Nonspecific Interactions in Transcription Regulation and Organization of Transcriptional Condensates.

Eukaryotic cells are characterized by a high degree of compartmentalization of their internal contents, which ensures precise and controlled regulation of intracellular processes. During many processes, including different stages of transcription, dynamic membraneless compartments termed biomolecular condensates are formed. Transcription condensates contain various transcription factors and RNA polymerase and are formed by high- and low-specificity interactions between the proteins, DNA, and nearby RNA. This review discusses recent data demonstrating important role of nonspecific multivalent protein-protein and RNA-protein interactions in organization and regulation of transcription.

Sample-Wise and Gene-Wise Comparisons Confirm a Greater Similarity of RNA and Protein Expression Data at the Level of Molecular Pathways and Suggest an Approach for the Data Quality Check in High-Throughput Expression Databases.

Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression.

Role of epigenetics and alterations in RNA metabolism in leukodystrophies.

Leukodystrophies are a class of rare heterogeneous disorders which affect the white matter of the brain, ultimately leading to a disruption in brain development and a damaging effect on cognitive, motor and social-communicative development. These disorders present a great clinical heterogeneity, along with a phenotypic overlap and this could be partially due to contributions from environmental stimuli. It is in this context that there is a great need to investigate what other factors may contribute to both disease insurgence and phenotypical heterogeneity, and novel evidence are raising the attention toward the study of epigenetics and transcription mechanisms that can influence the disease phenotype beyond genetics. Modulation in the epigenetics machinery including histone modifications, DNA methylation and non-coding RNAs dysregulation, could be crucial players in the development of these disorders, and moreover an aberrant RNA maturation process has been linked to leukodystrophies. Here, we provide an overview of these mechanisms hoping to supply a closer step toward the analysis of leukodystrophies not only as genetically determined but also with an added level of complexity where epigenetic dysregulation is of key relevance. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNA RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development.

3D exploration of gene expression in chicken embryos through combined RNA fluorescence in situ hybridization, immunofluorescence, and clearing.

BACKGROUND: Fine characterization of gene expression patterns is crucial to understand many aspects of embryonic development. The chicken embryo is a well-established and valuable animal model for developmental biology. The period spanning from the third to sixth embryonic days (E3 to E6) is critical for many organ developments. Hybridization chain reaction RNA fluorescent in situ hybridization (HCR RNA-FISH) enables multiplex RNA detection in thick samples including embryos of various animal models. However, its use is limited by tissue opacity. RESULTS: We optimized HCR RNA-FISH protocol to efficiently label RNAs in whole mount chicken embryos from E3.5 to E5.5 and adapted it to ethyl cinnamate (ECi) tissue clearing. We show that light sheet imaging of HCR RNA-FISH after ECi clearing allows RNA expression analysis within embryonic tissues with good sensitivity and spatial resolution. Finally, whole mount immunofluorescence can be performed after HCR RNA-FISH enabling as exemplified to assay complex spatial relationships between axons and their environment or to monitor GFP electroporated neurons. CONCLUSIONS: We could extend the use of HCR RNA-FISH to older chick embryos by optimizing HCR RNA-FISH and combining it with tissue clearing and 3D imaging. The integration of immunostaining makes possible to combine gene expression with classical cell markers, to correlate expressions with morphological differentiation and to depict gene expressions in gain or loss of function contexts. Altogether, this combined procedure further extends the potential of HCR RNA-FISH technique for chicken embryology.

Unveiling the mystery of nuclear RNA homeostasis.

How nuclear RNA homeostasis impacts cellular functions remains elusive. In this issue of Cell Stem Cell, Han et al.1 utilized a controllable protein degradation system targeting EXOSC2 to perturb RNA homeostasis in mouse pluripotent embryonic stem cells, revealing its vital role in orchestrating crucial nuclear events for cellular fitness.

A comprehensive analysis of spermatozoal RNA elements in idiopathic infertile males undergoing fertility treatment.

