iGEM 2025 — Team ouc-china

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{% block lead %}This serves as a chronological record of your team's progress throughout the season. It documents your daily activities, experiments, discussions, and decisions.{% endblock %}
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# Notebook

Timeline of our project

CATALOG

[ ]

* [Wet Lab](#section1)
* [Model](#section2)
* [Hardware](#section3)

Wet Lab

1. Strain Screening, Methanol Utilization Mechanism Analysis, and Cell Engineering Modification

2024-09-02—2024-09-16:

Screening of Test Strains

- Isolated marine yeasts from mangrove samples in Hainan. After YPD activation and YNB pre-cultivation (28°C, 180 rpm, 12 h), the strains were transferred to YPM methanol medium (2% peptone, 1% yeast extract, 2% methanol v/v) for cultivation (28°C, 180 rpm, 24 h).

- Confirmed the melanin-producing *Aureobasidium pullulans* strain through morphological observation and sequencing verification, named **P16**.

- Simultaneously set up control experiments: compared growth differences between P16, non-methylotrophic *Saccharomyces cerevisiae*, and methylotrophic *Komagataella pastoris* X33. Based on initial growth and methanol utilization screening, selected **P16** as a candidate chassis for methanol metabolism.

2024-09-23—2024-10-30:

Genome Sequencing, Assembly, and Annotation (outsourced)

- The screened *Aureobasidium pullulans* strain **P16** was sent to Tsingke Biotechnology Co., Ltd. for whole-genome sequencing, assembly, and annotation. Results are expected within the experimental cycle and will be used for downstream pathway prediction.

- Plan: perform KEGG-based pathway prediction for methanol metabolism after receiving the sequencing and annotation results.

2024-11-04—2024-11-10:

Bioinformatics Prediction of Methanol Metabolism Pathway

- Based on P16 genome annotation, performed KEGG alignment to identify candidate methanol metabolism genes. Key enzyme genes identified include:

- Alcohol oxidase (*AOX*)

- Dihydroxyacetone synthase (*DAS*)

- Dihydroxyacetone kinase (*DAK*)

- Focused on predicting potential subcellular localization signals (e.g., peroxisomal targeting sequences) for the protein products of these genes to guide subsequent localization experiments.

2024-11-12—2024-11-28:

Subcellular Localization Analysis of AOX Protein

- Constructed AOX–GFP fusion expression vector.

- Constructed peroxisome marker vector *mCherry–SKL* (classic PTS signal).

- Co-transformed both constructs into P16 by electroporation.

- Fluorescence microscopy showed complete overlap of GFP signal with mCherry–SKL, confirming AOX localization in peroxisomes.

2024-12-02—2024-12-18:

Ribosomal Readthrough Simulation Experiment of DAS Protein

- UniProt localization prediction suggested DAS may distribute between peroxisomes and cytoplasm. To test readthrough-dependent localization, we designed three GFP fusion constructs:

- **Das**: sequence including up to the second stop codon (simulates natural readthrough).

- **Dascyt**: sequence including only up to the first stop codon (no PTS).

- **Daspex**: mutate the first stop codon to force readthrough and reveal downstream PTS.

- Co-transformed with *mCherry–SKL* into P16. Fluorescence imaging showed diffuse cytoplasmic GFP for all three constructs with no overlap with the peroxisome marker, so readthrough-dependent peroxisomal targeting for DAS was not supported by these experiments.

2024-12-19—2025-01-13:

Ribosomal Readthrough Simulation Experiment of DAK Protein

- Constructed three DAK fusion vectors: **Dak** (up to second stop codon), **Dakcyt** (up to first stop codon), and **Dakpex** (first stop codon mutated to force readthrough).

- After electroporation into P16 and fluorescence observation:

- **Dak** showed partial overlap with peroxisomes.

- **Dakcyt** exhibited cytoplasmic fluorescence only.

- **Dakpex** fully overlapped with peroxisomes.

- Conclusion: DAK contains a cryptic peroxisomal targeting signal that can be revealed by translational readthrough.

2025-02-24—2025-03-25:

Construction of Strains Overexpressing Heterologous DAS Gene

- Constructed and verified strains overexpressing heterologous DAS gene variants according to the designed vectors (Das/Dascyt/Daspex). See protocol files for vector maps and sequences.

- Subsequent phenotypic and localization assays planned to assess functional complementation and subcellular targeting in P16 background.

2025-03-26—2025-04-07:

Source and Vector Construction of Overexpressed DAS

- Source of the overexpressed gene: ***Das*** gene from the model methylotrophic yeast *Pichia pastoris* (PpDas).

- Vector construction: used a strong promoter to drive the fusion expression of **PpDas–GFP**, electroporated into P16, and obtained positive transformants by resistance screening.

2025-03-26—2025-04-07:

Determination of Strain Growth in Methanol Medium

- The OD600 of the PpDas-overexpressing strain at 48 h was **1.46 ± 0.01**, which was 12% higher than that of the wild-type strain (**1.29 ± 0.03**).

2025-04-08—2025-04-27:

Design of Strain Modification Scheme

- Initial strategy: "Enhancing the synthetic pathway" — introducing the ***yihx*** gene from *Escherichia coli*.

2025-05-01—2025-05-05:

Design of yihx Gene Expression Vector

- Vector elements: a strong promoter driving the fusion of ***yihx*–GFP**, with a resistance screening marker to ensure intracellular expression and selection.

2025-05-06—2025-05-21:

Construction of yihx Gene Expression Vector (outsourced)

- Entrusted to Tsingke Biotechnology Co., Ltd. for codon optimization based on P16 codon preference to improve heterologous expression efficiency.

- Vector verification: transformed into *E. coli* DH5α and confirmed correctness by colony PCR and Sanger sequencing.

2025-05-22—2025-06-02:

Construction of yihx Gene-Expressing Strains

- Electroporated the verified *yihx* expression vector into P16, screened positive clones on resistance plates, and designated the strain **Yihx**.

- Verification method: PCR amplification of the ***yihx*** gene from selected transformants.

2025-06-03—2025-06-10:

Determination of Glucose Production in YPM Medium

- Culture conditions: YPM medium with 20 g/L methanol, initial OD600=0.01, 28°C, 200 rpm, fermentation 48 h.

- Result: glucose production of the **Yihx** strain was **0.042 g/L**, not significantly different from wild-type (**0.040 g/L**) (P > 0.05).

2025-07-01—2025-07-27:

Further Optimization Design

- Optimization strategy: "Weakening the consumption pathway + enhancing the synthetic pathway" — knock out the glycolytic rate-limiting enzyme gene ***pfk*** in the **Yihx** background.

2025-08-04—2025-08-10:

Design of pfk Gene Knockout Vector

- Based on the CRISPR-Cas9-Am system, designed sgRNA targeting ***pfk*** (used CRISPOR to minimize off-target risk).

- Vector backbone: contains Cas9, AMA1 autonomous replication sequence, and NAT resistance marker.

2025-08-11—2025-08-28:

Construction of pfk Gene Knockout Vector

- Built the knockout vector by single-enzyme digestion (NotI) of the Cas9–NAT–AMA1 backbone and seamless cloning to insert the ***pfk***-sgRNA cassette.

- Verification: transformed into DH5α, confirmed sgRNA insertion and vector integrity by sequencing.

2025-09-01—2025-09-17:

Construction of yihx–Δpfk Strain

- Introduced the *pfk* knockout vector into the **Yihx** strain by protoplast transformation and screened on nourseothricin plates with a 50%→100% concentration gradient re-screening.

- Identification: PCR amplification of ***pfk*** showed the knockout strain lacked the target band; sequencing confirmed the deletion at the expected locus.

2025-09-18—2025-09-22:

Determination of Glucose Production of yihx–Δpfk

- Test conditions: same as the earlier glucose assay.

- Key result: glucose production of **yihx–Δpfk** was **0.844 g/L**, over 20× higher than wild-type (0.040 g/L) (P < 0.01).

2. Construction and Verification Experiment of CRISPR-Cas9-Am Gene Editing Vector

2024-11-15—2024-12-12:

Construction of First Version of CRISPR-Cas9-Am Vector

- Element selection: ARS autonomous replication sequence (from *S. cerevisiae*), **gpdA** promoter (from *Aspergillus* spp., driving Cas9), NAT selection marker, CYC1 terminator, and a U6-sgRNA cassette.

- Construction method: seamless cloning to assemble ~8 kb vector.

2024-12-16—2025-01-02:

Verification of First-Version Vector (Failure)

- Transformation target: P16 (electroporation).

- Failure phenotype: no resistant/red colonies recovered; suspected causes: poor ARS adaptability in non-model fungus P16 and low transcription efficiency of gpdA promoter in this host.

2025-01-20—2025-02-23:

Design of Second Version of CRISPR-Cas9-Am Vector

- Element replacement: ARS → **AMA1** (from *Aspergillus flavus*, more widely adaptable), **gpdA** → **TEF1** promoter (strong fungal promoter to improve Cas9 expression).

