Biology subjects

Ji, Y.

Publications and source records attributed to Ji, Y..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Motion correction with subspace-based self-navigation for combined angiography, perfusion and structural imaging

Motion artifacts are problematic in many MRI modalities. "Self-navigating" approaches are desirable, since no additional scan time or hardware is required. However, the generation of a navigator image, to estimate and correct motion, is difficult in cases where the tissue contrast is changing during the navigator acquisition window, such as in magnetization- prepared methods. Here we propose a subspace approach to reconstruct accurate navigators in the presence of time-varying tissue contrast and apply it to a combined angiography, perfusion and structural imaging method using a golden ratio 3D cones trajectory. This arterial spin labeling-based pulse sequence relies on subtraction of label and control images to isolate the relatively weak blood signal, making it particularly susceptible to motion corruption. An inversion pulse leads to time-varying tissue contrast across the readout train, but by reconstructing subspace coefficient maps directly, artifacts due to the varying contrast were alleviated. This resulted in high-quality navigator images that were subsequently registered to estimate and correct for motion. In addition, a split-update method was proposed to efficiently reconstruct from mismatched label/control k-space data with locally low rank regularization enforced on the difference image. The correction process was tested with numerical simulation and in vivo data from 8 healthy subjects with and without cued motion. In numerical simulation, the subspace- based navigator achieved an 84% reduction in RMSE of residual motion compared to without motion correction. In vivo, motion correction resulted in noise-like and background artifacts being greatly reduced and vessel sharpness being noticeably improved. Correlation of angiography, perfusion and structural images with motion-free reference images also increased by 159%, 53% and 12%, respectively, after motion correction. These results show that subspace-based navigators can effectively improve the motion robustness of MR imaging in contrast-varying acquisitions.

neuroscience↗

bioRxiv2024

Scalable and universal prediction of cellular phenotypes

Biological systems can be interrogated by perturbing individual components and observing the consequences across molecular, cellular, and phenotypic levels. The vast combinatorial space of possible perturbations and responses makes exhaustive experimentation infeasible. Recent advances in machine learning have shown that training on diverse datasets enables transfer learning across tasks, capturing patterns that generalize and improving performance on previously unseen problems. Inspired by this principle, we present Prophet, a transformer-based model pretrained on a vast, heterogeneous collection of perturbation experiments. This pretraining allows Prophet to predict the outcomes of untested genetic or chemical perturbations in novel cellular contexts, spanning phenotypes such as gene expression, viability, and morphology. By leveraging shared structure across apparently disconnected assays, Prophet provides a scalable framework for large-scale virtual screening and prioritization of informative experiments. Prophet consistently outperforms baseline models, including those trained on single phenotypes, showing that transfer learning between phenotypes not only is possible but improves predictive accuracy. Its capabilities extends to in vivo developmental systems, where it recapitulates known lineage biology and proposes new candidates. In a large-scale in silico screen for melanoma, Prophet identified and experimentally validated compounds with selective activity that mirrored clinically approved therapies, demonstrating its ability to transform perturbation biology into a predictive and scalable engine for therapeutic discovery.

cell biology↗

bioRxiv2024

Pertpy: an end-to-end framework for perturbation analysis

Advances in single-cell technology have enabled the measurement of cell-resolved molecular states across a variety of cell lines and tissues under a plethora of genetic, chemical, environmental, or disease perturbations. Current methods focus on differential comparison or are specific to a particular task in a multi-condition setting with purely statistical perspectives. The quickly growing number, size, and complexity of such studies requires a scalable analysis framework that takes existing biological context into account. Here, we present pertpy, a Python-based modular framework for the analysis of large-scale perturbation single-cell experiments. Pertpy provides access to harmonized perturbation datasets and metadata databases along with numerous fast and user-friendly implementations of both established and novel methods such as automatic metadata annotation or perturbation distances to efficiently analyze perturbation data. As part of the scverse ecosystem, pertpy interoperates with existing libraries for the analysis of single-cell data and is designed to be easily extended.

bioinformatics↗

bioRxiv2024

MOSTPLAS: A Self-correction Multi-label Learning Model for Plasmid Host Range Prediction

