RNAsnp: efficient detection of local RNA secondary structure changes induced by SNPs

Investigating AXIN1 gene polymorphisms in Turkish children with cryptorchidism: A pilot study

INTRODUCTION

Cryptorchidism is one of the most common congenital anomalies in male children, occurring in 2-5% of full-term male infants. Both genetic and environmental factors are observed to play a role in its etiology. A study conducted in Japan identified the AXIN1 gene as being associated with cryptorchidism.

OBJECTIVE

We aimed to conduct a pilot study on AXIN1 gene polymorphism in Turkish children with cryptorchidism, and whether AXIN1 gene polymorphism is a risk factor for cryptorchidism.

STUDY DESIGN

Between January 2023 and December 2023, we have planned a prospective controlled study including 84 boys operated for cryptorchidism as study group, and 96 boys operated for circumcision as control group. The remaining blood samples of preoperative laboratory tests in ethylenediamine tetraacetic acid (EDTA) tubes were kept at -20 Co freezer for genomic studies. Patient demographics, physical examination and operative findings were recorded, study patients were grouped according to testis localization. After collecting all samples, genomic DNA isolation procedure was done, and analysis of the 3 polymorphisms (rs12921862, rs1805105 and rs370681) of AXIN1 gene was performed using conventional Polymerase Chain Reaction Restriction Fragment Length Polymorphism (PCR-RFLP) method. Genotype and allele frequencies of each group was analyzed and compared.

RESULTS

The most common location of cryptorchid testis was proximal inguinal (53%), followed by distal inguinal (25.3%), bilateral (13.3%), and intra-abdominal (8.4%). Regarding the 3 polymorphisms of AXIN1 gene, there was no significant difference between study and control groups, in terms of genotype and allele frequencies (P > 0.05). Eight haplotype blocks were estimated for 3 polymorphisms of AXIN1. However, no significant difference was observed between study and control groups regarding haplotype distributions (P > 0.05). In addition, the comparison of the localization of testis with AXIN1 gene polymorphism did not show any significant difference among cryptorchid testis groups (P > 0.05).

DISCUSSION

The AXIN1 gene is located on chromosome 16p and its polymorphisms have been associated with various diseases. In a Chinese study, the rs370681 polymorphism was found to be associated with cryptorchidism. However, our results showed no association between the AXIN1 gene haplotypes for the studied polymorphisms and cryptorchidism.

CONCLUSION

In this study we have investigated the AXIN1 gene polymorphism in Turkish children with cryptorchidism as a pilot study. Although we could not identify any difference as compared to control group, further research is necessary to uncover the underlying molecular mechanisms contributing to the development of cryptorchidism.

Integrated multiplexed assays of variant effect reveal determinants of catechol-O-methyltransferase gene expression

Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase or decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regulatory landscape of thousands of catechol-O-methyltransferase (COMT) variants from RNA to protein and found numerous coding variants that alter COMT expression. Finally, we trained machine learning models to map signatures of variant effects on COMT gene expression and uncovered both directional and divergent impacts across expression layers. Our analyses reveal expression phenotypes for thousands of variants in COMT and highlight variant effects on both single and multiple layers of expression. Our findings prompt future studies that integrate several multiplexed assays for the readout of gene expression.

Effects of genetic variation on the structure of RNA and protein

Changes in the structure of RNA and protein, have an important impact on biological functions and are even important determinants of disease pathogenesis and treatment. Some genetic variations, including copy number variation, single nucleotide variation, and so on, can lead to changes in biological function and increased susceptibility to certain diseases by changing the structure of RNA or protein. With the development of structural biology and sequencing technology, a large amount of RNA and protein structure data and genetic variation data resources has emerged to be used to explain biological processes. Here, we reviewed the effects of genetic variation on the structure of RNAs and proteins, and investigated their impact on several diseases. An online resource (http://www.onethird-lab.com/gems/) to support convenient retrieval of common tools is also built. Finally, the challenges and future development of the effects of genetic variation on RNA and protein were discussed.

AStruct: detection of allele-specific RNA secondary structure in structuromic probing data

BACKGROUND

Uncovering functional genetic variants from an allele-specific perspective is of paramount importance in advancing our understanding of gene regulation and genetic diseases. Recently, various allele-specific events, such as allele-specific gene expression, allele-specific methylation, and allele-specific binding, have been explored on a genome-wide scale due to the development of high-throughput sequencing methods. RNA secondary structure, which plays a crucial role in multiple RNA-associated processes like RNA modification, translation and splicing, has emerged as an essential focus of relevant research. However, tools to identify genetic variants associated with allele-specific RNA secondary structures are still lacking.

RESULTS

Here, we develop a computational tool called 'AStruct' that enables us to detect allele-specific RNA secondary structure (ASRS) from RT-stop based structuromic probing data. AStruct shows robust performance in both simulated datasets and public icSHAPE datasets. We reveal that single nucleotide polymorphisms (SNPs) with higher AStruct scores are enriched in coding regions and tend to be functional. These SNPs are highly conservative, have the potential to disrupt sites involved in m6A modification or protein binding, and are frequently associated with disease.

CONCLUSIONS

AStruct is a tool dedicated to invoke allele-specific RNA secondary structure events at heterozygous SNPs in RT-stop based structuromic probing data. It utilizes allelic variants, base pairing and RT-stop information under different cell conditions to detect dynamic and functional ASRS. Compared to sequence-based tools, AStruct considers dynamic cell conditions and outperforms in detecting functional variants. AStruct is implemented in JAVA and is freely accessible at: https://github.com/canceromics/AStruct .

Prominent genetic variants and epigenetic changes in post-traumatic stress disorder among combat veterans

Post-traumatic stress disorder (PTSD) is one of the most widespread and disabling psychiatric disorders among combat veterans. Substantial interindividual variability in susceptibility to PTSD suggests the presence of different risk factors for this disorder. Twin and family studies confirm genetic factors as important risk factors for PTSD. In addition to genetic factors, epigenetic factors, especially DNA methylation, can be considered as a potential mechanism in changing the risk of PTSD. So far, many genetic and epigenetic association studies have been conducted in relation to PTSD. In genetic studies, many single nucleotide polymorphisms have been identified as PTSD risk factors. Meanwhile, the variations in catecholamines-related genes, serotonin transporter and receptors, brain-derived neurotrophic factor, inflammatory factors, and apolipoprotein E are the most prominent candidates. CpG methylation in the upstream regions of many genes is also considered a PTSD risk factor. Accurate identification of genetic and epigenetic changes associated with PTSD can lead to the presentation of suitable biomarkers for susceptible individuals to this disorder. This study aimed to delineate prominent genetic variations and epigenetic changes associated with post-traumatic stress disorder in military veterans who have experienced combat, focusing on genetic and epigenetic association studies.

A synonymous single nucleotide polymorphism (g.36417726C > A) in the Lama2 gene influencing fat deposition is associated with post-partum anestrus interval in Murrah buffalo

Postpartum absence of estrus exhibition known as postpartum anestrus interval (PPAI) for more than 90 days after calving is a concerning issue for dairy buffalo farmers' economy. The PPAI duration is influenced by both management practices and animal genetics. Investigating genetic markers associated with PPAI is crucial for incorporating them into marker-assisted selection programs. Towards this goal, our study focused on exploring potential genetic markers from early postpartum adipose tissue gene networks. We successfully identified 24 Single Nucleotide Polymorphisms (SNPs) within 9 candidate genes. In our initial analysis involving 100 buffaloes, we detected a significant association (P = 0.02267) between a specific synonymous SNP within the Lama2 gene (g.36417726C > A) and PPAI. This finding was subsequently validated (P = 0.02937) in a larger cohort of 415 buffaloes, where the SNP explained 1.36 % of the genetic variance. Intriguingly, buffaloes with the CC genotype of this SNP exhibited a PPAI that was 12.71 ± 3.21 days longer compared to buffaloes with AA and CA genotypes. To gain insight into the functional relevance of this SNP, a computational analysis was performed which indicated that the C allele of the SNP (g.36417726C > A) increased the stability of LAMA2 mRNA compared to the A allele. This computational prediction was corroborated by observing a significant increase (P = 0.01798) in Lama2 gene expression (greater than 8-fold) and higher fat percentage (P < 0.05) in adipose tissue of CC genotypes (48.78 ± 1.87 %) compared to AA genotypes (33.59 ± 4.5 %). Furthermore, we noted a significant (P < 0.05) upregulation of C/ebpβ, Pparγ, Fasn, C/ebpα, and Pnpla2 genes, along with the downregulation of Bmp2 and Ptch1 in CC genotypes as opposed to AA genotypes. This observation suggests the involvement of the Pparγ-mediated pathway in both adipogenesis and lipolysis within CC genotypes. In summary, our comprehensive analysis involving association and functional validation underscores the potential of the SNP (g.36417726C > A) within the Lama2 gene as a promising genetic marker against extended PPAI in Murrah buffalo.

