Frequency of KFERQ-like motifs in the human proteome

Using the KFERQ-like motif construction rules experimentally defined originally [2] (Fig 1A), we analyzed all sequences of the human UniProt reference proteome (uniprot.org, UP000005640, 70,952 proteins) for the presence of KFERQ-like motifs. The analysis was then restricted to reviewed Swiss-Prot entries (20,165 proteins), excluding proteins transcribed from open reading frames without direct experimental confirmation (TrEMBL). This filtering step strongly reduced the number of short proteins (<250 amino acids, S1A Fig) that are often protein fragments.

It is not unusual that proteins contain more than one KFERQ-like motif. However, because it has been experimentally demonstrated that increasing the number of motifs in a protein does not accelerate its rate of degradation by CMA [19], we imposed a hierarchy on the types of motifs for protein grouping purposes. Canonical motifs are those already present in the unmodified protein sequence and are the best characterized. Phosphorylation- and acetylation-generated motifs (putative) are less well described and require PTMs in the protein sequence. Consequently, we categorized the results into four groups: (i) proteins containing at least one canonical motif, without factoring in the presence of putative motifs (canonical); (ii) proteins without canonical motifs but containing at least one phosphorylation-generated motif (phosphorylation-generated); (iii) proteins with only acetylation-generated motifs; and (iv) proteins without any motif. Although in specific circumstances the flanking Q residue in a motif can be replaced by asparagine (N) [24], due to the current lack of experimental information on the permissibility of this replacement, we did not include those motifs in our analysis (for more details, see the Discussion section). In the restricted data set, 45.98% of sequences contained at least one canonical motif, 20.31% contained no canonical motif but a phosphorylation-generated, and 9.17% contained only acetylation-generated motifs (Fig 1B). A similar overall motif distribution was found in the complete UniProt human proteome, including unreviewed TrEMBL entries, although the percentage of proteins without motif was higher (S1B Fig).

We next analyzed, within each group, the occurrence of additional motifs and found that, for proteins bearing at least one canonical motif, about 20% also contain phosphorylation-generated motifs, 20% acetylation-generated motifs, and almost half of them (46.5%) contain all three (Fig 1C). In the case of proteins with only putative motifs, one third contained both phosphorylation- and acetylation-generated motifs (Fig 1C). Analysis of the number of motifs of each type in proteins grouped according to Fig 1B revealed that the most common occurrence was for proteins to carry only one motif, independently of the motif type (S1C Fig). This indicates that when proteins contain motifs of different classes, most of the time, each class is only represented once. To determine whether the presence of more than one motif was more common in longer proteins, we analyzed a possible correlation between protein length and number of canonical motifs (Fig 1D). The low correlation coefficient (R² = 0.234) indicates that protein length is a poor predictor for the number of motifs per protein.

Position of motifs within proteins

The number of experimentally validated CMA substrate proteins (<50) is still too low to identify possible preferences in the position of the KFERQ-like motifs within the protein sequence. S2A and S2B Fig shows the absolute and relative position of motifs in a subset of 16 of the validated CMA substrates (summarized in [4]). However, taking advantage of our proteome-wide search for these motifs, we analyzed the distribution of canonical and putative motifs along the protein length. We anticipated that only very strong position preferences might become evident because the tested proteins have highly variable sizes, domain structure, and function. For the group of proteins with canonical KFERQ-like motifs, we observed a largely uniform motif distribution along the protein length (Fig 2A). Interestingly, we also noticed a decrease in the number of motifs close to the N-terminus of proteins compared with the C-terminus (Fig 2B). This difference is not due to the presence of an initiator methionine, because it was still noticeable even when N-terminal methionine residues were not counted towards protein length (S2C Fig). Similar results were obtained for the putative phosphorylation- and acetylation-generated motifs that were equally distributed along the protein length, with a decrease in frequency close to the N-terminus of proteins compared with the C-terminus (S2D Fig). The mean number of canonical motifs in the first 2.5% of the protein length was 42% lower than in the remaining 97.5% of the protein. This reduction was 35% for phosphorylation-generated and 43% for acetylation-generated motifs, respectively. Future studies on the preference for the C-terminal region may shed new light on the mechanism of substrate recognition by HSC70 or on the dynamics of the chaperone/substrate complex once reaching the lysosomal membrane.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 2. Distribution of KFERQ-like motifs within protein sequences.

