The Genetics of Transcription Factor DNA Binding Variation

Comprehensive mapping of genetic variation at Epromoters reveals pleiotropic association with multiple disease traits

There is growing evidence that a wide range of human diseases and physiological traits are influenced by genetic variation of cis-regulatory elements. We and others have shown that a subset of promoter elements, termed Epromoters, also function as enhancer regulators of distal genes. This opens a paradigm in the study of regulatory variants, as single nucleotide polymorphisms (SNPs) within Epromoters might influence the expression of several (distal) genes at the same time, which could disentangle the identification of disease-associated genes. Here, we built a comprehensive resource of human Epromoters using newly generated and publicly available high-throughput reporter assays. We showed that Epromoters display intrinsic and epigenetic features that distinguish them from typical promoters. By integrating Genome-Wide Association Studies (GWAS), expression Quantitative Trait Loci (eQTLs) and 3D chromatin interactions, we found that regulatory variants at Epromoters are concurrently associated with more disease and physiological traits, as compared with typical promoters. To dissect the regulatory impact of Epromoter variants, we evaluated their impact on regulatory activity by analyzing allelic-specific high-throughput reporter assays and provided reliable examples of pleiotropic Epromoters. In summary, our study represents a comprehensive resource of regulatory variants supporting the pleiotropic role of Epromoters.

Multiome-wide Association Studies: Novel Approaches for Understanding Diseases

The rapid development of multiome (transcriptome, proteome, cistrome, imaging, and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases. Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective. This review provides a detailed categorization and summary of the statistical models, use cases, and advantages of recent multiome-wide association studies. In addition, to illustrate gene-disease association studies based on transcriptome-wide association study (TWAS), we collected 478 disease entries across 22 categories from 235 manually reviewed publications. Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS, indicating its potential to deepen our understanding of the genetic architecture of complex diseases. In summary, this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.

Identification of co-localised transcription factors based on paired motifs analysis

The interaction of transcription factors (TFs) with DNA precisely regulates gene transcription. In mammalian cells, thousands of TFs often interact with DNA cis-regulatory elements in a combinatorial manner rather than act alone. The identification of cooperativity between TFs can help to explore the mechanism of transcriptional regulation. However, little is known about the cooperative patterns of TFs in the genome. To identify which TFs prefer co-localisation, the authors conducted a paired motif analysis in the accessible regions of the human genome based on the Poisson background model. Especially, the authors distinguish the cooperative binding TFs and the competitive binding TFs according to the distance between TF motifs. In the K562 cell line, the authors find that TFs from a same family are always competing the same binding sites, such as FOS_JUN family, whereas KLF family TFs show significant cooperative binding in the adjacency region. Furthermore, the comparative analysis across 16 human cell lines indicates that most TF combination patterns are conserved, but there are still some cell-line-specific patterns. Finally, in human prostate cancer cells (PC-3) and human prostate normal cells (RWPE-2), the authors investigate the specific TF combination patterns in the disease cell and normal cell. The results show that the cooperative binding TF pairs shared by PC-3 and RWPE-2 account for over 90%. Simultaneously, the authors also identify 26 specific TF combination pairs in PC-3 cancer cells.

Enhancing disease risk gene discovery by integrating transcription factor-linked trans-variants into transcriptome-wide association analyses

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-variants to enhance model building for TF downstream target genes. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these prediction models to large GWAS datasets for breast, prostate, lung cancers and other diseases. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene expression prediction models and identifying disease-associated genes, as shown by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study shed new light on several genetically driven key TF regulators and their associated TF-gene regulatory networks underlying disease susceptibility.

Perspectives on Codebook: sequence specificity of uncharacterized human transcription factors

We describe an effort ("Codebook") to determine the sequence specificity of 332 putative and largely uncharacterized human transcription factors (TFs), as well as 61 control TFs. Nearly 5,000 independent experiments across multiple in vitro and in vivo assays produced motifs for just over half of the putative TFs analyzed (177, or 53%), of which most are unique to a single TF. The data highlight the extensive contribution of transposable elements to TF evolution, both in cis and trans, and identify tens of thousands of conserved, base-level binding sites in the human genome. The use of multiple assays provides an unprecedented opportunity to benchmark and analyze TF sequence specificity, function, and evolution, as further explored in accompanying manuscripts. 1,421 human TFs are now associated with a DNA binding motif. Extrapolation from the Codebook benchmarking, however, suggests that many of the currently known binding motifs for well-studied TFs may inaccurately describe the TF's true sequence preferences.

Massively parallel binding assay (MPBA) reveals limited transcription factor binding cooperativity, challenging models of specificity

DNA-binding domains (DBDs) within transcription factors (TFs) recognize short sequence motifs that are highly abundant in genomes. In vivo, TFs bind only a small subset of motif occurrences, which is often attributed to the cooperative binding of interacting TFs at proximal motifs. However, large-scale testing of this model is still lacking. Here, we describe a novel method allowing parallel measurement of TF binding to thousands of designed sequences within yeast cells and apply it to quantify the binding of dozens of TFs to libraries of regulatory regions containing clusters of binding motifs, systematically mutating all motif combinations. With few exceptions, TF occupancies were well explained by independent binding to individual motifs, with motif cooperation being of only limited effects. Our results challenge the general role of motif combinatorics in directing TF genomic binding and open new avenues for exploring the basis of protein-DNA interactions within cells.

Disease-Linked Regulatory DNA Variants and Homeostatic Transcription Factors in Epidermis

Identifying noncoding single nucleotide variants ( SNVs ) in regulatory DNA linked to polygenic disease risk, the transcription factors ( TFs ) they bind, and the target genes they dysregulate is a goal in polygenic disease research. Massively parallel reporter gene analysis ( MPRA ) of 3,451 SNVs linked to risk for polygenic skin diseases characterized by disrupted epidermal homeostasis identified 355 differentially active SNVs ( daSNVs ). daSNV target gene analysis, combined with daSNV editing, underscored dysregulated epidermal differentiation as a pathomechanism shared across common polygenic skin diseases. CRISPR knockout screens of 1772 human TFs revealed 108 TFs essential for epidermal progenitor differentiation, uncovering novel roles for ZNF217, CXXC1, FOXJ2, IRX2 and NRF1. Population sampling CUT&RUN of 27 homeostatic TFs identified allele-specific DNA binding ( ASB ) differences at daSNVs enriched near epidermal homeostasis and monogenic skin disease genes, with notable representation of SP/KLF and AP-1/2 TFs. This resource implicates dysregulated differentiation in risk for diverse polygenic skin diseases.

Benchmarking and building DNA binding affinity models using allele-specific and allele-agnostic transcription factor binding data

BACKGROUND

Transcription factors (TFs) bind to DNA in a highly sequence-specific manner. This specificity manifests itself in vivo as differences in TF occupancy between the two alleles at heterozygous loci. Genome-scale assays such as ChIP-seq currently are limited in their power to detect allele-specific binding (ASB) both in terms of read coverage and representation of individual variants in the cell lines used. This makes prediction of allelic differences in TF binding from sequence alone desirable, provided that the reliability of such predictions can be quantitatively assessed.

RESULTS

We here propose methods for benchmarking sequence-to-affinity models for TF binding in terms of their ability to predict allelic imbalances in ChIP-seq counts. We use a likelihood function based on an over-dispersed binomial distribution to aggregate evidence for allelic preference across the genome without requiring statistical significance for individual variants. This allows us to systematically compare predictive performance when multiple binding models for the same TF are available. To facilitate the de novo inference of high-quality models from paired-end in vivo binding data such as ChIP-seq, ChIP-exo, and CUT&Tag without read mapping or peak calling, we introduce an extensible reimplementation of our biophysically interpretable machine learning framework named PyProBound. Explicitly accounting for assay-specific bias in DNA fragmentation rate when training on ChIP-seq yields improved TF binding models. Moreover, we show how PyProBound can leverage our threshold-free ASB likelihood function to perform de novo motif discovery using allele-specific ChIP-seq counts.

