Nucleotide sequence composition adjacent to intronic splice sites improves splicing efficiency via its effect on pre-mRNA local folding in fungi

Impact on splicing in Saccharomyces cerevisiae of random 50-base sequences inserted into an intron

Intron splicing is a key regulatory step in gene expression in eukaryotes. Three sequence elements required for splicing-5' and 3' splice sites and a branchpoint-are especially well-characterized in Saccharomyces cerevisiae, but our understanding of additional intron features that impact splicing in this organism is incomplete, due largely to its small number of introns. To overcome this limitation, we constructed a library in S. cerevisiae of random 50-nt (N50) elements individually inserted into the intron of a reporter gene and quantified canonical splicing and the use of cryptic splice sites by sequencing analysis. More than 70% of approximately 140,000 N50 elements reduced splicing by at least 20%. N50 features, including higher GC content, presence of GU repeats, and stronger predicted secondary structure of its pre-mRNA, correlated with reduced splicing efficiency. A likely basis for the reduced splicing of such a large proportion of variants is the formation of RNA structures that pair N50 bases-such as the GU repeats-with other bases specifically within the reporter pre-mRNA analyzed. However, multiple models were unable to explain more than a small fraction of the variance in splicing efficiency across the library, suggesting that complex nonlinear interactions in RNA structures are not accurately captured by RNA structure prediction methods. Our results imply that the specific context of a pre-mRNA may determine the bases allowable in an intron to prevent secondary structures that reduce splicing. This large data set can serve as a resource for further exploration of splicing mechanisms.

Long-range RNA structures in the human transcriptome beyond evolutionarily conserved regions

RNA structure has been increasingly recognized as a critical player in the biogenesis and turnover of many transcripts classes. In eukaryotes, the prediction of RNA structure by thermodynamic modeling meets fundamental limitations due to the large sizes and complex, discontinuous organization of eukaryotic genes. Signatures of functional RNA structures can be found by detecting compensatory substitutions in homologous sequences, but a comparative approach is applicable only within conserved sequence blocks. Here, we developed a computational pipeline called PHRIC, which is not limited to conserved regions and relies on RNA contacts derived from RNA in situ conformation sequencing (RIC-seq) experiments. It extracts pairs of short RNA fragments surrounded by nested clusters of RNA contacts and predicts long, nearly perfect complementary base pairings formed between these fragments. In application to a panel of RIC-seq experiments in seven human cell lines, PHRIC predicted ~12,000 stable long-range RNA structures with equilibrium free energy below -15 kcal/mol, the vast majority of which fall outside of regions annotated as conserved among vertebrates. These structures, nevertheless, show some level of sequence conservation and remarkable compensatory substitution patterns in other clades. Furthermore, we found that introns have a higher propensity to form stable long-range RNA structures between each other, and moreover that RNA structures tend to concentrate within the same intron rather than connect adjacent introns. These results for the first time extend the application of proximity ligation assays to RNA structure prediction beyond conserved regions.

Genomic Analysis of Multiplex Consanguineous Families Reveals Causes of Neurodevelopmental Disorders with Epilepsy

INTRODUCTION

Neurodevelopmental disorders (NDD) are a diverse group of disorders that affect the development of the nervous system. Epilepsy is a common phenotypic aspect of NDD.

METHODS

We recruited eight consanguineous families from Pakistan which segregated recessively inherited NDD with epilepsy. Magnetic Resonance imaging (MRI) and Electroencephalogram (EEG) were completed. Exome sequencing was carried out for selected participants from each family. The exome data were analyzed for exonic and splice-site variants that had allele frequencies of less than 0.01 in public databases.

RESULTS

Clinical investigations determined that developmental delay, intellectual disability and seizures were manifested by most patients in early childhood. EEG findings were abnormal in the participants of four families. MRI revealed demyelination orcerebral atrophic changes in multiple participants. We identified four novel homozygous variants including nonsense andmissense variants in OCLN, ALDH7A1, IQSEC2 and COL3A1, segregating with the phenotypes in the participants of four families. Previously reported homozygous variants of CNTNAP2, TRIT1 and NARS1 were found in individuals from three families. Clinical utility was observed in directing treatment in case of patients with an ALDH7A1 variant which included pyridoxine administration and enabling accurate counseling about the natural history and recurrence risk.

