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Chen, W.

Publications and source records attributed to Chen, W..

At least 343 records · Page 19Linked to original sources

Uni-Fold Symmetry: Harnessing Symmetry in Folding Large Protein Complexes

Deep folding models have revolutionized the conventional methods of protein complex prediction. However, applying them to large protein oligomers is not easy. These models generally require copying the sequences of identical subunits to capture the in-between relationships. Accordingly, the scales of target protein complexes are strictly limited due to the cubic complexity of these models. To address this issue, we propose UF-Symmetry (Uni-Fold Symmetry), which is extricated from the need of sequence copying via harnessing the intrinsic symmetry of large protein oligomers. Taking the sequences of the asymmetric unit (AU) and a pre-specified symmetry group, UF-Symmetry learns to fold the AU and to assemble the complex structure in an end-to-end manner. By reducing the input scales from entire assemblies to AUs, UF-Symmetry allows to predict much larger assemblies with significant acceleration: for a complex of 4-fold cyclic symmetry (C4) and AU size of 512, UF-Symmetry achieves approximately 20 times acceleration to current methods. On a benchmark of recently released PDB multimers, UF-Symmetry approximately halves the failure rate of current methods and achieves approaching accuracy on commonly successful cases.

bioinformatics↗

Wounding promotes root regeneration through a cell wall integrity sensor, the receptor kinase FERONIA

Wounding caused by various stresses is the initial event of plant regeneration. However, the mechanisms underlying the early wounding responses to promote plant regeneration remain largely unknown. Here, we report that the receptor kinase FERONIA (FER) interacts with Topless/Topless-related proteins (TPL/TPRs) to regulate the expression of regeneration-related genes to modulate root tip regeneration. One ligand of FER, rapid alkalinization factor 33 (RALF33), is stimulated by wounding and functions together with FER to promote regeneration. Single-cell sequencing data showed that the low-differentiation cell types in the stele may account for the enhanced regeneration ability in the fer mutant, especially in the columella and quiescent center (QC). Further interaction assays and analysis of the gene expression patterns in low-differentiation cell types confirmed that FER interacts with TPL/TPRs to regulate the expression of downstream regeneration-related genes. One of their downstream targets, an essential transcription factor (TF) in root regeneration, ERF115, acts downstream of FER-TPL/TPRs to control regeneration. Our results suggested a signaling pathway between the early wounding response and regeneration processes in roots. One-sentence summaryRALF33-FER serves as an early signaling module between wounding and regeneration by functioning with TPL/TPRs in roots.

cell biology↗

Role of mPFC DUSP1 in regulating the sensitivity to cocaine in adolescent cocaine-exposed mice of adulthood

Adolescent cocaine abuse increases the risk for developing addiction in later life, which remains a big public health concern, but the underlying molecular mechanism is poorly understood. Here, adolescent cocaine-exposed (ACE) male mice models were established by administrating cocaine during adolescent period. When growing to adult age, mice were subjected to conditioned place preference (CPP) to evaluate the sensitivity to cocaine, then potential molecule of dual specificity phosphatase 1 (DUSP1) were screened out by transcriptomic sequencing. Subthreshold dose of cocaine (sdC), that is insufficient to produce CPP, was used to induce CPP in adulthood. The sdC treatment effectively induced CPP in ACE mice during adulthood, accompanied with the more triggered CaMKII-positive neurons, and induced higher Dusp1 gene, lower DUSP1 protein, lower DUSP1 activity and lower DUSP1 expression on CaMKII-positive neurons (DUSP1CaMKII) in medial prefrontal cortex (mPFC). Overexpressing DUSP1CaMKII suppressed CaMKII-positive neuronal activation, and ultimately blocked sdC-induced CPP in ACE mice during adulthood. While, knocking-down DUSP1CaMKII activated more CaMKII-positive neurons, and aggravated sdC-preferred behavior in ACE mice during adulthood. ERK1/2 might be potential subsequent signal for DUSP1 in the process. Collectively, our findings reveal a novel molecular mechanism underlying adolescent drug abuse-induced susceptibility to addiction during adulthood, and mPFC DUSP1CaMKII is a promising pharmacological target to predict or treat addiction, especially caused by adolescent substance use. SummaryAdolescent cocaine exposure causes higher cocaine-preferred behaviors during adulthood, along with evoked mPFC activity in response to cocaine challenge. Locally overexpressing but not knocking-down the dual specificity phosphatase 1 (DUSP1) on CaMKII-positive neurons (DUSP1CaMKII) suppresses mPFC activation, and ultimately rescues the higher sensitivity to cocaine during adulthood.

