Anti-inflammatory effects of cold atmospheric plasma irradiation on the THP-1 human acute monocytic leukemia cell line.

Cold atmospheric plasma (CAP) has been studied and clinically applied to treat chronic wounds, cancer, periodontitis, and other diseases. CAP exerts cytotoxic, bactericidal, cell-proliferative, and anti-inflammatory effects on living tissues by generating reactive species. Therefore, CAP holds promise as a treatment for diseases involving chronic inflammation and bacterial infections. However, the cellular mechanisms underlying these anti-inflammatory effects of CAP are still unclear. Thus, this study aimed to elucidate the anti-inflammatory mechanisms of CAP in vitro. The human acute monocytic leukemia cell line, THP-1, was stimulated with lipopolysaccharide and irradiated with CAP, and the cytotoxic effects of CAP were evaluated. Time-course differentiation of gene expression was analyzed, and key transcription factors were identified via transcriptome analysis. Additionally, the nuclear localization of the CAP-induced transcription factor was examined using western blotting. The results indicated that CAP showed no cytotoxic effects after less than 70 s of irradiation and significantly inhibited interleukin 6 (IL6) expression after more than 40 s of irradiation. Transcriptome analysis revealed many differentially expressed genes (DEGs) following CAP irradiation at all time points. Cluster analysis classified the DEGs into four distinct groups, each with time-dependent characteristics. Gene ontology and gene set enrichment analyses revealed CAP-induced suppression of IL6 production, other inflammatory responses, and the expression of genes related to major histocompatibility complex (MHC) class II. Transcription factor analysis suggested that nuclear factor erythroid 2-related factor 2 (NRF2), which suppresses intracellular oxidative stress, is the most activated transcription factor. Contrarily, regulatory factor X5, which regulates MHC class II expression, is the most suppressed transcription factor. Western blotting revealed the nuclear localization of NRF2 following CAP irradiation. These data suggest that CAP suppresses the inflammatory response, possibly by promoting NRF2 nuclear translocation

Surgical Treatment for Colorectal Cancer Partially Restores Gut Microbiome and Metabolome Traits

Accumulating evidence indicates that the gut microbiome and metabolites are associated with colorectal cancer (CRC). However, the influence of surgery for CRC treatment on the gut microbiome and metabolites and how it relates to CRC risk in postoperative CRC patients remain partially understood. Here, we collected 170 fecal samples from 85 CRC patients pre- and approximately 1 year post-surgery and performed shotgun metagenomic sequencing and capillary electrophoresis-time of flight mass spectrometry-based metabolomics analyses to characterize alterations between pre- and postsurgery. We determined that the relative abundance of 114 species was altered postsurgery (P IMPORTANCE The gut microbiome and metabolites are associated with CRC progression and carcinogenesis. Postoperative CRC patients are reported to be at an increased CRC risk; however, how gut microbiome and metabolites are related to CRC risk in postoperative patients remains only partially understood. In this study, we investigated the influence of surgical CRC treatment on the gut microbiome and metabolites. We found that the CRC-associated species Fusobacterium nucleatum was decreased postsurgery, whereas carcinogenesis-associated DCA and its producing species and genes were increased postsurgery. We developed methods to estimate postoperative CRC risk based on the gut microbiome and metabolomic compositions. We applied methods to compare the estimated CRC risk between two groups according to the presence of large adenoma or tumors after 5 years postsurgery. To our knowledge, this study is the first report on differences between pre- and postsurgery using metagenomics and metabolomics data analysis. Our methods might be used for CRC risk assessment in postoperative patients.</p

Relating drug-protein interaction network with drug side effects.

[Motivation]: Identifying the emergence and underlying mechanisms of drug side effects is a challenging task in the drug development process. This underscores the importance of system–wide approaches for linking different scales of drug actions; namely drug-protein interactions (molecular scale) and side effects (phenotypic scale) toward side effect prediction for uncharacterized drugs. [Results]: We performed a large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the co-occurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis. The analysis of 658 drugs with the two profiles for 1368 proteins and 1339 side effects led to the extraction of 80 correlated sets. Enrichment analyses using KEGG and Gene Ontology showed that most of the correlated sets were significantly enriched with proteins that are involved in the same biological pathways, even if their molecular functions are different. This allowed for a biologically relevant interpretation regarding the relationship between drug–targeted proteins and side effects. The extracted side effects can be regarded as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. The proposed method is expected to be useful for predicting potential side effects of new drug candidate compounds based on their protein-binding profiles

An improved method for identifying functionally linked proteins using phylogenetic profiles-0

<p><b>Copyright information:</b></p><p>Taken from "An improved method for identifying functionally linked proteins using phylogenetic profiles"</p><p>http://www.biomedcentral.com/1471-2105/8/S4/S7</p><p>BMC Bioinformatics 2007;8(Suppl 4):S7-S7.</p><p>Published online 22 May 2007</p><p>PMCID:PMC1892086.</p><p></p>s ("matches") in three runs while genes 3 and 4 have four matches in a single run. We hypothesize that genes 1 and 2 are more likely to be truly co-evolving while genes 3 and 4 are likely to be just lineage-specific

An improved method for identifying functionally linked proteins using phylogenetic profiles-4

<p><b>Copyright information:</b></p><p>Taken from "An improved method for identifying functionally linked proteins using phylogenetic profiles"</p><p>http://www.biomedcentral.com/1471-2105/8/S4/S7</p><p>BMC Bioinformatics 2007;8(Suppl 4):S7-S7.</p><p>Published online 22 May 2007</p><p>PMCID:PMC1892086.</p><p></p> but poorly in the runs-informed metric as determined by smallest ratios of unweighted hypergeometric -value without runs to our runs-using score (taking both on a linear and not on a logarithmic scale). Not surprisingly, the matches between these profiles are concentrated in few runs. We find that the protein pairs here are not closely related functionally according to our snapshot of GO, and so they are likely false positives for the runs-oblivious unweighted hypergeometric model

An improved method for identifying functionally linked proteins using phylogenetic profiles-2

<p><b>Copyright information:</b></p><p>Taken from "An improved method for identifying functionally linked proteins using phylogenetic profiles"</p><p>http://www.biomedcentral.com/1471-2105/8/S4/S7</p><p>BMC Bioinformatics 2007;8(Suppl 4):S7-S7.</p><p>Published online 22 May 2007</p><p>PMCID:PMC1892086.</p><p></p>g to the unweighted hypergeometric metric without runs and one from our runs-employing two-term model. We see that the unweighted hypergeometric network contains many more edges of high degree. In particular, nodes with more than 40 edges are almost completely absent from the runs network while being abundant in the unweighted hypergeometric network. This suggests that the runs-informed network contains smaller and more interpretable clusters