Shaun Mahony

Segmental duplications (SDs) are a pervasive feature of eukaryotic genomes, enabling genomic innovation via the duplication of genes and cis -regulatory elements. However, the regulatory mechanisms that govern these regions of the genome after duplication are not well understood. The repetitive nature of SDs pose problems for short-read assays such as ChIP-seq and DNase-seq as they create reads that map to more than one location along the genome. Moreover, the newest Telomere-to-Telomere (T2T) human genome assembly has revealed previously unknown segmental duplications. In this study, we used the Telomere-to-Telomere genome assembly along with the probabilistic allocation of multi-mapped reads by our software Allo to better understand the regulatory mechanisms governing segmental duplications in the human genome. Using 134 transcription factor ChIP-seq datasets in GM12878, we found that transcription factor binding sites within segmental duplications are rarely conserved across paralogous copies. Similarly, chromatin states defined via histone ChIP-seq datasets are also poorly conserved across segmentally duplicated regions. In contrast, paralogous genes within SDs exhibit highly correlated expression patterns. Hi-C analysis revealed that many SD paralogs have higher than expected spatial proximity, suggesting that three-dimensional genome organization may buffer transcriptional divergence following duplication despite the loss of cis -regulatory regions. Together, our results suggest that regulatory regions undergo rapid turnover following segmental duplication, but the regulatory impact may be buffered by spatial proximity within the nucleus. Tissue-specific genes and transcription factors showed greater divergence, highlighting the role of SDs in expanding specialized cellular functions.

Interpreting genomics deep learning models remains challenging. Existing feature attribution methods are largely restricted to one-hot DNA inputs and therefore cannot assess the influence of more general genomic features such as chromatin states or genomic repeats. Concept attribution methods offer an input-agnostic global interpretation framework, yet they have not been systematically applied to interpret neural network applications in genomics. We present the first application of concept attribution to interpret genomics deep learning models by adapting the Testing with Concept Activation Vectors (TCAV) method. We improve upon the original TCAV method by incorporating a PCA-based decorrelation transformation to address correlated and redundant embedding features commonly observed in genomics deep learning models, resulting in the Testing with PCA-projected Concept Activation Vectors (TPCAV) approach. We also introduce a strategy for extracting concept-specific input attribution maps. We evaluate our approach by interpreting influential biological concepts across a diverse set of genomics models spanning multiple input representations and prediction tasks. We demonstrate that TPCAV provides comparable motif feature interpretation to TF-MoDISco on one-hot encoded DNA-based transcription factor binding prediction models. TPCAV also enables robust interpretive analysis of how more general biological concepts such as repetitive elements and chromatin state annotations contribute towards predictions. TPCAV uniquely generalizes to interpret features learned by tokenized foundation models as well as models incorporating chromatin signals as inputs. We further show that TPCAV can identify representative regions associated with specific concepts, motivating downstream investigation of distinct regulatory mechanisms. TPCAV provides a flexible and robust complement to existing model interpretation techniques.

The interdependence of chromatin states and transcription factor (TF) binding in eukaryotic genomes is critical for the proper regulation of gene expression. In this study, we explore the connection between TFs and chromatin states in the human malaria parasite, Plasmodium falciparum , throughout its 48-hour asexual intraerythrocytic developmental cycle (IDC). Most P. falciparum genes are expressed in a periodic manner during the IDC, accompanied by dynamic shifts in histone modifications and chromatin accessibility. Leveraging genome-wide profiles of chromatin accessibility, histone modifications, and Heterochromatin Protein 1 (HP1) occupancy, we characterize chromatin state dynamics during the IDC. Our results indicate that several chromatin states remain stable throughout the lifecycle, while others are dynamic and are linked to gene activation or repression. We further characterize chromatin state dynamics at the genome-wide DNA binding sites for a selection of Plasmodium TFs, allowing us to group TFs according to their chromatin preferences. By correlating changes in chromatin accessibility, histone modifications, and TF binding, we provide a global overview of the chromatin state dynamics that coordinate P. falciparum asexual blood stage development.

By combining chromatin immunoprecipitation (ChIP) with an exonuclease digestion of protein-bound DNA fragments, ChIP-exo characterizes genome-wide protein–DNA interactions at near basepair resolution. However, the widespread adoption of ChIP-exo has been hindered by several technical challenges, including lengthy protocols, the need for multiple custom reactions, and incompatibilities with recent Illumina sequencing platforms. To address these barriers, we systematically optimized and adapted the ChIP-exo library construction protocol for the unique requirements of mammalian cells and current sequencing technologies. We introduce a mammalian-optimized ChIP-exo (MO-ChIP-exo) protocol that builds upon previous ChIP-exo protocols with systematic optimization of crosslinking, harvesting, and library construction. We validate MO-ChIP-exo by comparing it with previously published ChIP-exo protocols and demonstrate its adaptability to both suspension (K562) and adherent (HepG2, mESC) cell lines. This improved protocol provides a more robust and efficient method for generating high-quality ChIP-exo libraries from mammalian cells.

