11 research outputs found
Computational Systems Biology of Psoriasis: Are We Ready for the Age of Omics and Systems Biomarkers?
No full textComputational biology and omics' systems sciences are greatly impacting research on common diseases such as cancer. By contrast, dermatology covering an array of skin diseases with high prevalence in society, has received relatively less attention from omics' and computational biosciences. We are focusing on psoriasis, a common and debilitating autoimmune disease involving skin and joints. Using computational systems biology and reconstruction, topological, modular, and a novel correlational analyses (based on fold changes) of biological and transcriptional regulatory networks, we analyzed and integrated data from a total of twelve studies from the Gene Expression Omnibus (sample size=534). Samples represented a comprehensive continuum from lesional and nonlesional skin, as well as bone marrow and dermal mesenchymal stem cells. We identified and propose here a JAK/STAT signaling pathway significant for psoriasis. Importantly, cytokines, interferon-stimulated genes, antimicrobial peptides, among other proteins, were involved in intrinsic parts of the proposed pathway. Several biomarker and therapeutic candidates such as SUB1 are discussed for future experimental studies. The integrative systems biology approach presented here illustrates a comprehensive perspective on the molecular basis of psoriasis. This also attests to the promise of systems biology research in skin diseases, with psoriasis as a systemic component. The present study reports, to the best of our knowledge, the largest set of microarray datasets on psoriasis, to offer new insights into the disease mechanisms with a proposal of a disease pathway. We call for greater computational systems biology research and analyses in dermatology and skin diseases in general
Computational prediction of associations between psoriasis, rheumatoid arthritis and osteoarthritis
No full textIntegration of multiple biological features yields high confidence human protein interactome
No full textThe biological function of a protein is usually determined by its physical interaction with other proteins. Protein-protein interactions (PPIs) are identified through various experimental methods and are stored in curated databases. The noisiness of the existing PPI data is evident, and it is essential that a more reliable data is generated. Furthermore, the selection of a set of PPIs at different confidence levels might be necessary for many studies. Although different methodologies were introduced to evaluate the confidence scores for binary interactions, a highly reliable, almost complete PPI network of Homo sapiens is not proposed yet. The quality and coverage of human protein interactome need to be improved to be used in various disciplines, especially in biomedicine. In the present work, we propose an unsupervised statistical approach to assign confidence scores to PPIs of H. sapiens. To achieve this goal PPI data from six different databases were collected and a total of 295,288 non-redundant interactions between 15,950 proteins were acquired. The present scoring system included the context information that was assigned to PPIs derived from eight biological attributes. A high confidence network, which included 147,923 binary interactions between 13,213 proteins, had scores greater than the cutoff value of 0.80, for which sensitivity, specificity, and coverage were 94.5%, 80.9%, and 82.8%, respectively. We compared the present scoring method with others for evaluation. Reducing the noise inherent in experimental PPIs via our scoring scheme increased the accuracy significantly. As it was demonstrated through the assessment of process and cancer subnetworks, this study allows researchers to construct and analyze context-specific networks via valid PPI sets and one can easily achieve subnetworks around proteins of interest at a specified confidence level. (C) 2016 Elsevier Ltd. All rights reserved
The role of protein interaction networks in systems biomedicine
No full textAbstractThe challenging task of studying and modeling complex dynamics of biological systems in order to describe various human diseases has gathered great interest in recent years. Major biological processes are mediated through protein interactions, hence there is a need to understand the chaotic network that forms these processes in pursuance of understanding human diseases. The applications of protein interaction networks to disease datasets allow the identification of genes and proteins associated with diseases, the study of network properties, identification of subnetworks, and network-based disease gene classification. Although various protein interaction network analysis strategies have been employed, grand challenges are still existing. Global understanding of protein interaction networks via integration of high-throughput functional genomics data from different levels will allow researchers to examine the disease pathways and identify strategies to control them. As a result, it seems likely that more personalized, more accurate and more rapid disease gene diagnostic techniques will be devised in the future, as well as novel strategies that are more personalized. This mini-review summarizes the current practice of protein interaction networks in medical research as well as challenges to be overcome
Computational Systems Biology of Psoriasis: Are We Ready for the Age of Omics and Systems Biomarkers?
