Synthetic Gene Network with Positive Feedback Loop Amplifies

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Synthetic gene network with positive feedback loop amplifies cellulase gene expression in Neurospora crassa Toru Matsu-ura, Andrey A. Dovzhenok, Samuel T. Coradetti, Krithika R. Subramanian, Daniel R. Meyer, Jaesang J. Kwon, Caleb Kim, Nathan Salomonis, N. Louise Glass, Sookkyung Lim, and Christian I. Hong ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.8b00011 • Publication Date (Web): 06 Apr 2018 Downloaded from http://pubs.acs.org on April 7, 2018

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ACS Synthetic Biology

Synthetic gene network with positive feedback loop amplifies cellulase gene expression in Neurospora crassa

Toru Matsu-ura1*#, Andrey A. Dovzhenok2*, Samuel T. Coradetti3, Krithika R. Subramanian1,4, Daniel R. Meyer5, Jaesang J. Kwon1, Caleb Kim1, Nathan Salomonis4, N. Louise Glass3, Sookkyung Lim2, and Christian I. Hong1,6#.

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Department of Pharmacology and Systems Physiology, University of Cincinnati, Cincinnati, OH 45267-0529, USA. 2

Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 45221-0025, USA. 3

Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720

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Department of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, OH 45229-3039, USA 5

Department of Biomedical, Chemical, and Environmental Engineering, University of Cincinnati, OH 45221-0012, USA 6

Division of Developmental Biology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, OH 45229-3039, USA

*These authors contributed equally to this work. #

Corresponding authors. E-mail: [email protected] (T.M.); [email protected] (C.I.H.)

Abstract Second-generation or lignocellulosic biofuels are a tangible source of renewable energy, which is critical to combat climate change by reducing the carbon footprint. Filamentous fungi secrete cellulose-degrading enzymes called cellulases, which are used for production of lignocellulosic biofuels. However, inefficient production of cellulases is a major obstacle for industrial-scale production of second-generation biofuels. We used computational simulations to design and implement synthetic positive feedback loops to increase gene expression of a key transcription factor, CLR-2, that activates a large number of cellulases in a filamentous fungus, Neurospora crassa. Overexpression of CLR-2 reveals previously unappreciated roles of CLR-2 in lignocellulosic gene network, which enabled simultaneous induction of approximately 50% of 78 lignocellulosic degradation-related genes in our engineered Neurospora strains. This engineering results in dramatically increased cellulase activity due to cooperative orchestration of multiple enzymes involved in the cellulose degradation pathway. Our work provides a proof of principle

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in utilizing mathematical modeling and synthetic biology to improve the efficiency of cellulase synthesis for second-generation biofuel production.

Keywords Second-generation biofuel, Lignocellulose, Cellulose, Positive feedback loop, Neurospora crassa

Introduction Cellulose in plant cell wall is one of the sources for biofuel production that does not compete with current food supplies. Hydrolysis of cellulose, however, is an economically expensive process due to the crystallinity of the substrate and the high cost of the production of enzymes needed to degrade cellulose. Therefore, it is critical to circumvent this problem for mass production of cellulosic biofuels. Importantly, efficient implementation of cellulosic biofuel production is critical to combat climate change; cellulosic biofuels are predicted to reduce greenhouse gas emissions more than 80% as compared to gasoline 1, 2. The model filamentous fungus, Neurospora crassa, is commonly found in tropical and sub-tropical regions on dead plants and secretes lignocellulolytic enzymes that hydrolyze plant biomass to simple carbohydrates3. N. crassa possesses twice as many predicted cellulase genes and has similar cellulase activity as Trichoderma reesei, an industrially relevant cellulaseproducing species 4,5, 6. Importantly, N. crassa has been used as a model organism to investigate fundamental questions ranging from genetics to circadian rhythms to plant cell wall degradation7. For this reason, there exists a wide range of genetic engineering techniques, including CRISPR/Cas98, for Neurospora, which posits N. crassa as an ideal organism for synthetic biology to implement genetic circuits to optimize enzyme production. Recent discoveries in N. crassa revealed a transcriptional network of cellulase regulation involving two key transcription factors, CLR-1 and CLR-2, which regulate cellulose-dependent induction of numerous cellulase genes9. Orthologs of clr-2 in other ascomycete fungi have subsequently been shown to be relevant for regulation of cellulolytic genes 10-12. Orchestrated induction of cellulase genes facilitates the degradation of cellulose and subsequent increase of intracellular glucose concentration, which activates cre-1, which encodes a key transcription factor for carbon catabolite repression (CCR) that down-regulates cellulase gene expression. Hence, deletion of cre-1 increases the activity of cellulases (endoglucanase) by 150% as compared to the wild type (WT) strain13. In N. crassa, deletion of the three major β-glucosidase genes (gh1-1, gh3-3, and gh3-4), that hydrolyze oligosaccharides to release glucose 14 plus deletion of cre-1 further increased cellulase activity (~200% of WT)15. These examples showcase the rational design of strains using gene deletions as a method to regulate cellulase control networks. In the current study, we constructed a mathematical model of cellulase regulation in N. crassa, and utilized model simulations to design a synthetic positive feedback loop that amplified the expression of genes encoding cellulase enzymes. We subsequently implemented transgenic Neurospora strains with a synthetic positive feedback loop that amplified the expression of the


