Open Access

Comparative metabolism of cellulose, sophorose and glucose in Trichoderma reeseiusing high-throughput genomic and proteomic analyses

  • Lilian dos Santos Castro1,
  • Wellington Ramos Pedersoli1,
  • Amanda Cristina Campos Antoniêto1,
  • Andrei Stecca Steindorff2,
  • Rafael Silva-Rocha1,
  • Nilce M Martinez-Rossi3,
  • Antonio Rossi1,
  • Neil Andrew Brown4,
  • Gustavo H Goldman4,
  • Vitor M Faça1,
  • Gabriela F Persinoti3 and
  • Roberto Nascimento Silva1Email author
Contributed equally
Biotechnology for Biofuels20147:41

DOI: 10.1186/1754-6834-7-41

Received: 14 January 2014

Accepted: 26 February 2014

Published: 21 March 2014



The filamentous fungus Trichoderma reesei is a major producer of lignocellulolytic enzymes utilized by bioethanol industries. However, to achieve low cost second generation bioethanol production on an industrial scale an efficient mix of hydrolytic enzymes is required for the deconstruction of plant biomass. In this study, we investigated the molecular basis for lignocellulose-degrading enzyme production T. reesei during growth in cellulose, sophorose, and glucose.


We examined and compared the transcriptome and differential secretome (2D-DIGE) of T. reesei grown in cellulose, sophorose, or glucose as the sole carbon sources. By applying a stringent cut-off threshold 2,060 genes were identified as being differentially expressed in at least one of the respective carbon source comparisons. Hierarchical clustering of the differentially expressed genes identified three possible regulons, representing 123 genes controlled by cellulose, 154 genes controlled by sophorose and 402 genes controlled by glucose. Gene regulatory network analyses of the 692 genes differentially expressed between cellulose and sophorose, identified only 75 and 107 genes as being specific to growth in sophorose and cellulose, respectively. 2D-DIGE analyses identified 30 proteins exclusive to sophorose and 37 exclusive to cellulose. A correlation of 70.17% was obtained between transcription and secreted protein profiles.


Our data revealed new players in cellulose degradation such as accessory proteins with non-catalytic functions secreted in different carbon sources, transporters, transcription factors, and CAZymes, that specifically respond in response to either cellulose or sophorose.


Trichoderma reesei RNA-seq DIGE Cellulases Bioethanol


The growing worldwide demand for energy and the desire to reduce dependency on finite fossil fuels has increased interest in alternative energy sources, especially liquid biofuels such as bioethanol and biodiesel. Ethanol obtained from lignocellulosic, non-food, feedstocks (for example, sugarcane bagasse or wheat straw) represents an attractive alternative due to its applicability in existing motor vehicles. In addition, the combustion of lignocellulosic-derived ethanol is considered cleaner than oil-based fuels [1]. Lignocellulosic biomass locks away approximately half of the energy produced by plants during photosynthesis and is the most abundant renewable organic carbon resource on Earth. Lignocellulose predominately consists of three polymers that are tightly interlinked, cellulose, hemicellulose and lignin, which correspond to approximately 98% of lignocellulose dry weight [2].

The production of fuel ethanol from lignocellulose requires biomass pretreatment, cellulose hydrolysis, hexose fermentation, separation, effluent treatment, and depending on the raw material, additional costs may occur [3]. In recent years, new technologies have been developed for the pretreatment of sugarcane bagasse such as the application of novel enzymes to increase the saccharification of cellulose/hemicellulose and specialized fermentation technologies, aiding in the development of second-generation (2G) bioethanol [4].

The filamentous fungus Trichoderma reesei is one of the main producers of cellulases and hemicellulases used in industrial scale [5] and is especially important for the production of 2G biofuels from lignocellulose [6]. Despite T. reesei being the most prominent lignocellulosic degrader among the genus Trichoderma, this species has a reduced number of cellulolytic enzymes compared to other lignocellulosic fungi [7]. This ability is attributed to T. reesei possessing efficient systems for the transport of nutrients and the induction/secretion of cellulases. Subsequently, the study of the cellulolytic system in T. reesei is of substantial interest to industrial biotechnology.

The T. reesei cellulolytic system consists of at least three different types of enzymes: exoglucanases (cellobiohydrolases EC, endoglucanases (EC and β-glucosidases (EC, which occur in various isoforms [8]. T. reesei produces at least two exo-acting cellobiohydrolases (CEL7A and CEL6A), five endo-acting cellulases (CEL7B, CEL5A, CEL5B, CEL12A, CEL45A), two characterized β-glucosidases (CEL3A and CEL1A), and an additional five predicted β-glucosidases (CEL3B, CEL3D, CEL1B, CEL3C, CEL3E) [9]. Besides the classic cellulases, new players involved in cellulose degradation were recently described in T. reesei, such as the expansin-like proteins swollenin (SWOI) and expansin/family 45 endoglucanase-like (EEL1, EEL2, and EEL3) [7, 10]. In addition, GH61 polysaccharide monooxygenases (PMOs), which were recently re-annotated as AA9 (Auxiliary family activity 9,, have been shown to enhance lignocellulose degradation by an oxidative mechanism [9].

The production of the main cellulases by T. reesei is controlled by a sophisticated regulation system that avoids energy expenditure on unrequired processes when readily metabolisable carbon sources are present [6]. Since the 1960s when Mary Mandels and Elwyn T. Reese [11], raised the question ‘Cellulases are adaptive enzymes, but the natural substrate – cellulose – is insoluble. So how does induction occur?’ many studies have been conducted in an attempt to discover the natural inducer of cellulase formation [1214]. It is now known that expression of cellulolytic genes in T. reesei are induced in the presence of cellulose and several disaccharides such as cellobiose (β-D-glucopyranosyl-(1 → 4)-β-D-glucopyranose), δ-cellobiono-1,5-lactone (β-D-glucopyranosyl-(1 → 4)-D-glucono-1,5-lactone), lactose (β-D-galactopyranosyl-(1 → 4)-D-glucose) and sophorose (2-O-β-D-glucopyranosyl-α-D-glucose)[15]. Sophorose is the strongest cellulase inducer and is considered to be a possible natural inducer. It is assumed that sophorose is formed by T. reesei during cellulose hydrolysis by a transglycosylation reaction [16]. However, additional low-molecular weight compounds have been reported to promote cellulase gene expression, such as l-arabitol and l-sorbose [17]. In contrast, the presence of easily metabolisable carbon sources such as glucose and fructose, represses the expression of cellulolytic genes [18].

The regulation of cellulase gene expression occurs at the transcriptional level in a coordinated manner and is dependent on the presence of the inducer [19]. This regulation is driven by specific transcriptional factors (TFs) that bind to cellulase gene promoters acting either in a positive or a negative way. So far, at least three transcriptional activators XYR1, ACE2, the HAP2/3/5 complex, as well as the two repressors CRE1 and ACE1 are involved in the regulation of cellulase gene expression in T. reesei[20].

Despite extensive studies attempting to answer the question raised by Mandels and Reese, neither the nature of the inducer nor how T. reesei senses the inducer and relays the cellulase induction signal, have been elucidated. In this study we report a comparison of the transcriptome (RNAseq) and secretome (two-dimensional Fluorescence Difference Gel Electrophoresis (2-D DIGE)) of T. reesei grown on cellulose, sophorose and glucose, in attempt to understand the molecular basis of lignocellulose-degrading enzyme induction. Our results provide new insights and revealed new players in cellulose degradation such as proteins with non-catalytic functions secreted in different carbon sources, transporters, transcription factors, carbohydrate active enzyme (CAZymes), and the regulatory network of T. reesei in response to cellulose and sophorose. These data will contribute to the development of industrial T. reesei strains by engineering its metabolism to produce high levels of cellulases for plant cell-wall degradation.


Global gene expression profiles of Trichoderma reeseigrown in three different carbon sources

T. reesei QM9414 was grown directly in three different carbon sources; glucose, sophorose, and cellulose as described (see Methods). We previously demonstrated the growth profiles and glucose consumption of T. reesei QM9414 in the presence of cellulose and glucose [21]. Based on these data, we designed the strategy to pool the time points of each condition before the sequencing. Nine barcoded libraries were sequenced using the Illumina Hiseq 2000 System, generating approximately 117 million 100-bp paired-end reads corresponding to 23.32 GB of sequence data (Additional file 1: Table S2). Reads were mapped to the T. reesei QM6a reference genome available from JGI (Trichoderma reesei v2.0) using the Bowtie aligner. Overall, 68% of reads mapped to the reference genome (Additional file 1: Table S2). There was a high correlation (Pearson correlation, r2 ≥ 0.71) between the three biological replicates of each condition used in the transcriptional analysis (Additional file 2: Figure S1A-B). After sample normalization, boxplots were constructed in order to determine if the conditions are comparable and the results are shown (see Additional file 2, Figure S1 C-D). The boxplots showed that both normalized samples and raw data displayed the same plot profile and no significant statistical difference (P <0.05), demonstrating that the samples are comparable.

