Efficient and highly reproducible production of red blood cell-derived extracellular vesicle mimetics for the loading and delivery of RNA molecules.

Extracellular vesicles (EVs) are promising natural nanocarriers for the delivery of therapeutic agents. As with any other kind of cell, red blood cells (RBCs) produce a limited number of EVs under physiological and pathological conditions. Thus, RBC-derived extracellular vesicles (RBCEVs) have been recently suggested as next-generation delivery systems for therapeutic purposes. In this paper, we show that thanks to their unique biological and physicochemical features, RBCs can be efficiently pre-loaded with several kinds of molecules and further used to generate RBCEVs. A physical vesiculation method, based on "soft extrusion", was developed, producing an extremely high yield of cargo-loaded RBCEV mimetics. The RBCEVs population has been deeply characterized according to the new guidelines MISEV2023, showing great homogeneity in terms of size, biological features, membrane architecture and cargo. In vitro preliminary results demonstrated that RBCEVs are abundantly internalized by cells and exert peculiar biological effects. Indeed, efficient loading and delivery of miR-210 by RBCEVs to HUVEC has been proven, as well as the inhibition of a known mRNA target. Of note, the bench-scale process can be scaled-up and translated into clinics. In conclusion, this investigation could open the way to a new biomimetic platform for RNA-based therapies and/or other therapeutic cargoes useful in several diseases.

The Origin of RNA and the Formose-Ribose-RNA Pathway.

Prebiotic pre-Darwinian reactions continued throughout biochemical or Darwinian evolution. Early chemical processes could have occurred on Earth between 4.5 and 3.6 billion years ago when cellular life was about to come into being. Pre-Darwinian evolution assumes the development of hereditary elements but does not regard them as self-organizing processes. The presence of biochemical self-organization after the pre-Darwinian evolution did not justify distinguishing between different types of evolution. From the many possible solutions, evolution selected from among those stable reactions that led to catalytic networks, and under gradually changing external conditions produced a reproducible, yet constantly evolving and adaptable, living system. Major abiotic factors included sunlight, precipitation, air, minerals, soil and the Earth's atmosphere, hydrosphere and lithosphere. Abiotic sources of chemicals contributed to the formation of prebiotic RNA, the development of genetic RNA, the RNA World and the initial life forms on Earth and the transition of genRNA to the DNA Empire, and eventually to the multitude of life forms today. The transition from the RNA World to the DNA Empire generated new processes such as oxygenic photosynthesis and the hierarchical arrangement of processes involved in the transfer of genetic information. The objective of this work is to unite earlier work dealing with the formose, the origin and synthesis of ribose and RNA reactions that were published as a series of independent reactions. These reactions are now regarded as the first metabolic pathway.

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.

Q-BioLiP: A Comprehensive Resource for Quaternary Structure-based Protein-ligand Interactions.

Since its establishment in 2013, BioLiP has become one of the widely used resources for protein-ligand interactions. Nevertheless, several known issues occurred with it over the past decade. For example, the protein-ligand interactions are represented in the form of single chain-based tertiary structures, which may be inappropriate as many interactions involve multiple protein chains (known as quaternary structures). We sought to address these issues, resulting in Q-BioLiP, a comprehensive resource for quaternary structure-based protein-ligand interactions. The major features of Q-BioLiP include: (1) representing protein structures in the form of quaternary structures rather than single chain-based tertiary structures; (2) pairing DNA/RNA chains properly rather than separation; (3) providing both experimental and predicted binding affinities; (4) retaining both biologically relevant and irrelevant interactions to alleviate the wrong justification of ligands' biological relevance; and (5) developing a new quaternary structure-based algorithm for the modelling of protein-ligand complex structure. With these new features, Q-BioLiP is expected to be a valuable resource for studying biomolecule interactions, including protein-small molecule interaction, protein-metal ion interaction, protein-peptide interaction, protein-protein interaction, protein-DNA/RNA interaction, and RNA-small molecule interaction. Q-BioLiP is freely available at https://yanglab.qd.sdu.edu.cn/Q-BioLiP/.