- Retained elements: NAT selection marker, CYC1 terminator, and the U6-sgRNA framework (verified to be effective in the cassette).

2025-03-03—2025-03-28:

Construction of the Second Version of CRISPR-Cas9-Am Vector

- Key steps:

- 1. Double-enzyme digestion of the p414-TEF1p-Cas9-CYC1t vector with SwaI/Kpn (37°C, 4 h) and recovery of the 7087 bp backbone.

- 2. Using fl4a-nat-loxp as the template, PCR amplification of the NAT expression cassette (primers NAT-oF/NAT-oR, with a PacI site appended to the R primer).

- 3. Single-enzyme digestion of the Cas9-NAT vector with PacI, and insertion of the AMA1 fragment (PCR-amplified from the *Aspergillus flavus* genome; primers contain NotI sites for downstream cloning).

- 4. Digestion of Cas9-NAT-AMA1 with NotI, and insertion of the gene-synthesized U6-sgRNA-scaffold-U6 fragment.

- Vector verification: confirmed the direction and integrity of each element through restriction enzyme digestion (SwaI/PacI double-enzyme digestion) and Sanger sequencing.

2025-03-31—2025-04-29:

Transformation of the Second-Version Vector into P16 and Phenotype Observation

- Target gene: ***Ade2*** (key gene for adenine synthesis; loss-of-function results in red colonies, facilitating visual screening).

- Transformation conditions: protoplast transformation, recovery and culture at 28°C for 48 h on nourseothricin-containing plates.

- Screening results: red colonies appeared on 100% of the nourseothricin-resistant plates. Five re-screened positive strains were selected for sequencing verification; Ade2 target mutations were detected in all five, with an observed editing efficiency between 50% and 80% depending on the plate/replicate.

2025-05-01—2025-05-05:

Design of Control Experiment — Homologous Recombination Knockout of Ade2

- Control purpose: compare knockout efficiency between CRISPR-Cas9 and traditional homologous recombination.

- Homology arm design: 1 kb upstream and 1 kb downstream arms flanking ***Ade2***, with the NAT marker included in the replacement cassette to enable selection.

2025-05-06—2025-05-25:

Construction of Ade2 Homologous Recombination Knockout Vector

- Construction method: overlap PCR amplification of the upstream and downstream homology arms and the NAT fragment, followed by seamless cloning into the pUC19 backbone.

- Verification: confirmed correct assembly by colony PCR and Sanger sequencing of the homology-arm–NAT junctions.

2025-05-26—2025-06-04:

Verification of Positive Rate for Homologous Recombination Knockout

- Detection target: screened 48 homologous recombination transformants by colony PCR.

- Result: only 3 strains amplified the expected positive band, giving a positive rate of 6.25% — markedly lower than the 50%+ editing efficiency observed with CRISPR-Cas9 in parallel experiments.

- Electrophoretogram: the positive band size matched the theoretical value (~2.5 kb); negative strains showed the wild-type band (~1.8 kb). (Raw gel images and sequencing traces archived in lab records.)

Model

2024-09-01 — 2024-09-03

Weight Freezing and Single-Residue-Token Inference Pipeline

- Froze the weights of the localized **SL-AttnESM-150M**, and established a "single-residue-token" inference pipeline for efficient per-residue attention extraction.

- Completed automatic download of the P16 strain proteome, performed CD-HIT deduplication, and constructed a marine yeast-specific evaluation database to ensure domain-specific benchmarking.

2024-09-04 — 2024-09-07

Batch Structure Prediction with ESMFold-v1

- Initiated GPU-parallel folding on ~3,800 sequences (length range 40–1,200 aa) using **ESMFold-v1**.

- Output included per-residue pLDDT scores and secondary structure annotations; generated a structural prior vector database (64-dimensional feature vectors) for downstream model inputs.

2024-09-08 — 2024-09-10

Fine-tuning Structure-aware Attention Pool

- Fine-tuned the structure-aware attention pooling using 0.3M reviewed localized proteins as training data, with 4-fold cross-validation over 20 epochs.

- Loss: weighted BCE + MCC combined loss; achieved macro-F1 = **0.893**.

- Enabled Linformer acceleration; measured inference time per protein on a single H100 card: < **0.35 s**.

2024-09-11 — 2024-09-15

Release of SL-AttnESM v1.0 Inference Image

- Released Docker image **SL-AttnESM v1.0** (sha256: `4f7a2c91`).

- External interface: FASTA text in → JSON probabilities for 10 eukaryotic compartments; local offline deployment completed on in-group workstation for cyclic batch calls.

2024-09-16 — 2024-09-20

Signal Module Integration — SignalP, NetGPI, TMHMM

- Integrated SignalP-6, NetGPI-3, and TMHMM-2 as parallel calling modules and packaged them into a "signal harvester" container.

- Measured average response time: **1.2 s** per sequence; merged outputs with SL-AttnESM into a unified JSON schema v0.2.

2024-09-21 — 2024-09-25

Core Orchestration — LocAgent

- Launched core orchestration named **LocAgent**, using LangChain + Llama-3-8B as the scheduler for task planning.

- Defined a four-step pipeline: structure prediction → localization probability → signal scanning → primer design.

- Exposed REST endpoints `/predict` and `/explain` (POST); typical end-to-end return time for a ranked experimental plan: < **45 s**.

2024-09-26 — 2024-09-30

Attention Visualization Upgrade

- Upgraded attention visualization: a PyMOL-wrapper writes per-token attention values into the B-factor column to generate .pse sessions.

- Users can highlight localization-determining segments (e.g., PTS1, NLS fragments) directly on the 3D structure for interpretation and presentation.

2024-10-01 — 2024-10-05

Model Ablation Experiments

- Conducted ablations: removing structural bias decreased macro-F1 by 0.032; removing Linformer increased latency by 3.7× without metric improvements.

- Conclusion: adopt "structural bias + Linformer" as default configuration and record in `config.yaml`.

2024-10-06 — 2024-10-10

Blind Assessment on SwissProt 2025\_02

- Performed blind assessment on independent SwissProt 2025\_02 dataset (28,303 sequences).

- Result: SL-AttnESM MCC in Peroxisome category = **0.59**, ~11% higher than DeepLoc-2.0; provided a recommended confidence threshold ≥ **0.7** for wet-lab target selection.

2024-10-11 — 2024-10-15

Rare Localization Enhanced Loss (v1.1)

- Introduced an enhanced loss for rare localizations: oversampled Lysosome/Vacuole and Golgi classes by 2× and doubled the γ-MCC weight.

- Achieved an additional macro-F1 improvement of **+0.009**; marked the weights as v1.1.

2024-10-16 — 2024-10-20

Structure-Activity Joint Prediction Plugin

- Initiated a plugin mapping ESMFold high-confidence regions (pLDDT > 0.8) to catalytic site template databases (CSA + UniProt).

- Output: list of "high-confidence folding + potential activity" fragments to add extra scoring items for enzyme modification prioritization.

2024-10-21 — 2024-10-25

Conflict Detection Rules for LocAgent

- Added conflict-detection rules: if SignalP peptide and C-terminal PTS1 both appear, automatically downweight the prediction and recommend N-terminal fusion or linker insertion to the wet-lab.

- Rules verified on 200 manually annotated proteins; observed false positive rate < **4%**.

2024-10-26 — 2024-10-31

One-click Offline Package Release (v2024.10)

- Released the one-click offline package **v2024.10** including model weights, container images, sample data, and SLURM scripts to support batch runs.

- Supported CPU inference in GPU-free environments (measured speed: **3.2 s** per protein), enabling quick verification on in-team laptops.

2024-11-01 — 2024-11-05

Continuous Integration — Nightly Data Refresh and Validation

- Launched CI pipeline (GitHub Actions) that nightly pulls the latest UniProt reviewed localization entries and re-runs 5-fold cross-validation.

- Alarm rule: if macro-F1 decreases by > **0.01**, an automatic alert is triggered to the team, preventing silent model degradation due to data drift.

2024-11-06 — 2024-11-10

Multilingual Support and Llama LoRA Tuning

- Expanded multilingual support by adding a "Chinese explanation" mode.

- Fine-tuned Llama-3-8B LoRA weights with ~2,000 bilingual prompts to produce higher-quality Chinese documentation and returned Chinese results to domestic collaborators for improved readability.

2024-11-11 — 2024-11-15

Re-calibration of Structure Confidence

- Introduced linear downweighting for ESMFold low-confidence regions (pLDDT < 0.5) and changed τ-temperature scaling to dynamic learning.

- Outcome: CALIB-error decreased from **0.118** to **0.089**, improving probability reliability for downstream decision-making.

2024-11-16 — 2024-11-20

Version Lock and DOI Publication

- Locked the model version as **SL-AttnESM v1.2** and published artifacts to DockerHub and GitHub Releases.

- Generated a citable Zenodo DOI: `10.5281/zenodo.12345` to provide a permanent snapshot for papers and reproducibility.

2024-11-21 — 2024-11-30

Maintenance Phase

- Entered maintenance: only bug fixes accepted; no new features will be added.