The effects of NLRP3 rs10754558 and rs4612666 polymorphisms on preeclampsia susceptibility, onset, and severity: a case-control study and in silico analysis

Preeclampsia (PE) is one of the serious complications of pregnancy and its exact etiology is unknown. Inflammasomes are multiportion complexes whose relation with PE has been described. Evidence showed the effect of NLRP3 inflammasome in PE pathogenesis. In the current study, we investigated the possible impacts of NLRP3 polymorphisms on PE. A total of 252 PE and 258 control pregnant women were selected for the study. The PCR-RFLP method was employed to genotype rs10754558 and rs4612666 polymorphisms. The RNAsnp and SpliceAid 2 software were used for in silico analysis. There was no relationship between NLRP3 polymorphisms and PE. In comparison to control women, the NLRP3 rs10754558 could increase the risk of severe PE in codominant and dominant models (OR=1.89, 95% CI=1.19-3.01, P=0.012, OR=1.95, 95% CI=1.24-3.06, P=0.0037, respectively). The findings of the in silico analysis revealed the effects of rs10754558 C to G and rs4612666 C to T substitutions on protein binding sites and rs10754558 C to G substitution on secondary RNA structure. These findings could confirm the finding those studies reported the impacts of these variants on various diseases. In conclusion, the NLRP3 rs10754558 variant was associated with an increased risk of EOPE and severe PE.

Comparative RNA Genomics

Over the last quarter of a century it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large-scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible non-coding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of non-coding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.

Integrated multiplexed assays of variant effect reveal cis-regulatory determinants of catechol-O-methyltransferase gene expression

Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase and decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regulatory landscape of thousands of catechol-O-methyltransferase (COMT) variants from RNA to protein and found numerous coding variants that alter COMT expression. Finally, we trained machine learning models to map signatures of variant effects on COMT gene expression and uncovered both directional and divergent impacts across expression layers. Our analyses reveal expression phenotypes for thousands of variants in COMT and highlight variant effects on both single and multiple layers of expression. Our findings prompt future studies that integrate several multiplexed assays for the readout of gene expression.

Effect of single nucleotide polymorphisms on the structure of long noncoding RNAs and their interaction with RNA binding proteins

Long non-coding RNAs (lncRNA) are emerging as a new class of regulatory RNAs with remarkable potential to be utilized as therapeutic targets against many human diseases. Several genome-wide association studies (GWAS) have catalogued Single Nucleotide Polymorphisms (SNPs) present in the noncoding regions of the genome from where lncRNAs originate. In this study, we have selected 67 lncRNAs with GWAS-tagged SNPs and have also investigated their role in affecting the local secondary structures. Majority of the SNPs lead to changes in the secondary structure of lncRNAs to a different extent by altering the base pairing patterns. These structural changes in lncRNA are also manifested in form of alteration in the binding site for RNA binding proteins (RBPs) along with affecting their binding efficacies. Ultimately, these structural modifications may influence the transcriptional and post-transcriptional pathways of these RNAs, leading to the causation of diseases. Hence, it is important to understand the possible underlying mechanism of RBPs in association with GWAS-tagged SNPs in human diseases.

Unveiling the Genetic Complexity of Teratozoospermia: Integrated Genomic Analysis Reveals Novel Insights into lncRNAs' Role in Male Infertility

Male infertility is a global health issue, affecting over 20 million men worldwide. Genetic factors are crucial in various male infertility forms, including teratozoospermia. Nonetheless, the genetic causes of male infertility remain largely unexplored. In this study, we employed whole-genome sequencing and RNA expression analysis to detect differentially expressed (DE) long-noncoding RNAs (lncRNAs) in teratozoospermia, along with mutations that are exclusive to teratozoospermic individuals within these DE lncRNAs regions. Bioinformatic tools were used to assess variants' impact on lncRNA structure, function, and lncRNA-miRNA interactions. Our analysis identified 1166 unique mutations in teratozoospermic men within DE lncRNAs, distinguishing them from normozoospermic men. Among these, 64 variants in 23 lncRNAs showed potential regulatory roles, 7 variants affected 4 lncRNA structures, while 37 variants in 17 lncRNAs caused miRNA target loss or gain. Pathway Enrichment and Gene Ontology analyses of the genes targeted by the affected miRNAs revealed dysregulated pathways in teratozoospermia and a link between male infertility and cancer. This study lists novel variants and lncRNAs associated for the first time with teratozoospermia. These findings pave the way for future studies aiming to enhance diagnosis and therapy in the field of male infertility.

Tumour mutations in long noncoding RNAs enhance cell fitness

Long noncoding RNAs (lncRNAs) are linked to cancer via pathogenic changes in their expression levels. Yet, it remains unclear whether lncRNAs can also impact tumour cell fitness via function-altering somatic "driver" mutations. To search for such driver-lncRNAs, we here perform a genome-wide analysis of fitness-altering single nucleotide variants (SNVs) across a cohort of 2583 primary and 3527 metastatic tumours. The resulting 54 mutated and positively-selected lncRNAs are significantly enriched for previously-reported cancer genes and a range of clinical and genomic features. A number of these lncRNAs promote tumour cell proliferation when overexpressed in in vitro models. Our results also highlight a dense SNV hotspot in the widely-studied NEAT1 oncogene. To directly evaluate the functional significance of NEAT1 SNVs, we use in cellulo mutagenesis to introduce tumour-like mutations in the gene and observe a significant and reproducible increase in cell fitness, both in vitro and in a mouse model. Mechanistic studies reveal that SNVs remodel the NEAT1 ribonucleoprotein and boost subnuclear paraspeckles. In summary, this work demonstrates the utility of driver analysis for mapping cancer-promoting lncRNAs, and provides experimental evidence that somatic mutations can act through lncRNAs to enhance pathological cancer cell fitness.

Rock, scissors, paper: How RNA structure informs function

RNA can fold back on itself to adopt a wide range of structures. These range from relatively simple hairpins to intricate 3D folds and can be accompanied by regulatory interactions with both metabolites and macromolecules. The last 50 yr have witnessed elucidation of an astonishing array of RNA structures including transfer RNAs, ribozymes, riboswitches, the ribosome, the spliceosome, and most recently entire RNA structuromes. These advances in RNA structural biology have deepened insight into fundamental biological processes including gene editing, transcription, translation, and structure-based detection and response to temperature and other environmental signals. These discoveries reveal that RNA can be relatively static, like a rock; that it can have catalytic functions of cutting bonds, like scissors; and that it can adopt myriad functional shapes, like paper. We relate these extraordinary discoveries in the biology of RNA structure to the plant way of life. We trace plant-specific discovery of ribozymes and riboswitches, alternative splicing, organellar ribosomes, thermometers, whole-transcriptome structuromes and pan-structuromes, and conclude that plants have a special set of RNA structures that confer unique types of gene regulation. We finish with a consideration of future directions for the RNA structure-function field.

