Biology subjects

Nguyen, L. H.

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

10 recordsLinked to original sources

bioRxiv2026

Enhanced multi-omic viral profiling from microbial community sequencing with BAQLaVa

Viruses are crucial components of microbial communities, both phage that infect bacterial community members as well as pathogenic and other eukaryotic viruses. However, they remain unobserved by most current technologies, due to combinations of experimental and analytical factors. To address the latter, we developed the BAQLaVa algorithm for high-resolution profiling of >120,000 viral species (viral genome bins, VGBs) via reference-based metagenome (MGX) or metatranscriptome (MTX) alignment to complementary nucleotide markers and proteome sets. In comprehensive benchmarking, BAQLaVa substantially outperformed alternatives, achieving species-level recall and precision regularly over 90%. We applied BAQLaVa to MGX and MTX samples from the HMP2 IBDMDB cohort to identify previously undescribed viral perturbations in inflammatory bowel diseases. Most notably, virome diversity was reduced in tandem with bacterial diversity during inflammation, in contrast to previous findings based on a narrower range of viral detection. A subset of viruses were enriched during IBD, associated with carriage of abortive infection anti-defense systems such as AbiL and PD-{lambda}-2, as well as genes involved in the regulation of lysogeny. Leveraging the corresponding viral profiles, we also inferred phage-host relationships using scalable co-occurrence and covariation signals, even in the absence of host references or genome annotations. By enabling high sensitivity and specificity viral profiling from metagenomes or metatranscriptomes, BAQLaVa provides a scalable framework for virome epidemiology and systematic analysis of virus-host interactions.

bioinformatics↗

bioRxiv2023

Profiling novel lateral gene transfer events in the human microbiome

Lateral gene transfer (LGT) is an important mechanism for genome diversification in microbial populations, including the human microbiome. While prior work has surveyed LGT events in human-associated microbial isolate genomes, the scope and dynamics of novel LGT events arising in personal microbiomes are not well understood, as there are no widely adopted computational methods to detect, quantify, and characterize LGT from complex microbial communities. We addressed this by developing, benchmarking, and experimentally validating a computational method (WAAFLE) to profile novel LGT events from assembled metagenomes. Applying WAAFLE to >2K human metagenomes from diverse body sites, we identified >100K putative high-confidence but previously uncharacterized LGT events ([~]2 per assembled microbial genome-equivalent). These events were enriched for mobile elements (as expected), as well as restriction-modification and transport functions typically associated with the destruction of foreign DNA. LGT frequency was quantifiably influenced by biogeography, the phylogenetic similarity of the involved taxa, and the ecological abundance of the donor taxon. These forces manifest as LGT networks in which hub species abundant in a community type donate unequally with their close phylogenetic neighbors. Our findings suggest that LGT may be a more ubiquitous process in the human microbiome than previously described. The open-source WAAFLE implementation, documentation, and data from this work are available at http://huttenhower.sph.harvard.edu/waafle.

bioinformatics↗

bioRxiv2023

The mTOR pathway genes mTOR, Rheb, Depdc5, Pten, and Tsc1 have convergent and divergent impacts on cortical neuron development and function

Brain somatic mutations in various components of the mTOR complex 1 (mTORC1) pathway have emerged as major causes of focal malformations of cortical development and intractable epilepsy. While these distinct gene mutations converge on excessive mTORC1 signaling and lead to common clinical manifestations, it remains unclear whether they cause similar cellular and synaptic disruptions underlying cortical network hyperexcitability. Here, we show that in utero activation of the mTORC1 activators, Rheb or mTOR, or biallelic inactivation of the mTORC1 repressors, Depdc5, Tsc1, or Pten in mouse medial prefrontal cortex leads to shared alterations in pyramidal neuron morphology, positioning, and membrane excitability but different changes in excitatory synaptic transmission. Our findings suggest that, despite converging on mTORC1 signaling, mutations in different mTORC1 pathway genes differentially impact cortical excitatory synaptic activity, which may confer gene-specific mechanisms of hyperexcitability and responses to therapeutic intervention.

neuroscience↗

bioRxiv2023

Binding of SARS-CoV-2 non-structured protein 1 to 40S ribosome inhibits mRNA translation

