Difference between revisions of "Team:elan vital korea/Project Overview"

 
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                     PROJECT <br> -Project Overview-
 
                     PROJECT <br> -Project Overview-
 
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                 </h4>
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                                    <a name="myAnchor" id="myAnchor"></a><br><br><br><br><br><br><br><br><br><font color="white">PROJECT OVERVIEW</font></h5> <br><br><br>
 
<P style="text-align:center;">
 
<font color="white">
 
Bacteria acquiring resistance to antibiotics pose serious health problem globally. Following last year’s example, <br>
 
the project of Elan Vital Korea for this year also is related to MRSA.  This year, however, we have focused <br>
 
on early detection of MRSA infection using quorum sensing.  Below, we have briefly described the health threats <br>
 
caused by MRSA, and have explained the quorum sensing method.  Then, we have proceeded to the description <br>
 
of how we designed and implemented our experiments, and what results we have obtained.  Finally, we have briefly <br>
 
outlined the implication of our results and future plans. <br><br><br>
 
  
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                    <font color="black">PROJECT OVERVIEW</font>
 
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                    <P style="text-align:left;">
Threats of Antibiotics-Resistant Bacteria
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  <font color="black">
</h5>  
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Antibiotic-resistant bacteria pose a serious problem for global medical community. Detecting antibiotic resistance as quickly as possible is crucial for determination of the correct treatment for patients and for setting up quarantines to prevent spreading. We hypothesized that it is possible to use quorum sensing (QS) to devise a rapid way for cells to report the existence of antibiotic-resistant bacteria.
<br><br><br>
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                    </font>
<P style="text-align:center;">
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                    </p>
Infection by antibiotic-resistant bacteria is a serious health threat worldwide including Korea and <br>
+
</div>
the United States of America. It is a serious threat primarily because, as the name suggests, <br>
+
bacteria have evolutionarily developed a resistance to antibiotics. It means, first of all, drugs don’t work.<br>
+
Furthermore, the spread of the antibiotic-resistant bacteria makes it more difficult to <br>
+
control or contain the spread of the infectious disease, because it undermines the effectiveness of treatment.<br>
+
And, it substantially increases the cost of healthcare, and the burden to society because it prolongs <br>
+
the treatment period and increases the likelihood of death. WHO declared that it “threatens the achievements of <br>
+
modern medicine” (Antimicrobial Resistance: Global Report on Surveillance 2014, WHO, 2014).  <br>
+
Antimicrobial resistance already causes 700,000 deaths every year, which number is expected to 10 million annually<br>
+
by 2050 (An international legal framework to address antimicrobial resistance, WHO, 2015).
+
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What makes the problem more pressing is that the data isbased on the reports of clinical samples from <br>
+
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laboratories, “predominantly in hospital settings” (Antimicrobial Resistance: Global Report on <br>
+
Here, we developed a reporter cell that expresses GFP in the presence of the QS signaling molecule acyl homoserine lactone (AHL). Our test cells (which act as a simulation of antibiotic-resistant bacteria) express lactonase, which breaks down AHL. In our experimental system, test cells should signify their presence by breaking down AHL and preventing GFP expression in reporter cells. Therefore, our project serves as a proof of principle and we hope that our work will serve as a basis for developing similar, more sophisticated quorum sensing-based detection systems for antibiotic-resistant bacteria in the future.
Surveillance 2014, WHO, 2014, p. 70), which means community-acquired (compared to health-care associated)<br>
+
infections and uncomplicated infections are underrepresented.
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Existing Methods Used for Detection
 
</h5>
 
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<P style="text-align:center;">
 
<br><br>
 
CDC’s efforts at outsmarting the antibiotic resistance focuses on 4 core actions: detect, respond, prevent <br>
 
and discover.  The project is called AR Initiative (Detect and Protect Against Antibiotic <br>
 
Resistance Initiative), which is an integral part of the CDC strategy to target investment aimed at AR. <br>
 
Among the AR initiative, detection is the first step that impacts the whole controlling process. <br>
 
Detecting antibiotic resistance quickly and effectively is crucial for determination of the treatment methods<br>
 
for different patients as well as for quarantines to prevent it from becoming epidemic. <br>
 
Currently, several methods are used for the detection of the antibiotic resistance.  Most common and traditional <br>
 
method is using growth inhibition assays performed in broth or by agar disc diffusion.  <br>
 
For clinically critical bacteria, diagnostic laboratories perform phenotypic-based analyses using standardized <br>
 
susceptibility testing methods, usually in accordance with the guidelines published by the Clinical <br>
 
and Laboratory Standards Institute. <br><br>
 
</font>
 
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<P style="text-align:center;">
 
<font color="white">
 
Bacteria acquiring resistance to antibiotics pose serious health problem globally. Following last year’s example, <br>
 
Using the culture-based approach, it can take 1—2 days to produce results for fast-growing bacteria such as <br>
 
Escherichia coli orSalmonella, but several weeks for slow-growing bacteria such as Mycobacterium tuberculosis. <br>
 
Moreover, culturing only works for a small fraction of microbes; although most pathogens can be cultured <br>
 
relatively easily thanks to years of accumulated experimental experiences, the vast majority of microbes cannot <br>
 
grow outside their host environment, including pathogens such as Chlamydia orTrypanosomes. <br><br>
 
 
LUsing newer molecular detection techniques for antibiotic resistance such as quantitative PCR (qPCR) or <br>
 
microarrays, we can determine the presence of specific resistance genes within hours, and we obtain improved <br>
 
diagnosis results.  However, these culture-independent approaches target well-known and well-studied<br>
 
pathogens or resistance-causing genes only, and cannot be easily used for broader spectrum screening. <br><br><br>
 