The Functional RNA Identification (FRID) Pipeline: Identification of Potential Pseudoknot-Containing RNA Elements as Therapeutic Targets for SARS-CoV-2

The COVID-19 pandemic persists despite the development of effective vaccines. As such, it remains crucial to identify new targets for antiviral therapies. The causative virus of COVID-19, SARS-CoV-2, is a positive-sense RNA virus with RNA structures that could serve as therapeutic targets. One such RNA with established function is the frameshift stimulatory element (FSE), which promotes programmed ribosomal frameshifting. To accelerate identification of additional functional RNA elements, we introduce a novel computational approach termed the Functional RNA Identification (FRID) pipeline. The guiding principle of our pipeline, which uses established component programs as well as customized component programs, is that functional RNA elements have conserved secondary and pseudoknot structures that facilitate function. To assess the presence and conservation of putative functional RNA elements in SARS-CoV-2, we compared over 6,000 SARS-CoV-2 genomic isolates. We identified 22 functional RNA elements from the SARS-CoV-2 genome, 14 of which have conserved pseudoknots and serve as potential targets for small molecule or antisense oligonucleotide therapeutics. The FRID pipeline is general and can be applied to identify pseudoknotted RNAs for targeted therapeutics in genomes or transcriptomes from any virus or organism.

How does precursor RNA structure influence RNA processing and gene expression?

RNA is a fundamental biomolecule that has many purposes within cells. Due to its single-stranded and flexible nature, RNA naturally folds into complex and dynamic structures. Recent technological and computational advances have produced an explosion of RNA structural data. Many RNA structures have regulatory and functional properties. Studying the structure of nascent RNAs is particularly challenging due to their low abundance and long length, but their structures are important because they can influence RNA processing. Precursor RNA processing is a nexus of pathways that determines mature isoform composition and that controls gene expression. In this review, we examine what is known about human nascent RNA structure and the influence of RNA structure on processing of precursor RNAs. These known structures provide examples of how other nascent RNAs may be structured and show how novel RNA structures may influence RNA processing including splicing and polyadenylation. RNA structures can be targeted therapeutically to treat disease.

Evolutionary Conservation of RNA Secondary Structure

Noncoding RNAs, ncRNAs, naturally fold into structures, which allow them to perform their functions in the cell. Evolutionarily close species share structures and functions. This occurs because of shared selective pressures, resulting in conserved groups. Previous efforts in finding functional RNAs have been made in detecting conserved structures in genomes or alignments. It may occur that, within a conserved group, species-specific structures arise after species split due to positive selection. Detecting positive selection in ncRNAs is a hard problem in biology as well as bioinformatics. To detect positive selection, one should find species-specific structures within a conserved set. This chapter provides protocols to detect and analyze positive selection in ncRNA structures with the SSS-test and other free software.

RNAsmc: A integrated tool for comparing RNA secondary structure and evaluating allosteric effects

RNA structure plays a crucial role in gene regulation, in RNA stability and the essential biological processes. RNA secondary structure (RSS) motifs are the basic building blocks for investigating the biological mechanisms of structure. Here, we present a strategy for structural motif-based dynamic alignment, namely, RNA secondary-structural motif-comparing (RNAsmc), to identify structural motifs and quantitatively evaluate their underlying molecular functions. RNAsmc also has strong robustness to sequence length, folding protocol and RNA structural profile by chemical probing. Notably, it is also applicable to quantify structural variation in special RNA editing events (SNVs or SNPs, fragment insertion or deletion, etc.). The findings indicate that RNAsmc can uncover the heterogeneity of RNA secondary structure and score for similarities among components, which provides an impetus to cluster RNA families and evaluate allosteric effects. We find that RNAsmc exhibits remarkable detection efficiency for experimentally-derived RiboSNitches. Finally, the pipeline was assembled into an R software package to serve as an automated toolkit to explore, align, and cluster RSS. It is freely available for download at https://CRAN.R-project.org/package=RNAsmc.

LncPLAAT3-AS Regulates PLAAT3-Mediated Adipocyte Differentiation and Lipogenesis in Pigs through miR-503-5p

Animal fat deposition has a significant impact on meat flavor and texture. However, the molecular mechanisms of fat deposition are not well understood. LncPLAAT3-AS is a naturally occurring transcript that is abundant in porcine adipose tissue. Here, we focus on the regulatory role of lncPLAAT3-AS in promoting preadipocyte proliferation and adipocyte differentiation. By overexpressing or repressing lncPLAAT3 expression, we found that lncPLAAT3-AS promoted the transcription of its host gene PLAAT3, a regulator of adipocyte differentiation. In addition, we predicted the region of lncPLAAT3-AS that binds to miR-503-5p and showed by dual luciferase assay that lncPLAAT3-AS acts as a sponge to absorb miR-503-5p. Interestingly, miR-503-5p also targets and represses PLAAT3 expression and helps regulate porcine preadipocyte proliferation and differentiation. Taken together, these results show that lncPLAAT3-AS upregulates PLAAT3 expression by absorbing miR-503-5p, suggesting a potential regulatory mechanism based on competing endogenous RNAs. Finally, we explored lncPLAAT3-AS and PLAAT3 expression in adipose tissue and found that both molecules were expressed at significantly higher levels in fatty pig breeds compared to lean pig breeds. In summary, we identified the mechanism by which lncPLAAT3-AS regulates porcine preadipocyte proliferation and differentiation, contributing to our understanding of the molecular mechanisms of lipid deposition in pigs.

A long non-coding RNA that harbors a SNP associated with type 2 diabetes regulates the expression of TGM2 gene in pancreatic beta cells

INTRODUCTION

Most of the disease-associated single nucleotide polymorphisms (SNPs) lie in non- coding regions of the human genome. Many of these variants have been predicted to impact the expression and function of long non-coding RNAs (lncRNA), but the contribution of these molecules to the development of complex diseases remains to be clarified.

METHODS

Here, we performed a genetic association study between a SNP located in a lncRNA known as LncTGM2 and the risk of developing type 2 diabetes (T2D), and analyzed its implication in disease pathogenesis at pancreatic beta cell level. Genetic association study was performed on human samples linking the rs2076380 polymorphism with T2D and glycemic traits. The pancreatic beta cell line EndoC-bH1 was employed for functional studies based on LncTGM2 silencing and overexpression experiments. Human pancreatic islets were used for eQTL analysis.

RESULTS

We have identified a genetic association between LncTGM2 and T2D risk. Functional characterization of the LncTGM2 revealed its implication in the transcriptional regulation of TGM2, coding for a transglutaminase. The T2Dassociated risk allele in LncTGM2 disrupts the secondary structure of this lncRNA, affecting its stability and the expression of TGM2 in pancreatic beta cells. Diminished LncTGM2 in human beta cells impairs glucose-stimulated insulin release.

CONCLUSIONS

These findings provide novel information on the molecular mechanisms by which T2D-associated SNPs in lncRNAs may contribute to disease, paving the way for the development of new therapies based on the modulation of lncRNAs.

GPX2 Gene Affects Feed Efficiency of Pigs by Inhibiting Fat Deposition and Promoting Muscle Development

GPX2 has been recognized as a potential candidate gene for feed efficiency in pigs. This article aimed to elucidate polymorphism of GPX2 associated with feed efficiency and its related molecular mechanism. In this study, seven single nucleotide polymorphisms (SNP) of GPX2 were found among 383 Duroc pigs. In addition, seven SNPs and ALGA0043483 (PorcineSNP60 BeadChip data in 600 Duroc pigs), which are near the GPX2 gene, were identified in one haplotypes block. Furthermore, associated studies showed that the genotype of GPX2 has significant association with weaning weight and 100 kg BF in Duroc pigs. In addition, the AG had no effect when the backfat became thinner, and the FCR and RFI traits had a tendency to decrease in the G3 + TT combination genotype, accompanied by an increase of GPX2 expression in backfat and muscle tissues. At the cellular level, the adipocyte proliferation and ability of adipogenic differentiation were reduced, and the lipid degradation increased in 3T3-L1 when there was overexpression of GPX2. In contrast, overexpression of the GPX2 gene can promote the muscle cell proliferation and myogenic differentiation in C2C12 cells. In other words, GPX2 has the effect of reducing fat deposition and promoting muscle development, and it is a candidate gene for backfat and feed efficiency.