CONCLUSION

Our results add to the clinical and molecular delineation of very rare NDD with epilepsy. The high success rate of exome sequencing is likely attributable to the expectation of homozygous variants in patients of consanguineous families, and in one case, the availability of positional mapping data that greatly aided the variant prioritization.

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.

Determinants of associations between codon and amino acid usage patterns of microbial communities and the environment inferred based on a cross-biome metagenomic analysis

Codon and amino acid usage were associated with almost every aspect of microbial life. However, how the environment may impact the codon and amino acid choice of microbial communities at the habitat level is not clearly understood. Therefore, in this study, we analyzed codon and amino acid usage patterns of a large number of environmental samples collected from diverse ecological niches. Our results suggested that samples derived from similar environmental niches, in general, show overall similar codon and amino acid distribution as compared to samples from other habitats. To substantiate the relative impact of the environment, we considered several factors, such as their similarity in GC content, or in functional or taxonomic abundance. Our analysis demonstrated that none of these factors can fully explain the trends that we observed at the codon or amino acid level implying a direct environmental influence on them. Further, our analysis demonstrated different levels of selection on codon bias in different microbial communities with the highest bias in host-associated environments such as the digestive system or oral samples and the lowest level of selection in soil and water samples. Considering a large number of metagenomic samples here we showed that microorganisms collected from similar environmental backgrounds exhibit similar patterns of codon and amino acid usage irrespective of the location or time from where the samples were collected. Thus our study suggested a direct impact of the environment on codon and amino usage of microorganisms that cannot be explained considering the influence of other factors.

Identification of Novel MET Exon 14 Skipping Variants in Non-Small Cell Lung Cancer Patients: A Prototype Workflow Involving in Silico Prediction and RT-PCR

Background and aims: The MET exon 14 skipping (METex14) is an oncogenic driver mutation that provides a therapeutic opportunity in non-small cell lung cancer (NSCLCs) patients. This event often results from sequence changes at the MET canonical splicing sites. We characterize two novel non-canonical splicing site variants of MET that produce METex14. Materials and Methods: Two variants were identified in three advanced-stage NSCLC patients in a next-generation sequencing panel. The potential impact on splicing was predicted using in silico tools. METex14 mutation was confirmed using reverse transcription (RT)-PCR and a Sanger sequencing analysis on RNA extracted from stained cytology smears. Results: The interrogated MET (RefSeq ID NM_000245.3) variants include a single nucleotide substitution, c.3028+3A>T, in intron 14 and a deletion mutation, c.3012_3028del, in exon 14. The in silico prediction analysis exhibited reduced splicing strength in both variants compared with the MET normal transcript. The RT-PCR and subsequent Sanger sequencing analyses confirmed METex14 skipping in all three patients carrying these variants. Conclusion: This study reveals two non-canonical MET splice variants that cause exon 14 skipping, concurrently also proposes a clinical workflow for the classification of such non-canonical splicing site variants detected by routine DNA-based NGS test. It shows the usefulness of in silico prediction to identify potential METex14 driver mutation and exemplifies the opportunity of routine cytology slides for RNA-based testing.

Improved recognition of splice sites in A. thaliana by incorporating secondary structure information into sequence-derived features: a computational study