</font>
 
</p>
 
 
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<a href="#top" rel="" id="top" class="anchorLink"><img class="displayed" src="https://static.igem.org/mediawiki/2015/b/b3/Scroll_arrow_top.PNG"></a> 
 
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<img class="displayed" src="https://static.igem.org/mediawiki/2015/6/6e/Hr_black.jpg" width="80px" height="2px">
 
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<h5 style="text-align:center;">
 
<font color="black">
 
Existing Methods Used for Detection
 
</font>
 
</h5>
 
<br><br>
 
 
 
<P style="text-align:center;">
 
<font color="black">
 
CDC’s efforts at outsmarting the antibiotic resistance focuses on 4 core actions: detect, respond, prevent <br>
 
and discover.  The project is called AR Initiative (Detect and Protect Against Antibiotic <br>
 
Resistance Initiative), which is an integral part of the CDC strategy to target investment aimed at AR. <br>
 
Among the AR initiative, detection is the first step that impacts the whole controlling process. <br>
 
Detecting antibiotic resistance quickly and effectively is crucial for determination of the treatment methods<br>
 
for different patients as well as for quarantines to prevent it from becoming epidemic. <br>
 
Currently, several methods are used for the detection of the antibiotic resistance.  Most common and traditional <br>
 
method is using growth inhibition assays performed in broth or by agar disc diffusion.  <br>
 
For clinically critical bacteria, diagnostic laboratories perform phenotypic-based analyses using standardized <br>
 
susceptibility testing methods, usually in accordance with the guidelines published by the Clinical <br>
 
and Laboratory Standards Institute.
 
<br><br><br>
 
</font>
 
</p>
 
 
<br>
 
 
<a href="#top" rel="" id="top" class="anchorLink"><img class="displayed" src="https://static.igem.org/mediawiki/2015/5/5b/Scroll_arrow_top_Black.png"></a> 
 
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<P style="text-align:center;">
 
<font color="white">
 
<br><br>
 
Using the culture-based approach, it can take 1—2 days to produce results for fast-growing bacteria such as <br>
 
Escherichia coli orSalmonella, but several weeks for slow-growing bacteria such as Mycobacterium tuberculosis. <br>
 
Moreover, culturing only works for a small fraction of microbes; although most pathogens can be cultured <br>
 
relatively easily thanks to years of accumulated experimental experiences, the vast majority of microbes cannot <br>
 
grow outside their host environment, including pathogens such as Chlamydia orTrypanosomes. <br><br>
 
 
LUsing newer molecular detection techniques for antibiotic resistance such as quantitative PCR (qPCR) or <br>
 
microarrays, we can determine the presence of specific resistance genes within hours, and we obtain improved <br>
 
diagnosis results.  However, these culture-independent approaches target well-known and well-studied<br>
 
pathogens or resistance-causing genes only, and cannot be easily used for broader spectrum screening. <br><br>
 
 
CDC dramatically innovated the detection process by adopting  the Advanced Molecular Detection (AMD), <br>
 
which combines the latest pathogen identification technologies with bioinformatics and <br>
 
advanced epidemiology to more effectively understand, prevent and control infectious diseases.  Using those <br>
 
technologies, it is possible to rapidly look for a microbe's match among thousands of reference samples<br>
 
in the microbe library.  If no match is found, the whole genomic sequence of the microbe's <br>
 
DNA code can be taken, then quickly analyzed using disease detective works and bioinformatics to answer <br>
 
critical disease-response questions. However, this new method, while it sounds very interesting,<br>
 
is not to be completed until 2020, and still requires incubation, as well as being expensive. <br>
 
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Morbi volutpat maximus dolor, non lacinia augue laoreet sit amet. Maecenas sed nulla diam. <br>
 
Integer imperdiet vulputate justo, tristique dictum purus tempus at. Etiam sit amet turpis urna. Proin nec <br>
 
nulla at libero euismod accumsan. Sed pulvinar velit eu purus condimentum rutrum ac in arcu. Nam <br>
 
fringilla nec dui at scelerisque. Nam lacus est, pretium eu nisi eget, congue vehicula diam. Curabitur pell<br>
 
entesque dapibus sollicitudin. Vestibulum nec eros eget nibh aliquam pharetra in sit amet diam. <br>
 
Aenean blandit neque non orci viverra iaculis. Donec ligula quam, ultrices at. <br><br>
 
 
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Donec at dui mi. Vestibulum purus elit, lacinia <br>
 
vitae elit nec, tempor rutrum purus. Duis ac tempus sem, eu sodales justo. Cras tristique <br>
 
lobortis efficitur. Donec sed turpis non ipsum sodales viverra. Proin vehicula quam in nisl laoreet finibus.<br>
 
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Latest revision as of 17:40, 17 September 2015








PROJECT
-Project Overview-









PROJECT OVERVIEW


Antibiotic-resistant bacteria pose a serious problem for global medical community. Detecting antibiotic resistance as quickly as possible is crucial for determination of the correct treatment for patients and for setting up quarantines to prevent spreading. We hypothesized that it is possible to use quorum sensing (QS) to devise a rapid way for cells to report the existence of antibiotic-resistant bacteria.



Here, we developed a reporter cell that expresses GFP in the presence of the QS signaling molecule acyl homoserine lactone (AHL). Our test cells (which act as a simulation of antibiotic-resistant bacteria) express lactonase, which breaks down AHL. In our experimental system, test cells should signify their presence by breaking down AHL and preventing GFP expression in reporter cells. Therefore, our project serves as a proof of principle and we hope that our work will serve as a basis for developing similar, more sophisticated quorum sensing-based detection systems for antibiotic-resistant bacteria in the future.



To The Top