Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes

<abstract> <p>Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, <italic>ERBB2, FN1, FYN, HSPA1A, HBA1</italic>, and <italic>ITGB1</italic> genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.</p> </abstract>

Revealing the Mechanism of Astragali Radix against Cancer-Related Fatigue by Network Pharmacology and Molecular Docking

Background. Cancer-related fatigue (CRF) is an increasingly appreciated complication in cancer patients, which severely impairs their quality of life for a long time. Astragali Radix (AR) is a safe and effective treatment to improve CRF, but the related mechanistic studies are still limited. Objective. To systematically analyze the mechanism of AR against CRF by network pharmacology. Methods. TCMSP was searched to obtain the active compounds and targets of AR. The active compound-target (AC-T) network was established and exhibited by related visualization software. The GeneCards database was searched to acquire CRF targets, and the intersection targets with AR targets were used to make the Venny diagram. The protein-protein interaction (PPI) network of intersection targets was established, and further, the therapeutic core targets were selected by topological parameters. The selected core targets were uploaded to Metascape for GO and KEGG analysis. Finally, AutoDock Vina and PyMOL were employed for molecular docking validation. Results. 16 active compounds of AR were obtained, such as quercetin, kaempferol, 7-O-methylisomucronulatol, formononetin, and isorhamnetin. 57 core targets were screened, such as AKT1, TP53, VEGFA, IL-6, and CASP3. KEGG analysis manifested that the core targets acted on various pathways, including 137 pathways such as TNF, IL-17, and the AGE-RAGE signaling pathway. Molecular docking demonstrated that active compounds docked well with the core targets. Conclusion. The mechanism of AR in treating CRF involves multiple targets and multiple pathways. The present study laid a theoretical foundation for the subsequent research and clinical application of AR and its extracts against CRF.

Tetrel Bonds between Phenyltrifluorosilane and Dimethyl Sulfoxide: Influence of Basis Sets, Substitution and Competition

Ab initio calculations have been performed for the complexes of DMSO and phenyltrifluorosilane (PTS) and its derivatives with a substituent of NH3, OCH3, CH3, OH, F, CHO, CN, NO2, and SO3H. It is necessary to use sufficiently flexible basis sets, such as aug’-cc-pVTZ, to get reliable results for the Si···O tetrel bonds. The tetrel bond in these complexes has been characterized in views of geometries, interaction energies, orbital interactions and topological parameters. The electron-donating group in PTS weakens this interaction and the electron-withdrawing group prominently strengthens it to the point where it exceeds that of the majority of hydrogen bonds. The largest interaction energy occurs in the p-HO3S-PhSiF3···DMSO complex, amounting to −122 kJ/mol. The strong Si···O tetrel bond depends to a large extent on the charge transfer from the O lone pair into the empty p orbital of Si, although it has a dominant electrostatic character. For the PTS derivatives of NH2, OH, CHO and NO2, the hydrogen bonded complex is favorable to the tetrel bonded complex for the NH2 and OH derivatives, while the σ-hole interaction prefers the π-hole interaction for the CHO and NO2 derivatives.

Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms’ representation

Background Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task. Results We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance. Conclusions Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale.

ACTIVE CONTROL OF THE TOPOLOGICAL PARAMETERS OF ISLAND CATALYSTS WHEN SOLID OXIDE ELECTROLYTES OF FUEL CELLS ARE DEPOSITED ON THE SURFACE

The method of active technological control of topological parameters of synthesized island catalysts on solid oxide fuel cell electrolytes is considered. The proposed method makes it possible to obtain a catalyst with a maximum active area and high adhesion to a solid oxide electrolyte, which contributes to an increase in the power of the fuel cell and an increase in the operational life. The models of two stages of formation of island films under vacuum and magnetron sputtering presented in this paper were used as a theoretical basis for the creation of a method for active technological control and control of the synthesis of island catalyst in solid oxide fuel cells. Experimental testing of the developed method has shown its efficiency and effectiveness in the creation of solid oxide fuel cells.

