http://informahealthcare.com/mbyISSN: 1040-841X (print), 1549-7828 (electronic)
Crit Rev Microbiol, Early Online: 1–14! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/1040841X.2013.875982
REVIEW ARTICLE
Antibiotrophs: The complexity of antibiotic-subsisting andantibiotic-resistant microorganisms
Yvon Woappi1, Prashant Gabani1, Arya Singh2, and Om V. Singh1
1Division of Biological and Health Sciences, University of Pittsburgh, Bradford, PA, USA and 2Department of Computer Science, Texas State
University, San Marcos, TX, USA
Abstract
Widespread overuse of antibiotics has led to the emergence of numerous antibiotic-resistantbacteria; among these are antibiotic-subsisting strains capable of surviving in environmentswith antibiotics as the sole carbon source. This unparalleled expansion of antibiotic resistancereveals the potent and diversified resistance abilities of certain bacterial strains. Moreover,these strains often possess hypermutator phenotypes and virulence transmissibility competentfor genomic and proteomic propagation and pathogenicity. Pragmatic and prospicientapproaches will be necessary to develop efficient therapeutic methods against such bacteriaand to understand the extent of their genomic adaptability. This review aims to reveal theniches of these antibiotic-catabolizing microbes and assesses the underlying factors linkingnatural microbial antibiotic production, multidrug resistance, and antibiotic-subsistence.
Keywords
Antibiotic-production, antibiotic-resistance,antibiotic-subsistence, antibiotrophs,extremophiles, gene-transfer, multi-drugresistance, systems biology
History
Received 1 October 2013Revised 6 December 2013Accepted 12 December 2013Published online 4 February 2014
Introduction
Bacterial survival has been optimized by fast reproduction
cycles, frequent genomic interchange and swift metabolic
adaptability. This metabolic flexibility can trigger the forma-
tion of secondary metabolites (e.g. antibiotics and toxins) that
are not essential for the growth and reproduction of the
organisms that produce them. An antibiotic has been defined
as an organic compound produced by one microorganism to
inhibit the growth of another; however, this definition does not
reflect non-microbial sources (e.g. plants). For any one
antibiotic, there is a specific group of microorganisms
comprising its inhibition spectrum, meaning they are sensitive
to the antibiotic at therapeutically possible dosage levels
(Canton & Morosini, 2011). However, the most common
antibiotics and respective microorganisms (i.e. bacteria and
fungi) in nature, as summarized in Table 1, may enforce long-
term selective pressure for the emergence of resistance,
generating another branch of extremophiles – antibiotic-
resistant extremophiles (ARE) (Gabani et al., 2012; Hawkey
& Jones, 2009; Helling et al., 2002; Kajander & Ciftcioglu,
1999; Nikaido, 2009).
The bacterial spectrum can be divided into five central
metabolic groupings: (i) antibiotic producers, (ii) antibiotic-
resistant, (iii) both antibiotic producer and resistant, (iv) non-
resistant, and (v) non-antibiotic-producing. The cellular
mechanisms for biosynthesis of secondary metabolites, i.e.
antibiotics, and the molecular genetics of the respective
microorganisms have been well studied over the years
(Helling et al., 2002; Nikaido, 2009). However, the metab-
olism of unmetabolized drugs from the extensive use of
antibiotics in humans and animals has yet to be understood.
Prolonged exposure to residual antibiotics under both in vivo
and in vitro conditions produces microorganisms that, under
selective pressure, evolve into AREs (Gabani et al., 2012).
Antibiotic resistance in bacteria could be defined as the
microbial ability to sustain and multiply in the presence of
antibiotics (Aminov & Mackie, 2007), which has raised
global concern due to its devastating impacts, such as spread
of infections across continents and prolonged illness. The
rapidity with which many resistances have appeared after the
introduction of new antibiotics suggests that resistance genes
were already present in nature prior to human use of
antibiotics. Exclusively antibiotic-resistant strains have been
identified and their swift resistance adaptation patterns have
been strongly linked to horizontal gene transfer (HGT)
(Canton & Morosini, 2011; Liebert et al., 1999). The
intensifying relevance of resistant pathogens has, however,
refocused contemporary studies toward resistance mechan-
isms, leaving a relatively small amount of research investigat-
ing natural antibiotic production and its links to evolving
intercellular communication toward resistance (Martin et al.,
2005). Hence, an understanding of antibiotic production
and resistance acquisition is more relevant today than ever
before.
Over the past several decades, the methods of pathogenic
eradication have proven largely inefficient and unrealistic.
In fact, they have promoted microbial propagation and have
Address for correspondence: Om V. Singh, University of Pittsburgh,Divison of Biological and Health Sciences, 300 Campus Drive,Bradford, PA 16701 USA. E-mail: [email protected], [email protected]
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Table 1. Biosynthesis of secondary metabolites, i.e. antibiotics, in most common bacterial and fungal origin*.
Bacterial originSecondary metabolites
(i.e. antibiotics) Fungal originSecondary metabolites
(i.e. antibiotics)
Amycolatopsis lactamdurans Cephamycin C Cephalosorium acremonium Cephalosporin CBacillus licheniformic Bacitracin Penicillium chrysogenum,
P. griseofulvin, P. urticaePenicillins, Griseofulvin, Patulin,
respectively.Bacillus subtillis Bacillin, Subtillin Aspergillus nidulans PenicillinErwinia chrysanthemi (Dickeya dadantii) Indigoidine Acremonium chrysogenum CephalosporinLysobacter lactamgenus Cephabacin Cercospora kikuchii CercosporinMicromonospora sp. Micromonosorin Cochliobolus carbonum HC-toxinNocardia uniformis Nocardicin A Pseudallescheria boydii PseudallinPseudomonas aureofaciens Pyrrolnitrin Mucor ramannianus RamycinStreptomyces antibioticus Actinomycin, Oleandomycin Carpenteles brefeldianum GriseofulvinStreptomyces griseus Indolmycin, Streptomycin,
CandicidinCordyceps militaris Cordycepin
Streptomyces kanamyceticus Kanamycin Giberella baccata Baccatin AStreptomyces fradiae Neomycin Nectria radicicola, Monosporium
bonordenRadicicol
Streptomyces albinogen Puromycin Chaetomium aureum, C. trilaterale OosporeinStreptomyces sioyaensis Siomycin C. cochliodes Chetomin, Orsenillic acidStreptomyces lavendulae Streptothricin Edothia parasitica DiaporthinStreptomyces cinnamonesis Monensin Neurospora crassa SpermineStreptomyces veneguelae Chloramphenicol Lambertela corni-maris, L.
