Sushil Devkota1, Hyobin Jeong2, Yunmi Kim1, Muhammad Ali1, Jae-Il Roh1, Daehee Hwang2, Han-Woong Lee1. 1. a Department of Biochemistry, College of Life Science and Biotechnology and Yonsei Laboratory Animal Research Center , Yonsei University , Seoul , Republic of Korea. 2. b Department of New Biology and Center for Plant Aging Research , Institute for Basic Science, DGIST , Daegu , Republic of Korea.
Abstract
Historically, the ubiquitin-proteasome system (UPS) and autophagy pathways were believed to be independent; however, recent data indicate that these pathways engage in crosstalk. To date, the players mediating this crosstalk have been elusive. Here, we show experimentally that EI24 (EI24, autophagy associated transmembrane protein), a key component of basal macroautophagy/autophagy, degrades 14 physiologically important E3 ligases with a RING (really interesting new gene) domain, whereas 5 other ligases were not degraded. Based on the degradation results, we built a statistical model that predicts the RING E3 ligases targeted by EI24 using partial least squares discriminant analysis. Of 381 RING E3 ligases examined computationally, our model predicted 161 EI24 targets. Those targets are primarily involved in transcription, proteolysis, cellular bioenergetics, and apoptosis and regulated by TP53 and MTOR signaling. Collectively, our work demonstrates that EI24 is an essential player in UPS-autophagy crosstalk via degradation of RING E3 ligases. These results indicate a paradigm shift regarding the fate of E3 ligases.
Historically, the ubiquitin-proteasome system (UPS) and autophagy pathways were believed to be independent; however, recent data indicate that these pathways engage in crosstalk. To date, the players mediating this crosstalk have been elusive. Here, we show experimentally that EI24 (EI24, autophagy associated transmembrane protein), a key component of basal macroautophagy/autophagy, degrades 14 physiologically important E3 ligases with a RING (really interesting new gene) domain, whereas 5 other ligases were not degraded. Based on the degradation results, we built a statistical model that predicts the RING E3 ligases targeted by EI24 using partial least squares discriminant analysis. Of 381 RING E3 ligases examined computationally, our model predicted 161 EI24 targets. Those targets are primarily involved in transcription, proteolysis, cellular bioenergetics, and apoptosis and regulated by TP53 and MTOR signaling. Collectively, our work demonstrates that EI24 is an essential player in UPS-autophagy crosstalk via degradation of RING E3 ligases. These results indicate a paradigm shift regarding the fate of E3 ligases.
The UPS and autophagy are 2 major protein degradation mechanisms, and they regulate nearly all aspects of cellular physiology. The proteasome system specifically recognizes ubiquitinated proteins. By contrast, engulfment of cytosolic contents by a phagophore (the precursor to the autophagosome) was considered to be nonselective. However, selective autophagy receptors, such as SQSTM1/p62 (sequestosome 1) and NBR1 (NBR1, autophagy cargo receptor), have recently been identified. These receptors specifically bind ubiquitin via the ubiquitin-associated domain (UBA) and deliver these ubiquitinated proteins to the phagophore through an interaction between MAP1LC3A/B (microtubule-associated protein 1, light chain 3 α/β; described as LC3 in the text hereafter) and the LC3-interacting region (LIR) motif. This discovery has fundamentally altered the perception that autophagy is a random cytosolic event and has established shared degradation mechanisms with the UPS. Furthermore, a recent report demonstrates autophagic degradation of the 26S proteasome in Arabidopsis, which supports the hypothesis that there is crosstalk between autophagy and the UPS. However, concrete evidence connecting the UPS with autophagy and the molecular players mediating their crosstalk in mammalian system is lacking.EI24 (EI24, autophagy-associated transmembrane protein) is a target gene of TP53/p53 with tumor suppressor activity that plays an important role in the negative regulation of cell growth. We have reported that EI24 suppresses the epithelial-to-mesenchymal transition (EMT) and tumor progression by suppressing RELA/NFKB p65 (RELA proto-oncogene, NF-kB subunit) activity, which induces autophagy-dependent degradation of RING (really interesting new gene) E3 ligases, including TRAF2 (TNF receptor associated factor 2) and TRAF5. We have also reported that EI24-induced degradation of a RING E3 ligase, TRIM41/RINCK1 (tripartite motif containing 41), results in PRKCA/PKCα (protein kinase C α) stabilization, and this signaling is important for the development of DMBA-TPA (7,12-dimethylbenz[a]-anthracene-12-O-tetradecanoylphorbol-13-acetate)-induced skin carcinogenesis in mice. Based on these studies illustrating EI24-mediated degradation of RING domain E3 ligases and recent reports describing EI24 as an essential autophagy gene in C. elegans and mice, we hypothesized that EI24 might degrade RING E3 ligases using the autophagy machinery, thus establishing a connection between autophagy and the UPS. This proposed autophagy-UPS crosstalk may be responsible for orchestrating diverse cellular processes.
Results
EI24 degrades TRIM41 by autophagy independent of the proteasome system
The involvement of EI24 in autophagy has first been identified while screening genes required for autophagy in C. elegans, and a follow-up study reports this gene as an essential component of basal autophagy in mammals. However, the functional role of EI24-induced autophagy remains to be elucidated.We used appearance of punctate LC3-II, a reliable marker for monitoring EI24-mediated autophagy activation. Consistent with previous reports, ectopic expression of EI24 resulted in the formation of punctate LC3-II (Fig. 1A). Furthermore, EI24 transfection in 293T cells resulted in increased LC3-II formation along with SQSTM1 degradation (Fig. 1B). Autophagy activation, induced by nutrient depletion using HBSS (Hank's balanced salt solution), was dysfunctional when EI24 expression was knocked down by an EI24-specific siRNA (Fig. 1C). Collectively, these data demonstrate that EI24 activates autophagy and is required for the proper execution of autophagic flux.
Figure 1.
EI24 activates autophagy. (A) H1299 cells, with or without EI24 overexpression, were transfected with GFP-LC3, and immunocytochemistry was performed. LC3, green; EI24, red; nucleus, blue (4′,6-diamidino-2-phenylindole, DAPI). Scale bar: 10 μm. (B) EI24 overexpression results in the formation of LC3-II and degradation of SQSTM1 in 293T cells. (C) SQSTM1 is not degraded when EI24 is knocked down. When EI24 levels decreased in 293T cells, SQSTM1 failed to degrade in HBSS-treated conditions.
EI24 activates autophagy. (A) H1299 cells, with or without EI24 overexpression, were transfected with GFP-LC3, and immunocytochemistry was performed. LC3, green; EI24, red; nucleus, blue (4′,6-diamidino-2-phenylindole, DAPI). Scale bar: 10 μm. (B) EI24 overexpression results in the formation of LC3-II and degradation of SQSTM1 in 293T cells. (C) SQSTM1 is not degraded when EI24 is knocked down. When EI24 levels decreased in 293T cells, SQSTM1 failed to degrade in HBSS-treated conditions.We have previously reported that EI24 binds and degrades TRIM41, resulting in PRKCA stabilization. However, we were unable to determine the detailed mechanism of TRIM41 degradation by EI24 at that time. Because TRIM41 self-ubiquitinates and undergoes proteasome-dependent degradation, we first examined whether EI24-mediated degradation of TRIM41 occurs via the UPS. EI24 overexpression reduced TRIM41-Flag level; however, the ubiquitinated TRIM41 signal also decreased (Fig. 2A). Furthermore, TRIM41 ubiquitination was not rescued when cells were treated with the proteasome inhibitor MG132, suggesting proteasome-independent degradation (Fig. 2A). Because the autophagy pathway is the alternate protein degradation machinery and EI24 is essential for basal autophagy, we examined the potential role of EI24 in autophagy-dependent TRIM41 degradation. EI24 overexpression induced TRIM41 degradation (Fig. 2B, lane 1 and 2), and TRIM41 ubiquitination and protein level were rescued when autophagy was inhibited using bafilomycin A1 (BAF) (Fig. 2B, lane 3, 4). Therefore, EI24 degrades TRIM41 through autophagy, independent of the UPS.
Figure 2.
EI24 degrades TRIM41 using autophagy. (A) TRIM41 is degraded by EI24, independent of the proteasome. 293T cells were transfected with TRIM41-Flag and 3HA-ubiquitin B (UBB), with or without EI24-GFP overexpression, and TRIM41 ubiquitination was observed. MG132 (10 μM, 6 h) was used as a proteasome inhibitor. (B) EI24 degrades TRIM41 in an autophagy-dependent manner. TRIM41 ubiquitination, with or without EI24 overexpression, was examined, and autophagy was inhibited using BAF (10 mM, 6 h).
EI24 degrades TRIM41 using autophagy. (A) TRIM41 is degraded by EI24, independent of the proteasome. 293T cells were transfected with TRIM41-Flag and 3HA-ubiquitin B (UBB), with or without EI24-GFP overexpression, and TRIM41 ubiquitination was observed. MG132 (10 μM, 6 h) was used as a proteasome inhibitor. (B) EI24 degrades TRIM41 in an autophagy-dependent manner. TRIM41 ubiquitination, with or without EI24 overexpression, was examined, and autophagy was inhibited using BAF (10 mM, 6 h).
The RING domain is essential for TRIM41 binding and degradation by EI24
To dissect the molecular mechanism of autophagy-dependent TRIM41 degradation by EI24, we investigated which domain in TRIM41 binds EI24. For this purpose, we generated Flag-tagged TRIM41 deletion constructs (Fig. 3A). Immunoprecipitation assays revealed that the B-box, coiled-coil 1 (CC1), CC2, and PTPN13-like protein, Y-linked (PRY) domains were dispensable, whereas the RING domain was required for EI24 binding (Fig. 3B). To functionally validate that the RING domain binds EI24, we examined whether TRIM41 with a deleted RING domain (TRIM41RINGΔ) is resistant to EI24-induced degradation. Consistent with our domain-mapping data, EI24 did not degrade TRIM41RINGΔ (Fig. 3C). To determine the role of endogenous EI24 in TRIM41 degradation, Flag-tagged TRIM41 and TRIM41RINGΔ were expressed in cells transfected with either control or EI24-specific siRNA, and protein level changes were determined by immunoblotting. Reduced EI24 expression increased the protein levels of TRIM41 but not TRIM41RINGΔ (Fig. 3D). Collectively, these results indicate that EI24 may recognize the RING domain, which is a common domain in E3 ubiquitin ligases, and degrade them using the autophagy pathway.
Figure 3.
EI24 binds and degrades TRIM41 through the RING domain. (A) Depiction of the Flag-tagged TRIM41 deletion constructs generated in this study. (B) The RING domain facilitates binding between EI24 and TRIM41. The constructs were individually transfected in 293T cells and immunoprecipitation was performed using an anti-Flag antibody. Bound MYC-tagged EI24 was detected by immunoblotting. HC and LC represent immunoglobulin heavy and light chains, respectively. (C, D) TRIM41 lacking the RING domain is resistant to EI24-mediated degradation. The indicated constructs were transfected in 293T cells and immunoblotting was used to determine the protein levels of TRIM41 constructs with or without EI24 overexpression (C) or knockdown (D).
EI24 binds and degrades TRIM41 through the RING domain. (A) Depiction of the Flag-tagged TRIM41 deletion constructs generated in this study. (B) The RING domain facilitates binding between EI24 and TRIM41. The constructs were individually transfected in 293T cells and immunoprecipitation was performed using an anti-Flag antibody. Bound MYC-tagged EI24 was detected by immunoblotting. HC and LC represent immunoglobulin heavy and light chains, respectively. (C, D) TRIM41 lacking the RING domain is resistant to EI24-mediated degradation. The indicated constructs were transfected in 293T cells and immunoblotting was used to determine the protein levels of TRIM41 constructs with or without EI24 overexpression (C) or knockdown (D).
EI24 degrades RING domain-containing E3 ligases
Previously, we have reported that EI24 degrades TRAF2/5 in a RING domain-dependent manner. Here, we observed that TRIM41, another E3 ligase, was also degraded by EI24 in a RING domain-dependent manner via the autophagy pathway. Because the RING domain is functional in most E3 ligases, we hypothesized that EI24-induced autophagy may regulate the E3 ligase protein levels. If this hypothesis held true, it would represent a paradigm shift regarding E3 ligase regulation. Currently, E3 ligase regulation is thought to be governed predominantly by self-ubiquitination and degradation by the proteasome and recycling. To test this hypothesis, we examined EI24-induced degradation of several E3 ligases in the TRIM (tripartite motif) family, including MID2/TRIM1, TRIM3, TRIM4, TRIM6, and TRIM21. Immunoblotting revealed that all 5 of the TRIMs tested were degraded by EI24 (Fig. 4A). To ensure that the autophagy pathway degraded the target proteins, we examined MID2 degradation by EI24, both with and without BAF, an autophagosome-lysosome fusion inhibitor. EI24-mediated degradation of MID2 was rescued in the presence of BAF, suggesting autophagic degradation (Fig. 4B). FBXO7 (F-Box protein 7) and STUB1/CHIP (STIP1 homology and U-box containing protein 1), which lack RING domains and belong to the F-box and U-box family of E3 ligases, respectively, were EI24-degradation resistant (Fig. 4C). These results indicate that the RING domain is required for degradation of target proteins by EI24.
Figure 4.
EI24 degrades RING domain-containing proteins. (A, D) EI24-mediated degradation of GFP-tagged TRIM family proteins was detected using immunoblotting. MID2, TRIM2, mouse TRIM3, TRIM4, TRIM5, TRIM6, TRIM8, TRIM20, TRIM21, and mouse TRIM26 were tested. (B) EI24 degrades MID2 using autophagy. EI24-mediated degradation of MID2 could be rescued by treatment with BAF (10 nM, 6 h). (C) Protein levels of FBXO7 and STUB1 lacking RING domains were examined for their susceptibility to EI24-mediated degradation. (E) The susceptibility of the RING domain-containing E3 ligases TRAF6, PARK2, XIAP, BIRC2, BIRC3, and MDM2 to EI24-mediated degradation.
EI24 degrades RING domain-containing proteins. (A, D) EI24-mediated degradation of GFP-tagged TRIM family proteins was detected using immunoblotting. MID2, TRIM2, mouseTRIM3, TRIM4, TRIM5, TRIM6, TRIM8, TRIM20, TRIM21, and mouseTRIM26 were tested. (B) EI24 degrades MID2 using autophagy. EI24-mediated degradation of MID2 could be rescued by treatment with BAF (10 nM, 6 h). (C) Protein levels of FBXO7 and STUB1 lacking RING domains were examined for their susceptibility to EI24-mediated degradation. (E) The susceptibility of the RING domain-containing E3 ligases TRAF6, PARK2, XIAP, BIRC2, BIRC3, and MDM2 to EI24-mediated degradation.To clearly establish if all RING domain-containing proteins can be degraded by EI24, we examined more TRIM proteins for their susceptibility to EI24-mediated degradation. Interestingly, immunoblotting revealed that TRIM2 and TRIM28 were degraded by EI24, whereas TRIM5 delta (TRIM5δ), TRIM8, and TRIM20 protein levels were unchanged (Fig. 4D).A previous study demonstrated that EI24 binds to the RING domain of TRAF2/5 and degrades them via the autophagy pathway. Therefore, we expanded our screen to determine whether other RING E3 ligases can be degraded by EI24. Immunoblotting revealed that EI24 overexpression induced the degradation of physiologically important E3 ligases, including TRAF6, BIRC2/CIAP1 (baculoviral IAP repeat containing 2), and MDM2 (MDM2 proto-oncogene), whereas PARK2 (parkin RBR E3 ubiquitin protein ligase), XIAP (X-linked inhibitor of apoptosis), and BIRC3/CIAP2 (baculoviral IAP repeat containing 3) were not degraded (Fig. 4E). These results indicate that the presence of a RING domain is not the only requirement for susceptibility to EI24-mediated degradation.
