Y. lipolytica is a distinctive oleaginous yeast strain whose metabolism is also being widely studied for its high capacity to produce cytosolic acetyl-CoA (Marella et al., 2018). Xu et al. (2016) rewired the central carbon metabolic pathway by overexpressing some alternative routes for the formation of acetyl-CoA, using plasmids containing the ePathBrick technology, a synthetic biology tool that is being used widely in the design of biosynthetic pathways. The authors were able to decouple nitrogen starvation from lipogenesis, enabling the biosynthesis of oleochemicals during the exponential growth phase. Interestingly, overexpression of a protein that exports acetyl-CoA from the mitochondria to the cytosol markedly improved lipid accumulation. Friedlander et al. (2016) further explored the Y. lipolytica lipid pathway, by examining when acetyl-CoA is converted to acyl-CoA. The authors overexpressed two heterologous enzymes (DGA1 and DGA2) responsible for the incorporation of acyl-CoA onto the diacylglycerol backbone to synthesize TAGs. This, along with a deletion from a lipase regulator, increased the lipid content to 77%. Both the studies reported the efficiency of using synthetic pathways to increase the production of fatty acids by Y. lipolytica, which later can be converted to oleochemicals that are important for industrial applications, including biodiesel, detergents, and bioplastics, among others (Marella et al., 2018).

Another industrially important molecule produced by Y. lipolytica is ß-carotene, which is widely used as a color additive and nutritional supplement (Gao et al., 2017). In this study, the authors redirected the carbon flux from acetyl-CoA to ß-carotene by fine-tuning the expression of 11 synthetic genes modulated by strong promoters. For this, a multiple-copy integration strategy was used, and the final product yield was surprisingly higher when compared with the organisms that are commonly used to produce ß-carotene industrially.

R. toruloides is a yeast strain that is growing in importance in the field of metabolic engineering. Remarkable results have been obtained in the production of fine chemicals through the process of degradation of complex substrates, such as xylose and aromatics, derived from lignin. Yaegashi et al. (2017) reported the high-level production of two terpenes by R. toruloides. Bisabolene is the precursor of bisabolane, which is a biosynthetic alternative to D2 diesel. Amorphadiene is the precursor of the antimalarial drug artemisinin. The production of these compounds was achieved by taking advantage of the large amounts of acetyl-CoA present in the cytosol of this organism and by expressing codon-optimized versions of the genes encoding bisabolene synthase (bis) and amorphadiene synthase (ads). Remarkably, the production of these chemicals increased when the organism grew using lignocellulose hydrolysates when compared with to purified substrates. A brief overview about the systems and synthetic biology strategies used for rewiring and dynamic control of metabolism in fungi are summarized in **Figure 3**.

### DYNAMIC CONTROL THROUGH GENETIC CIRCUITS

Even after considering the aforementioned accomplishments, it is important to note that there is still a huge difference between static and dynamic metabolism. Biological systems are much more complex than a physical, steady system. Results are hard to reproduce and, thus, hard to predict. This is especially true if we only consider a limited part of the system, such as enzymes in a metabolic pathway (Chubukov et al., 2016). Those components are not static, as their levels are always in response to changes in cellular environment. Synthetic biology can explore the fine-tuning of key metabolic steps through genetic circuits, which can optimize cell factories and bypass the any given specific limitation. This can also be seen under the light of a systems biology approach, not only for coupling chemical production with growth but also because one can use stoichiometric modeling to help in host engineering (Venayak et al., 2015; Chubukov et al., 2016; Teixeira et al., 2017).

One constraint that can be circumvented by the use of genetic circuits is the rate of fungal biomass production vs. the amount of the desired products generated (and also vs. all undesired by-products). An example of a study that took advantage of genetic circuits was performed by Williams et al. (2015). The authors constructed an ON-OFF circuit to overcome the metabolic burden by separating growth and compound production. This system was based on quorum sensing, using a pheromone and RNAi methodology. In this system, genes related to the production of the compound of interest were kept OFF, while genes related to growth were ON. When the population reached an optimal growth, genes related to biomass production were knocked down by RNAi while those related to the metabolic pathway of interest were activated. The authors used this strategy to increase the yield of p-hydroxybenzoic acid (PHDA) in S. cerevisiae and reported the highest PHDA yield ever achieved in yeast. Still, in this sense, ON-OFF genetic circuits can also be achieved by adding responses to temperature or other inducers, such as isopropyl β-D-1-thiogalactopyranoside (Venayak et al., 2015).

Continuous genetic circuits can also be constructed for the dynamic control of target metabolic pathways, coupling gene expression to the sensing of a specific metabolite. Metabolite sensors allow the circuit to respond in accordance with the cellular environment so that they become sensitive to variations. As an example, Xu et al. (2014) developed a continuous circuit triggered by malonyl-CoA concentrations. The promoters that were activated by malonyl-CoA induced a consumption pathway, while the same substrate repressed a production pathway. Thus, this intermediate compound was used to regulate the entire fatty acid biosynthetic pathway.

Another strategy for the dynamic control of metabolic pathways was employed by Teixeira et al. (2017). In this case, the authors studied the production of fatty alcohols from free fatty acids. For this, they dynamically expressed the fatty acyl-CoA synthase gene faa1 under the control of different promoters to prevent the accumulation of free fatty acids in industrial mutant strains, thus avoiding the loss of precursors to the extracellular medium. This approach enhanced the production of fatty alcohols and expanded the knowledge regarding the control of metabolite flux of this pathway. This is an excellent example of the use of dynamic control to increase industrial production. Venayak et al. (2015) supplied other examples to explain all the benefits and drawbacks of this approach. Algorithms for stoichiometric metabolic models are currently available to help with the understanding of how cell network contributes to the yield of the final product. Some examples are OptStrain and OptForce, which can be used to find additional reactions that can be targeted and to identify the pathways that need to be engineered. In this sense, these algorisms can be applied to guide host engineering approaches and to enhance the benefits of building genetic circuits (Chubukov et al., 2016). In a generic manner, dynamic regulatory circuits can be combined with all the previously cited tools and strategies to provide a more refined and productive mutant fungal strain to address the biotechnological demand in industrial processes.

### SYNTHETIC BIOLOGY APPLIED TO SECONDARY METABOLISM

In the last 15 years, the research community has used two approaches to produce fine chemicals in fungi. One approach is the implementation of new metabolic pathways through recombinant DNA techniques. The second approach is the engineering of existing pathways to enhance the yield and purity of existing metabolites. While in this review, we focus on the production of fine chemicals, molecules with low aggregated value and high-volume production have been a point of interest in of recent studies, as summarized in **Figure 4**.

The bioproduction of lipid molecules in fungi can provide novel renewable and sustainable material for the production of food ingredients independent of plant cultivation, climate changes, and seasonal availability. Lipids extracted from plants, such as the cocoa tree (Theobroma cacao), can be utilized for

industrial purposes. The lipids extracted from T. cacao beans are the basic component of cocoa butter, a valuable ingredient that is becoming increasingly scarce in the market. In this sense, Wei et al. (2018) reported the application of rational metabolic engineering to produce a CBL product in yeast. To obtain a strain producing a CBL product, the authors precisely determined the genes that are responsible for the production of the TAGs that compose cocoa butter, so that these genes could be functionally expressed in S. cerevisiae. The main composition of this triacylglycerol product is 1,3-dipalmitoyl-2-oleoylglycerol, 1-palmitoyl-3-stearoyl-2-oleoylglycerol, and 1,3-distearoyl-2 oleoylglycerol. These TAGs fail to naturally accumulate in large amounts in S. cerevisiae. Wei et al. (2018) proposed that the enzymes responsible for the synthesis of TAGs from T. cacao could be cloned into S. cerevisiae, favoring the production of the adequate TAG to obtain the (CBL) product.

To investigate this hypothesis, the authors cloned cocoa glycerol-3-phosphate acyltransferase, lysophospholipid acyltransferase, and diacylglycerol acyltransferase in S. cerevisiae through Gibson assembly into the pBS01A plasmid (Wei et al., 2018). Interestingly, some of those genes were amplified from cocoa cDNA and showed different sequences from the previously annotated genes. Additionally, a valuable use of this approach is the possibility of directly characterizing and testing the activity of plant genes in yeast. As a means to optimize the production of CBL, the authors combined the expression of the cocoa genes with two previously reported genes for the production of CBL compound (Wei et al., 2017b). The resulting strains were able to produce significantly higher amounts of the TAGs of interest than the control strain, with an increased total fatty acid production up to 84%.

#### Alkaloids

Alkaloids play a significant role in human health, and these are structurally and functionally diverse molecules. Their health applications are diverse, ranging from the treatment of pain (opioids) to cancer (vinblastine and vincristine). Owing to their structural complexity, most of those important chemicals are produced through extraction or semi-synthesis from plant species. Addressing problems like seasonal variations in crop yields and batch variation of Papaver somniferum, a plant that produces morphine and other opioids, Galanie et al. (2015) developed a proof-of-concept synthetic circuit composed of more than 20 genes that can produce opioids in S. cerevisiae. Similar advances were made by Li and Smolke (2016) for the production of noscapine, an anticancer drug isolated from P. somniferum.

The assembly and production of these molecules in yeast allowed the establishment of new sources for these key molecules. They also enable researchers to use yeast as a platform for enzyme engineering, to generate new tools that can be used in drug discovery. The study conducted by Li and Smolke (2016) also raised important concerns regarding the safety and ethics of producing psychoactive drugs in an easy manner in organisms such as S. cerevisiae. Although the yield obtained by Galanie et al. (2015) was low (which makes licit and illicit applications of this technology unlikely), advances in the yeast biosynthesis of other non-alkaloid psychoactive compounds such as 19-tetrahydrocanabinolic acid (THCA), a precursor of the main psychoactive Cannabis constituent (tetrahydrocannabinol (THC)) (Zirpel et al., 2015), has raised concerns about the safety of microbial production of addictive active pharmaceutical ingredients.

#### Nonribosomal Peptides

The production of nonribosomal peptides (NRP) is of interest for their use in synthetic biology approaches, since the modularity of NRP synthetases (NRPSs) allows enhanced compound production or the synthesis of entirely new structures. A milestone in the application of synthetic biology to produce NRP using S. cerevisiae was reported by Awan et al. (2017). They managed to express the whole biosynthetic pathway of benzylpenicillin in this yeast, thus, validating a screening method for antibiotic NRP production. The benzylpenicillin biosynthetic pathway consists of five enzymes (encoded by pcbAB, npgA, pcbC, pclA, and penDE). pcbAB and npgA are responsible for the synthesis of the benzylpenicillin (PEN) precursor, amino-adipylcysteine-valine (ACV), while the products of pcbC, pclA, and penDE are responsible for the conversion of ACV to PEN.

Although the heterologous production of ACV in S. cerevisiae and of PEN in Hansenula polymorpha were previously reported by (Gidijala et al., 2009; Siewers et al., 2009), the approach used to express the whole PEN pathway in S. cerevisiae was a simple, inexpensive, yet, powerful platform to screen relevant NRP pathways. This approach enabled the development of new NRP through the combinatorial assembly of NRPS pathways and construction of chimeras (Awan et al., 2017).

#### Flavonoids

Flavonoids are a class of polyphenolic compounds produced by plants. They have a variety of uses in modern and traditional health practices, with benefits including antimicrobial and antioxidant activities (Marín et al., 2015; Skrovankova et al., 2015). Breviscapine is a flavonoid extract that is used in Chinese medicine. This flavonoid is composed mainly of two molecules, scutellarin and apigenin 7-O-glucuronide. It is obtained through the extraction of vegetal tissues from Erigeron breviscapus, which has resulted in scarce supply as its popularity has increased over the past 30 years. Therefore, new methodologies to produce the active molecule are needed to ensure the supply of this active pharmaceutical ingredient. In an attempt to produce the main components of breviscapine, Liu X. et al. (2018) identified and expressed components of the breviscapine pathway in yeast, and they were able to produce the flavonoids scutellarin and apigenin 7-O-glucuronide from glucose. Their success exemplifies the potential of synthetic biology as a metabolic pathway elucidation tool, which, in this example, aided the researchers to elucidate the breviscapine biosynthetic pathway. This example shows how synthetic biology can have a huge impact on the biosynthesis of flavonoids, therefore, establishing a constant supply chain of such natural products through fungal bioproduction.

#### Glycosides

The burgeoning prevalence of metabolic syndrome, obesity, and diabetes is increasing the need for alternatives to sugar. Steviosides are safe sweeteners that are extracted from the leaves of Stevia rebaudiana. They are glucosides composed of diterpenoids that are covalently bonded to three glucose molecules. A collateral effect of those sweetener molecules is a bitter off-flavor. In this sense, researchers from the biotechnology company Evolva (Olsson et al., 2016) developed strains of S. cerevisiae that produce novel next-generation stevioside with a reduced bitter taste. Through homology modeling and identification of key target amino acids present in the glucosyltransferase UGT76G1, the authors were able to obtain S. cerevisiae strains with increased accumulation of rebaudioside D and M steviosides with a less bitter taste and enhanced sweetness. Their success indicates the potential of this approach for yeast metabolic engineering of sugar replacements.

### EUKARYOTIC PROMOTERS AND TRANSCRIPTION FACTORS: THE BLOCKS TO CONTROL GENE EXPRESSION

The role of TFs and TF motifs can directly or indirectly drive transcription. These proteins control transcription by transforming physiological and environmental signals into patterns of gene expression (Weingarten-Gabbay and Segal, 2014), thus, acting as biosensors to turn transcription ON or OFF (D'Ambrosio and Jensen, 2017). Transcription factors recognize specific sequences in the DNA, collectively abbreviated as TFBSs. With the aid of computational approaches, TFBSs can be represented as position weight matrices (PWMs) in an attempt to represent the statistical or binding energy of the DNAprotein interaction depending on the data type that originated from the PWM (Schipper and Gordân, 2016). Additionally, more comprehensive models that include Bayesian networks or support vector machine based models have been reported recently, which are reviewed in Boeva (2016).

