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                         <b>Map of pCKI Optimization Plasmid features with enhanced FRET pair (mTurquoise ; cp173Venus) sequences</b><br>
 
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                           <b>In silico digests to release fluorescent reporters </b><br>
 
                           <b>In silico digests to release fluorescent reporters </b><br>
 
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T3: <a href="http://parts.igem.org/Part:BBa_K3646009:Design" style="color:darkblue;"><b>BBa_K3646009</b></a> Is a composite project part composed of basic parts  BBa_I712004 (CMV promoter), <a href="http://parts.igem.org/Part:BBa_K3646007" style="color:darkblue;">BBa_K3646007<b></b></a> (eCFP), <a href="http://parts.igem.org/Part:BBa_K3646008" style="color:darkblue;">BBa_K3646008<b></b></a> (FKBP) and <a href="http://parts.igem.org/Part:BBa_K3646006" style="color:darkblue;">BBa_K3646006 (hSTAT2)</b></a></b><br> <br>  
 
T3: <a href="http://parts.igem.org/Part:BBa_K3646009:Design" style="color:darkblue;"><b>BBa_K3646009</b></a> Is a composite project part composed of basic parts  BBa_I712004 (CMV promoter), <a href="http://parts.igem.org/Part:BBa_K3646007" style="color:darkblue;">BBa_K3646007<b></b></a> (eCFP), <a href="http://parts.igem.org/Part:BBa_K3646008" style="color:darkblue;">BBa_K3646008<b></b></a> (FKBP) and <a href="http://parts.igem.org/Part:BBa_K3646006" style="color:darkblue;">BBa_K3646006 (hSTAT2)</b></a></b><br> <br>  
  
 
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                         <b><br>Plasmid Maps for FRaPPe project parts showing the tentative design for test and control constructs. Created using SnapGene.
 
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Revision as of 09:48, 9 November 2020

Poster: IISER_Berhampur

Authors
Sukanya Chakraborty1, Gokul Bhaskaran1, Vinayak S1, Harish Kumar Senapati1, Chitrak Bhowmik1, Sudev Pradhan1, Gyanaranjan Parida1, Deepjyoti Satpathy1, Gokul Madhav1, Neehar Verma1, Tanishta Bhattacharya1, U Abinash Patro1, Kingkini Roychoudhury1, Prachiti Vithole1, Sayantani Biswas1

2Indian Institute of Science Education and Research, Berhampur, India

Abstract


FRaPPe: A FRET based Ranker for Proteins and Peptides
FRaPPe by team iGEM IISER Berhampur, is a reporter system which aims to validate efficiency of therapeutics targeting protein-protein interactions (PPIs). This reporter, assembled with mammalian promoters, modulatory domains (Chemically Induced Dimerization modules), fluorescence tags (Fluorescence Resonance Energy Transfer modules) and the coding sequences of proteins of interest will be developed in E. coli and transfected into Human Embryonic Kidney cells. This tool will enable modulation of the extent of PPIs and their quantification via fluorescence readout, offering possibilities for a high-throughput screening system for drug efficiency in attenuating these interactions in vitro.The utility of FRaPPe will be demonstrated using interference peptides that will disrupt the Dengue Virus Non-Structural Protein and host STAT2 interaction thus modulating the host interferon signalling pathway. We propose FRaPPe as a one-stop solution for studying host-viral PPIs and also a tool to screen inhibitors of these interactions against several viral diseases.
Goals
1. Our first goal is create a synthetic system to study protein protein interactions in viral infections which can be used as a screen for drug discovery.

FRaPPe: a tool to study protein-protein interaction Rapid:
  • Revolutionize the field of drug discovery, by speeding up time taken for initial exploratory basic science research.
  • Proteins of interest are expressed in human cells coupled with our reporter using simple molecular cloning techniques.

Versatile:
  • Interaction between ANY two proteins of interest can be studied.
  • Our mission is to target protein-protein interactions employed by the deadly Dengue virus against its host. As a proof of concept our proteins of interest are Dengue NS5 and human STAT2.

High Throughput System:
  • Use of CID coupled with the FRET system will efficiently quantify the interaction of the proteins of interest enabling scale-up to screen several samples of inhibitors at once.