The T. reesei gene expression profiles obtained from the different carbon sources were analyzed using R Bioconductor DESeq. Of the 9,129 genes encoded by the T. reesei genome, 1,788 genes were identified as being differentially expressed (P <0.05) on glucose/cellulose, 2,545 genes on sophorose/cellulose and 2,481 genes on sophorose/glucose (Figure 1A-C).
Figure 1

Comparison of full-genome expression profiles of QM9414 strain grown in cellulose, sophorose, and glucose as the carbon source, measured by RNA-seq. (A) Cellulose/glucose. (B) Cellulose/sophorose. (C) Sophorose/glucose. Differentially expressed genes identified by DESeq package are plotted in red (P <0.05).

Appling a two-fold change (that is, log2 fold change ≥1 or ≤ -1) and an adjusted P-value ≤0.05 as a threshold, 2,060 genes were identified as differentially expressed in at least one of the respective carbon source comparisons. Figure 2A shows that 1,886 genes were differentially expressed in glucose and in cellulose, as represented by 703 and 491 genes being up- and downregulated exclusively in glucose, and 254 and 102 genes being up- and downregulated exclusively in cellulose, respectively. On the other hand, 1,889 genes were differentially expressed in sophorose and in cellulose, with 321 and 405 being up- or downregulated in sophorose, and 262 and 97 being up- or downregulated in cellulose, respectively (Figure 2B). Yet, in glucose and sophorose 1,670 genes were modulated, with 262 and 245 being up- or downregulated in sophorose, and 505 and 473 up- or downregulated genes in glucose, respectively (Figure 2C). Interestingly, the number of transcriptionally modulated genes in sophorose (726) was greater than that in cellulose (359), when both were compared to glucose.
Figure 2

Venn diagram representing the number of differentially expressed genes in the QM9414 strain. (A) QM9414 strain growth in cellulose and glucose as carbon source. (B) QM9414 strain growth in cellulose and sophorose. (C) QM9414 strain growth in sophorose and glucose. The red arrows indicate the number of upregulated genes and green arrows the number of downregulated genes in the conditions analyzed. The numbers below the given Venn diagram represent the total number of regulated genes. The number in the lower right of rectangle indicates the number of transcripts in the T. reesei genome. Thresholds for calling differentially expressed genes were (P ≤0.05) and ≥ two-fold change, that is, log2-fold change ≥1 or ≤ -1.

Hierarchical clustering of the 2,060 differentially expressed genes identified in the comparisons cellulose versus glucose (cel/glu), sophorose versus cellulose (soph/cel) and sophorose versus glucose (soph/glu), allowed the identification of three possible regulons, representing 123 genes modulated by cellulose, 154 genes modulated by sophorose and 402 genes modulated by glucose, totaling 679 genes (Figure 3A; Additional file 3: Table S3). Gene Ontology (GO) annotation of the 679 carbon source-specific genes revealed that 46%, 34% and 39% of the genes from the cellulose, glucose and sophorose regulons respectively were genes of unknown function. These results emphasize the potential for the discovery of genes involved in the cellulase production in T. reesei during growth under inducing or repressing conditions.
Figure 3

Gene expression profile of T. reesei , QM9414 strain, during grown in the presence of cellulose, sophorose and glucose as the carbon source. Expression scale is represented as Log2 Fold Change. (A) Hierarchical clustering analysis was performed using Mev v.4.6.1, with the average linkage method for cluster generation, and uncentered correlation as the similarity metric. Euclidian distance was used to measure the differences in gene expression among the 2,060 genes and the groups (QM9414 Cellulose/Glucose; QM9414 Sophorose/Cellulose and QM9414 Sophorose/Glucose) based on the distance between the centroids of the groups, P <0.05. (B) Genes upregulated by cellulose, (C) genes upregulated by glucose and (D) genes upregulated by sophorose. In (B), (C) and (D) the summary of the Gene Ontology annotation are also represented.

In order to further evaluate the carbon source-specific regulons shown in Figure 3, the top 10 genes differentially expressed on cellulose, glucose, and sophorose were identified (Table 1). The top 10 upregulated genes in cellulose included the glycoside hydrolases (GH) GH5, GH31 and GH16, the carbohydrate esterase CE5, an oxidase, a specific Major facilitator superfamily (MFS) permease and five proteins of unknown function (Table 1). The top 10 upregulated genes in sophorose included a GH76 hydrolase, four oxidoreductases, two MFS permeases and three proteins of unknown function (Table 1). It is interesting to note that there are more GHs in the top 10 upregulated genes of cellulose than in sophorose. As expected, the top 10 differentially expressed genes in glucose did not show any genes encoding hydrolytic enzymes (Table 1). These results indicate a specific gene expression in response to the available carbon source in T. reesei.
Table 1

Log 2 fold change (FC) of the top 10 genes differentially expressed in cellulose, glucose and sophorose


Protein ID


GO term

FC Cel/Glu

FC Sph/Cel

FC Sph/Glu



MFS permease

Cellular component





GH5 β-Mannanase MAN1

Biological process





GH31 α-xylosidase/α-glucosidase

Biological process





CE5 acetyl xylan esterase AXE1

Molecular function





Unknown protein






Unknown protein






Multicopper oxidases

Molecular function











Unique protein






GH16 glucan endo-1,3(4)-β-D-glucosidase







Unknown protein; secreted

Molecular function





short chain dehydrogenase/reductase

Molecular function





Aldehyde reductase AKR7

Molecular function





Zinc-containing alcohol dehydrogenase superfamily

Molecular function






Molecular function





Unknown protein






Dienelactone hydrolase






Isoflavone reductase

Molecular function





Unknown protein






MFS permease

Cellular component






short chain dehydrogenase/reductase

Molecular function





Unknown protein






MFS maltose permease

Cellular component





FAD-containing oxidoreductase

Molecular function





Unknown protein

Cellular component





Zinc-binding oxidoreductase

Molecular function





Glucose oxidase

Molecular function





Unknown protein

Biological process





MFS permease

Cellular component





GH76 α-1,6-mannanase

Molecular function




GO, Gene Ontology; MFS, Major facilitator superfamily; GH, glycoside hyrolase; CE, carbohydrate esterase; AXE, acetyl xylan esterase; SSCRP, Small secreted cysteine-rich protein; Cel, cellulose; Glu, glucose; Sph, sophorose.


The mean FPKM (fragments per kilobase of exon per million fragments mapped) for all the genes within a single GH family were calculated. The total of all the FPKM means for each GH family when cultured in glucose, cellulose and sophorose were utilized to demonstrate the overall enzymatic potential and global transcriptional response (Figure 4). During growth in glucose the overall transcription of GH encoding genes was low, whereas growth in the presence of cellulose or sophorose resulted in a dramatic induction of a wide array of GH families, reflecting the transcriptional induction of the CAZYome. Similarly, cellulose and sophorose resulted in a greater transcriptional induction of cellobiohydrolase members from the GH6 and GH7 families.
Figure 4

Carbohydrate active enZymes (CAZy) genes and expression data from RNA-seq analysis. Fragments per kilobase of exon per million fragments mapped (FPKM) means for each glycolic hydrase (GH) family when cultured in glucose, cellulose and sophorose. CAZy classification was performed based on re-annotation of CAZy genes of T. reesei according to Hakkinen et al. [9].

Looking in more detail, Table S4 (Additional file 4) shows the enzymes that are differentially upregulated in sophorose and cellulose. Twenty GHs and one CE were upregulated in response to the presence of sophorose whereas 23 GHs and two CEs were induced in the presence of cellulose (Additional file 4: Table S4A and B). Interestingly, genes of enzymes involved in xylan degradation, such as xylanases (xyn2, xyn3, xyn4), acetyl xylan esterase (axe1), xyloglucanase (cel74a), α-xylosidase (GH31) and arabinoxylans degradation, such as arabinofuranosidase (ABF1 and ABF2) were preferentially expressed in the presence of cellulose but not in sophorose (Additional file 4: Table S4B). Furthermore, a polysaccharide monooxygenase (cel61A) was upregulated only in cellulose, in accordance with a role in the cellulose oxidation process. These results were also observed by Bischof et al. [22] when transcriptional data from wheat straw was compared to lactose. On the other hand, eight genes encoding α - and β-glucosidases (including cel3c, cel3b and cel1b), and a candidate for α-amylase and α-1,6-mannanase (while on cellulose a β-mannanase was expressed), were upregulated in sophorose (Additional file 4: Table S4A). Interestingly, in both sophorose and cellulose, enzymes that degrade trehalose were induced indicating that the fungus may catabolize stored trehalose, producing glucose, during cellulase production.

When comparing the fold change in gene expression among the three conditions we observed that even in the presence of glucose, 17 GHs were upregulated (Additional file 4: Table S4C). These genes encoded for enzymes such as endoglucanase (cel5b), β-1,4-glucanase (GH5), β-1,3-glucanosyltransferase (GH72), and an uncharacterized GH (Trire2_121136) appeared not to be subject to carbon catabolite repression.

