Available via license: CC BY 4.0
Content may be subject to copyright.
Sadia Afrin Bristy currently works in Bioinformatics and Biomedical Research Network of Bangladesh, Dhaka, Bangladesh. Her research interests include
computational biology, system biology, drug design and bioinformatics.
Md. Arju Hossain is a thesis student (Master of Science) of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University. He
focuses on his study in numerous areas, including System Biology, Bioinformatics, Pharmacogenomics, Immunology, Metabolomics and Microbiology.
Dr. Md Habibur Rahman has received his B.Sc in Computer Science and Engineering (CSE) from the Dept. of Computer Science and Engineering, Islamic
University, Kushtia-7003, Bangladesh in 2006. Later on, he has finished his M.Sc. in CSE from the same University in 2007. Recently, he has received hisPhDin
Pattern Recognition and Intelligent Systems from The University of Chinese Academy of Sciences, Beijing, China in 2020. His research interest encompasses
Bioinformatics, Computational Biology, Machine Learning, Deep Learning and Drug Discovery. He is currently working as an Associate Professor at theDept.of
CSE, Islamic University, Kushtia-7003, Bangladesh. He is also investigating some projects as principal investigator and co-investigator and supervising several
undergraduate and post-graduate students. He has presented several papers in peer-reviewed journals and has published numerous studies in science cited
journals. He was an Awardee of the CAS-TWAS Presidential Fellowship, from 2016 to 2020.
Md Imran Hasan is a graduate student of the Department of Computer Science and Engineering, Islamic University. His research priorities include Bioinformatics,
Machine learning, Deep learning, Medical Imaging, and Networking Dr. S. M. Hasan Mahmud received a Ph.D. degree in Computer Science and Technology from
the Universityof Electronic Science and Technology of China, China. He received his B.Sc.degree in software engineering from the Shenyang University of
Chemical Technology, China, and his M.Sc. degree in software engineering from Hohai University, China. He is working as an Assistant professor with the
Department of Computer Science, American International University Bangladesh (AIUB). Besides he is also a research scientist at Mahidol University, Thailand.
Before joining AIUB, he served as a Lecturer in the Software Engineering department at Daffodil International University, Bangladesh. He has published many
research articles in reputed journals and conferences. His research interests include machine learning, deep learning, bioinformatics, drug discovery, and pattern
recognition. He received several Best Paper Awards from the IEEE Conferences.
Dr Mohammad Ali Moni holds a PhD in Artificial Intelligence & Clinical Informatics in 2015 from the University of Cambridge, UK followed by postdoctoral
training at the University of New South Wales, University of Sydney Vice-chancellor fellowship, and Senior Data Scientist at the University of Oxford. Dr Moni
then joined as a research senior lecturer University of Queensland in 2021. He is an Artificial Intelligence, Computer Vision & Machine learning, Digital Health
Data Science, Health Informatics and Bioinformatics researcher developing interpretable and clinical applicable machine learning and deep learning models to
increase the performance and transparency of AI-based automated decision making systems. He has published over 200 journal articles in many top tier journals.
Received: September 14, 2022. Revised: January 21, 2023. Accepted: January 25, 2023
© The Author(s) 2023. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Briefings in Functional Genomics, 2023, 1–17
https://doi.org/10.1093/bfgp/elad005
Protocol Article
An integrated complete-genome sequencing and
systems biology approach to predict antimicrobial
resistance genes in the virulent bacterial strains of
Moraxella catarrhalis
Sadia Afrin Bristy†,*,Md. Arju Hossain†,Md Imran Hasan,S. M. Hasan Mahmud, Mohammad Ali Moni and
Md Habibur Rahman *
*Corresponding authors: Mohammad Ali Moni, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The Universityof
Queensland, St Lucia, QLD 4072, Australia. E-mail: m.moni@uq.edu.au, Md Habibur Rahman, Department of Computer Science and Engineering, Islamic
University, Kushtia 7003, Bangladesh. E-mail: habib@iu.ac.bd
†Equal contributions
Abstract
Moraxella catarrhalis is a symbiotic as well as mucosal infection-causing bacterium unique to humans. Currently, it is considered as one of
the leading factors of acute middle ear infection in children. As M. catarrhalis is resistant to multiple drugs, the treatment is unsuccessful;
therefore, innovative and forward-thinking approaches are required to combat the problem of antimicrobial resistance (AMR). To better
comprehend the numerous processes that lead to antibiotic resistance in M. catarrhalis, we have adopted a computational method in
this study. From the NCBI-Genome database, we investigated 12 strains of M. catarrhalis. We explored the interaction network comprising
74 antimicrobial-resistant genes found by analyzing M. catarrhalis bacterial strains. Moreover, to elucidate the molecular mechanism
of the AMR system, clustering and the functional enrichment analysis were assessed employing AMR gene interactions networks.
According to the findings of our assessment, the majority of the genes in the network were involved in antibiotic inactivation; antibiotic
target replacement, alteration and antibiotic efflux pump processes. They exhibit resistance to several antibiotics, such as isoniazid,
ethionamide, cycloserine, fosfomycin, triclosan, etc.Additionally, rpoB, atpA, fusA, groEL and rpoL have the highest frequency of relevant
interactors in the interaction network and are therefore regarded as the hub nodes. These genes can be exploited to create novel
medications by serving as possible therapeutic targets. Finally, we believe that our findings could be useful to advance knowledge of
the AMR system present in M. catarrhalis.
Keywords: Moraxella catarrhalis; clustering analysis; functional enrichment analysis; antimicrobial resistance system; AMR genes; gene
ontology
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
2|Briefings in Functional Genomics, 2023
Introduction
Antimicrobial resistance (AMR) in pathogenic bacterial strains is
currently a serious problem causing a lot of death and suffering to
mankind around the globe. It has a negative impact on diagnostic,
therapeutic and financial consequences, with implications vary-
ing from a patient’s failure to retaliate to treatment. Moreover,the
rising incidence of multidrug-resistant (MDR) bacterial pathogens
causing clinical and community-acquired diseases is restricting
antibiotic treatment choices [1]. There are several pathogens
found in the intensive care unit that can develop antibiotic
resistance, but gram-negative strains of bacteria are the most
prone to developing barriers to various kinds of antibiotics [2].
The most frequent methods of resistance to β-lactam in gram-
negative bacteria are antimicrobials obliteration via beta-
lactamases; insulative properties, which include the shutdown
of gene encoding channels in the bacterial cell membrane; and
antibiotic deformation by efflux pumps [3]. To better understand
the numerous antibiotic resistance systems that lead to AMR in
Moraxella catarrhalis, we employed a computational method in our
current study (Figure 1).
Moraxella catarrhalis is a gram-negative, gamma-proteobacterium
and oxygenated ubiquitous bacteria which generates acute otitis
media (AOM) in youngsters and reduced respiratory tract
problems in adults, putting a strain on medical infrastructures
across the world [4]. Previously, this microorganism was referred
to as Neisseria catarrhalis or Micrococcus catarrhalis [5]. It is
commonly encountered as a top lung system pathogen in
humans [6] and also a prevalent source of otitis media (OM) in
newborns and children, contributing to 15–20% of severe OM
episodes [7]. Moreover, M. catarrhalis-induced OM is thought to
be moderate in compared with pneumococcal illness, multiple
potential pathogens have already been discovered and it has been
demonstrated that some epidermal constituents of M. catarrhalis
produce inflammatory responses [8]. This bacterium can easily
attach to the epithelium of a variety of nasal surfaces, including
the lungs as well as the nasopharynx and elicits a powerful
chronic inflammation defined by incursions of macrophages,
lymphocytes and neutrophils into diseased tissue following
infection, that is thought to be the etiology of OM and COPD
relapses [9]. OM is common and widespread in developing nations
and is a prominent reason of illness and death in children
below the age of five [10]. It can cause major abnormalities
in children’s linguistic, intellectual, academic and psychosocial
development [11]. Additionally, M. cattarhalis is currently a good
cause of around 10% of acute inflammatory comorbid conditions
in individuals, the chronic obstructive pulmonary disease (COPD)
[9]. In the United States, this bacterium is expected to trigger
2–4 million cases of chronic degenerative pulmonary disease in
people annually [7].
In Bangladesh, several studies found substantial pervasive-
ness of OM in rural, urban and elementary school youngsters,
with frequency rates of 43.2/1000, 32.6/1000 and 16.3/1000, corre-
spondingly [11]. The detection of M. catarrhalis by several fatalities
receptors (TLRs) including TLR4 and TLR9 induces the generation
of erythrogenic mediators IL-6 and TNF- by the host defenses,
according to several research studies [12,13]. Also severe and
recurring AOM infections have long been believed to include
bacterial survival inside a biofilm due to their extremely resistant
feature [14,15]. Direct detection of bacterial biofilms in inpa-
tient clinical specimens and the chinchilla model system of OM
provide medical confirmation of bacterial biofilms [16,17]. Sev-
eral processes, along with epigenetic modif ication heterogeneity
and slower spread of microorganisms inside this biofilm, post-
poned antibiotic infiltration through composite content, and the
existence of viable cells [18] can dramatically increase antibiotic
susceptibility in microbes within a biofilm congregation [19].
Moreover, some other preliminary research stated that M.
catarrhalis exhibits a variety of antibiotic susceptibility strategies,
including membrane porosity, active eff lux mechanisms and
alterations in antibacterial sites [20]. This bacteria shows that
these strategies of susceptibility to beta-lactam antibiotics are
usually related to type A serine β-lactamase enzyme [20,21].
