A truncated form of the p27 CDK inhibitor translated from pre-mRNA causes cell cycle arrest at G2 phase

Pre-mRNA splicing is indispensable for eukaryotic gene expression. Splicing inhibition causes cell cycle arrest and cell death, which are the reasons of potent anti-tumor activity of splicing inhibitors. Here, we found that truncated proteins are involved in cell cycle arrest and cell death upon splicing inhibition. We analyzed pre-mRNAs accumulated in the cytoplasm where translation occurs, and found that a truncated form of the p27 CDK inhibitor, named p27*, is translated from pre-mRNA and accumulated in G2 arrested cells. Overexpression of p27* caused G2 phase arrest through inhibiting CDK-cyclin complexes. Conversely, knockout of p27* accelerated resumption of cell proliferation after washout of splicing inhibitor. Interestingly, p27* was resistant to proteasomal degradation. We propose that cells produce truncated proteins with different nature to the original proteins via pre-mRNA translation only under splicing deficient conditions to response to the splicing deficient conditions.

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.

Spatial transcriptomics: Gene expression in space, time and numbers

Eukaryotic gene expression is an array of complex processes that must fine-tune with cellular needs to maintain homeostasis, yet flexible enough to respond to external cues and signals. Transcriptomics is the average output of cellular gene expression wherein messenger RNA copy the information inscribed in the DNA to instruct protein synthesis by the cellular ribosomes in the cytoplasm. So far, the knowledge gained from the transcriptomic-based studies was similar to the Heisenberg uncertainty dilemma. Information about these transcripts could be quantitated with utmost accuracy at the cost of losing information about their cellular spatial coordinates. In other words, we may quantitate the transcripts by PCR or the modern-day next-generation sequencing but cannot comment on where these transcripts originated or are located in the cells/tissues. Suppose we, however, accurately try to localize these transcripts using in-situ hybridization-like techniques; in that case, we are uncertain about its actual copy numbers due to the semi-quantitative nature of these methods. Its genuinely a Heisenbergian tradeoff that baffled the biologist for a long time until now; an innovative solution seems to be in place. Spatial transcriptomics (ST) is the perfect marriage between localization and quantitation without compromising either. It is a method that allows simultaneous visualization and quantitative analysis of the transcriptome by performing histological sections on glass slides incubated with oligonucleotides containing positional barcodes to generate high-quality cDNA libraries, which may be used for quantitation by RNA-sequencing. Increasingly, the scientific community seems to realize this technology’s full potential, as evidenced by the sheer number of publications in the past 2 years. This technology is the first of its kind to provide an unbiased whole transcriptome analysis with anatomical information from tissue sections where these transcripts are expressed. A time laspse serial longitudinal ST is powerful to identify the temporal transcript oscillations and decay at unprecedented accuracy. Cells of different types are spatially and structurally organized within the tissue matrix to perform their complex functions. Uncovering the complex spatial architecture of heterogenous tissue is crucial for our understanding of the disease’s pathology. Understanding the disease is the primary step towards its remedy, and ST is a powerful tool to address this with precision.

Proximal-end bias from in-vitro reconstituted nucleosomes and the result on downstream data analysis

The most basic level of eukaryotic gene regulation is the presence or absence of nucleosomes on DNA regulatory elements. In an effort to elucidate in vivo nucleosome patterns, in vitro studies are frequently used. In vitro, short DNA fragments are more favorable for nucleosome formation, increasing the likelihood of nucleosome occupancy. This may in part result from the fact that nucleosomes prefer to form on the terminal ends of linear DNA. This phenomenon has the potential to bias in vitro reconstituted nucleosomes and skew results. If the ends of DNA fragments are known, the reads falling close to the ends are typically discarded. In this study we confirm the phenomenon of end bias of in vitro nucleosomes. We describe a method in which nearly identical libraries, with different known ends, are used to recover nucleosomes which form towards the terminal ends of fragmented DNA. Finally, we illustrate that although nucleosomes prefer to form on DNA ends, it does not appear to skew results or the interpretation thereof.

Improved prediction of smoking status via isoform-aware RNA-seq deep learning models

Most predictive models based on gene expression data do not leverage information related to gene splicing, despite the fact that splicing is a fundamental feature of eukaryotic gene expression. Cigarette smoking is an important environmental risk factor for many diseases, and it has profound effects on gene expression. Using smoking status as a prediction target, we developed deep neural network predictive models using gene, exon, and isoform level quantifications from RNA sequencing data in 2,557 subjects in the COPDGene Study. We observed that models using exon and isoform quantifications clearly outperformed gene-level models when using data from 5 genes from a previously published prediction model. Whereas the test set performance of the previously published model was 0.82 in the original publication, our exon-based models including an exon-to-isoform mapping layer achieved a test set AUC (area under the receiver operating characteristic) of 0.88, which improved to an AUC of 0.94 using exon quantifications from a larger set of genes. Isoform variability is an important source of latent information in RNA-seq data that can be used to improve clinical prediction models.