The discovery of new TF motifs is a step forward in understanding gene regulation, which can serve as a basis for new bioengineering applications. However, finding TFBS motifs is a difficult task, since primary nucleotide sequence is not the unique characteristic that specifies a TF target. Additional factors, such as multiple modes of DNA binding, DNA modification, DNA shape, genomic context, and coding and noncoding (genetic) variation

can change TF nucleotide sequence preferences, as reviewed in Inukai et al. (2017) and Siggers and Gordân (2014).

Studies using high-throughput data regarding TF binding specifity are another source of valuable information on parameters affecting gene regulators. In this sense, Gordân et al. (2011) suggested that several TFs of S. cerevisiae have a primary and secondary binding motif, which may even perform distinct regulatory functions. These findings indicate a property that could be further explored in motif search algorithms that focus on yeasts. Even with many experimental approaches that generate high-throughput data, it is impossible to test all the environmental conditions that a natural regulatory network is able to respond to. Despite this limitation, several methods and approaches have been used to identify motifs in high-throughput data.  Particularly, rGADEM, HOMER, POSMO, Dimont, and ChIPMunk are tools with good performance (Boeva, 2016; Jayaram et al., 2016).

#### Promoters

Bioinformatics research is being allied with synthetic biology for the discovery of new modules and their interactions. Yeast promoters have motifs/sequences that are necessary for promoter function and for assembly of the preinitiation complex, which leads to the recruitment of the entire cellular transcription machinery (Thomas and Chiang, 2006). The core elements in yeast promoters are nucleosome-free regions located approximately 140 base pairs (bp) at the beginning and at the end of the genes that are rich in adenine (A) or thymine (T). Additionally, transcription start sites (TSSs), TATA-box, and upstream activation sites located several hundred bp upstream of the TSS are required. Analogously, upstream repressing sequences can also be present in natural promoters (Venters and Pugh, 2009; Sesma and von der Haar, 2014)**.**

Promoter prediction is a hurdle. Transcriptional initiation is the first step in gene expression and, so, is an important control point. Despite its importance, eukaryotic promoter prediction is not simple because of the structural complexity of natural cis-regulatory elements (Pedersen et al., 1999; Yella and Bansal, 2017). In the past years, promoter prediction tools have improved, which have been driven by new high-throughput data generated by next-generation sequencing and by the application of analytical machine learning methods, such as the support vector machine, neural networks, and naïve Bayesian classifier (Singh et al., 2015). However, until now, no precise prediction tools are available. Yet, several software use eukaryotic promoter characteristics that are specific to a given specie or to animals, which are tools that work with a specific methodology and precision.

Owing to the complexity of promoter prediction in general, we presents prediction below some useful tools that have been applied in fungi/yeast or that could be adapted to these organisms. For example, Shahmuradov et al. (2017) attained better performance than previous programs by using a novel prediction tool for plant promoters, which was named TSSPlant. This tool was created by using large promoter collections from plant promoter databases to identify most relevant promoter cisregulatory elements. This was followed by the use of a neural network with back propagation to create a promoter classifier, thus, allowing an improvement in accuracy. Another notable approach to solve the prediction bottleneck was described by Umarov and Solovyev (2017). The authors developed a general method for the recognition of promoters by constructing a predictive model with convolutional neural networks. To test the method and to prove its universality, the authors used promoter sequences from distinct organisms (bacteria, human, mouse, and plant). For each organism, the best accuracy was achieved when it was compared with the existing tools.

In addition to the aforementioned prediction methods that deal with pre existing data, several studies have used transcriptomics to find putative TSSs. With specific RNA-seq protocols to obtain 5′ regions (Gowda et al., 2007; de Hoon and Hayashizaki, 2008), the reads can be compared with the available genome annotation data. Analysis of nearby regions could then be performed using alignment and conventional motif discovery tools (discussed in detail below). This approach provided useful information about the core promoters of Schizosaccharomyces pombe and A. nidulans (Sibthorp et al., 2013; Li et al., 2015). These studies highlight a relevant experimental/computational strategy to obtain data regarding regulatory elements in fungal promoters. Yet, since this study, no recent reports about fungal promoter prediction tools have been published. Hence, we suggest that these aforementioned notable approaches could be integrated to aid the creation of new software and new pipelines of promoter discovery in these biotechnologically relevant organisms (**Figure 5**).

### Bioinformatic Approaches for the Identification of TFBS

Considering current efforts in analyzing large amounts of data, Yu et al. (2016) proposed a new algorithm, PairMotifChIP, for this purpose. This tool can identify motifs by extracting combining pairs of an "l" width in the input sequences that have small Hamming distance, distinguishing the motifs from random overrepresented sequences by probabilistic analysis and then combines the remaining sequences to form motifs. This tool runs very fast and does not require previous user information (Yu et al., 2016). Caldonazzo Garbelini et al. (2018) created a new approach for motif discovery by making use of a genetic algorithm to escape from optimal local solutions. The algorithm was designated as Memetic Framework for Motif Discovery (MFMD). The study made use of a version of the semi-greedy heuristic to build initial solution population and genetic algorithms (as a global optimizer) to develop these initial solutions (Caldonazzo Garbelini et al., 2018). Another tool that

was also built to escape from optimal local solutions is weaklysupervised motif discovery (WSMD). The WSMD algorithm uses a latent support vector machine optimization strategy and learns PWM in a continuous space, which reduces the loss of information. Thus, the quality of the motifs is improved (Zhang et al., 2017). All of these studies feature a comparative analysis with the currently used motif discovery tools and seem to have better performance.

While these tools may help in studies to overcome limitations in motif discovery, working with high-throughput data remains a bottleneck. To deal with this, Guzman and D'Orso (2017) developed CIPHER, a framework that integrates complex bioinformatics tools to analyze different types of next-generation sequencing data (e.g., ChIP-seq, RNA-seq, DNase-seq). This tool provides several types of analyses that include differential gene expression, peak annotation, and reasoning (with HOMER) in an easy-to-use way. This tool also allows parallelization of processing for the better use of local hardware, as well as a quality control module to identify possible errors, contaminations, and bias in input sequences, which generates accurate results (Guzman and D'Orso, 2017).

#### CONCLUSIONS

Systems and synthetic biology are playing a pivotal role in the development of tools for engineering yeast and filamentous fungi. Studies based on them have shed light on how single modules can influence an entire and robust system. Besides, the particularity of each tool presented in this review, the approaches to engineer fungi as cell factories for biotechnological industrial processes, can be successfully combined to guarantee a viable and cost-efficient strategy. The tools described here can cover a wide area of applications, substantially improving the already existing methodologies to engineer these organisms. Still, despite the great promises of synthetic biology, the genetic manipulation of filamentous fungi remains to be one of the major challenges for industrial applications. The difficulty of manipulating their genomes still hinders the use of metabolic engineering for filamentous species. However, synthetic biology

#### REFERENCES

approaches used for yeast are significantly contributing to the spread of methodologies and tools for engineering microbes to produce high value-added products at an achievable cost and to benefit equipoise. In general, synthetic biology approaches have presented successful examples of management and the acquisition of mutant strains that meet industrial demand. Finally, we anticipate that novel computational tools, especially for the investigation and design of regulatory elements, will play a pivotal role in future engineering attempts of these remarkable organisms.

#### AUTHOR CONTRIBUTIONS

LM-S and RS-R conceived the work. LM-S, LN, AS-M, GL, MC, and RS-R assembled the first draft of the manuscript. LM-S and RS-R revised the final version of the work. All the authors have read and approved the final version of the manuscript.

#### FUNDING

RS-R was supported by the São Paulo Research Foundation (FAPESP, award number 2012/22921-8). LM-S was supported by FAPESP PhD Fellowship (award number 2017/17924-1). LN was supported by FAPESP Master Fellowship (award number 2016/03763-3). AS-M, GL, and MC were supported by FAPESP Scientific Initiation Fellowship (award numbers 2015/22386-3, 2016/11093-8, and 2017/04217-5).

#### ACKNOWLEDGMENTS

The authors would like to thank their lab colleagues for their insightful discussion about this work.

expression, and network inference. BioTechniques 44(Suppl.), 627–632. doi: 10.2144/000112802

cerevisiae by expression of selected cocoa genes. AMB Express 7, 1–11. doi: 10.1186/s13568-017-0333-1

**Conflict of Interest Statement:** 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.

The reviewer EB and handling Editor declared their shared affiliation.

Copyright © 2018 Martins-Santana, Nora, Sanches-Medeiros, Lovate, Cassiano and Silva-Rocha. 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.

# Modulating Transcriptional Regulation of Plant Biomass Degrading Enzyme Networks for Rational Design of Industrial Fungal Strains

#### Ebru Alazi and Arthur F. J. Ram\*

Molecular Microbiology and Biotechnology, Institute of Biology Leiden, Leiden University, Leiden, Netherlands

#### Edited by:

Gustavo Henrique Goldman, Universidade de São Paulo, Brazil

#### Reviewed by:

Ramón Alberto Batista-García, Universidad Autónoma del Estado de Morelos, Mexico Bernhard Seiboth, Technische Universität Wien, Austria

\*Correspondence:

Arthur F. J. Ram<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Bioenergy and Biofuels, a section of the journal Frontiers in Bioengineering and Biotechnology

Received: 28 March 2018 Accepted: 05 September 2018 Published: 25 September 2018

#### Citation:

Alazi E and Ram AFJ (2018) Modulating Transcriptional Regulation of Plant Biomass Degrading Enzyme Networks for Rational Design of Industrial Fungal Strains. Front. Bioeng. Biotechnol. 6:133. doi: 10.3389/fbioe.2018.00133 Filamentous fungi are the most important microorganisms for the industrial production of plant polysaccharide degrading enzymes due to their unique ability to secrete these proteins efficiently. These carbohydrate active enzymes (CAZymes) are utilized industrially for the hydrolysis of plant biomass for the subsequent production of biofuels and high-value biochemicals. The expression of the genes encoding plant biomass degrading enzymes is tightly controlled. Naturally, large amounts of CAZymes are produced and secreted only in the presence of the plant polysaccharide they specifically act on. The signal to produce is conveyed via so-called inducer molecules which are di- or mono-saccharides (or derivatives thereof) released from the specific plant polysaccharides. The presence of the inducer results in the activation of a substrate-specific transcription factor (TF), which is required not only for the controlled expression of the genes encoding the CAZymes, but often also for the regulation of the expression of the genes encoding sugar transporters and catabolic pathway enzymes needed to utilize the released monosaccharide. Over the years, several substrate-specific TFs involved in the degradation of cellulose, hemicellulose, pectin, starch and inulin have been identified in several fungal species and systems biology approaches have made it possible to uncover the enzyme networks controlled by these TFs. The requirement for specific inducers for TF activation and subsequently the expression of particular enzyme networks determines the choice of feedstock to produce enzyme cocktails for industrial use. It also results in batch-to-batch variation in the composition and amounts of enzymes due to variations in sugar composition and polysaccharide decorations of the feedstock which hampers the use of cheap feedstocks for constant quality of enzyme cocktails. It is therefore of industrial interest to produce specific enzyme cocktails constitutively and independently of inducers. In this review, we focus on the methods to modulate TF activities for inducer-independent production of CAZymes and highlight various approaches that are used to construct strains displaying constitutive expression of plant biomass degrading enzyme networks. These approaches and combinations thereof are also used to construct strains displaying increased expression of CAZymes under inducing conditions, and make it possible to design strains in which different enzyme mixtures are simultaneously produced independently of the carbon source.

Keywords: CAZyme, plant biomass degradation, strain design, industrial fungi, transcriptional regulation, overexpression of transcription factors, constitutively active transcription factors, inducer accumulation

### INTRODUCTION

Plant biomass is the most abundant renewable carbon source in the world and represents the major natural substrate for fungi (Kowalczyk et al., 2014). Plant biomass mainly consists of plant cell wall material, which contains the polysaccharides, i.e., cellulose, hemicellulose and pectin, lignin, and structural proteins (Loqué et al., 2015). Cellulose, the most abundant plant cell wall polysaccharide, is a linear chain of glucose (Kolpak and Blackwell, 1976). Hemicelluloses are complex heteropolysaccharides with xylan- (linear chain of xylose), glucan- (linear chain of glucose), glucomannan- (linear chain of glucose and mannose), or mannan- (linear chain of mannose) backbones, with several different types of monomers attached to the backbone to give e.g., arabinoxylan or xyloglucan (Scheller and Ulvskov, 2010). Pectins are other complex polysaccharides containing D-galacturonic acid (GA) as the main sugar acid in their backbones. Polygalacturonic acid (PGA) is a linear chain of GA and the most abundant pectin substructure. Other pectin substructures are rhamnogalacturonan I (linear chain of alternating GA and rhamnose residues), rhamnogalacturonan II and xylogalacturonan, and contain several different types of monomers or polymers attached to their backbones (Caffall and Mohnen, 2009). Plant biomass also contains the plant cell storage polysaccharides starch, a linear (amylose) or branched (amylopectin) polymer of glucose, and inulin, a polymer of fructose residues with a terminal glucose residue (Gidley, 2001; Ritsema and Smeekens, 2003).

Polysaccharides present in plant biomass are the target of the degrading enzymes secreted abundantly by filamentous fungi. Fungal carbohydrate active enzymes (CAZymes) are also utilized industrially for the hydrolysis of plant biomass for the subsequent production of mainly bioethanol, and highvalue biochemicals (Kowalczyk et al., 2014; Gupta et al., 2016; Benocci et al., 2017). Plant biomass degrading enzymes are classified into families based on their sequence, such as glycoside hydrolases, polysaccharide lyases and carbohydrate esterases, abbreviated as GH, PL and CE, respectively (Carbohydrate Active Enzymes database, http://www.cazy.org/) (Lombard et al., 2014). Filamentous fungi secrete large amounts of CAZymes only in the presence of the plant polysaccharide they specifically act on. For a review on the substrate specificity of CAZymes we refer to Kowalczyk et al. (2014) and de Vries et al. (2017). The signal for tailor-made production of CAZymes is conveyed via so-called inducer molecules which are di- or mono-saccharides (or derivatives thereof) released from the specific plant polysaccharides. The presence of the inducer results in the activation of a substrate-specific transcription factor (TF), which is required for the controlled expression of the genes encoding not only the CAZymes, but usually also the transporters and catabolic pathway enzymes needed to utilize the released monosaccharide (Culleton et al., 2013; Benocci et al., 2017).