Beta-lactamases are enzymes generated by the preponderance
of strains isolated from M. catarrhalis that allow them to
withstand beta-lactam medications such as penicillin, amoxicillin
and cephalosporins [22,23]. In addition to this, the exterior
layer porin M35 of M. catarrhalis is the factor that determines
whether or not the bacteria are susceptible to aminopenicillins
[24]. The resistance frequency of M. catarrhalis identified in
infants to Beta-lactam antibiotics has attained 99% in China
as a result of therapeutic experimental usage of antibiotics
[25]. Furthermore, according to reports, M. catarrhalis appeared
extremely susceptible to macrolide antibiotics, erythromycin
and rokitamycin [25,26]. In addition, some investigations have
demonstrated that M. catarrhalis is sensitive to the antibiotics cefa-
clor, clarithromycin, azithromycin, doxycycline, co-trimoxazole,
cefuroxime, cefixime and ceftriaxone, as well as ofloxacin and
ciprofloxacin [27]. The percentage of resistance of M. catarrhalis to
tetracycline has attained 65.7%, representing a remarkable rise in
resistance [28].
In our current study, we gathered entire genomic sequences
of M. catarrhalis strains from the NCBI Genome resource and
built a phylogenetic tree to better comprehend the biological
and developmental connection among the M. catarrhalis strains.
We additionally obtained antibiotic resistance genes (AMR) of
M. catarrhalis strains from many databases, including Compre-
hensive Antibiotic Resistance Database (CARD), Pathway-systems
Resource Investigation Center (PATRIC) and ResFinder, as well as
built a gene interaction network to analyze the multidrug suscep-
tibility systems by utilizing those AMR genes. The application of
gene interaction-based network is to identify whether the influ-
ence of genomic outcomes on biological activities is becoming
progressively pertinent [29]. On the contrary, researchers have
increasingly been interested in gene interaction networking inves-
tigations, which are thought to be valuable in understanding mul-
tidrug susceptibility in infectious and exploitative microorgan-
isms, as well as other biological disorders [30,31].Currently, one of
the most promising approaches to research the roles of genes and
proteins, as well as their related collaborators, is to use gene inter-
actions. It aids in the discovery of relevant biological information
about AMR processes, which in turn assists in the identification
of critical candidate genes or proteins in the cycle, as well as
the development of innovative medications to combat ailments
triggered by AMR virulent strains [32,33]. The molecular linkages
and processes of the AMR genes have been explored in this
work, which will be crucial in the development of innovative and
effective medications for the disease’s therapy. Moreover, we per-
formed protein–protein interactions (PPIs) among the antibiotic-
resistant genes, cluster investigation, recognition of hub proteins
and pathway assessment to unveil the complicated biological
framework, as well as gene linkage with resistance mechanisms
and drug class. We employed a combination of clustering and
topological techniques to uncover the physiologically significant
genes involved in drug susceptibility pathways. Thus,we were able
to pinpoint the origin of the antimicrobial-resistant genes as well
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
Bristy et al. |3
Figure 1. This workflow illustrates the whole exploration of how we predicted antibiotic resistance in M. catarrhalis by applying multiple
computational techniques, depending on complete-genome sequencing and systems biology approaches.
as these gene interactions that occurred among the M. catarrhalis
strains. All of these things were carried out to establish an affili-
ation in gene expression patterns in M. catarrhalis.However,the
genes identified as prospective pharmacological targets can be
employed to create novel molecules with pharmaceutical uses to
reduce M. catarrhalis outbreaks. We anticipate that our findings
will improve the knowledge about the molecular underpinnings of
multidrug resistance pathways in M. catarrhalis bacterial strains.
Materials and methods
Genome information of M. Catarrhalis
From the NCBI Genome database (https://www.ncbi.nlm.nih.gov/
genome), we have analyzed 215 strains of M. catarrhalis, but for the
further analysis, we chose only the complete genome sequences
of 12 strains of M. catarrhalis. The genome database is a compre-
hensive resource of NCBI that includes genome sequences and
assembly metadata as well as mapping enrichment data such as
variants, and indicators, including epigenomics data [34]. During
the selection of genome sequences, chromosomes, scaffolds and
contigs were not evaluated; only complete genome sequences
were considered. Those strains were available from 1980 to 2022.
Furthermore, most of the strains were from human middle ear
and sputum sample.
Identification of AMR genes
After retrieving the complete genomes of M. Catarrhalis,we
extracted AMR genes from those complete genomes using
repositories including the CARD, PATRIC as well as ARDB and
investigated them. Here, CARD or Comprehensive Antibiotic
Resistance Database (https://card.mcmaster.ca/)isanelevated
data source concerning the underlying mechanism of antibiotic
resistance genes. It is a vetted platform that provides standard
DNA and protein sequences, identification models and com-
putational tools in a regulated ontology that is the Antibiotic
Resistance Ontology, which is established by CARD’s biocuration
group for program configuration [35,36]. Another side, the PATRIC
[https://www.patricbrc.org/] provides a collection of potential
pathogens information kinds that have been combined from
various data sources. PATRIC is a collaborative initiative between
the Bioinformatics Resource Center and the National Institute
of Allergy and Infectious Diseases [37]. ResFinder (https://cge.
cbs.dtu.dk/services/ResFinder/) is a repository that indexes
antibacterial resistant genes discovered in the whole genome of
bacteria. This is accomplished through the usage of BLAST [38]. In
the end, we compiled the resistance mechanisms and drug classes
associated with these AMR genes. Then, we employed Venny
2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) to gather only the
unique AMR gene. Venny 2.1 is a tool for mapping and comparing
gene lists that may be used interactively [39,40]. Then, we used
these unique AMR genes to build gene interaction networks and
for other further analysis.
Phylogenetic tree construction
Most biological research requires an understanding of evolution-
ary interconnections between species. A reliable phylogenetic tree
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
4|Briefings in Functional Genomics, 2023
is essential for presuming the provenance of novel genes, identify-
ing biochemical transformation, comprehension of morphological
feature progression as well as recreating psychographic trends in
diverged species [41]. We previously noted that we obtained a total
of 12 complete-genome sequences of M. catarrhalis. The reference
sequence was from the CCRI-195ME strain of M. catarrhalis.We
employed Mega v11 software (https://www.megasoftware.net/)
to do the phylogenetic investigation to determine the develop-
mental and evolutionary connection between the M. catarrhalis
strains. MEGA (Molecular Evolutionary Genetics Analysis) is a
computer-based program for statistically analyzing molecular
development, determining evolutionary process length and build-
ing phylogenetic relationships. It is open concerning safety and
offers GDPR (General Data Protection Regulation) insurance to
people all around the world [42]. However, the evolutionary history
was estimated using the Neighbor-Joining statistical approach
using 1000 bootstraps and then exported into iTOL (v. 6) (https://
itol.embl.de/) for improved display. We also provided the length
of each branch from the root. Here, the nodes in the phylogenetic
tree indicate isolated strains, and the edges indicate the hamming
distance between two strains.
PPI network construction and visualization
PPIs regulate a vast variety of biological activities, and physio-
logical activities notably tissue connectivity as well as develop-
mental management [43]. To build the PPI network and identify
the associated genes or protein databases, we utilized a well-
known search program STRING (http://string-db.org). The STRING
database plays an important role in assembling, evaluating and
disseminating PPI data in a user-friendly and extensive way [44].
As a starting point, we provided STRING with a list of unique
AMR genes so that it could look for their neighboring interactors.
The extracted PPI network was generated with medium confi-
dence (>0.40) in STRING. Finally, we employed Cytoscape_v3.9.1
to create a visual representation of the target network. Cytoscape
(https://cytoscape.org/) is a prominent bioinformatics program for
visualizing biological interactions and integrating data.
Cluster formation and hub proteins extraction
Cluster analysis is a comprehensive method for combining
expression profiles with protein–protein-interacting networks.
In systems biology, it has a vital role in identifying regulatory
components and estimating protein expression. We utilized
the MCODE (https://baderlab.org/Software/MCODE) plug-in in
Cytoscape to form the clusters. The MCODE plugin is intended
to find densely connected zone also known as clusters in a
biological network. In our current study, cluster formation was
carried out applying the default parameter including degree
score cutoff of 2, node score cutoff of 0.2 and K-Core of 2,
and the maximum depth of 100 in the MCODE to verify the
efficacy of interactive collaborators in the context of AMR gene
expression. On the other hand, hub proteins, also known as key
proteins, are characterized as proteins that have a significant
degree of association on a wide range throughout the PPI
network. In our ongoing study, we utilized the Cytoscape plug-
in cytohubba (http://apps.cytoscape.org/apps/cytohubba) to find
highly interconnected protein nodes as well as to investigate the
network topology. The cytoHubba plugin is employed to obtain
the protein nodes that are largely attributable inside the PPI
network. Eleven topology analytical techniques are accessible
in cytoHubba [45]. Our study included six analytical techniques
from the Cytohubba plugin, including three locally ranked
methodologies: degree, maximum neighborhood component
(MNC) and maximum clique centrality (MCC), as well as three
globally ranked methodologies: closeness centrality, betweenness
and also the stress method. In the following step, the collected
genes from the cytoHubba were submitted to jvenn (an interactive
Venn diagram analyzer) (http://jvenn.toulouse.inra.fr/)formore
analysis and the genes that were intersected among the six
approaches of cytohubba were designated as significant hub
proteins.
