Uncovering tissue-specific binding features from differential deep learning

Ubiquitous MEIS transcription factors actuate lineage-specific transcription to establish cell fate

Control of gene expression is commonly mediated by distinct combinations of transcription factors (TFs). This cooperative action allows the integration of multiple biological signals at regulatory elements, resulting in highly specific gene expression patterns. It is unclear whether combinatorial binding is also necessary to bring together TFs with distinct biochemical functions, which collaborate to effectively recruit and activate RNA polymerase II. Using a cardiac differentiation model, we find that the largely ubiquitous homeodomain proteins MEIS act as actuators, fully activating transcriptional programs selected by lineage-restricted TFs. Combinatorial binding of MEIS with lineage-enriched TFs, GATA, and HOX, provides selectivity, guiding MEIS to function at cardiac-specific enhancers. In turn, MEIS TFs promote the accumulation of the methyltransferase KMT2D to initiate lineage-specific enhancer commissioning. MEIS combinatorial binding dynamics, dictated by the changing dosage of its partners, drive cells into progressive stages of differentiation. Our results uncover tissue-specific transcriptional activation as the result of ubiquitous actuator TFs harnessing general transcriptional activator at tissue-specific enhancers, to which they are directed by binding with lineage- and domain-specific TFs.

Synergizing metabolomics and artificial intelligence for advancing precision oncology

Metabolomics has emerged as a transformative tool in precision oncology, with substantial potential for advancing biomarker discovery, monitoring treatment responses, and aiding drug development. Integrating artificial intelligence (AI) into metabolomics optimizes data acquisition and analysis, facilitating the interpretation of complex metabolic networks and enabling more effective multiomics integration. In this opinion, we explore recent advances in the application of metabolomics within precision oncology, emphasizing the unique advantages that AI-driven metabolomics offers. We propose that AI not only complements but also amplifies the potential of current platforms, accelerating research progress and ultimately improving patient outcomes. Finally, we discuss the opportunities and challenges involved in translating AI-driven metabolomics into clinical practice for precision oncology.

Identifying transcription factors with cell-type specific DNA binding signatures

BACKGROUND

Transcription factors (TFs) bind to different parts of the genome in different types of cells, but it is usually assumed that the inherent DNA-binding preferences of a TF are invariant to cell type. Yet, there are several known examples of TFs that switch their DNA-binding preferences in different cell types, and yet more examples of other mechanisms, such as steric hindrance or cooperative binding, that may result in a "DNA signature" of differential binding.

RESULTS

To survey this phenomenon systematically, we developed a deep learning method we call SigTFB (Signatures of TF Binding) to detect and quantify cell-type specificity in a TF's known genomic binding sites. We used ENCODE ChIP-seq data to conduct a wide scale investigation of 169 distinct TFs in up to 14 distinct cell types. SigTFB detected statistically significant DNA binding signatures in approximately two-thirds of TFs, far more than might have been expected from the relatively sparse evidence in prior literature. We found that the presence or absence of a cell-type specific DNA binding signature is distinct from, and indeed largely uncorrelated to, the degree of overlap between ChIP-seq peaks in different cell types, and tended to arise by two mechanisms: using established motifs in different frequencies, and by selective inclusion of motifs for distint TFs.

CONCLUSIONS

While recent results have highlighted cell state features such as chromatin accessibility and gene expression in predicting TF binding, our results emphasize that, for some TFs, the DNA sequences of the binding sites contain substantial cell-type specific motifs.

JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles

JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs' structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TF binding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.

