iGEM 2025 — Team estonia-tubi

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# Engineering

Our project aimed to engineer yeast strains with enhanced electron
transfer capacity to serve as the main component of microbial fuel
cells. Our strategy combined insights from published research, hardware
design, computational modeling, and our own experimental data. In this
section of the Wiki, we present the rationale behind our design strategy
to modify yeast metabolism for improved extracellular electron transfer
and to evaluate microbial fuel cell performance.

## The aims and uncertainties

The primary goal of our project is to engineer yeast with an increased cytosolic pool of nicotinamide adenine dinucleotide (NADH) and to use them as whole-cell biocatalysts for electron transfer in microbial fuel cells (MFCs). In MFCs, yeast transfer electrons to mediators, which, in turn, pass them to the anode [1]. From there, the electrons flow through an external circuit to the cathode, while protons pass through a proton exchange membrane to maintain charge balance [2] (Figure 1).

![](https://static.igem.wiki/teams/5944/description/description-1-light-new.avif)
![](https://static.igem.wiki/teams/5944/description/description-1-dark-new.avif)

**Figure 1.**  The anode chamber contains microorganisms that
metabolize organic compounds and release electrons and protons.
Electrons are transferred to the anode either directly or with the
aid of electron transfer mediators (e.g., methylene blue). The anode
and cathode chambers are separated by a proton exchange membrane
(PEM), which allows protons to migrate from the anode to the cathode
chamber. Meanwhile, electrons flow through an external circuit to
the cathode, where they participate in the reduction of a terminal
electron acceptor (e.g., potassium hexacyanoferrate).

By deleting certain genes, which related to NADH-consuming pathways (GPD1/2 and ADH1,5) and external mitochondrial dehydrogenases (NDE1/2), and by introducing a fungal cellobiose dehydrogenase (CDH) gene, we aim to redirect an enhanced electron flow toward the anode [3]. This would create increased current output and higher efficiency of electricity generation from microbial-based systems.

However, there are several uncertainties related to the success of our idea:

1. Can blocking glycerol production actually increase the NADH pool? How to deal with the redirection of excess NADH into ethanol production, given that cells tend to compensate to maintain redox balance?e?
2. How can we test the NADH/NAD+ shift in engineered yeast? Can certain methods capture the dynamic of intracellular redox changes and directly link them to extracellular electron transfer and current output?
3. Can modified intracellular metabolism actually increase extracellular electron transfer? Can cytosolic NADH be utilised to increase the amount of electrons which would reach the anode? Which methods would allow us to accurately measure NADH/NAD+ ratio?
4. Can *Phanerochaete chrysosporium* cellobiose dehydrogenase (CDH) be heterologously expressed in *S. cerevisiae*? Could CDH expressed on the yeast cell surface enhance the efficiency of electron transfer from the intracellular NADH pool to an external anode? Could this modification negatively affect cell fitness?
5. Would the hardware used for the yeast fuel cells be sustainable and compatible? How can the risk of yeast cell precipitation at the proton exchange membrane—which could reduce electron transfer efficiency—be minimized?

## Design

Exoelectrogenic bacteria are the most commonly used and studied for the
purposes of creating MFC [4]. However, yeast has certain advantages over
bacterial cells: yeast’s metabolism is well studied [5]; multiple
efficient tools for genetic engineering exist [6]; yeast can utilize
many different substrates for its growth that makes them good candidates
for efficient waste-water treatment; yeast-based devices are
eco-friendly; yeast can grow under low pH [7], high ethanol level [8]
and can tolerate osmotic stress [9]. All mentioned above makes yeast
attractive organisms for MFC.

However, in wild-type yeast, a lot of the reducing power is consumed internally in fermentation pathways (ethanol, glycerol production) or channeled into the mitochondrial respiratory chain. NADH molecules, as key intracellular electron carriers, can serve as donors of electrons to external circuits in microbial fuel cells (MFCs), enabling the conversion of chemical energy into electrical energy. Increasing the cytosolic NADH pool could therefore enhance electron transfer efficiency and overall MFC performance.

To increase cytosolic NADH pool, several gene-deletion strains of S. cerevisiae were engineered (Figure 2):

* **Glycerol-3-phosphate dehydrogenase (GPD1, GPD2)** : key enzyme
  of glycerol synthesis.It reduces dihydroxyacetone phosphate to
  glycerol-3-phosphate using NADH [10];
* **Alcohol dehydrogenase (ADH1, ADH5)** : converts acetaldehyde
  into ethanol, consuming NADH [11];
* **NADH dehydrogenase (NDE1, NDE2)** : catalyzes the oxidation of
  cytosolic NADH, providing cytosolic NADH to the mitochondrial
  respiratory chain [12].

