iGEM 2021 — Team William_and_Mary

[![](https://static.igem.org/mediawiki/2021/8/8b/HQ_page_logo.jpg)](https://2021.igem.org)

# Team:William and Mary/Notebook

* [Home](https://2021.igem.org/Team%3AWilliam_and_Mary)
* Project
  + [Description](https://2021.igem.org/Team%3AWilliam_and_Mary/Description)
  + [Design](https://2021.igem.org/Team%3AWilliam_and_Mary/Design)
  + [Results](https://2021.igem.org/Team%3AWilliam_and_Mary/Results)
  + [Engineering Success](https://2021.igem.org/Team%3AWilliam_and_Mary/Engineering)
  + [Proposed Implementation](https://2021.igem.org/Team%3AWilliam_and_Mary/Implementation)
  + [Proof of Concept](https://2021.igem.org/Team%3AWilliam_and_Mary/Proof_Of_Concept)

- [Model](https://2021.igem.org/Team%3AWilliam_and_Mary/Model)
- [Measurement](https://2021.igem.org/Team%3AWilliam_and_Mary/Measurement)
- Judging
  * [Medal Requirements](https://2021.igem.org/Team%3AWilliam_and_Mary/Medal_Requirements)
  * [Contribution](https://2021.igem.org/Team%3AWilliam_and_Mary/Contribution)
  * [Parts](https://2021.igem.org/Team%3AWilliam_and_Mary/Parts)
  * [Part Collection](https://2021.igem.org/Team%3AWilliam_and_Mary/Part_Collection)
- [Collaborations](https://2021.igem.org/Team%3AWilliam_and_Mary/Collaborations)
- Team
  * [Meet the Team](https://2021.igem.org/Team%3AWilliam_and_Mary/Team)
  * [Attributions](https://2021.igem.org/Team%3AWilliam_and_Mary/Attributions)
  * [Notebook](https://2021.igem.org/Team%3AWilliam_and_Mary/Notebook)
  * [Inclusivity](https://2021.igem.org/Team%3AWilliam_and_Mary/Inclusivity)
- [Safety and Security](https://2021.igem.org/Team%3AWilliam_and_Mary/Safety)
- Outreach
  * [Integrated Human Practices](https://2021.igem.org/Team%3AWilliam_and_Mary/Human_Practices)
  * [Education & Communication](https://2021.igem.org/Team%3AWilliam_and_Mary/Communication)
  * [Education](https://2021.igem.org/Team%3AWilliam_and_Mary/Education)
- [Excellence in Another Area](https://2021.igem.org/Team%3AWilliam_and_Mary/Excellence_in_Another_Area)

**[Spring Semester 2021](#Spring)**
**[Summer 2021](#Summer)**
**[Fall Semester 2021](#Fall)**

# Notebook

.

## Spring Semester 2021:

### 2/14 - 2/20:

* First meeting: discussed basic tenets and principles of synthetic biology
* Discussed past iGEM projects that included wetlab and modelling

### 2/21 - 2/27:

* Discussed what makes a great iGEM project
* Discussed basic tenets and principles of synthetic biology
* Reviewed medal criteria
* Presented on past iGEM projects that stood out to us

### 2/28 - 3/06:

* Read papers on bioremediation and synthetic biology applications
* Continued discussion on synthetic biology fundamentals

### 3/07 - 3/13:

* Started brainstorming project ideas
* Started literature search for topics
* Discussed many applications of synthetic biology: bioremediation for aquatic systems and pharmaceutical waste

### 3/14 - 3/20:

* Compiled ideas from project brainstorming
* Continued idea research
* Continued discussion of synthetic biology fundamentals

### 3/21 - 3/27:

* Discussed oyster parasitic infection ideas
* Continued idea research - discussion of orthogonality

### 3/28 - 4/03:

* Continued oyster discussion
* Continued orthogonality discussion
* Continued marine bioremediation discussion
* Continued idea research

### 4/04 - 4/10:

* Continued idea research: orthogonality
* Arranged for summer housing
* Prepared for outreach BioEngineering presentation to students

### 4/11 - 4/17:

* Discussed bacterial plastic remediating
* Continued idea research

### 4/18 - 4/24:

* Conducted BioEngineering presentation
* Discussed orthogonality
* Continued discussing plastic and microplastic remediation

### 4/25 - 5/01

* Read and searched for literature on orthogonality
* Continued discussing microplastic remediation

### 5/02 - 5/08

* Discussed diatoms for microplastic remediation
* Continued discussing orthogonality

### 5/09 - 5/15

* Made individual iGEM accounts under team name
* Decided on orthogonality focus

### 5/16 - 5/22

* Searched Addgene for interesting circuits in *E. coli*

+ Reviewed details and decided on favorites

* Ordered Addgene circuits in *E. coli* for testing over the summer

### 5/23 - 5/29

* Assigned circuits to people and working groups for testing

+ Reviewed strains and feasibility of testing

* Researched literature on orthogonality in Synthetic Biology
* Completed CITI research training modules online

.

## Summer 2021:

### 5/30 - 6/05

* Completed basic laboratory training
* Conducted a series of minipreps to confirm plasmids
* Finished institutional research training
* Developed protocols for equipment in the lab
* Reviewed papers concerning orthogonality
* Created Powerpoint presentation for outside our lab

### 6/06 - 6/12

* Performed restriction digests on minipreps to confirm plasmids
* Conducted team wide discussions on orthogonality papers
* Looked into previous iGEM projects involving orthogonality
* Reached out to the Second Sundays organization to present at their festival
* Wrote our Meet the Team Page
* Formed Wiki work groups

### 6/13 - 6/19

* Performed restriction digests on minipreps to confirm presence of plasmids
* Wrote protocols to confirm functionality of plasmids
* Filled out safety form
* Designed primers to confirm plasmids
* Searched the literature for RNA-sequencing of circuits

### 6/20 - 6/26

* Began working on confirming plasmid functionality
* Conducted team discussion on papers related to orthogonality that used RNA-seq
* Started brainstorming larger picture ideas of how to apply our orthogonality model
* Miniprepped and made official glycerol stocks from AddGene colonies
* Submitted our Safety Form
* Finished Powerpoint for outside lab
* Worked on Impact Grant application

### 6/27 - 7/03

* Continued working on confirming plasmid functionality
* Completed surveys posted by other iGEM teams
* Began looking for RNA extraction protocols suited for our experiments
* Began working on the script for the Promotional Video
* Started planning for the Mid-Atlantic Meetup
* Reached out to retirement homes
* Worked on identifying circuit backbones
* Started discussing design ideas for the diagnostic circuit

### 6/27 - 7/03

* Continued working on confirming plasmid functionality
* Completed surveys posted by other iGEM teams
* Began looking for RNA extraction protocols suited for our experiments
* Began working on the script for the Promotional Video
* Started planning for the Mid-Atlantic Meetup
* Reached out to retirement homes
* Worked on identifying circuit backbones
* Started discussing design ideas for the diagnostic circuit

### 7/04 - 7/10

* Worked on animations for Promotional Video
* Worked on the script for the Promotional Video
* Began looking for speakers for the Mid-Atlantic Meetup
* Decided which circuits to continue working with for RNA extraction

### 7/11 - 7/17

* Worked on animations for Promotional Video
* Started creating databases for papers/projects mentioning orthogonality
* Continued protocol for circuits whose RNA we planned to sequence
* Began drafting IHP emails
* Started planning for this year’s Mid-Atlantic Meetup

### 7/18 - 7/24

* Planned for this year’s Mid-Atlantic Meetup
* Finalized editing for Promotional Video
* Analyzed DE genes from genetic circuits in the literature
* Froze cell samples for RNA extraction from pDawn-Ag43 in *E. coli* JS006
* Continued work on databases for papers/projects mentioning orthogonality

### 7/25 - 7/31

* Designed schematic for sensor circuit, looked into individual parts
* Added marker genes to host model, found parameters, started working on PTM model
* Researched PTMs with regard to metabolism and heat shock response
* Drafted IHP emails to researchers and stakeholders with updates to reflect circuit design
* Finished database for ACS SynBio papers published in 2020

### 8/01 - 8/07

* Continued search for the individual parts of our circuit
* Hosted the Mid-Atlantic Meetup

+ Prepared for animation and data science seminar
+ Prepared for team presentation

* Continued analysis of DE genes from genetic circuits, considering potential environmental interactions
* Researched population models including colony formation

### 8/08 - 8/14

* Started reviewing ACS SynBio database and 2019 iGEM project database
* Wrote team Attributions page for Wiki
* Assigned responsibilities for Wiki pages and started drafting
* Started drafting Review
* Inoculated samples of untransformed DH5-alpha and JS006

+ Spun down samples for RNA extraction

* John Marken IHP Interview
* Aptamer literature search

+ Searched for new aptamer for *in vivo* transcription sensor circuit
+ Searched for optimal promoter

* Began transcription, translation, and post translational modification literature search for *in vitro* system
* Began genome integration literature search
* Had IHP meeting with John Marken

### 8/15 - 8/21

* Edited and revised Wiki drafts
* Aqib Hasnain IHP interview
* Continued ACS SynBio and 2019 iGEM database reviews
* Updated transcriptional sensor circuit parts
* Dr. Barrick IHP interview
* Ordered transcriptional sensor circuits from IDT
* Continued translational sensor circuit literature review
* Worked on differential gene Venn Diagram
* Presented at WindsorMeade retirement home

### 8/22 - 8/28

* Dr. Barrick follow up interview
* Researched Beyond Central Dogma differential genes
* Continued ACS SynBio and 2019 iGEM database reviews
* Ordered translational sensor circuits from IDT
* Designed Gibson primers for sensor circuit assembly
* Made protocol for transcriptional circuit testing

.

## Fall Semester 2021:

### 8/29 - 9/04

* Designed Beyond Central Dogma circuits to detect differential gene expression
* Finished review of ACS SynBio article spreadsheet
* Finished full drafts of Wiki pages
* Reviewed education pamphlet
* Ran double digest on pSB1C3 vector to confirm sequence
* Received translation and posttranslational modification circuits
* Made protocols for resuspension of gBlocks and oligonucleotides
* Performed PCR purification of pSB1C3 vector
* Performed Gibson assembly of circuits 11, 12, 13, and 16 and transformed into DH5-alpha
* Installed GENOCad

### 9/05 - 9/11

* Designed circuits to detect expression from *lon* promoter and *hslUV* promoter
* Reviewed education pamphlet
* Worked on designing in vitro sensor
* Transformed PTM circuit
* Recorded video for promotion of iGEM team
* Performed colony PCR on Gibsoned and transformed circuits
* Transformed circuit 15 into NEB 5-alpha for cloning
* Tested fluorescence of translational circuits in plate reader
* Performed primer resuspension of primers 702 and 703
* Sent out circuits 11, 12, and 16 for sequencing with primers 1, 16, 17, and 18
* Tested fluorescence of translational circuits using the plate reader
* Transformed circuits 13 and 16 into BL21(DE3)
* Edited wiki page drafts
* Created presentation for SDG Conference
* Learned and wrote code and instructions for differential gene expression analysis
* Calculated figures from spreadsheet of iGEM projects addressing orthogonality
* Continued genome integration research

### 9/12 - 9/18

* Continued *in vitro* sensor circuit design

+ Decided on *in vitro* aptamer

* Miniprepped transcriptional and translational circuits
* Finalized sensor circuit testing protocol
* Dr. Smith IHP interview
* Colony PCR on colonies 13, 15, and 16
* Finalized and ordered *lon*, *hslUV*, and *clpB* circuits
* Conducted NovaVax interview

### 9/19 - 9/25

* Began fluorescence testing of designed sensor circuits
* Gibsoned, transformed, and colony PCRed remaining circuits from IDT
* Sent out IHP emails

### 9/26 - 10/02

* Previewed Wiki pages and coding on site
* Continued testing designed sensor circuits
* Transformed translational circuits into BL21(DE3)
* Finalized project abstract

### 10/03 - 10/09

* Transformed Ceroni circuits into NEB 5-alpha and cotransformed with pBbB8k curli fiber circuit
* Began fluorescence testing Ceroni sensor circuits
* Dr. Renda IHP interview
* Started analyzing fluorescence data

### 10/10 - 10/16

* Continued and finished circuit fluorescence testing
* Continued data analysis
* Luis Ortiz IHP interview
* Transcriptional circuit functionality testing
* Converted fluorescence measurements to number of molecules

+ Input collected data into model

### 10/17 - 10/21

* Finalized Wiki
* Paul Maschoff IHP Interview

## See our Benchling Pages:

* [Alana and Matt](https://static.igem.org/mediawiki/2021/3/3c/T--William_and_Mary--File_Alana_Matt_Benchling.zip)
* [Avery](https://static.igem.org/mediawiki/2021/2/2b/T--William_and_Mary--Notebook_Avery_Benchling.zip)
* [Beteel, Linda, and Ubaid](https://static.igem.org/mediawiki/2021/9/95/T--William_and_Mary--Notebook_Beteel_Linda_Ubaid_Benchling.zip)
* [Julia and KC](https://static.igem.org/mediawiki/2021/d/d7/T--William_and_Mary--Notebook_Julia_KC_Benchling.zip)
* [Justin](https://static.igem.org/mediawiki/2021/a/a2/T--William_and_Mary--Notebook_Justin_Benchling.zip)
* [Pinar](https://static.igem.org/mediawiki/2021/a/a3/T--William_and_Mary--Notebook_Pinar_Benchling.zip)

[![](https://static.igem.org/mediawiki/2021/8/8b/HQ_page_logo.jpg)](https://2021.igem.org)

