Studies of Complex Biological Systems with Applications to Molecular Medicine: The Need to Integrate Transcriptomic and Proteomic Approaches

Abstract

Omics approaches to the study of complex biological systems with potential applications to molecular medicine are attracting great interest in clinical as well as in basic biological research. Genomics, transcriptomics and proteomics are characterized by the lack of an a priori definition of scope, and this gives sufficient leeway for investigators (a) to discern all at once a globally altered pattern of gene/protein expression and (b) to examine the complex interactions that regulate entire biological processes. Two popular platforms in “omics” are DNA microarrays, which measure messenger RNA transcript levels, and proteomic analyses, which identify and quantify proteins. Because of their intrinsic strengths and weaknesses, no single approach can fully unravel the complexities of fundamental biological events. However, an appropriate combination of different tools could lead to integrative analyses that would furnish new insights not accessible through one-dimensional datasets. In this review, we will outline some of the challenges associated with integrative analyses relating to the changes in metabolic pathways that occur in complex pathophysiological conditions (viz. ageing and altered thyroid state) in relevant metabolically active tissues. In addition, we discuss several new applications of proteomic analysis to the investigation of mitochondrial activity.

A post-publication analysis of the idealized upper reference value of 2.5 mIU/L for TSH: Time to support the thyroid axis with magnesium and iron especially in the setting of reproduction medicine

“…In 2017 we are still working with chemical elements as well as with refined techniques that go into the “OMICS” of body function [52].Consulting any current textbook on endocrinology one will find that the involved chemical messengers of the thyroid function axis include the peripheral thyroid hormones, i.e. fT3 and fT4, and the regulatory pituitary hormone TSH.…”

Application of Machine Learning to Proteomics Data: Classification and Biomarker Identification in Postgenomics Biology

“…These methods, including transcriptomics, metabolomics, and lipidomics, can also be used in conjunction with proteomics to gain a broader view of the biological system under analysis ( Joyce and Palsson, 2006;Silvestri et al, 2011;Tan et al, 2009).…”

New Challenges for Old Diseases: The Impact of -Omics Technologies in the Understanding of Allergic Diseases

“…Omics approaches to the study of complex biological systems with potential applications to molecular medicine are attracting great interest in clinical as well as in basic biological research. Genomics, transcriptomics and proteomics are characterized by the lack of an a priori definition of scope, and this gives sufficient leeway for investigators (a) to discern all at once a globally altered pattern of gene/protein expression and (b) to examine the complex interactions that regulate entire biological processes (Silvestri et al, 2011). All classes of biological compounds, from genes through mRNA to proteins and metabolites, can be analyzed by the respective "omic" approaches, namely, genomics (Study of genomes and the complete collection of genes that they contain), transcriptomics (or functional genomics, attempts to analyze patterns of gene expression and to correlate the patterns with the underlying biology), epigenomic (the large-scale study of epigenetic modifications), proteomics (examine the collection of proteins to determine how, when and where they are expressed) or metabolomics (or metabonomics, is a large-scale approach to characterize and to quantify the compounds involved in cellular processes in a single assay to derive metabolic profiles).…”

“…Because of their intrinsic strengths and weaknesses, no single approach can fully unravel the complexities of fundamental biological events. However, an appropriate combination of different tools could lead to integrative analyses that would furnish new insights not accessible through one-dimensional datasets (Silvestri et al, 2011). Indeed, while the data obtained from genomics may explain the disposition of diseases (i.e., increasing risk of acquiring a certain disease), several other mechanisms that are not gene mediated may be involved in the onset of disease.…”