ENGINEERING
“Mistakes are a fact of life. It is the response to error that counts.”
Nikki Giovanni

The engineering of our project followed the Design-Build-Test-Learn approach. DBTL approach allowed us@ to refine our projects iteratively, learning from each cycle. This process ensured our final biological systems are reliable, functional and robust.

Ideation

Once we decided to develop a non-invasive treatment to endometriosis, we brainstormed multiple ways of doing it. The very phase of ideation involved the delivery of miRNA using exosomes to endometriotic cells. We looked at literature to choose the target and selected CTGF initially, to prevent fibrosis that takes root later in the lifetime of the patient, delivered using our exosomes. However, we soon found out that we didn’t know the sequence of Extra Domain A of Fibronectin, and hence we can’t use exosomes.


While going through literature to find alternatives, we came across IL10, with evidence supporting its suppression of Endometriosis. This made us delve deeper into Interleaukins associated with Endometriosis. After extensive review, we realised that IL8 is a more promising target. However, questions remained about the implementation of the same.


Consulting with Dr Rachit helped us, as we realised LNPs are more versatile and easier to use carriers, with proven applications in the recent pandemic. It was just a matter of time before we connected the dots and decided to use mRNA as our solution, as was the original use case with LNPs.


Now, only one challenge remained. IL8 is found everywhere in the body and blocking it everywhere would be fatal. It was then that our original idea came to our rescue, as we found out about the Extra Domain B of fibronectin, overexpressed in Endometriotic tissues. We found in literature, Aptides, a magic peptide sequence that targets EDB of Fn specifically, which made it possible to implement our solution.


Later on, we explored some other pathways and molecules too, which can be exploited to treat similar conditions. This stemmed from our conversations with various stakeholders as we realised immune dysregulation a major culprit behind Endometriosis. Hence, we targeted macrophages, by activating their CD36 receptor, downregulated in Endometriotic patients. This would also have allowed us to increase the duration our treatment remained effective, as macrophages get recycled inside the body.


Hence, our ideation was a step-by-step stacking of small improvements, which finally brought our project in the present form.

Wet Lab
Dry Lab

Key

Design
Build
Test
Learn

Transformation

Design
Preparation of Competent E. coli TG1 cells using CaCl2 solution and Heat shock Transformation with GFP plasmid.

Build
Link to protocol for transformation.

Test
Plating of transformed and non-transformed control bacteria in ampicillin plates. Successful Transformation was achieved. However, the plates have lawns.

Learn
The transformation efficiency seems to be quite high and hence the incubation or/and the volume of solution must be reduced.


Design
Same Protocol, with lower incubation and halving the volume of transformant cells to be plated.

Build
Same as mentioned in the above cycle.

Test
Plating of transformed and non-transformed control bacteria in kanamycin plates. Individual colonies of transformed bacteria were obtained.


Motivation
A lab session with our instructors led us to a new realization: a combination of MgCl2 and CaCl2 is more potent for competency and is more stable compared to simple CaCl2 competency, due to the larger atomic size of Mg2+.

Design
Preparation of competent cells with MgCl2 at first suspension and CaCl2 in the second suspension. Transformation as usual.

Build
Same as mentioned in the above cycle.

Test
Plating of transformed and non-transformed control bacteria in kanamycin agar plates. Successful, with marked increase in the number of single colonies.

Protein Expression

The gene of interest (anti-IL-8) in the plasmid is regulated by the lac operon. IPTG (Isopropyl β-d-1-thiogalactopyranoside), a molecular mimic of allolactose can act as the promoter and is often used because it is not metabolized but as a con, it also has a toxic effect on cells.

Design
IPTG Induction of transformed bacteria.

Build
1mM concentration of IPTG was used to induce E. coli BL21 transformed with GFP AmpR plasmid, cultured to mid-log phase.

Test
The cells are pelleted down, and the supernatant is used to run a western blot (Since the protein is extracytoplasmic). Low expression observed.

Learn
  • IPTG concentration is not enough.
  • IPTG is not the best inducer in this case.
  • The stock IPTG is too old to promote good enough expression.


