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Review

Antimicrobial resistance genes of Escherichia coli, a bacterium of “One Health” importance in South Africa: Systematic review and meta-analysis

  • Received: 20 December 2022 Revised: 07 February 2023 Accepted: 08 February 2023 Published: 13 February 2023
  • This is a systematic review and meta-analysis that evaluated the prevalence of Escherichia coli antibiotic-resistant genes (ARGs) in animals, humans, and the environment in South Africa. This study followed Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines to search and use literature published between 1 January 2000 to 12 December 2021, on the prevalence of South African E. coli isolates' ARGs. Articles were downloaded from African Journals Online, PubMed, ScienceDirect, Scopus, and Google Scholar search engines. A random effects meta-analysis was used to estimate the antibiotic-resistant genes of E. coli in animals, humans, and the environment. Out of 10764 published articles, only 23 studies met the inclusion criteria. The obtained results indicated that the pooled prevalence estimates (PPE) of E. coli ARGs was 36.3%, 34.4%, 32.9%, and 28.8% for blaTEM-M-1, ampC, tetA, and blaTEM, respectively. Eight ARGs (blaCTX-M, blaCTX-M-1, blaTEM, tetA, tetB, sul1, sulII, and aadA) were detected in humans, animals and the environmental samples. Human E. coli isolate samples harboured 38% of the ARGs. Analyzed data from this study highlights the occurrence of ARGs in E. coli isolates from animals, humans, and environmental samples in South Africa. Therefore, there is a necessity to develop a comprehensive “One Health” strategy to assess antibiotics use in order to understand the causes and dynamics of antibiotic resistance development, as such information will enable the formulation of intervention strategies to stop the spread of ARGs in the future.

    Citation: Tsepo Ramatla, Mpho Tawana, Kgaugelo E. Lekota, Oriel Thekisoe. Antimicrobial resistance genes of Escherichia coli, a bacterium of “One Health” importance in South Africa: Systematic review and meta-analysis[J]. AIMS Microbiology, 2023, 9(1): 75-89. doi: 10.3934/microbiol.2023005

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  • This is a systematic review and meta-analysis that evaluated the prevalence of Escherichia coli antibiotic-resistant genes (ARGs) in animals, humans, and the environment in South Africa. This study followed Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines to search and use literature published between 1 January 2000 to 12 December 2021, on the prevalence of South African E. coli isolates' ARGs. Articles were downloaded from African Journals Online, PubMed, ScienceDirect, Scopus, and Google Scholar search engines. A random effects meta-analysis was used to estimate the antibiotic-resistant genes of E. coli in animals, humans, and the environment. Out of 10764 published articles, only 23 studies met the inclusion criteria. The obtained results indicated that the pooled prevalence estimates (PPE) of E. coli ARGs was 36.3%, 34.4%, 32.9%, and 28.8% for blaTEM-M-1, ampC, tetA, and blaTEM, respectively. Eight ARGs (blaCTX-M, blaCTX-M-1, blaTEM, tetA, tetB, sul1, sulII, and aadA) were detected in humans, animals and the environmental samples. Human E. coli isolate samples harboured 38% of the ARGs. Analyzed data from this study highlights the occurrence of ARGs in E. coli isolates from animals, humans, and environmental samples in South Africa. Therefore, there is a necessity to develop a comprehensive “One Health” strategy to assess antibiotics use in order to understand the causes and dynamics of antibiotic resistance development, as such information will enable the formulation of intervention strategies to stop the spread of ARGs in the future.



    The idea of statistical convergence was given by Zygmund [1] in the first edition of his monograph published in Warsaw in 1935. The concept of statistical convergence was introduced by Steinhaus [2] and Fast [3] and then reintroduced independently by Schoenberg [4]. Over the years and under different names, statistical convergence has been discussed in the Theory of Fourier Analysis, Ergodic Theory, Number Theory, Measure Theory, Trigonometric Series, Turnpike Theory and Banach Spaces. Later on it was further investigated from the sequence spaces point of view and linked with summability theory by Bilalov and Nazarova [5], Braha et al. [6], Cinar et al. [7], Colak [8], Connor [9], Et et al. ([10,11,12,13,14]), Fridy [15], Isik et al. ([16,17,18]), Kayan et al. [19], Kucukaslan et al. ([20,21]), Mohiuddine et al. [22], Nuray [23], Nuray and Aydın [24], Salat [25], Sengul et al. ([26,27,28,29]), Srivastava et al. ([30,31]) and many others.

    The idea of statistical convergence depends upon the density of subsets of the set N of natural numbers. The density of a subset E of N is defined by

    δ(E)=limn1nnk=1χE(k),

    provided that the limit exists, where χE is the characteristic function of the set E. It is clear that any finite subset of N has zero natural density and that

    δ(Ec)=1δ(E).