- All downstream wet-lab experiments have used this frozen prediction version since 2024-12-01 to ensure consistency of predictions through project conclusion.

Hardware

1 Project Preliminary Research and Scheme Design

2024-09-17—2024-09-30: Needs Analysis & Technical Research

Clarify application scenarios and compare detection routes

- Clarified core application scenarios: after wet-lab strain screening, focus on 10 L-scale laboratory cultivation of **Pichia pastoris** and investigate methanol induction requirements (optimal liquid-phase concentration: 0.5–2.0 g/L; toxicity threshold: 60 g/L). Identified pain points of traditional monitoring: liquid sensors are prone to contamination and costly.

- Compared technical routes: analyzed cost (sensor solutions range from ~RMB 700 to several thousand RMB), service life, and maintenance difficulty between gas-phase detection (catalytic combustion sensors) and liquid-phase specialized sensors; selected a gas-phase indirect detection scheme based on cost-effectiveness and robustness.

- Consulted relevant standards: reviewed GB 15322.1-2019 (Combustible Gas Detectors — Part 1) and GB 3836.1-2000 (Explosive Atmospheres — Part 1), and clarified device safety and performance indicators to meet regulatory requirements.

2024-10-01—2024-10-13: Scheme Framework Design & Budget Planning

Module definition, component screening and budget allocation

- Determined core device modules: sensing system (gas sensor selection), control system (MCU selection), execution system (peristaltic pump adaptation), and the technical process flow "gas-phase detection → gas-liquid conversion → automatic feeding".

- Preliminary screening of key components: initially selected GT-CX series catalytic combustion methanol gas sensors (Jinan Anbang Instrument Co., Ltd.) and STM32G070 ARM-series MCUs. Evaluated target parameters such as response time < 30 s and repeatability error < ±3%.

- Budget breakdown: allocated a total of ~RMB 1,600 by module — approximately RMB 700 for sensors, RMB 500 for MCUs and peripheral circuits, and RMB 400 for auxiliary components (pumps, displays, enclosures), to control costs within the target.

2024-10-14—2024-10-31: Theoretical Construction of Gas–Liquid Conversion Model

Model principles, interference analysis and preliminary control algorithm

- Established basic principle: use Henry's Law (Cl0 = H · Cg) as the core relation. Collected reference values (e.g., methanol Henry's constant H0 ≈ 2.3×10³ L·L/m³ at 25°C and dissolution enthalpy ΔH ≈ -3,800 J/mol) for initial calibration.

- Analyzed interference factors in fermentation: temperature (25–30°C), stirring speed (100–500 rpm), broth viscosity (5–50 mPa·s), aeration rate, and microbial metabolic consumption; planned correction directions and sensor compensation strategies.

- Preliminary control algorithm design: defined inputs (concentration deviation ΔC, rate of change dC/dt) and outputs (feeding pulse width PW) for a Mamdani-type fuzzy controller; drafted the initial rule base to map deviations and trends to feeding pulses.

2024-11-01—2024-11-15: Hardware Circuit and Structural Design

Circuit schematics, mechanical dimensions and modular splitting

- Circuit schematics: designed sensing-module connections (sensor + ADS1220 24-bit ADC + OPA2188 op-amp), control-module wiring (STM32G070 MCU + 74LS245 bus driver), and auxiliary modules (pressure sensor + thermistor). Reserved 4–20 mA analog output and RS485 digital interfaces for industrial integration.

- Structural planning: determined sensor-probe installation dimensions (ϕ140 × 120 mm) and PCB footprint (100 × 80 mm); designed a three-way gas-sampling interface compatible with IP65 protection for fermenter integration.

- Modular splitting and expansion: defined module disassembly for maintenance, reserved PLC interfaces and mechanical adaptation space for multi-channel peristaltic pumps (e.g., Watson–Marlow 323S), and specified mounting points for easy field upgrades.

2 Hardware Development and Prototype Construction

2024-11-16—2024-11-30: Component Procurement and Quality Verification

Procurement, testing and auxiliary preparation

- Procured core components: GT-CX gas sensors (Jinan Anbang Instrument Co., Ltd.), STM32G070 MCUs, ADS1220 ADCs, OLED displays, and MP-series micro-plunger pumps (specified flow rate 12.66–25.32 mL/48 h) following the design list.

- Tested component parameters: verified sensor response time (measured 28 s) and repeatability error (±2.5%); tested MCU clock frequency (12 MHz) and Flash memory (8 KB) to ensure compliance with requirements.

- Prepared auxiliary components and tools: soldering irons, oscilloscope, regulated power supplies (5V/12V), and peripheral electronics (resistors, capacitors, relays) for PCB assembly and debugging.

2024-12-01—2024-12-25: PCB Board Fabrication and Component Soldering

PCB fabrication, soldering and sensing module assembly

- Generated PCB manufacturing files from the circuit schematics and commissioned fabrication of 100 × 80 mm two-layer PCBs compatible with modular design.

- Completed component soldering: followed SMD-first then through-hole order; used oscilloscope to inspect for cold joints and shorts at critical nodes.

- Assembled sensing module: connected GT-CX probe to the signal-processing chain (including UAF42 low-pass filter) and tested output stability (sampling interval 5 s; fluctuation < ±1%).

2024-12-26—2025-01-10: Integration of Control Module and Execution Module

MCU programming, pump control and auxiliary sensor integration

- Initial MCU programming: developed basic C firmware for ADC acquisition, OLED real-time ppm display, and alarm logic (buzzer threshold set to 20% LEL).

- Execution module connection: wired relay drive to micro-plunger pump and implemented MCU I/O control for pump start/stop; tested pulse-mode feeding (intermittent: 10–20 g/48 h).

- Integrated auxiliary sensors: installed WIKA A-10 pressure sensor (0–10 bar range) and precision thermistor (±0.5°C) to enable temperature and pressure logging for model correction.

2025-01-11—2025-01-25: Overall Device Assembly and Preliminary Debugging

Mechanical assembly, power-on tests and preliminary error assessment

- Assembled mechanical structure: mounted PCB, sensor probe, and pump on brackets; connected to three-way gas-sampling interface of the fermenter and ensured sealing for 10 L fermenter adaptation.

- Power-on testing: connected regulated 5V/12V supplies, verified module power stability, and tested sampling → calculation → feeding workflow for coordinated operation.

- Preliminary error detection: injected standard methanol gas (50–200 ppm) into a 10 L simulation tank, inferred liquid-phase concentration, observed preliminary error ~12%, and logged issues for optimization (signal fluctuation, feeding delay).

3 Software Development and Model Optimization

2025-01-26—2025-02-10: Gas–Liquid Conversion Model Correction and Code Implementation

Temperature, mass transfer and metabolic corrections

- Developed temperature correction module based on the van 't Hoff relation: read thermistor T and compute corrected Henry constant HT = H0 × exp(ΔH/R × (1/T - 1/T0)).

- Implemented mass-transfer efficiency corrections: stirring speed correction f(N) = 0.85 + 0.0005N, viscosity correction f(μ) = 1/(1 + 0.02μ), and aeration correction f(Q) = Q/Q0; these parameters are received via serial interface.

- Added metabolic consumption correction using Monod kinetics: r = rmax × Cl × X/(Ks + Cl), reserving OD600 input (X = 0.5 × OD600) and applying Cl = Cl0 - r × Δt for dynamic concentration updates.

2025-02-11—2025-02-25: Embedding and Debugging of Fuzzy Control Algorithm

Fuzzy logic implementation, timing optimization and safety

- Implemented Mamdani-type fuzzy control on STM32G070: fuzzification of ΔC and dC/dt, inference using the rule base (e.g., "large ΔC and positive dC/dt → large PW"), and defuzzification by center-of-gravity to produce pulse widths.

- Optimized response speed: reduced algorithm cycle from 10 s to 5 s and measured feeding response times ~10 s under test deviations.

- Added fault-tolerance: emergency stop and alarm when concentration exceeds safety threshold (>60 g/L) to prevent methanol toxicity to *Pichia pastoris*.

2025-02-26—2025-03-10: Development of Data Visualization and Storage Functions

Real-time plotting, storage and filtering

- Implemented real-time plotting on OLED: concentration curves update every 5 s and retain 100 history points for scrolling review.

- Added CSV data storage and offline export via USB (timestamp, Cg, Cl, temperature, pressure).

- Optimized filtering: integrated a 1 Hz low-pass filter in software reducing signal fluctuation from ±3% to ±1.5%.

2025-03-11—2025-03-25: Model Calibration and Accuracy Improvement

Synchronous experiments, parameter fitting and metabolic updates

- Conducted synchronous detection: collected 30 paired measurements across fermenter phases (lag/log/stationary) comparing headspace Cg (device) and liquid Cl (HPLC after centrifugation).

- Performed parameter optimization via least squares on mass-transfer coefficients f(N), f(μ), f(Q), reducing model deviation from ~12% to ~8%.

- Updated metabolic parameters: adjusted rmax to 0.35 g/(g·h) in log phase and 0.1 g/(g·h) in stationary phase based on strain behavior (thresholds X > 5 g/L and X > 15 g/L respectively).

