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

Abstract 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.

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A gain-of-function single nucleotide variant creates a new promoter which acts as an orientation-dependent enhancer-blocker

Nature Communications ◽

10.1038/s41467-021-23980-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Yavor K. Bozhilov ◽

Damien J. Downes ◽

Jelena Telenius ◽

A. Marieke Oudelaar ◽

Emmanuel N. Olivier ◽

...

Keyword(s):

Gene Expression ◽

Genetic Diseases ◽

Regulatory Elements ◽

Base Change ◽

Dependent Manner ◽

Globin Genes ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Super Enhancer ◽

Single Base Change

AbstractMany single nucleotide variants (SNVs) associated with human traits and genetic diseases are thought to alter the activity of existing regulatory elements. Some SNVs may also create entirely new regulatory elements which change gene expression, but the mechanism by which they do so is largely unknown. Here we show that a single base change in an otherwise unremarkable region of the human α-globin cluster creates an entirely new promoter and an associated unidirectional transcript. This SNV downregulates α-globin expression causing α-thalassaemia. Of note, the new promoter lying between the α-globin genes and their associated super-enhancer disrupts their interaction in an orientation-dependent manner. Together these observations show how both the order and orientation of the fundamental elements of the genome determine patterns of gene expression and support the concept that active genes may act to disrupt enhancer-promoter interactions in mammals as in Drosophila. Finally, these findings should prompt others to fully evaluate SNVs lying outside of known regulatory elements as causing changes in gene expression by creating new regulatory elements.

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RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Genome Biology ◽

10.1186/s13059-019-1847-4 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 5

Author(s):

Hai Lin ◽

Katherine A. Hargreaves ◽

Rudong Li ◽

Jill L. Reiter ◽

Yue Wang ◽

...

Keyword(s):

Rna Splicing ◽

Evolutionary Conservation ◽

Random Forest Classifier ◽

Training Data ◽

Reporter Assay ◽

Single Nucleotide Variants ◽

Excellent Performance ◽

Computational Framework ◽

Single Nucleotide ◽

The Impact

AbstractSingle nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.

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Reference exome data for a Northern Brazilian population

Scientific Data ◽

10.1038/s41597-020-00703-y ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Alexia L. Weeks ◽

Richard W. Francis ◽

Joao I. C. F. Neri ◽

Nathaly M. C. Costa ◽

Nivea M. R. Arrais ◽

...

Keyword(s):

Rare Diseases ◽

Sequence Data ◽

Genetic Diseases ◽

Specific Reference ◽

Single Nucleotide Variants ◽

Data Set ◽

Single Nucleotide ◽

Rare Genetic Diseases ◽

Genetics And Genomics ◽

Brazilian Cohort

Abstract Exome sequencing is widely used in the diagnosis of rare genetic diseases and provides useful variant data for analysis of complex diseases. There is not always adequate population-specific reference data to assist in assigning a diagnostic variant to a specific clinical condition. Here we provide a catalogue of variants called after sequencing the exomes of 45 babies from Rio Grande do Nord in Brazil. Sequence data were processed using an ‘intersect-then-combine’ (ITC) approach, using GATK and SAMtools to call variants. A total of 612,761 variants were identified in at least one individual in this Brazilian Cohort, including 559,448 single nucleotide variants (SNVs) and 53,313 insertion/deletions. Of these, 58,111 overlapped with nonsynonymous (nsSNVs) or splice site (ssSNVs) SNVs in dbNSFP. As an aid to clinical diagnosis of rare diseases, we used the American College of Medicine Genetics and Genomics (ACMG) guidelines to assign pathogenic/likely pathogenic status to 185 (0.32%) of the 58,111 nsSNVs and ssSNVs. Our data set provides a useful reference point for diagnosis of rare diseases in Brazil. (169 words).