ReadZS detects developmentally regulated RNA processing programs in single cell RNA-seq and defines subpopulations independent of gene expression

Post-transcriptional regulation of RNA processing (RNAP), including splicing and alternative polyadenylation (APA), controls eukaryotic gene function. Conservative estimates based on bulk tissue studies conclude that at least 50% of mammalian genes undergo APA. Single-cell RNA sequencing (scRNA-seq) could enable a near complete estimate of the extent, function, and regulation of these and other forms of RNA processing. Yet, statistical methods to detect regulated RNAP are limited in their detection power because they suffer from reliance on (a) incomplete annotations of 3' untranslated regions (3' UTRs), (b) peak calling heuristics, (c) analysis based on measurements collapsed over all cells in a cell type (pseudobulking), or (d) APA-specific detection. Here, we introduce ReadZS, a computationally-efficient, and annotation-free statistical approach to identify regulated RNAP, including but not limited to APA, in single cells. ReadZS rediscovers and substantially extends the scope of known cell type-specific RNAP in the human lung and during human spermatogenesis. The unique single-cell resolution and statistical properties of ReadZS enable discovery of new evolutionarily conserved, developmentally regulated RNAP and subpopulations of lung-resident macrophages, homogenous by gene expression alone.

Shaping the Landscape of Eukaryotic Gene Expression: Horizontal Gene Transfer

Horizontal gene transfer (HGT) in prokaryotes refers to the movement of genes and genetic information between two organisms. This usually results in the spread of antibiotic resistance genes among bacteria. Vertical gene transfer(VGT), on the other hand, refers to the flow of genetic information from parents to offsprings. Until recently, HGT was an exclusive prerogative of the prokaryotes. These are obvious due to the distinct nuclear membrane enclosure of eukaryote genomes that are shielded from outside interferences. VGT can cross species barriers and may even allow the transmission of genes across the kingdoms of life. HGT is now an emerging idea in eukaryotic genomes, challenging previous assertions that HGT is restricted to prokaryotes. It is now accepted that HGT can profoundly influence host metabolic pathways and alter gene expressions even in eukaryotes. HGT, is also fundamentally important during development, origin of human diseases, such as cancer, and neurodegenerative disorders. It may also influence therapeutic outcome by promoting resistant phenotypes. HGT is recently documented in prokaryote to eukaryote HGT is the tardigrade case though an analysis of a draft tardigrade genome suggested that HGT contributed to up to ~17 % of the gene. Further analysis performed after whole genome pair-wise alignments between human genome as well as 53 vertebrate genomes, it was observed that nearly 1500 human genome regions involving 642 known genes, most of which are enriched with ion binding to be conserved with non-mammals than with most mammals. This indicated horizontal gene transfer is more common than we expected in the human genome. It’s a matter of time or maybe a tip of iceberg to know the full extent and implications of HGT. Surprisingly its seems that the eukaryotic genome has many more ways to update itself to vastly expand its repertoire of expression and usability. HGT is just another feather in the crown.

Principles of mRNA targeting via the Arabidopsis m6A-binding protein ECT2

Specific recognition of N6-methyladenosine (m6A) in mRNA by RNA-binding proteins containing a YT521-B homology (YTH) domain is important in eukaryotic gene regulation. The Arabidopsis YTH-domain protein ECT2 is thought to bind to mRNA at URU(m6A)Y sites, yet RR(m6A)CH is the canonical m6A consensus site in all eukaryotes and ECT2 functions require m6A binding activity. Here, we apply iCLIP (individual-nucleotide resolution cross-linking and immunoprecipitation) and HyperTRIBE (targets of RNA-binding proteins identified by editing) to define high-quality target sets of ECT2, and analyze the patterns of enriched sequence motifs around ECT2 crosslink sites. Our analyses show that ECT2 does in fact bind to RR(m6A)CH. Pyrimidine-rich motifs are enriched around, but not at m6A-sites, reflecting a preference for N6-adenosine methylation of RRACH/GGAU islands in pyrimidine-rich regions. Such motifs, particularly oligo-U and UNUNU upstream of m6A sites, are also implicated in ECT2 binding via its intrinsically disordered region (IDR). Finally, URUAY-type motifs are enriched at ECT2 crosslink sites, but their distinct properties suggest function as sites of competition between binding of ECT2 and as yet unidentified RNA-binding proteins. Our study provides coherence between genetic and molecular studies of m6A-YTH function in plants, and reveals new insight into the mode of RNA recognition by YTH-domain-containing proteins.

Arabidopsis OXIDATIVE STRESS 3 enhances stress tolerance in Schizosaccharomyces pombe by promoting histone subunit replacement that upregulates drug-resistant genes

Histone replacement in chromatin-remodeling plays an important role in eukaryotic gene expression. New histone variants replacing their canonical counterparts often lead to a change in transcription, including responses to stresses caused by temperature, drought, salinity, and heavy metals. In this study, we describe a chromatin-remodeling process triggered by eviction of Rad3/Tel1-phosphorylated H2Aα, in which a heterologous plant protein AtOXS3 can subsequently bind fission yeast HA2.Z and Swc2, a component of the SWR1 complex, to facilitate replacement of H2Aα with H2A.Z. The histone replacement increases occupancy of the oxidative stress-responsive transcription factor Pap1 at the promoters of at least three drug-resistant genes, which enhances their transcription and hence primes the cell for higher stress tolerance.