iGEM 2024 — Team william-and-mary

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# **Spring 2024**

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**January**

* Dry Lab

+ Reviewed past iGEM Projects and began to read primary synthetic biology literature to introduce the team to iGEM, synthetic biology and to get ideas for potential iGEM Projects.

**February 1 - February 4**

* Dry Lab

+ Continued to review past iGEM Projects and primary literature for potential iGEM projects; focused on primary literature to identify/develop projects. Discussed numerous ideas with the entire team contributing multitude of ideas.

**February 5 - February 11**

* Dry Lab

+ Began to settle on a few project ideas, particularly the underutilized potential of satellite phages. Reviewed past iGEM Projects and literature related to using satellite phage systems for control for narrowing down potential iGEM Projects.
+ Looked for available satellite systems to work with.

**February 12 - February 18**

* Dry Lab

+ Looked for available satellite systems to work with and phage metagenomics software.
+ Assigned individual project responsibilities and did our respective literature reviews.

**February 19 - February 25**

* Dry Lab

+ Using Phaster to screen for partial prophage (satellites) in strains of *Mycobacteria*.
+ Beginning to use Satellite Finder to search for satellites. Searched for other software; documented issues with existing software.

**February 26 - March 3**

* Dry Lab

+ Wet lab team has meetings about different satellite systems we could work with and applications that could make a good iGEM project. Discussed wide range of applications.
+ Discussing what our model should focus on.

**March 4 - March 10**

* Dry Lab

+ Writing literature reviews on all relevant satellite phage literature.
+ Wet lab team meets to discuss satellite systems to potentially work with.

**March 11 - March 17**

* Dry Lab

+ Continued looking into more relevant applications of satellites including biofilm destruction and creating novel satellites from pieces of DNA.

**March 18 - March 24**

* Wet Lab

+ Began lab training and preparations for the meeting with Noblis and the Hackathon.

* Outreach and IHP

+ Presented at and met with Noblis.
+ Hosted outreach event with Jamestown High School.

**March 25 - March 31**

* Dry Lab

+ Reviewed literature on satellite phage isolation and satellites available for purchase. Intensive search for what was available.

**April 1 - April 7**

* Dry Lab

+ Utilizing DePhT to identify partial prophage, potential satellites, in strains of *Mycobacteria*.
+ Started working with CarveMe, a software for formulating genome scale metabolic models.

* Outreach and IHP

+ Prepared our presentation for the 2024 Future of Biology Conference.

**April 8 - April 14**

* Wet Lab

+ Reviewed protocols ahead of summer lab session; continued training.

* Outreach and IHP

+ Prepared workshop for the Hackathon.

**April 15 - April 21**

* Wet Lab

+ Reviewed protocols ahead of the summer lab session; continued training on basic techniques.

* Outreach and IHP

+ Presented on “Introduction to Genome Scale Metabolic Modeling” at the Future of Biology 2024 Conference.
+ Hosted a presentation and workshop on machine learning and AI applications to biology for the TribeHacks William & Mary Hackathon.

**April 22 - April 28**

* Wet Lab

+ Found the P2/P4 system designed by Dr. Fa-arun to purchase.
+ Initial discussions of model colon design for hardware - began initial plans.

* Dry Lab

+ Began discussions for an alternative software tool to Satellite Finder (to become SaPhARI).

* Outreach and IHP

+ Presented at a William & Mary Biomath seminar.

**April 29 - May 5**

* Wet Lab

+ More discussions of P2/P4 system applications and Mycobacterium satellites.

* Dry Lab

+ Identified expectations for modeling and software projects.
+ Decided to move away from a GEM as part of the modeling.
+ Began coding web scraper for satellites in the NCBI database.
+ Completed safety training modules.
+ Officially divided up tasks for the project and established end goals.

**May 6 - May 12**

* Wet Lab

+ Met with Jonathan Frey to begin selecting materials for the model colon.

* Dry Lab

+ Began compiling tail fiber interaction database for *E. coli* and *Mycobacteria*.
+ Made improvements to the NCBI satellite web scraper.
+ Working on literature reviews on project sub-topics.

# **May Break 2024**

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**May 13 - May 19**

* Wet Lab

+ Drafted protocols to prepare for summer session.
+ Drafted designs for model colon at different cost levels; determined volumes and retention times for each chamber.

**May 20 - May 26**

* Wet Lab

+ Completed literature reviews on project sub-topics.
+ Developed engineering plan for the P2/P4 system.
+ Finalized materials list and began to discuss choice of inoculum for model colon.
+ Began designing soil microcosms.

* Dry Lab

+ Began to discuss using Prodigal for the initial ORF identification in SaPhARI.
+ Deciding on metagenomic softwares to use for analysis.

# **Summer 2024**

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**May 27 - June 2**

* Wet Lab

+ Continued lab training.
+ Met with Jonathan Frey to discuss mixing mechanism and cover design for model colon.
+ Began design of P4 Cosmid with RFP insert (P4-RFP) and detailed steps for assembly.

* Dry Lab

+ Utilizing Prodigal and Pyrodigal for annotation of BLAST+ outputs for SaPhARI.
+ Running Kraken2/Bracken and Metalign on metagenomic nucleotide database.
+ Working with BBMerge for metagenomic read merging.
+ Writing preliminary safety forms.

**June 3 - June 9**

* Wet Lab

+ Created and quantified transducing units.
+ Met about experimental design for screening our engineered systems *in vitro* and in the colon and soil models.
+ Finalized flow rates and feeding schedule for the model colon.
+ Decided to use a kanamycin resistance gene as the gene target proxy to evaluate P4 cosmid system.

* Dry Lab

+ Curated database to contain more information and altered NCBI web scraper to better target certain satellite families.
+ Attempting to use VirFinder for satellite identification in metagenomic data, but realized it is not suitable for large amounts of data.
+ Submitted preliminary safety forms.

**June 10 - June 16**

* Wet Lab

+ Found that the transducing units had degraded within a week exponentially.
+ Beginning to make *M. smegmatis* lysogens.
+ Switched from Raspberry Pi to Arduino to control the model colon and began pump calibration.
+ Began working with Mycobacterium satellites

* Dry Lab

+ Continued developing annotation pipeline for SaPhARI.
+ Finished compiling tail fiber interaction database for *E. coli*.
+ Using AlphaFold3 to find shared proteins between P1 and P2 tail fibers.
+ Began developing predictive and generative software for phage-host interaction.
+ Running Metalign with different cutoff scores.
+ Wrote project description.

**June 17 - June 23**

* Wet Lab

+ Created a new batch of transducing units.
+ Quantified the titer of the transducing units, which did not deteriorate.
+ Enrichment plating of soil samples.
+ First cloning of kanamycin targeting sequence into P4 cosmid, first transformation failed, second succeeded.
+ Continued making *M. smegmatis* lysogens.
+ Worked with isolation/characterization of Mycobacterium satellites.
+ Attempted assembly and transformation of P4-RFP cosmid.

* Dry Lab

+ Assembled a pipeline for SaPhARI that analyzes identified open reading frames in bacterial genomes with known satellites and performs DIAMOND BLAST.
+ Began using InSilicoSeq for satellite metagenomics.

* Outreach and IHP

+ Preparing for the BLAST program.
+ Began mentoring a local high school senior.

**June 24 - June 30**

* Wet Lab

+ Created a replicative system with the P4 transducing units.
+ Phage purification of potential satellites.
+ Completed final pump calibration for model colon.
+ Analytical work including colony PCR and digest to check for successful clones of kanamycin targeting P4 cosmid.
+ Continued making *M. smegmatis* lysogens.

* Dry Lab

+ Conducting literature review on satellite structure to refine SaPhARI pipeline.
+ Analyzing phagelet genomes for potential phage tail proteins to use for host expansion.
+ Running predictive model for phage-host interaction.
+ Finished compiling tail fiber interaction database for *Mycobacteria*.

* Outreach and IHP

+ Hosted the BLAST Camp program for two full days.

**July 1 - July 7**

* Wet Lab

+ Began attempting to resurrect the phagelets.
+ Phage purification of potential satellites.
+ Designed soil microcosms.
+ Began *in vitro* measurements of kanamycin targeting P4 cosmid.
+ Finished *in vitro* testing of *M. smegmatis* lysogens.
+ Began physical screening for satellites in *M. aichiense*.

* Dry Lab

+ Debugging generative model for phage-host interaction.
+ Strengthening training data for predictive and generative phage-host interaction models.
+ Began running MegaHIT and MetaSPAdes.

* Outreach and IHP

+ Had an IHP meeting with Dr. Fa-arun.

**July 8 - July 14**

* Wet Lab

+ Phage purification of potential satellites.
+ Began design of automated pH control system for the model colon.
+ Continued physical screening for satellites in *M. aichiense*.
+ Due to lack of fluorescence, redesigned P4-RFP.

* Dry Lab

+ Conducting literature review on PICMI satellites to compile parameters for SaPhARI.
+ Conducted tests of the SaPhARI annotation pipeline.
+ Began drafting organizational code for SaPhARI.
+ Determining which gene in phage HerbertWM to target for successful tail fiber engineering with phagelets.
+ Gained access to phage-host interaction predictive model source code and modifying model.
+ Analyzing MegaHIT outputs.

* Outreach and IHP

+ Had an IHP meeting with Dr. Otchy.
+ Had an IHP meeting with Dr. Sheila Van Cuyk and Dr. Anand Kumar.

**July 15 - July 21**

* Wet Lab

+ Continued plating the phagelets.
+ Screening for satellites in soil samples collected on campus.
+ Repeated enrichment plating with *E. coli*.
+ Planned experimental designs for model colon and soil microcosms.
+ Designed a single-chamber ‘mini’ model colon.
+ Had to restart measurement work because indicator *E. coli* strain showed weak chloramphenicol resistance independent of any plasmid.
+ Continued physical screening for satellites in *M. aichiense*.
+ Began physical screening for satellites in *M. smegmatis* lysogens.

* Dry Lab

+ Began analysis of the satellite database.
+ Finalized training data for phage-host interaction predictive model.
+ Decided to use the A2 phage, L5, for tail fiber engineering with HerbertWM.
+ Improving metagenomic pipeline code for efficiency and finalized threading.
+ We presented our project to Vice Provost of Research Dennis Manos.

**July 22 - July 28**

* Wet Lab

+ Ran experiments on the phagelets to determine their sizes and properties.
+ PCI extraction of potential satellites.
+ Began building the soil microcosms and designed rainfall mechanism.
+ Altered model colon from one five-chamber model to two separate three-chamber models.
+ Began preliminary work with pdCas9; began assembly of dCas9 Cosmid.
+ Assembled and transformed P4-RFP.

* Dry Lab

+ Began initial coding for the wiki.
+ Analyzing initial SaPhARI output.
+ Filtering contigs from soil metagenomes.

**July 29 - August 4**

* Wet Lab

+ Made transducing units with P1 tail fibers.
+ Began first run of the model colon.
+ Finished cloning of dCas9 Cosmid.
+ Evaluated growth of HL 713 (KanR *E. coli* strain for colon and soil measurement) in colon and soil-like conditions.
+ Verified P4-RFP assembly via colony PCR.
+ Reattempted *E. coli* potential satellite DNA extractions for higher purities.

* Dry Lab

+ Debugging SaPhARI code.
+ Designing chimeric tail fibers with L5 and HerbertWM.
+ Automating assembly of SRA metagenomes.

# **August Break 2024**

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**August 5 - August 11**

* Wet Lab

+ Sent *E. coli* potential satellites for sequencing.
+ Attempted transformation of *Mycobacterium aichiense* by electroporation.
+ Finished constructing soil microcosms and rainfall mechanism.
+ Maintenance of the model colon.
+ Assembling chimeric tail fibers.

* Dry Lab

+ Automating assembly of SRA metagenomes.
+ Worked on wiki and write-ups.
+ Writing promotional video script.

**August 12 - August 18**

* Wet Lab

+ Maintenance and measurement of the model colon.
+ Assembling chimeric tail fibers.

* Outreach and IHP

+ Had an IHP meeting with Shanthi Parkar from Seed Health.

* Dry Lab

+ Worked on wiki and write-ups.
+ Continued running metagenomes and improving SaPhARI

# **Fall 2024**

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**August 26 - September 1**

* Wet Lab

+ Maintenance and measurement of model colon.
+ Making graphics and animations for promotional video.
+ Began assembly of dCas9-w cosmid & inserting kanamycin targeting spacer insert into dCas9 cosmid.

* Dry Lab

+ Debugging SaPhARI code.
+ Merged and assembled sequencing returns for phagelets and putative *E. coli* satellite phage.
+ Creating graphics and editing project promotional video.

* Outreach and IHP

+ Had an IHP meeting with Dr. Katherine Jennings from Noblis.
+ Had an IHP meeting with Dr. Negron from Noblis.

**September 2 - September 8**

* Wet Lab

+ Created transducing units and grew out phagelets for gut and soil inoculation.
+ Maintenance and measurement of model colon.
+ Taking soil samples and bacterial plating from soil microcosms.
+ Preliminary work for CI dependent terminator characterization.
+ Made P4-RFP transducing units.

* Dry Lab

+ Running SaPhARI on metagenomic samples.
+ Continuing assembly of phagelet backbone with chimeric tail fiber inserts.
+ Finalizing metagenomic assemblies for phagelet and putative *E. coli* satellite sequencing data.
+ Submitted final safety forms.
+ Submitted project promotional video.

**September 9 - September 15**

* Wet Lab

+ Taking samples from soil microcosms and bacterial plating.
+ Inoculated soil microcosms 9-12 with phagelets and 13-16 with phage.
+ Began phage plating of samples from soil microcosms 9-16 with *M. aichiense*
+ Maintenance and measurement of model colon.
+ Designing engineering plan for inserting the chimeric tail fibers into the pKW08 plasmid.

* Dry Lab

+ Running SaPhARI on metagenomic samples.
+ Reassembling the putative *E. coli* satellite phage reads.

* Outreach and IHP

+ Had a booth at the William & Mary SCI-FRI community event.
+ Had an IHP meeting with Dr. Birgit Scharf.
+ Presented at a Willaim & Mary Biomath seminar.
+ Finalized Gut Busters board game.

**September 16 - September 22**

* Wet Lab

+ Taking samples from soil microcosms and bacterial plating.
+ Continuing to phage plate of samples from soil microcosms 9-16 with *M. aichiense*
+ Maintenance and measurement of model colon.
+ Inoculated soil microcosms with P4-RFP transducing units.
+ Attempting to insert chimeric tail fibers into pKW08.

* Dry Lab

+ Running SaPhARI on metagenomic samples.
+ Writing up wiki pages.

* Outreach and IHP

+ Had an IHP meeting with Dr. Fa-arun.
+ Had an IHP meeting with Dr. Bradley Abrahamson and Dr. Daniel Negrón from Noblis.

**September 23 - September 29**

* Wet Lab

+ Used TEM to attempt to image the two systems of phage satellites.
+ Maintenance and measurement of model colon.
+ Taking samples from soil microcosms and bacterial plating.
+ Continuing to phage plate samples from soil microcosms 9-16 with *M. aichiense*
+ Attempting to insert chimeric tail fibers into pKW08.

* Dry Lab

+ Running SaPhARI on metagenomic samples.
+ Writing up wiki pages.

**September 30 - October 2**

* Wet Lab

+ Taking samples from soil microcosms and bacterial plating.
+ Continuing to phage plate samples from soil microcosms 9-16 with *M. aichiense*
+ Maintenance and measurement of model colon.

* Dry Lab

+ Running SaPhARI on metagenomic samples.
+ Completing wiki.

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# Summary of Engineering Success

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Throughout the 2024 iGEM season, we pursued engineering in every aspect of our project in **discovery** of novel satellites, both bioinformatically and physically, and in our three **applications**: gene delivery using the P4 cosmid, using novel mycobacteria satellites to overcome prophage immunity, and host range expansion using tail fiber engineering with novel mycobacteriophage satellites. In the process of achieving engineering success in each of these facets, we utilized mathematical modeling as a framework to inform the engineering process at every step, and the engineering informed the parameters to refine the modeling.

---

#### For detailed information about our experiments and results, please visit our [experiments page](https://2024.igem.wiki/william-and-mary/experiments) and [results page](https://2024.igem.wiki/william-and-mary/results).

---

# Bioinformatic Discovery of Novel Satellites

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#### For more information about our software, please visit our [software page](https://2024.igem.wiki/william-and-mary/software).

---

#### **Final SaPhARI Pipeline:**

The SaPhARI pipeline was developed to provide a comprehensive, automated solution for the identification and classification of satellite families in bacteriophages. Leveraging a range of powerful bioinformatics tools, including Prodigal, DIAMOND BLAST, Aragorn, Barrnap, tRNAscan-SE, and custom-built Python and shell scripts, the pipeline offers researchers a high degree of flexibility and precision in annotating and clustering protein sequences. The final version allows users to seamlessly customize database searches, apply functional filters, and group proteins based on their target families, streamlining the discovery and characterization of satellite prophages.

![Sample Image](https://static.igem.wiki/teams/5179/graphics/software/final-pipeline-schematic.svg)

#### **Early Development and Tool Integration:**

The initial phase of SaPhARI's development focused on integrating key tools for accurate protein annotation and functional grouping. We aimed to build a robust pipeline comparable to PHASTEST, capable of clustering proteins and offering detailed functional insights. The workflow was initially designed to process nucleotide sequences, filter out prophage regions, and pass the data through a sequence of specialized tools. The ultimate goal was to deliver high-quality gene annotations alongside functional protein clusters to facilitate downstream analysis.

---

##### The core tools integrated in this early version included:

* Prodigal: A gene prediction tool used for identifying protein-coding regions from nucleotide sequences.
* DEPhT: Initially incorporated for prophage detection but later removed from the pipeline to streamline processing.
* DIAMOND BLAST: A high-speed protein alignment tool used for comparing sequences against large databases.
* BLASTn: Facilitates nucleotide sequence alignments for DNA region comparisons.
* Aragorn, tRNAscan-SE, and Barrnap: These specialized tools were incorporated for the detection of non-coding RNA elements such as tmRNA, tRNA, and rRNA genes, ensuring thorough gene annotation.

To manage the command-line interface (CLI) tools and optimize workflow efficiency, we utilized Nextflow as the core pipeline orchestration tool, enabling seamless integration and scalability.

---

#### **Design-Build-Test Cycle: From Initial to Final Pipeline**

The development of the SaPhARI pipeline was guided by an iterative Design-Build-Test cycle. Each component of the pipeline was tested and improved to achieve a robust system for bacteriophage satellite discovery. The following sections outline the design rationale, construction, and iterative testing of the pipeline's key tools and processes.

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### Click here! Prodigal Integration for Open Reading Frame (ORF) Prediction

**Design**: Prodigal was chosen for its accuracy in predicting open reading frames (ORFs), making it a critical first step in identifying potential satellite prophage regions. The objective was to predict ORFs from nucleotide FASTA files of experimentally verified prophages, such as EcCIEDL933 and EcCICFT073 in our software experiments, while capturing essential annotations needed for downstream analysis.