Assessment of gene enrichment
Gene enrichment is a process of analyzing collections of genes
using the gene ontology categorization system, whereby genes are
classified into preset groups based on their operational features.
Gene Ontology is categorized into three distinct activities: biologi-
cal activities, cellular activities and molecular activities.Here, the
term biological activities refer to the major cellular or metabolic
significance of genes in coordination with other genes, cellular
activities refer to the role of gene products within the cell, whereas
molecular activities refer to the specific molecular function (MF)
of a gene [46]. On the other hand, Kyoto Encyclopedia of Genes and
Genome (KEGG) is also a biological explanatory scientific route
database. KEGG pathway analysis aids in the discovery of linkages
between core activities of critical genes, as well as in gaining a
thorough understanding of the fundamental activities of genes
[26,47]. In this work, we retrieved GO keywords and KEGG pathway
data from the STRING database and then utilized SRplot—Science
and Research online plot (http://www.bioinformatics.com.cn/en)
to display and further analyze them.
Genes correlation with antibiotic resistance
mechanism and drug class
Microbes develop methods to defend themselves against antimi-
crobial compounds, which are known as AMR mechanisms. These
mechanisms have developed in bacteria due to a variety of
reasons. A few of them include modifications of the permeability
in the bacterial cell that constrain bacterial direct exposure
to target areas, alternations of the enzyme’s catalytic activity,
oversaturation of the intended enzymes, antimicrobial drugs
modification and deterioration, development of metabolic
processes other than those blocked by the medication, active
eff lux pump and so on [48].On the other hand, penicillin and beta-
lactam were the first antibacterial compounds identified [49].
These antibiotics were successful in treating bacterial infectious
diseases. Other antibiotics, such as macrolides, aminoglycosides,
chloramphenicol, tetracycline and streptothricin, as well as
sulfonamide and trimethoprim, perform a significant function
in the diagnosis and therapies of microbial pathogens [49]. These
agents can repress the antimicrobial protein production while
also interfering with DNA and RNA production, negatively affect
the microbial cell wall production and prevent microbial cell
energy biosynthesis [50]. In our ongoing study, during the process
of collecting AMR genes from the CARD and PATRIC databases,
we also put together a list of the resistance mechanisms and
drug classes. Afterward, we reorganized all of the information,
which included distinct genes, drug classes as well as resistance
mechanisms, to execute the sunbursts plot using python.
Python is a programming language that is employed to develop
computer programs. Additionally, it is frequently utilized to
perform automated operations, as well as statistical exploration
[51]. However, this analysis helps to dig out the causes behind
susceptibility, the enhanced strategy of detecting resistance when
it emerges, alternative therapeutic choices for diseases triggered
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
Bristy et al. |5
Figure 2. The number of AMR genes found in M. catarrhalis.TheX-axis indicates the number of AMR genes, while the Y-axis indicates the name of the
M. catarrhalis genomes.
Tab l e 1. Details annotation information of bacterial (M. catarrhalis) genome
Sl. No Accession No. Strain Name of
M. catarrhalis
Genome
Coverage
Genome size
(Mb)
Host/source Genes
number
Ref.
1GCF_002080125.1 CCRI-195ME
(Reference)
Not provided 1.9 Homo sapiens/middle ear 1901 NCBI:txid480
2GCA_000740455.1 25 240 317×1.9 Unknown 1777 NCBI:txid480
3GCA_000766665.1 25 239 211×1.8 H. sapiens 1752 NCBI:txid480
4GCA_002073215.2 FDAARGOS_213 1000.94×1.9 H. sapiens 1747 NCBI:txid480
5GCA_002984125.1 FDAARGOS_304 825.126×1.9 H. sapiens/nose of a
healthy pediatric carrier
1788 NCBI:txid480
6GCA_003971285.1 74P50B1 30×1.8 H. sapiens/sputum 1681 NCBI:txid480
7GCA_003971305.1 142P87B1 26×1.9 H. sapiens/sputum 1769 NCBI:txid480
8GCA_003971325.1 46P58B1 24×2.05 H. sapiens/sputum 1933 NCBI:txid480
9GCA_003971345.1 74P58B1 31×1.8 H. sapiens/sputum 1681 NCBI:txid480
10 GCA_003971365.1 5P47B2 29×1.9 H. sapiens/sputum 1771 NCBI:txid480
11 GCA_900476075.1 NCTC11020 100×1.9 Unknown 1748 NCBI:txid480
12 GCA_000092265.1 BBH18 Not provided 1.863 Unknown 1722 NCBI:txid1236608
by resistant organisms, as well as attempts to mitigate and
regulate the formation [52,53].
Results
Collection of AMR gene
We retrieved a total of 288 AMR genes from these 12 strains of
bacteria (Figure 2). Among these AMR genes, 18 were from the
card, 264 were from PATRIC and 6 were from ResFinder. Out of
288 collected resistance genes, 74 entries were found to be unique
(Table S1 available online at http://bib.oxfordjournals.org/). How-
ever, these unique AMR genes were implemented to conduct addi-
tional exploration of this current study. The detailed information
of bacterial genome size, genome coverage and gene number was
provided in Tab l e 1 .
Phylogenetic tree analysis
We have indicated that there were 215 strains of M. catarrhalis in
the NCBI up to 5 October 2022, but we analyzed only 12 complete
strains with genome coverage of ≥20×, and size of the analyzed
genomes varied from 1.8 to 2.05 Mbp (Tab l e 1 ). We constructed
a phylogenetic tree relying on the 12 complete genomes of M.
catarrhalis and revealed the ancestral connection among them.
Figure 3A and B represents the rooted and circular view of the
phylogenetic tree, respectively. Among 12 strains, the phylogenetic
tree revealed two major clades and one outgroup. Clade 1 consists
of two strains and clade 2 consists of 9 strains of M. catarrhalis.
Moreover, the out group strain was M. catarrhalis FDAARGOS_304
which was less linked to other strains. The reference sequence of
M. catarrhalis, strain CCRI-195ME, has been highlighted in red in
both images.
PPI network analysis
In our present study, STRING was used to generate the PPI network
through the unique genes. In a PPI network, proteins or genes are
denoted as nodes, while interconnections between these nodes
are denoted as edges. The PPI network that we extracted in our
current analysis contains 43 nodes and 288 edges. Moreover,
the clustering coefficient of the network was 0.414. Figure 4A
depicts the PPI network. It represents the connectivity of folE, FabF,
argG, RocD, ung, fabG and GalE genes to other nodes within the
network.
Cluster analysis and hub proteins identification
We detected three significant clusters in the PPI network employ-
ing the MCODE plugin of Cytoscape. Within the clusters, the 1st
cluster (C1) comprised 11 nodes and 192 edges (score: 25.517),
the 2nd cluster (C2) comprised 3 nodes and 8 edges (score 2) as
well as the 3rd cluster (C3) comprised 2 nodes and 4 edges (score
2). The three clusters are shown in Figure 4B–D, respectively. In
addition, Table 2 displays the genes involved within those three
clusters. Furthermore, we discovered hub proteins by employing
six Cytohubba plugin methodologies, comprising Stress,Between-
ness, Closeness, Degree, MNC and MCC (Figure 5). Then, Jvenn
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
6|Briefings in Functional Genomics, 2023
Figure 3. Phylogenetic tree (12 strains of M. catarrhalis).(A) Rooted view and (B) Circular view. The spreading arrangement of two views of phylogenetic
tree indicates how bacterial strains emerged from a prevalent origin. This phylogenetic tree was built using the genomes of 12 different M. catarrhalis
strains using the Neighbor-Joining method and 1000 bootstraps. The strains are divided into two separate clades by the tree network (Clade 1 and
Clade 2). In both views, the reference sequence M. catarrhalis, strain CCRI-195ME is marked (red). The edges of the phylogenetic tree indicate the
hamming distance between two strains, and each node indicates a single strain.
Tab l e 2. Identification of gene clusters (C1–C3) of M. catarrhalis from the PPI network
Cluster Score (density) Nodes Edges Gene name
C1 25.517 11 192 rpsJ, rpoC, dnaK, atpD, atpA, atpA, fusA, groEL, rplF, rplJ, rpsL.
C2 2 3 8 mdh, gyrB,ileS.
C3 2 2 4 SucB, fumC.
Note: Bold symbol indicates the potential hub genes.
analysis was performed on six classes of hub proteins obtained
using the aforementioned methods. From the Jvenn analysis, we
noticed that five proteins were shared by all methods namely
rpoB, atpA, fusA, groEL and rpoL. These five proteins were iden-
tified as signif icant hub proteins. The Jvenn diagram is shown
in Figure 6,andTable 3 shows the topological properties of sig-
nificant hub proteins. On the contrary, these hub proteins were
also detected in cluster 1, cluster 2 and cluster 3, indicating that
they were the most crucial hub proteins. In Table 2,wehave
highlighted the hub genes which were presented in those three
clusters.
Functional enrichment analysis
Functional enrichment was performed by employing unique AMR
genes. In this present study, GO and KEGG pathway findings
revealed some biological, cellular and molecular processes as
well as some multidrug susceptible mechanisms that were
strongly linked with AMR genes of M. catarrhalis. In this case,
the outputs of GO enrichment provided a biological process (BP)
that was significantly correlated with cellular metabolic process,
cellular biosynthetic process, organic substance metabolic
process, primary metabolic process, Cellular nitrogen compound
biosynthetic, etc.MFs enriched with catalytic activity, organic
cyclic compound binding, heterocyclic compound binding,
anion binding and purine ribonucleoside triphosphate binding.
