Human transcription factor and protein kinase gene fusions in human cancer.

Oncogenic gene fusions are estimated to account for up-to 20% of cancer morbidity. Recently sequence-level studies have established oncofusions throughout all tissue types. However, the functional implications of the identified oncofusions have often not been investigated. In this study, identified oncofusions from a fusion detection approach (DEEPEST) were analyzed in detail. Of the 28,863 oncofusions, we found almost 30% are expected to produce functional proteins with features from both parent genes. Kinases and transcription factors were the main gene families of the protein producing fusions. Considering their role as initiators, actors, and termination points of cellular signaling pathways, we focused our in-depth analyses on them. Domain architecture of the fusions and their wild-type interactors suggests that abnormal molecular context of protein domains caused by fusion events may unlock the oncogenic potential of the wild type counterparts of the fusion proteins. To understand overall oncofusion effects, we performed differential expression analysis using TCGA cancer project samples. Results indicated oncofusion-specific alterations in gene expression levels, and lower expression levels of components of key cellular pathways, in particular signal transduction and transcription regulation. The sum of results suggests that kinase and transcription factor oncofusions deregulate cellular signaling, possibly via acquiring novel functions.

Combined proximity labeling and affinity purification-mass spectrometry workflow for mapping and visualizing protein interaction networks.

Affinity purification coupled with mass spectrometry (AP-MS) and proximity-dependent biotinylation identification (BioID) methods have made substantial contributions to interaction proteomics studies. Whereas AP-MS results in the identification of proteins that are in a stable complex, BioID labels and identifies proteins that are in close proximity to the bait, resulting in overlapping yet distinct protein identifications. Integration of AP-MS and BioID data has been shown to comprehensively characterize a protein's molecular context, but interactome analysis using both methods in parallel is still labor and resource intense with respect to cell line generation and protein purification. Therefore, we developed the Multiple Approaches Combined (MAC)-tag workflow, which allows for both AP-MS and BioID analysis with a single construct and with almost identical protein purification and mass spectrometry (MS) identification procedures. We have applied the MAC-tag workflow to a selection of subcellular markers to provide a global view of the cellular protein interactome landscape. This localization database is accessible via our online platform ( http://proteomics.fi ) to predict the cellular localization of a protein of interest (POI) depending on its identified interactors. In this protocol, we present the detailed three-stage procedure for the MAC-tag workflow: (1) cell line generation for the MAC-tagged POI; (2) parallel AP-MS and BioID protein purification followed by MS analysis; and (3) protein interaction data analysis, data filtration and visualization with our localization visualization platform. The entire procedure can be completed within 25 d.

An AP-MS- and BioID-compatible MAC-tag enables comprehensive mapping of protein interactions and subcellular localizations.

Protein-protein interactions govern almost all cellular functions. These complex networks of stable and transient associations can be mapped by affinity purification mass spectrometry (AP-MS) and complementary proximity-based labeling methods such as BioID. To exploit the advantages of both strategies, we here design and optimize an integrated approach combining AP-MS and BioID in a single construct, which we term MAC-tag. We systematically apply the MAC-tag approach to 18 subcellular and 3 sub-organelle localization markers, generating a molecular context database, which can be used to define a protein's molecular location. In addition, we show that combining the AP-MS and BioID results makes it possible to obtain interaction distances within a protein complex. Taken together, our integrated strategy enables the comprehensive mapping of the physical and functional interactions of proteins, defining their molecular context and improving our understanding of the cellular interactome.

Sequence analysis of pooled bacterial samples enables identification of strain variation in group A streptococcus.

Knowledge of the genomic variation among different strains of a pathogenic microbial species can help in selecting optimal candidates for diagnostic assays and vaccine development. Pooled sequencing (Pool-seq) is a cost effective approach for population level genetic studies that require large numbers of samples such as various strains of a microbe. To test the use of Pool-seq in identifying variation, we pooled DNA of 100 Streptococcus pyogenes strains of different emm types in two pools, each containing 50 strains. We used four variant calling tools (Freebayes, UnifiedGenotyper, SNVer, and SAMtools) and one emm1 strain, SF370, as a reference genome. In total 63719 SNPs and 164 INDELs were identified in the two pools concordantly by at least two of the tools. Majority of the variants (93.4%) from six individually sequenced strains used in the pools could be identified from the two pools and 72.3% and 97.4% of the variants in the pools could be mined from the analysis of the 44 complete Str. pyogenes genomes and 3407 sequence runs deposited in the European Nucleotide Archive respectively. We conclude that DNA sequencing of pooled samples of large numbers of bacterial strains is a robust, rapid and cost-efficient way to discover sequence variation.