Unraveling the determinants of microRNA mediated regulation using a massively parallel reporter assay

Multiplexed assays of human disease-relevant mutations reveal UTR dinucleotide composition as a major determinant of RNA stability

Untranslated regions (UTRs) contain crucial regulatory elements for RNA stability, translation and localization, so their integrity is indispensable for gene expression. Approximately 3.7% of genetic variants associated with diseases occur in UTRs, yet a comprehensive understanding of UTR variant functions remains limited due to inefficient experimental and computational assessment methods. To systematically evaluate the effects of UTR variants on RNA stability, we established a massively parallel reporter assay on 6555 UTR variants reported in human disease databases. We examined the RNA degradation patterns mediated by the UTR library in two cell lines, and then applied LASSO regression to model the influential regulators of RNA stability. We found that UA dinucleotides and UA-rich motifs are the most prominent destabilizing element. Gain of UA dinucleotide outlined mutant UTRs with reduced stability. Studies on endogenous transcripts indicate that high UA-dinucleotide ratios in UTRs promote RNA degradation. Conversely, elevated GC content and protein binding on UA dinucleotides protect high-UA RNA from degradation. Further analysis reveals polarized roles of UA-dinucleotide-binding proteins in RNA protection and degradation. Furthermore, the UA-dinucleotide ratio of both UTRs is a common characteristic of genes in innate immune response pathways, implying a coordinated stability regulation through UTRs at the transcriptomic level. We also demonstrate that stability-altering UTRs are associated with changes in biobank-based health indices, underscoring the importance of precise UTR regulation for wellness. Our study highlights the importance of RNA stability regulation through UTR primary sequences, paving the way for further exploration of their implications in gene networks and precision medicine.

NaP-TRAP reveals the regulatory grammar in 5'UTR-mediated translation regulation during zebrafish development

The cis-regulatory elements encoded in an mRNA determine its stability and translational output. While there has been a considerable effort to understand the factors driving mRNA stability, the regulatory frameworks governing translational control remain more elusive. We have developed a novel massively parallel reporter assay (MPRA) to measure mRNA translation, named Nascent Peptide Translating Ribosome Affinity Purification (NaP-TRAP). NaP-TRAP measures translation in a frame-specific manner through the immunocapture of epitope tagged nascent peptides of reporter mRNAs. We benchmark NaP-TRAP to polysome profiling and use it to quantify Kozak strength and the regulatory landscapes of 5' UTRs in the developing zebrafish embryo and in human cells. Through this approach we identified general and developmentally dynamic cis-regulatory elements, as well as potential trans-acting proteins. We find that U-rich motifs are general enhancers, and upstream ORFs and GC-rich motifs are global repressors of translation. We also observe a translational switch during the maternal-to-zygotic transition, where C-rich motifs shift from repressors to prominent activators of translation. Conversely, we show that microRNA sites in the 5' UTR repress translation following the zygotic expression of miR-430. Together these results demonstrate that NaP-TRAP is a versatile, accessible, and powerful method to decode the regulatory functions of UTRs across different systems.

Quantitative design of cell type-specific mRNA stability from microRNA expression data

Limiting expression to target cell types is a longstanding goal in gene therapy, which could be met by sensing endogenous microRNA. However, an unclear association between microRNA expression and activity currently hampers such an approach. Here, we probe this relationship by measuring the stability of synthetic microRNA-responsive 3'UTRs across 10 cell lines in a library format. By systematically addressing biases in microRNA expression data and confounding factors such as microRNA crosstalk, we demonstrate that a straightforward model can quantitatively predict reporter stability purely from expression data. We use this model to design constructs with previously unattainable response patterns across our cell lines. The rules we derive for microRNA expression data selection and processing should apply to microRNA- responsive devices for any environment with available expression data.

Decoding biology with massively parallel reporter assays and machine learning

Massively parallel reporter assays (MPRAs) are powerful tools for quantifying the impacts of sequence variation on gene expression. Reading out molecular phenotypes with sequencing enables interrogating the impact of sequence variation beyond genome scale. Machine learning models integrate and codify information learned from MPRAs and enable generalization by predicting sequences outside the training data set. Models can provide a quantitative understanding of cis-regulatory codes controlling gene expression, enable variant stratification, and guide the design of synthetic regulatory elements for applications from synthetic biology to mRNA and gene therapy. This review focuses on cis-regulatory MPRAs, particularly those that interrogate cotranscriptional and post-transcriptional processes: alternative splicing, cleavage and polyadenylation, translation, and mRNA decay.