neuroscience↗

Synthetic fibrous hydrogels as a platform to decipher cell-matrix mechanical interactions

The interactions between cells and their direct environment are crucial for cell fate but biochemically and mechanically highly complex, and therefore, poorly understood. Despite recent advances that exposed the impact of a range of different factors, real progress remains challenging, since appropriate controllable matrices and quantitative analysis techniques that cover a range of time and length scales are unavailable. Here, we use a synthetic fibrous hydrogel with nonlinear mechanics to mimic and tailor the bi-directional cell-matrix interactions. Using advanced microscopy-based approaches, we acquire a comprehensive picture of how cellular traction forces, fiber remodeling, matrix stiffening, matrix properties and cellular behavior interact, highlighting for instance, the importance of a fibrous architecture and nonlinear mechanics of the matrix. Complete mapping of cell-matrix interactions at the cellular length scale provides indispensable information for the rational design of biomimetic materials to recreate realistic in vitro cell environments.

biophysics↗

β1-adrenergic receptor on pyramidal neurons of mPFC regulates METH-preferred behaviors in METH-sired male mice

Paternal abuse of drugs, such as methamphetamine (METH), elevates the risk of developing addiction in subsequent generations, however, its underlying molecular mechanism remains poorly understood. Male adult mice (F0) were exposed to METH for 30 days, followed by mating with naive female mice to create the first-generation mice (F1). When growing to adulthood, F1 were subjected to conditioned place preference (CPP) test. Subthreshold dose of METH, insufficient to induce CPP normally, were used in F1 (METHF1). Selective antagonist (betaxolol) for {beta}1-adrenergic receptor (ADRB1) or its knocking-down virus were administrated into mPFC to regulate ADRB1 function and expression on CaMKII-positive neurons. METH-sired male F1 acquired METHF1-induced CPP, indicating that paternal METH exposure induce higher sensitivity to METH in male F1. Compared with saline (SAL)-sired male F1, CaMKII-positive neuronal activity was normal without METHF1, but strongly evoked after METHF1 treatment in METH-sired male F1 during adulthood. METH-sired male F1 had higher ADRB1 levels without METHF1, which was kept at higher levels after METHF1 treatment in mPFC. Either inhibiting ADRB1 function with betaxolol, or knocking-down ADRB1 level on CaMKII-positive neurons (ADRB1CaMKII) with virus transfection efficiently suppressed METHF1-evoked mPFC acyivation, and ultimately blocked METHF1-induced CPP in METH-sired male F1. In the process, the p-ERK1/2 and {Delta}FosB may be potential subsequent signals of mPFC ADRB1CaMKII. The mPFC ADRB1CaMKII mediates paternal METH exposure-induced higher sensitivity to drug addiction in male offspring, raising a promising pharmacological target for predicting or treating transgenerational addiction. HIGHLIGHTSO_LIPaternal methamphetamine (METH) exposure induces higher sensitivity to METH in male F1 during adulthood, accompanied with higher ADRB1 level in mPFC. C_LIO_LIMETH use in F1 (METHF1) evokes more CaMKII-positive neurons in mPFC of METH-sired than saline-sired male F1. C_LIO_LIInhibiting ADRB1 function or knocking-down ADRB1 level on CaMKII-positive neurons (ADRB1CaMKII) efficiently suppresses METHF1-evoked mPFC activation, and ultimately rescues transgenerational susceptibility to addiction in male F1. C_LI

neuroscience↗

Transformer Enables Reference Free And Unsupervised Analysis of Spatial Transcriptomics