Several substrate-specific TFs involved in plant biomass degradation have been identified in filamentous fungal species. For a review on the TFs involved in plant biomass degradation we refer to Huberman et al. (2016) and Benocci et al. (2017). For example, it has been shown that induction of expression of the genes encoding cellulases when cellulose is present requires the presence of the transcriptional activators Clr-1 and Clr-2 in Neurospora crassa, ClrB in Aspergillus nidulans and Penicillium oxalicum, ManR in A. oryzae and Xyr1 in Trichoderma reesei (Stricker et al., 2006; Coradetti et al., 2012; Ogawa et al., 2013; Li et al., 2015). Clr-2, ClrB and ManR are orthologs (Kunitake and Kobayashi, 2017), while Xyr1 shows significant sequence similarity to the TFs involved in xylan degradation, XlnR in A. niger and Xlr-1 in N. crassa (Rauscher et al., 2006). The enzyme families controlled by orthologous transcription factors can vary. Although Xyr1 in T. reesei is essential for the expression of cellulases and xylanases, Xyr1 orthologs (named XlnR or Xlr-1 in other species) are essential only for the expression of xylanases in filamentous fungal species, such as A. oryzae (on xylose), A. niger (on xylan), A. nidulans (on xylan), Talaromyces cellulolyticus (on xylan), P. oxalicum (on cellulose), N. crassa (on xylan), Fusarium oxysporum (on xylan or wheat cell walls), and Myceliophthora thermophila (van Peij et al., 1998; Marui et al., 2002b; Stricker et al., 2006; Brunner et al., 2007; Tamayo et al., 2008; Sun et al., 2012; Fujii et al., 2014; Li et al., 2015; Wang et al., 2015). In A. oryzae, A. niger, T. cellulolyticus, and P. oxalicum, contribution of XlnR to the expression of cellulases during growth on cellulose and/or xylose/xylan has also been reported (Gielkens et al., 1999; Marui et al., 2002; Li et al., 2015; Okuda et al., 2016). Arabinan, present in the side chains of some form of hemicellulose, such as arabinogalactan, or some pectins, is degraded via CAZymes that are transcriptionally regulated by AraR in A. niger (Battaglia et al., 2011; Kowalczyk et al., 2017). Arabinan degradation in T. reesei is partially regulated via Xyr1 (Akel et al., 2009) and via Ara1 (Benocci et al., 2018). The TF GaaR, required for the expression of CAZymes degrading pectin, especially PGA, has been identified in both Botrytis cinerea and A. niger (Alazi et al., 2016; Zhang et al., 2016). Recently, Pdr-1 in N. crassa was described as a TF required for the

**Abbreviations:** CAZyme, carbohydrate active enzyme; GA, D-galacturonic acid; PGA, polygalacturonic acid; TF, transcription factor; CCR, carbon catabolite repression.

expression of the genes encoding CAZymes that degrade several pectin substructures including PGA and rhamnogalacturonan I (Thieme et al., 2017). Transcriptional control of the genes involved in starch degradation is conducted by AmyR in A. niger and A. nidulans (Tani et al., 2001; vanKuyk et al., 2012).

The transcriptional activators involved in plant biomass degradation mentioned above were identified either via classical approaches, such as mutant complementation (xlnR in A. niger) and gene cloning (xlnR and amyR in A. nidulans and amyR A. niger), or via post-genomic approaches, such as yeast onehybrid screening (gaaR in B. cinerea), screening TF deletion mutant collections (pdr-1 and clr-2 in N. crassa, manR in A. oryzae, and clrB and xlnR in P. oxalicum), and also based on sequence similarity (clrB in A. nidulans, araR and gaaR in A. niger, xyr1 in T. reesei and M. thermophila, and xlnR in F. oxysporum, T. cellulolyticus and A. oryzae) or their expression levels in the transcriptomics data (xlr-1 in N. crassa) (van Peij et al., 1998; Tani et al., 2001; Marui et al., 2002b; Rauscher et al., 2006; Brunner et al., 2007; Battaglia et al., 2011; Coradetti et al., 2012; Ogawa et al., 2012; Sun et al., 2012; vanKuyk et al., 2012; Fujii et al., 2014; Li et al., 2015; Wang et al., 2015; Alazi et al., 2016; Zhang et al., 2016; Thieme et al., 2017). Positively acting TFs involved in controlling expression of plant cell wall degrading enzymes belong to the Zn2Cys<sup>6</sup> type family of TFs. TFs belonging to this family are found specifically in fungi (both yeasts and filamentous fungi) and contain a DNA-binding domain with six cysteine residues bound to two zinc atoms, usually close to their NH2-terminal end. Most of the Zn2Cys<sup>6</sup> type TFs also contain a fungal-specific TF domain, known as the middle homology region, with a proposed role in regulating the activity of the TF (MacPherson et al., 2006). Apart from activators several repressor proteins involved in plant biomass degradation have been identified including wide domain repressors, such as the carbon catabolite repressor protein CreA/Cre1, or substrate specific repressors which are discussed in paragraphs 1.2 and 2.3, respectively.

### Diversity in the Control of Transcription Factor Activities

The mechanisms by which the activity of TFs is regulated in response to stimuli can be diverse. Firstly, the protein level of the TFs might be controlled. This can be realized by regulation at the level of transcription, mRNA stability or protein stability resulting in low protein levels under noninducing conditions and higher levels under inducing conditions. Secondly, the subcellular localization (nuclear import/export), DNA binding activity, and/or transcriptional activity of the TFs might be regulated by post-transcriptional modifications, such as phosphorylation, and/or via protein-protein or proteinmetabolite interactions (MacPherson et al., 2006; Chang and Ehrlich, 2013; Tani et al., 2014). Finally, DNA site occupancy of TFs might depend on their cooperation or competition with other proteins and the chromatin accessibility (Granek and Clarke, 2005; Biggin, 2011).

Our knowledge about regulation of the activity of the TFs involved in biomass degradation is limited, yet growing. The amount of Clr-2 in N. crassa is regulated at the level of transcription by Clr-1, which gets activated and positively regulates clr-2 expression only under inducing conditions (on insoluble crystalline cellulose Avicel or cellobiose). The expression of the clr-2 ortholog in A. nidulans, clrB, on the other hand, is not drastically induced under inducing conditions (on Avicel) and the Clr-1 ortholog ClrA is not essential but only contributes to cellulase gene expression (Coradetti et al., 2012). This comparison indicates that activation mechanisms of even orthologous transcription factors differ between fungal species.

The expression of xyr1 in T. reesei is subject to Cre1 dependent carbon catabolite repression (CCR) (see below) and induced on carbon sources inducing cellulase expression (e.g., lactose, sophorose, or cellulose), but not on carbon sources inducing xylanase expression (e.g., xylose) (Mach-Aigner et al., 2008; Portnoy et al., 2011a; Lichius et al., 2014). Xyr1 was shown to accumulate in the nucleus during growth on an inducing carbon source (i.e., sophorose or low concentration of xylose), whereas it is degraded in the nucleus during growth on a repressing carbon source (i.e., glucose or a high concentration of xylose). The increased amount of nuclear Xyr1 correlates with the increased expression level of cellulase gene cbh1 on sophorose, and that of xylanase gene xyn2 on a low concentration of xylose (Lichius et al., 2014). Similar to T. reesei xyr1, the expression of xlnR in A. nidulans and P. oxalicum is also subject to CreA-dependent CCR (see below), whereas xlnR in A. niger is not regulated at the level of transcription, but constitutively transcribed at low levels (Tamayo et al., 2008; Mach-Aigner et al., 2012; Li et al., 2015). Moreover, XlnR was found to be localized in the nucleus regardless of the presence of inducer in A. niger (Hasper, 2004). In F. oxysporum, xlnR is transcriptionally regulated by both CCR and induction on xylose/xylan (Calero-Nieto et al., 2007). XlnR in A. oryzae is reversibly phosphorylated in response to xylose, which does not affect its protein stability and correlates with the expression of XlnR target genes (Noguchi et al., 2011). Recently, Kunitake and Kobayashi proposed that a conserved sequence in XlnR is involved in the xylose-mediated phosphorylation of XlnR in A. oryzae, implying a conserved mechanism regulating XlnR activity among Ascomycete fungi. Moreover, the observation that XlnR in A. oryzae is not phosphorylated in response to cellobiose, but required for cellulase expression, indicates that its activity is regulated by a different mechanism on cellobiose (Noguchi et al., 2011; Kunitake and Kobayashi, 2017).

The activity of the GA-responsive transcriptional activator GaaR in A. niger is regulated by a different mechanism. It is suggested to be inhibited by the repressor protein GaaX via protein-protein interaction under non-inducing conditions. Under inducing conditions (in the presence of GA), the inducer is proposed to bind to GaaX, resulting in a free and active form of GaaR (Niu et al., 2017). The amount of GaaR is not significantly regulated at the level of transcription (Alazi et al., 2016, 2018). In addition, GaaR-eGFP was shown to be constitutively localized in the nucleus (Alazi et al., 2018). Transcription of pdr-1 in N. crassa is induced under inducing conditions (on rhamnose) and repressed under repressing conditions. Moreover, the activity of Pdr-1 is suggested to be post-transcriptionally regulated, as its nuclear accumulation and transcriptional activity requires the presence of rhamnose in a strain overexpressing pdr-1 (Thieme et al., 2017).

The transcription of amyR is upregulated on inducing carbon sources (e.g., starch or maltose) and is subject to CreA-dependent CCR (see below) in A. nidulans (Tani et al., 2001). Further, AmyR was shown to localize in the nucleus in an inducer (i.e., isomaltose)-dependent manner and activate the expression of its target genes (Makita et al., 2009).

### The Role of the Carbon Catabolite Repressor in the Production of Plant Biomass Degrading Enzymes

Apart from being upregulated under inducing conditions, the expression of CAZymes might also be controlled via CCR when a more energetically favorable carbon source, such as glucose, compared to plant biomass polysaccharides is available for fungi. The Cys2His<sup>2</sup> type TF CreA/Cre1/Cre-1 (in Aspergillus species, T. reesei, and N. crassa, respectively) is the main transcriptional repressor contributing to CCR. Regulation of CreA activity has not been fully understood, but several studies have indicated that post-transcriptional modifications, such as ubiquitination and phosphorylation, affect CreA protein stability, subcellular localization and/or DNA binding activity (Cziferszky et al., 2002; Ries et al., 2016; see Adnan et al., 2017 for a recent review). CreA not only represses the expression of genes encoding CAZymes, but it might also repress the expression of some transcriptional activators, such as clrB and xlnR in P. oxalicum, xyr1/xlnR in T. reesei, A. nidulans and F. oxysporum, and amyR in A. nidulans, that are required for the expression of CAZymes (Tani et al., 2001; Calero-Nieto et al., 2007; Mach-Aigner et al., 2008; Tamayo et al., 2008; Li et al., 2015). Furthermore, CreA might repress the expression of CAZymes under high xylose concentrations, too, as was shown to be the case for XlnR and its target genes in A. nidulans, and for XlnR itself in A. oryzae (Tamayo et al., 2008; Ichinose et al., 2017). Elimination of CCR due to a lack of function of CreA and/or CreB, a ubiquitin-specific protease involved in CCR, or their orthologs has been reported to result in an increased expression of CAZymes degrading cellulose, hemicellulose, pectin or starch, under inducing and/or non-inducing conditions in filamentous fungi (Prathumpai et al., 2004; Mach-Aigner et al., 2008; Nakari-Setälä et al., 2009; Denton and Kelly, 2011; Sun and Glass, 2011; Fujii et al., 2013; Ichinose et al., 2014, 2017; Niu et al., 2015; Yang et al., 2015). However, even when CreA-dependent CCR is circumvented, the expression of CAZymes might require the presence of active transcriptional activators, which is normally achieved in the presence of inducing metabolites. For example, in a Cre1-negative T. reesei strain (Rut-C30), the full expression of Xyr1 target genes requires the presence of the inducer (i.e., xylose) (Mach-Aigner et al., 2008). Another study revealed that deletion of cre1 in T. reesei results in elevated production of cellulases and xylanases under inducing (on lactose) and, to a lesser extent, under non-inducing conditions (on glucose) (Nakari-Setälä et al., 2009). Genome-wide studies in T. reesei have also indicated that only a few of the cellulolytic genes were upregulated in a 1cre1 strain during growth on glucose (Portnoy et al., 2011b; Antoniêto et al., 2014). This indicates that the majority of these cellulolytic genes are not induced simply by derepression, but require additional inducing condition for expression. Similarly, although the expression of pectinases is subject to CreA-dependent CCR in A. niger, deletion of creA is not sufficient for an increased production of polygalacturonases under non-inducing conditions. Polygalacturonases are only produced in the presence of the inducer (i.e., GA) or when gaaX is deleted, showing that GA-responsive gene expression requires the presence of active GaaR relieved from GaaX inhibition even in a 1creA strain (Niu et al., 2015, 2017).

