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RNA splicing, the process of intron removal from pre-mRNA, is essential for the regulation of gene expression. It is controlled by the spliceosome, a megadalton RNA–protein complex that assembles de novo on each pre-mRNA intron through an ordered assembly of intermediate complexes1,2. Spliceosome activation is a major control step that requires substantial protein and RNA rearrangements leading to a catalytically active complex1–5. Splicing factor 3B subunit 1 (SF3B1) protein—a subunit of the U2 small nuclear ribonucleoprotein6—is phosphorylated during spliceosome activation7–10, but the kinase that is responsible has not been identified. Here we show that cyclin-dependent kinase 11 (CDK11) associates with SF3B1 and phosphorylates threonine residues at its N terminus during spliceosome activation. The phosphorylation is important for the association between SF3B1 and U5 and U6 snRNAs in the activated spliceosome, termed the Bactcomplex, and the phosphorylation can be blocked by OTS964, a potent and selective inhibitor of CDK11. Inhibition of CDK11 prevents spliceosomal transition from the precatalytic complex B to the activated complex Bactand leads to widespread intron retention and accumulation of non-functional spliceosomes on pre-mRNAs and chromatin. We demonstrate a central role of CDK11 in spliceosome assembly and splicing regulation and characterize OTS964 as a highly selective CDK11 inhibitor that suppresses spliceosome activation and splicing.

Mapping the network of proteins provides a powerful means to investigate the function of disease genes and to unravel the molecular basis of phenotypes. We present an automated informatics-aided and bioluminescence resonance energy transfer-based approach (iBRET) enabling high-confidence detection of protein−protein interactions in living mammalian cells. A screen of the ABCD1 protein, which is affected in X-linked adrenoleukodystrophy (X-ALD), against an organelle library of peroxisomal proteins demonstrated applicability of iBRET for large-scale experiments. We identified novel protein–protein interactions for ABCD1 (with ALDH3A2, DAO, ECI2, FAR1, PEX10, PEX13, PEX5, PXMP2, and PIPOX), mapped its position within the peroxisomal protein–protein interaction network, and determined that pathogenic missense variants in ABCD1alter the interaction with selected binding partners. These findings provide mechanistic insights into pathophysiology of X-ALD and may foster the identification of new disease modifiers.

Tissue repair after spinal cord injury requires the mobilization of immune and glial cells to form a protective barrier that seals the wound and facilitates debris clearing, inflammatory containment and matrix compaction. This process involves corralling, wherein phagocytic immune cells become confined to the necrotic core, which is surrounded by an astrocytic border. Here we elucidate a temporally distinct gene signature in injury-activated microglia and macrophages (IAMs) that engages axon guidance pathways. Plexin-B2 is upregulated in IAMs and is required for motor sensory recovery after spinal cord injury. Plexin-B2 deletion in myeloid cells impairs corralling, leading to diffuse tissue damage, inflammatory spillover and hampered axon regeneration. Corralling begins early and requires Plexin-B2 in both microglia and macrophages. Mechanistically, Plexin-B2 promotes microglia motility, steers IAMs away from colliding cells and facilitates matrix compaction. Our data therefore establish Plexin-B2 as an important link that integrates biochemical cues and physical interactions of IAMs with the injury microenvironment during wound healing.

The RNase Regnase-1 is a master RNA regulator in macrophages and T cells that degrades cellular and viral RNA upon NF-κB signaling. The roles of its family members, however, remain largely unknown. Here, we analyzed Regnase-3–deficient mice, which develop hypertrophic lymph nodes. We used various mice with immune cell–specific deletions of Regnase-3 to demonstrate that Regnase-3 acts specifically within myeloid cells. Regnase-3 deficiency systemically increased IFN signaling, which increased the proportion of immature B and innate immune cells, and suppressed follicle and germinal center formation. Expression analysis revealed that Regnase-3 and Regnase-1 share protein degradation pathways. Unlike Regnase-1, Regnase-3 expression is high specifically in macrophages and is transcriptionally controlled by IFN signaling. Although direct targets in macrophages remain unknown, Regnase-3 can bind, degrade, and regulate mRNAs, such as Zc3h12a (Regnase-1), in vitro. These data indicate that Regnase-3, like Regnase-1, is an RNase essential for immune homeostasis but has diverged as key regulator in the IFN pathway in macrophages.

