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Article

Analysis of Retrospective Laboratory Data on the Burden of Bacterial Pathogens Isolated at the National Veterinary Research Institute Nigeria, 2018–2021

by
Dennis Kabantiyok
1,*,
Moses D. Gyang
1,
Godwin O. Agada
1,
Alice Ogundeji
1,
Daniel Nyam
1,
Uchechi G. Uhiara
1,
Elmina Abiayi
1,
Yakubu Dashe
1,
Sati Ngulukun
1,
Maryam Muhammad
1,
Oyelola A. Adegboye
2,3,* and
Theophilus I. Emeto
3,4
1
Diagnostic Laboratory Services Division, National Veterinary Research Institute NVRI, PMB 01, Vom 930010, Nigeria
2
Menzies School of Public Health, Charles Darwin University, Casuarina, NT 0811, Australia
3
Public Health & Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD 4811, Australia
4
World Health Organization Collaborating Center for Vector-Borne and Neglected Tropical Diseases, James Cook University, Townsville, QLD 4811, Australia
*
Authors to whom correspondence should be addressed.
Vet. Sci. 2023, 10(8), 505; https://doi.org/10.3390/vetsci10080505
Submission received: 31 May 2023 / Revised: 16 July 2023 / Accepted: 3 August 2023 / Published: 5 August 2023
(This article belongs to the Special Issue The Effects of Microbiota on Animal Health)
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Abstract

:

Simple Summary

Farm animals are important in food security and safety. The presence of disease-causing bacteria in farm animals is a key indicator of animal health and food safety. To understand the distribution and treatment outcome of these microorganisms in farm animals, data from the Bacteriology Laboratory of the National Veterinary Research Institute, Nigeria, from 2018 to 2021 were analysed. The influence of different types of antibiotic intervention from 2018 to 2021 on these bacteria was investigated. The findings showed that avian species were the predominant farm animals. The study revealed that the most isolated bacteria were Escherichia coli; Salmonella spp.; Klebsiella spp.; Staphylococcus spp.; Proteus spp.; and Pseudomonas spp. Additionally, it was noticed that the number of bacteria isolated increased steadily through the years, accompanied by the resistance to common antibiotics used during the period. Noteworthy is the resistance of Klebsiella spp. to cephalosporins—an important second-generation antibiotic. This is worrisome because the increasing resistance of animal bacteria portends more resistance to antimicrobial resistance in humans through several means. To address this growing concern prudent use of antimicrobials, effective surveillance, and stricter biosecurity measures (to minimise the need for antimicrobials) in farms must be observed to understand this problem better and minimise the spread of these infectious agents.

Abstract

Farm animals harbour bacterial pathogens, which are often viewed as important indicators of animal health and determinants of food safety. To better understand the prevalence and inform treatment, we audited laboratory data at the Bacteriology Laboratory of the NVRI from 2018–2021. Antibiotics were classified into seven basic classes: quinolones, tetracyclines, beta-lactams, aminoglycosides, macrolides, nitrofuran, and cephalosporins. Trends were analysed using a generalised linear model with a log link function for the Poisson distribution, comparing proportions between years with an offset to account for the variability in the total number of organisms per year. Avian (73.18%) samples were higher than any other sample. The major isolates identified were Escherichia. coli, Salmonella spp., Klebsiella spp., Staphylococcus spp., Proteus spp., and Pseudomonas spp. We found that antimicrobial resistance to baseline antibiotics increased over the years. Of particular concern was the increasing resistance of Klebsiella spp. to cephalosporins, an important second-generation antibiotic. This finding underscores the importance of farm animals as reservoirs of pathogens harbouring antimicrobial resistance. Effective biosecurity, surveillance, and frugal use of antibiotics in farms are needed because the health of humans and animals is intricately connected.

1. Introduction

Microbe-host interactions can have either beneficial or pathogenic outcomes for both the microbe and the host. One of the major public health concerns related to these interactions is the presence of food-borne pathogens (viruses, bacteria, parasites, fungi) and toxins in farm animals and their products [1]. These pathogens and toxins not only affect the safety and quality of food but also pose a threat to human health through zoonotic transmission [2]. The ecology of microbes around farm animals and their products is influenced by several factors, such as biosecurity measures, microbial flora of livestock, hygiene practices, and environmental conditions [2].
Bacterial agents are predominant among the food-borne pathogens from animal sources; for example, Salmonella spp., Staphylococcus aureus, Escherichia coli (E. coli), and Listeria monocytogenes [3,4]. Food animals serve as reservoirs of zoonotic pathogens, hence crucial in transmitting infection from food sources [1]. For example, E. coli, a commensal bacterium, does not normally cause disease in food animals. However, it has been documented as a promiscuous microbe that can acquire and disseminate plasmid-mediated antimicrobial resistance genes [5,6,7]. Similar phenomena have been observed in other microorganisms identified in food animals [8,9], such as Brucella abortus, Clostridium difficile, Campylobacter jejuni, Enterococcus spp., and Aeromonas hydrophilia [10,11].
In Nigeria, previous studies on animal products have isolated pathogenic microbes with zoonotic/public health potentials, such as methicillin-resistant Staphylococcus aureus (MRSA), Campylobacter spp., Shigella spp., and Listeria monocytogenes [11,12,13,14]. These pathogens can negatively impact farm animal health and production, resulting in economic losses. Extensive usage of antimicrobials in farm animals has compounded the deleterious effect of food-borne pathogens through the emergence of resistant microorganisms. A study by Van Boeckel et al. (2017) estimated that up to 73% of antimicrobials manufactured globally are used in farm animals [12], leading to environmental contamination by residual antimicrobials and the eventual buildup of more resistance [13]. The seriousness of antimicrobial resistance in veterinary practice has brought about efforts by regional groups such as the European Medicines Agency’s European Surveillance of Veterinary Consumption (ESVAC), the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS), and the Japanese Veterinary Antimicrobial Monitoring System (JVARM) [14]. The situation of antimicrobial resistance (AMR) in Africa is complicated by several factors, including lack of access to appropriate antimicrobials, poor legislation on antimicrobial use, weak surveillance systems, and insufficient knowledge of treatment guidelines [15,16].
Food-borne pathogens are a major public health concern worldwide, causing up to 3 million deaths annually [17]. Global food-borne infections of bacterial origin in 2015 were estimated at 226,526,634 [18], with food sourced from animals as a major contributor to these infections [19]. This data is expected to rise with the increasing human population. However, this estimate may not reflect the true burden of the problem, as many cases are underreported in developing countries, including Nigeria [20,21]. As the global population and the demand for animal protein increases, so does the need for food safety and security. The livestock industry in Nigeria contributes between 6 and 8% of the country’s GDP and employs many smallholder farmers and nomads [22]. The poultry sector alone has an estimated number of 180 million birds, ranking second in Africa after South Africa [23].
It is projected that the poultry population in Nigeria will grow to 1.3 billion heads by 2050 [24]. Therefore, health authorities must implement measures that ensure safer food production and consumption for the expanding population and create wealth through safe food. This data audit covers a period of four years and aims to provide a snapshot of the microorganisms isolated at the bacteriology laboratory of the National Veterinary Research Institute (NVRI), Vom, Nigeria. It also seeks to understand these pathogens’ distribution and their antimicrobial resistance.

