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Biology subjects

Nguyen, N. H.

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

17 recordsLinked to original sources

Soil Resistomes in a Tropical Watershed are Indirectly Structured by Bacterial Community Interactions with Soil Properties

Soils are recognized as reservoirs of antibiotic resistance genes (ARGs) with the potential to transfer to clinical pathogens, creating antimicrobial resistance (AMR) that poses a threat to human health. While large-scale AMR surveys have profiled how diverse biomes shape soil resistomes, less is known about the influence of specific soil properties. Here, we combined metagenomics and 16S rRNA amplicon sequencing with isolate-based approaches to investigate drivers of soil AMR across a tropical watershed from beach to mountaintop in Waimea Valley, Oahu, Hawai{square}i. We leveraged functional- and taxonomic-classification of resistances to unravel how soil properties interact with bacterial taxa to structure resistomes. Metagenomic- and isolate-resistomes showed remarkable consistency, including a general gradient of increasing AMR from ridge to beach. Resistome functional composition was significantly correlated with total bacterial community structure. The relationship between resistances and soil properties was primarily dictated by taxonomic composition of each resistance. Rifampin- and Vancomycin-ARGs associated with Actinomycetes negatively correlated with soil physical properties, while resistant genes and isolates from Gammaproteobacteria positively correlated with enzymatic activity metrics. These findings indicate that soil properties structure the resistome indirectly through taxonomic filtering of microbial hosts and challenge the notion that AMR is decoupled from phylogenetic relatedness.

ecology↗

Multi-level, multi-body atomic interaction graphs for machine learning-based prediction of protein-ligand binding energies

Accurate prediction of binding affinity is crucial for rational drug design and discovery. Traditional computational methods often rely on complex scoring functions that incorporate a multitude of physical and chemical descriptors, leading to high computational demands and sometimes limited generalizability. In this work, we propose a novel scoring function that models multi-level, multi-body atomic interactions using graph-based representations. Our method constructs comprehensive interaction graphs that incorporate both pairwise and triplet-wise atomic features that help capture cooperative spatial patterns essential for binding affinity prediction. By employing a feature fusion strategy, GMI-Score maintains model simplicity while enhancing accuracy. Extensive evaluation across multiple datasets, such as PDBbind v2013, PDBbind v2016, PDBbind v2020, CSAR-NRC-HiQ, and PDBbind-Redocked, demonstrates that our model consistently outperforms state-of-the-art scoring functions, achieving Pearson correlation coefficients up to 0.877. Furthermore, it retains strong predictive power under strict data leakage controls and realistic docking conditions to high-light its robustness and generalizability. Scientific ContributionIn this study, we present a scoring methodology that systematically captures higher-order atomic interactions within a unified graph framework, making a conceptual shift in cheminformatics scoring functions. Its consistent outperformances of existing methods and strong validity under redocked and withheld atascenarios demonstrate its utility for broad-scale molecular modeling applications and open heminformaticsworkflows.

bioinformatics↗

Reduced precipitation alters microbial availability and redistribution of rhizosphere carbon

Climate change intensifies grassland drying, which is predicted to decrease soil organic carbon (SOC) storage. Yet how reduced soil water affects the transport and processing of carbon in the rhizosphere -- a major source of SOC supporting microbial activities -- remains poorly resolved under field conditions. We traced living root-derived carbon (rhizodeposit C) into soils and microbes using {superscript 1}3CO2 labeling in a multi-year precipitation manipulation experiment in a California Mediterranean annual grassland dominated by Avena barbata. Under halved precipitation, season-averaged soil water content was 20% lower and soil matric potential more than threefold lower, without inducing detectable signs of plant stress. Quantitative stable isotope probing (qSIP) of rhizosphere microbial DNA revealed that up to 68% more bacterial and 13% more fungal taxa incorporated rhizodeposit C, and that microbial uptake of rhizodeposit C increased more over time under reduced precipitation, even as total microbial biomass and community composition were unchanged. {superscript 1}3C-informed co-occurrence networks of these consumers were larger and more connected under reduced precipitation. The surrounding soils contained less 13C, and modeled solute transport by soil water was 32-40% lower, implying that rhizodeposit C was retained and intensively processed nearer to roots rather than transported to the wider soil matrix. Our evidence supports that reduced precipitation concentrates rhizodeposit C near roots, increasing the number of distinct consumers and intensifying associations among them. This represents an overlooked mechanism linking precipitation reduction to rhizodeposit C cycling. These findings show that precipitation reduction may reshape rhizosphere C processing before apparent plant stress response, with potential consequences for the fate of rhizodeposit C in grassland soils.

microbiology↗

Global population structures and demographic history of Suillus luteus, a pine co-introduced ectomycorrhizal fungus associated with exotic forestry and invasion.