Furthermore, intracellular, cytoplasm, cellular anatomical
entity and tricarboxylic acid cycle enzyme complex principally
involved with the cellular component. Meanwhile, the KEGG
pathway enrichment study discovered that metabolic pathways,
citrate cycle (TCA cycle), RNA degradation, carbon metabolism,
biosynthesis of secondary metabolites, etc., were substantially
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
Bristy et al. |7
Figure 4. (A) PPI network. In this network, AMR genes are indicated as white nodes and gene connections are indicated by blue edges. Furthermore,
cluster analysis was performed to produce relevant and persistent sets of comparable genes for biological identification and evaluation. (B) Cluster C1
(11 nodes, 192 edges), (C) Cluster C2 (3 nodes, 8 edges) and (D) Cluster C3 (2 nodes, 4 edges) show the highly connected proteins among the PPI network.
related to the AMR genes (Figure 7). Furthermore, Tables 4 and
5and Table S2, available online at http://bib.oxfordjournals.org/,
provided the tabular representation of the GO and KEGG pathway.
Genes correlation with resistance mechanisms
and drug class analysis
We used a sunburst plot to identify the genes associated
with resistance mechanisms and drug classes in M. catarrhalis.
According to the plot, various M. catarrhalis strains contain
many genes and employ various mechanisms and techniques
to increase their resistance capacities against various mul-
tiple medications. Antibiotic inactivation, antibiotic target
replacement and alteration, reduced permeability to antibiotics
and antibiotic efflux pump are some of the resistance mech-
anisms employed by M. catarrhalis.Figure 8A and B indicates
the different types of resistance mechanisms and drug classes
exploited by M. catarrhalis, as well as their numbers. Antibiotic
target replacement mechanism is regulated by fabG-1 gene,
antibiotic target alteration is regulated by ICR-Mc and antibiotic
efflux pumps mechanism regulated by eptA, pgsA and OxyR
genes. Furthermore, we observed that M. catarrhalis has resistance
activity in a variety of antibiotics, including beta-lactam, peptide,
penam, tetracyclines, rifamycins, aminoglycosides, fosfomycin
and others, according to the CARD and PATRIC databases (Table
S3 available online at http://bib.oxfordjournals.org/). Figure 9A–C
represents the whole relationship between the genes with
resistance mechanisms and drug classes.
Discussion
AMR is the inability of microbes to adapt to various antibiotics,
which makes it more difficult to prevent infectious diseases
and also raises the overall probability of disease transmission,
serious morbidity and fatality. Several AMR genes perform a
vital function in developing this resilience to several anti-disease
medications in bacterial pathogens. Currently, it has become a
persistent danger to our capacity to cure prevalent diseases due
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
8|Briefings in Functional Genomics, 2023
Figure 5. Hub gene identification using six Cytohubba plugins in Cytoscape. (A)Stress,(B) Betweenness, (C) Closeness, (D) Degree, (E)MNCand(F)
MCC. These hub genes are referred to as highly linked fundamental nodes in a large-scale-free PPI network that includes diverse functional partners
that combine various network components. Color gradients indicate the higher to lower value from red to yellow.
Tab l e 3. Topological characteristics of unique hub genes
Gene Name MCODE Cluster Stress Betweenness Closeness Degree MNC MCC
rpoB NIC 8824 249.4637201 28.58333333 38 19 49 842
dnaK C1 8640 181.5186545 24.16666667 22 10 5769
atpA C1 5896 197.1844322 28.33333333 38 19 47 752
clpP NIC 5584 188.6512432 22 14 512
fusA C1 5584 138.2553313 26.91666667 32 15 50 427
mdh C2 5320 153.4120443 23.3333 20 9173
atpD C1 4904 115.8893319 26.08333333 30 15 6676
groEL C1 4880 106.0333377 26.66666667 30 15 47 654
rpsL C1 4512 122.0596986 25.66666667 30 14 43 939
thyA NIC 4200 103.3791209 20.5 12 410
rpoC C1 3072 75.43951 25.91666667 30 15 49 706
gyrB C2 3512 66.69975 25.41666667 26 13 8070
gyrA NIC 2476 54.03081 24.58333333 24 12 2168
rpsJ C1 400 8.7243 23.3333 22 11 41 784
rplF C1 224 3.08944 23 20 10 41 784
rplJ C1 184 3.00269 22.83333 20 10 41 760
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
Bristy et al. |9
Figure 6. Interpretation of JVENN analysis. The overlapped region comprises five genes (rpoB, atpA, fusA, groEL and rpoL) common among hub genes
gathered using six Cytohubba approaches (Stress, Betweenness, Closeness, Degree, MNC and MCC).
Tab l e 4. Most significant relevant pathway of each GO term of AMR genes
Category GO ID GO Terms
BP GO:0008152 Metabolic process
GO:0044237 Cellular metabolic process
GO:0071704 Organic substance metabolic process
GO:0009987 Cellular process
GO:0044249 Cellular biosynthetic process
GO:1901576 Organic substance biosynthetic process
GO:0044238 Primary metabolic process
GO:0044281 Small molecule metabolic process
GO:0044271 Cellular nitrogen compound biosynthetic process
GO:0034641 Cellular nitrogen compound metabolic process
Molecular function GO:0036094 Small molecule binding
GO:0003824 Catalytic activity
GO:0097159 Organic cyclic compound binding
GO:1901363 Heterocyclic compound binding
GO:0043168 Anion binding
GO:0000166 Nucleotide binding
GO:0005488 Binding
GO:0017076 Purine nucleotide binding
GO:0043167 Ion binding
GO:0035639 Purine ribonucleoside triphosphate binding
Cellular function GO:0005622 Intracellular
GO:0005737 Cytoplasm
GO:0110165 Cellular anatomical entity
GO:0045239 Tricarboxylic acid cycle enzyme complex
to the formation and transmission of drug-resistant bacteria.
In the current work, we have explored the AMR system in the
pathogenic bacterial strain M. catarrhalis.Moraxella catarrhalis
is typically detected in humans as a symbiotic and mucosal
parasite that might cause OM, chronic obstructive pulmonary
disease (COPD), ocular infection, sinusitis as well as infrequently
laryngitis in humans. This bacterium has shown that it can
block the passage of drugs into the cell by a variety of distinct
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
10 |Briefings in Functional Genomics, 2023
Figure 7. Functional Enrichment analysis (Gene Ontology and KEGG pathway) of AMR genes. The top 24 GO keywords and top 6 KEGG functional
pathways are shown by the study of AMR genes. In the bubble plot, circular-shaped indicates BP; triangle-shaped represents cellular component (CP);
plus sign indicates MF and square-shaped indicates KEEG pathway.
Tab l e 5 . Most significant relevant pathway KEGG pathway
analysis along with term ID and description
Pathway Te rm ID Term description
KEGG mct01100 Metabolic pathways
mct00020 Citrate cycle (TCA cycle)
mct03018 RNA degradation
mct01200 Carbon metabolism
mct01110 Biosynthesis of secondary metabolites
mct03010 Ribosome
pathways, demonstrating that it has various strategies that it
has adapted to do so. As previously mentioned, we gathered 288
AMR genes from the strains of M. catarrhalis via several databases;
then, we conducted phylogenetic analysis, protein interaction
network analysis, clustering analysis, hub protein identification
and functional enrichment analysis of AMR genes, to determine
the drug resistance mechanisms. In addition to this, we made
an approach to identify how AMR genes are linked to diverse
antibiotic resistance strategies as well as different categories of
antibiotics.
Environmental microorganisms, including other species on
the earth, are vulnerable to the processes of molecular diversity.
Genome sequencing provides information on the molecular
diversity of organisms through the analysis of those sequences.
More than that, significant perspectives on microbiological as well
as pragmatic applications such as disease identification might be
gained by integrating the DNA sequencing of microorganisms
and developmental modeling with phylogenetic studies [54]. The
phylogenetic analysis of the data from our current investigation
indicated the link between the M. catarrhalis strains. The strain
CCRI-195ME of M. catarrhalis, which was considered a reference
sequence, was highly correlated with other strains found in
GenBank such as M. catarrhalis FDAARGOS 304, M. catarrhalis
NCTC11020, M. catarrhalis 142P87B1, M. catarrhalis 74P58B1, M.
catarrhalis 46P58B1 and M. catarrhalis BBH18. Among these strains,
M. catarrhalis CCRI-195ME was the first fully sequenced genome
with the modM3 allele, derived from the inner ear of a 16-month
aged infant predisposed to OM [55]. About 80% of the total
children have experienced a minimum of one episode of OM by the
time they are 3 years old, resulting in the most frequent pediatric
illnesses [56]. Additionally, this bacterium is currently regarded to
be the 2nd highest prevalent reason for the deterioration in COPD
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
Bristy et al. |11
Figure 8. Moraxella catarrhalis significantly utilize several kinds of AMR mechanisms and drug classes. (A) Resistance mechanisms count and (B) Drug
Class count.
[57]. Unfortunately, the specific function of bacteria is not well
known and is a contentious topic. Although the importance of
developing a vaccine that is effective against M. catarrhalis,none
of the contenders have advanced to the medical testing stage.
