Impaired Inflammatory Response to LPS in Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is a severe health problem worldwide, reaching epidemic levels. High susceptibility to infections of T2DM patients indicates dysregulated immune responses to pathogens. However, innate immune responses, including monocyte functions, in T2DM are poorly investigated. Therefore, in this study we aimed to assess lipopolysaccharide- (LPS-) induced immune responses of circulating monocytes from T2DM patients. The results showed that monocytes from T2DM were hyporesponsive to LPS challenge as reflected by significantly suppressed secretion of TNFα (p<0.01) and expression of CD11b (p<0.001) and TLR4 (p<0.001) compared to those in monocytes from healthy subjects. Furthermore, LPS-induced IL-10 levels were similar in diabetic and healthy supernatants, while expression levels of CD163 were found to be downregulated on monocytes from T2DM (p<0.001) suggesting impaired ability of monocytes to switch their phenotype to anti-inflammatory. Taken together, our results suggest compromised function of monocytes in T2DM, which may explain, at least partly, high incidence of infection in these patients

Genotoxic potential of selected medicinal plant extracts in human whole blood cultures

Introduction: Many plant-derived products despite wide usage are not scientifically evaluated for their safety and efficacy. Therefore, in the present study, we aimed to evaluate the cytotoxic and genotoxic activities of Polygonum aviculare L., Equisetum arvense L., Plantago lanceolata L. and Artemisia absinthium L. ethanolic extracts in human white blood cells. Methods: Cell viability was assayed by trypan blue exclusion method, while the genotoxicity was tested by cytokinesis-block micronucleus (CBMN) assay upon cells stimulation with noncytotoxic concentrations of the plant extracts. Results: None of the plant extracts showed high cytotoxic activity. At the same time, only extract of P. lanceolata did not present any mutagenic activity, while E. arvense, P. aviculare and A. absinthium were clearly genotoxic. Conclusion: Caution is advice in the case of long-term use of E. arvense, P. aviculare and A. absinthium herbal medicines by population

Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes

In silico (quantitative) structure–activity relationship modeling is an approach that provides a fast and cost-effective alternative to assess the genotoxic potential of chemicals. However, one of the limiting factors for model development is the availability of consolidated experimental datasets. In the present study, we collected experimental data on micronuclei in vitro and in vivo, utilizing databases and conducting a PubMed search, aided by text mining using the BioBERT large language model. Chemotype enrichment analysis on the updated datasets was performed to identify enriched substructures. Additionally, chemotypes common for both endpoints were found. Five machine learning models in combination with molecular descriptors, twelve fingerprints and two data balancing techniques were applied to construct individual models. The best-performing individual models were selected for the ensemble construction. The curated final dataset consists of 981 chemicals for micronuclei in vitro and 1309 for mouse micronuclei in vivo, respectively. Out of 18 chemotypes enriched in micronuclei in vitro, only 7 were found to be relevant for in vivo prediction. The ensemble model exhibited high accuracy and sensitivity when applied to an external test set of in vitro data. A good balanced predictive performance was also achieved for the micronucleus in vivo endpoint

Top pathways with dysregulated sinks.

<p>Top pathways with dysregulated sinks.</p

Functional annotation of biological processes associated with spots and their contribution to diseases.

<p>The pink (left) and blue (right) background indicate the two clusters of disease obtained by spot-similarity analysis. The sinks in each spot were annotated with Webgestalt and REVIGO programs to reveal associated GO term and summarize them with non-redundant descriptors.</p

Global landscape of pathway dysregulations in diseases.

<p>The summary SOM portrait with spots transferred from individual disease portraits. The seven spots detected in the SOM portraits are assigned the letters A-G (in the center of figure). PSF-activity profile barplots (left and right panels) represent the mean PSF-signal for a given spot across the diseases. If that value passes the defined threshold the spot is marked with a “+” sign for up-regulation and “-” sign for downregulation. Bar coloring indicates the relation of diseases to similarity clusters (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187572#pone.0187572.g003" target="_blank">Fig 3</a>).</p

Description of the samples and datasets included in the study.

<p>Description of the samples and datasets included in the study.</p

Disease specific SOM portraits.

<p>PSF profiles for each disease were mapped on a 35x35 SOM grid, and visualized as 2D maps, where colors indicate the actual state of pathway sink dysregulations. Red to green color gradient indicates up-regulation, while blue to green one indicates down-regulation.</p

The spot-similarity graph of the diseases.

<p>The nodes represent the diseases; the edges connect the disease portraits sharing the highest number of spots. The portraits are provided as thumbnails to each node. The graph is divided into the pink and the blue clusters using the walktrap community search algorithm, implemented in R package “igraph” [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187572#pone.0187572.ref044" target="_blank">44</a>].</p

Sink distribution of top dysregulated pathways in spots.

<p>The pathways with the highest proportions of dysregulated sinks are represented in the graph. The proportion of dysregulated sinks in each spot is depicted in the heights of respective bars. Each pathway is indicated with corresponding color.</p