Experiments have shown that non-structural protein 1 (NSP1) of SARS-CoV-2 is a factor that restricts cellular gene expression and prevents mRNA translation in the ribosome 40S subunit. However, the molecular mechanism of this phenomenon remains unclear. To clarify this issue, all-atom steered molecular dynamics and coarse-grained alchemical simulations were used to compare the binding affinity of mRNA to 40S ribosome in the absence and presence of NSP1. We found that NSP1 binding to the 40S ribosome dramatically increases the binding affinity of mRNA, which, in agreement with experiment, suggests that NSP1 can stall mRNA translation. The mRNA translation has been found to be driven by electrostatic mRNA-40S ribosome interactions. Water molecules have been demonstrated to play an important role in stabilizing the mRNA-40S ribosome complex. The NSP1 residues that are critical in triggering a translation arrest have been identified.

biophysics↗

bioRxiv2021

Genetic expression of 4E-BP1 in juvenile mice alleviates mTOR-induced neuronal dysfunction and epilepsy

Hyperactivation of mTOR signaling during fetal neurodevelopment alters neuron structure and function, leading to focal malformation of cortical development (FMCD) and intractable epilepsy. Recent evidence suggests increased cap-dependent translation downstream of mTOR contributes to FMCD formation and seizures. However, whether reducing overactive translation once the developmental pathologies are established reverses neuronal abnormalities and seizures is unknown. Here, we found that the translational repressor 4E-BP1, which is inactivated by mTOR-mediated phosphorylation, is hyperphosphorylated in patient FMCD tissue and in a mouse model of FMCD. Expressing constitutive active 4E-BP1 to repress aberrant translation in juvenile mice with FMCD reduced neuronal cytomegaly and corrected several electrophysiological alterations, including depolarized resting membrane potential, irregular firing pattern, and aberrant HCN4 channel expression. This was accompanied by improved cortical spectral activity and decreased seizures. Although mTOR controls multiple pathways, our study shows that targeting 4E-BP1-mediated translation alone is sufficient to alleviate neuronal dysfunction and ongoing epilepsy.

neuroscience↗

bioRxiv2021

{-}Multivariable Association Discovery in Population-scale Meta-omics Studies

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses general linear models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g. counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel disease (IBD) across multiple time points and omics profiles.

microbiology↗

bioRxiv2020

Population Structure Discovery in Meta-Analyzed Microbial Communities and Inflammatory Bowel Disease

Microbial community studies in general, and of the human microbiome in inflammatory bowel disease (IBD) in particular, have now achieved a scale at which it is practical to associate features of the microbiome with environmental exposures and health outcomes across multiple large-scale populations. This permits the development of rigorous meta-analysis methods, of particular importance in IBD as a means by which the heterogeneity of disease etiology and treatment response might be explained. We have thus developed MMUPHin (Meta-analysis Methods with a Uniform Pipeline for Heterogeneity in microbiome studies) for joint normalization, meta-analysis, and population structure discovery using microbial community taxonomic and functional profiles. Applying this method to ten IBD cohorts (5,151 total samples), we identified a single consistent axis of microbial associations among studies, including newly associated taxa such as Acinetobacter and Turicibacter detected due to the sensitivity of meta-analysis. Linear random effects models further revealed associations with medications, disease location, and interaction effects consistent within and between studies. Finally, multiple unsupervised clustering metrics and dissimilarity measures agreed on a lack of discrete microbiome "types" in the IBD gut microbiome. These results thus provide a benchmark for consistent characterization of the IBD gut microbiome and a general framework applicable to meta-analysis of any microbial community types.

genomics↗

bioRxiv2020

Elucidating the differences in the molecular mechanism of receptor binding between 2019-nCoV and the SARS-CoV viruses using computational tools

