iGEM 2025 — Team william-and-mary

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* [Recruitment](#recruitment)
* [Spring Semester 2025](#spring_semester)
* [May Break 2025](#may_break)
* [Summer Session 2025](#summer_session)
* [August Break 2025](#august_break)
* [Fall Semester 2025](#fall_2025)

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# Recruitment: (November 2024 - December 2024)

##### **November 2024 - December 2024**

* Hung recruitment fliers in many buildings across campus
* Reached out to various biology classes along campus
* Read applications, conducted interviews, and sent out acceptance/rejection letters

# Spring Semester 2025

##### **Week 1: January 27 - February 2**

* Conducted preliminary literature reviews about SynBio applications in water and finalized project topic
* Began trainings in wetlab techniques
* Researched literature data to understand water synbio software needs and relevant mathematical models

##### **Week 2: February 3 - February 9**

* Developed subproject ideas and experiments
* Continued trainings in wetlab techniques, understanding safety guidelines
* Looked into metagenomics data and modeling literature pertaining to subprojects

##### **Week 3: February 10 - February 16**

* Developed case study topics for wet-lab proof of concept, while simultaneously conducting wetlab trainings
* Conducted literature reviews on detection method, phage dynamics, and reviewed structure of metagenomic data

##### **Week 4: February 17 - February 23**

* Researched cyanobacterial strains from ATCC, UTEX, and Carolina, and reviewed existing bacterial and species databases.
* Expanded literature review on phage–bacteria dynamics and modeling, while refining earlier searches for detection methods.

##### **Week 5: February 24 - March 2**

* Continued literature reviews for current methods
* Decided on creating a predictive model
* Began writing code for simulations of nonlinear systems of differential equations.

##### **Week 6: March 3 - March 9**

* Reviewing literature for cyanophage engineering techniques
* Reviewing machine learning models pertaining to desired applications
* Focused on mcy gene expression and HABs

##### **Week 7: March 10 - March 16**

* Ordered *M. aeruginosa* strain LE3 (UTEX 3037), began planning setup for growth conditions and inoculation
* Conducted literature reviews on a biosensor and *B. subtilis* MICI biofilms
* Confirmed data availability for machine learning model training. Decided on creating database to train a model on
* Conducted simulations of phage-bacteria system of differential equations

##### **Week 8: March 17 - March 23**

* Refining method for sensor implementation and application in real world
* Refined initial software idea, decided on lat/long as essential inputs

##### **Week 9: March 24 - March 30**

* Ordered reagents for making BG-11 media for growing cyanobacteria while continuing wetlab training
* Looked more in depth as to how the parameters affect the phage-bacteria dynamics
* Reviewed essential python packages for software development

##### **Week 10: March 31 - April 6**

* Wrote up protocols for sensor implementation and conducted microscopy training
* Looked at the sra database for what it offers.

##### **Week 11: April 7 - April 13**

* Received initial orders of *M. aeruginosa*. Inoculated subcultures and determined a preliminary setup for culture growth, additionally made BG-11 media.
* Began collection of studies that perform metaT and do DE gene analysis in HABs
* Looked at optimal conditions datasets as features.
* Started working on phage diffusion in biofilm model

##### **Week 12: April 14 - April 20**

* Looked into a toehold riboswitch literature database
* Inoculated new *M. aeruginosa* cultures
* Decided on using real-world environmental data over lab data
* Designed circuits for *B. subtilis* for corrosion prevention

##### **Week 13: April 21 - April 27**

* Reanalyzed the geometry of the biofilm-phage mode
* Started *M. aeruginosa* subcultures, tested growth in BG-11 media from different sources (premade by Carolina vs. UTEX vs. in house)
* Continued database/dataset collection

##### **Week 14: April 28 - May 4**

* Continued investigation of toehold riboswitch literature database
* Found four useful databases
* Looked into numerical methods for simulating partial differential equations

##### **Week 15: May 5 - May 11**

* NASBA primers creation, targeted regions of mcy genome, and ordered plasmids
* Determined procedures for creating BG-11 plates and growing *M. aeruginosa* lawns
* Started deciding essential features for the software

# May Break 2025

##### **Week 16: May 12 - May 18**

* Designed circuits and ordered primers and plasmids
* Designed experiments and determined assay techniques
* Verified partial differential equations numerical simulations aligned with analytical solutions
* Created BG-11 agar plates
* Attempted to streak and spread *M. aeruginosa* on BG-11 agar plates and to grow lawns of *M. aeruginosa*

##### **Week 17: May 19 - May 25**

* Designed circuits and ordered primers and plasmids
* Designed experiments and determined assay techniques
* Preparing media for culturing *Bacillus subtilis*
* Shortlisting database/website extraction points.

# Summer Session 2025

##### **Week 18: May 26 - June 1**

* Began design of an in silico riboswitch for target region
* Decided on prioritizing true metagenomics over 16s
* Engineered plasmid encoding for a functional cellulase for mycobacterial biofilm degradation
* Constructed diffusion model of phage infection of a biofilm
* Brought up freeze-dried bacteria received from ATCC
* Miniprepped plasmids received from Addgene
* Determined procedure for taking *M. aeruginosa* OD measurements, began testing culture growth under different culturing conditions
* Collected environmental samples, started cyanophage enrichments

##### **Week 19: June 2 - June 8**

* Inoculated plasmids for riboswitch
* Finalized databases to extract from
* Attempted and failed to transform plasmid with cellulase into mycobacteria
* Extracted genomic DNA for *B. subtilis* 168 and *B. subtilis* 3610
* Successfully began growing *M. aeruginosa* on agar plates
* Validated identity of *M. aeruginosa* cultures via culture PCR
* Began first cyanophage plating assays
* Re-analyzed geometry of system for biofilm-phage infection system of PDEs to a 1D cartesian model

##### **Week 20: June 9 - June 15**

* Assembly of mcy trigger plasmid
* Began growing phage to a high titer
* Created web scraping scripts for each database
* Amplify coding regions from *B. subtilis* genomic DNA
* Mutated out BsaI restriction cut sites in a coding region and assembled them into an empty plasmid
* Began first batch of liquid cyanophage assays
* Constructed 1D advection simulations that demonstrated cyanophage is limited in treatment ability
* Collected lakewater samples, attempted culture PCR on samples to search for presence of *M. aeruginosa*

##### **Week 21: June 16 - June 22**

* Collecting trigger plasmid, confirming success
* Finalized extraction
* Confirmed success of the coding region plasmid
* Ordered *A. baylyi* from Addgene (ADP1-ISx - Bacterial strain 216551)
* Engineered *B. subtilis* integration vector to make it compatible with golden gate assembly
* Attempted 16S culture PCR from environmental samples
* Attempted environmental DNA extraction from lakewater samples
* Analyzed mathematical model for phage-biofilm effectiveness

##### **Week 22: June 23 - June 29**

* Assembling switch plasmid
* Cleaning extracted data for merging.
* Confirmed success of integration vector.
* Conducted a session of BLAST science outreach program
* Identified potential cyanophage plaque on an agar assay
* Collected more environmental samples, started more enrichments and assays
* Adapted Acinetobacter and parameters into mathematical model for simulations
* Constructed novel mathematical model of Acinetobacter-*M. aeruginosa* dynamics

##### **Week 23: June 30 - July 6**

* Confirming success of switch plasmid assembly
* Looked into how to process our extracted samples for quantitative data
* Assembled the promoter, RBS, CDS, terminator into the integration vector for *B. subtilis*
* Made *B. subtilis* competent cells
* Sequenced the assembly for *B. subtilis* and found a problem for the RBS
* Visualized portion of potential cyanophage plaque on SEM, while conducting more cyanophage assays
* Tested DNA and RNA extraction procedures on *M. aeruginosa*
* Conducted parameter sweeps on Acinetobacter-*M. aeruginosa* dynamics

##### **Week 24: July 7 - July 13**

* Redo-ing switch plasmid assembly, DE gene search
* Chose Kraken and Bracken as our method of taxonomic processing
* Ordered RBS as DNA oligos and redo the assembly into the integration vector
* Ran existing *M. aeruginosa* sequences through Phastest software, compared existing *M. aeruginosa* cyanophage sequences with BLAST
* Advection-reaction PDE models on Acinetobacter-HAB dynamics

##### **Week 25: July 14 - July 20**

* New reporter addition to switch plasmid
* Created metagenomic pipeline
* Confirmed success of the integration vector assembly
* Tested TRIzol procedure for *M. aeruginosa* RNA extraction
* Observed substantial plaques on cyanophage assay plate, attempted to propagate plaques
* Reaction-diffusion equations for phage transport in biofilms expanded for analysis

##### **Week 26: July 21 - July 27**

* Cotransforming mcy switch and trigger
* Ran samples through metagenomic pipeline
* Transformed the constructed integration into *B. subtilis*
* Conducted a small-scale RNA extraction *M. aeruginosa* and *A. baylyi* coculture experiment
* Observed conditions for proper deployment of phage in pipes
* Planned lakewater microcosm experiment

##### **Week 27: July 28 - August 3**

* Beginning inducing of mcy co-transformation
* Merged metagenomic pipeline sample output into a single matrix
* Confirmed the success of the integration vector integrated into *B. subtilis* genome
* Made specific media for *B. subtilis* biofilm assays
* Inoculated preliminary lakewater microcosms and began preliminary RNA sequencing experiment
* Wrote up models of phage pipe transport and HAB suppression

# August Break 2025

##### **Week 28: August 4 - August 10**

* Grew sterile biofilms on PVC pipes
* Mutated single nucleotide mutations in switches
* Merged taxonomic abundance result with previously extracted metadata
* Literature reviews for *B. subtilis* biofilm assays
* Sample collection for preliminary lakewater microcosm experiment
* Created a differential equation for *B. subtilis* deployment & detachment

##### **Week 29: August 11 - August 17**

* Began construction of pipe microcosm and 3D printing prototypes of the microcosm parts
* Grew nonsterile biofilms on PVC pipes for microcosm innoculation
* Redid mcy switch co-transformations
* Created initial aquery database
* Designed microcosms for *B. subtilis* corrosion protection ability

##### **Week 30: August 18 - August 24**

* Set up final household pipe microcosms
* Began inducing second mcy co-transformation
* Reviewed machine learning models and started predictor script creation
* Attempted RNA extractions from preliminary lakewater microcosm samples
* Compiled the python files of the mathematical modeling

# Fall Semester 2025

##### **Week 31: August 25 - August 31**

* Began pipe microcosm experiment with flushings and bacterial titer sampling
* Did a DE gene search in order to prepare to design DE gene (gdhA and cpcB switches)
* Updated predictor script to utilize three classification models
* Tested growth curve and biofilm morphology for *B. subtilis*
* Attempted more RNA extractions from preliminary lakewater microcosm experiments
* Compiled information and writeups for mathematical models

##### **Week 32: September 1 - September 7**

* Further prepping to design DE gene (gdhA and cpcB switches)
* Continued pipe microcosm experiment with titer measurements
* Flushed pipe microcosm with phage
* Further analyzed model for *B. subtilis* biofilm detachmen
* Trained model on matrix containing environmental features and species abundance
* Measured the motility for engineered *B. subtilis* strains
* Inoculated new lakewater microcosms, began new microcosm experiment

##### **Week 33: September 8 - September 14**

* Finished pipe microcosm experiment
* Compiling info for wiki
* Created database SQL format, along with streamlit application and api
* Made microcosms for *B. subtilis* and took SEM pictures for the steel plates treated in microcosms
* Collected samples for lakewater microcosm experiment
* Extracted DNA and ran 16S PCRs for microcosm experiment samples
* Tested new RNA extraction procedure for microcosm samples
* Incorporated fitness score from the software into mathematical model framework

##### **Week 34: September 15 - September 21**

* Flushed biofilms on pipes with phage and placed under HIROX microscope
* Worked on RNA seq database, began running Transcriptomic pipeline on Galaxy
* Created predictive model frontend scripts
* Extracted RNA for RNA seq for all 3 case studies
* Conducted more DNA extractions and PCRs for lakewater microcosm experiment
* Compiled simulations and graphs from the mathematical model

##### **Week 35: September 22 - September 28**

* Continuing work on RNA seq database, continued running Transcriptomic pipeline on Galaxy, functional analyses w/DAVID
* Looked into metatranscriptomic and what it offers. Created metagenomic and metatranscriptomic comparison scripts
* Extract 16S DNA for all 3 case studies, sent 16S for sequencing
* Presented mathematical models on synthetic biology in aquatic systems

##### **Week 36: September 29 - October 5**

* Analyzing 16S data for all 3 case studies
* Writing up wiki pages
* Utilizing David & Galaxy for RNA-seq analysis
* Began AI-based T and metaT analysis
* Saving models publicly available repository
* Compiled graphs and heatmaps and simulations of mathematical models

##### **Week 37: October 6 - Wiki Freeze!**

* Analyzed RNA-Seq data for all three case studies
* Completing wiki
* Continued AI-based T and metaT analysis
* Completing wiki and pushing software tools

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* [Executive Summary](#overview)
* [Aquatic Case Study I: Corrosion Prevention Using Engineered *Bacillus subtilis* in Marine Environments](#corrosion)
* [Aquatic Case Study II: Freshwater Algal Bloom Remediation](#habs)
* [Aquatic Case Study III: Removal of Biofilms in Household Pipes](#pipes)
* [Bioinformatics](#bioinformatics)
* [References](#references)

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# **Executive Summary**

---

Aquarius applied engineering principles to the development and testing of SynBio solutions to three major aquatic “case studies” of focus. Each case study required multiple engineering cycles: We redesigned circuits after initial testing, altered our microcosm construction and sampling methods after preliminary experimentation, and, most importantly, reassessed the feasibility of our engineering solutions based on novel genomic and transcriptomic data generated through microcosm experiments.

Ultimately, our engineering results provide important considerations about the effects of water-associated factors on chassis behavior and survival and inform future engineering for aquatic environments.

### **I. Marine Corrosion Prevention with *Bacillus subtilis***

We conducted several rounds of redesign and engineering to construct functional biofilm-enhancing circuits in multiple strains of *B. subtilis*. We verified successful incorporation of engineered constructs via iodine staining and assessed the corrosion-prevention capabilities of engineered strains through motility assays and microcosm experiments. A large number of genes, including various metabolic and flagellar assembly genes, were upregulated under microcosm conditions, indicating altered chassis functionality. Future engineering should address the effects of realistic aquatic conditions on engineered *B. subtilis*’s corrosion-prevention capabilities.

### **II. Freshwater Harmful Algal Bloom (HAB) Remediation with Cyanophage and *Acinetobacter baylyi***

We developed, conducted, and adapted procedures for cyanophage isolation assays with the goal of engineering novel cyanophages for HAB remediation. We found strong evidence of cyanophage presence on an agar assay, but we were unable to consistently propagate any phage. We concluded that cyanophage isolation may have limited feasibility due to unique characteristics of *M. aeruginosa* and HAB ecology. We pivoted to testing the HAB remediation potential of the naturally algicidal chassis bacterium *A. baylyi*. We developed and adapted lakewater microcosms for simulated real-world testing through multiple rounds of experimentation. Colony counts, 16S abundances, and transcriptomic data indicated that *A. baylyi*’s survival was reduced and behavior altered under realistic conditions. Future engineering should address *A. baylyi* survival limitations associated with real-world deployment.

### **III. Household Pipe Biofilm Removal Using Phage Therapy**

We designed a targeted phage cocktail for mycobacterial biofilm removal and tested its anti-biofilm effectiveness under laboratory conditions and in simulated pipe-environment microcosms. We developed, 3D-printed, and adapted multiple pipe microcosm systems that realistically simulate elements of plumbing conditions, such as regular water flushing. We identified strategies for effective mycobacterial biofilm growth (for example, we found that biofilms grew more effectively under nonsterile conditions) to inform future mycobacterial microcosm development and experimentation with phage.

---

### **Bioinformatics & RNA-Seq Meta-Analysis**

We engineered a bioinformatic pipeline for accessing and processing large amounts of raw metagenomic data. We iterated through several design-build-test-learn cycles in order to efficiently extract large amounts of sequence data from NCBI in FASTQ format and run it through Kraken and Braken for taxonomic processing. We used various techniques to tune and train a predictive model for species survival in aquatic environments. Finally, we designed and redesigned methods for our meta-analysis of existing RNAseq data, troubleshooting the time constraints of analyzing massive datasets from scratch by instead querying AI chatbots with careful and highly specific prompt engineering (and thorough manual vetting steps) to obtain a comprehensive analysis.

# **Aquatic Case Study I: Corrosion Prevention Using Engineered *Bacillus subtilis* in Marine Environments**

---

## **1. Plasmid Engineering**

**Summary:** To test the differences in *Bacillus subtilis* biofilm growth between laboratory conditions and natural seawater environments, and their different ability of preventing corrosion, we constructed vectors for *Bacillus subtilis* strains to enhance their biofilm-forming capabilities to improve corrosion resistance on steel surfaces.

Design:▼

We selected four genetic targets known to influence biofilm properties:

* *SinI* – to promote biofilm thickness by alleviating SinR-mediated repression
* *BslA* – to enhance surface hydrophobicity through the formation of a protective protein layer
* *TapA-SipW-TasA* operon and TasA – to strengthen biofilm architecture via amyloid fiber production

The experimental strategy involved individual overexpression of each gene to assess their respective contributions to biofilm morphology and corrosion inhibition. Detailed plasmid construct includes:

* A xylose-inducible promoter (xylA) from the 2012 LMU-Munich iGEM team (Radeck et al., 2013)
* A ribosome binding site (RBS) adapted from the SpoVG RBS in the iGEM distribution kit
* Coding sequences (CDSs) for the four target genes, PCR-amplified from *B. subtilis* genomic DNA
* The B0015 terminator from the iGEM distribution kit

The backbone vector was the pBS1C plasmid (also from the 2012 LMU-Munich iGEM team), which was modified to conform to iGEM Type IIS assembly standards. Each construct carried a single target gene under an inducible promoter for regulated expression. Assembled plasmids were transformed into *B. subtilis* 168 and integrated into the *amyE* locus of the genome.

Build:▼

To comply with the iGEM type IIS assembly standard, we first modified the pBS1C plasmid that was originally constructed to fit BioBricks standards. A point mutation was introduced via Gibson assembly to eliminate a BsaI site within the ampicillin resistance region. We then replaced the whole RFP BioBrick segment with a GFP BioBrick from pJump28 using restriction digestion and ligation.
This segment includes BsaI sites after the BioBrick prefix (EcoRI, XbaI) and before the suffix (PstI, SpeI), enabling compatibility with both BioBrick and iGEM Type IIS assembly. The GFP reporter gene enables us to visually confirm successful gene insertion. The resulting plasmid was named pBS1C-N. Next, we PCR-amplified xylA and four coding sequences (CDSs), adding BsaI recognition sites and standard fusion sequences according to iGEM Type IIS rules. One CDS—the tapA-sipW-TasA operon—contained two illegal BsaI sites. To resolve this, we split the operon into three fragments near the restriction sites, PCRed the fragments, introduced point mutations to remove the BsaI sites, and added SapI sites with fusion sequences. These fragments were assembled into the iGEM L0 SapI-J04450 vector using SapI-based Golden Gate assembly. Once all parts were prepared, we performed the final Golden Gate assembly using BsaI to insert promoter-RBS-CDS-terminator into pBS1C-N.

