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Identification of microRNA-like RNAs in the filamentous fungus Trichoderma reesei by Solexa sequencing

ABSTRACT: MicroRNAs (miRNAs) are small non-coding RNAs capable of negatively regulating gene expression. Trichoderma reesei is an industrial filamentous fungus that can secrete abundant hydrolases for cellulosic biofuels. Recently, microRNA-like RNAs (milRNAs) were discovered in several filamentous fungi rather than T. reesei. The purpose of this study was to explore the presence of milRNA in T. reesei, to characterize the differential expression of T. reesei milRNA under cellulose induction, and to reveal the target genes of milRNA involved in cellulase production. Two small RNA libraries of cellulose induction (IN) or non-induction (CON) were generated and sequenced using Solexa sequencing technology. A total of 664,463 and 529,545 unique sequences, representing 1,271 and 1,021 unique small RNAs, were obtained from the IN and CON samples, respectively. Thirteen milRNAs were finally identified in T. reesei using the hairpin structure analysis. The milRNAs profiles obtained in deep sequencing were validated by RT-qPCR assay. The miRanda program predicts a number of potential targets for T. reesei milRNAs, including several hydrolases and carbon catabolite repressor Cre1.The presence and differential expression of T. reesei milRNAs, along with their predicted targets indicate that milRNAs might play a regulatory role in cellulase induction. This work lays foundation for further functional study of fungal milRNAs and their industrial application.

ORGANISM(S): Trichoderma reesei

PROVIDER: GSE46679 | GEO | 2014/12/31

SECONDARY ACCESSION(S): PRJNA201504

REPOSITORIES: GEO

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Project description:The filamentous fungus Trichoderma reesei is the main industrial cellulytic enzymes producer. Several strains have been developed in the past using random mutagenesis, and despite impressive performance enhancements, the pressure for low-cost cellulases has stimulated continuous research in the field. In this context, comparative study of the lower and higher producer strains obtained through random mutagenesis using systems biology tools (genome sequencing, transcriptome) can shed light on the mechanisms of cellulase production and help identify genes linked to performance. Previously, our group published comparative genome sequencing of the lower and higher producer strains NG14 and RUTM-BM- C30. In this follow-up work, we examined how these mutations affect phenotype at the level of the transcriptome and cultivation behavior.M-BM- We performed kinetic transcriptome analysis of the NG14 and RUTM-BM- C30 strains of early cellulase production induced by lactose using bioreactor cultivations close to industrial conditions. RUTM-BM- C30 exhibited both earlier onset of cellulase production and higher steady-state productivity. A rather lowM-BM- number of genes were regulated, most of them being specific to the NG14 strains. Clustering of these genes highlighted similar behavior for some functional categories, and allowed to distinguish between induction-related genes and productivity-related genes. The lower number of genes regulated in RUTM-BM- C30 could not formally be linked to the relief in catabolic repression that is characteristic of this strain. Cross-comparison of our transcriptome data with mutations previously identified revealed that most genes from our dataset have not been mutated. Interestingly, the few mutated genes belong to the same clusters, suggesting these clusters contain genes playing a role in strain performance. This is the first kinetic transcriptome study carried out in industry-relevant conditions with two related strains of T. reesei showing distinctive performances. Our study sheds some light on some of the events occurring in these strains following induction by lactose. The fact that few regulated genes have been affected by mutagenesis suggest that the induction mechanism is essentially intact and that there is room for further improvement of T. reesei. We also provide some potential target for further genetic improvement of these strains. Two biological pool by condition in dye switch. For the two biological replicates on each four experiments we apply on the pretreated results the linear modeling approach implemented by lmFit and the empirical Bayes statistics implemented by eBayes from the limma R package (Smyth 2004). We select the list of statistically regulated genes using a 5% significance threshold.

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Identification of microRNA-like RNAs in the filamentous fungus Trichoderma reesei by Solexa sequencing

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