Identification of splice sites is an important aspect with regard to the prediction of gene structure. In most of the existing splice site prediction studies, machine learning algorithms coupled with sequence-derived features have been successfully employed for splice site recognition. However, the splice site identification by incorporating the secondary structure information is lacking, particularly in plant species. Thus, we made an attempt in this study to evaluate the performance of structural features on the splice site prediction accuracy in Arabidopsis thaliana. Prediction accuracies were evaluated with the sequence-derived features alone as well as by incorporating the structural features into the sequence-derived features, where support vector machine (SVM) was employed as prediction algorithm. Both short (40 base pairs) and long (105 base pairs) sequence datasets were considered for evaluation. After incorporating the secondary structure features, improvements in accuracies were observed only for the longer sequence dataset and the improvement was found to be higher with the sequence-derived features that accounted nucleotide dependencies. On the other hand, either a little or no improvement in accuracies was found for the short sequence dataset. The performance of SVM was further compared with that of LogitBoost, Random Forest (RF), AdaBoost and XGBoost machine learning methods. The prediction accuracies of SVM, AdaBoost and XGBoost were observed to be at par and higher than that of RF and LogitBoost algorithms. While prediction was performed by taking all the sequence-derived features along with the structural features, a little improvement in accuracies was found as compared to the combination of individual sequence-based features and structural features. To the best of our knowledge, this is the first attempt concerning the computational prediction of splice sites using machine learning methods by incorporating the secondary structure information into the sequence-derived features. All the source codes are available at https://github.com/meher861982/SSFeature.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-03036-8.

Identification of cis-regulatory motifs in first introns and the prediction of intron-mediated enhancement of gene expression in Arabidopsis thaliana

BACKGROUND

Intron mediated enhancement (IME) is the potential of introns to enhance the expression of its respective gene. This essential function of introns has been observed in a wide range of species, including fungi, plants, and animals. However, the mechanisms underlying the enhancement are as of yet poorly understood. The goal of this study was to identify potential IME-related sequence motifs and genomic features in first introns of genes in Arabidopsis thaliana.

RESULTS

Based on the rationale that functional sequence motifs are evolutionarily conserved, we exploited the deep sequencing information available for Arabidopsis thaliana, covering more than one thousand Arabidopsis accessions, and identified 81 candidate hexamer motifs with increased conservation across all accessions that also exhibit positional occurrence preferences. Of those, 71 were found associated with increased correlation of gene expression of genes harboring them, suggesting a cis-regulatory role. Filtering further for effect on gene expression correlation yielded a set of 16 hexamer motifs, corresponding to five consensus motifs. While all five motifs represent new motif definitions, two are similar to the two previously reported IME-motifs, whereas three are altogether novel. Both consensus and hexamer motifs were found associated with higher expression of alleles harboring them as compared to alleles containing mutated motif variants as found in naturally occurring Arabidopsis accessions. To identify additional IME-related genomic features, Random Forest models were trained for the classification of gene expression level based on an array of sequence-related features. The results indicate that introns contain information with regard to gene expression level and suggest sequence-compositional features as most informative, while position-related features, thought to be of central importance before, were found with lower than expected relevance.

CONCLUSIONS

Exploiting deep sequencing and broad gene expression information and on a genome-wide scale, this study confirmed the regulatory role on first-introns, characterized their intra-species conservation, and identified a set of novel sequence motifs located in first introns of genes in the genome of the plant Arabidopsis thaliana that may play a role in inducing high and correlated gene expression of the genes harboring them.

Alternative RNA structures formed during transcription depend on elongation rate and modify RNA processing

Most RNA processing occurs co-transcriptionally. We interrogated nascent pol II transcripts by chemical and enzymatic probing and determined how the "nascent RNA structureome" relates to splicing, A-I editing and transcription speed. RNA folding within introns and steep structural transitions at splice sites are associated with efficient co-transcriptional splicing. A slow pol II mutant elicits extensive remodeling into more folded conformations with increased A-I editing. Introns that become more structured at their 3' splice sites get co-transcriptionally excised more efficiently. Slow pol II altered folding of intronic Alu elements where cryptic splicing and intron retention are stimulated, an outcome mimicked by UV, which decelerates transcription. Slow transcription also remodeled RNA folding around alternative exons in distinct ways that predict whether skipping or inclusion is favored, even though it occurs post-transcriptionally. Hence, co-transcriptional RNA folding modulates post-transcriptional alternative splicing. In summary, the plasticity of nascent transcripts has widespread effects on RNA processing.

