Heterogeneous RNA Research Articles

Bulk and single-cell RNA-seq analyses reveal canonical RNA editing associated with microglia homeostasis and its role in sepsis-associated encephalopathy

Previous studies have demonstrated the roles of both microglia homeostasis and RNA editing in sepsis-associated encephalopathy (SAE), yet their relationship remains to be elucidated. In this study, we analyzed bulk and single-cell RNA-seq (scRNA) datasets containing 107 brain tissue and microglia samples from mice with microglial depletion and repopulation to explore canonical RNA editing associated with microglia homeostasis and evaluate its role in SAE. Analysis of mouse brain RNA-Seq revealed hallmarks of microglial repopulation, including peak expressions of Apobec1 and Apobec3 at Day 5 of repopulation and dramatically altered B2m RNA editing. Significant time-dependent changes in brain RNA editing during microglial depletion and repopulation were primarily observed in synapse-related genes, such as Tbc1d24 and Slc1a2. ScRNA-Seq revealed heterogeneous RNA editing among microglia subpopulations and their distinct changes associated with microglia homeostasis. Moreover, repopulated microglia from lipopolysaccharide (LPS)-induced sepsis mice exhibited intensified up-regulation of Apobec1 and Apobec3, with distinct RNA editing responses to LPS, mainly involved in immune-related pathways. The hippocampus from sepsis mice induced by peritoneal contamination and infection showed upregulated Apobec1 and Apobec3 expression, and altered RNA editing in immune-related genes, such as B2m and Mier1, and nervous-related lncRNA Meg3 and Snhg11, both of which were repressed by microglial depletion. Furthermore, the expression of complement-related genes, such as C4b and Cd47, was substantially correlated with RNA editing activity in microglia homeostasis and SAE. Our study demonstrates canonical RNA editing associated with microglia homeostasis and provides new insights into its potential role in SAE.

Single-Particle Spectroscopic Chromatography Reveals Heterogeneous RNA Loading and Size Correlations in Lipid Nanoparticles.

Lipid nanoparticles (LNP) have emerged as pivotal delivery vehicles for RNA therapeutics. Previous research and development usually assumed that LNPs are homogeneous in population, loading density, and composition. Such perspectives are difficult to examine due to the lack of suitable tools to characterize these physicochemical properties at the single-nanoparticle level. Here, we report an integrated spectroscopy-chromatography approach as a generalizable strategy to dissect the complexities of multicomponent LNP assembly. Our platform couples cylindrical illumination confocal spectroscopy (CICS) with single-nanoparticle free solution hydrodynamic separation (SN-FSHS) to simultaneously profile population identity, hydrodynamic size, RNA loading levels, and distributions of helper lipid and PEGylated lipid of LNPs at the single-particle level and in a high-throughput manner. Using a benchmark siRNA LNP formulation, we demonstrate the capability of this platform by distinguishing seven distinct LNP populations, quantitatively characterizing size distribution and RNA loading level in wide ranges, and more importantly, resolving composition-size correlations. This SN-FSHS-CICS analysis provides critical insights into a substantial degree of heterogeneity in the packing density of RNA in LNPs and size-dependent loading-size correlations, explained by kinetics-driven assembly mechanisms of RNA LNPs.

Isolation of short RNAs with homogenous 3’-ends using quaternary-amine anion exchange chromatography

Abstract Visualizing RNA-protein interactions through structural approaches requires the use of RNA molecules purified to homogeneity. We describe here a simple and effective method, free of acrylamide contamination and without using UV radiation, to separate in vitro synthesized, heterogeneous RNA transcripts (up to ∼15 nucleotides) at single nucleotide resolution by quaternary-amine anion exchange chromatography. The quality of short RNAs isolated through this method is validated by gel electrophoresis, mass spectrometry, and crystallization with a protein binding partner.