**Build**: Prodigal was configured to accept nucleotide sequences and output either nucleotide or amino acid ORFs with comprehensive notations, including ORF number, strand direction, start and stop positions, and GC content. Additionally, metadata such as partial gene status and start codon types were retained to provide full context for each ORF.

**Test**: Early tests revealed that Prodigal’s raw output could not be directly utilized by downstream tools like BLAST+ due to inconsistent formatting in the headers. The outputs lacked essential notations making it impossible to trace back ORFs to their genomic locations.

**Learn**: We introduced a Nextflow process (formateHeaders) to clean Prodigal’s output, specifically reformatting headers to preserve ORF annotations. This ensured that all downstream tools could correctly parse ORF data and maintain accurate linkages between ORF predictions and genomic coordinates.

---

### Click here! BLAST Integration for Protein Sequence Alignment

**Design**: BLASTn and DIAMOND BLAST were selected for their high-speed sequence alignment capabilities against curated databases (e.g., PHASTEST). The goal was to enable comprehensive functional annotation of predicted ORFs by identifying homologous proteins across viral and bacterial datasets, critical for satellite phage analysis.

**Build**: We configured BLAST+ with a customizable output format (outfmt 6), presenting key alignment details in a tabular structure. This format was selected to streamline downstream analysis, providing essential information such as alignment length, bit scores, e-values, and taxonomic classification.

**Test**: During initial runs, BLAST produced large, complex outputs that were difficult to parse for biologically meaningful insights. The volume of data slowed downstream processes and overwhelmed manual interpretation efforts, particularly when dealing with large datasets.

**Learn**: To optimize the output, we introduced user-configurable filters, allowing the contents of static parameters—e-value, percent identity, percent coverage, and the number of matches—to be adjusted. This enhancement allowed users to focus on the top hits and tailor the alignment depth, ensuring that only the most relevant homologs were included in the results. This reduced processing time and made the outputs more manageable and meaningful for further analysis.

---

### Click here! Functional Annotation from BLAST Output

**Design**: Functional annotation of proteins is a critical step in satellite prophage identification. Our objective was to automate the assignment of protein functions based on BLAST results, while minimizing the inclusion of hypothetical proteins, which contextually contribute limited biological insight.

**Build**: A custom Python script (extract.py) was developed to parse BLAST outputs and assign functions to each ORF. The script works by selecting the majority function from the top BLAST hits, excluding hypothetical proteins where possible. If a consensus function is found, it is assigned to the ORF. If all matches are hypothetical, the ORF is flagged accordingly.

**Test**: Initial tests revealed that the script struggled with ambiguous BLAST results, especially when low-quality or irrelevant matches dominated the output. In cases where no clear majority function could be determined, the script either failed to assign a function or assigned inconsistent annotations.

**Learn**: To improve accuracy, we introduced a scoring system that ranks protein functions based on the order of top BLAST hits. If no clear majority function exists, the highest-scoring match is selected. In cases where all hits are hypothetical, the function is explicitly labeled as hypothetical, distinguishing these proteins from poorly characterized ones. This refinement significantly improved the consistency and completeness of functional annotations, making the results more reliable for downstream analysis.

---

### Click here! Custom Database for Improved Protein Detection

**Design**: In designing our pipeline, we aimed to leverage existing public databases for protein detection, expecting that PHASTEST’s focus on viral and bacterial proteins would efficiently identify relevant homologs, while the comprehensive scope of NCBI’s Non-Redundant Protein Database(NR) would ensure broader protein coverage. Our goal was to enable accurate detection of satellite prophage proteins without prematurely building a custom database.

**Build**: We implemented PHASTEST and NR as our primary databases for protein detection, integrating them into our pipeline for sequence alignment and functional annotation using BLASTn and DIAMOND BLAST. PHASTEST was chosen for its specificity, while NR provided extensive coverage of non-redundant proteins.

**Test**: During the initial tests with satellites EcCIEDL933 and EcCICFT073, we evaluated PHASTEST and NR separately. PHASTEST demonstrated faster alignment but failed to capture key satellite proteins such as AlpA, leading to detection gaps. In contrast, NR provided better protein coverage but proved to be computationally expensive, significantly slowing down the pipeline due to its large size.

**Learn**: To address these issues, we developed a custom database incorporating the non-redundant bacterial, viral, and archaeal protein sequences from RefSeq, as well as a curated set of satellite-specific proteins from PLE’s. Additionally, we refined our analysis by shifting the focus to benchmarking with DIAMOND BLAST, which provides significantly faster alignment speeds while maintaining high accuracy. BLASTn is still available as an option in SaPhARI, but we prioritize DIAMOND for its efficiency. This shift, combined with our custom database, resulted in reduced processing times and improved detection accuracy for key satellite proteins, ultimately enhancing both performance and coverage.

---

### Click here! DEPhT Prophage Detection Integration

**Design**: DEPhT, a prophage detection tool, was integrated into the pipeline to screen genomes for potential prophage elements as a pre-screening step before satellite detection to prevent false positive detection. The objective was to assess DEPhT's effectiveness in detecting wide host-range prophages.

**Build**: A process was developed to input genome sequences into DEPhT, enabling the collection of predictions regarding prophage regions. These predictions were then integrated into the broader SaPhARI workflow for subsequent satellite analysis.

**Test**: Although DEPhT yielded reliable results for certain prophages, challenges arose in managing and training the tool on diverse bacterial strains, leading to inconsistent performance.

**Learn**: Given its performance limitations, DEPhT was ultimately removed from the pipeline in favor of direct satellite protein clustering utilizing BLAST and Prodigal outputs. This decision was made to allow researchers greater flexibility in identifying satellite prophages without the constraints imposed by a pre-screening process in the pipeline, which primarily focused on excluding prophage sequences rather than aiding in their identification.

---

### Click here! RNA Element Detection with Aragorn, Barrnap, and tRNAscan-SE

**Design**: Non-coding RNA elements, including tRNAs, rRNAs, and tmRNAs, play crucial roles in prophage biology and serve as important indicators of satellite elements. The objective was to integrate specialized tools—Aragorn for tmRNA detection, Barrnap for rRNA, and tRNAscan-SE for highly accurate tRNA prediction—to effectively annotate these non-coding elements.

**Build**: Separate Nextflow processes were developed for Aragorn, Barrnap, and tRNAscan-SE, allowing adjustable parameters tailored to specific detection needs. Score thresholds were established for tRNAscan-SE, while e-value, rejection, and length cutoffs were defined for Barrnap.

**Test**: Initial tests demonstrated successful detection of non-coding RNA elements, with parameters adjustable by users to optimize detection based on specific research needs. This flexibility allowed for enhanced accuracy in identifying non-coding elements within the genomic context.

---

### Click here! Development of the Satellite Class for Flexible Protein Grouping

**Design**: To classify satellite prophages based on genomic protein clusters, we developed a Python class titled Satellite. This class allows for flexible grouping of proteins, configurable aliases (e.g., treating 'major capsid' and 'major head' as synonyms), and customizable thresholds for the number of proteins required to elicit a match. Protein searching is performed through string-based matching of the protein titles from the extract.py outfile, which we opted for as it is easier for users to work with. By treating the protein titles as strings, the system can easily identify clusters based on similar or synonymous protein names. This approach also helps account for convergent evolution in proteins, where similar functions may arise independently, allowing for more flexible classification. The goal was to create a user-friendly framework for classifying satellites into distinct families.

**Build**: The class was built to manage unordered protein sets, with customization through aliases. Key parameters, which include minimum protein thresholds for family assignment, exclusion of forbidden proteins, inter-protein distance limits, and maximum satellite length, were incorporated to boost flexibility.

**Test**: Initial implementations encountered issues where multiple sets containing the same proteins were detected, leading to inconsistent outputs based solely on variations in size.

**Learn**: To resolve this, the class was restructured to enhance alias matching and ensure the identification of the largest unique protein cluster. Extensive unit tests were implemented to validate the logic for grouping proteins and ensure accurate family classification across various input conditions. To further verify our approach, we used nucleotide FASTA genomes from individual satellite families in our novel database to test whether SaPhARI could successfully identify satellites. Additionally, we conducted negative control tests to ensure SaPhARI did not falsely identify satellites in non-satellite genomes, further confirming its accuracy.

**Performance Evaluation**: The find\_it() method in the Satellite Python class is designed to identify and extract specific protein regions from an annotated SaPhARI file based on parameters defined for a satellite family. The method processes the file by reading all lines and extracting protein names along with their respective positions. A nested function checks for forbidden proteins while iterating through the lines to collect potential protein regions within the specified maximum length. The algorithm keeps track of distinct proteins found and ensures uniqueness of regions by avoiding subsets of previously identified regions. Additionally, it incorporates functionality to include flanking genes around each identified region, thus providing a comprehensive output. The overall time complexity of this approach is O(n²) in the worst case, attributed to the nested loops iterating through the lines; however, practical performance may vary depending on the content of the input genome.

---

### Click here! Pipeline Deployment and Customization

**Design**: Simplicity in deployment was a top priority, aiming to allow users to run the entire SaPhARI pipeline with minimal configuration, while still providing options for customization.

**Build**: A Python script was developed as the primary interface for SaPhARI. This script captures user input for essential parameters, such as database selection, e-value thresholds, and output location, and passes them to shell scripts for execution either locally or on an HPC system. The Python script automates the setup and execution of the pipeline, enabling users to customize parameters without needing to modify the underlying shell scripts directly.

**Test**: Early feedback from users indicated that while the pipeline functioned as intended, new users often found the setup process overwhelming, particularly those unfamiliar with Python environments or manual configuration.

**Learn**: To enhance usability, we introduced a configuration template that walks users through setting key parameters and choosing workflows. The Python script was also updated with pre-configured defaults for common use cases, allowing novice users to run the pipeline with minimal setup. Advanced users still have the flexibility to fine-tune parameters directly within the template as needed.

---

### Click here! Output Cleaning

**Design**: Initially, the pipeline was designed to output only the identified regions containing core proteins, with the goal of providing users with a concise summary of detected satellite families.

**Build**: The output structure focused on providing clear results for each detected satellite region. Users could define core proteins and set thresholds for family classification. The output was generated in .txt format, summarizing the identified regions containing the specified core proteins.

**Test**: During testing, it became evident that focusing solely on the identified regions provided insufficient genomic context. Users found it challenging to interpret how the detected satellite regions fit into the broader genomic landscape, especially when analyzing large genomes with complex prophage structures.

**Learn**: To address this, we enhanced the Satellite class output by including the five flanking genes both upstream and downstream of each identified region. This provided valuable genomic context around the core protein regions, making it easier to understand the surrounding genomic structure. The refined output remains in .txt format, but now includes detailed context to offer a more complete view of each detected satellite region.

---

### **Analysis****:**

The iterative Design-Build-Test cycle led to the evolution of SaPhARI into a powerful and flexible tool for the discovery and classification of satellite prophages. By integrating tools such as Prodigal and BLAST, and refining the pipeline through continuous feedback and testing, SaPhARI has become a robust system that balances precision and performance. Each step, from open reading frame prediction to functional annotation and satellite classification, was meticulously designed to address the unique challenges of large-scale genomic analysis.

The incorporation of a custom database, dynamic filtering, and enhanced context through flanking gene inclusion ensures that researchers are provided with biologically relevant insights in a manageable format. With its streamlined deployment and high degree of customization, SaPhARI is now well-equipped to handle complex datasets, offering valuable genomic context and improving the accuracy of satellite prophage detection.

Ultimately, the final version of the SaPhARI pipeline stands as a versatile, scalable, and user-friendly platform, empowering researchers to uncover novel satellite prophage elements and deepen their understanding of bacteriophage satellite biology in diverse bacterial hosts and metagenomics.

# Applications

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![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/engineering-cycle-diagram.svg)

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The traditional *(1) Design - (2) Build - (3) Test - (4) Learn* model that investigators iGEM teams and SynBio researchers typically follow involves testing engineered circuits under laboratory conditions, where bacteria are provided with an environment conducive to growth. While extremely valuable and an absolutely necessary first step, the engineering cycle should also progress to another level of testing for constructs that will eventually be used in real-world environments. This level entails testing in and understanding how these circuits behave in environments that mimic those in which the devices will be deployed. For our experiments this entailed testing in complex and less predictable environments such as the colon and soil microbiomes, which is a vital component to expanding SynBio beyond the lab setting. Due to this, we have decided to utilize two cycles with *(1) Design - (2) Build - (3) Test in vitro - (4) Learn - (5) Design - (6) Build - (7) Test in situ - (8) Learn* cycle for our 2024 project. Under this two-cycle approach, researchers progress beyond the engineering process at the *in vitro* level to the *in situ*  (real world mimics) level – for our experiments a model colon and soil microcosm systems once their constructs are shown to work in the lab setting.

# Gene Delivery

![Summary](https://static.igem.wiki/teams/5179/graphics/engineering/p4engineeringchart.png)

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### Click here! Original P4 Cosmid

The P4 cosmid system was originally designed by Dr. Fa-arun at the University of Edinburgh. The original purpose of this system was to induce CRISPR cas9 mediated cell death for disease-causing strains of bacteria such as *E. coli* O157:H7 and *Shigella flexneri*. Aspects of the P4 packaging mechanism were used to create transducing units with a CRISPR cassette. In order to create a versatile tool that could be used for both *E. coli* and *Shigella*, Dr. Fa-arun also created chimeric tail fibers to expand the host range of these transducing units. The CRISPR cas9 used in the original study targeted shiga toxins in the bacterial genomes, causing a double stranded break in the genome and thus killing the bacteria.

**Design**: In order to make the P2 lysogen package the P4 cosmid with a wide selection of tail fibers, the genes encoding for the tail fibers were removed from the genome. The P2 wild type tail fibers were compatible with the indicator strain *E. coli* EMG2-K12. Therefore, the plasmid with the P2 tail fibers is necessary for the packaging of the P4 cosmid. Also, the system was designed in such a way that the P2 lysogen would only package and lyse the P4 cosmid in the presence of a sugar called L-rhamnose for higher levels of system control.  The cas9 on this cosmid lacks a crRNA target, so the transducing units would have no killing effect. However, they do contain a chloramphenicol selection, so the indicator strain would convey this antibiotic selection after being infected with transducing units.

**Build**: Initially, the bacteria carrying the P2 prophage was made chemically competent and then transformed with both the P4 cosmid with the non-targeting CRISPR and the P2 wild type tail fibers. Then that cell was forced to lyse with the presence of L-rhamnose and the lysate was collected.

**Test**: The indicator strain, *E. coli* EMG2-K12, was infected with the P4 transducing units and selected for chloramphenicol resistance to estimate the titer, quantifying the transducing units.

**Learn**: During the initial trial, the lysate degraded within a week period, decreasing the titer 10-fold per day. However, after another replicate, the titer remained stable after a two week period at about 3.4x109, which is higher than that of a standard phage. That supports the argument that satellite phage have a higher titer than other phage and thus a higher efficiency.

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### Click here! RFP Cosmid

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image4-new.png)

The P4 cosmid system’s engineerable host range was initially very intriguing to us, we reasoned that it would be much easier for future users of this system to assay the potency of transducing agents with modified host range if the cosmid contained a reporter device to allow researchers a positive visual cue for transduction. We elected to replace the Cas9 cassette in the cosmid with a reporter device, rather than simply inserting one, to conform to the size limitations of the P4 cosmid system. After an unsatisfactory first version we re-examined the promoter we used in the RFP cosmid and made changes so the sequence ahead of the promoter conformed to the consensus promoter UP-element (BBa\_J428201) in the parts registry. Ultimately, we were able to successfully transduce the second version of the RFP cosmid produced this way both in vitro, and when deployed in soil microcosms. RFP Cosmid transductants show potent fluorescence visible with the naked eye after in vitro transduction, and after transduction in the biologically stressful, heterogeneous, and dynamic conditions of our soil environments.

**Design**: After deciding that a version of the cosmid with a reporter device would be desirable, we evaluated alternatives for which reporter we would use. We decided on the fluorescent protein mRFP1 as it has been shown to be a reliable reporter and was readily available. The P4 cosmid, as with all transducing vectors, has a limited capacity for delivery of genetic circuits and simply inserting a fluorescent protein into the cosmid would have caused it to exceed the size of the wild type P4 genome, which we were advised against during our first IHP meeting with Dr. Fa-Arun. Ultimately we decided on a design wherein we replaced the Cas9 cassette on the original cosmid with a mRFP reporter device.

**Build**: To assemble this version of the cosmid we amplified fragments of the P4 cosmid and the 2022 Interlab construct, Test Device 2 (TD2), via PCR. We then joined these fragments via a gibson assembly and transformed into DH5 alpha cells.

**Test**: The first version of the RFP cosmid did not show a level of fluorescence visible to the naked eye when transformed into DH5 alpha. This vexed us, as colonies transformed with Test Device 2 showed visible fluorescence under the same conditions. We verified the sequence of the first version of the RFP cosmid and performed a number of small experiments to rule out possible confounding factors which could produce the drastic difference in fluorescence we observed between the two constructs.

**Learn**: After ruling out confounding factors, analysis of our sequence-confirmed construct showed us that our assembly excluded a small part of the consensus UP element ahead of the J23101 constitutive promoter. Though UP elements are not generally required for E. coli promoters to successfully initiate transcription, in this case exclusion of the complete UP element abolished transcription from J23101 to a level where fluorescence could not be observed with the naked eye, nor in a plate reader when compared to untransformed E. coli DH5 alpha.

**Redesign**: We redesigned our construct to include the consensus UP element ahead of the J23101 constitutive promoter.

**Build**: To assemble this version of the cosmid, we amplified fragments of the P4 cosmid and TD2 via PCR. We then joined these fragments via a Gibson assembly and transformed it into DH5 alpha cells. The transformed DH5 alpha cells had visible fluorescence after incubation for 48 hours. To ensure that fluorescence was not due to solely the TD2 plasmid without the P4 cosmid backbone, we confirmed our results via sequencing of each fluorescent colony. The results came back with 100% alignment to our predicted engineered product. Following sequencing, we transformed both the cosmid and the chimeric P2 tail fiber plasmid, created by Dr. Fa-Arun, into P2 lysogen cells. This resulted in fluorescent colonies, visible to the naked eye. We grew up, concentrated, and resuspended these co-transformed cells in media supplemented with L-Rhamnose. Induction with L-Rhamnose starts a positive feedback loop of late gene expression in the co-transformed cells resulting in cell lysis and the production of viable transducing agents. We then filtered that lysate with 0.22nm filters to produce transducing agent filtrate suitable for use in our experiments.

**Test**: We performed spot titer assays of our transducing units to measure the potency of these vectors in E. coli HL 713, the indicator strain used in our soil microcosms. This resulted in an average titer of 8.37x106, significantly lower than the 3.4x109 titer of the original P4 cosmid. This assay also served as a test of function, as visible fluorescence could be seen in the infected HL 713. We additionally tested the P4/RFP cosmid system in our soil environments. Ten samples were collected from distinct colonies from soil microcosm plating on LB with kanamycin and chloramphenicol.

