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Petrovic, J.

Publications and source records attributed to Petrovic, J..

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γδ T cells do not contribute to peripheral inflammatory pain

Circulating immune cells, which are recruited to the site of injury/disease, secrete various inflammatory mediators that are critical to nociception and pain. The role of tissue-resident immune cells, however, remains poorly characterized. One of the first cells to be activated in peripheral tissues following injury are {gamma}{delta} T cells, which serve important roles in infection and disease. Using a transgenic mouse line lacking these cells, we sought to identify their contribution to inflammatory pain. Three distinct models of inflammatory pain were used: intraplantar injection of formalin and incisional wound (as models of acute inflammatory pain) and intraplantar injection of complete Freunds adjuvant (as a model of chronic inflammatory pain). Our results show that absence of these cells does not alter baseline sensitivity, nor does it result in changes to mechanical or thermal hypersensitivity after tissue injury. These results were consistent in both male and female mice, suggesting that there are no sex differences in these outcomes. This comprehensive characterization suggests that {gamma}{delta} T cells do not contribute to basal sensitivity or the development and maintenance of inflammatory pain.

neuroscience

Differential Integration of Transcriptome and Proteome Identifies Pan-cancer Prognostic Biomarkers

High-throughput analysis of the transcriptome and proteome individually are used to interrogate complex oncogenic processes in cancer. However, an outstanding challenge is how to combine these complementary, yet partially disparate data sources to accurately identify tumor-specific gene-programs and clinical biomarkers. Here, we introduce inteGREAT for robust and scalable differential integration of high-throughput measurements. With inteGREAT, each data source is represented as a co-expression network, which is analyzed to characterize the local and global structure of each node across networks. inteGREAT scores the degree by which the topology of each gene in both transcriptome and proteome networks are conserved within a tumor type, yet different from other normal or malignant cells. We demonstrated the high performance of inteGREAT based on several analyses: deconvolving synthetic networks, rediscovering known diagnostic biomarkers, establishing relationships between tumor lineages, and elucidating putative prognostic biomarkers which we experimentally validated. Furthermore, we introduce the application of a clumpiness measure to quantitatively describe tumor lineage similarity. Together, inteGREAT not only infers functional and clinical insights from the integration of transcriptomic and proteomic data sources in cancer, but also can be readily applied to other heterogeneous high-throughput data sources. inteGREAT is open source and available to download from https://github.com/faryabib/inteGREAT.

bioinformatics