordance of molecular detection of serogroups (Fig. 2) and the serological method for all samples were 100%. Other samples were in different groups, and the matching results of the two molecular and serological serotypes were higher than 93% for all samples. The detection of B (rfb), C2 (wzxC2), and D (tyv) showed that the concordance of molecular serotyping and antisera method was 93.3%, 94.9%, and 94.2%, respectively. Eighteen samples were identified by neither serological nor molecular methods, and were named as unidentified. The D and C2 Salmonella serogroups were the most abundant. The match between molecular serotyping and serology methods for unidentified samples was 94.4%. Also, 10.4% of the samples were not identified.

The accuracy of detecting the flics gene by m-PCR and antigen H by the serological method significantly correlated with the contingency table test (P < 0.01). In the Receiver-Operating Characteristic (ROC) diagram, the sensitivity and specificity of the m-PCR were 92.3% and 95.2%, respectively, compared to the serological method (as gold standard). In evaluating H antigenic subgroups (including Ha, Hb, Hc, and Hj), the same results were obtained with both m-PCR and serological methods for a matching rate of 100%. Therefore, the relationship between methods was significant in the contingency table test (P < 0.01). Figure 3 shows the m-PCR product band to identify the subgroup H1 Salmonella genus (Ha, Hb, Hd, Hj).

2.2. Multiplex PCR to Serovar Typing Salmonella

Figure 4 depicts percentage of abundance of different Salmonella serovars among Salmonella samples and Fig. 5 shows the m-PCR identifying the Salmonella serovars. The molecular serotyping of seven serovars using nine pairs of primers was performed on samples positively identified as Salmonella. The prevalence was determined among positive samples and confirmed by molecular and cultivation approach as S. enterica serovar Enteritidis 50 (28.9%), S. enterica serovar Infantis 22 (12.7%), S. enterica serovar Kentucky 19 (11%), S. enterica serovar Hadar 15 (8.7%), S. enterica serovar Typhimurium 13 (7.5%), S. enterica serovar Choleraesuis 12 (6.9%), S. enterica serovar Heidelberg 5 (2.9%), and another serovar 37 (21.4%).

Salmonella is a life-threatening food-borne zoonotic pathogen with more than 2,500 serotypes. Over 95% of the strains cause infections in humans and animals to belong to serogroups A to D (Diep et al., 2019). Identification of Salmonella is necessary for the prevention, surveillance, and control of food-borne diseases. Therefore, there is a need for rapid detection, identification of sources, control of outbreaks, and identification of emerging serotypes of Salmonella. In this study, traditional (culture and serology) and molecular methods were used to detect Salmonella isolates from poultry farms in Iran. Serogroups and serovars were compared to determine the best fast and valid method.

In this study, 7.5% (173/2300) of the isolates were identified as Salmonella by culturing and 7.2% (166/2300) were identified by PCR (invA). The current study exhibited a lower prevalence of Salmonella than broiler poultry farms in Bangladesh where prevalence ranged from 23–38% (35/100; 36/123; 106/503) (Alam et al., 2020). A longitudinal Salmonella surveillance study was conducted in raw chicken meat in Mexico on 1160 samples collected between 2016–2018 (Regalado-Pineda et al., 2020). The study revealed a significantly higher prevalence (p < 0.0001) of S. entertica in supermarkets (27.2%, 158/580) than in wet markets (9.0%, 52/580) The prevalence of S. entertica was observed in other regions of the world and included Venezuela, the USA, Canada, Wales, Australia, Brazil, Belgium, China, Columbia, Ecuador, Portugal, and Spain, where infection levels ranged between 9.5–65% (Regalado-Pineda et al., 2020). The lower prevalence of Salmonella observed in the current study could be attributed to the sample size (Persoons et al., 2011), where larger samples were compared in previous studies. The sampling sources could be another factor (Taylor, Khush, Peletz, & Kumpel, 2018) as the previous studies included various sampling locations and sources such as cloacal swabs, litter, chicken meat, feed. In comparison, the current study only included fecal samples from poultry farms. Additionally, the geographical locations of the studies could be another factor that influences the current findings (Shah, Sachdev, Coggon, & Hossain, 2011).

Target genes used in our study were previously validated in several studies using PCR and m-PCR assays to detect Salmonella serogroup A-E (Farahani, Ehsani, Ebrahimi-Rad, & Khaledi, 2018). The present study implemented m-PCR of 878–897 gene to identify S. enterica serovar Infantis following previous studies where m-PCR was used on the same gene to identify S. enterica serovar Infantis in spiked chicken feces and meat samples. Furthermore, the current study used STM4492 and fliC genes to identify S. enterica serovar Typhimurium and S. enterica serovar Choleraesuis, respectively. These findings are consistent with previous reports where STM4492 was used as a target marker gene to identify S. enterica serovar Typhimurium and exhibited high specificity and differentiation between the Salmonella serovars (McCarthy et al., 2009). Studies have shown that the STM4492 gene discriminated S. enterica serovar Typhimurium from S. enterica serovar Enteritidis in broiler and chicken meat samples (Paião et al., 2013; Saeki, Alves, Bonfante, Hirooka, & de Oliveira, 2013). The fliC gene is the other target gene for S. enterica serovar Typhimurium and S. enterica serovar Choleraesuis detection that encodes the phase 1 flagellin protein (H1), which is the most frequently used gene to differentiate Typhimurium serovar from the others (TELLI, 2018). Researchers at Konya (Turkey) used the fliC gene to isolate S. enterica serovar Typhimurium from chicken meat and giblets (Telli, Biçer, Kahraman, Telli, & Doğruer, 2018). Furthermore, studies identified Salmonella spp. from pediatric patients and S. enterica serovar Choleraesuis by targeting the FliC gene (Filsner, 2018).