The outbreak of the 2019-nCoV coronavirus causing severe acute respiratory syndrome which can be fatal, especially in elderly population, has been declared a pandemic by the World Health Organization. Many biotechnology laboratories are rushing to develop therapeutic antibodies and antiviral drugs for treatment of this viral disease. The viral CoV spike (S) glycoprotein is one of the main targets for pharmacological intervention. Its receptor-binding domain (RBD) interacts with the human ACE2 receptor ensuring the entry of the viral genomes into the host cell. In this work, we report on the differences in the binding of the RBD of the previous coronavirus SARS-CoV and of the newer 2019-nCoV coronavirus to the human ACE2 receptor using atomistic molecular dynamics techniques. Our results show major mutations in the 2019-nCoV RBD with respect to the SARS-CoV RBD occurring at the interface of RBD-ACE2 complex. These mutations make the 2019-nCoV RBD protein backbone much more flexible, hydrophobic interactions are reduced and additional polar/charged residues appear at the interface. We observe that higher flexibility of the 2019-nCoV RBD with respect to the SARS-CoV RBD leads to a bigger binding interface between the 2019-nCoV RBD and ACE2 and to about 20% more contacts between them in comparison with SARS-CoV. Taken together, the 2019-nCoV RBD shows more stable binding interface and higher binding affinity for the ACE2 receptor. The mutations not only stabilize the binding interface, they also lead to overall more stable 2019-nCoV RBD protein structure, even far from the binding interface. Our results on the molecular differences in the binding between the two viruses can provide important inputs for development of appropriate antiviral treatments of the new viruses, addressing the necessity of ongoing pandemics.

molecular biology↗

bioRxiv2019

Gut microbiota plasticity is correlated with sustained weight loss on a low-carb or low-fat dietary intervention

BackgroundObesity is a complex global health challenge. Although both low-carbohydrate (low-carb) and low-fat diets can lead to weight loss, there is typically substantial variability in weight and related outcomes for both diet approaches among obese but otherwise healthy adults. Elucidating individual characteristics that might contribute to sustained weight loss is critical to developing effective dietary intervention strategies. We hypothesized that structural differences in the gut microbiota explained some portion of the weight loss variability among people randomized to either a low-carb or low-fat diet, possibly related to its effects on dietary compliance.\n\nResultsOur study included two staggered cohorts of obese adults enrolled in the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) study - a randomized clinical trial of either a low-fat or low-carb diet. In the discovery cohort (n=66), 161 pre-diet fecal samples were sequenced in addition to 157 samples collected after 10-weeks of dietary intervention. In the validation cohort (n = 56), 106 pre-diet fecal samples were sequenced. Pre-diet taxonomic features, such as the Prevotella/Bacteroides ratio, correlated to weight loss in the discovery cohort were not confirmed in the validation cohort. The most robust finding in the discovery cohort indicated that gut microbiota plasticity was linked to 12-month weight loss in a diet-dependent manner; subjects with higher sustained weight loss on a low-fat diet had higher pre-diet daily plasticity, whereas those most successful on the low-carb diet had greater microbiota plasticity over 10 weeks of dietary intervention. Unfortunately, because sample frequency and timing was quite different in the validation cohort, the relationship between plasticity and weight loss could not be studied in this group.\n\nConclusionsThese findings suggest the potential importance of gut microbiota plasticity in sustained weight loss. We highlight the importance of evaluating kinetic trends and in assessing reproducibility in studies of the gut microbiota.

microbiology↗

bioRxiv2017

Bayesian Unidimensional Scaling for visualizing uncertainty in high dimensional datasets with latent ordering of observations

BackgroundDetecting patterns in high-dimensional multivariate datasets is non-trivial. Clustering and dimensionality reduction techniques often help in discerning inherent structures. In biological datasets such as microbial community composition or gene expression data, observations can be generated from a continuous process, often unknown. Estimating data points natural ordering and their corresponding uncertainties can help researchers draw insights about the mechanisms involved.\n\nResultsWe introduce a Bayesian Unidimensional Scaling (BUDS) technique which extracts dominant sources of variation in high dimensional datasets and produces their visual data summaries, facilitating the exploration of a hidden continuum. The method maps multivariate data points to latent one-dimensional coordinates along their underlying trajectory, and provides estimated uncertainty bounds. By statistically modeling dissimilarities and applying a DiSTATIS registration method to their posterior samples, we are able to incorporate visualizations of uncertainties in the estimated data trajectory across different regions using confidence contours for individual data points. We also illustrate the estimated overall data density across different areas by including density clouds. One-dimensional coordinates recovered by BUDS help researchers discover sample attributes or covariates that are factors driving the main variability in a dataset. We demonstrated usefulness and accuracy of BUDS on a set of published microbiome 16S and RNA-seq and roll call data.\n\nConclusionsOur method effectively recovers and visualizes natural orderings present in datasets. Automatic visualization tools for data exploration and analysis are available at: https://nlhuong.shinyapps.io/visTrajectory/.

bioinformatics↗