Current approaches to diagnosing male infertility inadequately assess the complexity of the male gamete. Beyond the paternal haploid genome, spermatozoa also deliver coding and non-coding RNAs to the oocyte. While sperm-borne RNAs have demonstrated potential involvement in embryo development, the underlying mechanisms remain unclear. In this study, 47 sperm samples from normozoospermic males undergoing fertility treatment using donor oocytes were sequenced and analyzed to evaluate associations between sperm RNA elements (exon-sized sequences) and blastocyst progression. A total of 366 RNA elements (REs) were significantly associated with blastocyst rate (padj < 0.05), some of which were linked to genes related to critical developmental processes, including mitotic spindle formation and both ectoderm and mesoderm specification. Of note, 27 RE-associated RNAs are predicted targets of our previously reported list of developmentally significant miRNAs. Inverse RE-miRNA expression patterns were consistent with miRNA-mediated down-regulation. This study provides a comprehensive set of REs which differ by the patient's ability to produce blastocysts. This knowledge can be leveraged to improve clinical screening of male infertility and ultimately reduce time to pregnancy.

Harnessing noncanonical crRNA for highly efficient genome editing.

The CRISPR-Cas12a system is more advantageous than the widely used CRISPR-Cas9 system in terms of specificity and multiplexibility. However, its on-target editing efficiency is typically much lower than that of the CRISPR-Cas9 system. Here we improved its on-target editing efficiency by simply incorporating 2-aminoadenine (base Z, which alters canonical Watson-Crick base pairing) into the crRNA to increase the binding affinity between crRNA and its complementary DNA target. The resulting CRISPR-Cas12a (named zCRISPR-Cas12a thereafter) shows an on-target editing efficiency comparable to that of the CRISPR-Cas9 system but with much lower off-target effects than the CRISPR-Cas9 system in mammalian cells. In addition, zCRISPR-Cas12a can be used for precise gene knock-in and highly efficient multiplex genome editing. Overall, the zCRISPR-Cas12a system is superior to the CRISPR-Cas9 system, and our simple crRNA engineering strategy may be extended to other CRISPR-Cas family members as well as their derivatives.

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models.

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.

Simultaneous Detection of Adenosine-to-Inosine Editing and N6-Methyladenosine at Identical RNA Sites through Deamination-Assisted Reverse Transcription Stalling.

Adenosine-to-inosine (A-to-I) editing and N6-methyladenosine (m6A) modifications are pivotal RNA modifications with widespread functional significance in physiological and pathological processes. Although significant effort has been dedicated to developing methodologies for identifying and quantifying these modifications, traditional approaches have often focused on each modification independently, neglecting the potential co-occurrence of A-to-I editing and m6A modifications at the same adenosine residues. This limitation has constrained our understanding of the intricate regulatory mechanisms governing RNA function and the interplay between different types of RNA modifications. To address this gap, we introduced an innovative technique called deamination-assisted reverse transcription stalling (DARTS), specifically designed for the simultaneous quantification of A-to-I editing and m6A at the same RNA sites. DARTS leverages the selective deamination activity of the engineered TadA-TadA8e protein, which converts adenosine residues to inosine, in combination with the unique property of Bst 2.0 DNA polymerase, which stalls when encountering inosine during reverse transcription. This approach enables the accurate quantification of A-to-I editing, m6A, and unmodified adenosine at identical RNA sites. The DARTS method is remarkable for its ability to directly quantify two distinct types of RNA modifications simultaneously, a capability that has remained largely unexplored in the field of RNA biology. By facilitating a comprehensive analysis of the co-occurrence and interaction between A-to-I editing and m6A modifications, DARTS opens new avenues for exploring the complex regulatory networks modulated by different RNA modifications.

Zinc N,N-bis(2-picolyl)amine Chelates Show Substitution-Dependent Cleavage of Phosphodiesters in Models as Well as of PNAzyme-RNA Bulges.

PNAzymes are a group of artificial enzymes which show promising results in selective and efficient cleavage of RNA targets. In the present study, we introduce a series of metal chelating groups based on N,N-bis(2-picolyl) groups (parent, 6-methyl and 6-amino substituted) as the active sites of novel PNAzymes. An improved synthetic route for the 6-amino analogues is described. The catalytic activity of the chelating groups for cleaving phosphodiesters were assessed with the model substrate 2-hydroxypropyl p-nitrophenyl phosphate (HPNPP), confirming that the zinc complexes have the reactivity order of parent < 2-methyl < 2-amino. The three ligands were conjugated to a PNA oligomer to form three PNAzymes which showed the same order of reactivity and some sensitivity to the size of the RNA bulge designed into the catalyst-substrate complex. This work demonstrates that the kinetic activity observed for the model substrate HPNPP could be translated onto the PNAzymes, but that more reactive Zn complexes are required for such PNAzymes to be viable therapeutic agents.

Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors.

Single-cell expression dynamics, from differentiation trajectories or RNA velocity, have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either overlook these expression dynamics or necessitate that cells be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph of cells, constructed from pseudotime or RNA velocity measurements. Additionally, Velorama enables the estimation of the speed at which TFs influence target genes. Applying Velorama, we uncover evidence that the speed of a TF's interactions is tied to its regulatory function. For human corticogenesis, we find that slow TFs are linked to gliomas, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to become a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.

Transfer learning enables identification of multiple types of RNA modifications using nanopore direct RNA sequencing.

Nanopore direct RNA sequencing (DRS) has emerged as a powerful tool for RNA modification identification. However, concurrently detecting multiple types of modifications in a single DRS sample remains a challenge. Here, we develop TandemMod, a transferable deep learning framework capable of detecting multiple types of RNA modifications in single DRS data. To train high-performance TandemMod models, we generate in vitro epitranscriptome datasets from cDNA libraries, containing thousands of transcripts labeled with various types of RNA modifications. We validate the performance of TandemMod on both in vitro transcripts and in vivo human cell lines, confirming its high accuracy for profiling m6A and m5C modification sites. Furthermore, we perform transfer learning for identifying other modifications such as m7G, Ψ, and inosine, significantly reducing training data size and running time without compromising performance. Finally, we apply TandemMod to identify 3 types of RNA modifications in rice grown in different environments, demonstrating its applicability across species and conditions. In summary, we provide a resource with ground-truth labels that can serve as benchmark datasets for nanopore-based modification identification methods, and TandemMod for identifying diverse RNA modifications using a single DRS sample.

Preclinical efficacy and pharmacokinetics of an RNA-encoded T cell-engaging bispecific antibody targeting human claudin 6.

We present the preclinical pharmacology of BNT142, a lipid nanoparticle (LNP)-formulated RNA (RNA-LNP) encoding a T cell-engaging bispecific antibody that monovalently binds the T cell marker CD3 and bivalently binds claudin 6 (CLDN6), an oncofetal antigen that is absent from normal adult tissue but expressed on various solid tumors. Upon BNT142 RNA-LNP delivery in cell culture, mice, and cynomolgus monkeys, RNA is translated, followed by self-assembly into and secretion of the functional bispecific antibody RiboMab02.1. In vitro, RiboMab02.1 mediated CLDN6 target cell-specific activation and proliferation of T cells, and potent target cell killing. In mice and cynomolgus monkeys, intravenously administered BNT142 RNA-LNP maintained therapeutic serum concentrations of the encoded antibody. Concentrations of RNA-encoded RiboMab02.1 were maintained longer in circulation in mice than concentrations of directly injected, sequence-identical protein. Weekly injections of mice with BNT142 RNA-LNP in the 0.1- to 1-µg dose range were sufficient to eliminate CLDN6-positive subcutaneous human xenograft tumors and increase survival over controls. Tumor regression was associated with an influx of T cells and depletion of CLDN6-positive cells. BNT142 induced only transient and low cytokine production in CLDN6-positive tumor-bearing mice humanized with peripheral blood mononuclear cells (PBMCs). No signs of adverse effects from BNT142 RNA-LNP administration were observed in mice or cynomolgus monkeys. On the basis of these and other findings, a phase 1/2 first-in-human clinical trial has been initiated to assess the safety and preliminary efficacy of BNT142 RNA-LNP in patients with CLDN6-positive advanced solid tumors (NCT05262530).

How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA.

Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.

RNA degradation in human mitochondria: the journey is not finished.