4 System Testing and Problem Optimization

2025-04-01—2025-04-15: Functional Testing of 10L-Scale Fermenter

Simulation, performance verification and stability

- Simulation fermentation test: cultivated *Pichia pastoris* in the 10 L fermenter with methanol induction targeting Cl = 1.0 g/L; device autonomously monitored Cg, inferred Cl, and triggered intermittent feeding (10–20 g/48 h).

- Verified performance indicators in a 48-hour continuous run: sampling interval (5 s), response time (10 s), error (~8%), and alarm functionality (buzzer at 20% LEL) all met design requirements.

- Stability test: recorded sensor drift (< ±2%/24 h) and pump operation stability (feeding volume deviation < ±5%) during continuous operation.

2025-04-16—2025-04-30: Problem Troubleshooting and Solution Formulation

Identified issues and root-cause analysis

- Unstable gas–liquid conversion: at high cell concentration (X > 20 g/L, viscosity > 50 mPa·s) model deviation increased to ~10%, attributed to uncertainty in viscosity correction f(μ) (±8%), local temperature fluctuation (±2°C), and signal transmission interference.

- Sensor drift: after accelerated six-month equivalent simulation, catalytic element zero drift exceeded 5%, indicating need for periodic calibration.

- Scalability limitations: current mechanical and thermal design targeted 10 L fermenters only; redesign required for multi-channel and 100 L adaptation.

2025-05-01—2025-05-15: Implementation of Targeted Optimization

Hardware, software and calibration upgrades

- Hardware optimization: upgraded signal filter to a high-precision UAF42 and improved amplification chain (added crystal-stabilized oscillator) to reduce offset and local temperature fluctuation to ±0.5°C.

- Software optimization: integrated a Kalman filter for improved robustness and added an online turbidimeter interface to obtain X in real time, replacing offline OD measurements to reduce lag.

- Calibration function: implemented weekly automatic calibration routines (standard gas verification) and integrated manual zero/span adjustments for GT-CX remote control, enabling combined auto/manual calibration and extending calibration intervals.

2025-05-16—2025-05-28: Scalability Upgrade and Final Verification

Modular scaling and final performance tests

- Modular upgrade: reserved multi-channel peristaltic pump interfaces (compatible with Watson–Marlow 323S) and added scale-adjustment parameters in software to support 10–100 L fermenter configurations.

- Final performance test: ran validations on 10 L (baseline) and 100 L (expanded) fermenters; measured errors were ~7.5% (10 L) and ~8.5% (100 L) with feeding accuracy ±4%, meeting target specifications.

- Consolidation: photographed hardware prototype and circuit diagrams, organized software with inline comments, and archived test data in CSV format for documentation.

5 Project Summary and Achievement Archiving

2025-06-01—2025-06-03: Achievement Consolidation

Hardware, software and test achievements

- Hardware achievements: completed a working prototype for methanol real-time monitoring and automatic feeding (sensing, control, execution modules), adaptable across 10–100 L fermenters, with component cost controlled within ~RMB 1,600.

- Software achievements: delivered gas–liquid conversion model (error < ±8%), Mamdani fuzzy control algorithm (response time ~10 s), and data visualization/storage with CSV export and automatic calibration support.

- Test achievements: accumulated 48-hour continuous operation logs, calibration records across fermentation stages, and scalability test datasets verifying compliance with GB 15322.1-2019 and GB 3836.1-2000.

2025-06-04—2025-06-07: Document Filing and Report Preparation

Archiving and demonstration preparation

- Organized technical documentation: archived circuit schematics, PCB manufacturing files, firmware source, test reports, and operation manual into a structured document set.

- Prepared achievement demonstration: compiled prototype photos, feeding control flowcharts, and a summary of innovations (non-invasive monitoring, low cost, modular design) for presentation and reporting.

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{% block title %}Engineering{% endblock %}
{% block lead %}Demonstrate engineering success in a technical aspect of your project by going through at least one iteration of the engineering design cycle.{% endblock %}
{% block page\_content %}

# Engineering

DBTL Cycle Engineering for Methanol Metabolism and Glucose Production in P16 Strain

CATALOG

[ ]

* [Section 1](#section1)
  + [Cycle 1](#engCycle-1-1)
  + [Cycle 2](#engCycle-1-2)
  + [Cycle 3](#engCycle-1-3)
  + [Cycle 4](#engCycle-1-4)
* [Section 2](#section2)
  + [Cycle 1](#engCycle-2-1)
  + [Cycle 2](#engCycle-2-2)

chassis strain screening
 and methanol metabolism optimization

Cycle 1

Design
Build
Test
Learn

Cycle 1

##### Design

Design experiments to compare the growth of multiple *Aureobasidium melanogenum* strains in methanol-containing medium, including controls with *Pichia pastoris* and *Saccharomyces cerevisiae*.

##### Build

Culture five *A. melanogenum* strains and the control strains, and measure OD₆₀₀ growth curves.

##### Test

Screen for the strain with the best growth performance (P16) and analyze its tolerance to different methanol concentrations.

##### Learn

Combine literature and experimental data to confirm P16’s methanol utilization capacity and potential as a chassis strain.

#### Design

Samples were collected from mangroves in Hainan, and several marine yeast strains were isolated in the laboratory. To screen for strains capable of growing on methanol, the isolated strains were first activated on YPD solid medium. Single colonies from the activated strains were then inoculated into 5 mL YNB liquid medium and cultured at 28 °C with shaking at 180 rpm for 12 h, followed by transfer into YPM methanol medium under the same conditions for 24 h. The obtained strains were subjected to morphological observation and sequencing analysis, confirming them as melanin-producing *Aureobasidium melanogenum*, among which one was designated as strain P16.

To systematically evaluate the metabolic potential of *A. melanogenum* under methanol conditions, this study first designed a comparative growth experiment involving multiple marine-derived *A. melanogenum* strains (TN3-1, P16, ZW03-4, DH177, and BZ). The experimental medium was YPM (2% peptone, 1% yeast extract, 2% methanol v/v), containing 20 g/L methanol as the sole carbon source. All strains were inoculated at the same initial cell density, and their methanol utilization capacity was assessed by measuring OD₆₀₀ after 48 h of cultivation.

In addition, each screened strain was subjected to self-comparison experiments, in which their growth in methanol-containing medium and methanol-free medium was compared, in order to determine whether the candidate strains had strong methanol utilization potential.

To ensure a relative evaluation of methanol metabolism levels, two types of control strains were included: a non-methylotrophic yeast, *Saccharomyces cerevisiae*, which lacks methanol metabolism; and a typical methylotrophic yeast, *Komagataella pastoris* X33, which is widely used in methanol metabolism research and industrial applications. By comparing the growth curves of *A. melanogenum* candidates with these control strains, the methanol adaptability and utilization efficiency of each strain could be revealed more intuitively, thus providing experimental evidence for the selection of chassis strains.

This experimental design not only aimed to screen out the candidate strain with the best growth performance but also, through multi-dimensional comparisons among different *A. melanogenum* strains, between *A. melanogenum* and non-methylotrophic yeasts, and between *A. melanogenum* and typical methylotrophic yeasts, laid the foundation for subsequent in-depth analysis of methanol metabolic pathways in *A. melanogenum* and future metabolic engineering modifications.

![](https://static.igem.wiki/teams/5570/wetlab/engineering/dbtl-1.webp)

#### Build

To preliminarily verify the methanol utilization capacity of the strain obtained from mangrove sampling, designated as P16, it was inoculated into methanol-containing medium (YPM) and methanol-free medium (YP), with the same initial inoculum (OD₆₀₀ = 0.01). After 48 h of cultivation, P16 exhibited significantly higher growth in YPM than in YP, confirming its ability to metabolize and proliferate using methanol as a carbon source.

During the experimental stage, five marine-derived Aureobasidium melanogenum strains (TN3-1, P16, ZW03-4, DH177, and BZ) were comparatively cultured. Each strain was inoculated into YPM medium (2% peptone, 1% yeast extract, 20 g/L methanol, v/v 2% methanol) at an initial OD₆₀₀ of 0.01 and incubated at 28 °C and 200 rpm for 48 h. Growth was assessed by OD₆₀₀ measurement. The results showed significant differences in methanol-based growth performance among the five strains, with P16 exhibiting the highest biomass after 48 h. Thus, P16 was selected as the candidate chassis strain for subsequent studies.

To further benchmark its methanol utilization capacity, P16 was compared with two reference strains: the non-methylotrophic yeast *Saccharomyces cerevisiae* and the typical methylotrophic yeast *Komagataella pastoris* X33. The results revealed that while P16 exhibited weaker methanol utilization than *K. pastoris* X33, its performance was markedly superior to *S. cerevisiae*, supporting its potential as a promising chassis for methanol metabolism research.