The RING domain is required for EI24-mediated degradation of E3 ligases
TRIM41 lacking the RING domain is resistant to EI24-mediated degradation (Fig. 3C), which suggests that the RING domain is important for orchestrating the connection between the UPS and autophagy pathways. Given that the RING domain is important for EI24-mediated degradation of E3 ligases, we generated constructs of several E3 ligases that lacked a RING domain and examined if they were resistant to EI24-induced degradation. Full-length TRAF2 and MKRN1 (makorin ring finger protein 1) were degraded by EI24, whereas TRAF2RINGΔ (Fig. 5A) and MKRN1RINGΔ (Fig. 5B) were not. In addition, EI24-induced MRKN1 degradation was rescued with BAF treatment, suggesting that EI24 mediates autophagic MKRN1 degradation (Fig. 5B). These data underscore the fact that the RING domain is essential for recognition and autophagic degradation of EI24-degraded proteins. Thus far, our study has unraveled, for the first time, that the crosstalk between the UPS and autophagy is based on the ability of the basal autophagy protein EI24 to bind and degrade E3 ligases with RING domains.
Figure 5.
The RING domain is required for EI24-mediated degradation of E3 ligases. Degradation of full-length and RING domain-deficient TRAF2 (A) and MKRN1 (B) by EI24 was examined using immunoblotting. The lack of RING domain confers resistance to EI24-mediated degradation. EI24-induced degradation of MKRN1 was rescued by BAF treatment (10 μM, 6 h).
The RING domain is required for EI24-mediated degradation of E3 ligases. Degradation of full-length and RING domain-deficient TRAF2 (A) and MKRN1 (B) by EI24 was examined using immunoblotting. The lack of RING domain confers resistance to EI24-mediated degradation. EI24-induced degradation of MKRN1 was rescued by BAF treatment (10 μM, 6 h).
Functional characterization of EI24-induced, autophagic E3 ligase degradation
The data thus far indicated that something other than the presence of a RING domain may define whether an E3 ligase is targeted by EI24 for autophagy-mediated degradation. To investigate this, we compared the RING domain sequences of the 14 E3 ligases degraded by EI24 (Group 1) with those of the 5 E3 ligases that were not degraded (Group 2). However, the multiple sequence alignment showed no apparent sequence motif related to EI24-mediated degradation susceptibility, because Groups 1 and 2 RING domain sequences have a high degree of similarity (e.g. BIRC2 in Group 1 and BIRC3 in Group 2; Fig. S1A and B).Another possibility is that the subcellular localization of protein targets could potentially indicate the target's susceptibility for EI24-mediated autophagic degradation. Thus, we compared subcellular localizations of E3 ligases in Groups 1 and 2 based on their GOCCs (gene ontology cellular components). The comparison showed no significant difference in the localization distribution between Groups 1 and 2 (Fig. S1C). Next, we considered if EI24 target proteins have common binding partners with EI24. We analyzed common EI24 binding partners that were also capable of binding E3 ligases in Group 1 and compared them with those in Group 2. To this end, we combined the EI24 interactors in interactome databases with experimentally verified EI24 binding partners to compile a list of EI24 interactors. The interactome analysis comparing Groups 1 and 2 revealed no EI24 interactors that preferentially bound targets in Group 1 compared with those in Group 2 (Fig. S1D).Gene expression differences between Group 1 and 2 E3 ligases could also potentially contribute to EI24-mediated autophagic degradation susceptibility. To investigate this hypothesis, we examined whether gene expression data can separate Groups 1 and 2 by applying principal component analysis (PCA) to EI24-related gene expression data previously reported and then found that the combined use of the following 2 gene expression datasets can provide effective separation of Groups 1 and 2: 1) GSE52508 in the GEO (gene expression omnibus) database collected from ZR-75-1 breast cancer cells after EI24 knockdown and 2) GSE67266 in the GEO database collected from MEF cells after treatment with etoposide, which induces EI24 expression (Fig. S2A, B, and C). The use of single data sets showed no separation between Groups 1 and 2 in the PCA space (Fig. S2A and B), but the use of both datasets showed a certain degree of the separation (Fig. S2C). For more effective separation captured by PCA with the 2 data sets, we applied MPLS-DA (multi-block partial least square-discriminant analysis) that can effectively integrate the 2 datasets for classification of Groups 1 and 2 as previously described. MPLS-DA successfully separated Group 1 from Group 2 (Fig. S2D and E). Using this MPLS-DA model, we then predicted those E3 ligases likely to be susceptible to EI24 degradation. Previous studies identified 689 potential E3 ligases, 381 of which possess RING domains. Those 381 E3 ligases were used as the starting point for our MPLS-DA analysis (Fig. S2F). The MPLS-DA model predicted 161 E3 ligases (predicted Group [pGroup] 1) to be EI24 targets and 64 E3 ligases (pGroup 2) to be nontargets (Fig. 6A; Table S1). The delineation of E3 ligases into targets and nontargets could potentially be used to predict the susceptibility of a particular E3 ligase to EI24-mediated degradation. Notably, the computationally generated pGroups 1 and 2 correctly categorized the previously tested E3 ligases into their respective experimentally identified Groups (Figs. 3 and 4).
Figure 6.
Functional characterization of E3 ligases targeted by EI24. (A) Projected scores (thatc) of predicted EI24 targets (pGroup 1) and nontargets (pGroup 2) for the first 3 MPLS-DA latent variables (LV1-3). Red and blue triangles represent experimentally identified Group 1 (targets) and Group 2 (nontargets) E3 ligases, respectively. Orange and light blue circles denote pGroup 1 (predicted targets) and pGroup 2 (predicted nontargets) E3 ligases, respectively. (B) Correlation coefficient distributions of E3 ligase gene expression levels in pGroups 1 and 2 were evaluated in 2 datasets collected following EI24 knockdown (EI24 KD) and etoposide treatment (EI24 induced). P values represent the significance of the difference between the pGroup 1 and 2 distributions. The GOBPs (C) and GOMFs (D) enriched in pGroups 1 and 2. Enrichment P values for the GOBPs and GOMFs were displayed as -log10(P values). The number of genes with the corresponding GOBP or GOMF in pGroups 1 and 2 is shown in parenthesis (pGroup 1/pGroup 2). Upstream transcription factors (TFs) (E) and kinases (F) enriched in pGroups 1 and 2. P values (P) represent the significance of TF targets and kinase substrates enriched in pGroups 1 and 2.
Functional characterization of E3 ligases targeted by EI24. (A) Projected scores (thatc) of predicted EI24 targets (pGroup 1) and nontargets (pGroup 2) for the first 3 MPLS-DA latent variables (LV1-3). Red and blue triangles represent experimentally identified Group 1 (targets) and Group 2 (nontargets) E3 ligases, respectively. Orange and light blue circles denote pGroup 1 (predicted targets) and pGroup 2 (predicted nontargets) E3 ligases, respectively. (B) Correlation coefficient distributions of E3 ligase gene expression levels in pGroups 1 and 2 were evaluated in 2 datasets collected following EI24 knockdown (EI24 KD) and etoposide treatment (EI24 induced). P values represent the significance of the difference between the pGroup 1 and 2 distributions. The GOBPs (C) and GOMFs (D) enriched in pGroups 1 and 2. Enrichment P values for the GOBPs and GOMFs were displayed as -log10(P values). The number of genes with the corresponding GOBP or GOMF in pGroups 1 and 2 is shown in parenthesis (pGroup 1/pGroup 2). Upstream transcription factors (TFs) (E) and kinases (F) enriched in pGroups 1 and 2. P values (P) represent the significance of TF targets and kinase substrates enriched in pGroups 1 and 2.EI24 target expression is likely to be correlated with EI24 expression. Therefore, we examined the correlation between pGroups and EI24 gene expression in the 2 data sets. Following EI24 knockdown or etoposide treatment, EI24 expression was more strongly correlated with pGroup 1 expression than pGroup 2 expression (Fig. 6B, Fig. S3A).We could not observe a difference in cellular localizations of proteins in Group 1 and Group 2 (Fig. S1C), which may be attributed to the small size of the samples analyzed (Group 1 sample size = 14, Group 2 sample size = 5). pGroup 1 (n = 161) and pGroup 2 (n = 64) can ensure sufficiently large sample sizes. Thus, we re-examined if there is any difference in the cellular localization between EI24 targets and nontargets using pGroup 1 and pGroup 2. With the varying stringency of probability of a particular E3 ligase belonging to Group 1 or Group 2, we examined GOCCs of the predicted E3 ligases and found that pGroup 1 and pGroup 2 candidates neatly aligned themselves in separate GOCC attributes (Fig. S3B). On the one hand, pGroup 1 members displayed the tendency to be primarily localized to cellular organelles or structures such as endosomes, ubiquitin ligase complexes, vacuoles, lysosomes, chromatin, and the cytoskeleton, most of which are involved in autophagy. On the other hand, pGroup 2 was related with perinuclear region of the cytoplasm and Golgi apparatus (Fig. S3B). These results illustrate that in addition to the presence of the RING-domain, the difference in the cellular localization of E3 ligases could be an additional factor that determines the susceptibility of a particular E3 ligase to be degraded by EI24.To evaluate fairly both sensitivity and specificity in prediction of EI24 targets, we randomly selected 5 predicted EI24 targets and 5 nontargets from pGroups 1 and 2, respectively, which include no E3 ligases in the training set, and then experimentally tested whether the selected E3 ligases are targeted by EI24. Consistent with the MPLS-DA prediction results, all the tested pGroup 1 E3 ligases such as RNF43, RNF6, RNF11, and PML (PML IV: 1–633 amino acids; PML VI: 1–560 amino acids) were truly degraded by EI24, whereas pGroup 2 members such as RNF128, RNF5, ZNF462, and TRIM72 were not (Fig. S4A and B). These experimental results indicate high degrees of sensitivity and specificity to our model. This result provides the credibility to our model in correctly predicting the susceptibility of a particular E3 ligase to EI24-mediated degradation. Interestingly, CBLC (Cbl proto-oncogene C) that belonged to pGroup 2 was also degraded by EI24 (Fig. S4A). When we examined the distribution of casitas B-lineage lymphoma (CBL)-like proteins in our prediction, we found that CBLB and CBLC belonged to pGroup 2 whereas CBLL1 was predicted to be pGroup1. A similar observation was made regarding CIAP isoforms (BIRC2 in Group 1 and BIRC3 in in Group 2, Fig. 4E). Thus, further studies need to focus to elucidate why similar isoforms categorize themselves in separate groups.To functionally characterize the pGroups, we performed an enrichment analysis of GOBPs (gene ontology biological functions) and GOMFs (molecular functions) on pGroups 1 and 2, and then compared GOBPs and GOMFs between the 2. GOBP analysis showed that proteolysis was enriched in pGroup 1 compared with pGroup 2 (Fig. 6C; Table S2). pGroup 1 also had higher enrichment for apoptosis, cell cycle, histone and chromatin modifications, regulation of RNA metabolic processes, and response to DNA damage stimulus functions. GOMF analysis showed that DNA and chromatin binding, zinc ion binding, and transcription activator/coactivator activities were enriched in pGroup 1 (Fig. 6D; Table S3). These data indicate that EI24 may also be associated with chromatin/histone modifications, RNA metabolism regulation, and transcription.The GOBP enrichment analysis suggested that EI24 could be functionally linked to cellular physiology regulation by degrading the E3 ligases involved in those processes. Therefore, we investigated upstream regulators that control EI24 targets and their associated cellular processes. First, we analyzed upstream transcriptional regulators of pGroups 1 and 2 using transcription factor enrichment analysis. pGroup 1 was enriched in binding sites for SP1 (Sp1 transcription factor), REPIN1 (replication initiator 1), TP53, and NFYB (nuclear transcription factor Y subunit β) (Fig. 6E). Furthermore, we performed kinase enrichment analysis to examine kinases that were upstream of EI24 targets. E3 ligases phosphorylated by ATM (ataxia telangiectasia mutated), CHEKs (checkpoint kinases), MTOR (mechanistic target of rapamycin [serine/threonine kinase]), and AKT1 (AKT serine/threonine kinase 1) were enriched in pGroup 1 (Fig. 6F). These data show functional and regulatory links between EI24 and physiologically important cellular processes through autophagic degradation of E3 ligases.
EI24-mediated degradation of TRAF2 and MDM2 regulates MTOR and TP53 signaling with implications in cellular bioenergetics and response to genotoxic stress
To elucidate if EI24-mediated degradation of E3 ligases regulates respective downstream signaling, we analyzed the relation of EI24 with the TRAF2 downstream-target MTOR and the MDM2 target TP53 in basal and autophagy-activated conditions.Previously, we have reported that EI24 mediates autophagy-dependent degradation of TRAF2 resulting in the activation of RELA signaling that is important for the suppression of EMT and metastasis in breast cancers and melanoma. Based on this observation, in the current study, we examined if B16F10 cells with stable Ei24 knockdown (Fig. 7A) containing higher levels of TRAF2, retain activated MTOR signaling. Immunoblotting assay revealed that compared to control cells, Ei24 knockdown cells displayed increased phosphorylation of RPS6KB/p70S6K, a reliable marker of activated MTOR signaling (Fig. 7B). Furthermore, an increased lipidated form of LC3 (LC3-II) and SQSTM1 accumulation indicated impaired autophagy flux in cells containing reduced expression of EI24 (Fig. 7B). Thus, our data are consistent with previous reports demonstrating EI24 as an essential component of basal autophagy and also with the well-established fact that activated MTOR-signaling acts as a strong negative regulator of autophagy.
Figure 7.
Functional validation of the role of EI24 in cellular bioenergetics and response to genotoxic stress by regulating MTOR and TP53 signaling. (A) Validation of Ei24 expression in B16F10 control and Ei24 knockdown cells by real-time qPCR. Ei24 expression was reduced by 90% in knockdown cells. (B, C) Activation of MTOR signaling and suppression of autophagy in Ei24 knockdown B16F10 cells in normal (B) and nutrient-depleted conditions (HBSS-treatment, 6 h (C). p-RPS6KB level was used as a marker of MTOR activity. I and II represent cytosolic and lipidated forms of LC3, respectively. Low indicates low-exposure and high indicates high-exposure of the blot. (D) Knockdown of EI24 stabilizes MDM2 and degradation of TP53 in HCT116 cells. (E) EI24 is required for mounting proper TP53 response in the presence of genotoxic stress in HCT116 cells. Cells were treated with cisplatin (10 μg/ml) for 24 h. CDKN1A protein level was used as a read-out of TP53 transcriptional activity. (F) EI24 is required for replenishing cellular ATP in nutrient-deprived conditions. ATP measurement in control and Ei24 knockdown B16F10 cells grown in basal and nutrient-depleted conditions (HBSS-treatment for 24 h). (G) EI24-induced autophagy is required for cell survival upon nutrient-deficiency. B16F10 control and Ei24 knockdown cells were subjected to nutrient-deprivation for 48 h and cell survival was measured by FACS analysis. X-axis and Y-axis represent FL2-H and events, respectively.
