International lineages of Salmonella enterica serovars isolated from chicken farms, Wakiso District, Uganda.

The growing occurrence of multidrug-resistant (MDR) Salmonella enterica in poultry has been reported with public health concern worldwide. We reported, recently, the occurrence of Escherichia coli and Salmonella enterica serovars carrying clinically relevant resistance genes in dairy cattle farms in the Wakiso District, Uganda, highlighting an urgent need to monitor food-producing animal environments. Here, we present the prevalence, antimicrobial resistance, and sequence type of 51 Salmonella isolates recovered from 379 environmental samples from chicken farms in Uganda. Among the Salmonella isolates, 32/51 (62.7%) were resistant to at least one antimicrobial, and 10/51 (19.6%) displayed multiple drug resistance. Through PCR, five replicon plasmids were identified among chicken Salmonella isolates including IncFIIS 17/51 (33.3%), IncI1α 12/51 (23.5%), IncP 8/51 (15.7%), IncX1 8/51 (15.7%), and IncX2 1/51 (2.0%). In addition, we identified two additional replicons through WGS (Whole Genome Sequencing; ColpVC and IncFIB). A significant seasonal difference between chicken sampling periods was observed (p = 0.0017). We conclude that MDR Salmonella highlights the risks posed to animals and humans. Implementing a robust, integrated surveillance system will aid in monitoring MDR zoonotic threats.

Introduction

Multidrug-resistant (MDR) Salmonella enterica remains a major public health concern as reported in food, animals, humans, and environmental settings, particularly in developing countries. The spread of antimicrobial resistance (AMR) is worldwide [1-6], leading to a high impact on public health and has been deemed a global threat (WHO).

In Uganda, antibiotics, such as tetracyclines and sulfonamides, are increasingly being used and not monitored or regulated in food-producing animals [7]. This practice is well established to select antibiotic-resistant strains that can spread to humans through the food chain. To address this concern and to consider the lack of information regarding AMR in developing countries, Uganda has plans for an integrated national surveillance system for foodborne pathogens using a One Health approach, which is included in their National Action Plan (NAP) for AMR [7].

In Uganda, the poultry production system is divided into two systems, indigenous and exotic flocks. Indigenous chicken, or local birds, make up 88% of the flocks in Uganda, whereas the exotic broilers, kuroilers, and layers make up the rest. There is no current data regarding the total population of poultry in Uganda; however, according to the United Bureau of Statistics (UBOS) in 2008, there were an estimated 52.27 million birds in the country [8]. Hatcheries, which are located in Uganda, are the main source of day-old birds as very few are imported [9]. There is also a lack of information regarding the import and export of live chicken and feed within Uganda as the last census update was conducted in 2005. Feed is supplied to farmers by local feed manufacturers, while a small amount of pre-mixed feed is imported [9]. Commercial poultry in Uganda is primarily kept indoors with screening for ventilation; a small number of chickens are raised at home in the out-of-doors and managed by women and children. Village and backyard production is mainly comprised of free-range poultry [8]. For this study, chickens consisted of broilers, layers, kuroilers, and local (crossbreed) which were housed indoors.

Therefore, we present a cross-sectional study developed in chicken farms in Uganda to investigate the prevalence, AMR, and molecular characterization of Salmonella enterica serovars.

Methods

Ethical statement

This research was field research on private farms in the Wakiso district of Uganda. There were no field permits required for the sample collection.

We did have an exemption waiver for an IRB for geographical locations used to analyze data of AMR from a geographical standpoint. This data was not used in this manuscript due to IRB ethical concerns. The North Carolina State IRB approval number is 17745.

Farm description and bacterial isolates

In our previous study, we reported on the phenotypic characterization of Salmonella isolates from cattle farms. Salmonella isolates were collected from chicken farms in parallel with the collection from cattle farms [5] as part of a cross-sectional study spanning one year. Sampling occurred over two seasons, the rainy season (March to May and September to November) and the dry season (December to February and June to August) [10]. Enrollment in the study occurred through individual contact with producers throughout the Wakiso district. Commercial farms were used in this study located on the west side of Kampala City, Uganda, consisting of rural and small-town farms. Types of chickens on-farm included broilers, layers, kuroilers, and local crossbred chickens, where most farms had two or more types of chickens in production. Most farms had other animals present, either domestic and/or wild, including cattle, horses, pigs, sheep, goats, egrets, turkey, ducks, cats, and dogs.

A total of 20 farms agreed to participate in the study. The first collection was conducted in June (dry seasons), while the second collection was conducted in September (rainy season). A total of 38 farm collections were completed as two farms dropped out of the study in the rainy season. Ten samples per farm were collected at each visit totaling 379 samples (one farm had nine samples).

Drag swabs (3” x 3” sterile gauze pads) in sterile skim milk was the preferred collection tool (Hardy Diagnostics, Inc., Santa Maria, CA) for farm sampling. The sampling was carried out to ensure maximum sampling of the house floor environment and included inside diagonals, feed and water containers, coops, and outer edge wall-to-wall samples. Swabs were individually placed in a sterile whirl-pak bag; the bag was kept on ice in a cooler prior to transport to the laboratory. Isolation of Salmonella was conducted as previously described by Fedorka-Cray et al. [11]. Presumptive-positive Salmonella was confirmed using slide agglutination and antisera for serogroup determination followed by identification of the invA gene (present in all Salmonella spp.) by polymerase chain reaction (PCR). All confirmed isolates were frozen in LB broth with 30% glycerol (Thermo Fisher Scientific Inc, Waltham, MA) and stored at -80°C.

Antimicrobial resistance and molecular characterization

A total of 51 Salmonella were isolated from chicken farms. For analyses, the isolates were retrieved from the -80 frozen stocks, plated on to Tryptic Soy Agar (TSA) with 5% sheep blood (BAP) (Thermo Fisher Scientific Inc, Waltham, MA) and incubated overnight at 37°C. Antimicrobial resistance testing was done using the National Antimicrobial Resistance Monitoring System (NARMS) Gram-negative panels (Thermo Fisher Scientific Inc, Waltham, MA) as described by Ball et al. [5]. Lysates were prepared by suspending a loopful of well-isolated colonies into 200 μl of molecular grade water and vortexed at maximum speed for several seconds. The suspension was boiled at 100°C for 10 minutes, centrifuged at 13 X 1000 rpm for 60 seconds, and the supernatant was collected for use as the DNA template. Plasmid detection using PCR was carried out as previously described in Ball et al. [5].

Whole-genome sequencing

Using the QiAMP commercial kit, DNA extraction was performed (QiAmp tissue, Qiagen, Germany) according to manufacturer’s guidelines. Genomic DNA (n = 51) were sequenced at a 300-bp paired-end-read using the Nextera XT library preparation kit at the MiSeq platform (Illumina, San Diego, CA). De novo assembly was achieved using CLC Genomics Workbench 10.1.1 (Qiagen). Resistome, plasmidome, and multilocus sequence types were identified using multiple public databases such as ResFinder 3.1, PlasmidFinder 2.0, and MLST 2.0, respectively, available from the Center for Genomic Epidemiology (http://genomicepidemiology.org/). Sequence data were deposited in the GenomeTrakr Project.

Statistical analysis

The prevalence of Salmonella was analyzed using WHONET and Microsoft Excel. A logistic regression model was used in SAS® software (SAS® Cary, NC), where season (rainy and dry) served as the factor. Farm was included as a random effect.

Results

From the 20 farms sampled once during each season, rainy and dry, 379 samples were collected, resulting in 51 positive Salmonella isolates. Eight of the 20 farms did not result in a positive sample for Salmonella during the study. None of the farms sampled in this study had free-range chickens; all chickens were housed indoors with screening as a source of ventilation. Table 1 displays the results by serotype, AMR phenotype, AMR genotype, and plasmid identification. The 51 Salmonella isolates (51/379; 13.5%) belonging to eight different serotypes: Salmonella serovar Enteritidis (31.3%); S. Kentucky (21.6%); S. Zanzibar and S. Virchow (15.7%); S. Newport and S. serovar 42:r:- (5.88%), S. Typhimurium (4%) and S. Barranquilla at (2.0%). The overall prevalence of Salmonella was higher in the rainy season (p = 0.0017). No interaction between serotype and season was observed.

Table 1

Antimicrobial resistance phenotype and genotype comparison of Salmonella from chickens in the Wakiso district of Uganda (n = 51).

