Biology subjects

Liu, Z.

Publications and source records attributed to Liu, Z..

At least 127 records · Page 7Linked to original sources

bioRxiv2026

Tartrazine clears live cells while preserving viability at high refractive indices and osmolality

Tissue-clearing techniques have transformed optical imaging of fixed specimens, yet their application to living systems remains limited by toxicity and removal of key tissue components. We recently demonstrated that absorbing molecules such as tartrazine can reversibly render live mouse skin transparent. Subsequently, it was reported that isotonic protein solutions can achieve ex vivo and in vivo cellular clearing. However, discrepancies remain regarding the optimal refractive index (RI) for live-cell clearing and the impact of elevated osmolality on cell viability. Here, using cultured mammalian cells, we systematically examine the dependence of optical contrast on medium RI and the effects of hyperosmolality. We find that, contrary to the recent report of an optimal RI of 1.36[~]1.37 for suspended cells, densely-packed adherent cells exhibit a monotonic decrease in phase contrast up to an RI of 1.41 with tartrazine. Moreover, even under highly hyperosmotic conditions ([~]1200 mOsm/kg), cultured cells exhibit minimal deformation and negligible loss of viability for up to 30 min in the clearing solution. These results demonstrate that tartrazine enables effective live-cell clearing at RI up to 1.41 while preserving viability under elevated osmolality, and motivate future studies to define optimal conditions for in vivo optical clearing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=44 SRC="FIGDIR/small/717314v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1ec9fbforg.highwire.dtl.DTLVardef@1ebe9e6org.highwire.dtl.DTLVardef@1492c3corg.highwire.dtl.DTLVardef@f75559_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2026

Designer indicators for two-photon recording of subthreshold voltage dynamics

Subthreshold voltage dynamics are critical for neuronal information integration, yet they remain understudied in vivo due to the limitations of current tools. While genetically encoded voltage indicators (GEVIs) offer a promising alternative, their application for deep-tissue recording using two-photon (2P) microscopy--a preferred method for deep-tissue recording--has been hindered by insufficient sensitivity for detecting millivolt-scale subthreshold signals. Here, we refined our multiparametric two-photon high-throughput screening platform to develop two novel GEVIs, JEDI3sub and JEDI3hyp, tailored explicitly for subthreshold voltage detection. Through fast 2P optical recording in awake, behaving mice, we demonstrated the superior sensitivity of JEDI3 indicators compared to JEDI-2P. We also showed that JEDI3sub can track population-level subthreshold optical tuning, while JEDI3hyp reliably captured subthreshold dynamics associated with sharp-wave ripple oscillations in hippocampal PV interneurons. Finally, JEDI3hyp facilitated extended imaging of brain-state-dependent, millivolt-scale subthreshold voltage changes across deep-layer somas, fine dendritic structures, and diverse cell types. By addressing the critical gap in 2P optical recording of subthreshold voltage dynamics, JEDI3 indicators open new avenues for studying neural information processing and its alterations in health and disease.

neuroscience↗

bioRxiv2026

Protenix-v2: Broadening the Reach of Structure Prediction and Biomolecular Design

Advances in biomolecular modeling have broadened the range of problems addressable by structure prediction and design models. Here, we present results from Protenix-v2, a system spanning high-accuracy structure prediction and biomolecular design. On the structure prediction side, Protenix-v2 achieves antibody-antigen success rates with up to 13-point gains over Protenix-v1, while 5-seed performance surpasses previous 1000-seed results. On the design side, Protenix-v2 demonstrates a 100% target-level success rate in novelty-controlled VHH-Fc campaigns, reaching hit rates up to 48%. Crucially, the model enables hit discovery on difficult GPCR targets with hit rates of 16%-88% (VHH-Fc) and up to 50% (mAb) under 16-30 testing budgets per target. Resulting hits show high developability and diversity. Beyond antibody tasks, we report improved ligand-related plausibility and successful cross-variant SARS-CoV-2 spike RBD mini-binder design. These results establish Protenix-v2 as a robust and powerful model for accelerated drug discovery.