2. Materials and Methods

2.1. Study Area and Data Collection

The National Veterinary Research Institute (NVRI) is a leading research institute in Nigeria that researches animal diseases, diagnosis, training, and production of biologicals for the livestock industry. The NVRI operates through six zonal stations and 17 laboratories across Nigeria [25]. One of these laboratories is the bacteriology laboratory at the Central Diagnostic Laboratory, located at the headquarters of the NVRI (Figure 1). This laboratory performs bacteriological investigations of specimens received from various sources, mainly from the Plateau State and its neighbouring regions. A large proportion of the specimens are submitted for postmortem examination to determine the probable cause of death in livestock. Each specimen is assigned a unique identification identifier that is linked to additional information such as farm location, flock size, and number of deaths. A flow chart of the data generated for this study is shown in Figure 2.

2.2. Key Pathogens

2.2.1. Escherichia coli

Escherichia coli is a bacterium that colonises the gut of all vertebrate animals as either a commensal or an opportunistic microbe. It provides digestive support by synthesising certain vitamins and essential biomolecules [26,27]. However, it can also cause various infections in both human and animal hosts, such as colibacillosis, hospital-acquired pneumonia, haemolytic uremic syndrome (HUS), meningitis, and urinary tract infection (UTI) [26,27]. Researchers often classify pathogenic strains of E. coli into pathotypes using acronyms that define the target organ, a virulence gene, or a specific gene such as extra-intestinal pathogenic E. coli (ExPEC), avian pathogenic E. coli (APEC), and Shiga toxin-producing E. coli (STEC) [28]. Some E. coli strains may carry plasmids that facilitate their ability to acquire and distribute antimicrobial resistance horizontally to other microbes or the environment. Previous studies have shown that APEC strains can serve as reservoirs of virulence genes for ExPEC [29], especially in poultry [30]. Because of their ubiquitous nature in mammals, E. coli is a useful indicator for monitoring antimicrobial resistance, as the frequency of antimicrobial resistance by E. coli can provide useful insight into selective pressure induced by drugs [31,32].

2.2.2. Staphylococcus spp.

Staphylococcus spp. is a group of bacteria that cause various diseases in humans and animals. These bacteria can infect the skin and mucous membranes, causing conditions such as otitis, pyoderma, and surgical wound infections [32]. Among the challenges posed by Staphylococci spp. is their ability to develop multidrug resistance, especially in Staphylococcus aureus, which is a common cause of hospital- and community-acquired infections and food poisoning [33,34]. Staphylococcus aureus belongs to the ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterobacter spp.) group of the pathogen which have been given a ‘priority status’ [35] in the fight against antimicrobial resistance (AMR) [36]. Moreover, Staphylococcus aureus can colonise or infect healthy, warm-blooded animals, increasing the risk of transmission to humans [37]. Knowledge of diseases caused by Staphylococcus aureus in animals may vary depending on the economic impact of the disease and the life expectancy of the animal. For instance, mastitis, which affects milk production and calf nutrition-with economic consequences on agribusinesses, gets more attention than osteomyelitis or endocarditis, which are often not noticeable because their progression outlives most feed animals [37].
Although the coagulase-negative group of Staphylococcus (CoNS) was previously considered less virulent than Staphylococcus aureus [38], recent studies have revealed that they possess a range of virulence factors, such as exfoliative toxins, hemolysins, enterotoxins, and toxic shock syndrome toxin [38,39,40,41]. CoNS have been associated with mastitis in animals and biofilm formation on biotic and abiotic surfaces, which facilitates colonisation and infection by other microbes [41].

2.2.3. Salmonella spp.

Salmonella is a genus of gram-negative, facultatively anaerobic, flagellated bacilli belonging to the family of Enterobacteriaceae [42]. The surface antigen of the cell’s lipopolysaccharide, flagella, and capsular polysaccharide is expressed as the O, H, and V antigens and is used to classify its serotypes [43]. Salmonella bacteria are ubiquitous and resilient in nature and can survive for several weeks in dry environments and several months in water [43]. The genus is currently divided into Salmonella bongori and Salmonella enterica, with a third species pending approval (Salmonella subterranean). Salmonella enterica has more than 2610 serovars that cause diseases in humans and animals [44]. This species is further divided into six subspecies: Salmonella enterica enterica, Salmonella enterica salamae, Salmonella enterica arizonae, Salmonella enterica diarizonae, Salmonella enterica houtenae, and Salmonella enterica indica. Two serovars of Salmonella enterica, Salmonella gallinarum and Salmonella pollurum, are the most commonly associated with clinical disease in poultry [44].
Salmonella is a bacteria pathogen that causes infectious diseases in humans and animals, with chickens being one of its main hosts [45]. It can cause chronic and acute diseases in poultry [46] and swine [47,48], resulting in serious economic losses for farmers and requiring large investments of private and public resources for testing and control [49]. In addition, salmonellosis can contaminate human food, posing a significant threat to public health [50,51].

2.2.4. Klebsiella spp.

Klebsiella spp. are a group of gram-negative, non-motile, lactose fermenting, facultative anaerobic negative rods belonging to the Enterobacteriaceae group [52]. The Klebsiella genus consists of various species, such as the Klebsiella pneumoniae species complex (KpSC) and several other genetically distinct species [53], which encompasses a broad range of species, including K. indica, K. terrigena, K. spallanzanii, K. huaxiensis, K. oxytoca, K. grimontii, K. pasteurii, and K. michiganensis [54]. These species share an average nucleotide identity of only 90% with KpSC. They are ubiquitous in the environment and are found in soil, water, manure, and plants; as such, they have been isolated from humans and animals alike [52,55].

2.3. Laboratory Investigation

The study employed standard laboratory procedures to isolate bacterial pathogens from the samples. These procedures involved basic cultural techniques, biochemical fermentation tests, and morphological observation of the isolates [56]. The samples were simultaneously inoculated on MacConkey and blood agar, except for those that required a pre-enrichment medium for specific investigations. Antimicrobial susceptibility testing (AST) was conducted using the Kirby-Bauer disc diffusion method [57] for all the identified bacterial isolates using validated in-house discs [58] impregnated with various classes of antibiotics. To facilitate data analysis, we classified the antibiotics into seven groups based on their primary classes, namely, quinolones, tetracyclines, beta-lactams, aminoglycosides, macrolides, nitrofuran, and cephalosporins. This classification was necessary because of the differences in the trademark names used over time.

2.4. Data Analysis

All data analysis and visualisation were conducted in R, version 4.1.1 (R Core Team, 2021). Trends were analysed using a generalised linear model with a log link function for the Poisson distribution, comparing proportions between years with an offset to account for the variability in the total number of organisms per year. Percentage increases and 95% CIs were estimated to determine significant changes in the relative proportion of bacterial pathogens.