CONCLUSION

Our work provides new strategies for predicting the functional impact of non-coding variants.

Fibrillarin reprograms glucose metabolism by driving the enhancer-mediated transcription of PFKFB4 in liver cancer

DNA- and RNA-binding proteins (DRBPs) are versatile proteins capable of binding to both DNA and RNA molecules. In this study, we identified fibrillarin (FBL) as a key DRBP that is upregulated in liver cancer tissues vs. normal tissues and is correlated with patient prognosis. FBL promotes the proliferation of liver cancer cells both in vitro and in vivo. Mechanistically, FBL interacts with the transcription factor KHSRP, thereby regulating the expression of genes involved in glucose metabolism and leading to the reprogramming of glucose metabolism. Specifically, FBL and KHSRP work together to transcriptionally activate the glycolytic enzyme PFKFB4 by co-occupying enhancer and promoter elements, thereby further promoting liver cancer growth. Collectively, these findings provide compelling evidence highlighting the role of FBL as a transcriptional regulator in liver cancer cells, working in conjunction with KHSRP. The FBL/KHSRP-PFKFB4 regulatory axis holds potential as both a prognostic indicator and a therapeutic target for liver cancer. SIGNIFICANCE: A novel role of FBL in the transcriptional activation of PFKFB4, leading to glucose metabolism reprogramming in liver cancer.

Genetic Variations in the NRF2 Microsatellite Contribute to the Regulation of Bovine Sperm-Borne Antioxidant Capacity

Nuclear factor (erythroid-derived 2)-like 2 (NRF2) is a transcription factor protein-coding gene, considered a master regulator of the cellular stress response. The genetic variations of the NRF2 could influence its transcriptional profile and, subsequently, the stress resilience in all cell types, including sperm cells. Therefore, the sperm-borne antioxidants abundance in association with the genetic variation of a GCC microsatellite located at the 5' upstream region of the NRF2 gene was investigated in young (n = 8) and old (n = 8) Holstein bulls' sperm cells at different seasons. The sperm DNA was sequenced using Sanger sequencing, while- the sperm-borne mRNA analysis was carried out using the synthesized cDNA and qPCR. The data were statistically analyzed using GraphPad Prism 10.0.2 software. The results showed that two bulls had a heterozygous genotype of eight and nine GCC repeats, while biallelic of eight, nine, and fifteen repeats were identified in two, ten, and two bulls, respectively. The computational in silico analysis revealed that the NRF2 upstream sequence with 15, 9, and 8 GCC repeats bound with 725, 709, and 707 DNA-binding transcription factor proteins, respectively. Lower quality of sperm DNA was detected in the spring season compared to other seasons and in young bulls compared to old ones, particularly in the summer and autumn seasons. The mRNA expression analysis revealed that the PRDX1 gene was the abundant transcript among the studied sperm-borne antioxidants and was significantly determined in old bulls' spermatozoa. Moreover, two transcripts of the NRF2 gene and antioxidant (SOD1, CAT, GPX1, TXN1, NQO1) genes displayed differential expression patterns between the age groups across seasons in an antioxidant-dependent manner. The bulls with a heterozygous GCC sequence exhibited elevated sperm-borne mRNA levels of NRF2 and PRDX1 transcripts. Taken together, the findings suggest that the NRF2-GCC microsatellite may contribute to the transcription regulation of NRF2 transcripts and their subsequent downstream antioxidants in bovine sperm cells.

Uncovering methylation-dependent genetic effects on regulatory element function in diverse genomes

A major goal in evolutionary biology and biomedicine is to understand the complex interactions between genetic variants, the epigenome, and gene expression. However, the causal relationships between these factors remain poorly understood. mSTARR-seq, a methylation-sensitive massively parallel reporter assay, is capable of identifying methylation-dependent regulatory activity at many thousands of genomic regions simultaneously, and allows for the testing of causal relationships between DNA methylation and gene expression on a region-by-region basis. Here, we developed a multiplexed mSTARR-seq protocol to assay naturally occurring human genetic variation from 25 individuals sampled from 10 localities in Europe and Africa. We identified 6,957 regulatory elements in either the unmethylated or methylated state, and this set was enriched for enhancer and promoter annotations, as expected. The expression of 58% of these regulatory elements was modulated by methylation, which was generally associated with decreased RNA expression. Within our set of regulatory elements, we used allele-specific expression analyses to identify 8,020 sites with genetic effects on gene regulation; further, we found that 42.3% of these genetic effects varied between methylated and unmethylated states. Sites exhibiting methylation-dependent genetic effects were enriched for GWAS and EWAS annotations, implicating them in human disease. Compared to datasets that assay DNA from a single European individual, our multiplexed assay uncovers dramatically more genetic effects and methylation-dependent genetic effects, highlighting the importance of including diverse individuals in assays which aim to understand gene regulatory processes.

Systematic dissection of sequence features affecting binding specificity of a pioneer factor reveals binding synergy between FOXA1 and AP-1

Despite the unique ability of pioneer factors (PFs) to target nucleosomal sites in closed chromatin, they only bind a small fraction of their genomic motifs. The underlying mechanism of this selectivity is not well understood. Here, we design a high-throughput assay called chromatin immunoprecipitation with integrated synthetic oligonucleotides (ChIP-ISO) to systematically dissect sequence features affecting the binding specificity of a classic PF, FOXA1, in human A549 cells. Combining ChIP-ISO with in vitro and neural network analyses, we find that (1) FOXA1 binding is strongly affected by co-binding transcription factors (TFs) AP-1 and CEBPB; (2) FOXA1 and AP-1 show binding cooperativity in vitro; (3) FOXA1's binding is determined more by local sequences than chromatin context, including eu-/heterochromatin; and (4) AP-1 is partially responsible for differential binding of FOXA1 in different cell types. Our study presents a framework for elucidating genetic rules underlying PF binding specificity and reveals a mechanism for context-specific regulation of its binding.

Analysis of plant pararetrovirus promoter sequence(s) for developing a useful synthetic promoter with enhanced activity in rice, pearl millet, and tobacco plants

Promoters are one of the most important components for many gene-based research as they can fine-tune precise gene expression. Many unique plant promoters have been characterized, but strong promoters with dual expression in both monocot and dicot systems are still lacking. In this study, we attempted to make such a promoter by combining specific domains from monocot-infecting pararetroviral-based promoters sugarcane bacilliform virus (SCBV) and banana streak virus (BSV) to a strong dicot-infecting pararetroviral-based promoter mirabilis mosaic virus (MMV). The generated chimeric promoters, MS, SM, MB, and BM, were tested in monocot and dicot systems and further validated in transgenic tobacco plants. We found that the developed chimeric promoters were species-specific (monocot or dicot), which depended on their respective core promoter (CP) region. Furthermore, with this knowledge, deletion-hybrid promoters were developed and evaluated, which led to the development of a unique dual-expressing promoter, MSD3, with high gene expression efficiency (GUS and GFP reporter genes) in rice, pearl millet, and tobacco plants. We conclude that the MSD3 promoter can be an important genetic tool and will be valuable in plant biology research and application.

ABCC2 induces metabolic vulnerability and cellular ferroptosis via enhanced glutathione efflux in gastric cancer

BACKGROUND

Although it is traditionally believed that ATP binding cassette subfamily C member 2 (ABCC2) is a multidrug resistance-associated protein correlated with a worse prognosis, our previous and several other studies demonstrated the contrary to be true in gastric cancer (GC). We aim to explore the underlying mechanism of this discovery.

