Systematic interrogation of human promoters

Sequence basis of transcription initiation in the human genome

Transcription initiation is a process that is essential to ensuring the proper function of any gene, yet we still lack a unified understanding of sequence patterns and rules that explain most transcription start sites in the human genome. By predicting transcription initiation at base-pair resolution from sequences with a deep learning-inspired explainable model called Puffin, we show that a small set of simple rules can explain transcription initiation at most human promoters. We identify key sequence patterns that contribute to human promoter activity, each activating transcription with distinct position-specific effects. Furthermore, we explain the sequence basis of bidirectional transcription at promoters, identify the links between promoter sequence and gene expression variation across cell types, and explore the conservation of sequence determinants of transcription initiation across mammalian species.

A universal system for boosting gene expression in eukaryotic cell-lines

We demonstrate a transcriptional regulatory design algorithm that can boost expression in yeast and mammalian cell lines. The system consists of a simplified transcriptional architecture composed of a minimal core promoter and a synthetic upstream regulatory region (sURS) composed of up to three motifs selected from a list of 41 motifs conserved in the eukaryotic lineage. The sURS system was first characterized using an oligo-library containing 189,990 variants. We validate the resultant expression model using a set of 43 unseen sURS designs. The validation sURS experiments indicate that a generic set of grammar rules for boosting and attenuation may exist in yeast cells. Finally, we demonstrate that this generic set of grammar rules functions similarly in mammalian CHO-K1 and HeLa cells. Consequently, our work provides a design algorithm for boosting the expression of promoters used for expressing industrially relevant proteins in yeast and mammalian cell lines.

Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences

The breakthrough high-throughput measurement of the cis-regulatory activity of millions of randomly generated promoters provides an unprecedented opportunity to systematically decode the cis-regulatory logic that determines the expression values. We developed an end-to-end transformer encoder architecture named Proformer to predict the expression values from DNA sequences. Proformer used a Macaron-like Transformer encoder architecture, where two half-step feed forward (FFN) layers were placed at the beginning and the end of each encoder block, and a separable 1D convolution layer was inserted after the first FFN layer and in front of the multi-head attention layer. The sliding k-mers from one-hot encoded sequences were mapped onto a continuous embedding, combined with the learned positional embedding and strand embedding (forward strand vs. reverse complemented strand) as the sequence input. Moreover, Proformer introduced multiple expression heads with mask filling to prevent the transformer models from collapsing when training on relatively small amount of data. We empirically determined that this design had significantly better performance than the conventional design such as using the global pooling layer as the output layer for the regression task. These analyses support the notion that Proformer provides a novel method of learning and enhances our understanding of how cis-regulatory sequences determine the expression values.

Rewriting regulatory DNA to dissect and reprogram gene expression

Regulatory DNA sequences within enhancers and promoters bind transcription factors to encode cell type-specific patterns of gene expression. However, the regulatory effects and programmability of such DNA sequences remain difficult to map or predict because we have lacked scalable methods to precisely edit regulatory DNA and quantify the effects in an endogenous genomic context. Here we present an approach to measure the quantitative effects of hundreds of designed DNA sequence variants on gene expression, by combining pooled CRISPR prime editing with RNA fluorescence in situ hybridization and cell sorting (Variant-FlowFISH). We apply this method to mutagenize and rewrite regulatory DNA sequences in an enhancer and the promoter of PPIF in two immune cell lines. Of 672 variant-cell type pairs, we identify 497 that affect PPIF expression. These variants appear to act through a variety of mechanisms including disruption or optimization of existing transcription factor binding sites, as well as creation of de novo sites. Disrupting a single endogenous transcription factor binding site often led to large changes in expression (up to -40% in the enhancer, and -50% in the promoter). The same variant often had different effects across cell types and states, demonstrating a highly tunable regulatory landscape. We use these data to benchmark performance of sequence-based predictive models of gene regulation, and find that certain types of variants are not accurately predicted by existing models. Finally, we computationally design 185 small sequence variants (≤10 bp) and optimize them for specific effects on expression in silico. 84% of these rationally designed edits showed the intended direction of effect, and some had dramatic effects on expression (-100% to +202%). Variant-FlowFISH thus provides a powerful tool to map the effects of variants and transcription factor binding sites on gene expression, test and improve computational models of gene regulation, and reprogram regulatory DNA.

Design and deep learning of synthetic B-cell-specific promoters

Synthetic biology and deep learning synergistically revolutionize our ability for decoding and recoding DNA regulatory grammar. The B-cell-specific transcriptional regulation is intricate, and unlock the potential of B-cell-specific promoters as synthetic elements is important for B-cell engineering. Here, we designed and pooled synthesized 23 640 B-cell-specific promoters that exhibit larger sequence space, B-cell-specific expression, and enable diverse transcriptional patterns in B-cells. By MPRA (Massively parallel reporter assays), we deciphered the sequence features that regulate promoter transcriptional, including motifs and motif syntax (their combination and distance). Finally, we built and trained a deep learning model capable of predicting the transcriptional strength of the immunoglobulin V gene promoter directly from sequence. Prediction of thousands of promoter variants identified in the global human population shows that polymorphisms in promoters influence the transcription of immunoglobulin V genes, which may contribute to individual differences in adaptive humoral immune responses. Our work helps to decipher the transcription mechanism in immunoglobulin genes and offers thousands of non-similar promoters for B-cell engineering.

Deep flanking sequence engineering for efficient promoter design using DeepSEED

Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.

Pan-viral ORFs discovery using Massively Parallel Ribosome Profiling

Unveiling the complete proteome of viruses is crucial to our understanding of the viral life cycle and interaction with the host. We developed Massively Parallel Ribosome Profiling (MPRP) to experimentally determine open reading frames (ORFs) in 20,170 designed oligonucleotides across 679 human-associated viral genomes. We identified 5,381 ORFs, including 4,208 non-canonical ORFs, and show successful detection of both annotated coding sequences (CDSs) and reported non-canonical ORFs. By examining immunopeptidome datasets of infected cells, we found class I human leukocyte antigen (HLA-I) peptides originating from non-canonical ORFs identified through MPRP. By inspecting ribosome occupancies on the 5'UTR and CDS regions of annotated viral genes, we identified hundreds of upstream ORFs (uORFs) that negatively regulate the synthesis of canonical viral proteins. The unprecedented source of viral ORFs across a wide range of viral families, including highly pathogenic viruses, expands the repertoire of vaccine targets and exposes new cis-regulatory sequences in viral genomes.