Transcription factors

Table S5 (see Additional file 5) shows the TF encoding genes that were induced in each condition. In this analysis, 7 TF encoding genes were upregulated in cellulose, 18 in sophorose and 18 other TF genes were specific to glucose. Within this group, we focused on the TF genes that were upregulated depending on the carbon source. For example, Trire2_105269 showed a high level of expression in the presence of cellulose, whereas Trire2_123881 showed a high level of expression in sophorose, and Trire2_ID 112499 was upregulated by glucose (Additional file 5: Table S5). TF encoding genes from the Zn2Cys6 subgroup known as C2H2 (one of the most common type of transcription factors found in eukaryotes) were only induced in the presence of cellulose and sophorose, but not in glucose-grown cells (Additional file 5: Table S5), suggesting a specific response of C2H2 to the presence of cellulase inducer molecules. TF encoding genes from the bZIP family, on the other hand, showed higher expression in cellulose (Trire2_110152), but were also present in sophorose (Trire2_73654) and glucose (Trire2_119759) (Additional file 5: Table S5).

The expression of TF encoding genes already characterized as being involved in the regulation of the expression of cellulases and hemicellulases is shown in Table 2. Among the positively acting TFs (XYR1, ACE2, CLR-1, CLR-2, and BglR), the gene for XYR1 showed the highest expression level, followed by CLR-1 and BglR (Table 2). The TF genes ACE2 and CLR-2 showed no significant modulation in expression (P <0.05) between the various carbon sources. These results reinforce the hypothesis that XYR1 is the major positive regulator of cellulases and hemicellulase gene expression. On the other hand, the TFs that negatively regulate hydrolytic enzyme gene transcription, such as ACE1 and CREI, showed a lower level of expression, compared to the positive-acting TFs, or were not transcriptionally modulated depending upon the carbon source, suggesting that these TFs may act in a cooperative manner or have a more effective mode of action. In addition, the gene for PacC (pH-responsive transcription factor) was regulated in a carbon source manner, showing a higher expression level in the presence of cellulose (Table 2). Other TFs that have been shown to have a regulatory role, such as HAP2/3 and AreA, showed no significant modulation in gene expression in any condition (log2 > 1 and P <0.05). Taken together, our results depict a complex system of TFs that regulate the expression of hydrolytic enzymes, while also revealing additional, uncharacterized, TFs that appear to play a role.
Table 2

Log 2 fold change of characterized transcriptional factor genes involved in the regulation of cellulase and hemicellulase genes

Protein ID




























































NS, non-significant at P <0.05; Cel, cellulose; Glu, glucose; Soph, sophorose


Genes that encode proteins involved in transport comprise about 5% (459 genes) of the T. reesei genome. Our results show that among these genes, 14 were regulated exclusively by cellulose, 14 by sophorose, and 30 by glucose, applying an adjusted P-value <0.05 as thresholds (Additional file 6: Table S6).

The MFS (Major facilitator superfamily) permeases are the most abundant proteins in the three analyzed conditions. These proteins enable the transport of essential nutrients and ions, plus the excretion of end products of metabolism and cell-environment communication [23]. The gene encoding for the MFS permease (Trire2_69957) that was specifically highly upregulated in cellulose may be involved in the transport of disaccharides, due to a high similarity with a putative maltose permease of the human pathogenic fungus Talaromyces marneffei[24]. Another maltose permease encoding gene (Trire2_48444) was also highly induced by sophorose. Conversely, the MFS permease gene Trire2_76641 was expressed at a higher level in glucose than on sophorose or cellulose (Table S6). A BlastP analysis of this MFS permease showed 85% sequence identity to a synaptic vesicle transporter SVOP and also shared structural similarity to the human glucose transporter 1 (Glut1) [25]. Interestingly, a gene encoding a potential galactose permease (Trire2_62380) that was specifically expressed in glucose and a MFS permease encoding gene (Trire2_76800) that was induced by cellulose, both resembled the 19 Saccharomyces cerevisiae transporters that when deleted, contribute to the total loss of hexose uptake [26].

In order to identify the MFS permeases shared by cellulose and sophorose, the expression results were normalized with the glucose condition (Table 3). From 85 MFS permeases annotated in the T. reesei genome, 22 of them seem to be shared by cellulose and sophorose (Table 3). Among them, the most expressed were: crt1, which has been shown to be required by T. reesei for growth in cellulose and lactose, but not in xylan [27]; hxt1, a glucose permease; the MFS gene Trire2_50894, a high affinity glucose transporter [28]; and an MFS gene related to cellulose signaling (Trire2_79202) [29]. Interestingly, the recently described stp1, which is involved in cellobiose and glucose transport [27], showed a higher level of expression in sophorose than in cellulose (Table 3), indicating a complex regulation on cellobiose/sophorose uptake by T. reesei.
Table 3

The log 2 fold change of sharing MFS permeases genes in cellulose and sophorose conditions

Protein ID





MFS permease (Crt1)




MFS permease (glucose permease HXT1)




MFS permease




MFS permease, associated with cellulose signalling




MFS H + sugar transporter




MFS permease




MFS permease




MFS permease




MFS permease




MFS permease (Stp1)




MFS permease (fucose permease)




MFS permease




MFS permease




MFS permease




MFS permease




MFS permease




MFS permease




MFS permease




MFS permease




MFS permease




MFS permease




MFS toxin efflux pump



Another family of proteins that showed carbon source-dependent transcriptional regulation were the ABC (ATP binding cassette) transporters, which were highly upregulated in cellulose and sophorose. The AAA family (ATPases associated with a variety of cellular activities) and aquaglyceroporin genes were highly expressed in sophorose, whereas the ADP/ATP carrier genes were highly expressed in glucose (Additional file 6: Table S6). In addition, amino acids, oligopeptide, and ion transporter genes were identified as being regulated by the three carbon sources, with a larger number of genes expressed in cellulose.

Deciphering the regulatory network of T. reeseiin response to cellulose/sophorose

Using the experimental setup described above, we were able to identify a specific set of genes differentially regulated by the analyzed carbon sources. Using these data, the regulatory network of the genes identified as being modulated in a carbon source-dependent manner was reconstructed (Figure 5). Extensive overlapping between the differentially expressed genes in cellulose and sophorose (710 genes) was observed. Additionally, genes specifically associated with each condition were identified, as exemplified by the large number of genes (441) whose expression was specifically modulated during growth in sophorose compared to glucose. These genes represent either genes silenced in glucose but induced by sophorose (upregulated: 154 genes), or genes that are necessary for growth in glucose but dispensable for growth in sophorose (downregulated, 287 genes). Accordingly the analysis of genes specific to cellulose showed an over-representation of upregulated genes (132 genes) that are related to the expression of cellulase genes as compared to glucose (201 in total) (Figure 5).
Figure 5

Gene regulatory network (GRN) of 2,060 differentially expressed genes in T. reesei QM9414 in each tested condition. Cellulose versus glucose (CelGlu), sophorose versus cellulose (SphCel) and sophorose versus glucose (SphGlu). Genes are represented as nodes (shown as squares), and interactions are represented as edges (shown as lines, that is, red indicates upregulated interactions and green indicates downregulated interactions), that connect the nodes: 3,385 interactions.

The comparison between the two inducing conditions provided additional information by revealing new differentially expressed genes that were not identified via the comparison with glucose. From the 692 genes differentially expressed between cellulose and sophorose, only 75 and 107 genes were assigned specifically to sophorose or cellulose respectively (Figure 5).

The majority of the genes identified from this network analysis were of unknown function. Importantly, a cellulose- or sophorose-specific enrichment of different gene classes was observed (Figure 6). During growth in cellulose, there was an enrichment of CAZy encoding genes (that is, GH64, GH 62, GH81, GH76, GH54), accessory proteins (Small secreted cysteine-rich protein (SSCRP), OOC1, and Epl1), transporters (most of them related to iron and metal transporters), TFs (lae1, C2H2 and Zn2Cys6 TFs) and a variety of proteins related to electron transport (Table S7). In contrast, there were only three CAZy encoding genes specific to growth in sophorose. However, four genes encoding Trichoderma species-specific proteins were only induced on sophorose, suggesting that Trichoderma possesses a specialized sophorose metabolism system (the complete list of differentially expressed genes is shown in Additional file 7: Table S7). The substantial overlap between the cellulose and sophorose transcriptomes supports the hypothesis that sophorose is a natural inducer of cellulase transcription, while the cellulose-specific enrichment for additional CAZymes and accessory proteins reflects the difficulty in the deconstruction of this insoluble substrate.
Figure 6

Gene Ontology (GO) enrichment analysis of different classes of genes upregulated in cellulose and sophorose in T. reseei. Significantly enriched categories (P ≤0.05) are shown. The complete list of differentially expressed genes is shown in Additional file 7: Table S7.

Quantitative real-time PCR (RT-qPCR) analysis

The RNA-seq data were validated using 20 genes with mRNA accumulation that was modulated when the following comparisons were performed: cellulose versus glucose; sophorose versus cellulose; and sophorose versus glucose. The 10 upregulated genes were predominantly glycoside hydrolases and 10 downregulated genes were randomly chosen (see Additional file 8: Table S8). The log2 fold change in gene expression between the three comparisons obtained by RNA-seq and RT-qPCR demonstrated significant Pearson correlation (r2 = 0.8882), indicating the reliability of the RNA-seq analysis (Figure 7).
Figure 7

Correlation between RNAseq and quantitative real-time PCR (RT-qPCR). Comparison of log2 fold change of 20 genes obtained by RNA-seq and RT-qPCR. Real-time PCR was performed using the amplified cDNA from each RNA-seq sample. Strong, statistically significant Pearson correlation is shown between the expression levels measured using real-time PCR and RNA-seq.