molecular biology↗

bioRxiv2026

PoolParty: streamlined design of DNA sequence libraries in Python

Background: Computationally designed DNA sequence libraries are essential components of massively parallel reporter assays (MPRAs), deep mutational scanning (DMS) experiments, and other multiplex assays of variant effect (MAVEs). They are also increasingly used in silico to analyze genomic AI models. Designing these libraries, however, remains tedious and error-prone due to the scarcity of purpose-built software. Results: Here we describe PoolParty, a Python package that streamlines the design of complex oligo pools using a simple but flexible API. In PoolParty, each library is represented by a computational graph that can be specified in just a few lines of code. Over 50 built-in operations cover nucleotide- and codon-level mutagenesis, motif insertion, barcode generation, and more. PoolParty automatically generates informative names for each sequence and provides "design cards" detailing how each sequence was generated. Visualization methods let users quickly audit library content and inspect the underlying graph. PoolParty thus transforms oligo pool design from a tedious task requiring custom functions and scripts into a structured, transparent, and reproducible process. Conclusions: PoolParty streamlines the design of DMS, MPRA, and other multiplex assay libraries, and the design cards it provides can help researchers systematically probe and interpret genomic AI models. PoolParty can also be extended to support new assays and analysis strategies as they emerge.

bioinformatics↗

bioRxiv2026

Genetic interaction between Adgrg6 and Sox9 reveals a feedforward mechanism for postnatal spinal stability

Adolescent idiopathic scoliosis (AIS) is the most common spinal disorder in children and is best understood as a complex polygenic condition. Genome-wide association studies (GWAS) have identified several AIS risk loci, including regions near ADGRG6 and SOX9, yet the functional mechanisms underlying AIS heritability remain poorly defined. Here, we use spatial transcriptomics to characterize altered gene expression in the spine of a conditional Adgrg6 mutant mouse model of AIS (Adgrg6-cKO), revealing reduced expression of Sox9 and several components of extracellular matrix organization in the intervertebral disc. We further show that SOX9 occupies regions of open chromatin within the Adgrg6 locus in cells isolated from the mouse intervertebral disc. Finally, we demonstrate a strong genetic interaction between Adgrg6-cKO and a hypomorphic Sox9 allele that increases both the penetrance and severity of AIS-like pathology in mice. Collectively, these findings support a self-reinforcing feedforward regulatory circuit, where Adgrg6 and Sox9 are co-regulated to maintain extracellular matrix gene expression in the annulus fibrosus and paraspinal tissues. These findings provide mechanistic insight into the functional significance of AIS-associated GWAS loci near ADGRG6 and SOX9 and establish combined Adgrg6-Sox9 insufficiency as a tractable model of polygenic scoliosis susceptibility. O_LIAdgrg6 maintains Sox9 and extracellular matrix gene expression in the intervertebral disc C_LIO_LISOX9 directly occupies open chromatin at the Adgrg6 locus in SOX9 skeletal cells C_LIO_LIAdgrg6 and Sox9 genetically interact to increase scoliosis incidence and severity C_LIO_LICombined loss of Adgrg6 and Sox9 drives ectopic cartilage formation and reduced vertebral bone mineral density in the thoracic spine C_LI

developmental biology↗

bioRxiv2026

Genetic background shapes AI-predicted variant effects

Predicting the consequences of genetic variants remains a major goal in biomedicine. Conventional approaches typically assess single-nucleotide variants in the context of a single reference genome, without accounting for genetic diversity that can modulate variant effects. Here we introduce the personalized variant effect predictor (pVEP) framework, which quantifies how genetic background across thousands of human genomes from globally diverse populations shapes computational predictions of clinical variant effects. Across deep learning models spanning protein structure, splicing, and noncoding regulation, pVEP reveals that many clinical variants exhibit heterogeneous predicted effects across haplotypes, with the same variant predicted to be pathogenic in some genetic backgrounds and benign in others. We find support for underlying molecular mechanisms, including shifts in predicted protein contacts and changes in splice-site recognition. Overall, personalized genomic context emerges as a systematically underappreciated variable in variant annotation and clinical interpretation, with particular implications for genetically diverse populations.