Design
Induction with lactose (skimmed milk) instead of IPTG.

Build
Use Skimmed Milk Broth (1% peptone + 1% NaCl + 0.5% Yeast Extract + 1% Skimmed milk powder in MilliQ water) as a culture medium cum inducer - since allolactose can directly induce lac operons.

Test
Curdling of Milk is seen when autoclaved.

Learn
Skimmed Milk broth can be sterilized after preparation; we have to prepare the media in a sterile environment.


Design
Skimmed Milk broth/ Lactose Induction

Build
Autoclave LB solution and add 1% sterile skimmed milk powder.

Test
The cells are pelleted down, and the supernatant is used to run a western blot (Since the protein is extracytoplasmic), which showed almost negligible induction.

Learn
Skimmed Milk is not a good promoter.


Design
We try a gradient of IPTG concentrations on different cultures to optimize induction. Fresh IPTG stocks (100mM) were procured.

Build
Link to Optimization of Expression under protein expression in experiments.

Test
The cells are pelleted down, and the supernatant is used to run a western blot (Since the protein is extracytoplasmic) which shows sufficient induction at 1mM.
A piece of paper in a clear plastic container
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Design
We now varied the temperature of final incubation to optimize the temperature requirements of our plasmid.

Build
Prepare cultures, induce with 1mM IPTG, and incubate for 12-14 hours at 10°, 15°, 20°, 25°, 30°, 37°C.

Test
We again ran a western blot with all types of supernatants and got the strongest signal at 25°C.

Learn
The optimized conditions for our plasmid to produce proteins in bacterial BL21 chassis is 1mM IPTG at 25°C at 230 rpm.

OIL8 Plasmid Strain Selection

Design
We compared TG1 and BL21 strains of E. coli to choose a chassis for protein expression. Both were transformed with OIL-8 plasmid.

Build
We induced transformed cultures of both strains with the same concentration of IPTG at the same conditions.

Test
BL21 showed considerably high expression in the Western Blot with cell lysates from both cultures.
A blue and yellow paint on a white surface
Description automatically generated

Learn
BL21 was chosen as the chassis for protein production.

Plasmid Extraction

Design
Kit-free Extraction to isolate plasmid from E. coli DH5-alpha bacteria containing OIL-8 plasmid (provided by AddGene).

Build
Link to Alkaline Lysate Protocol .

Test
No pellet was left over at the end of the protocol.

Learn
The isopropanol necessary to precipitate the plasmid is light-sensitive, also the reason why it is kept in colored bottles. So, the very old stocks we had were possibly quite ineffective.


Design
Same as before, with new isopropanol procured from the chemistry lab.

Build
Same as mentioned in the above cycle.

Test
No pellet is obtained, leading to a search for better procedures since our experience meant we didn’t have optimally pure reagents or conditions necessary to carry out the process with enough precision to give us a workable yield.


Design
We resort to using ThermoFisher MiniPrep Kit for plasmid extraction.

Build
Link to MiniPrep Kit Protocol.

Test
Successful, with viable concentrations under Nanodrop - about 25µg/mL.

Protein Expression for Anti-IL8

Design
Since this was a secreted protein, buffers used in training runs wouldn't have good efficiency with the exact same composition. So, we needed the buffer selection runs.

Build
pI= 5.7 → pH > pI
Trial Buffers: Phosphate (pH=8), Tris Cl (pH=8), PBS (pH=7.4)
Additives: PMSF [Protease Inhibition], β-mercaptoethanol [Reducing Agent], Glycerol [10%]
Comparison: Yield [Nanodrop], Purity [SDS Page], Stability [Aggregation]
Salt: NaCl

Test
We ran SDS PAGE's with 1. Varying buffers, 2. Varying salt concentrations

Learn
Unclear data. Salt concentration chosen 300mM.


Design
Western Blot.

Build
We run the cell lysate to see if the protein is produced.

Test
The gel is successful: visible bands are formed at 31kDa.

Learn
Protein is successfully produced by plasmid.


