Biology subjects

Zhong, C.

Publications and source records attributed to Zhong, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Disease-associated NK-cell state remodeling reveals transcriptional profiles predictive of clinical phenotypes

Natural killer (NK) cells adapt their cellular states to diverse tissue and inflammatory environments, yet how this plasticity manifests across pathological contexts and relates to patient-level disease phenotypes remains poorly defined. Here we established the Human NK-cell Atlas (HNKA) from over 1 million NK cells, including samples from 1,632 donors spanning 50 diseases. Within a shared NK-cell state architecture, analyses of state abundance and matched-reference exceedance distinguished disease-biased redistribution from transcriptional displacement. Fine-grained analyses localized disease-associated remodeling to specific NK-cell states, including CD56dim NK ISG15 in infection and autoimmunity and CD56bright NK ITM2C in tumors. These patterns were accompanied by recurrent interferon responses and state-specific regulatory activity. Cytokine perturbations recapitulated key transcriptional features of disease-associated NK-cell states. We developed NKceiver, a model based on state-specific NK-cell expression profiles for patient-level prediction. It distinguished 18 diseases and predicted COVID-19 severity and response to immune checkpoint inhibitors, with predictive performance retained in unseen patients and studies. Collectively, our results reveal that disease-associated remodeling within the diverse NK-cell state repertoire resolved by HNKA is biologically interpretable and clinically informative, supporting disease discrimination and prediction of clinical phenotypes.

immunology↗

bioRxiv2026

RegDesign-Bench: Benchmarking computational methods for cell-type-specific regulatory DNA design

The de novo design of regulatory DNA sequences that drive cell-type-specific gene expression could enable precise gene control in synthetic biology, cell engineering and gene therapy. Various computational methods have been developed for this task, yet systematic comparisons that guide method selection for different design objectives remain limited. Here, we introduce RegDesign-Bench, a standardized framework for evaluating cell-type-specific cis-regulatory element design through a comprehensive set of computational analyses. Among the methods tested, strong predicted activity was not consistently accompanied by cell-type-selective accessibility or prominent cell-type-associated motif signals. Similarity to reference sequence distributions also did not ensure high predicted target-cell activity or specificity. These findings demonstrate that favourable performance on individual measurements does not necessarily extend across regulatory properties and library characteristics, revealing application-dependent strengths and limitations of current design methods. Overall, RegDesign-Bench offers a general-purpose computational framework for comparing and selecting methods to design regulatory DNA that drives cell-type-specific gene expression.

genomics↗

bioRxiv2026

Automatic bioinformatic software named entity recognition from literature

Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hybrid named entity recognition framework designed to automatically identify bioinformatics software and database (SW/DB) names from biomedical texts. SNAIL integrates complementary lexical and semantic modeling strategies. The lexical component captures orthographic patterns and contextual cues characteristic of SW/DB names, while the semantic component leverages contextual embeddings generated by transformer-based language models such as SciBERT, combined with an explicit token-masking strategy to enhance entity-focused representations. A large training corpus was constructed automatically through a hybrid pipeline that integrates citation-hinted extraction with large language model-assisted distillation. Evaluation on two independent benchmark datasets and real-world research articles demonstrates that SNAIL substantially outperforms existing approaches, including domain-specific methods such as bioNerDS2 and general-purpose large language models such as ChatGPT, Gemini, Grok and Claude. Applying SNAIL to large-scale literature analysis further reveals distinct journal-level preferences across bioinformatics subfields. These results demonstrate that SNAIL provides an accurate and scalable solution for identifying bioinformatics resources in scientific texts and enables systematic meta-analysis of tool usage and research trends.

bioinformatics↗

bioRxiv2026

Reconstructing sequence-grammar trajectories enables interpretable and tunable cis- regulatory element design