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key transcription factor, clr-2, by 40-fold, resulting in increased expression of a large number of cellulolytic genes. As a result, cellulase activity of our engineered strain was increased by 10fold as compared to the WT progenitor strain. Such an engineering strategy could dramatically reduce the cost of production of cellulases from filamentous fungi for second-generation biofuel production. Our results underscore the potential synergy by integrating model simulations and synthetic biology for biomaterial production.

Results Mathematical model of the Neurospora cellulase regulation The mathematical model of cellulase gene expression on microcrystalline cellulose (Avicel) substrate is based on the wiring diagram shown in Figure 1a. In WT cells, basal expression of cellulases initiates the breakdown of Avicel to oligosaccharides such as cellobiose, which is transported into the cell and activates CLR-1 for induction of target genes including clr-1, clr-2, and genes encoding β-glucosidase enzymes9. Subsequently, CLR-2 activates increased cellulase gene expression for Avicel deconstruction, resulting in the production of cellodextrins and glucose. Cellodextrins and cellobiose are hydrolyzed to glucose by β-glucosidase enzymes. This increased abundance of glucose activates CRE-1, which is the main transcription factor implicated in CCR in Neurospora. CRE-1 suppresses expression of clr-1, clr-2, β-glucosidase enzymes, and cellulase genes 13, 15. To develop a realistic mathematical model, we performed qRT-PCR to measure transcripts of clr-1, clr-2, and a cellulase gene, cbh-1 (exoglucanase), in the WT and cre-1 deletion (∆cre-1) strains grown on glucose for 24 h and then transferred to glucose or Avicel for 4 h; these results were used to adjust the parameters in the model to reproduce the observed phenotypes (Figure 1b). As previously reported, we observed increased expression of clr-1, clr-2, and cbh-1 on Avicel in the WT strain9. Interestingly, increased expression of clr-2 in the ∆cre-1 mutant on glucose media did not translate to higher abundance of cbh-1 (Figure 1b), which suggests that there are other CCR components in addition to CRE-1 that repress cellulase gene expression.

Cellulase model suggested CLR-2 is the key transcription factor to increase expression level of cellulases Although both clr-1 and clr-2 are necessary for the induction of cellulases, the way they affect cellulase gene expression is different. The qRT-PCR results showed 8-fold induction of clr-1 on Avicel in wild type (Figure 1b). In contrast, the induction of clr-2 and cbh-1 were 250- and 150fold, respectively (Figure 1b). We chose appropriate parameter space and functions to reproduce these experimental data. For example, the model required Michaelis-Menten type of saturation function to describe the mode of action of CLR-1. As a result of fitting the above experimental phenotypes, the model demonstrated that the amount of cellulase correlates well with clr-2 but not with clr-1 expression levels (Figure 1c). This prediction was confirmed by additional qRTPCR experiments. Although we observed a weak correlation between clr-1 induction levels and


