Biology subjects

Dong, S.

Publications and source records attributed to Dong, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Complete genomes of a multi-generational pedigree to expand studies of genetic and epigenetic inheritance

Pedigree analysis remains the gold standard for rare disease diagnostics, yet whole genome sequencing studies typically omit critical regions like centromeres, telomeres, and acrocentric chromosome p-arms. Here, we present telomere-to-telomere (T2T) reference genomes for four self-identified African American individuals of admixed ancestry spanning three generations. Our parent-of-origin assigned, chromosome-level assemblies revealed precise meiotic recombination breakpoints in previously inaccessible regions, including recombination events across acrocentric and subtelomeric sequences. Centromeric regions were highly stable, with multi-megabase arrays inherited intact across three generations, while the position of kinetochore assembly sites remained consistent and predominantly associated with the p-arm proximal region. The relative lengths of telomeres on individual chromosomes were maintained across generations. Using a targeted rDNA assembly approach, we reconstructed a complete megabase-scale ribosomal DNA (rDNA) array corresponding to the paternal chromosome 14. This openly available pedigree provides a benchmark dataset for studying recombination and genetic and epigenetic variation across the complete genome.

genomics↗

bioRxiv2025

HINS: HLA-mediated immunogenic neoantigen score for robust prediction of immune checkpoint blockade response

Immune checkpoint blockade (ICB) therapy response prediction provides remarkable genomic and transcriptomic gains and has successfully understood the molecular mechanisms underlying various cancers. However, only a subset of patients with advanced tumors currently benefits from ICB therapies, which at times incur considerable side effects and costs. Developing predictive tools for ICB response has remained a serious challenge because of the complexity of the immune response and the shortage of large cohorts of ICB-treated patients that include both omics and response data. Here, based on a pooled genomic dataset of 919 patients across multiple solid tumors treated with anti-PD-(L)1 or anti-CTLA-4, we constructed human leukocyte antigen class I (HLA-I)-mediated immunogenic neoantigen score (HINS). HINS is a predictor of ICB response using neoantigen quality and HLA evolutionary divergence to integrate the factors associated with immune activation and evasion. It yielded an overall accuracy of AUC=0.853, outperforming existing predictors and capturing more than 75% true responders. Experimental analysis indicated that patients with higher HINS were more likely to undergo survival benefits following ICB therapy. Our results highlighted the transcriptional and genomic correlations between HINS-identified molecule features and ICB response, such as immunoresponsive gene enrich pathways, favorable and unfavorable genomic subgroups, and hotspot somatic events in driver genes. This study presented an interpretable, accurate deep-learning method using meaningful predictive features to predict the response to ICB, providing biological insights into the complexity of the determinants underlying immunotherapy.

bioinformatics↗

bioRxiv2025

Disruption of Cell-Type-Specific Molecular Programs of Medium Spiny Neurons in Autism

Autism spectrum disorders (ASD) are highly heritable neurodevelopmental conditions with major contributions from rare genetic variants. Most studies have focused on cortical mechanisms; even growing evidence implicates subcortical circuits in ASD etiology. To systematically map developmental and molecular alterations beyond the cortex, we profiled lineage relationships across five brain regions in an ASD mouse model. Most prominent changes emerged in the striatum, a hub for learning and motor control. Furthermore, we performed single-nucleus multiomic profiling of human putamen from ASD and neurotypical donors revealed cell-type-specific transcriptomic and regulatory alterations. Differential expression converged on synaptic and energy metabolic dysfunctions in D1 striosome medium spiny neurons (MSNs), coupled with astrocytic remodeling of synaptic support. Gene regulatory network analysis identified EGR3 and EGR1 as key transcriptional regulators of ASD-associated programs of D1 MSNs. Together, these results establish the striatum as a central node of ASD convergence and provide a multiomic resource for dissecting its subcortical mechanisms.

neuroscience↗

bioRxiv2025

Comprehensive benchmarking of somatic single-nucleotide variant and indel detection at ultra-low allele fractions using short- and long-read data

