Factors affecting splicing strength of yeast genes

Splicing factor Prp18p promotes genome-wide fidelity of consensus 3'-splice sites

The fidelity of splice site selection is critical for proper gene expression. In particular, proper recognition of 3'-splice site (3'SS) sequences by the spliceosome is challenging considering the low complexity of the 3'SS consensus sequence YAG. Here, we show that absence of the Prp18p splicing factor results in genome-wide activation of alternative 3'SS in S. cerevisiae, including highly unusual non-YAG sequences. Usage of these non-canonical 3'SS in the absence of Prp18p is enhanced by upstream poly(U) tracts and by their potential to interact with the first intronic nucleoside, allowing them to dock in the spliceosome active site instead of the normal 3'SS. The role of Prp18p in 3'SS fidelity is facilitated by interactions with Slu7p and Prp8p, but cannot be fulfilled by Slu7p, identifying a unique role for Prp18p in 3'SS fidelity. This fidelity function is synergized by the downstream proofreading activity of the Prp22p helicase, but is independent from another late splicing helicase, Prp43p. Our results show that spliceosomes exhibit remarkably relaxed 3'SS sequence usage in the absence of Prp18p and identify a network of spliceosomal interactions centered on Prp18p which are required to promote the fidelity of the recognition of consensus 3'SS sequences.

Spliceosomal mutations decouple 3' splice site fidelity from cellular fitness

The fidelity of splice site selection is thought to be critical for proper gene expression and cellular fitness. In particular, proper recognition of 3'-splice site (3'SS) sequences by the spliceosome is a daunting task considering the low complexity of the 3'SS consensus sequence YAG. Here we show that inactivating the near-essential splicing factor Prp18p results in a global activation of alternative 3'SS, many of which harbor sequences that highly diverge from the YAG consensus, including some highly unusual non-AG 3'SS. We show that the role of Prp18p in 3'SS fidelity is promoted by physical interactions with the essential splicing factors Slu7p and Prp8p and synergized by the proofreading activity of the Prp22p helicase. Strikingly, structure-guided point mutations that disrupt Prp18p-Slu7p and Prp18p-Prp8p interactions mimic the loss of 3'SS fidelity without any impact on cellular growth, suggesting that accumulation of incorrectly spliced transcripts does not have a major deleterious effect on cellular viability. These results show that spliceosomes exhibit remarkably relaxed fidelity in the absence of Prp18p, and that new 3'SS sampling can be achieved genome-wide without a major negative impact on cellular fitness, a feature that could be used during evolution to explore new productive alternative splice sites.

The SR-protein Npl3 is an essential component of the meiotic splicing regulatory network in Saccharomyces cerevisiae

The meiotic gene expression program in Saccharomyces cerevisiae involves regulated splicing of meiosis-specific genes via multiple splicing activators (e.g. Mer1, Nam8, Tgs1). Here, we show that the SR protein Npl3 is required for meiotic splicing regulation and is essential for proper execution of the meiotic cell cycle. The loss of Npl3, though not required for viability in mitosis, caused intron retention in meiosis-specific transcripts, inefficient meiotic double strand break processing and an arrest of the meiotic cell cycle. The targets of Npl3 overlapped in some cases with other splicing regulators, while also having unique target transcripts that were not shared. In the absence of Npl3, splicing defects for three transcripts (MER2, HOP2 and SAE3) were rescued by conversion of non-consensus splice sites to the consensus sequence. Methylation of Npl3 was further found to be required for splicing Mer1-dependent transcripts, indicating transcript-specific mechanisms by which Npl3 supports splicing. Together these data identify an essential function for the budding yeast SR protein Npl3 in meiosis as part of the meiotic splicing regulatory network.

Development of a sandwich ELISA for the detection of Chinese sacbrood virus infection

Chinese sacbrood disease (CSBD) is a highly pathogenic infectious disease in bees that is caused by Chinese sacbrood virus (CSBV). Although several molecular detection methods have been developed for CSBV, there are no commercially available enzyme-linked immunosorbent assay (ELISA) kits. We therefore developed a sandwich ELISA to detect CSBV antigens. To this end, monoclonal antibodies were produced using VP2 as an immunogen and subsequently characterized. Hybridomas were screened for the secretion of immunoglobulin G (IgG). Using an unlabeled monoclonal antibody (mAb) for coating and a horseradish peroxidase (HRP)-labeled mAb for detection, a CSBV sandwich ELISA method was established. This method showed specificity for CSBV and did not show cross-reactivity with other bee viruses. The detection limit of the sandwich ELISA was 3.675 × 104 copies/µL. Sixty bee larvae were tested using our sandwich ELISA method, and the presence of CSBV was verified by reverse transcription polymerase chain reaction (RT-PCR). The total coincidence rate was 90%. Thus, a sandwich ELISA method with high specificity and accuracy and a detection limit of 3.675 × 104 copies/µL has been successfully developed and can be used for the clinical detection of CSBV. This method will support rapid diagnosis, real-time monitoring, and early warning of CSBD.