3. Results

A total of 2362 tests were conducted between 2018 and 2021, with avian samples accounting for the majority (73.18%), followed by bovine and canine samples (5.67% and 5.27%, respectively), while Feline, Equine, and Pisces samples were the least. There was a yearly increase in samples except for 2020 (Table 1).
Poisson regression analysis showed that Klebsiella spp. and Proteus spp. were the only isolates that demonstrated a significant increase in their number of isolates between 2019 and 2020. All other isolates demonstrated a decrease in their number, with E. coli showing a similar number of isolates compared to the previous year (Table 2). In contrast, all the isolates increased by at least two-fold between 2020 and 2021. The major pathogens isolated are E. coli, Staphylococcus spp., Proteus spp., Salmonella spp., Pseudomonas spp., and Klebsiella spp. (Figure 3), which are the leading pathogens. Regardless of the dip in 2020, there was a general increase in isolates from 2018–2021.
The antimicrobial resistance of the selected isolates to selected antibiotics showed that Escherichia coli and Salmonella spp. exhibited higher resistance to tetracyclines and macrolides across all samples (Figure 4). Whereas Klebsiella spp. alone showed susceptibility to nitrofurans and aminoglycosides, cephalosporins and aminoglycosides were relatively effective against most pathogens.
A majority of the samples originated from Plateau State, Nigeria, as depicted in Figure 5. The number of samples increased alongside their distribution; Bauchi and Kogi were the leading states after Plateau in 2021. In 2020, there was an appreciable reduction in the number of cases and isolates compared to previous years. While in 2021, there was a significant rise in both cases and isolates, with a more extensive geographical reach covering multiple states.

4. Discussion

Our study highlights the consistent increase of antimicrobial-resistant pathogens in food animals. Furthermore, the most common pathogens identified (Escherichia coli, Staphylococcus aureus, Salmonella spp., Pseudomonas spp., and Klebsiella spp.) showed high levels of resistance to common classes of antibiotics except for aminoglycosides and cephalosporins, which is alarming. Resistance to these antibiotics is alarming due to their crucial importance as first-line therapeutics [59] and the role of animal reservoirs of antimicrobial resistance in the spread and emergence of resistant species in the environment [60,61]. While resistance to quinolones, macrolides, tetracyclines, and beta-lactams was common among all isolates, Klebsiella spp. was an exception, as it was only susceptible to nitrofuran and aminoglycoside. The data from this study showed that Klebsiella spp. had developed significant resistance to this drug—an important second-generation antimicrobial often used for treating veterinary infections. This is corroborated by a study by De Oliveira et al. (2020), who reported that Klebsiella spp. resistance to cephalosporins is a global phenomenon [36].
The analysis of the samples revealed the dominant pathogens and their AMR profiles, which have important implications for food safety, public health, and business. Additionally, most of the samples (62%) were from poultry, which reflects the growing popularity and importance of poultry farming in Nigeria [22]. However, this also means that the data were skewed towards avian species, which might limit the generalisability and accuracy of the findings for other animal pathogens. Therefore, future studies in settings with other species are needed to obtain a more comprehensive picture of the AMR situation in farm animals.

4.1. AMR Spilling from Animal to Humans

Antimicrobial resistance has been described as a slow-pandemic, with estimated deaths from antimicrobial-related complications projected to reach up to 10 million by the year 2050 [31]. The contribution of livestock to global antimicrobial resistance is significantly documented [62], and it has been estimated that about 73% of global antimicrobial use is in livestock [63]. The US alone consumes an estimated 10.9 million kg of antibiotics in domestic livestock production systems [64]. Thus, livestock play an important role in the dissemination of antimicrobial resistance, and they do this through a variety of means: food value chain [65], direct contact with farm owners and workers, and byproducts such as manure [66,67]. This means it is imperative to keep up with data on antimicrobial resistance in animals. Unfortunately, data on antimicrobial use in low- and middle-income countries(LMIC) is inadequate because only 6% of countries have a system that monitors antibiotic resistance in animals [68]. And then there are other factors that complicate AMR in LMIC, such as poor regulation/control, irrational use to make up for poor animal husbandry, and biosecurity [69]. The presence of resistant bacteria in animal reservoirs, particularly those carrying resistance to antimicrobial agents that are critically important for human therapy, such as aminoglycosides, fluoroquinolones, and third- and fourth-generation cephalosporins, poses a serious threat to public health [70]. There are several complex scenarios where AMR from livestock is capable of getting to humans. In a study by Nikola et al. (2020) [71], the R plasmids typical to E. coli from birds fed with tetracycline-supplemented feed were isolated from the human faeces of individuals who have worked on the same farm. A variety of health conditions in humans has been connected to the effect of antimicrobial usage, including bone marrow toxicity, hepatotoxicity, carcinogenicity, and immunological and reproductive disorders [72]. Hence, it is relatable to say that man is already living with the consequences of antimicrobial resistance. This view is supported by the fact that the rising antimicrobial resistance in animals is often a sign of excessive use or abuse, which can have indirect negative consequences on human health. Although it is difficult to definitively link these negative consequences to the excessive use of antimicrobials, the deteriorating health condition in developing countries may be compounded by this practice. Therefore, it can be argued that the burden of antimicrobial resistance in humans and animals is intricately connected.

4.2. The Influence of the COVID-19 Pandemic on the Livestock Industry

The majority of the samples in this study were from Plateau State, Nigeria, which may be attributable to the proximity of the laboratory to immediate communities. There was a steady increase in both isolates and samples over the years, except for a noticeable drop in 2020. This decline might be attributed to the COVID-19 pandemic and lockdown, which affected the agribusiness and livelihood of farmers. This finding is consistent with previous studies that reported the negative impacts of the pandemic on the agricultural sector [73,74]. However, there was a significant rise in both cases and isolates, with a more extensive geographical reach covering multiple states in 2021. This was possibly due to the return to normalcy following the easing of restrictions during COVID.
The most common pathogens isolated from the samples were Escherichia coli, Staphylococcus aureus, Salmonella spp., Pseudomonas spp., and Klebsiella spp. The diversity and frequency of these bacteria increased steadily from 2018 to 2021, and except for a slight decline in 2020, the percentage yearly increase of isolates also followed a similar trend. Notably, all the members of the ESKAPE group (Enterococcus, Staphylococcus, Klebsiella, Pseudomonas, and Enterobacter) were represented in the isolates, with Staphylococcus being the most prevalent, followed by Klebsiella and Pseudomonas. These bacteria are known to contribute to the emergence and spread of AMR through their acquisition and distribution of resistance genes through genetic mutation and mobile genetic elements [35,75]. Moreover, the presence of Salmonella and Staphylococcus spp. portends a significant risk to food safety, public health, and agribusinesses as potential carriers of food-borne infections and intoxications [76,77,78].
The spatial distribution of samples shows a strong clustering around Plateau State (likely because the NVRI is domiciled in Plateau State), with few cases reported from the neighbouring states/regions. Nonetheless, a greater diversity of cases was observed in 2021 compared to previous years. It should be noted that the data presented in this study are limited to the cases handled by the bacteriology laboratory in Plateau State and do not represent an exhaustive account of all the cases treated by state and zonal branches.