The sdf gene, a chromosome region related to invasion and infection of poultry and eggs, are used for the detection of S. enterica serovar Enteritidis in humans and animals (Del Serrone, 2019). To detect S. enterica serovar Hadar, S. enterica serovar Kentucky, and S. enterica serovar Heidelberg, had, gly, and heli genes were used, respectively. Martínez-Ballesteros et al. (Martínez-Ballesteros et al., 2012) detected had gene by an improved m-PCR method to detected S. enterica serovar Hadar and typing them as S. enterica serovar Hadar. Furthermore, in another study by Ahmed et al. (Ahmed, Younis, Ishida, & Shimamoto, 2009) in Egypt, the had gene was used to detect multidrug resistance in Salmonella spp. isolated from diarrheic calves. The P1-P2 primer pair targeting the oriC gene was included as an internal control in all m-PCR reactions.

The present study found the highest prevalence of S. enterica serovar Enteritidis in fecal samples from poultry in Iran. The lowest prevalence was associated with S. Heidelberg, indicating that live poultry was the source of S. enterica serovar Enteritidis for contamination of raw chicken meat in the primary part of the chain production. The motile Salmonella spp. are mainly associated with food products, and they are the significant causes of salmonellosis in humans (Whiley & Ross, 2015). In our study, approximately 7.5% of Salmonella isolates were confirmed as S. enterica serovar Typhimurium. Similar results were found by Barua et al. (Barua, Biswas, Olsen, Shil, & Christensen, 2013), where 11% of commercial broiler chicken farm isolates were motile Salmonella and Islam et al. (Islam, Mahbub-E-Elahi, Ahmed, & Hasan, 2016) in Bangladesh, where 15.91% of isolates were S. enterica serovar Typhimurium. Alam et al. (Alam et al., 2020) showed that 85.7% of the isolates from Bangladesh were confirmed as motile Salmonella, which is higher than our results. In another study conducted from 154 commercial poultry layer farms in the Southern part of India, a total of 1215 samples containing poultry meat, tissues, egg, and environmental samples were screened for non-typhoidal Salmonella (NTS) serovars. Multiplex-PCR, allele-specific PCR, enterobacterial repetitive intergenic consensus (ERIC) PCR, and pulse field gel electrophoresis (PFGE) revealed 21/1215 (1.73 %) samples positive for NTS (Saravanan et al., 2015). Similarly, during disease outbreaks (40–80% mortality) in poultry farms in Lagos, Ogun and Oyo states, Nigeria, PCR and serotyping conducted on chicken organ samples collected at postmortem examinations identified motile Salmonella serotypes primarily represented by S. enterica serovar Zega (34.14%), S. enterica serovar Kentucky (24.32%), S. enterica serovar Herston (16.22%), S. enterica serovar Nima (10.81%), S. enterica serovar Colindale (2.70%), S. enterica serovar Telelkebir (8.11%) and S. enterica serovar Tshiongwe (2.70%) (Mshelbwala et al., 2017).

Bacterial culture-based techniques are time-consuming, laborious, and have a lower discriminatory capacity. Simultaneously, molecular methods such as m-PCR are crucial in detecting, typing, speciating, and classifying Salmonella at the genus level, serogroups, and serovars. The m-PCR assay is a sensitive, reliable, specific, and highly effective diagnostic test for the simultaneous identification of Salmonella and its serogroups and serovars. However, the cultivation-based PCR-dependent technique has certain limitations, such as the less abundant microbes could not be grown easily, and uncultivable microorganisms are not retrieved, resulting in the wrong interpretation of the result. Conversely, the cultivation-independent PCR-dependent technique is more reliable as it involves the PCR of the metagenome directly retrieved from the environment, devoid of any prior cultivation (Ghosh, 2015). This system could significantly reduce reliance on the tedious conventional serotyping. However, the main issues to be considered are the cost scale-up of these advanced methods and the regulatory necessities. Although the present results are preliminary, the m-PCR assay could offer a valuable alternative to traditional typing methods (culture and serological) to identify and differentiate the most Salmonella spp. in diverse samples. Further investigations should embark on the whole genome sequencing, functional genomics, extraction, and purification of the bioactive compounds from these isolate, which could contribute to understanding the mechanism of infections.

Acknowledgment

Thanks to the Pasteur Institute of Iran, we had a chance to do this study.

Ethical Approval: Not applicable

Consent to Participate: All authors consented to participate

Consent to Publish: All authors consented to publish the manuscript.

Author contributions:

RKF and AHKF were designed the study. RKF, AGL, and MM performed the laboratory procedures and experiments. Safoora. G analyzed the data. MM wrote the manuscript. SG and AHKF edited the manuscript. All authors read the latest version of the manuscript and confirmed that.

Funding: Not applicable

Competing Interests: There were not any competing interests.

Availability of data and materials: Not applicable

Code availability: Not applicable

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Due to technical limitations, Table 2 is only available as a download in the Supplemental Files section.

Table2.docx

Evaluation of the Different Methods to Detect Salmonella in Poultry Feces Samples

Status:

Journal Publication

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Abstract

Figures

Introduction

1. Materials And Methods

1.1. Collection of Samples and Isolation of Salmonella

1.2. Dna Extraction And Molecular Detection Of Salmonella Genus

1.3. Multiplex PCR to Serogroup Typing Salmonella

1.4. Multiplex PCR to Serovar Typing Salmonella

1.5. Statistical Analysis:

2. Results

2.1. Salmonella Genus And Serogroup Detection

2.2. Multiplex PCR to Serovar Typing Salmonella

3. Discussion

Declarations

References

Tables

Supplementary Files

Status:

Journal Publication

Version 1