The role of the 5' sensing function of ribonuclease E in cyanobacteria.

RNA degradation is critical for synchronising gene expression with changing conditions in prokaryotic and eukaryotic organisms. In bacteria, the preference of the central ribonucleases RNase E, RNase J and RNase Y for 5'-monophosphorylated RNAs is considered important for RNA degradation. For RNase E, the underlying mechanism is termed 5' sensing, contrasting to the alternative 'direct entry' mode, which is independent of monophosphorylated 5' ends. Cyanobacteria, such as Synechocystis sp. PCC 6803 (Synechocystis), encode RNase E and RNase J homologues. Here, we constructed a Synechocystis strain lacking the 5' sensing function of RNase E and mapped on a transcriptome-wide level 283 5'-sensing-dependent cleavage sites. These included so far unknown targets such as mRNAs encoding proteins related to energy metabolism and carbon fixation. The 5' sensing function of cyanobacterial RNase E is important for the maturation of rRNA and several tRNAs, including tRNAGluUUC. This tRNA activates glutamate for tetrapyrrole biosynthesis in plant chloroplasts and in most prokaryotes. Furthermore, we found that increased RNase activities lead to a higher copy number of the major Synechocystis plasmids pSYSA and pSYSM. These results provide a first step towards understanding the importance of the different target mechanisms of RNase E outside Escherichia coli.

Heterogenous nuclear ribonucleoprotein D-like controls endothelial cell functions.

HnRNPs are ubiquitously expressed RNA-binding proteins, tightly controlling posttranscriptional gene regulation. Consequently, hnRNP networks are essential for cellular homeostasis and their dysregulation is associated with cancer and other diseases. However, the physiological function of hnRNPs in non-cancerous cell systems are poorly understood. We analyzed the importance of HNRNPDL in endothelial cell functions. Knockdown of HNRNPDL led to impaired proliferation, migration and sprouting of spheroids. Transcriptome analysis identified cyclin D1 (CCND1) and tropomyosin 4 (TPM4) as targets of HNRNPDL, reflecting the phenotypic changes after knockdown. Our findings underline the importance of HNRNPDL for the homeostasis of physiological processes in endothelial cells.

Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

Transcriptome-wide in vivo mapping of cleavage sites for the compact cyanobacterial ribonuclease E reveals insights into its function and substrate recognition.

Ribonucleases are crucial enzymes in RNA metabolism and post-transcriptional regulatory processes in bacteria. Cyanobacteria encode the two essential ribonucleases RNase E and RNase J. Cyanobacterial RNase E is shorter than homologues in other groups of bacteria and lacks both the chloroplast-specific N-terminal extension as well as the C-terminal domain typical for RNase E of enterobacteria. In order to investigate the function of RNase E in the model cyanobacterium Synechocystis sp. PCC 6803, we engineered a temperature-sensitive RNase E mutant by introducing two site-specific mutations, I65F and the spontaneously occurred V94A. This enabled us to perform RNA-seq after the transient inactivation of RNase E by a temperature shift (TIER-seq) and to map 1472 RNase-E-dependent cleavage sites. We inferred a dominating cleavage signature consisting of an adenine at the -3 and a uridine at the +2 position within a single-stranded segment of the RNA. The data identified mRNAs likely regulated jointly by RNase E and an sRNA and potential 3' end-derived sRNAs. Our findings substantiate the pivotal role of RNase E in post-transcriptional regulation and suggest the redundant or concerted action of RNase E and RNase J in cyanobacteria.

The temperature-regulated DEAD-box RNA helicase CrhR interactome: Autoregulation and photosynthesis-related transcripts.