Massively parallel screen uncovers many rare 3' UTR variants regulating mRNA abundance of cancer driver genes

Understanding the function of rare non-coding variants represents a significant challenge. Using MapUTR, a screening method, we studied the function of rare 3' UTR variants affecting mRNA abundance post-transcriptionally. Among 17,301 rare gnomAD variants, an average of 24.5% were functional, with 70% in cancer-related genes, many in critical cancer pathways. This observation motivated an interrogation of 11,929 somatic mutations, uncovering 3928 (33%) functional mutations in 155 cancer driver genes. Functional MapUTR variants were enriched in microRNA- or protein-binding sites and may underlie outlier gene expression in tumors. Further, we introduce untranslated tumor mutational burden (uTMB), a metric reflecting the amount of somatic functional MapUTR variants of a tumor and show its potential in predicting patient survival. Through prime editing, we characterized three variants in cancer-relevant genes (MFN2, FOSL2, and IRAK1), demonstrating their cancer-driving potential. Our study elucidates the function of tens of thousands of non-coding variants, nominates non-coding cancer driver mutations, and demonstrates their potential contributions to cancer.

Computational design of mRNA vaccines

mRNA technology has emerged as a successful vaccine platform that offered a swift response to the COVID-19 pandemic. Accumulating evidence shows that vaccine efficacy, thermostability, and other important properties, are largely impacted by intrinsic properties of the mRNA molecule, such as RNA sequence and structure, both of which can be optimized. Designing mRNA sequence for vaccines presents a combinatorial problem due to an extremely large selection space. For instance, due to the degeneracy of the genetic code, there are over 10632 possible mRNA sequences that could encode the spike protein, the COVID-19 vaccines' target. Moreover, designing different elements of the mRNA sequence simultaneously against multiple objectives such as translational efficiency, reduced reactogenicity, and improved stability requires an efficient and sophisticated optimization strategy. Recently, there has been a growing interest in utilizing computational tools to redesign mRNA sequences to improve vaccine characteristics and expedite discovery timelines. In this review, we explore important biophysical features of mRNA to be considered for vaccine design and discuss how computational approaches can be applied to rapidly design mRNA sequences with desirable characteristics.

Using Synthetic DNA Libraries to Investigate Chromatin and Gene Regulation

Despite the recent explosion in genome-wide studies in chromatin and gene regulation, we are still far from extracting a set of genetic rules that can predict the function of the regulatory genome. One major reason for this deficiency is that gene regulation is a multi-layered process that involves an enormous variable space, which cannot be fully explored using native genomes. This problem can be partially solved by introducing synthetic DNA libraries into cells, a method that can test the regulatory roles of thousands to millions of sequences with limited variables. Here, we review recent applications of this method to study transcription factor (TF) binding, nucleosome positioning, and transcriptional activity. We discuss the design principles, experimental procedures, and major findings from these studies and compare the pros and cons of different approaches.

Design and Analysis of Massively Parallel Reporter Assays Using FORECAST

Machine learning is revolutionizing molecular biology and bioengineering by providing powerful insights and predictions. Massively parallel reporter assays (MPRAs) have emerged as a particularly valuable class of high-throughput technique to support such algorithms. MPRAs enable the simultaneous characterization of thousands or even millions of genetic constructs and provide the large amounts of data needed to train models. However, while the scale of this approach is impressive, the design of effective MPRA experiments is challenging due to the many factors that can be varied and the difficulty in predicting how these will impact the quality and quantity of data obtained. Here, we present a computational tool called FORECAST, which can simulate MPRA experiments based on fluorescence-activated cell sorting and subsequent sequencing (commonly referred to as Flow-seq or Sort-seq experiments), as well as carry out rigorous statistical estimation of construct performance from this type of experimental data. FORECAST can be used to develop workflows to aid the design of MPRA experiments and reanalyze existing MPRA data sets.