DeepVelo: deep learning extends RNA velocity to multi-lineage systems with cell-specific kinetics

Existing RNA velocity estimation methods strongly rely on predefined dynamics and cell-agnostic constant transcriptional kinetic rates, assumptions often violated in complex and heterogeneous single-cell RNA sequencing (scRNA-seq) data. Using a graph convolution network, DeepVelo overcomes these limitations by generalizing RNA velocity to cell populations containing time-dependent kinetics and multiple lineages. DeepVelo infers time-varying cellular rates of transcription, splicing, and degradation, recovers each cell’s stage in the differentiation process, and detects functionally relevant driver genes regulating these processes. Application to various developmental and pathogenic processes demonstrates DeepVelo’s capacity to study complex differentiation and lineage decision events in heterogeneous scRNA-seq data.

Alternatives to Remdesivir: Drug repurposing for inhibition of SARS-CoV2 RNA dependent RNA polymerase

Background & Objectives: Even after more than a year of the beginning of COVID-19 pandemic, a specific treatment for the disease has not been discovered. Vaccination programmes are being rolled out as the fastest pace possible but achievement of herd immunity will take time. Many drugs like favipiravir, remdesivir and tocilizumab are being used for the treatment of this disease but reports published by the World Health Organization and the New England Journal of Medicine shows that they do not produce any significant clinical results. In this study, by molecular docking a large set of drugs has been used to replace remdesivir in RdRp protein so that they can produce the same action and therefore provide suitable alternatives for clinical trials and emergency use. Materials & Methods: The receptor i.e. the RdRp protein was first processed in Drug Discovery studio by removal of heterogenous atoms, water molecules, prime and template RNA strands and remdesivir. This gave us the clean RdRp molecule. Using PyRx docking software, it was converted automatically into an Autodock macromolecule. Around 750 drug molecules were loaded into PyRx using Open Babel plugin which were then converted into Autodock ligands by minimization of their energies, addition of hydrogen atoms and addition of partial charges. Using Autodock Vina ligands were docked into the restricted search space containing the target amino acids. Results: The drugs identified in the study are saquinavir, cefoperazone, gliquidone, nelfinavir, 5-methyltetrahyrofolate among various others. Conclusion: The drugs obtained above have also been identified in other in-silico studies as potential inhibitors of SARS-CoV2 by different mechanisms. They must be tried in-vitro because of the limitations of in-silico docking.

Pattern of changes in morpho-densitometric parameters of magnocellular neurons of the hypothalamic paraventricular nucleus under restraint stress of different duration

The aim of the work was to define the nature of changes in morpho-densitometric parameters of magnocellular neurons of the hypothalamic paraventricular nucleus in rats at 6, 15 and 21 weeks of restricted living space. Materials and methods. A total of 55 male Wistar rats aged 6-10 months were used and divided into 4 groups (1 – intact control, 10 rats; 2, 3 and 4 – experimental groups of 15 rats each, which were in conditions of restricted environmental space for 6, 15 and 21 weeks, respectively). Morpho-densitometric analysis of 5-μm-thick rat brain sections was performed after preliminary standard histological processing and 48 hours of halocyanine-chrome staining by Einarson using an AxioImager-M2 microscope (Carl Zeiss, Germany). Images of magnocellular neurons of the hypothalamic paraventricular nuclei (PVN), which were topographically identified according to the stereotaxic atlas of the rat brain, were captured using a highly sensitive AxioCam-ERc 5s video camera (Carl Zeiss, Germany) and recorded as a computer file using AxioVision 40 V 4.8.2.0 software (license number 3005339). Quantitative data on the area of neurons, their nuclei and nucleoli, the content of heterogeneous DNA and RNA in the cell cytoplasm, nuclei and nucleoli were obtained in a semi-automatic mode using the open source software ImageJ (National Institutes of Health, USA). Results. Chronic stress condition modelled in rats for 6, 15, and 21 weeks (limited living space, inactivity, and complicated communications) resulted in a decrease in the area of cytoplasm and nuclei of magnocellular neurons of the hypothalamic PVN. The decrease was directly proportional to restrain stress duration: cytoplasm and nuclei by 18 % and 9 % at week 6 and by 40 % and 25 % at week 21, respectively, and only cytoplasm by 23 % at 15 weeks. On the contrary, the area of neuronal nuclei was increased by 31 % at 6 weeks of stress and 33 % at 15 weeks, and by 15 % at 21 weeks of stress compared to the control. The content of nucleic acids in the cytoplasm of magnocellular neurons of the hypothalamic PVN in the group of rats with 6-week restriction was 39 % less than in the control group, 34 % less than that in the group with 15-week restriction, and 42 % less than that in the group of 21-week restrain stress. In the nuclei of cells after 6 and 15 weeks of restriction, the content was decreased approximately equally by 37 % and 35 %, respectively, and long-term restriction (21 weeks) contributed to a 41 % decrease. In the neuron nucleoli, the content of nucleic acids was changed similarly: in the group of 6 weeks, a decrease of 40 % was characteristic, after 15 weeks – by 39 %, and after 21 weeks – by 43 %. Conclusions. Changes in the morphometric parameters of magnocellular neurons of the hypothalamic PVN in rats with long-term limitation of living space are characterized by time dependence and multidirectionality, which is related to the functional features of the neuron structural components (cytoplasm, nucleus and nucleoli). There is a progressive decrease in the cytoplasmic area of PVN neurons and their nuclei, while the nucleolar area increases with increasing length of living space limitation. The content of nucleic acids in the studied structures of the hypothalamic PVN magnocellular neurons after prolonged stress at each time point (6, 15, 21 weeks) becomes less than the control values by more than a third. Comparison of nucleic acid content in the neuron cytoplasm, nuclei, and nucleoli shows a narrow range of variations in the parameters of the experimental group rats between different periods.

KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations.

Recent studies have demonstrated the significant role that circRNA plays in the progression of human diseases. Identifying circRNA-disease associations (CDA) in an efficient manner can offer crucial insights into disease diagnosis. While traditional biological experiments can be time-consuming and labor-intensive, computational methods have emerged as a viable alternative in recent years. However, these methods are often limited by data sparsity and their inability to explore high-order information. In this paper, we introduce a novel method named Knowledge Graph Encoder from Transformer for predicting CDA (KGETCDA). Specifically, KGETCDA first integrates more than 10 databases to construct a large heterogeneous non-coding RNA dataset, which contains multiple relationships between circRNA, miRNA, lncRNA and disease. Then, a biological knowledge graph is created based on this dataset and Transformer-based knowledge representation learning and attentive propagation layers are applied to obtain high-quality embeddings with accurately captured high-order interaction information. Finally, multilayer perceptron is utilized to predict the matching scores of CDA based on their embeddings. Our empirical results demonstrate that KGETCDA significantly outperforms other state-of-the-art models. To enhance user experience, we have developed an interactive web-based platform named HNRBase that allows users to visualize, download data and make predictions using KGETCDA with ease. The code and datasets are publicly available at https://github.com/jinyangwu/KGETCDA.

Accurate integration of multiple heterogeneous single-cell RNA-seq data sets by learning contrastive biological variation.

For most biological and medical applications of single-cell transcriptomics, an integrative study of multiple heterogeneous single-cell RNA sequencing (scRNA-seq) data sets is crucial. However, present approaches are unable to integrate diverse data sets from various biological conditions effectively because of the confounding effects of biological and technical differences. We introduce single-cell integration (scInt), an integration method based on accurate, robust cell-cell similarity construction and unified contrastive biological variation learning from multiple scRNA-seq data sets. scInt provides a flexible and effective approach to transfer knowledge from the already integrated reference to the query. We show that scInt outperforms 10 other cutting-edge approaches using both simulated and real data sets, particularly in the case of complex experimental designs. Application of scInt to mouse developing tracheal epithelial data shows its ability to integrate development trajectories from different developmental stages. Furthermore, scInt successfully identifies functionally distinct condition-specific cell subpopulations in single-cell heterogeneous samples from a variety of biological conditions.

Longitudinal Effects of 1-Year Smoking Cessation on Human Bronchial Epithelial Transcriptome

Longitudinal Effects of 1-Year Smoking Cessation on Human Bronchial Epithelial Transcriptome

An Unanticipated Modulation of Cyclin-Dependent Kinase Inhibitors: The Role of Long Non-Coding RNAs.

