Biology subjects

Wong, Y.

Publications and source records attributed to Wong, Y..

15 recordsLinked to original sources

bioRxiv2026

Pulmonary delivery of antigen-enhanced BCG overcomes safety barriers in immunocompromised hosts and protects against TB in the absence of adaptive immunity.

The low efficacy of Bacille Calmette-Guerin (BCG) in preventing pulmonary tuberculosis (TB) underscores the need for improved TB vaccines. Recombinant BCG (rBCG) strains secreting the virulence-associated effector molecule ESAT-6 from Mycobacterium tuberculosis (Mtb) markedly improve efficacy and immunogenicity in animal models of TB but have been considered unsuitable for clinical translation due to safety concerns identified in intravenous SCID mouse models. Here, we demonstrate that pulmonary delivery fundamentally reshapes the safety and protective efficacy of the ESAT-6-secreting rBCG strains, BCG::RD1 and BCG::ESAT6-PE25SS. In sharp contrast to intravenous delivery, pulmonary administration was markedly better tolerated, improved survival, and reduced systemic dissemination and brain pathology of severely immunocompromised mice. Strikingly, pulmonary rBCG vaccination also conferred superior protection against aerosol Mtb challenge in wild-type, type 2 diabetic, and adaptive immunity-deficient Rag1-/- and Rag2-/-Il2rg-/- mice, with BCG::RD1 showing the strongest adaptive immunity-independent protection. Mechanistically, pulmonary rBCG vaccination promoted lung innate immune activation, expansion of myeloid-biased progenitors in the bone marrow, and enhanced antimycobacterial activity of macrophages, consistent with trained innate immunity. Collectively, these findings reveal that pulmonary vaccination largely overcomes safety concerns of ESAT-6-secreting rBCG strains and provide preclinical evidence for a viable strategy to improve protection against TB in immunocompromised individuals.

immunology↗

bioRxiv2026

Piezo1 couples fluid shear stress to adaptive genome dynamics by integrating cytoplasmic-nuclear mechanotransduction

Fluid shear stress (FSS) regulates endothelial morphology and function through flow-responsive gene expression programs. Yet, how mechanical forces are transmitted across cytoplasmic and nuclear compartments to regulate genome adaptive response remains unclear. Here, we show that FSS induces rapid nuclear remodeling characterized by nuclear compaction and apical nuclear indentations. These changes are driven by reorganization of perinuclear actin and microtubule cytoskeleton into apical linear cytoskeletal cables that constrain the nuclear surface. Concurrently, the mechanosensitive ion channel Piezo1 redistributes from the plasma membrane to these perinuclear deformations. Quantitative molecular imaging under flow reveals a transient adaptive cell state characterized by chromatin reorganization and epigenetic remodeling, accompanied by altered mobility of the flow-responsive transcription factor KLF2. Pharmacological inhibition of Piezo1 abolishes FSS-induced nuclear deformation and uncouples chromatin reorganization from KLF2 dynamic changes. Together, these findings reveal that endothelial mechanotransduction exploits physical principles of nuclear organization to regulate transcription factor behavior and adaptive genome responses across biological scales.

Cell Biology↗

bioRxiv2026

Tracing the evolutionary histories of ultra-rare variants using variational dating of large ancestral recombination graphs

Ultra-rare variants dominate whole-genome sequencing datasets, yet their interpretation is limited by allele frequency, which provides little information at very low counts and is highly sensitive to uneven ancestry representation. Allele age offers an ancestry-agnostic alternative but existing methods do not scale to biobank-sized cohorts. Here we present a scalable variational algorithm for dating Ancestral Recombination Graphs (ARGs), implemented in tsdate, together with new distributed methods enabling practical biobank-scale ARG inference using tsinfer. Applied to 47,535 genomes from the Genomics England 100,000 Genomes Project, we infer contiguous ARGs spanning 206 Mb and estimate ages for 23.2 million variants, including 11.8 million singletons. ARG-based allele ages remain accurate under extreme sampling imbalance and, in real data, reveal signatures of purifying selection and clinically relevant heterogeneity among variants with identical observed frequencies. Estimates for recent mutations are precise only at large sample sizes, highlighting the information accessible in the haplotype structure of large datasets. Biobank-scale ARGs therefore enable robust, ancestry-agnostic age estimation for ultra-rare variation with broad utility for statistical and clinical genomics.