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.

Combining TSS-MPRA and sensitive TSS profile dissimilarity scoring to study the sequence determinants of transcription initiation

Cis-regulatory elements (CREs) can be classified by the shapes of their transcription start site (TSS) profiles, which are indicative of distinct regulatory mechanisms. Massively parallel reporter assays (MPRAs) are increasingly being used to study CRE regulatory mechanisms, yet the degree to which MPRAs replicate individual endogenous TSS profiles has not been determined. Here, we present a new low-input MPRA protocol (TSS-MPRA) that enables measuring TSS profiles of episomal reporters as well as after lentiviral reporter chromatinization. To sensitively compare MPRA and endogenous TSS profiles, we developed a novel dissimilarity scoring algorithm (WIP score) that outperforms the frequently used earth mover's distance on experimental data. Using TSS-MPRA and WIP scoring on 500 unique reporter inserts, we found that short (153 bp) MPRA promoter inserts replicate the endogenous TSS patterns of ∼60% of promoters. Lentiviral reporter chromatinization did not improve fidelity of TSS-MPRA initiation patterns, and increasing insert size frequently led to activation of extraneous TSS in the MPRA that are not active in vivo. We discuss the implications of our findings, which highlight important caveats when using MPRAs to study transcription mechanisms. Finally, we illustrate how TSS-MPRA and WIP scoring can provide novel insights into the impact of transcription factor motif mutations and genetic variants on TSS patterns and transcription levels.

Evaluating single-cell variability in proteasomal decay

Gene expression is a stochastic process that leads to variability in mRNA and protein abundances even within an isogenic population of cells grown in the same environment. This variation, often called gene-expression noise, has typically been attributed to transcriptional and translational processes while ignoring the contributions of protein decay variability across cells. Here we estimate the single-cell protein decay rates of two degron GFPs in Saccharomyces cerevisiae using time-lapse microscopy. We find substantial cell-to-cell variability in the decay rates of the degron GFPs. We evaluate cellular features that explain the variability in the proteasomal decay and find that the amount of 20s catalytic beta subunit of the proteasome marginally explains the observed variability in the degron GFP half-lives. We propose alternate hypotheses that might explain the observed variability in the decay of the two degron GFPs. Overall, our study highlights the importance of studying the kinetics of the decay process at single-cell resolution and that decay rates vary at the single-cell level, and that the decay process is stochastic. A complex model of decay dynamics must be included when modeling stochastic gene expression to estimate gene expression noise.

Short tandem repeats are important contributors to silencer elements in T cells

The action of cis-regulatory elements with either activation or repression functions underpins the precise regulation of gene expression during normal development and cell differentiation. Gene activation by the combined activities of promoters and distal enhancers has been extensively studied in normal and pathological contexts. In sharp contrast, gene repression by cis-acting silencers, defined as genetic elements that negatively regulate gene transcription in a position-independent fashion, is less well understood. Here, we repurpose the STARR-seq approach as a novel high-throughput reporter strategy to quantitatively assess silencer activity in mammals. We assessed silencer activity from DNase hypersensitive I sites in a mouse T cell line. Identified silencers were associated with either repressive or active chromatin marks and enriched for binding motifs of known transcriptional repressors. CRISPR-mediated genomic deletions validated the repressive function of distinct silencers involved in the repression of non-T cell genes and genes regulated during T cell differentiation. Finally, we unravel an association of silencer activity with short tandem repeats, highlighting the role of repetitive elements in silencer activity. Our results provide a general strategy for genome-wide identification and characterization of silencer elements.

Transcription factor binding site orientation and order are major drivers of gene regulatory activity

The gene regulatory code and grammar remain largely unknown, precluding our ability to link phenotype to genotype in regulatory sequences. Here, using a massively parallel reporter assay (MPRA) of 209,440 sequences, we examine all possible pair and triplet combinations, permutations and orientations of eighteen liver-associated transcription factor binding sites (TFBS). We find that TFBS orientation and order have a major effect on gene regulatory activity. Corroborating these results with genomic analyses, we find clear human promoter TFBS orientation biases and similar TFBS orientation and order transcriptional effects in an MPRA that tested 164,307 liver candidate regulatory elements. Additionally, by adding TFBS orientation to a model that predicts expression from sequence we improve performance by 7.7%. Collectively, our results show that TFBS orientation and order have a significant effect on gene regulatory activity and need to be considered when analyzing the functional effect of variants on the activity of these sequences.

Systematic discovery of recombinases for efficient integration of large DNA sequences into the human genome

Large serine recombinases (LSRs) are DNA integrases that facilitate the site-specific integration of mobile genetic elements into bacterial genomes. Only a few LSRs, such as Bxb1 and PhiC31, have been characterized to date, with limited efficiency as tools for DNA integration in human cells. In this study, we developed a computational approach to identify thousands of LSRs and their DNA attachment sites, expanding known LSR diversity by >100-fold and enabling the prediction of their insertion site specificities. We tested their recombination activity in human cells, classifying them as landing pad, genome-targeting or multi-targeting LSRs. Overall, we achieved up to seven-fold higher recombination than Bxb1 and genome integration efficiencies of 40-75% with cargo sizes over 7 kb. We also demonstrate virus-free, direct integration of plasmid or amplicon libraries for improved functional genomics applications. This systematic discovery of recombinases directly from microbial sequencing data provides a resource of over 60 LSRs experimentally characterized in human cells for large-payload genome insertion without exposed DNA double-stranded breaks.

Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers

BACKGROUND

The largest sequence-based models of transcription control to date are obtained by predicting genome-wide gene regulatory assays across the human genome. This setting is fundamentally correlative, as those models are exposed during training solely to the sequence variation between human genes that arose through evolution, questioning the extent to which those models capture genuine causal signals.

RESULTS

Here we confront predictions of state-of-the-art models of transcription regulation against data from two large-scale observational studies and five deep perturbation assays. The most advanced of these sequence-based models, Enformer, by and large, captures causal determinants of human promoters. However, models fail to capture the causal effects of enhancers on expression, notably in medium to long distances and particularly for highly expressed promoters. More generally, the predicted impact of distal elements on gene expression predictions is small and the ability to correctly integrate long-range information is significantly more limited than the receptive fields of the models suggest. This is likely caused by the escalating class imbalance between actual and candidate regulatory elements as distance increases.