genomics↗

bioRxiv2026

Traditional Physical Practice Participation and Vision-Related Quality of Life in Adolescents: The Serial Mediating Roles of Exercise Self-Efficacy and Visual Function Anomalies

PurposeThis study examined the association between traditional physical practice participation and vision-related quality of life among junior secondary school students and tested the mediating roles of exercise self-efficacy and visual function anomalies within a serial mediation framework. MethodsA four-wave time-lagged survey was conducted among 1,579 students in Grades 7-9 from schools implementing traditional physical practice activities. Variables were assessed at two-week intervals. Mediation effects were tested using the bias-corrected percentile bootstrap method with 5,000 resamples. ResultsThe total effect of traditional physical practice participation on vision-related quality of life was significant ({beta} = 0.591, p < .001). After including the mediators, the direct effect remained significant ({beta} = 0.404, 95% CI [0.348, 0.457]), accounting for 68.36% of the total effect. The total indirect effect was significant ({beta} = 0.187, 95% CI [0.160, 0.218]), representing 31.64% of the total effect. The indirect effect via exercise self-efficacy was significant ({beta} = 0.088, 95% CI [0.068, 0.112], 14.89%), as was the indirect effect via visual function anomalies ({beta} = 0.065, 95% CI [0.048, 0.086], 11.00%). The serial mediation pathway through exercise self-efficacy and visual function anomalies was also significant ({beta} = 0.034, 95% CI [0.025, 0.045], 5.75%). All confidence intervals excluded zero, supporting partial mediation. ConclusionTraditional physical practice participation was associated with vision-related quality of life both directly and indirectly through exercise self-efficacy and visual function anomalies, including a significant serial mediation pathway. The findings highlight the combined psychological and functional mechanisms underlying adolescents vision-related quality of life.

scientific communication and education↗

bioRxiv2026

Integrative Multi-cohort Transcriptomics and Network Pharmacology Analysis Reveals Key Network Nodes and Potential Drug Clues in PCOS Granulosa Cells

BackgroundPolycystic ovary syndrome (PCOS) is a prevalent endocrine disorder with complex pathophysiology and limited therapeutic options. Identifying key molecular drivers and potential drug candidates is critical for improving clinical outcomes. MethodsWe integrated multi-cohort transcriptomics (GSE155489, GSE138518, GSE226146) with weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, and drug repurposing. Differential expression analysis identified 1,039 DEGs, and WGCNA identified 10 PCOS-associated modules. Intersection of DEGs with module genes yielded 498 core candidate genes, which were subjected to functional enrichment, PPI network analysis, and connectivity map-based drug repurposing (CLUE/LINCS). Candidate drugs were further evaluated by molecular docking and ADMET prediction using a triple intersection strategy (hub genes, high differential expression, drug-target evidence). ResultsFunctional enrichment revealed significant enrichment in cell adhesion and TGF-beta signaling. PPI network analysis identified CD44 as the top hub gene (degree=42). Drug repurposing identified 106 candidate drugs, including troglitazone and enzalutamide. Using the triple intersection strategy, five genes (ID2, NR4A1, GJA5, ID1, MYH11) were prioritized for molecular docking. GJA5 showed strong predicted binding affinity with flufenamic acid (-7.88 kcal/mol), and cytosporone B exhibited favorable druglikeness (0 Lipinski violations). ConclusionThis study systematically characterizes PCOS-associated gene networks and provides a prioritized set of candidate targets and drugs through a purely computational framework. CD44 emerges as a key network node with potential relevance in PCOS pathophysiology. These findings offer testable hypotheses for future mechanistic studies and drug discovery efforts in PCOS.

systems biology↗

bioRxiv2026

Metabolic Drivers of Disease Activity and Complications in Crohns Disease: A Retrospective Cross-Sectional Study