Human colonization since the 19th century has resulted in the global spread of pines across the Southern Hemisphere, well beyond their original northern boreal distribution. Such introductions moved not only the pines but also expanded the distribution of their symbiotic partners. Although the introduction of pines is documented through historical records, little is known about the introduction history of their ectomycorrhizal fungi, which are critical symbionts for the survival and invasion of pines. Using Suillus luteus as an example, population genomic analyses of 208 individuals across both native and introduced ranges showed that all introductions originated from Europe, likely mediated by human activities along with pine introductions. With the exception of North America, introduced populations were genetically differentiated from the Europe population, with varying magnitudes of population expansion in different introduced regions, often linked to forestry practices. Genetic variation within the native European population followed isolation by distance, but not in the introduced range, highlighting the disparity in the spatial genetic patterns of native versus exotic habitats. This study provides insight into the population genetics of a globally introduced ectomycorrhizal fungus whose introduction process is likely applicable to other pine-co-introduced ectomycorrhizal fungi.

evolutionary biology↗

A Near Infrared Fluorescent Nanosensor for Spatial and Dynamic Measurements of Auxin, Indole-3-Acetic Acid, in Planta

Auxins, particularly indole-3-acetic acid (IAA), is a phytohormone critical for plant growth, development, and response to environmental stimuli. Despite its importance, there is a lack of species-independent sensors that allow direct and reversible detection of IAA. Herein, we introduce a novel near infrared fluorescent nanosensor for spatial and temporal measurement of IAA in planta using Corona Phase Molecular Recognition. The IAA nanosensor shows high specificity to IAA in vitro and was validated to localize and function in plant cells. The sensor works across different plant species without optimization and allows visualization of dynamic changes to IAA distribution and movement in leaf tissues. The results highlighted the utility of IAA nanosensor for understanding IAA dynamics in planta.

bioengineering↗

Rating Pome Fruit Quality Traits Using Deep Learning and Image Processing

Quality assessment of pome fruits (i.e. apples and pears) is used not only crucial for determining the optimal harvest time, but also the progression of fruit-quality attributes during storage. Therefore, it is typical to repeatedly evaluate fruits during the course of a postharvest experiment. This evaluation often includes careful visual assessments of fruit for apparent defects and physiological symptoms. A general best practice for quality assessment is to rate fruit using the same individual rater or group of individuals raters to reduce bias. However, such consistency across labs, facilities, and experiments is often not feasible or attainable. Moreover, while these visual assessments are critical empirical data, they are often coarse-grained and lack consistent objective criteria. Granny, is a tool designed for rating fruit using machine-learning and image-processing to address rater bias and improve resolution. Additionally, Granny supports backwards compatibility by providing ratings compatible with long-established standards and references, promoting research program continuity. Current Granny ratings include starch content assessment, rating levels of peel defects, and peel color analyses. Integrative analyses enhanced by Grannys improved resolution and reduced bias, such as linking fruit outcomes to global scale-omics data, environmental changes, and other quantitative fruit quality metrics like soluble solids content and flesh firmness, will further enrich our understanding of fruit quality dynamics. Lastly, Granny is open-source and freely available.

