Biology subjects

Wei, Y.

Publications and source records attributed to Wei, Y..

At least 145 records · Page 8Linked to original sources

bioRxiv2023

Association between recovering from tempo perturbations and reading measurements.

A strong correlation between auditory temporal processing and reading proficiency has been consistently observed across clinical and nonclinical populations, spanning various age groups and languages. Specifically, rhythm sensitivity in both the music and speech domains has been considered to be fundamental for accurately tracking the hierarchical acoustic components in speech, playing a central role in the development of reading skills. However, the empirical validation of this hypothesis has primarily utilized stimuli with an isochronous underlying beat structure, which is limited in its ability to capture the nonlinearity inherent in the perception of timing within the speech and music domains. In our current study, we introduced perturbation stimuli and demonstrated a relationship between sensorimotor synchronization performance and reading measurements in the neurotypical adult population. Specifically, the current study highlighted that sensorimotor synchronization during the post-perturbation time window yields notably superior predictive value across a wide array of reading measurements when compared to the pre-perturbation time window, which, in contrast, did not predict reading measurements. Furthermore, our novel curve fitting analysis effectively captured the nonlinear aspects of participants sensorimotor synchronization performance when recovering from tempo perturbation, providing further insight into their auditory temporal processing abilities when responding to timing changes in auditory signals--a phenomenon commonly encountered in both speech and music contexts.

neuroscience↗

bioRxiv2023

ThermoLink: Bridging Disulfide Bond and EnzymeThermostability through Database Construction andMachine Learning Prediction

Disulfide bonds (SS bonds), covalently formed by sulfur atoms in cysteine residues, play a crucial role in protein folding and structure stability. Due to their significance, artificial disulfide bonds are often introduced to enhance the thermostability of proteins. Although an increasing number of tools can assist with this task, significant amounts of time and resources were often wasted due to inadequate consideration. To enhance the accuracy and efficiency of designing disulfide bonds for protein thermostability improvement, we first collected disulfide-bond data with protein thermostability data from extensive literature sources. Then, various sequence- and structure-based features were extracted, and machine learning models were constructed to predict whether a disulfide bond could improve protein thermostability. Among all models, the neighborhood context model using the Adaboost-DT algorithm performs the best, and the AUC-ROC score and accuracy are 0.773 and 0.714, respectively. Alongside this, we also found that AlphaFold2 exhibits a high superiority in predicting disulfide bonds, and the coevolutionary relationship between residue pairs, to some extent, could also guide artificial disulfide-bond design. The SS-bond data has been integrated into an online server, named ThermoLink, available at guolab.mpu.edu.mo/thermoLink. O_TEXTBOXKey MessagesO_LIWe manually curated a database of disulfide bonds and their impacts on protein thermostability from the literature. C_LIO_LIAlphaFold2 exhibits a high superiority in predicting disulfide bonds, and to some extent, the conservation and co-evolutionary information of the residue pairs involved in disulfide bonds could also guide artificial disulfide-bond design. C_LIO_LIMachine learning models were developed to predict potential disulfide bonds with the aim of improving protein thermostability, offering an extended design strategy beginning with existing SS-bond prediction tools to optimize variant selection. C_LIO_LIThis work provides valuable data and theoretical support for a more precise design of disulfide bonds for improving protein thermostability. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2023

Epigenetic reader ZMYND11 noncanonical function restricts HNRNPA1-mediated stress granule formation and oncogenic activity

ZMYND11 encodes an epigenetic reader of histone methylation, functioning as a transcriptional corepressor. However, whether and how ZMYND11 contributes to cancer progression and therapy remains unclear. Here we report that ZMYND11 downregulation is prevalent in cancers and profoundly correlates with adverse prostate cancer patient outcomes. Depletion of ZMYND11 promotes tumor cell growth, migration and invasion in vitro as well as tumor formation and metastasis in vivo. Mechanistically, we find that ZMYND11 exhibits tumor suppressive roles through recognizing arginine-194-methylated HNRNPA1 dependent on its MYND domain, thereby squeezing HNRNPA1 in nucleus and inhibiting the formation of stress granules in cytoplasm. Furthermore, ZMYND11 antagonizes HNRNPA1-driven high PKM2/PKM1 ratio and counteracts PKM2-induced aggressive tumor phenotype. Remarkably, ZMYND11 recognition of HNRNPA1 could be disrupted by pharmaceutical inhibition of arginine methyltransferase PRMT5 while ZMYND11 low-expressing tumors are sensitive to the treatment of PRMT5 inhibitors. Collectively, our study unravels a novel and noncanonical function of ZMYND11 as the nonhistone methyl reader and discovers a mechanism for the requirement of arginine-methylation-mediated ZMYND11-HNRNPA1 association to restrict tumor progression and offers cancer therapeutic targets and potential biomarkers.

