Integrative Analysis of Genomics and Transcriptome Data to Identify Potential Functional Genes of BMDs in Females

Identification of bone mineral density associated genes with shared genetic architectures across multiple tissues: Functional insights for EPDR1, PKDCC, and SPTBN1

Recent studies suggest a shared genetic architecture between muscle and bone, yet the underlying molecular mechanisms remain elusive. This study aims to identify the functionally annotated genes with shared genetic architecture between muscle and bone using the most up-to-date genome-wide association study (GWAS) summary statistics from bone mineral density (BMD) and fracture-related genetic variants. We employed an advanced statistical functional mapping method to investigate shared genetic architecture between muscle and bone, focusing on genes highly expressed in muscle tissue. Our analysis identified three genes, EPDR1, PKDCC, and SPTBN1, which are highly expressed in muscle tissue and previously unlinked to bone metabolism. About 90% and 85% of filtered Single-Nucleotide Polymorphisms were in the intronic and intergenic regions for the threshold at P≤5×10-8 and P≤5×10-100, respectively. EPDR1 was highly expressed in multiple tissues, including muscles, adrenal glands, blood vessels, and the thyroid. SPTBN1 was highly expressed in all 30 tissue types except blood, while PKDCC was highly expressed in all 30 tissue types except the brain, pancreas, and skin. Our study provides a framework for using GWAS findings to highlight functional evidence of crosstalk between multiple tissues based on shared genetic architecture between muscle and bone. Further research should focus on functional validation, multi-omics data integration, gene-environment interactions, and clinical relevance in musculoskeletal disorders.

Weighted gene co-expression network analysis for hub genes in colorectal cancer

BACKGROUND

This study is designed to explore hub genes participating in colorectal cancer (CRC) development through weighted gene co-expression network analysis (WGCNA).

METHODS

Expression profiles of CRC and normal samples were retrieved from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA), and were subjected to WGCNA to filter differentially expressed genes with significant association with CRC. Functional enrichment analysis and protein-protein interaction (PPI) analysis were carried out to filter the candidate genes, further and survival analysis was performed for the candidate genes to obtain potential regulatory hub genes in CRC. Expression analysis was conducted for the candidate genes and a multifactor model was established.

RESULTS

After differential analysis and WGCNA, 289 candidate genes were filtered from the GEO and TCGA. Further functional enrichment analysis demonstrated possible regulatory pathways and functions. PPI analysis filtered 15 hub genes and survival analysis indicated a significant correlation of CLCA1, CLCA4, and CPT1A with prognosis of patients with CRC. The multifactor Cox risk model established based on the three genes revealed that if the three genes were a gene set, they had well predictive capacity for the prognosis of patients with CRC.

CONCLUSIONS

CLCA1, CLCA4, and CPT1A express at low levels in CRC and function as core anti-tumor genes. As a gene set, they can predict prognosis well.

Identification of key regulatory genes and their working mechanisms in type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of beta cells in pancreatic islets. Identification of the key genes involved in T1D progression and their mechanisms of action may contribute to a better understanding of T1D.

METHODS

The microarray profile of T1D-related gene expression was searched using the Gene Expression Omnibus (GEO) database. Then, the expression data of two messenger RNAs (mRNAs) were integrated for Weighted Gene Co-Expression Network Analysis (WGCNA) to generate candidate genes related to T1D. In parallel, T1D microRNA (miRNA) data were analyzed to screen for possible regulatory miRNAs and their target genes. An miRNA-mRNA regulatory network was then established to predict the key regulatory genes and their mechanisms.

RESULTS

A total of 24 modules (i.e., clusters/communities) were selected using WGCNA analysis, in which three modules were significantly associated with T1D. Further correlation analysis of the gene module revealed 926 differentially expressed genes (DEGs), of which 327 genes were correlated with T1D. Analysis of the miRNA microarray showed that 13 miRNAs had significant expression differences in T1D. An miRNA-mRNA network was established based on the prediction of miRNA target genes and the combined analysis of mRNA, in which the target genes of two miRNAs were found in T1D correlated genes.

CONCLUSION

An miRNA-mRNA network for T1D was established, based on which 2 miRNAs and 12 mRNAs were screened, suggesting that they may play key regulatory roles in the initiation and development of T1D.

Integrative analysis of GWAS and transcriptome reveals p53 signaling pathway mediates resistance to visceral white-nodules disease in large yellow croaker

Visceral white-nodules disease (VWND), caused by Pseudomonas plecoglossicida, is one of the primary causes of morbidity and mortality in large yellow croaker aquaculture. Host disease resistance is a heritable trait that involves complex regulatory processes. However, the regulatory mechanism of bacterial resistance in large yellow croaker is still unclear. This study attempted to systematically evaluate the major genetic loci and transcriptional regulatory mechanisms associated with the resistance to VWND in large yellow croaker by crossover method studies. A large population of large yellow croaker was challenged with P. plecoglossicida, with survival time recorded and samples were taken for genotyping. Meanwhile, spleen samples that were used for RNA-seq to compare their transcriptomic profiles before and after infection were taken from resistant populations (RS) and susceptible control populations (CS) bred using the genomic selection (GS) technique. Genome-wide association analyses using 46 K imputed SNP genotypes highlighted that resistance is a polygenic trait. The integrative analysis results show the co-localization of the cd82a gene between disease resistance-related genetic loci and comparative transcriptional analysis. And functional enrichment analysis showed differential enrichment of the p53 signaling pathway in RS and CS groups, suggesting that there may be cd82a-mediated p53 signaling pathway activation for VWND resistance. This large-scale study provides further evidence for the heritability and transcriptional regulatory mechanisms of host inheritance of VWND resistance.