Farm	Sample IDBiosample #	Season	Serovar	ST	Resistance profile (MIC)	Resistance genes	gyrA	parC	Plasmids
1	SALM-01SAMN06240035	Dry	Enteritidis	11	Pansusceptible	strA, strB, aadA1, blaTEM-1B, sul2, sul3, tetA	none	none	IncFII(S), IncFIB (S), ColpVC
1	SALM-02SAMN06240034	Dry	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S), ColpVC
1	SALM-03SAMN06240033	Dry	Enteritidis	11	Pansusceptible	sul2	none	none	IncFII(S), IncFIB (S), ColpVC
1	SALM-04SAMN06240032	Rainy	Typhimurium	19	AMP, SOX	aadA1, blaTEM-1B, qacL, sul3	none	none	Incl1, IncFII(S), IncFIB (S), ColpVC
1	SALM-05SAMN06240031	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S), ColpVC
1	SALM-06SAMN06240030	Rainy	Typhimurium	19	AMP, SOX	aadA1, blaTEM-1B, qacL, sul3	none	none	Incl1, IncFII(S), IncFIB (S), ColpVC
1	SALM-07SAMN06240029	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S), ColpVC
3	SALM-08SAMN06240028	Dry	Kentucky	198	AMP, CIP, NAL, STR, SOX, TCY, SXT	aadA1, aph(6)-Id, strA, strB, blaTEM-1B, dfrA14, qacL, sul2, sul3, tet(A)	S83F/D87N	S80I	ColpVC, Incl1
3	SALM-09SAMN06240027	Dry	Kentucky	198	CIP, NAL	Pansusceptible	S83F/D87N	S80I	ColpVC
3	SALM-10SAMN06240026	Dry	Kentucky	198	CIP, NAL, STR, SOX, TCY	aph(3'')-Ib, aph(6)-Id, strA, strB, sul2, tet(A)	S83F/D87N	S80I	ColpVC
3	SALM-11SAMN06240025	Dry	Kentucky	198	CIP, NAL, STR, SOX, TCY	aph(3'')-Ib, aph(6)-Id, strA, strB, sul2, tet(A)	S83F/D87N	S80I	ColpVC
3	SALM-12SAMN06240024	Rainy	Kentucky	198	CIP, NAL, STR, SOX, TCY	aph(3'')-Ib, aph(6)-Id, strA, strB, sul2, tet(A)	S83F/D87N	S80I	ColpVC
3	SALM-13SAMN06240023	Rainy	Kentucky	198	CIP, NAL, STR, SOX, TCY	aph(3'')-Ib, aph(6)-Id, strA, strB, sul2, tet(A)	S83F/D87N	S80I	ColpVC
3	SALM-14SAMN06240022	Rainy	Kentucky	198	CIP, NAL, STR, SOX, TCY	aph(3'')-Ib, aph(6)-Id, strA, strB, sul2, tet(A)	S83F/D87N	S80I	ColpVC
3	SALM-15SAMN06240021	Rainy	Kentucky	198	CIP, NAL, STR, SOX, TCY	aph(3'')-Ib, aph(6)-Id, strA, strB, sul2, tet(A)	S83F/D87N	S80I	ColpVC
3	SALM-16SAMN06240020	Rainy	Kentucky	198	CHL, CIP, NAL, STR, SOX, TCY, SXT	qnrS1, aadA1, aadA2, aph(6)-Id, strA, strB, cmlA1, dfrA14, sul2, sul3, tet(A)	S83F/D87N	S80I	ColpVC, Incl1
3	SALM-17SAMN06240019	Rainy	Kentucky	198	CIP, NAL, STR, SOX, TCY	strA, strB, sul2, tetA	none	none	ColpVC
3	SALM-18SAMN06240018	Rainy	Kentucky	198	AMP, CIP, NAL, SOX	aadA1, blaTEM-1B, sul3	S83F/D87N	S80I	ColpVC, Incl1
4	SALM-19SAMN06240017	Dry	Zanzibar	466	TCY	tetA	none	none	Incl1
4	SALM-20SAMN06240016	Dry	Zanzibar	466	TCY	tetA	none	none	Incl1
4	SALM-21SAMN06240015	Dry	Zanzibar	466	TCY	tetA	none	none	ColpVC, Incl1
4	SALM-22SAMN06240014	Dry	Zanzibar	466	TCY	tetA	none	none	Incl1
4	SALM-23SAMN06240013	Dry	Zanzibar	466	TCY	tetA	none	none	ColpVC, Incl1
4	SALM-24SAMN06240012	Dry	Zanzibar	466	Pansusceptible	Pansusceptible	none	none	ColpVC
4	SALM-25SAMN06240092	Dry	Zanzibar	466	TCY	tetA	none	none	Incl1
4	SALM-26SAMN06240091	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S)
4	SALM-27SAMN06240090	Rainy	Zanzibar	466	TCY	tetA	none	none	Incl1
4	SALM-28SAMN06240089	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S)
4	SALM-29SAMN06240088	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S)
4	SALM-30SAMN06240087	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S)
4	SALM-31SAMN06240086	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S)
4	SALM-32SAMN06240085	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S)
5	SALM-33SAMN06240084	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S)
8	SALM-34SAMN06240083	Rainy	Virchow	16	NAL, TCY	tetA	S83Y	none	none
8	SALM-35SAMN06240082	Rainy	Virchow	16	NAL, TCY	tetA	S83Y	none	none
8	SALM-36SAMN06240081	Rainy	Virchow	16	NAL, TCY	tetA	S83Y	none	none
8	SALM-37SAMN06238262	Rainy	Virchow	16	NAL, TCY	tetA	S83Y	none	none
8	SALM-38SAMN06238261	Rainy	Virchow	16	NAL, TCY	tetA	S83Y	none	none
8	SALM-39SAMN06238260	Rainy	Virchow	16	NAL, TCY	tetA	S83Y	none	none
8	SALM-40SAMN06238259	Rainy	Virchow	16	NAL, TCY	tetA	S83Y	none	none
9	SALM-41SAMN06238258	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S), Col440I
9	SALM-42SAMN06238257	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	none
9	SALM-43SAMN06238276	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S), Col440I
10	SALM-44SAMN06238275	Dry	42:r:-	1208	STR	Pansusceptible	none	none	Col440I
14	SALM-45SAMN06238274	Rainy	Newport	166	NAL, TCY	qnrS1, tetA	none	none	IncX2
14	SALM-46SAMN06238273	Rainy	42:r:-	1208	STR	Pansusceptible	none	none	none
15	SALM-47SAMN06238272	Dry	Barranquilla	3807	Pansusceptible	Pansusceptible	none	none	none
17	SALM-48SAMN06238271	Rainy	Virchow	16	NAL, TCY	tetA	S83Y	none	none
17	SALM-49SAMN06238270	Rainy	Enteritidis	11	Pansusceptible	Pansusceptible	none	none	IncFII(S), IncFIB (S)
18	SALM-50SAMN06238269	Dry	Newport	46	Pansusceptible	Pansusceptible	none	none	none
20	SALM-51SAMN06238268	Rainy	42:r:-	1208	STR	Pansusceptible	none	none	none

Antibiotics: AMC = Amoxicillin-Clavulanic Acid, AMP = Ampicillin, AZM = Azithromycin, FOX = Cefoxitin, TIO = Ceftiofur, CRO = Ceftriaxone, CHL = Chloramphenicol, CIP = Ciprofloxacin, GEN = Gentamicin, NAL = Nalidixic Acid, STR = Streptomycin, SOX = Sulfisoxazole, TCY = Tetracycline, SXT = Trimethoprim-Sulfamethoxazole

Farms that are not displayed were negative for Salmonella (2,6,7,11,12,13,16,19). All farms that show a negative sign in the table were negative for Salmonella for that particular season. Farm 11 was negative for Salmonella for the dry season and did not participate for the rainy season; therefore, both are not shown in the table. Farm 15 only participated in the dry season, as shown in the table.

The isolates displayed resistance to eight antimicrobials including tetracycline (51%), nalidixic acid (37.3%), sulfisoxazole (23.5%), ciprofloxacin (21.6%), streptomycin (13.7%), ampicillin (7.8%), sulfamethoxazole (3.9%), and chloramphenicol (2%). Phenotypically, all Salmonella Enteriditis were pan-susceptible, and all except one Salmonella Kentucky were MDR isolates. No interaction was observed between serotype and season (Table 2).

Table 2

Serotype distribution on farms by season.