hicoriaeLambertellin
Streptomyces verticillatus Mitomycin Mollisia caesia, M. gallens MollisinStreptomyces caelestis Celesticetin Blennoria sp. CitrininStreptomyces cacaoi Polyonins L & M Pestalotia ramulosa RamulosinStreptomyces spinosus Spinosad Candida albicans MycobacillinStreptomyces hygnoscopicus Rapamycin Achorion gypseum AchoricineStreptomyces pencetius Avermectin Aspergillus giganteus Alpha-SarcinStreptomyces erythrea Erythromycin A. amstelodami, A. proliferans, A.
restrictus, A. variecolor, A.nidulans, A. flavipes, A. humi-cola, A. niger, A. velutinus, A.oryzae
Amodins A&B, Proliferin,Restrictosin, Variecolin,Cordycepin, Flavipin,Humicolin, Jawaharene,Velutinin, Hydroxyaspergillicacid, respectively.
Streptomyces thermotolerans Carbomycin A. fumigatus Helvonic acid, Fumigallin,Trypacidin
Streptomyces peucetius Daunorubicin Beauveria bassiana OosporeinStreptomyces argillaceus Mithramycin Cephalosporium cellulens,
C. salmosynnematumSellenin (Cellulenin),
CephalosporinsStreptomyces natalensis Pimaricin Cephalosporium sp. CephalothecinStreptomyces ambophaciens Spiramycin Clavariopsis aquatic CitrininStreptomyces fradiae Tylosin Dendrodochium toxicum DendrodochinStreptomyces coelicolor Actinorhodin Fusidium coccineum FucidinStreptomyces clavuligerus Cephamycin C Paecilomyces persicinus, P. var-
ioti, P. brefeldianum, P. fre-quentans, P. cyaneum, P.canescens, P. albidum, P. nota-tum, P. paxilli,
Cephalosporin N, Variotin,Brefeldin, Frequentin, Cyanein,Canescin, Albidin,Xanthocillin, Nordin,respectively.
Streptomyces maritimus Enterocin Oospora aurantia, O. colorans, O.virescens
Oosporein, Virescin, respectively.
Streptomyces roseofulvum Frenolicin Cephalosporium sp. SynnematinStreptomyces lincolnensis Lincomycin Fusarium lateritium, Fusarium sp. Locabiotal, Chlamydosporins,
respectively.Streptomyces coelicolor MethylenomycinStreptomyces rimosus TetracyclinStreptomyces pristinaespiralis PristinamycinStreptomyces alboniger PuromycinStreptomyces glaucescens TetracenomycinStreptomyces cattleya ThienamycinStreptomyces sp. NovobiocinStreptomyces sp. P-8648 ViridogriseinThermophilic actinomycetes Thermomycin, Thermocyridin,
Refcin (anthracin)
*Data collected from multiple articles available at http://www.ncbi.nlm.nih.gov/pubmed and http://www.scopus.com/home.url using key words forbacteria and fungal origin antibiotics.
2 Y. Woappi et al. Crit Rev Microbiol, Early Online: 1–14
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resulted in more cases of multi-drug-resistant strains (MDR)
(Nakata & Tang, 2008, Sahoo et al., 2010, Magiorakos et al.,
2012). Combating MDR with proficient, pragmatic strategies
will be necessary to put a halt to the expansion of resistance
and to develop adequate therapies. In this review, we assess
the underlying factors linking natural microbial antibiotic
production, multidrug resistance, and antibiotic subsistence.
We also present promising approaches to combat nosocomial
pathogens and panresistant strains.
Perspectives of antibiotic catabolism and origin ofresistance
The ethnopharmacolic focus drastically changed in the 20th
century as microorganisms with various antibiotic-producing
capabilities were identified (Goldberg et al., 1999; Rahmati
et al., 2002). It eventually became apparent that, in nature,
actinomyces and bacteria undertake the vast majority of
antibiotic synthesis. Currently, over 80% of clinically used
antibiotics are derived from soil-dwelling bacteria, and the
benefits have been manifested through their applications in
surgical transplants, everyday infections treatment, and as
chemotherapeutic agents and agrochemicals (Besier et al.,
2007; Effmert et al., 2012; Woappi et al., 2013).
The appearance of antibiotic-resistant microbes within
health care centers was the first indication of an evolving
microbial resistance. This paved the way for the development
of several synthetic and semisynthetic antibiotics, such as
Prontosil (reviewed by Barr, 2011; Foster & Woodruff, 2010).
In the 1950s, numerous strains of MDR were identified in
hospitals and clinics around the world. This was driven by the
exposure of pathogens to sub-inhibitory antibiotic levels,
which increased the clinical demand for more potent
antibiotics and abruptly halted the search for natural anti-
biotics. The research refocus was almost entirely centered on
designing novel synthetic antibiotics, with the hope that their
chemical compositions would be foreign enough to nature to
prevent bacteria from developing resistance.
In 1959, a promising synthetic antibiotic, methicillin, was
designed and introduced in clinics and hospitals worldwide.
A chronicle of these occurrences was elegantly presented and
reviewed (Foster & Woodruff, 2010). Such antibiotics were
further complemented by the advent of quinolones, but this
approach was again disappointingly interrupted with reported
cases of nalidixic acid-resistant strains, then extremely drug-
resistant strains (XDR) such as vancomycin-resistant enter-
ococci (VRE), penicillin-resistant Streptococcus pneumonia
(PSRP), and eventually totally resistant strains (TDR), notably
methicillin-resistant Staphylococcus aureus (MRSA) (Canton
& Morosini, 2011; Magiorakos et al., 2012; McAdam et al.,
2012; Tenover, 2006). Over half a century later, TDR strains
remain the leading cause of nosocomial infections in the
world (Maal-Bared et al., 2013), confirming the ineffective-
ness of synthetic and semisynthetic antibiotics as a long-term
solution against antibiotic resistance.