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

Nucleic Acids Research ◽

10.1093/nar/gkaa331 ◽

2020 ◽

Vol 48 (W1) ◽

pp. W287-W291

Author(s):

Milad Miladi ◽

Martin Raden ◽

Sven Diederichs ◽

Rolf Backofen

Keyword(s):

Rna Structure ◽

Web Server ◽

Regulatory Elements ◽

Intramolecular Interactions ◽

Messenger Rnas ◽

Base Pairing ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Induced Changes ◽

The Impact

Abstract 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.

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Dynamic Molecular Epidemiology Reveals Lineage-Associated Single-Nucleotide Variants That Alter RNA Structure in Chikungunya Virus

Genes ◽

10.3390/genes12020239 ◽

2021 ◽

Vol 12 (2) ◽

pp. 239

Author(s):

Thomas Spicher ◽

Markus Delitz ◽

Adriano de Bernardi Schneider ◽

Michael T. Wolfinger

Keyword(s):

Molecular Epidemiology ◽

South African ◽

Rna Structure ◽

Chikungunya Virus ◽

Rna Structures ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Web Based ◽

The Stability ◽

Human Infections

Chikungunya virus (CHIKV) is an emerging Alphavirus which causes millions of human infections every year. Outbreaks have been reported in Africa and Asia since the early 1950s, from three CHIKV lineages: West African, East Central South African, and Asian Urban. As new outbreaks occurred in the Americas, individual strains from the known lineages have evolved, creating new monophyletic groups that generated novel geographic-based lineages. Building on a recently updated phylogeny of CHIKV, we report here the availability of an interactive CHIKV phylodynamics dataset, which is based on more than 900 publicly available CHIKV genomes. We provide an interactive view of CHIKV molecular epidemiology built on Nextstrain, a web-based visualization framework for real-time tracking of pathogen evolution. CHIKV molecular epidemiology reveals single nucleotide variants that change the stability and fold of locally stable RNA structures. We propose alternative RNA structure formation in different CHIKV lineages by predicting more than a dozen RNA elements that are subject to perturbation of the structure ensemble upon variation of a single nucleotide.

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The integrity of the U12 snRNA 3′ stem–loop is necessary for its overall stability

Nucleic Acids Research ◽

10.1093/nar/gkab048 ◽

2021 ◽

Vol 49 (5) ◽

pp. 2835-2847

Author(s):

Antto J Norppa ◽

Mikko J Frilander

Keyword(s):

Steady State ◽

Early Onset ◽

Human Population ◽

Genetic Diseases ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Stem Loop ◽

Snrna Gene ◽

Small Nuclear Rnas ◽

Overall Stability

Abstract Disruption of minor spliceosome functions underlies several genetic diseases with mutations in the minor spliceosome-specific small nuclear RNAs (snRNAs) and proteins. Here, we define the molecular outcome of the U12 snRNA mutation (84C>U) resulting in an early-onset form of cerebellar ataxia. To understand the molecular consequences of the U12 snRNA mutation, we created cell lines harboring the 84C>T mutation in the U12 snRNA gene (RNU12). We show that the 84C>U mutation leads to accelerated decay of the snRNA, resulting in significantly reduced steady-state U12 snRNA levels. Additionally, the mutation leads to accumulation of 3′-truncated forms of U12 snRNA, which have undergone the cytoplasmic steps of snRNP biogenesis. Our data suggests that the 84C>U-mutant snRNA is targeted for decay following reimport into the nucleus, and that the U12 snRNA fragments are decay intermediates that result from the stalling of a 3′-to-5′ exonuclease. Finally, we show that several other single-nucleotide variants in the 3′ stem-loop of U12 snRNA that are segregating in the human population are also highly destabilizing. This suggests that the 3′ stem-loop is important for the overall stability of the U12 snRNA and that additional disease-causing mutations are likely to exist in this region.

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DeepSVP: Integration of genotype and phenotype for structural variant prioritization using deep learning

10.1101/2021.01.28.428557 ◽

2021 ◽

Author(s):

Azza Althagafi ◽

Lamia Alsubaie ◽

Nagarajan Kathiresan ◽

Katsuhiko Mineta ◽

Taghrid Aloraini ◽

...

