MicroRNAs: Beyond Post-transcriptional Regulation of mRNAs

MicroRNAs (miRNAs), small non-coding RNAs, participate in the transcriptional and post-transcriptional regulation of eukaryotic genes, and are potential biomarkers for diseases. Mature miRNAs can be located in both the nucleus and cytoplasm, where they perform their regulatory function. The discovery of new miRNAs and the identification of their targets and functions are fundamental to understanding the biological processes regulated by them, as well as the role they play in diseases. This present study researched miRNAs function at nuclear level and as circulating molecules.

A slow dynamic RNA switch regulates processing of microRNA-21

The microRNAs are non-coding RNAs which post-transcriptionally regulate the expression of a majority of eukaryotic genes, and whose dysregulation is a driver of many human diseases. Here we report the discovery of a very slow (0.1 sec) conformational rearrangement at the Dicer cleavage site of pre-miR-21 which regulates the relative concentration of readily processed and inefficiently processed structural states. We show this dynamic switch is affected by single nucleotide mutations and can be biased by small molecule and peptide ligands, which can direct the microRNA to occupy the inefficiently processed state and reduce processing efficiency. This result reveals a new mechanism of RNA regulation and suggests a chemical approach to suppressing or activating pathogenic microRNAs by selective stabilization of the unprocessed or processed state.

Structural basis of INTAC-regulated transcription

For the majority of expressed eukaryotic genes, RNA polymerase II (Pol II) forms a paused elongation complex (PEC) and undergoes promoter-proximal pausing downstream of the transcription start site. The polymerase either proceeds into productive elongation or undergoes promoter-proximal premature transcription termination. It remains incompletely understood how transcription is regulated at this stage. Here, we determined the structure of PEC bound to INTAC, an Integrator-containing PP2A complex, at near-atomic resolution. The structure shows that INTAC partially wraps around PEC through multiple contacts, permitting the memetic nascent RNA to run into substrate-entry tunnel of the endonuclease subunit INTS11 of INTAC for cleavage. Pol II C-terminal domain (CTD) winds over INTAC backbone module through multiple anchors and is suspended above the phosphatase of INTAC for dephosphorylation. Biochemical analysis shows that INTAC-PEC association requires unphosphorylated CTD and could tolerate CTD phosphorylation, suggesting an INTAC-mediated persistent CTD dephosphorylation followed by reinforcement of the INTAC-PEC complex. Our study reveals how INTAC binds PEC and orchestrates RNA cleavage and CTD dephosphorylation, two critical events in generating premature transcription termination.

Whokaryote: distinguishing eukaryotic and prokaryotic contigs in metagenomes based on gene structure

Metagenomics has become a prominent technology to study the functional potential of all organisms in a microbial community. Most studies focus on the bacterial content of these communities, while ignoring eukaryotic microbes. Indeed, many metagenomics analysis pipelines silently assume that all contigs in a metagenome are prokaryotic. However, because of marked differences in gene structure, prokaryotic gene prediction tools fail to accurately predict eukaryotic genes. Here, we developed a classifier that distinguishes eukaryotic from prokaryotic contigs based on foundational differences between these taxa in gene structure. We first developed a random forest classifier that uses intergenic distance, gene density and gene length as the most important features. We show that, with an estimated accuracy of 97%, this classifier with principled features grounded in biology can perform almost as well as the classifiers EukRep and Tiara, which use k-mer frequencies as features. By re-training our classifier with Tiara predictions as additional feature, weaknesses of both types of classifiers are compensated; the result is an enhanced classifier that outperforms all individual classifiers, with an F1-score of 1.00 on precision, recall and accuracy for both eukaryotes and prokaryotes, while still being fast. In a reanalysis of metagenome data from a disease-suppressive plant endosphere microbial community, we show how using Whokaryote to select contigs for eukaryotic gene prediction facilitates the discovery of several biosynthetic gene clusters that were missed in the original study. Our enhanced classifier, which we call ′Whokaryote′, is wrapped in an easily installable package and is freely available from https://git.wageningenur.nl/lotte.pronk/whokaryote.

Reduction in gene expression noise by targeted increase in accessibility at gene loci

Many eukaryotic genes are expressed in randomly initiated bursts that are punctuated by periods of quiescence. Here, we show that the intermittent access of the promoters to transcription factors through relatively impervious chromatin contributes to this “noisy” transcription. We tethered a nuclease-deficient Cas9 fused to a histone acetyl transferase at the promoters of two endogenous genes in HeLa cells. An assay for transposase-accessible chromatin using sequencing showed that the activity of the histone acetyl transferase altered the chromatin architecture locally without introducing global changes in the nucleus and rendered the targeted promoters constitutively accessible. We measured the gene expression variability from the gene loci by performing single-molecule fluorescence in situ hybridization against mature messenger RNAs (mRNAs) and by imaging nascent mRNA molecules present at active gene loci in single cells. Because of the increased accessibility of the promoter to transcription factors, the transcription from two genes became less noisy, even when the average levels of expression did not change. In addition to providing evidence for chromatin accessibility as a determinant of the noise in gene expression, our study offers a mechanism for controlling gene expression noise which is otherwise unavoidable.