The development of spatial transcriptomics technologies makes it possible to study tissue heterogeneity at the scale of spatial expressed microenvironment. However, most of the previous methods collapse the spatial patterns in the low spatial resolution. Existing reference based deconvolution methods integrate single-cell reference and spatial transcriptomics data to predict the proportion of cell-types, but the availability of suitable single-cell reference is often limited. In this paper, we propose a novel Transformer based model (TransfromerST) to integrate the spatial gene expression measurements and their spatial patterns in the histology image (if available) without single cell reference. TransfromerST enables the learning of the locally realistic and globally consistent constituents at nearly single cell resolution. TransfromerST firstly uses a transformer based variational autoencoder to explore the latent representation of gene expression, which is further embedded with the spatial relationship learned from adaptive graph Transformer model. The super-resolved cross-scale graph network improves the model-fit to enhanced structure-functional interactions. The public and in-house experimental results with multimodal spatial transcriptomics data demonstrate TransfromerST could highlight the tissue structures at nearly single cell resolution and detect the spatial variable genes and meta gene for each spatial domain. In summary, TransfromerST provides an effective and efficient alternative for spatial transcriptomics tissue clustering, super-resolution and gene expression prediction from histology image.

bioinformatics↗

A reductase in the lipid metabolism at cross roads between cuticular wax production and jasmonic acid-mediated defenses in maize

Cuticular wax is the first physical layer to protect plants from herbivore infestations. Although wax and jasmonic acid (JA) biosynthesis are the two most studied branches of lipid metabolism, the mechanism how cuticular wax production modulates plant chemical defenses is unclear. Here, we show that the maize (Zea mays) GL8 gene, encoding 3-ketoacyl reductase during fatty acid elongation in the biosynthesis of very long chain fatty acids (VLCFA), functions as a turning point between wax production and JA-mediated defenses. The fall armyworm (Spodoptera frugiperda) larvae consumed more tissue but gained a lower performance on gl8/Ye478 mutant plants. gl8/Ye478 mutant displayed higher JA-mediated defenses constitutively, and also more inducible by herbivore stimulation. The comprehensive transcriptomic and lipidomic analyses further demonstrated that ZmGL8 mutation up-regulates the JA biosynthesis pathway by promoting the accumulation of lysophospholipids and modulation of galactolipid synthase genes ZmDGD and ZmMGD. The phenotypic and transcription comparisons of the maize and Arabidopsis wax-deficient mutants suggest a conserved wide-spread trade-off between wax production and chemical defense in both 18:3 and 16:3 plants. These results illustrate a critical role of VLCFA metabolism as a switch to control the balance between cuticular wax physical barrier and JA-mediated chemical defenses during plant-herbivore coevolution history.

plant biology↗

Uni-Fold: An Open-Source Platform for Developing Protein Folding Models beyond AlphaFold

Recent breakthroughs on protein structure prediction, namely AlphaFold, have led to unprecedented new possibilities in related areas. However, the lack of training utilities in its current open-source code hinders the community from further developing or adapting the model. Here we present Uni-Fold as a thoroughly open-source platform for developing protein folding models beyond AlphaFold. We reimplemented AlphaFold and AlphaFold-Multimer in the PyTorch framework, and reproduced their from-scratch training processes with equivalent or better accuracy. Based on various optimizations, Uni-Fold achieves about 2.2 times training acceleration compared with AlphaFold under similar hardware configuration. On a benchmark of recently released multimeric protein structures, Uni-Fold outperforms AlphaFold-Multimer by approximately 2% on the TM-Score. Uni-Fold is currently the only open-source repository that supports both training and inference of multimeric protein models. The source code, model parameters, test data, and web server of Uni-Fold are publicly available3.

bioinformatics↗

TSNAdb v2.0: the updated version of tumor-specific neoantigen database

Tumor neoantigens have been well-acknowledged as ideal targets for tumor immunotherapy in recent years. With the deepening of research on neoantigen-based tumor immunotherapy, comprehensive neoantigen databases are urgently needed to meet the growing demand for clinical studies. We have built the Tumor-Specific NeoAntigen Database (TSNAdb v1.0) previously, which has attracted wide attention. In this study, we provide an updated version of the Tumor-Specific NeoAntigen Database (TSNAdb v2.0) with several new features including 1) taking stricter criteria for neoantigen identification. 2) providing predicted neoantigens derived from three types of somatic mutations. 3) collecting experimentally validated neoantigens and classifying them according to the evidence. TSNAdb v2.0 is freely available at https://pgx.zju.edu.cn/tsnadb/.

