Predicting RNA-RNA Interactions Using RNAstructure

The human SMAD9 (GCC) repeat links to natural selection and late-onset neurocognitive disorders

INTRODUCTION

Whereas (GCC)-repeats are overrepresented in genic regions, and mutation hotspots, they are largely unexplored with regard to their link with natural selection. Across numerous primate species and tissues, SMAD9 (SMAD Family Member 9) reaches highest level of expression in the human brain. This gene contains a (GCC)-repeat in the interval between + 1 and + 60 of the transcription start site, which is in the high-ranking (GCC)-repeats with respect to length.

METHODS

Here we sequenced this (GCC)-repeat in 396 Iranian individuals, consisting of late-onset neurocognitive disorder (NCD) (N = 181) and controls (N = 215).

RESULTS

We detected two predominantly abundant alleles of 7 and 9 repeats, forming 96.2% of the allele pool. The (GCC)7/(GCC)9 ratio was in the reverse order in the NCD group versus controls (p = 0.005), resulting from excess of (GCC)7 in the NCD group (p = 0.003) and (GCC)9 in the controls (p = 0.01). Five genotypes, predominantly consisting of (GCC)7 and lacking (GCC)9 were detected in the NCD group only (p = 0.008). The patients harboring those genotypes received the diagnoses of Alzheimer's disease (AD) and vascular dementia (VD). Five genotypes consisting of (GCC)9 and lacking (GCC)7 were detected in the control group only (p = 0.002). The group-specific genotypes formed approximately 4% of the genotype pool in the human samples studied.

CONCLUSION

We propose natural selection and a novel locus for late-onset AD and VD at the SMAD9 (GCC)-repeat in humans.

DinoKnot: Duplex Interaction of Nucleic Acids With PseudoKnots

Interaction of nucleic acid molecules is essential for their functional roles in the cell and their applications in biotechnology. While simple duplex interactions have been studied before, the problem of efficiently predicting the minimum free energy structure of more complex interactions with possibly pseudoknotted structures remains a challenge. In this work, we introduce a novel and efficient algorithm for prediction of Duplex Interaction of Nucleic acids with pseudoKnots, DinoKnot follows the hierarchical folding hypothesis to predict the secondary structure of two interacting nucleic acid strands (both homo- and hetero-dimers). DinoKnot utilizes the structure of molecules before interaction as a guide to find their duplex structure allowing for possible base pair competitions. To showcase DinoKnots's capabilities we evaluated its predicted structures against (1) experimental results for SARS-CoV-2 genome and nine primer-probe sets, (2) a clinically verified example of a mutation affecting detection, and (3) a known nucleic acid interaction involving a pseudoknot. In addition, we compared our results against our closest competition, RNAcofold, further highlighting DinoKnot's strengths. We believe DinoKnot can be utilized for various applications including screening new variants for potential detection issues and supporting existing applications involving DNA/RNA interactions, adding structural considerations to the interaction to elicit functional information.

A Hypermutable Region in the DISP2 Gene Links to Natural Selection and Late-Onset Neurocognitive Disorders in Humans

(CCG) short tandem repeats (STRs) are predominantly enriched in genic regions, mutation hotspots for C to T truncating substitutions, and involved in various neurological and neurodevelopmental disorders. However, intact blocks of this class of STRs are widely overlooked with respect to their link with natural selection. The human neuron-specific gene, DISP2 (dispatched RND transporter family member 2), contains a (CCG) repeat in its 5' untranslated region. Here, we sequenced this STR in a sample of 448 Iranian individuals, consisting of late-onset neurocognitive disorder (NCD) (N = 203) and controls (N = 245). We found that the region spanning the (CCG) repeat was highly mutated, resulting in several flanking (CCG) residues. However, an 8-repeat of the (CCG) repeat was predominantly abundant (frequency = 0.92) across the two groups. While the overall distribution of genotypes was not different between the two groups (p > 0.05), we detected four genotypes in the NCD group only (2% of the NCD genotypes, Mid-p = 0.02), consisting of extreme short alleles, 5- and 6-repeats, that were not detected in the control group. The patients harboring those genotypes received the diagnoses of probable Alzheimer's disease and vascular dementia. We also found six genotypes in the control group only (2.5% of the control genotypes, Mid-p = 0.01) that consisted of the 8-repeat and extreme long alleles, 9- and 10-repeats, of which the 10-repeat was not detected in the NCD group. The (CCG) repeat specifically expanded in primates. In conclusion, we report an indication of natural selection at a novel hypermutable region in the human genome and divergent alleles and genotypes in late-onset NhCDs and controls. These findings reinforce the hypothesis that a collection of rare alleles and genotypes in a number of genes may unambiguously contribute to the cognition impairment component of late-onset NCDs.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

LncRNA CCTT-mediated RNA-DNA and RNA-protein interactions facilitate the recruitment of CENP-C to centromeric DNA during kinetochore assembly

Kinetochore assembly on centromeres is central for chromosome segregation, and defects in this process cause mitotic errors and aneuploidy. Besides the well-established protein network, emerging evidence suggests the involvement of regulatory RNA in kinetochore assembly; however, it has remained elusive about the identity of such RNA, let alone its mechanism of action in this critical process. Here, we report CCTT, a previously uncharacterized long non-coding RNA (lncRNA) transcribed from the arm of human chromosome 17, which plays a vital role in kinetochore assembly. We show that CCTT highly localizes to all centromeres via the formation of RNA-DNA triplex and specifically interacts with CENP-C to help engage this blueprint protein in centromeres, and consequently, CCTT loss triggers extensive mitotic errors and aneuploidy. These findings uncover a non-centromere-derived lncRNA that recruits CENP-C to centromeres and shed critical lights on the function of centromeric DNA sequences as anchor points for kinetochore assembly.