Secretome analysis by two-dimensional DIGE

The T. reesei secretome when grown in glucose, sophorose and cellulose were analyzed by quantitative proteomics (two-dimensional DIGE), followed by liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. The gels shown in Figure 8 are representative of all three independent gels and three biological replicates. The distribution of the spots indicates that most of the secreted proteins have isoelectric points <6.0 and a molecular weight >30 kDa. In some cases, the molecular weights and isoelectric points observed in the two-dimensional gels were higher than expected, probably due to post-translational changes. Another observation was that various different spots were assigned to the same protein, suggesting the presence of a number of isoforms or possibly degraded forms of the protein (Table S9.1 and S9.2, see Additional file 9 and Additional file 10, respectively).
Figure 8

Differential gel electrophoresis (DIGE) analysis of T. reesei secretome grown in different carbon sources. (A) DIGE of cellulose (green spots) versus glucose (red spots). (B) DIGE of cellulose (green spots) versus sophorose (red spots). (C) Venn diagram from analysis of cellulose versus glucose. (D) Venn diagram from analysis of cellulose versus sophorose. The numbers in white indicate the spots subjected to liquid chromatography tandem mass spectrometry analysis. Protein IDs of identified spots are listed in Additional files 9 and 10: Table S.9.1 and S9.2).

The comparative analysis between the cellulose and glucose secretomes showed a total of 170 spots automatically detected by the software PDQuest (BioRad). Among these, 130 spots were statistically validated using a differential abundance ratio ≥2.0-fold (P ≤0.05). In total 89 spots were exclusively expressed in cellulose (Figure 8C) however, only 36 spots could be identified by mass spectrometry (MS) (Table S9.1). The identified proteins included classical cellulases (CEL7A and CEL6A), such as β-1-3-glucanosyltransferase, α-L-arabinofuranosidase, β-1,3-glucanase, α-1,2-mannosidase, Xylanase 4, β-xylosidase and isoforms, as well as a non-hydrolytic CIPI (Cellulose-induced protein). Furthermore, some proteases were also identified as being expressed during growth in cellulose, as well as an protein of unknown function (Trire2_55887) and a novel protein isoamyl alcohol oxidase (Trire2_73631), (see Additional file 9: Table S9.1). On the other hand, 41 spots were exclusively expressed during growth in glucose (Figure 8C). Among them, 28 spots were identified by MS (Additional file 9: Table S9.1): an acid phosphatase-like protein, various isoforms of isoamyl alcohol oxidase, subtilisin-like protease PPRC1, cell-wall glucanosyltransferase, amidase, a cerato-platanin, Epl1/Sm1 as well an SSCRP, and a unique protein (Trire2_121136).

When the sophorose secretome was compared to cellulose, the software PDQuest detected 183 spots, with 30 spots being exclusive to sophorose (≥2.0-fold; P ≤0.05), 37 exclusive to cellulose (≥2.0-fold; and P ≤0.05), and 116 spots common between the two conditions (Figure 8D). After MS analysis 20 spots from sophorose and 31 spots from cellulose were identified (see Additional file 10: Table S9.2). Among the enzymes classified as glycoside hydrolases, families GH3 (BXL1 β-xylosidase, β-glucosidase BGL1/CEL3a), GH64 (endo-β-1,3-glucanase), GH7 (Cellobiohydrolase CBH1/CEL7a, Endo-β-1,4-glucanase EGL1/CEL7b), GH72 (β-1-3-glucanosyltransferase), GH17 (glucan endo-1,3-β-glucosidase), GH28 (exo-rhamnogalacturonase RGX1), GH61 (polysaccharide monooxygenase CEL61a), GH74 (Xyloglucanase CEL74a), and GH30 (endo-β-1,4-xylanase XYN4) were found to be exclusive in cellulose cultures, whereas only the GH13 (α-amylase) was detected in sophorose. Furthermore, a larger number of proteins not related to glycoside hydrolase functions were detected in sophorose than in cellulose, such as SSCRP, lipoate-binding, ribonuclease T2, ubiquinol cytochrome reductase, DNAse, cell wall glucanosyltransferase, CIP1, ceramidase family protein, amidase, isoamyl alcohol oxidase, and ubiquitin fusion protein. Another interesting feature of the secretome was that both unknown proteins and Trichoderma species-specific proteins with described functions were found in both cellulose and sophorose, demonstrating the complexity and uniqueness of the Trichoderma secretome during cellulose degradation.

In order to correlate the gene expression data with the secretome, the fold change from cellulose versus glucose in both datasets was compared. There was 70.17% correlation between gene expression and secreted protein profiles (see Additional file 9: Table S9.1). When cellulose and sophorose cultures were compared, lower correlation was observed (47%), possibly due to many non-significant differences in expression (P <0.05) in RNAseq analysis (see Additional file 10: Table S9.2).


In natural environments, free-living organisms are continuously challenged with rapidly changing conditions that have a considerable impact on their lifestyle. Genomic and post-genomic techniques have revealed that free-living organisms dedicate a large percentage of their genes to sensing environmental signals and the subsequent coordination of gene expression in response to such cues. How the fungus T. reesei recognizes its substrate and activates the transcription of genes encoding transporters and TFs that culminate in the production of hydrolytic enzymes has been a subject of speculation since the 1960s. By using high-throughput genomic and proteomic approaches we describe both repressing (glucose) and de-repressing (cellulose or sophorose) conditions, identifying new players in cellulose degradation in T. reesei. In addition, the comparison between cellulose and sophorose, the hypothesized natural inducers of cellulase production, revealed a striking similarity in the global profiles.

The transcriptome study of T. reesei identified 123 genes that were specifically induced by cellulose, 154 by sophorose and 402 by glucose (Figure 3). Within these gene sets, 8 permease/transporter genes were induced in cellulose, 6 in sophorose and 11 in glucose (see Additional file 3: Table S3 and Additional file 8: Table S8 respectively). Of these 25 transporters, 10 showed possible homology to N. crassa homologues and one of them, MFS permease (Trire2_76800) (highly induced in the presence of cellulose), allowed S. cerevisiae to transport xylose [30, 31]. Furthermore, a gene encoding a putative galactose permease (Trire2_62380) found in glucose and another MFS permease (Trire2_76800) regulated by cellulose, showed similarity to S. cerevisiae transporters involved in hexose uptake [26]. Additional transporters were induced by both cellulose and sophorose, suggesting that sophorose could be the natural inducer of cellulase gene transcription in T. reesei. Despite that, the functions of these transporters in T. reesei remain obscure. For instance, the transporter Trire2_3405 was recently identified to be specifically involved in cellulase induction by lactose [29], but has also been described as being involved in cellobiose transport [27]. Furthermore, the same transporter was upregulated during growth on wheat straw [22, 28], cellulose or sophorose (Table 3). This lack of specificity by transporters could be explained by the close structure of cellobiose/lactose/sophorose or by the fact that some transporters can act as transporters and nutrient sensors. However, more detailed studies will be needed to characterize these transporters and generate a better understanding of the inducer/repressor transport system in T. reesei.

Global gene expression analysis by RNA-seq enabled the construction of gene regulatory networks (GRN) that enhanced the understanding of the interaction between different genes during the degradation and metabolism of cellulose. Studies on the control of catabolic genes related to the metabolism of simple substrates (such as glucose) performed in model organisms have revealed very complex GRN, thus, an even more sophisticated network controlling catabolic functions related to the metabolism of complex substrates, such as cellulose, could have been anticipated [32]. In the T. reesei model for cellulose degradation, the deep knowledge of the catabolic activities related to cellulose metabolism is accompanied by a very limited understanding of the regulatory pathways responsible for controlling gene expression [22, 32, 33]. In fact, despite the TFs, XYR1 and CRE1 [34, 35], which regulate the induction or repression of the cellulolytic enzymes respectively, and a few more specific regulators (ACE1, ACE2, BGLR) [36, 37] that have been experimentally characterized, there remains a lack of information on how, and to what extent, the expression of these enzymes are connected to the core GRN of T. reesei[32]. This is important as GRN in free-living organisms are usually densely connected and the final decision on the expression of a particular gene set is generally controlled by many different external/internal signals [38]. The collection of omics data provided here tries to fill this gap by providing a global analysis of T. reesei grown in three different substrates (cellulose, sophorose and glucose). From the analysis provided, we started building a bona fide regulatory network for this organism through the identification of 43 TF genes specifically induced in some particular growth conditions (see Additional file 5: Table S5). The GRN revealed that some of factors are exclusively induced in response to cellulose or sophorose (see Additional file 7: Table S7). For instance, the methyltransferase LAE1 has already been described as controlling the expression of cellulases, auxiliary factors for cellulose degradation, β-glucosidases and xylanases [39], proteins commonly found in response to inducers, cellulose, lactose and wheat straw [22, 28]. However, our results showed that LAE1 is preferentially expressed in response to cellulose, indicating that the fungus has specific signaling for the metabolism of cellulose. This hypothesis is supported by the fact that recent study showed that LAE1 affects other components of cellulose degradation, such as non-ribosomal peptide synthases, ankyrin-repeat proteins, iron uptake, PTH11-receptors, and oxidases/monoxygenases [40], genes that were also upregulated in the presence of cellulose in our data and in the presence of wheat straw [22]. Another TF gene upregulated in response to cellulose (Trire2_120698) showed homology to the Aspergillus nidulans pH-responsive transcription factor pacC. It is known that this TF controls a range of functions in filamentous fungi [41]. Although studies have shown that pH is involved in cellulase production in T. reesei[42], the regulation of cellulase genes by any pH-responsive TF is still unknown.

