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Background Trichoderma reesei is a soft rot Ascomycota fungus utilised for industrial production of secreted enzymes, especially lignocellulose degrading enzymes. About 30 carbohydrate active enzymes (CAZymes) of T. reesei have been biochemically characterised. Genome sequencing has revealed a large number of novel candidates for CAZymes, thus increasing the potential for identification of enzymes with novel activities and properties. Plenty of data exists on the carbon source dependent regulation of the characterised hydrolytic genes. However, information on the expression of the novel CAZyme genes, especially on complex biomass material, is very limited. Results In this study, the CAZyme gene content of the T. reesei genome was updated and the annotations of the genes refined using both computational and manual approaches. Phylogenetic analysis was done to assist the annotation and to identify functionally diversified CAZymes. The analyses identified 201 glycoside hydrolase genes, 22 carbohydrate esterase genes and five polysaccharide lyase genes. Updated or novel functional predictions were assigned to 44 genes, and the phylogenetic analysis indicated further functional diversification within enzyme families or groups of enzymes. GH3 β-glucosidases, GH27 α-galactosidases and GH18 chitinases were especially functionally diverse. The expression of the lignocellulose degrading enzyme system of T. reesei was studied by cultivating the fungus in the presence of different inducing substrates and by subjecting the cultures to transcriptional profiling. The substrates included both defined and complex lignocellulose related materials, such as pretreated bagasse, wheat straw, spruce, xylan, Avicel cellulose and sophorose. The analysis revealed co-regulated groups of CAZyme genes, such as genes induced in all the conditions studied and also genes induced preferentially by a certain set of substrates. Conclusions In this study, the CAZyme content of the T. reesei genome was updated, the discrepancies between the different genome versions and published literature were removed and the annotation of many of the genes was refined. Expression analysis of the genes gave information on the enzyme activities potentially induced by the presence of the different substrates. Comparison of the expression profiles of the CAZyme genes under the different conditions identified co-regulated groups of genes, suggesting common regulatory mechanisms for the gene groups.

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.

Background Trichoderma reesei is the main industrial producer of cellulases and hemicellulases that are used to depolymerize biomass in a variety of biotechnical applications. Many of the production strains currently in use have been generated by classical mutagenesis. In this study we characterized genomic alterations in high-producing mutants of T. reesei by high-resolution array comparative genomic hybridization (aCGH). Our aim was to obtain genome-wide information which could be utilized for better understanding of the mechanisms underlying efficient cellulase production, and would enable targeted genetic engineering for improved production of proteins in general. Results We carried out an aCGH analysis of four high-producing strains (QM9123, QM9414, NG14 and Rut-C30) using the natural isolate QM6a as a reference. In QM9123 and QM9414 we detected a total of 44 previously undocumented mutation sites including deletions, chromosomal translocation breakpoints and single nucleotide mutations. In NG14 and Rut-C30 we detected 126 mutations of which 17 were new mutations not documented previously. Among these new mutations are the first chromosomal translocation breakpoints identified in NG14 and Rut-C30. We studied the effects of two deletions identified in Rut-C30 (a deletion of 85 kb in the scaffold 15 and a deletion in a gene encoding a transcription factor) on cellulase production by constructing knock-out strains in the QM6a background. Neither the 85 kb deletion nor the deletion of the transcription factor affected cellulase production. Conclusions aCGH analysis identified dozens of mutations in each strain analyzed. The resolution was at the level of single nucleotide mutation. High-density aCGH is a powerful tool for genome-wide analysis of organisms with small genomes e.g. fungi, especially in studies where a large set of interesting strains is analyzed.

A variety of functionally important protein properties, such as secondary structure, transmembrane topology and solvent accessibility, can be encoded as a labeling of amino acids. Indeed, the prediction of such properties from the primary amino acid sequence is one of the core projects of computational biology. Accordingly, a panoply of approaches have been developed for predicting such properties; however, most such approaches focus on solving a single task at a time. Motivated by recent, successful work in natural language processing, we propose to use multitask learning to train a single, joint model that exploits the dependencies among these various labeling tasks. We describe a deep neural network architecture that, given a protein sequence, outputs a host of predicted local properties, including secondary structure, solvent accessibility, transmembrane topology, signal peptides and DNA-binding residues. The network is trained jointly on all these tasks in a supervised fashion, augmented with a novel form of semi-supervised learning in which the model is trained to distinguish between local patterns from natural and synthetic protein sequences. The task-independent architecture of the network obviates the need for task-specific feature engineering. We demonstrate that, for all of the tasks that we considered, our approach leads to statistically significant improvements in performance, relative to a single task neural network approach, and that the resulting model achieves state-of-the-art performance.