bioinformatics↗

Type 17 Follicular Helper T (Tfh17) Cells are Superior for Memory Maintenance

A defining feature of successful vaccination is the ability to induce long-lived antigen- specific memory cells. Follicular helper T (Tfh) cells specialize in providing help to B cells in mounting protective humoral immunity in infection and after vaccination. Memory Tfh cells that retain the CXCR5 expression can confer protection through enhancing humoral response upon antigen re-exposure but how they are maintained is poorly understood. CXCR5+ memory Tfh cells in human blood are divided into Tfh1, Tfh2 and Tfh17 cells by the expression of chemokine receptors CXCR3 and CCR6 associated with Th1 and Th17 respectively. Here, we developed a new method to induce Tfh1, Tfh2 and Tfh17-like (iTfh1, iTfh2 and iTfh17) cells in vitro. Although all three iTfh subsets efficiently support antibody responses in recipient mice with immediate immunization, iTfh17 cells are superior to iTfh1 and iTfh2 cells in supporting antibody response to a later immunization after extended resting in vivo to mimic memory maintenance. Notably, the counterpart human Tfh17 cells are selectively enriched in CCR7+ central memory Tfh (TfhCM) with survival and proliferative advantages. Furthermore, the analysis of multiple human cohorts that received different vaccines for HBV, influenza virus, tetanus toxin or measles revealed that vaccine-specific Tfh17 cells outcompete Tfh1 or Tfh2 cells for the persistence in memory phase. Therefore, the complementary mouse and human results showing the advantage of Tfh17 cells in maintenance and memory function supports the notion that Tfh17-induced immunization might be preferable in vaccine development to confer long-term protection.

immunology↗

Epigenome-wide meta-analysis of BMI in nine population-based cohorts: examining the utility of methylation as a biomarker of BMI

This study sought to examine the association between DNA methylation and body mass index (BMI) and the potential utility of these cytosine-phosphate-guanine (CpG) sites in predicting metabolic health. We pooled summary statistics from six trans-ethnic EWAS of BMI representing nine cohorts (n=17058), replicated these findings in the Womens Health Initiative (WHI, n=4822) and developed an epigenetic prediction score of BMI. In the pooled EWAS, 1265 CpG sites were associated with BMI (p<1E-7), and 1238 replicated in the WHI (FDR < 0.05). We performed several stratified analyses to examine whether these associations differed between individuals of European descent and individuals of African descent. We found five CpG sites had a significant interaction with BMI by race/ethnicity. To examine the utility of the significant CpG sites in predicting BMI, we used elastic net regression to predict log normalized BMI in the WHI (80% training/20% testing). This model found 397 sites could explain 32% of the variance in BMI in the WHI test set. Individuals whose methylome-predicted BMI overestimated their BMI (high epigenetic BMI) had significantly higher glucose and triglycerides, and lower HDL-cholesterol and LDL-cholesterol compared to accurately predicted BMI. Individuals whose methylome-predicted BMI underestimated their BMI (low epigenetic BMI) had significantly higher HDL-cholesterol and lower glucose and triglycerides. This study identified 553 previously identified and 685 novel CpG sites associated with BMI. Participants with high epigenetic BMI had poorer metabolic health suggesting that the overestimation may be driven in part by cardiometabolic derangements characteristic of metabolic syndrome.

genetics↗

Cohort-mean measured macromolecules lead to more robust linear-combination modeling than subject-specific or parameterized ones

Expert consensus recommends linear-combination modeling (LCM) of 1H MR spectra with sequence-specific simulated metabolite basis function and experimentally derived macromolecular (MM) basis functions. Measured MM basis functions have been derived from metabolite-nulled spectra averaged across a small cohort. The use of subject-specific instead of cohort-averaged measured MM basis functions has not been studied. Furthermore, measured MM basis functions are not widely available to non-expert users, who commonly rely on parameterized MM signals internally simulated by LCM software. To investigate the impact of the choice of MM modeling, this study, therefore, compares metabolite level estimates between different MM modeling strategies (cohort-mean measured; subject-specific measured; parameterized) in a lifespan cohort and characterizes its impact on metabolite-age associations. 100 conventional (TE = 30 ms) and metabolite-nulled (TI = 650 ms) PRESS datasets, acquired from the medial parietal lobe in a lifespan cohort (20-70 years of age), were analyzed in Osprey. Short-TE spectra were modeled in Osprey using six different strategies to consider the macromolecular baseline. Fully tissue- and relaxation-corrected metabolite levels were compared between MM strategies. Model performance was evaluated by model residuals, the Akaike information criterion (AIC), and the impact on metabolite-age associations. The choice of MM strategy had a significant impact on the mean metabolite level estimates and no major impact on variance. Correlation analysis revealed moderate-to-strong agreement between different MM strategies (r > 0.6). The lowest relative model residuals and AIC values were found for the cohort-mean measured MM. Metabolite-age associations were consistently found for two major singlet signals (tCr, tCho) for all MM strategies, however, findings for highly J-coupled metabolites it was depended on the MM strategy. A variance partition analysis indicated that up to 44% of the total variance was related to the choice of MM strategy. Additionally, the variance partition analysis reproduced the metabolite-age association for tCr and tCho found in the simpler correlation analysis. In summary, the inclusion of a single high-SNR MM basis function (cohort-mean) leads to more robust metabolite estimation (lower model residuals and AIC values) compared to MM strategies with more degrees of freedom (Gaussian parametrization) or subject-specific MM information. Integration of multiple LCM analyses into a single statistical model potentially improves the robustness in the detection of underlying effects (e.g. metabolite vs age), reduces algorithm-based bias, and estimates algorithm-related variance.