CONCLUSIONS

Our results suggest that sequence-based models have advanced to the point that in silico study of promoter regions and promoter variants can provide meaningful insights and we provide practical guidance on how to use them. Moreover, we foresee that it will require significantly more and particularly new kinds of data to train models accurately accounting for distal elements.

Massively parallel characterization of transcriptional regulatory elements in three diverse human cell types

The human genome contains millions of candidate cis-regulatory elements (CREs) with cell-type-specific activities that shape both health and myriad disease states. However, we lack a functional understanding of the sequence features that control the activity and cell-type-specific features of these CREs. Here, we used lentivirus-based massively parallel reporter assays (lentiMPRAs) to test the regulatory activity of over 680,000 sequences, representing a nearly comprehensive set of all annotated CREs among three cell types (HepG2, K562, and WTC11), finding 41.7% to be functional. By testing sequences in both orientations, we find promoters to have significant strand orientation effects. We also observe that their 200 nucleotide cores function as non-cell-type-specific 'on switches' providing similar expression levels to their associated gene. In contrast, enhancers have weaker orientation effects, but increased tissue-specific characteristics. Utilizing our lentiMPRA data, we develop sequence-based models to predict CRE function with high accuracy and delineate regulatory motifs. Testing an additional lentiMPRA library encompassing 60,000 CREs in all three cell types, we further identified factors that determine cell-type specificity. Collectively, our work provides an exhaustive catalog of functional CREs in three widely used cell lines, and showcases how large-scale functional measurements can be used to dissect regulatory grammar.

The origin of genetic and metabolic systems: Evolutionary structuralinsights

DNA is derived from reverse transcription and its origin is related to reverse transcriptase, DNA polymerase and integrase. The gene structure originated from the evolution of the first RNA polymerase. Thus, an explanation of the origin of the genetic system must also explain the evolution of these enzymes. This paper proposes a polymer structure model, termed the stable complex evolution model, which explains the evolution of enzymes and functional molecules. Enzymes evolved their functions by forming locally tightly packed complexes with specific substrates. A metabolic reaction can therefore be considered to be the result of adaptive evolution in this way when a certain essential molecule is lacking in a cell. The evolution of the primitive genetic and metabolic systems was thus coordinated and synchronized. According to the stable complex model, almost all functional molecules establish binding affinity and specific recognition through complementary interactions, and functional molecules therefore have the nature of being auto-reactive. This is thermodynamically favorable and leads to functional duplication and self-organization. Therefore, it can be speculated that biological systems have a certain tendency to maintain functional stability or are influenced by an inherent selective power. The evolution of dormant bacteria may support this hypothesis, and inherent selectivity can be unified with natural selection at the molecular level.

Strand Asymmetries Across Genomic Processes

Across biological systems, a number of genomic processes, including transcription, replication, DNA repair, and transcription factor binding, display intrinsic directionalities. These directionalities are reflected in the asymmetric distribution of nucleotides, motifs, genes, transposon integration sites, and other functional elements across the two complementary strands. Strand asymmetries, including GC skews and mutational biases, have shaped the nucleotide composition of diverse organisms. The investigation of strand asymmetries often serves as a method to understand underlying biological mechanisms, including protein binding preferences, transcription factor interactions, retrotransposition, DNA damage and repair preferences, transcription-replication collisions, and mutagenesis mechanisms. Research into this subject also enables the identification of functional genomic sites, such as replication origins and transcription start sites. Improvements in our ability to detect and quantify DNA strand asymmetries will provide insights into diverse functionalities of the genome, the contribution of different mutational mechanisms in germline and somatic mutagenesis, and our knowledge of genome instability and evolution, which all have significant clinical implications in human disease, including cancer. In this review, we describe key developments that have been made across the field of genomic strand asymmetries, as well as the discovery of associated mechanisms.

Widespread perturbation of ETS factor binding sites in cancer

Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted.

DeePromClass: Delineator for Eukaryotic Core Promoters Employing Deep Neural Networks

Computational promoter identification in eukaryotes is a classical biological problem that should be refurbished with the availability of an avalanche of experimental data and emerging deep learning technologies. The current knowledge indicates that eukaryotic core promoters display multifarious signals such as TATA-Box, Inr element, TCT, and Pause-button, etc., and structural motifs such as G-quadruplexes. In the present study, we combined the power of deep learning with a plethora of promoter motifs to delineate promoter and non-promoters gleaned from the statistical properties of DNA sequence arrangement. To this end, we implemented convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network architecture for five model systems with [-100 to +50] segments relative to the transcription start site being the core promoter. Unlike previous state-of-the-art tools, which furnish a binary decision of promoter or non-promoter, we classify a chunk of 151mer sequence into a promoter along with the consensus signal type or a non-promoter. The combined CNN-LSTM model; we call "DeePromClass", achieved testing accuracy of 90.6%, 93.6%, 91.8%, 86.5%, and 84.0% for S. cerevisiae, C. elegans, D. melanogaster, Mus musculus, and Homo sapiens respectively. In total, our tool provides an insightful update on next-generation promoter prediction tools for promoter biologists.

The sound of silence: Transgene silencing in mammalian cell engineering

To elucidate principles operating in native biological systems and to develop novel biotechnologies, synthetic biology aims to build and integrate synthetic gene circuits within native transcriptional networks. The utility of synthetic gene circuits for cell engineering relies on the ability to control the expression of all constituent transgene components. Transgene silencing, defined as the loss of expression over time, persists as an obstacle for engineering primary cells and stem cells with transgenic cargos. In this review, we highlight the challenge that transgene silencing poses to the robust engineering of mammalian cells, outline potential molecular mechanisms of silencing, and present approaches for preventing transgene silencing. We conclude with a perspective identifying future research directions for improving the performance of synthetic gene circuits.

Identification and functional analysis of genetic variants of ISL1 gene promoter in human atrial septal defects

BACKGROUND

Atrial septal defect (ASD) is a common type of congenital heart disease. A gene promoter plays pivotal role in the disease development. This study was designed to investigate the pathological role of variants of the ISL1 gene promoter region in ASD patients.