Plasmids play an essential role in horizontal gene transfer among diverse microorganisms, aiding their host bacteria in acquiring beneficial traits like antibiotic and metal resistance. Identifying the host bacteria where a plasmid can transfer, replicate or persist provides insights into how plasmids promote bacterial evolution. Plasmid host range prediction tools can be categorized as alignment-based and learning-based. Alignment-based tools have high precision but fail to align many newly sequenced plasmids with characterized ones in reference databases. In contrast, learning-based tools help predict the host range of these newly discovered plasmids. Although previous researches have demonstrated the existence of broad-host-range (BHR) plasmids, there is no database providing their detailed and complete host labels. Without adequate well-annotated training samples, learning-based tools fail to extract discriminative feature representations and obtain limited performance. To address this problem, we propose a self-correction multi-label learning model called MOSTPLAS. We design a pseudo label learning algorithm and a self-correction asymmetric loss to facilitate the training of multi-label learning model with samples containing some unknown missing positive labels. Experimental results on multi-host plasmids generated from the NCBI RefSeq database, metagenomic data, and real-world plasmid sequences with experimentally determined host range demonstrate the superiority of MOSTPLAS.

bioinformatics↗

bioRxiv2024

Dynamic B0 field shimming for improving pseudo-continuous arterial spin labeling at 7 Tesla

PurposeB0 inhomogeneity within the brain-feeding arteries is a major issue for pseudo-continuous arterial spin labeling (PCASL) at 7T because it reduces labeling efficiency and leads to a loss of perfusion signal. This study aimed to develop a vessel-specific dynamic B0 field shimming method for 7T PCASL to enhance labeling efficiency by correcting off-resonance in the arteries within the labeling region. MethodsWe implemented a PCASL sequence with dynamic B shimming at 7T that compensates for B0 field offsets at the brain-feeding arteries by updating linear shimming terms and adding a phase increment to the PCASL RF pulses. Rapidly acquired vessel-specific B field maps were used to calculate dynamic shimming parameters. We evaluated both 2D and 3D variants of our method, comparing their performance against established global frequency offset and optimal-encoding-scheme (OES)-based corrections. Cerebral blood flow (CBF) maps were quantified before and after corrections. CBF values from different methods in the whole brain, white matter, and grey matter regions were compared. ResultsAll off-resonance correction methods significantly enhanced perfusion signals across the brain. The proposed vessel-specific dynamic B shimming method improved labeling efficiency while maintaining optimal static shimming in the imaging region. Perfusion-weighted images demonstrated the superiority of 3D dynamic B shimming method compared to global or 2D-based correction approaches. CBF analysis revealed that 3D dynamic B shimming significantly increased CBF values relative to the other methods. ConclusionOur proposed dynamic B0 shimming method offers a significant advancement in PCASL robustness and effectiveness, enabling full utilization of 7T ASLs high sensitivity and spatial resolution.

biophysics↗

bioRxiv2024

Confidence-Enhanced Semi-supervised Learning for Mediastinal Neoplasm Segmentation

Automated segmentation of mediastinal neoplasms in preoperative computed tomography (CT) scans is critical for accurate diagnosis. Though convolutional neural networks (CNNs) have proven effective in medical imaging analysis, the segmentation of mediastinal neoplasms, which vary greatly in shape, size, and texture, presents a unique challenge due to the inherent local focus of convolution operations. To address this limitation, we propose a confidence-enhanced semi-supervised learning framework for mediastinal neoplasm segmentation. Specifically, we introduce a confidence-enhanced module that improves segmentation accuracy over indistinct tumor boundaries by assessing and excluding unreliable predictions simultaneously, which can greatly enhance the efficiency of exploiting unlabeled data. Additionally, we implement an iterative learning strategy designed to continuously refine the estimates of prediction reliability throughout the training process, ensuring more precise confidence assessments. Quantitative analysis on a real-world dataset demonstrates that our model significantly improves the performance by leveraging unlabeled data, surpassing existing semi-supervised segmentation benchmarks. Finally, to promote more efficient academic communication, the analysis code is publicly available at https://github.com/fxiaotong432/CEDS. Author summaryIn clinical practice, computed tomography (CT) scans can aid in the detection and evaluation of mediastinal tumors. The early detection of mediastinal tumors plays a crucial role in formulating appropriate treatment plans and improving patient survival rates. To reduce the high cost of manual annotation, researchers have attempted to employ convolutional neural networks (CNNs) for efficient automatic segmentation. However, the significant challenges arise due to the considerable variation in shape, size, and texture of mediastinal tumors, posing difficulties for the segmentation task. In this study, we introduce a confidence-enhanced module with a semi-supervised learning framework. By evaluating the models prediction confidence and selecting high-confidence predictions, we improve the efficiency and quality of data utilization. This approach demonstrates the achievement of accurate mediastinal tumor segmentation with only a minimal amount of labeled data. Our research not only provides an effective technical approach for automatic segmentation of mediastinal tumors but also opens up new possibilities for optimizing strategies in semi-supervised learning methods.