Functional validation of the role of EI24 in cellular bioenergetics and response to genotoxic stress by regulating MTOR and TP53 signaling. (A) Validation of Ei24 expression in B16F10 control and Ei24 knockdown cells by real-time qPCR. Ei24 expression was reduced by 90% in knockdown cells. (B, C) Activation of MTOR signaling and suppression of autophagy in Ei24 knockdown B16F10 cells in normal (B) and nutrient-depleted conditions (HBSS-treatment, 6 h (C). p-RPS6KB level was used as a marker of MTOR activity. I and II represent cytosolic and lipidated forms of LC3, respectively. Low indicates low-exposure and high indicates high-exposure of the blot. (D) Knockdown of EI24 stabilizes MDM2 and degradation of TP53 in HCT116 cells. (E) EI24 is required for mounting proper TP53 response in the presence of genotoxic stress in HCT116 cells. Cells were treated with cisplatin (10 μg/ml) for 24 h. CDKN1A protein level was used as a read-out of TP53 transcriptional activity. (F) EI24 is required for replenishing cellular ATP in nutrient-deprived conditions. ATP measurement in control and Ei24 knockdown B16F10 cells grown in basal and nutrient-depleted conditions (HBSS-treatment for 24 h). (G) EI24-induced autophagy is required for cell survival upon nutrient-deficiency. B16F10 control and Ei24 knockdown cells were subjected to nutrient-deprivation for 48 h and cell survival was measured by FACS analysis. X-axis and Y-axis represent FL2-H and events, respectively.Since MTOR signaling is the master nutrient sensor and Ei24 knockdown resulted in accumulation of TRAF2 and activation of MTOR signaling in basal conditions (Fig. 7B), we next examined EI24-TRAF2-MTOR signaling in nutrient-depleted conditions (HBSS treatment). Control cells that were deprived of nutrients for 6 h displayed suppressed MTOR signaling and consequent activation of autophagy as indicated by decreased phosphorylation of the MTOR target RPS6KB (Fig. 7C). However, Ei24 knockdown cells retained higher levels of TRAF2 and activated MTOR signaling and impaired autophagy-flux even in nutrient-depleted conditions (Fig. 7C). Thus, EI24-induced degradation of RING-domain E3 ligase TRAF2 results in the regulation of MTOR signaling in both basal (Fig. 7B) and nutrient-depleted conditions (Fig. 7C).Next, we sought to determine if EI24-mediated degradation of MDM2 results in the activation of TP53 signaling. For this purpose, we used HCT116 cells retaining functional TP53 (HCT116TP53 WT cells). Immunoblotting revealed that cells with reduced expression of EI24 displayed accumulated MDM2 that was accompanied with the reduction in the protein levels of TP53 (Fig. 7D). TP53 is a transcription factor that acts as a master tumor suppressor by responding to oncogenic insults and genotoxic stress. MDM2 maintains the protein level of TP53 in balance, by its promotion of proteasome-dependent degradation to avert aberrant apoptosis. Since EI24 degraded MDM2 that resulted in TP53 accumulation in basal conditions (Fig. 7D), we next examined EI24-MDM2-TP53 signaling in the presence of genotoxic stress. Upon cisplatin treatment, control cells responded by stabilizing TP53 and inducing its transcriptional target CDKN1A whereas EI24 knockdown cells failed to do so (Fig. 7E). These data indicate that EI24-mediated degradation of MDM2 is important for proper TP53 response against genotoxic stress. Collectively, molecular analysis of TP53 and MTOR signaling in basal and autophagy-activated conditions revealed the importance of EI24-mediated degradation of E3 ligases in regulating these pathways with implications in maintaining cellular bioenergetics (through MTOR signaling) and genomic integrity (through TP53 signaling).Because EI24 regulated MTOR signaling (Fig. 7B and C) and was involved in autophagy-mediated degradation of several E3 ligases, we examined if EI24 regulates cellular bioenergetics. For this purpose, we first determined ATP content in B16F10 control and Ei24 knockdown cells grown in normal media and HBSS-treated conditions. We did not observe significant differences in ATP content in control and Ei24 knockdown cells grown in ad libitum medium. However, total ATP content in Ei24 knockdown cells in HBSS-treated conditions was significantly lower than that of control cells (Fig. 7F). To examine if cells with reduced expression of EI24 display increased vulnerability toward lack of nutrient due to depleted ATP content, we measured cell viability of B16F10 control and Ei24 knockdown cells in nutrient-depleted conditions. Compared to approximately 80% cell-death in Ei24 knockdown cells, only approximately 50% cell-death was observed in control cells when subjected to nutrient deficiency for 48 h (Fig. 7G). This result indicates that EI24-mediated autophagic degradation of target proteins regulates cellular bioenergetics to act as a backup mechanism for replenishing ATP in nutrient-deprived conditions. Our observation is consistent with previous reports demonstrating that end products of the autophagy process are channeled into the tricarboxylic acid cycle to generate energy in nutrient-deprived conditions and substantiating the physiological function of autophagy as a cytoprotective mechanism during metabolic stresses.Collectively, our work demonstrates that EI24 is an important mediator orchestrating crosstalk between the UPS and autophagy by targeting RING E3 ligases for autophagic degradation. This degradation is functionally linked to the regulation of several cellular processes, which represents a paradigm shift regarding the fate of E3 ligase degradation.
Discussion
In the present study, we showed that the RING domain, which is present in the majority of E3 ligases, acts as an ‘eat-me' signal for EI24-mediated autophagic degradation. We propose the autophagy machinery is integrated with the UPS, indicating that these protein degradation pathways are not as independent as previously suggested. The proposed model clearly represents a paradigm shift regarding our understanding of E3 ligase fate-determination.We also utilized the screening data and combined it with computational methods to determine potential biological functions for the RING domain E3 ligases that are degraded by EI24. Using these methods, we elucidated that EI24 potentially works cooperatively with the master tumor suppressor TP53 and the central nutrient-sensing mediator MTOR to regulate various cellular processes.Our study revealed that in addition to the presence of RING-domain, cellular localization of E3 ligases could be also a contributing factor to determine the susceptibility to be degraded by EI24. pGroup 1 members were primarily localized to the endosome, ubiquitin ligase complex, vacuole, lysosome, chromatin, and cytoskeleton. Most of these cellular organelles are directly involved in the execution of autophagy process or maintain high-autophagy activity, so from the molecular standpoint, it makes sense that pGroup 1 E3-ligases reside in these organelles. One exception is the localization of some of pGroup 1 E3 ligases in the nuclear-compartment, because the autophagy process is largely cytosolic. However, exclusivity of the cytosolic nature of the autophagy process has been challenged by recent reports describing the degradation of nuclear proteins and degradation of the nucleus itself by autophagy. Conversely, pGroup 2 was related with the perinuclear region of the cytoplasm and Golgi apparatus. In addition, we observed that similar protein isoforms aligned themselves in separate groups (BIRC2 in Group 1 and BIRC3 in Group 2, CBLB and CBLC in Group 2 and CBLL1 in Group 1). Although detailed experimental validation has to be done to prove the hypothesis, our data illustrate that in addition to the presence of RING-domain, the difference in the cellular localization of E3 ligases could be an additional factor that determines the susceptibility of a particular E3 ligase to be degraded by EI24.The data we describe here are consistent with previously reported associations between EI24 and AKT1, CHEK, TP53, and MTOR in autophagy pathways. It also suggests potential associations between EI24 and SP1, REPIN1, NFYB, and ATM pathways. Comparative analyses of predicted EI24 targets and nontargets suggest functional and regulatory links between EI24 and physiologically important cellular processes, such as proteolysis, apoptosis, chromatin/histone modifications, and transcription, through autophagic degradation of the E3 ligases involved in these processes (Fig. 6). By combining our analysis results with previously reported molecular interactions in the interactome databases (human protein reference database) and the Kyoto encyclopedia of genes and genomes, we developed a network model of the functional and regulatory links (Fig. 8). In the network model, the kinase module shows that upstream kinases of EI24 targets (Fig. 6F, Fig. 8) are members of the AKT1-MTOR and ATM-CHEK1/2 pathways, and these pathways crosstalk with each other through a link between AKT1 and CHEK1/2. The transcription factor module shows that TP53 is an upstream transcription factor of EI24 targets (Fig. 6E), and TP53 is regulated negatively and positively by the AKT1-MTOR and ATM-CHECK1/2 pathways, respectively (Fig. 8). Moreover, TP53 transcriptionally regulates the gene expression of both EI24 and its targets. The EI24 target module shows that EI24 degrades E3 ligase targets that are involved in physiologically important cellular processes (Fig. 6C). Furthermore, some of these targets regulate the upstream kinases and transcription factors via feedback mechanisms. For example, EI24 degrades MDM2, which negatively regulates TP53, and it also degrades TRAF2, which positively regulates MTOR (Fig. 8). The feed-forward and feedback links between the upstream regulators and E3 ligase targets of EI24 suggest a complex mechanism regulating EI24-dependent autophagic degradation and E3 ligase-associated cellular processes. Collectively, the numerous interactions represented in the network model demonstrate that EI24 connects all the network modules at the molecular level, indicating that EI24 is a central player in the crosstalk between the UPS and autophagy.
Figure 8.
Network model delineating functional and regulatory links between EI24 and kinases, transcription factors (TF), E3 ligase targets, and cellular processes. Kinase and TF modules in the network model include the upstream TFs (Fig. 6E) and kinases (Fig. 6F) whose targets and substrates, respectively, are enriched in pGroup 1 (EI24 targets). The E3 ligase module includes E3 ligase targets of EI24, such as MDM2 and TRAF2. The arrows and repression symbols represent activation and inhibition, respectively, in the regulatory relationships. Solid and dotted lines denote direct and indirect interactions, respectively, in the regulatory reactions. Cellular processes include the GOBPs represented by EI24 target E3 ligases (Fig. 6C). PPIs, protein-protein interactions.
Network model delineating functional and regulatory links between EI24 and kinases, transcription factors (TF), E3 ligase targets, and cellular processes. Kinase and TF modules in the network model include the upstream TFs (Fig. 6E) and kinases (Fig. 6F) whose targets and substrates, respectively, are enriched in pGroup 1 (EI24 targets). The E3 ligase module includes E3 ligase targets of EI24, such as MDM2 and TRAF2. The arrows and repression symbols represent activation and inhibition, respectively, in the regulatory relationships. Solid and dotted lines denote direct and indirect interactions, respectively, in the regulatory reactions. Cellular processes include the GOBPs represented by EI24 target E3 ligases (Fig. 6C). PPIs, protein-protein interactions.One of the central questions of this study was why do cells need to degrade E3 ligases via autophagy when they are already being degraded by the proteasome. One possible explanation is energy conservation. ATP is essential for various steps during protein degradation via the UPS, and protein unfolding, fueled by ATP hydrolysis, ensures the smooth passage of substrates through the proteasome tunnel. The amount of ATP required for autophagy is not well studied; however, the consensus is that autophagy is “cheaper” than the UPS with respect to ATP. From the cellular point of view, E3 ligase protein levels need to be tightly regulated at all times, and autophagic degradation seems to be the economical choice. Another explanation for the crosstalk between autophagy and UPS is that the cell could use autophagy as a backup mechanism for protein degradation. E3 ligases control whether a protein will be degraded or not. Thus, dysfunctional regulation in E3 ligase protein level could have severe ramifications for cells. In this context, based on our model, autophagy may serve as a maintenance mechanism for E3 ligase homeostasis in cells.We previously tested and verified the physiological significance of EI24-mediated degradation of E3 ligases at the functional level using in vivo mouse models. We demonstrated that EI24 binds and degrades TRIM41, an E3 ligase of PRKCA. Thus, loss of EI24 in the mouse resulted in TRIM41 accumulation and reduced PRKCA protein levels. Because PRKCA is required for skin carcinogenesis, we have found that mice with reduced EI24 expression has an attenuated response to DMBA-TPA-induced skin carcinogenesis. In another study, we report that EI24 degrades TRAF2 and TRAF5 via autophagy based on its recognition of the E3 ligase RING domain. Because TRAF signaling lies upstream of the RELA/NFKB p65 pathway, reduced EI24 expression results in RELA signaling activation, increased expression of proinflammatory cytokines, the emergence of EMT, and tumor metastasis. Thus, the molecular model we propose here has already been verified with respect to skin cancers and tumor metastasis. Furthermore, we have revealed that EI24-induced degradation of TRAF2 suppresses MTOR signaling resulting in the activation of autophagy. Autophagy-mediated proteolysis channels amino acids into the tricarboxylic acid cycle to generate energy that is required for cell survival in nutrient-deprived conditions. Consistent with this paradigm, we found that cells with reduced expression of EI24 that cannot mount a proper autophagy response contain decreased ATP levels in HBSS-treated conditions. As a consequence of inability to replenish ATP, Ei24 knockdown cells displayed increased cell death in nutrient-deprived conditions. Increased susceptibility of Ei24 knockdown cells that lack autophagy-inducing activity is consistent with previous reports demonstrating the protective nature of autophagy process during metabolic stress.In this study, we used MPLS-DA to predict E3 ligases whose degradation is regulated by EI24. PLS-DA has been applied to mRNA expression data for classification of the samples. In PLS-DA, the data matrixes are commonly auto-scaled. In the sample classification problems, the auto-scaling of mRNA expression data is usually performed across genes or proteins. However, in this study, our goal was to classify E3 ligases (gene classification), not the samples (sample classification). For the gene classification, we first explored whether protein sequences (Fig. S1A and B), cellular localizations (Fig. S1C), known binding partners (Fig. S1D), and mRNA expression levels (Fig. S2A to C) of E3 ligases showed any correlation with Group memberships (i.e., Group 1 [EI24 targets] and Group 2 [nontargets]). We found that only mRNA expression levels showed significant correlation with Group memberships (Fig. S2A to C). Thus, considering E3 ligases as independent observations defined by their mRNA expression levels (attributes), we formed n × m data matrix (X-block) for n E3 ligases and m samples and then auto-scaled the matrix for each column to have zero mean and unit variance.When the performance of a prediction model is evaluated, it is common not to include the training set in the test set. In this study, however, the training set (16 E3 ligases) used to build the MPLS-DA model was included in the test set (342 array probes for 308 E3 ligases). In our gene classification, we used the mixture modeling and probabilistic prediction of EI24 targets based on the mixture model, both of which require inclusion of the training set to assure accurate estimation of the mixture model and also to reliably determine the cutoff of the probability of being EI24 targets. For the gene classification, we first built the MPLS-DA model using the expression data matrixes (X1, X2, and Y) auto-scaled for the 16 E3 ligases (training set). In the MPLS-DA model, the latent variables (LVs) were determined to optimally separate Groups 1 and 2 (11 and 5 of 16 E3 ligases, respectively) in the training set on the PLS space. Next, to predict whether 308 E3 ligases (342 probes) in the test set are EI24 targets (Y_pred = 1) or not (Y_pred = 0), we auto-scaled the expression data matrixes (X1pred and X2pred) for the 342 probes and then estimated Y_pred values of the 342 probes using the LVs determined for the 16 E3 ligases. The application of the MPLS-DA LVs identified from the 16 E3 ligases to the auto-scaled X1pred and X2pred for the 342 probes will result in a suboptimal separation of EI24 targets (pGroup 1) and nontargets (pGroup 2) on the PLS space. To improve the classification accuracy by resolving this problem, we thus additionally applied a Gaussian mixture modeling to estimate 2 Gaussian distributions for pGroups 1 and 2, respectively, and then statistically determined whether each of 342 probes belongs to pGroup 1 by calculating the probability of each E3 ligase being a EI24 target [P(x ∈ Group 1|Y_pred)] using the 2 Gaussian distributions based on Bayesian rule. During the mixture modeling, the use of 11 EI24 targets (Group 1) and 5 nontargets (Group 2) in the training set enables reliable estimation of the 2 Gaussian distributions.Moreover, to determine the cutoff of the probability for the targets, the use of Groups 1 and 2 would be important because it can guarantee that pGroups 1 and 2 determined by the cutoff can include Groups 1 and 2 in the training set, respectively. PeptideProphet software used a similar approach involving linear discriminant analysis of a training set followed by a mixture modeling of predicted values for a test set for statistical peptide identification. However, to evaluate sensitivity and specificity in the prediction based on the MPLS-DA model, we randomly selected 5 predicted EI24 targets and 5 nontargets from pGroups 1 and 2, respectively, which include no E3 ligases in the training set, and then experimentally tested whether the selected E3 ligases are targeted by EI24. The experimental results showed that all 5 predicted EI24 targets were found to be truly EI24 targets while all 4 predicted nontargets were to be truly nontargets, indicating high degrees of sensitivity and specificity to our model. CBLC that belonged to pGroup 2 was also degraded by EI24 (Fig. S4A). Further studies must focus to elucidate why similar protein isoforms aligned themselves in separate groups (BIRC2 in Group 1 and BIRC3 in Group 2, CBLL1 in Group 1 and CBLB and CBLC in Group 2).Overall, our study revealed that EI24-mediated autophagic degradation of RING E3 ligases allows crosstalk between autophagy and the UPS. This crosstalk presents the opportunity to manage protein degradation machineries and cellular bioenergetics, both in cancers and other human diseases, when cell physiology goes awry.