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/eng-rfp-table.png)

A1: Negative control - uninfected E. coli HL 713, A2: Positive control - E. coli HL 713 infected with RFP, A3: Negative control - water. A4: Sample 1, A5: Sample 2, A6: Sample 3, A7: Sample 4, A8: Sample 5, A9: Sample 6, A10: Sample 7, A11: Sample 8, A12: Sample 9, B1-11: Empty wells, B12: Sample 10.

The above table is a red fluorescence protein measurement for the 10 colonies from soil samples. Sample 6 showed a significantly higher measurement than the negative control suggesting that this sample was fluorescing.

**Learn**: We learned that engineering the P4 cosmid can be useful as a reporter system for the presence of bacteria with a compatible host range. We predict that the lower titer is due to the size difference between the original cosmid (11544 bp) and our engineered system (7742 bp), resulting in less efficient packaging.

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### Click here! Replicative System for P4 Cosmid

An argument for using phage satellites instead of standard phages is that they tend to have a higher titer, and thus would be more efficient in real world environments. They also provide a certain level of safety, since they are unable to replicate without a helper phage. However, this could prove to be indesirable in certain applications.

**Design**: The P4 cosmid system relies on the P2 prophage to package the transducing units, but the tail fiber genes have been removed from this prophage. Therefore, in order to produce transducing units that are able to infect the target strain of bacteria, the bacteria with the P2 prophage must be transformed with the tail fibers that accompany P2. Therefore, infecting the bacteria with the P2 lysogen that was transformed with the tail fiber plasmid and forcing that cell to lyse would create more transducing units. The system was designed to only lyse in the presence of a sugar called L-rhamnose, which increases levels of control when using this system since no further transducing units can be packaged if L-rhamnose is no longer supplemented to the system. Therefore, if the effects of the P4 transducing units do not reach the desired extent, the system could be further grown in vitro by adding a combination of the bacteria with the P2 prophage and the transformed tail fibers as well as L-rhamnose.

**Build**: The P2 lysogenic bacteria and the P2 wild type tail fiber plasmid was designed to accompany the P4 cosmid. The bacteria with the P2 lysogen was made chemically competent in order to be transformed with the plasmid. The tail fiber plasmid created for this system has several variations: one with wild type P2 and two more chimeric tail fibers that are able to vary the host range of the transducing units. The tail fiber compatible with our indicator strain was the wild type P2. Therefore, in order to create a replicative system, we transformed competent P2 lysogens with P2 wild type tail fibers and infected that with transducing units.

**Test**: The titer of the transducing units was quantified, and infected the transformed cell. Then that cell was lysed and the lysate was quantified.

**Learn**: The result was that the titer increased exponentially, meaning that the replicative system is efficient even after only one round of infection. The ability with satellites to be supplemented with bacteria for repackaging could be a useful tool when employing them in real world environments.

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### Click here! Kanamycin Resistance Targeting P4 Cosmid

In order to put the P4 cosmid to the test in the environments it was designed for, we needed a suitable experimental design that would allow us to easily assay its sequence specific killing effect in our simulated colon model. The Kanamycin Resistance Targeting P4 cosmid was born out of this need. It uses a gRNA target for Neomycin Phosphotransferace to induce killing only in an E. coli variant engineered to have a genome integrated kanamycin resistance gene (HL 713). The point of using KanR as a target because we are specifically interested in killing bacteria with this gene, but rather KanR works as a useful proxy for an arbitrary toxin gene or virulence factor of clinical or scientific interest. This choice enhances the biosafety of our system as it only induces a killing effect in the specially engineered bacterial target used in our experiment and not wild type bacteria. The kanamycin resistance gene in HL 713 also doubles as a screening marker which allows us to differentiate our target bacteria from others in our simulated colon or soil environments.

We chose a target site within neomycin phosphotransferase with the help of CHOPCHOP, a web tool for identifying suitable Cas9 target sites in a given DNA sequence (Labun et al., 2019). After choosing a target site we designed a crRNA spacer insert with flanking BsaI cut sites for insertion into the P4 cosmid via a golden gate reaction. We cloned the kanamycin targeting spacer insert into the P4 cosmid and measured its killing effect and transduction efficiency on HL 713 and a standard lab strain of E. coli in vitro. These experiments showed us that the Kanamycin Targeting Cosmid was effective in causing sequence specific killing in HL 713. Additional measurement confirmed that the observed killing effect on HL 713 was indeed resulting from the Cosmid directly and not simply a product of knocking out KanR and screening on Kanamycin. These results were promising, so we pushed the P4 cosmid system even further by scaling it up and testing it in a simulated human colon. Though smaller in magnitude than our in vitro experiments, we observed a significant killing effect by the KanR targeting cosmid in our simulated colon, indicating this system indeed shows promise in its intended role of sequence specific killing of pathogenic bacteria in vivo. This experiment was also the first to use the P4 cosmid system which targets a specifically engineered bacteria. Its success in this role indicated to us that it, or a similar system, could one day be used to help remediate a bioengineered bacteria which has breached containment while leaving wild-type bacteria unharmed.

**Design**: Choosing a gene target and target site was not trivial. We needed a gene which we could be certain is present in one indicator strain, and not another, as choosing a target present in all or most strains of E. coli would prevent us from accurately measuring transducing unit titer, as any killing effect would artificially deflate the measured titer of a functioning cosmid. We eventually decided to use an engineered strain, E. coli HL 713, and choose its genome integrated Kanamycin Resistance gene as our target. This would decrease the likelihood of off-target killing effects when measuring transducing agent titer, and allow us to screen for our target bacteria when used in our simulated colon and soil environments.

**Build**: We deviated from standard protocols for cloning gRNAs into this CRISPR cassette so that we didn’t need to use a polynucleotide kinase, which we did not have on hand. Instead we designed and ordered synthetic DNA oligos, which, when annealed, could be cut into the proper insert by BsaI during a golden gate reaction. After cloning the new gRNA into the cosmid via a golden gate reaction we transformed the new cosmid into E. coli, harvested it with a miniprep, and confirmed its sequence with nanopore sequencing.

We then co-transformed the KanR targeting cosmid with a plasmid containing the wild type P2 tail fiber into the P2 lysogen created by Dr. Fa-Arun, E. coli EMG C5545 ∆cosσε ∆HG. We grew up, concentrated, and resuspended these co-transformed cells in media supplemented with L-Rhamnose. Induction with L-Rhamnose starts a positive feedback loop of late gene expression in the co-transformed cells resulting in cell lysis and the production of viable transducing agents. We then filtered that lysate with 0.22nm filters to produce transducing agent filtrate suitable for use in our experiments. We repeated this protocol as needed for each experiment.

**Test**: Experiments in vitro showed a significant and large killing effect, when compared with the “empty” P4 cosmid, specifically on HL 713, even in the absence of kanamycin. When evaluated in our synthetic colon, treatment with KanR Cosmid filtrate significantly lowered the population of HL 713 in the colon when compared to pretreatment measurements.

**Learn**: The P4 cosmid performed well in its role of inducing sequence specific killing in vitro and in our simulated colon. During our experiments we determined the cosmid did not have a 100 percent efficient killing effect, although it was very close to it, yielding a small number of HL 713 transductants which were still viable after transduction, albeit with diminished fitness. We reasoned, after consulting with Dr. Fa-Arun, the creator of the original P4 cosmid, this was due to flaws in the transducing agent production phase which naturally occurred at a low level. Alternatively, some of these colonies could have survived due to a silent mutation in the protospacer adjacent motif of their KanR target site. Either way, this result led us to wonder if the P4 cosmid had hidden potential to perform genetic manipulation beyond sequence-specific killing, and iterate on it further.

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### Click here! dCas9 Cosmid

The dCas9 P4 Cosmid was the first construct born out of our curiosity to see if the P4 cosmid could be used in a wider variety of genetic manipulations, while still retaining its desirable characteristics. Through replacing its Cas9 cassette with a catalytically dead variant (dCas9), we have altered the P4s cosmid functionally from inducing sequence-specific killing of its transductants to causing targeted silencing of gene expression.

**Design**: The dCas9 variant of the P4 cosmid employs a Cas9 variant with 2 amino acid substitutions which renders it catalytically dead and unable to cleave its target site (Bikard, 2013). Its targeted DNA binding activity is retained however, allowing it to silence genes by blocking transcription. We elected to use a dCas9 cassette as similar as possible to the one in the original cosmid, both to make cloning easier and because the cosmid was already at an ideal size for packing into P4 transducing agents and we didn’t want to alter it too dramatically and risk making an inferior transducing vector.

**Build**: Rather than using site-directed mutagenesis we elected to use fragments of an existing dCas9 containing plasmid. We PCR amplified the essential region of the P4 cosmid and left it in two homologous arms at the beginning and end of the CRISPR cassette. We also amplified part of our new dCas9 gene, including the 2 amino acid substituted regions. We joined these fragments via Gibson Assembly and transformed them into DH5 alpha before harvesting and sequence confirming the cosmid. We then co-transformed the dCas9 cosmid into EMG C5545 ∆cosσε ∆HG with a plasmid containing the wild type P2 tail fiber and followed the same protocol used for the other cosmid variants to produce transducing agents with the modified cosmid.

**Test**: We reasoned that we could use the same crRNA spacer insert used to create the KanR targeting P4 cosmid to characterize the silencing ability of the dCas9 P4 cosmid. If the dCas9 cosmid was working as intended, HL 713 transduced with the P4 cosmid should exhibit reduced resistance to kanamycin, as dCas9 would inhibit transcription elongation past its binding site, this reducing expression of neomycin phosphotransferase.

We inserted the neomycin phosphotransferase crRNA into the dCas9 cosmid via a IIS reaction with BsaI, transformed into E. coli DH5-alpha, harvested the construct and confirmed its sequence with nanopore sequencing. We then followed the same procedure as with previous experiments to produce transducing agent filtrate containing the dCas9 P4 cosmid (with added KanR targeting crRNA), as well as original P4 cosmid filtrate as a negative control.

Treatment with dCas9 Cosmid (with added KanR targeting crRNA) transducing agent filtrate produced fewer kanamycin resistant cosmid transductants compared to a control group treated with original P4 cosmid transducing agent filtrate. Overall the experiment was not sufficiently powered to draw any definitive conclusions about the magnitude of the dCas9 cosmid’s silencing effect, but is intriguing enough to potentially motivate further characterization of this part.

**Learn**: For future characterization of this part we aim to use crRNAs which specify a target sequence in the promoter region of a gene target for silencing rather than using an in-frame target.

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### Click here! dCas9-⍵ Cosmid

The rationale for the dCas9-⍵ Cosmid (pronounced “dCas9 Omega”) followed much the same reasoning for the dCas9 cosmid. It functions to induce targeted upregulation via promoting assembly of RNA polymerase near its target site upstream of a promoter region. It accomplishes this by coding for a dCas9 variant which is fused to the Omega subunit of RNA polymerase on its C terminus (Bikard, 2013).

**Design**: We elected to use a C terminal RNAP omega fusion rather than an N terminal fusion as it was more expedient to create via gibson assembly. We also took care to try and minimize the size difference between the dCas9-w cosmid and original dCas9 cosmid so as to not interfere with P4’s packaging mechanism, potentially risking sabotaging the titer of transducing agents produced with this cosmid. In the end, the dCas9-w cosmid ended up being slightly closer to the wild type P4 genome than the original cosmid.

**Build**: In order to create the Cas9-RNAP omega cosmid we PCR amplified the backbone of the P4 cosmid as well as a fragment from an RNAP omega fused dCas9 variant from pWJ66 (AddGene Plasmid #46570). We assembled each of the fragments together via a gibson assembly.

**Test**: Following the same procedure as we did with the dCas9 cosmid, we transformed the assembly into E. coli DH5 alpha, harvested the assembled cosmid, and confirmed its sequence via nanopore sequencing.

**Learn**: Though we used a C terminal fused RNAP omega subunit for the dCas9-omega cosmid an N terminal fused dCas9 may also be useful for upregulating certain targets if they do not respond well to the C terminal fused variant. The success of this assembly also inspired us to consider other, smaller Cas proteins as potentially interesting payloads for future uses of the P4 cosmid, as the dCas9-omega cosmid is nearing what we believe to be the maximum size for a P4 cosmid variant.

# Overcoming Prophage Immunity

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### Click here! Engineering Protocols For Reviving Mycobacteria Satellite Phage

“Phagelets,” phage satellites in a strain of bacteria called *Mycobacterium aichiense*, were discovered years ago at William & Mary. *M. aichiense* is a strain of bacteria that is known to carry strong immunity to other phages, and these phage satellites were the only phage that could ever infect this strain to our knowledge. Due to their fragile nature, they degraded and were thought to be lost for six years. Our team “resurrected” these “phagelets” and grew them out to a high titer, and then employed them in the soil microcosm to fully test their effects.

**Design**: In order to search for at least one viable mycobacteria satellite in degraded lysate, large quantities need to be screened. We used a procedure similar to screening for satellites in the soil, in order to find lysate that is not degraded.

**Build/Test**: We plated several milliliters of 20 different degraded lysates at each time.

**Learn**: Using our protocols, our team managed to grow out four “phagelets,” two of which were not sequenced or characterized before. What we found was one was in fact not a phage satellite and was a standard phage that shared large similarities with the prophage in *M. aichiense* called HerbertWM. While the satellites worked to excise HerbertWM, there was no evidence of HerbertWM when the phage lysed the cell.

Originally, these phage satellites showed one or two plaques after the initial plating of large quantities of old lysate, but were grown out to a titer of 107 to 108. The non-satellite phage reached a titer of 109.

Some of the soil microcosms were inoculated with these “phagelets” while others contained the phage that was previously thought to be a satellite for comparison. In these microcosms, we tested for the mycobacteria satellite phages’ and the phage’s killing effect on *M. aichiense*, and continued to test the presence of phage as well.

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### Click here! Environmental Sensitivity of Mycobacteria Satellites

“Phagelets” have historically been extremely fragile and degraded quickly. After achieving the desired titer, we began plating them less frequently. After taking several days between collecting the lysate and plating, they stopped growing.

**Design**: The procedure we have developed for reviving phage satellites was used to continue to grow them at a slower rate due to an ideal titer being achieved. However, they stopped forming plaques.

**Build/Test**: After plating as described above and the plates forming perfect lawns and no plaques in the incubator at 37℃, they were left to sit untouched at room temperature for 2 days on the lab bench.

**Learn**: After 2 days being left at room temperature, webbed lysis plaques formed on each plate except for the negative control. The reason for this could be temperature sensitivity, although previously they formed plaques in the incubator at 37ºC. The negative control with no “phagelets” added to the *M. aichiense* did not form any plaques. What we learned is that these phage satellites may still be viable but not under any conditions and assuming degradation is not accurate.

# Host Range Expansion

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### Click here! Chimeric Tail Proteins

We have engineered chimeric tail proteins that we believe may expand the host range of HerbertWM and its “phagelet” to include *M. smeg*. To test these chimeric tail genes, we proposed two potential methods for introduction to HerbertWM. Rather than attempting to directly engineer the genome of the helper phage, we want to allow the new tail proteins to be expressed nearby during the packing of the Herbert-”phagelet” systems, allowing them to potentially pick up the new protein during the last stages of assembly of the tail.  The first component of development for this approach was a comprehensive literature review.

Gene Product 6 (GP6) has been identified among an array of minor tail proteins in the A2 phage L5 (Hatfull, 1993). Via BLAST analysis, minor tail genes with high sequence similarity and genomic location to those listed in the paper exist in virtually all A2 phage. HerbertWM’s corresponding minor tail protein coding genes show similarly high homology, with the exception of gene product 3, which corresponds to GP6 in L5. Gp3 shows remarkably low sequence similarity to that of all other A2 phages, which are also all known to infect *M. smegmatis*. GP6 has also been identified as potentially involved in surface binding of *M. smegmatis;* GP6 immobilized on gold surfaces showed specificity in capture of exclusively *M. smeg* cells as compared to other *Mycobacterium* species after incubation (Arutyunov, 2014), suggesting GP6 as a strong candidate for *M. smeg*-specific detection.

The inability of HerbertWM to infect *Mycobacterium smegmatis* is a defining characteristic of the lysogenic phage, which inspired us to look further into this specific tail protein. Below, we describe the approach and analysis that informed our designs.

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image3.png)

**Design**: We began our design process via sequence analysis. In the lab, we isolated a novel bacteriophage distinct from HerbertWM, with the same observed host range of exclusively *M. aichiense*. Sequencing analysis of gene product 3 in this phage showed a 92% identity to that of HerbertWM, further suggesting that this gene plays a role in determination of infection range. Using the standard phage analysis software DNAMaster, we identified the ribosome binding sites associated with corresponding genes Gp3 and Gp6 in HerbertWM and L5, respectively. We also identified the region of the HerbertWM Gp3 gene with the highest sequence homology to the corresponding protein in other A2 phages, which we ended up switching in when designing the chimeric tail proteins.

**Build**: We constructed these genes, pictured above, in the lab using standard assembly methods. First, we amplified specific regions of both the HerbertWM Gp3 and L5 Gp6 phage genes via PCR. We then added ‘overhangs’ to the ends of each fragment using PCR, creating overlaps that were then used for final gibson assembly of the complete constructs.

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### Click here! Constructs for Testing of Tail Proteins

**Design**: Our first approach to testing these novel proteins involved insertion of the desired gene directly into the “phagelet” genome. This meant ligating the insert into a linearized region of the “phagelet” genome, in hopes that the protein will be expressed and available during packaging.

**Build/Test/Learn**: We amplified the entirety of the “phagelet” genome via PCR, then performed overlap PCR to extend the ends of the “phagelet” fragments, forming homologous arms with the tail fiber inserts. We attempted gibson assembly repeatedly using a huge variety of variations to standard protocols including incubation time and vector:insert ratio. We performed a diagnostic PCR each time to amplify any potential product, which showed no success in the assembly.

From this, we learned that “phagelet” genomes cannot be directly modified via standard assembly methods due to a variety of unique characteristics, including unusually high GC content and repeat-heavy sequences.

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image9.png)

**Redesign**: Our next approach involved cloning of the tail protein inserts into the pKW08 plasmid. This is a construct designed for reliable gene expression *in vitro*. The pre-designed pKW08 plasmid includes a promoter, terminator, and hygromycin resistance gene. Our intention is to ultimately use this plasmid alongside traditional “phagelet” infection of *M. aichiense*.

**Build**: We amplified and added restriction cut sites to the ends of the plasmid backbone and tail protein inserts. This allows for a double digest followed by ligation of cohesive sticky ends and transformation into competent *E. coli* for replication, which we are currently in the process of performing in the lab. To test this construct, we plan to transform the plasmid into *M. aichiense* and confirm its presence via hygromycin resistance. We will then use our protocols for phage plating of the transformed cells with “phagelet” lysate on hygromycin.