No full textIDEEA: information diffusion model for integrating gene expression and EEG data in identifying Alzheimer’s disease markers
No full textUnderstanding the genetic components of Alzheimer’s disease (AD) via transcriptome analysis often necessitates the use of invasive methods. This work focuses on overcoming the difficulties associated with the invasive process of collecting brain tissue samples in order to measure and investigate the transcriptome behavior of AD. Our approach called IDEEA ( I nformation D iffusion model for integrating gene E xpression and E EG data in identifying A lzheimer’s disease markers) involves systematically linking two different but complementary modalities: transcriptomics and electroencephalogram (EEG) data. We preprocess these two data types by calculating the spectral and transcriptional sample distances, over 11 brain regions encompassing 6 distinct frequency bands. Subsequently, we employ a genetic algorithm approach to integrate the distinct features of the preprocessed data. Our experimental results show that IDEEA converges rapidly to local optima gene subsets, in fewer than 250 iterations. Our algorithm identifies novel genes along with genes that have previously been linked to AD. It is also capable of detecting genes with transcription patterns specific to individual EEG bands as well as those with common patterns among bands. In particular, the alpha2 (10–13 Hz) frequency band yielded 8 AD-associated genes out of the top 100 most frequently selected genes by our algorithm, with a p -value of 0.05. Our method not only identifies AD-related genes but also genes that interact with AD genes in terms of transcription regulation. We evaluated various aspects of our approach, including the genetic algorithm performance, band-pair association and gene interaction topology. Our approach reveals AD-relevant genes with transcription patterns inferred from EEG alone, across various frequency bands, avoiding the risky brain tissue collection process. This is a significant advancement toward the early identification of AD using non-invasive EEG recordings
<i>In silico</i> analysis of substitution mutations in the β-globin gene in Turkish population of β-thalassemia
No full textBeta-thalassemia is a genetic blood disorder represented by anomalies in hemoglobin’s beta chain production. Most hemoglobin defects are a result of mutations of the structural β-globin gene. Many diseases, including β-thalassemia, benefit from computational studies that aid researchers in investigating the association of genotype and phenotype. In this study, the alanine substitution mutations of the β-globin protein sub-units in the Turkish population (Hb Ankara, Hb Siirt and Hb Izmir) and the effects of those mutations on the β-globin protein structure and performance are examined using molecular dynamics simulation. While Hb Ankara variant showed a non-conservative mutation, Hb Siirt and Hb Izmir showed a semi-conservative mutation. RMSF values of Hb Siirt, between residues 95 and 99, were higher than wild-type and the other mutant proteins. The residues of Hb Ankara showed lower fluctuation compared to the other structures. The mean ROG values were 1.47 nm, 1.46 nm, 1.49 nm and 1.48 and the average number of the hydrogen bonds were 92, 100, 99, and 89 for Hb Ankara, Hb Siirt and Hb Izmir, respectively. Moreover, a significant increase in overall motion in Hb Siirt was observed based on PCA analysis. Hb Siirt substitution mutation might cause an effect in β-globin proteins which could impact the protein function. This indicates a major role on beta globin subunit’s stability for alanine on 27th position. However, Hb Ankara and Hb Izmir variants may act as a silent mutation, since these two mutations did not show a large change in the dynamics of the protein. Communicated by Ramaswamy H. Sarma</p
Systems biomarkers in psoriasis: Integrative evaluation of computational and experimental data at transcript and protein levels
