The benefits of yeast being used as a biosensor have opened new avenues for drug discovery, understanding molecular pathways involved in disease pathogenesis, protein–protein interaction studies, understanding of the molecular architecture of complex protein assemblies, identifying mutations in proteins that have significance in determining the functional differences, and detecting pollutants from the environment. Yeast has already proved its benefits in studying protein–protein interactions, drug screening against several diseases, including cancer, Alzheimer’s disease, Parkinson’s disease, and others, detection of pollutants, and diagnosis of diseases. 

Because endocrine disrupting compounds are usually found in minimal amounts (ng/L-1) in the water, one of the most common techniques for their detection is liquid chromatography-tandem mass spectrometry (LC-MS). But this approach is very expensive to perform on a routine basis, requiring both skilled personnel and a robust quality assurance/control program. Maybe biology is the answer... The YES-YEAST (yeast estrogen sensor) are a genetically modified strain of Saccharomyces cerevisiae (W303) that contain Human Estrogen Receptor (HER). They act as a biosensor: the input is estrogen and the output is a yellow color change. More importantly, the YES yeast are extensions of our bodies: what binds to their receptors also bind to ours, demonstrating a DIRECT biological response to xenoestrogens on our bodies. The same process of estrogen binding and activation is reproduced in the yeast. This bioassay detection method is more sensitive than the chemical approach either detecting estrogenic target compounds at lower concentrations, other non-target compounds, and synergistic effects that chemical methods and machines fail to detect.

This is both a "part two" of the Open Source Estrogen project as well as the Final Project for HTGAA, which combines lectures (1) "Synthetic Minimal Cells" with Kate Adamala, (2) "Bio-production" with Patrick Boyle, (3) "Computational Protein Design" with Srivatsan Raman, and (4) "Tools, Automation, and Open Hardware.

A badge that helps hospital lab workers better track their daily radiation exposure, enabling a faster assessment of tissue damage that could lead to cancer. Rather than building portable cellars or ovens, Purdue University researchers have engineered yeast "microbreweries" within disposable badges made of freezer paper, aluminum, and tape. Simply adding a drop of water activates the yeast to show radiation exposure as read by an electronic device.
The success of the badge lies in the quick and measurable response of yeast to radiation: The higher the radiation dose, the higher the percentage of yeast cells that die. Wetting the badge activates the cells that are still alive to eat glucose and release carbon dioxide—the same fermentation process responsible for brewing beer and making bread rise.

You 11:52 PM (Edited)
I am interested in plastic degrading fungi. How can I apply synthetic bio to this concept? I have a few ideas but it will be nice to get expert advice.

TA, Val Thompson, ChiTownBio, Chicago 11:57 PM
You could look at the pathways responsible for making the enzymes in mushrooms like oysters, that break down plastic, like lactase, manganese peroxidase, and lignin peroxidase, and maybe boost one of those pathways in some manner? The specifics beyond that are above me at this point in time.

While fungi offer superior PTMs and secretion, they often have longer cell cycles (12–24 hours) compared to bacteria (20–60 minutes), and their genetic toolkit is often considered less developed compared to Escherichia coli.

Doing synthetic biology in fungi, particularly filamentous fungi and yeasts, offers several advantages over bacteria (such as E. coli) due to their eukaryotic nature, metabolic complexity, and specialized secretion systems. Key advantages include superior protein folding and secretion, a vast repertoire of secondary metabolites, and higher environmental robustness.

How can you train your mycelium to degrade synthetic petroleum derived polymers and environmental pollutants in order to harness its biocomputational capacity for soft robotic actuation and control? This project addresses the growing crisis of plastic pollution by exploring enzymatic degradation and mycoremediation processes as both an environmental and technological solution. How to train your mycelium investigates natural and engineered fungal systems as platforms for bioremediation and as living components in biohybrid robotics. The hypothesis is that enhancing or introducing plastic-degrading enzyme pathways—through selective exposure or through genetic modification will alter metabolic activity and produce distinct electrophysiological patterns that can be used as control inputs for actuating a soft robotic system. The overall objective is to train mycelium to metabolize synthetic polymers and pollutants in a controlled in vitro environment, while exploring and harnessing the biocomputational capacity of its electrical signaling as a biological interface for soft robotic control and actuation. 