Mosaic mutations in normal tissues occur at low variant allele fractions (VAFs), complicating detection. To benchmark strategies, the SMaHT Network created a cell-line mixture (1:49) and produced ultra-deep whole-genome sequencing using short and long reads (five centers, 180-500x each). We assembled a reference of 44,008 mosaic SNVs and 2,059 Indels, cross-validation between platforms to expose limits of short-read analysis. We also partitioned the genome by mappability to examine the impact of genomic context, added a negative reference set, and accounted for culture-derived mutations. When seven institutions applied eleven algorithms to mixture data, call sets were largely discordant across tools and replicates, partly reflecting stochastic presence of low-VAF mutations in biological replicants. For >2% VAF SNVs, sensitivity and precision approached [~]80% at [≥]300x, with little gain from additional sequencing. This work provides a comprehensive framework for reliable detection of low-VAF mutations in non-cancer tissues and a valuable resource for the community.

bioinformatics↗

bioRxiv2025

Multi-platform framework for mapping somatic retrotransposition in human tissues

Mobile element insertions (MEI) shape the human genome in both germline and somatic tissues. While inherited MEIs are well characterized, mapping somatic MEIs (sMEI) in non-cancer tissues remains challenging due to their low allelic fraction and repetitive nature. We established an integrative framework for sMEI analysis leveraging modern sequencing technologies and analytical innovations. We first benchmarked sMEI detection and demonstrated advantages of long-read and MEI-targeted sequencing for ultra-low-frequency events using a mixture of well-established cell lines. We then showed that haplotype phasing and donor-specific assemblies refine sMEI detection, effectively distinguishing from germline and false signals in in-silico tumor-normal mixtures. We further developed a source-tracing strategy based on internal sequence variation, expanding the catalogue of active source elements beyond traditional transduction-based methods. Applying this framework to donor tissues, we identified 18 rare somatic L1 insertions, revealing structural and source diversity. Our work provides a foundational framework and biological insight into sMEIs.

genomics↗

bioRxiv2025

A Pangenomic Method for Establishing a Somatic Variant Detection Resource in HapMap Mixtures

Somatic mosaicism is essential in human biology and disease, yet robust benchmarks are scarce. The SMaHT Consortium mixed six HapMap cell lines to create artificial somatic variants spanning 0.25% to 16.5% variant allele fractions. We developed a technology-agnostic method that builds pangenome graphs from individual assemblies to create unified benchmarking sets: > 6M single-nucleotide variants, 1.8M small insertions/deletions, 49K structural variations, and 10K mobile element insertions across autosomes, X, and mitochondrial chromosomes. We validated the variants using ultra-deep simulated reads and developed a binomial-based model to estimate coverage requirements for variant detection. Evaluating multiple callers showed CHM13 alignment improves structural variant detection and offers advantages in difficult-to-map regions compared to GRCh38. Systematic characterization showed regions with low detection rate are enriched in centromeres, satellite sequences, tandem repeats, and falsely duplicated genes. This accurate, versatile resource enables systematic evaluation of somatic variant detection technologies.

genomics↗

bioRxiv2025

GeoGAT-site: A Face-Centered Geometric Graph Attention Network for Protein-Protein Interface Prediction

