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Characterization of GPR101 transcript structure and expression patterns.

ABSTRACT: We recently showed that Xq26.3 microduplications cause X-linked acrogigantism (X-LAG). X-LAG patients mainly present with growth hormone and prolactin-secreting adenomas and share a minimal duplicated region containing at least four genes. GPR101 was the only gene highly expressed in their pituitary lesions, but little is known about its expression patterns. In this work, GPR101 transcripts were characterized in human tissues by 5'-Rapid Amplification of cDNA Ends (RACE) and RNAseq, while the putative promoter was bioinformatically predicted. We investigated GPR101 mRNA and protein expression by RT-quantitative PCR (qPCR), whole-mount in situ hybridization, and immunostaining, in human, rhesus monkey, rat and zebrafish. We identified four GPR101 isoforms characterized by different 5'-untranslated regions (UTRs) and a common 6.1kb long 3'UTR. GPR101 expression was very low or absent in almost all adult human tissues examined, except for specific brain regions. Strong GPR101 staining was observed in human fetal pituitary and during adolescence, whereas very weak/absent expression was detected during childhood and adult life. In contrast to humans, adult monkey and rat pituitaries expressed GPR101, but in different cell types. Gpr101 is expressed in the brain and pituitary during rat and zebrafish development; in rat pituitary, Gpr101 is expressed only after birth and shows sexual dimorphism. This study shows that different GPR101 transcripts exist and that the brain is the major site of GPR101 expression across different species, although divergent species- and temporal-specific expression patterns are evident. These findings suggest an important role for GPR101 in brain and pituitary development and likely reflect the very different growth, development and maturation patterns among species.

SUBMITTER: Trivellin G

PROVIDER: S-EPMC4959428 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Characterization of GPR101 transcript structure and expression patterns.

Trivellin Giampaolo G Bjelobaba Ivana I Daly Adrian F AF Larco Darwin O DO Palmeira Leonor L Faucz Fabio R FR Thiry Albert A Leal Letícia F LF Rostomyan Liliya L Quezado Martha M Schernthaner-Reiter Marie Helene MH Janjic Marija M MM Villa Chiara C Wu T John TJ Stojilkovic Stanko S SS Beckers Albert A Feldman Benjamin B Stratakis Constantine A CA

Journal of molecular endocrinology 20160609 2

We recently showed that Xq26.3 microduplications cause X-linked acrogigantism (X-LAG). X-LAG patients mainly present with growth hormone and prolactin-secreting adenomas and share a minimal duplicated region containing at least four genes. GPR101 was the only gene highly expressed in their pituitary lesions, but little is known about its expression patterns. In this work, GPR101 transcripts were characterized in human tissues by 5'-Rapid Amplification of cDNA Ends (RACE) and RNAseq, while the pu ...[more]

PMID: 27282544

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Project description:BackgroundLong-term survival in numerous cancers often correlates with specific whole transcriptome profiles or the expression patterns of smaller numbers of transcripts. In some instances, these are better predictors of survival than are standard classification methods such as clinical stage or hormone receptor status in breast cancer. Here, we have used the method of "t-distributed stochastic neighbor embedding" (t-SNE) to show that, collectively, the expression patterns of small numbers of functionally-related transcripts from fifteen cancer pathways correlate with long-term survival in the vast majority of tumor types from The Cancer Genome Atlas (TCGA). We then ask whether the sequential application of t-SNE using the transcripts from a second pathway improves predictive capability or whether t-SNE can be used to refine the initial predictive power of whole transcriptome profiling.MethodsRNAseq data from 10,227 tumors in TCGA were previously analyzed using t-SNE-based clustering of 362 transcripts comprising 15 distinct cancer-related pathways. After showing that certain clusters were associated with differential survival, each relevant cluster was re-analyzed by t-SNE with a second pathway's transcripts. Alternatively, groups with differential survival identified by whole transcriptome profiling were subject to a second, t-SNE-based analysis.ResultsSequential analyses employing either t-SNE➔t-SNE or whole transcriptome profiling➔t-SNE analyses were in many cases superior to either individual method at predicting long-term survival. We developed a dynamic and intuitive R Shiny web application to explore the t-SNE based transcriptome clustering and survival analysis across all TCGA cancers and all 15 cancer-related pathways in this analysis. This application provides a simple interface to select specific t-SNE clusters and analyze survival predictability using both individual or sequential approaches. The user can recreate the relationships described in this analysis and further explore many different cancer, pathway, and cluster combinations. Non-R users can access the application on the web at https://chpupsom19.shinyapps.io/Survival_Analysis_tsne_umap_TCGA. The application, R scripts performing survival analysis, and t-SNE clustering results of TCGA expression data can be accessed on GitHub enabling users to download and run the application locally with ease (https://github.com/RavulaPitt/Sequential-t-SNE/).ConclusionsThe long-term survival of patients correlated with expression patterns of 362 transcripts from 15 cancer-related pathways. In numerous cases, however, survival could be further improved when the cohorts were re-analyzed using iterative t-SNE clustering or when t-SNE clustering was applied to cohorts initially segregated by whole transcriptome-based hierarchical clustering.

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Characterization of GPR101 transcript structure and expression patterns.

Publications

Characterization of GPR101 transcript structure and expression patterns.

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