4.3. Veterinary and Food Safety Implications of Bacterial Pathogens on Public Health

Klebsiella infections in humans are often described as a hospital-acquired infection prevalent in immunocompromised patients. Infections outside the clinical settings involve diverse routes, such as ingesting contaminated food and water and direct contact with companion animals [79]. Common transmission routes in livestock and companion animals include consuming contaminated food and water and through the genitourinary and intramammary pathways [80]. Although most infections with Klebsiella go unnoticed, pathogenic Klebsiella infections in humans produce harmful effects that are sometimes fatal, such as blood infection, respiratory diseases, abscesses, and encephalitis [81].
Klebsiella spp. acquisition of genetic elements and its ability to initiate mutations that provide resistance to antimicrobials is a formidable trait associated with its virulence. This can lead to the emergence of convergent clones known as multidrug-resistant and hypervirulent (MDR-hv) Klebsiella spp. [82] These hypervirulent strains continue to proliferate due to limited treatment options, thereby setting the stage for the emergence of multidrug resistance (MDR) superbugs with dire public health consequences [83]. This is not surprising, seeing the level of antimicrobial use in feed and companion animals.
E. coli is a bacterium that causes various diseases in farm and companion animals, posing significant challenges to veterinary practice and food safety. For example, avian pathogenic APEC causes colibacillosis in poultry, leading to serious respiratory distress [84]. Enterotoxigenic Escherichia coli (ETEC) produces enterotoxins that result in diarrhea in pigs and calves, often with fatal outcomes [32]. Moreover, E. coli is associated with over 80% of mastitis cases in cattle, affecting the quality of milk and the nutrition of young calves. E. coli often co-infects with other pathogens such as Staphylococcus aureus, Streptococcus agalactiae, and Streptococcus uberis [32]. The emergence of extended-spectrum beta-lactamase (ESBL) Escherichia coli is of great concern to veterinarians, as most of the antibiotics approved for veterinary use belong to the class of beta-lactams and cephalosporins. The high rate of antimicrobial resistance acquisition and dissemination by Escherichia coli reduces the effectiveness of treatment options [31].
Staphylococcus spp. has been isolated from various animal species, but they cause the most severe diseases in ruminants. Cattle are considered a major reservoir of Staphylococcus aureus, which is the most prevalent species among different climates and animals. However, swine have also emerged as an important reservoir of methicillin-resistant Staphylococcus aureus (MRSA), a strain that seriously threatens human health [85]. A specific MRSA strain, ST398, has been found in swine, humans, and birds [86]. The contact between humans and animals, such as farm owners and veterinarians, facilitates the transmission and spread of MRSA in the community. The extent of diseases caused by Staphylococcus spp. in animals may depend on the economic impact of the disease and the life expectancy of the animal. For example, mastitis, which affects milk production and calf nutrition, receives more attention than osteomyelitis or endocarditis, which may not be noticeable in feed animals [37].
Salmonellosis is a major food-borne disease affecting millions worldwide [87]. It can be classified into typhoidal and non-typhoidal forms, depending on the serotype of Salmonella bacteria involved. Both forms are endemic in many developing countries and are mainly transmitted through food and water contaminated with fecal matter. According to global estimates, non-typhoidal salmonellosis causes about 93.8 million cases of gastroenteritis [87] and 155,000 deaths annually [88], especially among vulnerable groups such as infants and elderly persons. The most common serotypes associated with human salmonellosis are Salmonella enteritidis and Salmonella typhimurium [89]. Salmonella bacteria can infect a wide range of hosts, including humans, reptiles, rodents, birds, and amphibians. Therefore, human salmonellosis can result from direct or indirect contact with infected animals or their products [90]. Poultry products, particularly undercooked meat and raw eggs, are the main sources of human salmonellosis [91]. However, the risk of Salmonella infection also depends on other factors, such as lifestyle, eating habits, and environmental conditions. People living in low-income settings with poor hygiene and limited access to safe water and food are more susceptible to Salmonella infection [75].

5. Conclusions

Food animals can harbour diverse bacterial pathogens that are resistant to antimicrobial agents critically important in human therapy. Through constant surveillance, monitoring for changes in their antimicrobial resistance pattern may intimate appropriate action. A combination of approaches, such as improved biosecurity at farms, the frugal use of antimicrobial agents, and a surveillance system for monitoring and reporting antimicrobial resistance in farms, hold the promise of mitigating antimicrobial resistance. As such, there is a need to heighten AMR surveillance in animal pathogens with the intention of developing baseline AMR data in animals due to their potential impact on public and human health.

Author Contributions

Conceptualisation, D.K., G.O.A., A.O., O.A.A. and T.I.E.; methodology, D.K., M.D.G., O.A.A. and T.I.E.; software, O.A.A. and T.I.E.; formal analysis and investigation, D.K., O.A.A. and T.I.E.; writing—original draft preparation, D.K., A.O., D.N., G.O.A. and U.G.U.; writing—review and editing, S.N., Y.D., M.M. and E.A.; supervision, S.N., O.A.A. and T.I.E.; funding acquisition, O.A.A. and T.I.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical approval for this study was obtained from the Animal Ethics Committee of the NVRI (NVRI AEC REF. No: AEC/03/122/23).

Informed Consent Statement

Not applicable.

Data Availability Statement

Data used for this study are unavailable to the public due ethical and privacy policy of the source.