The overproduction, overuse and misuse of antibiotics
have made it commonplace for microbial populations to be in
contact with sub-therapeutic levels of antibiotics in the
environment. These sub-inhibitory exposures have allowed
strains possessing intrinsic antibiotic resistance traits to
gradually adapt and develop mechanisms for voiding anti-
biotic-induced toxicity, thus progressively improving fitness
cost. It is now required to deal with antibiotic metabolizers
and panresistant strains – microbes, which have been exposed
to all known clinical antibiotics and have demonstrated
resistance to every single one of them (Dhar & McKinney,
2007). These bacteria reveal a new cohort of resistant
microorganisms, as they differ from the naturally occurring,
primeval antibiotic-resistant microbiota.
Emergence of antibiotrophs
The commonality of antibiotic resistance in microorganisms
over the past several decades reveals the frightening tenacious
pliability of microbes, including pathogens. The World Health
Organization (WHO), the Centers for Disease Control (CDC),
and private groups such as the Infectious Disease Society of
America (IDSA) have recognized antibiotic resistance as a
pandemic (Anderson, 1999; Magiorakos et al., 2012; Robicsek,
2005). Dantas et al. (2008) isolated microbes that were
resistant to a plethora of antibiotics and capable of catabolizing
non-synthetic, synthetic, and semi-synthetic antibiotics,
including amphenicols, at concentrations 50 times higher
than what is considered normal resistance threshold. The
isolates not only escaped the cytotoxic effects of the peptides,
but also were able to catabolize and subsist under them in an
environment free of alternative carbon source. Recently,
numerous strains of Salmonella featuring antibiotic-subsisting
abilities were isolated (Barnhill et al., 2010), and chloram-
phenicol-subsisting isolates were identified later in Asia (Xin
et al., 2012). The variety of bacteria adapting to proliferate
under harsh environmental conditions, e.g. extreme dosages of
antibiotics lethal to normal microorganisms, could be referred
to as ‘‘antibiotic-resistant extremophiles (AREs)’’ (Gabani
et al., 2012). Here we propose the term antibiotrophs, from
the Greek anti (against), bio (life) and trophe (food), to
depict microorganisms capable of subsisting in environments
containing abnormally elevated antibiotic concentrations or
antibiotics as the sole carbon source.
Antibiotrophic niches
Microorganisms (i.e. bacteria and fungi) possessing resistance
and catabolic capacities against specific antibiotics and/or bio-
chemicals within unusual microenvironments (Barnhill et al.,
2010; Dantas et al., 2008; Frank-Petersise et al., 2011; Gabani
et al., 2012; Singh & Walker 2006; Xin et al., 2012) can be
referred to as antibiotrophs. Anthropocentric activities have
undoubtedly been the paramount catalysts in the development
of this resistant cohort. Increases in intercontinental travels,
farming, and soil exchange within human settings have
exposed these strains – along with the rest of the resistance-
bearing microbial flora in nature – to human therapeutics
(Baquero et al., 2013; Barbe et al., 2004; Canton & Morosini,
2011; Falagas & Billiziotis, 2007). Through these exposures,
antibiotrophs likely developed metabolic enzymes while
retaining their unique catabolic competence (Figure 1).
The absence of alternative nutritional carbon-resources in
environments with high human presence is quite rare
and was therefore believed to be a poor driving criterion for
the emergence of antibiotic-subsisting bacteria. However,
DOI: 10.3109/1040841X.2013.875982 Antibiotrophs: The complexity of antibiotic-subsisting 3
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subsistence on antibiotics as a sole carbon source has been
shown to be the central benchmark of their existence
(Maruyama et al., 2006; Nybom et al., 2008; Xin et al.,
2012). The antibiotrophic grouping is evidently much larger
than originally anticipated, and embodies microorganisms
capable not only of subsistence, but also of MDR gene
transmission, antibiotic detoxification, and even antibiotic
production (Barnhill et al., 2010, Xin et al., 2012, Walsh
et al., 2013, Martinez & Rojo, 2011) (Table 2). They
principally differ from most resistant microbes by their
ability to both neutralize and catabolize antibiotics for
nutrition and cellular homeostasis (Barnhill et al., 2010;
Martinez, 2006; Romero et al., 2011; Xin et al., 2012). This
cohort certainly incorporates microorganisms able to subsist
on abnormally elevated antibiotic gradients, whether present
as the sole carbon sources or not.
In past, studies have revealed the diversified microbio-
sphere upsurge in the evolutionary development of new
antibiotic resistant microorganisms (Dhar & McKinney, 2007;
Gabani et al., 2012; Magiorakos et al., 2012; Singh & Walker,
2006). A prominent antibiotrophic niche has been studies
within the veterinary cohort (Barnhill et al., 2010). Dantas
et al. (2008) observed higher antibiotrophic activity in urban
soils compared to soil obtained from farmlands, with most
isolates belonging to the Proteobacteria, Antinobacteria and
Bacteroidetes phyla, with Burkholderiales, Pseudomonadales
and Enterobacteriales representing the most populous orders.
Health care centers, clinics, and agricultural environments are
locations likely harboring the most elevated populations of
these microbes. Given their resistance, antibiotrophs’ gen-
omic profile likely possesses an enhanced degree of trans-
missibility and a widespread pathogenic spectrum (Hawkey &
Jones, 2009; Maal-Bared et al., 2013).
Mechanisms driving antibiotic resistance inantibiotrophs
The paramount mechanisms underlying the development of
antibiotic resistance include gene mutations and the acquisi-
tion of exogenous genetic material, such as plasmids which
contain a variety of resistance genes (Baquero et al., 2013;
Martinez & Rojo, 2011; Rice, 2012). Most genes involved in
antibiotic resistance possess auxiliary functions in bacterial
species. Many antibiotics naturally produced by microbes
Figure 1. Schematic illustrating four centralanthropocentric hubs of the antibiotic-subsisting resistance (not to scale).Commensal, industrial, and waste sites pro-vide an abundant nutritional source forantibiotrophs; the latter eventually find theirway to human communities via farm manuretranslocations, hospital visits, and industrialwaste dumping.