### APPROACHES FOR INCREASED OR CONSTITUTIVE EXPRESSION OF PLANT BIOMASS DEGRADING ENZYMES BY MODULATING THEIR TRANSCRIPTIONAL REGULATION

Plant biomass with varying sugar composition present in waste streams from agriculture, forestry and food industries represent a cheap, sustainable and renewable feedstock for the production of the CAZymes, and subsequently, valuable chemicals (Sweeney and Xu, 2012; Meyer et al., 2015). However, the composition of the enzyme cocktail will vary because of variation in the composition of the plant biomass. It is therefore of industrial interest to produce specific fungal enzyme cocktails constitutively, independently of inducers and the substrate used. Fungal production strains, such as A. niger CBS513.88, T. reesei Rut-C30, and P. oxalicum JU-A10- T, have been for a long time improved via multiple rounds of classical mutagenesis and screening approaches that can be time-consuming (Montenecourt and Eveleigh, 1979; Pel et al., 2007; Fang et al., 2010; van Hanh et al., 2010; Ho and Ho, 2015; Yao et al., 2015). However, emergence of the -omics era (genomics, transcriptomics etc.) and advances in recombinant technologies allow nowadays efficient strain improvement via genetic engineering approaches with minimal alterations in the genome (Meyer et al., 2010; Liu et al., 2013). In the remaining part of this review, we discuss the approaches to modulate transcriptional regulation in order to rationally design fungal strains with increased or constitutive production of plant biomass degrading enzymes, examples of which are given in **Table 1**. As will become clear in the following sections, the success of the approach used highly depends on the mechanism regulating the activity of the targeted TF.

#### Overexpression of TFs

Increasing the amount of a TF at the level of transcription can be achieved by expressing multiple copies of the TF via its endogenous promoter, or (multiple copies of) the TF via a strong inducible/constitutive promoter. Overexpression of several TFs in Saccharomyces cerevisiae has been shown to result in an increased expression of the TF target genes, even under noninducing conditions (Chua et al., 2006).

#### TABLE 1 | Examples of rational design of industrial fungal strains with increased or constitutive production of CAZymes.

Inducer-independent production of cellulases in N. crassa was achieved by constitutively overexpressing clr-2 via the ccg-1 promoter (Coradetti et al., 2013). While deletion of clr-2 resulted in a drastic decrease in the expression of most of the genes encoding cellulases under inducing conditions [i.e., on insoluble crystalline cellulose (Avicel)], overexpression of clr-2 resulted in an increased expression of cellulases under inducing conditions and constitutive expression under non-inducing conditions. Expression of the genes encoding cellulases was higher under starvation (no carbon) conditions than on sucrose highlighting the effect of CCR on cellulase encoding genes under repressing conditions (i.e., on sucrose) even when clr-2 was overexpressed (Coradetti et al., 2013).

Unlike overexpression of clr-2 in N. crassa, overexpression of the clr-2 ortholog clrB in A. nidulans or in P. oxalicum, both via the constitutive A. nidulans gpdA promoter, did not result in an increased expression of CAZymes encoding genes under non-inducing conditions, but only under inducing conditions (on Avicel or cellulose, respectively) (Coradetti et al., 2013; Li et al., 2015). It was shown that deletion of creA in combination with clrB overexpression allows increased expression of cellulases under non-inducing conditions in P. oxalicum, indicating that the strong CreA-dependent repression on cellulase genes overrules ClrB-dependent induction (Li et al., 2015).

ManR in A. oryzae is the N. crassa clr-2 ortholog, regulating the expression of genes encoding cellulose and hemicellulose (including mannan, but not xylan) degrading enzymes in A. oryzae (Ogawa et al., 2012, 2013). Overexpression of ManR via the tef1 promoter resulted in an increased expression of cellulases and hemicellulases involved in mannan degradation under inducing conditions (i.e., on Avicel and mannan, respectively) (Ogawa et al., 2012, 2013). The effect of overexpression of ManR under non-inducing conditions has not been reported.

As mentioned before, in T. reesei, Xyr1 controls the transcriptional regulation of both cellulase encoding and xylanase encoding genes. Expression of xyr1 is subject to CCR and xyr1 expression is upregulated on carbon sources inducing cellulase production. Overexpression of xyr1 via the tcu1 promoter resulted in an inducer-independent production of cellulases, even under repressing conditions (i.e., on glucose) (Lv et al., 2015). All reported T. reesei Xyr1 orthologs in other filamentous fungal species regulate mainly the transcription of hemicellulase encoding genes and contribute less to the transcriptional regulation of cellulase encoding genes. Overexpression of xlnR via gpdA promoter in T. cellulolyticus resulted in an increased production of cellulases on cellulose, but not that of xylanases (Okuda et al., 2016). In A. niger, increased expression of cellulases and xylanases on xylose was observed in a strain carrying multiple copies of xlnR (Gielkens et al., 1999). Similarly, an A. oryzae strain overexpressing xlnR via the tef1 promoter showed an increased production of both cellulases and xylanases when grown on carbon sources known to induce cellulase (i.e., Avicel or cellobiose) or xylanases (i.e., xylose or xylan) production (Marui et al., 2002; Noguchi et al., 2009).

The studies mentioned above reported on the increased expression of xylanases in fungal strains overexpressing xlnR or its orthologs under inducing conditions. Only a few studies have reported the effect of overexpression of xlnR on the expression of xylanases under non-inducing conditions. For instance, xlnR overexpression via the constitutive gpdA promoter in A. nidulans or in F. oxysporum yields to increased production of xylanases encoding genes only in the presence of inducing carbon sources (e.g., xylose or xylan) (Calero-Nieto et al., 2007; Tamayo et al., 2008). It was also reported that when GFP-XlnR is overproduced in A. oryzae, it localizes constitutively in the nucleus, but its target genes are expressed only in the presence of xylose (Noguchi et al., 2011). These results indicate that XlnR in these organisms gets activated by an inducer-mediated post-transcriptional mechanism and that XlnR activation under non-inducing conditions cannot simply be achieved by overexpression. In M. thermophila, overexpression of xyr1 via the pdc promoter resulted in an increased expression of xylanases under both inducing (i.e., corncob) and non-inducing conditions (i.e., glucose) (Wang et al., 2015). This indicates that the mechanism of Xyr1 activation in M. thermophila differ from other filamentous fungi.

Another example of inducer-independent production of CAZymes by overexpression of TFs was given by a recent study in A. niger. Here it was shown that overexpression of gaaR in A. niger via the A. nidulans gpdA promoter leads to constitutive expression of the genes encoding pectinases, as well as GA transporters and GA catabolic pathway enzymes (Alazi et al., 2018). Deletion of creA further enhanced pectinase production under mildly repressing conditions (i.e., on fructose) indicating competing roles of GaaR and CreA to control the expression of GA-induced genes (Alazi et al., 2018). The effect of overexpression of gaaR on the expression of its target genes under non-inducing conditions is likely caused by its specific activation mechanism. Regulation of the activity of GaaR includes a specific repressor protein (GaaX) (Niu et al., 2017). It has been proposed that under non-inducing conditions the activity of GaaR is controlled through direct interaction by GaaX which prevents GaaR to be active (Niu et al., 2017). Modulating the amount of GaaR by overexpression affects the balance of GaaR-GaaX and results in the presence of uncomplexed active GaaR even under non-inducing conditions (Alazi et al., 2018).

The regulation of GA-induced gene expression shows some striking similarities with the regulation of genes involved in quinic acid catabolism. Quinic acid is present as an aromatic compound in the plant cell and can be released from tannins, which are water-soluble polyphenols, by the action of tannases known to be secreted by filamentous fungi (Wagh, 2010). Similar to regulation of GA utilization in A. niger, regulation of quinic acid utilization in A. nidulans involves a Zn2Cys<sup>6</sup> type transcriptional activator (QutA) and a repressor (QutR), and strains expressing multiple copies of qutA displayed constitutive expression of the genes encoding quinic acid catabolic pathway enzymes (Lamb et al., 1996). The QutR and GaaX repressor proteins are both multidomain proteins with sequence similarity to the three C-terminal domains of AROM, indicating that these repressors share a common evolutionary origin (Niu et al., 2017).

On the other hand, overexpression of pdr-1, the pectin degradation regulator in N. crassa, via the gpd promoter of M. thermophila resulted in elevated expression of its target genes only under inducing conditions (on rhamnose). Therefore, it was proposed that Pdr-1 activity is regulated post-transcriptionally in a manner depending on the presence of the inducer but not on the amount of Pdr-1 (Thieme et al., 2017).

An A. niger strain carrying multiple copies of amyR displayed increased expression of AmyR target genes, such as CAZymes acting on starch as well as on cellulose and hemicellulose, under inducing conditions (i.e., on maltose, starch, and low concentration of glucose), but not under non-inducing condition (vanKuyk et al., 2012). This result is in line with the observation that in A. nidulans, nuclear localization of AmyR and thereby activation of its target genes is inducer-dependent (Makita et al., 2009).

In conclusion, we have seen that overexpression of TFs involved in plant biomass degradation can result in increased expression of their target genes under inducing conditions. However, in most cases overexpression of TFs, such as XlnR, Pdr-1 and AmyR, does not result in inducer-independent expression of their target genes. In these cases it is likely that an inducer molecule is required to activate the TF. The exact activation mechanism of XlnR, Pdr-1, and AmyR are currently unknown, and could involve direct interaction of the TF with its inducer, or could be related to post-translational modifications connected to the presence of inducers. Overexpression of TFs like GaaR and QutA results in inducer-independent expression of GaaR and QutA target genes most likely by titrating away the corresponding repressor proteins (GaaX and QutR, respectively). This illustrates that different mechanisms controlling TF activities result in different outcomes of overexpression of TFs under non-inducing conditions.

#### Constitutive Activation of TFs

The activity of a TF can be controlled in different ways, including post-transcriptional modifications affecting its localization, DNA binding or interaction with repressor protein(s). Over the past 15 years, mutations resulting in changes in amino acid sequences and thereby in constitutively active TFs have been identified via classical mutagenesis and screening approaches, or via predesigned amino acid substitutions, domain removal or protein truncation analyses.

The first reported constitutively active form of XlnR (XlnRV756F) was identified in A. niger via a forward genetic screen. Expression of xlnRV756<sup>F</sup> resulted in a constitutive expression of xylanases even under repressing conditions (i.e., on fructose or glucose; Hasper, 2004; Hasper et al., 2004). Later, in T. reesei, a point mutation in Xyr1 (Xyr1A824V) introduced via UV mutagenesis was found to result in a constitutively active Xyr1 and constitutive expression of cellulases and xylanases even in the presence of a repressing carbon source (Derntl et al., 2013). In addition, overexpression of xyr1A824<sup>V</sup> in T. reesei was shown to be more effective in increasing cellulase production than overexpression of xyr1 under inducing conditions (e.g., Avicel) (Jiang et al., 2016). Both amino acids changes (V756F in XlnR and A824V in Xyr1) are located within the same predicted αhelix in the fungal-specific TF domain of XlnR/Xyr1 (Derntl et al., 2013). Although overexpression of xlr-1 in N. crassa via the ccg-1 promoter did not yield to constitutive expression of xylanases, overexpression of xlr-1A828V, which carries the homologous mutation as in xyr1A824V, resulted in a constitutive and increased production of xylanases under both inducing (on xylan) and noninducing conditions (Craig et al., 2015). Similarly, overexpression of xlnR carrying the homologous point mutation (xlnRA871V) using the PDE\_02864 promoter in a P. oxalicum strain that lacks creA and overexpresses clrB (see above), enabled even more increased production of cellulases and xylanases under inducing conditions (on wheat bran) (Gao et al., 2017a).

Recently, via a forward genetic screen, several different point mutations throughout AraR were found to give rise to a constitutively active AraR and constitutive expression of AraR target genes (abfA, abfB, and abfC). Unlike the mutations in XlnR that gave rise to inducer-independent and constitutive expression of its target genes, the mutations in AraR lead to constitutive expression of AraR target genes only under derepressed conditions. Deletion of creA improved the production of CAZymes that degrade arabinan to a large extent, indicating the strong CCR on the genes encoding these CAZymes under repressing conditions on fructose (Reijngoud et al., manuscript in preparation).

We recently conducted a large forward genetic screen for mutants with constitutive expression of pectinases (Niu et al., 2017). Apart from identifying GaaX as a repressor for GAinduced genes expression, it also brought about the identification of a constitutive allele of GaaR (GaaRW361R) resulting in constitutive expression of pectinases under even repressing conditions (on glucose or fructose). Within GaaR, W361 is situated in the fungal-specific TF domain and is highly conserved among Aspergillus species (Alazi et al., in press).

Deletion of the C-terminal regions of several TFs involved in plant biomass degradation was shown to result in constitutive activation of the TFs indicating that these C-terminal parts contain an inhibitory domain. For example, in A. niger, truncation of XlnR from L668 resulted in a constitutive expression of xylanases (Hasper et al., 2004), and truncation of AraR from P646 was reported to cause constitutive activation of AraR (Jiang et al., 2016). Expression of the C-terminally truncated AmyR1−<sup>514</sup> and AmyR1−<sup>511</sup> resulted in constitutive localization of AmyR in the nucleus both in A. nidulans and A. oryzae, respectively. However, while constitutive amylase production was observed in A. nidulans (Makita et al., 2009), loss of expression was observed in A. oryzae (Suzuki et al., 2015), indicating that the effect of the truncation can also be species-specific.

### Deletion or Down-Regulation of Specific Repressors

It has been shown that besides the general CCR, specific repressors might play a role in the transcriptional regulation of the genes encoding CAZymes. For instance, the Cys2His<sup>2</sup> type TF Ace1 represses the expression of both cellulase and hemicellulase (i.e., xylanase) encoding genes, as well as the expression of xyr1 in T. reesei (Aro et al., 2003; Wang et al., 2013). Furthermore, Ace1 was shown to compete with Xyr1 to bind to the same sequence in the promoter of the xylanase gene xyn1 (Rauscher et al., 2006). Deletion of ace1 in the wild type background resulted in an increased expression of the genes encoding cellulases and xylanases only under inducing conditions (on cellulose), and gene silencing of ace1 led to an increase in constitutive expression of these genes when combined with the overexpression of xyr1 via the pdc promoter in a Cre1-negative background (Rut-C30) (Aro et al., 2003; Wang et al., 2013). Recently, another repressor, the Zn2Cys<sup>6</sup> type TF Rce1, was identified in T. reesei that is involved in the repression of the genes encoding cellulases, but not xylanases. It was shown that Rce1 is constitutively localized in the nucleus and competes with Xyr1 to bind to the same sequence in the promoter of the cellulase gene cbh1 (Cao et al., 2017). Deletion of rce1 resulted in an increased production of cellulases under inducing conditions (on cellulose) (Cao et al., 2017). Deletion of a basic helix-loop-helix TF xpp1, xylanase promoter-binding protein 1, in T. reesei resulted in an increased expression of hemicellulase (i.e., xylanase) encoding genes, but not cellulase encoding genes, at late stages of cultivation under inducing conditions (on xylan) (Derntl et al., 2015). However, Xpp1 was later described as a general regulator of both primary and secondary metabolism and therefore not a factor specifically controlling xylanases expression (Derntl et al., 2017). In addition, the Zn2Cys<sup>6</sup> type transcriptional repressor SxlR was found to bind to the promoters of specific (GH11 family) xylanase genes, and deletion of sxlR resulted in an increased expression of these xylanase genes under inducing conditions (on Avicel, xylan or lactose) (Liu et al., 2017).

