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.

Parallel DNA/RNA NGS Using an Identical Target Enrichment Panel in the Analysis of Hereditary Cancer Predisposition.

Germline DNA testing using the next-gene-ration sequencing (NGS) technology has become the analytical standard for the diagnostics of hereditary diseases, including cancer. Its increasing use places high demands on correct sample identification, independent confirmation of prioritized variants, and their functional and clinical interpretation. To streamline these processes, we introduced parallel DNA and RNA capture-based NGS using identical capture panel CZECANCA, which is routinely used for DNA analysis of hereditary cancer predisposition. Here, we present the analytical workflow for RNA sample processing and its analytical and diagnostic performance. Parallel DNA/RNA analysis allowed credible sample identification by calculating the kinship coefficient. The RNA capture-based approach enriched transcriptional targets for the majority of clinically relevant cancer predisposition genes to a degree that allowed analysis of the effect of identified DNA variants on mRNA processing. By comparing the panel and whole-exome RNA enrichment, we demonstrated that the tissue-specific gene expression pattern is independent of the capture panel. Moreover, technical replicates confirmed high reproducibility of the tested RNA analysis. We concluded that parallel DNA/RNA NGS using the identical gene panel is a robust and cost-effective diagnostic strategy. In our setting, it allows routine analysis of 48 DNA/RNA pairs using NextSeq 500/550 Mid Output Kit v2.5 (150 cycles) in a single run with sufficient coverage to analyse 226 cancer predisposition and candidate ge-nes. This approach can replace laborious Sanger confirmatory sequencing, increase testing turnaround, reduce analysis costs, and improve interpretation of the impact of variants by analysing their effect on mRNA processing.

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.

Editorial: The RNA revolution and cancer.

RNA biology has revolutionized cancer understanding and treatment, especially in endocrine-related malignancies. This editorial highlights RNA's crucial role in cancer progression, emphasizing its influence on tumor heterogeneity and behavior. Processes like alternative splicing and noncoding RNA regulation shape cancer biology, with microRNAs, long noncoding RNAs, and circular RNAs orchestrating gene expression dynamics. Aberrant RNA signatures hold promise as diagnostic and prognostic biomarkers in endocrine-related cancers. Recent findings, such as aberrant PI3Kδ splice isoforms and epithelial-mesenchymal transition-related lncRNA signatures, unveil potential therapeutic targets for personalized treatments. Insights into m6A-associated lncRNA prognostic models and the function of lncRNA LINC00659 in gastric cancer represents ongoing research in this field. As understanding of RNA's role in cancer expands, personalized therapies offer transformative potential in managing endocrine-related malignancies. This signifies a significant stride towards precision oncology, fostering innovation for more effective cancer care.

TransAC4C-a novel interpretable architecture for multi-species identification of N4-acetylcytidine sites in RNA with single-base resolution.

N4-acetylcytidine (ac4C) is a modification found in ribonucleic acid (RNA) related to diseases. Expensive and labor-intensive methods hindered the exploration of ac4C mechanisms and the development of specific anti-ac4C drugs. Therefore, an advanced prediction model for ac4C in RNA is urgently needed. Despite the construction of various prediction models, several limitations exist: (1) insufficient resolution at base level for ac4C sites; (2) lack of information on species other than Homo sapiens; (3) lack of information on RNA other than mRNA; and (4) lack of interpretation for each prediction. In light of these limitations, we have reconstructed the previous benchmark dataset and introduced a new dataset including balanced RNA sequences from multiple species and RNA types, while also providing base-level resolution for ac4C sites. Additionally, we have proposed a novel transformer-based architecture and pipeline for predicting ac4C sites, allowing for highly accurate predictions, visually interpretable results and no restrictions on the length of input RNA sequences. Statistically, our work has improved the accuracy of predicting specific ac4C sites in multiple species from less than 40% to around 85%, achieving a high AUC > 0.9. These results significantly surpass the performance of all existing models.

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.

A comparison of five methods to maximize RNA and DNA isolation yield from adipose tissue.