Because of this, we must interpret the diversity of M. catarrhalis to
develop an improved insight of the epidemiological data as well
as the propagation of genes involved in pathogenicity aspects,
which will assist in the development of vaccines [57].
Furthermore, the PPI network that was analyzed in this study
showed that the genes folE, FabF, argG, RocD and fabG are all
connected to neighboring nodes in the network. Previous findings
have suggested that the folE motif might act as a potent riboswitch
for the structural intergenic RNAs found in microbial noncoding
regions [58]. This folE motif, which is typically located on the
proximal of folE genes, follows a straightforward construction
[59]. Furthermore, these genes encode enzyme that catalyzes
the initial stage of the de novo folate manufacturing process
in bacteria [58]. FabF and fabG perform a critical part in the
bacterial process of fatty acid production. For the discovery
of novel antibacterial drugs, the fatty acid production route
is substantially underutilized. Initially, FabH catalyzes the
condensation process between acetyl CoA and malonyl-acetyl
carrying molecule to create acetoacetyl-ACP, which is crucial for
the commencement of fatty acid biosynthesis [60]. In this cycle,
FabF generates -ketoacyl-ACP, which boosts the rate of fatty acid
biosynthesis of different lengths for activation by the microor-
ganism [61,62]. But by producing butyryl-ACP, fabG reduces
the rate of fatty acid biosynthesis [60]. Other side, argG gene,
which encodes argininosuccinate synthetase enzyme, is required
in arginine biosynthesis in bacteria [63]. Arginine performs a
significant function in the metabolic process of M. catarrhalis [64].
In Bacillus subtilis, a gene named rocD was discovered adjacent
the rocR gene which was also present in M. catarrhalis [65]. The
rocD gene produces an enzyme that is structurally analogous
to eukaryotic ornithine aminotransferases [66]. Therefore, these
genes perform many crucial roles in the metabolic, cellular and
BPs that take place in microorganisms. In our investigation, GO
keywords relating to antibiotic-resistant processes including BPs,
cellular components and molecular activities were significantly
elevated. BPs were mostly related to the cellular metabolic process
(GO:0044237), organic substance metabolic process (GO:0071704),
cellular biosynthetic process (GO:0044249), organic substance
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
12 |Briefings in Functional Genomics, 2023
Figure 9. Sunburst plots were used to build interactions between different gene classes, drug classes and resistance mechanisms in networks. (A) The
relationship between resistance mechanisms corresponding to their genes. (B) The relationship between drug classes corresponding to their genes. (C)
The relationship between resistance mechanism and drug class.
cellular biosynthetic process (GO:0044249), organic substance
biosynthetic process (GO:1901576), primary metabolic process
(GO:0044238), small molecule metabolic process (GO:0044281),
cellular nitrogen compound biosynthetic process (GO:0044271)
and cellular nitrogen compound metabolic process (GO:0034641).
Typically, bacteria’s cellular metabolic process determines their
resistance to antibiotics, so drug potency could be increased
by modifying bacteria’s metabolic condition [67]. Additionally,
disruptions in bacterial metabolic equilibrium have substantial
side effects on therapy concerning drugs [67]. On the other hand,
the primary metabolic process entails biochemical events and
mechanisms which generate substances during regular anabolic
and catabolic events, and the organic substance metabolic
process entails the series of biochemical events that an organic
matter,which can be thought of as like any molecule or other unit
that contains carbon, is involved in [68,69].
Remarkably, both the cellular nitrogen compound biosynthetic
process and the cellular nitrogen compound metabolic process
are implicated in the formation of nitrogen, a crucial component
for the production of, amino acids, proteins, different types of
enzymes, DNA and RNA in all microorganisms [70]. Among the
cellular function terms cellular anatomical entity (GO:0110165),
tricarboxylic acid cycle enzyme complex (GO:0045239), cytoplasm
(GO:0005737), etc., were mostly enriched. Similarly, the follow-
ing MFs were found to be accumulated: small molecule binding
(GO:0036094), catalytic activity (GO:0003824), organic cyclic com-
pound binding (GO:1901363), nucleotide binding (GO:0000166),
organic cyclic compound binding (GO:0097159), heterocyclic com-
pound binding (GO:1901363) anion binding (GO:0043168), purine
nucleotide binding (GO:0017076), purine ribonucleoside triphos-
phate binding (GO:0035639) and ion binding (GO:0043167). Cur-
rently, nanomaterial-based therapeutics are intriguing methods
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
Bristy et al. |13
for combating complicated bacterial infestations, as they can cir-
cumvent established processes linked with accumulated antibi-
otic resistance [71]. Moreover, heterocyclic compounds, such as
thiazole, benzothiazole and thiazolidinone, have been produced
over the previous decades in an effort to acquire novel antibiotics
capable of treating conditions triggered by antimicrobial resistant
bacterial strains [72]. We also uncovered KEGG pathways associ-
ated with the citrate cycle (TCA cycle), RNA degradation, carbon
metabolism and secondary metabolite production. In microor-
ganisms, RNA can be degraded via multiple processes. Controlling
gene expression relies heavily on RNA synthesis and degradation,
which plays an important part in the molecular process [73]. Addi-
tionally, carbon metabolism is essential for bacterial proliferation
[74]. Since bacteria cannot manufacture their food, they must rely
on the source of carbon for the synthesis of energy and metabolic
substances [75]. These substances are required for the production
of anabolic subunits, which are then transformed into polymers,
including organic molecules (proteins, nucleotides), as well as
elements of the complicated cell membrane [75,76]. Mainly the
citrate cycle also referred to as the Krebs cycle, is the fundamental
route through which cells obtain their supply of energy and is
an essential component of cellular breathing. This cycle is also
employed as the foundation for secondary metabolite synthesis
because its byproducts are utilized as substrates in the production
of metabolites; numerous molecular systems are linked to the
CAC, forming a biochemical circuit [77].
Aforementioned, we identified three important clusters, which
are referred to as C1, C2 and C3, and also detected hub proteins
using six Cytohubba plugin approaches. In addition, we detected
a total of 11 hub genes that were unique among six approaches
including rpoB, dnaK, atpA, clpP, fusA, mdh, atpD, groEL, rpsL, thyA,
rpoC, gyrB, gyrA, rpsJ, rplF and rplJ. Among these genes, dnaK, atpA,
fusA, atpD, groEL, rpsL, rpoC, rpsJ, rplF and rplJ were present in C1 as
well as mdh and gyrB were present in C2. We found that these hub
genes significantly correlated with various types of drug resis-
tance mechanisms along with drug classes. These mechanisms
are carried on by changes to the drug, changes to the antimicrobial
targets, restricted access to the target, or even a mixture of
these processes [78–80]. Because of its capacity to manufacture
BRO-lactamase, M. catarrhalis is reported to be resistant to peni-
cillin as well as the foundation follows cephalosporins; never-
theless, it is normally sensitive to additional drugs, notably flu-
oroquinolones [81,82]. However, in our analysis, we found some
significant resistance mechanisms such as antibiotic inactivation,
antibiotic target replacement, drug target alteration,reduced per-
meability to antibiotics, and antibiotic efflux pump. Antibiotic
inactivation was carried out by 36 genes (fabI, inhA, gyrB, kasA,
rpoB, rpsJ, rpoC, folA, S10p, fabF, gidB, gyrA, ileS, murA, rho, rplF, fusA,
rpsL, tuf, tufA_1, tufA_2, folP, BRO-1, Ddl, Dfr, EF-G, EF-Tu, fabG, Iso-
tRNA, OxyR, S12p, alr, bro-2, ddlB, dfrA and dxr). These genes inacti-
vated the antibiotics including isoniazid, triclosan, diaminopyrim-
idines, aminoglycosides, ethionamide, peptide antibiotics, tetra-
cyclines, fluoroquinolones, fosfomycin, penam, rifamycins, sul-
fonamide, beta-lactam antibiotics, etc.Antibiotic target replace-
ment and alternation were performed by fabG_1 and ICR-Mc,
respectively. They showed resistance to triclosan and peptide
antibiotic. Moreover, PgsA and rsmG diminished the permeability
to antibiotics. As a result, they exhibit resistance to peptide and
aminoglycoside antibiotics. Then, PgsA, OxyR and eptA possessed
antibiotic efflux pumps that were resistant to the drugs isoniazid
and polymixins. Various studies concluded that the presence of
efflux pumps, which potentially provide resistance to macrolides,
b-lactams, macrolides, tetracyclines, aminoglycosides and f luoro-
quinolones, is common in MDR microorganisms [83,84].
To prevent the propagation of the infection, prompt detection
with appropriate management is crucial. Singpanomchai et al.
[85] and Guitor [86] have indicated in their studies that infec-
tions that are prominent in respiratory regions cause significant
rpoB gene alterations in RNA polymerase during RNA produc-
tion, which is associated with rifamycin susceptibility. Mujawar
et al. [87] revealed that DnaK is essential for antibiotic resistance
through a variety of assessments using microbial extracts. The
nucleotide alterations from cytosine to adenine on DnaK gene
were identified by the SNPs discovered in the individual samples
that potentially caused protein denaturing [87]. In the same way,
the DnaK-GroEL interaction may play a significant contribution to
antibiotic resistance in pathogenic microorganisms [88]. The GyrA
and GyrB genes encode DNA gyrase enzyme that contributes to
the key methods of quinolone (QN) susceptibility and comprises
a reduction in interaction propensity to QNs caused by amino acid
change in the QRDR (quinolone resistance-determining region)
[89]. On the other hand, through the evaluation of the gene’s
genomic structure, many researchers demonstrated in their paper
that the rpsL, ClpP,rpsJ and rplJ function as potential antibiotic
resistance genes [90–94].