Designing synthetic cis-regulatory elements (CREs) with cell-type-specific activity remains challenging, and optimization is usually treated as a black box, obscuring how regulatory grammar emerges and why design trajectories fail. Here, we present GO-CRE (Guided Optimization of Cis-Regulatory Elements), which combines efficient sequence generation, predictor-guided reinforcement learning, and trajectory-level interpretation. GO-CRE reconstructs iterative sequence changes in a shared sequence-grammar landscape and identifies coordinated update programs corresponding to search, commitment, and optimization. Productive trajectories in HepG2 and K562 progressively acquired cell-type-associated grammar, whereas SK-N-SH trajectories remained confined to local basins. In HepG2, trajectory analysis revealed a low-complexity polyG trap; introducing a polyG penalty redirected optimization toward HNF/FOXA-associated features. Final designs retained sequence diversity while converging on cell-type-associated motif patterns. Lentiviral MPRA validated cell-type-specific activity in K562 and HepG2 and showed higher average activity of HepG2 designs than endogenous CREs. Together, these findings establish sequence-grammar trajectory reconstruction as a basis for interpretable and tunable synthetic CRE design.

genomics↗

bioRxiv2026

MZF1-mediated GAPDH overexpression drives glycolytic reprogramming and neuroendocrine progression in advanced prostate cancer

Drug resistance to the androgen receptor (AR) antagonist is a critical obstacle in the clinic for advanced prostate cancers. Especially, AR antagonist treatment-induced neuroendocrine progression represents a lethal and therapy-resistant subtype. Although transcriptional and epigenetic lineage plasticity have been extensively implicated in treatment-induced neuroendocrine progression, the contribution of metabolic adaptation remains incompletely understood. Here, we identified a previously unrecognized metabolic reprogramming mechanism induced by AR antagonists in castration-resistant prostate cancer (CRPC) models. AR antagonist treatment markedly enhanced glycolytic activity and induced glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression. Genetic depletion of GAPDH suppressed AR antagonist-induced glycolytic activation, altered transcriptomic and metabolic programs, reduced neuroendocrine-associated marker expression, and inhibited xenograft tumor growth. Mechanistically, GAPDH promoter pulldown coupled with mass spectrometry, siRNA screening, and chromatin immunoprecipitation assays identified myeloid zinc finger-1 (MZF1) as a key transcription factor for Enzalutamide-induced GAPDH gene expression. Pharmacological inhibition of GAPDH using koningic acid (KA) or penta-O-galloyl-{beta}-D-glucopyranose (PGG) significantly suppressed tumor growth and attenuated neuroendocrine-associated molecular programs in CRPC cell-derived xenograft and patient-derived t-NEPC xenograft models. Collectively, our findings identify an AR antagonist-induced MZF1-GAPDH signaling axis that promotes glycolytic activation and neuroendocrine-associated metabolic adaptation during treatment resistance. These results support targeting GAPDH-dependent metabolic reprogramming as a potential therapeutic strategy for treatment-resistant prostate cancer. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/729867v1_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@10480b2org.highwire.dtl.DTLVardef@9d2ed8org.highwire.dtl.DTLVardef@16e8705org.highwire.dtl.DTLVardef@1627ad2_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2026

Genome compartments guide protamine replacement and genome stability during spermiogenesis

Compartment-scale genome organisation persists in mammalian sperm, yet how histone-protamine replacement is orchestrated in space and time during spermiogenesis remains unclear. Here we combine stage-resolved purification of mouse spermatids with spike-in-normalised ATAC-seq and PRM1 CUT&Tag to map chromatin accessibility and protamine incorporation across spermiogenesis. We uncover a transient, genome-wide hyper-accessible phase coincident with replacement that is uncoupled from transcription and suppressed in catalytic PHF7-mutant spermatids. PRM1 loading initiates within accessible A-compartment chromatin and later spreads across both A- and B-compartments, whereas protamine-null mice demonstrated that PRM1/ PRM2 deficiency selectively destabilises A-compartment closure. Sequencing of short DNA fragments from Prm1/Prm2 dosage-reduced epididymal sperm reveals early enrichment of breakage at protamine-targeted A-compartment regions, which dissipates as fragmentation becomes genome-wide during epididymal transit. Together, our data place histone-protamine replacement within a compartment-centred framework that links 3D genome domains to the timing, targeting and integrity of the sperm genome.

genomics↗

bioRxiv2026

Stage-adaptive integration of polydopamine promotes hPSC-derived alveolar organoids differentiation and maturation