Adipose tissue in the human body occurs in various forms with different functions. It is an energy store, a complex endocrine organ, and a source of cells used in medicine. Many molecular analyses require the isolation of nucleic acids, which can cause some difficulties connected with the large amount of lipids in adipocytes. Ribonucleic acid isolation is particularly challenging due to its low stability and easy degradation by ribonucleases. The study aimed to compare and evaluate five RNA and DNA isolation methods from adipose tissue. The tested material was subcutaneous porcine adipose tissue subjected to different homogenization methods and RNA or DNA purification. A mortar and liquid nitrogen or ceramic beads were used for homogenization. The organic extraction (TriPure Reagent), spin columns with silica-membrane (RNeasy Mini Kit or High Pure PCR Template Preparation Kit), and the automatic MagNA Pure system were used for the purification. Five combinations were compared for RNA and DNA isolation. Obtained samples were evaluated for quantity and quality. The methods were compared in terms of yield (according to tissue mass), purity (A260/280 and A260/230), and nucleic acid degradation (RNA Integrity Number, RIN; DNA Integrity Number, DIN). The results were analyzed statistically. The average RNA yield was highest in method I, which used homogenization with ceramic beads and organic extraction. Low RNA concentration didn't allow us to measure degradation for all samples in method III (homogenization with ceramic beads and spin-column purification). The highest RNA quality was achieved with method IV using homogenization in liquid nitrogen and spin column purification, which makes it the most effective for RNA isolation from adipose tissue. Required values of DNA yield, purity, and integrity were achieved only with spin column-based methods (III and IV). The most effective method for DNA isolation from adipose tissue is method III, using spin-columns without additional homogenization.

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.

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.

Classified Dynamic Programming in RNA Structure Analysis.

Analysis of the folding space of RNA generally suffers from its exponential size. With classified Dynamic Programming algorithms, it is possible to alleviate this burden and to analyse the folding space of RNA in great depth. Key to classified DP is that the search space is partitioned into classes based on an on-the-fly computed feature. A class-wise evaluation is then used to compute class-wide properties, such as the lowest free energy structure for each class, or aggregate properties, such as the class' probability. In this paper we describe the well-known shape and hishape abstraction of RNA structures, their power to help better understand RNA function and related methods that are based on these abstractions.

Classification and Identification of Non-canonical Base Pairs and Structural Motifs.

The 3D structures of many ribonucleic acid (RNA) loops are characterized by highly organized networks of non-canonical interactions. Multiple computational methods have been developed to annotate structures with those interactions or automatically identify recurrent interaction networks. By contrast, the reverse problem that aims to retrieve the geometry of a look from its sequence or ensemble of interactions remains much less explored. In this chapter, we will describe how to retrieve and build families of conserved structural motifs using their underlying network of non-canonical interactions. Then, we will show how to assign sequence alignments to those families and use the software BayesPairing to build statistical models of structural motifs with their associated sequence alignments. From this model, we will apply BayesPairing to identify in new sequences regions where those loop geometries can occur.

LocARNA 2.0: Versatile Simultaneous Alignment and Folding of RNAs.

Generating accurate alignments of non-coding RNA sequences is indispensable in the quest for understanding RNA function. Nevertheless, aligning RNAs remains a challenging computational task. In the twilight-zone of RNA sequences with low sequence similarity, sequence homologies and compatible, favorable (a priori unknown) structures can be inferred only in dependency of each other. Thus, simultaneous alignment and folding (SA&F) remains the gold-standard of comparative RNA analysis, even if this method is computationally highly demanding. This text introduces to the recent release 2.0 of the software package LocARNA, focusing on its practical application. The package enables versatile, fast and accurate analysis of multiple RNAs. For this purpose, it implements SA&F algorithms in a specific, lightweight flavor that makes them routinely applicable in large scale. Its high performance is achieved by combining ensemble-based sparsification of the structure space and banding strategies. Probabilistic banding strongly improves the performance of LocARNA 2.0 even over previous releases, while simplifying its effective use. Enabling flexible application to various use cases, LocARNA provides tools to globally and locally compare, cluster, and multiply aligned RNAs based on optimization and probabilistic variants of SA&F, which optionally integrate prior knowledge, expressible by anchor and structure constraints.

