Establishing multiple omics baselines for three Southeast Asian populations in the Singapore Integrative Omics Study

Abstract

The Singapore Integrative Omics Study provides valuable insights on establishing population reference measurement in 364 Chinese, Malay, and Indian individuals. These measurements include > 2.5 millions genetic variants, 21,649 transcripts expression, 282 lipid species quantification, and 284 clinical, lifestyle, and dietary variables. This concept paper introduces the depth of the data resource, and investigates the extent of ethnic variation at these omics and non-omics biomarkers. It is evident that there are specific biomarkers in each of these platforms to differentiate between the ethnicities, and intra-population analyses suggest that Chinese and Indians are the most biologically homogeneous and heterogeneous, respectively, of the three groups. Consistent patterns of correlations between lipid species also suggest the possibility of lipid tagging to simplify future lipidomics assays. The Singapore Integrative Omics Study is expected to allow the characterization of intra-omic and inter-omic correlations within and across all three ethnic groups through a systems biology approach.

Alterations of the Gut Microbiome Composition and Lipid Metabolic Profile in Radiation Enteritis

“…Currently, single omics study is insufficient to systematically explain the occurrence and development of the disease, conducting integrated multi-omics studies are increasingly becoming an irresistible trend. Recent years, it is well established that gut microbiome and their metabolites can widely regulate host metabolism, but the interactions of the molecular interface among them are highly dynamic and complex, which are still less well understood (Saw et al, 2017;McGrail et al, 2018;Lloyd-Price et al, 2019). Based on 16S datasets, we predicted that multiple metabolic processes of the host, including intestinal epithelial lipid metabolism, may all change as a consequence of RE-related dysbacteriosis.…”

LipidCreator workbench to probe the lipidomic landscape

“…To the best of our knowledge, due to the lack of isotope-coded standards there are currently no large-scale methods available to cope with occurring stereoisomers. The isomers existence and quantity should be reported individually based on their retention time 35 or summed up and reported as one species as it was done here and in previous studies 14,51 . In the end, the user has to decide whether these issues pose a potential challenge for their analysis regarding a specific chromatographic setup, target MS platform and acquisition scheme.…”

“…9). This difference might reflect the complex interaction of plasma lipidome, ethnicity, diet and life style 14,52 but would also be caused by sample collection as well as the chosen quantification strategy ( Supplementary Fig. 9).…”

“…Despite the rapid growth of the field, there is a lack of a comprehensive software toolbox for lipidomics, which focuses on the reproducible, quantitative analysis of a subset of lipids of interest. MS techniques such as selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) are employed for lipid quantification 13,14 . However, such targeted analyses widely applied in proteomics have led to software developments such as Skyline 15,16 , which was further advanced by tools such as Panorama, and MSStats 17,18 .…”

Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project

“…Single nucleotide polymorphism (SNP) data of more than 10,000 participants have also been analyzed by our custom array system (Japonica Array ® ) (Kawai et al., ). In addition, metabolome analysis is becoming an indispensable method to investigate molecular phenotypes in cohort studies (Gieger et al., ; Harada et al., ; Kettunen et al., ; Loeffler et al., ; Rhee et al., ; Saw et al., ; Schmidt et al., ; Shin et al., ; Tigchelaar et al., ). The metabolome is an intermediate phenotype connecting genotypes and phenotypes and mediates the effects of environmental and lifestyle factors to phenotypes.…”