<template>
<div class="iterative-cycle">
<div class="module-tabs">
<div
v-for="(module, index) in modules"
:key="index"
:class="['module-tab', { active: selectedModule === index }]"
@click="selectModule(index)"
>
{{ module.title }}
</div>
</div>
<div v-if="selectedModule !== null" class="collapsible-cards">
<h2>{{ modules[selectedModule].title }}</h2>
<div v-html="modules[selectedModule].description" class="module-description"></div>
<div
v-for="(cycle, index) in modules[selectedModule].cycles"
:key="index"
class="card"
>
<h4 @click="toggleCycle(index)">
{{ cycle.title }}
<span v-if="isCycleOpen(index)">▲</span>
<span v-else>▼</span>
</h4>
<div v-if="isCycleOpen(index)" class="content">
<h3>Phases:</h3>
<ul>
<li v-for="(phase, phaseIndex) in cycle.phases" :key="phaseIndex">
<strong>{{ phase.title }}:</strong>
<div v-html="phase.description"></div>
</li>
</ul>
</div>
</div>
</div>
</div>
</template>
<script>
export default {
data() {
return {
selectedModule: 0,
openCycles: [],
modules: [
{
title: 'Module 1: Assembly and Transformation of Contruct into V.Natriegens',
description: `
<p>This module consisted of assembling the construct, and took up most of the lab work, as the team had to overcome multiple obstacles. Numerous changes were made, with the methods, the construct and pathway, throughout the 15 Gibson Assembly attempts.</p>
<p>Transformation of the final construct into Vibrio natriegens was attempted, however it has not yielded conclusive results as of this time. Plans have been made to continue research in hopes of completing successful transformation.</p>
`,
cycles: [
{
title: 'Cycle 1: Assembly Process',
phases: [
{
title: 'Design',
description: `
<p><b>Nitrate Reduction Pathways</b></p>
<p>Nitrates can be removed from water through several bacterial metabolic processes. The most prevalent pathway is denitrification, in which NO₃⁻ is sequentially reduced to NO₂⁻ and then to N₂, which is released into the atmosphere (Zhao et al., 2018). Other important nitrate reduction pathways include dissimilatory NO₃⁻ reduction to NH₄⁺ (DNRA) and NO₃⁻ assimilation (Moreno-Vivián et al., 1999).</p>
<p>DNRA, typically utilised by bacteria in anaerobic conditions for the purposes of energy conservation, involves converting NO₃⁻ into NH₄⁺ in a two-step reaction via the NO₂⁻ intermediate (Herrmann & Taubert, 2022). While DNRA retains nitrogen in its bioavailable form (NH₄⁺), it does not directly incorporate it into organic compounds. Thus, both denitrification and DNRA result in the loss of available nitrogen—either as atmospheric nitrogen in the case of denitrification or as NH₄⁺ that is not assimilated into biomass in the case of DNRA.</p>
<p>In contrast, the assimilatory NO₃⁻ pathway leads to the incorporation of nitrogen into organic compounds, such as amino acids, conserving it within the organism (Moreno-Vivián & Flores, 2007; Jiang & Jiao, 2015). These amino acids can then be used to produce single-cell proteins (SCPs). The assimilatory pathway not only retains nitrogen but also contributes to the production of microbial biomass, thus providing a more efficient means of nitrogen utilisation.</p>
<p>The assimilatory pathway in bacteria comprises several steps. First, NO₃⁻ is captured and internalised from the extracellular environment to the intracellular space. This step is mediated by a NO₃⁻-transporter, which is most commonly an ATP-binding cassette (ABC)-type transporter located in the cytoplasmic membrane (Moreno-Vivián & Flores, 2007). The transporter consists of three subunits: a periplasmic protein that binds NO₃⁻ with high affinity (even at low extracellular concentrations of NO₃⁻), a transmembrane protein that facilitates the transport of NO₃⁻ across the membrane, and a cytoplasmic ATPase anchored to the membrane, which hydrolyses ATP to provide energy for the process (Lin & Stewart, 1997; Moreno-Vivián & Flores, 2007).</p>
<p>Once NO₃⁻ is internalised, it is then reduced to NO₂⁻ by assimilatory nitrate reductase (Nas). This enzyme is NADH-dependent and has two subunits: a large catalytic subunit, which contains the essential active site for the reduction of NO₃⁻, and a small NADH oxidoreductase subunit, which facilitates the transfer of electrons to the active site (Lin & Stewart, 1997; Moreno-Vivián & Flores, 2007). In the following step, NO₂⁻ is further reduced to NH₄⁺ by the monomeric nitrite reductase (Nir). Afterwards, the produced NH₄⁺ is incorporated into amino acids, specifically glutamine and glutamate, through the GS-GOGAT and GDH pathways (Moreno-Vivián & Flores, 2007; van Heeswijk et al., 2013).</p>
