The rapid advancement in available -omics technologies is expected to greatly aid the development and application of personalized, precise and preventive medicine. In particular, novel medical insights are expected through the integration of genomic information through the global monitoring of changes in molecular components, such as transcriptomic, proteomic and metabolomic information in conjunction with monitoring varying physiological states. This necessitates the development of new methodology and tools to handle the analysis and integration of multiple omic data with a view towards medical applicability. The proposed research of this 4-year K99/R00 proposal will begin at the laboratory of Dr. Michael Snyder, a renowned expert in genetics and multiple omics, who will act as the mentor for Dr. George Mias throughout the K99 year of the award. Foremost in this proposal is the development of a framework to integrate dynamic data obtained from monitoring multiple omic information over multiple time points. The innovative methodology involves the development of statistical tools to analyze different omics technologies, including raw data processing, combining the individual omic data, categorizing the data in medically relevant categories, and ascertaining biological significance through pathway analyses. Considerations such as uneven sampling of data, lack of replicates and noise, inherent in obtaining samples from patients will be addressed during the K99 period. Additionally, the interactions between multiple omics components will be inferred through changes in expression, and time-varying networks will be defined that can be analyzed for detail explanation of transitions between physiological states. In order to develop the computational methodology, highly precise experiments will be performed, by monitoring gene, protein and metabolite expression over multiple time points in stimulated B-cells [lymphoblasts in K99 period and primary B cells in R00 period], with focus on the important and complex Nuclear Factor kappa B (NF?B) signaling pathways. Furthermore, developed tools, algorithms and data will be disseminated to the scientific community in the R00 period. The proposed learning activities of the K99/R00 award will allow Dr. Mias to adapt his research approaches to omics integration across multiple platforms, and acquire the necessary experimental background for independent investigations. The unique nature of Dr. Snyder's lab will provide Dr. Mias the opportunity to closely interact and collaborate with experimental experts conducting research at the forefront of omics research in genetics. Moreover, the lab's extensive resources readily available to Dr. Mias will greatly facilitate his research and academic endeavors. The K99/R00 award will enable Dr. Mias to smoothly transition into an independent faculty position in the R00 phase and successfully lead novel investigations in his own laboratory.
Technological developments now make it possible to measure dynamically the levels thousands of omics components (eg. proteins, metabolites and transcripts). The collection and integration of such information from individuals monitored through varying physiological states is expected to offer insights into the applicability of the emerging field of personalized medicine. The current project aims at designing a unique framework for processing such omics data and applying novel statistical and computational methods to simultaneously study the interplay between molecular components as these vary in time, through physiological changes. The obtained information will be used in the proposed investigations to probe the changes in genetic, proteomic and metabolic pathways that respond to varying conditions. Such information of how changes in physiological states are correlated to the monitored molecular component changes will have applications towards diagnosis of complex diseases, in a personalized and more precise medical implementation.
|Roushangar, R; Mias, G I (2017) MathIOmica-MSViewer: a dynamic viewer for mass spectrometry files for Mathematica. J Mass Spectrom 52:315-318|
|Mias, George I; Yusufaly, Tahir; Roushangar, Raeuf et al. (2016) MathIOmica: An Integrative Platform for Dynamic Omics. Sci Rep 6:37237|
|Marcobal, Angela; Yusufaly, Tahir; Higginbottom, Steven et al. (2015) Metabolome progression during early gut microbial colonization of gnotobiotic mice. Sci Rep 5:11589|
|Snyder, Michael; Mias, George; Stanberry, Larissa et al. (2014) Metadata checklist for the integrated personal OMICS study: proteomics and metabolomics experiments. OMICS 18:81-5|
|Kolker, Eugene; Özdemir, Vural; Martens, Lennart et al. (2014) Toward more transparent and reproducible omics studies through a common metadata checklist and data publications. OMICS 18:10-4|
|Mias, George I; Chen, Rui; Zhang, Yan et al. (2013) Specific plasma autoantibody reactivity in myelodysplastic syndromes. Sci Rep 3:3311|
|Chen, Rui; Giliani, Silvia; Lanzi, Gaetana et al. (2013) Whole-exome sequencing identifies tetratricopeptide repeat domain 7A (TTC7A) mutations for combined immunodeficiency with intestinal atresias. J Allergy Clin Immunol 132:656-664.e17|
|Stanberry, Larissa; Mias, George I; Haynes, Winston et al. (2013) Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile. Metabolites 3:741-60|