cancer biology↗

bioRxiv2023

PPARγ controls ESCRT-dependent fibroblast-like synoviocyte exosome biogenesis and Alleviates Chondrocyte Osteoarthritis Mediated by Exosomal ANXA1

Osteoarthritis (OA) is characterized by synovitis, cartilage degeneration and exercise therapy has been recognized as first line therapy. The exercise related exosome involved in the interaction between fibroblast-like synoviocytes (FLSs) and chondrocytes could be a novel promising strategy for treating OA. In this study, PPAR{gamma} was upregulated in FLSs under exercise by single cell transcriptome sequencing. Then, we investigated the underlying mechanisms of PPAR{gamma}-treated FLSs derived exosome on OA in vivo and vitro. Our data revealed that overexpression PPAR{gamma} FLSs derived exosome could ameliorate the OA severity in vivo and vitro. But knockdown PPAR{gamma} FLSs derived exosome aggravate OA. Moreover, we found PPAR{gamma} controls the endosomal sorting complex required for the transport (ESCRT)-dependent exosome biogenesis pathway. Annexin A1 (ANXA1) was enriched in OE-PPAR{gamma} exosome by quantitative proteomics. By Chip-qPCR and Co-IP methods, PPAR{gamma} and its coactivator -1 (PGC-1) acts with ESCRT subunits including HRS, STAM1, TSG101, CHMP7 and promotes their association to cargo ANXA1. As a therapeutic cargo, exosomal ANXA1 was confirmed be internalization by chondrocyte via exosome labeled experiment and ANXA1 could inhibit the phospharylation of ERK to activate the autophagy and decrease chondrocyte apoptosis. While the ANXA1 receptor blocker BOC-2 could reverse the therapic effect. In conclusion, PPAR{gamma}/ESCRT - FLSs exosomal ANXA1 - ERK axis provides a deeper theoretical basis for exercise therapy of OA and a new idea for the clinical transformation of exosomes into OA therapy.

cell biology↗

bioRxiv2023

Fast and accurate local ancestry inference with Recomb-Mix

The availability of large genotyped cohorts brings new opportunities for revealing the high-resolution genetic structure of admixed populations via local ancestry inference (LAI), the process of identifying the ancestry of each segment of an individual haplotype. Though current methods achieve high accuracy in standard cases, LAI is still challenging when reference populations are more similar (e.g., intra-continental), when the number of reference populations is too numerous, or when the admixture events are deep in time, all of which are increasingly unavoidable in large biobanks. Here, we present a new LAI method, Recomb-Mix. Recomb-Mix integrates the elements of existing methods of the site-based Li and Stephens model and introduces a new graph collapsing trick to simplify counting paths with the same ancestry label readout. Through comprehensive benchmarking on various simulated datasets, we show that Recomb-Mix is more accurate than existing methods in diverse sets of scenarios while being competitive in terms of resource efficiency. We expect that Recomb-Mix will be a useful method for advancing genetics studies of admixed populations.

bioinformatics↗

bioRxiv2023

A New Paradigm for Applying Deep Learning to Protein-Ligand Interaction Prediction