# Team:William and Mary/Engineering

* [Home](https://2021.igem.org/Team%3AWilliam_and_Mary)
* Project
  + [Description](https://2021.igem.org/Team%3AWilliam_and_Mary/Description)
  + [Design](https://2021.igem.org/Team%3AWilliam_and_Mary/Design)
  + [Results](https://2021.igem.org/Team%3AWilliam_and_Mary/Results)
  + [Engineering Success](https://2021.igem.org/Team%3AWilliam_and_Mary/Engineering)
  + [Proposed Implementation](https://2021.igem.org/Team%3AWilliam_and_Mary/Implementation)
  + [Proof of Concept](https://2021.igem.org/Team%3AWilliam_and_Mary/Proof_Of_Concept)

- [Model](https://2021.igem.org/Team%3AWilliam_and_Mary/Model)
- [Measurement](https://2021.igem.org/Team%3AWilliam_and_Mary/Measurement)
- Judging
  * [Medal Requirements](https://2021.igem.org/Team%3AWilliam_and_Mary/Medal_Requirements)
  * [Contribution](https://2021.igem.org/Team%3AWilliam_and_Mary/Contribution)
  * [Parts](https://2021.igem.org/Team%3AWilliam_and_Mary/Parts)
  * [Part Collection](https://2021.igem.org/Team%3AWilliam_and_Mary/Part_Collection)
- [Collaborations](https://2021.igem.org/Team%3AWilliam_and_Mary/Collaborations)
- Team
  * [Meet the Team](https://2021.igem.org/Team%3AWilliam_and_Mary/Team)
  * [Attributions](https://2021.igem.org/Team%3AWilliam_and_Mary/Attributions)
  * [Notebook](https://2021.igem.org/Team%3AWilliam_and_Mary/Notebook)
  * [Inclusivity](https://2021.igem.org/Team%3AWilliam_and_Mary/Inclusivity)
- [Safety and Security](https://2021.igem.org/Team%3AWilliam_and_Mary/Safety)
- Outreach
  * [Integrated Human Practices](https://2021.igem.org/Team%3AWilliam_and_Mary/Human_Practices)
  * [Education & Communication](https://2021.igem.org/Team%3AWilliam_and_Mary/Communication)
  * [Education](https://2021.igem.org/Team%3AWilliam_and_Mary/Education)
- [Excellence in Another Area](https://2021.igem.org/Team%3AWilliam_and_Mary/Excellence_in_Another_Area)

**[Improvement of a Part](#Improving)**
**[Engineering Success of Individual Circuits](#Individual)**
**[Transcriptional Burden Sensor Circuits](#Transcriptional)**
**[Translational Burden Sensor Circuits](#Translational)**
**[Post-Translational Modification Sensor Circuits](#Post-Translational)**
**[Beyond Burden Circuits](#Beyond)**

# Engineering Success

The W&M 2021 iGEM team had significant engineering success this season.  We are able to demonstrate engineering success with circuits to assess burden at all levels of the central dogma. These include the following circuits: 3WJ Dimeric Broccoli Circuit, translational burden sensors, phosphorylation sensors, *lon* protease sensor and *hslVU* protease sensor.

We were also able to demonstrate engineering success with circuits that assess orthogonality beyond burden. These include: *dnaK* and *dnaJ* heat shock protein sensor and *clpB* (ATPase) sensor. As explained in detail below, our team followed the engineering cycle repeatedly throughout our project in order to design, construct, and test our circuit system.

.

## Improvement of a Part

This year, our team was able to improve part BBa\_K769001 from iGEM12\_Tokyo-NoKoGen. This part consists of the ompC promoter, the B0034 RBS, GFP, and the B0015 terminator. To improve this part, our team created construct BBa\_K3773515 which replaces GFP with an sfGFP sequence from Ceroni et al. 2015.

The  *ompC*  promoter is a part of the EnvZ/OmpR system. In  *E. coli* , this system senses changes in the osmolarity of a cell's surrounding environment. In high osmolarity environments, ompC is produced. (Cai et al., 2002) Therefore, in order to confirm the functionality of our circuit, our team grew bacteria (E. coli BL21 and NEB5-alpha) transformed with this construct overnight in LB with a 20% concentration of sucrose. The next morning, after around 16 hours of growth, we used a plate reader to quantify the fluorescence output of our circuit. We found that our construct produced an average output of 73,913.25 RFU and 32,334 RFU in NEB5-alpha and BL21, respectively. To determine if our circuit was an improved version of the iGEM12\_Tokyo-NoKoGen iGEM construct, our team compared our data to the data that Universitas Indonesia published on the parts page for part BBa\_K769001. When grown in media with approximately the same osmolarity, their fluorescence outputs averaged 7,505.25 RFU in DH5-alpha (a similar strain to NEB5-alpha) and 7,408.72 in BL21. Therefore, our part was able to provide a greater fluorescence output than the original part, proving that our team was able to improve the original composite part.

.

## Engineering Success of Individual Circuits

For clarity, we have divided our circuits into the following categories, although they may fit into multiple categories.

## Transcriptional Burden Sensor Circuits

.

### **F30-Broccoli Circuits**

**Design:** Using the F30-Broccoli aptamer, we designed two transcriptional burden sensors. Our first design, WM21\_011, places control of F30-Broccoli expression under the strong, constitutive Anderson promoter BBa\_J23119, without incorporation of any RBS or spacers. Our second design, WM21\_012, places control of F30-Broccoli expression under the strong, constitutive P70a promoter without incorporation of any RBS or spacers. In both circuits, F30-Broccoli is directly followed by the strong, synthetic, bidirectional terminator BBa\_B1006.

**Build:** In order to construct these circuits, we ordered both inserts (including promoter, F30-Broccoli aptamer sequence, and terminator) flanked by unique nucleotide sequences (UNSs) 1 and 10, as gBlocks from IDT. We then used Gibson Assembly to join our insert with the vector pSB1C3 flanked by UNSs 1 and 10. Following Gibson Assembly, we transformed our transcriptional burden sensor circuits individually into *E. coli* DH5-alpha using a standard heat shock method.

**Test:** After transformations, we attempted to confirm the functionality of our circuits. We did this by growing a culture of transformed DH5-alpha cells overnight (37°C and 250 RPM), diluting to an OD600 of 0.4, and following 2-4 hours of growth, incubating the cells with the aptamer-activating dye DFHBI-1T at room temperature for 45 minutes. After the 45-minute incubation period, we tested for aptamer fluorescence in our culture using a plate reader and comparing its fluorescence to samples of untransformed cells and LB (negative controls) and samples of our translational burden sensor circuit (positive control). However, we were unable to confirm the functionality of our circuits, as the fluorescence levels of our samples transformed with transcriptional burden sensor circuits was similar to that of the negative controls. This is most likely due to synthesis difficulties, as F30-Broccoli itself contains repeats within its sequence as well as a hairpin within its sequence. This part was successfully sequenced by Epoch Life Science and sequence-confirmed using Benchling tools.

### **3WJ Dimeric Broccoli Circuit**

**Design**: We used the 3WJ dimeric Broccoli (3WJdb) circuit (pUC19-P70a-3WJdB-T), which was designed and constructed entirely by researchers Burke et al. This circuit contains the dimeric Broccoli aptamer placed within the three-way junction (3WJ) RNA motif, and this aptamer forms a secondary structure that is able to bind to a membrane-permeable, fluorescent dye known as DFHBI-1T. DFHBI-1T only fluoresces after binding to the Broccoli aptamer. This circuit places 3WJdb under the control of the constitutive P70a promoter.

**Build:** We acquired this circuit from Addgene (#87311).

**Test:** To test this circuit, we transformed it individually into BL21(DE3), which is the bacterial strain used in the original paper in which this circuit was constructed and tested (Alam et al., 2017). After growing a culture of transformed cells overnight (37°C and 250 RPM) and incubating with DFHBI-1T at room temperature for 45 minutes, we tested for aptamer fluorescence in our culture using a plate reader by comparing the fluorescence of the aptamer to samples of other transcriptional circuit sensors that were incubated with dye for the same amount of time, including the Split Broccoli system (Alam et al., 2017) and untransformed cells and LB (negative controls) and samples of our translational burden sensor circuit (positive control). The results of our test showed that 3WJ-Broccoli is able to bind to DFHBI-1T and produced a fluorescent signal *in vivo*. The next step of circuit testing was then to set up an experiment with 3WJ Broccoli and our test circuit, pBbB8k-csg-amylase, an arabinose-inducible curli fiber production circuit designed by researchers Birnbaum et al. In order to complete this step, we needed to do a cotransformation with the 3WJ-Broccoli circuit and our test circuit in *E. coli* BL21(DE3). After overnight incubation (37°C and 250 RPM), we made subcultures of our overnight cultures and grew all samples in plate reader plates. We compared the fluorescence of samples of 3WJ Broccoli individually transformed into the host, 3WJ Broccoli and our test circuit cotransformed into the host (uninduced), 3WJ Broccoli and our test circuit cotransformed into the host (induced with arabinose), untransformed BL21(DE3) cells, and LB. We measured the fluorescence of all overnight samples, all subcultures right before induction of the test circuit (T0), 1 hour after induction, 6 hours after induction, 12 hours after induction, 24 hours after induction, and 48 hours after induction. We repeated this experiment in plates a total of three times. This part was successfully sequenced by Epoch Life Science and sequence-confirmed using Benchling tools.

.

## **Translational Burden Sensor Circuits**

**Design:**For our translational burden sensor circuits, we tested two designs. The first version of this circuit, WM21\_016, was designed by researchers Ceroni et al. (Ceroni et al., 2015) to test translational burden of genetic constructs within a cell. However, while researchers Ceroni et al. incorporated their circuit into the host genome, we utilized their circuit in plasmid form. Researchers Ceroni et al. used the strong constitutive promoter BBa\_J23100 and an RBS designed (using an RBS calculator) specifically for codon optimized *sfGFP*. The sequence for codon optimized *sfGFP* was created for high expression levels using DNA2.0, and sfGFP was followed by a spacer and the synthetic terminator BBa\_B1002. Based on Ceroni et al. circuit concept and design, our team designed WM21\_013, a translational burden sensor consisting of the strong, constitutive Anderson promoter BBa\_J23119, the RBS B0034 with a spacer included, the codon optimized *sfGFP* sequence used by researchers Ceroni et al. (2015), and the strong synthetic, bidirectional terminator BBa\_B1006.

**Build:**In order to construct these circuits, we ordered both inserts (including promoter, RBS, codon-optimized *sfGFP* and terminator) flanked by unique nucleotide sequences (UNSs) 1 and 10, as gBlocks from IDT. We then used Gibson Assembly to join our insert with the vector pSB1C3 flanked by UNSs 1 and 10. Following Gibson Assembly, we transformed our translational burden sensor circuits individually into *E. coli* DH5-alpha using a standard heat shock method.

**Test:** To test the transcriptional sensor circuits, we transformed them individually into *E. coli* DH5-alpha. After growing a culture of transformed cells overnight (37°C and 250 RPM), we tested for sfGFP fluorescence using a plate reader by comparing the fluorescence of our circuits to untransformed cells and LB (negative controls) and samples of our translational burden sensor circuit (positive control). The results of our test showed that both circuits are functional and produce high levels of *sfGFP.* The next step of circuit testing was then to set up an experiment with our translational burden sensor circuits and our test circuit, pBbB8k-csg-amylase, an arabinose-inducible curli fiber production circuit designed by researchers Birnbaum et al. In order to complete this step, we needed to do a cotransformation with the translational burden sensor circuits and our test circuit in *E. coli* DH5-alpha. After overnight incubation (37°C and 250 RPM), we made subcultures of our overnight cultures and grew all samples in plate reader plates. We compared the fluorescence of samples of our translational burden sensor circuits individually transformed into the host, our translational burden sensor circuits and our test circuit cotransformed into the host (uninduced), our translational burden sensor circuits and our test circuit cotransformed into the host (induced with arabinose), untransformed *E. coli* DH5-alpha cells, and LB. We measured the fluorescence of all overnight samples, all subcultures right before induction of the test circuit (T0), 1 hour after induction, 6 hours after induction, 12 hours after induction, 24 hours after induction, and 48 hours after induction. We repeated these experiments in flasks a total of three times per translational burden sensor circuit. This part was successfully sequenced by Epoch Life Science and sequence-confirmed using Benchling tools.

.

## **Post-Translational Modification Sensor Circuits**

### **Phosphorylation Circuit**

**Design:** Our team designed WM21\_014 with the intent of quantifying differences in phosphorylation levels in host cells with and without an additional circuit. WM21\_014 utilizes the EnvZ/OmpR osmoregulation system of *E. coli* to measure changes in phosphorylation via a fluorescence output (sfGFP). This circuit design is taken from Schmidl et al.

**Build**: In order to build this circuit, our team utilized the sequence in Schmidl *et al*. We ordered this sequence as a gene block from IDT DNA with UNS1 and UNS10 sequences flanking the gene block in order to insert it into the 1C3 plasmid backbone. Once our gene block arrived, we Gibsoned it into the appropriate backbone and transformed it into NEB5-alpha cells. To confirm the efficacy of our transformation, we performed a colony PCR using a forward primer of UNS1 and a reverse primer using UNS10. Once the PCR was run, we performed a gel electrophoresis on the product to confirm that the insert was the appropriate length. Our gel electrophoresis was able to confirm that all tested colonies contained the appropriate plasmid lengths. These confirmed colonies were grown overnight and converted into glycerol stocks, which were then used for the rest of the experiment.

**Test**: In order to test the functionality of WM21\_014, our team performed a series of tests to determine fluorescence. As this system is supposed to fluoresce in response to changes in osmolarity, our team planned to grow this circuit in LB media with a 20% sucrose concentration overnight. After overnight growth, our team planned to measure the fluorescence of the culture with a plate reader. However, after our transformations, our team encountered an issue. Our colonies transformed with WM21\_014 were fluorescent on the plate without having been induced. Obviously, our circuit was not working properly and our team decided to design a new circuit. This part was successfully sequenced by Epoch Life Science and sequence-confirmed using Benchling tools.

