Mendelian Randomization Identifies CpG Methylation Sites With Mediation Effects for Genetic Influences on BMD in Peripheral Blood Monocytes

Bone Trans-omics: Integrating Omics to Unveil Mechanistic Molecular Networks Regulating Bone Biology and Disease

PURPOSE OF REVIEW

Recent advancements in "omics" technologies and bioinformatics have afforded researchers new tools to study bone biology in an unbiased and holistic way. The purpose of this review is to highlight recent studies integrating multi-omics data gathered from multiple molecular layers (i.e.; trans-omics) to reveal new molecular mechanisms that regulate bone biology and underpin skeletal diseases.

RECENT FINDINGS

Bone biologists have traditionally relied on single-omics technologies (genomics, transcriptomics, proteomics, and metabolomics) to profile measureable differences (both qualitative and quantitative) of individual molecular layers for biological discovery and to investigate mechanisms of disease. Recently, literature has grown on the implementation of integrative multi-omics to study bone biology, which combines computational and informatics support to connect multiple layers of data derived from individual "omic" platforms. This emerging discipline termed "trans-omics" has enabled bone biologists to identify and construct detailed molecular networks, unveiling new pathways and unexpected interactions that have advanced our mechanistic understanding of bone biology and disease. While the era of trans-omics is poised to revolutionize our capacity to answer more complex and diverse questions pertinent to bone pathobiology, it also brings new challenges that are inherent when trying to connect "Big Data" sets. A concerted effort between bone biologists and interdisciplinary scientists will undoubtedly be needed to extract physiologically and clinically meaningful data from bone trans-omics in order to advance its implementation in the field.

Elucidating the genetic architecture of DNA methylation to identify promising molecular mechanisms of disease

DNA methylation commonly occurs at cytosine-phosphate-guanine sites (CpGs) that can serve as biomarkers for many diseases. We analyzed whole genome sequencing data to identify DNA methylation quantitative trait loci (mQTLs) in 4126 Framingham Heart Study participants. Our mQTL mapping identified 94,362,817 cis-mQTLvariant-CpG pairs (for 210,156 unique autosomal CpGs) at P < 1e-7 and 33,572,145 trans-mQTL variant-CpG pairs (for 213,606 unique autosomal CpGs) at P < 1e-14. Using cis-mQTL variants for 1258 CpGs associated with seven cardiovascular disease (CVD) risk factors, we found 104 unique CpGs that colocalized with at least one CVD trait. For example, cg11554650 (PPP1R18) colocalized with type 2 diabetes, and was driven by a single nucleotide polymorphism (rs2516396). We performed Mendelian randomization (MR) analysis and demonstrated 58 putatively causal relations of CVD risk factor-associated CpGs to one or more risk factors (e.g., cg05337441 [APOB] with LDL; MR P = 1.2e-99, and 17 causal associations with coronary artery disease (e.g. cg08129017 [SREBF1] with coronary artery disease; MR P = 5e-13). We also showed that three CpGs, e.g., cg14893161 (PM20D1), are putatively causally associated with COVID-19 severity. To assist in future analyses of the role of DNA methylation in disease pathogenesis, we have posted a comprehensive summary data set in the National Heart, Lung, and Blood Institute's BioData Catalyst.

Integrative analysis of multi-omics data to detect the underlying molecular mechanisms for obesity in vivo in humans

BACKGROUND

Obesity is a complex, multifactorial condition in which genetic play an important role. Most of the systematic studies currently focuses on individual omics aspect and provide insightful yet limited knowledge about the comprehensive and complex crosstalk between various omics levels.

SUBJECTS AND METHODS

Therefore, we performed a most comprehensive trans-omics study with various omics data from 104 subjects, to identify interactions/networks and particularly causal regulatory relationships within and especially those between omic molecules with the purpose to discover molecular genetic mechanisms underlying obesity etiology in vivo in humans.

RESULTS

By applying differentially analysis, we identified 8 differentially expressed hub genes (DEHGs), 14 differentially methylated regions (DMRs) and 12 differentially accumulated metabolites (DAMs) for obesity individually. By integrating those multi-omics biomarkers using Mendelian Randomization (MR) and network MR analyses, we identified 18 causal pathways with mediation effect. For the 20 biomarkers involved in those 18 pairs, 17 biomarkers were implicated in the pathophysiology of obesity or related diseases.

