Biology subjects

Nishino, T.

Publications and source records attributed to Nishino, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Neural Crest-Derived Endocardial Cells Required for Heart Valve Development

Abnormalities in cardiac valves, which maintain unidirectional blood flow through the heart, affect 2-3% of people worldwide and are the most common form of structural heart disease (1).Yet the cell types that make up the valve, and how they communicate to direct development and homeostasis, remain poorly understood. Here, we identify a rare, essential population of highly secretory valve endocardial cells in mouse and human that derive from migrating neural crest cells. Ablation of these cells in mice caused perinatal lethality with thickened leaflets, stenosis, and regurgitation, mirroring human valve disease. This subset of endocardial cells uniquely expresses the secreted signaling protein parathyroid hormone-like hormone (Pthlh). Global disruption of Pthlh caused valve stenosis and lethality and neural crest-specific deletion produced similar valve disease, with impaired maturation of valvular interstitial cells and disrupted bone morphogenetic protein signaling. These findings identify a previously unrecognized cell type and signaling pathway essential for valve morphogenesis, with implications for the pathogenesis and treatment of human valve defects.

developmental biology↗

bioRxiv2026

Multimodal phenotyping reveals plant-food-related dietary information in serum Raman spectra of community-dwelling Japanese adults

Healthy dietary patterns are associated with long-term health, but diet-quality scores do not reveal how habitual dietary information is represented in human biological phenotypes. Here we linked habitual dietary assessment with preceding health records and contemporaneous clinical chemistry, serum metabolomics and serum Raman spectra in community-dwelling Japanese adults. Higher diet quality corresponded to a more favorable preceding health profile, but did not define discrete states across contemporary multivariate phenotypes. Instead, specific dietary information was recoverable across these layers: Raman spectra provided predictive information complementary to clinical and metabolomic measurements for multiple dietary variables. Raman variation corresponding to {beta}-carotene equivalent could be traced to carotenoid-assigned bands and aligned with a broader plant-food-related dietary dimension. These findings show that habitual diet is represented not as a single biological state but through biologically traceable dietary dimensions distributed across complementary phenotypic layers.

bioinformatics↗

bioRxiv2026

NECTIN4 defines a subtype-dependent tumor state coupling enfortumab vedotin targetability to antitumor immunity in bladder cancer

NECTIN4 is the target of enfortumab vedotin (EV), but its biological role in bladder cancer (BC) and its relationship to EV plus PD-1 blockade remain unclear. Integrating clinical multi-omics, spatial and single-cell immune profiling, and experimental models, we identify NECTIN4 as a subtype-dependent determinant of tumor state and antitumor immunity. In basal BC cells, NECTIN4 suppressed inflammatory and mesenchymal programs. Consistently, NECTIN4-high basal/squamous muscle-invasive BCs showed favorable survival, higher checkpoint inhibitor response rates, and a coordinated CD8+ T cell-inflamed microenvironment with no comparable associations in luminal papillary tumors. In orthotopic models, murine Nectin4 promoted CD8+ T cell-dependent tumor control, whereas human NECTIN4 conferred EV sensitivity. EV also attenuated NECTIN4-dependent TIGIT binding. Conversely, NECTIN4-low models were preferentially sensitive to HDAC inhibition. NECTIN4 therefore couples antibody-drug conjugate targetability to a subtype-specific tumor-immune state permissive for checkpoint blockade, whereas the NECTIN4-low state defines a distinct inflammatory-mesenchymal state with an alternative therapeutic vulnerability.

cancer biology↗

bioRxiv2026

Standardizing image-derived fish length-frequency distributions to reference measurements using bin-specific error matrices

Artificial intelligence (AI)-based image analysis can efficiently estimate fish length, but differences in devices, imaging conditions, operators, and AI models limit comparability among surveys. We propose a standardization framework that estimates a bin-specific error matrix from paired reference measurements and AI-derived lengths and applies it to standardize (correct) AI-derived length-frequency distributions. The Richardson-Lucy expectation-maximization algorithm was used, with the number of iterations selected via cross-validation. Simulations based on empirical length-frequency data from 110 species showed that standardization reduced relative bias and distributional discrepancy; median relative-bias and root mean square error ratios were below 1, and the performance was more affected by the amount of paired data than by the number of cross-validation folds. In real data from 957 Japanese jack mackerel, standardized AI-derived distributions approached human-observer histograms, although discrepancies remained in the range of 160-230 mm. The proposed framework provides a practical approach for improving the comparability of image-derived length-frequency data using paired calibration data, without retraining the underlying AI model.

