Biology subjects

Ngo, V.

Publications and source records attributed to Ngo, V..

13 recordsLinked to original sources

bioRxiv2026

Deprivation of dietary fiber promotes low-grade neuroinflammation and anxiety-like behavior in mice

Background: Intestinal health is an important determinant of brain function in that an array of gut perturbants, especially those targeting microbiota, influence behavior. Fermentable dietary fiber nourishes gut microbiota and, consequently, promotes gut health. We hypothesized that depriving mice of fermentable dietary fiber would impact their brain and behavior. Methods: Male C57BL/6J mice were fed standard grain-based chow (GBC), a compositionally defined diet (CDD) which mimics macronutrient content of GBC but lacks fermentable fiber, or CDD supplemented with the fermentable fiber inulin (CDD+INU). Mice were subjected to behavioral testing 6-8 weeks later followed by analysis of the gut brain axis. Results: Relative to GBC-fed mice, CDD-fed mice exhibited low-grade gut inflammation and a reduction in open-field center exploration, signaling heightened anxiety-like behavior. CDD+INU fully reversed these abnormalities arguing they were a consequence of fiber deprivation. Analysis of the gut microbiome and serum metabolome showed that CDD+INU increased oxidative phosphorylation and immune suppressive metabolites. Flow cytometric and transcriptomic analyses of the brain revealed an array of pro-inflammatory alterations in myeloid cells, CD4+ T-cells, microglia and astrocytes in mice fed CDD but not CDD+INU. Most notably, microglia and astrocytes from mice fed CDD, but not CDD+INU, displayed reduced mitochondrial respiration and greater glycolytic activity. CDD, but not CDD+INU consumption also reduced the abundance of oligodendrocytes and neurons, and increased hippocampal pro-inflammatory gene expression. Conclusions: Deprivation of fermentable fiber results in coordinated changes in brain immune composition and glial metabolic state that is associated with increased anxiety-like behavior.

neuroscience↗

bioRxiv2026

Transformer-based framework uncovers state-dependent modular organization and conformational landscapes of the β-arrestin 1 C-terminal tail

{beta}-arrestins ({beta}arr) regulate signaling and trafficking of G protein-coupled receptors (GPCRs) across diverse physiological and pathological processes. However, mechanistic understanding of how ligand-activated GPCRs engage and activate {beta}arr remains limited, with the conformation of the entire {beta}arr tail in the active state still unknown. Here, by comparatively analyzing temperature replica-exchange molecular dynamics simulation data of {beta}arr1 in basal and active states, we investigated the conformational landscape of the {beta}arr1 tail to elucidate its role in activation. To overcome limitation of conventional analyses in characterizing the vast conformational space sampled by the 62-residue tail, we developed a transformer-based autoencoder (TAE) framework that integrates attention-derived residue relationships and latent-space clustering to identify the tails modular organization and conformational substates, providing an interpretable description of how local residue interactions couple to large-scale conformational rearrangements. Using the conformationally constrained basal state as a control, we validated the framework by showing that it recovers interpretable conformational features. In the active state, the framework revealed a reorganized modular architecture, recovered key interactions identified through manual analysis, and uncovered segment-specific dynamics inaccessible to conventional approaches. Our findings show that, in the active state, the {beta}arr1 tail preferentially engages the back side of the main body and forms substates in which the middle segment occupies the central crest crevice, suggesting that the released tail can self-engage functionally critical surfaces and influence the balance between tail-only and core-engaged receptor complexes. Together, this work establishes a TAE framework for analyzing large-scale conformational ensembles and advances our understanding of {beta}arr1 activation.

biophysics↗

bioRxiv2026

Gut Microbiota Dysbiosis Drives Myocardial Hypertrophy Through GBP2b/GBP1-Mediated Immune Reprogramming and Exosomal Signaling in Chronic Colitis

