Zebrafish as a Model for Lipidomics and Similar Investigations.

Zebrafish (Danio rerio) has emerged as a pivotal model organism in vertebrate development research over several decades. Beyond its contributions to developmental biology, zebrafish have increasingly played a crucial role in the field of lipidomics. Lipidomics, a comprehensive analysis of lipids within biological systems, offers profound insights into lipid metabolism and signaling pathways. This chapter explores the zebrafish's unique attributes that make it an ideal candidate for lipidomics studies. With a genome sharing numerous genetic similarities with humans, zebrafish serve as a powerful model for dissecting lipid metabolism and unraveling the complexities of lipid mediator-related diseases. In this chapter, we delve into specific protocols tailored for utilizing zebrafish in lipidomics research and similar investigations. Through a comprehensive exploration of zebrafish as a model organism, this chapter aims to provide researchers with valuable insights and methodologies for advancing lipidomics studies using zebrafish.

Epigenome-wide methylation study identified two novel CpGs associated with T2DM risk and a network of co-methylated CpGs capable of patient's classifications.

Prevention of Type 2 diabetes mellitus (T2DM) pandemic needs markers that can precisely predict the disease risk in an individual. Alterations in DNA methylations due to exposure towards environmental risk factors are widely sought markers for T2DM risk prediction. To identify such individual DNA methylation signatures and their effect on disease risk, we performed an epigenome-wide association study (EWAS) in 844 Indian individuals of Indo-European origin. We identified and validated methylation alterations at two novel CpG sites in MIR1287 (cg01178710) and EDN2-SCMH1 (cg04673737) genes associated with T2DM risk at the epigenome-wide-significance-level (P < 1.2 × 10-7). Further, we also replicated the association of two known CpG sites in TXNIP, and CPT1A in the Indian population. With 535 EWAS significant CpGs (P < 1.2 × 10-7) identified in the discovery phase samples, we created a co-methylation network using weighted correlation network analysis and identified four modules among the CpGs. We observed that methylation of one of the module associates with T2DM risk factors (e.g. BMI, insulin and C-peptide) and can be used as markers to segregate T2DM patients with good glycemic control (e.g. low HbA1c) and dyslipidemia (low HDL and high TG) from the other patients. Additionally, an intronic SNP (rs6503650) in the JUP gene, a member of the same module, associated with methylation at all the 14 hub CpG sites of that module as methQTL. Our network-assisted EWAS is the first to systematically explore DNA methylation variations conferring risks to T2DM in Indians and use the identified risk CpG sites for patient segregation with different clinical outcomes. These findings can be useful for better stratification of patients to improve the clinical management and treatment effects.

Multifaceted genome-wide study identifies novel regulatory loci in SLC22A11 and ZNF45 for body mass index in Indians.

Obesity, a risk factor for multiple diseases (e.g. diabetes, hypertension, cancers) originates through complex interactions between genes and prevailing environment (food habit and lifestyle) that varies across populations. Indians exhibit a unique obesity phenotype with high abdominal adiposity for a given body weight compared to matched white populations suggesting presence of population-specific genetic and environmental factors influencing obesity. However, Indian population-specific genetic contributors for obesity have not been explored yet. Therefore, to identify potential genetic contributors, we performed a two-staged genome-wide association study (GWAS) for body mass index (BMI), a common measure to evaluate obesity in 5973 Indian adults and the lead findings were further replicated in 1286 Indian adolescents. Our study revealed novel association of variants-rs6913677 in BAI3 gene (p = 1.08 × 10-8) and rs2078267 in SLC22A11 gene (p = 4.62 × 10-8) at GWAS significance, and of rs8100011 in ZNF45 gene (p = 1.04 × 10-7) with near GWAS significance. As genetic loci may dictate the phenotype through modulation of epigenetic processes, we overlapped genetic data of identified signals with their DNA methylation patterns in 236 Indian individuals and performed methylation quantitative trait loci (meth-QTL) analysis. Further, functional roles of discovered variants and underlying genes were speculated using publicly available gene regulatory databases (ENCODE, JASPAR, GeneHancer, GTEx). The identified variants in BAI3 and SLC22A11 genes were found to dictate methylation patterns at unique CpGs harboring critical cis-regulatory elements. Further, BAI3, SLC22A11 and ZNF45 variants were located in repressive chromatin, active enhancer, and active chromatin regions, respectively, in human subcutaneous adipose tissue in ENCODE database. Additionally, these genomic regions represented potential binding sites for key transcription factors implicated in obesity and/or metabolic disorders. Interestingly, GTEx portal identify rs8100011 as a robust cis-expression quantitative trait locus (cis-eQTL) in subcutaneous adipose tissue (p = 1.6 × 10-7), and ZNF45 gene expression in skeletal muscle of Indian subjects showed an inverse correlation with BMI indicating its possible role in obesity. In conclusion, our study discovered 3 novel population-specific functional genetic variants (rs6913677, rs2078267, rs8100011) in 2 novel (SLC22A11 and ZNF45) and 1 earlier reported gene (BAI3) for BMI in Indians. Our study decodes key genomic loci underlying obesity phenotype in Indians that may serve as prospective drug targets in future.

DNA methylation profiling reveals the presence of population-specific signatures correlating with phenotypic characteristics.

Phenotypic characteristics are known to vary substantially among different ethnicities around the globe. These variations are mediated by number of stochastic events and cannot be attributed to genetic architecture alone. DNA methylation is a well-established mechanism that sculpts our epigenome influencing phenotypic variation including disease manifestation. Since DNA methylation is an important determinant for health issues of a population, it demands a thorough investigation of the natural differences in genome wide DNA methylation patterns across different ethnic groups. This study is based on comparative analyses of methylome from five different ethnicities with major focus on Indian subjects. The current study uses hierarchical clustering approaches, principal component analysis and locus specific differential methylation analysis on Illumina 450K methylation data to compare methylome of different ethnic subjects. Our data indicates that the variations in DNA methylation patterns of Indians are less among themselves compared to other global population. It empirically correlated with dietary, cultural and demographical divergences across different ethnic groups. Our work further suggests that Indians included in this study, despite their genetic similarity with the Caucasian population, are in close proximity with Japanese in terms of their methylation signatures.