(A) Distribution of canonical KFERQ-like motifs along the protein length (normalized to a scale from 0 [N-terminus] to 1 [C-terminus]). The histograms show the count of motifs at the relative position with a bin size of 0.02. (B) The first 10% (N-terminus; top) and last 10% (C-terminus; bottom) of the normalized protein length in Fig 2A, shown here with a bin size of 0.001. The C-terminal plot (bottom) is mirrored for easier comparison. The red line indicates the slope of the reduction in KFERQ-like motifs. (C, D) Bar plots showing the average of exposed amino acids, as predicted from the primary sequence, using JPred4 for proteins validated as CMA substrates (C) or proteins in the human proteome harboring one canonical motif (D). For each protein, a region ±30 amino acids around the central amino acid of the motifs was isolated and aligned on the KFERQ-like motifs. The percentage of exposed residues was then calculated for each position. The red line indicates the mean percentage of exposure for all amino acids in all investigated proteins. Amino acids that are part of the KFERQ-like motifs are highlighted in blue. (E-H) Examples of domain localization and experimentally confirmed PTMs in KFERQ-like motifs of DJ-1 (E), alpha-synuclein (F), CHK1 (G) and PLIN3 (H). Canonical motifs are marked as yellow bars, phosphorylation-generated in blue, and acetylation-generated in green. Protein structures were obtained from the RCSBPDB protein data bank (rcsb.org) using PBD IDs 1j42 (for DJ-1 [25]); 1XQ8, 2KKW, and 2N0A (for alpha-synuclein [26]); 4FSM (for Chk1 [27]); and 1SZI (for PLIN3 [28]). The structures of the KFERQ-like motifs are shown as strings and ribbons colored based on amino acid properties. PTMs shown: ubiquitylation (ub), phosphorylation (P), and oxidation (Ox). Arrows: location of the motif in the protein structure. The cartoon in (G) depicts the conformational change in Chk1 that releases autoinhibition of its catalytic activity. CHK1, checkpoint kinase 1; CMA, chaperone-mediated autophagy; Memb. Bind., Membrane Binding; Ox, oxidation; P, phosphorylation; PARK7, Parkinsonism associated deglycase; PAT, perilipin/ADRP/TIP47; PLIN3, perilipin 3; PTM, posttranslational modification; ub, ubiquitylation.

https://doi.org/10.1371/journal.pbio.3000301.g002

We also considered the location of the motif in the fully folded proteins, because for HSC70 binding, the motif should be accessible at the protein surface. We followed the approach used to study the relation between protein structure and motif location for other small linear motifs (SLiMs) [29] that share characteristics with KFERQ-like motifs, such as their short lengths (average of six amino acids) and degenerated sequences [30]. To estimate the accessibility of a motif, we predicted the solvent accessibility of the amino acids in the motif and the surrounding protein region with the JPred4 algorithm [31]. Using four experimentally confirmed CMA substrates, we validated that the classification of residues into buried and exposed predicted by the JPred4 algorithm closely follows the classification obtained from their protein crystal structures (S2E Fig). We next investigated the motifs and their surrounding regions in 24 experimentally confirmed CMA substrates (S1 Table) using the predicted solvent accessibility. Fig 2C shows the average percentage of exposed amino acids in KFERQ-like motifs (marked in blue) with a flanking region of ±30 amino acids around the central motif position. The mean percentage of exposed residues over all investigated proteins (red line) is close to 50%, in line with an equal probability for an amino acid to be classified as exposed or buried. Interestingly, the amino acids in the motif and in close proximity upstream of the motif (approximately 8 residues) are more frequently classified as exposed. This is especially striking for the flanking amino acids inside the motif, while the central amino acid is less exposed (Fig 2C). Very similar accessibility properties in the residues at the motif were observed when we performed the same type of analysis in >1,000 proteins in the human proteome bearing one canonical motif (Fig 2D). In this case, while the solvent exposure of amino acids outside of the KFERQ-like motif was closer to the baseline distribution of 50%, the flanking amino acids that form part of the motif were still clearly more frequently exposed and the central amino acid was again more buried (Fig 2D). These results support a critical function to the flanking amino acids of the motif, in agreement with the experimental observation that mutation of the flanking Q and the amino acid next to it is sufficient to disrupt HSC70 binding [8, 11–13, 15].