Activation of immune cells results in rapid functional changes, but how such fast changes are accomplished remains enigmatic. By combining time courses of 4sU-seq, RNA-seq, ribosome profiling (RP), and RNA polymerase II (RNA Pol II) ChIP-seq during T cell activation, we illustrate genome-wide temporal dynamics for ∼10,000 genes. This approach reveals not only immediate-early and posttranscriptionally regulated genes but also coupled changes in transcription and translation for >90% of genes. Recruitment, rather than release of paused RNA Pol II, primarily mediates transcriptional changes. This coincides with a genome-wide temporary slowdown in cotranscriptional splicing, even for polyadenylated mRNAs that are localized at the chromatin. Subsequent splicing optimization correlates with increasing Ser-2 phosphorylation of the RNA Pol II carboxy-terminal domain (CTD) and activation of the positive transcription elongation factor (pTEFb). Thus, rapid de novo recruitment of RNA Pol II dictates the course of events during T cell activation, particularly transcription, splicing, and consequently translation.

We determined the effect of p53 activation on de novoprotein synthesis using quantitative proteomics (pulsed stable isotope labeling with amino acids in cell culture/pSILAC) in the colorectal cancer cell line SW480. This was combined with mRNA and noncoding RNA expression analyses by next generation sequencing (RNA-, miR-Seq). Furthermore, genome-wide DNA binding of p53 was analyzed by chromatin-immunoprecipitation (ChIP-Seq). Thereby, we identified differentially regulated proteins (542 up, 569 down), mRNAs (1258 up, 415 down), miRNAs (111 up, 95 down) and lncRNAs (270 up, 123 down). Changes in protein and mRNA expression levels showed a positive correlation (r= 0.50, p< 0.0001). In total, we detected 133 direct p53 target genes that were differentially expressed and displayed p53 occupancy in the vicinity of their promoter. More transcriptionally induced genes displayed occupied p53 binding sites (4.3% mRNAs, 7.2% miRNAs, 6.3% lncRNAs, 5.9% proteins) than repressed genes (2.4% mRNAs, 3.2% miRNAs, 0.8% lncRNAs, 1.9% proteins), suggesting indirect mechanisms of repression. Around 50% of the down-regulated proteins displayed seed-matching sequences of p53-induced miRNAs in the corresponding 3′-UTRs. Moreover, proteins repressed by p53 significantly overlapped with those previously shown to be repressed by miR-34a. We confirmed up-regulation of the novel direct p53 target genes LINC01021, MDFI, ST14and miR-486and showed that ectopic LINC01021expression inhibits proliferation in SW480 cells. Furthermore, KLF12, HMGB1and CITmRNAs were confirmed as direct targets of the p53-induced miR-34a, miR-205 and miR-486–5p, respectively. In line with the loss of p53 function during tumor progression, elevated expression of KLF12, HMGB1and CITwas detected in advanced stages of cancer. In conclusion, the integration of multiple omics methods allowed the comprehensive identification of direct and indirect effectors of p53 that provide new insights and leads into the mechanisms of p53-mediated tumor suppression.

High concentrations (> 100 µM) of the ribonucleoside analog 4-thiouridine (4sU) is widely used in methods for RNA analysis like photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) and nascent messenger (m)RNA labeling (4sU-tagging). Here, we show that 4sU-tagging at low concentrations ≤ 10 µM can be used to measure production and processing of ribosomal (r)RNA. However, elevated concentrations of 4sU (> 50 µM), which are usually used for mRNA labeling experiments, inhibit production and processing of 47S rRNA. The inhibition of rRNA synthesis is accompanied by nucleoplasmic translocation of nucleolar nucleophosmin (NPM1), induction of the tumor suppressor p53, and inhibition of proliferation. We conclude that metabolic labeling of RNA by 4sU triggers a nucleolar stress response, which might influence the interpretation of results. Therefore, functional ribosome biogenesis, nucleolar integrity, and cell cycle should be addressed in 4sU labeling experiments.