Farm ID	Season
	Dry	Rainy
1	S. Enteritidis (n = 2), S. Typhimurium (n = 2)	S. Enteritidis (n = 3)
3	S. Kentucky (n = 7)	S. Kentucky (n = 4)
4	S. Zanzibar (n = 1), S. Enteritidis (n = 6)	S. Zanzibar (n = 7)
5	S. Enteritidis (n = 1)	-
8	-	S. Virchow (n = 7)
9	-	S. Enteritidis (n = 3)
10	S. 42:r- (n = 1)	-
14	-	S. 42:r- (n = 1), S. Newport (n = 1)
15	S. Barranquilla (n = 1)	ND
17	-	S. Virchow (n = 1), S. Enteritidis (n = 1)
18	S. Newport (n = 1)	-
20	-	S. 42:r- (n = 1)

Farms that are not displayed were negative for Salmonella (2,6,7,11,12,13,16,19). All farms that show a negative sign in the table were negative for Salmonella for that particular season. Farm 11 was negative for Salmonella for the dry season and did not participate for the rainy season; therefore, both are not shown in the table. Farm 15 only participated in the dry season, as shown in the table.

Table 3 and Table 4 display the AMR phenotypes by class of antibiotics as well as the frequency (%) of resistance patterns. Ten isolates (all of which are Salmonella Kentucky) displayed MDR (resistant to three or more classes), as seen in Table 3. Resistance to both nalidixic acid and tetracycline only occurred within Salmonella serovars Virchow and Newport. Other patterns observed included TCY (S. Zanzibar), STR (S. 42:r-), and AMP-SOX (S. Typhimurium) resistance. All other patterns were observed among S. Kentucky isolates.

Table 3

MDR resistance of Salmonella from chicken (n = 51).

Resistance Pattern	N (%)
No Resistance Detected	19 (37.3)
Resistance = 1 CLSI Class1	11 (21.6)
Resistance = 2 CLSI Classes1	11 (21.6)
Resistance = 3 CLSI Classes1	1 (2.0)
Resistance = 4 CLSI Classes1	7 (13.7)
Resistance = 5 CLSI Classes1	2 (3.9)

Clinical and Laboratory Standards Institute Class1: Antibiotic class including penicillin

Table 4

Top resistance patterns for Salmonella from chicken (n = 51).

Resistance pattern	N (%)
NAL TCY	9 (17.6)
TCY	7 (13.7)
CIP NAL STR SOX TCY	7 (13.7)
STR	3 (5.9)
AMP SOX	2 (3.9)
CIP NAL	1 (2.0)
AMP CIP NAL SOX	1 (2.0)
CHL CIP NAL STR SOX TCY SXT	1 (2.0)
AMP CIP NAL STR SOX TCY SXT	1 (2.0)

Antibiotics: AMC = Amoxicillin-Clavulanic Acid, AMP = Ampicillin, AZM = Azithromycin, FOX = Cefoxitin, TIO = Ceftiofur, CRO = Ceftriaxone, CHL = Chloramphenicol, CIP = Ciprofloxacin, GEN = Gentamicin, NAL = Nalidixic Acid, STR = Streptomycin, SOX = Sulfisoxazole, TCY = Tetracycline, SXT = Trimethoprim-Sulfamethoxazole

Whole-genome sequencing analysis revealed the presence of resistance genes to tetracycline [tetA; 53%], sulfonamides [sul2 (21.5%); sul3 (11.7%)], streptomycin [strA (19.6%); strB (19.6%)], aminoglycosides [aph(6)-Id (15.6%); aph(3'')-Ib (11.7%); aadA1 (11.7%); aadA2 (2%)], β-lactams [blaTEM-1B; 9.8%], quaternary ammonium [qacL; 5.8%], quinolones [qnrS1; 5.8%] and trimethoprim [dfrA14; 4%]. Genes were noted as quinolone resistance determining regions (QRDR) with point mutations in gyrA and parC (Table 1). Ten isolates (19.6%) showed a double amino acid mutation in GyrA (GyrA-S83F-D87N), whereas eight isolates (15.6%) showed a single amino acid substitution of serine to tyrosine at codon 83. For QRDR in parC (n = 10; 19.6%), only one substitution in serine to isoleucine at codon 80 was observed. Sequencing identified six plasmids. IncFII(S)-IncFIB (S)-ColpVC were most common in S. Enteritidis; Incl1-ColpVC in S. Kentucky and S. Zanzibar; IncX2 in S. Newport; Incl1-IncFII(S)-IncFIB (S)-ColpVC in S. Typhimurium and Col440I in S. serovar 42:r:-. Nine sequence types (ST), namely ST11, ST198, ST466, ST16, ST166, ST46, ST19, ST1208, and ST3807 were associated with S. Enteritidis, S. Kentucky, S. Zanzibar, S. Virchow, S. Newport, S. Newport, S. Typhimurium, S. serovar 42:r:- and S. Barranquilla, respectively. Five of the 28 plasmids that were screened through PCR were observed in multiple isolates: IncFIIS (17/51; 33.3%), IncI1α (12/51; 23.5%), IncP (8/51; 15.7%), 193 IncX1 (8/51; 15.7%), and IncX2 (1/51; 2.0%). After analyzing the WGS sequences for plasmids, 12 isolates were found to harbor IncI1α, with seven of the 12 having an additional plasmid (ColpVC) that was not detected by PCR (ColpVC was not included in the PCR kit) and two with IncFIIS plasmid. Seventeen isolates carried the IncFIIS plasmid. These same 17 isolates also presented IncFIB (S) plasmids, and ColpVC and Col4401 were identified in seven and two isolates, respectively. IncX2 and IncP were not identified in the WGS analysis and by PCR. PCR did not detect plasmids in ten isolates, but WGS detected ColpVC in nine isolates and Col4401 in one isolate. IncFIIS was the most common plasmid identified at 33.3% (17/51). Overall, it was seen how the use of WGS presented a more robust and accurate data analysis for resistance genes present in the isolates. Phenotypic data will not always allow for a good representation of what genes are present as genotypic data.

Based on the output provided for this study, there was a significance (p = 0.0017) seen during the rainy seasons as compared to the dry with a higher presence of positive Salmonella.

Discussion

The percent prevalence of Salmonella (13.5%) in this study highlights the potential risk to humans in Ugandan households, particularly those engaging in poultry production. There is a lack of reports on the prevalence of Salmonella on farm; the percentage reported in this study is slightly higher than the 11% reported by Afema et al. [12] and comparable to the farms in Nigeria at 2–26%[13]]. As the majority of chickens from the farms in this study end up for sale at the live market, the prevalence is likely in concordance with what is seen on farm. This heightens the concern that food-animals are a possible source of Salmonella for Ugandan consumers, regardless of AMR status, further highlighting the need for control of zoonotic pathogens, including Salmonella.

We also learned that there was a seasonal effect associated with the recovery of Salmonella. Uganda typically has a rainy season that occurs between March to May and September to November [10]. Recovery of Salmonella was higher during the rainy season, and the use of screening does not allow for temperature control. Therefore, it is likely that the higher humidity and moisture allowed for better dispersal or survival of Salmonella as observed for several bacterial species in poultry[14]. Further, grass is not commonly seen around production buildings and during a rain event, as the environment is mostly mud. It is also possible that human traffic during daily chores resulted in higher traffic of Salmonella into the facility. Additional environmental studies are warranted.

Comparable to the United States (US) [15], Salmonella serovars Enteritidis and Kentucky were most often recovered from chicken samples. Serovar Kentucky has previously been reported in Uganda in humans, poultry, and the environment [12] However, there were no similarities between Salmonella serovars reported in humans compared with the serovars observed in our study. Afema et al. [12], reported that Salmonella Haifa was most commonly seen in samples collected from wastewater treatment plants in Kampala city, along with S. Stanleyville, S. Kentucky, S. Heidelberg, and S. 42r:- rounding out the top five; however, Salmonella Enteritidis was not detected in human samples from this study [12]. While there are similarities between serovars from the wastewater treatment plants, the source of the isolates is unknown. It should be noted that most of the housing outside of Kampala proper does not include indoor plumbing and outhouses are prevalent. Further, at the live market, particularly in small villages, flush gutters are used and animals are dressed on-site with waste commonly ending up in the gutter. The gutters are also used for dumping wash water, garbage, and other waste as well for the passage of human waste. Environmental studies would be quite complex, and multiple factors would need to be controlled for. This highlights the complexity and crucial component of the environment in determining the source of pathogens in Uganda.

Approximately 38% of the isolates were resistant to two or more classes of antibiotics, including two isolates that were resistant to seven antimicrobials. Interestingly, there was no resistance to third-generation cephalosporins. This was also noted from cattle samples, as described in our previous report [5]. As third-generation cephalosporins are the treatment of choice when indicated for salmonellosis, surveillance for emerging resistance is warranted and may aid in identifying the source of infection.