<p>The GS-GOGAT pathway consists of two key steps. First, the enzyme glutamine synthetase (GS) catalyses an ATP-dependent reaction that converts glutamate to glutamine by incorporating an ammonium ion. Following this, glutamate synthase (GOGAT) transfers the amide group from glutamine to 2-oxoglutarate, resulting in the production of two glutamate molecules. In contrast, the GDH pathway employs a more direct approach. The enzyme glutamate dehydrogenase (GDH) catalyses the incorporation of an ammonium ion (NH₄⁺) directly into 2-oxoglutarate, forming glutamate in a single step. The resulting amino acids, glutamate and glutamine, undergo further transamidation and transamination, yielding various amino acids, which then serve as building blocks for the biosynthesis of proteins during translation (van Heeswijk et al., 2013).</p>
<img src="https://static.igem.wiki/teams/5306/aa-pathway.png" alt="Amino Acid Pathway" style="width:100%; height:auto;" />
<figcaption style="font-style: italic;">
Figure X. The pathways that result in the biosynthesis of glutamine and glutamate. The GDH pathway is shown in the left panel. The GS-GOGAT pathway is shown in the right panel.
Created with <a href="https://www.biorender.com/" target="_blank">BioRender.com</a>.
</figcaption>
`
},
{
title: 'Build',
description: `
<p>Performing the Gibson assembly procedure.</p>
<p>This step combines the designed DNA fragments into a plasmid construct.</p>
`
},
{
title: 'Test',
description: `
<p>Validating the assembly results through sequencing.</p>
<p>Analyzing whether the intended constructs have been successfully created.</p>
`
},
{
title: 'Learn',
description: `
<p>Refining assembly methods based on test results.</p>
<p>This feedback loop is essential for improving future designs and assemblies.</p>
`
},
]
},
{
title: 'Cycle 2: Optimization',
phases: [
{
title: 'Design',
description: `
<p>Reassessing and improving the design of DNA fragments.</p>
<p>Ensuring sequences are optimized for assembly efficiency.</p>
`
},
{
title: 'Build',
description: `
<p>Executing the optimized assembly protocols.</p>
<p>Combining DNA fragments with refined conditions to enhance yield.</p>
`
},
{
title: 'Test',
description: `
<p>Testing the results of the optimized assembly.</p>
<p>Performing sequencing and functional assays to ensure success.</p>
`
},
{
title: 'Learn',
description: `
<p>Documenting lessons learned from optimization tests.</p>
<p>Using this information to inform future cycles of assembly.</p>
`
},
]
},
{
title: 'Cycle 3: Finalization',
phases: [
{
title: 'Design',
description: `
<p>Finalizing designs for the constructs to be used in experiments.</p>
<p>This step includes ensuring all parts are ready for testing.</p>
`
},
{
title: 'Build',
description: `
<p>Final assembly of the constructs.</p>
<p>Using all accumulated knowledge to achieve the best results.</p>
`
},
{
title: 'Test',
description: `
<p>Conducting final tests on the assembled constructs.</p>
<p>Verifying that the constructs perform as intended in biological systems.</p>
`
},
{
title: 'Learn',
description: `
<p>Gathering all data and insights from the final testing phase.</p>
<p>Planning next steps based on the outcomes of the assembly process.</p>
`
},
]
},
]
},
{
title: 'Module 2: Characterization',
description: `
<p>While developing the chemical pathway and genetic design of engineered organism, a method was considered in order to verify the working of the GMO. </p>
<p>The functionality and efficiency of the engineered V. natriegens can be tested by probing the efficacy of the implanted enzymes. Colour reactions coupled with spectrophotometry is an effective way to establish the concentration of the various analytes. </p>
<p>However, due to the time constraints and difficulties with the assembly of our construct (See above), the verification and characterisation of the functioning of the pathway inserted into V. natriegens was not able to be carried out. </p>
`,
cycles: [
{
title: 'Cycle 1: Characterization Process',
phases: [
{
title: 'Design',
description: `
<p>Chemical compounds are able to absorb and refract light at different wavelengths depending on their structures, more precisely because of their ability to delocalise their π-electrons. The more freedom a π-electron has to move around the structure, the shorter the wavelength it can absorb, and the longer the wavelength which can be refracted. The ability of a compound to delocalize its π-electrons is determined by the consecutive overlap of multiple p-type orbitals generated by the presence of consecutive double (or triple) bonds. Compounds with an high degree of conjugation, like Beta-carotene (Fig. 1 Beta-carotene) absorb light in the blue region of the visible light spectrum (~450 nm) and refract on the opposite side of the spectrum, the red region (~750 nm), giving molecules their characteristic colours. </p>
<!-- Image with figure caption -->
<figure style="text-align: center; margin: 20px 0;">