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cbh-1 gene expression over a wide range of strains and different media conditions (R = 0.01, Figure 1d, Supporting Information Dataset S1 for the details of strains and conditions), clr-2 expression was strongly correlated with cbh-1 expression (R = 0.84, Figure 1d). Consistent with previous observations, these results show that CLR-2 is the major transcription factor for the regulation of cellulase genes, even in the absence of inducer 9, 10 and indicate that the level of CLR-2 is directly related to the level of cellulase gene expression. Based on the above results, we designed a synthetic gene circuit for Neurospora to increase cellulase gene expression. We first tested a positive feedback circuit for the expression of clr-1 or clr-2 in the model. To produce a positive feedback loop in the model, we assumed that the expression of clr-1 and clr-2 was regulated by their protein products, CLR-1 and CLR-2, respectively. Simulations demonstrated that positive feedback on clr-1 and clr-2 resulted in a dramatic induction of clr-1 or clr-2 gene expression (Supporting Information Figure S1). However, the over-expression of clr-1 did not result in the over-expression of cellulases (Figure 1e). In contrast, the over-expression of clr-2 via a positive feedback loop was predicted to efficiently induce cellulase gene expression (Figure 1e).

Synthetic positive feedback gene circuits increase the expression of cellulases To implement the positive feedback circuit for the expression of clr-1 or clr-2 in N. crassa, we engineered Neurospora strains with rationally designed genetic circuits. In addition to the native clr-1 and clr-2 genes (Figure 2a, these engineered strains have an additional copy of clr-1 or clr2 gene under the control of the promoter of cbh-1 (Pcbh-1), which is a target of CLR-2 (Figure 2b and c). Therefore, the above synthetic gene network establishes a positive feedback circuit to amplify the expression of the cbh-1 promoter driven clr-1 or clr-2. We developed two Neurospora strains: a strain bearing a positive feedback loop for CLR-1 (Pcbh-1-clr-1) and a strain bearing a positive feedback loop for CLR-2 (Pcbh-1-clr-2) (Figure 2b and c). To test the expression of target genes, we performed qRT-PCR with the RNA samples extracted at 4 h after the media shift from glucose to Avicel. These synthetic positive feedback loops amplified the expression of clr-1 and clr-2 genes in the Pcbh-1-clr-1 and Pcbh-1-clr-2 strains, respectively (Figure 2d). As predicted by the mathematical model, the induction of clr-2 and major cellulase genes (exoglucanase cbh-1 and endoglucanases gh6-2) were modest in Pcbh-1-clr-1 strain (Figure 2d). On the other hand, the synthetic positive feedback loop for clr-2 significantly amplified the expression of cellulase genes in the Pcbh-1-clr-2 strain (Figure 2d). The expression of clr-2 was increased even in glucose media compared to WT, and was further amplified over 4,900-fold when shifted to Avicel in the Pcbh-1-clr-2 strain (Figure 2d). Similar to the results observed in the ∆cre-1 mutant (Figure 1b), the increased expression of clr-2 translated to a higher expression of cellulases when the Pcbh-1-clr-2 strain was shifted to Avicel media, but showed a modest increase on glucose media (Figure 2d). Compared to WT strain on glucose media, the induction of cellulase gene expression was 17,000, 14,000, and 39,000-fold higher for cbh-1, gh6-2, and gh5-1, respectively, when Pcbh-1-clr-2 strain was shifted from glucose to Avicel media (Table 1). Unexpectedly, we observed an increased expression of clr-1 in the Pcbh-1-clr-2 strain (Figure 2d), which suggests that CLR-2 regulates the expression of clr-1. This additional regulatory loop could further increase cellulase gene expression in the Pcbh-1-clr-2 strain.


