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Lu, M.

Publications and source records attributed to Lu, M..

At least 91 records · Page 5Linked to original sources

Targeted mutations in IFNα2 improve its antiviral activity against various viruses

During viral infections, type I interferons (IFN) are induced and play a key role in counteracting initial viral spread. Twelve different human IFN subtypes exist that bind the same receptor; however, they elicit unique host responses and display distinct potencies of antiviral activities. Our previous studies on HIV and HBV demonstrated that the clinically used IFN2 is not the most effective one among the IFN subtypes. By sequence modeling, we identified a region in helix B with mainly conserved residues at the outside facing IFNAR1, but variable residues at the inside facing the core of IFN, potentially representing a putative tunable anchor to tune pleiotropic IFN responses. Using site-directed mutagenesis various mutations were introduced into the IFN2b backbone targeting sites which are important for binding to IFNAR1 and IFNAR2, the putative tunable anchor, or outside these three regions. Selected mutations were based on sequence differences to high antiviral subtypes IFN6 and IFN14. Treatment assays against HBV and HIV identified several critical residues for the antiviral activity of IFN mainly in the IFNAR1 binding region. Combined mutations of the IFN2 IFNAR1/2 binding sites or the IFNAR1 binding region plus the putative tunable anchor by those of IFN14 further augmented activation of different downstream signaling cascades providing a molecular correlate for the enhanced antiviral activity. We describe here important functional residues within IFN subtype molecules, which enabled us to design novel and innovative drugs that may have the potential to be used in clinical trials against a variety of different viral infections. ImportanceThe potency of IFN to restrict viruses was already discovered in 1957. However, until today only IFN2 out of the 12 distinct human IFN subtypes has been therapeutically used against chronic viral infections. There is convincing evidence that other IFN subtypes are far more efficient than IFN2 against many viruses. In order to identify critical antiviral residues within the IFN subtype sequence, we designed hybrid molecules based on the IFN2 backbone with individual sequence motifs from the more potent subtypes IFN6 and IFN14. In different antiviral assays with HIV or HBV, residues binding to IFNAR1 as well as combinations of residues in the IFNAR1 binding region, the putative tunable anchor, and residues outside these regions were identified to be crucial for the antiviral activity of IFN. Thus, we designed artificial IFN molecules, based on the clinically approved IFN2 backbone, but with highly improved antiviral activity against several viruses.

immunology↗

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a novel optimization procedure to identify the optimal network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression. SignificanceA combined bioinformatics and systems-biology modeling approach is designed to model a transcriptional regulatory network for AML with IDH mutations. Network modeling identifies key regulators DNMT1 and E2F1, which is supported by patient survival data.

systems biology↗

Human receptive endometrial organoid for deciphering the implantation window

Human endometrial receptivity is a critical determinant of pregnancy success; however, in vivo studies of its features and regulation are particularly challenging due to ethical restriction. Recently, the development of human endometrial assembloids has provided a powerful model to investigate this intricate biological process. In this study, we established a specialized human window-of-implantation (WOI) endometrial assembloid system that mimics the in vivo receptive endometrium. It not only reproduces the structural attributes of pinopodes and cilia, but also molecular characteristics of mid-secretory endometrium. Furthermore, the WOI endometrial assembloid exhibits hormone responsiveness, an energy metabolism profile characterized by larger and functionally enhanced mitochondria, increased ciliary assembly and motility, and epithelial-mesenchymal transition (EMT), as well as promising potential for embryo implantation. As such, WOI assembloids hold great promise as a platform to unravel the intricate mechanisms governing the regulation of endometrial receptivity, maternal-fetal interactions, and associated pathologies, ultimately driving impactful advancements in the field.

cell biology↗

Structural basis for auto-inhibition and activation of a short prokaryotic Argonaute associated TIR-APAZ defense system