Partial protein unfolding, often associated with protein damage or aberrant synthesis, might make KFERQ-like motifs accessible to HSC70 for targeting to CMA as part of its role in protein quality control. However, recent studies support that timely removal of still functional proteins by CMA is behind the ability of this autophagic pathway to regulate multiple intracellular processes (i.e., glycolysis, lipolysis, cell cycle arrest, etc.) [11, 18]. In those instances, because HSC70 recognizes the motif in the fully folded protein, it is likely that location in specific protein domains and/or PTMs in the motifs and in nearby areas contribute to modulating HSC70 binding. To start gaining insights into this regulated substrate recognition, we used 24 experimentally validated substrates (72 motifs total) and evaluated the protein domains where motifs localize and their possible PTMs (S2 Table). Despite the small sample size, we found that close to 40% of motifs were in protein domains known to mediate protein–protein interactions (S2 Table). We also identified motifs in regions described to be important for protein structure (27%) or protein activity (31%). Analysis of experimentally validated PTMs—which can generate or disrupt existing motifs—revealed abundance of ubiquitylation and acetylation events in the KFERQ-motifs of this subset of CMA substrates (S2 Table). Other modifications, such as sumoylation, methylation, succinylation, and neddylation, that will disrupt recognition of the motif by HSC70 were also identified (S2 Table). Fig 2E–2H shows a series of vignettes illustrating how the information on location and PTMs of KFERQ-like motifs could be used to infer their possible impact on regulated degradation of a protein. For example, in the case of the transcriptional regulator DJ-1 (PARK7 locus) (Fig 2E) the overlapping acetylation-generated motif and the canonical motif are in an alpha helix of the core structure region, 10 residues upstream of the cysteine (¹⁰⁶C) shown to be key for activation of DJ-1 by free radicals [32]. It is possible that the oxidative status of ¹⁰⁶C changes the exposure of the two K residues in the motif to promote HSC70 binding, or to prevent it through their already described ubiquitylation (⁸⁹K [33] or ⁹³K [34]). The only canonical motif present in α-synuclein, a protein tightly related with Parkinson disease pathology, is right at the transitional region between the α-helix structured polymerization region and the disorganized C terminus (Fig 2F). Upon analysis of the position of this motif in currently available structures of this protein, we noticed that association of α-synuclein to membranes (as part of its physiological function [35]) buries the KFERQ-like motif (Fig 2F). Membrane binding masking the KFERQ-like motif may prevent the degradation of vesicle-associated α-synuclein, whereas ubiquitylation in ⁹⁶K [36] and protein partners known to bind this region may prevent degradation by CMA of free soluble α-synuclein. Interestingly, the KFERQ-like motif is also masked in the structure of α-synuclein fibers (Fig 2F), supporting the previous experimental findings that, once in this oligomeric state, the protein is no longer amenable for CMA [8]. Checkpoint kinase 1 (CHK1) KFERQ-motifs and structure are depicted in Fig 2G as an example of how motifs in different protein domains could contribute to the degradation of functionally different forms of the same protein. Although structural information is only available for the kinase region of CHK1, it is well accepted that activation requires release of self-inhibition [37] through conformational changes dependent on phosphorylation of ³⁴⁵S, separated only by four amino acids from the canonical KFERQ-like motif in CHK1 (Fig 2G). Changes in the already-reported phosphorylation of the serine residues flanking the motif [38] may prevent or promote HSC70 recognition and subsequent degradation. In fact, we have previously reported that CHK1 degradation by CMA is modulated by phosphorylation [11]. Similarly, ubiquitylation/deubiquitylation events in the putative motif of the catalytic region may modulate degradation of the still inactive CHK1 (Fig 2G). Phosphorylation has also shown to be a triggering event in degradation of the lipid droplet-associated protein perilipin 3 (PLIN3) by CMA [12], which is a limiting step to initiate lipolysis. One of the PLIN3 motifs is in the perilipin/ADRP/TIP47 (PAT) region—used for association to lipid droplets—making it likely buried in the lipid surface (Fig 2H). Phosphorylation in two residues downstream of the motif (¹⁰⁶S) may facilitate its exposure. In fact, phosphorylation of ²⁴⁵S right before the second motif—located in the beginning of the four-helix bundle region—has been described to induce a conformational change for interaction with the hormone-sensitive lipase that will initiate lipolysis [39]. It is attractive to propose that those conformational changes will modulate HSC70 access to that region and contribute to modulating PLIN3 degradation by CMA. The information on position and PTMs of KFERQ-like motifs provided in this work may help guide future experimental studies on regulated protein degradation.

Analysis of amino acids within KFERQ-like motifs

In our effort to analyze all conceivable KFERQ-like motifs, we studied every amino acid permutation allowed within the rules that determine the architecture of the motifs (Fig 1A). However, some arrangements of amino acids may be better suited for binding to HSC70 than others. In light of the current lack of experimental information on the physical and structural basis for HSC70 binding, we decided to analyze if specific amino acid arrangements inside the KFERQ-like motif are found in the human proteome more frequently.

We calculated for each amino acid its relative percentage at the four positions in the motif (because Q is in a fixed position). To allow superimposition, all motifs were aligned with a downstream glutamine independently of their original orientation in the protein. Fig 3A shows the results for the analysis of all canonical motifs in the human proteome. The relative percentage of hydrophobic amino acids was lowest at the position furthest away from the glutamine (<23%, position −4) and highest at position −2 (>27%). In contrast, the acidic amino acids were more frequent at position −4 (30%) and less commonly found in position −2 (21%). The basic amino acids showed no clear location preference within the motif. A similar analysis in putative motifs revealed that, in phosphorylation- (Fig 3B) and acetylation-generated motifs (Fig 3C), position −2 was still the most common for hydrophobic amino acids. Acidic residues in acetylation-generated motifs maintained similar preference for position −4 and low frequency in position −2, as observed in the canonical motifs (Fig 3C), while among the phosphorylation amenable amino acids used in phosphorylation-generated motifs, only serine showed a weak trend in this direction (Fig 3B). Despite some differences in relative frequency at each position, when grouped by categories, the higher frequencies of an acidic residue in −4 and a hydrophobic residue in −2 were still observed for all three types of motifs (Fig 3D). Overall, these findings suggest a more important role of both acidic and hydrophobic residues in motif construction. Their preferred placement in the motif’s borders is in agreement with our previous result from the prediction of solvent exposure that the more exposed amino acids are at the motif’s borders and the less exposed are in the motif’s core (Fig 2C and 2D).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 3. Amino acid positioning and frequencies within KFERQ-like motifs.