Protein-ligand interaction prediction poses a significant challenge in the field of drug design. Numerous machine learning and deep learning models have been developed to identify the most accurate docking poses of ligands and active compounds against specific targets. However, the current models often suffer from inadequate accuracy and lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that leverages the geometric information of protein-ligand complexes as input for predicting the root mean square deviation (RMSD) of docking poses and the binding strength (the negative value of the logrithm of binding affinity, pKd) with the same prediction framework. By incorporating the geometric information, IGModel ensures that its scores carry intuitive meaning. The performance of IGModel has been extensively evaluated on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core, and DISCO set, consistently achieving state-of-theart accuracies. Furthermore, we assess IGModels generalization ability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for deep learning-based prediction of protein-ligand interactions, contributing to the advancement of this field. O_TEXTBOXKey MessagesO_LIWe introduce the first framework for simultaneously predicting the RMSD of the ligand docking pose and its binding strength to the target. C_LIO_LIIGModel can effectively improve the accuracy of identifying the near-native binding poses of the ligands, and can still outperform most baseline models in scoring power, ranking power and screening power tasks. C_LIO_LIIGModel is still ahead of other state-of-the-art models in the unbiased data set and the target structure predicted by AlphaFold2, proving its excellent generalization ability. C_LIO_LILatent space provided by IGModel learns the physical interactions, thus indicating the robustness of the model. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2023

Single cell transcriptomic profiling identifies tumor-acquired and therapy-resistant cell states in pediatric rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is a pediatric tumor that resembles undifferentiated muscle cells; yet the extent to which cell state heterogeneity and molecular features are shared with human development have not been fully ascribed. Here, we report a single-cell/nucleus RNA sequencing atlas derived from 72 datasets that includes patient tumors, patient-derived xenografts, primary in vitro cultures, and established cell lines. We report four dominant muscle-lineage cell states in RMS: progenitors, proliferative, differentiated, and ground cells. We stratify these RMS cells along the continuum of human muscle development and show that RMS cells share expression patterns with fetal/embryonal myogenic precursors rather than postnatal satellite cells. Indeed, fusion-negative RMS (FN-RMS) have a discrete stem cell hierarchy that faithfully recapitulates fetal muscle development. We also identify therapy-resistant FN-RMS progenitor cells that share transcriptomic similarity with bipotent skeletal mesenchymal cells, while a subset of fusion-positive (FP) RMS have tumor-acquired cells states, including a neuronal cell state, that are not found in development. Chemotherapy induced upregulation of progenitor signatures in FN-RMS while the neuronal gene programs were retained after therapy in FP-RMS. Taken together, this work identifies new cell state heterogeneity including unique treatment-resistant and tumor-acquired cell states that differ across RMS subtypes.

cancer biology↗

bioRxiv2023

Endocannabinoids and their receptors modulate endometriosis pathogenesis and immune response

Endometriosis (EM), characterized by the presence of endometrial-like tissue outside the uterus, is the leading cause of chronic pelvic pain and infertility in females of reproductive age. Despite its high prevalence, the molecular mechanisms underlying EM pathogenesis remain poorly understood. The endocannabinoid system (ECS) is known to influence several cardinal features of this complex disease including pain, vascularization, and overall lesion survival, but the exact mechanisms are not known. Utilizing CNR1 knockout (k/o), CNR2 k/o and wild type (WT) mouse models of EM, we reveal contributions of ECS and these receptors in disease initiation, progression, and immune modulation. Particularly, we identified EM-specific T cell dysfunction in the CNR2 k/o mouse model of EM. We also demonstrate the impact of decidualization- induced changes on ECS components, and the unique disease-associated transcriptional landscape of ECS components in EM. Imaging Mass Cytometry (IMC) analysis revealed distinct features of the microenvironment between CNR1, CNR2, and WT genotypes in the presence or absence of decidualization. This study, for the first time provides an in-depth analysis of the involvement of the ECS in EM pathogenesis and lays the foundation for the development of novel therapeutic interventions to alleviate the burden of this debilitating condition.