![](https://static.igem.wiki/teams/5944/description/description-2-light-new.avif)
![](https://static.igem.wiki/teams/5944/description/description-2-dark-new.avif)

**Figure 2. Modifications in the yeast central metabolism.**
GPD1, GPD2, ADH1,5 and NDE1,2 genes were deleted to increase the
cytosolic pool of NADH

However, expected increase in NADH pool might not result in the enhanced electron transfer from the cells toward anode. In order to improve yeast capacity for electron transfer, we decided to employ another strategy: express  **cellobiose dehydrogenase (CDH)**  from fungus *Phanerochaete chrysosporium* in *S. cerevisiae* [13]. This enzyme is an extracellular oxidoreductase responsible for cellobiose utilization that can enhance the yeast’s capacity for wastewater treatment and to further facilitate electron transfer to the anode. CDH overexpression broadens the range of substrates that the cell can metabolize. In the context of microbial fuel cells, expressing CDH enables  *S. cerevisiae*  to catalyze the oxidation of various organic compounds while directly transferring electrons to the electrode, either through direct electron transfer or via redox mediators. This should significantly improve the efficiency of bioelectricity generation and enhance the degradation of organic pollutants in wastewater [14]. Extracellular expression of CDH might enhance electron transfer toward anode in the presence of cellobiose. In addition, yeast cells displaying fully functional CDH on their surface may eliminate the need for electron transfer mediators such as methylene blue, or at least significantly reduce their required amount, making the entire system more eco-friendly.

Initially, the yeast codon-optimized sequence of WT CDH was designed as
synthetic DNA and contained overhangs, compatible with cloning using
MoClo yeast toolkit [15]. However, after further literature review, we
decided to revise our design because the native CDH activity in
 *S. cerevisiae*  appears to be very low/undetectable. The new
design was based on [16]. In this case, CDH was fused with Aga2 protein
for cell surface expression and three mutations were introduced into the
CDH coding sequence (D20N, A64T, V592M). The authors claimed that it
resulted in much higher activity of the mutated chimeric protein.

Together, all genetic modifications are intended to increase the cytosolic pool of NADH, enhancing the availability of this key electron donor to boost current generation in MFCs. In addition, the surface expression of CDH is expected to increase the yeast cells’ capacity for electron transfer.

## Build

In order to use the CRISPR/Cas9 tool for gene deletion, our first objective was to introduce Cas9 plasmid (pCfB2312) into the DOM0090 yeast strain. This was the primary step for the majority of further lab work. We checked colonies after transformation by PCR for the presence of Cas9 plasmid. For further work, strains containing Cas9 plasmid were maintained on the plates with G-418 antibiotic.

#### gRNA plasmid design

At the initial stage of the project, we planned to generate multiple deletion mutants. To better understand the impact of each target gene on MFC efficiency, single deletion strains *Δgpd1*, *Δgpd2*, *Δnde1*, and *Δnde2* were created. To improve the efficiency of gene deletions and facilitate subsequent strain engineering, CRISPR/Cas9 was selected as the primary tool.

We started with the design of gRNA (single guide RNA) and assembly of
gRNA plasmids. The purpose of gRNA is to provide the guiding sequence
for Cas9 protein so that it can be directed to the target gene site.

gRNA part of the plasmid consists of:

* Promoter – controls gRNA expression.
* gRNA - single RNA molecule consisting of a 20 bp spacer sequence
  (located adjacent to the Protospacer Adjacent Motif, or PAM, in the
  genome) and a structural RNA region that activates Cas9 and guides it
  to the specific genomic site defined by the spacer sequence.
* Terminator – signals gRNA transcription termination [17].

Since initially we wanted to study the effect of single gene deletions, four plasmids containing single gRNAs for  *GPD1, GPD2, NDE1, NDE2*  genes were designed as following:

1. Benchling.com CRISPR/Cas9 design tool was used for screening target
   genes for the presence of potential gRNA sequences.
2. Two gRNA oligonucleotides were designed and ordered for each of the four target genes (forward and reverse strands, each was 24 bp in length, including cloning overhangs). A list of all designed oligonucleotide pairs used for gRNA plasmid construction is provided in Table 1. The forward and reverse oligos were then phosphorylated and annealed prior to cloning.
3. The annealed oligo pairs were cloned into the pSBME133 backbone using
   the Golden Gate (GG) cloning and the Eco31I restriction enzyme (Figure
   3).
4. Turbo competent  *E. coli* cells were transformed with the GG reaction mixture.
5. Colonies carrying plasmids were selected on LB plates containing
   ampicillin. An additional color-based selection was used to identify
   colonies containing gRNA plasmids. During the Golden Gate (GG)
   assembly, the gRNA sequence replaces the mCherry dropout cassette;
   therefore, desired colonies appear white, while those carrying the
   original plasmid without insertion remain pink. Several colonies from
   each transformation were screened by colony PCR across the gRNA
   insertion site to confirm the expected amplicon size.
6. Positive colonies carrying desired plasmids were used to grow cultures
   for miniprep.