BACKGROUNDPatients with inflammatory bowel disease (IBD) are at increased risk of cardiovascular disease, yet the mechanisms linking chronic intestinal inflammation to cardiac dysfunction remain poorly understood. IBD is characterized by profound gut microbiota dysbiosis, which we hypothesize drives systemic immune dysregulation and contributes to cardiac dysfunction. METHODSA chronic colitis mouse model was used to assess gut microbiota dysbiosis, systemic immune cell metabolism, and cardiac remodeling. Cardiac outcomes were evaluated by echocardiography, histology, and molecular analyses. Mechanisms were examined using fecal microbiota transplantation, immune cell depletion, exosome transfer, bone marrow chimeras, RNA-seq, co-immunoprecipitation, confocal microscopy, and siRNA-mediated gene silencing. RESULTSChronic DSS colitis induced cardiac dysfunction, hypertrophy, and fibrosis in mice. These changes were accompanied by sustained gut microbiota dysbiosis, metabolic reprogramming, and mitochondrial dysfunction in circulating immune cells. Fecal microbiota transfer experiments demonstrated that colitis-associated microbiota were sufficient to reprogram systemic immune cells and promote cardiac dysfunction. Immune cell depletion studies identified macrophages as key mediators of colitis-associated cardiac injury. Colitis increased systemic lipopolysaccharide (LPS) translocation, bone marrow chimera experiments demonstrated that hematopoietic TLR4 signaling was required for immune cell metabolic remodeling and cardiac dysfunction during chronic colitis. Transcriptomic analysis identified guanylate-binding protein 2b (GBP2b/GBP1, hereafter referred to as GBP1) as a key downstream effector of LPS-TLR4 signaling. Upon LPS stimulation, GBP1 localized to mitochondria, where it interacted with DRP1 and FIS1 to promote mitochondrial fission, oxidative stress, and enhanced immune cell migration into the heart. In addition, GBP1 was secreted via exosomes, which were taken up by cardiomyocytes and contributed to hypertrophic remodeling, and cardiac dysfunction. CONCLUSIONSThese findings establish the LPS-TLR4-GBP1 axis as a key driver of colitis-associated cardiovascular dysfunction and highlight this pathway as a promising therapeutic target for reducing cardiovascular risk in patients with IBD. Novelty and SignificanceO_ST_ABSWhat Is Known?C_ST_ABSO_LIPatients with inflammatory bowel disease have an increased risk of cardiovascular dysfunction that cannot be fully explained by traditional cardiovascular risk factors. C_LIO_LIGut microbiota dysbiosis and chronic innate immune activation are hallmarks of inflammatory bowel disease, but their direct contribution to cardiac remodeling remains unclear. C_LI What New Information Does This Article Contribute?O_LIChronic colitis-associated gut microbiota dysbiosis induces systemic immune cell metabolic and mitochondrial reprogramming that is sufficient to drive cardiomyocyte hypertrophy and cardiac dysfunction. C_LIO_LIHematopoietic Toll-like receptor 4 signaling links colitis associated gut microbiota to immune metabolic dysfunction and cardiac impairment, establishing a causal gut-immune-heart axis. C_LIO_LIGuanylate-binding protein 2b (GBP2b/GBP1) is identified as a critical downstream effector that promotes mitochondrial fission, oxidative stress, immune cell cardiac infiltration, and exosome-mediated cardiac remodeling. C_LI

pathology↗

bioRxiv2025

Climate warming and urbanization may expand dengue transmission risk in California

BackgroundWhile primarily a disease of tropical and subtropical regions, dengue outbreaks are increasing in non-endemic regions due to environmental change and increasing travel and trade. For these non-endemic regions, estimating the risk of dengue is challenging as transmission is driven by both local environmental conditions and the introduction of viremic travelers. In this study, we aimed to estimate current and future dengue risk in California, USA--a region that has recently experienced its first cases of locally-acquired dengue. MethodsWe modeled dengue risk as the product of three key components needed for local transmission--vector presence, temperature-suitability for pathogen transmission, and viral introductions via travel-associated cases--estimated using vector and case surveillance, sociodemographic, and environmental data. We estimated risk for locations and months where local transmission was reported in 2023-2024 to define a threshold level of risk. We then projected monthly, census tract-level risk under both current conditions and future scenarios of climate warming and urban expansion. FindingsApproximately 18.2 million (95% CI: 17.9-18.3) California residents--primarily in the Central Valley and the Los Angeles and San Diego metropolitan areas--currently live in areas where peak monthly dengue risk exceeds levels estimated during observed local transmission. Under moderate scenarios of climate warming and urban expansion, an additional 4.1 million (95% CI: 3.7-4.6) California residents may be at risk by mid-century, with the largest increase in risk estimated for September and for the Sacramento Valley and coastal southern California regions. Outside the summer months and beyond the Central Valley and southern California, current and future risk remains low due to one or more major bottlenecks to transmission. InterpretationOur study identifies the specific regions and months conducive to dengue transmission in the non-endemic setting of California. At present, this covers a substantial portion of the state and is projected to expand under on-going climate warming and urbanization. Our results underscore the need for sustained vector control, and timely detection and management of travel-associated cases. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSDengue is considered endemic in over 125 countries and rapidly expanding its range, aided by climate warming, urbanization, and global travel and trade. Estimating transmission risk in newly emerging regions is critical for public health preparedness and depends on both local environmental conditions and the introduction of viremic travelers. We searched PubMed from database inception to May 8, 2025, for articles published in English using search terms "dengue", "model", "non-endemic", and their common textual variants. We identified 75 relevant studies modeling dengue transmission risk in non-endemic settings. However, nearly all were focused on one or two major determinants of transmission (eg, climate, vector population dynamics, or case importations) and/or did not include future projections. We found no studies that developed and validated a model of dengue transmission risk in non-endemic settings that incorporated vector, pathogen, and human suitability factors, and applied this model to project future risk. Added value of this studyThis study provides a novel approach to model dengue transmission risk in emerging regions that integrates the major factors driving transmission--vector presence, temperature suitability, and travel-associated cases. We apply this model to California--an emerging center of transmission risk in the continental USA--to identify the times and regions where risk exceeds levels observed during recent local transmission. We found that approximately 18.2 million California residents may be at risk based on this threshold, with an additional 4.1 million potentially at risk by mid-century under a moderate scenario of warming and urban expansion. Implications of all the available evidenceOur study identifies the hotspots of dengue transmission risk at a fine spatial and temporal resolution (census tract, month) in a highly populous and globally-connected region of emerging dengue risk. These risk estimates, and the regionally-specific bottlenecks to transmission that we identify can inform targeted disease surveillance and prevention strategies. Further, our findings have implications for other emerging regions including the southern USA and southern Europe, suggesting that the risk of local dengue transmission may increase under ongoing climate warming, urbanization, and global travel.

