Difference between revisions of "Team:UC San Diego/Applied Design"

 
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         <h2>Product Design</h2>
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         <h2>Project Design</h2>
        <img src="" alt="prodDes" class="deviceIconMain" />
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         <h3>Overview and Introduction</h3>
         <h3>Our Thought Process</h3>
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         <p>Liquid biopsy refers to sampling and analysis of non-solid biological tissue, and is used for disease diagnostics and prognosis. In cancer diagnostics and prognostics, liquid biopsy is mainly used to quantify and characterize circulating tumor DNA (ctDNA) in blood. ctDNA is short fragment (~180bp) of double stranded DNA derived from tumor cells. When patients develop, ctDNA enriches in blood, containing variable information about cancers such as genetic mutation and methylation. </p>
         <p>As our team endeavored to take our idea of the wetlab and tailor it to real world parameters, we realized that it would be important to gain a fundamental understanding of the problem that we were trying to solve, examine some of the clear flaws with the status quo and existing solutions, and then develop a solution that addresses these pain points. Along the way, we have to revise our designs to incorporate our stakeholders’ needs and address them in a comprehensive manner. In addition, considerations of the broader implementation and lifecycle use also helped optimize our design decisions. As a result of our many interactions, we were able to create Epinoma, a modular beginning-to-end workflow for non-invasive cancer detection that uses machine learning to aid biomarker discovery, a functional assay that uses engineered proteins and principles of synthetic biology to detect specific epigenetic determinants, and a digital health platform that helps streamline doctor-patient communication. This journey would have been impossible without the input of many domain experts immersed in the diagnostic pathway, digital health experts, graduate students in the BLUE LINC incubator, industry professionals (researchers and department heads at Genentech and Roche), social entrepreneurs and innovators at the TATA Institute of Genetics in Society.</p>
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        <p>Liquid biopsy has become an exciting field of biomedical research since it has several advantages compared to common tissue biopsy. As liquid biopsy analyzes blood rather than cancer tissue, it is much less invasive and causes less pain during sample collections. As a result, multiple liquid tests could be carried out in different times to monitor the progress of disease and treatment. Moreover, many cancers have no detectable solid tissues in the early stage, rendering tissue biopsy impossible. By contrast, liquid biopsy could detect a trace of ctDNA released from small amounts of cancer cells even tumor is not present, gaining it huge power for diagnostics. </p>
         <img src="https://static.igem.org/mediawiki/2018/e/ec/T--UC_San_Diego--productw.png" alt="prodDesFlow" class="deviceIMmg" />
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        <h3>Framing the problem and examining existing solutions</h3>
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        <p>Inspired by this emerging field, our original goal was to develop a general platform that could be used to (1) identify potential diagnostic biomarkers and (2) rapidly detect cancer from a given sample. While doing our literature research and talking to clinical researchers, we came across the idea of using promoter methylation as a quantifiable diagnostic metric. Most analyses of the methylation signal rely on a procedure known as bisulfite sequencing. It begins with converting unmethylated cytosine to uracil with sodium bisulfite, leaving methylated cytosine alone. Converted ctDNA undergoes multiple rounds of polymerase chain reaction (PCR) to selectively amplify methylation regions. In the end, amplified product is analyzed, typically by next-generation sequencing (NGS). Although bisulfite sequencing remains the gold standard for ctDNA methylation analysis, we quickly identified several limitations of this method:</p>
         <p>Talking to clinical researchers and diagnostic experts throughout the UC San Diego Health System helped us identify some of the fundamental problems with tissue specimen analysis. One of the primary concerns that healthcare professionals have is that tissue specimen analysis is unable to capture the inherent  molecular heterogeneity of tumors and the ability of cancer genomes to evolve. From a diagnostician’s perspective, this decreases the method’s predictive value which makes it suboptimal. In addition, doctors must often make a decision regarding a patient’s biopsy, even though it carries the inherent risk of spreading the tumor and further complicating the issue. From a patient’s perspective, tissue biopsy is often very invasive and painful, and can also pose a serious economic burden in the current healthcare system. </p>
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         <p>As a result, our team turned to research in the liquid biopsy space, which focuses in non-invasive cancer detection techniques. After examining some of the main players and methods in the liquid biopsy space, we realized that although the liquid biopsy space addressed the pain point of physical pain, it still did not fix the issue of diagnostic accuracy. Our team identified several bottlenecks in the commercialization of liquid biopsy tests including: (1) Analysis of cell-free DNA in urine, blood, and saliva was often inaccurate because the low concentrations of DNA could not be properly analyzed given existing technologies, (2) Analysis of the methylation signal often relied on DNA sample treatment with sodium bisulfite, which can induce random breaks in DNA fragments and lead to incomplete deamination and inaccurate results. Talking to several medical institutions  led us to realize that our methodology should eliminate the chemical treatment method while still retaining a quantitative output. </p>
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        <ol>