immunology↗

bioRxiv2023

Lipid discovery enabled by sequence statistics and machine learning

Bacterial membranes are complex and dynamic, arising from an array of evolutionary pressures. One enzyme that alters membrane compositions through covalent lipid modification is MprF. We recently identified that Streptococcus agalactiae MprF synthesizes lysyl-phosphatidylglycerol (Lys-PG) from anionic PG, and a novel cationic lipid, lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG), from neutral glycolipid Glc-DAG. This unexpected result prompted us to investigate whether Lys-Glc-DAG occurs in other MprF-containing bacteria, and whether other novel MprF products exist. Here, we studied protein sequence features determining MprF substrate specificity. First, pairwise analyses identified several streptococ-cal MprFs synthesizing Lys-Glc-DAG. Second, a restricted Boltzmann machine-guided approach led us to discover an entirely new substrate for MprF in Enterococcus, diglucosyl-diacylglycerol (Glc2-DAG), and an expanded set of organisms that modify glycolipid substrates using MprF. Overall, we combined the wealth of available sequence data with machine learning to model evolutionary constraints on MprF sequences across the bacterial domain, thereby identifying a novel cationic lipid.

microbiology↗

bioRxiv2023

Spatial transcriptomics and single-nucleus RNA sequencing reveal a transcriptomic atlas of adult human spinal cord

Despite the recognized importance of the spinal cord in sensory processing, motor behaviors, and neural diseases, the underlying organization of neuronal clusters and their spatial location remain elusive. Recently, several studies have attempted to define the neuronal types and functional heterogeneity in the spinal cord using single-cell or single-nucleus RNA sequencing in animal models or developing humans. However, molecular evidence of cellular heterogeneity in the adult human spinal cord is limited. Here, we classified spinal cord neurons into 21 subclusters and determined their distribution from nine human donors using single-nucleus RNA sequencing and spatial transcriptomics. Moreover, we compared the human findings with previously published single-nucleus data of the mouse adult spinal cord, which revealed an overall similarity in the neuronal composition of the spinal cord between the two species while simultaneously highlighting some degree of heterogeneity. Additionally, we examined the sex differences in the spinal neuronal subclusters. Several genes, such as SCN10A and HCN1, showed sex differences in motor neurons. Finally, we classified human dorsal root ganglia (DRG) neurons using spatial transcriptomics and explored the putative interactions between DRG and spinal cord neuronal subclusters. In summary, these results illustrate the complexity and diversity of spinal neurons in humans and provide an important resource for future research to explore the molecular mechanisms underlying spinal cord physiology and diseases.

neuroscience↗

bioRxiv2023

KRAS inhibition activates an actionable CD24 do-not-eat-me signal in pancreas cancer

KRASG12C inhibitors (G12Ci) have produced encouraging, albeit modest and transient, clinical benefit in pancreatic ductal adenocarcinoma (PDAC). Identifying and targeting resistance mechanisms to G12Ci treatment is therefore crucial. To better understand the function of KRASG12C and possible G12Ci bypass mechanisms, we developed an autochthonous KRASG12C- driven PDAC model. Compared to the classical KRASG12D PDAC model, the G12C model exhibits slower tumor growth, yet similar histopathological and molecular features. Aligned with clinical experience, G12Ci treatment of KRASG12C tumors produced modest impact despite stimulating a hot tumor immune microenvironment. Immunoprofiling revealed that CD24, a dont eat me signal, is significantly upregulated on cancer cells upon G12Ci treatment. Blocking CD24 enhanced macrophage phagocytosis of cancer cells and significantly sensitized tumors to G12Ci treatment. Similar findings were observed in KRASG12D-driven PDAC. Together, this study reveals common and distinct oncogenic KRAS allele-specific biology and identifies a clinically actionable adaptive mechanism that may improve the efficacy of oncogenic KRAS inhibitor therapy in PDAC. SignificanceGeneration of an autochthonous KRASG12C-driven pancreatic cancer model enabled elucidation of specific effects of KRASG12C during tumor development, revealing CD24 as an actionable adaptive mechanism in cancer cells induced upon KRASG12C inhibition.

cancer biology↗

bioRxiv2023

MMD-DTA: A multi-modal deep learning framework for drug-target binding affinity and binding region prediction

