BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool.

Multi-omics data offer unprecedented insights into complex biological systems, yet their high dimensionality, sparsity, and intricate interactions pose significant analytical challenges. Network-based approaches have advanced multi-omics research by effectively capturing biologically relevant relationships among molecular entities. While these methods are powerful for representing molecular interactions, there remains a need for tools specifically designed to effectively utilize these network representations across diverse downstream analyses.

DNA Methylation Smoking Scores and Risk of Islet Autoimmunity and Type 1 Diabetes.

Objective: Multiple studies have reported an inverse association between self-reported smoking during pregnancy and offspring type 1 diabetes (T1D) risk. We investigated the association between DNA methylation (DNAm) smoke exposure scores, parental self-reported smoking, and islet autoimmunity (IA) and T1D risk in children at high risk of T1D.

Research Design And Methods: We used longitudinal data from the Diabetes Autoimmunity Study in the Young cohort, including 205 IA case and 206 control participants (87 and 88 were T1D case and control participants, respectively), matched by age, race/ethnicity, and sample availability.

Differences in immune cell profiles around the time of islet autoimmunity seroconversion in children with and without type 1 diabetes: Immune cell changes around islet autoimmunity.

Seroconversion (SV) marks the initiation of islet autoimmunity (IA) and pre-clinical phase of type 1 diabetes, yet the contributions of immune cells beyond cytotoxic T cells remain unclear. We applied high-resolution immune cell-type deconvolution using peripheral blood DNA methylation data from nested case-control samples of the Diabetes Autoimmunity Study in the Young (DAISY; n=151) and The Environmental Determinants of Diabetes in the Young (TEDDY; n=166) to estimate immune cell proportions across pre-SV and SV timepoints and construct functional ratios, such as the neutrophil-to-lymphocyte ratio (NLR). Using linear models, we evaluated differences between type 1 diabetes cases and controls at pre-SV, SV, and the change across timepoints.

Omic Risk Scores are Associated with COPD-related Traits Across Three Cohorts.

Background: Chronic obstructive pulmonary disease (COPD) exhibits marked heterogeneity in lung function decline, mortality, exacerbations, and other disease-related outcomes. Omic risk scores (ORS) estimate the cumulative contribution of omics, such as the transcriptome, proteome, and metabolome, to a particular trait. This study evaluates the predictive value of ORS for COPD-related traits in both smoking-enriched and general population cohorts.

A generalized higher-order correlation analysis framework for multi-omics network inference.

Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality.

Matrix Linear Models for Connecting Metabolite Composition to Individual Characteristics.

High-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how association of metabolite levels with individual (sample) characteristics, such as sex or treatment, depend on metabolite characteristics such as pathways. Typically, this is done using a two-step process.

Identification of Plasma Metabolites Responding to Oxycodone Exposure in Rats.

Background: Oxycodone has an elevated abuse liability profile compared to other prescription opioid medications. However, many human and rodent metabolomics studies have not been specifically focused on oxycodone.

Objectives: Investigating metabolomics changes associated with oxycodone exposure can provide insights into biochemical mechanisms of the addiction cycle and prognosis prediction.

Prediction and Characterization of Genetically Regulated Expression of Target Tissues in Asthma.

Background: Genetic control of gene expression in asthma-related tissues is not well-characterized, particularly for African-ancestry populations, limiting advancement in our understanding of the increased prevalence and severity of asthma in those populations.

Objective: To create novel transcriptome prediction models for asthma tissues (nasal epithelium and CD4+ T cells) and apply them in transcriptome-wide association study (TWAS) to discover candidate asthma genes.

Methods: We developed and validated gene expression prediction databases for unstimulated CD4+ T cells (CD4+T) and nasal epithelium using an elastic net framework.

DisCo P-ad: Distance-Correlation-Based -Value Adjustment Enhances Multiple Testing Corrections for Metabolomics.

Background: Due to scientific advancements in high-throughput data production technologies, omics studies, such as genomics and metabolomics, often give rise to numerous measurements per sample/subject containing several noisy variables that potentially cloud the true signals relevant to the desired study outcome(s). Therefore, correcting for multiple testing is critical while performing any statistical test of significance to minimize the chances of false or missed discoveries. Such correction practice is commonplace in genome-wide association studies (GWAS) but is also becoming increasingly relevant to metabolome-wide association studies (MWAS).

Death and survival of gut CD4 T cells following HIV-1 infection ex vivo.