The interdependence of chromatin states and transcription factor (TF) binding in eukaryotic genomes is critical for the proper regulation of gene expression. In this study, we explore the connection between TFs and chromatin states in the human malaria parasite, Plasmodium falciparum , throughout its 48-hour asexual intraerythrocytic developmental cycle (IDC). Most P. falciparum genes are expressed in a periodic manner during the IDC, accompanied by dynamic shifts in histone modifications and chromatin accessibility. Leveraging genome-wide profiles of chromatin accessibility, histone modifications, and Heterochromatin Protein 1 (HP1) occupancy, we characterize chromatin state dynamics during the IDC. Our results indicate that several chromatin states remain stable throughout the lifecycle, while others are dynamic and are linked to gene activation or repression. We further characterize chromatin state dynamics at the genome-wide DNA binding sites for a selection of Plasmodium TFs, allowing us to group TFs according to their chromatin preferences. By correlating changes in chromatin accessibility, histone modifications, and TF binding, we provide a global overview of the chromatin state dynamics that coordinate P. falciparum asexual blood stage development.

By combining chromatin immunoprecipitation (ChIP) with an exonuclease digestion of protein-bound DNA fragments, ChIP-exo characterizes genome-wide protein-DNA interactions at near base-pair resolution. However, the widespread adoption of ChIP-exo has been hindered by several technical challenges, including lengthy protocols, the need for multiple custom reactions, and incompatibilities with recent Illumina sequencing platforms. To address these barriers, we systematically optimized and adapted the ChIP-exo library construction protocol for the unique requirements of mammalian cells and current sequencing technologies. We introduce a Mammalian-Optimized ChIP-exo (MO-ChIP-exo) protocol that builds upon previous ChIP-exo protocols with systematic optimization of crosslinking, harvesting, and library construction. We validate MO-ChIP-exo by comparing it to previously published ChIP-exo protocols and demonstrate its adaptability to both suspension (K562) and adherent (HepG2, mESC) cell lines. This improved protocol provides a more robust and efficient method for generating high-quality ChIP-exo libraries from mammalian cells. SUMMARY: ChIP-exo is a genome-wide protein-DNA binding assay with unrivalled resolution, but its widespread adoption has been hindered by technical challenges, particularly when applied to mammalian cells or when used with recent sequencing platforms. We introduce a Mammalian-Optimized ChIP-exo (MO-ChIP-exo) protocol with key modifications that overcome previous technical hurdles. We demonstrate that our optimized protocol produces high-quality data comparable to previously published protocols and is adaptable for use with both suspension (K562) and adherent (HepG2, mESC) cell lines.

Summary: Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges toward solving this fundamental problem. We describe how cis-regulatory elements are mapped with various genomics assays and how studies of the 3D chromatin organization could help identifying long-range regulatory effects. We discuss how the cis-regulatory sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also describe current methods for mapping gene regulatory networks to describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals.

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-regulatory elements are mapped and how their sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also discuss how studies of the 3D chromatin organization could help identifying long-range regulatory effects and how current methods for mapping gene regulatory networks could better describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals.

CCR4-NOT regulates multiple steps in gene regulation and has been well studied in budding yeast, but much less is known about the human complex. Auxin-induced degradation was used to rapidly deplete the scaffold subunit CNOT1, and CNOT4, to characterize the functions of human CCR4-NOT in gene regulation. Depleting CNOT1 increased RNA levels and caused a widespread decrease in RNA decay. In contrast, CNOT4 depletion only modestly changed steady-state RNA levels and, surprisingly, led to a global acceleration in mRNA decay. Further, depleting either subunit resulted in a global increase in RNA synthesis. In contrast to most of the genome, the transcription of KRAB-Zinc-Finger-protein (KZNFs) genes, especially those on chromosome 19, was repressed. KZNFs are transcriptional repressors of retrotransposable elements (rTEs), and consistent with the decreased KZNFs expression, rTEs, mainly Long Interspersed Nuclear Elements (LINEs), were activated. These data establish CCR4-NOT as a global regulator of gene expression and a novel silencer of rTEs.

Development of the malaria parasite, Plasmodium falciparum, is regulated by a limited number of sequence-specific transcription factors (TFs). However, the mechanisms by which these TFs recognize genome-wide binding sites is largely unknown. To address TF specificity, we investigated the binding of two TF subsets that either bind CACACA or GTGCAC DNA sequence motifs and further characterized two additional ApiAP2 TFs, PfAP2-G and PfAP2-EXP, which bind unique DNA motifs (GTAC and TGCATGCA). We also interrogated the impact of DNA sequence and chromatin context on P. falciparum TF binding by integrating high-throughput in vitro and in vivo binding assays, DNA shape predictions, epigenetic post-translational modifications, and chromatin accessibility. We found that DNA sequence context minimally impacts binding site selection for paralogous CACACA-binding TFs, while chromatin accessibility, epigenetic patterns, co-factor recruitment, and dimerization correlate with differential binding. In contrast, GTGCAC-binding TFs prefer different DNA sequence context in addition to chromatin dynamics. Finally, we determined that TFs that preferentially bind divergent DNA motifs may bind overlapping genomic regions due to low-affinity binding to other sequence motifs. Our results demonstrate that TF binding site selection relies on a combination of DNA sequence and chromatin features, thereby contributing to the complexity of P. falciparum gene regulatory mechanisms.