ecology↗

bioRxiv2026

CATaN maps gene regulatory programs that shape genetic risk across complex diseases

Causal variants of complex traits are enriched at transcription factor (TF) binding sites and are thought to contribute to pathology by disrupting TF activity and thereby causing transcriptome dysregulation. However, existing approaches typically address TF-mediated gene regulatory networks (TF-GRNs) and transcriptomes separately, and methods that jointly leverage both to systematically assess disease heritability remain limited. We aimed to develop a framework that jointly leverages TF-GRNs and transcriptomes to assess disease heritability. Here, we constructed a matrix encoding TF-GRNs and developed an unsupervised analytical pipeline, Canonical correlation Analysis of Transcriptome and TF-gene regulatory Networks (CATaN). CATaN applies canonical correlation analysis (CCA) to extract canonical correlation (CC) components, i.e., shared variation components between transcriptomes and TF-GRNs, and converts them into genome-wide functional annotation scores connected to stratified LD score regression (S-LDSC) for heritability analysis. We applied CATaN to eight datasets, including 19,198 bulk samples and 611,772 single cells from human and mouse sources, identifying 588 CC components that are significantly enriched for SNP heritability across 69 complex traits. Notably, functional annotation tracks based on these TF-GRNs are distinct from transcriptome signatures prioritized by LDSC-SEG, with greater heritability enrichment for a subset of traits. Finally, we suggest that CATaN may help prioritize candidate causal variants for experimental fine-mapping using genome editing. Together, integrating TF-GRNs with transcriptomes reveals disease-relevant regulatory programs that are not fully captured by transcriptome-based analyses alone.

genetics↗

bioRxiv2026

Estimation of the curved body length of tuna larvae from microscope images using a zero-shot model and image processing techniques

Fixed larval specimens often shrink and curve, making length measurement labor-intensive. Although recent studies have demonstrated efficient fish-length estimation from images using deep learning, methods for estimating curved length remain limited. Furthermore, although deep learning is a powerful method for object detection in images, an essential step for length measurement, it requires preparing large amounts of training data, which can hinder practical implementation. In this study, we used a zero-shot model that requires no training to detect fish in an image. The curved length was then estimated using an image-processing approach that combines image thinning with Bezier curve approximation, and its accuracy was evaluated. We analyzed 1,040 larvae from five tuna species captured in stereomicroscope images. Manual measurements (notochord length or standard length; 1.5-8.5 mm) were conducted by two measurers and served as reference values. Fish regions were detected using GroundedSAM, and curved body centerlines were extracted through image thinning and approximated with Bezier curves. The curve length was used as the estimated body length. Estimation accuracy was assessed using bias and standard deviation between estimated and manual measurements. GroundedSAM detected all 1,040 fish, although there were 49 overdetections. Overdetection was caused by the double-detection of a single fish or by the misidentification of debris and light reflections as fish. Although the standard deviation of the differences between manual measurements and the image analysis-based (IAB) method was larger than the inter-measurer differences, the bias for [≤]5 mm was comparable to or smaller than the inter-measurer bias. According to the strength-of-agreement criteria for the concordance correlation coefficient, the IAB method demonstrated substantial agreement in the [≤]5.0-mm range. The IAB method accurately measured most curved tuna larvae without prior training, particularly in the [≤]5.0-mm range. Combining the IAB method with manual remeasuring can improve the efficiency of curved-length measurement tasks.