Acknowledgments

The authors would like to acknowledge the assistance received through transcription and preparation of reagents by the following staff: Fatima B. Liman, Angelina Dalyop, Elizabeth Oladejo, and Thomas Dauda.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Abebe, E.; Gugsa, G.; Ahmed, M. Review on Major Food-Borne Zoonotic Bacterial Pathogens. J. Trop. Med. 2020, 2020, 4674235. [Google Scholar] [CrossRef]
  2. Prieto, M.; Colin, P.; Fernández-Escámez, P.; Alvarez-Ordóñez, A. Editorial Epidemiology, Detection, and Control of Foodborne Microbial Pathogens. BioMed Res. Int. 2015, 2015, 617417. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Argaw, S.; Addis, M. A Review on Staphylococcal Food Poisoning. Food Sci. Qual. Manag. 2015, 40, 59–72. [Google Scholar]
  4. Pillai, N.; Ramkumar, M.; Nanduri, B. Artificial Intelligence Models for Zoonotic Pathogens: A Survey. Microorgisms 2022, 10, 1911. [Google Scholar] [CrossRef] [PubMed]
  5. Caniça, M.; Igrejas, G. Escherichia coli as Commensal and Pathogenic Bacteria among Food-Producing Animals: Health Implications of Extended Spectrum β-Lactamase (ESBL) Production. Animals 2020, 10, 2239. [Google Scholar]
  6. Landers, T.F.; Cohen, B.; Wittum, T.E.; Larson, E.L. A Review of Antibiotic Use in Food Animals: Perspective, Policy, and Potential. Public Health Rep. 2012, 127, 4–22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Almshawt, N.F.; Hiblu, M.A.; Abid, A.S.; Abbassi, M.S.; Elkady, A.A.; Abouzeed, Y.M.; Ahmed, M.O. Antimicrobial resistance among commensal enteric bacteria isolated from healthy cattle in Libya. PAMJ-OH 2020, 13, 3. [Google Scholar] [CrossRef]
  8. De Jong, A.; Simjee, S.; Rose, M.; Moyaert, H.; El Garch, F.; Youala, M. Antimicrobial resistance monitoring in commensal enterococci from healthy cattle, pigs and chickens across Europe during 2004–14 (EASSA Study). J. Antimicrob. Chemother. 2019, 74, 921–930. [Google Scholar] [CrossRef]
  9. Aasmäe, B.; Häkkinen, L.; Kaart, T.; Kalmus, P. Antimicrobial resistance of Escherichia coli and Enterococcus spp. isolated from Estonian cattle and swine from 2010 to 2015. Acta Vet. Scand. 2019, 61, 5. [Google Scholar] [CrossRef]
  10. Bhunia, A.K. Foodborne Microbial Pathogens: Mechanisms and Pathogenesis; Springer: Berlin/Heidelberg, Germany, 2018. [Google Scholar] [CrossRef]
  11. De Vries, S.P.W.; Vurayai, M.; Holmes, M.; Gupta, S.; Bateman, M.; Goldfarb, D.; Maskell, D.J.; Matsheka, M.I.; Grant, A.J. Phylogenetic analyses and antimicrobial resistance profiles of Campylobacter spp. from diarrhoeal patients and chickens in Botswana. PLoS ONE 2018, 13, e0194481. [Google Scholar] [CrossRef] [Green Version]
  12. Van Boeckel, T.P.; Glennon, E.E.; Chen, D.; Gilbert, M.; Robinson, T.P.; Grenfell, B.T.; Levin, S.A.; Bonhoeffer, S.; Laxminarayan, R. Reducing antimicrobial use in food animals. Science (80-) 2017, 357, 1350–1352. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Muhammad, J.; Khan, S.; Su, J.Q.; Hesham, A.E.L.; Ditta, A.; Nawab, J.; Ali, A. Antibiotics in poultry manure and their associated health issues: A systematic review. J. Soils Sediments 2020, 20, 486–497. [Google Scholar] [CrossRef]
  14. Van Boeckel, T.P.; Brower, C.; Gilbert, M.; Grenfell, B.T.; Levin, S.A.; Robinson, T.P.; Teillant, A.; Laxminarayan, R. Global trends in antimicrobial use in food animals. Proc. Natl. Acad. Sci. USA 2015, 112, 5649–5654. [Google Scholar] [CrossRef] [PubMed]
  15. Gemeda, B.A.; Amenu, K.; Magnusson, U.; Dohoo, I.; Hallenberg, G.S.; Alemayehu, G.; Desta, H.; Wieland, B. Antimicrobial Use in Extensive Smallholder Livestock Farming Systems in Ethiopia: Knowledge, Attitudes, and Practices of Livestock Keepers. Front. Vet. Sci. 2020, 7, 55. [Google Scholar] [CrossRef] [PubMed]
  16. Grace, D. Review of Evidence on Antimicrobial Resistance and Animal Agriculture in Developing Countries Delia Grace; CABI Publishing: Wallingford, UK, 2015. [Google Scholar]
  17. EFSA. The European Union Summary Report on antimicrobial resistance in zoonotic and indicator bacteria from humans, animals and food in 2010. EFSA J. 2012, 10, 7209. [Google Scholar] [CrossRef]
  18. World Health Organization. WHO Estimates of the Global Burden of Foodborne Diseases; World Health Organization: Geneva, Switzerland, 2007. [Google Scholar]
  19. Ejo, M.; Garedew, L.; Alebachew, Z.; Worku, W. Prevalence and Antimicrobial Resistance of Salmonella Isolated from Animal-Origin Food Items in Gondar, Ethiopia. BioMed Res. Int. 2016, 2016, 4290506. [Google Scholar] [CrossRef] [Green Version]
  20. Estimating the Burden of Foodborne Diseases. Available online: https://www.who.int/activities/estimating-the-burden-of-foodborne-diseases (accessed on 24 December 2022).
  21. Hedman, H.D.; Vasco, K.A.; Zhang, L. A Review of Antimicrobial Resistance in Poultry Farming within Low-Resource Settings. Animals 2020, 10, 1264. [Google Scholar] [CrossRef]
  22. Caleb Ike, E. A Review of Small holder Farming in Nigeria: Need for Transformation. Agric. Eng. Res. J. 2015, 5, 19–26. [Google Scholar] [CrossRef]
  23. Odunze, E.; Boussini, H.; Mikecz, O.; Pica-Ciamarra, U. Livestock and livelihoods spotlight Nigeria: Cattle and poultry sectors. Food Agric. Organ. Afr. Sustain. Livest. 2018, 2050, 11–12. [Google Scholar]
  24. The Future of Livestock in Nigeria: Opportunities and Challenges in the Face of Uncertainty—Nigeria|ReliefWeb. Available online: https://reliefweb.int/report/nigeria/future-livestock-nigeria-opportunities-and-challenges-face-uncertainty (accessed on 24 December 2022).
  25. Contact—Contact. Available online: https://nvri.gov.ng/contact (accessed on 28 April 2023).
  26. Denamur, E.; Clermont, O.; Bonacorsi, S.; Gordon, D. The population genetics of pathogenic Escherichia coli. Nat. Rev. Microbiol. 2021, 19, 37–54. [Google Scholar] [CrossRef]
  27. Alkeskas, A.; Ogrodzki, P.; Saad, M.; Masood, N.; Rhoma, N.R.; Moore, K.; Farbos, A.; Paszkiewicz, K. The molecular characterisation of Escherichia coli K1 isolated from neonatal nasogastric feeding tubes. BMC Infect. Dis. 2015, 15, 449. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Croxen, M.A.; Finlay, B.B. Molecular mechanisms of Escherichia coli pathogenicity. Nat. Rev. Microbiol. 2010, 8, 26–38. [Google Scholar] [CrossRef]
  29. Rodriguez-Siek, K.E.; Giddings, C.W.; Doetkott, C.; Johnson, T.J.; Fakhr, M.K.; Nolan, L.K. Comparison of Escherichia coli isolates implicated in human urinary tract infection and avian colibacillosis. Microbiology 2005, 151, 2097–2110. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Casella, T.; Nogueira, M.; Saras, E.; Haenni, M.; Madec, J.Y. High prevalence of ESBLs in retail chicken meat despite reduced use of antimicrobials in chicken production, France. Int. J. Food Microbiol. 2017, 257, 271–275. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Salgado-Caxito, M.; Benavides, J.A.; Adell, A.D.; Paes, A.C.; Moreno-Switt, A.I. Global prevalence and molecular characterization of extended-spectrum β-lactamase producing-Escherichia coli in dogs and cats—A scoping review and meta-analysis. One Health 2021, 12, 100236. [Google Scholar] [CrossRef]
  32. Urban-Chmiel, R.; Marek, A.; Stępień-Pyśniak, D.; Wieczorek, K.; Dec, M.; Nowaczek, A.; Osek, J. Antibiotic Resistance in Bacteria—A Review. Antibiotics 2022, 11, 1079. [Google Scholar] [CrossRef]
  33. Bengtsson-Palme, J.; Kristiansson, E.; Larsson, D.G.J. Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiol. Rev. 2018, 42, 68–80. [Google Scholar] [CrossRef]
  34. World Health Organization. Foodborne Disease Outbreaks: Guidelines for Investigation and Control; World Health Organization: Geneva, Switzerland, 2008. [Google Scholar]
  35. Shrivastava, S. World health organization releases global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics. J. Med. Soc. 2018, 32, 76–77. [Google Scholar] [CrossRef]