High-throughput identification of RNA localization elements in neuronal cells

Hundreds of RNAs are enriched in the projections of neuronal cells. For the vast majority of them, though, the sequence elements that regulate their localization are unknown. To identify RNA elements capable of directing transcripts to neurites, we deployed a massively parallel reporter assay that tested the localization regulatory ability of thousands of sequence fragments drawn from endogenous mouse 3' UTRs. We identified peaks of regulatory activity within several 3' UTRs and found that sequences derived from these peaks were both necessary and sufficient for RNA localization to neurites in mouse and human neuronal cells. The localization elements were enriched in adenosine and guanosine residues. They were at least tens to hundreds of nucleotides long as shortening of two identified elements led to significantly reduced activity. Using RNA affinity purification and mass spectrometry, we found that the RNA-binding protein Unk was associated with the localization elements. Depletion of Unk in cells reduced the ability of the elements to drive RNAs to neurites, indicating a functional requirement for Unk in their trafficking. These results provide a framework for the unbiased, high-throughput identification of RNA elements and mechanisms that govern transcript localization in neurons.

A massively parallel reporter assay reveals focused and broadly encoded RNA localization signals in neurons

Asymmetric subcellular mRNA localization allows spatial regulation of gene expression and functional compartmentalization. In neurons, localization of specific mRNAs to neurites is essential for cellular functioning. However, it is largely unknown how transcript sorting works in a sequence-specific manner. Here, we combined subcellular transcriptomics and massively parallel reporter assays and tested ∼50 000 sequences for their ability to localize to neurites. Mapping the localization potential of >300 genes revealed two ways neurite targeting can be achieved: focused localization motifs and broadly encoded localization potential. We characterized the interplay between RNA stability and localization and identified motifs able to bias localization towards neurite or soma as well as the trans-acting factors required for their action. Based on our data, we devised machine learning models that were able to predict the localization behavior of novel reporter sequences. Testing this predictor on native mRNA sequencing data showed good agreement between predicted and observed localization potential, suggesting that the rules uncovered by our MPRA also apply to the localization of native full-length transcripts.

Relative specificity as an important consideration in the big data era

Technological breakthroughs such as high-throughput methods, genomics, single-cell studies, and machine learning have fundamentally transformed research and ushered in the big data era of biology. Nevertheless, current data collections, analyses, and modeling frequently overlook relative specificity, a crucial property of molecular interactions in biochemical systems. Relative specificity describes how, for example, an enzyme reacts with its many substrates at different rates, and how this discriminatory action alone is sufficient to modulate the substrates and downstream events. As a corollary, it is not only important to comprehensively identify an enzyme’s substrates, but also critical to quantitatively determine how the enzyme interacts with the substrates and to evaluate how it shapes subsequent biological outcomes. Genomics and high-throughput techniques have greatly facilitated the studies of relative specificity in the 21st century, and its functional significance has been demonstrated in complex biochemical systems including transcription, translation, protein kinases, RNA-binding proteins, and animal microRNAs (miRNAs), although it remains ignored in most work. Here we analyze recent findings in big data and relative specificity studies and explain how the incorporation of relative specificity concept might enhance our mechanistic understanding of gene functions, biological phenomena, and human diseases.

A systematic dissection of determinants and consequences of snoRNA-guided pseudouridylation of human mRNA

RNA can be extensively modified post-transcriptionally with >170 covalent modifications, expanding its functional and structural repertoire. Pseudouridine (Ψ), the most abundant modified nucleoside in rRNA and tRNA, has recently been found within mRNA molecules. It remains unclear whether pseudouridylation of mRNA can be snoRNA-guided, bearing important implications for understanding the physiological target spectrum of snoRNAs and for their potential therapeutic exploitation in genetic diseases. Here, using a massively parallel reporter based strategy we simultaneously interrogate Ψ levels across hundreds of synthetic constructs with predesigned complementarity against endogenous snoRNAs. Our results demonstrate that snoRNA-mediated pseudouridylation can occur on mRNA targets. However, this is typically achieved at relatively low efficiencies, and is constrained by mRNA localization, snoRNA expression levels and the length of the snoRNA:mRNA complementarity stretches. We exploited these insights for the design of snoRNAs targeting pseudouridylation at premature termination codons, which was previously shown to suppress translational termination. However, in this and follow-up experiments in human cells we observe no evidence for significant levels of readthrough of pseudouridylated stop codons. Our study enhances our understanding of the scope, 'design rules', constraints and consequences of snoRNA-mediated pseudouridylation.