    A sequence x=(xk)kN is said to be statistically convergent to L if, for every ε>0, we have

    δ({kN:|xkL|ε})=0.

    In this case, we write \newline

    xkstatLaskorSlimkxk=L.

    In 1932, Agnew [32] introduced the concept of deferred Cesaro mean of real (or complex) valued sequences x=(xk) defined by

    (Dp,qx)n=1qnpnqnk=pn+1xk,n=1,2,3,

    where p=(pn) and q=(qn) are the sequences of non-negative integers satisfying

    pn<qnandlimnqn=. (1)

    Let K be a subset of N and denote the set {k:k(pn,qn],kK} by Kp,q(n).

    Deferred density of K is defined by

    δp,q(K)=limn1(qnpn)|Kp,q(n)|, provided the limit exists

    where, vertical bars indicate the cardinality of the enclosed set Kp,q(n). If qn=n, pn=0, then the deferred density coincides with natural density of K.

    A real valued sequence x=(xk) is said to be deferred statistically convergent to L, if for each ε>0

    limn1(qnpn)|{k(pn,qn]:|xkL|ε}|=0.

    In this case we write Sp,q-limxk=L. If qn=n, pn=0, for all nN, then deferred statistical convergence coincides with usual statistical convergence [20].

    In this section, we give some inclusion relations between statistical convergence of order α, deferred strong Cesàro summability of order α and deferred statistical convergence of order α in general metric spaces.

    Definition 1. Let (X,d) be a metric space, (pn) and (qn) be two sequences as above and 0<α1. A metric valued sequence x=(xk) is said to be Sd,αp,q-convergent (or deferred d-statistically convergent of order α) to x0 if there is x0X such that

    limn1(qnpn)α|{k(pn,qn]:xkBε(x0)}|=0,

    where Bε(x0)={xX:d(x,x0)<ε} is the open ball of radius ε and center x0. In this case we write Sd,αp,q-limxk=x0 or xkx0(Sd,αp,q). The set of all Sd,αp,q-statistically convergent sequences will be denoted by Sd,αp,q. If qn=n and pn=0, then deferred d-statistical convergence of order α coincides d -statistical convergence of order α denoted by Sd,α. In the special cases qn=n,pn=0 and α=1 then deferred d -statistical convergence of order α coincides d-statistical convergence denoted by Sd.

    Definition 2. Let (X,d) be a metric space, (pn) and (qn) be two sequences as above and 0<α1. A metric valued sequence x=(xk) is said to be strongly wd,αp,q-summable (or deferred strongly d-Ces àro summable of order α) to x0 if there is x0X such that

    limn1(qnpn)αqnk=pn+1d(xk,x0)=0.

    In this case we write wd,αp,q-limxk=x0 or xkx0(wd,αp,q). The set of all strongly wd,αp,q-summable sequences will be denoted by wd,αp,q. If qn=n and pn=0, for all nN, then deferred strong d-Cesàro summability of order α coincides strong d-Cesàro summability of order α denoted by wd,α. In the special cases qn=n,pn=0 and α=1 then deferred strong d-Cesàro summability of order α coincides strong d-Ces àro summability denoted by wd.

    Theorem 1. Let (pn) and (qn) be sequences of non-negative integers satisfying the condition (1), (X,d) be a linear metric space and x=(xk),y=(yk) be metric valued sequences, then

    (i) If Sd,αp,q-limxk=x0 and Sd,αp,q-limyk=y0, then Sd,αp,q-lim(xk+yk)=x0+y0,

    (ii)If Sd,αp,q-limxk=x0 and cC, then Sd,αp,q-lim(cxk)=cx0,

    (iii) If Sd,αp,q-limxk=x0,Sd,αp,q-limyk=y0 and x,y(X), then Sd,αp,q-lim(xkyk)=x0y0.

    Proof. Omitted.

    Theorem 2. Let (pn) and (qn) be sequences of non-negative integers satisfying the condition (1) and α and β be two real numbers such that 0<αβ1. If a sequence x=(xk) is deferred strongly d-Cesàro summable of order α to x0, then it is deferred d-statistically convergent of order β to x0, but the converse is not true.

    Proof. First part of the proof is easy, so omitted. For the converse, take X=R and choose qn=n,pn=0 (for all nN),d(x,y)=|xy| and define a sequence x=(xk) by

    xk={3n,k=n20,kn2.

