Biology subjects

Lin, X.

Publications and source records attributed to Lin, X..

At least 181 records · Page 10Linked to original sources

bioRxiv2023

Utilizing Non-Invasive Prenatal Test Sequencing Data Resource for Human Genetic Investigation

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. With exceptional sensitivity, specificity, and safety, NIPT has gained global adoption, exceeding ten million tests, establishing it as one of the largest human genetic resources. This resource holds immense potential for exploring population genetic variations and their correlations with phenotypes. Here, we present comprehensive methods tailored for analyzing large, low-depth NIPT genetic datasets, involving customized algorithms and software for genetic variation detection, genotype imputation, and genome-wide association analysis. Through evaluations, we demonstrate that, when integrated with appropriate probabilistic models and population-specific haplotype reference panels, accurate allele frequency estimation and high genotype imputation accuracy (0.8 to 0.9) are achievable for genetic variants with alternative allele frequencies between 0.01 and 0.05, at sequencing depths of 0.1x to 0.25x. Additionally, we attained an R-square exceeding 0.9 for estimating genetic effect sizes across various sequencing platforms. These findings establish a robust theoretical and practical foundation for leveraging NIPT data in advancing medical genetic studies, not only in realms of maternal and child health, but also for long-term health outcomes. HighlightsO_LIIntroduction of probabilistic model integration for analyzing large-scale, low-pass non-invasive prenatal test (NIPT) sequencing data C_LIO_LIEvaluation of protocols for variant detection, genotype imputation, and genome-wide association analyses with NIPT data C_LI

bioinformatics↗

bioRxiv2023

ProTokens: A Machine-Learned Language for Compact and Informative Encoding of Protein 3D Structures

Understanding functions of proteins and designing proteins committed to specific functions in silico are highly valuable for science, industry and therapeutics. However, there is a long-standing divergence in how to present function-related protein structures to the machine learning models: Although the 1-dimensional (1D) representation of proteins via Anfinsens tokens (i.e., amino acids) is sufficient in principle and more machine-friendly, it is less successful in structure-oriented protein design compared to symmetry-constrained 3D representation (i.e., atom coordinates). Aiming to bridge the gap between 1D and 3D protein representations and harvest the advantages of the two, we develop probabilistic tokenization theory for metastable protein structures. We present an unsupervised learning strategy, which conjugates inverse folding with structure prediction, to encode protein structures into artificial amino-acid tokens (ProTokens) and decode them back to atom coordinates. We show that tokenizing protein structures variationally can lead to compact and informative representations. Compared to amino acids -- the Anfinsens tokens -- ProTokens are easier to detokenize and more descriptive of finer conformational ensembles. Therefore, protein structures can be efficiently compressed, stored, aligned and compared in the form of ProTokens. By unifying the discrete and continuous representations of protein structures, ProTokens also enable all-atom protein structure design via various generative models without the concern of symmetry or modality mismatch, and allows scalable foundation models to perceive, process and explore the microscopic structures of biomolecules effectively.

bioinformatics↗

bioRxiv2023

Millimeter-scale magnetic implants paired with a fully integrated wearable device for wireless biophysical and biochemical sensing

Implantable sensors can directly interface with various organs for precise evaluation of health status. However, extracting signals from such sensors must rely on transcutaneous wires, integrated circuit chips, or cumbersome readout equipment, which increases the risks of infection, reduces the biocompatibility, or limits the portability. Here, we develop a set of millimeter-scale, chip-less and battery-less magnetic implants that can measure biophysical and biochemical signals wirelessly. In particular, the implants form two-way communications with a fully integrated wearable device, where the wearable device can induce a large-amplitude damped vibration of the magnetic implants and capture their subsequent motions in a wireless manner. Such damped vibrations reflect not only the biophysical conditions surrounding the implants movements, but also the concentration of a specific biochemical depending on the surface modification. Experiments in rat models demonstrate the capabilities in measuring cerebrospinal fluid (CSF) viscosity, intracranial pressure (ICP), and CSF glucose levels. This miniaturized system opens possibility for continuous, wireless monitoring of a wide range of biophysical and biochemical conditions within the living organism.