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### Click here! Phage-Bacteria Interaction Prediction Model

While the new and exciting field of machine-learning has drawn interest in novel approaches to biological problems, there have only been a few publications that explore the specific application of phage “host range” (the selection of bacteria that a given bacteriophage is able to infect), working off training sets pulled from public databases en masse. We have compiled a novel, nearly exhaustive ongoing database of confirmed interaction data and complete genomes for phages infecting both *E. coli* strains and mycobacteria species. We use this data to train and evaluate current machine-learning models from the literature, identifying and addressing their weaknesses and building a basis for future improvement to this approach.

**Design**: Rather than build a new program completely, we utilized various open-source code from the literature (Ataee, 2020; Leite, 2018), in which evaluation of models and parameters for processing phage-bacteria interaction data has been done through mass-scale optimization.

The model processes the provided phage and bacteria genomes through one-hot encoding (similar to binary) using representative vectors for individual base pairs, allowing the interaction category and genomic information to pass through a series of complex neural networks to train the model.

**Build**: The key to having a good model is a good training set - here we go through some of the key considerations we made when building our datasets that set our model apart from others like it.

The following are limitations set by previous models in the literature, and how we addressed them.

##### **1) Generation of “putative non-interacting pairs” (Ataee, 2020) due to the absence of information on negative interactions between phage-bacteria pairs in public databases.**

As can be seen in the database we have compiled, many papers testing the host range of novel phages often found significant numbers of positive interactions with bacteria outside of their species of origin, and even outside their genus. This means the accuracy of this ‘negative’ interaction dataset is likely questionable, and not fit to be used in training data as none of it is experimentally confirmed.

To address this, we performed a near-exhaustive manual literature review of *experimentally confirmed* phage-bacteria interaction data, including both positive and negative interactions; approximately 52% of the documented interactions in our dataset are negative.

##### **2) Database consisting of interactions between phage and solely the host they were isolated from.**

In real-life applications like phage therapy, hundreds of phage may end up being screened for effective killing of a treatment-resistant host (LeMieux, 2020). A dataset with the limitation of only the host bacteria of initial isolation is unlikely to capture less ‘obvious’ interactions between phage and completely different hosts with distinct serotypes.

Many of the interactions documented in our database are taken from papers characterizing novel phage, where host range evaluations spanned a wide range of bacterial genus and species.

##### **3) Over-representation of certain bacterial host species (Leite, 2018).**

While the authors chose to oversample the less represented hosts to prevent the model from being reliant on the presence or absence of *M. smeg* in a pair, this resulted in 86% of the public dataset being represented by only 149 interactions from 94 bacteria.

We compiled our training set of *Mycobacteria* interactions in a similar way, while compiling our *E. coli* interaction database with a more evenly spread range of pairs. The difference in model performance we observed indicates the importance of representative and robust training sets.

##### **4) Choosing to build a database consisting of phage with less diversity (i.e. Pseudomonas phage) than of that seen in other families.**

Phage are incredibly diverse, and failing to capture this within a training set also likely fails to capture various interactions that may lay ground for new discoveries. A more predictable family of phage will have more predictable interactions in any model.

Our *E. coli* interaction database captures a much more diverse span of pairs, with 160+ strains and serotypes and over 130 unique phages, including more well-characterized phages like T7 or P2 as well as novel genetic elements from recent literature.

**Test/Learn**: We trained the model on a random section of our dataset each time, then tested its ability to predict whether a phage-bacterium pair can interact with the remaining data. The accuracy score each time was recorded and averaged, which we found to be around 68% with p < 0.001. For a broader picture: out of the top 20 highest probability interactions chosen by the model each time, 14 were confirmed interactions.

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image11.png)

While indicating some ability to pick up on patterns in available interactions and genomes, the fact that the over 10*31* phage on earth still remain largely unexplored is the main reason for the current prediction accuracy; with more robust training sets that will come with future work on phage, this approach will increase in relevance and useability.

This serves as inspiration for further characterization and discovery of phage systems; physical isolation, discovery of phage-like elements, and characterization of host range are a core pillars of our project, and we show in our handbook that this is a very accessible method of contribution to the field.

Additionally, our prediction model and database were both used in selecting helper phage gene sources for the engineering of chimeric tail proteins in our host expansion experiments.

# Model Colon and Soil Microcosms

---

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#### For more information about our hardware and modeling, please visit our [hardware page](https://2024.igem.wiki/william-and-mary/hardware) and [modeling page](https://2024.igem.wiki/william-and-mary/model).

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### Click here! Model Colon

We designed and built a model colon to test the efficacy of the non-replicative kanamycin targeting CRISPR Cas9 system and the non-replicative non-targeting CRISPR system in a realistic model of the human colon environment. We designed this to approximate the fluid dynamics, temperature, and motion of a human colon. We also tested it with a probiotic to simulate a multimicrobial system.

**Design**:  We designed a model colon based on previous models of the human gastrointestinal tract, particularly the SHIME (Simulator of Human Intestinal Microbial Ecosystem). Our original design consisted of a five chamber system for the stomach, small intestine, ascending colon, transverse colon, and descending colon. Each chamber is an enteral feeding bag, a flexible and cost effective alternative to the glass containers typically used in model colons. We wanted a flexible chamber to better represent the method of mixing in the human digestive system. Each chamber would be pumped to the next segment of the colon by peristaltic pumps and have ports to allow for sampling. This entire system of bags is in a heated fish tank to allow for motion of the bags while efficiently maintaining a temperature of 37ºC. We designed the model colon to allow for nitrogen purging to keep each chamber anaerobic to better represent the conditions of the human colon.

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image7.jpg)

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image10.jpg)

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**Build**:  We assembled a preliminary system of the pumps and enteral feeding bags.

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image16.jpg)

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image17.jpg)

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image14.jpg)

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image22.jpg)

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**Test**: We tested and calibrated the pumps to the appropriate flow rates.

**Build**: We finished building the model colon. This includes coating the enteral feeding bags in mucin agar to increase the adhesion and colonization to the chambers of the colon model. This system was heated in the fish tank.

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image20.jpg)

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image21.jpg)

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image24.jpg)

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image15.jpg)

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**Design**: In order to test more systems in the model colon, we re-designed the model colon to have two three chamber models of the ascending colon, transverse colon, and descending colon as well as a “mini colon” which is a single chamber model.

**Build**: We built the additional colon chambers and added three additional pumps and one additional enteral feeding bag to accommodate the changes to the design.

**Test**: We inoculated the colon with the indicator strain HL 713 for the E. coli systems. We took measurements daily to monitor the growth and persistence of the indicator strain.

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![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image13.jpg)

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**Learn**: This initial inoculation was unsuccessful, yielding no indicator strain surviving in the model colon. We concluded that although E. coli HL 713 can in theory survive anaerobically, it was not able to colonize or persist in the model colon with regular nitrogen purges.

**Design/Build**: We stopped regular nitrogen purges and allowed small amounts of oxygen into the model colon. This should allow E. coli to consume the oxygen and the colon will result in a semi-anaerobic environment.

**Test**: We re-inoculated the model colon with HL 713 allowing small amounts of oxygen. We continued daily measurements to determine the growth and persistence of the indicator strain. The E. coli indicator strain successfully colonized and persisted in the model colon. However, the mucin agar stopped adhering to the walls of the enteral feeding bags resulting in clogging of the tubing.

**Learn**: The small amounts of oxygen are necessary for the model colon to be colonized. Mucin did not perform as expected and will not be used in future experiments with the model colon.

**Design/Build**: We restarted the model colon without mucin agar and re-inoculated with E. coli HL 713 still including a small amount of oxygen to promote colonization and persistence.

**Test**: We took regular measurements of the population of E. coli HL 713 to determine colonization and persistence. The model colon was later inoculated with transducing units for the kanamycin targeting Cas9 CRISPR system and empty CRISPR system to test the efficacy of these systems in the colon environment.

**Learn**: The E. coli HL 713 colonized successfully without the inclusion of mucin agar. The kanamycin targeting Cas9 CRISPR system and empty CRISPR system showed an effect. The kanamycin targeting Cas9 CRISPR system exhibited a significant killing effect. The empty CRISPR system conveyed chloramphenicol resistance. However, this was only measurable in the ascending colon chamber suggesting that the non-replicative empty CRISPR system did not have enough of an effect size to disperse throughout the entire colon.

**Test**: We re-inoculated the model colon with E. coli HL 713 and the Seed Health DS-01 Daily Probiotic to test our phage systems in a multimicrobial environment.

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### Click here! Soil Microcosms

The soil microcosms were designed to demonstrate horizontal movement of the bacteria and satellite phages, allow for watering, and included plants to increase the survival of E. coli and make the soil microcosms more representative of potential agricultural applications.

**Design**: The soil microcosms are designed according to three key features of our experimental design: measurement of horizontal movement of bacteria and satellite phages, rainfall simulation, and inclusion of plants. Therefore we built our soil microcosms to be roughly 12” x 18” x 6” with layers of soil, mesh, and gravel to allow appropriate drainage when watering. We also planted bush bean and lettuce seeds, both fast growing plants, to provide a sufficient root system which is important for the growth of E. coli in the soil. This also is a better representation of potential agricultural applications. The initial design of the rainfall apparatus used the same aluminum drip pans which the plastic soil containers were placed in.

**Build**: We assembled the microcosms according to our design. The rainfall apparatus was made by poking 1/16” holes in every indent on the bottom of the drip pans. A stand for the watering pan was made out of scrap wood to rest on the containers’ edges.

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![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image12.jpg)

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image23.jpg)

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**Test**: The drip pans were ineffective at evenly spacing water droplets across the microcosms, as the water would weigh down the middle of the pan and most of the ‘rain’ would fall into the center of the microcosms. The stand also did not provide enough height above the microcosms, which was important because we planned on planting bean and lettuce seeds in each microcosm, which grow fast and tall. The size of the holes also was also too small which caused water to pool in the pans without dropping out.

**Learn**: The initial rainfall design had several issues which informed the next design iteration. The pans were not strong enough to support the weight of the water, the small holes didn’t allow water to drop out, and the rainmaker needed to be higher above the microcosms.

**Design**: The next rainmaker was designed with stronger materials, and included a stand which would rest on the same ground as the microcosm rather than the microcosm container.

**Build**: A new rainmaker was built from a sheet of ¼” thick plexiglass. The sheet was cut to create a small container, with a waterproof glue sealing the pieces together. The holes of 5/64” diameter were drilled into the bottom, and a stand was made from spare gantry system pieces to sufficiently elevate the rainmaker above the microcosm.

**Test**: The new rainmaker was much sturdier and stable; the larger holes allowed water to drip smoothly out of the apparatus to mimic rainfall. We inoculated microcosms 1-8 with HL 713, the indicator strain for our P2 P4 cosmid systems and inoculated microcosms 9-16 with *M. aichiense*. Microcosms 1-4 were inoculated with the non-targeting CRISPR system, 5-8 were inoculated with the RFP system, 9-12 were inoculated with a mycobacteria phage, and 13-16 were inoculated with the novel mycobacteria satellite phage.

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![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image8.jpg)

---

---

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### Click here! PDE Model

We wrote several PDE models of each of our systems for the model colon and soil microcosms to predict results and inform experimental design. We used preliminary results to inform experimental design. We adjusted the model to preliminary experimental data to improve accuracy.

**Design**: We have written several partial differential equations (PDEs) which model the growth, fluid flow, and the infection dynamics of satellites. For both the soil microcosms and model colon experiments, we wrote a PDE model for each of our systems both replicative and non-replicative kanamycin targeting CRISPR system, non-targeting CRISPR system, and RFP.

**Build**: These are based on previous modeling of satellite systems, fluid dynamics, and population dynamics. This includes reaction-diffusion dynamics:

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![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image1.png)

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This models the flow rate of water and diffusion. These models use Monod growth kinetics which limits the growth by a growth limiting substrate, in this case carbon.

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![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image2.png)

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**Test**: An important early result that we found from a preliminary model was that given the titer and efficiency of the transducing units, the non-replicative systems would not have as significant of an effect as the replicative system. Therefore, we tested both the non-replicative and replicative systems to confirm this result and show a more significant effect.

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![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/image19.png)

Figure. Early result of the PDE model for the non-replicative kanamycin targeting CRISPR system in the model colon.

**Learn**: Results from the preliminary sun of the soil models were compared to preliminary results from the soil microcosm and informed new parameters to improve the accuracy of the model.

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# **References**

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# **Summary of Experiments**

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For our 2024 project, we conducted both Dry Lab and Wet Lab experiments centered around two main goals: (1) Developing innovative tools for **discovery** of novel satellite phage for use in SynBio, and (2) Employing satellite phage in three **applications** to demonstrate their potential for SynBio.

Our experiments for the **discovery** of novel satellite phage are split into two categories: (1a) bioinformatic discovery and (1b) physical discovery.

* In discovering novel satellites bioinformatically

+ We conducted runs of our novel satellite phage identification software, SaPhARI.
+ We trained a phage-host interaction predictive model with an extensive phage-host interaction database.

* In discovering novel satellites physically we screened for satellites in *E. coli*, *M. smegmatis*, and *M. aichiense*.

Our experiments for **applications** of Satellite Phage are separated into three categories: (2a) gene delivery, (2b) overcoming prophage immunity, and (2c) host expansion. In these categories, we conducted experiments at the *in vitro* and *in situ* levels (that is, using realistic environments) in our model colon and soil microcosms.

---

#### At the *in vitro* level our experiments include:

* Testing gene delivery:

+ We created and quantified transducing units with the original P2/P4 system from Dr. Fa-arun.
+ We created and quantified transducing units with a kanamycin-targeting CRISPR insert and tested for killing effect.
+ We created and quantified transducing units with a dCas9 insert and tested for silencing effect.
+ We created and quantified transducing units with a red fluorescent protein insert and tested for fluorescent output.

* Testing ability to overcome prophage immunity:

+ We resurrected the mycobacteria satellite phage from six-year-old phage lysate and characterized them.

* Testing phage host range expansion:

+ We plated phage lysate to test the infection range of phages HerbertWM and a non-satellite mycobacteriophage.
+ We characterized HerbertWM and mycobacteria satellite phage packaging mechanisms.
+ We assembled chimeric tail fibers that can theoretically expand host range of HerbertWM via the mycobacteria satellite phage.

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#### At the *in situ* level our experiments include:

* Testing gene delivery:

+ We inoculated the model colon with the CRISPR transducing units and sampled the affluent to test for a killing effect.
+ We inoculated soil microcosms 5-8 with the RFP transducing units and conducted bacterial plating to test for fluorescent output.

* Testing ability to overcome prophage immunity:

+ We inoculated soil microcosms with the mycobacteria satellite phage and conducted phage plating to test for a killing effect and persistence over time.

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#### A detailed description of experiments is provided below.

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#### For full protocols for our experiments [CLICK HERE](https://static.igem.wiki/teams/5179/pdfs/protocols/igem-2024-protocols-2.pdf)

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#### For detailed information about our results, please visit our [RESULTS PAGE](https://2024.igem.wiki/william-and-mary/results)

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# **Discovery**

## Bioinformatic Discovery

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#### **Detailed relevant protocols:** Searching for Phage Satellites in Metagenomic Data

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### **Experiment: Searching through Metagenomic Samples for Satellite Phage Protein Clusters using SaPhARI (CLICK HERE)**

**Rationale:** Metagenome samples exist from a wide variety of environments, ranging from wastewater, to soil, to rat guts. Many studies seek to leverage metagenomic sequencing as a means to understand microbial dynamics within natural environments, thus providing a seemingly endless amount of genetic information for researchers to utilize. (Meyer et al., 2022) Mining the wide breadth of these data sets is crucial to our project, as our mission is to categorize and locate the extremely diverse and ubiquitous families of satellite phage

**Procedure:** To search for phage satellites in a wide variety of metagenomes , we built a custom pipeline using open source software that downloads, assembles, and sorts metagenomic data for subsequent annotation and analysis using our custom software SaPhARI (Satellite Phage Algorithmic Recognition and Interpretation)

**Results:** We assembled, annotated, and analyzed 7,659 metagenomic contigs from 37 respective metagenomes derived from the NCBI Sequence Read Archive (SRA) . We tested each family within the satellite class (cfPICI, PICI, PICMI, Phagelet, P4like, and PLE) across five different stringency levels. These levels ranged from requiring all proteins defined in each model to be present in order to classify a protein cluster as a satellite, to requiring only half of the proteins defined in each model to be present. Through this analysis, we have identified 87 putative satellite protein clusters: 61 from the PICMI model, 21 from the cfPICI model , 2 from the P4like model, 2 from the PICI model, and 1 from the Phagelet model. Each satellite sequence is available on our GitLab in .csv format for viewing and further analysis, containing detailed information about sample origin and identified proteins within the sample.

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## Physical Discovery

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Phage satellites, though incredibly abundant at an estimated 3.2 x 1026, are widely uncharacterized (Eppley et al., 2022). Furthermore, comprehensive protocols to screen for phage satellites have not been developed. We developed procedures to screen for phage satellites using bacterial lysogens and applied these in Mycobacterium and E. coli. To discover phage satellites, we used bacterial lysogens, to screen for satellites that can exploit the prophage’s packaging machinery. The general steps to discover a phage satellite include 1) obtaining/creating a lysogen, 2) collecting a soil sample, 3) enrichment plating, 4) purifying the potential satellite by plaque assays and 5) Phage DNA extraction by PCI.

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments1.png)

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#### **Detailed relevant protocols:** Sample Collection and Enrichment Plating, Purification by Plaque Assay, Phage DNA Extraction Using PCI, Making a Lysogen

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### **Experiment: Screened for E. coli satellite phages  (CLICK HERE)**

**Rationale:** In addition to the overall abundance of undiscovered phage satellites, there are no known phage satellites that parasitize lambda. We screened for phage satellites that could infect *E. coli* strain EMG2 K-12, a lambda lysogen, to search for phage satellites that therefore may hijack lambda’s packaging machinery.

**Procedure:** We collected 4 samples of soil and water mixtures on William and Mary’s campus. Taking these samples, we made 4 enrichments of the samples and liquid culture of E. coli EMG2 K-12, an E. coli strain that is a lambda lysogen, in LB media. After 48 hours of incubation, these enrichments were filtered and plated. Two of the enrichment plates showed plaques.

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments2.png)

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments3.png)

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Enrichment plates that show plaques and indicate potential phage satellites. Each plate shows a distinct plaque morphology.

We performed one round of plaque purification of each potential phage satellite by picking a single plaque from each enrichment plate and phage plating dilutions with E. coli EMG2 K-12. The plates from round 1 of purification were flooded with phage buffer and lysate was collected after leaving overnight for phage elution in buffer.

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments4.png)

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments5.png)

Phage purification plates that were flooded to collect lysate. Purification of the potential satellite from soil sample 1 resulted in plates that did not show clear plaques. When held up to light, turbid plaques could be seen on this purification plaque.

We then performed multiple DNA extractions to extract phage satellite DNA using the phenol:chloroform:isoamyl alcohol method (Göller et al., 2020; Spilsberg et al., 2020). DNA purities were consistently low despite repeating the chloroform:isoamyl alcohol step in an attempt to wash away phenol contamination.