Mitochondria are vital organelles present in almost all eukaryotic cells. Although most of the mitochondrial proteins are nuclear-encoded, mitochondria contain their own genome, whose proper expression is necessary for mitochondrial function. Transcription of the human mitochondrial genome results in the synthesis of long polycistronic transcripts that are subsequently processed by endonucleases to release individual RNA molecules, including precursors of sense protein-encoding mRNA (mt-mRNA) and a vast amount of antisense noncoding RNAs. Because of mitochondrial DNA (mtDNA) organization, the regulation of individual gene expression at the transcriptional level is limited. Although transcription of most protein-coding mitochondrial genes occurs with the same frequency, steady-state levels of mature transcripts are different. Therefore, post-transcriptional processes are important for regulating mt-mRNA levels. The mitochondrial degradosome is a complex composed of the RNA helicase SUV3 (also known as SUPV3L1) and polynucleotide phosphorylase (PNPase, PNPT1). It is the best-characterized RNA-degrading machinery in human mitochondria, which is primarily responsible for the decay of mitochondrial antisense RNA. The mechanism of mitochondrial sense RNA decay is less understood. This review aims to provide a general picture of mitochondrial genome expression, with a particular focus on mitochondrial RNA (mtRNA) degradation.

Cymoxanil disrupts RNA synthesis through inhibiting the activity of dihydrofolate reductase.

The agricultural fungicide cymoxanil (CMX) is commonly used in the treatment of plant pathogens, such as Phytophthora infestans. Although the use of CMX is widespread throughout the agricultural industry and internationally, the exact mechanism of action behind this fungicide remains unclear. Therefore, we sought to elucidate the biocidal mechanism underlying CMX. This was accomplished by first performing a large-scale chemical-genomic screen comprising the 4000 haploid non-essential gene deletion array of the yeast Saccharomyces cerevisiae. We found that gene families related to de novo purine biosynthesis and ribonucleoside synthesis were enriched in the presence of CMX. These results were confirmed through additional spot-test and colony counting assays. We next examined whether CMX affects RNA biosynthesis. Using qRT-PCR and expression assays, we found that CMX appears to target RNA biosynthesis possibly through the yeast dihydrofolate reductase (DHFR) enzyme Dfr1. To determine whether DHFR is a target of CMX, we performed an in-silico molecular docking assay between CMX and yeast, human, and P. infestans DHFR. The results suggest that CMX directly interacts with the active site of all tested forms of DHFR using conserved residues. Using an in vitro DHFR activity assay we observed that CMX inhibits DHFR activity in a dose-dependent relationship.

Dynamic changes in RNA m6A and 5 hmC influence gene expression programs during macrophage differentiation and polarisation.

RNA modifications are essential for the establishment of cellular identity. Although increasing evidence indicates that RNA modifications regulate the innate immune response, their role in monocyte-to-macrophage differentiation and polarisation is unclear. While m6A has been widely studied, other RNA modifications, including 5 hmC, remain poorly characterised. We profiled m6A and 5 hmC epitranscriptomes, transcriptomes, translatomes and proteomes of monocytes and macrophages at rest and pro- and anti-inflammatory states. Transcriptome-wide mapping of m6A and 5 hmC reveals enrichment of m6A and/or 5 hmC on specific categories of transcripts essential for macrophage differentiation. Our analyses indicate that m6A and 5 hmC modifications are present in transcripts with critical functions in pro- and anti-inflammatory macrophages. Notably, we also discover the co-occurrence of m6A and 5 hmC on alternatively-spliced isoforms and/or opposing ends of the untranslated regions (UTR) of mRNAs with key roles in macrophage biology. In specific examples, RNA 5 hmC controls the decay of transcripts independently of m6A. This study provides (i) a comprehensive dataset to interrogate the role of RNA modifications in a plastic system (ii) a resource for exploring different layers of gene expression regulation in the context of human monocyte-to-macrophage differentiation and polarisation, (iii) new insights into RNA modifications as central regulators of effector cells in innate immunity.

Programmable RNA targeting with CRISPR-Cas13.

The RNA-targeting CRISPR-Cas13 system has enabled precise engineering of endogenous RNAs, significantly advancing our understanding of RNA regulation and the development of RNA-based diagnostic and therapeutic applications. This review aims to provide a summary of Cas13-based RNA targeting tools and applications, discuss limitations and challenges of existing tools and suggest potential directions for further development of the RNA targeting system.