The Salmonella serovar Kentucky isolates were resistant to over five (ciprofloxacin, nalidixic acid, streptomycin, sulfisoxazole, and tetracycline) or seven (chloramphenicol, ampicillin, ciprofloxacin, nalidixic acid, streptomycin, sulfisoxazole, tetracycline, and trimethoprim-sulfamethoxazole) antibiotics. All S. Kentucky isolates were resistant to ciprofloxacin, and all originated from one farm; however, the only antibiotic used on that farm was oxytetracycline with water as the route of administration. The source of the ciprofloxacin resistance is unknown as it is not used in poultry production; the only other animal on this farm were dogs. Since the early 2000s, ciprofloxacin resistance in Salmonella serovar Kentucky has been on the rise, especially from travelers to northern and eastern Africa [16]. Rickert-Hartman et al. [16] found that 9% of the Salmonella serovar Kentucky isolates from travelers were ciprofloxacin-resistant. Poultry was thought to be a reservoir for these resistant strains [16, 17]. Ciprofloxacin-resistant S. Kentucky was attributed to illness in seven people and one death in the US after traveling from India [16]. In this regard, the emergence of S. Kentucky ST198, which is resistant to a number of critically important antibiotics, poses a major threat to public health worldwide since it is highly drug-resistant [18] and has been reported from different sources including retail chicken carcasses [19]. The presence of the mutation can be useful for tracking the pandemic ciprofloxacin-resistant S. Kentucky strain ST198 from geographically distinct regions [18].

Other serotypes exhibiting MDR includes Salmonella Newport, which has recently been reported by the Centers for Disease Control and Prevention as having ciprofloxacin and azithromycin resistance in the US; the origin was soft cheese and beef from the US and Mexico, respectively [20]. Globally, MDR has also been reported for DT104 S. Typhimurium [21, 22].

The AMR field is moving to utilize WGS for detecting resistant genes worldwide. We sequenced all isolates to identify resistance genes and compare them to the observed AMR phenotype. With WGS, the β-lactamase gene blaTEM-1B was identified in five isolates that were not identified by PCR. In previous studies [23], discrepancies were also seen between phenotypic resistance patterns and genotypic analysis using WGS. It was reported that a MIC might not reach the breakpoint even though resistance genes were present [23]. In some cases, in this study, the gene was not present but was expressed phenotypically, which is not typically seen. Little research is done as to why this happens and will need further investigation.

WGS was also used to detect plasmids and compare the results with PCR. All results were in concordance with PCR and WGS, except for S. Virchow isolates. As stated above, AMR genes were not present by WGS but were observed phenotypically for S. Virchow isolates contained IncP and IncX1 plasmids. As with the AMR genes, false positives may explain this phenomenon, but further testing needs to elucidate the differences between the PCR and WGS results.

In this study, IncFIIS was the most common plasmid identified (33.3% (17/51)). Studies have shown that bacterial isolates containing blaCTX-M-1, harbor the IncFIIS plasmid along with other incompatibility plasmids [24]. Inc1 plasmids are known to be distributed throughout many serotypes of Salmonella and predominate in both E.coli and Salmonella [25-27]. Inc1α was observed among Salmonella serovars Zanzibar, Kentucky, and Typhimurium. IncP and IncX1 were the next most common plasmids detected by PCR. Both were present in the Salmonella serovar Virchow isolates. It has been reported that IncP plasmids can spread via conjugative transfer and that they code for a broad range of antimicrobial resistance. IncP is highly likely to be found in manure, wastewater, and soil [28]. IncX1 is commonly found as a narrow host-range plasmid in Enterobacteriaceae, also spreading to other bacteria via conjugative transfer [29].

Although traditional tools have been considered the gold standard to study Salmonella, WGS has been applied as an alternative in providing more detailed and accurate data. In this regard, WGS identifies antimicrobial resistance profile, MLST, and evolutionary groupings that could precisely determine the differences between Salmonella strains. We observed that the main drivers for characterization analysis were serotype, sequence types, and resistance profile. These isolates were clustered together by these characteristics and not by a period of isolation, source, or geographic location. To endorse these results, we have done pulsed-field gel electrophoreses (results not included), which are in agreement with the WGS results. Our study shows how WGS inspection constitutes a useful means to characterize Salmonella isolates.

Conclusion

In summary, we present in this study eight Salmonella enterica serovars displaying resistance to clinically important antibiotics. Of these, the presence of international lineages as ciprofloxacin-resistant S. Kentucky sequence type 198 in chicken farms presents public concern given that fluoroquinolones are the first treatment choice. Our findings suggest the occurrence of epidemic dissemination of resistant serovars, adding valuable information and justification for establishing a robust epidemiological One Health integrated surveillance program in Uganda. Therefore, these results may encourage additional genomic surveillance studies in this region to aid the development of mitigation strategies and to limit the global distribution of these multi-drug resistant Salmonella enterica isolates.

19 Aug 2019

PONE-D-19-19750

International lineages of Salmonella enterica serovars isolated from chicken farms, Wakiso District, Uganda

PLOS ONE

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**********

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Reviewer #1: Review of International lineages of Salmonella enterica serovars isolated from chicken farms, Wakiso District, Uganda, by Ball et al.

In this manuscript, 51 isolates of Salmonella from chicken farms were characterized with respect to antimicrobial resistance, PCR testing and whole genome sequencing. The work seems to have been carried out carefully, but some details are missing in the materials and methods. Logistic regression was carried out with season as main factor, but it is not clear if only the total numbers of Salmonella strains were considered as response factor or also different genotypes and antimicrobial resistance factors. The main problem I have with the current manuscript is that differences were observed between seasons for total Salmonella counts, but it is not clear if the genotypic and phenotypic compositions of the Salmonella isolates were different between the rainy and dry seasons.

I made detailed comments in the manuscript itself; a scanned copy will be attached.

Title:

Replace Review of International Lineages by: Internationally recognized lineages.

This is not a review, and the lineages are all from Uganda.

Abstract:

Generally ok, but see comments in the attachment.

Introduction:

OK, but add a paragraph about the methods of chicken raising in Uganda. Are the farms large-scale indoor chicken coops or are the chickens outdoors? This is important to be able to understand the differences between the rainy and dry seasons. Mention also about the movement of Salmonella between habitats (soil, feed, water, chickens, manure, floor, walls etc.) (Semenov et al., 2010).

Semenov, A.M., Kupriyanov, A.A. and van Bruggen, A.H.C. 2010. Transfer of enteric pathogens to successive habitats as part of microbial cycles. Microb. Ecol. 60: 239-249.

Methodology:

The rainy season and dry season overlap (line 64). Was the period of overlap between seasons avoided during sampling? Or were some samples considered in both categories?

The isolation method by Fedorka-Cray et al. was used; this is a classical method with the possibility that some colonies were considered Salmonella, but actually were not. How was the identity of all these isolates checked? By PCR? Which primers were used? That needs to be mentioned. See also Klerks et al., 2006.

Klerks, M.M., van Bruggen, A.H.C., Zijlstra, C., Donnikov M., and de Vos, R. 2006. Comparison of methods of extracting Salmonella enterica serovar Enteritidis DNA from environmental substrates and quantification of organisms by using a general internal procedural control. Appl. Environ. Microbiol. 72: 3879-3886.

Results:

It is important to understand the differences between the rainy and dry seasons. I suggest indicating in Table 1 which samples were collected in each of the seasons. Then you can mention which serotypes occurred in the rainy season and which in the dry season. Now, the numbers may be too low in the dry season, but then it is important to mention that these are preliminary data, and that additional research would be needed to distinguish between seasons.

The outcome of the statistical analysis has not been presented in detail. I expect a multivariate graph, including all observations. Hopefully those will group out into two ‘clouds’, one for the rainy season and one for the dry season.

Discussion:

To better understand the results, in particular the differences between dry and rainy seasons, you need to refer back to the way chickens are raised in Uganda (that’s why you need to add a paragraph about this in the introduction, so that you can refer to it in the discussion).

Which serotypes were endemic and which were introduced? In the conclusion you mention endemic dissemination. In the rest of the manuscript, nothing was mentioned about dissemination. Are chickens imported or exported? Is the feed imported or exported? It is not really clear where all these serotypes and multi-drug-resistances came from. Or did the drug resistance develop in Uganda? It is important to give more general information about chicken production and the possible routes of dissemination of Salmonella.

Reviewer #2: The article by Ball et al. tackles the vital subject of antimicrobial resistance food-producing animals in a region from where very little is known regarding this subject. It is a welcome addition to the body of knowledge, although the article is missing information and data analysis.

1- The farms a poorly described both, in a geographical sense and relation to each other. Furthermore, the type of production, size, and information on different types of animals on the farm are not mentioned but are necessary.

