Similarity regression predicts evolution of transcription factor sequence specificity

Mechanisms of stearoyl CoA desaturase inhibitor sensitivity and acquired resistance in cancer

The lipogenic enzyme stearoyl CoA desaturase (SCD) plays a key role in tumor lipid metabolism and membrane architecture. SCD is often up-regulated and a therapeutic target in cancer. Here, we report the unexpected finding that median expression of SCD is low in glioblastoma relative to normal brain due to hypermethylation and unintentional monoallelic co-deletion with phosphatase and tensin homolog (PTEN) in a subset of patients. Cell lines from this subset expressed undetectable SCD, yet retained residual SCD enzymatic activity. Unexpectedly, these lines evolved to survive independent of SCD through unknown mechanisms. Cell lines that escaped such genetic and epigenetic alterations expressed higher levels of SCD and were highly dependent on SCD for survival. Last, we identify that SCD-dependent lines acquire resistance through a previously unknown FBJ murine osteosarcoma viral oncogene homolog B (FOSB)-mediated mechanism. Accordingly, FOSB inhibition blunted acquired resistance and extended survival of tumor-bearing mice treated with SCD inhibitor.

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations

Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the largest human epigenomic map from ENCODE and Roadmap consortia and implemented a deep-learning-based convolutional neural network (CNN) model to predict the regulatory roles of genetic variants across a comprehensive list of epigenomic modifications. Our model, called DeepFun, was built on DNA accessibility maps, histone modification marks, and transcription factors. DeepFun can systematically assess the impact of non-coding variants in the most functional elements with tissue or cell-type specificity, even for rare variants or de novo mutations. By applying this model, we prioritized trait-associated loci for 51 publicly-available GWAS studies. We demonstrated that CNN-based analyses on dense and high-resolution epigenomic annotations can refine important GWAS associations in order to identify regulatory loci from background signals, which yield novel insights for better understanding the molecular basis of human complex disease. We anticipate our approaches will become routine in GWAS downstream analysis and non-coding variant evaluation.

Human Virus Transcriptional Regulators

Viral genomes encode transcriptional regulators that alter the expression of viral and host genes. Despite an emerging role in human diseases, a thorough annotation of human viral transcriptional regulators (vTRs) is currently lacking, limiting our understanding of their molecular features and functions. Here, we provide a comprehensive catalog of 419 vTRs belonging to 20 different virus families. Using this catalog, we characterize shared and unique cellular genes, proteins, and pathways targeted by particular vTRs and discuss the role of vTRs in human disease pathogenesis. Our study provides a unique and valuable resource for the fields of virology, genomics, and human disease genetics.

CTCF As an Example of DNA-Binding Transcription Factors Containing Clusters of C2H2-Type Zinc Fingers

In mammals, most of the boundaries of topologically associating domains and all well-studied insulators are rich in binding sites for the CTCF protein. According to existing experimental data, CTCF is a key factor in the organization of the architecture of mammalian chromosomes. A characteristic feature of the CTCF is that the central part of the protein contains a cluster consisting of eleven domains of C2H2-type zinc fingers, five of which specifically bind to a long DNA sequence conserved in most animals. The class of transcription factors that carry a cluster of C2H2-type zinc fingers consisting of five or more domains (C2H2 proteins) is widely represented in all groups of animals. The functions of most C2H2 proteins still remain unknown. This review presents data on the structure and possible functions of these proteins, using the example of the vertebrate CTCF protein and several well- characterized C2H2 proteins in Drosophila and mammals.

Single-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers

While the majority of cells contain a single nucleus, cell types such as trophoblasts, osteoclasts, and skeletal myofibers require multinucleation. One advantage of multinucleation can be the assignment of distinct functions to different nuclei, but comprehensive interrogation of transcriptional heterogeneity within multinucleated tissues has been challenging due to the presence of a shared cytoplasm. Here, we utilized single-nucleus RNA-sequencing (snRNA-seq) to determine the extent of transcriptional diversity within multinucleated skeletal myofibers. Nuclei from mouse skeletal muscle were profiled across the lifespan, which revealed the presence of distinct myonuclear populations emerging in postnatal development as well as aging muscle. Our datasets also provided a platform for discovery of genes associated with rare specialized regions of the muscle cell, including markers of the myotendinous junction and functionally validated factors expressed at the neuromuscular junction. These findings reveal that myonuclei within syncytial muscle fibers possess distinct transcriptional profiles that regulate muscle biology.