METHODS

Total DNA extracted from 625 subjects, including 332 ASD patients and 293 healthy controls, was sequenced to identify variants in the promoter region of ISL1 gene. Further functional analyses of the variants were performed with dual luciferase reporter assay and electrophoretic mobility shift assay (EMSA). All possible binding sites of transcription factor affected by the identified variants were predicted using the JASPAR database.

RESULTS

Four variants in the ISL1 gene promoter were found only in patients with ASD by sequencing. Three of the four variants [g.4923 G > C (rs541081886), g.5079 A > G (rs1371835943) and g.5309 G > A (rs116222082)] significantly decreased the transcriptional activities compared with the wild-type ISL1 gene promoter (p < 0.05). The EMSA revealed that these variants [g.4923 G > C (rs541081886), g.5079 A > G (rs1371835943) and g.5309 G > A (rs116222082)] in the ISL1 gene promoter affected the number and affinity of binding sites of transcription factors. Further analysis with the online JASPAR database demonstrated that a cluster of putative binding sites for transcription factors may be altered by these variants.

CONCLUSIONS

These sequence variants identified from the promoter region of ISL1 gene in ASD patients are probably involved in the development of ASD by affecting the transcriptional activity and altering ISL1 levels. Therefore, these findings may provide new insights into the molecular etiology and potential therapeutic strategy of ASD.

Performance of abiotic stress-inducible synthetic promoters in genetically engineered hybrid poplar (Populus tremula × Populus alba)

Abiotic stresses can cause significant damage to plants. For sustainable bioenergy crop production, it is critical to generate resistant crops to such stress. Engineering promoters to control the precise expression of stress resistance genes is a very effective way to address the problem. Here we developed stably transformed Populus tremula × Populus alba hybrid poplar (INRA 717-1B4) containing one-of-six synthetic drought stress-inducible promoters (SDs; SD9-1, SD9-2, SD9-3, SD13-1, SD18-1, and SD18-3) identified previously by transient transformation assays. We screened green fluorescent protein (GFP) induction in poplar under osmotic stress conditions. Of six transgenic lines containing synthetic promoter, three lines (SD18-1, 9-2, and 9-3) had significant GFP expression in both salt and osmotic stress treatments. Each synthetic promoter employed heptamerized repeats of specific and short cis-regulatory elements (7 repeats of 7-8 bases). To verify whether the repeats of longer sequences can improve osmotic stress responsiveness, a transgenic poplar containing the synthetic promoter of the heptamerized entire SD9 motif (20 bases, containing all partial SD9 motifs) was generated and measured for GFP induction under osmotic stress. The heptamerized entire SD9 motif did not result in higher GFP expression than the shorter promoters consisting of heptamerized SD9-1, 9-2, and 9-3 (partial SD9) motifs. This result indicates that shorter synthetic promoters (~50 bp) can be used for versatile control of gene expression in transgenic poplar. These synthetic promoters will be useful tools to engineer stress-resilient bioenergy tree crops in the future.

Repetitive DNA symmetry elements negatively regulate gene expression in embryonic stem cells

Transcription factor (TF) binding to genomic DNA elements constitutes one of the key mechanisms that regulates gene expression program in cells. Both consensus and nonconsensus DNA sequence elements influence the recognition specificity of TFs. Based on the analysis of experimentally determined c-Myc binding preferences to genomic DNA, here we statistically predict that certain repetitive, nonconsensus DNA symmetry elements can relatively reduce TF-DNA binding preferences. This is in contrast to a different set of repetitive, nonconsensus symmetry elements that can increase the strength of TF-DNA binding. Using c-Myc enhancer reporter system containing consensus motif flanked by nonconsensus sequences in embryonic stem cells, we directly demonstrate that the enrichment in such negatively regulating repetitive symmetry elements is sufficient to reduce the gene expression level compared with native genomic sequences. Negatively regulating repetitive symmetry elements around consensus c-Myc motif and DNA sequences containing consensus c-Myc motif flanked by entirely randomized sequences show similar expression baseline. A possible explanation for this observation is that rather than complete repression, negatively regulating repetitive symmetry elements play a regulatory role in fine-tuning the reduction of gene expression, most probably by binding TFs other than c-Myc.

Generating specificity in genome regulation through transcription factor sensitivity to chromatin

Cell type-specific gene expression relies on transcription factors (TFs) binding DNA sequence motifs embedded in chromatin. Understanding how motifs are accessed in chromatin is crucial to comprehend differential transcriptional responses and the phenotypic impact of sequence variation. Chromatin obstacles to TF binding range from DNA methylation to restriction of DNA access by nucleosomes depending on their position, composition and modification. In vivo and in vitro approaches now enable the study of TF binding in chromatin at unprecedented resolution. Emerging insights suggest that TFs vary in their ability to navigate chromatin states. However, it remains challenging to link binding and transcriptional outcomes to molecular characteristics of TFs or the local chromatin substrate. Here, we discuss our current understanding of how TFs access DNA in chromatin and novel techniques and directions towards a better understanding of this critical step in genome regulation.

Compatibility rules of human enhancer and promoter sequences

Gene regulation in the human genome is controlled by distal enhancers that activate specific nearby promoters1. A proposed model for this specificity is that promoters have sequence-encoded preferences for certain enhancers, for example, mediated by interacting sets of transcription factors or cofactors2. This 'biochemical compatibility' model has been supported by observations at individual human promoters and by genome-wide measurements in Drosophila3-9. However, the degree to which human enhancers and promoters are intrinsically compatible has not yet been systematically measured, and how their activities combine to control RNA expression remains unclear. Here we design a high-throughput reporter assay called enhancer × promoter self-transcribing active regulatory region sequencing (ExP STARR-seq) and applied it to examine the combinatorial compatibilities of 1,000 enhancer and 1,000 promoter sequences in human K562 cells. We identify simple rules for enhancer-promoter compatibility, whereby most enhancers activate all promoters by similar amounts, and intrinsic enhancer and promoter activities multiplicatively combine to determine RNA output (R2 = 0.82). In addition, two classes of enhancers and promoters show subtle preferential effects. Promoters of housekeeping genes contain built-in activating motifs for factors such as GABPA and YY1, which decrease the responsiveness of promoters to distal enhancers. Promoters of variably expressed genes lack these motifs and show stronger responsiveness to enhancers. Together, this systematic assessment of enhancer-promoter compatibility suggests a multiplicative model tuned by enhancer and promoter class to control gene transcription in the human genome.