TALE transcription factors: Cofactors no more

Exd/PBX, Hth/MEIS and PREP proteins belong to the TALE (three-amino-acid loop extension) superclass of transcription factors (TFs) with an atypical homedomain (HD). Originally discovered as "cofactors" to HOX proteins, revisiting their traditional role in light of genome-wide experiments reveals a strong and reproducible pattern of HOX and TALE co-occupancy across diverse embryonic tissues. While confirming that TALE increases HOX specificity and selectivity in vivo, this wider outlook also reveals novel aspects of HOX:TALE collaboration, namely that HOX TFs generally require pre-bound TALE factors to access their functional binding sites in vivo. In contrast to the restricted expression domains of HOX TFs, TALE factors are largely ubiquitous, and PBX and PREP are expressed at the earliest developmental stages. PBX and MEIS control development of many organs and tissues and their dysregulation is associated with congenital disease and cancer. Accordingly, many instances of TALE cooperation with non HOX TFs have been documented in various systems. The model that emerges from these studies is that TALE TFs create a permissive chromatin platform that is selected by tissue-restricted TFs for binding. In turn, HOX and other tissue-restricted TFs selectively convert a ubiquitous pool of low affinity TALE binding events into high confidence, tissue-restricted binding events associated with transcriptional activation. As a result, TALE:TF complexes are associated with active chromatin and domain/lineage-specific gene activity. TALE ubiquitous expression and broad genomic occupancy, as well as the increasing examples of TALE tissue-specific partners, reveal a universal and obligatory role for TALE in the control of tissue and lineage-specific transcriptional programs, beyond their initial discovery as HOX co-factors.

Transcription factor-binding k-mer analysis clarifies the cell type dependency of binding specificities and cis-regulatory SNPs in humans

BACKGROUND

Transcription factors (TFs) exhibit heterogeneous DNA-binding specificities in individual cells and whole organisms under natural conditions, and de novo motif discovery usually provides multiple motifs, even from a single chromatin immunoprecipitation-sequencing (ChIP-seq) sample. Despite the accumulation of ChIP-seq data and ChIP-seq-derived motifs, the diversity of DNA-binding specificities across different TFs and cell types remains largely unexplored.

RESULTS

Here, we applied MOCCS2, our k-mer-based motif discovery method, to a collection of human TF ChIP-seq samples across diverse TFs and cell types, and systematically computed profiles of TF-binding specificity scores for all k-mers. After quality control, we compiled a set of TF-binding specificity score profiles for 2,976 high-quality ChIP-seq samples, comprising 473 TFs and 398 cell types. Using these high-quality samples, we confirmed that the k-mer-based TF-binding specificity profiles reflected TF- or TF-family dependent DNA-binding specificities. We then compared the binding specificity scores of ChIP-seq samples with the same TFs but with different cell type classes and found that half of the analyzed TFs exhibited differences in DNA-binding specificities across cell type classes. Additionally, we devised a method to detect differentially bound k-mers between two ChIP-seq samples and detected k-mers exhibiting statistically significant differences in binding specificity scores. Moreover, we demonstrated that differences in the binding specificity scores between k-mers on the reference and alternative alleles could be used to predict the effect of variants on TF binding, as validated by in vitro and in vivo assay datasets. Finally, we demonstrated that binding specificity score differences can be used to interpret disease-associated non-coding single-nucleotide polymorphisms (SNPs) as TF-affecting SNPs and provide candidates responsible for TFs and cell types.

CONCLUSIONS

Our study provides a basis for investigating the regulation of gene expression in a TF-, TF family-, or cell-type-dependent manner. Furthermore, our differential analysis of binding-specificity scores highlights noncoding disease-associated variants in humans.

Uncovering the Relationship between Tissue-Specific TF-DNA Binding and Chromatin Features through a Transformer-Based Model

Chromatin features can reveal tissue-specific TF-DNA binding, which leads to a better understanding of many critical physiological processes. Accurately identifying TF-DNA bindings and constructing their relationships with chromatin features is a long-standing goal in the bioinformatic field. However, this has remained elusive due to the complex binding mechanisms and heterogeneity among inputs. Here, we have developed the GHTNet (General Hybrid Transformer Network), a transformer-based model to predict TF-DNA binding specificity. The GHTNet decodes the relationship between tissue-specific TF-DNA binding and chromatin features via a specific input scheme of alternative inputs and reveals important gene regions and tissue-specific motifs. Our experiments show that the GHTNet has excellent performance, achieving about a 5% absolute improvement over existing methods. The TF-DNA binding mechanism analysis shows that the importance of TF-DNA binding features varies across tissues. The best predictor is based on the DNA sequence, followed by epigenomics and shape. In addition, cross-species studies address the limited data, thus providing new ideas in this case. Moreover, the GHTNet is applied to interpret the relationship among TFs, chromatin features, and diseases associated with AD46 tissue. This paper demonstrates that the GHTNet is an accurate and robust framework for deciphering tissue-specific TF-DNA binding and interpreting non-coding regions.

