Dissecting genetic regulation of metabolic coordination.

Understanding genetic regulation of metabolism is critical for gaining insights into the causes of metabolic diseases. Traditional metabolome-based genome-wide association studies (mGWAS) focus on static associations between single nucleotide polymorphisms (SNPs) and metabolite levels, overlooking the changing relationships caused by genotypes within the metabolic network. Notably, some metabolites exhibit changes in correlation patterns with other metabolites under certain physiological conditions while maintaining their overall abundance level.

Caffeine Sodium Benzoate Promotes Endothelial Dysfunction of Human Umbilical Vein Endothelial Cells by Promoting M1 Macrophage Polarization.

Our previous study uncovered that long-term abuse of caffeine sodium benzoate (CSB) could lead to dysfunction in human umbilical vein endothelial cells (HUVECs). However, the mechanism by which CSB induced endothelial dysfunction remains largely unstudied. CSB containing serum (CSB-CS) was collected from patients under long-term CSB inhalation.

A unified framework for cell-type-specific eQTL prioritization by integrating bulk and scRNA-seq data.

Genome-wide association studies (GWASs) have identified numerous genetic variants associated with complex traits, yet the biological interpretation remains challenging, especially for variants in non-coding regions. Expression quantitative trait locus (eQTL) studies have linked these variations to gene expression, aiding in identifying genes involved in disease mechanisms. Traditional eQTL analyses using bulk RNA sequencing (bulk RNA-seq) provide tissue-level insights but suffer from signal loss and distortion due to unaddressed cellular heterogeneity.

ADM: adaptive graph diffusion for meta-dimension reduction.

Dimension reduction is essential for analyzing high-dimensional data, with various techniques developed to address diverse data characteristics. However, individual methods often struggle to capture all intricate patterns and complex structures simultaneously. To overcome this limitation, we introduce ADM (Adaptive graph Diffusion for Meta-dimension reduction), a novel meta-dimension reduction method grounded in graph diffusion theory.

A robust statistical approach for finding informative spatially associated pathways.

Spatial transcriptomics offers deep insights into cellular functional localization and communication by mapping gene expression to spatial locations. Traditional approaches that focus on selecting spatially variable genes often overlook the complexity of biological pathways and the interactions among genes. Here, we introduce a novel framework that shifts the focus towards directly identifying functional pathways associated with spatial variability by adapting the Brownian distance covariance test in an innovative manner to explore the heterogeneity of biological functions over space.

A new framework for exploratory network mediator analysis in omics data.

Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome.

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches.

Severe disease during both primary and secondary dengue virus infections in pediatric populations.

Dengue is a global epidemic causing over 100 million cases annually. The clinical symptoms range from mild fever to severe hemorrhage and shock, including some fatalities. The current paradigm is that these severe dengue cases occur mostly during secondary infections due to antibody-dependent enhancement after infection with a different dengue virus serotype.

Bayesian nonparametric method for genetic dissection of brain activation region.

Biological evidence indicewates that the brain atrophy can be involved at the onset of neuropathological pathways of Alzheimer's disease. However, there is lack of formal statistical methods to perform genetic dissection of brain activation phenotypes such as shape and intensity. To this end, we propose a Bayesian hierarchical model which consists of two levels of hierarchy.

MM-GANN-DDI: Multimodal Graph-Agnostic Neural Networks for Predicting Drug-Drug Interaction Events.

Personalized treatment of complex diseases relies on combined medication. However, the occurrence of unexpected drug-drug interactions (DDIs) in these combinations can lead to adverse effects or even fatalities. Although recent computational methods exhibit promising performance in DDI screening, their practical implementation faces two significant challenges: (i) the availability of comprehensive datasets to support clinical application, and (ii) the ability to infer DDI types for new drugs beyond the existing dataset coverage.

Long-term abuse of caffeine sodium benzoate induces endothelial cells injury and leads to coagulation dysfunction.

Our hospital admitted a patient who had difficulty in coagulation even after blood replacement, and the patient had abused caffeine sodium benzoate (CSB) for more than 20 years. Hence, we aimed to explore whether CSB may cause dysfunction in vascular endothelial cells and its possible mechanism. Low, medium, and high concentrations of serum of long-term CSB intake patients were used to treat HUVECs, with LPS as the positive control.

Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features.

Random Forest (RF) is a widely used machine learning method with good performance on classification and regression tasks. It works well under low sample size situations, which benefits applications in the field of biology. For example, gene expression data often involve much larger numbers of features (p) compared to the size of samples (n).

An integrated deep learning framework for the interpretation of untargeted metabolomics data.

Untargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathways to reveal biological mechanisms. One of the key roadblocks in data analysis is not well-addressed, which is the problem of matching uncertainty between data features and known metabolites.

Metabolomic analysis of the sera of patients with the long-term inhalation of caffeine-sodium benzoate using LC-MS.

The present study aimed to systematically assess the potential biomarkers in the serum samples of patients with long-term inhalation of caffeine-sodium benzoate (CSB). LC-MS was applied to analyze the metabolic profiles of serum samples of patients with the long-term intake of CSB (n = 35) and other volunteers with no intake of CSB treated as the control group (n = 35). The raw data of metabolic profiles were analyzed via principal component analysis, partial least squares analysis, and orthogonal partial least squares analysis.

Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data.

Motivation: Testing for pathway enrichment is an important aspect in the analysis of untargeted metabolomics data. Due to the unique characteristics of untargeted metabolomics data, some key issues have not been fully addressed in existing pathway testing algorithms: (i) matching uncertainty between data features and metabolites; (ii) lacking of method to analyze positive mode and negative mode liquid chromatography-mass spectrometry (LC/MS) data simultaneously on the same set of subjects; (iii) the incompleteness of pathways in individual software packages.