**Failure & Redesign****:** In response to the presence of fluorescence without induction of circuit WM21\_014, our team designed WM21\_015. This circuit is based off of Tokyo-NoKoGen’s 2012 circuit (BBa\_K769001). However, instead of using GFP, our team used codon-optimized sfGFP (Ceroni et al., 2015). Our team used the same building and testing process, including the sequencing process, on WM21\_015 as WM21\_014 and was able to functionally confirm it.

### **Glycosylation Circuit**

**Design:** We designed WM21\_022 to quantify differences in glycosylation levels in host cells with or without an additional circuit. It makes use of the ftsLp2 promoter that initiates transcription of genes involved in glycosylation of peptidoglycan in *E. coli*, such as *murG*, a glycosyltransferase, and *murC*, a ligase that binds MurNAc to an amino acid (Barreteau et al, 2008). The promoter, which is regulated by the repressor LexA and was identified by Vicente et al, would assess glycosylation in the host cell via a fluorescence output by expressing sfGFP. The circuit featured strong RBS BBa\_B0034 with a spacer (which had been successful with sfGFP expression in E. coli), codon optimized *sfGFP*, and strong terminator BBa\_B1006. These parts were all used in the design of translation sensor circuit WM21\_013 and had proven to be successful.

**Build:** In order to build this circuit, our team combined the sequence from the E. coli genome at the position indicated by Vicente et al., BBa\_0034, *sfGFP*, and BBa\_B1006. We ordered this sequence as a gene block from IDT DNA with UNS1 and UNS10 sequences flanking the gene block in order to insert it into the 1C3 plasmid backbone. Once our gene block arrived, we performed a Gibson assembly to insert the fragment into the 1C3 backbone and transformed it into NEB5-alpha cells. To confirm the efficacy of our transformation, we performed a colony PCR using a forward primer for UNS1 and a reverse primer for UNS10. Once the PCR was run, we performed a gel electrophoresis on the product to confirm that the insert was the appropriate length. Our gel electrophoresis was able to confirm that all tested colonies contained the appropriate plasmid lengths. These confirmed colonies were grown overnight and converted into glycerol stocks, which were then used for the rest of the experiment.

**Test:** Once we confirmed the transformation of the circuit, we tested its functionality by measuring its fluorescence compared to untransformed cells and successfully fluorescing cells. When these tests showed that the circuit was not fluorescing above the levels of the untransformed cells, we set about inducing the SOS response in the cells to derepress LexA from the circuit’s ftsLp2 promoter. The transformed cells were exposed to UV light and subinhibitory levels of Ciprofloxacin (Sassanfar et al, 1990, Baharoglu et al, 2011), but all attempts were unsuccessful at making induced, transformed cells consistently and significantly more fluorescent than untransformed cells. This part was successfully sequenced by Epoch Life Science and sequence-confirmed using Benchling tools.

**Failure:** We designed this circuit with the goal of assessing another common mode of post translational modification besides phosphorylation in the host cells. Searching in the literature yielded no prior attempts to do this. This may be related to the fact that there is currently no characterization of protein glycosylation in non-pathogenic strains of *E. coli.* Glycosylation in *E. coli* is typically associated with toxicity to humans because glycosylated auto-transporter proteins aid in biofilm aggregation and binding to human cells (Sherlock et al, 2006). Knowing this, we decided our best option to represent glycosylation was the synthesis of peptidoglycan, so we selected the ftsLp2 promoter that initiates transcription of the *mur* operon, involved with peptidoglycan synthesis. However, we could find no prior use of this promoter in synthetic biology to design a circuit, so we were unsure if this design would be functional. The sequence was estimated by the location of LexA-binding site sequence homology analysis and S1 nuclease mapping analysis, but we believe it is most likely that the sequence we used for our design was incomplete or inaccurate.

### **Proteolysis Circuits**

#### ***lon*****Protease**

**Design**: We designed the *lon* sensor circuit, WM21\_023, to report expression of the promoter which codes for the protease Lon in *E. coli.* This circuit places codon-optimized *sfGFP* after the promoter and the strong RBS BBa\_B0034, allowing us to report expression via green fluorescence. The terminator BBa\_B1002 is also used. Finally, between the RBS and *sfGFP* is a spacer which has been shown to allow for expression with BBa\_B0034 and sfGFP (Clifton et al., 2018). Additionally, we flanked the circuit with the UNS 1 and UNS 10 sequences from Torella et al., 2014 in order to allow for easy Gibson assembly (Torella et al., 2014).

**Build**: After ordering our circuit from IDT as a gene block, we inserted it into the 1C3 plasmid backbone. We transformed the plasmid into NEB5-alpha cells, which we chose due to its high MiniPrep concentrations and success with single transformations and cotransformations with circuits in the past. We then confirmed the transformations by growing cells on plates with selection for chlor-resistance, which is conferred by the 1C3 backbone. Furthermore, we performed a colony PCR on four colonies and confirmed that they contained plasmids of the correct length using gel electrophoresis. All four bands were confirmed, and we made MiniPreps and corresponding glycerol stocks of cells from two of the colonies. The initial MiniPrep returned concentrations of below 50 ng/uL, but a second attempt gave a concentration of 89.4 ng/uL for Colony 1 and 62.5 ng/uL for Colony 2.

**Test**: We confirmed that the circuit produced green fluorescence by inoculating cells from our two glycerol stocks overnight and placing them in the plate reader along with two negative controls (LB alone and untransformed NEB5-alpha cells) and a positive control (Circuit 13, which we had previously seen to fluoresce). Although the positive control failed to fluoresce in this case for unknown reasons, cells from both colonies containing WM21\_023 fluoresced far more than the negative controls. Additionally, the positive control did fluoresce during functional confirmation of the *clpB* and *hslVU* sensors; when compared to these fluorescence values, *lon* caused fluorescence that was about half as high as that of the positive control for both colonies. As a result, we decided to use our Colony 1 as our main source of cells, as its MiniPrep had the highest concentration of the two MiniPrepped colonies. This part was successfully sequenced by Epoch Life Science and sequence-confirmed using Benchling tools.

#### ***hslVU*** **Protease**

**Design**: We designed the *hslVU* sensor circuit, Circuit 24, to report expression of the promoter which codes for the protease complex hslVU in *E. coli.* This circuit places codon-optimized *sfGFP* after the promoter and the strong RBS BBa\_B0034, allowing us to report expression via green fluorescence. The terminator BBa\_B1002 is also used. Finally, between the RBS and *sfGFP* is a spacer which has been shown to allow for expression with BBa\_B0034 and sfGFP (Clifton et al., 2018). Additionally, we flanked the circuit with the UNS 1 and UNS 10 sequences from Torella et al., 2014 in order to allow for easy Gibson assembly (Torella et al., 2014).

**Build**: After ordering our circuit from IDT as a gene block, we inserted it into the 1C3 plasmid backbone. We transformed the plasmid into NEB5-alpha cells, which we chose due to its high MiniPrep concentrations and success with single transformations and cotransformations with circuits in the past. We then confirmed the transformations by growing cells on plates with selection for chlor-resistance, which is conferred by the 1C3 backbone. Furthermore, we performed a colony PCR on four colonies and confirmed that they contained plasmids of the correct length using gel electrophoresis. All four bands were confirmed, and we made MiniPreps and corresponding glycerol stocks of cells from two of the colonies. Cells from Colony 1 provided a MiniPrep concentration of 155.5, and cells from Colony 2 provided a concentration of 187.9, albeit with lower purity.

**Test**: We confirmed that the circuit produced green fluorescence by inoculating cells from our two glycerol stocks overnight and placing them in the plate reader along with two negative controls (LB alone and untransformed NEB5-alpha cells) and a positive control (Circuit 13, which we had previously seen to fluoresce). Cells from both colonies containing the *hslVU* sensor fluoresced at a similar level to the positive control. Additionally, we heat shocked an additional sample of cells from each colony to confirm the circuit’s sensitivity to some form of stress, as authors who have used the promoter in a circuit before have seen an increase in fluorescence after heat shock (Lien et al., 2009). We placed the cells, which were initially at 37ºC, into a 42ºC heating block for 20 minutes. We then measured green fluorescence in the plate reader and saw that, on average, Colony 1 induced cells fluoresced about 30% more than uninduced cells. Colony 2 induced cells fluoresced about 20% more. As a result, we decided to use our Colony 1 as our main source of cells; additionally, its MiniPrep had the highest concentration of the two MiniPrepped colonies. It is worth noting that if we had waited for a longer time after heat shock before measuring fluorescence, the increase would likely have been higher (Lien et al., 2004). This part was successfully sequenced by Epoch Life Science and sequence-confirmed using Benchling tools.

.

## **”Beyond Burden” Circuits**

### **Heat Shock Response Proteins**

#### ***dnaK and dnaJ (dnaKJ)*****Sensor**

**Design**: With the goal of quantifying the changes in expression of both *dnaK* and *dnaJ* through fluorescence output, we designed a single sensor circuit, WM21\_021, consisting of the constitutive *dnaKJ* operon promoter (Cowing et al.,1985), the *sfGFP* coding region, and the terminator BBa\_B1006. To be able to do Gibson Assembly with ease, we added UNS1 and UNS10 sequences to the ends of our insert design before ordering. We also made sure to find multiple sources that used the same promoter sequence and tested its functionality *in vivo*. When ordering the circuit, we took advantage of the IDT design tools to confirm that IDT is able to produce these gene blocks.

**Build**: To build our plasmids, we first inserted the circuit using Gibson Assembly into the 1C3 plasmid backbone, which has chloramphenicol antibiotic resistance. We then transformed our plasmids into NEB5-alpha cells which we chose because of its high MiniPrep concentration yield and success with single transformations and cotransformations based on results from circuit WM21\_015 (the phosphorylation circuit). To confirm that the cells had the plasmids with the right inserts, after selecting for the cells with the specific antibiotic resistance on the LB agar plates with chloramphenicol, we ran a colony PCR on 4 different single colonies from the successful overnight growth plate. After amplification of the inserts using colony PCR with a forward primer at UNS1 and a reverse primer at UNS10 (which encapsulated our insert), we then conducted gel electrophoresis to determine whether the inserted gene blocks were of the appropriate length according to what we had designed. For the WM21\_021 circuit, which has a length of 1057 bp, all 4 colonies showed the correct bands close to the 1000 bp line. Three colonies were chosen for making MiniPreps and glycerol stocks with the last colony solution, made during colony PCR preparation, being put aside as a backup.

**Test**: To test whether the insert was functional, we needed to check if there was fluorescence production in cells with our circuit. Taking the heat-shocking protocol from Cha et al. (1999) as a model, we conducted a plate reader experiment to measure the fluorescence and OD600 levels of transformed cells that were heat shocked from 37 C to 42 C, cells that were not heat-shocked, untransformed NEB5-alpha cells (negative control), LB supplemented with chloramphenicol (negative control), and NEB5-alpha cells transformed with circuit WM21\_013 which we had previously functionally confirmed to be producing green fluorescence constitutively (positive control). Interestingly, the heat shocked cells had fluorescence readings lower than those that were not heat-shocked. This was probably due to the adjustments we made to the model protocol by Cha et al. (1999) where they had originally been cold-shocked at 30 C and then taken to 42 C for the heat-shock for a longer time which may have been why they observed higher fluorescence for the heat-shocked cells in their results. Regardless, our results showed that the heat shocked cells and the non-heat shocked cells had relative fluorescence levels 5- to 10-fold higher than the controls, indicating that the dnaKJ operon promoter is indeed functional and allows for the expression of sfGFP. This part was successfully sequenced by Epoch Life Science and sequence-confirmed using Benchling tools.

#### ***clpB*** **(ATPase) Sensor**

**Design**: We designed the *clpB* sensor circuit to report expression of the promoter which codes for the heat shock protein *clpB* in *E. coli.* Our initial design of this circuit, WM21\_017, placed codon-optimized *sfGFP* after the promoter and the RBS which normally precedes *clpB* in *E. coli*, allowing us to report expression via green fluorescence. We included this RBS because the same was done by Kitagawa *et al.,* 1991 (Kitagawa et al.,1991). The terminator BBa\_B1002 was also used. Finally, between the RBS and *sfGFP* was a spacer which has been shown to allow for expression with BBa\_B0034 and sfGFP (Clifton et al., 2018). Additionally, we flanked the circuit with the UNS 1 and UNS 10 sequences from Torella et al., 2014 in order to allow for easy Gibson assembly (Torella et al., 2014).

**Build**: After ordering our circuit from IDT as a gene block, we inserted it into the 1C3 plasmid backbone. We transformed the plasmid into NEB5-alpha cells, which we chose due to its high MiniPrep concentrations and success with single transformations and cotransformations with circuits in the past. We then confirmed the transformations by growing cells on plates with selection for chlor-resistance, which is conferred by the 1C3 backbone. Furthermore, we performed a colony PCR on four colonies and confirmed that they contained plasmids of the correct length using gel electrophoresis. All four bands were confirmed, and we made MiniPreps and corresponding glycerol stocks of cells from two colonies. Cells from Colony 1 provided a MiniPrep concentration of 103.7, and cells from Colony 2 provided a concentration of 192.3, albeit with lower purity.

**Test**: We confirmed that the circuit produced green fluorescence by inoculating cells from our two glycerol stocks overnight and placing them in the plate reader along with two negative controls (LB alone and untransformed NEB5-alpha cells) and a positive control (Circuit 13, which we had previously seen to fluoresce). Cells from both colonies containing WM21\_017 fluoresced about half as much as the positive control, which was not as high as we had hoped. Additionally, we heat shocked an additional sample of cells from each colony to confirm the circuit’s sensitivity to some form of stress, as authors who have used the promoter in a circuit before have seen an increase in fluorescence after heat shock (Cha et al., 1999). We placed the cells, which were initially at 37ºC, into a 42ºC heating block for 20 minutes. We then measured green fluorescence in the plate reader and saw that, on average, Colony 1 induced cells fluoresced about 10% more than uninduced cells. Colony 2 induced cells fluoresced about 60% more. It is worth noting that if we had waited for a longer time after heat shock before measuring fluorescence, the increase would likely have been higher (Cha et al., 1999).