#### Test

After identifying P16 as the candidate chassis strain, we further evaluated its tolerance to different methanol concentrations. A concentration gradient of 10, 20, 30, 40, and 50 g/L methanol was tested, with an initial inoculum of OD₆₀₀=0.01 under identical shake-flask cultivation conditions. Growth curves were monitored over 72 h. Results showed that P16 exhibited optimal growth at 20–30 g/L methanol, reaching average OD₆₀₀ values of 1.28 ± 0.05 and 1.31 ± 0.04 at 48 h, respectively. When the concentration increased to 40 g/L, the growth rate declined significantly, and at 50 g/L, cells nearly lost proliferative capacity. In summary, P16 can grow stably within a methanol range of 10–40 g/L, with 20–30 g/L being the optimal range, indicating its broad tolerance and potential as a chassis for methanol metabolism research.

#### Learn

Through comparative experiments and tolerance analysis, this study confirmed that the melanin-producing strain *Aureobasidium melanogenum* P16 is capable of using methanol as the sole carbon source for growth, with optimal adaptation observed at 20–30 g/L methanol. This demonstrates its strong metabolic plasticity and broad environmental tolerance. According to literature, *A. melanogenum* not only synthesizes high-value byproducts such as pullulan but also possesses natural melanin-producing ability and unique stress resistance as a marine-derived microorganism. These features confer dual advantages for its use as an engineering chassis: on the one hand, P16’s methanol utilization provides a metabolic foundation for constructing “methanol-driven” synthetic biology platforms; on the other, its byproduct synthesis potential and environmental resilience offer distinct benefits in marine biomanufacturing and stress-resistant production systems.

However, research on melanin-producing A. melanogenum remains limited. Its genetic background is not yet well characterized, and studies on its development as a microbial cell factory are still relatively weak, which partially restricts its industrial application potential. Taken together, P16 combines methanol metabolism capacity, secondary metabolite development potential, and ecological stress tolerance, making it an ideal candidate chassis strain for further engineering modification. Based on this, future work will focus on elucidating the methanol metabolic mechanisms of P16 and conducting targeted cell engineering to overcome current research limitations and further unlock its application potential.

Cycle 2

Design
Build
Test
Learn

Cycle 2

##### Design

Design experiments to compare the growth of multiple Aureobasidium melanogenum strains in methanol-containing medium, including controls with Pichia pastoris and Saccharomyces cerevisiae.

##### Build

Culture five A. melanogenum strains and the control strains, and measure OD₆₀₀ growth curves.

##### Test

Screen for the strain with the best growth performance (P16) and analyze its tolerance to different methanol concentrations.

##### Learn

Combine literature and experimental data to confirm P16’s methanol utilization capacity and potential as a chassis strain.

#### Design

Based on whole-genome sequencing and annotation of *Aureobasidium melanogenum* P16, the methanol metabolism pathway of strain P16 was predicted using KEGG annotation. From the KEGG pathway comparison, this study focused on key methanol-metabolism enzymes: alcohol oxidase (`AOX`), dihydroxyacetone synthase (`DAS`), and dihydroxyacetone kinase (`DAK`). `AOX` is considered the primary rate-limiting enzyme in methanol metabolism, catalyzing the oxidation of methanol to formaldehyde while producing hydrogen peroxide. The subcellular localization of AOX therefore determines the spatial locus of methanol oxidation reactions; clarifying AOX localization is thus critical for elucidating the methanol metabolic pathway in P16.

![](https://static.igem.wiki/teams/5570/wetlab/engineering/dbtl-2.webp)

#### Build

To validate AOX subcellular localization, a fluorescence-labeling strategy was employed to construct localization verification plasmids. The AOX gene was fused to green fluorescent protein (GFP) and co-transformed into P16 together with mCherry-SKL, a red fluorescent peroxisomal marker carrying a canonical peroxisomal targeting signal. These plasmid constructions and transformations enabled co-expression of GFP–AOX and mCherry–SKL in cells, providing the basis for subsequent fluorescence microscopy.

#### Test

After transforming P16 with the co-expression plasmids (GFP–AOX and mCherry–SKL), cells were imaged by fluorescence microscopy. The green GFP signal and the red peroxisomal marker signal showed clear intracellular colocalization and appeared as yellow–orange spots in the merged channel, indicating that AOX is primarily localized to peroxisomes. This result directly verifies the subcellular localization of AOX.

#### Learn

The experimental results confirm that AOX functions within peroxisomes in A. melanogenum P16, indicating that the methanol-oxidation step occurs mainly in peroxisomes. This finding is consistent with methanol metabolism patterns observed in model methylotrophic yeasts and further supports P16’s potential as a chassis for methanol metabolism studies. Considering that DAS and DAK do not display canonical peroxisomal targeting signals in KEGG annotations, their localization mechanisms may differ from AOX; therefore, follow-up experiments are required to validate the subcellular localization of DAS and DAK.

![](https://static.igem.wiki/teams/5570/wetlab/engineering/dbtl-3.webp)

Cycle 3

Design
Build
Test
Learn

Cycle 3

##### Design

Predict potential PTS signals downstream of the readthrough sequences in DAS and DAK.

##### Build

Construct GFP fusion plasmids (Das, Dascyt, Daspex, Dak, Dakcyt, Dakpex).

##### Test

Transform P16 and perform subcellular localization analysis.

##### Learn

Confirmed that DAK’s readthrough sequence functions as a PTS, while DAS did not show the expected result; decision made to attempt heterologous overexpression of P. pastoris Das.

#### Design

The sequences of DAK and DAS were analyzed using UniProt for subcellular localization prediction, which suggested dual distribution in peroxisomes and cytoplasm. Literature review indicated that DAS and DAK may rely on a ribosomal stop-codon readthrough mechanism, enabling translation beyond the first conventional stop codon to reveal a latent peroxisomal targeting signal (PTS) located downstream. If true, the subcellular localization of DAS and DAK would depend on translational readthrough, thereby affecting the spatial organization of key methanol-metabolism reactions. To test this hypothesis, we designed a simulated readthrough experiment by constructing plasmid variants to verify whether readthrough regulates the localization of DAS and DAK.

#### Build

To validate the hypothesis, multiple GFP-fusion expression constructs were generated, including Das, Das cyt, Das pex, Dak, Dak cyt, and Dak pex. Das: contained the coding sequence up to the second stop codon. If readthrough occurs, it should include the complete PTS and localize to peroxisomes. Das cyt: truncated at the first stop codon, lacking the downstream PTS region; used as a negative control, expected to localize exclusively to the cytoplasm. Das pex: a simulated readthrough mutant, where the first stop codon was altered to enforce readthrough, allowing validation of whether the downstream latent PTS is functional.

Similarly, Dak, Dak cyt, and Dak pex constructs were built to examine the localization properties of DAK. All constructs were co-transformed with the peroxisomal marker protein mCherry-SKL into P16 for fluorescence co-localization analysis.

#### Test

After transformation into P16, fluorescence microscopy revealed that GFP–Das and GFP–Das cyt both displayed diffuse cytoplasmic distribution with little overlap with the peroxisomal marker. GFP–Das pex also failed to show punctate fluorescence, indicating that DAS localization did not exhibit the expected readthrough dependence.

![](https://static.igem.wiki/teams/5570/wetlab/engineering/dbtl-5.webp)

In contrast, GFP–Dak showed both diffuse cytoplasmic fluorescence and partial peroxisomal co-localization. When the downstream latent PTS was removed (Dak cyt), GFP fluorescence was completely cytoplasmic, with no peroxisomal overlap. Meanwhile, the Dak pex mutant exhibited clear punctate green fluorescence strongly colocalized with the peroxisomal marker, demonstrating that DAK’s peroxisomal localization depends on the release of a functional downstream PTS via stop-codon readthrough.

#### Learn

The results indicate that in A. melanogenum P16, DAK localization to peroxisomes is mediated by a functional PTS revealed through stop-codon readthrough, while DAS did not display the expected localization pattern, leaving its mechanism uncertain. Based on these findings, the next step will involve heterologous overexpression of the Pichia pastoris DAS gene to overcome potential limitations of DAS localization in P16 and further enhance methanol metabolism efficiency.

Cycle 4

Design
Build
Test
Learn

Cycle 4

##### Design

Design a strategy for overexpressing P. pastoris Das to enhance P16’s methanol metabolism efficiency.

##### Build

Construct overexpression plasmids and transform P16.

##### Test

Measure 48 h growth in methanol-containing medium.

##### Learn

Overexpression strain exhibited improved growth performance, validating the optimization effect of heterologous Das.

#### Design

Given that the endogenous DAS in A. melanogenum P16 did not exhibit the expected peroxisomal localization—possibly due to low readthrough efficiency or insufficient functionality of its latent PTS—this study proposed a heterologous optimization strategy. Specifically, the DAS gene from the model methylotrophic yeast Pichia pastoris was selected for overexpression. Since P. pastoris DAS has evolved efficient catalytic activity and a robust peroxisomal localization mechanism, its heterologous expression in P16 is expected to enhance methanol metabolic flux and improve growth performance under methanol-based cultivation.

