Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers

DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues

Homeodomains (HDs) are the second largest class of DNA binding domains (DBDs) among eukaryotic sequence-specific transcription factors (TFs) and are the TF structural class with the largest number of disease-associated mutations in the Human Gene Mutation Database (HGMD). Despite numerous structural studies and large-scale analyses of HD DNA binding specificity, HD-DNA recognition is still not fully understood. Here, we analyze 92 human HD mutants, including disease-associated variants and variants of uncertain significance (VUS), for their effects on DNA binding activity. Many of the variants alter DNA binding affinity and/or specificity. Detailed biochemical analysis and structural modeling identifies 14 previously unknown specificity-determining positions, 5 of which do not contact DNA. The same missense substitution at analogous positions within different HDs often exhibits different effects on DNA binding activity. Variant effect prediction tools perform moderately well in distinguishing variants with altered DNA binding affinity, but poorly in identifying those with altered binding specificity. Our results highlight the need for biochemical assays of TF coding variants and prioritize dozens of variants for further investigations into their pathogenicity and the development of clinical diagnostics and precision therapies.

UV irradiation remodels the specificity landscape of transcription factors

Somatic mutations are highly enriched at transcription factor (TF) binding sites, with the strongest trend being observed for ultraviolet light (UV)-induced mutations in melanomas. One of the main mechanisms proposed for this hypermutation pattern is the inefficient repair of UV lesions within TF-binding sites, caused by competition between TFs bound to these lesions and the DNA repair proteins that must recognize the lesions to initiate repair. However, TF binding to UV-irradiated DNA is poorly characterized, and it is unclear whether TFs maintain specificity for their DNA sites after UV exposure. We developed UV-Bind, a high-throughput approach to investigate the impact of UV irradiation on protein-DNA binding specificity. We applied UV-Bind to ten TFs from eight structural families, and found that UV lesions significantly altered the DNA-binding preferences of all the TFs tested. The main effect was a decrease in binding specificity, but the precise effects and their magnitude differ across factors. Importantly, we found that despite the overall reduction in DNA-binding specificity in the presence of UV lesions, TFs can still compete with repair proteins for lesion recognition, in a manner consistent with their specificity for UV-irradiated DNA. In addition, for a subset of TFs, we identified a surprising but reproducible effect at certain nonconsensus DNA sequences, where UV irradiation leads to a high increase in the level of TF binding. These changes in DNA-binding specificity after UV irradiation, at both consensus and nonconsensus sites, have important implications for the regulatory and mutagenic roles of TFs in the cell.

Long read sequencing of Toona sinensis (A. Juss) Roem: A chromosome-level reference genome for the family Meliaceae

Chinese mahogany (Toona sinensis) is a woody plant that is widely cultivated in China and Malaysia. Toona sinensis is important economically, including as a nutritious food source, as material for traditional Chinese medicine and as a high-quality hardwood. However, the absence of a reference genome has hindered in-depth molecular and evolutionary studies of this plant. In this study, we report a high-quality T. sinensis genome assembly, with scaffolds anchored to 28 chromosomes and a total assembled length of 596 Mb (contig N50 = 1.5 Mb and scaffold N50 = 21.5 Mb). A total of 34,345 genes were predicted in the genome after homology-based and de novo annotation analyses. Evolutionary analysis showed that the genomes of T. sinensis and Populus trichocarpa diverged ~99.1-103.1 million years ago, and the T. sinensis genome underwent a recent genome-wide duplication event at ~7.8 million years and one more ancient whole genome duplication event at ~71.5 million years. These results provide a high-quality chromosome-level reference genome for T. sinensis and confirm its evolutionary position at the genomic level. Such information will offer genomic resources to study the molecular mechanism of terpenoid biosynthesis and the formation of flavour compounds, which will further facilitate its molecular breeding. As the first chromosome-level genome assembled in the family Meliaceae, it will provide unique insights into the evolution of members of the Meliaceae.

The phytochrome-interacting transcription factor CsPIF8 contributes to cold tolerance in citrus by regulating superoxide dismutase expression

As one of the subtropical and tropical fruit trees, Citrus sinensis is sensitive to cold stress. However, most transcription factors (TFs) that regulate cold tolerance in citrus have not yet been reported. A phytochrome-interacting transcription factor (PIF) gene (CsPIF8) in citrus was significantly upregulated under cold stress. Overexpression of CsPIF8 increased cold tolerance in transgenic tomato plants and grapefruit callus, whereas virus-induced gene silencing-mediated suppression of PIF8 increased cold sensitivity in seedlings of Poncirus trifoliata. Superoxide dismutase (SOD) reduces the superoxide anion (O₂^-) level to enhance cold tolerance in plants. Chromatin immunoprecipitation combined with high-throughput sequencing, yeast one hybrid, electrophoretic mobility shift and dual luciferase assays showed that CsPIF8 directly bound the E-box (CANNTG) of CsSOD promoter and activated the promoter of CsSOD. Furthermore, the expression level of CsSOD and CsSOD activity were significantly increased, whereas the level of O₂^- was significantly reduced in the transgenic lines. The Poncirus trifoliata seedlings with VIGS-mediated suppression of PIF8 exhibited the opposite effects. These results have shown that CsPIF8 improved cold tolerance in citrus through regulating the expression level of SOD and SOD activity. These findings may provide novel insights into the regulation of PIF8 in the response to cold stress in citrus.