Additionally, when we cotransformed WM21\_017 with pBbB8k-csg-amylase into NEB5-alpha cells, we saw that fluorescence was consistently higher than when WM21\_017 was transformed alone into cells. We had designed this circuit to show increased fluorescence when cotransformed with a second circuit such as pBbB8k-csg-amylase. This part was successfully sequenced by Epoch Life Science and sequence-confirmed using Benchling tools.

**Failure & Redesign**: Although it was not a failure, we hoped to improve upon the fluorescence of WM21\_017, which was only half as fluorescent as Circuit 13. More importantly, WM21\_017 did not succeed at producing higher fluorescence when cotransformed alongside another circuit. Fortunately, we had considered that WM21\_017 might have produced lower fluorescence than was possible, and therefore designed a different version of the *clpB* sensor circuit which includes the RBS BBa\_B0034 rather than the RBS native to *E. coli.* This design, WM21\_018, is more in line with our Circuit 13, which successfully produced fluorescence and includes BBa\_B0034.

**Build:** We built this circuit in the same way as WM21\_017, transforming it into cells and confirming that plasmids of the correct length were present through colony PCR and gel electrophoresis. The bands for Colonies 3 and 4 looked the most clearly confirmed, so we made MiniPreps and corresponding glycerol stocks of cells from those colonies. Cells from Colony 3 provided a MiniPrep concentration of 212.3, while cells from Colony 4 provided a concentration of 181.1. We chose to use cells from Colony 3 due to the higher MiniPrep concentration.

**Test:** We functionally confirmed WM21\_018 in the same way as was used for WM21\_017. Cells from both colonies containing WM21\_018 fluoresced over twice as much as the positive control before heat shock, indicating an improvement from WM21\_017. After heat shock, cells from Colony 3 did not fluoresce any more than non-heat-shocked cells. Although we had hoped that heat shock would cause an increase in fluorescence, we believed that if we had measured fluorescence after a longer time had elapsed after heat shock, there would be a larger increase, as this would have more closely reflected the methods used by Cha et al., 1999 to observe an increase in fluorescence (Cha et al., 1999).

When we cotransformed WM21\_018 with pBbB8k-csg-amylase into NEB5-alpha cells, we did see that fluorescence was higher over time for cotransformed cells than cells with the circuit alone. This suggests that, unlike WM21\_017, WM21\_018 successfully responds to the presence of pBbB8k-csg-amylase with increased sfGFP expression.

### **Secretion (AG43) Sensors**

**Design:** We designed two circuits using two different promoters for the flu gene (*flup1* and *flup2*) with the intent to sense cellular secretion as a response to the insertion of a circuit.

**Build:** We then built these circuits using Gibson Assembly after ordering a gene block for each circuit that was bookended by UNS1 and UNS10 for easy insertion into the 1C3 backbone. After transformation into NEB5-alpha cells, we proceeded to run a colony PCR, where we selected 4 colonies per circuit to verify. After amplification of the inserts using colony PCR with a forward primer at UNS1 and a reverse primer at UNS10 (which encapsulated our insert), we then conducted gel electrophoresis to determine whether the inserted gene blocks were of the appropriate length according to what we had designed. After verifying the length of the gene blocks, we then inoculated using the PCR tubes and let the cultures grow overnight. The next day, we conducted MiniPreps to create working -20ºC and -80ºC glycerol stocks for each of our plasmids.

**Test:** To test the functionality of our circuits, we inoculated from our -20ºC glycerol stocks and let the cultures grow overnight. We then pipetted three wells, each containing 200 µL of our culture, into a 96-well plate, along with three wells of the same volume of each of our positive and negative controls. Our positive control was WM21\_013, a circuit that we had already found to exhibit incredibly high fluorescence values, and our negative control was LB, as the medium we used should not have been fluorescent. We then ran the plate through a Synergy H-1 plate reader, and determined whether the wells containing our plasmid cultures were more fluorescent than the negative control, with the positive control as a benchmark for the expected high level of fluorescence given our use of sfGFP. This part was successfully sequenced by Epoch Life Science and sequence-confirmed using Benchling tools.

**Failure & Redesign:** Both of the secretion circuits failed to be functionally confirmed. We hypothesize that this may be due to the fact that the promoters we used were not well-characterized, given that the rest of these circuits were identical to the corresponding parts of other functional circuits we had tested. If we were to redesign this circuit in the future, it would be prudent to find a promoter for the *flu* gene that has been better characterized, although it is possible that such a promoter does not currently exist in the literature.

Please see [here](https://static.igem.org/mediawiki/2021/9/97/T--William_and_Mary--Protocols.zip) for our laboratory protocols.

\*Our team understands that many of these circuits can fit into both the Central Dogma sensor circuit category and "Beyond Burden" sensor circuit category; therefore, these circuits were grouped together for clarity.

## References

Alam, K. K., Tawiah, K. D., Lichte, M. F., Porciani, D., & Burke, D. H. (2017). A Fluorescent Split Aptamer for Visualizing RNA-RNA Assembly In Vivo. ACS synthetic biology, 6(9), 1710–1721. https://doi.org/10.1021/acssynbio.7b00059

Baharoglu, Z., Mazel, D. (2011). Vibrio cholerae Triggers SOS and Mutagenesis in Response to a Wide Range of Antibiotics: a Route towards Multiresistance. Antimicrobial Agents and Chemotherapy, 55(5), 2438-2441.

Barreteau, H., Kovac, A., Boniface, A., Sova, M., Gobec, S., & Blanot, D. (2008). Cytoplasmic steps of peptidoglycan biosynthesis. FEMS Microbiology Reviews, 32(2), 168–207.

Birnbaum, D. P., Manjula-Basavanna, A., Kan, A., Tardy, B. L., Joshi, N. S., Hybrid Living Capsules Autonomously Produced by Engineered Bacteria. Adv. Sci. 2021, 8, 2004699. https://doi.org/10.1002/advs.202004699

Cai, S. and Inouye, M. (2002). EnvZ-OmpR Interaction and Osmoregulation in Escherichia coli. Journal of Biological Chemistry. https://doi.org/10.1074/jbc.M110715200

Ceroni, F., Algar, R., Stan, G., & Ellis, T. (2015). Quantifying cellular capacity identifies gene expression designs with reduced burden. Nature Methods, 12(5):415-418. Doi: 10.1038/nmeth.3339

Cha, H., Srivastava, R., Vakharia, V., Rao, G., & Bentley, W (1999). Green Fluorescent Protein as a Noninvasive Stress Probe in Resting Escherichia coli Cells. Applied and Environmental Microbiology, 65(2):409-411. https://doi.org/10.1128/AEM.65.2.409-414.1999

Clifton, K. P., Jones, E. M., Paudel, S., Marken, J. P., Monette, C. E., Halleran, A. D., ... & Saha, M. S. (2018). The genetic insulator RiboJ increases expression of insulated genes. Journal of biological engineering, 12(1), 1-6.

Cowing,D.W., Bardwell,J.C., Craig,E.A., Woolford,C., Hendrix,R.W. & Gross,C.A. Consensus sequence for Escherichia coli heat shock gene promoters.(1985) Proc. Natl. Acad. Sci. U.S.A. 82(9), 2679-2683

Kitagawa, M. A. S. A. N. A. R. I., Wada, C., Yoshioka, S., & Yura, T. (1991). Expression of ClpB, an analog of the ATP-dependent protease regulatory subunit in Escherichia coli, is controlled by a heat shock sigma factor (sigma 32). Journal of Bacteriology, 173(14), 4247-4253.

Lien, H. Y., Yu, C. H., Liou, C. M., & Wu, W. F. (2009). Regulation of clpQ+ Y+ (hslV+ U+) gene expression in Escherichia coli. The Open Microbiology Journal, 3, 29.

Sassanfar, M., Roberts, J. (1990). Nature of the SOS-inducing Signal in Escherichia coli. Journal of Molecular Biology, 212(1), 79-96.

Schmidl, S., Sheth, R., Wu, A., & Tabor, J. (2014). Refactoring and Optimization of Light-Switchable Escherichia coli Two-Component Systems. ACS Synthetic Biology. 3(11): 820-831. https://doi.org/10.1021/sb500273n

Sherlock, O., Dobrindt, U., Jensen, J. B., Vejborg, R. M., Klemm, P. (2006). Glycosylation of the Self-Recognizing Escherichia coli Ag43 Autotransporter Protein. Journal of Bacteriology, 188(5), 1798-1807.

Torella, J. P., Boehm, C. R., Lienert, F., Chen, J. H., Way, J. C., & Silver, P. A. (2014). Rapid construction of insulated genetic circuits via synthetic sequence-guided isothermal assembly. Nucleic Acids Research, 42(1), 681-689.

Vicente, M., Gomez, M. J., & Ayala, J. A. (1998). Regulation of transcription of cell division genes in the Escherichia coli dcw cluster. Cellular and Molecular Life Sciences, 54(4), 317-324.

[![](https://static.igem.org/mediawiki/2021/8/8b/HQ_page_logo.jpg)](https://2021.igem.org)

# Team:William and Mary/Experiments

# Experiments

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).

### What should this page contain?

* Protocols
* Experiments

### Inspiration

* [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)

[![](https://static.igem.org/mediawiki/2021/8/8b/HQ_page_logo.jpg)](https://2021.igem.org)

# Team:William and Mary/Results

* [Home](https://2021.igem.org/Team%3AWilliam_and_Mary)
* Project
  + [Description](https://2021.igem.org/Team%3AWilliam_and_Mary/Description)
  + [Design](https://2021.igem.org/Team%3AWilliam_and_Mary/Design)
  + [Results](https://2021.igem.org/Team%3AWilliam_and_Mary/Results)
  + [Engineering Success](https://2021.igem.org/Team%3AWilliam_and_Mary/Engineering)
  + [Proposed Implementation](https://2021.igem.org/Team%3AWilliam_and_Mary/Implementation)
  + [Proof of Concept](https://2021.igem.org/Team%3AWilliam_and_Mary/Proof_Of_Concept)

- [Model](https://2021.igem.org/Team%3AWilliam_and_Mary/Model)
- [Measurement](https://2021.igem.org/Team%3AWilliam_and_Mary/Measurement)
- Judging
  * [Medal Requirements](https://2021.igem.org/Team%3AWilliam_and_Mary/Medal_Requirements)
  * [Contribution](https://2021.igem.org/Team%3AWilliam_and_Mary/Contribution)
  * [Parts](https://2021.igem.org/Team%3AWilliam_and_Mary/Parts)
  * [Part Collection](https://2021.igem.org/Team%3AWilliam_and_Mary/Part_Collection)
- [Collaborations](https://2021.igem.org/Team%3AWilliam_and_Mary/Collaborations)
- Team
  * [Meet the Team](https://2021.igem.org/Team%3AWilliam_and_Mary/Team)
  * [Attributions](https://2021.igem.org/Team%3AWilliam_and_Mary/Attributions)
  * [Notebook](https://2021.igem.org/Team%3AWilliam_and_Mary/Notebook)
  * [Inclusivity](https://2021.igem.org/Team%3AWilliam_and_Mary/Inclusivity)
- [Safety and Security](https://2021.igem.org/Team%3AWilliam_and_Mary/Safety)
- Outreach
  * [Integrated Human Practices](https://2021.igem.org/Team%3AWilliam_and_Mary/Human_Practices)
  * [Education & Communication](https://2021.igem.org/Team%3AWilliam_and_Mary/Communication)
  * [Education](https://2021.igem.org/Team%3AWilliam_and_Mary/Education)
- [Excellence in Another Area](https://2021.igem.org/Team%3AWilliam_and_Mary/Excellence_in_Another_Area)

[**Overview**](#Overview)
[**Overview of Results & Successful Proof of Concept**](#SuccessfulProof)
[**Math Model - Simulated Data**](#SimulatedData)
[**Math Model - Our Actual Data**](#OurActualData)
[**Results of Individual Circuits**](#IndividualCircuits)
[**References**](#References)

# Results

.

## Overview

Below we provide our results of our project organized as follows:

1. Overview of Results & Successful Proof of Concept
2. Results of Mathematical Model Using Simulated Data
3. Results of Mathematical Model Using Our Actual Data

1. Mechanistic Model
2. Data-Driven Model

4. Results of Individual Circuits

1. Confirmation of Test Circuit Functionality
2. Sensor Circuit Results

.

## 1. Overview of Results & Successful Proof of Concept

The goal of our project was to develop an accessible toolkit to assess the orthogonality of genetic circuits to the host. This toolkit consists of [1] a system of sensor circuits designed to measure aspects of orthogonality and [2] models into which measurements from our circuits can be inputted to provide an assessment of a circuit’s overall orthogonality. Our models include: [a] a mechanistic model of ODEs coupling classical models of metabolic burden in *E. coli* and markers of orthogonality derived from analyzing RNA-sequencing data; this model incorporated additional markers of burden including post- translational modifications; and [b] a purely data-driven model developed from RNA-seq datasets which outputs a quantitative evaluation of orthogonality that goes beyond burden using inputs from our circuits. We were able to develop this system, take measurements from our sensor circuits, input the values into our model, and provide an assessment of the orthogonality of a genetic circuit, thus providing successful proof of concept.