Design and Build
We decided to carry out ELISA with our Antibody as a secondary and IL8 as primary, using HRP conjugation to quantify the interactions between IL8 and O-IL8-15.

Test
Unsuccessful, with no color changes.

Learn
Ineffective protein folding might be the reason for failure. So, we looked into literature, and concluded there could be two issues: one, disulfide linkages not being formed properly; two, protein being secreted in the form of Inclusion Bodies.


Design
Ammonium Sulphate Precipitation to concentrate supernatant.

Build
Link to ammonium sulphate precipitation protocol.

Test
Ran the above at varying concentrations to find the optimum saturation.

Learn
70% saturation was found to be best.


Design
BLI with Tris Cl as a buffer.

Build
BLI gives us a quantitative measure of the binding affinity of IL8 and Anti IL8.

Test
We get unclear data.

Learn
The buffer might have led to improper binding. Hence, we change the buffer to PBS.


Design
BLI with PBS as buffer.

Build
Quantitative test of binding affinity of IL8 and Anti IL8.

Test
Worse binding compared to Tris-Cl.

Learn
Ineffective folding might be the issue. We changed our buffer back to Tris-Cl. All further steps used Tris-Cl pH 7.4, unless mentioned.


Design
Urea precipitation to check presence of protein in IBs.

Build
Link to urea precipitation protocol..

Test
SDS-PAGE showed no presence of protein in purified pellet.

Learn
Protein may not have been properly produced, due to the lack of formation of disulfide linkages. Should either use shuffle cells or target protein to the periplasmic space using PelB signal peptide.


Design
Switch to SHuffle Cells. We transfect SHuffle Cells using the original plasmid as well as we have. These cells are designed to produce necessary disulfide linkage as and where required, thus solving our issue.

Test
To Be Done


Design
We designed a new plasmid with PelB signaling peptide instead of OmpA.

Build
We intend to transform the chassis with the modified plasmid to send the protein first to the periplasmic space, to render the necessary disulfide bonds.

Test
To Be Done

Lipid Nanoparticle Formulation and Characterization

Design
LNPs with the below composition were fabricated using the iLNP chip at flow rate ratio (mRNA:lipid) =3:1 i.e 240 and 80 ul/min using a 20 baffle mixer. Nishant, a PhD mentor, suggested that we use a 20 baffle mixer (instead of 30 or 10) since they were pre-optimised for 100nm LNPs. Based on our inputs from Dr. Pradipta, the LNPs were formulated with four lipids: ALC-0315, mal-PEG 2000, cholesterol and DSPC. ALC-0315 was the cationic lipid. It was necessary as RNA is negatively charged and the cationic lipid helps in the uptake of the RNA. The molar ratios used were ionizable cationic lipid: neutral lipid: cholesterol: PEG-ylated lipid:: 50: 10: 38.5: 1.5. DSPC is the neutral lipid.

Build
Link to protocol.

Test
DLS (Dynamic Light Scattering) analysis of the first LNP batch yielded results showing a radius of around 180nm.

Learn
Tested LNPs are close to required characteristics. Nishanth, who helped us in this process, advised us to redo the fabrication of the LNPs at high speed to reduce the LNP sizes to near about 100nm.


Test
Second run of DLS yielded LNP sizes of about 500nm.

Learn
The high speeds used could have damaged the microfluidic device, resulting in a larger average radius due to improper mixing. We hypothesized the cause for the radius mismatch as lack of information and optimization of the speed necessary for the LNP.

Signal Peptide for Mammalian Cells

Design
Use TPA as a mammalian signal peptide, as it is the mostly used in genetic constructs.

Test
Use SignalP 6.0 to check efficiency of signaling.

Learn
tPA shows good secretion probability on SignalP 6.0.


Design
Based on feedback from PhD students and on literature review, we decided to investigate the multiple signal peptides.

Build
We moved our focus to using CD33 as a mammalian signal peptide.