The first aim of my final project is to explore the enzymatic degradation process of mycelium to break down different types of synthetic petroleum derived polymers (plastic) and other environmental pollutants while training my mycelium (Pleutotus Ostreatus) through exposure (naturally) vs synthetically through genetically optimising enzymatic degradation process in strains of mycelia that do not posses this enzymatic capability. I plan to do so by utilising mycelium cultivation techniques with a variety of scaffolds (substrates) containing plastics and pollutants as well as DNA sequencing, writing and editing.

The development aim of the project is to record enzymatic degradation capacity and analyse it via measuring bioelectrical activity via electrodes, and interfacing and translating these signals into actuation with microcontroller-driven soft robotic systems. The finaldevelopmental aim is to create a 3D printable or casted silicone soft robot like the ones below to combine with my mycelium brain.

Harnessing sensing and mycoremediation capabilities of mycelium to produce and utilise regenerative  forms of energy from the enzymatic degradation process. Enabling mycelium to be utilised  as the living component in biohybrid robotics. Enabling future application experimentation around fungal materials through engineering additional sensing capabilities for microplastic biosensing and bioremediation.

Pleurotus ostreatus as a Dual-Function Plastic-Degrading and Electrophysiological Biosensor Platform

Background

Literature Context
Austin et al. (2018) first characterized Ideonella sakaiensis PETase, demonstrating that a bacterially derived enzyme could hydrolyze PET under mild aqueous conditions, producing TPA and mono(2-hydroxyethyl) terephthalate (MHET) as measurable products. Lu et al. (2022) subsequently applied machine learning-guided directed evolution to generate FAST-PETase, a five-mutation variant with dramatically enhanced activity at ambient temperatures (~50°C), representing the current state-of-the-art for biological PET degradation. Mishra et al. (2024) demonstrated that mycelial electrophysiological signals in Ganoderma sessile can be recorded extracellularly, decoded computationally, and used to mediate sensorimotor control of robotic platforms, showing that fungal spike trains carry actionable environmental information with reproducible frequency and amplitude characteristics correlated with substrate contact events. Together, these three studies establish both the enzymatic toolkit for PET degradation and the electrophysiological framework for fungal environmental sensing, yet no study has combined these capabilities in a single engineered organism — a gap this project directly addresses.

Innovation
This project is the first to propose expressing FAST-PETase in a macroscopic, self-propagating fungal mycelial network rather than a purified enzyme or bacterial host. By leveraging P. ostreatus as a chassis, the system is self-sustaining, scalable through substrate colonization, and capable of operating in unstructured environments without bioreactor containment. The integration of electrophysiological biosensing with enzymatic function, inspired by Mishra et al. (2024), creates a genuinely novel class of living material that both acts on and reports its environment.

Significance
Global PET production exceeds 70 million tonnes annually, and less than 30% is recycled, with the remainder entering landfills or ecosystems where it persists for centuries. Enzymatic degradation at ambient temperature offers a low-energy alternative to thermal recycling, but deployment at scale requires a self-sustaining biological chassis rather than purified protein. P. ostreatus is already used industrially for lignocellulosic waste degradation and is genetically tractable, making it an ideal platform for environmental biotechnology. The electrophysiological sensing layer adds a real-time monitoring capability that could transform passive bioremediation into an active, data-generating environmental intervention. This project also advances the broader field of living materials by demonstrating that fungi can be engineered to simultaneously perform a chemical function and report on that function through intrinsic bioelectrical signals.

Bioethical Considerations
Ethics: Engineering P. ostreatus to express a heterologous enzyme raises questions about the release of genetically modified fungi into open environments. Any field deployment would require rigorous ecological risk assessment, including assessment of horizontal gene transfer potential, competitive fitness relative to wild-type strains, and impact on native fungal communities. Transparent public engagement and regulatory compliance with EPA and USDA biosafety frameworks are essential before any environmental application.
Risk Mitigation and Responsible Implementation: Laboratory work will be conducted under BSL-1 containment with no environmental release. Containment strategies such as auxotrophic kill switches or synthetic nutrient dependencies will be incorporated in Aim 2 to ensure that engineered strains cannot persist outside controlled conditions. All electrophysiology and robotics integration work in Aim 3 will occur in closed laboratory systems. Collaboration with SecureDNA for sequence screening and with Ginkgo Bioworks for biosafety review is recommended prior to any scale-up.