Exploring Potential Signals of Selection for Disordered Residues in Prokaryotic and Eukaryotic Proteins

Intrinsically disordered proteins (IDPs) are an important class of proteins in all domains of life for their functional importance. However, how nature has shaped the disorder potential of prokaryotic and eukaryotic proteins is still not clearly known. Randomly generated sequences are free of any selective constraints, thus these sequences are commonly used as null models. Considering different types of random protein models, here we seek to understand how the disorder potential of natural eukaryotic and prokaryotic proteins differs from random sequences. Comparing proteome-wide disorder content between real and random sequences of 12 model organisms, we noticed that eukaryotic proteins are enriched in disordered regions compared to random sequences, but in prokaryotes such regions are depleted. By analyzing the position-wise disorder profile, we show that there is a generally higher disorder near the N- and C-terminal regions of eukaryotic proteins as compared to the random models; however, either no or a weak such trend was found in prokaryotic proteins. Moreover, here we show that this preference is not caused by the amino acid or nucleotide composition at the respective sites. Instead, these regions were found to be endowed with a higher fraction of protein–protein binding sites, suggesting their functional importance. We discuss several possible explanations for this pattern, such as improving the efficiency of protein–protein interaction, ribosome movement during translation, and post-translational modification. However, further studies are needed to clearly understand the biophysical mechanisms causing the trend.

ChimeraUGEM: unsupervised gene expression modeling in any given organism

MOTIVATION

Regulation of the amount of protein that is synthesized from genes has proved to be a serious challenge in terms of analysis and prediction, and in terms of engineering and optimization, due to the large diversity in expression machinery across species.

RESULTS

To address this challenge, we developed a methodology and a software tool (ChimeraUGEM) for predicting gene expression as well as adapting the coding sequence of a target gene to any host organism. We demonstrate these methods by predicting protein levels in seven organisms, in seven human tissues, and by increasing in vivo the expression of a synthetic gene up to 26-fold in the single-cell green alga Chlamydomonas reinhardtii. The underlying model is designed to capture sequence patterns and regulatory signals with minimal prior knowledge on the host organism and can be applied to a multitude of species and applications.

AVAILABILITY AND IMPLEMENTATION

Source code (MATLAB, C) and binaries are freely available for download for non-commercial use at http://www.cs.tau.ac.il/~tamirtul/ChimeraUGEM/, and supported on macOS, Linux and Windows.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Widespread non-modular overlapping codes in the coding regions

Messenger RNAs (mRNAs) consist of a coding region (open reading frame (ORF)) and two untranslated regions (UTRs), 5'UTR and 3'UTR. Ribosomes travel along the coding region, translating nucleotide triplets (called codons) to a chain of amino acids. The coding region was long believed to mainly encode the amino acid content of proteins, whereas regulatory signals reside in the UTRs and in other genomic regions. However, in recent years we have learned that the ORF is expansively populated with various regulatory signals, or codes, which are related to all gene expression steps and additional intracellular aspects. In this paper, we review the current knowledge related to overlapping codes inside the coding regions, such as the influence of synonymous codon usage on translation speed (and, in turn, the effect of translation speed on protein folding), ribosomal frameshifting, mRNA stability, methylation, splicing, transcription and more. All these codes come together and overlap in the ORF sequence, ensuring production of the right protein at the right time.

Mating-Type-Specific Ribosomal Proteins Control Aspects of Sexual Reproduction in Cryptococcus neoformans

The MAT locus of Cryptococcus neoformans has a bipolar organization characterized by an unusually large structure, spanning over 100 kb. MAT genes have been characterized by functional genetics as being involved in sexual reproduction and virulence. However, classical gene replacement failed to achieve mutants for five MAT genes (RPL22, RPO41, MYO2, PRT1, and RPL39), indicating that they are likely essential. In the present study, targeted gene replacement was performed in a diploid strain for both the α and a alleles of the ribosomal genes RPL22 and RPL39 Mendelian analysis of the progeny confirmed that both RPL22 and RPL39 are essential for viability. Ectopic integration of the RPL22 allele of opposite MAT identity in the heterozygous RPL22a/rpl22αΔ or RPL22α/rpl22aΔ mutant strains failed to complement their essential phenotype. Evidence suggests that this is due to differential expression of the RPL22 genes, and an RNAi-dependent mechanism that contributes to control RPL22a expression. Furthermore, via CRISPR/Cas9 technology, the RPL22 alleles were exchanged in haploid MATα and MATa strains of C. neoformans These RPL22 exchange strains displayed morphological and genetic defects during bilateral mating. These results contribute to elucidating functions of C. neoformans essential mating type genes that may constitute a type of imprinting system to promote inheritance of nuclei of both mating types.