(A-C) Frequency of amino acids at the four variable positions in canonical (A), phosphorylation-generated (B), and acetylation-generated (C) motifs in the human proteome. To allow superimposition, all motifs were aligned with a downstream glutamine. The amino acid positions are given relative to the glutamine (−1 = closest and −4 = furthest away). For each amino acid, the counts at each position are normalized as the percentage of the sum of all four positions. The phosphorylation acceptors serine, threonine, and tyrosine (red) are classified as acidic because they appear as an acidic residue once phosphorylated. Red boxes highlight consistent changes in abundance across motif types (see text for details). (D) Frequency of amino acids grouped by biochemical properties (basic, hydrophobic, acidic) at the four variable positions. The groups are the same three type of KFERQ-like motifs as shown in Fig 3A–3C. (E) Comparison of amino acid frequencies at each position in canonical motifs from the human proteome and from a permutated proteome. Amino acid counts from A are divided by the counts in motifs from permutated proteins. Means are from 40 random samples of 10% of the data sets each. ***p < 0.001, **p < 0.01, *p < 0.05. The p-values from two-sided t tests are corrected (Bonferroni) by the number of comparisons (n = 32). hydroph., hydrophobic;

https://doi.org/10.1371/journal.pbio.3000301.g003

To analyze if some amino acids preferentially occur in KFERQ-like motifs, we corrected their frequencies in the motifs by their relative abundance in the total proteome. Therefore, we generated a baseline for the amino acid frequency in KFERQ-like pentapeptides using motifs extracted from randomized sequences with the same amino acid composition as the human proteome. This comparison showed that leucine is enriched among hydrophobic residues both in the preferred and less common positions (15% increase, position −2), while the presence of valine and phenylalanine is below baseline (20% depletion, position −4) (Fig 3E). Among the acidic residues, glutamic acid is the most frequent amino acid at any given position (30% increase, position −4), and for the basic residues, lysine is slightly more enriched compared with arginine (Fig 3E). Similar trends are observed for phosphorylation- and acetylation-generated motifs, although the amino acid preferences for phosphorylation-generated motifs are less pronounced compared with the other kinds of motifs (S3A and S3B Fig). Importantly, the frequency of amino acids involved in the formation of KFERQ-like motifs is the same for proteins harboring a KFERQ-like motif as for proteins without a motif (S3C Fig). Future experimental studies are needed to test whether preference of amino acid usage and location in a KFERQ-like motif are predictive of HSC70 binding affinity.

Evolutionary conservation of KFERQ-like motifs

In contrast with other types of autophagy conserved from yeast to mammals, CMA is of a relatively late evolutionary development [40]. The recent discovery that a different type of autophagy, eMI, also requires KFERQ-like motifs for protein degradation and the fact that, in addition to mammals, eMI has also been described as early as Drosophila melanogaster [41] make it impossible to establish a link between CMA and abundance of KFERQ-motifs in proteomes of different species. However, because LAMP-2A, the essential component of CMA, is absent in flies, rendering them unable of performing CMA, we still considered of interest comparing KFERQ-motifs in the proteomes of species with different CMA capability. Thus, we analyzed the proteomes of Mus musculus (capable of both CMA and eMI), Drosophila melanogaster (capable of eMI but not CMA), and Saccharomyces cerevisiae (unable to perform either). We found that, although these organisms differ in their ability to perform each of these types of selective autophagy, the overall percentages of proteins bearing each of the types of KFERQ-like motifs are comparable (Fig 4A). We noticed, however, a trend towards a higher proportion of proteins with KFERQ-like motifs, specifically canonical ones, in species that could perform at least one of these types of autophagy (Fig 4A). Studies targeted to identify if HSC70 binds to proteins with motifs in S. cerevisiae and the fate of those proteins could help in identifying an alternative to these selective forms of autophagy in yeast.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 4. Conservation of KFERQ-like motifs and CMA components among species.

