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.

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 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.

RNA tailing machinery drives amyloidogenic phase transition.

The RNA tailing machinery adds nucleotides to the 3'-end of RNA molecules that are implicated in various biochemical functions, including protein synthesis and RNA stability. Here, we report a role for the RNA tailing machinery as enzymatic modifiers of intracellular amyloidogenesis. A targeted RNA interference screen identified Terminal Nucleotidyl-transferase 4b (TENT4b/Papd5) as an essential participant in the amyloidogenic phase transition of nucleoli into solid-like Amyloid bodies. Full-length-and-mRNA sequencing uncovered starRNA, a class of unusually long untemplated RNA molecules synthesized by TENT4b. StarRNA consists of short rRNA fragments linked to long, linear mixed tails that operate as polyanionic stimulators of amyloidogenesis in cells and in vitro. Ribosomal intergenic spacer noncoding RNA (rIGSRNA) recruit TENT4b in intranucleolar foci to coordinate starRNA synthesis driving their amyloidogenic phase transition. The exoribonuclease RNA Exosome degrades starRNA and functions as a general suppressor of cellular amyloidogenesis. We propose that amyloidogenic phase transition is under tight enzymatic control by the RNA tailing and exosome axis.

Sequencing accuracy and systematic errors of nanopore direct RNA sequencing.

BACKGROUND: Direct RNA sequencing (dRNA-seq) on the Oxford Nanopore Technologies (ONT) platforms can produce reads covering up to full-length gene transcripts, while containing decipherable information about RNA base modifications and poly-A tail lengths. Although many published studies have been expanding the potential of dRNA-seq, its sequencing accuracy and error patterns remain understudied. RESULTS: We present the first comprehensive evaluation of sequencing accuracy and characterisation of systematic errors in dRNA-seq data from diverse organisms and synthetic in vitro transcribed RNAs. We found that for sequencing kits SQK-RNA001 and SQK-RNA002, the median read accuracy ranged from 87% to 92% across species, and deletions significantly outnumbered mismatches and insertions. Due to their high abundance in the transcriptome, heteropolymers and short homopolymers were the major contributors to the overall sequencing errors. We also observed systematic biases across all species at the levels of single nucleotides and motifs. In general, cytosine/uracil-rich regions were more likely to be erroneous than guanines and adenines. By examining raw signal data, we identified the underlying signal-level features potentially associated with the error patterns and their dependency on sequence contexts. While read quality scores can be used to approximate error rates at base and read levels, failure to detect DNA adapters may be a source of errors and data loss. By comparing distinct basecallers, we reason that some sequencing errors are attributable to signal insufficiency rather than algorithmic (basecalling) artefacts. Lastly, we generated dRNA-seq data using the latest SQK-RNA004 sequencing kit released at the end of 2023 and found that although the overall read accuracy increased, the systematic errors remain largely identical compared to the previous kits. CONCLUSIONS: As the first systematic investigation of dRNA-seq errors, this study offers a comprehensive overview of reproducible error patterns across diverse datasets, identifies potential signal-level insufficiency, and lays the foundation for error correction methods.

RNA m5C methylation: a potential modulator of innate immune pathways in hepatocellular carcinoma.

RNA 5-methylcytosine (m5C) methylation plays a crucial role in hepatocellular carcinoma (HCC). As reported, aberrant m5C methylation is closely associated with the progression, therapeutic efficacy, and prognosis of HCC. The innate immune system functions as the primary defense mechanism in the body against pathogenic infections and tumors since it can activate innate immune pathways through pattern recognition receptors to exert anti-infection and anti-tumor effects. Recently, m5C methylation has been demonstrated to affect the activation of innate immune pathways including TLR, cGAS-STING, and RIG-I pathways by modulating RNA function, unveiling new mechanisms underlying the regulation of innate immune responses by tumor cells. However, research on m5C methylation and its interplay with innate immune pathways is still in its infancy. Therefore, this review details the biological significance of RNA m5C methylation in HCC and discusses its potential regulatory relationship with TLR, cGAS-STING, and RIG-I pathways, thereby providing fresh insights into the role of RNA methylation in the innate immune mechanisms and treatment of HCC.

Computational approaches and challenges in the analysis of circRNA data.

Circular RNAs (circRNA) are a class of non-coding RNA, forming a single-stranded covalently closed loop structure generated via back-splicing. Advancements in sequencing methods and technologies in conjunction with algorithmic developments of bioinformatics tools have enabled researchers to characterise the origin and function of circRNAs, with practical applications as a biomarker of diseases becoming increasingly relevant. Computational methods developed for circRNA analysis are predicated on detecting the chimeric back-splice junction of circRNAs whilst mitigating false-positive sequencing artefacts. In this review, we discuss in detail the computational strategies developed for circRNA identification, highlighting a selection of tool strengths, weaknesses and assumptions. In addition to circRNA identification tools, we describe methods for characterising the role of circRNAs within the competing endogenous RNA (ceRNA) network, their interactions with RNA-binding proteins, and publicly available databases for rich circRNA annotation.