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To further increase the induction of cellulase gene expression, we established Pcbh-1-clr2 in a quadruple deletion mutant strain carrying deletions of the three major β-glucosidase genes (gh1-1, gh3-3, and gh3-4) and cre-1 (3βG∆cre-1), which previously showed a dramatic increase of cellulase expression and enzyme production 15. Deletion of cre-1 significantly decreases the effect of CCR, and deletion of genes encoding the β-glucosidase enzymes reduces intracellular glucose concentration and increases cellobiose concentration, resulting in activation of CLR-1 (Supporting Information Figure S2). The resultant strain (Pcbh-1-clr-2 / 3βG∆cre-1) demonstrated increased expression of cellulases compared to its parental quadruple deletion strain (3βG∆cre-1). However, our engineered strain (Pcbh-1-clr-2 / 3βG∆cre-1) showed similar or slightly higher expression of cellulases compared to Pcbh-1-clr-2 (21,000, 13,000, and 39,000-fold for cbh-1, gh6-2, and gh5-1, respectively) (Figure 2e, Table 1). These data indicate that engineering the positive feedback loop for clr-2 (Pcbh-1-clr-2) was sufficient to drive the increase of cellulase genes. Transcriptome analysis of the transgenic strains To profile genome wide gene expression changes induced by the synthetic gene circuit strains, we performed RNA-sequencing (RNA-Seq) of WT, Pcbh-1-clr-1, Pcbh-1-clr-2, and Pcbh-1-clr2 / 3βG∆cre-1 grown on glucose for 24 h and then transferred to Avicel for 4 h. The heat map of transcriptomes from the four strains showed distinct mRNA expression patterns with 8 different clusters (Figure 3a and Supporting Information Dataset S2). The WT strain exposed to Avicel showed higher expression of genes in clusters B and C, which are unrelated to cellulose degradation components. On the other hand, we observed induction of cluster G in both Pcbh-1clr-2, and Pcbh-1-clr-2 / 3βG∆cre-1 strains including genes encoding cellulose binding proteins (Figure 3a). It is interesting to note distinct expression pattern of transcriptomes between Pcbh1-clr-1, Pcbh-1-clr-2, and Pcbh-1-clr-2 / 3βG∆cre-1 strains, which suggests different aspects of gene regulatory roles of CLR-1, CLR-2, and CRE-1 (Figure 3a and Supporting Information Figure S4a-b). The global changes of gene expression pattern by the insertion of transgene and the gene deletions suggest a possible expression changes of genes relating to production of enzymes. We described the details in the supporting information (Supporting results). In contrast to the global gene expression profiles (Figure 3a), we observed a large overlap in expression patterns of lignocellulose degradation-related genes in Pcbh-1-clr-1, Pcbh1-clr-2, and Pcbh-1-clr-2 / 3βG∆cre-1 strains; 22, 32, and 42 out of 78 genes for Pcbh-1-clr-1, Pcbh-1-clr-2, and Pcbh-1-clr-2 / 3βG∆cre-1, respectively. Eleven genes were overexpressed among all three strains (Figure 3b, Table 2, Supporting Information Figure S4c, and Dataset S3). Similar to the results of qRT-PCR experiments (Figure 2), the expression of genes involved in lignocellulose degradation was higher in the Pcbh-1-clr-2 and Pcbh-1-clr-2 / 3βG∆cre-1 strains as compared to Pcbh-1-clr-1 (Figure 4a-d). We observed overexpression of various cellulases and cellulose degradation-related genes, including genes encoding β-glucosidase enzymes, cellobiose dehydrogenases, exoglucanases, endoglucanase, lytic polysaccharide monooxygenases, and cellulose binding proteins (Figure 4a-d, Table 2, and Supporting Information Figure S5a). In addition, we observed over-expression of cellodextrin transporter-2


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(cdt-2) in the Pcbh-1-clr-2 / 3βG∆cre-1 strain, suggesting CCR acts to regulate cdt-2, as suggested by a previous study (Supporting Information Figure S5b)16 .

Expression of hemicellulase, galacturonase, and laccase genes in the transgenic strains Plant cell wall consists of cellulose microfibrils embedded in the matrix of hemicellulose, pectin, and proteins that support the structure of a plant. In addition, lignin in the plant cell wall strengthens its structure. The transcription factor, xylan degradation regulator-1 (XLR-1) is essential for the expression of genes encoding hemicellulases, which are required for degradation of hemicellulose17. We observed an over-expression of xlr-1 in the Pcbh-1-clr-2 / 3βG∆cre-1 strain, but not in the Pcbh-1-clr-1 and Pcbh-1-clr-2 strains, suggesting that CCR acts on xlr-1 (Supporting Information Figure S5c). Importantly, we also observed overexpression of hemicellulase genes in all of the transgenic strains (2-8 out of 11 hemicellulase genes, Figure 4f, and Table 2), which suggests a cooperative role of CLR-1, CLR-2, and XLR-1 for the expression of genes encoding hemicellulases9. Unexpectedly, the Pcbh-1-clr-1, Pcbh-1-clr-2, and Pcbh-1-clr-2 / 3βG∆cre-1 strains showed overexpression of genes encoding galacturonases, which degrade pectin (Figure 4g and Table 2). In addition, Neurospora secretes laccases that modify lignin, which enables its growth on burnt wood after forest fires18. We observed a modest increase of a laccase gene (NCU09023) in Pcbh-1-clr-2 / 3βG∆cre-1 strain, but not in Pcbh-1clr-1 and Pcbh-1-clr-2 strains, suggesting that CCR also acts on NCU09023 expression (Figure 4h and Table 2). These results indicate the global role of CLR-1, CLR-2, and CRE-1 for the expression of lignocellulose degrading enzymes in N. crassa.