(A) Percentage of proteins with the indicated types of KFERQ-like motifs in the referenced proteomes of M. musculus, D. melanogaster and S. cerevisiae. Only reviewed Swiss-Prot entries are included. The occurrence of motifs is ranked as canonical > phosphorylation-generated > acetylation-generated, and proteins with a combination of motifs are assigned to a group based on their highest-ranking motif. (B) Scatterplot of the conservation of motifs from human proteins with a single canonical motif in orthologs from the list of species predicted to be able or unable to perform CMA based on detection of LAMP-2A (S4C Fig). Sequences are aligned in MUSCLE (drive5.com/muscle) and motifs identified in the pentapeptides match the exact position of the human motif. The conservation score was calculated as follows: , where _partial = species with motifs of a different type and n_noOrth = species with no ortholog identified. A conservation score >0 indicates that it is more likely than not to find an ortholog with a motif at the same position as the human protein. (C) Conservation of CMA machinery across species. Proteins involved in CMA are grouped based on their function (effector and modulators) and localization (lysosomal and extra-lysosomal). The colored disk next to the name of each element represents the conservation between CMA-able and CMA-unable species, as indicated by the lateral color bar. Positive and negative symbols indicate their function as activators or inhibitors of CMA activity. AKT1, RAC-alpha serine/threonine-protein kinase; Cath A, lysosomal protective protein/cathepsin A; CMA, chaperone-mediated autophagy; eF1α, Elongation factor 1-alpha; GFAP, Glial fibrillary acidic protein; HSC70, Heat shock cognate 71 kDa protein; HSP40, DnaJ homolog subfamily B member 1; HSP90, Heat shock protein HSP 90; LAMP-2A, lysosome-associated membrane protein type 2A; NFAT, nuclear factor of activated T cells; NRF-2, nuclear factor erythroid 2-related factor 2; PHLPP1, PH domain leucine-rich repeat-containing protein phosphatase 1; Rab11, Ras-related protein Rab-11; RAC1, Ras-related C3 botulinum toxin substrate 1; RARα, Retinoic acid receptor alpha.

https://doi.org/10.1371/journal.pbio.3000301.g004

To begin to systematically gather similar information across additional species, we grouped them based on their potential to perform CMA. Because CMA depends on the presence of a spliced variant of the LAMP2 gene, we took advantage of the two unique features in the C-terminus of this variant that differentiates it from the other two LAMP2 isoforms in mammals: (1) the presence of three to four basic amino acids in the proximal region of the C-terminus cytosolic tail [42] and (2) the sequence GYEQF at the end of the C-terminus (S4A Fig). Furthermore, because experimental studies have demonstrated that the addition of a short stretch of amino acids (i.e., a 7–amino acid hemagglutinin tag) to the C-terminus of LAMP-2A was enough to disrupt its CMA ability [42], we also imposed the criterion that GYEQF-homology sequences should be present at the end of the C-terminus. Using the Basic Local Alignment Search Tool (BLAST) we searched for homologous sequences to the LAMP-2A tail that conform to these criteria and found that the presence of LAMP-2A isoforms is limited to mammalian species together with some bird [40] and reptile species (S4B Fig). Note that although a recent theoretical commentary proposes that CMA occurs in some types of fish, based on the sequence analysis of a spliced variant of the LAMP2 gene containing GYEQF followed by an additional amino acid [43] (S4C Fig), we decided against allowing that variation in our search criteria until experimental evidence that this isoform is required for CMA is generated. Intriguingly, not all branches within the class Mammalia possess LAMP-2A. For example, no homologous sequences were found in Metatheria indicating that these may constitute a separate line in the development of selective autophagy.

Using this information in combination with a list of organisms for which rich proteomic information is available (treefam.org, March 2013) [44], we constructed a set of 50 species classified by the presence or absence of a LAMP-2A homologue that we generically named CMA-able or CMA-unable, respectively (S4C Fig). Orthologs of human proteins harboring a single canonical KFERQ-like motif were identified using the EggNOG database of orthologous groups (eggnogdb.embl.de), and all sequences of those orthologs within the 50 CMA-able or -unable species were selected. After alignment of the sequences, the positions corresponding to the KFERQ-like motif in the human sequence were analyzed for the presence of a CMA-targeting motif. A conservation score was calculated for the species grouped by their capability to perform CMA to determine which motifs are selectively conserved in species with CMA. We found that about 45% of the investigated motifs are more conserved in CMA-proficient species over CMA-deficient species (Fig 4B; a conservation score above zero indicates that it is more likely to find an ortholog with a motif at the same position as the human protein than not). In CMA-unable species, the score is often negative because many of them have no clear orthologs to human proteins. However, no motif is fully conserved in CMA-able species either. When setting a score of >0 (more than average conservation) as threshold, the motifs of 503 proteins are selectively conserved in CMA-able species and not in CMA-unable ones (S3 Table). We speculate that this difference in conservation suggests a higher number of true positive KFERQ-like motifs in this group. Interestingly, analysis of the frequency of amino acids involved in the formation of KFERQ, as in S3C Fig but across species, revealed that amino acid percentages are also conserved among CMA-able species, while the percentages are noticeably more variable for the less closely related group of species unable to perform CMA (S3D Fig).

To estimate the conservation of other CMA components (effectors and regulators) across evolution, we used the division on CMA-able and CMA-unable species established from the conservation analysis of the LAMP-2A cytosolic tail. We found that 73% of CMA components (including core machinery [effectors] and lysosomal and extra-lysosomal regulators) are highly conserved across all species (Fig 4C and S4 Table). Only two of the extra-lysosomal CMA regulators (nuclear factor of activated T cells [NFAT] and nuclear factor erythroid 2-related factor 2 [NRF-2]) and two of the lysosomal regulators (Glial fibrillary acidic protein [GFAP] and Humanin) showed partial conservation (Figs 4C and S4C and S4 Table). This is in line with the hypothesis that the LAMP-2A isoform plays a critical role for CMA and its absence is sufficient to make a species unable to perform CMA.

