Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities

Postpartum Depression: A Clinical Review of Impact and Current Treatment Solutions

Depression during the first year postpartum (postpartum depression) impacts millions of women and their families worldwide. In this narrative review, we provide a summary of postpartum depression, examining the etiology and consequences, pharmacological and psychological treatments, and potential mechanisms of change and current barriers to care. Psychological treatments are effective and preferred by many perinatal patients over medications, but they often remain inaccessible. Key potential mechanisms underlying their effectiveness include treatment variables (e.g., dosage and therapeutic alliance) and patient behaviors (e.g., activation and avoidance and emotional regulation). Among pharmacological treatments, the selective serotonin reuptake inhibitor (SSRI) sertraline is generally the first-line antidepressant medication recommended to women in the postpartum period due to its minimal passage into breastmilk and the corresponding decades of safety data. Importantly, most antidepressant drugs are considered compatible with breastfeeding. Neurosteroids are emerging as an effective treatment for postpartum depression, although currently this treatment is not widely available. Barriers to widespread access to treatment include those that are systematic (e.g., lack of specialist providers), provider-driven (e.g., lack of flexibility in treatment delivery), and patient-driven (e.g., stigma and lack of time for treatment engagement). We propose virtual care, task-sharing to non-specialist treatment providers, and collaborative care models as potential solutions to enhance the reach and scalability of effective treatments to address the growing burden of postpartum depression worldwide and its negative impact on families and society.

BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS

Genetic interactions have the potential to modulate phenotypes, including human disease. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions; however, traditional methods for identifying them, which tend to focus on testing individual variant pairs, lack statistical power. In this protocol, we describe a novel computational approach, called Bridging Gene sets with Epistasis (BridGE), for discovering genetic interactions between biological pathways from GWAS data. We present a Python-based implementation of BridGE along with instructions for its application to a typical human GWAS cohort. The major stages include initial data processing and quality control, construction of a variant-level genetic interaction network, measurement of pathway-level genetic interactions, evaluation of statistical significance using sample permutations and generation of results in a standardized output format. The BridGE software pipeline includes options for running the analysis on multiple cores and multiple nodes for users who have access to computing clusters or a cloud computing environment. In a cluster computing environment with 10 nodes and 100 GB of memory per node, the method can be run in less than 24 h for typical human GWAS cohorts. Using BridGE requires knowledge of running Python programs and basic shell script programming experience.

Polymorphism of IL-12/IL-23 axis is associated with coronary heart disease

IL12B encodes the shared p40 subunit (IL-12p40) of IL-12 and IL-23, which have diverse immune functions and are closely related to the occurrence and development of atherosclerosis (AS). However, the exact role of IL12B in coronary heart disease (CHD) was still unknown. A case-control association analysis was performed between five single nucleotide polymorphisms (SNPs) of IL12B (rs1003199, rs3212219, rs2569254, rs2853694 and rs3212227) and CHD in Chinese Han population (1824 patients with CHD vs. 2784 controls). Logistic regression analyses were used to study the relationships between SNPs and CHD, while multiple linear regression analyses were used to test the association between the SNP and the severity of CHD. In addition, the plasma IL12B concentration of CHD patients were detected by ELISA. We detected a significant association between one of the SNPs, rs2853694-G and CHD (padj  = 2.075 × 10-5 , OR, 0.773 [95% CI, 0.686-0.870]). Stratified analysis showed that rs2853694 was associated with CHD in both male and female populations and was significantly associated with both early- and late-onset CHD. In addition, rs2853694 is also related to the different types of CHD including clinical-CHD and anatomical-CHD. Moreover, there are significant differences in serum IL12B concentrations between rs2853694-TT carriers and rs2853694-GT carriers in CHD patients (p = 0.010). A common variant of IL12B was found significantly associated with CHD and its subgroups. As a shared subunit of IL-12 and IL-23, IL-12p40 may play a key role in IL-12/IL-23 axis mediated AS, which is expected to be an effective therapeutic target for CHD.

SPP2 plays a role in the tumorigenesis of hepatocellular carcinoma: A bioinformatic based analysis

OBJECTIVES

Hepatocellular carcinoma (HCC) patients at the same stage exhibit different prognosis, and the underlying molecular mechanism remains unclear. This study aims to identify the key genes impacting the prognosis of HCC patients.

METHODS

Differentially expressed gene analyses were performed between HCC samples and normal ones, and between patients with long overall survival (OS) and those with short OS, in TCGA-LIHC and GSE14520 datasets. The Kaplan-Meier method with log-rank test was used to evaluate the role of secreted phosphoprotein 2 (SPP2) in the prognosis of HCC patients. Gene set enrichment analysis (GSEA) was used to understand the difference of enriched signaling pathways between SPP2-stratified HCC subgroups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to predict the potential functional pathways in which SPP2 might participate.