In contrast, the major cause of antibiotic resistance may also
be mutations. The terminology ‘mutation rate’ is used in inves-
tigations of biological evolvement to provide estimates of the
percentage of genetic mutation for every nucleotide, for every
region or subsequently for the entire genotype. Here, preferen-
tially beneficial, detrimental or neutrality changes are all taken
into consideration. The probability in which identif iable muta-
tions appear in a microbial species that exposed to a specific
therapeutic dose shows how the evolution rate is usually charac-
terized in the perspective of antimicrobial sensitivity. Numerous
investigations have demonstrated that even an one amino acid
change can result in the development of beta-lactam resistance
in isolates that are extremely sensitive to penicillin have addi-
tional penicillin-binding proteins changes than bacteria that are
intermediately resistant [95]. Furthermore, Jacobs and Micheael R
showed in their study the activation of an erythromycin ribosomal
methylation gene leads in transcription factors alteration of 23S
ribosomal RNA, which prevents the macrolide from attaching
to the ribosome [96]. They discovered that it is the source of
the majority of pneumococcal macrolide susceptibility. However,
many clinical studies have revealed that M. catarrhalis isolates
are extremely sensitive to macrolides. Even several studies have
found that a mutation in the TonB-dependent receptor expressing
gene MCR 0492 may be linked to macrolides susceptibility in M.
catarrhalis strains [97]. Kasai et al. [98] used naturally occurring
erythromycin-resistant variants to study the impact of alterations
in the 23S rRNA gene and also the L4 and L22 ribosomal subunits.
Additionally, the development of rising macrolide-susceptibility
M. catarrhalis may be attributed to the A2330T Mutation in the
23S rRNA gene [99]. Therefore, we may conclude that alterations
in 23S rRNA are the major cause of macrolide resistance. On the
other hand, f luoroquinolone resistance is induced by significant
mutations in the DNA gyrase enzymes, which can potentially be
induced by the emergence of efflux pumps within the bacterium
[100]. Warner et al. [101] attempted to demonstrate in their work
that therapeutically significant mutations that induce derepres-
sion of the Neisseria gonorrhoeae MtrC-MtrD-MtrE Efflux pump sys-
tem provide various rates of antimicrobial sensitivity. Likewise,
mutation in Mycobacterium tuberculosis may be the cause of the
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
14 |Briefings in Functional Genomics, 2023
individual’s inherent resistance to numerous antibiotics, which
reduces the amount of drugs that are accessible for therapy [102].
Antibiotic resistance mutations may have cytoprotective impacts,
resulting in a decrease in bacterial viability, as measured, for
example, by a decrease in in laboratory multiplication efficiency.
Thus, our findings provide a comprehensive portrayal of antibi-
otic resistance mechanisms as well as the relevant entities par-
ticipating in the interaction network. We believe that these obser-
vations will offer clarity on the underlying process that leads to
AMR in M. catarrhalis. In near future, we will be validated these
antibiotic susceptibility patterns in wet lab experiments. Firstly,
we will collect sample from patients or pure culture in hospitals
or clinic. Secondly, we will analyze bacterial growth curve for
the test of antibiorgram susceptibility patterns and then vali-
date the gene expression patterns (potential hub gene) through
Polymerase Chain Reaction analysis. Finally, we will sequence the
potential bacteria based on 16sRNA analysis for the identification
of possible new bacterial strains.
Conclusion
Antibiotic resistance in virulent bacteria is a prominent cause
for concern all around the world. It is a persistent issue in the
medical sector. In this work, we investigated the AMR system in
the virulent strain of M. catarrhalis using genomic interaction sys-
tem biology. The AMR genes, in conjunction with their functional
interacting partners, mostly manifest their antibiotic resistance
through the inactivation of antibiotics, target replacement of
antibiotics, target alteration, reduced permeability to antibiotics
and also antibiotic eff lux pump. Various antibiotics, notably beta-
lactam, tetracyclines, glycylcyclines, rifamycins, aminoglycosides
and fosfomycin are sensitive to these mechanisms. In addition,
the clustering approach uncovered gene sets that are intricately
linked to one another. The genes rpoB, atpA, fusA, groEL and rpoL
have quite a significant contact as well as can be more important
for figuring out how the AMR genes work together at the genomic
level. Therefore, we believe that the findings that we have pro-
vided in this study will provide researchers with a solid foundation
upon which to build their investigations into novel therapeu-
tic approaches for the management of M. catarrhalis epidemics.
We have analyzed only complete annotation of Genbank files
datasets. In addition,we have searched three antibiotic resistance
database, while several databases are available. There is also a
lack of systematic antibiotic resistance gene collection predicted
by different databases and further evaluate the reliability of wet
lab experiment.
Key Points
•The fundamental underpinnings of antibiotic suscep-
tibility in the pathogenic bacterial strain Moraxella
catarrhalis were discovered using genomic interaction
study relying on system biology.
• The PPI network uncovered important hub-proteins that
might be explored to develop potential pharmacological
candidates against antimicrobial-resistant bacteria.
•Cluster analysis was used to generate useful and
long-lasting groupings of similar factors for biological
characterization and assessment that coincided with
resistance mechanisms along with corresponding drug
classes.
• Functional enrichment was carried out to identify sev-
eral biological processes, cellular functions and molecu-
lar processes as well as some multidrug-sensitive mech-
anisms which were closely associated with AMR genes
of M. catarrhalis.
• The molecular connections underlying mechanisms of
the AMR genes were investigated in this study, which
is important in the creation of new and efficient treat-
ments for infectious diseases.
Supplementary Data
Supplementary data are available online at http://bib.oxfordjournals.
org/.
Data Availability Statement
The used datasets are publicly available and the research data can
be accessible on request.
Acknowledgements
We would like to thank the team members of the Center for
Advanced Bioinformatics Artificial Intelligent Research headed by
Dr Md Habibur Rahman who helped us and provided Valuable
insight into the research.
Funding
There is no funding of interest.
Author Contribution
Conceptualization: Md Habibur Rahman, Md. Arju Hossain; Inves-
tigation: Sadia Afrin Bristy, Md Imran Hasan, Md. Arju Hossain;
Writing (Original Draft): Sadia Afrin Bristy; Writing (Review and
Editing): Md Habibur Rahman and Md. Arju Hossain. Supervision:
Mohammad Ali Moni and Md Habibur Rahman.
References
1. Cassir N, Rolain JM, Brouqui P. A new strategy to fight antimi-
crobial resistance: the revival of old antibiotics. Front Microbiol
2014;5:551.
2. Brusselaers N, Vogelaers D, Blot S. The rising problem of antimi-
crobial resistance in the intensive care unit. Ann Intensive Care
2011;1(1):1–7.
3. Karam G, Chastre J, Wilcox MH, Vincent JL. Antibiotic strategies
in the era of multidrug resistance. Crit Care 2016;20(1):1–9.
4. Ren D, Pichichero ME. Vaccine targets against Moraxella
catarrhalis.Expert Opin Ther Targets 2016;20(1):19–33.
5. Verduin CM, Hol C, Fleer A, et al. Moraxella catarrhalis:from
emerging to established pathogen. Clin Microbiol Rev 2002;15(1):
125–44.
6. Davie JJ, Earl J, de Vries SP, et al. Comparative analysis and
supragenome modeling of twelve Moraxella catarrhalis clinical
isolates. BMC Genomics 2011;12(1):1–7.
7. Goldstein EJ, Murphy TF, Parameswaran GI. Moraxella
catarrhalis, a human respiratory tract pathogen. Clin Infect
Dis 2009;49(1):124–31.
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
Bristy et al. |15
8. Aebi C. Moraxella catarrhalis–pathogen or commensal? In: Hot
Topics in Infection and Immunity in Children VII,Advances in Exper-
imental Medicine and Biology, New York, NY: Springer, 2011, 697:
107–16.
9. Alnahas S, Hagner S, Raifer H, et al. IL-17 and TNF-αare
key mediators of Moraxella catarrhalis triggered exacerbation of
allergic airway inflammation. Front Immunol 2017;8:1562.
10. Berman S. Otitis media in developing countries. Pediatrics
1995;96(1):126–31.
11. Shaheen MM, Raquib A, Ahmad SM. Prevalence and associated
socio-demographic factors of chronic suppurative otitis media
among rural primary school children of Bangladesh. Int J Pediatr
Otorhinolaryngol 2012;76(8):1201–4.
12. de Vries SP, Eleveld MJ, Hermans PW, Bootsma HJ. Char-
acterization of the molecular interplay between Moraxella
catarrhalis and human respiratory tract epithelial cells. PloS one.
2013;8(8):e72193.
13. Ferretti S, Bonneau O, Dubois GR, et al. IL-17, produced by lym-
phocytes and neutrophils, is necessary for lipopolysaccharide-
induced airway neutrophilia: IL-15 as a possible trigger. J
Immunol 2003;170(4):2106–12.
14. Bakaletz LO. Bacterial biofilms in otitis media: evidence and
relevance. Pediatr Infect Dis J 2007;26(10):S17–9.