  36. De Oliveira, D.M.P.; Forde, B.M.; Kidd, T.J.; Harris, P.N.A.; Schembri, M.A.; Beatson, S.A.; Paterson, D.L.; Walker, M.J. Antimicrobial resistance in ESKAPE pathogens. Clin. Microbiol. Rev. 2020, 33, 10–1128. [Google Scholar] [CrossRef]
  37. Peton, V.; Le Loir, Y. Staphylococcus aureus in veterinary medicine. Infect. Genet. Evol. 2014, 21, 602–615. [Google Scholar] [CrossRef]
  38. Michalik, M.; Samet, A.; Podbielska-Kubera, A.; Savini, V.; Międzobrodzki, J.; Kosecka-Strojek, M. Coagulase-negative staphylococci (CoNS) as a significant etiological factor of laryngological infections: A review. Ann. Clin. Microbiol. Antimicrob. 2020, 19, 26. [Google Scholar] [CrossRef] [PubMed]
  39. Nawrotek, P.; Karakulska, J.; Fijałkowski, K. The Staphylococcal Panton-Valentine leukocidin (PVL). In Pet-to-Man Travelling Staphylococci; Academic Press: Cambridge, MA, USA, 2018. [Google Scholar]
  40. Huebner, J.; Goldmann, D.A. Coagulase-Negative Staphylococci: Role as Pathogens. Annu. Rev. Med. 2003, 50, 223–236. [Google Scholar] [CrossRef] [PubMed]
  41. Heilmann, C.; Ziebuhr, W.; Becker, K. Are coagulase-negative staphylococci virulent? Clin. Microbiol. Infect. 2019, 25, 1071–1080. [Google Scholar] [CrossRef] [PubMed]
  42. Salmonella (Non-Typhoidal). Available online: https://www.who.int/news-room/fact-sheets/detail/salmonella-(non-typhoidal) (accessed on 9 January 2023).
  43. Ferrari, R.G.; Rosario, D.K.A.; Cunha-Neto, A.; Mano, S.B.; Figueiredo, E.E.S.; Conte-Juniora, C.A. Worldwide epidemiology of Salmonella serovars in animal-based foods: A meta-analysis. Appl. Environ. Microbiol. 2019, 85, e00591-19. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Mezal, E.H.; Sabol, A.; Khan, M.A.; Ali, N.; Stefanova, R.; Khan, A.A. Isolation and molecular characterization of Salmonella enterica serovar Enteritidis from poultry house and clinical samples during 2010. Food Microbiol. 2014, 38, 67–74. [Google Scholar] [CrossRef]
  45. Wang, X.; Wang, H.; Li, T.; Liu, F.; Cheng, Y.; Guo, X.; Zhang, X. Characterization of Salmonella spp. isolated from chickens in Central China. BMC Vet. Res. 2020, 16, 258. [Google Scholar] [CrossRef]
  46. Nair, D.V.T.; Kollanoor, J.A. Salmonella in Poultry Meat Production. In Food Safety in Poultry Meat Production; Springer: Berlin/Heidelberg, Germany, 2019; pp. 1–24. [Google Scholar] [CrossRef]
  47. Antunes, P.; Mourão, J.; Campos, J. Salmonellosis: The role of poultry meat. Clin. Microbiol. Infect. 2016, 22, 110–121. [Google Scholar] [CrossRef] [Green Version]
  48. Scallan, E.; Hoekstra, R.; Angulo, F. Foodborne illness acquired in the United States. Emerg. Infect. Dis. 2011, 17, 7–15. [Google Scholar] [CrossRef] [PubMed]
  49. Gast, R.K.; Porter, R.E., Jr. Salmonella Infections. In Diseases of Poultry, 13th ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2017; pp. 675–736. [Google Scholar] [CrossRef]
  50. Sylejmani, D.; Musliu, A. Associations between the Level of Biosecurity and Occurrence of Dermanyssus gallinae and Salmonella spp. in Layer Farms. Avian Dis. 2016, 60, 454–459. [Google Scholar] [CrossRef]
  51. Salmonella Homepage|CDC. Available online: https://www.cdc.gov/salmonella/index.html (accessed on 9 January 2023).
  52. Ribeiro, M.G.; de Morais, A.B.C.; Alves, A.C.; Bolaños, C.A.D.; de Paula, C.L.; Portilho, F.V.R.; de Nardi Júnior, G.; Lara, G.H.B.; de Souza Araújo Martins, L.; Moraes, L.S.; et al. Klebsiella-induced infections in domestic species: A case-series study in 697 animals (1997–2019). Braz. J. Microbiol. 2022, 53, 455–464. [Google Scholar] [CrossRef]
  53. Wyres, K.; Lam, M. Population genomics of Klebsiella pneumoniae. Nat. Rev. Microbiol. 2020, 18, 344–359. [Google Scholar] [CrossRef] [PubMed]
  54. Dong, N.; Yang, X.; Chan, E.W.C.; Zhang, R.; Chen, S. Klebsiella species: Taxonomy, hypervirulence and multidrug resistance. EBioMedicine 2022, 79, 103998. [Google Scholar] [CrossRef] [PubMed]
  55. Quinn, P.J.; Markey, B.K.; Carter, M.E.; Donnelly, W.J.C.; Leonard, F.C. Veterinary microbiology and microbial disease. Vet. Microbiol. Microb. Dis. 2002, 44, 986. [Google Scholar]
  56. Washington, J. Laboratory Procedures in Clinical Microbiology; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2012. [Google Scholar]
  57. Bauer, A.W.; Perry, D.M.; Kirby, W.M.M. Single-Disk Antibiotic-Sensitivity Testing of Staphylococci: An Analysis of Technique and Results. AMA Arch. Intern. Med. 1959, 104, 208–216. [Google Scholar] [CrossRef]
  58. Vineetha, N.; Sridhar, D.; Vignesh, R.A. Preparation, Standardization of Antibiotic Discs and Study of Resistance Pattern for First-Line Antibiotics in Isolates from Clinical Samples. Int. J. Appl. Res. 2015, 1, 624–631. [Google Scholar]
  59. Benklaouz, M.B.; Aggad, H.; Benameur, Q. Resistance to multiple first-line antibiotics among Escherichia coli from poultry in Western Algeria. Vet. World 2020, 13, 290–295. [Google Scholar] [CrossRef]
  60. Samreen Ahmad, I.; Malak, H.A.; Abulreesh, H.H. Environmental antimicrobial resistance and its drivers: A potential threat to public health. J. Glob. Antimicrob. Resist. 2021, 27, 101–111. [Google Scholar] [CrossRef] [PubMed]
  61. Wee, B.A.; Muloi, D.M.; van Bunnik, B.A.D. Quantifying the transmission of antimicrobial resistance at the human and livestock interface with genomics. Clin. Microbiol. Infect. 2020, 26, 1612–1616. [Google Scholar] [CrossRef]
  62. Magouras, I.; Carmo, L.P.; Stärk, K.D.C.; Schüpbach-Regula, G. Antimicrobial usage and -resistance in livestock: Where should we focus? Front. Vet. Sci. 2017, 4, 148. [Google Scholar] [CrossRef] [Green Version]
  63. Van Boeckel, T.P.; Pires, J.; Silvester, R.; Zhao, C.; Song, J.; Criscuolo, N.G.; Gilbert, M.; Bonhoeffer, S.; Laxminarayan, R. Global trends in antimicrobial resistance in animals in low- and middle-income countries. Science (80-) 2019, 365, eaaw1944. [Google Scholar] [CrossRef] [Green Version]
  64. Innes, G.K.; Randad, P.R.; Korinek, A.; Davis, M.F.; Price, L.B.; So, A.D.; Heaney, C.D. External societal costs of antimicrobial resistance in humans attributable to antimicrobial use in livestock. Annu. Rev. Public Health 2019, 41, 141–157. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. McEwen, S.A.; Collignon, P.J. Antimicrobial Resistance: A One Health Perspective. Microbiol. Spectr. 2018, 6, 521–547. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Tian, M.; He, X.; Feng, Y.; Wang, W.; Chen, H.; Gong, M.; Liu, D.; Clarke, L.J.; van Eerde, A. Pollution by Antibiotics and Antimicrobial Resistance in LiveStock and Poultry Manure in China, and Countermeasures. Antibiotics 2021, 10, 539. [Google Scholar] [CrossRef]
  67. Heuer, H.; Schmitt, H.; Smalla, K. Antibiotic resistance gene spread due to manure application on agricultural fields. Curr. Opin. Microbiol. 2011, 14, 236–243. [Google Scholar] [CrossRef]
  68. Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report: 2021. Available online: https://www.who.int/publications/i/item/9789240027336 (accessed on 5 July 2023).
  69. Magnusson, U.; Moodley, A.; Osbjer, K. Antimicrobial resistance at the livestock–human interface: Implications for Veterinary Services. OIE Rev. Sci. Tech. 2021, 40, 511–521. [Google Scholar] [CrossRef] [PubMed]
  70. Suzuki, Y.; Ono, H.K.; Shimojima, Y.; Kubota, H.; Kato, R.; Kakuda, T.; Hirose, S.; Hu, D.-L.; Nakane, A.; Takai, S.; et al. A novel staphylococcal enterotoxin SE02 involved in a staphylococcal food poisoning outbreak that occurred in Tokyo in 2004. Food Microbiol. 2020, 92, 103588. [Google Scholar] [CrossRef]
  71. Vidovic, N.; Vidovic, S. Antimicrobial resistance and food animals: Influence of livestock environment on the emergence and dissemination of antimicrobial resistance. Antibiotics 2020, 9, 52. [Google Scholar] [CrossRef] [Green Version]
  72. Bacanlı, M.; Başaran, N. Importance of antibiotic residues in animal food. Food Chem. Toxicol. 2019, 125, 462–466. [Google Scholar] [CrossRef]