Integrated analysis of single-cell RNA sequencing and bulk RNA data reveals gene regulatory networks and targets in dilated cardiomyopathy.

Dilated cardiomyopathy (DCM) is a common cause of heart failure, thromboembolism, arrhythmias, and sudden cardiac death. The quality of life and long-term survival rates of patients with dilated DCM have greatly improved in recent decades. Nevertheless, the clinical prognosis for DCM patients remains unfavorable. The primary driving factors underlying the pathogenesis of DCM remain incompletely understood. The present study aimed to identify driving factors underlying the pathogenesis of DCM from the perspective of gene regulatory networks. Single-cell RNA sequencing data and bulk RNA data were obtained from the Gene Expression Omnibus (GEO) database. Differential gene analysis, single-cell genomics analysis, and functional enrichment analysis were conducted using R software. The construction of Gene Regulatory Networks was performed using Python. We used the pySCENIC method to analyze the single-cell data and identified 401 regulons. Through variance decomposition, we selected 19 regulons that showed significant responsiveness to DCM. Next, we employed the ssGSEA method to assess regulons in two bulk RNA datasets. Significant statistical differences were observed in 9 and 13 regulons in each dataset. By intersecting these differentiated regulons and identifying shared targets that appeared at least twice, we successfully pinpointed three differentially expressed targets across both datasets. In this study, we assessed and identified 19 gene regulatory networks that were responsive to the disease. Furthermore, we validated these networks using two bulk RNA datasets of DCM. The elucidation of dysregulated regulons and targets (CDKN1A, SAT1, ZFP36) enhances the molecular understanding of DCM, aiding in the development of tailored therapies for patients.

CircR-loop: a novel RNA:DNA interaction on genome instability.

CircR-loop, a recently unearthed regulatory mechanism situated at the crossroads of circular RNA and DNA interactions, constitute a subset of R-loop. This circR-loop have emerged as a crucial player in pivotal regulatory functions within both animal and plant systems. The journey into the realm of circR-loop commenced with their discovery within the human mitochondrial genome, where they serve as critical directors of mitochondrial DNA replication. In the plant kingdom, circR-loop wield influence over processes such as alternative splicing and centromere organization, impacting the intricacies of floral development and genome stability, respectively. Their significance extends to the animal domain, where circR-loop has captured attention for their roles in cancer-related phenomena, exerting control over transcription, chromatin architecture, and orchestrating responses to DNA damage. Moreover, their involvement in nuclear export anomalies further underscores their prominence in cellular regulation. This article summarizes the important regulatory mechanisms and physiological roles of circR-loop in plants and animals, and offers a comprehensive exploration of the methodologies employed for the identification, characterization, and functional analysis of circR-loop, underscoring the pressing need for innovative approaches that can effectively distinguish them from their linear RNA counterparts while elucidating their precise functions. Lastly, the article sheds light on the challenges and opportunities that lie ahead in the field of circR-loop research, emphasizing the vital importance of continued investigations to uncover their regulatory roles and potential applications in the realm of biology. In summary, circR-loop represents a captivating and novel regulatory mechanism with broad-reaching implications spanning the realms of genetics, epigenetics, and disease biology. Their exploration opens new avenues for comprehending gene regulation and holds significant promise for future therapeutic interventions.

An eQTL-based approach reveals candidate regulators of LINE-1 RNA levels in lymphoblastoid cells.