VarGibbs Usage in the Optimization of Nearest-Neighbor Parameters and Prediction of Melting Temperature of RNA Duplexes.

The nearest-neighbor (NN) model is a general tool for the evaluation for oligonucleotide thermodynamic stability. It is primarily used for the prediction of melting temperatures but has also found use in RNA secondary structure prediction and theoretical models of hybridization kinetics. One of the key problems is to obtain the NN parameters from melting temperatures, and VarGibbs was designed to obtain those parameters directly from melting temperatures. Here we will describe the basic workflow from RNA melting temperatures to NN parameters with the use of VarGibbs. We start by a brief revision of the basic concepts of RNA hybridization and of the NN model and then show how to prepare the data files, run the parameter optimization, and interpret the results.

RNA Secondary Structure Modeling Following the IPANEMAP Workflow.

The structure of RNA molecules and their complexes are crucial for understanding biology at the molecular level. Resolving these structures holds the key to understanding their manifold structure-mediated functions ranging from regulating gene expression to catalyzing biochemical processes. Predicting RNA secondary structure is a prerequisite and a key step to accurately model their three dimensional structure. Although dedicated modelling software are making fast and significant progresses, predicting an accurate secondary structure from the sequence remains a challenge. Their performance can be significantly improved by the incorporation of experimental RNA structure probing data. Many different chemical and enzymatic probes have been developed; however, only one set of quantitative data can be incorporated as constraints for computer-assisted modelling. IPANEMAP is a recent workflow based on RNAfold that can take into account several quantitative or qualitative data sets to model RNA secondary structure. This chapter details the methods for popular chemical probing (DMS, CMCT, SHAPE-CE, and SHAPE-Map) and the subsequent analysis and structure prediction using IPANEMAP.

Assessing the Quality of Cotranscriptional Folding Simulations.

Structural changes in RNAs are an important contributor to controlling gene expression not only at the posttranscriptional stage but also during transcription. A subclass of riboswitches and RNA thermometers located in the 5' region of the primary transcript regulates the downstream functional unit - usually an ORF - through premature termination of transcription. Not only such elements occur naturally, but they are also attractive devices in synthetic biology. The possibility to design such riboswitches or RNA thermometers is thus of considerable practical interest. Since these functional RNA elements act already during transcription, it is important to model and understand the dynamics of folding and, in particular, the formation of intermediate structures concurrently with transcription. Cotranscriptional folding simulations are therefore an important step to verify the functionality of design constructs before conducting expensive and labor-intensive wet lab experiments. For RNAs, full-fledged molecular dynamics simulations are far beyond practical reach because of both the size of the molecules and the timescales of interest. Even at the simplified level of secondary structures, further approximations are necessary. The BarMap approach is based on representing the secondary structure landscape for each individual transcription step by a coarse-grained representation that only retains a small set of low-energy local minima and the energy barriers between them. The folding dynamics between two transcriptional elongation steps is modeled as a Markov process on this representation. Maps between pairs of consecutive coarse-grained landscapes make it possible to follow the folding process as it changes in response to transcription elongation. In its original implementation, the BarMap software provides a general framework to investigate RNA folding dynamics on temporally changing landscapes. It is, however, difficult to use in particular for specific scenarios such as cotranscriptional folding. To overcome this limitation, we developed the user-friendly BarMap-QA pipeline described in detail in this contribution. It is illustrated here by an elaborate example that emphasizes the careful monitoring of several quality measures. Using an iterative workflow, a reliable and complete kinetics simulation of a synthetic, transcription-regulating riboswitch is obtained using minimal computational resources. All programs and scripts used in this contribution are free software and available for download as a source distribution for Linux® or as a platform-independent Docker® image including support for Apple macOS® and Microsoft Windows®.