2- Spatial and temporal information on the tables is missing, and it is barely described in the results, and not discussed.

3- The presence of resistance genes without phenotypic testing is an indication of resistance, but the isolates cannot be described as resistant as it is the case in the discussion.

5- Information on the quality of the sequencing obtained for each isolate should be documented. If they met Genometrackr standards, please estate this fact, and their current minimum.

4-Exact information on the bioinformatics pipeline used to analyze the samples Is crucial for each isolate. Where different databases used every time? Where there any differences between the databases?

Reviewer #3: This paper describes a cross sectional study of Salmonella in poultry farms in Uganda conducted over the dry and rainy seasons. The authors undertook antibiogram phenotyping of the isolate collection and used whole genome sequence analysis to determine the serotype, AMR genotype, plasmid replicon type and in some cases sequence type of the isolates. A high proportion of isolates were found to be MDR and to contain plasmids of significant impact to human health and alarmingly, fluoroquinolone resistance was very high (21.6%). Whilst the study design is excellent, the techniques used appropriate and the results are significant, the paper is let down by being poorly written in parts. Also, it is not stated whether the study population are broilers or layers (or mixed?). I would also like to see more discussion about the very high rate of fluoroquinolone resistance and its relationship to antimicrobial use on farm. The absence of resistance to third generation cephalosporins is also an important finding.

Specific comments below:

Line 45-47: This is a poor sentence that needs rewriting

Methods: Were these broiler farms or egg-producing farms or a combination of the two? This is a very important distinction due to the restrictions on which antimicrobials can be used in each system.

Line 113: Tetracycline spelt incorrectly. It is not the AMR phenotype displaying resistance to eight antimicrobials but the isolate collection.

Line 120: Other than acquiring resistance genes does not make sense, This should be “Point mutations identified in the QRDR of fluoroquinolone target genes included……”

The section from Line 120 to Line 127 needs a careful rewrite as the sentence construction is poor.

Line 128 to 130 is poorly written

Reviewer #4: Multiple drug resistance (MDR) foodborne pathogens have been reported from multiple sources, especially from poultry worldwide. In this article, Ball et al. characterized 51 Salmonella enterica isolates, isolated from 400 environmental samples collected form poultry farms in Uganda by using whole genome sequencing (WGS). In addition, they also identified replicon plasmid through PCR. Among 51 isolates, 32 isolates were resistant to at least one antimicrobial and 10 isolates were assigned as MDR. Ball et al. also found a significant correlation between prevalence of Salmonella and seasonal difference. This article is underlining the importance of the surveillance systems, using One Health approach, to control MDR foodborne pathogens world wide. However, there are some minor issues that should be addressed and clarified before prior to publication.

Minor issues:

- Footnote, describing abbreviations of antimicrobials, should be added to Table 1. In addition, to describe temporal and spatial relatedness of isolates, new columns, representing where and when isolates were collected, might help.

- For plasmid description, there are some inconsistent results, obtained from WGS and PCR. These results should be discussed in details.

- For antimicrobial resistance profiling, there are also some inconsistent results. For examples, some isolates (i.e., SALM-9, SALM-1) represented resistance by using MIC, but no gene, related to this phenotype, was found from WGS or vice versa. These results should be discussed in details.

- In general, more discussion should be added.

Reviewer #5: Please see attachment for suggestions, comments and questions. The data are of interest, but the wording of the manuscript needs improvement.

Reviewer #6: The authors have presented result of a study of Salmonella on chicken farms in Uganda. They report data on the distribution of serotypes, antimicrobial resistance profiles and plasmids. There are a number of interesting findings, but several issues that need to be addressed.

Lines 63-65 Sampling occurred over two seasons, but the time intervals for the dry and rainy seasons overlap. For samples collected from June- September, how was the season determined?

Line 66 It would be simpler just to say 2o chicken farms.

Lines 106-112 More description of the results would be useful. There were 20 chicken farms included. How many had positive samples? What was the distribution of serotypes by farm? Did some farms have a greater diversity than others? What was the distribution by season? Showing a significant p value, without presenting the underlying data is not useful. In line 107 it should be stated that the positive rate is for samples from chicken farms, not from chickens.

Line 113 There were multiple AMR phenotypes identified. Presenting the % of isolates resistant to each antibiotic is not that same as characterizing phenotypes.

Line 120 The sentence beginning “other than acquiring…” makes no sense as written.

Line 150 Although there is very likely a risk for Salmonella transmission from poultry in Uganda, the 13.5% positive rate of swab samples from chicken farms is not the same as a 13.5% prevalence of Salmonella on chicken carcasses or chicken meat samples at retail.

Line 158 As suggested above, there is need for more detail in the distribution of results by farm and season.

Table 1 An additional table that aggregates data by farm and season would be useful.

**********

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Submitted filename: Comments on International lineages of Salmonella manuscript.pdf

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Submitted filename: Review Salmonella manuscript.docx

Click here for additional data file.

Submitted filename: The article by Ball et al.pdf

Click here for additional data file.

9 Oct 2019

Reviewer #1: Review of International lineages of Salmonella enterica serovars isolated from chicken farms, Wakiso District, Uganda, by Ball et al.

In this manuscript, 51 isolates of Salmonella from chicken farms were characterized with respect to antimicrobial resistance, PCR testing and whole genome sequencing. The work seems to have been carried out carefully, but some details are missing in the materials and methods. Logistic regression was carried out with season as main factor, but it is not clear if only the total numbers of Salmonella strains were considered as response factor or also different genotypes and antimicrobial resistance factors. The main problem I have with the current manuscript is that differences were observed between seasons for total Salmonella counts, but it is not clear if the genotypic and phenotypic compositions of the Salmonella isolates were different between the rainy and dry seasons.

I made detailed comments in the manuscript itself; a scanned copy will be attached.

Title:

Replace Review of International Lineages by: Internationally recognized lineages.

This is not a review, and the lineages are all from Uganda. Completed

Abstract:

Generally ok,but see comments in the attachment. Addressed all comments in the attachment

Introduction:

OK, but add a paragraph about the methods of chicken raising in Uganda. Are the farms large-scale indoor chicken coops or are the chickens outdoors? This is important to be able to understand the differences between the rainy and dry seasons. Added information

Mention also about the movement of Salmonella between habitats (soil, feed, water, chickens, manure, floor, walls etc.) (Semenov et al., 2010). All housing was indoors.

Semenov, A.M., Kupriyanov, A.A. and van Bruggen, A.H.C. 2010. Transfer of enteric pathogens to successive habitats as part of microbial cycles. Microb. Ecol. 60: 239-249.

Methodology:

The rainy season and dry season overlap (line 64). Was the period of overlap between seasons avoided during sampling? Or were some samples considered in both categories? Corrected dates

The isolation method by Fedorka-Cray et al. was used; this is a classical method with the possibility that some colonies were considered Salmonella, but actually were not. How was the identity of all these isolates checked? By PCR? Which primers were used? That needs to be mentioned. See also Klerks et al., 2006.

Klerks, M.M., van Bruggen, A.H.C., Zijlstra, C., Donnikov M., and de Vos, R. 2006. Comparison of methods of extracting Salmonella enterica serovar Enteritidis DNA from environmental substrates and quantification of organisms by using a general internal procedural control. Appl. Environ. Microbiol. 72: 3879-3886. Added confirmation of methods

Results:

It is important to understand the differences between the rainy and dry seasons. I suggest indicating in Table 1 which samples were collected in each of the seasons. Then you can mention which serotypes occurred in the rainy season and which in the dry season. Now, the numbers may be too low in the dry season, but then it is important to mention that these are preliminary data, and that additional research would be needed to distinguish between seasons. Add rainy and dry season information in the table. Also added farm information. Added a second table to focus on season and serotypes

The outcome of the statistical analysis has not been presented in detail. I expect a multivariate graph, including all observations. Hopefully those will group out into two ‘clouds’, one for the rainy season and one for the dry season. The output provided for the stats was only the P-value.

Discussion:

To better understand the results, in particular the differences between dry and rainy seasons, you need to refer back to the way chickens are raised in Uganda (that’s why you need to add a paragraph about this in the introduction, so that you can refer to it in the discussion). Added information on the type of production Uganda uses

Which serotypes were endemic and which were introduced? Assumption made that all are endemic as recovery from wastewater and this study verify similarity. In the conclusion you mention endemic dissemination. In the rest of the manuscript, nothing was mentioned about dissemination. Are chickens imported or exported? Is the feed imported or exported? It is not really clear where all these serotypes and multi-drug-resistances came from. Or did the drug resistance develop in Uganda? It is important to give more general information about chicken production and the possible routes of dissemination of Salmonella. Origin of chickens is unknown. See additions in paper

Reviewer #2: The article by Ball et al. tackles the vital subject of antimicrobial resistance food-producing animals in a region from where very little is known regarding this subject. It is a welcome addition to the body of knowledge, although the article is missing information and data analysis.