Protein-protein interactions (PPIs) underpin the intricate machinery of cellular life, orchestrating processes from signal transduction to metabolic regulation, yet their precise interface prediction remains a cornerstone challenge in structural biology, demanding scalable computational paradigms that balance accuracy and efficiency. Herein, we present GeoGAT-site, a pioneering geometric graph attention network that leverages face-centered surface fingerprints extracted from three-dimensional protein architectures to forecast interaction sites. Diverging from conventional vertex-centric approaches, our face-centered methodology achieves a 3.64-fold acceleration in patch generation, mitigating computational bottlenecks while preserving granular surface descriptors. At its core, GeoGAT-site incorporates a bespoke attention mechanism that adaptively modulates inter-facial distances and normal vector orientations, synergistically integrating spatial geometries with physicochemical attributes for nuanced interface delineation. Harnessing a meticulously curated dataset of 150 million face-centered fingerprints from over 20,000 structurally diverse proteins, the model attains robust generalization across heterogeneous interaction motifs. Empirical validation on an independent cohort of 167 protein complexes yields a ROC AUC of 0.89, surpassing established benchmarks including MaSIF-site (0.845), SPPIDER (0.65), and PSIVER (0.63). By furnishing high-fidelity interface annotations, GeoGAT-site augments downstream structural modeling paradigms through targeted constraints, thereby providing a versatile scaffold for unraveling PPI dynamics with profound implications for therapeutic discovery, protein redesign, and molecular epistemology.

bioinformatics↗

bioRxiv2025

A Rapid Multiplex LAMP Assay for Point-of-Care Detection of CT, NG, TV, and Fluoroquinolone Resistance in NG

Rapid point-of-care (POC) diagnostics are essential tools for improving timely treatment and reducing the transmission of sexually transmitted infections (STIs). The STI NG Plus Assay is a rapid multiplex LAMP (loop-mediated isothermal amplification) NAAT (nucleic acid amplification test) capable of simultaneously detecting Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), Trichomonas vaginalis (TV), and fluoroquinolone resistance-associated mutations in NG (gyrA S91F). In this study, we assessed the STI NG Plus assay primer design and analytical sensitivity. Using a bioinformatically optimized primer design pipeline and empirical screening, the assay demonstrated high inclusivity and specificity, with no cross-reactivity to 48 urogenital organisms or the human genome. Analytical sensitivity testing showed reliable detection of all targets in both lysis buffer and clinical matrix. Limits of detection were lower than those of an existing FDA-cleared test. The assays robustness, speed, and sensitivity support its potential for decentralized STI testing with integrated antimicrobial resistance profiling.

molecular biology↗

bioRxiv2025

Unveiling the biochemical catalysis and regulatory mechanisms of the E3-independent E2 enzyme hUBE2O

UBE2O, an E3-independent E2 ubiquitin-conjugation enzyme, directly engages substrates to mediate Ubiquitin conjugation and ligation. Despite its critical role in ubiquitination of multiple substrates, the catalytic and regulatory mechanisms of Homo sapiens UBE2O (hUBE2O) remain incompletely understood. Here, combining domain truncation, systematic mutagenesis and well-designed biochemical approaches, we demonstrate that hUBE2O mediates both mono- and polyubiquitination through a unique catalytic architecture. While none cysteines function for hUBE2Os E3 activity, the coiled-coil (CC) and C-terminal regulatory (CTR) domains maintain catalytic competence, with the N-terminal regions impose activity constraints. Interestingly, hUBE2O activity is refractory to its self-ubiquitination and phosphorylation state. Instead, specific non-cysteine residues (H939, T995, S1042A, S1046A, S1060A and H1130) emerge as critical regulators of substrate selectivity and catalytic optimization. Surprisingly, zinc ions emerge as potent allosteric inhibitors that bind cysteines of hUBE2O, sterically occluding the access of catalytic site C1040 to Ubiquitin. Our findings reveal that hUBE2O-mediated E3-independent ubiquitination is governed by dynamic interdomain cooperation and allosteric modulation, establishing a mechanistic framework for understanding non-canonical ubiquitination and informing the development of targeted hUBE2O modulators.

biochemistry↗

bioRxiv2025

THLANet: A Deep Learning Framework for Predicting TCR-pHLA Binding in Immunotherapy Applications

