A specific type of Argonaute phosphorylation regulates binding to microRNAs during C. elegans development.

Argonaute proteins are at the core of the microRNA-mediated gene silencing pathway essential for animals. In C. elegans, the microRNA-specific Argonautes ALG-1 and ALG-2 regulate multiple processes required for proper animal developmental timing and viability. Here we identified a phosphorylation site on ALG-1 that modulates microRNA association. Mutating ALG-1 serine 642 into a phospho-mimicking residue impairs microRNA binding and causes embryonic lethality and post-embryonic phenotypes that are consistent with alteration of microRNA functions. Monitoring microRNA levels in alg-1 phosphorylation mutant animals shows that microRNA passenger strands increase in abundance but are not preferentially loaded into ALG-1, indicating that the miRNA binding defects could lead to microRNA duplex accumulation. Our genetic and biochemical experiments support protein kinase A (PKA) KIN-1 as the putative kinase that phosphorylates ALG-1 serine 642. Our data indicate that PKA triggers ALG-1 phosphorylation to regulate its microRNA association during C. elegans development.

Nanopore sequencing reveals endogenous NMD-targeted isoforms in human cells.

BACKGROUND: Nonsense-mediated mRNA decay (NMD) is a eukaryotic, translation-dependent degradation pathway that targets mRNAs with premature termination codons and also regulates the expression of some mRNAs that encode full-length proteins. Although many genes express NMD-sensitive transcripts, identifying them based on short-read sequencing data remains a challenge. RESULTS: To identify and analyze endogenous targets of NMD, we apply cDNA Nanopore sequencing and short-read sequencing to human cells with varying expression levels of NMD factors. Our approach detects full-length NMD substrates that are highly unstable and increase in levels or even only appear when NMD is inhibited. Among the many new NMD-targeted isoforms that our analysis identifies, most derive from alternative exon usage. The isoform-aware analysis reveals many genes with significant changes in splicing but no significant changes in overall expression levels upon NMD knockdown. NMD-sensitive mRNAs have more exons in the 3΄UTR and, for those mRNAs with a termination codon in the last exon, the length of the 3΄UTR per se does not correlate with NMD sensitivity. Analysis of splicing signals reveals isoforms where NMD has been co-opted in the regulation of gene expression, though the main function of NMD seems to be ridding the transcriptome of isoforms resulting from spurious splicing events. CONCLUSIONS: Long-read sequencing enables the identification of many novel NMD-sensitive mRNAs and reveals both known and unexpected features concerning their biogenesis and their biological role. Our data provide a highly valuable resource of human NMD transcript targets for future genomic and transcriptomic applications.

Protease-mediated processing of Argonaute proteins controls small RNA association.

Small RNA pathways defend the germlines of animals against selfish genetic elements, yet pathway activities need to be contained to prevent silencing of self genes. Here, we reveal a proteolytic mechanism that controls endogenous small interfering (22G) RNA activity in the Caenorhabditis elegans germline to protect genome integrity and maintain fertility. We find that DPF-3, a P-granule-localized N-terminal dipeptidase orthologous to mammalian dipeptidyl peptidase (DPP) 8/9, processes the unusually proline-rich N termini of WAGO-1 and WAGO-3 Argonaute (Ago) proteins. Without DPF-3 activity, these WAGO proteins lose their proper complement of 22G RNAs. Desilencing of repeat-containing and transposon-derived transcripts, DNA damage, and acute sterility ensue. These phenotypes are recapitulated when WAGO-1 and WAGO-3 are rendered resistant to DPF-3-mediated processing, identifying them as critical substrates of DPF-3. We conclude that N-terminal processing of Ago proteins regulates their activity and promotes silencing of selfish genetic elements by ensuring Ago association with appropriate small RNAs.

Gene Expression Signatures of a Preclinical Mouse Model during Colorectal Cancer Progression under Low-Dose Metronomic Chemotherapy.

Understanding the molecular signatures of colorectal cancer progression under chemotherapeutic treatment will be crucial for the success of future therapy improvements. Here, we used a xenograft-based mouse model to investigate, how whole transcriptome signatures change during metastatic colorectal cancer progression and how such signatures are affected by LDM chemotherapy using RNA sequencing. We characterized mRNAs as well as non-coding RNAs such as microRNAs, long non-coding RNAs and circular RNAs in colorectal-cancer bearing mice with or without LDM chemotherapy. Furthermore, we found that circZNF609 functions as oncogene, since over-expression studies lead to an increased tumor growth while specific knock down results in smaller tumors. Our data represent novel insights into the relevance of non-coding and circRNAs in colorectal cancer and provide a comprehensive resource of gene expression changes in primary tumors and metastases. In addition, we present candidate genes that could be important modulators for successful LDM chemotherapy.

Aberrant interaction of FUS with the U1 snRNA provides a molecular mechanism of FUS induced amyotrophic lateral sclerosis.

Mutations in the RNA-binding protein Fused in Sarcoma (FUS) cause early-onset amyotrophic lateral sclerosis (ALS). However, a detailed understanding of central RNA targets of FUS and their implications for disease remain elusive. Here, we use a unique blend of crosslinking and immunoprecipitation (CLIP) and NMR spectroscopy to identify and characterise physiological and pathological RNA targets of FUS. We find that U1 snRNA is the primary RNA target of FUS via its interaction with stem-loop 3 and provide atomic details of this RNA-mediated mode of interaction with the U1 snRNP. Furthermore, we show that ALS-associated FUS aberrantly contacts U1 snRNA at the Sm site with its zinc finger and traps snRNP biogenesis intermediates in human and murine motor neurons. Altogether, we present molecular insights into a FUS toxic gain-of-function involving direct and aberrant RNA-binding and strengthen the link between two motor neuron diseases, ALS and spinal muscular atrophy (SMA).