The expression level of the cre1 gene was low even in the presence of glucose. One explanation for this result is the fact that some TFs can act either directly on CAZyme encoding genes or indirectly by regulating other TFs that in turn regulate the expression of CAZyme genes. Here we identified some TF genes that are candidates for the indirectly transcriptional regulation, in a carbon source-dependent manner (see Additional file 5: Table S5). Some of these TFs could play an important role in the coordination of gene expression downstream in the network, either in association with the previously identified general factors at the target promoters or in isolation, in a sort of cascade signaling pathway. Additionally, the identified TFs could work as check points for the integration of different physiological/environmental signals, such as metabolic status of the cell, levels of light, presence of stresses, et cetera. [43, 44]. The TFs identified here are candidates for further investigation into the mechanisms of signal integration in this biotechnologically relevant fungus. Understanding these missing regulatory interactions is pivotal for future attempts to synthetically engineer T. reesei for enhanced cellulolytic functions.

Analyses of the T. reesei secretome has commonly focused on growth in cellulose or lactose [4547]. Besides the classical cellulases already described, our differential secretome showed the presence of polysaccharide monooxygenase, xyloglucanase CEL74a, and xylanases, induced by cellulose, whereas in sophorose, amidase, amylase and isoamyl alcohol oxidase they were described for the first time. The strong correlation between transcriptome and secretome data in the presented study is consistent with other comparable studies [29, 48, 49]. Furthermore, a comparison of the cellulose and sophorose transcriptome and differential secretome data did not detect a massive difference in any analyzed category of proteins. This observation suggest that the signaling for cellulose and sophorose to induce cellulase formation is very conserved and thus sophorose still remains a strong candidate as natural inducer.

Despite extensive work related to the regulation of cellulases in T. reesei, the real identity of the natural inducer is not yet established. New evidence has recognized cellobiose and cellodextrins as strong candidates for natural inducers [50]. Indeed, studies with N. crassa[51] and A. niger[52] have discredited sophorose as the natural inducer. It is known that T. reesei possesses a different mechanism for the regulation of cellulase production in response to sophorose when compared to other lignocellulose-degrading fungi [5]. Our GRN data showed little differences in the regulation of gene expression by the inducers cellulose and sophorose, suggesting that sophorose could be a natural cellulase inducer. But how did this divergence between T. reesei and other fungi occur? Comparative genomics between T. atroviride, T. virens and T. reesei suggest that the ancestral state of Hypocrea/Trichoderma was indeed a mycoparasitic, possibly of wood-degrading basidiomycetes [5]. T. reesei subsequently may have kept the mycoparasitic characteristic for substrate competition, converting cellobiose to sophorose by a transglycosylation reaction and then metabolizing sophorose. This hypothesis can be supported by the fact that new species-specific proteins were upregulated only in sophorose and by the fact that cellobiose and sophorose are transported and metabolized at different rates [50]. For this reason, we propose that both cellobiose and sophorose act as co-inducers of cellulase formation in T. reesei. These facts could explain why among lignocellulose-degrading fungi, T. reesei is the more efficient degrader, despite its smaller enzymatic arsenal.


Our study shows little difference between gene expression and the secretome during the growth of T. reesei in cellulose and sophorose. The difference in gene expression is associated with CAZymes, accessory proteins, transporters, TFs, and electron transport. Together with recent literature, the results shown here suggest that both cellobiose and sophorose act as co-inducers of cellulase production in T. reesei. Further functional genomic investigations of the new players identified to be involved in growth in cellulose will open up new lines of research into clarifying cellulase and hemicellulase regulation in T. reesei. In addition, the data shown in this study will contribute to the construction of industrial strains of T. reesei that produce high levels of cellulase for plant cell-wall degradation thus facilitating its application in 2G-bioethanol production.


Strain and growth conditions

T. reesei strain QM9414 (ATCC 26921) was obtained from the Molecular Biotechnology Laboratory, Institute, TU Vienna, Austria. The strain was maintained on MEX medium (malt extract 3% (w/v) and agar-agar 2% (w/v)) at 4°C. QM9414 was grown on MEX medium at 28°C for 7 to 10 days to complete sporulation. For gene expression assays, a spore suspension containing approximately 107 cells mL-1 was inoculated into 200 mL of Mandels-Andreotti medium [53] containing 1% (w/v) of cellulose (Avicel), or 2% (w/v) of glucose, or 1 mM of sophorose, as the sole carbon source. The cultures were incubated on an orbital shaker (200 rpm) at 28°C for 24, 48 and 72 hours using cellulose; for 24 and 48 hours with glucose; and 2, 4 and 6 hours with sophorose, as the carbon source. In the latter, the mycelium was previously grown on glycerol 1% (w/v) for 24 hours. After this time, the mycelium was washed with Mandels-Andreotti medium without peptone and then transferred to 20 mL of Mandels-Andreotti medium without peptone containing sophorose 1 mM. All experiments were performed in three biological replicates. The resulting mycelia were collected by filtration, frozen and stored at -80°C until RNA extraction and the supernatants were used for secretome analysis.

RNA extraction

Total RNA was extracted from mycelia of each sample using TRIzol® RNA kit (Invitrogen Life Technologies, Carlsbad, CA, USA), according to the manufacturer’s instructions. RNAs concentrations were determined by spectrophotometric OD 260/280 and RNA integrity was verified by both the Agilent 2100 Bioanalyzer (Agilent Technologies, Waldbroon, Germany) and gel electrophoresis in 1% agarose.

High-throughput sequencing (RNA-seq)

Total RNA of three biological replicates, cellulose (24, 48 and 72 hours), sophorose (2, 4 and 6 hours) and glucose (24 and 48 hours) were time points that were pooled, resulting in nine samples for the preparation of next-generation sequencing libraries using the TruSeq RNA Sample Prep kit (Illumina, San Diego, CA, USA). The total RNA samples obtained from T. reesei were lyophilized and stored using the RNAstable tube kit (Biomatrica, San Diego, CA, USA) in order to maintain the RNAs integrity for sequencing. Nine barcoded libraries (cel1-3, gluc 1–3 and soph 1–3) were prepared and sequenced by LGC Genomics GmbH (Berlin/Germany) using the Illumina Hiseq 2000 platform.

Data analysis

The Illumina Hiseq 2000 system was used to sequence approximately 117 million 100 bp paired-end reads. These sequences were quality-filtered and mapped to the Trichoderma reesei 2.0 reference genome, available from the JGI Genome Portal (, using the Bowtie aligner version 0.12.8 [54], allowing for two mismatches and only unique alignments. After alignment, Samtools version 0.1.18 [55] was used to process the alignments files, which were visualized using the Integrative Genomics Viewer [56]. The genes were annotated using Trichoderma reesei 2.0 reference genome and a local database provided by Professor CP Kubicek (TU, Vienna). Unknown proteins were defined as proteins that have yet to be assigned a function in any ascomycete and T. reesei species-specific proteins were defined to be proteins that did not occur in any other Pezizomycotina [57]. Bioconductor DESeq package version 1.10.1 [58] was utilized for the differential expression analysis, using two-fold change cutoff, that is, log2 fold change ≥1 or ≤ -1 and an adjusted P-value ≤0.05 as thresholds. Samples were normalized using median log deviation DESeq, available in the Bioconductor package. Cluster analysis was carried out using the software Mev v.4.6.1 to identify cellulose, sophorose and glucose regulons. The average linkage method was used for cluster generation, with uncentered correlation as the similarity metric. Functional enrichment analysis of differentially expressed genes was performed using GO terms was performed using the BayGO algorithm [59]. GO terms significantly enriched, (that is, with P-values ≤0.05) were analyzed further. Raw sequence data and count data for all samples are available at [GEO: GSE53629]. CAZy classification was performed based upon the re-annotation of CAZy genes of T. reesei according to Hakkinen et al. [9].

Regulation network of T. reesei

In order to reconstruct the regulatory network of T. reesei under the experimental condition analyzed, a table using the following information was generated: inducing condition (QMCelGlu, QMSphCel and QMSphGlu, selecting differentially expressed genes, up- and downregulated in each condition, P ≤0.05), the interaction type (up- or downregulated) and the target gene (that is, the protein ID of each gene affected). This analysis provides a network representation for all the genes (2,060 in total) shown in the heat map of Figure 2. The regulatory network was then generated using the Cytoscape 3.0.1 software [60].