Similarly, the Cys2His<sup>2</sup> type TF Hcr-1 in N. crassa was shown to repress the expression of the genes encoding xylanases, but not the ones encoding cellulases. A 1hcr-1 strain exhibited an increased xylanase production under inducing conditions (on xylan or Avicel) (Li et al., 2014).

Recently a new TF, MhR1, was identified in M. thermophila as the regulator of cellulase and xylanase genes. Gene silencing of mhr1 resulted in an increased production of cellulases and xylanases, as well as increased expression of xyr1 and the genes encoding cellulases under inducing conditions (on wheat straw) (Wang et al., 2018).

As mentioned before, the activity of GaaR in A. niger is inhibited by the repressor protein GaaX, the amount of which is regulated at the level of transcription by induction on GA. GaaX was proposed to bind to- and inhibit GaaR under noninducing conditions, and bind to the inducer molecule and release GaaR under inducing conditions. Deletion of gaaX resulted in a constitutive expression of pectinases, providing additional evidence for the proposed model of regulation of GA-responsive gene expression in A. niger (Niu et al., 2017). Similarly, deletion of QutR, the repressor protein involved in controlling the expression of quinic acid-responsive genes, also results in constitutive expression of at least eight genes involved in quinic acid uptake and metabolism (Levett et al., 2000).

#### Accumulation of Inducers

The intracellular accumulation of inducers is another effective method to boost the production of CAZymes by fungi. Cellulase production by N. crassa is greatly induced on insoluble, crystalline cellulose (Avicel). However, cellulase production is not observed on cellobiose, which is the soluble degradation product of cellulose, possibly due to rapid action of βglucosidases converting cellobiose to glucose and subsequent glucose-mediated CCR. As first shown in N. crassa, deletion of three genes encoding the major β-glucosidases (intracellular enzyme Gh1-1, and extracellular enzymes Gh3-3 and Gh3-4) disrupted the hydrolysis of the inducer, cellobiose, and resulted in higher levels of expression of cellulases on cellobiose compared to the wild type strain grown on cellobiose, and similar to the wild type strain grown on Avicel. Moreover, deletion of cre-1 further increased cellulase production on cellobiose (Znameroski et al., 2012). Similarly, deletion of the major intracellular β-glucosidase bgl2 in a carbon catabolite de-repressed (1creA) P. oxalicum strain that overexpresses clrB using the A. nidulans gpdA promoter, gave rise to higher levels of cellulase and hemicellulase (i.e., xylanase) production on cellulose compared to the wild type strain. These were similar to the levels of the industrial strain JU-A10-T grown on wheat bran (Yao et al., 2015). A similar effect of deleting bgl2 on cellulase and hemicellulase production was observed in P. oxalicum grown on cellulose (Chen et al., 2013).

Expression of hemicellulases, specifically xylanases, in T. reesei is induced both on xylose and arabinose. Although the catabolic pathways assimilating xylose and arabinose are interconnected, the physiological inducers triggering hemicellulase production appear to be different and were suggested to be xylose and L-arabitol, respectively. Expression of xylanases increased dramatically in single or double xylose/arabinose catabolic pathway enzyme deletion mutants, indicating the effect of accumulation of physiological inducers in these mutants (Seiboth et al., 2012a; Herold et al., 2013).

One of the GA catabolic pathway intermediates, 2-keto-3 deoxy-L-galactonate, was recently shown to be the physiological inducer of the genes encoding pectinases in A. niger. It was demonstrated that deletion of gaaC, the gene encoding 2-keto-3 deoxy-L-galactonate aldolase, results in accumulation of 2-keto-3-deoxy-L-galactonate and thereby elevated expression levels of pectinase encoding genes (Alazi et al., 2018). Inducer molecules that are responsible to the activation of TFs represent therefore another interesting target to enhance expression of CAZymes by constructing strains in which the inducer accumulates either due to prevention of rapid hydrolysis of the inducer or due to inactivation of the metabolic genes that function downstream of the enzyme that forms the inducer.

## FUTURE PERSPECTIVES AND CONCLUDING REMARKS

Increased expression of plant cell wall degrading enzymes can be achieved by overexpression of specific transcription factors or by identifying mutations in TFs leading to constitutive activation, and combinations thereof. It is also well established that CreA-dependent CCR has in many cases a negative effect on the production of enzymes and therefore CreA is an important target for improving enzyme production under both inducing and non-inducing conditions. Apart from the approaches described above in the previous paragraphs, modulating chromatin accessibility is yet an under-utilized approach to increase the expression of CAZyme encoding genes in filamentous fungi. Chromatin remodeling of the promoters of CAZyme encoding genes through the actions of the histone acetyltransferase Gcn5, the CCAAT-binding complex, and possibly the putative protein methyltransferase Lae1 is required for the full expression of these CAZymes in T. reesei (Zeilinger et al., 1998; Xin et al., 2013; Aghcheh and Kubicek, 2015; Li et al., 2016). Overexpression of a putative GCN5-related N-acetyltransferase (gene ID 123668) via the A. nidulans gpdA promoter or lae1 via the tef1 promoter resulted in an increased expression of cellulase encoding genes under inducing conditions (on lactose) (Seiboth et al., 2012b; Häkkinen et al., 2014). More recently, Cre1 was shown to be involved in chromatin accessibility of xyr1 promoter (Mello-de-Sousa et al., 2016).

As our knowledge about regulation of TF activities accumulates, various possibilities for rational design of industrial fungal strains emerge, such as combinations of different approaches as already mentioned above, or the use of synthetic TFs. Recently, inducer-independent production of CAZymes by filamentous fungi was achieved via synthetic TFs. For instance, overexpression via pdc1 promoter of a synthetic TF (xyr1-cre1<sup>b</sup> ) consisting of Xyr1 DNA-binding and effector domains, and Cre1 DNA-binding domain, resulted in inducerindependent production of cellulases and hemicellulases (i.e., xylanases) in the Cre1-negative T. reesei strain Rut-C30 (Zhang et al., 2017). Another example of synthetic TFs (CXC-S) was shown in P. oxalicum, where the DNA-binding domain of the constitutively active XlnRA871V was replaced with that of ClrB.

#### REFERENCES

This synthetic TF was overexpressed using the A. nidulans gpdA promoter, yielding to inducer-independent, but still glucoserepressed, expression of cellulases and xylanases (Gao et al., 2017a,b).

To conclude, fungal strains with increased or constitutive production of plant biomass degrading enzymes can be rationally designed by tuning the transcriptional regulatory systems. Mutations that lead to constitutive expression of enzymes are difficult to predict and so far only identified via genetic screens. Once identified, these mutations can be successfully transferred to industrial strains or related species. In this review, we also highlighted that regulation of activities of orthologues TFs, or the set of genes regulated by orthologues TFs might be speciesspecific. It is therefore important that detailed studies on how TFs are activated are performed not only in a single species, which is subsequently used as a blue print to predict the activation mechanism in other fungal species, but performed in several representative fungal species across the filamentous fungi.

#### AUTHOR CONTRIBUTIONS

EA and AR designed the content of the manuscript and collected literature. EA wrote the manuscript and AR critically commented on the manuscript.

#### ACKNOWLEDGMENTS

EA was supported by a grant from BE-Basic (Flagship 10). We acknowledge the helpful comments of Dr. Jaap Visser and Prof. Dr. Peter J. Punt on the manuscript.

of xylanase genes and virulence. Mol. Plant Microbe Interact. 20, 977–985. doi: 10.1094/MPMI-20-8-0977

transduction within the QUTR transcription repressor protein. Biochem. J. 350, 189–197. doi: 10.1042/bj3500189

Industrial Microbiology and Biotechnology, eds R. Baltz, A. Demain, J. Davies, A. Bull, B. Junker, L. Katz et al. (Washington, DC: ASM Press), 318–329.

**Conflict of Interest Statement:** 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.

Copyright © 2018 Alazi and Ram. 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.

# Analysis of the Transcriptome in Aspergillus tamarii During Enzymatic Degradation of Sugarcane Bagasse

Glaucia Emy Okida Midorikawa<sup>1</sup> , Camila Louly Correa<sup>1</sup> , Eliane Ferreira Noronha<sup>1</sup> , Edivaldo Ximenes Ferreira Filho<sup>1</sup> , Roberto Coiti Togawa<sup>2</sup> , Marcos Mota do Carmo Costa<sup>2</sup> , Orzenil Bonfim Silva-Junior <sup>2</sup> , Priscila Grynberg<sup>2</sup> and Robert Neil Gerard Miller <sup>1</sup> \*

<sup>1</sup> Departamento de Biologia Celular, Universidade de Brasília, Brasília, Brazil, <sup>2</sup> Embrapa Recursos Genéticos e Biotecnologia, Parque Estação Biológica, Brasília, Brazil

#### Edited by:

Roberto Silva, Universidade de São Paulo, Brazil

#### Reviewed by:

Baskar Gurunathan, St. Joseph's College of Engineering, India Ching Man Wai, Michigan State University, United States

> \*Correspondence: Robert Neil Gerard Miller<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Bioenergy and Biofuels, a section of the journal Frontiers in Bioengineering and Biotechnology

Received: 06 July 2018 Accepted: 20 August 2018 Published: 18 September 2018

#### Citation:

Midorikawa GEO, Correa CL, Noronha EF, Filho EXF, Togawa RC, Costa MMdC, Silva-Junior OB, Grynberg P and Miller RNG (2018) Analysis of the Transcriptome in Aspergillus tamarii During Enzymatic Degradation of Sugarcane Bagasse. Front. Bioeng. Biotechnol. 6:123. doi: 10.3389/fbioe.2018.00123 The production of bioethanol from non-food agricultural residues represents an alternative energy source to fossil fuels for incorporation into the world's economy. Within the context of bioconversion of plant biomass into renewable energy using improved enzymatic cocktails, Illumina RNA-seq transcriptome profiling was conducted on a strain of Aspergillus tamarii, efficient in biomass polysaccharide degradation, in order to identify genes encoding proteins involved in plant biomass saccharification. Enzyme production and gene expression was compared following growth in liquid and semi-solid culture with steam-exploded sugarcane bagasse (SB) (1% w/v) and glucose (1% w/v) employed as contrasting sole carbon sources. Enzyme production following growth in liquid minimum medium supplemented with SB resulted in 0.626 and 0.711 UI.mL−<sup>1</sup> xylanases after 24 and 48 h incubation, respectively. Transcriptome profiling revealed expression of over 7120 genes, with groups of genes modulated according to solid or semi-solid culture, as well as according to carbon source. Gene ontology analysis of genes expressed following SB hydrolysis revealed enrichment in xyloglucan metabolic process and xylan, pectin and glucan catabolic process, indicating up-regulation of genes involved in xylanase secretion. According to carbohydrate-active enzyme (CAZy) classification, 209 CAZyme-encoding genes were identified with significant differential expression on liquid or semi-solid SB, in comparison to equivalent growth on glucose as carbon source. Up-regulated CAZyme-encoding genes related to cellulases (CelA, CelB, CelC, CelD) and hemicellulases (XynG1, XynG2, XynF1, XylA, AxeA, arabinofuranosidase) showed up to a 10-fold log2FoldChange in expression levels. Five genes from the AA9 (GH61) family, related to lytic polysaccharide monooxygenase (LPMO), were also identified with significant expression up-regulation. The transcription factor gene XlnR, involved in induction of hemicellulases, showed up-regulation on liquid and semi-solid SB culture. Similarly, the gene ClrA, responsible for regulation of cellulases, showed increased expression on liquid SB culture. Over 150 potential transporter genes were also identified with increased expression on liquid and semi-solid SB culture. This first comprehensive analysis of the transcriptome of A. tamarii contributes to our understanding of genes and regulatory systems involved in cellulose and hemicellulose degradation in this fungus, offering potential for application in improved enzymatic cocktail development for plant biomass degradation in biorefinery applications.

Keywords: Aspergillus tamarii, transcriptome, carbohydrate-active enzymes, XlnR, ClrA, sugar transporters, lignocellulose, bioethanol

#### INTRODUCTION

The production of renewable energy is one of the greatest challenges of the twenty-first Century. Whilst dependency upon fossil fuels is associated with depleting oil reserves and greenhouse gas emissions, plant biomass, by contrast, with its' global abundancy, represents a sustainable and environmentally clean energy source (Goldemberg, 2007; Tan et al., 2016).

The production of bioethanol from non-food agricultural residues such as lignocellulosic trash, grasses and woods, is known as second-generation (2G) ethanol (Alvira et al., 2010), and is considered a promising alternative energy source to fossil fuels for incorporation into the world's economy. Brazil is currently one of the principal agricultural producers, as an important supplier of both food and industrial crops. Sugarcane is planted over an area of almost 9 million hectares, with an annual production of over 620 million tons (CONAB, 2018). Whilst around 45% of the crop production is employed for sugar extraction, the majority is used in the bioethanol industry, with estimates of production of 28 billion liters of anhydrous and hydrated ethanol for 2018/2019 (CONAB, 2018). Bioethanol production in Brazil is based almost exclusively on first-generation technologies, whereby the sucrose content of the plant is converted into ethanol. In this process, sugarcane bagasse will accumulate as an agricultural residue (Goldemberg, 2008). Whilst the burning of bagasse currently serves as an energy source in bioethanol mills, as this biomass represents approximately one-third of the energy content of the crop, the conversion of the lignocellulose component of the cell wall into fermentable hexose (glucose) and pentose (e.g., D-xylose and L-arabinose) sugars offers considerable potential for increased 2G ethanol production, potentially by up to 40% (Amorim et al., 2011). Two Brazilian cellulosic ethanol plants came into operation in 2014, with capacities planned for production of up to 1 billion liters of ethanol per year from bagasse (Silva et al., 2017).