Adaptive immunity is a targeted immune response that enables the body to identify and eliminate foreign pathogens, playing a critical role in the anti-tumor immune response. Tumor cell expression of antigens forms the foundation for inducing this adaptive response. However, the human leukocyte antigens (HLA)-restricted recognition of antigens by T-cell receptors (TCR) limits their ability to detect all neoantigens, with only a small subset capable of activating T-cells. Accurately predicting neoantigen binding to TCR is, therefore, crucial for assessing their immunogenic potential in clinical settings. We present THLANet, a deep learning model designed to predict the binding specificity of TCR to neoantigens presented by class I HLAs. THLANet employs evolutionary scale modeling-2 (ESM-2), replacing the traditional embedding methods to enhance sequence feature representation. Using scTCR-seq data, we obtained the TCR immune repertoire and constructed a TCR-pHLA binding database to validate THLANets clinical potential. The models performance was further evaluated using clinical cancer data across various cancer types. Additionally, by analyzing divided complementarity-determining region (CDR3) sequences and simulating alanine scanning of antigen sequences, we unveiled the 3D binding conformations of TCRs and antigens. Predicting TCR-neoantigen pairing remains a significant challenge in immunology, THLANet provides accurate predictions using only the TCR sequence (CDR3{beta}), antigen sequence, and class I HLA, offering novel insights into TCR-antigen interactions. Author summaryT-cell receptor (TCR) recognition of peptide-human leukocyte antigen (pHLA) complexes is fundamental to immune responses. However, predicting their binding poses a significant challenge due to the intricate dynamics of their interactions. We developed THLANet, a novel deep learning model, to address this challenge by integrating the ESM-2 and Transformer-Encoder modules. This approach enhances sequence feature encoding, improving the models generalization capability and enabling accurate predictions of TCR-pHLA binding in clinical datasets. Using data processed from open-source databases, THLANet outperformed existing methods, such as PanPep and pMTnet, in precision-recall metrics across multiple epitopes. Additionally, THLANet demonstrates superior capability in identifying critical binding sites within 3D structures, providing structural insights into TCR-pHLA interactions. THLANet offers a robust framework for advancing immunotherapy research, with potential applications in the development of personalized medicine.

bioinformatics↗

bioRxiv2025

Cold-activated hepatocyte derived exosomes promote BAT thermogenesis through miR-293-5p

The liver-adipose axis serves as a fundamental regulatory network governing systemic lipid homeostasis, with hepatic-derived signals orchestrating adipose tissue plasticity through multimodal mechanisms. Comprehensive elucidation of these bidirectional crosstalk mechanisms may reveal novel therapeutic strategies for metabolic disorders. Our investigation demonstrates that cold exposure induces hepatic secretion of exosomes that potentiate adipose thermogenic activation in both in vitro and in vivo models. This thermogenic potentiation is mechanistically linked to cold-induced upregulation of hepatocyte-derived exosomal miR-293-5p. Notably, pharmacological administration of miR-293-5p agomir significantly attenuates diet- induced obesity and associated metabolic dysregulation in murine models. Through mechanistic interrogation, we identified Tet1 as a direct downstream target of miR- 293-5p, where ectopic Tet1 expression disrupts brown adipose tissue (BAT) thermogenic programming independently of miR-293-5p modulation. Our findings establish cold-activated hepatocyte exosomes as endocrine signaling mediators containing thermogenic microRNA cargos, with miR-293-5p emerging as a regulatory nexus coordinating mitochondrial bioenergetics and systemic energy equilibrium through liver-adipose cross-talk.

genetics↗

bioRxiv2025

Enhancing Tissue-Specific Antiviral Immunity to Disrupt Arbovirus Transmission by Mosquitoes