Short prokaryotic Ago accounts for most prokaryotic Argonaute (pAgo) and is involved in defending bacteria against invading nucleic acids. Short prokaryotic Ago associated with APAZ-TIR (SPARTA) has been shown to oligomerize and deplete NAD+ upon guide-mediated target DNA recognition. However, the molecular basis of SPARTA inhibition and activation remains unknown. Here we determine the cryo-EM structures of Crenotalea thermophila SPARTA in its inhibited, transient, as well as activated states. The SPARTA is auto-inhibited by its acidic tail, which occupies the guide-target binding channel. Guide mediated target binding expels this acidic tail and triggers substantial conformational changes to expose Ago-Ago dimerization interface. As a result, SPARTA assembles into an active TIR-APAZ4/short Ago4 octamer, where the four TIR domains are rearranged and packed to form NADase active sites. Together with biochemical evidence, our results provide a panoramic vision explaining SPARTA auto-inhibition and activation, and expand understanding of pAgo mediated bacterial defense systems.

biochemistry↗

Entropy-based decoy generation methods for accurate FDR estimation in large-scale metabolomics annotations.

Large-scale metabolomics research faces challenges in accurate metabolite annotation and false discovery rate (FDR) estimation. Recent progress in addressing these challenges has leveraged experience from proteomics and inspiration from other sciences. Although the target-decoy strategy has been applied to metabolomics, generating reliable decoy libraries is difficult due to the complexity of metabolites. Additionally, continuous bioinformatic efforts are necessary to increase the utilization of growing spectra resources while reducing false identifications. Here we introduce the concept of ion entropy and present two entropy-based decoy generation methods. The assessment of public spectral databases using ion entropy validated it as a good metric for ion information content in massive metabolomics data. The decoy generation method developed based on this concept outperformed current representative decoy strategies in metabolomics and achieved the best FDR estimation performance. We analyzed 47 public metabolomics datasets using the constructed workflow to provide instructive suggestions. Finally, we present MetaPhoenix, a tool equipped with a well-constructed FDR estimation workflow that facilitates the development of accurate FDR-controlled analysis in the metabolomics field.

bioinformatics↗

Endodermal BRD4 mediates epithelial-mesenchymal crosstalk during lung development

Lung morphogenesis relies on diverse cell intrinsic and extrinsic mechanisms to ensure proper cellular differentiation and compartmentalization. Using genetic mouse models, tissue explants, and transcriptomic analysis, we demonstrate that the epigenetic reader Bromodomain Containing Protein 4 (BRD4) is required for lung morphogenesis and perinatal survival. Endodermal BRD4 deletion impairs epithelial-mesenchymal crosstalk, leading to disrupted proximal-distal patterning and branching morphogenesis. These early defects result in dilated airways and the formation of cystic distal airway structures, containing both airway and alveolar features. Moreover, BRD4 deficient lungs exhibit abnormal airway and alveolar epithelial cell lineage allocation and differentiation. Restoration of SHH signaling partially rescues defects due to loss of BRD4, suggesting a role for BRD4 in regulating the SHH-FGF signaling axis. Together, these data identify the essential role of Brd4 in lung development to ensure proper intercellular crosstalk to enable proper lineage specification, identity, and maturation.

developmental biology↗

AnnoPRO: an innovative strategy for protein function annotation based on image-like protein representation and multimodal deep learning

Protein function annotation has been one of the longstanding issues, which is key for discovering drug targets and understanding physiological or pathological process. A variety of computational methods have therefore been constructed to facilitate the research developments in this particular direction. However, the annotation of protein function based on computational methods has been suffering from the serious "long-tail problem", and it remains extremely challenging for existing methods to improve the prediction accuracies for protein families in tail label levels. In this study, an innovative strategy, entitled AnnoPRO, for protein function annotation was thus constructed. First, a novel method enabling image-like protein representations was proposed. This method is unique in capturing the intrinsic correlations among protein features, which can greatly favor the application of the state-of-the-art deep learning methods popular in image classification. Second, a multimodal framework integrating multichannel convolutional neural network and long short-term memory neural network was constructed to realize a deep learning-based protein functional annotation. Since this framework was inspired by a reputable method used in image classification for dealing with its long-tail problem, our AnnoPRO was expected to significantly improve the annotation performance of the protein families in tail label level. Multiple case studies based on benchmark were also conducted, which confirmed the superior performance of AnnoPRO among the existing methods. All source codes and models of AnnoPRO were freely available to all users at https://github.com/idrblab/AnnoPRO, and would be essential complement to existing methods.

