HoxA13 Regulates Phenotype Regionalization of Human Pregnant Myometrium.

CONTEXT: Bipedalism separates humans from most other animal species, but results in significant physiologic challenges, particularly with respect to the maintenance of pregnancy and induction of parturition. A contracted lower uterine segment (LUS) and a relaxed uterine fundal myometrium (FUN) during pregnancy are required to prevent pressure on the cervix from the fetal head due to gravity. With the onset of labor, this regionalization of myometrial function must be reversed, allowing descent of the fetus, dilation of the cervix, and expulsion of the fetus through the birth canal. However, the molecular mechanisms remain unclear. OBJECTIVE AND DESIGN: This study sought to identify phenotypic regionalization of LUS and FUN during pregnancy, RNA sequencing was performed to analyze the human myometrial transcriptome. Real-time PCR and immunoblotting were applied to validate sequencing results. Cell contraction/adhesion assays and gene microarrays were used to study the cellular functions of the identified genes. RESULTS: Homeobox A13 (HoxA13), prostacyclin synthase (PTGIS), and periostin (POSTN) genes are more highly expressed in LUS than FUN of nonlaboring, but not laboring, myometrial cells at term. HoxA13 up-regulates transcription of PTGIS and POSTN genes. Elevated HoxA13 expression enhances myometrial cell contractility and cell-cell adhesion. Gene microarray studies show that HoxA13-regulated genes are associated with immune response, gap junction/cell adhesion, and pregnancy. CONCLUSION: The LUS expresses higher levels of HoxA13, PTGIS, and POSTN, and is more contractile than the FUN at term prior to labor. This pregnancy-maintaining regionalization of myometrial function may be mediated by HoxA13.

A pan-cancer analysis of alternative splicing events reveals novel tumor-associated splice variants of matriptase.

High-throughput transcriptome sequencing allows identification of cancer-related changes that occur at the stages of transcription, pre-messenger RNA (mRNA), and splicing. In the current study, we devised a pipeline to predict novel alternative splicing (AS) variants from high-throughput transcriptome sequencing data and applied it to large sets of tumor transcriptomes from The Cancer Genome Atlas (TCGA). We identified two novel tumor-associated splice variants of matriptase, a known cancer-associated gene, in the transcriptome data from epithelial-derived tumors but not normal tissue. Most notably, these variants were found in 69% of lung squamous cell carcinoma (LUSC) samples studied. We confirmed the expression of matriptase AS transcripts using quantitative reverse transcription PCR (qRT-PCR) in an orthogonal panel of tumor tissues and cell lines. Furthermore, flow cytometric analysis confirmed surface expression of matriptase splice variants in chinese hamster ovary (CHO) cells transiently transfected with cDNA encoding the novel transcripts. Our findings further implicate matriptase in contributing to oncogenic processes and suggest potential novel therapeutic uses for matriptase splice variants.

Bioinformatics analysis identify novel OB fold protein coding genes in C. elegans.

BACKGROUND: The C. elegans genome has been extensively annotated by the WormBase consortium that uses state of the art bioinformatics pipelines, functional genomics and manual curation approaches. As a result, the identification of novel genes in silico in this model organism is becoming more challenging requiring new approaches. The Oligonucleotide-oligosaccharide binding (OB) fold is a highly divergent protein family, in which protein sequences, in spite of having the same fold, share very little sequence identity (5-25%). Therefore, evidence from sequence-based annotation may not be sufficient to identify all the members of this family. In C. elegans, the number of OB-fold proteins reported is remarkably low (n=46) compared to other evolutionary-related eukaryotes, such as yeast S. cerevisiae (n=344) or fruit fly D. melanogaster (n=84). Gene loss during evolution or differences in the level of annotation for this protein family, may explain these discrepancies. METHODOLOGY/PRINCIPAL FINDINGS: This study examines the possibility that novel OB-fold coding genes exist in the worm. We developed a bioinformatics approach that uses the most sensitive sequence-sequence, sequence-profile and profile-profile similarity search methods followed by 3D-structure prediction as a filtering step to eliminate false positive candidate sequences. We have predicted 18 coding genes containing the OB-fold that have remarkably partially been characterized in C. elegans. CONCLUSIONS/SIGNIFICANCE: This study raises the possibility that the annotation of highly divergent protein fold families can be improved in C. elegans. Similar strategies could be implemented for large scale analysis by the WormBase consortium when novel versions of the genome sequence of C. elegans, or other evolutionary related species are being released. This approach is of general interest to the scientific community since it can be used to annotate any genome.

Predicting protein complexes by data integration of different types of interactions.

The explosion of high throughput interaction data from proteomics studies gives us the opportunity to integrate Protein-Protein Interactions (PPI) from different type of interactions. These methods rely on the assumption that proteins within a complex have more interactions across the different data sets which translate into the identification of dense subgraphs. However, the relative importance of the types of interaction are not equivalent in their reliability and accuracy consequently they should be analysed separately. Here we propose a method that use graph theory and mathematical modelling to solve this problem. Our approach has four steps that: i) score independently each type of interaction; ii) build an interaction specific networks for each type; iii) weight the specific networks; and iv) combine and normalise the scores. Using this approach to the BRCA1 Associated genome Surveillance Complex (BASC), we correctly identified the known core components of the complex and subcomplexes that have solved structures as well as predicted new interactions and core complexes. The method presented in this study is of general use. It is flexible enough to allow the development of any scoring system and can be applied to any protein complex to provide the latest knowledge in its interactions and structure.