Cell Death Proteomics Database: Consolidating Proteomics Data on

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Cell Death Proteomics database: consolidating proteomics data on cell death Magnus Øverlie Arntzen, Vibeke Hervik Bull, and Bernd Thiede J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr4000703 • Publication Date (Web): 28 Mar 2013 Downloaded from http://pubs.acs.org on April 2, 2013

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Journal of Proteome Research

Cell Death Proteomics database: consolidating proteomics data on cell death

Magnus Ø. Arntzen*, Vibeke H. Bull and Bernd Thiede

The Biotechnology Centre of Oslo, University of Oslo, 0317 Oslo, Norway

Running title: A Cell Death Proteomics database

*Corresponding author: Magnus Ø. Arntzen, The Biotechnology Centre of Oslo, University of Oslo, P. O. Box 1125 Blindern, N-0317 Oslo, Norway, Email: [email protected]; Tel: +47-22840512; Fax: +47-22840501

ACS Paragon Plus Environment

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Abstract: Programmed cell death is a ubiquitous process of utmost importance for the development and maintenance of multicellular organisms. More than ten different types of programmed cell death forms have been discovered. Several proteomics analyses have been performed to gain insight in proteins involved in the different forms of programmed cell death. To consolidate these studies, we have developed the cell death proteomics (CDP) database which comprehends data from apoptosis, autophagy, cytotoxic granule-mediated cell death, excitotoxicity, mitotic catastrophe, paraptosis, pyroptosis, and Wallerian degeneration. The CDP database is available as a web-based database to compare protein identifications and quantitative information across different experimental setups. The proteomics data of 73 publications were integrated and unified with protein annotations from UniProt-KB and gene ontology (GO). Currently, more than 6,500 records of more than 3,700 proteins were included in CDP. Comparing apoptosis and autophagy using overrepresentation analysis of GO terms, the majority of enriched processes were found in both, but also some clear differences were perceived. Furthermore, the analysis revealed differences and similarities of the proteome between autophagosomal and overall autophagy. The CDP database represents a useful tool to consolidate data

from

proteome

analyses

of

programmed

cell

death

and

is

available

at

http://celldeathproteomics.uio.no.

Keywords: ApoptoProteomics, apoptosis, autophagy, cell death, database, proteomics.


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Introduction Programmed cell death includes a set of complex and highly regulated processes in multi-cellular organisms and plays important roles in growth, development, tissue homeostasis and immune response1-3. Furthermore, dysregulation of cell death has implications for a number of diseases, including cancer, autoimmune diseases and neurodegenerative diseases4-6. Although apoptosis is the most investigated form of cell death, different types of cell death exists in higher organisms. The Nomenclature Committee on Cell Death (NCCD) have published guidelines and classification of 8 (in 2005), 12 (in 2009) and 11 (in 2012) modalities of cell death depending on biochemical markers and morphology7-9. In addition to extrinsic and intrinsic apoptosis, the latest publication included autophagic cell death, regulated necrosis, mitotic catastrophe, anoikis, entosis,

parthanatos,

pyroptosis,

netosis

and

cornification7.

Excitotoxicity,

Wallerian

degeneration and paraptosis were excluded in the last publication. Many proteomics studies have been performed to study cell death. Initially, 2-DE gel techniques were utilised to quantitatively study protein behaviour in response to specific apoptosis inducers. Differences in gel spot intensities provided a ratio between dead and live cells. In recent years, several elegant mass spectrometry-based techniques for quantifications have been developed utilizing stable isotopes for metabolic labelling of proteins or peptides10-12. A large number of publications described quantitatively how distinct proteins were up- or downregulated in response to treatment by different apoptosis-inducing drugs. To consolidate this proteomic information, we created the ApoptoProteomics database (APdb), a database for storage, browsing and cross-comparing identifications and quantification of more than 1,500 unique proteins collected from 52 different studies13. We showed a high level of agreement between the experimental directional change and the expected apoptosis function, based on gene