bioengineering↗

bioRxiv2023

The unique function of Runx1 in skeletal muscle differentiation and regeneration is mediated by an ETS interaction domain

The conserved Runt-related (RUNX) transcription factor family are well-known master regulators of developmental and regenerative processes. Runx1 and Runx2 are both expressed in satellite cells (SC) and skeletal myotubes. Conditional deletion of Runx1 in adult SC negatively impacted self-renewal and impaired skeletal muscle maintenance. Runx1- deficient SC retain Runx2 expression but cannot support muscle regeneration in response to injury. To determine the unique molecular functions of Runx1 that cannot be compensated by Runx2 we deleted Runx1 in C2C12 that retain Runx2 expression and established that myoblasts differentiation was blocked in vitro due in part to ectopic expression of Mef2c, a target repressed by Runx1. Structure-function analysis demonstrated that the Ets-interacting MID/EID region of Runx1, absent from Runx2, is critical to regulating myoblasts proliferation, differentiation, and fusion. Analysis of in-house and published ChIP-seq datasets from Runx1 (T-cells, muscle) versus Runx2 (preosteoblasts) dependent tissue identified enrichment for a Ets:Runx composite site in Runx1-dependent tissues. Comparing ATACseq datasets from WT and Runx1KO C2C12 cells showed that the Ets:Runx composite motif was enriched in peaks open exclusively in WT cells compared to peaks unique to Runx1KO cells. Thus, engagement of a set of targets by the RUNX1/ETS complex define the non-redundant functions of Runx1.

developmental biology↗

bioRxiv2023

An automated archival single-nucleus total RNA sequencing platform mapping integrative and retrospective cell atlas of gliomas

Single-cell RNA sequencing (scRNA-seq) has dramatically transformed biomedical research within laboratory settings. It has been extensively employed to investigate the heterogeneity and plasticity of glioma, the most prevalent brain tumor. However, the clinical diagnosis and treatment of glioma remain complex and challenging, highlighting the need for comprehensive cancer research. Currently available scRNA-seq platforms are insufficient to fulfill the demands posed by large-scale clinical applications. Here, we present an automated high-throughput single-nucleus total RNA sequencing platform, known as AAsnRandom-seq. This platform integrates automated single-nucleus isolation and droplet barcoding systems with the random primer-based scRNA-seq chemistry, designed to accommodate a diverse range of sample types. The performance and versatility of AAsnRandom-seq are validated using over one hundred clinical FFPE and frozen samples. AAsnRandom-seq was applied to archival FFPE samples of various glioma subtypes, including rare clinical samples, and matched primary-recurrent glioblastomas (GBMs), delving into the comprehensive molecular characteristic of glioma at single-cell level. Abundant non-coding RNAs (ncRNAs) with distinct expression profiles within different glioma clusters are detected. Promising recurrence-related targets and pathways are identified from the matched primary-recurrent GBMs. AAsnRandom-seq holds significant application value on large-scale integrative and retrospective clinical research using archived specimens.

bioengineering↗

bioRxiv2023

Disrupted sleep-wake regulation in the MCI-Park mouse model of Parkinson's Disease

Disrupted sleep has a profound adverse impact on lives of Parkinsons disease (PD) patients and their caregivers. Sleep disturbances are exceedingly common in PD, with substantial heterogeneity in type, timing, and severity. Among the most common sleep-related symptoms reported by PD patients are insomnia, excessive daytime sleepiness, and sleep fragmentation, characterized by interruptions and decreased continuity of sleep. Alterations in brain wave activity, as measured on the electroencephalogram (EEG), also occur in PD, with changes in the pattern and relative contributions of different frequency bands of the EEG spectrum to overall EEG activity in different vigilance states consistently observed. The mechanisms underlying these PD-associated sleep-wake abnormalities are poorly understood, and they are ineffectively treated by conventional PD therapies. To help fill this gap in knowledge, a new progressive model of PD - the MCI-Park mouse - was studied. Near the transition to the parkinsonian state, these mice exhibited significantly altered sleep-wake regulation, including increased wakefulness, decreased non-rapid eye movement (NREM) sleep, increased sleep fragmentation, reduced rapid eye movement (REM) sleep, and altered EEG activity patterns. These sleep-wake abnormalities mirror those identified in PD patients. Thus, this model may help elucidate the circuit mechanisms underlying sleep disruption in PD and identify targets for novel therapeutic approaches.