For economically viable 2G ethanol production, complete hydrolysis, or saccharification, of plant biomass is required. Such plant material is composed mainly of polysaccharide crystalline microfibers of cellulose (40–50%), followed in abundance by a matrix of various hemicelluloses and pectins (25–35%), in addition to the polyaromatic lignin (15–20%) (Lin and Tanaka, 2006; Ragauskas et al., 2006; Jordan et al., 2012; Guerriero et al., 2016). Efficient biorefinary-based conversion of this material is hampered due to the recalcitrance of lignocelluloses (Chundawat et al., 2011). In the case of sugarcane bagasse, lignocellulose sugars vary in terms of identity and branching, comprising residues of glucose (60%), xylose (13%), arabinose (6%), mannose (3%), galactose (1,5%), and less than 1% fructose and rhamnose (Häkkinen et al., 2012).

As cellulases and hemicellulases remain costly, increasing the costs of 2G bioethanol production, a continued characterization of sources of such enzymes, together with an improved understanding of the mechanisms involved in enzyme secretion and enzyme efficiency are of fundamental importance for the biofuel industry. Hydrolytic enzymes appropriate for fermentation of available sugars in lignocelluloses are known to be secreted by a wide variety of bacteria and filamentous fungi, with the latter often producing not only a diverse array of extracellular lignocellulolitic enzymes, but also displaying efficiency in secretion of such enzymes in high quantities (Phitsuwan et al., 2013). For this reason, fungi are today the principal source of hydrolytic enzymes for this industrial application (Sims et al., 2010; Couturier et al., 2012).

Lignocellulolytic fungi typically produce extensive sets of carbohydrate-active enzymes (CAZymes) that correlate with their geographical origin habitat (Van Den Brink and De Vries, 2011). A number of ascomycete fungi, notably species members of the genera Trichoderma and Aspergillus, produce a range of cellulases and hemicellulases, which are today applied across numerous relevant industries for production of food, feed, paper, textiles and pharmaceuticals (Archer, 2000; de Souza et al., 2011). Whilst Aspergillus niger and Trichoderma reseei are currently employed in the production of commercial enzymatic cocktails for lignocellulosic biomass deconstruction (Singhania, 2011; Mohanram et al., 2013), the identification of additional sources of carbohydrate active enzymes will likely increase efficiency in the deconstruction of this biomass. As such, additional species have recently been screened as potential sources of cellulases, hemicellulases and accessory proteins for optimized industrial enzyme production (Brown et al., 2016; Cong et al., 2017; de Gouvêa et al., 2018).

The availability of whole genome sequences for fungi has improved understanding of fungal biodiversity with respect to plant cell wall degradation. Aspergillus nidulans, considered the model species of the genus given its' well-elucidated sexual cycle, possesses a genome sequence of 30.06 MB, with 9396 predicted genes (Galagan et al., 2005). Other characterized Aspergillus species of importance for the food, textile, pulp and paper industries, and potentially in 2G ethanol production, include A. oryzae and A. niger. A. oryzae has a total genome size of 37.12 MB, with 12336 predicted genes (Machida et al., 2005). Similarly, A. niger possesses a genome of 37.2 MB, with 14600 predicted genes (Pel et al., 2007). Comparison of gene sequences against the Carbohydrate-Active Enzymes Database (http://www.cazy. org/) (Cantarel et al., 2009) has revealed 186 genes related to polysaccharide hydrolysis in A. nidulans, 217 in A. oryzae and 171 in A. niger (Delmas et al., 2012).

In addition to gene discovery, the annotated genome sequences for these species serve as resources for analysis of the transcriptome in additional Aspergillus species without available genome sequences. Such analysis of transcriptional regulation of genes encoding hydrolytic enzymes in Aspergillus has been studied in relation to growth on different sugar carbon sources (Andersen et al., 2008; Jørgensen et al., 2009; Salazar et al., 2009). In relation to fermentation of sugarcane bagasse, microarray analysis provided information on gene expression modulation in A. niger (Guillemette et al., 2007; de Souza et al., 2011), with cellulases, hemicellulases and transporters identified with increased expression during growth on sugarcane bagasse in comparison to fructose. Subsequent RNAseq analysis of the A. niger transcriptome, following growth on wheat straw compared to simple sugars, revealed a CAZy gene representation change from 3% of total mRNA on 1% glucose to 19% on wheat straw, representing numerous enzymes from the classes of Glycoside Hydrolases (GH), Carbohydrate Esterases (CE), and Polysaccharide Lyases (PL) (Delmas et al., 2012). Further RNAseq-based analysis of gene expression in Aspergillus species following growth on sugarcane bagasse as carbon source has also revealed important information regarding regulatory mechanisms and genes encoding plant cell wall degrading enzymes, accessory proteins and transporters (Pullan et al., 2014; Brown et al., 2016; Borin et al., 2017; Cong et al., 2017; de Gouvêa et al., 2018).

The continued characterization of hydrolases, accessory proteins and the regulation of their expression in Aspergillus species that display efficiency in degradation of lignocellulose will further our understanding of their roles in saccharification. Given the importance of Aspergillus tamarii as an efficient producer of enzymes such as xylanases (El-Gindy et al., 2015; Monclaro et al., 2016), we utilized an Illumina RNA-seq approach to analyze the transcriptome in this fungus following semisolid and liquid cultivation on steam-exploded bagasse (SB) compared gene expression following growth on glucose (G). Genes encoding cellulases and hemicellulases, transcription factors and transporters are characterized in relation to their differential expression following fungal growth on each carbon source. Data will benefit the development of improved fungal strains with increased ability to deconstruct lignocellulose and generate value-added bioproducts.

#### MATERIALS AND METHODS

#### Strain and Culture Conditions

A stock culture of a strain of A. tamarii, code BLU37, was provided by the fungal culture collection at the Enzymology Laboratory, University of Brasilia, Brazil (genetic heritage number 010237/2015-1). The strain was originally isolated into pure culture from natural composting cotton textile waste material in the Vale do Itajaí, Santa Catarina, Brazil (Siqueira et al., 2009) and maintained in the culture collection at −80◦C in 50% glycerol.

Species identity reconfirmation was conducted by sequence analysis of the nuclear ribosomal DNA (rDNA) ITS1-5.8S-ITS2 region, together with specific regions of the β-tubulin and calmodulin genes. Genomic DNA was extracted according to Raeder and Broda (1985) from a 3 day old liquid culture in Czapek Yeast Extract medium (CYA) (Pitt and Hocking, 2009) incubated on an orbital shaker at 28◦C. Each PCR reaction contained 10 ng genomic DNA, 2,5 mmol−<sup>1</sup> of each primer, 1 mmol−<sup>1</sup> dNTPs, 4 mmol−<sup>1</sup> MgCl2, 1U of Taq Platinum <sup>R</sup> polymerase (Invitrogen) and 1 x Taq Platinum <sup>R</sup> polymerase buffer (Invitrogen). Ribosomal DNA ITS regions were amplified using primers ITS5 and ITS4 (White et al., 1990), a β-tubulin gene region with primers Bt2a and Bt2b (Glass and Donaldson, 1995), and a calmodulin gene region amplified with primers Cmd5 and Cmd6 (Hong et al., 2006). PCR cycling was performed with the following programs: initial denaturation at 94◦C for 4 min, 30 cycles of denaturation at 94◦C for 1 min, primer annealing for 1 min, at 50◦C for primers ITS5 and ITS4, and at 60◦C for primers Bt2a, Bt2b, Cmd5 and Cmd6, extension at 72◦C for 1 min, and a final extension period at 72◦C for 5 min. PCR products were purified using ExoSAP-IT <sup>R</sup> (USB, Cleveland, Ohio, USA) and sequenced using Big Dye <sup>R</sup> Terminator v3.1 Cycle Sequencing chemistry (Applied Biosystems, Foster City, CA, USA) on an ABI 3700 DNA sequencer (Applied Biosystems, Foster City, CA, USA). For molecular identification, sequences were compared against the nucleotide database NCBI using the BLASTn algorithm (Altschul et al., 1990). Ribosomal DNA ITS, β-tubulin and calmodulin gene sequences were deposited in GenBank under accession numbers MH540359, MH544272 and MH544273, respectively.

For analysis of gene expression in BLU37 following exposure to SB or glucose as carbon source, the strain was grown in either liquid or semi-solid minimal medium (KH2PO<sup>4</sup> 7 g; K2HPO<sup>4</sup> 2 g; MgSO<sup>4</sup> 0.4 g; (NH4)2SO<sup>4</sup> 1.6 g, pH 7.0, per liter of distilled water), containing SB (1% w/v) or glucose (1% w/v) (Sigma Aldrich) as exclusive carbon source. In order to guarantee elimination of reducing sugars, prior to fungal inoculation, SB was repeatedly washed with deionized water until reducing sugars were no longer detectable by the colorimetric dinitrosalicylic acid (DNS) assay (Miller, 1959). Liquid cultures were grown in 100 mL of media in Erlenmeyer flasks, whilst semi-solid cultures were grown on petri plates with media supplemented with agar (15 g L−<sup>1</sup> ). Fungal spores at a concentration of 1 × 10<sup>8</sup> conidiospores mL−<sup>1</sup> were used as inocula, with cultures then incubated at 28◦C and 150 rpm for 36 and 48 h. Fungal cultures were arranged in a randomized block design, with three replicates for each treatment and time point. Growth treatments were labeled as follows: liquid medium, SB carbon source, 36 h incubation (LB36); liquid medium, glucose carbon source, 36 h incubation (LG36); liquid medium, SB carbon source, 48 h incubation (LB48); liquid medium, glucose carbon source, 48 h incubation (LG48); semi-solid medium SB carbon source, 36 h incubation (SB36); semi-solid medium, glucose carbon source, 36 h incubation (SG36); semi-solid medium, SB carbon source, 48 h incubation (SB48); semi-solid medium, glucose carbon source, 48 h incubation (SG48).

#### Analysis of Enzymatic Activities

Analysis of hydrolytic enzyme secretion was conducted following fungal growth in the liquid minimal medium with SB (1% w/v) or glucose (1% w/v) as carbon source. Enzyme activities were evaluated over a 10 day period at 24 h intervals. Xylanase, CMCase, pectinase and FPase assays were determined using the DNS assay at pH 5.0. Each assay comprised 10 µl of the fungal secretome, together with xylan (1% w/v), carboxy methylcellulose (1% w/v), or pectin (1% w/v) as substrate. An assay for FPase activity, as a measurement of total cellulases, was conducted according to Ghose (1987). All assays were conducted with at least three replicates. Quantification of reduced sugars from the assays was conducted using a spectrophotometer at an absorbance of 540 nm (Spectra Max), calibrated using standard curves of glucose, xylose and galacturonic acid. Absorbance values for each assay were calculated in international units (UI), where 1 UI was defined as the amount of enzyme necessary to release 1 µmol of reducing sugars per minute per liter by hydrolysis of each crude substrate.

#### Vizualization of Fungal Colonization of SB by Scanning Electron Microscopy

Following 36 and 48 h mycelial growth, liquid minimum medium culture supplemented with SB was filtered with Whatman <sup>R</sup> filter paper n◦ 1 and washed with Karnovsky buffer (0.05M; pH 7.2). Samples were fixed for 4 h in a fresh solution of 0.05M cacodylate buffer at pH 7.4. Following dehydration with acetone, samples were postfixed for 1 h using 1% osmium tetroxide. Samples were washed in liquid CO<sup>2</sup> at 4◦C, dried in a critical point drier (Emitech K850, Kent, UK), mounted on copper stubs then sputter coated with 20 nm gold particles. Prepared samples were observed under scanning electron microscopy using a Zeiss DSM 962 scanning electron microscope.

#### Total RNA Extraction and Illumina RNA-seq

Following 36 and 48 h incubation, total RNA was extracted from each culture according to a standard phenol/chloroform method (Brasileiro et al., 2015). Mycelia from liquid media was collected by filtration with Whatman <sup>R</sup> filter paper n◦ 1, whilst from semi-solid cultures mycelia from the surface of each agar plate was collected manually using a sterilized spatula. Extracted total RNA was quantified and integrity determined using an Agilent 2100 Bioanalyzer and RNA LabChip <sup>R</sup> kit system (Agilent Technologies, Santa Clara, CA, USA). Isolation of mRNA, cDNA library construction and Illumina RNA-seq were conducted by Eurofins MWG Operon (Louisville, KY, USA). All treatments from the replicate bioassays were paired-end sequenced (2 × 100 bases) using TruSeq RNA Chemistry v3 on two flow cell channels of an Illumina Hiseq2000 system (Illumina Inc., San Diego CA, USA).

#### Bioinformatics Analysis Read Mapping and Assembly

Quality was determined for sequence reads from each cDNA library using ea-utils (Aronesty, 2011). For assignment of reads to gene models for the genus, high quality sequences (Fastq QC > 30) were mapped against the reference annotated genome sequence for the phylogenetically related species A. oryzae, strain RIB40 (National Research Institute of Brewing Stock Culture and ATCC-42149 (Machida et al., 2005), publically available at DOGAN (http://www.bio.nite.go.jp/dogan/Top). Alignment and assembly were conducted using the programs TopHat (Trapnell et al., 2009) and Cufflinks (Trapnell et al., 2010).

