Regnase-1 and Roquin Regulate a Common Element in Inflammatory mRNAs by Spatiotemporally Distinct Mechanisms.

Regnase-1 and Roquin are RNA binding proteins essential for degradation of inflammation-related mRNAs and maintenance of immune homeostasis. However, their mechanistic relationship has yet to be clarified. Here, we show that, although Regnase-1 and Roquin regulate an overlapping set of mRNAs via a common stem-loop structure, they function in distinct subcellular locations: ribosome/endoplasmic reticulum and processing-body/stress granules, respectively. Moreover, Regnase-1 specifically cleaves and degrades translationally active mRNAs and requires the helicase activity of UPF1, similar to the decay mechanisms of nonsense mRNAs. In contrast, Roquin controls translationally inactive mRNAs, independent of UPF1. Defects in both Regnase-1 and Roquin lead to large increases in their target mRNAs, although Regnase-1 tends to control the early phase of inflammation when mRNAs are more actively translated. Our findings reveal that differential regulation of mRNAs by Regnase-1 and Roquin depends on their translation status and enables elaborate control of inflammation.

Protein Synthesis in the Developing Neocortex at Near-Atomic Resolution Reveals Ebp1-Mediated Neuronal Proteostasis at the 60S Tunnel Exit.

Protein synthesis must be finely tuned in the developing nervous system as the final essential step of gene expression. This study investigates the architecture of ribosomes from the neocortex during neurogenesis, revealing Ebp1 as a high-occupancy 60S peptide tunnel exit (TE) factor during protein synthesis at near-atomic resolution by cryoelectron microscopy (cryo-EM). Ribosome profiling demonstrated Ebp1-60S binding is highest during start codon initiation and N-terminal peptide elongation, regulating ribosome occupancy of these codons. Membrane-targeting domains emerging from the 60S tunnel, which recruit SRP/Sec61 to the shared binding site, displace Ebp1. Ebp1 is particularly abundant in the early-born neural stem cell (NSC) lineage and regulates neuronal morphology. Ebp1 especially impacts the synthesis of membrane-targeted cell adhesion molecules (CAMs), measured by pulsed stable isotope labeling by amino acids in cell culture (pSILAC)/bioorthogonal noncanonical amino acid tagging (BONCAT) mass spectrometry (MS). Therefore, Ebp1 is a central component of protein synthesis, and the ribosome TE is a focal point of gene expression control in the molecular specification of neuronal morphology during development.

A Conserved Noncoding Locus Regulates Random Monoallelic Xist Expression across a Topological Boundary.

cis-Regulatory communication is crucial in mammalian development and is thought to be restricted by the spatial partitioning of the genome in topologically associating domains (TADs). Here, we discovered that the Xist locus is regulated by sequences in the neighboring TAD. In particular, the promoter of the noncoding RNA Linx (LinxP) acts as a long-range silencer and influences the choice of X chromosome to be inactivated. This is independent of Linx transcription and independent of any effect on Tsix, the antisense regulator of Xist that shares the same TAD as Linx. Unlike Tsix, LinxP is well conserved across mammals, suggesting an ancestral mechanism for random monoallelic Xist regulation. When introduced in the same TAD as Xist, LinxP switches from a silencer to an enhancer. Our study uncovers an unsuspected regulatory axis for X chromosome inactivation and a class of cis-regulatory effects that may exploit TAD partitioning to modulate developmental decisions.

Liam tackles complex multimodal single-cell data integration challenges.

Multi-omics characterization of single cells holds outstanding potential for profiling the dynamics and relations of gene regulatory states of thousands of cells. How to integrate multimodal data is an open problem, especially when aiming to combine data from multiple sources or conditions containing both biological and technical variation. We introduce liam, a flexible model for the simultaneous horizontal and vertical integration of paired single-cell multimodal data and mosaic integration of paired with unimodal data. Liam learns a joint low-dimensional representation of the measured modalities, which proves beneficial when the information content or quality of the modalities differ. Its integration accounts for complex batch effects using a tunable combination of conditional and adversarial training, which can be optimized using replicate information while retaining selected biological variation. We demonstrate liam's superior performance on multiple paired multimodal data types, including Multiome and CITE-seq data, and in mosaic integration scenarios. Our detailed benchmarking experiments illustrate the complexities and challenges remaining for integration and the meaningful assessment of its success.