The potential consequences of bidirectional promoter methylation on GLA and HNRNPH2 expression in Fabry disease phenotypes in a family of patients carrying a GLA deletion variant

Fabry disease (FD) is a rare inherited disease characterized by a wide range of symptoms attributed to GLA mutations resulting in defective α-galactosidase A (α-Gal A) and accumulation of glycosphingolipids. The GLA locus is paired in a divergent manner with the heterogeneous nuclear ribonucleoprotein HNRNPH2 locus mapped in the RPL36A-HNRNPH2 readthrough locus. As a follow-up to our recent finding of the co-regulation of GLA and HNRNPH2 via a bidirectional promoter (BDP) in normal kidney and skin cells, the potential accumulative influence of BDP methylation and GLA mutation on the severity of FD in patients from the same family, two males and two females carrying a GLA deletion mutation, c.1033_1034delTC (p.Ser345Argfs) was addressed in the present study. The molecular analyses of the FD patients compared with the control revealed that the expression of GLA was significantly low (P<0.05), and HNRNPH2 showed a tendency of low expression (P=0.1) when BDP methylation was elevated in FD patients, compared with low BDP methylation and high GLA expression (P<0.05), and a high trend of HNRNPH2 expression in normal individuals. The accumulative effects of the mutation and BDP methylation with the severity of the disease were observed in three patients. One male FD patient, a member of the FD family diagnosed with progressive loss of kidney function, hypertension, and eventually a stroke, and the lowest level of α-Gal A enzyme activity showed the highest BDP DNA methylation level. It is concluded that the DNA methylation of GLA-HNRNPH2 BDP may serve a role in diagnosing and treating FD.

A synthetic synthesis to explore animal evolution and development

Identifying the general principles by which genotypes are converted into phenotypes remains a challenge in the post-genomic era. We still lack a predictive understanding of how genes shape interactions among cells and tissues in response to signalling and environmental cues, and hence how regulatory networks generate the phenotypic variation required for adaptive evolution. Here, we discuss how techniques borrowed from synthetic biology may facilitate a systematic exploration of evolvability across biological scales. Synthetic approaches permit controlled manipulation of both endogenous and fully engineered systems, providing a flexible platform for investigating causal mechanisms in vivo. Combining synthetic approaches with multi-level phenotyping (phenomics) will supply a detailed, quantitative characterization of how internal and external stimuli shape the morphology and behaviour of living organisms. We advocate integrating high-throughput experimental data with mathematical and computational techniques from a variety of disciplines in order to pursue a comprehensive theory of evolution.

This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

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.

Molecular and experimental tools to design synthetic enhancers

Understanding the grammar of enhancers and how they regulate gene expression is key for both basic research and for the pharma and biotech industries. The design and characterization of synthetic enhancers can expand the known regulatory space. This is achieved by the utilization of DNA Oligo Libraries (OLs), which facilitates screening of as many as millions of synthetic enhancer variants simultaneously. This review includes the latest commercial DNA OL synthesis technology and its capabilities, and a general 'know-how' guide for the design, construction, and analysis of OL-based synthetic enhancer characterization experiments. Specifically, we focus on synthetic-enhancer-based massively parallel reporter assay, Sort-seq methodologies (e.g. flow cytometry, deep sequencing), and a brief description of machine learning-based attempts for OL-analysis and follow-up validation experiments.

Bioinformatic Design of Dendritic Cell-Specific Synthetic Promoters

Next-generation DNA vectors for cancer immunotherapies and vaccine development require promoters eliciting predefined transcriptional activities specific to target cell types, such as dendritic cells (DCs), which underpin immune response. In this study, we describe the de novo design of DC-specific synthetic promoters via in silico assembly of cis-transcription factor response elements (TFREs) that harness the DC transcriptional landscape. Using computational genome mining approaches, candidate TFREs were identified within promoter sequences of highly expressed DC-specific genes or those exhibiting an upregulated expression during DC maturation. Individual TFREs were then screened in vitro in a target DC line and off-target cell lines derived from skeletal muscle, fibroblast, epithelial, and endothelial cells using homotypic (TFRE repeats in series) reporter constructs. Based on these data, a library of heterotypic promoter assemblies varying in the TFRE composition, copy number, and sequential arrangement was constructed and tested in vitro to identify DC-specific promoters. Analysis of the transcriptional activity and specificity of these promoters unraveled underlying design rules, primarily TFRE composition, which govern the DC-specific synthetic promoter activity. Using these design rules, a second library of exclusively DC-specific promoters exhibiting varied transcriptional activities was generated. All DC-specific synthetic promoter assemblies exhibited >5-fold activity in the target DC line relative to off-target cell lines, with transcriptional activities ranging from 8 to 67% of the nonspecific human cytomegalovirus (hCMV-IE1) promoter. We show that bioinformatic analysis of a mammalian cell transcriptional landscape is an effective strategy for de novo design of cell-type-specific synthetic promoters with precisely controllable transcriptional activities.

Massively parallel reporter perturbation assays uncover temporal regulatory architecture during neural differentiation

Gene regulatory elements play a key role in orchestrating gene expression during cellular differentiation, but what determines their function over time remains largely unknown. Here, we perform perturbation-based massively parallel reporter assays at seven early time points of neural differentiation to systematically characterize how regulatory elements and motifs within them guide cellular differentiation. By perturbing over 2,000 putative DNA binding motifs in active regulatory regions, we delineate four categories of functional elements, and observe that activity direction is mostly determined by the sequence itself, while the magnitude of effect depends on the cellular environment. We also find that fine-tuning transcription rates is often achieved by a combined activity of adjacent activating and repressing elements. Our work provides a blueprint for the sequence components needed to induce different transcriptional patterns in general and specifically during neural differentiation.

How gene regulatory elements regulate gene expression during cellular differentiation remains largely unknown. Here the authors use perturbation-based massively parallel reporter assays at early time points of neural differentiation to systematically characterize how regulatory elements and motifs within them guide different transcriptional patterns.

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).