#### Analysis of Gene Expression Levels for Normalized Data

Genes with statistically significant differential expression between the evaluated growth conditions were identified on the basis of comparisons of in silico data. Read counts aligned to each gene were determined using the Python script HTseq-count (Anders et al., 2015). Differences in gene expression levels between treatments were calculated using the DEGseq program (Wang et al., 2010a). Differentially expressed genes (DEGs) between evaluated treatments were considered to be significant if a log2 fold change (FC) was at least ≥2-fold and at a probability level of p ≤ 0.01.

#### Gene Ontology and Analysis of Enrichment

Using the program FUNC (Prüfer et al., 2007), a hypergeometric test enabled analysis of both over- and under-representation of DEGs according to gene function classification within gene ontology categories (GO). Redundancy in category terms was eliminated using the program REVIGO (http://revigo.irb.hr/).

#### Carbohydrate-Active Enzymes

Identification of genes encoding hydrolytic enzymes was conducted through alignment against the Carbohydrate-Active Enzymes (CAZymes) database (http://www.cazy.org/) (Cantarel et al., 2009), to enable classification of glycosyl hydrolases, glycosyltransfereases, carbohydrate-binding modules and carbohydrate esterases.

#### Transcription Factors

Transcription factors were identified following alignment of gene sequences against the Fungal Transcription Factor Database (FTFD) (http://ftfd.snu.ac.kr/index.php?a=view).

### Validation of RNAseq-Derived DEGs by RT-qPCR

To validate DEGs identified on the basis of in silico transcriptome data, a RT-qPCR analysis was conducted according to MIQE guidelines (Bustin et al., 2009). All specific primers were designed with Primer Express <sup>R</sup> software (Applied Biosystems) and evaluated at OligoAnalyzer 3.1 – IDT (https://www. idtdna.com/calc/analyzer). All cDNA libraries were synthesized using the same RNA samples employed for RNAseq analysis. Three independent biological replicates were analyzed for each carbon source and time point treatment, with three technical replicates per amplification. Total RNA was treated with 2U of Amplification Grade DNase I (Invitrogen, Carlsbad, CA, USA) and cDNA was synthesized with Oligo(dT)20 primers (Invitrogen, Carlsbad, CA, USA) and SuperScript <sup>R</sup> II Reverse Transcriptase (Invitrogen, Carlsbad, CA, USA). PCR was carried on a Step One Plus Real Time PCR System (Applied Biosystems) using a Platinum <sup>R</sup> SYBR <sup>R</sup> Green qPCR Super Mix-UDG w/ROX kit (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's recommendations and 1 µL of template cDNA. Thermocycling was performed with 40 cycles of denaturation at 95◦C for 15 s, followed by primer annealing and extension at 60◦C for 30 s. GAPDH (Wang et al., 2010b) and β-tubulin (Mckelvey and Murphy, 2010) were employed as stable reference genes. Cycle threshold (Ct) values were determined using the program SDS 2.2.2 (Applied Biosystems, Foster City, USA), with specificity of PCR products for each primer set verified according to the Tm (dissociation) of the amplified products. Individual amplification efficiencies were calculated with the program LinRegPCR, version 2013.0, using a window-of-linearity. Gene expression values were calculated according to the 2-11CT method (Livak and Schmittgen, 2001).

### RESULTS

### Fungal Growth on Steam-Exploded Sugarcane Bagasse

Scanning electron microscopy-based observation of liquid minimal medium culture supplemented with SB following inoculation with A. tamarii conidia revealed evidence for degradation of sugarcane bagasse after incubation with the fungus (**Figure 1**). Homogeneous SB parenchyma fragments prior to inoculation can be observed in **Figure 1A**, with mycelial growth and resultant disruption of the parenchyma surface clearly visible in **Figures 1B,C**, following 36 and 48 h fungal colonization.

#### Evaluation of Enzyme Production

In order to gain an understanding of the genes and pathways involved in xylanase and cellulase induction following exposure to SB, analysis of enzyme secretion in A. tamarii BLU37 was firstly conducted over a 10 day period following growth on liquid minimal medium culture supplemented with SB, as well as on the same medium with a glucose carbon source as control (**Figure 2**). Xylanase activity was shown to rapidly increase during the first 24 h on SB (0.626 ± 0.001), in contrast to growth on glucose (0.000 ± 0.004), with activity remaining relatively constant over the 10 day period. The activities of CMCases, pectinases and FPases at 24 h on SB were inferior in comparison to xylanases (0.096 ± 0.002, 0.039 ± 0.004, and 0.159 ± 0.037, respectively), although they were clearly induced by SB when compared to values for these enzymes following growth in glucose (0.000 ± 0.002, 0.006 ± 0.007, and 0.000 ± 0.088, respectively). Again, the expression of these enzymes remained relatively constant throughout the 10 day evaluation time course. Given the kinetics of xylanase production on SB, together with evidence for carbon catabolic repression during the first 48 h of growth on glucose, 36 and 48 h were selected as time points for analysis of the

FIGURE 1 | Scanning electron microscopy images of (A) non-inoculated steam-exploded sugarcane bagasse parenchyma in liquid minimal medium culture; (B) steam-exploded sugarcane bagasse parenchyma in liquid minimal medium culture following 36 h incubation with A. tamarii BLU37; (C) steam-exploded sugarcane bagasse parenchyma in liquid minimal medium culture following 48 h incubation with A. tamarii BLU37. Bars = 100µm; 200X magnification.

transcriptome of A. tamarii BLU37 in response to SB or glucose as carbon source.

#### Transcriptome of Aspergillus Tamarii BLU37 in Response to Sugarcane Bagasse Sequence Metrics

Illumina Hiseq2000-sequenced cDNA libraries resulted in a total of 634,255,527 reads with lengths between 75 and 85 bp, totaling 83.82 Gb of raw data. Illumina quality filtering indicated no information loss on the basis of passfilter data. High quality sequences (Fastq QC>30) averaged 80.34% after adapter trimming (**Supplementary Table 1**). The total of paired and unpaired reads mapping to the reference genome A. oryzae RIB40 was 94 and 84%, respectively. An indel percentage of approximately 0.002% was observed in quality filtered sequences, with a low total of chimeric reads, at 0.000949%. All Illumina RNAseq data was deposited at the NCBI Sequence Read Archive (SRA) database (BioProject ID PRJNA479954, SRA accession: SRP152413).

#### Gene Expression Modulation

Sequence data generated from each of the cDNA libraries successfully aligned to over 7120 of the 12074 gene models in the reference A. oryzae RIB40 genome. Such homogeneity in characterized genes across cDNA libraries indicates a high quality and coverage of sequenced mRNA.

Analysis of fold change in gene expression was conducted on read counts aligned to each gene model in the reference A. oryzae RIB40 genome. DEGs with significant fold change (at least ≥2-fold and at a probability level of p ≤ 0.01) between treatments were identified through comparison of mapped read counts for genes expressed in cultures grown on SB as carbon source, in contrast read counts following growth on respective glucose controls. Differential gene expression analysis was also conducted in relation to culture

format (liquid or semi-solid) and growth period (36 or 48 h).

A global heatmap representation of gene expression profiles in A. tamarii BLU37 following growth on SB in comparison to glucose revealed considerable modulation of gene expression following growth on this source of plant cell wall polysaccharides (**Figure 3**). Modulated genes on SB, in comparison to glucose, that grouped exclusively according to culture format (liquid or semi-solid culture), and independent of the growth period, can be observed in **Figure 3**, region 1. Modulated genes after growth on SB were also identified that grouped according to both culture format and growth period (**Figure 3**, region 2), according to SB as carbon source, independent of culture format or growth period (**Figure 3**, region 3), or exclusively according to growth period (**Figure 3**, region 4). Comparison of expression levels in the predicted genes identified in each of the treatments, whether in growth media supplemented with SB or glucose, revealed that gene expression in A. tamarii can also be influenced exclusively according to growth period or culture format, regardless of carbon source (**Supplementary Figure 1**).

Numerous genes were detected with significant expression fold change on SB as sole carbon source in comparison to glucose at an equivalent growth time point. For LB36, a total of 621 DEGs were detected with significant expression up-regulation, plus a total of 919 DEGs showing down-regulation. For LB48, numbers were greater, with 1187 DEGs up-regulated and 1229 down-regulated. The overall number of DEGs observed following growth on semi-solid media was generally lower than observed on liquid media. For SB36, a total of 755 DEGs were significantly up-regulated, with 449 down-regulated. Similarly for SB48, a total of 914 DEGs were detected as up-regulated and 445 as down-regulated.

#### Gene Ontology Enrichment Analysis of Differentially Expressed Genes

Differentially expressed genes were analyzed for over- and under-representation according to gene function classification within gene ontology categories (GO) (**Figure 4**). The majority of annotations for DEGs were classified in biological process subcategories, followed by molecular function and cellular component. Biological process GO terms enriched in DEGs up-regulated following SB hydrolysis included those involved in xylan (GO:0045493), pectin (GO:0045490) and glucan (GO:0009251) catabolic process, with enrichment of up to 4.2 times in all treatments supplemented with SB. Xyloglucan metabolic process (GO:0010411) was the most pronounced of such GO terms, with an enrichment of 8.4 times for the treatment SB48. By contrast, glycolytic process (GO:0006096), glucose catabolic process (GO:0006007), fatty acid (GO:0006633) and ergosterol biosynthetic process (GO:0006696) were abundant terms for down-regulated DEGs following growth in SB-supplemented treatments, indicating that genes classified under these GO terms are more highly expressed in the treatments supplemented with glucose. With regard to molecular function, subcategories cellulase (GO:0008810), galactosidase (GO:0015925), pectate lyase (GO:0030570), alpha-L-arabinofuranosidase (GO:0046556), carboxylic ester hydrolase (GO:0052689) and glucosidase activity (GO:0015926) were all enriched in up-regulated DEGs following growth on SB-supplemented treatments, providing further evidence for A. tamarii as a promising fungal species for secretion of cellulases and hemicellulases.

#### CAZyme-Encoding Genes

With regard to plant cell wall degradation in SB, global analysis of gene expression following fungal growth in the four treatments LB36, LB48, SB36, and SB48 revealed the presence of a total of 311, 314, 337, and 324 expressed CAZyme-encoding genes, respectively, after growth in treatments with this complex carbon source. Differential expression modulation in relation to equivalent treatments with glucose is summarized in **Figure 5**. A. tamarii BLU37 clearly expressed important genes related to SB degradation, with a total of 209 CAZy-encoding genes with statistically significant differential expression on SB, either in liquid or semi-solid treatments, in comparison to equivalent growth treatments with glucose as carbon source (**Table 1**; **Supplementary Table 2**). In terms of those with significant modulation of gene expression, genes encoding GH family proteins were the most abundant, with a total of 141 DEGs observed, followed by 15 DEGs encoding CEs, 15 DEGs encoding PLs, four DEGs encoding AA proteins and 32 DEGs encoding

GTs. A Venn representation of total numbers of CAZymeencoding DEGs in treatments LB36, LB48, SB36 and SB48 revealed 50 genes that were common to all four treatments and likely representing those essential in hydrolysis of the complex carbon source SB (**Figure 6**).

DEGs involved in cellulose depolymerization included β-1,4-endoglucanases, cellobiohydrolases, β-glucosidases, and lytic polysaccharide mono-oxygenases (LPMO). Genes with expression modulation of up to 10-fold were also observed amongst the DEGs, including numerous encoding hemicellulases related to xylan depolymerization such as xylanase, endoxylanase, endo-β-1,4-xylanase, α-Larabinofuranosidase, arabinofuranosidase, and feruloyl esterase. Genes related to pectin depolymerization were also highly expressed, such as for those encoding polygalacturonase, pectinesterase, pectin and pectate lyase. A total of 40 CAZyme-encoding DEGs were exclusive to specific treatments with sugarcane. Interestingly, most were expressed positively in the treatment SB36 and were related to pectin hydrolysis. These included, for example, orthologos for genes encoding a polygalacturonase (AO090009000470), a rhamnogalacturonan hydrolase (AO090102000139), a pectin lyase (AO090010000030), and a β-glucosidase (AO090701000841).

#### Differentially Expressed Transcription Factor and Transporter-Encoding Genes

A total of 80 transcription factor genes were differentially expressed in A. tamarii BLU37 following growth on sugarcane in comparison with equivalent treatments with glucose (padj < 0.01) (**Supplementary Table 3**). These comprised genes with transcription factor activity domains in families such as Zn2/Cys6 DNA-binding domain, Homeodomain, C2H2 zinc fingers, helix-loop-helix DNA-binding domain, GATA zinc finger and Basic-leucine zipper (bZIP). An ortholog for the

Transcription Factor-encoding genes; Gray dots, un-annotated genes.

transcription factor gene XlnR (AO090012000267), known to be responsible for activation of genes involved in xylan and cellulose degradation, showed an increased expression of up to 1-fold in LB48 and 1.46-fold in SB36. Additionally, the XlnR gene ortholog AO090003001292, showed increased expression of 2.06-fold in LB48 and 1.75-fold in SB48. Similarly, an ortholog of the ClrA transcription factor gene AO090011000944, which is positively regulated by XlnR, was differentially modulated 2.33-fold in LB36, 2.34-fold in LB48 and 2.07-fold in SB36. Two transcription factors controlling pectinase-encoding genes, namely RhaR (AO090005000121) and AraR (AO090003001292), were also up-regulated, 1.80 fold in LB48, 2.60-fold in SB36, 2.05-fold in LB48 and 1.74 in SB48. Interestingly, an ortholog of the BlrA gene (AO090005001041), a DNA binding transcription factor involved in asexual sporulation in fungi, displayed an early up-regulation in the treatments on sugarcane bagasse, with a 1.11-fold increase in expression in LB36 followed by a 0.73-fold decrease in LB48, together with a 7.40-fold increase in expression in SB36 and subsequent 1.70-fold decrease in gene expression in SB48. Transcription factor genes that are known to act in repression of cellulases and hemicellulases, such as the zincfinger carbon catabolite repressor transcription factor CreA (AO090026000464) (Ruijter and Visser, 1997), together with Ace1 (AO090005001502) and Ace2 (AO090003000678), whilst observed amongst the unigenes identified in the A. tamarii transcriptome, were not significantly differentially expressed on sugarcane bagasse in comparison to growth on glucose as carbon source.