  73. Awoyomi, O.J.; Olasoju, M.I.; Kehinde, O.O.; Adebowale, O.O.; Awoyomi, F.S.O.; Agbajelola, V.I.; Ogugua, A.J. Impact of COVID-19 on livestock production and marketing in Nigeria. Niger. J. Anim. Prod. 2022, 49, 195–212. [Google Scholar] [CrossRef]
  74. Rahimi, P.; Islam, M.S.; Duarte, P.M.; Tazerji, S.S.; Sobur, M.A.; El Zowalaty, M.E.; Ashour, H.M.; Rahman, T. Impact of the COVID-19 pandemic on food production and animal health. Trends Food. Sci. Technol. 2022, 121, 105–113. [Google Scholar] [CrossRef]
  75. Galgallo, D.A.; Roka, Z.G.; Boru, W.G.; Abill, K.; Ransom, J. Investigation of a typhoid fever epidemic in Moyale Sub-County, Kenya, 2014–2015. J. Health Popul. Nutr. 2018, 37, 105–113. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Bamidele, O.; Amole, T.A. Impact of COVID-19 on Smallholder Poultry Farmers in Nigeria. Sustainability 2021, 13, 11475. [Google Scholar] [CrossRef]
  77. Morens, D.M.; Folkers, G.K.; Fauci, A.S. The challenge of emerging and re-emerging infectious diseases. Nature 2004, 430, 242–249. [Google Scholar] [CrossRef] [Green Version]
  78. Okolie, C.C.; Ogundeji, A.A. Effect of COVID-19 on agricultural production and food security: A scientometric analysis. Hum. Soc. Sci. Commun. 2022, 9, 64. [Google Scholar] [CrossRef]
  79. Sykes, J.E.; Rankin, S.C.; Papich, M.G.; Weese, J.S.; Little, S.E. Greene’s Infectious Diseases of the Dog and Cat; Springer: Berlin/Heidelberg, Germany, 1799. [Google Scholar]
  80. Schukken, Y.; Chuff, M.; Moroni, P.; Gurjar, A.; Santisteban, C.; Welcome, F.; Zadoks, R. The “Other” Gram-Negative Bacteria in Mastitis. Klebsiella, Serratia, and More. Vet. Clin. N. Am. Food Anim. Pract. 2012, 28, 239–256. [Google Scholar] [CrossRef] [PubMed]
  81. Shon, A.S.; Bajwa, R.P.S.; Russo, T.A. Hypervirulent (hypermucoviscous) Klebsiella pneumoniae: A new and dangerous breed. Virulence 2013, 4, 107–118. [Google Scholar] [CrossRef] [Green Version]
  82. Gu, D.; Dong, N.; Zheng, Z.; Lin, D.; Huang, M.; Wang, L.; Chan, E.W.-C.; Shu, L.; Yu, J.; Zhang, R.; et al. A fatal outbreak of ST11 carbapenem-resistant hypervirulent Klebsiella pneumoniae in a Chinese hospital: A molecular epidemiological study. Lancet Infect. Dis. 2018, 18, 37–46. [Google Scholar] [CrossRef]
  83. Marr, C.M.; Russo, T.A. Hypervirulent Klebsiella pneumoniae: A new public health threat. Expert Rev. Anti-Infect. Ther. 2019, 17, 71–73. [Google Scholar] [CrossRef] [Green Version]
  84. Nolan, L.K.; John, B.H.; Vaillancourt, J.P.; Abdul-Aziz, T.; Logue, C.M. Colibacillosis. In Diseases of Poultry, 13th ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2017; pp. 751–805. [Google Scholar] [CrossRef]
  85. Smith, T.C.; Male, M.J.; Harper, A.L.; Kroeger, J.S.; Tinkler, G.P.; Moritz, E.D.; Capuano, A.W.; Herwaldt, L.A.; Diekema, D.J. Methicillin-resistant Staphylococcus aureus (MRSA) strain ST398 is present in midwestern U.S. swine and swine workers. PLoS ONE 2009, 4, e4258. [Google Scholar] [CrossRef] [Green Version]
  86. Nemati, M.; Hermans, K.; Lipinska, U.; Denis, O.; Deplano, A.; Struelens, M.; Devriese, L.A.; Pasmans, F.; Haesebrouck, F. Antimicrobial resistance of old and recent Staphylococcus aureus isolates from poultry: First detection of livestock-associated methicillin-resistant strain ST398. Antimicrob. Agents Chemother. 2008, 52, 3817–3819. [Google Scholar] [CrossRef] [Green Version]
  87. Uro, T.W. International Journal of Advanced Research in Biological Sciences Salmonellosis: A Review. Int. J. Adv. Res. Biol. Sci. 2019, 6, 79–88. [Google Scholar] [CrossRef]
  88. Ngogo, F.A.; Joachim, A.; Abade, A.M.; Rumisha, S.F.; Mizinduko, M.M.; Majigo, M.V. Factors associated with Salmonella infection in patients with gastrointestinal complaints seeking health care at Regional Hospital in Southern Highland of Tanzania. BMC Infect. Dis. 2020, 20, 135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  89. Xue, Z.; Ge, Y.; Guo, F. Salmonella bacteremia and its disseminated infection should not be neglected. Zhonghua Nei Ke Za Zhi 2021, 60, 102–105. [Google Scholar] [PubMed]
  90. Haeusler, G.M.; Curtis, N. Non-typhoidal salmonella in children: Microbiology, epidemiology and treatment. Adv. Exp. Med. Biol. 2013, 764, 13–26. [Google Scholar] [CrossRef]
  91. Barua, H.; Biswas, P.K.; Olsen, K.E.P.; Christensen, J.P. Prevalence and characterization of motile Salmonella in commercial layer poultry farms in Bangladesh. PLoS ONE 2012, 7, e35914. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Vetsci 10 00505 g001
Figure 1. The map of Nigeria showing the location of the NVRI, Vom, Plateau State. The red dot represents the location of NVRI.
Figure 1. The map of Nigeria showing the location of the NVRI, Vom, Plateau State. The red dot represents the location of NVRI.
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Figure 2. Flowchart of data. Others#: Listeria spp., Citrobacter spp., Bacillus spp., Aeromonas spp., Pseudomonas spp., Enterobacter spp., Shigella spp., Streptococcus spp., Yersinia spp., Edwardsiella spp., Leptospira spp., Bacteroides.
Figure 2. Flowchart of data. Others#: Listeria spp., Citrobacter spp., Bacillus spp., Aeromonas spp., Pseudomonas spp., Enterobacter spp., Shigella spp., Streptococcus spp., Yersinia spp., Edwardsiella spp., Leptospira spp., Bacteroides.
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Figure 3. Cases of bacterial isolates identified at the NVRI bacteriology laboratory between 2018 and 2021.
Figure 3. Cases of bacterial isolates identified at the NVRI bacteriology laboratory between 2018 and 2021.
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Figure 4. Antibiotic resistant trends of the antibiotics. The bars represent the number of selected isolates, while the lines show the percentage of antimicrobial resistance to specific antibiotics.
Figure 4. Antibiotic resistant trends of the antibiotics. The bars represent the number of selected isolates, while the lines show the percentage of antimicrobial resistance to specific antibiotics.
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Figure 5. Geographical distribution of samples reported to NVRI from 2018–2021.
Figure 5. Geographical distribution of samples reported to NVRI from 2018–2021.
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Table 1. Descriptive summary of the sources of the sample.
Table 1. Descriptive summary of the sources of the sample.
Sample2018201920202021
Avian310383323468
Bovine29332429
Canine19242727
Caprine8111115
Equine1107
Feed1410
Feline1003
Laprine22232127
Ovine10101316
Pisces2024
Porcine693258
Grand Total409498464654
Table 2. Percentage yearly increases of isolates between the years 2018–2021.
Table 2. Percentage yearly increases of isolates between the years 2018–2021.
Microorganisms% Bacterial Isolates for Each AnimalNumber of Reports 2018–2021% Annual Increase (95% CI)
AvBovCanCapEquFelLapOvPcsPorTotalMeanSD
Escherichia coli5431303633040492541112028062.0915.83 (0.54, 21.13)
Salmonella spp.9.50.904.42200025114235.512.2311.31 (−3.44, 26.19)
Klebsiella spp.4.58.752.20258140814135.310.5918.04 (3.16, 33.14)
Proteus spp.1010162.20011120172446120.717.19 (5.88, 28.63)
Staphylococcus spp.131824222221512252236190.329.497.88 (−1.36, 17.16)
Streptococcus spp.0.65.212.2000001297.254.2720.95 (−11.83, 55.01)
Psuedomonas spp.2.45.234.4003.24.1047318.35.3210.44 (−10.11, 31.24)
Shighella spp.0.5010000002102.52.650.00 (−56.64, 56.64)
Enterobacter spp.0.90.902.2001.1200235.752.3630.33 (−6.84, 69.89)
Other organisms 56214154.0730.33 (−6.84, 69.89)
Av.; Avian, Bov.; Bovine, Can.; Canine, Cap.; Caprine, Fel.; Feline, Lap.; Laprine, Ov.; Ovine, Pisces.; Pisces, Por.; Porcine.
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Kabantiyok, D.; Gyang, M.D.; Agada, G.O.; Ogundeji, A.; Nyam, D.; Uhiara, U.G.; Abiayi, E.; Dashe, Y.; Ngulukun, S.; Muhammad, M.; et al. Analysis of Retrospective Laboratory Data on the Burden of Bacterial Pathogens Isolated at the National Veterinary Research Institute Nigeria, 2018–2021. Vet. Sci. 2023, 10, 505. https://doi.org/10.3390/vetsci10080505