Quantitative qRT-PCR analysis

Differentially expressed genes identified by the RNA-seq analysis were further analyzed by qRT-PCR in order to validate their expression. In this analysis, the same RNA samples, utilized for the RNA sequencing experiments were re-used. Approximately, 1 μg of RNA was treated with DNAseI (Thermo scientific) and reverse-transcribed to cDNA using the First Strand cDNA kit Maxima™ Synthesis according to manufacturer’s instructions. The cDNA was diluted to 1/50 fold and used for real-time PCR analysis in the Bio-Rad CFX96™ System, using SsoFast™EvaGreen®Supermix (Bio-Rad, San Francisco, CA, USA) for signal detection in accordance with the manufacturer’s instructions. Genes encoding actin (act) and a small GTPase SAR/ARF-type (sar1) were used as endogenous controls according to [61]. Twenty genes, including up- and downregulated genes in cellulose compared to glucose samples (see Additional file 11: Table S1), were used for qRT-PCR analysis. The following amplification reaction was used: 95°C for 10 minutes followed by 39 cycles of 95°C for 10 seconds, 60°C for 30 seconds followed by a dissociation curve of 60°C to 95°C with an increment of 0.5°C for 10 seconds. Gene expression values were calculated according to the 2-ΔΔCT method [62] using the QM9414 strain growth on glucose as the reference sample. Data analysis was performed using GraphPad Prism v 5.1 software.

Sample preparation for proteomic analysis

The protein concentration was determined using the kit Bio-Rad Protein Assay, based on the Bradford method. Protein concentration was adjusted to 1 μg /μl, and 150 μg used for in two-dimensional DIGE and 300 μg in two-dimensional SDS-PAGE. Samples were precipitated using 10% tricarboxylic acid (TCA) in acetone and incubated at -20°C overnight. Samples were centrifuged at 10,000 g for 10 minutes at 4°C and the supernatant removed. β-mercaptanol was added (0.07%) in acetone and centrifuged at 10,000 g for 10 minutes at 4°C. This was repeated three times, discarding each supernatant after centrifugation. After precipitation, the pellet was purified using Ettan2D Clean-Up Kit (GE Healthcare, Waukesha, WI, USA).

Two-dimensional differential gel electrophoresis

The proteins (150 μg) secreted by T. reesei under different conditions were labeled with 400 pmol CyDyes (Cy3 or Cy5) according to the manufacturer’s instructions (GE Healthcare, Waukesha, WI, USA). An internal pool generated by equal amounts of all samples was labeled with Cy2. The isoelectric focusing was carried out on 18-cm linear IPG strips, pH 4–7, with the addition of 1.2% DeStreak and 1% IPG buffer 4–7 (GE Healthcare). Isoelectric focusing was performed on IPGphor III in four steps: 500 V for 60 minutes, 1000 V for 60 minutes, 8000 V for four hours and 8000 V for six hours. The strips were reduced (1.5% w/v dithioerythritol) and alkylated (2.5% w/v iodocetamide) in equilibration buffer (6 M urea, 50 mM Tris–HCl, pH 6.8, 30% glycerol, 2% SDS). Equilibrated strips were run on homogeneous 12.5% polyacrylamide gels using an Ettan DALTsix electrophoresis (GE Healthcare). All the experiments resulted in three independent replicates for each experimental condition. The preparative gels were stained using colloidal Coomassie and destained with Milli-Q water to remove excess Coomassie particles. Gels were scanned using the laser scanner Pharos FX Plus (Bio-Rad) and Quantite One software (Bio-Rad) using a resolution of 100 μm and the appropriate wavelength. The images were analyzed with the software PDQuest Advanced 2-D Analysis Software (Bio-Rad). Differential expression was determined by statistical analyses using the t-test, as the parameter of significance (P ≤0.05).

Protein identification by mass spectrometry

Spots which increased or decreased in volume (protein content) by two-fold or more were manually excised from the gels and washed four times with 50 mM NH4HCO3 containing 50% v/v acetonitrile (ACN) to remove SDS and dye. They were then washed with ACN and completely dried in a SpeedVac (Savant Instrument, Farmingdale, NY, USA). Each spot was rehydrated with 20 μl 50 mM NH4HCO3 containing 0.3 μg of sequencing grade modified trypsin (Promega, Madison, WI, USA). After 30 minutes of rehydration with the trypsin solution, spots were covered with 50 mM NH4HCO3. The hydrolysis reaction was carried out at 37°C for 24 hours and stopped by the addition of 10 μl formic acid 1%. Peptides were extracted twice from the gel with 40 μl 0.1% v/v formic acid solution containing 50% v/v ACN for 1 hour. Extracts were dried in a SpeedVac and resuspended in 35 μl 0.1% v/v formic acid solution containing 5% v/v ACN for MS injection. Samples were then analyzed in an XEVO-TQS mass spectrometer (Waters) coupled with a UPLC chromatography system (Waters). Liquid chromatography separation was performed in a 15 cm column (ACQUITY UPLC HSS C18, 100 Å, 1.8 μm, 1 mm × 150 mm, Waters) using a 30-minute linear gradient from 5 to 30% of ACN in 0.1% formic acid at 150 μl/minute. The spectra were acquired in a data-dependent mode in an m/z range of 400 to 1,500, with selection of the two most abundant ions of each MS spectrum for MS/MS analysis. MS parameters were as follows: capillary voltage of 3.5 KV and capillary temperature of 400°C. Acquired raw data were converted to mzXML and automatically processed by an in-house installation of Labkey Server v12, using theX!Tandem search algorithm [63]. The minimum criterion for peptide matching was performed using a Peptide Prophet [64] score greater than 0.8. Peptides that met these criteria were further grouped to protein sequences using the Protein Prophet [65] algorithm and only proteins with an error rate of 5% or less and two peptides sequences identified were considered as valid identifications.






Auxiliary family activity 9


ATP binding cassette






acetyl xylan esterase


base pairs


carbohydrate active enzyme


carbohydrate esterase


Cellulose-induced protein


differential gel electrophoresis




fragments per kilobase of exon per million fragments mapped


glycoside hydrolase




gene regulatory network


liquid chromatography tandem mass spectrometry


malt extract medium


Major facilitator superfamily


mass spectrometry


polysaccharide monooxygenase


quantitative real-time PCR


Small secreted cysteine-rich protein




transcriptional factor





This work was supported by The State of São Paulo Research Foundation (FAPESP) (proc. 2010/15683-8). We are grateful to Professor Christian Kubicek (TU-Wien) for helping in the experimental design of this study.

Authors’ Affiliations

Department of Biochemistry and Immunology, Ribeirão Preto Medical School, University of São Paulo
Departamento de Biologia Celular, Universidade de Brasília, Asa Norte
Department of Genetics, Ribeirão Preto Medical School, University of São Paulo
Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, São Paulo, Laboratório Nacional de Ciência e Tecnologia do Bioetanol