Amplified cellulase, hemicellulase, and galacturonase activities in the transgenic strains Filamentous fungi secrete cellulases (e.g. endo- and exoglucanases, and β-glucosidases) to degrade cellulose present in the external environment. To measure cellulase activity, WT, Pcbh1-clr-2, and Pcbh-1-clr-2 / 3βG∆cre-1 strains were cultured on Avicel, and the culture media was collected at days 1, 2, 3, and 7. The culture supernatants of WT, Pcbh-1-clr-2, and Pcbh-1clr-2 / 3βG∆cre-1 strains were mixed with 2% Avicel, and reducing sugars were measured 3 h after the start of incubation at 50℃. As expected, we observed significantly elevated levels of reducing sugars from the Pcbh-1-clr-2 strain compared to the WT strain (Figure 5a). We also measured the cellulase activity in the culture media from Pcbh-1-clr-2 strain compared to purified fraction of cellulases from Trichoderma reesei to estimate the yield of cellulase production: 1.41 ± 0.56 g cellulase per 1L or 0.36 ± 0.14 g cellulase per 1 gram of fungal material (dry weight) (Figure S6a, cultured for three days with 2% Avicel). In contrast to the increased production of cellulase enzymes, we found reduced growth of all three engineered strains compared to WT strain both on the agar plates containing glucose or cellulose as the sole carbon source, suggesting that there is a trade-off between the growth of Neurospora and the production of cellulase enzymes (Figure S7). However, 10-fold over-production of cellulase enzymes in Pcbh-1-clr-2 strain compared to WT strain (Figure 5a) clearly indicates that its capacity for the cellulase production outweighs the reduced growth rates.


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Both Pcbh-1-clr-2 and Pcbh-1-clr-2 / 3βG∆cre-1 strains showed similar induction of representative cellulase genes (cbh-1, gh6-2, and gh5-1), but the Pcbh-1-clr-2 / 3βG∆cre-1 strain showed a significantly lower concentration of reduced sugars in the cellulase assay, likely due to the absence of the three major β-glucosidase enzymes (Figure 5a). However, we predicted that the reduction of β-glucosidase enzyme activity in the Pcbh-1-clr-2 / 3βG∆cre-1 strain would result in the accumulation of sugar in the culture media. To test this hypothesis, we measured the reducing sugar concentration in the growth media and observed dramatically higher levels in the Pcbh-1-clr-2 / 3βG∆cre-1 strain as compared to the WT and Pcbh-1-clr-2 strains (Figure 5b and c). The peak reducing sugar concentration in the media of Pcbh-1-clr-2 / 3βG∆cre-1 reached 47fold of WT (Figure 5c). The reducing sugar concentration decreased over time in the Pcbh-1clr-2 / 3βKO-∆cre-1 strain after 4 days of culturing, presumably due to lack of nutrients under batch culture conditions. The transgenic strains also showed higher hemicellulase and galacturonase activities (Figure 5d and e) as expected by mRNA expression profiles of the corresponding genes from RNA-Seq analysis (Figure 4e and f).

