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Multi-omics Analysis of Umbilical Cord Hematopoietic Stem Cells from a Multi-ethnic Cohort of Hawaii Reveals the Intergenerational Effect of Maternal Pre-Pregnancy Obesity and Risk Prediction for Cancers

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE273075 | GEO | 2025/02/24

REPOSITORIES: GEO

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Multi-omics Analysis of Umbilical Cord Hematopoietic Stem Cells from a Multi-ethnic Cohort of Hawaii Reveals the Intergenerational Effect of Maternal Pre-Pregnancy Obesity and Risk Prediction for Cancers

Project description:Maternal obesity is a health concern that may predispose newborns to a high risk of medical problems later in life. To understand the transgenerational effect of maternal obesity, we conducted a multi-omics study, using DNA methylation and gene expression in the CD34+/CD38-/Lin- umbilical cord blood hematopoietic stem cells (uHSCs) and metabolomics of the cord blood, all from a multi-ethnic cohort (n=72) from Kapiolani Medical Center for Women and Children in Honolulu, Hawaii (collected between 2016 and 2018). Differential methylation (DM) analysis unveiled a global hypermethylation pattern in the maternal pre-pregnancy obese group (BH adjusted p<0.05), adjusting for major clinical confounders. Functional analysis revealed significant associations of differentially methylated sites with downregulated cell cycle, lipid synthesis, immune signaling, and metabolic pathways. Utilizing Shannon entropy to evaluate uHSCs methylation levels, we discerned a notable impact of maternal obesity on higher quiescence of uHSCs. Further, the multi-omics integration revealed dysfunction in adipogenesis, erythropoietin production, cell differentiation and DNA repair. This study reveals a significant correlation between pre-pregnancy maternal obesity and multi-omics level molecular changes in the uHSCs of offspring, particularly in DNA methylation. These findings suggest that maternal obesity can lead to alterations that affect physiological pathways in the offspring.

2025-02-24 | GSE273074 | GEO

2025-02-24 | GSE272978 | GEO

Multi-omics Analysis of Umbilical Cord Hematopoietic Stem Cells from a Multi-ethnic Cohort of Hawaii Reveals the Transgenerational Effect of Maternal Pre-Pregnancy Obesity.

Project description:BackgroundMaternal obesity is a health concern that may predispose newborns to a high risk of medical problems later in life. To understand the transgenerational effect of maternal obesity, we conducted a multi-omics study, using DNA methylation and gene expression in the CD34+/CD38-/Lin- umbilical cord blood hematopoietic stem cells (uHSCs) and metabolomics of the cord blood, all from a multi-ethnic cohort (n=72) from Kapiolani Medical Center for Women and Children in Honolulu, Hawaii (collected between 2016 and 2018).ResultsDifferential methylation (DM) analysis unveiled a global hypermethylation pattern in the maternal pre-pregnancy obese group (BH adjusted p<0.05), after adjusting for major clinical confounders. Comprehensive functional analysis showed hypermethylation in promoters of genes involved in cell cycle, protein synthesis, immune signaling, and lipid metabolism. Utilizing Shannon entropy on uHSCs methylation, we discerned notably higher quiescence of uHSCs impacted by maternal obesity. Additionally, the integration of multi-omics data-including methylation, gene expression, and metabolomics-provided further evidence of dysfunctions in adipogenesis, erythropoietin production, cell differentiation, and DNA repair, aligning with the findings at the epigenetic level. Furthermore, the CpG sites associated with maternal obesity from these pathways also predicted highly accurately (average AUC = 0.8687) between cancer vs. normal tissues in 14 cancer types in The Cancer Genome Atlas (TCGA).ConclusionsThis study revealed the significant correlation between pre-pregnancy maternal obesity and multi-omics level molecular changes in the uHSCs of offspring, particularly in DNA methylation. Moreover, these maternal obesity epigenetic markers in uHSCs may predispose offspring to higher cancer risks.

| S-EPMC11302719 | biostudies-literature

Project description:Multi-omics Analysis of Umbilical Cord Hematopoietic Stem Cells from a Multi-ethnic Cohort of Hawaii Reveals the Intergenerational Effect of Maternal Pre-Pregnancy Obesity and Risk Prediction for Cancers

| PRJNA1139938 | ENA

| PRJNA1139739 | ENA

| PRJNA1139940 | ENA

Genotype and phylogenetic characterization of hepatitis B virus among multi-ethnic cohort in Hawaii.