Long interspersed element 1 (LINE-1; L1) are a family of transposons that occupy ~17% of the human genome. Though a small number of L1 copies remain capable of autonomous transposition, the overwhelming majority of copies are degenerate and immobile. Nevertheless, both mobile and immobile L1s can exert pleiotropic effects (promoting genome instability, inflammation, or cellular senescence) on their hosts, and L1's contributions to aging and aging diseases is an area of active research. However, because of the cell type-specific nature of transposon control, the catalogue of L1 regulators remains incomplete. Here, we employ an eQTL approach leveraging transcriptomic and genomic data from the GEUVADIS and 1000Genomes projects to computationally identify new candidate regulators of L1 RNA levels in lymphoblastoid cell lines. To cement the role of candidate genes in L1 regulation, we experimentally modulate the levels of top candidates in vitro, including IL16, STARD5, HSD17B12, and RNF5, and assess changes in TE family expression by Gene Set Enrichment Analysis (GSEA). Remarkably, we observe subtle but widespread upregulation of TE family expression following IL16 and STARD5 overexpression. Moreover, a short-term 24-hour exposure to recombinant human IL16 was sufficient to transiently induce subtle, but widespread, upregulation of L1 subfamilies. Finally, we find that many L1 expression-associated genetic variants are co-associated with aging traits across genome-wide association study databases. Our results expand the catalogue of genes implicated in L1 RNA control and further suggest that L1-derived RNA contributes to aging processes. Given the ever-increasing availability of paired genomic and transcriptomic data, we anticipate this new approach to be a starting point for more comprehensive computational scans for regulators of transposon RNA levels.

The role of RNA methylation in tumor immunity and its potential in immunotherapy.

RNA methylation, a prevalent post-transcriptional modification, has garnered considerable attention in research circles. It exerts regulatory control over diverse biological functions by modulating RNA splicing, translation, transport, and stability. Notably, studies have illuminated the substantial impact of RNA methylation on tumor immunity. The primary types of RNA methylation encompass N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1-methyladenosine (m1A), and N7-methylguanosine (m7G), and 3-methylcytidine (m3C). Compelling evidence underscores the involvement of RNA methylation in regulating the tumor microenvironment (TME). By affecting RNA translation and stability through the "writers", "erasers" and "readers", RNA methylation exerts influence over the dysregulation of immune cells and immune factors. Consequently, RNA methylation plays a pivotal role in modulating tumor immunity and mediating various biological behaviors, encompassing proliferation, invasion, metastasis, etc. In this review, we discussed the mechanisms and functions of several RNA methylations, providing a comprehensive overview of their biological roles and underlying mechanisms within the tumor microenvironment and among immunocytes. By exploring how these RNA modifications mediate tumor immune evasion, we also examine their potential applications in immunotherapy. This review aims to provide novel insights and strategies for identifying novel targets in RNA methylation and advancing cancer immunotherapy efficacy.

Single-Molecule RNA Imaging in Live Cells with an Avidity-Based Fluorescent Light-Up Aptamer biRhoBAST.

Observing individual RNA molecules provides valuable insights into their regulation, interactions with other cellular components, organization, and functions. Although fluorescent light-up aptamers (FLAPs) have recently shown promise for RNA imaging, their wider applications have been mostly hindered by poor brightness and photostability. We recently developed an avidity-based FLAP known as biRhoBAST that allows for single-molecule RNA imaging in live or fixed cells and tracking individual mRNA molecules in living cells due to its excellent photostability and high brightness. Here, we present step-by-step detailed protocols starting from cloning biRhoBAST repeats into the target RNA sequence, to imaging dynamics of single mRNA molecules. Additionally, we address the validation of single-molecule imaging experiments through single-molecule fluorescence in situ hybridization (smFISH) and colocalization studies.

Altered nucleocytoplasmic export of adenosine-rich circRNAs by PABPC1 contributes to neuronal function.

Circular RNAs (circRNAs) are upregulated during neurogenesis. Where and how circRNAs are localized and what roles they play during this process have remained elusive. Comparing the nuclear and cytoplasmic circRNAs between H9 cells and H9-derived forebrain (FB) neurons, we identify that a subset of adenosine (A)-rich circRNAs are restricted in H9 nuclei but exported to cytosols upon differentiation. Such a subcellular relocation of circRNAs is modulated by the poly(A)-binding protein PABPC1. In the H9 nucleus, newly produced (A)-rich circRNAs are bound by PABPC1 and trapped by the nuclear basket protein TPR to prevent their export. Modulating (A)-rich motifs in circRNAs alters their subcellular localization, and introducing (A)-rich circRNAs in H9 cytosols results in mRNA translation suppression. Moreover, decreased nuclear PABPC1 upon neuronal differentiation enables the export of (A)-rich circRNAs, including circRTN4(2,3), which is required for neurite outgrowth. These findings uncover subcellular localization features of circRNAs, linking their processing and function during neurogenesis.