Table 2. Microbial species possessing antibiotic resistant and subsisting abilities.
AntibiotrophicMicroorganisms/family Mechanism of degradation Subsistence characteristics Reference
Streptomyces* MDR efflux pump Tetracycline producer Barnhill et al., 2010;D’Costa et al., 2011
Salmonella enterica* Veterinary associations Barnhill et al., 2010Burkhodelriales N/A Fenitrothion degradation D’Costa et al., 2011Enterobacteriales Inactivation by beta-lactamases Elevated antibiotic resistanceActinomycetales Ribonucleic acid mutations Elevated antibiotic resistanceRhizobiales N/A Elevated antibiotic resistancePseudomonadales N/A Elevated antibiotic resistance Zhongli et al., 2001Sphingobacteriales Inactivation by beta-lactamases Elevated antibiotic resistance Chopra et al., 2003;
D’Costa et al., 2011Sphingosinicella microcystinivorans Microcystin degradation by
HydrolysisElevated antibiotic resistance Chopra et al., 2003
Citrobacter sp. Erythromycin degradation bybeta-lactamases
Antibiotic Inactivation Foster & Woodruff, 2010
*Several orders; N/A: not available
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have been shown to be involved in microbial survival
pathways (e.g. streptomycin) (Martinez & Rojo, 2011;
Meroueh et al., 2003). Therefore, it can be assumed that in
nature, the function of antibiotics goes beyond inhibiting the
growth of the competing microbial flora, and that many genes
responsible for antibiotic resistance also have metabolic roles
in the survival of the microorganism. In Providencia stuartii,
intrinsic chromosomal acetyltransferases are involved in
aminoglycoside resistance, but their primary role, prior to
the introduction of aminoglycosides in clinics, was to
acetylate peptidoglycan for structural modifications (Barlow
& Hall, 2002; Goldberg et al., 1999). Some of the mechan-
isms underlying the process of antibiotic resistance acquisi-
tion are elaborated below.
Horizontal gene transfer
Horizontal gene transfer (HGT) is the process by which genes
from one species of organism are transferred to another
species. In bacteria, HGT mostly occurs with genes located on
plasmids. Transformation (acquisition of exogenous DNA
often located in the environment), conjugation (acquisition of
DNA directly from another bacteria), and bacteriophage
transduction (acquisition of DNA via viral infection) are
major modes of HGT. It has been widely accepted that
plasmids can harbor a vast variety of antibiotic resistance
genes. Modern biotechnology and refined therapeutics for
blocking HGT can allow us to halt the spread of antibiotic
resistance genes such as R plasmids, which have been known
to include genes for resistance to chloramphenicol, tetracyc-
line, aminoglycosides, and sulfonamides as well as other
classes of antibiotics (Hammami et al., 2009; Islam et al.,
2012). The R plasmids, originally found in organisms that
produced antibiotics, have found ways to naturally transform
pathogenic bacteria (Kopmann et al., 2013).
In plasmids, the resistant genes are components of
transposons, which are able to insert themselves in the
middle of any DNA sequence (Liebert et al., 1999). It has
been reported that a plasmid-mediated quinolone resistance
gene, qnrA1, was present on a mobile plasmid and was able to
spread to other species of bacteria that were under the
selective pressure of fluoroquinolones and b-lactam anti-
biotics (Chowdhury et al., 2011). Cantas et al. (2012)
confirmed that in Aeromonas hydrophila, the R-plasmid
pRAS1 carrying resistance genes TcR, TmR, and SuR was
transferred to other species of bacteria by conjugative transfer.
In E. coli, the IncF plasmids, carrying the blaCTX-M-14 gene
responsible for ceftazidime and defotaxime resistance, are
known to spread via HGT (Kim et al., 2011). Several other
plasmid-encoded genes, such as b-lactamases, are known to
have been acquired by pathogenic bacteria via HGT (Meroueh
et al., 2003). It has been suggested that the frequency of
conjugative transfer of resistance genes in nature is several
magnitudes higher than under laboratory conditions (Tenover,
2006). In addition to conjugative transfer, in Streptococci,
Meningococci, and related genera, the exchange of genetic
material occurs mainly via transformation (Springman et al.,
2009). In Acinetobacter sp. ADP1, it was shown that the strain
was exceptionally capable of acquiring DNA from its
environment (Barbe et al., 2004).
MDR efflux pumps
The evolution of MDR efflux pumps in the development of
pathogenic antibiotic resistance has raised concerns (Poole,
2005). MDR efflux pumps are a common way that microbes
expel physiological substrates, non-antibiotic substrates, and
antibiotics outside the cell (Brown et al., 1999; Liang et al.,
1995). Five different protein-based efflux pumps have been
identified: (i) the multidrug and toxic compound extrusion
(MATE) (Brown et al., 1999), (ii) the adenosine triphosphate
(ATP) binding cassette (ABC) (Springman et al., 2009), (iii)
the small multidrug resistance (Paulsen et al., 1996), (iv) the
major facilitator superfamily (Marger & Saier, 1993), and (v)
the resistance nodulation cell division (Saier et al., 1994).
It has been thought that MDR efflux pumps originally evolved
in antibiotic-producing microbes to exocytose the antibiotics
from their cytoplasm as well also evade other naturally
produced toxic molecules, thereby allowing the microbe to
survive in its ecological niche (Brown et al., 1999; Rahmati
et al., 2002). Helling et al. (2002) reported that the natural
function of efflux pumps in E. coli was to remove metabolic
products and toxins and to buffer the organism against surges
in pools of potentially toxic metabolites. In addition, there
have been studies confirming the role of efflux pumps in cell-
to-cell signaling, such as quorum sensing (Rahmati et al.,
2002). The HGT of MDR efflux pumps is now being thought
of as an important mechanism by which many pathogenic
bacteria have become resistant to a wide range of antibiotics.