No full textPsoriasis is a complex autoimmune disease with multiple genes and proteins being involved in its pathogenesis. Despite the efforts performed to understand mechanisms of psoriasis pathogenesis and to identify diagnostic and prognostic targets, disease-specific and effective biomarkers were still not available. This study is compiled regarding clinical validation of computationally proposed biomarkers at gene and protein expression levels through qRT-PCR and ELISA techniques using skin biopsies and blood plasma. We identified several gene and protein clusters as systems biomarkers and presented the importance of gender difference in psoriasis. A gene cluster comprising of P13, IRF9, IFIT1 and NMI were found as positively correlated and differentially co-expressed for women, whereas SUB1 gene was also included in this cluster for men. The differential expressions of IRF9 and NMI in women and SUB1 in men were validated at gene expression level via qRT-PCR. At protein level, PI3 was abundance in disease states of both genders, whereas PC4 protein and WIF1 protein were significantly higher in healthy states than disease states of male group and female group, respectively. Regarding abundancy of PI3 and WIF1 proteins in women, and PI3 and PC4 in men may be assumed as systems biomarkers at protein level
Tissue-Specific Molecular Biomarker Signatures of Type 2 Diabetes: An Integrative Analysis of Transcriptomics and Protein-Protein Interaction Data
No full textType 2 diabetes mellitus is a major global public health burden. A complex metabolic disease, type 2 diabetes affects multiple different tissues, demanding a systems medicine approach to biomarker and novel diagnostic discovery, not to mention data integration across omics-es. In the present study, transcriptomics data from different tissues including beta-cells, pancreatic islets, arterial tissue, peripheral blood mononuclear cells, liver, and skeletal muscle of 228 samples were integrated with protein-protein interaction data and genome scale metabolic models to unravel the molecular and tissue-specific biomarker signatures of type 2 diabetes mellitus. Classifying differentially expressed genes, reconstruction and topological analysis of active protein-protein interaction subnetworks indicated that genomic reprogramming depends on the type of tissue, whereas there are common signatures at different levels. Among all tissue and cell types, Mannosidase Alpha Class 1A Member 2 was the common signature at genome level, and activation-ppara reaction, which stimulates a nuclear receptor protein, was found out as the mutual reporter at metabolic level. Moreover, miR-335 and miR-16-5p came into prominence in regulation of transcription at different tissues. On the other hand, distinct signatures were observed for different tissues at the metabolome level. Various coenzyme-A derivatives were significantly enriched metabolites in pancreatic islets, whereas skeletal muscle was enriched for cholesterol, malate, L-carnitine, and several amino acids. Results have showed utmost importance concerning relations between T2D and cancer, blood coagulation, neurodegenerative diseases, and specific metabolic and signaling pathways
Uncovering potent natural phytochemicals targeting SARS-COV-2 spike protein variants: molecular dynamics insights
No full textAbstract SARS-CoV-2 remains a critical global health concern due to its high transmissibility, evolving variants, and the absence of a universally effective treatment. Phytocompounds, known for their antiviral, anti-inflammatory, and immunomodulatory properties, continue to be explored as potential therapeutic agents. This study evaluated 20 phytocompounds and four approved antiviral drugs, Remdesivir, Favipiravir, Hydroxychloroquine, and Ivermectin, against nine SARS-CoV-2 spike glycoprotein structures, including five wild-type and four variants (Alpha, Beta, Delta, and Omicron). Molecular docking using two software platforms identified ursolic acid, betulinic acid, β-sitosterol, and ivermectin as top candidates, with binding affinities ranging from − 6.7 to − 9.6 kcal/mol. These compounds also displayed favorable pharmacokinetic properties and druggability. 100 ns molecular dynamics simulations were performed on the highest-affinity complexes to assess stability. Betulinic acid and β-sitosterol demonstrated stable binding interactions, with low RMSD values (~ 0.2–0.3 nm) and consistent hydrogen bonding (1–3 bonds), suggesting structural stability. In contrast, ursolic acid and ivermectin showed unstable binding and higher structural fluctuations during simulation. Overall, the study highlights betulinic acid and β-sitosterol as presumptive SARS-CoV-2 inhibitors, warranting further experimental validation through in vitro and in vivo studies to confirm their therapeutic potential