CRISPR/Cas12a-Powered Amplification-Free RNA Diagnostics by Integrating T7 Exonuclease-Assisted Target Recycling and Split G-Quadruplex Catalytic Signal Output.

Rapid and sensitive RNA detection is of great value in diverse areas, ranging from biomedical research to clinical diagnostics. Existing methods for RNA detection often rely on reverse transcription (RT) and DNA amplification or involve a time-consuming procedure and poor sensitivity. Herein, we proposed a CRISPR/Cas12a-enabled amplification-free assay for rapid, specific, and sensitive RNA diagnostics. This assay, which we termed T7/G4-CRISPR, involved the use of a T7-powered nucleic acid circuit to convert a single RNA target into numerous DNA activators via toehold-mediated strand displacement reaction and T7 exonuclease-mediated target recycling amplification, followed by activating Cas12a trans-cleavage of the linker strands inhibiting split G-Quadruplex (G4) assembly, thereby inducing fluorescence attenuation proportion to the input RNA target. We first performed step-by-step validation of the entire assay process and optimized the reaction parameters. Using the optimal conditions, T7/G4-CRISPR was capable of detecting as low as 3.6 pM target RNA, obtaining ∼100-fold improvement in sensitivity compared with the most direct Cas12a assays. Meanwhile, its excellent specificity could discriminate single nucleotide variants adjacent to the toehold region and allow species-specific pathogen identification. Furthermore, we applied it for analyzing bacterial 16S rRNA in 40 clinical urine samples, exhibiting a sensitivity of 90% and a specificity of 100% when validated by RT-quantitative PCR. Therefore, we envision that T7/G4-CRISPR will serve as a promising RNA sensing approach to expand the toolbox of CRISPR-based diagnostics.

Release of CD36-associated cell-free mitochondrial DNA and RNA as a hallmark of space environment response.

A detailed understanding of how spaceflight affects human health is essential for long-term space exploration. Liquid biopsies allow for minimally-invasive multi-omics assessments that can resolve the molecular heterogeneity of internal tissues. Here, we report initial results from the JAXA Cell-Free Epigenome Study, a liquid biopsy study with six astronauts who resided on the International Space Station (ISS) for more than 120 days. Analysis of plasma cell-free RNA (cfRNA) collected before, during, and after spaceflight confirms previously reported mitochondrial dysregulation in space. Screening with 361 cell surface marker antibodies identifies a mitochondrial DNA-enriched fraction associated with the scavenger receptor CD36. RNA-sequencing of the CD36 fraction reveals tissue-enriched RNA species, suggesting the plasma mitochondrial components originated from various tissues. We compare our plasma cfRNA data to mouse plasma cfRNA data from a previous JAXA mission, which had used on-board artificial gravity, and discover a link between microgravity and the observed mitochondrial responses.

Electrophoretic mobility shift assays (EMSAs) for in vitro detection of protein-nucleic acid interactions.

Protein-nucleic acid interactions drive some of the most important physiological events in cells. Here, we present a protocol for detecting protein-DNA or protein-RNA interactions in vitro. We describe steps for labeling nucleic acid species and electrophoretic mobility shift assays (EMSAs). This protocol can be used to confirm suspected in vivo interactions using recombinantly expressed/purified proteins of interest and a nucleic acid substrate. It can further be used to investigate mutations that can disrupt interaction or compensatory mutations that restore it. For complete details on the use and execution of this protocol, please refer to Mansouri-Noori et al.1.

RNATACs: Multispecific small molecules targeting RNA by induced proximity.