It is believed that MDR efflux pumps have auxiliary
physiological roles including the expelling of toxic endogen-
ous metabolites as well as exogenous chemicals. The
observation that most MDR efflux pumps are chromosomally
encoded indicates that they did not appear due to the
widespread use of antibiotics (Nikaido, 2009). This is
exemplified by the acrB gene of E. coli, which is used for
protection against bile salts and other detergents abundant in
the E. coli microenvironment (i.e. the intestinal tracts of
vertebrates) (Zeibell et al., 2007). In P. aeruginosa, which
lives mainly in the soil, the MDR efflux pumps are involved
in pumping toxic compounds produced by other soil micro-
organisms (Kang & Gross, 2005). These pumps have also
been reported to secrete secondary metabolites produced by
plants, as well as toxic metals such as Co2+, Ni2+, Cd2+, and
Zn2+ (Goldberg et al., 1999).
Effects of mutations
Overall, antibiotic resistance in bacteria is influenced by the
colony’s physiological status. It is believed that the first few
cells that start a colony ultimately determine the final status of
the colony at the end, as all cells within a colony are clones of
each other. Similarly, if a population of bacteria is exposed to
antibiotics and a few survive, their genome will ultimately
determine the characteristics of the future colonies they
generate. In such instances, when a bacterial population is
exposed to antibiotics, a population is always left behind.
This strategy used by most bacterial populations is now
thought to be a way by which bacterial populations generate
strains able to survive extreme changes in their environment
(Dhar & McKinney, 2007). This phenomenon, termed the
founder effect, is thought to give rise to antibiotic-resistant
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populations of bacteria when they are exposed to antibiotics
produced by naturally occurring bacteria. This effect is
different from what is known as persistence, in which cells
may survive the first treatment of antibiotics, but are still
sensitive to the antibiotic and will be killed with a second
treatment (Kumarasamy et al., 2010). The founder effect,
on the other hand, has been linked to the ability of microbes
to go through gain-of-function mutations (hypermutator
phenotype) and acquired resistance.
Many bacterial species possess a special phenotype
termed the hypermutator phenotype, which allows bacteria
to quickly adapt by mutating their genomes, allowing them
to survive in a quickly changing environment. Kenna et al.
(2007) reported that P. aeruginosa contained a mutS gene
that allowed it to swiftly adapt to antibiotic treatments in
cystic fibrosis (CF) patients. In addition to P. aeruginosa,
isolates of H. influenza and S. aureus from CF patients have
a high proportion of mutators. Small colony variants of
S. aureus have been isolated from a variety of drug-resistant
chronic infections including CF (Besier et al., 2007).
A thymidine-dependent SCV isolated from CF patients has
been shown to be a hypermutator and antibiotic-resistant
(Besier et al., 2008).
The majority of naturally occurring mutants have defects in
the methyl-directed mismatch repair (MMS) system proteins
(Oliver et al., 2004). Several other genes encoding beta-clamp
proteins, mutH, mutL, and mutU, have also been studied
(Chopra et al., 2003). In Bacillus anthracis, the knockouts of
mutY and mutM combination resulted in high mutation rates,
but mutY and mutM single knockouts were weak mutants
(Zeibell et al., 2007). In addition to mut genes, the DNA
adenine methyltransferase (Dam) genes play a role in repair
mechanisms (Nikaido, 2009). Organisms lacking Dam are
hypersensitive to DNA-damaging agents and reactive oxygen
species (Zaleski and Piekarowicz 2004), while organisms with
this hypermutator phenotype can acquire antibiotic resistance
if faced with stresses such as high antibiotic levels.
Risks of an antibiotic-subsisting resistance
The unsettling concept of antibiotic subsistence subsequent to
antibiotic resistance has been examined by several groups
(Barnhill et al., 2010; Lee et al., 2008; Walsh et al., 2013;
Xin et al., 2012). As explained above, the acquisition of
resistance is a natural phenomenon that can serve to maintain
balance within the microbiome. The problem, however,
presents itself when the resistance is acquired by strains
whose pathogenicity affects humans or animals and crops
closely associated with humans. Furthermore, contemporary
classifications fail to properly categorize resistant strains, and
often encompass ‘‘intrinsic’’ MDR strains and ‘‘acquired’’
MDR strains alike, even though a strong genomic distinction
distinguishes the two (Magiorakos et al., 2012). This leads to
a distorted theoretical approach, since hypermutator charac-
teristics often alter therapeutic targets (Chopra et al., 2003,
Lee et al., 2008). We must be aware that ‘‘intrinsic’’ MDR
strains are principally resistant gene propagators within the
microbiome (i.e. the totality of microbes), as they generally
represent antibiotic-resistant populations present prior the
introduction of antimicrobials in clinics (Allen et al., 2010).
‘‘Acquired’’ MDR strains, however, are usually derived
moieties whose resistant abilities were obtained through
HGT, this includes a series of pathogenic strains possessing a
cutting-edge antibiotic-resistant profile (Thomson & Amyes,
1992; Wright, 2005).
Antibiotrophs are likely the result of perpetual gene
interchange, and typical methodologies for antibiotic resist-
ance inhibition may prove particularly inefficient against this
subset of resistant microbes. This is made evident by the
concept of ‘‘co-resistance’’ as described by Canton et al.
(2011), and by the realization that certain bacteria not only
resist antibiotics they are exposed to, but can also derive an
advantage from that environment (Canton & Morosini, 2011).
It is expected that future findings will reveal several TDR
strains as antibiotrophs.
One promising discovery is the identification of peptides
with metabolic-hindering properties (Rajgarhia & Strohl,
1997). This is unexpectedly the case for tetracycline, whose
polyketide origins have revealed anti-catabolic properties
against nearly 600 antibiotic-subsisting isolates (Barnhill
et al., 2010). Although one of the earliest antibiotics
introduced in clinic, tetracycline was one of the latest
antibiotics to have a clinical case of resistance reported
against it. In contrast, recent findings analyzing the evolution
of antibiotic resistance have demonstrated drastically
increased rates of vancomycin, nalidixic acid, and strepto-
mycin resistance (Arias & Murray, 2012; Robicsek et al.,
2005). An inventory of antibiotics commonly metabolized
and subsisted on by antibiotrophic strains is summarized
in Table 3.
