Global Survey of Organ and Organelle Protein Expression in Mouse: Combined Proteomic and Transcriptomic Profiling

SummaryOrgans and organelles represent core biological systems in mammals, but the diversity in protein composition remains unclear. Here, we combine subcellular fractionation with exhaustive tandem mass spectrometry-based shotgun sequencing to examine the protein content of four major organellar compartments (cytosol, membranes [microsomes], mitochondria, and nuclei) in six organs (brain, heart, kidney, liver, lung, and placenta) of the laboratory mouse, Mus musculus. Using rigorous statistical filtering and machine-learning methods, the subcellular localization of 3274 of the 4768 proteins identified was determined with high confidence, including 1503 previously uncharacterized factors, while tissue selectivity was evaluated by comparison to previously reported mRNA expression patterns. This molecular compendium, fully accessible via a searchable web-browser interface, serves as a reliable reference of the expressed tissue and organelle proteomes of a leading model mammal

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project

Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines

Abstract Background Onco-proteogenomics aims to understand how changes in a cancer’s genome influences its proteome. One challenge in integrating these molecular data is the identification of aberrant protein products from mass-spectrometry (MS) datasets, as traditional proteomic analyses only identify proteins from a reference sequence database. Methods We established proteomic workflows to detect peptide variants within MS datasets. We used a combination of publicly available population variants (dbSNP and UniProt) and somatic variations in cancer (COSMIC) along with sample-specific genomic and transcriptomic data to examine proteome variation within and across 59 cancer cell-lines. Results We developed a set of recommendations for the detection of variants using three search algorithms, a split target-decoy approach for FDR estimation, and multiple post-search filters. We examined 7.3 million unique variant tryptic peptides not found within any reference proteome and identified 4771 mutations corresponding to somatic and germline deviations from reference proteomes in 2200 genes among the NCI60 cell-line proteomes. Conclusions We discuss in detail the technical and computational challenges in identifying variant peptides by MS and show that uncovering these variants allows the identification of druggable mutations within important cancer genes

Proteomic Analysis of Cancer-Associated Fibroblasts Reveals a Paracrine Role for MFAP5 in Human Oral Tongue Squamous Cell Carcinoma

Bidirectional communication between cells and their microenvironment is crucial for both normal tissue homeostasis and tumor growth. During the development of oral tongue squamous cell carcinoma (OTSCC), cancer-associated fibroblasts (CAFs) create a supporting niche by maintaining a bidirectional crosstalk with cancer cells, mediated by classically secreted factors and various nanometer-sized vesicles, termed as extracellular vesicles (EVs). To better understand the role of CAFs within the tumor stroma and elucidate the mechanism by which secreted proteins contribute to OTSCC progression, we isolated and characterized patient-derived CAFs from resected tumors with matched adjacent tissue fibroblasts (AFs). Our strategy employed shotgun proteomics to comprehensively characterize the proteomes of these matched fibroblast populations. Our goals were to identify CAF-secreted factors (EVs and soluble) that can functionally modulate OTSCC cells in vitro and to identify novel CAF-associated biomarkers. Comprehensive proteomic analysis identified 4247 proteins, the most detailed description of a pro-tumorigenic stroma to date. We demonstrated functional effects of CAF secretomes (EVs and conditioned media) on OTSCC cell growth and migration. Comparative proteomics identified novel proteins associated with a CAF-like state. Specifically, MFAP5, a protein component of extracellular microfibrils, was enriched in CAF secretomes. Using in vitro assays, we demonstrated that MFAP5 activated OTSCC cell growth and migration via activation of MAPK and AKT pathways. Using a tissue microarray of richly annotated primary human OTSCCs, we demonstrated an association of MFAP5 expression with patient survival. In summary, our proteomics data of patient-derived stromal fibroblasts provide a useful resource for future mechanistic and biomarker studies

Proteomic Analysis of Cancer-Associated Fibroblasts Reveals a Paracrine Role for MFAP5 in Human Oral Tongue Squamous Cell Carcinoma