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ontology (GO). Furthermore, several proteins with no known apoptotic function according to GO were identified by independent studies to be regulated upon apoptosis. This data consolidation could contribute to shed new light on the involvement of these proteins in the apoptotic process. Considering cell death in general, proteomics has predominantly been used in studies of apoptotic cell death, and only to a minor extent in some of the less studied types of cell death. However, several large-scale proteomics studies exist for autophagy. Macroautophagy (hereafter referred to as autophagy) is one of the major intracellular catabolic pathways and it is crucial for generating nutrients under conditions of starvation or growth factor deprivation. Although it is mainly considered to be a cytoprotective response, the process has also been shown to induce cell death under certain conditions14-16. Since autophagic cell death has been controversial, the simplest definition at present is “the cell death which can be prevented by inhibition of essential autophagy proteins”. Autophagy is characterized by formation of autophagosomes, doublemembrane vesicles, where the cellular content is embedded and further degraded. Autophagosome formation is initiated and carefully regulated by the autophagy-related (ATG) proteins, class III phosphoinositide 3-kinase and lipidation of MAP1LC3. Although apoptosis is a self-killing process and autophagy is a self-digestion process, the two processes have been shown to have several points of interactions and cross-talk17. Several antiapoptotic Bcl-2 family members (such as Bcl-2 and Bcl-xL) have been shown to sequester and inhibit Beclin 1, which is crucial for autophagy initiation18, 19. Atg5 is another autophagy marker necessary for vesicle elongation. However, upon apoptotic stimuli, it can be cleaved by the endoprotease calpain, and the truncated version has been shown to bind Bcl-xL with subsequent induction of apoptosis20. Furthermore, established apoptosis regulators such as p53 and death associated protein kinase (DAPK), have also been shown to regulate induction of autophagy under certain circumstances21-24.


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Here, we describe the consolidation of proteomics data from seven cell death modalities. The data has been collected into a database called the Cell Death Proteomics (CDP) database. Nine publications which present proteomics data for the autophagic process were included and compared with data about apoptosis to find proteins shared by the two processes and to uncover more about the field where cell death and autophagy meets.

Experimental section Database curation, integration and implementation Publications describing proteomic studies of cell death were manually extracted from PubMed and considered for the CDP database. For apoptosis, only large scale publications where at least ten proteins were reported to have changed during cell death were accepted. For the other modalities, all publications were accepted due to the low number of published proteomics data. The 52 publications already in APdb were transferred to the CDP database and 21 additional publications were included. From each of the accepted publications protein name, accession number, reported change, reported direction, PubMed ID, first author, journal, year of publication, cell death inducer used, type of study and death modality, cell types used, organism, subcellular fractionation, protein and peptide separation techniques, quantification techniques, type of mass spectrometer used, and PRIDE accession number were extracted. Reported regulation was always kept on the form cell death / cell survival for consistency on the directionality of regulation. All protein identifiers were mapped to UniProt accession numbers using either the ‘ID Mapping’ or Blastp within UniProt-KB or Protein Identifier Cross-Reference Service (PICR)25 located at http://www.ebi.ac.uk/Tools/picr/. All proteins in the CDP database were then integrated with updated metadata from UniProt-KB, the CAspase Substrate dataBAse


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Homepage (CASBAH)26, Deathbase27, PubMed, Gene Ontology (GO) version 1.2962, GO Annotations (GOA) version 1.231 (human), 2012_05 (mouse and rat) and PhosphoSitePlus. GOA files

were

downloaded

from

the

http://www.geneontology.org/GO.downloads.annotations.shtml

GO

Consortium and

EBI

at at

ftp://ftp.ebi.ac.uk/pub/databases/GO/goa, respectively. The CDP web implementation was created based on the APdb and hence all the same functionality exists with a few adjustments. In brief, searching and filtering can be limited to specific database fields such as protein name, journal, cell type, etc. In addition, the CDP database has an extra field to limit the search to specific death modalities. Advanced filtering can also be applied to retrieve only proteins where all studies report the same directionality of regulation or whether the reported regulation agrees or disagrees with available GO metadata. When viewing a protein, comparisons between the different studies and modalities can be performed, and links to further analysis tools such as PRIDE, DASty28 and PeptideAtlas29 are available. Links to PRIDE Biomart are available for viewing protein scores, peptides identified and even MS/MS spectra if PRIDE data are associated with a protein. Regarding posttranslational modifications (PTMs), data from PhosphoSitePlus30 (http://www.phosphosite.org) describing type of PTM, residue, sequence, PTM effect on cell death and PubMed-evidence are shown when available for the current protein. Furthermore, while browsing a protein in the CDP database, publications regarding this proteins involvement in cell death stored in PubMed are simultaneously displayed for easy comparison between experiments and literature. Furthermore, it also support browsing of the signalling pathways of extrinsic and intrinsic apoptosis as well as for autophagic cell death based on the information available from the NCCD7. The web interface also supports text export and the database content can be queried from scripting environments such as Perl, Python and Ruby using URL switches. Permalinks to proteins residing in the CDP