Topoisomerase 1 inhibition therapy protects against SARS-CoV-2-induced inflammation and death in animal models

The ongoing pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is currently affecting millions of lives worldwide. Large retrospective studies indicate that an elevated level of inflammatory cytokines and pro-inflammatory factors are associated with both increased disease severity and mortality. Here, using multidimensional epigenetic, transcriptional, in vitro and in vivo analyses, we report that Topoisomerase 1 (Top1) inhibition suppresses lethal inflammation induced by SARS-CoV-2. Therapeutic treatment with two doses of Topotecan (TPT), a FDA-approved Top1 inhibitor, suppresses infection-induced inflammation in hamsters. TPT treatment as late as four days post-infection reduces morbidity and rescues mortality in a transgenic mouse model. These results support the potential of Top1 inhibition as an effective host-directed therapy against severe SARS-CoV-2 infection. TPT and its derivatives are inexpensive clinical-grade inhibitors available in most countries. Clinical trials are needed to evaluate the efficacy of repurposing Top1 inhibitors for COVID-19 in humans.

Gene Regulatory Networks of Penicillium echinulatum 2HH and Penicillium oxalicum 114-2 Inferred by a Computational Biology Approach

Penicillium echinulatum 2HH and Penicillium oxalicum 114-2 are well-known cellulase fungal producers. However, few studies addressing global mechanisms for gene regulation of these two important organisms are available so far. A recent finding that the 2HH wild-type is closely related to P. oxalicum leads to a combined study of these two species. Firstly, we provide a global gene regulatory network for P. echinulatum 2HH and P. oxalicum 114-2, based on TF-TG orthology relationships, considering three related species with well-known regulatory interactions combined with TFBSs prediction. The network was then analyzed in terms of topology, identifying TFs as hubs, and modules. Based on this approach, we explore numerous identified modules, such as the expression of cellulolytic and xylanolytic systems, where XlnR plays a key role in positive regulation of the xylanolytic system. It also regulates positively the cellulolytic system by acting indirectly through the cellodextrin induction system. This remarkable finding suggests that the XlnR-dependent cellulolytic and xylanolytic regulatory systems are probably conserved in both P. echinulatum and P. oxalicum. Finally, we explore the functional congruency on the genes clustered in terms of communities, where the genes related to cellular nitrogen, compound metabolic process and macromolecule metabolic process were the most abundant. Therefore, our approach allows us to confer a degree of accuracy regarding the existence of each inferred interaction.

Multiple Isolated Transcription Factors Act as Switches and Contribute to Species Uniqueness

Mammals have variable numbers (1300-2000) of transcription factors (TFs), but the reasons for this large variation are unclear. To investigate general TF patterns, we de novo identified 156,906 TFs from 96 mammalian species. We identified more than 500 human isolated TFs that are rarely reported in human TF-to-TF networks. Mutations in the genes of these TFs were less lethal than those of connected TFs. Consequently, these isolated TFs are more tolerant of changes and have become unique during speciation. They may also serve as a source of variation for TF evolution. Reconciliation of TF-family phylogenetic trees with a mammalian species tree revealed an average of 37.8% TF gains and 15.0% TF losses over 177 million years, which implies that isolated TFs are pervasive in mammals. Compared with non-TF interacting genes, TF-interacting genes have unique TF profiles and have higher expression levels in mice than in humans. Different expression levels of the same TF-interacting gene contribute to species-specific phenotypes. Formation and loss of isolated TFs enabling unique TF profiles may provide variable switches that adjust divergent expression profiles of target genes to generate species-specific phenotypes, thereby making species unique.