METHODS

Our study utilized whole-exome sequencing (WES), RNA sequencing, and droplet digital PCR (ddPCR) analysis of 80 gastric cancer samples, along with comprehensive immunohistochemical (IHC) analysis of 1044 human GC tissue samples.By utilizing CRISPRCas9 to genetically modify cell lines with the ABCC2-24C > T (rs717620) point mutation and conducting dual-luciferase reporter assays, we identified that transcription factors SOX9 and ETS1 serve as negative regulators of ABCC2 expression. Seahorse assay and mass spectrometry were used to discover altered metabolic patterns. Gain and loss-of-function experiments in GC cell lines and preclinical models were carried out to validate ABCC2 biological function.

RESULTS

ABCC2 high expression correlated with better prognosis, and rs717620 can influence ABCC2 expression by disrupting the binding of ETS1 and SOX9. Gain and loss-of-function experiments in GC cell lines demonstrated amino acid deprivation reduces proliferation, migration, and drug resistance in ABCC2-high GC cells. ABCC2 leads to reduced intracellular amino acid pools and disruption of cellular energy metabolism. This phenomenon depended on ABCC2-mediated GSH extrusion, resulting in alterations in redox status, thereby increasing the cell's susceptibility to ferroptosis. Furthermore, patient-derived organoids and patient-derived tumor-like cell clusters were used to observe impact of ABCC2 on therapeutic effect. In the xenograft model with high ABCC2 expression, we observed that constricting amino acid intake in conjunction with GPX4 inactivation resulted in notable tumor regression.

CONCLUSIONS

Our findings demonstrate a significant role of ABCC2 in amino acid metabolism and ferroptosis by mediating GSH efflux in GC. This discovery underlines the potential of combining multiple ferroptosis targets as a promising therapeutic strategy for GC with high ABCC2 expression.

HIGHLIGHTS

ABCC2 plays a crucial role in inducing metabolic vulnerability and ferroptosis in gastric cancer through enhanced glutathione efflux. The ABCC2 24C > T polymorphism is a key factor influencing its expression. These results highlight the potential of ABCC2 as a predictive biomarker and therapeutic target in gastric cancer.

New Drosophila promoter-associated architectural protein Mzfp1 interacts with CP190 and is required for housekeeping gene expression and insulator activity

In Drosophila, a group of zinc finger architectural proteins recruits the CP190 protein to the chromatin, an interaction that is essential for the functional activity of promoters and insulators. In this study, we describe a new architectural C2H2 protein called Madf and Zinc-Finger Protein 1 (Mzfp1) that interacts with CP190. Mzfp1 has an unusual structure that includes six C2H2 domains organized in a C-terminal cluster and two tandem MADF domains. Mzfp1 predominantly binds to housekeeping gene promoters located in both euchromatin and heterochromatin genome regions. In vivo mutagenesis studies showed that Mzfp1 is an essential protein, and both MADF domains and the CP190 interaction region are required for its functional activity. The C2H2 cluster is sufficient for the specific binding of Mzfp1 to regulatory elements, while the second MADF domain is required for Mzfp1 recruitment to heterochromatin. Mzfp1 binds to the proximal part of the Fub boundary that separates regulatory domains of the Ubx and abd-A genes in the Bithorax complex. Mzfp1 participates in Fub functions in cooperation with the architectural proteins Pita and Su(Hw). Thus, Mzfp1 is a new architectural C2H2 protein involved in the organization of active promoters and insulators in Drosophila.

Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-located variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-located variants to enhance model building. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these models to large GWAS datasets for breast, prostate, and lung cancers. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene prediction models and identifying disease-associated genes, as evidenced by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study have shed new light on several genetically driven key regulators and their associated regulatory networks underlying disease susceptibility.

DNA methylation patterns of transcription factor binding regions characterize their functional and evolutionary contexts

BACKGROUND

DNA methylation is an important epigenetic modification which has numerous roles in modulating genome function. Its levels are spatially correlated across the genome, typically high in repressed regions but low in transcription factor (TF) binding sites and active regulatory regions. However, the mechanisms establishing genome-wide and TF binding site methylation patterns are still unclear.

RESULTS

Here we use a comparative approach to investigate the association of DNA methylation to TF binding evolution in mammals. Specifically, we experimentally profile DNA methylation and combine this with published occupancy profiles of five distinct TFs (CTCF, CEBPA, HNF4A, ONECUT1, FOXA1) in the liver of five mammalian species (human, macaque, mouse, rat, dog). TF binding sites are lowly methylated, but they often also have intermediate methylation levels. Furthermore, biding sites are influenced by the methylation status of CpGs in their wider binding regions even when CpGs are absent from the core binding motif. Employing a classification and clustering approach, we extract distinct and species-conserved patterns of DNA methylation levels at TF binding regions. CEBPA, HNF4A, ONECUT1, and FOXA1 share the same methylation patterns, while CTCF's differ. These patterns characterize alternative functions and chromatin landscapes of TF-bound regions. Leveraging our phylogenetic framework, we find DNA methylation gain upon evolutionary loss of TF occupancy, indicating coordinated evolution. Furthermore, each methylation pattern has its own evolutionary trajectory reflecting its genomic contexts.

CONCLUSIONS

Our epigenomic analyses indicate a role for DNA methylation in TF binding changes across species including that specific DNA methylation profiles characterize TF binding and are associated with their regulatory activity, chromatin contexts, and evolutionary trajectories.

A transcriptional enhancer regulates cardiac maturation

Cardiomyocyte maturation is crucial for generating adult cardiomyocytes and the application of human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs). However, regulation at the cis-regulatory element level and its role in heart disease remain unclear. Alpha-actinin 2 (ACTN2) levels increase during CM maturation. In this study, we investigated a clinically relevant, conserved ACTN2 enhancer's effects on CM maturation using hPSC and mouse models. Heterozygous ACTN2 enhancer deletion led to abnormal CM morphology, reduced function and mitochondrial respiration. Transcriptomic analyses in vitro and in vivo showed disrupted CM maturation and upregulated anabolic mammalian target for rapamycin (mTOR) signaling, promoting senescence and hindering maturation. As confirmation, ACTN2 enhancer deletion induced heat shock protein 90A expression, a chaperone mediating mTOR activation. Conversely, targeting the ACTN2 enhancer via enhancer CRISPR activation (enCRISPRa) promoted hPSC-CM maturation. Our studies reveal the transcriptional enhancer's role in cardiac maturation and disease, offering insights into potentially fine-tuning gene expression to modulate cardiomyocyte physiology.

The 330 risk loci known for systemic lupus erythematosus (SLE): a review

An in-depth literature review of up to 2023 reveals 330 risk loci found by genetic association at p ≤ 5 × 10-8, with systemic lupus erythematosus (SLE) in at least one study of 160 pertinent publications. There are 225 loci found in East Asian (EAS), 106 in European (EU), 11 in African-American (AA), 18 Mixed American (MA), and 1 in Egyptian ancestries. Unexpectedly, most of these associations are found to date at p ≤ 5 × 10-8 in a single ancestry. However, the EAS and EU share 40 risk loci that are independently established. The great majority of the identified loci [250 (75.8%) of 330] do not contain a variant that changes an amino acid sequence. Meanwhile, most overlap with known regulatory elements in the genome [266 (80.6%) of 330], suggesting a major role for gene regulation in the genetic mechanisms of SLE. To evaluate the pathways altered by SLE-associated variants, we generated gene sets potentially regulated by SLE loci that consist of the nearest genes, published attributions, and genes predicted by computational tools. The most useful insights, at present, suggest that SLE genetic mechanisms involve (1) the regulation of both adaptive and innate immune responses including immune cell activation and differentiation; (2) the regulation of production and response to cytokines, including type I interferon; (3) apoptosis; (4) the sensing and removal of immune complexes and apoptotic particles; and (5) immune response to infections, including Epstein-Barr Virus, and symbiont microorganisms. These mechanisms affected by SLE genes involve multiple cell types, including B cells/plasma cells, T cells, dendritic cells, monocytes/macrophages, natural killer cells, neutrophils, and endothelial cells. The genetics of SLE from GWAS data reveal an incredibly complex profusion of interrelated molecular processes and interacting cells participating in SLE pathogenesis, mostly unified in the molecular regulation of inflammatory responses. These genetic associations in lupus and affected molecular pathways not only give us an understanding of the disease pathogenesis but may also help in drug discoveries for SLE treatment.