Arboviruses, including dengue virus (DENV), Zika virus (ZIKV), and chikungunya virus (CHIKV), pose a significant global health and economic burden, with Aedes aegypti serving as their primary vector. Arbovirus infection in Ae. aegypti progresses sequentially through the midgut (MG), carcass (CA), and salivary glands (SG), with each tissue exhibiting distinct antiviral responses. Here, we investigate tissue-specific antiviral mechanisms, focusing on the small interfering RNA (siRNA) pathway in SGs. Our results reveal that SGs possess weaker antiviral defense and are more susceptible to arboviral infection compared to MGs and CAs. Notably, overexpression of Dicer2 (Dcr2), a key component of the siRNA pathway, in SGs leads to a significant decrease in arboviral replication. Conversely, Dcr2 overexpression in fat bodies, the primary tissue in CAs, only moderately suppresses DENV2 infection and has no notable effect on Mayaro virus (MAYV) infection. Remarkably, the simultaneous overexpression of Dcr2 in both MGs and SGs enhances antiviral activity, effectively blocking the transmission of multiple arboviruses. These findings reveal the tissue-specific dynamics of mosquito antiviral immunity and underscore the potential for targeting SG-specific immunity to disrupt arbovirus transmission, providing a promising approach for controlling mosquito-borne diseases.

microbiology↗

bioRxiv2024

Ancient cyanobacterial proto-circadian clock adapted to short day-night cycles ~ 0.95 billion years ago

In the early history, the Earth rotation was much faster; however, how ancient organisms adapted to the day-night cycles of that time remains unclear. We resurrected the ancestor KaiABC (anKaiABC) genes circa 0.95 billion years (Ga) ago when the daily-night cycling period was [~] 18 h. Compared with its contemporary counterpart KaiC, the anKaiC protein shows subtle structural differences, and the activities of kinase, phosphatase activities, and adenosine triphosphatase (ATPase) of anKaiC are lower. The anKaiAB proteins are less effective in regulating KaiC/anKaiC phosphorylation status. The anKaiABC system does not sustain an endogenous oscillation but it can be entrained by an 18-h light/dark cycle. The strain expressing anKaiABC shows better adaptation under 9-h light/9-h dark cycles (LD9:9) which mimic the 18-h day-night cycles. These findings suggest that the ancient cyanobacterial proto-circadian system may not be endogenous, but it conferred the capability to adapt to daily cycles [~] 0.95 Ga ago.

evolutionary biology↗

bioRxiv2024

In vivo Auto-tuning of Antibody-Drug Conjugate Delivery for Effective Immunotherapy using High-Avidity, Low-Affinity Antibodies

Antibody-drug conjugates (ADCs) have experienced a surge in clinical approvals in the past five years. Despite this success, a major limitation to ADC efficacy in solid tumors is poor tumor penetration, which leaves many cancer cells untargeted. Increasing antibody doses or co-administering ADC with an unconjugated antibody can improve tumor penetration and increase efficacy when target receptor expression is high. However, it can also reduce efficacy in low-expression tumors where ADC delivery is limited by cellular uptake. This creates an intrinsic problem because many patients express different levels of target between tumors and even within the same tumor. Here, we generated High-Avidity, Low-Affinity (HALA) antibodies that can automatically tune the cellular ADC delivery to match the local expression level. Using HER2 ADCs as a model, HALA antibodies were identified with the desired HER2 expression-dependent competitive binding with ADCs in vitro. Multi-scale distribution of trastuzumab emtansine and trastuzumab deruxtecan co-administered with the HALA antibody were analyzed in vivo, revealing that the HALA antibody increased ADC tumor penetration in high-expression systems with minimal reduction in ADC uptake in low-expression tumors. This translated to greater ADC efficacy in immunodeficient mouse models across a range of HER2 expression levels. Furthermore, Fc-enhanced HALA antibodies showed improved Fc-effector function at both high and low expression levels and elicited a strong response in an immunocompetent mouse model. These results demonstrate that HALA antibodies can expand treatment ranges beyond high expression targets and leverage strong immune responses.

bioengineering↗

bioRxiv2024

SBT5.2s are the major active extracellular subtilases processing IgG antibody 2F5 in the Nicotiana benthamiana apoplast

