Anti-Biotin polyclonal antibody [AP]

Background: Exosomes are extracellular vesicles containing a variety of biological molecules including microRNAs (miRNAs). We have recently demonstrated that certain miRNA species are selectively and highly enriched in pancreatic cancer exosomes with miR-1246 being the most abundant. Exosome miRNAs have been shown to mediate intercellular communication in the tumor microenvironment and promote cancer progression. Therefore, understanding how exosomes selectively enrich specific miRNAs to initiate exosome miRNA signaling in cancer cells is critical to advancing cancer exosome biology. Results: The aim of this study was to identify RNA binding proteins responsible for selective enrichment of exosome miRNAs in cancer cells. A biotin-labeled miR-1246 probe was used to capture RNA binding proteins (RBPs) from PANC-1 cells. Among the RBPs identified through proteomic analysis, SRSF1, EIF3B and TIA1 were highly associated with the miR-1246 probe. RNA immunoprecipitation (RIP) and electrophoretic mobility shift assay (EMSA) confirmed the binding of SRSF1 to miR-1246. Lentivirus shRNA knockdown of SRSF1 in pancreatic cancer cells selectively reduced exosome miRNA enrichment whereas GFP-SRSF1 overexpression enhanced the enrichment as analyzed by next generation small RNA sequencing and qRT-PCR. miRNA sequence motif analysis identified a common motif shared by 36/45 of SRSF1-associated exosome miRNAs. EMSA confirmed that shared motif decoys inhibit the binding of SRSF1 to the miR-1246 sequence. Conclusions: We conclude that SRSF1 mediates selective exosome miRNA enrichment in pancreatic cancer cells by binding to a commonly shared miRNA sequence motif.

Author summary We have used several methods of analysis of a high-throughput transposon library dataset for the organismZymomonas mobilisin order to improve a newly developed genome-scale metabolic model. Data from individual experiments within this dataset were compared to predictions made by our draft model to determine areas of inaccuracies in the model. Correlations within the dataset were investigated in the framework of metabolic modules in the model enabling us to identify, and subsequently experimentally confirm, genes for several reactions in the model that previously lacked an associated gene. Finally, we looked at which metabolic modules correlate poorly, highlighting where metabolic knowledge gaps inZ.mobilismay still reside. Our metabolic model,iZM4_478, is one of the most complete forZ.mobilisZM4 to date, and we expect it will be a valuable tool for investigating the unique, streamlined metabolism of this organism. The data analysis approaches that we used can be easily applied by other investigators in the development of metabolic models for other organisms with similar datasets, leading to more complete assignment of genes to reactions and more accurate metabolic models. Genome-scale metabolic models have been utilized extensively in the study and engineering of the organisms they describe. Here we present the analysis of a published dataset from pooled transposon mutant fitness experiments as an approach for improving the accuracy and gene-reaction associations of a metabolic model forZymomonas mobilisZM4, an industrially relevant ethanologenic organism with extremely high glycolytic flux and low biomass yield. Gene essentiality predictions made by the draft model were compared to data from individual pooled mutant experiments to identify areas of the model requiring deeper validation. Subsequent experiments showed that some of the discrepancies between the model and dataset were caused by polar effects, mis-mapped barcodes, or mutants carrying both wild-type and transposon disrupted gene copies-highlighting potential limitations inherent to data from individual mutants in these high-throughput datasets. Therefore, we analyzed correlations in fitness scores across all 492 experiments in the dataset in the context of functionally related metabolic reaction modules identified within the model via flux coupling analysis. These correlations were used to identify candidate genes for a reaction in histidine biosynthesis lacking an annotated gene and highlight metabolic modules with poorly correlated gene fitness scores. Additional genes for reactions involved in biotin, ubiquinone, and pyridoxine biosynthesis inZ.mobiliswere identified and confirmed using mutant complementation experiments. These discovered genes, were incorporated into the final model,iZM4_478, which contains 747 metabolic and transport reactions (of which 612 have gene-protein-reaction associations), 478 genes, and 616 unique metabolites, making it one of the most complete models ofZ.mobilisZM4 to date. The methods of analysis that we applied here with theZ.mobilistransposon mutant dataset, could easily be utilized to improve future genome-scale metabolic reconstructions for organisms where these, or similar, high-throughput datasets are available.