CONCLUSIONS

The integration of trans-omics and MR analyses may provide us a holistic understanding of the underlying functional mechanisms, molecular regulatory information flow and the interactive molecular systems among different omic molecules for obesity risk and other complex diseases/traits.

DNA comethylation analysis reveals a functional association between BRCA1 and sperm DNA fragmentation

OBJECTIVES

To identify the DNA comethylation patterns associated with sperm DNA fragmentation (SDF) and to explore the potential associations of hub genes with SDF.

DESIGN

Prospective study.

SETTING

University-affiliated reproductive medicine center.

PATIENT(S)

A total of 300 male patients consulting for couple infertility were recruited from the First Affiliated Hospital of Wenzhou Medical University.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Comethylation network analysis based on the genome-wide methylation profile of spermatozoal DNA from 20 men was performed to identify hub modules and genes involved in SDF. Human spermatozoa were used for targeted bisulfite amplicon sequencing (267 men) or droplet digital polymerase chain reaction (45 men). The potential role of Brca1 in DNA damage was explored in mouse GC2 spermatocyte cells. Oxidative damage to spermatocytes was modeled by incubating GC2 cells with H₂O₂ (25 mM) for 90 minutes.

RESULT(S)

BRCA1 was identified as a hub gene in SDF. Promoter hypermethylation of BRCA1 was observed in those samples with a high DNA fragmentation index (DFI) compared to those with a low DFI. Concomitantly, BRCA1 mRNA expression was lower in samples with a high DFI than with a low DFI. In the GC2 cell model, Brca1 knockdown reduced cell proliferation and increased sensitivity to oxidative stress. Moreover, it increased double-strand breaks and decreased the protein levels of the DNA repair genes MRE11 and RAD51.

CONCLUSION(S)

A prominent cluster of comethylated patterns associated with SDF was identified. BRCA1 may be the hub gene involved in sperm DNA damage.

Epigenetic regulation of bone mass

Osteoporosis, characterised by low bone mass, poor bone structure, and an increased risk of fracture, is a major public health problem. There is increasing evidence that the influence of the environment on gene expression, through epigenetic processes, contributes to variation in BMD and fracture risk across the lifecourse. Such epigenetic processes include DNA methylation, histone and chromatin modifications and non-coding RNAs. Examples of associations with phenotype include DNA methylation in utero linked to maternal vitamin D status, and to methylation of target genes such as OPG and RANKL being associated with osteoporosis in later life. Epigenome-wide association studies and multi-omics technologies have further revealed susceptibility loci, and histone acetyltransferases, deacetylases and methylases are being considered as therapeutic targets. This review encompasses recent advances in our understanding of epigenetic mechanisms in the regulation of bone mass and osteoporosis development, and outlines possible diagnostic and prognostic biomarker applications.

Genetic impacts on DNA methylation: research findings and future perspectives

Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.

A novel computational strategy for DNA methylation imputation using mixture regression model (MRM)

BACKGROUND

DNA methylation is an important heritable epigenetic mark that plays a crucial role in transcriptional regulation and the pathogenesis of various human disorders. The commonly used DNA methylation measurement approaches, e.g., Illumina Infinium HumanMethylation-27 and -450 BeadChip arrays (27 K and 450 K arrays) and reduced representation bisulfite sequencing (RRBS), only cover a small proportion of the total CpG sites in the human genome, which considerably limited the scope of the DNA methylation analysis in those studies.

RESULTS

We proposed a new computational strategy to impute the methylation value at the unmeasured CpG sites using the mixture of regression model (MRM) of radial basis functions, integrating information of neighboring CpGs and the similarities in local methylation patterns across subjects and across multiple genomic regions. Our method achieved a better imputation accuracy over a set of competing methods on both simulated and empirical data, particularly when the missing rate is high. By applying MRM to an RRBS dataset from subjects with low versus high bone mineral density (BMD), we recovered methylation values of ~ 300 K CpGs in the promoter regions of chromosome 17 and identified some novel differentially methylated CpGs that are significantly associated with BMD.

CONCLUSIONS

Our method is well applicable to the numerous methylation studies. By expanding the coverage of the methylation dataset to unmeasured sites, it can significantly enhance the discovery of novel differential methylation signals and thus reveal the mechanisms underlying various human disorders/traits.