Biology subjects

Vu, T. D.

Publications and source records attributed to Vu, T. D..

3 recordsLinked to original sources

bioRxiv2026

Structural basis of canonical TIR-NLR activation in plant innate immunity

In plants, intracellular NLRs (nucleotide-binding leucine-rich repeat receptors) detect pathogen effector proteins, form oligomeric resistosomes, and activate ETI (effector-triggered immunity). NLRs contain N-terminal signaling, central NB-ARC (nucleotide-binding) and C-terminal LRR (leucine-rich repeat) domains. NLRs with N-terminal TIR (Toll/interleukin-1 receptor) domains (TNLs) hydrolyze NAD+ (nicotinamide adenine dinucleotide) to generate signaling molecules. We determined cryo-EM structures of flax M, a canonical non C-JID (C-terminal jellyroll/Ig-like domain) TNL, in both monomeric autoinhibited conformation, and tetrameric resistosome after activation by its rust fungal effector AvrM-A. AvrM-A homodimers dissociate into monomers to bind directly to the LRR and NB-ARC domains in the M resistosome. The resistosome structure includes a non-hydrolyzable NAD+ analogue, revealing the substrate NAD+ recognition mechanism by the TIR domains. M cleaves NAD+ and generates the same signaling compounds as the related flax TNL, L6. Our findings explain the mechanism of TNL signaling, and provide a basis for rational engineering of disease-resistant crops.

plant biology↗

bioRxiv2024

Tensor Kernel Learning for Classification of Alzheimer's Conditions using Multimodal Data

Early, timely, and accurate assessment of Alzheimers disease (AD), particularly at its earlier stage - mild cognitive impairment (MCI) -, is central to detecting, managing, and potentially treating the disease. The biological underpinnings of AD, however, is multifaceted, from genetic variations, abnormal protein accumulation, to irregular brain functions and structure. A joint analysis of these data, therefore, may offer potentially new insights about AD-related biomarkers and AD prediction. But such explorations must confront the complexity of these data: heterogeneity, multimodality and high-dimensionality. Here, to address these challenges, we propose a new machine-learning method, namely the tensor kernel learning (TKL), leveraging tensor methods and kernel learning, to enhance AD assessment by enhancing multi-modal data integration. More specifically, TKL first uses CP/PARAFAC decomposition and graph diffusion to fuse multiple kernels learned from four complementary data modalities (MRI, PET, CSF, and SNP data). We then used a supervised kernel for a kernel SVM classifier to identify potential patients. To evaluate the effectiveness of TKL, we apply it to data from the Alzheimers Disease Neuroimaging Initiative (ADNI; the sample size n = 331 subjects), including cognitively normal individuals (CN), MCI subjects, and AD patients. TKL improves AD classification performance in both linear and nonlinear combinations, achieving accuracies of 91.31% for CN vs. AD, 81.45% for CN vs. MCI, and 78.27% for AD vs. MCI, compared to 85.48%, 70.89%, and 73.51% using the best single modality. Additionally, TKL reveals clearer, more structured patterns in the data, enhancing interpretation and understanding of the relationships among different modalities. Detailed implementation details of our method can be found at: https://github.com/thanhvd18/Tensor-Kernel-Learning-matlab.

neuroscience↗

bioRxiv2021

Genetic diversity of the pathogenic banana Fusarium wilt in northern Viet Nam

Fusarium is one of the most important fungal genera of plant pathogens that affect the cultivation of a wide range of crops. Agricultural losses caused by Fusarium oxysporum f. sp. cubense (Foc) have a direct effect on the income, subsistence and nourishment of thousands of smallholder farmers worldwide. In addition, also commercial growers are strongly affected. For Viet Nam, predictions on the impact of Foc for the future are dramatic with an estimated loss in banana production area of 8% within the next 5 years and up to 71% within the next 25 years. In the current study we applied a combined morphological - molecular approach to assess the taxonomic identity and phylogenetic position of the different Foc isolates that were collected in northern Viet Nam. In addition, we aim to estimate the proportion of the different Fusarium races that are infecting bananas in northern Viet Nam. The morphology of the isolates was investigated by growing the collected Fusarium isolates on four distinct nutritious media (PDA, SNA, CLA, and OMA). Molecular phylogenetic relationships were inferred by sequencing partial rpb1, rpb2 and tef1a genes and adding the obtained sequences into a large phylogenetic framework. The present study showed that Foc Race 1 is the most common strain in northern Viet Nam, causing 74% of all the infections. A more in-depth molecular characterization shows that of the Foc Race 1 infections, 92% are caused by Fusarium tardichlamydosporum and 8% by F. duoseptatum. Compared to Foc Race 1, Foc TR4 (represented by F. odoratissimum) account for only 10.5% of the Fusarium wilting in northern Viet Nam demonstrating that Foc TR4 is not yet a dominant strain in the region. Foc Race 2 infections of Vietnamese bananas also account for 10.5% of the Fusarium wilting in northern Viet Nam. One of the isolates cultured from diseased bananas collected in northern Viet Nam was phylogenetically not positioned within the F. oxysporum species complex (FOSC), but in contrast fell within the Fusarium fujikuroi species complex (FFSC). As a result, it is possible that a new pathogen for bananas has been found. Besides being present on several ABB Tay banana, Foc Race 1 was also found as pathogen on wild Musa lutea, showing the importance of wild bananas as possible sink for Foc.

evolutionary biology↗