ecology↗

bioRxiv2026

Parallel processing chains span cytoarchitectures to organize association cortex

Task fMRI1 and electrophysiology2 have revealed distributed, linked cortical patches with shared category preferences (e.g., faces, objects, places)1,3-5, smaller than cytoarchitectonic areas. Resting-state functional connectivity (RSFC) similarly showed that somato-cognitive action network (SCAN) nodes interleave with effectors (foot, hand, mouth), subdividing the precentral gyrus6. Here, using multiple precision functional mapping (PFM) modalities (RSFC, task, lags), we discovered that most of association cortex is organized like face processing and SCAN, with small, discrete patches interconnected into chains. Such patch-chains densely tile prefrontal cortex but are largely absent from primary cortex. Cortico-striatal connectivity is organized such that patches of the same chain connect to the same striatal location. Within chains, infra-slow fMRI signals are ordered in time. RSFC-defined chains align with task fMRI localizers (e.g., visual, motor, pain). Chains are absent at birth and emerge in the first year of life, suggesting their formation is at least partially experience-driven. Cytoarchitectonic areas are subdivided by patches, and patches in the same chain are distributed across different cytoarchitectures. Chains represent parallel ordered processing streams that are separated by information domain and behavioral goals, not cytoarchitectonics. Functional subdivision of architectonics into smaller patches, interlinked to form cross-architecture chains, enable greater parallelization and flexible specialization of processing.

neuroscience↗

bioRxiv2025

Cellular and molecular fine mapping pinpoints new immunopathology of lupus

Systemic lupus erythematosus (SLE) is a complex autoimmune disease with an unknown etiology. To pinpoint new disease-relevant cell states and their molecular profiles, we performed an in-depth investigation of multimodal single-cell datasets comprising [~]2.1 million peripheral blood mononuclear cells from 346 donors. By resolving 123 fine-grained cell states across 27 cell types, we identified previously uncharacterized populations distinctively associated with clinical severity and treatment status, including GZMK+GZMH+HLA-DR+ effector memory CD8+ T cells (double-positive [DP] EMCD8) and FOXO1+ARHGAP15+ T cells. Through extensive statistical frameworks and multimodal approaches, we delineated their aberrant immune signaling networks, transcriptional regulators, key surface proteins, T cell receptor repertoires, and genetic/epigenetic landscapes, underscoring them as candidate drivers of SLE immunopathology. These findings provide new insights into therapeutic target discovery in SLE.

immunology↗

bioRxiv2025

Niche-targeted therapy via YAP/TAZ activation enhances hematopoietic regeneration

The distinctive milieu of the bone marrow (BM), known as the BM niche, supports hematopoietic stem cells (HSCs) and serves as a foundation for hematopoietic regeneration. Myeloablative stress disrupts not only hematopoietic stem and progenitor cells but also essential BM niche components, including endothelial cells (ECs) and mesenchymal stromal cells (MSCs); disruption of the latter impairs efficient hematopoietic recovery. However, therapeutic strategies targeting niche restoration remain largely underdeveloped. Here, we demonstrate that the Hippo pathway effectors YAP/TAZ are critical for enabling ECs and MSCs to respond to BM injury, and that YAP/TAZ activation accelerates BM niche recovery, thereby promoting hematopoietic regeneration. We found that YAP/TAZ are rapidly activated in both MSCs and ECs following myeloablative stress, maintaining MSC multipotency and orchestrating vascular remodeling. Mechanistically, YAP/TAZ function as transcriptional hubs in MSCs, regulating key transcriptional factors such as Ebf1 and Ebf3. This regulation preserves MSC identity by preventing osteogenic and fibrogenic differentiation while promoting the expression of hematopoietic factors such as Cxcl12 and angiogenic factors. In parallel, YAP/TAZ signaling in ECs facilitates pro-angiogenic responses and drives sinusoidal vessel remodeling after injury. These YAP/TAZ-mediated niche responses are essential for HSC retention and hematopoietic regeneration following diverse myelosuppressive therapies. Notably, pharmacological activation of YAP/TAZ enhances BM niche reorganization and augments hematopoietic regeneration following myeloablative therapies. These findings establish YAP/TAZ as central regulators of BM niche resilience, providing a rationale for niche-targeted therapeutic strategies to enhance hematopoietic regeneration. Key points{blacksquare} YAP/TAZ enable BM niche cells to sense injury and restore their structure and function, thereby promoting hematopoietic regeneration. {blacksquare}Pharmacological activation of YAP/TAZ enhances BM niche resilience and accelerates hematopoietic recovery after myelosuppressive therapies.

cell biology↗

bioRxiv2025

A cardiac microphysiological system for screening lipid nanoparticle/mRNA complexes predicts in vivo efficacy for heart transfection