Secondary metabolites’ influence on exaptation also plays
a central role in the localization of microbial niches, as the
effects of cell-to-cell signaling through quorum sensing can
enable natural microbial dispersions (Martinez, 2006). Within
the microbiosphere, antibiotics can serve as warning signs,
and can induce biofilm formations in neighboring microbial
populations (Tsui et al., 2011). Such metabolites can also
serve as chemoattractants that appeal to certain antibiotrophs,
concomitantly selecting strains needed in specific micro-
environmental milieus (Romero et al., 2011; Woappi et al.,
2013; Zhang et al., 2011). Hence, human commensals, health/
research centers and agricultural/farm soils, again, are
attractive niches for these microorganisms, as they can readily
gain access to the elevated antibiotic concentrations common
in those environments.
Catabolic conduits
A microbe’s ability to catabolize extreme antibiotic levels
requires its genomic adaptation to stretch beyond the mere
procurement of MDR pumps. Their potential swarming
motility and chemoattractant patterns are revealed by their
appeal toward elevated antibiotic gradients in microfluidic
devices, which also provide insights about their probable
pathogenic mode of action (MOA) (Hawkey & Jones, 2009;
Martinez & Rojo, 2011; Zhang et al., 2011). In actuality,
antibiotrophs may necessitate ‘‘intake pumps’’ – modified
efflux pumps driven by mechanisms analogous to the
phosphotransferase system (PTS), but used to ingest extra-
cellular antibiotics – while possessing expulsive efflux pumps
6 Y. Woappi et al. Crit Rev Microbiol, Early Online: 1–14
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only for antibiotics they cannot metabolize, or for concentra-
tions surpassing their intake threshold (Figure 2). For this
reason, proton motive force protein such as secF, seen in the
potentially prominent antibiotroph order Burkholderiales, in
concert with antibiotic-metabolizing genes may be red flags
for antibiotrophism (Chopra et al., 2003; Hawkey & Jones,
2009). Furthermore, molecular and genomic elements
involved in the regulation of carbon and nitrogen utilization
should be investigated (Martinez & Rojo, 2011; Qu & Spain,
2011).
Antibiotic production versus antibiotrophism
The origins of microbial antibiotic production stemmed from
the diversification of prehistoric biomes. Antibiotic resistance
arose afterwards as a self-protective mechanism against
Figure 2. Hypothetical antibiotic-metaboliz-ing pathway of ciprofloxacin, a second-generation fluoroquinolone frequentlydegraded by antibiotrophs. Subsistence onantibiotics is enabled by phosphatases andhydrolases capable of efficient antibioticdeactivation and degradation inter and intra-cellularly; degraded antibiotics are consumedas carbon source and excess contents areexpelled via MDR efflux pumps.
Table 3. Antibiotic inventory frequently subsisted on by antibiotrophs.
Most-often degraded (High MIC) Reference Often degraded (Low MIC) Reference
Erythromycin (120 mg/ml) Frank-Petersise et al., 2011 Rifampin (40 mg/l) Foster & Woodruff, 2010;Singh & Walker, 2006
Ampicilin (41 mg/ml) Barnhill et al., 2010 Tetracylcline (51 mg/ml) Barnhill et al., 2010Clavulanic acid (41 mg/ml) Cefepime (51 mg/ml)Sulfisoxazole (41 mg/ml) Florfenicol (51 mg/ml)Trimethoprimb (41 g/ml) Amikacin (51 mg/ml)Kanamycin (1 g/l) Dantas et al., 2008; Barnhill et al., 2010 Ceftiofur (51 mg/ml)Vancomycin (1 g/l) Levofloxacin (1 g/l) Aminov & Mackie, 2007Penicillin G (1 g/l) Dantas et al., 2008 Sulfisoxazole (1 g/l)Ciprofloxacinb (1 g/l) Sulfamethizole (1 g/l)Mafenide (1 g/l) Clindamycin (20 mg/ml)Trimethoprim (1 g/l) Novobiocin (20 mg/ml) Chowdhury et al., 2011Carbenicillin (1 g/l)Chloramphenicol (1 g/l)Sisomicin (1 g/l)Amikacin (1 g/l)Daptomycin (1 g/l) Chowdhury et al., 2011Rifampicin (1 g/l)Trimethoprimb (51mg/ml)Vancomycin (51mg/ml)
b¼ cited both as highly degraded and as least degraded; NA¼Not Available.
DOI: 10.3109/1040841X.2013.875982 Antibiotrophs: The complexity of antibiotic-subsisting 7
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microbes’ own metabolites, and as a superficial protective
mechanism against extraneous environmental toxins (Davies
& Davies, 2010; Tenover, 2006; Wright, 2005). The presence
of resistant genes was mostly trivial in antibiotic-producing
microbes. Over time, however, the antibiotic resistance
genome has acquired elements that often enable it to confer
virulence (Baquero et al., 2013; Martinez, 2012). It has been
found that MDR efflux pumps, for instance, originated in
microbes that produced antibiotics and parvomes (Allen et al.,
2010; Dhar & McKinney, 2007). Streptomyces rimosus, a
tetracycline producer, possesses MDR efflux pumps, which it
uses to expel antibiotics (Petkovic et al., 2006). More recently,
the coexistence of antibiotic production and resistance
functions has been confirmed by studying the gene nexus
within antibiotic-producing cassettes (D’Costa et al., 2011;
Islam et al., 2012; Nikaido, 2009). This presents a pertinent
concern, as it reveals some of the antibiotic-producing
microbiota to be part of the antibiotrophic strains, with
virulence transmissibility and pathogenicity competence
(Gabani et al., 2012; Gillings, 2013; Palmer & Keller, 2010;
Singh & Walker, 2006; Torres-Cortes et al., 2011).
The implications of antibiotic production and antibiotic
subsistence raise some interesting questions. Could certain
antibiotrophic strains be self-sufficient under stressful condi-
tions by producing antibiotics they can also digest? Are
antibiotrophs catabolically restricted to exogenous metabol-
ites? Genomic mining and metabolic mapping can provide an
enormous amount of information relevant to such queries.