PML::RARA and GATA2 proteins interact via DNA templates to induce aberrant self-renewal in mouse and human hematopoietic cells

The underlying mechanism(s) by which the PML::RARA fusion protein initiates acute promyelocytic leukemia is not yet clear. We defined the genomic binding sites of PML::RARA in primary mouse and human hematopoietic progenitor cells with V5-tagged PML::RARA, using anti-V5-PML::RARA chromatin immunoprecipitation sequencing and CUT&RUN approaches. Most genomic PML::RARA binding sites were found in regions that were already chromatin-accessible (defined by ATAC-seq) in unmanipulated, wild-type promyelocytes, suggesting that these regions are "open" prior to PML::RARA expression. We found that GATA binding motifs, and the direct binding of the chromatin "pioneering factor" GATA2, were significantly enriched near PML::RARA binding sites. Proximity labeling studies revealed that PML::RARA interacts with ~250 proteins in primary mouse hematopoietic cells; GATA2 and 33 others require PML::RARA binding to DNA for the interaction to occur, suggesting that binding to their cognate DNA target motifs may stabilize their interactions. In the absence of PML::RARA, Gata2 overexpression induces many of the same epigenetic and transcriptional changes as PML::RARA. These findings suggested that PML::RARA may indirectly initiate its transcriptional program by activating Gata2 expression: Indeed, we demonstrated that inactivation of Gata2 prior to PML::RARA expression prevented its ability to induce self-renewal. These data suggested that GATA2 binding creates accessible chromatin regions enriched for both GATA and Retinoic Acid Receptor Element motifs, where GATA2 and PML::RARA can potentially bind and interact with each other. In turn, PML::RARA binding to DNA promotes a feed-forward transcriptional program by positively regulating Gata2 expression. Gata2 may therefore be required for PML::RARA to establish its transcriptional program.

The role of transcription factors in the pathogenesis and therapeutic targeting of vascular diseases

Transcription factors (TFs) constitute an essential component of epigenetic regulation. They contribute to the progression of vascular diseases by regulating epigenetic gene expression in several vascular diseases. Recently, numerous regulatory mechanisms related to vascular pathology, ranging from general TFs that are continuously activated to histiocyte-specific TFs that are activated under specific circumstances, have been studied. TFs participate in the progression of vascular-related diseases by epigenetically regulating vascular endothelial cells (VECs) and vascular smooth muscle cells (VSMCs). The Krüppel-like family (KLF) TF family is widely recognized as the foremost regulator of vascular diseases. KLF11 prevents aneurysm progression by inhibiting the apoptosis of VSMCs and enhancing their contractile function. The presence of KLF4, another crucial member, suppresses the progression of atherosclerosis (AS) and pulmonary hypertension by attenuating the formation of VSMCs-derived foam cells, ameliorating endothelial dysfunction, and inducing vasodilatory effects. However, the mechanism underlying the regulation of the progression of vascular-related diseases by TFs has remained elusive. The present study categorized the TFs involved in vascular diseases and their regulatory mechanisms to shed light on the potential pathogenesis of vascular diseases, and provide novel insights into their diagnosis and treatment.

Combined SNPs sequencing and allele specific proteomics capture reveal functional causality underpinning the 2p25 prostate cancer susceptibility locus

Genome wide association studies (GWASs) have identified numerous risk loci associated with prostate cancer, yet unraveling their functional significance remains elusive. Leveraging our high-throughput SNPs-seq method, we pinpointed rs4519489 within the multi-ancestry GWAS-discovered 2p25 locus as a potential functional SNP due to its significant allelic differences in protein binding. Here, we conduct a comprehensive analysis of rs4519489 and its associated gene, NOL10, employing diverse cohort data and experimental models. Clinical findings reveal a synergistic effect between rs4519489 genotype and NOL10 expression on prostate cancer prognosis and severity. Through unbiased proteomics screening, we reveal that the risk allele A of rs4519489 exhibits enhanced binding to USF1, a novel oncogenic transcription factor (TF) implicated in prostate cancer progression and prognosis, resulting in elevated NOL10 expression. Furthermore, we elucidate that NOL10 regulates cell cycle pathways, fostering prostate cancer progression. The concurrent expression of NOL10 and USF1 correlates with aggressive prostate cancer characteristics and poorer prognosis. Collectively, our study offers a robust strategy for functional SNP screening and TF identification through high-throughput SNPs-seq and unbiased proteomics, highlighting the rs4519489-USF1-NOL10 regulatory axis as a promising biomarker or therapeutic target for clinical diagnosis and treatment of prostate cancer.

Genetic association and functional validation of ZFP36L2 in non-syndromic orofacial cleft subtypes

BACKGROUND

Non-syndromic orofacial cleft (NSOC) is one of the most common craniofacial malformations with complex etiology. This study aimed to explore the role of specific SNPs in ZFP36L2 and its functional relevance in zebrafish models.

METHODS

We analyzed genetic data of the Chinese Han population from two previous GWAS, comprising of 2512 cases and 2255 controls. Based on the Hardy-Weinberg Equilibrium (HWE) and minor allele frequency (MAF), SNPs in the ZFP36L2 were selected for association analysis. In addition, zebrafish models were used to clarify the in-situ expression pattern of zfp36l2 and the impact of its Morpholino-induced knockdown.

RESULTS

Via association analysis, rs7933 in ZFP36L2 was significantly associated with various non-syndromic cleft lip-only subtypes, potentially conferring a protective effect. Zebrafish embryos showed elevated expression of zfp36l2 in the craniofacial region during critical stages of oral cavity formation. Furthermore, Morpholino-induced knockdown of zfp36l2 led to craniofacial abnormalities, including cleft lip, which was partially rescued by the addition of zfp36l2 mRNA.

CONCLUSION

Our findings highlight the significance of ZFP36L2 in the etiology of NSOC, supported by both human genetic association data and functional studies in zebrafish. These results pave the way for further exploration of targeted interventions for craniofacial malformations.

rs822336 binding to C/EBPβ and NFIC modulates induction of PD-L1 expression and predicts anti-PD-1/PD-L1 therapy in advanced NSCLC

Efficient predictive biomarkers are needed for immune checkpoint inhibitor (ICI)-based immunotherapy in non-small cell lung cancer (NSCLC). Testing the predictive value of single nucleotide polymorphisms (SNPs) in programmed cell death 1 (PD-1) or its ligand 1 (PD-L1) has shown contrasting results. Here, we aim to validate the predictive value of PD-L1 SNPs in advanced NSCLC patients treated with ICIs as well as to define the molecular mechanisms underlying the role of the identified SNP candidate. rs822336 efficiently predicted response to anti-PD-1/PD-L1 immunotherapy in advanced non-oncogene addicted NSCLC patients as compared to rs2282055 and rs4143815. rs822336 mapped to the promoter/enhancer region of PD-L1, differentially affecting the induction of PD-L1 expression in human NSCLC cell lines as well as their susceptibility to HLA class I antigen matched PBMCs incubated with anti-PD-1 monoclonal antibody nivolumab. The induction of PD-L1 expression by rs822336 was mediated by a competitive allele-specificity binding of two identified transcription factors: C/EBPβ and NFIC. As a result, silencing of C/EBPβ and NFIC differentially regulated the induction of PD-L1 expression in human NSCLC cell lines carrying different rs822336 genotypes. Analysis by binding microarray further validated the competitive allele-specificity binding of C/EBPβ and NFIC to PD-L1 promoter/enhancer region based on rs822336 genotype in human NSCLC cell lines. These findings have high clinical relevance since identify rs822336 and induction of PD-L1 expression as novel biomarkers for predicting anti-PD-1/PD-L1-based immunotherapy in advanced NSCLC patients.

Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease

Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome; however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability of our workflow, we considered the multifactorial disorder migraine as a model to follow our step-by-step approach. We annotated the overlap of selected SNPs with regulatory elements and assessed their potential impact on gene expression based on publicly available prediction algorithms and functional genomics information. Some migraine risk loci have been hypothesised to reside in non-coding regions and to be implicated in the neurotransmission pathway. In this study, we used a set of 22 non-coding SNPs from neurotransmission and synaptic machinery-related genes previously suggested to be involved in migraine susceptibility based on our candidate gene association studies. After prioritising these SNPs, we focused on non-reported ones that demonstrated high regulatory potential: (1) VAMP2_rs1150 (3' UTR) was predicted as a target of hsa-mir-5010-3p miRNA, possibly disrupting its own gene expression; (2) STX1A_rs6951030 (proximal enhancer) may affect the binding affinity of zinc-finger transcription factors (namely ZNF423) and disturb TBL2 gene expression; and (3) SNAP25_rs2327264 (distal enhancer) expected to be in a binding site of ONECUT2 transcription factor. This study demonstrated the applicability of our practical workflow to facilitate the prioritisation of potentially relevant non-coding SNPs and predict their functional impact in multifactorial neurological diseases.

Temporospatial hierarchy and allele-specific expression of zygotic genome activation revealed by distant interspecific urochordate hybrids

Zygotic genome activation (ZGA) is a universal process in early embryogenesis of metazoan, when the quiescent zygotic nucleus initiates global transcription. However, the mechanisms related to massive genome activation and allele-specific expression (ASE) remain not well understood. Here, we develop hybrids from two deeply diverged (120 Mya) ascidian species to symmetrically document the dynamics of ZGA. We identify two coordinated ZGA waves represent early developmental and housekeeping gene reactivation, respectively. Single-cell RNA sequencing reveals that the major expression wave exhibits spatial heterogeneity and significantly correlates with cell fate. Moreover, allele-specific expression occurs in a species- rather than parent-related manner, demonstrating the divergence of cis-regulatory elements between the two species. These findings provide insights into ZGA in chordates.

Thermodynamic Allosteric Switch-Actuated 3D DNA Nanomachine for Ultrasensitive Electrochemical/Fluorescent Dual-Mode Biosensing of a Transcription Factor

Herein, we reported an innovative thermodynamic allosteric switch-actuated 3D DNA nanomachine for selective, sensitive, and accurate electrochemical (EC)/fluorescent (FL) dual-mode biosensing of a microphthalmia-associated transcription factor (MITF). The thermodynamic allosteric switch was ingeniously customized as a hairpin probe (HP) that was in dynamic equilibrium but rapidly interconverting conformations. At the "inactive state", the MITF-binding region and the switch part were "sequestered". Upon the introduction of MITF, an MITF-HP complex promptly formed, and the equilibrium of HP thermodynamically inclined from the "inactive state" toward the "active state" conformation. Immediately, the exposed switch on HP effectively actuated the 3D DNA nanomachine and synchronously produced the restriction site for Nb.BbvCI nicking endonuclease. After the autonomous conveying of the 3D DNA nanomachine by means of the high-efficiency circularly nicking endonuclease signal amplification (NESA), not only was MB-S1 in the supernatant used for FL measurements but also MB-SP/MNs/S2 in the precipitate was adapted for EC analysis, significantly improving the utilization of output products derived from the 3D DNA nanomachine. Accordingly, benefiting from the efficient DNA nanomachine signal amplification manner and the self-calibration function of a dual-mode bioassay, the constructed biosensor exhibits superior sensitivity and accuracy for MITF determination.

Cell-type-directed design of synthetic enhancers

Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes1. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Here we show that deep learning models2-6, can be used to efficiently design synthetic, cell-type-specific enhancers, starting from random sequences, and that this optimization process allows detailed tracing of enhancer features at single-nucleotide resolution. We evaluate the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We further exploit enhancer design to create 'dual-code' enhancers that target two cell types and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the state space searches towards local optima, we characterize enhancer codes through the strength, combination and arrangement of transcription factor activator and transcription factor repressor motifs. Finally, we apply the same strategies to successfully design human enhancers, which adhere to enhancer rules similar to those of Drosophila enhancers. Enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data

Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.

RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci

BACKGROUND

As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources.

RESULTS

We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs.

CONCLUSIONS

RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.

Quantifying negative selection in human 3' UTRs uncovers constrained targets of RNA-binding proteins

Many non-coding variants associated with phenotypes occur in 3' untranslated regions (3' UTRs), and may affect interactions with RNA-binding proteins (RBPs) to regulate gene expression post-transcriptionally. However, identifying functional 3' UTR variants has proven difficult. We use allele frequencies from the Genome Aggregation Database (gnomAD) to identify classes of 3' UTR variants under strong negative selection in humans. We develop intergenic mutability-adjusted proportion singleton (iMAPS), a generalized measure related to MAPS, to quantify negative selection in non-coding regions. This approach, in conjunction with in vitro and in vivo binding data, identifies precise RBP binding sites, miRNA target sites, and polyadenylation signals (PASs) under strong selection. For each class of sites, we identify thousands of gnomAD variants under selection comparable to missense coding variants, and find that sites in core 3' UTR regions upstream of the most-used PAS are under strongest selection. Together, this work improves our understanding of selection on human genes and validates approaches for interpreting genetic variants in human 3' UTRs.

Refine localizations of functional variants affecting eggshell color of Lueyang black-boned chicken in the SLCO1B3

Table eggs with color-uniformity shell are visually attractive for consumers. Lueyang black-boned chicken (LBC) lays colorful eggs, which is undesirable for sale of table eggs, but provides a segregating population for mapping functional variants affecting eggshell color. SLCO1B3 was identified as the causative gene for blue eggs in the Dongxiang and Araucana chickens. The aim of this study is to map functional variants associated with chicken eggshell color in the SLCO1B3. Eggshell color of LBC (n = 383) was measured using the L*a*b color space. SLCO1B3 was resequencing using a subset (n = 30) of 383 samples. Linkage disequilibrium among 139 SNP was analyzed. Association of 16 SNP in the SLCO1B3 and 8 in CPOX, ALAS1, and ABCG2 genes with L*a*b were tested by a polygenic model (LMM) and a polygenic/oligogenic mixed model (BSLMM). Chromatin state annotations were retrieved from the UCSC database. Effect of SLCO1B3 variants distributed in mapping and upstream 1.6-kb regions on promoter activities were analyzed using dual-luciferase reporter assay. One hundred and thirty-nine variants maintained low linkage disequilibrium with 80% of r2 less than 0.226. Fifteen SLCO1B3 variants were significantly associated with a*, of which 1B3_SNP108 was showed the strongest association and the largest effect on a*. In the BSLMM, 1B3_SNP108 alone appeared in the Markov chain Monte Carlo as major variants in 100% of posterior inclusion probability. None of variants in CPOX, ALAS1, and ABCG2 were significantly associated with color indexes except that 2 ALAS1 variants were associated with L*. 1B3_SNP108 distributes in the Intron4 where 6 active enhancers and 1 ATAC island were enriched. However, 1B3_SNP108-containing constructs showed negligible activities in the reporter assay. No significant differences of activities between haplotypes were found for five 5'-deleted promoter constructs. The data recognizes 1B3_SNP108 as a valuable marker for breeding of eggshell color. Functional variants are localized in the region adjacent to the 1B3_SNP108 due to low linkage disequilibrium in the LBC. Our findings extend the role of SLCO1B3 from a causative gene for blue eggs to a major regulator driving continuous variation of LBC eggshell color.

