Abstract
Boolean gates, the fundamental components of digital circuits, have been widely investigated in synthetic biology because they permit the fabrication of biosensors and facilitate biocomputing. This study was conducted to design and construct Boolean gates in the yeast Saccharomyces cerevisiae, the main component of which was the RNA interference pathway (RNAi) that is naturally absent from the budding yeast cells. We tested different expression cassettes for the siRNA precursor (a giant hairpin sequence, a DNA fragment—flanked by one or two introns—between convergent promoters or transcribed separately in the sense and antisense directions) and placed different components under the control of the circuit inputs (i.e., the siRNA precursor or proteins such as the Dicer and the Argonaute). We found that RNAi-based logic gates are highly sensitive to promoter leakage and, for this reason, challenging to implement in vivo. Convergent-promoter architecture turned out to be the most reliable solution, even though the overall best performance was achieved with the most difficult design based on the siRNA precursor as a giant hairpin.
Introduction
Small interfering RNA (siRNA) molecules are noncoding RNAs that play a significant role in the regulation of gene expression () at the translational level in eukaryotic cells. siRNAs are fully complementary to the transcript of the target genes and are silenced through a mechanism known as RNA interference (RNAi) (). Initially, a siRNA precursor, which is usually a double-stranded RNA (dsRNA) molecule up to 100 nt long, is cleaved in the cytoplasm into smaller (21–23-nt long (Zamore et al., 2000)) pieces (the siRNAs) by the Dicer (Dcr) enzyme. siRNAs then become part of the RNA-induced silencing complex (RISC), where their passenger (sense) strand is removed. The guide (antisense) strand, in contrast, directs the RISC to the target mRNA () that is finally cut by the Argonaute (Ago) endonuclease and is then degraded by the cell. Due to their ability to silence specific genes, siRNAs have attracted significant attention as potent instruments for targeted drug delivery and disease treatment ().
The yeast Saccharomyces cerevisiae, a commonly used organism in molecular biological research and biotechnology applications (), does not naturally express a complete RNAi pathway (). However, upon introduction of Dcr and Ago in the yeast genome, RNA interference can be re-established (Wang et al., 2006; ) and employed in synthetic gene circuits to downregulate protein expression at the mRNA level as an alternative to ribozymes/riboswitches (; ; ), PUF proteins (), and CRISPR-(d)Cas systems (d: DNase-deficient) (; Zhang et al., 2022; Yu and Marchisio, 2023).
Drinnenberg and colleagues () were the first to show how to re-engineer RNAi in S. cerevisiae. They implemented a circuit where the siRNA precursor was synthesized, as a giant hairpin, under the GAL1 promoter (pGAL1) that is activated by galactose and repressed by glucose. siRNA molecules, which were generated by the action of Dcr1 and Ago1 from Saccharomyces castelli on the siRNA precursor, targeted a region of the green fluorescent protein (GFP) transcript. Ideally, galactose (input: 1) would trigger the RNAi pathway that determines the degradation of the GFP mRNA, causing a low fluorescence output (0). Glucose (input: 0), in contrast, would stop RNAi and determine a high GFP expression (output: 1). Therefore, the circuit should behave like a logical “NOT” gate. However, the circuit always exhibited low fluorescence both with and without galactose in the cell culture. Later research focusing on RNA interference in S. cerevisiae did not consider chemically inducible/repressible or protein-regulated promoters to express the siRNA precursor or any other RNAi components. The constitutive promoters of different strengths were used inside the original circuit by Drinnenberg and colleagues (; ). Alternatively, novel strategies to generate the siRNA precursor (such as convergent promoters or separate sense–antisense gene expression—; ) were adopted. Hence, RNAi was never employed again to realize Boolean gates or more complex digital circuits in yeast.
Digital circuits have emerged as a significant branch of synthetic biology, finding widespread application in medical diagnosis, environmental care, and biocomputing (; ). S. cerevisiae cells have been turned into Boolean gates, mainly by using orthogonal bacterial transcription factors such as TetR (; ), LacI (), LexA (), and the CRISPR-dCas9 system (). At the translation level, ribozymes (Win and Smolke, 2008) and riboswitches () have been engineered to respond to specific input signals such as theophylline and tetracycline. Moreover, a consortia of yeast cells exchanging pheromones have been shown to mimic what are effectively logic gates sensing up to three inputs (). Logic circuits provide a clear, compact representation of the relationship between their inputs (usually chemical species) and output (e.g., fluorescence) in a truth table which contains only 0/1 values and from which two Boolean formulae (one in the conjunctive, the other in the disjunctive normal form) are derived (). Inputs can be categorized into inducer and corepressor groups. Inducers promote transcription by inactivating repressors or activating activators, whereas corepressors prevent RNA synthesis by activating repressors or inactivating activators ().
In this study, we assessed the feasibility of constructing Boolean gates that sense one or two chemicals by harnessing RNAi in S. cerevisiae. First, we tried to elucidate why the original circuit by Drinnenberg and colleagues failed to reproduce a NOT logic function. We then determined how to modify it and build a working NOT gate. Moreover, we constructed other Boolean gates (“YES” and “IMPLY”) based on inducible convergent promoters and separate sense–antisense transcription. The comparison of the performance of the different circuit designs led us to conclude that the convergent-promoter architecture is the most reliable solution for assembling RNAi-based digital circuits in budding yeast.