Xylose is present with glucose in lignocellulosic streams available for valorisation to biochemicals. Saccharomyces cerevisiae has excellent characteristics as a host for the bioconversion, except that it strongly prefers glucose to xylose, and the co-consumption remains a challenge. Further, since xylose is not a natural substrate of S. cerevisiae, the regulatory response it induces in an engineered strain cannot be expected to have evolved for its utilisation. Xylose-induced effects on metabolism and gene expression during anaerobic growth of an engineered strain of S. cerevisiae on medium containing both glucose and xylose medium were quantified. The gene expression of S. cerevisiae with an XR-XDH pathway for xylose utilisation was analysed throughout the cultivation: at early cultivation times when mainly glucose was metabolised, at times when xylose was co-consumed in the presence of low glucose concentrations, and when glucose had been depleted and only xylose was being consumed. Cultivations on glucose as a sole carbon source were used as a control. Genome-scale dynamic flux balance analysis models were simulated to analyse the metabolic dynamics of S. cerevisiae. The simulations quantitatively estimated xylose-dependent flux dynamics and challenged the utilisation of the metabolic network. A relative increase in xylose utilisation was predicted to induce the bi-directionality of glycolytic flux and a redox challenge even at low glucose concentrations. Remarkably, xylose was observed to specifically delay the glucose-dependent repression of particular genes in mixed glucose-xylose cultures compared to glucose cultures. The delay occurred at a cultivation time when the metabolic flux activities were similar in the both cultures.

In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict protein interactions in other, related species. In our methodology, we combine several state of the art machine learning approaches, namely, multiple kernel learning (MKL), pairwise kernels and kernelized structured output prediction in the supervised graph inference framework. For MKL, we apply recently proposed centered kernel alignment and p-norm path following approaches to integrate several feature sets describing the proteins, demonstrating improved performance. For graph inference, we apply input-output kernel regression (IOKR) in supervised and semi-supervised modes as well as output kernel trees (OK3). In our experiments simulating increasing genetic distance, Input-Output Kernel Regression proved to be the most robust prediction approach. We also show that the MKL approaches improve the predictions compared to uniform combination of the kernels. We evaluate the methods on the task of predicting protein-protein-interactions in the secretion pathways in fungi, S.cerevisiae, baker’s yeast, being the source, T. reesei being the target of the inter-species transfer learning. We identify completely novel candidate secretion proteins conserved in filamentous fungi. These proteins could contribute to their unique secretion capabilities.

Retroviral LTRs, paired or single, influence the transcription of both retroviral and non-retroviral genomic sequences. Vertebrate genomes contain many thousand endogenous retroviruses (ERVs) and their LTRs. Single LTRs are difficult to detect from genomic sequences without recourse to repetitiveness or presence in a proviral structure. Understanding of LTR structure increases understanding of LTR function, and of functional genomics. Here we develop models of orthoretroviral LTRs useful for detection in genomes and for structural analysis. Although mutated, ERV LTRs are more numerous and diverse than exogenous retroviral (XRV) LTRs. Hidden Markov models (HMMs), and alignments based on them, were created for HML- (human MMTV-like), general-beta-, gamma- and lentiretroviruslike LTRs, plus a general-vertebrate LTR model. Training sets were XRV LTRs and RepBase LTR consensuses. The HML HMM was most sensitive and detected 87% of the HML LTRs in human chromosome 19 at 96% specificity. By combining all HMMs with a low cutoff, for screening, 71% of all LTRs found by RepeatMasker in chromosome 19 were found. HMM consensus sequences had a conserved modular LTR structure. Target site duplications (TG-CA), TATA (occasionally absent), an AATAAA box and a T-rich region were prominent features. Most of the conservation was located in, or adjacent to, R and U5, with evidence for stem loops. Several of the long HML LTRs contained long ORFs inserted after the second A rich module. HMM consensus alignment allowed comparison of functional features like transcriptional start sites (sense and antisense) between XRVs and ERVs. The modular conserved and redundant orthoretroviral LTR structure with three A-rich regions is reminiscent of structurally relaxed Giardia promoters. The five HMMs provided a novel broad range, repeat-independent, ab initio LTR detection, with prospects for greater generalisation, and insight into LTR structure, which may aid development of LTR-targeted pharmaceuticals.