bioengineering↗

bioRxiv2024

PlasGO: enhancing GO-based function prediction for plasmid-encoded proteins based on genetic structure

Plasmid, as a mobile genetic element, plays a pivotal role in facilitating the transfer of traits, such as antimicrobial resistance, among the bacterial community. Annotating plasmid-encoded proteins with the widely used Gene Ontology (GO) vocabulary is a fundamental step in various tasks, including plasmid mobility classification. However, GO prediction for plasmid-encoded proteins faces two major challenges: the high diversity of functions and the limited availability of high-quality GO annotations. Thus, we introduce PlasGO, a tool that leverages a hierarchical architecture to predict GO terms for plasmid proteins. PlasGO utilizes a powerful protein language model to learn the local context within protein sentences and a BERT model to capture the global context within plasmid sentences. Additionally, PlasGO allows users to control the precision by incorporating a self-attention confidence weighting mechanism. We rigorously evaluated PlasGO and benchmarked it against six state-of-the-art tools in a series of experiments. The experimental results collectively demonstrate that PlasGO has achieved commendable performance. PlasGO significantly expanded the annotations of the plasmid-encoded protein database by assigning high-confidence GO terms to over 95% of previously unannotated proteins, showcasing impressive precision of 0.8229, 0.7941, and 0.8870 for the three GO categories, respectively, as measured on the novel protein test set.

bioinformatics↗

bioRxiv2024

Ultra-high temporal resolution 4D angiography using arterial spin labeling with subspace reconstruction

PurposeTo achieve ultra-high temporal resolution non-contrast 4D angiography with improved spatiotemporal fidelity. MethodsContinuous data acquisition using 3D golden-angle sampling following an arterial spin labeling preparation allows for flexibly reconstructing 4D dynamic angiograms at arbitrary temporal resolutions. However, k-space data is often temporally "binned" before image reconstruction, negatively affecting spatiotemporal fidelity and limiting temporal resolution. In this work, a subspace was extracted by linearly compressing a dictionary constructed from simulated curves of an angiographic kinetic model. The subspace was used to represent and reconstruct the voxelwise signal timecourse at the same temporal resolution as the data acquisition without temporal binning. Physiological parameters were estimated from the resulting images using a Bayesian fitting approach. A group of 8 healthy subjects were scanned and the in-vivo results reconstructed by different methods were compared. Due to the difficulty of obtaining ground truth 4D angiograms with ultra-high temporal resolution, the in-vivo results were cross-validated with numerical simulations. ResultsThe proposed method enables 4D time-resolved angiography with much higher temporal resolution (14.7 ms) than previously reported ([~]50 ms) whilst maintaining high spatial resolution (1.1 mm isotropic). Blood flow dynamics were depicted in greater detail, thin vessel visibility was improved, and the estimated physiological parameters also exhibited more realistic spatial patterns with the proposed method. ConclusionIncorporating a subspace compressed kinetic model into the reconstruction of 4D ASL angiograms notably improved the temporal resolution and spatiotemporal fidelity, which was subsequently beneficial for accurate physiological modeling.

neuroscience↗

bioRxiv2024

Human Accelerated Regions regulate gene networks implicated in apical-to-basal neural progenitor fate transitions

The evolution of the human cerebral cortex involved modifications in the composition and proliferative potential of the neural stem cell (NSC) niche during brain development. Human Accelerated Regions (HARs) exhibit a significant excess of human-specific sequence changes and have been implicated in human brain evolution. Multiple studies support that HARs include neurodevelopmental enhancers with novel activities in humans, but their biological functions in NSCs have not been empirically assessed at scale. Here we conducted a direct-capture Perturb-seq screen repressing 180 neurodevelopmentally active HARs in human iPSC-derived NSCs with single-cell transcriptional readout. After profiling >188,000 NSCs, we identified a set of HAR perturbations with convergent transcriptional effects on gene networks involved in NSC apicobasal polarity, a cellular process whose precise regulation is critical to the developmental emergence of basal radial glia (bRG), a progenitor population that is expanded in humans. Across multiple HAR perturbations, we found convergent dysregulation of specific apicobasal polarity and adherens junction regulators, including PARD3, ABI2, SETD2, and PCM1. We found that the repression of one candidate from the screen, HAR181, as well as its target gene CADM1, disrupted apical PARD3 localization and NSC rosette formation. Our findings reveal interconnected roles for HARs in NSC biology and cortical development and link specific HARs to processes implicated in human cortical expansion.