![Corrosion Plasmid](https://static.igem.wiki/teams/5687/eng/corrosion-1.webp)

Figure: pBS1C-N plasmid design.

Test:▼

The constructed vectors were validated using colony PCR and restriction enzyme digestion. All pBS1C-N constructs passed both tests and were confirmed by sequencing. However, only 2 out of 24 L0 SapI-J04450-tapA-sipW-TasA constructs were successful. Sequencing confirmed correct assembly in these two cases. None of the L1 plasmids passed validation; sequencing revealed missing elements such as promoter, RBS, CDS, or terminator in various combinations.

Learn:▼

SapI-based assembly may suffer from low ligation efficiency due to its short three-base-pair fusion sites, resulting in a high possibility of mismatch. Further investigation is needed to determine the causes of failure in the L1 constructs.

Redesign:▼

Since all sequenced L1 vectors lacked the RBS component, we strongly suspected that the SpoVG RBS obtained from the distribution kit is problematic. This led us to send the SpoVG plasmid from the distribution kit for sequencing and purchased SpoVG as a set of oligonucleotides.

![Corrosion Plasmid](https://static.igem.wiki/teams/5687/eng/corrosion-2.webp)

Figure: Constructed L1 pBS1C plasmid, where BslA is the coding region.

Build/Test:▼

We bought SpoVG oligos with BsaI cut sites and fusion sites at both ends. We sequenced the SpoVG plasmid. At the same time, we combined the ordered SpoVG oligos into dsDNA.

Learn:▼

The SpoVG RBS in the iGEM distribution kit did not contain the expected sequence noted in the iGEM registry.

Redesign:▼

We incorporated our ordered SpoVG segment into iGEM Type IIS assembly standard and built the vector. The integration vector was designed to integrate into the AmyE locus in the *Bacillus subtilis* genome. Upon successful transformation, the vector disrupts the native amylase gene, thereby stopping the strain’s ability to hydrolyze starch. This disruption enables a straightforward functional screening method: colonies with successful genomic integration will fail to degrade starch and appear dark blue when stained with iodine solution, whereas wild-type colonies will be orange due to starch breakdown.

Build:▼

Using the bought SpoVG oligonucleotides, we assembled four coding sequences into the pBS1C-N backbone via Golden Gate assembly. Chemically competent *B. subtilis* 168 and 3610 cells were prepared for transformation. All four constructed integration vectors were transformed into both strains.

Test:▼

Post-transformation, we verified the constructs using colony PCR, restriction enzyme digestion, and sequencing. Transformation into *B. subtilis* 168 yielded colonies for all four constructs on selective antibiotic plates, no colonies were obtained from *B. subtilis* 3610. To further test for genomic integration, we plated the modified *B. subtilis* 168 strains on starch agar alongside the unmodified *B. subtilis* 168 and *B. subtilis* 3610 controls. After 24 hours of incubation, iodine staining revealed a uniformly blue stain in all engineered strains, confirming the loss of starch degradation ability. Further validation was performed using colony PCR with primers specific to the AmyE locus, which confirmed successful integration of the constructs into the genome.

Learn:▼

Due to the natural incompetence of *B. subtilis* 3610, transformation of the engineered vectors into the cells was unsuccessful. The combination of iodine-starch staining and colony PCR provided robust evidence for successful genomic integration in *B. subtilis* 168. As a result, we obtained four genetically modified strains that were designed to prevent steel corrosion.

## **2. Morphology analysis**

**Summary:** The natural strain, laboratory strain, and four engineered variants of *Bacillus subtilis* displayed distinct differences in biofilm morphology and motility. To evaluate their difference in biofilm-forming capacity and overall performance, we systematically examined each strain’s motility, colony morphology, and growth dynamics under various conditions.

Design:▼

Brück et al. (2019) demonstrated that *Bacillus subtilis* 168 loses its ability to produce extracellular polymeric substances (EPS) during domestication, resulting in decreased biofilm formation. The wild-type strain *B. subtilis* 3610 retains robust biofilm-forming capabilities. We compared the wild-type strain (*B. subtilis* 3610), the laboratory strain (*B. subtilis* 168), and the four engineered variants in terms of motility, growth, and biofilm formation. Our modifications included:

* *SinI*: Overall upregulates biofilm-related genes and suppresses growth rate
* *BslA*: Enhances surface hydrophobicity.
* *TasA & TapA-SipW-TasA*: Contribute to amyloid fibers production in the biofilm.

Build/Test:▼

To evaluate growth dynamics, overnight cultures were subcultured into 96-well plates containing LB medium with xylose at induction levels ranging from 0.001% to 2% (v/v). Growth curves were recorded at 24 and 36 hours using a microplate reader, enabling comparisons across strains and induction conditions.

Motility was assessed using semi-solid agar plates (0.3%, 0.7%, and 1.5% agar) supplemented with 0.2% xylose. Movement on 0.3% agar reflects flagellum-dependent swarming, while diffusion on 0.7% agar indicates EPS-dependent sliding. Spreading diameters were measured after 12 hours.

To evaluate biofilm formation and colony morphology, 3 μL of overnight LB cultures were inoculated onto various media:

* LB (baseline growth)
* LB + 0.03% xylose (non-biofilm-inducing)
* MSgg (biofilm-promoting minimal medium)
* MSgg + surface sprayed 0.03% xylose
* MSgg + 0.03% xylose mixed into agar (non-uniform inducer distribution)
* MSgg + 0.2% xylose (to assess promoter response at higher inducer levels)

![Corrosion Plate](https://static.igem.wiki/teams/5687/eng/corrosion-3.webp)

Figure: MSgg agar plate supplemented with 0.03% xylose inducer, with each *Bacillus subtilis* strain directly spotted onto the agar surface.

Learn:▼

In non-biofilm-promoting media, varying xylose concentrations had minimal impact on growth rate. The four engineered strains showed similar growth rates to *B. subtilis* 168, all lower than the wild-type *B. subtilis* 3610. Only *B. subtilis* 3610 exhibited both swarming sliding motility. Among engineered strains:

* *B. subtilis* 168 and *B. subtilis* 168 *SinI* variant showed strong swarming.
* *B. subtilis* 168 *BslA* and *TapA-SipW-TasA* variants displayed moderate swarming.
* *B. subtilis* 168 *TasA* variant showed neither swarming nor sliding, possibly due to toxicity from *TasA* overexpression.

*B. subtilis* 3610 formed a distinct, slightly brown biofilm. Only *SinI+* exhibited biofilm-like folds and wavy margins, *Bacillus subtilis* 168 (lab strain) showed a faint wavy margin, other strains formed smooth colonies without biofilm features. LB medium did not support biofilm formation, it should be used for culture rather than biofilm assays. MSgg with surface-sprinkled inducers led to uneven colony growth due to difference distribution of inducer concentration. Adding 0.03% or 0.2% xylose to MSgg did not substantially affect biofilm development, likely due to insufficient concentration differences to influence promoter activity.

## **3. Microcosm construction**

**Summary:** To investigate differences between natural and laboratory environments, we constructed marine microcosms that simulate real-world conditions. The lab strain *B.subtilis* 168, natural strain *B. subtilis* 3610 and the 4 modified strains were introduced into these systems, and steel chips were placed in to the microcosms assess their corrosion resistance in mimicked natural environment.We performed molecular-level analyses as 16S rRNA sequencing and RNA-seq to explore microbial community dynamics and gene expression profiles associated with biofilm formation and corrosion prevention.

Design:▼

We evaluated the corrosion prevention capabilities of various *Bacillus subtilis* strains under simulated real-world conditions. Specifically, we compared the performance of the natural strain, laboratory strain, and four genetically modified variants. To achieve this, we constructed microcosms containing steel plates to mimic marine environments.

Build:▼

We recycled 1000 mL pipette tip boxes as microcosm water tanks. To simulate seawater with an average salinity of 35‰, we dissolved 35 g of Instant Ocean salt in 1 L of deionized water. Each microcosm was filled with 200 mL of this artificial seawater.

Seven microcosms were prepared:

* One served as a control with untreated seawater.
* Six were inoculated with the following strains: *B. subtilis* 3610 (wild-type), *B. subtilis* 168 (lab strain), *B. subtilis SinI+*, *BslA+*, *TasA+*, and *TapA-SipW-TasA+*. Each strain was induced with 0.2% xylose, and bacterial biomass was added at 0.5% (v/v) of the seawater volume(1ml).

We used A36 steel—commonly employed in marine applications—and cut it into 1.5 × 1.5 cm chips suitable for SEM analysis. Three chips were placed in each microcosm. We created three additional microcosms by adding 50 mL of overnight bacterial culture (with 0.2% xylose) to 150 mL of seawater, resulting in a 25% (v/v) bacterial concentration. Steel chips were also added to these microcosms. These setups were used for RNA extraction. Based on prior performance, we selected *B. subtilis* 168 *SinI+*, *BslA+*, and 3610 for RNA analysis.

![Corrosion Microcosms](https://static.igem.wiki/teams/5687/eng/corrosion-4.webp)

Figure: Microcosms that mimic corrosion on steel in seawater.

![Corrosion Microcosms](https://static.igem.wiki/teams/5687/eng/corrosion-5.webp)

Figure: Steel chip in microcosm with 0.5% (1mL) of *B.subtilis* 168 *BslA+* culture added and incubated for a day.

Test:▼

Steel chips were removed from each microcosm after 3 and 7 days. To prepare for imaging, chips were cleaned with acetone and alcohol to remove surface biofilms. We documented their appearance using phone photography and conducted surface analysis via SEM. Simultaneously, we extracted RNA from the three high-concentration microcosms using the NEB RNA extraction kit. The same strains were also cultured in a shaking incubator under laboratory conditions, and RNA was extracted from these cultures. RNA sequencing was performed on all six samples to compare gene expression between natural and laboratory environments. Sequence data were processed through the Galaxy platform: adapters were removed using Trim Galore!, reads were aligned to the appropriate *B.subtilis* reference genome (NC\_000964.3 for *B.subtilis* 168-based strains and CP020102.1 for *B.subtilis* 3610) using HISAT2, alignments were sorted with SAMtools, read counts were generated with HTSeq, and differential expression analysis was conducted with DESeq2. We used DAVID(Huang et al., 2009; Sherman et al., 2022) to achieve gene function annotation for the DESeq2 output.Additionally, we pelleted bacteria from both 0.5% and 25% microcosms of *B. subtilis* 168 *SinI+*, *BslA+*, and 3610. We did PCR amplification using 16S primers, and sequenced the results to assess strain survival and microbial population in the microcosms. This analysis helped determine how the 3 strains survive in simulated environments under different concentrations and whether microbial dynamics within the microcosms contributed to the antimicrobial effects observed in SEM imaging.

Learn:▼

Visually, the seawater control group, which was not inoculated with *B. subtilis*, exhibited a thick oxidation layer on the steel surface, while moderate oxidation was observed in the *B. subtilis* 168 treated samples. Oxidation on the remaining samples was minimal and much less apparent. Scanning electron microscopy (SEM) further revealed extensive corrosion on both the three-day and seven-day control plates, consistent with severe surface degradation. In contrast, the three-day *B. subtilis* 168 sample displayed distinct corrosion lines interspersed with deposits of irregularly shaped compounds—patterns similar to nano-structured calcium silicate(Johnston et al., 2008). Similar surface features were also present in the three-day *B. subtilis* 3610 sample and showed up in a region of the seven-day *SinI* plate. Notably, in other regions of the three-day *B. subtilis* 3610 plate, as well as on the three-day and seven-day *BslA+* and *SinI+* samples, we observed a novel surface structure: parallel, unidirectional metallic crystals forming across the steel. These formations differ markedly from previously published SEM images of *B. subtilis*–related corrosion (Guo et al., 2017; Wang et al., 2020), which were obtained under sterile laboratory conditions. In contrast, our study employed non-sterile microcosms designed to mimic natural marine environments showcasing that these unique crystalline structures may arise from species interactions in aqueous environments.

16S rRNA gene sequencing revealed substantial colonization by non-*Bacillus subtilis* species across all seawater microcosm samples, indicating that mixed microbial communities developed under non-sterile conditions. Among the three tested strains—wild-type *B. subtilis* 3610, *SinI+*, and *BslA+*—the wild-type strain showed minimal persistence within these communities, whereas both engineered strains displayed moderate survival. Community composition varied strongly with inoculum concentration. In microcosms inoculated with 25% bacterial culture, Ochrobactrum dominated the population and emerged as the most abundant non-Bacillus genus. In contrast, microcosms inoculated with only 0.5% bacterial culture were consistently enriched in Acinetobacter. These results suggest that inoculum concentration not only shapes community composition but may also influence competitive interactions among bacteria communities, providing a potential ecological explanation for the observed differences in corrosion-protection outcomes described in Experiment 4.

RNA sequencing results revealed that sporulation-related genes were upregulated in all three *B. subtilis* strains under marine microcosm conditions. In contrast, metabolic genes were downregulated across all strains, with the *SinI*+ and *BslA*+ variants showing increased expression of genes involved in ATP binding, rRNA binding, and translation. Notably, the *BslA*+ strain exhibited specific downregulation of genes associated with flagellar assembly, suggesting an enhanced motility response to the seawater environment. The RNA sequence strongly supports that strains were behaving differentially in simulated natural environments and lab environments.

# **Aquatic Case Study II: Freshwater Algal Bloom Remediation**

---

## **4. Microcystis Cyanophage Isolation for Harmful Algal Bloom Remediation**

**Summary:** Cyanophage, viruses that infect cyanobacteria, are a promising tool for HAB remediation, however, 1) few *M. aeruginosa* cyanophages have been isolated and 2) cyanophage deployment in real lakewater environments may be challenging. Our engineering process aimed to 1) isolate novel *M. aeruginosa* cyanophages for use in HAB remediation, 2) test their anti-algal effectiveness in flowing water environments, and 3) identify design principles to inform cyanophage engineering for HAB remediation in water systems.

Design:▼

We developed an initial WetLab experimental pipeline for cyanophage isolation from environmental samples before beginning our experiments.

Build:▼

We collected environmental samples, started cyanophage enrichments, and assayed for cyanophage presence via a traditional “phage plating” method with agar plates. Phage plating typically involves pipetting a mixture of 1 ml bacterial culture, 1 ml potential phage filtrate, and 2 ml warm “top agar” onto an agar plate. The experimenter typically monitors the lawns over time for evidence of plaque formation that could indicate phage infection.

![Water Sample Collection](https://static.igem.wiki/teams/5687/eng/hab-1.webp)

![Water Sample Collection](https://static.igem.wiki/teams/5687/eng/hab-2.webp)

Figure: Environmental sample collection and filtration prior to cyanophage enrichment

Test:▼

*M. aeruginosa* did not grow when we plated only 2 ml of bacterial culture.

Learn:▼

We found that we could produce uniform *M. aeruginosa* lawns by concentrating an initial 30 ml of healthy bacterial culture into 2 ml via centrifugation prior to plating.

![Cyanophage Plate Assay](https://static.igem.wiki/teams/5687/eng/hab-3.webp)

Figure: Cyanophage plating assays

Redesign:▼

Phage plating was too resource-intensive to perform on a large scale. Given *M. aeruginosa*’s slow growth rate, we could not generate enough bacteria (30 ml per assay) to conduct phage plating assays in large quantities. To maximize our assay count (and to increase the probability of phage isolation), we began to conduct small liquid assays, which consisted of 4 ml bacterial culture and 1 ml potential phage filtrate, in parallel with plating assays.

![Liquid Cyanophage Assay](https://static.igem.wiki/teams/5687/eng/hab-4.webp)

Figure: Liquid cyanophage assays

Build:▼

We monitored liquid assays for evidence of bacterial clearing or cell death indicative of phage infection, and we screened our plating assays for cyanophage plaque formation.

Test:▼

We found evidence of cyanophage plaque formation on two out of 65 agar plates and evidence of bacterial clearing in 39 out of 205 liquid assays.

![Putative Cyanophage Plate](https://static.igem.wiki/teams/5687/eng/hab-5.webp)

Figure: Putative cyanophage plaque formation on an agar plate.

We collected putative cyanophage lysates through 0.22 um filtration of cleared assays and by “flooding” with cyanophage buffer a plate that displayed evidence of plaques. We tested the re-infection potential of putative lysates by “infecting” new assays with the putative lysates. However, we were unable to consistently re-propagate any phage.

Learn:▼

Drs. Steven Wilhelm and Gary LeCleir, University of Tennessee microbiologists with expertise in Microcystis ecology, told us that our results are consistent with a broader (though hidden) trend in the field: Microcystis phages are difficult to isolate and propagate. While our team’s preliminary literature review found 13 reports of successful Microcystis phage isolation, the Wilhelm Lab has contacted each associated research group and found that all but one of the phages (*M. aeruginosa* phage Ma-LMM01) were eventually “lost” after publication — i.e., they could not be continually propagated by their isolators. Dr. Wilhelm and Dr. LeCleir suggested that *M. aeruginosa* may evolve phage resistance at the level of a single culture or assay. Without an infectable bacterial population, labs have no means to propagate their stock of phage, which eventually degrades in the absence of a host.

Our results support existing evidence that *M. aeruginosa* possesses strong anti-phage defense systems—likely including frequent genomic rearrangements and phage immunity provided by viral lysogens—that complicate phage isolation and could limit the feasibility of phage-based HAB treatments in the field. Importantly, about 7% of *M. aeruginosa*’s genome encodes transposases, which allow for rapid rearrangement of the species’s abundant mobile genetic elements (Frangeul et al., 2008). Frequent genomic rearrangement events may allow *M. aeruginosa* to rapidly evolve phage resistance in the wild and limit propagation *in vitro*. Additionally, phages often enter the lysogenic state (integration and latency within the host genome) during Microcystis blooms (Huang et al., 2025). Lysogeny could limit the number of free-floating phages available for isolation, provide bacteria with phage immunity that prevents reinfection, and complicate the in vitro phage propagation process.

Given *M. aeruginosa*’s strong anti-phage defenses (both *in vivo* and *in vitro*) and the difficulty of isolating Microcystis cyanophages and maintaining them in the lab, we concluded that cyanophage are not a viable tool for HAB remediation in the short term. Developing cyanophage for effective remediation will require two approaches:

1. Successful isolation of more novel Microcystis phages, which may require a longer experimental time course and larger “n” of assays than we conducted. We have developed a brief guidebook on Microcystis and *M. aeruginosa* phage isolation to help labs without experience in cyanobacterial cultivation get started.
2. Engineering cyanophages to bypass host defense mechanisms, e.g., by modifying lysogeny genes or expanding phage host ranges. (Of course, Microcystis cyanophage engineering requires access to phages that can readily infect the host, so an engineering approach is reliant on successful phage isolation, per the previous goal.)

In the short term, other biological HAB control strategies (e.g., remediation via naturally algicidal bacteria) may be more effective and simpler to develop.

## **5. Comparison of *Acinetobacter baylyi* Behavior in Laboratory vs Simulated Real-World Microcosms**

**Summary:** Engineered bacteria—particularly, species that already possess natural algicidal properties—may present a more immediate alternative to phage that is easier to cultivate and optimize for *in vivo* HAB treatment. We chose to pivot our focus to *Acinetobacter baylyi* ADP1, a common lab strain with algicidal properties and potential applications for HAB remediation.