Lipid nanoparticle (LNP)/mRNA complexes have tremendous potential for treating cardiac diseases but lack the transfection efficiency required for successful heart therapeutics. A challenge preventing the development of cardiac LNPs is the lack of in vitro screening platforms that maximize transfection and predict efficacy. Here, we demonstrate that a phenotypic cardiac microphysiological system (MPS), constructed from a human induced pluripotent stem cell cardiomyocytes (hiPSC-CM) Cre-reporter line, can identify LNP/mRNA complexes that diffuse efficiently within 3D cardiac micromuscles and transfect cardiomyocytes with high efficiency. Successful LNP formulations contained an acid-degradable PEG (ADP)-lipid, where ADP-LNPs containing 10% PEG had enhanced diffusion and gene editing efficiency in the cardiac MPS. The in vivo delivery of LNP/mRNA complexes, including luciferase and CRE mRNA, into Ai6 mice confirmed the cardiac MPS screening outcomes. It revealed that ADP-LNPs achieved notably superior transfection in the heart with reduced off-target liver uptake compared to a standard LNP formulation. The cardiac MPS showed strong LNP transfection in vitro and pinpointed a promising formulation for in vivo mRNA delivery to the heart.

bioengineering↗

bioRxiv2024

Defensive fungal symbiosis on insect hindlegs

Tympanal organs as "insect ears" have evolved repeatedly. Dinidorid stinkbugs were reported to possess a conspicuous tympanal organ on females hindlegs. Here we report an unexpected discovery that the stinkbugs "tympanal organ" is actually a novel symbiotic organ. The stinkbugs "tympanum" is not membranous but a porous cuticle, where each pore connects to glandular secretory cells. In reproductive females, the hindleg organ is covered with fungal hyphae growing out of the pores. Upon oviposition, the females skillfully transfer the fungi from the organ to the eggs. The eggs are quickly covered with hyphae and physically protected against wasp parasitism. The fungi are mostly benign Cordycipitaceae entomopathogens and show considerable diversity among insect individuals and populations, indicating environmental acquisition of specific fungal associates. These results uncover a novel external fungal symbiosis in which hosts elaborate morphological, physiological and behavioral specializations underpin the selective recruitment of benign entomopathogens for a defensive purpose.

microbiology↗

bioRxiv2024

Effects of input image size on the accuracy of fish identification using deep learning.

The length composition of catches by species is important for stock assessment. However, length measurement is performed manually, jeopardizing the future of continuous measurement because of likely labor shortages. We focused on applying deep learning to estimate length composition by species from images of fish caught for sustainable management. In this study, input image sizes were varied to evaluate the effect of input image size on detection and classification accuracy, as a method for improving the accuracy. The images (43,226 fish of 85 classes) were captured on conveyor belts to sort set-net catches. Fish detection and classification were performed using Mask R-CNN. The effect of input image size on accuracy was examined using three image sizes of 1333x888, 2000x1333, and 2666x1777 pixels, achieving an mAP50-95 of 0.580 or higher. The accuracy improved with increasing image size, attaining a maximum improvement of 4.3% compared to the smallest size. However, increasing the image size too far from the default size may not improve the accuracy of models with fine-tuning. Improvements in accuracy were primarily observed for the species with low accuracy at the smallest image size. Increasing image size would be a useful and simple way to improve accuracy for these species.

ecology↗

bioRxiv2023

Surface-engineered extracellular vesicles to modulate antigen-specific T cell expansion for cancer immunotherapy

Extracellular vesicles (EVs), including exosomes, are emerging as novel mediators of cell-cell communications, involved in various processes such as immune activation and immunosuppression. Despite the recent development of several EVs-based cancer immunotherapies, their clinical efficacy remained limited. Here, using fusion with tetraspanin as one of the EV engineering techniques, we created antigen-presenting extracellular vesicles (AP-EVs) to reproduce the functional characteristics of professional antigen-presenting cells (APCs). AP-EVs were also equipped with surface-bound IL-2, a feature not inherent to APCs, which facilitated selective delivery of IL-2 to antigen-specific CD8+ T cells. AP-EVs were engineered to express a peptide-major histocompatibility class I (pMHCI) complex, a costimulatory CD80 molecule, and IL-2, allowing the simultaneous presentation of multiple immune modulators to antigen-specific CD8+ T cells. This promoted the clonal expansion and differentiation of antigen-specific cytotoxic T lymphocytes, leading to potent anticancer immune responses. Combination therapy with AP-EVs and anti-PD-1 demonstrated enhanced anticancer immunity against established tumors compared with anti-PD-1 monotherapy. Our engineered EVs represent a novel effective strategy for cancer immunotherapy.