Omics Data and Data Representations for Deep Learning-Based Predictive Modeling

Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data.

Host-pathogen protein-nucleic acid interactions: A comprehensive review

Recognition of pathogen-derived nucleic acids by host cells is an effective host strategy to detect pathogenic invasion and trigger immune responses. In the context of pathogen-specific pharmacology, there is a growing interest in mapping the interactions between pathogen-derived nucleic acids and host proteins. Insight into the principles of the structural and immunological mechanisms underlying such interactions and their roles in host defense is necessary to guide therapeutic intervention. Here, we discuss the newest advances in studies of molecular interactions involving pathogen nucleic acids and host factors, including their drug design, molecular structure and specific patterns. We observed that two groups of nucleic acid recognizing molecules, Toll-like receptors (TLRs) and the cytoplasmic retinoic acid-inducible gene (RIG)-I-like receptors (RLRs) form the backbone of host responses to pathogen nucleic acids, with additional support provided by absent in melanoma 2 (AIM2) and DNA-dependent activator of Interferons (IFNs)-regulatory factors (DAI) like cytosolic activity. We review the structural, immunological, and other biological aspects of these representative groups of molecules, especially in terms of their target specificity and affinity and challenges in leveraging host-pathogen protein-nucleic acid interactions (HP-PNI) in drug discovery.

Protein-DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data

With the development of artificial intelligence technologies and the availability of large amounts of biological data, computational methods for proteomics have undergone a developmental process from traditional machine learning to deep learning. This review focuses on computational approaches and tools for the prediction of protein-DNA/RNA interactions using machine intelligence techniques. We provide an overview of the development progress of computational methods and summarize the advantages and shortcomings of these methods. We further compiled applications in tasks related to the protein-DNA/RNA interactions, and pointed out possible future application trends. Moreover, biological sequence-digitizing representation strategies used in different types of computational methods are also summarized and discussed. This article is protected by copyright. All rights reserved.

Discovering differential genome sequence activity with interpretable and efficient deep learning

Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two black-box methods that can interpret genome regulatory sequences for cell type-specific or condition specific patterns. We show that these methods identify relevant transcription factor motifs and spacings that are predictive of cell state-specific chromatin accessibility. Finally, we integrate these methods into framework that is readily accessible to non-experts and available for download as a binary or installed via PyPI or bioconda at https://cgs.csail.mit.edu/deepaccess-package/.

HOX paralogs selectively convert binding of ubiquitous transcription factors into tissue-specific patterns of enhancer activation

Gene expression programs determine cell fate in embryonic development and their dysregulation results in disease. Transcription factors (TFs) control gene expression by binding to enhancers, but how TFs select and activate their target enhancers is still unclear. HOX TFs share conserved homeodomains with highly similar sequence recognition properties, yet they impart the identity of different animal body parts. To understand how HOX TFs control their specific transcriptional programs in vivo, we compared HOXA2 and HOXA3 binding profiles in the mouse embryo. HOXA2 and HOXA3 directly cooperate with TALE TFs and selectively target different subsets of a broad TALE chromatin platform. Binding of HOX and tissue-specific TFs convert low affinity TALE binding into high confidence, tissue-specific binding events, which bear the mark of active enhancers. We propose that HOX paralogs, alone and in combination with tissue-specific TFs, generate tissue-specific transcriptional outputs by modulating the activity of TALE TFs at selected enhancers.

Enhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study

Despite the success and fast adaptation of deep learning models in biomedical domains, their lack of interpretability remains an issue. Here, we introduce Enhanced Integrated Gradients (EIG), a method to identify significant features associated with a specific prediction task. Using RNA splicing prediction as well as digit classification as case studies, we demonstrate that EIG improves upon the original Integrated Gradients method and produces sets of informative features. We then apply EIG to identify A1CF as a key regulator of liver-specific alternative splicing, supporting this finding with subsequent analysis of relevant A1CF functional (RNA-seq) and binding data (PAR-CLIP).