The genomes of hybrid organisms, such as lager yeast ( Saccharomyces cerevisiae  ×  Saccharomyces eubayanus ), contain orthologous genes, the functionality and effect of which may differ depending on their origin and copy number. How the parental subgenomes in lager yeast contribute to important phenotypic traits such as fermentation performance, aroma production, and stress tolerance remains poorly understood. Here, three de novo lager yeast hybrids with different ploidy levels (allodiploid, allotriploid, and allotetraploid) were generated through hybridization techniques without genetic modification. The hybrids were characterized in fermentations of both high gravity wort (15 °P) and very high gravity wort (25 °P), which were monitored for aroma compound and sugar concentrations. The hybrid strains with higher DNA content performed better during fermentation and produced higher concentrations of flavor-active esters in both worts. The hybrid strains also outperformed both the parent strains. Genome sequencing revealed that several genes related to the formation of flavor-active esters ( ATF1 , ATF2 ¸ EHT1 , EEB1 , and BAT1 ) were present in higher copy numbers in the higher ploidy hybrid strains. A direct relationship between gene copy number and transcript level was also observed. The measured ester concentrations and transcript levels also suggest that the functionality of the S. cerevisiae - and S. eubayanus -derived gene products differs. The results contribute to our understanding of the complex molecular mechanisms that determine phenotypes in lager yeast hybrids and are expected to facilitate targeted strain development through interspecific hybridization.

We describe here the identification and characterization of two novel enzymes belonging to the IlvD/EDD protein family, the D-xylonate dehydratase from Caulobacter crescentus , Cc XyDHT, (EC 4.2.1.82), and the L-arabonate dehydratase from Rhizobium leguminosarum bv . trifolii , Rl ArDHT (EC 4.2.1.25), that produce the corresponding 2-keto-3-deoxy-sugar acids. There is only a very limited amount of characterization data available on pentonate dehydratases, even though the enzymes from these oxidative pathways have potential applications with plant biomass pentose sugars. The two bacterial enzymes share 41 % amino acid sequence identity and were expressed and purified from Escherichia coli as homotetrameric proteins. Both dehydratases were shown to accept pentonate and hexonate sugar acids as their substrates and require Mg 2+ for their activity. Cc XyDHT displayed the highest activity on D-xylonate and D-gluconate, while Rl ArDHT functioned best on D-fuconate, L-arabonate and D-galactonate. The configuration of the OH groups at C2 and C3 position of the sugar acid were shown to be critical, and the C4 configuration also contributed substantially to the substrate recognition. The two enzymes were also shown to contain an iron–sulphur [Fe–S] cluster. Our phylogenetic analysis and mutagenesis studies demonstrated that the three conserved cysteine residues in the aldonic acid dehydratase group of IlvD/EDD family members, those of C60, C128 and C201 in Cc XyDHT, and of C59, C127 and C200 in Rl ArDHT, are needed for coordination of the [Fe–S] cluster. The iron–sulphur cluster was shown to be crucial for the catalytic activity (k cat ) but not for the substrate binding (K m ) of the two pentonate dehydratases.

An open reading frame CC1225 from the Caulobacter crescentus CB15 genome sequence belongs to the Gfo/Idh/MocA protein family and has 47 % amino acid sequence identity with the glucose-fructose oxidoreductase from Zymomonas mobilis (Zm GFOR). We expressed the ORF CC1225 in the yeast Saccharomyces cerevisiae and used a yeast strain expressing the gene coding for Zm GFOR as a reference. Cell extracts of strains overexpressing CC1225 (renamed as Cc aaor) showed some Zm GFOR type of activity, producing D-gluconate and D-sorbitol when a mixture of D-glucose and D-fructose was used as substrate. However, the activity in Cc aaor expressing strain was >100-fold lower compared to strains expressing Zm gfor. Interestingly, C. crescentus AAOR was clearly more efficient than the Zm GFOR in converting in vitro a single sugar substrate D-xylose (10 mM) to xylitol without an added cofactor, whereas this type of activity was very low with Zm GFOR. Furthermore, when cultured in the presence of D-xylose, the S. cerevisiae strain expressing Cc aaor produced nearly equal concentrations of D-xylonate and xylitol (12.5 g D-xylonate l⁻¹ and 11.5 g D-xylitol l⁻¹ from 26 g D-xylose l⁻¹), whereas the control strain and strain expressing Zm gfor produced only D-xylitol (5 g l⁻¹). Deletion of the gene encoding the major aldose reductase, Gre3p, did not affect xylitol production in the strain expressing Cc aaor, but decreased xylitol production in the strain expressing Zm gfor. In addition, expression of Cc aaor together with the D-xylonolactone lactonase encoding the gene xylC from C. crescentus slightly increased the final concentration and initial volumetric production rate of both D-xylonate and D-xylitol. These results suggest that C. crescentus AAOR is a novel type of oxidoreductase able to convert the single aldose substrate D-xylose to both its oxidized and reduced product.