Improved biomarker discovery through a plot twist in transcriptomic data analysis

Background

Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

Results

In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

Conclusions

We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01398-w.

Spectrins and human diseases

Spectrin, as one of the major components of a plasma membrane-associated cytoskeleton, is a cytoskeletal protein composed of the modular structure of α and β subunits. The spectrin-based skeleton is essential for preserving the integrity and mechanical characteristics of the cell membrane. Moreover, spectrin regulates a variety of cell processes including cell apoptosis, cell adhesion, cell spreading, and cell cycle. Dysfunction of spectrins is implicated in various human diseases including hemolytic anemia, neurodegenerative diseases, ataxia, heart diseases, and cancers. Here, we briefly discuss spectrins function as well as the clinical manifestations and currently known molecular mechanisms of human diseases related to spectrins, highlighting that strategies for targeting regulation of spectrins function may provide new avenues for therapeutic intervention for these diseases.

Screening of Important Markers in Peripheral Blood Mononuclear Cells to Predict Female Osteoporosis Risk Using LASSO Regression Algorithm and SVM Method

Background:

Osteoporosis is a bone disease that increases the patient’s risk of fracture. We aimed to identify robust marker genes related to osteoporosis based on different bioinformatic methods and multiple datasets.

Methods:

Three datasets from Gene Expression Omnibus (GEO) were utilized for analysis separately. Significantly differentially expressed genes (DEGs) from comparing high hip and low hip low bone mineral density (BMD) groups in the first dataset were identified for Gene Ontology (GO), Gene set enrichment analysis (GSEA) and Kyoto encyclopedia of genes and genomes (KEGG) to investigate the discrepantly enriched biological processes between high hip and low hip group. Last absolute shrinkage and selection operator (LASSO), SVM model and protein-protein interaction (PPI) regulatory network were performed and generated robust marker genes for downstream TF-target and miRNA-target prediction.

Results:

Several DEGs between high hip BMD group and low hip BMD group were obtained. And the metabolism-related pathways such as metabolic pathways, carbon metabolism, glyoxylate and dicarboxylate metabolism shown enrichment in these DEGs. Integration with LASSO regression analysis, 8 differential expression genes ( SH3BP1, NARF, ANKRD34B, RNF40, ZNF473, AKT1, SHMT1, and VASH1) in GSE62402 were identified as the optimal differential genes combination. Moreover, the SVM validation analysis in GSE56814 and GSE56815 datasets showed that the characteristic gene combinations presented high diagnostic effects, and the model AUC areas for GSE56814 was 0.899 and for GSE56815 was 0.921. Furthermore, the subcellular localization analysis of the 8 genes revealed that 4 proteins were located in the cytoplasm, 3 proteins were located in the nucleus, and 1 protein was located in the mitochondria. Additionally, the related TFs and miRNAs by performing TF-target and miRNA-target prediction for 5 genes ( AKT1, SHMT1, ZNF473, RNF40 and VASH1) were investigated from PPI network.

Conclusion:

The optimal differential genes combination ( SH3BP1, NARF, ANKRD34B, RNF40, ZNF473, AKT1, SHMT1, and VASH1) presented high diagnostic effect for osteoporosis risk.

Comparative transcriptome analysis of winter yaks in plateau and plain

The yak is an important source for the people living and ecological environment in the Qinghai-Tibet Plateau. In every winter, many domestic yaks will lose bodyweight or dead under cold and food scarcity. Moving the plateau yaks to farm in the plain is a useful approach to reduce their environmental stress and gain more production. In this study, we measured growth, slaughter and beef quality traits every month and sequenced mRNA expression levels of muscles of two groups yaks living in plateau and plain respectively. We found there is significant difference (p-value <0.01) in the third (60 days), fourth (90 days), fifth (120 days) and sixth (150 days) weights between subpopulations in the plateau and plain. We identified 540 different expressed genes (DEGs) including 123 known genes and 417 unknown genes. Using the weighted correlation network analysis (WGCNA) to build a co-express network, the modules were strong relative to weight traits. The findings highlighted the underlying way and a relative network to yield a new view about gene expression between the yaks living plateau and plain.

Advance toward isolation, extraction, metabolism and health benefits of kaempferol, a major dietary flavonoid with future perspectives

As a major ubiquitous secondary metabolite, flavonoids are widely distributed in planta. Among flavonoids, kaempferol is a typical natural flavonol in diets and medicinal plants with myriad bioactivities, such as anti-inflammatory activity, anti-cancer activity, antioxidant activity, and anti-diabetic activity. However, the natural sources, absorption and metabolism as well as the bioactivities of kaempferol have not been reviewed comprehensively and systematically. This review highlights the latest research progress and the effect of kaempferol in the prevention and treatment of various chronic diseases, as well as its protective health effects, and provides a theoretical basis for future research to be used in nutraceuticals. Further, comparison of the different extraction and analytical methods are presented to highlight the most optimum for PG recovery and its detection in plasma and body fluids. Such review aims at improving the value-added applications of this unique dietary bioactive flavonoids at commercial scale and to provide a reference for its needed further development.