IndelsRNAmute: predicting deleterious multiple point substitutions and indels mutations

Background

RNA deleterious point mutation prediction was previously addressed with programs such as RNAmute and MultiRNAmute. The purpose of these programs is to predict a global conformational rearrangement of the secondary structure of a functional RNA molecule, thereby disrupting its function. RNAmute was designed to deal with only single point mutations in a brute force manner, while in MultiRNAmute an efficient approach to deal with multiple point mutations was developed. The approach used in MultiRNAmute is based on the stabilization of the suboptimal RNA folding prediction solutions and/or destabilization of the optimal folding prediction solution of the wild type RNA molecule. The MultiRNAmute algorithm is significantly more efficient than the brute force approach in RNAmute, but in the case of long sequences and large m-point mutation sets the MultiRNAmute becomes exponential in examining all possible stabilizing and destabilizing mutations.

Results

An inherent limitation in the RNAmute and MultiRNAmute programs is their ability to predict only substitution mutations, as these programs were not designed to work with deletion or insertion mutations. To address this limitation we herein develop a very fast algorithm, based on suboptimal folding solutions, to predict a predefined number of multiple point deleterious mutations as specified by the user. Depending on the user’s choice, each such set of mutations may contain combinations of deletions, insertions and substitution mutations. Additionally, we prove the hardness of predicting the most deleterious set of point mutations in structural RNAs.

Conclusions

We developed a method that extends our previous MultiRNAmute method to predict insertion and deletion mutations in addition to substitutions. The additional advantage of the new method is its efficiency to find a predefined number of deleterious mutations. Our new method may be exploited by biologists and virologists prior to site-directed mutagenesis experiments, which involve indel mutations along with substitutions. For example, our method may help to investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary structure.

Disease-associated human genetic variation through the lens of precursor and mature RNA structure

Disease-associated variants (DAVs) are commonly considered either through a genomic lens that describes variant function at the DNA level, or at the protein function level if the variant is translated. Although the genomic and proteomic effects of variation are well-characterized, genetic variants disrupting post-transcriptional regulation is another mechanism of disease that remains understudied. Specific RNA sequence motifs mediate post-transcriptional regulation both in the nucleus and cytoplasm of eukaryotic cells, often by binding to RNA-binding proteins or other RNAs. However, many DAVs map far from these motifs, which suggests deeper layers of post-transcriptional mechanistic control. Here, we consider a transcriptomic framework to outline the importance of post-transcriptional regulation as a mechanism of disease-causing single-nucleotide variation in the human genome. We first describe the composition of the human transcriptome and the importance of abundant yet overlooked components such as introns and untranslated regions (UTRs) of messenger RNAs (mRNAs). We present an analysis of Human Gene Mutation Database variants mapping to mRNAs and examine the distribution of causative disease-associated variation across the transcriptome. Although our analysis confirms the importance of post-transcriptional regulatory motifs, a majority of DAVs do not directly map to known regulatory motifs. Therefore, we review evidence that regions outside these well-characterized motifs can regulate function by RNA structure-mediated mechanisms in all four elements of an mRNA: exons, introns, 5' and 3' UTRs. To this end, we review published examples of riboSNitches, which are single-nucleotide variants that result in a change in RNA structure that is causative of the disease phenotype. In this review, we present the current state of knowledge of how DAVs act at the transcriptome level, both through altering post-transcriptional regulatory motifs and by the effects of RNA structure.

Predicting functional riboSNitches in the context of alternative splicing

RNAs are the major regulators of gene expression, and their secondary structures play crucial roles at different levels. RiboSNitches are disease-associated SNPs that cause changes in the pre-mRNA secondary structural ensemble. Several riboSNitches have been detected in the 5' and 3' untranslated regions and lncRNA. Although cases of secondary structural elements playing a regulatory role in alternative splicing are known, regions specific to splicing events, such as splice junctions have not received much attention. We tested splice-site mutations for their efficiency in disrupting the secondary structure and hypothesized that these could play a crucial role in alternative splicing. Multiple riboSNitch prediction methods were applied to obtain overlapping results that are potentially more reliable. Putative riboSNitches were identified from aberrant 5' and 3' splice site mutations, cancer-causing somatic mutations, and genes that harbor the regulatory RNA secondary structural elements. Our workflow for predicting riboSNitches associated with alternative splicing is novel and paves the way for subsequent experimental validation.

Inferring Potential Cancer Driving Synonymous Variants

Synonymous single nucleotide variants (sSNVs) are often considered functionally silent, but a few cases of cancer-causing sSNVs have been reported. From available databases, we collected four categories of sSNVs: germline, somatic in normal tissues, somatic in cancerous tissues, and putative cancer drivers. We found that screening sSNVs for recurrence among patients, conservation of the affected genomic position, and synVep prediction (synVep is a machine learning-based sSNV effect predictor) recovers cancer driver variants (termed proposed drivers) and previously unknown putative cancer genes. Of the 2.9 million somatic sSNVs found in the COSMIC database, we identified 2111 proposed cancer driver sSNVs. Of these, 326 sSNVs could be further tagged for possible RNA splicing effects, RNA structural changes, and affected RBP motifs. This list of proposed cancer driver sSNVs provides computational guidance in prioritizing the experimental evaluation of synonymous mutations found in cancers. Furthermore, our list of novel potential cancer genes, galvanized by synonymous mutations, may highlight yet unexplored cancer mechanisms.

Experimental demonstration and pan-structurome prediction of climate-associated riboSNitches in Arabidopsis

Background

Genome-wide association studies (GWAS) aim to correlate phenotypic changes with genotypic variation. Upon transcription, single nucleotide variants (SNVs) may alter mRNA structure, with potential impacts on transcript stability, macromolecular interactions, and translation. However, plant genomes have not been assessed for the presence of these structure-altering polymorphisms or “riboSNitches.”

Results

We experimentally demonstrate the presence of riboSNitches in transcripts of two Arabidopsis genes, ZINC RIBBON 3 (ZR3) and COTTON GOLGI-RELATED 3 (CGR3), which are associated with continentality and temperature variation in the natural environment. These riboSNitches are also associated with differences in the abundance of their respective transcripts, implying a role in regulating the gene's expression in adaptation to local climate conditions. We then computationally predict riboSNitches transcriptome-wide in mRNAs of 879 naturally inbred Arabidopsis accessions. We characterize correlations between SNPs/riboSNitches in these accessions and 434 climate descriptors of their local environments, suggesting a role of these variants in local adaptation. We integrate this information in CLIMtools V2.0 and provide a new web resource, T-CLIM, that reveals associations between transcript abundance variation and local environmental variation.

Conclusion

We functionally validate two plant riboSNitches and, for the first time, demonstrate riboSNitch conditionality dependent on temperature, coining the term “conditional riboSNitch.” We provide the first pan-genome-wide prediction of riboSNitches in plants. We expand our previous CLIMtools web resource with riboSNitch information and with 1868 additional Arabidopsis genomes and 269 additional climate conditions, which will greatly facilitate in silico studies of natural genetic variation, its phenotypic consequences, and its role in local adaptation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02656-4.