Design:▼

We hypothesized that *A. baylyi* would display lower survival and different patterns of gene expression when tested in realistic water conditions as compared to traditional cocultures. We aimed to 1) identify differences in *A. baylyi* persistence and functioning in realistic versus *in vitro* conditions and 2) identify design principles that would allow us to adapt the species for future real-world deployment.

We designed water microcosms to simulate two aspects of a lakewater environment: 1) turbulent water flow and 2) diverse microbial communities.

Build:▼

To simulate water flow, we equipped our microcosms with water pumps that generated consistent bubbling and water movement over the course of the experiment. To simulate microbial diversity, we filled our microcosm with water from Lake Matoaka, a local lake. Control cultures were static and grown in sterile BG-11 media. Microcosms and cocultures were both inoculated with *A. baylyi* and *M. aeruginosa*.

![HAB Microcosms](https://static.igem.wiki/teams/5687/eng/hab-6.webp)

Figure: Preliminary lakewater microcosms and control cocultures containing *A. baylyi* and *M. aeruginosa*.

![Lake Matoaka, Williamsburg VA](https://static.igem.wiki/teams/5687/eng/hab-7.webp)

Figure: Lake Matoaka, Williamsburg, VA. We used lake water from Lake Matoaka to simulate realistic microbial communities in lake microcosms.

Test:▼

For our preliminary microcosm experiment, we collected, pelletted via centrifugation, and flash froze 4 ml samples from each microcosm and control culture at each timepoint over a 21-day time course. We attempted DNA and RNA extractions using a standard PCI procedure and TRIzol reagent, respectively.

![HAB Bacterial Pellet](https://static.igem.wiki/teams/5687/eng/hab-8.webp)

Figure: A bacterial pellet from the preliminary microcosm experiment. Initial pellets were too small for reliable nucleic acid extraction.

Learn:▼

The 4 ml sample pellets yielded limited nucleic acid material, but we successfully amplified and sequenced 16S fragments for preliminary species/genus abundance analysis over the experimental time course.

Redesign/Build:▼

To improve nucleic acid yield, we increased our sample volumes to 15 ml (prior to pelleting) and switched from TRIzol to the NEB Monarch Spin RNA Isolation Kit for RNA extraction. We scaled up the volumes of our control cultures to accommodate for volume loss with the collection of larger sample volumes.

![Bacterial Inoculation](https://static.igem.wiki/teams/5687/eng/hab-9.webp)

![Bacterial Inoculation](https://static.igem.wiki/teams/5687/eng/hab-10.webp)

Figure: Preparation and centrifugation of large volumes of *M. aeruginosa* and *A. baylyi* prior to microcosm inoculation

Test:▼

Our new methods consistently yielded bright RNA bands. We amplified new 16S fragments for parallel species abundance analysis.

![HAB Microcosm](https://static.igem.wiki/teams/5687/eng/hab-11.webp)

Figure: Sample collection during our second lakewater microcosm experiment.

Learn:▼

16S data and colony counts indicated that *Acinetobacter baylyi* predominated upon initial inoculation of lakewater microcosms. Within a week, the *A. baylyi* population was largely outcompeted by native lakewater organisms, despite a high initial inoculation density. By comparison, *A. baylyi* predominated in sterile, static control cocultures over the course of the experiment, suggesting that the microcosms’ water flow and microbial competitors reduced A*. baylyi*’s survival. Additionally, preliminary analysis of RNA sequencing data indicated that a number of *A. baylyi*’s genes were significantly differentially expressed between microcosm and control coculture conditions approximately 24 hours post-inoculation. Differential gene expression suggests that specific aspects of *A. baylyi*’s behavior and functioning are concretely affected by real world conditions.

Our results indicate that *A. baylyi* is not yet ready for real world anti-HAB deployment. While the species may effectively persist and eliminate Microcystis in cocultures, its survival and functioning may be limited in a lake environment. Synthetic biologists should consider the effects of water flow and microbial community dynamics on chassis survival when adapting *A. baylyi* and other bacterial chassis for HAB remediation purposes and deployment in water environments.

# **Aquatic Case Study III: Removal of Biofilms in Household Pipes**

---

## **6. Phage Cocktail Selection**

**Summary:** To exploit the evolutionary adaptations of bacteriophages developed through their co-evolutionary dynamics with bacterial hosts, we formulated a phage cocktail aimed at maximizing biofilm disruption and bacterial lysis efficiency. The selected phages were chosen based on their genetic characteristics, lytic capabilities, and potential for biofilm interference.

Design:▼

**Phage Raid** is a member of the A1 subcluster of mycobacteriophages. Phages within this subcluster have demonstrated the ability to inhibit *GroEL1*, a gene implicated in biofilm maturation (Ojha, 2005). This inhibition occurs via lysogenic integration into the *GroEL1* locus, thereby disrupting its function.

**Phage CrimD**, belonging to subcluster K1, is notable for being the first sequenced phage in its cluster. It possesses robust lytic machinery, including genes encoding for the enzymes LysA and LysB, which target the mycobacterial cell wall. Additionally, K1 phages have been successfully genetically modified using CRISPY-BRED (Bacteriophage Recombineering of Electroporated DNA), a technique that enables precise genome editing. This method holds promise for enhancing lytic efficiency by removing integrase genes to prevent lysogeny.

**Phage Neighly**, classified within subcluster K3, is genetically similar to Phage Larvae (K5 subcluster), differing by only a single nucleotide. Comparative genomic analysis suggests that Neighly also encodes LysA and LysB. Furthermore, Neighly exhibits high phage titers, reaching concentrations of approximately 10¹³ PFU/mL, indicating strong replication and lytic potential.

Build:▼

Phages were isolated from the William & Mary Phage Lab. High-titer lysates were generated by plating phages on *Mycobacterium smegmatis* lawns using top agar, followed by flooding with phage buffer and filtration to obtain purified lysate.

Test:▼

Phage efficacy was assessed under two conditions: controlled laboratory settings and simulated environmental microcosms.

Laboratory Assays: Plaque assays were conducted to evaluate lytic activity across a range of phage dilutions. This allowed for quantification of plaque-forming units and assessment of dose-dependent lysis efficiency.

Microcosm Assays: To simulate real-world conditions, phages were introduced into tap water and flushed through PVC pipes colonized with mycobacterial biofilms. The cocktail included all three phages. Efficacy was measured via bacterial titer reduction, sustained phage presence, and microscopic analysis of biofilm degradation.

Learn:▼

Phages demonstrated significantly higher lytic efficiency under controlled laboratory conditions compared to PVC pipe microcosms. These findings show the complexity of translating *in vitro* efficacy to *in situ* applications and highlight the need for further optimization of phage delivery and stability in real-world settings.

## **7. Microcosm Construction**

**Summary:** To evaluate bacteriophage-mediated biofilm lysis under conditions that mimic household plumbing, we developed a custom microcosm system using PVC piping, tap water flow, and engineered components.

![Showerhead Microcosms](https://static.igem.wiki/teams/5687/eng/pipes-1.webp)

Figure: Microcosms simulating household pipes with clear PVC pipes and 3D printed adaptors. The PVC pipes have mycobacterial biofilms grown in nonsterile environments.

#### **7a. 3D-Printed Adaptors for Microcosm Assembly**

Design:▼

To simulate the hydraulic behavior of household PVC plumbing, we designed 3D-printed adaptors that enable controlled flushing and effluent collection. The adaptors were engineered to slow water exit velocity, allowing for prolonged interaction between phage and biofilm. Computer-Aided Design (CAD) models were created using *Fusion360*.

Build:▼

Design files were sliced using *Bambu Studio* and printed with the *Bambu Lab X1 Carbon* 3D printer. PETG HF filament was selected for its chemical inertness and compatibility with microbial growth environments.

Test:▼

Prototype adaptors were fitted to 2-inch PVC pipe segments and flushed with tap water to assess flow consistency and structural stability. Effluent velocity was calculated to ensure the system mimicked realistic plumbing conditions.

Learn:▼

The adaptors successfully regulated water flow and maintained system integrity, validating their use in downstream biofilm lysis experiments.

#### **7b. Single-Species Biofilm Development on PVC**

Design:▼

To visualize biofilm formation, we used transparent PVC pipes and a strain of *Mycobacterium smegmatis* expressing red fluorescent protein (RFP). To decrease microbial competition and promote selective growth, biofilms were cultivated in media supplemented with hygromycin.

Build:▼

Sterile bottles containing 7H9 media and hygromycin were inoculated with *M. smegmatis* at a 1:20 dilution. PVC segments (~2 inches) were submerged to promote biofilm attachment.

![M. smeg Biofilms](https://static.igem.wiki/teams/5687/eng/pipes-2.webp)

Figure: PVC pipes containing *M. smegmatis* biofilms grown in hygromycin-containing media to promote single-species biofilm growth.

Test:▼

Following incubation, pipes were transferred to the microcosm adaptors. However, biofilm adhesion to PVC was minimal; most biomass remained suspended in the media and detached during handling.

Learn:▼

Sterile conditions did not support sufficient biofilm adhesion to PVC surfaces, limiting the feasibility of flushing-based assays.

Redesign:▼

Literature suggests that *Mycobacterium* spp. form more robust biofilms when co-cultured with other microbes (Gomez-Smith et al., 2015). We hypothesized that nonsterile conditions would enhance biofilm adhesion through multispecies interactions and extracellular matrix production.

Build:▼

PVC segments were placed in nonsterile trays containing unsupplemented 7H9 media, allowing natural microbial colonization without antibiotic selection.

![M. smeg Biofilms](https://static.igem.wiki/teams/5687/eng/pipes-3.webp)

Figure: (Left) PVC pipes containing *M. smegmatis* biofilms grown in a nonsterile environment and with media with no antibiotic selection to promote multispecies biofilm growth. (Right) Single PVC pipe grown in conditions that allow multispecies biofilm formation.

Test:▼

Biofilms cultivated under nonsterile conditions demonstrated strong adhesion and structural integrity, remaining firmly attached to PVC surfaces even after repeated flushing with tap water. To investigate the microbial community structure and dynamics within these complex biofilms, we performed 16S rRNA metagenomic sequencing on samples grown under nonsterile conditions.

Learn:▼

Cultivating biofilms under nonsterile conditions significantly enhanced their structural resilience and surface adhesion. In addition, this strategy of biofilm development made them more representative of environmental biofilms and better suited for phage treatment testing. Within these biofilms, distinct color variations—particularly pink (attributed to RFP-transformed *Mycobacterium smegmatis*) and black—indicates the presence of multiple microbial species and likely symbiotic interactions. The 16s sequencing results suggested that the biofilm largely consisted of acinetobacter, but contained various other species. However, this sequencing strategy is limited to bacterial species and does not offer full insight into the community structure of these biofilms.

# **Bioinformatics**

---

## **8. Pipeline Engineering**

**Summary:** Metagenomic samples yield large FASTQ files that contain raw DNA sequence information. Given the immense size and quantity of these files, full local storage and processing were deemed impractical. Our engineering solution was designed to bypass this significant data burden by streamlining the acquisition process; we selectively accessed the necessary raw data and fed it directly into the classifier, eliminating the need for extensive local FASTQ file storage.

#### **8a. Integration of FASTQ file retrieval tool**

Design:▼

The NCBI SRA database is already widely used and referenced in SynBio and bioinformatics. We decided to use SRA accession numbers as the identification for each sample to allow integration with literature data and future projects uploaded to various genomic databases.

Build:▼

During development, the ESearch tool was the first step of the pipeline, used for retrieval of FASTQ files from the NCBI SRA database.

Test:▼

The integration of the ESearch tool was then tested using a small subset of retrieved samples which was associated with an SRA identification number.

Learn:▼

After a few tests, we realized that the ESearch tool was not suited for FASTQ retrieval, and we removed it. Instead, we moved forward with using the A toolkit, with its fasterq-dump tool, which was ideal for reading in SRA accession numbers and retrieving the associated FASTQ files to be processed in later steps.

#### **8b. Integration of Kraken + Bracken for taxonomic processing**

Design:▼

To create a database relating species abundance data (for evaluating community structure) and extracted environmental abiotic metadata, we required a pipeline that transforms metagenomic input into taxonomic abundance output. Kraken2 and Bracken were essential final steps, pulling the necessary taxonomic abundances from the metagenomic DNA sequences.

Build:▼

First, Kraken2 and Bracken were installed from the official Kraken GitHub. Additionally, a Kraken database was installed and used for processing.

Test:▼

After implementation of Kraken2 and Braken, we tested different Kraken reference databases, such as the standard and core\_nt databases, aiming to assess mapping success and the associated computational expense.

Learn:▼

Following testing, we found that the core\_nt was too computationally heavy to realistically implement for every sample and for future user integration. Additionally, the more compact databases, such as the 8gb and 16gb databases, are computationally lighter to work with, but we found these databases to be too sparse to map k-mers successfully. After testing, we decided that the standard database would be the best option for implementation, since the larger database would hinder reproducibility due to computational overhead, and the smaller database would lead to loss of valuable data through an excess of unmapped reads.

## **9. Predictive Model Tuning**

**Summary:** To ensure AQUIRE achieved maximum predictive accuracy, we employed the Design-Build-test cycle. This iterative engineering approach was essential for model enhancement and tuning, allowing us to systematically compare model accuracy and utilize appropriate classification models best suited for a species.

#### **9a. Assembling three candidate classification models**

Design:▼

We initially aimed for a simple machine learning classifier, implementing logistic regression to avoid an overly complex model for predicting species survival based on taxonomic abundance and environmental abiotic variables.

Build:▼

We implemented the predictive model using a Python script and the scikit-learn package. This script leverages a logistic regression algorithm, which we trained using a portion of the known data from our assembled database, AQUERY.

Test:▼

We tested the trained model by running the remainder of the known AQUERY samples through the classification process. Scoring revealed a significant lack of data linearity, suggesting that the simple logistic regression model was insufficient for accurately classifying chassis survival based on abundance and environmental conditions.

Learn:▼

Recognizing that logistic regression was not an optimal classifier, we decided to test alternative, more robust models to better assess chassis survival. We elected to implement a Random Forest classifier and XGBoost, both of which are powerful and widely utilized machine learning algorithms.

#### **9b. Assessing model accuracy using evaluation metrics**

Design:▼

To properly review model accuracy, we implemented a model evaluation step. The initial choice for this metric was F1 score, to enable a rough comparison between different model parameter configurations.

Build:▼

We included a dedicated post-training step in the Python script, to output F1 scores. This integration allowed standardized and quantitative model evaluation.

Test:▼

Multiple training iterations were performed on different model architectures. The resulting F1 scores were systematically recorded and reviewed to assess each model's initial performance against predefined benchmarks.

Learn:▼

Based on the results from the testing phase, we decided to switch from F1 score to an AUC metric (area under the curve). Unlike F1 score, AUC provides a more robust approach for comparing the general performance of different models, primarily because it is threshold-independent, not requiring the selection of an arbitrary classification cutoff like F1​ scoring does.

## **10. RNA-Seq Meta-Analysis**

**Summary:** We conducted an RNA-Seq meta-analysis to find design principles by identifying differentially expressed (DE) genes in chassis organisms across laboratory vs. natural environments. Initially, we used custom bioinformatic pipelines, but this proved too slow and resource-intensive. We then redesigned our process to integrate AI tools (Copilot, Gemini, ChatGPT) with our pipelines. By carefully vetting and curating the AI results, this hybrid approach allowed us to generate a more comprehensive and consistent analysis in significantly less time.

Design:▼

Given that we did not find a comprehensive assessment of differentially expressed (DE) genes and associated pathways in the literature from which we could extract design principles, we sought to generate this as part of our project. We did so by identifying literature and databases that compared chassis gene expression between laboratory conditions and more natural environments, and by identifying the gene lists through our own pipeline.

Our first design iteration focused exclusively on developing our own pipelines for:

1. Analysis of transcriptomic data comparing laboratory versus environmental samples
2. Analysis of metatranscriptomic data comparing laboratory versus environmental samples
3. Functional analysis of differentially expressed genes

If a study already included a DE gene list, we incorporated it into our analysis; if not, we ran the pipeline ourselves.

Build:▼

We built pipelines for:

1. Analyzing transcriptomic data using Galaxy and a Python-based pipeline (Fastq → TrimGalore → HiSat2 → htseq-count → DESeq2)
2. Analyzing metatranscriptomic data
3. Performing functional analysis using DAVID Bioinformatics Resources

Test:▼

We tested the pipelines using numerous datasets from the studies that performed lab and environment comparisons. For each dataset, we evaluated the pipeline’s ability to successfully process raw sequencing data from FASTQ files through to DE outputs, to accurately identify DE genes, and to integrate functional enrichment results from DAVID into interpretable pathway-level insights.

Learn:▼

We learned that conducting all of this analysis from scratch—particularly for studies that did not include DE gene lists—was taking an inordinate amount of time, and we were barely scratching the surface of the available data. In addition, while this approach was valuable, it did not capture studies that focused on a small number of genes, such as those utilizing qRT-PCR for more targeted analyses of gene expression differences between field and laboratory conditions.

Redesign:▼

We therefore redesigned our meta-analysis to include AI tools (Microsoft Copilot, Google Gemini, and OpenAI ChatGPT—both version 4 and ChatGPT+) using specific, targeted prompts and current methods for prompt engineering. Given the limitations of AI (e.g., hallucinations and errors), our redesign also included protocols for vetting AI outputs at each step.

Build/Test:▼

We integrated our own data analyses with the results produced by AI to generate a more comprehensive analysis in a much shorter timeframe.

Learn:▼

Integrating results generated by AI agents with those produced by our own pipelines—while consistently applying careful vetting and curation—yielded more complete, current, and consistent results in a fraction of the time.

# References

---

{% endblock %}

{% extends "layout.html" %}
{% set header\_image = 'https://static.igem.wiki/teams/5687/banners/experiments.webp' %}
{% block title %}Experiments{% endblock %}
{% block lead %}{% endblock %}
{% block page\_content %}

* [Executive Summary](#summary)
* [Aquatic Case Study I: Corrosion Prevention Using Engineered Bacillus subtilis in Marine Environments](#corrosion)
* [Aquatic Case Study II: Freshwater Algal Bloom Remediation](#habs)
* [Aquatic Case Study III: Removal of Biofilms in Household Pipes](#pipes)
* [RNA-Seq Meta-Analysis](#rna)
* [References](#references)

---

# **Executive Summary**

---

Aquarius explores differences in chassis performance in laboratory conditions as compared to real-world aquatic environments. Through wet lab and dry lab experimentation, we aimed to clarify the effects of environmental complexities—such as microbial competition, water flow, and nutrient variability—on the survival, behavior, and functioning of engineered microorganisms.

We focused on three aquatic case studies, each designed to test and compare chassis effectiveness in controlled laboratory conditions versus simulated natural environments:

### **I. Marine Corrosion Prevention with *Bacillus subtilis***

We engineered biofilm-enhancing variants of *Bacillus subtilis* and compared their growth, motility, biofilm formation, and corrosion protection in lab media versus seawater microcosms. Experiments included strain construction, growth curve analysis, motility assays, biofilm morphology characterization, and corrosion testing on steel surfaces.