**Table 1. List of oligonucleotide pairs used for gRNA plasmid
construction**

| Oligonucleotide name | Comment |
| --- | --- |
| GPD1\_gRNA1\_F | Oligo pair used to generate the gRNA sequence targeting the GPD1 gene |
| GPD1\_gRNA1\_R |
| GPD2\_gRNA1\_F | gRNA sequence targeting the GPD2 gene |
| GPD2\_gRNA1\_R |
| NDE1\_gRNA1\_F | Oligo pair used to generate the gRNA sequence targeting the NDE1 gene. |
| NDE1\_gRNA1\_R |
| NDE2\_gRNA1\_F | Oligo pair used to generate the gRNA sequence targeting the NDE2 gene. |
| NDE2\_gRNA1\_R |

![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/fig3-light.avif)
![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/fig3-dark.avif)

**Figure 3. Single gRNA Plasmid assembly using GG.**
Two gRNA oligonucleotides were designed for each of the four target genes (forward and reverse strands, each was 24 bp in length, including cloning overhangs). After phosphorylation and annealing, they were cloned into the pSBME133 backbone using the Golden Gate (GG) assembly method with Eco31I restriction enzyme.

We also aimed to investigate the cumulative effect of multiple gene deletions on MFC performance. To this end, we constructed plasmids carrying two gRNAs for simultaneous deletion of two genes. Two sets of multi-gRNA plasmids were designed to target *NDE1* + *NDE2*; *GPD1* + *GPD2* (Figure 4). For the triple gene deletion (*ADH1, ADH2, ADH5*) we used ready plasmid that had gRNA for all three ADH genes. Although ADH2 catalyzes the reverse reaction—the oxidation of ethanol to acetaldehyde—it should remain inactive under MFC conditions, as it is glucose-repressible and becomes active only when sugars are depleted. Therefore, including ADH2 in the triple gRNA plasmid (targeting *ADH1/2/5*) should not compromise our system.

![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/fig4-light-yes.avif)
![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/fig4-dark.avif)

**Figure 4. Multi gRNA plasmid assembly using GG.**
For the double-gRNA plasmid assembly, two gRNA oligonucleotides were designed for each of the four target genes, containing overhangs compatible with the multiple gRNA assembly plasmids. After phosphorylation and annealing, the gRNAs were assembled with the pSBME134 and pSBME136 backbone vectors to get pSBME134-GPD1\_gRNA, pSBME136-GPD2\_gRNA, pSBME134-NDE1\_gRNA, and pSBME136-NDE2\_gRNA. Assembly was performed using GG with the Eco31I restriction enzyme. For the second GG reaction with Esp3I enzyme, plasmids containing gRNA for NDE1 and NDE1, or GPD1 and GPD2 genes (products of the first assembly) were mixed with pSBME121 (contains assembly connector sequence) and pSBME106 to get double-gRNA plasmid vectors.

For the multiple gRNA plasmids, the gRNA oligos for each target gene were designed as described above. The only difference was the presence of different overhangs, compatible with other target plasmids. “Intermediate” plasmids each carrying one gRNA expression cassette were first created using pSBME134 and pSBME136 as backbone vectors (pSBME134-*GPD1\_gRNA*, pSBME136-*GPD2\_gRNA*, pSBME134-*NDE1\_gRNA*, and pSBME136-*NDE2\_gRNA*). Assembly was performed using GG with the Eco31I restriction enzyme. For the second GG reaction with Esp3I enzyme, plasmids containing gRNA for *NDE1* and *NDE1*, or *GPD1* and *GPD2* genes (products of the first assembly) were mixed with pSBME121 (contains assembly connector sequence) and pSBME106 to get double-gRNA plasmid vectors. Transformation was performed in E. coli, followed by selection on ampicillin-containing LB plates.

