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Molecular Tools for the Yeast Papiliotrema terrestris LS28 and Identification of Yap1 as a Transcription Factor Involved in Biocontrol Activity.

ABSTRACT: Fungal attacks on stored fruit and vegetables are responsible for losses of products. There is an active research field to develop alternative strategies for postharvest disease management, and the use of biocontrol agents represents a promising approach. Understanding the molecular bases of the biocontrol activity of these agents is crucial to potentiate their effectiveness. The yeast Papiliotrema terrestris is a biocontrol agent against postharvest pathogens. Phenotypic studies suggest that it exerts its antagonistic activity through competition for nutrients and space, which relies on its resistance to oxidative and other cellular stresses. In this study, we developed tools for genetic manipulation in P. terrestris to perform targeted gene replacement and functional complementation of the transcription factors Yap1 and Rim101. In vitro phenotypic analyses revealed a conserved role of Yap1 and Rim101 in broad resistance to oxidative stress and alkaline pH sensing, respectively. In vivo analyses revealed that P. terrestris yap1Δ and rim101Δ mutants display decreased ability to colonize wounded fruit compared to that of the parental wild-type (WT) strain; the yap1Δ mutant also displays reduced biocontrol activity against the postharvest pathogens Penicillium expansum and Monilinia fructigena, indicating an important role for resistance to oxidative stress in timely wound colonization and biocontrol activity of P. terrestris In conclusion, the availability of molecular tools developed in the present study provides a foundation to elucidate the genetic mechanisms underlying biocontrol activity of P. terrestris, with the goal of enhancing this activity for the practical use of P. terrestris in pest management programs based on biological and integrated control.IMPORTANCE The use of fungicides represents the most effective and widely used strategy for controlling postharvest diseases. However, their extensive use has raised several concerns, such as the emergence of plant pathogens' resistance as well as the health risks associated with the persistence of chemical residues in fruit, in vegetables, and in the environment. These factors have brought attention to alternative methods for controlling postharvest diseases, such as the utilization of biocontrol agents. In the present study, we developed genetic resources to investigate at the molecular level the mechanisms involved in the biocontrol activity of Papiliotrema terrestris, a basidiomycete yeast that is an effective biocontrol agent against widespread fungal pathogens, including Penicillium expansum, the etiological agent of blue mold disease of pome fruits. A deeper understanding of how postharvest biocontrol agents operate is the basic requirement to promote the utilization of biological (and integrated) control for the reduction of chemical fungicides.

SUBMITTER: Castoria R

PROVIDER: S-EPMC8091616 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Medulloblastomas, the most frequent malignant brain tumours affecting children, comprise at least 4 distinct clinicogenetic subgroups. Aberrant sonic hedgehog (SHH) signalling is observed in approximately 25% of tumours and defines one subgroup. Although alterations in SHH pathway genes (e.g. PTCH1, SUFU) are observed in many of these tumours, high throughput genomic analyses have identified few other recurring mutations. Here, we have mutagenised the Ptch+/- murine tumour model using the Sleeping Beauty transposon system to identify additional genes and pathways involved in SHH subgroup medulloblastoma development. RESULTS:Mutagenesis significantly increased medulloblastoma frequency and identified 17 candidate cancer genes, including orthologs of genes somatically mutated (PTEN, CREBBP) or associated with poor outcome (PTEN, MYT1L) in the human disease. Strikingly, these candidate genes were enriched for transcription factors (p=2x10-5), the majority of which (6/7; Crebbp, Myt1L, Nfia, Nfib, Tead1 and Tgif2) were linked within a single regulatory network enriched for genes associated with a differentiated neuronal phenotype. Furthermore, activity of this network varied significantly between the human subgroups, was associated with metastatic disease, and predicted poor survival specifically within the SHH subgroup of tumours. Igf2, previously implicated in medulloblastoma, was the most differentially expressed gene in murine tumours with network perturbation, and network activity in both mouse and human tumours was characterised by enrichment for multiple gene-sets indicating increased cell proliferation, IGF signalling, MYC target upregulation, and decreased neuronal differentiation. CONCLUSIONS:Collectively, our data support a model of medulloblastoma development in SB-mutagenised Ptch+/- mice which involves disruption of a novel transcription factor network leading to Igf2 upregulation, proliferation of GNPs, and tumour formation. Moreover, our results identify rational therapeutic targets for SHH subgroup tumours, alongside prognostic biomarkers for the identification of poor-risk SHH patients.

Project description:BackgroundTranscriptional regulation of gene expression in eukaryotes is usually accomplished by cooperative transcription factors (TFs). Computational identification of cooperative TF pairs has become a hot research topic and many algorithms have been proposed in the literature. A typical algorithm for predicting cooperative TF pairs has two steps. (Step 1) Define the targets of each TF under study. (Step 2) Design a measure for calculating the cooperativity of a TF pair based on the targets of these two TFs. While different algorithms have distinct sophisticated cooperativity measures, the targets of a TF are usually defined using ChIP-chip data. However, there is an inherent weakness in using ChIP-chip data to define the targets of a TF. ChIP-chip analysis can only identify the binding targets of a TF but it cannot distinguish the true regulatory from the binding but non-regulatory targets of a TF.ResultsThis work is the first study which aims to investigate whether the performance of computational identification of cooperative TF pairs could be improved by using a more biologically relevant way to define the targets of a TF. For this purpose, we propose four simple algorithms, all of which consist of two steps. (Step 1) Define the targets of a TF using (i) ChIP-chip data in the first algorithm, (ii) TF binding data in the second algorithm, (iii) TF perturbation data in the third algorithm, and (iv) the intersection of TF binding and TF perturbation data in the fourth algorithm. Compared with the first three algorithms, the fourth algorithm uses a more biologically relevant way to define the targets of a TF. (Step 2) Measure the cooperativity of a TF pair by the statistical significance of the overlap of the targets of these two TFs using the hypergeometric test. By adopting four existing performance indices, we show that the fourth proposed algorithm (PA4) significantly out performs the other three proposed algorithms. This suggests that the computational identification of cooperative TF pairs is indeed improved when using a more biologically relevant way to define the targets of a TF. Strikingly, the prediction results of our simple PA4 are more biologically meaningful than those of the 12 existing sophisticated algorithms in the literature, all of which used ChIP-chip data to define the targets of a TF. This suggests that properly defining the targets of a TF may be more important than designing sophisticated cooperativity measures. In addition, our PA4 has the power to predict several experimentally validated cooperative TF pairs, which have not been successfully predicted by any existing algorithms in the literature.ConclusionsThis study shows that the performance of computational identification of cooperative TF pairs could be improved by using a more biologically relevant way to define the targets of a TF. The main contribution of this study is not to propose another new algorithm but to provide a new thinking for the research of computational identification of cooperative TF pairs. Researchers should put more effort on properly defining the targets of a TF (i.e. Step 1) rather than totally focus on designing sophisticated cooperativity measures (i.e. Step 2). The lists of TF target genes, the Matlab codes and the prediction results of the four proposed algorithms could be downloaded from our companion website http://cosbi3.ee.ncku.edu.tw/TFI/.

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Molecular Tools for the Yeast Papiliotrema terrestris LS28 and Identification of Yap1 as a Transcription Factor Involved in Biocontrol Activity.

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