Identification of transcriptional macromolecular associations in human bone using browser based in silico analysis in a giant correlation matrix

In silico discovery of blood cell macromolecular associations

Background

Physical molecular interactions are the basis of intracellular signalling and gene regulatory networks, and comprehensive, accessible databases are needed for their discovery. Highly correlated transcripts may reflect important functional associations, but identification of such associations from primary data are cumbersome. We have constructed and adapted a user-friendly web application to discover and identify putative macromolecular associations in human peripheral blood based on significant correlations at the transcriptional level.

Methods

The blood transcriptome was characterized by quantification of 17,328 RNA species, including 341 mature microRNAs in 105 clinically well-characterized postmenopausal women. Intercorrelation of detected transcripts signal levels generated a matrix with > 150 million correlations recognizing the human blood RNA interactome. The correlations with calculated adjusted p-values were made easily accessible by a novel web application.

Results

We found that significant transcript correlations within the giant matrix reflect experimentally documented interactions involving select ubiquitous blood relevant transcription factors (CREB1, GATA1, and the glucocorticoid receptor (GR, NR3C1)). Their responsive genes recapitulated up to 91% of these as significant correlations, and were replicated in an independent cohort of 1204 individual blood samples from the Framingham Heart Study. Furthermore, experimentally documented mRNAs/miRNA associations were also reproduced in the matrix, and their predicted functional co-expression described. The blood transcript web application is available at http://app.uio.no/med/klinmed/correlation-browser/blood/index.php and works on all commonly used internet browsers.

Conclusions

Using in silico analyses and a novel web application, we found that correlated blood transcripts across 105 postmenopausal women reflected experimentally proven molecular associations. Furthermore, the associations were reproduced in a much larger and more heterogeneous cohort and should therefore be generally representative. The web application lends itself to be a useful hypothesis generating tool for identification of regulatory mechanisms in complex biological data sets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01077-3.

Expression Quantitative Trait Locus Study of Bone Mineral Density GWAS Variants in Human Osteoclasts

Osteoporosis is a complex disease with a strong genetic component. Genomewide association studies (GWAS) have been very successful at identifying common genetic variants associated with bone parameters. A recently published study documented the results of the largest GWAS for bone mineral density (BMD) performed to date (n = 142,487), identifying 307 conditionally independent single-nucleotide polymorphisms (SNPs) as associated with estimated BMD (eBMD) at the genomewide significance level. The vast majority of these variants are non-coding SNPs. Expression quantitative trait locus (eQTL) studies using disease-specific cell types have increasingly been integrated with the results from GWAS to identify genes through which the observed GWAS associations are likely mediated. We generated a unique human osteoclast-specific eQTL data set using cells differentiated in vitro from 158 participants. We then used this resource to characterize the 307 recently identified BMD GWAS SNPs for association with nearby genes (±500 kb). After correction for multiple testing, 24 variants were found to be significantly associated with the expression of 32 genes in the osteoclast-like cells. Bioinformatics analysis suggested that these variants and those in strong linkage disequilibrium with them are enriched in regulatory regions. Several of the eQTL associations identified are relevant to genes that present strongly as having a role in bone, particularly IQGAP1, CYP19A1, CTNNB1, and COL6A3. Supporting evidence for many of the associations was obtained from publicly available eQTL data sets. We have also generated strong evidence for the presence of a regulatory region on chromosome 15q21.2 relevant to both the GLDN and CYP19A1 genes. In conclusion, we have generated a unique osteoclast-specific eQTL resource and have used this to identify 32 eQTL associations for recently identified BMD GWAS loci, which should inform functional studies of osteoclast biology. © 2018 American Society for Bone and Mineral Research.

Effects of threshold on the topology of gene co-expression networks

Several developments regarding the analysis of gene co-expression profiles using complex network theory have been reported recently. Such approaches usually start with the construction of an unweighted gene co-expression network, therefore requiring the selection of a suitable threshold defining which pairs of vertices will be connected. We aimed at addressing such an important problem by suggesting and comparing five different approaches for threshold selection. Each of the methods considers a respective biologically-motivated criterion for electing a potentially suitable threshold. A set of 21 microarray experiments from different biological groups was used to investigate the effect of applying the five proposed criteria to several biological situations. For each experiment, we used the Pearson correlation coefficient to measure the relationship between each gene pair, and the resulting weight matrices were thresholded considering several values, generating respective adjacency matrices (co-expression networks). Each of the five proposed criteria was then applied in order to select the respective threshold value. The effects of these thresholding approaches on the topology of the resulting networks were compared by using several measurements, and we verified that, depending on the database, the impact on the topological properties can be large. However, a group of databases was verified to be similarly affected by most of the considered criteria. Based on such results, it can be suggested that when the generated networks present similar measurements, the thresholding method can be chosen with greater freedom. If the generated networks are markedly different, the thresholding method that better suits the interests of each specific research study represents a reasonable choice.

Identification of a novel locus on chromosome 2q13, which predisposes to clinical vertebral fractures independently of bone density

OBJECTIVES

To identify genetic determinants of susceptibility to clinical vertebral fractures, which is an important complication of osteoporosis.