1- The farms a poorly described both, in a geographical sense and relation to each other. Furthermore, the type of production, size, and information on different types of animals on the farm are not mentioned but are necessary. Added information on general area of farm location. We cannot supply geographical coordinates due to our IRB. Added information on other animals that were included on the farms and the type of production employed.

2- Spatial and temporal information on the tables is missing, and it is barely described in the results, and not discussed. Month of collection is described. Spatial location of farms is not possible

3- The presence of resistance genes without phenotypic testing is an indication of resistance, but the isolates cannot be described as resistant as it is the case in the discussion.

This information is in the table under resistant profile (MIC). Isolates were tested phenotypically as described in the paper. We are unsure why the reviewer is querying.

5- Information on the quality of the sequencing obtained for each isolate should be documented. If they met Genometrackr standards, please estate this fact, and their current minimum. Samples are submitted to Genometrakr as mentioned in the methods section. The submission is indication that they are accepted by the program.

4-Exact information on the bioinformatics pipeline used to analyze the samples Is crucial for each isolate. Where different databases used every time? Where there any differences between the databases? Databases used added in the methods section.

Reviewer #3: This paper describes a cross sectional study of Salmonella in poultry farms in Uganda conducted over the dry and rainy seasons. The authors undertook antibiogram phenotyping of the isolate collection and used whole genome sequence analysis to determine the serotype, AMR genotype, plasmid replicon type and in some cases sequence type of the isolates. A high proportion of isolates were found to be MDR and to contain plasmids of significant impact to human health and alarmingly, fluoroquinolone resistance was very high (21.6%). Whilst the study design is excellent, the techniques used appropriate and the results are significant, the paper is let down by being poorly written in parts. Also, it is not stated whether the study population are broilers or layers (or mixed?). The chickens ranged from broilers, layers, Kuroilers and local birds were added I would also like to see more discussion about the very high rate of fluoroquinolone resistance and its relationship to antimicrobial use on farm. Added in the discussion. The only antibiotic used on most farms was oxytetracycline. The absence of resistance to third generation cephalosporins is also an important finding.

Specific comments below:

Line 45-47: This is a poor sentence that needs rewriting rewritten

Methods: Were these broiler farms or egg-producing farms or a combination of the two? This is a very important distinction due to the restrictions on which antimicrobials can be used in each system. The farm types were defined. Only Oxytet is used as described

Line 113: Tetracycline spelt incorrectly. It is not the AMR phenotype displaying resistance to eight antimicrobials but the isolate collection. Did not observe misspelling. Reworded the sentence

Line 120: Other than acquiring resistance genes does not make sense, This should be “Point mutations identified in the QRDR of fluoroquinolone target genes included……”

The section from Line 120 to Line 127 needs a careful rewrite as the sentence construction is poor. Rewritten

Line 128 to 130 is poorly written Rewritten

Reviewer #4: Multiple drug resistance (MDR) foodborne pathogens have been reported from multiple sources, especially from poultry worldwide. In this article, Ball et al. characterized 51 Salmonella enterica isolates, isolated from 400 environmental samples collected form poultry farms in Uganda by using whole genome sequencing (WGS). In addition, they also identified replicon plasmid through PCR. Among 51 isolates, 32 isolates were resistant to at least one antimicrobial and 10 isolates were assigned as MDR. Ball et al. also found a significant correlation between prevalence of Salmonella and seasonal difference. This article is underlining the importance of the surveillance systems, using One Health approach, to control MDR foodborne pathogens world wide. However, there are some minor issues that should be addressed and clarified before prior to publication.

Minor issues:

- Footnote, describing abbreviations of antimicrobials, should be added to Table 1. In addition, to describe temporal and spatial relatedness of isolates, new columns, representing where and when isolates were collected, might help. Added antimicrobials, added timeframe of collection. We cannot disclosed geographical coordinates but samples were collected in western rural and small town villages of the wakiso district, in Kampala Uganda.

- For plasmid description, there are some inconsistent results, obtained from WGS and PCR. These results should be discussed in details. Both methods were used to identify plasmids and for the comparison. WGS identified plasmids that were identified in the PCR method, but also identified plasmids not in the PCR kit.

- For antimicrobial resistance profiling, there are also some inconsistent results. For examples, some isolates (i.e., SALM-9, SALM-1) represented resistance by using MIC, but no gene, related to this phenotype, was found from WGS or vice versa. These results should be discussed in details. AMR phenotype only displays resistance when expressed whereas the gene could still be there. In the case of these isolates we observed cases when there was no resistance expressed but the gene was present. We also observed when the gene was not there but it was expressed phenotypically - this was interesting and needs more work as stated.

- In general, more discussion should be added.

Reviewer #5: Please see attachment for suggestions, comments and questions. The data are of interest, but the wording of the manuscript needs improvement. please see responses to attached suggestions

The manuscript would be more insightful if the authors would have been able to find out which antibiotics were used on the farms the samples came from. This was added in the manuscript. The only antibiotic used on the farms was oxytetracycline.

Are there any data on which serovars are common in humans in Uganda? There is limited work looking at the common serotypes in humans. A part of this project was to do a parallel study in clinical samples with the MOH. But due to limited funding that part of the project was dropped until further funding. However, added in the paper was research on wastewater treatment plants and the findings of most common serotypes.

Are the MDR levels higher or lower than those observed in other parts of the world? Because this study is a small-scale study, it is hard to compare to what is observed globally.

The manuscript contains numerous language issues and unclear sentences. Below are suggestions for improvements and questions that should be addressed.

L.25: The growing occurrence of multidrug-resistant (MDR) Salmonella enterica in poultry is a public health concern worldwide. The present study, the prevalence, antimicrobial resistance, and sequence type [sequence of what?] of 51 Salmonella isolates recovered from 400 environmental samples from chicken farms in Uganda. reworded

L. 33: Five plasmid replicons were identified among all isolates, including IncFIIS 17/51 35 (33.3%), IncI1α 12/51 (23.5%), IncP 8/51 (15.7%), IncX1 8/51 (15.7%), and IncX2 1/51 (2.0%). In addition, ColpVC and IncFIB replicons were identified through WGS [spell out]. Spelled out WGS

L. 45: Additionally, international lineages [what are these?] have been readily spread …reworded

L. 48: In Uganda, antibiotics [what kind?] are increasingly being used [based on what information?] and not monitored or regulated in food-producing animals. Added information and citation

L. 50: To address this concern and considering the lack of information regarding antimicrobial resistance (AMR) in developing countries, Uganda ….reworded

L. 55: Therefore, we present a cross-sectional study involving chicken farms in Uganda to investigate the prevalence, AMR, and their?? [referring to what?] genomic aspects [what is meant by “aspects”?] of Salmonella enterica serovars. reworded

L. 60: Salmonella isolates were collected from chicken farms in parallel with the collection from cattle farms (5) as part of a cross-sectional study spanning one year. reworded

L. 76: Isolation of Salmonella was done as described by Fedorka-Cray et al. (8).reworded

L. 81: [Move this sentence to the previous section.] The isolates were frozen in LB broth with 30% glycerol (Thermo Fisher Scientific Inc, Waltham, MA) at -80o C. moved

L. 78: [Combine the sections.] Antimicrobial Resistance testing and molecular characterization of …[what?] Combined

A total of 51 Salmonella were obtained from chicken farms. For analyses, the isolates were retrieved from frozen stock, plated on Tryptic Soy Agar (TSA) with 5% sheep blood (BAP) (Thermo Fisher Scientific Inc, Waltham, MA) and incubated overnight at 37oC. AMR testing was done using the National Antimicrobial Resistance Monitoring System (NARMS) gram-negative panels (Thermo Fisher Scientific Inc, Waltham, MA) as described by Ball et al. (5). Lysates were prepared by suspending a loopful of well-isolated colonies in 200 µl of molecular grade water and vortexing at maximum speed for several seconds. The suspension was boiled at 100°C for 10 minutes, centrifuged at ..x g for 60 seconds, and the supernatant was collected for use as the DNA template. PCR screening [for what?] and whole genome sequencing were carried out as described in Ball et al. (manuscript submitted). [Details should be provided in case publication is delayed.]reworded