ecology↗

bioRxiv2025

Conformational dynamics of the active state of β-arrestin 1

{beta}-arrestins ({beta}arr) regulate the signaling and trafficking of G protein-coupled receptors (GPCRs) in numerous physiological processes and have been implicated in various diseases. Structural and kinetic insights into how ligand-mediated GPCR activation drives {beta}arr coupling and activation remain limited, with the binding mechanism of the phosphorylated GPCR C-terminal tails, such as that of the vasopressin receptor-2 (V2Rpp), and the conformation of the entire {beta}arr tail in the active state still unknown. Here, we simulated both the basal and V2Rpp-bound states of {beta}arr1 with temperature replica-exchange molecular dynamics (TREMD) simulations to probe the activation mechanism of {beta}arr1. Compared to conventional MD, our TREMD simulations, employing an unprecedented 200-replica setup, significantly broadened conformational sampling while preserving the basal state. Our analysis showed that, without the bound Fab30 antibody fragment, the main body of V2Rpp-bound {beta}arr1 tended to transition toward the basal conformation; however, binding of V2Rpp in the N-domain groove allosterically oriented the finger loop to point upward for core engagement with a GPCR. Furthermore, V2Rpp dissociation events suggest that its binding involves a sliding movement along the N-domain groove, during which its phosphorylated residues p3 and p4 transiently occupy the S5 site to facilitate repositioning of p5 into the S5 site, thereby triggering a zippering process of p1 to p3. The dynamic 62-residue {beta}arr1 tail explored a vast conformational space, forming transient secondary structures, and could favorably anchor on the main bodys back side and within the central crest crevice. These findings elucidate key mechanistic steps underlying {beta}arr1 activation.

biophysics↗

bioRxiv2024

Gut-resident C. perfringens impedes rotavirus vaccine efficacy

Background & AimsThe extent to which live orally-administered rotavirus (RV) vaccines elicit protective immunity is highly heterogeneous. We hypothesized microbiota composition might influence vaccine efficacy. MethodsWe tested this concept by examining extent to which colonizing mice with segmented filamentous bacteria (SFB) influenced RV vaccine efficacy.Influence of human microbiomes on RV vaccination was studied via administering germ-free mice fecal microbial transplants (FMT) from children with robust or minimal RV vaccine responsiveness. Post-FMT, mice were subjected to vaccination and challenge doses of RV. ResultsSFB administration resulted in a phenotype reminiscent of RV vaccine failure, i.e. minimal generation of RV antigens and, consequently, lack of anti-RV antibodies resulting in proneness to RV challenge once SFB levels diminished. Transplant of microbiomes from children to mice recapitulated donor vaccination phenotype. Specifically, mice receiving FMT from high-responding children exhibited high levels of fecal RV antigen shedding and RV antibodies in response to RV vaccination and, concomitantly, were impervious to RV challenge. In contrast, mice receiving FMT from children who had not responded to RV vaccination exhibited only modest responses to RV challenge and, accordingly, remained prone to RV challenge. Microbiome analysis ruled out a role for SFB but suggested that RV vaccine failure might involve Clostridium perfringens. Oral administration of cultured C. perfringens to gnotobiotic mice partially recapitulated the RV vaccine non-responder phenotype. Analysis of previously-reported microbiome data found C. perfringens abundance in children associated with RV vaccine failure. ConclusionMicrobiota composition influences RV vaccine virus infection and, consequently, protective immunity. C. perfringens may be one, perhaps of many, bacterial species harbored in the intestine of RV-vaccine non-responders that influences RV vaccine outcomes.