Human pluripotent stem cell (hPSC)-derived alveolar organoids (ALOs) have emerged as a powerful tool for modeling human lung development and disease, and accelerating respiratory drug discovery. However, achieving the functional maturation of ALOs remains challenging. Polydopamine (PDA) is a mussel-inspired polyphenolic biomaterial with antioxidant and adhesive properties that can be deployed as surface coatings and nanoparticles (NPs) in cell culture systems. Here, we integrate PDA coatings and NPs sequentially in a stage-adaptive manner throughout the hPSC-derived ALOs differentiation system and study their contributions to ALOs maturation. Our results demonstrated PDA coating yielded more anterior foregut endoderm (AFE) spheroids by strengthening the interaction between Matrigel and substrate. Bulk RNA-seq revealed enrichment of cell-cell and cell-extracellular matrix interactions by PDA. The subsequent incorporation of PDA NPs in Matrigel at lung progenitor cells (LPCs) stage significantly mitigated reactive oxygen species (ROS) accumulation and enhanced LPCs generation. Functionally, AT2 cells in ALOs exhibit characteristic lysosome-to-lamellar body (LB) maturation due to the traffic of internalized PDA NPs to endolysosome. Transcriptomics further indicated enrichment of endocytic-phagosome and epithelium development pathways by PDA treatment. Together, our study establishes a stage-adaptive-integrated PDA strategy throughout hPSC-to-ALOs differentiation and demonstrates that PDA robustly enhances ALOs maturation and secretory function. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/708928v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@88208dorg.highwire.dtl.DTLVardef@1111590org.highwire.dtl.DTLVardef@9ea9b0org.highwire.dtl.DTLVardef@969fad_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2026

AI-driven discovery and engineering of human endogenous nanocage proteins for mRNA delivery

Safe and effective gene delivery remains a central challenge for therapeutic applications. While non-viral and viral vectors have enabled substantial progress, their reliance on non-human components often triggers immune responses, limiting their use in chronic treatments. Here, we developed DeepDelivery, an artificial intelligence-driven platform to repurpose human proteins for mRNA delivery. An unbiased screening of the human proteome nominated 512 candidates, with experimental validation confirming that 80% of top-ranked hits form mRNA-encapsulating particles and mediate efficient functional delivery in human cells without provoking detectable inflammation. Notably, multiple tripartite motif (TRIM) family proteins, typically linked to antiviral responses, exhibited strong assembly and delivery activity. Quantitative analysis and interpretation of the model revealed structural domains that govern nanocage formation, enabling domain-guided engineering of TRIM25 variants with enhanced function. Our work establishes a generalizable framework for discovering human-derived delivery vehicles and provides a path toward programmable, non-immunogenic mRNA therapeutics.

synthetic biology↗

bioRxiv2026

Reduced Complex III assembly dampens host inflammation as an adaptive response to viral infection

Mitochondrial electron transport and oxidative respiration are required for immunity and host tolerance. How the ETC contributes to viral infections - where a proinflammatory anti-viral response needs to be finely balanced with host-protective anti-inflammatory mechanisms - remains unclear. Here, we demonstrate that following infection by H1N1 influenza virus, murine macrophages reduce Complex III levels by downregulating the early CIII assembly factor called the COMB complex. To determine the effect of CIII suppression, we utilized Brawnin (Br or UQCC6) knockout mice, which lack COMB complexes and have reduced Complex III. Following H1N1 infection, Br KO bone marrow-derived macrophages (BMDMs) had reduced inflammation due to reduced CIII Qo site ROS. Br KO mice infected with a non-lethal dose of H1N1 had reduced lung immune pathology, viral burden and enhanced recovery following H1N1 infection. Single-cell RNAseq analysis revealed that H1N1-infected Br KO lungs had reduced abundance of hyper-inflammatory monocytes known to cause severe respiratory disease and reduced monocyte chemotaxis. Our study demonstrates that ETC Complex III suppression is part of an innate immune response to viral infection, and is a potential strategy to control host inflammation in acute respiratory viral infections.

immunology↗

bioRxiv2026

A general and extensible algorithmic framework to biological sequence alignment across scales and applications