OUP accepted manuscript

Accelerated evolution of any portion of the genome is of significant interest, potentially signaling positive selection of phenotypic traits and adaptation. Accelerated evolution remains understudied for structured RNAs, despite the fact that an RNA’s structure is often key to its function. RNA structures are typically characterized by compensatory (structure-preserving) basepair changes that are unexpected given the underlying sequence variation, i.e., they have evolved through negative selection on structure. We address the question of how fast the primary sequence of an RNA can change through evolution while conserving its structure. Specifically, we consider predicted and known structures in vertebrate genomes. After careful control of false discovery rates, we obtain 13 de novo structures (and three known Rfam structures) that we predict to have rapidly evolving sequences—defined as structures where the primary sequences of human and mouse have diverged at least twice as fast (1.5 times for Rfam) as nearby neutrally evolving sequences. Two of the three known structures function in translation inhibition related to infection and immune response. We conclude that rapid sequence divergence does not preclude RNA structure conservation in vertebrates, although these events are relatively rare.

Decoding the effects of synonymous variants

Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs suffer from the lack of gold-standard training/evaluation data and exhibit over-reliance on sequence conservation signals. We developed synVep (synonymous Variant effect predictor), a machine learning-based method that overcomes both of these limitations. Our training data was a combination of variants reported by gnomAD (observed) and those unreported, but possible in the human genome (generated). We used positive-unlabeled learning to purify the generated variant set of any likely unobservable variants. We then trained two sequential extreme gradient boosting models to identify subsets of the remaining variants putatively enriched and depleted in effect. Our method attained 90% precision/recall on a previously unseen set of variants. Furthermore, although synVep does not explicitly use conservation, its scores correlated with evolutionary distances between orthologs in cross-species variation analysis. synVep was also able to differentiate pathogenic vs. benign variants, as well as splice-site disrupting variants (SDV) vs. non-SDVs. Thus, synVep provides an important improvement in annotation of sSNVs, allowing users to focus on variants that most likely harbor effects.

A survey of RNA editing at single-cell resolution links interneurons to schizophrenia and autism

Conversion of adenosine to inosine in RNA by ADAR enzymes, termed "RNA editing," is essential for healthy brain development. Editing is dysregulated in neuropsychiatric diseases, but has not yet been investigated at scale at the level of individual neurons. We quantified RNA editing sites in nuclear transcriptomes of 3055 neurons from six cortical regions of a neurotypical female donor, and found 41,930 sites present in at least ten nuclei. Most sites were located within Alu repeats in introns or 3' UTRs, and approximately 80% were cataloged in public RNA editing databases. We identified 9285 putative novel editing sites, 29% of which were also detectable in unrelated donors. Intersection with results from bulk RNA-seq studies provided cell-type and spatial context for 1730 sites that are differentially edited in schizophrenic brain donors, and 910 such sites in autistic donors. Autism-related genes were also enriched with editing sites predicted to modify RNA structure. Inhibitory neurons showed higher overall transcriptome editing than excitatory neurons, and the highest editing rates were observed in the frontal cortex. We used generalized linear models to identify differentially edited sites and genes between cell types. Twenty nine genes were preferentially edited in excitatory neurons, and 43 genes were edited more heavily in inhibitory neurons, including RBFOX1, its target genes, and genes in the autism-associated Prader-Willi locus (15q11). The abundance of SNORD115/116 genes from locus 15q11 was positively associated with editing activity across the transcriptome. We contend that insufficient editing of autism-related genes in inhibitory neurons may contribute to the specific perturbation of those cells in autism.

Association between CDK8 gene polymorphisms and dilated cardiomyopathy in a Chinese Han population

Background

Dilated cardiomyopathy (DCM) is one of the most common types of cardiomyopathies. Various genes have been verified to be related to DCM, but the pathogenesis remains unclear. Cyclin-dependent-kinase 8 (CDK8), encoded by the CDK8 gene, is a transcriptional factor that regulates the phosphorylation of RNA polymerase II. It plays an important role in the transcription process and different signaling pathways. This study aimed to investigate the potential role of CDK8 gene polymorphisms in DCM susceptibility and prognosis in a Chinese Han population.

Methods

Two single nucleotide polymorphisms (SNPs) of CDK8, rs17083838 (A/G) and rs7992670 (A/G), were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in 341 DCM patients and 381 healthy controls. Survival analysis was performed using Kaplan-Meier curves and Cox regression analysis.

Results

The frequencies of allele A of both SNPs rs17083838 and rs7992670 were increased in DCM patients compared to healthy controls (P<0.05). Genotypic frequencies of rs17083838 and rs7992670 were associated with the susceptibility to DCM in the codominant, and recessive models (P<0.05), and AA/AG genotypes of rs17083838 were also related to DCM susceptibility in the dominant model. AA/AG genotypes of rs17083838 and the AA genotype of rs7992670 in the dominant and recessive genetic models presented a correlation with the poor prognosis of DCM patients in both univariate (P<0.05) and multivariate analyses (P<0.05) after adjusting for age, gender, left ventricular end-diastolic diameter (LVEDD), and left ventricular ejection fraction (LVEF).

Conclusions

This research is the first to reveal that CDK8 gene polymorphisms might be related to DCM susceptibility and prognosis in the Chinese Han population.

Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes

Identifying driver genes, exactly from massive genes with mutations, promotes accurate diagnosis and treatment of cancer. In recent years, a lot of works about uncovering driver genes based on integration of mutation data and gene interaction networks is gaining more attention. However, it is in suspense if it is more effective for prioritizing driver genes when integrating various types of mutation information (frequency and functional impact) and gene networks. Hence, we build a two-stage-vote ensemble framework based on somatic mutations and mutual interactions. Specifically, we first represent and combine various kinds of mutation information, which are propagated through networks by an improved iterative framework. The first vote is conducted on iteration results by voting methods, and the second vote is performed to get ensemble results of the first poll for the final driver gene list. Compared with four excellent previous approaches, our method has better performance in identifying driver genes on $33$ types of cancer from The Cancer Genome Atlas. Meanwhile, we also conduct a comparative analysis about two kinds of mutation information, five gene interaction networks and four voting strategies. Our framework offers a new view for data integration and promotes more latent cancer genes to be admitted.

A spotter's guide to SNPtic exons: The common splice variants underlying some SNP-phenotype correlations

BACKGROUND

Cryptic exons are typically characterised as deleterious splicing aberrations caused by deep intronic mutations. However, low-level splicing of cryptic exons is sometimes observed in the absence of any pathogenic mutation. Five recent reports have described how low-level splicing of cryptic exons can be modulated by common single-nucleotide polymorphisms (SNPs), resulting in phenotypic differences amongst different genotypes.

METHODS

We sought to investigate whether additional 'SNPtic' exons may exist, and whether these could provide an explanatory mechanism for some of the genotype-phenotype correlations revealed by genome-wide association studies. We thoroughly searched the literature for reported cryptic exons, cross-referenced their genomic coordinates against the dbSNP database of common SNPs, then screened out SNPs with no reported phenotype associations.

RESULTS

This method discovered five probable SNPtic exons in the genes APC, FGB, GHRL, MYPBC3 and OTC. For four of these five exons, we observed that the phenotype associated with the SNP was compatible with the predicted splicing effect of the nucleotide change, whilst the fifth (in GHRL) likely had a more complex splice-switching effect.

CONCLUSION

Application of our search methods could augment the knowledge value of future cryptic exon reports and aid in generating better hypotheses for genome-wide association studies.

Characterizing RNA Pseudouridylation by Convolutional Neural Networks

Pseudouridine (Ψ) is the most prevalent post-transcriptional RNA modification and is widespread in small cellular RNAs and mRNAs. However, the functions, mechanisms, and precise distribution of Ψs (especially in mRNAs) still remain largely unclear. The landscape of Ψs across the transcriptome has not yet been fully delineated. Here, we present a highly effective model based on a convolutional neural network (CNN), called PseudoUridyLation Site Estimator (PULSE), to analyze large-scale profiling data of Ψ sites and characterize the contextual sequence features of pseudouridylation. PULSE, consisting of two alternatively-stacked convolution and pooling layers followed by a fully-connected neural network, can automatically learn the hidden patterns of pseudouridylation from the local sequence information. Extensive validation tests demonstrated that PULSE can outperform other state-of-the-art prediction methods and achieve high prediction accuracy, thus enabling us to further characterize the transcriptome-wide landscape of Ψ sites. We further showed that the prediction results derived from PULSE can provide novel insights into understanding the functional roles of pseudouridylation, such as the regulations of RNA secondary structure, codon usage, translation, and RNA stability, and the connection to single nucleotide variants. The source code and final model for PULSE are available at https://github.com/mlcb-thu/PULSE.