neuroscience↗

bioRxiv2023

Dense Sampling of Taxa and Genomes Untangles the Phylogenetic Backbone of a Non-model Plant Lineage Rife with Deep Hybridization and Allopolyploidy

Phylogenetic networks, rather than purely bifurcating trees, more accurately depict the intricate evolutionary dynamics of most lineages, especially those characterized by extensive hybridization and allopolyploidization events. However, the challenges of achieving complete taxon sampling, and limited financial resources for studying non-model plant lineages, have hindered comprehensive and robust estimation of phylogenetic backbones with guidance from networks. The bellflower tribe, Campanuleae, characterized by a reticulate evolutionary history, serves as an ideal model to investigate how to diagnose nested ancient reticulation events. Here, by integrating multiple genomic data sources and a range of phylogenetic inference methods, we produced a robust phylogenetic backbone for the tribe Campanuleae. Our investigation of reticulate evolution indicates that hybridization and allopolyploidization were instrumental in shaping the diversity of the bellflower tribe, particularly during the initial diversification of the subtribe Phytematinae. Additionally, we ascertained that conflicting topologies resulting from distinct genomic datasets and inference methodologies significantly impact downstream estimates of divergence dating, ancestral area construction, and diversification rates. This study offers a universally relevant framework for deciphering how to use network-based phylogenetic structures using various genomic sources and inference methods. [Campanulaceae, Campanuleae, Cytonuclear discordance, paralog, phylogenomics, reticulate evolution]

plant biology↗

bioRxiv2023

scDILT: a model-based and constrained deep learning framework for single-cell Data Integration, Label Transferring, and clustering

The scRNA-seq technology enables high-resolution profiling and analysis of individual cells. The increasing availability of datasets and advancements in technology have prompted researchers to integrate existing annotated datasets with newly sequenced datasets for a more comprehensive analysis. It is important to ensure that the integration of new datasets does not alter the cell clusters defined in the old/reference datasets. Although several methods have been developed for scRNA-seq data integration, there is currently a lack of tools that can simultaneously achieve the aforementioned objectives. Therefore, in this study, we have introduced a novel tool called scDILT, which leverages a conditional autoencoder and deep embedding clustering to effectively remove batch effects among different datasets. Moreover, scDILT utilizes homogeneous constraints to preserve the cell-type/clustering patterns observed in the reference datasets, while employing heterogeneous constraints to map cells in the new datasets to the annotated cell clusters in the reference datasets. We have conducted extensive experiments to demonstrate that scDILT outperforms other methods in terms of data integration, as confirmed by evaluations on simulated and real datasets. Furthermore, we have shown that scDILT can be successfully applied to integrate multi-omics single-cell datasets. Based on these findings, we conclude that scDILT holds great promise as a tool for integrating single-cell datasets derived from different batches, experiments, times, or interventions.

bioinformatics↗

bioRxiv2023

Ensembled best subset selection using summary statistics for polygenic risk prediction

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, yet existing methods face a tradeoff between predictive power and computational efficiency. We introduce ALL-Sum, a fast and scalable PRS method that combines an efficient summary statistic-based L0L2 penalized regression algorithm with an ensembling step that aggregates estimates from different tuning parameters for improved prediction performance. In extensive large-scale simulations across a wide range of polygenicity and genome-wide association studies (GWAS) sample sizes, ALL-Sum consistently outperforms popular alternative methods in terms of prediction accuracy, runtime, and memory usage. We analyze 27 published GWAS summary statistics for 11 complex traits from 9 reputable data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen, evaluated using individual-level UKBB data. ALL-Sum achieves the highest accuracy for most traits, particularly for GWAS with large sample sizes. We provide ALL-Sum as a user-friendly command-line software with pre-computed reference data for streamlined user-end analysis.

