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Highlights in BioScience             

ISSN: 2682-4043 

DOI:10.36462/H.BioSci.20223                                                                                                          
 

 

Highlights in BioScience                                                                                                                                                                  September 2020| Volume 3 

http://bioscience.highlightsin.org/ 

Page 1 of 7 

                                         

Research Article 

 

Open Access 

 
 

 

 

 

1The Oke- Ogun Polytechnic, Saki Oyo State 

Nigeria, Department of Science Laboratory 

Technology, Microbiology option.  
2Ladoke Akintola University of Technology, 

Ogbomosho, P.M.B 4000, Department of 

Pure & Applied Biology.  
3Department of Microbiology and Botany, 

University of Ibadan, Ibadan, Nigeria.  
4Department of Zoology, Parasitology Unit, 

University of Ibadan, Nigeria. 
5Cellular Parasitology Unit, Department of 

Zoology, University of Ibadan, Nigeria.  
6Department Food Science Technology, the 

Oke-Ogun Polytechnic, Saki.  
7Lead City University, Ibadan, Faculty of 

Medical Science, Chemical Science 

(Biochemistry Unit). 

 

Contacts of Authors  

 

* To whom correspondence should be 

addressed: Adeoti Olatunde Micheal 

Citation: Adeoti Olatunde Micheal, Oni 

Abosede Catherine, Adeoye Kafilat Adenike, 

Adeoti Oluwole Adeola, Adeoye Basirat 

Adedamola and Adesina David Ademola 

(2020). Predictive comparative antibiotic 

resistance profiles of rhizobacteria genes using 

CARD: a bioinformatics approach. Highlights 

in BioScience Volume 3. Article ID 20223. 

dio:10.36462/H.BioSci.20223 

 

Received:  June 23, 2020 

 

Accepted:  September 7, 2020 

 

Published: September 26, 2020 
 

Copyright: © 2020 Micheal et al. This is an 

open access article distributed under the terms 

of the Creative Commons Attribution License, 

which permits unrestricted use, distribution, 

and reproduction in any medium, provided the 

original author and source are credited. 

 

Data Availability Statement: All relevant data 

are within the paper and supplementary 

materials 

 

Funding: The authors have no support or 

funding to report. 

 

Competing interests: The authors declare that 

they have no competing interests. 

 

 

 

Predictive comparative antibiotic resistance (AMR) 

profiles of rhizobacteria genes using CARD: a 

bioinformatics approach 

 
Adeoti Olatunde Micheal

1,2,5
*, Oni Abosede Catherine

1
, Adeoye Kafilat 

Adenike
1,4

, Adeoti Oluwole Adeola
6
, Adeoye Basirat Adedamola

7
 and 

Adesina David Ademola
1,3

  

 

 

Abstract 

        Members of the Plant Growth Promoting Rhizobacteria (PGPR) 

have been severally implicated as excellent growth enhancers, yield 

promoters as well as bio-fertilizers. A study on antibiotics surveillance of 

PGPR is urgently needed as caution towards its continued usage in Bio-

science and Agro-allied. Antimicrobial resistance has become a great 

concern in agriculture and public health. The detection and 

characterization of antimicrobial resistance move from targeted culture 

and enzyme-based reaction to high-throughput metagenomics; acceptable 

resources for the analysis of large-scale information area unit as an 

expected rescue. The excellent bioinformatics tool newly curated for 

Antibiotic Resistance information (CARD; https://card.mcmaster.ca) 

could be a curated hub and resource-providing-referenced server for 

deoxyribonucleic acid and protein sequences as well as detection models 

on the molecular radar for antimicrobial resistance. This study employed 

CARD as pathogenomics repertoires for high-quality reference 

information on retrieving antibiotics resistance information on twenty-

two carefully-selected members of Rhizobacter from NCBI. NCBI and 

CARD on-line platform were employed in polishing of antiobitics 

resistance info of selected PGPR genera such as Leguminosarum, 

Azotobacter, Azospirillum, Erwinia, Mesorhizobium, Flavobacterium 

Paenibacillus Polymyxa, Bacilli mycoides, B. subtilis, and Burkholderia 

pseudomallei among others. The data generated showed evidence that 

these rhizobacteria could be resistant to certain drug classes under a 

different Antimicrobial Resistance (AMR) Gene families using different 

phyto-pathogenic genes (ARO terms) using different resistance 

mechanisms. This distinctive platform provides bioinformatics tool that 

bridges antibiotic resistance considerations, which could be a fallback for 

policies in healthcare, agriculture and the environment.  