Materials and methods
Cell culture and transfection
293T, HeLa, B16F10, and HCT116 cells were cultured in Dulbecco's modified Eagle's medium (Thermo Fisher Scientific, 11965092) supplemented with 10% fetal bovine serum (Hyclone, SH30919.03), 100 units/ml penicillin, and 100 μg/ml streptomycin (Thermo Fisher Scientific, 15070-063). Cells were grown at 37°C in a humidified chamber containing 5% CO2. Cells were transfected with polyethylenimine (Sigma-Aldrich, 764647) at a ratio of 6 µg polyethylenimine/µg DNA. For siRNA transfection, Lipofectamine RNAiMAX (Thermo Fisher Scientific, 13778150) was used. The EI24 knockdown siRNA sequence was: 5′-GCAAGAGAGUGAGCCACGUAUUGUUTT-3′.
Immunocytochemistry and ATP measurement
Immunocytochemistry was performed as described previously with slight modifications. Briefly, cells, with or without EI24 overexpression, were transfected with GFP-LC3 plasmids and seeded on gelatin-coated coverslips (Deckglasser, 0111520). Following a 4% paraformaldehyde (Sigma-Aldrich, P6148) in PBS (HyClone, SH30256.01) fixation step, cells were permeabilized with 0.5% Triton X-100 (Sigma-Aldrich, X100). Nonspecific signals were blocked with 1% normal goat serum (Thermo Fisher Scientific, 16210072) in PBS with 0.1% Triton X-100 for 30 min. Cells were then stained for 1 h at room temperature with their respective antibodies. Primary fluorescent signals were detected using Alexa Fluor 488 (Thermo Fisher Scientific, A-11008)- or Alexa Fluor 568 (Thermo Fisher Scientific, A-11008)-conjugated secondary antibodies. The data were imaged as described previously. Cellular ATP content in B16F10 control and Ei24 knockdown cells grown in normal media or HBSS (Thermo Fisher Scientific, 14025-092)-treated conditions (12 h) was determined using the Mitochondrial ToxGlo Assay Kit following manufacturer's protocol (Promega, G8000).
FACS analysis
FACS analysis was performed as described previously. Briefly, B16F10 control and Ei24 knockdown cells grown in normal medium or in HBSS (48 h) were fixed in 70% ethanol, treated with RNase A (Sigma-Aldrich, R4875), and stained with propidium iodide (Sigma-Aldrich, P4170) to stain DNA. FACS analysis was conducted using a FACS calibur apparatus 342975 (BD Biosciences, San Jose, CA, USA) to generate plots depicting counts (Y-axis) versus FL2-H (X-axis).
Plasmids and constructs
Flag-tagged TRIM41/RINCK1 was provided by Prof. Alexandra Newton (University of California, San Diego) and GFP-tagged TRIMs were provided by Profs. Germana Meroni (Cluster in Biomedicine, Italy) and Andrea Ballabio (Telethon Institute of Genetics and Medicine, Italy). HA-tagged Ub, MKRN1, MKRN1RINGΔ, and STUB1 as well as Flag-tagged TRAF2, TRAF2RINGΔ, TRAF6, and ZFPL1 were provided by Prof. Jaewhan Song (Yonsei University, Republic of Korea). Flag-tagged FBXO7 and XIAP, MYC-tagged BIRC2 and BIRC3, and GFP-tagged PARK2 were provided by Prof. Kwang Chul Chung (Yonsei University, Republic of Korea). HA-tagged PML ([1-633 amino-acids: PML-IV] and [1-560 amino-acids: PML-VI]) were a kind gift from Prof. Jin Hyun Ahn (Sungkyunkwan University School of Medicine, Republic of Korea). HA-tagged CBLC was provided by Prof. Kwang Youl Lee (Chonnam National University, Republic of Korea). Prof. Young-Gyu Ko (Korea University, Republic of Korea) provided MYC-tagged TRIM72 construct. MYC-tagged EI24 has been reported previously and was transferred to a pEGFPN1 vector (Clontech, 6085-1) to generate a GFP-tagged EI24 construct. TRIM41/RINCK1-Flag deletion constructs were generated as described previously.
Ubiquitination assay
Cells were harvested in PBS containing 2 mM N-ethylmaleimide (NEM; Sigma-Aldrich, E3876) and lysed in Tris-buffered saline (Sigma-Aldrich, T6664) containing 1% SDS (Sigma-Aldrich, L3771) and 20 mM NEM 30 h after transfection. The lysate was boiled, sonicated, and centrifuged at 14,000 × g for 15 min. The supernatant was diluted in NP-40 buffer containing 2 mM NEM, and immunoprecipitation was carried out using standard methods.
Immunoprecipitation and immunoblotting
Immunoprecipitation was performed as described previously with slight modifications. Briefly, cell lysates were prepared in NP-40 buffer comprised of 20 mM Tris (Sigma-Aldrich, 252859)-HCl (Sigma-Aldrich, 258148), 137 mM NaCl (Sigma-Aldrich, S9888), 1% NP-40 (Sigma-Aldrich, NP40S), 2 mM EDTA (Sigma-Aldrich, E9884), 10% glycerol (Sigma-Aldrich, G5516), 1 mM PMSF (Sigma-Aldrich, P7626), 2 mM sodium fluoride (Sigma-Aldrich, 201154), 1 mM sodium vanadate (Sigma-Aldrich, S6508), 1 mM β-glycerophosphate (Sigma-Aldrich, G9422), and 20 μg/ml each aprotinin (Sigma-Aldrich, A6106), pepstatin (Sigma-Aldrich, P5318), and leupeptin (Sigma-Aldrich, L2884). After centrifugation at 14,000 ×g for 15 min at 4°C, the supernatant underwent immunoprecipitation using 1 μg of the respective antibodies. After 3 h, 30 μl of protein G agarose beads (Thermo Fisher Scientific, 15920010) were added to the supernatant and incubated for 3 h. The beads were washed in the cell lysis buffer and boiled in an equal volume of 2× SDS sample buffer. For immunoblotting, cells were lysed in a radioimmunoprecipitation assay buffer consisting of 50 mM Tris-HCl, 150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate (Sigma-Aldrich, D6750), 0.1% SDS, and 2 mM EDTA along with protease and phosphatase inhibitors.
Antibodies
The following antibodies were used: HA (Santa Cruz Biotechnology, sc-805), GAPDH (Santa Cruz Biotechnology, sc-25778), ACTB (Santa Cruz Biotechnology, sc-8432), SQSTM1 (Santa Cruz Biotechnology, sc-28359), TP53 (Santa Cruz Biotechnology, sc-126), CDKN1A (Santa Cruz Biotechnology, sc-6246), GFP (Santa Cruz Biotechnology, sc-9996), Flag (Sigma-Aldrich, F1804), MYC (Cell Signaling Technology, 2276), LC3B (Cell Signaling Technology, 2775), TRAF2 (Cell Signaling Technology, 4724S), RPS6KB/p70S6K (Cell Signaling Technology, 2708), p-RPS6KB/p-p70S6K (Cell Signaling Technology, 9205), MDM2 (Cell Signaling Technology, 323), RNF6 (abcam, ab80427), and ZNF462 (abcam, ab117771). The EI24rabbit polyclonal antibody has been reported previously.
Multi-block partial least square-discriminatory analysis (MPLS-DA)
We performed MPLS-DA on gene expression levels to build a model that can separate Groups 1 and 2 and predict pGroups 1 and 2 using the previously reported MPLS algorithm. Of linear, nonparametric, and nonlinear classification methods, we used MPLS-DA because it effectively integrates multiple different datasets for classification of Groups 1 and 2 and enables us to interpret how different expression data contribute to the separation of Group 1 from Group 2, as previously demonstrated. For the analysis, we obtained gene expression data previously generated from ZR-75-1 breast cancer cells after EI24 knockdown (GSE52508 in GEO database) and MEF cells after etoposide treatment (GSE67266 in GEO database). We transformed the measured intensities into log2-intensity and then normalized the log2-intensity data using the quantile normalization. Of the 381 E3 ligases with RING domains, 354 were included in the gene expression data, and 354 included 11 of the 14 E3 ligases in Group 1 and all 5 E3 ligases in Group 2. For the 11 targets and 5 nontargets, we generated 2 X-block data matrixes: a 16×8 X1-block (X1) and a 16×6 X2-block (X2) containing mRNA expression levels of the 16 E3 ligases measured in data sets 1 and 2, respectively (Fig. S2D and Table S4). We also generated a 16×1 Y-block data matrix (Y) designating “1” for EI24 targets and “0” for nontargets (Table S4). All data matrixes were auto-scaled such that each column had zero mean and unit standard deviation. MPLS-DA was then applied to the auto-scaled X1, X2, and Y (Fig. S2D). After MPLS-DA, we selected 4 PLS latent variables (LVs) based on leave-one-out cross-validation. Before predicting EI24 targets, of 354 E3 ligases (396 array probes), we first selected 308 (342 probes) with MAD>25% to focus on E3 ligases with reasonable information contents. To predict E3 ligases targeted by EI24, we then generated a 342×8 X1-block (X1pred) and a 342×6 X2-block (X2pred) containing mRNA expression levels of all the E3 ligases measured in datasets 1 and 2 (Table S5). We then applied the MPLS-DA model to the X1- and X2-blocks, after auto-scaling the data matrixes (X1pred and X2pred) as described above, and obtained ‘predicted Y' values for the 342 probes (Y_pred in Fig. S2F). Finally, a mixture Gaussian model was applied to Y_pred values, and we used the Bayesian rule to calculate the probability of each E3 ligase (x) being a EI24 target [P(x ∈ Group 1|Y_pred)] or a nontargets [P(x ∈ Group 2|Y_pred)] using the Gaussian distributions estimated for EI24 targets and nontargets. The EI24 targets (pGroup 1) and nontargets (pGroup 2) were identified as E3 ligases with P(x ∈ Group 1|Y_pred) > 0.75 and P(x ∈ Group 2|Y_pred) > 0.75, respectively. See Supplementary Materials and Methods for implementation of MPLS-DA and mixture modeling.
Upstream regulator analysis
We performed TF (transcription factor) and kinase enrichment analyses to identify upstream regulators of EI24 targets. For these analyses, we first collected experimentally verified TF-target interactions and kinase-substrate interactions. For each regulator (TF or kinase), the null distributions for the numbers of targets (TF targets or kinase substrates) that overlapped with pGroups 1 and 2 were estimated by randomly sampling the same numbers of proteins in pGroups 1 and 2. To determine the observed numbers of targets for the pGroup 1 or 2 regulator, the significance (P values) of the targets enriched in pGroup 1 or 2 was computed using a one-tailed test of the observed number of targets based on the null distribution for the corresponding group (pGroup 1 or 2). Finally, the regulators with P value < 0.05 and the number of targets ≥ 5 were selected as the upstream regulators whose targets or substrates were enriched in pGroup 1 or 2. Of the upstream TFs, the ones showing similar expression patterns with EI24 could be desired. Thus, for each of the 2 data sets, after normalizing mRNA expression data using quantile normalization, we calculated Pearson correlation coefficients between the normalized expression data of each TF and EI24, as well as the significance of the correlation coefficient, using the MATLAB ‘corr.m' function (MathWorks Inc., Natick, MA, USA). To generate a final set of enriched TFs, we combined P values from the 2 datasets using a previously reported integrative statistical method and then selected the TFs with the combined P value <0.05.