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database can also be made. The database is located at http://celldeathproteomics.uio.no and will be updated twice per year to incorporate new cell death proteomics related publications, as well as changes to UniProt-KB, PubMed, CASBAH, Deathbase, GO, GOA and PhosphoSitePlus.

Bioinformatic data analysis and visualization For visualisation of protein overlap between the different death modalities, we utilized Cytoscape31 version 2.7.0 with weighted spring-embedded layout. For functional enrichment analysis and comparison between apoptosis and autophagy, we utilised the g:Profiler package32, 33 (available at http://biit.cs.ut.ee/gprofiler/welcome.cgi) where UniProt accession numbers were first converted to gene names using g:Convert and then further analysed for comparative enrichment using g:Cocoa which employ Fisher’s one-tailed test and g:SCS threshold for multiple hypothesis testing correction.

Results Description of the Cell Death Proteomics database. The CDP database is a manually curated database consisting of the whole APdb (52 apoptosis proteomics studies) as basis and updated with four publications describing apoptosis, nine describing autophagy, two describing excitotoxicity, two describing pyroptosis, and one publication each describing paraptosis, Wallerian degeneration, mitotic catastrophe and cytotoxic granule-mediated cell death (Table S1). In total, CDPdb currently covers 6,550 records of 3,772 unique proteins from 73 studies deriving from human, mouse and rat. This dataset more than doubles the original APdb in terms of unique proteins. The contribution from all the different death modalities is illustrated in table 1. The CDP database enables detailed and easy cross-comparisons at the protein level between


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single studies, and even between death modalities. A selected protein from one study can be compared to other studies, by comparison of information of e.g., reported regulation, death inducer used, death modality studied and proteomics workflow (Figure 1). The different studies in the comparison are colour-coded based on the cell death modality. Furthermore, the reported directional change is colour coded based on agreement with GO or not. In brief, if a protein has a GO annotation as a negative regulator of cell death and the regulation during death is down, then there exist an agreement and green colour highlights this. For the opposite, red colour denotes disagreement with GO. All GO terms for the protein under comparison are retrieved and the terms associated with cell death (the term being a child-term of ‘cell death’, GO:0008219) are highlighted and clickable for direct link to the evidence describing the term association. All information in the CDP database can be exported to tab-separated text files for further analysis with other bioinformatics tools. New apoptosis studies. Four new publications were added for apoptosis34-37 providing 100 new records of 82 unique proteins. 56 of these proteins were previously reported in other proteomics studies of apoptosis and for these the agreement on regulation directionality, that is up- or downregulation in response to apoptosis, was 75%. In addition, the four new publications reported 26 new proteins not previously mentioned in APdb, and only 15% of them have any known apoptosis recognition based on GO. Proteins found in more than one death modality. To visualize the protein overlap between the different modalities, we used Cytoscape and made each modality a node with the overlapping proteins as edges (Figure 2). Considering this illustration, we observed that all modalities have proteins in common with at least three other subroutines. The overlap between apoptosis and autophagy comprehended 637 proteins while 38 and 68 common proteins were found between apoptosis-pyroptosis and apoptosis-mitotic catastrophe, respectively. 45 and 78 common proteins