Intricacies of single-cell multi-omics data integration.

A wealth of single-cell protocols makes it possible to characterize different molecular layers at unprecedented resolution. Integrating the resulting multimodal single-cell data to find cell-to-cell correspondences remains a challenge. We argue that data integration needs to happen at a meaningful biological level of abstraction and that it is necessary to consider the inherent discrepancies between modalities to strike a balance between biological discovery and noise removal. A survey of current methods reveals that a distinction between technical and biological origins of presumed unwanted variation between datasets is not yet commonly considered. The increasing availability of paired multimodal data will aid the development of improved methods by providing a ground truth on cell-to-cell matches.

Ythdf is a N6-methyladenosine reader that modulates Fmr1 target mRNA selection and restricts axonal growth in Drosophila.

N6-methyladenosine (m6 A) regulates a variety of physiological processes through modulation of RNA metabolism. This modification is particularly enriched in the nervous system of several species, and its dysregulation has been associated with neurodevelopmental defects and neural dysfunctions. In Drosophila, loss of m6 A alters fly behavior, albeit the underlying molecular mechanism and the role of m6 A during nervous system development have remained elusive. Here we find that impairment of the m6 A pathway leads to axonal overgrowth and misguidance at larval neuromuscular junctions as well as in the adult mushroom bodies. We identify Ythdf as the main m6 A reader in the nervous system, being required to limit axonal growth. Mechanistically, we show that the m6 A reader Ythdf directly interacts with Fmr1, the fly homolog of Fragile X mental retardation RNA binding protein (FMRP), to inhibit the translation of key transcripts involved in axonal growth regulation. Altogether, this study demonstrates that the m6 A pathway controls development of the nervous system and modulates Fmr1 target transcript selection.

Cluster-independent marker feature identification from single-cell omics data using SEMITONES.

Identification of cell identity markers is an essential step in single-cell omics data analysis. Current marker identification strategies typically rely on cluster assignments of cells. However, cluster assignment, particularly for developmental data, is nontrivial, potentially arbitrary, and commonly relies on prior knowledge. In response, we present SEMITONES, a principled method for cluster-free marker identification. We showcase and evaluate its application for marker gene and regulatory region identification from single-cell data of the human haematopoietic system. Additionally, we illustrate its application to spatial transcriptomics data and show how SEMITONES can be used for the annotation of cells given known marker genes. Using several simulated and curated data sets, we demonstrate that SEMITONES qualitatively and quantitatively outperforms existing methods for the retrieval of cell identity markers from single-cell omics data.

Deep neural networks for interpreting RNA-binding protein target preferences.

Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its strength to learn complex features from possibly multiple sources of raw data. However, the interpretability of these models, which is crucial to improve our understanding of RBP binding preferences and functions, has not yet been investigated in significant detail. We have designed a multitask and multimodal deep neural network for characterizing in vivo RBP targets. The model incorporates not only the sequence but also the region type of the binding sites as input, which helps the model to boost the prediction performance. To interpret the model, we quantified the contribution of the input features to the predictive score of each RBP. Learning across multiple RBPs at once, we are able to avoid experimental biases and to identify the RNA sequence motifs and transcript context patterns that are the most important for the predictions of each individual RBP. Our findings are consistent with known motifs and binding behaviors and can provide new insights about the regulatory functions of RBPs.

Human promoters are intrinsically directional.

Divergent transcription, in which reverse-oriented transcripts occur upstream of eukaryotic promoters in regions devoid of annotated genes, has been suggested to be a general property of active promoters. Here we show that the human basal RNA polymerase II transcriptional machinery and core promoter are inherently unidirectional and that reverse-oriented transcripts originate from their own cognate reverse-directed core promoters. In vitro transcription analysis and mapping of nascent transcripts in HeLa cells revealed that sequences at reverse start sites are similar to those of their forward counterparts. The use of DNase I accessibility to define proximal promoter borders revealed that about half of promoters are unidirectional and that unidirectional promoters are depleted at their upstream edges of reverse core promoter sequences and their associated chromatin features. Divergent transcription is thus not an inherent property of the transcription process but rather the consequence of the presence of both forward- and reverse-directed core promoters.