<img src="https://static.igem.wiki/teams/5306/engineering/beta-carotene-2d-skeletal-svg.webp" alt="Beta Carotene" style="width:100%; height:auto;" />
<figcaption style="font-style: italic;">Figure 17. Beta-carotene. Image sourced from Wikipedia</figcaption>
</figure>
<p>This concept can be used to generate highly conjugated molecules using the target compound that has to be quantified as the starting material using a spectrophotometer and the Beer-Lambert law. This mathematical model allows the calculation of the concentration of a compound depending on its light absorbance.</p>
<p>Two tests have been selected for our quantification protocols, the Griess test and the indophenol blue test. </p>
<p>The Griess test is used for the detection and quantification of nitrates, and will be able to tell us if the nitrate reductase enzyme works. The Griess test is based on the diazotization reaction of sulfanilinic acid, where the nitrite will react with the amino moiety on the phenyl ring to form a diazonium salt intermediate. The diazonium salt is extremely electrophilic and will readily react with the ring of an aromatic compound. Depending from the aromatic compound chosen, different colours can be obtained, the most common are pink (given by 1-naphtylamine), orange (given by 1-naphtol) or yellow (given by phenol). This azo coupling reaction makes use of the nitrite substrate as a nucleophilic reagent to yield a highly conjugated system that extends from the sulfanilinic end to the napthaline moiety, able to absorb very short wavelengths and resulting in bright and warm colours (Griess reaction).</p>
<!-- Image with figure caption -->
<figure style="text-align: center; margin: 20px 0;">
<img src="https://static.igem.wiki/teams/5306/engineering/griess-test-reaction-svg.webp" alt="Griess Test" style="width:100%; height:auto;" />
<figcaption style="font-style: italic;">Figure 18. Griess test reaction scheme. Image sourced from Wikipedia</figcaption>
</figure>
<p>The indophenol blue test is used to detect the presence of ammonia to check the functionality of the nitrite reductase. It exploits the electrophilicity of the ammonium ion to link to phenol molecules, thus creating a conjugated system that is able to absorb in the red region of the visible light, yielding a deep-blue dye. The reaction starts by treating a sample suspected to contain ammonium with methanol, keeping the ammonia in its cationic form, and sodium hypochlorite to convert the ammonium in chloramine gas, which will stay dissolved in the methanol solution. The chloramine in methanol solution is then added to a mixture containing phenol to yield a chloramine to phenol molar ratio of 1:2 and sodium nitroprusside, which will serve as a catalyst. The reaction mixture then turns blue to signal that it has come to an end (Indophenol reaction).</p>
<!-- Image with figure caption -->
<figure style="text-align: center; margin: 20px 0;">
<img src="https://static.igem.wiki/teams/5306/engineering/indophenol-formation-park-et-al-2009.png" alt="Indophenol Test" style="width:100%; height:auto;" />
<figcaption style="font-style: italic;">Figure 19. Indophenol blue reaction scheme. Image sourced from Sasongko, A. (2018).</figcaption>
</figure>
<p>The concentration of both nitrites or ammonia can then be measured by measuring the absorbance of the solution and comparing to a previously made calibration curve to know exactly the amount of substrate measured.</p>
<!-- Image with figure caption -->
<figure style="text-align: center; margin: 20px 0;">
<img src="https://static.igem.wiki/teams/5306/engineering/beer-lambert-law.jpg" alt="Beer Lambert Law" style="width:100%; height:auto;" />
<figcaption style="font-style: italic;">Figure 20. Beer-Lambert law. Image sourced from Bhuyan, S., (2024)..</figcaption>
</figure>
`
},
{
title: 'Build',
description: `
<p>With an outline of the theory behind the mechanism of the tests, the following plan was constructed.</p>
<p>A known concentration of engineered V. natriegens is to be placed in a solution that mimics the environmental conditions of the places where our GMO is meant to be implemented. This solution will contain a nitrate salt, to simulate the nitrate compounds from fertilisers leeched into the water. Nitrate sulfate would be optimal, due to the low interference of the sulfate ion on both the Griess test and the indophenol test. Additionally, it will contain some nourishment to allow the bacteria to survive for the time of the test.</p>
<p>Samples of the bacteria growing in the above described solution would then be collected at spaced increments of time, filtered from the bacteria via centrifugation and filtration, yielding a solution containing the compounds of interest (nitrites and ammonia).</p>