AbstractThe prediction of drug-target affinity (DTA) plays an important role in the development of drugs and the discovery of potential drug targets. In recent years, computer-assisted DTA prediction has become an important method in this field. In this work, we propose a multi-modal deep learning framework for drug-target binding affinity and binding region prediction, namely MMD-DTA. The model can predict DTA while unsupervised learning of drug-target binding regions. The experimental results show that MMD-DTA performs better than the existing models on the main evaluation metrics. In addition, external validation results show that MMD-DTA improves the generalization ability of the model by integrating sequence information and structural information of drugs and targets, and the model trained on the benchmark dataset can be well generalized to independent virtual screening tasks. Visualization of drug-target binding region prediction shows the powerful interpretability of MMD-DTA, which has important implications for exploring the functional regions of drug molecules acting on proteins.

bioinformatics↗

bioRxiv2023

Overexpression of ZmSTOP1-A Enhances Aluminum Tolerance in Arabidopsis by Stimulating Organic Acid Secretion and Reactive Oxygen Species Scavenging

Aluminum (Al) toxicity and low pH are major factors limiting plant growth in acidic soils. Sensitive to Proton Rhizotoxicity 1 (STOP1) transcription factor respond to these stresses by regulating the expression of multiple Al- or low pH-responsive genes. ZmSTOP1-A, a STOP1-like protein from maize (Zea mays), was localized to the nucleus and had transactivation activity. ZmSTOP1-A was expressed moderately in both roots and shoots of maize seedlings, but was not induced by Al stress or low pH. Overexpression of ZmSTOP1-A in Arabidopsis Atstop1 mutant partially restored Al tolerance and completely low pH tolerance with respect to root growth. Regarding Al tolerance, ZmSTOP1-A/Atstop1 plants showed clear upregulation of organic acid transporter genes, and leading to increased organic acid secretion and reduced Al accumulation in roots. Besides, the antioxidant enzyme activity in roots and shoots of ZmSTOP1-A/Atstop1 plants was significantly enhanced, ultimately alleviating Al toxicity via scavenging reactive oxygen species. Similarly, ZmSTOP1-A could directly activate ZmMATE1 expression in maize, positively correlated with the number of Al-responsive GGNVS cis-element in the ZmMATE1 promoter. Our results revealed that ZmSTOP1-A antagonizes Al toxicity by enhancing organic acid secretion and reactive oxygen species scavenging in Arabidopsis, demonstrating that it is an important transcription factor conferring Al tolerance. Our findings help to comprehensively elucidate the role of STOP1-like transcript factor in enabling plants to detoxifying Al.

molecular biology↗

bioRxiv2023

Improved dual-color GRAB sensors for monitoring dopaminergic activity in vivo

Dopamine (DA) plays multiple roles in a wide range of physiological and pathological processes via a vast network of dopaminergic projections. To fully dissect the spatiotemporal dynamics of DA release in both dense and sparsely innervated brain regions, we developed a series of green and red fluorescent GPCR activation-based DA (GRABDA) sensors using a variety of DA receptor subtypes. These sensors have high sensitivity, selectivity, and signal-to-noise properties with subsecond response kinetics and the ability to detect a wide range of DA concentrations. We then used these sensors in freely moving mice to measure both optogenetically evoked and behaviorally relevant DA release while measuring neurochemical signaling in the nucleus accumbens, amygdala, and cortex. Using these sensors, we also detected spatially resolved heterogeneous cortical DA release in mice performing various behaviors. These next-generation GRABDA sensors provide a robust set of tools for imaging dopaminergic activity under a variety of physiological and pathological conditions.

neuroscience↗

bioRxiv2023

Phylogenomic analyses support a new infrageneric classification of Pourthiaea (Maleae, Rosaceae) using multiple inference methods and extensive taxon sampling

In this comprehensive study, we conducted extensive taxon sampling and performed phylogenomic analyses based on plastome and nuclear ribosomal DNA (nrDNA) datasets. We employed multiple inference methods, including concatenated and coalescent-based strategies, to generate an accurate phylogeny of the woody Rosaceae genus Pourthiaea. The nrDNA phylogeny of Pourthiaea strongly supported three major clades, which were consistent with morphology. However, the plastid tree provided an alternative phylogenetic topology, indicating cytonuclear discordance. Frequent hybridizations between and among the species of Pourthiaea could explain the cytonuclear discordance. Considering the evidence from morphology and phylogenomic data, we propose a new infrageneric classification for Pourthiaea, consisting of three sections: P. sect. Pourthiaea, P. sect. Amphidoxae B.B.Liu, and P. sect. Impressivenae B.B.Liu.