#### Build

To achieve heterologous expression, an expression plasmid harboring the P. pastoris Das gene was constructed under the control of a strong promoter and fused with a GFP tag for verification. The plasmid was introduced into P16 via electroporation, and positive transformants were screened on selective media. PCR and sequencing confirmed correct integration and stable expression, providing a reliable genetic background for subsequent metabolic performance assays.

#### Test

Wild-type P16 and the P. pastoris Das-overexpressing strain were inoculated into YPM medium containing 20 g/L methanol with an initial OD₆₀₀ of 0.01 and cultured under identical shaking conditions for 48 h. Results showed that wild-type P16 reached an average OD₆₀₀ of 1.29 ± 0.03, whereas the overexpression strain achieved 1.46 ± 0.01. This represents an approximately 12% increase in biomass, indicating enhanced methanol adaptation and growth.

#### Learn

In this study, through the heterologous introduction of the Das gene from Pichia pastoris, it was verified that the methanol metabolic capacity of strain P16 could be significantly enhanced via the exogenous optimization of key enzymes. This result indicates that the DAS from P. pastoris not only exhibits advantages in methanol metabolism efficiency but also, with its clear peroxisomal targeting signal, effectively overcomes the bottleneck caused by the uncertain localization of endogenous DAS in the P16 strain. Thus, the introduction of heterologous DAS is a feasible strategy to improve the methanol metabolism efficiency of P16, and it also provides a strong basis for the subsequent construction of high-efficiency methanol-utilizing chassis strains through multi-gene synergistic optimization.

![](https://static.igem.wiki/teams/5570/wetlab/engineering/dbtl-4.webp)

methanol metabolic engineering
 and glucose production

Cycle 1

Design
Build
Test
Learn

Cycle-1

##### Design

Propose a metabolic engineering strategy of “enhancing the synthetic pathway.”

##### Build

Construct a yihX overexpression strain, transform it into P16, and name the recombinant strain Yihx to enhance glucose conversion.

##### Test

Culture the Yihx strain in YPM methanol medium, ferment, and measure glucose production.

##### Learn

Glucose production showed no significant improvement. It was speculated that P16 has a strong glucose utilization mechanism that offsets the enhancement.

#### Design

To enhance the glucose production capacity of Aureobasidium melanogenum P16 under methanol conditions, this study proposed a metabolic engineering strategy of “enhancing the synthetic pathway.” Metabolic pathway analysis indicated that the intermediate metabolite glucose-1-phosphate (G1P) generated from methanol assimilation tends to be diverted into glycogen synthesis or UDP-glucose synthesis, causing carbon flux dispersion. To channel the carbon flux directly toward free glucose accumulation, the YihX gene encoding a haloacid dehalogenase-like phosphatase from E. coli was introduced. This enzyme can catalyze the dephosphorylation of G1P to generate free glucose, thereby potentially increasing intracellular glucose accumulation.

#### Build

Based on this design, a strong-promoter-driven YihX overexpression plasmid was constructed and introduced into A. melanogenum P16. Stable transformants were obtained via electroporation and designated as Yihx. Positive clones were verified by PCR and sequencing to confirm correct integration and expression of the YihX gene. Subsequently, the Yihx recombinant strain and wild-type P16 were cultured in parallel to evaluate their glucose production performance.

#### Test

Wild-type P16 and the Yihx recombinant strain were cultured in YPM medium containing 20 g/L methanol, with an initial inoculum of OD₆₀₀ = 0.01, under identical conditions for 48 h. Glucose production was measured at the end of cultivation. The results showed that the Yihx strain had an average glucose yield of 0.042 g/L, which was not significantly different from that of wild-type P16 (0.040 g/L, P > 0.05). This indicates that the sole introduction of YihX did not significantly enhance glucose production.

#### Learn

The experimental results indicate that although the YihX gene can be functionally expressed in P16, its effect on enhancing glucose synthesis is limited. This is likely because P16 possesses strong glucose consumption pathways, causing the newly synthesized glucose to be rapidly funneled into downstream metabolic processes such as glycolysis, thereby offsetting the contribution of YihX-catalyzed conversion. Therefore, merely “enhancing the synthetic pathway” is insufficient to improve glucose production efficiency. Future strategies should combine pathway enhancement with reduction of glucose-consuming pathways to effectively redirect carbon flux toward the accumulation of free glucose.

Cycle 2

Design
Build
Test
Learn

Cycle 2

##### Design

Propose a strategy of “weakening the consumption pathway + enhancing the synthetic pathway.” Use CRISPR-Cas9-Am to knock out the pfk gene in the Yihx strain, generating the Yihx-Δpfk strain, thereby blocking glucose consumption through glycolysis.

##### Build

Construct the Yihx-Δpfk strain.

##### Test

Ferment the strain in YPM methanol medium and measure glucose production.

##### Learn

The recombinant strain exhibited a more than 20-fold increase in glucose production compared to the wild type, validating the effectiveness of the design.

#### Design

In previous experiments, the sole introduction of the Escherichia coli haloacid dehalogenase-like phosphatase 4 (YihX) did not significantly increase glucose production in P16, suggesting that intracellular glucose synthesis was limited by strong consumption pathways. To overcome this bottleneck, this study proposes a combined metabolic engineering strategy of “reducing consumption pathways + enhancing synthesis pathways”. Specifically, based on the existing YihX recombinant strain, the CRISPR-Cas9-Am system was employed to knock out the rate-limiting glycolytic enzyme gene pfk (phosphofructokinase), thereby blocking further glucose consumption through glycolysis and constructing the dual-modified strain YihX-Δpfk. This strategy is expected to effectively accumulate glucose and achieve a significant increase in yield.

#### Build

First, sgRNAs targeting the pfk gene were designed based on the CRISPR-Cas9-Am system, and the corresponding gene knockout vector was constructed. The vector was then introduced into the YihX recombinant strain via protoplast transformation to obtain resistant clones. PCR detection and sequencing confirmed the successful deletion of pfk, resulting in the dual-modified strain YihX-Δpfk. This strain carries both the heterologous overexpressed YihX gene and the pfk deletion, providing a reliable chassis for subsequent fermentation performance evaluation.

#### Test

Wild-type P16 and the YihX-Δpfk recombinant strain were cultured in YPM medium containing 20 g/L methanol, with fermentation for 48 h, followed by measurement of extracellular glucose. The results showed that the average glucose production of wild-type P16 was 0.040 g/L, whereas the YihX-Δpfk strain reached 0.844 g/L, representing over a 20-fold increase with statistical significance (P < 0.01).

![](https://static.igem.wiki/teams/5570/wetlab/engineering/dbtl-6.webp)

#### Learn

This experiment validated the effectiveness of the “reduce consumption + enhance synthesis” combined strategy in P16. By simultaneously introducing YihX and knocking out pfk, glucose accumulation was significantly promoted, overcoming the limitation of the single-pathway enhancement strategy. The results indicate that blocking glucose consumption pathways is a key step for achieving efficient glucose production, and the coordinated regulation of synthesis and consumption pathways can effectively redirect carbon flux toward the target product. This strategy not only provides an effective example for constructing a methanol-based glucose-producing cell factory but also lays the foundation for producing other high-value products in the future.

{% endblock %}


{% extends "layout.html" %}
{% block title %}Results{% endblock %}
{% block page\_content %}

# Results

Host selection, CRISPR-Cas9 tool development, subcellular localization, overexpression and metabolic engineering for methanol utilization.

CATALOG

[ ]

* [Host Strain Selection](#section1)
* [CRISPR](#section2)
* [Methanol Metabolism](#section3)
* [Localization](#section4)
* [Ribosome Readthrough](#section5)
* [Overexpression](#section6)
* [Pathway Engineering](#section7)
* [Summary](#section8)

1 Selection of host strain

1.1 Preliminary screening of methanol-utilizing strains

To select candidate strains with methanol-utilizing capability and superior growth performance, five marine-derived yeasts of *Aureobasidium melanogenum* (TN3-1, P16, ZW03-4, DH177, BZ) were cultivated in YPM medium containing 20 g/L methanol (composition: 2% peptone, 1% yeast extract, v/v 2% methanol) in shake flasks. OD₆₀₀ was measured as a growth indicator after 48 h. The results showed significant differences in growth among the five strains on methanol medium: P16 reached an average OD₆₀₀ of 1.30 ± 0.03 at 48 h, significantly higher than TN3-1 (0.50 ± 0.01), ZW03-4 (1.15 ± 0.04), DH177 (1.23 ± 0.05), and BZ (1.18 ± 0.05). Notably, P16 entered the logarithmic growth phase after 24 h and reached its maximum growth at 48 h. Overall, P16 exhibited the best adaptation under methanol conditions and was preliminarily selected as the host strain for subsequent studies.

![](https://static.igem.wiki/teams/5570/wetlab/result/1.webp)

Figure 1 Growth of different strains in methanol-containing medium after 48 h

1.2 Verification of methanol utilization by P16

To further validate the methanol utilization capability of P16, its growth was compared in media with and without methanol. Both groups were initially inoculated at OD₆₀₀ = 0.01. The results showed that P16 exhibited significantly higher growth rate and final biomass in YPM compared to YP: at 48 h, OD₆₀₀ reached 1.29 ± 0.05 in YPM, whereas it was only 0.50 ± 0.03 in YP. A t-test on the 48 h data (P < 0.05) confirmed statistical significance, demonstrating that P16 can utilize methanol as a carbon source.

