Biology subjects

Niknafs, Y.

Publications and source records attributed to Niknafs, Y..

3 recordsLinked to original sources

bioRxiv2025

Identification and Characterization of PLUTO-201, a Novel Long Non-Coding RNA Associated with Poor Outcomes in Prostate Cancer

Background: Despite extensive investigation, the factors promoting aggressive prostate cancer are poorly understood. In particular, despite a few prominent examples, the role of long non-coding RNAs (lncRNAs) is largely unknown. Methods: We performed a comprehensive analysis of whole-genome transcriptome data to identify differential expression across 1,567 patients with prostate cancer, then correlated differential expression with risk of metastatic recurrence. We characterized the lncRNA most associated with metastasis in vitro and in vivo to investigate the mechanism by which it promotes aggressive prostate cancer. Results: We have identified a novel lncRNA, Prostate Cancer Associated hnRNPK Interacting Transcript (PCAHIT), which is strongly associated with metastasis and poor overall survival in men with prostate cancer. We find that overexpression/knockdown of PCAHIT in pre-clinical models of prostate cancer modulates proliferation rates and markers of an aggressive phenotype through regulation of steroid biosynthesis and expression of the MHC class I complex, driving increased growth in androgen-depleted conditions and decreased susceptibility to T cell-mediated cytotoxicity. We further find that the heterogeneous nuclear ribonucleoprotein hnRNPK directly binds PCAHIT and is indispensable for its activity. Conclusions: Overall, our findings indicate that PCAHIT is a driver of aggressive prostate cancer phenotypes and poor clinical outcomes through suppression of the immune response and increased androgen-independent cancer growth. PCAHIT is a potential biomarker of aggressive disease.

cancer biology↗

bioRxiv2019

Regulation of heterotopic ossification through local inflammatory monocytes in a mouse model of aberrant wound healing

Heterotopic ossification (HO) is an aberrant regenerative process with ectopic bone induction in response to musculoskeletal trauma, in which mesenchymal stem cells (MSC) differentiate into osteochondrogenic cells instead of myocytes or tenocytes. Despite frequent cases of hospitalized musculoskeletal trauma, the inflammatory responses and cell population dynamics that regulate subsequent wound healing and tissue regeneration are still unclear. Here we examine, using a mouse model of trauma-induced HO, the local microenvironment of the initial post-injury inflammatory response. Single cell transcriptome analyses identify distinct monocyte/macrophage populations at the injury site, with their dynamic changes over time elucidated using trajectory analyses. Mechanistically, transforming growth factor beta-1 (TGF{beta}1)-producing monocytes/macrophages are associated with HO and aberrant chondrogenic progenitor cell differentiation, while CD47-activating peptides that reduce systemic TGF{beta} levels help ameliorate HO. Our data thus implicate CD47 activation as a novel therapeutic approach for modulating monocyte/macrophage phenotypes, MSC differentiation and HO formation during wound healing.

immunology↗

bioRxiv2018

MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions

MotivationLong non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nucleotides that do not get translated into proteins. Often these transcripts are processed (spliced, capped, polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA.\n\nApproachHere we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined p-value.\n\nResultsWe applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29, ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays.\n\nAvailabilityMechRNA is available for download at https://bitbucket.org/compbio/mechrna\n\nContactbackofen@informatik.uni-freiburg.de, cenksahi@indiana.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