Transcriptome-scale methods for uncovering subcellular RNA localization mechanisms

Across a variety of systems, thousands of RNAs are localized to specific subcellular locations. However, for the vast majority of these RNAs, the mechanisms that underlie their transport are unknown. Historically, these mechanisms were uncovered for a single transcript at a time by laboriously testing the ability of RNA fragments to direct transcript localization. Recently developed methods profile the content of subcellular transcriptomes using high-throughput sequencing, allowing the analysis of the localization of thousands of transcripts at once. By identifying commonalities shared among multiple localized transcripts, these methods have the potential to rapidly expand our understanding of RNA localization mechanisms.

Cloning of DNA oligo pools for in vitro expression

Oligo library pools are powerful tools for systematic investigation of genetic and transcriptomic machinery such as promoter function and gene regulation, non-coding RNAs, or RNA modifications. Here, we provide a detailed protocol for cloning DNA oligo pools made up of tens of thousands of different constructs, aiming to preserve the complexity of the pools. This system would be suitable for expression in cell lines and can be followed up by next-generation sequencing analysis.

For complete details on the use and execution of this profile, please refer to Uzonyi et al. (2021).

•

Restriction-based cloning of DNA pools

•

Preservation of complexity of thousands of constructs

•

Used to investigate genetic and transcriptomic machineries

•

To be expressed in cell lines and follow up by NGS analysis

Conservation of Differential Animal MicroRNA Processing by Drosha and Dicer

In complex biochemical systems, an enzyme, protein, or RNA, symbolized as E, has hundreds or thousands of substrates or interacting partners. The relative specificity hypothesis proposes that such an E would differentially interact with and influence its many distinct, downstream substrates, thereby regulating the underlying biological process (es). The importance of relative specificity has been underappreciated, and evidence of its physiological consequences particularly lacking. Previously we showed that human Drosha and Dicer ribonucleases (RNases) both discriminate their respective microRNA (miRNA) substrates, and that differential cleavage by Drosha contributes to global differential miRNA expression. If relative specificity is an important biological mechanism, it should be evolutionarily conserved. To test this hypothesis, we hereby examined the cleavage of hundreds of zebrafish and fruitfly miRNA intermediates by Drosha and Dicer and the impact on miRNA biogenesis in these organisms. We showed that Drosha action regulates differential miRNA expression in zebrafish and fruitflies and identified the conserved secondary structure features and sequences in miRNA transcripts that control Drosha activity and miRNA expression. Our results established the conservation of miRNA processing mechanisms and regulatory functions by Drosha and Dicer, greatly strengthened the evidence for the physiological consequences of relative specificity as well as demonstrated its evolutionary significance.

A regulatory role of circRNA-miRNA-mRNA network in osteoblast differentiation

Osteoblast differentiation is an important process in skeletal development and bone remodelling. Serious bone diseases occur from any delay, defect, or imbalance in osteoblastic differentiation. Non-coding RNAs (ncRNAs) play a regulatory role in controlling the expression of proteins under physiological and pathological conditions via inhibiting mRNA translation or degrading mRNA. Circular RNAs (circRNAs) and microRNAs (miRNAs) are the long and small ncRNAs, respectively, which have been reported to regulate the expression of osteoblast marker genes directly or indirectly. Also, recent studies identified the regulatory mechanisms involving the crosstalk among circRNAs, miRNAs, and mRNAs during osteoblast differentiation. Understanding these regulatory mechanisms behind osteoblastic differentiation would help to diagnose or treat bone and bone-related disorders. Hence, the current review comprehensively discussed the regulatory relationship of circRNAs, miRNAs and mRNAs, and their functional role as circRNA-miRNA-mRNA axis in osteoblast differentiation.

Effects of sequence motifs in the yeast 3' untranslated region determined from massively parallel assays of random sequences

BACKGROUND

The 3' untranslated region (UTR) plays critical roles in determining the level of gene expression through effects on activities such as mRNA stability and translation. Functional elements within this region have largely been identified through analyses of native genes, which contain multiple co-evolved sequence features.