Discussion Scientific advancements have dramatically improved selection process of specific genetic traits through the technical development of genetics and molecular biology. Genetic engineering of microorganisms is a routine process to overexpress the desired proteins including enhancement of cellulase production in the industrially relevant species Penicillium oxalicum 11. However, it has been only recently that one could design and implement sophisticated synthetic gene circuits to produce biodiesels in Escherichia coli19 or to eliminate cancer cells with a tunable molecular switch that targets and kills cancer cells20. A positive feedback loop is one of the common regulatory circuits in living organisms to control molecular responses and is usually used to amplify gene expression21. Based on previous work revealing the molecular mechanism of cellulase gene regulation in N. crassa9, 15, 13, we developed a mathematical model to reproduce the expression of cellulases in different genetic backgrounds, and rationally designed a synthetic gene network that incorporated a positive feedback loop. The resultant engineered Neurospora strains demonstrated overexpression of numerous lignocellulose degradation-related genes. The process of cellulose degradation has multiple steps including disassembly of cellulose and oligosaccharides by the enzymatic action of endoglucanases, exoglucanases, lytic polysaccharide monooxygenases, and breakdown of cellobiose by β-glucosidases. Therefore, overexpression of multiple types of cellulase enzymes is necessary for the efficient degradation of cellulose. We observed amplification of all of these classes of enzymes in the engineered Neurospora strains containing synthetic positive feedback loop driving the overexpression of clr-2. This approach led to a dramatic increase of cellulase activity due to simultaneous induction of these genes. In addition to cellulose, plant cell walls consist of several additional components that are difficult to degrade: hemicellulose, pectin, and lignin. Our data showed that the Neurospora strains with synthetic gene circuits with a positive feedback loop also amplified the expression of hemicellulases, galacturonases, and laccases (Figure 6). Therefore, these Neurospora strains are appropriate to produce enzymes that degrade


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lignocellulosic materials from agricultural wastes and food wastes. Tangentially, laccases produced by Pcbh-1-clr-2 / 3βG∆cre-1 may be used to degrade dioxins, which are toxic byproducts of industrial incineration that share identical structure as lignin 22, 23. In addition, cellulase enzyme secretion is another critical step that can be engineered to increase the production of cellulases. Previous studies have shown that secretion capacity is important for the growth of N. crassa in the media containing cellulose as the sole carbon source 24, 25 . To assess whether we can further improve the cellulase activity by increasing secretion, we tested tween-80, which is known to enhance secretion from fungi 26, in the culture media. We observed a significant increase of cellulase activity from the cultured media containing Pcbh-1clr-2 strain with 0.2% tween-80 (Figure S6b), which suggests that the production of cellulases could be further augmented by engineering secretion pathway. In this study, we focused on clr-1 and clr-2 to establish the synthetic positive feedback circuits. However, there are other transcriptional factors that are known to regulate lignocellulosic degradation. XLR-1 and cross pathway control-1 (CPC-1) are essential transcriptional factors to control the expression of hemicellulases and induction of laccases, respectively17, 27. Therefore, additional synthetic positive feedback circuits amplifying the expression of xlr-1 and cpc-1 may produce more hemicellulases and laccases, respectively. In addition to increased cellulase activity by the synthetic positive circuits, a strain carrying a triple deletion of genes encoding β-glucosidases enables accumulation of simple carbohydrates in the growth media in Pcbh-1-clr-2 / 3βG∆cre-1 strain, due to an inability to covert cellobiose to glucose via extracellular β-glucosidase enzymes. Thus, the enzyme mixture from such an engineered strain could be used to increase ethanol production in yeast strains expressing a synthetic cellodextrin transporter and an intracellular β-glucosidase28, which we predict would result in increased biofuel production. The industrially relevant species, T. reesei, P. oxalicum and Aspergillus niger are currently used for production of cellulases, and recent studies report existence of clr-2 homolog in these fungi10, 12, 29, 30. Unlike Neurospora, however, overexpression of clr-2 does not dramatically increase cellulase activity in T. reesei 31. On the other hand, the importance of CLR2 homolog, CLR-B, for cellulase expression was shown in both P. oxalicum and A. niger 12, 30. These data suggest that different designs and engineering approaches may be needed for different organisms. Neurospora was shown to have comparable cellulase activity to these fungi5, 6 while harnessing the extra advantage as a model organism with tools that enable efficient genetic engineering. Until recently, there was a lack of efficient techniques to modify the genomic information of in filamentous fungi. However, advancements in genetic engineering technology with CRISPR/Cas9 system has enabled efficient gene editing in various organisms including filamentous fungi8, 32 with the simple design of guide RNA to target homologous recombination at specific genomic locus33-35 (reviewed by Hsu et al. 36). Therefore, our clr-2 based synthetic gene circuit could also be implemented for the efficient production of cellulases in other filamentous fungal species that have identical regulatory mechanisms of cellulase gene expression as in Neurospora crassa. In conclusion, we demonstrated a proof-of-principle of rational design and execution of synthetic circuits to amplify the expression of cellulases.


