iGEM 2023 — Team iiser-tirupati-india

{% extends "layout.html" %}
{% block title %}Notebook{% endblock %}
{% block lead %}Document the dates you worked on your project. This should be a detailed account of the work done each
day for your project.{% endblock %}
{% block page\_content %}

![Header Image](https://static.igem.wiki/teams/4836/wiki/lab-notebook/lab-notebook.png)

![Header Image](https://static.igem.wiki/teams/4836/wiki/lab-notebook/lab-notebook.png)

* [Module 1](#1)
* [Module 2](#2)
* [Module 3](#3)
* [Module 4](#4)
* [Module 5](#5)
* [Module 6](#6)
* [Module 7](#7)

# Module 1

# Module 2

# Module 3

# Module 4

# Module 5

# Module 6

# Module 7

[![](https://static.igem.org/mediawiki/2021/b/bd/T--IISER-Pune-India--pngs--Back-to-top.png "Go to top")](#top)

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block lead %}Demonstrate engineering success in a part of your project by going through at least one iteration of the
engineering design cycle.{% endblock %}
{% block page\_content %}

![Header Image](https://static.igem.wiki/teams/4836/wiki/contribution/engineering-success.png)

![Header Image](https://static.igem.wiki/teams/4836/wiki/contribution/engineering-success.png)

* [Wet lab Modules](#1)
* [Testing of Module 4:](#2)
* [Constructing the ODE Model of the Probiotic](#3)

# Wet lab Modules

The design of the wet lab modules has undergone several iterations, leading to changes in the experimental
setups.

## Iteration 1

### **Design:**

In the initial phase, we designed four gene constructs to demonstrate the core proof of concept in our wet
lab experiments. These constructs were specifically engineered to address key aspects of our project:

1. **Expression of the LasR Transcription Factor:** A gene construct was designed to test the
   expression of the LasR transcription factor.
2. **SERT Expression:** Another construct aimed to validate the expression of SERT.
3. **Functionality Testing:** A complex construct was created to assess the functionality of
   SERT, SNAT, and COMT proteins.
4. **pLasI Promoter Leaky Expression:** The fourth construct was designed to investigate the
   leaky expression of the pLasI promoter.

During the design phase, we focused on conceptualizing these constructs to align with the project goals and
experimental requirements.

![4 modules.png](https://static.igem.wiki/teams/4836/wiki/4-modules.png)

### **Build (In Silico Assembly):**

Following the design phase, we utilized bioinformatics tools such as �Snapgene� and �Benchling� for in-silico
assembly. This step involved digitally assembling the gene constructs to assess compatibility and plan cloning
strategies.

### **Test (Theoretical Validation):**

To validate our designs, we engaged in comprehensive discussions with experienced peers, including seniors
and Principal Investigators (PIs). These discussions were pivotal in scrutinizing the theoretical aspects of
our construct designs. The insights provided by these professionals served as a critical form of theoretical
testing, validating our designs before practical implementation.

### **Learn and Iterative Improvements:**

Upon evaluation, we identified areas for improvement:

1. **Spacer DNA sequence Inclusion:** It became evident that a spacer DNA sequence was necessary
   between the gene cassettes within a construct, which was initially overlooked.
2. **Fluorescent Protein Selection:** We realized the impracticality of using GFP due to its
   interference with the plasmid backbone's sfGFP, hindering the distinction between empty and cloned
   vectors after cloning.
3. **Missing Constructs:** There was a gap in our prepared constructs, specifically regarding
   the expression analysis of Serotonin N-acetyl transferase and Caffeic Acid O-Methyl Transferase proteins.

   Through this iterative DBTL cycle, we are refining our wet lab modules for a more comprehensive and
   effective implementation of our project.

## Iteration 2:

### **Design:**

Building upon the insights gained from the first iteration, our second design phase focused on refinement and
expansion. We introduced a new gene construct, aiming to validate the expression of Serotonin N-Acetyl
Transferase (SNAT) and Caffeic Acid O-Methyl Transferase protein (COMT). Additionally, we made strategic
changes to enhance the constructs: replacing GFP with TagBFP to prevent Fluorescence Resonance Energy Transfer
(FRET) by ensuring non-overlapping excitation and emission wavelengths.