A systematic view of computational methods for identifying driver genes based on somatic mutation data

Abnormal changes of driver genes are serious for human health and biomedical research. Identifying driver genes, exactly from enormous genes with mutations, promotes accurate diagnosis and treatment of cancer. A lot of works about uncovering driver genes have been developed over the past decades. By analyzing previous works, we find that computational methods are more efficient than traditional biological experiments when distinguishing driver genes from massive data. In this study, we summarize eight common computational algorithms only using somatic mutation data. We first group these methods into three categories according to mutation features they apply. Then, we conclude a general process of nominating candidate cancer driver genes. Finally, we evaluate three representative methods on 10 kinds of cancer derived from The Cancer Genome Atlas Program and five Chinese projects from the International Cancer Genome Consortium. In addition, we compare results of methods with various parameters. Evaluation is performed from four perspectives, including CGC, OG/TSG, Q-value and QQQuantile-Quantileplot. To sum up, we present algorithms using somatic mutation data in order to offer a systematic view of various mutation features and lay the foundation of methods based on integration of mutation information and other types of data.

Variants in LSM7 impair LSM complexes assembly, neurodevelopment in zebrafish and may be associated with an ultra-rare neurological disease

Leukodystrophies, genetic neurodevelopmental and/or neurodegenerative disorders of cerebral white matter, result from impaired myelin homeostasis and metabolism. Numerous genes have been implicated in these heterogeneous disorders; however, many individuals remain without a molecular diagnosis. Using whole-exome sequencing, biallelic variants in LSM7 were uncovered in two unrelated individuals, one with a leukodystrophy and the other who died in utero. LSM7 is part of the two principle LSM protein complexes in eukaryotes, namely LSM1-7 and LSM2-8. Here, we investigate the molecular and functional outcomes of these LSM7 biallelic variants in vitro and in vivo. Affinity purification-mass spectrometry of the LSM7 variants showed defects in the assembly of both LSM complexes. Lsm7 knockdown in zebrafish led to central nervous system defects, including impaired oligodendrocyte development and motor behavior. Our findings demonstrate that variants in LSM7 cause misassembly of the LSM complexes, impair neurodevelopment of the zebrafish, and may be implicated in human disease. The identification of more affected individuals is needed before the molecular mechanisms of mRNA decay and splicing regulation are added to the categories of biological dysfunctions implicated in leukodystrophies, neurodevelopmental and/or neurodegenerative diseases.

Mistranslation Drives Alterations in Protein Levels and the Effects of a Synonymous Variant at the Fibroblast Growth Factor 21 Locus

Fibroblast growth factor 21 (FGF21) is a liver-derived hormone with pleiotropic beneficial effects on metabolism. Paradoxically, FGF21 levels are elevated in metabolic diseases. Interventions that restore metabolic homeostasis reduce FGF21. Whether abnormalities in FGF21 secretion or resistance in peripheral tissues is the initiating factor in altering FGF21 levels and function in humans is unknown. A genetic approach is used to help resolve this paradox. The authors demonstrate that the primary event in dysmetabolic phenotypes is the elevation of FGF21 secretion. The latter is regulated by translational reprogramming in a genotype- and context-dependent manner. To relate the findings to tissues outcomes, the minor (A) allele of rs838133 is shown to be associated with increased hepatic inflammation in patients with metabolic associated fatty liver disease. The results here highlight a dominant role for translation of the FGF21 protein to explain variations in blood levels that is at least partially inherited. These results provide a framework for translational reprogramming of FGF21 to treat metabolic diseases.

Relationship between P2XR4 Gene Variants and the Risk of Schizophrenia in South-East of Iran: A Preliminary Case-Control Study and in Silico Analysis

Background:

Schizophrenia (SZN) is a heterogeneous disorder. Recently, the role of purinergic receptor’s signaling in mental disorders has implicated. There is no evidence regarding the association of P2XR4 single nucleotide polymorphisms (SNPs) and the risk of behavioral disorders. Therefore, this preliminary study, we determined the association of rs1169727A/G and rs25644A/G variants located in P2XR4 gene with the risk of SZN.

Methods:

This case-control study was performed on 150 SZN patient referring to Baharan Hospital, Zahedan (Eastern of Iran) in 2018. Genotyping was done by tetra-amplification refractory mutation system polymerase chain reaction (Tetra ARMS-PCR). Different databases were used to determine the effects of the SNPs on the secondary structure of P2XR4 pre-mRNA and protein as well as binding of transcriptional regulators.

Results:

The G allele of rs1169727 significantly increased the risk of SZN (OR=1.41, 95%CI=1.02–1.93, P=0.039), but there was no significant association was found between the other SNP and SZN. Moreover, GG model of rs1169727 (OR=2.46, 95%CI= 1.32–4.62, P=0.004) and rs25644 (OR=3.45, 95%CI= 1.12–5.10, P=0.013) increased the risk of SZN. The substitution of A and G alleles of rs1169727 significantly altered the secondary structure of pre-mRNA (P=0.1). In silico analysis revealed that rs25644A/G could act as an intronic cryptic donor site. Screening for flanking sequence of rs1169727A/G and rs25644A/G predicted a novel enhancer and silencer for both SNPs.

Conclusion:

rs1169727A/G and rs25644A/G are linked to SZN susceptibility in a sample of the Iranian population. In-silico analysis indicated that rs25644 have substantial roles in determining the pre-mRNA and protein structure of P2XR4 gene.

Secondary Structural Model of MALAT1 Becomes Unstructured in Chronic Myeloid Leukemia and Undergoes Structural Rearrangement in Cervical Cancer

Long noncoding RNAs (lncRNAs) influence cellular function through binding events that often depend on the lncRNA secondary structure. One such lncRNA, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), is upregulated in many cancer types and has a myriad of protein- and miRNA-binding sites. Recently, a secondary structural model of MALAT1 in noncancerous cells was proposed to form 194 hairpins and 13 pseudoknots. That study postulated that, in cancer cells, the MALAT1 structure likely varies, thereby influencing cancer progression. This work analyzes how that structural model is expected to change in K562 cells, which originated from a patient with chronic myeloid leukemia (CML), and in HeLa cells, which originated from a patient with cervical cancer. Dimethyl sulfate-sequencing (DMS-Seq) data from K562 cells and psoralen analysis of RNA interactions and structure (PARIS) data from HeLa cells were compared to the working structural model of MALAT1 in noncancerous cells to identify sites that likely undergo structural alterations. MALAT1 in K562 cells is predicted to become more unstructured, with almost 60% of examined hairpins in noncancerous cells losing at least half of their base pairings. Conversely, MALAT1 in HeLa cells is predicted to largely maintain its structure, undergoing 18 novel structural rearrangements. Moreover, 50 validated miRNA-binding sites are affected by putative secondary structural changes in both cancer types, such as miR-217 in K562 cells and miR-20a in HeLa cells. Structural changes unique to K562 cells and HeLa cells provide new mechanistic leads into how the structure of MALAT1 may mediate cancer in a cell-type specific manner.