bioengineering↗

bioRxiv2023

Length estimation of fish detected as non-occluded using a smartphone application and deep learning techniques

Uncertainty in stock assessment can be reduced if accurate and precise length composition of catch is available. Length data are usually manually collected, although this method is costly and time-consuming. Recently, some studies have estimated fish species and length from images using deep learning by installing camera systems in fishing vessels or a fish auction center. Once the deep learning model is properly trained, it does not require expensive and time-consuming manual labor. However, several previous studies have focused on monitoring fishing practices using an electronic monitoring system (EMS); therefore, it is necessary to solve many challenges, such as counting the total number of fish in the catch. In this study, we proposed a new deep learning-based method to estimate fish length using images. Species identification was not performed by the model, and images were taken manually by the measurers; however, length composition was obtained only for non-occluded fish detected by the model. A smartphone application was developed to calculate scale information (cm/pixel) from a known size fish box in fish images, and the Mask R-CNN (Region-based convolutional neural networks) model was trained using 76,161 fish to predict non-occluded fish. Two experiments were conducted to confirm whether the proposed method resulted in errors in the length composition. First, we manually measured the total length (TL) for each of the five fish categories and estimated the TL using deep learning and calculated the bias. Second, multiple fish in a fish box were photographed simultaneously, and the difference between the mean TL estimated from the non-occluded fish and the true TL from all fish was calculated. The results indicated that the biases of all five species categories were within {+/-} 3%. Moreover, the difference was within {+/-} 1.5% regardless of the number of fish in the fish box. In the proposed method, deep learning was used not to replace the measurer but to increase their measurement efficiency. The proposed method is expected to increase opportunities for the application of deep learning-based fish length estimation in areas of research that are different from the scope of conventional EMS.

ecology↗

bioRxiv2023

Chromatin Remodeling Drives Immune-Fibroblast Crosstalk in Heart Failure Pathogenesis

Chronic inflammation and tissue fibrosis are common stress responses that worsen organ function, yet the molecular mechanisms governing their crosstalk are poorly understood. In diseased organs, stress-induced changes in gene expression fuel maladaptive cell state transitions and pathological interaction between diverse cellular compartments. Although chronic fibroblast activation worsens dysfunction of lung, liver, kidney, and heart, and exacerbates many cancers, the stress-sensing mechanisms initiating the transcriptional activation of fibroblasts are not well understood. Here, we show that conditional deletion of the transcription co-activator Brd4 in Cx3cr1-positive myeloid cells ameliorates heart failure and is associated with a dramatic reduction in fibroblast activation. Analysis of single-cell chromatin accessibility and BRD4 occupancy in vivo in Cx3cr1-positive cells identified a large enhancer proximal to Interleukin-1 beta (Il1b), and a series of CRISPR deletions revealed the precise stress-dependent regulatory element that controlled expression of Il1b in disease. Secreted IL1B functioned non-cell autonomously to activate a p65/RELA-dependent enhancer near the transcription factor MEOX1, resulting in a profibrotic response in human cardiac fibroblasts. In vivo, antibody-mediated IL1B neutralization prevented stress-induced expression of MEOX1, inhibited fibroblast activation, and improved cardiac function in heart failure. The elucidation of BRD4-dependent crosstalk between a specific immune cell subset and fibroblasts through IL1B provides new therapeutic strategies for heart disease and other disorders of chronic inflammation and maladaptive tissue remodeling.