15. Post JC, Hiller NL, Nistico L, et al. The role of biofilms in
otolaryngologic infections: update 2007. Curr Opin Otolaryngol
Head Neck Surg 2007;15(5):347–51.
16. Hong W, Mason K, Jurcisek J, et al. Phosphorylcholine decreases
early inflammation and promotes the establishment of stable
biofilm communities of nontypeable Haemophilus inf luenzae
strain 86-028NP in a chinchilla model of otitis media. Infect
Immun 2007;75(2):958–65.
17. Jurcisek J, Greiner L, Watanabe H, et al. Role of sialic acid and
complex carbohydrate biosynthesis in biofilm formation by
nontypeable Haemophilus influenzae in the chinchilla middle
ear. Infect Immun 2005;73(6):3210–8.
18. Jefferson KK, Goldmann DA, Pier GB. Use of confocal
microscopy to analyze the rate of vancomycin penetra-
tion through Staphylococcus aureus biofilms. Antimicrob Agents
Chemother 2005;49(6):2467–73.
19. Ceri H, Olson ME, Stremick C, et al. The Calgary biofilm device:
new technology for rapid determination of antibiotic suscepti-
bilities of bacterial biofilms. J Clin Microbiol 1999;37(6):1771–6.
20. Kyd MJ, McGrath J, Krishnamurthy A. Mechanisms of bacterial
resistance to antibiotics in infections of COPD patients. Curr
Drug Targets 2011;12(4):521–30.
21. Perez AC, Pang B, King LB, et al. Residence of Streptococ-
cus pneumoniae and Moraxella catarrhalis within polymicrobial
biofilm promotes antibiotic resistance and bacterial persis-
tence in vivo. Pathog Dis 2014;70(3):280–8.
22. Narinesingh SP, Whitby DJ, Davenport PJ. Moraxella catarrhalis:
an unrecognized pathogen of the oral cavity? Cleft Palate Cran-
iofac J 2011;48(4):462–4.
23. Hoban D, Felmingham D. The PROTEKT surveillance study:
antimicrobial susceptibility of Haemophilus influenzae and
Moraxella catarrhalis from community-acquired respiratory
tract infections. J Antimicrob Chemother 2002;50(suppl_2):49–59.
24. Jetter M, Heiniger N, Spaniol V, et al. Outer membrane
porin M35 of Moraxella catarrhalis mediates susceptibility to
aminopenicillins. BMC Microbiol 2009;9(1):1–3.
25. Zhang Z, Yang Z, Xiang X, et al. Mutation of TonB-dependent
receptor encoding gene MCR_0492 potentially associates with
macrolides resistance in Moraxella catarrhalis isolates. Infect
Drug Resist 2022;15:2419–26.
26. Chen L, Zhang YH, Wang S, et al. Prediction and analysis of
essential genes using the enrichments of gene ontology and
KEGG pathways. PloS one 2017;12(9):e0184129.
27. Berk SL, Kalbf leisch JH, Chemotherapy APCGJJOA. Antibiotic
susceptibility patterns of community-acquired respiratory iso-
lates of Moraxella catarrhalis in Western Europe and in the USA.
Journal of Antimicrobial Chemotherapy 1996;38(Suppl_A):85–96.
28. Sun X, Zhang B, Xu G, et al. In vitro activity of the novel tetra-
cyclines, tigecycline, eravacycline, and omadacycline, against
Moraxella catarrhalis.Ann Lab Med 2021;41(3):293–301.
29. Miryala SK, Anbarasu A, Ramaiah S. Discerning molecular
interactions: a comprehensive review on biomolecular inter-
action databases and network analysis tools. Gene 2018;642:
84–94.
30. Anitha P, Anbarasu A, Ramaiah S. Gene network analy-
sis reveals the association of important functional partners
involved in antibiotic resistance: a report on an important
pathogenic bacterium Staphylococcus aureus.Gene 2016;575(2):
253–63.
31. Anitha P, Bag S, Anbarasu A, Ramaiah S. Gene and protein
network analysis of AmpC βlactamase. Cell Biochem Biophys
2015;71(3):1553–67.
32. Gonzalez MW, Kann MG. Chapter 4: protein interactions and
disease. PLoS Comput Biol 2012;8(12):e1002819.
33. Bag S, Ramaiah S, Anbarasu A. fabp4 is central to eight obesity
associated genes: a functional gene network-based polymor-
phic study. J Theor Biol 2015;364:344–54.
34. Tatusova T, DiCuccio M, Badretdin A, et al. NCBI prokary-
otic genome annotation pipeline. Nucleic Acids Res 2016;44(14):
6614–24.
35. McArthur AG, Waglechner N, Nizam F, et al. The comprehen-
sive antibiotic resistance database. Antimicrob Agents Chemother
2013;57(7):3348–57.
36. Jia B, Raphenya AR, Alcock B, et al. CARD 2017: expansion
and model-centric curation of the comprehensive antibiotic
resistance database. Nucleic Acids Res 2016;45:gkw1004.
37. Wattam AR, Abraham D, Dalay O, et al. PATRIC, the bacterial
bioinformatics database and analysis resource. Nucleic Acids Res
2014;42(D1):D581–91.
38. Zankari E, Hasman H, Cosentino S, et al. Identification of
acquired antimicrobial resistance genes. J Antimicrob Chemother
2012;67(11):2640–4.
39. Sun F, Mu C, Kwok HF, et al. Capivasertib restricts SARS-CoV-2
cellular entry: a potential clinical application for COVID-19. Int
JBiolSci2021;17(9):2348.
40. Bardou P, Mariette J, Escudié F, et al. jvenn: an interactive Venn
diagram viewer. BMC Bioinformatics 2014;15(1):1–7.
41. Kapli P, Yang Z, Telford MJ. Phylogenetic tree building in the
genomic age. Nat Rev Genet 2020;21(7):428–44.
42. Kumar S, Stecher G, Li M, et al. MEGA X: molecular evolutionary
genetics analysis across computing platforms. MolBiolEvol
2018;35(6):1547.
43. Bristy SA, Islam AH, Andalib KS, et al. Determination of
molecular signatures and pathways common to brain tis-
sues of autism spectrum disorder: insights from comprehen-
sive bioinformatics approach. Inform Med Unlocked 2022;29:
100871.
44. Rahman MH, Rana HK, Peng S, et al. Bioinformatics and
machine learning methodologies to identify the effects of cen-
tral nervous system disorders on glioblastoma progression.
Brief Bioinform 2021;22(5):bbaa365.
45. Rahman MH, Peng S, Xiyuan H, et al. A network-based bioin-
formatics approach to identify molecular biomarkers for type
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
16 |Briefings in Functional Genomics, 2023
2 diabetes that are linked to the progression of neurological
diseases. Int J Environ Res Public Health 2020;17(3):1035.
46. Rahman MH, Peng S, Xiyuan H, et al. Bioinformatics method-
ologies to identify interactions between type 2 diabetes and
neurological comorbidities. IEEE Access 2019;7:183948–70.
47. Rahman MR, Islam T, Huq F, et al. Identification of molecular
signatures and pathways common to blood cells and brain
tissue of amyotrophic lateral sclerosis patients. Inform Med
Unlocked 2019;16:100193.
48. Rahman MH, Rana HK, Seng Peng M, et al. Bioinformatics
and system biology approaches to identify pathophysiological
impact of COVID-19 to the progression and severity of neuro-
logical diseases. Comput Biol Med 2021;138:104859.
49. Van Hoek AH, Mevius D, Guerra B, et al. Acquired antibiotic
resistance genes: an overview. Front Microbiol 2011;2:203.
50. Lima TB, Pinto MF, Ribeiro SM, et al. Bacterial resistance mech-
anism: what proteomics can elucidate. FASEB J 2013;27(4):
1291–303.
51. Zelle JM. Python Programming: An Introduction to Computer Science.
Franklin: Beedle & Associates, Inc., 2004.
52. Hasan MI, Hossain MA, Piplu Bhuiyan M, et al. Asystem
biology approach to determine therapeutic targets by iden-
tifying molecular mechanisms and key pathways for type 2
diabetes that are linked to the development of tuberculosis and
rheumatoid arthritis. Life Sci 2022;IF(5.037):120483.
53. Holmes AH, Moore LS, Sundsfjord A, et al. Understanding the
mechanisms and drivers of antimicrobial resistance. Lancet
2016;387(10014):176–87.
54. Darling AE, Jospin G, Lowe E, et al. PhyloSift: phylogenetic
analysis of genomes and metagenomes. PeerJ 2014;2:e243.
55. Tan A, Blakeway LV, Bakaletz LO, et al. Complete genome
sequence of Moraxella catarrhalis strain CCRI-195ME, isolated
from the middle ear. Genome Announc 2017;5(21):e00384–17.
56. Morris DE, Osman KL, Cleary DW, Clarke SC. The characteri-
zation of Moraxella catarrhalis carried in the general population.
Microbial Genomics 2022;8(5):000820.
57. Wilkinson TM, Aris E, Bourne S, et al. A prospective, obser-
vational cohort study of the seasonal dynamics of airway
pathogens in the aetiology of exacerbations in COPD. Thorax
2017;72(10):919–27.
58. Chen X, Arachchilage GM, Breaker RR. Biochemical validation
of a second class of tetrahydrofolate riboswitches in bacteria.
RNA 2019;25(9):1091–7.