Moving forward, our focus shifted to designing synthetic donor DNA fragments to repair the DNA break that the Cas9 introduces. These donor DNAs provide templates for homology-directed repair in order to eliminate mutations and preserve genome structural organisation. The synthetic DNA fragments were designed with appropriate homology arms (around 100 bp) and cloned into a plasmid containing AmilCP chromoprotein (was used as a reporter for color selection of the colonies carrying the insert). Finally the donor DNA fragments were PCR-amplified from pAmilCP plasmids and used for transformation of DOM90 + pCfB2312 (Cas9-containing plasmid) yeast strains along with the corresponding gRNA plasmids. The transformed cultures were grown on plates, which contained selection markers for both the Cas9 and gRNA plasmid. To verify the success and efficiency of gene deletions, colony PCR was performed.

To verify successful deletions, we designed primers binding
approximately 100 bp upstream and 100 bp downstream of each target
locus. In the DOM90 control strain, where no deletion occurred, this
setup amplified a fragment corresponding to the intact gene sequence in
addition to the 200 bp flanking regions. In contrast, in correctly
edited strains, the amplification product was reduced to the ~200 bp
flanking sequence only, confirming that the intervening gene had been
removed. In addition, for single-gene deletions, several colonies were
chosen to perform sequencing and thereby ensure that the donor DNA was
correctly inserted in a place of the deleted genes.

For heterologous expression of CDH, synthetic DNA with overhangs compatible with cloning into the plasmids of the MoClo kit was designed [15]. Two versions of CDH plasmids were created (WT *CDH* version and *Aga2-CDHmut*) (Figure 5). The rationale behind the use of every variant is explained above in the Design section.

![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/fig5a-light.avif)
![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/fig5a-dark.avif)

![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/fig5b-light.avif)
![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/fig5b-dark.avif)

**Figure 5. GG assembly of the plasmids containing CDH (A) or Aga2-CDHmut (B) transcriptional units.**
The yeast codon-optimized sequence of *Phanerochaete chrysosporium* WT CDH was designed as synthetic DNA with type 3 pars overhangs, compatible with pYTK001 plasmid from MoClo yeast toolkit [15]. pYTK001-CDH custom part was used for the transcriptional unit assembly with Eco31I restriction enzyme. (B) The yeast codon-optimized sequence of CDHmut (D20N, A64T, V592M) was designed as synthetic DNA with type 3b overhangs, compatible with pYTK001 plasmid from MoClo yeast toolkit, while Aga2 sequence was designed as type 3a overhangs (was also initially cloned into pYTK001). After transcriptional unit assembly, Aga2 was fused in one ORF with CDHmut.

## Test

To test the effect of our gene deletion on the growth, glycerol and ethanol production, a time course sample collection experiment was performed. Two colonies from each of the single gene deletion strains along with the DOM90 + Cas9 as a control strain were grown in CSM + 2% glucose. OD600 measurements were performed and samples for HPLC and NADH/NAD+ measurement assay were collected at the following time points: 0, 1.5, 3, 4.5, 6.5, 8.5, 10.5, 12, 24, and 48 hours.

#### Growth experiment and HPLC analysis

OD measurements indicated that most of the engineered strains exhibited impaired growth, with the slowest growth observed in the *Δgpd1* and *Δgpd2* strains (Figure 6). After 48h, OD600 in *Δgpd1\_7*, *Δgpd2\_1*, and *Δgpd2\_1* strains was about 3 times lower than in one of the controls. However, the *Δnde1* and *Δnde2* strains, as well as the *Δgpd1\_8* mutant, did not show such substantial differences compared to the control.

![](https://static.igem.wiki/teams/5944/hplc-new/growth-curve-light-new.avif)
![](https://static.igem.wiki/teams/5944/hplc-new/growth-curve-dark-new.avif)

**Figure 6. Time course of cell growth for engineered and control
yeast strains over 48 hours.**
The absorbance of yeast cultures at every time point was measured at
600 nm to monitor cell growth.

HPLC analysis of glycerol production over a 48-hour time course demonstrated the impact of our gene deletions. As shown in the graph, both *Δgpd1* strains exhibited complete suppression of glycerol accumulation. The *Δgpd2* and *Δnde2* strains accumulated glycerol more slowly during the initial stages of growth, but by the end of the experiment, their glycerol levels were similar to those of the control strain. In contrast, the *Δnde1* mutant showed enhanced glycerol accumulation compared to the control, likely reflecting the need to reduce cytosolic NADH due to impaired mitochondrial uptake—directly indicating the intended effect of the mutation (Figure 7).

![](https://static.igem.wiki/teams/5944/hplc-new/glycerol-data-light-new.avif)
![](https://static.igem.wiki/teams/5944/hplc-new/glycerol-data-dark-new.avif)

**Figure 7. Glycerol production over time.** Glycerol accumulation in the culture medium was measured during the growth of the parental DOM90 strain and deletion strains (*Δgpd1, Δgpd2, Δnde1, Δnde2*). Differences between strains demonstrates the role of the targeted genes in glycerol biosynthesis.

