A unified model for yeast transcript definition

RNAirport: a deep neural network-based database characterizing representative gene models in plants

A 5'-leader, known initially as the 5'-untranslated region, contains multiple isoforms due to alternative splicing (aS) and alternative transcription start site (aTSS). Therefore, a representative 5'-leader is demanded to examine the embedded RNA regulatory elements in controlling translation efficiency. Here, we develop a ranking algorithm and a deep-learning model to annotate representative 5'-leaders for five plant species. We rank the intra-sample and inter-sample frequency of aS-mediated transcript isoforms using the Kruskal-Wallis test-based algorithm and identify the representative aS-5'-leader. To further assign a representative 5'-end, we train the deep-learning model 5'leaderP to learn aTSS-mediated 5'-end distribution patterns from cap-analysis gene expression data. The model accurately predicts the 5'-end, confirmed experimentally in Arabidopsis and rice. The representative 5'-leader-contained gene models and 5'leaderP can be accessed at RNAirport (http://www.rnairport.com/leader5P/). The Stage 1 annotation of 5'-leader records 5'-leader diversity and will pave the way to Ribo-Seq open-reading frame annotation, identical to the project recently initiated by human GENCODE.

Regulatory activity is the default DNA state in eukaryotes

Genomes encode for genes and non-coding DNA, both capable of transcriptional activity. However, unlike canonical genes, many transcripts from non-coding DNA have limited evidence of conservation or function. Here, to determine how much biological noise is expected from non-genic sequences, we quantify the regulatory activity of evolutionarily naive DNA using RNA-seq in yeast and computational predictions in humans. In yeast, more than 99% of naive DNA bases were transcribed. Unlike the evolved transcriptome, naive transcripts frequently overlapped with opposite sense transcripts, suggesting selection favored coherent gene structures in the yeast genome. In humans, regulation-associated chromatin activity is predicted to be common in naive dinucleotide-content-matched randomized DNA. Here, naive and evolved DNA have similar co-occurrence and cell-type specificity of chromatin marks, challenging these as indicators of selection. However, in both yeast and humans, extreme high activities were rare in naive DNA, suggesting they result from selection. Overall, basal regulatory activity seems to be the default, which selection can hone to evolve a function or, if detrimental, repress.

Antisense transcription from lentiviral gene targeting linked to an integrated stress response in colorectal cancer cells

Advances in gene therapy research have resulted in the successful development of new therapies for clinical use. Here, we explored a gene targeting approach to deplete ephrinB2 from colorectal cancer cells using an inducible lentiviral vector. EphrinB2, a transmembrane ephrin ligand, promotes colorectal cancer cell growth and viability and predicts poor patient survival when expressed at high levels in colorectal cancer tissues. We discovered that lentiviral vector integration and expression in the host DNA frequently drive divergent host gene transcription, generating antisense reads coupled with splicing events and generation of chimeric vector/host transcripts. Antisense transcription of host DNA was linked to development of an integrated stress response and cell death. Despite recent successes, off-target effects remain a concern in genetic medicine. Our results provide evidence that divergent gene transcription is a previously unrecognized off-target effect of lentiviral vector integration with built-in properties for regulation of gene expression.

Systematic analysis of bypass suppression of essential genes

Essential genes tend to be highly conserved across eukaryotes, but, in some cases, their critical roles can be bypassed through genetic rewiring. From a systematic analysis of 728 different essential yeast genes, we discovered that 124 (17%) were dispensable essential genes. Through whole-genome sequencing and detailed genetic analysis, we investigated the genetic interactions and genome alterations underlying bypass suppression. Dispensable essential genes often had paralogs, were enriched for genes encoding membrane-associated proteins, and were depleted for members of protein complexes. Functionally related genes frequently drove the bypass suppression interactions. These gene properties were predictive of essential gene dispensability and of specific suppressors among hundreds of genes on aneuploid chromosomes. Our findings identify yeast's core essential gene set and reveal that the properties of dispensable essential genes are conserved from yeast to human cells, correlating with human genes that display cell line-specific essentiality in the Cancer Dependency Map (DepMap) project.

Genomic Considerations for the Modification of Saccharomyces cerevisiae for Biofuel and Metabolite Biosynthesis

The growing global population and developing world has put a strain on non-renewable natural resources, such as fuels. The shift to renewable sources will, thus, help meet demands, often through the modification of existing biosynthetic pathways or the introduction of novel pathways into non-native species. There are several useful biosynthetic pathways endogenous to organisms that are not conducive for the scale-up necessary for industrial use. The use of genetic and synthetic biological approaches to engineer these pathways in non-native organisms can help ameliorate these challenges. The budding yeast Saccharomyces cerevisiae offers several advantages for genetic engineering for this purpose due to its widespread use as a model system studied by many researchers. The focus of this review is to present a primer on understanding genomic considerations prior to genetic modification and manipulation of S. cerevisiae. The choice of a site for genetic manipulation can have broad implications on transcription throughout a region and this review will present the current understanding of position effects on transcription.