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Authors: Jessica Severin; Andrew M Waterhouse; Hideya Kawaji; Timo Lassmann; Erik van Nimwegen; Piotr J Balwierz; Michiel Jl de Hoon; David A Hume; Piero Carninci; Yoshihide Hayashizaki; Harukazu Suzuki; Carsten O Daub; Alistair Rr Forrest Journal: Genome Biol Date: 2009-04-19 Impact factor: 13.583
Authors: Daniel J Klionsky; Amal Kamal Abdel-Aziz; Sara Abdelfatah; Mahmoud Abdellatif; Asghar Abdoli; Steffen Abel; Hagai Abeliovich; Marie H Abildgaard; Yakubu Princely Abudu; Abraham Acevedo-Arozena; Iannis E Adamopoulos; Khosrow Adeli; Timon E Adolph; Annagrazia Adornetto; Elma Aflaki; Galila Agam; Anupam Agarwal; Bharat B Aggarwal; Maria Agnello; Patrizia Agostinis; Javed N Agrewala; Alexander Agrotis; Patricia V Aguilar; S Tariq Ahmad; Zubair M Ahmed; Ulises Ahumada-Castro; Sonja Aits; Shu Aizawa; Yunus Akkoc; Tonia Akoumianaki; Hafize Aysin Akpinar; Ahmed M Al-Abd; Lina Al-Akra; Abeer Al-Gharaibeh; Moulay A Alaoui-Jamali; Simon Alberti; Elísabet Alcocer-Gómez; Cristiano Alessandri; Muhammad Ali; M Abdul Alim Al-Bari; Saeb Aliwaini; Javad Alizadeh; Eugènia Almacellas; Alexandru Almasan; Alicia Alonso; Guillermo D Alonso; Nihal Altan-Bonnet; Dario C Altieri; Élida M C Álvarez; Sara Alves; Cristine Alves da Costa; Mazen M Alzaharna; Marialaura Amadio; Consuelo Amantini; Cristina Amaral; Susanna Ambrosio; Amal O Amer; Veena Ammanathan; Zhenyi An; Stig U Andersen; Shaida A Andrabi; Magaiver Andrade-Silva; Allen M Andres; Sabrina Angelini; David Ann; Uche C Anozie; Mohammad Y Ansari; Pedro Antas; Adam Antebi; Zuriñe Antón; Tahira Anwar; Lionel Apetoh; Nadezda Apostolova; Toshiyuki Araki; Yasuhiro Araki; Kohei Arasaki; Wagner L Araújo; Jun Araya; Catherine Arden; Maria-Angeles Arévalo; Sandro Arguelles; Esperanza Arias; Jyothi Arikkath; Hirokazu Arimoto; Aileen R Ariosa; Darius Armstrong-James; Laetitia Arnauné-Pelloquin; Angeles Aroca; Daniela S Arroyo; Ivica Arsov; Rubén Artero; Dalia Maria Lucia Asaro; Michael Aschner; Milad Ashrafizadeh; Osnat Ashur-Fabian; Atanas G Atanasov; Alicia K Au; Patrick Auberger; Holger W Auner; Laure Aurelian; Riccardo Autelli; Laura Avagliano; Yenniffer Ávalos; Sanja Aveic; Célia Alexandra Aveleira; Tamar Avin-Wittenberg; Yucel Aydin; Scott Ayton; Srinivas Ayyadevara; Maria Azzopardi; Misuzu Baba; Jonathan M Backer; Steven K Backues; Dong-Hun Bae; Ok-Nam Bae; Soo Han Bae; Eric H Baehrecke; Ahruem Baek; Seung-Hoon Baek; Sung Hee Baek; Giacinto Bagetta; Agnieszka Bagniewska-Zadworna; Hua Bai; Jie Bai; Xiyuan Bai; Yidong Bai; Nandadulal Bairagi; Shounak Baksi; Teresa Balbi; Cosima T Baldari; Walter Balduini; Andrea Ballabio; Maria Ballester; Salma Balazadeh; Rena Balzan; Rina Bandopadhyay; Sreeparna Banerjee; Sulagna Banerjee; Ágnes Bánréti; Yan Bao; Mauricio S Baptista; Alessandra Baracca; Cristiana Barbati; Ariadna Bargiela; Daniela Barilà; Peter G Barlow; Sami J Barmada; Esther Barreiro; George E Barreto; Jiri Bartek; Bonnie Bartel; Alberto Bartolome; Gaurav R Barve; Suresh H Basagoudanavar; Diane C Bassham; Robert C Bast; Alakananda Basu; Henri Batoko; Isabella Batten; Etienne E Baulieu; Bradley L Baumgarner; Jagadeesh Bayry; Rupert Beale; Isabelle Beau; Florian Beaumatin; Luiz R G Bechara; George R Beck; Michael F Beers; Jakob Begun; Christian Behrends; Georg M N Behrens; Roberto Bei; Eloy Bejarano; Shai Bel; Christian Behl; Amine Belaid; Naïma Belgareh-Touzé; Cristina Bellarosa; Francesca Belleudi; Melissa Belló Pérez; Raquel Bello-Morales; Jackeline Soares de Oliveira Beltran; Sebastián Beltran; Doris Mangiaracina Benbrook; Mykolas Bendorius; Bruno A Benitez; Irene Benito-Cuesta; Julien Bensalem; Martin W Berchtold; Sabina Berezowska; Daniele Bergamaschi; Matteo Bergami; Andreas Bergmann; Laura Berliocchi; Clarisse Berlioz-Torrent; Amélie Bernard; Lionel Berthoux; Cagri G Besirli; Sebastien Besteiro; Virginie M Betin; Rudi Beyaert; Jelena S Bezbradica; Kiran Bhaskar; Ingrid Bhatia-Kissova; Resham Bhattacharya; Sujoy Bhattacharya; Shalmoli Bhattacharyya; Md Shenuarin Bhuiyan; Sujit Kumar Bhutia; Lanrong Bi; Xiaolin Bi; Trevor J Biden; Krikor Bijian; Viktor A Billes; Nadine Binart; Claudia Bincoletto; Asa B Birgisdottir; Geir Bjorkoy; Gonzalo Blanco; Ana Blas-Garcia; Janusz Blasiak; Robert Blomgran; Klas Blomgren; Janice S Blum; Emilio Boada-Romero; Mirta Boban; Kathleen Boesze-Battaglia; Philippe Boeuf; Barry Boland; Pascale Bomont; 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Florin Burada; Joseph R Burgoyne; M Isabel Burón; Victor Bustos; Sabrina Büttner; Elena Butturini; Aaron Byrd; Isabel Cabas; Sandra Cabrera-Benitez; Ken Cadwell; Jingjing Cai; Lu Cai; Qian Cai; Montserrat Cairó; Jose A Calbet; Guy A Caldwell; Kim A Caldwell; Jarrod A Call; Riccardo Calvani; Ana C Calvo; Miguel Calvo-Rubio Barrera; Niels Os Camara; Jacques H Camonis; Nadine Camougrand; Michelangelo Campanella; Edward M Campbell; François-Xavier Campbell-Valois; Silvia Campello; Ilaria Campesi; Juliane C Campos; Olivier Camuzard; Jorge Cancino; Danilo Candido de Almeida; Laura Canesi; Isabella Caniggia; Barbara Canonico; Carles Cantí; Bin Cao; Michele Caraglia; Beatriz Caramés; Evie H Carchman; Elena Cardenal-Muñoz; Cesar Cardenas; Luis Cardenas; Sandra M Cardoso; Jennifer S Carew; Georges F Carle; Gillian Carleton; Silvia Carloni; Didac Carmona-Gutierrez; Leticia A Carneiro; Oliana Carnevali; Julian M Carosi; Serena Carra; Alice Carrier; Lucie Carrier; Bernadette Carroll; A Brent Carter; Andreia Neves Carvalho; Magali Casanova; Caty Casas; Josefina Casas; Chiara Cassioli; Eliseo F Castillo; Karen Castillo; Sonia Castillo-Lluva; Francesca Castoldi; Marco Castori; Ariel F Castro; Margarida Castro-Caldas; Javier Castro-Hernandez; Susana Castro-Obregon; Sergio D Catz; Claudia Cavadas; Federica Cavaliere; Gabriella Cavallini; Maria Cavinato; Maria L Cayuela; Paula Cebollada Rica; Valentina Cecarini; Francesco Cecconi; Marzanna Cechowska-Pasko; Simone Cenci; Victòria Ceperuelo-Mallafré; João J Cerqueira; Janete M Cerutti; Davide Cervia; Vildan Bozok Cetintas; Silvia Cetrullo; Han-Jung Chae; Andrei S Chagin; Chee-Yin Chai; Gopal Chakrabarti; Oishee Chakrabarti; Tapas Chakraborty; Trinad Chakraborty; Mounia Chami; Georgios Chamilos; David W Chan; Edmond Y W Chan; Edward D Chan; H Y Edwin Chan; Helen H Chan; Hung Chan; Matthew T V Chan; Yau Sang Chan; Partha K Chandra; Chih-Peng Chang; Chunmei Chang; Hao-Chun Chang; Kai Chang; Jie Chao; Tracey Chapman; Nicolas Charlet-Berguerand; Samrat Chatterjee; Shail K Chaube; Anu Chaudhary; Santosh Chauhan; Edward Chaum; Frédéric Checler; Michael E Cheetham; Chang-Shi Chen; Guang-Chao Chen; Jian-Fu Chen; Liam L Chen; Leilei Chen; Lin Chen; Mingliang Chen; Mu-Kuan Chen; Ning Chen; Quan Chen; Ruey-Hwa Chen; Shi Chen; Wei Chen; Weiqiang Chen; Xin-Ming Chen; Xiong-Wen Chen; Xu Chen; Yan Chen; Ye-Guang Chen; Yingyu Chen; Yongqiang Chen; Yu-Jen Chen; Yue-Qin Chen; Zhefan Stephen Chen; Zhi Chen; Zhi-Hua Chen; Zhijian J Chen; Zhixiang Chen; Hanhua Cheng; Jun Cheng; Shi-Yuan Cheng; Wei Cheng; Xiaodong Cheng; Xiu-Tang Cheng; Yiyun Cheng; Zhiyong Cheng; Zhong Chen; Heesun Cheong; Jit Kong Cheong; Boris V Chernyak; Sara Cherry; Chi Fai Randy Cheung; Chun Hei Antonio Cheung; King-Ho Cheung; Eric Chevet; Richard J Chi; Alan Kwok Shing Chiang; Ferdinando Chiaradonna; Roberto Chiarelli; Mario Chiariello; Nathalia Chica; Susanna Chiocca; Mario Chiong; Shih-Hwa Chiou; Abhilash I Chiramel; Valerio Chiurchiù; Dong-Hyung Cho; Seong-Kyu Choe; Augustine M K Choi; Mary E Choi; Kamalika Roy Choudhury; Norman S Chow; Charleen T Chu; Jason P Chua; John Jia En Chua; Hyewon Chung; Kin Pan Chung; Seockhoon Chung; So-Hyang Chung; Yuen-Li Chung; Valentina Cianfanelli; Iwona A Ciechomska; Mariana Cifuentes; Laura Cinque; Sebahattin Cirak; Mara Cirone; Michael J Clague; Robert Clarke; Emilio Clementi; Eliana M Coccia; Patrice Codogno; Ehud Cohen; Mickael M Cohen; Tania Colasanti; Fiorella Colasuonno; Robert A Colbert; Anna Colell; Miodrag Čolić; Nuria S Coll; Mark O Collins; María I Colombo; Daniel A Colón-Ramos; Lydie Combaret; Sergio Comincini; Márcia R Cominetti; Antonella Consiglio; Andrea Conte; Fabrizio Conti; Viorica Raluca Contu; Mark R Cookson; Kevin M Coombs; Isabelle Coppens; Maria Tiziana Corasaniti; Dale P Corkery; Nils Cordes; Katia Cortese; Maria do Carmo Costa; Sarah Costantino; Paola Costelli; Ana Coto-Montes; Peter J Crack; Jose L Crespo; Alfredo Criollo; Valeria Crippa; Riccardo Cristofani; Tamas Csizmadia; Antonio Cuadrado; Bing Cui; Jun Cui; 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James DeGregori; Benjamin Dehay; Gabriel Del Rio; Joe R Delaney; Lea M D Delbridge; Elizabeth Delorme-Axford; M Victoria Delpino; Francesca Demarchi; Vilma Dembitz; Nicholas D Demers; Hongbin Deng; Zhiqiang Deng; Joern Dengjel; Paul Dent; Donna Denton; Melvin L DePamphilis; Channing J Der; Vojo Deretic; Albert Descoteaux; Laura Devis; Sushil Devkota; Olivier Devuyst; Grant Dewson; Mahendiran Dharmasivam; Rohan Dhiman; Diego di Bernardo; Manlio Di Cristina; Fabio Di Domenico; Pietro Di Fazio; Alessio Di Fonzo; Giovanni Di Guardo; Gianni M Di Guglielmo; Luca Di Leo; Chiara Di Malta; Alessia Di Nardo; Martina Di Rienzo; Federica Di Sano; George Diallinas; Jiajie Diao; Guillermo Diaz-Araya; Inés Díaz-Laviada; Jared M Dickinson; Marc Diederich; Mélanie Dieudé; Ivan Dikic; Shiping Ding; Wen-Xing Ding; Luciana Dini; Jelena Dinić; Miroslav Dinic; Albena T Dinkova-Kostova; Marc S Dionne; Jörg H W Distler; Abhinav Diwan; Ian M C Dixon; Mojgan Djavaheri-Mergny; Ina Dobrinski; Oxana Dobrovinskaya; Radek Dobrowolski; Renwick C J Dobson; Jelena Đokić; Serap Dokmeci Emre; Massimo Donadelli; Bo Dong; Xiaonan Dong; Zhiwu Dong; Gerald W Dorn Ii; Volker Dotsch; Huan Dou; Juan Dou; Moataz Dowaidar; Sami Dridi; Liat Drucker; Ailian Du; Caigan Du; Guangwei Du; Hai-Ning Du; Li-Lin Du; André du Toit; Shao-Bin Duan; Xiaoqiong Duan; Sónia P Duarte; Anna Dubrovska; Elaine A Dunlop; Nicolas Dupont; Raúl V Durán; Bilikere S Dwarakanath; Sergey A Dyshlovoy; Darius Ebrahimi-Fakhari; Leopold Eckhart; Charles L Edelstein; Thomas Efferth; Eftekhar Eftekharpour; Ludwig Eichinger; Nabil Eid; Tobias Eisenberg; N Tony Eissa; Sanaa Eissa; Miriam Ejarque; Abdeljabar El Andaloussi; Nazira El-Hage; Shahenda El-Naggar; Anna Maria Eleuteri; Eman S El-Shafey; Mohamed Elgendy; Aristides G Eliopoulos; María M Elizalde; Philip M Elks; Hans-Peter Elsasser; Eslam S Elsherbiny; Brooke M Emerling; N C Tolga Emre; Christina H Eng; Nikolai Engedal; Anna-Mart Engelbrecht; Agnete S T Engelsen; Jorrit M Enserink; Ricardo Escalante; Audrey Esclatine; Mafalda Escobar-Henriques; Eeva-Liisa Eskelinen; Lucile Espert; Makandjou-Ola Eusebio; Gemma Fabrias; Cinzia Fabrizi; Antonio Facchiano; Francesco Facchiano; Bengt Fadeel; Claudio Fader; Alex C Faesen; W Douglas Fairlie; Alberto Falcó; Bjorn H Falkenburger; Daping Fan; Jie Fan; Yanbo Fan; Evandro F Fang; Yanshan Fang; Yognqi Fang; Manolis Fanto; Tamar Farfel-Becker; Mathias Faure; Gholamreza Fazeli; Anthony O Fedele; Arthur M Feldman; Du Feng; Jiachun Feng; Lifeng Feng; Yibin