Plants offer a powerful platform for recombinant protein production but degradation of recombinant proteins by endogenous proteases is causing severe yield losses. Here, we introduce triple knockout lines for SBT5.2, the major active subtilases in the apoplast of agroinfiltrated Nicotiana benthamiana. HIV-neutralising IgG antibody 2F5 is no longer cleaved in the apoplast of sbt5.2 mutants and these mutants accumulate 3-fold more 2F5 upon transient expression but grow normally. Remarkably, however, 2F5 does not accumulate in the apoplast and is not exposed to SBT5.2 when transiently expressed, uncovering an important controversy regarding the subcellular localisation of IgGs in agroinfiltrated plants.

plant biology↗

bioRxiv2023

AlphaMissense is better correlated with functional assays of missense impact than earlier prediction algorithms

Missense variants that alter a single amino acid in the encoded protein contribute to many human disorders but pose a substantial challenge in interpretation. Though these variants can be reliably identified through sequencing, distinguishing the clinically significant ones remains difficult, such that "Variants of Unknown Significance" outnumber those classified as "Pathogenic" or "Likely Pathogenic." Numerous in silico approaches have been developed to predict the functional impact of missense variants to inform clinical interpretation, the latest being AlphaMissense, which uses artificial intelligence methods trained on predicted protein structure. To independently assess the performance of AlphaMissense and 38 other predictors of missense severity, we compared predictions to data from multiplexed assays of variant effect (MAVE). MAVE experiments generate almost every possible individual amino acid change in a gene and measure their functional impact using a high-throughput assay. Assessing 17,696 variants across five genes (DDX3X, MSH2, PTEN, KCNQ4, and BRCA1), we find that AlphaMissense is consistently one of the top five algorithms based on correlation with functional impact and is the best-correlated algorithm for two genes. We conclude that AlphaMissense represents the current best-in-class predictor by this metric; however, the improvement over other algorithms is modest. We note that multiple missense predictors, including AlphaMissense, appear to overcall variants as pathogenic despite minimal functional impact and that substantially more high-quality training data, including consistently analyzed patient cohorts and MAVE analyses, are required to improve accuracy.

genetics↗

bioRxiv2023

Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences

The availability of natural protein sequences synergized with generative artificial intelligence (AI) provides new paradigms to create enzymes. Although active enzyme variants with numerous mutations have been produced using generative models, their performance often falls short compared to their wild-type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate of designed single mutants is [~]50% to improve either luciferase activity or stability. These finding highlights natures ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in Renilla luciferase towards emitting blue light that holds advantages in terms of water penetration compared to other light spectrum. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.

bioinformatics↗

bioRxiv2023

Optimising a self-assembling peptide hydrogel as a Matrigel alternative for 3-dimensional mammary epithelial cell culture

Three-dimensional (3D) organoid models have been instrumental in understanding molecular mechanisms responsible for many cellular processes and diseases. However, established organic biomaterial scaffolds used for 3D hydrogel cultures, such as Matrigel, are biochemically complex and display significant batch variability, limiting reproducibility in experiments. Recently, there has been significant progress in the development of synthetic hydrogels for in vitro cell culture that are reproducible, mechanically tuneable, and biocompatible. Self-assembling peptide hydrogels (SAPHs) are synthetic biomaterials that can be engineered to be compatible with 3D cell culture. Here we investigate the ability of PeptiGel(R) SAPHs to model the mammary epithelial cell (MEC) microenvironment in vitro. The positively charged PeptiGel(R)Alpha4 supported MEC viability, but did not promote formation of polarised acini. Modifying the stiffness of PeptiGel(R) Alpha4 stimulated changes in MEC viability and changes in protein expression associated with altered MEC function, but did not fully recapitulate the morphologies of MECs grown in Matrigel. To supply the appropriate biochemical signals for MEC organoids, we supplemented PeptiGels(R) with laminin. Laminin was found to require negatively charged PeptiGel(R) Alpha7 for functionality, but was then able to provide appropriate signals for correct MEC polarisation and expression of characteristic proteins. Thus, optimisation of SAPH composition and mechanics allows tuning to support tissue-specific organoids.

cell biology↗