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Identification of keratinases from Fervidobacterium islandicum AW-1 using dynamic gene expression profiling.

ABSTRACT: Keratin degradation is of great interest for converting agro-industrial waste into bioactive peptides and is directly relevant for understanding the pathogenesis of superficial infections caused by dermatophytes. However, the mechanism of this process remains unclear. Here, we obtained the complete genome sequence of a feather-degrading, extremely thermophilic bacterium, Fervidobacterium islandicum AW-1 and performed bioinformatics-based functional annotation. Reverse transcription PCR revealed that 57 putative protease-encoding genes were differentially expressed in substrate-dependent manners. Consequently, 16 candidate genes were highly expressed under starvation conditions, when keratin degradation begun. Subsequently, the dynamic expression profiles of these 16 selected genes in response to feathers, as determined via quantitative real-time PCR, suggested that they included four metalloproteases and two peptidases including an ATP-dependent serine protease, all of which might act as key players in feather decomposition. Furthermore, in vitro keratinolytic assays supported the notion that recombinant enzymes enhanced the decomposition of feathers in the presence of cell extracts. Therefore, our genome-based systematic and dynamic expression profiling demonstrated that these identified metalloproteases together with two additional peptidases might be primarily associated with the decomposition of native feathers, suggesting that keratin degradation can be achieved via non-canonical catalysis of several membrane-associated metalloproteases in cooperation with cytosolic proteases.

SUBMITTER: Kang E

PROVIDER: S-EPMC7017815 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Project description:BackgroundImaging techniques can provide information about the tumor non-invasively and have been shown to provide information about the underlying genetic makeup. Correlating image-based phenotypes (radiomics) with genomic analyses is an emerging area of research commonly referred to as "radiogenomics" or "imaging-genomics". The purpose of this study was to assess the potential for using an automated, quantitative radiomics platform on magnetic resonance (MR) breast imaging for inferring underlying activity of clinically relevant gene pathways derived from RNA sequencing of invasive breast cancers prior to therapy.MethodsWe performed quantitative radiomic analysis on 47 invasive breast cancers based on dynamic contrast enhanced 3 Tesla MR images acquired before surgery and obtained gene expression data by performing total RNA sequencing on corresponding fresh frozen tissue samples. We used gene set enrichment analysis to identify significant associations between the 186 gene pathways and the 38 image-based features that have previously been validated.ResultsAll radiomic size features were positively associated with multiple replication and proliferation pathways and were negatively associated with the apoptosis pathway. Gene pathways related to immune system regulation and extracellular signaling had the highest number of significant radiomic feature associations, with an average of 18.9 and 16 features per pathway, respectively. Tumors with upregulation of immune signaling pathways such as T-cell receptor signaling and chemokine signaling as well as extracellular signaling pathways such as cell adhesion molecule and cytokine-cytokine interactions were smaller, more spherical, and had a more heterogeneous texture upon contrast enhancement. Tumors with higher expression levels of JAK/STAT and VEGF pathways had more intratumor heterogeneity in image enhancement texture. Other pathways with robust associations to image-based features include metabolic and catabolic pathways.ConclusionsWe provide further evidence that MR imaging of breast tumors can infer underlying gene expression by using RNA sequencing. Size and shape features were appropriately correlated with proliferative and apoptotic pathways. Given the high number of radiomic feature associations with immune pathways, our results raise the possibility of using MR imaging to distinguish tumors that are more immunologically active, although further studies are necessary to confirm this observation.

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Identification of keratinases from Fervidobacterium islandicum AW-1 using dynamic gene expression profiling.

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