Feng; Yuchen Feng; Wei Feng; Thais Fenz Araujo; Thomas A Ferguson; Álvaro F Fernández; Jose C Fernandez-Checa; Sonia Fernández-Veledo; Alisdair R Fernie; Anthony W Ferrante; Alessandra Ferraresi; Merari F Ferrari; Julio C B Ferreira; Susan Ferro-Novick; Antonio Figueras; Riccardo Filadi; Nicoletta Filigheddu; Eduardo Filippi-Chiela; Giuseppe Filomeni; Gian Maria Fimia; Vittorio Fineschi; Francesca Finetti; Steven Finkbeiner; Edward A Fisher; Paul B Fisher; Flavio Flamigni; Steven J Fliesler; Trude H Flo; Ida Florance; Oliver Florey; Tullio Florio; Erika Fodor; Carlo Follo; Edward A Fon; Antonella Forlino; Francesco Fornai; Paola Fortini; Anna Fracassi; Alessandro Fraldi; Brunella Franco; Rodrigo Franco; Flavia Franconi; Lisa B Frankel; Scott L Friedman; Leopold F Fröhlich; Gema Frühbeck; Jose M Fuentes; Yukio Fujiki; Naonobu Fujita; Yuuki Fujiwara; Mitsunori Fukuda; Simone Fulda; Luc Furic; Norihiko Furuya; Carmela Fusco; Michaela U Gack; Lidia Gaffke; Sehamuddin Galadari; Alessia Galasso; Maria F Galindo; Sachith Gallolu Kankanamalage; Lorenzo Galluzzi; Vincent Galy; Noor Gammoh; Boyi Gan; Ian G Ganley; Feng Gao; Hui Gao; Minghui Gao; Ping Gao; Shou-Jiang Gao; Wentao Gao; Xiaobo Gao; Ana Garcera; Maria Noé Garcia; Verónica E Garcia; Francisco García-Del Portillo; Vega Garcia-Escudero; Aracely Garcia-Garcia; Marina Garcia-Macia; Diana García-Moreno; Carmen Garcia-Ruiz; Patricia García-Sanz; Abhishek D Garg; Ricardo Gargini; Tina Garofalo; Robert F Garry; Nils C Gassen; Damian Gatica; Liang Ge; Wanzhong Ge; Ruth Geiss-Friedlander; Cecilia Gelfi; Pascal Genschik; Ian E Gentle; Valeria Gerbino; Christoph Gerhardt; Kyla Germain; Marc Germain; David A Gewirtz; Elham Ghasemipour Afshar; Saeid Ghavami; Alessandra Ghigo; Manosij Ghosh; Georgios Giamas; Claudia Giampietri; Alexandra Giatromanolaki; Gary E Gibson; Spencer B Gibson; Vanessa Ginet; Edward Giniger; Carlotta Giorgi; Henrique Girao; Stephen E Girardin; Mridhula Giridharan; Sandy Giuliano; Cecilia Giulivi; Sylvie Giuriato; Julien Giustiniani; Alexander Gluschko; Veit Goder; Alexander Goginashvili; Jakub Golab; David C Goldstone; Anna Golebiewska; Luciana R Gomes; Rodrigo Gomez; Rubén Gómez-Sánchez; Maria Catalina Gomez-Puerto; Raquel Gomez-Sintes; Qingqiu Gong; Felix M Goni; Javier González-Gallego; Tomas Gonzalez-Hernandez; Rosa A Gonzalez-Polo; Jose A Gonzalez-Reyes; Patricia González-Rodríguez; Ing Swie Goping; Marina S Gorbatyuk; Nikolai V Gorbunov; Kıvanç Görgülü; Roxana M Gorojod; Sharon M Gorski; Sandro Goruppi; Cecilia Gotor; Roberta A Gottlieb; Illana Gozes; Devrim Gozuacik; Martin Graef; Markus H Gräler; Veronica Granatiero; Daniel Grasso; Joshua P Gray; Douglas R Green; Alexander Greenhough; Stephen L Gregory; Edward F Griffin; Mark W Grinstaff; Frederic Gros; Charles Grose; Angelina S Gross; Florian Gruber; Paolo Grumati; Tilman Grune; Xueyan Gu; Jun-Lin Guan; Carlos M Guardia; Kishore Guda; Flora Guerra; Consuelo Guerri; Prasun Guha; Carlos Guillén; Shashi Gujar; Anna Gukovskaya; Ilya Gukovsky; Jan Gunst; Andreas Günther; Anyonya R Guntur; Chuanyong Guo; Chun Guo; Hongqing Guo; Lian-Wang Guo; Ming Guo; Pawan Gupta; Shashi Kumar Gupta; Swapnil Gupta; Veer Bala Gupta; Vivek Gupta; Asa B Gustafsson; David D Gutterman; Ranjitha H B; Annakaisa Haapasalo; James E Haber; Aleksandra Hać; Shinji Hadano; Anders J Hafrén; Mansour Haidar; Belinda S Hall; Gunnel Halldén; Anne Hamacher-Brady; Andrea Hamann; Maho Hamasaki; Weidong Han; Malene Hansen; Phyllis I Hanson; Zijian Hao; Masaru Harada; Ljubica Harhaji-Trajkovic; Nirmala Hariharan; Nigil Haroon; James Harris; Takafumi Hasegawa; Noor Hasima Nagoor; Jeffrey A Haspel; Volker Haucke; Wayne D Hawkins; Bruce A Hay; Cole M Haynes; Soren B Hayrabedyan; Thomas S Hays; Congcong He; Qin He; Rong-Rong He; You-Wen He; Yu-Ying He; Yasser Heakal; Alexander M Heberle; J Fielding Hejtmancik; Gudmundur Vignir Helgason; Vanessa Henkel; Marc Herb; Alexander Hergovich; Anna Herman-Antosiewicz; Agustín Hernández; Carlos Hernandez; Sergio Hernandez-Diaz; Virginia Hernandez-Gea; Amaury Herpin; Judit Herreros; Javier H Hervás; Daniel Hesselson; Claudio Hetz; Volker T Heussler; Yujiro Higuchi; Sabine Hilfiker; Joseph A Hill; William S Hlavacek; Emmanuel A Ho; Idy H T Ho; Philip Wing-Lok Ho; Shu-Leong Ho; Wan Yun Ho; G Aaron Hobbs; Mark Hochstrasser; Peter H M Hoet; Daniel Hofius; Paul Hofman; Annika Höhn; Carina I Holmberg; Jose R Hombrebueno; Chang-Won Hong Yi-Ren Hong; Lora V Hooper; Thorsten Hoppe; Rastislav Horos; Yujin Hoshida; I-Lun Hsin; Hsin-Yun Hsu; Bing Hu; Dong Hu; Li-Fang Hu; Ming Chang Hu; Ronggui Hu; Wei Hu; Yu-Chen Hu; Zhuo-Wei Hu; Fang Hua; Jinlian Hua; Yingqi Hua; Chongmin Huan; Canhua Huang; Chuanshu Huang; Chuanxin Huang; Chunling Huang; Haishan Huang; Kun Huang; Michael L H Huang; Rui Huang; Shan Huang; Tianzhi Huang; Xing Huang; Yuxiang Jack Huang; Tobias B Huber; Virginie Hubert; Christian A Hubner; Stephanie M Hughes; William E Hughes; Magali Humbert; Gerhard Hummer; James H Hurley; Sabah Hussain; Salik Hussain; Patrick J Hussey; Martina Hutabarat; Hui-Yun Hwang; Seungmin Hwang; Antonio Ieni; Fumiyo Ikeda; Yusuke Imagawa; Yuzuru Imai; Carol Imbriano; Masaya Imoto; Denise M Inman; Ken Inoki; Juan Iovanna; Renato V Iozzo; Giuseppe Ippolito; Javier E Irazoqui; Pablo Iribarren; Mohd Ishaq; Makoto Ishikawa; Nestor Ishimwe; Ciro Isidoro; Nahed Ismail; Shohreh Issazadeh-Navikas; Eisuke Itakura; Daisuke Ito; Davor Ivankovic; Saška Ivanova; Anand Krishnan V Iyer; José M Izquierdo; Masanori Izumi; Marja Jäättelä; Majid Sakhi Jabir; William T Jackson; Nadia Jacobo-Herrera; Anne-Claire Jacomin; Elise Jacquin; Pooja Jadiya; Hartmut Jaeschke; Chinnaswamy Jagannath; Arjen J Jakobi; Johan Jakobsson; Bassam Janji; Pidder Jansen-Dürr; Patric J Jansson; Jonathan Jantsch; Sławomir Januszewski; Alagie Jassey; Steve Jean; Hélène Jeltsch-David; Pavla Jendelova; Andreas Jenny; Thomas E Jensen; Niels Jessen; Jenna L Jewell; Jing Ji; Lijun Jia; Rui Jia; Liwen Jiang; Qing Jiang; Richeng Jiang; Teng Jiang; Xuejun Jiang; Yu Jiang; Maria Jimenez-Sanchez; Eun-Jung Jin; Fengyan Jin; Hongchuan Jin; Li Jin; Luqi Jin; Meiyan Jin; Si Jin; Eun-Kyeong Jo; Carine Joffre; Terje Johansen; Gail V W Johnson; Simon A Johnston; Eija Jokitalo; Mohit Kumar Jolly; Leo A B Joosten; Joaquin Jordan; Bertrand Joseph; Dianwen Ju; Jeong-Sun Ju; Jingfang Ju; Esmeralda Juárez; Delphine Judith; Gábor Juhász; Youngsoo Jun; Chang Hwa Jung; Sung-Chul Jung; Yong Keun Jung; Heinz Jungbluth; Johannes Jungverdorben; Steffen Just; Kai Kaarniranta; Allen Kaasik; Tomohiro Kabuta; Daniel Kaganovich; Alon Kahana; Renate Kain; Shinjo Kajimura; Maria Kalamvoki; Manjula Kalia; Danuta S Kalinowski; Nina Kaludercic; Ioanna Kalvari; Joanna Kaminska; Vitaliy O Kaminskyy; Hiromitsu Kanamori; Keizo Kanasaki; Chanhee Kang; Rui Kang; Sang Sun Kang; Senthilvelrajan Kaniyappan; Tomotake Kanki; Thirumala-Devi Kanneganti; Anumantha G Kanthasamy; Arthi Kanthasamy; Marc Kantorow; Orsolya Kapuy; Michalis V Karamouzis; Md Razaul Karim; Parimal Karmakar; Rajesh G Katare; Masaru Kato; Stefan H E Kaufmann; Anu Kauppinen; Gur P Kaushal; Susmita Kaushik; Kiyoshi Kawasaki; Kemal Kazan; Po-Yuan Ke; Damien J Keating; Ursula Keber; John H Kehrl; Kate E Keller; Christian W Keller; Jongsook Kim Kemper; Candia M Kenific; Oliver Kepp; Stephanie Kermorgant; Andreas Kern; Robin Ketteler; Tom G Keulers; Boris Khalfin; Hany Khalil; Bilon Khambu; Shahid Y Khan; Vinoth Kumar Megraj Khandelwal; Rekha Khandia; Widuri Kho; Noopur V Khobrekar; Sataree Khuansuwan; Mukhran Khundadze; Samuel A Killackey; Dasol Kim; Deok Ryong Kim; Do-Hyung Kim; Dong-Eun Kim; Eun Young Kim; Eun-Kyoung Kim; Hak-Rim Kim; Hee-Sik Kim; Jeong Hun Kim; Jin Kyung Kim; Jin-Hoi Kim; Joungmok Kim; Ju Hwan Kim; Keun Il Kim; Peter K Kim; Seong-Jun Kim; Scot R Kimball; Adi Kimchi; Alec C Kimmelman; Tomonori Kimura; Matthew A King; Kerri J Kinghorn; Conan G Kinsey; Vladimir Kirkin; Lorrie A Kirshenbaum; Sergey L Kiselev; Shuji Kishi; Katsuhiko Kitamoto; Yasushi Kitaoka; Kaio Kitazato; Richard N Kitsis; Josef T Kittler; Ole Kjaerulff; Peter S Klein; Thomas Klopstock; Jochen Klucken; Helene Knævelsrud; Roland L Knorr; Ben C B Ko; Fred Ko; Jiunn-Liang Ko; Hotaka Kobayashi; Satoru Kobayashi; Ina Koch; Jan C Koch; Ulrich Koenig; Donat Kögel; Young Ho Koh; Masato Koike; Sepp D Kohlwein; Nur M Kocaturk; Masaaki Komatsu; Jeannette König; Toru Kono; Benjamin T Kopp; Tamas Korcsmaros; Gözde Korkmaz; Viktor I Korolchuk; Mónica Suárez Korsnes; Ali Koskela; Janaiah Kota; Yaichiro Kotake; Monica L Kotler; Yanjun Kou; Michael I Koukourakis; Evangelos Koustas; Attila L Kovacs; Tibor Kovács; Daisuke Koya; Tomohiro Kozako; Claudine Kraft; Dimitri Krainc; Helmut Krämer; Anna D Krasnodembskaya; Carole Kretz-Remy; Guido Kroemer; Nicholas T Ktistakis; Kazuyuki Kuchitsu; Sabine Kuenen; Lars Kuerschner; Thomas Kukar; Ajay Kumar; Ashok Kumar; Deepak Kumar; Dhiraj Kumar; Sharad Kumar; Shinji Kume; Caroline Kumsta; Chanakya N Kundu; Mondira Kundu; Ajaikumar B Kunnumakkara; Lukasz Kurgan; Tatiana G Kutateladze; Ozlem Kutlu; SeongAe Kwak; Ho Jeong Kwon; Taeg Kyu Kwon; Yong Tae Kwon; Irene Kyrmizi; Albert La Spada; Patrick Labonté; Sylvain Ladoire; Ilaria Laface; Frank Lafont; Diane C Lagace; Vikramjit Lahiri; Zhibing Lai; Angela S Laird; Aparna Lakkaraju; Trond Lamark; Sheng-Hui Lan; Ane Landajuela; Darius J R Lane; Jon D Lane; Charles H Lang; Carsten Lange; Ülo Langel; Rupert Langer; Pierre Lapaquette; Jocelyn Laporte; Nicholas F LaRusso; Isabel Lastres-Becker; Wilson Chun Yu Lau; Gordon W Laurie; Sergio Lavandero; Betty Yuen Kwan Law; Helen Ka-Wai Law; Rob Layfield; Weidong Le; Herve Le Stunff; Alexandre Y Leary; Jean-Jacques Lebrun; Lionel Y W Leck; Jean-Philippe Leduc-Gaudet; Changwook Lee; Chung-Pei Lee; Da-Hye Lee; Edward B Lee; Erinna F Lee; Gyun Min Lee; He-Jin Lee; Heung Kyu Lee; Jae Man Lee; Jason S Lee; Jin-A Lee; Joo-Yong Lee; Jun Hee Lee; Michael Lee; Min Goo Lee; Min Jae Lee; Myung-Shik Lee; Sang Yoon Lee; Seung-Jae Lee; Stella Y Lee; Sung Bae Lee; Won Hee Lee; Ying-Ray Lee; Yong-Ho Lee; Youngil Lee; Christophe Lefebvre; Renaud Legouis; Yu L Lei; Yuchen Lei; Sergey Leikin; Gerd Leitinger; Leticia Lemus; Shuilong Leng; Olivia Lenoir; Guido Lenz; Heinz Josef Lenz; Paola Lenzi; Yolanda León; Andréia M Leopoldino; Christoph Leschczyk; Stina Leskelä; Elisabeth Letellier; Chi-Ting Leung; Po Sing Leung; Jeremy S Leventhal; Beth Levine; Patrick A Lewis; Klaus Ley; Bin Li; Da-Qiang Li; Jianming Li; Jing Li; Jiong Li; Ke Li; Liwu Li; Mei Li; Min Li; Min Li; Ming Li; Mingchuan Li; Pin-Lan Li; Ming-Qing Li; Qing Li; Sheng Li; Tiangang Li; Wei Li; Wenming Li; Xue Li; Yi-Ping Li; Yuan Li; Zhiqiang Li; Zhiyong Li; Zhiyuan Li; Jiqin Lian; Chengyu Liang; Qiangrong Liang; Weicheng Liang; Yongheng Liang; YongTian Liang; Guanghong Liao; Lujian Liao; Mingzhi Liao; Yung-Feng Liao; Mariangela Librizzi; Pearl P Y Lie; Mary A Lilly; Hyunjung J Lim; Thania R R Lima; Federica Limana; Chao Lin; Chih-Wen Lin; Dar-Shong Lin; Fu-Cheng Lin; Jiandie D Lin; Kurt M Lin; Kwang-Huei Lin; Liang-Tzung Lin; Pei-Hui Lin; Qiong Lin; Shaofeng