METHODS

Here we conduct a genome-wide association study in 1553 postmenopausal women with clinical vertebral fractures and 4340 controls, with a two-stage replication involving 1028 cases and 3762 controls. Potentially causal variants were identified using expression quantitative trait loci (eQTL) data from transiliac bone biopsies and bioinformatic studies.

RESULTS

A locus tagged by rs10190845 was identified on chromosome 2q13, which was significantly associated with clinical vertebral fracture (P=1.04×10-9) with a large effect size (OR 1.74, 95% CI 1.06 to 2.6). Bioinformatic analysis of this locus identified several potentially functional SNPs that are associated with expression of the positional candidate genes TTL (tubulin tyrosine ligase) and SLC20A1 (solute carrier family 20 member 1). Three other suggestive loci were identified on chromosomes 1p31, 11q12 and 15q11. All these loci were novel and had not previously been associated with bone mineral density or clinical fractures.

CONCLUSION

We have identified a novel genetic variant that is associated with clinical vertebral fractures by mechanisms that are independent of BMD. Further studies are now in progress to validate this association and evaluate the underlying mechanism.

Identifying dysregulated pathways in postmenopausal osteoporosis through investigation of crosstalk between pathways

The present study aimed to identify potential dysregulated pathways to further reveal the molecular mechanisms of postmenopausal osteoporosis (PMOP) based on pathway‑interaction network (PIN) analysis, which considers crosstalk between pathways. Protein‑protein interaction (PPI) data and pathway information were derived from STRING and Reactome Pathway databases, respectively. According to the gene expression profiles, pathway data and PPI information, a PIN was constructed with each node representing a biological pathway. Principal component analysis was used to compute the pathway activity for each pathway, and the seed pathway was selected. Subsequently, dysregulated pathways were extracted from the PIN based on the seed pathway and the increased classification accuracy, which was measured using the area under the curve (AUC) index according to 5‑fold cross validation. A PIN comprising 2,725 interactions was constructed, which was used to detect dysregulated pathways. Notably, the 'mitotic prometaphase' pathway was selected and defined as a seed pathway. Starting with the seed pathway, network‑based analysis successfully identified one pathway set for PMOP comprising eight dysregulated pathways (such as mitotic prometaphase, resolution of sister chromatid cohesion, mRNA splicing and mRNA splicing‑major) with an AUC score of 0.85, which may provide potential biomarkers for targeted therapy for PMOP.

Omics analysis of human bone to identify genes and molecular networks regulating skeletal remodeling in health and disease

The skeleton is a metabolically active organ throughout life where specific bone cell activity and paracrine/endocrine factors regulate its morphogenesis and remodeling. In recent years, an increasing number of reports have used multi-omics technologies to characterize subsets of bone biological molecular networks. The skeleton is affected by primary and secondary disease, lifestyle and many drugs. Therefore, to obtain relevant and reliable data from well characterized patient and control cohorts are vital. Here we provide a brief overview of omics studies performed on human bone, of which our own studies performed on trans-iliacal bone biopsies from postmenopausal women with osteoporosis (OP) and healthy controls are among the first and largest. Most other studies have been performed on smaller groups of patients, undergoing hip replacement for osteoarthritis (OA) or fracture, and without healthy controls. The major findings emerging from the combined studies are: 1. Unstressed and stressed bone show profoundly different gene expression reflecting differences in bone turnover and remodeling and 2. Omics analyses comparing healthy/OP and control/OA cohorts reveal characteristic changes in transcriptomics, epigenomics (DNA methylation), proteomics and metabolomics. These studies, together with genome-wide association studies, in vitro observations and transgenic animal models have identified a number of genes and gene products that act via Wnt and other signaling systems and are highly associated to bone density and fracture. Future challenge is to understand the functional interactions between bone-related molecular networks and their significance in OP and OA pathogenesis, and also how the genomic architecture is affected in health and disease.

Distinct DNA methylation profiles in bone and blood of osteoporotic and healthy postmenopausal women

DNA methylation affects expression of associated genes and may contribute to the missing genetic effects from genome-wide association studies of osteoporosis. To improve insight into the mechanisms of postmenopausal osteoporosis, we combined transcript profiling with DNA methylation analyses in bone. RNA and DNA were isolated from 84 bone biopsies of postmenopausal donors varying markedly in bone mineral density (BMD). In all, 2529 CpGs in the top 100 genes most significantly associated with BMD were analyzed. The methylation levels at 63 CpGs differed significantly between healthy and osteoporotic women at 10% false discovery rate (FDR). Five of these CpGs at 5% FDR could explain 14% of BMD variation. To test whether blood DNA methylation reflect the situation in bone (as shown for other tissues), an independent cohort was selected and BMD association was demonstrated in blood for 13 of the 63 CpGs. Four transcripts representing inhibitors of bone metabolism-MEPE, SOST, WIF1, and DKK1-showed correlation to a high number of methylated CpGs, at 5% FDR. Our results link DNA methylation to the genetic influence modifying the skeleton, and the data suggest a complex interaction between CpG methylation and gene regulation. This is the first study in the hitherto largest number of postmenopausal women to demonstrate a strong association among bone CpG methylation, transcript levels, and BMD/fracture. This new insight may have implications for evaluation of osteoporosis stage and susceptibility.