bioinformatics↗

Aird-ComboComp: A combinable compressor framework with a dynamic-decider for lossy mass spectrometry data compression

Mass spectrum (MS) data volumes increase with an improved ion acquisition ratio and a highly accurate mass spectrometer. However, the most widely used data format, mzML, does not take advantage of compression methods and improved read performances. Several compression algorithms have been proposed in recent years, and they consider a number of factors, including, numerical precision, metadata read strategies and the compression performance. Due to limited compression ratio, the high-throughput MS data format is still quite large. High bandwidth and memory requirements severely limit the applicability of MS data analysis in cloud and mobile computing. ComboComp is a comprehensive improvement to the Aird data format. Instead of using the general-purpose compressor directly, ComboComp uses two integer-purpose compressors and four general-purpose compressors, and obtains the best compression combination with a dynamic decider, achieving the most balanced compression ratio among all the numerous varieties of compressors. ComboComp supports a seamless extension of the new integer and generic compressors, making it an evolving compression framework. The improvement of compression rate and decoding speed greatly reduces the cost of data exchange and real-time decompression, and effectively reduces the hardware requirements of MS data analysis. Analyzing mass spectrum data on IoT devices can be useful in real-time quality control, decentralized analysis, collaborative auditing, and other scenarios. We tested ComboComp on 11 datasets generated by commonly used MS instruments. Compared with Aird-ZDPD, the compression size can be reduced by an average of 12.9%. The decompression speed is increased by an average of 27.1%. The average compression time is almost the same as that of ZDPD. The high compression rate and decoding speed make the Aird format effective for data analysis on small memory devices. This will enable MS data to be processed normally even on IoT devices in the future. We provide SDKs in three languages, Java, C# and Python, which offer optimized interfaces for the various acquisition modes. All the SDKs can be found on Github: https://github.com/CSi-Studio/Aird-SDK.

bioinformatics↗

An unsupervised learning approach uncovers divergent mesenchymal-like gene expression programs across human neuroblastoma tumors, preclinical models, and chemotherapy-exposed tumors.

Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. Here, we generated single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We developed an unsupervised machine learning approach ( automatic consensus nonnegative matrix factorization (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirmed a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly however, this weak-mesenchymal-like program was maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 hours, suggesting an uncharacterized therapy-escape mechanism. Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

cancer biology↗

How much storage precision can be lost: Guidance for near-lossless compression of untargeted metabolomics mass spectrometry data

The size of high-resolution mass spectrometry (HRMS) data has been increasing significantly. Several lossy compressors have been developed for higher compression rates. Currently, a comprehensive evaluation of what and how MS data (m/z and intensities) with precision losses would affect data processing (i.e., feature detection and compound identification) is absent. Here, we set an error threshold at 1% to assess the significance of the difference between two files in feature and compound detection results obtained from MZmine3. First, we examined that mzML files with both m/z and intensity encoded in 32-bit precision appear to be a preferred combination via msConvert, which has smaller file size and minor variation with other combinations of storage precision (<0.13%). We then identified that the absolute error of 10-4 for m/z had a feature detection error of 0.57% and compound detection error of 1.1%. For intensities, the relative error group of 2x10-2 had an error of 4.65% for features and 0.98% for compounds, compared with precision-lossless files. Taken together, we provided a reasonable scene-accuracy proposal, with a maximum absolute error of 10-4 for m/z and a maximum relative error of 2x10-2 for intensity. This guidance aimed to help researchers in improving lossy compression algorithms and minimizing the negative effects of precision losses on downstream data processing.