Sox17 and β-catenin co-occupy Wnt-responsive enhancers to govern the endoderm gene regulatory network

Lineage specification is governed by gene regulatory networks (GRNs) that integrate the activity of signaling effectors and transcription factors (TFs) on enhancers. Sox17 is a key transcriptional regulator of definitive endoderm development, and yet, its genomic targets remain largely uncharacterized. Here, using genomic approaches and epistasis experiments, we define the Sox17-governed endoderm GRN in Xenopus gastrulae. We show that Sox17 functionally interacts with the canonical Wnt pathway to specify and pattern the endoderm while repressing alternative mesectoderm fates. Sox17 and β-catenin co-occupy hundreds of key enhancers. In some cases, Sox17 and β-catenin synergistically activate transcription apparently independent of Tcfs, whereas on other enhancers, Sox17 represses β-catenin/Tcf-mediated transcription to spatially restrict gene expression domains. Our findings establish Sox17 as a tissue-specific modifier of Wnt responses and point to a novel paradigm where genomic specificity of Wnt/β-catenin transcription is determined through functional interactions between lineage-specific Sox TFs and β-catenin/Tcf transcriptional complexes. Given the ubiquitous nature of Sox TFs and Wnt signaling, this mechanism has important implications across a diverse range of developmental and disease contexts.

Disease-associated KIF3A variants alter gene methylation and expression impacting skin barrier and atopic dermatitis risk

Single nucleotide polymorphisms (SNPs) in the gene encoding kinesin family member 3A, KIF3A, have been associated with atopic dermatitis (AD), a chronic inflammatory skin disorder. We find that KIF3A SNP rs11740584 and rs2299007 risk alleles create cytosine-phosphate-guanine sites, which are highly methylated and result in lower KIF3A expression, and this methylation is associated with increased transepidermal water loss (TEWL) in risk allele carriers. Kif3a^K14∆/∆ mice have increased TEWL, disrupted junctional proteins, and increased susceptibility to develop AD. Thus, KIF3A is required for skin barrier homeostasis whereby decreased KIF3A skin expression causes disrupted skin barrier function and promotes development of AD.

Genetic variants in KIF3A are associated with atopic dermatitis (AD). Here, the authors identify two AD-risk alleles that show high methylation resulting in lower KIF3A expression. Mice with epidermis-specific loss of Kif3a show disrupted skin barrier homeostasis and increased AD susceptibility.

Dynamic transcriptional and chromatin accessibility landscape of medaka embryogenesis

Medaka (Oryzias latipes) has become an important vertebrate model widely used in genetics, developmental biology, environmental sciences, and many other fields. A high-quality genome sequence and a variety of genetic tools are available for this model organism. However, existing genome annotation is still rudimentary, as it was mainly based on computational prediction and short-read RNA-seq data. Here we report a dynamic transcriptome landscape of medaka embryogenesis profiled by long-read RNA-seq, short-read RNA-seq, and ATAC-seq. By integrating these data sets, we constructed a much-improved gene model set including about 17,000 novel isoforms and identified 1600 transcription factors, 1100 long noncoding RNAs, and 150,000 potential cis-regulatory elements as well. Time-series data sets provided another dimension of information. With the expression dynamics of genes and accessibility dynamics of cis-regulatory elements, we investigated isoform switching, as well as regulatory logic between accessible elements and genes, during embryogenesis. We built a user-friendly medaka omics data portal to present these data sets. This resource provides the first comprehensive omics data sets of medaka embryogenesis. Ultimately, we term these three assays as the minimum ENCODE toolbox and propose the use of it as the initial and essential profiling genomic assays for model organisms that have limited data available. This work will be of great value for the research community using medaka as the model organism and many others as well.

Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study

BACKGROUND

Positional weight matrix (PWM) is a de facto standard model to describe transcription factor (TF) DNA binding specificities. PWMs inferred from in vivo or in vitro data are stored in many databases and used in a plethora of biological applications. This calls for comprehensive benchmarking of public PWM models with large experimental reference sets.

RESULTS

Here we report results from all-against-all benchmarking of PWM models for DNA binding sites of human TFs on a large compilation of in vitro (HT-SELEX, PBM) and in vivo (ChIP-seq) binding data. We observe that the best performing PWM for a given TF often belongs to another TF, usually from the same family. Occasionally, binding specificity is correlated with the structural class of the DNA binding domain, indicated by good cross-family performance measures. Benchmarking-based selection of family-representative motifs is more effective than motif clustering-based approaches. Overall, there is good agreement between in vitro and in vivo performance measures. However, for some in vivo experiments, the best performing PWM is assigned to an unrelated TF, indicating a binding mode involving protein-protein cooperativity.