Identification and validation of potential novel biomarkers to predict distant metastasis in differentiated thyroid cancer

Background

Distant metastasis (DM) is not common in differentiated thyroid cancer (DTC). However, it is associated with a significantly poor prognosis. Early detection of high-risk DTC patients is difficult, and the molecular mechanism is still unclear. Therefore, the present study aims to establish a novel predictive model based on clinicopathological parameters and DM-related gene signatures to provide guidelines for clinicians in decision making.

Methods

Weighted gene co-expression network analysis (WGCNA) was performed to discover co-expressed gene modules and hub genes associated with DM. Univariate and multivariate analyses were carried out to identify independent clinicopathological risk factors based on The Cancer Genome Atlas (TCGA) database. An integrated nomogram prediction model was established. Finally, real hub genes were validated using the GSE60542 database and various thyroid cell lines.

Results

The midnightblue module was most significantly positively correlated with DM (R=0.56, P=9e-06) by as per WGCNA. DLX5 (AUC: 0.769), COX6B2 (AUC: 0.764), and LYPD1 (AUC: 0.760) were determined to be the real hub genes that play a crucial role in predicting DM. Meanwhile, univariate and multivariate analyses demonstrated that T-stage (OR, 15.03; 95% CI, 1.75-319.40; and P=0.024), histologic subtype (OR, 0.17; 95% CI, 0.03-0.92; and P=0.042) were the independent predictors of DM. Subsequently, a nomogram model was constructed based on gene signatures and independent clinical risk factors exhibited good performance. Additionally, the mRNA expressions of real hub genes in the GSE60542 dataset were consistent with TCGA.

Conclusions

The present study has provided a reliable model to predict DM in patients with DTC. This model is likely to serve as an individual risk assessment tool in therapeutic decision-making.

Identification of Atrial Fibrillation-Associated Genes ERBB2 and MYPN Using Genome-Wide Association and Transcriptome Expression Profile Data on Left-Right Atrial Appendages

More reliable methods are needed to uncover novel biomarkers associated with atrial fibrillation (AF). Our objective is to identify significant network modules and newly AF-associated genes by integrative genetic analysis approaches. The single nucleotide polymorphisms with nominal relevance significance from the AF-associated genome-wide association study (GWAS) data were converted into the GWAS discovery set using ProxyGeneLD, followed by merging with significant network modules constructed by weighted gene coexpression network analysis (WGCNA) from one expression profile data set, composed of left and right atrial appendages (LAA and RAA). In LAA, two distinct network modules were identified (blue: p = 0.0076; yellow: p = 0.023). Five AF-associated biomarkers were identified (ERBB2, HERC4, MYH7, MYPN, and PBXIP1), combined with the GWAS test set. In RAA, three distinct network modules were identified and only one AF-associated gene LOXL1 was determined. Using human LAA tissues by real-time quantitative polymerase chain reaction, the differentially expressive results of ERBB2, MYH7, and MYPN were observed (p < 0.05). This study first demonstrated the feasibility of fusing GWAS with expression profile data by ProxyGeneLD and WGCNA to explore AF-associated genes. In particular, two newly identified genes ERBB2 and MYPN via this approach contribute to further understanding the occurrence and development of AF, thereby offering preliminary data for subsequent studies.

The mTORC2 Regulator Homer1 Modulates Protein Levels and Sub-Cellular Localization of the CaSR in Osteoblast-Lineage Cells

We recently found that, in human osteoblasts, Homer1 complexes to Calcium-sensing receptor (CaSR) and mediates AKT initiation via mechanistic target of rapamycin complex (mTOR) complex 2 (mTORC2) leading to beneficial effects in osteoblasts including β-catenin stabilization and mTOR complex 1 (mTORC1) activation. Herein we further investigated the relationship between Homer1 and CaSR and demonstrate a link between the protein levels of CaSR and Homer1 in human osteoblasts in primary culture. Thus, when siRNA was used to suppress the CaSR, we observed upregulated Homer1 levels, and when siRNA was used to suppress Homer1 we observed downregulated CaSR protein levels using immunofluorescence staining of cultured osteoblasts as well as Western blot analyses of cell protein extracts. This finding was confirmed in vivo as the bone cells from osteoblast specific CaSR-/- mice showed increased Homer1 expression compared to wild-type (wt). CaSR and Homer1 protein were both expressed in osteocytes embedded in the long bones of wt mice, and immunofluorescent studies of these cells revealed that Homer1 protein sub-cellular localization was markedly altered in the osteocytes of CaSR-/- mice compared to wt. The study identifies additional roles for Homer1 in the control of the protein level and subcellular localization of CaSR in cells of the osteoblast lineage, in addition to its established role of mTORC2 activation downstream of the receptor.