immunology↗

bioRxiv2023

Intestinal microbiota programming of alveolar macrophages influences severity of respiratory viral infection

Susceptibility to respiratory virus infections (RVIs) varies widely across individuals. Because the gut microbiome impacts immune function, we investigated the influence of intestinal microbiota composition on RVI and determined that segmented filamentous bacteria (SFB), naturally acquired or exogenously administered, protected mice against influenza virus (IAV) infection. Such protection, which also applied to respiratory syncytial virus and SARS-CoV-2, was independent of interferon and adaptive immunity but required basally resident alveolar macrophages (AM). In SFB-negative mice, AM were quickly depleted as RVI progressed. In contrast, AM from SFB-colonized mice were intrinsically altered to resist IAV-induced depletion and inflammatory signaling. Yet, AM from SFB-colonized mice were not quiescent. Rather, they directly disabled IAV via enhanced complement production and phagocytosis. Accordingly, transfer of SFB-transformed AM into SFB-free hosts recapitulated SFB-mediated protection against IAV. These findings uncover complex interactions that mechanistically link the intestinal microbiota with AM functionality and RVI severity. One sentence summaryIntestinal segmented filamentous bacteria reprogram alveolar macrophages promoting nonphlogistic defense against respiratory viruses.

immunology↗

bioRxiv2023

Functional gene delivery to and across brain vasculature of systemic AAVs with endothelial-specific tropism in rodents and broad tropism in primates

Delivering genes to and across the brain vasculature efficiently and specifically across species remains a critical challenge for addressing neurological diseases. We have evolved adeno-associated virus (AAV9) capsids into vectors that transduce brain endothelial cells specifically and efficiently following systemic administration in wild-type mice with diverse genetic backgrounds and rats. These AAVs also exhibit superior transduction of the CNS across non-human primates (marmosets and rhesus macaques), and ex vivo human brain slices although the endothelial tropism is not conserved across species. The capsid modifications translate from AAV9 to other serotypes such as AAV1 and AAV-DJ, enabling serotype switching for sequential AAV administration in mice. We demonstrate that the endothelial specific mouse capsids can be used to genetically engineer the blood-brain barrier by transforming the mouse brain vasculature into a functional biofactory. Vasculature-secreted Hevin (a synaptogenic protein) rescued synaptic deficits in a mouse model.

bioengineering↗

bioRxiv2022

Predicting regional somatic mutation rates using DNA motifs

How the locus-specificity of epigenetic modifications is regulated remains an unanswered question. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs). Using these motifs to predict biological outputs depending on local epigenetic state such as somatic mutation rates would confirm their functionality. Here, we used DNA motifs including known TF motifs and epi-motifs as a surrogate of epigenetic signals to predict somatic mutation rates in 13 cancers at an average 23kbp resolution. We implemented an interpretable neural network model, called contextual regression, to successfully learn the universal relationship between mutations and DNA motifs, and uncovered motifs that are most impactful on the regional mutation rates such as TP53 and epi-motifs associated with H3K9me3. Furthermore, we identified genomic regions with significantly higher mutation rates than the expected values in each individual tumor and demonstrated that such cancer-specific regions can accurately predict cancer types. (The code is available from https://github.com/Wang-lab-UCSD/SomaticMutation) Significance StatementThe relationship between DNA motifs and somatic mutation rates in cancers is not fully understood, especially at high resolution. Here we developed an interpretable neural network model to successfully predict somatic mutation rates using DNA motifs in 13 diverse cancers and identified the most informative motifs. Furthermore, we showed that the genomic regions with significant higher mutation rates than the predicted values can be used for cancer classification.

