Biology subjects

Rietman, E. A.

Publications and source records attributed to Rietman, E. A..

4 recordsLinked to original sources

bioRxiv2024

A Systems Biology Analysis of Chronic Lymphocytic Leukemia

Whole-genome sequencing has revealed that TP53, NOTCH1, ATM, SF3B1, BIRC3, ABL, NXF1, BCR, ZAP70 are often mutated in CLL, but not consistently across all CLL patients. This paper employs a statistical thermo-dynamics approach in combination with the systems biology of the CLL protein-protein interaction networks to identify the most significant participant proteins in the cancerous transformation. Betti number (a topology of complexity) estimates highlight a protein hierarchy, primarily in the Wnt pathway known for aberrant CLL activation. These individually identified proteins suggest a network-targeted strategy over single-target drug development. The findings advocate for a multi-target inhibition approach, limited to several key proteins to minimize side effects, thereby providing a foundation for designing therapies. This study emphasizes a shift towards a comprehensive, multi-scale analysis to enhance personalized treatment strategies for CLL, which could be experimentally validated using siRNA or small molecule inhibitors. The result is not just the identification of these proteins but their rank-order, offering a potent signal amplification in the context of the 20,000 proteins produced by the human body, thus providing a strategic basis for therapeutic intervention in CLL, underscoring the necessity for a more holistic, cellular, chromosomal, and genome-wide study to develop tailored treatments for CLL patients. Author SummaryChronic Lymphocytic Leukemia (CLL) is a unique and slowly progressing cancer affecting white blood cells, and research on CLL has highlighted the inconsistency of gene mutations across patients. Using a novel approach that merges statistical thermodynamics and systems biology, this research examines the CLL protein-protein interaction networks to pinpoint proteins integral to the onset of the disease. Betti number (a topology of complexity) estimates, which measure the importance of individual proteins when removed from the network, helped identify numerous potential therapeutic targets, notably within the Wnt signaling pathway, a pathway implicated in various cellular processes and known for its defective expression in CLL. The finding advocates for a multi-target inhibition approach, focusing on several key proteins to minimize side effects, thereby laying a foundation for designing more effective therapies for CLL. This paper emphasizes the potential benefits of a comprehensive study, spanning cellular to genome-wide scales, to design personalized treatments for CLL patients.

systems biology↗

bioRxiv2019

Personalized Therapy Design for Systemic Lupus Erythematosus Based on the Analysis of Protein-Protein Interaction Networks

We analyzed protein expression data for Lupus patients, which have been obtained from publicly available databases. A combination of systems biology and statistical thermodynamics approaches was used to extract topological properties of the associated protein-protein interaction networks for each of the 291 patients whose samples were used to provide the molecular data. We have concluded that among the many proteins that appear to play critical roles in this pathology, most of them are either ribosomal proteins, ubiquitination pathway proteins or heat shock proteins. We propose some of the proteins identified in this study to be considered for drug targeting.

systems biology↗

bioRxiv2019

Gibbs Free Energy, a thermodynamic measure of protein-protein interactions, correlates with neurologic disability

Background: Modern network science has been used to reveal new and often fundamental aspects of brain network organization in physiological as well as pathological conditions. As a consequence, these discoveries, which relate to network hierarchy, hubs and network interactions, begun to change the paradigms of neurodegenerative disorders. We therefore explored the use of thermodynamics for protein-protein network interactions in Alzheimer disease (AD), Parkinson disease (PD), multiple sclerosis (MS), traumatic brain injury and epilepsy. Methods: To assess the validity of using network interactions in neurological disease, we investigated the relationship between network thermodynamics and molecular systems biology for these neurological disorders. In order to uncover whether there was a correlation between network organization and biological outcomes, we used publicly available RNA transcription data from individual patients with these neurological conditions, and correlated these molecular profiles with their respective individual disability scores. Results: We found a linear correlation (Pearson correlation of -0.828) between disease disability (a clinically validated measurement of a person's functional status), and Gibbs free energy (a thermodynamic measure of protein-protein interactions). In other words, we found an inverse relationship between disease entropy and thermodynamic energy. Interpretation: Because a larger degree of disability correlated with a larger negative drop in Gibbs free energy in a linear, disability-dependent fashion, it could be presumed that the progression of neuropathology such as is seen in Alzheimer Disease, could potentially be prevented by therapeutically correcting the changes Gibbs free energy.

biophysics↗

bioRxiv2018

Gibbs Free Energy of Protein-Protein Interactions Correlates with ATP Production in Cancer Cells

In this paper we analyze several cancer cell types from two seemingly independent angles: (a) the over-expression of various proteins participating in protein-protein interaction networks and (b) a metabolic shift from oxidative phosphorylation to glycolysis. We use large data sets to obtain a thermodynamic measure of the protein-protein interaction network, namely the associated Gibbs free energy. We find a reasonably strong inverse correlation between the percentage of energy production via oxidative phosphorylation and the Gibbs free energy of the protein networks. The latter is a measure of functional dysregulation within the cell. Our findings corroborate earlier indications that signaling pathway upregulation in cancer cells are linked to the metabolic shift known as the Warburg effect, hence these two seemingly independent characteristics of cancer phenotype may be interconnected.

biophysics↗