Results and discussion
Reconstructing the first S. cerevisiae synthetic gene circuit based on a reengineered RNAi pathway
The starting point of our work was to build a gene circuit as close as possible to that of Drinnenberg and colleagues (). Four transcription units (TUs) were assembled (Figure 1A). pGAL1 controlled the expression of the siRNA precursor that was designed as a giant hairpin, with a stem comprising 276 nt, whereas 67 nt were present in the loop. The siRNA molecules generated from the siRNA precursor targeted the yeast-enhanced green fluorescent protein (yEGFP) transcript (). As in our reference work (Wang et al., 2006), Ago1 and Dcr1 were placed downstream of the TEF1 (pTEF1) and TEF2 promoter (pTEF2), respectively. Unlike the original circuit (Wang et al., 2006), we used a relatively weak synthetic promoter (designed in our lab) termed “Tsynth8_pCYC1noTATA” () to lead yEGFP expression (see Supplementary Table S1 for a comparison of the strengths the promoters used in this work).
FIGURE 1
As shown in Figure 1B, we reproduced the same trend as in
Drinnenberg and colleagues proposed two possible explanations for this unexpected result: 1) the presence of a constitutive antisense promoter downstream of the siRNA precursor sequence; 2) pGAL1 leakage—that is, its transcriptional activity in the presence of glucose. Before proceeding with the construction of new logic circuits, we conducted a series of experiments to understand what hindered the reproduction of a NOT logic function via the synthetic RNAi pathway.
To force the termination of the transcription started by a possible antisense promoter on chromosome V (where we integrated the TU expressing the siRNA precursor), we inserted the strong DEG1 terminator (DEG1t) (
We performed a different test by placing pGAL1-siRNA_ precursor-CYC1t on the centromeric vector pRSII416 (
Our previous tests were performed despite the fact that the CYC1 terminator is classified as bidirectional (
On the basis of these results, we can exclude the production of the siRNA precursor by pGAL1 in the OFF state being due to the activity of an antisense promoter. In the absence of other plausible explanations, we conclude that pGAL1 leakage is responsible for a non-negligible synthesis of the siRNA precursor in a glucose-containing cell-growth solution which fully activates the RNAi pathway. According to our fluorescence measurement and mRNA quantification (Supplementary Table S6), pGAL1 leakage in the presence of glucose corresponds to less than 0.5% of its activity in a 2%-galactose-supplied medium. Whenever we used pGAL1 to express proteins in different kinds of synthetic gene circuits, its leakage always turned out to be insignificant for the circuit’s functionality (
A new circuit design: inducible expression of the Dicer enzyme
Moving from the observations we made on GAL1 promoter leakage, we designed an alternative version of the circuit in Figure 1A, where the siRNA precursor was constitutively transcribed under the strong GPD promoter (pGPD), whereas Dcr1 expression became galactose-inducible (Figure 2A). In the presence of glucose, the limited number of Dcr1 proteins that were produced did not provoke any dramatic reduction in fluorescence expression with respect to the control circuit (where the Dcr1-expressing cassette was missing). In contrast, the fluorescence signal dropped remarkably in the presence of galactose (Figure 2B). With an OFF/ON ratio of 0.06, the circuit behaved very efficiently as a NOT gate. In order to build more basic Boolean gates that are responsive to different inputs, we first replaced pGAL1 with the copper-inducible CUP1 promoter (
FIGURE 2

Inducible synthesis of Dcr1. (A) Schematic representation of NOT gates sensing either galactose or CuSO4. pGAL1 or pCUP1 regulates the expression of the Dcr1 protein. The siRNA precursor is constitutively expressed under pGPD. (B) Fluorescence intensity profile of galactose-responsive NOT gate. Cell cultures were supplied with 2% galactose. (C) Performance of copper-sensing NOT gate. The “1” concentration of CuSO4 corresponded to 0.5 mM (*: p-value <0.05, **: p-value <0.01, ***: p-value <0.001, ****: p-value <0.0001; two-sided Welch’s t-test, see Supplementary Table S7).
Building a two-input IMPLY gate
In previous work from our lab (Yu and Marchisio, 2023), we implemented two versions of an IMPLY gate based on mRNA degradation, hosting both pCUP1 and pMET25. The mRNA cleavage was due to the action of type V dCas12a proteins on their direct repeat (previously added to the target transcript), which was not affected by the leakage of the two promoters. Thus, we tried to determine whether the combination of pGAL1 or pCUP1 with pMET25 would permit engineering an IMPLY gate based on RNAi (the importance of this kind of gate is explained in
FIGURE 3

RNAi-based IMPLY gate. (A) Circuit scheme. The two inputs are methionine and galactose. The former represses the expression of Ago1, and the latter enhances that of Dcr1. (B) Fluorescence levels from the strain byMM 1900, which hosts the IMPLY gate, by varying the concentration of the two inputs (“1” concentration of methionine: 10 mM; “1” galactose amount: 2%). The minimal “1” and the only “0” output are statistically significantly different (**: p-value <0.01; two-sided Welch’s t-test—see Supplementary Table S9) yielding ρ = 3.27.