genetics↗

bioRxiv2025

Differential Regulation of APOE4-Mediated Astrocytic Lipid Metabolism by BMP Signaling Exacerbates Alzheimers Disease Pathologies in Neurons

The two greatest risk factors for Alzheimers Disease (AD) are aging and Apolipoprotein E4 (APOE4) polymorphism, yet how these factors interact remain unclear. In this study, we investigate how bone morphogenetic protein (BMP) signaling, which increases with age, contributes to APOE4-induced lipid metabolic dysfunctions using induced-pluripotent stem cell (iPSC)-derived astrocytes and cocultured neurons. Surprisingly, BMP signaling differentially altered lipid droplet formation, cholesterol synthesis and breakdown, and fatty acid-oxidation in APOE4 compared to APOE3 astrocytes, and increased secretion of oxidized LDL (oxLDL). Furthermore, neurons cocultured with BMP4-treated APOE4 astrocytes showed altered transcriptomic profiles based on scRNA-seq as well as increased tau phosphorylation (p-tau). oxLDL treatment similarly increased p-tau and reduced neuronal survival. Conversely, lipid uptake inhibition in neurons rescued the BMP4/APOE4 astrocyte-induced neuronal phenotype. These data demonstrate key interactions between APOE4 and aging-associated molecular signaling in AD pathogenesis and establish a causal linkage between astrocytic lipid metabolism and neuronal tau hyperphosphorylation.

neuroscience↗

bioRxiv2025

Accessible, realistic genome simulation with selection using stdpopsim

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsims core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

evolutionary biology↗

bioRxiv2023

A general and efficient representation of ancestral recombination graphs

As a result of recombination, adjacent nucleotides can have different paths of genetic inheritance and therefore the genealogical trees for a sample of DNA sequences vary along the genome. The structure capturing the details of these intricately interwoven paths of inheritance is referred to as an ancestral recombination graph (ARG). Classical formalisms have focused on mapping coalescence and recombination events to the nodes in an ARG. This approach is out of step with modern developments, which do not represent genetic inheritance in terms of these events or explicitly infer them. We present a simple formalism that defines an ARG in terms of specific genomes and their intervals of genetic inheritance, and show how it generalises these classical treatments and encompasses the outputs of recent methods. We discuss nuances arising from this more general structure, and argue that it forms an appropriate basis for a software standard in this rapidly growing field.

genetics↗

bioRxiv2023

Towards Pandemic-Scale Ancestral Recombination Graphs of SARS-CoV-2

Millions of SARS-CoV-2 genome sequences were collected during the COVID-19 pandemic, forming a dataset of unprecedented richness. Estimated genealogies are fundamental to understanding this ocean of data and form the primary input to many downstream analyses. A basic assumption of methods to infer genealogies from viral genetic data is that recombination is negligible and the genealogy is a tree. However, recombinant lineages have risen to global prevalence, and simple tree representations are therefore incomplete and potentially misleading. We present sc2ts, a method to infer reticulate genealogies as an Ancestral Recombination Graph (ARG) in real time at pandemic scale. We infer an ARG for 2.48 million SARS-CoV-2 genomes, which leverages the widely used tskit software ecosystem to support further analyses and visualisation. This rich and validated resource clarifies the relationships among recombinant lineages, quantifies the rate of recombination over time, and provides a lower bound on detectable recombination.

evolutionary biology↗

bioRxiv2023

Integrative proteomics and transcriptomics of human T-cells reveals temporal metabolic reprogramming following TCR-induced activation