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Restriction-based cloning of DNA pools

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Preservation of complexity of thousands of constructs

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Used to investigate genetic and transcriptomic machineries

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

Sequence determinants of human gene regulatory elements

DNA can determine where and when genes are expressed, but the full set of sequence determinants that control gene expression is unknown. Here, we measured the transcriptional activity of DNA sequences that represent an ~100 times larger sequence space than the human genome using massively parallel reporter assays (MPRAs). Machine learning models revealed that transcription factors (TFs) generally act in an additive manner with weak grammar and that most enhancers increase expression from a promoter by a mechanism that does not appear to involve specific TF–TF interactions. The enhancers themselves can be classified into three types: classical, closed chromatin and chromatin dependent. We also show that few TFs are strongly active in a cell, with most activities being similar between cell types. Individual TFs can have multiple gene regulatory activities, including chromatin opening and enhancing, promoting and determining transcription start site (TSS) activity, consistent with the view that the TF binding motif is the key atomic unit of gene expression.

Analysis of massively parallel reporter assays measuring the transcriptional activity of DNA sequences indicates that most transcription factor (TF) activity is additive and does not rely on specific TF–TF interactions. Individual TFs can have different gene regulatory activities.

Functional and HRE motifs count analysis of induction of selected hypoxia-responsive genes by HIF-1 and HIF-2 in human umbilical endothelial cells

We analyzed the effects of selective knockdown of either HIF-1α or HIF-2α on the transcriptional response to hypoxia of human umbilical endothelial cells at two time-points (2 h and 8 h) of hypoxia. We focused on 13 previously identified hypoxia-responsive genes, pre-selected to have different activation kinetics and different proportions of HRE motifs annotated to either HIF-1 or HIF-2 in open promoters - open chromatin DNase-hypersensitive sites (DHS) regions within ±1 kb of the gene start. We report that genes activated by both HIF-1 and 2 tend to be activated earlier than genes activated by HIF-1 only, which, in turn, tend to be activated earlier than genes activated by HIF-2 only. Moreover, for the 13 analyzed genes, we found that the effect of silencing HIF1A on the gene induction by hypoxia is greater for the genes with more HRE motifs annotated to HIF-1 in their promoter open chromatin DHS regions within ±1 kb and also within ±10 kb of the gene start. We corroborated and extended this finding by showing that among 232 genes previously identified as activated by hypoxia, the genes with ChIP-seq peak(s) for HIF-1α within a ±10 kb flank of the gene start contain more HRE motifs annotated to HIF-1 in the DHS regions within this flank than the genes with no ChIP-seq peaks. Also in the whole genome, the DHS regions intersecting ChIP-seq peaks for HIF-1α contain more HRE motifs annotated to HIF-1 than the DHS regions not intersecting the ChIP-seq peaks. This suggests a mechanism, by which higher promoter content of HRE motifs in DHS regions increases HIF-1 binding, which in turn increases gene induction by hypoxia.

Genome-wide mRNA profiling identifies X-box-binding protein 1 (XBP1) as an IRE1 and PUMA repressor

Accumulation of misfolded proteins in ER activates the unfolded protein response (UPR), a multifunctional signaling pathway that is important for cell survival. The UPR is regulated by three ER transmembrane sensors, one of which is inositol-requiring protein 1 (IRE1). IRE1 activates a transcription factor, X-box-binding protein 1 (XBP1), by removing a 26-base intron from XBP1 mRNA that generates spliced XBP1 mRNA (XBP1s). To search for XBP1 transcriptional targets, we utilized an XBP1s-inducible human cell line to limit XBP1 expression in a controlled manner. We also verified the identified XBP1-dependent genes with specific silencing of this transcription factor during pharmacological ER stress induction with both an N-linked glycosylation inhibitor (tunicamycin) and a non-competitive inhibitor of the sarco/endoplasmic reticulum Ca²⁺ ATPase (SERCA) (thapsigargin). We then compared those results to the XBP1s-induced cell line without pharmacological ER stress induction. Using next‐generation sequencing followed by bioinformatic analysis of XBP1-binding motifs, we defined an XBP1 regulatory network and identified XBP1 as a repressor of PUMA (a proapoptotic gene) and IRE1 mRNA expression during the UPR. Our results indicate impairing IRE1 activity during ER stress conditions accelerates cell death in ER-stressed cells, whereas elevating XBP1 expression during ER stress using an inducible cell line correlated with a clear prosurvival effect and reduced PUMA protein expression. Although further studies will be required to test the underlying molecular mechanisms involved in the relationship between these genes with XBP1, these studies identify a novel repressive role of XBP1 during the UPR.

ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation

Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understanding gene expression patterns. While there are many attempts to develop computational promoter and enhancer identification methods, reliable tools to analyze long genomic sequences are still lacking. Prediction methods often perform poorly on the genome-wide scale because the number of negatives is much higher than that in the training sets. To address this issue, we propose a dynamic negative set updating scheme with a two-model approach, using one model for scanning the genome and the other one for testing candidate positions. The developed method achieves good genome-level performance and maintains robust performance when applied to other vertebrate species, without re-training. Moreover, the unannotated predicted regulatory regions made on the human genome are enriched for disease-associated variants, suggesting them to be potentially true regulatory elements rather than false positives. We validated high scoring "false positive" predictions using reporter assay and all tested candidates were successfully validated, demonstrating the ability of our method to discover novel human regulatory regions.

Quantitative imaging of transcription in living Drosophila embryos reveals the impact of core promoter motifs on promoter state dynamics

Genes are expressed in stochastic transcriptional bursts linked to alternating active and inactive promoter states. A major challenge in transcription is understanding how promoter composition dictates bursting, particularly in multicellular organisms. We investigate two key Drosophila developmental promoter motifs, the TATA box (TATA) and the Initiator (INR). Using live imaging in Drosophila embryos and new computational methods, we demonstrate that bursting occurs on multiple timescales ranging from seconds to minutes. TATA-containing promoters and INR-containing promoters exhibit distinct dynamics, with one or two separate rate-limiting steps respectively. A TATA box is associated with long active states, high rates of polymerase initiation, and short-lived, infrequent inactive states. In contrast, the INR motif leads to two inactive states, one of which relates to promoter-proximal polymerase pausing. Surprisingly, the model suggests pausing is not obligatory, but occurs stochastically for a subset of polymerases. Overall, our results provide a rationale for promoter switching during zygotic genome activation.