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Efficient synthesis of cellulases will reduce the overall cost of second-generation biofuel production and reduce global carbon footprint.

Methods Mathematical model. The mathematical model of cellulase gene expression on microcrystalline cellulose (Avicel) substrate is based on the wiring diagram shown in Figure 1. The details of the procedures are given in the SI Materials Methods. Strains. Strains used for the experiments are a wild type (74-OR23-1V, Mating Type: A, FGSC 2489), ∆clr-1 (NCU07705, Mating Type: a, FGSC 11029), ∆clr-2 (NCU08042, Mating Type: a, FGSC 15835), and ∆cre-1 (NCU08807, Mating Type: a, FGSC 10372) from the Fungal Genetics Stock Center (FGSC). Construction of Pcbh-1-clr-1and Pcbh-1-clr-2 donor plasmid vectors. Pcbh-1-clr-1, Pcbh-1-clr-2 donor plasmid vectors were modified from the yeast shuttle vector pRS426. These vectors were constructed using PCR and yeast recombination. The full-plasmid sequences are shown in Supporting Information Figure S8. Transformation of N. crassa with CRISPR-Cas9. A suspension of conidia (2.0 x 109 per ml) was prepared in 1 M sorbitol. A solution containing 5 µg of circular donor plasmid and 5 µg each of Cas9 (p415-PtrpC-Cas9-TtrpC-CYC1t) and gRNA (p426-SNR52p-gRNA.csr-1.YSUP4t) circular plasmids 8 were mixed with 50 µl of the conidial suspension and electroporated. After the electroshock, the solution was spread on agar medium containing Ignite (400 µg/ml) and Cyclosporine A (5 µg/ml) 37. The details of the procedures are given in the SI Materials Methods. RNA extraction, Quantitative RT-PCR, and RNA sequencing. Total RNA was isolated using Tri Reagent (Molecular Research Center, Inc.), and treated with RQ1 RNase-free DNase (Promega). Quantitative RT-PCR (qRT-PCR) was performed as previously described 38. For RNA sequencing, RNA was isolated with RNeasy Mini Kit (QIAGEN) as manufacturer’s instruction. RNA sequencing was performed by DNA sequencing and Genotyping core at Cincinnati Children’s Hospital Medical Center. The details of the procedures are given in the SI Materials Methods. Data analysis for RNA sequencing result. To calculate gene expression estimates pseudoalignment and transcript per million estimates were calculated by the Kallisto (version 0.42.1), available through AltAnalyze (version 2.0.9.4) 39, 40. The details of the procedures are given in the Supporting Information Materials Methods. Sugars in culturing media and enzymatic Assays. Reducing sugar concentration in cultured media was measured with Benedict’s reagent (Ricca Chemical). For enzymatic assay, Neurospora strains were cultured with 2% Avicel containing LCM for 1-10 days. The cultured media was centrifuged down, and the supernatant was used as enzymatic assays. Cellulase activity was measured as Avicel hydrolysis activity. The reducing sugar concentration in the reaction mixture was measured with Benedict’s reagent. For hemicellulase and


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galacturonase activity, cultured media collected at day 3 was used. For hemicellulase activity, an assay based on the use of a soluble chromogenic xylan was used 41. The absorbance at 590 nm of the supernatant was measured as enzymatic activity. For galacturonase activity, 1% (wt/vol) of pectin (Sigma-Aldrich) was added to 1 ml of cultured media and incubated at 37 ℃ for 3 h. The reducing sugar concentration in the reaction mixture was measured with Benedict’s reagent. The details of the procedures are given in the Supporting Information Materials Methods.