Lin; Su-Ju Lin; Wenyu Lin; Xueying Lin; Yao-Xin Lin; Yee-Shin Lin; Rafael Linden; Paula Lindner; Shuo-Chien Ling; Paul Lingor; Amelia K Linnemann; Yih-Cherng Liou; Marta M Lipinski; Saška Lipovšek; Vitor A Lira; Natalia Lisiak; Paloma B Liton; Chao Liu; Ching-Hsuan Liu; Chun-Feng Liu; Cui Hua Liu; Fang Liu; Hao Liu; Hsiao-Sheng Liu; Hua-Feng Liu; Huifang Liu; Jia Liu; Jing Liu; Julia Liu; Leyuan Liu; Longhua Liu; Meilian Liu; Qin Liu; Wei Liu; Wende Liu; Xiao-Hong Liu; Xiaodong Liu; Xingguo Liu; Xu Liu; Xuedong Liu; Yanfen Liu; Yang Liu; Yang Liu; Yueyang Liu; Yule Liu; J Andrew Livingston; Gerard Lizard; Jose M Lizcano; Senka Ljubojevic-Holzer; Matilde E LLeonart; David Llobet-Navàs; Alicia Llorente; Chih Hung Lo; Damián Lobato-Márquez; Qi Long; Yun Chau Long; Ben Loos; Julia A Loos; Manuela G López; Guillermo López-Doménech; José Antonio López-Guerrero; Ana T López-Jiménez; Óscar López-Pérez; Israel López-Valero; Magdalena J Lorenowicz; Mar Lorente; Peter Lorincz; Laura Lossi; Sophie Lotersztajn; Penny E Lovat; Jonathan F Lovell; Alenka Lovy; Péter Lőw; Guang Lu; Haocheng Lu; Jia-Hong Lu; Jin-Jian Lu; Mengji Lu; Shuyan Lu; Alessandro Luciani; John M Lucocq; Paula Ludovico; Micah A Luftig; Morten Luhr; Diego Luis-Ravelo; Julian J Lum; Liany Luna-Dulcey; Anders H Lund; Viktor K Lund; Jan D Lünemann; Patrick Lüningschrör; Honglin Luo; Rongcan Luo; Shouqing Luo; Zhi Luo; Claudio Luparello; Bernhard Lüscher; Luan Luu; Alex Lyakhovich; Konstantin G Lyamzaev; Alf Håkon Lystad; Lyubomyr Lytvynchuk; Alvin C Ma; Changle Ma; Mengxiao Ma; Ning-Fang Ma; Quan-Hong Ma; Xinliang Ma; Yueyun Ma; Zhenyi Ma; Ormond A MacDougald; Fernando Macian; Gustavo C MacIntosh; Jeffrey P MacKeigan; Kay F Macleod; Sandra Maday; Frank Madeo; Muniswamy Madesh; Tobias Madl; Julio Madrigal-Matute; Akiko Maeda; Yasuhiro Maejima; Marta Magarinos; Poornima Mahavadi; Emiliano Maiani; Kenneth Maiese; Panchanan Maiti; Maria Chiara Maiuri; Barbara Majello; Michael B Major; Elena Makareeva; Fayaz Malik; Karthik Mallilankaraman; Walter Malorni; Alina Maloyan; Najiba Mammadova; Gene Chi Wai Man; Federico Manai; Joseph D Mancias; Eva-Maria Mandelkow; Michael A Mandell; Angelo A Manfredi; Masoud H Manjili; Ravi Manjithaya; Patricio Manque; Bella B Manshian; Raquel Manzano; Claudia Manzoni; Kai Mao; Cinzia Marchese; Sandrine Marchetti; Anna Maria Marconi; Fabrizio Marcucci; Stefania Mardente; Olga A Mareninova; Marta Margeta; Muriel Mari; Sara Marinelli; Oliviero Marinelli; Guillermo Mariño; Sofia Mariotto; Richard S Marshall; Mark R Marten; Sascha Martens; Alexandre P J Martin; Katie R Martin; Sara Martin; Shaun Martin; Adrián Martín-Segura; Miguel A Martín-Acebes; Inmaculada Martin-Burriel; Marcos Martin-Rincon; Paloma Martin-Sanz; José A Martina; Wim Martinet; Aitor Martinez; Ana Martinez; Jennifer Martinez; Moises Martinez Velazquez; Nuria Martinez-Lopez; Marta Martinez-Vicente; Daniel O Martins; Joilson O Martins; Waleska K Martins; Tania Martins-Marques; Emanuele Marzetti; Shashank Masaldan; Celine Masclaux-Daubresse; Douglas G Mashek; Valentina Massa; Lourdes Massieu; Glenn R Masson; Laura Masuelli; Anatoliy I Masyuk; Tetyana V Masyuk; Paola Matarrese; Ander Matheu; Satoaki Matoba; Sachiko Matsuzaki; Pamela Mattar; Alessandro Matte; Domenico Mattoscio; José L Mauriz; Mario Mauthe; Caroline Mauvezin; Emanual Maverakis; Paola Maycotte; Johanna Mayer; Gianluigi Mazzoccoli; Cristina Mazzoni; Joseph R Mazzulli; Nami McCarty; Christine McDonald; Mitchell R McGill; Sharon L McKenna; BethAnn McLaughlin; Fionn McLoughlin; Mark A McNiven; Thomas G McWilliams; Fatima Mechta-Grigoriou; Tania Catarina Medeiros; Diego L Medina; Lynn A Megeney; Klara Megyeri; Maryam Mehrpour; Jawahar L Mehta; Alfred J Meijer; Annemarie H Meijer; Jakob Mejlvang; Alicia Meléndez; Annette Melk; Gonen Memisoglu; Alexandrina F Mendes; Delong Meng; Fei Meng; Tian Meng; Rubem Menna-Barreto; Manoj B Menon; Carol Mercer; Anne E Mercier; Jean-Louis Mergny; Adalberto Merighi; Seth D Merkley; Giuseppe Merla; Volker Meske; Ana Cecilia Mestre; Shree Padma Metur; Christian Meyer; Hemmo Meyer; Wenyi Mi; Jeanne Mialet-Perez; Junying Miao; Lucia Micale; Yasuo Miki; Enrico Milan; Małgorzata Milczarek; Dana L Miller; Samuel I Miller; Silke Miller; Steven W Millward; Ira Milosevic; Elena A Minina; Hamed Mirzaei; Hamid Reza Mirzaei; Mehdi Mirzaei; Amit Mishra; Nandita Mishra; Paras Kumar Mishra; Maja Misirkic Marjanovic; Roberta Misasi; Amit Misra; Gabriella Misso; Claire Mitchell; Geraldine Mitou; Tetsuji Miura; Shigeki Miyamoto; Makoto Miyazaki; Mitsunori Miyazaki; Taiga Miyazaki; Keisuke Miyazawa; Noboru Mizushima; Trine H Mogensen; Baharia Mograbi; Reza Mohammadinejad; Yasir Mohamud; Abhishek Mohanty; Sipra Mohapatra; Torsten Möhlmann; Asif Mohmmed; Anna Moles; Kelle H Moley; Maurizio Molinari; Vincenzo Mollace; Andreas Buch Møller; Bertrand Mollereau; Faustino Mollinedo; Costanza Montagna; Mervyn J Monteiro; Andrea Montella; L Ruth Montes; Barbara Montico; Vinod K Mony; Giacomo Monzio Compagnoni; Michael N Moore; Mohammad A Moosavi; Ana L Mora; Marina Mora; David Morales-Alamo; Rosario Moratalla; Paula I Moreira; Elena Morelli; Sandra Moreno; Daniel Moreno-Blas; Viviana Moresi; Benjamin Morga; Alwena H Morgan; Fabrice Morin; Hideaki Morishita; Orson L Moritz; Mariko Moriyama; Yuji Moriyasu; Manuela Morleo; Eugenia Morselli; Jose F Moruno-Manchon; Jorge Moscat; Serge Mostowy; Elisa Motori; Andrea Felinto Moura; Naima Moustaid-Moussa; Maria Mrakovcic; Gabriel Muciño-Hernández; Anupam Mukherjee; Subhadip Mukhopadhyay; Jean M Mulcahy Levy; Victoriano Mulero; Sylviane Muller; Christian Münch; Ashok Munjal; Pura Munoz-Canoves; Teresa Muñoz-Galdeano; Christian Münz; Tomokazu Murakawa; Claudia Muratori; Brona M Murphy; J Patrick Murphy; Aditya Murthy; Timo T Myöhänen; Indira U Mysorekar; Jennifer Mytych; Seyed Mohammad Nabavi; Massimo Nabissi; Péter Nagy; Jihoon Nah; Aimable Nahimana; Ichiro Nakagawa; Ken Nakamura; Hitoshi Nakatogawa; Shyam S Nandi; Meera Nanjundan; Monica Nanni; Gennaro Napolitano; Roberta Nardacci; Masashi Narita; Melissa Nassif; Ilana Nathan; Manabu Natsumeda; Ryno J Naude; Christin Naumann; Olaia Naveiras; Fatemeh Navid; Steffan T Nawrocki; Taras Y Nazarko; Francesca Nazio; Florentina Negoita; Thomas Neill; Amanda L Neisch; Luca M Neri; Mihai G Netea; Patrick Neubert; Thomas P Neufeld; Dietbert Neumann; Albert Neutzner; Phillip T Newton; Paul A Ney; Ioannis P Nezis; Charlene C W Ng; Tzi Bun Ng; Hang T T Nguyen; Long T Nguyen; Hong-Min Ni; Clíona Ní Cheallaigh; Zhenhong Ni; M Celeste Nicolao; Francesco Nicoli; Manuel Nieto-Diaz; Per Nilsson; Shunbin Ning; Rituraj Niranjan; Hiroshi Nishimune; Mireia Niso-Santano; Ralph A Nixon; Annalisa Nobili; Clevio Nobrega; Takeshi Noda; Uxía Nogueira-Recalde; Trevor M Nolan; Ivan Nombela; Ivana Novak; Beatriz Novoa; Takashi Nozawa; Nobuyuki Nukina; Carmen Nussbaum-Krammer; Jesper Nylandsted; Tracey R O'Donovan; Seónadh M O'Leary; Eyleen J O'Rourke; Mary P O'Sullivan; Timothy E O'Sullivan; Salvatore Oddo; Ina Oehme; Michinaga Ogawa; Eric Ogier-Denis; Margret H Ogmundsdottir; Besim Ogretmen; Goo Taeg Oh; Seon-Hee Oh; Young J Oh; Takashi Ohama; Yohei Ohashi; Masaki Ohmuraya; Vasileios Oikonomou; Rani Ojha; Koji Okamoto; Hitoshi Okazawa; Masahide Oku; Sara Oliván; Jorge M A Oliveira; Michael Ollmann; James A Olzmann; Shakib Omari; M Bishr Omary; Gizem Önal; Martin Ondrej; Sang-Bing Ong; Sang-Ging Ong; Anna Onnis; Juan A Orellana; Sara Orellana-Muñoz; Maria Del Mar Ortega-Villaizan; Xilma R Ortiz-Gonzalez; Elena Ortona; Heinz D Osiewacz; Abdel-Hamid K Osman; Rosario Osta; Marisa S Otegui; Kinya Otsu; Christiane Ott; Luisa Ottobrini; Jing-Hsiung James Ou; Tiago F Outeiro; Inger Oynebraten; Melek Ozturk; Gilles Pagès; Susanta Pahari; Marta Pajares; Utpal B Pajvani; Rituraj Pal; Simona Paladino; Nicolas Pallet; Michela Palmieri; Giuseppe Palmisano; Camilla Palumbo; Francesco Pampaloni; Lifeng Pan; Qingjun Pan; Wenliang Pan; Xin Pan; Ganna Panasyuk; Rahul Pandey; Udai B Pandey; Vrajesh Pandya; Francesco Paneni; Shirley Y Pang; Elisa Panzarini; Daniela L Papademetrio; Elena Papaleo; Daniel Papinski; Diana Papp; Eun Chan Park; Hwan Tae Park; Ji-Man Park; Jong-In Park; Joon Tae Park; Junsoo Park; Sang Chul Park; Sang-Youel Park; Abraham H Parola; Jan B Parys; Adrien Pasquier; Benoit Pasquier; João F Passos; Nunzia Pastore; Hemal H Patel; Daniel Patschan; Sophie Pattingre; Gustavo Pedraza-Alva; Jose Pedraza-Chaverri; Zully Pedrozo; Gang Pei; Jianming Pei; Hadas Peled-Zehavi; Joaquín M Pellegrini; Joffrey Pelletier; Miguel A Peñalva; Di Peng; Ying Peng; Fabio Penna; Maria Pennuto; Francesca Pentimalli; Cláudia Mf Pereira; Gustavo J S Pereira; Lilian C Pereira; Luis Pereira de Almeida; Nirma D Perera; Ángel Pérez-Lara; Ana B Perez-Oliva; María Esther Pérez-Pérez; Palsamy Periyasamy; Andras Perl; Cristiana Perrotta; Ida Perrotta; Richard G Pestell; Morten Petersen; Irina Petrache; Goran Petrovski; Thorsten Pfirrmann; Astrid S Pfister; Jennifer A Philips; Huifeng Pi; Anna Picca; Alicia M Pickrell; Sandy Picot; Giovanna M Pierantoni; Marina Pierdominici; Philippe Pierre; Valérie Pierrefite-Carle; Karolina Pierzynowska; Federico Pietrocola; Miroslawa Pietruczuk; Claudio Pignata; Felipe X Pimentel-Muiños; Mario Pinar; Roberta O Pinheiro; Ronit Pinkas-Kramarski; Paolo Pinton; Karolina Pircs; Sujan Piya; Paola Pizzo; Theo S Plantinga; Harald W Platta; Ainhoa Plaza-Zabala; Markus Plomann; Egor Y Plotnikov; Helene Plun-Favreau; Ryszard Pluta; Roger Pocock; Stefanie Pöggeler; Christian Pohl; Marc Poirot; Angelo Poletti; Marisa Ponpuak; Hana Popelka; Blagovesta Popova; Helena Porta; Soledad Porte Alcon; Eliana Portilla-Fernandez; Martin Post; Malia B Potts; Joanna Poulton; Ted Powers; Veena Prahlad; Tomasz K Prajsnar; Domenico Praticò; Rosaria Prencipe; Muriel Priault; Tassula Proikas-Cezanne; Vasilis J Promponas; Christopher G Proud; Rosa Puertollano; Luigi Puglielli; Thomas Pulinilkunnil; Deepika Puri; Rajat Puri; Julien Puyal; Xiaopeng Qi; Yongmei Qi; Wenbin Qian; Lei Qiang; Yu Qiu; Joe Quadrilatero; Jorge Quarleri; Nina Raben; Hannah Rabinowich; Debora Ragona; Michael J Ragusa; Nader Rahimi; Marveh Rahmati; Valeria Raia; Nuno Raimundo; Namakkal-Soorappan Rajasekaran; Sriganesh Ramachandra Rao; Abdelhaq Rami; Ignacio Ramírez-Pardo; David B Ramsden; Felix Randow; Pundi N Rangarajan; Danilo Ranieri; Hai Rao; Lang Rao; Rekha Rao; Sumit Rathore; J Arjuna Ratnayaka; Edward A Ratovitski; Palaniyandi Ravanan; Gloria Ravegnini; Swapan K Ray; Babak Razani; Vito Rebecca; Fulvio Reggiori; Anne Régnier-Vigouroux; Andreas S Reichert; David Reigada; Jan H Reiling; Theo Rein; Siegfried Reipert; Rokeya Sultana Rekha; Hongmei Ren; Jun Ren; Weichao Ren; Tristan Renault; Giorgia Renga; Karen Reue; Kim Rewitz; Bruna Ribeiro de Andrade Ramos; S Amer Riazuddin; Teresa M Ribeiro-Rodrigues; Jean-Ehrland Ricci; Romeo Ricci; Victoria Riccio; Des R Richardson; Yasuko Rikihisa; Makarand V Risbud; Ruth M Risueño; Konstantinos Ritis; Salvatore Rizza; Rosario Rizzuto; Helen C Roberts; Luke D Roberts; Katherine J Robinson; Maria