The CELLmicrocosmos PathwayIntegration (CmPI) was developed to support and visualize the subcellular localization prediction of protein-related data such as protein-interaction networks. From the start it was possible to manually analyze the localizations by using an interactive table. It was, however, quite complicated to compare and analyze the different localization results derived from data integration as well as text-mining-based databases. The current software release provides a new interactive visual workflow, the Subcellular Localization Charts. As an application case, a MUPP1-related protein-protein interaction network is localized and semi-automatically analyzed. It will be shown that the workflow was dramatically improved and simplified. In addition, it is now possible to use custom protein-related data by using the SBML format and get a view of predicted protein localizations mapped onto a virtual cell model.
Analysis of molecular markers in addition to cytological analysis of fine-needle aspiration (FNA) samples is a promising way to improve the preoperative diagnosis of thyroid nodules. Nonetheless, in clinical practice, applications of existing diagnostic solutions based on the detection of somatic mutations or analysis of gene expression are limited by their high cost and difficulties with clinical interpretation. The aim of our work was to develop an algorithm for the differential diagnosis of thyroid nodules on the basis of a small set of molecular markers analyzed by real-time PCR.A total of 494 preoperative FNA samples of thyroid goiters and tumors from 232 patients with known histological reports were analyzed: goiter, 105 samples (50 patients); follicular adenoma, 101 (48); follicular carcinoma, 43 (28); Hürthle cell carcinoma, 25 (11); papillary carcinoma, 121 (56); follicular variant of papillary carcinoma, 80 (32); and medullary carcinoma, 19 (12). Total nucleic acids extracted from dried FNA smears were analyzed for five somatic point mutations and two translocations typical of thyroid tumors as well as for relative concentrations of HMGA2 mRNA and 13 microRNAs and the ratio of mitochondrial to nuclear DNA by real-time PCR. A decision tree-based algorithm was built to discriminate benign and malignant tumors and to type the thyroid cancer. Leave-p-out cross-validation with five partitions was performed to estimate prediction quality. A comparison of two independent samples by quantitative traits was carried out via the Mann-Whitney U test.A minimum set of markers was selected (levels of HMGA2 mRNA and miR-375, - 221, and -146b in combination with the mitochondrial-to-nuclear DNA ratio) and yielded highly accurate discrimination (sensitivity = 0.97; positive predictive value = 0.98) between goiters with benign tumors and malignant tumors and accurate typing of papillary, medullary, and Hürthle cell carcinomas. The results support an alternative classification of follicular tumors, which differs from the histological one.The study shows the feasibility of the preoperative differential diagnosis of thyroid nodules using a panel of several molecular markers by a simple PCR-based method. Combining markers of different types increases the accuracy of classification.
Recent findings indicate that the microbiome may have significant impact on the development of lung cancer by its effects on inflammation, dysbiosis or genome damage. The aim of this study was to compare the sputum microbiome of lung cancer (LC) patients with the chromosomal aberration (CA) and micronuclei (MN) frequency in peripheral blood lymphocytes. In the study, the taxonomic composition of the sputum microbiome of 66 men with untreated LC were compared with 62 control subjects with respect to CA and MN frequency and centromere fluorescence in situ hybridisation analysis. Results showed a significant increase in CA (4.11 ± 2.48% versus 2.08 ± 1.18%) and MN (1.53 ± 0.67% versus 0.87 ± 0.49%) frequencies, respectively, in LC patients as compared to control subjects. The higher frequency of centromeric positive MN of LC patients was mainly due to aneuploidy. A significant increase in Streptococcus, Bacillus, Gemella and Haemophilus in LC patients was detected, in comparison to the control subjects while 18 bacterial genera were significantly reduced, which indicates a decrease in the beta diversity in the microbiome of LC patients. Although, the CA frequency in LC patients is significantly associated with an increased presence of the genera Bacteroides, Lachnoanaerobaculum, Porphyromonas, Mycoplasma and Fusobacterium in their sputum, and a decrease for the genus Granulicatella after application of false discovery rate correction, significance was not any more present. The decrease of MN frequency of LC patients is significantly associated with an increase in Megasphaera genera and Selenomonas bovis. In conclusion, a significant difference in beta diversity of microbiome between LC and control subjects and association between the sputum microbiome composition and genome damage of LC patients was detected, thus supporting previous studies suggesting an etiological connection between the airway microbiome and LC.