RNA helicases play crucial functions in RNA biology. In plants, RNA helicases are encoded by large gene families, performing roles in abiotic stress responses, development, the post-transcriptional regulation of gene expression as well as house-keeping functions. Several of these RNA helicases are targeted to the organelles, mitochondria and chloroplasts. Cyanobacteria are the direct evolutionary ancestors of plant chloroplasts. The cyanobacterium Synechocystis 6803 encodes a single DEAD-box RNA helicase, CrhR, that is induced by a range of abiotic stresses, including low temperature. Though the ΔcrhR mutant exhibits a severe cold-sensitive phenotype, the physiological function(s) performed by CrhR have not been described. To identify transcripts interacting with CrhR, we performed RNA co-immunoprecipitation with extracts from a Synechocystis crhR deletion mutant expressing the FLAG-tagged native CrhR or a K57A mutated version with an anticipated enhanced RNA binding. The composition of the interactome was strikingly biased towards photosynthesis-associated and redox-controlled transcripts. A transcript highly enriched in all experiments was the crhR mRNA, suggesting an auto-regulatory molecular mechanism. The identified interactome explains the described physiological role of CrhR in response to the redox poise of the photosynthetic electron transport chain and characterizes CrhR as an enzyme with a diverse range of transcripts as molecular targets.

RNAProt: an efficient and feature-rich RNA binding protein binding site predictor.

BACKGROUND: Cross-linking and immunoprecipitation followed by next-generation sequencing (CLIP-seq) is the state-of-the-art technique used to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression, which can be highly variable between conditions and thus cannot provide a complete picture of the RBP binding landscape. This creates a demand for computational methods to predict missing binding sites. Although there exist various methods using traditional machine learning and lately also deep learning, we encountered several problems: many of these are not well documented or maintained, making them difficult to install and use, or are not even available. In addition, there can be efficiency issues, as well as little flexibility regarding options or supported features. RESULTS: Here, we present RNAProt, an efficient and feature-rich computational RBP binding site prediction framework based on recurrent neural networks. We compare RNAProt with 1 traditional machine learning approach and 2 deep-learning methods, demonstrating its state-of-the-art predictive performance and better run time efficiency. We further show that its implemented visualizations capture known binding preferences and thus can help to understand what is learned. Since RNAProt supports various additional features (including user-defined features, which no other tool offers), we also present their influence on benchmark set performance. Finally, we show the benefits of incorporating additional features, specifically structure information, when learning the binding sites of an hairpin loop binding RBP. CONCLUSIONS: RNAProt provides a complete framework for RBP binding site predictions, from data set generation over model training to the evaluation of binding preferences and prediction. It offers state-of-the-art predictive performance, as well as superior run time efficiency, while at the same time supporting more features and input types than any other tool available so far. RNAProt is easy to install and use, comes with comprehensive documentation, and is accompanied by informative statistics and visualizations. All this makes RNAProt a valuable tool to apply in future RBP binding site research.

StoatyDive: Evaluation and classification of peak profiles for sequencing data.

BACKGROUND: The prediction of binding sites (peak-calling) is a common task in the data analysis of methods such as cross-linking immunoprecipitation in combination with high-throughput sequencing (CLIP-Seq). The predicted binding sites are often further analyzed to predict sequence motifs or structure patterns. When looking at a typical result of such high-throughput experiments, the obtained peak profiles differ largely on a genomic level. Thus, a tool is missing that evaluates and classifies the predicted peaks on the basis of their shapes. We hereby present StoatyDive, a tool that can be used to filter for specific peak profile shapes of sequencing data such as CLIP. FINDINGS: With StoatyDive we are able to classify peak profile shapes from CLIP-seq data of the histone stem-loop-binding protein (SLBP). We compare the results to existing tools and show that StoatyDive finds more distinct peak shape clusters for CLIP data. Furthermore, we present StoatyDive's capabilities as a quality control tool and as a filter to pick different shapes based on biological or technical questions for other CLIP data from different RNA binding proteins with different biological functions and numbers of RNA recognition motifs. We finally show that proteins involved in splicing, such as RBM22 and U2AF1, have potentially sharper-shaped peaks than other RNA binding proteins. CONCLUSION: StoatyDive finally fills the demand for a peak shape clustering tool for CLIP-Seq data that fine-tunes downstream analysis steps such as structure or sequence motif predictions and that acts as a quality control.

