Quantitation and Identification of Thousands of Human Proteoforms below 30 kDa

Top-down proteomics is capable of identifying and quantitating unique proteoforms through the analysis of intact proteins. We extended the coverage of the label-free technique, achieving differential analysis of whole proteins <30 kDa from the proteomes of growing and senescent human fibroblasts. By integrating improved control software with more instrument time allocated for quantitation of intact ions, we were able to collect protein data between the two cell states, confidently comparing 1577 proteoform levels. To then identify and characterize proteoforms, our advanced acquisition software, named Autopilot, employed enhanced identification efficiency in identifying 1180 unique Swiss-Prot accession numbers at 1% false-discovery rate. This coverage of the low mass proteome is equivalent to the largest previously reported but was accomplished in 23% of the total acquisition time. By maximizing both the number of quantified proteoforms and their identification rate in an integrated software environment, this work significantly advances proteoform-resolved analyses of complex systems

Quantitation and Identification of Thousands of Human Proteoforms below 30 kDa

Top-down proteomics is capable of identifying and quantitating unique proteoforms through the analysis of intact proteins. We extended the coverage of the label-free technique, achieving differential analysis of whole proteins <30 kDa from the proteomes of growing and senescent human fibroblasts. By integrating improved control software with more instrument time allocated for quantitation of intact ions, we were able to collect protein data between the two cell states, confidently comparing 1577 proteoform levels. To then identify and characterize proteoforms, our advanced acquisition software, named Autopilot, employed enhanced identification efficiency in identifying 1180 unique Swiss-Prot accession numbers at 1% false-discovery rate. This coverage of the low mass proteome is equivalent to the largest previously reported but was accomplished in 23% of the total acquisition time. By maximizing both the number of quantified proteoforms and their identification rate in an integrated software environment, this work significantly advances proteoform-resolved analyses of complex systems

Quantitation and Identification of Thousands of Human Proteoforms below 30 kDa

Top-down proteomics is capable of identifying and quantitating unique proteoforms through the analysis of intact proteins. We extended the coverage of the label-free technique, achieving differential analysis of whole proteins <30 kDa from the proteomes of growing and senescent human fibroblasts. By integrating improved control software with more instrument time allocated for quantitation of intact ions, we were able to collect protein data between the two cell states, confidently comparing 1577 proteoform levels. To then identify and characterize proteoforms, our advanced acquisition software, named Autopilot, employed enhanced identification efficiency in identifying 1180 unique Swiss-Prot accession numbers at 1% false-discovery rate. This coverage of the low mass proteome is equivalent to the largest previously reported but was accomplished in 23% of the total acquisition time. By maximizing both the number of quantified proteoforms and their identification rate in an integrated software environment, this work significantly advances proteoform-resolved analyses of complex systems

Prognostic role of Rcade-derived p53-targets in cancer.

<p><b>(A)</b> Heatmap summarizing RPA for Rcade-derived p53 targets in the RIS condition for four cancer datasets indicated: Lung [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005053#pgen.1005053.ref031" target="_blank">31</a>], Breast_ER+ and ER- [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005053#pgen.1005053.ref032" target="_blank">32</a>], Prostate [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005053#pgen.1005053.ref033" target="_blank">33</a>]. Blue and red denote worse and better survival, when expression of genes is high. The fifth column depicts the ratios of gene expression in p53 mutant (mt) vs. wild-type (wt) tumors in separate breast cancer datasets [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005053#pgen.1005053.ref038" target="_blank">38</a>]. <i>CDKN1A</i> (<i>p21</i>), <i>MDM2</i>, and <i>SCD</i> are highlighted. <i>SCD</i> was also p53-repressive in the pApo condition. <b>(B)</b> Kaplan–Meier plots in the indicated cohorts for patients with high and low <i>SCD</i> levels.</p

Phenotype-associated p53-responsive gene expression in IMR90 cells.

<p><b>(A)</b> Schematic of the p53-associated phenotypes. <b>(B)</b> Cell viability, senescence-associated ß-galactosidase activity (SA-ß-gal), and BrdU incorporation (mean ± SEM; n = 3) were measured for each condition as indicated in (A). In addition, DNA damage-induced senescence (DDIS) was included for comparison: cells were treated with etoposide (100 μM) for two days, and maintained for an additional five days in drug-free media. <b>(C, D)</b> Immunoblot analyses for the proteins indicated for total lysates and chromatin fractions from the cells as labeled. Cyclin A2, a cell cycle marker; HMGA proteins, senescence markers. d1 and d7 correspond to acDDR and DDIS, respectively (C). Core histones (C, D) and HMGA proteins (C) were stained with Coomassie blue. The arrow indicates non-specific bands (the Cyclin A2 blot in (C)). <b>(E)</b> Immunoblot analysis in the indicated cells for chromatin fractions for p53. sh and v, sh-p53#1 and corresponding lentiviral vector (a miR30 design), respectively. For acDDR, sh-p53 was introduced first for at least 5 days before administration of etoposide. For RIS and pApo, sh-p53 was introduced after the phenotype establishment. Core histones were stained with Coomassie blue. <b>(F)</b> Venn diagram showing the numbers of differentially expressed (DE) genes upon p53 depletion with lentivirus-mediated RNAi (sh-p53#1) compared to vector, in the indicated conditions. <b>(G)</b> Pathway heatmap for differentially expressed genes upon p53 depletion.</p

High connectivity within the (putative) p53 direct target genes.