bioinformatics↗

Multi-omics of the gut microbial ecosystem in the immunotherapy resistance in microsatellite instability-high gastrointestinal cancer patients

Despite the encouraging efficacy of anti-PD-1/PD-L1 immunotherapy in microsatellite instability-high/deficient mismatch repair (MSI-H/dMMR) advanced gastrointestinal cancer, many patients exhibit primary or acquired resistance. Using multi-omics approaches, we interrogated gut microbiome, blood metabolome and cytokines/chemokines of MSI-H/dMMR advanced gastrointestinal cancer patients (N=77) and identified a number of microbes (e.g. Alistipes putredinis) and metabolites (e.g. arginine and SCFA) highly associated with primary resistance. Fecal microbiota transplantation of patients stool replicated the clinical responsiveness as well as certain molecular signatures. Based on the clinical microbiome data, we developed a predictive machine learning model for primary resistance and achieved accuracy at 0.83 on an external validation set. Furthermore, several microbes were pinpointed which gradually changed during the process of acquired resistance. In summary, our study demonstrated the essential role of gut microbiome in drug resistance, and this could be utilized as a preventative diagnosis tool as well as therapeutic targets in the future.

cancer biology↗

Comparative transcriptomics reveals divergence in pathogen response gene families amongst twenty forest tree species

Forest trees provide critical ecosystem services for humanity that are under threat due to ongoing global change. Measuring and characterizing genetic diversity is key to understanding adaptive potential and developing strategies to mitigate negative consequences arising from climate change. In the area of forest genetic diversity, genetic divergence caused by large-scale changes at the chromosomal level has been largely understudied. In this study, we used the RNA-seq data of twenty co-occurring forest trees species from genera including Acer, Alnus, Amelanchier, Betula, Cornus, Corylus, Dirca, Fraxinus, Ostrya, Populus, Prunus, Quercus, Ribes, Tilia, and Ulmus sampled from Upper Peninsula of Michigan. These data were used to infer the origin and maintenance of gene family variation, species divergence time, as well as gene family expansion and contraction. We identified a signal of common whole genome duplication events shared by core eudicots. We also found rapid evolution, namely fast expansion or fast contraction of gene families, in plant-pathogen interaction genes amongst the studied diploid species. Finally, the results lay the foundation for further research on the genetic diversity and adaptive capacity of forest trees, which will inform forest management and conservation policies.

plant biology↗

An intact amber-free HIV-1 system for in-virus protein bioorthogonal click labeling that delineates envelope conformational dynamics

The HIV-1 envelope (Env) glycoprotein is conformationally dynamic and mediates membrane fusion required for cell entry. Single-molecule fluorescence resonance energy transfer (smFRET) of Env using peptide tags has provided mechanistic insights into the dynamics of Env conformations. Nevertheless, using peptide tags risks potential effects on structural integrity. Here, we aim to establish minimally invasive smFRET systems of Env on the virus by combining genetic code expansion and bioorthogonal click chemistry. Amber stop-codon suppression allows site-specifically incorporating noncanonical/unnatural amino acids (ncAAs) at introduced amber sites into proteins. However, ncAA incorporation into Env (or other HIV-1 proteins) in the virus context has been challenging due to low copies of Env on virions and incomplete amber suppression in mammalian cells. Here, we developed an intact amber-free virus system that overcomes impediments from preexisting ambers in HIV-1. Using this system, we successfully incorporated dual ncAAs at amber-introduced sites into Env on intact virions. Dual-ncAA incorporated Env retained similar neutralization sensitivities to neutralizing antibodies as wildtype. smFRET of click-labeled Env on intact amber-free virions recapitulated conformational profiles of Env. The amber-free HIV-1 infectious system also permits in-virus protein bioorthogonal labeling, compatible with various advanced microscopic studies of virus entry, trafficking, and egress in living cells. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/530526v1_ufig1.gif" ALT="Figure 1000"> View larger version (30K): org.highwire.dtl.DTLVardef@10e7728org.highwire.dtl.DTLVardef@b1078aorg.highwire.dtl.DTLVardef@65f874org.highwire.dtl.DTLVardef@9ab0e8_HPS_FORMAT_FIGEXP M_FIG C_FIG Amber-free HIV-1 infectious systems actualized minimal invasive Env tagging for smFRET, versatile for in-virus bioorthogonal click labeling in advanced microscopic studies of virus-host interactions.