2. Use of CID coupled with the FRET system will efficiently quantify the interaction of the proteins of interest enabling scale-up to screen several samples of inhibitors at once.
Inspiration
  • Slow Drug Development pipeline- To understand the predicament faced by humanity in developing effective therapeutics against deadly diseases, one need not look further than the COVID-19 pandemic waging around us right now. One might have often heard that it takes at least 10 years for a drug to be discovered and commercialised. However the actual journey might even take three times this time period! Our project arose out of this need for more effective techniques to speed up drug development. We realised that in vitro systems for testing drugs is of paramount importance.
  • Viruses, a potent enemy- For viruses, in particular, the problems for drug development are many-fold. The arsenal is much more limited when compared to other infectious agents such as bacteria. Moreover, any drug that targets a part of the viral life cycle, could end up harming the host in the process since viruses hijack the cells of our own body to feed their replication machinery. And then comes the issue of diversity among viruses. Investing a huge deal of money and resources to study every unique virus stretches out the time needed immensely.
  • Dengue- a socio-economic issue in the tropics and sub-tropics- Dengue is a critical disease that has now reached 129 countries and has put nearly half the world population at risk. Moreover this virus shows a diverse clinical picture making accurate diagnosis difficult. The existence of four different DENV serotypes makes it even more difficult to produce an effective, commercialised antiviral drug or vaccine. Yet there remains very little awareness about the mechanisms of its spread among the common people. Keeping these points in mind, we decided to focus on dengue as a proof-of-concept for our main project.

Problem
What is the problem your team is working to solve? How does it affect the world?
Design
In order to study our target interaction, we would need a viral infected cell. This is where Synthetic Biology came to our rescue. Instead of actual viral infection (which would require specialized laboratory set up and additional safety protocols), we decided to assemble the viral protein and the host protein in plasmid constructs which consist of the following modules:

Module 1 : CID (Chemically Induced Dimerization)
Purpose : Bring the two proteins in proximity and keep control on their interaction.
Module 2: FRET (Fluorescence Resonance Energy Transfer)
Purpose : Detect PPI and offer a quantifiable measure of strength of interaction in presence or absence of Peptide Inhibitor.
Module 3 : POI (Proteins of Interest)
Purpose : Gene sequences for hSTAT2 and DENV NS-5 will encode the proteins whose interaction we want to observe and attenuate.

The end goal : a FRET-CID coupled system where, after the CID reporter ensures the PPI, the FRET reporter will measure the extent of interaction and also act as a measure for the peptide inhibitor efficiency by showing a change in the acceptor fluorophore’s emission spectra. The measurable parameters generated from the FRET system, namely the efficiency and dynamic range will be our tools in gauging the interaction and its subsequent inhibition.
Dry Lab
We took an approach of ‘Imagine each step -> Ask Questions: Why? How? -> Modelling to answer the questions -> Integrate the answers into the project -> Proceed ahead’ for the designing our overall project.
WHY DENGUE? → Epidemiological Studies
WHICH ONE TO TARGET: NON-STRUCTURAL OR STRUCTURAL PROTEINS? → Mutational Analysis

Figure Legend: Mutation Profile for DENV1

Conclusion: Evolutionarily speaking, targeting NSPs for iPEP designing is better than targeting SPs as the latter is more prone to mutations.

WHICH PPI TO TARGET? → Protein-Protein Interaction Studies (i) Aim 1: To select a particular PPI as the target.

Figure Legend: Representative Interactome of DENV NS5

Conclusion: Interaction between DENV NS5 and hSTAT2 was selected as target PPI.

(ii) Aim 2: Building structures for DENV NS5- hSTAT2 complex.

Figure Legend: Model of DENV NS5- hSTAT2 complex (Colour Codes: DENV NS5- Blue, hSTAT2- Green) obtained through Docking


Figure Legend: Model of DENV NS5 - hSTAT2 complex (Individual Structures were first obtained through Homology Modelling and then were docked. The insights from this model were helpful in FRaPPe plasmid designing) (Colour Codes: DENV NS5- Blue, hSTAT2-Green, White Sphere- N-terminal residues, Red Spheres- C-terminal residues)

HOW TO INHIBIT TARGET PPI? → Peptide Inhibitor Designing
Peptides binding to DENV NS5 and inhibiting DENV NS5 - hSTAT2 interactions were designed in-silico and were characterised in-silico for various physicochemical properties and their ability to bind to the target site.