 

 

Keywords: Plant Growth Promoting Rhizobacteria (PGPR), Comprehensive    

Antibiotic Resistance Database (CARD), Antimicrobial Resistance (AMR), 

Phyto-pathogenic, Metagenomics, Bio-fertilizers 

 

https://creativecommons.org/licenses/by/4.0/


 

 

Micheal et al. 2020                                                                                          Predictive  antibiotic resistance (AMR) of rhizobacteria genes  using CARD 

 

 

Highlights in BioScience                                                                                                                                                                  September 2020| Volume 3 

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Introduction  

Rhizobacteria are a group of agronomic bacteria that 

form a mutualistic symbiotic association, which is 

beneficial to both parties. Members of rhizobacteria are an 

essential group of microorganisms used as bio-fertilizer [1, 

2, 3] and plant growth promoters, which are often referred 

to as PGPRs [4, 5, 6]. PGPR enhance plant growth by 

adding nutrients which act as inoculants during bio 

remediation, phytostimulation and biological control are 

classified based on their essential roles in the plant they 

inhabit [7, 8, 9]. The most common of PGPR species which 

are present in the rhizosphere are members of the 

genus Azospirillum [10, 11], Bacillus spp. and 

Pseudomonas spp [12, 13, 14, 15]. The increasing reliance 

on plants as major sources of pharmaceuticals, cosmetics, 

and fragrance flavours to meet with the increasing rise in 

the world population hence the urgent need to investigate its 

potential antibiotic potentials if barriers of its non-

pathogenicity are trespassed [16,17,18]. 

Recent advancements in pathogenomics have 

advanced the phenomenon of antimicrobial resistance has 

gained benchmarking astute as a world number one public 

health threat [19, 20]. Therefore, efforts should be 

dissipated toward the characterization of antimicrobial 

resistance with increasing attention at the international level 

and proven to global acceptance in recent United Nations 

radars. The enzyme based polymerase chain reaction (PCR) 

characterizes and effectively serve as indicator of 

pathogenic microorganisms such as Escherichia coli, 

Salmonella. Non-pathogenicity is associated with 

Antimicrobial Resistance whereas culture- and PCR-based 

may have provided the necessary sigh into the prevalence of 

resistance. These techniques had thus enhance our ability to 

review both the evolution and ecology of antimicrobial 

resistance among the entire microbiota population level [21, 

22]. 

There are several web servers exist nowadays that 

completely identify ontological AMR genes. These 

customized in silico resources are primarily designed for 

screening of one ordination or many assembled contigs of 

Antibiotic Resistance metaphysics (ARO) developed by 

excellent antibiotic resistance information [21], which is a 

notable improvement in AMR bio curation. Such an 

annotational classification is incredibly useful for useful 

description. The CARD includes bioinformatics tools that 

modify the identification of antibiotic resistance genes from 

whole- or partial-genome sequence information together 

with unannotated raw sequence assembly contigs [23].  

Over the ages, because of the evolutionary and 

ecological drift, there are many bacteria of different genera 

that have migrated beyond the coast of their ecological 

niches in an attempt to seek new abode and survival. Some 

of these organisms include members of Pseudomonas, 

Serratia, and Escherichia, which have variously acquired 

resistance complexity along the wheel of change. With this 

in mind, PGPRs are beneficial today because of their 

various roles in agriculture and food security, hence the 

need for predictive genomic resistance profile as a tool for 

antibiotic resistance for future age. 