microbiology↗

InjectionDesign: Plate Design with Optimized Stratified Block Randomization for Modern LC/GC-MS-Based Sample Preparation

Plate Design is a necessary and time-consuming operation for GC/LC-MS based sample preparation. The implementation of the inter-batch balancing algorithm and the intra-batch randomization algorithm can have a significant impact on the final analysis results. For researchers without programming skills, a stable and efficient online service for plate design is necessary. However, most exist products do not currently have online services, and are not optimized for GC/LC-MS instruments with custom injection capabilities. Here we describe InjectionDesign, a free online plate design service focus on GC/LC-MS-based multi-omics experiment design. It offers the ability to separate the position design from the sequence design, making the output more compatible with the requirements of a modern mass spectrometer-based laboratory. In addition, it has implemented an optimized block randomization algorithm, which can be better applied to sample stratification with block randomization for unbalanced distribution. It is easy to use, with built-in support for common instrument models and quick export to worksheet. InjectionDesign is an open-source project based on Java. Researchers can get the source code of the project from Github: https://github.com/CSi-Studio/InjectionDesign. A free web service is also provided: http://www.injection.design. Demo Projecthttp://www.injection.design/#/project/detail?projectId=Test

bioinformatics↗

Broadly effective ACE2 decoy proteins protect mice from lethal SARS-CoV-2 infection

As SARS-CoV-2 variants have been causing increasingly serious drug resistance problem, development of broadly effective and hard-to-escape anti-SARS-CoV-2 agents is in urgent need. Here we describe further development and characterization of two SARS-CoV-2 receptor decoy proteins, ACE2-Ig-95 and ACE2-Ig-105/106. We found that both proteins had potent and robust in vitro neutralization activities against diverse SARS-CoV-2 variants including Omicron, with an average IC50 of up to 37 pM. In a stringent lethal SARS-CoV-2 infection mouse model, both proteins lowered lung viral load by up to [~]1000 fold, prevented the emergence of clinical signs in >75% animals, and increased animal survival rate from 0% (untreated) to >87.5% (treated). These results demonstrate that both proteins are good drug candidates for protecting animals from severe COVID-19. In a head-to-head comparison of these two proteins with five previously-described ACE2-Ig constructs, we found that two of these constructs, each carrying five surface mutations in the ACE2 region, had partial loss of neutralization potency against three SARS-CoV-2 variants. These data suggest that extensively mutating ACE2 residues near the RBD-binding interface should be avoided or performed with extra caution. Further, we found that both ACE2-Ig-95 and ACE2-Ig-105/106 could be produced to gram/liter level, demonstrating the developability of them as biologic drug candidates. Stress-condition stability test of them further suggests that more studies are required in the future to improve the stability of these proteins. These studies provide useful insight into critical factors for engineering and preclinical development of ACE2 decoys as broadly effective therapeutics against diverse ACE2-utilizing coronaviruses. Abstract ImportanceEngineering soluble ACE2 proteins that function as a receptor decoy to block SARS-CoV-2 infection is a very attractive approach to broadly effective and hard-to-escape anti-SARS-CoV-2 agents. This study here describes development of two antibody-like soluble ACE2 proteins that broadly block diverse SARS-CoV-2 variants including Omicron. In a stringent COVID-19 mouse model, both proteins successfully protected >87.5% animals from lethal SARS-CoV-2 infection. In addition, a head-to-head comparison of the two constructs developed in this study with five previously-described ACE2 decoy constructs were performed here. Two previously-described constructs with relatively more ACE2-surface mutations were found with less robust neutralization activities against diverse SARS-CoV-2 variants. Further, the developability of the two proteins as biologic drug candidates was also assessed here. This study provides two broadly anti-SARS-CoV-2 drug candidates and useful insight into critical factors for engineering and preclinical development of ACE2 decoy as broadly effective therapeutics against diverse ACE2-utilizing coronaviruses. TweetTwo antibody-like ACE2 decoy proteins could block diverse SARS-CoV-2 variants and prevent animals from severe COVID-19.