Terminal exon characterization with TECtool reveals an abundance of cell-specific isoforms.

Sequencing of RNA 3' ends has uncovered numerous sites that do not correspond to the termination sites of known transcripts. Through their 3' untranslated regions, protein-coding RNAs interact with RNA-binding proteins and microRNAs, which regulate many properties, including RNA stability and subcellular localization. We developed the terminal exon characterization (TEC) tool ( http://tectool.unibas.ch ), which can be used with RNA-sequencing data from any species for which a genome annotation that includes sites of RNA cleavage and polyadenylation is available. We discovered hundreds of previously unknown isoforms and cell-type-specific terminal exons in human cells. Ribosome profiling data revealed that many of these isoforms were translated. By applying TECtool to single-cell sequencing data, we found that the newly identified isoforms were expressed in subpopulations of cells. Thus, TECtool enables the identification of previously unknown isoforms in well-studied cell systems and in rare cell types.

High-throughput identification of C/D box snoRNA targets with CLIP and RiboMeth-seq.

High-throughput sequencing has greatly facilitated the discovery of long and short non-coding RNAs (ncRNAs), which frequently guide ribonucleoprotein complexes to RNA targets, to modulate their metabolism and expression. However, for many ncRNAs, the targets remain to be discovered. In this study, we developed computational methods to map C/D box snoRNA target sites using data from core small nucleolar ribonucleoprotein crosslinking and immunoprecipitation and from transcriptome-wide mapping of 2΄-O-ribose methylation sites. We thereby assigned the snoRNA guide to a known methylation site in the 18S rRNA, we uncovered a novel partially methylated site in the 28S ribosomal RNA, and we captured a site in the 28S rRNA in interaction with multiple snoRNAs. Although we also captured mRNAs in interaction with snoRNAs, we did not detect 2΄-O-methylation of these targets. Our study provides an integrated approach to the comprehensive characterization of 2΄-O-methylation targets of snoRNAs in species beyond those in which these interactions have been traditionally studied and contributes to the rapidly developing field of 'epitranscriptomics'.

Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data.

BACKGROUND: Understanding the regulation of gene expression, including transcription start site usage, alternative splicing, and polyadenylation, requires accurate quantification of expression levels down to the level of individual transcript isoforms. To comparatively evaluate the accuracy of the many methods that have been proposed for estimating transcript isoform abundance from RNA sequencing data, we have used both synthetic data as well as an independent experimental method for quantifying the abundance of transcript ends at the genome-wide level. RESULTS: We found that many tools have good accuracy and yield better estimates of gene-level expression compared to commonly used count-based approaches, but they vary widely in memory and runtime requirements. Nucleotide composition and intron/exon structure have comparatively little influence on the accuracy of expression estimates, which correlates most strongly with transcript/gene expression levels. To facilitate the reproduction and further extension of our study, we provide datasets, source code, and an online analysis tool on a companion website, where developers can upload expression estimates obtained with their own tool to compare them to those inferred by the methods assessed here. CONCLUSIONS: As many methods for quantifying isoform abundance with comparable accuracy are available, a user's choice will likely be determined by factors such as the memory and runtime requirements, as well as the availability of methods for downstream analyses. Sequencing-based methods to quantify the abundance of specific transcript regions could complement validation schemes based on synthetic data and quantitative PCR in future or ongoing assessments of RNA-seq analysis methods.

mpMoRFsDB: a database of molecular recognition features in membrane proteins.

SUMMARY: Molecular recognition features (MoRFs) are small, intrinsically disordered regions in proteins that undergo a disorder-to-order transition on binding to their partners. MoRFs are involved in protein-protein interactions and may function as the initial step in molecular recognition. The aim of this work was to collect, organize and store all membrane proteins that contain MoRFs. Membrane proteins constitute ∼30% of fully sequenced proteomes and are responsible for a wide variety of cellular functions. MoRFs were classified according to their secondary structure, after interacting with their partners. We identified MoRFs in transmembrane and peripheral membrane proteins. The position of transmembrane protein MoRFs was determined in relation to a protein's topology. All information was stored in a publicly available mySQL database with a user-friendly web interface. A Jmol applet is integrated for visualization of the structures. mpMoRFsDB provides valuable information related to disorder-based protein-protein interactions in membrane proteins. AVAILABILITY: http://bioinformatics.biol.uoa.gr/mpMoRFsDB

A disease annotation study of gene signatures in a breast cancer microarray dataset.

Breast cancer is a complex disease with heterogeneity between patients regarding prognosis and treatment response. Recent progress in advanced molecular biology techniques and the development of efficient methods for database mining lead to the discovery of promising novel biomarkers for prognosis and prediction of breast cancer. In this paper, we applied three computational algorithms (RFE-LNW, Lasso and FSMLP) to one microarray dataset for breast cancer and compared the obtained gene signatures with a recently described disease-agnostic tool, the Genotator. We identified a panel of 152 genes as a potential prognostic signature and the ERRFI1 gene as possible biomarker of breast cancer disease.