Codon bias confers stability to human mRNAs

Codon bias has been implicated as one of the major factors contributing to mRNA stability in several model organisms. However, the molecular mechanisms of codon bias on mRNA stability remain unclear in humans. Here, we show that human cells possess a mechanism to modulate RNA stability through a unique codon bias. Bioinformatics analysis showed that codons could be clustered into two distinct groups-codons with G or C at the third base position (GC3) and codons with either A or T at the third base position (AT3): the former stabilizing while the latter destabilizing mRNA. Quantification of codon bias showed that increased GC3-content entails proportionately higher GC-content. Through bioinformatics, ribosome profiling, and in vitro analysis, we show that decoupling the effects of codon bias reveals two modes of mRNA regulation, one GC3- and one GC-content dependent. Employing an immunoprecipitation-based strategy, we identify ILF2 and ILF3 as RNA-binding proteins that differentially regulate global mRNA abundances based on codon bias. Our results demonstrate that codon bias is a two-pronged system that governs mRNA abundance.

Transcriptional profile of a bioethanol production contaminant Candida tropicalis

The fermentation process is widely used in the industry for bioethanol production. Even though it is widely used, microbial contamination is unpredictable and difficult to control. The problem of reduced productivity is directly linked to competition for nutrients during contamination. Yeasts representing the Candida species are frequently isolated contaminants. Elucidating the behavior of a contaminant during the fermentation cycle is essential for combatting the contamination. Consequently, the aim of the current study was to better understand the functional and transcriptional behavior of a contaminating yeast Candida tropicalis. We used a global RNA sequencing approach (RNA-seq/MiSeq) to analyze gene expression. Genes with significantly repressed or induced expression, and related to the fermentations process, such as sugar transport, pyruvate decarboxylase, amino acid metabolism, membrane, tolerance to high concentrations of ethanol and temperatures, nutrient suppression), and transcription-linked processes, were identified. The expression pattern suggested that the functional and transcriptional behavior of the contaminating yeast during fermentation for bioethanol production is similar to that of the standard yeast Saccharomyces cerevisiae. In addition, the analysis confirmed that C. tropicalis is an important contaminant of the alcoholic fermentation process, generating bioethanol and viability through its tolerance to all the adversities of a fermentation process essential for the production of bioethanol. According on the gene expression profile, many of these mechanisms are similar to those of S. cerevisiae strains currently used for bioethanol production. These mechanisms can inform studies on antimicrobials, to combat yeast contamination during industrial bioethanol production.

Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking

Background

The regulation of all gene expression steps (e.g., Transcription, RNA processing, Translation, and mRNA Degradation) is known to be primarily encoded in different parts of genes and in genomic regions in proximity to genes (e.g., promoters, untranslated regions, coding regions, introns, etc.). However, the entire gene expression codes and the genomic regions where they are encoded are still unknown.

Results

Here, we employ an unsupervised approach to estimate the concentration of gene expression codes in different non-coding parts of genes and transcripts, such as introns and untranslated regions, focusing on three model organisms (Escherichia coli, Saccharomyces cerevisiae, and Schizosaccharomyces pombe). Our analyses support the conjecture that regions adjacent to the beginning and end of ORFs and the beginning and end of introns tend to include higher concentration of gene expression information relatively to regions further away. In addition, we report the exact regions with elevated concentration of gene expression codes. Furthermore, we demonstrate that the concentration of these codes in different genetic regions is correlated with the expression levels of the corresponding genes, and with splicing efficiency measurements and meiotic stage gene expression measurements in S. cerevisiae.