Benchmarking DNA binding affinity models using allele-specific transcription factor binding data

Transcription factors (TFs) bind to DNA in a highly sequence-specific manner. This specificity can manifest itself in vivo at heterozygous loci as a difference in TF occupancy between the two alleles. When applied on a genomic scale, functional genomic assays such as ChIP-seq typically lack the statistical power to detect allele-specific binding (ASB) at the level of individual variants. To address this, we propose a framework for benchmarking sequence-to-affinity models for TF binding in terms of their ability to predict allelic imbalances in ChIP-seq counts. We show that a likelihood function based on an over-dispersed binomial distribution can aggregate evidence for allelic preference across the genome without requiring statistical significance for individual variants. This allows us to systematically compare predictive performance when multiple binding models for the same TF are available. We introduce PyProBound, an easily extensible reimplementation of the ProBound biophysically interpretable machine learning framework. Configuring PyProBound to explicitly account for a confounding sequence-specific bias in DNA fragmentation rate yields improved TF binding models when training on ChIP-seq data. We also show how our likelihood function can be leveraged to perform de novo motif discovery on the raw allele-aware ChIP-seq counts.

Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks

BACKGROUND

Transcription factors regulate gene expression by binding to transcription factor binding sites (TFBSs). Most models for predicting TFBSs are based on position weight matrices (PWMs), which require a specific motif to be present in the DNA sequence and do not consider interdependencies of nucleotides. Novel approaches such as Transcription Factor Flexible Models or recurrent neural networks consequently provide higher accuracies. However, it is unclear whether such approaches can uncover novel non-canonical, hitherto unexpected TFBSs relevant to human transcriptional regulation.

RESULTS

In this study, we trained a convolutional recurrent neural network with HT-SELEX data for GRHL1 binding and applied it to a set of GRHL1 binding sites obtained from ChIP-Seq experiments from human cells. We identified 46 non-canonical GRHL1 binding sites, which were not found by a conventional PWM approach. Unexpectedly, some of the newly predicted binding sequences lacked the CNNG core motif, so far considered obligatory for GRHL1 binding. Using isothermal titration calorimetry, we experimentally confirmed binding between the GRHL1-DNA binding domain and predicted GRHL1 binding sites, including a non-canonical GRHL1 binding site. Mutagenesis of individual nucleotides revealed a correlation between predicted binding strength and experimentally validated binding affinity across representative sequences. This correlation was neither observed with a PWM-based nor another deep learning approach.

CONCLUSIONS

Our results show that convolutional recurrent neural networks may uncover unanticipated binding sites and facilitate quantitative transcription factor binding predictions.

Quantification of absolute transcription factor binding affinities in the native chromatin context using BANC-seq

Transcription factor binding across the genome is regulated by DNA sequence and chromatin features. However, it is not yet possible to quantify the impact of chromatin context on transcription factor binding affinities. Here, we report a method called binding affinities to native chromatin by sequencing (BANC-seq) to determine absolute apparent binding affinities of transcription factors to native DNA across the genome. In BANC-seq, a concentration range of a tagged transcription factor is added to isolated nuclei. Concentration-dependent binding is then measured per sample to quantify apparent binding affinities across the genome. BANC-seq adds a quantitative dimension to transcription factor biology, which enables stratification of genomic targets based on transcription factor concentration and prediction of transcription factor binding sites under non-physiological conditions, such as disease-associated overexpression of (onco)genes. Notably, whereas consensus DNA binding motifs for transcription factors are important to establish high-affinity binding sites, these motifs are not always strictly required to generate nanomolar-affinity interactions in the genome.

Prioritizing cardiovascular disease-associated variants altering NKX2-5 and TBX5 binding through an integrative computational approach

Cardiovascular diseases (CVDs) are the leading cause of death worldwide and are heavily influenced by genetic factors. Genome-wide association studies have mapped >90% of CVD-associated variants within the noncoding genome, which can alter the function of regulatory proteins, such as transcription factors (TFs). However, due to the overwhelming number of single-nucleotide polymorphisms (SNPs) (>500,000) in genome-wide association studies, prioritizing variants for in vitro analysis remains challenging. In this work, we implemented a computational approach that considers support vector machine (SVM)-based TF binding site classification and cardiac expression quantitative trait loci (eQTL) analysis to identify and prioritize potential CVD-causing SNPs. We identified 1535 CVD-associated SNPs within TF footprints and putative cardiac enhancers plus 14,218 variants in linkage disequilibrium with genotype-dependent gene expression in cardiac tissues. Using ChIP-seq data from two cardiac TFs (NKX2-5 and TBX5) in human-induced pluripotent stem cell-derived cardiomyocytes, we trained a large-scale gapped k-mer SVM model to identify CVD-associated SNPs that altered NKX2-5 and TBX5 binding. The model was tested by scoring human heart TF genomic footprints within putative enhancers and measuring in vitro binding through electrophoretic mobility shift assay. Five variants predicted to alter NKX2-5 (rs59310144, rs6715570, and rs61872084) and TBX5 (rs7612445 and rs7790964) binding were prioritized for in vitro validation based on the magnitude of the predicted change in binding and are in cardiac tissue eQTLs. All five variants altered NKX2-5 and TBX5 DNA binding. We present a bioinformatic approach that considers tissue-specific eQTL analysis and SVM-based TF binding site classification to prioritize CVD-associated variants for in vitro analysis.

Gene as a dynamical notion: An extensive and integrative vision. Redefining the gene concept, from traditional to genic-interaction, as a new dynamical version

The current concept of gene has been very useful during the 20th and 21st centuries. However, recent advances in molecular biology and bioinformatics, which have further diversified the functional and adaptive profile of genetic information and its integration with cell physiology and environmental response, have contributed to focusing on additional new gene properties besides the traditional definition. Considering the inherent complexity of gene expression, whose adaptive objective must be referred to the Tortoise-Hare model, in which two tendencies converge, one focused on rapid adaptation to achieve survival, and the other that prevents an over-adaptation effect. In this context, a revision of the gene concept must be made, which must include these new mechanisms and approaches. In this paper, we propose a new conception of the idea of a gene that moves from a static and defined version of hereditary information to a dynamic idea that preponderates gene interaction (circumscribed to that established between protein-protein, protein-nucleic acid, and nucleic acid-nucleic acid) and the selection it exerts, as the irreducible element that works in a coordinated way in a genomic regulatory network (GRN).

Genome-wide expression QTL mapping reveals the highly dynamic regulatory landscape of a major wheat pathogen

BACKGROUND

In agricultural ecosystems, outbreaks of diseases are frequent and pose a significant threat to food security. A successful pathogen undergoes a complex and well-timed sequence of regulatory changes to avoid detection by the host immune system; hence, well-tuned gene regulation is essential for survival. However, the extent to which the regulatory polymorphisms in a pathogen population provide an adaptive advantage is poorly understood.

RESULTS

We used Zymoseptoria tritici, one of the most important pathogens of wheat, to generate a genome-wide map of regulatory polymorphism governing gene expression. We investigated genome-wide transcription levels of 146 strains grown under nutrient starvation and performed expression quantitative trait loci (eQTL) mapping. We identified cis-eQTLs for 65.3% of all genes and the majority of all eQTL loci are within 2kb upstream and downstream of the transcription start site (TSS). We also show that polymorphism in different gene elements contributes disproportionally to gene expression variation. Investigating regulatory polymorphism in gene categories, we found an enrichment of regulatory variants for genes predicted to be important for fungal pathogenesis but with comparatively low effect size, suggesting a separate layer of gene regulation involving epigenetics. We also show that previously reported trait-associated SNPs in pathogen populations are frequently cis-regulatory variants of neighboring genes with implications for the trait architecture.

CONCLUSIONS

Overall, our study provides extensive evidence that single populations segregate large-scale regulatory variation and are likely to fuel rapid adaptation to resistant hosts and environmental change.