The present study is an in silico surveillance attempt 

to predict AMR molecules among farmers friendly PGPR 

bacteria, although presently environmental opportunistic 

isolates. PGPR influence plant growth in two different 

ways: direct and indirect. The direct promotion of plant 

growth by PGBR is by the synthesis of phyto-hormones, 

thereby facilitating the uptake of certain nutrients from the 

environment. Indirect plant growth promotion occurs when 

PGBR lessen or the risk of phytopathogenic organisms, 

making them serve as antibiotic by producing bio-control 

synthesis. We aimed at using bioinformatics tool to predict 

the antibiotic resistance genes in PGBR. 

Materials and Methods 

Retrieval of complete genomes from NCBI: To analyze 

antimicrobial resistance genes, twenty-two complete 

genomes of selected Plant Growth Promoting Rhizobacteria 

(PGPR) complete sequences were randomly retrieved from 

NCBI database. The retrieved sequences were in FASTA 

format, which was copied from the National Center for 

Biotechnology Information NCBI) website; https://www. 

ncbi.nlm.nih.gov as well as their accession numbers of 

PGPR. 

Analyzing nucleotide sequence on CARD: Nucleotide 

sequences of twenty two members of PGPR was imported 

into the CARD analyzing software from genbank using 

custom software developed specifically for the retention of 

all annotations, NCBI accession numbers and taxonomy 

identification (ID) numbers of the PGPR. The importation 

of these follows a process in which sequences were first 

acquired from genbank in FASTA format 

(https://www.ncbi.nlm.nih.gov/) and then loaded into the 

CARD’s Chado database [24]. By convention, CARD uses 

only the subset of the available NCBI that is relevant to 

antibiotic-resistant bacteria. Individual Antibiotic 

Resistance Ontology (ARO) terms in the CARD have been 

associated with specific computational tools and models 

[25, 26, 14]. 

Results 

Chart representation of RGI results for AMR genes and 

AMR family as retrieved by CARD on 22 selected PGPR 

(Figure 1). 

 

 

file:///C:/wiki/Biofertilizer


 

 

Micheal et al. 2020                                                                                          Predictive  antibiotic resistance (AMR) of rhizobacteria genes  using CARD 

 

 

Highlights in BioScience                                                                                                                                                                  September 2020| Volume 3 

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Figure 1. Continued 

a:  AMR gene family of CP0422721.1 b: AMR gene family of CP021965.1 c: AMR Gene family of CP045291.1  

d: AMR gene family of CP019171.1  e: AMR gene family for CP010313.1  f: AMR gene family of CP029603.1  

g: AMR gene family for CP033430.1  h: AMR gene family of BX571966.1 i: AMR gene family of CP040552.1 



 

 

Micheal et al. 2020                                                                                          Predictive  antibiotic resistance (AMR) of rhizobacteria genes  using CARD 

 

 

Highlights in BioScience                                                                                                                                                                  September 2020| Volume 3 

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Figure 1. Continued 

j: AMR gene family of CP018228.1 k: AMR gene family for CP004015.1 l:  AMR gene family for CP18228.1 

m: AMR gene family for CP000133.1  n: AMR gene family for CP037992.1 o: AMR gene family for CP027116.1 

p: AMR gene family for CP0.107209.1 q: AMR gene family for CP037992.1 r: AMR gene family for CP025542.1 



 

 

Micheal et al. 2020                                                                                          Predictive  antibiotic resistance (AMR) of rhizobacteria genes  using CARD 

 

 

Highlights in BioScience                                                                                                                                                                  September 2020| Volume 3 

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Figure 1. ARO classification tags for the selected twenty-two PGRD bacteria a-v: The assemly contigs retains the RGI into perfect (green), strict (flexible )and loose( spurious partial) 

resistomes.

s: AMR gene family for AP025149.2 

t: AMR gene family for CP000438.1 u: AMR gene family for CP019702.2 v: AMR gene family for CP034395.1 



 

 

Micheal et al. 2020                                                                                          Predictive  antibiotic resistance (AMR) of rhizobacteria genes  using CARD 

 

 

Highlights in BioScience                                                                                                                                                                  September 2020| Volume 3 

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Table 1. Prevalence of AMR gene family among PGPRs. 