neuroscience↗

bioRxiv2024

Accelerating CAR T cell manufacturing with an automated next-day process

The traditional method of CAR T cell production involves lengthy ex vivo culture times which can result in the reduction of crucial naive T cell subsets. Moreover, traditional CAR T cell therapy manufacturing processes can prolong time-to-patient and contribute to disease progression. In this study, we describe an innovative and automated 24-hour CAR T manufacturing process that yields a higher percentage of naive/stem-cell like T cells which have increased cytotoxic activity and cytokine release. The data supports the feasibility of implementing this streamlined manufacturing process in clinics. This approach also has the potential to enhance CAR T therapy efficacy and improve patient access to therapy.

bioengineering↗

bioRxiv2024

Trajectory-centric Framework TrajAtlas reveals multi-scale differentiation heterogeneity among cells, genes, and gene module in osteogenesis

Osteoblast differentiation is crucial for bone formation and maintaining skeletal integrity. Although it is now understood that this process exhibits significant heterogeneity across developmental stages and tissue microenvironments, the underlying mechanisms remain largely unexplored. In the present study, we introduce TrajAtlas, a comprehensive framework that addresses this gap in knowledge. TrajAtlas comprises four modules: a reference atlas (Differentiation Atlas), a differentiation model (Differentiation Model), a tool for differential pseudotime analysis (TrajDiff), and a method for pseudotemporal gene module detection (TRAVMap). By leveraging single-cell technologies, TrajAtlas offers a systematic approach to exploring the multi-scale heterogeneity among cells, genes, and gene modules within population-level trajectories across diverse tissues and age groups. We systematically investigate the impact of age and injury on osteogenesis, providing new insights into osteoporosis and bone regeneration. In conclusion, our comprehensive framework offers novel insights into osteogenesis and provides a valuable resource for understanding the complexities of bone formation. Author SummaryOsteoblasts, the cells responsible for bone formation, can originate from various cellular sources. However, its unclear how different progenitor cells differentiate into osteoblasts, and how this process is influenced by factors such as age and tissue location. This knowledge gap stems from the lack of comprehensive databases and tools to decipher the differentiation process. In this study, we introduce TrajAtlas, a comprehensive framework designed to bridge this gap. To explore the cellular origins of osteoblasts, we constructed an atlas centered on osteogenesis. To answer how progenitor cells differentiate to osteoblasts, we developed a model that reveals the dynamic regulatory landscape during this process. To elucidate the influence of age and tissue location on differentiation, we built a tool for differential analysis. Furthermore, to identify conserved patterns of differentiation, we developed an approach to detect pseudotemporal gene modules. We validated the effectiveness of this framework by applying it to more datasets, unveiling novel cell states associated with injury. Notably, this framework focuses on dynamic processes, with the potential for broader applications in studying cell differentiation and complementing cell-centric analyses.

bioinformatics↗

bioRxiv2024

The polyadenosine RNA binding protein Nab2 regulates alternative splicing and intron retention during Drosophila melanogaster brain development

The regulation of cell-specific gene expression patterns requires the coordinated actions of hundreds of proteins, including transcription factors, processing enzymes, and many RNA binding proteins (RBPs). RBPs often become associated with an mRNA immediately after its production and can coordinate processing and quality control steps. Since RBPs can regulate multiple post-transcriptional processing steps, mutations within RBP-encoding genes often lead to pleiotropic effects that alter the physiology of multiple cell types. Thus, identifying how an RBP functions during RNA processing can provide a better understanding of both tissue physiology and mechanisms of disease. In the current study, we investigated how the Drosophila RNA binding protein Nab2, an evolutionary conserved ortholog of human ZC3H14, coordinates mRNA splicing and polyadenylation. ZC3H14 loss in human patients has previously been linked to alterations in nervous system function and disease. Both fly Nab2 and vertebrate ZC3H14 bind to polyadenosine RNA and have been implicated in the control of poly(A) tail length. Here we show that Nab2 loss from developing fly brain tissue also causes widespread changes in alternative splicing and intron retention. Retained introns in Nab2 null larval brains are most often first introns, are surrounded by adenosine-rich sequences, and are often associated with extended poly(A) tails, suggesting that Nab2 coordinates the splicing and polyadenylation of target transcripts. Alterations in splicing patterns ultimately lead to changes in protein abundance in Nab2 null developing brains. Overall, these studies highlight the importance of Nab2 and ZC3H14 in the post-transcriptional regulation of gene expression and provide critical insight into how this family of RBPs coordinates RNA processing during brain development.

