The Heartbreak of Wheat-Related Disorders
Stephen T. Sinatra, Mark C. Houston in Nutritional and Integrative Strategies in Cardiovascular Medicine, 2022
Such a differentiated approach identifying sources of inflammation is in line with the growing appreciation and ongoing introduction of precision medicine in cardiology.26 Precision medicine incorporates standard clinical and health record data with advanced panomics (i.e., genomics, transcriptomics, epigenomics, proteomics, metabolomics, microbiomics) for deep phenotyping.27 These phenotypic data can then be analyzed within the framework of molecular interaction (interactome) networks to uncover previously unrecognized disease phenotypes, relationships between comorbidities, and unique inflammatory triggers to the individual.28 Functional medicine addresses chronic disease by delivering precision medicine with an emphasis on reducing the triggers of inflammation. The ability to deliver precision medicine relies on one’s capability to not only collect data, but also organize it in a way that extracts an understanding of a patient’s biological processes and then maps these processes to human disease.29,30
Definition, risk factors, and epidemiology of osteoporosis
Peter V. Giannoudis, Thomas A. Einhorn in Surgical and Medical Treatment of Osteoporosis, 2020
The development of “-omics” technologies, enabling the large-scale, rapid study of genomes, exomes, proteomes, and differential gene expression, has expanded our understanding of the molecular processes involved in multifactorial diseases. New areas of research such as the genome, transcriptome, metabolome, and interactome constitute a challenge for translational research and an area of immense promise for the development of systems medicine. In this respect, studies have obtained encouraging results, improving our understanding of the osteoporotic process. Thus, a major comparative gene expression study of 13,463 genes in patients with osteoporosis and osteoarthritis revealed significant differences in gene expression profiles in 241 CpG methylation regions of DNA from bone (60). The affected 228 genes were associated with cell differentiation factors and bone transcription embryogenesis. Moreover, the lower DNA methylation detected in 217 of these genes was associated with osteoporosis rather than osteoarthritis.
Computational Biology and Bioinformatics in Anti-SARS-CoV-2 Drug Development
Debmalya Barh, Kenneth Lundstrom in COVID-19, 2022
Selection of host proteins as drug targets and/or the discovery of drug candidates depends on the knowledge of the SARS-CoV-2/human interactome [101–103]. MS-assisted proteomics represents an important means for a better understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein–protein interactions, and post-translational modifications. Bittremieux et al. describe freely available data and computational resources that can be used to facilitate mass spectrometry-based analysis of SARS-CoV-2 [104]. Important information on the potentially druggable host proteins and the molecular mechanisms at play during infection can also be retrieved from the comparisons of SARS-CoV-2 with other viruses [105].
Interactomics and tick vaccine development: new directions for the control of tick-borne diseases
Published in Expert Review of Proteomics, 2018
Sara Artigas-Jerónimo, José De La Fuente, Margarita Villar
Interactomics can be defined as the study of the interactome network, understood as the complete set of protein–protein and other macromolecules physical and functional interactions (PPIs) involved in cellular processes and biological functions within an organism’s cell [34]. Proteins play very important roles in a variety of biological processes that take place in a cell and the different biological functions that regulate these processes, which are controlled by a large number of direct and indirect interactions between them [35]. The knowledge of the interactomics maps supposes a challenge and a great advance in molecular biology, because not only the proteome is characterized but also its complete functionality can be elucidated. One of the main applications of interactomics is the understanding and knowledge of disease biology. Interactomics allow observing the whole scene, comprising biological processes within a cell and providing information to target proteins facilitating the discovery of biomarkers for early diagnosis, surveillance, prognosis, and drug targets for prevention and treatment of diseases [35].
Hot-spot analysis for drug discovery targeting protein-protein interactions
Published in Expert Opinion on Drug Discovery, 2018
Mireia Rosell, Juan Fernández-Recio
The complete set of interactions in a living organism defines the so-called interactome [11], whose description is key to understand the behavior of the entire biological system, especially when integrated with other data generated from the ‘omics’ sciences (genomics, proteomics, transcriptomics, etc.). Recent studies have tried to describe the complete interactome of different organisms, but this is a highly challenging task due to experimental limitations. Indeed, the number of PPIs in human is highly uncertain, with some estimations widely ranging between 130,000 and 650,000 [12,13]. In a recent study, a total of 14,000 human interactions were experimentally identified with high reproducibility [11]. Using information from several public databases, the number of reliable interactions (i.e. those found in more than one database and showing reliable evidence that they are binary) can increase up to 93,000 PPIs (http://interactome3d.irbbarcelona.org/) [14]. This shows that there is still a significant degree of uncertainty in the description of the human protein–protein interactome.
Effect of nitric oxide reduction on arterial thrombosis
Published in Scandinavian Cardiovascular Journal, 2019
Dario Costa, Giuditta Benincasa, Roberta Lucchese, Teresa Infante, Giovanni Francesco Nicoletti, Claudio Napoli
Since the most of molecular factors exert their functions by interacting with other cellular components (interactome), any disease is rarely a consequence of a single gene defect; rather it reflects a perturbation of the complex intracellular network [16,17,73,74]. In the era of network medicine, the analysis of topological properties of protein-protein interactions (PPIs), regulatory, as well as co-expression networks guide research from cellular-molecular level of protein-protein interactions to correlational studies of gene expression in biological samples [16,17,73,74]. All of these networks can be viewed as maps where disorders are represented with localized perturbation within a specific module (pathway) of interactome. In essence, a network is a set of nodes and edges wherein the nodes are linked if there is a significant functional interaction between them. In PPI networks (e.g., GenePanda, DIAMOnD, PRINCE, PRODIGE), the nodes are proteins that are linked to each other by physical interaction; in regulatory networks (e.g., MMI-Network and PANDA), the directed links represent regulatory relationships between a transcription factor and a gene; in co-expression networks (e.g., SWIM and WGCNA), genes with similar co-expression profiles are linked [16,17,73,74]. The final goal is both to increase the global knowledge of the interactome-related perturbations resulting in diseases and to translate computational data into real clinical applications.
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