### **II. Freshwater Harmful Algal Bloom (HAB) Remediation with Cyanophage and *Acinetobacter baylyi***

We attempted to isolate (and ultimately engineer) novel *M. aerguinosa* cyanophages, viruses that infect the cyanobacterium *M. aeruginosa*, through multiple large scale assay-based experiments. We also tested the persistence and algicidal activity of *Acinetobacter baylyi*, a chassis with natural algicidal capabilities, in static laboratory cocultures and flowing lakewater microcosms. We assessed *A. baylyi*’s survival and gene expression via colony counts, 16S amplicon sequencing, and transcriptomic analysis.

### **III. Household Pipe Biofilm Removal Using Phage Therapy**

We evaluated phage efficacy in lysing *Mycobacterium smegmatis* biofilms under lab conditions and in PVC pipe microcosms simulating household plumbing. Experiments included phage titer estimation, survivability testing, biofilm imaging, and gene expression analysis of biofilm-forming bacteria.

---

### **Bioinformatics & RNA-Seq Meta-Analysis**

To complement experimental data, we conducted a meta-analysis of transcriptomic and metatranscriptomic datasets comparing lab and environmental conditions. Using differential expression analysis and functional enrichment tools, we identified key genes and pathways that inform design principles for deploying synthetic biology in aquatic systems.

# **Aquatic Case Study I:**

## **Corrosion Prevention Using Engineered *Bacillus subtilis* in Marine Environments**

*Overarching Goal: To compare ability of engineered *B. subtilis* to prevent corrosion in lab conditions versus simulated real-world marine seawater conditions.*

---

**Overall Rationale:**

Domesticated bacterial strains commonly used as synthetic biology chassis are optimized for laboratory performance but often lose key traits during the domestication process. To ensure their effectiveness for real-world implementations, domesticated strains must be compatible with the pathways they are engineered to regulate. For instance, while natural *Bacillus subtilis* strains can form robust biofilms, competent lab strains like *B. subtilis* 168 have lost their ability to secrete extracellular polymeric substances (EPS), which are critical for biofilm development (McLoon et al., 2011).

Microbial-Induced Corrosion Inhibition (MICI) presents a promising, sustainable, and self-healing approach to reducing corrosion. However, current MICI strategies lack systematic experimental validation, and there is limited research comparing biofilm behavior in laboratory versus natural seawater environments—raising questions about their real-world feasibility.

We hypothesized that natural *B. subtilis* biofilms may not be sufficiently robust to prevent corrosion in realistic conditions. We aimed to engineer *B. subtilis* strains to have enhanced biofilm-forming capabilities by upregulating EPS-related genes. Using the competent but biofilm-deficient lab strain *B. subtilis* 168, we tested multiple regulatory pathways to identify those most effective at promoting robust biofilm formation and corrosion resistance in real-world environments.

## **Experiment 1:**

### **Construction of Engineered *B. subtilis* Strains**

---

**Rationale:** Gao et al. (2022) suggested that *Bacillus subtilis* strains that produce thicker biofilms will more effectively prevent corrosion than those with weaker biofilm-forming capabilities. We selected 4 genes from distinct regulatory pathways in *B. subtilis* (strain 168) for targeted editing to create thicker and denser biofilms for corrosion prevention on steel surfaces. We used an integration vector to ensure stable genomic incorporation, minimize risk of construct loss during cell passage, and prevent horizontal transfer of antibiotic resistance genes typically required for plasmid-based engineering. We also employed inducible promoters to maintain controlled gene expression.

**Procedure:** We selected 4 genetic targets known to influence biofilm properties: *SinI* to promote biofilm thickness by alleviating *SinR*-mediated repression, *BslA* to enhance surface hydrophobicity through the formation of a protective protein layer, and the *TapA-SipW-TasA* operon and *TasA* to strengthen biofilm architecture via amyloid fiber production. We constructed a vector that overexpresses each gene to assess their respective contributions to biofilm morphology and corrosion inhibition.

**Summary Results:** We successfully transformed *B. subtilis* 168 with the integration vector using homemade chemically competent cells. We confirmed the transformation’s success with Starch-Iodine staining. Attempts to transform *B. subtilis* 3610 were unsuccessful due to the strain’s lack of natural competence.

## **Experiment 2:**

### **Establishing growth curves to compare growth dynamics of *B. subtilis* 168, 3610, and the 4 engineered 168 variants under laboratory conditions**

---

**Rationale:** Studies suggest that upregulating certain biofilm-formation genes adversely affects bacterial growth rates (Bai et al., 1993). To confirm that our engineered constructs do not impose substantial burden on the chassis, we conducted multiple growth curve measurements with the engineered strains and under varying concentrations of xylose (the inducer) in LB media.

**Procedure:** Strains were cultured in LB medium with varying xylose induction levels in 96-well plates. A 24-hour growth curve and a 36-hour growth curve were conducted using a microplate reader for downstream comparison of growth rates between the lab strain, natural strain, and engineered variants under different inducer concentrations.

**Summary Results:** Both the 24- and 36- hour growth curves showed that the xylose inducer concentration had no substantial effect on bacterial growth in normal LB media (which does not promote biofilm growth). The 4 modified strains and the unmodified lab strain of *Bacillus subtilis* 168 did not differ substantially in growth rate, while the growth rate of the natural strain *B. subtilis* 3610 was substantially greater than all other strains.

[For detailed results, click here!](results)

## **Experiment 3:**

### **Evaluating the motility of *B. subtilis* 168, 3610, and the 4 engineered 168 variants**

---

**Rationale:** Studies found that the laboratory strain *Bacillus subtilis* 168 lacks extracellular polymeric substances (EPS), a key biofilm component, and therefore does not exhibit EPS-dependent sliding motility (Dergham et al., 2021). We aimed to determine whether specific biofilm-enhancing genetic modifications restore or alter motility behavior in *Bacillus subtilis* 168.

**Procedure:** Motility was assessed using semi-solid agar plates (0.3%, 0.7%, and 1.5% agar) all supplemented with a 0.2% concentration of xylose inducer. Movement on 0.3% agar reflects flagellum-dependent swarming, while diffusion on 0.7% agar indicates EPS-dependent sliding. Spreading diameters were measured after 12 hours. The bacterial strain is considered to be mobile when the diameter of the spread region is at least 20% greater than the diameter of the colony of the same strain developed in the 1.5% agar medium that does not allow any movement (Bartolini & Grau, 2019)

**Summary Results:** Among all tested strains, only *Bacillus subtilis* 3610 exhibited both swarming and sliding motility, highlighting its robust native biofilm-associated movement. Among the engineered variants, *B. subtilis* 168 and the *SinI*-modified strain demonstrated strong swarming capability, while the *BslA+* and *TapA-SipW-TasA+* variants showed moderate swarming. In contrast, the *TasA+* variant exhibited neither swarming nor sliding motility, which may be attributed to potential toxicity arising from *TasA* overexpression.

[For detailed results, click here!](results)

## **Experiment 4:**

### **Determining effect of our genetic modifications on biofilm robustness and structure through characterization of biofilm and colony morphology of laboratory and engineered strains of *Bacillus subtilis***

---

**Rationale:** Brück et al. (2019) demonstrated that *Bacillus subtilis* 168 loses its ability to produce extracellular polymeric substances (EPS) during domestication, resulting in diminished biofilm formation. Therefore, we aimed to test the effect of our genetic modifications on biofilm morphology and robustness.

**Procedure:** 3 μL of overnight LB culture inoculation from each strain was spotted onto various agar media, including: LB, LB supplemented with 0.03% xylose, MSgg (biofilm-inducing minimal medium), MSgg with 0.03% xylose spread on the surface, MSgg with 0.03% xylose mixed into the agar prior to pouring, and MSgg with 0.2% xylose. Plates were incubated at 37 °C for 48 hours. Colony and biofilm morphology were documented using phone photography and Hyrox microscopy for higher-resolution imaging.

**Summary Results:** Biofilm formation varied significantly across strains and conditions. *Bacillus subtilis* 3610 (natural strain) was the only strain to produce a distinct biofilm, while the engineered and laboratory strains showed minimal or no biofilm characteristics. Media choice played a critical role: LB supported basic growth but was unsuitable for biofilm development, whereas MSgg enabled biofilm formation. However, minimal differences in xylose concentration had little effect on biofilm enhancement, while varying methods of inducer distribution had an effect on colony morphology, suggesting that more precise control of induction conditions is necessary for consistent biofilm formation.

[For detailed results, click here!](results)

## **Experiment 5:**

### **Evaluating corrosion protection abilities of each strain on steel surfaces in a simulated marine environment**

---

**Rationale:** The effectiveness of corrosion prevention mechanisms likely differs between sterile laboratory conditions and natural environments due to microbial community dynamics. In seawater microcosms, native bacteria may infiltrate *Bacillus subtilis* biofilms, potentially disrupting their uniformity and compromising protective function. This experiment evaluates whether engineered enhancements to biofilm structure improve corrosion resistance, and how each strain—lab strain 168, natural strain 3610, and engineered variants *Sini+* and *BslA+* —performs in a simulated marine setting that reflects real-world conditions. *SinI+* and *BslA+* were selected from the 4 engineered strains based on their demonstrated motility and potential for biofilm formation.

**Procedure:** We used recycled 1000 mL pipette tip boxes as microcosm water tanks, each containing 200 mL of artificial seawater (35 g Instant Ocean salt / L). Seven microcosms were prepared: one control and six inoculated with *Bacillus subtilis* strains (3610, 168, *SinI+*, *BslA+*, *TasA+*, and *TapA–SipW–TasA+*), each induced with 0.2% xylose and inoculated at 0.5% (v/v) of the seawater(1ml). Three additional high-biomass microcosms with 50 mL of overnight bacterial culture (with 0.2% xylose) to 150 mL of seawater(15% (v/v)) were prepared for RNA extraction using *B. subtilis* 168 *SinI+*, *BslA+*, and 3610. A36 steel plates were thoroughly cleaned, painted with bacterial strains, and incubated in seawater microcosms. After 3 to 7 days of incubation, corrosion levels were visually assessed using phone pictures and via scanning electron microscopy (SEM).

**Summary Results:** The experiment revealed notable differences in corrosion protection among natural, lab, and engineered *Bacillus subtilis* strains under realistic conditions. The uninoculated seawater control showed heavy oxidation, while samples treated with *B. subtilis* 168 exhibited moderate corrosion. Novel surface features—including irregular deposits and parallel, unidirectional metallic crystals—were observed across various samples, suggesting that microbial activity and strain-specific interactions can influence surface mineralization. This novel structure, which has not been observed in the literature, may be attributed to species interactions in biofilm formation.

[For detailed results, click here!](results)

## **Experiment 6:**

### **Analysis of gene expression differences between lab and microcosm environments in strains with strong corrosion protection**

---

**Rationale:** We aimed to use RNA-seq to uncover transcriptional changes that explain the observed differences in biofilm formation and corrosion inhibition capabilities of engineered and non-engineered *B. subtilis* strains. This would provide insight into the genetic and regulatory mechanisms underlying biofilm production in simulated natural environments, allowing future synthetic biologists to rationally design Bacillus strains with optimized biofilm and surface-protective properties. We also use RNA-seq and 16S analyses to enable direct comparison between strain-specific transcriptional profiles and community-level metagenomic data, to showcase the ability of *B. subtilis* to survive in a community dynamic environment.

**Procedure:** Three strains: *SinI* overexpressing *B. subtilis* 168, *BslA* overexpressing *B. subtilis* 168, and the natural strain *B. subtilis* 3610, were selected for transcriptomic analysis via RNA sequencing based on the morphology test assays, motility test assays and their suggested corrosion inhibition ability as shown in the SEM image. RNA was extracted from cultures grown under sterile laboratory conditions and from samples retrieved from seawater microcosms to compare gene expression profiles across environments. Additionally, 16S DNA sequencing was performed on microcosm samples inoculated with varying concentrations (0.5% and 25%) of each strain. This analysis aimed to identify the microbial community composition within the microcosms and assess whether population structure influences the corrosion protection efficacy observed in Experiment 4.

**Summary Results:** Across all samples, a substantial presence of non-*Bacillus subtilis* bacteria was detected, indicating the establishment of mixed microbial communities. The wild-type *B. subtilis* 3610 showed limited persistence within these communities, whereas both engineered strains exhibited moderate survival. In the microcosms inoculated with 25% bacterial culture, Ochrobactrum emerged as the dominant contaminant and the most abundant genus overall. The 0.5% inoculum microcosms consistently contained substantial populations of Acinetobacter, suggesting that the amount of Bacillus added influences microbial community composition and competitive interactions. RNA-seq showed that all three *B. subtilis* strains activates sporulation genes but suppressed metabolic ones in marine microcosms. The *BslA*+ strain also downregulated flagellar assembly genes. The data confirm distinct gene-expression behaviors between microcosm and laboratory conditions.

[For detailed results, click here!](results)

# **Aquatic Case Study II:**

## **Freshwater Algal Bloom Remediation**

*Overarching Goal: To compare behavior and ability of cyanophages and/or Acinetobacter baylyi to remediate HABs in lab conditions versus simulated real-world conditions.*

---

## **Experiment 7:**

### **Microcystis Cyanophage Isolation for Harmful Algal Bloom Remediation**

---

**Experimental Goal:** To isolate (and potentially engineer) novel Microcystis cyanophages for harmful algal bloom (HAB) remediation as a case study to compare phage behavior in laboratory conditions versus simulated real-world environments.

**Rationale:** Cyanophage, viruses that infect cyanobacteria, may represent a promising tool for HAB remediation (Grasso et al., 2022; Bhatt et al., 2023; Aranda et al., 2023), however 1) few *M. aeruginosa* cyanophages have been isolated (Guo et al., 2024) and 2) cyanophage deployment in real lakewater environments may be challenging given dynamic factors such as water flow and unique features of HAB structure and ecology— e.g., host resistance evolution and phage lysogeny during blooms (Grasso et al., 2022; Huang et al., 2025; Bhatt et al., 2023; Aranda et al., 2023).

To address the lack of readily available phages infecting *M. aeruginosa*, we aimed to isolate novel Microcystis cyanophages for future use in HAB remediation and for downstream optimization via engineering. To clarify the effect of water flow on cyanophage persistence and infection of *M. aeruginosa*, we aimed to test novel cyanophages under simulated lakewater conditions. Our ultimate goal was to identify and implement design principles that would enhance phage viability for HAB mitigation in real-world water environments.

**Procedure:** The general procedure for isolating Microcystis cyanophages includes 1) collection of water samples, 2) filtration of water samples and enrichment in Microcystis liquid culture, 3) enrichment filtration (0.22 μm) for phage particles, and 4) phage infection assays via two methods:

A) Liquid Assays: *M. aeruginosa* was cultured with sample filtrate in 35 mm dishes and monitored for bacterial clearing indicative of infection. Cleared assays were filtered (0.22 μm) and filtrate was used to conduct re-infection assays.

B) Plaque Assays: Concentrated *M. aeruginosa* was mixed with sample filtrate and plated on BG-11 plates. Microcystis lawns were monitored for phage plaque formation. Plaques were resuspended in liquid Microcystis culture and monitored for clearing. A plate containing prominent plaques was flooded and lysate harvested and used for more reinfection assays.

**Summary Results:** We observed bacterial clearing compared to control assays in 39 of 205 liquid assays and putative cyanophage plaque formation on 2 of 65 agar assays. However, we were unable to consistently propagate any phage, likely due to strong host defense systems. Following the guidance of IHP experts as well as our mathematical modeling, we then transitioned to investigating the use of *Acinetobacter baylyi*, a commonly-used chassis with algicidal capabilities, for HAB remediation.

[For detailed results, click here!](results)

## **Experiment 8:**

### **Comparison of *Acinetobacter baylyi* Behavior in Laboratory vs Simulated Real-World Microcosms**

---

**Experimental Goal:** To test habitat remediation with *Acinetobacter baylyi* ADP1-ISx as a case study comparing bacterial behavior (persistence and gene expression) in laboratory conditions versus simulated real-world environments in order to inform the development of design principles for synthetic biology applications in aquatic systems.

**Overall Rationale:** Bacteria with natural algicidal capabilities offer an alternative HAB mitigation option that may be easier to engineer and deploy than phage. Lab-based co-culture experiments have suggested that Acinetobacter species can almost completely eliminate an *M. aeruginosa* culture within as few as 5 days (Li et al., 2016, Yi et al., 2015). While algicidal bacteria are highly effective in a lab setting (Kong et al., 2022), our meta-analysis indicated that bacterial species, including engineered strains, are unlikely to function optimally in real-world lakewater environments, where they are subjected to stress due to mechanical flow and the presence of diverse, competing microbial communities.

To more fully understand the effects of *in vivo*, water-associated complexities on the functioning of engineered, lab strain algicidal bacteria and assess the feasibility of real-world deployment, we measured the persistence and gene expression of *A. baylyi* ADP1-ISx in flowing lakewater HAB microcosms as compared to static control co-cultures.

We inoculated three lakewater microcosms and three control flasks with equivalent ratios of approximately log phase *A. baylyi* and *M. aeruginosa*. Microcosms contained water from a local lake and were equipped with water pumps to simulate turbulent lakewater flow. Control flasks were static and contained BG-11 media. For details on the microcosm setup, see our Results section.

## **Experiment 8a:**

### **Estimating *A. baylyi* survival in microcosms and control cocultures via colony counts**

---

**Rationale:** We hypothesized that realistic *in vivo* lakewater conditions (including water flow and microbial communities) would reduce *A. baylyi* survival and could limit real-world deployment potential. To estimate the extent to which realistic conditions alter *A. baylyi* survival over time, we grew spread plates and quantified *A. baylyi* colony formation (from microcosms and control cultures) at each experimental timepoint.

**Procedure:** At each timepoint, we spread 100 μL of fluid from each microcosm and control culture on LB agar plates. After approximately 48 hours of incubation at 37°C, we stored plates at 4°C for downstream *A. baylyi* population estimation via colony counts. Colony counts were generated by Google Gemini using AI image analysis. We validated Gemini’s counts visually and adjusted values to match our observations.

**Summary Results:** Cocultures consistently yielded uniform *A. baylyi* lawns, while microcosms yielded decreasing *A. baylyi* colony counts over the course of the experiment.

[For detailed results, click here!](results)

## **Experiment 8b:**

### **Quantifying species abundance in microcosms and control cocultures via 16S amplicon sequencing**

---

**Rationale:** Real-world aquatic environments contain dense microbial communities that we hypothesized would adversely affect chassis survival and functioning during *in vivo* deployment. To quantify the extent to which native lakewater species outcompete or interfere with *A. baylyi* survival under realistic conditions, we used 16S data to approximate relative species abundances in the microcosms and control cocultures over the course of the experiment.

**Procedure:** We extracted total genomic DNA via phenol chloroform isoamyl alcohol (PCI) extraction from frozen sample pellets collected on day 1 and day 7 for each microcosm and control culture. We amplified 16S fragments via PCR and purified them using the NEB PCR cleanup kit. Purified samples were sequenced by Plasmidsaurus.