To test our system, we used the pBbB8k-csg-amylase “test” circuit. We obtained fluorescent measurements from each of our circuits, converted them to the number of molecules, and input the measurements from our sensor circuits into the mechanistic/metabolic model. After the data from our sensor circuits was put into the model, we ran the model and obtained the following results.

The model predicts that after 6 hours, mRNA transcripts encoding the following major protein groups were upregulated in response to circuit induction: metabolic proteins, housekeeping proteins, transport proteins, and ribosomes. In addition, our transcriptional aptamer circuit shows upregulation in response to circuit induction. These data suggest that the cells did not experience significant burden at the transcriptional level.

Our model also predicted that, on the translational level, after 12 hours, most metabolic proteins, transport proteins, and ribosomes were predicted to be downregulated while housekeeping proteins were predicted to be upregulated. This suggests significant translational burden on the cell. Finally, the model predicted that on the level of post-translational modifications (as assayed by protease activity) remains relatively stable.

We also used our circuit measurements as input for the second data driven (from RNA-seq data) model which outputs a quantitative evaluation of orthogonality that goes beyond burden. Basically we developed an equation that assigned an orthogonality score between 0 (orthogonal) and 1 (non-orthogonal) from the weighted values of the input from the genes in our circuits. To determine the weight of each gene, we used RNA-seq data. We plotted the fold change of the marker gene for a given circuit versus the product of the average fold change of all the differentially expressed genes and the number of DE (differentially expressed) genes in that particular RNA-seq experiment. A function was generated to fit the data and was used to determine the gene’s weight for the equation to determine the orthogonality score.

After inputting our values from our circuits, we obtained an orthogonality score of 0.18 suggesting that, while translational burden played a significant role in overall non-orthogonality, the pBbB8k-csg-amylase circuit did demonstrate some degree of non-orthogonality due to factors beyond classic burden.

The summary data for these conclusions and proof of concept are shown below.

#### Model A: a mechanistic model of ODEs coupling classical models of metabolic burden in *E. coli* and markers of orthogonality derived from analyzing RNA-sequencing data; this model incorporated additional markers of burden including post- translational modifications.

![](https://static.igem.org/mediawiki/2021/9/91/T--William_and_Mary--all_genes%2BsfGFP.png)

**Figure 1:** *Left: number of proteins, transcripts, mRNAs, and mRNA-protein complexes for various proteins in the cell for cells containing only the sensor circuit (top) or containing the sensor and induced test circuit (bottom) based on parameters from the literature. Right: number of proteins for markers in the host cell containing only the sensor circuit (top) or containing the sensor and induced test circuit (bottom) based on measurements from our circuits.*

![](https://static.igem.org/mediawiki/2021/5/54/T--William_and_Mary--vector_relative_number_of_aptamers.svg)

**Figure 2:** *This figure shows the relative number of fluorescent aptamers (y axis) over time (x axis) for the 3WJ Broccoli circuit alone (sensor), untransformed BL21(DE3) cells in addition to the 3WJ Broccoli circuit (untransformed+sensor), and the 3WJ Broccoli circuit cotransformed with the induced test circuit (sensor+induced).*

#### Model B: a purely data-driven model developed from RNA-seq datasets which outputs a quantitative evaluation of orthogonality that goes beyond burden using inputs from our circuits

![](https://static.igem.org/mediawiki/2021/8/83/T--William_and_Mary--BeyondBurdenclpB.png)

**Figure 3:** *x-axis: fold change of clpB, y-axis: y = Bx+C, where x equals the fold change of clpB and B and C represent fitting parameters, r2 = 0.2 with a p value of 0.9*

![](https://static.igem.org/mediawiki/2021/8/88/T--William_and_Mary--BeyondBurdendnaKJ.png)

**Figure 4:** *x-axis: fold change of dnaKJ, y-axis: y = Dx+F where x equals the fold change of dnaKJ and D and F are fitting parameters, r2 = 1.0 with a p value of 0.005*

![](https://static.igem.org/mediawiki/2021/3/3c/T--William_and_Mary--BeyondBurdengroSL.png)

**Figure 5:** *x-axis: fold change of groSL; y-axis: y = Aex where x equals the fold change of groSL and A is a fitting constant; r2 = 0.7, p = 0.1*

![](https://static.igem.org/mediawiki/2021/a/a1/T--William_and_Mary--BeyondBurdenibpAB.png)

**Figure 6:**  *x-axis: fold change of ibpAB; y-axis: y = Gx+H where x equals the fold change of ibpAB and G and H are fitting constants; r2 = 1.0, p = 0.02*

### Future Directions:

* Test a wider range of circuits using our sensor system to improve our model’s accuracy and to gather comparative data
* Incorporate additional RNA-seq data sets into the model to increase the robustness of the gene weights (and gene selection) for the “beyond burden” model

.

## 2. Results of Mathematical Model Using Simulated Data

The model was calibrated by sampling simulated data points from within a range of Weiße’s empirically derived parameters for the repressilator circuit (Weiße et. al., 2015), on the assumption that induction of other arbitrary circuits would create burden within the same order of magnitude. The sampled simulated data points served as via scaling factors for translation rates we found from the literature for each marker gene and overall translation rates for the proteome as a whole (affecting the ribosome, metabolic, transport, and housekeeping protein sectors). In the simulations, we found that setting the scaling factor too high with a dynamic translation rate compounds the extreme values, creating a stiff system requiring extremely small step sizes to simulate accurately, or creating discontinuities in the numerical methods due to being pushed too far in an unstable state.

![](https://static.igem.org/mediawiki/2021/8/83/T--William_and_Mary--baseline.png)

**Figure 7:** *Baseline outputs with all parameters taken directly from the literature. Right: number of various proteins and their associated mRNAs, along with nutrient concentrations. Left: number of markers correlated with increased differential gene expression.*

![](https://static.igem.org/mediawiki/2021/2/21/T--William_and_Mary--unphysical.png)

**Figure 8:** *Non-physical outputs when translation rates are pushed outside their usual physiological range.*

We also introduced a noise term into ribosome binding rates, as Weiße's empirical data indicated that ribosome binding rates decrease upon induction of a burdensome circuit. We observed that this captured a fairly broad range of behaviors as we varied these from higher to lower rates due to binding rates being factored into growth rate, which affects almost every concentration in our model. Since measurements from our sensor circuit collate the effects of translational and transcriptional burden by examining final fluorescence outputs, we assumed that these changes were reflected in our fitted parameters from the data and thus did not introduce similar changes to ribosome binding rates in our empirically-based model.

![](https://static.igem.org/mediawiki/2021/5/51/T--William_and_Mary--rib.png)

**Figure 9:** *Outputs under conditions of high (top row) and low (bottom row) ribosome binding rates.*

Introducing proteases at the right rate introduces oscillatory behavior in overall protein concentration and marker genes. To avoid non-physical negative protease concentrations, we establish a lower bound of 0 for protease production rates. Proteases scale with the amount of total protein within the cell and are thus are a dynamic source of degradation for each concentration of protein, including both the general proteome and the marker genes. At low translation rates, initially small amounts of protein are produced and trigger small amounts of protease production, which degrades proteins at a lower rate than translation produces them. This leads to the amount of protein concentration increasing fairly rapidly, which in turn triggers more production of proteases. This higher amount of protease will then degrade the existing amounts of protein, and in doing so slow its own production. Thus, protein concentrations decrease but will again increase as protease production slows. This leads to oscillations in protein concentration since the rate of degradation due to proteases is dynamic and will never reach a fixed ratio with translation rate where they exactly cancel each other out, which would lead to a steady state. However, when translation rate is significantly higher than the protease production rate, the effect of degradation from proteases is negligibly small, and thus protein concentration will be almost solely determined by translation rate and the natural protein degradation rate, which is not nonlinearly dynamic and thus will achieve a steady state.

![](https://static.igem.org/mediawiki/2021/0/05/T--William_and_Mary--pplane1.png)

**Figure 10:** *Stable oscillatory behavior in lower translation scaling rates for simulated data. Left shows concentrations of marker gene ibpA, right shows solution trajectories of ibpA plotted against ribosome concentration forming a limit cycle.*

![](https://static.igem.org/mediawiki/2021/6/68/T--William_and_Mary--pplane2.png)

**Figure 11:** *Decaying oscillations leading to a stable steady state in higher translation scaling rates when using simulated data points.*

.

## 3. Results of Mathematical Model Using Our Actual Data

### 3a. Model A

![](https://static.igem.org/mediawiki/2021/9/91/T--William_and_Mary--all_genes%2BsfGFP.png)

**Figure 12:** *Left: number of proteins, transcripts, mRNAs, and mRNA-protein complexes for various proteins in the cell for cells containing only the sensor circuit (top) or containing the sensor and induced test circuit (bottom) based on parameters from the literature. Right: number of proteins for markers in the host cell containing only the sensor circuit (top) or containing the sensor and induced test circuit (bottom) based on measurements from our circuits.*

To test our system, we used the pBbB8k-csg-amylase “test” circuit. We obtained fluorescent measurements from each of our circuits, converted them to the number of molecules ("Quantitation of Green Fluorescent Protein," 2006), and input the measurements from our sensor circuits into the mechanistic/metabolic model. After the data from our sensor circuits was put into the model, we ran the model and obtained the following results.

The model predicts that after 6 hours, mRNA transcripts encoding the following major protein groups were upregulated in response to circuit induction: metabolic proteins, housekeeping proteins, transport proteins, and ribosomes. In addition, our transcriptional aptamer circuit shows upregulation in response to circuit induction. These data suggest that the cells did not experience significant burden at the transcriptional level.

Our model also predicted that, on the translational level, after 12 hours, most metabolic proteins, transport proteins, and ribosomes were predicted to be downregulated while housekeeping proteins were predicted to be upregulated. This suggests significant translational burden on the cell. Finally, the model predicted that on the level of post-translational modifications (as assayed by protease activity) remains relatively stable.

The summary data for these conclusions and proof of concept are shown below.

#### Model A: a mechanistic model of ODEs coupling classical models of metabolic burden in *E. coli* and markers of orthogonality derived from analyzing RNA-sequencing data; this model incorporated additional markers of burden including post- translational modifications.

![](https://static.igem.org/mediawiki/2021/9/91/T--William_and_Mary--all_genes%2BsfGFP.png)

**Figure 13:** *Left: number of proteins, transcripts, mRNAs, and mRNA-protein complexes for various proteins in the cell for cells containing only the sensor circuit (top) or containing the sensor and induced test circuit (bottom) based on parameters from the literature. Right: number of proteins for markers in the host cell containing only the sensor circuit (top) or containing the sensor and induced test circuit (bottom) based on measurements from our circuits.*

![](https://static.igem.org/mediawiki/2021/5/54/T--William_and_Mary--vector_relative_number_of_aptamers.svg)

**Figure 14:** *This figure is showing number of fluorescent aptamers (y axis) over time (x axis) for the 3WJ Broccoli circuit alone (sensor), untransformed BL21(DE3) cells in addition to the 3WJ Broccoli circuit (untransformed+sensor), and the 3WJ Broccoli circuit cotransformed with the induced test circuit (sensor+induced).*

To convert fluorescence of the 3WJ Broccoli circuit to a relative number of fluorescent aptamers, we adapted the formula y = 623.5x − 1580 from Figure 1 of the source “Quantitation of Green Fluorescent Protein in Microplates Using the FL600” and scaled to the molecular weight of the dye DFHBI-1T, which binds to the 3WJ Broccoli aptamer. We then normalized the relative number of fluorescent aptamers to the approximate number of cells per sample.

### 3b. Model B

While a mechanistic model of certain known cell processes is useful to characterize burden, moving from burden to orthogonality requires a way to reliably characterize unknown processes in the circuit-host complex. A burdensome process, by definition, draws from the resource pool of a cell, and thus makes quantification of burden caused by a circuit relatively straightforward; it is the change in the resource pool of a cell due to a circuit. However, orthogonality asks for all of the unintended interactions in the host cell, including those that are not rooted in the metabolic processes of the cell. As a result, properly considering orthogonality requires finding a way to move beyond burden. For our project, we chose to look at the fundamental processes of the cell in order to quantify orthogonality by looking at transcriptomics data, specifically RNA-seq.

#### RNA-Seq Analysis

[In our analysis of consistently DE genes correlated with induction of a circuit](https://2021.igem.org/Team%3AWilliam_and_Mary/Design), we identified several patterns of differentially expressed genes. Investigating these particular genes provides us with the necessary basis to investigate the correlation between individual gene expression and overall differential expression. Unfortunately, RNA-seq datasets that reported the complete spread of everything are not prevalent, but the data from our analysis allowed us to plot the efficacy of these select ’marker genes’ as a measurement for overall differential expression. We plotted the product of the average absolute fold change and the total number of differentially expressed genes as a measure of orthogonality for all of our marker genes and calculated the correlation coefficients, as shown in Fig. 12-15. Due to the limited size of our data set, in many cases we were unable to see an overarching functional form. As a result, we fit all of our data points to a linear model to generate fit parameters for assessing the overall orthogonality, giving us the total equation

\[O=\frac{1}{5}\frac{1}{733.5} (\left(\textrm{groSL FC}\right)\cdot A+B+\left(\textrm{clpB FC}\right)\cdot C+D+F\left(\textrm{dnaKJ FC}\right)+G+H\left(\textrm{ibpAB FC}\right)+I)\]

Where FC refers to fold change and O is orthogonality and A,B,C,D,F,G and H are the fit parameters \(A=81.1,B=21.4,C=13.7, D=234.23,F=62.9,G=0,H=8.17\*ibpAB+38.0\). H is set to 0 as the fit predicts a non-physical negative intercept for dnaKJ. The \(\frac{1}{5}\) term arises from the fact that this is the average of 5 marker genes and the \(\frac{1}{733.5}\) is a scaling term designed to provide a reasonable limit to non-orthogonality, as derived from our analysis of the literature on RNA-seq. This equation provides the framework and the general idea for calculating orthogonality from the fold change of a select genes, but the relatively small size of the data points available means it is highly sensitive to outliers and non-physical results had to be abstracted away from the fit in order to produce an equation. Nevertheless, as the available data increases this model is simple to implement by refitting to multiple points.

