Journal: Scientific Reports

Article Title: Mitofusin 1 silencing decreases the senescent associated secretory phenotype, promotes immune cell recruitment and delays melanoma tumor growth after chemotherapy

doi: 10.1038/s41598-024-51427-7

Figure Lengend Snippet: Mfn1 silencing in melanoma cells affects the senescence associated secretory phenotype (SASP). B16-F1 cells were transduced with lentiviral particles carrying shScr or shMfn1 and selected with puromycin. ( a ) Mfn1 expression was determined by RT-qPCR and reported relative to control value. Unpaired t-test, two-tails, ***P < 0.0001. ( b ) Representative images of mitofusin 1 (MFN1) protein levels, assessed by immunocytochemistry. Unpaired t-test, two-tails, ***P < 0.0001 (n = 3). ( c ) ShScr and shMfn1 cells were then exposed twice to TMZ (200 μM) or the vehicle DMSO for five hours with a 24 h interval. Samples were analyzed five days after the last exposure to the drug. A schematic representation of the different groups obtained after transduction and treatment with TMZ or vehicle is shown. ( d ) mRNA levels of Cdkn1a (p21). Two-way ANOVA, main effects of treatment (P < 0.0001), shRNA (P = 0.67) and their interaction (P = 0.41). Tukey post-hoc for multiple comparisons, different letters are significantly different (P ≤ 0.0008). ( e ) Number of live cells in the culture assessed with Trypan blue at different time points after the last treatment. Two-way ANOVA and Tukey post-hoc for multiple comparisons, ***P < 0.0001 ShScr TMZ vs ShScr DMSO, and ShMfn1TMZ vs ShScr DMSO. ( f ) SA-β-galactosidase activity. The percentage of positive cells is shown. Two-way ANOVA, main effects of treatment (P < 0.0001), shRNA (P < 0.0001) and their interaction (P < 0.0001). Tukey post-hoc for multiple comparisons, different letters are significantly different (P < 0. 0001). In ( a , b , d , e , f ) results are the mean ± SD (n = 3–4). ( g ) Conditioned media was obtained as described in Methods. Protein content was analyzed by shotgun proteomics, and a heatmap generated with the Clustergram module from PatternLab for Proteomics software. ( h ) Principal components analysis (PCA) was performed with the Buzios module from PatternLab for Proteomics software.

Article Snippet: Protein content was analyzed by shotgun proteomics, and a heatmap generated with the Clustergram module from PatternLab for Proteomics software. ( h ) Principal components analysis (PCA) was performed with the Buzios module from PatternLab for Proteomics software.

Techniques: Transduction, Expressing, Quantitative RT-PCR, Control, Immunocytochemistry, shRNA, Activity Assay, Generated, Software

Journal: Frontiers in Microbiology

Article Title: Identification of microbial metabolic functional guilds from large genomic datasets

doi: 10.3389/fmicb.2023.1197329

Figure Lengend Snippet: Resulting clustergram plot on the presence/absence pathway data for our composite dataset (red = present, black = absent) using a cut height of 0.9 with rows (genomes) and columns (functions) clustered based on Jaccard distance.

Article Snippet: We also analyzed our composite dataset using an agglomerative hierarchical clustering method using the clustergram function from the Statistics and Machine Learning toolbox v12.1 from MATLAB R2021a (The Math Works, ).

Techniques:

Journal: Frontiers in Microbiology

Article Title: Identification of microbial metabolic functional guilds from large genomic datasets

doi: 10.3389/fmicb.2023.1197329

Figure Lengend Snippet: The distribution of guild sizes (number of functions) and the number of mapback genomes for guilds generated with clustergram at cut heights of 0.9 (blue square) and 1 (purple plus sign) as well as for AB. AB approach 1 (red circle) defined guilds using a fixed size of 5 functions while AB approach 2 (green triangle) defined guilds using a minimum mapback genome cut-off of 100. Points were jittered using the built-in position_jitter function in the ggplot2 package v3.4.2 with h = 0.1, w = 0.35 using the random seed 123.

Article Snippet: We also analyzed our composite dataset using an agglomerative hierarchical clustering method using the clustergram function from the Statistics and Machine Learning toolbox v12.1 from MATLAB R2021a (The Math Works, ).

Techniques: Generated