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Summary Transcription factors (TFs) are thought to function with partners to achieve specificity and precise quantitative outputs. In the developing heart, heterotypic TF interactions, such as between the T-box TF TBX5 and the homeodomain TF NKX2-5, have been proposed as a mechanism for human congenital heart defects. We report extensive and complex interdependent genomic occupancy of TBX5, NKX2-5, and the zinc finger TF GATA4 coordinately controlling cardiac gene expression, differentiation, and morphogenesis. Interdependent binding serves not only to co-regulate gene expression but also to prevent TFs from distributing to ectopic loci and activate lineage-inappropriate genes. We define preferential motif arrangements for TBX5 and NKX2-5 cooperative binding sites, supported at the atomic level by their co-crystal structure bound to DNA, revealing a direct interaction between the two factors and induced DNA bending. Complex interdependent binding mechanisms reveal tightly regulated TF genomic distribution and define a combinatorial logic for heterotypic TF regulation of differentiation. [ABSTRACT FROM AUTHOR]

The bendability of genomic DNA impacts chromatin packaging and protein-DNA binding. However, we do not have a comprehensive understanding of the motifs influencing DNA bendability. Recent high-throughput technologies such as Loop-Seq offer an opportunity to address this gap but the lack of accurate and interpretable machine learning models still remains. Here we introduce DeepBend, a convolutional neural network model with convolutions designed to directly capture the motifs underlying DNA bendability and their periodic occurrences or relative arrangements that modulate bendability. DeepBend consistently performs on par with alternative models while giving an extra edge through mechanistic interpretations. Besides confirming the known motifs of DNA bendability, DeepBend also revealed several novel motifs and showed how the spatial patterns of motif occurrences influence bendability. DeepBend's genome-wide prediction of bendability further showed how bendability is linked to chromatin conformation and revealed the motifs controlling the bendability of topologically associated domains and their boundaries., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

Spatial transcriptomics (ST) can catalyze discoveries linking tissue function with spatial organization of cell types. However, technical variations and subtle differences between cell subtypes make this a challenging goal. Recent algorithms that carefully incorporate the details of the ST data generation process show the promise to overcome these challenges., Competing Interests: Declaration of interests The author declares no competing interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Single-cell spatial transcriptomics (sc-ST) holds the promise to elucidate architectural aspects of complex tissues. Such analyses require modeling cell types in sc-ST datasets through their integration with single-cell RNA-seq datasets. However, this integration, is nontrivial since the two technologies differ widely in the number of profiled genes, and the datasets often do not share many marker genes for given cell types. We developed a neural network model, spatial transcriptomics cell-types assignment using neural networks (STANN), to overcome these challenges. Analysis of STANN's predicted cell types in mouse olfactory bulb (MOB) sc-ST data delineated MOB architecture beyond its morphological layer-based conventional description. We find that cell-type proportions remain consistent within individual morphological layers but vary significantly between layers. Notably, even within a layer, cellular colocalization patterns and intercellular communication mechanisms show high spatial variations. These observations imply a refinement of major cell types into subtypes characterized by spatially localized gene regulatory networks and receptor-ligand usage., Competing Interests: Declarations of interests Conflicts, J.F.M. is a founder of and owns shares in Yap Therapeutics., (Copyright © 2021 Elsevier Inc. All rights reserved.)

DNA shape adds specificity to sequence motifs but has not been explored systematically outside this context. We hypothesized that DNA-binding proteins (DBPs) preferentially occupy DNA with specific structures ("shape motifs") regardless of whether or not these correspond to high information content sequence motifs. We present ShapeMF, a Gibbs sampling algorithm that identifies de novo shape motifs. Using binding data from hundreds of in vivo and in vitro experiments, we show that most DBPs have shape motifs and can occupy these in the absence of sequence motifs. This "shape-only binding" is common for many DBPs and in regions co-bound by multiple DBPs. When shape and sequence motifs co-occur, they can be overlapping, flanking, or separated by consistent spacing. Finally, DBPs within the same protein family have different shape motifs, explaining their distinct genome-wide occupancy despite having similar sequence motifs. These results suggest that shape motifs not only complement sequence motifs but also facilitate recognition of DNA beyond conventionally defined sequence motifs., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Computational models of enhancer function generally assume that transcription factors (TFs) exert their regulatory effects independently, modeling an enhancer as a "bag of sites." These models fail on endogenous loci that harbor multiple enhancers, and a "two-tier" model appears better suited: in each enhancer TFs work independently, and the total expression is a weighted sum of their expression readouts. Here, we test these two opposing views on how cis-regulatory information is integrated. We fused two Drosophila blastoderm enhancers, measured their readouts, and applied the above two models to these data. The two-tier mechanism better fits these readouts, suggesting that these fused enhancers comprise multiple independent modules, despite having sequence characteristics typical of single enhancers. We show that short-range TF-TF interactions are not sufficient to designate such modules, suggesting unknown underlying mechanisms. Our results underscore that mechanisms of how modules are defined and how their outputs are combined remain to be elucidated., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

To understand the relationship between an enhancer DNA sequence and quantitative gene expression, thermodynamics-driven mathematical models of transcription are often employed. These "sequence-to-expression" models can describe an incomplete or even incorrect set of regulatory relationships if the parameter space is not searched systematically. Here, we focus on an enhancer of the Drosophila gene ind and demonstrate how a systematic search of parameter space can reveal a more comprehensive picture of a gene's regulatory mechanisms, resolve outstanding ambiguities, and suggest testable hypotheses. We describe an approach that generates an ensemble of ind models; all of these models are technically acceptable solutions to the sequence-to-expression problem in light of wild-type data, and some represent mechanistically distinct hypotheses about the regulation of ind. This ensemble can be restricted to biologically plausible models using requirements gleaned from in vivo perturbation experiments. Biologically plausible models make unique predictions about how specific ind enhancer sequences affect ind expression; we validate these predictions in vivo through site mutagenesis in transgenic Drosophila embryos., (Copyright © 2015 Elsevier Inc. All rights reserved.)