Journal: Oxidative Medicine and Cellular Longevity

Article Title: Molecular Evolution of the Activating Transcription Factors Shapes the Adaptive Cellular Responses to Oxidative Stress

doi: 10.1155/2022/2153996

Figure Lengend Snippet: The activating transcription factors are expressed in human tissues. The expression data of the indicated genes across different human tissues from the GTEx consortium .

Article Snippet: Genotype-Tissue Expression (GTEx) database is maintained by the National Center for Biotechnology Information (NCBI) to investigate the link between genetic diversity and gene expression in normal human tissues [ ].

Techniques: Expressing

Journal: Oxidative Medicine and Cellular Longevity

Article Title: Molecular Evolution of the Activating Transcription Factors Shapes the Adaptive Cellular Responses to Oxidative Stress

doi: 10.1155/2022/2153996

Figure Lengend Snippet: ATF differ across human tissue types but are correlated among tissues types. (a) Distribution of activating transcription factors (ATF) across 24 GTEx tissue types .

Article Snippet: Genotype-Tissue Expression (GTEx) database is maintained by the National Center for Biotechnology Information (NCBI) to investigate the link between genetic diversity and gene expression in normal human tissues [ ].

Techniques:

Journal: Communications Biology

Article Title: Artificial intelligence-rationalized balanced PPARα/γ dual agonism resets dysregulated macrophage processes in inflammatory bowel disease

doi: 10.1038/s42003-022-03168-4

Figure Lengend Snippet: a An interactive web-based platform allows the querying of paths of gene clusters in the IBD map [ref. ; see Supplementary Fig. ] to pick high-value targets with a few mouse clicks and generate a comprehensive automated target ‘report card’. The components of a ‘target report card’ is shown (right): predicted ‘therapeutic index’ (likelihood of Phase III success), IBD outcome (prognostic potential in UC and/or CD), network-prioritized mouse model, estimation of gender bias, and predicted tissue cell type of action. b – h Components of a target report card for PPARA and PPARG are displayed. Bar plot (b) displays the rank ordering of normal vs . ulcerative colitis (UC) /Crohn’s Disease (CD) patient samples using the average gene expression patterns of the two genes: PPARG/PPARA . Samples are arranged from highest (left) to lowest (right) levels. ROC-AUC statistics were measured for determining the classification strength of normal vs IBD. Violin plots ( b ) display the differences in the average expression of the two genes in normal, UC, and CD samples in the test cohort that was used to build the IBD-map in . Bar plots in panel c – d show the rank ordering of either normal vs . IBD samples ( c ) or responder vs. non-responder (R vs. NR; d ), or active vs . inactive disease, or neoplastic progression in quiescent UC (qUC vs. nUC; d ) across numerous cohorts based on gene expression patterns of PPARG and PPARA , from highest (left) to lowest (right) levels. Classification strength within each cohort is measured using ROC-AUC analyses. Bar plots in panel ( e ) show the rank ordering of either normal vs IBD samples across numerous published murine models of IBD based on gene expression patterns of PPARG and PPARA as in ( d ). ACT adoptive T cell transfer. Classification strength within each cohort is measured using ROC-AUC analyses. Bulk = whole distal colon; epithelium = sorted epithelial cells. Schematic in ( f ) summarizes the computational prediction of the cell type of action for potential PPARA/G -targeted therapy, as determined using Boolean implication analysis. GSEID# of multiple publicly available databases of the different cell types and colorectal datasets used to make sure predictions are cited. Red boxes/circles denote that PPARA/G -targeted therapeutics are predicted to work on monocytes/macrophages and crypt-top enterocytes. Computationally generated therapeutic index (see Methods) is represented as a line graph in ( g ). The annotated numbers represent Boolean implication statistics. PPARA and PPARG align with other targets of FDA-approved drugs on the right of threshold (0.1). Two FDA-approved targets (green; ITGB1 , 0.046; JAK2 , 0.032), two abandoned targets (red; SMAD7 , 0.33; IL11, 0.16), PPARA (gray, 0.064), PPARG (gray, 0.04), and the threshold (black, 0.1) are shown in the scale. Box plot in panel h shows that the level of PPARA/G expression is similar in the colons of both genders in health and in IBD, and hence, PPARα/γ-targeted therapeutics are predicted to have little/no gender predilection. The diamond square is the mean, and the arrows around it are 95% confidence interval.

Article Snippet: Publicly available human colon tissue gene expression databases were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus website (GEO) – .

Techniques: Gene Expression, Expressing, Generated

Journal: Communications Biology

Article Title: Artificial intelligence-rationalized balanced PPARα/γ dual agonism resets dysregulated macrophage processes in inflammatory bowel disease

doi: 10.1038/s42003-022-03168-4

Figure Lengend Snippet: a Schematic summarizing the workflow for testing PPARα/γ-targeted therapeutics in C. rodentium -induced colitis. Mice were gavaged with C. rodentium on day 0 and subsequently treated daily with PPAR agonists. Fecal pellets were collected to test viable bacterial burden, as determined by dilution plating and colony counting. Colons were excised on day 7 and 18 and analyzed using the indicated readouts. b – d Line graphs ( b ) display time series of the burden of viable bacteria in feces. Scatter plots with bar graphs ( c ) compare the peak burden of viable bacteria in feces on day 7. Scatter plots with bar graphs ( d ) display the area under the curve (AUC) for the line graph in b. CFU, colony forming units. Data points were plotted as black or white simply to improve visibility. e Images display representative fields from H&E-stained colon tissues of 4–5 mice in each group. Mag = 100x (top) and 200x (bottom); Scale bars, 100 μm. White arrowheads point to immune cell infiltrates. f – j Bar graphs display four parameters of inflammation that were quantified in H&E stained colon tissues in ( e ). Statistics: All results are displayed as mean ± SEM. Significance was tested on the cumulative histological score using one-way ANOVA followed by Tukey’s test for multiple comparisons. Significance: n.s., not significant, p -value < 0.05 was considered significant. k Violin plots (left) display the deviation of expression of genes in Clusters #1-2-3 in the IBD network, as determined by RNA Seq on murine colons. Bar plot (right) displays the rank ordering of the samples. Welch’s t -test was used to determine statistical significance. Significance: n.s., not significant, p -value < 0.05 was considered significant. l Pre-ranked GSEA based on pairwise differential expression analyses (DMSO vs PAR5359 groups) are displayed as enrichment plots for epithelial tight (top) and adherens (middle) junction signatures and balanced macrophage processes (bottom). See also Supplementary Fig. for the day #7 results in the C. rodentium -induced colitis model, Supplementary Fig. for extended GSEA analyses, and Supplementary Fig. for the effect of PAR5359 on DSS-induced colitis in mice.

Article Snippet: Publicly available human colon tissue gene expression databases were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus website (GEO) – .

Techniques: Bacteria, Staining, Expressing, RNA Sequencing, Quantitative Proteomics