 
Accession No 

Prevalence of AMR genes 

 Perfect (%) Strict (%) Loose (%) 

1 CP 042272.1 33 34 33 

2 CP021965.1 30 40 30 

3 CP045291.1 20 60 20 

4 CP019171.1 35 35 30 

5 CP010313.1 25 50 25 

6 CP029603.1 25 50 25 

7 CP033430.1 30 40 30 

8 BX571966.1 25 50 25 

9 CP040552.1 25 50 25 

10 CP018228.1 30 40 30 

11 CP004015.1 44 28 28 

12 CP018228.1 23 38 29 

13 CP000133.1 35 30 35 

14 CP037992.1 25 50 25 

15 CP027116.1 33 43 24 

16 AP007209.1 40 25 35 

17 CP037992.1 23 54 23 

18 CP025542.1 13 74 14 

19 CP025149.2 38 38 24 

20 CP000438.1 48 48 04 

21 CP019702.2 25 50 25 

22 CP034395.1 34 44 22 

Discussion 

This study employed CARD ARO and AMR 

resistomes classification in agreement with earlier studies. 

The RGI genes in CARD predict resistomes for genomic 

and metagenomics data for gene mutations from NCBI 

retrieved nucleotide sequences by using a combination of 

open reading frame prediction [29, 30]. In consonance with 

earlier studies, in this study, the perfect algorithm predicted 

AMR proteins with exact homology (100%) with a query on 

the CARD reference sequences. In the same vein, members 

of PGPR on CARD algorithm showed strict genes under 

AMR/ARO curation because strict RGI genes are more 

flexible by allowing flexible variation from the genome 

reference sequence within the curated BLAST cut-off, which 

is useful for detection of previously unreported variants of 

antibiotics target changes through altered sequences [31].   

The loose algorithm under CARD was an indication of 

resistance genes, which work outside the detection of target, 

which could enable the detection of new, emerging risks and 

more distant homologs of antibiotics resistance genes. The 

loose portends a computational novel AMR gene discovery 

[32]. The study is somewhat pioneer  that employed CARD 

as surveillance algorithm for PGPR, it is however a  novel 

study because all the twenty-two selected PGBR clearly 

showed the three AMR variants into perfect, strict and loose. 

This is an outright indication that the bacteria under study 

harbour resistance genes. The degree of prevalence of these 

genes varies among the PGBR.   In consonance with earlier 

studies, drug efflux accounted for 67% as the mechanism of 

gene resistance with other mechanism. This study is in 

contrast with earlier studies leading to curation paradigm of 

CARD resistomes, which operates on four primary AMR 

gene family to, which resistance is conferred on specificity 

and sensitivity of antibiotics, which rest on the experimental 

interpretation of Minimum inhibitory concentration of the 

antibiotics.  This study implicated LimA 23SrRNA 

methyltransferase in Paenibacillus Polymyxa and P. 

odorifer strain responsible for resistance to lincosamide 

antibiotics, while adeF genes are predictably responsible for 

resistance to fluoroquinolone and tetracycline antibiotics 

Delftia spp and Bradyrhizobium species.  

The most variant genes (adeF, OXA-59, omp 38, amrA 

and amrB) of antibiotic resistance through antibiotic efflux 

pump. In the same vein, members of Rhizobium species use 

adeF genes for resistance to fluoroquinolone and 

tetracycline antibiotics family while Rhizobium species in 

this study use BcII, FosB genes for resistance to fosfomycin, 

cephalosporin, penam, phenicol antibiotics family [27, 28]. 

Conclusion 

CARD as technological metagenomics tools here has 

further demonstrated its wider applicability as primarily 

better curation paradigm over ResFinder [31], ARG-

ANNOT and even catalog of resistance alleles in NCBI 

[32]. The new CARD rules allow diversity inclusion of 

experimentally-proven data on axiomatic variation in 

agricultural or environmental isolates. 

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