Carmela Roccheri; Stephane Rocchi; George G Rodney; Tiago Rodrigues; Vagner Ramon Rodrigues Silva; Amaia Rodriguez; Ruth Rodriguez-Barrueco; Nieves Rodriguez-Henche; Humberto Rodriguez-Rocha; Jeroen Roelofs; Robert S Rogers; Vladimir V Rogov; Ana I Rojo; Krzysztof Rolka; Vanina Romanello; Luigina Romani; Alessandra Romano; Patricia S Romano; David Romeo-Guitart; Luis C Romero; Montserrat Romero; Joseph C Roney; Christopher Rongo; Sante Roperto; Mathias T Rosenfeldt; Philip Rosenstiel; Anne G Rosenwald; Kevin A Roth; Lynn Roth; Steven Roth; Kasper M A Rouschop; 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Alberto Sanz; Pascual Sanz; Shweta Saran; Marco Sardiello; Timothy J Sargeant; Apurva Sarin; Chinmoy Sarkar; Sovan Sarkar; Maria-Rosa Sarrias; Surajit Sarkar; Dipanka Tanu Sarmah; Jaakko Sarparanta; Aishwarya Sathyanarayan; Ranganayaki Sathyanarayanan; K Matthew Scaglione; Francesca Scatozza; Liliana Schaefer; Zachary T Schafer; Ulrich E Schaible; Anthony H V Schapira; Michael Scharl; Hermann M Schatzl; Catherine H Schein; Wiep Scheper; David Scheuring; Maria Vittoria Schiaffino; Monica Schiappacassi; Rainer Schindl; Uwe Schlattner; Oliver Schmidt; Roland Schmitt; Stephen D Schmidt; Ingo Schmitz; Eran Schmukler; Anja Schneider; Bianca E Schneider; Romana Schober; Alejandra C Schoijet; Micah B Schott; Michael Schramm; Bernd Schröder; Kai Schuh; Christoph Schüller; Ryan J Schulze; Lea Schürmanns; Jens C Schwamborn; Melanie Schwarten; Filippo Scialo; Sebastiano Sciarretta; Melanie J Scott; Kathleen W Scotto; A Ivana Scovassi; Andrea Scrima; Aurora Scrivo; David Sebastian; Salwa Sebti; Simon Sedej; 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Bruno J de Andrade Silva; Johnatas D Silva; Eduardo Silva-Pavez; Sandrine Silvente-Poirot; Rachel E Simmonds; Anna Katharina Simon; Hans-Uwe Simon; Matias Simons; Anurag Singh; Lalit P Singh; Rajat Singh; Shivendra V Singh; Shrawan K Singh; Sudha B Singh; Sunaina Singh; Surinder Pal Singh; Debasish Sinha; Rohit Anthony Sinha; Sangita Sinha; Agnieszka Sirko; Kapil Sirohi; Efthimios L Sivridis; Panagiotis Skendros; Aleksandra Skirycz; Iva Slaninová; Soraya S Smaili; Andrei Smertenko; Matthew D Smith; Stefaan J Soenen; Eun Jung Sohn; Sophia P M Sok; Giancarlo Solaini; Thierry Soldati; Scott A Soleimanpour; Rosa M Soler; Alexei Solovchenko; Jason A Somarelli; Avinash Sonawane; Fuyong Song; Hyun Kyu Song; Ju-Xian Song; Kunhua Song; Zhiyin Song; Leandro R Soria; Maurizio Sorice; Alexander A Soukas; Sandra-Fausia Soukup; Diana Sousa; Nadia Sousa; Paul A Spagnuolo; Stephen A Spector; M M Srinivas Bharath; Daret St Clair; Venturina Stagni; Leopoldo Staiano; Clint A Stalnecker; Metodi V Stankov; 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Motomasa Tanaka; Daolin Tang; Jingfeng Tang; Tie-Shan Tang; Isei Tanida; Zhipeng Tao; Mohammed Taouis; Lars Tatenhorst; Nektarios Tavernarakis; Allen Taylor; Gregory A Taylor; Joan M Taylor; Elena Tchetina; Andrew R Tee; Irmgard Tegeder; David Teis; Natercia Teixeira; Fatima Teixeira-Clerc; Kumsal A Tekirdag; Tewin Tencomnao; Sandra Tenreiro; Alexei V Tepikin; Pilar S Testillano; Gianluca Tettamanti; Pierre-Louis Tharaux; Kathrin Thedieck; Arvind A Thekkinghat; Stefano Thellung; Josephine W Thinwa; V P Thirumalaikumar; Sufi Mary Thomas; Paul G Thomes; Andrew Thorburn; Lipi Thukral; Thomas Thum; Michael Thumm; Ling Tian; Ales Tichy; Andreas Till; Vincent Timmerman; Vladimir I Titorenko; Sokol V Todi; Krassimira Todorova; Janne M Toivonen; Luana Tomaipitinca; Dhanendra Tomar; Cristina Tomas-Zapico; Sergej Tomić; Benjamin Chun-Kit Tong; Chao Tong; Xin Tong; Sharon A Tooze; Maria L Torgersen; Satoru Torii; Liliana Torres-López; Alicia Torriglia; Christina G Towers; Roberto Towns; Shinya Toyokuni; Vladimir Trajkovic; Donatella Tramontano; Quynh-Giao Tran; Leonardo H Travassos; Charles B Trelford; Shirley Tremel; Ioannis P Trougakos; Betty P Tsao; Mario P Tschan; Hung-Fat Tse; Tak Fu Tse; Hitoshi Tsugawa; Andrey S Tsvetkov; David A Tumbarello; Yasin Tumtas; María J Tuñón; Sandra Turcotte; Boris Turk; Vito Turk; Bradley J Turner; Richard I Tuxworth; Jessica K Tyler; Elena V Tyutereva; Yasuo Uchiyama; Aslihan Ugun-Klusek; Holm H Uhlig; Marzena Ułamek-Kozioł; Ilya V Ulasov; Midori Umekawa; Christian Ungermann; Rei Unno; Sylvie Urbe; Elisabet Uribe-Carretero; Suayib Üstün; Vladimir N Uversky; Thomas Vaccari; Maria I Vaccaro; Björn F Vahsen; Helin Vakifahmetoglu-Norberg; Rut Valdor; Maria J Valente; Ayelén Valko; Richard B Vallee; Angela M Valverde; Greet Van den Berghe; Stijn van der Veen; Luc Van Kaer; Jorg van Loosdregt; Sjoerd J L van Wijk; Wim Vandenberghe; Ilse Vanhorebeek; Marcos A Vannier-Santos; Nicola Vannini; M Cristina Vanrell; Chiara Vantaggiato; Gabriele Varano; Isabel Varela-Nieto; Máté Varga; M Helena Vasconcelos; Somya Vats; Demetrios G Vavvas; Ignacio Vega-Naredo; Silvia Vega-Rubin-de-Celis; Guillermo Velasco; Ariadna P Velázquez; Tibor Vellai; Edo Vellenga; Francesca Velotti; Mireille Verdier; Panayotis Verginis; Isabelle Vergne; Paul Verkade; Manish Verma; Patrik Verstreken; Tim Vervliet; Jörg Vervoorts; Alexandre T Vessoni; Victor M Victor; Michel Vidal; Chiara Vidoni; Otilia V Vieira; Richard D Vierstra; Sonia Viganó; Helena Vihinen; Vinoy Vijayan; Miquel Vila; Marçal Vilar; José M Villalba; Antonio Villalobo; Beatriz Villarejo-Zori; Francesc Villarroya; Joan Villarroya; Olivier Vincent; Cecile Vindis; Christophe Viret; Maria Teresa Viscomi; Dora Visnjic; Ilio Vitale; David J Vocadlo; Olga V Voitsekhovskaja; Cinzia Volonté; Mattia Volta; Marta Vomero; Clarissa Von Haefen; Marc A Vooijs; Wolfgang Voos; Ljubica Vucicevic; Richard Wade-Martins; Satoshi Waguri; Kenrick A Waite; Shuji Wakatsuki; David W Walker; Mark J Walker; Simon A Walker; Jochen Walter; Francisco G Wandosell; Bo Wang; Chao-Yung Wang; Chen Wang; Chenran Wang; Chenwei Wang; Cun-Yu Wang; Dong Wang; Fangyang Wang; Feng Wang; Fengming Wang; Guansong Wang; Han Wang; Hao Wang; Hexiang Wang; Hong-Gang Wang; Jianrong Wang; Jigang Wang; Jiou Wang; Jundong Wang; Kui Wang; Lianrong Wang; Liming Wang; Maggie Haitian Wang; Meiqing Wang; Nanbu Wang; Pengwei Wang; Peipei Wang; Ping Wang; Ping Wang; Qing Jun Wang; Qing Wang; Qing Kenneth Wang; Qiong A Wang; Wen-Tao Wang; Wuyang Wang; Xinnan Wang; Xuejun Wang; Yan Wang; Yanchang Wang; Yanzhuang Wang; Yen-Yun Wang; Yihua Wang; Yipeng Wang; Yu Wang; Yuqi Wang; Zhe Wang; Zhenyu Wang; Zhouguang Wang; Gary Warnes; Verena Warnsmann; Hirotaka Watada; Eizo Watanabe; Maxinne Watchon; Anna Wawrzyńska; Timothy E Weaver; Grzegorz Wegrzyn; Ann M Wehman; Huafeng Wei; Lei Wei; Taotao Wei; Yongjie Wei; Oliver H Weiergräber; Conrad C Weihl; Günther Weindl; Ralf Weiskirchen; Alan Wells; Runxia H Wen; Xin Wen; Antonia Werner; Beatrice Weykopf; Sally P Wheatley; J Lindsay Whitton; Alexander J Whitworth; Katarzyna Wiktorska; Manon E Wildenberg; Tom Wileman; Simon Wilkinson; Dieter Willbold; Brett Williams; Robin S B Williams; Roger L Williams; Peter R Williamson; Richard A Wilson; Beate Winner; Nathaniel J Winsor; Steven S Witkin; Harald Wodrich; Ute Woehlbier; Thomas Wollert; Esther Wong; Jack Ho Wong; Richard W Wong; Vincent Kam Wai Wong; W Wei-Lynn Wong; An-Guo Wu; Chengbiao Wu; Jian Wu; Junfang Wu; Kenneth K Wu; Min Wu; Shan-Ying Wu; Shengzhou Wu; Shu-Yan Wu; Shufang Wu; William K K Wu; Xiaohong Wu; Xiaoqing Wu; Yao-Wen Wu; Yihua Wu; Ramnik J Xavier; Hongguang Xia; Lixin Xia; Zhengyuan Xia; Ge Xiang; Jin Xiang; Mingliang Xiang; Wei Xiang; Bin Xiao; Guozhi Xiao; Hengyi Xiao; Hong-Tao Xiao; Jian Xiao; Lan Xiao; Shi Xiao; Yin Xiao; Baoming Xie; Chuan-Ming Xie; Min Xie; Yuxiang Xie; Zhiping Xie; Zhonglin Xie; Maria Xilouri; Congfeng Xu; En Xu; Haoxing Xu; Jing Xu; JinRong Xu; Liang Xu; Wen Wen Xu; Xiulong Xu; Yu Xue; Sokhna M S Yakhine-Diop; Masamitsu Yamaguchi; Osamu Yamaguchi; Ai Yamamoto; Shunhei Yamashina; Shengmin Yan; Shian-Jang Yan; Zhen Yan; Yasuo Yanagi; Chuanbin Yang; Dun-Sheng Yang; Huan Yang; Huang-Tian Yang; Hui Yang; Jin-Ming Yang; Jing Yang; Jingyu Yang; Ling Yang; Liu Yang; Ming Yang; Pei-Ming Yang; Qian Yang; Seungwon Yang; Shu Yang; Shun-Fa Yang; Wannian Yang; Wei Yuan Yang; Xiaoyong Yang; Xuesong Yang; Yi Yang; Ying Yang; Honghong Yao; Shenggen Yao; Xiaoqiang Yao; Yong-Gang Yao; Yong-Ming Yao; Takahiro Yasui; Meysam Yazdankhah; Paul M Yen; Cong Yi; Xiao-Ming Yin; Yanhai Yin; Zhangyuan Yin; Ziyi Yin; Meidan Ying; Zheng Ying; Calvin K Yip; Stephanie Pei Tung Yiu; Young H Yoo; Kiyotsugu Yoshida; Saori R Yoshii; Tamotsu Yoshimori; Bahman Yousefi; Boxuan Yu; Haiyang Yu; Jun Yu; Jun Yu; Li Yu; Ming-Lung Yu; Seong-Woon Yu; Victor C Yu; W Haung Yu; Zhengping Yu; Zhou Yu; Junying Yuan; Ling-Qing Yuan; Shilin Yuan; Shyng-Shiou F Yuan; Yanggang Yuan; Zengqiang Yuan; Jianbo Yue; Zhenyu Yue; Jeanho Yun; Raymond L Yung; David N Zacks; Gabriele Zaffagnini; Vanessa O Zambelli; Isabella Zanella; Qun S Zang; Sara Zanivan; Silvia Zappavigna; Pilar Zaragoza; Konstantinos S Zarbalis; Amir Zarebkohan; Amira Zarrouk; Scott O Zeitlin; Jialiu Zeng; Ju-Deng Zeng; Eva Žerovnik; Lixuan Zhan; Bin Zhang; Donna D Zhang; Hanlin Zhang; Hong Zhang; Hong Zhang; Honghe Zhang; Huafeng Zhang; Huaye Zhang; Hui Zhang; Hui-Ling Zhang; Jianbin Zhang; Jianhua Zhang; Jing-Pu Zhang; Kalin Y B Zhang; Leshuai W Zhang; Lin Zhang; Lisheng Zhang; Lu Zhang; Luoying Zhang; Menghuan Zhang; Peng Zhang; Sheng Zhang; Wei Zhang; Xiangnan Zhang; Xiao-Wei Zhang; Xiaolei Zhang; Xiaoyan Zhang; Xin Zhang; Xinxin Zhang; Xu Dong Zhang; Yang Zhang; Yanjin Zhang; Yi Zhang; Ying-Dong Zhang; Yingmei Zhang; Yuan-Yuan Zhang; Yuchen Zhang; Zhe Zhang; Zhengguang Zhang; Zhibing Zhang; Zhihai Zhang; Zhiyong Zhang; Zili Zhang; Haobin Zhao; Lei Zhao; Shuang Zhao; Tongbiao Zhao; Xiao-Fan Zhao; Ying Zhao; Yongchao Zhao; Yongliang Zhao; Yuting Zhao; Guoping Zheng; Kai Zheng; Ling Zheng; Shizhong Zheng; Xi-Long Zheng; Yi Zheng; Zu-Guo Zheng; Boris Zhivotovsky; Qing Zhong; Ao Zhou; Ben Zhou; Cefan Zhou; Gang Zhou; Hao Zhou; Hong Zhou; Hongbo Zhou; Jie Zhou; Jing Zhou; Jing Zhou; Jiyong Zhou; Kailiang Zhou; Rongjia Zhou; Xu-Jie Zhou; Yanshuang Zhou; Yinghong Zhou; Yubin Zhou; Zheng-Yu Zhou; Zhou Zhou; Binglin Zhu; Changlian Zhu; Guo-Qing Zhu; Haining Zhu; Hongxin Zhu; Hua Zhu; Wei-Guo Zhu; Yanping Zhu; Yushan Zhu; Haixia Zhuang; Xiaohong Zhuang; Katarzyna Zientara-Rytter; Christine M Zimmermann; Elena Ziviani; Teresa Zoladek; Wei-Xing Zong; Dmitry B Zorov; Antonio Zorzano; Weiping Zou; Zhen Zou; Zhengzhi Zou; Steven Zuryn; Werner Zwerschke; Beate Brand-Saberi; X Charlie Dong; Chandra Shekar Kenchappa; Zuguo Li; Yong Lin; Shigeru Oshima; Yueguang Rong; Judith C Sluimer; Christina L Stallings; Chun-Kit Tong Journal: Autophagy Date: 2021-02-08 Impact factor: 13.391
Authors: E Servián-Morilla; M Cabrera-Serrano; E Rivas-Infante; A Carvajal; P J Lamont; A L Pelayo-Negro; G Ravenscroft; R Junckerstorff; J M Dyke; S Fletcher; A M Adams; F Mavillard; M A Fernández-García; J L Nieto-González; N G Laing; C Paradas Journal: Acta Neuropathol Commun Date: 2019-03-01 Impact factor: 7.801
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