Convergent promoters to generate dsRNA molecules
In the previous circuits, the siRNA precursor was synthesized as a giant hairpin. A different strategy to generate siRNA-precursor-like structures—double-stranded RNAs of different sizes—relies on convergent promoter architecture (
Testing transcriptional interference in S. cerevisiae: protein production
We conducted a preliminary investigation on the effects of transcriptional interference due to convergent promoters on the expression of a reporter protein. In the initial test, we used two constitutive promoters (pGPD on the sense strand and pTEF1 on the antisense strand) to express the yEGFP (Figure 4A). Compared to a strain yeast containing the only sense TU (i.e., pGPD-yEGFP-CYC1t), the convergent promoter design caused a significant 46.98% decrease in the fluorescence level (Figure 4B). We then replaced pGPD with pGAL1 (realizing, in this way, a YES Boolean gate—see Figure 4C). The comparison with a single-directed TU (pGAL1-yEGFP-CYC1t) highlighted a remarkable drop in fluorescence both in the induced (41.20%) and uninduced (94.73%) conditions (Figure 4D). Overall, transcriptional interference proved to be a highly efficient mechanism for reducing protein synthesis.
FIGURE 4

Preliminary tests on convergent promoters. (A) Schematic representation of the uninduced TU containing convergent promoters. The 100-nt-long spacer (spacer100, denoted as s100) was placed between Tsynth8’ (the reverse synthetic Tsynth8 terminator—
RNA interference from constitutive convergent promoters
After verifying the effects of transcriptional interference on protein expression, we tested the convergent promoter architecture inside circuits harnessing RNA interference. We used the TU in Figure 4A to express a fragment (
FIGURE 5

Convergent promoters expressing fragments of the yEGFP extended with one or two RAD9 introns. (A) Circuit diagram. yEGFPf indicates a fragment of the yEGFP. “I” represents an RAD9 intron. [I’] is the intron missing in the one-intron circuit. (B) Circuit performance in the presence of two RAD9 introns. Four complete circuits were constructed on two control strain (byMM254 and byMM283, sharing the same genetic content). Of these, byMM307 constructed on byMM283 demonstrated the most favorable outcome (Supplementary Table S11). (C) Circuit performance in the presence of a single RAD9 intron (*: p-value <0.05; **: p-value <0.01; ***: p-value< 0.001; two-sided Welch’s t-test).
RNAi-interference from galactose-inducible convergent promoters
After demonstrating that convergent promoters could be used to effectively silence protein synthesis, we replaced the galactose-inducible giant-hairpin expression cassette in the original circuit in Figure 1A with a galactose-inducible convergent-promoter system (where pGPD was replaced by pGAL1—see Figure 6A). The original circuit failed to work as a NOT gate because of pGAL1 leakage that provoked a dramatic drop in fluorescence even in the absence of galactose. In contrast, the convergent promoter architecture appeared much less affected by pGAL1 leakage. Efficient NOT gates were built using both one (OFF/ON ratio: 0.28 and 0.39) and two (0.14 and 0.22) RAD9 introns (Figures 6B, C).
FIGURE 6

NOT gates based on galactose-inducible convergent promoters. Four complete circuits were constructed on the control strain byMM283. (A) Schematic NOT gate representation. (B) Circuit performance when two RAD9 introns flank the yEGFP fragment. Two circuits were obtained from the control strain byMM283: byMM305, which gave an OFF/ON ratio equal to 0.22, and byMM306 that reached OFF/ON = 0.14. (C) The best OFF/ON ratio in the presence of a single RAD9 intron corresponded to 0.28 (byMM334). However, the NOT gate in byMM333 (OFF/ON = 0.39) appeared to be less affected by pGAL1 leakage (**, p-value <0.01, ****: p-value< 0.0001; two-sided Welch’s t-test, see Supplementary Table S12).
Boolean gates hosting constitutive convergent promoters
When dealing with a giant hairpin as a source of the siRNA precursor, we managed to implement working logic circuits only by expressing it constitutively and controlling the synthesis of the Dicer enzyme with an input chemical instead. We also applied this strategy to Boolean circuits hosting convergent promoters. In these tests, the yEGFP fragment was always accompanied by two RAD9 introns (Figure 7A). The new variant of the galactose-sensing NOT gate registered a moderately high OFF/ON ratio (0.41—see Figure 7B). The copper-responsive circuit failed to work as a Boolean gate because it returned the same fluorescence level in both the presence and absence of the CuSO4 (Supplementary Figure S7). In contrast, the methionine-sensing YES gate turned out to work effectively (ON/OFF ratio: 2.94—Figure 7C and Eq. 1 in Materials and Methods). Hence, promoter leakage seems to considerably affect the working of RNAi-based digital circuits even when they employ convergent promoters. The higher leakage of pCUP1 (compared to that of pMET25—see Supplementary Table S6) appears to be the most plausible reason for the failure in the construction of a NOT gate regulated by CuSO4 (Supplementary Figure S7).
FIGURE 7

Basic Boolean gates hosting convergent promoters. (A) Schematic diagram of the circuits, incorporating convergent promoters and chemically regulated Dcr1 expression. Fluorescence intensity from (B) a NOT gate responding to 2% galactose and (C) a methionine-controlled YES gate (“1” level of methionine corresponded to a concentration of 10 mM ***: p-value< 0.001; ****: p-value <0.0001; two-sided Welch’s t-test, see Supplementary Table S13).
Finally, we also implemented a version of the IMPLY gate that contained convergent promoters. Here, methionine repressed Ago1 production, whereas galactose induced the synthesis of Dcr1 expression (Figure 8A). The fluorescence levels returned by this implementation of the IMPLY gate effectively reflected the circuit truth table. However, the ρ value was rather low (1.60) because of the pMET25 leakage that determined a decrease in the fluorescence corresponding to the “11” truth-table entry (Figure 8B). This result further confirms the negative influence of promoter leakage in circuits whose function depends on the RNAi pathway.