<p>Integrative networks of Rcade-derived p53 targets were generated utilizing >300 external high-throughput genomic and proteomic datasets. Nodes are colored by the Rcade B-value, which represents the probability of genes being direct p53-targets. Nodes are spatially organized by ‘Degree’ (local connectivity) and the size of the nodes represents their ‘Betweeness centrality’ (global connectivity). Edge colors indicate the data types used. p53 represents the central node (the highest Degree) in both conditions. A random list of genes (including p53) was chosen to generate a control network using the same methods (inset). For each condition, a graph plotting the two network topology measures (global and local) was generated, representing overall connectivity. For simplicity only Rcade genes positively regulated by p53 are shown.</p

‘Chronic’ p53 preferably associates with CpG islands, whereas ‘acute’ p53 exhibits a diverse genomic distribution.

<p><b>(A, B)</b> Number (A) and genomic features (B) of high confidence (HC) p53 ChIP-seq peaks from the indicated conditions. The number of HC peaks from (A) is also shown in (B). <b>(C)</b> Peak-width distribution in each condition. <b>(D)</b> Proportion of peaks associated with CpG islands (CGIs) to our HC peaks (black bars) and total peaks from external datasets: red bar, IMR90 cells [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005053#pgen.1005053.ref021" target="_blank">21</a>]; blue bars, MCF7 cells [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005053#pgen.1005053.ref020" target="_blank">20</a>]. The external data are derived from single ChIP-seq experiments, thus ‘Total’ peaks were used. <b>(E)</b> Proportion of CGI-type peaks vs. all peaks within the promoter regions. <b>(F)</b> Canonical p53 motif enrichment determined by p53 specific position weight matrices. <b>(G)</b> Histogram of p53 motif occurrence position distribution in HC-peak as determined by PscanChip in the RIS condition. Frequency of p53 motifs in each 50 bp window across 6148 CpG and 3700 non-CpG peaks is plotted across +/- 1000 bp regions around the TSS. <b>(H)</b> Genome browser snapshot of ChIP-seq for p53 and the indicated histone marks at the <i>p21</i> locus in each condition. The vertical lines labeled #1 and #2 indicate the classical distal and proximal p53REs. Two representative RefSeq transcripts encode the same protein. The vertical scaling of ChIP-seq tracks for each antibody is identical between conditions.</p

The p53 regulome reveals an extensive self-regulatory hub.

<p><b>(A)</b> Hubs were generated by integrating (putative) p53 targets from Rcade analysis with pathway information, protein-protein interactions and literature mining. <b>(B)</b> Circos plots showing physical interactions between all hub genes. Red, p53-interactions; blue, MDM2-interactions; gray, other component-interactions.</p

Simplified schematic of <i>de novo</i> fatty acid synthesis.

<p>Gene products of Rcade-derived p53-targets are colored. Red and blue represent genes positively and negatively regulated by p53, respectively. Oncogenic signals activate fatty acid (FA) biogenesis in part through the activation of SREBF1. Typically oncogenic signals also stimulate the p53 pathway. In addition to <i>SCD</i>, we found several Rcade genes (at least in one condition) involved in this process, suggesting that p53 regulates <i>de novo</i> FA synthesis at multiple levels. FA-CoA, fatty acyl-CoA; MUFAs, monounsaturated fatty acids.</p

A novel <i>Atg5</i>-shRNA mouse model enables temporal control of Autophagy <i>in vivo</i>

<p>Macroautophagy/autophagy is an evolutionarily conserved catabolic pathway whose modulation has been linked to diverse disease states, including age-associated disorders. Conventional and conditional whole-body knockout mouse models of key autophagy genes display perinatal death and lethal neurotoxicity, respectively, limiting their applications for <i>in vivo</i> studies. Here, we have developed an inducible shRNA mouse model targeting <i>Atg5</i>, allowing us to dynamically inhibit autophagy <i>in vivo</i>, termed ATG5i mice. The lack of brain-associated shRNA expression in this model circumvents the lethal phenotypes associated with complete autophagy knockouts. We show that ATG5i mice recapitulate many of the previously described phenotypes of tissue-specific knockouts. While restoration of autophagy in the liver rescues hepatomegaly and other pathologies associated with autophagy deficiency, this coincides with the development of hepatic fibrosis. These results highlight the need to consider the potential side effects of systemic anti-autophagy therapies.</p