  1. Pessoa-Jr A, Roberto IC, Menossi M, dos Santos RR, Filho SO, Penna TC: Perspectives on bioenergy and biotechnology in Brazil. Appl Biochem Biotechnol. 2005, 121–124: 59-70.View ArticleGoogle Scholar
  2. Mosier N, Wyman C, Dale B, Elander R, Lee YY, Holtzapple M, Ladisch M: Features of promising technologies for pretreatment of lignocellulosic biomass. Bioresour Technol. 2005, 96: 673-686. 10.1016/j.biortech.2004.06.025.View ArticleGoogle Scholar
  3. Ojeda K, Kafarov V: Exergy analysis of enzymatic hydrolysis reactors for transformation of lignocellulosic biomass to bioethanol. Chem Eng J. 2009, 154: 390-395. 10.1016/j.cej.2009.05.032.View ArticleGoogle Scholar
  4. Soccol CR, Vandenberghe LPD, Medeiros ABP, Karp SG, Buckeridge M, Ramos LP, Pitarelo AP, Ferreira-Leitao V, Gottschalk LMF, Ferrara MA, Bon EPD, de Moraes LMP, Araujo JD, Torres FAG: Bioethanol from lignocelluloses: Status and perspectives in Brazil. Bioresource Technology. 2010, 101: 4820-4825. 10.1016/j.biortech.2009.11.067.View ArticleGoogle Scholar
  5. Kubicek CP, Herrera-Estrella A, Seidl-Seiboth V, Martinez DA, Druzhinina IS, Thon M, Zeilinger S, Casas-Flores S, Horwitz BA, Mukherjee PK, Mukherjee M, Kredics L, Alcaraz LD, Aerts A, Antal Z, Atanasova L, Cervantes-Badillo MG, Challacombe J, Chertkov O, McCluskey K, Coulpier F, Deshpande N, von Dohren H, Ebbole DJ, Esquivel-Naranjo EU, Fekete E, Flipphi M, Glaser F, Gomez-Rodriguez EY, Gruber S, et al: Comparative genome sequence analysis underscores mycoparasitism as the ancestral life style of Trichoderma. Genome biology. 2011, 12: R40-10.1186/gb-2011-12-4-r40.View ArticleGoogle Scholar
  6. Schuster A, Schmoll M: Biology and biotechnology of Trichoderma. Appl Microbiol Biotechnol. 2010, 87: 787-799. 10.1007/s00253-010-2632-1.View ArticleGoogle Scholar
  7. Martinez D, Berka RM, Henrissat B, Saloheimo M, Arvas M, Baker SE, Chapman J, Chertkov O, Coutinho PM, Cullen D, Danchin EG, Grigoriev IV, Harris P, Jackson M, Kubicek CP, Han CS, Ho I, Larrondo LF, de Leon AL, Magnuson JK, Merino S, Misra M, Nelson B, Putnam N, Robbertse B, Salamov AA, Schmoll M, Terry A, Thayer N, Westerholm-Parvinen A, et al: Genome sequencing and analysis of the biomass-degrading fungus Trichoderma reesei (syn. Hypocrea jecorina). Nat Biotechnol. 2008, 26: 553-560. 10.1038/nbt1403.View ArticleGoogle Scholar
  8. Saloheimo M, Nakari-Setala T, Tenkanen M, Penttila M: cDNA cloning of a Trichoderma reesei cellulase and demonstration of endoglucanase activity by expression in yeast. Eur J Biochem. 1997, 249: 584-591. 10.1111/j.1432-1033.1997.00584.x.View ArticleGoogle Scholar
  9. Hakkinen M, Arvas M, Oja M, Aro N, Penttila M, Saloheimo M, Pakula TM: Re-annotation of the CAZy genes of Trichoderma reesei and transcription in the presence of lignocellulosic substrates. Microb Cell Fact. 2012, 11: 134-10.1186/1475-2859-11-134.View ArticleGoogle Scholar
  10. Verbeke J, Coutinho P, Mathis H, Quenot A, Record E, Asther M, Heiss-Blanquet S: Transcriptional profiling of cellulase and expansin-related genes in a hypercellulolytic Trichoderma reesei. Biotechnol Lett. 2009, 31: 1399-1405. 10.1007/s10529-009-0030-5.View ArticleGoogle Scholar
  11. Mandels M, Parrish FW, Reese ET: Sophorose as an inducer of cellulase in Trichoderma viride. J Bacteriol. 1962, 83: 400-408.Google Scholar
  12. Kubicek CP, Messner R, Gruber F, Mach RL, Kubicek-Pranz EM: The Trichoderma cellulase regulatory puzzle: from the interior life of a secretory fungus. Enzyme Microb Technol. 1993, 15: 90-99. 10.1016/0141-0229(93)90030-6.View ArticleGoogle Scholar
  13. Seiboth B, Hakola S, Mach RL, Suominen PL, Kubicek CP: Role of four major cellulases in triggering of cellulase gene expression by cellulose in Trichoderma reesei. J Bacteriol. 1997, 179: 5318-5320.Google Scholar
  14. Foreman PK, Brown D, Dankmeyer L, Dean R, Diener S, Dunn-Coleman NS, Goedegebuur F, Houfek TD, England GJ, Kelley AS, Meerman HJ, Mitchell T, Mitchinson C, Olivares HA, Teunissen PJ, Yao J, Ward M: Transcriptional regulation of biomass-degrading enzymes in the filamentous fungus Trichoderma reesei. The Journal of biological chemistry. 2003, 278: 31988-31997. 10.1074/jbc.M304750200.View ArticleGoogle Scholar
  15. Schmoll M, Kubicek CP: Regulation of trichoderma cellulase formation: lessons in molecular biology from an industrial fungus.A review. Acta Microbiol Immunol Hung. 2003, 50: 125-145. 10.1556/AMicr.50.2003.2-3.3.View ArticleGoogle Scholar
  16. El-Gogary S, Leite A, Crivellaro O, Eveleigh DE, El-Dorry H: Mechanism by which cellulose triggers cellobiohydrolase I gene expression in Trichoderma reesei. Proc Natl Acad Sci USA. 1989, 86: 6138-6141. 10.1073/pnas.86.16.6138.View ArticleGoogle Scholar
  17. Nogawa M, Goto M, Okada H, Morikawa Y: L-Sorbose induces cellulase gene transcription in the cellulolytic fungus Trichoderma reesei. Curr Genet. 2001, 38: 329-334. 10.1007/s002940000165.View ArticleGoogle Scholar
  18. Chambergo FS, Bonaccorsi ED, Ferreira AJ, Ramos AS, Ferreira Junior JR, Abrahao-Neto J, Farah JP, Farah JP, El-Dorry H: Elucidation of the metabolic fate of glucose in the filamentous fungus Trichoderma reesei using expressed sequence tag (EST) analysis and cDNA microarrays. J Biol Chem. 2002, 277: 13983-13988. 10.1074/jbc.M107651200.View ArticleGoogle Scholar
  19. Kubicek CP: Systems biological approaches towards understanding cellulase production by Trichoderma reesei. J Biotechnol. 2013, 163: 133-142. 10.1016/j.jbiotec.2012.05.020.View ArticleGoogle Scholar
  20. Kubicek CP, Mikus M, Schuster A, Schmoll M, Seiboth B: Metabolic engineering strategies for the improvement of cellulase production by Hypocrea jecorina. Biotechnol Biofuels. 2009, 2: 19-10.1186/1754-6834-2-19.View ArticleGoogle Scholar
  21. Castro LD, Antonieto AC, Pedersoli WR, Silva-Rocha R, Persinoti GF, Silva RN: Expression pattern of cellulolytic and xylanolytic genes regulated by transcriptional factors XYR1 and CRE1 are affected by carbon source in Trichoderma reesei. Gene Expr Patterns. 2014, 14: 88-95. 10.1016/j.gep.2014.01.003.View ArticleGoogle Scholar
  22. Bischof R, Fourtis L, Limbeck A, Gamauf C, Seiboth B, Kubicek CP: Comparative analysis of the Trichoderma reesei transcriptome during growth on the cellulase inducing substrates wheat straw and lactose. Biotechnol Biofuels. 2013, 6: 127-10.1186/1754-6834-6-127.View ArticleGoogle Scholar
  23. Pao SS, Paulsen IT, Saier MH: Major facilitator superfamily. Microbiol Mol Biol R. 1998, 62: 1-34.Google Scholar
  24. Boyce KJ, Andrianopoulos A: Morphogenetic circuitry regulating growth and development in the dimorphic pathogen Penicillium marneffei. Eukaryot Cell. 2013, 12: 154-160. 10.1128/EC.00234-12.View ArticleGoogle Scholar
  25. Yao J, Bajjalieh SM: SVOP is a nucleotide binding protein. PLoS One. 2009, 4: e5315-10.1371/journal.pone.0005315.View ArticleGoogle Scholar
  26. Wieczorke R, Krampe S, Weierstall T, Freidel K, Hollenberg CP, Boles E: Concurrent knock-out of at least 20 transporter genes is required to block uptake of hexoses in Saccharomyces cerevisiae. FEBS Lett. 1999, 464: 123-128. 10.1016/S0014-5793(99)01698-1.View ArticleGoogle Scholar
  27. Zhang W, Kou Y, Xu J, Cao Y, Zhao G, Shao J, Wang H, Wang Z, Bao X, Chen G, Liu W: Two major facilitator superfamily sugar transporters from trichoderma reesei and their roles in induction of cellulase biosynthesis. J Biol Chem. 2013, 288: 32861-32872. 10.1074/jbc.M113.505826.View ArticleGoogle Scholar
  28. Ries L, Pullan ST, Delmas S, Malla S, Blythe MJ, Archer DB: Genome-wide transcriptional response of Trichoderma reesei to lignocellulose using RNA sequencing and comparison with Aspergillus niger. BMC Genomics. 2013, 14: 541-10.1186/1471-2164-14-541.View ArticleGoogle Scholar
  29. Ivanova C, Baath JA, Seiboth B, Kubicek CP: Systems analysis of lactose metabolism in Trichoderma reesei identifies a lactose permease that is essential for cellulase induction. PLoS One. 2013, 8: e62631-10.1371/journal.pone.0062631.View ArticleGoogle Scholar
  30. Du J, Li S, Zhao H: Discovery and characterization of novel d-xylose-specific transporters from Neurospora crassa and Pichia stipitis. Mol Biosyst. 2010, 6: 2150-2156. 10.1039/c0mb00007h.View ArticleGoogle Scholar
  31. Sun J, Tian C, Diamond S, Glass NL: Deciphering transcriptional regulatory mechanisms associated with hemicellulose degradation in Neurospora crassa. Eukaryot Cell. 2012, 11: 482-493. 10.1128/EC.05327-11.View ArticleGoogle Scholar