FIGURE 8

IMPLY gate based on convergent promoters. (A) Schematic representation of the circuit. (B) Fluorescence expression. 10 mM methionine and 2% galactose were used as “1” input values. (***: p-value <0.001; two-sided Welch’s t-test, see Supplementary Table S14).
Generating an siRNA precursor via separate sense and antisense transcription of a DNA fragment
Another way to generate an siRNA precursor relies on two separate TUs that transcribe the same DNA fragment—one in the sense and the other in the antisense direction. This design, inspired by a similar one reported in
FIGURE 9

Galactose-responsive NOT gates with separate sense–antisense yEGFP fragments (A) NOT gate diagram. The sense fragment contains one or two introns. (B) Performance of a NOT gate hosting two RAD9 introns. The circuit was obtained from the control strain byMM283 and returned an OFF/ON ratio equal to 0.52. (C) Performance of NOT gates hosting a single RAD9 intron. Two circuits, byMM352 and byMM348, were obtained using byMM254 and byMM283 as control strains, respectively. They returned the same OFF/ON ratio: 0.35 (**, p-value <0.01, ***: p-value <0.001, ****: p-value <0.0001; two-sided Welch’s t-test, see Supplementary Table S15).
Conclusion
Back in 2009, Drinnenberg and colleagues (
Through a series of tests, we confirmed that pGAL1 leakage, although minimal, completely disrupted the logic behavior of the original circuit by Drinnenberg and colleagues. By placing the Dicer enzyme under pGAL1 and expressing the giant hairpin siRNA precursor constitutively, we successfully constructed a working NOT and IMPLY gate. The former exhibited a remarkably low OFF/ON ratio, equal to 0.06. However, the number of strains transformed with a properly working logic gate hosting a giant hairpin-like siRNA precursor was consistently extremely low, if not zero. Hence, it was necessary to identify more reliable expression systems for the siRNA precursor that would not overly burden the engineered cells.
The convergent-promoter architecture guaranteed higher reliability and performance. In general, it appeared less susceptible to promoter leakage and performed well when the DNA fragment between the two promoters was expressed either constitutively or under chemical control. Additionally, gate performance could be modulated by including one or two introns in the siRNA-precursor-like sequence to enhance RNA stability. We also constructed NOT gates based on separate sense and antisense transcription of the same DNA fragment (see Table 1 for an overview of our best artifacts and results). This solution, however, required integrating one or more genes into the yeast genome than in the previous designs. Moreover, gate performance was lower than expected based on previously published data (
TABLE 1
| Strains | Content | p-value | Ratio | Comments | |
|---|---|---|---|---|---|
| Giant hairpin: inducible expression | byMM280 | pGAL1-siRNA_precursor pTEF1-Ago1 pTEF2-Dcr1 Tsynth8_pCYC1noTATA-yEGFP | 0.0001 (***) | 0.62 | - NOT gate - Too low ON state: 79.40 A.U. (control: 1597.60 A.U.) |
| byMM1854 | pGAL1-siRNA_precursor-poly(T) pTEF1-Ago1 pTEF2-Dcr1 Tsynth8_pCYC1noTATA-yEGFP | 0.0177 (*) | 0.29 | - NOT gate - Too low ON state: 57.46 A.U. (control: 1441.40 A.U.) | |
| Giant hairpin: inducible expression of the Dicer enzyme | byMM1699 | pGPD-siRNA_precursor-Tsynth6 pTEF1-Ago1 pGAL1-Dcr1-Tsynth6 Tsynth8_pCYC1noTATA-yEGFP | 0.0004 (***) | 0.06 | - NOT gate - ON state: 209.58 A.U. (control: 809.40 A.U.) |
| byMM1864 | pGPD-siRNA_precursor-Tsynth6 pTEF1-Ago1 pCUP1-Dcr1-Xbal-Tsynth6 Tsynth8_pCYC1noTATA-yEGFP | <0.0001 (****) | 0.12 | - NOT gate - Too low ON state: 36.45 A.U. (control: 1765.59 A.U.) | |
| Giant hairpin: inducible expression of the Dcr and Ago | byMM1900 | pGPD-siRNA_precursor-Tsynth6 pMET25-Ago1 pGAL1-Dcr1-Tsynth6 Tsynth8_pCYC1noTATA-yEGFP | 0.0012 (**) | 3.27 (-value) | - IMPLY gate - Low ONL state: 142.50 A.U. (ONH state: 981.51 A.U.) |
| Convergent promoter-based circuits | byMM307 | Tsynth8’_sp100-pGPD-intronRAD9-yEGFPfragment-intronRAD9′-pTEF1’ pTEF1-Ago1 pTEF2- Dcr1 Tsynth8_pCYC1noTATA-yEGFP | 0.0096 (**) | 0.15 | - No logic function |
| byMM313 | Tsynth8’_sp100-pGPD-intronRAD9-yEGFPfragment-pTEF1′ pTEF1-Ago1 pTEF2- Dcr1 Tsynth8_pCYC1noTATA-yEGFP | 0.0001 (***) | 0.002 | - No logic function | |
| byMM306 | Tsynth8’_sp100-pGAL1-intronRAD9-yEGFPfragment-intronRAD9′-pTEF1’ pTEF1-Ago1 pTEF2- Dcr1 Tsynth8_pCYC1noTATA-yEGFP | <0.0001 (****) | 0.14 | - NOT gate | |
| byMM333 | Tsynth8’_sp100-pGAL1-intronRAD9-yEGFPfragment-pTEF1’ pTEF1-Ago1 pTEF2- Dcr1 Tsynth8_pCYC1noTATA-yEGFP | <0.0001 (****) | 0.39 | - NOT gate - High ON state: 2408.61 A.U. (control: 1673.64 A.U.) | |
| byMM1917 | Tsynth8′-sp100-pGPD-intronRAD9-yEGFPfragment-intronRAD9′-pTEF1′ pTEF1-Ago1 pGAL1-Dcr1-Tsynth6 Tsynth8_pCYC1noTATA-yEGFP | <0.0001 (****) | 0.41 | - NOT gate | |
| byMM1964 | Tsynth8′-sp100-pGPD-intronRAD9-yEGFPfragment-intronRAD9′-pTEF1′ pTEF1-Ago1 pMET25-Dcr1-Tsynth6 Tsynth8_pCYC1noTATA-yEGFP | 0.0004 (***) | 2.94 | - YES gate | |
| byMM1918 | Tsynth8′-sp100-pGPD-intronRAD9-yEGFPfragment-intronRAD9′-pTEF1’ pMET25-Ago1 pGAL1-Dcr1-Tsynth6 Tsynth8_pCYC1noTATA-yEGFP | 0.0001 (***) | 1.60 (-value) | - IMPLY gate Low ONL state: 1437.37 A.U. (ONH state: 1863.41 A.U.) | |