         <p>With regards to the disease that we were were working with, hepatocellular carcinoma, we also wanted to quantify the potential impact that our solution would have. With 700,000 existing cases of HCC with an additional 43,000 cases in America predicted in the upcoming year, we felt that it would be important . Current healthcare economics and reimbursement strategies mean that it costs almost almost $500 for a simple needle biopsy and nearly $4000 for surgical biopsies; with our predicted price point of $250, we would be able to prevent a significant economic burden for many individuals as well as prevent a preventative measure on various healthcare systems around the world. </p>
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          <li><p> Bisulfite treatment causes degradation of most ctDNA and induce incomplete cytosine conversion. Bisulfite treatment typically requires overnight treatment of ctDNA samples.</p></li>
         <img src="https://static.igem.org/mediawiki/2018/6/61/T--UC_San_Diego--need.png" alt="biopsy" class="deviceIMmg" />
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          <li><p> Multiple rounds of PCR are time-consuming and suffers from <b>amplification bias</b></p>.</li>
         <img src="https://static.igem.org/mediawiki/2018/a/ac/T--UC_San_Diego--bis.png" alt="chemical treatment" class="deviceIMmg" />
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          <li><p> NGS is expensive and not always possible for clinical settings </p> </li>
        <h3>Determining crucial criteria and incorporating synthetic biology into our product design</h3>
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        </ol>
         <p>After reflecting on our initial stakeholder interactions, our team started thinking about how to address the issues at hand. After brainstorming several solutions, we determined that the first crucial pivot that we would have to make is to identify and develop a diagnostic metric that would serve as a more consistent, useful assessment of an individual’s health. Thus, we decided that our solution would need to eliminate the invasiveness of tissue biopsy, be cost-effective, have clinically accurate levels of diagnostic accuracy, provide a quantitative output, and pose no threat in case of environmental release.</p>
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         <p>As we learned more about hepatocellular carcinoma, we realized that our tool would be extremely applicable in local and international low-resource communities. To understand the specifics of what it would take to develop a clinically-viable test in a variety of environments, we talked to Mr. Manoj Kumar, head at the TATA Institute of Genetics and Society, a NGO and philanthropic social incubator that seeks to bring better healthcare tools to low-resource communities in India. From this, we learned about the ASSURED criteria that are crucial to deploying point-of-care devices in low-resource communities; although we were not necessarily planning on a POC device, we felt that many of these guidelines could guide our design criteria. As such, we realized our device had to be affordable, sensitive (low rates of false positives), specific (low rates of false negatives), user-friendly, robust (rigorously tested in different settings), and . Because a large number of these cases are brought to centralized government hospitals and associated clinical labs, we felt that the equipment-free requirement was not as crucial for the deployment of our project. In fact, given that the desired readout was a quantitative signal, it would be almost impossible to meet that requirement.</p>
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         <img src="https://static.igem.org/mediawiki/2018/2/21/T--UC_San_Diego--bisulfite.png" alt="bisulfateSeq" class="designImg" />
         <p>Our team decided that incorporating synthetic biology would be essential. Engineering a methyl-binding domain protein with a fluorescent reporter gene would be the core of our biosensor as it allowed for a easy, safe transduction element that did not pose issues that existing alternatives had, including invasiveness and inaccurate readout.</p>
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         <p>For us, it was also crucial that we come out with a solution that would be easy to acquire and analyze. The most important factors for that depended on turnaround time, ease of materials acquisition, and overall detection accuracy.</p>
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         <img src="https://static.igem.org/mediawiki/2018/e/e0/T--UC_San_Diego--smartphone.png" alt="smartphone diagnostics " class="deviceIMmg" />
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<h5> Figure 1. Overview of bisulfite treatment and sequencing flow </h5>.
        <img src="https://static.igem.org/mediawiki/2018/5/5c/T--UC_San_Diego--hydrog.png" alt="hydrogel " class="deviceIMmg" />
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         <img src="https://static.igem.org/mediawiki/2018/6/65/T--UC_San_Diego--nanop.png" alt="gold nanoparticle  " class="deviceIMmg" />
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         <p>In addition, the lack of commercially available diagnostic tests is due to the fact that it is quite difficult to determine potential biomarkers; outside of existing literature, there are few resources out there for clinicians and oncologists to rely on when performing a liquid biopsy and choosing a particular gene of interest. Here is just a simple flowchart of all the possible methods that researchers can use in order to gain information about methylation patterns.</p>
         <img src="https://static.igem.org/mediawiki/2018/8/86/T--UC_San_Diego--logof.png" alt="Epinoma design  " class="deviceIMmg" />
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    <img src="https://static.igem.org/mediawiki/2018/b/b2/T--UC_San_Diego--aff.png" alt="Epinoma design  " class="deviceIMmg" />
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        <h3>Designing our Protein-Based System</h3>
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         <p>To address the first problem that we had identified, our team decided to use synthetic biology coupled with nanomaterials to determine methylation levels of specific sites. A clinically useful assay to profile methylation sites ultimately requires output of a quantitative value. Through literature research, we decided to use beta methylation values which are commonly used In other detection methods such as bisulfite treatment coupled with NGS.</p>
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        <p>An ideal design should be able to quantitatively measure two values: frequency of total target ctDNA and frequency of methylated target ctDNA. Our design could quantify these two parameters on a single platform by two separate fluorescent readouts.</p>