plant biology↗

bioRxiv2023

A fibroblast-derived TGFβ/sFRP2 noncanonical Wnt signaling axis underlies epithelial metaplasia in idiopathic pulmonary fibrosis

Reciprocal interactions between alveolar fibroblasts and epithelial cells are crucial for lung homeostasis, injury repair, and fibrogenesis, but underlying mechanisms remain unclear. To investigate this, we administered the fibroblast-selective TGF{beta}1 signaling inhibitor, epigallocatechin gallate (EGCG), to Interstitial Lung Disease (ILD) patients undergoing diagnostic lung biopsy and conducted single-cell RNA sequencing on spare tissue. Unexposed biopsy samples showed higher fibroblast TGF{beta}1 signaling compared to non-disease donor or end-stage ILD tissues. In vivo, EGCG significantly downregulated TGF{beta}1 signaling and several pro-inflammatory and stress pathways in biopsy samples. Notably, EGCG reduced fibroblast secreted Frizzle-like Receptor Protein 2 (sFRP2), an unrecognized TGF{beta}1 fibroblast target gene induced near type II alveolar epithelial cells (AEC2s). In human AEC2-fibroblast coculture organoids, sFRP2 was essential for AEC2 trans-differentiation to basal cells. Precision cut lung slices (PCLS) from normal donors demonstrated that TGF{beta}1 promoted KRT17 expression and AEC2 morphological change, while sFRP2 was necessary for KRT5 expression in AEC2-derived basaloid cells. Wnt-receptor Frizzled 5 (Fzd5) expression and downstream calcineurin-related signaling in AEC2s were required for sFRP2-induced KRT5 expression. These findings highlight stage-specific TGF{beta}1 signaling in ILD, the therapeutic potential of EGCG in reducing IPF-related transcriptional changes, and identify the TGF{beta}1-non-canonical Wnt pathway crosstalk via sFRP2 as a novel mechanism for dysfunctional epithelial signaling in Idiopathic Pulmonary Fibrosis/ILD.

cell biology↗

bioRxiv2023

Multi-color live-cell optical nanoscopy using phasor analysis

Stimulated emission depletion microscopy (STED) is a powerful tool for studying nanoscale cell structure and activity, but the difficulties it encounters in multicolor imaging limit its application in biological research. To overcome the disadvantages of limited number of channels and high cost of multicolor STED imaging based on spectral identity, we introduced lifetime into live-cell multicolor STED imaging by separating selected dyes of the same spectrum by phasor analysis. Experimental results show that our method enables live-cell STED imaging with at least 4 colors, enabling observation of cellular activity beyond the diffraction limit.

cell biology↗

bioRxiv2023

Modeling Human Spine-Spinal Cord Organogenesis by hPSC-Derived Neuromesodermal Progenitors

Human trunk development, including spine and spinal cord organogenesis, is a coordinated, orderly, and interdependent process with spatiotemporal tissue patterning. However, the underlying cellular and molecular mechanisms remain largely unclear due to the lack of an effective model that can simulate the early development of human body axis. Here, we reported the long-term patterning and dynamic morphogenesis of human trunk through the formation of spine-spinal cord organoids (SSCOs) self-organized from three-dimensional culture of human PSC-derived neuromesodermal progenitors (NMPs). The SSCOs resembled the morphogenetic features of spine and spinal cord along the anterior-posterior axis, and showed the chondro-osteogenic and neural trajectories consistent with developmental dynamics of spine and spinal cord in gestational embryo through single-cell RNA sequencing (scRNA-seq). In addition, we identified a new HMMR+ bipotent cell population with self-renewal ability and neural/mesodermal competence but distinct from NMPs, which may be involved in trunk development and represent an invaluable tool for disease modeling of spine- and spinal cord-related disorders. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/549829v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@14bfd30org.highwire.dtl.DTLVardef@1db7fe8org.highwire.dtl.DTLVardef@1aa66a1org.highwire.dtl.DTLVardef@380932_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