HRIBO: high-throughput analysis of bacterial ribosome profiling data.

MOTIVATION: Ribosome profiling (Ribo-seq) is a powerful approach based on deep sequencing of cDNA libraries generated from ribosome-protected RNA fragments to explore the translatome of a cell, and is especially useful for the detection of small proteins (50-100 amino acids) that are recalcitrant to many standard biochemical and in silico approaches. While pipelines are available to analyze Ribo-seq data, none are designed explicitly for the automatic processing and analysis of data from bacteria, nor are they focused on the discovery of unannotated open reading frames (ORFs). RESULTS: We present HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing and quality control steps. Importantly, HRIBO outputs annotation-independent ORF predictions based on two complementary bacteria-focused tools, and integrates them with additional feature information and expression values. This facilitates the rapid and high-confidence discovery of novel ORFs and their prioritization for functional characterization. AVAILABILITY AND IMPLEMENTATION: HRIBO is a free and open source project available under the GPL-3 license at: https://github.com/RickGelhausen/HRIBO.

Galaxy CLIP-Explorer: a web server for CLIP-Seq data analysis.

BACKGROUND: Post-transcriptional regulation via RNA-binding proteins plays a fundamental role in every organism, but the regulatory mechanisms lack important understanding. Nevertheless, they can be elucidated by cross-linking immunoprecipitation in combination with high-throughput sequencing (CLIP-Seq). CLIP-Seq answers questions about the functional role of an RNA-binding protein and its targets by determining binding sites on a nucleotide level and associated sequence and structural binding patterns. In recent years the amount of CLIP-Seq data skyrocketed, urging the need for an automatic data analysis that can deal with different experimental set-ups. However, noncanonical data, new protocols, and a huge variety of tools, especially for peak calling, made it difficult to define a standard. FINDINGS: CLIP-Explorer is a flexible and reproducible data analysis pipeline for iCLIP data that supports for the first time eCLIP, FLASH, and uvCLAP data. Individual steps like peak calling can be changed to adapt to different experimental settings. We validate CLIP-Explorer on eCLIP data, finding similar or nearly identical motifs for various proteins in comparison with other databases. In addition, we detect new sequence motifs for PTBP1 and U2AF2. Finally, we optimize the peak calling with 3 different peak callers on RBFOX2 data, discuss the difficulty of the peak-calling step, and give advice for different experimental set-ups. CONCLUSION: CLIP-Explorer finally fills the demand for a flexible CLIP-Seq data analysis pipeline that is applicable to the up-to-date CLIP protocols. The article further shows the limitations of current peak-calling algorithms and the importance of a robust peak detection.

Nephronophthisis gene products display RNA-binding properties and are recruited to stress granules.

Mutations of cilia-associated molecules cause multiple developmental defects that are collectively termed ciliopathies. However, several ciliary proteins, involved in gating access to the cilium, also assume localizations at other cellular sites including the nucleus, where they participate in DNA damage responses to maintain tissue integrity. Molecular insight into how these molecules execute such diverse functions remains limited. A mass spectrometry screen for ANKS6-interacting proteins suggested an involvement of ANKS6 in RNA processing and/or binding. Comparing the RNA-binding properties of the known RNA-binding protein BICC1 with the three ankyrin-repeat proteins ANKS3, ANKS6 (NPHP16) and INVERSIN (NPHP2) confirmed that certain nephronophthisis (NPH) family members can interact with RNA molecules. We also observed that BICC1 and INVERSIN associate with stress granules in response to translational inhibition. Furthermore, BICC1 recruits ANKS3 and ANKS6 into TIA-1-positive stress granules after exposure to hippuristanol. Our findings uncover a novel function of NPH family members, and provide further evidence that NPH family members together with BICC1 are involved in stress responses to maintain tissue and organ integrity.