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were found between autophagy-pyroptosis and autophagy-mitotic catastrophe, respectively. For the other modalities, only < 20 proteins were detected to overlap with both apoptosis and autophagy. However, the vast majority of all proteins (81%) were only found in one death modality while 16%, 2% and 0.4% were identified in two, three or four modalities, respectively. Those proteins common for four different death modalities encompass 13 proteins (Table 2) and they all show varying regulation between the modalities. Ten of them can be connected to apoptosis based on GO and literature, and ten of them were identified as protease substrates. Cell death and autophagy related proteins. The CDP database contains a large proportion of proteins with association to cell death and autophagy. All these proteins were categorized into groups with common protein family. These groups encompass 26 protein families derived from 179 proteins (Figure 3 and table S2). For these proteins, we extracted the reported regulation by the different studies (which varied between the studies) as well as GO and literature annotation describing cell death or autophagy. An interesting finding, although maybe biased towards the larger studies of apoptosis and autophagy, are overlaps between protein families and death modalities. For example, heat shock proteins, 14-3-3 family proteins, and known degradation products were detected in five different modalities. Similarities and differences between apoptosis and autophagy. The CDP database contains 1,526 and 2,711 unique proteins found in studies of apoptosis and autophagy, respectively. To detect and visualise similarities and differences between these two processes, we used functional enrichment analysis utilizing the g:Profiler tool g:Cocoa which is able to perform enrichment analysis on multiple gene lists and compare the results visually. For autophagy, we separated between publications describing general proteome changes during autophagy and those describing autophagosome content. Previously, we showed that not just the proteins directly known to be involved in apoptosis were enriched in proteomic studies of apoptosis, but in fact all


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cellular processes that occur during apoptosis13. This includes ‘cytoskeleton organisation’, ‘regulation of translation’, ‘RNA processing’ and more. When comparing these known apoptosisenriched processes to autophagy (clusters in figure 3 from reference

13

), the majority of these

processes were also found to be enriched upon autophagy (Figure 4A). However, some clear differences between apoptosis and autophagy were observed as ‘chromatin assembly or disassembly’, ‘regulation of cysteine-type endopeptidase activity’ and ‘RNA stabilization’ were not significantly enriched in the list of autophagy-related proteins (Figure 4A). We extracted also processes specifically affected by autophagy, and as expected the processes/terms ‘autophagy’, ‘membrane organization’ and ‘response to starvation’ were found enriched to this process and not to apoptosis (Figure 4B). In addition to these expected terms, we found several processes highly enriched in autophagy compared to apoptosis, and also between overall cellular proteome during autophagy and autophagosome content (Figure 4B). This included the processes ‘regulation of ligase activity’, ‘signal transduction by p53 class mediator’, ‘signal transduction in response to DNA damage’, ‘signal transduction involved in cell cycle checkpoint’, ‘regulation of cellular amine metabolic process’, ‘generation of precursor metabolites and energy’, ‘amine metabolic process’, ‘Golgi vesicle transport’, ‘protein polyubiquitination’, ‘mitotic cell cycle checkpoint’, ‘vesicle organization’, ‘regulation of cellular ketone metabolic process’, ‘cell communication’, ‘proteolysis’, ‘antigen processing and presentation’, ‘small GTPase mediated signal transduction’, ‘secretion by cell’, ‘cellular respiration’, ‘ncRNA metabolic process’, ‘membrane budding’ and ‘cofactor metabolic process’, all unique to autophagy. A complete list of the comparing enrichment analysis, also including GO Molecular Function and GO Cellular Component, KEGG pathway and Reactome pathway, is provided in figure S1.


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Discussion We have created the Cell Death Proteomics database, a proteomics consolidation database to enable comparisons of proteins identified in different experiments across cell death modalities and to extract further biological information. The database currently contains 6,550 records of 3,772 unique proteins from 73 studies describing eight cell death modalities, however, it must be noted that 95% derived from studies of apoptosis or autophagy. Previously, we have reported analysis based solely on apoptosis of 1,502 unique proteins13. The four new reports about apoptosis included in this study corroborated this dataset both on identification (in 68% of the cases) and for these, also on the reported direction of regulation (75% of the cases). For APdb, the proteins reported more than once were only 403 proteins, i.e. 27%. We postulated that this number would increase as more publications were added to the database. With the addition of these four new apoptosis publications, 19 proteins which were only mentioned once in APdb are now re-mentioned and the reported directional change was confirmed in 58% of the cases. We believe that this number will continue to grow as more data are consolidated. Furthermore, a list of 79 candidate proteins was presented as well where several different studies agreed on the reported direction of regulation although no apoptosis involvement were known according to GO. By the addition of these four publications, this list is now extended to 86 proteins. Interestingly, three of the 79 proteins previously unrecognized in apoptosis (by GO) now have a recognized involvement in apoptosis. Certainly, GO annotation will improve as more experiments are performed, and more proteins in the database might be involved and recognized in apoptosis in the future. Although the vast majority of the proteins in the CDP db were unique to one cell death modality, 13 proteins overlapping were found in four death modalities. As we have shown