BackgroundThis study aims to examine the independent relationships between individual components of metabolic syndrome (MetS) and two key clinical outcomes in patients with Crohns disease (CD): disease activity, as quantified by the Crohns Disease Activity Index (CDAI), and the occurrence of complications. MethodsThis retrospective cross-sectional study included 376 adults with newly diagnosed Crohns disease. Multiple linear regression was used to examine associations between metabolic parameters and CDAI scores, while multivariate logistic regression assessed links to complications. Analyses were also based on clinical CDAI cut-offs. Predictive nomograms were developed and internally validated via bootstrap resampling. ResultsMultiple linear regression indicated that higher CDAI scores were independently associated with lower BMI (B = -5.866, P < 0.001), lower HDL-C levels (B = -81.770, P < 0.001), higher triglycerides (B = 15.618, P = 0.001), and lower ESR (B = -0.375, P = 0.03). Multivariate logistic regression established low HDL-C (OR = 0.042, P < 0.001), low BMI (OR = 0.915, P = 0.034), and high triglycerides (OR = 1.792, P = 0.007) as significant independent risk factors for complications. The developed nomograms demonstrated strong predictive performance, with an adjusted R2 of 0.207 for the CDAI model and an AUC of 0.765 for the complication model. For both predictive tasks, the model incorporating separate TG and HDL-C measurements significantly outperformed the TG/HDL-C ratio model. ConclusionMetabolic disturbances demonstrate a significant association with increased disease severity and a higher risk of complication development in Crohns disease. Core tipO_LIDual-outcome study reveals HDL-C and TG differentially link to CD inflammation and complications, pointing to distinct mechanisms. C_LIO_LILow HDL-C is the strongest independent predictor for CD complications, underscoring its protective role beyond cholesterol transport. C_LIO_LIIndividual TG and HDL-C metrics outperform their ratio in prediction, challenging its use and suggesting independent pathways in CD. C_LIO_LILow BMI independently associates with both adverse outcomes, refining the "obesity paradox" and highlighting malnutritions key role. C_LIO_LIA practical, validated nomogram (AUC=0.765) integrates HDL-C, TG, and BMI to stratify complication risk, aiding clinical decision-making. C_LI

immunology↗

bioRxiv2026

A Conserved Metabolic Oxidative Axis Underlies Immune Cell Cryo-vulnerability

Immunotherapy has emerged as a transformative approach for treating cancer and other diseases, yet its widespread deployment requires effective cryopreservation strategies to enable scalable global distribution. However, many immune cell types remain acutely vulnerable to freeze-thaw stress, and the underlying mechanisms of this cryo-vulnerability are not well defined. In this study, we combined metabolic flux analysis, ROS quantification, lipidomics, and preclinical xenograft models to investigate how metabolic state influences cryopreservation outcomes. We found that immune cell activation induces a metabolic shift characterized by elevated glucose utilization and excessive ROS production, leading to profound post-thaw loss of viability and function, as demonstrated by a [~]25% survival rate in natural killer (NK) cells. Targeted pretreatments--including inhibitors of glucose metabolism, antioxidants, and suppression of lipid peroxidation--restored post-thaw recovery to nearly 90% while preserving effector activity and antitumor efficacy. Similar protective effects were observed across {beta} T cells, {gamma}{delta} T cells, and macrophages, defining a conserved metabolic-oxidative pathway of cryo-vulnerability and offering applicable strategies to enhance immune cell preservation.

immunology↗

bioRxiv2026

Wnt/β-catenin signaling restrains developmental beiging and imprints long-term energy expenditure