neuroscience↗

Noninvasive cancer detection by extracting and integrating multi-modal data from whole-methylome sequencing of plasma cell-free DNA

AbstractPlasma cell-free DNA (cfDNA) methylation and fragmentation signatures have been shown to be valid biomarkers for blood-based cancer detection. However, conventional methylation sequencing assays are inapplicable for fragmentomic profiling due to bisulfite-induced DNA damage. Here using enzymatic conversion-based low-pass whole-methylome sequencing (WMS), we developed a novel approach to comprehensively interrogate the genome-wide plasma methylation, fragmentation, and copy number profiles for sensitive and noninvasive multi-cancer detection. With plasma WMS data from a clinical cohort comprising 497 healthy controls and 780 patients with both early- and advanced-stage cancers of the breast, colorectum, esophagus, stomach, liver, lung, or pancreas, genomic features including methylation, fragmentation size, copy number alteration, and fragment end motif were extracted individually and subsequently integrated to develop an ensemble cancer classifier, called THEMIS, using machine learning algorithms. THEMIS outperformed individual biomarkers for differentiating cancer patients of all seven types from healthy individuals and achieved a combined area under the curve value of 0.971 in the independent test cohort, translating to a sensitivity of 86% and early-stage (I and II) sensitivity of 77% at 99% specificity. In addition, we built a cancer signal origin classifier with true-positive cancer samples at 100% specificity based on methylation and fragmentation profiling of tissue-specific accessible regulatory elements, which localized cancer-like signal to a limited number of clinically informative sites with 66% accuracy. Overall, this proof-of-concept work demonstrates the feasibility of extracting and integrating multi-modal biomarkers from a single WMS run for noninvasive detection and localization of common cancers across stages.

cancer biology↗

Vast human gut virus diversity uncovered by combined short- and long-read sequencing

Current metagenome-assembled human phage catalogs contained mostly fragmented genomes. Here, we developed a vigorous phage detection method involving phage enrichment and long-read sequencing and applied to 135 fecal samples. With ~10 times more efficient in obtaining complete genomes (~34%) than the Gut Virome Database, we identified the first megabasephage (~1.03Mb), and revealed the hidden diversity of the gut phageome including dozens of phages more prevalent than the crAssphages and Gubaphages.

bioinformatics↗

A Multi-subject, Multi-session, and Multi-task database for EEG-based Biometrics Challenge

EEG signals exhibit commonality and variability across subjects, sessions, and tasks. But most existing EEG studies focus on mean group effects (commonality) by averaging signals over trials and subjects. The substantial intra- and inter-subject variability of EEG has often been overlooked. The recently significant technological advances in machine learning, especially deep learning, have brought technological innovations to EEG signal application in many aspects, but there are still great challenges in cross-session, cross-task, and cross-subject EEG decoding. In this work, an EEG-based biometric competition based on a large-scale M3CV (A Multi-subject, Multi-session, and Multi-task Database for investigation of EEG Commonality and Variability) database was launched to better characterize and harness the intra- and inter-subject variability and promote the development of machine learning algorithm in this field. In the M3CV database, EEG signals were recorded from 106 subjects, of which 95 subjects repeated two sessions of the experiments on different days. The whole experiment consisted of 6 paradigms, including resting-state, transient-state sensory, steady-state sensory, cognitive oddball, motor execution, and steady-state sensory with selective attention with 14 types of EEG signals, 120,000 epochs. With the learning tasks of the identification and verification, the performance metrics and baseline methods were introduced in the competition. In general, the proposed M3CV dataset and the EEG-based biometric competition aim to provide the opportunity to develop advanced machine learning algorithms for achieving an in-depth understanding of the commonality and variability of EEG signals across subjects, sessions, and tasks.