Deep learning for prediction of population health costs.

BACKGROUND: Accurate prediction of healthcare costs is important for optimally managing health costs. However, methods leveraging the medical richness from data such as health insurance claims or electronic health records are missing. METHODS: Here, we developed a deep neural network to predict future cost from health insurance claims records. We applied the deep network and a ridge regression model to a sample of 1.4 million German insurants to predict total one-year health care costs. Both methods were compared to existing models with various performance measures and were also used to predict patients with a change in costs and to identify relevant codes for this prediction. RESULTS: We showed that the neural network outperformed the ridge regression as well as all considered models for cost prediction. Further, the neural network was superior to ridge regression in predicting patients with cost change and identified more specific codes. CONCLUSION: In summary, we showed that our deep neural network can leverage the full complexity of the patient records and outperforms standard approaches. We suggest that the better performance is due to the ability to incorporate complex interactions in the model and that the model might also be used for predicting other health phenotypes.

Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data.

BACKGROUND: Co-localized combinations of histone modifications ("chromatin states") have been shown to correlate with promoter and enhancer activity. Changes in chromatin states over multiple time points ("chromatin state trajectories") have previously been analyzed at promoter and enhancers separately. With the advent of time series Hi-C data it is now possible to connect promoters and enhancers and to analyze chromatin state trajectories at promoter-enhancer pairs. RESULTS: We present TimelessFlex, a framework for investigating chromatin state trajectories at promoters and enhancers and at promoter-enhancer pairs based on Hi-C information. TimelessFlex extends our previous approach Timeless, a Bayesian network for clustering multiple histone modification data sets at promoter and enhancer feature regions. We utilize time series ATAC-seq data measuring open chromatin to define promoters and enhancer candidates. We developed an expectation-maximization algorithm to assign promoters and enhancers to each other based on Hi-C interactions and jointly cluster their feature regions into paired chromatin state trajectories. We find jointly clustered promoter-enhancer pairs showing the same activation patterns on both sides but with a stronger trend at the enhancer side. While the promoter side remains accessible across the time series, the enhancer side becomes dynamically more open towards the gene activation time point. Promoter cluster patterns show strong correlations with gene expression signals, whereas Hi-C signals get only slightly stronger towards activation. The code of the framework is available at https://github.com/henriettemiko/TimelessFlex . CONCLUSIONS: TimelessFlex clusters time series histone modifications at promoter-enhancer pairs based on Hi-C and it can identify distinct chromatin states at promoter and enhancer feature regions and their changes over time.

Deciphering human ribonucleoprotein regulatory networks.

RNA-binding proteins (RBPs) control and coordinate each stage in the life cycle of RNAs. Although in vivo binding sites of RBPs can now be determined genome-wide, most studies typically focused on individual RBPs. Here, we examined a large compendium of 114 high-quality transcriptome-wide in vivo RBP-RNA cross-linking interaction datasets generated by the same protocol in the same cell line and representing 64 distinct RBPs. Comparative analysis of categories of target RNA binding preference, sequence preference, and transcript region specificity was performed, and identified potential posttranscriptional regulatory modules, i.e. specific combinations of RBPs that bind to specific sets of RNAs and targeted regions. These regulatory modules represented functionally related proteins and exhibited distinct differences in RNA metabolism, expression variance, as well as subcellular localization. This integrative investigation of experimental RBP-RNA interaction evidence and RBP regulatory function in a human cell line will be a valuable resource for understanding the complexity of post-transcriptional regulation.

Beyond Read-Counts: Ribo-seq Data Analysis to Understand the Functions of the Transcriptome.