Enrichment of KFERQ-like motif classes in functional terms

When considering the coexistence of both constitutive and posttranslational-generated KFERQ-like motifs in a given proteome, a possible prediction would be that proteins with buried motifs (either in their structure or through protein–protein or protein–membrane interactions) will be more likely to have canonical motifs, whereas proteins in which the motif is in an easily accessible region may use generation of motifs through PTMs to prevent continuous degradation by CMA. As a consequence, we expect that proteins harboring particular motifs would be better suited for some but not other cellular processes.

To analyze if different types of KFERQ-like motifs associate with specific cellular processes, we performed an enrichment analysis using biological process annotations from gene ontology (GO; geneontology.org, S5 Table). Applying the previously mentioned motif hierarchy (first, canonical; next, phosphorylation-generated; followed by acetylation-generated motifs; Fig 1B), we observed clear differences in the biological processes enriched in each type of motif (S5A Fig and S6 Table). Canonical motifs are highly enriched in cytoskeleton-associated terms (cytoskeleton organization, cell projection organization), proteins with phosphorylation-generated motifs are frequently associated with transport-related terms, and proteins with acetylation-generated motifs with metabolism-related terms. Strikingly, the group of proteins without a CMA-targeting motif shows only weak enrichment in biological processes, and we confirmed that this is not an effect of particularly low annotation density in this group (S5B Fig). To further expand the analysis of a possible association between KFERQ-like motifs and cellular functions, we performed a similar analysis of hierarchy-arranged motifs with a protein localization database (COMPARTMENTS, https://compartments.jensenlab.org) and compared the cellular distribution of KFERQ-bearing proteins with the full proteome distribution. Interestingly, we found significant enrichment of proteins bearing canonical motifs in cytoskeleton, cytosol, and endosomes and of proteins with phosphorylation-generated motifs in mitochondria, when compared with the overall proteome distribution (S5C Fig and S6 Table). Also, we noted a significant decrease of KFERQ-like motifs of any type in proteins in the extracellular space (S5C Fig and S6 Table).

The unexpected high percentage of the proteome that contained more than one KFERQ-like motif (Fig 1C) made us consider whether specific combinations of motifs may also be associated with some biological process. Further analysis of the enrichment of the biological process in proteins grouped according to their combinations of motifs confirmed that this was indeed the case and that some combinations of motifs are more frequently found together in certain functional terms. For example, inside the group of proteins with canonical motifs, those also bearing an acetylation-generated motif were enriched in cytoskeleton-associated processes (S5D Fig and S7 Table). This observation motivated us to perform a network analysis of GO term enrichment, but this time without applying a hierarchy for motif types. Using this strategy, we could show that several cellular functions (i.e., cell cycle, gene expression, signal transduction, cellular localization, cellular metabolic processes, and cell death) formed clusters associated with all kind of motifs (Fig 5A, black circles). Interesting, a small subset of clusters was associated with unique motif types (Fig 5A, color coded circles; and S6A–S6C Fig for higher magnification). Thus, proteins with canonical motifs were associated with protein phosphorylation (including both regulation of kinase enzymes activity and of phosphate metabolism) (S6A Fig) and proteins with phosphorylation-generated motifs associate with nucleic acid metabolism and transcription (S6B Fig), whereas proteins with acetylation-generated motifs associated with RNA transport and localization (S6C Fig). These findings strengthen the idea that functionally related proteins may also share common signals for their regulated selective degradation by CMA.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 5. Enrichment of proteins with KFERQ-like motifs in biological processes.

(A) Enrichment map analysis of the association of proteins with KFERQ-like motifs with biological processes. The nodes are radial heat maps in which size shows the number of proteins within the given annotation and intensity of color indicates association with a specific kind of motif (the position of each type of motif in the radial heat map is indicated in the legend). Edges represent similarity between nodes, and color coded circles indicate clusters associated with a specific motif type (yellow for canonical, blue for phosphorylation-generated, and green for acetylation-generated motifs). (B,C) Enrichment for human proteins annotated under cellular components biogenesis (B) and under protein catabolic processes (C), grouped by relative content of KFERQ-like motif classes. For each protein, the fractional content (0% to 100%) of canonical, phosphorylation-, and acetylation-generated motifs is calculated. Proteins are binned by motif composition using a 5% bin size for each dimension. The combined score for enrichment (−log_e(p-value)*z-score) of the proteins in each bin (small triangles within the plot area) is color coded from blue (low) to red (high). Acet., acetylation; Phosph., phosphorylation.