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<br>
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        <p>The central biological part in our design is methyl-CpG-binding domain (MBD domain). MBD domain comes from a family of proteins called MBD proteins and retains its functionality when single domain is present. It specifically recognizes symmetrically methylated CpG dinucleotides on double-stranded DNA (dsDNA). On the other hand, MBD domain has much lower affinity toward non-methylated CpG and hemi-methylated CpG. Coupled with signal generation biological parts such as eGFP and Horseradish peroxidase (HRP), MBD domain has the potential to detect and quantify methylation levels of ctDNA. </p>
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<br>
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        <p>Current ctDNA methylation methods, such as bisulfite treatment coupled with NGS, are able to quantify methylation levels of specific methylation site. Using MBD domain seems to pose a problem as it is reported to be sequence-independent: MBD domain could not differentiate methylations from different methylation locus. Ambitious to build a simpler, cheaper, and faster device while maintaining its ability to be site specific, we use direct hybridization method based on Yu’s idea. </p>
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         <p>As Yu et al. reported, target-complementary DNA probes are designed to have single methylation on target locus. When target DNA hybridizes to DNA probe, MBD could differentiate site-specific methylation status by binding to symmetrically methylated duplex but not the hemi-symmetrical ones. However, this MBD method requires modification of target DNA with fluorescent dye, which is difficult when other nonspecific DNA is present in the sample. Moreover, the method failed to quantify the frequency of total target DNA, a crucial parameter for beta value calculation mentioned above. This year, UCSD iGEM team has successfully built upon the MBD method to build a clinically relevant platform for DNA methylation testing. </p>
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        <img src="https://static.igem.org/mediawiki/2018/c/cf/T--UC_San_Diego--origidea.png" alt="original" class="designImg" />
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<h5> Figure 2. Preliminary visualization for providing specificity to the MBD protein </h5>
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<br>
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        <h3>Part I. GO Immobilization and Fluorescence Quenching</h3>
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        <p>Cheap and accessible, graphene oxide (GO) has shown many Interesting properties under nanoscale. Previous research has demonstrated that graphene oxide has a strong affinity toward single-stranded DNA (ssDNA) and it quenches fluorescent dyes (e.g. FAM) whenever dyes get close to surface.</p>
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        <p>Inspired by this previous research, we designed multiple DNA probes with a structure shown below. Each DNA probe is attached with a fluorescent dye at 3' end and contains two distinct regions: GO Interacting region and target recognition region. GO Interacting region is designed to contain high GC content which strengthens probe-GO Interaction based on literature. Target recognition region Is complementary to part of target ctDNA; It also contains a 5' methylated cytosine for later detection purpose (see part IV). Before applying ctDNA samples, DNA probes are pre-incubated with GO In solution. Noncovalent interaction between ssDNA and GO bring fluorescent dyes close to GO surface, and therefore effectively quench fluorescent signal.</p>
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         <img src="https://static.igem.org/mediawiki/2018/4/4e/T--UC_San_Diego--probe.png" alt="probe" class="designImg" />
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<h5> Figure 3. Sample target probe design </h5>
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        <h3>Part II. Hybridization of target ctDNA with probes recovers fluorescence</h3>
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         <img src="https://static.igem.org/mediawiki/2018/2/2f/T--UC_San_Diego--exo.png" alt="exoiii" class="designImg" />
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<h5> Figure 4. Using exonuclease to digest DNA probe and amplify signal </h5>
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         <p>A typical silicon gel-purified ctDNA sample contains at least three DNA entities: target DNA with methylation, target DNA without methylation, and nonspecific DNA. Target ctDNA, no matter methylated or not, will specifically hybridize with recognition region of probe DNA to form partial dsDNA duplex. Such duplex significantly weakens DNA-GO interaction and therefore pushes fluorescent dye away from GO surface. This process recovers fluorescent signal, which could be used to quantify target ctDNA concentration. On the other hand, non-specific DNA could not form duplex with DNA probes. GO could absorb this non specific ssDNA to minimize false positive signal.</p>
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<br>
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        <h3>Part III. Signal amplification via ExoIII digestion of excess DNA probes </h3>
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         <p>Fluorescent dye is sensitive toward DNA detection in general. However, ctDNA exists in a much lower concentration than most biosensor targets. As a result, we employ two enzymatic amplification strategies in our design to improve device sensitivity. The first strategy is exonuclease III (Exo III) digestion. (See Part V for second strategy). E.coli Exo III specifically digest dsDNA duplex from a blunt or recessive 3’ end. ctDNA typically has a length of 180 bp, while DNA probe in our design has a length of ~25bp. As a result, DNA probe will mostly form recessive 3’ end, which is the substrate of exonuclease III. Exo III digests DNA probe and releases single-stranded target DNA and fluorescent dye. Free target DNA could form a duplex with more DNA probes and generate more fluorescent dyes by Exo III. The maximum fluorescent signal could be achieved in 90 min and Exo III could be quenched by 70-degree heating. Impurities such as Exo III can then be washed away prior to performing subsequent steps.</p>
