bioRxiv Science⌕ Search

Biology subjects

Jha, N.

Publications and source records attributed to Jha, N..

2 recordsLinked to original sources

PITAR, a DNA damage-inducible Cancer/Testis long noncoding RNA, inactivates p53 by binding and stabilizing TRIM28 mRNA

In tumors with WT p53, alternate mechanisms of p53 inactivation are reported. Here, we have identified a long noncoding RNA, PITAR (p53 Inactivating TRIM28 associated RNA), as an inhibitor of p53. PITAR is an oncogenic Cancer/testis lncRNA and is highly expressed in glioblastoma (GBM) and glioma stem-like cells (GSC). We establish that TRIM28 mRNA, which encodes a p53-specific E3 ubiquitin ligase, is a direct target of PITAR. PITAR interaction with TRIM28 RNA stabilized TRIM28 mRNA, which resulted in increased TRIM28 protein levels and reduced p53 steady-state levels due to enhanced p53 ubiquitination. DNA damage activated PITAR, in addition to p53, in a p53-independent manner, thus creating an incoherent feedforward loop to inhibit the DNA damage response by p53. While PITAR silencing inhibited the growth of WT p53 containing GSCs in vitro and reduced glioma tumor growth in vivo, its overexpression enhanced the tumor growth in a TRIM28-dependent manner and promoted resistance to Temozolomide. Thus, we establish an alternate way of p53 inactivation by PITAR, which maintains low p53 levels in normal cells and attenuates the DNA damage response by p53. Finally, we propose PITAR as a potential GBM therapeutic target.

cancer biology↗

A visual atlas of genes tissue-specific pathological roles

Dysregulation of a genes function, either due to mutations or impairments in regulatory networks, often triggers pathological states in the affected tissue. Comprehensive mapping of these apparent gene-pathology relationships is an ever daunting task, primarily due to genetic pleiotropy and lack of suitable computational approaches. With the advent of high throughput genomics platforms and community scale initiatives such as the Human Cell Landscape (HCL) project [1], researchers have been able to create gene expression portraits of healthy tissues resolved at the level of single cells. However, a similar wealth of knowledge is currently not at our finger-tip when it comes to diseases. This is because the genetic manifestation of a disease is often quite heterogeneous and is confounded by several clinical and demographic covariates. To circumvent this, we mined ~18 million PubMed abstracts published till May 2019 and selected ~6.1 million of them that describe the pathological role of genes in different diseases. Further, we employed a word embedding technique from the domain of Natural Language Processing (NLP) to learn vector representation of entities such as genes, diseases, tissues, etc., in a way such that their relationship is preserved in a vector space. Notably, Pathomap, by the virtue of its underpinning theory, also learns transitive relationships. Pathomap provided a vector representation of words indicating a possible association between DNMT3A/BCOR with CYLD cutaneous syndrome (CCS). The first manuscript reporting this finding was not part of our training data. Key pointsO_LIWe mined ~18 million PubMed abstracts to extract latent knowledge pertaining to tissue specific pathological roles of genes. C_LIO_LIWe found well-defined gene modules implicated in disease pathogenesis in anatomically proximal tissues. C_LIO_LIWe demonstrated an ahead of time discovery of the association between DNMT3A/BCOR with CYLD cutaneous syndrome (CCS), as a knowledge synthesis use-case. C_LI

bioinformatics↗