Furthermore, traditional molecular cloning approaches
through the insertion of specific resistant cassettes upstream
from a macrolide promoter may provide a swift method of
determining minimal genomic requirements and mechanisms
underpinning antibiotic production, molecule transportation,
and subsistence acquisition in several resistant strains
(Bergstrom et al., 2004; Chowdhury et al., 2011;
Kumarasamy et al., 2010; Torres-Cortes et al., 2011).
Therefore, in addition to catabolic patterns, the use of
molecular determinants is necessary to accurately describe
subsistence in bacteria. Studies analyzing catabolic behaviors
in resistant strains, for instance, have shed light on the scarcity
of truly, solely, antibiotic-subsisting bacteria (Walsh et al.,
2013), emphasizing the need for genomic and systems biology
in characterizing subsistence.
Bioinformatics of antibiotic resistance
Global in silico approaches, precise transcriptomics (i.e.
global study of mRNA/transcripts) and advanced metage-
nomic systems (i.e. global gene profiles of uncultivable
bacteria) can now enable us to identify members of the
antibiotroph subset based on specific molecular profiles.
Consequently, instead of looking for the mere ability to
subsist in carbon-free environments under elevated antibiotic
concentrations, we can mark specific genomic elements as
strong indicators of antibiotrophism. By doing so, it is
possible to establish solid foresight strategies and to accur-
ately anticipate possible expansions and transmutations within
the microbial resistome (i.e. antibiotic resistance genes).
The identification of the SG11 integron and secF gene
in several antibiotic-subsisting isolates, for instance, has
enabled microbiologists to tag commonly recognized gene
clusters as probable antibiotic-catabolizing elements
(Barnhill et al., 2010; Kopmann et al., 2013; Tsui et al.,
2011). Another example is L-tryptophan, whose degradation
in antibiotic-producing microbes unleashes its indole func-
tional group and subsequently confers antibiotic resistance
(Lee & Collins, 2012). Such mechanisms reinforce the
pleiotropic characteristics of subsistence and indicate a
potential presence of auxiliary antibiotic-subsisting genes
camouflaged within ordinary antibiotic-resistant clusters
(Canton & Ruiz-Garbajosam, 2011).
Furthermore, comparative proteomic (i.e. global study of
proteins) analysis could help reveal the MOA of several
antibiotrophic strains. The application of PM47 treatment, as
demonstrated by Wenzel & Bandow (2011), has shown
distinctive proteomics markers underlining antibiotic-resistant
MOA in several species. Another comparative proteomic
study reveals bacteria resistant to different antibiotic drugs
with distinct mechanisms of action, along with an overview of
the proteins possibly related to the resistance process (Lima
et al., 2013). Analogous gene-specific approaches would
provide an extremely dynamic scaffold for antibiotrophic
genomic mining, which could enable us to determine whether
genes, in synergy with antibiotic-inducing elements such as
S12 ribosomal proteins, are concrete indicators of subsist-
ence. These uncharacteristic arrangements can readily be
assessed through pyrosequencing if performed with primers
hypersensitive to subsisting genes (Table 4).
Proteogenomic expression profiles are increasingly
important in the search for functional biomolecules (i.e.
metabolites) that may have potential uses in eliminating
antibiotic resistance. The vast amount of data produced from
genome and proteome data sets needs to be analyzed, and one
must understand the analytical platforms that have been used
to obtain the data and the statistical principles. The current
infrastructure of bioinformatics allows the generation, storing,
analysis, and interpretation of these data sets. In general, data
interpretations of antimicrobial drugs in terms of genomics
and proteomics have been rendered through three major
bioinformatics approaches: (i) mathematical modeling (i.e.
data mining, statistical analysis, genetic algorithms, and graph
matching); (ii) computation search alignment techniques (i.e.
comparing new proteomes and metabolomes against the set
of known proteins and metabolites); and (iii) a combination
of critical mathematical modeling and search techniques.
Among the search tools, there are a number of databases
of antibiotic peptides treating microbial infections
(e.g. ANTIMIC, PhytAMP, APD2 and CAMP) that provide
an overview of antibiotic resistance (Brahmachary et al.,
2004; Chowdhury et al., 2011; Thomas et al., 2010;
Wang et al., 2009).
The antibiotic resistance gene database (ARDB) unifies
most of the publicly available information on antibiotic
resistance (Liu & Pop, 2009). The antimicrobial drug
database (AMDD) provides a comprehensive platform with
the potential to help analyze antimicrobial agents and develop
new candidates to overcome drug resistance (Danishuddin
et al., 2012). Developments in the Internet have allowed
researchers to have immediate access to information in almost
every scientific field. Table 5 summarizes a list of Internet
8 Y. Woappi et al. Crit Rev Microbiol, Early Online: 1–14
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resources that may be useful to practitioners and scientists
with research interests in antimicrobial resistance (adopted
and modified from (Falagas & Karveli, 2006).
Challenges and future perspectives
Our dependence on antibiotics for agriculture and disease
treatment is higher today than it has ever been. Nonetheless,
we concurrently face the challenge of an unprecedented,
evolving microbial resistance. Unlike most microorganisms
and pests, our story is intertwined with that of bacteria.
The symbiotic relationship that exists between humans and
microbes will prevent us from eradicating several microbial
strains, even if hypothetically feasible. Not by virtue of
coincidence, human commensal is a frequent recipient
of pathogenic traits (Marshall & Ochieng, 2009). This is
seen in ESBL-producing E. coli, a pivotal mediator of
resistant gene interchange between environmental micro-
organisms, commensals, and nosocomial pathogens (Kurihara
et al., 2013; Sengupta et al., 2013). The combination of HGT
and gene mutations allows such microbes to quickly generate
advantageous variances and to produce strains with swift
antibiotic resistance propensities.
Although a general consensus on the description of
antibiotic-subsisting microorganisms is provided in this
review, it is important to halt bacterial evolution and develop
a next generation of antibiotics that would quickly eliminate
resistant strains, while subsequently voiding their genomic
content to prevent its acquisition by nearby microbes.