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        <h3>Part IV. MBD fusion proteins recognize symmetrically methylated sites</h3>
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        <h4> A. Designing MBD fusion proteins</h4>
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        <p>This summer, we were able to express and purify three different MBD fusion proteins to recognize DNA methylation. The first construct we came out with is mMBD-eGFP, which has been expressed and applied in previous research. In this construct, attached eGFP could give a direct fluorescent readout once MBD bind to methylated CpG and the excess is washed away. Avi tag is a 15 amino acid region specifically recognized by biotin ligase BirA. Once biotin is attached, multiple signal amplification strategies could be used to increase device sensitivity (references, see section B).</p>
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         <p>Based on this characterized BioBrick, which served as our baseline genetic circuit, we started brainstorming more optimal genetic circuits. Much of our attention has been focused on sensitivity, as the original MBD method was not sensitive enough for ctDNA quantitation. We drew out lessons from a concept called “avidity”. When multiple MBD domains are fused to a single sequence, each domain could interact with methylated CpG. Avidity describes the apparent affinity of such multivalent interaction and it is significantly higher than affinity of single MBD-methylated CpG island. However, increasing numbers of MBD on a single sequence also increases protein size and the possibility of interference between domains. As a result, we chose to fuse two MBD domains with a flexible Gly4Ser2 linker in our improved BioBricks. Combining previous literature and our experimental attempts, we found out although human MBD has higher affinity, mouse MBD is much easier to purified and gives higher yield. As a consequence, we chose to use hybrid human MBD and mouse MBD fusion protein, hoping to maximize protein avidity but in the meantime simplify cloning and protein expression difficulties. </p>
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         <p>Two improved constructs shown below represent two ways to amplify signal, each with its own merit. Hybrid MBD concatenated with HRP could detect methylated CpG in a single step; it also avoids the need to biotinylate the expressed protein, which is necessary for Hybrid MBD with avi-tag. However, Hybrid MBD with avi-tag is a much smaller protein and therefore less likely to exhibit steric hindrance between domains, which could compromise MBD affinity. As we anticipate MBD-methylated CpG recognition could be applied to scenarios other than ctDNA, we design and validate these different constructs to fulfill different applications. </p>
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         <img src="https://static.igem.org/mediawiki/2018/9/97/T--UC_San_Diego--iconconstructs.png" alt="constructs" class="designImg" />
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<h5> Figure 5. Overview of MBD-based genetic circuit for promoter methylation detection </h5>
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         <h4>B. Discrimination between methylated target and unmethylated sequences</h4>
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        <p>Once the impurities are washed away (see Part III), MBD fusion proteins are applied and incubated. If target CpG is methylated, resulting DNA duplex will form symmetrically methylated CpG as the substrate for MBD domain binding; otherwise resulting DNA duplex is semi-methylated and MBD proteins won’t bind. Excess MBD proteins could be washed away by buffers. </p>
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        <p>For MBD-eGFP protein, methylated CpG signal could be directly quantified with eGFP fluorescence. Hybrid MBD fused with HRP or avi-tag (with biotin attached) proteins requires the application of substrate and hydrogen peroxide (see part V). Hybrid MBD-avi tag protein needs two extra steps: incubation of commercially available streptavidin-HRP and washing excess streptavidin-HRP.</p>
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         <h3>Part V.  HRP amplifies fluorescent signal by enzymatic oxidation of substrate</h3>
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        <p>Our second signal amplification idea comes from the ELISA-type assays. HRP is commonly used to oxidize substrates in the presence of hydrogen peroxide. An oxidized substrate, in this case bi-p,p’-4-hydroxyphenylacetic acid, gives intense fluorescent signal with excitation at 320nm and emission at 406nm.</p>
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        <img src="https://static.igem.org/mediawiki/2018/3/3f/T--UC_San_Diego--hrp.png" alt="hrp" class="designImg" style="width:60%; margin-left:20%;"/>
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<h5> Figure 6. Visualization of the HRP mechanism </h5>
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        <p>To improve the diagnostic accuracy of our assay (the sensitivity and specificity), we also felt that synthetic biology provided some inherent advantages. Discussions with individuals in academia and clinical labs gave us confidence that our signal amplification strategies, including an exonuclease-driven strategy, would also work ; it also allowed us to design several optimized circuits in addition to our baseline MBD-GFP. Foundational literature suggested that multimerization of the protein would enhance binding ability for our MBD protein which would be crucial in detection at the attomolar levels. Discussions with material scientists also pointed us in the direction of implementation for a graphene oxide platform that could help boost our signal-to-noise ratio in the assay. A follow-up conversation with several doctors associated with the TIGS initiative also allowed us to implement a microfluidic system to enable high-throughput analysis and reduce the overall complexity of our workflow. </p>
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<h3>References</h3>
        <h3>Expanding our workflow and developing novel use cases</h3>
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<ol>  
        <p>However, our initial product consisted of a single diagnostic assay; although several technological innovations strengthened the predictive power, our team was dedicated to addressing some of the other key lags in the development of clinically available liquid biopsy tests. Our interactions with the following individuals really brought some fresh perspective on broadening our impact.</p>
