Current Genomics - Volume 19, Issue 4, 2018

Perilipins, an ancient family of lipid droplet-associated proteins, are embedded in a phospholipid monolayer of intracellular lipid droplets. The core of lipid droplets is composed of neutral fat, which mainly includes triglyceride and cholesterol ester. Perilipins are closely related to the function of lipid droplets, and they mediate lipid metabolism and storage. Therefore, perilipins play an important role in the development of obesity, diabetes, cancer, hepatic diseases, atherosclerosis, and carcinoma, which are caused by abnormal lipid metabolism. Accumulation of lipid droplets is a common phenomenon in tumor cells. Available data on the pathophysiology of perilipins and the relationship of perilipins with endocrine metabolic diseases and cancers are summarized in this mini-review. The research progress on this family offers novel insights into the therapeutic strategies for these diseases.

Lung cancer continues to be the leading topic concerning global mortality rate caused by cancer; it needs to be further investigated to reduce these dramatic unfavorable statistic data. Noncoding RNAs (ncRNAs) have been shown to be important cellular regulatory factors and the alteration of their expression levels has become correlated to extensive number of pathologies. Specifically, their expression profiles are correlated with development and progression of lung cancer, generating great interest for further investigation. This review focuses on the complex role of non-coding RNAs, namely miRNAs, piwi-interacting RNAs, small nucleolar RNAs, long non-coding RNAs and circular RNAs in the process of developing novel biomarkers for diagnostic and prognostic factors that can then be utilized for personalized therapies toward this devastating disease. To support the concept of personalized medicine, we will focus on the roles of miRNAs in lung cancer tumorigenesis, their use as diagnostic and prognostic biomarkers and their application for patient therapy.

In recent years, genomic, animal and cell biology studies have implicated deficiencies in retromer-mediated trafficking of proteins in an increasing number of neurodegenerative diseases including Alzheimer's Disease (AD), Parkinson's Disease (PD) and Frontotemporal Lobar Degeneration (FTLD). The retromer complex, which is highly conserved across all eukaryotes, regulates the sorting of transmembrane proteins out of endosomes to the cell surface or to the trans-Golgi network. Within retromer, cargo selection and binding are performed by a trimer of the Vps26, Vps29 and Vps35 proteins, named the “Cargo-Selective Complex (CSC)”. Sorting of cargo into tubules for distribution to the trans-Golgi network or the cell surface is achieved through the dimeric sorting nexin (SNX) component of retromer and accessory proteins such as the WASH complex which mediates the formation of discrete endosomal tubules enabling the sorting of cargo into distinct pathways through production of filamentous actin patches. In the present article, we review the molecular structure and function of the retromer and summarize the evidence linking retromer dysfunction to neurodegenerative disease.

Background: Transcription Factors (TFs) control actuation of genes in the genome and are key mediators of complex processes such as obesity. Master Regulators (MRs) are the genes at the top of a regulation hierarchy which regulate other genes. Objective: To elucidate clusters of highly co-expressed TFs (modules), involved pathways, highly interconnected TFs (hub-TFs) and MRs leading to obesity and leanness, using porcine model for human obesity. Methods: We identified 817 expressed TFs in RNA-Sequencing dataset representing extreme degrees of obesity (DO; lean, obese). We built a single Weighted Transcription Factor Co-expression Network (WTFCN) and TF sub-networks (based on the DO). Hub-TFs and MRs (using iRegulon) were identified in biologically relevant WTFCNs modules. Results: Single WTFCN detected the Red module significantly associated with DO (P < 0.03). This module was enriched for regulation processes in the immune system, e.g.: Immune system process (Padj = 2.50E-06) and metabolic lifestyle disorders, e.g. Circadian rhythm - mammal pathway (Padj = 2.33E-11). Detected MR, hub-TF SPI1 was involved in obesity, immunity and osteoporosis. Within the obese sub-network, the Red module suggested possible associations with immunity, e.g. TGF-beta signaling pathway (Padj = 1.73E-02) and osteoporosis, e.g. Osteoclast differentiation (Padj = 1.94E- 02). Within the lean sub-network, the Magenta module displayed associations with type 2 diabetes, obesity and osteoporosis e.g. Notch signaling pathway (Padj = 2.40E-03), osteoporosis e.g. hub-TF VDR (a prime candidate gene for osteoporosis). Conclusion: Our results provide insights into the regulatory network of TFs and biologically relevant hub TFs in obesity.

Background: Neurodegeneration is a progressive/irreversible loss of neurons, building blocks of our nervous system. Their degeneration gradually collapses the entire structural and functional system manifesting in myriads of clinical disorders categorized as Neurodegenerative Disorders (NDs) such as Alzheimer's Disease, (AD), Parkinson's Disease (PD), Frontotemporal Dementia (FTD) and Amyotrophic Lateral Sclerosis (ALS). NDs are characterized by a puzzling interplay of molecular and cellular defects affecting subset of neuronal populations in specific affected brain areas. Objective: In present study, comparative in silico analysis was performed by utilizing gene expression datasets of AD, PD, FTD and ALS to identify potential common features to gain insights into complex molecular pathophysiology of the selected NDs. Methods: Gene expression data of four disorders were subjected to the identification of Differential Gene Expression (DEG) and their mapping on biological processes, KEGG pathways and molecular functions. Detailed comparative analysis was performed to highlight the common grounds of these disorders at various stages. Results: Astoundingly, 106 DEGs were found to be common across all disorders. Alongwith in total 100 GO terms and 7 KEGG pathways were found to be significantly enriched across all disorders. EGFR, CDC42 and CREBBP have been identified as the significantly interacting nodes in gene-gene interaction and in Protein-Protein Interaction (PPI) network as well. Furthermore, interaction of common DEGs targets with miRNA's has been scrutinized. Conclusion: The complex molecular underpinnings of these disorders are currently elusive. Despite heterogeneous clinical and pathological expressions, common features have been recognized in many NDs which provide evidence of their convergence.

Background: AhR, a ubiquitously expressed ligand-activated transcription factor, upon its encounter with the foreign ligands activates the transcriptional machinery of genes encoding for biotransformation enzymes like CYP1A1 hence, mediating the metabolism of Poly aromatic hydrocarbons and nitrosamines which account for the maximally found carcinogen in cigarette smoke. Polymorphic variants of AhR play a significant role and are held responsible for disposing the individuals with greater chances of acquiring lung cancer. Objective: To study the role of AhR variants (rs2282885, rs10250822, rs7811989, rs2066853) in affecting lung cancer susceptibility. Methods: 297 cases and 320 controls have been genotyped using PCR-RFLP technique. In order to find out the association, unconditional logistic regression approach was used. To analyze high order interactions Multifactor Dimensionality Reduction and Classification and regression tree was used. Results: Subjects carrying the variant genotype for AhR rs7811989 showed a two-fold risk (p=0.007) and a marginal risk was also seen in case of individuals carrying either single or double copy of susceptible allele for rs102550822 (p=0.02). Whereas the variant allele for rs2066853 showcased a strong protective effect (p=0.003). SQCC individuals with mutant genotype of rs2066853 also exhibited a protective effect towards lung cancer (OR=0.30, p=0.0013). The association of rs7811989 mutant genotype and rs10250822 mutant genotype was evident especially in smokers as compared to nonsmokers. AhR rs2066853 showed a decreased risk in smokers with mutant genotype (p=0.002). MDR approach gave the best interaction model of AhR rs2066853 and smoking (CVC=10/10, prediction error= 0.42). Conclusion: AhR polymorphic variations can significantly contribute towards lung cancer predisposition.