Identification of Sitogluside as a Potential Skin-Pigmentation-Reducing Agent through Network Pharmacology

Many traditional Chinese medicines (TCMs) with skin-whitening properties have been recorded in the Ben-Cao-Gang-Mu and in folk prescriptions, and some literature confirms that their extracts do have the potential to inhibit pigmentation. However, no systematic studies have identified the specific regulatory mechanisms of the potential active ingredients. The aim of this study was to screen the ingredients in TCMs that inhibit skin pigmentation through a network pharmacology system and to explore underlying mechanisms. We identified 148 potential active ingredients from 14 TCMs, and based on the average “degree” of the topological parameters, the top five TCMs (Fructus Ligustri Lucidi, Hedysarum multijugum Maxim., Ampelopsis japonica, Pseudobulbus Cremastrae Seu Pleiones, and Paeoniae Radix Alba) that were most likely to cause skin-whitening through anti-inflammatory processes were selected. Sitogluside, the most common ingredient in the top five TCMs, inhibits melanogenesis in human melanoma cells (MNT1) and murine melanoma cells (B16F0) and decreases skin pigmentation in zebrafish. Furthermore, mechanistic research revealed that sitogluside is capable of downregulating tyrosinase (TYR) expression by inhibiting the ERK and p38 pathways and inhibiting TYR activity. These results demonstrate that network pharmacology is an effective tool for the discovery of natural compounds with skin-whitening properties and determination of their possible mechanisms. Sitogluside is a novel skin-whitening active ingredient with dual regulatory effects that inhibit TYR expression and activity.

DETERMINATION OF THE DIMENSIONAL AND TOPOLOGICAL PARAMETERS OF THE GRAPHITE PHASE IN CAST IRON BY FRACTAL ANALYSIS OF IMAGES OF ITS MICROSTRUCTURE

The basics of metallography and modern systems used for studying and analyzing the structures of materials are described. Special attention is paid to the techniques of quantitative microscopy, as a kind of ancestress of modern microstructure analysis systems. The analysis of modern computer programs used to analyze images of microstructures obtained from digital microscopes is presented. The basics of fractal analysis as a highly effective tool for calculating numerical values of parameters of geometrically complex objects are outlined. Using the example of the analysis of graphitized cast iron structure, the application of the fractal analysis method to determine such characteristics of the graphite phase as the shape of graphite inclusions and their distribution in the alloy volume is demonstrated. As part of the study, various classes of cast iron have been studied, such as graphitic pig iron with flaked graphite, cast iron with vermicular graphite, and high-grade cast iron with spheroidal graphite. To determine the shape of graphite inclusions, a fractal dimension has been proposed to be used, and the unevenness of the distribution has been estimated using such a function as lacunarity. The individual stages of determining these characteristics using a specialized FracLac module applied in the structure of the ImageJ program are presented. The obtained results showed high adequacy. Despite positive assessment, the shortcomings identified during the research on the use of fractal analysis methods for identifying geometrically complex dimensional and topological parameters inherent in the graphite phase in cast iron are noted. The ways for further improvement of these methods for solving a wide range of problems in metallography of alloys are proposed.

Assessment of Disrupted Brain Structural Connectome in Depressive Patients With Suicidal Ideation Using Generalized Q-Sampling MRI

Suicide is one of the leading causes of mortality worldwide. Various factors could lead to suicidal ideation (SI), while depression is the predominant cause among all mental disorders. Studies have shown that alterations in brain structures and networks may be highly associated with suicidality. This study investigated both neurological structural variations and network alterations in depressed patients with suicidal ideation by using generalized q-sampling imaging (GQI) and Graph Theoretical Analysis (GTA). This study recruited 155 participants and divided them into three groups: 44 depressed patients with suicidal ideation (SI+; 20 males and 24 females with mean age = 42, SD = 12), 56 depressed patients without suicidal ideation (Depressed; 24 males and 32 females with mean age = 45, SD = 11) and 55 healthy controls (HC; nine males and 46 females with mean age = 39, SD = 11). Both the generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA) values were evaluated in a voxel-based statistical analysis by GQI. We analyzed different topological parameters in the graph theoretical analysis and the subnetwork interconnections in the Network-based Statistical (NBS) analysis. In the voxel-based statistical analysis, both the GFA and NQA values in the SI+ group were generally lower than those in the Depressed and HC groups in the corpus callosum and cingulate gyrus. Furthermore, we found that the SI+ group demonstrated higher global integration and lower local segregation among the three groups of participants. In the network-based statistical analysis, we discovered that the SI+ group had stronger connections of subnetworks in the frontal lobe than the HC group. We found significant structural differences in depressed patients with suicidal ideation compared to depressed patients without suicidal ideation and healthy controls and we also found several network alterations among these groups of participants, which indicated that white matter integrity and network alterations are associated with patients with depression as well as suicidal ideation.