RNA levels in a cell are determined by the relative rates of RNA synthesis and decay. State-of-the-art transcriptional analyses only employ total cellular RNA. Therefore, changes in RNA levels cannot be attributed to RNA synthesis or decay, and temporal resolution is poor. Recently, it was reported that newly transcribed RNA can be biosynthetically labeled for 1-2 h using thiolated nucleosides, purified from total cellular RNA and subjected to microarray analysis. However, in order to study signaling events at molecular level, analysis of changes occurring within minutes is required. We developed an improved approach to separate total cellular RNA into newly transcribed and preexisting RNA following 10-15 min of metabolic labeling. Employing new computational tools for array normalization and half-life determination we simultaneously study short-term RNA synthesis and decay as well as their impact on cellular transcript levels. As an example we studied the response of fibroblasts to type I and II interferons (IFN). Analysis of RNA transcribed within 15-30 min at different times during the first three hours of interferon-receptor activation resulted in a >10-fold increase in microarray sensitivity and provided a comprehensive profile of the kinetics of IFN-mediated changes in gene expression. We identify a previously undisclosed highly connected network of short-lived transcripts selectively down-regulated by IFNgamma in between 30 and 60 min after IFN treatment showing strong associations with cell cycle and apoptosis, indicating novel mechanisms by which IFNgamma affects these pathways.

Motivation: Two important questions for the analysis of gene expression measurements from different sample classes are (1) how to classify samples and (2) how to identify meaningful gene signatures (ranked gene lists) exhibiting the differences between classes and sample subsets. Solutions to both questions have immediate biological and biomedical applications. To achieve optimal classification performance, a suitable combination of classifier and gene selection method needs to be specifically selected for a given dataset. The selected gene signatures can be unstable and the resulting classification accuracy unreliable, particularly when considering different subsets of samples. Both unstable gene signatures and overestimated classification accuracy can impair biological conclusions. Methods: We address these two issues by repeatedly evaluating the classification performance of all models, i.e. pairwise combinations of various gene selection and classification methods, for random subsets of arrays (sampling). A model score is used to select the most appropriate model for the given dataset. Consensus gene signatures are constructed by extracting those genes frequently selected over many samplings. Sampling additionally permits measurement of the stability of the classification performance for each model, which serves as a measure of model reliability. Results: We analyzed a large gene expression dataset with 78 measurements of four different cartilage sample classes. Classifiers trained on subsets of measurements frequently produce models with highly variable performance. Our approach provides reliable classification performance estimates via sampling. In addition to reliable classification performance, we determined stable consensus signatures (i.e. gene lists) for sample classes. Manual literature screening showed that these genes are highly relevant to our gene expression experiment with osteoarthritic cartilage. We compared our approach to others based on a publicly available dataset on breast cancer. Availability: R package at http://www.bio.ifi.lmu.de/~davis/edaprakt Contact: ralf.zimmer@bio.ifi.lmu.de

Motivation: Discovery of host and pathogen genes expressed at the plant–pathogen interface often requires the construction of mixed libraries that contain sequences from both genomes. Sequence identification requires high-throughput and reliable classification of genome origin. When using single-pass cDNA sequences difficulties arise from the short sequence length, the lack of sufficient taxonomically relevant sequence data in public databases and ambiguous sequence homology between plant and pathogen genes. Results: A novel method is described, which is independent of the availability of homologous genes and relies on subtle differences in codon usage between plant and fungal genes. We used support vector machines (SVMs) to identify the probable origin of sequences. SVMs were compared to several other machine learning techniques and to a probabilistic algorithm (PF-IND) for expressed sequence tag (EST) classification also based on codon bias differences. Our software (Eclat) has achieved a classification accuracy of 93.1% on a test set of 3217 EST sequences from Hordeum vulgare and Blumeria graminis, which is a significant improvement compared to PF-IND (prediction accuracy of 81.2% on the same test set). EST sequences with at least 50 nt of coding sequence can be classified using Eclat with high confidence. Eclat allows training of classifiers for any host–pathogen combination for which there are sufficient classified training sequences. Availability: Eclat is freely available on the Internet (http://mips.gsf.de/proj/est) or on request as a standalone version. Contact: friedel@informatik.uni-muenchen.de