Identifying Gene Interaction Networks

SLC11A1 genetic variation and low expression may cause immune response impairment in TB patients

Tuberculosis (TB) is caused by Mycobacterium tuberculosis. Host genetic factors are important for the detection of TB susceptibility. SLC11A1 is located in monocyte phagolysosomes that help to limit M. tuberculosis growth by transferring divalent cations across the membrane. Genetic variation in SLC11A1 may alter its expression and increase the susceptibility of individuals to TB. The current study aimed to provide insight into host genetic variations and gene expression in TB patients. A total of 164 TB patients and 85 healthy controls were enrolled in this study. SLC11A1 polymorphisms were detected by PCR-RFLP. Real-time qPCR was used for SLC11A1 gene expression, and ELISA was used for protein estimation. GTEx Portal was used for quantitative trait loci analysis, while the STRING (v.11) web platform was used for gene interactive network construction. Data were analyzed using SPSS, GraphPad Prism, Haploview, and SNPstats. SLC11A1 polymorphisms and combinatorial genotypes were strongly associated with TB susceptibility, which may explain the greater prevalence of tuberculosis in the local population. Polymorphisms in SLC11A1 have also been linked to gene expression variation. Furthermore, the expression of SLC11A1 was downregulated in TB patients, which may influence the function of other associated genes and may impair the immunological response to tuberculosis.

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer

The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.

MMS22L Expression as a Predictive Biomarker for the Efficacy of Neoadjuvant Chemoradiotherapy in Oesophageal Squamous Cell Carcinoma

Long-term survival in oesophageal squamous cell carcinoma (ESCC) is related with pathological response after neoadjuvant chemoradiotherapy (NCRT) followed by surgery. However, effective biomarkers to predict the pathologic response are still lacking. Therefore, a systematic analysis focusing on genes associated with the efficacy of chemoradiotherapy in ESCC will provide valuable insights into the regulation of molecular processes. By screening publications deposited in PubMed, we collected genes associated with the efficacy of chemoradiotherapy. A specific subnetwork was constructed using the Steiner minimum tree algorithm. Survival analysis in Kaplan-Meier Plotter online resources was performed to explore the relationship between gene mRNA expression and the prognosis of patients with ESCC. Quantitative real-time polymerase chain reaction (qRT-PCR), Western blotting, and immunohistochemical staining (IHC) were used to evaluate the expression of key genes in cell lines and human samples. The areas under the receiver operating characteristic (ROC) curves (AUCs) were used to describe performance and accuracy. Transwell assays assessed cell migration, and cell viability was detected using the Cytotoxicity Assay. Finally, we identified 101 genes associated with efficacy of chemoradiotherapy. Additionally, specific molecular networks included some potential related genes, such as CUL3, MUC13, MMS22L, MME, UBC, VAPA, CYP1B1, and UGDH. The MMS22L mRNA expression level showed the most significant association with the ESCC patient outcome (p < 0.01). Furthermore, MMS22L was downregulated at both the mRNA (p < 0.001) and protein levels in tumour tissues compared with that in normal tissues. Lymph node metastasis was significantly associated with low MMS22L expression (p < 0.01). MMS22L levels were inversely correlated with the NCRT response in ESCC (p < 0.01). The resulting area under the ROC curve was 0.847 (95% CI: 0.7232 to 0.9703; p < 0.01). In conclusion, low expression of MMS22L is associated with poor response to NCRT, worse survival, lymph node metastasis, and enhanced migration of tumour cells in ESCC.

Systematic review of Pharmacogenomics Knowledgebase evidence for pharmacogenomic links to the dopamine reward pathway for heroin dependence

Genetics play an important role in opioid use disorder (OUD); however, few specific gene variants have been identified. Therefore, there is a need to further understand the pharmacogenomics influences on the pharmacodynamics of opioids. The Pharmacogenomics Knowledgebase (PharmGKB), a database that links genetic variation and drug interaction in the body, was queried to identify polymorphisms associated with heroin dependence in the context of opioid related disorders/OUD. Eight genes with 22 variants were identified as linked to increased risk of heroin dependence, with three genes and variants linked to decreased risk, although the level of evidence was moderate to low. Therefore, continued exploration of biomarker influences on OUD, reward pathways and other contributing circuitries is necessary to understand the true impact of genetics on OUD before integration into clinical guidelines.

Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review

Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.