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|  |  |  |  |
| --- | --- | --- | --- |
| DNA sample | Concentration | A240/A260 purity | A280/A260 purity |
| 1A | 163.8 | 1.69 | 0.58 |
| 1B | 588 | 1.69 | 0.61 |
| 4A | 196.1 | 1.73 | 0.87 |
| 4B | 579.9 | 1.68 | 0.79 |

---

DNA samples were sent to Oklahoma Medical Research Foundation for Illumina sequencing and the phage genomes were assembled using the Shovill genome assembler pipeline.

**Results:**  Two novel E. coli bacteriophages were discovered, a tailed phage and a filamentous phage, though these bacteriophages do not appear to be satellite phages. Both soil samples contained the filamentous phage, which shows genetic similarity with Ff filamentous phages based on its proteins. Soil sample 4 contained the novel tailed bacteriophage, which consistently lysed E. coli EMG2 K-12, as shown by clear plaques at each round of purification.

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### **Experiment: Created lysogens in Mycobacterium smegmatis (CLICK HERE)**

**Rationale:** Traditionally, the golden rule for phage screening is to never use a lysogen to screen on; our project, however, our project developed a protocol to create lysogens to identify phage satellites. Once a phage is isolated, trying to create a stable lysogen is the second necessary step in satellite phage discovery. If a lysogen can be successfully created, we are able to proceed with screening for satellites in the future.

**Procedure:** We created a bacterial lawn using *M. smegmatis* on top agar. Through the course of the summer, 60 lysates from William and Mary’s freshman phage lab. We performed spot test, incubated plates at 37° C for 24-48 hours, checked for mesas after incubation.

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments6.jpg)

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments7.png)

Initial spot tests of lysates. Left shows an example of a plate negative for mesas, characterized by its see through appearance where spotted. The right image shows an example of a plate positive for mesas. There is a clearing where the lysate solution was spotted with a cloudy spot within it, indicating some resistance to the phage.

If mesas were present, we streaked cells from the mesa onto a new plate and incubated at 37°C for 2-4 days. Then, we took colonies from the streaked mesa plates and performed a patch assay. After 1-4 days, we checked for halos present surrounding the patch, indicating phage release.

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments8.jpg)

We streaked out positive patch assay colonies for three rounds to purify. Following purification, we inoculated colonies in liquid culture and performed a final patch assay using the same previous protocol.Finally, we tested the superinfection (homoimmunity) abilities of the putative lysogen. incubated for 24 – 48 hours at 37° and assessed the homoimmunity by plate comparison.

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments9.jpg)

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments10.jpg)

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A good lysogen has little to no homoimmunity, such as the lysogen candidate above. The left image shows the control (lysate dilutions spotted on M. smegmatis). The right image shows the homoimmunity assay (lysate dilutions spotted on the putative lysogens). This had no plaques, showing perfect homoimmunity for this lysogen.

**Results:** Out of the 55 tested lysates, five passed all of the lysogeny tests and resulted in a stable lysogen, ready to be used for satellite screening. These phages included: JollyBabz, StubbySaigey, KingChadwick, DocMcStuffins, and SucetteDeLMort in M. smegmatis. We used these to screen for satellites and tentatively identified one satellite phage in M. smegmatis.

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### **Experiment: Transmission electron microscope (TEM) on putative phage satellites (CLICK HERE)**

**Rationale:** To further understand the qualities of satellite phage, we used transmission electron microscopy to image mycobacteria phage satellites.

**Procedure:**  We prepared copper grids with lysate from each of the mycobacteria phage satellites, both putative and confirmed, and the novel E. coli phages that we discovered, and sent the grids to a nearby facility with access to a TEM.

**Results:**  TEM images at 50nm and 200nm (in order: ChrisB, EllieK, Putative mycobacteria satellite, E.coli filamentous phage).

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/chris-b.webp)

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/ellie-k.webp)

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/putative-satellite.webp)

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/filamentous.webp)

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### **Experiment: Discovered novel satellites in Mycobacterium aichiense (CLICK HERE)**

**Rationale:***Mycobacterium aichiense* is a strain of bacteria that conveys strong immunity to other phages due to the presence of a prophage called HerbertWM. In other words, no other known phage has been able to infect this strain of bacteria because of a temperate phage that resides in this bacteria. However, a group of high school students found a series of phage satellites that were able to infect *M. aichiense* and trigger the lysis of the prophage HerbertWM, killing the cell. Finding phage satellites that are able to infect a strain of bacteria that conveys phage immunity has many applications in phage therapy, and thus developing a procedure to screen for phage satellites in bacteria with prophage immunity could have many uses.

**Procedure:** We collected soil from the environment and enriched it with *M. aichiense.* We then filtered it to get rid of the soil debris and the bacteria, leaving only media and phages. We added *M. aichiense* at a 1:1 ratio to the "phagelets" and then plated with top agar. After letting it grow for 24-48 hours until visible plaques, we purify plaques by taking a plug from a plaque and resuspending in phage buffer. Afterwards, we filtered and plated the full resuspended plug with *M. aichiense* on top agar again until webbed lysis is reached. Then the plate was flooded and we filtered the lysate and plated again with dilutions until the ideal titer was reached.

**Results:**  The procedure developed by our lab was given to a freshman program within the school that works with SEA-Phages to discover new phage. The students in this lab had found a putative phage satellite, but we are working on growing it to a higher titer and getting it sequenced.

The protocol was also given to an advanced phage lab in the university, and screening for phage satellites with a prophage led a member of that lab to find a novel phage that infects *M. aichiense*. However, characterizing this novel phage has proved difficult thus far, and after several attempts as sequencing it seems that a large part of its genome aligns with a pathogenic bacteria called Cupriavidus gilardii, which is in not only a different species but also a different phyla of bacteria than *M. aichiense*.

We also resurrected several satellites from six years ago that were thought to be degraded and lost, some of which have not been sequenced or characterized. One of those we found to be another phage satellite. The other, more interestingly, is a standard phage that bypasses homo- and hetero-immunity in *M. aichiense* and lyses the bacteria without excising the prophage HerbertWM. Interestingly enough, it seems to have large parts of the genome of HerbertWM but is lytic while HerbertWM is temperate. This novel phage was used in the soil microcosm as a way to compare the efficiency of phage satellites with standard phage, while the newly characterized satellite along with the other resurrected phage satellites were used to test the killing effect of *M. aichiense* in the soil microcosms.

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# **Applications**

## Sequence Specific Population Level Control

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#### **Detailed relevant protocols:** Creating Transducing Units, Quantifying Cas9 Killing Effect, Sampling Bacterial Plating and Phage Plating of Soil Microcosms, Running the Model Colon

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### **Experiment: Created and quantified titer of P4 transducing units (CLICK HERE)**

**Rationale**: The P4 cosmid system was originally designed by Dr. Fa-arun in University of Edinburgh (Fa-arun et al., 2023). The original purpose of this system was for CRISPR cas9 mediated cell death for disease-causing strains of bacteria such as *E. coli* O157:H7 and *Shigella flexneri*. Aspects of the P4 packaging mechanism were used to create transducing units with a CRISPR cassette. In order to create a versatile tool that could be used for both *E. coli* and *Shigella*, Dr. Fa-arun also created chimeric tail fibers to expand the host range of these transducing units. The CRISPR cas9 used in the original study targeted shiga toxins in the bacterial genomes, causing a double stranded break in the genome and thus killing the bacteria.

In order to make the P2 lysogen package the P4 cosmid with a wide selection of tail fibers, the genes encoding for the tail fibers were removed from the genome. The P2 wild type tail fibers were compatible with the indicator strain *E. coli* EMG2-K12. Therefore, the plasmid with the P2 tail fibers is necessary for the packaging of the P4 cosmid. Also, the system was designed in such a way that the P2 lysogen would only package and lyse the P4 cosmid.

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments11.png)

**Procedure:** First, we made chemically competent cell out of the P2 lysogen. Then, we transformed the newly competent cell with the P4 cosmid and tail fiber plasmid. Afterwards, we created a lysate by adding L-rhamnose and allowing the cells to lyse, collecting the P4 transducing units that they produce. Lastly, we quantified the transducing units by infecting the *E. coli* EMG2-K12 indicator strain with the lysate from the previous step

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments12.png)

**Results:**  During the initial trial, the lysate degraded within a week period, decreasing the titer 10-fold per day. However, after another replicate, the titer remained stable after a two week period at about 3.4\*109, which is higher than that of a standard phage. That supports the argument that satellite phage have a higher titer than other phage and thus a higher efficiency.

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### **Experiment: Made replicative transducing units (CLICK HERE)**

**Rationale:** Satellites have the unique quality of being nonreplicative in the absence of their helper phage, providing a level of safety that standard phages do not offer. However, if there is degradation of these satellites, their inability to replicate can prevent them from having the desired effect on the system. Therefore, developing a way to maintain the high titer of the P4 transducing units can make them more efficient long-term. Since P4 requires the P2 packaging mechanism to lyse, infecting a P2 lysogen transformed with the necessary tail fibers with the P4 transducing unit and triggering lysis should produce a higher titer of transducing units

**Procedure:** First, we transformed electrocompetent bacteria carrying the P2 lysogen with the P2 wild type tail fiber, which has ampicillin selection. Then, we added 100uL of 10x fold diluted transducing units to 100uL of 20x concentrated bacteria. Afterwards, we incubated with shaking for 4 hours and spread 100uL on a plate with ampicillin to select for the tail fiber plasmid and chloramphenicol to select for the P4 cosmid. We then picked colonies and grew them in LB with the presence of both antibiotics, and supplement with L-rhamnose to trigger cell lysis. We collected and filtered the lysate and infected E. coli EMG2 K-12 with the lysate, diluted, and plated to quantify the transducing units.

**Results:**  After one round of replication, there was a large increase in titer. The titer of the original transducing units was 109, but after infection with only 100uL of 10x diluted transducing units, the resulting titer was 1011. Therefore, there was a 100 fold increase in the titer of transducing units.

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### **Experiment: Characterized KanR Targeting P4 Cosmid** ***in vitro* (CLICK HERE)**

**Rationale:** Before evaluating the efficacy of sequence specific killing based on a neomycin phosphotransferase gene target in the synthetic colon we needed to ensure that transducing units carrying the P4 cosmid with appropriate spacer inserts could do so in vitro. Our method for doing this is largely based on the method(s) used by Dr. Fa-Arun to characterize the original P4 cosmid.

We also wanted to ensure that any killing effect we observed was an authentic killing effect rather than one arising from a knockout of HL 713’s resistance marker while screening on kanamycin, so we adapted our procedure to account for this.

**Procedure:** We created transducing agent filtrate by resuspending concentrated EMG C5545 ∆cosσε ∆HG co-transformed with the Kanamycin resistance targeting P4 cosmid and a plasmid containing the wild type P2 tail fiber, in media containing L-rhamnose, which induced production of P4 transducing agents and cell lysis. We completed the procedure by filter sterilizing the lysate to produce a transducing agent filtrate free of bacteria. We verified the sterility of each filtrate by plating a portion of it on nonselective LB agar. We also created non-targeting transducing agent filtrate with the same procedure only using cells with the original P4 cosmid (i.e. lacking a targeting crRNA insert).

After producing transducing agents with the Kanamycin Resistance targeting p4 cosmid and the original P4 cosmid as a negative control, we quantified the titer of each filtrate by mixing a portion of each with a concentrated solution of the E. coli indicator strain EMG2 K-12 (ATCC 23716), performing a serial dilution of the incubated cells, and finally plating on LB plates containing chloramphenicol to screen for cosmid transductants. We used these data to equalize the titer of each transducing agent filtrate we produced for these experiments to 10^9 transducing agents per milliliter.

We measured the killing effect of each type of transducing agent filtrate by incubating 100uL of dilute transducing agent filtrate with 100uL HL713 bacterial culture diluted in SM buffer to a concentration of 10^7 cells per milliliter. HL 713 was grown up to the end of log phase as indicated by an OD600 of between 0.5-0.6 before being diluted to the desired concentration.

After incubation, we serially diluted the incubated cells ten fold in LB, and plated on LB agar plates lacking antibiotics, as well as LB with antibiotics. By comparing CFU between targeting and non-targeting treatments on LB without antibiotics, Cas9 killing effect can be evaluated. Doing the same on chloramphenicol containing LB quantifies killing efficiency among transductants only.

**Results:** We observed a statistically significant decrease (at the 95% confidence interval) in CFU (6.26 mean fold difference) after treatment with kanamycin targeting transducing agents when compared to treatment with non-targeting transducing agents, at a multiplicity of infection of 100 - meaning estimated 100 transducing agents per HL713 cell. Screening on chloramphenicol indicated a killing efficiency among transductants of over 98% for most measurements.

---

### **Experiment: Deployed Cas9 gene targeting system into the model colon (CLICK HERE)**

**Rationale**: The Cas9 gene targeting system was tested in the model colon to determine the fieldability of the system and differences in killing effect in a natural environment.

**Procedure:** The model colon was set up and inoculated with 100 mL HL713 in feed, made as previously described in Van den Abbeele et al. to allow the indicator strain to colonize the entire colon before inoculation with the transducing units(2010). The effluent of the model colon was regularly sampled to establish a baseline population by plating a serial dilution (100, 10-2, 10-4, 10-6) on LB agar plates and LB agar plates with kanamycin to screen for HL713. The model colon was inoculated with 12 mL of Cas9 targeting transducing units into the top of the first chamber, the ascending colon.

**Results:**  For the two model colons we inoculated with Cas9 targeting transducing units there was a statistically significant decrease in population for the effluent of model colon #1. However, while there was a decrease in the mini colon (which models one chamber of the colon), it was not statistically significant.

---

### **Experiment: Deployed RFP gene delivery system into the soil microcosms (CLICK HERE)**

**Rationale**: If successful, the transducing units with the RFP insert can be used as a reporter system for the presence of E. coli within the soil. When the transducing units are deployed, the infected bacteria will express a red fluorescent protein that will be visualized when bacteria plating the soil microcosms.

**Procedure:**  The microcosms were designed and rooted with bush bean and lettuce plants. After sufficient root growth, soil was inoculated with 18.25mLof 12.5X concentrated HL713 on August 31st. After allowing the indicator strain to colonize the soil microcosm, transducing units were inoculated. The soil microcosms were regularly sampled from the ‘center’ (where the microcosm was inoculated) and ‘edge’ (the other end of the microcosm) and plated at a 1e-4 and 1e-2 dilution respectively. The microcosms were inoculated with transducing units and sampling continued after inoculation with transducing units.

**We used the Opentrons OT-2 for preparing the 96 well plate for fluorescence measurement.**

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/opentrons-min.png)

**Results:**  Ten samples were collected from distinct colonies from soil microcosm plating on LB with kanamycin and chloramphenicol.

|  |  |  |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| A | 1196 | 32188 | 42 | 534 | 685 | 820 | 611 | 555 | 2826 | 695 | 191 | 477 |
| B | 38 | 45 | 46 | 31 | 36 | 37 | 30 | 43 | 37 | 34 | 37 | 473 |

A1: Negative control - uninfected E. coli HL713, A2: Positive control - E. coli HL713 infected with RFP, A3: Negative control - water. A4: Sample 1, A5: Sample 2, A6: Sample 3, A7: Sample 4, A8: Sample 5, A9: Sample 6, A10: Sample 7, A11: Sample 8, A12:Sample 9. B12: Sample 10.

The above table is a red fluorescence protein measurement for the 10 colonies from soil samples. Sample 6 showed a significantly higher measurement than the negative control suggesting that this sample was fluorescing.

---

### **Experiment: Characterization of dCas9 P4 cosmid (CLICK HERE)**

**Rationale** : We reasoned that we could use the same crRNA spacer insert used to create the KanR targeting P4 cosmid to characterize the silencing ability of the dCas9 P4 cosmid. If the dCas9 cosmid was working as intended HL713 transduced with the P4 cosmid should exhibit reduced resistance to kanamycin, as dCas9 would inhibit transcription elongation past its binding site, this reducing expression of neomycin phosphotransferase.

**Procedure:** We inserted the neomycin phosphotransferase crRNA into the dCas9 cosmid via a IIS reaction with BsaI, transformed into E. coli DH5-alpha, harvested the construct and confirmed its sequence with nanopore sequencing.

We then followed the same procedure as with previous experiments to produce transducing agent filtrate containing the dCas9 P4 cosmid (with added KanR targeting crRNA), as well as original P4 cosmid filtrate as a negative control.

**Results:**  Treatment with dCas9 Cosmid (with added KanR targeting crRNA) transducing agent filtrate produced fewer kanamycin resistant cosmid transductants compared to a control group treated with original P4 cosmid transducing agent filtrate. Overall the experiment was not sufficiently powered to draw any definitive conclusions about the magnitude of the dCas9 cosmid’s silencing effect, but is intriguing enough to potentially motivate further characterization of this part.

---

## Overcoming Prophage Immunity

---

#### **Detailed relevant protocols:** Phage Plating, Purification by Plaque Assay, Collecting Plate Lysates, Long Range PCR, Gel Electrophoresis, NEB PCR Cloning, Sampling Bacterial Plating and Phage Plating of Soil Microcosms.

---

### **Experiment: Confirming Mycobacteria aichiense colonies using colony PCR (CLICK HERE)**

**Rationale**: In order to confirm the accuracy of our soil microcosm results, we used colony PCR to amplify regions of DNA on what we counted as Mycobacterium aichiense from bacterial samples extracted from the soil. M. aichiense has a distinct yellow color and a more uneven shape, but can occasionally be more pale, and this variation can affect the estimation of M. aichiense presence in the soil.

**Procedure:** We collected colonies resembling the shape and color of traditional M. aichiense and suspended them in nuclease free water. The cells were broken apart by vortexing on a high setting and boiling for five minutes. A sample that was known to be M. aichiense was used as a positive control and a sample of M. smeg was used as a negative control. Then, standard PCR protocol was followed, and the fragments were imaged on an electrophoresis gel.

**Results:**  Two out of the seven colonies randomly selected from bacterial soil platings had bands on the gel at the expected length, identifying M. aichiense presence in these samples. The positive control had an expected band and the negative had no band.

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiment-thaw.webp)

---

### **Experiment: Resurrected novel satellites found in Mycobacterium aichiense (CLICK HERE)**

**Rationale**: Several “phagelets,” phage satellites in a species of bacteria called *Mycobacterium aichiense*, were first isolated by high school students in an iGEM outreach event but remained uncharacterized. *M. aichiense* is a strain of bacteria that is known to carry strong immunity to other phages, and these phage satellites were the only phage that could ever infect this strain to our knowledge. Due to their fragile nature, they degraded and were thought to be lost for six years. Our team “resurrected” these "phagelets" and grew them out to a high titer, and then employed them in the soil microcosm to fully test their effects.