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.

[Research advances in nucleic acid drugs developed based on circular RNAs].

The development and clinical application of nucleic acid drugs has been a trendy field. One of the notable examples is mRNA vaccines, which have been used in the fighting against SARS-CoV-2. With short development cycles and mature preparation processes, mRNA vaccines demonstrate advantages in the global supply and in response to virus mutations. Circular RNAs (circRNAs) are a group of nucleic acid molecules with more stable structure, longer half-life, and weaker immunogenicity than mRNAs. Studies have proven that circRNAs can efficiently express protein products, indicating their potential in drug development. Despite extensive studies on the biogenesis and biological functions of circRNAs, there is limited research on developing nucleic acid drugs based on circRNAs. This article provides an overview of circRNAs, including their basic information, synthesis routes, and mechanisms, and discusses the future development directions of this field, hoping to provide inspiration for the research and development of drugs based on circRNAs.

The RNA-DNA world and the emergence of DNA-encoded heritable traits.

The RNA world hypothesis confers a central role to RNA molecules in information encoding and catalysis. Even though evidence in support of this hypothesis has accumulated from both experiments and computational modelling, the transition from an RNA world to a world where heritable genetic information is encoded in DNA remains an open question. Recent experiments show that both RNA and DNA templates can extend complementary primers using free RNA/DNA nucleotides, either non-enzymatically or in the presence of a replicase ribozyme. Guided by these experiments, we analyse protocellular evolution with an expanded set of reaction pathways made possible through the presence of DNA nucleotides. By encapsulating these reactions inside three different types of protocellular compartments, each subject to distinct modes of selection, we show how protocells containing DNA-encoded replicases in low copy numbers and replicases in high copy numbers can dominate the population. This is facilitated by a reaction that leads to auto-catalytic synthesis of replicase ribozymes from DNA templates encoding the replicase after the chance emergence of a replicase through non-enzymatic reactions. Our work unveils a pathway for the transition from an RNA world to a mixed RNA-DNA world characterized by Darwinian evolution, where DNA sequences encode heritable phenotypes.

Contact lenses as novel tear fluid sampling vehicles for total RNA isolation, precipitation, and amplification.

The tear fluid is a readily accessible, potential source for biomarkers of disease and could be used to monitor the ocular response to contact lens (CL) wear or ophthalmic pathologies treated by therapeutic CLs. However, the tear fluid remains largely unexplored as a biomarker source for RNA-based molecular analyses. Using a rabbit model, this study sought to determine whether RNA could be collected from commercial CLs and whether the duration of CL wear would impact RNA recovery. The results were referenced to standardized strips of filtered paper (e.g., Shirmer Strips) placed in the inferior fornix. By performing total RNA isolation, precipitation, and amplification with commercial kits and RT-PCR methods, CLs were found to have no significant differences in RNA concentration and purity compared to Schirmer Strips. The study also identified genes that could be used to normalize RNA levels between tear samples. Of the potential control genes or housekeeping genes, GAPDH was the most stable. This study, which to our knowledge has never been done before, provides a methodology for the detection of RNA and gene expression changes from tear fluid that could be used to monitor or study eye diseases.

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®.

How Parameters Influence SHAPE-Directed Predictions.

The structure of an RNA sequence encodes information about its biological function. Dynamic programming algorithms are often used to predict the conformation of an RNA molecule from its sequence alone, and adding experimental data as auxiliary information improves prediction accuracy. This auxiliary data is typically incorporated into the nearest neighbor thermodynamic model22 by converting the data into pseudoenergies. Here, we look at how much of the space of possible structures auxiliary data allows prediction methods to explore. We find that for a large class of RNA sequences, auxiliary data shifts the predictions significantly. Additionally, we find that predictions are highly sensitive to the parameters which define the auxiliary data pseudoenergies. In fact, the parameter space can typically be partitioned into regions where different structural predictions predominate.

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.

Developing Complex RNA Design Applications in the Infrared Framework.

Applications in biotechnology and bio-medical research call for effective strategies to design novel RNAs with very specific properties. Such advanced design tasks require support by computational tools but at the same time put high demands on their flexibility and expressivity to model the application-specific requirements. To address such demands, we present the computational framework Infrared. It supports developing advanced customized design tools, which generate RNA sequences with specific properties, often in a few lines of Python code. This text guides the reader in tutorial format through the development of complex design applications. Thanks to the declarative, compositional approach of Infrared, we can describe this development as a step-by-step extension of an elementary design task. Thus, we start with generating sequences that are compatible with a single RNA structure and go all the way to RNA design targeting complex positive and negative design objectives with respect to single or even multiple target structures. Finally, we present a "real-world" application of computational design to create an RNA device for biotechnology: we use Infrared to generate design candidates of an artificial "AND" riboswitch, which activates gene expression in the simultaneous presence of two different small metabolites. In these applications, we exploit that the system can generate, in an efficient (fixed-parameter tractable) way, multiple diverse designs that satisfy a number of constraints and have high quality w.r.t. to an objective (by sampling from a Boltzmann distribution).