Biology subjects

Elnitski, L.

Publications and source records attributed to Elnitski, L..

5 recordsLinked to original sources

bioRxiv2025

Identification of RNA binding proteins that mediate a quality control mechanism of splicing

Accurate splicing, which involves the controlled removal of non-coding sequences (introns) from precursor messenger RNAs (pre-mRNAs), is essential for producing correct mature mRNAs that encode functional proteins. Within pre-mRNAs, latent splice sites (LSSs) resemble proper splice sites but are usually not used because their activation can introduce in-frame STOP codons. The nuclear suppression of splicing (SOS) mechanism prevents the use of LSSs. Although the SOS mechanism is not fully understood, recent studies have identified initiator-tRNA and the NCL protein as key components. To discover additional regulators, we performed a genetic screen targeting RNA-binding proteins (RBPs) with an siRNA library and a luminescence reporter for latent splice site activation. This identified five RBPs -- ALYREF (THOC4), PPIE, DDX41, DHX38, and HNRNPA2B1 -- whose knockdown significantly increased LSS usage in the reporter. RNA-Seq analysis after knocking down each of these RBPs confirmed these results, showing widespread LSS activation in hundreds of mRNAs. Among these, we focused on ALYREF, a conserved protein involved in mRNA export and splicing. Using fPAR-CLIP, we found that U5 snRNA is ALYREFs main binding partner. Overexpressing ALYREF deletion mutants activated latent splicing, and affinity purification confirmed its interaction with U5 snRNA. These mutants exhibited different binding properties, highlighting the importance of specific structural elements within ALYREF in SOS regulation. Our findings reveal that nuclear RBPs play a key role in suppressing LSS activation and suggest that ALYREF has a novel role in maintaining splicing accuracy within the spliceosome, advancing our understanding of the SOS mechanism.

genomics↗

bioRxiv2021

Characterization and clustering of kinase isoform expression in metastatic melanoma

Mutations to the human kinome are known to play causal roles in cancer. The kinome regulates numerous cell processes including growth, proliferation, differentiation, and apoptosis. In addition to aberrant expression, aberrant alternative splicing of cancer-driver genes is receiving increased attention as it could create loss or gain of functional domains, altering a kinases downstream effects. The present study quantifies changes in gene expression and isoform ratios in the kinome of metastatic melanoma cells relative to primary tumors. We contrast 538 total kinases and 3042 known kinase isoforms between 103 primary tumor and 367 metastatic samples from The Cancer Genome Atlas (TCGA). We find strong evidence of differential expression (DE) at the gene level in 123 genes (23%). Additionally, of the 468 genes with alternative isoforms, 60 (13%) had differential isoform ratios (DIR). Notably, DE and DIR have little correlation; for instance, although DE highlights enrichment in receptor tyrosine kinases (RTKs), DIR identifies altered splicing in non-receptor tyrosine kinases (nRTKs). Using exon junction mapping, we identify five examples of splicing events favored in metastatic samples. We cluster isoform expression data and identify subgroups that correlate with genomic subtypes and anatomic tumor locations. Notably, distinct DE and DIR patterns separate samples with BRAF hotspot mutations and (N/K/H)RAS hotspot mutations, the latter of which lacks effective kinase inhibitor treatments. DE in RAS mutants concentrates in CMGC kinases (a group including cell cycle and splicing regulators) rather than RTKs as in BRAF mutants. Furthermore, isoforms in the RAS kinase subgroup show enrichment for cancer-related processes such as angiogenesis and cell migration. Our results reveal a new approach to therapeutic target identification and demonstrate how different mutational subtypes may respond differently to treatments highlighting possible new driver events in cancer.

genomics↗

bioRxiv2020

A Novel Role for Nucleolin in Splice Site Selection

Latent 5 splice sites are highly abundant in human introns, yet, are apparently not normally used. Splicing at most of these sites would incorporate in-frame stop codons generating nonsense mRNAs. Importantly, under stress and in cancer, splicing at latent sites is activated generating nonsense mRNAs from thousands of genes. Previous studies point to an unresolved RNA quality control mechanism that suppresses latent splicing independently of NMD. They further demonstrated a pivotal role for initiator-tRNA in this mechanism, through its interaction with the AUG codon, independent of its role in protein translation. To further elucidate this mechanism, here we searched for nuclear proteins directly bound to initiator-tRNA in the nucleus. We identified nucleolin (NCL), a multifunctional, abundant, and conserved protein, as a novel regulator of splice site selection. Starting with UV crosslinking, we show that NCL is directly and specifically interacting with initiator-tRNA in the nucleus, but not in the cytoplasm. In support of NCL involvement in this mechanism, we show activation of latent splicing in hundreds of transcripts upon NCL knockdown, disrupting gene transcripts involved in several important cellular pathways and cell metabolism functions (e.g. transcription factors, oncogenes, kinases, splicing factors, translation factors, and genes affecting cell motility, proliferation, and cellular trafficking). We thus propose NCL, a component of the endogenous spliceosome, through its direct interaction with initiator-tRNA and its effect on latent splicing as the first documented protein of a nuclear quality control mechanism that regulates splice site selection to protect cells from latent splicing that would generate defective mRNAs.