**Procedure:** We took the thought to be degraded lysate and plated the maximum amount with *M. aichiense* and top agar. We then purified plaques by taking a plug from a plaque and resuspended in phage buffer. We filtered and plated the resuspended plug with *M. aichiense* on top agar. We repeated until webbed lysis was reached. Once webbed lysis was achieved, we flooded the plate with phage buffer, we let it sit for 12 hours at 4 ℃, and plated with dilutions of 10-1, 10-3, and 10-5 to estimate titer. We then flooded the 10-1 plate, filtered the lysate, and plate again with dilutions. We kept repeating this process until the titer no longer increased.

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments13.png)

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiments14.png)

**Results:**  Our team managed to grow out four “phagelets,” two of which were not sequenced or characterized before. What we found was one was in fact not a phage satellite and was a standard phage that shared large similarities with the prophage in *M. aichiense* called HerbertWM. While the satellites worked to excise HerbertWM, there was no evidence of HerbertWM when the phage lysed the cell.

Originally, these phage satellites showed one or two plaques after the initial plating of large quantities of old lysate, but were grown out to a titer of 107 to 108. The non-satellite phage reached a titer of 109.

Some of the soil microcosms were inoculated with these “phagelets” while others contained the phage that was previously thought to be a satellite for comparison. In these microcosms, we tested for the "phagelets" and the phage’s killing effect on *M. aichiense*, and continued to test the presence of phage as well.

---

### **Experiment: Tested environmental sensitivity of** ***M. aichiense*** **satellites (CLICK HERE)**

**Rationale**: The mycobacteria satellite phage, "Phagelets", have historically been extremely fragile and degraded quickly. After achieving the desired titer, we began plating them less frequently. After taking several days between collecting the lysate and plating, they stopped growing.

**Procedure:** After plating as described above and the plates forming perfect lawns and no plaques in the incubator at 37℃, they were left to sit untouched at room temperature for 2 days on the lab bench.

**Results:**  After 2 days being left at room temperature, webbed lysis plaques formed on each plate except for the negative control. The reason for this could be temperature sensitivity, although previously they formed plaques in the incubator at 37ºC. The negative control with no "phagelets" added to the *M. aichiense* did not form any plaques.

---

### **Experiment: Amplified large sections of novel mycobacteria phage satellite genomes for future use (CLICK HERE)**

**Rationale:** We wanted to determine if entire "phagelet" genomes can be amplified via PCR for potential use in future cloning reactions or for potential preservation strategies. "Phagelet" DNA is highly GC-rich, so it may be difficult to amplify via PCR and be used for future engineering.

**Procedure:** We designed a set of back-to-back primers (facing in opposite directions) to amplify the entirety of the "phagelet" genome. Then, we ran long-range PCR on PCI-extracted "phagelet" DNA using these primers and waterwards we ran the product on an agarose gel along the GeneRuler 1 kb ladder and a high molecular weight ladder to get the size of the amplified PCR product.

**Results:** The band on the high molecular gel was the size of the "phagelet" at about 11kb, meaning the "phagelet" genomes were successfully amplified.

---

### **Experiment: Cloned linearized mycobacteria phage satellite amplicons into plasmids for long-term storage (CLICK HERE)**

**Rationale:** In order to preserve these *Mycobacterium* satellites long-term, we can clone them onto a plasmid designed for storage.

**Procedure:** We used the NEB PCR Cloning Kit, which provided the pMiniT™ 2.0 Vector for cloning. We first ligated the linearized satellite DNA and a linearized version of the plasmid vector. Then, we transformed into NEB 10-beta Competent *E. coli* and plated on ampicillin plates to select for the plasmid.

**Results:**  The procedure was unsuccessful and no colonies grew on the ampicillin plate. This was likely due to the size of the insert, since it was 11 kb, causing a large strain on the vector.

---

### **Experiment: Tested activity and persistence of mycobacteria phage satellites in soil microcosms (CLICK HERE)**

**Rationale:** Innovations in synthetic biology are often only tested *in vitro*, but the concepts fail in more complex systems and real world environments (Brooks & Alper, 2021). Therefore, although the “phagelet” phage satellites lyse *M. aichiense* and overcome prophage immunity efficiently in a petri dish, they may not do so in the soil where they would be directly utilized. Employing these “phagelets” in soil microcosms would show their efficiency outside the lab and make them more applicable to SynBio.

**Procedure:** 8 soil microcosms were inoculated with 11mL of 12.5x fold concentrated *M. aichiense* in the center of the microcosm. We quantified *M. aichiense* persistence and spread by sampling from the center (location of inoculation) and from the edge of the microcosm. After several days, we inoculated 4 microcosms with 6.5 mL of phage satellites that infect *M. aichiense* and 4 microcosms with standard phage for comparison. After phage and satellite phage inoculation, we continued quantifying *M. aichiense* in the center and edge of each soil microcosm as well as phage plating with *M. aichiense* to quantify phage and satellite phage persistence.Plates were left in the incubator for 48 hours and record the *M. aichiense* colonies and recorded plaques seen on the phage plates.

**Results:**

|  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
| Microcosm | Antibiotic | Test | H0 | Ha | p-value |
| 9-12 C | 7H9 | Two sample t-test comparing population before and after inoculation | Population before = population after | Population before ≠ population after | p-value = 0.03938 |
|  |  | Two sample t-test comparing population before and after inoculation | Population before = population after | Population before > population after | p-value = 0.9803 |
| 13-16 C | 7H9 | Two sample t-test comparing population before and after inoculation | Population before = population after | Population before ≠ population after | p-value = 0.2022 |
|  |  | Two sample t-test comparing population before and after inoculation | Population before = population after | Population before > population after | p-value = 0.8989 |

These results indicate that neither the mycobacteria phage satellite nor the non-satellite mycobacteria phage had a significant killing effect on M. aichiense.

---

## Host Expansion

---

#### **Detailed relevant protocols:** Collecting Plate Lysates, Phage DNA Extraction Using PCI, PCR, Electroporation of *M. aichiense*, Gel Electrophoresis

---

### **Experiment: Tested for circularity of the mycobacteria satellite phage genomes (CLICK HERE)**

**Rationale:**We wanted to determine whether the “novel mycobacteria satellite phage”, called "phagelets", have circular or linear DNA. This will allow us to understand the mechanism, which we can utilize to simulate the packaging of HerbertWM while inserting our tail fiber plasmids.

**Procedure:** During this experiment, we designed a set of primers to amplify a large section of the “phagelet” genome and another set to cover the leftover region that was not amplified with some overlap with the previous primer. After this, we tested both primer sets on extracted DNA from two “phagelets”, ChrisB and WillG, and ran them on a gel to determine their size.

**Results:**  We observed amplicons of the expected size, with one of each, confirming the "phagelet" genomes to be circular.

---

### **Experiment: Tested infection range of HerbertWM and the novel mycobacteria satellite phage (CLICK HERE)**

**Rationale:** In order to expand the host range of HerbertWM, we need to confirm that HerbertWM does not infect *M. smeg* to create chimeric tail fibers that would be compatible with *M. smeg*.

**Procedure:** For this experiment, we used viable mycobacteria phage lysate that has high titers when plated with *M. aichiense*. We added *M. smeg* to this lysate while still using *M. aichiense* as a control and plated with top agar on plates to look for plaques.

**Results:** No plaques formed on the *M. smeg* plates with each mycobacteria satellite, but each formed plaques on the plates containing *M. aichiense*, meaning that the phage satellites were present but were not compatible with *M. smeg.*

---

### **Experiment: Isolated HerbertWM DNA on an Electrophoresis Gel (CLICK HERE)**

**Rationale:** The full genome for HerbertWM, the prophage in *M. aichiense*, was found when sequencing the lysate, however it has not been confirmed via gel electrophoresis. Using a high molecular weight ladder and running a gel with the DNA extracted from the lysate of the "phagelets", we should be able to see two bands: one with the "phagelet" and one with the "phagelet" each at their respective size.

**Procedure:** We flooded plates showing webbed lysis as a result of the mycobacteria satellite phage-induced killing. Then, we extracted the DNA from the lysates, ran this extracted DNA on a low percent agarose gel with a high molecular weight ladder, and performed PCR on the samples with both HerbertWM-specific primers and "phagelet"-specific primers for diagnostics

**Results:**  We observed bands at 50kb and 11kb, which match the genome sizes of HerbertWM and the "phagelets", respectively. After PCR, we observed clear bands for each, confirming the presence of both of the constructs present in the lysate.

---

### **Experiment: Electroporated novel mycobacteria satellite phage DNA into** ***M. aichiense*** (CLICK HERE)

**Rationale:** In order for HerbertWM to use our chimeric tail fibers, it would need to be excised and packaged. We wanted to determine if extracted and purified DNA taken from "phagelet" lysate could be transformed into competent *M. aichiense* and have a killing effect; this would act as a positive control to show that HerbertWM would lyse the cell regardless of the presence of the chimeric tail fiber plasmid.

**Procedure:** We combined electrocompetent *M. aichiense* and isolated "phagelet" DNA into a pre-chilled 0.1 cm electro gap cuvette. We delivered a pulse at 1.25kV. Then, we incubated on ice for 5-10 minutes before re-suspending cells in 1mL 7H9 recovery media and afterwards incubated at 37ºC with shaking for 2 hours. Lastly, we plated transformation on 7H9 plates and incubated at 37ºC for 3 days.

**Results:** Electroporation of “phagelet” DNA was unsuccessful as there were no colonies after 3 days.

---

### **Experiment: Trained and tested the phage-bacteria interaction prediction model with known data** (CLICK HERE)

**Rationale:** In order to use the prediction model, the training data must be processed and run first.

**Procedure:** To process the training data, each bacteria and phage genome was converted to an array of vectors using binary to code for each nucleotide base. The array was then be “padded” with empty zero vectors to reach a standardized input length (i.e. 500,000 bp for phage), allowing the shape of the input to match the shape of the output for each intermediate neural network layer. The interaction data was categorized into a dataframe of phage-bacteria pair (input) and interaction value (0=FALSE, 1=TRUE). We ran the data through the model using either a graphics card or externally hosted GPU.

For testing the model, known phage-bacteria interactions that were not used in the training dataset were run through the model, and the prediction accuracy was determined after averaging the accuracy outputs with varying distribution of data for training and test sets each time.

**Results:** After 20 full runs, the model was found to have an accuracy of 68% with p < 0.001, using 50% as the expected prediction accuracy for a totally random model where our distribution of positive and negative interaction pairs in the test set was even.

---

# References

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# **Summary of Results**

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For our 2024 project, we conducted both Dry Lab and Wet Lab experiments, and achieved results in two major categories: (1) Discovery and (2) Application of satellite phage.

Our results for the discovery of novel satellite phage are split into two categories: (1a) bioinformatic discovery and (1b) physical discovery.

* In discovering novel satellites bioinformatically

+ We collected data from our runs of our novel satellite phage identification software, SaPhARI.

* In discovering novel satellites physically we screened for satellites in *E. coli*, *M. smegmatis*, and *M. aichiense*, and sequenced phages discovered.

Our results for Applications of Satellite Phage are separated into three categories: (2a) gene delivery, (2b) overcoming prophage immunity, and (2c) host expansion. In these categories, we conducted experiments at the *in silico, in vitro*, and simulated *in situ* levels in our model colon and soil microcosms.

---

#### At the *in silico* level our results include:

* The predictions of the mathematical model to describe the functionality of phage satellites in simulated real-world environments.
* We collected results from a phage-host interaction predictive model with an extensive phage-host interaction database.

---

#### At the *in vitro* level our results include:

* Results of gene delivery:

+ Quantifying transducing units in both nonreplicative and replicative systems.
+ Testing killing effect of kanamycin targeting cosmid.
+ Testing silencing effect of dCas9 cosmid.
+ Testing fluorescence output of RFP cosmid.

* Results of ability to overcome prophage immunity:

+ Resurrecting Mycobacterium aichiense phage satellites, termed “phagelets”, from six-year-old phage lysate and characterized these satellites.

* Results of phage host range expansion:

+ Plating phage lysate to test the infection range of phages HerbertWM and a novel mycobacteria phage.
+ Assembling chimeric tail fibers that can theoretically expand host range of HerbertWM via the phagelets.

---

#### At the simulated *in situ* level our results include:

* Quantifying effects of transducing units:

+ Sampled the effluent of the model colon to test for a killing effect model colon with the CRISPR transducing units.
+ We inoculated soil microcosms 5-8 with the RFP transducing units and conducted bacterial plating to test for fluorescent output.

* Results of overcoming prophage immunity:

+ Conducted phage plating to test for a killing effect and persistence over time after inoculating soil microcosms with Mycobacterium phage satellites

---

#### For detailed information about our experiments, please visit our [experiments page](https://2024.igem.wiki/william-and-mary/experiments).

---

# **Discovery**

---

We discovered new phage satellites with two methods, bioinformatic and physical, and developed novel protocols for discovery which can be used by other laboratories.

## **Bioinformatic Discovery**

---

### **Searching through Metagenomic Samples for Satellite Phage Protein Clusters using SaPhARI (CLICK HERE)**

**Experiment**: In order to gain deeper insight into the microbial dynamics of satellite phage in natural environments, we took advantage of the wealth of available metagenomic sequences to categorize the extremely diverse and ubiquitous satellite phage families, while simultaneously deepening our understanding of satellite genetic structure We searched through metagenomic samples for satellite phage protein clusters using SaPhARI, assessing its ability to find and tag protein clusters.

---

#### **Results: Identified Protein Clusters**

Using SaPhARI at varied protein parameter stringencies, we searched through 7,659 metagenomic contigs, which were less than 20,000bp, representing 37 metagenomic samples. We identified 87 putative satellite protein phage clusters representing 5 out of the 6 developed satellite family models:

* PICMIs - 41
* cfPICI - 21
* PICI - 2
* P4like - 2
* Phagelet - 1

The only family not identified were PLE’s, which are only categorized in Vibrio cholerae.

---

#### **Explanation of Stringency Score**

![Summary](https://static.igem.wiki/teams/5179/graphics/results/satellitemodelexample.png)

For each satellite model, the parameters are defined in the “proteins” variable, which is a Python list of potential proteins that each respective satellite family is expected to contain. For example, the above PICI model has 5 protein parameters —primase, integrase, decor, either “major head” or “major capsid”, and either 'alpa' or 'icd-like'—. To define stringency in our experiment, we changed the “minnumber” variable for each model, which defines the minimum number of these proteins that need to be present in order for SaPhARI to tag the cluster as that satellite family. For the satellite group defined in photo above, the protein cluster will be identified as a PICI if it has 3 of the 5 protein groups per defined in the model. A stringency score of 0 would define this minnumber variable as 5 and a stringency score of -1 would define this minnumber variable as 4.

We ran five iterations of stringency, decreasing the minimum required proteins by one in each run until the models reached or exceeded half of the total proteins. The stringency scores were: 0, -1, -2, -3, -4, and -5. Not every model was run with the higher stringency scores, as the highest stringency score used for each model never exceeded the equivalent of ½ of the number of protein parameters defined in the model. For example, with the PICI model, the highest stringency model used was -2, thus requiring 3 out of 5 proteins to be present.

---

PICMI had the most lenient model, with three potential protein parameters—primase, integrase, and either 'alpa' or 'icd-like'—all located within 15,000 bp of each other. 4 putative PICMIs strictly adhered to the requirement of all three protein parameters, while the remaining 37 were found with a stringency score of -1, requiring only two of the parameters.

2 putative PICIs were detected at a stringency score of -2, with three of the five possible protein parameters to be present within 15,000 bp of each other, making it the second least stringent model. The P4-like family required the highest stringency for detection, at -5, due to having twelve possible protein parameters within 12,000 bp, making it the strictest satellite family model. The 2 putative P4-like satellites were detected at a stringency score of -5.

A single cf-PICI was found at a stringency score of -3, with seven of the ten protein parameters to be present within 15,000 bp of each other, making it the second most stringent model. The remaining 20 cf-PICI’s were found at a stringency score of -4. Lastly, the sole Mycobacterium phage satellite, "phagelet," was found at a stringency score of -3, requiring three of the six protein parameters to be present within 12,000 bp of each other.

---

#### **Data Analysis:**

To assess whether putative satellites could be full phages, we analyzed all contigs containing identified satellites using PHASTEST to infer prophage status, as well as analyzed each contig using BLAST to assess similarity to known species. The results were compiled into a CSV file accessible below:

[##### Click here to download CSV](https://static.igem.wiki/teams/5179/data/metagenomicsatellites.csv)

---

Within the 82 contigs analyzed with PHASTEST, 44 prophage/phage regions were identified. Of these, 20 were classified as "complete" by the PHASTEST algorithm. Notably, 12 of the 20 "complete" prophage regions were identified as cfPICIs. Additionally, 14 regions were categorized as "questionable," spanning various satellite families—5 were cfPICIs, 8 were PICMIs, and 1 was a “phagelet”. All 9 regions classified as "incomplete" were identified as PICMIs.

Interestingly, 37 of the 43 satellites not detected by PHASTEST were PICMIs. This was expected, as 29 of the 41 PICMIs had a query coverage of less than 10% for their top hit in a nucleotide BLAST, highlighting how poorly characterized these phage satellite sequences are. Furthermore, only 18 of the 82 metagenomic contigs analyzed by BLAST had both query coverage and identity greater than 90%, further demonstrating the lack of characterization in satellite phages.

Reliance solely on nucleotide similarity can yield limited results and overlook numerous satellite phages hidden within bacterial genomes. SaPhARI addresses the urgent need for novel bioinformatic approaches that move beyond nucleotide similarity.

All contig fasta files, original SaPhARI outputs, contig BLAST results, and PHASTEST results are available in our GitLab within the folder titled “METAGENOMIC\_SATELLITES”

---

#### **Intepretation**:

The discovery of these 87 putative satellite phage clusters only scratches the surface of the capabilities that SaPhARI has to offer through the identification of satellite protein clusters within metagenomic data. Our team continues to analyze more metagenomic data daily, seeking to characterize more satellite phage systems for future applications within synthetic biology.

## **Physical Discovery**

---

Phage satellites, though incredibly abundant at an estimated 3.2 x 1026, are widely uncharacterized (Eppley et al., 2022). Furthermore, comprehensive protocols to screen for phage satellites have not been developed. We developed procedures to screen for phage satellites using bacterial lysogens and applied these in Mycobacterium and E. coli. To discover phage satellites, we used bacterial lysogens, to screen for satellites that can exploit the prophage’s packaging machinery. The general steps to discover a phage satellite include 1) obtaining/creating a lysogen, 2) collecting a soil sample, 3) enrichment plating, 4) purifying the potential satellite by plaque assays and 5) Phage DNA extraction by PCI.

Phage screening does not typically use bacterial lysogens; however, to screen for satellite phages that can exploit a prophage’s packaging machinery, we used bacterial lysogens. The general steps to discover a phage satellite include 1) obtaining/creating a lysogen, 2) collecting a soil sample, 3) enrichment plating, 4) purifying the potential satellite by plaque assays and 5) Phage DNA extraction by PCI.