![5 modules.png](https://static.igem.wiki/teams/4836/wiki/5-modules.png)

### **Build (In Silico Assembly and Cloning Strategies):**

In the build phase, we employed bioinformatics tools, including Benchling and Snapgene, for in-silico
assembly. This step allowed us to assess the compatibility of genetic components and finalize cloning
strategies. Attention was given to optimizing the assembly process and ensuring the integrity of our gene
constructs.

### **Test (Expert Review and Validation):**

To validate our enhanced designs, we sought expert opinions once again. Engaging with our seniors and
Principal Investigators, we underwent a rigorous review process. They provided essential feedback, confirming
the validity of our modifications and ensuring alignment with project objectives.

### **Learn:**

Following expert evaluation, we identified areas that required further attention:

1. **LasR-pLasI Interaction:** To comprehensively demonstrate the project�s proof of concept, we
   recognized the need for a construct specifically testing the ability of the LasR transcription factor to
   activate the pLasI promoter independently. This experiment was crucial for a detailed understanding of pLasI
   promoter functionality.
2. **SERT-pLasI Interaction:** Considering the non-native nature of SERT in E. coli, we
   acknowledged the potential interference of SERT protein with the functioning of the pLasI promoter. A
   dedicated construct was required to investigate whether SERT could bind to the pLasI promoter, ensuring a
   thorough evaluation of promoter induction.

## Iteration 3:

### Design (Iterative Refinement):

Drawing on the insights from previous iterations, our final design phase comprised seven modules. The newly
added designed modules aimed to check the following aspects:

1. Expression and Binding Analysis: A module was dedicated to evaluating the expression of CUW\_0748, the
   bacterial analog of the Human Serotonin Transporter (SERT), in E. coli. Additionally, it aimed to
   investigate the potential binding of SERT with the pLasI promoter.
2. LasR Expression and Basal pLasI Activity: Another module focused on assessing LasR expression and
   analyzing the basal activity of the pLasI promoter in the presence of the LasR transcription factor alone.

![7 modules.png](https://static.igem.wiki/teams/4836/wiki/7-modules.png)

### Build (In Silico Assembly and Cloning Strategies):

In the build phase, we harnessed sophisticated bioinformatics tools, including Benchling and Snapgene, to
conduct in-silico assembly. This step ensured the compatibility of genetic components and allowed for the
meticulous planning of our cloning strategies.

### Test (Expert Evaluation):

Our designs underwent thorough evaluation through consultations with esteemed seniors and Principal
Investigators. Expert opinions were sought to scrutinize our gene constructs and explore potential assembly
strategies. These discussions contributed significantly to the validation of our designs and the selection of
optimal assembly techniques.

### Learn and Implementation Strategy:

Upon expert evaluation, we made an informed decision to proceed with practical implementation. Considering
various cloning strategies such as Biobrick assembly, Gibson Assembly, Golden Gate Assembly, and Infusion
cloning, we opted for the Gate Gate assembly strategy. This strategic choice was made based on its perceived
efficiency, especially given the complexity of inserting multiple components into the plasmid vector.

# Testing of Module 4:

### **Design:**

In the design phase, a gene construct was meticulously devised to address critical objectives:

1. **Verification of SERT and LasR Protein Functionality:** The construct aimed to validate the
   functionality of SERT and LasR proteins within the bacterial cell.
2. **Evaluation of pLasI Activation with Serotonin+LasR Complex:** External serotonin,
   facilitated by the expressed SERT membrane protein, was designed to bind to the LasR Transcription factor.
   The resulting LasR+Serotonin complex would induce the pLasI promoter.

### **Build:**

During the build phase, essential Biobricks were synthesized de novo. All inserts were meticulously assembled
into the pJUMP 28-1A plasmid using the Golden Gate assembly strategy. Precise cloning was achieved utilizing
BsaI (Type IIS Restriction Enzyme) to insert the components into the plasmid.