59. Bertacine Dias MV, Santos JC, Libreros-Zúñiga GA, et al. Folate
biosynthesis pathway: mechanisms and insights into drug
design for infectious diseases. Future Med Chem 2018;10(8):
935–59.
60. Chung PY. Novel targets of pentacyclic triterpenoids in
Staphylococcus aureus: A systematic review. Phytomedicine
2020;73:152933.
61. Waldrop GL. Acetyl-CoA carboxylase as a target for antibac-
terial development. In: Antimicrobial Drug Discovery: Emerging
Strategies, 2012, 208–19.
62. Fujita Y, Matsuoka H, Hirooka K. Regulation of fatty acid
metabolism in bacteria. Mol Microbiol 2007;66(4):829–39.
63. Schramm T, Lempp M, Beuter D, et al. High-throughput enrich-
ment of temperature-sensitive argininosuccinate synthetase
for two-stage citrulline production in E. coli.Metab Eng 2020;60:
14–24.
64. Otsuka T, Kirkham C, Brauer A, et al. The vaccine candidate
substrate binding protein SBP2 plays a key role in arginine
uptake, which is required for growth of Moraxella catarrhalis.
Infect Immun 2016;84(2):432–8.
65. Zaprasis A, Hoffmann T, Wünsche G, et al. Mutational activa-
tion of the RocR activator and of a cryptic rocDEF promoter
bypass loss of the initial steps of proline biosynthesis in Bacil-
lus subtilis. Environ Microbiol 2014;16(3):701–17.
66. Gardan R, Rapoport G, Débarbouillé M. Expression of therocDE-
FOperon involved in arginine catabolism in Bacillus subtilis. J
Mol Biol 1995;249(5):843–56.
67. Stokes JM, Lopatkin AJ, Lobritz MA, Collins JJ. Bacterial
metabolism and antibiotic eff icacy. Cell Metab 2019;30(2):
251–9.
68. Martínez I, Mohamed ME, Santos VE, et al. Metabolic and
process engineering for biodesulfurization in gram-negative
bacteria. J Biotechnol 2017;262:47–55.
69. Manskaya SM, Drozdova TV. Geochemistry of Organic Substances:
International Series of Monographs in Earth Sciences. V.I Verned-
skii Institute of Geochemistry and Analytical Chemistry, USSR
Academy of Science, Elsevier, 2013.
70. Carpinelli EC, Telatin A, Vitulo N, et al. Chromosome
scale genome assembly and transcriptome profiling of
Nannochloropsis gaditana in nitrogen depletion. Mol Plant
2014;7(2):323–35.
71. Makabenta JM, Nabawy A, Li CH, et al. Nanomaterial-based
therapeutics for antibiotic-resistant bacterial infections. Nat
Rev Microbiol 2021;19(1):23–36.
72. Cascioferro S, Parrino B, Carbone D, et al. Thiazoles, their ben-
zofused systems, and thiazolidinone derivatives: versatile and
promising tools to combat antibiotic resistance. J Med Chem
2020;63(15):7923–56.
73. Arraiano CM, Andrade JM, Domingues S, et al. The critical role
of RNA processing and degradation in the control of gene
expression. FEMS Microbiol Rev 2010;34(5):883–923.
74. Muñoz-Elías EJ, McKinney JD. Carbon metabolism of intracel-
lular bacteria. Cell Microbiol 2006;8(1):10–22.
75. Eisenreich W, Dandekar T, Heesemann J, Goebel W. Carbon
metabolism of intracellular bacterial pathogens and possible
links to virulence. Nat Rev Microbiol 2010;8(6):401–12.
76. Ishii SI, Suzuki S, Norden-Krichmar TM, et al. Microbial popula-
tion and functional dynamics associated with surface potential
and carbon metabolism. ISME J 2014;8(5):963–78.
77. Kumar P, Dubey KK. Citric acid cycle regulation: back bone for
secondary metabolite production. InNew and future develop-
ments in microbial biotechnology and bioengineering. Elsevier
2019;165–81.
78. Ghannoum MA, Rice LB. Antifungal agents: mode of action,
mechanisms of resistance, and correlation of these mecha-
nisms with bacterial resistance. Clin Microbiol Rev 1999;12(4):
501–17.
79. Aslam B, Wang W, Arshad MI, et al. Antibiotic resistance: a
rundown of a global crisis. Infect Drug Resist 2018;11:1645.
80. Liu B, Pop M. ARDB—antibiotic resistance genes database.
Nucleic Acids Res 2009;37(suppl_1):D443–7.
81. Saito R, Nonaka S, Fujinami Y, et al. The frequency of BRO β-
lactamase and its relationship to antimicrobial susceptibility
and serum resistance in Moraxella catarrhalis.J Infect Chemother
2014;20(1):6–8.
82. Khan MA, Northwood JB, Levy F, et al. Bro β-lactamase and
antibiotic resistances in a global cross-sectional study of
Moraxella catarrhalis from children and adults. J Antimicrob
Chemother 2010;65(1):91–7.
83. Hernando-Amado S, Blanco P, Alcalde-Rico M, et al. Multidrug
efflux pumps as main players in intrinsic and acquired
resistance to antimicrobials. Drug Resist Updat 2016;28:
13–27.
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
Bristy et al. |17
84. Normark BH, Normark S. Evolution and spread of antibiotic
resistance. J Intern Med 2002;252(2):91–106.
85. Singpanomchai N, Akeda Y, Tomono K, et al. Rapid detection
of multidrug-resistant tuberculosis based on allele-specific
recombinase polymerase amplification and colorimetric detec-
tion. PloS one 2021;16(6):e0253235.
86. Guitor AK, Wright GD. Antimicrobial resistance and respiratory
infections. Chest 2018;154(5):1202–12.
87. Mujawar S, El-Aal A, Ahmed AA, Lahiri C. Variant analy-
sis from bacterial isolates affirms DnaK crucial for mul-
tidrug resistance. In: International Work-Conference on Bioinfor-
matics and Biomedical Engineering, 2020. pp. 237–48. Springer,
Cham.
88. Vanghele LM, Ionescu M, Coste H, Ganea E. Association of
DnaK and GroEL with antimicrobial resistance in Salmonella
Abortusovis. International Journal of Veterinary Medicine: Research
& Reports 2016 (2016), 1–11.
89. Yamada K, Saito R. Molecular analysis of low-level f luoro-
quinolone resistance in clinical isolates of Moraxella catarrhalis.
J Med Microbiol 2014;63(8):1066–70.
90. Attia AS, Lafontaine ER, Latimer JL, et al. The UspA2 protein
of Moraxella catarrhalis is directly involved in the expression of
serum resistance. Infect Immun 2005;73(4):2400–10.
91. Thomy D, Culp E, Adamek M, et al. The ADEP biosynthetic
gene cluster in Streptomyces hawaiiensis NRRL 15010 reveals an
accessory clpP gene as a novel antibiotic resistance factor. Appl
Environ Microbiol 2019;85(20):e01292–19.
92. Culp EJ, Sychantha D, Hobson C, et al. ClpP inhibitors are
produced by a widespread family of bacterial gene clusters.Nat
Microbiol 2022;7(3):451–62.
93. Sekyere JO, Asante J. Emerging mechanisms of antimicro-
bial resistance in bacteria and fungi: advances in the era of
genomics. Future Microbiol 2018;13(2):241–62.
94. Kaur P, Agarwal S, Datta S. Delineating bacteriostatic and
bactericidal targets in mycobacteria using IPTG inducible anti-
sense expression. PloS one. 2009;4(6):e5923.
95. Martinez JL, Baquero F. Mutation frequencies and
antibiotic resistance. Antimicrob Agents Chemother 2000;44(7):
1771–7.
96. Jacobs MR. Worldwide trends in antimicrobial resistance
among common respiratory tract pathogens in children. Pediatr
Infect Dis J 2003;22(8):S109–19.
97. Zhang Z, Yang Z, Xiang X, et al. Mutation of TonB-dependent
receptor encoding gene MCR_0492 potentially associates with
macrolides resistance in Moraxella catarrhalis isolates. Infect
Drug Resist 2022;15:2419.
98. Kasai A, Ohta A, Maeda Y, et al. Novel mechanism responsible
for high-level macrolide resistance in Moraxella catarrhalis.Infect
Drug Resist 2018;11:2137.
99. LiuY,XuH,XuZ,et al. High-level macrolide-resistant Moraxella
catarrhalis and development of an allele-specific PCR assay
for detection of 23S rRNA gene A2330T mutation: a three-
year study at a Chinese tertiary hospital. Microb Drug Resist
2015;21(5):507–11.
100. Fàbrega A, Madurga S, Giralt E, Vila J. Mechanism of action
of and resistance to quinolones. J Microbial Biotechnol 2009;2(1):
40–61.
101. Warner DM, Shafer WM, Jerse AE. Clinically relevant muta-
tions that cause derepression of the Neisseria gonorrhoeae
MtrC-MtrD-MtrE efflux pump system confer different levels
of antimicrobial resistance and in vivo fitness. Mol Microbiol
2008;70(2):462–78.
102. Gygli SM, Borrell S, Trauner A, Gagneux S. Antimicro-
bial resistance in Mycobacterium tuberculosis: mechanistic
and evolutionary perspectives. FEMS Microbiol Rev 2017;41(3):
354–73.
Downloaded from https://academic.oup.com/bfg/advance-article/doi/10.1093/bfgp/elad005/7067527 by guest on 09 March 2023