Specificity landscapes unmask submaximal binding site preferences of transcription factors

We have developed Differential Specificity and Energy Landscape (DiSEL) analysis to comprehensively compare DNA-protein interactomes (DPIs) obtained by high-throughput experimental platforms and cutting edge computational methods. While high-affinity DNA binding sites are identified by most methods, DiSEL uncovered nuanced sequence preferences displayed by homologous transcription factors. Pairwise analysis of 726 DPIs uncovered homolog-specific differences at moderate- to low-affinity binding sites (submaximal sites). DiSEL analysis of variants of 41 transcription factors revealed that many disease-causing mutations result in allele-specific changes in binding site preferences. We focused on a set of highly homologous factors that have different biological roles but "read" DNA using identical amino acid side chains. Rather than direct readout, our results indicate that DNA noncontacting side chains allosterically contribute to sculpt distinct sequence preferences among closely related members of transcription factor families.

Ancient mechanisms for the evolution of the bicoid homeodomain's function in fly development

The ancient mechanisms that caused developmental gene regulatory networks to diversify among distantly related taxa are not well understood. Here we use ancestral protein reconstruction, biochemical experiments, and developmental assays of transgenic animals carrying reconstructed ancestral genes to investigate how the transcription factor Bicoid (Bcd) evolved its central role in anterior-posterior patterning in flies. We show that most of Bcd's derived functions are attributable to evolutionary changes within its homeodomain (HD) during a phylogenetic interval >140 million years ago. A single substitution from this period (Q50K) accounts almost entirely for the evolution of Bcd's derived DNA specificity in vitro. In transgenic embryos expressing the reconstructed ancestral HD, however, Q50K confers activation of only a few of Bcd's transcriptional targets and yields a very partial rescue of anterior development. Adding a second historical substitution (M54R) confers regulation of additional Bcd targets and further rescues anterior development. These results indicate that two epistatically interacting mutations played a major role in the evolution of Bcd's controlling regulatory role in early development. They also show how ancestral sequence reconstruction can be combined with in vivo characterization of transgenic animals to illuminate the historical mechanisms of developmental evolution.

Direct Promoter Repression by BCL11A Controls the Fetal to Adult Hemoglobin Switch

Fetal hemoglobin (HbF, α₂γ₂) level is genetically controlled and modifies severity of adult hemoglobin (HbA, α₂β₂) disorders, sickle cell disease, and β-thalassemia. Common genetic variation affects expression of BCL11A, a regulator of HbF silencing. To uncover how BCL11A supports the developmental switch from γ- to β- globin, we use a functional assay and protein binding microarray to establish a requirement for a zinc-finger cluster in BCL11A in repression and identify a preferred DNA recognition sequence. This motif appears in embryonic and fetal-expressed globin promoters and is duplicated in γ-globin promoters. The more distal of the duplicated motifs is mutated in individuals with hereditary persistence of HbF. Using the CUT&RUN approach to map protein binding sites in erythroid cells, we demonstrate BCL11A occupancy preferentially at the distal motif, which can be disrupted by editing the promoter. Our findings reveal that direct γ-globin gene promoter repression by BCL11A underlies hemoglobin switching.

Comparison of discriminative motif optimization using matrix and DNA shape-based models

BACKGROUND

Transcription factor (TF) binding site specificity is commonly represented by some form of matrix model in which the positions in the binding site are assumed to contribute independently to the site's activity. The independence assumption is known to be an approximation, often a good one but sometimes poor. Alternative approaches have been developed that use k-mers (DNA "words" of length k) to account for the non-independence, and more recently DNA structural parameters have been incorporated into the models. ChIP-seq data are often used to assess the discriminatory power of motifs and to compare different models. However, to measure the improvement due to using more complex models, one must compare to optimized matrix models.

RESULTS

We describe a program "Discriminative Additive Model Optimization" (DAMO) that uses positive and negative examples, as in ChIP-seq data, and finds the additive position weight matrix (PWM) that maximizes the Area Under the Receiver Operating Characteristic Curve (AUROC). We compare to a recent study where structural parameters, serving as features in a gradient boosting classifier algorithm, are shown to improve the AUROC over JASPAR position frequency matrices (PFMs). In agreement with the previous results, we find that adding structural parameters gives the largest improvement, but most of the gain can be obtained by an optimized PWM and nearly all of the gain can be obtained with a di-nucleotide extension to the PWM.

CONCLUSION

To appropriately compare different models for TF bind sites, optimized models must be used. PWMs and their extensions are good representations of binding specificity for most TFs, and more complex models, including the incorporation of DNA shape features and gradient boosting classifiers, provide only moderate improvements for a few TFs.