However, under our experimental conditions, complete suppression of glycerol biosynthesis had also an adverse effect on growth as it was revealed by both OD measurements and HPLC analysis. Consistent with their lower growth rates, *Δgpd1\_7, Δgpd2\_1*, and *Δgpd2\_1* strains exhibited reduced glucose consumption compared to the DOM0090 control. Glucose was not fully depleted from the culture medium even after 48 hours of growth, with approximately half of the initial glucose amount remaining (Figure 8).

![](https://static.igem.wiki/teams/5944/hplc-new/glucose-data-light-new.avif)
![](https://static.igem.wiki/teams/5944/hplc-new/glucose-data-dark-new.avif)

**Figure 8. Glucose consumption of engineered and control yeast
strains over 48 hours measured by HPLC.**
The decrease in glucose concentration in the medium was measured by HPLC as the parental DOM90 strain and deletion strains (*Δgpd1, Δgpd2, Δnde1, Δnde2*) grew. The graph illustrates glucose uptake by each strain during different cell growth phases.

All the *Δgpd1\_7, Δgpd2\_1*, and *Δgpd2\_1* strains also exhibited decreased ethanol production during growth, whereas other deletion mutants showed ethanol synthesis rates similar to the DOM0090 control. However, differences were observed in ethanol consumption after 24 hours, with the DOM0090 strain displaying more intensive ethanol utilization (Figure 9).

![](https://static.igem.wiki/teams/5944/hplc-new/ethanol-data-light-new.avif)
![](https://static.igem.wiki/teams/5944/hplc-new/ethanol-data-dark-new.avif)

**Figure 9. Ethanol production in engineered and control yeast
strains over 48 hours measured by HPLC.**

Ethanol accumulation in the culture medium was measured throughout the growth of the parental DOM90 strain and deletion strains (*Δgpd1, Δgpd2, Δnde1, Δnde2*). The graph depicts the fermentative activity of each strain and the impact of specific gene deletions on ethanol production over time.

#### NADH/NAD+ assay

Since the ultimate goal of our gene deletions was to increase the cytosolic NADH pool in yeast cells, we measured NADH/NAD+ levels using Amplite® Colorimetric NADH/NAD+ Ratio Assay Kit. Samples for the assay were also collected during the growth experiment.

![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/fig10-light.avif)
![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/fig10-dark.avif)

**Figure 10. Calibration curve constructed from absorbance
measurements at 460 nm of standard NADH solutions. The results of
the second measurement were used to build the curve.**

However, the sample measurements did not yield meaningful results. In some cases, the absorbance measured for total NADH + NAD+ samples was lower than that of NADH alone (Table 3). We therefore concluded that these results are unreliable and that further optimization of the protocol is required.

**Table 3. NADH/NAD+ ratio measurements using Amplite® Colorimetric NADH/NAD+ Ratio Assay Kit.**

![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/nadh1-light.avif)
![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/nadh1-dark.avif)

![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/nadh2-light.avif)
![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/nadh2-dark.avif)

Although the NADH/NAD+ measurements did not provide any reliable data, we can still conclude that the cytosolic NADH pool in our engineered yeast strains increased, as evidenced by the suppression of glycerol production.

To evaluate the performance of our engineered yeast strains as
biocatalysts for electron transfer in MFCs, we designed custom-built
hardware and tested multiple yeast strains under these conditions. The
results of testing our engineered strains in the
[MFC hardware](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27hardware%27%29%20%7D%7D)
showed that the Δgpd1 strain performed slightly better than the DOM90
control as a catalyst for electron transfer.

#### Hardware

To evaluate the performance of our engineered yeast strains as biocatalysts for electron transfer in MFCs, we designed custom-built hardware and tested multiple yeast strains under these conditions. The results of testing our engineered strains in the MFC hardware showed that the *Δgpd1* strain performed slightly better than the DOM90 control as a catalyst for electron transfer. These results confirm that suppressing glycerol biosynthesis following *GPD1* deletion increases the cytosolic NADH pool, thereby enhancing electron transfer to the electrodes, and validate our hypothesis that boosting NADH levels is an effective strategy for engineering yeast as a high-performance cell factory. Thus, the measurement results obtained from our hardware directly validate our genetic design by linking the metabolic modifications in yeast to measurable improvements in MFC performance.