Patterns of stability and change in the maize genome: a case study of small RNA transcriptomes in two recombinant inbred lines and their progenitors

Small RNAs (sRNAs) are epigenetic regulators of eukaryotic genes and transposable elements (TEs). Diverse sRNA expression patterns exist within a species, but how this diversity arises is not well understood. To provide a window into the dynamics of maize sRNA patterning, sRNA and mRNA transcriptomes were examined in two related Zea mays recombinant inbred lines (RILs) and their inbred parents. Analysis of these RILs revealed that most clusters of sRNA expression retain the parental sRNA expression level. However, expression states that differ from the parental allele were also observed, predominantly reflecting decreases in sRNA expression. When RIL sRNA expression differed from the parental allele, the new state was frequently similar between the two RILs, and similar to the expression state found at the allele in the other parent. Novel sRNA expression patterns, distinct from either parent, were rare. Additionally, examination of sRNA expression over TEs revealed one TE family, Gyma, that showed consistent enrichment for RIL sRNA expression differences compared to those found at parental alleles. These findings provide insights into how sRNA silencing might evolve over generations and suggest that further inquiry into the molecular nature of sRNA trans regulators is warranted.

The spread of the first introns in proto-eukaryotic paralogs

Spliceosomal introns are a unique feature of eukaryotic genes. Previous studies have established that many introns were present in the protein-coding genes of the last eukaryotic common ancestor (LECA). Intron positions shared between genes that duplicated before LECA could in principle provide insight into the emergence of the first introns. In this study we use ancestral intron position reconstructions in two large sets of duplicated families to systematically identify these ancient paralogous intron positions. We found that 20-35% of introns inferred to have been present in LECA were shared between paralogs. These shared introns, which likely preceded ancient duplications, were widespread across different functions, with the notable exception of nuclear transport. Since we observed a clear signal of pervasive intron loss prior to LECA, it is likely that substantially more introns were shared at the time of duplication than we can detect in LECA. The large extent of shared introns indicates an early origin of introns during eukaryogenesis and suggests an early origin of a nuclear structure, before most of the other complex eukaryotic features were established.

The citron homology domain as a scaffold for Rho1 signaling

Aspergillus fumigatus is a human opportunistic pathogen showing emerging resistance against a limited repertoire of antifungal agents available. The GTPase Rho1 has been identified as an important regulator of the cell wall integrity signaling pathway that regulates the composition of the cell wall, a structure that is unique to fungi and serves as a target for antifungal compounds. Rom2, the guanine nucleotide exchange factor to Rho1, contains a C-terminal citron homology (CNH) domain of unknown function that is found in many other eukaryotic genes. Here, we show that the Rom2 CNH domain interacts directly with Rho1 to modulate β-glucan and chitin synthesis. We report the structure of the Rom2 CNH domain, revealing that it adopts a seven-bladed β-propeller fold containing three unusual loops. A model of the Rho1–Rom2 CNH complex suggests that the Rom2 CNH domain interacts with the Rho1 Switch II motif. This work uncovers the role of the Rom2 CNH domain as a scaffold for Rho1 signaling in fungal cell wall biosynthesis.

Actively translated uORFs reduce translation and mRNA stability independent of NMD in Arabidopsis

ABSTRACTUpstream ORFs (uORFs) are widespread cis-regulatory elements in the 5’ untranslated regions of eukaryotic genes. Translation of uORFs could negatively regulate protein synthesis by repressing main ORF (mORF) translation and by reducing mRNA stability presumably through nonsense-mediated decay (NMD). While the above expectations were supported in animals, they have not been extensively tested in plants. Using ribosome profiling, we systematically identified 2093 Actively Translated uORFs (ATuORFs) in Arabidopsis seedlings and examined their roles in gene expression regulation by integrating multiple genome-wide datasets. Compared with genes without uORFs, we found ATuORFs result in 38%, 14%, and 43% reductions in translation efficiency, mRNA stability, and protein levels, respectively. The effects of predicted but not actively translated uORFs are much weaker than those of ATuORFs. Interestingly, ATuORF-containing genes are also expressed at higher levels and encode longer proteins with conserved domains, features that are common in evolutionarily older genes. Moreover, we provide evidence that uORF translation in plants, unlike in vertebrates, generally does not trigger NMD. We found ATuORF-containing transcripts are degraded through 5’ to 3’ decay, while NMD targets are degraded through both 5’ to 3’ and 3’ to 5’ decay, suggesting uORF-associated mRNA decay and NMD have distinct genetic requirements. Furthermore, we showed ATuORFs and NMD repress translation through separate mechanisms. Our results reveal that the potent inhibition of uORFs on mORF translation and mRNA stability in plants are independent of NMD, highlighting a fundamental difference in gene expression regulation by uORFs in the plant and animal kingdoms.

MAAPER: model-based analysis of alternative polyadenylation using 3′ end-linked reads

Most eukaryotic genes express alternative polyadenylation (APA) isoforms. A growing number of RNA sequencing methods, especially those used for single-cell transcriptome analysis, generate reads close to the polyadenylation site (PAS), termed nearSite reads, hence inherently containing information about APA isoform abundance. Here, we present a probabilistic model-based method named MAAPER to utilize nearSite reads for APA analysis. MAAPER predicts PASs with high accuracy and sensitivity and examines different types of APA events with robust statistics. We show MAAPER’s performance with both bulk and single-cell data and its applicability in unpaired or paired experimental designs.