Survey of variation in human transcription factors reveals prevalent DNA binding changes

Sequencing of exomes and genomes has revealed abundant genetic variation affecting the coding sequences of human transcription factors (TFs), but the consequences of such variation remain largely unexplored. We developed a computational, structure-based approach to evaluate TF variants for their impact on DNA binding activity and used universal protein-binding microarrays to assay sequence-specific DNA binding activity across 41 reference and 117 variant alleles found in individuals of diverse ancestries and families with Mendelian diseases. We found 77 variants in 28 genes that affect DNA binding affinity or specificity and identified thousands of rare alleles likely to alter the DNA binding activity of human sequence-specific TFs. Our results suggest that most individuals have unique repertoires of TF DNA binding activities, which may contribute to phenotypic variation.

A DNA-binding-site landscape and regulatory network analysis for NAC transcription factors in Arabidopsis thaliana

Target gene identification for transcription factors is a prerequisite for the systems wide understanding of organismal behaviour. NAM-ATAF1/2-CUC2 (NAC) transcription factors are amongst the largest transcription factor families in plants, yet limited data exist from unbiased approaches to resolve the DNA-binding preferences of individual members. Here, we present a TF-target gene identification workflow based on the integration of novel protein binding microarray data with gene expression and multi-species promoter sequence conservation to identify the DNA-binding specificities and the gene regulatory networks of 12 NAC transcription factors. Our data offer specific single-base resolution fingerprints for most TFs studied and indicate that NAC DNA-binding specificities might be predicted from their DNA-binding domain's sequence. The developed methodology, including the application of complementary functional genomics filters, makes it possible to translate, for each TF, protein binding microarray data into a set of high-quality target genes. With this approach, we confirm NAC target genes reported from independent in vivo analyses. We emphasize that candidate target gene sets together with the workflow associated with functional modules offer a strong resource to unravel the regulatory potential of NAC genes and that this workflow could be used to study other families of transcription factors.

A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data

Understanding gene regulation is a key challenge in today's biology. The new technologies of protein-binding microarrays (PBMs) and high-throughput SELEX (HT-SELEX) allow measurement of the binding intensities of one transcription factor (TF) to numerous synthetic double-stranded DNA sequences in a single experiment. Recently, Jolma et al. reported the results of 547 HT-SELEX experiments covering human and mouse TFs. Because 162 of these TFs were also covered by PBM technology, for the first time, a large-scale comparison between implementations of these two in vitro technologies is possible. Here we assessed the similarities and differences between binding models, represented as position weight matrices, inferred from PBM and HT-SELEX, and also measured how well these models predict in vivo binding. Our results show that HT-SELEX- and PBM-derived models agree for most TFs. For some TFs, the HT-SELEX-derived models are longer versions of the PBM-derived models, whereas for other TFs, the HT-SELEX models match the secondary PBM-derived models. Remarkably, PBM-based 8-mer ranking is more accurate than that of HT-SELEX, but models derived from HT-SELEX predict in vivo binding better. In addition, we reveal several biases in HT-SELEX data including nucleotide frequency bias, enrichment of C-rich k-mers and oligos and underrepresentation of palindromes.

Determining the quality and complexity of next-generation sequencing data without a reference genome

We describe an open-source kPAL package that facilitates an alignment-free assessment of the quality and comparability of sequencing datasets by analyzing k-mer frequencies. We show that kPAL can detect technical artefacts such as high duplication rates, library chimeras, contamination and differences in library preparation protocols. kPAL also successfully captures the complexity and diversity of microbiomes and provides a powerful means to study changes in microbial communities. Together, these features make kPAL an attractive and broadly applicable tool to determine the quality and comparability of sequence libraries even in the absence of a reference sequence. kPAL is freely available at https://github.com/LUMC/kPAL webcite.

Predicting tissue specific transcription factor binding sites

BACKGROUND

Studies of gene regulation often utilize genome-wide predictions of transcription factor (TF) binding sites. Most existing prediction methods are based on sequence information alone, ignoring biological contexts such as developmental stages and tissue types. Experimental methods to study in vivo binding, including ChIP-chip and ChIP-seq, can only study one transcription factor in a single cell type and under a specific condition in each experiment, and therefore cannot scale to determine the full set of regulatory interactions in mammalian transcriptional regulatory networks.

RESULTS

We developed a new computational approach, PIPES, for predicting tissue-specific TF binding. PIPES integrates in vitro protein binding microarrays (PBMs), sequence conservation and tissue-specific epigenetic (DNase I hypersensitivity) information. We demonstrate that PIPES improves over existing methods on distinguishing between in vivo bound and unbound sequences using ChIP-seq data for 11 mouse TFs. In addition, our predictions are in good agreement with current knowledge of tissue-specific TF regulation.

CONCLUSIONS

We provide a systematic map of computationally predicted tissue-specific binding targets for 284 mouse TFs across 55 tissue/cell types. Such comprehensive resource is useful for researchers studying gene regulation.