---

### **Screening for** ***E. coli*** **Satellite Phages  (CLICK HERE)**

**Experiment:** Applying our phage satellite screening procedure, we discovered two novel bacteriophages that infect E. coli strain EMG2 K-12, a tailed bacteriophage and filamentous bacteriophage. These discoveries exemplify how our detailed screening protocols can lead to new, unexpected discoveries, particularly by our discovery of new E. coli bacteriophages despite how well-studied E. coli is.

![Summary](https://static.igem.wiki/teams/5179/data/image35.png)

#### Figure. Experimental process from collection of soil sample 1, which showed plaquing on enrichment plate, to final phage genomes obtained from sequencing.

---

![Summary](https://static.igem.wiki/teams/5179/data/image37.png)

#### Figure. Experimental process from collection of soil sample 4, which showed plaquing on enrichment plate, to final phage genomes obtained from sequencing.

---

#### **Raw Data:**

DNA isolation of phages were performed after a series of steps to purify the phage from the original enrichment plates which showed plaques (see Figures 1 and 2). Double stranded phage DNA was obtained by performing a DNA extraction of this lysate with phenol/chloroform/isoamyl alcohol. More detail on the experimental process can be found on the [Experiments page](https://2024.igem.wiki/william-and-mary/experiments).

DNA samples were sent to the Oklahoma Medical Research Foundation for Illumina Sequencing. Raw sequencing data was assembled using the Shovill pipeline for de novo assembly of phage genomes (Turner et al., 2021). To look for phage, the raw sequencing data was then assembled into contiguous sequences using the Shovill assembly pipeline for The Shovill pipeline allowed assembly of our phage genomes by allowing us to specify contig length and implementing error correction. DNA sample 1 resulted in 12 contigs of various sizes. DNA sample 4 resulted in 2 contigs of 7,432 bp and 31,664 bp.

---

##### [Putative *E. coli* filamentous phage raw sequence](https://static.igem.wiki/teams/5179/data/putative-e-coli-filamentous-phage-sequence.pdf)

##### [Putative *E. coli* tailed phage raw sequence](https://static.igem.wiki/teams/5179/data/putative-e-coli-tailed-phage-sequence.pdf)

---

#### **Data Analysis/Interpretation:**

##### **Novel Putative Enterobacteria Filamentous Phage**

---

We identified a novel 7kb filamentous phage that infects E. coli strain EMG2 K-12 by isolation from soil samples on W&M campus.

Unexpectedly, this putative phage was likely present in both initial soil samples collected. Both DNA samples showed nearly-identical contiguous sequences of about 7,000 bp (7432 and 7420) after assembly of the reads. Also, both samples showed identical bands around 9kb on a 1% agarose gel. Though the size appears to differ, filamentous phage genomes are single-stranded and circular, and therefore, may appear higher on a gel than expected by size (Hay 2019). Together, these results suggest that both soil samples contained this 7kb phage.

![Summary](https://static.igem.wiki/teams/5179/data/filamentous-phage.png)

#### Figure. *E. coli* filamentous phage Benchling linear map with Prokka protein annotations.

Prokka genome annotation of the 7kb contig suggests that the putative phage contains key functions for filamentous phage function from Enterobacteria filamentous phages: f1 phage, M13 phage, and IF1 phage. The virion export protein gene, involved in filamentous phage extrusion, and gene 1 proteins share homology with that of f1 phage; attachment protein G3P and head virion protein genes share homology with that of phage M13; and the capsid protein G8P and DNA-binding protein G5P share homology with that of IF1 phage. This discovered phage appears similar to the Ff filamentous phage family, which have M13 and f1, which share 98.5% genetic similarity (Mai-Prochnow 2015, Hay 2019). The M13 phage has important applications: half of the 2018 Chemistry Nobel prize was awarded for “phage display of peptides and antibodies” which primarily uses filamentous phage M13 (Barderas 2019).

The plaque morphology of this phage during purification further supports our hypothesis that we have discovered a filamentous phage. When undergoing plaque purification, the phage from sample 1 showed decreasing numbers of plaques, which are transparent areas in a bacterial lawn indicating phage lysis of bacteria. Initially, assuming that we were working with a tailed phage, we hypothesized that the phage was degrading in lysate form. However, while tailed phage lyse bacteria and can be detected by plaques, filamentous phage do not lyse their bacterial hosts. Filamentous phages instead extrude from their hosts through channels in the bacterial membrane, and as such, filamentous phages plaque at varying degrees as a result of reduced or slower bacterial growth (Gavric 2022). If this phage is a filamentous phage, a possibility is that the phage titer decreased considerably by the third round of purification, resulting in no observation of plaque, despite the phage infecting E. coli at this titer.

![Summary](https://static.igem.wiki/teams/5179/data/image36.png)

#### Figure. At dilutions of 10-1 to 10-11, phage plating of dilutions of the phage from soil sample 1 showed no plaques in the third round of purification. Since discovering that this phage is a filamentous phage, we may question if the discovered filamentous phage was present on these plates but simply did not show plaques.

---

##### **Novel Putative Enterobacteria Tailed Bacteriophage**

---

Additionally, we identified a novel tailed bacteriophage that infects *E. coli* strain EMG2 K-12.

![Summary](https://static.igem.wiki/teams/5179/data/novel-tailed-phage.png)

#### Figure. *E. coli* tailed phage Benchling linear map with Prokka protein annotations.

We identified a 31kb contiguous sequence present in the DNA isolated from soil sample 4 as a bacteriophage based on the presence of a capsid protein, virion proteins, and tail proteins. This bacteriophage has not been previously isolated–using NCBI Blast did not yield any phage with high similarity. Yet, this phage may be present in multiple E. coli strains as a prophage. A NCBI Nucleotide blast of the phage genome resulted in a 97.95% identity match and 84% query cover with E. coli strain ETEC1721, and high similarity to other *Escherichia* species and E. coli strains including *Escherichia albertii* Strain sample 168, E. coli strain RHB39-C23, and E. coli strain E2528-C1.

### **Transmission electron microscope (TEM) on putative phage satellites (CLICK HERE)**

**Experiment:** Experiment: Did transmission electron microscope (TEM) on putative phage satellites physically discovered in the lap using our procedures.

---

#### **Raw Data:**

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/chris-b.webp)

#### Figure. TEM image at 50nm of ChrisB, a putative mycobacteria satellite phage.

---

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/ellie-k.webp)

#### Figure. TEM image at 50nm of EllieK, a putative mycobacteria satellite phage.

---

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/putative-satellite.webp)

#### Figure. TEM image at 50nm of a putative mycobacteria satellite phage.

---

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/filamentous.webp)

#### Figure. TEM image at 200nm of a novel E.coli filamentous phage.

---

#### **Data Analysis:**

The mycobacteria satellite phage were smaller in scale when compared to the expected size of a standard phage, and the structure of the tail looked similar to that of HerbertWM. The putative mycobacteria phage satellite has a strange structure with what seems to be several capsid heads grouped together. The suspected filamentous phage has a long shape, and also has what look like standard phage with capsid heads and tails.

---

#### **Interpretation:**

For the mycobacteria phage satellites, this further suggests that the satellites encode for their own capsid heads, due to the disparity in size. The strange structure of the putative mycobacteria phage satellite is consistent with the irregularity of the sequencing results. The images of the filamentous phage confirms our theory that there are two phages in the lysate: one that is causing the lysing and one that is filamentous and does not lyse the bacteria it infects.

### Created Lysogens in Mycobacterium smegmatis (CLICK HERE)

**Experiments:** We created lysogens in Mycobacterium smegmatis in order to screen for satellite phages. To test lysates potential to form stable lysogens, we performed a lengthy and robust procedure, testing the titer, phage release, and superinfection efficiency.

#### **Raw Data:**

---

Lysogen Data Table. Cells highlighted in blue represent phage names while gray cells are the isolators' initials. If a lysate fails one of the tests, it can be repeated up to 3 times, with yellow representing 1 failed attempt, orange representing 2 failed attempts, and red representing 3 failed attempts. If a lysate passes a second or third attempt, the lysate continues to the next experiment.

More detail on the experimental process can be found on the [experiments page](https://2024.igem.wiki/william-and-mary/experiments).

---

#### **Data Analysis/Interpretation:**

Out of the 55 tested lysates, five passed all of the lysogeny tests and resulted in a stable lysogen, ready to be used for satellite screening. We successfully created 5 stable lysogens of phages JollyBabz, StubbySaigey, KingChadwick, and DocMcStuffins, and SucetteDeLMort in *M. smegmatis*.

### **Discovered novel satellites in *Mycobacterium aichiense* (CLICK HERE)**

**Experiment:** We screened for novel satellites in *Mycobacterium aichiense*, a strain of bacteria that conveys strong immunity to other phages due to the presence of a prophage called HerbertWM.

---

#### **Raw Data:**

3 phages, including 2 potential satellite phages, one discovered by Phage Lab and one of the novel “phagelets”, and the standard phage were sent for sequencing to better characterize these phage. We received Illumina short, paired-end reads for each sample, which were evaluated and trimmed to ensure the highest possible quality for further assembly. Quality and adapter trimming was performed utilizing FASTQC and trim\_galore, while assembly was performed utilizing the Shovill assembly pipeline.

---

#### **Data Analysis:**

Using the phage satellite screening procedure that our iGEM team developed, W&M Phage lab successfully discovered a putative phage satellite that infects M. aichiense. However, characterizing this putative satellite has proved difficult thus far. Several attempts at sequencing show that a large part of the putative satellite’s genome aligns with a pathogenic bacteria called Giardia, which is not only a different species but also a different phyla than M. aichiense.

Importantly, our team has characterized a group of putative satellite phages that infect M. aichiense, which we term “phagelets”, by increasing their titer and sequencing one of these phage satellites for further analysis. These putative satellites were discovered over 6 years ago by Jamestown High School students, but the phage lysates were thought to be degraded and were not initially characterized.

We have additionally characterized a novel M. aichiense standard phage, also discovered previously by Jamestown High School students. This phage bypasses homo- and hetero- immunity in M. aichiense and lyses the bacteria without excising the prophage HerbertWM.

In our soil microcosms, we utilized both the novel M. aichiense “phagelets” and novel M. aichiense phage, to compare spread and killing efficiency of satellite phages to phages. These results are described in detail below.

---

#### **Interpretation:**

Two samples of a novel M. aichiense phage were sequenced, which resulted in assemblies of about 60,000bp each. The two largest contigs (23,000bp each) from the assemblies showed 72% query coverage and 88% identities to HerbertWM, and 99.99% identity to each other. The similarity to HerbertWM supports our theory that the novel mycobacterium phage is a close relative HerbertWM. We hypothesize further that this similarity, yet few differences, plays a key role in this novel phage’s ability to excise the temperate phage HerbertWM.

The other Mycobacterium phage satellites also showed significant similarity to HerbertWM, as well as other Mycobacterium phage such as Caroline.

# **Applications**

---

## **Using Satellites for Gene Delivery**

### **Produced and Quantified Titer of P4 Transducing Units (CLICK HERE)**

**Experiment:** The P4 cosmid system was originally designed by Dr. Fa-arun at the University of Edinburgh. The original purpose of this system was to induce Cas9 mediated cell death for disease-causing strains of bacteria such as E. coli O157:H7 and *Shigella flexneri*. Aspects of the P4 packaging mechanism were used to create transducing units carrying a plasmid with a Cas9 cassette. In order to create a versatile tool that could be used for both E. coli and *Shigella*, Dr. Fa-arun also created chimeric tail fibers to expand the host range of these transducing units. The Cas9 used in the original study targeted shiga toxins in the bacterial genomes, causing a double stranded break in the genome, thus killing the bacteria.

---

#### **Raw Data**:

![Summary](https://static.igem.wiki/teams/5179/data/created-and-quantified-titer-of-p4-transducing-units-data-1.png)
![Summary](https://static.igem.wiki/teams/5179/data/created-and-quantified-titer-of-p4-transducing-units-data-2.png)

---

#### **Data Analysis**:

After the first attempt to create transducing units we observed their degradation over a week-long period. The titer decreased approximately 10-fold each day of quantification. The second attempt at creating transducing units did not degrade over a two week period, and the titer was consistently around 2.6\*109.

---

#### **Interpretation**:

During the initial trial, the lysate degraded within a week period, decreasing the titer 10-fold per day. However, after another replicate, the titer remained stable after a two week period at about 3.4\*109, which is higher than that of a standard phage. That supports the argument that satellite phage have a higher titer than other phage and thus a higher efficiency.

### **Made Replicative Transducing Units (CLICK HERE)**

**Experiment**:  Satellites have the unique quality to be nonreplicative in the absence of their helper phage, providing enhanced biosafety compared to standard phages. However, if there is degradation of these satellites, their inability to replicate can prevent them from having the desired effect on the system. Therefore, developing a way to maintain the high titer of the P4 transducing units can make them more efficient long-term. Since P4 requires the P2 packaging mechanism to lyse, infecting a P2 lysogen transformed with the necessary tail fibers with the P4 transducing unit and triggering lysis should produce a higher titer of transducing units. The result was a 100-fold increase in transducing units with an average titer of 1.9\*1011.

---

#### **Raw Data**:

![Summary](https://static.igem.wiki/teams/5179/data/made-replicative-tu-new.png)

---

#### **Data Analysis**:

After one round of replication, we observed a large increase in titer. The titer of the original transducing units was about 109, but after infection with only 100uL of 10x diluted transducing units, the resulting titer was about 1011. Therefore, there was a 100 fold increase in the titer of transducing units.

---

#### **Interpretation**:

If after one round of replication the titer increased 100-fold, then the P4 transducing units and the replicative bacteria could be utilized as a system in vivo to increase the effectiveness of the transducing units due to constant production of new transducing units.

### **Quantification of KanR Targeting P4 Cosmid Killing Effect on HL713 in Vitro (CLICK HERE)**

**Experiment**: After producing transducing agents containing the Kanamycin Resistance (KanR) targeting P4 cosmid, as well as transducing units containing the original P4 cosmid lacking a targeting crRNA as a negative control, we measured the titer of each on an E. Coli indicator strain lacking kanamycin resistance. We used these data to equalize the titer of each transducing agent filtrate we produced for these experiments to 109 transducing agents per milliliter.

We measured the killing effect of each type of transducing agent filtrate by incubating 100uL of dilute transducing agent filtrate with 100uL HL 713 bacterial culture diluted in SM buffer to a concentration of 107 cells per milliliter. Thus, all experiments were conducted at a multiplicity of infection of 100 transducing agents per target cell. HL 713 was grown up to the end of log phase as indicated by an OD600 of between 0.5-0.6 before being diluted to the desired concentration.

To determine absolute killing effect post-treatment cells were serially diluted in LB and plated on nonselective LB agar, then incubated at 37 C for 16 hours. Colonies were then counted at each dilution level to estimate post treatment bacterial titer.

---

#### **Raw Data**:

![Summary](https://static.igem.wiki/teams/5179/data/bba-k5179101-results-of-quantification-of-cas9-killing-effect.webp)
![Summary](https://static.igem.wiki/teams/5179/data/data-tables/kanr-killing-effect-data-table.png)

#### Table. HL713 titer was 107 per mL, transducing agent titer was 109 per mL for all measurements

---

#### **Data Analysis**:

A two sample t-test was used with the alternative hypothesis that the true difference in means between KanR transducing agent treated and control groups is less than zero.

---

#### **Interpretation**:

Our experimental data was sufficient to reject the null hypothesis at a significance level of 0.04 with an effect size of 6.26 fold difference between post treatment colony forming units of each group.

### **Quantification of Killing Efficiency (CLICK HERE)**

**Experiment**: By repeating the same procedure used to quantify KanR cosmid killing effect on HL 713 and plating on chloramphenicol containing LB we can screen for cosmid transductants only. Since the same titer of transducing agents and target bacteria was used between measurements, a similar number of transductants should be generated in each round of measurement, thus any differences in the number of chloramphenicol resistant colonies between KanR targeting and non-targeting treatments arises from the killing effect of Cas9 mediated cleavage induced by the KanR targeting cosmid.

![Summary](https://static.igem.wiki/teams/5179/data/data-tables/kanr-killing-efficency-data-table.png)

---

#### **Data Analysis**:

From three trials, we concluded that there was a killing efficiency between 0.50 and 0.99. Meaning in cases of successful transduction the KanR targeting cosmid kills its host between 50% to 99% percent of the time. Cases of HL 713 colonies surviving transduction with the KanR targeting P4 cosmid could be due to a variety of factors, including the possibility of HL 713 escaping the effects of sequence specific killing through a mutation to the protospacer adjacent motif in its kanamycin resistance marker, which would effectively prevent Cas9 from cleaving HL 713’s chromosome.

---

#### **Interpretation**:

We chose the target site specified by the crRNA spacer in the KanR targeting cosmid in specifically so that silent mutations (mutations which do not change how a protein is translated) in the relevant protospacer adjacent motif were impossible, thus immunity to the sequence specific killing effect of the KanR targeting P4 cosmid and full kanamycin resistance should be mutually exclusive. Additional analysis of the same measurements indicate this design strategy may have been successful, as when screened for cosmid transductants which have functional kanamycin resistance genes (by screening on LB containing both kanamycin and chloramphenicol) the killing efficiency recovers to high levels (96-99%).

### **Characterization of dCas9 P4 Cosmid (CLICK HERE)**

**Experiment**: To characterize the silencing ability of the dCas9 cosmid we inserted the neomycin phosphotransferase targeting crRNA into the dCas9 cosmid via a golden gate reaction with BsaI, transformed into E. coli DH5-alpha, harvested the construct and confirmed its sequence with nanopore sequencing.

We then followed the same procedure as with previous experiments to produce transducing agent filtrate containing the dCas9 P4 cosmid (with added KanR targeting crRNA), as well as original P4 cosmid filtrate as a negative control. We treated cultures of HL 713 with each, serially diluted in LB, and plated on LB agar plates with chloramphenicol, kanamycin and chloramphenicol, and no antibiotics. We incubated the plates for 16 hours at 37 C and counted colonies at each dilution level.

---

#### **Raw Data**:

![Summary](https://static.igem.wiki/teams/5179/data/data-tables/dcas9-data-table.png)

---

#### **Data Analysis**:

Overall treatments with the dCas9 cosmid with a KanR targeting crRNA spacer insert produced a smaller percentage of transductants with resistance to both chloramphenicol and kanamycin, while producing a comparable number of chloramphenicol resistant colonies. First, this indicates that the dCas9 cosmid can be packaged into transducing units as effectively as the original P4 cosmid. Additionally, it suggests some silencing effect on HL 713’s kanamycin resistance among transductants.

---

#### **Interpretation**:

Though this experiment was not sufficiently powered to draw any firm quantitative conclusions about the potency of the dCas9 cosmid’s silencing effect on this target, we wanted to characterize it somewhat, and this result is encouraging enough to motivate further characterization of this device with a different experimental design.

### **Deployed Cas9 Gene Targeting System into the Model Colon (CLICK HERE)**

**Experiment**: We deployed the kanamycin targeting P4 Cosmid system into the model colon in order to determine the effect and persistence of the system in conditions simulating a human colon.