### **Test:**

The cloned plasmid was introduced into *E. coli* Dh5-alpha. Screening for positive colonies was
performed through Colony PCR. Plasmids from the confirmed colonies were isolated from *E. coli*
Dh5-alpha. Further validation included Restriction Digestion using EcoRI, SpeI, and NheI. The plasmid was
subsequently transformed into *E. coli* Bl21 to assess mCherry expression. The BL21 cells were then
inoculated in two media: one containing Serotonin and the other without Serotonin.

### **Learn:**

Under the Learn phase, BL21 strain cells were observed using a Confocal microscope:

1. **In the Absence of Serotonin in the media:** Observation revealed minimal mCherry
   fluorescence intensity, indicating the absence of LasR+Serotonin complex formation. Consequently, the pLasI
   promoter remained uninduced without serotonin. The observed fluorescence potentially stemmed from the leaky
   expression of the pLasI promoter.
2. **In the Presence of Serotonin in the media:** Observations reveal increased mCherry
   fluorescence intensity indicating the functionality of the Serotonin transporter and the formation of the
   LasR+Serotonin complex.

# Constructing the ODE Model of the Probiotic

## Iteration 1

### Design

Probiotic, We wanted to predict the outcomes of wet-lab results, experiments done on similar systems. We
wanted to design an ODE model which extensively describes the system.

### Build

A series of Ordinary Differential Equations (ODEs) were formulated to mathematically model the circuit's
behavior as per the initial design. The following are the initial ODEs that were formulated after a good deal
of literature review and discussion with experts. Certain equations like the induced expression of SNAT and
COMT were taken from the iGEM Modeling webinars of [2020,](https://2020.igem.org/Measurement/Webinars) [2021](https://2021.igem.org/Engineering/Webinars) & [2022](https://technology.igem.org/engineering/webinars) and by tracing back their literature.

$
\frac{dS\_e}{dt} = LSP - k\_{in}(S\_e - S\_i)
$

$
\frac{d[LasR]}{dt} = \frac{k\_{1LasR}.k\_{2LasR}.C\_N}{d\_{mLasR}} - k\_{f}[Si][LasR] +k\_b[LS] - d\_{2LasR}.[LasR]
$

$
\frac{d[NAS]}{dt} = (k\_{21}[SNAT])\frac{[S\_i]}{[S\_i]+k\_{m1}} - (k\_{22}[COMT])\frac{[NAS]}{[NAS]+k\_{m2}}
$

$
\frac{d[M\_i]}{dt} = (k\_{22}[COMT])\frac{[NAS]}{[NAS]+k\_{m2}} - k\_{eff}(M\_i - M\_e)
$

$
[SNAT] = \frac{k\_{2SNAT} \cdot k\_{1LasI}}{d\_{mSNAT}}\: \cdot C\_N\: \cdot \left( \beta\_{0SNAT} + \frac{(1 -
\beta\_{0SNAT}).[LS\_i]^n}{[LS\_i]^n + (K\_{dLasI})^n } \right)
$

$
[COMT] = \frac{K\_{2COMT} \cdot K\_{1LasI}}{d\_{mCOMT}}\: \cdot C\_N\: \cdot \left( \beta\_{0COMT} + \frac{(1 -
\beta\_{0COMT}) \cdot [LS\_i]^n}{[LS\_i]^n + (K\_{dLasI})^n } \right)
$

$
\frac{d[LS\_i]}{dt} = k\_f[S\_i][LasR] - k\_b[LS\_i]
$

$
\frac{dM\_e}{dt} = \frac{NV\_c}{V\_l}k\_{eff}(M\_i - M\_e)
$

### Test

We did a parameter hunt with variety of methods. A rough protocol was followed for finding the parameters.

