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.

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.

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.

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.

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.

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.

Adipocyte hypertrophy in mesenchymal stem cells from infants of mothers with obesity.

Objective: Fat content of adipocytes derived from infant umbilical cord mesenchymal stem cells (MSCs) predicts adiposity in children through 4 to 6 years of age. This study tested the hypothesis that MSCs from infants born to mothers with obesity (Ob-MSCs) exhibit adipocyte hypertrophy and perturbations in genes regulating adipogenesis compared with MSCs from infants of mothers with normal weight (NW-MSCs).

Methods: Adipogenesis was induced in MSCs embedded in three-dimensional hydrogel structures, and cell size and number were measured by three-dimensional imaging.

Large scale proteomic studies create novel privacy considerations.

Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA).

Effective Subject Representation based on Multi-omics Disease Networks using Graph Embedding.

The study of complex behavior of biological systems has become increasingly dependent on evolutionary network modeling. In particular, multi-omics networks capture interactions between biomolecules such as proteins and metabolites, providing a basis for predicting relationships between such biomolecules and various phenotypic traits of complex diseases. In this paper, we introduce an integrative framework that given a multi-omics network representing a cohort of subjects, learns expressive representations for network nodes, and combines the learned nodes representations with the biological profiles of individual subjects for enriched representation of the subjects.

NetSHy: network summarization via a hybrid approach leveraging topological properties.

Motivation: Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e.

Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function.

Multi-omics subtyping pipeline for chronic obstructive pulmonary disease.

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects).

Plasma Metabolomic Signatures of Chronic Obstructive Pulmonary Disease and the Impact of Genetic Variants on Phenotype-Driven Modules.

Small studies have recently suggested that there are specific plasma metabolic signatures in chronic obstructive pulmonary disease (COPD), but there have been no large comprehensive study of metabolomic signatures in COPD that also integrate genetic variants. Fresh frozen plasma from 957 non-Hispanic white subjects in COPDGene was used to quantify 995 metabolites with Metabolon's global metabolomics platform. Metabolite associations with five COPD phenotypes (chronic bronchitis, exacerbation frequency, percent emphysema, post-bronchodilator forced expiratory volume at one second [FEV]/forced vital capacity [FVC], and FEV percent predicted) were assessed.

Metabolomic Profiling Reveals Sex Specific Associations with Chronic Obstructive Pulmonary Disease and Emphysema.

Susceptibility and progression of lung disease, as well as response to treatment, often differ by sex, yet the metabolic mechanisms driving these sex-specific differences are still poorly understood. Women with chronic obstructive pulmonary disease (COPD) have less emphysema and more small airway disease on average than men, though these differences become less pronounced with more severe airflow limitation. While small studies of targeted metabolites have identified compounds differing by sex and COPD status, the sex-specific effect of COPD on systemic metabolism has yet to be interrogated.

Placental Insulin/IGF-1 Signaling, PGC-1α, and Inflammatory Pathways Are Associated With Metabolic Outcomes at 4-6 Years of Age: The ECHO Healthy Start Cohort.

An adverse intrauterine environment is associated with the future risk of obesity and type 2 diabetes. Changes in placental function may underpin the intrauterine origins of adult disease, but longitudinal studies linking placental function with childhood outcomes are rare. Here, we determined the abundance and phosphorylation of protein intermediates involved in insulin signaling, inflammation, cortisol metabolism, protein glycosylation, and mitochondrial biogenesis in placental villus samples from healthy mothers from the Healthy Start cohort.

Patterns of relatedness and genetic diversity inferred from whole genome sequencing of archival blood fluke miracidia (Schistosoma japonicum).

Genomic approaches hold great promise for resolving unanswered questions about transmission patterns and responses to control efforts for schistosomiasis and other neglected tropical diseases. However, the cost of generating genomic data and the challenges associated with obtaining sufficient DNA from individual schistosome larvae (miracidia) from mammalian hosts have limited the application of genomic data for studying schistosomes and other complex macroparasites. Here, we demonstrate the feasibility of utilizing whole genome amplification and sequencing (WGS) to analyze individual archival miracidia.

Hypertensive Disorders of Pregnancy and DNA Methylation in Newborns.

Hypertensive disorders of pregnancy (HDP) are associated with low birth weight, shorter gestational age, and increased risk of maternal and offspring cardiovascular diseases later in life. The mechanisms involved are poorly understood, but epigenetic regulation of gene expression may play a part. We performed meta-analyses in the Pregnancy and Childhood Epigenetics Consortium to test the association between either maternal HDP (10 cohorts; n=5242 [cases=476]) or preeclampsia (3 cohorts; n=2219 [cases=135]) and epigenome-wide DNA methylation in cord blood using the Illumina HumanMethylation450 BeadChip.

Meta-analysis of peripheral blood gene expression modules for COPD phenotypes.

Chronic obstructive pulmonary disease (COPD) occurs typically in current or former smokers, but only a minority of people with smoking history develops the disease. Besides environmental factors, genetics is an important risk factor for COPD. However, the relationship between genetics, environment and phenotypes is not well understood.

High-Risk Human Papillomavirus E7 Alters Host DNA Methylome and Represses HLA-E Expression in Human Keratinocytes.

Human papillomavirus (HPV) infection distinctly alters methylation patterns in HPV-associated cancer. We have recently reported that HPV E7-dependent promoter hypermethylation leads to downregulation of the chemokine CXCL14 and suppression of antitumor immune responses. To investigate the extent of gene expression dysregulated by HPV E7-induced DNA methylation, we analyzed parallel global gene expression and DNA methylation using normal immortalized keratinocyte lines, NIKS, NIKS-16, NIKS-18, and NIKS-16∆E7.