**Summary Results:** 16S percent abundance data indicated that *Acinetobacter baylyi* was outcompeted by native lakewater organisms within a week after initial microcosm inoculation. The average microcosm *A. baylyi* population declined from an initial 73.58% abundance (on day 1) to 7.83% abundance (day 7). Control abundances on day 7 were approximately comparable to those measured on day 1 (on average at 77.41% on day 7 as compared to 81.33% on day 1).

[For detailed results, click here!](results)

## **Experiment 8c:**

### **Quantifying differential gene expression of *A. baylyi* in microcosms and control cocultures via RNA sequencing**

---

**Rationale:** We hypothesized that aquatic environments may affect specific aspects of chassis behavior and functioning in addition to altering survival. To quantify the effect of simulated real-world deployment conditions on *A. baylyi* functioning and HAB remediation potential, we extracted RNA samples for downstream differential gene expression analysis comparing simulated *in vivo microcosm* and *in vitro* coculture conditions.

**Procedure:** We extracted total RNA via the NEB Monarch Spin RNA Isolation Kit from frozen sample pellets collected on day 1, day 5, and day 7 for each microcosm and control culture. We conducted preliminary analysis of RNAseq data using DEseq2 on the Galaxy platform.

**Summary Results:** Preliminary RNA sequencing analysis indicated that a number of *A. baylyi* genes displayed significant (p-adjusted ≤ 0.05) differential gene expression across microcosm and control coculture conditions approximately 24 hours post-inoculation. The existence of differentially expressed genes across the two conditions provides evidence that *A. baylyi* behaves differently in lakewater conditions than in a traditional lab setting and may suggest that real-world conditions could limit the chassis’s HAB remediation potential.

We are currently in the process of analyzing and interpreting RNAseq data to quantify *A. baylyi* differential gene expression between microcosms and control cocultures at all three timepoints.

[For detailed results, click here!](results)

# **Aquatic Case Study III:**

## **Removal of Biofilms in Household Pipes**

*Overarching Goal: To compare ability of phages to lyse biofilms in lab conditions versus real world conditions of household pipes.*

---

## **Experiment 9:**

### **Determining efficiency of specific mycobacteriophages to lyse mycobacteria under laboratory conditions**

---

**Rationale:** Ultimately, we wish to test the ability of phage to treat biofilms in pipes, but to do so we need to ensure that the phage lyse bacteria effectively under controlled lab conditions. Therefore, for our first experiment we tested the ability of phage to successfully lyse mycobacteria under controlled lab conditions. This would allow us to generate comparative results between the efficiency of phage in lysing mycobacteria in the form of a lawn on a petri dish and in the form of a biofilm on a pipe.

**Procedure:** We conducted several experiments to estimate the titer of phage and to simultaneously increase titer in preparation for further experiments outside of standard lab conditions. Phage CrimD, Phage Neighly, and Phage Raid were filtered and diluted with a phage buffer solution. Dilutions (10^-2 to 10^-10) were combined with *Mycobacterium smegmatis* and plated with top agar, and incubated overnight. Dilutions were determined based on plaque assays. Titers were estimated by counting plaque forming units (PFU). Plates with bacterial lawns with webbed lysis phage patterns were flooded with a phage buffer, and filtered to generate high titer phage lysate.

**Summary Results:** Phage titers ranged from approximately 10^10 to 10^13 PFU per milliliter of lysate. Phage CrimD and Phage Neighly formed webbed lysis at 10^10 dilutions and Phage Raid at 10^8 dilutions, successfully killing most of the mycobacterial lawn.

[For detailed results, click here!](results)

## **Experiment 10:**

### **Determining efficiency of phage to lyse of biofilm-forming mycobacteria on PVC pipes simulating household plumbing conditions**

---

**Experimental Goal:** To evaluate whether phages can effectively lyse mycobacteria under conditions that mimic household water systems—beyond controlled laboratory settings—and to assess their potential as a viable strategy for biofilm removal in pipes.

**Rationale:** In order to compare with laboratory conditions, we evaluated the effectiveness of phage to lyse biofilms in more dynamic, complex environments simulating household environments compared to controlled lab conditions and the ability of phage to penetrate a biofilm. We constructed microcosms mimicking household water systems where mycobacterial biofilms are often found and administered phage as treatment of these biofilms and compared these results with phage lysing efficiency under lab conditions. We did so by the following types of measurements:

![Pipe Biofilm Case Study Experimental Design](https://static.igem.wiki/teams/5687/msc/exp-pipe-fig.webp)

Figure: Experimental design for the microcosm that was used for experiments 10a, 10b, and 10c.

## **Experiment 10a:**

### **Determining titer of viable bacteria in the microcosm effluent before and after phage infection**

---

**Rationale:** Bacterial population estimates are difficult to evaluate in the biofilm due to the complex structure of the extracellular matrix and the adhesion of the bacteria to the biofilm. Therefore, we sampled from the microcosm effluent to determine the bacterial titers in the effluent.

**Procedure:** Biofilms with *Mycobacterium smegmatis* were grown out on PVC pipes in a nonsterile environment, and placed into 3D adaptors to simulate a showerhead. The pipes were flushed once a day with tap water, and bacterial titers from the effluent were taken after each flushing. The *M. smegmatis* was transformed with a red fluorescent protein (RFP) before biofilm growth with a plasmid that displayed hygromycin resistance, so bacterial titers were evaluated on hygromycin resistant plates. After several days, phage was added to the tap water and bacterial titers were taken every day after phage treatment.

**Summary Results:** Overall, populations of hygromycin resistant bacteria did not decrease after phage treatment, implying that phage were unable to infect and lyse mycobacteria in the biofilm along the pipe efficiently enough to make a significant impact.

[For detailed results, click here!](results)

## **Experiment 10b:**

### **Determining phage survivability in microcosm conditions**

---

**Rationale:** Real world environments present challenges to the survivability of bacteriophage, since they are largely susceptible to degradation, with the presence of proteases in drinking water. We aimed to determine if there would be degradation of phage in tap water by testing its survivability in microcosm effluent after 24 hours.

**Procedure:** To determine if phage can not only lyse mycobacteria but survive in real world conditions without complete degradation, phage titer was compared before and after microcosm flushing. Phage of known titer was diluted with standard tap water and flushed through the microcosm. The effluent was collected and let stand for 24 hours, and the effluent was filtered and spotted on a mycobacterial lawn formed with top agar.

**Summary Results:** After 24 hours, there was a decrease in PFU, signifying that there is degradation of phage under microcosm conditions.

[For detailed results, click here!](results)

## **Experiment 10c:**

### **Testing phage presence in microcosm effluent at the final time point**

---

**Rationale:** When phage was flushed through the biofilm, we predicted through literature and mathematical modeling that the phage would adhere to the biofilm and continuously replicate while lysing the bacteria. Therefore, phage presence should have persisted throughout the time after inoculation since we still saw a persistence of bacterial presence, although decreased.

**Procedure:** Microcosm effluent was collected and filtered. It was combined with *M. smegmatis*, plated with top agar, and incubated overnight. Plaques were quantified.

**Summary Results:** There was no phage presence in the effluent at the final time point, meaning that phage did not maintain presence in the biofilm a week after treatment.

[For detailed results, click here!](results)

## **Experiment 10d:**

### **Determining the structure of the biofilm in the pipe following phage treatment**

---

**Rationale:** Mycobacterial biofilms are largely made up of sugars and polysaccharides, which comprise the extracellular matrix (Esteban et al., 2018). In order to understand how the structure of the biofilm was affected by phage treatment, we placed it under a microscope in order to assess the differences in biofilm structure and visual composition with and without phage treatment.

**Procedure:** In order to evaluate the effect of phage on the biofilm structure, we constructed separate microcosms, where two were treated with phage and two were not. 36 hours after flushing either with or without phage, a slice of the PVC pipe was viewed under the Hirox RV 2000 microscope to generate a 3-dimensional image of the biofilm. Because the mycobacteria was transformed with a RFP, mycobacteria was identified by the expression of red fluorescence under the microscope camera. The average height of the biofilm as well as surface area covered by biofilm on the PVC pipe were compared between pipes with and without phage treatment.

**Summary Results:** There was not significant variation in maximum height of the biofilm, but the average height of the biofilm was lesser in pipes exposed to phage treatment. There was also a formation of plaque-like clearings on the PVC pipe, with a significant decrease in surface area covered by biofilm.

[For detailed results, click here!](results)

## **Experiment 10e:**

### **Determining gene expression differences of mycobacteria in laboratory versus microcosm conditions**

---

**Rationale:** Mycobacterial gene activation in biofilm formation can largely inhibit phage infection, with the extracellular matrix creating difficulty for phage accessibility to the bacteria. Some phage have adapted to this by inhibiting genes that regulate biofilm maturation such as *GroEL1* (Ojha et al., 2005). For more informed phage selection for biofilm treatment, RNA from biofilm forming mycobacteria was extracted and sequenced. Biofilms were grown in a nonsterile environment in the presence of PVC material and tap water in order to create conditions more similar to those inside of a household pipe.

**Procedure:** *Mycobacterium smegmatis* culture was diluted with tap water and placed in a nonsterile tray containing PVC pipe fragments and incubated for 7 days. RNA was extracted using the NEB Monarch Spin RNA Isolation Kit.

**Summary Results:** We are in the process of analyzing RNA sequencing data to determine the genes expressed in mycobacteria while it is forming biofilms in a nonsterile environment.

[For detailed results, click here!](results)

## **Experiment 10f:**

### **Determining bacterial community structure of microcosm biofilm**

---

**Rationale:** Findings in literature suggest that mycobacteria most efficiently forms biofilms in the presence of other species and adheres most efficiently to biofilms produced by other bacteria or yeast (Gomez-Smith et al., 2015). Therefore, the biofilms were grown on PVC pipes in a nonsterile setting to allow for more effective adherence to the PVC material. To determine the bacterial species present in the biofilm, we performed 16s rRNA metagenomic sequencing.

**Procedure:** Biofilm was scraped off the inside of a pipe and suspended in TE buffer. The solution was then bead beat and boiled to release DNA. 16s PCR was performed and the amplicons were sequenced. Raw sequence data was analyzed using Kraken2 and Bracken tools on Galaxy.

**Summary Results:** The 16s sequencing results suggest that the biofilm largely consisted of acinetobacter, but contained various other species. There is no mycobacteria sequence identified, although there is actinobacterium sequence identified, which may be the mycobacteria.

[For detailed results, click here!](results)

# **RNA-Seq Meta-Analysis**

*Overarching Goal: To identify differentially expressed genes and pathways in chassis organisms by comparing laboratory versus environmental transcriptomic and metatranscriptomic data.*

---

## **Experiment 11:**

### **To identify and assemble a comprehensive set of synthetic biology chassis genes and pathways that are differentially expressed (both up- and down-regulated) in laboratory versus natural environments, with the ultimate goal of using these data to develop fundamental design principles for synthetic biology in aquatic systems**

---

**Rationale:** As stated in our Project Description, despite the ever-increasing severity of problems facing aquatic systems and the ineffectiveness and/or harmful side effects of current solutions, synthetic biology solutions—despite their potential—have not been widely deployed. While many synthetic biology solutions have been developed and function under laboratory conditions, there is an overall lack of knowledge on how these chassis and their engineered circuits would perform in realistic environments.

The objective of our project is to establish a rigorous knowledge base for conducting synthetic biology safely, predictably, and effectively in aquatic systems. To this end, we have employed an experimental approach (also presented in this section) in which we rigorously test these differences at all levels. However, these are only three examples. Therefore, it is equally important—if not more so—to conduct a thorough meta-analysis of current literature and to extract as many relevant existing datasets as possible to establish a comprehensive assessment of genes, pathways, and patterns that are differentially expressed (DE) between laboratory and environmental conditions, with the ultimate goal of establishing fundamental design principles for synthetic biology.

**Procedure:** The first step of this bioinformatic experiment was to establish an exhaustive list of DE genes. For the bioinformatic analysis, we performed a structured survey of the primary literature and public transcriptome repositories (e.g., NCBI SRA) to identify studies profiling the same aquatic chassis under laboratory and more natural or simulated environmental conditions. When DE results were provided by the original authors, we incorporated them directly; otherwise, we reprocessed raw reads through a standardized workflow culminating in DESeq2 for DE analysis. Functional interpretation relied on gene-set and pathway enrichment against Gene Ontology and KEGG catalogues, implemented via DAVID tools. In all cases, we attempted to pair cultured-strain transcriptomes with *in situ* metatranscriptomes from the same or closely related taxa wherever possible.

**Our experiment can be divided into five stages:**

1. Analysis of transcriptomic data comparing laboratory versus environmental samples
2. Analysis of metatranscriptomic data comparing laboratory versus environmental samples
3. Functional analysis of DE genes
4. AI Analysis of DE Genes:

* As described above, while we devoted significant effort to the bioinformatic analyses, our team also began studying prompt engineering to more effectively and accurately employ AI for our analyses. We were able to significantly enhance our catalog of DE genes with specific prompts for both generally DE genes and those specific to a given genus or species. For our analysis, we employed Microsoft Copilot, Google Gemini, and OpenAI ChatGPT (both version 4 and ChatGPT+).

5. Curation and Vetting of AI Results:

* While AI tools are improving at an exponential rate, they still hallucinate and make mistakes. It is therefore absolutely essential to vet the results produced by AI agents. To do so, we not only checked the published literature but also analyzed the outcomes using our own bioinformatics pipelines.

**Summary Results:** As described in more detail in the Results section, integrating all five approaches allowed us to generate not only specific lists of genes and pathways differentially expressed when comparing laboratory conditions versus natural environmental conditions for the species in which we generated experimental data, but also a more generalizable list of genes and pathways that served as a starting point for developing a set of design principles.

[For detailed results, click here!](results)

[For detailed protocols, click here!](results)

# References

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{% set header\_image = 'https://static.igem.wiki/teams/5687/banners/results.webp' %}
{% block title %}Results{% endblock %}
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{% block page\_content %}

* [Executive Summary](#summary)
* [Aquatic Case Study I: Corrosion Prevention Using Engineered *Bacillus subtilis* in Marine Environments](#corrosion)
* [Aquatic Case Study II: Freshwater Algal Bloom Remediation](#habs)
* [Aquatic Case Study III: Removal of Biofilms in Household Pipes](#pipes)
* [RNA-Seq Meta-Analysis](#rna)
* [References](#references)

---

# **Executive Summary**

---

Aquarius investigates differences in engineered chassis behavior in laboratory conditions versus simulated real-world aquatic environments. Through three case studies, we clarify critical differences in survival, gene expression, and functional performance of chassis under controlled laboratory and simulated natural conditions.

### **I. Marine Corrosion Prevention with *Bacillus subtilis***

Engineered *B. subtilis* strains showed enhanced biofilm traits in lab media but failed to form robust biofilms or prevent corrosion in simulated seawater environments. Natural strain 3610 outperformed engineered variants. RNA-seq and 16S analyses revealed environmental stress and microbial competition significantly impacted gene expression and chassis persistence.

### **II. Freshwater Harmful Algal Bloom (HAB) Remediation with Cyanophage and *Acinetobacter baylyi***

*A. baylyi* persisted in static laboratory cocultures but was outcompeted by microbial competitors in flowing lakewater microcosms. Under microcosm conditions, 16S amplicon sequencing and colony counts indicated a reduction in *A. baylyi* survival that corresponded with an increase in the relative abundance of competitor species. Preliminary RNA sequencing results identified differentially expressed genes that indicate differences in *A. baylyi* functioning across the two conditions.

### **III. Biofilm Removal in Household Pipes Using Phage Therapy**

Phages lysed mycobacteria effectively in lab assays but degraded quickly and failed to persist or reduce biofilms in PVC pipe microcosms. Microscopy and 16S sequencing revealed complex biofilm communities and limited phage impact. We are analyzing novel RNA-seq data to identify biofilm-associated gene expression that may inhibit phage efficacy.

---

### **Bioinformatics & RNA-Seq Meta-Analysis**

We conducted a meta-analysis of transcriptomic and metatranscriptomic data to identify genes and pathways differentially expressed between lab and environmental conditions. These insights are guiding our development of design principles for synthetic biology in aquatic systems.

# **Aquatic Case Study I:**

## **Corrosion Prevention Using Engineered *Bacillus subtilis* in Marine Environments**

---

*Overarching Goal: To compare ability of engineered B. subtilis to prevent corrosion in lab conditions versus simulated real-world marine seawater conditions.*

## **Experiment 1:**

### **Construction of Engineered *B. subtilis* Strains**

---

Raw Data:▼

![Gel](https://static.igem.wiki/teams/5687/results/results-1.webp)

Figure: Gel electrophoresis results for a colony PCR for pBS1C

![Plates](https://static.igem.wiki/teams/5687/results/results-2.webp)

Figure: Starch-iodine stain to check whether the constructed integration vector pBS1C got successfully integrated into *B.subtilis* 168 genome

**Data Analysis/Interpretation:**

We conducted colony PCR for the constructed plasmid L0 SapI-J04450-TapA-SipW-TasA, only 2 out of 24 PCR reactions yielded a band of the expected length, suggesting that the SapI insert has a low ligation efficiency, possibly due to its short three-base-pair fusion sites, resulting in a high possibility of mismatch.

The integration vector pBS1C-N was designed to integrate into the AmyE locus in the *Bacillus subtilis* genome. Upon successful transformation, the vector disrupts the native amylase gene, thereby stopping the strain’s ability to hydrolyze starch. This disruption enables a straightforward functional screening method: colonies with successful genomic integration will fail to degrade starch and appear dark blue when stained with iodine solution, whereas wild-type colonies will be orange due to starch breakdown. Transformation into *B. subtilis* 168 yielded colonies for all four constructs showed dark blue in iodine staining, confirming the loss of starch degradation, showing that the vectors were successfully integrated into *B. subtilis* 168 genome. When labeling Petri dishes, the *B. subtilis* 168 *tapA–sipW–tasA+* strain was abbreviated as *TST* for convenience.

## **Experiment 2:**

### **Establishing growth curves to compare growth dynamics of *B. subtilis* 168, 3610, and the 4 engineered 168 variants under laboratory conditions**

---

Raw Data:▼

![Growth Curve](https://static.igem.wiki/teams/5687/results/results-3.webp)
![Growth Curve](https://static.igem.wiki/teams/5687/results/results-4.webp)
![Growth Curve](https://static.igem.wiki/teams/5687/results/results-5.webp)
![Growth Curve](https://static.igem.wiki/teams/5687/results/results-6.webp)
![Growth Curve](https://static.igem.wiki/teams/5687/results/results-7.webp)
![Growth Curve](https://static.igem.wiki/teams/5687/results/results-8.webp)

Figures: The 36-hour growth curve of B. subtilis 168, 3610, BslA+, SinI+ under different inducer levels.

**Data Analysis/Interpretation:**

Both the 24- and 36-hour growth curves indicated that the concentration of the xylose inducer had no significant effect on bacterial growth in standard LB medium, which does not promote biofilm formation. The dark blue line shows that the absence of xylose resulted in no major difference compared to other concentrations. The four engineered strains and the unmodified laboratory strain *Bacillus subtilis* 168 exhibited similar growth rates, whereas the natural strain *B. subtilis 3*610 grew substantially faster. Additionally, after the experiment, visible cell clusters were observed in several wells containing *B. subtilis SinI+* and *B. subtilis* *BslA+* strains, which may explain the pronounced fluctuations in OD readings toward the end of the growth curve.