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<li> Hikoya Hayatsu, Kazuo Negishi, Masahiko Shiraishi, Katsumi Tsuji, Kei Moriyama; Chemistry of Bisulfite Genomic Sequencing; Advances and Issues, Nucleic Acids Symposium Series, Volume 51, Issue 1, 1 November 2007, Pages 47–48 </li>
        <h4>Dr. Jian Dai, senior data scientist at Genentech</h4>
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<li> Genereux, Diane P. et al. “Errors in the Bisulfite Conversion of DNA: Modulating Inappropriate- and Failed-Conversion Frequencies.” Nucleic Acids Research 36.22 (2008): e150. PMC. Web. 18 Oct. 2018. </li>
        <p>Part of our needs-finding had uncovered a <b>critical lag in the development of liquid biopsy tests</b>. Researchers and clinicians were unsure of which biomarkers to analyze for different diseases. Our team wanted to take the step of addressing this crucial gap in existing liquid biopsy workflows. Talking to Dr. Dai helped give us additional perspective in the drylab, and gave us <b>awareness of the tools</b> that are needed for data analysis. After talking to Dr. Dai, our team was able to come up with a methodology for an unsupervised machine learning framework that would aid in biomarker discovery. By using techniques such as <b>Random Forest and Lasso-Cox</b>, we would be able to discover the optimal gene panel combinations to detect promoter methylation across a subset of patients based on the disease of interest. This aspect of our workflow can be integrate any existing methylome dataset and will provide disease-specific markers. </p>
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<li>Brandon W. Heimer, Brooke E. Tam, Hadley D. Sikes; Characterization and directed evolution of a methyl-binding domain protein for high-sensitivity DNA methylation analysis, Protein Engineering, Design and Selection, Volume 28, Issue 12, 1 December 2015, Pages 543–551 </li>
        <h4>Dr. Mikael Eliasson, head of Global Product Development & Strategic Innovation at Genentech</h4>
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<li> B.W. Heimer, T.A. Shatova, J.K. Lee, K. Kaastrup and H.D. Sikes. “Evaluating the Sensitivity of Hybridization-Based Epigenotyping using a Methyl Binding Domain Protein,” Analyst, 2014, 139 (15): 3695-3701. </li>
        <p>Our conversation with Dr. Eliasson also identified another critical lag in the traditional diagnostics journey. In post-treatment therapy, there is a significant decrease in communications between doctors and patients, and this can hinder one’s ability to determine if the initial treatment was successful. In talking with Dr. Eliasson, we realized we could capitalize on the trends of digital health and implement a digital health platform. In addition, we developed a completely novel use case of using hypermethylation as a continuous variable that could be correlated to tumor burden and assess the effectiveness of a patient’s treatment.</p>
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</ol>
        <h4>Dr. Matthias Essenpreis, CTO at Roche Diagnostics</h4>
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        <p>We also had an opportunity to meet with the CTO of Roche Diagnostics, Dr. Matthias Essenpreis. By talking to him, we started to understand the value of a platform-based business that could facilitate exchange of information and services between two different stakeholder groups. The ability to analyze patient data (both at an early clinical-screening and post-therapy stage) could be given to healthcare professionals to guide their decision-making, and it could also plug into pharmaceuticals’ companies in order to develop more effective treatments going forward. Dr. Essenpreis explained that a multi-sided platform business model would help address stakeholder needs more effectively, and emphasized that the value is in the insight created by the analysis of the data.</p>
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        <h4>From this, we were able to construct two thorough lifecycle cases and the full clinical protocol.</h4>
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        <p>These interactions helped expand our workflow from a technologically innovative diagnostic assay into a full-scale diagnostics platform that is able to be implemented at multiple points in the patient care journey, and (1) aids in biomarker discovery and addresses an industry-wide lag, and (2) uses digital health data collection and analysis to generate clinical utility that goes beyond the initial interaction. </p>
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        <h4>Feasibility Analysis and Broader Considerations</h4>
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        <P>In addition to having our clinical framework validated by academia and industry professionals, we also were fortunate enough to meet with Dr. Mike Pellini, a venture capitalist who also has a clinical practice. He was able to provide further validation of our entire workflow and found it interesting in its work with post-therapy response via promoter methylation monitoring. The implementation of the cell-free system was an interesting but acceptable way of addressing biosecurity concerns, and he thought that using synthetic biology to advance liquid biopsy and shift cancer diagnosis paradigms was an elegant solution. </P>
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        <p>Although our project has the potential for broad, positive social impact because of its applications to other researchers and ability for relatively easy implementation in low-resource communities, it was almost important to realize some of the negative consequences of wrongful use of our product, especially in the last component of our workflow.</p>
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        <p>Medical data and privacy are growing concerns, and discussions with industry professionals have consistently reiterated that point. Our team’s consultations with Harry Gandhi, founder of Medella Health, and his domain knowledge about medical data privacy helped us choose specific security protocols to ensure that data would be properly handled. However, a large concern still remains as there need to be proper monitoring and handling of patient data when being used for drug and treatment development in the future. </p>
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Latest revision as of 01:27, 18 October 2018

