Biology subjects

Nguyen, Q. H.

Publications and source records attributed to Nguyen, Q. H..

8 recordsLinked to original sources

bioRxiv2026

Experimental and computational analyses of KEA3-dependent photosynthetic responses uncover species-specific molecular variation

- NPQ relaxation is thought to limit crop biomass and yield under natural, fluctuating light. The thylakoid K/H exchanger KEA3 accelerates NPQ relaxation by lowering the lumen proton concentration and thus should affect biomass and yield under frequent light fluctuations. - To investigate the impact of KEA3-dependent NPQ relaxation on Solanum lycopersicum (tomato), we generated knock-out and over-expression lines and measured NPQ, gas exchange, biomass and yield. We constructed a mathematical model to compare species-specific NPQ relaxation dynamics in tomato and the model plant Arabidopsis thaliana. - In polytunnel and controlled fluctuating-light conditions, KEA3-mediated NPQ relaxation did not translate into consistent differences in tomato biomass or yield. Although KEA3 levels strongly affected NPQ relaxation, more so than in A.thaliana, the resulting effects on CO2 assimilation were minor. Modeling results indicated that, besides minor changes in KEA3 regulation, the activity of PsbS, the primary NPQ trigger, largely determines the species-specific differences in NPQ relaxation. Structural models from both species predict sequence variation with impact on PsbS activation. - Manipulating KEA3 levels in tomato did not link NPQ relaxation with productivity. However, the results support robust cross-species mathematical model that accurately predicts the molecular interactions governing NPQ dynamics.

plant biology↗

bioRxiv2026

GreenSloth: a curated database and executable platform for mechanistic photosynthesis models

Mechanistic models of photosynthesis have expanded substantially over the past decades, covering processes from light reactions to carbon fixation. However, these models remain fragmented across the literature, inconsistently implemented, and difficult to reproduce or reuse, limiting their adoption beyond the research group that developed them. Here, we present GreenSloth, a freely accessible web-based database of 22 published mechanistic photosynthesis models, reimplemented as standardized, executable Python objects within MxlPy, an open-source framework for mechanistic biological modeling. Although the database is primarily designed for dynamic mechanistic models formulated as ordinary differential equations, the current implementation also includes the fields most widely cited steady-state mechanistic model and its variants. GreenSloth provides a structured environment for model discovery, comparison, and reuse, addressing reproducibility challenges in the field and enabling integration into emerging hybrid modeling approaches. It is also interactive: each model runs directly in the browser, with no installation, environment setup, or programming required. The resource is openly accessible and designed for long-term community maintenance, hoping to position itself as foundational infrastructure for the photosynthesis modeling community. Database URLhttps://greensloth.rwth-aachen.de/

systems biology↗

bioRxiv2025

Preserving tissue structure through density-based spatial analysis with scider

In spatial transcriptomics, most existing approaches for spatial domain detection analyze slide-wide gene expression patterns while integrating information from local neighborhoods or nearest neighbors. However, such global strategies can overlook tissue organization and obscure regional heterogeneity. To address this, we present scider, a framework for spatial data analysis to preserve tissue structures. scider defines spatial domains from local cellular organization and distribution, instead of relying on global clustering of gene expression and neighborhood profiles. Kernel density estimation (KDE) is used to characterize the spatial distribution of cells, enabling the robust and unbiased identification of regions of interest (ROIs) beyond histological annotations. We show that ROIs make multi-sample analysis possible and reveal spatial patterns that are undetectable in single-sample analysis. Additionally, KDE-derived contour lines define regions of similar cell density, supporting cell type composition and differential expression analyses along continuous spatial gradients.

bioinformatics↗

bioRxiv2024

A surface lipoprotein on Pasteurella multocida binds complement factor I to promote immune evasion.

Pasteurella multocida is the leading cause of wound infections in humans following animals bites or scratches. This bacterium is also commonly found in the respiratory tract of many mammals and can cause serious diseases resulting in the brutal rapid death of infected animals, especially cattle. To prevent these infections in cattle, a subunit-based vaccine utilizing the surface lipoprotein PmSLP was developed and showed remarkable protection with a single dose administration. Here, we report that PmSLP binds host complement factor I (FI) and facilitates cleavage of complement components C3b and C4b independently of any cofactors (e.g FH, C4BP), thereby allowing the pathogen to evade host defence. Cryo-EM structure of PmSLP bound to FI reveals that PmSLP stimulates FI enzymatic activity by stabilizing the catalytic domain. This is the first time that a bacterial protein has been shown to directly activate FI independent of complement cofactors and target all arms of the complement cascade.