## **Experiment 3:**

### **Evaluating the motility of *B. subtilis* 168, 3610, and the 4 engineered 168 variants**

---

Raw Data:▼

![Plates](https://static.igem.wiki/teams/5687/results/results-9.webp)

Figure: B. subtilis 3610 on 0.3%, 0.7%, 1.5% agar plates with 0.2% xylose.

![Plates](https://static.igem.wiki/teams/5687/results/results-10.webp)

Figure: B.subtilis 168 BslA+ on 0.3%, 0.7%, 1.5% agar plates with 0.2% xylose.

![Plates](https://static.igem.wiki/teams/5687/results/results-11.webp)

Figure: B. subtilis 168 SinI+ on 0.3%, 0.7%, 1.5% agar plates with 0.2% xylose.

![Plates](https://static.igem.wiki/teams/5687/results/results-12.webp)

Figure: B. subtilis 168 TasA+ on 0.3%, 0.7%, 1.5% agar plates with 0.2% xylose.

![Plates](https://static.igem.wiki/teams/5687/results/results-13.webp)

Figure: B. subtilis 168 Tap-SipW-TasA+ on 0.3%, 0.7%, 1.5% agar plates with 0.2% xylose.

**Data Analysis/Interpretation:**

Only *Bacillus subtilis* 3610 exhibited both swarming and sliding motility, confirming that it remains the only strain possessing both EPS-driven sliding and flagellum-driven spreading. None of the engineered modifications produced sufficient EPS to support sliding motility. Among the engineered strains, *B. subtilis* 168 and its *SinI*+ variant spread across the entire Petri plate, demonstrating strong flagellum-dependent swarming. The *B. subtilis* 168 *BslA*+ and *TapA–SipW–TasA*+ variants covered approximately 67% of the plate surface, showing moderate swarming. In contrast, the *B. subtilis* 168 *TasA* variant displayed neither swarming nor sliding, possibly due to cellular toxicity associated with *TasA* overexpression.

## **Experiment 4:**

### **Determining effect of our genetic modifications on biofilm robustness and structure through characterization of biofilm and colony morphology of laboratory and engineered strains of *Bacillus subtilis***

---

Raw Data:▼

![Plates](https://static.igem.wiki/teams/5687/results/results-14.webp)

Figure: MSgg agar plate with 0.03% xylose inducer spread on top, with each Bacillus subtilis strain directly spotted onto the agar surface

![Plates](https://static.igem.wiki/teams/5687/results/results-15.webp)

Figure: MSgg agar plate supplemented with 0.03% xylose inducer, with each Bacillus subtilis strain directly spotted onto the agar surface.

![Plates](https://static.igem.wiki/teams/5687/results/results-16.webp)

Figure: MSgg agar plate supplemented with 0.2% xylose inducer, with each Bacillus subtilis strain directly spotted onto the agar surface

![Plates](https://static.igem.wiki/teams/5687/results/results-17.webp)

Figure: LB agar plate supplemented with 0.03% xylose inducer, with each Bacillus subtilis strain directly spotted onto the agar surface

**Data Analysis/Interpretation:**

*Bacillus subtilis* 3610 formed a distinct, slightly brown biofilm, while only the *SinI*+ variant exhibited biofilm-like folds and wavy colony margins. All other engineered strains produced smooth, flat colonies lacking biofilm characteristics.

When comparing media, LB medium did not support biofilm development and is therefore suitable for general culture rather than biofilm assays. In contrast, both *SinI*+ and *B. subtilis* 3610 formed biofilm structures on MSgg medium.

Regarding inducer application, sprinkled xylose on MSgg agar surface resulted in uneven colony morphology, likely due to heterogeneous inducer distribution across the surface. In contrast, adding xylose into the MSgg medium resulted in structured colonies. Adding 0.03% or 0.2% xylose produced no significant change in biofilm development, indicating that these concentrations were insufficient to elicit measurable promoter activation difference or alter biofilm formation dynamics.

## **Experiment 5:**

### **Evaluating corrosion protection abilities of each strain on steel surfaces in a simulated marine environment**

---

Raw Data:▼

Phone pictures:

![Steel Plates](https://static.igem.wiki/teams/5687/results/results-18.webp)

Figure: Steel chips taken out of microcosms after 3 days

![Steel Plates](https://static.igem.wiki/teams/5687/results/results-19.webp)

Figure: Steel chips taken out of microcosms after 7 days

SEM Images:

![SEM](https://static.igem.wiki/teams/5687/results/results-20.webp)
![SEM](https://static.igem.wiki/teams/5687/results/results-21.webp)
![SEM](https://static.igem.wiki/teams/5687/results/results-22.webp)
![SEM](https://static.igem.wiki/teams/5687/results/results-23.webp)
![SEM](https://static.igem.wiki/teams/5687/results/results-24.webp)

Figure: SEM pictures of steel chips taken out of microcosms after 3 days and 7 days.

**Data Analysis/Interpretation:**

Visually, the seawater control group, which was not inoculated with *B. subtilis*, exhibited a thick oxidation layer on the steel surface, while moderate oxidation was observed in the *B. subtilis* 168 treated samples. Oxidation on the remaining samples was minimal and much less apparent. Scanning electron microscopy (SEM) further revealed extensive corrosion on both the three-day and seven-day control plates. Dennis Manos, the CSX Professor of Applied Science in William and Mary, interpreted the structures as oxidation structures along with tetrahedral crystal habit of magnetites and needle-like lepidocrocite structures. In contrast, the three-day *B. subtilis* 168 sample displayed distinct corrosion lines interspersed with deposits of irregularly shaped compounds—patterns similar to nano-structured calcium silicate(Johnston et al., 2008). Similar surface features were also present in the three-day *B. subtilis* 3610 sample and showed up in a region of the seven-day *SinI*+ plate. Notably, in other regions of the three-day *B. subtilis* 3610 plate, as well as on the three-day and seven-day *BslA*+ and *SinI*+ samples, we observed a novel surface structure: parallel, unidirectional apparently inorganic crystals forming across the steel in an anisotropic morphology that appears to follow the underlying substrate grains. Our phone and SEM pictures suggested the *Bacillus subtilis* in the microcosms noticeably altered the corrosion behavior and protected against corrosion. These formations differ markedly from previously published SEM images of *B. subtilis*–related corrosion (Guo et al., 2017; Wang et al., 2020), which were obtained under sterile laboratory conditions. In contrast, our study employed non-sterile microcosms designed to mimic natural marine environments, which showcased that these unique crystalline structures may arise from species interactions in aqueous environments.

## **Experiment 6:**

### **Analysis of gene expression differences between lab and microcosm environments in strains with strong corrosion protection**

---

Raw Data:▼

![Abundance Graph](https://static.igem.wiki/teams/5687/results/results-25.webp)

Figure: Relative abundance of bacterial species in seawater microcosms inoculated with different concentrations of Bacillus subtilis 168 BslA+.

![Abundance Graph](https://static.igem.wiki/teams/5687/results/results-26.webp)

Figure: Relative abundance of bacterial species in seawater microcosms inoculated with different concentrations of Bacillus subtilis 168 SinI+.

![Abundance Graph](https://static.igem.wiki/teams/5687/results/results-27.webp)

Figure: Relative abundance of bacterial species in seawater microcosms inoculated with different concentrations of Bacillus subtilis 3610.

**Data Analysis/Interpretation:**

16S sequencing revealed substantial colonization by non-*Bacillus subtilis* species across all seawater microcosm samples, indicating that mixed microbial communities developed under non-sterile conditions. Among the three tested strains: wild-type *B. subtilis* 3610, *SinI*+, and *BslA*+, the wild-type strain showed minimal persistence within these communities, whereas both engineered strains displayed moderate survival. Community composition varied strongly with inoculum concentration. In microcosms inoculated with 25% bacterial culture, Ochrobactrum dominated the population and emerged as the most abundant non-Bacillus genus. In contrast, microcosms inoculated with only 0.5% bacterial culture were consistently enriched in Acinetobacter. These results suggest that inoculum concentration not only shapes community composition but may also influence competitive interactions among resident taxa, providing a potential ecological explanation for the observed differences in corrosion-protection outcomes described in Experiment 4.

RNA sequencing results revealed that sporulation-related genes were upregulated in all three Bacillus subtilis strains under marine microcosm conditions. In contrast, metabolic genes were downregulated across all strains, with the SinI+ and BslA+ variants showing increased expression of genes involved in ATP binding, rRNA binding, and translation. Notably, the BslA+ strain exhibited specific downregulation of genes associated with flagellar assembly, suggesting an enhanced motility response to the seawater environment. The RNA sequence strongly supports that strains were behaving differentially in simulated natural environments and lab environments.

Additional Figures:▼

![DAVID Output](https://static.igem.wiki/teams/5687/results/results-28.webp)
![DAVID Output](https://static.igem.wiki/teams/5687/results/results-29.webp)

Figures: A sample of genes downregulated in microcosms compared to controls

![DAVID Output](https://static.igem.wiki/teams/5687/results/results-30.webp)

Figure: A sample of genes upregulated in microcosms compared to controls

# **Aquatic Case Study II:**

## **Freshwater Algal Bloom Remediation**

---

*Overarching Goal: To compare behavior and ability of cyanophages and/or Acinetobacter baylyi to remediate HABs in lab conditions versus simulated real-world conditions.*

## **Experiment 7:**

### **Microcystis Cyanophage Isolation for Harmful Algal Bloom Remediation**

---

**Experimental Goal:**

To isolate (and potentially engineer) novel Microcystis cyanophages for harmful algal bloom (HAB) remediation as a case study to compare phage behavior in laboratory conditions versus simulated real-world environments.

Raw Data:▼

![Clearing/Plaques](https://static.igem.wiki/teams/5687/results/results-31.webp)

Figure: Samples of raw data for observation of bacterial clearing and plaque formation in liquid and plaque assays, respectively

We observed bacterial clearing compared to control assays in 39 of 205 liquid assays. Putative cyanophage plaque formation, a strong indication of phage presence, occurred on 2 of 65 agar assays. One assay (see below) displayed multiple turbid plaques.

![Plates](https://static.igem.wiki/teams/5687/results/results-32.webp)

Figure: Substantial plaque formation on an agar assay

Resuspension of plaques in 5 mL liquid Microcystis culture yielded potential clearing in 3 of 4 instances. However, filtrate from the cleared assays did not produce re-clearing of a second round of liquid assays. Lysate harvested from the plaque-containing plate did not yield re-clearing.

![Clearing/Plaques](https://static.igem.wiki/teams/5687/results/results-33.webp)

Figure: Frequency of M. aeruginosa clearing and plaquing in liquid and phage plating cyanophage assays

Figure: Examples of clearing over time in a liquid assay “infected” with potential phage filtrate (left) versus a control assay “infected” with NFW (right).

![Plates](https://static.igem.wiki/teams/5687/results/results-34.webp)

Above: Filtrate was obtained from a previous assay. By day 5, the reinfection assay (left) was almost fully clear, while the control (right) retained its green coloration.

![Plates](https://static.igem.wiki/teams/5687/results/results-35.webp)

Above: Filtrate was obtained from a cyanophage enrichment. By day 3, the enrichment assay (left) had begun yellowing, and by day 4 had almost fully cleared. The control (right) retained approximately the same coloration throughout the experiment.

**Data Analysis:**

Plaque formation on a Microcystis lawn is a strong indicator of phage presence, however we were unable to consistently propagate any putative cyanophage.

**Interpretation:**

Our results highlight a need for SynBio solutions that consider the ecological complexities of real-world water systems in which they must ultimately be deployed. Our attempts to isolate and engineer cyanophage offer a case-study in the effects of real-world chassis behavior and community dynamics on the effectiveness of SynBio-based aquatic remediation solutions.

Importantly, our results indicate that *M. aeruginosa* may have strong anti-phage defense systems that complicate phage isolation and could limit the feasibility of phage-based HAB treatments in the field. We have identified two major limitations of cyanophage isolation and cyanophage-based HAB treatment:

First, *M. aeruginosa* may rapidly evolve phage resistance. About 7% of *M. aeruginosa*’s genome is composed of transposases that can rearrange the species’ abundant mobile genetic elements (Frangeul et al., 2008) and drive evolution in the field and in the lab (Stark et al., 2024). Constant re-shuffling of the genome can rapidly generate novel phage-resistant genotypes—and our discussion with IHP experts indicated that resistance evolution can occur even at the level of a single culture. Rapid host resistance evolution at the culture level may prevent continual propagation of newly-isolated phages in the lab, and analogous evolution at the HAB population level would likely limit *in vivo* deployment.

Second, *M. aeruginosa* phages may exhibit high rates of lysogeny (integration and latency within the host genome), particularly during HAB formation (Huang et al., 2025). Lysogeny could limit novel cyanophage isolation, as there may be few free-floating phage available to isolate. Interestingly, the turbidity of our plaques may indicate a level of lysogeny, which could explain the putative cyanophage’s inconsistent propagation. Lysogeny often provides host cells with immunity to other infecting phages (Buchner et al., 2024), and lysogeny’s prevalence in HABs could limit cyanophage treatment viability *in vivo* by preventing reinfection.

## **Experiment 8:**

### **Comparison of *Acinetobacter baylyi* Behavior in Laboratory vs Simulated Real-World Microcosms**

---

**Overall Experimental Goal:**To test habitat remediation with *Acinetobacter baylyi* ADP1-ISx as a case study comparing bacterial behavior (persistence and gene expression) in laboratory conditions versus simulated real-world environments in order to inform the development of design principles for synthetic biology applications in aquatic systems.

## **Experiment 8a:**

### **Estimating *A. baylyi* survival in microcosms and control cocultures via colony counts**

---

Raw Data:▼

![Colony Growth Graph](https://static.igem.wiki/teams/5687/results/results-36.webp)

Figure: A. baylyi colony growth on LB agar spread plates vs. Days Post-Inoculation. Bacterial lawns were arbitrarily assigned the value of 600 colonies.

![Plates](https://static.igem.wiki/teams/5687/results/results-37.webp)

Figure: Spread plates approximately 1 day post-inoculation (left) versus 7 days post-inoculation (right). The top row of plates in each image were spread using samples from lakewater microcosms, the bottom row from control cocultures.

**Data Analysis:**

Control coculture spread plates consistently formed *A. baylyi* lawns throughout the time course, while microcosm spread plates yielded decreasing *A. baylyi* colony counts over the span of the experiment.

**Interpretation:**

The reduction in microcosm *A. baylyi* colony formation over time (and as compared with the control) suggests that the microcosm conditions adversely affected A. baylyi survival over the course of the experiment.

## **Experiment 8b:**

### **Quantifying species abundance in microcosms and control cocultures via 16S amplicon sequencing**

---

Raw Data:▼

![Species Abundance Data](https://static.igem.wiki/teams/5687/results/results-38.webp)

Figure: Samples of total species abundance data in microcosms and control cocultures at timepoints 1 and 7

![Gel](https://static.igem.wiki/teams/5687/results/results-39.webp)

Figure: Microcosm 16S amplicons after cleanup

**Data Analysis:**

The average microcosm *A. baylyi* population declined from an initial 73.58% abundance (on day 1) to 7.83% abundance (day 7). Control abundances on day 7 were approximately comparable to those measured on day 1 (at 77.41% on day 7 as compared to 81.33% on day 1).

![Relative Abundance Data](https://static.igem.wiki/teams/5687/results/results-40.webp)

Figure: Mean A. baylyi Relative Abundance in Cocultures and Microcosms vs Days Post-Inoculation. Error bars display 95% confidence intervals for each mean.

**Interpretation:**

The substantial reduction in microcosm *A. baylyi* relative abundance over the course of the experiment (and as compared to the control) suggests that *A. baylyi* was outcompeted by native lakewater organisms within a week after initial microcosm inoculation.

![Microscopy Image](https://static.igem.wiki/teams/5687/results/results-41.webp)

Figure: A sample of native Lake Matoaka organisms, including various diatoms, cyanobacteria, and green algae under a standard light microscope. Our 16S results suggest that native microbes outcompeted A. baylyi in lakewater microcosms.

## **Experiment 8c:**

### **Quantifying differential gene expression of *A. baylyi* in microcosms and control cocultures via RNA sequencing**

---

Raw Data:▼

![DE Gene Data](https://static.igem.wiki/teams/5687/results/results-42.webp)

Figure: Genes displaying statistically significant differential expression across control and coculture conditions. Positive log2(FC) values indicate upregulation in microcosms as compared to control cultures, while negative values indicate downregulation.

![Gel](https://static.igem.wiki/teams/5687/results/results-43.webp)

Figure: Successful microcosm RNA extractions

**Data Analysis:**

After 24 hours, the most highly upregulated genes in *A. baylyi* microcosms as compared to control cocultures (indicated by high log2 fold change values) included genes encoding an NAD(P)/FAD-dependent oxidoreductase, an ATP-dependent Clp protease ATP-binding subunit, a mechanosensitive ion channel, and a PA1571 family protein. *A. baylyi* genes significantly downregulated in the microcosms included genes encoding LyrR family transcriptional regulators, a MATE family efflux transporter, and the epoxyqueuosine reductase QueH.

**Interpretation:**

Preliminary evidence of differential gene expression after 24 hours suggests that specific aspects of *A. baylyi*’s behavior and functioning are concretely affected by real world conditions and that *A. baylyi* likely does not function as effectively in a real world environment as it does in traditional *in vitro* cocultures. We are currently in the process of analyzing and interpreting RNAseq data to quantify *A. baylyi* differential gene expression between microcosms and control cocultures at all three timepoints.

**Overall Interpretation:**

Our results illustrate that engineered chassis may not persist and function as effectively in real-world environments compared with the lab settings in which they are typically tested. Synthetic biologists should test their constructs in simulated real-world environments in order to identify and address limitations associated with the effects of *in vivo* conditions prior to deployment.

We conclude that *Acinetobacter baylyi’s* survival and functioning may be limited in a real lakewater environment, despite effective persistence in cocultures. We observed a substantial decline in *A. baylyi* relative abundance (according to 16S sequence data) under simulated lakewater conditions, indicating that *A. baylyi* was outcompeted by native lake species. Colony counts further suggest that *A. baylyi*’s population declined over the course of the microcosm experiment, suggesting that lakewater conditions adversely affected survival.

# **Aquatic Case Study III:**

## **Removal of Biofilms in Household Pipes**

---

*Overarching Goal: To compare ability of phages to lyse biofilms in lab conditions versus real world conditions of household pipes*

## **Experiment 9:**

### **Determining efficiency of specific mycobacteriophages to lyse mycobacteria under laboratory conditions**

---

Raw Data:▼

![Phage Titers](https://static.igem.wiki/teams/5687/results/results-44.webp)

Table: Final phage titers for all three phage that were used for the microcosm experiments.

![Plaque Assays](https://static.igem.wiki/teams/5687/results/screenshot-20251208-192243-keep-notes.avif)

Figure: Plaque assays with Phage CrimD, Phage Neighly, and Phage Raid at 10^-6, 10^-8, and 10^-10 dilutions.

**Data Analysis/Interpretation:**

Phage titers ranged from approximately 10^10 to 10^13 PFU per milliliter of lysate. Phage CrimD and Phage Neighly formed webbed lysis at 10^10 dilutions and Phage Raid at 10^8 dilutions, successfully killing most of the mycobacterial lawn. This illustrated the ability of phage to kill mycobacteria even at high dilutions.