It is now definitively established that a large part of the human genome is transcribed. However, only a scarce percentage of the transcriptome (about 1.2%) consists of RNAs that are translated into proteins, while the large majority of transcripts include a variety of RNA families with different dimensions and functions. Within this heterogeneous RNA world, a significant fraction consists of sequences with a length of more than 200 bases that form the so-called long non-coding RNA family. The functions of long non-coding RNAs range from the regulation of gene transcription to the changes in DNA topology and nucleosome modification and structural organization, to paraspeckle formation and cellular organelles maturation. This review is focused on the role of long non-coding RNAs as regulators of cyclin-dependent kinase inhibitors’ (CDKIs) levels and activities. Cyclin-dependent kinases are enzymes necessary for the tuned progression of the cell division cycle. The control of their activity takes place at various levels. Among these, interaction with CDKIs is a vital mechanism. Through CDKI modulation, long non-coding RNAs implement control over cellular physiology and are associated with numerous pathologies. However, although there are robust data in the literature, the role of long non-coding RNAs in the modulation of CDKIs appears to still be underestimated, as well as their importance in cell proliferation control.

Chemical reversible crosslinking enables measurement of RNA 3D distances and alternative conformations in cells

Three-dimensional (3D) structures dictate the functions of RNA molecules in a wide variety of biological processes. However, direct determination of RNA 3D structures in vivo is difficult due to their large sizes, conformational heterogeneity, and dynamics. Here we present a method, Spatial 2′-Hydroxyl Acylation Reversible Crosslinking (SHARC), which uses chemical crosslinkers of defined lengths to measure distances between nucleotides in cellular RNA. Integrating crosslinking, exonuclease (exo) trimming, proximity ligation, and high throughput sequencing, SHARC enables transcriptome-wide tertiary structure contact maps at high accuracy and precision, revealing heterogeneous RNA structures and interactions. SHARC data provide constraints that improves Rosetta-based RNA 3D structure modeling at near-nanometer resolution. Integrating SHARC-exo with other crosslinking-based methods, we discover compact folding of the 7SK RNA, a critical regulator of transcriptional elongation. These results establish a strategy for measuring RNA 3D distances and alternative conformations in their native cellular context.

The Functional Role of Long Non-Coding RNAs in Melanoma.

Simple SummaryLong non-coding RNAs (lncRNAs) are heterogeneous RNA molecules that can regulate a plethora of cellular processes including proliferation, differentiation, migration, invasion and apoptosis through diverse mechanisms. Growing evidence indicates that aberrant expression of lncRNA leads to the development and progression of cancer, including melanoma, and might contribute to the acquisition of drug resistance. Moreover, their tightly-regulated expression and their high tissue and disease specificity make them promising biomarkers in non-invasive diagnostics of cancer. This review summarizes current knowledge regarding the involvement of lncRNAs in melanoma along with their possible use as diagnostic biomarkers.Melanoma is the most lethal skin cancer, with increasing incidence worldwide. The molecular events that drive melanoma development and progression have been extensively studied, resulting in significant improvements in diagnostics and therapeutic approaches. However, a high drug resistance to targeted therapies and adverse effects of immunotherapies are still a major challenge in melanoma treatment. Therefore, the elucidation of molecular mechanisms of melanomagenesis and cancer response to treatment is of great importance. Recently, many studies have revealed the close association of long noncoding RNAs (lncRNAs) with the development of many cancers, including melanoma. These RNA molecules are able to regulate a plethora of crucial cellular processes including proliferation, differentiation, migration, invasion and apoptosis through diverse mechanisms, and even slight dysregulation of their expression may lead to tumorigenesis. lncRNAs are able to bind to protein complexes, DNA and RNAs, affecting their stability, activity, and localization. They can also regulate gene expression in the nucleus. Several functions of lncRNAs are context-dependent. This review summarizes current knowledge regarding the involvement of lncRNAs in melanoma. Their possible role as prognostic markers of melanoma response to treatment and in resistance to therapy is also discussed

Integrated cross-study datasets of genetic dependencies in cancer

CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.