SPTBN1 Prevents Primary Osteoporosis by Modulating Osteoblasts Proliferation and Differentiation and Blood Vessels Formation in Bone

Osteoporosis is a common systemic skeletal disorder that leads to increased bone fragility and increased risk of fracture. Although βII-Spectrin (SPTBN1) has been reported to be involved in the development of various human cancers, the function and underlying molecular mechanisms of SPTBN1 in primary osteoporosis remain unclear. In this study, we first established a primary osteoporosis mouse model of senile osteoporosis and postmenopausal osteoporosis. The results showed that the expression of SPTBN1 was significantly downregulated in primary osteoporosis mice model compared with the control group. Furthermore, silencing of SPTBN1 led to a decrease in bone density, a small number of trabecular bones, wider gap, decreased blood volume fraction and number of blood vessels, as well as downregulation of runt-related transcription factor 2 (Runx2), Osterix (Osx), Osteocalcin (Ocn), and vascular endothelial growth factor (VEGF) in primary osteoporosis mice model compared with the control group. Besides, the silencing of SPTBN1 inhibited the growth and induced apoptosis of mouse pre-osteoblast MC3T3-E1 cells compared with the negative control group. Moreover, the silencing of SPTBN1 significantly increased the expression of TGF-β, Cxcl9, and the phosphorylation level STAT1 and Smad3 in MC3T3-E1 cells compared with the control group. As expected, overexpression of SPTBN1 reversed the effect of SPTBN1 silencing in the progression of primary osteoporosis both in vitro and in vivo. Taken together, these results suggested that SPTBN1 suppressed primary osteoporosis by facilitating the proliferation, differentiation, and inhibition of apoptosis in osteoblasts via the TGF-β/Smad3 and STAT1/Cxcl9 pathways. Besides, overexpression of SPTBN1 promoted the formation of blood vessels in bone by regulating the expression of VEGF. This study, therefore, provided SPTBN1 as a novel therapeutic target for osteoporosis.

βII spectrin (SPTBN1): biological function and clinical potential in cancer and other diseases

βII spectrin, the most common isoform of non-erythrocyte spectrin, is a cytoskeleton protein present in all nucleated cells. Interestingly, βII spectrin is essential for the development of various organs such as nerve, epithelium, inner ear, liver and heart. The functions of βII spectrin include not only establishing and maintaining the cell structure but also regulating a variety of cellular functions, such as cell apoptosis, cell adhesion, cell spreading and cell cycle regulation. Notably, βII spectrin dysfunction is associated with embryonic lethality and the DNA damage response. More recently, the detection of altered βII spectrin expression in tumors indicated that βII spectrin might be involved in the development and progression of cancer. Its mutations and disorders could result in developmental disabilities and various diseases. The versatile roles of βII spectrin in disease have been examined in an increasing number of studies; nonetheless, the exact mechanisms of βII spectrin are still poorly understood. Thus, we summarize the structural features and biological roles of βII spectrin and discuss its molecular mechanisms and functions in development, homeostasis, regeneration and differentiation. This review highlight the potential effects of βII spectrin dysfunction in cancer and other diseases, outstanding questions for the future investigation of therapeutic targets. The investigation of the regulatory mechanism of βII spectrin signal inactivation and recovery may bring hope for future therapy of related diseases.

Postmenopausal osteoporosis is a musculoskeletal disease with a common genetic trait which responds to strength training: a translational intervention study

Background:

Clinical evidence suggests that body muscle mass is positively associated with bone mass, of significance for the elderly population at risk of osteoporosis (OP). Furthermore, muscle and bone interact mechanically and functionally, via local interactions as well as remotely via secreted components. Thus, it was of interest to compare muscle transcriptomes in postmenopausal OP and healthy women, and study effects of strength training on the muscle transcriptome, muscle stress proteins and bone mineral density (BMD).

Methods:

Skeletal muscle histological and genetic properties were compared in postmenopausal healthy (n = 18) and OP (n = 17) women before and after heavy-load strength training for 13–15 weeks. The cohorts were of similar age and body mass index without interfering diseases, medication or difference in lifestyle factors. Muscle biopsies obtained before and after intervention were studied histologically, and stress proteins and transcriptomes analyzed.

Results:

The OP women showed distinct muscle transcription profiles when compared with healthy women and had higher levels of the stress proteins HSP70 and α-β-crystalline. A set of 12 muscle transcripts, including ACSS3, FZD4, GNAI1 and IGF1, were differentially expressed before and after intervention (false discovery rate ⩽0.10, p ⩽0.001), and their corresponding bone transcripts were associated with BMD. Experimental data underline and describe the functionality of these genes in bone biology. OP women had 8% (p <0.01) higher proportion of type I fibres, but muscle fibre cross-sectional area did not differ. Muscle strength increased in both groups (p <0.01).

Conclusions:

Postmenopausal healthy and OP women have distinct muscle transcriptomes [messenger ribonucleic acids (mRNAs) and microRNAs] that are modulated by strength training, translating into key protein alterations and muscle fibre changes. The function of common skeletal muscle and bone genes in postmenopausal OP is suggestive of a shared disease trait.

Characterizing rhizome bud dormancy in Polygonatum kingianum: Development of novel chill models and determination of dormancy release mechanisms by weighted correlation network analysis

This study was conducted to explore specific chill models and the mechanisms underlying rhizome bud dormancy break in Polygonatum kingianum. Rhizome buds were subjected to various chilling temperatures for different duration and then transferred to warm conditions for germination and subsequent evaluation of their response to temperature and chilling requirements. A CUkingianum model was constructed to describe the contribution of low temperature to the chill unit, and it was suggested that 2.97°C was the optimum temperature and that 11.54°C was the upper limit for bud release. The CASkingianum model showed the relationship between chilling accumulation and sprouting percentage; therefore, rhizome bud development could be predicted through the model. Weighted correlation network analysis (WGCNA) of transcriptomic data of endo-, eco- and nondormant rhizome buds generated 33 gene modules, 6 of which were significantly related to bud sprouting percentage. In addition, 7 significantly matched transcription factors (TFs) were identified from the promoters of 17 "real" hub genes, and DAG2 was the best matched TF that bound to AAAG element to regulate gene expression. The current study is valuable for developing a highly efficient strategy for seedling cultivation and provides strong candidates for key genes related to rhizome bud dormancy in P. kingianum.

