The Epithelial adhesin 1 tandem repeat region mediates protein display through multiple mechanisms

In-Silico Analysis Highlights the Existence in Members of Burkholderia cepacia Complex of a New Class of Adhesins Possessing Collagen-like Domains

Burkholderia cenocepacia is a multi-drug-resistant lung pathogen. This species synthesizes various virulence factors, among which cell-surface components (adhesins) are critical for establishing the contact with host cells. This work in the first part focuses on the current knowledge about the adhesion molecules described in this species. In the second part, through in silico approaches, we perform a comprehensive analysis of a group of unique bacterial proteins possessing collagen-like domains (CLDs) that are strikingly overrepresented in the Burkholderia species, representing a new putative class of adhesins. We identified 75 CLD-containing proteins in Burkholderia cepacia complex (Bcc) members (Bcc-CLPs). The phylogenetic analysis of Bcc-CLPs revealed the evolution of the core domain denominated "Bacterial collagen-like, middle region". Our analysis remarkably shows that these proteins are formed by extensive sets of compositionally biased residues located within intrinsically disordered regions (IDR). Here, we discuss how IDR functions may increase their efficiency as adhesion factors. Finally, we provided an analysis of a set of five homologs identified in B. cenocepacia J2315. Thus, we propose the existence in Bcc of a new type of adhesion factors distinct from the described collagen-like proteins (CLPs) found in Gram-positive bacteria.

Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence

Bladder cancer (BLCA) is one of the most common cancers worldwide with high recurrence rate. Hence, we intended to establish a recurrence-related long non-coding RNA (lncRNA) model of BLCA as a potential biomarker based on multi-omics analysis. Multi-omics data including copy number variation (CNV) data, mutation annotation files, RNA expression profiles and clinical data of The Cancer Genome Atlas (TCGA) BLCA cohort (303 cases) and GSE31684 (93 cases) were downloaded from public database. With multi-omics analysis, twenty lncRNAs were identified as the candidates related with BLCA recurrence, CNVs and mutations in training set. Ten-lncRNA signature were established using least absolute shrinkage and selection operation (LASSO) and Cox regression. Then, various survival analysis was used to assess the power of lncRNA model in predicting BLCA recurrence. The results showed that the recurrence-free survival time of high-risk group was significantly shorter than that of low-risk group in training and testing sets, and the predictive value of ten-lncRNA signature was robust and independent of other clinical variables. Gene Set Enrichment Analysis (GSEA) showed this signature were associated with immune disorders, indicating this signature may be involved in tumor immunology. After compared with the other reported lncRNA signatures, ten-lncRNA signature was validated as a superior prognostic model in predicting the recurrence of BLCA. The effectiveness of the model was also evaluated in bladder cancer samples via qRT-PCR. Thus, the novel ten-lncRNA signature, constructed based on multi-omics data, had robust prognostic power in predicting the recurrence of BLCA and potential clinical implications as biomarkers.