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previously for apoptosis, the ten most frequently reported proteins were caspase substrates, with vimentin as the most reported protein which was identified 19 times to be up- or downregulated13. The same trend was now observed for other types of cell deaths as well, as ten of the 13 common proteins are identified as protease substrates. The question that arises is if these proteins, albeit being protease substrates, plays a general role in cell death, regardless of cell death programme. Experimentally, protease substrates were only reported from studies of apoptosis, cytotoxic granule-mediated cell death and pyroptosis, and all these three modalities utilise proteases during the death programme. In apoptosis the caspases -3, -6, -7 are potent executors of apoptosis. During cytotoxic granule-mediated cell death, cytotoxic granule content is released into the immunological synapse formed between the killer cell and its target, a process which might induce apoptosis via caspase-independent pathways utilizing the granzymes A, B and C38. Furthermore, caspase-1 is believed to be activated by the inflammasome or pyroptosome and further activate the caspase-7 cascade with limited caspase-3 involvement in pyroptosis7, 39-41. The proteins that were detected as protease substrates in these three modalities were also identified in other cell death modalities, such as mitotic catastrophe. But since mitotic catastrophe is not believed to be a ‘pure’ cell death, but rather an oncosuppressive mechanism with outcome similar to apoptosis or necrosis7, this could possibly explain why these six experimentally detected protease substrates were also found in mitotic catastrophe. More targeted experiments would be needed to reveal if these 13 proteins are truly common stress signal sensors during cell death or if they may exhibit different roles under different circumstances. Comparing autophagy to apoptosis, several biological processes were found to be enriched in both, but also processes specific to each cell death programme. However, it should be noted that some of the publications describing autophagy in the CDP database are based on analysis of the content in autophagosomes rather than the overall cellular proteome. As autophagosomes are not


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formed during apoptosis, it would be expected that these studies resulted in enrichment of unique processes. Therefore, these studies were kept separately in the analysis and revealed minor differences and similarities of the proteome between autophagosomal and overall autophagy, which showed the two most significant differences in the presence of proteins involved in cell communication and the absence of proteins involved in cellular respiration in the autophagosomes. Another interesting observation was that proteins annotated to autophagy were only significantly enriched in studies of overall apoptosis and not in autophagosomal content. Processes enriched in overall proteome analysis, but not for autophagosomes, may reflect proteins that were affected by autophagy but not being sequestered in the autophagosomes. We have previously shown that upon apoptosis, proteins belonging to several affected processes and not only apoptotic players are regulated upon apoptotic stimuli13. Actually, the same processes with a few exceptions were also found to be enriched for autophagy. Examples for exceptions were e.g., ‘chromatin assembly or disassembly’ which was found only enriched for apoptosis and not for autophagy, probably because chromatin condensation is a hallmark of apoptosis and not the typical outcome of autophagy. The same pattern can also be noticed for ‘regulation of cysteine-type endopeptidase activity’ which could be explained by caspase activity in apoptosis. On the other hand, a few processes were found to be specific for autophagy/autophagosomes, such as ‘vesicle organization’ and ‘generation of precursor metabolites and energy’. These processes were expected to be found more enriched in autophagy due to the nature of this cellular process. However, several processes that were found through enrichment analysis to be unique to autophagy were unexpected e.g., ‘protein polyubiquitination’, a process known for protein degradation through the proteasome. This was an unexpected finding as the ubiquitin-proteasome has been shown to regulate apoptosis42, and breaks down short-lived regulatory proteins tagged by poly-ubiquitin chains while long-lived structures such as proteins


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and organelles are degraded by autophagy43. However, recently Dengjel and colleagues44 described interplay between these two degradation pathways showing that proteasomal proteins were abundant in autophagosomes independent of autophagy-inducing stimuli. And that activation of autophagy decreased the cellular proteasome level. Considering figure 4B, the term ‘protein polyubiquitination’ revealed a similar enrichment in both overall autophagy and in autophagosomes and hence suggests that if the proteasome is selectively sequestered in autophagosomes, the ubiquitin ligases may not be (see also same pattern for ‘regulation of ligase activity’). In conclusion, the CDP database is a useful tool in consolidating proteomics data and may shed new light on the processes governing the different forms of programmed cell death. One use of the database is to detect proteins shared by different death modalities, and as proteomics experiments will grow, new connections are anticipated to be made. Furthermore, determination of the relationship between autophagy and apoptosis could be supported by the proteomics experiments included in CDP which revealed similarities but also several differences between these processes based on GO analysis.