L. 96: Resistomes, plasmidomes and multilocus sequence types (MLST) were identified using ResFinder 3.1, PlasmidFinder 2.0, and MLST 2.0, respectively, available from the Center for Genomic Epidemiology (http://genomicepidemiology.org/). Sequence data were deposited in the GenomeTrakr Project. [I could not find any data on a project from Uganda. Please make sure it is available as you state.] Added the BIOsample number in table 1

L. 107: Fifty-one Salmonella were isolated (51/379; 13.5%) belonging eight different serovars: Enteritidis (31.3%); S. Kentucky (21.6%); S. Zanzibar and S. Virchow (15.7%); S. Newport and 42:r:- (5.88%), Typhimurium (4%) and S. Barranquilla (2.0%). The prevalence of Salmonella was higher in the rainy season (p=0.0017). The isolates displayed resistance to eight antimicrobials including tetracylcine (51%), …. reworded

L. 120: Other than acquiring resistance genes???[I do not understand this part of the sentence.] were assigned as quinolone resistance determining regions (QRDR) with point mutation in gyrA and parC (Table 1). Ten isolates (19.6%) showed a double amino acid mutation in GyrA (GyrA-S83F-D87N), whereas eight isolates (15.6%) showed a single amino acid substitution of serine to tyrosine at codon 126. For QRDR in parC (n=10; 19.6%) only one substitution of serine by isoleucine at codon 80 was observed. No mutations were found in gyrB and parE. Sequencing identified six plasmids. IncFII(S)-IncFIB (S)- ColpVC were the most common in S. Enteritidis; Incl1-ColpVC in S. Kentucky and S. Zanzibar; IncX2 in S. Newport; Incl1-IncFII(S)-IncFIB (S)-ColpVC in S. Typhimurium and Col440I in serovar 42:r:-. Nine sequence types, namely ST11, ST198, ST466, ST16, ST166, ST46, ST19, ST1208 and ST3807 were associated with S. Enteritidis, S. Kentucky, S. Zanzibar, 135 S. Virchow, S. Newport, S. Newport, S. Typhimurium, S. serovar 42:r:- and S. 136 Barranquilla, respectively. reworded

L. 150: The percent prevalence of Salmonella (13.5%) in this study highlights the potential risk to humans to Ugandan households. This percentage is higher than that reported by Afema et al. (9) of 6.6% Salmonella in samples from live birds markets within Kampala, Uganda. [How meaningful is this comparison? Farms vs. live birds?] [Explain why the percentage might be higher.] reworded

There was a seasonal effect in the recovery of Salmonella. Uganda typically has a rainy season that occurs between March to May and October to December (10). Recovery of Salmonella was higher during the rainy season, possibly because the higher humidity and moisture allowed better dispersal or survival of Salmonella as observed for other bacterial species in poultry (11). Comparable to the situation in the US (12), Salmonella serovars Enteritidis and Kentucky were most often recovered. Serovar Kentucky has previously been reported in Uganda in humans, poultry, and the environment (9). reworded

Among chicken isolates, Salmonella presented with MDR phenotypes to the 168 antimicrobials tested. [New paragraph] Approximately 38% of the isolates were resistant to two or more classes of antimicrobials? [antibiotics?], including two isolates resistant to seven antimicrobials. The Salmonella serovar Kentucky isolates were resistant to over five (ciprofloxacin, nalidixic acid, streptomycin, sulfisoxazole, and tetracycline) or seven (chloramphenicol, ampicillin, ciprofloxacin, nalidixic acid, streptomycin, sulfisoxazole, tetracycline, and trimethoprim-sulfamethoxazole) antibiotics. All Salmonella serovar Kentucky isolates were resistant to ciprofloxacin. Since the early 2000s, ciprofloxacin resistant Salmonella serovar Kentucky have been on the rise, especially in travelers to northern and eastern Africa (13). Rickert-Hartman et al. (..??) found that 9% of the Salmonella serovar Kentucky isolated from travelers were ciprofloxacin resistant. Poultry was thought to be a reservoir for these resistant strains (13, 14). Ciprofloxacin-resistant Kentucky have caused seven persons to become ill and one death in the US after the carriers had travelled to India (13). In this regard, the emergence of S. Kentucky ST198 poses a major threat to public health worldwide since it is highly drug-resistant (15) and has been reported in different sources including retail chicken carcasses (16). reworded

The presence of mutation can be useful for tracking the pandemic ciprofloxacin-resistant S. Kentucky strain ST198 from geographically distinct regions (15). Using WGS TEM-1B (beta lactamase?) was identified in five isolates that PCR methods did not identify. In previous studies (17), discrepancies were also seen between phenotypic resistance and genotypic analysis using WGS. It was reported that an MIC might not reach the breakpoint even though resistance genes were present (17). Five of the 28 plasmids that were screened through PCR were observed in multiple isolates: IncFIIS (17/51; 33.3%), IncI1α (12/51; 23.5%), IncP (8/51; 15.7%), 193 IncX1 (8/51; 15.7%), and IncX2 (1/51; 2.0%). After analyzing the WGS sequences for plasmids, 12 isolates were found to harbor IncI1α, with seven of the 12 having an additional plasmid (ColpVC) that was not detected by PCR and two with IncFIIS plasmid. Seventeen isolates carried the IncFIIS plasmid. These same 17 isolates also presented IncFIB (S) plasmids, and ColpVC and Col4401 were identified in seven and two isolates, respectively. IncX2 and IncP were not identified in the WGS analysis and by PCR. PCR did not detect plasmids in ten isolates, but WGS detected ColpVC 201 in nine isolates and Col4401 in one isolate. IncFIIS was the most common plasmid identified at 33.3% (17/51). Studies have shown that isolates containing blaCTX-M-1 harbor IncFIIS along with other incompatibility plasmids (18). Inc1 plasmids are known to be distributed throughout many serotypes of Salmonella and predominate in both E. coli and Salmonella 206 (19-21). In this study, Inc1α was observed among Salmonella serovars Zanzibar, Kentucky, and Typhimurium. All isolates from Salmonella serovar Kentucky and Typhimurium came from the same farm [what does this observation suggest?]. IncP and IncX1 were the next most common plasmids detected by PCR. Both were present in the Salmonella serovar Virchow isolates. It has been reported that IncP plasmids can spread via conjugative transfer and that they code for a broad range antimicrobial resistances. IncP is highly likely to be found in manure, wastewater, and soil (22). IncX1 is commonly found as a narrow host-range plasmid in Enterobacteriaceae, also spreading to other bacteria via conjugative transfer (23). reworded

L. 216: In summary, we present in this study the clonal??[how do you know this?] distribution of eight Salmonella enterica serovars displaying resistance to clinically important antibiotics. Of these, the presence of international lineages such as ciprofloxacin-resistant S. Kentucky sequence type 198 in chicken farms raises a public concern given that fluoroquinolones are the first treatment choice. Our findings suggest that endemic dissemination of resistant serovars …[incomplete sentence], adding valuable information to the epidemiological surveillance in Uganda. Therefore, these results may encourage addition genomic surveillance studies in this region to aid in the development of mitigation strategies to limit the global distribution of these multidrug resistant Salmonella enterica. reworded

Reviewer #6: The authors have presented result of a study of Salmonella on chicken farms in Uganda. They report data on the distribution of serotypes, antimicrobial resistance profiles and plasmids. There are a number of interesting findings, but several issues that need to be addressed.

Lines 63-65 Sampling occurred over two seasons, but the time intervals for the dry and rainy seasons overlap. For samples collected from June- September, how was the season determined? Corrected the dates

Line 66 It would be simpler just to say 2o chicken farms. reworded

Lines 106-112 More description of the results would be useful. There were 20 chicken farms included. How many had positive samples? What was the distribution of serotypes by farm? Did some farms have a greater diversity than others? What was the distribution by season? Showing a significant p value, without presenting the underlying data is not useful. In line 107 it should be stated that the positive rate is for samples from chicken farms, not from chickens. Please see an updated table one and table 2 for this information

Line 113 There were multiple AMR phenotypes identified. Presenting the % of isolates resistant to each antibiotic is not that same as characterizing phenotypes. Added table 3 and 4 to show more on AMR phenotype

Line 120 The sentence beginning “other than acquiring…” makes no sense as written. reworded

Line 150 Although there is very likely a risk for Salmonella transmission from poultry in Uganda, the 13.5% positive rate of swab samples from chicken farms is not the same as a 13.5% prevalence of Salmonella on chicken carcasses or chicken meat samples at retail. reworked.

Line 158 As suggested above, there is need for more detail in the distribution of results by farm and season. Information was added in table 1

Table 1 An additional table that aggregates data by farm and season would be useful. Tabel 2 was added to show this information.

Submitted filename: Response to reviewers.docx

Click here for additional data file.