Defining Functional Genic Regions in the Human Genome through Integration of Biochemical, Evolutionary, and Genetic Evidence

The human genome is dominated by large tracts of DNA with extensive biochemical activity but no known function. In particular, it is well established that transcriptional activities are not restricted to known genes. However, whether this intergenic transcription represents activity with functional significance or noise is under debate, highlighting the need for an effective method of defining functional genomic regions. Moreover, these discoveries raise the question whether genomic regions can be defined as functional based solely on the presence of biochemical activities, without considering evolutionary (conservation) and genetic (effects of mutations) evidence. Here, computational models integrating genetic, evolutionary, and biochemical evidence are established that provide reliable predictions of human protein-coding and RNA genes. Importantly, in addition to sequence conservation, biochemical features allow accurate predictions of genic sequences with phenotypic evidence under strong purifying selection, suggesting that they can be used as an alternative measure of selection. Moreover, 18.5% of annotated noncoding RNAs exhibit higher degrees of similarity to phenotype genes and, thus, are likely functional. However, 64.5% of noncoding RNAs appear to belong to a sequence class of their own, and the remaining 17% are more similar to pseudogenes and random intergenic sequences that may represent noisy transcription.

Bidirectional terminators: an underestimated aspect of gene regulation

Recent experimental and computational work revealed that transcriptional terminators in Saccharomyces cerevisiae can terminate transcription coming from both directions. This mechanism helps budding yeast cope with the pervasive nature of transcription by limiting aberrant transcription from invading neighboring genes.

The Ground State and Evolution of Promoter Region Directionality

Eukaryotic promoter regions are frequently divergently transcribed in vivo, but it is unknown whether the resultant antisense RNAs are a mechanistic by-product of RNA polymerase II (Pol II) transcription or biologically meaningful. Here, we use a functional evolutionary approach that involves nascent transcript mapping in S. cerevisiae strains containing foreign yeast DNA. Promoter regions in foreign environments lose the directionality they have in their native species. Strikingly, fortuitous promoter regions arising in foreign DNA produce equal transcription in both directions, indicating that divergent transcription is a mechanistic feature that does not imply a function for these transcripts. Fortuitous promoter regions arising during evolution promote bidirectional transcription and over time are purged through mutation or retained to enable new functionality. Similarly, human transcription is more bidirectional at newly evolved enhancers and promoter regions. Thus, promoter regions are intrinsically bidirectional and are shaped by evolution to bias transcription toward coding versus non-coding RNAs.

In silico identification of enhancers on the basis of a combination of transcription factor binding motif occurrences

Enhancers interact with gene promoters and form chromatin looping structures that serve important functions in various biological processes, such as the regulation of gene transcription and cell differentiation. However, enhancers are difficult to identify because they generally do not have fixed positions or consensus sequence features, and biological experiments for enhancer identification are costly in terms of labor and expense. In this work, several models were built by using various sequence-based feature sets and their combinations for enhancer prediction. The selected features derived from a recursive feature elimination method showed that the model using a combination of 141 transcription factor binding motif occurrences from 1,422 transcription factor position weight matrices achieved a favorably high prediction accuracy superior to that of other reported methods. The models demonstrated good prediction accuracy for different enhancer datasets obtained from different cell lines/tissues. In addition, prediction accuracy was further improved by integration of chromatin state features. Our method is complementary to wet-lab experimental methods and provides an additional method to identify enhancers.

Strand-specific, high-resolution mapping of modified RNA polymerase II

Reversible modification of the RNAPII C‐terminal domain links transcription with RNA processing and surveillance activities. To better understand this, we mapped the location of RNAPII carrying the five types of CTD phosphorylation on the RNA transcript, providing strand‐specific, nucleotide‐resolution information, and we used a machine learning‐based approach to define RNAPII states. This revealed enrichment of Ser5P, and depletion of Tyr1P, Ser2P, Thr4P, and Ser7P in the transcription start site (TSS) proximal ~150 nt of most genes, with depletion of all modifications close to the poly(A) site. The TSS region also showed elevated RNAPII relative to regions further 3′, with high recruitment of RNA surveillance and termination factors, and correlated with the previously mapped 3′ ends of short, unstable ncRNA transcripts. A hidden Markov model identified distinct modification states associated with initiating, early elongating and later elongating RNAPII. The initiation state was enriched near the TSS of protein‐coding genes and persisted throughout exon 1 of intron‐containing genes. Notably, unstable ncRNAs apparently failed to transition into the elongation states seen on protein‐coding genes.