Columns 1–2:   TPA standard curve (0, 1, 5, 10, 25, 50, 100 µM — n=4 replicates each)
Columns 3–4:   Wild-type P. ostreatus lysate (negative control)
Columns 5–6:   Purified FAST-PETase protein 1 µg/mL (positive control)
Columns 7–22:  Transformant lysates (16 transformants × 2 replicates)
Columns 23–24: Buffer-only blank

LOCUS       pGPDA_FASTpetase_HIS     5521 bp    DNA     circular SYN
DEFINITION  Pleurotus ostreatus expression vector encoding codon-optimized
            FAST-PETase with C-terminal 6xHis epitope tag under gpdA promoter.
ACCESSION   .
FEATURES             Location/Qualifiers
     promoter        1..1000
                     /label="gpdA promoter (A. nidulans)"
                     /note="Constitutive fungal promoter active in basidiomycetes"
     CDS             1001..1921
                     /label="FAST-PETase-6xHis (codon-optimized for P. ostreatus)"
                     /codon_start=1
                     /product="FAST-PETase variant (Lu et al. 2022)"
                     /note="5 key mutations: N233K/R224Q/S121E/D186H/R280A"
                     /note="C-terminal 6xHis epitope tag for Western blot detection"
     terminator      1922..2221
                     /label="trpC terminator (A. nidulans)"
     CDS             2300..3358
                     /label="Hygromycin resistance cassette (hph)"
                     /note="Fungal selection marker"
     rep_origin      3500..4163
                     /label="pUC ori"
     CDS             4300..5160
                     /label="KanR"
                     /note="Kanamycin resistance for E. coli propagation"
ORIGIN
        1 [Full sequence to be completed by Twist Bioscience whole plasmid synthesis]
//

Expanded Technique 1 — Label-Free TPA Fluorescence Assay

Terephthalic acid (TPA) is the primary small molecule product released when FAST-PETase hydrolyzes PET plastic. Crucially, TPA exhibits native fluorescence at excitation 315 nm and emission 425 nm, meaning no fluorescent label or reporter gene is required — the assay directly measures enzymatic degradation activity through the accumulation of a reaction product. This makes the assay highly specific: only genuine PET hydrolysis generates TPA signal, eliminating false positives from non-specific fluorescence. The assay is fully compatible with 384-well miniaturization, enabling screening of 16 transformants with replicates and controls in a single plate run using the Spark Plate Reader at Ginkgo Bioworks. Combined with the 6xHis Western blot — which confirms protein presence — the TPA assay provides the second and functionally critical line of evidence: that the expressed protein is enzymatically active.

Expanded Technique 2 — Fungal Electrophysiology (Mishra et al. 2024 Framework)

Mishra et al. (2024) demonstrated that Ganoderma sessile mycelial networks generate spontaneous and stimulus-evoked extracellular action potential-like spikes that can be recorded with non-invasive AgCl electrodes and decoded to control robotic actuators in real time. The key finding was that spike train frequency and amplitude change reproducibly and specifically in response to substrate contact, chemical stimuli, and light, suggesting that mycelial electrophysiology encodes environmental information in a manner analogous to peripheral nervous system signaling. Applying this framework to engineered P. ostreatus expressing FAST-PETase would allow the mycelial network to simultaneously degrade PET and generate a bioelectrical signal reporting on substrate contact — creating a self-monitoring bioremediation system. This technique requires only standard electrophysiology hardware (AgCl microelectrodes, differential amplifier, analog-to-digital converter) and open-source spike sorting software, making it accessible for laboratory implementation in Aim 3.