#### Modeling

Microbial fuel cell (MFC) design has traditionally relied on
trial-and-error strain screening, lacking a predictive framework to link
genetics with power output. Here, we introduce a mechanistic multi-scale
model that addresses this gap. Cellular NADH flux models predict
electron surplus resulting from specific gene deletions, biofilm
kinetics translate single-cell metabolism into collective current
dynamics, and reactor optimization maximizes power across biological and
electrochemical timescales.

The key innovation is the integration of redox balance constraints at the molecular level with population dynamics and electrochemical processes. By measuring only five strains, our framework can predict the performance of novel genotypes and validate these predictions using bootstrap-based uncertainty quantification. We identified statistically significant electron surplus, demonstrating that the model accurately captures real metabolic constraints. Most critically, the model prevented generation of double mutants *Δgpd1Δnde1* and *Δgpd2Δnde2*, which had been planned initially.

This approach transforms MFCs from an empirical biotechnology into a predictive bioengineering platform, providing a genotype-to-watts pipeline for systematic optimization [Modeling](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27model%27%29%20%7D%7D).

## Learn

Our experiments lead to several important conclusions. Firstly, the CRISPR/Cas9 system was successfully implemented in DOM90 yeast, as confirmed by colony PCR and sequencing of single- and double-gene deletions. This establishes a robust platform for genome editing in our strains and provides a foundation for more efficient and complex modifications.

Second, our experiments with engineered strains revealed measurable
physiological changes following gene deletions. HPLC analysis showed
altered ethanol and glycerol production, as well as reduced glucose
consumption in the engineered strains compared to the DOM90 control. The
reductions in ethanol and glycerol production suggest a possible
increase in the intracellular NADH pool.

However, NADH/NAD+ ratio measurements did not yield reliable data suitable for quantification. The measurements were repeated twice. In the first attempt, no meaningful results were obtained; only the standard curve samples were measured, showing a low correlation between concentration and absorbance. To optimize the protocol, we reviewed the literature and modified the sample handling.

When ordering the kit, we were aware that it might not be optimal for yeast cells, as it was primarily designed for mammalian and bacterial systems. Although the kit documentation mentioned potential applicability to plant cells with tough cell walls, it only suggested that it could possibly be used for yeast. Nevertheless, we proceeded with the measurements, and the second attempt produced somewhat improved results. We hypothesize that the main limitation of the assay is the robust yeast cell wall. Considering the high instability of NADH and NAD+ during handling [18], it is likely that a significant fraction of these metabolites was degraded during cell lysis before the addition of the measurement reagents. Before the second measurement, we added glass beads to the samples along with lysis buffer and used a FastPrep-24 homogenizer prior to incubation at 37 °C, which resulted in a significant improvement in the measurements. The improvement observed in the second attempt suggests that there is still room for further optimization. Another potential approach would be to employ protocols specifically developed for measuring the NADH/NAD+ ratio in yeast cells [19].

Based on our laboratory data and findings from the scientific
literature, we developed models to guide further optimization of MFC
engineering. Gene deletions often impose a fitness cost, which can
increase when multiple genes are deleted. Our model allows us to predict
the consequences of different gene deletion combinations, including
their impact on cell viability, growth, and potential benefits for MFC
performance. It is important to note that growth conditions during the
time-course experiments differ significantly from those within MFC
hardware, which involve anaerobic conditions, limited space, and
constraints on electron transfer. Understanding these differences is
essential for accurately translating experimental findings into improved
MFC designs.

The modeling approach provided practical advantages over traditional experimental screening. Testing all candidate strains electrochemically in MFC reactors would require 8-12 weeks per strain (reactor assembly, biofilm formation, stabilization, and performance characterization), consuming significant resources and laboratory time. By predicting MFC performance from 24-hour fermentation assays, we compressed the screening timeline to under one week while correctly identifying top candidates (NDE1, GPD1) and avoiding experimental investment in failed designs. Most critically, the model prevented generation of double mutants *Δgpd1Δnde1* and *Δgpd2Δnde2*, which had been planned initially.

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# EXPERIMENTS

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# Results

Our project aimed to engineer yeast strains with enhanced electron transfer capacity to serve as the main component of microbial fuel cells. Our strategy combined insights from published research, hardware design, computational modeling, and our own experimental data. In this section of the Wiki, we present the rationale behind our design strategy to modify yeast metabolism for improved extracellular electron transfer and to evaluate microbial fuel cell performance.

## Test

To test the effect of our gene deletion on the growth, glycerol and
ethanol production, a time course sample collection experiment was
performed. Two colonies from each of the single gene deletion strains
along with the DOM90 + Cas9 as a control strain were grown in CSM + 2%
glucose. OD600 measurements were performed and samples for HPLC and
NADH/NAD measurement assay were collected at the following time points:
0, 1.5, 3, 4.5, 6.5, 8.5, 10.5, 12, 24, and 48 hours.