genetics↗

bioRxiv2023

Single-cell mitochondrial variant enrichment resolved clonal tracking and spatial architecture in human embryonic hematopoiesis

The ability to perform lineage tracing at the single-cell level is critical to reconstructing dynamic transitions during cell differentiation. However, prospective tracing approaches inevitably encounter outstanding challenges including barcoding precision, barcode diversity, and detection efficiency, which can skew inferred lineage relationships. Human pluripotent stem cells (hPSCs) even face risks of DNA-damage-induced toxicity-related cell death. We explored the use of naturally occurring somatic mutations in mitochondrial transcripts detected in single-cell RNA-seq as genetic lineage barcodes in hPSCs. In this study, we used an enrichment of scRNA-seq mitochondrial reads and a robust computational method to identify clonally relevant mitochondrial variants as endogenous genetic barcodes for clonal tracking of early embryonic hematopoiesis from hPSC. We modeled the development of embryonic tissues from hPSCs and delineated cell fate specification by integrating synthetic barcoding with mitochondrial lineage tracing. Using a biophysical model, we reconstructed the sequential transcriptional logic of fate specification and its underlying regulatory network. We further applied mitochondrial lineage tracing to spatial transcriptomics, which enabled us to identify the spatial clonal architecture of human embryonic organoids. Our analysis revealed that this spatial zonation was orchestrated by NOTCH-mediated crosstalk between stromal cells and hematopoietic progenitors. Our multi-modal framework links clonal dynamics with niche-specific fate decisions, providing a generalizable approach to dissecting tissue organization in development and disease. This study underscores the utility of mitochondrial variants as endogenous markers for high-resolution spatial clonal tracking in stem cell-derived organoid models of human development.

developmental biology↗

bioRxiv2023

Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

genetics↗

bioRxiv2023

Rof is a key regulator of Rho and transcription termination in bacteria

Transcription is crucial for the expression of genetic information and its efficient and accurate termination is required for all living organisms. Rho-dependent termination could rapidly terminate unwanted premature RNAs and play important roles in bacterial adaptation to changing environments. Although Rho has been discovered for about five decades, the regulation mechanisms of Rho-dependent termination are still not fully elucidated. Here we report the cryogenic electron microscopy structure of Rho-Rof antitermination complex. The structure shows that Rof binds to the open-ring Rho hexamer and inhibits the initiation of Rho-dependent termination. Rofs N-terminal -helix is key in facilitating Rof-Rho interactions. Rof binds to Rhos primary binding site (PBS) and excludes Rho from binding with PBS ligand RNA at the initiation step. Further in vivo assays in Salmonella Typhimurium show that Rof is required for virulence gene expression and host cell invasion, unveiling a novel physiological function of Rof and transcription termination in bacterial pathogenesis.

microbiology↗

bioRxiv2023

Iterative In Silico Screening for Optimizing Stable Conformation of Anti-SARS-CoV-2 Nanobodies