## **Experiment 10a:**

### **Determining titer of viable bacteria in the microcosm effluent before and after phage infection**

---

Raw Data:▼

![Titers Table](https://static.igem.wiki/teams/5687/results/results-45.webp)

Table: Bacterial titers evaluated from microcosm effluent throughout each time point.

![Titers Graph](https://static.igem.wiki/teams/5687/results/results-46.webp)

Figure: Average titer of effluent in CFU/mL on a log scale throughout a time period of 12 days. The microcosms were treated with phage on day 5.

**Data Analysis:**

There was not a significant difference in the bacterial titer before and after phage infection (p-value = 0.604), which was determined using a Welch t-test.

**Interpretation:**

Phage treatment did not have a significant effect on bacterial titer in the effluent, suggesting that one treatment of phage was not significant in decreasing bacterial population in the biofilm.

## **Experiment 10b:**

### **Determining phage survivability in microcosm conditions**

---

**Raw Data:**

Initial phage titer inoculated uniformly throughout the microcosms: 4.5\*10^12

![Titers Table](https://static.igem.wiki/teams/5687/results/results-47.webp)

Figure: Phage titer from effluent after 24 hours.

**Data Analysis/Interpretation:**

Phage titer decreases substantially after 24 hours in microcosm effluent, likely due to the proteases and other degradation factors that are inherent to realistic and dynamic environments.

## **Experiment 10c:**

### **Testing phage presence in microcosm effluent at the final time point**

---

**Raw Data:**

There were no visible plaques in the plaque assays from the final effluent.

**Data Analysis/Interpretation:**

The lack of phage presence in the effluent at the final time point signifies that phage did not adhere strongly enough to the biofilm or phage no longer were able to sustain themselves in the biofilm. This may be because of the decrease in bacterial population or the structure of the biofilm preventing the phage from surviving for long periods of time.

## **Experiment 10d:**

### **Determining the structure of the biofilm in the pipe following phage treatment**

---

Raw Data:▼

We used the Hirox RV 2000 microscope to generate a 3-dimensional image of the biofilm, both in PVC pipes with and without phage treatment.

![Hirox](https://static.igem.wiki/teams/5687/results/results-48.webp)

Figure: HIROX microscope image generated from a sample that was not treated with phage. The top image is in the original color while the bottom image is the pseudocolor assigned based on height.

![Hirox](https://static.igem.wiki/teams/5687/results/results-49.webp)

Figure: HIROX microscope image generated from a sample that was treated with phage. The top image is in the original color while the bottom image is the pseudocolor assigned based on height.

![Hirox table](https://static.igem.wiki/teams/5687/results/results-50.webp)

Table: Percentage of each color distribution the pseudocolor image of the pipe.

**Data Analysis:**

![Hirox table](https://static.igem.wiki/teams/5687/results/results-51.webp)

Figure: Average color distributions on biofilm surfaces with phage and without phage treatment.

**Interpretation:**

The PVC pipes that were treated with phage had what resembled plaque-like holes in biofilm formation, with less surface area covered by biofilm compared to the biofilm that was flushed with standard tap water with no phage added. There were also less visible red bacteria, signifying that the RFP-transformed mycobacteria was less present after phage treatment.

The colors represent the distribution of height throughout the biofilm. Since we saw no significant differences in maximum and minimum heights, we chose to focus on the height distribution based on the pseudocolors that were assigned by the HIROX microscope program. Blue represents the lowest height range, green being the second lowest, yellow being greater, and red being the highest height range.

There is a trend (p-value = 0.067) of lower range colors being predominant in the pipes treated with phage, meaning that although the range of biofilm height remains relatively consistent, the height distribution is skewed towards the lower end after phage treatment. The lack of disparity in maximum height is consistent with literature that suggests that phage clusters in regions upon infection of the biofilm, and the extracellular matrix itself is not heavily affected by phage treatment (Testa, 2019).

## **Experiment 10f:**

### **Determining bacterial community structure of microcosm biofilm**

---

**Raw Data:**

![Kraken/Braken](https://static.igem.wiki/teams/5687/results/results-52.webp)

Table: Outputs from running the 16s sequence through Kraken2 and Bracken with species and abundances.

**Data Analysis:**

The 16s sequencing results suggest that the biofilm largely consisted of acinetobacter, but contained various other species. There is no mycobacteria sequence identified, although there is actinobacterium sequence identified, which may be the mycobacteria.

**Interpretation:**

Because the biofilm was grown in a nonsterile environment, there were expected to be other species present in the biofilm to closely simulate household water system conditions. However, 16s sequencing is limited to bacterial species, and does not provide full insight into the community structure of the biofilm.

# **RNA-Seq Meta-Analysis**

---

*Overarching Goal: To identify differentially expressed genes and pathways in chassis organisms by comparing laboratory versus environmental transcriptomic and metatranscriptomic data*

## **Experiment 11a:**

### **Analysis of transcriptomic data comparing laboratory versus environmental samples**

---

**Raw Data:**

DESeq2 Outputs from Galaxy workflow and literature DE gene datasets.

![Lit Review](https://static.igem.wiki/teams/5687/results/results-53.webp)

**Data Analysis/Interpretation:**

![DE Gene Data](https://static.igem.wiki/teams/5687/results/results-54.webp)

Differential expression analysis showed transcriptional changes between lab and environmental conditions. These results emphasize the importance of designing chassis with environmental adaptation in mind.

## **Experiment 11b:**

### **Analyzing differences between metagenomic and metatranscriptomic taxonomic results**

---

**Raw Data:**

Final outputs of running our comparison pipeline (metaG\_metaT)

![metaT/metaG](https://static.igem.wiki/teams/5687/results/results-55.webp)

Figure: Taxonomic results of running metagenomic and metatranscriptomic FASTQ files.

**Data Analysis/Interpretation:**

The comparison reflects the yield differences between a metagenomic and metatranscriptomic processing done in the sample location from a singular study. This result reflects the importance of running both metagenomic and metatranscriptomic sequencing to understand both historical and current species presence within a sampling location.

## **Experiment 11c:**

### **Functional analysis of DE genes**

---

**Raw Data:**

Complete DAVID functional analysis output, unfiltered

![](https://static.igem.wiki/teams/5687/results/results-56.webp)

**Data Analysis/Interpretation:**

After applying a Benjamini-Hochberg correction (α = 0.05), we observed a number of significant p-values, but relatively fewer adjusted p-values meeting the cutoff. Nonetheless, the pathways that did remain significant, primarily related to stress response, membrane transport, and metabolic adaptation aligned with those identified through AI-assisted analyses. These correlations suggest that our meta-analysis is capturing biologically meaningful trends, though further data expansion and processing are needed to strengthen statistical power and confirm emerging patterns.

![](https://static.igem.wiki/teams/5687/results/results-57.webp)

## **Experiment 11d/e:**

### **Al analysis of DE genes & Curation and Vetting of AI results**

---

**Summary Results:**

**Differentially Expressed Genes When Comparing Bacterial Gene Expression in the Lab vs Natural Aquatic Environments**

Here we present summary results for differentially expressed genes when comparing bacterial gene depression in laboratory conditions and in natural aquatic environments.

Additional Information:▼

**Summary Results:**

**Differentially Expressed Genes When Comparing Bacterial Gene Expression in the Lab vs Natural Aquatic Environments**

Here we present summary results for differentially expressed genes when comparing bacterial gene depression in laboratory conditions and in natural aquatic environments.

---

1. **Whole-transcriptome shifts in natural waters**

Seawater vs. lab exponential growth (E. coli): After 20 h in seawater, ~937 genes were upregulated and ~~320 downregulated relative to lab medium; stress and starvation programs dominated the response (microarray). This shows broad re-wiring away from growth programs you typically assume in the lab.

2. **Global regulators override engineered promoters in the wild**

Global stress regulators (e.g., RpoS/stringent response) gate expression when nutrients or conditions fluctuate—common in lakes/estuaries—so payload promoters that look “constitutive” in rich media can attenuate or become heterogeneous in situ. Recent reporter libraries that track multiple E. coli global regulators over time are useful references to explain these shifts.

3. **Inducible systems behave differently outside the flask**

Arabinose (PBAD) “all-or-none” induction: intermediate inducer levels create mixed ON/OFF subpopulations; fixing uptake (araE overexpression) yields more homogeneous induction—but in aquatic settings, uneven inducer diffusion/consumption often re-creates heterogeneity, weakening engineered outputs.

Cross-talk & performance drift: Work improving PBAD compatibility with IPTG (AraC evolution) underscores how inducer mixtures affect circuits; such mixtures and degradations are typical in environmental water, not in lab broth.

4. **Community interactions and quorum effects**

In mixed aquatic communities, quorum signaling and quorum-quenching alter transcription of motility, biofilm, nutrient uptake and public-goods genes—this cross-talk can silence or mis-time engineered outputs that looked clean in monoculture. Reviews focused on marine systems and WWTPs document these QS-mediated expression changes.

5. **"Bottle effects” & sampling artifacts**

How you collect the sample changes the expression you measure. A comparison of traditional Niskin bottle collection vs. in situ filtration showed different community gene-expression profiles, highlighting that ex-situ handling can distort conclusions about “field” expression of engineered strains.

6. **Plasmid retention & engineered-gene stability drop without selection**

In the lab, antibiotics/auxotrophies maintain plasmids; in natural waters, no selection → rapid plasmid loss or mutation, reducing reporter/cargo expression. Multiple methodological studies quantify higher plasmid loss rates without selection and discuss stabilization strategies (TA modules, CRISPRi-based maintenance, antibiotic-free systems)—all relevant because environmental release removes antibiotic selection.

7. **Real-world aquatic microcosm tests of engineered reporters**

GFP-tagged E. coli microcosms in freshwater/marine-like conditions showed reduced persistence and signal vs. lab culture, with survival and detectable fluorescence dropping over time—consistent with stress-driven and plasmid-loss explanations.

Engineered biosensors deployed in untreated water samples often require adapting induction/assay protocols because inducer availability and matrix effects blunt reporter expression (e.g., lead/PFOA biosensors).

---

Overall: There is lower mean expression, more heterogeneity, and time-dependent drift in natural waters, driven by stress responses, QS cross-talk, and plasmid/circuit instability. Characterization in realistic matrices (filtered lake/sea water, defined low-nutrient media, redox/UV cycles) + field-mimicking induction schemes is essential before in situ use. Reviews on environmental deployment and genetic-stability design patterns summarize practical strategies (kill-switches, metabolic load reduction, chromosomal integration, resource-insulated parts).

---

#### **Environment-enriched (often ↑ in seawater/soil/hosts/particles vs LB/glucose lab)**

* Iron scavenging (Fe-limitation)

*tonB–exbB–exbD (energize OM receptors), OM receptors fepA/cirA/fecA/fhuA/fpvA/hasR, siderophores entCEBA (enterobactin), pvd/pch (pyoverdine/pyochelin), dhb (bacillibactin).*

* Phosphate starvation (Pho regulon)

*High-affinity uptake pstSCAB, regulators phoBR/phoPR, phosphatases phoA/phoD, organo-P phn (phosphonate).*

* Nitrogen & sulfur scavenging

*amtB–glnK (NH4+), nitrate/nitrite narGHI/nirBD/nas, taurine/alkanesulfonate ssuEADCB, tauABCD.*

* Osmoprotection / desiccation

*Release valves mscL/mscS, compatible solutes uptake proU/proVWX, opuA/opuC/opuD, synthesis betT–betIBA (GB), ectABC (ectoine), otsAB (trehalose).*

* Oxidative/UV stress defenses

*Peroxide/superoxide: katG/katE, sodA/sodB, ahpCF, dps, msrA/B, trxA/B, gor; DNA repair uvrABC, phr, recA. Regulators oxyR/soxRS, perR, sigB.*

* Motility & chemotaxis (foraging)

*Flagella program flhDC/fliA/fli, chemotaxis cheA/cheY and MCPs, motors motA/motB.*

* Adhesion/biofilm & surface sticking

*Gram-: EPS/export wza/wzc/wzb, curli csg, pel/psl/algD (Pseudomonas). Gram+: epsA-O, tapA–sipW–tasA (Bacillus). Often under QS: luxS/AI-2, las/rhl/pqs, aphA/opaR (Vibrio).*

* Broad substrate uptake & catabolism

*ABC/TRAP/outer porins (dctPQM, dctA, oprD), complex carbon: aromatic ring cleavage ben/cat/pca, chitin/cellulose/hemicellulose hydrolases (chiA/chb, celAB, xylAB).*

* Membrane remodeling for stress

*FA tuning: desA/desC (desaturases), cfa (cyclopropane FA), hopanoids hpn (e.g., Z. mobilis).*

* Defense & phage pressure

*CRISPR–Cas, RM systems, toxin–antitoxin (mazEF, relBE).*

* Competence / gene exchange (*in situ* cues)

*comEA/EC/FA (Gram+), type IV pili/pilus assembly; tfoX/Y (Vibrio on chitin).*

* Storage polymers

*ppk/ppx (poly-P), glycogen glgABC, PHB phbCAB.*

#### **Lab-enriched (often ↑ in rich, aerated exponential culture)**

* Fast growth / translation machinery

*rRNA operons rrn, ribosomal proteins (rpl, rps), tRNA synthetases, elongation factors fusA, tufA/B.*

* Central carbon for rapid glycolysis

*ptsG/manXYZ (PTS sugar uptake), pgi/pfkA/pykF, TCA/respiration modules; overflow pta–ackA, ldhA (acetate/lactate).*

* Amino acid & nucleotide biosynthesis

*ilv/leu/aro, pur/pyr (especially in minimal but nutrient-replete growth).*

* Cell division & envelope biogenesis

*ftsZ/ftsA/zipA (divisome), mur/fab (peptidoglycan/FA biosynthesis), LPS core/synthesis in Gram-.*

* Housekeeping chaperones (heat shock during fast growth)

*rpoH (σ32), dnaKJ, groESL, ibpAB.*

* Motility/Biofilm OFF

*Many lab conditions repress costly flagella/EPS; QS modules often low.*

**Using Differentially Expressed Gene Lists to Suggest Engineering Approaches to Make Engineered Chassis Fieldable in Aquatic Systems**

Here we provide genes and target modules that repeatedly show *in situ* up-regulation in natural aquatic environments as well as down regulated in natural aquatic environments — and suggestions on what to engineer. Below are practical edit ideas associated with pathways known to flip between lab and field. We list gene families (examples) and why they matter in water.

Additional Information:▼

**Using Differentially Expressed Gene Lists to Suggest Engineering Approaches to Make Engineered Chassis Fieldable in Aquatic Systems**

Here we provide genes and target modules that repeatedly show *in situ* up-regulation in natural aquatic environments as well as down regulated in natural aquatic environments — and suggestions on what to engineer. Below are practical edit ideas associated with pathways known to flip between lab and field. We list gene families (examples) and why they matter in water.

---

#### **Up-Regulated in Aquatic Environments:**

1. Membrane fluidity & pressure/cold adaptation

Desaturases (desA/desC, fabA/fabB regulators), cyclopropane synthase (cfa), Psp stress operon: tune unsaturated/cyclopropane fatty acids to keep membranes functional under cold/high hydrostatic pressure. Overexpress desaturases; inducible cfa for transient shocks.

Chassis tip: *Shewanella piezotolerans*/*baltica* show clear transcript shifts under cold/HHP; their parts and promoters are a great starting library.

2. Osmoregulation & ion balance (salinity swings)

Compatible solute synthesis/transport: ectoine (ectABC), glycine betaine transporters (proVWX, betT/betAB), mechanosensitive channels (mscL/mscS). Install tunable uptake + synthesis; add an osm promoter to couple growth to external salinity profiles (marine ↔ brackish).

3. Light & photoprotection (surface waters, algae/cyanobacteria)

NPQ/photoprotection modules in diatoms and algae: LHCX family (e.g., lhcx1/2), carotenoid cycle genes. LHCX2 responds sensitively to iron limitation; wire this to a promoter reporting Fe stress to regulate antenna size or repair.

DNA photolyase (phr), uvrABC excision repair: upweight for high UV.

4. Nutrient scavenging at oceanic concentrations

Phosphate: pstSCAB, phoBR regulon; Iron: TonB-dependent receptors, siderophore uptake; diatom Fe-inducible loci (e.g., ISIP1, co-expressed with LHCX2 under Fe limitation). Overexpress high-affinity transporters; use riboswitches for dynamic uptake.

Nitrogen: amtB (NH4+), urea (ureABCDEFG), cyanate (cynS), nitrate/nitrite transporters; glnB (PII) links C–N balance in cyanobacteria—use as a sensor or controller node for C/N flux.

5. Oxidative stress & UV-derived ROS

Peroxiredoxins (ahpC), catalase-peroxidase (katG), superoxide dismutases (sodA/sodB), thioredoxin systems. Build a layered ROS defense with a fast peroxide sensor controlling membranes and repair enzymes.

6. Community interactions: competition, cooperation, viruses

Bacteriocin/bacteriocin-like peptides & export: *Prochlorococcus* co-culture DEGs reveal small exported peptide families (e.g., CCRG genes) induced by neighbors—use such motifs for programmable interference or communication.

Quorum sensing (LuxI/LuxR) wired to resource uptake or motility; biofilm/EPS (e.g., wza/wzc, pel/psl) for particle/surface association at the right time.

Phage defense: CRISPR-Cas, BREX/DISARM as swappable cassettes to harden open-water deployments.

7. Motility & microscale foraging

Chemotaxis/flagella (cheA/cheY, motAB, fli genes): program “forage and settle” behaviors—chemotaxis on labile DOM or phosphate gradients, then EPS-mediated attachment.

#### **Down-Regulated in Aquatic Environments:**

1. High-rate translation & ribosome biogenesis

Genes/modules: rrn rRNA operons; ribosomal proteins (rpl/rps loci), translation factors (tufA/B, fusA, tsf, infA/B/C), tRNA synthetases. Slow growth dominates in oligotrophic waters; the stringent response ((p)ppGpp) suppresses rRNA/tRNA and ribosome biogenesis, shifting resources to stress survival. Growth-law work shows ribosome fraction scales with growth rate, so low μ in the field ⇒ low ribosome program.

2. DNA replication initiation & cell division

Genes/modules: Initiation/elongation (dnaA, dnaN, dnaG, gyrA/B), divisome (ftsZ, ftsA, zipA, ftsQLB).

Why down *in situ*: Nutrient limitation triggers (p)ppGpp-mediated suppression of replication initiation; explicit starvation studies in *Vibrio* show replication initiation shut-off during stringent response. With slower cycles, division genes fall too.

3. PTS sugar uptake & lab-sugar catabolism

Genes/modules: PTS core (ptsH/ptsI/crr), glucose PTS (ptsG), mannose PTS (manXYZ), lab sugar regulons (lac, mal, etc.).

Why down *in situ*: In the ocean, ABC/SBP transport dominates (high-affinity scavenging) while PTS is rare/inefficient at nM substrates; copiotrophs that carry PTS down-shift it in low-nutrient seawater. Comparative genomics & theory show an ABC↔PTS rate-affinity tradeoff underlying oligotroph vs copiotroph lifestyles.