31 Oct 2019

PONE-D-19-19750R1

International lineages of Salmonella enterica serovars isolated from chicken farms, Wakiso District, Uganda

PLOS ONE

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Reviewers' comments:

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Comments to the Author

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Reviewer #3: All comments have been addressed

Reviewer #5: (No Response)

Reviewer #6: (No Response)

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Yes

Reviewer #5: Yes

Reviewer #6: Partly

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Reviewer #3: Yes

Reviewer #5: I Don't Know

Reviewer #6: No

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Reviewer #3: Yes

Reviewer #5: (No Response)

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: (No Response)

Reviewer #5: The manuscript has been improved considerably. The one comment I have now that Table 1 is available concerns n = 51. Is it possible that you included the same strains multiple times? The strains could have been isolated from different sites at a farm or even at different times. Would inclusion of the same strains not skew the percentages of resistance to particular antibiotics?

Did the whole genome sequencing effort clearly demonstrate that all 51 isolates were different?

Reviewer #6: Authors have revised paper in response to previous comments, but still need to address concerns. The over point of the study is to highlight the need for integrated surveillance for Salmonella using a One Health approach. I certainly concur with that, but think there is considerable restructuring of the paper needed to draw out that point.

In this relatively small data set, all of the MDR strains were S. Kentucky ST 198, which likely represent the expansion of a globally emerging strain. The authors note that these were from one farm that did not use fluoroquinolones. The spread of emerging strains across borders is a critical reason for integrated surveillance.

Specific comments:

Line 34 delete “all”

Line 61 does “chicken” imply live animals or poultry meat? Please clarify.

Line 106 delete “all”

Line 139-140 It is important to expand on this analysis. 6/20 farms were positive in the dry season and 9/18 were positive during the wet season. The rate of Salmonella positivity appears to be about 9% for dry season, and 19% for rainy season. These are the data that are more interesting than the p-value that should be presented.

Line 142-145 It should be noted that all SE were pansusceptible, and all MDR isolates were S. Kentucky. The resistance data follow clonal strains of Salmonella.

Line 145 Table 2 does not include resistance data.

Line 170 Note ass are S. Kentucky

Line 209-213. See comments for lines 139-140. There is no point to mentioning the logistic regression if none of the output of the regression is presented.

Lines 182-208 There is a lot of detail presented here, but the context is not clear, and the reader easily gets lost. What is the bottom line? This is an opportunity to show how WGS can aid surveillance, but that does not clearly emerge from the presentation.

Line 237-239 Does this mean that surveillance data for human illnesses were compared to results of this study? If so, that should be more thoroughly described and discussed. It would be useful to do so to highlight the One Health approach.

Line 258 and beyond… The presence of MDR Kentucky likely represents movement of clonal strains. This is the good justification of One Health integrated surveillance.

**********

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17 Dec 2019

Response to editors, Manuscript PONE-D-19-19750.R2

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Response: We sincerely appreciate the editorial board and reviewers for their careful review and constructive suggestions. We believe that the manuscript was substantially improved after making the suggested edits. We have responded to specific queries below.

Reviewer #3: (No Response)

Reviewer #5: The manuscript has been improved considerably.

Response: We sincerely thank the reviewer for their favorable comments and interest in our work.

The one comment I have now that Table 1 is available concerns n = 51. Is it possible that you included the same strains multiple times? The strains could have been isolated from different sites at a farm or even at different times. Would inclusion of the same strains not skew the percentages of resistance to particular antibiotics?

Response: There were isolates collected on the same farms but at different sample sites (feed, water, litter, entryway, etc.) and different months. Most farms had new flocks in the area or in the process of getting new flocks between visits. The object of the entire study (E. coli and Salmonella for Cattle and Chicken) was to see if the prevalence and AMR results differ between the seasons and to identify what would be found. Every strain that was collected was presented in Table one to see all phenotypic and genotypic differences in both seasons. To avoid the percentages skew, we did not include the same strains multiple times, as mentioned above.

Did the whole genome sequencing effort clearly demonstrate that all 51 isolates were different?

Response: Although traditional tools have been considered the gold standards to study Salmonella, the whole-genome sequencing has been applied as an alternative to that after its establishment. In this regard, WGS recognizes antimicrobial resistance profile, MLST and evolutionary groupings that could precisely identify the differences between Salmonella strains. In this backdrop, we observed that the main drivers for cluster analysis were serotype, sequence types, and resistance profile since all isolates were clustered together by these characteristics and not by the period of isolation, source, or geographic location. We do have PFGE results not included in this manuscript for these isolates. They were not included due to the inclusion of other strains, not in this study. However, these PFGE results are in line with the WGS sequence data.

Reviewer #6: Authors have revised paper in response to previous comments, but still need to address concerns. The over point of the study is to highlight the need for integrated surveillance for Salmonella using a One Health approach. I certainly concur with that, but think there is considerable restructuring of the paper needed to draw out that point.

In this relatively small data set, all of the MDR strains were S. Kentucky ST 198, which likely represent the expansion of a globally emerging strain. The authors note that these were from one farm that did not use fluoroquinolones. The spread of emerging strains across borders is a critical reason for integrated surveillance.

Specific comments:

Line 34 delete “all”

Response: Addressed

Line 61 does “chicken” imply live animals or poultry meat? Please clarify.

Response: Live birds. Addressed

Line 106 delete “all”

Response: Addressed

Line 139-140 It is important to expand on this analysis. 6/20 farms were positive in the dry season and 9/18 were positive during the wet season. The rate of Salmonella positivity appears to be about 9% for dry season, and 19% for rainy season. These are the data that are more interesting than the p-value that should be presented.

Response: Remove the logistic regression statement. We agree that it is essential to expand this study. Indeed, these results may serve as a baseline study for future surveys.

Line 142-145 It should be noted that all SE were pansusceptible, and all MDR isolates were S. Kentucky. The resistance data follow clonal strains of Salmonella.

Response: Addressed

Line 145 Table 2 does not include resistance data.

Response: Addressed

Line 170 Note ass are S. Kentucky

Response: Addressed

Line 209-213. See comments for lines 139-140. There is no point to mentioning the logistic regression if none of the output of the regression is presented.

Response: Addressed

Lines 182-208 There is a lot of detail presented here, but the context is not clear, and the reader easily gets lost. What is the bottom line? This is an opportunity to show how WGS can aid surveillance, but that does not clearly emerge from the presentation.

Response: Statement was included “Although traditional tools have been considered the gold standard to study Salmonella, WGS has been applied as an alternative in providing more detailed and accurate data. In this regard, WGS recognizes antimicrobial resistance profile, MLST, and evolutionary groupings that could precisely identify the differences between Salmonella strains. We observed that the main drivers for characterization analysis were serotype, sequence types, and resistance profile. These isolates were clustered together by these characteristics and not by a period of isolation, source, or geographic location. To endorse these results, we have done pulsed-field gel electrophoreses (results not included), which are in agreement with the WGS results. Our study shows how WGS inspection constitutes a useful means to characterize Salmonella isolates.”

Line 237-239 Does this mean that surveillance data for human illnesses were compared to results of this study? If so, that should be more thoroughly described and discussed. It would be useful to do so to highlight the One Health approach.

Response: There were no human results compared in parallel to this study. The study mentioned in the manuscript was a project in Uganda looking at a few human cases, not for surveillance. The idea for our study was to get human data in parallel to compare, but resources were limited to do so. The statement in this line was implying that we did not see the same type of serovars as they saw in the Afema article. Clarification was added

Line 258 and beyond… The presence of MDR Kentucky likely represents movement of clonal strains. This is the good justification of One Health integrated surveillance.

Response: Addressed

Submitted filename: Response to editors 2.docx

Click here for additional data file.

7 Jan 2020

International lineages of Salmonella enterica serovars isolated from chicken farms, Wakiso District, Uganda

PONE-D-19-19750R2

Dear Dr. Ball,

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PLOS ONE

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Comments to the Author

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Reviewer #5: All comments have been addressed

Reviewer #6: All comments have been addressed

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Reviewer #5: Yes

Reviewer #6: (No Response)

**********

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Reviewer #5: I Don't Know

Reviewer #6: (No Response)

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Reviewer #5: Yes

Reviewer #6: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #5: Yes

Reviewer #6: (No Response)

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #5: My concerns have been addressed to the extent possible. The limitations of the study are now clearer.

Reviewer #6: (No Response)

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Reviewer #6: No

15 Jan 2020

PONE-D-19-19750R2

International lineages of Salmonella enterica serovars isolated from chicken farms, Wakiso District, Uganda

Dear Dr. Ball:

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