Test
We used SignalP 6.0 to check efficiency of signaling, along with a few other signal peptides:
Leader sequence NameSequence
Human OSMMGVLLTQRTLLSLVLALLFPSMASM
VSV-GMKCLLYLAFLFIGVNC
Mouse Ig KappaMETDTLLLWVLLLWVPGSTGD
Mouse Ig HeavyMGWSCIILFLVATATGVHS
BM40MRAWIFFLLCLAGRALA
SecreconMWWRLWWLLLLLLLLWPMVWA
Human IgKVIIIMDMRVPAQLLGLLLLWLRGARC
CD33MPLLLLLPLLWAGALA
tPAMDAMKRGLCCVLLLCGAVFVSPS
Human ChymotrypsinogenMAFLWLLSCWALLGTTFG
Human trypsinogen-2MNLLLILTFVAAAVA
Human IL-2MYRMQLLSCIALSLALVTNS
Gaussia lucMGVKVLFALICIAVAEA
Albumin(HSA)MKWVTFISLLFSSAYS
Influenza HaemagglutininMKTIIALSYIFCLVLG
Human insulinMALWMRLLPLLALLALWGPDPAAA
Silkworm Fibroin LCMKPIFLVLLVVTSAYA
image description
The probability of secretion is highest with CD33.

Learn
Good efficiency is seen for CD33, significantly higher than in tPA. So we selected it as our signal peptide for mammalian protein expression.

dAb Design

Design
The standard procedure Camelization i.e. removal of the entirely of the sequence except the VH (Variable head), followed by the replacement of four hydrophobic with more hydrophilic amino acids to avoid the exposure of such a sizeable hydrophobic region to solvent, and other similar changes to increase stability. Link to modeling.

Build
To make a preliminary version of such a molecule, do the following:
  • Take the VH and VL regions and go through the sequences. Change the amino acid positions to the ones mentioned above if it is not already this.
  • Join the 2 sequences with a linker: GGGGSGGGGSGGGGS.
This simple change is all it takes to get a preliminary scFv!

Test
Checked our results with docking and GRAMM, which gave very good results!

PK/QSP

Aim of PK/PD
To determine the movement of the LNP-based drug through the body and determine the body’s biological response to it.

Design and Build
We designed a lipid nanoparticle which would carry the mRNA inside the endometrial cells. We determined the characteristics of the LNPs prepared. We further use this data to run a simulation.

Test
Using the PK-Sim software on Open Systems Pharmacology, we get data regarding concentrations of the drug in the endometrial tissue over time.

Learn
We use this data to optimize the characteristics of the lipid nanoparticle.

Homology and Docking

Aim of Docking
Aim of docking: To determine the structural interactions between IL-8 and Anti IL-8, between the Aptide and the Fibronectin EDB, between PD1 and anti-PD1, IL6 and anti-IL6 and nanobody versions of all antibodies.

Design
We determined the sequences of all the required proteins and collected the relevant data for the models from the respective PDB files. One protein was to be the ligand and the other the receptor. The ligand and receptor files were combined to form a complex.pdb file.

Build
We ran the ligand and receptor files through GRAMM web docking server and got 10 different models of possible docking orientations. We combined the first ligand model with the receptor model to get a ligand-receptor complex model.

Test
We used PRODIGY to determine the ‘predicted binding affinity’ of the proteins.

Learn
The data obtained can be used as reference for wet labs. We also plan to try out all the 10 different models received to figure out which gives results most relevant to the wet lab results.

Primer Design

Motivation
Our interaction with Debajyoti, a PhD at Prof. Raghavan’s lab, made us realize that we had a finite stock of DNA, if we weren’t using a plasmid. On further feedback from seniors and mentors, we decided to go ahead with PCR.

Design
Taking aid from Addgene’s handbook (plasmids 101) and their primer design website, we set out to design a plasmid with the following properties:
  • Length of 18-24 bases.
  • 40-60% G/C content.
  • Start and end with 1-2 G/C pairs.
  • Melting temperature (Tm) of 50-60°C.
  • Primer pairs should have a Tm within 5°C of each other.
  • Primer pairs should not have complementary regions.