<p>The results of the test can be quantified by converting the absorbance of the sample, measured in a spectrophotometer against a blank, into concentration and compared to a calibration curve. The concentration the will be plotted over the time they were collected to establish the rate of conversion from nitrate to ammonia to finally determine the efficiency of the ANRA pathway of the newly engineered Vibrio natriegens.</p>
`
},
]
},
]
},
{
title: 'Module 3: Genomic Integration',
description: `
<p>Chemical compounds are able to absorb and refract light at different wavelengths depending on their structures, more precisely because of their ability to delocalise their π-electrons. The more freedom a π-electron has to move around the structure, the shorter the wavelength it can absorb, and the longer the wavelength which can be refracted. The ability of a compound to delocalize its π-electrons is determined by the consecutive overlap of multiple p-type orbitals generated by the presence of consecutive double (or triple) bonds. Compounds with an high degree of conjugation, like Beta-carotene (Fig. 1 Beta-carotene) absorb light in the blue region of the visible light spectrum (~450 nm) and refract on the opposite side of the spectrum, the red region (~750 nm), giving molecules their characteristic colours. </p>
<p>Integrating the construct into the genome would eliminate the risk of the organism dropping maintenance of the plasmid, as well as remove the need of growing it in antibiotic-supplemented media.</p>
<p>While the team was unable to reach this phase of our lab work, extensive research was carried out in order to plan the ideal method, the sum of which is listed below.</p>
`,
cycles: [
{
title: 'Cycle 1: Vector Development',
phases: [
{
title: 'Design',
description: `
<p>Firstly we considered a plasmid with the R6K ORI, pJUMP21-1A (Valenzuela-Ortega and French, 2021). Rakowski & Filutowicz (2013) suggest that in the absence of a π protein, replication of the plasmid would not be possible in organisms. This forces them to integrate the plasmid (containing genes coding for antibiotic resistance as well as selected genes) into its genome when grown on antibiotic-supplemented media (Martin-Pascual et al., 2021). Following further research however, it found that research carried out by Tschirhart et al. (2019) suggested this ORI was not transformable into V. natriegens.</p>
<!-- Image with figure caption -->
<figure style="text-align: center; margin: 20px 0;">
<img src="https://static.igem.wiki/teams/5306/engineering/pseva261-map.webp" alt="pSEVA261 Map" style="width:100%; height:auto;" />
<figcaption style="font-style: italic;">Figure 21: pJUMP21-1A plasmid. Sourced from SnapGene.</figcaption>
</figure>
<p>The overexpression of tFoX gene and induction of natural competence for genome integration was also considered, with Dalia et al. (2017) and Specht et al. (2024) both outlining the possibilities this route opens up. However, this option was discarded as it was considered beyond our reach to carry out engineering on V. natriegens to induce natural competence prior to having a completed assembly without integration in V. natriegens, considering the factor of the time available to us.</p>
<!-- Image with figure caption -->
<figure style="text-align: center; margin: 20px 0;">
<img src="https://static.igem.wiki/teams/5306/engineering/natural-competence-image.webp" alt="pSEVA261 Map" style="width:100%; height:auto;" />
<figcaption style="font-style: italic;">Figure 22. A comparison of the Natural Competence Transformation workflow (A) made possible via tFoX gene overexpression, and the standard Heat Shock and Electroporation workflows (B). Sourced from Specht et al. (2024)</figcaption>
</figure>
<p>The large size of the insert shifted the focus to serine integrases since it was shown that these enzymes can successfully insert up to 10k bases of inserts during a reaction, with high efficiency, unidirectionally and no need for additional proteins (Snoeck et al., 2019). Serine integrases work by recognising attachment sites on the plasmid and the genome of the host, attB and attP, which tend to be around 50bp of nucleotides flanking the area of interest (Merrick et al., 2018; Muroi et al., 2012). The small size of the att sites make them easier to insert via homologous recombination, making it possible to use integrases that are not native to the chosen chassis.</p>
<!-- Image with figure caption -->
<figure style="text-align: center; margin: 20px 0;">
<img src="https://static.igem.wiki/teams/5306/engineering/intergation-flowchart.webp" alt="pSEVA261 Map" style="width:100%; height:auto;" />
<figcaption style="font-style: italic;">Figure 23. Image illustrating the working mechanism of integrases, taken from Merrick et al., 2018. </figcaption>
</figure>
