Scheme of tests assessing statistical significance of the accuracy of RBP^plus model to predict protein abundance and association of accuracy with genomic features.

<p>For each gene, 1000 randomized versions of the RBP^plus model were obtained either by permuting the RBP protein levels across samples (left side), or by randomly sampling a number of protein predictors equal to the number of actual RBPs inferred to bind the mRNA UTRs (right side). The two randomization tests were run in parallel for each gene. Each randomized model was fitted with Ridge penalized linear regression using nested cross-validation (CV). In the nested cross-validation scheme, test samples are held out for accuracy estimation in the outer layer of CV, and penalty parameters are tuned in the inner layer of CV within training samples only. The p-value of the RBP^plus model of each gene was defined by the probability of sampling a R² value from the empirical null distribution higher than the R² observed for the actual RBP^plus model. False Discovery Rate was estimated by Storey’s q-value method.</p

Control of Gene Expression by RNA Binding Protein Action on Alternative Translation Initiation Sites

<div><p>Transcript levels do not faithfully predict protein levels, due to post-transcriptional regulation of gene expression mediated by RNA binding proteins (RBPs) and non-coding RNAs. We developed a multivariate linear regression model integrating RBP levels and predicted RBP-mRNA regulatory interactions from matched transcript and protein datasets. RBPs significantly improved the accuracy in predicting protein abundance of a portion of the total modeled mRNAs in three panels of tissues and cells and for different methods employed in the detection of mRNA and protein. The presence of upstream translation initiation sites (uTISs) at the mRNA 5’ untranslated regions was strongly associated with improvement in predictive accuracy. On the basis of these observations, we propose that the recently discovered widespread uTISs in the human genome can be a previously unappreciated substrate of translational control mediated by RBPs.</p></div

Inferred RBP-mRNA interactions improve accuracy in predicting protein abundance of a portion of the total modeled mRNAs in three panels of tissues and cell lines.

<p>While RBP^plus models improve accuracy (R²) in predicted protein abundance over RNA^only models, improvements attained by RBPs were not distinguishable from those by randomly sampled proteins, for the majority of genes considered in the three panels. The proportion of genes where actual RBPs produced higher accuracy than random protein predictors (q < 0.05) increases from 0.65% in the NCI-60 panel to 4.2% in the normal tissue panel. <b>(A)</b> Distribution of R² coefficients for the actual RNA^only and RBP^plus models as well as for the RBP^plus models randomized either by permuting sample labels (RBP^plus_r.by.sample) or by randomly sampling proteins in place of actual RBPs (RBP^plus_r.by.RBP). <b>(B)</b> Histogram of statistical significance estimates for the RBP^plus models which were obtained randomizing the actual RBP^plus models by randomly sampling proteins. <b>(C)</b> Histogram of statistical significance estimates for the RBP^plus models which were obtained randomizing the actual RBP^plus models by permuting sample labels. Dashed line corresponds to the number of genes expected in each bin under the assumption of a uniform distribution.</p

Post-transcriptional features quantified in modeled genes.

<p>Post-transcriptional features quantified in modeled genes.</p

Prioritization of RNA binding proteins.

<p>Candidate RBPs are identified analysing the binding sites of each RBP in the RBP binding sites situated nearest to the uTISs of the mRNAs where the RBP^plus model improves accuracy pf predicted protein abundance. <b>(A)</b> The heat map displays the percentages of genes where each RBP showed the minimal distance between a RBP binding site and an uTIS. <b>(B)</b> The inset displays the criterion of minimal distance between RBP binding sites and uTISs used to identify RBPs. RBPs are shown if they resulted to recognize the binding sites closest to the uTISs of mRNAs in at least one of the three panels.</p

MOESM1 of Autophagy inhibition improves the cytotoxic effects of receptor tyrosine kinase inhibitors