T-cells are critical components of the adaptive immune system. Upon activation, they acquire effector functions through a complex interplay between mRNA transcripts and proteins, the landscape of which remains to be fully elucidated. In this resource article, we present an integrative temporal proteomic and transcriptomic analysis of primary human CD4+ and CD8+ T-cells following ex vivo activation with anti-CD3/CD28 Dynabeads. Our data reveal a time-dependent dissociation between the T-cell transcriptome and proteome during activation. A transient downregulation of GLUT1, the central glucose transporter in T-cells, marked the onset of reprogramming in both CD4+ and CD8+ T-cells. At late activation, CD4+ T-cells upregulated enzymes associated with degradation of fatty acids while CD8+ T-cells preferentially upregulated enzymes in the metabolism of cofactors and vitamins. Surprisingly, we found that activated CD4+ and CD8+ T-cells became transcriptionally more divergent at the same time their proteome became more similar. In addition to the metabolic reprogramming highlighted in our analysis, this dataset provides a public resource for understanding temporal molecular changes governing the acquisition of effector functions by T-cells.

systems biology↗

bioRxiv2023

Insights into the mechanism of oligodendrocyte protection and remyelination enhancement by the integrated stress response

CNS inflammation triggers activation of the integrated stress response (ISR). We previously reported that prolonging the ISR protects remyelinating oligodendrocytes and promotes remyelination in the presence of inflammation (Chen et al., eLife, 2021). However, the exact mechanisms through which this occurs remain unknown. Here, we investigated whether the ISR modulator Sephin1 in combination with the oligodendrocyte differentiation enhancing reagent bazedoxifene (BZA) is able to accelerate remyelination under inflammation, and the underlying mechanisms mediating this pathway. We find that the combined treatment of Sephin1 and BZA is sufficient to accelerate early-stage remyelination in mice with ectopic IFN-{gamma} expression in the CNS. IFN-{gamma}, which is a critical inflammatory cytokine in multiple sclerosis (MS), inhibits oligodendrocyte precursor cell (OPC) differentiation in culture and triggers a mild ISR. Mechanistically, we further show that BZA promotes OPC differentiation in the presence of IFN-{gamma}, while Sephin1 enhances the IFN-{gamma}-induced ISR by reducing protein synthesis and increasing RNA stress granule formation in differentiating oligodendrocytes. Finally, the ISR suppressor 2BAct is able to partially lessen the beneficial effect of Sephin1 on disease progression, in an MS mouse model of experimental autoimmune encephalitis (EAE). Overall, our findings uncover distinct mechanisms of action of BZA and Sephin1 on oligodendrocyte lineage cells under inflammatory stress, suggesting that a combination therapy may effectively promote restoring neuronal function in MS patients.

neuroscience↗

bioRxiv2022

Expanding the stdpopsim species catalog, and lessons learned forrealistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

bioinformatics↗

bioRxiv2021

Efficient ancestry and mutation simulation with msprime 1.0

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and tskit library. We summarise msprimes many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

genetics↗

bioRxiv2021

A unified genealogy of modern and ancient genomes

The sequencing of modern and ancient genomes from around the world has revolutionised our understanding of human history and evolution1,2. However, the general problem of how best to characterise the full complexity of ancestral relationships from the totality of human genomic variation remains unsolved. Patterns of variation in each data set are typically analysed independently, and often using parametric models or data reduction techniques that cannot capture the full complexity of human ancestry3,4. Moreover, variation in sequencing technology5,6, data quality7 and in silico processing8,9, coupled with complexities of data scale10, limit the ability to integrate data sources. Here, we introduce a non-parametric approach to inferring human genealogical history that overcomes many of these challenges and enables us to build the largest genealogy of both modern and ancient humans yet constructed. The genealogy provides a lossless and compact representation of multiple datasets, addresses the challenges of missing and erroneous data, and benefits from using ancient samples to constrain and date relationships. Using simulations and empirical analyses, we demonstrate the power of the method to recover relationships between individuals and populations, as well as to identify descendants of ancient samples. Finally, we show how applying a simple non-parametric estimator of ancestor geographical location to the inferred genealogy recapitulates key events in human history. Our results demonstrate that whole-genome genealogies are a powerful means of synthesising genetic data and provide rich insights into human evolution.