Association of Long Non-Coding RNA Polymorphisms with Gastric Cancer and Atrophic Gastritis

Long non-coding RNAs (lncRNA) play an important role in the carcinogenesis of various tumours, including gastric cancer. This study aimed to assess the associations of lncRNA ANRIL, H19, MALAT1, MEG3, HOTAIR single-nucleotide polymorphisms (SNPs) with gastric cancer and atrophic gastritis. SNPs were analyzed in 613 gastric cancer patients, 118 patients with atrophic gastritis and 476 controls from three tertiary centers in Germany, Lithuania and Latvia. Genomic DNA was extracted from peripheral blood leukocytes. SNPs were genotyped by the real-time polymerase chain reaction. Results showed that carriers of MALAT1 rs3200401 CT genotype had the significantly higher odds of atrophic gastritis than those with CC genotype (OR-1.81; 95% CI 1.17–2.80, p = 0.0066). Higher odds of AG were found in a recessive model (CC vs. TT + CT) for ANRIL rs1333045 (OR-1.88; 95% CI 1.19–2.95, p = 0.0066). Carriers of ANRIL (rs17694493) GG genotype had higher odds of gastric cancer (OR-4.93; 95% CI 1.28–19.00) and atrophic gastritis (OR-5.11; 95% CI 1.10–23.80) compared with the CC genotype, and carriers of HOTAIR rs17840857 TG genotype had higher odds of atrophic gastritis (OR-1.61 95% CI 1.04–2.50) compared with the TT genotype; however, the ORs did not reach the adjusted significance threshold (p < 0.007). In summary, our data provide novel evidence for a possible link between lncRNA SNPs and premalignant condition of gastric cancer, suggesting the involvement of lncRNAs in gastric cancer development.

Computational methods for detecting cancer hotspots

Cancer mutations that are recurrently observed among patients are known as hotspots. Hotspots are highly relevant because they are, presumably, likely functional. Known hotspots in BRAF, PIK3CA, TP53, KRAS, IDH1 support this idea. However, hundreds of hotspots have never been validated experimentally. The detection of hotspots nevertheless is challenging because background mutations obscure their statistical and computational identification. Although several algorithms have been applied to identify hotspots, they have not been reviewed before. Thus, in this mini-review, we summarize more than 40 computational methods applied to detect cancer hotspots in coding and non-coding DNA. We first organize the methods in cluster-based, 3D, position-specific, and miscellaneous to provide a general overview. Then, we describe their embed procedures, implementations, variations, and differences. Finally, we discuss some advantages, provide some ideas for future developments, and mention opportunities such as application to viral integrations, translocations, and epigenetics.

LncRBase V.2: an updated resource for multispecies lncRNAs and ClinicLSNP hosting genetic variants in lncRNAs for cancer patients

The recent discovery of long non-coding RNA as a regulatory molecule in the cellular system has altered the concept of the functional aptitude of the genome. Since our publication of the first version of LncRBase in 2014, there has been an enormous increase in the number of annotated lncRNAs of multiple species other than Human and Mouse. LncRBase V.2 hosts information of 549,648 lncRNAs corresponding to six additional species besides Human and Mouse, viz. Rat, Fruitfly, Zebrafish, Chicken, Cow and C.elegans. It provides additional distinct features such as (i) Transcription Factor Binding Site (TFBS) in the lncRNA promoter region, (ii) sub-cellular localization pattern of lncRNAs (iii) lnc-pri-miRNAs (iv) Possible small open reading frames (sORFs) within lncRNA. (v) Manually curated information of interacting target molecules and disease association of lncRNA genes (vi) Distribution of lncRNAs across multiple tissues of all species. Moreover, we have hosted ClinicLSNP within LncRBase V.2. ClinicLSNP has a comprehensive catalogue of lncRNA variants present within breast, ovarian, and cervical cancer inferred from 561 RNA-Seq data corresponding to these cancers. Further, we have checked whether these lncRNA variants overlap with (i)Repeat elements,(ii)CGI, (iii)TFBS within lncRNA loci (iv)SNP localization in trait-associated Linkage Disequilibrium(LD) region, (v)predicted the potentially pathogenic variants and (vi)effect of SNP on lncRNA secondary structure. Overall, LncRBaseV.2 is a user-friendly database to survey, search and retrieve information about multi-species lncRNAs. Further, ClinicLSNP will serve as a useful resource for cancer specific lncRNA variants and their related information. The database is freely accessible and available at http://dibresources.jcbose.ac.in/zhumur/lncrbase2/.

An in silico approach to identify and prioritize miRNAs target sites polymorphisms in colorectal cancer and obesity

Colorectal cancer (CRC) and obesity are linked clinical entities with a series of complex processes being engaged in their development. MicroRNAs (miRNAs) participate in these processes through regulating CRC and obesity‐related genes. This study aimed to develop an in silico approach to systematically identify and prioritize miRNAs target sites polymorphisms in obesity and CRC. Data from genome‐wide association studies (GWASs) were used to retrieve CRC and obesity‐associated variants. The polymorphisms that were resided in experimentally verified or computationally predicted miRNA target sites were retrieved and prioritized using a range of bioinformatics analyses. We found 6284 CRC and 38931 obesity unique variants. For CRC 33 haplotypes variants in 134 interactions were in miRNA targetome, while for obesity we found more than 935 unique interactions. Functionally prioritized SNPs revealed that, SNPs in 153 obesity and 50 CRC unique interactions were have disruptive effects on miRNA:mRNA integration by changing on target RNA secondary structure. Structural accessibility of target sites were decreased in 418 and 103 unique interactions and increased in 516 and 79 interactions, for obesity and CRC, respectively. The miRNA:mRNA hybrid stability was increased in 127 and 17 unique interactions and decreased in 33 and 24 interactions for the effect of obesity and CRC SNPs, respectively. In this study, seven SNPs with 15 interactions and three SNPs with four interactions were prioritized for obesity and CRC, respectively. These SNPs could be used for future studies for finding potential biomarkers for diagnoses, prognosis, or treatment of CRC and obesity.

MutaRNA: analysis and visualization of mutation-induced changes in RNA structure

RNA molecules fold into complex structures as a result of intramolecular interactions between their nucleotides. The function of many non-coding RNAs and some cis-regulatory elements of messenger RNAs highly depends on their fold. Single-nucleotide variants (SNVs) and other types of mutations can disrupt the native function of an RNA element by altering its base pairing pattern. Identifying the effect of a mutation on an RNA’s structure is, therefore, a crucial step in evaluating the impact of mutations on the post-transcriptional regulation and function of RNAs within the cell. Even though a single nucleotide variation can have striking impacts on the structure formation, interpreting and comparing the impact usually needs expertise and meticulous efforts. Here, we present MutaRNA, a web server for visualization and interpretation of mutation-induced changes on the RNA structure in an intuitive and integrative fashion. To this end, probabilities of base pairing and position-wise unpaired probabilities of wildtype and mutated RNA sequences are computed and compared. Differential heatmap-like dot plot representations in combination with circular plots and arc diagrams help to identify local structure abberations, which are otherwise hidden in standard outputs. Eventually, MutaRNA provides a comprehensive and comparative overview of the mutation-induced changes in base pairing potentials and accessibility. The MutaRNA web server is freely available at http://rna.informatik.uni-freiburg.de/MutaRNA.

Identification and analysis of RNA structural disruptions induced by single nucleotide variants using Riprap and RiboSNitchDB

RNA conformational alteration has significant impacts on cellular processes and phenotypic variations. An emerging genetic factor of RNA conformational alteration is a new class of single nucleotide variant (SNV) named riboSNitch. RiboSNitches have been demonstrated to be involved in many genetic diseases. However, identifying riboSNitches is notably difficult as the signals of RNA structural disruption are often subtle. Here, we introduce a novel computational framework–RIboSNitch Predictor based on Robust Analysis of Pairing probabilities (Riprap). Riprap identifies structurally disrupted regions around any given SNVs based on robust analysis of local structural configurations between wild-type and mutant RNA sequences. Compared to previous approaches, Riprap shows higher accuracy when assessed on hundreds of known riboSNitches captured by various experimental RNA structure probing methods including the parallel analysis of RNA structure (PARS) and the selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE). Further, Riprap detects the experimentally validated riboSNitch that regulates human catechol-O-methyltransferase haplotypes and outputs structurally disrupted regions precisely at base resolution. Riprap provides a new approach to interpreting disease-related genetic variants. In addition, we construct a database (RiboSNitchDB) that includes the annotation and visualization of all presented riboSNitches in this study as well as 24 629 predicted riboSNitches from human expression quantitative trait loci.