[Figure Legend: iPEP library generated in-silico to target DENV NS5- hSTAT2 interaction]

HOW THE TARGET PROTEINS INTERACT? → MDS Studies

Molecular Dynamics Simulations were performed to study the interaction between DENV NS5 and hSTAT2.

[Figure Legend: Plot showing distance between the centre of mass of DENV NS5 and hSTAT2]

Contributions
1. Complex Models

  • Docked Model of DENV NS5 and hSTAT2 structures (not the whole protein) obtained from PDB
  • Docked Model of DENV NS5 and hSTAT2 structures (whole protein) obtained from homology modelling.


2. iPEP library containing potential peptide inhibitors targeting DENV NS5 - hSTAT2 interaction

3. Tips and Tricks for GROMACS troubleshooting while performing MDS on PPIs.

Wet Lab
Owing to the pandemic, we did not have access to lab facilities during the lockdown. We have started our preliminary optimization experiments recently.

A FRET reporter, pCKI-PYRATES (Bulusu et al., 2017), that was available in the Department of Biological Sciences (DBS), IISER Berhampur was used to carry out restriction digestion with three sets of enzymes and validate it. We also performed in silico digests with the circular pCKI sequence. Since it contains coding sequences of the CFP and YFP variants, we decided to excise these fragments out for additional FRaPPe constructs. We aim to use these variants as they are more stable forms than conventional CFP and YFP to generate enhanced FRET sensors. We have also planned how we would be conducting the experiments if the opportunity came up.


Schematic for Optimization Experiments

Map of pCKI Optimization Plasmid features with enhanced FRET pair (mTurquoise ; cp173Venus) sequences


In silico digests to release fluorescent reporters

Restriction digestion of pCKI with EcoRI and HindIII, 0.8% agarose gel, 1X TBE
CODE CONTROL CONSTRUCTS CODE TEST CONSTRUCTS
FRaPPe C1 FKBP-CFP FRaPPe T1 FKBP C-hSTAT2(FL)-N CFP
FRaPPe C2 FRB-YFP FRaPPe T2 FRB C-NS-5(FL)-N YFP
FRaPPe C3 C-hSTAT2(FL)-N CFP FRaPPe T3 CFP FKBP N-hSTAT2(FL)-C
FRaPPe C4 C-NS-5(FL)-N YFP FRaPPe T4 YFP FRB N-NS-5(FL)-C
FRaPPe C5 FKBP-N-hSTAT2(FL)-C
FRaPPe C6 FRB-N-NS-5(FL)-C
Table of constructs to be prepared. Cloning for NS-5 (GenBank : KR919821.1, DENV TSV08 Serotype 1) into Addgene plasmid # 20148 using HindIII at 5’ and SalI at 3’ and for hSTAT2 (GenBank : U18671.1) into Addgene plasmid # 20160 using XhoI at 5’ and EcoRI at 3’ to create test constructs.

Overview of the Reporter System showing the overall test procedure for experimentation and characterization of its working.


Dose dependent Rapamycin assay and corresponding FRET measurement to choose an optimum Rapa concentration. FRaPPe T2 and T3 co-transfected cells will be treated with the inhibitor and the efficiency of each peptide inhibitor in inhibiting the PPI will be quantified based on the FRET readout with respect to different concentrations of the iPEPs.




Parts



The proposed constructs that will be generated as a part of project FRaPPe.The designed constructs T2 and T3 have been registered with the standard registry of biological parts.

T2: BBa_K3646010 Is a composite project part composed of basic parts BBa_I712004 (CMV promoter), BBa_K3646002 (eYFP), BBa_K3646000 (DENV NS-5) and BBa_K3646004 (FRB)
T3: BBa_K3646009 Is a composite project part composed of basic parts BBa_I712004 (CMV promoter), BBa_K3646007 (eCFP), BBa_K3646008 (FKBP) and BBa_K3646006 (hSTAT2)




Plasmid Maps for FRaPPe project parts showing the tentative design for test and control constructs. Created using SnapGene.


Future Perspective
Frappe, goes to Clinic

The proposed end-use of our tool is an HTS system in the drug discovery field. Compared to several sophisticated instruments, it's simple and easy to use, requiring basic molecular cloning and cell culture procedures.

Initial stages of studying the basic biology of the disease can be accelerated, combined with preliminary drug screening.

We will collaborate with researchers as discussed with NCBS, to take this forward in a research setting.