#### Growth experiment and HPLC analysis

OD measurements indicated that most of the engineered strains exhibited
impaired growth, with the slowest growth observed in the Δgpd1 and Δgpd2
strains (Figure 6). After 48h, OD600 in Δgpd1\_7, Δgpd2\_1, and Δgpd2\_1
strains was about 3 times lower than in one of the controls. However,
the Δnde1 and Δnde2 strains, as well as the Δgpd1\_8 mutant, did not show
such substantial differences compared to the control.

![](https://static.igem.wiki/teams/5944/hplc-new/growth-curve-light-new.avif)
![](https://static.igem.wiki/teams/5944/hplc-new/growth-curve-dark-new.avif)

**Figure 6. Time course of cell growth for engineered and control
yeast strains over 48 hours.**
The absorbance of yeast cultures at every time point was measured at
600 nm to monitor cell growth.

HPLC analysis of glycerol production over a 48-hour time course
demonstrated the impact of our gene deletions. As shown in the graph,
both Δgpd1 strains exhibited complete suppression of glycerol
accumulation. The Δgpd2 and Δnde2 strains accumulated glycerol more
slowly during the initial stages of growth, but by the end of the
experiment, their glycerol levels were similar to those of the control
strain. In contrast, the Δnde1 mutant showed enhanced glycerol
accumulation compared to the control, likely reflecting the need to
reduce cytosolic NADH due to impaired mitochondrial uptake—directly
indicating the intended effect of the mutation (Figure 7).

![](https://static.igem.wiki/teams/5944/hplc-new/glycerol-data-light-new.avif)
![](https://static.igem.wiki/teams/5944/hplc-new/glycerol-data-dark-new.avif)

**Figure 7. Glycerol production over time.** Glycerol
accumulation in the culture medium was measured during the growth of
the parental DOM90 strain and deletion strains (Δgpd1, Δgpd2, Δnde1,
Δnde2). Differences between strains demonstrates the role of the
targeted genes in glycerol biosynthesis.

However, under our experimental conditions, complete suppression of
glycerol biosynthesis had also an adverse effect on growth as it was
revealed by both OD measurements and HPLC analysis. Consistent with
their lower growth rates, Δgpd1\_7, Δgpd2\_1, and Δgpd2\_1 strains
exhibited reduced glucose consumption compared to the DOM0090 control.
Glucose was not fully depleted from the culture medium even after 48
hours of growth, with approximately half of the initial glucose amount
remaining (Figure 8).

![](https://static.igem.wiki/teams/5944/hplc-new/glucose-data-light-new.avif)
![](https://static.igem.wiki/teams/5944/hplc-new/glucose-data-dark-new.avif)

**Figure 8. Glucose consumption of engineered and control yeast
strains over 48 hours measured by HPLC.**
The decrease in glucose concentration in the medium was measured by
HPLC as the parental DOM90 strain and deletion strains (Δgpd1,
Δgpd2, Δnde1, Δnde2) grew. The graph illustrates glucose uptake by
each strain during different cell growth phases.

All the Δgpd1\_7, Δgpd2\_1, and Δgpd2\_1 strains also exhibited decreased
ethanol production during growth, whereas other deletion mutants showed
ethanol synthesis rates similar to the DOM0090 control. However,
differences were observed in ethanol consumption after 24 hours, with
the DOM0090 strain displaying more intensive ethanol utilization (Figure
9).

![](https://static.igem.wiki/teams/5944/hplc-new/ethanol-data-light-new.avif)
![](https://static.igem.wiki/teams/5944/hplc-new/ethanol-data-dark-new.avif)

**Figure 9. Ethanol production in engineered and control yeast
strains over 48 hours measured by HPLC.**

Ethanol accumulation in the culture medium was measured throughout the
growth of the parental DOM90 strain and deletion strains (Δgpd1, Δgpd2,
Δnde1, Δnde2). The graph depicts the fermentative activity of each
strain and the impact of specific gene deletions on ethanol production
over time.

#### NAD/NADH assay

Since the ultimate goal of our gene deletions was to increase the
cytosolic NADH pool in yeast cells, we measured NADH/NAD levels using
Amplite® Colorimetric NAD/NADH Ratio Assay Kit. Samples for the assay
were also collected during the growth experiment. The measurements were
repeated twice. In parallel with the sample measurements, the standard
curve for different NADH concentrations was built (Figure 10).