Comprehensive Analysis of the Genetic and Epigenetic Mechanisms of Osteoporosis and Bone Mineral Density

Osteoporosis is a skeletal disorder characterized by a systemic impairment of bone mineral density (BMD). Genome-wide association studies (GWAS) have identified hundreds of susceptibility loci for osteoporosis and BMD. However, the vast majority of susceptibility loci are located in non-coding regions of the genome and provide limited information about the genetic mechanisms of osteoporosis. Herein we performed a comprehensive functional analysis to investigate the genetic and epigenetic mechanisms of osteoporosis and BMD. BMD and osteoporosis are found to share many common susceptibility loci, and the corresponding susceptibility genes are significantly enriched in bone-related biological pathways. The regulatory element enrichment analysis indicated that BMD and osteoporosis susceptibility loci are significantly enriched in 5′UTR and DNase I hypersensitive sites (DHSs) of peripheral blood immune cells. By integrating GWAS and expression Quantitative Trait Locus (eQTL) data, we found that 15 protein-coding genes are regulated by the osteoporosis and BMD susceptibility loci. Our analysis provides new clues for a better understanding of the pathogenic mechanisms and offers potential therapeutic targets for osteoporosis.

Distinct and Overlapping Expression Patterns of the Homer Family of Scaffolding Proteins and Their Encoding Genes in Developing Murine Cephalic Tissues

In mammals Homer1, Homer2 and Homer3 constitute a family of scaffolding proteins with key roles in Ca²⁺ signaling and Ca²⁺ transport. In rodents, Homer proteins and mRNAs have been shown to be expressed in various postnatal tissues and to be enriched in brain. However, whether the Homers are expressed in developing tissues is hitherto largely unknown. In this work, we used immunohistochemistry and in situ hybridization to analyze the expression patterns of Homer1, Homer2 and Homer3 in developing cephalic structures. Our study revealed that the three Homer proteins and their encoding genes are expressed in a wide range of developing tissues and organs, including the brain, tooth, eye, cochlea, salivary glands, olfactory and respiratory mucosae, bone and taste buds. We show that although overall the three Homers exhibit overlapping distribution patterns, the proteins localize at distinct subcellular domains in several cell types, that in both undifferentiated and differentiated cells Homer proteins are concentrated in puncta and that the vascular endothelium is enriched with Homer3 mRNA and protein. Our findings suggest that Homer proteins may have differential and overlapping functions and are expected to be of value for future research aiming at deciphering the roles of Homer proteins during embryonic development.

Identification of gene modules associated with survival of diffuse large B-cell lymphoma treated with CHOP-based chemotherapy

Diffuse Large B-cell Lymphoma (DLBCL), a heterogeneous disease, is influenced by complex network of gene interactions. Most previous studies focused on individual genes, but ignored the importance of intergenic correlations. In current study, we aimed to explore the association between gene networks and overall survival (OS) of DLBCL patients treated with CHOP-based chemotherapy (cyclophosphamide combination with doxorubicin, vincristine and prednisone). Weighted gene co-expression network analysis was conducted to obtain insights into the molecular characteristics of DLBCL. Ten co-expression gene networks (modules) were identified in training dataset (n = 470), and their associations with patients' OS after chemotherapy were tested. The results were validated in four independent datasets (n = 802). Gene ontology (GO) biological function enrichment analysis was conducted with Metascape. Three modules (purple, brown and red), which were enriched in T-cell immune, cell-cell adhesion and extracellular matrix (ECM), respectively, were found to be related to longer OS. Higher expression of several hub genes within these three co-expression modules, for example, LCP2 (HR = 0.77, p = 5.40 × 10^-2), CD2 (HR = 0.87, p = 6.31 × 10^-2), CD3D (HR = 0.83, p = 6.94 × 10^-3), FYB (HR = 0.82, p = 1.40 × 10^-2), GZMK (HR = 0.92, p = 1.19 × 10^-1), FN1 (HR = 0.88, p = 7.06 × 10^-2), SPARC (HR = 0.82, p = 2.06 × 10^-2), were found to be associated with favourable survival. Moreover, the associations of the modules and hub genes with OS in different molecular subtypes and different chemotherapy groups were also revealed. In general, our research revealed the key gene modules and several hub genes were upregulated correlated with good survival of DLBCL patients, which might provide potential therapeutic targets for future clinical research.

A road map for understanding molecular and genetic determinants of osteoporosis

Osteoporosis is a highly prevalent disorder characterized by low bone mineral density and an increased risk of fracture, termed osteoporotic fracture. Notably, bone mineral density, osteoporosis and osteoporotic fracture are highly heritable; however, determining the genetic architecture, and especially the underlying genomic and molecular mechanisms, of osteoporosis in vivo in humans is still challenging. In addition to susceptibility loci identified in genome-wide association studies, advances in various omics technologies, including genomics, transcriptomics, epigenomics, proteomics and metabolomics, have all been applied to dissect the pathogenesis of osteoporosis. However, each technology individually cannot capture the entire view of the disease pathology and thus fails to comprehensively identify the underlying pathological molecular mechanisms, especially the regulatory and signalling mechanisms. A change to the status quo calls for integrative multi-omics and inter-omics analyses with approaches in 'systems genetics and genomics'. In this Review, we highlight findings from genome-wide association studies and studies using various omics technologies individually to identify mechanisms of osteoporosis. Furthermore, we summarize current studies of data integration to understand, diagnose and inform the treatment of osteoporosis. The integration of multiple technologies will provide a road map to illuminate the complex pathogenesis of osteoporosis, especially from molecular functional aspects, in vivo in humans.