Recently identified antibacterial reagents capable of evicting
antibiotic-resistant plasmids from a cell, such as apramycin,
may prove particularly appealing for this approach (Denap
et al., 2004). In addition, it has been recognized that the mass
production of antibiotics has generated an array of logistic
challenges that have resulted in their overuse and misuse. As
noted by the CDC, the unmonitored dumping and distribution
of ineffective antibiotics in developing nations provides an
enormous nutritional bank for antibiotic-resistant microbes,
and is a major driving factor for the expansion of the pan-
resistant resistome (Okeke et al., 1999). Although inconveni-
ent, an approach averse to modern antibiotics will need to be
introduced within the next decade. Ingenuity and foresight
should be encouraged in the planning of the next therapeutic
models. The use of bacterial repellents, for instance (i.e.
biotoxic and microtoxic inhibitors), may be a better thera-
peutic strategy than the current bactericidal approach. Such
cytotoxin neutralizers can prove particularly effective since
they do not threaten colony viability and hence do not activate
resistance-favorable mutations, ultimately decreasing the
propagation of resistant genes (Baquero et al., 2013;
Williams & Hergenrother, 2008; Williams et al., 2011).
Multiple microbial metabolic mechanisms for drug toler-
ance reveal an ongoing evolution towards extremophiles,
tolerating environments that are otherwise lethal to a normally
occurring bacterium in nature. The limited research on
naturally occurring extremophiles has not allowed us to
fully understand the particularities of their niches. However,
in-depth studies of antibiotic-resistant organisms that fall into
Table 4. Prospective molecular elements conferring subsistence.
Molecular agent(s) Host Mode of action (MOA) Reference
SGI1 integron Salmonella enterica Genomic island carrying resistancegene cassettes
Barnhill et al., 2010
Sul1 Salmonella (*) Gene conferring sulfonamideresistance
L-tryptophan S. aureus Degradation-dependent indole aidsefflux pump functioning
Lee & Collins, 2012
L-arginine Streptococcus sp., Escherichia coli,Klebsiella sp
Ferton reaction suppression andcatalase activation
Zhao-Lai et al., 2012;Lee & Collins, 2012
CTX-Ms Enterobacteriaceae b-lactamase-driven hydrolysis Thomson & Amyes, 19922NI nitrohydrolase Nocardia mesenterica, Pseudomonas
Fluorescens, Streptomyces eurocidicusHydrolytic denitration Qu & Spain, 2011
secF Burkholderiales Protein translocation throughproton motive force
Barnhill, 2010; Kazuya et al., 2011
MlrAMlrBMlrCMlrD
Sphingomonas/Sphingomonadaceae Hydrolysis. MCLR transport Kormas & Lymperopoulou, 2013;Borgia et al., 2012
NDM-1 Enterobacteriaceae Carbapene degradation Borgia et al., 2012CbrA Pseudomonas aeruginosa Sensor kinase driving metabolic
switching in swarmer cellsJin et al., 2002
OPH Pseudomonas sp. Organophosphate degradation Singh & Walker, 2006pehA Burkholderia caryophilli Gene responsible for polygalactur-
onase expressionglp A&B Pseudomonas sp. Invovlved in commensalism
adaptationstet(S) Staphylococcus aureus, Bacillus sp. Gene driving tetracycline-efflux
transporter expression andcatalysis of Na+ and K+
Falagas & Bliziotis, 2007;Lee & Collins, 2012
mphAmphBmphBM
E. coliE. coliS. aureus
Macrolide inactivation Wright, 2005
*Several orders.
DOI: 10.3109/1040841X.2013.875982 Antibiotrophs: The complexity of antibiotic-subsisting 9
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shS
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tern
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ciet
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HP
A:
Hea
lth
Pro
tect
ion
Agen
cy.
DOI: 10.3109/1040841X.2013.875982 Antibiotrophs: The complexity of antibiotic-subsisting 11
Cri
tical
Rev
iew
s in
Mic
robi
olog
y D
ownl
oade
d fr
om in
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ahea
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on 0
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pers
onal
use
onl
y.
the extremophile category (i.e. antibiotrophs) would help us
locate thriving properties of many other extremophiles and
resistant bacterial strains. In addition, it would be advanta-
geous for future studies to be consistent with terminologies
such as subsistence, antibiotic-catabolism, antibiotrophism,
and sole-carbon source utilization. This will aid facilitate the
analytical correlation between various subsisting gene acqui-
sition patterns, and will improve surveillance culture
screening.
To fully understand the spectrum of this subsistance-based
resistance, the focus should also be orientated towards
nosocomial environments. The novel antibiotic arsenals and
well-known antibiotics associated with resistant strains could
be administered solely through compact portable drug pumps
(PDP) attached to patients. Studies have suggested, ‘‘anti-
biotic cycling,’’ which involves the periodic replacement of
antibiotics with alternative classes of compounds in an
attempt to reduce the selection pressure for resistance
(Masterton, 2005). However, this does not provide a
long-term solution, since the complete eradication of multi-
drug-resistant strains is practically impossible. An equally
promising and currently used approach is the combinations
of various antibiotics with different modes of action.
One demonstration of this strategy resulted in great success
with clavulanic acid and related compounds in combination
with b-lactam antibiotics (Jariyawat et al., 1999). However,
microbial strains resistant to this mechanism have been found,
and recent studies have reported clavulanic acid as one of the
most readily catabolized antibiotics when screened against
antibiotrophic strains (Barnhill et al., 2010).
As microbiosphere ‘‘cleaning machines’’, the unique
metabolism observed in antibiotrophs also has some valuable
and practical potential. Through gene manipulation, non-
pathogenic antibiotrophs can be engineered for pharmaceut-
ical, agricultural, and clinical uses. Their chemoattractant
patterns could be employed to identify hotspots of antibiotic
outflow, while their genomic profiles could help hone
CRISPR interference and could serve as substrates for the
next generation of molecular cloning applications.
Acknowledgements
The authors thank Institutional assistance for making this
research possible.
Declaration of interest
The authors report no conflicts of interest.
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