RNA-Seq approach for accurate characterization of splicing efficiency of yeast introns

Introns in different genes, or even different introns within the same gene, often have different splice sites and differ in splicing efficiency (SE). One expects mass-transcribed genes to have introns with higher SE than weakly transcribed genes. However, such a simple expectation cannot be tested directly because variable SE for these genes is often not measured. Mechanistically, SE should depend on signal strength at key splice sites (SS) such as 5'SS, 3'SS and branchpoint site (BPS), i.e., SE = F(5'SS, 3'SS, BPS). However, without SE, we again cannot model how these splice sites contribute to SE. Here I present an RNA-Seq approach to quantify SE for each of the 304 introns in yeast (Saccharomyces cerevisiae) genes, including 24 in the 5'UTR, by measuring 1) number of reads mapped to exon-exon junctions (N_EE) as a proxy for the abundance of spliced form, and 2) number of reads mapped to exon-intron junction (N_EI5 and N_EI3 at 5' and 3' ends of intron) as a proxy for the abundance of unspliced form. The total mRNA is N_Total = N_EE + p * N_EI5 + (1-p) * N_EI3, with the simplest p = 0.5 but statistical methods were presented to estimate p from data. An estimated p is needed because N_EI5 is expected to be smaller than N_EI3 due to 1) step 1 splicing occurs before step 2 so EI5 is broken before EI3, 2) enrichment of poly(A) mRNA by oligo-dT, and 3) 5' degradation. SE is defined as the proportion (N_EE/N_Total). Application of the method shows that ribosomal protein messages are efficiently and mostly cotranscriptionally spliced. Yeast genes with long introns are also spliced efficiently. HAC1/YFL031W is poorly spliced partly because its splicing involves a nonspliceosome mechanism and partly because Ire1p, which participate in splicing HAC1, is hardly expressed. Many putative yeast genes have low SE, and some splice sites are incorrectly annotated.

Bioinformatics and Translation Elongation

Codon usage depends on mutation bias, tRNA-mediated selection, and the need for high efficiency and accuracy in translation. One codon in a synonymous codon family is often strongly over-used, especially in highly expressed genes, which often leads to a high dN/dS ratio because dS is very small. Many different codon usage indices have been proposed to measure codon usage and codon adaptation. Sense codon could be misread by release factors and stop codons misread by tRNAs, which also contribute to codon usage in rare cases. This chapter outlines the conceptual framework on codon evolution, illustrates codon-specific and gene-specific codon usage indices, and presents their applications. A new index for codon adaptation that accounts for background mutation bias (Index of Translation Elongation) is presented and contrasted with codon adaptation index (CAI) which does not consider background mutation bias. They are used to re-analyze data from a recent paper claiming that translation elongation efficiency matters little in protein production. The reanalysis disproves the claim.

The splicing code

This issue dedicated to the code of life tackles very challenging and open questions in Biology. The genetic code, brilliantly uncovered over 50 years ago is an example of a univocal biological code. In fact, except for very few and marginal variations, it is the same from bacteria to man, the RNA stretch: 5' GUGUUC 3' reads as the dipeptide: Val-Phe in bacteria, in yeast, in Arabidopsis, in zebra fish, in mouse and in human. A degree of ambiguity is possible if mutations are introduced in the tRNAs in a way that the anticodon reads one amino acid but the aminoacyl-transferase attaches a different one onto the tRNA. These were the very useful suppressor genes that aided greatly the study of bacterial genetics. Other biological codes however, are more akin to social codes and are less amenable to an unambiguous deciphering. Legal and ethical codes, weather we like it or not, are flexible and depend on the structure and history of the society that has produced them, as well as a specific point in time. The codes that govern RNA splicing have similar characteristics. In fact, the splicing code depends on a myriad of different factors that in part are influenced by the background in which they are read such as different cells, tissues or developmental stages. Given the complexity of the splicing process, the construction of an algorithm that can define exons or their fate with certainty has not yet been achieved. However a substantial amount of information towards the deciphering of the splicing code has been gathered and in this manuscript we summarize the point reached.

DAMBE6: New Tools for Microbial Genomics, Phylogenetics, and Molecular Evolution

DAMBE is a comprehensive software workbench for data analysis in molecular biology, phylogenetics, and evolution. Several important new functions have been added since version 5 of DAMBE: 1) comprehensive genomic profiling of translation initiation efficiency of different genes in different prokaryotic species, 2) a new index of translation elongation (I_TE) that takes into account both tRNA-mediated selection and background mutation on codon–anticodon adaptation, 3) a new and accurate phylogenetic approach based on pairwise alignment only, which is useful for highly divergent sequences from which a reliable multiple sequence alignment is difficult to obtain. Many other functions have been updated and improved including PWM for motif characterization, Gibbs sampler for de novo motif discovery, hidden Markov models for protein secondary structure prediction, self-organizing map for nonlinear clustering of transcriptomic data, comprehensive sequence alignment, and phylogenetic functions. DAMBE features a graphic, user-friendly and intuitive interface, and is freely available from http://dambe.bio.uottawa.ca.

Bioinformatics and Drug Discovery

Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanismbased drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules and metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data and software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.