AMA Style

Kabantiyok D, Gyang MD, Agada GO, Ogundeji A, Nyam D, Uhiara UG, Abiayi E, Dashe Y, Ngulukun S, Muhammad M, et al. Analysis of Retrospective Laboratory Data on the Burden of Bacterial Pathogens Isolated at the National Veterinary Research Institute Nigeria, 2018–2021. Veterinary Sciences. 2023; 10(8):505. https://doi.org/10.3390/vetsci10080505

Chicago/Turabian Style

Kabantiyok, Dennis, Moses D. Gyang, Godwin O. Agada, Alice Ogundeji, Daniel Nyam, Uchechi G. Uhiara, Elmina Abiayi, Yakubu Dashe, Sati Ngulukun, Maryam Muhammad, and et al. 2023. "Analysis of Retrospective Laboratory Data on the Burden of Bacterial Pathogens Isolated at the National Veterinary Research Institute Nigeria, 2018–2021" Veterinary Sciences 10, no. 8: 505. https://doi.org/10.3390/vetsci10080505

APA Style

Kabantiyok, D., Gyang, M. D., Agada, G. O., Ogundeji, A., Nyam, D., Uhiara, U. G., Abiayi, E., Dashe, Y., Ngulukun, S., Muhammad, M., Adegboye, O. A., & Emeto, T. I. (2023). Analysis of Retrospective Laboratory Data on the Burden of Bacterial Pathogens Isolated at the National Veterinary Research Institute Nigeria, 2018–2021. Veterinary Sciences, 10(8), 505. https://doi.org/10.3390/vetsci10080505

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