| Sense and antisense transcription-based circuits | byMM2018 | pGAL1-intronRAD9-yEGFPfragment-intronRAD9-CYC1t-pGPD-(RAD9-yEGFPfragment)’-Tsynth8 pTEF1-Ago1 pTEF2- Dcr1 Tsynth8_pCYC1noTATA-yEGFP | <0.0001 (****) | 0.52 | - NOT gate |
| byMM348 | pGAL1-intronRAD9-yEGFPfragment-CYC1t-pGPD-(intronRAD9-yEGFPfragment)’-Tsynth8 pTEF1-Ago1 pTEF2- Dcr1 Tsynth8_pCYC1noTATA-yEGFP | 0.0003 (***) | 0.35 | - NOT gate | |
Overview of top-performing gates constructed here.
Overall, RNAi-based logic gates appear more susceptible to promoter leakage than transcriptional networks. Furthermore, they require more integration steps since the Argonaute and Dicer genes are absent from S. cerevisiae chromosomes. Even though they can guarantee high performance, particularly when introns are employed, their complex implementation has so far limited their widespread use in budding yeast synthetic biology.
Materials and methods
Plasmid construction
The plasmids utilized in this study are documented in Supplementary Table S16. These were constructed by employing the pRSII40X yeast integrating shuttle vector collection (
Two distinct methods were adopted in the construction of novel plasmids. The first involved enzymatic digestion and ligation. Initially, the backbone and the insert-containing plasmid were subjected to overnight digestion. Subsequently, the purified DNA fragments (extracted from the agarose gel with the AxyPrep DNA Gel Extraction Kit #AP-GX-250) were ligated by using T4 DNA ligase (NEB-M0202S) at 16 °C for more than 8 h. The second method for plasmid construction was isothermal assembly. Initially, standard biological parts including promoters, coding regions, and terminators were amplified via touchdown PCR using Q5 High-Fidelity DNA Polymerase (NEB-M0491S). The purified PCR products were then combined with a cut-open backbone (specifically, a pRSII40X plasmid devoid of the multiple cloning sequence) in equimolar quantities. This mixture was incubated in a thermal cycler at 50 °C for 1 h (
All constructed plasmids in this study were introduced via a 30 s heat shock at 42 °C, into Escherichia coli cells (DH5α, Life Technology 18,263-012) and preserved in a glycerol storage solution. The DNA sequences of all DNA parts can be found in Supplementary Table S17. To ensure accuracy, each plasmid underwent Sanger sequencing at Genewiz Inc. in Suzhou, China.
Yeast transformation
All genetically modified strains utilized in this research were derived from the S. cerevisiae strain CEN. PK2-1C (MATa; his3Δ1; leu2-3_112; ura3-52; trp1-289; MAL2-8c; SUC2) obtained from EUROSCARF (Johann Wolfgang Goethe University, Frankfurt, Germany—#30000A). The PEG/LiAc method was employed for yeast transformation (
Fluorescence measurement
In the absence of an inducer in the growth medium, S. cerevisiae cells were cultured at 30 °C in a synthetic defined complete medium (SDC) supplemented with 2% glucose for 16 h. Conversely, when (2%) galactose was used as an inducer, the incubation period was extended to 24 h. For SDC supplemented with methionine (10 mM) or copper sulfate (0.5 mM), the incubation period ranged from 22 h to 24 h. Prior to fluorescence-activated cell sorting (FACS) experiments, the yeast cells were diluted 1:20 in SDC. Fluorescence intensity measurements were performed using a BD FACSVerse flow cytometer equipped with a blue laser (488 nm) and an emission filter (527/32). The FACS machine settings were validated through a quality check (QC) procedure utilizing fluorescent beads (BD FACS quite CS&T Research beads-17495). Each yeast strain was analyzed in triplicate on different days, representing independent experiments. In each experiment, 30,000 events were collected for each sample at low flow rates, ensuring a threshold rate below 2000 events per second. The resulting raw data obtained from the BD FACSVerse instrument were analyzed using the flowcore R-Bioconductor software package (
RT-qPCR
RNA extraction and purification from yeast cells were performed via the YeaStar RNA kit (Zymo Research-R1002). The obtained cDNA, which served as the template for qPCR, was synthesized using the Hifair III 1st Strand cDNA Synthesis Kit (YEASEN-1141ES60). The primers used for amplifying yEGFP and the reference ACT1 gene are listed in Supplementary Table S19. For qPCR, a total volume of 20 μL qPCR solution was prepared, consisting of 10 μL Hieff qPCR SYBR Green Master Mix (No Rox) (YEASEN-11201ES08), 0.4 μL of 10 μM forward and reverse primers, a variable amount of cDNA (ranging from 20 to 50 ng), and RNase-free water. The following program was set up on a Roche LightCycler96 machine: (1) hold stage: 5 min at 95 °C; (2) PCR stage: 10 s at 95 °C, followed by 30 s at 60 °C. The PCR stage was repeated for 45 cycles. The threshold cycle (CT) values for ACT1 and yEGFP were determined using qPCR. Each sample was analyzed in triplicate. The relative mRNA expression levels were calculated using the Pfaffl formula (
Logic circuit analysis
NOT gates are components that convert a low input signal into a high output signal and vice versa. YES gates are devices that produce an output identical to their input. The evaluation of YES or NOT gates is commonly performed by calculating ON/OFF or OFF/ON ratios.