RESULTS

SPP2 was significantly down-regulated in tumors when compared with normal tissues, or in tumor samples with short OS when compared with those with long OS [fold change (FC)>2 and false discovery rate (FDR)<0.05]. Low expression of SPP2 was associated with worse clinicopathological features like vascular invasion (P=1.6e-05), poor cancer status (with tumor, P=0.021), advanced T stage (T3 or T4, P=4.5e-04), advanced TNM stage (stage III or IV, P=3.1e-04), and with unfavorable prognosis (shorter OS, P=0.002). Gene enrichment analyses revealed that SPP2 might involve in the metabolic homeostasis of HCC and in the development of liver fibrosis and cirrhosis.

CONCLUSIONS

SPP2 might inhibit the development of liver fibrosis and cirrhosis and the tumorigenesis of HCC, and analogs of SPP2 might be potential drugs in the prevention of these diseases.

bioGWAS: A Simple and Flexible Tool for Simulating GWAS Datasets

Genome-wide association studies (GWAS) have proven to be a powerful tool for the identification of genetic susceptibility loci affecting human complex traits. In addition to pinpointing individual genes involved in a particular trait, GWAS results can be used to discover relevant biological processes for these traits. The development of new tools for extracting such information from GWAS results requires large-scale datasets with known biological ground truth. Simulation of GWAS results is a powerful method that may provide such datasets and facilitate the development of new methods. In this work, we developed bioGWAS, a simple and flexible pipeline for the simulation of genotypes, phenotypes, and GWAS summary statistics. Unlike existing methods, bioGWAS can be used to generate GWAS results for simulated quantitative and binary traits with a predefined set of causal genetic variants and/or molecular pathways. We demonstrate that the proposed method can recapitulate complete GWAS datasets using a set of reported genome-wide associations. We also used our method to benchmark several tools for gene set enrichment analysis for GWAS data. Taken together, our results suggest that bioGWAS provides an important set of functionalities that would aid the development of new methods for downstream processing of GWAS results.

Stage-specific coexpression network analysis of Myc in cohorts of renal cancer

The present study investigates the molecular dynamics of Myc in normal precursors and in different stages (I/II/III/IV) of cohorts of renal cancer using two distinct yet complementary approaches: gene expression and gene coexpression. We also analysed the variation of coexpression networks of Myc through the stage-wise progression of renal cancer cohorts. Myc expression is significantly higher in stage I compared to normal tissue but changed inconsistently across stages of renal cancer. We identified that Myc consistently coexpressed with fourteen genes in the KIPAN [Pan-kidney cohort (KICH + KIRC + KIRP)] and eight in the KIRC (Kidney renal clear cell carcinoma) across all stages, providing potential prognostic and diagnostic biomarkers. Coexpression network complexity decreased from normal precursor tissues to associated tumour stage I in KIPAN and KIRC but was inconsistent after that. In the process of cancer development, there is generally lower cross-tissue cancer network homology observed among coexpressed genes with Myc during the normal to the stage I compared to the stage-wise progression of cancer. Overall, this research provides novel perceptions of the molecular causes of kidney cancer. It also highlights potential genes and pathways crucial for diagnosing and treating this disease.

The muscle proteome reflects changes in mitochondrial function, cellular stress and proteolysis after 14 days of unilateral lower limb immobilization in active young men

Skeletal muscle unloading due to joint immobilization induces muscle atrophy, which has primarily been attributed to reductions in protein synthesis in humans. However, no study has evaluated the skeletal muscle proteome response to limb immobilization using SWATH proteomic methods. This study characterized the shifts in individual muscle protein abundance and corresponding gene sets after 3 and 14 d of unilateral lower limb immobilization in otherwise healthy young men. Eighteen male participants (25.4 ±5.5 y, 81.2 ±11.6 kg) underwent 14 d of unilateral knee-brace immobilization with dietary provision and following four-weeks of training to standardise acute training history. Participant phenotype was characterized before and after 14 days of immobilization, and muscle biopsies were obtained from the vastus lateralis at baseline (pre-immobilization) and at 3 and 14 d of immobilization for analysis by SWATH-MS and subsequent gene-set enrichment analysis (GSEA). Immobilization reduced vastus group cross sectional area (-9.6 ±4.6%, P <0.0001), immobilized leg lean mass (-3.3 ±3.9%, P = 0.002), unilateral 3-repetition maximum leg press (-15.6 ±9.2%, P <0.0001), and maximal oxygen uptake (-2.9 ±5.2%, P = 0.044). SWATH analyses consistently identified 2281 proteins. Compared to baseline, two and 99 proteins were differentially expressed (FDR <0.05) after 3 and 14 d of immobilization, respectively. After 14 d of immobilization, 322 biological processes were different to baseline (FDR <0.05, P <0.001). Most (77%) biological processes were positively enriched and characterized by cellular stress, targeted proteolysis, and protein-DNA complex modifications. In contrast, mitochondrial organization and energy metabolism were negatively enriched processes. This study is the first to use data independent proteomics and GSEA to show that unilateral lower limb immobilization evokes mitochondrial dysfunction, cellular stress, and proteolysis. Through GSEA and network mapping, we identify 27 hub proteins as potential protein/gene candidates for further exploration.