microbiology↗

A novel rotavirus reverse genetics platform supports flexible insertion of exogenous genes and enables rapid development of a high-throughput neutralization assay

Despite the success of rotavirus vaccines, rotaviruses are still one of the leading causes of diarrheal diseases, resulting in significant childhood morbidity and mortality especially in low- and middle-income countries. Reverse genetics system enables the manipulation of the rotavirus genome and opens the possibility of using rotavirus as an expression vector for heterologous proteins such as vaccine antigens and therapeutic payloads. Here, we identified three positions in rotavirus genome, the C terminus of NSP1, NSP3 and NSP5, for reporter gene insertion. By using rotavirus expressing GFP, we developed a high-throughput neutralization assay and revealed the pre-existing immunity against rotavirus in human and other animal species. Our work shows the plasticity of rotavirus genome and establishes a high-throughput assay for interrogating humoral immune responses, benefiting the design of next-generation rotavirus vaccines and the development of rotavirus-based expression platforms.

microbiology↗

How useful is genomic data for predicting maladaptation to future climate?

Methods using genomic information to forecast potential population maladaptation to climate change are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare the validation of maladaptation estimates derived from two methods - Gradient Forests (GFoffset) and the Risk Of Non-Adaptedness (RONA) - using exome capture pool-seq data from 35 to 39 populations across three conifer taxa: two Douglas-fir varieties and jack pine. We evaluate sensitivity of these algorithms to the source of input loci (markers selected from genotype-environment associations [GEA] or those selected at random). We validate these methods against two-year and 52-year growth and mortality measured in independent transplant experiments. Overall, we find that both methods often better predict transplant performance than climatic or geographic distances. We also find that GFoffset and RONA models are surprisingly not improved using GEA candidates. Even with promising validation results, variation in model projections to future climates makes it difficult to identify the most maladapted populations using either method. Our work advances understanding of the sensitivity and applicability of these approaches, and we discuss recommendations for their future use.

evolutionary biology↗

B3GALT6 Promotes Dormant Breast Cancer Cell Survival and Recurrence by Enabling Heparan Sulfate-Mediated FGF Signaling

Breast cancer mortality results primarily from incurable recurrent tumors seeded by dormant, therapy-refractory residual tumor cells (RTCs). Understanding the mechanisms enabling dormant RTC survival is therefore essential for improving patient outcomes. We derived a dormancy-associated RTC signature that mirrors the transcriptional response to neoadjuvant chemotherapy in patients and is enriched for extracellular matrix-related pathways. In vivo CRISPR-Cas9 screening of dormancy-associated candidate genes identified the galactosyltransferase B3GALT6 as a functional regulator of RTC fitness. B3GALT6 covalently attaches glycosaminoglycans (GAGs) to proteins to generate proteoglycans and its germline loss-of-function causes skeletal dysplasias. We determined that B3GALT6-mediated biosynthesis of the GAG heparan sulfate predicts poor patient outcomes, promotes tumor recurrence by enhancing dormant RTC survival in multiple contexts, and does so via a B3GALT6-heparan sulfate/HS6ST1-heparan 6-O-sulfation/FGF1-FGFR2 signaling axis. These findings identify a role for B3GALT6 in cancer and suggest targeting FGF signaling as a novel approach to preventing recurrence by eradicating dormant RTCs.

cancer biology↗