genomics↗

bioRxiv2020

Dynamic visualisation of million-tip trees: the OneZoom project

O_LIThe complete tree of life is now available, but methods to visualise it are still needed to meet needs in research, teaching and science communication. Dynamic visualisation of million-tip trees requires many challenges in data synthesis, data handling and computer graphics to be overcome. C_LIO_LIOur approach is to automate data processing, synthesise data from a wide range of available sources, then to feed these data to a client-side visualisation engine in parts. We develop a way to store the whole tree topology locally in a highly compressed form, then dynamically populate metadata such as text and images as the user explores. C_LIO_LIThe result is a seamless and smooth way to explore the complete tree of life, including images and metadata, even on relatively old mobile devices. C_LIO_LIThe underlying methods developed have applications that transcend tree of life visualisation. For the whole complete tree, we describe automated ID mappings between well known resources without resorting to taxonomic name resolution, automated methods to collate sets of public domain representative images for higher taxa, and an index to measure public interest of individual species. C_LIO_LIThe visualisation layout and the client user interface are both abstracted components of the codebase enabling other zoomable tree layouts to be swapped in, and supporting multiple applications including exhibition kiosks and digital art. C_LIO_LIAfter ten years of work, our tree of life explorer is now broadly complete, it has attracted nearly 1.5 million online users, and is backed by a novel long-term sustainability plan. We conclude our description of the OneZoom project by suggesting the next challenges that need to be solved in this field: extinct species and guided tours around the tree. C_LI

evolutionary biology↗

bioRxiv2020

Wnt-regulated lncRNA discovery enhanced by in vivo identification and CRISPRi functional validation

BackgroundWnt signaling is an evolutionarily conserved developmental pathway that is frequently hyperactivated in cancer. While multiple protein-coding genes regulated by Wnt signaling are known, the functional lncRNAs regulated by Wnt signaling have not been systematically characterized. ResultsWe comprehensively mapped lncRNAs from an orthotopic Wnt-addicted pancreatic cancer model, identifying 3,633 lncRNAs, of which 1,503 were regulated by Wnt signaling. We found lncRNAs were much more sensitive to changes in Wnt signaling in xenografts than in cultured cells. To functionally validate Wnt-regulated lncRNAs, we performed CRISPRi screens to assess their role in cancer cell proliferation. Consistent with previous genome-wide lncRNA CRISPRi screens, around 1% (13/1,503) of the Wnt-regulated lncRNAs could modify cancer cell growth in vitro. This included CCAT1 and LINC00263, previously reported to regulate cancer growth. Using an in vivo CRISPRi screen, we doubled the discovery rate, identifying twice as many Wnt-regulated lncRNAs (25/1,503) that had a functional effect on cancer cell growth. ConclusionsOur study demonstrates the value of studying lncRNA functions in vivo, provides a valuable resource of lncRNAs regulated by Wnt signaling and establishes a framework for systematic discovery of functional lncRNAs.

genomics↗

bioRxiv2018

Inferring the ancestry of everyone

A central problem in evolutionary biology is to infer the full genealogical history of a set of DNA sequences. This history contains rich information about the forces that have influenced a sexually reproducing species. However, existing methods are limited: the most accurate is unable to cope with more than a few dozen samples. With modern genetic data sets rapidly approaching millions of genomes, there is an urgent need for efficient inference methods to exploit such rich resources. We introduce an algorithm to infer whole-genome history which has comparable accuracy to the state-of-the-art but can process around four orders of magnitude more sequences. Additionally, our method results in an \"evolutionary encoding\" of the original sequence data, enabling efficient access to genealogies and calculation of genetic statistics over the data. We apply this technique to human data from the 1000 Genomes Project, Simons Genome Diversity Project and UK Biobank, showing that the genealogies we estimate are both rich in biological signal and efficient to process.

evolutionary biology↗