By mapping the positions of millions of translating ribosomes in the cell, ribosome profiling (Ribo-seq) has established its role as a powerful tool to study gene expression. Several laboratories have introduced modifications to the experimental protocol and expanded the repertoire of biochemical methods to study translation transcriptome-wide. However, the diversity of protocols highlights a need for standardization. At the same time, different computational analysis strategies have used Ribo-seq data to identify the set of translated sequences with high confidence. In this review we present an overview of such methodologies, outlining their assumptions, data requirements, and availability. At the interface between RNA and proteins, Ribo-seq can complement data from multiple omics approaches, zooming in on the central role of translation in the molecular cell.

DDX54 regulates transcriptome dynamics during DNA damage response.

The cellular response to genotoxic stress is mediated by a well-characterized network of DNA surveillance pathways. The contribution of post-transcriptional gene regulatory networks to the DNA damage response (DDR) has not been extensively studied. Here, we systematically identified RNA-binding proteins differentially interacting with polyadenylated transcripts upon exposure of human breast carcinoma cells to ionizing radiation (IR). Interestingly, more than 260 proteins, including many nucleolar proteins, showed increased binding to poly(A)+ RNA in IR-exposed cells. The functional analysis of DDX54, a candidate genotoxic stress responsive RNA helicase, revealed that this protein is an immediate-to-early DDR regulator required for the splicing efficacy of its target IR-induced pre-mRNAs. Upon IR exposure, DDX54 acts by increased interaction with a well-defined class of pre-mRNAs that harbor introns with weak acceptor splice sites, as well as by protein-protein contacts within components of U2 snRNP and spliceosomal B complex, resulting in lower intron retention and higher processing rates of its target transcripts. Because DDX54 promotes survival after exposure to IR, its expression and/or mutation rate may impact DDR-related pathologies. Our work indicates the relevance of many uncharacterized RBPs potentially involved in the DDR.

Genome-wide identification of regulatory elements in Sertoli cells.

A current goal of molecular biology is to identify transcriptional networks that regulate cell differentiation. However, identifying functional gene regulatory elements has been challenging in the context of developing tissues where material is limited and cell types are mixed. To identify regulatory sites during sex determination, we subjected Sertoli cells from mouse fetal testes to DNaseI-seq and ChIP-seq for H3K27ac. DNaseI-seq identified putative regulatory sites around genes enriched in Sertoli and pregranulosa cells; however, active enhancers marked by H3K27ac were enriched proximal to only Sertoli-enriched genes. Sequence analysis identified putative binding sites of known and novel transcription factors likely controlling Sertoli cell differentiation. As a validation of this approach, we identified a novel Sertoli cell enhancer upstream of Wt1, and used it to drive expression of a transgenic reporter in Sertoli cells. This work furthers our understanding of the complex genetic network that underlies sex determination and identifies regions that potentially harbor non-coding mutations underlying disorders of sexual development.

The Chloroplast RNA Binding Protein CP31A Has a Preference for mRNAs Encoding the Subunits of the Chloroplast NAD(P)H Dehydrogenase Complex and Is Required for Their Accumulation.

Chloroplast RNA processing requires a large number of nuclear-encoded RNA binding proteins (RBPs) that are imported post-translationally into the organelle. Most of these RBPs are highly specific for one or few target RNAs. By contrast, members of the chloroplast ribonucleoprotein family (cpRNPs) have a wider RNA target range. We here present a quantitative analysis of RNA targets of the cpRNP CP31A using digestion-optimized RNA co-immunoprecipitation with deep sequencing (DO-RIP-seq). This identifies the mRNAs coding for subunits of the chloroplast NAD(P)H dehydrogenase (NDH) complex as main targets for CP31A. We demonstrate using whole-genome gene expression analysis and targeted RNA gel blot hybridization that the ndh mRNAs are all down-regulated in cp31a mutants. This diminishes the activity of the NDH complex. Our findings demonstrate how a chloroplast RNA binding protein can combine functionally related RNAs into one post-transcriptional operon.

The mRNA-bound proteome of the early fly embryo.