Project Design

Overview and Introduction

Liquid biopsy refers to sampling and analysis of non-solid biological tissue, and is used for disease diagnostics and prognosis. In cancer diagnostics and prognostics, liquid biopsy is mainly used to quantify and characterize circulating tumor DNA (ctDNA) in blood. ctDNA is short fragment (~180bp) of double stranded DNA derived from tumor cells. When patients develop, ctDNA enriches in blood, containing variable information about cancers such as genetic mutation and methylation.

Liquid biopsy has become an exciting field of biomedical research since it has several advantages compared to common tissue biopsy. As liquid biopsy analyzes blood rather than cancer tissue, it is much less invasive and causes less pain during sample collections. As a result, multiple liquid tests could be carried out in different times to monitor the progress of disease and treatment. Moreover, many cancers have no detectable solid tissues in the early stage, rendering tissue biopsy impossible. By contrast, liquid biopsy could detect a trace of ctDNA released from small amounts of cancer cells even tumor is not present, gaining it huge power for diagnostics.

Inspired by this emerging field, our original goal was to develop a general platform that could be used to (1) identify potential diagnostic biomarkers and (2) rapidly detect cancer from a given sample. While doing our literature research and talking to clinical researchers, we came across the idea of using promoter methylation as a quantifiable diagnostic metric. Most analyses of the methylation signal rely on a procedure known as bisulfite sequencing. It begins with converting unmethylated cytosine to uracil with sodium bisulfite, leaving methylated cytosine alone. Converted ctDNA undergoes multiple rounds of polymerase chain reaction (PCR) to selectively amplify methylation regions. In the end, amplified product is analyzed, typically by next-generation sequencing (NGS). Although bisulfite sequencing remains the gold standard for ctDNA methylation analysis, we quickly identified several limitations of this method:

  1. Bisulfite treatment causes degradation of most ctDNA and induce incomplete cytosine conversion. Bisulfite treatment typically requires overnight treatment of ctDNA samples.

  2. Multiple rounds of PCR are time-consuming and suffers from amplification bias

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  3. NGS is expensive and not always possible for clinical settings

bisulfateSeq
Figure 1. Overview of bisulfite treatment and sequencing flow
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In addition, the lack of commercially available diagnostic tests is due to the fact that it is quite difficult to determine potential biomarkers; outside of existing literature, there are few resources out there for clinicians and oncologists to rely on when performing a liquid biopsy and choosing a particular gene of interest. Here is just a simple flowchart of all the possible methods that researchers can use in order to gain information about methylation patterns.