Identification of a key gene module associated with glucocorticoid- induced derangement in bone mineral density in patients with asthma

Derangement in bone mineral density (BMD) caused by glucocorticoid is well-known. The present study aimed to find key biological pathways associated with low BMD after glucocorticoid treatment in asthmatics using gene expression profiles of peripheral blood cells. We utilized immortalized B cells (IBCs) from 32 childhood asthmatics after multiple oral glucocorticoid bursts and peripheral blood mononuclear cells (PBMCs) from 17 adult asthmatics after a long-term use of oral glucocorticoid. We searched co-expressed gene modules significantly related with the BMD Z score in childhood asthmatics and tested if these gene modules were preserved and significantly associated with the BMD Z score in adult asthmatics as well. We identified a gene module composed of 199 genes significantly associated with low BMD in both childhood and adult asthmatics. The structure of this module was preserved across gene expression profiles. We found that the cellular metabolic pathway was significantly enriched in this module. Among 18 hub genes in this module, we postulated that 2 genes, CREBBP and EP300, contributed to low BMD following a literature review. A novel biologic pathway identified in this study highlighted a gene module and several genes as playing possible roles in the pathogenesis of glucocorticoid- induced derangement in BMD.

PPWD1 is associated with the occurrence of postmenopausal osteoporosis as determined by weighted gene co‑expression network analysis

Postmenopausal osteoporosis (PMO) is the most common type of primary osteoporosis (OP), a systemic skeletal disease. Although many factors have been revealed to contribute to the occurrence of PMO, specific biomarkers for the early diagnosis and therapy of PMO are not available. In the present study, a weighted gene co-expression network analysis (WGCNA) was performed to screen gene modules associated with menopausal status. The turquoise module was verified as the clinically significant module, and 12 genes (NUP133, PSMD12, PPWD1, RBM8A, CRNKL1, PPP2R5C, RBM22, PIK3CB, SKIV2L2, PAPOLA, SRSF1 and COPS2) were identified as ‘real’ hub genes in both the protein-protein interaction (PPI) network and co-expression network. Furthermore, gene expression analysis by microarray in blood monocytes from pre- and post-menopausal women revealed an increase in the expression of these hub genes in postmenopausal women. However, only the expression of peptidylprolyl isomerase domain and WD repeat containing 1 (PPWD1) was correlated with bone mineral density (BMD) in postmenopausal women. In the validation set, a similar expression pattern of PPWD1 was revealed. Functional enrichment analysis revealed that the fatty acid metabolism pathway was significantly abundant in the samples that exhibited a higher expression of PPWD1. Collectively, PPWD1 is indicated as a potential diagnostic biomarker for the occurrence of PMO.

Identifying Hub Genes for Heat Tolerance in Water Buffalo (Bubalus bubalis) Using Transcriptome Data

Heat stress has a detrimental effect on the physiological and production performance of buffaloes. Elucidating the underlying mechanisms of heat stress is challenging, therefore identifying candidate genes is urgent and necessary. We evaluated the response of buffaloes (n = 30) to heat stress using the physiological parameters, ELISA indexes, and hematological parameters. We then performed mRNA and microRNA (miRNA) expression profiles analysis between heat tolerant (HT, n = 4) and non-heat tolerant (NHT, n = 4) buffaloes, as well as the specific modules, significant genes, and miRNAs related to the heat tolerance identified using the weighted gene co-expression network analysis (WGCNA). The results indicated that the buffaloes in HT had a significantly lower rectal temperature (RT) and respiratory rate (RR) and displayed a higher plasma heat shock protein (HSP70 and HSP90) and cortisol (COR) levels than those of NHT buffaloes. Differentially expressed analysis revealed a total of 753 differentially expressed genes (DEGs) and 16 differentially expressed miRNAs (DEmiRNAs) were identified between HT and NHT. Using the WGCNA analysis, these DEGs assigned into 5 modules, 4 of which were significantly correlation with the heat stress indexes. Interestingly, 158 DEGs associated with heat tolerance in the turquoise module were identified, 35 of which were found within the protein-protein interaction network. Several hub genes (IL18RAP, IL6R, CCR1, PPBP, IL1B, and IL1R1) were identified that significantly enriched in the Cytokine-cytokine receptor interaction. The findings may help further elucidate the underlying mechanisms of heat tolerance in buffaloes.

Identification of a metabolomic signature associated with feed efficiency in beef cattle

BACKGROUND

Ruminants play a great role in sustainable livestock since they transform pastures, silage, and crop residues into high-quality human food (i.e. milk and beef). Animals with better ability to convert food into animal protein, measured as a trait called feed efficiency (FE), also produce less manure and greenhouse gas per kilogram of produced meat. Thus, the identification of high feed efficiency cattle is important for sustainable nutritional management. Our aim was to evaluate the potential of serum metabolites to identify FE of beef cattle before they enter the feedlot.