https://doi.org/10.1371/journal.pbio.3000301.g005

In the case of proteins with multiple motifs, we also calculated the exact motif composition expressed as a percentage of each motif type for different biological processes. For example, a protein with two canonical, one phosphorylation-, and one acetylation-generated motifs will be assigned a fractional content of 50% canonical, 25% phosphorylation-generated, and 25% acetylation-generated motifs. To display the enrichment of proteins grouped by their fractional content, we utilized triangle plots that display the three motif dimensions in a two-dimensional space (as illustrated by the cartoon in S7A Fig). This method confirmed the association of some GO terms with particular multiple-motif combinations. For example, in the case of proteins with at least one canonical motif, the term “cellular component biogenesis” was enriched in proteins with a generally high content of canonical motifs and intermediate content of phosphorylation-generated motifs (Fig 5B). In comparison, proteins participating in protein catabolic process had a higher ratio of canonical and acetylation-generated and fewer phosphorylation-generated motifs (Fig 5C). In the case of proteins with only putative motifs, terms like transmembrane transport were clearly associated with phosphorylation-generated motifs (S7B Fig), whereas proteins involved in mRNA catabolic process were enriched in acetylation-generated motifs (S7C Fig). These results support an association between the type and combination of KFERQ-like motifs in proteins and their participation in specific biological processes, thus suggesting a differential regulatory role for CMA in biological function through this elaborated code of protein targeting motifs.

The KFERQ-finder computational tool

To allow other researchers to utilize these analyses for their work, we have developed the “KFERQ finder” (S8 Fig), a free web-based tool for performing KFERQ-like motif searches in proteins of interest. Protein sequences are input using their UniProt ID or as FASTA sequence, and the tool searches them against all possible pentapeptides containing a KFERQ-like motif. The “KFERQ finder” menu gives the possibility of (1) analyzing individual or in-bulk inputs, (2) selecting the type of KFERQ-like motif to search for, and (3) allowing additional options such as advanced motifs (to include replacement of Q by N) or inactivation of the motif through ubiquitylation. We recommend caution when using the Q by N replacement search because, as indicated in the previous sections, those motifs may need still unknown features to lead to CMA targeting (they are often necessary but not sufficient). The results are presented as (i) entry name (UniProt ID/sequence name), (ii) status of the protein in the UniProt Database, (iii) protein names, (iv) gene name, (v) protein length, (vi) motif composition, (vii) motif position (start amino acid), and (viii) type of motif (“KFERQ finder,” S8 Fig).

To validate the use of this tool for the identification of KFERQ-containing proteins and potential CMA substrates, we experimentally analyzed the degradation properties of proteins identified in silico by the search tool as having KFERQ-like motifs but never previously reported as CMA substrates. As input in the KFERQ finder, we used a list of proteins for which we had access to antibodies (>200) and randomly selected six proteins bearing a KFERQ-like motif and three without motif to analyze their degradation (S9 Fig). We treated NIH3T3 cells with inhibitors of lysosomal proteolysis (NH₄Cl and leupeptin) to determine the fraction of protein undergoing lysosomal degradation and used NIH3T3 cells knocked down (KD) for LAMP-2A to analyze the CMA dependence of their degradation (S9A Fig). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a well-characterized CMA substrate, was included as positive control, and the efficiency of lysosomal proteolysis inhibition was confirmed by measuring the degradation of microtubule-associated proteins 1A/1B light chain 3B (LC3-II) that takes place via macroautophagy (S9B Fig). We confirmed that GAPDH and the other five KFERQ-bearing proteins all underwent lysosomal degradation, albeit at different rates (S9A Fig). Degradation of three of the new proteins was dependent on LAMP-2A (1) (lysosomal degradation through CMA), whereas two of the new KFERQ-containing proteins still underwent degradation in LAMP-2A KD cells (2) (their lysosomal degradation could instead be mediated through eMI, which also utilizes this targeting motif) (S9A Fig). None of the proteins that rendered negative for the presence of KFERQ-like motif in their sequence were degraded in lysosomes (3) (S9A Fig). Although additional experiments are needed to fully confirm that the new proteins are bona fide CMA substrates (i.e., by eliminating the KFERQ-like motif or reproducing their direct transport into isolated lysosomes [45]), this experiment validates the use of the KFERQ finder to identify putative CMA substrates.

Software and data resources

The Homo sapiens (UP000005640), M. musculus (UP000000589), D. melanogaster (UP000000803), and S. cerevisiae (UP000002311) proteomes were downloaded from UniProt.org (retrieved January 16, 2018). Data analyses were performed using R [34] (3.3.2) together with the packages dplyr [50] (0.7.4), stringr [51] (1.2.0), gplots [52] (3.0.1), ggplot2 [53] (2.2.1), jsonlite [54] (1.5), and bio3d [55] (2.3–3). Protein structures were obtained from the RCSBPDB protein data bank (rcsb.org) and PTMs from the PhoshoSitePlus [56]. All code used for analyses can be found at https://github.com/PhilippKirchner/KFERQ_analysis.

Analysis of amino acid frequencies in KFERQ-like motifs

The frequency for each amino acid at the four variable positions in the motifs (shown as percentage of total counts) was calculated and compared with a baseline frequency calculated for motifs identified in the same proteome after scrambling each protein sequence. Means and standard deviations were derived by repeatedly (40 times) sampling 10% of each data set. Statistical tests were performed using two-sided t tests with a Bonferroni correction of the p-values for multiple testing (n = 32).