---

#### **Raw Data**:

---

---

#### **Data Analysis**:

![Summary](https://static.igem.wiki/teams/5179/data/deployed-cas9-analysis-table-1.png)

---

#### **Interpretation**:

The Cas9 kanamycin targeting killing effect is significant with a p-value of 0.04 in the 3-chamber colon (EF1) and has an effect with a p-value of 0.15 in the single chamber colon (termed “Mini”). This suggests that the P4 cosmid system can induce sequence specific killing at the bacterial population level, even subject to the conditions of our simulated colon.

---

### **Quantifying RFP Fluorescent Output *in vitro* (CLICK HERE)**

**Experiment**: We engineered the P4 cosmid to include the red fluorescence protein, and afterwards quantified the fluorescence of E. coli strain HL 713 transduced with the RFP cosmid.

---

#### **Raw Data**:

![Summary](https://static.igem.wiki/teams/5179/data/quantifying-rfp-fluorescent-output-in-vitro-data-1.png)

#### Table. Fluorescence measurement of E. coli RFP results from the soil microcosms. Sample 1: Negative Control - LB, Sample 2: Positive Control - E. coli HL 713 with RFP

---

#### **Data Analysis**:

The E. coli HL 713 transduced with the RFP cosmid was significantly more fluorescent than the negative control of LB.

---

#### **Interpretation**:

The E. coli HL 713 was successfully infected with the P4 RFP cosmid and showed a fluorescent phenotype.

### **Quantifying RFP Fluorescent Output in situ (CLICK HERE)**

**Experiment**: We deployed the RFP cosmid into soil microcosms 5-8 in order to determine the efficacy and persistence of the RFP cosmid in the natural environment of the soil.

---

#### **Raw Data**:

Ten samples were collected from distinct colonies from soil microcosm plating on LB with kanamycin and chloramphenicol.

![Sample Image](https://static.igem.wiki/teams/5179/graphics/engineering/eng-rfp-table.png)

#### A1: Negative control - uninfected E. coli HL 713, A2: Positive control - E. coli HL 713 infected with RFP, A3: Negative control - water. A4: Sample 1, A5: Sample 2, A6: Sample 3, A7: Sample 4, A8: Sample 5, A9: Sample 6, A10: Sample 7, A11: Sample 8, A12: Sample 9, B1-11: Empty wells, B12: Sample 10.

---

#### **Data Analysis**:

The above table is a red fluorescence protein measurement for the 10 colonies from soil samples. Sample 6 showed a significantly higher measurement than the negative control showing that this sample was fluorescing, but not to its fullest extent.

---

#### **Interpretation**:

This, in combination with the results from in-vitro testing, indicate that many RFP cosmid transductants do not show a fluorescent phenotype after re-isolation from soil. A likely explanation is that the stressors of the soil environment cause E. coli HL 713 to express the RFP at lower levels than when observed in more favorable conditions.

---

## **Overcoming Prophage Immunity**

### **Resurrected Novel Satellites Found in *Mycobacterium aichiense* (CLICK HERE)**

**Experiment**: We plated novel mycobacteria phage satellites, which we call “phagelets”, that were stored in lysates for 6 years, to increase their titer.

---

#### **Raw Data**:

Out of the 14 “phagelets” that we attempted to use to infect *M. aichiense* by phage plating, four formed plaques.

Originally, these phage satellites showed one or two plaques after the initial plating of large quantities of old lysate, but were grown out to a titer of 107 to 108. The non-satellite phage reached a titer of 1 x 109.

---

#### **Data Analysis**:

Our team managed to increase the titer of four “phagelets,” two of which were not sequenced or characterized before. What we found was one was in fact not a phage satellite and was a standard phage that shared large similarities with the prophage in M. aichiense called HerbertWM (more on it in discovery). While the satellites worked to excise HerbertWM, there was no evidence of HerbertWM when the phage lysed the cell.

---

#### **Interpretation**:

Although all mycobacteria satellites were not revived, the fact that four were still viable shows the ability of our developed procedures to revive denatured lysate.

### **Colony PCR on Mycobacterium aichiense (CLICK HERE)**

**Experiment**: We confirmed Mycobacteria aichiense colonies in the soil microcosms by using colony PCR.

---

#### **Raw Data**:

---

![Summary](https://static.igem.wiki/teams/5179/graphics/experiments/experiment-thaw.webp)

---

#### **Data Analysis**:

Two out of the seven colonies randomly selected from bacterial soil platings had bands on the gel at the expected length, identifying M. aichiense presence in these samples. The positive control had an expected band and the negative had no band.

---

#### **Interpretation**:

The results confirm the shape and size of the colonies that are M. aichiense. We recounted the colonies accordingly to yield more accurate results.

### ***In situ* Tests of Novel Mycobacteria Satellites** (CLICK HERE)

**Experiment**: *M. aichiense* and the “phagelets”, novel mycobacteria satellites, were deployed into soil microcosms 13-16. *M. aichiense* and a novel mycobacteria phage were deployed into microcosms 9-12. This experiment was to determine the efficacy of the phage satellites and their relative effectiveness to an *M. aichiense* phage.

---

#### **Raw Data**:

---

#### **Data Analysis**:

![Summary](https://static.igem.wiki/teams/5179/data/in-situ-tests-of-phagelets-analysis-1.png)

These results indicate that neither the mycobacteria phage satellite nor the non-satellite mycobacteria phage had a significant killing effect on M. aichiense.

---

#### **Interpretation**:

There was no significant effect of the “phagelets” on the population of M. aichiense in the soil microcosms. However, the phagelets continued to survive in the soil due to their confirmed presence via phage plating.

---

## **Host Expansion**

---

**### Phagelet System Characterization: Circularity (CLICK HERE)**

**Experiment**: We wanted to determine whether the “phagelets” have circular or linear DNA. We amplified a large section of the phagelet genome to confirm the expected size, as well as a smaller overlapping section to cover the rest of the genome from the other direction.

---

#### **Raw Data**:

![Summary](https://static.igem.wiki/teams/5179/data/host-expansion-1.png)

#### Figure. Electrophoresis gel from long range PCR. Ladder is Generuler 1kb+. Lane of interest is labeled “ChrisB + Will G Long”

---

![Summary](https://static.igem.wiki/teams/5179/data/host-expansion-2.png)

#### Figure. Electrophoresis gel from short range PCR. Ladder is Generuler 1kb+. Lane of interest is labeled “ChrisB.”

---

#### **Data Analysis**:

On gel 1, we expected a band for “Chris + Will G Long” at around 11000bp, and we observed a band right around 11kb. On gel 2, we expected a band for ChrisB at 2200 bp, and observed a band at around 2200bp.

---

#### **Interpretation**:

We observed amplicons of the expected size, confirming that the “phagelet” genomes to be circular.

### **Tested Infection Range of HerbertWM and the “Phagelets” (CLICK HERE)**

**Experiment**: In order to expand the host range of HerbertWM, we confirmed that HerbertWM does not infect M. smegmatis to create chimeric tail fibers that would be compatible with M. smeg.

---

#### **Raw Data**:

No plaques formed on the M. smeg plates with each mycobacteria satellite, but each formed plaques on the plates containing M. aichiense, meaning that the phage satellites were present but were not compatible with M. smeg.

![Summary](https://static.igem.wiki/teams/5179/data/host-expansion-3.png)

#### Figure. Novel satellite phages, termed “phagelets”, plated with M. aichiense. Clear plaques visible in the image indicate cell lysis.

---

#### **Data Analysis**:

Plaques are visible for found with both “phagelets” for M. aichiense infection.

---

#### **Interpretation**:

Both HerbertWM and our novel phage were capable of M. aichiense infection, and negative for interaction with M. smeg.

### **"Phagelet" System Characterization: Isolating HerbertWM DNA on an Electrophoresis Gel (CLICK HERE)**

**Experiment**: We wanted to confirm phagelet infection was associated with excision and packaging of HerbertWM.

---

#### **Raw Data**:

![Summary](https://static.igem.wiki/teams/5179/data/host-expansion-4.png)

#### Figure. Electrophoresis gel. Lane 1 contains a high MW ladder, and lanes 2-4 contain phagelet lysate extracted DNA (labeled ChrisB) or novel M. aichiense phage (labeled EllieK).

---

#### **Data Analysis**:

We observed bands at 50kb and 11kb for the HerbertWM and “phagelet” lysate. After PCR, we observed clear bands for each of these amplicons.

---

#### **Interpretation**:

The amplicons match the genome sizes of HerbertWM and the phagelets, confirming the presence of both of the constructs in the lysate.

### **Electroporating Mycobacteria Phage Satellite DNA into *M. aichiense* (CLICK HERE)**

**Experiment**: In order for HerbertWM to use our chimeric tail fibers, it would need to be excised and packaged. We wanted to determine if extracted and purified DNA taken from “phagelet” lysate could be transformed into competent M. aichiense and have a killing effect; this would act as a positive control to show that HerbertWM would lyse the cell regardless of the presence of the chimeric tail fiber plasmid.

---

#### **Raw Data**:

No plaques formed on the plate. There was a full M. aichiense lawn on the plate but no plaques formed after three days.

---

#### **Data Analysis/Interpretation**:

Electroporation of “phagelet” DNA was unsuccessful and the phage satellite is unable to lyse the bacteria by electroporation.

### **Training and Testing the Phage-Bacteria Interaction Prediction Model with Known Data (CLICK HERE)**

**Experiment**: We wanted to test whether a machine-learning model could accurately predict interactions between bacteria and phage using unbiased and balanced datasets.

---

#### **Raw Data**:

Below is an excerpt of interactions predicted by the model.

![Summary](https://static.igem.wiki/teams/5179/data/training-and-testing-the-phage-bacteria-interaction-prediction-model-with-known-data-1.png)

#### Figure. Top 20 phage-bacterium pairs predicted as able to interact by the machine-learning model trained with our novel interaction database.

---

#### **Data Analysis**:

After 20 full runs with different allocations of training and test set data each time (test set size = 220, training set size = 800), the model was found to have an accuracy of 68% with p < 0.001, using 50% as the expected prediction accuracy for a totally random model where our distribution of true and false interaction pairs in the test and training sets was even.

---

#### **Interpretation**:

The model’s predictions are not random, indicating an ability to pick up on patterns in phage-bacteria interaction using genomic information.

## PDE Model

---

#### For more information about our modeling, please visit our [modeling page](https://2024.igem.wiki/william-and-mary/model).

---

### **Colon Models Key Results: (CLICK HERE)**

* Both replicative and non-replicative of each system significant impact the indicator strain population showing an infected population on the order of 1e5 for the RFP and non-targeting CRISPR system and a similarly effective killing effect for the kanamycin targeting CRISPR system.
* A preliminary result indicated that the non-replicative system would have less of an effect than the replicative system which informed our experimental design to test the replicative system.
* The replicative system has more of an effect than the non-replicative system.
* The effects of each system in the model human colon did not disperse to the end of the colon which aligns with some of our experimental results.

We developed a novel partial differential equation (PDE) model for our satellite phage systems in the human colon. This model uses advection-diffusion fluid dynamics, Monod growth kinematics, and population dynamics to describe the interactions and efficacy of our satellite phage systems. Our results demonstrate that both our replicative and non-replicative systems have a measurable effect and our replicative systems have more of an effect than the non-replicative satellite part systems.

#### Influence on Experimental Design

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An important early result that we found from a preliminary model of the colon was that given the titer and efficiency of the transducing units, the non-replicative systems would not have a significant effect. Therefore, we tested both the non-replicative and replicative systems to confirm this result and show a more significant effect.

![Summary](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-68.png)

#### Figure. Early result of the PDE model for the non-replicative kanamycin targeting CRISPR system in the model colon.

The models of the colon, particularly the RFP systems, clearly demonstrate significant infectivity reaching an infected population on the order of 1e5 CFU in a one week time span. This PDE model also aligns with our experimental findings that the RFP system was effective, however it did not reach as significantly to later segments of the colon. This result is also seen in the soil microcosm models with infected populations reaching 1e7 CFU.

![Summary](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-69.png)

#### Figure. **Difference between infection of replicative and non-replicative systems.** Left: *E. coli* RFP replicative system infected indicator strain in the colon. Right: *E. coli* RFP non-replicative system infected indicator strain in the colon.

The model of the colon shows significant efficacy for the satellite phage systems. The replicative model for the RFP system in the colon shows higher infectivity than the non-replicative system.

![Summary](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-70.png)

#### Figure. **Model of** ***E. coli*** **Replicative RFP System in the Colon**. In order the subplots are the indicator strain (CFU), infected indicator strain (CFU), satellite (PFU), replicative system (CFU), infected replicative system (CFU), colon microbial community (CFU), and nutrient (%).

![Summary](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-71.png)

#### Figure. **Model of** ***E. coli*** **Non-Replicative RFP System in the Colon.** In order the subplots are the indicator strain (CFU), infected indicator strain (CFU), satellite (PFU), colon microbial community (CFU), and nutrient (%).

Interestingly, in contrast to the results of the previous models, the *E. coli* replicative kanamycin targeting CRISPR system has a higher population of indicator strain (CFU) than the non-replicative system.

![Summary](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-72.png)

#### Figure. **Model of** ***E. coli*** **Replicative Kanamycin Targeting CRISPR System in the Colon.** In order the subplots are the indicator strain (CFU), satellite (PFU), replicative system (CFU), infected replicative system (CFU), colon microbial community (CFU), and nutrient (%).

![Summary](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-73.png)

#### Figure. **Model of** ***E. coli*** **Non-Replicative Kanamycin Targeting CRISPR System in the Colon.** In order the subplots are the indicator strain (CFU), satellite (PFU), colon microbial community (CFU), and nutrient (%).

### **Soil Microcosm Models Key Results: (CLICK HERE)**

* Both replicative and non-replicative of each system significant impact with infected populations ranging from 2e5 to 3.5e5
* The replicative system has more of an effect than the non-replicative system. The replicative and non-replicative RFP models show clear results that the non-replicative system has less effect than the replicative system showing peak populations of approximately 2e5 and 2.5e5 CFU of infected indicator strain respectively.

We developed a novel partial differential equation (PDE) model for our satellite phage systems in the soil. This model uses advection-diffusion fluid dynamics, Monod growth kinematics, and population dynamics to describe the interactions and efficacy of our satellite phage systems. Our results demonstrate that both our replicative and non-replicative systems have a measurable effect and our replicative systems have more of an effect than the non-replicative satellite part systems.

Similar results can be seen in the models of the soil microcosms as in the results for the colon models. The replicative and non-replicative RFP models show clear results that the non-replicative system has less effect than the replicative system showing peak populations of approximately 2e5 and 2.5e5 CFU of infected indicator strain respectively.

![Summary](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-74.png)

#### Figure. **Difference between infection of replicative and non-replicative systems.** Left: Indicator population of *E. coli* Replicative Kanamycin Targeting CRISPR System. Right: Indicator population of *E. coli* Non-Replicative Kanamycin Targeting CRISPR System in Soil.

The soil microcosm models of the *E. coli* replicative and non-replicative kanamycin targeting CRISPR system show similar higher killing efficiency for the replicative system than the non-replicative system.

![Summary](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-75.png)

#### Figure. **Model of** ***E. coli*** **Replicative RFP System in Soil.** In order the subplots are the indicator strain (CFU), attached indicator strain (CFU), infected indicator strain (CFU), attached infected indicator strain (CFU), satellite (PFU), replicative system (CFU), attached replicative system (CFU), soil microbial community (CFU), and nutrient (%).

![Summary](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-76.png)

#### Figure. **Model of** ***E. coli*** **Non-Replicative RFP System in Soil.** In order the subplots are the indicator strain (CFU), attached indicator strain (CFU), infected indicator strain (CFU), attached infected indicator strain (CFU), satellite (PFU), soil microbial community (CFU), and nutrient (%).

The above two figures show the results for the replicative and non-replicative *E. coli* RFP system in the soil microcosms. The non-replicative system has a peak population of infected indicator strain between 2e5 and 2.5e5. The replicative system has a peak population of infected indicator strain between 2.5e5 and 3e5.

![Summary](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-77.png)

#### Figure. **Model of** ***E. coli*** **Replicative Kanamycin Targeting CRISPR System/** ***M. aichiense*** **and Phagelet System in Soil.** In order the subplots are the indicator strain (CFU), attached indicator strain (CFU), satellite (PFU), replicative system (CFU), attached replicative system (CFU), soil microbial community (CFU), and nutrient (%).

![Summary](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-78.png)

#### Figure. **Model of** ***E. coli*** **Non-Replicative Kanamycin Targeting CRISPR System in Soil.** In order the subplots are the indicator strain (CFU), attached indicator strain (CFU), satellite (PFU), soil microbial community (CFU), and nutrient (%).

The two above figures show the replicative and non-replicative kanamycin targeting CRISPR system. The replicative system has a greater killing effect than the non-replicative system with a minimum population of indicator strain around 3e5 and 3.5e5 respectively.

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#### Refined parameters from comparison with Microcosm Data

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After preliminary results from the soil microcosms we were able to adjust the model parameters to better represent the dynamics.

![Summary](https://static.igem.wiki/teams/5179/graphics/results/math-table-new.png)

The diffusion rate of bacteria and transducing units was determined by spread from the ‘center’ to the ‘edge’ measurements using the following equation to estimate the diffusion coefficient

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![Sample Image](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-96.png)

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The burst size was re-estimated from the effect size. The diffusion rate of nutrients was re-adjusted accordingly to the new diffusion rate of bacteria and transducing units.

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![Sample Image](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-83.png)
![Sample Image](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-84.png)
![Sample Image](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-85.png)

Figure. Model of E. coli Replicative RFP System in Soil. In order the subplots are the indicator strain (CFU), attached indicator strain (CFU), infected indicator strain (CFU), attached infected indicator strain (CFU), satellite (PFU), replicative system (CFU), attached replicative system (CFU), soil microbial community (CFU), and nutrient (%).

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![Sample Image](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-86.png)
![Sample Image](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-87.png)

Figure. Model of E. coli Non-Replicative RFP System in Soil. In order the subplots are the indicator strain (CFU), attached indicator strain (CFU), infected indicator strain (CFU), attached infected indicator strain (CFU), satellite (PFU), soil microbial community (CFU), and nutrient (%).

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![Sample Image](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-88.png)
![Sample Image](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-89.png)

Figure. Model of E. coli Replicative Kanamycin Targeting CRISPR System in Soil. In order the subplots are the indicator strain (CFU), attached indicator strain (CFU), satellite (PFU), replicative system (CFU), attached replicative system (CFU), soil microbial community (CFU), and nutrient (%).

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![Sample Image](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-90.png)
![Sample Image](https://static.igem.wiki/teams/5179/graphics/modeling/modeling-91.png)

Figure. Model of E. coli Non-Replicative Kanamycin Targeting CRISPR System in Soil. Model of E. coli Non-Replicative Kanamycin Targeting CRISPR System in Soil. In order the subplots are the indicator strain (CFU), attached indicator strain (CFU), satellite (PFU), soil microbial community (CFU), and nutrient (%).

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These new parameters from the soil microcosm experiments show a further spread which matches our experimental results.

# References

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