RESULTS

A total of 3598 and 4210 m/z features was detected in negative and positive ionization modes via liquid chromatography-mass spectrometry. A single feature was different between high and low FE groups. Network analysis (WGCNA) yielded the detection of 19 and 20 network modules of highly correlated features in negative and positive mode respectively, and 1 module of each acquisition mode was associated with RFI (r = 0.55, P < 0.05). Pathway enrichment analysis (Mummichog) yielded the Retinol metabolism pathway associated with feed efficiency in beef cattle in our conditions.

CONCLUSION

Altogether, these findings demonstrate the existence of a serum-based metabolomic signature associated with feed efficiency in beef cattle before they enter the feedlot. We are now working to validate the use of metabolites for identification of feed efficient animals for sustainable nutritional management.

Dissecting the Genetics of Osteoporosis using Systems Approaches

Osteoporosis is a condition characterized by low bone mineral density (BMD) and an increased risk of fracture. Traits contributing to osteoporotic fracture are highly heritable, indicating that a comprehensive understanding of bone requires a thorough understanding of the genetic basis of bone traits. Towards this goal, genome-wide association studies (GWASs) have identified over 500 loci associated with bone traits. However, few of the responsible genes have been identified, and little is known of how these genes work together to influence systems-level bone function. In this review, we describe how systems genetics approaches can be used to fill these knowledge gaps.

System network analysis of genomics and transcriptomics data identified type 1 diabetes-associated pathway and genes

Genome-wide association studies (GWASs) have discovered >50 risk loci for type 1 diabetes (T1D). However, those variations only have modest effects on the genetic risk of T1D. In recent years, accumulated studies have suggested that gene-gene interactions might explain part of the missing heritability. The purpose of our research was to identify potential and novel risk genes for T1D by systematically considering the gene-gene interactions through network analyses. We carried out a novel system network analysis of summary GWAS statistics jointly with transcriptomic gene expression data to identify some of the missing heritability for T1D using weighted gene co-expression network analysis (WGCNA). Using WGCNA, seven modules for 1852 nominally significant (P ≤ 0.05) GWAS genes were identified by analyzing microarray data for gene expression profile. One module (tagged as green module) showed significant association (P ≤ 0.05) between the module eigengenes and the trait. This module also displayed a high correlation (r = 0.45, P ≤ 0.05) between module membership (MM) and gene significant (GS), which indicated that the green module of co-expressed genes is of significant biological importance for T1D status. By further describing the module content and topology, the green module revealed a significant enrichment in the "regulation of immune response" (GO:0050776), which is a crucially important pathway in T1D development. Our findings demonstrated a module and several core genes that act as essential components in the etiology of T1D possibly via the regulation of immune response, which may enhance our fundamental knowledge of the underlying molecular mechanisms for T1D.

Identification of potential functional genes in papillary thyroid cancer by co-expression network analysis

Interactions between multiple genes are involved in the development of complex diseases. However, there are few analyses of gene interactions associated with papillary thyroid cancer (PTC). Weighted gene co-expression network analysis (WGCNA) is a novel and powerful method that detects gene interactions according to their co-expression similarities. In the present study, WGCNA was performed in order to identify functional genes associated with PTC using R package. First, differential gene expression analysis was conducted in order to identify the differentially expressed genes (DEGs) between PTC and normal samples. Subsequently, co-expression networks of the DEGs were constructed for the two sample groups, respectively. The two networks were compared in order to identify a poorly preserved module. Concentrating on the significant module, validation analysis was performed to confirm the identified genes and combined functional enrichment analysis was conducted in order to identify more functional associations of these genes with PTC. As a result, 1062 DEGs were identified for network construction. A brown module containing 118 highly related genes was selected as it exhibited the lowest module preservation. After validation analysis, 61 genes in the module were confirmed to be associated with PTC. Following the enrichment analysis, two PTC-related pathways were identified: Wnt signal pathway and transcriptional misregulation in cancer. LRP4, KLK7, PRICKLE1, ETV4 and ETV5 were predicted to be candidate genes regulating the pathogenesis of PTC. These results provide novel insights into the etiology of PTC and the identification of potential functional genes.

Assessing the Associations of Blood Metabolites With Osteoporosis: A Mendelian Randomization Study

Context

Osteoporosis is a metabolic bone disease. The effect of blood metabolites on the development of osteoporosis remains elusive.

Objective

To explore the relationship between blood metabolites and osteoporosis.

Design and Methods

We used 2286 unrelated white subjects for the discovery samples and 3143 unrelated white subjects from the Framingham Heart Study (FHS) for the replication samples. The bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry. Genome-wide single nucleotide polymorphism (SNP) genotyping was performed using Affymetrix Human SNP Array 6.0 (for discovery samples) and Affymetrix SNP 500K and 50K array (for FHS replication samples). The SNP sets significantly associated with blood metabolites were obtained from a reported whole-genome sequencing study. For each subject, the genetic risk score of the metabolite was calculated from the genotype data of the metabolite-associated SNP sets. Pearson correlation analysis was conducted to evaluate the potential effect of blood metabolites on the variations in bone phenotypes; 10,000 permutations were conducted to calculate the empirical P value and false discovery rate.

Results

We analyzed 481 blood metabolites. We identified multiple blood metabolites associated with hip BMD, such as 1,5-anhydroglucitol (Pdiscovery < 0.0001; Preplication = 0.0361), inosine (Pdiscovery = 0.0018; Preplication = 0.0256), theophylline (Pdiscovery = 0.0048; Preplication = 0.0433, gamma-glutamyl methionine (Pdiscovery = 0.0047; Preplication = 0.0471), 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6; Pdiscovery = 0.0018; Preplication = 0.0390), and X-12127 (Pdiscovery = 0.0002; Preplication = 0.0249).