Structure of a type IV CRISPR-Cas ribonucleoprotein complex

SummaryWe reveal the cryo-electron microscopy structure of a type IV-B CRISPR ribonucleoprotein (RNP) complex (Csf) at 3.9-Å resolution. The complex best resembles the type III-A CRISPR Csm effector complex, consisting of a Cas7-like (Csf2) filament intertwined with a small subunit (Cas11) filament, but the complex lacks subunits for RNA processing and target DNA cleavage. Surprisingly, instead of assembling around a CRISPR-derived RNA (crRNA), the complex assembles upon heterogeneous RNA of a regular length arranged in a pseudo-A-form configuration. These findings provide a high-resolution glimpse into the assembly and function of enigmatic type IV CRISPR systems, expanding our understanding of class I CRISPR-Cas system architecture, and suggesting a function for type IV-B RNPs that may be distinct from other class 1 CRISPR-associated systems.

Characterizing miRNA-lncRNA Interplay.

Long noncoding RNAs (lncRNAs) are noncoding transcripts, usually longer than 200nt, that constitute one of the largest and significantly heterogeneous RNA families. The annotation of lncRNAs and the characterization of their function is a constantly evolving field. LncRNA interplay with microRNAs (miRNAs) is thoroughly studied in several physiological and disease states. miRNAs are small noncoding RNAs (~22nt) that posttranscriptionally regulate the expression of protein coding genes, through mRNA target cleavage, degradation or direct translational suppression. miRNAs can affect lncRNA half-life by promoting their degradation, or lncRNAs can act as miRNA "sponges," reducing miRNA regulatory effect on target mRNAs. This chapter outlines the miRNA-lncRNA interplay and provides hands-on methodologies for experimentally supported and in silico-guided analyses. The proposed techniques are a valuable asset to further understand lncRNA functions and can be appropriately adapted to become the backbone for further downstream analyses.

MiRNA and LncRNA as Potential Biomarkers in Triple-Negative Breast Cancer: A Review.

Noncoding RNAs (ncRNAs) include a diverse range of RNA species, including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs). MiRNAs, ncRNAs of approximately 19–25 nucleotides in length, are involved in gene expression regulation either via degradation or silencing of the messenger RNAs (mRNAs) and have roles in multiple biological processes, including cell proliferation, differentiation, migration, angiogenesis, and apoptosis. LncRNAs, which are longer than 200 nucleotides, comprise one of the largest and most heterogeneous RNA families. LncRNAs can activate or repress gene expression through various mechanisms, acting alone or in combination with miRNAs and other molecules as part of various pathways. Until recently, most research has focused on individual lncRNA and miRNA functions as regulators, and there is limited available data on ncRNA interactions relating to the tumor growth, metastasis, and therapy of cancer, acting either on mRNA alone or as competing endogenous RNA (ceRNA) networks. Triple-negative breast cancer (TNBC) represents approximately 10%–20% of all breast cancers (BCs) and is highly heterogenous and more aggressive than other types of BC, for which current targeted treatment options include hormonotherapy, PARP inhibitors, and immunotherapy; however, no targeted therapies for TNBC are available, partly because of a lack of predictive biomarkers. With advances in proteomics, new evidence has emerged demonstrating the implications of dysregulation of ncRNAs in TNBC etiology. Here, we review the roles of lncRNAs and miRNAs implicated in TNBC, including their interactions and regulatory networks. Our synthesis provides insight into the mechanisms involved in TNBC progression and has potential to aid the discovery of new diagnostic and therapeutic strategies.