A complete or closed logic circuit (Lc) displays a binary output where fluorescence can be either high (“1”) or low (“0”). A control circuit (Cc) lacks at least a component that, in our case, prevents RNAi. Therefore, the fluorescence level of the control circuit should always be high.
The background fluorescence originating from the strain CEN. PK2-1C (byMM584), which serves as a chassis for our circuits, was subtracted from the average fluorescence value of both closed and control circuits. This adjusted fluorescence value will be denoted as F* in the formulae below.
A YES gate is a logic circuit that exhibits a high fluorescence value (ON state) when induced with an input chemical (inp) while displaying a low fluorescence value (OFF state) in the absence of the input from the growth medium (grm). To quantify the performance of a YES gate, the relative circuit fluorescence—the ON/OFF ratio—is calculated as follows (Yu and Marchisio, 2021):
The bigger the ON/OFF ratio, the higher the efficiency of the circuit
A NOT gate is a logic circuit that returns a high fluorescence value (ON) in the absence of the input from the growth medium and a low fluorescence level (OFF) in the presence of the input. The performance of a NOT gate demands the calculation of the OFF/ON ratio:
The lower the OFF/ON ratio, the higher the efficiency of the gate
Boolean gates that take n inputs (n > 1) are characterized by truth tables that are composed of 2n entries, each associated with a high or low output (fluorescence level). The performance of the circuit is quantified with the value (
Statements
Data availability statement
FACS data are available at Flowrepository.org at the following links: http://flowrepository.org/id/RvFrJUacvKoYBpBliD5Mup1O7aZ9mG3fFgEIeQWjhFnU0ElLQhjdji3KrjNQQl7Ohttp://flowrepository.org/id/RvFrrA8hlcPn1Xa036vZSC8yXYohfZxgFNbkVDqusly0blT9BKX9r4yOvvAdAii2
Author contributions
XT: writing–original draft, investigation, and formal analysis. AV: writing–original draft, investigation, and formal analysis. MM: writing–review and editing, supervision, and conceptualization.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Acknowledgments
We thank all students of the Synthetic Biology laboratory at the School of Pharmaceutical Science and Technology at Tianjin University for their help. We also want to thank Zhi Li and Xiangyang Zhang for their assistance in the FACS experiments.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbioe.2024.1392967/full#supplementary-material
References
1
AbrahaB. W.MarchisioM. A. (2022). NOT gates based on protein degradation as a case study for a new modular modeling via SBML level 3—comp package. Front. Bioeng. Biotechnol.10, 845240. 10.3389/fbioe.2022.845240
2
AgrawalN.DasaradhiP.MohmmedA.MalhotraP.BhatnagarR. K.MukherjeeS. K. (2003). RNA interference: biology, mechanism, and applications. Microbiol. Mol. Biol. Rev.67, 657–685. 10.1128/mmbr.67.4.657-685.2003
3
BabiskinA. H.SmolkeC. D. (2011). A synthetic library of RNA control modules for predictable tuning of gene expression in yeast. Mol. Syst. Biol.7, 471. 10.1038/msb.2011.4
4
BellíG.GaríE.PiedrafitaL.AldeaM.HerreroE. (1998). An activator/repressor dual system allows tight tetracycline-regulated gene expression in budding yeast [published erratum appears in Nucleic Acids Res 1998 Apr 1;26(7):following 1855]. Nucleic Acids Res.26, 942–947. 10.1093/nar/26.4.942
5
BenensonY. (2012). Biomolecular computing systems: principles, progress and potential. Nat. Rev. Genet.13, 455–468. 10.1038/nrg3197
6