Project Validation
Validation Choice
The primary validation experiment is the TPA fluorescence assay comparing engineered P. ostreatus transformants to wild-type controls in the automated 384-well pipeline. This experiment directly tests the central hypothesis — that FAST-PETase expressed in P. ostreatus confers measurable PET-degrading activity — and produces quantitative, instrument-verified data that is orthogonal to the Western blot protein detection result.
Step-by-Step Validation Protocol
Grow fungal cultures: Inoculate 3 confirmed FAST-PETase transformants and 1 wild-type P. ostreatus strain into 50 mL PDB liquid culture. Incubate at 25°C for 5 days with shaking at 150 rpm.
Harvest and lyse: Collect mycelium by filtration. Lyse by bead beating (0.5 mm glass beads, 3 × 30 s cycles) in 50 mM glycine-NaOH pH 9.0 buffer. Clarify lysate by centrifugation at 10,000 × g for 10 min using the HiG Centrifuge.
Prepare PET nanoparticle substrate: Resuspend PET nanoparticles at 1 mg/mL in assay buffer immediately before use.
Prepare TPA standard curve: Dispense TPA standards (0, 1, 5, 10, 25, 50, 100 µM) into columns 1–2 of a 384 Greiner black clear-bottom plate using the Echo525.
Dispense samples: Use Bravo-384 to dispense 50 µL of each lysate into designated wells per the plate map (columns 3–22). Include buffer-only blank in columns 23–24.
Add substrate: Use Bravo-384 to add 10 µL PET nanoparticle suspension to all sample and control wells.
Seal plate: Seal with Plateloc thermal sealer to prevent evaporation during incubation.
Incubate: Place sealed plate in Inheco Plate Incubator at 50°C for 24 hours.
Unseal plate: Remove seal using XPeel.
Read fluorescence: Measure fluorescence on Spark Plate Reader at Ex 315 nm / Em 425 nm.
Quantify TPA: Calculate TPA concentration in each well from the standard curve using plate reader software.
Normalize to protein: Run BCA assay on remaining lysate in a parallel 384-well plate using PHERAstar FSX. Calculate specific activity as µmol TPA released / mg total protein / hour.
Statistical analysis: Apply one-way ANOVA with Dunnett's post-test comparing each transformant to wild-type. Significance threshold p < 0.05.
Techniques Used
The validation combines automated liquid handling (Echo525, Bravo-384), thermal incubation (Inheco), and multimode plate reading (Spark, PHERAstar FSX) into a single integrated pipeline that minimizes manual variability. The TPA fluorescence readout is label-free and directly reports on enzymatic activity rather than protein abundance, making it functionally specific. The BCA normalization step ensures that differences in TPA signal reflect genuine differences in specific enzymatic activity rather than differences in total protein loaded. Statistical analysis with Dunnett's test controls for multiple comparisons across transformants, ensuring that any positive hit is rigorously distinguished from background.
Hypothetical Data and Graph Concept
Hypothetical result: Wild-type P. ostreatus produces 2 µM TPA (background hydrolysis). Transformant 1 produces 4 µM TPA (not significant). Transformant 2 produces 18 µM TPA (p < 0.05). Transformant 3 produces 11 µM TPA (p < 0.05). Purified FAST-PETase positive control produces 45 µM TPA.

Troubleshooting
If no TPA signal is detected above wild-type, the first step is to confirm protein expression by Western blot — if the His-tag band is absent, the issue is upstream of enzyme activity (transformation, transcription, or translation failure) rather than catalytic function. If the Western blot shows protein but TPA signal is absent, the enzyme may be misfolded in the fungal host; adding a flexible (Gly₄Ser)₃ linker between the His tag and the coding sequence, or switching to a lower-expression promoter to reduce aggregation, would be the next troubleshooting step. If wild-type background TPA signal is unexpectedly high, the PET nanoparticle substrate should be checked for chemical contamination with free TPA, and a no-substrate control should be added to the plate layout to subtract background. If transformant-to-transformant variability is very high, this likely reflects positional effects of random genomic integration, and targeted integration into a defined safe-harbor locus should be considered for Aim 2.
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PROJECT MAIN GOAL: Increase the stability of the L protein

GROUP PROPOSAL: We will use the same workflow than in previous HW (e.g. mutagenesis) but adapt it to specific aim(s) based on HW reading material of WEEK 04 (e.g.  shorten the L protein to make it not dependant on bacterial chaperone DnaJ anymore).

METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLLEAVIRTVTTLQQLLT

MAKQDYYEILGVSKTAEEREIRKAYKRLAMKYHPDRNQGDKEAEAKFKEIKEAYEVLTDSQKRAAYDQYGHAAFEQGGMGGGGFGGGADFSDIFGDVFGDIFGGGRGRQRAARGADLRYNMELTLEEAVRGVTKEIRIPTLEECDVCHGSGAKPGTQPQTCPTCHGSGQVQMRQGFFAVQQTCPHCQGRGTLIKDPCNKCHGHGRVERSKTLSVKIPAGVDTGDRIRLAGEGEAGEHGAPAGDLYVQVQVKQHPIFEREGNNLYCEVPINFAMAALGGEIEVPTLDGRVKLKVPGETQTGKLFRMRGKGVKSVRGGAQGDLLCRVVVETPVGLNERQKQLLQELQESFGGPTGEHNSPRSKSFFDGVKKFFDDLTR