Designing our Protein-Based System

To address the first problem that we had identified, our team decided to use synthetic biology coupled with nanomaterials to determine methylation levels of specific sites. A clinically useful assay to profile methylation sites ultimately requires output of a quantitative value. Through literature research, we decided to use beta methylation values which are commonly used In other detection methods such as bisulfite treatment coupled with NGS.

An ideal design should be able to quantitatively measure two values: frequency of total target ctDNA and frequency of methylated target ctDNA. Our design could quantify these two parameters on a single platform by two separate fluorescent readouts.


The central biological part in our design is methyl-CpG-binding domain (MBD domain). MBD domain comes from a family of proteins called MBD proteins and retains its functionality when single domain is present. It specifically recognizes symmetrically methylated CpG dinucleotides on double-stranded DNA (dsDNA). On the other hand, MBD domain has much lower affinity toward non-methylated CpG and hemi-methylated CpG. Coupled with signal generation biological parts such as eGFP and Horseradish peroxidase (HRP), MBD domain has the potential to detect and quantify methylation levels of ctDNA.


Current ctDNA methylation methods, such as bisulfite treatment coupled with NGS, are able to quantify methylation levels of specific methylation site. Using MBD domain seems to pose a problem as it is reported to be sequence-independent: MBD domain could not differentiate methylations from different methylation locus. Ambitious to build a simpler, cheaper, and faster device while maintaining its ability to be site specific, we use direct hybridization method based on Yu’s idea.

As Yu et al. reported, target-complementary DNA probes are designed to have single methylation on target locus. When target DNA hybridizes to DNA probe, MBD could differentiate site-specific methylation status by binding to symmetrically methylated duplex but not the hemi-symmetrical ones. However, this MBD method requires modification of target DNA with fluorescent dye, which is difficult when other nonspecific DNA is present in the sample. Moreover, the method failed to quantify the frequency of total target DNA, a crucial parameter for beta value calculation mentioned above. This year, UCSD iGEM team has successfully built upon the MBD method to build a clinically relevant platform for DNA methylation testing.

original
Figure 2. Preliminary visualization for providing specificity to the MBD protein

Part I. GO Immobilization and Fluorescence Quenching

Cheap and accessible, graphene oxide (GO) has shown many Interesting properties under nanoscale. Previous research has demonstrated that graphene oxide has a strong affinity toward single-stranded DNA (ssDNA) and it quenches fluorescent dyes (e.g. FAM) whenever dyes get close to surface.

Inspired by this previous research, we designed multiple DNA probes with a structure shown below. Each DNA probe is attached with a fluorescent dye at 3' end and contains two distinct regions: GO Interacting region and target recognition region. GO Interacting region is designed to contain high GC content which strengthens probe-GO Interaction based on literature. Target recognition region Is complementary to part of target ctDNA; It also contains a 5' methylated cytosine for later detection purpose (see part IV). Before applying ctDNA samples, DNA probes are pre-incubated with GO In solution. Noncovalent interaction between ssDNA and GO bring fluorescent dyes close to GO surface, and therefore effectively quench fluorescent signal.

probe
Figure 3. Sample target probe design

Part II. Hybridization of target ctDNA with probes recovers fluorescence

exoiii
Figure 4. Using exonuclease to digest DNA probe and amplify signal

A typical silicon gel-purified ctDNA sample contains at least three DNA entities: target DNA with methylation, target DNA without methylation, and nonspecific DNA. Target ctDNA, no matter methylated or not, will specifically hybridize with recognition region of probe DNA to form partial dsDNA duplex. Such duplex significantly weakens DNA-GO interaction and therefore pushes fluorescent dye away from GO surface. This process recovers fluorescent signal, which could be used to quantify target ctDNA concentration. On the other hand, non-specific DNA could not form duplex with DNA probes. GO could absorb this non specific ssDNA to minimize false positive signal.


Part III. Signal amplification via ExoIII digestion of excess DNA probes

Fluorescent dye is sensitive toward DNA detection in general. However, ctDNA exists in a much lower concentration than most biosensor targets. As a result, we employ two enzymatic amplification strategies in our design to improve device sensitivity. The first strategy is exonuclease III (Exo III) digestion. (See Part V for second strategy). E.coli Exo III specifically digest dsDNA duplex from a blunt or recessive 3’ end. ctDNA typically has a length of 180 bp, while DNA probe in our design has a length of ~25bp. As a result, DNA probe will mostly form recessive 3’ end, which is the substrate of exonuclease III. Exo III digests DNA probe and releases single-stranded target DNA and fluorescent dye. Free target DNA could form a duplex with more DNA probes and generate more fluorescent dyes by Exo III. The maximum fluorescent signal could be achieved in 90 min and Exo III could be quenched by 70-degree heating. Impurities such as Exo III can then be washed away prior to performing subsequent steps.