Keyword(s):

Research Group ◽

Genetic Diseases ◽

Computational Method ◽

Structural Variants ◽

Single Nucleotide Variants ◽

Structural Genomic ◽

Single Nucleotide ◽

Coding Regions ◽

Molecular Features ◽

Gene Functions

AbstractMotivationStructural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in different ways from single nucleotide variants. Interpreting the phenotypic consequences of structural variants relies on information about gene functions, haploinsufficiency or triplosensitivity, and other genomic features. Phenotype-based methods to identifying variants that are involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been applied successfully to single nucleotide variants, as well as short insertions and deletions, the complexity of structural variants makes it more challenging to link them to phenotypes. Furthermore, structural variants can affect a large number of coding regions, and phenotype information may not be available for all of them.ResultsWe developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic information with information about gene functions. We incorporate phenotypes linked to genes, functions of gene products, gene expression in individual celltypes, and anatomical sites of expression, and systematically relate them to their phenotypic consequences through ontologies and machine learning. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.Availabilityhttps://github.com/bio-ontology-research-group/[email protected]

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False Alarms in Consumer Genomics Add to Public Fear and Potential Health Care Burden

Journal of Personalized Medicine ◽

10.3390/jpm10040187 ◽

2020 ◽

Vol 10 (4) ◽

pp. 187

Author(s):

Xiaoming Liu ◽

Deborah Cragun ◽

Jinyong Pang ◽

Swamy R. Adapa ◽

Renee Fonseca ◽

...

Keyword(s):

Genetic Diseases ◽

False Alarms ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Potential Health ◽

Single Nucleotide ◽

Direct To Consumer ◽

Success Stories ◽

Health Care Burden ◽

Genotyping Microarray

We have entered an era of direct-to-consumer (DTC) genomics. Patients have relayed many success stories of DTC genomics about finding causal mutations of genetic diseases before showing any symptoms and taking precautions. However, consumers may also take unnecessary medical actions based on false alarms of “pathogenic alleles”. The severity of this problem is not well known. Using publicly available data, we compared DTC microarray genotyping data with deep-sequencing data of 5 individuals and manually checked each inconsistently reported single nucleotide variants (SNVs). We estimated that, on average, a person would have ~5 “pathogenic” alleles reported due to wrongly reported genotypes if using a 23andMe genotyping microarray. We also found that the number of wrongly classified “pathogenic” alleles per person is at least as significant as those due to wrongly reported genotypes. We show that the scale of the false alarm problem could be large enough that the medical costs will become a burden to public health.

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Identification and Characterization of Splicing Defects by Single-Molecule Real-Time Sequencing Technology (PacBio)

Journal of Neuromuscular Diseases ◽

10.3233/jnd-200523 ◽

2020 ◽

Vol 7 (4) ◽

pp. 477-481

Author(s):

Marco Savarese ◽

Talha Qureshi ◽

Annalaura Torella ◽

Pia Laine ◽

Teresa Giugliano ◽

...

Keyword(s):

Real Time ◽

Single Molecule ◽

Genetic Diseases ◽

Disease Genes ◽

Test Results ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Dna Test ◽

Splicing Defects

Although DNA-sequencing is the most effective procedure to achieve a molecular diagnosis in genetic diseases, complementary RNA analyses are often required. Reverse-Transcription polymerase chain reaction (RT-PCR) is still a valuable option when the clinical phenotype and/or available DNA-test results address the diagnosis toward a gene of interest or when the splicing effect of a single variant needs to be assessed. We use Single-Molecule Real-Time sequencing to detect and characterize splicing defects and single nucleotide variants in well-known disease genes (DMD, NF1, TTN). After proper optimization, the procedure could be used in the diagnostic setting, simplifying the workflow of cDNA analysis.

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