Predicting miRNA-lncRNA interactions on plant datasets based on bipartite network embedding method

The research of miRNA-lncRNA interactions (MLIs) has received great attention recently due to their vital roles in microbiology and profound significance in diseases. Currently, many related studies mainly focus on animals and the link prediction problem on plants is rarely discussed comprehensively. Motivated by this, we achieve link prediction task based on the concept of bipartite graph and verify encouraging performance of our conclusions by conducting experiments on plant datasets. In this work, we firstly extract attribute information and structure information as base features and further process these information for network embedding. Intra-partition and inter-partition proximity modelling are conducted to construct the loss function, which facilitates the training of parameters. Finally, the superiority of our presented approach is shown by carrying out experiments on four plant datasets, which reflects the significance of this work to the research of microbiology and disease.

Thoughts on how to think (and talk) about RNA structure

Recent events have pushed RNA research into the spotlight. Continued discoveries of RNA with unexpected diverse functions in healthy and diseased cells, such as the role of RNA as both the source and countermeasure to a severe acute respiratory syndrome coronavirus 2 infection, are igniting a new passion for understanding this functionally and structurally versatile molecule. Although RNA structure is key to function, many foundational characteristics of RNA structure are misunderstood, and the default state of RNA is often thought of and depicted as a single floppy strand. The purpose of this perspective is to help adjust mental models, equipping the community to better use the fundamental aspects of RNA structural information in new mechanistic models, enhance experimental design to test these models, and refine data interpretation. We discuss six core observations focused on the inherent nature of RNA structure and how to incorporate these characteristics to better understand RNA structure. We also offer some ideas for future efforts to make validated RNA structural information available and readily used by all researchers.

fSHAPE, fSHAPE-eCLIP, and SHAPE-eCLIP probe transcript regions that interact with specific proteins

Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) structure probing techniques characterize the secondary structure of RNA molecules, which influence their functions and interactions. A variation of SHAPE, footprinting SHAPE (fSHAPE), probes RNA in the presence and absence of protein to identify RNA bases that hydrogen-bond with protein. SHAPE or fSHAPE coupled with enhanced crosslinking and immunoprecipitation (SHAPE-eCLIP or fSHAPE-eCLIP) pulls down RNAs bound by any protein of interest and returns their structure or protein interaction information, respectively. Here, we describe detailed protocols for SHAPE-eCLIP and fSHAPE-eCLIP and an analysis protocol for fSHAPE. For complete details on the use and execution of these protocols, please refer to Corley et al. (2020).

Circular RNAs in Human Cancer

Circular RNAs (circRNAs) are a class of endogenous single-stranded covalently closed RNAs, primarily produced from pre-mRNAs via non-canonical back-splicing. circRNAs are highly conserved, stable, and expressed in tissue- and development-specific pattern. circRNAs play essential roles in physiological process as well as cancer biology. By the advances of deep sequencing and bioinformatics, the number of circRNAs have increased explosively. circRNAs function as miRNA/protein sponge, protein scaffold, protein recruitment, enhancer of protein function, as well as templates for translation involved in the regulation of transcription/splicing, translation, protein degradation, and pri-miRNA processing in human cancers and contributed to the pathogenesis of cancer. Numerous circRNAs may function in diverse manners. In this review, we survey the current understanding of circRNA functions in human cancer including miRNA sponge, circRNA-protein interaction, and circRNA-encoded protein, and summarize available databases for circRNA annotation and functional prediction.

A phosphorylation-regulated eIF3d translation switch mediates cellular adaptation to metabolic stress

Shutoff of global protein synthesis is a conserved response to cellular stresses. This general phenomenon is accompanied by the induction of distinct gene programs tailored to each stress. Although the mechanisms driving repression of general protein synthesis are well characterized, how cells reprogram the translation machinery for selective gene expression remains poorly understood. Here, we found that the noncanonical 5' cap-binding protein eIF3d was activated in response to metabolic stress in human cells. Activation required reduced CK2-mediated phosphorylation near the eIF3d cap-binding pocket. eIF3d controls a gene program enriched in factors important for glucose homeostasis, including members of the mammalian target of rapamycin (mTOR) pathway. eIF3d-directed translation adaptation was essential for cell survival during chronic glucose deprivation. Thus, this mechanism of translation reprogramming regulates the cellular response to metabolic stress.