The Meaning of NMD: Translate or Perish

Premature translation termination leads to a reduced mRNA level in all types of organisms. In eukaryotes, the phenomenon is known as nonsense-mediated mRNA decay (NMD). This is commonly regarded as the output of a specific surveillance and destruction mechanism that is activated by the presence of a premature translation termination codon (PTC) in an atypical sequence context. Despite two decades of research, it is still unclear how NMD discriminates between PTCs and normal stop codons. We suggest that cells do not possess any such mechanism and instead propose a new model in which this mRNA depletion is a consequence of the appearance of long tracts of mRNA that are unprotected by scanning ribosomes.

Small Genetic Circuits and MicroRNAs: Big Players in Polymerase II Transcriptional Control in Plants

RNA Polymerase II (Pol II) regulatory cascades involving transcription factors (TFs) and their targets orchestrate the genetic circuitry of every eukaryotic organism. In order to understand how these cascades function, they can be dissected into small genetic networks, each containing just a few Pol II transcribed genes, that generate specific signal-processing outcomes. Small RNA regulatory circuits involve direct regulation of a small RNA by a TF and/or direct regulation of a TF by a small RNA and have been shown to play unique roles in many organisms. Here, we will focus on small RNA regulatory circuits containing Pol II transcribed microRNAs (miRNAs). While the role of miRNA-containing regulatory circuits as modular building blocks for the function of complex networks has long been on the forefront of studies in the animal kingdom, plant studies are poised to take a lead role in this area because of their advantages in probing transcriptional and posttranscriptional control of Pol II genes. The relative simplicity of tissue- and cell-type organization, miRNA targeting, and genomic structure make the Arabidopsis thaliana plant model uniquely amenable for small RNA regulatory circuit studies in a multicellular organism. In this Review, we cover analysis, tools, and validation methods for probing the component interactions in miRNA-containing regulatory circuits. We then review the important roles that plant miRNAs are playing in these circuits and summarize methods for the identification of small genetic circuits that strongly influence plant function. We conclude by noting areas of opportunity where new plant studies are imminently needed.

TIPR: transcription initiation pattern recognition on a genome scale

MOTIVATION

The computational identification of gene transcription start sites (TSSs) can provide insights into the regulation and function of genes without performing expensive experiments, particularly in organisms with incomplete annotations. High-resolution general-purpose TSS prediction remains a challenging problem, with little recent progress on the identification and differentiation of TSSs which are arranged in different spatial patterns along the chromosome.

RESULTS

In this work, we present the Transcription Initiation Pattern Recognizer (TIPR), a sequence-based machine learning model that identifies TSSs with high accuracy and resolution for multiple spatial distribution patterns along the genome, including broadly distributed TSS patterns that have previously been difficult to characterize. TIPR predicts not only the locations of TSSs but also the expected spatial initiation pattern each TSS will form along the chromosome-a novel capability for TSS prediction algorithms. As spatial initiation patterns are associated with spatiotemporal expression patterns and gene function, this capability has the potential to improve gene annotations and our understanding of the regulation of transcription initiation. The high nucleotide resolution of this model locates TSSs within 10 nucleotides or less on average.

AVAILABILITY AND IMPLEMENTATION

Model source code is made available online at http://megraw.cgrb.oregonstate.edu/software/TIPR/.

CONTACT

megrawm@science.oregonstate.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Determination and inference of eukaryotic transcription factor sequence specificity

Transcription factor (TF) DNA sequence preferences direct their regulatory activity, but are currently known for only ∼1% of eukaryotic TFs. Broadly sampling DNA-binding domain (DBD) types from multiple eukaryotic clades, we determined DNA sequence preferences for >1,000 TFs encompassing 54 different DBD classes from 131 diverse eukaryotes. We find that closely related DBDs almost always have very similar DNA sequence preferences, enabling inference of motifs for ∼34% of the ∼170,000 known or predicted eukaryotic TFs. Sequences matching both measured and inferred motifs are enriched in chromatin immunoprecipitation sequencing (ChIP-seq) peaks and upstream of transcription start sites in diverse eukaryotic lineages. SNPs defining expression quantitative trait loci in Arabidopsis promoters are also enriched for predicted TF binding sites. Importantly, our motif "library" can be used to identify specific TFs whose binding may be altered by human disease risk alleles. These data present a powerful resource for mapping transcriptional networks across eukaryotes.