    Then for every ε>0, we have

    1(qnpn)α|{k(pn,qn]:xkBε(0)}|[n]nα0, as n,

    where 12<α1, that is xk0(Sd,αp,q). At the same time, we get

    1(qnpn)αqnk=pn+1d(xk,0)[n][3n]nα1

    for α=16 and

    1(qnpn)αqnk=pn+1d(xk,0)[n][3n]nα

    for 0<α<16, i.e., xk0(wd,αp,q) for 0<α16.

    From Theorem 2 we have the following results.

    Corollary 1. ⅰ) Let (pn) and (qn) be sequences of non-negative integers satisfying the condition (1) and α be a real number such that 0<α1. If a sequence x=(xk) is deferred strongly d-Cesàro summable of order α to x0, then it is deferred d-statistically convergent of order α to x0, but the converse is not true.

    ⅱ) Let (pn) and (qn) be sequences of non-negative integers satisfying the condition (1) and α be a real number such that 0<α1. If a sequence x=(xk) is deferred strongly d-Cesàro summable of order α to x0, then it is deferred d-statistically convergent to x0, but the converse is not true.

    ⅲ) Let (pn) and (qn) be sequences of non-negative integers satisfying the condition (1). If a sequence x=(xk) is deferred strongly d-Cesàro summable to x0, then it is deferred d-statistically convergent to x0, but the converse is not true.

    Remark Even if x=(xk) is a bounded sequence in a metric space, the converse of Theorem 2 (So Corollary 1 i) and ii)) does not hold, in general. To show this we give the following example.

    Example 1. Take X=R and choose qn=n,pn=0 (for all nN),d(x,y)=|xy| and define a sequence x=(xk) by

    xk={1k,kn30,k=n3n=1,2,....

    It is clear that x and it can be shown that xSd,αwd,α for 13<α<12.

    In the special case α=1, we can give the followig result.

    Theorem 3. Let (pn) and (qn) be sequences of non-negative integers satisfying the condition (1) and x=(xk) is a bounded sequence in a metric space. If a sequence x=(xk) is deferred d-statistically convergent to x0, then it is deferred strongly d-Cesàro summable to x0.

    Proof. Let x=(xk) be deferred d-statistically convergent to x0 and ε>0 be given. Then there exists x0X such that

    limn1(qnpn)|{k(pn,qn]:xkBε(x0)}|=0,

    Since x=(xk) is a bounded sequence in a metric space X, there exists x0X and a positive real number M such that d(xk,x0)<M for all kN. So we have

    1(qnpn)qnk=pn+1d(xk,x0)=1(qnpn)qnk=pn+1d(xk,x0)εd(xk,x0)+1(qnpn)qnk=pn+1d(xk,x0)<εd(xk,x0)M(qnpn)|{k(pn,qn]:xkBε(x0)}|+ε

    Takin limit n, we get wdp,q-limxk=x0.

    Theorem 4. Let (pn) and (qn) be sequences of non-negative integers satisfying the condition (1) and α be a real number such that 0<α1. If liminfnqnpn>1, then Sd,αSd,αp,q.

    Proof. Suppose that liminfnqnpn>1; then there exists a ν>0 such that qnpn1+ν for sufficiently large n, which implies that

    (qnpnqn)α(ν1+ν)α1qαnνα(1+ν)α1(qnpn)α.

    If xkx0(Sd,α), then for every ε>0 and for sufficiently large n, we have

    1qαn|{kqn:xkBε(x0)}|1qαn|{k(pn,qn]:xkBε(x0)}|να(1+ν)α1(qnpn)α|{k(pn,qn]:xkBε(x0)}|.

    This proves the proof.

    Theorem 5. Let (pn) and (qn) be sequences of non-negative integers satisfying the condition (1) and α and β be two real numbers such that 0<αβ1. If limn(qnpn)αqβn=s>0, then Sd,αSd,βp,q.

    Proof. Let limn(qnpn)αqβn=s>0. Notice that for each ε>0 the inclusion

    {kqn:xkBε(x0)}{k(pn,qn]:xkBε(x0)}

    is satisfied and so we have the following inequality

    1qαn|{kqn:xkBε(x0)}|1qαn|{k(pn,qn]:xkBε(x0)}|1qβn|{k(pn,qn]:xkBε(x0)}|=(qnpn)αqβn1(qnpn)α|{k(pn,qn]:xkBε(x0)}|(qnpn)αqβn1(qnpn)β|{k(pn,qn]:xkBε(x0)}|.

    Therefore Sd,αSd,βp,q.

    Theorem 6. Let (pn),(qn),(pn) and (qn) be four sequences of non-negative real numbers such that

    pn<pn<qn<qn for all nN, (2)

    and α,β be fixed real numbers such that 0<αβ1, then

    (i) If

    limn(qnpn)α(qnpn)β=a>0 (3)

    then Sd,βp,qSd,αp,q,

    (ii) If

    limnqnpn(qnpn)β=1 (4)

    then Sd,αp,qSd,βp,q.