BlountB. A.WeeninkT.EllisT. (2012). Construction of synthetic regulatory networks in yeast. FEBS Lett.586, 2112–2121. 10.1016/j.febslet.2012.01.053
7
BorchardtE. K.VandorosL. A.HuangM.LackeyP. E.MarzluffW. F.AsokanA. (2015). Controlling mRNA stability and translation with the CRISPR endoribonuclease Csy4. RNA21, 1921–1930. 10.1261/rna.051227.115
8
BordoyA. E.VaranasiU. S.CourtneyC. M.ChatterjeeA. (2016). Transcriptional interference in convergent promoters as a means for tunable gene expression. ACS Synth. Biol.5, 1331–1341. 10.1021/acssynbio.5b00223
9
BrambillaA.MainieriD.CarboneM. A. (1997). A simple signal element mediates transcription termination and mRNA 3′ end formation in the DEG1 gene of Saccharomyces cerevisiae. Mol. Gen. Genet.254, 681–688. 10.1007/s004380050466
10
CheeM. K.HaaseS. B. (2012). New and redesigned pRS plasmid shuttle vectors for genetic manipulation of Saccharomyces cerevisiae. G3 Genes., Genomes, Genet.2, 515–526. 10.1534/g3.111.001917
11
ChengA. A.LuT. K. (2012). Synthetic biology: an emerging engineering discipline. Annu. Rev. Biomed. Eng.14, 155–178. 10.1146/annurev-bioeng-071811-150118
12
CrookN.SunJ.MorseN.SchmitzA.AlperH. S. (2016). Identification of gene knockdown targets conferring enhanced isobutanol and 1-butanol tolerance to Saccharomyces cerevisiae using a tunable RNAi screening approach. Appl. Microbiol. Biotechnol.100, 10005–10018. 10.1007/s00253-016-7791-2
13
CrookN. C.SchmitzA. C.AlperH. S. (2014). Optimization of a yeast RNA interference system for controlling gene expression and enabling rapid metabolic engineering. ACS Synth. Biol.3, 307–313. 10.1021/sb4001432
14
CurranK. A.MorseN. J.MarkhamK. A.WagmanA. M.GuptaA.AlperH. S. (2015). Short synthetic terminators for improved heterologous gene expression in yeast. ACS Synth. Biol.4, 824–832. 10.1021/sb5003357
15
DrinnenbergI. A.WeinbergD. E.XieK. T.MowerJ. P.WolfeK. H.FinkG. R.et al (2009). RNAi in budding yeast. Science326, 544–550. 10.1126/science.1176945
16
FarzadfardF.PerliS. D.LuT. K. (2013). Tunable and multifunctional eukaryotic transcription factors based on CRISPR/Cas. ACS Synth. Biol.2, 604–613. 10.1021/sb400081r
17
FeldmannH. (2011) Yeast: molecular and cell biology. Hoboken: John Wiley & Sons.
18
GibsonD. G.YoungL.ChuangR.-Y.VenterJ. C.Hutchison IIIC. A.SmithH. O. (2009). Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods6, 343–345. 10.1038/nmeth.1318
19
GietzR. D.WoodsR. A. (2002). “Transformation of yeast by lithium acetate/single-stranded carrier DNA/polyethylene glycol method,” in Methods in enzymology. Editors GuthrieC.FinkG. R. (London: Academic Press), 87–96.
20
GroherA.-C.JagerS.SchneiderC.GroherF.HamacherK.SuessB. (2018). Tuning the performance of synthetic riboswitches using machine learning. ACS Synth. Biol.8, 34–44. 10.1021/acssynbio.8b00207
21
HanZ.MooreG. A.MitterR.MartinezD. L.WanL.SvejstrupA. B. D.et al (2023). DNA-directed termination of RNA polymerase II transcription. Mol. Cell.83, 3253–3267.e7. 10.1016/j.molcel.2023.08.007
22
JacksonJ. S.HoushmandiS. S.LebanF. L.OlivasW. M. (2004). Recruitment of the Puf3 protein to its mRNA target for regulation of mRNA decay in yeast. RNA10, 1625–1636. 10.1261/rna.7270204
23
KötterP.WeigandJ. E.MeyerB.EntianK.-D.SuessB. (2009). A fast and efficient translational control system for conditional expression of yeast genes. Nucleic Acids Res.37, e120. 10.1093/nar/gkp578
24
LamJ. K.ChowM. Y.ZhangY.LeungS. W. (2015). siRNA versus miRNA as therapeutics for gene silencing. Mol. Ther. Nucleic Acids4, e252. 10.1038/mtna.2015.23
25
LewinB.KrebsJ.KilpatrickS. T.GoldsteinE. S. (2011) Lewin's genes X. Burlington: Jones & Bartlett Learning.