bioinformatics↗

bioRxiv2022

Active vision during prey-capture in wild marmoset monkeys

Here, we examined prey-capture in wild common marmosets (Callithrix jacchus) to explicate the active role of primate vision for high-precision, goal-directed motor actions in challenging natural environments. We observed distinct marmoset hunting strategies that each relied on the close integration of visual processes and dynamic biomechanical motor movements, but differed based on several factors including prey size/speed, substrate, and their relative distance to the prey. Marmoset positional behavior in these complex arboreal settings often involves extending the body, including inversions, while relying on vision to guide prey capture. Applying markerless pose estimation technology, analyses revealed details of how primate vision both guides and corrects rapid motor actions in real-time during prey-capture in the wild. These findings illustrate the significance of active vision during primate ethological behaviors in response to the very challenges the visual system evolved to solve.

animal behavior and cognition↗

bioRxiv2019

Motto: Representing motifs in consensus sequences with minimum information loss

Sequence analysis frequently requires intuitive understanding and convenient representation of motifs. Typically, motifs are represented as position weight matrices (PWMs) and visualized using sequence logos. However, in many scenarios, representing motifs by wildcard-style consensus sequences is compact and sufficient for interpreting the motif information and search for motif match. Based on mutual information theory and Jenson-Shannon Divergence, we propose a mathematical framework to minimize the information loss in converting PWMs to consensus sequences. We name this representation as sequence Motto and have implemented an efficient algorithm with flexible options for converting motif PWMs into Motto from nucleotides, amino acids, and customized alphabets. Here we show that this representation provides a simple and efficient way to identify the binding sites of 1156 common TFs in the human genome. The effectiveness of the method was benchmarked by comparing sequence matches found by Motto with PWM scanning results found by FIMO. On average, our method achieves 0.81 area under the precision-recall curve, significantly (p-value < 0.01) outperforming all existing methods, including maximal positional weight, Douglas and minimal mean square error. We believe this representation provides a distilled summary of a motif, as well as the statistical justification.\n\nAVAILABILITYMotto is freely available at http://wanglab.ucsd.edu/star/motto.

bioinformatics↗

bioRxiv2019

Identification of DNA motifs that regulate DNA methylation

DNA methylation is an important epigenetic mark but how its locus-specificity is decided in relation to DNA sequence is not fully understood. Here, we have analyzed 34 diverse whole-genome bisulfite sequencing datasets in human and identified 313 motifs, including 92 and 221 associated with methylation (methylation motifs, MMs) and unmethylation (unmethylation motifs, UMs), respectively. The functionality of these motifs is supported by multiple lines of evidences. First, the methylation levels at the MM and UM motifs are respectively higher and lower than the genomic background. Second, these motifs are enriched at the binding sites of methylation modifying enzymes including DNMT3A and TET1, indicating their possible roles of recruiting these enzymes. Third, these motifs significantly overlap with SNPs associated with gene expression and those with DNA methylation. Fourth, disruption of these motifs by SNPs is associated with significantly altered methylation level of the CpGs in the neighbor regions. Furthermore, these motifs together with somatic SNPs are predictive of cancer subtypes and patient survival. We revealed some of these motifs were also associated with histone modifications, suggesting possible interplay between the two types of epigenetic modifications. We also found some motifs form feed forward loops to contribute to DNA methylation dynamics.

bioinformatics↗

bioRxiv2019

Scalable and Accurate Drug-target Prediction Based on Heterogeneous Bio-linked Network Mining

MotivationDespite the existing classification- and inference-based machine learning methods that show promising results in drug-target prediction, these methods possess inevitable limitations, where: 1) results are often biased as it lacks negative samples in the classification-based methods, and 2) novel drug-target associations with new (or isolated) drugs/targets cannot be explored by inference-based methods. As big data continues to boom, there is a need to study a scalable, robust, and accurate solution that can process large heterogeneous datasets and yield valuable predictions. ResultsWe introduce a drug-target prediction method that improved our previously proposed method from the three aspects: 1) we constructed a heterogeneous network which incorporates 12 repositories and includes 7 types of biomedical entities (#20,119 entities, # 194,296 associations), 2) we enhanced the feature learning method with Node2Vec, a scalable state-of-art feature learning method, 3) we integrate the originally proposed inference-based model with a classification model, which is further fine-tuned by a negative sample selection algorithm. The proposed method shows a better result for drug-target association prediction: 95.3% AUC ROC score compared to the existing methods in the 10-fold cross-validation tests. We studied the biased learning/testing in the network-based pairwise prediction, and conclude a best training strategy. Finally, we conducted a disease specific prediction task based on 20 diseases. New drug-target associations were successfully predicted with AUC ROC in average, 97.2% (validated based on the DrugBank 5.1.0). The experiments showed the reliability of the proposed method in predicting novel drug-target associations for the disease treatment.

bioinformatics↗