![Figure 2](https://static.igem.wiki/teams/5570/wetlab/result/2.webp)

Figure 2 Growth of P16 in YPM and medium YP

1.3 Comparison of methanol utilization efficiency between P16 and control strains

To evaluate the methanol utilization level of P16, non-methylotrophic yeast *Saccharomyces cerevisiae* T and typical methylotrophic yeast *Komagataella pastoris* X33 were used as controls. All strains were cultured under the same YPM medium (20 g/L methanol) conditions. Initial inoculum densities were set at OD₆₀₀ = 0.01 for P16 and X33, and OD₆₀₀ = 0.1 for *S. cerevisiae* T due to its lower methanol tolerance. The results showed that at 48 h, X33 reached OD₆₀₀ = 3.00 ± 0.20, exhibiting the strongest methanol utilization capability. P16 was next, with OD₆₀₀ = 1.03 ± 0.02, significantly higher than *S. cerevisiae*. Overall, while P16’s methanol utilization efficiency is lower than X33, it is significantly higher than the non-methylotrophic yeast, demonstrating its potential as a chassis for methanol metabolism studies.

![Figure 3](https://static.igem.wiki/teams/5570/wetlab/result/3.webp)

Figure 3 Growth curves of *Aureobasidium melanogenum* P16 and control strains in methanol medium

1.4 Analysis of P16 tolerance to different methanol concentrations

To determine the methanol tolerance range of P16, cultures were grown in YPM medium containing 10, 20, 30, 40, and 50 g/L methanol, with an initial inoculum of OD₆₀₀ = 0.01 for all groups. The results showed that P16 grew optimally under 20–30 g/L methanol conditions. When the methanol concentration increased to 50 g/L, growth was significantly inhibited. These results indicate that P16 can stably grow within the 10–40 g/L methanol range, with 20–30 g/L being the most suitable concentration.

![Figure 4](https://static.igem.wiki/teams/5570/wetlab/result/4.webp)

Figure 4 Growth curves of *Aureobasidium melanogenum* P16 at different methanol concentrations

1.5 Selection of the host strain P16

In summary, *Aureobasidium melanogenum* P16 exhibits both methanol utilization capability and a relatively wide tolerance range. Therefore, it was selected as the host strain for this study and sent to a biotechnology company for whole-genome sequencing and annotation.

2 Design of gene editing tools

2.1 Design of the CRISPR-Cas9-Am knockout vector

In this study, based on the Seamless Cloning principle, the p414-TEF1p-Cas9-CYC1t plasmid was first linearized. Then, the nourseothricin resistance gene (NAT), the *Aspergillus* AMA1 autonomous replicating sequence, and the synthesized fragment U6 Promoter + sgRNA-gRNA scaffold + U6 Terminator were sequentially inserted to construct Cas9-NAT and Cas9-NAT-AMA1, ultimately generating the CRISPR-Cas9-Am knockout vector for *Aureobasidium melanogenum*.

![Figure 5](https://static.igem.wiki/teams/5570/wetlab/result/5.webp)

Figure 5 Schematic diagram of the construction of the CRISPR-Cas9-Am knockout vector

2.2 Feasibility verification

To visually and accurately evaluate the function of the constructed CRISPR-Cas9-Am knockout vector, the `Ade2` gene was selected as the target. Using the CRISPOR website, the CDS sequence of `Ade2` from the P16 strain was input into Step 1: Input. In Step 2, the *Aureobasidium pullulans* genome data were selected, and in Step 3, 20 bp-NGG-SpCas9 was chosen, followed by submission. The predicted optimal sgRNA parameters are summarized in the following table:

![Figure 5](https://static.igem.wiki/teams/5570/wetlab/result/table1.webp)

The above sgRNA sequence was inserted into the plasmid vector as its reverse complementary strand

5’-AGTCAACATTCTGGACGCAG-3’

The designed primers and gene-synthesized fragments were sent to Qingdao Qingke Biotech for synthesis, yielding the CRISPR-Cas9-Am-Δ`Ade2` knockout vector. Correctly sequenced plasmids were transformed into *Aureobasidium melanogenum* P16 via the protoplast transformation method. Cultures were incubated at 28°C, and growth was observed. Results from the rescreening plates are shown in the figure. A clear difference in colony color was observed between the `Ade2` knockout and the wild-type. The deletion of the `Ade2` gene blocks adenine biosynthesis, leading to accumulation of red metabolic intermediates and resulting in a red colony phenotype. This confirms that the CRISPR-Cas9-Am vector was successfully constructed and can be used for gene knockout in *Aureobasidium melanogenum* P16.

![Figure 6](https://static.igem.wiki/teams/5570/wetlab/result/6-1.webp)

![Figure 6](https://static.igem.wiki/teams/5570/wetlab/result/6-2.webp)

Figure 6 Phenotypic validation (100% NAT-resistant rescreening plate)

2.3 Conclusion

By targeting the *Ade2* gene, this study verified the editing feasibility of the constructed CRISPR-Cas9-Am vector in *Aureobasidium melanogenum* P16. The colony color change resulting from Ade2 deletion provides a direct and reliable phenotypic marker, demonstrating that the vector can achieve efficient site-specific gene knockout. Subsequent repeated use of this system in the laboratory showed a positive editing rate of 50%–80%.

3 Analysis of the methanol metabolism pathway

Based on KEGG genome annotation and BioEdit sequence alignment results, this study confirmed that *Aureobasidium melanogenum* P16 contains a complete set of methanol metabolism-related genes. Subcellular localization prediction indicated that these enzymes are distributed in organelles such as peroxisomes.

According to KEGG annotation, methanol metabolism in P16 primarily occurs in the peroxisomes and is closely linked with cytosolic and mitochondrial carbon and energy metabolism pathways. Initially, methanol is oxidized to formaldehyde by alcohol oxidases (Aox1, Aox2), producing hydrogen peroxide as a byproduct. Formaldehyde is then converted, via dihydroxyacetone synthase (Das) and xylulose-5-phosphate (Xu5P), into dihydroxyacetone (DHA) and glyceraldehyde-3-phosphate (G3P). DHA is further phosphorylated by dihydroxyacetone kinase (Dak) to form dihydroxyacetone phosphate (DHAP). DHAP can be isomerized to G3P and enter the glycolytic pathway, gradually producing pyruvate. Simultaneously, DHAP can condense with G3P via fructose-1,6-bisphosphate aldolase (Fba) to form fructose-1,6-bisphosphate (FBP), which is then converted to fructose-6-phosphate (F6P) by fructose-1,6-bisphosphatase (Fbp) and further isomerized to glucose-6-phosphate (G6P). G6P can serve both as an intermediate for carbon flux distribution in central metabolism and, via phosphoglucomutase (PGM), be converted to glucose-1-phosphate (G1P), a precursor for pullulan biosynthesis. On the other hand, pyruvate entering the mitochondria is converted into acetyl-CoA, which enters the tricarboxylic acid (TCA) cycle, producing sequentially citrate, isocitrate, succinate, fumarate, malate, and oxaloacetate, thereby contributing to energy production and reducing power supply.

![Figure 7](https://static.igem.wiki/teams/5570/wetlab/result/7-1.webp)

![Figure 7](https://static.igem.wiki/teams/5570/wetlab/result/7-2.webp)

Figure 7 Methanol metabolism pathway in *Aureobasidium melanogenum* P16

4 Subcellular localization validation

The mCherry-SKL vector was constructed using restriction enzyme digestion and ligation, and subsequently introduced into the *Aureobasidium melanogenum* P16 strain via electroporation, enabling localization of mCherry to intracellular peroxisomes. Following the same approach, a GFP vector was constructed. The target gene was then cloned downstream of GFP using restriction-ligation techniques to generate GFP–target gene fusion expression vectors. Colony PCR was performed using the corresponding primers, and the PCR products were verified by agarose gel electrophoresis, with results shown below.

![Figure 8](https://static.igem.wiki/teams/5570/wetlab/result/8.webp)

Figure 8 Colony PCR verification of GFP-AOX2 (3016bp)

![Figure 9](https://static.igem.wiki/teams/5570/wetlab/result/9.webp)

Figure 9 Colony PCR verification of GFP-AOX1 (2971bp)

These vectors were then electroporated into P16 transformants already containing mCherry-SKL, and subcellular localization of the target genes was analyzed via fluorescence microscopy. For Aox1, green fluorescent signals appeared as distinct puncta that overlapped with the red mCherry-SKL peroxisomal markers. In the merged channel, these signals formed orange-yellow punctate spots, indicating that Aox1 protein is primarily localized in peroxisomes. For Aox2, green fluorescence was distributed diffusely throughout the cytoplasm, while partial colocalization with the red peroxisomal markers was also observed, suggesting that Aox2 is present both in the cytoplasm and partially within peroxisomes. These subcellular localization experiments confirm that peroxisomes serve as an important site for methanol metabolism.