Nanobodies (Nbs or VHHs) are single-domain antibodies (sdAbs) derived from camelid heavy-chain antibodies. The variable region of these nanobodies has special and unique characteristics, such as small size, good tissue penetration, and cost-effective production, making nanobodies a good candidate for the diagnosis and treatment of viruses. Identifying effective nanobodies against the COVID-19 would help us defeat this dangerous virus or other unknown variants in future. Herein, we introduce an in silico screening strategy for optimizing stable conformation of anti-SARS-CoV-2 nanobodies. Firstly, various complexes containing nanobodies were downloaded from the RCSB database, which were identified from immunized llamas. The primary docking between nanobodies and the SARS-CoV-2 spike protein receptor-binding domain was performed through ClusPro program, with the manually screening that leaving the reasonable conformation to the next step. Then, the binding distances of atoms between the antigen-antibody interfaces were measured through the NeighborSearch algorithm. Finally, filtered nanobodies were acquired according to HADDOCK scores through HADDOCK docking the COVID spike protein with nanobodies under restrictions of calculated molecular distance between active residues and antigenic epitopes less than 4.5 [A]. In this way, those nanobodies which with more reasonable conformation and with stronger neutralizing efficacy were acquired. To validate the efficacy ranking of the nanobodies we obtained, we calculated the binding affinities ({Delta}G) and dissociation constants (Kd) of all screened nanobodies using the PRODIGY web tool, and predicted the stability changes induced by all possible point mutations in nanobodies using the MAESTROWeb server. Furthermore, we examined the performance of the relationship between nanobodies ranking and their number of mutation-sensitive sites (Spearman correlation > 0.68), the results revealed a robust correlation, indicating that the superior nanobodies identified through our screening process exhibited fewer mutation hotspots and higher stability. This correlation analysis demonstrates the validity of our screening criteria, underscoring the suitability of these nanobodies for future development and practical implementation. In conclusion, this three-step screening strategy iteratively in silico greatly improved the accuracy of screening desired nanobodies compared to using only ClusPro docking or default HADDOCK docking settings. It provides new ideas for the screening of novel antibodies and computer-aided screening methods.

bioinformatics↗

bioRxiv2023

RACER-m Leverages Structural Features for Sparse T Cell Specificity Prediction

Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated primarily by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen structural interface to accurately predict the MHC-allele-restricted TCR-peptide binding interactions remained challenging. Here, we present RACER-m, a coarse-grained structural template model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. We find explicit inclusion of structural content substantially reduces the required number of training examples for reliable prediction of TCR-recognition specificity and sensitivity across diverse biological contexts. We demonstrate that our structural model capably identifies biophysically meaningful point-mutants that affect overall binding affinity, distinguishing its ability in predicting TCR specificity of point mutants peptides from alternative sequence-based methods. Collectively, our approach combines biophysical and inferential learning-based methods to predict TCR-peptide binding events using sparse training data. Its application is broadly applicable to studies involving both closely-related and structurally diverse TCR-peptide pairs.

biophysics↗

bioRxiv2023

Biogenic phosphonate utilization by globally distributed diatom Thalassiosira

Phosphonate is a class of enigmatic organic phosphorus compounds that contribute [~]25% of total dissolved organic phosphorus (DOP). Recent studies reveal the important role of phosphonate mediated by prokaryotes in the marine P redox cycle, however, its bioavailability and metabolic process by eukaryotic phytoplankton are under debate. 2-Aminoethylphosphonic acid and (2-AEP) and 2-amino-3-phosphonopropionic acid (2-AP3) are two biogenic phosphonates that ubiquitously exist in marine environment. Here, we report that Thalassiosira pseudonana, a dominant diatom species in the ocean, are able to recover growth from phosphorus starvation with individual supplement of 2-AEP and 2-AP3. Moreover, the cellular stoichiometric C:P and N:P ratios of cells grown under 2-AEP are in-between the P-depleted and DIP-replete groups. This study provided evidence that biogenic phosphonates can be adopted as alternative phosphorus sources to support diatom growth and might have a profound effect on the elemental stoichiometry in the oligotrophic ocean. Scientific significance statementPhosphorus (P) is a major limiting macronutrient for primary productivity in the ocean, whilst the biologically mediated cycling of different P forms is less understood compared to carbon and nitrogen. Accounting for 25% of the marine dissolved organic phosphorus pool, phosphonate bioavailability by eukaryotic phytoplankton still remains discrepancies. In line with our hypothesis, here we report that a cosmopolitan diatom Thalassiosira pseudonana can utilize biogenic phosphonates 2-aminoethylphosphonic acid and 2-amino-3-phosphonopropionic acid to support cell growth, with deviated cellular C:N:P in comparison to that of P-replete cells. Furthermore, cells grown under lower temperature exhibit physiological adaptation (K-selection strategy) with the benefit of 2-AEP supplied in the medium. We provide the evidence that utilization of biogenic phosphonate is ubiquitous in diatom and it might have profound effect on elemental stoichiometry ratios in the environment. Filling in this missing puzzle piece provides us with a fresh perspective to elucidate the role of phosphonates in marine phosphorus cycle and biological pump.