4. Overflow metabolism (acetate/lactate “Crabtree-like”)

Genes/modules: pta–ackA (acetate), poxB (pyruvate oxidase), ldhA (lactate).

Why down *in situ*: Overflow pathways are hallmarks of glucose-rich, aerated lab growth; under carbon-limited seawater they’re unnecessary and typically repressed.

5. Flagellar assembly & motility (pelagic starvation context)

Genes/modules: Master/flagella (flhDC, fliA, fliC), motors (motA/B), chemotaxis (cheA/cheY).

Why down *in situ*: Under prolonged starvation in seawater, several vibrios damp flagellum assembly to save ATP; motility often re-emerges near particles/blooms (exception).

6. Low-affinity phosphate transport (Pi-replete lab bias)

Genes/modules: Pit system (pitA, pitB) vs high-affinity PstSCAB–PhoU.

Why down *in situ*: In low-P waters, cells induce Pst and Pho regulon while low-affinity Pit is disfavored/curtailed; Pit tends to operate under Pi-replete lab conditions.

7. Heat-shock (σ³²/RpoH) program at 37 °C

Genes/modules: rpoH (σ³²), chaperones dnaKJ, groESL, proteases (hslUV, lon, ftsH).

Why down *in situ*: Ambient seawater (~4–25 °C) rarely triggers the RpoH/heat-shock axis that is prominent at 30–37 °C lab growth; cold-shock modules may be used instead.

#### **Core, conserved toolbox (works across many bacteria)**

1. Osmotic shock / salinity

Release valves: mscL, mscS → OE (low-leak) to prevent lysis in rapid hypo-osmotic dips.

Compatible solutes (uptake & synthesis): betT, betIBA (choline → glycine betaine), proVWX/proU (GB/Pro uptake), opuA/opuC (Firmicutes), opuD (GB uptake), ectABC (ectoine), gbsAB (GB synthesis in Gram+).

→ REG by salinity/σS/σB or QS; preload pools for shocks.

2. Iron acquisition (Fe-poor)

Energy for outer-membrane receptors: tonB–exbB–exbD → mild OE (1–2×).

Siderophore biosynthesis/uptake (species-specific) + OM receptors (fepA/cirA/fecA/fhuA, fpvA, etc.) → REG by Fe limitation (Fur/PerR logic) with guardrails vs. ROS.

3. ROS/UV

Peroxides/•OH/superoxide: katG/katE, sodA/sodB, ahpC/ahpF, trxA/trxB, gor → basal OE of katG/sodA, stress-inducible ahpC/trx.

DNA repair: uvrABC, phr, recA → constitutive/UV-inducible.

Regulators: OxyR/SoxRS (Gram-), PerR/SigB (Gram+).

4. Phosphate scavenging

pstSCAB–phoBR (Gram-) / pstSCAB–phoPR (Gram+) → REG via Pho promoters for low-P; add phoA/phn where organo-P needed.

5. Membrane tuning / motility & sticking

FA remodeling: desA/desC (desaturases), cfa (cyclopropane FA) → OE or REG (cold/pressure/solvent); avoid over-stiffening.

Motility: flhDC (master), cheA/cheY, motA/motB → REG/QS toggle for particle-association vs. free-living.

EPS/export: wza/wzc/wzb (Gram-), eps/tapA–sipW–tasA (Bacillus matrix), pel/psl (Pseudomonas).

6. General stress programs

rpoS (σS) in many Gram-, sigB (σB) in Firmicutes → REG to gate costly defenses.

Heat/cold shock: rpoH (σ32), groESL, dnaKJ, ibpAB, cspA → inducible modules.

### Aquatic Laboratory vs Environment Differential Gene Expression ---

**Specific Case Study: *Bacillus subtilis***

Here we present a summary of genes differentially regulated between lab and natural aquatic systems, and potential engineering approaches to enhance survival and persistence, of *Bacillus subtilis* in natural environments, for one of our case studies: the use of Bacillus subtilis to prevent or at least diminish corrosion.

Additional Information:▼

**Specific Case Study: *Bacillus subtilis***

Here we present a summary of genes differentially regulated between lab and natural aquatic systems, and potential engineering approaches to enhance survival and persistence, of *Bacillus subtilis* in natural environments, for one of our case studies: the use of Bacillus subtilis to prevent or at least diminish corrosion.

---

**Case Study: *Acinetobacter baylyi* ADP1**

Here we present a summary of genes differentially regulated between lab and natural aquatic systems, and potential engineering approaches to enhance survival and persistence, of *Acinetobacter baylyi* ADP1 in natural environments, for one of our case studies: the use of *Acinetobacter baylyi* ADP1 to remediate HABS.

Additional Information:▼

**Case Study: *Acinetobacter baylyi* ADP1**

Here we present a summary of genes differentially regulated between lab and natural aquatic systems, and potential engineering approaches to enhance survival and persistence, of *Acinetobacter baylyi* ADP1 in natural environments, for one of our case studies: the use of *Acinetobacter baylyi* ADP1 to remediate HABS.

---

#### ***Acinetobacter baylyi* ADP1 suggested to up tune in field:**

1. Osmoprotection

ADP1 uses BCCT transporters (e.g., ACIAD3460) for glycine betaine; it has osmo-dependent and osmo-independent choline transporters; GB uptake improves high-salt survival.

2. Choline→GB & regulation

betIBA and two BetT-like routes (osmo-independent BetT1 vs. osmo-dependent BetT2) described; wiring them to salinity/choline sensors matches physiology.

3. Aromatic DOM catabolism

Native β-ketoadipate island (ben/cat/pca; pobA; BenM/CatM/PcaU) is a hallmark of ADP1 and is routinely up when aromatic substrates are present.

4. Fe acquisition

In Acinetobacter spp., iron uptake relies on TonB–ExbB–ExbD and numerous TBDRs; A. baumannii uses acinetobactin, while ADP1 is generally viewed as a non-pathogenic environmental strain that can pirate exogenous siderophores—so emphasize TBDRs/Feo and Fur-logic rather than siderophore biosynthesis. (Inference drawn from genus-level reviews + ADP1 genome resources.)

5. ROS/UV & DNA repair

ADP1 shows ROS-linked responses under oxidative/biocide stress; pair catalases/SOD with PerR-like peroxide defenses and NER/photolyase for surface waters.

6. Outer-membrane porins

Nutrient vs. toxin influx trade-offs (OmpA/CarO/OprD-like) documented for Acinetobacter; use modest, context-dependent tuning.

7. Lab-biased storage lipids

ADP1’s wax ester pathway (atfA, acr1) is prominent under high C/N in the lab—handy for production, but usually a carbon sink in dilute waters.

8. Domestication context

Recent work catalogs lab domestication mutations in ADP1 that alter competence, carbon use, and aggregation—useful when reconciling lab vs. field behavior.

#### **Acinetobacter baylyi ADP1 Lab biased/upregulated (suggested to down tune in field)**

- High rate translation & ribosome biogenesis

Genes: rrn operons; rpl/rps; tuf/fus/tsf/inf

Field Action: Keep native control; repress in field via ppGpp; lab only induction

Bench Promoter: Inducible bench promoters

Hints: ppGpp ON in field

Guardrail: Proteome burden in oligotrophy

Why: Ribosome fraction tracks growth rate

- DNA replication & cell division

Genes: dnaA/dnaN; gyrA/B; ftsZ/ftsA/zipA/sepF

Field Action: Avoid constitutive OE; allow starvation/dormancy to suppress divisome

Bench Promoter: Inducible only

Hints: Starvation sensors gate OFF

Guardrail: Division push causes stress in dilute waters

Why: Replication/division downshift during limitation

- PTS/simple sugar shortcuts

Genes: PTS core (if present) + high flux glycolysis

Field Action: Default OFF in field; prefer high affinity ABC/SBPs

Bench Promoter: Induce with lab sugars only

Hints: Carbon limitation gates OFF

Guardrail: Inefficient at nM sugars

Why: Natural DOM is dilute/complex

- Overflow metabolism

Genes: acetate/lactate routes

Field Action: Keep OFF in field; favor respiration

Bench Promoter: Carbon/oxygen sensors

Hints: Repress when C limited

Guardrail: Low yield without benefit

Why: Overflow is lab artifact of high carbon + aeration

- Heat shock at 30 37 °C

Genes: dnaKJ; groESL; grpE; clpC/P (HrcA/CtsR)

Field Action: Keep inducible; avoid high basal at ambient water temps

Bench Promoter: Native heat shock promoters

Hints: Only when >30 37°C

Guardrail: Chronic load taxes proteome

Why: Field programs skew to cold/oxidative

**Specific Case Study: *Mycobacterium smegmatis***

Although we are using phage to remediate Mycobacteria biofilms in pipes, there is insufficient information regarding phage behavior of phage in laboratory and natural environments. Therefore we present gene analysis between page behavior in laboratory and natural environments. Here we present a summary of genes differentially regulated between lab and natural aquatic systems, and potential engineering approaches to enhance survival and persistence, of *Mycobacteria smegmatis* in natural environments, for one of our case studies: the use of mycobacteria phage to destroy mycobacterial biofilms in household plumbing. This is still relevant given that the biofilms may likely behave differently in actual aquatic (household plumbing) environments compared to laboratory settings.

Additional Information:▼

**Specific Case Study: *Mycobacterium smegmatis***

Although we are using phage to remediate Mycobacteria biofilms in pipes, there is insufficient information regarding phage behavior of phage in laboratory and natural environments. Therefore we present gene analysis between page behavior in laboratory and natural environments. Here we present a summary of genes differentially regulated between lab and natural aquatic systems, and potential engineering approaches to enhance survival and persistence, of *Mycobacteria smegmatis* in natural environments, for one of our case studies: the use of mycobacteria phage to destroy mycobacterial biofilms in household plumbing. This is still relevant given that the biofilms may likely behave differently in actual aquatic (household plumbing) environments compared to laboratory settings.

---

Below is a list of modules that are up repeatedly up in natural waters vs lab that are good targets to overexpress and modules that are typically down in natural waters which are good to reducing expression.

#### **Environment-enriched modules**

1. Iron acquisition (Fe-poor water)

Mycobactin / carboxymycobactin biosynthesis: mbt clusters (mbtI–mbtN). Action: REG by low-Fe (IdeR-off); mild OE ceiling. Why: core siderophores for Fe capture.

Exochelin pathway (M. smegmatis-specific): fxbA (+ exochelin biosynthetic/transport genes). Action: REG by low-Fe (IdeR-off). Why: additional extracellular siderophore in M. smegmatis.

Siderophore import/reduction: irtAB (inner-membrane ABC importer; reduces Fe-cMBT). Action: OE 1–2× with low-Fe promoter.

Type VII secretion for Fe uptake: ESX-3 locus (esxG/H, ecc, mycP3, etc.). Action: keep REG by Fe/Zn; avoid KO. Why: required for mycobactin-mediated iron acquisition.

Regulator: IdeR (DtxR-family). Action: keep intact; use IdeR-responsive promoters for “Fe-on” logic. Why: master Fe homeostasis; couples Fe uptake with oxidative stress defenses.

2. Outer access

MspA/MspC porins. Action: OE mspA (± mspC) to raise solute/Fe flux; use native σ^A promoter or stress-tunable. Why: major hydrophilic conduit; porin loss reduces Fe/phosphate/glucose uptake.

3. Phosphate scavenging (P-poor water/particles)

High-affinity transport: pstSCAB–phoU1/phoU2. Action: REG via the SenX3–RegX3 system; avoid constitutive OE. Why: dedicated low-P uptake; two PhoU paralogs tune signaling.

Aux systems under low P: phnDCE (phosphonate). Action: REG by low-P; pair with P starvation reporters.

4. Carbon/osmoprotection tied to the mycomembrane

Trehalose recycling/uptake: LpqY–SugABC; treS, otsA/otsB for trehalose metabolism. Action: REG by stress; consider OE of LpqY–SugABC to save ATP in envelope turnover; “field-mode” trehalose pool.

Mechanosensitive channel (osmotic dips): mscL (and MscS-like homologs). Action: keep a low-leak OE baseline. Why: emergency solute dump under hypo-osmotic shocks.

5. ROS & redox defenses (surface waters / metal stress)

Peroxides & superoxide: katG (I/II/III), sodA, ahpC/D, tpx, trx/gor. Action: basal OE of katG/sodA; put ahpC/tpx under ROS-inducible control. Why: M. smegmatis encodes multiple katG; ROS stress strongly induces this axis.

6. Hypoxia / energy limitation (stratified lakes, biofilms)

Dormancy/hypoxia regulon: dosR–dosS/dosT ± PrrAB cross-talk. Action: keep DosR circuit intact; you can drive “low-O₂ mode” cassettes from DosR targets. Why: essential for hypoxic survival; stabilizes ribosomes.

7. Envelope remodeling / permeability (nutrient-poor water)

Porins: mspA/mspC OE as above. Why: improves influx of hydrophilic solutes/Fe.

Mycolic-acid desaturation: desA1, desA2 (regulated by MadR). Action: REG to tune fluidity; modest OE for cold/low-energy regimes; avoid over-stiffening. Why: desaturation is essential; MadR links envelope stress to desA1/2.

Cyclopropanation/methyltransferases: cmaA1/2, pcaA, mmaA1/2. Action: REG/KO to adjust permeability vs robustness; (pathogenesis-linked effects mostly from Mtb, but orthologs inform tuning in M. smegmatis).

GPL biosynthesis & export: mps/tmtpC/gpl cluster, mmpL11; groEL1 assists biofilm matrix. Action: REG by surface cues; OE to increase sticking when desired. Why: GPL acetylation affects sliding/biofilm; mmpL11 exports MA-lipids needed for biofilm.

#### **Lab-enriched modules (often down in natural waters)**

1. High-rate translation & ribosome biogenesis

rrn, rpl/rps, EF/Tu (tuf), EF-G (fus). Action: make repressible in “field mode.” Why: slow growth in situ; stringent response suppresses these. (General growth-law logic; also reflected under DosR programs in mycobacteria.)

2. DNA replication/division

dnaA/dnaN, ftsZ/ftsA/zipA. Action: avoid constitutive overdrive; keep native control for low-μ. (DosR-linked dormancy reduces replication pressure.)

3. Heat-shock at 30–37 °C (classic lab temps)

dnaKJ, groEL/ES (keep but don’t force high basal expression for field mode). Why: ambient waters rarely trigger strong heat-shock; oxidative/cold responses dominate. (General)

4. Low-affinity phosphate transport

pitA/B (when present). Action: prefer PstSCAB–PhoU–RegX3 under low-P; avoid Pit-only reliance.

5. Overflow pathways (acetate/lactate) from sugar-rich shake flasks

Prefer respiration/fatty-acid utilization in field mode; don’t hard-wire pta–ackA/ldhA overflows. (General lab-bias.)

---

#### **Overall Summary:**

Mycobacterium smegmatis Field mode (environment enriched)

1. Iron acquisition (mycobactin/exochelin)

Genes: mbtI mbtN (mycobactin/cMBT); fxbA (exochelin biosynth/transport); irtAB (siderophore import/reduction); ESX 3 (esxG/H, ecc, mycP3); IdeR (regulator)

Action: REG siderophore biosynth/import by low Fe; keep ESX 3 intact; mild OE irtAB

Promoter: IdeR OFF promoters (Fe limitation); ESX 3 native promoters

Hints: Fe sensor (IdeR OFF) AND ROS LOW gate

Guardrail: Couple Fe uptake to ROS LOW to avoid Fenton overload

Why: Core Fe capture in dilute waters; ESX 3 required for mycobactin Fe use

2. Outer permeability porins

Genes: mspA, mspC

Action: Moderate OE (tunable promoter) to increase hydrophilic influx

Promoter: A like constitutive or mild inducible

Hints: Cap expression; combine with toxin sensitivity monitor

Guardrail: Higher porin levels increase antibiotic/toxin influx cap OE

Why: Porins dominate small solute entry; boosts Fe/P/glucose uptake in poor media

3. Phosphate scavenging

Genes: pstSCAB phoU1/phoU2; SenX3 RegX3 (Pho control); phnDCE (phosphonate)

Action: REG Pst via RegX3; enable phosphonate transport when Pi scarce

Promoter: PpstS/Pho responsive promoters

Hints: Use Pho activity reporter to meter AP load

Guardrail: Avoid constant Pho ON burden from secreted enzymes

Why: Low P waters select high affinity uptake and phosphonate salvage

4. Trehalose recycling & metabolism (envelope/osmoprotection)

Genes: LpqY SugABC (trehalose uptake/recycling); treS; otsA/otsB

Action: REG stress responsive; OE LpqY SugABC to recover trehalose from envelope turnover

Promoter: Native trehalose module promoters; stress responsive logic

Hints: Cap trehalose pool with ceiling or drain

Guardrail: Trehalose overaccumulation can slow growth

Why: Saves ATP and supports mycomembrane maintenance/osmoprotection

5. Mechanosensitive channel (osmotic dips)

Genes: mscL (± MscS like)

Action: Low leak baseline OE

Promoter: Weak constitutive promoter

Hints: Leakage reduces turgor if too high

Guardrail: Avoid chronic leak

Why: Prevents lysis during rapid hypo osmotic shocks

Mycobacterium smegmatis Lab biased (down tune in field)

1. High rate translation & ribosome biogenesis

Genes: rrn operons; rpl/rps; tuf/fus/tsf/inf

Action: Keep native control; repress in field via ppGpp; lab only induction

Promoter: Bench: Pami (acetamide) / Tet; Field: stringent responsive rrn promoters

Hints: ppGpp sensor gate for OFF in field

Guardrail: Avoid proteome/ATP burden in oligotrophy

Why: Ribosome fraction tracks ; low in situ growth rates

2. DNA replication & cell division

Genes: dnaA/dnaN; gyrA/B; ftsZ/ftsA/zipA/ftsQLB

Action: Avoid constitutive OE; allow starvation/dormancy to suppress divisome

Promoter: Bench inducible (Pami/Tet); field OFF via starvation cues

Hints: Prevent filamentation/stress at low nutrients

Guardrail: Constitutive push stresses cells in dilute waters

Why: Replication/division are downshifted in natural waters

3. Heat shock program (30 37 °C bias)

Genes: dnaKJ; groEL/ES; hsp; proteases (lon, ftsH, clp)

Action: Keep inducible; avoid high basal in field

Promoter: Native heat shock promoters only

Hints: Reduce basal at ambient temps

Guardrail: Chronic load taxes proteome

Why: Field conditions emphasize cold/oxidative, not strong heat shock

4. Low affinity phosphate transport

Genes: pitA/pitB (if present)

Action: De emphasize; rely on Pst/RegX3 under low P

Promoter: Pho responsive control (PpstS)

Hints: Disable in Pi poor water

Guardrail: Pit inefficient at low Pi

Why: Low P environments select high affinity Pst

5. Overflow pathways (shake flask artifact)

Genes: pta ackA; ldhA (if present); poxB like routes

Action: Keep OFF in field; favor respiration and FA utilization

Promoter: Carbon/oxygen sensors; bench only inducible

Hints: Avoid acid stress/low yield in low C waters

Guardrail: Overflow gives low yield with little benefit in situ

Why: Overflow requires high sugar + aeration; uncommon in natural waters

### Combined Species Table Laboratory vs. Environment Differential Gene Expression ---

# References

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