Genome wide analysis of protein production load in Trichoderma reesei

Abstract

Background: The filamentous fungus Trichoderma reesei (teleomorph Hypocrea jecorina) is a widely used industrial host organism for protein production. In industrial cultivations, it can produce over 100 g/l of extracellular protein, mostly constituting of cellulases and hemicellulases. In order to improve protein production of T. reesei the transcriptional regulation of cellulases and secretory pathway factors have been extensively studied. However, the metabolism of T. reesei under protein production conditions has not received much attention.

Results: To understand the physiology and metabolism of T. reesei under protein production conditions we carried out a well-controlled bioreactor experiment with extensive analysis. We used minimal media to make the data amenable for modelling and three strain pairs to cover different protein production levels. With RNA-sequencing transcriptomics we detected the concentration of the carbon source as the most important determinant of the transcriptome. As the major transcriptional response concomitant to protein production we detected the induction of selected genes that were putatively regulated by xyr1 and were related to protein transport, amino acid metabolism and transcriptional regulation. We found novel metabolic responses such as production of glycerol and a cellotriose-like compound. We then used this cultivation data for flux balance analysis of T. reesei metabolism and demonstrate for the first time the use of genome wide stoichiometric metabolic modelling for T. reesei. We show that our model can predict protein production rate and provides novel insight into the metabolism of protein production. We also provide this unprecedented cultivation and transcriptomics data set for future modelling efforts.

Conclusions: The use of stoichiometric modelling can open a novel path for the improvement of protein production in T. reesei. Based on this we propose sulphur assimilation as a major limiting factor of protein production. As an organism with exceptional protein production capabilities modelling of T. reesei can provide novel insight also to other less productive organisms.

Keywords: Flux balance analysis; Hypocrea jecorina; Metabolic modelling; Protein production; RNA sequencing; Stoichiometric model; Transcriptomics; Trichoderma reesei.

Fig. 1

Parameters measured from the bioreactor cultures. The volumetric amount of fungal biomass, extracellular protein, cellobiose and activity against MUL substrate is shown in the panels on the left (a–d) and the corresponding specific rates (per biomass amount and time) in the panels on the right (e–h). The error bars indicate the standard error of the mean (SEM). Futher parameters are shown in Fig. 2

Fig. 2

Further bioreactor batch cultivation parameters. The volumetric amount of cellotriose-like compound, glucose and glycerol shown in the panels on the left (a–c) and the corresponding specific rates (per biomass amount and time) in the panels on the right (d–f). The exact identity of the cellotriose-like (a, d) compound is not known. The error bars indicate the standard error of the mean (SEM)

Fig. 3

Dependencies between key cultivation parameters. Each number signifies the sampling time (h) during cultivations of a strain. Strains are specified with colored boxes. The red line is a linear regression model and the black line a generalized additive model, surrounded by a grey region of one standard deviation. Panels (a–f) show dependencies between a biomass (g/l) and cellobiose (g/l), b biomass (1/h) and cellobiose [mmol/(g CDW h)], c CER [mol/(g CDW h)] and OUR [mol/(g CDW h)], d OUR [mol/(g CDW h)] and biomass (1/h), e extracellular protein [g/(g CDW h)] and biomass (1/h) and f extracellular protein [g/(g CDW h] and OUR [mol/(g CDW h)]

Fig. 4

Selected gene expression clusters. On y-axes the average gene expression level expressed as rlog2 which is log2 like transformation of normalized counts calculated by DESeq2 [40]. Thick line is cluster average and thin lines individual genes. On x-axes time points (16–64 h) from the cultivations of the six strains

Fig. 5

Overlaps of groups of genes. Overlap of groups of genes correlated significantly with a cultivation parameter (Table 3) (a) with genes in expression clusters and ( b) with groups of differentially expressed genes. Heatmap coloring shows the negative log p value of overlap with a cut-off of two i.e. only overlaps with $p\le 0.01$ are shown. Red cell notes show the actual overlapping count of genes. In (b) 'High’ refers to strain producing protein well (Cel4dCt, LipPr4d and LipPr4dCt) and 'Low’ to strains producing less protein (Cel4d, CutCBHd and CutCBdCt). 'Down’ signifies the direction of regulation i.e. the group of genes is expressed at significantly lower level in ’High’ strains, than in 'Low’ strains. ’16 h’ specifies the time point i.e. sample taken at 16  h

Fig. 6

Expression levels of individual genes. On y-axis the average gene expression level expressed as rlog2 which is log2 like transformation of normalized counts calculated by DESeq2 [40]. On x-axis time points (16–64 h) from the cultivations of the six strains. a main cellulases of T. reesei. b Central genes of protein secretion. c Selected differentially expressed regulators. d Heterologous product genes cutinase (Cut) and lipase (Lip). Main cellulase signal in cellulase deletion strain Cel4d is a technical artefact created by the DESeq2 process as exemplified by its lack of variation in Cel4d samples

Fig. 7

Correlation of predicted and measured specific protein production rate. Each number signifies the sampling time (h) during cultivations of a strain. Strains are specified with colored boxes. The red line is a linear regression model and the black line a generalized additive model, surrounded by a grey region of one standard deviation

Fig. 8

Selected flux clusters a Cluster 3; b Cluster 4; c Cluster 13. On y-axis flux. On x-axis time points (16–64 h) from the cultivations of the six strains. Thick line is cluster average and thin lines individual reactions

Fig. 9

Cysteine and methionine metabolism. Enzymes which reactions belong to flux clusters are colored, for example C13 is flux cluster 13. Enzymes which genes are significantly correlated with protein production rate are encircled in red: 120176, EC:1.13.11.20, cysteine dioxygenase; 56350, EC: 2.5.1.47, cysteine synthase; 68036, EC:2.5.1.48, cystathionine gamma-synthase; 3823, EC: 2.1.1.14 methionine synthase. Enzyme(s) found in gene expression cluster 24 is encircled with green: 53091 EC: 4.3.1.17, in cluster 28 in blue: 76018 EC: 2.5.1.47 and in cluster 21 in yellow: 5233 EC: 2.6.1.1, 81089 EC: 4.2.1.22. For each gene expression and flux cluster the profile of strain LipPr4d shown. Pathway map from KEGG [107]