ecology↗

bioRxiv2023

Patient-derived organoids assist personalized treatment for a patient with multiple metastatic neuroendocrine carcinoma of the cervix

Neuroendocrine carcinoma of the cervix (NECC) is a rare subtype of cervical cancer with rapid metastasis. Lack of treatment regimens for metastatic NECC highlights the need for preclinical models and personalized treatment. Here, we established two patient-derived NECC organoid lines from lung metastases from the same patient, one of which was drug-free and the other exhibited tolerance to carboplatin plus etoposide (EP). The two organoids recapitulated the histopathology and genomic spectrum of the original tumors. Whole-genome sequencing revealed genomic structural variations (SVs), including HPV16 infection, NEUROD2, and ERBB2 amplification. The sensitivity phenotype of organoids to ERBB2 inhibitors coincides with the genomic features of ERBB2 amplification. In addition, the patient after relapse responded well to the paclitaxel plus carboplatin (TC) regimen as recommended by organoid drug screening, underscoring the utility of organoids as preclinical models. We first provide two NECC organoids of lung metastases with comprehensive molecular characteristics as valuable resources for this rare cancer and give a typical application for personalized treatment based on organoids.

cancer biology↗

bioRxiv2023

Human fallopian tube single-cell analysis reveals monocytic transcriptional and interaction changes in high-grade serous ovarian cancers

Tumorigenesis for most high-grade serous ovarian cancers (HGSCs) likely initiates from fallopian tube (FT) epithelia. While epithelial subtypes have been characterized using single-cell RNA- sequencing (scRNA-Seq), heterogeneity of other cellular compartments and their involvement in tumor progression are poorly defined. Integrated analysis of human FT scRNA-Seq data and other relevant tissues, including HGSC tumors, revealed greater transcriptional diversity of immune and stromal cells. We identify an unprecedented abundance of monocytes in human FT myeloid cells across two independent donor cohorts. The ratio of macrophages to monocytes are relatively similar between benign FTs, ovaries, and adjacent normal tissues, but is significantly greater in tumor. FT-defined monocyte and macrophage signatures, cell-cell communication, and gene set enrichment analysis identified monocyte- and macrophage-specific ligand-receptor interactions and functional pathways in tumors and adjacent normal tissue. Further reanalysis of tumor scRNA-Seq from HGSC patients suggested different monocyte and macrophage subsets associated with neoadjuvant chemotherapy treatment. Taken together, our work provides evidence that an altered FT immune composition could inform early detection markers in HGSCs.

cancer biology↗

bioRxiv2023

A framework for Frizzled-G protein coupling and implications to the Wnt-PCP signaling pathways

The ten Frizzled receptors (FZDs) are essential in Wnt signaling and play important roles in embryonic development and tumorigenesis. Among these, FZD6 is closely associated with lens development. Understanding FZD activation mechanism is key to unlock these emerging targets. Here we present the cryo-EM structures of FZD6 and FZD3 which are known to relay non-canonical Wnt-PCP (planar cell polarity) signaling pathways as well as FZD1 in their G protein-coupled (active) and G protein-free (inactive) states, respectively. Comparison of the three inactive/active pairs unveiled a shared activation framework among all ten FZDs. Mutagenesis along with imaging and functional analysis on the human lens epithelial tissues suggested potential crosstalk between G-protein binding and Wnt-PCP signaling pathways. Together, this study provides an integrated understanding of FZD structure and function, and lays the foundation for developing therapeutic modulators to activate or inhibit FZD signaling for a range of disorders including cancers and cataracts.

biochemistry↗