A neuron-specific microexon ablates the novel DNA-binding function of a histone H3K4me0 reader PHF21A

How cell-type-specific chromatin landscapes emerge and progress during metazoan ontogenesis remains an important question. Transcription factors are expressed in a cell-type-specific manner and recruit chromatin-regulatory machinery to specific genomic loci. In contrast, chromatin-regulatory proteins are expressed broadly and are assumed to exert the same intrinsic function across cell types. However, human genetics studies have revealed an unexpected vulnerability of neurodevelopment to chromatin factor mutations with unknown mechanisms. Here, we report that 14 chromatin regulators undergo evolutionary-conserved neuron-specific splicing events involving microexons. Of the 14 chromatin regulators, two are integral components of a histone H3K4 demethylase complex; the catalytic subunit LSD1 and an H3K4me0-reader protein PHF21A adopt neuron-specific forms. We found that canonical PHF21A (PHF21A-c) binds to DNA by AT-hook motif, and the neuronal counterpart PHF21A-n lacks this DNA-binding function yet maintains H3K4me0 recognition intact. In-vitro reconstitution of the canonical and neuronal PHF21A-LSD1 complexes identified the neuronal complex as a hypomorphic H3K4 demethylating machinery with reduced nucleosome engagement. Furthermore, an autism-associated PHF21A missense mutation, 1285 G>A, at the last nucleotide of the common exon immediately upstream of the neuronal microexon led to impaired splicing of PHF21A -n. Thus, ubiquitous chromatin regulatory complexes exert unique intrinsic functions in neurons via alternative splicing of their subunits and potentially contribute to faithful human brain development.

KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites

This paper addresses the crucial task of identifying DNA/RNA binding sites, which has implications in drug/vaccine design, protein engineering, and cancer research. Existing methods utilize complex neural network structures, diverse input types, and machine learning techniques for feature extraction. However, the growing volume of sequences poses processing challenges. This study introduces KDeep, employing a CNN-LSTM architecture with a novel encoding method called 2Lk. 2Lk enhances prediction accuracy, reduces memory consumption by up to 84%, reduces trainable parameters, and improves interpretability by approximately 79% compared to state-of-the-art approaches. KDeep offers a promising solution for accurate and efficient binding site prediction.

Human-genome single nucleotide polymorphisms affecting transcription factor binding and their role in pathogenesis

Single nucleotide polymorphisms (SNPs) are the most common type of variation in the human genome. The vast majority of SNPs identified in the human genome do not have any effect on the phenotype; however, some can lead to changes in the function of a gene or the level of its expression. Most SNPs associated with certain traits or pathologies are mapped to regulatory regions of the genome and affect gene expression by changing transcription factor binding sites. In recent decades, substantial effort has been invested in searching for such regulatory SNPs (rSNPs) and understanding the mechanisms by which they lead to phenotypic differences, primarily to individual differences in susceptibility to diseases and in sensitivity to drugs. The development of the NGS (next-generation sequencing) technology has contributed not only to the identification of a huge number of SNPs and to the search for their association (genome-wide association studies, GWASs) with certain diseases or phenotypic manifestations, but also to the development of more productive approaches to their functional annotation. It should be noted that the presence of an association does not allow one to identify a functional, truly disease-associated DNA sequence variant among multiple marker SNPs that are detected due to linkage disequilibrium. Moreover, determination of associations of genetic variants with a disease does not provide information about the functionality of these variants, which is necessary to elucidate the molecular mechanisms of the development of pathology and to design effective methods for its treatment and prevention. In this regard, the functional analysis of SNPs annotated in the GWAS catalog, both at the genome-wide level and at the level of individual SNPs, became especially relevant in recent years. A genome-wide search for potential rSNPs is possible without any prior knowledge of their association with a trait. Thus, mapping expression quantitative trait loci (eQTLs) makes it possible to identify an SNP for which - among transcriptomes of homozygotes and heterozygotes for its various alleles - there are differences in the expression level of certain genes, which can be located at various distances from the SNP. To predict rSNPs, approaches based on searches for allele-specific events in RNA-seq, ChIP-seq, DNase-seq, ATAC-seq, MPRA, and other data are also used. Nonetheless, for a more complete functional annotation of such rSNPs, it is necessary to establish their association with a trait, in particular, with a predisposition to a certain pathology or sensitivity to drugs. Thus, approaches to finding SNPs important for the development of a trait can be categorized into two groups: (1) starting from data on an association of SNPs with a certain trait, (2) starting from the determination of allele-specific changes at the molecular level (in a transcriptome or regulome). Only comprehensive use of strategically different approaches can considerably enrich our knowledge about the role of genetic determinants in the molecular mechanisms of trait formation, including predisposition to multifactorial diseases.

Epromoters are new players in the regulatory landscape with potential pleiotropic roles

Precise spatiotemporal control of gene expression during normal development and cell differentiation is achieved by the combined action of proximal (promoters) and distal (enhancers) cis-regulatory elements. Recent studies have reported that a subset of promoters, termed Epromoters, works also as enhancers to regulate distal genes. This new paradigm opened novel questions regarding the complexity of our genome and raises the possibility that genetic variation within Epromoters has pleiotropic effects on various physiological and pathological traits by differentially impacting multiple proximal and distal genes. Here, we discuss the different observations pointing to an important role of Epromoters in the regulatory landscape and summarize the evidence supporting a pleiotropic impact of these elements in disease. We further hypothesize that Epromoter might represent a major contributor to phenotypic variation and disease.

Prioritizing Cardiovascular Disease-Associated Variants Altering NKX2-5 Binding through an Integrative Computational Approach

Cardiovascular diseases (CVDs) are the leading cause of death worldwide and are heavily influenced by genetic factors. Genome-wide association studies (GWAS) have mapped > 90% of CVD-associated variants within the non-coding genome, which can alter the function of regulatory proteins, like transcription factors (TFs). However, due to the overwhelming number of GWAS single nucleotide polymorphisms (SNPs) (>500,000), prioritizing variants for in vitro analysis remains challenging. In this work, we implemented a computational approach that considers support vector machine (SVM)-based TF binding site classification and cardiac expression quantitative trait loci (eQTL) analysis to identify and prioritize potential CVD-causing SNPs. We identified 1,535 CVD-associated SNPs that occur within human heart footprints/enhancers and 9,309 variants in linkage disequilibrium (LD) with differential gene expression profiles in cardiac tissue. Using hiPSC-CM ChIP-seq data from NKX2-5 and TBX5, two cardiac TFs essential for proper heart development, we trained a large-scale gapped k-mer SVM (LS-GKM-SVM) predictive model that can identify binding sites altered by CVD-associated SNPs. The computational predictive model was tested by scoring human heart footprints and enhancers in vitro through electrophoretic mobility shift assay (EMSA). Three variants (rs59310144, rs6715570, and rs61872084) were prioritized for in vitro validation based on their eQTL in cardiac tissue and LS-GKM-SVM prediction to alter NKX2-5 DNA binding. All three variants altered NKX2-5 DNA binding. In summary, we present a bioinformatic approach that considers tissue-specific eQTL analysis and SVM-based TF binding site classification to prioritize CVD-associated variants for in vitro experimental analysis.

GATA2 co-opts TGFβ1/SMAD4 oncogenic signaling and inherited variants at 6q22 to modulate prostate cancer progression

BACKGROUND

Aberrant somatic genomic alteration including copy number amplification is a hallmark of cancer genomes. We previously profiled genomic landscapes of prostate cancer (PCa), yet the underlying causal genes with prognostic potential has not been defined. It remains unclear how a somatic genomic event cooperates with inherited germline variants contribute to cancer predisposition and progression.

