Poly(A) binding protein nuclear 1 levels affect alternative polyadenylation.

The choice for a polyadenylation site determines the length of the 3'-untranslated region (3'-UTRs) of an mRNA. Inclusion or exclusion of regulatory sequences in the 3'-UTR may ultimately affect gene expression levels. Poly(A) binding protein nuclear 1 (PABPN1) is involved in polyadenylation of pre-mRNAs. An alanine repeat expansion in PABPN1 (exp-PABPN1) causes oculopharyngeal muscular dystrophy (OPMD). We hypothesized that previously observed disturbed gene expression patterns in OPMD muscles may have been the result of an effect of PABPN1 on alternative polyadenylation, influencing mRNA stability, localization and translation. A single molecule polyadenylation site sequencing method was developed to explore polyadenylation site usage on a genome-wide level in mice overexpressing exp-PABPN1. We identified 2012 transcripts with altered polyadenylation site usage. In the far majority, more proximal alternative polyadenylation sites were used, resulting in shorter 3'-UTRs. 3'-UTR shortening was generally associated with increased expression. Similar changes in polyadenylation site usage were observed after knockdown or overexpression of expanded but not wild-type PABPN1 in cultured myogenic cells. Our data indicate that PABPN1 is important for polyadenylation site selection and that reduced availability of functional PABPN1 in OPMD muscles results in use of alternative polyadenylation sites, leading to large-scale deregulation of gene expression.

The identification of informative genes from multiple datasets with increasing complexity.

BACKGROUND: In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. RESULTS: In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. CONCLUSIONS: We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events.

Quasispecies composition and evolution of a typical Zika virus clinical isolate from Suriname.

The arthropod-borne Zika virus (ZIKV) is currently causing a major international public health threat in the Americas. This study describes the isolation of ZIKV from the plasma of a 29-year-old female traveler that developed typical symptoms, like rash, fever and headache upon return from Suriname. The complete genome sequence including the 5' and 3' untranslated regions was determined and phylogenetic analysis showed the isolate clustering within the Asian lineage, close to other viruses that have recently been isolated in the Americas. In addition, the viral quasispecies composition was analyzed by single molecule real time sequencing, which suggested a mutation frequency of 1.4 × 10-4 for this ZIKV isolate. Continued passaging of the virus in cell culture led to the selection of variants with mutations in NS1 and the E protein. The latter might influence virus binding to cell surface heparan sulfate.