26
LiJ.XuZ.ChupalovA.MarchisioM. A. (2018). Anti-CRISPR-based biosensors in the yeast S. cerevisiae. J. Biol. Eng.12, 11–14. 10.1186/s13036-018-0101-z
27
LiuY.GeH.MarchisioM. A. (2023). Hybrid Boolean gates show that Cas12c controls transcription activation effectively in the yeast S. cerevisiae. Front. Bioeng. Biotechnol.11, 1267174. 10.3389/fbioe.2023.1267174
28
MarchisioM. A. (2014). In silico design and in vivoimplementation of yeast gene Boolean gates. J. Biol. Eng.8, 6–15. 10.1186/1754-1611-8-6
29
MarchisioM. A.StellingJ. (2011). Automatic design of digital synthetic gene circuits. PLoS Comput. Biol.7, e1001083. 10.1371/journal.pcbi.1001083
30
MazumderM.McMillenD. R. (2014). Design and characterization of a dual-mode promoter with activation and repression capability for tuning gene expression in yeast. Nucleic Acids Res.42, 9514–9522. 10.1093/nar/gku651
31
MøllerT. S.HayJ.SaxtonM. J.BuntingK.PetersenE. I.KjærulffS.et al (2017). Human β-defensin-2 production from S. cerevisiae using the repressible MET17 promoter. Microb. Cell. Fact.16, 11. 10.1186/s12934-017-0627-7
32
MurphyK. F.BalázsiG.CollinsJ. J. (2007). Combinatorial promoter design for engineering noisy gene expression. Proc. Natl. Acad. Sci.104, 12726–12731. 10.1073/pnas.0608451104
33
PfafflM. W. (2001). A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acids Res.29, e45. 10.1093/nar/29.9.e45
34
RantasaloA.KuivanenJ.PenttilaM.JanttiJ.MojzitaD. (2018). Synthetic toolkit for complex genetic circuit engineering in Saccharomyces cerevisiae. ACS Synth. Biol.7, 1573–1587. 10.1021/acssynbio.8b00076
35
RegotS.MaciaJ.CondeN.FurukawaK.KjellénJ.PeetersT.et al (2011). Distributed biological computation with multicellular engineered networks. Nature469, 207–211. 10.1038/nature09679
36
SemizarovD.FrostL.SarthyA.KroegerP.HalbertD. N.FesikS. W. (2003). Specificity of short interfering RNA determined through gene expression signatures. Proc. Natl. Acad. Sci.100, 6347–6352. 10.1073/pnas.1131959100
37
ShearwinK. E.CallenB. P.EganJ. B. (2005). Transcriptional interference–a crash course. Trends Genet.21, 339–345. 10.1016/j.tig.2005.04.009
38
SheffM. A.ThornK. S. (2004). Optimized cassettes for fluorescent protein tagging in Saccharomyces cerevisiae. Yeast21, 661–670. 10.1002/yea.1130
39
ShettyR. S.DeoS. K.LiuY.DaunertS. (2004). Fluorescence‐based sensing system for copper using genetically engineered living yeast cells. Biotechnol. Bioeng.88, 664–670. 10.1002/bit.20331
40
SiT.LuoY.BaoZ.ZhaoH. (2015). RNAi-assisted genome evolution in Saccharomyces cerevisiae for complex phenotype engineering. ACS Synth. Biol.4, 283–291. 10.1021/sb500074a
41
SikorskiR. S.HieterP. (1989). A system of shuttle vectors and yeast host strains designed for efficient manipulation of DNA in Saccharomyces cerevisiae. Genetics122, 19–27. 10.1093/genetics/122.1.19
42
SongW.LiJ.LiangQ.MarchisioM. A. (2016). Can terminators be used as insulators into yeast synthetic gene circuits?J. Biol. Eng.10, 19–13. 10.1186/s13036-016-0040-5
43
SontheimerE. J. (2005). Assembly and function of RNA silencing complexes. Nat. Rev. Mol. Cell. Biol.6, 127–138. 10.1038/nrm1568
44
UwimanaN.CollinP.JeronimoC.Haibe-KainsB.RobertF. (2017). Bidirectional terminators in Saccharomyces cerevisiae prevent cryptic transcription from invading neighboring genes. Nucleic Acids Res.45, 6417–6426. 10.1093/nar/gkx242
45
WangX.-H.AliyariR.LiW.-X.LiH.-W.KimK.CarthewR.et al (2006). RNA interference directs innate immunity against viruses in adult Drosophila. Science312, 452–454. 10.1126/science.1125694
46
WinM. N.SmolkeC. D. (2008). Higher-order cellular information processing with synthetic RNA devices. Science322, 456–460. 10.1126/science.1160311
47
YuL.MarchisioM. A. (2021). Saccharomyces cerevisiae synthetic transcriptional networks harnessing dCas12a and type VA anti-CRISPR proteins. ACS Synth. Biol.10, 870–883. 10.1021/acssynbio.1c00006
48
YuL.MarchisioM. A. (2023). CRISPR-associated type V proteins as a tool for controlling mRNA stability in S. cerevisiae synthetic gene circuits. Nucleic Acids Res.51, 1473–1487. 10.1093/nar/gkac1270
49
ZamoreP. D.TuschlT.SharpP. A.BartelD. P. (2000). RNAi: double-stranded RNA directs the ATP-dependent cleavage of mRNA at 21 to 23 nucleotide intervals. Cell.101, 25–33. 10.1016/S0092-8674(00)80620-0
50
ZhangY.GeH.MarchisioM. A. (2022). A mutated Nme1Cas9 is a functional alternative RNase to both LwaCas13a and RfxCas13d in the yeast S. cerevisiae. Front. Bioeng. Biotechnol.10, 922949. 10.3389/fbioe.2022.922949
Summary
Keywords
siRNA precursor, Boolean gates, convergent promoters, antisense transcription, Saccharomyces cerevisiae
Citation
Tian X, Volkovinskiy A and Marchisio MA (2024) RNAi-based Boolean gates in the yeast Saccharomyces cerevisiae. Front. Bioeng. Biotechnol. 12:1392967. doi: 10.3389/fbioe.2024.1392967
Received
28 February 2024
Accepted
06 May 2024
Published
04 June 2024
Volume
12 - 2024
Edited by
Genlin Zhang, Shihezi University, China
Reviewed by
Xiaochen Liu, Xi’an Polytechnic University, China
Hu-Hu Liu, Hunan Agricultural University, China
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© 2024 Tian, Volkovinskiy and Marchisio.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Mario Andrea Marchisio, mario@tju.edu.cn, mamarchisio@yahoo.com
‡ These authors have contributed equally to this work
ORCID: Ximing Tian, orcid.org/0000-0002-2722-929X; Mario Andrea Marchisio, orcid.org/0000-0002-5102-1069
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