Sequence alignment underpins nearly every facet of modern genomics, from genetic testing and cancer profiling to functional genome annotation. Yet, despite decades of algorithmic innovation, most existing aligners remain narrowly optimized for specific tasks, fragmenting analytical workflows and limiting reproducibility. Here we introduce the Versatile Alignment Toolkit (VAT), a unified algorithmic framework that generalizes existing seeding and genome-indexing strategies within a single, transparent architecture. VAT employs a novel multi-view indexing scheme that integrates multiple seeding strategies and supports run-time seed-length parameterization without reindexing. A radix clustering algorithm based on hardware-efficient in-register bitonic sort accelerates multi-view table construction, ensuring scalability across large datasets. VAT delivers consistently high performance across diverse alignment tasks, including short- and long-read mapping, homology search, and whole-genome alignment, while maintaining algorithmic simplicity and adaptability. By bridging previously isolated alignment paradigms, VAT substantially reduces workflow complexity, enhances computational efficiency, and establishes an extensible foundation for future sequencing technologies. We anticipate this unification will set a new standard for flexible and reproducible sequence alignment in biomedical research and clinical genomics.

bioinformatics↗

bioRxiv2025

Benchmarking Perturbation Tools for the Noncoding Genome

Deciphering the functionality of the noncoding genome which includes important cis-regulatory elements (CREs) and transcribed noncoding RNA genes remains technically challenging. Here, using massively parallel genetic screening, we systematically benchmark the performance of five representative loss-of-function perturbation tools, including single guide RNA (gRNA) mediated SpCas9 cleavage or CRISPR interference, and paired gRNA (pgRNA) involved dual-SpCas9, Big Papi (paired SpCas9 and SaCas9) or dual-enAsCas12a fragment deletion methods, in decoding the roles of the noncoding genome. For targeting CREs such as enhancer, dual-SpCas9 outperforms other methods with superior efficiency of destroying functional genomic regions. For perturbing noncoding RNA genes, in addition to dual-SpCas9, other RNA-targeting methods such as RNA interference are recommended to discriminate transcript-dependent or -independent roles. A deep learning model DeepDC with associated web server is built to facilitate optimal dual-SpCas9 pgRNA design for efficiently deleting a genomic fragment. Together, our work provides practical guidance on selecting appropriate loss-of-function tools to resolve the functional complexity of the noncoding genome.

genetics↗

bioRxiv2025

Bio-Inspired Ultrasound-Driven Ultrafast Soft Microgripper

Acoustically actuated soft matter offers potential for agile microscale manipulation, yet acoustic-soft matter interaction at the microscale remains poorly understood. Here, we explore the mechanism of ultrasound-soft matter interaction by developing a bio-inspired ultrasound-driven soft hydrogel microgripper. This exploration allows to delve deeper into the understanding of nonlinear dynamics, mode coupling, and energy transfer. The developed microgripper ([≤] 120 {micro}m) overcomes key challenges of existing grippers, including complex fabrication, reliance on additives or external wiring, rigid structures, slow or poorly controllable responses, and risks of sample damage or contamination. Interacting with acoustic actuation, soft microgrippers oscillate and deform, while adjusting acoustic parameters and microgrippers structures allows for programmable interactions. The optimized acoustic actuation of the soft microgripper enables precise, ultrafast ([~]2 ms) gripping and handling of distinct delicate objects. This work advances the integration of soft matter with acoustic actuation especially at the microscale, offering a versatile, reliable, and scalable solution for microrobotics, targeted drug delivery, and lab-on-a-chip applications. TeaserAcoustic-soft matter interaction validated on bio-inspired ultrasound-driven ultrafast soft microgrippers.

bioengineering↗

bioRxiv2025

ODFormer: a Virtual Organoid for Predicting Personalized Therapeutic Responses in Pancreatic Cancer