After inputting our values from our circuits, we obtained an orthogonality score of 0.18 suggesting that, while translational burden played a significant role in overall non-orthogonality, the pBbB8k-csg-amylase circuit did demonstrate some degree of non-orthogonality due to factors beyond classic burden.

#### Model B: a purely data-driven model developed from RNA-seq datasets which outputs a quantitative evaluation of orthogonality that goes beyond burden using inputs from our circuits

![](https://static.igem.org/mediawiki/2021/8/83/T--William_and_Mary--BeyondBurdenclpB.png)

**Figure 15:** *x-axis: fold change of clpB, y-axis: y = Bx+C, where x equals the fold change of clpB and B and C represent fitting parameters, r2 = 0.2 with a p value of 0.9*

![](https://static.igem.org/mediawiki/2021/8/88/T--William_and_Mary--BeyondBurdendnaKJ.png)

**Figure 16:** *x-axis: fold change of dnaKJ, y-axis: y = Dx+F where x equals the fold change of dnaKJ and D and F are fitting parameters, r2 = 1.0 with a p value of 0.005*

![](https://static.igem.org/mediawiki/2021/3/3c/T--William_and_Mary--BeyondBurdengroSL.png)

**Figure 17:** *x-axis: fold change of groSL; y-axis: y = Aex where x equals the fold change of groSL and A is a fitting constant; r2 = 0.7, p = 0.1*

![](https://static.igem.org/mediawiki/2021/a/a1/T--William_and_Mary--BeyondBurdenibpAB.png)

**Figure 18:**  *x-axis: fold change of ibpAB; y-axis: y = Gx+H where x equals the fold change of ibpAB and G and H are fitting constants; r2 = 1.0, p = 0.02*

### Future Directions:

* Test a wider range of circuits using our sensor system to improve our model’s accuracy and to gather comparative data
* Incorporate additional RNA-seq data sets into the model to increase the robustness of the gene weights (and gene selection) for the “beyond burden” model

.

## 4. Results of Individual Circuits

### 4a. Confirmation of Test Circuit Functionality

We confirmed the functionality of test circuit pBbB8k-csg-amylase using the Congo Red spin-down assay protocol from Harvard iGEM 2017 (Protocols). We found that in *E. coli* DH5-alpha, curli fibers were produced after 48 hours of incubation (37°C, 250 RPM), as the uninduced samples bound less Congo Red dye than the induced samples after 48 hours of incubation. Normalization of these values was done by subtracting the absorbance at 490 nm of a control (1X PBS and Congo Red dye).

The results of our Congo Red spin-down assay can be found below:
![](https://static.igem.org/mediawiki/2021/b/b6/T--William_and_Mary--Curli_Fiber_Spin_Down_Assay.png)

### 4b. Sensor Circuit Results

#### Burden Sensor Circuits

##### Transcriptional Burden Sensor Circuit

Hypothesis:

For the transcriptional burden sensor circuit 3WJ Broccoli (Alam et al., 2017), we hypothesized that the sensor circuit transformed individually in the host would produce higher levels of fluorescence than the sensor and test circuit cotransformed, and that the induced samples would have lower levels of fluorescence than the uninduced samples, as we hypothesized that since induction of the circuit would lead to curli fiber formation, it would inflict a significant amount of transcriptional burden upon the host cell, resulting in a decrease in fluorescence.

Actual Results:

For the transcriptional burden sensor circuit, the fluorescence levels of the uninduced samples were slightly higher than the induced samples (cotransformed with both our sensor and test circuits) 24 hours after induction. The fluorescence of the sensor circuit individually is higher than the induced and uninduced samples at most time points, but decreases to a level lower than induced and uninduced samples at 48 hours after induction. The difference in fluorescence between uninduced and induced samples are not statistically significant at any time point, likely due to the fact that only three replicates of this experiment were performed. However, before making subcultures using the overnight cultures (T-1) and 12 hours after induction, there was a statistically significant (p-value < 0.05) difference in fluorescence between induced and untransformed samples. The fluorescence of the induced samples was higher than the fluorescence of the untransformed samples at this time point. In addition, 6 hours after induction as well as 12 hours after induction, there was a statistically significant (p-value < 0.05) difference in fluorescence between induced samples and samples transformed with the sensor circuit individually. This is likely due to the test circuit placing a load on the host transcriptional resources and creating transcriptional burden, which is causing the induced samples to have lower fluorescence than the samples containing the sensor alone. Therefore, we can conclude that this **sensor circuit is functional**  and that the **test circuit is moderately burdensome** at the transcriptional level, as induced samples were less fluorescent than uninduced samples and both uninduced and induced samples were less fluorescent than samples containing the sensor circuit alone at most time points. P-values found after conducting an unpaired t-test comparing all three replicates of this experiment can be found in the table below.

In summary:

* Fluorescence of the sensor circuit alone is higher than the induced and uninduced samples at most time points
* Difference in fluorescence between uninduced and induced samples are not statistically significant at any time point
* We can conclude that this **sensor circuit is functional** and that the **test circuit is moderately burdensome**  at the transcriptional level

![](https://static.igem.org/mediawiki/2021/7/7c/T--William_and_Mary--3wj_t_test.png)

**Please check the experience tab of this circuit’s parts page for graphical representation of the data.** Raw fluorescence data for this circuit can be found [here](https://static.igem.org/mediawiki/2021/8/8c/T--William_and_Mary--WM21_3WJ.zip).

##### Translational Burden Sensor Circuits

Hypothesis:

For the translational burden sensor circuits, we hypothesized that the sensor circuit transformed individually in the host would produce higher levels of fluorescence than the sensor and test circuit cotransformed for both the induced and uninduced samples. We also hypothesized that the samples in which the test circuit was induced with arabinose would produce higher fluorescence than the uninduced samples, as we hypothesized that since induction of the circuit would lead to curli fiber formation, it would inflict a significant amount of translational burden upon the host cell, resulting in a decrease in fluorescence.

**Translational Burden Sensor (WM21\_016) - Designed by Ceroni et al. (2015)**

Actual Results:

For the translational burden sensor circuit designed by Ceroni et al. (2015), the uninduced test circuit cotransformed with the sensor circuit produces higher fluorescence than the induced test circuit cotransformed with the sensor circuit at most time points, consistently for all three replicates of the experiment. In addition, for most time points across all three replicates of the experiment, the sensor circuit transformed individually in the host produced higher fluorescence than the cotransformed host (both induced and uninduced). The level of fluorescence produced by our untransformed cells was consistently much lower than that of all transformed cells, remaining at around 5,000 to 8,000 RFU throughout the 48 hour time period. Therefore, we can conclude that this translational burden sensor circuit is functional and that our hypothesis has been supported. P-values found after conducting an unpaired t-test comparing all three replicates of this experiment can be found in the table below.

In summary:

* Uninduced test circuit cotransformed with the sensor circuit produces higher fluorescence than the induced test circuit cotransformed with the sensor circuit at most time points
* The level of fluorescence produced by our untransformed cells was consistently much lower than that of all transformed cells
* Uninduced test circuit cotransformed with the sensor circuit produces higher fluorescence than the induced test circuit cotransformed with the sensor circuit at most time points
* We can conclude that this translational burden sensor circuit is functional and that our hypothesis has been supported

![](https://static.igem.org/mediawiki/2021/9/9c/T--William_and_Mary--016_t_test.png)

**Please check the experience tab of this circuit’s parts page for graphical representation of the data.** Raw fluorescence data for this circuit can be found [here](https://static.igem.org/mediawiki/2021/f/fd/T--William_and_Mary--WM21_016.zip).

**Translational Burden Sensor - Alternate Design (WM21\_013)**

Actual Results:

Host cells cotransformed with both the sensor and test circuits that were induced with arabinose had consistently low levels of fluorescence that were very similar to the fluorescence levels of the uninduced samples. From the point right before induction (T0) up to 12 hours after induction (T12), the difference in fluorescence produced between induced and uninduced samples is not statistically significant, although it is statistically significant (p-value < 0.05) 24 hours after induction (T24). The difference in fluorescence produced between the induced cells and the cells containing the sensor circuit individually is not statistically significant from the point right before induction (T0) to 24 hours after induction (T24). However, the difference in fluorescence produced between the induced cells and cells containing only our sensor were only statistically significant (p-value < 0.05) 48 hours after induction (T48). For the difference in fluorescence produced by our induced samples and our untransformed samples, it was only statistically significant 24 hours and 48 hours after induction (T24 and T48)

Although host cells transformed with our sensor circuits individually had slightly higher levels of fluorescence than the induced and uninduced cells, the level of fluorescence produced by the circuit itself is very low. This indicates that our design of the translational sensor circuit is **not functional** . Therefore, our hypothesis was refuted. P-values found after conducting an unpaired t-test comparing all three replicates of this experiment can be found in the table below.

In summary:

* From the point right before induction (T0) up to 12 hours after induction (T12), the difference in fluorescence produced between induced and uninduced samples is not statistically significant
* The difference in fluorescence produced between the induced cells and cells containing only our sensor were only statistically significant (p-value < 0.05) 48 hours after induction (T48).
* The level of fluorescence produced by the circuit itself is very low.
* Our design of the translational sensor circuit is **not functional**.

*P-values for Translational Burden Sensor Circuit (WM21\_013)*

![](https://static.igem.org/mediawiki/2021/2/2e/T--William_and_Mary--p13.png)

**Please check the experience tab of this circuit’s parts page for graphical representation of the data.** Raw fluorescence data for this circuit can be found [here](https://static.igem.org/mediawiki/2021/a/a2/T--William_and_Mary--WM21_013.zip).

##### Post-Translational Burden Sensor Circuits

**Phosphorylation Sensor**

WM21\_015 (our phosphorylation sensor) is based on the EnvZ/OmpR system. This system responds to changes in osmolarity of the environment by phosphorylation of the OmpR protein which then triggers production of either OmpC in high molarity states or OmpF in low osmolarity states. This circuit utilizes the *ompC* promoter to drive production of sfGFP. Therefore, this fluorescence output is indicative of phosphorylation rates.

Our team expected that our phosphorylation sensor would decrease in fluorescence output when an additional circuit was added to the host due to the additional burden caused by the results. The results we received were not exactly as predicted. This circuit actually produced more fluorescence when induced than when uninduced; however, there were no statistically significant differences between these values over time.

Due to the increase in fluorescence in the induced vs the uninduced, the osmolarity of the system must have increased. Given that the circuit our team used as our test plasmid was a biofilm producing plasmid, our team hypothesizes that biofilm production created an increase in the osmolarity of the surrounding environment. This increase in osmolarity triggered the EnvZ/OmpR system to increase phosphorylation rates. Overall, there was no significant difference in the amount of fluorescence output between our sensor circuit with an additional plasmid, induced or uninduced.

In Summary:

* Our team received unexpected results for our phosphorylation sensor
* There was no statistically significant difference between expression between the induced and uninduced sensors
* The level of fluorescence produced by the circuit itself is very low.
* This circuit produced more fluorescence when induced than when uninduced

*P-values for Phosphorylation Sensor Circuit (WM21\_015)*
![](https://static.igem.org/mediawiki/2021/5/56/T--William_and_Mary--p15.png)

**Please check the experience tab of this circuit’s parts page for graphical representation of the data.** Raw fluorescence data for this circuit can be found [here](https://static.igem.org/mediawiki/2021/0/04/T--William_and_Mary--WM21_015.zip).

##### ”Beyond Burden” Sensor Circuits

**WM21\_017 (*clpB* sensor, original design)**

Our analysis of differentially expressed genes told us that *clpB* was frequently upregulated upon introduction of a heterologous circuit. As a result, our first prediction was that when transformed into cells alongside another circuit such as pBbB8k-csg-amylase, the *clpB* sensor circuit would fluoresce more than when it was transformed into cells alone. Additionally, our second prediction was that the *clpB* sensor would produce increasing fluorescence over time, especially in cotransformed cells, as we believed pBbB8k-csg-amylase would become more productive and thereby cause more effects on the cell over time. Third, we predicted that cotransformed cells would fluoresce more when induced than otherwise. This is because we expected pBbB8k-csg-amylase, which is inducible by arabinose, to have more effects on regular cell functions if its expression levels were increased.

Our cultures in fact appeared to fluoresce more on average when only the *clpB* sensor was present than when it was alongside pBbB8k-csg-amylase, refuting our first prediction. On average, all of our cultures that were transformed with the sensor circuit alone did increase in fluorescence over time, confirming the first half of our second prediction; however, our cotransformed cultures did not increase faster than those containing the *clpB* sensor alone, indicating that the circuit was not fluorescing more due to to the presence of pBbB8k-csg-amylase. Finally, of our cotransformed cultures, induced cells were more often higher in fluorescence than uninduced cells. This somewhat confirms our third prediction, although the difference was small, inconsistent across replicates, and not evident after converting raw fluorescence values to number of sfGFP molecules per cell.

It is important to note that these results are not statistically significant (p-value>0.05), however, largely because we were only able to obtain three replicates of data. We performed a t-test to evaluate the difference between: A) uninduced versus induced, cotransformed cells; B) induced cells versus untransformed cells, the negative control; and C) induced, cotransformed cells versus cells containing only the sensor circuit. Only B was consistently statistically significant (p-value<0.05) across time points. Although not statistically significant, we were able to observe the aforementioned, suggested trends, which might become clearer with more testing.