RESULTS

To explore the effects of 3' UTR sequence elements outside of native sequence contexts, we analyze hundreds of thousands of random 50-mers inserted into the 3' UTR of a reporter gene in the yeast Saccharomyces cerevisiae. We determine relative protein expression levels from the fitness of transformants in a growth selection. We find that the consensus 3' UTR efficiency element significantly boosts expression, independent of sequence context; on the other hand, the consensus positioning element has only a small effect on expression. Some sequence motifs that are binding sites for Puf proteins substantially increase expression in the library, despite these proteins generally being associated with post-transcriptional downregulation of native mRNAs. Our measurements also allow a systematic examination of the effects of point mutations within efficiency element motifs across diverse sequence backgrounds. These mutational scans reveal the relative in vivo importance of individual bases in the efficiency element, which likely reflects their roles in binding the Hrp1 protein involved in cleavage and polyadenylation.

CONCLUSIONS

The regulatory effects of some 3' UTR sequence features, like the efficiency element, are consistent regardless of sequence context. In contrast, the consequences of other 3' UTR features appear to be strongly dependent on their evolved context within native genes.

Genome-wide functional screen of 3'UTR variants uncovers causal variants for human disease and evolution

3' untranslated region (3'UTR) variants are strongly associated with human traits and diseases, yet few have been causally identified. We developed the massively parallel reporter assay for 3'UTRs (MPRAu) to sensitively assay 12,173 3'UTR variants. We applied MPRAu to six human cell lines, focusing on genetic variants associated with genome-wide association studies (GWAS) and human evolutionary adaptation. MPRAu expands our understanding of 3'UTR function, suggesting that simple sequences predominately explain 3'UTR regulatory activity. We adapt MPRAu to uncover diverse molecular mechanisms at base pair resolution, including an adenylate-uridylate (AU)-rich element of LEPR linked to potential metabolic evolutionary adaptations in East Asians. We nominate hundreds of 3'UTR causal variants with genetically fine-mapped phenotype associations. Using endogenous allelic replacements, we characterize one variant that disrupts a miRNA site regulating the viral defense gene TRIM14 and one that alters PILRB abundance, nominating a causal variant underlying transcriptional changes in age-related macular degeneration.

Uncovering the impact of noncoding variants in neurodegenerative brain diseases

Neurodegenerative brain diseases (NBDs) are characterized by cognitive decline and movement impairments caused by neuronal loss in different brain regions. A large fraction of the genetic heritability of NBDs is not explained by the current known mutations. Genome-wide association studies identified novel disease-risk loci, adding to the genetic basis of NBDs. Many of the associated variants reside in noncoding regions with distinct molecular functions. Genetic variation in these regions can alter functions and contribute to disease pathogenesis. Here, we discuss noncoding variants associated with NBDs. Methods for better functional interpretation of noncoding variation will expand our knowledge of the genetic architecture of NBDs and broaden the routes for therapeutic strategies.

Deciphering the principles of the RNA editing code via large-scale systematic probing

Adenosine-to-inosine editing is catalyzed by ADAR1 at thousands of sites transcriptome-wide. Despite intense interest in ADAR1 from physiological, bioengineering, and therapeutic perspectives, the rules of ADAR1 substrate selection are poorly understood. Here, we used large-scale systematic probing of ∼2,000 synthetic constructs to explore the structure and sequence context determining editability. We uncover two structural layers determining the formation and propagation of A-to-I editing, independent of sequence. First, editing is robustly induced at fixed intervals of 35 bp upstream and 30 bp downstream of structural disruptions. Second, editing is symmetrically introduced on opposite sites on a double-stranded structure. Our findings suggest a recursive model for RNA editing, whereby the structural alteration induced by the editing at one site iteratively gives rise to the formation of an additional editing site at a fixed periodicity, serving as a basis for the propagation of editing along and across both strands of double-stranded RNA structures.