Conclusion

We suggest that these discoveries improve our understanding of gene expression regulation and evolution; they can also be used for developing improved models of genome/gene evolution and for engineering gene expression in various biotechnological and synthetic biology applications.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1497-z) contains supplementary material, which is available to authorized users.

On the Ribosomal Density that Maximizes Protein Translation Rate

During mRNA translation, several ribosomes attach to the same mRNA molecule simultaneously translating it into a protein. This pipelining increases the protein translation rate. A natural and important question is what ribosomal density maximizes the protein translation rate. Using mathematical models of ribosome flow along both a linear and a circular mRNA molecules we prove that typically the steady-state protein translation rate is maximized when the ribosomal density is one half of the maximal possible density. We discuss the implications of our results to endogenous genes under natural cellular conditions and also to synthetic biology.

Selection on Position of Nonsense Codons in Introns

Introns occasionally remain in mature messenger RNAs (mRNAs) due to splicing errors and the translated, aberrant proteins that result represent a metabolic cost and may have other deleterious consequences. The nonsense-mediated decay (NMD) pathway degrades aberrant mRNAs, which it recognizes by the presence of an in-frame premature termination codon (PTC). We investigated whether selection has shaped the location of PTCs in introns to reduce waste and facilitate NMD. We found across seven model organisms, that in both first and last introns, PTCs occur earlier in introns than expected by chance, suggesting that selection favors earlier position. This pattern is more pronounced in species with larger effective population sizes. The pattern does not hold for last introns in the two mammal species, however, perhaps because in these species NMD is not initiated from 3'-terminal introns. We conclude that there is compelling evidence that the location of PTCs is shaped by selection for reduced waste and efficient degradation of aberrant mRNAs.

Technology development for natural product biosynthesis in Saccharomyces cerevisiae

The explosion of genomic sequence data and the significant advancements in synthetic biology have led to the development of new technologies for natural products discovery and production. Using powerful genetic tools, the yeast Saccharomyces cerevisiae has been engineered as a production host for natural product pathways from bacterial, fungal, and plant species. With an expanding library of characterized genetic parts, biosynthetic pathways can be refactored for optimized expression in yeast. New engineering strategies have enabled the increased production of valuable secondary metabolites by tuning metabolic pathways. Improvements in high-throughput screening methods have facilitated the rapid identification of variants with improved biosynthetic capabilities. In this review, we focus on the molecular tools and engineering strategies that have recently empowered heterologous natural product biosynthesis.

Transcriptome-wide RNA processing kinetics revealed using extremely short 4tU labeling

BACKGROUND

RNA levels detected at steady state are the consequence of multiple dynamic processes within the cell. In addition to synthesis and decay, transcripts undergo processing. Metabolic tagging with a nucleotide analog is one way of determining the relative contributions of synthesis, decay and conversion processes globally.

RESULTS

By improving 4-thiouracil labeling of RNA in Saccharomyces cerevisiae we were able to isolate RNA produced during as little as 1 minute, allowing the detection of nascent pervasive transcription. Nascent RNA labeled for 1.5, 2.5 or 5 minutes was isolated and analyzed by reverse transcriptase-quantitative polymerase chain reaction and RNA sequencing. High kinetic resolution enabled detection and analysis of short-lived non-coding RNAs as well as intron-containing pre-mRNAs in wild-type yeast. From these data we measured the relative stability of pre-mRNA species with different high turnover rates and investigated potential correlations with sequence features.

CONCLUSIONS

Our analysis of non-coding RNAs reveals a highly significant association between non-coding RNA stability, transcript length and predicted secondary structure. Our quantitative analysis of the kinetics of pre-mRNA splicing in yeast reveals that ribosomal protein transcripts are more efficiently spliced if they contain intron secondary structures that are predicted to be less stable. These data, in combination with previous results, indicate that there is an optimal range of stability of intron secondary structures that allows for rapid splicing.