Project description:AimHepatitis B virus (HBV) genomes in carriers from Hawaii have not been evaluated previously. The aim of the present study was to evaluate the distribution of HBV genotypes and their clinical relevance in Hawaii.MethodsGenotyping of HBV among 61 multi-ethnic carriers in Hawaii was performed by genetic methods. Three complete genomes and 61 core promoter/precore regions of HBV were sequenced directly.ResultsHBV genotype distribution among the 61 carriers was 23.0% for genotype A, 14.7% for genotype B and 62.3% for genotype C. Genotypes A, B and C were obtained from the carriers whose ethnicities were Filipino and Caucasian, Southeast Asian, and various Asian and Micronesian, respectively. All cases of genotype B were composed of recombinant strains with genotype C in the precore plus core region named genotype Ba. HBeAg was detected more frequently in genotype C than in genotype B (68.4% vs 33.3%, P<0.05) and basal core promoter (BCP) mutation (T1762/A1764) was more frequently found in genotype C than in genotype B. Twelve of the 38 genotype C strains possessed C at nucleotide (nt) position 1858 (C-1858). However there was no significant difference in clinical characteristics between C-1858 and T-1858 variants. Based on complete genome sequences, phylogenetic analysis revealed one patient of Micronesian ethnicity as having C-1858 clustered with two isolates from Polynesia with T-1858. In addition, two strains from Asian ethnicities were clustered with known isolates in carriers from Southeast Asia.ConclusionGenotypes A, B and C are predominant types among multi-ethnic HBV carriers in Hawaii, and distribution of HBV genotypes is dependent on the ethnic background of the carriers in Hawaii.

| S-EPMC4724971 | biostudies-literature

PAGE Multi-Ethnic Cohort Study

Project description:PAGE Multi-Ethnic Cohort Study

| PRJNA75617 | ENA

The pregnancy metabolome from a multi-ethnic pregnancy cohort

Project description: Not available

| ST001827 | MetabolomicsWorkbench

Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.

Project description:Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, we developed interpretable predictive models for multi-omics data by employing neural networks informed by prior biological knowledge, referred to as visible networks. These neural networks offer insights into the decision-making process and can unveil novel perspectives on the underlying biological mechanisms associated with traits and complex diseases. We tested the performance, interpretability and generalizability for inferring smoking status, subject age and LDL levels using genome-wide RNA expression and CpG methylation data from the blood of the BIOS consortium (four population cohorts, Ntotal = 2940). In a cohort-wise cross-validation setting, the consistency of the diagnostic performance and interpretation was assessed. Performance was consistently high for predicting smoking status with an overall mean AUC of 0.95 (95% CI: 0.90-1.00) and interpretation revealed the involvement of well-replicated genes such as AHRR, GPR15 and LRRN3. LDL-level predictions were only generalized in a single cohort with an R2 of 0.07 (95% CI: 0.05-0.08). Age was inferred with a mean error of 5.16 (95% CI: 3.97-6.35) years with the genes COL11A2, AFAP1, OTUD7A, PTPRN2, ADARB2 and CD34 consistently predictive. For both regression tasks, we found that using multi-omics networks improved performance, stability and generalizability compared to interpretable single omic networks. We believe that visible neural networks have great potential for multi-omics analysis; they combine multi-omic data elegantly, are interpretable, and generalize well to data from different cohorts.

| S-EPMC11297229 | biostudies-literature

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