Identification of variants of ISL1 gene promoter and cellular functions in isolated ventricular septal defects

Ventricular septal defects (VSDs) are the most common congenital heart defects (CHDs). Studies have documented that ISL1 has a crucial impact on cardiac growth, but the role of variants in the ISL1 gene promoter in patients with VSD has not been explored. In 400 subjects (200 patients with isolated and sporadic VSDs: 200 healthy controls), we investigated the ISL1 gene promoter variant and performed cellular functional experiments by using the dual-luciferase reporter assay to verify the impact on gene expression. In the ISL1 promoter, five variants were found only in patients with VSD by sequencing. Cellular functional experiments demonstrated that three variants decreased the transcriptional activity of the ISL1 promoter (P < 0.05). Further analysis with the online JASPAR database demonstrated that a cluster of putative binding sites for transcription factors may be altered by these variants, possibly resulting in change of ISL1 protein expression and VSD formation. Our study has, for the first time, identified novel variants in the ISL1 gene promoter region in the Han Chinese patients with isolated and sporadic VSD. In addition, the cellular functional experiments, electrophoretic mobility shift assay, and bioinformatic analysis have demonstrated that these variants significantly alter the expression of the ISL1 gene and affect the binding of transcription factors, likely resulting in VSD. Therefore, this study may provide new insights into the role of the gene promoter region for a better understanding of genetic basis of the formation of CHDs and may promote further investigations on mechanism of the formation of CHDs.

Genetic and epigenetic features of promoters with ubiquitous chromatin accessibility support ubiquitous transcription of cell-essential genes

Gene expression is controlled by regulatory elements within accessible chromatin. Although most regulatory elements are cell type-specific, a subset is accessible in nearly all the 517 human and 94 mouse cell and tissue types assayed by the ENCODE consortium. We systematically analyzed 9000 human and 8000 mouse ubiquitously-accessible candidate cis-regulatory elements (cCREs) with promoter-like signatures (PLSs) from ENCODE, which we denote ubi-PLSs. These are more CpG-rich than non-ubi-PLSs and correspond to genes with ubiquitously high transcription, including a majority of cell-essential genes. ubi-PLSs are enriched with motifs of ubiquitously-expressed transcription factors and preferentially bound by transcriptional cofactors regulating ubiquitously-expressed genes. They are highly conserved between human and mouse at the synteny level but exhibit frequent turnover of motif sites; accordingly, ubi-PLSs show increased variation at their centers compared with flanking regions among the ∼186 thousand human genomes sequenced by the TOPMed project. Finally, ubi-PLSs are enriched in genes implicated in Mendelian diseases, especially diseases broadly impacting most cell types, such as deficiencies in mitochondrial functions. Thus, a set of roughly 9000 mammalian promoters are actively maintained in an accessible state across cell types by a distinct set of transcription factors and cofactors to ensure the transcriptional programs of cell-essential genes.

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.

Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters

Targeted engineering of plant gene expression holds great promise for ensuring food security and for producing biopharmaceuticals in plants. However, this engineering requires thorough knowledge of cis-regulatory elements to precisely control either endogenous or introduced genes. To generate this knowledge, we used a massively parallel reporter assay to measure the activity of nearly complete sets of promoters from Arabidopsis, maize and sorghum. We demonstrate that core promoter elements-notably the TATA box-as well as promoter GC content and promoter-proximal transcription factor binding sites influence promoter strength. By performing the experiments in two assay systems, leaves of the dicot tobacco and protoplasts of the monocot maize, we detect species-specific differences in the contributions of GC content and transcription factors to promoter strength. Using these observations, we built computational models to predict promoter strength in both assay systems, allowing us to design highly active promoters comparable in activity to the viral 35S minimal promoter. Our results establish a promising experimental approach to optimize native promoter elements and generate synthetic ones with desirable features.

Current Practice in Bicistronic IRES Reporter Use: A Systematic Review

Bicistronic reporter assays have been instrumental for transgene expression, understanding of internal ribosomal entry site (IRES) translation, and identification of novel cap-independent translational elements (CITE). We observed a large methodological variability in the use of bicistronic reporter assays and data presentation or normalization procedures. Therefore, we systematically searched the literature for bicistronic IRES reporter studies and analyzed methodological details, data visualization, and normalization procedures. Two hundred fifty-seven publications were identified using our search strategy (published 1994-2020). Experimental studies on eukaryotic adherent cell systems and the cell-free translation assay were included for further analysis. We evaluated the following methodological details for 176 full text articles: the bicistronic reporter design, the cell line or type, transfection methods, and time point of analyses post-transfection. For the cell-free translation assay, we focused on methods of in vitro transcription, type of translation lysate, and incubation times and assay temperature. Data can be presented in multiple ways: raw data from individual cistrons, a ratio of the two, or fold changes thereof. In addition, many different control experiments have been suggested when studying IRES-mediated translation. In addition, many different normalization and control experiments have been suggested when studying IRES-mediated translation. Therefore, we also categorized and summarized their use. Our unbiased analyses provide a representative overview of bicistronic IRES reporter use. We identified parameters that were reported inconsistently or incompletely, which could hamper data reproduction and interpretation. On the basis of our analyses, we encourage adhering to a number of practices that should improve transparency of bicistronic reporter data presentation and improve methodological descriptions to facilitate data replication.

Fish-Ing for Enhancers in the Heart

Precise control of gene expression is crucial to ensure proper development and biological functioning of an organism. Enhancers are non-coding DNA elements which play an essential role in regulating gene expression. They contain specific sequence motifs serving as binding sites for transcription factors which interact with the basal transcription machinery at their target genes. Heart development is regulated by intricate gene regulatory network ensuring precise spatiotemporal gene expression program. Mutations affecting enhancers have been shown to result in devastating forms of congenital heart defect. Therefore, identifying enhancers implicated in heart biology and understanding their mechanism is key to improve diagnosis and therapeutic options. Despite their crucial role, enhancers are poorly studied, mainly due to a lack of reliable way to identify them and determine their function. Nevertheless, recent technological advances have allowed rapid progress in enhancer discovery. Model organisms such as the zebrafish have contributed significant insights into the genetics of heart development through enabling functional analyses of genes and their regulatory elements in vivo. Here, we summarize the current state of knowledge on heart enhancers gained through studies in model organisms, discuss various approaches to discover and study their function, and finally suggest methods that could further advance research in this field.