<p>With this background research done the team planned to use homologous recombination to insert the phiC31 att sites into the genome of V. natriegens making them compatible for the use of phiC31 integrase (Merrick et al., 2018). The location of the insertion sites were chosen based on the research done by a previous iGEM team VibriGens since they have already identified suitable regions with low transcription numbers, non-coding areas and 500bp of space between coding sequences (Marburg, 2018). Followed by the integration of a GFP gene to be able to easily track and test the success of the integration. The desired operon would have been inserted only after the location and presence of the att sites were confirmed. However, due to time and financial constraints the team was not able to carry out this phase of the planned research, and instead focused on successfully assembling the traditional plasmid and transforming the V. natriegens.</p>
<p>Even if we were not able to carry out this research ourselves, we are hoping that this information will prove to be useful for future iGEM or research teams. </p>
`
},
]
},
]
},
{
title: 'Module 4: Bioinformatics',
description: `
<p>As part of our future engineering efforts, we intend to optimise the growth of the engineered Vibrio natriegens by utilising the GenomeSPOT (Genome-based Salinity, pH, Oxygen Tolerance, and Temperature for Bacteria and Archaea) Python package (Barnum et al., 2024). This powerful computational tool, introduced to us through our collaboration with Cultivarium, predicts optimal growth conditions based on bacterial and archeal genome sequences with no need for functional annotations (Barnum et al., 2024). </p>
`,
cycles: [
{
title: 'Cycle 1: Bioinformatics Analysis',
phases: [
{
title: 'Design',
description: `
<p>Our objective is to utilise GenomeSPOT to compare the computationally predicted optimal growth conditions with experimentally determined conditions for both the native and genetically modified strain of Vibrio natriegens after integration of the construct into the genome. First, it is necessary to obtain complete genome sequences for both of the strains. These will serve as input data for the GenomeSPOT package and will be processed to generate predictions for key factors affecting bacterial growth, such as temperature, salinity, pH, and oxygen levels. This computational model is based on well-studied correlations between amino acid frequencies and growth factors (e.g., tryptophan frequency and positive correlation with oxygen tolerance associated with it) (Barnum et al., 2024). </p>
<p>After simulating the optimal conditions, we will conduct laboratory experiments to test both strains under various conditions and determine if the experimental results match the predictions. The growth of both bacterial strains will be monitored under conditions provided by GenomeSPOT by measuring optical density (OD600) at regular intervals. In addition, the strains will be subjected to various alternative conditions to determine those that result in the most optimal growth rates. Any discrepancies between the experimental and computationally predicted results will be carefully examined. This analysis will allow us to understand if the modified V. natriegens has distinct requirements for optimal growth compared to the native strain. Furthermore, it will allow for an assessment of the accuracy of GenomeSPOT in predicting optimal growth conditions for engineered microbial strains. </p>
`
},
]
},
]
},
]
};
},
methods: {
toggleCycle(index) {
if (this.openCycles.includes(index)) {
this.openCycles = this.openCycles.filter(i => i !== index);
} else {
this.openCycles.push(index);
}
},
isCycleOpen(index) {
return this.openCycles.includes(index);
},
selectModule(index) {
this.selectedModule = index;
this.openCycles = [];
}
}
};
</script>
<style scoped>
.iterative-cycle {
max-width: 800px;
margin: 20px auto;
padding: 20px;
border: 1px solid #ddd;
border-radius: 8px;
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
}
/* Module Selector Tabs */
.module-tabs {
display: flex;
justify-content: center;
margin-bottom: 20px;
}
.module-tab {
padding: 10px 20px;
margin: 0 10px;
font-size: 16px;
background-color:#73aba2;
border-radius: 5px;
cursor: pointer;
transition: background-color 0.3s;
}
.module-tab.active {
background-color: #396d7e;
color: white;
}
/* Collapsible Cards */
.card {
background-color: #e3f2fd;
border: 1px solid #ccc;
border-radius: 10px;
padding: 20px;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
}
.card h4 {
cursor: pointer;
margin: 0;
display: flex;
justify-content: space-between;
}
.card .content {
padding-top: 10px;
}
/* Module Description */
.module-description {
margin-bottom: 20px;
}
/* Styling for paragraphs */
.card p {
margin: 10px 0; /* Adds space between paragraphs */
}
</style>