Members of the genus Streptomyces are Gram-positive bacteria that are used as important cell factories to produce secondary metabolites and secrete heterologous proteins. They possess some of the largest bacterial genomes and thus proteomes. Understanding their complex proteomes and metabolic regulation will improve any genetic engineering approach. Here, we performed a comprehensive annotation of the subcellular localization of the proteome of Streptomyces lividans TK24 and developed the Subcellular Topology of Polypeptides in Streptomyces database (SToPSdb) to make this information widely accessible. We first introduced a uniform, improved nomenclature that re-annotated the names of ~ 4000 proteins based on functional and structural information. Then protein localization was assigned de novo using prediction tools and edited by manual curation for 7494 proteins, including information for 183 proteins that resulted from a recent genome re-annotation and are not available in current databases. The S. lividans proteome was also linked with those of other model bacterial strains including Streptomyces coelicolor A3(2) and Escherichia coli K-12, based on protein homology, and can be accessed through an open web interface. Finally, experimental data derived from proteomics experiments have been incorporated and provide validation for protein existence or topology for 579 proteins. Proteomics also reveals proteins released from vesicles that bleb off the membrane. All export systems known in S. lividans are also presented and exported proteins assigned export routes, where known. SToPSdb provides an updated and comprehensive protein localization annotation resource for S. lividans and other streptomycetes. It forms the basis for future linking to databases containing experimental data of proteomics, genomics and metabolomics studies for this organism.

Mantle cell lymphoma (MCL) is a rare, incurable, and aggressive B-cell non-Hodgkin lymphoma (NHL). Early MCL diagnosis and treatment is critical and puzzling due to inter/intra-tumoral heterogeneity and limited understanding of the underlying molecular mechanisms. We developed and applied a multifaceted analysis of selected publicly available transcriptomic data of well-defined MCL stages, integrating network-based methods for pathway enrichment analysis, co-expression module alignment, drug repurposing, and prediction of effective drug combinations. We demonstrate the “butterfly effect” emerging from a small set of initially differentially expressed genes, rapidly expanding into numerous deregulated cellular processes, signaling pathways, and core machineries as MCL becomes aggressive. We explore pathogenicity-related signaling circuits by detecting common co-expression modules in MCL stages, pointing out, among others, the role of VEGFA and SPARC proteins in MCL progression and recommend further study of precise drug combinations. Our findings highlight the benefit that can be leveraged by such an approach for better understanding pathobiology and identifying high-priority novel diagnostic and prognostic biomarkers, drug targets, and efficacious combination therapies against MCL that should be further validated for their clinical impact.