Bidirectional communication between cells and their microenvironment is crucial for both normal tissue homeostasis and tumor growth. During the development of oral tongue squamous cell carcinoma (OTSCC), cancer-associated fibroblasts (CAFs) create a supporting niche by maintaining a bidirectional crosstalk with cancer cells, mediated by classically secreted factors and various nanometer-sized vesicles, termed as extracellular vesicles (EVs). To better understand the role of CAFs within the tumor stroma and elucidate the mechanism by which secreted proteins contribute to OTSCC progression, we isolated and characterized patient-derived CAFs from resected tumors with matched adjacent tissue fibroblasts (AFs). Our strategy employed shotgun proteomics to comprehensively characterize the proteomes of these matched fibroblast populations. Our goals were to identify CAF-secreted factors (EVs and soluble) that can functionally modulate OTSCC cells in vitro and to identify novel CAF-associated biomarkers. Comprehensive proteomic analysis identified 4247 proteins, the most detailed description of a pro-tumorigenic stroma to date. We demonstrated functional effects of CAF secretomes (EVs and conditioned media) on OTSCC cell growth and migration. Comparative proteomics identified novel proteins associated with a CAF-like state. Specifically, MFAP5, a protein component of extracellular microfibrils, was enriched in CAF secretomes. Using in vitro assays, we demonstrated that MFAP5 activated OTSCC cell growth and migration via activation of MAPK and AKT pathways. Using a tissue microarray of richly annotated primary human OTSCCs, we demonstrated an association of MFAP5 expression with patient survival. In summary, our proteomics data of patient-derived stromal fibroblasts provide a useful resource for future mechanistic and biomarker studies

Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma

<div><p>Renal cell carcinoma comprises 2 to 3% of malignancies in adults with the most prevalent subtype being clear-cell RCC (ccRCC). This type of cancer is well characterized at the genomic and transcriptomic level and is associated with a loss of <i>VHL</i> that results in stabilization of HIF1. The current study focused on evaluating ccRCC stage dependent changes at the proteome level to provide insight into the molecular pathogenesis of ccRCC progression. To accomplish this, label-free proteomics was used to characterize matched tumor and normal-adjacent tissues from 84 patients with stage I to IV ccRCC. Using pooled samples 1551 proteins were identified, of which 290 were differentially abundant, while 783 proteins were identified using individual samples, with 344 being differentially abundant. These 344 differentially abundant proteins were enriched in metabolic pathways and further examination revealed metabolic dysfunction consistent with the Warburg effect. Additionally, the protein data indicated activation of ESRRA and ESRRG, and HIF1A, as well as inhibition of FOXA1, MAPK1 and WISP2. A subset analysis of complementary gene expression array data on 47 pairs of these same tissues indicated similar upstream changes, such as increased HIF1A activation with stage, though ESRRA and ESRRG activation and FOXA1 inhibition were not predicted from the transcriptomic data. The activation of ESRRA and ESRRG implied that HIF2A may also be activated during later stages of ccRCC, which was confirmed in the transcriptional analysis. This combined analysis highlights the importance of HIF1A and HIF2A in developing the ccRCC molecular phenotype as well as the potential involvement of ESRRA and ESRRG in driving these changes. In addition, cofilin-1, profilin-1, nicotinamide N-methyltransferase, and fructose-bisphosphate aldolase A were identified as candidate markers of late stage ccRCC. Utilization of data collected from heterogeneous biological domains strengthened the findings from each domain, demonstrating the complementary nature of such an analysis. Together these results highlight the importance of the VHL/HIF1A/HIF2A axis and provide a foundation and therapeutic targets for future studies. (Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.)</p></div

Correlation of differentially abundant proteins and respective gene expression levels in matched samples.

<p>(A) There were 725 proteins identified which had 1764 corresponding probes in the corresponding transcriptomic data. Pearson's linear correlation coefficient was used to correlate normalized spectral count levels and RMA normalized microarray data in matched samples (94 samples). The average Pearson's linear correlation coefficient (<i>r</i>) was 0.157 (dotted line). (B) Distribution of <i>r</i> for just the 344 differentially abundant proteins, of which 318 were also measured in the corresponding transcriptomic study. The average <i>r</i> was 0.347 (dotted line).</p

Differential protein abundance and gene expression between tumor and normal-adjacent ccRCC tissues.

<p>(A) Heatmap of 344 proteins with differential abundance between tumor and normal-adjacent samples (moderated <i>t</i>-test, Benjamini-Hockberg adjusted <i>p</i>-value < 0.05). (B) Heatmap of 1003 genes with differential expression between 94 tumor and normal-adjacent samples (moderated <i>t</i>-test BH adjusted <i>p</i> < 0.001 and absolute fold-change ≥ 4). Scale bar is standard deviation units around the mean of each protein abundance or gene expression level.</p