genetics↗

bioRxiv2024

Methyltransferase DNMT3B promotes colorectal cancer cell proliferation by inhibiting PLCG2

BackgroundAberrant DNA methylation patterns in the promoter region of PLCG2 have been associated with dysregulated signaling pathways and cellular functions. Its role in colorectal cancer cells is still unknown. MethodsqRT-PCR was used to examine DNMT3B expression in colorectal cancer. Western blot and immunohistochemistry were used to analyze DNMT3B and PLCG2 protein levels in colorectal tissues and cell lines. The cell counting kit-8 and colony experiments were used to identify the proliferation of colorectal cancer cells. Methylation-specific PCR (MSP) and bisulfite-sequencing PCR (BSP) was used to measure DNA methylation levels. ResultsDNMT3B is overexpressed in colorectal cell in TCGA datasets and Kaplan-Meier plots. DNMT3B is significantly overexpressed in tumor tissues compared to adjacent non-tumor tissues. Western blotting results demonstrated high expression of DNMT3B in tumor tissues. Compared to normal colonic epithelial cells, colorectal cancer cell lines exhibited elevated levels of PLCG2 methylation. oePLCG2 effectively prevented the in vivo xenograft tumor growth of colorectal cancer. ConclusionsPLCG2 is identified as a key downstream regulatory protein of DNMT3B in colorectal cancer. DNMT3B Inhibits PLCG2 transcription through methylation of the PLCG2 promoter region. DNMT3B controls colorectal cancer cell proliferation through the PLCG2, which is useful for creating therapeutic approaches that target PLCG2 expression for the treatment of colorectal cancer.

cancer biology↗

bioRxiv2024

Anatomics MLT, an AI tool for large scale quantification of ultrastructural traits

The ever increasing breadth of biological knowledge has led to recent efforts to combine information from various fields into cell- or tissue atlases. Anatomical features are the structural basis for such efforts, but unfortunately large scale analysis of subcellular anatomical traits is currently a missing feature. Similarly, small phenotypic alterations of organelle- or cell-specific anatomical traits, such as an increase of the total volume or the number of mitochondria in response to certain stimuli, are currently hard to quantify. To provide tools to extract quantitative information from available 3D microscopic datasets generated with methods such as serial block face scanning electron microscopy we a) developed much improved fixation and embedding protocols for plants to drastically reduce processing artifacts and b) generated an easy-to-use AI tool for quantitative analysis and visualization of large-scale data sets. We make this tool available as open source.

cell biology↗

bioRxiv2024

Integrated multiomic analysis identifies TRIP13 as a mediator of alveolar epithelial type II cell dysfunction in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a lethal progressive lung disease urgently needing new therapies. Current treatments only delay disease progression, leaving lung transplant as the sole remaining option. Recent studies support a model whereby IPF arises because alveolar epithelial type II (AT2) cells, which normally mediate distal lung regeneration, acquire airway and/or mesenchymal characteristics, preventing proper repair. Mechanisms driving this abnormal differentiation remain unclear. We performed integrated transcriptomic and epigenomic analysis of purified AT2 cells which revealed genome-wide alterations in IPF lungs. The most prominent epigenetic alteration was activation of an enhancer in thyroid receptor interactor 13 (TRIP13), coinciding with TRIP13 upregulation. TRIP13 is broadly implicated in epithelial-mesenchymal plasticity and transforming growth factor-{beta} signaling. In cultured human AT2 cells and lung slices, small molecule TRIP inhibitor DCZ0415 prevented acquisition of the mesenchymal gene signature characteristic of IPF, suggesting TRIP13 inhibition as a potential therapeutic approach to fibrotic disease.

