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PROJECT
-Project Overview-

PROJECT OVERVIEW

Bacteria acquiring resistance to antibiotics pose serious health problem globally. Following last year’s example,
the project of Elan Vital Korea for this year also is related to MRSA. This year, however, we have focused
on early detection of MRSA infection using quorum sensing. Below, we have briefly described the health threats
caused by MRSA, and have explained the quorum sensing method. Then, we have proceeded to the description
of how we designed and implemented our experiments, and what results we have obtained. Finally, we have briefly
outlined the implication of our results and future plans.

Threats of Antibiotics-Resistant Bacteria

Infection by antibiotic-resistant bacteria is a serious health threat worldwide including Korea and
the United States of America. It is a serious threat primarily because, as the name suggests,
bacteria have evolutionarily developed a resistance to antibiotics. It means, first of all, drugs don’t work.
Furthermore, the spread of the antibiotic-resistant bacteria makes it more difficult to
control or contain the spread of the infectious disease, because it undermines the effectiveness of treatment.
And, it substantially increases the cost of healthcare, and the burden to society because it prolongs
the treatment period and increases the likelihood of death. WHO declared that it “threatens the achievements of
modern medicine” (Antimicrobial Resistance: Global Report on Surveillance 2014, WHO, 2014).
Antimicrobial resistance already causes 700,000 deaths every year, which number is expected to 10 million annually
by 2050 (An international legal framework to address antimicrobial resistance, WHO, 2015).

What makes the problem more pressing is that the data isbased on the reports of clinical samples from
laboratories, “predominantly in hospital settings” (Antimicrobial Resistance: Global Report on
Surveillance 2014, WHO, 2014, p. 70), which means community-acquired (compared to health-care associated)
infections and uncomplicated infections are underrepresented.

Existing Methods Used for Detection

CDC’s efforts at outsmarting the antibiotic resistance focuses on 4 core actions: detect, respond, prevent
and discover. The project is called AR Initiative (Detect and Protect Against Antibiotic
Resistance Initiative), which is an integral part of the CDC strategy to target investment aimed at AR.
Among the AR initiative, detection is the first step that impacts the whole controlling process.
Detecting antibiotic resistance quickly and effectively is crucial for determination of the treatment methods
for different patients as well as for quarantines to prevent it from becoming epidemic.
Currently, several methods are used for the detection of the antibiotic resistance. Most common and traditional
method is using growth inhibition assays performed in broth or by agar disc diffusion.
For clinically critical bacteria, diagnostic laboratories perform phenotypic-based analyses using standardized
susceptibility testing methods, usually in accordance with the guidelines published by the Clinical
and Laboratory Standards Institute.

Using the culture-based approach, it can take 1—2 days to produce results for fast-growing bacteria such as
Escherichia coli orSalmonella, but several weeks for slow-growing bacteria such as Mycobacterium tuberculosis.
Moreover, culturing only works for a small fraction of microbes; although most pathogens can be cultured
relatively easily thanks to years of accumulated experimental experiences, the vast majority of microbes cannot
grow outside their host environment, including pathogens such as Chlamydia orTrypanosomes.

Using newer molecular detection techniques for antibiotic resistance such as quantitative PCR (qPCR) or
microarrays, we can determine the presence of specific resistance genes within hours, and we obtain improved
diagnosis results. However, these culture-independent approaches target well-known and well-studied
pathogens or resistance-causing genes only, and cannot be easily used for broader spectrum screening.

CDC dramatically innovated the detection process by adopting the Advanced Molecular Detection (AMD),
which combines the latest pathogen identification technologies with bioinformatics and
advanced epidemiology to more effectively understand, prevent and control infectious diseases.
Using those technologies, it is possible to rapidly look for a microbe's match among
thousands of reference samples in the microbe library. If no match is found, the whole genomic sequence
of the microbe's DNA code can be taken, then quickly analyzed using disease detective works and
bioinformatics to answer critical disease-response questions. However, this new method, while it sounds
very interesting, is not to be completed until 2020, and still requires incubation, as well as being expensive.

Our Hypothesis: Possibility of Using Quorum Sensing for Early Detection

Our team, Elan Vital Korea, addressed the problem of rapidly detecting antibiotic-resistant bacteria.
We were interested in a rapid and efficient method of antibiotic resistance detection, and we believed that
such a method could be engineered using quorum sensing. Our hypothesis was that we would be able to
use quorum sensing – a method bacteria use to communicate with each other – to make the cells quickly report
the existence of antibiotic-resistant bacteria

By quorum sensing, bacteria can perform many cooperative functions, such as biofilm formation, antibiotic
production, motility, swarming, virulence, and much more. While most quorum sensing takes place
between bacteria of the same species, there are cases of interspecies quorum sensing. Auto-inducers affect
the gene expression of the bacteria once they reach a certain concentration threshold. Bacteria using quorum
sensing usually produce small amounts of auto-inducers, so that the concentration of auto-inducers
are affected by the concentration of the bacteria. In other words, quorum sensing, in essence, regulates gene
expression in response to cell density. Using quorum sensing, bacteria are able to act in unison,
as if they were a single organism.

Quorum sensing is widely used by various bacteria for various functions, so each uses a slightly different
auto-inducer so the signals are not mixed up. In general, gram-negative bacteria use
a class of molecules called N-acyl homoserine lactones, or AHL, while gram-positive bacteria use short
processed polypeptides. For example, the picture below represents the quorum sensing mechanism
in the bacteria vibrio fisheri. Vibrio fisheri is a bacteria that produces bioluminescence,
and is famous for revealing quorum sensing for the first time. Vibrio fisheri uses quorum sensing to produce
light in high cell density, and researchers first discovered quorum sensing from examining
vibrio fisheri.

In vibrio fishri, quorum sensing involves LuxI and LuxR as well as AHL. LuxI is the protein that produces AHL,
and LuxR forms a complex with AHL to affect the regulation of genes. In this case, it
produces luciferase, which produces bioluminescence. Furthermore, the process also boosts the production
of LuxI, which creates a positive feedback loop. This AHL-LuxR quorum sensing mechanism is
one of the most well known gram-negative quorum sensing pathways, and it can be engineered to affect almost
any coding sequence we like.

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