plant biology↗

Genome mining yields new disease-associated ROMK variants with distinct defects

Bartter syndrome is a group of rare genetic disorders that compromise kidney function by impairing electrolyte reabsorption. Left untreated, the resulting hyponatremia, hypokalemia, and dehydration can be fatal. Although there is no cure for this disease, specific genes that lead to different Bartter syndrome subtypes have been identified. Bartter syndrome type II specifically arises from mutations in the KCNJ1 gene, which encodes the renal outer medullary potassium channel, ROMK. To date, over 40 Bartter syndrome-associated mutations in KCNJ1 have been identified. Yet, their molecular defects are mostly uncharacterized. Nevertheless, a subset of disease-linked mutations compromise ROMK folding in the endoplasmic reticulum (ER), which in turn results in premature degradation via the ER associated degradation (ERAD) pathway. To identify uncharacterized human variants that might similarly lead to premature degradation and thus disease, we mined three genomic databases. First, phenotypic data in the UK Biobank were analyzed using a recently developed computational platform to identify individuals carrying KCNJ1 variants with clinical features consistent with Bartter syndrome type II. In parallel, we examined ROMK genomic data in both the NIH TOPMed and ClinVar databases with the aid of a computational algorithm that predicts protein misfolding and disease severity. Subsequent phenotypic studies using a high throughput yeast screen to assess ROMK function--and analyses of ROMK biogenesis in yeast and human cells--identified four previously uncharacterized mutations. Among these, one mutation uncovered from the two parallel approaches (G228E) destabilized ROMK and targeted it for ERAD, resulting in reduced protein expression at the cell surface. Another ERAD-targeted ROMK mutant (L320P) was found in only one of the screens. In contrast, another mutation (T300R) was ERAD-resistant, but defects in ROMK activity were apparent after expression and two-electrode voltage clamp measurements in Xenopus oocytes. Together, our results outline a new computational and experimental pipeline that can be applied to identify disease-associated alleles linked to a range of other potassium channels, and further our understanding of the ROMK structure-function relationship that may aid future therapeutic strategies. Author SummaryBartter syndrome is a rare genetic disorder characterized by defective renal electrolyte handing, leading to debilitating symptoms and, in some patients, death in infancy. Currently, there is no cure for this disease. Bartter syndrome is divided into five types based on the causative gene. Bartter syndrome type II results from genetic variants in the gene encoding the ROMK protein, which is expressed in the kidney and assists in regulating sodium, potassium, and water homeostasis. Prior work established that some disease-associated ROMK mutants misfold and are destroyed soon after their synthesis in the endoplasmic reticulum (ER). Because a growing number of drugs have been identified that correct defective protein folding, we wished to identify an expanded cohort of similarly misshapen and unstable disease-associated ROMK variants. To this end, we developed a pipeline that employs computational analyses of human genome databases with genetic and biochemical assays. Next, we both confirmed the identity of known variants and uncovered previously uncharacterized ROMK variants associated with Bartter syndrome type II. Further analyses indicated that select mutants are targeted for ER-associated degradation, while another mutant compromises ROMK function. This work sets-the-stage for continued mining for ROMK loss of function alleles as well as other potassium channels, and positions select Bartter syndrome mutations for correction using emerging pharmaceuticals.

genetics↗

Legume-nodulating rhizobia are widespread in soils and plants across the island of O'ahu, Hawai'i

Legumes and their interaction with rhizobia represent one of the most well-characterized symbioses that are widespread across both natural and agricultural environments. However, larger distribution patterns and host associations on isolated Pacific islands with many native and introduced hosts have not been well-documented. Here, we used molecular and culturing techniques to characterize rhizobia from soils and 24 native and introduced legume species on the island of Oahu, Hawaii. We chose two of these isolates to inoculated an endemic legume tree, Erythina sandwicensis to measure nodulation potentials and host benefits. We found that all rhizobia genera can be found in the soil, where only Cupriavidus was found at all sites, although at lower abundance relative to other more common genera such as Rhizobium (and close relatives), Bradyzhizobium, and Devosia. Bradyrhizobium was the most common nodulator of plants, where the strain Bradyrhizobium sp. strain JA1 is a generalist capable of forming nodules on nine different host species, including two native species. In greenhouse nursery inoculations, the two different Bradyrhizobium strains successfully nodulate the endemic E. sandwicensis; both strains equally and significantly increased seedling biomass in nursery inoculations. Overall, this work provides a molecular-based framework in which to study potential native and introduced rhizobia on one of the most isolated archipelagos on the planet.

ecology↗

Personalized Risk Prediction for Cancer Survivors: A Bayesian Semi-parametric Recurrent Event Model with Competing Outcomes

Multiple primary cancers are increasingly more frequent due to improved survival of cancer patients. Characteristics of the first primary cancer largely impact the risk of developing subsequent primary cancers. Hence, model-based risk characterization of cancer survivors that captures patient-specific variables is needed for healthcare policy making. We propose a Bayesian semi-parametric framework, where the occurrence processes of the competing cancer types follow independent non-homogeneous Poisson processes and adjust for covariates including the type and age at diagnosis of the first primary. Applying this framework to a historically collected cohort with families presenting a highly enriched history of multiple primary tumors and diverse cancer types, we have derived a suite of age-to-onset penetrance curves for cancer survivors. This includes penetrance estimates for second primary lung cancer, potentially impactful to ongoing cancer screening decisions. Using Receiver Operating Characteristic (ROC) curves, we have validated the good predictive performance of our models in predicting second primary lung cancer, sarcoma, breast cancer, and all other cancers combined, with areas under the curves (AUCs) at 0.89, 0.91, 0.76 and 0.68, respectively. In conclusion, our framework provides covariate-adjusted quantitative risk assessment for cancer survivors, hence moving a step closer to personalized health management for this unique population.