DAMBE5: a comprehensive software package for data analysis in molecular biology and evolution

Since its first release in 2001 as mainly a software package for phylogenetic analysis, data analysis for molecular biology and evolution (DAMBE) has gained many new functions that may be classified into six categories: 1) sequence retrieval, editing, manipulation, and conversion among more than 20 standard sequence formats including MEGA, NEXUS, PHYLIP, GenBank, and the new NeXML format for interoperability, 2) motif characterization and discovery functions such as position weight matrix and Gibbs sampler, 3) descriptive genomic analysis tools with improved versions of codon adaptation index, effective number of codons, protein isoelectric point profiling, RNA and protein secondary structure prediction and calculation of minimum folding energy, and genomic skew plots with optimized window size, 4) molecular phylogenetics including sequence alignment, testing substitution saturation, distance-based, maximum parsimony, and maximum-likelihood methods for tree reconstructions, testing the molecular clock hypothesis with either a phylogeny or with relative-rate tests, dating gene duplication and speciation events, choosing the best-fit substitution models, and estimating rate heterogeneity over sites, 5) phylogeny-based comparative methods for continuous and discrete variables, and 6) graphic functions including secondary structure display, optimized skew plot, hydrophobicity plot, and many other plots of amino acid properties along a protein sequence, tree display and drawing by dragging nodes to each other, and visual searching of the maximum parsimony tree. DAMBE features a graphic, user-friendly, and intuitive interface and is freely available from http://dambe.bio.uottawa.ca (last accessed April 16, 2013).

Functional motifs in Escherichia coli NC101

Escherichia coli (E. coli) bacteria can damage DNA of the gut lining cells and may encourage the development of colon cancer according to recent reports. Genetic switches are specific sequence motifs and many of them are drug targets. It is interesting to know motifs and their location in sequences. At the present study, Gibbs sampler algorithm was used in order to predict and find functional motifs in E. coli NC101 contig 1. The whole genomic sequence of Escherichia coli NC101 contig 1 were retrieved from http://www.ncbi.nlm.nih.gov (NCBI Reference sequence: NZ_AEFA01000001.1) in order to be analyzed with DAMBE software and BLAST. The results showed that the 6-mer motif is CUGGAA in most sequences (genes1-3, 8, 9, 12, 14-18, 20-23, 25, 27, 29, 31-34), CUUGUA for gene 4 , CUGUAA for gene 5, CUGAUG for gene 6, CUGAUA for gene7, CUGAAA for genes 10, 11, 13, 26, 28, and CUGGAG for gene 19, and CUGGUA for gene30 in E. coli NC101 contig 1. It is concluded that the 6-mer motif is CUGGAA in most sequences in E. coli NC101 contig1. The present study may help experimental studies on elucidating the pharmacological and phylogenic functions of the motifs in E. coli.

Position weight matrix, gibbs sampler, and the associated significance tests in motif characterization and prediction

Position weight matrix (PWM) is not only one of the most widely used bioinformatic methods, but also a key component in more advanced computational algorithms (e.g., Gibbs sampler) for characterizing and discovering motifs in nucleotide or amino acid sequences. However, few generally applicable statistical tests are available for evaluating the significance of site patterns, PWM, and PWM scores (PWMS) of putative motifs. Statistical significance tests of the PWM output, that is, site-specific frequencies, PWM itself, and PWMS, are in disparate sources and have never been collected in a single paper, with the consequence that many implementations of PWM do not include any significance test. Here I review PWM-based methods used in motif characterization and prediction (including a detailed illustration of the Gibbs sampler for de novo motif discovery), present statistical and probabilistic rationales behind statistical significance tests relevant to PWM, and illustrate their application with real data. The multiple comparison problem associated with the test of site-specific frequencies is best handled by false discovery rate methods. The test of PWM, due to the use of pseudocounts, is best done by resampling methods. The test of individual PWMS for each sequence segment should be based on the extreme value distribution.

An improved implementation of effective number of codons (nc)

The effective number of codons (N(c)) is a widely used index for characterizing codon usage bias because it does not require a set of reference genes as does codon adaptation index (CAI) and because of the freely available computational tools such as CodonW. However, N(c), as originally formulated has many problems. For example, it can have values far greater than the number of sense codons; it treats a 6-fold compound codon family as a single-codon family although it is made of a 2-fold and a 4-fold codon family that can be under dramatically different selection for codon usage bias; the existing implementations do not handle all different genetic codes; it is often biased by codon families with a small number of codons. We developed a new N(c) that has a number of advantages over the original N(c). Its maximum value equals the number of sense codons when all synonymous codons are used equally, and its minimum value equals the number of codon families when exactly one codon is used in each synonymous codon family. It handles all known genetic codes. It breaks the compound codon families (e.g., those involving amino acids coded by six synonymous codons) into 2-fold and 4-fold codon families. It reduces the effect of codon families with few codons by introducing pseudocount and weighted averages. The new N(c) has significantly improved correlation with CAI than the original N(c) from CodonW based on protein-coding genes from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli, Bacillus subtilis, Micrococcus luteus, and Mycoplasma genitalium. It also correlates better with protein abundance data from the yeast than the original N(c).