Background: Reverse transcription quantitative real-time PCR (RT-qPCR) is a well-established method for analysing gene expression. Most RT-qPCR experiments in the field of microbiology aim for the detection of transcriptional changes by relative quantification, which means the comparison of the expression level of a specific gene between different samples by the application of a calibration condition and internal reference genes. Due to the numerous data processing procedures and factors that can influence the final result, relative expression analysis and interpretation of RT-qPCR data are still not trivial and often necessitate the use of multiple separate software packages capable of performing specific functions. Results: Here we present qRAT, a stand-alone desktop application based on R that automatically processes raw output data from any qPCR machine using well-established and state-of-the-art statistical and graphical techniques. The ability of qRAT to analyse RT-qPCR data was evaluated using two example datasets generated in our laboratory. The tool successfully completed the procedure in both cases, returning the expected results. The current implementation includes functionalities for parsing, filtering, normalizing and visualisation of relative RT-qPCR data, like the determination of the relative quantity and the fold change of differentially expressed genes as well as the correction of inter-plate variation for multiple-plate experiments. Conclusion: qRAT provides a comprehensive, straightforward, and easy-to-use solution for the relative quantification of RT-qPCR data that requires no programming knowledge or additional software installation. All application features are available for free and without requiring a login or registration.

Trichoderma fungi support sustainable agriculture by suppressing plant diseases and improving crop performance. However, emerging pathogenicity of Trichoderma warrants further ecological and genetic characterization. Here we used machine learning to correlate genomic data from 37 Trichoderma strains with over 140 phenotypic traits, spanning metabolic versatility, biotic interactions, stress tolerance and reproductive strategies. We determined Trichoderma to be an ancient, genetically cohesive and physiologically diverse genus with spores capable of germination in water and dispersal via air and water droplets. Metabolic preferences indicate universal adaptation to mycoparasitism and to niches like arboreal microbial mats, alongside broader saprotrophic versatility. Our analyses are consistent with character displacement among close relatives and convergent evolution in distant lineages, with both processes shaping ecological plasticity and traits including dispersal modes, terrestrialization or endophytism. Our findings reveal that while some Trichoderma species show traits of biosafety concern, its vast ecophysiological diversity enables the development of safe, targeted bioeffectors.

Many studies aim at maximizing fungal secondary metabolite production but the influence of light during cultivation has often been neglected. Here, we combined an untargeted isotope-assisted liquid chromatography–high-resolution mass spectrometry-based metabolomics approach with standardized cultivation of Trichoderma atroviride under three defined light regimes (darkness (PD), reduced light (RL) exposure, and 12/12 h light/dark cycle (LD)) to systematically determine the effect of light on secondary metabolite production. Comparative analyses revealed a similar metabolite profile upon cultivation in PD and RL, whereas LD treatment had an inhibiting effect on both the number and abundance of metabolites. Additionally, the spatial distribution of the detected metabolites for PD and RL was analyzed. From the more than 500 detected metabolites, only 25 were exclusively produced upon fungal growth in darkness and 85 were significantly more abundant in darkness. The majority were detected under both cultivation conditions and annotation revealed a cluster of substances whose production followed the pattern observed for the well-known T. atroviride metabolite 6-pentyl-alpha-pyrone. We conclude that cultivation of T. atroviride under RL can be used to maximize secondary metabolite production.

Background: Trichoderma atroviride is a competitive soil-borne mycoparasitic fungus with extensive applications as a biocontrol agent in plant protection. Despite its importance and application potential, reference genes for RT-qPCR analysis in T. atroviride have not been evaluated. Light exerts profound effects on physiology, such as growth, conidiation, secondary metabolism, and stress response in T. atroviride, as well as in other fungi. In this study, we aimed to address this gap by identifying stable reference genes for RT-qPCR experiments in T. atroviride under different light conditions, thereby enhancing accurate and reliable gene expression analysis in this model mycoparasite. We measured and compared candidate reference genes using commonly applied statistical algorithms. Results: Under cyclic light–dark cultivation conditions, tbp and rho were identified as the most stably expressed genes, while act1, fis1, btl, and sar1 were found to be the least stable. Similar stability rankings were obtained for cultures grown under complete darkness, with tef1 and vma1 emerging as the most stable genes and act1, rho, fis1, and btl as the least stable genes. Combining the data from both cultivation conditions, gapdh and vma1 were identified as the most stable reference genes, while sar1 and fis1 were the least stable. The selection of different reference genes had a significant impact on the calculation of relative gene expression, as demonstrated by the expression patterns of target genes pks4 and lox1. Conclusion: The data emphasize the importance of validating reference genes for different cultivation conditions in fungi to ensure accurate interpretation of gene expression data.