Expression and Localization of microRNAs in Perinatal Rat Pancreas: Role of miR-21 in Regulation of Cholesterol Metabolism

OBJECTIVE: To investigate the expression of pancreatic microRNAs (miRNAs) during the period of perinatal beta-cell expansion and maturation in rats, determine the localization of these miRNAs and perform a pathway analysis with predicted target mRNAs expressed in perinatal pancreas. RESEARCH DESIGN AND METHODS: RNA was extracted from whole pancreas at embryonic day 20 (E20), on the day of birth (P0) and two days after birth (P2) and hybridized to miRNA microarrays. Differentially expressed miRNAs were verified by northern blotting and their pancreatic localization determined by in situ hybridization. Pathway analysis was done using regulated sets of mRNAs predicted as targets of the miRNAs. Possible target genes were tested using reporter-gene analysis in INS-1E cells. RESULTS: Nine miRNAs were differentially expressed perinatally, seven were confirmed to be regulated at the level of the mature miRNA. The localization studies showed endocrine localization of six of these miRNAs (miR-21, -23a, -29a, -125b-5p, -376b-3p and -451), and all were expressed in exocrine cells at one time point at least. Pathways involving metabolic processes, terpenoid and sterol metabolism were selectively affected by concomitant regulation by miRNAs and mRNAs, and Srebf1 was validated as a target of miR-21. CONCLUSIONS: The findings suggest that miRNAs are involved in the functional maturation of pancreatic exocrine and endocrine tissue following birth. Pathway analysis of target genes identify changes in sterol metabolism around birth as being selectively affected by differential miRNA expression during this period

Identification of DHX9 as a cell cycle regulated nucleolar recruitment factor for CIZ1

CIP1-interacting zinc finger protein 1 (CIZ1) is a nuclear matrix associated protein that facilitates a number of nuclear functions including initiation of DNA replication, epigenetic maintenance and associates with the inactive X-chromosome. Here, to gain more insight into the protein networks that underpin this diverse functionality, molecular panning and mass spectrometry are used to identify protein interaction partners of CIZ1, and CIZ1 replication domain (CIZ1-RD). STRING analysis of CIZ1 interaction partners identified 2 functional clusters: ribosomal subunits and nucleolar proteins including the DEAD box helicases, DHX9, DDX5 and DDX17. DHX9 shares common functions with CIZ1, including interaction with XIST long-non-coding RNA, epigenetic maintenance and regulation of DNA replication. Functional characterisation of the CIZ1-DHX9 complex showed that CIZ1-DHX9 interact in vitro and dynamically colocalise within the nucleolus from early to mid S-phase. CIZ1-DHX9 nucleolar colocalisation is dependent upon RNA polymerase I activity and is abolished by depletion of DHX9. In addition, depletion of DHX9 reduced cell cycle progression from G1 to S-phase in mouse fibroblasts. The data suggest that DHX9-CIZ1 are required for efficient cell cycle progression at the G1/S transition and that nucleolar recruitment is integral to their mechanism of action

A Machine Learning Approach to Test Data Generation:A Case Study in Evaluation of Gene Finders

Abstract. Programs for gene prediction in computational biology are examples of systems for which the acquisition of authentic test data is difficult as these require years of extensive research. This has lead to test methods based on semiartificially produced test data, often produced by ad hoc techniques complemented by statistical models such as Hidden Markov Models (HMM). The quality of such a test method depends on how well the test data reflect the regularities in known data and how well they generalize these regularities. So far only very simplified and generalized, artificial data sets have been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here, showing parts of a model under development for genetic sequences and indicate first initial experiments producing test data for evaluation of existing gene finders, exemplified by GENSCAN, HMMGene and genemark.hmm.