Biology subjects

Koh, C.

Publications and source records attributed to Koh, C..

7 recordsLinked to original sources

bioRxiv2025

Spatio-temporal factors shape mosquito virome diversity and composition in a host and virus phylogeny-dependent manner

Beyond the arboviruses they transmit to humans and animals, mosquitoes harbour diverse viral communities that shape infection and immunity dynamics. Understanding how these communities shift within mosquito populations across time and space has important implications for how we anticipate and manage arboviral disease epidemics. Here, we examined the viromes of rural and sylvatic mosquito species sampled longitudinally across two years from countries representing South America, Africa, and Southeast Asian geographies to quantify the relative contributions of host and ecological factors in shaping their virome dynamics. We found that virus diversity and virome composition are strongly influenced by host phylogeny and spatial factors and show limited temporal variation. By examining virus similarity networks across mosquito species and biogeographies, we observed frequent instances of within- and cross-genus Culex-Anopheles virus sharing in Madagascar. Taken together, these findings inform the development and implementation strategies of any virome-based vector control technology and raise new evolutionary questions on how host generalism shapes the spatial prevalence of viruses and their potential for disease emergence.

microbiology↗

bioRxiv2025

Computational Modeling of Ternary Complex Formation and Targeted Protein Degradation Mediated by Molecular Glues

The rational design of molecular glue degraders is challenging because glue-mediated protein degradation depends on a complex interplay of molecular mechanisms: an effective molecular glue must simultaneously bring together two distinct proteins to form an interface that is both stable and accessible for ubiquitination by E3 ligases. We present GlueMap, a computational platform for discovering and optimizing molecular glues (MGs) through integrative structural and pharmacodynamic modeling. GlueMap combines molecular dynamics (MD) simulations and machine learning (ML) to model targeted protein degradation (TPD) mediated by molecular glues. We validated GlueMap by applying it to two case studies: DDB1-dependent degradation of CDK12 and CRBN-dependent degradation of GSPT1. In the DDB1-CDK12 system, GlueMap revealed strong correlations between ternary-complex stability, quantified through rigorous free-energy calculations, and experimental degradation efficiency. Notable outliers underscored the importance of incorporating conformational dynamics within assembled Cullin-RING ligase complexes. Using a supervised variational autoencoder combined with attention-based regression, GlueMap achieved high predictive accuracy for degradation potency from structural features. For CRBN-GSPT1, GlueMap reproduced known ternary-complex crystal structures and identified previously unrecognized protein-protein interactions (PPIs) that may guide the design of next-generation glues. Hierarchical virtual screening of more than 12 billion compounds successfully recovered known degraders and uncovered nine new potent MGs with DC50 values below 50 nM. Across both systems, GlueMap outperformed Boltz-1 and Boltz-2, state-of-the-art structure-prediction models that failed to reproduce the CDK12-DDB1 crystal structure and captured substantially less PPI diversity. By integrating structural, thermodynamic, and ubiquitination metrics, GlueMap establishes a multi-criteria framework for rational molecular glue design. These results demonstrate that GlueMap provides both mechanistic insight and practical acceleration of molecular glue discovery and optimization across diverse therapeutic targets.

bioinformatics↗

bioRxiv2022

Design of a systemic small molecule clinical STING agonist using physics-based simulations and artificial intelligence

The protein STING (stimulator of interferon genes) is a central regulator of the innate immune system and plays an important role in antitumor immunity by inducing the production of cytokines such as type I interferon (IFN). Activation of STING stems from the selective recognition of endogenous cyclic dinucleotides (CDNs) by the large, polar, and flexible binding site, thus posing challenges to the design of small molecule agonists with drug-like physicochemical properties. In this work we present the design of SNX281, a small molecule STING agonist that functions through a unique self-dimerizing mechanism in the STING binding site, where the ligand dimer approximates the size and shape of a cyclic dinucleotide while maintaining drug-like small molecule properties. SNX281 exhibits systemic exposure, STING-mediated cytokine release, strong induction of type I IFN, potent in vivo antitumor activity, durable immune memory, and single-dose tumor elimination in mouse models via a Cmax-driven pharmacologic response. Bespoke computational methods - a combination of quantum mechanics, molecular dynamics, binding free energy simulations, and artificial intelligence - were developed during the course of the project to design SNX281 by explicitly accounting for the unique self-dimerization mechanism and the large-scale conformational change of the STING protein upon activation. Over the course of the project, we explored millions of virtual molecules while synthesizing and testing only 208 molecules in the lab. This work highlights the value of a multifaceted computationally-driven approach anchored by methods tailored to address target-specific problems encountered along the project progression from initial hit to the clinic.

cancer biology↗

bioRxiv2022

The E2 glycoprotein holds key residues for Mayaro virus adaptation to the urban Aedes aegypti mosquito