Beige adipocytes that emerge during the peri-weaning period support sympathetic nervous system (SNS)-independent thermogenesis, yet the mechanisms governing this spontaneous beiging remain unclear. Here, by integrating transcriptomic profiling with adipocyte-targeted Ctnnb1 deletion in mice, we identify the canonical Wnt signaling as an endogenous brake on developmental beige thermogenesis. Peri-weaning inguinal fat from adipocyte Ctnnb1 knockout mice exhibits enhanced beige adipocyte biogenesis, with increased thermogenesis-related gene expression and mitochondrial oxidative capacity, which programs durable activation of adaptive thermogenesis and augmented whole-body energy expenditure into adulthood. Mechanistically, suppression of Wnt/{beta}-catenin signaling induces a non-canonical Wnt5a-Ca2-AMPK axis that promotes triglyceride lipolysis and subsequent PPAR-driven fatty acid oxidation, thereby fueling mitochondrial respiration. Genetic or pharmacological disruption of this axis blunts thermogenic responses induced by {beta}-catenin inhibition in both murine and human subcutaneous adipocytes, indicating that Wnt5a-Ca2-AMPK axis is required for the cell-autonomous activation of beige fat. These results reveal Wnt/{beta}-catenin signaling as a developmental constraint on beige adipocyte formation and suggest an SNS-independent route to sustainably raise energy expenditure and improve metabolic health.

physiology↗

bioRxiv2026

Reference genomes of four miniature and non-miniature cypriniform fishes inhabiting acidic peat-swamp forest blackwaters of Southeast Asia

The acidic blackwaters of Southeast Asias peat-swamp forests represent some of the most extreme freshwater environments on Earth. Despite their very low pH values, limited nutrients, and hypoxic conditions, these blackwater habitats harbor a remarkable diversity of freshwater fishes, including multiple lineages that have independently adapted to these extreme conditions and, in some cases, exhibiting extreme body miniaturization. These replicate evolutionary lineages therefore provide a powerful comparative framework to investigate adaptation to extreme environments and the genomic basis of miniaturization. Here, we present high-quality, annotated reference genomes for four cypriniform species endemic to these peat-swamp forest ecosystems: Paedocypris sp., Sundadanio atomus, Boraras brigittae, and Rasbora kalochroma. The first two are progenetic miniatures, including Paedocypris, comprising the smallest known fish, while B. brigittae represents a proportioned dwarf and R. kalochroma a non-miniature taxon. Genome sizes ranged from 401-1,290 Mb and heterozygosity from 0.34-1.7%. All genome assemblies achieved pseudo-chromosome-level contiguity, high k-mer completeness (>99%), and high BUSCO completeness (94.5-98.9%). Repeat analyses revealed lineage-specific differences in transposable element landscapes and abundances, while gene annotation identified notable intron length reduction in progenetic miniatures.

genomics↗

bioRxiv2026

Microfluidic low-input profiling reveals lncRNA roles in disease

Long noncoding RNAs (lncRNAs) regulate gene expression through binding to DNA, various RNAs, and proteins, playing potentially important but poorly understood roles in diseases. Existing approaches for profiling lncRNA-chromatin interactions at the genome scale require large quantities of input material (e.g., 100 million cells per assay). Applying these technologies to tissue samples has been challenging especially when examination of a specific cell type is desired. Here we demonstrate a low-input microfluidic technology based on Chromatin Isolation by RNA Purification (ChIRP) for mapping lncRNA-chromatin interactions using as few as 50,000 cells. We validate our technology, muChIRP-seq, on two lncRNAs of different sizes (GOMAFU and TERC) in human and mouse cell lines and in brain tissues. Furthermore, we profile neuronal nuclei from postmortem human brain tissues of schizophrenia and control subjects. Our profiling data reveal distinct roles and levels of involvement for the two lncRNAs in contribution to schizophrenia. Our multimodal integrative analysis suggests coordination between lncRNA binding and other epigenomic mechanisms such as histone modifications in schizophrenia pathogenesis. Our technology enables lncRNA studies in tissue samples and in a cell-type-specific fashion, unlocking new opportunities to screen and understand lncRNA involvement in diseases.

genomics↗

bioRxiv2026

RNAGAN: Train One and Get Four, Multipurpose Human RNA-Seq Analysis Tool with Enhanced Interpretability and Small Data Size Capability