![Determine the parameter_page-0001.jpg](https://static.igem.wiki/teams/4836/wiki/drylab-flowchart.jpg)

The goal is to scrutinise the output in reference to standard studies. The reference study considered was
done by Byeon et al. Their study is about SNAT and COMT dual expression. Expected plots:

![NAS vs time](https://static.igem.wiki/teams/4836/wiki/paper1.png)

NAS vs Time

![Untitled](https://static.igem.wiki/teams/4836/wiki/paper2.png)

A range of simulations were conducted using Python to solve the ODEs. The parameters resulted in the
following graphs **Concentration(uM) vs Time(s):**

![Untitled](https://static.igem.wiki/teams/4836/wiki/old1.png)

![Untitled](https://static.igem.wiki/teams/4836/wiki/old2.png)

![Untitled](https://static.igem.wiki/teams/4836/wiki/old3.png)

![Untitled](https://static.igem.wiki/teams/4836/wiki/old4.png)

![Untitled](https://static.igem.wiki/teams/4836/wiki/old5.png)

The expected outcomes and the actual data didn�t match. The time scales and the trend do not match.
Importantly SNAT and COMT have significant concentration differences.

### Learn

We discussed these results and tried to troubleshoot the entire process. We seeked out for help from Experts
in Computational Biology and Modelling. We cornered out the cause for this discrepancy to be inaccuracy in
parameters.

## Iteration 2

### Design:

The ODEs were redesigned to the following after implementation of quasi-static assumptions.

### Build:

We are left with 5 equations and 3 ODEs.

$S\_e=S\_i(=S)$

$M\_e=M\_i(=M)$

$L=\frac{\beta\_1}{d\_1} C\_N$

${[SNAT]=\frac{\beta\_2}{d\_2}\left(\frac{1}{k\_{e q}^n L^n+1}\right)}$

$\frac{\beta\_2}{d\_2}\left(\frac{1}{k\_{e q}^n L^n+1}\right) ; k\_{e q}=\frac{k\_f}{k\_b}$

$\frac{d N A S}{d t}=-k\_1[S N A T]\left(\frac{S}{S+k\_{m 1}}\right)$

$\frac{d M}{d t}=k\_2[C O M T]\left(\frac{N A S}{N A S+k\_{m 2}}\right)$

### Test:

We determined that Extracellular serotonin, Intracellular serotonin and N-acetyl Serotonin(NAS) as the most
important and tangible variables. Their plots in the modified model are:

![Untitled (3).png](https://static.igem.wiki/teams/4836/wiki/model/ser-plot.png)

![Untitled (2).png](https://static.igem.wiki/teams/4836/wiki/model/nas-plot.png)

![Untitled (3).png](https://static.igem.wiki/teams/4836/wiki/model/mel-plot.png)

With appropriate values considered for SNAT and COMT, the modified model fitted well into the reference
experiment.

### Learn:

Owing to the fact that our probiotic system is complex in nature with a myriad of factors involved at every
step in the process, a little change in the value of parameters can give a completely different solutions.
Discussions with our PIs were extremely helpful in this regard.

We learnt that exactly determining the dynamics exactly of each and every component of a complex system is
not feasible. Even if the parameters seem accurate enough on their own regard, their inaccuracy compounds out
over all the parameters in question.

To deal with complex systems it is necessary to approximate the system by conveniently and tactically
avoiding the variables that are redundant to the total outcome of the system. Here we introduced the concept
of quasi-static approximations into our model. Dr Raghunath O Ramabhadran played a key role in this endeavour
of solving and approximating the system.

[![](https://static.igem.org/mediawiki/2021/b/bd/T--IISER-Pune-India--pngs--Back-to-top.png "Go to top")](#top)

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block lead %}Describe the research, experiments, and protocols you used in your iGEM project.{% endblock %}
{% block page\_content %}

## What should this page contain?

---

Describe the research, experiments, and protocols you used in your iGEM project. These should be detailed enough for another team to repeat your experiments.

If you made Parts this year, please remember to put all information, characterization, and measurement data on the Part's Main Page on the [Registry](http://parts.igem.org/Main_Page).