Because the average fluorescence of cotransformed cells both was lower than and increased slower than the average fluorescence of cells containing the sensor circuit alone, we conclude that the circuit does not successfully report an increase in P*clpB* expression when a second circuit is present. Additionally, it appears that pBbB8k-csg-amylase is so burdensome to the expression of the *clpB* sensor that rather than causing its promoter to be upregulated as intended, it uses up cellular resources needed to produce sfGFP. One explanation is that this version of the *clpB* sensor simply does not produce enough fluorescence to compete with pBbB8k-csg-amylase. We notice that WM21\_018, which appeared four times as fluorescent as WM21\_017 during functional confirmation, does show greater fluorescence when cotransformed alongside pBbB8k-csg-amylase than otherwise. It may be that circuits that produce a low level of sfGFP are more easily outcompeted by pBbB8k-csg-amylase.

In summary:

* Unexpectedly, cotransformations fluoresced less than sensor-alone cells
* Transformed cells expectedly increased in fluorescence over time
* Induced cotransformations may have fluoresced more than uninduced cotransformations, which would be expected
* We conclude that this circuit is not a successful reporter of differential expression of *clpB*; it is likely burdened by and unable to compete with pBbB8k-csg-amylase for cell resources

*P-values for *clpB* Sensor Circuit, original design (WM21\_017)*

![](https://static.igem.org/mediawiki/2021/8/8a/T--William_and_Mary--017_t_test.png)

**Please check the experience tab of this circuit’s parts page for graphical representation of the data.** Raw fluorescence data for this circuit can be found [here](https://static.igem.org/mediawiki/2021/3/34/T--William_and_Mary--WM21_017.zip).

**WM21\_018 (*clpB* sensor, improved design)**

We had the same expectations for WM21\_018 as we had had for the earlier version of it, WM21\_017. First, we expected this new version of the *clpB* sensor to fluoresce more in cotransformed cells than cells with the sensor alone. Second, we predicted that fluorescence of all transformed cells would increase over time, and that the fluorescence of cotransformed cells would increase faster than that of those with the sensor alone. Finally, we expected cotransformed cells to fluoresce most after being induced.

Uninduced, cotransformed cells did generally appear to fluoresce more on average than cells containing the sensor circuit alone, supporting the first prediction. Induced, cotransformed cells appeared to have a similar result only after converting fluorescence to number of sfGFP molecules per cell. This is likely because induced cells took longer to grow in Replicates 1 and 2, according to our OD600 measurements at each time point. It may be that with more replicates, this issue would be resolved, but it is likely that the burden imposed on an induced, cotransformed cell places limitations on growth rate. The low average fluorescence of induced cells contradicts our third prediction. Average fluorescence of all transformed cells did increase over time, fulfilling our second prediction, although fluorescence of induced cells increased slower than that of other cells, likely due to the previously mentioned reasons. Fluorescence later decreased on average for uninduced cells and cells with the sensor circuit alone; these cells were likely unable to sustain a high production of sfGFP over such a long period of time, especially with the presence of two circuits.

As with the earlier version of this circuit, it is important to note that many of the differences between fluorescence measurements were not statistically significant (p-value>0.05) according to a t-test. Once again, only the comparison between induced versus untransformed cells returned a p-value of less than 0.05 for multiple time points. If we were able to perform more replicates, the aforementioned trends suggested by the data might become more clear.

Because cotransformed cells produced more sfGFP over time than WM21\_018 alone, we concluded that WM21\_018 is an improved version of WM21\_017, and could plausibly be used to report the unintended interactions occurring between a secondary circuit and the host cell. This success, while limited by our number of replicates, is likely due to the high fluorescence of WM21\_018, which was shown to fluoresce about four times as much as WM21\_017 during functional confirmation. This allows us to measure the upregulation of the *clpB* promoter when a circuit such as pBbB8k-csg-amylase is present.

In summary:

* Only uninduced cotransformations clearly fluoresced more than sensor-only cells
* Transformed cells expectedly increased in fluorescence over time
* Induced cotransformations unexpectedly fluoresced less than uninduced cotransformations
* We conclude that this circuit is a successful reporter of differential expression of *clpB*; although induced cells grow slower and therefore fluoresce less, this is an improvement upon WM21\_017

*P-values for clpB Sensor Circuit, improved design (WM21\_018)*

![](https://static.igem.org/mediawiki/2021/2/28/T--William_and_Mary--p18.png)

**Please check the experience tab of this circuit’s parts page for graphical representation of the data.** Raw fluorescence data for this circuit can be found [here](https://static.igem.org/mediawiki/2021/8/8f/T--William_and_Mary--WM21_018.zip).

**WM21\_023 (*lon* sensor)**

Our analysis of differentially expressed genes told us that *lon* was frequently upregulated upon introduction of a heterologous circuit. As a result, we expected that when transformed into cells alongside another circuit such as pBbB8k-csg-amylase, WM21\_023 would fluoresce more than when it was transformed into cells alone. Second, we expected the *lon* sensor to produce increasing fluorescence over time, especially in cotransformed cells, as we believed pBbB8k-csg-amylase would become more productive and thereby cause more effects on the cell over time. Third, we predicted that cotransformed cells would fluoresce more when induced than otherwise. This is because we expected pBbB8k-csg-amylase, which is inducible by arabinose, to have more effects on regular cell functions if its expression levels were increased.

On average, our cultures in fact appeared to fluoresce far more, and increase in fluorescence far faster, when only the *lon* sensor was present than when it was alongside pBbB8k-csg-amylase. This contradicts our first prediction. While average fluorescence appeared to increase over time for all transformed cells, supporting our second prediction, when converted to number of sfGFP molecules per cell, this was not the case; average sfGFP increased for the sensor circuit alone and from about 6 to 12 hours after induction for induced cells, but average fluorescence decreased until 48 hours for uninduced cells. Finally, of our cotransformed cultures, induced cells were on average consistently higher in both fluorescence and number of sfGFP molecules per cell than uninduced cells after 6 hours. This confirms our third prediction.

It is important to note that these results are not totally statistically significant, however, largely because we were only able to obtain three replicates of data. We performed a t-test to evaluate the difference in fluorescence between: A) uninduced versus induced, cotransformed cells; B) induced cells versus untransformed cells, the negative control; and C) induced, cotransformed cells versus cells containing only the *lon* sensor. Only B and C were consistently statistically significant (p-value<0.05), and only after one or six hours, respectively, when fluorescence values diverged most strongly. Although not fully statistically significant, we were able to observe the aforementioned, suggested trends, which might become clearer with more testing.

Because the average fluorescence of cotransformed cells both was lower than and increased slower than the fluorescence of cells containing the *lon* sensor alone, we conclude that the circuit does not successfully report an increase in P*lon* expression when a second circuit is present. pBbB8k-csg-amylase may be so burdensome to the expression of the sensor circuit that rather than causing its promoter to be upregulated as intended, it uses up cellular resources needed to produce sfGFP. The *lon* sensor circuit showed only about half as much fluorescence as WM21\_018 during functional confirmation, so it may be that the *lon* sensor simply does not produce enough fluorescence to compete with pBbB8k-csg-amylase. However, as time passed, the fluorescence for the *lon* sensor alone here reached even higher values (above two million RFU) than those obtained for WM21\_018 alone. Therefore, there may be some other factor at play that prevents the *lon* sensor circuit from maintaining access to cell resources. However, induced cells are clearly more fluorescent than uninduced cells, suggesting that this circuit may be able to report increased *lon* expression caused only by induction of a circuit.

In summary:

* The cotransformations unexpectedly fluoresced less than sensor-alone cells
* Transformed cells expectedly increased in fluorescence over time
* Induced cotransformations expectedly fluoresced more than uninduced cotransformations.
* We conclude that this circuit is a successful reporter of differential expression of *clpB* when comparing induced to uninduced cells, but not when comparing induced cells to those containing only the sensor

*P-values for lon Sensor Circuit (WM21\_023)*

![](https://static.igem.org/mediawiki/2021/3/38/T--William_and_Mary--p23.png)

**Please check the experience tab of this circuit’s parts page for graphical representation of the data.** Raw fluorescence data for this circuit can be found [here](https://static.igem.org/mediawiki/2021/d/df/T--William_and_Mary--WM21_023.zip).

**WM21\_024 (hslVU sensor)**

Our DE gene analysis revealed that *hslVU* would be upregulated in response to the presence of an orthogonal circuit within the cell. We predicted that the pBbB8k-csg-amylase circuit would decrease the orthogonality of the cell, and the cotransformation of both the pBbB8k-csg-amylase circuit plasmid and our sensor circuit plasmid into a cell would have lower orthogonality than the insertion of the sensor circuit by itself, leading to higher average fluorescence values. Our second prediction was that we would observe higher average fluorescence in the transformed and cotransformed cells, as compared to the untransformed cells that did not contain the sfGFP coding region. Our final prediction was that average fluorescence values in the transformed and cotransformed cells would increase over time, as the pBbB8k-csg-amylase circuit imposed greater and greater strain on the host circuit functions.

For our statistical analysis, we performed three sets of unpaired t-tests at each time point we measured. These three comparisons were: induced vs. uninduced, induced vs. untransformed, and induced vs. the sensor alone. We were unable to demonstrate statistical significance between the uninduced and induced cells, indicating that induction does not make much of a difference in the orthogonality of this circuit, as measured by this sensor. The induced cotransformed cells had statistically significant average fluorescence values from the untransformed cells at certain time points (p-value<0.05). However, whether the uninduced cells fluoresced more than the cells containing only the sensor circuit varied from replicate to replicate. We were also unable to find statistical significance between the induced contransformed cells and the cells containing only our sensor circuit.

It was difficult to achieve statistical significance in our data due to a low number of trials, however we observed several interesting trends. The untransformed NEB5-alpha cells stayed at a consistently low fluorescence given their lack of a fluorescence gene, establishing them as a good negative control for our experiment. We also generally observed an increase in fluorescence at a decreasing rate over time after subculturing at t0.

We likely obtained these results because the marginal strain imposed on the host cell by the pBbB8k-csg-amylase circuit was not higher than the strain already imposed by our sensor circuit. The high average fluorescence values in transformed and cotransformed cells indicate that our sensor circuit was functional, but we would have to conduct additional trials to determine whether or not our sensor circuit was truly able to sense the difference in orthogonality due to the circuit of interest. Further trials would also elucidate whether the temporal differences in significance persist when comparing induced vs. untransformed cells.

In summary:

* The *hslVU* sensor circuit cotransformed with the induced pBbB8k circuit was expected to have increased fluorescence over time, with greater fluorescence than the culture of *hslVU* with uninduced pBbB8k and the culture of *hslVU* alone
* The average fluorescence of the induced cotransformed cells was significantly higher than that of the untransformed negative control
* Average fluorescence for all cells containing the *hslVU* sensor circuit generally increased over time
* We were unable to statistically determine whether there was a difference in average fluorescence between the transformed and cotransformed cells, regardless of the state of induction
* These results suggest that the pBbB8k-csg-amylase circuit may not have a more detrimental effect on the cell than that imposed by the *hslVU* sensor circuit by itself.

*P-values for hslVU Sensor Circuit (WM21\_024)*

![](https://static.igem.org/mediawiki/2021/a/af/T--William_and_Mary--p24.png )

**Please check the experience tab of this circuit’s parts page for graphical representation of the data.** Raw fluorescence data for this circuit can be found [here](https://static.igem.org/mediawiki/2021/3/30/T--William_and_Mary--WM21_024.zip).

**WM21\_021 (dnaKJ Sensor)**

Our analysis of the differentially expressed genes informed us that heat-shock response genes *dnaK* and *dnaJ* were highly upregulated upon introduction of a heterologous circuit. Our first prediction was that if the sensor circuit was functional, meaning the *dnaKJ* operon promoter is expressed to produce sfGFP, we expected to observe an increase in average fluorescence in both co-transformed and single-transformed cells when compared to untransformed cells. Our second prediction was that having an additional plasmid, pBbB8k-csg-amylase, would yield higher average fluorescence results in the co-transformed cells (both induced and uninduced) when compared to cells transformed only with the *dnaKJ* sensor. Lastly, our third prediction was that induction of pBbB8k-csg-amylase would lead to higher average fluorescence values in cells compared to uninduced ones as Birnbaum et al. (2021) had previously characterized the induction-dependent activity of pBbB8k-csg-amylase. With the activated plasmid, we would expect a higher degree of interaction between the host cell and the circuit, which could potentially induce the *dnaKJ* operon promoter more, indicating that the cells are under stress conditions due to heterologous gene expression.

Looking at the results of our flask experiments, the average fluorescence is higher at every time point for the induced and uninduced co-transformed, and single-transformed cell samples compared to the untransformed NEB5-alpha cells. This supports our first prediction that the *dnaKJ* sensor is indeed functional in cells after transformation and leads to an increased fluorescence production.

When comparing the average fluorescence values from cells transformed only with the *dnaKJ* sensor and those co-transformed with the *dnaKJ* sensor and pBbB8k-csg-amylase (induced and uninduced), the cells with only our sensor circuit had significantly higher average fluorescence at the 6 hour mark compared to the induced pBbB8k-csg-amylase and the sensor circuit containing cells (p-value<0.05). Although a statistical significance was not found at other time points for this comparison, the graph of our fluorescence data shows consistently higher fluorescence for cells with the sensor circuit alone at the 1, 6, 12, and 48 hour marks as well. These results do not support our second prediction that the co-transformed cells would exhibit a higher average fluorescence overall.

Interestingly, we saw that the co-transformed cells with induced and uninduced pBbB8k-csg-amylase had a higher average fluorescence than the cells with the *dnaKJ* sensor alone at the 24 hour time point. This led us to consider the growth dynamics of induced and uninduced cells for explaining the possible delay in sfGFP accumulation. Looking at our OD600 data, we observed a higher growth rate for the cells with only the *dnaKJ* sensor, between hours 1 and 48, compared to both induced and uninduced co-transformed cells for all samples. This could explain the high average fluorescence values for cells with the *dnaKJ* sensor alone at most time points as those cells are expected to reach the log phase for growth earlier than the co-transformed cells, which could lead to an earlier onset of stress-response at the *dnaKJ* operon due to unwanted interactions between the host cell and the sensor circuit.