The advent of artificial intelligence (AI) has brought revolutionary tools for biomedical transcriptomic (RNA-level) research. However, there are persistent constraints including limited interpretations with biomedical concepts such as functional pathways, small sample sizes and substantial time and computing power requirements for AI training. To overcome these limitations, we developed RNAGAN (https://github.com/ZhaozhengHou-HKU/RNAGAN-1.0.git), an AI tool with a generative adversarial network (GAN) structure with the objective of enhancing transcriptomic analysis. The network was established based on public human datasets comprising 4.6 million single cells from multiple organs and 5,900 sequenced samples of various cancer types with normal references. A specialized pathway neural layer was embedded to extract activities of predefined pathways from the Human Molecular Signatures Database (MSigDB), or newly learned pathways from single-cell data. The structure of RNAGAN (generator and discriminator) enables four applications after one shared training procedure: 1. single-cell and bulk-level patient stratification or differential diagnosis; 2. analysis of the gene and pathway markers in a selected disease; 3. pseudo data generation when sample size is limited for downstream analysis; 4. vectorization with gene and pathway-level features learned from multiple data sets. RNGAN contributes to the efficient utilization of limited data for transcriptomic studies.

bioinformatics↗

bioRxiv2026

Quinone reductase 2 reads H3 serotonylation to support neuronal maturation

Histone H3 Gln5 serotonylation (H3Q5ser) is a recently described posttranslational modification1 that plays important roles in guiding transcriptional permissiveness in brain and peripheral systems2-5. H3Q5ser has been implicated in diverse physiological and pathological processes ranging from neural differentiation1 to sensory processing6, circadian rhythmicity7, stress responsivity8, placental gene regulation9, and tumorigenesis10-19. Since H3Q5ser can occur in combination with H3 Lys4 trimethylation (H3K4me3), most mechanistic studies to date have focused on H3Q5sers roles in modulating H3K4me3 reader interactions, where it has been shown to potentiate TAF3/TFIID binding to H3K4me31,20,21 and inhibit the recruitment of K4me3 demethylases21; however, whether H3 serotonylation functions as an autonomous chromatin signaling mark through dedicated reader proteins has remained unknown. Here, using a combination of proteomic-, structural-, molecular-, epigenomic-, and cellular-based approaches, we demonstrate that the Quinone reductase 2 (QR2) enzyme reads H3Q5ser independently of H3K4me3. CRISPR-Cas9-mediated disruption of H3 serotonylation or QR2s binding to the mark in human induced pluripotent stem cell-derived neurons impairs the establishment of neuronal transcriptional programs, alters synaptic connectivity, and disrupts electrophysiological maturation. These findings thus uncover an H3 serotonylation-dependent chromatin signaling axis that is essential for human neurodevelopment.

molecular biology↗

bioRxiv2026

A biobank-scale method for learning modulators of gene-environment interaction underlying human complex traits from multiple environmental exposures

It is increasingly recognized that genetic effects on complex traits and diseases are shaped by environmental context. Biobanks that measure diverse environmental exposures alongside genotypes and phenotypes at scale enable systematic study of gene-environment (GxE) interactions. Existing approaches, however, are limited in their ability to accurately model polygenic GxE involving many exposures across genome-wide genetic variants. It is unclear which exposure combinations are relevant for a given trait while distinguishing true interactions from environment-dependent heteroskedastic noise. To address these challenges, we develop Efficient multi-eNvironmental Gene-environment Interaction iNference Estimator (ENGINE), a supervised variance-component framework that learns an embedding that combines multiple environmental exposures while jointly estimating additive, GxE, and heteroskedastic noise components. To enable biobank-scale inference, ENGINE makes a single pass over the genotype matrix to cache genotype-dependent summaries, then assembles normal-equation components and gradients at each iteration. In simulations, ENGINE controls type I error rates, achieves high power, and accurately recovers the environmental embedding while remaining efficient at biobank-scale. Applied to five complex traits paired with lifestyle exposures in N = 291,273 unrelated white British individuals and M = 454,207 common SNPs (MAF> 0.01) from the UK Biobank, ENGINE recovered GxE variance that was on average 1.4-fold larger than that captured by a single exposure and 5.5-fold larger than that captured by the first principal component of the exposures.