Early embryogenesis is characterized by the maternal to zygotic transition (MZT), in which maternally deposited messenger RNAs are degraded while zygotic transcription begins. Before the MZT, post-transcriptional gene regulation by RNA-binding proteins (RBPs) is the dominant force in embryo patterning. We used two mRNA interactome capture methods to identify RBPs bound to polyadenylated transcripts within the first 2 h of Drosophila melanogaster embryogenesis. We identified a high-confidence set of 476 putative RBPs and confirmed RNA-binding activities for most of 24 tested candidates. Most proteins in the interactome are known RBPs or harbor canonical RBP features, but 99 exhibited previously uncharacterized RNA-binding activity. mRNA-bound RBPs and TFs exhibit distinct expression dynamics, in which the newly identified RBPs dominate the first 2 h of embryonic development. Integrating our resource with in situ hybridization data from existing databases showed that mRNAs encoding RBPs are enriched in posterior regions of the early embryo, suggesting their general importance in posterior patterning and germ cell maturation.

Detecting actively translated open reading frames in ribosome profiling data.

RNA-sequencing protocols can quantify gene expression regulation from transcription to protein synthesis. Ribosome profiling (Ribo-seq) maps the positions of translating ribosomes over the entire transcriptome. We have developed RiboTaper (available at https://ohlerlab.mdc-berlin.de/software/), a rigorous statistical approach that identifies translated regions on the basis of the characteristic three-nucleotide periodicity of Ribo-seq data. We used RiboTaper with deep Ribo-seq data from HEK293 cells to derive an extensive map of translation that covered open reading frame (ORF) annotations for more than 11,000 protein-coding genes. We also found distinct ribosomal signatures for several hundred upstream ORFs and ORFs in annotated noncoding genes (ncORFs). Mass spectrometry data confirmed that RiboTaper achieved excellent coverage of the cellular proteome. Although dozens of novel peptide products were validated in this manner, few of the currently annotated long noncoding RNAs appeared to encode stable polypeptides. RiboTaper is a powerful method for comprehensive de novo identification of actively used ORFs from Ribo-seq data.

SSMART: sequence-structure motif identification for RNA-binding proteins.

Motivation: RNA-binding proteins (RBPs) regulate every aspect of RNA metabolism and function. There are hundreds of RBPs encoded in the eukaryotic genomes, and each recognize its RNA targets through a specific mixture of RNA sequence and structure properties. For most RBPs, however, only a primary sequence motif has been determined, while the structure of the binding sites is uncharacterized. Results: We developed SSMART, an RNA motif finder that simultaneously models the primary sequence and the structural properties of the RNA targets sites. The sequence-structure motifs are represented as consensus strings over a degenerate alphabet, extending the IUPAC codes for nucleotides to account for secondary structure preferences. Evaluation on synthetic data showed that SSMART is able to recover both sequence and structure motifs implanted into 3'UTR-like sequences, for various degrees of structured/unstructured binding sites. In addition, we successfully used SSMART on high-throughput in vivo and in vitro data, showing that we not only recover the known sequence motif, but also gain insight into the structural preferences of the RBP. Availability and implementation: SSMART is freely available at https://ohlerlab.mdc-berlin.de/software/SSMART_137/. Supplementary information: Supplementary data are available at Bioinformatics online.

Integrative regulatory mapping indicates that the RNA-binding protein HuR couples pre-mRNA processing and mRNA stability.

RNA-binding proteins coordinate the fates of multiple RNAs, but the principles underlying these global interactions remain poorly understood. We elucidated regulatory mechanisms of the RNA-binding protein HuR, by integrating data from diverse high-throughput targeting technologies, specifically PAR-CLIP, RIP-chip, and whole-transcript expression profiling. The number of binding sites per transcript, degree of HuR association, and degree of HuR-dependent RNA stabilization were positively correlated. Pre-mRNA and mature mRNA containing both intronic and 3' UTR binding sites were more highly stabilized than transcripts with only 3' UTR or only intronic binding sites, suggesting that HuR couples pre-mRNA processing with mature mRNA stability. We also observed HuR-dependent splicing changes and substantial binding of HuR in polypyrimidine tracts of pre-mRNAs. Comparison of the spatial patterns surrounding HuR and miRNA binding sites provided functional evidence for HuR-dependent antagonism of proximal miRNA-mediated repression. We conclude that HuR coordinates gene expression outcomes at multiple interconnected steps of RNA processing.