Self-similarity study based on the particle sizes of coal-series diatomite
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ABSTRACT:
SUBMITTER: Cheng L
PROVIDER: S-EPMC10980820 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundMutations of different genes often result in clinically similar diseases. Among the datasets of similar diseases, we analyzed the 'phenotypic series' from Online Mendelian Inheritance in Man and examined the similarity of the diseases that belong to the same phenotypic series, because we hypothesize that clinical similarity may unveil shared pathogenic mechanisms.MethodsSpecifically, for each pair of diseases, we quantified their similarity, based on both number and information content of the shared clinical phenotypes. Then, we assembled the disease similarity network, in which nodes represent diseases and edges represent clinical similarities.ResultsOn average, diseases have high similarity with other diseases of their own phenotypic series, even though about one third of diseases have their maximal similarity with a disease of another series. Consequently, the network is assortative (i.e., diseases belonging to the same series link preferentially to each other), but the series differ in the way they distribute within the network. Specifically, heterophobic series, which minimize links to other series, form islands at the periphery of the network, whereas heterophilic series, which are highly inter-connected with other series, occupy the center of the network.ConclusionsThe finding that the phenotypic series display not only internal similarity (assortativity) but also varying degrees of external similarity (ranging from heterophobicity to heterophilicity) calls for investigation of biological mechanisms that might be shared among different series. The correlation between the clinical and biological similarities of the phenotypic series is analyzed in Part II of this study1.
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Project description:Concerns about the potential adverse health effects of engineered nanoparticles stems in part from the possibility that some materials display unique chemical and physical properties at nanoscales which could exacerbate their biological activity. However, studies that have assessed the effect of particle size across a comprehensive set of biological responses have not been reported. Using a macrophage cell model, we demonstrate that the ability of unopsonized amorphous silica particles to stimulate inflammatory protein secretion and induce macrophage cytotoxicity scales closely with the total administered particle surface area across a wide range of particle diameters (7-500 nm). Whole genome microarray analysis of the early gene expression changes induced by 10- and 500-nm particles showed that the magnitude of change for the majority of genes affected correlated more tightly with particle surface area than either particle mass or number. Gene expression changes that were particle size-specific were also identified. However, the overall biological processes represented by all gene expression changes were nearly identical, irrespective of particle diameter. Direct comparison of the cell processes represented in the 10- and 500-nm particle gene sets using gene set enrichment analysis revealed that among 1009 total biological processes, none were statistically enriched in one particle size group over the other. The key mechanisms involved in silica nanoparticle-mediated gene regulation and cytotoxicity have yet to be established. However, our results suggest that on an equivalent nominal surface area basis, common biological modes of action are expected for nano- and supranano-sized silica particles.
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Project description:BackgroundDespite being caused by mutations in different genes, diseases in the same phenotypic series are clinically similar, as reported in Part I of this study. Here, in Part II, we hypothesized that the phenotypic series too might be clinically similar. Furthermore, on the assumption that gene mutations indirectly cause clinical phenotypes by directly affecting biological functions, we hypothesized that clinically similar phenotypic series might be biologically similar as well.MethodsTo test these hypotheses, we generated a clinical similarity network and a set of biological similarity networks. In both types of network, the nodes represent the phenotypic series, and the edges linking the nodes indicate the similarity of the linked phenotypic series. The weight of each edge is proportional to a similarity coefficient, which depends on the clinical phenotypes and the biological features that are shared by the linked phenotypic series, in the clinical and biological similarity networks, respectively.ResultsAfter assembling and analyzing the networks, we raised the threshold for the similarity coefficient, to retain edges of progressively greater weight. This way all the networks were gradually split into fragments, composed of phenotypic series with increasingly greater degrees of similarity. Finally, by comparing the fragments from the two types of network, we defined subsets of phenotypic series with varying types and degrees of clinical and biological correlation.ConclusionsLike the individual diseases, the phenotypic series too are clinically and biologically similar to each other. Furthermore, our findings unveil different modalities of correlation between the clinical manifestations and the biological features of the inherited diseases.
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