Overcoming the design, build, test bottleneck for synthesis of nonrepetitive protein-RNA cassettes

We apply an oligo-library and machine learning-approach to characterize the sequence and structural determinants of binding of the phage coat proteins (CPs) of bacteriophages MS2 (MCP), PP7 (PCP), and Qβ (QCP) to RNA. Using the oligo library, we generate thousands of candidate binding sites for each CP, and screen for binding using a high-throughput dose-response Sort-seq assay (iSort-seq). We then apply a neural network to expand this space of binding sites, which allowed us to identify the critical structural and sequence features for binding of each CP. To verify our model and experimental findings, we design several non-repetitive binding site cassettes and validate their functionality in mammalian cells. We find that the binding of each CP to RNA is characterized by a unique space of sequence and structural determinants, thus providing a more complete description of CP-RNA interaction as compared with previous low-throughput findings. Finally, based on the binding spaces we demonstrate a computational tool for the successful design and rapid synthesis of functional non-repetitive binding-site cassettes.

Phage-coat proteins can be used to build synthetic biology parts. Here the authors use an oligo library and machine learning to predict and verify sequences based on binding scores.

The Role of MicroRNAs in Development and Function of Regulatory T Cells - Lessons for a Better Understanding of MicroRNA Biology

MicroRNAs (miRNAs) have emerged as critical posttranscriptional regulators of the immune system, including function and development of regulatory T (Treg) cells. Although this critical role has been firmly demonstrated through genetic models, key mechanisms of miRNA function in vivo remain elusive. Here, we review the role of miRNAs in Treg cell development and function. In particular, we focus on the question what the study of miRNAs in this context reveals about miRNA biology in general, including context-dependent function and the role of individual targets vs. complex co-targeting networks. In addition, we highlight potential technical pitfalls and state-of-the-art approaches to improve the mechanistic understanding of miRNA biology in a physiological context.

Dynamic RNA acetylation revealed by quantitative cross-evolutionary mapping

N4-acetylcytidine (ac4C) is an ancient and highly conserved RNA modification that is present on tRNA and rRNA and has recently been investigated in eukaryotic mRNA1-3. However, the distribution, dynamics and functions of cytidine acetylation have yet to be fully elucidated. Here we report ac4C-seq, a chemical genomic method for the transcriptome-wide quantitative mapping of ac4C at single-nucleotide resolution. In human and yeast mRNAs, ac4C sites are not detected but can be induced-at a conserved sequence motif-via the ectopic overexpression of eukaryotic acetyltransferase complexes. By contrast, cross-evolutionary profiling revealed unprecedented levels of ac4C across hundreds of residues in rRNA, tRNA, non-coding RNA and mRNA from hyperthermophilic archaea. Ac4C is markedly induced in response to increases in temperature, and acetyltransferase-deficient archaeal strains exhibit temperature-dependent growth defects. Visualization of wild-type and acetyltransferase-deficient archaeal ribosomes by cryo-electron microscopy provided structural insights into the temperature-dependent distribution of ac4C and its potential thermoadaptive role. Our studies quantitatively define the ac4C landscape, providing a technical and conceptual foundation for elucidating the role of this modification in biology and disease4-6.

High-throughput interrogation of programmed ribosomal frameshifting in human cells

Programmed ribosomal frameshifting (PRF) is the controlled slippage of the translating ribosome to an alternative frame. This process is widely employed by human viruses such as HIV and SARS coronavirus and is critical for their replication. Here, we developed a high-throughput approach to assess the frameshifting potential of a sequence. We designed and tested >12,000 sequences based on 15 viral and human PRF events, allowing us to systematically dissect the rules governing ribosomal frameshifting and discover novel regulatory inputs based on amino acid properties and tRNA availability. We assessed the natural variation in HIV gag-pol frameshifting rates by testing >500 clinical isolates and identified subtype-specific differences and associations between viral load in patients and the optimality of PRF rates. We devised computational models that accurately predict frameshifting potential and frameshifting rates, including subtle differences between HIV isolates. This approach can contribute to the development of antiviral agents targeting PRF.

MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

Dissecting splicing decisions and cell-to-cell variability with designed sequence libraries

Most human genes are alternatively spliced, allowing for a large expansion of the proteome. The multitude of regulatory inputs to splicing limits the potential to infer general principles from investigating native sequences. Here, we create a rationally designed library of >32,000 splicing events to dissect the complexity of splicing regulation through systematic sequence alterations. Measuring RNA and protein splice isoforms allows us to investigate both cause and effect of splicing decisions, quantify diverse regulatory inputs and accurately predict (R² = 0.73–0.85) isoform ratios from sequence and secondary structure. By profiling individual cells, we measure the cell-to-cell variability of splicing decisions and show that it can be encoded in the DNA and influenced by regulatory inputs, opening the door for a novel, single-cell perspective on splicing regulation.

Alternative splicing is regulated by multiple mechanisms. Here the authors employed designed splice site libraries and massively parallel reporter assays to dissect the regulatory complexity and cell-to-cell variability of splicing decisions and to build accurate predictive models.

Sequence determinants of polyadenylation-mediated regulation

The cleavage and polyadenylation reaction is a crucial step in transcription termination and pre-mRNA maturation in human cells. Despite extensive research, the encoding of polyadenylation-mediated regulation of gene expression within the DNA sequence is not well understood. Here, we utilized a massively parallel reporter assay to inspect the effect of over 12,000 rationally designed polyadenylation sequences (PASs) on reporter gene expression and cleavage efficiency. We find that the PAS sequence can modulate gene expression by over five orders of magnitude. By using a uniquely designed scanning mutagenesis data set, we gain mechanistic insight into various modes of action by which the cleavage efficiency affects the sensitivity or robustness of the PAS to mutation. Furthermore, we employ motif discovery to identify both known and novel sequence motifs associated with PAS-mediated regulation. By leveraging the large scale of our data, we train a deep learning model for the highly accurate prediction of RNA levels from DNA sequence alone (R = 0.83). Moreover, we devise unique approaches for predicting exact cleavage sites for our reporter constructs and for endogenous transcripts. Taken together, our results expand our understanding of PAS-mediated regulation, and provide an unprecedented resource for analyzing and predicting PAS for regulatory genomics applications.

Differential Inhibition of Target Gene Expression by Human microRNAs

microRNAs (miRNAs) exert their functions by repressing the expression of their target genes, but most miRNA target genes are unknown, and the degree to which a miRNA differentially inhibits the expression of its targets is underappreciated. We selected human miR-1, miR-122, and miR-124 as representatives to investigate the reliability of miRNA target predictions and examine how miRNAs suppress their targets. We constructed miRNA target gene reporter libraries based on prediction programs TargetScan, miRanda, and PicTar, and performed large-scale reporter assays to directly evaluate whether and how strongly a predicted target gene is repressed by its miRNA. We then performed statistical analyses to examine parameters that contributed to the miRNA inhibition of target genes. We found that the three programs have approximately 72–85% success rates in predicting genuine targets and that the miRNA inhibition of different targets varies in extent. We also identified parameters that could predict the degrees of miRNA repression, and further showed that differential miR-124 repression might contribute to differential gene expression in vivo. Our studies systematically investigated hundreds of miRNA target genes, shed light on factors influencing miRNA functions, and suggested a new mechanism by which differential target repression by miRNAs regulates endogenous gene expression.

Characterising the genetic basis of immune response variation to identify causal mechanisms underlying disease susceptibility

Over the last 10 years, genome-wide association studies (GWAS) have identified hundreds of susceptibility loci for autoimmune diseases. However, despite increasing power for the detection of both common and rare coding variants affecting disease susceptibility, a large fraction of disease heritability has remained unexplained. In addition, a majority of the identified loci are located in noncoding regions, and translation of disease-associated loci into new biological insights on the etiology of immune disorders has been lagging. This highlights the need for a better understanding of noncoding variation and new strategies to identify causal genes at disease loci. In this review, I will first detail the molecular basis of gene expression and review the various mechanisms that contribute to alter gene activity at the transcriptional and post-transcriptional level. I will then review the findings from 10 years of functional genomics studies regarding the genetics on gene expression, in particular in the context of infection. Finally, I will discuss the extent to which genetic variants that modulate gene expression at transcriptional and post-transcriptional level contribute to disease susceptibility and present strategies to leverage this information for the identification of causal mechanisms at disease loci in the era of whole genome sequencing.