Regulation of long non-coding RNA in cartilage injury of osteoarthritis

To summarize the regulatory effect of long non-coding RNA (lncRNA) on osteoarthritis (OA) cartilage injury. The molecular functions and mechanisms of lncRNA were introduced and its regulatory effects on the pathological processes of OA were elaborated by referring to the relevant literature at domestic and abroad in recent years. The pathological characteristics of OA are degeneration of articular cartilage and inflammation of synovial tissue, but its etiology and pathological mechanism have not been clarified. lncRNA is a kind of heterogeneous non-coding RNA, which plays a regulatory role in many inflammation-related diseases and exerts a wide range of biological functions. lncRNA is a regulator involved in the pathogenesis of OA, and is abnormally expressed in OA cartilage, leading to the degeneration of the extracellular matrix of cartilage. At present, there have been preliminary studies on the pathological effects of lncRNA in regulating OA and the biological functions of chondrocytes. However, the pathogenesis of lncRNA and its regulatory network in OA and the way in which it regulates inflammatory pathways are still unclear, and further exploration is needed.

Long non-coding RNA and Notch regulation in non-small cell lung cancer

Non-small cell lung cancer is one of the most commonly diagnosed cancer with a very high mortality rate. Trying to understand the mechanisms underlying the progression of this type of cancer, it is necessary to evaluate the changes occurring at molecular level in cancer cells. Besides the widely studied signaling pathways and genes which are dysregulated in NSCLC, there is a large group of non-coding RNAs involved in cancer pathogenesis. Those RNAs are tissue specific heterogeneous class of RNAs that play many functions in physiological condition in cells, nevertheless current data has shown that lncRNAs are also functional in different types of cancer. Moreover, it has been suggest that lncRNAs are involved in cancer progression by controlling key signaling pathways involved in diverse types of tumors. Notch signaling is one of those pathways, very often deregulated in NSCLC. Therefore in this review I summarized recent outcomes according the importance of lncRNAs in regulation of Notch pathway in the pathogenesis of NSCLC.

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples.

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding of the basis of various diseases as well as resistance and/or susceptibility mechanisms. However, FFPE tissues, which represent the most common method for preserving tissue morphology in clinical specimens, are not the best sources for gene expression profiling analysis. The RNA obtained from such samples is often degraded, fragmented, and chemically modified, which leads to suboptimal sequencing libraries. In turn, these generate poor quality sequence data that may not be reliable for gene expression analysis and mutation discovery. In order to make the most of FFPE samples and obtain the best possible data from low quality samples, it is important to take certain precautions while planning experimental design, preparing sequencing libraries, and during data analysis. This includes the use of appropriate metrics for precise sample quality control (QC), identifying the best methods for various steps during the sequencing library generation, and careful library QC. In addition, applying correct software tools and parameters for sequence data analysis is critical in order to identify artifacts in RNA-seq data, filter out contamination and low quality reads, assess uniformity of gene coverage, and measure the reproducibility of gene expression profiles among biological replicates. These steps can ensure high accuracy and reproducibility for profiling of very heterogeneous RNA samples. Here we describe the various steps for sample QC, library preparation and QC, sequencing, and data analysis that can help to increase the amount of useful data obtained from low quality RNA, such as that obtained from FFPE-RNA tissues.

ORCA/LRWD1 Regulates Homologous Recombination at ALT-Telomeres by Modulating Heterochromatin Organization.

SummaryTelomeres are maintained by telomerase or in a subset of cancer cells by a homologous recombination (HR)-based mechanism, Alternative Lengthening of Telomeres (ALT). The mechanisms regulating telomere-homeostasis in ALT cells remain unclear. We report that a replication initiator protein, Origin Recognition Complex-Associated (ORCA/LRWD1), by localizing at the ALT-telomeres, modulates HR activity. ORCA's localization to the ALT-telomeres is facilitated by its interaction to SUMOylated shelterin components. The loss of ORCA in ALT-positive cells elevates the levels of two mediators of HR, RPA and RAD51, and consistent with this, we observe increased ALT-associated promyelocytic leukemia body formation and telomere sister chromatid exchange. ORCA binds to RPA and modulates the association of RPA to telomeres. Finally, the loss of ORCA causes global chromatin decondensation, including at the telomeres. Our results demonstrate that ORCA acts as an inhibitor of HR by modulating RPA binding to ssDNA and inducing chromatin compaction.