CONCLUSIONS

In an all-against-all setting, we compute more than 18 million performance measure values for different PWM-experiment combinations and offer these results as a public resource to the research community. The benchmarking protocols are provided via a web interface and as docker images. The methods and results from this study may help others make better use of public TF specificity models, as well as public TF binding data sets.

Epigenetic regulation of neuronal cell specification inferred with single cell "Omics" data

The brain is a highly complex organ consisting of numerous types of cells with ample diversity at the epigenetic level to achieve distinct gene expression profiles. During neuronal cell specification, transcription factors (TFs) form regulatory modules with chromatin remodeling proteins to initiate the cascade of epigenetic changes. Currently, little is known about brain epigenetic regulatory modules and how they regulate gene expression in a cell-type specific manner. To infer TFs involved in neuronal specification, we applied a recursive motif search approach on the differentially methylated regions identified from single-cell methylomes. The epigenetic transcription regulatory modules (ETRM), including EGR1 and MEF2C, were predicted and the co-expression of TFs in ETRMs were examined with RNA-seq data from single or sorted brain cells using a conditional probability matrix. Lastly, computational predications were validated with EGR1 ChIP-seq data. In addition, methylome and RNA-seq data generated from Egr1 knockout mice supported the essential role of EGR1 in brain epigenome programming, in particular for excitatory neurons. In summary, we demonstrated that brain single cell methylome and RNA-seq data can be integrated to gain a better understanding of how ETRMs control cell specification. The analytical pipeline implemented in this study is freely accessible in the Github repository (https://github.com/Gavin-Yinld/brain_TF).

Identification and evolution of gene regulatory networks: insights from comparative studies in plants

The availability of genome sequences, genome-wide assays of transcription factor binding, and accessible chromatin maps have unveiled gene regulatory landscapes in plants. This understanding has ushered in comparative gene regulatory network studies that assess network rewiring between species, across time, and between biological tissues. Comparisons of cis-regulatory elements across the plant kingdom have uncovered examples of conserved sequences, but also of divergence, indicating that selective pressures can vary in different plant families. Transcription factor duplication, followed by spatiotemporal expression divergence of the duplicates, also appears to be a key mechanism of network evolution. Here, we review recent literature describing the regulation of gene expression in plants, and how comparative studies provide insights into how these regulatory interactions change and lead to gene regulatory network rewiring.

Sharing DNA-binding information across structurally similar proteins enables accurate specificity determination

We are now in an era where protein-DNA interactions have been experimentally assayed for thousands of DNA-binding proteins. In order to infer DNA-binding specificities from these data, numerous sophisticated computational methods have been developed. These approaches typically infer DNA-binding specificities by considering interactions for each protein independently, ignoring related and potentially valuable interaction information across other proteins that bind DNA via the same structural domain. Here we introduce a framework for inferring DNA-binding specificities by considering protein-DNA interactions for entire groups of structurally similar proteins simultaneously. We devise both constrained optimization and label propagation algorithms for this task, each balancing observations at the individual protein level against dataset-wide consistency of interaction preferences. We test our approaches on two large, independent Cys2His2 zinc finger protein-DNA interaction datasets. We demonstrate that jointly inferring specificities within each dataset individually dramatically improves accuracy, leading to increased agreement both between these two datasets and with a fixed external standard. Overall, our results suggest that sharing protein-DNA interaction information across structurally similar proteins is a powerful means to enable accurate inference of DNA-binding specificities.

