Depletion of Ric-8B leads to reduced mTORC2 activity.

mTOR, a serine/threonine protein kinase that is involved in a series of critical cellular processes, can be found in two functionally distinct complexes, mTORC1 and mTORC2. In contrast to mTORC1, little is known about the mechanisms that regulate mTORC2. Here we show that mTORC2 activity is reduced in mice with a hypomorphic mutation of the Ric-8B gene. Ric-8B is a highly conserved protein that acts as a non-canonical guanine nucleotide exchange factor (GEF) for heterotrimeric Gαs/olf type subunits. We found that Ric-8B hypomorph embryos are smaller than their wild type littermates, fail to close the neural tube in the cephalic region and die during mid-embryogenesis. Comparative transcriptome analysis revealed that signaling pathways involving GPCRs and G proteins are dysregulated in the Ric-8B mutant embryos. Interestingly, this analysis also revealed an unexpected impairment of the mTOR signaling pathway. Phosphorylation of Akt at Ser473 is downregulated in the Ric-8B mutant embryos, indicating a decreased activity of mTORC2. Knockdown of the endogenous Ric-8B gene in cultured cell lines leads to reduced phosphorylation levels of Akt (Ser473), further supporting the involvement of Ric-8B in mTORC2 activity. Our results reveal a crucial role for Ric-8B in development and provide novel insights into the signals that regulate mTORC2.

A guide for building biological pathways along with two case studies: hair and breast development.

Genomic information is being underlined in the format of biological pathways. Building these biological pathways is an ongoing demand and benefits from methods for extracting information from biomedical literature with the aid of text-mining tools. Here we hopefully guide you in the attempt of building a customized pathway or chart representation of a system. Our manual is based on a group of software designed to look at biointeractions in a set of abstracts retrieved from PubMed. However, they aim to support the work of someone with biological background, who does not need to be an expert on the subject and will play the role of manual curator while designing the representation of the system, the pathway. We therefore illustrate with two challenging case studies: hair and breast development. They were chosen for focusing on recent acquisitions of human evolution. We produced sub-pathways for each study, representing different phases of development. Differently from most charts present in current databases, we present detailed descriptions, which will additionally guide PESCADOR users along the process. The implementation as a web interface makes PESCADOR a unique tool for guiding the user along the biointeractions, which will constitute a novel pathway.

ICRmax: an optimized approach to detect tumor-specific interchromosomal rearrangements for clinical application.

Somatically acquired chromosomal rearrangements occur at early stages during tumorigenesis and can be used to indirectly detect tumor cells, serving as highly sensitive and tumor-specific biomarkers. Advances in high-throughput sequencing have allowed the genome-wide identification of patient-specific chromosomal rearrangements to be used as personalized biomarkers to efficiently assess response to treatment, detect residual disease and monitor disease recurrence. However, sequencing and data processing costs still represent major obstacles for the widespread application of personalized biomarkers in oncology. We developed a computational pipeline (ICRmax) for the cost-effective identification of a minimal set of tumor-specific interchromosomal rearrangements (ICRs). We examined ICRmax performance on sequencing data from rectal tumors and simulated data achieving an average accuracy of 68% for ICR identification. ICRmax identifies ICRs from low-coverage sequenced tumors, eliminates the need to sequence a matched normal tissue and significantly reduces the costs that limit the utilization of personalized biomarkers in the clinical setting.

PESCADOR, a web-based tool to assist text-mining of biointeractions extracted from PubMed queries.

BACKGROUND: Biological function is greatly dependent on the interactions of proteins with other proteins and genes. Abstracts from the biomedical literature stored in the NCBI's PubMed database can be used for the derivation of interactions between genes and proteins by identifying the co-occurrences of their terms. Often, the amount of interactions obtained through such an approach is large and may mix processes occurring in different contexts. Current tools do not allow studying these data with a focus on concepts of relevance to a user, for example, interactions related to a disease or to a biological mechanism such as protein aggregation. RESULTS: To help the concept-oriented exploration of such data we developed PESCADOR, a web tool that extracts a network of interactions from a set of PubMed abstracts given by a user, and allows filtering the interaction network according to user-defined concepts. We illustrate its use in exploring protein aggregation in neurodegenerative disease and in the expansion of pathways associated to colon cancer. CONCLUSIONS: PESCADOR is a platform independent web resource available at: http://cbdm.mdc-berlin.de/tools/pescador/

Uncovering the mouse olfactory long non-coding transcriptome with a novel machine-learning model.

Very little is known about long non-coding RNAs (lncRNAs) in the mammalian olfactory sensory epithelia. Deciphering the non-coding transcriptome in olfaction is relevant because these RNAs have been shown to play a role in chromatin modification and nuclear architecture reorganization, processes that accompany olfactory differentiation and olfactory receptor gene choice, one of the most poorly understood gene regulatory processes in mammals. In this study, we used a combination of in silico and ex vivo approaches to uncover a comprehensive catalogue of olfactory lncRNAs and to investigate their expression in the mouse olfactory organs. Initially, we used a novel machine-learning lncRNA classifier to discover hundreds of annotated and unannotated lncRNAs, some of which were predicted to be preferentially expressed in the main olfactory epithelium and the vomeronasal organ, the most important olfactory structures in the mouse. Moreover, we used whole-tissue and single-cell RNA sequencing data to discover lncRNAs expressed in mature sensory neurons of the main epithelium. Candidate lncRNAs were further validated by in situ hybridization and RT-PCR, leading to the identification of lncRNAs found throughout the olfactory epithelia, as well as others exquisitely expressed in subsets of mature olfactory neurons or progenitor cells.