Prediction of solvent exposure from primary sequence

The relative solvent exposure was calculated from crystal structures (pdb.org) or predicted from amino acid sequence using JPred4 [31] (compbio.dundee.ac.uk/jpred). JPred4 accepts inputs with a maximum length of 800 amino acids. Therefore, all proteins with a length above this limit were removed from the analysis to avoid unknown behavior of truncated input sequences. An amino acid with a relative solvent accessibility <0.25 was considered buried.

Identification of LAMP-2A isoforms

Isoforms of the LAMP-2A protein were identified in a Protein BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi) search using the last 100 amino acids of each protein in the human proteome. The results were then filtered using the regular expression “[K,R,H]{3,4}*{1,3}GYEQF$” to match the required features of the LAMP-2A C terminus tail.

Alignment of orthologs and calculation of conservation scores

Using the information of LAMP-2A expression, species from different branches of the phylogenetic tree (treefam.org) were classified as CMA-able and CMA-unable. For the analysis of motif conservation across species, orthologs were identified from the EggNOG database (eggnogdb.embl.de) for a set of human proteins with one canonical motif. Species from the treefam.org database not well represented in the EggNOG database were manually corrected to a better-covered subspecies of the same species when possible. If more than one possible ortholog was returned, the two with the highest alignment score (minimum expectation value and maximum alignment score, BLAST) were selected. All orthologs for a specific protein were aligned using MUSCLE [57] (https://www.drive5.com/muscle/), and motifs were identified in the pentapeptide matching the motif position in the human protein. After identification of motifs in the isolated pentapeptides, the conservation score was calculated as follows: , where _partial = species with motifs of a different type and n_noOrth = species with no ortholog identified.

Analysis of association with biological processes

GO terms were manually selected using the PANTHER GO-slim set (http://www.pantherdb.org/panther/ontologies.jsp) as a starting point to cover a wide range of intracellular processes with general terms. To improve annotation density, annotations for all available GO terms were mapped to the custom group using the map2slim algorithm (go.princeton.edu/cgi-bin/GOTermMapper). For each group of proteins, enrichment probabilities were calculated using the Fisher exact test. Z-scores were calculated by repeated (40 times) random resampling of GO term annotations. The combined score was then derived as −log_e(p-value)*z-score. Ternary plots [58] were made using the python-ternary package in Python 3.7 and the Python scientific stack [59, 60], colbert [61]. Enrichment map analysis was performed using Cytoscape 3.7.1 [62] and Enrichment map 3.1.0 [63].

Analysis of protein degradation

NIH-3T3 mouse fibroblasts (American Type Culture Collection, Manassas, VA, ATCC CRL-1658) were cultured in a 37°C incubator with 5% CO₂ in DMEM supplemented with 10% newborn calf serum. For lysosomal proteolysis assays, a combination of two lysosomal proteolysis inhibitors (lys inh), ammonium chloride (20 mM; Sigma-Aldrich, St. Louis, MO, A9434) and leupeptin (100 μM; Fisher Scientific, Hampton, NH, BP26621), were added directly to the media, and cells were incubated for 12 h or 24 h (as indicated) in serum-free media to induce CMA activity. At the end of the incubation, cells were lysed in 0.25 M sucrose buffer (pH 7.2) supplemented with protease inhibitors, and samples were subjected to electrophoresis and immunoblot in nitrocellulose membranes. Sources of primary antibodies and dilutions used were as follows: against LC3 (1:1,000; Cell Signaling, Danvers, MA, 3868), mouse LAMP-2A (1:3,000, Thermo Scientific, Whaltman, MA, 512200), Alix (1:1,000, Cell Signaling, 2171S), CYLD (1:1,000; Sigma-Aldrich, SAB4200060), ATF6a (1:1,000, Novus Biologicals, Centennial, CO, nbp1-40256), ACACA (1:1,000, Cell Signaling 3676), ATGL (1:1,000, Cell Signaling, 2138), CDKN2A (1:1,000, Abcam, Cambridge, UK, ab109199), H2A.X (1:1,000, Cell Signaling, 2595), BCL2 (1:1,000, Cell Signaling, 2870), PSA5 (1:1,000, Biomol GmbH, Hamburg, Germany, pw8125), and GAPDH (1:1,000, Abcam, ab8245). The proteins of interest were visualized by chemiluminescence using peroxidase-conjugated secondary antibodies in G-BOX Chemi XX6 (Imgen, Alexandria, VA), and red ponceau was used as loading control. Sources of other chemicals were as described before [12, 18].

Data availability

All raw data (individual numerical values that underlie the summary data displayed in main and supplementary figure panels) have been deposited in the publicly available repository, GitHub, and can be accessed through this link: https://github.com/PhilippKirchner/KFERQ_analysis/tree/master/raw_data_figures.