METHODS

We applied integrated genomic and clinical data, experimental models and bioinformatic analysis to identify GATA2 as a highly prevalent metastasis-associated genomic amplification in PCa. Biological roles of GATA2 in PCa metastasis was determined in vitro and in vivo. Global chromatin co-occupancy and co-regulation of GATA2 and SMAD4 was investigated by coimmunoprecipitation, ChIP-seq and RNA-seq assays. Tumor cellular assays, qRT-PCR, western blot, ChIP, luciferase assays and CRISPR-Cas9 editing methods were performed to mechanistically understand the cooperation of GATA2 with SMAD4 in promoting TGFβ1 and AR signaling and mediating inherited PCa risk and progression.

RESULTS

In this study, by integrated genomics and experimental analysis, we identified GATA2 as a prevalent metastasis-associated genomic amplification to transcriptionally augment its own expression in PCa. Functional experiments demonstrated that GATA2 physically interacted and cooperated with SMAD4 for genome-wide chromatin co-occupancy and co-regulation of PCa genes and metastasis pathways like TGFβ signaling. Mechanistically, GATA2 was cooperative with SMAD4 to enhance TGFβ and AR signaling pathways, and activated the expression of TGFβ1 via directly binding to a distal enhancer of TGFβ1. Strinkingly, GATA2 and SMAD4 globally mediated inherited PCa risk and formed a transcriptional complex with HOXB13 at the PCa risk-associated rs339331/6q22 enhancer, leading to increased expression of the PCa susceptibility gene RFX6.

CONCLUSIONS

Our study prioritizes causal genomic amplification genes with prognostic values in PCa and reveals the pivotal roles of GATA2 in transcriptionally activating the expression of its own and TGFβ1, thereby co-opting to TGFβ1/SMAD4 signaling and RFX6 at 6q22 to modulate PCa predisposition and progression.

Noncanonical binding of transcription factors: time to revisit specificity?

Transcription factors (TFs) are one of the most studied classes of DNA-binding proteins that have a direct functional impact on gene transcription and thus, on human physiology and disease. The mechanisms that TFs use for recognizing target DNA binding sites have been studied for nearly five decades, yet they remain poorly understood. It is classically assumed that a TF recognizes a specific sequence pattern, or motif, as its binding sites. However, recent studies are consistently finding examples of noncanonical binding, that is, TFs binding at sites that do not resemble their sequence motifs. Here we review the current literature on four major types of noncanonical TF binding, namely binding based on DNA shape readout, at Guanine-quadruplex structures, at repeat sequences, and bispecific binding. These examples point to a critical need for studies to unify our current observations, many of which are at odds with the "one TF, one motif" view, into a more comprehensive definition of the DNA-binding specificity of TFs.

Multiomic analysis of cohesin reveals that ZBTB transcription factors contribute to chromatin interactions

One bottleneck in understanding the principles of 3D chromatin structures is caused by the paucity of known regulators. Cohesin is essential for 3D chromatin organization, and its interacting partners are candidate regulators. Here, we performed proteomic profiling of the cohesin in chromatin and identified transcription factors, RNA-binding proteins and chromatin regulators associated with cohesin. Acute protein degradation followed by time-series genomic binding quantitation and BAT Hi-C analysis were conducted, and the results showed that the transcription factor ZBTB21 contributes to cohesin chromatin binding, 3D chromatin interactions and transcriptional repression. Strikingly, multiomic analyses revealed that the other four ZBTB factors interacted with cohesin, and double degradation of ZBTB21 and ZBTB7B led to a further decrease in cohesin chromatin occupancy. We propose that multiple ZBTB transcription factors orchestrate the chromatin binding of cohesin to regulate chromatin interactions, and we provide a catalog of many additional proteins associated with cohesin that warrant further investigation.

Blood cell traits' GWAS loci colocalization with variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants

Genome-wide association studies (GWASs) have uncovered numerous trait-associated loci across the human genome, most of which are located in noncoding regions, making interpretation difficult. Moreover, causal variants are hard to statistically fine-map at many loci because of widespread linkage disequilibrium. To address this challenge, we present a strategy utilizing transcription factor (TF) binding quantitative trait loci (bQTLs) for colocalization analysis to identify trait associations likely mediated by TF occupancy variation and to pinpoint likely causal variants using motif scores. We applied this approach to PU.1 bQTLs in lymphoblastoid cell lines and blood cell trait GWAS data. Colocalization analysis revealed 69 blood cell trait GWAS loci putatively driven by PU.1 occupancy variation. We nominate PU.1 motif-altering variants as the likely shared causal variants at 51 loci. Such integration of TF bQTL data with other GWAS data may reveal transcriptional regulatory mechanisms and causal noncoding variants underlying additional complex traits.

Noncoding variants alter GATA2 expression in rhombomere 4 motor neurons and cause dominant hereditary congenital facial paresis

Hereditary congenital facial paresis type 1 (HCFP1) is an autosomal dominant disorder of absent or limited facial movement that maps to chromosome 3q21-q22 and is hypothesized to result from facial branchial motor neuron (FBMN) maldevelopment. In the present study, we report that HCFP1 results from heterozygous duplications within a neuron-specific GATA2 regulatory region that includes two enhancers and one silencer, and from noncoding single-nucleotide variants (SNVs) within the silencer. Some SNVs impair binding of NR2F1 to the silencer in vitro and in vivo and attenuate in vivo enhancer reporter expression in FBMNs. Gata2 and its effector Gata3 are essential for inner-ear efferent neuron (IEE) but not FBMN development. A humanized HCFP1 mouse model extends Gata2 expression, favors the formation of IEEs over FBMNs and is rescued by conditional loss of Gata3. These findings highlight the importance of temporal gene regulation in development and of noncoding variation in rare mendelian disease.

Candidate SNP Markers Significantly Altering the Affinity of TATA-Binding Protein for the Promoters of Human Hub Genes for Atherogenesis, Atherosclerosis and Atheroprotection

Atherosclerosis is a systemic disease in which focal lesions in arteries promote the build-up of lipoproteins and cholesterol they are transporting. The development of atheroma (atherogenesis) narrows blood vessels, reduces the blood supply and leads to cardiovascular diseases. According to the World Health Organization (WHO), cardiovascular diseases are the leading cause of death, which has been especially boosted since the COVID-19 pandemic. There is a variety of contributors to atherosclerosis, including lifestyle factors and genetic predisposition. Antioxidant diets and recreational exercises act as atheroprotectors and can retard atherogenesis. The search for molecular markers of atherogenesis and atheroprotection for predictive, preventive and personalized medicine appears to be the most promising direction for the study of atherosclerosis. In this work, we have analyzed 1068 human genes associated with atherogenesis, atherosclerosis and atheroprotection. The hub genes regulating these processes have been found to be the most ancient. In silico analysis of all 5112 SNPs in their promoters has revealed 330 candidate SNP markers, which statistically significantly change the affinity of the TATA-binding protein (TBP) for these promoters. These molecular markers have made us confident that natural selection acts against underexpression of the hub genes for atherogenesis, atherosclerosis and atheroprotection. At the same time, upregulation of the one for atheroprotection promotes human health.

A survey on algorithms to characterize transcription factor binding sites

Transcription factors (TFs) are key regulatory proteins that control the transcriptional rate of cells by binding short DNA sequences called transcription factor binding sites (TFBS) or motifs. Identifying and characterizing TFBS is fundamental to understanding the regulatory mechanisms governing the transcriptional state of cells. During the last decades, several experimental methods have been developed to recover DNA sequences containing TFBS. In parallel, computational methods have been proposed to discover and identify TFBS motifs based on these DNA sequences. This is one of the most widely investigated problems in bioinformatics and is referred to as the motif discovery problem. In this manuscript, we review classical and novel experimental and computational methods developed to discover and characterize TFBS motifs in DNA sequences, highlighting their advantages and drawbacks. We also discuss open challenges and future perspectives that could fill the remaining gaps in the field.