    Proof. (i) Let (3) be satisfied. For given ε>0 we have

    {k(pn,qn]:xkBε(x0)}{k(pn,qn]:xkBε(x0)},

    and so

    1(qnpn)β|{k(pn,qn]:xkBε(x0)}|(qnpn)α(qnpn)β1(qnpn)α|{k(pn,qn]:xkBε(x0)}|.

    Therefore Sd,βp,qSd,αp,q.

    (ii) Let (4) be satisfied and x=(xk) be a deferred d-statistically convergent sequence of order α to x0. Then for given ε>0, we have

    1(qnpn)β|{k(pn,qn]:xkBε(x0)}|1(qnpn)β|{k(pn,pn]:xkBε(x0)}|+1(qnpn)β|{k(qn,qn]:xkBε(x0)}|+1(qnpn)β|{k(pn,qn]:xkBε(x0)}|pnpn+qnqn(qnpn)β+1(qnpn)β|{k(pn,qn]:xkBε(x0)}|=(qnpn)(qnpn)(qnpn)β+1(qnpn)β|{k(pn,qn]:xkBε(x0)}|(qnpn)(qnpn)β(qnpn)β+1(qnpn)β|{k(pn,qn]:xkBε(x0)}|(qnpn(qnpn)β1)+1(qnpn)α|{k(pn,qn]:xkBε(x0)}|

    Therefore Sd,αp,qSd,βp,q.

    Theorem 7. Let (pn),(qn),(pn) and (qn) be four sequences of non-negative integers defined as in (2) and α,β be fixed real numbers such that 0<αβ1.

    (i) If (3) holds then wd,βp,qwd,αp,q,

    (ii) If (4) holds and x=(xk) be a bounded sequence, then wd,αp,qwd,βp,q.

    Proof.

    i) Omitted.

    ii) Suppose that wd,αp,q-limxk=x0 and (xk)(X). Then there exists some M>0 such that d(xk,x0)<M for all k, then

    1(qnpn)βqnk=pn+1d(xk,x0)=1(qnpn)β[pnk=pn+1+qnk=pn+1+qnk=qn+1]d(xk,x0)pnpn+qnqn(qnpn)βM+1(qnpn)βqnk=pn+1d(xk,x0)(qnpn)(qnpn)β(qnpn)βM+1(qnpn)αqnk=pn+1d(xk,x0)=(qnpn(qnpn)β1)M+1(qnpn)αqnk=pn+1d(xk,x0)

    Theorem 8. Let (pn),(qn),(pn) and (qn) be four sequences of non-negative integers defined as in (2) and α,β be fixed real numbers such that 0<αβ1. Then

    (i) Let (3) holds, if a sequence is strongly wd,βp,q-summable to x0, then it is Sd,αp,q-convergent to x0,

    (ii) Let (4) holds and x=(xk) be a bounded sequence in (X,d), if a sequence is Sd,αp,q-convergent to x0 then it is strongly wd,βp,q-summable to x0.

    Proof. (i) Omitted.

    (ii) Suppose that Sd,αp,q-limxk=x0 and (xk). Then there exists some M>0 such that d(xk,x0)<M for all k, then for every ε>0 we may write

    1(qnpn)βqnk=pn+1d(xk,x0)=1(qnpn)βqnpnk=qnpn+1d(xk,x0)+1(qnpn)βqnk=pn+1d(xk,x0)(qnpn)(qnpn)(qnpn)βM+1(qnpn)βqnk=pn+1d(xk,x0)(qnpn)(qnpn)β(qnpn)βM+1(qnpn)βqnk=pn+1d(xk,x0)(qnpn(qnpn)β1)M+1(qnpn)βqnk=pn+1d(xk,x0)εd(xk,x0)+1(qnpn)βqnk=pn+1d(xk,x0)<εd(xk,x0)(qnpn(qnpn)β1)M+M(qnpn)α|{k(pn,qn]:d(xk,x0)ε}|+qnpn(qnpn)βε.

    This completes the proof.

    The authors declare that they have no conflict of interests.



    Conflict of interest



    We declare that there are no conflicts of interest.

    Author contributions



    TR, KEL and OT conceived and designed the study. TR performed the literature review and extraction of data. TR and MT analyzed and interpreted the data, created figures and tables and drafted the manuscript. OT and KEL offered mentorship and guidance on antimicrobial resistance, as well as reviewing the manuscript. All authors read, commented and approved the final manuscript.

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