Lastly, evaluating the effect of inducing pBbB8k-csg-amylase on the *dnaKJ* operon promoter, our results indicated that, only at 24 hours post-induction, the induced cells had a significantly higher average fluorescence compared to uninduced cells (p-value<0.05). At other time points, the average fluorescence of the uninduced cells were consistently higher than those of induced. These results do not support our third prediction that the induction of pBbB8k-csg-amylase would lead to higher expression of the *dnaKJ* operon promoter than the uninduced samples which would have led to higher fluorescence values.

Overall, these results confirm the functionality of the *dnaKJ* sensor in both single-transformed and co-transformed cells. Although our results do not support that cells with plasmids co-transformed induce higher levels of expression at the *dnaKJ* operon promoter compared to cells with only the sensor circuit transformed, we could explain this difference by inferring temporal variance of the degree of circuit-host cell interactions based on the different growth rates of samples. Additionally, our observation that the uninduced cells led to *dnaKJ* operon promoter expression at higher levels than the induced cells did not support our prediction of active transcription of a circuit imposing a higher stress, thus higher *dnaKJ* operon promoter activity. We would have to conduct further experiments with a larger sample size to fully understand the behavior of the *dnaKJ* sensor in response to co-transformation of two plasmids.

In summary:

* Our design for the *dnaKJ* sensor (WM21\_021) was confirmed to be functional in responding to an addition of a plasmid which in our design was co-transformation with pBbB8k-csg-amylase into NEB5-alpha cells
* The sensor detected *dnaKJ* operon promoter expression levels that were higher in cells with only the *dnaKJ* sensor alone compared to both the induced and uninduced co-transformed cells
* Induced co-transformed cells overall exhibited less expression at the *dnaKJ* operon promoter compared to the uninduced cells.

*P-values for dnaKJ Sensor Circuit*

![](https://static.igem.org/mediawiki/2021/8/8e/T--William_and_Mary--p21.png)

**Please check the experience tab of this circuit’s parts page for graphical representation of the data.** Raw fluorescence data for this circuit can be found [here](https://static.igem.org/mediawiki/2021/b/be/T--William_and_Mary--WM21_021.zip).

**ibpAB-GFP Sensor [by Ceroni *et al.* (2018)]**

Our analysis of the differentially expressed genes informed us that heat-shock response genes *ibpA* and *ibpB* were highly upregulated upon introduction of a heterologous circuit. Our first prediction was that if the sensor circuit was functional which would be in support for results from Ceroni *et al.* (2018), meaning the *ibpAB* operon promoter is expressed to produce sfGFP, we expected to observe an increase in average fluorescence in both co-transformed and single-transformed cells when compared to untransformed cells. Our second prediction was that having an additional plasmid, pBbB8k-csg-amylase, would yield higher average fluorescence results in the co-transformed cells (both induced and uninduced) when compared to cells transformed only with the ibpAB-GFP sensor. Lastly, our third prediction was that induction of pBbB8k-csg-amylase would lead to higher average fluorescence values in cells compared to uninduced ones as Birnbaum *et al.* (2021) had previously characterized the induction-dependent activity of pBbB8k-csg-amylase. With the activated plasmid, we would expect a higher degree of interaction between the host cell and the circuit, which could potentially induce the *ibpAB* operon promoter more, indicating that the cells are under stress conditions due to heterologous gene expression.

Looking at the results of our flask experiments, the average fluorescence is higher at every time point for the induced and uninduced co-transformed, and ibpAB-GFP sensor single-transformed cell samples compared to the untransformed NEB5-alpha cells. This supports our first prediction that the ibpAB-GFP sensor is indeed functional in cells after transformation and leads to an increased fluorescence production.

Comparing the induced co-transformed cells and the single-transformed cells, there was significantly higher average fluorescence at time points 12 and 24 for the induced co-transformed samples. Although not significant, from time points 6 to 48, the induced cells consistently seem to have the highest average fluorescence values, with the uninduced cells having the second highest average fluorescence values and the cells with only the ibpAB-GFP sensor having the lowest average fluorescence in comparison. These results support our second prediction that having an additional plasmid with our sensor circuit would induce higher expression at the *ibpAB* operon promoter due to heat-shock response resulting from increased load of host-circuit interactions.

Lastly, when comparing the induced and uninduced cells co-transformed with pBbB8k-csg-amylase and the ibpAB-GFP sensor, our results indicate that only at time point 12, the average fluorescence of the induced cells were higher than those of uninduced. At other time points, though not statistically significant, the average fluorescence values for the induced cells were consistently higher than the uninduced cells between time points 12 to 48. Although these time points are past the healthy log-phase growth for *E. coli* cells, the overall comparison could potentially indicate a greater accumulation of sfGFP in the induced cells, which is also supported by our number of sfGFP molecules per cell data, by the end of the log-phase growth. Overall, these results support our third prediction that induction of the pBbB8k-csg-amylase in cells would increase potential interactions between the circuit and the cell more than the uninduced cells, thus leading to higher expression levels at the *ibpAB* operon promoter as indicated by sfGFP production.

Overall, these results functionally confirm the ibpAB-GFP sensor plasmid which was designed and previously tested by Ceroni *et al.* (2018). Our results further confirm the functionality of this sensor when it is co-transformed with the pBbB8k-csg-amylase plasmid. When comparing the cells with only the ibpAB-GFP sensor to those with both induced and uninduced co-transformed cells, our data showed that both the induced and uninduced cells exhibited higher fluorescence than the cells with the sensor alone which indicated that the presence of a second circuit induced ibpAB-GFP sensor expression. Lastly, the highest fluorescence was observed in the induced cells which supported that induced and actively expressed plasmid were leading to higher levels of *ibpAB* operon promoter expression than when they were not induced. Together, these results suggest that the ibpAB-GFP construct designed by Ceroni *et al.* (2018) has potential to be used as a sensor of *ibpAB operon*, containing genes *ibpA* and *ibpB*, expression which was a highly common DE gene from our RNA-seq analysis.

In summary:

* The ibpAB-GFP sensor construct (Ceroni et al., 2018) was confirmed to be functional in responding to an addition of a plasmid which in our design was co-transformation with pBbB8k-csg-amylase into NEB5-alpha cells
* The sensor detected *ibpAB* operon promoter expression levels that were higher in cells with both the induced and uninduced co-transformed cells compared to only the ibpAB-GFP sensor alone
* Induced co-transformed cells overall exhibited higher expression at the *ibpAB* operon promoter compared to the uninduced cells

*P-values for ibpAB-GFP Sensor Circuit*

![](https://static.igem.org/mediawiki/2021/c/c8/T--William_and_Mary--pibpABceroni.png)

**Please check the experience tab of this circuit’s parts page for graphical representation of the data.** Raw fluorescence data for this circuit can be found [here](https://static.igem.org/mediawiki/2021/8/89/T--William_and_Mary--WM21_ibpAB.zip).

**groSL-sfGFP Sensor [by Ceroni *et al.* (2018)]**

Our analysis of differential expression of *groS*, a heat-shock gene, showed that it was another gene that is upregulated when introduced to a heterologous circuit. Therefore, the expected trend in fluorescence was that the co-transformation of groSL and our test circuit pBbB8k-csg-amylase would display increasing fluorescence over time. It was also expected that the sample of groSL transformed with induced pBbB8k would fluoresce at a higher level than the groSL transformed with uninduced pBbB8k. This is because the arabinose-induced pBbB8k circuit would be promoting curli fiber production in the bacteria more than the uninduced circuit would, and this increased cell activity could cause a higher stress response in the host cells. Additionally, we expected the co-transformed cells to show greater fluorescence than the sensor circuit alone, due to the data suggesting that *groS* is upregulated in the presence of another circuit, which should promote the translation of more sfGFP in this circuit.

The fluorescence data does illustrate an increase in fluorescence over time in the induced co-transformed sample, where the fluorescence generally stayed low and more constant for the first 12 hours after induction, then increased steadily after that 12 hour point. This trend was consistent with our prediction of increasing fluorescence in the induced co-transformed sample, and was observed through all 3 test replicates. The uninduced co-transformed sample also shows an increasing trend, but different from our hypothesis, had a higher fluorescence than the transformed sample from 6 to 48 hours, and across all of our replicates. Again different from our hypothesis, through all replicates the culture of the *groSL* sensor circuit alone had greater fluorescence than the induced cotransformation, but approached or dropped below the level of fluorescence of the uninduced co-transformation over time. The *groSL* circuit alone sample increased at the greatest rate from the 6 to 12 hour time points, but for there is an average slight decrease in fluorescence at 12 hours, after which the trend is not consistent between replicates, but remains at a similar fluorescence to the uninduced cotransformation.

Although not all differences between sample fluorescence are statistically significant, much of this insignificance can likely be attributed to our N of only 3, and conducting more replicates may have differentiated more clearly between the fluorescence of samples. Evaluated through t-tests, based on the average fluorescence values between replicates at each time point, the differences between the fluorescence of the induced and uninduced co-transformations were only significant at the time T=12h, and the difference between the induced cotransformation and *groSL* circuit alone only significant at T=6h and T=12h (p-value<0.05). The difference between the induced co-transformation and the untranslated NEB5-alpha cells had more consistently significant results, at T=-1h, 1h, 12h, and 48h (p-value<0.05).

While these results were unexpected, the conversions of molecules of sfGFP, which used the optical density (OD) measurements to find the number of molecules per cell, helps to explain the trends. The induced and uninduced co-transformations showed very similar counts of molecules for T=-1 and T=0. However, from T=1, the first measurement point after induction, to T=6, the induced cells have a greater magnitude of sfGFP molecules, which follows with the expected behavior of the *groSL* circuit. This suggests that the individual cells of the induced co-transformation fluoresced more, but a lower density of cells produced less overall fluorescence in the sample. This also fits with the OD data, which indicates that the growth across all replicates of the induced co-transformation was lower than all other samples, and nearly constant, until after the 12 hour time point. Through all time points, the molecule amounts of the groSL circuit alone in NEB5-alpha were very similar to the molecule amounts of groSL transformed with uninduced pBbB8k. This again can be understood with the OD data, which shows that the *groSL* circuit alone experiences a greater rate of growth than the induced or uninduced co-transformation in only the first 24 hours of the measurement, which could explain why this sensor circuit alone fluoresced more in the first 24 hours of measurement, but varied against the fluorescence of the induced and uninduced samples from 24 to 48 hours. Although the molecule conversion data suggests that the *groSL* circuit may have fluoresced as expected, the discrepancy in the fluorescence data also suggests that the *groSL* circuit’s functionality is inhibited by the limits it places on cell growth after cotransformation with a heterologous circuit, displaying decreased orthogonality.

In summary:

* The *groSL* sensor circuit cotransformed with the induced pBbB8k circuit was expected to increased fluorescence over time, with greater fluorescence than the culture of groSL with uninduced pBbB8k and the culture of groSL alone
* GroSL with induced pBbB8k experienced increasing fluorescence over time, but less fluorescence than either groSL with uninduced pBbB8k or groSL alone
* The molecule conversions of sfGFP per cell suggest that groSL with induced pBbB8k had greater fluorescence than the uninduced and groSL samples in certain time frames, but the overall fluorescence measured was decreased by less culture growth
* These results suggest that the *groSL* circuit functions as intended on the cell level, but its reduction in orthogonality within a cotransformation prevents effective measurement as intended

*P-values for groSL-sfGFP Sensor Circuit*

![](https://static.igem.org/mediawiki/2021/8/85/T--William_and_Mary--gproslceroni.png)

**Please check the experience tab of this circuit’s parts page for graphical representation of the data.** Raw fluorescence data for this circuit can be found [here](https://static.igem.org/mediawiki/2021/2/2d/T--William_and_Mary--WM21_groSL.zip).

.

## References

Alam, K. K., Tawiah, K. D., Lichte, M. F., Porciani, D., & Burke, D. H. (2017). A Fluorescent Split Aptamer for Visualizing RNA-RNA Assembly In Vivo. ACS synthetic biology, 6(9), 1710–1721. https://doi.org/10.1021/acssynbio.7b00059

Birnbaum, D., Manjula-Basavanna, A., Kan, A., Tardy, B., & Joshi, N. (2021). Hybrid Living Capsules Autonomously Produced by Engineered Bacteria. *Advanced Science*. *8*(11), https://doi.org/10.1002/advs.202004699

Ceroni, F., Algar, R., Stan, G., & Ellis, T. (2015). Quantifying cellular capacity identifies gene expression designs with reduced burden. Nature Methods, 12(5):415-418. Doi: 10.1038/nmeth.3339

Ceroni, F., Furini, S., Gorochowski, T.E., Boo, A., Borkowski, O., Ladak, Y., Awan, A.R., Gilbert, C., Stan, G.V., & Ellis, T. (2018). Burden-driven feedback control of gene expression. *Nat Methods*, *15*, 387–393. https://doi.org/10.1038/nmeth.4635

*Protocols*. Harvard OPTI POLY.https://2017.igem.org/Team:Harvard/Protocols.

Quantitation of Green Fluorescent Protein in Microplates using the FL600. (2006). Retrieved October 18, 2021, from www.biotek.com/resources/docs/FL600\_Quantitation\_of\_Grn\_Fluorescent\_Proteins\_in\_Microplates.pdf

Weiße, A. Y., Oyarzún, D. A., Danos, V., &amp; Swain, P. S. (2015). Mechanistic links between cellular trade-offs, gene expression, and growth. Proceedings of the National Academy of Sciences, 112(9).