An in vitro peptide complementation assay for CYT-18-dependent group I intron splicing reveals a new role for the N-terminus

Interconnected roles of fungal nuclear- and intron-encoded maturases: at the crossroads of mitochondrial intron splicing

Group I and II introns are large catalytic RNAs (ribozymes) that are frequently encountered in fungal mitochondrial genomes. The discovery of respiratory mutants linked to intron splicing defects demonstrated that for the efficient removal of organellar introns there appears to be a requirement of protein splicing factors. These splicing factors can be intron-encoded proteins with maturase activities that usually promote the splicing of the introns that encode them (cis-acting) and/or nuclear-encoded factors that can promote the splicing of a range of different introns (trans-acting). Compared to plants organellar introns, fungal mitochondrial intron splicing is still poorly explored, especially in terms of the synergy of nuclear factors with intron-encoded maturases that has direct impact on splicing through their association with intron RNA. In addition, nuclear-encoded accessory factors might drive the splicing impetus through translational activation, mitoribosome assembly, and phosphorylation-mediated RNA turnover. This review explores protein-assisted splicing of introns by nuclear and mitochondrial-encoded maturases as a means of mitonuclear interplay that could respond to environmental and developmental factors promoting phenotypic adaptation and potentially speciation. It also highlights key evolutionary events that have led to changes in structure and ATP-dependence to accommodate the dual functionality of nuclear and organellar splicing factors.

Structural Divergence of the Group I Intron Binding Surface in Fungal Mitochondrial Tyrosyl-tRNA Synthetases That Function in RNA Splicing

The mitochondrial tyrosyl-tRNA synthetases (mtTyrRSs) of Pezizomycotina fungi, a subphylum that includes many pathogenic species, are bifunctional proteins that both charge mitochondrial tRNA(Tyr) and act as splicing cofactors for autocatalytic group I introns. Previous studies showed that one of these proteins, Neurospora crassa CYT-18, binds group I introns by using both its N-terminal catalytic and C-terminal anticodon binding domains and that the catalytic domain uses a newly evolved group I intron binding surface that includes an N-terminal extension and two small insertions (insertions 1 and 2) with distinctive features not found in non-splicing mtTyrRSs. To explore how this RNA binding surface diverged to accommodate different group I introns in other Pezizomycotina fungi, we determined x-ray crystal structures of C-terminally truncated Aspergillus nidulans and Coccidioides posadasii mtTyrRSs. Comparisons with previous N. crassa CYT-18 structures and a structural model of the Aspergillus fumigatus mtTyrRS showed that the overall topology of the group I intron binding surface is conserved but with variations in key intron binding regions, particularly the Pezizomycotina-specific insertions. These insertions, which arose by expansion of flexible termini or internal loops, show greater variation in structure and amino acids potentially involved in group I intron binding than do neighboring protein core regions, which also function in intron binding but may be more constrained to preserve mtTyrRS activity. Our results suggest a structural basis for the intron specificity of different Pezizomycotina mtTyrRSs, highlight flexible terminal and loop regions as major sites for enzyme diversification, and identify targets for therapeutic intervention by disrupting an essential RNA-protein interaction in pathogenic fungi.

Pre-mRNA splicing is modulated by antifungal drugs in the filamentous fungus Neurospora crassa

For this study, we sought to identify pre‐mRNA processing events modulated by changes in extracellular pH, inorganic phosphate, and antifungal drugs. We examined genes with at least four putative introns whose transcriptional level responded to these effectors. We showed that the intron retention levels of genes encoding asparagine synthetase 2, C6‐zinc finger regulator (fluffy), and a farnesyltransferase respond to amphotericin B, ketoconazole, and other effectors. In general, the assayed antifungals promoted the disruption of the structural domains of these proteins probably leading to their inactivation, which emphasize the complexity of the metabolic modulation exerted by antifungal signaling.

Identifying novel protein phenotype annotations by hybridizing protein-protein interactions and protein sequence similarities

Studies of protein phenotypes represent a central challenge of modern genetics in the post-genome era because effective and accurate investigation of protein phenotypes is one of the most critical procedures to identify functional biological processes in microscale, which involves the analysis of multifactorial traits and has greatly contributed to the development of modern biology in the post genome era. Therefore, we have developed a novel computational method that identifies novel proteins associated with certain phenotypes in yeast based on the protein-protein interaction network. Unlike some existing network-based computational methods that identify the phenotype of a query protein based on its direct neighbors in the local network, the proposed method identifies novel candidate proteins for a certain phenotype by considering all annotated proteins with this phenotype on the global network using a shortest path (SP) algorithm. The identified proteins are further filtered using both a permutation test and their interactions and sequence similarities to annotated proteins. We compared our method with another widely used method called random walk with restart (RWR). The biological functions of proteins for each phenotype identified by our SP method and the RWR method were analyzed and compared. The results confirmed a large proportion of our novel protein phenotype annotation, and the RWR method showed a higher false positive rate than the SP method. Our method is equally effective for the prediction of proteins involving in all the eleven clustered yeast phenotypes with a quite low false positive rate. Considering the universality and generalizability of our supporting materials and computing strategies, our method can further be applied to study other organisms and the new functions we predicted can provide pertinent instructions for the further experimental verifications.