JASPAR 2020: update of the open-access database of transcription factor binding profiles

JASPAR (http://jaspar.genereg.net) is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) for TFs across multiple species in six taxonomic groups. In this 8th release of JASPAR, the CORE collection has been expanded with 245 new PFMs (169 for vertebrates, 42 for plants, 17 for nematodes, 10 for insects, and 7 for fungi), and 156 PFMs were updated (125 for vertebrates, 28 for plants and 3 for insects). These new profiles represent an 18% expansion compared to the previous release. JASPAR 2020 comes with a novel collection of unvalidated TF-binding profiles for which our curators did not find orthogonal supporting evidence in the literature. This collection has a dedicated web form to engage the community in the curation of unvalidated TF-binding profiles. Moreover, we created a Q&A forum to ease the communication between the user community and JASPAR curators. Finally, we updated the genomic tracks, inference tool, and TF-binding profile similarity clusters. All the data is available through the JASPAR website, its associated RESTful API, and through the JASPAR2020 R/Bioconductor package.

Evolutionary Dynamics of the SKN-1 → MED → END-1,3 Regulatory Gene Cascade in Caenorhabditis Endoderm Specification

Gene regulatory networks and their evolution are important in the study of animal development. In the nematode, Caenorhabditis elegans, the endoderm (gut) is generated from a single embryonic precursor, E. Gut is specified by the maternal factor SKN-1, which activates the MED → END-1,3 → ELT-2,7 cascade of GATA transcription factors. In this work, genome sequences from over two dozen species within the Caenorhabditis genus are used to identify MED and END-1,3 orthologs. Predictions are validated by comparison of gene structure, protein conservation, and putative cis-regulatory sites. All three factors occur together, but only within the Elegans supergroup, suggesting they originated at its base. The MED factors are the most diverse and exhibit an unexpectedly extensive gene amplification. In contrast, the highly conserved END-1 orthologs are unique in nearly all species and share extended regions of conservation. The END-1,3 proteins share a region upstream of their zinc finger and an unusual amino-terminal poly-serine domain exhibiting high codon bias. Compared with END-1, the END-3 proteins are otherwise less conserved as a group and are typically found as paralogous duplicates. Hence, all three factors are under different evolutionary constraints. Promoter comparisons identify motifs that suggest the SKN-1, MED, and END factors function in a similar gut specification network across the Elegans supergroup that has been conserved for tens of millions of years. A model is proposed to account for the rapid origin of this essential kernel in the gut specification network, by the upstream intercalation of duplicate genes into a simpler ancestral network.

Insights Into the Regulation of the Expression Pattern of Calvin-Benson-Bassham Cycle Enzymes in C3 and C4 Grasses

C₄ photosynthesis is characterized by the compartmentalization of the processes of atmospheric uptake of CO₂ and its conversion into carbohydrate between mesophyll and bundle-sheath cells. As a result, most of the enzymes participating in the Calvin-Benson-Bassham (CBB) cycle, including RubisCO, are highly expressed in bundle-sheath cells. There is evidence that changes in the regulatory sequences of RubisCO contribute to its bundle-sheath-specific expression, however, little is known about how the spatial-expression pattern of other CBB cycle enzymes is regulated. In this study, we use a computational approach to scan for transcription factor binding sites in the regulatory regions of the genes encoding CBB cycle enzymes, SBPase, FBPase, PRK, and GAPDH-B, of C₃ and C₄ grasses. We identified potential cis-regulatory elements present in each of the genes studied here, regardless of the photosynthetic path used by the plant. The trans-acting factors that bind these elements have been validated in A. thaliana and might regulate the expression of the genes encoding CBB cycle enzymes. In addition, we also found C₄-specific transcription factor binding sites in the genes encoding CBB cycle enzymes that could potentially contribute to the pathway-specific regulation of gene expression. These results provide a foundation for the functional analysis of the differences in regulation of genes encoding CBB cycle enzymes between C₃ and C₄ grasses.

Origin and evolution of eukaryotic transcription factors

Transcription factors (TFs) have a central role in genome regulation directing gene transcription through binding specific DNA sequences. Eukaryotic genomes encode a large diversity of TF classes, each defined by unique DNA-interaction domains. Recent advances in genome sequencing and phylogenetic placement of diverse eukaryotic and archaeal species are re-defining the evolutionary history of eukaryotic TFs. The emerging view from a comparative genomics perspective is that the Last Eukaryotic Common Ancestor (LECA) had an extensive repertoire of TFs, most of which represent eukaryotic evolutionary novelties. This burst of TF innovation coincides with the emergence of genomic nuclear segregation and complex chromatin organization.