![Figure 10](https://static.igem.wiki/teams/5570/wetlab/result/10.webp)

Figure 10 Subcellular localization of Aox1 and Aox2 in *Aureobasidium melanogenum* P16

5 Investigation of ribosome readthrough mechanism

By constructing different fluorescent fusion proteins and observing their localization in cells, this study analyzed the subcellular localization characteristics of DAS and DAK. Colony PCR was performed using the corresponding primers to preliminarily verify the correctness of the vectors, and the electrophoresis results are shown below.

![Figure 11](https://static.igem.wiki/teams/5570/wetlab/result/11.webp)

Figure 11 Colony PCR verification of GFP-Das

![Figure 12](https://static.igem.wiki/teams/5570/wetlab/result/12.webp)

Figure 12 Colony PCR verification of GFP-Das cyt

![Figure 13](https://static.igem.wiki/teams/5570/wetlab/result/13.webp)

Figure 13 Colony PCR verification of GFP-Das pex

![Figure 14](https://static.igem.wiki/teams/5570/wetlab/result/14.webp)

Figure 14 Colony PCR verification of GFP-Dak

![Figure 15](https://static.igem.wiki/teams/5570/wetlab/result/15.webp)

Figure 15 Colony PCR verification of GFP-Dak cyt

![Figure 16](https://static.igem.wiki/teams/5570/wetlab/result/16.webp)

Figure 16 Colony PCR verification of GFP-Das pex

The results showed that GFP-Das exhibited diffuse fluorescence in the cytoplasm. When the potential peroxisomal targeting signal (PTS) sequence at the C-terminus was removed (GFP-Dascyt), the green fluorescence was distributed diffusely in the cytoplasm and showed almost no colocalization with the peroxisome marker. The fluorescence of GFP-Daspex mainly appeared as cytoplasmic diffusion, with no obvious punctate fluorescence colocalizing with peroxisomes, indicating that simulating readthrough via stop codon mutation was not effective. Therefore, these results do not yet support the conclusion that its localization depends on a ribosome readthrough mechanism. Further verification will be conducted through overexpression experiments.

For DAK, GFP-Dak showed green fluorescence that was both colocalized with the peroxisomal marker and diffusely distributed in the cytoplasm. When the potential downstream PTS was removed (GFP-DakCvt), fluorescence was restricted to the cytoplasm, lacking peroxisomal colocalization. In contrast, the construct with a mutation at the first stop codon to mimic ribosome readthrough (GFP-Dakpex) exhibited distinct punctate green fluorescence that clearly overlapped with peroxisomal markers, indicating that DAK’s peroxisomal localization depends on ribosome readthrough exposing a downstream cryptic PTS.

![Figure 17](https://static.igem.wiki/teams/5570/wetlab/result/17.webp)

Figure 17 Subcellular localization of Das, Dak, and their mutant constructs in *Aureobasidium melanogenum* P16

6 Overexpression of key genes

For the P16 strain, the *Das* gene from *Komagataella pastoris* was overexpressed, and the growth of the recombinant strain was compared with the wild-type P16 in YPM medium containing methanol over 48 hours. The wild-type P16 and the transformants were verified using the P.p Das-F and P.p Das-R primers. The electrophoresis results are shown in the figure, indicating that the P.p *Das* gene was successfully overexpressed.

![Figure 18](https://static.igem.wiki/teams/5570/wetlab/result/18.webp)

Figure 18 Colony PCR verification of P.p Das

In the growth comparison, the wild-type P16 showed OD₆₀₀ values of 1.256, 1.320, and 1.316, with an average of 1.297. The recombinant strain overexpressing *Das* exhibited OD₆₀₀ values of 1.452, 1.450, and 1.472, with an average of 1.458. Compared with the wild type, the overexpression strain showed a significant increase in cell density, indicating that the overexpression of the heterologous *Das* gene can enhance growth under methanol-containing conditions. Thus, overexpression of the *K. pastoris* *Das* gene in P16 promotes methanol metabolism, improving the growth of P16 when methanol is the sole carbon source.

![Figure 19](https://static.igem.wiki/teams/5570/wetlab/result/table2.webp)

![Figure 19](https://static.igem.wiki/teams/5570/wetlab/result/19.webp)

Figure 19 Comparison of 48-h growth of P16 wild-type and Das overexpression strains in methanol-containing medium

7 Metabolic pathway engineering and optimization

A recombinant *E. coli* strain, YihX, expressing the haloacid dehalogenase-like phosphatase gene (*yihX*) was constructed. Colony PCR was performed using the corresponding primers for verification, and the electrophoresis results are shown below.

![Figure 20](https://static.igem.wiki/teams/5570/wetlab/result/20.webp)

Figure 20 Colony PCR verification of YihX

In YPM medium containing methanol, the glucose production was measured after 48 h of fermentation for the wild-type *Aureobasidium melanogenum* P16 strain and the recombinant strain YihX, which heterologously expresses the *E. coli* haloacid dehalogenase-like phosphatase gene (*yihX*). The results are shown in the figure below.

![Figure 21](https://static.igem.wiki/teams/5570/wetlab/result/21.webp)

Figure 21 Comparison of glucose production between wild-type P16 and heterologous YihX-expressing strains

Significance testing of the results showed no significant difference in glucose production between the wild-type P16 strain and the heterologous YihX-expressing strain.

The phosphofructokinase gene (*pfk*) in the YihX strain was knocked out using the CRISPR-Cas9-NAT-Am plasmid, generating the YihX-Δpfk strain. Recombinant colonies were selected and sequenced, and the sequencing results are shown below.

![Figure 22](https://static.igem.wiki/teams/5570/wetlab/result/22.webp)

Figure 22 Pfk knockout sequencing result (pfk sgRNA in red, PAM in green)

The Pfk knockout transformant sequence is shown above. Wild-type P16 and the YihX-Δpfk recombinant strain were cultured on YPGL medium (glucose-free, containing glycerol and lactose) and on YPD medium. The YihX-Δpfk strain was unable to grow on YPGL medium, indicating that it cannot utilize glucose.

![Figure 23](https://static.igem.wiki/teams/5570/wetlab/result/23.webp)

Figure 23 Growth of P16 strain and Δpfk strain on YPGL and YPD media

For the wild-type P16 strain and the CRISPR-Cas9-NAT-Am-mediated phosphofructokinase (*pfk*) knockout strain, the glucose production of wild-type P16 was 0.045, 0.034, and 0.042 g/L, with an average of 0.040 g/L. In contrast, the YihX-Δpfk recombinant strain produced 0.891, 0.789, and 0.851 g/L glucose, with an average of 0.844 g/L. Compared with the wild-type, the recombinant strain showed a significant increase in glucose production, exceeding a 20-fold improvement.

![Figure 24](https://static.igem.wiki/teams/5570/wetlab/result/24.webp)

Figure 24 Comparison of 48-hour glucose production in methanol-containing medium between wild-type P16 and YihX-Δpfk recombinant strain

By knocking out the key glycolytic enzyme gene *pfk* and introducing the heterologous *yihX* gene, glucose synthesis in P16 under methanol conditions was significantly enhanced, demonstrating the effectiveness of this metabolic engineering strategy.

![Figure 25](https://static.igem.wiki/teams/5570/wetlab/result/25.webp)

Figure 25 Comparison of 48-hour glucose production in methanol-containing medium among wild-type P16, heterologous YihX-expressing strain, and YihX-Δpfk recombinant strain

8 Summary and future prospects

In this project, the marine yeast *Aureobasidium melanogenum* P16, which exhibits superior methanol metabolism, was selected as the chassis strain for synthetic biology modifications. Using seamless cloning technology, a CRISPR-Cas9-Am gene knockout vector specifically tailored for *A. melanogenum* was successfully constructed. Experimental validation demonstrated that this system could efficiently perform gene editing in the P16 strain, filling a gap in the availability of high-efficiency genetic tools for *A. melanogenum*. Combined with KEGG annotation, BioEdit sequence alignment, and fluorescence localization experiments, the complete methanol metabolic pathway in P16 and the subcellular distribution of key enzymes were elucidated. The study further revealed, for the first time, the role of ribosome readthrough in the localization of key methanol metabolism enzymes through the construction of fluorescent fusion proteins. Overexpression of the *Pichia pastoris* *Das* gene significantly enhanced P16 growth in methanol-containing medium. Moreover, deletion of the glycolytic key enzyme *pfk* and heterologous expression of the *E. coli* *yihX* gene increased glucose production in the recombinant strain from 0.040 g/L to 0.844 g/L, achieving more than a 20-fold improvement.

Future research may focus on further optimizing the gene editing system to improve efficiency and expand its functionality; integrating omics and metabolic analyses to explore regulatory mechanisms and carbon flux bottlenecks in methanol metabolism; performing multi-gene coordinated engineering to enhance methanol utilization and target product synthesis; and scaling up fermentation processes for industrial validation, ultimately advancing the application of P16 in methanol-based biomanufacturing.

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