A total of 155 transporter genes were differentially expressed across the four treatments on sugarcane, in comparison to glucose (**Supplementary Table 4**). Highly expressed genes related to sugar transport were, in the majority, those known to be positively regulated by the transcription factor XlnR and coding for Major Facilitator Superfamily (MFS) proteins. Orthologs of the genes AO090003000782, AO090001000069, and AO090003001277 were the most positively differentially expressed of such genes in all treatments on sugarcane, in comparison to glucose. The putative D-xylose transmembrane transporter XtrD (AO090001000069) was also highly expressed on sugarcane bagasse (log2FC 5.02 in LB36, 5.57 in LB48, 7.16 in SB36 and 4.68 in SB48), as was the putative cellobiose transporter cdt-2 gene ortholog (AO090003001277), expressed at log2FC levels 6.53 in LB36, 9.10 in LB48, 8.48 in SB36 and 6.07 in SB48.

#### Validation of RNAseq Analysis by RT-qPCR

In order to validate Illumina RNAseq-derived gene expression data, a total of 14 highly expressed CAZyme-encoding genes were selected for expression profile analysis by RT-qPCR. These gene orthologs, which are known to be positively regulated by XlnR, comprised cellobiohydrolases (AO090001000348, AO090012000941, AO090038000439), endoxylanases (AO090103000423, AO090120000026, AO090001000111), feruloyl esterase (AO090701000884), pectinesterase (AO090102000010), polygalacturonase (AO090102000011), arabinofuranosidase (AO090103000120), β-xylosidase (AO090005000986), exoarabinase (AO090011000141), endoglucanase (AO090026000102), and a Lytic Polysaccharide Mono-Oxygenase (LPMO) (AO090023000787). Both RNAseq and RT-qPCR gene expression values are represented in log2FoldChange, showing broad agreement in expression pattern tendencies to up- or down-regulation in each of the four treatment comparisons (**Figure 7**). Primer sequences for target genes selected for validation via RT-qPCR are available in **Supplementary Table 5**.

## DISCUSSION

Efficient 2G bioethanol production systems require the complete hydrolysis of hexose and pentose sugars present in plant biomass. In this study, a global analysis of the transcriptome revealed considerable ligninolytic potential in the ascomycete fungus A. tamarii BLU37. With an abundance of expressed CAZyme-encoding genes, data on the transcriptional response to contrasting carbon sources of sugarcane bagasse and glucose both increase understanding of the mechanisms involved in enzyme secretion and efficiency, and serve as a resource for exploitation of genes encoding transcription factors, transporters and enzymes involved in the degradation of plant biomass for engineering of improved microorganisms.

As plant pathogens or saprophytes, many filamentous fungi are efficient in hydrolytic extracellular enzyme secretion for plant biomass depolymerization. A number of species belonging to the genus Aspergillus are efficient in secretion of a large repertoire of glycosyl hydrolases appropriate for industrial application in lignocellulosic biomass degradation (Machida et al., 2005; Duarte et al., 2012; Jaramillo et al., 2013; Pirota et al., 2014). The strain examined in this study was originally isolated from composting cotton textile waste material (Siqueira et al., 2009). Typically, this species is associated with plant biomass degradation as a spoilage fungus, with reports of isolation from food and feed products (Rodrigues et al., 2011; Midorikawa et al., 2014; Martins et al., 2017; Prencipe et al., 2018). Previous studies have also highlighted the potential in this species in depolymerization of xylan, through secretion of β-1,4-endoxylanases (Gouda and Abdel-Naby, 2002; da Silva et al., 2014). Efficient secretion of such xylanases has also been reported in the strain A. tamarii BLU37 (Duarte et al., 2012; Monclaro et al., 2016). Whilst the transcriptome in different Aspergillus species during degradation of plant biomass has been the subject of recent investigation (de Souza et al., 2011; Delmas et al., 2012; Pullan et al., 2014; van Munster et al., 2014; Miao et al., 2015; Borin et al., 2017), despite the clear potential of A. tamarii as a xylanolytic species, there has been no investigation, prior to the current study, of the transcriptome of this species during saccharification of plant biomass.

The A. tamarii BLU37 transcriptome in response to SB was mapped to the annotated reference A. oryzae RIB40 genome, with the total number of expressed genes detected (7126 genes) similar to that previously observed in A. niger (7359 genes) when grown on this same lignocellulosic substrate as sole carbon source (Borin et al., 2017). Global analysis of gene expression revealed DEGs not only induced or repressed according to culture treatments with SB or C, but also according to semi-solid versus liquid culture (**Supplementary Figure 1**). Culture format has previously also been reported to influence enzyme secretion in Aspergillus species, with greater protein production in A. oryzae following solid-state fermentation (Wang et al., 2010b). Similarly, in the case of A. niger, solid-state fermentation of pretreated sugarcane bagasse was reported to favor endoglucanase and xylanase secretion, with submerged fermentation favoring βglucosidase production (Vasconcellos et al., 2015).

Enzymes involved in the degradation of plant cell wall polysaccharides are distributed in different classes of glycosidases based on their primary amino acid sequence and related catalytic modules. Currently, these enzymes are classified as carbohydrate esterases (CEs), polysaccharide lyases (PLs), glycoside hydrolases (GHs), glycosyltransferases (GTs), as enzymes with the auxiliary activities (AAs), and as carbohydratebinding modules (CBMs). As lignocellulose deconstruction requires the activities of a large number of these CAZymes, analysis of DEGs included a comparison of CAZyme-encoding genes. Considering that Aspergillus sp. are able to colonize a variety of plant biomass polysaccharides, and given that sugarcane bagasse has been shown to comprise approximately 35% cellulose, 24% hemicellulose and 22% lignin (Rezende et al., 2011), it was expected that A. tamarii BLU37 would secrete many enzymes from different CAZy families when growing on SB as carbon source. The majority of the CAZyme-encoding genes observed amongst DEGs on SB were those encoding proteins classified across 36 GH families. The total of 141 GH family DEGs reflects the similar numbers identified in the genomes of A. oryzae, A. terreus, A. niger and A. nidulans, with respective totals of 194, 186, 157, and 172 predicted GH family CAZyme-encoding genes. Greatest numbers of genes with differential expression on SB included those from GH families 3, 5, 31, 43, and 92. CAZyme-encoding DEGs classified in CE families (15 genes) and PL families (15 genes) were also similar in numbers to those observed in the A. oryzae genome, where 14 CE and 20 PL genes have been annotated (Benoit et al., 2015). DEGs encoding GTs were generally less expressed on SB in comparison to glucose, indicating a probable lack of involvement in degradation of this biomass. GTs represent a diverse family of enzymes that function in the cell in many activities relating to structure, storage and signaling. Numerous GTs can transfer sugar residues from an activated sugar donor residue to specific acceptor molecules, TABLE 1 | CAZyme-encoding genes with significant increased expression after growth in treatments with steam exploded bagasse, in comparison with glucose.

down-regulated in each treatment.

resulting in the formation of glycosidic bonds. As such, this family of enzymes can enable the biosynthesis of an infinite number of oligosaccharides, polysaccharides and glycoconjugates (Taniguchi et al., 2002; Coutinho et al., 2003). As such, in contrast to sugarcane bagasse, where enzyme activity is involved in the degradation of plant cell wall polysaccharides, an increased expression of GTs on glucose may be expected, likely resulting in the biosynthesis of oligosaccharides and polysaccharides required for fungal metabolism. Such observations have also been reported in A. fumigatus on different lignocellulosic biomass sources (Miao et al., 2015; de Gouvêa et al., 2018), with many such GTs involved in the biosynthesis of fungal cell wall chitins and ergosterol glycosylation (Klutts et al., 2006; Castell-Miller et al., 2016). Additional DEGs likely to be not involved in the plant cell wall degradation included six GH18 putative chitinases, with activity likely associated with cell wall dynamics during fungal growth, as also observed in the ascomycete fungus Malbranchea cinnamomea (Hüttner et al., 2017).

Genes related to cellulose and hemicellulose deconstruction were highly expressed during liquid and semi-solid culture on SB (**Supplementary Table 2**), including cellobiohydrolases (CBHs) (celC - AO090001000348, celD - AO090012000941 and chbC - AO090038000439), endoglucanases (EGs) (celA - AO090026000102, celB - AO090010000314 and eglA - AO090005001553), and β-glucosidases (BGLs) (bgl3 - AO090003000497, bglD - AO090701000274 and bgl5 - AO090001000544). Two putative β-glucosidases (AO090701000244 and AO090005000337) and one putative exoglucanase (AO090005000423) were also observed with high levels of expression during liquid and semi-solid culture. In the case of the CAZy family AA9, classified as a member of the copper-dependent lytic polysaccharide monooxygenases (LPMOs) and responsible for cellulose cleavage through an oxidative reaction, two AA9 genes (AO090023000787 and AO090023000159) and two AA9 genes linked by a carbohydrate-binding module (CBM1) (AO090103000087 and AO090005000531) were observed amongst the highly expressed DEGs on SB, as also reported in A. niger (Borin et al., 2017). Cellulose-binding CBMs are frequently observed amongst fungal enzymes, playing roles in anchoring enzymes to crystalline cellulose (Igarashi et al., 2009), conferring enzyme and substrate specificity and enhancing enzyme activity (Crouch et al., 2016). CBM1s have been estimated to represent over 30% of CBMs in Ascomycete and Basidiomycetes (Várnai et al., 2014).

Whilst sugarcane bagasse hemicellulose and pectin have structures composed of xylan, galactan and arabinan polymers, many of the highly expressed DEGs from liquid and semisolid cultures grown on sugarcane bagasse were related to xylan saccharification, corroborating enzyme activity data and previous studies on A. tamarii. The endoxylanases (xynG1 - AO090001000111, xynF1 - AO090103000423) and xylanase (xynG2 - AO090120000026), for example, were upregulated more than 10-fold in SB. Xylosidases involved in xylooligomer hydrolysis into xylose, such as α-xylosidases (AO090005000768 and AO090005000767) and β-xylosidases (xylA - AO090005000986 and xylB - AO090005000698), also showed high expression levels.

Genes that encode accessory enzymes involved in galactan breakdown, such as β-galactosidase and α-galactosidase (AO090012000135 and AO090011000063), as well as those involved in xylan side chain removal, such as arabinofuranosidases (AO090701000886 and AO090701000885) and feruloyl esterases (AO090701000884 and AO090023000158), were all also highly expressed in liquid and semi-solid SB culture.

Fungi from the genus Aspergillus possess a variety of genes encoding enzymes related to pectin degradation. A. niger possesses over 60 such genes and A. oryzae over 90 genes encoding for pectinolytic enzymes (Coutinho et al., 2009; Martens-Uzunova and Schaap, 2009). In our study, we detected numerous pectin-related DEGs with increased expression on SB. Genes with increased expression on semi-solid SB included a number from the PL1 family (AO090010000087, AO090011000673, and AO090102000072), the PL9 family (AO090038000131), pectinase-encoding genes from the GH28 family (exopolygalacturonase - AO090005001400; polygalacturonase - AO090009000470 and AO090138000086; and rhamnogalacturonan hydrolase - AO090102000139), as well as one gene from the PL1 family, a pectin lyase (AO090010000030). In contrast, a number of PL1 family DEGs displayed increased expression exclusively in liquid SB culture, namely AO090012000451, AO090701000321, and AO090010000706. Such data reveal that culture format of this carbon source, in addition to cultivation time, can influence gene expression. Although pectin is less abundant in sugarcane bagasse in comparison to xylan, in terms of chemical composition (de Souza et al., 2013), the presence of PL family members amongst the DEGs also indicates pectin breakdown in SB. Similar increased expression has previously been observed in

PL4 family member genes in A. niger and A. fumigatus grown on SB (Borin et al., 2017; de Gouvêa et al., 2018).

Transcription factors perform major roles in the regulation of gene expression. Our data revealed a total of 80 differentially expressed transcription factor genes, from the families Zn2/Cys6 DNA-binding domain, Homeodomain-like, C2H2 zinc fingers, helix-loop-helix DNA-binding domain, GATA zinc finger and Basic-leucine zipper (bZIP) transcription factor. Amongst the DEGs, a total of 42 transcription factors were identified, from Zn clusters, a family commonly found across fungal species (Shelest, 2017). Of these, the transcription factor XlnR is known to play an important role in xylanolytic transcriptional activation in fungi (van Peij et al., 1998), controlling expression of a wide range of genes, including those encoding xylanases and enzymes in the D-xylose metabolic pathway (Hasper et al., 2000; de Groot et al., 2007). This TF is also involved in regulation of expression of genes encoding endocellulases (Gielkens et al., 1999). In the case of Aspergilli specifically, XlnR is known, in the presence of D-xylose, to induce xylanases, βxylosidases, cellobiohydrolases, endoglucanases, galactosidases, arabinofuranosidases and carbohydrate esterases (Mach-Aigner et al., 2012). In this study, XlnR was up-regulated according to SB substrate, as observed previously in A. niger in response to SB, over a similar time course (Borin et al., 2017), as well as in A. fumigatus on rice straw (Miao et al., 2015). Up-regulation was also observed in the ClrA gene following growth in LB36 and LB48. Together with XlnR, this gene is also known to act positively in cellulase production in A. niger (Raulo et al., 2016). Two transcription factors controlling pectinase-encoding genes, namely RhaR and AraR were also up-regulated in LB and SB. The major pectinase-encoding genes in A. niger have been shown to be under the control of the transcription factors RhaR, AraR, and GaaR (Kowalczyk et al., 2017), suggesting that the up-regulation of the RhaR and AraR observed in our study, also indicates a role in induction of pectinase-encoding gene expression in A. tamarii, releasing lignin associated with pectin in the sugarcane bagasse cell wall.