Pancreatic cancer (PC) patient-derived organoids (PDOs) faithfully recapitulate therapeutic responses but face clinical translation barriers, including high costs and technical complexity. To address these problems and the lack of frameworks for PDO-based drug-response assays, we developed ODFormer, a computational framework that simulates PC PDOs to predict clinically actionable, patient-specific drug responses by integrating transcriptomic and mutational profiles. ODFormer first employed two encoders, pretrained on 30,000 pan-cancer bulk transcriptomics and 1 million PC single-cell profiles respectively, to distil tissue-and organoid-specific representations. Then, trained on our curated 14,000 PDO drug-response assay (across 183 PDOs and 98 drugs) using a transformer-augmented hybrid contrastive network, ODFormer significantly outperformed state-of-the-art methods, notably achieving a PCC >0.9 in predicting standardized drug response. Multi-cohort retrospective analyses further demonstrated that ODFormer-guided personalized therapy significantly improves clinical outcomes, without requiring physical organoid assays. Furthermore, ODFormer identified novel clinico-biological PC subtypes and revealed therapy resistance biomarkers by stratifying predicted responders and non-responders. These were validated using independent datasets including TCGA-PDAC. Notably, ODFormer-guided treatment efficacy showed high concordance with prospective clinical responses by CA19-9.

bioinformatics↗

bioRxiv2025

Targeting protein glycosylation and cholesterol metabolism in chemoresistant pancreatic cancer

Chemotherapy remains the cornerstone of pancreatic ductal adenocarcinoma (PDAC) treatment. However, most cases of PDAC finally progress to advanced chemoresistant disease. This highlights an urgent need to develop new pharmacological strategies to overcome chemotherapy resistance using clinical grade inhibitors. Here, we established a biobank comprising 260 organoid lines derived from 322 pancreatic cancer patients. These organoids underwent extensive multi-omics profiling and drug sensitivity testing for both chemotherapy and targeted therapy. Increased protein glycosylation and cholesterol metabolism signaling pathways were especially enriched in chemoresistant organoid lines. Importantly, chemoresistant PDAC organoids were effectively targeted with statins, inhibitors of cholesterol synthesis. Statins treatment attenuated protein glycosylation, cholesterol levels, and the epithelial-to-mesenchymal transition (EMT) signature in PDAC organoids. We further conducted a phase 2 clinical trial (NCT06241352) combining atorvastatin with chemotherapy in patients with advanced chemoresistant pancreatic cancer. Among 29 patients, 21 (72.4%) demonstrated a response, with tumor markers decreasing by more than 20%, suggesting durable responses and potential clinical benefits in this challenging patient population. ClinicalTrials.gov identifier: NCT06241352.

cancer biology↗

bioRxiv2025

Characterizing the Microbiome of 'Sterile' Organs in Experimental Mice

Recent studies have shown that microorganisms can be present in organs or blood traditionally considered sterile, both in healthy and diseased individuals. In this study, we investigated the presence of microorganisms in the "sterile" organs (brain, heart, kidney, liver, lung, and spleen) of commercially available experimental mice using culturomics and metagenomics. To avoid potential environmental contamination, strict disinfection assays were performed. Among 104 mice of the C57BL/6J, BALB/c, and ICR strains purchased from three major experimental animal suppliers in China, 23 mice (22.1%) exhibited a high microbial burden (>1 x 104 CFU/g tissue) in at least one organ, and 42 mice (40.4%) exhibited >1 x 103 CFU/g tissue in at least one organ. In total, 216 microbial species were identified through culturomics. Of them, 56 microbial species were found in the organs of at least three mice of the 42 mice (>1 x 103 CFU/g tissue in at least one organ). Metagenomics analysis of 10 high-burden mice verified the existence of microbes in the mouse organs and identified 262 species. Several species, including Acinetobacter sp., Alcaligenes faecalis, and Ligilactobacillus murinus, were identified as the most abundant microbes across both culturomics and metagenomics assays. 16S FISH staining assays verify the presence of microbes in the mouse organs. Feces Metagenomics analysis and detection of the wide distribution of L. murinus in the mouse mesenteric lymph nodes (MLNs) imply that microbial cells could be translocated from the gut to the MLNs and then to the remote tissues. Together, these findings reveal the presence of non-pathogenic microorganisms in the "sterile" tissues of experimental mice, raising concerns about potential variability in experimental outcomes when using such animals in research.

microbiology↗

bioRxiv2025

Apolipoprotein E ε4 exacerbates microglia-mediated complement-dependent synapse loss caused by neuronal Tpk deficiency