microbiology↗

bioRxiv2021

Pre-neoplastic stromal cells drive BRCA1-mediated breast tumorigenesis

Women with germline mutations in BRCA1 (BRCA1+/mut) have increased risk for developing hereditary breast cancer1, 2. Cancer initiation in BRCA1+/mut is associated with pre-malignant changes in the breast epithelium including altered differentiation3-5, proliferative stress6 and genomic instability7. However, the role of the epithelium- associated stromal niche during BRCA1-driven tumor initiation remains unclear. Here, we show that the pre-malignant stromal niche promotes epithelial proliferation and BRCA1- driven cancer initiation in trans. Using single-cell RNAseq (scRNAseq) analysis of human pre-neoplastic BRCA1+/mut and control breast tissues, we show that stromal cells provide numerous pro-proliferative paracrine signals inducing epithelial proliferation. We identify a subpopulation of pre-cancer associated fibroblasts (pre-CAFs) that produces copious amounts of pro-tumorigenic factors including matrix metalloproteinase 3 (MMP3)8, 9, and promotes BRCA1-driven tumorigenesis in vivo. Our gene-signature analysis and mathematical modeling of epithelial differentiation reveals that stromal-induced proliferation leads to the accumulation of luminal progenitor cells with altered differentiation, and thus contributes to increased breast cancer risk in BRCA1+/mut. Our results demonstrate how alterations in cell-cell communication can induce imbalances in epithelial homeostasis ultimately leading to cancer initiation. We anticipate our results to form the foundation for novel disease monitoring and therapeutic strategies to improve patient management in hereditary breast cancer. For example, pre-CAF specific proteins may serve as biomarkers for pre-cancerous disease initiation to inform whether radical bilateral mastectomy is needed. In addition, MMP inhibitors could be re-indicated for primary cancer prevention treatment in women with high-risk BRCA1 mutations.

cancer biology↗

bioRxiv2020

stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues

Spatial Transcriptomics is an emerging technology that adds spatial dimensionality and tissue morphology to the genome-wide transcriptional profile of cells in an undissociated tissue. Integrating these three types of data creates a vast potential for deciphering novel biology of cell types in their native morphological context. Here we developed innovative integrative analysis approaches to utilise all three data types to first find cell types, then reconstruct cell type evolution within a tissue, and search for tissue regions with high cell-to-cell interactions. First, for normalisation of gene expression, we compute a distance measure using morphological similarity and neighbourhood smoothing. The normalised data is then used to find clusters that represent transcriptional profiles of specific cell types and cellular phenotypes. Clusters are further sub-clustered if cells are spatially separated. Analysing anatomical regions in three mouse brain sections and 12 human brain datasets, we found the spatial clustering method more accurate and sensitive than other methods. Second, we introduce a method to calculate transcriptional states by pseudo-space-time (PST) distance. PST distance is a function of physical distance (spatial distance) and gene expression distance (pseudotime distance) to estimate the pairwise similarity between transcriptional profiles among cells within a tissue. We reconstruct spatial transition gradients within and between cell types that are connected locally within a cluster, or globally between clusters, by a directed minimum spanning tree optimisation approach for PST distance. The PST algorithm could model spatial transition from non-invasive to invasive cells within a breast cancer dataset. Third, we utilise spatial information and gene expression profiles to identify locations in the tissue where there is both high ligand-receptor interaction activity and diverse cell type co-localisation. These tissue locations are predicted to be hotspots where cell-cell interactions are more likely to occur. We detected tissue regions and ligand-receptor pairs significantly enriched compared to background distribution across a breast cancer tissue. Together, these three algorithms, implemented in a comprehensive Python software stLearn, allow for the elucidation of biological processes within healthy and diseased tissues.

bioinformatics↗

bioRxiv2020

An In Vitro Vascularized Micro-Tumor Model of Human Colorectal Cancer Recapitulates In Vivo Drug Responses

Around 95% of anti-cancer drugs that show promise during preclinical study fail to gain FDA-approval for clinical use. This failure of the preclinical pipeline highlights the need for improved, physiologically-relevant in vitro models that can better serve as reliable drug-screening tools. The vascularized micro-tumor (VMT) is a novel three-dimensional model system that recapitulates the complex human tumor microenvironment, including perfused vasculature, within a transparent microfluidic device, allowing real-time study of drug responses and tumor-stromal interactions. Here we have validated the VMT platform for the study of colorectal cancer (CRC), the second leading cause of cancer-related deaths, by showing that gene expression, tumor heterogeneity, and treatment response in the VMT more closely model CRC tumor clinicopathology than current standard drug screening modalities, including 2-dimensional (2D) monolayer culture and 3-dimensional (3D) spheroids.

cancer biology↗

bioRxiv2017

Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics and epigenetics data

Genome sequences for hundreds of mammalian species are available, but an understanding of their genomic regulatory regions, which control gene expression, is only beginning. A comprehensive prediction of potential active regulatory regions is necessary to functionally study the roles of the majority of genomic variants in evolution, domestication, and animal production. We developed a computational method to predict regulatory DNA sequences (promoters, enhancers and transcription factor binding sites) in production animals (cows and pigs) and extended its broad applicability to other mammals. The method utilizes human regulatory features identified from thousands of tissues, cell lines, and experimental assays to find homologous regions that are conserved in sequences and genome organization and are enriched for regulatory elements in the genome sequences of other mammalian species. Importantly, we developed a filtering strategy, including a machine learning classification method, to utilize a very small number of species-specific experimental datasets available to select for the likely active regulatory regions. The method finds the optimal combination of sensitivity and accuracy to unbiasedly predict regulatory regions in mammalian species. Furthermore, we demonstrated the utility of the predicted regulatory datasets in cattle for prioritizing variants associated with multiple production and climate change adaptation traits, and identifying potential genome editing targets.

genomics↗