genetics↗

A systematic review and meta-analysis of genetic parameters for complex quantitative traits in aquatic animal species

A systematic review and meta-analysis of genetic parameters underlying inheritance and complex biological relationships for quantitative traits are not available for aquatic animal species. I synthesised and conducted a comprehensive meta-analysis of the published information from 1985 to 2017 on heritability, common full-sib effects and genetic correlations for quantitative characters of biological importance (growth, carcass and flesh quality, disease resistance, deformity and reproduction) for aquaculture species. A majority of the studies (73.5%) focussed on growth related traits (body weight), followed by those on disease resistance (15.9%), whereas only a limited number of studies (10.6%) reported heritability estimates for carcass and flesh quality, deformity or reproduction characteristics. The weighted means of heritability for growth (weight, food utilisation efficiency, maturity) and carcass (fillet weight and yield) traits were moderate. Resistance against various bacteria, virus and parasites were moderately to highly heritable. Across aquatic animal species, the weighted heritability for a range of deformity measures and reproductive traits (fecundity, early survival) was low and not significantly different from zero. The common full-sibs (c2) accounted for a large proportion of total variance for body traits but it was of smaller magnitude in later phase of the growth development. The c2 effects however were not significant or in many cases they were not reported for carcass and flesh quality attributes as well as survival and deformity. The maternal genetic effects were not available for all traits studied especially for reproductive and early growth characters. Genetic correlations between body and carcass traits were high and positive, suggesting that selection for rapid growth can improve fillet weight, a carcass trait of paramount importance. Body weight, the most commonly used selection criterion in aquatic animals, showed non-significant genetic correlation with disease resistance, likely because both positive and negative genetic associations between the two types of traits. Interestingly the genetic associations between growth and reproductive performance (fecundity) and fry traits (fry weight, fry survival) were favourable. To date, there are still no published data on genetic relationships of carcass and flesh quality with disease resistance or reproductive performance in any aquaculture species. Additionally, the present study discussed new traits, including functional, immunological, behavioural and social interaction as well as uniformity that are emerging as potential selection criteria and which can be exploited in future genetic improvement programs for aquatic animals.

genetics↗

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Assessments of genomic prediction accuracies using artificial intelligence (AI) algorithms (i.e., machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e., pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a non-linear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e., ML-KAML) and deep learning (i.e., DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP and BayesR) were conducted for two main disease resistance traits (i.e., survival status coded as 0 and 1 and survival time, i.e., days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 Single Nucleotide Polymorphism (SNPs). The results using 6470 SNPs after quality control showed that AI methods outperformed PBLUP, GBLUP and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1 - 15.4%. However, the prediction accuracies obtained from AI methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3 - 19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3 - 5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318 - 400 SNPs for survival status and 1362 - 1589 SNPs for survival time) were somewhat lower (0.3 to 15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that there are prospects for the application of genomic selection to increase disease resistance to Edwardsiella ictaluri in striped catfish breeding programs.

genetics↗

Fungal communities from geothermal soils in Yellowstone National Park

Geothermal soils offer unique insight into the way extreme environmental factors shape communities of organisms. However, little is known about the fungi growing in these environments and in particular how localized steep abiotic gradients affect fungal diversity. We used metabarcoding to characterize soil fungi surrounding a hot spring-fed thermal creek with water up to 84 {degrees}C and pH 10 in Yellowstone National Park. We found a significant association between fungal communities and soil variable principal components, and we identify the key trends in co-varying soil variables that explain the variation in fungal community. Saprotrophic and ectomycorrhizal fungi community profiles followed, and were significantly associated with, different soil variable principal components, highlighting potential differences in the factors that structure these different fungal trophic guilds. In addition, in vitro growth experiments in four target fungal species revealed a wide range of tolerances to pH levels but not to heat. Overall, our results documenting turnover in fungal species within a few hundred meters suggest many co-varying environmental factors structure the diverse fungal communities found in the soils of Yellowstone National Park.

ecology↗

Community RNA-Seq: Multi-kingdom responses to living versus decaying root inputs in soil