Abbreviations 2-DE, 2-Dimensional electrophoresis; APdb , ApoptoProteomics database; CASBAH, Caspase substrate database homepage; CDP, Cell Death Proteomics; GO, Gene ontology; GOA, Gene ontology annotation; NCCD, Nomenclature Committee on Cell Death; SILAC, Stable isotope labelling of amino acids in cell culture.


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Acknowledgements This work was supported by the National Program for Research in Functional Genomics in Norway of the Norwegian Research Council (FUGE-Øst) to BT. The authors would like to thank Ian Donaldson, The Biotechnology Centre of Oslo, University of Oslo for discussions on data analysis and visualisation techniques.

Supporting Information Available This material is available free of charge via the Internet at http://pubs.acs.org.”


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quantitative proteomic analysis and genetic screens. Mol. Cell. Proteomics 2012, 11, (3), M111.014035.


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Tables

Table 1 Contribution of the different cell death modalities. The contribution to the CDP database is not equal for all modalities, in fact 95 % derived from apoptosis and autophagy. Although several proteins are shared between the modalities, autophagy revealed the highest fraction of exclusive proteins.

Unique

Proteins exclusive to

proteins

Records

Publications

this modality

Apoptosis

1526

2480

57

57 %

Autophagy

2711

3815

9

75 %

Cytotoxic granule-mediated cell death

4

4

1

0%

Excitotoxicity

14

16

2

43 %

Mitotic catastrophe

130

137

1

34 %

Paraptosis

8

8

1

13 %

Pyroptosis

73

74

2

32 %

Wallerian degeneration

16

16

1

63 %


21


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Page 22 of 30

Table 2 Table of proteins found in in common for four death modalities. Arrows indicates up- or down-regulation, S: Protease substrate, #: Identified in autophagosome or as part of the autophagy network. Ap: Apoptosis, Au: Autophagy, Cy: Cytotoxic granule-mediated cell death, Ex: Excitotoxicity, Mi: Mitotic catastrophe, Pa: Paraptosis, Py: Pyroptosis, Wd: Wallerian degeneration. Numbers in PubMed column are PMIDs where the evidence was found. Literature search was only performed if no GO information was found. CDP: Cell death proteomics database.

UniProt Acc.no.

Protein name

Ap Au Cy Ex Mi Pa

P62258

14-3-3 protein epsilon

↕S

↓#

↓

Py

Wd

↕

Cell death-GO or

CDP

Cell death (PubMed)

count

apoptotic process; induction of

11

apoptosis by extracellular signals P60710

Actin, cytoplasmic 1

↕

#

S

↑

Apoptosis (7794260);

5

Necrosis-like (15371523) P06733

Alpha-enolase

↕

↕#

↓

S

P06576

ATP synthase subunit beta, mitochondrial

↕

↓#

↑

S

P14625

Endoplasmin

↕S

↓#

↑

S

P52907

F-actin-capping protein subunit alpha-1

↑

↓#

↑

S

Apoptosis (11973636)

17 8

anti-apoptosis


12 5

22

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48


P08107

Heat shock 70 kDa protein 1A/1B

↕

#

↓

P08238

Heat shock protein HSP 90-beta

↑

↓#

↑

P09651

Heterogeneous nuclear ribonucleoprotein A1

↑S

↓

↑

P05787

Keratin, type II cytoskeletal 8

↑S

↓#

P35232

Prohibitin

↕

↓#

↑

P14618

Pyruvate kinase isozymes M1/M2

↕

↓#

↓

P68363

Tubulin alpha-1B chain

↑

↓#

↑

S

↑

anti-apoptosis

9

S

Apoptosis (21859842;21751262)

10

S

Apoptosis (11673663;9774422)

14

Apoptosis (21220329;15194421)

11

regulation of apoptotic process

11

programmed cell death

11

↑

↑

S ↕


8

23


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Page 24 of 30

Figure legends Figure 1. The Cell Death Proteomics database. Figure shows parts of the web-output of the comparing analysis of peroxiredoxin-5, mitochondrial (P30044), a cell death associated protein identified in 7 studies of human cells. Three of the studies are from apoptosis (blue), three from autophagy (green) and one from excitotoxicity (red), all showing varying regulation in response to different death inducers. Gene ontology terms associated with cell death are highlighted (yellow) and clickable for direct link to evidence.