Part IV. MBD fusion proteins recognize symmetrically methylated sites

A. Designing MBD fusion proteins

This summer, we were able to express and purify three different MBD fusion proteins to recognize DNA methylation. The first construct we came out with is mMBD-eGFP, which has been expressed and applied in previous research. In this construct, attached eGFP could give a direct fluorescent readout once MBD bind to methylated CpG and the excess is washed away. Avi tag is a 15 amino acid region specifically recognized by biotin ligase BirA. Once biotin is attached, multiple signal amplification strategies could be used to increase device sensitivity (references, see section B).

Based on this characterized BioBrick, which served as our baseline genetic circuit, we started brainstorming more optimal genetic circuits. Much of our attention has been focused on sensitivity, as the original MBD method was not sensitive enough for ctDNA quantitation. We drew out lessons from a concept called “avidity”. When multiple MBD domains are fused to a single sequence, each domain could interact with methylated CpG. Avidity describes the apparent affinity of such multivalent interaction and it is significantly higher than affinity of single MBD-methylated CpG island. However, increasing numbers of MBD on a single sequence also increases protein size and the possibility of interference between domains. As a result, we chose to fuse two MBD domains with a flexible Gly4Ser2 linker in our improved BioBricks. Combining previous literature and our experimental attempts, we found out although human MBD has higher affinity, mouse MBD is much easier to purified and gives higher yield. As a consequence, we chose to use hybrid human MBD and mouse MBD fusion protein, hoping to maximize protein avidity but in the meantime simplify cloning and protein expression difficulties.

Two improved constructs shown below represent two ways to amplify signal, each with its own merit. Hybrid MBD concatenated with HRP could detect methylated CpG in a single step; it also avoids the need to biotinylate the expressed protein, which is necessary for Hybrid MBD with avi-tag. However, Hybrid MBD with avi-tag is a much smaller protein and therefore less likely to exhibit steric hindrance between domains, which could compromise MBD affinity. As we anticipate MBD-methylated CpG recognition could be applied to scenarios other than ctDNA, we design and validate these different constructs to fulfill different applications.

constructs
Figure 5. Overview of MBD-based genetic circuit for promoter methylation detection

B. Discrimination between methylated target and unmethylated sequences

Once the impurities are washed away (see Part III), MBD fusion proteins are applied and incubated. If target CpG is methylated, resulting DNA duplex will form symmetrically methylated CpG as the substrate for MBD domain binding; otherwise resulting DNA duplex is semi-methylated and MBD proteins won’t bind. Excess MBD proteins could be washed away by buffers.

For MBD-eGFP protein, methylated CpG signal could be directly quantified with eGFP fluorescence. Hybrid MBD fused with HRP or avi-tag (with biotin attached) proteins requires the application of substrate and hydrogen peroxide (see part V). Hybrid MBD-avi tag protein needs two extra steps: incubation of commercially available streptavidin-HRP and washing excess streptavidin-HRP.

Part V. HRP amplifies fluorescent signal by enzymatic oxidation of substrate

Our second signal amplification idea comes from the ELISA-type assays. HRP is commonly used to oxidize substrates in the presence of hydrogen peroxide. An oxidized substrate, in this case bi-p,p’-4-hydroxyphenylacetic acid, gives intense fluorescent signal with excitation at 320nm and emission at 406nm.

hrp
Figure 6. Visualization of the HRP mechanism

References

  1. Hikoya Hayatsu, Kazuo Negishi, Masahiko Shiraishi, Katsumi Tsuji, Kei Moriyama; Chemistry of Bisulfite Genomic Sequencing; Advances and Issues, Nucleic Acids Symposium Series, Volume 51, Issue 1, 1 November 2007, Pages 47–48
  2. Genereux, Diane P. et al. “Errors in the Bisulfite Conversion of DNA: Modulating Inappropriate- and Failed-Conversion Frequencies.” Nucleic Acids Research 36.22 (2008): e150. PMC. Web. 18 Oct. 2018.
  3. Brandon W. Heimer, Brooke E. Tam, Hadley D. Sikes; Characterization and directed evolution of a methyl-binding domain protein for high-sensitivity DNA methylation analysis, Protein Engineering, Design and Selection, Volume 28, Issue 12, 1 December 2015, Pages 543–551
  4. B.W. Heimer, T.A. Shatova, J.K. Lee, K. Kaastrup and H.D. Sikes. “Evaluating the Sensitivity of Hybridization-Based Epigenotyping using a Methyl Binding Domain Protein,” Analyst, 2014, 139 (15): 3695-3701.