genomics↗

bioRxiv2019

A Methylation Density Binary Classifier for Predicting and Optimizing the Performance of Methylation Biomarkers in Clinical Samples

BackgroundVariation in intercellular methylation patterns can complicate the use of methylation biomarkers for clinical diagnostic applications such as blood-based cancer testing. Here, we describe development and validation of a methylation density binary classification method called EpiClass (available for download at https://github.com/Elnitskilab/EpiClass), that can be used to predict and optimize the performance of methylation biomarkers, particularly in challenging, heterogeneous samples such as liquid biopsies. This approach is based upon leveraging statistical differences in single-molecule sample methylation density distributions to identify ideal thresholds for sample classification. ResultsWe developed and tested the classifier using reduced representation bisulfite sequencing (RRBS) data derived from ovarian carcinoma tissue DNA and controls. We used these data to perform in silico simulations using methylation density profiles from individual epiallelic copies of ZNF154, a genomic locus known to be recurrently methylated in numerous cancer types. From these profiles, we predicted the performance of the classifier in liquid biopsies for the detection of epithelial ovarian carcinomas (EOC). In silico analysis indicated that EpiClass could be leveraged to better identify cancer-positive liquid biopsy samples by implementing precise thresholds with respect to methylation density profiles derived from circulating cell-free DNA (cfDNA) analysis. These predictions were confirmed experimentally using DREAMing to perform digital methylation density analysis on a cohort of low volume (1-mL) plasma samples obtained from 26 EOC-positive and 41 cancer-free women. EpiClass performance was then validated in an independent cohort of 24 plasma specimens, derived from a longitudinal study of 8 EOC-positive women, and 12 plasma specimens derived from 12 healthy women, respectively, attaining a sensitivity/specificity of 91.7%/100.0%. Direct comparison of CA-125 measurements with EpiClass demonstrated that EpiClass was able to better identify EOC-positive women than standard CA-125 assessment. Finally, we used independent whole genome bisulfite sequencing (WGBS) datasets to demonstrate that EpiClass can also identify other cancer types as well or better than alternative methylation-based classifiers. ConclusionsOur results indicate that assessment of intramolecular methylation density distributions calculated from cfDNA facilitate the use of methylation biomarkers for diagnostic applications. Furthermore, we demonstrated that EpiClass analysis of ZNF154 methylation was able to outperform CA-125 in the detection of etiologically-diverse ovarian carcinomas, indicating the broad utility of ZNF154 for use as a biomarker of ovarian cancer.

bioinformatics↗

bioRxiv2017

Significant Associations Between Driver Gene Mutations And DNA Methylation Alterations Across Many Cancer Types

Recent evidence shows that mutations in several driver genes can cause aberrant methylation patterns, a hallmark of cancer. In light of these findings, we hypothesized that the landscapes of tumor genomes and epigenomes are tightly interconnected. We measured this relationship using principal component analyses and methylation-mutation associations applied at the nucleotide level and with respect to genome-wide trends. We found a few mutated driver genes were associated with genome-wide patterns of aberrant hypomethylation or CpG island hypermethylation in specific cancer types. We identified associations between 737 mutated driver genes and site-specific methylation changes. Moreover, using these mutation-methylation associations, we were able to distinguish between two uterine and two thyroid cancer subtypes. The driver gene mutation-associated methylation differences between the thyroid cancer subtypes were linked to differential gene expression in JAK-STAT signaling, NADPH oxidation, and other cancer-related pathways. These results establish that driver-gene mutations are associated with methylation alterations capable of shaping regulatory network functions. In addition, the methodology presented here can be used to subdivide tumors into more homogeneous subsets corresponding to their underlying molecular characteristics, which could improve treatment efficacy.\n\nAuthor summaryMutations that alter the function of driver genes by changing DNA nucleotides have been recognized as a key player in cancer progression. Recent evidence showed that DNA methylation, a molecular signature that is used for controlling gene expression and that consists of cytosine residues with attached methyl groups in the context of CG dinucleotides, is also highly dysregulated in cancer and contributes to carcinogenesis. However, whether those methylation alterations correspond to mutated driver genes in cancer remains unclear. In this study, we analyzed 4,302 tumors from 18 cancer types and demonstrated that driver gene mutations are inherently connected with the aberrant DNA methylation landscape in cancer. We showed that those driver gene-associated methylation patterns can classify heterogeneous tumors in a cancer type into homogeneous subtypes and have the potential to influence the genes that contribute to tumor growth. This finding could help us to better understand the fundamental connection between driver gene mutations and DNA methylation alterations in cancer and to further improve the cancer treatment.

cancer biology↗