Troubleshooting and optimising the tool, is our next focus before making it available for researchers.





Safety

The FraPPe screening system is made with the HEK293 cell lines. It should be only used by researchers with at least BSL-2 infrastructure.

E.coli K12 derived DH5alpha; the bacterial strains we are using for cloning, are considered to be non-colonizing and non-pathogenic to humans or animals.

Based on the IHP, we will also be cloning FRaPPe in lentiviral construct.Lentiviruses belong to the class of biosafety level 2 material.
This work will be done in our BSL2+ facility and all safety guidelines relevant to cell cultures will be followed.


Challenges

The transition from the research lab to the clinic is by far the biggest challenge. A suitable therapeutic should meet several criteria including target populations (adults and children), dosage and pill burden, pharmacokinetics, safety, stability and cost.

Testing Antivirals would require more than several thousand patients in order to draw a statistically sound conclusion which may be taken up as a future initiative.

Human Practices
Interviews

We got in touch with experts from different fields including researchers, medical professionals, and government authorities from whom we got valuable insights into our project design and outreach activities. The takeaways from these interviews were implemented into our project lifecycle which helped in improving our results.

Outreach

We conducted a crowdfunding campaign to provide mosquito nets, masks, and information brochures to people living in low-resource areas. Fortunately, we were able to collect around 800 USD for this cause through which we distributed amenities to 70 families in Berhampur. At another level, we spread awareness about Dengue through our social media handles in the form of posters and threads.

Education

In collaboration with team iGEM IISER Pune, we conducted an interactive online workshop for middle school students on the prevention of Mosquito-borne diseases. We contributed several articles spanning domains of Synthetic Biology to different established blogs, designed pamphlets, posters, and comic strips. We also created an online forum named 'Syntillate', engaging writers from different disciplines of our home institute.

Splash

By creating this colouring book, called ‘Splash’, we tried to make an impact on primary class students by introducing to them some concepts of biology that they were able to learn in a fun way. To kindle their curiosity, we invited questions from them and tried to answer those in our Q&A forum. We received an enthusiastic response in this venture.

References
Bulusu V et al., (2017) Spatiotemporal Analysis of a Glycolytic Activity Gradient Linked to Mouse Embryo Mesoderm Development. Dev Cell. 40(4):331-341.

Banaszynski, L. A., Liu, C. W., & Wandless, T. J. (2005). Characterization of the FKBP⊙ Rapamycin⊙ FRB Ternary Complex. Journal of the American Chemical Society, 127(13), 4715-4721.

Ganeshkumar P, Murhekar MV, Poornima V, Saravanakumar V, Sukumaran K, et al. (2018) Dengue infection in India: A systematic review and meta-analysis. PLOS Neglected Tropical Diseases 12(7): e0006618. doi:10.1371/journal.pntd.0006618

PopulationPyramid.net, India-website severe

Jain, A.,Gupta, N., Srivastava, S., & Chaturvedi, U. C. (2012). Dengue in India. The Indian journal of medical research,2012 Sep;136(3):373-90.

Hadfield et al., Nextstrain: real-time tracking of pathogen evolution, Bioinformatics (2018)Nextstrain: real-time tracking of pathogen evolution

Ashour, Joseph, et al. "Mouse STAT2 restricts early dengue virus replication." Cell host & microbe 8.5 (2010): 410-421.

Ashour, Joseph, et al. "NS5 of dengue virus mediates STAT2 binding and degradation." Journal of virology 83.11 (2009): 5408-5418.

El Sahili, Abbas, and Julien Lescar. "Dengue virus non-structural protein 5." Viruses 9.4 (2017): 91.

Lee, Andy Chi-Lung, et al. "A comprehensive review on current advances in peptide drug development and design." International journal of molecular sciences 20.10 (2019): 2383.

Acknowledgement
P.I.- Dr. R Selvi Bharathavikru, IISER Berhampur

Dr. Sandeep Chatterjee, IISER Berhampur

Dr. Vinay Bulusu, IISER Berhampur

Dr. Malay Kumar Rana, IISER Berhampur

Dr. Sunita Patel, CEBS Mumbai

Prof. Sudhir Krishna, NCBS

Dr. Gulam H Syed, Institute of Life Sciences

Dr. Rindu Raveendran, DM WIMS medical college

Dr. Puja Singh, CCMB

Dr. R Jagadeesh Pattnaik, DMO Ganjam