neuroscience↗

Loss of growth differentiation factor 9 causes an arrest of early folliculogenesis in zebrafish - a novel insight into its action mechanism

Growth differentiation factor 9 (GDF9) is the best characterized growth factor released by the oocyte; however, most information about GDF9 functions comes from studies in the mouse model. In this study, we created a mutant for Gdf9 gene (gdf9-/-) in zebrafish using TALEN approach. The loss of Gdf9 caused a complete arrest of follicle development at primary growth (PG) stage. These follicles eventually degenerated, and all mutant females gradually changed to males through sex reversal, which could be prevented by mutation of the male-promoting gene dmrt1. Interestingly, the phenotypes of gdf9-/- could be rescued by mutation of inhibin (inha-/-) but not estradiol, suggesting a potential role for the activin-inhibin system in Gdf9 actions. In gdf9 null follicles, activin {beta}Aa (inhbaa) expression decreased dramatically; however, its expression rebounded in the double mutant (gdf9-/-;inha-/-). These results clearly indicate that although endocrine hormones such as follicle-stimulating hormone (FSH) are important for folliculogenesis, the activation of PG follicles requires intrinsic factors from the oocyte, such as Gdf9, which in turn works on the neighboring follicle cells to trigger follicle activation, probably via activins.

developmental biology↗

mBAT-combo: a more powerful test to detect gene-trait associations from GWAS data

Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries. Since genes have a direct biological link to downstream function, gene-based test results are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of{chi} 2 statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects (e.g., when the product of the effects of two SNPs and their linkage disequilibrium (LD) correlation is negative). Here, we introduce "mBAT-combo", a new set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,175 gene-trait pairs, indicating that masking effects in complex traits is common. We further validate the improved power of our method in height, body mass index and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with larger sample sizes (average sample size increased by 1.7-fold). For instance, LRRC4B is significant only in our method for schizophrenia, which has been shown to play a role in presynaptic pathology using genetic fine-mapping and evidence-based synaptic annotations. As a more powerful gene-based method, mBAT-combo is expected to improve the downstream pathway analysis or tissue and cell-type enrichment analysis that takes genes identified from GWAS data as input to understand the biological mechanisms of the trait or disease. Despite our focus on genes in this study, the framework of mBAT-combo is general and can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.

genetics↗

A Chemical-mechanical Coupled Model Predicts Roles of Spatial Distribution of Morphogen in Maintaining Tissue Growth

The exact mechanism controlling cell growth remains a grand challenge in developmental biology and regenerative medicine. The Drosophila wing disc tissue serves as an ideal biological model to study growth regulation due to similar features observed in other developmental systems. The mechanism of growth regulation in the wing disc remains a subject of intense debate. Most existing models to study tissue growth focus on either chemical signals or mechanical forces only. Here we developed a multiscale chemical-mechanical coupled model to test a growth regulation mechanism depending on the spatial range of the morphogen gradient. By comparing the spatial distribution of cell division and the overall shape of tissue obtained in the coupled model with experimental data, our results show that the distribution of the Dpp morphogen can be critical in resulting tissue size and shape. A larger tissue size with a faster growth rate and more symmetric shape can be achieved if the Dpp gradient spreads in a larger domain. Together with the absorbing boundary conditions, the feedback regulation that downregulates Dpp receptors on the cell membrane allows the further spread of the morphogen away from its source region, resulting in prolonged tissue growth at a more spatially homogeneous growth rate. Summary StatementA multiscale chemical-mechanical model was developed by coupling submodels representing dynamics of a morphogen gradient at the tissue level, intracellular chemical signals, and mechanical properties at the subcellular level. By applying this model to study the Drosophila wing disc, it was found that the spatial range of the morphogen gradient affected tissue growth in terms of the growth rate and the overall shape.

developmental biology↗