Changes In Expression Programs Research Articles

Decoding and Recoding of mRNA Sequences by the Ribosome.

Faithful translation of messenger RNA (mRNA) into protein is essential to maintain protein homeostasis in the cell. Spontaneous translation errors are very rare due to stringent selection of cognate aminoacyl transfer RNAs (tRNAs) and the tight control of the mRNA reading frame by the ribosome. Recoding events, such as stop codon readthrough, frameshifting, and translational bypassing, reprogram the ribosome to make intentional mistakes and produce alternative proteins from the same mRNA. The hallmark of recoding is the change of ribosome dynamics. The signals for recoding are built into the mRNA, but their reading depends on the genetic makeup of the cell, resulting in cell-specific changes in expression programs. In this review, I discuss the mechanisms of canonical decoding and tRNA-mRNA translocation; describe alternative pathways leading to recoding; and identify the links among mRNA signals, ribosome dynamics, and recoding.

MiRCoop: Identifying Cooperating miRNAs via Kernel Based Interaction Tests.

Although miRNAs can cause widespread changes in expression programs, single miRNAs typically induce mild repression on their targets. Cooperativity among miRNAs is reported as one strategy to overcome this constraint. Expanding the catalog of synergistic miRNAs is critical for understanding gene regulation and for developing miRNA-based therapeutics. In this study, we develop miRCoop to identify synergistic miRNA pairs that have weak or no repression on the target mRNA individually, but when act together, induce strong repression. miRCoop uses kernel-based statistical interaction tests, together with miRNA and mRNA target information. We apply our approach to patient data of two different cancer types. In kidney cancer, we identify 66 putative triplets. For 64 of these triplets, there is at least one common transcription factor that potentially regulates all participating RNAs of the triplet, supporting a functional association among them. Furthermore, we find that identified triplets are enriched for certain biological processes that are relevant to kidney cancer. Some of the synergistic miRNAs are very closely encoded in the genome, hinting a functional association among them. In applying the method on tumor data with the primary liver site, we find 3105 potential triplet interactions. We believe miRCoop can aid our understanding of the complex regulatory interactions in different health and disease states of the cell and can help in designing miRNA-based therapies. Matlab code for the methodology is provided in https://github.com/guldenolgun/miRCoop.

Robust and accurate deconvolution of tumor populations uncovers evolutionary mechanisms of breast cancer metastasis.

MotivationCancer develops and progresses through a clonal evolutionary process. Understanding progression to metastasis is of particular clinical importance, but is not easily analyzed by recent methods because it generally requires studying samples gathered years apart, for which modern single-cell sequencing is rarely an option. Revealing the clonal evolution mechanisms in the metastatic transition thus still depends on unmixing tumor subpopulations from bulk genomic data.MethodsWe develop a novel toolkit called robust and accurate deconvolution (RAD) to deconvolve biologically meaningful tumor populations from multiple transcriptomic samples spanning the two progression states. RAD uses gene module compression to mitigate considerable noise in RNA, and a hybrid optimizer to achieve a robust and accurate solution. Finally, we apply a phylogenetic algorithm to infer how associated cell populations adapt across the metastatic transition via changes in expression programs and cell-type composition.ResultsWe validated the superior robustness and accuracy of RAD over alternative algorithms on a real dataset, and validated the effectiveness of gene module compression on both simulated and real bulk RNA data. We further applied the methods to a breast cancer metastasis dataset, and discovered common early events that promote tumor progression and migration to different metastatic sites, such as dysregulation of ECM-receptor, focal adhesion and PI3k-Akt pathways.Availability and implementationThe source code of the RAD package, models, experiments and technical details such as parameters, is available at https://github.com/CMUSchwartzLab/RAD.Supplementary information Supplementary data are available at Bioinformatics online.

A comprehensive study of epigenetic alterations in hepatocellular carcinoma identifies potential therapeutic targets

A causal link has recently been established between epigenetic alterations and hepatocarcinogenesis, indicating that epigenetic inhibition may have therapeutic potential. We aimed to identify and target epigenetic modifiers that show molecular alterations in hepatocellular carcinoma (HCC). We studied the molecular-clinical correlations of epigenetic modifiers including bromodomains, histone acetyltransferases, lysine methyltransferases and lysine demethylases in HCC using The Cancer Genome Atlas (TCGA) data of 365 patients with HCC. The therapeutic potential of epigenetic inhibitors was evaluated in vitro and in vivo. RNA sequencing analysis and its correlation with expression and clinical data in the TCGA dataset were used to identify expression programs normalized by Jumonji lysine demethylase (JmjC) inhibitors. Genetic alterations, aberrant expression, and correlation between tumor expression and poor patient prognosis of epigenetic enzymes are common events in HCC. Epigenetic inhibitors that target bromodomain (JQ-1), lysine methyltransferases (BIX-1294 and LLY-507) and JmjC lysine demethylases (JIB-04, GSK-J4 and SD-70) reduce HCC aggressiveness. The pan-JmjC inhibitor JIB-04 had a potent antitumor effect in tumor bearing mice. HCC cells treated with JmjC inhibitors showed overlapping changes in expression programs related with inhibition of cell proliferation and induction of cell death. JmjC inhibition reverses an aggressive HCC gene expression program that is also altered in patients with HCC. Several genes downregulated by JmjC inhibitors are highly expressed in tumor vs. non-tumor parenchyma, and their high expression correlates with a poor prognosis. We identified and validated a 4-gene expression prognostic signature consisting of CENPA, KIF20A, PLK1, and NCAPG. The epigenetic alterations identified in HCC can be used to predict prognosis and to define a subgroup of high-risk patients that would potentially benefit from JmjC inhibitor therapy. In this study, we found that mutations and changes in expression of epigenetic modifiers are common events in human hepatocellular carcinoma, leading to an aggressive gene expression program and poor clinical prognosis. The transcriptional program can be reversed by pharmacological inhibition of Jumonji enzymes. This inhibition blocks hepatocellular carcinoma progression, providing a novel potential therapeutic strategy.