Results: We developed an innovative R/Bioconductor package: metabolic pathway testing with positive and negative mode data (metapone), which can perform two novel statistical tests that take matching uncertainty into consideration-(i) a weighted gene set enrichment analysis-type test and (ii) a permutation-based weighted hypergeometric test.

Evaluation of the Use of Saliva Metabolome as a Surrogate of Blood Metabolome in Assessing Internal Exposures to Traffic-Related Air Pollution.

In the omics era, saliva, a filtrate of blood, may serve as an alternative, noninvasive biospecimen to blood, although its use for specific metabolomic applications has not been fully evaluated. We demonstrated that the saliva metabolome may provide sensitive measures of traffic-related air pollution (TRAP) and associated biological responses via high-resolution, longitudinal metabolomics profiling. We collected 167 pairs of saliva and plasma samples from a cohort of 53 college student participants and measured corresponding indoor and outdoor concentrations of six air pollutants for the dormitories where the students lived.

Sphinganine is associated with 24-h MAP in the non-sleepy with OSA.

Introduction: Excessive daytime sleepiness is a debilitating symptom of obstructive sleep apnea (OSA) linked to cardiovascular disease, and metabolomic mechanisms underlying this relationship remain unknown. We examine whether metabolites from inflammatory and oxidative stress-related pathways that were identified in our prior work could be involved in connecting the two phenomena.

Methods: This study included 57 sleepy (Epworth Sleepiness Scale (ESS) ≥ 10) and 37 non-sleepy (ESS < 10) participants newly diagnosed and untreated for OSA that completed an overnight in-lab or at home sleep study who were recruited from the Emory Mechanisms of Sleepiness Symptoms Study (EMOSS).

AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments.

In the integrative analyses of omics data, it is often of interest to extract data representation from one data type that best reflect its relations with another data type. This task is traditionally fulfilled by linear methods such as canonical correlation analysis (CCA) and partial least squares (PLS). However, information contained in one data type pertaining to the other data type may be complex and in nonlinear form.

The Oxidative Potential of Fine Particulate Matter and Biological Perturbations in Human Plasma and Saliva Metabolome.

Particulate oxidative potential may comprise a key health-relevant parameter of particulate matter (PM) toxicity. To identify biological perturbations associated with particulate oxidative potential and examine the underlying molecular mechanisms, we recruited 54 participants from two dormitories near and far from a congested highway in Atlanta, GA. Fine particulate matter oxidative potential ("FPMOP") levels at the dormitories were measured using dithiothreitol assay.

HK2 Is a Crucial Downstream Regulator of miR-148a for the Maintenance of Sphere-Forming Property and Cisplatin Resistance in Cervical Cancer Cells.

The acquisition of cancer stem-like properties is believed to be responsible for cancer metastasis and therapeutic resistance in cervical cancer (CC). CC tissues display a high expression level of hexokinase 2 (HK2), which is critical for the proliferation and migration of CC cells. However, little is known about the functional role of HK2 in the maintenance of cancer stem cell-like ability and cisplatin resistance of CC cells.

Feature selection and classification over the network with missing node observations.

Jointly analyzing transcriptomic data and the existing biological networks can yield more robust and informative feature selection results, as well as better understanding of the biological mechanisms. Selecting and classifying node features over genome-scale networks has become increasingly important in genomic biology and genomic medicine. Existing methods have some critical drawbacks.

The exposome in practice: an exploratory panel study of biomarkers of air pollutant exposure in Chinese people aged 60-69 years (China BAPE Study).

The exposome overhauls conventional environmental health impact research paradigms and provides a novel methodological framework that comprehensively addresses the complex, highly dynamic interplays of exogenous exposures, endogenous exposures, and modifiable factors in humans. Holistic assessments of the adverse health effects and systematic elucidation of the mechanisms underlying environmental exposures are major scientific challenges with widespread societal implications. However, to date, few studies have comprehensively and simultaneously measured airborne pollutant exposures and explored the associated biomarkers in susceptible healthy elderly subjects, potentially resulting in the suboptimal assessment and management of health risks.

Immunologic mechanisms of seasonal influenza vaccination administered by microneedle patch from a randomized phase I trial.

In a phase 1 randomized, single-center clinical trial, inactivated influenza virus vaccine delivered through dissolvable microneedle patches (MNPs) was found to be safe and immunogenic. Here, we compare the humoral and cellular immunologic responses in a subset of participants receiving influenza vaccination by MNP to the intramuscular (IM) route of administration. We collected serum, plasma, and peripheral blood mononuclear cells in 22 participants up to 180 days post-vaccination.

Elevated levels of inflammatory plasma biomarkers are associated with risk of HIV infection.

Background: To determine if individuals, from HIV-1 serodiscordant couple cohorts from Rwanda and Zambia, who become HIV-positive have a distinct inflammatory biomarker profile compared to individuals who remain HIV-negative, we compared levels of biomarkers in plasma of HIV-negative individuals who either seroconverted (pre-infection) and became HIV-positive or remained HIV-negative (uninfected).

Results: We observed that individuals in the combined cohort, as well as those in the individual country cohorts, who later became HIV-1 infected had significantly higher baseline levels of multiple inflammatory cytokines/chemokines compared to individuals who remained HIV-negative. Genital inflammation/ulceration or schistosome infections were not associated with this elevated profile.