Nimbus: a design-driven analyses suite for amplicon-based NGS data.

Motivation: PCR-based DNA enrichment followed by massively parallel sequencing is a straightforward and cost effective method to sequence genes up to high depth. The full potential of amplicon-based sequencing assays is currently not achieved as analysis methods do not take into account the source amplicons of the detected variants. Tracking the source amplicons has the potential to identify systematic biases, enhance variant calling and improve the designs of future assays. Results: We present Nimbus, a software suite for the analysis of amplicon-based sequencing data. Nimbus includes tools for data pre-processing, alignment, single nucleotide polymorphism (SNP), insertion and deletion calling, quality control and visualization. Nimbus can detect SNPs in its alignment seeds and reduces alignment issues by the usage of decoy amplicons. Tracking the amplicons throughout analysis allows easy and fast design optimization by amplicon performance comparison. It enables detection of probable false positive variants present in a single amplicon from real variants present in multiple amplicons and provides multiple sample visualization. Nimbus was tested using HaloPlex Exome datasets and outperforms other callers for low-frequency variants. The variants called by Nimbus were highly concordant between twin samples and SNP-arrays. The Nimbus suite provides an end-to-end solution for variant calling, design optimization and visualization of amplicon-derived next-generation sequencing datasets. Availability and implementation: https://github.com/erasmus-center-for-biomics/Nimbus. Supplementary information: Supplementary data are available at Bioinformatics online.

A kinase inhibitor screen reveals MEK1/2 as a novel therapeutic target to antagonize IGF1R-mediated antiestrogen resistance in ERα-positive luminal breast cancer.

Antiestrogen resistance of breast cancer has been related to enhanced growth factor receptor expression and activation. We have previously shown that ectopic expression and subsequent activation of the insulin-like growth factor-1 receptor (IGF1R) or the epidermal growth factor receptor (EGFR) in MCF7 or T47D breast cancer cells results in antiestrogen resistance. In order to identify novel therapeutic targets to prevent this antiestrogen resistance, we performed kinase inhibitor screens with 273 different inhibitors in MCF7 cells overexpressing IGF1R or EGFR. Kinase inhibitors that antagonized antiestrogen resistance but are not directly involved in IGF1R or EGFR signaling were prioritized for further analyses. Various ALK (anaplastic lymphoma receptor tyrosine kinase) inhibitors inhibited cell proliferation in IGF1R expressing cells under normal and antiestrogen resistance conditions by preventing IGF1R activation and subsequent downstream signaling; the ALK inhibitors did not affect EGFR signaling. On the other hand, MEK (mitogen-activated protein kinase kinase)1/2 inhibitors, including PD0325901, selumetinib, trametinib and TAK-733, selectively antagonized IGF1R signaling-mediated antiestrogen resistance but did not affect cell proliferation under normal growth conditions. RNAseq analysis revealed that MEK inhibitors PD0325901 and selumetinib drastically altered cell cycle progression and cell migration networks under IGF1R signaling-mediated antiestrogen resistance. In a group of 219 patients with metastasized ER + breast cancer, strong pMEK staining showed a significant correlation with no clinical benefit of first-line tamoxifen treatment. We propose a critical role for MEK activation in IGF1R signaling-mediated antiestrogen resistance and anticipate that dual-targeted therapy with a MEK inhibitor and antiestrogen could improve treatment outcome.

An interaction network of mental disorder proteins in neural stem cells.

Mental disorders (MDs) such as intellectual disability (ID), autism spectrum disorders (ASD) and schizophrenia have a strong genetic component. Recently, many gene mutations associated with ID, ASD or schizophrenia have been identified by high-throughput sequencing. A substantial fraction of these mutations are in genes encoding transcriptional regulators. Transcriptional regulators associated with different MDs but acting in the same gene regulatory network provide information on the molecular relation between MDs. Physical interaction between transcriptional regulators is a strong predictor for their cooperation in gene regulation. Here, we biochemically purified transcriptional regulators from neural stem cells, identified their interaction partners by mass spectrometry and assembled a protein interaction network containing 206 proteins, including 68 proteins mutated in MD patients and 52 proteins significantly lacking coding variation in humans. Our network shows molecular connections between established MD proteins and provides a discovery tool for novel MD genes. Network proteins preferentially co-localize on the genome and cooperate in disease-relevant gene regulation. Our results suggest that the observed transcriptional regulators associated with ID, ASD or schizophrenia are part of a transcriptional network in neural stem cells. We find that more severe mutations in network proteins are associated with MDs that include lower intelligence quotient (IQ), suggesting that the level of disruption of a shared transcriptional network correlates with cognitive dysfunction.