Acknowledgments The authors thank DNA sequencing and Genotyping core at Cincinnati Children’s Hospital Medical Center (Cincinnati, OH, USA) for RNA-Seq experiments of the transgenic Neurospora strains included in this study. The authors thank Fungal Genetic Stock Center (Manhattan, KS, USA) for providing the following Neurospora strains: 74A, ∆clr-1, ∆clr-2, and ∆cre-1. This work was supported by Department of Interior grant No. D12AP00005 to C.I.H. and S.L. and the University of Cincinnati Technology Commercialization Accelerator funding program to C.I.H. and T.M. and a grant from the Energy Biosciences Institute (UC-Berkeley) to N.L.G. Author contributions T.M., A.A.D., S.L., and C.I.H. designed the experiments. A.A.D. performed mathematical modeling. T.M., S.T.C., D.M., J.J.K, C.K. performed the experiments. T.M., K.K., and N.S. analyzed RNA sequence data. T.M., A.A.D., N.S., N.L.G., S.L., and C.I.H. wrote the manuscript. T.M., S.L. and C.I.H. supervised the overall experiments. Competing financial interests The authors declare competing financial interests: details are available in the online version of the paper.

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Figure legends

Figure 1. Mathematical modeling and simulation of cellulase expression in N. crassa. (a) Schematic representation of gene network of cellulase regulation. Cellulase expression is induced by CLR-1 and CLR-2. CLR-1 is activated in the presence of cellodextrins (such as cellobiose) and promotes the expression of clr-2 and β-glucosidase genes as well as its own expression. CLR-2 then induces expression of cellulases enzymes that breakdown extracellular Avicel into cellodextrins and glucose. Increased abundance of cellobiose and glucose activates CCR that includes CRE-1, CRE-1-independent (CCRi) and cellulase-specific (CCRii) repression. (b) Comparison of mRNA expression levels of clr-1, clr-2, and cbh-1 (cellulase gene) in 74A (WT) and ∆cre-1 strains cultured in different carbon sources. (c) Simulated relationship between mRNA expression levels of cellulase genes vs clr-1 or clr-2. (d) Relationship between mRNA expression levels of cbh-1 vs clr-1 or clr-2 in 74A (WT) and ∆cre-1 strains cultured in media with Avicel or under no-carbon conditions. (e) Mathematical simulation predicted increased expression of cellulase genes by overexpression of CLR-1 or CLR-2.

Figure 2. Synthetic positive feedback circuits to amplify the expression of CLR-1 or CLR-2. (a) Transcriptional gene network of cellulase gene expression in WT. Grey arrows indicate induction. (b and c) Genetically modified N. crassa strains with extra clr-1 (b) or clr-2 (c) genes regulated by the cbh-1 promoter. Red arrows indicate synthetic positive feedback loop. (d) mRNA expression of cellulase genes in 74A (WT), Pcbh-1-clr-1, and Pcbh-1-clr-2 strains. The expression of clr-1, clr-2, cbh-1, gh6-2, and gh5-1 are shown. Each strain is cultured in the media containing 2% glucose or 2% Avicel as a sole carbon source. Expression data were normalized by to expression levels in 74A (WT). **: p < 0.01, Tukey’s test. Comparisons were done between 74A and transgenic strains cultured in glucose or Avicel media. Error bars corresponds to the SEM. (e) mRNA expression of cellulase genes in 3βG∆cre-1 and Pcbh-1-clr2 / 3βG∆cre-1 strains. The expression of clr-1, clr-2, cbh-1, gh6-2, and gh5-1 are shown. Each strain is cultured in the media containing 2 % glucose or 2 % Avicel as a sole carbon source. **: p < 0.01 student’s t-test. Comparisons were done between 3βG∆cre-1 and Pcbh-1-clr-2 / 3βG∆cre-1 strains cultured in glucose or Avicel media. Error bars correspond to the SEM.

Figure 3. Transcriptional profiles of transgenic strains. (a) Expression patterns for the 1,917 genes that displayed more than 4 times expression difference between WT, Pcbh-1-clr-1, Pcbh1-clr-2, and Pcbh-1-clr-2 / 3βG∆cre-1 strains when cultured in 2% Avicel containing liquid culture media (LCM), which contains Vogel’s medium (pH 5.8), 0.5% arginine, and 50 ng/mL biotin with 2% (wt/vol) glucose, for 4 h. Relative expression differences were calculated relative to the mean expression of all strains for each gene. Expression patterns reveal 8 different clusters represented by different colors on right bar (cluster A-H), and biological processes relating to the clusters are shown as Gene Ontology (GO) terms. Differential expression analyses were performed using a moderated t-test p

Synthetic Gene Network with Positive Feedback Loop Amplifies

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