Build
The primer sequence was arrived at by using SnapGene.
PrimersSequenceTm (as predicted by Snapgene, in °C)
fwd CD36 without Restriction sitesGAATTCGCGGCCGCTTCTA58
bkwd CD36 with Restriction sites (Spe1, Drd1)TGACTCACTAGTCCACTCAGACTTTATTCAAAGACC55
Bkwd IL8 without Restriction sitesACTAGTGCCGCCCACTCAGA58
Fwd IL8 without Restriction sitesGAATTCGCGGCCGCTTCTA59
Melting points and other properties were analysed.

Learn
SnapGene cannot check for secondary structures, and hence, we were suggested to try out bioinformatics.org.


Test
The sequence was assessed using bioinformatics.org.
Anti-IL8 primers
CD36 primers

Learn
The primer sequence seems to be perfect for manufacturing, based on these online tools.

Biobricks

Design
Can we scrap data from biobricks pages to generate descriptions?

Build
Write a script to scrape part pages and parse their content to extract a "description."

Test
A rule-based approach is not practical due to the variety of parts and the lack of consistency in parts pages.

Learn
We can use AI to create a more generalized solution in the absence of well-defined rules.


Design
Can we use few-shot learning to make LLMs? Use the parts page summary as an input for generating descriptions?

Build
We select good and poor descriptions and use few-shot prompting using good descriptions and corresponding summaries to generate for poor descriptions.

Test
Since we were limited to open-sourced models like TS, the prompt space is too small for adding more than a couple of examples. Very unsatisfactory results due to noisy and repetitive completions.

Learn
We can fine-tune the model if few-shot prompting is not feasible.


Design
Can we fine-tune the model using “good” descriptions?

Build
We fine-tune the model with “good” descriptions and corresponding summaries.

Test
We get better results, but the output is still somewhat noisy and repetitive.

Learn
We should make the criteria for "good" less strict so that we have many more examples for fine-tuning.


Design
Can we use the part pages to directly generate descriptions?

Build
We use an LLM for summarizing the content from the parts pages.

Test
The quality of descriptions is poor since there is a lot of verbosity in the descriptions; many good descriptions are now worse.


Test
The results obtained were improved in terms of relevance and coverage, based on feedback by team members.

Learn
We need more realistic data on what descriptions are good. This data can later also be used to train a model for reinforcement learning.

Toxicity Estimation

Design
Firstly, we decide what software to use and what toxicities to check. Collect all data regarding the proteins, including its mRNA sequence, components of the LNP, and relevant structural data.

Build
We decide on using toxicity estimation software tools. We try to input our components and check their toxicities.

Test
On trying to input, we first fail as the TEST does not present an option to enter our mRNA sequence. Further, we are unable to incorporate structural features of our protein and LNP.

Learn
We learn the limitations of the software and decide to break down our composite LNP-mRNA into its components and analyze them piecewise.


Design
We now look for programs that may analyze the toxicity of our protein/mRNA sequences. We find CSM-toxin as relevant software for our purpose.

Build
We input our mRNA sequence into CSM-Toxin. Meanwhile, we input various components of our LNPs into the TEST software to estimate individual toxicities.

Test
CSM-Toxin verifies that our protein is non-toxic. TEST reveals various results for components of our LNP in terms of immunogenicity, developmental toxicity, LD50, etc.

Learn
We see that TEST is unable to directly analyze some components of the LNP, and we must find similar, known components to analyze.


Design
We utilize TEST's existing database to find molecules similar to our LNP's components to analyze them.

Build
We run in-depth checks for all similar molecules.

Test
Similar molecules return a variety of results that we document and save on our Git-Repo.

Learn
Despite individual analyses, TEST and CSM-Toxin fail to analyze the toxicity of our mRNA-LNP as a whole.


Beyond this, a plan remains to find more comprehensive ways to check the toxicity of our composite mRNA-LNP based on even more parameters and come up with an upper limit for the safe dosage of our therapeutic into the human body. On a more ambitious scale, in silico and in vivo methods could be run simultaneously to check the accuracy of all these computational toxicity prediction tools and give inputs for further improvements based on in vivo data.
Wet Lab
Dry Lab