The gastrointestinal tract is ground zero for the massive and sustained CD4 T cell depletion during acute HIV-1 infection. To date, the molecular mechanisms governing this fundamental pathogenic process remain unclear. HIV-1 infection in the gastrointestinal tract is associated with chronic inflammation due to a disrupted epithelial barrier that results in microbial translocation.

Metabolic Aging as an Increased Risk for Chronic Obstructive Pulmonary Disease.

Background/objectives: Both aging and chronic obstructive pulmonary disease (COPD) are strongly associated with changes in the metabolome; however, it is unknown whether there are common aging/COPD metabolomic signatures and if accelerated aging is associated with COPD.

Methods: Plasma from 5704 subjects from the Genetic Epidemiology of COPD study (COPDGene) and 2449 subjects from Subpopulations and intermediate outcome measures in COPD study (SPIROMICS) were profiled using the Metabolon global metabolomics platform (1013 annotated metabolites). Post-bronchodilator spirometry measures of airflow obstruction (forced expiratory volume at one second (FEV)/forced vital capacity (FVC) < 0.

Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics.

Modeling the network topology of the human brain within the mesoscale has become an increasing focus within the neuroscientific community due to its variation across diverse cognitive processes, in the presence of neuropsychiatric disease or injury, and over the lifespan. Much research has been done on the creation of algorithms to detect these mesoscopic structures, called communities or modules, but less has been done to conduct inference on these structures. The literature on analysis of these community detection algorithms has focused on comparing them within the same subject.

Prenatal black carbon exposure and DNA methylation in umbilical cord blood.

Background/objectives: Prenatal exposure to ambient air pollution is associated with adverse cardiometabolic outcomes in childhood. We previously observed that prenatal black carbon (BC) was inversely associated with adiponectin, a hormone secreted by adipocytes, in early childhood. Changes to DNA methylation have been proposed as a potential mediator linking in utero exposures to lasting health impacts.

The predictive utility of the in utero exposome for childhood adiposity in independent and integrated frameworks.

Objectives: To assess the predictive potential of the in utero exposome in relation to childhood adiposity as indicated by body mass index z-scores (BMIz) and the fourth versus first quartile of % fat mass (FM) at median age of 4.6 years.

Methods: We leveraged data on clinical risk factors for childhood obesity during the perinatal period, along with cord blood per/polyfluoroalkyl substances (PFAS) and cord blood DNA methylation, in 268 mother-offspring pairs.

Differentially methylated regions interrogated for metastable epialleles associate with offspring adiposity.

Assess if cord blood differentially methylated regions (DMRs) representing human metastable epialleles (MEs) associate with offspring adiposity in 588 maternal-infant dyads from the Colorado Health Start Study. DNA methylation was assessed via the Illumina 450K array (~439,500 CpG sites). Offspring adiposity was obtained via air displacement plethysmography.

Smccnet 2.0: a comprehensive tool for multi-omics network inference with shiny visualization.

Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable.

Limitations of Clustering with PCA and Correlated Noise.

It is now common to have a modest to large number of features on individuals with complex diseases. Unsupervised analyses, such as clustering with and without preprocessing by Principle Component Analysis (PCA), is widely used in practice to uncover subgroups in a sample. However, in many modern studies features are often highly correlated and noisy (e.

Dynamic and prognostic proteomic associations with FEV decline in chronic obstructive pulmonary disease.

Rationale: Identification and validation of circulating biomarkers for lung function decline in COPD remains an unmet need.

Objective: Identify prognostic and dynamic plasma protein biomarkers of COPD progression.

Methods: We measured plasma proteins using SomaScan from two COPD-enriched cohorts, the Subpopulations and Intermediate Outcomes Measures in COPD Study (SPIROMICS) and Genetic Epidemiology of COPD (COPDGene), and one population-based cohort, Multi-Ethnic Study of Atherosclerosis (MESA) Lung.

Longitudinal changes in DNA methylation during the onset of islet autoimmunity differentiate between reversion versus progression of islet autoimmunity.

Background: Type 1 diabetes (T1D) is preceded by a heterogenous pre-clinical phase, islet autoimmunity (IA). We aimed to identify pre vs. post-IA seroconversion (SV) changes in DNAm that differed across three IA progression phenotypes, those who lose autoantibodies (reverters), progress to clinical T1D (progressors), or maintain autoantibody levels (maintainers).

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration.

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity.

Proteomic Networks and Related Genetic Variants Associated with Smoking and Chronic Obstructive Pulmonary Disease.

Background: Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features.

Methods: Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.