genetics↗

bioRxiv2026

Transcriptomic data and biomedical literature synergize in finding pharmacologic gene regulators

Most disorders caused by a deficiency or excess of one gene product lack targeted therapies. Since these disorders can be modeled with a gene overexpression, knockout, or knockdown, drugs that oppose the transcriptomic effects of such perturbations may be promising therapeutic candidates. RNA-Sequencing (RNA-Seq) studies can fuel this drug-prioritization, but their labels, written in plain language, must be annotated manually. Hence, we introduce Signature-based Networks from Automatically Curated Knockout, Knockdown, and Small-molecule Studies (SNACKKSS), which automatically curates gene-disruption and drug-treatment studies from the Gene Expression Omnibus and, in partnership with uniformly computed read count datasets, feeds the labels and RNA-Seq data directly into regulatory relationship predictions. Through cross-validation, we show that SNACKKSS predictions (specifically, from a variation called "SA4") make a unique contribution to finding protein-inhibiting compounds, even alongside existing predictors. We demonstrate the benefit of aggregating multiple predictive tools, and provide this powerful ensemble alongside SNACKKSS. Importantly, we advise researchers to test complex machine learning models on multiple devices, since the hardware architecture can affect their output. Nonetheless, the downstream predictive ability was striking, and our findings support the value of integrating automatically curated RNA-Seq signatures with literature- and co-expression-based predictors for drug-repurposing prioritization.

genomics↗

bioRxiv2026

TMEM106B mediates ACE2-independent replication of the SARS-CoV-2 S-E484D variant in airway-derived cell models

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to cause significant respiratory disease, particularly in vulnerable populations. Although ACE2 is the primary receptor for viral entry, previous studies have identified a naturally occurring, ACE2-independent entry pathway in certain airway-derived cell lines. Utilization of this pathway depends on surface heparan sulfates and requires the E484D substitution within the receptor-binding domain of the viral Spike (S) protein. In this study, we expand the panel of airway-derived cell lines that support ACE2-independent, S-E484D-dependent replication and identify the host lysosomal transmembrane protein TMEM106B as a critical host factor for infection. Knockout of TMEM106B completely abolishes infection by SARS-CoV-2 SE484D in NCI-H522 and NCI-H661 cells. Moreover, ectopic expression of the luminal C-terminal domain (CTD) of TMEM106B-either alone or redirected to the plasma membrane - is sufficient to enable viral entry and infection in otherwise non-permissive cells. We further show that Fc-TMEM106B-CTD decoy protein blocks infection by SARS-CoV-2 SE484D, supporting a direct interaction between the S-E484D protein and TMEM106B-CTD. Finally, passaging experiments with a chimeric VSV-SARS-CoV-2 SE484D identify additional mutations within the heptad repeat 1 that enhance TMEM106B utilization and viral spread in the ACE2-independent cell models. Together, these findings demonstrate that TMEM106B is a key mediator of a naturally occurring ACE2-independent pathway in multiple airway-derived cells lines and suggest that variation in the Spike protein can expand receptor usage by SARS-CoV-2. IMPORTANCESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to acquire mutations in the viral spike (S) protein as it circulates in humans. How these mutations influence the cell and tissue tropism of SARS-CoV-2 remains poorly understood. While ACE2 is the canonical host cell receptor for SARS-CoV-2, previous studies revealed the presence of a naturally occurring ACE2-independent entry pathway in multiple airway-derived cell lines that can be utilized only by a SARS-CoV-2 variant bearing the E484D substitution within the viral S protein. Here we expand the airway-derived cell line models that support ACE2-independent and S-E484D-dependent entry and show that the host lysosomal transmembrane protein, TMEM106B, is an essential viral entry factor. Although the E484D substitution is currently rare in human isolates, our findings raise the possibility that small mutations in SARS-CoV-2 S can allow the utilization of alternative entry pathways that broadens its cell and tissue tropism.

microbiology↗