Cholesterol is an essential structural component of cell membranes and a precursor of vitamin D, as well as steroid hormones. Humans and other animal species can absorb cholesterol from food. Cholesterol is also synthesized de novo in the cells of many tissues. We have previously reconstructed the gene network regulating intracellular cholesterol levels, which included regulatory circuits involving transcription factors from the SREBP (Sterol Regulatory Element-Binding Proteins) subfamily. The activity of SREBP transcription factors is regulated inversely depending on the intracellular cholesterol level. This mechanism is implemented with the participation of proteins SCAP, INSIG1, INSIG2, MBTPS1/S1P and MBTPS2/S2P. This group of proteins, together with the SREBP factors, is designated as “cholesterol sensor”. An elevated cholesterol level is a risk factor for the development of cardiovascular diseases and may also be observed in obesity, diabetes and other pathological conditions. Systematization of information about the molecular mechanisms controlling the activity of SREBP factors and cholesterol biosynthesis in the form of a gene network and building new knowledge about the gene network as a single object is extremely important for understanding the molecular mechanisms underlying the predisposition to diseases. With a computer tool, ANDSystem, we have built a gene network regulating cholesterol biosynthesis. The gene network included data on: (1) the complete set of enzymes involved in cholesterol biosynthesis; (2) proteins that function as part of the “cholesterol sensor”; (3) proteins that regulate the activity of the “cholesterol sensor”; (4) genes encoding proteins of these groups; (5) genes whose transcription is regulated by SREBP factors (SREBP target genes). The gene network was analyzed and feedback loops that control the activity of SREBP factors were identified. These feedback loops involved the PPARG , NR0B2 / SHP1 , LPIN1 , and AR genes and the proteins they encode. Analysis of the phylostratigraphic age of the genes showed that the ancestral forms of most human genes encoding the enzymes of cholesterol biosynthesis and the proteins of the “cholesterol sensor” may have arisen at early evolutionary stages ( Cellular organisms (the root of the phylostratigraphic tree) and the stages of Eukaryota and Metazoa divergence). However, the mechanism of gene transcription regulation in response to changes in cholesterol levels may only have formed at later evolutionary stages, since the phylostratigraphic age of the genes encoding the transcription factors SREBP1 and SREBP2 corresponds to the stage of Vertebrata divergence.
The network for differetially expressed proteins for both EXP0039 and EXP00050 experiments. The file can be opened with the ANDSystem tool ( http://pbiosoft.com/en/andsystem/andsystem-free ). (ANDZ 21Â kb)
The metabolomic profiles of glioblastoma and surrounding brain tissue, comprising 17 glioblastoma samples and 15 peritumoral tissue samples, were thoroughly analyzed in this investigation. The LC-MS/MS method was used to analyze over 400 metabolites, revealing significant variations in metabolite content between tumor and peritumoral tissues. Statistical analyses, including the Mann–Whitney and Cucconi tests, identified several metabolites, particularly ceramides, that showed significant differences between glioblastoma and peritumoral tissues. Pathway analysis using the KEGG database, conducted with MetaboAnalyst 6.0, revealed a statistically significant overrepresentation of sphingolipid metabolism, suggesting a critical role of these lipid molecules in glioblastoma pathogenesis. Using computational systems biology and artificial intelligence methods implemented in a cognitive platform, ANDSystem, molecular genetic regulatory pathways were reconstructed to describe potential mechanisms underlying the dysfunction of sphingolipid metabolism enzymes. These reconstructed pathways were integrated into a regulatory gene network comprising 15 genes, 329 proteins, and 389 interactions. Notably, 119 out of the 294 proteins regulating the key enzymes of sphingolipid metabolism were associated with glioblastoma. Analysis of the overrepresentation of Gene Ontology biological processes revealed the statistical significance of 184 processes, including apoptosis, the NF-kB signaling pathway, proliferation, migration, angiogenesis, and pyroptosis, many of which play an important role in oncogenesis. The findings of this study emphasize the pivotal role of sphingolipid metabolism in glioblastoma development and open new prospects for therapeutic approaches modulating this metabolism.
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