![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/nadh-graph-light.avif)
![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/nadh-graph-dark.avif)

**Figure 10. Calibration curve constructed from absorbance
measurements at 460 nm of standard NADH solutions. The results of
the second measurement were used to build the curve.**

However, the sample measurements did not yield meaningful results. In
some cases, the absorbance measured for total NAD⁺/NADH samples was
lower than that of NADH alone (Table 3). We therefore concluded that
these results are unreliable and that further optimization of the
protocol is required.

**Table 3. NAD/NADH ratio measurements using Amplite® Colorimetric
NAD/NADH Ratio Assay Kit.**

![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/nadh1-light.avif)
![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/nadh1-dark.avif)

![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/nadh2-light.avif)
![](https://static.igem.wiki/teams/5944/images/experiments/nad-nadh/nadh2-dark.avif)

Although the NAD/NADH measurements did not provide any reliable data, we
can still conclude that the cytosolic NADH pool in our engineered yeast
strains increased, as evidenced by the suppression of glycerol
production.

To evaluate the performance of our engineered yeast strains as
biocatalysts for electron transfer in MFCs, we designed custom-built
hardware and tested multiple yeast strains under these conditions. The
results of testing our engineered strains in the
[MFC hardware](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27hardware%27%29%20%7D%7D)
showed that the Δgpd1 strain performed slightly better than the DOM90
control as a catalyst for electron transfer.

## Modeling

Microbial fuel cell (MFC) design has traditionally relied on
trial-and-error strain screening, lacking a predictive framework to link
genetics with power output. Here, we introduce a mechanistic multi-scale
model that addresses this gap. Cellular NADH flux models predict
electron surplus resulting from specific gene deletions, biofilm
kinetics translate single-cell metabolism into collective current
dynamics, and reactor optimization maximizes power across biological and
electrochemical timescales.

The key innovation is the integration of redox balance constraints at
the molecular level with population dynamics and electrochemical
processes. By measuring only five strains, our framework can predict the
performance of novel genotypes and validate these predictions using
bootstrap-based uncertainty quantification. We identified statistically
significant electron surplus, demonstrating that the model accurately
captures real metabolic constraints.

This approach transforms MFCs from an empirical biotechnology into a
predictive bioengineering platform, providing a genotype-to-watts
pipeline for systematic optimization
[Modeling](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27model%27%29%20%7D%7D).

## Learn

Our experiments lead to several important conclusions. Firstly, the
CRISPR - Cas9 system was successfully implemented in DOM90 yeast, as
confirmed by colony PCR verification and sequencing of single- and
double-gene deletions. This establishes a robust platform for genome
editing in our strains and provides a foundation for more efficient and
complex modifications.

Second, our experiments with engineered strains revealed measurable
physiological changes following gene deletions. HPLC analysis showed
altered ethanol and glycerol production, as well as reduced glucose
consumption in the engineered strains compared to the DOM90 control. The
reductions in ethanol and glycerol production suggest a possible
increase in the intracellular NADH pool.

However, NAD/NADH ratio measurements did not yield reliable data
suitable for quantification. The measurements were repeated twice. In
the first attempt, no meaningful results were obtained; only the
standard curve samples were measured, showing a low correlation between
concentration and absorbance. To optimize the protocol, we reviewed the
literature and modified the sample handling.

When ordering the kit, we were aware that it might not be optimal for
yeast cells, as it was primarily designed for mammalian and bacterial
systems. Although the kit documentation mentioned potential
applicability to plant cells with tough cell walls, it only suggested
that it could possibly be used for yeast. Nevertheless, we proceeded
with the measurements, and the second attempt produced somewhat improved
results. We hypothesize that the main limitation of the assay is the
robust yeast cell wall. Considering the high instability of NADH and NAD
during handling [19], it is likely that a significant fraction of these
metabolites was degraded during cell lysis before the addition of the
measurement reagents. Before the second measurement, we added glass
beads to the samples along with lysis buffer and used a FastPrep-24
homogenizer prior to incubation at 37 °C, which resulted in a
significant improvement in the measurements. The improvement observed in
the second attempt suggests that there is still room for further
optimization. Another potential approach would be to employ protocols
specifically developed for measuring the NADH/NAD ratio in yeast cells
[20].

Based on our laboratory data and findings from the scientific
literature, we developed models to guide further optimization of MFC
engineering. Gene deletions often impose a fitness cost, which can
increase when multiple genes are deleted. Our model allows us to predict
the consequences of different gene deletion combinations, including
their impact on cell viability, growth, and potential benefits for MFC
performance. It is important to note that growth conditions during the
time-course experiments differ significantly from those within MFC
hardware, which involve anaerobic conditions, limited space, and
constraints on electron transfer. Understanding these differences is
essential for accurately translating experimental findings into improved
MFC designs.

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