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Novel eye genes systematically discovered through an integrated analysis of mouse transcriptomes and phenome.

ABSTRACT: In the last few decades, reverse genetic and high throughput approaches have been frequently applied to the mouse (Mus musculus) to understand how genes function in tissues/organs and during development in a mammalian system. Despite these efforts, the associated phenotypes for the majority of mouse genes remained to be fully characterized. Here, we performed an integrated transcriptome-phenome analysis by identifying coexpressed gene modules based on tissue transcriptomes profiled with each of various platforms and functionally interpreting these modules using the mouse phenotypic data. Consequently, >15,000 mouse genes were linked with at least one of the 47 tissue functions that were examined. Specifically, our approach predicted >50 genes previously unknown to be involved in mice (Mus musculus) visual functions. Fifteen genes were selected for further analysis based on their potential biomedical relevance and compatibility with further experimental validation. Gene-specific morpholinos were introduced into zebrafish (Danio rerio) to target their corresponding orthologs. Quantitative assessments of phenotypes of developing eyes confirmed predicted eye-related functions of 13 out of the 15 genes examined. These novel eye genes include: Adal, Ankrd33, Car14, Ccdc126, Dhx32, Dkk3, Fam169a, Grifin, Kcnj14, Lrit2, Ppef2, Ppm1n, and Wdr17. The results highlighted the potential for this phenome-based approach to assist the experimental design of mutating and phenotyping mouse genes that aims to fully reveal the functional landscape of mammalian genomes.

SUBMITTER: Chiang CY

PROVIDER: S-EPMC6951830 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMicroarray technology enables a standardized, objective assessment of oncological diagnosis and prognosis. However, such studies are typically specific to certain cancer types, and the results have limited use due to inadequate validation in large patient cohorts. Discovery of genes commonly regulated in cancer may have an important implication in understanding the common molecular mechanism of cancer.Methods and findingsWe described an integrated gene-expression analysis of 2,186 samples from 39 studies to identify and validate a cancer type-independent gene signature that can identify cancer patients for a wide variety of human malignancies. The commonness of gene expression in 20 types of common cancer was assessed in 20 training datasets. The discriminative power of a signature defined by these common cancer genes was evaluated in the other 19 independent datasets including novel cancer types. QRT-PCR and tissue microarray were used to validate commonly regulated genes in multiple cancer types. We identified 187 genes dysregulated in nearly all cancerous tissue samples. The 187-gene signature can robustly predict cancer versus normal status for a wide variety of human malignancies with an overall accuracy of 92.6%. We further refined our signature to 28 genes confirmed by QRT-PCR. The refined signature still achieved 80% accuracy of classifying samples from mixed cancer types. This signature performs well in the prediction of novel cancer types that were not represented in training datasets. We also identified three biological pathways including glycolysis, cell cycle checkpoint II and plk3 pathways in which most genes are systematically up-regulated in many types of cancer.ConclusionsThe identified signature has captured essential transcriptional features of neoplastic transformation and progression in general. These findings will help to elucidate the common molecular mechanism of cancer, and provide new insights into cancer diagnostics, prognostics and therapy.

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Novel eye genes systematically discovered through an integrated analysis of mouse transcriptomes and phenome.

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