  32. Glass NL, Palma-Guerrero J, Jonkers W, Leeder A, Hall C, Kowbel D, Taylor JW, Brem R: Revealing fungal communication modules by genomics, population genomics, and genome wide association studies in Neurospora crassa. Phytopathology. 2013, 103: 186-187.Google Scholar
  33. Mukherjee PK, Horwitz BA, Herrera-Estrella A, Schmoll M, Kenerley CM: Trichoderma research in the genome era. Annu Rev Phytopathol. 2013, 51: 105-129. 10.1146/annurev-phyto-082712-102353.View ArticleGoogle Scholar
  34. Furukawa T, Shida Y, Kitagami N, Mori K, Kato M, Kobayashi T, Okada H, Ogasawara W, Morikawa Y: Identification of specific binding sites for XYR1, a transcriptional activator of cellulolytic and xylanolytic genes in Trichoderma reesei. Fungal Genet Biol. 2009, 46: 564-574. 10.1016/j.fgb.2009.04.001.View ArticleGoogle Scholar
  35. Strauss J, Mach RL, Zeilinger S, Hartler G, Stoffler G, Wolschek M, Kubicek CP: Cre1, the carbon catabolite repressor protein from Trichoderma reesei. FEBS Lett. 1995, 376: 103-107. 10.1016/0014-5793(95)01255-5.View ArticleGoogle Scholar
  36. Portnoy T, Margeot A, Seidl-Seiboth V, Le Crom S, Ben Chaabane F, Linke R, Seiboth B, Kubicek CP: Differential regulation of the cellulase transcription factors XYR1, ACE2, and ACE1 in Trichoderma reesei strains producing high and low levels of cellulase. Eukaryot Cell. 2011, 10: 262-271. 10.1128/EC.00208-10.View ArticleGoogle Scholar
  37. Nitta M, Furukawa T, Shida Y, Mori K, Kuhara S, Morikawa Y, Ogasawara W: A new Zn(II)(2)Cys(6)-type transcription factor BglR regulates beta-glucosidase expression in Trichoderma reesei. Fungal Genet Biol. 2012, 49: 388-397. 10.1016/j.fgb.2012.02.009.View ArticleGoogle Scholar
  38. Martinez-Antonio A, Collado-Vides J: Identifying global regulators in transcriptional regulatory networks in bacteria. Curr Opin Microbiol. 2003, 6: 482-489. 10.1016/j.mib.2003.09.002.View ArticleGoogle Scholar
  39. Seiboth B, Karimi RA, Phatale PA, Linke R, Hartl L, Sauer DG, Smith KM, Baker SE, Freitag M, Kubicek CP: The putative protein methyltransferase LAE1 controls cellulase gene expression in Trichoderma reesei. Mol Microbiol. 2012, 84: 1150-1164. 10.1111/j.1365-2958.2012.08083.x.View ArticleGoogle Scholar
  40. Karimi-Aghcheh R, Bok JW, Phatale PA, Smith KM, Baker SE, Lichius A, Omann M, Zeilinger S, Seiboth B, Rhee C, Keller NP, Freitag M, Kubicek CP: Functional analyses of Trichoderma reesei LAE1 reveal conserved and contrasting roles of this regulator. G3 (Bethesda). 2013, 3: 369-378. 2013.View ArticleGoogle Scholar
  41. Rossi A, Cruz AH, Santos RS, Silva PM, Silva EM, Mendes NS, Martinez-Rossi NM: Ambient pH sensing in filamentous fungi: pitfalls in elucidating regulatory hierarchical signaling networks. IUBMB Life. 2013, 65: 930-935. 10.1002/iub.1217.View ArticleGoogle Scholar
  42. Li C, Yang Z, He Can Zhang R, Zhang D, Chen S, MA L: Effect of pH on cellulase production and morphology of Trichoderma reesei and the application in cellulosic material hydrolysis. J Biotechnol. 2013, 168: 470-477. 10.1016/j.jbiotec.2013.10.003.View ArticleGoogle Scholar
  43. Mach RL, Zeilinger S: Regulation of gene expression in industrial fungi: Trichoderma. Appl Microbiol Biotechnol. 2003, 60: 515-522. 10.1007/s00253-002-1162-x.View ArticleGoogle Scholar
  44. Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, Brown PO: Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell. 2000, 11: 4241-4257. 10.1091/mbc.11.12.4241.View ArticleGoogle Scholar
  45. Herpoel-Gimbert I, Margeot A, Dolla A, Jan G, Molle D, Lignon S, Mathis H, Sigoillot JC, Monot F, Asther M: Comparative secretome analyses of two Trichoderma reesei RUT-C30 and CL847 hypersecretory strains. Biotechnol Biofuels. 2008, 1: 18-10.1186/1754-6834-1-18.View ArticleGoogle Scholar
  46. Jun H, Kieselbach T, Jonsson LJ: Enzyme production by filamentous fungi: analysis of the secretome of Trichoderma reesei grown on unconventional carbon source. Microb Cell Fact. 2011, 10: 68-10.1186/1475-2859-10-68.View ArticleGoogle Scholar
  47. Adav SS, Chao LT, Sze SK: Quantitative secretomic analysis of Trichoderma reesei strains reveals enzymatic composition for lignocellulosic biomass degradation. Mol Cell Proteomics. 2012, 11: M111 012419-10.1074/mcp.M111.012419.View ArticleGoogle Scholar
  48. Tisch D, Kubicek CP, Schmoll M: New insights into the mechanism of light modulated signaling by heterotrimeric G-proteins: ENVOY acts on gna1 and gna3 and adjusts cAMP levels in Trichoderma reesei (Hypocrea jecorina). Fungal Genet Biol. 2011, 48: 631-640. 10.1016/j.fgb.2010.12.009.View ArticleGoogle Scholar
  49. Druzhinina IS, Shelest E, Kubicek CP: Novel traits of Trichoderma predicted through the analysis of its secretome. FEMS Microbiol Lett. 2012, 337: 1-9. 10.1111/j.1574-6968.2012.02665.x.View ArticleGoogle Scholar
  50. Zhou QX, Xu JT, Kou YB, Lv XX, Zhang X, Zhao GL, Zhang WX, Chen GJ, Liu WF: Differential involvement of beta-glucosidases from hypocrea jecorina in rapid induction of cellulase genes by cellulose and cellobiose. Eukaryot Cell. 2012, 11: 1371-1381. 10.1128/EC.00170-12.View ArticleGoogle Scholar
  51. Znameroski EA, Coradetti ST, Roche CM, Tsai JC, Iavarone AT, Cate JHD, Glass NL: Induction of lignocellulose-degrading enzymes in Neurospora crassa by cellodextrins. Proc Natl Acad Sci USA. 2012, 109: 6012-6017. 10.1073/pnas.1118440109.View ArticleGoogle Scholar
  52. Gielkens MM, Dekkers E, Visser J, de Graaff LH: Two cellobiohydrolase-encoding genes from Aspergillus niger require D-xylose and the xylanolytic transcriptional activator XlnR for their expression. Appl Environ Microbiol. 1999, 65: 4340-4345.Google Scholar
  53. Schmoll M, Schuster A, Silva Rdo N, Kubicek CP: The G-alpha protein GNA3 of Hypocrea jecorina (Anamorph Trichoderma reesei) regulates cellulase gene expression in the presence of light. Eukaryot Cell. 2009, 8: 410-420. 10.1128/EC.00256-08.View ArticleGoogle Scholar
  54. Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009, 25: 1754-1760. 10.1093/bioinformatics/btp324.View ArticleGoogle Scholar
  55. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Proc GPD: The sequence alignment/Map format and SAMtools. Bioinformatics. 2009, 25: 2078-2079. 10.1093/bioinformatics/btp352.View ArticleGoogle Scholar
  56. Thorvaldsdottir H, Robinson JT, Mesirov JP: Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform. 2013, 14: 178-192. 10.1093/bib/bbs017.View ArticleGoogle Scholar
  57. Atanasova L, Jaklitsch WM, Komon-Zelazowska M, Kubicek CP, Druzhinina IS: Clonal species Trichoderma parareesei sp. nov. likely resembles the ancestor of the cellulase producer Hypocrea jecorina/T. reesei. Appl Environ Microbiol. 2010, 76: 7259-7267. 10.1128/AEM.01184-10.View ArticleGoogle Scholar
  58. Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biol. 2010, 11: R106-10.1186/gb-2010-11-10-r106.View ArticleGoogle Scholar
  59. Vencio RZ, Koide T, Gomes SL, Pereira CA: BayGO: bayesian analysis of ontology term enrichment in microarray data. BMC Bioinformatics. 2006, 7: 86-10.1186/1471-2105-7-86.View ArticleGoogle Scholar
  60. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13: 2498-2504. 10.1101/gr.1239303.View ArticleGoogle Scholar
  61. Steiger MG, Mach RL, Mach-Aigner AR: An accurate normalization strategy for RT-qPCR in Hypocrea jecorina (Trichoderma reesei). J Biotechnol. 2010, 145: 30-37. 10.1016/j.jbiotec.2009.10.012.View ArticleGoogle Scholar
  62. Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(T)(-Delta Delta C) method. Methods. 2001, 25: 402-408. 10.1006/meth.2001.1262.View ArticleGoogle Scholar
  63. MacLean B, Eng JK, Beavis RC, McIntosh M: General framework for developing and evaluating database scoring algorithms using the TANDEM search engine. Bioinformatics. 2006, 22: 2830-2832. 10.1093/bioinformatics/btl379.View ArticleGoogle Scholar
  64. Keller A, Nesvizhskii AI, Kolker E, Aebersold R: Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem. 2002, 74: 5383-5392. 10.1021/ac025747h.View ArticleGoogle Scholar
  65. Nesvizhskii AI, Keller A, Kolker E, Aebersold R: A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem. 2003, 75: 4646-4658. 10.1021/ac0341261.View ArticleGoogle Scholar


© dos Santos Castro et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.