What do Transcription Factors Interact With?

Although we have made significant progress, we still possess a limited understanding of how genomic and epigenomic information directs gene expression programs through sequence-specific transcription factors (TFs). Extensive research has settled on three general classes of TF targets in metazoans: promoter accessibility via chromatin regulation (e.g., SAGA), assembly of the general transcription factors on promoter DNA (e.g., TFIID), and recruitment of RNA polymerase (Pol) II (e.g., Mediator) to establish a transcription pre-initiation complex (PIC). Here we discuss TFs and their targets. We also place this in the context of our current work with Saccharomyces (yeast), where we find that promoters typically lack an architecture that supports TF function. Moreover, yeast promoters that support TF binding also display interactions with cofactors like SAGA and Mediator, but not TFIID. It is unknown to what extent all genes in metazoans require TFs and their cofactors.

Orthogonal control of mean and variability of endogenous genes in a human cell line

Stochastic fluctuations at the transcriptional level contribute to isogenic cell-to-cell heterogeneity in mammalian cell populations. However, we still have no clear understanding of the repercussions of this heterogeneity, given the lack of tools to independently control mean expression and variability of a gene. Here, we engineer a synthetic circuit to modulate mean expression and heterogeneity of transgenes and endogenous human genes. The circuit, a Tunable Noise Rheostat (TuNR), consists of a transcriptional cascade of two inducible transcriptional activators, where the output mean and variance can be modulated by two orthogonal small molecule inputs. In this fashion, different combinations of the inputs can achieve the same mean but with different population variability. With TuNR, we achieve low basal expression, over 1000-fold expression of a transgene product, and up to 7-fold induction of the endogenous gene NGFR. Importantly, for the same mean expression level, we are able to establish varying degrees of heterogeneity in expression within an isogenic population, thereby decoupling gene expression noise from its mean. TuNR is therefore a modular tool that can be used in mammalian cells to enable direct interrogation of the implications of cell-to-cell variability.

Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems

A crucial step towards engineering biological systems is the ability to precisely tune the genetic response to environmental stimuli. In the case of Escherichia coli inducible promoters, our incomplete understanding of the relationship between sequence composition and gene expression hinders our ability to predictably control transcriptional responses. Here, we profile the expression dynamics of 8269 rationally designed, IPTG-inducible promoters that collectively explore the individual and combinatorial effects of RNA polymerase and LacI repressor binding site strengths. We then fit a statistical mechanics model to measured expression that accurately models gene expression and reveals properties of theoretically optimal inducible promoters. Furthermore, we characterize three alternative promoter architectures and show that repositioning binding sites within promoters influences the types of combinatorial effects observed between promoter elements. In total, this approach enables us to deconstruct relationships between inducible promoter elements and discover practical insights for engineering inducible promoters with desirable characteristics.

Unexplored regulatory sequences of divergently paired GLA and HNRNPH2 loci pertinent to Fabry disease in human kidney and skin cells: Presence of an active bidirectional promoter

Fabry disease (FD) is a rare hereditary disorder characterized by a wide range of symptoms caused by a variety of mutations in the galactosidase α (GLA) gene. The heterogeneous nuclear ribonucleoprotein (HNRNPH2) gene is divergently paired with GLA on chromosome X and is thought to be implicated in FD. However, insufficient information is available on the regulatory mechanisms associated with the expression of HNRNPH2 and the GLA loci. Therefore, the current study performed bioinformatics analyses to assess the GLA and HNRNPH2 loci and investigate the regulatory mechanisms involved in the expression of each gene. The regulatory mechanisms underlying GLA and HNRNPH2 were revealed. The expression of each gene was associated with a bidirectional promoter (BDP) characterized by the absence of TATA box motifs and the presence of specific transcription factor binding sites (TFBSs) and a CpG Island (CGI). The nuclear run-on transcription assay confirmed the activity of BDP GLA and HNRNPH2 transcription in 293T. Methylation-specific PCR analysis demonstrated a statistically significant variation in the DNA methylation pattern of BDP in several cell lines, including human adult epidermal keratinocytes (AEKs), human renal glomerular endothelial cells, human renal epithelial cells and 293T cells. The highest observed significance was demonstrated in AEKs (P<0.05). The results of the chromatin-immunoprecipitation assay using 293T cells identified specific TFBS motifs for Yin Yang 1 and nuclear respiratory factor 1 transcription factors in BDPs. The National Center for Biotechnology Information-single nucleotide polymorphism database revealed pathogenic variants in the BDP sequence. Additionally, a previously reported variant associated with a severe heterozygous female case of GLA FD was mapped in BDP. The results of the present study suggested that the expression of the divergent paired loci, GLA and HNRNPH2, were controlled by BDP. Mutations in BDP may also serve a role in FD and may explain clinical disease diversity.

Molecular and evolutionary processes generating variation in gene expression

Heritable variation in gene expression is common within and between species. This variation arises from mutations that alter the form or function of molecular gene regulatory networks that are then filtered by natural selection. High-throughput methods for introducing mutations and characterizing their cis- and trans-regulatory effects on gene expression (particularly, transcription) are revealing how different molecular mechanisms generate regulatory variation, and studies comparing these mutational effects with variation seen in the wild are teasing apart the role of neutral and non-neutral evolutionary processes. This integration of molecular and evolutionary biology allows us to understand how the variation in gene expression we see today came to be and to predict how it is most likely to evolve in the future.

Systematic identification of cis-regulatory variants that cause gene expression differences in a yeast cross

Sequence variation in regulatory DNA alters gene expression and shapes genetically complex traits. However, the identification of individual, causal regulatory variants is challenging. Here, we used a massively parallel reporter assay to measure the cis-regulatory consequences of 5832 natural DNA variants in the promoters of 2503 genes in the yeast Saccharomyces cerevisiae. We identified 451 causal variants, which underlie genetic loci known to affect gene expression. Several promoters harbored multiple causal variants. In five promoters, pairs of variants showed non-additive, epistatic interactions. Causal variants were enriched at conserved nucleotides, tended to have low derived allele frequency, and were depleted from promoters of essential genes, which is consistent with the action of negative selection. Causal variants were also enriched for alterations in transcription factor binding sites. Models integrating these features provided modest, but statistically significant, ability to predict causal variants. This work revealed a complex molecular basis for cis-acting regulatory variation.