## Inspirations

---

* [2019 Nantes](https://2019.igem.org/Team%3ANantes/Experiments)
* [2019 TU Eindhoven](https://2019.igem.org/Team%3ATU_Eindhoven/Experiments)
* [2019 Mingdao](https://2019.igem.org/Team%3AMingdao/Demonstrate)
* [2020 Amsterdam](https://2020.igem.org/Team%3AAmsterdam/Experiments)
* [2020 NCTU Formosa](https://2020.igem.org/Team%3ANCTU_Formosa/Experiments)
* [2020 USAFA](https://2020.igem.org/Team%3AUSAFA/Experiments)

{% endblock %}

{% extends "layout.html" %}
{% block title %}Page Name{% endblock %}
{% block lead %}Page Description goes here in like a line or so{% endblock %}
{% block page\_content %}

![Header Image](https://static.igem.wiki/teams/4836/wiki/results/results.png)

![Header Image](https://static.igem.wiki/teams/4836/wiki/results/results.png)

* [Basal Expression of LasI (Module 1)](#1)
* [Expression of SNAT and COMT (Module 6)](#2)
* [The Serotonin Induced regulatory system (Module 4)](#3)
* [Bacterial Growth in the Presence of Serotonin.(Module
  7)](#4)

# Basal Expression of LasI (Module 1)

## Cloning the construct and Confirmation

We intend to find the Basal expression of LasI. We
cloned our genetic circuit into pJUMP, our destination plasmid, with the golden gate assembly.

The size of the cloned plasmid is 3499bp.

We did colony PCR to confirm whether bacteria had
taken
up the cloned plasmid.

We designed colony PCR primers in a way where we had
two
amplicon sizes within the plasmid.

The expected sizes of amplicons are �-

Plasmid Forward and Insert Reverse 528bp

Insert Forward and Plasmid Reverse 824bp

![](https://static.igem.wiki/teams/4836/wiki/results/m11.jpg)

## Plasmid Isolation and Restriction Digestion

We performed restriction digestion with SpeI, EcoRV,
and
EcoRI enzymes to further confirm the colony.

The Expected sizes are 2469, 855, 175.

![](https://static.igem.wiki/teams/4836/wiki/results/m12.jpg)

## BL-21 Transformation and Confocal microscopy

We also checked under an epifluorescence microscope to
confirm the positive colonies.

![](https://static.igem.wiki/teams/4836/wiki/results/m13.jpg)

We used a confocal microscope to check the basal
expression.

![](https://static.igem.wiki/teams/4836/wiki/results/m14.jpg)

We inferred that there is some basal expression of
LasI.

# Expression of SNAT and COMT (Module 6)

## Cloning the construct and Confirmation

We planned to check the expression of SNAT and COMT by
tagging them with TagBFP and mCherry respectively. We cloned our genetic circuit into pJUMP by Golden Gate
assembly using the BsaI restriction enzyme.

* The size of the cloned plasmid is 5833bp.

* We performed colony PCR to check the positive colony.

The expected sizes of amplicons are

Plasmid forward and insert reverse 1248bp

Insert forward and Plasmid Reverse 1063bp

![](https://static.igem.wiki/teams/4836/wiki/results/m15.jpg)

## Plasmid Isolation and Restriction Digestion

We performed restriction digestion with HindIII and
EcoRV single-cutter enzymes to further confirm the colony, both enzyme sites were present in the insert at
different locations.

The expected band size is 5833bp.

![](https://static.igem.wiki/teams/4836/wiki/results/image.jpg)

## BL-21 Transformation and Confocal Microscopy

The plasmid was transformed into BL-21 and colonies
were
observed. The culture was induced by the addition of IPTG and the expression of SNAT and COMT with IPTG
induction was checked by confocal microscope.

![](https://static.igem.wiki/teams/4836/wiki/results/image-1.jpg)

We concluded that SNAT and COMT can be expressed under
an inducible promoter like pLasI.