Thiamine pyrophosphokinse-1 (TPK) is a key enzyme that converts thiamine to functional thiamine diphosphate (TDP). TPK insufficiency and hence TDP reduction in neurons induced by amyloid-{beta} deposition and diabetes, an independent risk factor of Alzheimers disease (AD), recapitulate multi-pathophysiological features in the brain of mice, similar to those in human AD. Apolipoprotein E {varepsilon}4 allele (APOE4) is the most well-known genetic risk factor for AD. Clinical trials by boosting TDP using benfotiamine, a derivative of thiamine that significantly elevates TDP level in human encrythrocytes, have shown the inferior clinical efficacies in APOE4 carriers compared to non-APOE4 carriers. Clarifying the relationship between APOE4 and TPK expression and multi-pathophysiological characteristics of AD induced by Tpk deficiency in neurons is imperative. Here, we find that humanized APOE4 didnt directly affect Tpk expression of mice, but markedly aggravates behavior abnormalities of Tpk-cKO mice. Pathologically, the Tpk-cKO mice with humanized APOE4 knock-in (AE-cKO mice) exhibit more synapse loss than the mice with only humanized APOE4 knock-in and the Tpk-cKO mice. Transcriptomics and pathologic analysis identified that APOE4 promoted the overactivation of microglia and the transition of microglia to a disease-associated and phagocytosis state via a complement-mediated pathway. Further, the C3aR antagonist significantly repressed microglia phagocytosis and synaptic elimination of the AE-cKO mice. Our results demonstrate that APOE4 exacerbates behavior dysfunction of Tpk-cKO mice through microglia-mediated complement-dependent synaptic elimination. These findings provide important insights into the role of APOE4 in the pathogenesis of AD.

neuroscience↗

bioRxiv2025

Engineered Living Glues for Autonomous Detection and On-Demand Treatment of Inflammatory Bowel Disease

Smart biomaterials capable of autonomously sensing pathological conditions and executing targeted medical interventions offer therapeutic advantages over conventional passive biomaterials. However, their development remains a considerable challenge. Here, we introduce a therapeutic "living glue" for the automatic detection and on-demand treatment of inflammatory bowel disease (IBD). This living glue employs a genetically engineered, non-pathogenic Escherichia coli with a highly sensitive blood-inducible gene circuit to monitor gastrointestinal bleeding, a key indicator of severe IBD. Upon detection, the bacteria respond by producing potent adhesive and therapeutic proteins around bleeding sites, enabling robust attachment to inflamed tissues and sustained treatment. In a dextran sulfate sodium (DSS)-induced mouse model, a single rectal administration of the living glue markedly improved weight recovery, reversed colonic shortening, and reduced intestinal bleeding. Additionally, the living glue decreased intestinal inflammation, promoted mucosal repair, and restored gut barrier integrity, demonstrating comprehensive therapeutic effects in alleviating IBD symptoms. This study highlights the potential of integrating programmable, living components into biomaterials for autonomous, targeted, and enhanced medical interventions.

synthetic biology↗

bioRxiv2024

A comprehensive benchmark for multiple highly efficient base editors with broad targeting scope

As the toolbox of base editors (BEs) expands, selecting appropriate BE and guide RNA (gRNA) to achieve optimal editing efficiency and outcome for a given target becomes challenging. Here, we construct a set of 10 adenine and cytosine BEs with high activity and broad targeting scope, and comprehensively evaluate their editing profiles and properties head-to-head with 34,040 BE-gRNA-target combinations using genomically integrated long targets and tiling gRNA strategies. Interestingly, we observe widespread non-canonical protospacer adjacent motifs (PAMs) for these BEs. Using this large-scale benchmark data, we build a deep learning model, named BEEP (Base Editing Efficiency Predictor), for predicting the editing efficiency and outcome of these BEs. Guided by BEEP, we experimentally test and validate the installment of 3,558 disease-associated single nucleotide variants (SNVs) via BEs, including 20.1% of target sites that would be generally considered as "uneditable", due to the lack of canonical PAMs. We further predict candidate BE-gRNA-target combinations for modeling 1,752,651 ClinVar SNVs. We also identify several cancer-associated SNVs that drive the resistance to BRAF inhibitors in melanoma. These efforts benchmark the performance and illuminate the capabilities of multiple highly useful BEs for interrogating functional SNVs. A practical webserver (http://beep.weililab.org/) is freely accessible to guide the selection of optimal BEs and gRNAs for a given target.

molecular biology↗