RNA-targeting small molecules (rSMs) have become an attractive modality to tackle traditionally undruggable proteins and expand the druggable space. Among many innovative concepts, RNA-targeting chimeras (RNATACs) represent a new class of multispecific, induced proximity small molecules that act by chemically bringing RNA targets into proximity with an endogenous RNA effector, such as a ribonuclease (RNase). Depending on the RNA effector, RNATACs can alter the stability, localization, translation, or splicing of the target RNA. Although still in its infancy, this new modality has the potential for broad applications in the future to treat diseases with high unmet need. In this review, we discuss potential advantages of RNATACs, recent progress in the field, and challenges to this cutting-edge technology.

Spatiotemporal constrained RNA-protein heterogeneous network for protein complex identification.

The identification of protein complexes from protein interaction networks is crucial in the understanding of protein function, cellular processes and disease mechanisms. Existing methods commonly rely on the assumption that protein interaction networks are highly reliable, yet in reality, there is considerable noise in the data. In addition, these methods fail to account for the regulatory roles of biomolecules during the formation of protein complexes, which is crucial for understanding the generation of protein interactions. To this end, we propose a SpatioTemporal constrained RNA-protein heterogeneous network for Protein Complex Identification (STRPCI). STRPCI first constructs a multiplex heterogeneous protein information network to capture deep semantic information by extracting spatiotemporal interaction patterns. Then, it utilizes a dual-view aggregator to aggregate heterogeneous neighbor information from different layers. Finally, through contrastive learning, STRPCI collaboratively optimizes the protein embedding representations under different spatiotemporal interaction patterns. Based on the protein embedding similarity, STRPCI reweights the protein interaction network and identifies protein complexes with core-attachment strategy. By considering the spatiotemporal constraints and biomolecular regulatory factors of protein interactions, STRPCI measures the tightness of interactions, thus mitigating the impact of noisy data on complex identification. Evaluation results on four real PPI networks demonstrate the effectiveness and strong biological significance of STRPCI. The source code implementation of STRPCI is available from https://github.com/LI-jasm/STRPCI.

HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data.

MOTIVATION: Tissue context and molecular profiling are commonly used measures in understanding normal development and disease pathology. In recent years, the development of spatial molecular profiling technologies (e.g. spatial resolved transcriptomics) has enabled the exploration of quantitative links between tissue morphology and gene expression. However, these technologies remain expensive and time-consuming, with subsequent analyses necessitating high-throughput pathological annotations. On the other hand, existing computational tools are limited to predicting only a few dozen to several hundred genes, and the majority of the methods are designed for bulk RNA-seq. RESULTS: In this context, we propose HE2Gene, the first multi-task learning-based method capable of predicting tens of thousands of spot-level gene expressions along with pathological annotations from H&E-stained images. Experimental results demonstrate that HE2Gene is comparable to state-of-the-art methods and generalizes well on an external dataset without the need for re-training. Moreover, HE2Gene preserves the annotated spatial domains and has the potential to identify biomarkers. This capability facilitates cancer diagnosis and broadens its applicability to investigate gene-disease associations. AVAILABILITY AND IMPLEMENTATION: The source code and data information has been deposited at https://github.com/Microbiods/HE2Gene.

CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites.

BACKGROUND: RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throughput methods can lack precision because of a high false-positive rate. RESULTS: We present CheRRI, the first tool to evaluate the biological relevance of putative RNA-RNA interaction sites. CheRRI filters candidates via a machine learning-based model trained on experimental RNA-RNA interactome data. Its unique setup combines interactome data and an established thermodynamic prediction tool to integrate experimental data with state-of-the-art computational models. Applying these data to an automated machine learning approach provides the opportunity to not only filter data for potential false positives but also tailor the underlying interaction site model to specific needs. CONCLUSIONS: CheRRI is a stand-alone postprocessing tool to filter either predicted or experimentally identified potential RNA-RNA interactions on a genomic level to enhance the quality of interaction candidates. It is easy to install (via conda, pip packages), use (via Galaxy), and integrate into existing RNA-RNA interaction pipelines.