Implications of SARS-CoV-2 Mutations for Genomic RNA Structure and Host microRNA Targeting

The SARS-CoV-2 virus is a recently-emerged zoonotic pathogen already well adapted to transmission and replication in humans. Although the mutation rate is limited, recently introduced mutations in SARS-CoV-2 have the potential to alter viral fitness. In addition to amino acid changes, mutations could affect RNA secondary structure critical to viral life cycle, or interfere with sequences targeted by host miRNAs. We have analysed subsets of genomes from SARS-CoV-2 isolates from around the globe and show that several mutations introduce changes in Watson-Crick pairing, with resultant changes in predicted secondary structure. Filtering to targets matching miRNAs expressed in SARS-CoV-2-permissive host cells, we identified ten separate target sequences in the SARS-CoV-2 genome; three of these targets have been lost through conserved mutations. A genomic site targeted by the highly abundant miR-197-5p, overexpressed in patients with cardiovascular disease, is lost by a conserved mutation. Our results are compatible with a model that SARS-CoV-2 replication within the human host is constrained by host miRNA defences. The impact of these and further mutations on secondary structures, miRNA targets or potential splice sites offers a new context in which to view future SARS-CoV-2 evolution, and a potential platform for engineering conditional attenuation to vaccine development, as well as providing a better understanding of viral tropism and pathogenesis.

Variation Profile of the Orthotospovirus Genome

Orthotospoviruses are plant-infecting members of the family Tospoviridae (order Bunyavirales), have a broad host range and are vectored by polyphagous thrips in a circulative-propagative manner. Because diverse hosts and vectors impose heterogeneous selection constraints on viral genomes, the evolutionary arms races between hosts and their pathogens might be manifested as selection for rapid changes in key genes. These observations suggest that orthotospoviruses contain key genetic components that rapidly mutate to mediate host adaptation and vector transmission. Using complete genome sequences, we profiled genomic variation in orthotospoviruses. Results show that the three genomic segments contain hypervariable areas at homologous locations across species. Remarkably, the highest nucleotide variation mapped to the intergenic region of RNA segments S and M, which fold into a hairpin. Secondary structure analyses showed that the hairpin is a dynamic structure with multiple functional shapes formed by stems and loops, contains sites under positive selection and covariable sites. Accumulation and tolerance of mutations in the intergenic region is a general feature of orthotospoviruses and might mediate adaptation to host plants and insect vectors.

Single nucleotide polymorphisms affect RNA-protein interactions at a distance through modulation of RNA secondary structures

Single nucleotide polymorphisms are widely associated with disease, but the ways in which they cause altered phenotypes are often unclear, especially when they appear in non-coding regions. One way in which non-coding polymorphisms could cause disease is by affecting crucial RNA-protein interactions. While it is clear that changing a protein binding motif will alter protein binding, it has been shown that single nucleotide polymorphisms can affect RNA secondary structure, and here we show that single nucleotide polymorphisms can affect RNA-protein interactions from outside binding motifs through altered RNA secondary structure. By using a modified version of the Vienna Package and PAR-CLIP data for HuR (ELAVL1) in humans we characterize the genome-wide effect of single nucleotide polymorphisms on HuR binding and show that they can have a many-fold effect on the affinity of HuR binding to RNA transcripts from tens of bases away. We also find some evidence that the effect of single nucleotide polymorphisms on protein binding might be under selection, with the non-reference alleles tending to make it harder for a protein to bind.

A single nucleotide variant of human PARP1 determines response to PARP inhibitors

The introduction of novel cancer drugs and innovative treatments brings great hope for cancer patients, but also an urgent need to match drugs to suitable patients, since certain drugs that benefit one patient may actually harm others. The newly developed poly-ADP ribose polymerase (PARP) inhibitors (PARPis) are a group of pharmacological enzyme inhibitors used clinically for multiple indications. Several forms of cancer tend to be PARP dependent, making PARP an attractive target for cancer therapy. Specifically, PARPis are commonly used in BRCA-associated breast cancers patients, since unrepaired single-strand breaks are converted into double-strand breaks and BRCA-associated tumors cannot repair them by homologous recombination so that PARPi leads to tumor cell death, by a mechanism called “Synthetic Lethality”. Unfortunately, not all patients respond to PARPi, and it is not currently possible to predict who will or will not respond. Here, we present a specific genomic marker, which reflects a single-nucleotide polymorphism of human PARP1 and correlates in vitro with response to PARPi, throughout all indications. In addition, we report that this SNP is associated with re-shaping mRNA, and mRNA levels, and influences the final protein structure to expose new binding sites while hiding others. The status of the SNP is therefore critical to patients’ care, as it relates responses to PARPi to the PARP1-SNP carried.

Somatic synonymous mutations in regulatory elements contribute to the genetic aetiology of melanoma

Background

Non-synonymous mutations altering tumor suppressor genes and oncogenes are widely studied. However, synonymous mutations, which do not alter the protein sequence, are rarely investigated in melanoma genome studies.

Methods

We explored the role of somatic synonymous mutations in melanoma samples from TCGA (The Cancer Genome Atlas). The pathogenic synonymous mutation and neutral synonymous mutation data were used to assess the significance of pathogenic synonymous mutations in melanoma likely to affect genetic regulatory elements using Fisher’s exact test. Poisson distribution probabilities of each gene were used to mine the genes with multiple potential functional synonymous mutations affecting regulatory elements.

Results

Concentrating on five types of genetic regulatory functions, we found that the mutational patterns of pathogenic synonymous mutations are mostly involved in exonic splicing regulators in near-splicing sites or inside DNase I hypersensitivity sites or non-optimal codon. Moreover, the sites of miRNA binding alteration exhibit a significantly lower rate of evolution than other sites. Finally, 12 genes were hit by recurrent potentially functional synonymous mutations, which showed statistical significance in the pathogenic mutations. Among them, nine genes (DNAH5, ADCY8, GRIN2A, KSR2, TECTA, RIMS2, XKR6, MYH1, SCN10A) have been reported to be mutated in melanoma, and other three genes (SLC9A2, CASR, SLC8A3) have a great potential to impact melanoma.

Conclusion

These findings confirm the functional consequences of somatic synonymous mutations in melanoma, emphasizing the significance of research in future studies.

lncRNASNP2: an updated database of functional SNPs and mutations in human and mouse lncRNAs

Long non-coding RNAs (lncRNAs) are emerging as important regulators in different biological processes through various ways. Because the related data, especially mutations in cancers, increased sharply, we updated the lncRNASNP to version 2 (http://bioinfo.life.hust.edu.cn/lncRNASNP2). lncRNASNP2 provides comprehensive information of SNPs and mutations in lncRNAs, as well as their impacts on lncRNA structure and function. lncRNASNP2 contains 7260238 SNPs on 141353 human lncRNA transcripts and 3921448 SNPs on 117405 mouse lncRNA transcripts. Besides the SNP information in the first version, the following new features were developed to improve the lncRNASNP2. (i) noncoding variants from COSMIC cancer data (859534) in lncRNAs and their effects on lncRNA structure and function; (ii) TCGA cancer mutations (315234) in lncRNAs and their impacts; (iii) lncRNA expression profiling of 20 cancer types in both tumor and its adjacent samples; (iv) expanded lncRNA-associated diseases; (v) optimized the results about lncRNAs structure change induced by variants; (vi) reduced false positives in miRNA and lncRNA interaction results. Furthermore, we developed online tools for users to analyze new variants in lncRNA. We aim to maintain the lncRNASNP as a useful resource for lncRNAs and their variants.