molecular biology↗

bioRxiv2022

A mind-body interface alternates with effector-specific regions in motor cortex

Primary motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down precentral gyrus from foot to face representations1,2. The motor homunculus has remained a textbook pillar of functional neuroanatomy, despite evidence for concentric functional zones3 and maps of complex actions4. Using our highest precision functional magnetic resonance imaging (fMRI) data and methods, we discovered that the classic homunculus is interrupted by regions with sharpy distinct connectivity, structure, and function, alternating with effector-specific (foot, hand, mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, and to prefrontal, insular, and subcortical regions of the Cingulo-opercular network (CON), critical for executive action5 and physiological control6, arousal7, and processing of errors8 and pain9. This interdigitation of action control-linked and motor effector regions was independently verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant, child) precision fMRI revealed potential cross-species analogues and developmental precursors of the inter-effector system. An extensive battery of motor and action fMRI tasks documented concentric somatotopies for each effector, separated by the CON-linked inter-effector regions. The inter-effector regions lacked movement specificity and co-activated during action planning (coordination of hands and feet), and axial body movement (e.g., abdomen, eyebrows). These results, together with prior work demonstrating stimulation-evoked complex actions4 and connectivity to internal organs (e.g., adrenal medulla)10, suggest that M1 is punctuated by an integrative system for implementing whole-body action plans. Thus, two parallel systems intertwine in motor cortex to form an integrate-isolate pattern: effector-specific regions (foot, hand, mouth) for isolating fine motor control, and a mind-body interface (MBI) for the integrative whole-organism coordination of goals, physiology, and body movement.

neuroscience↗

bioRxiv2022

Single Cell Epigenetics Reveal Cell-Cell Communication Networks in Normal and Abnormal Cardiac Morphogenesis

Communication between myriad cell types during organ formation underlies proper morphogenesis1. In cardiac development, reciprocal signaling between mesoderm progenitors and neural crest cells is essential, and its disruption leads to congenital heart malformations, the most common human birth defect. However, mechanistic interrogation of temporal gene networks and cis regulatory elements in this crosstalk is limited2,3. Here, we integrated single cell chromatin accessibility and transcriptomics to establish an unbiased and temporal epigenomic map of the embryonic mouse heart over multiple stages and developed machine learning models to predict enhancers for heart and neural crest. We leveraged these advances to determine the consequences of dysregulated signaling at single cell resolution caused by deletion of TBX1, a transcription factor that causes morphogenetic defects of the cardiac outflow tract in humans and functions non-cell autonomously in cardiac mesodermal progenitors to direct pharyngeal neural crest differentiation4-6. Loss of Tbx1 in mice led to broad closure of chromatin regions enriched in cardiac progenitor transcription factor motifs within a narrow subset of cardiac mesodermal progenitors and correlated with diminished expression of numerous members of the fibroblast growth factor, retinoic acid, Notch and Semaphorin pathways. In affected progenitors, ectopic accessibility and expression of posterior heart field factors in the anterior heart field suggested impaired axial patterning. In response, a subset of cardiac neural crest cells displayed epigenomic and transcriptional defects, indicating a failure of differentiation corresponding to dysregulation of the anterior-posterior gradient of pharyngeal Hox gene expression. This study demonstrates that single-cell genomics and machine learning can generate a mechanistic model for how disruptions in cell communication selectively affect spatiotemporally dynamic regulatory networks in cardiogenesis.

developmental biology↗

bioRxiv2022

Single Cell Multimodal Analyses Reveal Epigenomic and Transcriptomic Basis for Birth Defects in Maternal Diabetes

Birth defects occur in [~]6% of all live births and can be caused by combinations of genetic and environmental influences1. Large-scale DNA sequencing efforts are revealing genetic influences2,3, but investigations into the contributions of environmental factors have largely been limited to association studies with limited mechanistic insight. Hyperglycemia present in pre-gestational diabetic mothers is among the most frequent environmental contributor to congenital defects and results in an increased incidence of congenital heart defects and craniofacial anomalies4. However, the cell types involved and underlying mechanisms by which maternal hyperglycemia affects these regions are unknown. Here, we utilized multi-modal single cell analyses to reveal that maternal diabetes affects the epigenomic and transcriptomic state of specific subsets of cardiac and craniofacial progenitors during embryogenesis. A previously unrecognized subpopulation of anterior heart field progenitors expressing Alx3 acquired a more posterior identity in response to maternal hyperglycemia, based on gene expression and chromatin status. Similarly, a sub-population of neural crest-derived cells in the second pharyngeal arch, which contributes to craniofacial structures, also displayed abnormalities in cell specification and patterning. Analysis of differentially accessible chromatin regions demonstrated that disrupted patterning was associated with increased intrinsic retinoic acid signaling in affected cell types in response to maternal diabetes and hyperglycemia. This work demonstrates how an environmental insult can have highly selective epigenomic consequences on discrete cell types leading to developmental patterning defects.

developmental biology↗