A Rapid CRISPR/Cas-based Mutagenesis Assay in Zebrafish for Identification of Genes Involved in Thyroid Morphogenesis and Function

The foregut endoderm gives rise to several organs including liver, pancreas, lung and thyroid with important roles in human physiology. Understanding which genes and signalling pathways regulate their development is crucial for understanding developmental disorders as well as diseases in adulthood. We exploited unique advantages of the zebrafish model to develop a rapid and scalable CRISPR/Cas-based mutagenesis strategy aiming at the identification of genes involved in morphogenesis and function of the thyroid. Core elements of the mutagenesis assay comprise bi-allelic gene invalidation in somatic mutants, a non-invasive monitoring of thyroid development in live transgenic fish, complementary analyses of thyroid function in fixed specimens and quantitative analyses of mutagenesis efficiency by Illumina sequencing of individual fish. We successfully validated our mutagenesis-phenotyping strategy in experiments targeting genes with known functions in early thyroid morphogenesis (pax2a, nkx2.4b) and thyroid functional differentiation (duox, duoxa, tshr). We also demonstrate that duox and duoxa crispants phenocopy thyroid phenotypes previously observed in human patients with bi-allelic DUOX2 and DUOXA2 mutations. The proposed combination of efficient mutagenesis protocols, rapid non-invasive phenotyping and sensitive genotyping holds great potential to systematically characterize the function of larger candidate gene panels during thyroid development and is applicable to other organs and tissues.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

A general method to derive robust organ-specific gene expression-based differentiation indices: application to thyroid cancer diagnostic.

Differentiation is central to development, while dedifferentiation is central to cancer progression. Hence, a quantitative assessment of differentiation would be most useful. We propose an unbiased method to derive organ-specific differentiation indices from gene expression data and demonstrate its usefulness in thyroid cancer diagnosis. We derived a list of thyroid-specific genes by selecting automatically those genes that are expressed at higher level in the thyroid than in any other organ in a normal tissue's genome-wide gene expression compendium. The thyroid index of a tissue was defined as the median expression of these thyroid-specific genes in that tissue. As expected, the thyroid index was inversely correlated with meta-PCNA, a proliferation metagene, across a wide range of thyroid tumors. By contrast, the two indices were positively correlated in a time course of thyroid-stimulating hormone (TSH) activation of primary thyrocytes. Thus, the thyroid index captures biological information not integrated by proliferation rates. The differential diagnostic of follicular thyroid adenomas and follicular thyroid carcinoma is a notorious challenge for pathologists. The thyroid index discriminated them as accurately as did machine-learning classifiers trained on the genome-wide cancer data. Hence, although it was established exclusively from normal tissue data, the thyroid index integrates the relevant diagnostic information contained in tumoral transcriptomes. Similar results were obtained for the classification of the follicular vs classical variants of papillary thyroid cancers, that is, tumors dedifferentiating along a different route. The automated procedures demonstrated in the thyroid are applicable to other organs.Oncogene advance online publication, 23 January 2012; doi:10.1038/onc.2011.626.JOURNAL ARTICLESCOPUS: ar.jinfo:eu-repo/semantics/publishe

An explication of uncertain evidence in Bayesian networks: likelihood evidence and probabilistic evidence

This paper is closed access.This paper proposes a systematized presentation and a terminology for observations in a Bayesian network. It focuses on the three main concepts of uncertain evidence, namely likelihood evidence and fixed and not-fixed probabilistic evidence, using a review of previous literature. A probabilistic finding on a variable is specified by a local probability distribution and replaces any former belief in that variable. It is said to be fixed or not fixed regarding whether it has to be kept unchanged or not after the arrival of observation on other variables. Fixed probabilistic evidence is defined by Valtorta et al. (J Approx Reason 29(1):71–106 2002) under the name soft evidence, whereas the concept of not-fixed probabilistic evidence has been discussed by Chan and Darwiche (Artif Intell 163(1):67–90 2005). Both concepts have to be clearly distinguished from likelihood evidence defined by Pearl (1988), also called virtual evidence, for which evidence is specified as a likelihood ratio, that often represents the unreliability of the evidence. Since these three concepts of uncertain evidence are not widely understood, and the terms used to describe these concepts are not well established, most Bayesian networks engines do not offer well defined propagation functions to handle them. Firstly, we present a review of uncertain evidence and the proposed terminology, definitions and concepts related to the use of uncertain evidence in Bayesian networks. Then we describe updating algorithms for the propagation of uncertain evidence. Finally, we propose several results where the use of fixed or not-fixed probabilistic evidence is required