Roots are the primary source of organic carbon inputs to most soils. Decomposition is a multi-trophic process involving multiple kingdoms of microbial life, but typically microbial ecology studies focus on one or two major lineages in isolation. We used Illumina shotgun RNA sequencing to conduct PCR-independent SSU rRNA community analysis ("community RNA-Seq") to simultaneously study the bacteria, archaea, fungi, and microfauna surrounding both living and decomposing roots of the annual grass, Avena fatua. Plants were grown in 13CO2-labeled microcosms amended with 15N-root litter. We identified rhizosphere substrate preferences for 13C-exudates versus 15N-litter using NanoSIMS microarray imaging (Chip-SIP). When litter was available, rhizosphere and bulk soil had significantly more Amoebozoa, which are potentially important yet often overlooked top-down drivers of detritusphere community dynamics and nutrient cycling. Bulk soil containing litter was depleted in Actinobacteria but had significantly more Bacteroidetes and Proteobacteria. While Actinobacteria were abundant in the rhizosphere, Chip-SIP showed Actinobacteria preferentially incorporated litter relative to root exudates, indicating this groups more prominent role in detritus elemental cycling in the rhizosphere. Our results emphasize that decomposition is a multi-trophic process involving cross-kingdom interactions, and the trajectory of carbon through this soil food web likely impacts the fate of carbon in soil.

microbiology↗

Does fungal competitive ability explain host specificity or rarity in ectomycorrhizal symbioses?

Two common ecological assumptions are that host generalist and rare species are poorer competitors relative to host specialist and more abundant counterparts. While these assumptions have received considerable study in both plant and animals, how they apply to ectomycorrhizal fungi remains largely unknown. To investigate how interspecific competition may influence the anomalous host associations of the rare ectomycorrhizal generalist fungus, Suillus subaureus, we conducted a seedling bioassay. Pinus strobus seedlings were inoculated in single- or two-species treatments of three Suillus species: S. subaureus, S. americanus, and S. spraguei. After 4 and 8 months of growth, seedlings were harvested and scored for mycorrhizal colonization as well as dry biomass. At both time points, we found a clear competitive hierarchy among these species: S. americanus > S. subaureus > S. spraguei, with the competitive inferior, S. spraguei, having significantly delayed colonization relative to S. americanus and S. subaureus. In the single-species treatments, we found no significant differences in the dry biomasses of seedlings colonized by each Suillus species, suggesting none of these species was a more effective plant symbiont relative to each other. Taken together, these results indicate that the rarity and anomalous host associations exhibited by S. subaureus in natural settings are not driven by inherently poor competitive ability or host growth promotion, but that the timing of colonization is a key factor determining the outcome of ectomycorrhizal fungal competitive interactions.

ecology↗

Feature-based Molecular Networking in the GNPS Analysis Environment

Molecular networking has become a key method used to visualize and annotate the chemical space in non-targeted mass spectrometry-based experiments. However, distinguishing isomeric compounds and quantitative interpretation are currently limited. Therefore, we created Feature-based Molecular Networking (FBMN) as a new analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN leverages feature detection and alignment tools to enhance quantitative analyses and isomer distinction, including from ion-mobility spectrometry experiments, in molecular networks.

bioinformatics↗

Repository-scale Co- and Re-analysis of Tandem Mass Spectrometry Data

Metabolomics data are difficult to find and reuse, even in public repositories. We, therefore, developed the Reanalysis of Data User (ReDU) interface (https://redu.ucsd.edu/), a community- and data-driven approach that solves this problem at the repository scale. ReDU enables public data discovery and co- or re-analysis via uniformly formatted, publicly available MS/MS data and metadata in the Global Natural Product Social Molecular Networking Platform (GNPS), consistent with findable, accessible, interoperable, and reusable (FAIR) principles.1

bioinformatics↗

Blind exploration of the unreferenced transcriptome reveals novel RNAs for prostate cancer diagnosis

The broad use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens is restricted by the limited power of conventional reference-based protocols relying on uniquely mapped reads and transcript annotations. Here, we implemented a blind reference-free computational protocol, DE-kupl, to directly infer RNA variations of any origin, including yet unreferenced RNAs, from high coverage total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into unannotated putative long noncoding (lnc)RNAs expressed in prostate cancer tissues. Through filtering and visual inspection of 1,179 candidates, we defined 21 lncRNA probes that were further validated for robust tumor-specific expression by NanoString single molecule-based RNA measurements in 144 tissue specimens. Predictive modeling yielded a restricted probe panel enabling over 90% of true positive detection of cancer in an independent dataset from The Cancer Genome Atlas. Remarkably, this clinical signature made of only 9 unannotated lncRNAs largely outperformed PCA3, the only RNA biomarker approved by the Food and Drug Administration agency, specifically, in detection of high-risk prostate tumors. The proposed reference-free computational workflow is modular, highly sensitive and robust and can be applied to any pathology and any clinical application.

genomics↗