bioinformatics↗

bioRxiv2024

Suppressing mortality and curbing cellular damage by targeting VHL

Mortality rate increases with age and can accelerate upon extrinsic or intrinsic damage to individuals. Identifying factors and mechanisms that curb population mortality rate has wide-ranging implications. Here, we show that targeting the VHL-1 (Von Hippel- Lindau) protein suppresses C. elegans mortality caused by distinct factors, including elevated reactive oxygen species, temperature, and APOE4, the genetic variant that confers high risks of neurodegeneration in Alzheimers diseases and all-cause mortality in humans. These mortality factors are of different physical-chemical nature, yet result in similar cellular dysfunction and damage that are suppressed by deleting VHL-1. Stabilized HIF-1 (hypoxia inducible factor), a transcription factor normally targeted for degradation by VHL-1, recapitulates the protective effects of deleting VHL-1. HIF-1 orchestrates a genetic program that defends against mitochondrial abnormalities, excess oxidative stress, cellular proteostasis dysregulation, and endo-lysosomal rupture, key events that lead to mortality. Genetic Vhl inhibition also alleviates cerebral vascular injury and synaptic lesions in APOE4 mice, supporting an evolutionarily conserved mechanism. Collectively, we identify the VHL-HIF axis as a potent modifier of APOE4 and mortality and propose that targeting VHL-HIF in non-proliferative animal tissues may suppress tissue injuries and mortality by broadly curbing cellular damage.

physiology↗

bioRxiv2024

The ZmHSF20-ZmHSF4-ZmCesA2 module regulates heat stress tolerance in maize

Temperature shapes the geographical distribution and behavior of plants. Understanding the regulatory mechanisms behind plant heat response is important for developing climate-resilient crops, including maize (Zea mays). To identify transcription factors that may contribute to heat response, we generated a dataset of short- and long-term transcriptome changes following a heat treatment time course in the maize inbred line B73. Co-expression network analysis highlighted several transcription factors, including the class B2a heat shock factor ZmHSF20. ZmHsf20 mutant seedlings exhibited enhanced tolerance of heat stress. Furthermore, DNA affinity purification sequencing and CUT&Tag assays demonstrated that ZmHSF20 binds the promoters of Cellulose synthase A2 (ZmCesA2) and three class A HSF genes, including ZmHSF4, repressing their transcription. We showed that ZmCesA2 and ZmHSF4 positively regulate heat response, with ZmHSF4 directly activating ZmCesA2 transcription. In agreement with the transcriptome analysis, ZmHSF20 negatively modulated cellulose accumulation and repressed the expression of cell wall-related genes. Importantly, the ZmHsf20 ZmHsf4 double mutant exhibited decreased thermotolerance, placing ZmHSF4 downstream of ZmHSF20. Based on our results, we propose an expanded model of the heat stress response in maize, whereby ZmHSF20 lowers heat tolerance of seedlings by repressing ZmHSF4 and ZmCesA2, thus balancing growth and defense at the seedling stage. One-sentence summaryZmHSF20, as a negative factor, acts upstream of ZmHSF4 and ZmCesA2, which are involved in positively regulating the cell wall development under heat stress, thereby improving maize heat tolerance.

plant biology↗

bioRxiv2024

Evolutionary innovation in conserved regulatory elements across the mammalian tree of life

Transcriptional enhancers orchestrate cell type- and time point-specific gene expression programs. Evolution of enhancer sequences can alter target gene expression without causing detrimental misexpression in other contexts. It has long been thought that this modularity allows evolutionary changes in enhancers to escape pleiotropic constraints, which is especially important for evolutionary constrained developmental patterning genes. However, there is still little data supporting this hypothesis. Here we identified signatures of accelerated evolution in conserved enhancer elements across the mammalian phylogeny. We found that pleiotropic genes involved in gene regulatory and developmental processes were enriched for accelerated sequence evolution within their enhancer elements. These genes were associated with an excess number of enhancers compared to other genes, and due to this they exhibit a substantial degree of sequence acceleration over all their enhancers combined. We provide evidence that sequence acceleration is associated with turnover of regulatory function. We studied one acceleration event in depth and found that its sequence evolution led to the emergence of a new enhancer activity domain that may be involved in the evolution of digit reduction in hoofed mammals. Our results provide tangible evidence that enhancer evolution has been a frequent contributor to modifications involving constrained developmental signaling genes in mammals.

evolutionary biology↗