A massively parallel 3' UTR reporter assay reveals relationships between nucleotide content, sequence conservation, and mRNA destabilization

Compared to coding sequences, untranslated regions of the transcriptome are not well conserved, and functional annotation of these sequences is challenging. Global relationships between nucleotide composition of 3′ UTR sequences and their sequence conservation have been appreciated since mammalian genomes were first sequenced, but the functional relevance of these patterns remain unknown. We systematically measured the effect on gene expression of the sequences of more than 25,000 RNA-binding protein (RBP) binding sites in primary mouse T cells using a massively parallel reporter assay. GC-rich sequences were destabilizing of reporter mRNAs and come from more rapidly evolving regions of the genome. These sequences were more likely to be folded in vivo and contain a number of structural motifs that reduced accumulation of a heterologous reporter protein. Comparison of full-length 3′ UTR sequences across vertebrate phylogeny revealed that strictly conserved 3′ UTRs were GC-poor and enriched in genes associated with organismal development. In contrast, rapidly evolving 3′ UTRs tended to be GC-rich and derived from genes involved in metabolism and immune responses. Cell-essential genes had lower GC content in their 3′ UTRs, suggesting a connection between unstructured mRNA noncoding sequences and optimal protein production. By reducing gene expression, GC-rich RBP-occupied sequences act as a rapidly evolving substrate for gene regulatory interactions.

Functional assays for transcription mechanisms in high-throughput

Dramatic increases in the scale of programmed synthesis of nucleic acid libraries coupled with deep sequencing have powered advances in understanding nucleic acid and protein biology. Biological systems centering on nucleic acids or encoded proteins greatly benefit from such high-throughput studies, given that large DNA variant pools can be synthesized and DNA, or RNA products of transcription, can be easily analyzed by deep sequencing. Here we review the scope of various high-throughput functional assays for studies of nucleic acids and proteins in general, followed by discussion of how these types of study have yielded insights into the RNA Polymerase II (Pol II) active site as an example. We discuss methodological considerations in the design and execution of these experiments that should be valuable to studies in any system.

Precise tuning of gene expression levels in mammalian cells

Precise, analogue regulation of gene expression is critical for cellular function in mammals. In contrast, widely employed experimental and therapeutic approaches such as knock-in/out strategies are more suitable for binary control of gene activity. Here we report on a method for precise control of gene expression levels in mammalian cells using engineered microRNA response elements (MREs). First, we measure the efficacy of thousands of synthetic MRE variants under the control of an endogenous microRNA by high-throughput sequencing. Guided by this data, we establish a library of microRNA silencing-mediated fine-tuners (miSFITs) of varying strength that can be employed to precisely control the expression of user-specified genes. We apply this technology to tune the T-cell co-inhibitory receptor PD-1 and to explore how antigen expression influences T-cell activation and tumour growth. Finally, we employ CRISPR/Cas9 mediated homology directed repair to introduce miSFITs into the BRCA1 3′UTR, demonstrating that this versatile tool can be used to tune endogenous genes.

Analogue regulation of gene expression is important for normal function in mammals but existing genetic technologies are designed to achieve ON/OFF control. Here the authors develop synthetic microRNA silencing-mediated fine-tuners (miSFITs) to precisely control target gene expression levels.

The sequence features that define efficient and specific hAGO2-dependent miRNA silencing guides

MicroRNAs (miRNAs) are ribonucleic acids (RNAs) of ∼21 nucleotides that interfere with the translation of messenger RNAs (mRNAs) and play significant roles in development and diseases. In bilaterian animals, the specificity of miRNA targeting is determined by sequence complementarity involving the seed. However, the role of the remaining nucleotides (non-seed) is only vaguely defined, impacting negatively on our ability to efficiently use miRNAs exogenously to control gene expression. Here, using reporter assays, we deciphered the role of the base pairs formed between the non-seed region and target mRNA. We used molecular modeling to reveal that this mechanism corresponds to the formation of base pairs mediated by ordered motions of the miRNA-induced silencing complex. Subsequently, we developed an algorithm based on this distinctive recognition to predict from sequence the levels of mRNA downregulation with high accuracy (r2 > 0.5, P-value < 10-12). Overall, our discovery improves the design of miRNA-guide sequences used to simultaneously downregulate the expression of multiple predetermined target genes.