Conclusions

Our results suggest a modest effect of blood metabolites on the variations of BMD and identified several candidate blood metabolites for osteoporosis.

Long non-coding RNA GAS5 and ZFAS1 are prognostic markers involved in translation targeted by miR-940 in prostate cancer

Identification of prognostic biomarkers helps facilitate the prediction of patient outcomes as well as guide treatments. Accumulating evidence now suggests that long non-coding RNAs (lncRNAs) play key roles in tumor progression with diagnostic and prognostic values. However, little is known about the biological functions of lncRNAs and how they contribute to the pathogenesis of cancer. Herein, we performed weighted correlation network analysis (WGCNA) on 380 RNA-seq samples from prostate cancer patients to create networks comprising of microRNAs, lncRNAs, and protein-coding genes. Our analysis revealed expression modules that associated with pathological parameters. More importantly, we identified a gene module that is involved in protein translation and is associated with patient survival. In this gene module, we explored the regulation axis involving GAS5, ZFAS1, and miR-940. We show that GAS5, ZFAS1, and miR-940 are up-regulated in tumors relative to normal prostate tissues, and high expression of either lncRNA is an indicator of poor patient outcome. Finally, we constructed a co-expression network involving GAS5, ZFAS1, and miR-940, as well as the targets of miR-940. Our results show that GAS5 and ZFAS1 are targeted by miR-940 via NAA10 and RPL28. Taken together, co-expression analysis of gene expression profiling from RNA-seq can accelerate the identification and functional characterization of novel prognostic markers in prostate cancer.

Cytosolic proteome profiling of monocytes for male osteoporosis

In male Caucasians with discordant hip bone mineral density (BMD), we applied the subcellular separation and proteome profiling to investigate the monocytic cytosol. Three BMD-associated proteins (ALDOA, MYH14, and Rap1B) were identified based on multiple omics evidence, and they may influence the pathogenic mechanisms of osteoporosis by regulating the activities of monocytes.

INTRODUCTION

Osteoporosis is a serious public health problem, leading to significant mortality not only in aging females but also in males. Peripheral blood monocytes (PBMs) play important roles in bone metabolism by acting as precursors of osteoclasts and producing cytokines important for osteoclast development. The first cytosolic sub-proteome profiling analysis was performed in male PBMs to identify differentially expressed proteins (DEPs) that are associated with BMDs and risk of osteoporosis.

METHODS

Here, we conducted a comparative proteomics analysis in PBMs from Caucasian male subjects with discordant hip BMD (29 low BMD vs. 30 high BMD). To decrease the proteome complexity and expand the coverage range of the cellular proteome, we separated the PBM proteome into several subcellular compartments and focused on the cytosolic fractions, which are involved in a wide range of fundamental biochemical processes.

RESULTS

Of the total of 3796 detected cytosolic proteins, we identified 16 significant (P < 0.05) and an additional 22 suggestive (P < 0.1) DEPs between samples with low vs. high hip BMDs. Some of the genes for DEPs, including ALDOA, MYH14, and Rap1B, showed an association with BMD in multiple omics studies (proteomic, transcriptomic, and genomic). Further bioinformatics analysis revealed the enrichment of DEPs in functional terms for monocyte proliferation, differentiation, and migration.

CONCLUSIONS

The combination strategy of subcellular separation and proteome profiling allows an in-depth and refined investigation into the composition and functions of cytosolic proteome, which may shed light on the monocyte-mediated pathogenic mechanisms of osteoporosis.

Relation of Transcriptional Factors to the Expression and Activity of Cytochrome P450 and UDP-Glucuronosyltransferases 1A in Human Liver: Co-Expression Network Analysis

Cytochrome P450 (CYPs) and UDP-glucuronosyltransferases (UGTs) play important roles in the metabolism of exogenous and endogenous compounds. The gene transcription of CYPs and UGTs can be enhanced or reduced by transcription factors (TFs). This study aims to explore novel TFs involved in the regulatory network of human hepatic UGTs/CYPs. Correlations between the transcription levels of 683 key TFs and CYPs/UGTs in three different human liver expression profiles (n = 640) were calculated first. Supervised weighted correlation network analysis (sWGCNA) was employed to define hub genes among the selected TFs. The relationship among 17 defined TFs, CYPs/UGTs expression, and activity were evaluated in 30 liver samples from Chinese patients. The positive controls (e.g., PPARA, NR1I2, NR1I3) and hub TFs (NFIA, NR3C2, and AR) in the Grey_sWGCNA Module were significantly and positively associated with CYPs/UGTs expression. And the cancer- or inflammation-related TFs (TEAD4, NFKB2, and NFKB1) were negatively associated with mRNA expression of CYP2C9/CYP2E1/UGT1A9. Furthermore, the effect of NR1I2, NR1I3, AR, TEAD4, and NFKB2 on CYP450/UGT1A gene transcription translated into moderate influences on enzyme activities. To our knowledge, this is the first study to integrate Gene Expression Omnibus (GEO) datasets and supervised weighted correlation network analysis (sWGCNA) for defining TFs potentially related to CYPs/UGTs. We detected several novel TFs involved in the regulatory network of hepatic CYPs and UGTs in humans. Further validation and investigation may reveal their exact mechanism of CYPs/UGTs regulation.