# The Serotonin Induced regulatory system (Module 4)

## Cloning the construct and Confirmation

We opted for Golden Gate assembly to assemble the
parts
in our plasmid. We used the BsaI restriction enzyme to produce overhangs and run the reaction in a
Thermocycler to complete the Golden Gate assembly using the protocol on our Protocols page.
We have ordered colony PCR primers to confirm our cloning through colony PCR and Gel electrophoresis. The
colony PCR results are as below for this module:

* Gel electrophoresis - we got 1:5 5th colony positive result with
  successful
  clones.

![](https://static.igem.wiki/teams/4836/wiki/results/untitled-25.png)

* Correct bands were observed in the 9th lane of the 1st gel (1.6 Kb) and
  the
  7th lane of the 2nd Gel(1.2 kb).

* 7th lane of 2nd Gel- 1:5 (Vector: Insert ratio), 5th Colony, Part a 9th
  lane of 1st gel- 1:5 (Vector: Insert ratio), 5th Colony, Part b

* **Colony 5 (1:5)- Positive**

## Plasmid Isolation and Restriction Digestion

Restriction Digestion of the isolated plasmid from
DH5-$\alpha$ with EcoRI, NdeI, and SpeI.

Gel electrophoresis results are as follows:

![]()

## BL-21 Transformation and Confocal Microscopy

The confirmed plasmid with the desired construct was
transformed successfully into E. coli BL21 cells to check the expression and functionality of the
regulatory
system components.
Confocal Microscopy was performed to confirm the fluorescence reporter mCherry present in this module
which
shows its expression after the Serotonin-LasR complex activates the transcription of downstream genes of
the
LasI promoter.
**Expected Results:** Either Basal expression or no expression should be observed in the
module
when no inducer molecule - Serotonin is added. An increase in the intensity of mCherry should be observed
when serotonin is added to the culture.
We inoculated the primary culture of BL-21 cells which had the confirmed construct and after the OD600 nm
reached 0.4 i.e., the exponential growth phase of the cells, inducer molecule - serotonin of 80 $\mu$M
concentration (physiological levels of serotonin in IBS-D patients) and 100 $\mu$L volume of this solution
is added into 10mL secondary culture. The confocal microscopy was performed at 0min, 40min, and 120mins
and
observed less significant change in the intensity after 40mins. All the parameters like exposure,
emission,
and absorption wavelengths were kept constant during the observations. The pictures below depict the
intensities observed.

![](https://static.igem.wiki/teams/4836/wiki/results/0-min-up.png)

![](https://static.igem.wiki/teams/4836/wiki/results/40-min-up.png)

![](https://static.igem.wiki/teams/4836/wiki/results/120-min-up.png)

### Inference - 1

The above result suggests that the regulatory system
induced by Serotonin works well. In the presence of serotonin, as it is clearly visible from the
Confocal
Microscopy results, the presence of mCherry protein is seen after the addition of serotonin.

### Inference - 2

The SERT analog CUW\_0748 acts slower than the
desired
efficiency. As there is no significant increase in the intensities at 40 mins and at 120 mins, we could
say
that CUW\_0748 should be expressed constitutively.

# Bacterial Growth in the Presence of Serotonin.(Module 7)

Serotonin has an impact on the growth of bacteria in
the
gut. Literature reveals that in the presence of high levels of serotonin, it is difficult for bacteria
to
grow properly. To test this we cloned the whole 7th module and transformed it into BL-21 after partial
confirmation of the presence of construct through restriction digestion. Colonies were observed in the
Kanamycin selective BL-21 plate which leads to 2 inferences:

1. The presence of a cassette in the plasmid due to the presence of colonies but no colony PCR
   confirmation
   was obtained and hence we infer there might be a problem with colony PCR primers.

2. Partially correct results in the restriction digestion also infer the presence of cassette.

However, we continued with the colonies we had and
checked the bacterial growth curve under the kanamycin selective marker and serotonin stress marker. The
graph below shows that at 80 $\mu$M concentration of serotonin, the cells� growth had no significant
impact.

![](https://static.igem.wiki/teams/4836/wiki/results/od600-vs-time-sec-2-1.png)

[![](https://static.igem.org/mediawiki/2021/b/bd/T--IISER-Pune-India--pngs--Back-to-top.png "Go to top")](#top)

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