Adaptation to mosquito vectors suited for transmission in urban settings is a major driver in the emergence of arboviruses. To better anticipate future emergence events, it is crucial to assess their potential to adapt to new vector hosts. In this work, we used two different experimental evolution approaches to study the adaptation process of an emerging alphavirus, Mayaro virus (MAYV), to Aedes aegypti, an urban mosquito vector of many arboviruses. We identified E2-T179N as a key mutation increasing MAYV replication in insect cells and enhancing transmission by live Aedes aegypti. In contrast, this mutation decreased viral replication and binding in human fibroblasts, a primary cellular target of MAYV in humans. We also showed that MAYV E2-T179N was attenuated in vivo in a mouse model. We then used structural and experimental analyses to show that MAYV E2-T179N bound less efficiently to human cells, though the decrease in replication or binding was not mediated through interaction with one of the host receptors, Mxra8. When this mutation was introduced in the closely related chikungunya virus, which has caused major outbreaks globally in the past two decades, we observed increased replication in both human and insect cells, suggesting E2 position 179 is an important determinant of alphavirus host-adaptation, although in a virus-specific manner. Collectively, these results indicate that adaptation at the T179 residue in MAYV E2 may result in increased vector competence - but coming at the cost of optimal replication in humans - and may represent a first step towards a future emergence event. Author SummaryMosquito-borne viruses must replicate in both mosquito and vertebrate hosts to be maintained in nature successfully. When viruses that are typically transmitted by forest dwelling mosquitoes enter urban environments due to deforestation or travel, they must adapt to urban mosquito vectors to transmit effectively. For mosquito-borne viruses, the need to also replicate in a vertebrate host like humans constrains this adaptation process. Towards understanding how the emerging alphavirus, Mayaro virus, might adapt to transmission by the urban mosquito vector, Aedes aegypti, we used natural evolution approaches to identify several viral mutations that impacted replication in both mosquito and vertebrate hosts. We show that a single mutation in the receptor binding protein increased transmission by Aedes aegypti but simultaneously reduced replication and disease in a mouse model, suggesting that this mutation alone is unlikely to be maintained in a natural transmission cycle between mosquitoes and humans. Understanding the adaptive potential of emerging viruses is critical to preventing future pandemics.

microbiology↗

bioRxiv2022

Decoding rRNA Sequences for Improved Metagenomics in Sylvatic Mosquito Species

As mosquito-borne virus epidemics are often preceded by undetected spillover events, surveillance and virus discovery studies in non-urban mosquitoes informs pre-emptive and responsive public health measures. RNA-seq metagenomics is a popular methodology but it is constrained by overabundant rRNA. The lack of reference sequences for most mosquito species is a major impediment against physical and computational removal of rRNA reads. We describe a strategy to assemble novel rRNA sequences from mosquito specimens, producing an unprecedented dataset of 234 full-length 28S and 18S rRNA sequences of 33 medically important species from countries with known histories of mosquito-borne virus circulation (Cambodia, the Central African Republic, Madagascar, and French Guiana). We also evaluate the utility of rRNA sequences as molecular barcodes relative to the mitochondrial cytochrome c oxidase I (COI) gene. We show that rRNA sequences can be used for species identification when COI sequences are ambiguous or unavailable, revealing evolutionary relationships concordant with contemporary mosquito systematics. This expansion of the rRNA reference library improves mosquito RNA-seq metagenomics by permitting the optimization of species-specific rRNA depletion protocols for a broader species range and streamlined species identification by rRNA barcoding. In addition, rRNA barcodes could serve as an additional tool for mosquito taxonomy and phylogeny.

bioinformatics↗

bioRxiv2019

Artificial selection finds new hypotheses for the mechanism of Wolbachia-mediated dengue blocking in mosquitoes

Wolbachia is an intracellular bacterium that blocks virus replication in insects and has been introduced into the mosquito, Aedes aegypti for the biocontrol of arboviruses including dengue, Zika and chikungunya. Despite ongoing research, the mechanism of Wolbachia-mediated virus blocking remains unclear. We recently used experimental evolution to reveal that Wolbachia-mediated dengue blocking could be selected upon in the A. aegypti host and showed evidence that strong levels of blocking could be maintained by natural selection. In this study, we investigate the genetic variation associated with blocking and use these analyses to generate testable hypotheses surrounding the mechanism of Wolbachia-mediated dengue blocking. From our results, we hypothesise that Wolbachia may block virus replication by increasing the regeneration rate of mosquito cells via the Notch signalling pathway. We also propose that Wolbachia modulates the hosts transcriptional pausing pathway either to prime the hosts anti-viral response or to directly inhibit viral replication.

evolutionary biology↗

bioRxiv2017

Coessentiality And Cofunctionality: A Network Approach To Learning Genetic Vulnerabilities From Cancer Cell Line Fitness Screens

Genetic interaction networks are a powerful approach for functional genomics, and the synthetic lethal interactions that comprise these networks offer a compelling strategy for identifying candidate cancer targets. As the number of published shRNA and CRISPR perturbation screens in cancer cell lines expands, there is an opportunity for integrative analysis that goes further than pairwise synthetic lethality and discovers genetic vulnerabilities of related sets of cell lines. We re-analyze over 100 high-quality, genome-scale shRNA screens in human cancer cell lines and derive a quantitative fitness score for each gene that accurately reflects genotype-specific gene essentiality. We identify pairs of genes with correlated essentiality profiles and merge them into a cancer coessentiality network, where shared patterns of genetic vulnerability in cell lines give rise to clusters of functionally related genes in the network. Network clustering discriminates among all three defined subtypes of breast cancer cell lines (basal, luminal, and Her2-amplified), and further identifies novel subsets of Her2+ and ovarian cancer cells. We demonstrate the utility of the network as a platform for both hypothesis-driven and data-driven discovery of context-specific essential genes and their associated biomarkers.

genomics↗