Figure 2. Protein overlaps between death modalities. Proteomics data from eight death modalities were visually compared for overlaps using Cytoscape. The colour and ID of the nodes represents the different modalities while the size of the nodes reflects the number of unique proteins reported. Lines/edges were drawn between modalities if overlapping proteins were found and the number and the thickness of the line reflect the protein overlap. The nodes were positioned based on an overlap-weighted spring-embedded layout.

Figure 3. Proteins in the CDP categorized into groups. The proteins in the CDP were grouped based on function or family to visualize overlaps between death modalities. Circle denotes positive identification and the fill colour represent the number of proteins detected ranging from 1 (white) to 40 (black). Ap: Apoptosis, Au: Autophagy, Cy: Cytotoxic granule-mediated cell death, Ex: Excitotoxicity, Mi: Mitotic catastrophe, Pa: Paraptosis, Py: Pyroptosis, Wd: Wallerian degeneration.


24

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Figure 4. Functional enrichment analysis of apoptosis related processes. Proteomics data from apoptosis (1,526 proteins), overall cellular autophagy (1,791 proteins) and autophagosome content during autophagy (1,364 proteins) were compared using the g:Profiler tool g:Cocoa to find similarities and differences. A) The processes which we previously have found to be enriched for studies of apoptosis13 were compared to the enrichment in autophagy. B) The processes with a clear difference between apoptosis and autophagy are presented. The colour represents the p-value while the number represents the number of proteins associated with each term. The category with lowest p-value is indicated with a black rectangle around the fill colour. The terms are hierarchically clustered based on the p-values.


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Page 26 of 30

Table of content:


26

Page 27 of 30

Figure 1

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Figure 2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58



Page 28 of 30

Page 29 of 30

Figure 3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58



Figure 4

327 513 399 35

43

21

56

64

18

44

41

35

34

18

6

30

24

16

11

7

2

54 94

91 123

46 70

34

40

15

46

51

14

182 224 124 50

65

39

29

50

42

186 253 189 141 189 141 35

56

49

109 110 82 282 336 276

Page 30 of 30

AUTOPHAGOSOMES

104 169 140

translation RNA processing nucleobase-containing small molecule metabolic process transport regulation of translation DNA repair regulation of cytoskeleton organization chromatin assembly or disassembly regulation of cysteine-type endopeptidase activity RNA stabilization cell cycle arrest regulation of cell cycle ribosome biogenesis DNA replication cell cycle protein folding regulation of protein ubiquitination cell death regulation of cell death proteosomal ubiquitin-dependent protein catabolic process cytoskeleton organization response to stress

AUTOPHAGY

74 31

APOPTOSIS

AUTOPHAGY

103 164 135 175

B

AUTOPHAGOSOMES

A APOPTOSIS

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58


regulation of ligase activity signal transduction by p53 class mediator 25 49 42 signal transduction in response to DNA damage 72 126 104 membrane organization 17 35 34 signal transduction involved in cell cycle checkpoint 15 38 36 regulation of cellular amine metabolic process 60 114 64 generation of precursor metabolites and energy 59 134 92 amine metabolic process 24 61 41 Golgi vesicle transport 23 46 41 protein polyubiquitination 26 45 36 mitotic cell cycle checkpoint 15 39 20 vesicle organization 20 54 38 regulation of cellular ketone metabolic process 321 433 387 cell communication 85 124 101 proteolysis 19 52 56 antigen processing and presentation 56 82 81 small GTPase mediated signal transduction 52 69 83 secretion by cell 21 59 15 cellular respiration 37 83 25 ncRNA metabolic process membrane budding 9 27 9 response to starvation 13 27 9 8 32 13 autophagy 27 65 24 cofactor metabolic process 19

42

37

20

46

40


Cell Death Proteomics Database: Consolidating Proteomics Data on

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