Additional file 1: Figure S1. Caspase-3 and PI measurement upon treatments. A) Apoptosis activation was evaluated by measuring the intensity of PARP cleavage (89 kDa proteolytic PARP fragment), and by evaluating a decrease in the pro-Caspase-3 (35 kDa protein) level. Active Caspase-3 forms are observed as cleaved proteins with molecular weight below 20 kDa. GAPDH served as a loading control protein. D = DMSO; Af—Afatinib (8 μM); Sor—Sorafenib (14 μM); TP—TP-0903 (0.15 μM). B) Percentage of PI positive cells with respect to total cell number was presented as mean ± SD out of triplicates. D—DMSO; Af—Afatinib; Sor—Sorafenib; TP—TP-0903. p value is marked as **p < 0.01; n.s.—non-significant. C) Changes in the proliferation rate upon treatment with the three RTKi were determined by means of PCNA expression. The numbers indicate relative expression (densitometry) obtained after normalization to GAPDH protein level, which was used a loading control, and with respect to DMSO control (DMSO = 1)

Additional file 2: of TrkA is amplified in malignant melanoma patients and induces an anti-proliferative response in cell lines

Figure S1Genomic copy number levels of CDKN2A in primary MM.Figure S2Bioinformatic analysis of TrkA mRNA expression and copy number in public database of MM cell lines and tumor samples.Figure S3 Expression of TrkA in MM cell lines.Figure S4Dose-response activation of AKT and MAPK following stimulation of NGF-TrkA signaling in MM cell lines.Figure S5Morphological and quantitative analysis of MM cells in response to NGF-TrkA signaling.Figure S6 Cell proliferation and apoptosis analysis of MM cells in the absence of active NGF-TrkA signaling following MAPK and AKT pathway inhibition.Figure S7Proliferation of MM cells in the absence of active NGF-TrkA signaling following MAPK and AKT pathway inhibition.Figure S8 Analysis of MAPK downstream target expression following NGF-TrkA signaling in MM cells.Figure S9 Analysis of MAPK activation and p21cip1 expression in MM cells in the absence of NGF-TrkA signaling following inhibition of MAPK pathway in MM cells.Figure S10 Morphological analysis of MM cells in the absence of NGF-TrkA signaling activation and following inhibition of MAPK pathway

Additional file 3: of EIF2A-dependent translational arrest protects leukemia cells from the energetic stress induced by NAMPT inhibition

Effects of CHS-828 and chemotherapeutics on protein translation. A) Jurkat cells were treated for 48 h with or without (Mock) the indicated concentration of CHS-828. Caspase 3/7 activity was quantified (using 5 μM of Camptothecin for 4 h as a positive control of apoptosis) and relative ATP levels were determined and then normalized to the number of viable cells. The levels of total AMPK, p-AMPK, total EIF2A and p-EIF2A, total 4EBP1, p-4EBP1 were evaluated by WB. Histogram shows the densitometric analysis of p-AMPK and p-EIF2A (* indicates p-value <0.05). Mean and SD of a biological triplicate. B) Jurkat cells were treated with the indicated concentration of drugs for 48 h and cell viability was measured by Cell Titer Glo. Data are represented as mean and SD of three independent experiments. C) Click-it chemistry based on the incorporation of an aminoacid analog (AHA) was used to monitor protein synthesis. Jurkat cells were treated for 48 h with or without (Mock) the indicated concentration of FK866, Rapamycin (RAPA), Doxorubicin (DOXO), Cisplatin (CIS) and Dexamethasone (DEXA). The histogram quantifies the % of AHA positive cells (active protein-synthesizing cells) in the viable cell population. Flow-cytometry experiments were carried out on two biological replicates and statistics were based on acquisition of 20000 events/sample. D) Jurkat cells were treated as in C and the level of p-EIF2A and p-4EBP1 was evaluated. Histogram shows the densitometric analysis of p-EIF2A (* indicates p-value <0.05). Mean and SD of a biological triplicate. E) Primary B-CLL cells were treated for 48 h with or without 30 nM FK866 in the presence or absence of 1 mM NA. Histogram shows the densitometric analysis of p-AMPK/AMPK. (PDF 691 kb

Additional file 1: of Cancer cell metabolic plasticity allows resistance to NAMPT inhibition but invariably induces dependence on LDHA

Table S2. List of RT-PCR primer sequences. (XLS 34 kb

Additional file 3: of Cancer cell metabolic plasticity allows resistance to NAMPT inhibition but invariably induces dependence on LDHA

Table S1. List of gene expression from genome wide analysis in CCRF-CEM cells. (XLSX 44303 kb